[ { "path": ".ci/update_windows/update.py", "content": "import pygit2\r\nfrom datetime import datetime\r\nimport sys\r\nimport os\r\nimport shutil\r\nimport filecmp\r\n\r\ndef pull(repo, remote_name='origin', branch='master'):\r\n for remote in repo.remotes:\r\n if remote.name == remote_name:\r\n remote.fetch()\r\n remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target\r\n merge_result, _ = repo.merge_analysis(remote_master_id)\r\n # Up to date, do nothing\r\n if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:\r\n return\r\n # We can just fastforward\r\n elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:\r\n repo.checkout_tree(repo.get(remote_master_id))\r\n try:\r\n master_ref = repo.lookup_reference('refs/heads/%s' % (branch))\r\n master_ref.set_target(remote_master_id)\r\n except KeyError:\r\n repo.create_branch(branch, repo.get(remote_master_id))\r\n repo.head.set_target(remote_master_id)\r\n elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:\r\n repo.merge(remote_master_id)\r\n\r\n if repo.index.conflicts is not None:\r\n for conflict in repo.index.conflicts:\r\n print('Conflicts found in:', conflict[0].path) # noqa: T201\r\n raise AssertionError('Conflicts, ahhhhh!!')\r\n\r\n user = repo.default_signature\r\n tree = repo.index.write_tree()\r\n repo.create_commit('HEAD',\r\n user,\r\n user,\r\n 'Merge!',\r\n tree,\r\n [repo.head.target, remote_master_id])\r\n # We need to do this or git CLI will think we are still merging.\r\n repo.state_cleanup()\r\n else:\r\n raise AssertionError('Unknown merge analysis result')\r\n\r\npygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)\r\nrepo_path = str(sys.argv[1])\r\nrepo = pygit2.Repository(repo_path)\r\nident = pygit2.Signature('comfyui', 'comfy@ui')\r\ntry:\r\n print(\"stashing current changes\") # noqa: T201\r\n repo.stash(ident)\r\nexcept KeyError:\r\n print(\"nothing to stash\") # noqa: T201\r\nexcept:\r\n print(\"Could not stash, cleaning index and trying again.\") # noqa: T201\r\n repo.state_cleanup()\r\n repo.index.read_tree(repo.head.peel().tree)\r\n repo.index.write()\r\n try:\r\n repo.stash(ident)\r\n except KeyError:\r\n print(\"nothing to stash.\") # noqa: T201\r\n\r\nbackup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))\r\nprint(\"creating backup branch: {}\".format(backup_branch_name)) # noqa: T201\r\ntry:\r\n repo.branches.local.create(backup_branch_name, repo.head.peel())\r\nexcept:\r\n pass\r\n\r\nprint(\"checking out master branch\") # noqa: T201\r\nbranch = repo.lookup_branch('master')\r\nif branch is None:\r\n try:\r\n ref = repo.lookup_reference('refs/remotes/origin/master')\r\n except:\r\n print(\"fetching.\") # noqa: T201\r\n for remote in repo.remotes:\r\n if remote.name == \"origin\":\r\n remote.fetch()\r\n ref = repo.lookup_reference('refs/remotes/origin/master')\r\n repo.checkout(ref)\r\n branch = repo.lookup_branch('master')\r\n if branch is None:\r\n repo.create_branch('master', repo.get(ref.target))\r\nelse:\r\n ref = repo.lookup_reference(branch.name)\r\n repo.checkout(ref)\r\n\r\nprint(\"pulling latest changes\") # noqa: T201\r\npull(repo)\r\n\r\nif \"--stable\" in sys.argv:\r\n def latest_tag(repo):\r\n versions = []\r\n for k in repo.references:\r\n try:\r\n prefix = \"refs/tags/v\"\r\n if k.startswith(prefix):\r\n version = list(map(int, k[len(prefix):].split(\".\")))\r\n versions.append((version[0] * 10000000000 + version[1] * 100000 + version[2], k))\r\n except:\r\n pass\r\n versions.sort()\r\n if len(versions) > 0:\r\n return versions[-1][1]\r\n return None\r\n latest_tag = latest_tag(repo)\r\n if latest_tag is not None:\r\n repo.checkout(latest_tag)\r\n\r\nprint(\"Done!\") # noqa: T201\r\n\r\nself_update = True\r\nif len(sys.argv) > 2:\r\n self_update = '--skip_self_update' not in sys.argv\r\n\r\nupdate_py_path = os.path.realpath(__file__)\r\nrepo_update_py_path = os.path.join(repo_path, \".ci/update_windows/update.py\")\r\n\r\ncur_path = os.path.dirname(update_py_path)\r\n\r\n\r\nreq_path = os.path.join(cur_path, \"current_requirements.txt\")\r\nrepo_req_path = os.path.join(repo_path, \"requirements.txt\")\r\n\r\n\r\ndef files_equal(file1, file2):\r\n try:\r\n return filecmp.cmp(file1, file2, shallow=False)\r\n except:\r\n return False\r\n\r\ndef file_size(f):\r\n try:\r\n return os.path.getsize(f)\r\n except:\r\n return 0\r\n\r\n\r\nif self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10:\r\n shutil.copy(repo_update_py_path, os.path.join(cur_path, \"update_new.py\"))\r\n exit()\r\n\r\nif not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):\r\n import subprocess\r\n try:\r\n subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path])\r\n shutil.copy(repo_req_path, req_path)\r\n except:\r\n pass\r\n\r\n\r\nstable_update_script = os.path.join(repo_path, \".ci/update_windows/update_comfyui_stable.bat\")\r\nstable_update_script_to = os.path.join(cur_path, \"update_comfyui_stable.bat\")\r\n\r\ntry:\r\n if not file_size(stable_update_script_to) > 10:\r\n shutil.copy(stable_update_script, stable_update_script_to)\r\nexcept:\r\n pass\r\n\r\n" }, { "path": ".ci/update_windows/update_comfyui.bat", "content": "@echo off\r\n..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\\r\nif exist update_new.py (\r\n move /y update_new.py update.py\r\n echo Running updater again since it got updated.\r\n ..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\ --skip_self_update\r\n)\r\nif \"%~1\"==\"\" pause\r\n" }, { "path": ".ci/update_windows/update_comfyui_stable.bat", "content": "@echo off\r\n..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\ --stable\r\nif exist update_new.py (\r\n move /y update_new.py update.py\r\n echo Running updater again since it got updated.\r\n ..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\ --skip_self_update --stable\r\n)\r\nif \"%~1\"==\"\" pause\r\n" }, { "path": ".ci/windows_amd_base_files/README_VERY_IMPORTANT.txt", "content": "As of the time of writing this you need this driver for best results:\r\nhttps://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html\r\n\r\nHOW TO RUN:\r\n\r\nIf you have a AMD gpu:\r\n\r\nrun_amd_gpu.bat\r\n\r\nIf you have memory issues you can try disabling the smart memory management by running comfyui with:\r\n\r\nrun_amd_gpu_disable_smart_memory.bat\r\n\r\nIF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\\models\\checkpoints\r\n\r\nYou can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors\r\n\r\n\r\nRECOMMENDED WAY TO UPDATE:\r\nTo update the ComfyUI code: update\\update_comfyui.bat\r\n\r\n\r\nTO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:\r\nIn the ComfyUI directory you will find a file: extra_model_paths.yaml.example\r\nRename this file to: extra_model_paths.yaml and edit it with your favorite text editor.\r\n\r\n\r\n\r\n" }, { "path": ".ci/windows_amd_base_files/run_amd_gpu.bat", "content": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build\r\npause\r\n" }, { "path": ".ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat", "content": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build --disable-smart-memory\r\npause\r\n" }, { "path": ".ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat", "content": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build --fast\r\npause\r\n" }, { "path": ".ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt", "content": "HOW TO RUN:\r\n\r\nif you have a NVIDIA gpu:\r\n\r\nrun_nvidia_gpu.bat\r\n\r\nif you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality):\r\n\r\nrun_nvidia_gpu_fast_fp16_accumulation.bat\r\n\r\n\r\nTo run it in slow CPU mode:\r\n\r\nrun_cpu.bat\r\n\r\n\r\n\r\nIF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\\models\\checkpoints\r\n\r\nYou can download the stable diffusion 1.5 one from: https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/blob/main/v1-5-pruned-emaonly-fp16.safetensors\r\n\r\n\r\nRECOMMENDED WAY TO UPDATE:\r\nTo update the ComfyUI code: update\\update_comfyui.bat\r\n\r\n\r\n\r\nTo update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.\r\nupdate\\update_comfyui_and_python_dependencies.bat\r\n\r\n\r\nTO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:\r\nIn the ComfyUI directory you will find a file: extra_model_paths.yaml.example\r\nRename this file to: extra_model_paths.yaml and edit it with your favorite text editor.\r\n" }, { "path": ".ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat", "content": "..\\python_embeded\\python.exe -s ..\\ComfyUI\\main.py --windows-standalone-build --disable-api-nodes\r\necho If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe\r\npause\r\n" }, { "path": ".ci/windows_nvidia_base_files/run_cpu.bat", "content": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --cpu --windows-standalone-build\r\npause\r\n" }, { "path": ".ci/windows_nvidia_base_files/run_nvidia_gpu.bat", "content": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build\r\necho If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe\r\npause\r\n" }, { "path": ".ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat", "content": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build --fast fp16_accumulation\r\necho If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest. If you get a c10.dll error you need to install vc redist that you can find: https://aka.ms/vc14/vc_redist.x64.exe\r\npause\r\n" }, { "path": ".coderabbit.yaml", "content": "# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json\nlanguage: \"en-US\"\nearly_access: false\ntone_instructions: \"Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code.\"\n\nreviews:\n profile: \"chill\"\n request_changes_workflow: false\n high_level_summary: false\n poem: false\n review_status: false\n review_details: false\n commit_status: true\n collapse_walkthrough: true\n changed_files_summary: false\n sequence_diagrams: false\n estimate_code_review_effort: false\n assess_linked_issues: false\n related_issues: false\n related_prs: false\n suggested_labels: false\n auto_apply_labels: false\n suggested_reviewers: false\n auto_assign_reviewers: false\n in_progress_fortune: false\n enable_prompt_for_ai_agents: true\n\n path_filters:\n - \"!comfy_api_nodes/apis/**\"\n - \"!**/generated/*.pyi\"\n - \"!.ci/**\"\n - \"!script_examples/**\"\n - \"!**/__pycache__/**\"\n - \"!**/*.ipynb\"\n - \"!**/*.png\"\n - \"!**/*.bat\"\n\n path_instructions:\n - path: \"**\"\n instructions: |\n IMPORTANT: Only comment on issues directly introduced by this PR's code changes.\n Do NOT flag pre-existing issues in code that was merely moved, re-indented,\n de-indented, or reformatted without logic changes. If code appears in the diff\n only due to whitespace or structural reformatting (e.g., removing a `with:` block),\n treat it as unchanged. Contributors should not feel obligated to address\n pre-existing issues outside the scope of their contribution.\n - path: \"comfy/**\"\n instructions: |\n Core ML/diffusion engine. Focus on:\n - Backward compatibility (breaking changes affect all custom nodes)\n - Memory management and GPU resource handling\n - Performance implications in hot paths\n - Thread safety for concurrent execution\n - path: \"comfy_api_nodes/**\"\n instructions: |\n Third-party API integration nodes. Focus on:\n - No hardcoded API keys or secrets\n - Proper error handling for API failures (timeouts, rate limits, auth errors)\n - Correct Pydantic model usage\n - Security of user data passed to external APIs\n - path: \"comfy_extras/**\"\n instructions: |\n Community-contributed extra nodes. Focus on:\n - Consistency with node patterns (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY)\n - No breaking changes to existing node interfaces\n - path: \"comfy_execution/**\"\n instructions: |\n Execution engine (graph execution, caching, jobs). Focus on:\n - Caching correctness\n - Concurrent execution safety\n - Graph validation edge cases\n - path: \"nodes.py\"\n instructions: |\n Core node definitions (2500+ lines). Focus on:\n - Backward compatibility of NODE_CLASS_MAPPINGS\n - Consistency of INPUT_TYPES return format\n - path: \"alembic_db/**\"\n instructions: |\n Database migrations. Focus on:\n - Migration safety and rollback support\n - Data preservation during schema changes\n\n auto_review:\n enabled: true\n auto_incremental_review: true\n drafts: false\n ignore_title_keywords:\n - \"WIP\"\n - \"DO NOT REVIEW\"\n - \"DO NOT MERGE\"\n\n finishing_touches:\n docstrings:\n enabled: false\n unit_tests:\n enabled: false\n\n tools:\n ruff:\n enabled: false\n pylint:\n enabled: false\n flake8:\n enabled: false\n gitleaks:\n enabled: true\n shellcheck:\n enabled: false\n markdownlint:\n enabled: false\n yamllint:\n enabled: false\n languagetool:\n enabled: false\n github-checks:\n enabled: true\n timeout_ms: 90000\n ast-grep:\n essential_rules: true\n\nchat:\n auto_reply: true\n\nknowledge_base:\n opt_out: false\n learnings:\n scope: \"auto\"\n" }, { "path": ".gitattributes", "content": "/web/assets/** linguist-generated\n/web/** linguist-vendored\ncomfy_api_nodes/apis/__init__.py linguist-generated\n" }, { "path": ".github/ISSUE_TEMPLATE/bug-report.yml", "content": "name: Bug Report\ndescription: \"Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)\"\nlabels: [\"Potential Bug\"]\nbody:\n - type: markdown\n attributes:\n value: |\n Before submitting a **Bug Report**, please ensure the following:\n\n - **1:** You are running the latest version of ComfyUI.\n - **2:** You have your ComfyUI logs and relevant workflow on hand and will post them in this bug report.\n - **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing\n `--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version.\n - **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact\n steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.\n\n ## Very Important\n\n Please make sure that you post ALL your ComfyUI logs in the bug report **even if there is no crash**. Just paste everything. The startup log (everything before \"To see the GUI go to: ...\") contains critical information to developers trying to help. For a performance issue or crash, paste everything from \"got prompt\" to the end, including the crash. More is better - always. A bug report without logs will likely be ignored.\n - type: checkboxes\n id: custom-nodes-test\n attributes:\n label: Custom Node Testing\n description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.\n options:\n - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)\n required: false\n - type: textarea\n attributes:\n label: Expected Behavior\n description: \"What you expected to happen.\"\n validations:\n required: true\n - type: textarea\n attributes:\n label: Actual Behavior\n description: \"What actually happened. Please include a screenshot of the issue if possible.\"\n validations:\n required: true\n - type: textarea\n attributes:\n label: Steps to Reproduce\n description: \"Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author.\"\n validations:\n required: true\n - type: textarea\n attributes:\n label: Debug Logs\n description: \"Please copy the output from your terminal logs here.\"\n render: powershell\n validations:\n required: true\n - type: textarea\n attributes:\n label: Other\n description: \"Any other additional information you think might be helpful.\"\n validations:\n required: false\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "blank_issues_enabled: true\ncontact_links:\n - name: ComfyUI Frontend Issues\n url: https://github.com/Comfy-Org/ComfyUI_frontend/issues\n about: Issues related to the ComfyUI frontend (display issues, user interaction bugs), please go to the frontend repo to file the issue\n - name: ComfyUI Matrix Space\n url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org\n about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).\n - name: Comfy Org Discord\n url: https://discord.gg/comfyorg\n about: The Comfy Org Discord is available for support and general discussion related to ComfyUI.\n" }, { "path": ".github/ISSUE_TEMPLATE/feature-request.yml", "content": "name: Feature Request\ndescription: \"You have an idea for something new you would like to see added to ComfyUI's core.\"\nlabels: [ \"Feature\" ]\nbody:\n - type: markdown\n attributes:\n value: |\n Before submitting a **Feature Request**, please ensure the following:\n\n **1:** You are running the latest version of ComfyUI.\n **2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea.\n **3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node.\n\n If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.\n - type: textarea\n attributes:\n label: Feature Idea\n description: \"Describe the feature you want to see.\"\n validations:\n required: true\n - type: textarea\n attributes:\n label: Existing Solutions\n description: \"Please search through available custom nodes / extensions to see if there are existing custom solutions for this. If so, please link the options you found here as a reference.\"\n validations:\n required: false\n - type: textarea\n attributes:\n label: Other\n description: \"Any other additional information you think might be helpful.\"\n validations:\n required: false\n" }, { "path": ".github/ISSUE_TEMPLATE/user-support.yml", "content": "name: User Support\ndescription: \"Use this if you need help with something, or you're experiencing an issue.\"\nlabels: [ \"User Support\" ]\nbody:\n - type: markdown\n attributes:\n value: |\n Before submitting a **User Report** issue, please ensure the following:\n\n **1:** You are running the latest version of ComfyUI.\n **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.\n\n If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.\n - type: checkboxes\n id: custom-nodes-test\n attributes:\n label: Custom Node Testing\n description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.\n options:\n - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)\n required: false\n - type: textarea\n attributes:\n label: Your question\n description: \"Post your question here. Please be as detailed as possible.\"\n validations:\n required: true\n - type: textarea\n attributes:\n label: Logs\n description: \"If your question relates to an issue you're experiencing, please go to `Server` -> `Logs` -> potentially set `View Type` to `Debug` as well, then copypaste all the text into here.\"\n render: powershell\n validations:\n required: false\n - type: textarea\n attributes:\n label: Other\n description: \"Any other additional information you think might be helpful.\"\n validations:\n required: false\n" }, { "path": ".github/PULL_REQUEST_TEMPLATE/api-node.md", "content": "\n\n## API Node PR Checklist\n\n### Scope\n- [ ] **Is API Node Change**\n\n### Pricing & Billing\n- [ ] **Need pricing update**\n- [ ] **No pricing update**\n\nIf **Need pricing update**:\n- [ ] Metronome rate cards updated\n- [ ] Auto‑billing tests updated and passing\n\n### QA\n- [ ] **QA done**\n- [ ] **QA not required**\n\n### Comms\n- [ ] Informed **Kosinkadink**\n" }, { "path": ".github/scripts/check-ai-co-authors.sh", "content": "#!/usr/bin/env bash\n# Checks pull request commits for AI agent Co-authored-by trailers.\n# Exits non-zero when any are found and prints fix instructions.\nset -euo pipefail\n\nbase_sha=\"${1:?usage: check-ai-co-authors.sh }\"\nhead_sha=\"${2:?usage: check-ai-co-authors.sh }\"\n\n# Known AI coding-agent trailer patterns (case-insensitive).\n# Each entry is an extended-regex fragment matched against Co-authored-by lines.\nAGENT_PATTERNS=(\n # Anthropic — Claude Code / Amp\n 'noreply@anthropic\\.com'\n # Cursor\n 'cursoragent@cursor\\.com'\n # GitHub Copilot\n 'copilot-swe-agent\\[bot\\]'\n 'copilot@github\\.com'\n # OpenAI Codex\n 'noreply@openai\\.com'\n 'codex@openai\\.com'\n # Aider\n 'aider@aider\\.chat'\n # Google — Gemini / Jules\n 'gemini@google\\.com'\n 'jules@google\\.com'\n # Windsurf / Codeium\n '@codeium\\.com'\n # Devin\n 'devin-ai-integration\\[bot\\]'\n 'devin@cognition\\.ai'\n 'devin@cognition-labs\\.com'\n # Amazon Q Developer\n 'amazon-q-developer'\n '@amazon\\.com.*[Qq].[Dd]eveloper'\n # Cline\n 'cline-bot'\n 'cline@cline\\.ai'\n # Continue\n 'continue-agent'\n 'continue@continue\\.dev'\n # Sourcegraph\n 'noreply@sourcegraph\\.com'\n # Generic catch-alls for common agent name patterns\n 'Co-authored-by:.*\\b[Cc]laude\\b'\n 'Co-authored-by:.*\\b[Cc]opilot\\b'\n 'Co-authored-by:.*\\b[Cc]ursor\\b'\n 'Co-authored-by:.*\\b[Cc]odex\\b'\n 'Co-authored-by:.*\\b[Gg]emini\\b'\n 'Co-authored-by:.*\\b[Aa]ider\\b'\n 'Co-authored-by:.*\\b[Dd]evin\\b'\n 'Co-authored-by:.*\\b[Ww]indsurf\\b'\n 'Co-authored-by:.*\\b[Cc]line\\b'\n 'Co-authored-by:.*\\b[Aa]mazon Q\\b'\n 'Co-authored-by:.*\\b[Jj]ules\\b'\n 'Co-authored-by:.*\\bOpenCode\\b'\n)\n\n# Build a single alternation regex from all patterns.\nregex=\"\"\nfor pattern in \"${AGENT_PATTERNS[@]}\"; do\n if [[ -n \"$regex\" ]]; then\n regex=\"${regex}|${pattern}\"\n else\n regex=\"$pattern\"\n fi\ndone\n\n# Collect Co-authored-by lines from every commit in the PR range.\nviolations=\"\"\nwhile IFS= read -r sha; do\n message=\"$(git log -1 --format='%B' \"$sha\")\"\n matched_lines=\"$(echo \"$message\" | grep -iE \"^Co-authored-by:\" || true)\"\n if [[ -z \"$matched_lines\" ]]; then\n continue\n fi\n\n while IFS= read -r line; do\n if echo \"$line\" | grep -iqE \"$regex\"; then\n short=\"$(git log -1 --format='%h' \"$sha\")\"\n violations=\"${violations} ${short}: ${line}\"$'\\n'\n fi\n done <<< \"$matched_lines\"\ndone < <(git rev-list \"${base_sha}..${head_sha}\")\n\nif [[ -n \"$violations\" ]]; then\n echo \"::error::AI agent Co-authored-by trailers detected in PR commits.\"\n echo \"\"\n echo \"The following commits contain Co-authored-by trailers from AI coding agents:\"\n echo \"\"\n echo \"$violations\"\n echo \"These trailers should be removed before merging.\"\n echo \"\"\n echo \"To fix, rewrite the commit messages with:\"\n echo \" git rebase -i ${base_sha}\"\n echo \"\"\n echo \"and remove the Co-authored-by lines, then force-push your branch.\"\n echo \"\"\n echo \"If you believe this is a false positive, please open an issue.\"\n exit 1\nfi\n\necho \"No AI agent Co-authored-by trailers found.\"\n" }, { "path": ".github/workflows/api-node-template.yml", "content": "name: Append API Node PR template\n\non:\n pull_request_target:\n types: [opened, reopened, synchronize, ready_for_review]\n paths:\n - 'comfy_api_nodes/**' # only run if these files changed\n\npermissions:\n contents: read\n pull-requests: write\n\njobs:\n inject:\n runs-on: ubuntu-latest\n steps:\n - name: Ensure template exists and append to PR body\n uses: actions/github-script@v7\n with:\n script: |\n const { owner, repo } = context.repo;\n const number = context.payload.pull_request.number;\n const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md';\n const marker = '';\n\n const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number });\n\n let templateText;\n try {\n const res = await github.rest.repos.getContent({\n owner,\n repo,\n path: templatePath,\n ref: pr.base.ref\n });\n const buf = Buffer.from(res.data.content, res.data.encoding || 'base64');\n templateText = buf.toString('utf8');\n } catch (e) {\n core.setFailed(`Required PR template not found at \"${templatePath}\" on ${pr.base.ref}. Please add it to the repo.`);\n return;\n }\n\n // Enforce the presence of the marker inside the template (for idempotence)\n if (!templateText.includes(marker)) {\n core.setFailed(`Template at \"${templatePath}\" does not contain the required marker:\\n${marker}\\nAdd it so we can detect duplicates safely.`);\n return;\n }\n\n // If the PR already contains the marker, do not append again.\n const body = pr.body || '';\n if (body.includes(marker)) {\n core.info('Template already present in PR body; nothing to inject.');\n return;\n }\n\n const newBody = (body ? body + '\\n\\n' : '') + templateText + '\\n';\n await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody });\n core.notice('API Node template appended to PR description.');\n" }, { "path": ".github/workflows/check-ai-co-authors.yml", "content": "name: Check AI Co-Authors\n\non:\n pull_request:\n branches: ['*']\n\njobs:\n check-ai-co-authors:\n name: Check for AI agent co-author trailers\n runs-on: ubuntu-latest\n\n steps:\n - name: Checkout code\n uses: actions/checkout@v4\n with:\n fetch-depth: 0\n\n - name: Check commits for AI co-author trailers\n run: bash .github/scripts/check-ai-co-authors.sh \"${{ github.event.pull_request.base.sha }}\" \"${{ github.event.pull_request.head.sha }}\"\n" }, { "path": ".github/workflows/check-line-endings.yml", "content": "name: Check for Windows Line Endings\n\non:\n pull_request:\n branches: ['*'] # Trigger on all pull requests to any branch\n\njobs:\n check-line-endings:\n runs-on: ubuntu-latest\n\n steps:\n - name: Checkout code\n uses: actions/checkout@v4\n with:\n fetch-depth: 0 # Fetch all history to compare changes\n\n - name: Check for Windows line endings (CRLF)\n run: |\n # Get the list of changed files in the PR\n CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }})\n\n # Flag to track if CRLF is found\n CRLF_FOUND=false\n\n # Loop through each changed file\n for FILE in $CHANGED_FILES; do\n # Check if the file exists and is a text file\n if [ -f \"$FILE\" ] && file \"$FILE\" | grep -q \"text\"; then\n # Check for CRLF line endings\n if grep -UP '\\r$' \"$FILE\"; then\n echo \"Error: Windows line endings (CRLF) detected in $FILE\"\n CRLF_FOUND=true\n fi\n fi\n done\n\n # Exit with error if CRLF was found\n if [ \"$CRLF_FOUND\" = true ]; then\n exit 1\n fi\n" }, { "path": ".github/workflows/pullrequest-ci-run.yml", "content": "# This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to ComfyUI, when the 'Run-CI-Test' label is added\n# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/\nname: Pull Request CI Workflow Runs\non:\n pull_request_target:\n types: [labeled]\n\njobs:\n pr-test-stable:\n if: ${{ github.event.label.name == 'Run-CI-Test' }}\n strategy:\n fail-fast: false\n matrix:\n os: [macos, linux, windows]\n python_version: [\"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n cuda_version: [\"12.1\"]\n torch_version: [\"stable\"]\n include:\n - os: macos\n runner_label: [self-hosted, macOS]\n flags: \"--use-pytorch-cross-attention\"\n - os: linux\n runner_label: [self-hosted, Linux]\n flags: \"\"\n - os: windows\n runner_label: [self-hosted, Windows]\n flags: \"\"\n runs-on: ${{ matrix.runner_label }}\n steps:\n - name: Test Workflows\n uses: comfy-org/comfy-action@main\n with:\n os: ${{ matrix.os }}\n python_version: ${{ matrix.python_version }}\n torch_version: ${{ matrix.torch_version }}\n google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}\n comfyui_flags: ${{ matrix.flags }}\n use_prior_commit: 'true'\n comment:\n if: ${{ github.event.label.name == 'Run-CI-Test' }}\n runs-on: ubuntu-latest\n permissions:\n pull-requests: write\n steps:\n - uses: actions/github-script@v6\n with:\n script: |\n github.rest.issues.createComment({\n issue_number: context.issue.number,\n owner: context.repo.owner,\n repo: context.repo.repo,\n body: '(Automated Bot Message) CI Tests are running, you can view the results at https://ci.comfy.org/?branch=${{ github.event.pull_request.number }}%2Fmerge'\n })\n" }, { "path": ".github/workflows/release-stable-all.yml", "content": "name: \"Release Stable All Portable Versions\"\n\non:\n workflow_dispatch:\n inputs:\n git_tag:\n description: 'Git tag'\n required: true\n type: string\n\njobs:\n release_nvidia_default:\n permissions:\n contents: \"write\"\n packages: \"write\"\n pull-requests: \"read\"\n name: \"Release NVIDIA Default (cu130)\"\n uses: ./.github/workflows/stable-release.yml\n with:\n git_tag: ${{ inputs.git_tag }}\n cache_tag: \"cu130\"\n python_minor: \"13\"\n python_patch: \"11\"\n rel_name: \"nvidia\"\n rel_extra_name: \"\"\n test_release: true\n secrets: inherit\n\n release_nvidia_cu128:\n permissions:\n contents: \"write\"\n packages: \"write\"\n pull-requests: \"read\"\n name: \"Release NVIDIA cu128\"\n uses: ./.github/workflows/stable-release.yml\n with:\n git_tag: ${{ inputs.git_tag }}\n cache_tag: \"cu128\"\n python_minor: \"12\"\n python_patch: \"10\"\n rel_name: \"nvidia\"\n rel_extra_name: \"_cu128\"\n test_release: true\n secrets: inherit\n\n release_nvidia_cu126:\n permissions:\n contents: \"write\"\n packages: \"write\"\n pull-requests: \"read\"\n name: \"Release NVIDIA cu126\"\n uses: ./.github/workflows/stable-release.yml\n with:\n git_tag: ${{ inputs.git_tag }}\n cache_tag: \"cu126\"\n python_minor: \"12\"\n python_patch: \"10\"\n rel_name: \"nvidia\"\n rel_extra_name: \"_cu126\"\n test_release: true\n secrets: inherit\n\n release_amd_rocm:\n permissions:\n contents: \"write\"\n packages: \"write\"\n pull-requests: \"read\"\n name: \"Release AMD ROCm 7.2\"\n uses: ./.github/workflows/stable-release.yml\n with:\n git_tag: ${{ inputs.git_tag }}\n cache_tag: \"rocm72\"\n python_minor: \"12\"\n python_patch: \"10\"\n rel_name: \"amd\"\n rel_extra_name: \"\"\n test_release: false\n secrets: inherit\n" }, { "path": ".github/workflows/release-webhook.yml", "content": "name: Release Webhook\n\non:\n release:\n types: [published]\n\njobs:\n send-webhook:\n runs-on: ubuntu-latest\n env:\n DESKTOP_REPO_DISPATCH_TOKEN: ${{ secrets.DESKTOP_REPO_DISPATCH_TOKEN }}\n steps:\n - name: Send release webhook\n env:\n WEBHOOK_URL: ${{ secrets.RELEASE_GITHUB_WEBHOOK_URL }}\n WEBHOOK_SECRET: ${{ secrets.RELEASE_GITHUB_WEBHOOK_SECRET }}\n run: |\n # Generate UUID for delivery ID\n DELIVERY_ID=$(uuidgen)\n HOOK_ID=\"release-webhook-$(date +%s)\"\n \n # Create webhook payload matching GitHub release webhook format\n PAYLOAD=$(cat <> ./python3${{ inputs.python_minor }}._pth\n curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py\n ./python.exe get-pip.py\n ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*\n\n grep comfy ../ComfyUI/requirements.txt > ./requirements_comfyui.txt\n ./python.exe -s -m pip install -r requirements_comfyui.txt\n rm requirements_comfyui.txt\n\n sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth\n\n if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then\n rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space\n rm ./Lib/site-packages/torch/lib/libprotoc.lib\n rm ./Lib/site-packages/torch/lib/libprotobuf.lib\n fi\n\n cd ..\n\n git clone --depth 1 https://github.com/comfyanonymous/taesd\n cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/\n\n mkdir ComfyUI_windows_portable\n mv python_embeded ComfyUI_windows_portable\n mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI\n\n cd ComfyUI_windows_portable\n\n mkdir update\n cp -r ComfyUI/.ci/update_windows/* ./update/\n cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./\n cp ../update_comfyui_and_python_dependencies.bat ./update/\n\n cd ..\n\n \"C:\\Program Files\\7-Zip\\7z.exe\" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable\n mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z\n\n - shell: bash\n if: ${{ inputs.test_release }}\n run: |\n cd ..\n cd ComfyUI_windows_portable\n python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu\n\n python_embeded/python.exe -s ./update/update.py ComfyUI/\n\n ls\n\n - name: Upload binaries to release\n uses: softprops/action-gh-release@v2\n with:\n files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z\n tag_name: ${{ inputs.git_tag }}\n draft: true\n overwrite_files: true\n" }, { "path": ".github/workflows/stale-issues.yml", "content": "name: 'Close stale issues'\non:\n schedule:\n # Run daily at 430 am PT\n - cron: '30 11 * * *'\npermissions:\n issues: write\n\njobs:\n stale:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/stale@v9\n with:\n stale-issue-message: \"This issue is being marked stale because it has not had any activity for 30 days. Reply below within 7 days if your issue still isn't solved, and it will be left open. Otherwise, the issue will be closed automatically.\"\n days-before-stale: 30\n days-before-close: 7\n stale-issue-label: 'Stale'\n only-labels: 'User Support'\n exempt-all-assignees: true\n exempt-all-milestones: true\n" }, { "path": ".github/workflows/test-build.yml", "content": "name: Build package\n\n#\n# This workflow is a test of the python package build.\n# Install Python dependencies across different Python versions.\n#\n\non:\n push:\n paths:\n - \"requirements.txt\"\n - \".github/workflows/test-build.yml\"\n\njobs:\n build:\n name: Build Test\n runs-on: ubuntu-latest\n strategy:\n fail-fast: false\n matrix:\n python-version: [\"3.10\", \"3.11\", \"3.12\", \"3.13\", \"3.14\"]\n steps:\n - uses: actions/checkout@v4\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v4\n with:\n python-version: ${{ matrix.python-version }}\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -r requirements.txt\n" }, { "path": ".github/workflows/test-ci.yml", "content": "# This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the master branch of ComfyUI\n# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/\nname: Full Comfy CI Workflow Runs\non:\n push:\n branches:\n - master\n - release/**\n paths-ignore:\n - 'app/**'\n - 'input/**'\n - 'output/**'\n - 'notebooks/**'\n - 'script_examples/**'\n - '.github/**'\n - 'web/**'\n workflow_dispatch:\n\njobs:\n test-stable:\n strategy:\n fail-fast: false\n matrix:\n # os: [macos, linux, windows]\n # os: [macos, linux]\n os: [linux]\n python_version: [\"3.10\", \"3.11\", \"3.12\"]\n cuda_version: [\"12.1\"]\n torch_version: [\"stable\"]\n include:\n # - os: macos\n # runner_label: [self-hosted, macOS]\n # flags: \"--use-pytorch-cross-attention\"\n - os: linux\n runner_label: [self-hosted, Linux]\n flags: \"\"\n # - os: windows\n # runner_label: [self-hosted, Windows]\n # flags: \"\"\n runs-on: ${{ matrix.runner_label }}\n steps:\n - name: Test Workflows\n uses: comfy-org/comfy-action@main\n with:\n os: ${{ matrix.os }}\n python_version: ${{ matrix.python_version }}\n torch_version: ${{ matrix.torch_version }}\n google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}\n comfyui_flags: ${{ matrix.flags }}\n\n # test-win-nightly:\n # strategy:\n # fail-fast: true\n # matrix:\n # os: [windows]\n # python_version: [\"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n # cuda_version: [\"12.1\"]\n # torch_version: [\"nightly\"]\n # include:\n # - os: windows\n # runner_label: [self-hosted, Windows]\n # flags: \"\"\n # runs-on: ${{ matrix.runner_label }}\n # steps:\n # - name: Test Workflows\n # uses: comfy-org/comfy-action@main\n # with:\n # os: ${{ matrix.os }}\n # python_version: ${{ matrix.python_version }}\n # torch_version: ${{ matrix.torch_version }}\n # google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}\n # comfyui_flags: ${{ matrix.flags }}\n\n test-unix-nightly:\n strategy:\n fail-fast: false\n matrix:\n # os: [macos, linux]\n os: [linux]\n python_version: [\"3.11\"]\n cuda_version: [\"12.1\"]\n torch_version: [\"nightly\"]\n include:\n # - os: macos\n # runner_label: [self-hosted, macOS]\n # flags: \"--use-pytorch-cross-attention\"\n - os: linux\n runner_label: [self-hosted, Linux]\n flags: \"\"\n runs-on: ${{ matrix.runner_label }}\n steps:\n - name: Test Workflows\n uses: comfy-org/comfy-action@main\n with:\n os: ${{ matrix.os }}\n python_version: ${{ matrix.python_version }}\n torch_version: ${{ matrix.torch_version }}\n google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}\n comfyui_flags: ${{ matrix.flags }}\n" }, { "path": ".github/workflows/test-execution.yml", "content": "name: Execution Tests\n\non:\n push:\n branches: [ main, master, release/** ]\n pull_request:\n branches: [ main, master, release/** ]\n\njobs:\n test:\n strategy:\n matrix:\n os: [ubuntu-latest, windows-latest, macos-latest]\n runs-on: ${{ matrix.os }}\n continue-on-error: true\n steps:\n - uses: actions/checkout@v4\n - name: Set up Python \n uses: actions/setup-python@v4\n with:\n python-version: '3.12'\n - name: Install requirements\n run: |\n python -m pip install --upgrade pip\n pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\n pip install -r requirements.txt\n pip install -r tests-unit/requirements.txt\n - name: Run Execution Tests\n run: |\n python -m pytest tests/execution -v --skip-timing-checks\n" }, { "path": ".github/workflows/test-launch.yml", "content": "name: Test server launches without errors\n\non:\n push:\n branches: [ main, master, release/** ]\n pull_request:\n branches: [ main, master, release/** ]\n\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - name: Checkout ComfyUI\n uses: actions/checkout@v4\n with:\n repository: \"Comfy-Org/ComfyUI\"\n path: \"ComfyUI\"\n - uses: actions/setup-python@v4\n with:\n python-version: '3.10'\n - name: Install requirements\n run: |\n python -m pip install --upgrade pip\n pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\n pip install -r requirements.txt\n pip install wait-for-it\n working-directory: ComfyUI\n - name: Start ComfyUI server\n run: |\n python main.py --cpu 2>&1 | tee console_output.log &\n wait-for-it --service 127.0.0.1:8188 -t 30\n working-directory: ComfyUI\n - name: Check for unhandled exceptions in server log\n run: |\n grep -v \"Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': \\\"ImportError: No module named 'triton'\\\", 'capabilities': \\[\\]}\" console_output.log | grep -v \"Found comfy_kitchen backend triton: {'available': False, 'disabled': False, 'unavailable_reason': \\\"ImportError: No module named 'triton'\\\", 'capabilities': \\[\\]}\" > console_output_filtered.log\n cat console_output_filtered.log\n if grep -qE \"Exception|Error\" console_output_filtered.log; then\n echo \"Unhandled exception/error found in server log.\"\n exit 1\n fi\n working-directory: ComfyUI\n - uses: actions/upload-artifact@v4\n if: always()\n with:\n name: console-output\n path: ComfyUI/console_output.log\n retention-days: 30\n" }, { "path": ".github/workflows/test-unit.yml", "content": "name: Unit Tests\n\non:\n push:\n branches: [ main, master, release/** ]\n pull_request:\n branches: [ main, master, release/** ]\n\njobs:\n test:\n strategy:\n matrix:\n os: [ubuntu-latest, windows-2022, macos-latest]\n runs-on: ${{ matrix.os }}\n continue-on-error: true\n steps:\n - uses: actions/checkout@v4\n - name: Set up Python \n uses: actions/setup-python@v4\n with:\n python-version: '3.12'\n - name: Install requirements\n run: |\n python -m pip install --upgrade pip\n pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\n pip install -r requirements.txt\n - name: Run Unit Tests\n run: |\n pip install -r tests-unit/requirements.txt\n python -m pytest tests-unit\n" }, { "path": ".github/workflows/update-api-stubs.yml", "content": "name: Generate Pydantic Stubs from api.comfy.org\n\non:\n schedule:\n - cron: '0 0 * * 1'\n workflow_dispatch:\n\njobs:\n generate-models:\n runs-on: ubuntu-latest\n \n steps:\n - name: Checkout repository\n uses: actions/checkout@v4\n \n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: '3.10'\n \n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install 'datamodel-code-generator[http]'\n npm install @redocly/cli\n \n - name: Download OpenAPI spec\n run: |\n curl -o openapi.yaml https://api.comfy.org/openapi\n \n - name: Filter OpenAPI spec with Redocly\n run: |\n npx @redocly/cli bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components\n \n - name: Generate API models\n run: |\n datamodel-codegen --use-subclass-enum --input filtered-openapi.yaml --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel\n \n - name: Check for changes\n id: git-check\n run: |\n git diff --exit-code comfy_api_nodes/apis || echo \"changes=true\" >> $GITHUB_OUTPUT\n \n - name: Create Pull Request\n if: steps.git-check.outputs.changes == 'true'\n uses: peter-evans/create-pull-request@v5\n with:\n commit-message: 'chore: update API models from OpenAPI spec'\n title: 'Update API models from api.comfy.org'\n body: |\n This PR updates the API models based on the latest api.comfy.org OpenAPI specification.\n \n Generated automatically by the a Github workflow.\n branch: update-api-stubs\n delete-branch: true\n base: master\n" }, { "path": ".github/workflows/update-ci-container.yml", "content": "name: \"CI: Update CI Container\"\n\non:\n release:\n types: [published]\n workflow_dispatch:\n inputs:\n version:\n description: 'ComfyUI version (e.g., v0.7.0)'\n required: true\n type: string\n\njobs:\n update-ci-container:\n runs-on: ubuntu-latest\n # Skip pre-releases unless manually triggered\n if: github.event_name == 'workflow_dispatch' || !github.event.release.prerelease\n steps:\n - name: Get version\n id: version\n run: |\n if [ \"${{ github.event_name }}\" = \"release\" ]; then\n VERSION=\"${{ github.event.release.tag_name }}\"\n else\n VERSION=\"${{ inputs.version }}\"\n fi\n echo \"version=$VERSION\" >> $GITHUB_OUTPUT\n\n - name: Checkout comfyui-ci-container\n uses: actions/checkout@v4\n with:\n repository: comfy-org/comfyui-ci-container\n token: ${{ secrets.CI_CONTAINER_PAT }}\n\n - name: Check current version\n id: current\n run: |\n CURRENT=$(grep -oP 'ARG COMFYUI_VERSION=\\K.*' Dockerfile || echo \"unknown\")\n echo \"current_version=$CURRENT\" >> $GITHUB_OUTPUT\n\n - name: Update Dockerfile\n run: |\n VERSION=\"${{ steps.version.outputs.version }}\"\n sed -i \"s/^ARG COMFYUI_VERSION=.*/ARG COMFYUI_VERSION=${VERSION}/\" Dockerfile\n\n - name: Create Pull Request\n id: create-pr\n uses: peter-evans/create-pull-request@v7\n with:\n token: ${{ secrets.CI_CONTAINER_PAT }}\n branch: automation/comfyui-${{ steps.version.outputs.version }}\n title: \"chore: bump ComfyUI to ${{ steps.version.outputs.version }}\"\n body: |\n Updates ComfyUI version from `${{ steps.current.outputs.current_version }}` to `${{ steps.version.outputs.version }}`\n\n **Triggered by:** ${{ github.event_name == 'release' && format('[Release {0}]({1})', github.event.release.tag_name, github.event.release.html_url) || 'Manual workflow dispatch' }}\n\n labels: automation\n commit-message: \"chore: bump ComfyUI to ${{ steps.version.outputs.version }}\"\n" }, { "path": ".github/workflows/update-version.yml", "content": "name: Update Version File\n\non:\n pull_request:\n paths:\n - \"pyproject.toml\"\n branches:\n - master\n - release/**\n\njobs:\n update-version:\n runs-on: ubuntu-latest\n # Don't run on fork PRs\n if: github.event.pull_request.head.repo.full_name == github.repository\n permissions:\n pull-requests: write\n contents: write\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v4\n\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.11\"\n\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n\n - name: Update comfyui_version.py\n run: |\n # Read version from pyproject.toml and update comfyui_version.py\n python -c '\n import tomllib\n\n # Read version from pyproject.toml\n with open(\"pyproject.toml\", \"rb\") as f:\n config = tomllib.load(f)\n version = config[\"project\"][\"version\"]\n\n # Write version to comfyui_version.py\n with open(\"comfyui_version.py\", \"w\") as f:\n f.write(\"# This file is automatically generated by the build process when version is\\n\")\n f.write(\"# updated in pyproject.toml.\\n\")\n f.write(f\"__version__ = \\\"{version}\\\"\\n\")\n '\n\n - name: Commit changes\n run: |\n git config --local user.name \"github-actions\"\n git config --local user.email \"github-actions@github.com\"\n git fetch origin ${{ github.head_ref }}\n git checkout -B ${{ github.head_ref }} origin/${{ github.head_ref }}\n git add comfyui_version.py\n git diff --quiet && git diff --staged --quiet || git commit -m \"chore: Update comfyui_version.py to match pyproject.toml\"\n git push origin HEAD:${{ github.head_ref }}\n" }, { "path": ".github/workflows/windows_release_dependencies.yml", "content": "name: \"Windows Release dependencies\"\n\non:\n workflow_dispatch:\n inputs:\n xformers:\n description: 'xformers version'\n required: false\n type: string\n default: \"\"\n extra_dependencies:\n description: 'extra dependencies'\n required: false\n type: string\n default: \"\"\n cu:\n description: 'cuda version'\n required: true\n type: string\n default: \"130\"\n\n python_minor:\n description: 'python minor version'\n required: true\n type: string\n default: \"13\"\n\n python_patch:\n description: 'python patch version'\n required: true\n type: string\n default: \"11\"\n# push:\n# branches:\n# - master\n\njobs:\n build_dependencies:\n runs-on: windows-latest\n steps:\n - uses: actions/checkout@v4\n - uses: actions/setup-python@v5\n with:\n python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}\n\n - shell: bash\n run: |\n echo \"@echo off\n call update_comfyui.bat nopause\n echo -\n echo This will try to update pytorch and all python dependencies.\n echo -\n echo If you just want to update normally, close this and run update_comfyui.bat instead.\n echo -\n pause\n ..\\python_embeded\\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2\n pause\" > update_comfyui_and_python_dependencies.bat\n\n grep -v comfyui requirements.txt > requirements_nocomfyui.txt\n python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir\n python -m pip install --no-cache-dir ./temp_wheel_dir/*\n echo installed basic\n ls -lah temp_wheel_dir\n mv temp_wheel_dir cu${{ inputs.cu }}_python_deps\n tar cf cu${{ inputs.cu }}_python_deps.tar cu${{ inputs.cu }}_python_deps\n\n - uses: actions/cache/save@v4\n with:\n path: |\n cu${{ inputs.cu }}_python_deps.tar\n update_comfyui_and_python_dependencies.bat\n key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}\n" }, { "path": ".github/workflows/windows_release_dependencies_manual.yml", "content": "name: \"Windows Release dependencies Manual\"\n\non:\n workflow_dispatch:\n inputs:\n torch_dependencies:\n description: 'torch dependencies'\n required: false\n type: string\n default: \"torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128\"\n cache_tag:\n description: 'Cached dependencies tag'\n required: true\n type: string\n default: \"cu128\"\n\n python_minor:\n description: 'python minor version'\n required: true\n type: string\n default: \"12\"\n\n python_patch:\n description: 'python patch version'\n required: true\n type: string\n default: \"10\"\n\njobs:\n build_dependencies:\n runs-on: windows-latest\n steps:\n - uses: actions/checkout@v4\n - uses: actions/setup-python@v5\n with:\n python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}\n\n - shell: bash\n run: |\n echo \"@echo off\n call update_comfyui.bat nopause\n echo -\n echo This will try to update pytorch and all python dependencies.\n echo -\n echo If you just want to update normally, close this and run update_comfyui.bat instead.\n echo -\n pause\n ..\\python_embeded\\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2\n pause\" > update_comfyui_and_python_dependencies.bat\n\n grep -v comfyui requirements.txt > requirements_nocomfyui.txt\n python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir\n python -m pip install --no-cache-dir ./temp_wheel_dir/*\n echo installed basic\n ls -lah temp_wheel_dir\n mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps\n tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps\n\n - uses: actions/cache/save@v4\n with:\n path: |\n ${{ inputs.cache_tag }}_python_deps.tar\n update_comfyui_and_python_dependencies.bat\n key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}\n" }, { "path": ".github/workflows/windows_release_nightly_pytorch.yml", "content": "name: \"Windows Release Nightly pytorch\"\n\non:\n workflow_dispatch:\n inputs:\n cu:\n description: 'cuda version'\n required: true\n type: string\n default: \"129\"\n\n python_minor:\n description: 'python minor version'\n required: true\n type: string\n default: \"13\"\n\n python_patch:\n description: 'python patch version'\n required: true\n type: string\n default: \"5\"\n# push:\n# branches:\n# - master\n\njobs:\n build:\n permissions:\n contents: \"write\"\n packages: \"write\"\n pull-requests: \"read\"\n runs-on: windows-latest\n steps:\n - uses: actions/checkout@v4\n with:\n fetch-depth: 30\n persist-credentials: false\n - uses: actions/setup-python@v5\n with:\n python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}\n - shell: bash\n run: |\n cd ..\n cp -r ComfyUI ComfyUI_copy\n curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip\n unzip python_embeded.zip -d python_embeded\n cd python_embeded\n echo 'import site' >> ./python3${{ inputs.python_minor }}._pth\n curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py\n ./python.exe get-pip.py\n python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir\n ls ../temp_wheel_dir\n ./python.exe -s -m pip install --pre ../temp_wheel_dir/*\n sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth\n\n rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space\n cd ..\n\n git clone --depth 1 https://github.com/comfyanonymous/taesd\n cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/\n\n mkdir ComfyUI_windows_portable_nightly_pytorch\n mv python_embeded ComfyUI_windows_portable_nightly_pytorch\n mv ComfyUI_copy ComfyUI_windows_portable_nightly_pytorch/ComfyUI\n\n cd ComfyUI_windows_portable_nightly_pytorch\n\n mkdir update\n cp -r ComfyUI/.ci/update_windows/* ./update/\n cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./\n cp -r ComfyUI/.ci/windows_nightly_base_files/* ./\n\n echo \"call update_comfyui.bat nopause\n ..\\python_embeded\\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2\n pause\" > ./update/update_comfyui_and_python_dependencies.bat\n cd ..\n\n \"C:\\Program Files\\7-Zip\\7z.exe\" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch\n mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z\n\n cd ComfyUI_windows_portable_nightly_pytorch\n python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu\n\n ls\n\n - name: Upload binaries to release\n uses: svenstaro/upload-release-action@v2\n with:\n repo_token: ${{ secrets.GITHUB_TOKEN }}\n file: ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z\n tag: \"latest\"\n overwrite: true\n" }, { "path": ".github/workflows/windows_release_package.yml", "content": "name: \"Windows Release packaging\"\n\non:\n workflow_dispatch:\n inputs:\n cu:\n description: 'cuda version'\n required: true\n type: string\n default: \"129\"\n\n python_minor:\n description: 'python minor version'\n required: true\n type: string\n default: \"13\"\n\n python_patch:\n description: 'python patch version'\n required: true\n type: string\n default: \"6\"\n# push:\n# branches:\n# - master\n\njobs:\n package_comfyui:\n permissions:\n contents: \"write\"\n packages: \"write\"\n pull-requests: \"read\"\n runs-on: windows-latest\n steps:\n - uses: actions/cache/restore@v4\n id: cache\n with:\n path: |\n cu${{ inputs.cu }}_python_deps.tar\n update_comfyui_and_python_dependencies.bat\n key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}\n - shell: bash\n run: |\n mv cu${{ inputs.cu }}_python_deps.tar ../\n mv update_comfyui_and_python_dependencies.bat ../\n cd ..\n tar xf cu${{ inputs.cu }}_python_deps.tar\n pwd\n ls\n\n - uses: actions/checkout@v4\n with:\n fetch-depth: 150\n persist-credentials: false\n - shell: bash\n run: |\n cd ..\n cp -r ComfyUI ComfyUI_copy\n curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip\n unzip python_embeded.zip -d python_embeded\n cd python_embeded\n echo 'import site' >> ./python3${{ inputs.python_minor }}._pth\n curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py\n ./python.exe get-pip.py\n ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*\n sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth\n\n rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space\n rm ./Lib/site-packages/torch/lib/libprotoc.lib\n rm ./Lib/site-packages/torch/lib/libprotobuf.lib\n cd ..\n\n git clone --depth 1 https://github.com/comfyanonymous/taesd\n cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/\n\n mkdir ComfyUI_windows_portable\n mv python_embeded ComfyUI_windows_portable\n mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI\n\n cd ComfyUI_windows_portable\n\n mkdir update\n cp -r ComfyUI/.ci/update_windows/* ./update/\n cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./\n cp ../update_comfyui_and_python_dependencies.bat ./update/\n\n cd ..\n\n \"C:\\Program Files\\7-Zip\\7z.exe\" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable\n mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z\n\n cd ComfyUI_windows_portable\n python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu\n\n python_embeded/python.exe -s ./update/update.py ComfyUI/\n\n ls\n\n - name: Upload binaries to release\n uses: svenstaro/upload-release-action@v2\n with:\n repo_token: ${{ secrets.GITHUB_TOKEN }}\n file: new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z\n tag: \"latest\"\n overwrite: true\n\n" }, { "path": ".gitignore", "content": "__pycache__/\n*.py[cod]\n/output/\n/input/\n!/input/example.png\n/models/\n/temp/\n/custom_nodes/\n!custom_nodes/example_node.py.example\nextra_model_paths.yaml\n/.vs\n.vscode/\n.idea/\nvenv*/\n.venv/\n/web/extensions/*\n!/web/extensions/logging.js.example\n!/web/extensions/core/\n/tests-ui/data/object_info.json\n/user/\n*.log\nweb_custom_versions/\n.DS_Store\nopenapi.yaml\nfiltered-openapi.yaml\nuv.lock\n" }, { "path": "CODEOWNERS", "content": "# Admins\n* @comfyanonymous @kosinkadink @guill\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing to ComfyUI\n\nWelcome, and thank you for your interest in contributing to ComfyUI!\n\nThere are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.\n\n## Asking Questions\n\nHave a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.\n\n## Providing Feedback\n\nYour comments and feedback are welcome, and the development team is available via a handful of different channels.\n\nSee the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.\n\n## Reporting Issues\n\nHave you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.\n\n\n### Look For an Existing Issue\n\nBefore you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.\n\nIf you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a \"+1\" comment:\n\n* 👍 - upvote\n* 👎 - downvote\n\nIf you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.\n\n\n### Creating Pull Requests\n\n* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.\n\n\n## Thank You\n\nYour contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.\n" }, { "path": "LICENSE", "content": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \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. 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If not, see .\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 Copyright (C) \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.\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.\n" }, { "path": "QUANTIZATION.md", "content": "# The Comfy guide to Quantization\n\n\n## How does quantization work?\n\nQuantization aims to map a high-precision value x_f to a lower precision format with minimal loss in accuracy. These smaller formats then serve to reduce the models memory footprint and increase throughput by using specialized hardware.\n\nWhen simply converting a value from FP16 to FP8 using the round-nearest method we might hit two issues:\n- The dynamic range of FP16 (-65,504, 65,504) far exceeds FP8 formats like E4M3 (-448, 448) or E5M2 (-57,344, 57,344), potentially resulting in clipped values\n- The original values are concentrated in a small range (e.g. -1,1) leaving many FP8-bits \"unused\"\n\nBy using a scaling factor, we aim to map these values into the quantized-dtype range, making use of the full spectrum. One of the easiest approaches, and common, is using per-tensor absolute-maximum scaling.\n\n```\nabsmax = max(abs(tensor))\nscale = amax / max_dynamic_range_low_precision\n\n# Quantization\ntensor_q = (tensor / scale).to(low_precision_dtype)\n\n# De-Quantization\ntensor_dq = tensor_q.to(fp16) * scale\n\ntensor_dq ~ tensor\n```\n\nGiven that additional information (scaling factor) is needed to \"interpret\" the quantized values, we describe those as derived datatypes.\n\n\n## Quantization in Comfy\n\n```\nQuantizedTensor (torch.Tensor subclass)\n ↓ __torch_dispatch__\nTwo-Level Registry (generic + layout handlers)\n ↓\nMixedPrecisionOps + Metadata Detection\n```\n\n### Representation\n\nTo represent these derived datatypes, ComfyUI uses a subclass of torch.Tensor to implements these using the `QuantizedTensor` class found in `comfy/quant_ops.py`\n\nA `Layout` class defines how a specific quantization format behaves:\n- Required parameters\n- Quantize method\n- De-Quantize method\n\n```python\nfrom comfy.quant_ops import QuantizedLayout\n\nclass MyLayout(QuantizedLayout):\n @classmethod\n def quantize(cls, tensor, **kwargs):\n # Convert to quantized format\n qdata = ...\n params = {'scale': ..., 'orig_dtype': tensor.dtype}\n return qdata, params\n \n @staticmethod\n def dequantize(qdata, scale, orig_dtype, **kwargs):\n return qdata.to(orig_dtype) * scale\n```\n\nTo then run operations using these QuantizedTensors we use two registry systems to define supported operations. \nThe first is a **generic registry** that handles operations common to all quantized formats (e.g., `.to()`, `.clone()`, `.reshape()`).\n\nThe second registry is layout-specific and allows to implement fast-paths like nn.Linear.\n```python\nfrom comfy.quant_ops import register_layout_op\n\n@register_layout_op(torch.ops.aten.linear.default, MyLayout)\ndef my_linear(func, args, kwargs):\n # Extract tensors, call optimized kernel\n ...\n```\nWhen `torch.nn.functional.linear()` is called with QuantizedTensor arguments, `__torch_dispatch__` automatically routes to the registered implementation.\nFor any unsupported operation, QuantizedTensor will fallback to call `dequantize` and dispatch using the high-precision implementation.\n\n\n### Mixed Precision\n\nThe `MixedPrecisionOps` class (lines 542-648 in `comfy/ops.py`) enables per-layer quantization decisions, allowing different layers in a model to use different precisions. This is activated when a model config contains a `layer_quant_config` dictionary that specifies which layers should be quantized and how.\n\n**Architecture:**\n\n```python\nclass MixedPrecisionOps(disable_weight_init):\n _layer_quant_config = {} # Maps layer names to quantization configs\n _compute_dtype = torch.bfloat16 # Default compute / dequantize precision\n```\n\n**Key mechanism:**\n\nThe custom `Linear._load_from_state_dict()` method inspects each layer during model loading:\n- If the layer name is **not** in `_layer_quant_config`: load weight as regular tensor in `_compute_dtype`\n- If the layer name **is** in `_layer_quant_config`: \n - Load weight as `QuantizedTensor` with the specified layout (e.g., `TensorCoreFP8Layout`)\n - Load associated quantization parameters (scales, block_size, etc.)\n\n**Why it's needed:**\n\nNot all layers tolerate quantization equally. Sensitive operations like final projections can be kept in higher precision, while compute-heavy matmuls are quantized. This provides most of the performance benefits while maintaining quality.\n\nThe system is selected in `pick_operations()` when `model_config.layer_quant_config` is present, making it the highest-priority operation mode.\n\n\n## Checkpoint Format\n\nQuantized checkpoints are stored as standard safetensors files with quantized weight tensors and associated scaling parameters, plus a `_quantization_metadata` JSON entry describing the quantization scheme.\n\nThe quantized checkpoint will contain the same layers as the original checkpoint but:\n- The weights are stored as quantized values, sometimes using a different storage datatype. E.g. uint8 container for fp8.\n- For each quantized weight a number of additional scaling parameters are stored alongside depending on the recipe.\n- We store a metadata.json in the metadata of the final safetensor containing the `_quantization_metadata` describing which layers are quantized and what layout has been used.\n\n### Scaling Parameters details\nWe define 4 possible scaling parameters that should cover most recipes in the near-future:\n- **weight_scale**: quantization scalers for the weights\n- **weight_scale_2**: global scalers in the context of double scaling\n- **pre_quant_scale**: scalers used for smoothing salient weights\n- **input_scale**: quantization scalers for the activations\n\n| Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale |\n|--------|---------------|--------------|----------------|-----------------|-------------|\n| float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) |\n\nYou can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS).\n\n### Quantization Metadata\n\nThe metadata stored alongside the checkpoint contains:\n- **format_version**: String to define a version of the standard\n- **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`. \n\nExample:\n```json\n{\n \"_quantization_metadata\": {\n \"format_version\": \"1.0\",\n \"layers\": {\n \"model.layers.0.mlp.up_proj\": \"float8_e4m3fn\",\n \"model.layers.0.mlp.down_proj\": \"float8_e4m3fn\",\n \"model.layers.1.mlp.up_proj\": \"float8_e4m3fn\"\n }\n }\n}\n```\n\n\n## Creating Quantized Checkpoints\n\nTo create compatible checkpoints, use any quantization tool provided the output follows the checkpoint format described above and uses a layout defined in `QUANT_ALGOS`.\n\n### Weight Quantization\n\nWeight quantization is straightforward - compute the scaling factor directly from the weight tensor using the absolute maximum method described earlier. Each layer's weights are quantized independently and stored with their corresponding `weight_scale` parameter.\n\n### Calibration (for Activation Quantization)\n\nActivation quantization (e.g., for FP8 Tensor Core operations) requires `input_scale` parameters that cannot be determined from static weights alone. Since activation values depend on actual inputs, we use **post-training calibration (PTQ)**:\n\n1. **Collect statistics**: Run inference on N representative samples\n2. **Track activations**: Record the absolute maximum (`amax`) of inputs to each quantized layer\n3. **Compute scales**: Derive `input_scale` from collected statistics\n4. **Store in checkpoint**: Save `input_scale` parameters alongside weights\n\nThe calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters." }, { "path": "README.md", "content": "
\n\n# ComfyUI\n**The most powerful and modular visual AI engine and application.**\n\n\n[![Website][website-shield]][website-url]\n[![Dynamic JSON Badge][discord-shield]][discord-url]\n[![Twitter][twitter-shield]][twitter-url]\n[![Matrix][matrix-shield]][matrix-url]\n
\n[![][github-release-shield]][github-release-link]\n[![][github-release-date-shield]][github-release-link]\n[![][github-downloads-shield]][github-downloads-link]\n[![][github-downloads-latest-shield]][github-downloads-link]\n\n[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white\n[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org\n[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat\n[website-url]: https://www.comfy.org/\n\n[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total\n[discord-url]: https://www.comfy.org/discord\n[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI\n[twitter-url]: https://x.com/ComfyUI\n\n[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver\n[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases\n[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat\n[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat\n[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest\n[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases\n\n![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)\n
\n\nComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.\n\n## Get Started\n\n### Local\n\n#### [Desktop Application](https://www.comfy.org/download)\n- The easiest way to get started.\n- Available on Windows & macOS.\n\n#### [Windows Portable Package](#installing)\n- Get the latest commits and completely portable.\n- Available on Windows.\n\n#### [Manual Install](#manual-install-windows-linux)\nSupports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).\n\n### Cloud\n\n#### [Comfy Cloud](https://www.comfy.org/cloud)\n- Our official paid cloud version for those who can't afford local hardware.\n\n## Examples\nSee what ComfyUI can do with the [newer template workflows](https://comfy.org/workflows) or old [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).\n\n## Features\n- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.\n- Image Models\n - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))\n - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)\n - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)\n - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)\n - Pixart Alpha and Sigma\n - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)\n - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)\n - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)\n - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)\n - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)\n - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)\n - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)\n - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)\n - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)\n- Image Editing Models\n - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)\n - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)\n - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)\n - [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model)\n- Video Models\n - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)\n - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)\n - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)\n - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)\n - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)\n - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)\n - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)\n- Audio Models\n - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)\n - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)\n- 3D Models\n - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)\n- Asynchronous Queue system\n- Many optimizations: Only re-executes the parts of the workflow that changes between executions.\n- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.\n- Works even if you don't have a GPU with: ```--cpu``` (slow)\n- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.\n- Safe loading of ckpt, pt, pth, etc.. files.\n- Embeddings/Textual inversion\n- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)\n- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)\n- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.\n- Saving/Loading workflows as Json files.\n- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.\n- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)\n- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.\n- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)\n- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)\n- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)\n- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)\n- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)\n- Latent previews with [TAESD](#how-to-show-high-quality-previews)\n- Works fully offline: core will never download anything unless you want to.\n- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview) disable with: `--disable-api-nodes`\n- [Config file](extra_model_paths.yaml.example) to set the search paths for models.\n\nWorkflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)\n\n## Release Process\n\nComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:\n\n1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**\n - Releases a new stable version (e.g., v0.7.0) roughly every week.\n - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.\n - Minor versions will be used for releases off the master branch.\n - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.\n - Commits outside of the stable release tags may be very unstable and break many custom nodes.\n - Serves as the foundation for the desktop release\n\n2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**\n - Builds a new release using the latest stable core version\n\n3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**\n - Weekly frontend updates are merged into the core repository\n - Features are frozen for the upcoming core release\n - Development continues for the next release cycle\n\n## Shortcuts\n\n| Keybind | Explanation |\n|------------------------------------|--------------------------------------------------------------------------------------------------------------------|\n| `Ctrl` + `Enter` | Queue up current graph for generation |\n| `Ctrl` + `Shift` + `Enter` | Queue up current graph as first for generation |\n| `Ctrl` + `Alt` + `Enter` | Cancel current generation |\n| `Ctrl` + `Z`/`Ctrl` + `Y` | Undo/Redo |\n| `Ctrl` + `S` | Save workflow |\n| `Ctrl` + `O` | Load workflow |\n| `Ctrl` + `A` | Select all nodes |\n| `Alt `+ `C` | Collapse/uncollapse selected nodes |\n| `Ctrl` + `M` | Mute/unmute selected nodes |\n| `Ctrl` + `B` | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |\n| `Delete`/`Backspace` | Delete selected nodes |\n| `Ctrl` + `Backspace` | Delete the current graph |\n| `Space` | Move the canvas around when held and moving the cursor |\n| `Ctrl`/`Shift` + `Click` | Add clicked node to selection |\n| `Ctrl` + `C`/`Ctrl` + `V` | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |\n| `Ctrl` + `C`/`Ctrl` + `Shift` + `V` | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |\n| `Shift` + `Drag` | Move multiple selected nodes at the same time |\n| `Ctrl` + `D` | Load default graph |\n| `Alt` + `+` | Canvas Zoom in |\n| `Alt` + `-` | Canvas Zoom out |\n| `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out |\n| `P` | Pin/Unpin selected nodes |\n| `Ctrl` + `G` | Group selected nodes |\n| `Q` | Toggle visibility of the queue |\n| `H` | Toggle visibility of history |\n| `R` | Refresh graph |\n| `F` | Show/Hide menu |\n| `.` | Fit view to selection (Whole graph when nothing is selected) |\n| Double-Click LMB | Open node quick search palette |\n| `Shift` + Drag | Move multiple wires at once |\n| `Ctrl` + `Alt` + LMB | Disconnect all wires from clicked slot |\n\n`Ctrl` can also be replaced with `Cmd` instead for macOS users\n\n# Installing\n\n## Windows Portable\n\nThere is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).\n\n### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)\n\nSimply download, extract with [7-Zip](https://7-zip.org) or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\\models\\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\\models\\\n\nIf you have trouble extracting it, right click the file -> properties -> unblock\n\nThe portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.\n\n#### Alternative Downloads:\n\n[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)\n\n[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).\n\n#### How do I share models between another UI and ComfyUI?\n\nSee the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.\n\n\n## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)\n\nYou can install and start ComfyUI using comfy-cli:\n```bash\npip install comfy-cli\ncomfy install\n```\n\n## Manual Install (Windows, Linux)\n\nPython 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported.\n\nPython 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12\n\ntorch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.\n\n### Instructions:\n\nGit clone this repo.\n\nPut your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints\n\nPut your VAE in: models/vae\n\n\n### AMD GPUs (Linux)\n\nAMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:\n\n```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```\n\nThis is the command to install the nightly with ROCm 7.2 which might have some performance improvements:\n\n```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2```\n\n\n### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.\n\nThese have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.\n\nRDNA 3 (RX 7000 series):\n\n```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/```\n\nRDNA 3.5 (Strix halo/Ryzen AI Max+ 365):\n\n```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```\n\nRDNA 4 (RX 9000 series):\n\n```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```\n\n### Intel GPUs (Windows and Linux)\n\nIntel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)\n\n1. To install PyTorch xpu, use the following command:\n\n```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```\n\nThis is the command to install the Pytorch xpu nightly which might have some performance improvements:\n\n```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```\n\n### NVIDIA\n\nNvidia users should install stable pytorch using this command:\n\n```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```\n\nThis is the command to install pytorch nightly instead which might have performance improvements.\n\n```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```\n\n#### Troubleshooting\n\nIf you get the \"Torch not compiled with CUDA enabled\" error, uninstall torch with:\n\n```pip uninstall torch```\n\nAnd install it again with the command above.\n\n### Dependencies\n\nInstall the dependencies by opening your terminal inside the ComfyUI folder and:\n\n```pip install -r requirements.txt```\n\nAfter this you should have everything installed and can proceed to running ComfyUI.\n\n### Others:\n\n#### Apple Mac silicon\n\nYou can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.\n\n1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).\n1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.\n1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).\n1. Launch ComfyUI by running `python main.py`\n\n> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).\n\n#### Ascend NPUs\n\nFor models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:\n\n1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.\n2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.\n3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.\n4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.\n\n#### Cambricon MLUs\n\nFor models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:\n\n1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)\n2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)\n3. Launch ComfyUI by running `python main.py`\n\n#### Iluvatar Corex\n\nFor models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:\n\n1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)\n2. Launch ComfyUI by running `python main.py`\n\n\n## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)\n\n**ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.\n\n### Setup\n\n1. Install the manager dependencies:\n ```bash\n pip install -r manager_requirements.txt\n ```\n\n2. Enable the manager with the `--enable-manager` flag when running ComfyUI:\n ```bash\n python main.py --enable-manager\n ```\n\n### Command Line Options\n\n| Flag | Description |\n|------|-------------|\n| `--enable-manager` | Enable ComfyUI-Manager |\n| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |\n| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |\n\n\n# Running\n\n```python main.py```\n\n### For AMD cards not officially supported by ROCm\n\nTry running it with this command if you have issues:\n\nFor 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```\n\nFor AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```\n\n### AMD ROCm Tips\n\nYou can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.\n\n```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```\n\nYou can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.\n\n# Notes\n\nOnly parts of the graph that have an output with all the correct inputs will be executed.\n\nOnly parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.\n\nDragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.\n\nYou can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\\\( or \\\\).\n\nYou can use {day|night}, for wildcard/dynamic prompts. With this syntax \"{wild|card|test}\" will be randomly replaced by either \"wild\", \"card\" or \"test\" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\\\{ or \\\\}.\n\nDynamic prompts also support C-style comments, like `// comment` or `/* comment */`.\n\nTo use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):\n\n```embedding:embedding_filename.pt```\n\n\n## How to show high-quality previews?\n\nUse ```--preview-method auto``` to enable previews.\n\nThe default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.\n\n## How to use TLS/SSL?\nGenerate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj \"/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname\"`\n\nUse `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.\n\n> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.\n

If you use a container, note that the volume mount `-v` can be a relative path so `... -v \".\\:/openssl-certs\" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.\n\n## Support and dev channel\n\n[Discord](https://comfy.org/discord): Try the #help or #feedback channels.\n\n[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).\n\nSee also: [https://www.comfy.org/](https://www.comfy.org/)\n\n## Frontend Development\n\nAs of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.\n\n### Reporting Issues and Requesting Features\n\nFor any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently.\n\n### Using the Latest Frontend\n\nThe new frontend is now the default for ComfyUI. However, please note:\n\n1. The frontend in the main ComfyUI repository is updated fortnightly.\n2. Daily releases are available in the separate frontend repository.\n\nTo use the most up-to-date frontend version:\n\n1. For the latest daily release, launch ComfyUI with this command line argument:\n\n ```\n --front-end-version Comfy-Org/ComfyUI_frontend@latest\n ```\n\n2. For a specific version, replace `latest` with the desired version number:\n\n ```\n --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2\n ```\n\nThis approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.\n\n### Accessing the Legacy Frontend\n\nIf you need to use the legacy frontend for any reason, you can access it using the following command line argument:\n\n```\n--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest\n```\n\nThis will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).\n\n# QA\n\n### Which GPU should I buy for this?\n\n[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)\n" }, { "path": "alembic.ini", "content": "# A generic, single database configuration.\n\n[alembic]\n# path to migration scripts\n# Use forward slashes (/) also on windows to provide an os agnostic path\nscript_location = alembic_db\n\n# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s\n# Uncomment the line below if you want the files to be prepended with date and time\n# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file\n# for all available tokens\n# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s\n\n# sys.path path, will be prepended to sys.path if present.\n# defaults to the current working directory.\nprepend_sys_path = .\n\n# timezone to use when rendering the date within the migration file\n# as well as the filename.\n# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.\n# Any required deps can installed by adding `alembic[tz]` to the pip requirements\n# string value is passed to ZoneInfo()\n# leave blank for localtime\n# timezone =\n\n# max length of characters to apply to the \"slug\" field\n# truncate_slug_length = 40\n\n# set to 'true' to run the environment during\n# the 'revision' command, regardless of autogenerate\n# revision_environment = false\n\n# set to 'true' to allow .pyc and .pyo files without\n# a source .py file to be detected as revisions in the\n# versions/ directory\n# sourceless = false\n\n# version location specification; This defaults\n# to alembic_db/versions. When using multiple version\n# directories, initial revisions must be specified with --version-path.\n# The path separator used here should be the separator specified by \"version_path_separator\" below.\n# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions\n\n# version path separator; As mentioned above, this is the character used to split\n# version_locations. The default within new alembic.ini files is \"os\", which uses os.pathsep.\n# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.\n# Valid values for version_path_separator are:\n#\n# version_path_separator = :\n# version_path_separator = ;\n# version_path_separator = space\n# version_path_separator = newline\n#\n# Use os.pathsep. Default configuration used for new projects.\nversion_path_separator = os\n\n# set to 'true' to search source files recursively\n# in each \"version_locations\" directory\n# new in Alembic version 1.10\n# recursive_version_locations = false\n\n# the output encoding used when revision files\n# are written from script.py.mako\n# output_encoding = utf-8\n\nsqlalchemy.url = sqlite:///user/comfyui.db\n\n\n[post_write_hooks]\n# post_write_hooks defines scripts or Python functions that are run\n# on newly generated revision scripts. See the documentation for further\n# detail and examples\n\n# format using \"black\" - use the console_scripts runner, against the \"black\" entrypoint\n# hooks = black\n# black.type = console_scripts\n# black.entrypoint = black\n# black.options = -l 79 REVISION_SCRIPT_FILENAME\n\n# lint with attempts to fix using \"ruff\" - use the exec runner, execute a binary\n# hooks = ruff\n# ruff.type = exec\n# ruff.executable = %(here)s/.venv/bin/ruff\n# ruff.options = check --fix REVISION_SCRIPT_FILENAME\n" }, { "path": "alembic_db/README.md", "content": "## Generate new revision\n\n1. Update models in `/app/database/models.py`\n2. Run `alembic revision --autogenerate -m \"{your message}\"`\n" }, { "path": "alembic_db/env.py", "content": "from sqlalchemy import engine_from_config\nfrom sqlalchemy import pool\n\nfrom alembic import context\n\n# this is the Alembic Config object, which provides\n# access to the values within the .ini file in use.\nconfig = context.config\n\n\nfrom app.database.models import Base, NAMING_CONVENTION\ntarget_metadata = Base.metadata\n\n# other values from the config, defined by the needs of env.py,\n# can be acquired:\n# my_important_option = config.get_main_option(\"my_important_option\")\n# ... etc.\n\n\ndef run_migrations_offline() -> None:\n \"\"\"Run migrations in 'offline' mode.\n This configures the context with just a URL\n and not an Engine, though an Engine is acceptable\n here as well. By skipping the Engine creation\n we don't even need a DBAPI to be available.\n Calls to context.execute() here emit the given string to the\n script output.\n \"\"\"\n url = config.get_main_option(\"sqlalchemy.url\")\n context.configure(\n url=url,\n target_metadata=target_metadata,\n literal_binds=True,\n dialect_opts={\"paramstyle\": \"named\"},\n )\n\n with context.begin_transaction():\n context.run_migrations()\n\n\ndef run_migrations_online() -> None:\n \"\"\"Run migrations in 'online' mode.\n In this scenario we need to create an Engine\n and associate a connection with the context.\n \"\"\"\n connectable = engine_from_config(\n config.get_section(config.config_ini_section, {}),\n prefix=\"sqlalchemy.\",\n poolclass=pool.NullPool,\n )\n\n with connectable.connect() as connection:\n context.configure(\n connection=connection,\n target_metadata=target_metadata,\n render_as_batch=True,\n naming_convention=NAMING_CONVENTION,\n )\n\n with context.begin_transaction():\n context.run_migrations()\n\n\nif context.is_offline_mode():\n run_migrations_offline()\nelse:\n run_migrations_online()\n" }, { "path": "alembic_db/script.py.mako", "content": "\"\"\"${message}\n\nRevision ID: ${up_revision}\nRevises: ${down_revision | comma,n}\nCreate Date: ${create_date}\n\n\"\"\"\nfrom typing import Sequence, Union\n\nfrom alembic import op\nimport sqlalchemy as sa\n${imports if imports else \"\"}\n\n# revision identifiers, used by Alembic.\nrevision: str = ${repr(up_revision)}\ndown_revision: Union[str, None] = ${repr(down_revision)}\nbranch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}\ndepends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}\n\n\ndef upgrade() -> None:\n \"\"\"Upgrade schema.\"\"\"\n ${upgrades if upgrades else \"pass\"}\n\n\ndef downgrade() -> None:\n \"\"\"Downgrade schema.\"\"\"\n ${downgrades if downgrades else \"pass\"}\n" }, { "path": "alembic_db/versions/0001_assets.py", "content": "\"\"\"\nInitial assets schema\nRevision ID: 0001_assets\nRevises: None\nCreate Date: 2025-12-10 00:00:00\n\"\"\"\n\nfrom alembic import op\nimport sqlalchemy as sa\n\nrevision = \"0001_assets\"\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ASSETS: content identity\n op.create_table(\n \"assets\",\n sa.Column(\"id\", sa.String(length=36), primary_key=True),\n sa.Column(\"hash\", sa.String(length=256), nullable=True),\n sa.Column(\"size_bytes\", sa.BigInteger(), nullable=False, server_default=\"0\"),\n sa.Column(\"mime_type\", sa.String(length=255), nullable=True),\n sa.Column(\"created_at\", sa.DateTime(timezone=False), nullable=False),\n sa.CheckConstraint(\"size_bytes >= 0\", name=\"ck_assets_size_nonneg\"),\n )\n op.create_index(\"uq_assets_hash\", \"assets\", [\"hash\"], unique=True)\n op.create_index(\"ix_assets_mime_type\", \"assets\", [\"mime_type\"])\n\n # ASSETS_INFO: user-visible references\n op.create_table(\n \"assets_info\",\n sa.Column(\"id\", sa.String(length=36), primary_key=True),\n sa.Column(\"owner_id\", sa.String(length=128), nullable=False, server_default=\"\"),\n sa.Column(\"name\", sa.String(length=512), nullable=False),\n sa.Column(\"asset_id\", sa.String(length=36), sa.ForeignKey(\"assets.id\", ondelete=\"RESTRICT\"), nullable=False),\n sa.Column(\"preview_id\", sa.String(length=36), sa.ForeignKey(\"assets.id\", ondelete=\"SET NULL\"), nullable=True),\n sa.Column(\"user_metadata\", sa.JSON(), nullable=True),\n sa.Column(\"created_at\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"updated_at\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"last_access_time\", sa.DateTime(timezone=False), nullable=False),\n sa.UniqueConstraint(\"asset_id\", \"owner_id\", \"name\", name=\"uq_assets_info_asset_owner_name\"),\n )\n op.create_index(\"ix_assets_info_owner_id\", \"assets_info\", [\"owner_id\"])\n op.create_index(\"ix_assets_info_asset_id\", \"assets_info\", [\"asset_id\"])\n op.create_index(\"ix_assets_info_name\", \"assets_info\", [\"name\"])\n op.create_index(\"ix_assets_info_created_at\", \"assets_info\", [\"created_at\"])\n op.create_index(\"ix_assets_info_last_access_time\", \"assets_info\", [\"last_access_time\"])\n op.create_index(\"ix_assets_info_owner_name\", \"assets_info\", [\"owner_id\", \"name\"])\n\n # TAGS: normalized tag vocabulary\n op.create_table(\n \"tags\",\n sa.Column(\"name\", sa.String(length=512), primary_key=True),\n sa.Column(\"tag_type\", sa.String(length=32), nullable=False, server_default=\"user\"),\n sa.CheckConstraint(\"name = lower(name)\", name=\"ck_tags_lowercase\"),\n )\n op.create_index(\"ix_tags_tag_type\", \"tags\", [\"tag_type\"])\n\n # ASSET_INFO_TAGS: many-to-many for tags on AssetInfo\n op.create_table(\n \"asset_info_tags\",\n sa.Column(\"asset_info_id\", sa.String(length=36), sa.ForeignKey(\"assets_info.id\", ondelete=\"CASCADE\"), nullable=False),\n sa.Column(\"tag_name\", sa.String(length=512), sa.ForeignKey(\"tags.name\", ondelete=\"RESTRICT\"), nullable=False),\n sa.Column(\"origin\", sa.String(length=32), nullable=False, server_default=\"manual\"),\n sa.Column(\"added_at\", sa.DateTime(timezone=False), nullable=False),\n sa.PrimaryKeyConstraint(\"asset_info_id\", \"tag_name\", name=\"pk_asset_info_tags\"),\n )\n op.create_index(\"ix_asset_info_tags_tag_name\", \"asset_info_tags\", [\"tag_name\"])\n op.create_index(\"ix_asset_info_tags_asset_info_id\", \"asset_info_tags\", [\"asset_info_id\"])\n\n # ASSET_CACHE_STATE: N:1 local cache rows per Asset\n op.create_table(\n \"asset_cache_state\",\n sa.Column(\"id\", sa.Integer(), primary_key=True, autoincrement=True),\n sa.Column(\"asset_id\", sa.String(length=36), sa.ForeignKey(\"assets.id\", ondelete=\"CASCADE\"), nullable=False),\n sa.Column(\"file_path\", sa.Text(), nullable=False), # absolute local path to cached file\n sa.Column(\"mtime_ns\", sa.BigInteger(), nullable=True),\n sa.Column(\"needs_verify\", sa.Boolean(), nullable=False, server_default=sa.text(\"false\")),\n sa.CheckConstraint(\"(mtime_ns IS NULL) OR (mtime_ns >= 0)\", name=\"ck_acs_mtime_nonneg\"),\n sa.UniqueConstraint(\"file_path\", name=\"uq_asset_cache_state_file_path\"),\n )\n op.create_index(\"ix_asset_cache_state_file_path\", \"asset_cache_state\", [\"file_path\"])\n op.create_index(\"ix_asset_cache_state_asset_id\", \"asset_cache_state\", [\"asset_id\"])\n\n # ASSET_INFO_META: typed KV projection of user_metadata for filtering/sorting\n op.create_table(\n \"asset_info_meta\",\n sa.Column(\"asset_info_id\", sa.String(length=36), sa.ForeignKey(\"assets_info.id\", ondelete=\"CASCADE\"), nullable=False),\n sa.Column(\"key\", sa.String(length=256), nullable=False),\n sa.Column(\"ordinal\", sa.Integer(), nullable=False, server_default=\"0\"),\n sa.Column(\"val_str\", sa.String(length=2048), nullable=True),\n sa.Column(\"val_num\", sa.Numeric(38, 10), nullable=True),\n sa.Column(\"val_bool\", sa.Boolean(), nullable=True),\n sa.Column(\"val_json\", sa.JSON(), nullable=True),\n sa.PrimaryKeyConstraint(\"asset_info_id\", \"key\", \"ordinal\", name=\"pk_asset_info_meta\"),\n )\n op.create_index(\"ix_asset_info_meta_key\", \"asset_info_meta\", [\"key\"])\n op.create_index(\"ix_asset_info_meta_key_val_str\", \"asset_info_meta\", [\"key\", \"val_str\"])\n op.create_index(\"ix_asset_info_meta_key_val_num\", \"asset_info_meta\", [\"key\", \"val_num\"])\n op.create_index(\"ix_asset_info_meta_key_val_bool\", \"asset_info_meta\", [\"key\", \"val_bool\"])\n\n # Tags vocabulary\n tags_table = sa.table(\n \"tags\",\n sa.column(\"name\", sa.String(length=512)),\n sa.column(\"tag_type\", sa.String()),\n )\n op.bulk_insert(\n tags_table,\n [\n {\"name\": \"models\", \"tag_type\": \"system\"},\n {\"name\": \"input\", \"tag_type\": \"system\"},\n {\"name\": \"output\", \"tag_type\": \"system\"},\n\n {\"name\": \"configs\", \"tag_type\": \"system\"},\n {\"name\": \"checkpoints\", \"tag_type\": \"system\"},\n {\"name\": \"loras\", \"tag_type\": \"system\"},\n {\"name\": \"vae\", \"tag_type\": \"system\"},\n {\"name\": \"text_encoders\", \"tag_type\": \"system\"},\n {\"name\": \"diffusion_models\", \"tag_type\": \"system\"},\n {\"name\": \"clip_vision\", \"tag_type\": \"system\"},\n {\"name\": \"style_models\", \"tag_type\": \"system\"},\n {\"name\": \"embeddings\", \"tag_type\": \"system\"},\n {\"name\": \"diffusers\", \"tag_type\": \"system\"},\n {\"name\": \"vae_approx\", \"tag_type\": \"system\"},\n {\"name\": \"controlnet\", \"tag_type\": \"system\"},\n {\"name\": \"gligen\", \"tag_type\": \"system\"},\n {\"name\": \"upscale_models\", \"tag_type\": \"system\"},\n {\"name\": \"hypernetworks\", \"tag_type\": \"system\"},\n {\"name\": \"photomaker\", \"tag_type\": \"system\"},\n {\"name\": \"classifiers\", \"tag_type\": \"system\"},\n\n {\"name\": \"encoder\", \"tag_type\": \"system\"},\n {\"name\": \"decoder\", \"tag_type\": \"system\"},\n\n {\"name\": \"missing\", \"tag_type\": \"system\"},\n {\"name\": \"rescan\", \"tag_type\": \"system\"},\n ],\n )\n\n\ndef downgrade() -> None:\n op.drop_index(\"ix_asset_info_meta_key_val_bool\", table_name=\"asset_info_meta\")\n op.drop_index(\"ix_asset_info_meta_key_val_num\", table_name=\"asset_info_meta\")\n op.drop_index(\"ix_asset_info_meta_key_val_str\", table_name=\"asset_info_meta\")\n op.drop_index(\"ix_asset_info_meta_key\", table_name=\"asset_info_meta\")\n op.drop_table(\"asset_info_meta\")\n\n op.drop_index(\"ix_asset_cache_state_asset_id\", table_name=\"asset_cache_state\")\n op.drop_index(\"ix_asset_cache_state_file_path\", table_name=\"asset_cache_state\")\n op.drop_constraint(\"uq_asset_cache_state_file_path\", table_name=\"asset_cache_state\")\n op.drop_table(\"asset_cache_state\")\n\n op.drop_index(\"ix_asset_info_tags_asset_info_id\", table_name=\"asset_info_tags\")\n op.drop_index(\"ix_asset_info_tags_tag_name\", table_name=\"asset_info_tags\")\n op.drop_table(\"asset_info_tags\")\n\n op.drop_index(\"ix_tags_tag_type\", table_name=\"tags\")\n op.drop_table(\"tags\")\n\n op.drop_constraint(\"uq_assets_info_asset_owner_name\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_owner_name\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_last_access_time\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_created_at\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_name\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_asset_id\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_owner_id\", table_name=\"assets_info\")\n op.drop_table(\"assets_info\")\n\n op.drop_index(\"uq_assets_hash\", table_name=\"assets\")\n op.drop_index(\"ix_assets_mime_type\", table_name=\"assets\")\n op.drop_table(\"assets\")\n" }, { "path": "alembic_db/versions/0002_merge_to_asset_references.py", "content": "\"\"\"\nMerge AssetInfo and AssetCacheState into unified asset_references table.\n\nThis migration drops old tables and creates the new unified schema.\nAll existing data is discarded.\n\nRevision ID: 0002_merge_to_asset_references\nRevises: 0001_assets\nCreate Date: 2025-02-11\n\"\"\"\n\nfrom alembic import op\nimport sqlalchemy as sa\n\nrevision = \"0002_merge_to_asset_references\"\ndown_revision = \"0001_assets\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # Drop old tables (order matters due to FK constraints)\n op.drop_index(\"ix_asset_info_meta_key_val_bool\", table_name=\"asset_info_meta\")\n op.drop_index(\"ix_asset_info_meta_key_val_num\", table_name=\"asset_info_meta\")\n op.drop_index(\"ix_asset_info_meta_key_val_str\", table_name=\"asset_info_meta\")\n op.drop_index(\"ix_asset_info_meta_key\", table_name=\"asset_info_meta\")\n op.drop_table(\"asset_info_meta\")\n\n op.drop_index(\"ix_asset_info_tags_asset_info_id\", table_name=\"asset_info_tags\")\n op.drop_index(\"ix_asset_info_tags_tag_name\", table_name=\"asset_info_tags\")\n op.drop_table(\"asset_info_tags\")\n\n op.drop_index(\"ix_asset_cache_state_asset_id\", table_name=\"asset_cache_state\")\n op.drop_index(\"ix_asset_cache_state_file_path\", table_name=\"asset_cache_state\")\n op.drop_table(\"asset_cache_state\")\n\n op.drop_index(\"ix_assets_info_owner_name\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_last_access_time\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_created_at\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_name\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_asset_id\", table_name=\"assets_info\")\n op.drop_index(\"ix_assets_info_owner_id\", table_name=\"assets_info\")\n op.drop_table(\"assets_info\")\n\n # Truncate assets table (cascades handled by dropping dependent tables first)\n op.execute(\"DELETE FROM assets\")\n\n # Create asset_references table\n op.create_table(\n \"asset_references\",\n sa.Column(\"id\", sa.String(length=36), primary_key=True),\n sa.Column(\n \"asset_id\",\n sa.String(length=36),\n sa.ForeignKey(\"assets.id\", ondelete=\"CASCADE\"),\n nullable=False,\n ),\n sa.Column(\"file_path\", sa.Text(), nullable=True),\n sa.Column(\"mtime_ns\", sa.BigInteger(), nullable=True),\n sa.Column(\n \"needs_verify\",\n sa.Boolean(),\n nullable=False,\n server_default=sa.text(\"false\"),\n ),\n sa.Column(\n \"is_missing\", sa.Boolean(), nullable=False, server_default=sa.text(\"false\")\n ),\n sa.Column(\"enrichment_level\", sa.Integer(), nullable=False, server_default=\"0\"),\n sa.Column(\"owner_id\", sa.String(length=128), nullable=False, server_default=\"\"),\n sa.Column(\"name\", sa.String(length=512), nullable=False),\n sa.Column(\n \"preview_id\",\n sa.String(length=36),\n sa.ForeignKey(\"assets.id\", ondelete=\"SET NULL\"),\n nullable=True,\n ),\n sa.Column(\"user_metadata\", sa.JSON(), nullable=True),\n sa.Column(\"created_at\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"updated_at\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"last_access_time\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"deleted_at\", sa.DateTime(timezone=False), nullable=True),\n sa.CheckConstraint(\n \"(mtime_ns IS NULL) OR (mtime_ns >= 0)\", name=\"ck_ar_mtime_nonneg\"\n ),\n sa.CheckConstraint(\n \"enrichment_level >= 0 AND enrichment_level <= 2\",\n name=\"ck_ar_enrichment_level_range\",\n ),\n )\n op.create_index(\n \"uq_asset_references_file_path\", \"asset_references\", [\"file_path\"], unique=True\n )\n op.create_index(\"ix_asset_references_asset_id\", \"asset_references\", [\"asset_id\"])\n op.create_index(\"ix_asset_references_owner_id\", \"asset_references\", [\"owner_id\"])\n op.create_index(\"ix_asset_references_name\", \"asset_references\", [\"name\"])\n op.create_index(\"ix_asset_references_is_missing\", \"asset_references\", [\"is_missing\"])\n op.create_index(\n \"ix_asset_references_enrichment_level\", \"asset_references\", [\"enrichment_level\"]\n )\n op.create_index(\"ix_asset_references_created_at\", \"asset_references\", [\"created_at\"])\n op.create_index(\n \"ix_asset_references_last_access_time\", \"asset_references\", [\"last_access_time\"]\n )\n op.create_index(\n \"ix_asset_references_owner_name\", \"asset_references\", [\"owner_id\", \"name\"]\n )\n op.create_index(\"ix_asset_references_deleted_at\", \"asset_references\", [\"deleted_at\"])\n\n # Create asset_reference_tags table\n op.create_table(\n \"asset_reference_tags\",\n sa.Column(\n \"asset_reference_id\",\n sa.String(length=36),\n sa.ForeignKey(\"asset_references.id\", ondelete=\"CASCADE\"),\n nullable=False,\n ),\n sa.Column(\n \"tag_name\",\n sa.String(length=512),\n sa.ForeignKey(\"tags.name\", ondelete=\"RESTRICT\"),\n nullable=False,\n ),\n sa.Column(\n \"origin\", sa.String(length=32), nullable=False, server_default=\"manual\"\n ),\n sa.Column(\"added_at\", sa.DateTime(timezone=False), nullable=False),\n sa.PrimaryKeyConstraint(\n \"asset_reference_id\", \"tag_name\", name=\"pk_asset_reference_tags\"\n ),\n )\n op.create_index(\n \"ix_asset_reference_tags_tag_name\", \"asset_reference_tags\", [\"tag_name\"]\n )\n op.create_index(\n \"ix_asset_reference_tags_asset_reference_id\",\n \"asset_reference_tags\",\n [\"asset_reference_id\"],\n )\n\n # Create asset_reference_meta table\n op.create_table(\n \"asset_reference_meta\",\n sa.Column(\n \"asset_reference_id\",\n sa.String(length=36),\n sa.ForeignKey(\"asset_references.id\", ondelete=\"CASCADE\"),\n nullable=False,\n ),\n sa.Column(\"key\", sa.String(length=256), nullable=False),\n sa.Column(\"ordinal\", sa.Integer(), nullable=False, server_default=\"0\"),\n sa.Column(\"val_str\", sa.String(length=2048), nullable=True),\n sa.Column(\"val_num\", sa.Numeric(38, 10), nullable=True),\n sa.Column(\"val_bool\", sa.Boolean(), nullable=True),\n sa.Column(\"val_json\", sa.JSON(), nullable=True),\n sa.PrimaryKeyConstraint(\n \"asset_reference_id\", \"key\", \"ordinal\", name=\"pk_asset_reference_meta\"\n ),\n )\n op.create_index(\"ix_asset_reference_meta_key\", \"asset_reference_meta\", [\"key\"])\n op.create_index(\n \"ix_asset_reference_meta_key_val_str\", \"asset_reference_meta\", [\"key\", \"val_str\"]\n )\n op.create_index(\n \"ix_asset_reference_meta_key_val_num\", \"asset_reference_meta\", [\"key\", \"val_num\"]\n )\n op.create_index(\n \"ix_asset_reference_meta_key_val_bool\",\n \"asset_reference_meta\",\n [\"key\", \"val_bool\"],\n )\n\n\ndef downgrade() -> None:\n \"\"\"Reverse 0002_merge_to_asset_references: drop new tables, recreate old schema.\n\n NOTE: Data is not recoverable. The upgrade discards all rows from the old\n tables and truncates assets. After downgrade the old schema will be empty.\n A filesystem rescan will repopulate data once the older code is running.\n \"\"\"\n # Drop new tables (order matters due to FK constraints)\n op.drop_index(\"ix_asset_reference_meta_key_val_bool\", table_name=\"asset_reference_meta\")\n op.drop_index(\"ix_asset_reference_meta_key_val_num\", table_name=\"asset_reference_meta\")\n op.drop_index(\"ix_asset_reference_meta_key_val_str\", table_name=\"asset_reference_meta\")\n op.drop_index(\"ix_asset_reference_meta_key\", table_name=\"asset_reference_meta\")\n op.drop_table(\"asset_reference_meta\")\n\n op.drop_index(\"ix_asset_reference_tags_asset_reference_id\", table_name=\"asset_reference_tags\")\n op.drop_index(\"ix_asset_reference_tags_tag_name\", table_name=\"asset_reference_tags\")\n op.drop_table(\"asset_reference_tags\")\n\n op.drop_index(\"ix_asset_references_deleted_at\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_owner_name\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_last_access_time\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_created_at\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_enrichment_level\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_is_missing\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_name\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_owner_id\", table_name=\"asset_references\")\n op.drop_index(\"ix_asset_references_asset_id\", table_name=\"asset_references\")\n op.drop_index(\"uq_asset_references_file_path\", table_name=\"asset_references\")\n op.drop_table(\"asset_references\")\n\n # Truncate assets (upgrade deleted all rows; downgrade starts fresh too)\n op.execute(\"DELETE FROM assets\")\n\n # Recreate old tables from 0001_assets schema\n op.create_table(\n \"assets_info\",\n sa.Column(\"id\", sa.String(length=36), primary_key=True),\n sa.Column(\"owner_id\", sa.String(length=128), nullable=False, server_default=\"\"),\n sa.Column(\"name\", sa.String(length=512), nullable=False),\n sa.Column(\"asset_id\", sa.String(length=36), sa.ForeignKey(\"assets.id\", ondelete=\"RESTRICT\"), nullable=False),\n sa.Column(\"preview_id\", sa.String(length=36), sa.ForeignKey(\"assets.id\", ondelete=\"SET NULL\"), nullable=True),\n sa.Column(\"user_metadata\", sa.JSON(), nullable=True),\n sa.Column(\"created_at\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"updated_at\", sa.DateTime(timezone=False), nullable=False),\n sa.Column(\"last_access_time\", sa.DateTime(timezone=False), nullable=False),\n sa.UniqueConstraint(\"asset_id\", \"owner_id\", \"name\", name=\"uq_assets_info_asset_owner_name\"),\n )\n op.create_index(\"ix_assets_info_owner_id\", \"assets_info\", [\"owner_id\"])\n op.create_index(\"ix_assets_info_asset_id\", \"assets_info\", [\"asset_id\"])\n op.create_index(\"ix_assets_info_name\", \"assets_info\", [\"name\"])\n op.create_index(\"ix_assets_info_created_at\", \"assets_info\", [\"created_at\"])\n op.create_index(\"ix_assets_info_last_access_time\", \"assets_info\", [\"last_access_time\"])\n op.create_index(\"ix_assets_info_owner_name\", \"assets_info\", [\"owner_id\", \"name\"])\n\n op.create_table(\n \"asset_cache_state\",\n sa.Column(\"id\", sa.Integer(), primary_key=True, autoincrement=True),\n sa.Column(\"asset_id\", sa.String(length=36), sa.ForeignKey(\"assets.id\", ondelete=\"CASCADE\"), nullable=False),\n sa.Column(\"file_path\", sa.Text(), nullable=False),\n sa.Column(\"mtime_ns\", sa.BigInteger(), nullable=True),\n sa.Column(\"needs_verify\", sa.Boolean(), nullable=False, server_default=sa.text(\"false\")),\n sa.CheckConstraint(\"(mtime_ns IS NULL) OR (mtime_ns >= 0)\", name=\"ck_acs_mtime_nonneg\"),\n sa.UniqueConstraint(\"file_path\", name=\"uq_asset_cache_state_file_path\"),\n )\n op.create_index(\"ix_asset_cache_state_file_path\", \"asset_cache_state\", [\"file_path\"])\n op.create_index(\"ix_asset_cache_state_asset_id\", \"asset_cache_state\", [\"asset_id\"])\n\n op.create_table(\n \"asset_info_tags\",\n sa.Column(\"asset_info_id\", sa.String(length=36), sa.ForeignKey(\"assets_info.id\", ondelete=\"CASCADE\"), nullable=False),\n sa.Column(\"tag_name\", sa.String(length=512), sa.ForeignKey(\"tags.name\", ondelete=\"RESTRICT\"), nullable=False),\n sa.Column(\"origin\", sa.String(length=32), nullable=False, server_default=\"manual\"),\n sa.Column(\"added_at\", sa.DateTime(timezone=False), nullable=False),\n sa.PrimaryKeyConstraint(\"asset_info_id\", \"tag_name\", name=\"pk_asset_info_tags\"),\n )\n op.create_index(\"ix_asset_info_tags_tag_name\", \"asset_info_tags\", [\"tag_name\"])\n op.create_index(\"ix_asset_info_tags_asset_info_id\", \"asset_info_tags\", [\"asset_info_id\"])\n\n op.create_table(\n \"asset_info_meta\",\n sa.Column(\"asset_info_id\", sa.String(length=36), sa.ForeignKey(\"assets_info.id\", ondelete=\"CASCADE\"), nullable=False),\n sa.Column(\"key\", sa.String(length=256), nullable=False),\n sa.Column(\"ordinal\", sa.Integer(), nullable=False, server_default=\"0\"),\n sa.Column(\"val_str\", sa.String(length=2048), nullable=True),\n sa.Column(\"val_num\", sa.Numeric(38, 10), nullable=True),\n sa.Column(\"val_bool\", sa.Boolean(), nullable=True),\n sa.Column(\"val_json\", sa.JSON(), nullable=True),\n sa.PrimaryKeyConstraint(\"asset_info_id\", \"key\", \"ordinal\", name=\"pk_asset_info_meta\"),\n )\n op.create_index(\"ix_asset_info_meta_key\", \"asset_info_meta\", [\"key\"])\n op.create_index(\"ix_asset_info_meta_key_val_str\", \"asset_info_meta\", [\"key\", \"val_str\"])\n op.create_index(\"ix_asset_info_meta_key_val_num\", \"asset_info_meta\", [\"key\", \"val_num\"])\n op.create_index(\"ix_asset_info_meta_key_val_bool\", \"asset_info_meta\", [\"key\", \"val_bool\"])\n" }, { "path": "alembic_db/versions/0003_add_metadata_job_id.py", "content": "\"\"\"\nAdd system_metadata and job_id columns to asset_references.\nChange preview_id FK from assets.id to asset_references.id.\n\nRevision ID: 0003_add_metadata_job_id\nRevises: 0002_merge_to_asset_references\nCreate Date: 2026-03-09\n\"\"\"\n\nfrom alembic import op\nimport sqlalchemy as sa\n\nfrom app.database.models import NAMING_CONVENTION\n\nrevision = \"0003_add_metadata_job_id\"\ndown_revision = \"0002_merge_to_asset_references\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n with op.batch_alter_table(\"asset_references\") as batch_op:\n batch_op.add_column(\n sa.Column(\"system_metadata\", sa.JSON(), nullable=True)\n )\n batch_op.add_column(\n sa.Column(\"job_id\", sa.String(length=36), nullable=True)\n )\n\n # Change preview_id FK from assets.id to asset_references.id (self-ref).\n # Existing values are asset-content IDs that won't match reference IDs,\n # so null them out first.\n op.execute(\"UPDATE asset_references SET preview_id = NULL WHERE preview_id IS NOT NULL\")\n with op.batch_alter_table(\n \"asset_references\", naming_convention=NAMING_CONVENTION\n ) as batch_op:\n batch_op.drop_constraint(\n \"fk_asset_references_preview_id_assets\", type_=\"foreignkey\"\n )\n batch_op.create_foreign_key(\n \"fk_asset_references_preview_id_asset_references\",\n \"asset_references\",\n [\"preview_id\"],\n [\"id\"],\n ondelete=\"SET NULL\",\n )\n batch_op.create_index(\n \"ix_asset_references_preview_id\", [\"preview_id\"]\n )\n\n # Purge any all-null meta rows before adding the constraint\n op.execute(\n \"DELETE FROM asset_reference_meta\"\n \" WHERE val_str IS NULL AND val_num IS NULL AND val_bool IS NULL AND val_json IS NULL\"\n )\n with op.batch_alter_table(\"asset_reference_meta\") as batch_op:\n batch_op.create_check_constraint(\n \"ck_asset_reference_meta_has_value\",\n \"val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL\",\n )\n\n\ndef downgrade() -> None:\n # SQLite doesn't reflect CHECK constraints, so we must declare it\n # explicitly via table_args for the batch recreate to find it.\n # Use the fully-rendered constraint name to avoid the naming convention\n # doubling the prefix.\n with op.batch_alter_table(\n \"asset_reference_meta\",\n table_args=[\n sa.CheckConstraint(\n \"val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL\",\n name=\"ck_asset_reference_meta_has_value\",\n ),\n ],\n ) as batch_op:\n batch_op.drop_constraint(\n \"ck_asset_reference_meta_has_value\", type_=\"check\"\n )\n\n with op.batch_alter_table(\n \"asset_references\", naming_convention=NAMING_CONVENTION\n ) as batch_op:\n batch_op.drop_index(\"ix_asset_references_preview_id\")\n batch_op.drop_constraint(\n \"fk_asset_references_preview_id_asset_references\", type_=\"foreignkey\"\n )\n batch_op.create_foreign_key(\n \"fk_asset_references_preview_id_assets\",\n \"assets\",\n [\"preview_id\"],\n [\"id\"],\n ondelete=\"SET NULL\",\n )\n\n with op.batch_alter_table(\"asset_references\") as batch_op:\n batch_op.drop_column(\"job_id\")\n batch_op.drop_column(\"system_metadata\")\n" }, { "path": "api_server/__init__.py", "content": "" }, { "path": "api_server/routes/__init__.py", "content": "" }, { "path": "api_server/routes/internal/README.md", "content": "# ComfyUI Internal Routes\n\nAll routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.\n" }, { "path": "api_server/routes/internal/__init__.py", "content": "" }, { "path": "api_server/routes/internal/internal_routes.py", "content": "from aiohttp import web\nfrom typing import Optional\nfrom folder_paths import folder_names_and_paths, get_directory_by_type\nfrom api_server.services.terminal_service import TerminalService\nimport app.logger\nimport os\n\nclass InternalRoutes:\n '''\n The top level web router for internal routes: /internal/*\n The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.\n Check README.md for more information.\n '''\n\n def __init__(self, prompt_server):\n self.routes: web.RouteTableDef = web.RouteTableDef()\n self._app: Optional[web.Application] = None\n self.prompt_server = prompt_server\n self.terminal_service = TerminalService(prompt_server)\n\n def setup_routes(self):\n @self.routes.get('/logs')\n async def get_logs(request):\n return web.json_response(\"\".join([(l[\"t\"] + \" - \" + l[\"m\"]) for l in app.logger.get_logs()]))\n\n @self.routes.get('/logs/raw')\n async def get_raw_logs(request):\n self.terminal_service.update_size()\n return web.json_response({\n \"entries\": list(app.logger.get_logs()),\n \"size\": {\"cols\": self.terminal_service.cols, \"rows\": self.terminal_service.rows}\n })\n\n @self.routes.patch('/logs/subscribe')\n async def subscribe_logs(request):\n json_data = await request.json()\n client_id = json_data[\"clientId\"]\n enabled = json_data[\"enabled\"]\n if enabled:\n self.terminal_service.subscribe(client_id)\n else:\n self.terminal_service.unsubscribe(client_id)\n\n return web.Response(status=200)\n\n\n @self.routes.get('/folder_paths')\n async def get_folder_paths(request):\n response = {}\n for key in folder_names_and_paths:\n response[key] = folder_names_and_paths[key][0]\n return web.json_response(response)\n\n @self.routes.get('/files/{directory_type}')\n async def get_files(request: web.Request) -> web.Response:\n directory_type = request.match_info['directory_type']\n if directory_type not in (\"output\", \"input\", \"temp\"):\n return web.json_response({\"error\": \"Invalid directory type\"}, status=400)\n\n directory = get_directory_by_type(directory_type)\n\n def is_visible_file(entry: os.DirEntry) -> bool:\n \"\"\"Filter out hidden files (e.g., .DS_Store on macOS).\"\"\"\n return entry.is_file() and not entry.name.startswith('.')\n\n sorted_files = sorted(\n (entry for entry in os.scandir(directory) if is_visible_file(entry)),\n key=lambda entry: -entry.stat().st_mtime\n )\n return web.json_response([entry.name for entry in sorted_files], status=200)\n\n\n def get_app(self):\n if self._app is None:\n self._app = web.Application()\n self.setup_routes()\n self._app.add_routes(self.routes)\n return self._app\n" }, { "path": "api_server/services/__init__.py", "content": "" }, { "path": "api_server/services/terminal_service.py", "content": "from app.logger import on_flush\nimport os\nimport shutil\n\n\nclass TerminalService:\n def __init__(self, server):\n self.server = server\n self.cols = None\n self.rows = None\n self.subscriptions = set()\n on_flush(self.send_messages)\n\n def get_terminal_size(self):\n try:\n size = os.get_terminal_size()\n return (size.columns, size.lines)\n except OSError:\n try:\n size = shutil.get_terminal_size()\n return (size.columns, size.lines)\n except OSError:\n return (80, 24) # fallback to 80x24\n\n def update_size(self):\n columns, lines = self.get_terminal_size()\n changed = False\n\n if columns != self.cols:\n self.cols = columns\n changed = True\n\n if lines != self.rows:\n self.rows = lines\n changed = True\n\n if changed:\n return {\"cols\": self.cols, \"rows\": self.rows}\n\n return None\n\n def subscribe(self, client_id):\n self.subscriptions.add(client_id)\n\n def unsubscribe(self, client_id):\n self.subscriptions.discard(client_id)\n\n def send_messages(self, entries):\n if not len(entries) or not len(self.subscriptions):\n return\n\n new_size = self.update_size()\n\n for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration\n if client_id not in self.server.sockets:\n # Automatically unsub if the socket has disconnected\n self.unsubscribe(client_id)\n continue\n\n self.server.send_sync(\"logs\", {\"entries\": entries, \"size\": new_size}, client_id)\n" }, { "path": "api_server/utils/file_operations.py", "content": "import os\nfrom typing import List, Union, TypedDict, Literal\nfrom typing_extensions import TypeGuard\nclass FileInfo(TypedDict):\n name: str\n path: str\n type: Literal[\"file\"]\n size: int\n\nclass DirectoryInfo(TypedDict):\n name: str\n path: str\n type: Literal[\"directory\"]\n\nFileSystemItem = Union[FileInfo, DirectoryInfo]\n\ndef is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:\n return item[\"type\"] == \"file\"\n\nclass FileSystemOperations:\n @staticmethod\n def walk_directory(directory: str) -> List[FileSystemItem]:\n file_list: List[FileSystemItem] = []\n for root, dirs, files in os.walk(directory):\n for name in files:\n file_path = os.path.join(root, name)\n relative_path = os.path.relpath(file_path, directory)\n file_list.append({\n \"name\": name,\n \"path\": relative_path,\n \"type\": \"file\",\n \"size\": os.path.getsize(file_path)\n })\n for name in dirs:\n dir_path = os.path.join(root, name)\n relative_path = os.path.relpath(dir_path, directory)\n file_list.append({\n \"name\": name,\n \"path\": relative_path,\n \"type\": \"directory\"\n })\n return file_list\n" }, { "path": "app/__init__.py", "content": "" }, { "path": "app/app_settings.py", "content": "import os\nimport json\nfrom aiohttp import web\nimport logging\n\n\nclass AppSettings():\n def __init__(self, user_manager):\n self.user_manager = user_manager\n\n def get_settings(self, request):\n try:\n file = self.user_manager.get_request_user_filepath(\n request,\n \"comfy.settings.json\"\n )\n except KeyError as e:\n logging.error(\"User settings not found.\")\n raise web.HTTPUnauthorized() from e\n if os.path.isfile(file):\n try:\n with open(file) as f:\n return json.load(f)\n except:\n logging.error(f\"The user settings file is corrupted: {file}\")\n return {}\n else:\n return {}\n\n def save_settings(self, request, settings):\n file = self.user_manager.get_request_user_filepath(\n request, \"comfy.settings.json\")\n with open(file, \"w\") as f:\n f.write(json.dumps(settings, indent=4))\n\n def add_routes(self, routes):\n @routes.get(\"/settings\")\n async def get_settings(request):\n return web.json_response(self.get_settings(request))\n\n @routes.get(\"/settings/{id}\")\n async def get_setting(request):\n value = None\n settings = self.get_settings(request)\n setting_id = request.match_info.get(\"id\", None)\n if setting_id and setting_id in settings:\n value = settings[setting_id]\n return web.json_response(value)\n\n @routes.post(\"/settings\")\n async def post_settings(request):\n settings = self.get_settings(request)\n new_settings = await request.json()\n self.save_settings(request, {**settings, **new_settings})\n return web.Response(status=200)\n\n @routes.post(\"/settings/{id}\")\n async def post_setting(request):\n setting_id = request.match_info.get(\"id\", None)\n if not setting_id:\n return web.Response(status=400)\n settings = self.get_settings(request)\n settings[setting_id] = await request.json()\n self.save_settings(request, settings)\n return web.Response(status=200)\n" }, { "path": "app/assets/api/routes.py", "content": "import asyncio\nimport functools\nimport json\nimport logging\nimport os\nimport urllib.parse\nimport uuid\nfrom typing import Any\n\nfrom aiohttp import web\nfrom pydantic import ValidationError\n\nimport folder_paths\nfrom app import user_manager\nfrom app.assets.api import schemas_in, schemas_out\nfrom app.assets.services import schemas\nfrom app.assets.api.schemas_in import (\n AssetValidationError,\n UploadError,\n)\nfrom app.assets.helpers import validate_blake3_hash\nfrom app.assets.api.upload import (\n delete_temp_file_if_exists,\n parse_multipart_upload,\n)\nfrom app.assets.seeder import ScanInProgressError, asset_seeder\nfrom app.assets.services import (\n DependencyMissingError,\n HashMismatchError,\n apply_tags,\n asset_exists,\n create_from_hash,\n delete_asset_reference,\n get_asset_detail,\n list_assets_page,\n list_tags,\n remove_tags,\n resolve_asset_for_download,\n update_asset_metadata,\n upload_from_temp_path,\n)\nfrom app.assets.services.tagging import list_tag_histogram\n\nROUTES = web.RouteTableDef()\nUSER_MANAGER: user_manager.UserManager | None = None\n_ASSETS_ENABLED = False\n\n\ndef _require_assets_feature_enabled(handler):\n @functools.wraps(handler)\n async def wrapper(request: web.Request) -> web.Response:\n if not _ASSETS_ENABLED:\n return _build_error_response(\n 503,\n \"SERVICE_DISABLED\",\n \"Assets system is disabled. Start the server with --enable-assets to use this feature.\",\n )\n return await handler(request)\n\n return wrapper\n\n\n# UUID regex (canonical hyphenated form, case-insensitive)\nUUID_RE = r\"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}\"\n\n\ndef get_query_dict(request: web.Request) -> dict[str, Any]:\n \"\"\"Gets a dictionary of query parameters from the request.\n\n request.query is a MultiMapping[str], needs to be converted to a dict\n to be validated by Pydantic.\n \"\"\"\n query_dict = {\n key: request.query.getall(key)\n if len(request.query.getall(key)) > 1\n else request.query.get(key)\n for key in request.query.keys()\n }\n return query_dict\n\n\n# Note to any custom node developers reading this code:\n# The assets system is not yet fully implemented,\n# do not rely on the code in /app/assets remaining the same.\n\n\ndef register_assets_routes(\n app: web.Application,\n user_manager_instance: user_manager.UserManager | None = None,\n) -> None:\n global USER_MANAGER, _ASSETS_ENABLED\n if user_manager_instance is not None:\n USER_MANAGER = user_manager_instance\n _ASSETS_ENABLED = True\n app.add_routes(ROUTES)\n\n\ndef disable_assets_routes() -> None:\n \"\"\"Disable asset routes at runtime (e.g. after DB init failure).\"\"\"\n global _ASSETS_ENABLED\n _ASSETS_ENABLED = False\n\n\ndef _build_error_response(\n status: int, code: str, message: str, details: dict | None = None\n) -> web.Response:\n return web.json_response(\n {\"error\": {\"code\": code, \"message\": message, \"details\": details or {}}},\n status=status,\n )\n\n\ndef _build_validation_error_response(code: str, ve: ValidationError) -> web.Response:\n errors = json.loads(ve.json())\n return _build_error_response(400, code, \"Validation failed.\", {\"errors\": errors})\n\n\ndef _validate_sort_field(requested: str | None) -> str:\n if not requested:\n return \"created_at\"\n v = requested.lower()\n if v in {\"name\", \"created_at\", \"updated_at\", \"size\", \"last_access_time\"}:\n return v\n return \"created_at\"\n\n\ndef _build_preview_url_from_view(tags: list[str], user_metadata: dict[str, Any] | None) -> str | None:\n \"\"\"Build a /api/view preview URL from asset tags and user_metadata filename.\"\"\"\n if not user_metadata:\n return None\n filename = user_metadata.get(\"filename\")\n if not filename:\n return None\n\n if \"input\" in tags:\n view_type = \"input\"\n elif \"output\" in tags:\n view_type = \"output\"\n else:\n return None\n\n subfolder = \"\"\n if \"/\" in filename:\n subfolder, filename = filename.rsplit(\"/\", 1)\n\n encoded_filename = urllib.parse.quote(filename, safe=\"\")\n url = f\"/api/view?type={view_type}&filename={encoded_filename}\"\n if subfolder:\n url += f\"&subfolder={urllib.parse.quote(subfolder, safe='')}\"\n return url\n\n\ndef _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResult) -> schemas_out.Asset:\n \"\"\"Build an Asset response from a service result.\"\"\"\n if result.ref.preview_id:\n preview_detail = get_asset_detail(result.ref.preview_id)\n if preview_detail:\n preview_url = _build_preview_url_from_view(preview_detail.tags, preview_detail.ref.user_metadata)\n else:\n preview_url = None\n else:\n preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata)\n return schemas_out.Asset(\n id=result.ref.id,\n name=result.ref.name,\n asset_hash=result.asset.hash if result.asset else None,\n size=int(result.asset.size_bytes) if result.asset else None,\n mime_type=result.asset.mime_type if result.asset else None,\n tags=result.tags,\n preview_url=preview_url,\n preview_id=result.ref.preview_id,\n user_metadata=result.ref.user_metadata or {},\n metadata=result.ref.system_metadata,\n job_id=result.ref.job_id,\n prompt_id=result.ref.job_id, # deprecated: mirrors job_id for cloud compat\n created_at=result.ref.created_at,\n updated_at=result.ref.updated_at,\n last_access_time=result.ref.last_access_time,\n )\n\n\n@ROUTES.head(\"/api/assets/hash/{hash}\")\n@_require_assets_feature_enabled\nasync def head_asset_by_hash(request: web.Request) -> web.Response:\n hash_str = request.match_info.get(\"hash\", \"\").strip().lower()\n try:\n hash_str = validate_blake3_hash(hash_str)\n except ValueError:\n return _build_error_response(\n 400, \"INVALID_HASH\", \"hash must be like 'blake3:'\"\n )\n exists = asset_exists(hash_str)\n return web.Response(status=200 if exists else 404)\n\n\n@ROUTES.get(\"/api/assets\")\n@_require_assets_feature_enabled\nasync def list_assets_route(request: web.Request) -> web.Response:\n \"\"\"\n GET request to list assets.\n \"\"\"\n query_dict = get_query_dict(request)\n try:\n q = schemas_in.ListAssetsQuery.model_validate(query_dict)\n except ValidationError as ve:\n return _build_validation_error_response(\"INVALID_QUERY\", ve)\n\n sort = _validate_sort_field(q.sort)\n order_candidate = (q.order or \"desc\").lower()\n order = order_candidate if order_candidate in {\"asc\", \"desc\"} else \"desc\"\n\n result = list_assets_page(\n owner_id=USER_MANAGER.get_request_user_id(request),\n include_tags=q.include_tags,\n exclude_tags=q.exclude_tags,\n name_contains=q.name_contains,\n metadata_filter=q.metadata_filter,\n limit=q.limit,\n offset=q.offset,\n sort=sort,\n order=order,\n )\n\n summaries = [_build_asset_response(item) for item in result.items]\n\n payload = schemas_out.AssetsList(\n assets=summaries,\n total=result.total,\n has_more=(q.offset + len(summaries)) < result.total,\n )\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True))\n\n\n@ROUTES.get(f\"/api/assets/{{id:{UUID_RE}}}\")\n@_require_assets_feature_enabled\nasync def get_asset_route(request: web.Request) -> web.Response:\n \"\"\"\n GET request to get an asset's info as JSON.\n \"\"\"\n reference_id = str(uuid.UUID(request.match_info[\"id\"]))\n try:\n result = get_asset_detail(\n reference_id=reference_id,\n owner_id=USER_MANAGER.get_request_user_id(request),\n )\n if not result:\n return _build_error_response(\n 404,\n \"ASSET_NOT_FOUND\",\n f\"AssetReference {reference_id} not found\",\n {\"id\": reference_id},\n )\n\n payload = _build_asset_response(result)\n except ValueError as e:\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", str(e), {\"id\": reference_id}\n )\n except Exception:\n logging.exception(\n \"get_asset failed for reference_id=%s, owner_id=%s\",\n reference_id,\n USER_MANAGER.get_request_user_id(request),\n )\n return _build_error_response(500, \"INTERNAL\", \"Unexpected server error.\")\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True), status=200)\n\n\n@ROUTES.get(f\"/api/assets/{{id:{UUID_RE}}}/content\")\n@_require_assets_feature_enabled\nasync def download_asset_content(request: web.Request) -> web.Response:\n disposition = request.query.get(\"disposition\", \"attachment\").lower().strip()\n if disposition not in {\"inline\", \"attachment\"}:\n disposition = \"attachment\"\n\n try:\n result = resolve_asset_for_download(\n reference_id=str(uuid.UUID(request.match_info[\"id\"])),\n owner_id=USER_MANAGER.get_request_user_id(request),\n )\n abs_path = result.abs_path\n content_type = result.content_type\n filename = result.download_name\n except ValueError as ve:\n return _build_error_response(404, \"ASSET_NOT_FOUND\", str(ve))\n except NotImplementedError as nie:\n return _build_error_response(501, \"BACKEND_UNSUPPORTED\", str(nie))\n except FileNotFoundError:\n return _build_error_response(\n 404, \"FILE_NOT_FOUND\", \"Underlying file not found on disk.\"\n )\n\n _DANGEROUS_MIME_TYPES = {\n \"text/html\", \"text/html-sandboxed\", \"application/xhtml+xml\",\n \"text/javascript\", \"text/css\",\n }\n if content_type in _DANGEROUS_MIME_TYPES:\n content_type = \"application/octet-stream\"\n\n safe_name = (filename or \"\").replace(\"\\r\", \"\").replace(\"\\n\", \"\")\n encoded = urllib.parse.quote(safe_name)\n cd = f\"{disposition}; filename*=UTF-8''{encoded}\"\n\n file_size = os.path.getsize(abs_path)\n size_mb = file_size / (1024 * 1024)\n logging.info(\n \"download_asset_content: path=%s, size=%d bytes (%.2f MB), type=%s, name=%s\",\n abs_path,\n file_size,\n size_mb,\n content_type,\n filename,\n )\n\n async def stream_file_chunks():\n chunk_size = 64 * 1024\n with open(abs_path, \"rb\") as f:\n while True:\n chunk = f.read(chunk_size)\n if not chunk:\n break\n yield chunk\n\n return web.Response(\n body=stream_file_chunks(),\n content_type=content_type,\n headers={\n \"Content-Disposition\": cd,\n \"Content-Length\": str(file_size),\n \"X-Content-Type-Options\": \"nosniff\",\n },\n )\n\n\n@ROUTES.post(\"/api/assets/from-hash\")\n@_require_assets_feature_enabled\nasync def create_asset_from_hash_route(request: web.Request) -> web.Response:\n try:\n payload = await request.json()\n body = schemas_in.CreateFromHashBody.model_validate(payload)\n except ValidationError as ve:\n return _build_validation_error_response(\"INVALID_BODY\", ve)\n except Exception:\n return _build_error_response(\n 400, \"INVALID_JSON\", \"Request body must be valid JSON.\"\n )\n\n # Derive name from hash if not provided\n name = body.name\n if name is None:\n name = body.hash.split(\":\", 1)[1] if \":\" in body.hash else body.hash\n\n result = create_from_hash(\n hash_str=body.hash,\n name=name,\n tags=body.tags,\n user_metadata=body.user_metadata,\n owner_id=USER_MANAGER.get_request_user_id(request),\n mime_type=body.mime_type,\n preview_id=body.preview_id,\n )\n if result is None:\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", f\"Asset content {body.hash} does not exist\"\n )\n\n asset = _build_asset_response(result)\n payload_out = schemas_out.AssetCreated(\n **asset.model_dump(),\n created_new=result.created_new,\n )\n return web.json_response(payload_out.model_dump(mode=\"json\", exclude_none=True), status=201)\n\n\n@ROUTES.post(\"/api/assets\")\n@_require_assets_feature_enabled\nasync def upload_asset(request: web.Request) -> web.Response:\n \"\"\"Multipart/form-data endpoint for Asset uploads.\"\"\"\n try:\n parsed = await parse_multipart_upload(request, check_hash_exists=asset_exists)\n except UploadError as e:\n return _build_error_response(e.status, e.code, e.message)\n\n owner_id = USER_MANAGER.get_request_user_id(request)\n\n try:\n spec = schemas_in.UploadAssetSpec.model_validate(\n {\n \"tags\": parsed.tags_raw,\n \"name\": parsed.provided_name,\n \"user_metadata\": parsed.user_metadata_raw,\n \"hash\": parsed.provided_hash,\n \"mime_type\": parsed.provided_mime_type,\n \"preview_id\": parsed.provided_preview_id,\n }\n )\n except ValidationError as ve:\n delete_temp_file_if_exists(parsed.tmp_path)\n return _build_error_response(\n 400, \"INVALID_BODY\", f\"Validation failed: {ve.json()}\"\n )\n\n if spec.tags and spec.tags[0] == \"models\":\n if (\n len(spec.tags) < 2\n or spec.tags[1] not in folder_paths.folder_names_and_paths\n ):\n delete_temp_file_if_exists(parsed.tmp_path)\n category = spec.tags[1] if len(spec.tags) >= 2 else \"\"\n return _build_error_response(\n 400, \"INVALID_BODY\", f\"unknown models category '{category}'\"\n )\n\n try:\n # Fast path: hash exists, create AssetReference without writing anything\n if spec.hash and parsed.provided_hash_exists is True:\n result = create_from_hash(\n hash_str=spec.hash,\n name=spec.name or (spec.hash.split(\":\", 1)[1]),\n tags=spec.tags,\n user_metadata=spec.user_metadata or {},\n owner_id=owner_id,\n mime_type=spec.mime_type,\n preview_id=spec.preview_id,\n )\n if result is None:\n delete_temp_file_if_exists(parsed.tmp_path)\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", f\"Asset content {spec.hash} does not exist\"\n )\n delete_temp_file_if_exists(parsed.tmp_path)\n else:\n # Otherwise, we must have a temp file path to ingest\n if not parsed.tmp_path or not os.path.exists(parsed.tmp_path):\n return _build_error_response(\n 400,\n \"MISSING_INPUT\",\n \"Provided hash not found and no file uploaded.\",\n )\n\n result = upload_from_temp_path(\n temp_path=parsed.tmp_path,\n name=spec.name,\n tags=spec.tags,\n user_metadata=spec.user_metadata or {},\n client_filename=parsed.file_client_name,\n owner_id=owner_id,\n expected_hash=spec.hash,\n mime_type=spec.mime_type,\n preview_id=spec.preview_id,\n )\n except AssetValidationError as e:\n delete_temp_file_if_exists(parsed.tmp_path)\n return _build_error_response(400, e.code, str(e))\n except ValueError as e:\n delete_temp_file_if_exists(parsed.tmp_path)\n return _build_error_response(400, \"BAD_REQUEST\", str(e))\n except HashMismatchError as e:\n delete_temp_file_if_exists(parsed.tmp_path)\n return _build_error_response(400, \"HASH_MISMATCH\", str(e))\n except DependencyMissingError as e:\n delete_temp_file_if_exists(parsed.tmp_path)\n return _build_error_response(503, \"DEPENDENCY_MISSING\", e.message)\n except Exception:\n delete_temp_file_if_exists(parsed.tmp_path)\n logging.exception(\"upload_asset failed for owner_id=%s\", owner_id)\n return _build_error_response(500, \"INTERNAL\", \"Unexpected server error.\")\n\n asset = _build_asset_response(result)\n payload_out = schemas_out.AssetCreated(\n **asset.model_dump(),\n created_new=result.created_new,\n )\n status = 201 if result.created_new else 200\n return web.json_response(payload_out.model_dump(mode=\"json\", exclude_none=True), status=status)\n\n\n@ROUTES.put(f\"/api/assets/{{id:{UUID_RE}}}\")\n@_require_assets_feature_enabled\nasync def update_asset_route(request: web.Request) -> web.Response:\n reference_id = str(uuid.UUID(request.match_info[\"id\"]))\n try:\n body = schemas_in.UpdateAssetBody.model_validate(await request.json())\n except ValidationError as ve:\n return _build_validation_error_response(\"INVALID_BODY\", ve)\n except Exception:\n return _build_error_response(\n 400, \"INVALID_JSON\", \"Request body must be valid JSON.\"\n )\n\n try:\n result = update_asset_metadata(\n reference_id=reference_id,\n name=body.name,\n user_metadata=body.user_metadata,\n owner_id=USER_MANAGER.get_request_user_id(request),\n preview_id=body.preview_id,\n )\n payload = _build_asset_response(result)\n except PermissionError as pe:\n return _build_error_response(403, \"FORBIDDEN\", str(pe), {\"id\": reference_id})\n except ValueError as ve:\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", str(ve), {\"id\": reference_id}\n )\n except Exception:\n logging.exception(\n \"update_asset failed for reference_id=%s, owner_id=%s\",\n reference_id,\n USER_MANAGER.get_request_user_id(request),\n )\n return _build_error_response(500, \"INTERNAL\", \"Unexpected server error.\")\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True), status=200)\n\n\n@ROUTES.delete(f\"/api/assets/{{id:{UUID_RE}}}\")\n@_require_assets_feature_enabled\nasync def delete_asset_route(request: web.Request) -> web.Response:\n reference_id = str(uuid.UUID(request.match_info[\"id\"]))\n delete_content_param = request.query.get(\"delete_content\")\n delete_content = (\n False\n if delete_content_param is None\n else delete_content_param.lower() not in {\"0\", \"false\", \"no\"}\n )\n\n try:\n deleted = delete_asset_reference(\n reference_id=reference_id,\n owner_id=USER_MANAGER.get_request_user_id(request),\n delete_content_if_orphan=delete_content,\n )\n except Exception:\n logging.exception(\n \"delete_asset_reference failed for reference_id=%s, owner_id=%s\",\n reference_id,\n USER_MANAGER.get_request_user_id(request),\n )\n return _build_error_response(500, \"INTERNAL\", \"Unexpected server error.\")\n\n if not deleted:\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", f\"AssetReference {reference_id} not found.\"\n )\n return web.Response(status=204)\n\n\n@ROUTES.get(\"/api/tags\")\n@_require_assets_feature_enabled\nasync def get_tags(request: web.Request) -> web.Response:\n \"\"\"\n GET request to list all tags based on query parameters.\n \"\"\"\n query_map = dict(request.rel_url.query)\n\n try:\n query = schemas_in.TagsListQuery.model_validate(query_map)\n except ValidationError as e:\n return _build_error_response(\n 400,\n \"INVALID_QUERY\",\n \"Invalid query parameters\",\n {\"errors\": json.loads(e.json())},\n )\n\n rows, total = list_tags(\n prefix=query.prefix,\n limit=query.limit,\n offset=query.offset,\n order=query.order,\n include_zero=query.include_zero,\n owner_id=USER_MANAGER.get_request_user_id(request),\n )\n\n tags = [\n schemas_out.TagUsage(name=name, count=count, type=tag_type)\n for (name, tag_type, count) in rows\n ]\n payload = schemas_out.TagsList(\n tags=tags, total=total, has_more=(query.offset + len(tags)) < total\n )\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True))\n\n\n@ROUTES.post(f\"/api/assets/{{id:{UUID_RE}}}/tags\")\n@_require_assets_feature_enabled\nasync def add_asset_tags(request: web.Request) -> web.Response:\n reference_id = str(uuid.UUID(request.match_info[\"id\"]))\n try:\n json_payload = await request.json()\n data = schemas_in.TagsAdd.model_validate(json_payload)\n except ValidationError as ve:\n return _build_error_response(\n 400,\n \"INVALID_BODY\",\n \"Invalid JSON body for tags add.\",\n {\"errors\": ve.errors()},\n )\n except Exception:\n return _build_error_response(\n 400, \"INVALID_JSON\", \"Request body must be valid JSON.\"\n )\n\n try:\n result = apply_tags(\n reference_id=reference_id,\n tags=data.tags,\n origin=\"manual\",\n owner_id=USER_MANAGER.get_request_user_id(request),\n )\n payload = schemas_out.TagsAdd(\n added=result.added,\n already_present=result.already_present,\n total_tags=result.total_tags,\n )\n except PermissionError as pe:\n return _build_error_response(403, \"FORBIDDEN\", str(pe), {\"id\": reference_id})\n except ValueError as ve:\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", str(ve), {\"id\": reference_id}\n )\n except Exception:\n logging.exception(\n \"add_tags_to_asset failed for reference_id=%s, owner_id=%s\",\n reference_id,\n USER_MANAGER.get_request_user_id(request),\n )\n return _build_error_response(500, \"INTERNAL\", \"Unexpected server error.\")\n\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True), status=200)\n\n\n@ROUTES.delete(f\"/api/assets/{{id:{UUID_RE}}}/tags\")\n@_require_assets_feature_enabled\nasync def delete_asset_tags(request: web.Request) -> web.Response:\n reference_id = str(uuid.UUID(request.match_info[\"id\"]))\n try:\n json_payload = await request.json()\n data = schemas_in.TagsRemove.model_validate(json_payload)\n except ValidationError as ve:\n return _build_error_response(\n 400,\n \"INVALID_BODY\",\n \"Invalid JSON body for tags remove.\",\n {\"errors\": ve.errors()},\n )\n except Exception:\n return _build_error_response(\n 400, \"INVALID_JSON\", \"Request body must be valid JSON.\"\n )\n\n try:\n result = remove_tags(\n reference_id=reference_id,\n tags=data.tags,\n owner_id=USER_MANAGER.get_request_user_id(request),\n )\n payload = schemas_out.TagsRemove(\n removed=result.removed,\n not_present=result.not_present,\n total_tags=result.total_tags,\n )\n except PermissionError as pe:\n return _build_error_response(403, \"FORBIDDEN\", str(pe), {\"id\": reference_id})\n except ValueError as ve:\n return _build_error_response(\n 404, \"ASSET_NOT_FOUND\", str(ve), {\"id\": reference_id}\n )\n except Exception:\n logging.exception(\n \"remove_tags_from_asset failed for reference_id=%s, owner_id=%s\",\n reference_id,\n USER_MANAGER.get_request_user_id(request),\n )\n return _build_error_response(500, \"INTERNAL\", \"Unexpected server error.\")\n\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True), status=200)\n\n\n@ROUTES.get(\"/api/assets/tags/refine\")\n@_require_assets_feature_enabled\nasync def get_tags_refine(request: web.Request) -> web.Response:\n \"\"\"GET request to get tag histogram for filtered assets.\"\"\"\n query_dict = get_query_dict(request)\n try:\n q = schemas_in.TagsRefineQuery.model_validate(query_dict)\n except ValidationError as ve:\n return _build_validation_error_response(\"INVALID_QUERY\", ve)\n\n tag_counts = list_tag_histogram(\n owner_id=USER_MANAGER.get_request_user_id(request),\n include_tags=q.include_tags,\n exclude_tags=q.exclude_tags,\n name_contains=q.name_contains,\n metadata_filter=q.metadata_filter,\n limit=q.limit,\n )\n payload = schemas_out.TagHistogram(tag_counts=tag_counts)\n return web.json_response(payload.model_dump(mode=\"json\", exclude_none=True), status=200)\n\n\n@ROUTES.post(\"/api/assets/seed\")\n@_require_assets_feature_enabled\nasync def seed_assets(request: web.Request) -> web.Response:\n \"\"\"Trigger asset seeding for specified roots (models, input, output).\n\n Query params:\n wait: If \"true\", block until scan completes (synchronous behavior for tests)\n\n Returns:\n 202 Accepted if scan started\n 409 Conflict if scan already running\n 200 OK with final stats if wait=true\n \"\"\"\n try:\n payload = await request.json()\n roots = payload.get(\"roots\", [\"models\", \"input\", \"output\"])\n except Exception:\n roots = [\"models\", \"input\", \"output\"]\n\n valid_roots = tuple(r for r in roots if r in (\"models\", \"input\", \"output\"))\n if not valid_roots:\n return _build_error_response(400, \"INVALID_BODY\", \"No valid roots specified\")\n\n wait_param = request.query.get(\"wait\", \"\").lower()\n should_wait = wait_param in (\"true\", \"1\", \"yes\")\n\n started = asset_seeder.start(roots=valid_roots)\n if not started:\n return web.json_response({\"status\": \"already_running\"}, status=409)\n\n if should_wait:\n await asyncio.to_thread(asset_seeder.wait)\n status = asset_seeder.get_status()\n return web.json_response(\n {\n \"status\": \"completed\",\n \"progress\": {\n \"scanned\": status.progress.scanned if status.progress else 0,\n \"total\": status.progress.total if status.progress else 0,\n \"created\": status.progress.created if status.progress else 0,\n \"skipped\": status.progress.skipped if status.progress else 0,\n },\n \"errors\": status.errors,\n },\n status=200,\n )\n\n return web.json_response({\"status\": \"started\"}, status=202)\n\n\n@ROUTES.get(\"/api/assets/seed/status\")\n@_require_assets_feature_enabled\nasync def get_seed_status(request: web.Request) -> web.Response:\n \"\"\"Get current scan status and progress.\"\"\"\n status = asset_seeder.get_status()\n return web.json_response(\n {\n \"state\": status.state.value,\n \"progress\": {\n \"scanned\": status.progress.scanned,\n \"total\": status.progress.total,\n \"created\": status.progress.created,\n \"skipped\": status.progress.skipped,\n }\n if status.progress\n else None,\n \"errors\": status.errors,\n },\n status=200,\n )\n\n\n@ROUTES.post(\"/api/assets/seed/cancel\")\n@_require_assets_feature_enabled\nasync def cancel_seed(request: web.Request) -> web.Response:\n \"\"\"Request cancellation of in-progress scan.\"\"\"\n cancelled = asset_seeder.cancel()\n if cancelled:\n return web.json_response({\"status\": \"cancelling\"}, status=200)\n return web.json_response({\"status\": \"idle\"}, status=200)\n\n\n@ROUTES.post(\"/api/assets/prune\")\n@_require_assets_feature_enabled\nasync def mark_missing_assets(request: web.Request) -> web.Response:\n \"\"\"Mark assets as missing when outside all known root prefixes.\n\n This is a non-destructive soft-delete operation. Assets and metadata\n are preserved, but references are flagged as missing. They can be\n restored if the file reappears in a future scan.\n\n Returns:\n 200 OK with count of marked assets\n 409 Conflict if a scan is currently running\n \"\"\"\n try:\n marked = asset_seeder.mark_missing_outside_prefixes()\n except ScanInProgressError:\n return web.json_response(\n {\"status\": \"scan_running\", \"marked\": 0},\n status=409,\n )\n return web.json_response({\"status\": \"completed\", \"marked\": marked}, status=200)\n" }, { "path": "app/assets/api/schemas_in.py", "content": "import json\nfrom dataclasses import dataclass\nfrom typing import Any, Literal\n\nfrom app.assets.helpers import validate_blake3_hash\nfrom pydantic import (\n BaseModel,\n ConfigDict,\n Field,\n conint,\n field_validator,\n model_validator,\n)\n\n\nclass UploadError(Exception):\n \"\"\"Error during upload parsing with HTTP status and code.\"\"\"\n\n def __init__(self, status: int, code: str, message: str):\n super().__init__(message)\n self.status = status\n self.code = code\n self.message = message\n\n\nclass AssetValidationError(Exception):\n \"\"\"Validation error in asset processing (invalid tags, metadata, etc.).\"\"\"\n\n def __init__(self, code: str, message: str):\n super().__init__(message)\n self.code = code\n self.message = message\n\n\n@dataclass\nclass ParsedUpload:\n \"\"\"Result of parsing a multipart upload request.\"\"\"\n\n file_present: bool\n file_written: int\n file_client_name: str | None\n tmp_path: str | None\n tags_raw: list[str]\n provided_name: str | None\n user_metadata_raw: str | None\n provided_hash: str | None\n provided_hash_exists: bool | None\n provided_mime_type: str | None = None\n provided_preview_id: str | None = None\n\n\nclass ListAssetsQuery(BaseModel):\n include_tags: list[str] = Field(default_factory=list)\n exclude_tags: list[str] = Field(default_factory=list)\n name_contains: str | None = None\n\n # Accept either a JSON string (query param) or a dict\n metadata_filter: dict[str, Any] | None = None\n\n limit: conint(ge=1, le=500) = 20\n offset: conint(ge=0) = 0\n\n sort: Literal[\"name\", \"created_at\", \"updated_at\", \"size\", \"last_access_time\"] = (\n \"created_at\"\n )\n order: Literal[\"asc\", \"desc\"] = \"desc\"\n\n @field_validator(\"include_tags\", \"exclude_tags\", mode=\"before\")\n @classmethod\n def _split_csv_tags(cls, v):\n # Accept \"a,b,c\" or [\"a\",\"b\"] (we are liberal in what we accept)\n if v is None:\n return []\n if isinstance(v, str):\n return [t.strip() for t in v.split(\",\") if t.strip()]\n if isinstance(v, list):\n out: list[str] = []\n for item in v:\n if isinstance(item, str):\n out.extend([t.strip() for t in item.split(\",\") if t.strip()])\n return out\n return v\n\n @field_validator(\"metadata_filter\", mode=\"before\")\n @classmethod\n def _parse_metadata_json(cls, v):\n if v is None or isinstance(v, dict):\n return v\n if isinstance(v, str) and v.strip():\n try:\n parsed = json.loads(v)\n except Exception as e:\n raise ValueError(f\"metadata_filter must be JSON: {e}\") from e\n if not isinstance(parsed, dict):\n raise ValueError(\"metadata_filter must be a JSON object\")\n return parsed\n return None\n\n\nclass UpdateAssetBody(BaseModel):\n name: str | None = None\n user_metadata: dict[str, Any] | None = None\n preview_id: str | None = None # references an asset_reference id, not an asset id\n\n @model_validator(mode=\"after\")\n def _validate_at_least_one_field(self):\n if all(\n v is None\n for v in (self.name, self.user_metadata, self.preview_id)\n ):\n raise ValueError(\n \"Provide at least one of: name, user_metadata, preview_id.\"\n )\n return self\n\n\nclass CreateFromHashBody(BaseModel):\n model_config = ConfigDict(extra=\"ignore\", str_strip_whitespace=True)\n\n hash: str\n name: str | None = None\n tags: list[str] = Field(default_factory=list)\n user_metadata: dict[str, Any] = Field(default_factory=dict)\n mime_type: str | None = None\n preview_id: str | None = None # references an asset_reference id, not an asset id\n\n @field_validator(\"hash\")\n @classmethod\n def _require_blake3(cls, v):\n return validate_blake3_hash(v or \"\")\n\n @field_validator(\"tags\", mode=\"before\")\n @classmethod\n def _normalize_tags_field(cls, v):\n if v is None:\n return []\n if isinstance(v, list):\n out = [str(t).strip().lower() for t in v if str(t).strip()]\n seen = set()\n dedup = []\n for t in out:\n if t not in seen:\n seen.add(t)\n dedup.append(t)\n return dedup\n if isinstance(v, str):\n return [t.strip().lower() for t in v.split(\",\") if t.strip()]\n return []\n\n\nclass TagsRefineQuery(BaseModel):\n include_tags: list[str] = Field(default_factory=list)\n exclude_tags: list[str] = Field(default_factory=list)\n name_contains: str | None = None\n metadata_filter: dict[str, Any] | None = None\n limit: conint(ge=1, le=1000) = 100\n\n @field_validator(\"include_tags\", \"exclude_tags\", mode=\"before\")\n @classmethod\n def _split_csv_tags(cls, v):\n if v is None:\n return []\n if isinstance(v, str):\n return [t.strip() for t in v.split(\",\") if t.strip()]\n if isinstance(v, list):\n out: list[str] = []\n for item in v:\n if isinstance(item, str):\n out.extend([t.strip() for t in item.split(\",\") if t.strip()])\n return out\n return v\n\n @field_validator(\"metadata_filter\", mode=\"before\")\n @classmethod\n def _parse_metadata_json(cls, v):\n if v is None or isinstance(v, dict):\n return v\n if isinstance(v, str) and v.strip():\n try:\n parsed = json.loads(v)\n except Exception as e:\n raise ValueError(f\"metadata_filter must be JSON: {e}\") from e\n if not isinstance(parsed, dict):\n raise ValueError(\"metadata_filter must be a JSON object\")\n return parsed\n return None\n\n\nclass TagsListQuery(BaseModel):\n model_config = ConfigDict(extra=\"ignore\", str_strip_whitespace=True)\n\n prefix: str | None = Field(None, min_length=1, max_length=256)\n limit: int = Field(100, ge=1, le=1000)\n offset: int = Field(0, ge=0, le=10_000_000)\n order: Literal[\"count_desc\", \"name_asc\"] = \"count_desc\"\n include_zero: bool = True\n\n @field_validator(\"prefix\")\n @classmethod\n def normalize_prefix(cls, v: str | None) -> str | None:\n if v is None:\n return v\n v = v.strip()\n return v.lower() or None\n\n\nclass TagsAdd(BaseModel):\n model_config = ConfigDict(extra=\"ignore\")\n tags: list[str] = Field(..., min_length=1)\n\n @field_validator(\"tags\")\n @classmethod\n def normalize_tags(cls, v: list[str]) -> list[str]:\n out = []\n for t in v:\n if not isinstance(t, str):\n raise TypeError(\"tags must be strings\")\n tnorm = t.strip().lower()\n if tnorm:\n out.append(tnorm)\n seen = set()\n deduplicated = []\n for x in out:\n if x not in seen:\n seen.add(x)\n deduplicated.append(x)\n return deduplicated\n\n\nclass TagsRemove(TagsAdd):\n pass\n\n\nclass UploadAssetSpec(BaseModel):\n \"\"\"Upload Asset operation.\n\n - tags: optional list; if provided, first is root ('models'|'input'|'output');\n if root == 'models', second must be a valid category\n - name: display name\n - user_metadata: arbitrary JSON object (optional)\n - hash: optional canonical 'blake3:' for validation / fast-path\n - mime_type: optional MIME type override\n - preview_id: optional asset_reference ID for preview\n\n Files are stored using the content hash as filename stem.\n \"\"\"\n\n model_config = ConfigDict(extra=\"ignore\", str_strip_whitespace=True)\n\n tags: list[str] = Field(default_factory=list)\n name: str | None = Field(default=None, max_length=512, description=\"Display Name\")\n user_metadata: dict[str, Any] = Field(default_factory=dict)\n hash: str | None = Field(default=None)\n mime_type: str | None = Field(default=None)\n preview_id: str | None = Field(default=None) # references an asset_reference id\n\n @field_validator(\"hash\", mode=\"before\")\n @classmethod\n def _parse_hash(cls, v):\n if v is None:\n return None\n s = str(v).strip()\n if not s:\n return None\n return validate_blake3_hash(s)\n\n @field_validator(\"tags\", mode=\"before\")\n @classmethod\n def _parse_tags(cls, v):\n \"\"\"\n Accepts a list of strings (possibly multiple form fields),\n where each string can be:\n - JSON array (e.g., '[\"models\",\"loras\",\"foo\"]')\n - comma-separated ('models, loras, foo')\n - single token ('models')\n Returns a normalized, deduplicated, ordered list.\n \"\"\"\n items: list[str] = []\n if v is None:\n return []\n if isinstance(v, str):\n v = [v]\n\n if isinstance(v, list):\n for item in v:\n if item is None:\n continue\n s = str(item).strip()\n if not s:\n continue\n if s.startswith(\"[\"):\n try:\n arr = json.loads(s)\n if isinstance(arr, list):\n items.extend(str(x) for x in arr)\n continue\n except Exception:\n pass # fallback to CSV parse below\n items.extend([p for p in s.split(\",\") if p.strip()])\n else:\n return []\n\n # normalize + dedupe\n norm = []\n seen = set()\n for t in items:\n tnorm = str(t).strip().lower()\n if tnorm and tnorm not in seen:\n seen.add(tnorm)\n norm.append(tnorm)\n return norm\n\n @field_validator(\"user_metadata\", mode=\"before\")\n @classmethod\n def _parse_metadata_json(cls, v):\n if v is None or isinstance(v, dict):\n return v or {}\n if isinstance(v, str):\n s = v.strip()\n if not s:\n return {}\n try:\n parsed = json.loads(s)\n except Exception as e:\n raise ValueError(f\"user_metadata must be JSON: {e}\") from e\n if not isinstance(parsed, dict):\n raise ValueError(\"user_metadata must be a JSON object\")\n return parsed\n return {}\n\n @model_validator(mode=\"after\")\n def _validate_order(self):\n if not self.tags:\n raise ValueError(\"at least one tag is required for uploads\")\n root = self.tags[0]\n if root not in {\"models\", \"input\", \"output\"}:\n raise ValueError(\"first tag must be one of: models, input, output\")\n if root == \"models\":\n if len(self.tags) < 2:\n raise ValueError(\n \"models uploads require a category tag as the second tag\"\n )\n return self\n" }, { "path": "app/assets/api/schemas_out.py", "content": "from datetime import datetime\nfrom typing import Any\n\nfrom pydantic import BaseModel, ConfigDict, Field, field_serializer\n\n\nclass Asset(BaseModel):\n \"\"\"API view of an asset. Maps to DB ``AssetReference`` joined with its ``Asset`` blob;\n ``id`` here is the AssetReference id, not the content-addressed Asset id.\"\"\"\n\n id: str\n name: str\n asset_hash: str | None = None\n size: int | None = None\n mime_type: str | None = None\n tags: list[str] = Field(default_factory=list)\n preview_url: str | None = None\n preview_id: str | None = None # references an asset_reference id, not an asset id\n user_metadata: dict[str, Any] = Field(default_factory=dict)\n is_immutable: bool = False\n metadata: dict[str, Any] | None = None\n job_id: str | None = None\n prompt_id: str | None = None # deprecated: use job_id\n created_at: datetime\n updated_at: datetime\n last_access_time: datetime | None = None\n\n model_config = ConfigDict(from_attributes=True)\n\n @field_serializer(\"created_at\", \"updated_at\", \"last_access_time\")\n def _serialize_datetime(self, v: datetime | None, _info):\n return v.isoformat() if v else None\n\n\nclass AssetCreated(Asset):\n created_new: bool\n\n\nclass AssetsList(BaseModel):\n assets: list[Asset]\n total: int\n has_more: bool\n\n\nclass TagUsage(BaseModel):\n name: str\n count: int\n type: str\n\n\nclass TagsList(BaseModel):\n tags: list[TagUsage] = Field(default_factory=list)\n total: int\n has_more: bool\n\n\nclass TagsAdd(BaseModel):\n model_config = ConfigDict(str_strip_whitespace=True)\n added: list[str] = Field(default_factory=list)\n already_present: list[str] = Field(default_factory=list)\n total_tags: list[str] = Field(default_factory=list)\n\n\nclass TagsRemove(BaseModel):\n model_config = ConfigDict(str_strip_whitespace=True)\n removed: list[str] = Field(default_factory=list)\n not_present: list[str] = Field(default_factory=list)\n total_tags: list[str] = Field(default_factory=list)\n\n\nclass TagHistogram(BaseModel):\n tag_counts: dict[str, int]\n" }, { "path": "app/assets/api/upload.py", "content": "import logging\nimport os\nimport uuid\nfrom typing import Callable\n\nfrom aiohttp import web\n\nimport folder_paths\nfrom app.assets.api.schemas_in import ParsedUpload, UploadError\nfrom app.assets.helpers import validate_blake3_hash\n\n\ndef normalize_and_validate_hash(s: str) -> str:\n \"\"\"Validate and normalize a hash string.\n\n Returns canonical 'blake3:' or raises UploadError.\n \"\"\"\n try:\n return validate_blake3_hash(s)\n except ValueError:\n raise UploadError(400, \"INVALID_HASH\", \"hash must be like 'blake3:'\")\n\n\nasync def parse_multipart_upload(\n request: web.Request,\n check_hash_exists: Callable[[str], bool],\n) -> ParsedUpload:\n \"\"\"\n Parse a multipart/form-data upload request.\n\n Args:\n request: The aiohttp request\n check_hash_exists: Callable(hash_str) -> bool to check if a hash exists\n\n Returns:\n ParsedUpload with parsed fields and temp file path\n\n Raises:\n UploadError: On validation or I/O errors\n \"\"\"\n if not (request.content_type or \"\").lower().startswith(\"multipart/\"):\n raise UploadError(\n 415, \"UNSUPPORTED_MEDIA_TYPE\", \"Use multipart/form-data for uploads.\"\n )\n\n reader = await request.multipart()\n\n file_present = False\n file_client_name: str | None = None\n tags_raw: list[str] = []\n provided_name: str | None = None\n user_metadata_raw: str | None = None\n provided_hash: str | None = None\n provided_hash_exists: bool | None = None\n provided_mime_type: str | None = None\n provided_preview_id: str | None = None\n\n file_written = 0\n tmp_path: str | None = None\n\n while True:\n field = await reader.next()\n if field is None:\n break\n\n fname = getattr(field, \"name\", \"\") or \"\"\n\n if fname == \"hash\":\n try:\n s = ((await field.text()) or \"\").strip().lower()\n except Exception:\n raise UploadError(\n 400, \"INVALID_HASH\", \"hash must be like 'blake3:'\"\n )\n\n if s:\n provided_hash = normalize_and_validate_hash(s)\n try:\n provided_hash_exists = check_hash_exists(provided_hash)\n except Exception as e:\n logging.exception(\n \"check_hash_exists failed for hash=%s: %s\", provided_hash, e\n )\n raise UploadError(\n 500,\n \"HASH_CHECK_FAILED\",\n \"Backend error while checking asset hash.\",\n )\n\n elif fname == \"file\":\n file_present = True\n file_client_name = (field.filename or \"\").strip()\n\n if provided_hash and provided_hash_exists is True:\n # Hash exists - drain file but don't write to disk\n try:\n while True:\n chunk = await field.read_chunk(8 * 1024 * 1024)\n if not chunk:\n break\n file_written += len(chunk)\n except Exception:\n raise UploadError(\n 500, \"UPLOAD_IO_ERROR\", \"Failed to receive uploaded file.\"\n )\n continue\n\n uploads_root = os.path.join(folder_paths.get_temp_directory(), \"uploads\")\n unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)\n os.makedirs(unique_dir, exist_ok=True)\n tmp_path = os.path.join(unique_dir, \".upload.part\")\n\n try:\n with open(tmp_path, \"wb\") as f:\n while True:\n chunk = await field.read_chunk(8 * 1024 * 1024)\n if not chunk:\n break\n f.write(chunk)\n file_written += len(chunk)\n except Exception:\n delete_temp_file_if_exists(tmp_path)\n raise UploadError(\n 500, \"UPLOAD_IO_ERROR\", \"Failed to receive and store uploaded file.\"\n )\n\n elif fname == \"tags\":\n tags_raw.append((await field.text()) or \"\")\n elif fname == \"name\":\n provided_name = (await field.text()) or None\n elif fname == \"user_metadata\":\n user_metadata_raw = (await field.text()) or None\n elif fname == \"id\":\n raise UploadError(\n 400,\n \"UNSUPPORTED_FIELD\",\n \"Client-provided 'id' is not supported. Asset IDs are assigned by the server.\",\n )\n elif fname == \"mime_type\":\n provided_mime_type = ((await field.text()) or \"\").strip() or None\n elif fname == \"preview_id\":\n provided_preview_id = ((await field.text()) or \"\").strip() or None\n\n if not file_present and not (provided_hash and provided_hash_exists):\n raise UploadError(\n 400, \"MISSING_FILE\", \"Form must include a 'file' part or a known 'hash'.\"\n )\n\n if (\n file_present\n and file_written == 0\n and not (provided_hash and provided_hash_exists)\n ):\n delete_temp_file_if_exists(tmp_path)\n raise UploadError(400, \"EMPTY_UPLOAD\", \"Uploaded file is empty.\")\n\n return ParsedUpload(\n file_present=file_present,\n file_written=file_written,\n file_client_name=file_client_name,\n tmp_path=tmp_path,\n tags_raw=tags_raw,\n provided_name=provided_name,\n user_metadata_raw=user_metadata_raw,\n provided_hash=provided_hash,\n provided_hash_exists=provided_hash_exists,\n provided_mime_type=provided_mime_type,\n provided_preview_id=provided_preview_id,\n )\n\n\ndef delete_temp_file_if_exists(tmp_path: str | None) -> None:\n \"\"\"Safely remove a temp file and its parent directory if empty.\"\"\"\n if tmp_path:\n try:\n if os.path.exists(tmp_path):\n os.remove(tmp_path)\n except OSError as e:\n logging.debug(\"Failed to delete temp file %s: %s\", tmp_path, e)\n try:\n parent = os.path.dirname(tmp_path)\n if parent and os.path.isdir(parent):\n os.rmdir(parent) # only succeeds if empty\n except OSError:\n pass\n" }, { "path": "app/assets/database/models.py", "content": "from __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\nfrom typing import Any\n\nfrom sqlalchemy import (\n JSON,\n BigInteger,\n Boolean,\n CheckConstraint,\n DateTime,\n ForeignKey,\n Index,\n Integer,\n Numeric,\n String,\n Text,\n)\nfrom sqlalchemy.orm import Mapped, foreign, mapped_column, relationship\n\nfrom app.assets.helpers import get_utc_now\nfrom app.database.models import Base\n\n\nclass Asset(Base):\n __tablename__ = \"assets\"\n\n id: Mapped[str] = mapped_column(\n String(36), primary_key=True, default=lambda: str(uuid.uuid4())\n )\n hash: Mapped[str | None] = mapped_column(String(256), nullable=True)\n size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)\n mime_type: Mapped[str | None] = mapped_column(String(255))\n created_at: Mapped[datetime] = mapped_column(\n DateTime(timezone=False), nullable=False, default=get_utc_now\n )\n\n references: Mapped[list[AssetReference]] = relationship(\n \"AssetReference\",\n back_populates=\"asset\",\n primaryjoin=lambda: Asset.id == foreign(AssetReference.asset_id),\n foreign_keys=lambda: [AssetReference.asset_id],\n cascade=\"all,delete-orphan\",\n passive_deletes=True,\n )\n\n # preview_id on AssetReference is a self-referential FK to asset_references.id\n\n __table_args__ = (\n Index(\"uq_assets_hash\", \"hash\", unique=True),\n Index(\"ix_assets_mime_type\", \"mime_type\"),\n CheckConstraint(\"size_bytes >= 0\", name=\"ck_assets_size_nonneg\"),\n )\n\n def __repr__(self) -> str:\n return f\"\"\n\n\nclass AssetReference(Base):\n \"\"\"Unified model combining file cache state and user-facing metadata.\n\n Each row represents either:\n - A filesystem reference (file_path is set) with cache state\n - An API-created reference (file_path is NULL) without cache state\n \"\"\"\n\n __tablename__ = \"asset_references\"\n\n id: Mapped[str] = mapped_column(\n String(36), primary_key=True, default=lambda: str(uuid.uuid4())\n )\n asset_id: Mapped[str] = mapped_column(\n String(36), ForeignKey(\"assets.id\", ondelete=\"CASCADE\"), nullable=False\n )\n\n # Cache state fields (from former AssetCacheState)\n file_path: Mapped[str | None] = mapped_column(Text, nullable=True)\n mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)\n needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)\n is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)\n enrichment_level: Mapped[int] = mapped_column(Integer, nullable=False, default=0)\n\n # Info fields (from former AssetInfo)\n owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default=\"\")\n name: Mapped[str] = mapped_column(String(512), nullable=False)\n preview_id: Mapped[str | None] = mapped_column(\n String(36), ForeignKey(\"asset_references.id\", ondelete=\"SET NULL\")\n )\n user_metadata: Mapped[dict[str, Any] | None] = mapped_column(\n JSON(none_as_null=True)\n )\n system_metadata: Mapped[dict[str, Any] | None] = mapped_column(\n JSON(none_as_null=True), nullable=True, default=None\n )\n job_id: Mapped[str | None] = mapped_column(String(36), nullable=True, default=None)\n created_at: Mapped[datetime] = mapped_column(\n DateTime(timezone=False), nullable=False, default=get_utc_now\n )\n updated_at: Mapped[datetime] = mapped_column(\n DateTime(timezone=False), nullable=False, default=get_utc_now\n )\n last_access_time: Mapped[datetime] = mapped_column(\n DateTime(timezone=False), nullable=False, default=get_utc_now\n )\n deleted_at: Mapped[datetime | None] = mapped_column(\n DateTime(timezone=False), nullable=True, default=None\n )\n\n asset: Mapped[Asset] = relationship(\n \"Asset\",\n back_populates=\"references\",\n foreign_keys=[asset_id],\n lazy=\"selectin\",\n )\n preview_ref: Mapped[AssetReference | None] = relationship(\n \"AssetReference\",\n foreign_keys=[preview_id],\n remote_side=lambda: [AssetReference.id],\n )\n\n metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship(\n back_populates=\"asset_reference\",\n cascade=\"all,delete-orphan\",\n passive_deletes=True,\n )\n\n tag_links: Mapped[list[AssetReferenceTag]] = relationship(\n back_populates=\"asset_reference\",\n cascade=\"all,delete-orphan\",\n passive_deletes=True,\n overlaps=\"tags,asset_references\",\n )\n\n tags: Mapped[list[Tag]] = relationship(\n secondary=\"asset_reference_tags\",\n back_populates=\"asset_references\",\n lazy=\"selectin\",\n viewonly=True,\n overlaps=\"tag_links,asset_reference_links,asset_references,tag\",\n )\n\n __table_args__ = (\n Index(\"uq_asset_references_file_path\", \"file_path\", unique=True),\n Index(\"ix_asset_references_asset_id\", \"asset_id\"),\n Index(\"ix_asset_references_owner_id\", \"owner_id\"),\n Index(\"ix_asset_references_name\", \"name\"),\n Index(\"ix_asset_references_is_missing\", \"is_missing\"),\n Index(\"ix_asset_references_enrichment_level\", \"enrichment_level\"),\n Index(\"ix_asset_references_created_at\", \"created_at\"),\n Index(\"ix_asset_references_last_access_time\", \"last_access_time\"),\n Index(\"ix_asset_references_deleted_at\", \"deleted_at\"),\n Index(\"ix_asset_references_preview_id\", \"preview_id\"),\n Index(\"ix_asset_references_owner_name\", \"owner_id\", \"name\"),\n CheckConstraint(\n \"(mtime_ns IS NULL) OR (mtime_ns >= 0)\", name=\"ck_ar_mtime_nonneg\"\n ),\n CheckConstraint(\n \"enrichment_level >= 0 AND enrichment_level <= 2\",\n name=\"ck_ar_enrichment_level_range\",\n ),\n )\n\n def __repr__(self) -> str:\n path_part = f\" path={self.file_path!r}\" if self.file_path else \"\"\n return f\"\"\n\n\nclass AssetReferenceMeta(Base):\n __tablename__ = \"asset_reference_meta\"\n\n asset_reference_id: Mapped[str] = mapped_column(\n String(36),\n ForeignKey(\"asset_references.id\", ondelete=\"CASCADE\"),\n primary_key=True,\n )\n key: Mapped[str] = mapped_column(String(256), primary_key=True)\n ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)\n\n val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True)\n val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True)\n val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)\n val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)\n\n asset_reference: Mapped[AssetReference] = relationship(\n back_populates=\"metadata_entries\"\n )\n\n __table_args__ = (\n Index(\"ix_asset_reference_meta_key\", \"key\"),\n Index(\"ix_asset_reference_meta_key_val_str\", \"key\", \"val_str\"),\n Index(\"ix_asset_reference_meta_key_val_num\", \"key\", \"val_num\"),\n Index(\"ix_asset_reference_meta_key_val_bool\", \"key\", \"val_bool\"),\n CheckConstraint(\n \"val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL\",\n name=\"has_value\",\n ),\n )\n\n\nclass AssetReferenceTag(Base):\n __tablename__ = \"asset_reference_tags\"\n\n asset_reference_id: Mapped[str] = mapped_column(\n String(36),\n ForeignKey(\"asset_references.id\", ondelete=\"CASCADE\"),\n primary_key=True,\n )\n tag_name: Mapped[str] = mapped_column(\n String(512), ForeignKey(\"tags.name\", ondelete=\"RESTRICT\"), primary_key=True\n )\n origin: Mapped[str] = mapped_column(String(32), nullable=False, default=\"manual\")\n added_at: Mapped[datetime] = mapped_column(\n DateTime(timezone=False), nullable=False, default=get_utc_now\n )\n\n asset_reference: Mapped[AssetReference] = relationship(back_populates=\"tag_links\")\n tag: Mapped[Tag] = relationship(back_populates=\"asset_reference_links\")\n\n __table_args__ = (\n Index(\"ix_asset_reference_tags_tag_name\", \"tag_name\"),\n Index(\"ix_asset_reference_tags_asset_reference_id\", \"asset_reference_id\"),\n )\n\n\nclass Tag(Base):\n __tablename__ = \"tags\"\n\n name: Mapped[str] = mapped_column(String(512), primary_key=True)\n tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default=\"user\")\n\n asset_reference_links: Mapped[list[AssetReferenceTag]] = relationship(\n back_populates=\"tag\",\n overlaps=\"asset_references,tags\",\n )\n asset_references: Mapped[list[AssetReference]] = relationship(\n secondary=\"asset_reference_tags\",\n back_populates=\"tags\",\n viewonly=True,\n overlaps=\"asset_reference_links,tag_links,tags,asset_reference\",\n )\n\n __table_args__ = (Index(\"ix_tags_tag_type\", \"tag_type\"),)\n\n def __repr__(self) -> str:\n return f\"\"\n" }, { "path": "app/assets/database/queries/__init__.py", "content": "from app.assets.database.queries.asset import (\n asset_exists_by_hash,\n bulk_insert_assets,\n get_asset_by_hash,\n get_existing_asset_ids,\n reassign_asset_references,\n update_asset_hash_and_mime,\n upsert_asset,\n)\nfrom app.assets.database.queries.asset_reference import (\n CacheStateRow,\n UnenrichedReferenceRow,\n bulk_insert_references_ignore_conflicts,\n bulk_update_enrichment_level,\n bulk_update_is_missing,\n bulk_update_needs_verify,\n convert_metadata_to_rows,\n delete_assets_by_ids,\n delete_orphaned_seed_asset,\n delete_reference_by_id,\n delete_references_by_ids,\n fetch_reference_and_asset,\n fetch_reference_asset_and_tags,\n get_or_create_reference,\n get_reference_by_file_path,\n get_reference_by_id,\n get_reference_with_owner_check,\n get_reference_ids_by_ids,\n get_references_by_paths_and_asset_ids,\n get_references_for_prefixes,\n get_unenriched_references,\n get_unreferenced_unhashed_asset_ids,\n insert_reference,\n list_all_file_paths_by_asset_id,\n list_references_by_asset_id,\n list_references_page,\n mark_references_missing_outside_prefixes,\n rebuild_metadata_projection,\n reference_exists,\n reference_exists_for_asset_id,\n restore_references_by_paths,\n set_reference_metadata,\n set_reference_preview,\n set_reference_system_metadata,\n soft_delete_reference_by_id,\n update_reference_access_time,\n update_reference_name,\n update_is_missing_by_asset_id,\n update_reference_timestamps,\n update_reference_updated_at,\n upsert_reference,\n)\nfrom app.assets.database.queries.tags import (\n AddTagsResult,\n RemoveTagsResult,\n SetTagsResult,\n add_missing_tag_for_asset_id,\n add_tags_to_reference,\n bulk_insert_tags_and_meta,\n ensure_tags_exist,\n get_reference_tags,\n list_tag_counts_for_filtered_assets,\n list_tags_with_usage,\n remove_missing_tag_for_asset_id,\n remove_tags_from_reference,\n set_reference_tags,\n validate_tags_exist,\n)\n\n__all__ = [\n \"AddTagsResult\",\n \"CacheStateRow\",\n \"RemoveTagsResult\",\n \"SetTagsResult\",\n \"UnenrichedReferenceRow\",\n \"add_missing_tag_for_asset_id\",\n \"add_tags_to_reference\",\n \"asset_exists_by_hash\",\n \"bulk_insert_assets\",\n \"bulk_insert_references_ignore_conflicts\",\n \"bulk_insert_tags_and_meta\",\n \"bulk_update_enrichment_level\",\n \"bulk_update_is_missing\",\n \"bulk_update_needs_verify\",\n \"convert_metadata_to_rows\",\n \"delete_assets_by_ids\",\n \"delete_orphaned_seed_asset\",\n \"delete_reference_by_id\",\n \"delete_references_by_ids\",\n \"ensure_tags_exist\",\n \"fetch_reference_and_asset\",\n \"fetch_reference_asset_and_tags\",\n \"get_asset_by_hash\",\n \"get_existing_asset_ids\",\n \"get_or_create_reference\",\n \"get_reference_by_file_path\",\n \"get_reference_by_id\",\n \"get_reference_with_owner_check\",\n \"get_reference_ids_by_ids\",\n \"get_reference_tags\",\n \"get_references_by_paths_and_asset_ids\",\n \"get_references_for_prefixes\",\n \"get_unenriched_references\",\n \"get_unreferenced_unhashed_asset_ids\",\n \"insert_reference\",\n \"list_all_file_paths_by_asset_id\",\n \"list_references_by_asset_id\",\n \"list_references_page\",\n \"list_tag_counts_for_filtered_assets\",\n \"list_tags_with_usage\",\n \"mark_references_missing_outside_prefixes\",\n \"reassign_asset_references\",\n \"rebuild_metadata_projection\",\n \"reference_exists\",\n \"reference_exists_for_asset_id\",\n \"remove_missing_tag_for_asset_id\",\n \"remove_tags_from_reference\",\n \"restore_references_by_paths\",\n \"set_reference_metadata\",\n \"set_reference_preview\",\n \"set_reference_system_metadata\",\n \"soft_delete_reference_by_id\",\n \"set_reference_tags\",\n \"update_asset_hash_and_mime\",\n \"update_is_missing_by_asset_id\",\n \"update_reference_access_time\",\n \"update_reference_name\",\n \"update_reference_timestamps\",\n \"update_reference_updated_at\",\n \"upsert_asset\",\n \"upsert_reference\",\n \"validate_tags_exist\",\n]\n" }, { "path": "app/assets/database/queries/asset.py", "content": "import sqlalchemy as sa\nfrom sqlalchemy import select\nfrom sqlalchemy.dialects import sqlite\nfrom sqlalchemy.orm import Session\n\nfrom app.assets.database.models import Asset, AssetReference\nfrom app.assets.database.queries.common import MAX_BIND_PARAMS, calculate_rows_per_statement, iter_chunks\n\n\ndef asset_exists_by_hash(\n session: Session,\n asset_hash: str,\n) -> bool:\n \"\"\"\n Check if an asset with a given hash exists in database.\n \"\"\"\n row = (\n session.execute(\n select(sa.literal(True))\n .select_from(Asset)\n .where(Asset.hash == asset_hash)\n .limit(1)\n )\n ).first()\n return row is not None\n\n\ndef get_asset_by_hash(\n session: Session,\n asset_hash: str,\n) -> Asset | None:\n return (\n (session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1)))\n .scalars()\n .first()\n )\n\n\ndef upsert_asset(\n session: Session,\n asset_hash: str,\n size_bytes: int,\n mime_type: str | None = None,\n) -> tuple[Asset, bool, bool]:\n \"\"\"Upsert an Asset by hash. Returns (asset, created, updated).\"\"\"\n vals = {\"hash\": asset_hash, \"size_bytes\": int(size_bytes)}\n if mime_type:\n vals[\"mime_type\"] = mime_type\n\n ins = (\n sqlite.insert(Asset)\n .values(**vals)\n .on_conflict_do_nothing(index_elements=[Asset.hash])\n )\n res = session.execute(ins)\n created = int(res.rowcount or 0) > 0\n\n asset = (\n session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))\n .scalars()\n .first()\n )\n if not asset:\n raise RuntimeError(\"Asset row not found after upsert.\")\n\n updated = False\n if not created:\n changed = False\n if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:\n asset.size_bytes = int(size_bytes)\n changed = True\n if mime_type and not asset.mime_type:\n asset.mime_type = mime_type\n changed = True\n if changed:\n updated = True\n\n return asset, created, updated\n\n\ndef bulk_insert_assets(\n session: Session,\n rows: list[dict],\n) -> None:\n \"\"\"Bulk insert Asset rows with ON CONFLICT DO NOTHING on hash.\"\"\"\n if not rows:\n return\n ins = sqlite.insert(Asset).on_conflict_do_nothing(index_elements=[Asset.hash])\n for chunk in iter_chunks(rows, calculate_rows_per_statement(5)):\n session.execute(ins, chunk)\n\n\ndef get_existing_asset_ids(\n session: Session,\n asset_ids: list[str],\n) -> set[str]:\n \"\"\"Return the subset of asset_ids that exist in the database.\"\"\"\n if not asset_ids:\n return set()\n found: set[str] = set()\n for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS):\n rows = session.execute(\n select(Asset.id).where(Asset.id.in_(chunk))\n ).fetchall()\n found.update(row[0] for row in rows)\n return found\n\n\ndef update_asset_hash_and_mime(\n session: Session,\n asset_id: str,\n asset_hash: str | None = None,\n mime_type: str | None = None,\n) -> bool:\n \"\"\"Update asset hash and/or mime_type. Returns True if asset was found.\"\"\"\n asset = session.get(Asset, asset_id)\n if not asset:\n return False\n if asset_hash is not None:\n asset.hash = asset_hash\n if mime_type is not None and not asset.mime_type:\n asset.mime_type = mime_type\n return True\n\n\ndef reassign_asset_references(\n session: Session,\n from_asset_id: str,\n to_asset_id: str,\n reference_id: str,\n) -> None:\n \"\"\"Reassign a reference from one asset to another.\n\n Used when merging a stub asset into an existing asset with the same hash.\n \"\"\"\n ref = session.get(AssetReference, reference_id)\n if ref and ref.asset_id == from_asset_id:\n ref.asset_id = to_asset_id\n\n session.flush()\n" }, { "path": "app/assets/database/queries/asset_reference.py", "content": "\"\"\"Query functions for the unified AssetReference table.\n\nThis module replaces the separate asset_info.py and cache_state.py query modules,\nproviding a unified interface for the merged asset_references table.\n\"\"\"\n\nfrom collections import defaultdict\nfrom datetime import datetime\nfrom decimal import Decimal\nfrom typing import NamedTuple, Sequence\n\nimport sqlalchemy as sa\nfrom sqlalchemy import delete, select\nfrom sqlalchemy.dialects import sqlite\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy.orm import Session, noload\n\nfrom app.assets.database.models import (\n Asset,\n AssetReference,\n AssetReferenceMeta,\n AssetReferenceTag,\n Tag,\n)\nfrom app.assets.database.queries.common import (\n MAX_BIND_PARAMS,\n apply_metadata_filter,\n apply_tag_filters,\n build_prefix_like_conditions,\n build_visible_owner_clause,\n calculate_rows_per_statement,\n iter_chunks,\n)\nfrom app.assets.helpers import escape_sql_like_string, get_utc_now\n\n\ndef _check_is_scalar(v):\n if v is None:\n return True\n if isinstance(v, bool):\n return True\n if isinstance(v, (int, float, Decimal, str)):\n return True\n return False\n\n\ndef _scalar_to_row(key: str, ordinal: int, value) -> dict:\n \"\"\"Convert a scalar value to a typed projection row.\"\"\"\n if isinstance(value, bool):\n return {\"key\": key, \"ordinal\": ordinal, \"val_bool\": bool(value)}\n if isinstance(value, (int, float, Decimal)):\n num = value if isinstance(value, Decimal) else Decimal(str(value))\n return {\"key\": key, \"ordinal\": ordinal, \"val_num\": num}\n if isinstance(value, str):\n return {\"key\": key, \"ordinal\": ordinal, \"val_str\": value}\n return {\"key\": key, \"ordinal\": ordinal, \"val_json\": value}\n\n\ndef convert_metadata_to_rows(key: str, value) -> list[dict]:\n \"\"\"Turn a metadata key/value into typed projection rows.\"\"\"\n if value is None:\n return []\n\n if _check_is_scalar(value):\n return [_scalar_to_row(key, 0, value)]\n\n if isinstance(value, list):\n if all(_check_is_scalar(x) for x in value):\n return [_scalar_to_row(key, i, x) for i, x in enumerate(value) if x is not None]\n return [{\"key\": key, \"ordinal\": i, \"val_json\": x} for i, x in enumerate(value) if x is not None]\n\n return [{\"key\": key, \"ordinal\": 0, \"val_json\": value}]\n\n\n\n\ndef get_reference_by_id(\n session: Session,\n reference_id: str,\n) -> AssetReference | None:\n return session.get(AssetReference, reference_id)\n\n\ndef get_reference_with_owner_check(\n session: Session,\n reference_id: str,\n owner_id: str,\n) -> AssetReference:\n \"\"\"Fetch a reference and verify ownership.\n\n Raises:\n ValueError: if reference not found or soft-deleted\n PermissionError: if owner_id doesn't match\n \"\"\"\n ref = get_reference_by_id(session, reference_id=reference_id)\n if not ref or ref.deleted_at is not None:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n if ref.owner_id and ref.owner_id != owner_id:\n raise PermissionError(\"not owner\")\n return ref\n\n\ndef get_reference_by_file_path(\n session: Session,\n file_path: str,\n) -> AssetReference | None:\n \"\"\"Get a reference by its file path.\"\"\"\n return (\n session.execute(\n select(AssetReference).where(AssetReference.file_path == file_path).limit(1)\n )\n .scalars()\n .first()\n )\n\n\ndef reference_exists_for_asset_id(\n session: Session,\n asset_id: str,\n) -> bool:\n q = (\n select(sa.literal(True))\n .select_from(AssetReference)\n .where(AssetReference.asset_id == asset_id)\n .where(AssetReference.deleted_at.is_(None))\n .limit(1)\n )\n return session.execute(q).first() is not None\n\n\ndef reference_exists(\n session: Session,\n reference_id: str,\n) -> bool:\n \"\"\"Return True if a reference with the given ID exists (not soft-deleted).\"\"\"\n q = (\n select(sa.literal(True))\n .select_from(AssetReference)\n .where(AssetReference.id == reference_id)\n .where(AssetReference.deleted_at.is_(None))\n .limit(1)\n )\n return session.execute(q).first() is not None\n\n\ndef insert_reference(\n session: Session,\n asset_id: str,\n name: str,\n owner_id: str = \"\",\n file_path: str | None = None,\n mtime_ns: int | None = None,\n preview_id: str | None = None,\n) -> AssetReference | None:\n \"\"\"Insert a new AssetReference. Returns None if unique constraint violated.\"\"\"\n now = get_utc_now()\n try:\n with session.begin_nested():\n ref = AssetReference(\n asset_id=asset_id,\n name=name,\n owner_id=owner_id,\n file_path=file_path,\n mtime_ns=mtime_ns,\n preview_id=preview_id,\n created_at=now,\n updated_at=now,\n last_access_time=now,\n )\n session.add(ref)\n session.flush()\n return ref\n except IntegrityError:\n return None\n\n\ndef get_or_create_reference(\n session: Session,\n asset_id: str,\n name: str,\n owner_id: str = \"\",\n file_path: str | None = None,\n mtime_ns: int | None = None,\n preview_id: str | None = None,\n) -> tuple[AssetReference, bool]:\n \"\"\"Get existing or create new AssetReference.\n\n For filesystem references (file_path is set), uniqueness is by file_path.\n For API references (file_path is None), we look for matching\n asset_id + owner_id + name.\n\n Returns (reference, created).\n \"\"\"\n ref = insert_reference(\n session,\n asset_id=asset_id,\n name=name,\n owner_id=owner_id,\n file_path=file_path,\n mtime_ns=mtime_ns,\n preview_id=preview_id,\n )\n if ref:\n return ref, True\n\n # Find existing - priority to file_path match, then name match\n if file_path:\n existing = get_reference_by_file_path(session, file_path)\n else:\n existing = (\n session.execute(\n select(AssetReference)\n .where(\n AssetReference.asset_id == asset_id,\n AssetReference.name == name,\n AssetReference.owner_id == owner_id,\n AssetReference.file_path.is_(None),\n )\n .limit(1)\n )\n .unique()\n .scalar_one_or_none()\n )\n if not existing:\n raise RuntimeError(\"Failed to find AssetReference after insert conflict.\")\n return existing, False\n\n\ndef update_reference_timestamps(\n session: Session,\n reference: AssetReference,\n preview_id: str | None = None,\n) -> None:\n \"\"\"Update timestamps and optionally preview_id on existing AssetReference.\"\"\"\n now = get_utc_now()\n if preview_id and reference.preview_id != preview_id:\n reference.preview_id = preview_id\n reference.updated_at = now\n\n\ndef list_references_page(\n session: Session,\n owner_id: str = \"\",\n limit: int = 100,\n offset: int = 0,\n name_contains: str | None = None,\n include_tags: Sequence[str] | None = None,\n exclude_tags: Sequence[str] | None = None,\n metadata_filter: dict | None = None,\n sort: str | None = None,\n order: str | None = None,\n) -> tuple[list[AssetReference], dict[str, list[str]], int]:\n \"\"\"List references with pagination, filtering, and sorting.\n\n Returns (references, tag_map, total_count).\n \"\"\"\n base = (\n select(AssetReference)\n .join(Asset, Asset.id == AssetReference.asset_id)\n .where(build_visible_owner_clause(owner_id))\n .where(AssetReference.is_missing == False) # noqa: E712\n .where(AssetReference.deleted_at.is_(None))\n .options(noload(AssetReference.tags))\n )\n\n if name_contains:\n escaped, esc = escape_sql_like_string(name_contains)\n base = base.where(AssetReference.name.ilike(f\"%{escaped}%\", escape=esc))\n\n base = apply_tag_filters(base, include_tags, exclude_tags)\n base = apply_metadata_filter(base, metadata_filter)\n\n sort = (sort or \"created_at\").lower()\n order = (order or \"desc\").lower()\n sort_map = {\n \"name\": AssetReference.name,\n \"created_at\": AssetReference.created_at,\n \"updated_at\": AssetReference.updated_at,\n \"last_access_time\": AssetReference.last_access_time,\n \"size\": Asset.size_bytes,\n }\n sort_col = sort_map.get(sort, AssetReference.created_at)\n sort_exp = sort_col.desc() if order == \"desc\" else sort_col.asc()\n\n base = base.order_by(sort_exp).limit(limit).offset(offset)\n\n count_stmt = (\n select(sa.func.count())\n .select_from(AssetReference)\n .join(Asset, Asset.id == AssetReference.asset_id)\n .where(build_visible_owner_clause(owner_id))\n .where(AssetReference.is_missing == False) # noqa: E712\n .where(AssetReference.deleted_at.is_(None))\n )\n if name_contains:\n escaped, esc = escape_sql_like_string(name_contains)\n count_stmt = count_stmt.where(\n AssetReference.name.ilike(f\"%{escaped}%\", escape=esc)\n )\n count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)\n count_stmt = apply_metadata_filter(count_stmt, metadata_filter)\n\n total = int(session.execute(count_stmt).scalar_one() or 0)\n refs = session.execute(base).unique().scalars().all()\n\n id_list: list[str] = [r.id for r in refs]\n tag_map: dict[str, list[str]] = defaultdict(list)\n if id_list:\n rows = session.execute(\n select(AssetReferenceTag.asset_reference_id, Tag.name)\n .join(Tag, Tag.name == AssetReferenceTag.tag_name)\n .where(AssetReferenceTag.asset_reference_id.in_(id_list))\n .order_by(AssetReferenceTag.tag_name.asc())\n )\n for ref_id, tag_name in rows.all():\n tag_map[ref_id].append(tag_name)\n\n return list(refs), tag_map, total\n\n\ndef fetch_reference_asset_and_tags(\n session: Session,\n reference_id: str,\n owner_id: str = \"\",\n) -> tuple[AssetReference, Asset, list[str]] | None:\n stmt = (\n select(AssetReference, Asset, Tag.name)\n .join(Asset, Asset.id == AssetReference.asset_id)\n .join(\n AssetReferenceTag,\n AssetReferenceTag.asset_reference_id == AssetReference.id,\n isouter=True,\n )\n .join(Tag, Tag.name == AssetReferenceTag.tag_name, isouter=True)\n .where(\n AssetReference.id == reference_id,\n AssetReference.deleted_at.is_(None),\n build_visible_owner_clause(owner_id),\n )\n .options(noload(AssetReference.tags))\n .order_by(Tag.name.asc())\n )\n\n rows = session.execute(stmt).all()\n if not rows:\n return None\n\n first_ref, first_asset, _ = rows[0]\n tags: list[str] = []\n seen: set[str] = set()\n for _ref, _asset, tag_name in rows:\n if tag_name and tag_name not in seen:\n seen.add(tag_name)\n tags.append(tag_name)\n return first_ref, first_asset, tags\n\n\ndef fetch_reference_and_asset(\n session: Session,\n reference_id: str,\n owner_id: str = \"\",\n) -> tuple[AssetReference, Asset] | None:\n stmt = (\n select(AssetReference, Asset)\n .join(Asset, Asset.id == AssetReference.asset_id)\n .where(\n AssetReference.id == reference_id,\n AssetReference.deleted_at.is_(None),\n build_visible_owner_clause(owner_id),\n )\n .limit(1)\n .options(noload(AssetReference.tags))\n )\n pair = session.execute(stmt).first()\n if not pair:\n return None\n return pair[0], pair[1]\n\n\ndef update_reference_access_time(\n session: Session,\n reference_id: str,\n ts: datetime | None = None,\n only_if_newer: bool = True,\n) -> None:\n ts = ts or get_utc_now()\n stmt = sa.update(AssetReference).where(AssetReference.id == reference_id)\n if only_if_newer:\n stmt = stmt.where(\n sa.or_(\n AssetReference.last_access_time.is_(None),\n AssetReference.last_access_time < ts,\n )\n )\n session.execute(stmt.values(last_access_time=ts))\n\n\ndef update_reference_name(\n session: Session,\n reference_id: str,\n name: str,\n) -> None:\n \"\"\"Update the name of an AssetReference.\"\"\"\n now = get_utc_now()\n session.execute(\n sa.update(AssetReference)\n .where(AssetReference.id == reference_id)\n .values(name=name, updated_at=now)\n )\n\n\ndef update_reference_updated_at(\n session: Session,\n reference_id: str,\n ts: datetime | None = None,\n) -> None:\n \"\"\"Update the updated_at timestamp of an AssetReference.\"\"\"\n ts = ts or get_utc_now()\n session.execute(\n sa.update(AssetReference)\n .where(AssetReference.id == reference_id)\n .values(updated_at=ts)\n )\n\n\ndef rebuild_metadata_projection(session: Session, ref: AssetReference) -> None:\n \"\"\"Delete and rebuild AssetReferenceMeta rows from merged system+user metadata.\n\n The merged dict is ``{**system_metadata, **user_metadata}`` so user keys\n override system keys of the same name.\n \"\"\"\n session.execute(\n delete(AssetReferenceMeta).where(\n AssetReferenceMeta.asset_reference_id == ref.id\n )\n )\n session.flush()\n\n merged = {**(ref.system_metadata or {}), **(ref.user_metadata or {})}\n if not merged:\n return\n\n rows: list[AssetReferenceMeta] = []\n for k, v in merged.items():\n for r in convert_metadata_to_rows(k, v):\n rows.append(\n AssetReferenceMeta(\n asset_reference_id=ref.id,\n key=r[\"key\"],\n ordinal=int(r[\"ordinal\"]),\n val_str=r.get(\"val_str\"),\n val_num=r.get(\"val_num\"),\n val_bool=r.get(\"val_bool\"),\n val_json=r.get(\"val_json\"),\n )\n )\n if rows:\n session.add_all(rows)\n session.flush()\n\n\ndef set_reference_metadata(\n session: Session,\n reference_id: str,\n user_metadata: dict | None = None,\n) -> None:\n ref = session.get(AssetReference, reference_id)\n if not ref:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n\n ref.user_metadata = user_metadata or {}\n ref.updated_at = get_utc_now()\n session.flush()\n\n rebuild_metadata_projection(session, ref)\n\n\ndef set_reference_system_metadata(\n session: Session,\n reference_id: str,\n system_metadata: dict | None = None,\n) -> None:\n \"\"\"Set system_metadata on a reference and rebuild the merged projection.\"\"\"\n ref = session.get(AssetReference, reference_id)\n if not ref:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n\n ref.system_metadata = system_metadata or {}\n ref.updated_at = get_utc_now()\n session.flush()\n\n rebuild_metadata_projection(session, ref)\n\n\ndef delete_reference_by_id(\n session: Session,\n reference_id: str,\n owner_id: str,\n) -> bool:\n stmt = sa.delete(AssetReference).where(\n AssetReference.id == reference_id,\n build_visible_owner_clause(owner_id),\n )\n return int(session.execute(stmt).rowcount or 0) > 0\n\n\ndef soft_delete_reference_by_id(\n session: Session,\n reference_id: str,\n owner_id: str,\n) -> bool:\n \"\"\"Mark a reference as soft-deleted by setting deleted_at timestamp.\n\n Returns True if the reference was found and marked deleted.\n \"\"\"\n now = get_utc_now()\n stmt = (\n sa.update(AssetReference)\n .where(\n AssetReference.id == reference_id,\n AssetReference.deleted_at.is_(None),\n build_visible_owner_clause(owner_id),\n )\n .values(deleted_at=now)\n )\n return int(session.execute(stmt).rowcount or 0) > 0\n\n\ndef set_reference_preview(\n session: Session,\n reference_id: str,\n preview_reference_id: str | None = None,\n) -> None:\n \"\"\"Set or clear preview_id and bump updated_at. Raises on unknown IDs.\"\"\"\n ref = session.get(AssetReference, reference_id)\n if not ref:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n\n if preview_reference_id is None:\n ref.preview_id = None\n else:\n if not session.get(AssetReference, preview_reference_id):\n raise ValueError(f\"Preview AssetReference {preview_reference_id} not found\")\n ref.preview_id = preview_reference_id\n\n ref.updated_at = get_utc_now()\n session.flush()\n\n\nclass CacheStateRow(NamedTuple):\n \"\"\"Row from reference query with cache state data.\"\"\"\n\n reference_id: str\n file_path: str\n mtime_ns: int | None\n needs_verify: bool\n asset_id: str\n asset_hash: str | None\n size_bytes: int | None\n\n\ndef list_references_by_asset_id(\n session: Session,\n asset_id: str,\n) -> Sequence[AssetReference]:\n return (\n session.execute(\n select(AssetReference)\n .where(AssetReference.asset_id == asset_id)\n .where(AssetReference.is_missing == False) # noqa: E712\n .where(AssetReference.deleted_at.is_(None))\n .order_by(AssetReference.id.asc())\n )\n .scalars()\n .all()\n )\n\n\ndef list_all_file_paths_by_asset_id(\n session: Session,\n asset_id: str,\n) -> list[str]:\n \"\"\"Return every file_path for an asset, including soft-deleted/missing refs.\n\n Used for orphan cleanup where all on-disk files must be removed.\n \"\"\"\n return list(\n session.execute(\n select(AssetReference.file_path)\n .where(AssetReference.asset_id == asset_id)\n .where(AssetReference.file_path.isnot(None))\n )\n .scalars()\n .all()\n )\n\n\ndef upsert_reference(\n session: Session,\n asset_id: str,\n file_path: str,\n name: str,\n mtime_ns: int,\n owner_id: str = \"\",\n) -> tuple[bool, bool]:\n \"\"\"Upsert a reference by file_path. Returns (created, updated).\n\n Also restores references that were previously marked as missing.\n \"\"\"\n now = get_utc_now()\n vals = {\n \"asset_id\": asset_id,\n \"file_path\": file_path,\n \"name\": name,\n \"owner_id\": owner_id,\n \"mtime_ns\": int(mtime_ns),\n \"is_missing\": False,\n \"created_at\": now,\n \"updated_at\": now,\n \"last_access_time\": now,\n }\n ins = (\n sqlite.insert(AssetReference)\n .values(**vals)\n .on_conflict_do_nothing(index_elements=[AssetReference.file_path])\n )\n res = session.execute(ins)\n created = int(res.rowcount or 0) > 0\n\n if created:\n return True, False\n\n upd = (\n sa.update(AssetReference)\n .where(AssetReference.file_path == file_path)\n .where(\n sa.or_(\n AssetReference.asset_id != asset_id,\n AssetReference.mtime_ns.is_(None),\n AssetReference.mtime_ns != int(mtime_ns),\n AssetReference.is_missing == True, # noqa: E712\n AssetReference.deleted_at.isnot(None),\n )\n )\n .values(\n asset_id=asset_id, mtime_ns=int(mtime_ns), is_missing=False,\n deleted_at=None, updated_at=now,\n )\n )\n res2 = session.execute(upd)\n updated = int(res2.rowcount or 0) > 0\n return False, updated\n\n\ndef mark_references_missing_outside_prefixes(\n session: Session,\n valid_prefixes: list[str],\n) -> int:\n \"\"\"Mark references as missing when file_path doesn't match any valid prefix.\n\n Returns number of references marked as missing.\n \"\"\"\n if not valid_prefixes:\n return 0\n\n conds = build_prefix_like_conditions(valid_prefixes)\n matches_valid_prefix = sa.or_(*conds)\n result = session.execute(\n sa.update(AssetReference)\n .where(AssetReference.file_path.isnot(None))\n .where(AssetReference.deleted_at.is_(None))\n .where(~matches_valid_prefix)\n .where(AssetReference.is_missing == False) # noqa: E712\n .values(is_missing=True)\n )\n return result.rowcount\n\n\ndef restore_references_by_paths(session: Session, file_paths: list[str]) -> int:\n \"\"\"Restore references that were previously marked as missing.\n\n Returns number of references restored.\n \"\"\"\n if not file_paths:\n return 0\n\n total = 0\n for chunk in iter_chunks(file_paths, MAX_BIND_PARAMS):\n result = session.execute(\n sa.update(AssetReference)\n .where(AssetReference.file_path.in_(chunk))\n .where(AssetReference.is_missing == True) # noqa: E712\n .where(AssetReference.deleted_at.is_(None))\n .values(is_missing=False)\n )\n total += result.rowcount\n return total\n\n\ndef get_unreferenced_unhashed_asset_ids(session: Session) -> list[str]:\n \"\"\"Get IDs of unhashed assets (hash=None) with no active references.\n\n An asset is considered unreferenced if it has no references,\n or all its references are marked as missing.\n\n Returns list of asset IDs that are unreferenced.\n \"\"\"\n active_ref_exists = (\n sa.select(sa.literal(1))\n .where(AssetReference.asset_id == Asset.id)\n .where(AssetReference.is_missing == False) # noqa: E712\n .where(AssetReference.deleted_at.is_(None))\n .correlate(Asset)\n .exists()\n )\n unreferenced_subq = sa.select(Asset.id).where(\n Asset.hash.is_(None), ~active_ref_exists\n )\n return [row[0] for row in session.execute(unreferenced_subq).all()]\n\n\ndef delete_assets_by_ids(session: Session, asset_ids: list[str]) -> int:\n \"\"\"Delete assets and their references by ID.\n\n Returns number of assets deleted.\n \"\"\"\n if not asset_ids:\n return 0\n total = 0\n for chunk in iter_chunks(asset_ids, MAX_BIND_PARAMS):\n session.execute(\n sa.delete(AssetReference).where(AssetReference.asset_id.in_(chunk))\n )\n result = session.execute(sa.delete(Asset).where(Asset.id.in_(chunk)))\n total += result.rowcount\n return total\n\n\ndef get_references_for_prefixes(\n session: Session,\n prefixes: list[str],\n *,\n include_missing: bool = False,\n) -> list[CacheStateRow]:\n \"\"\"Get all references with file paths matching any of the given prefixes.\n\n Args:\n session: Database session\n prefixes: List of absolute directory prefixes to match\n include_missing: If False (default), exclude references marked as missing\n\n Returns:\n List of cache state rows with joined asset data\n \"\"\"\n if not prefixes:\n return []\n\n conds = build_prefix_like_conditions(prefixes)\n\n query = (\n sa.select(\n AssetReference.id,\n AssetReference.file_path,\n AssetReference.mtime_ns,\n AssetReference.needs_verify,\n AssetReference.asset_id,\n Asset.hash,\n Asset.size_bytes,\n )\n .join(Asset, Asset.id == AssetReference.asset_id)\n .where(AssetReference.file_path.isnot(None))\n .where(AssetReference.deleted_at.is_(None))\n .where(sa.or_(*conds))\n )\n\n if not include_missing:\n query = query.where(AssetReference.is_missing == False) # noqa: E712\n\n rows = session.execute(\n query.order_by(AssetReference.asset_id.asc(), AssetReference.id.asc())\n ).all()\n\n return [\n CacheStateRow(\n reference_id=row[0],\n file_path=row[1],\n mtime_ns=row[2],\n needs_verify=row[3],\n asset_id=row[4],\n asset_hash=row[5],\n size_bytes=int(row[6]) if row[6] is not None else None,\n )\n for row in rows\n ]\n\n\ndef bulk_update_needs_verify(\n session: Session, reference_ids: list[str], value: bool\n) -> int:\n \"\"\"Set needs_verify flag for multiple references.\n\n Returns: Number of rows updated\n \"\"\"\n if not reference_ids:\n return 0\n total = 0\n for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):\n result = session.execute(\n sa.update(AssetReference)\n .where(AssetReference.id.in_(chunk))\n .values(needs_verify=value)\n )\n total += result.rowcount\n return total\n\n\ndef bulk_update_is_missing(\n session: Session, reference_ids: list[str], value: bool\n) -> int:\n \"\"\"Set is_missing flag for multiple references.\n\n Returns: Number of rows updated\n \"\"\"\n if not reference_ids:\n return 0\n total = 0\n for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):\n result = session.execute(\n sa.update(AssetReference)\n .where(AssetReference.id.in_(chunk))\n .values(is_missing=value)\n )\n total += result.rowcount\n return total\n\n\ndef update_is_missing_by_asset_id(\n session: Session, asset_id: str, value: bool\n) -> int:\n \"\"\"Set is_missing flag for ALL references belonging to an asset.\n\n Returns: Number of rows updated\n \"\"\"\n result = session.execute(\n sa.update(AssetReference)\n .where(AssetReference.asset_id == asset_id)\n .where(AssetReference.deleted_at.is_(None))\n .values(is_missing=value)\n )\n return result.rowcount\n\n\ndef delete_references_by_ids(session: Session, reference_ids: list[str]) -> int:\n \"\"\"Delete references by their IDs.\n\n Returns: Number of rows deleted\n \"\"\"\n if not reference_ids:\n return 0\n total = 0\n for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):\n result = session.execute(\n sa.delete(AssetReference).where(AssetReference.id.in_(chunk))\n )\n total += result.rowcount\n return total\n\n\ndef delete_orphaned_seed_asset(session: Session, asset_id: str) -> bool:\n \"\"\"Delete a seed asset (hash is None) and its references.\n\n Returns: True if asset was deleted, False if not found or has a hash\n \"\"\"\n asset = session.get(Asset, asset_id)\n if not asset:\n return False\n if asset.hash is not None:\n return False\n session.execute(\n sa.delete(AssetReference).where(AssetReference.asset_id == asset_id)\n )\n session.delete(asset)\n return True\n\n\nclass UnenrichedReferenceRow(NamedTuple):\n \"\"\"Row for references needing enrichment.\"\"\"\n\n reference_id: str\n asset_id: str\n file_path: str\n enrichment_level: int\n\n\ndef get_unenriched_references(\n session: Session,\n prefixes: list[str],\n max_level: int = 0,\n limit: int = 1000,\n) -> list[UnenrichedReferenceRow]:\n \"\"\"Get references that need enrichment (enrichment_level <= max_level).\n\n Args:\n session: Database session\n prefixes: List of absolute directory prefixes to scan\n max_level: Maximum enrichment level to include (0=stubs, 1=metadata done)\n limit: Maximum number of rows to return\n\n Returns:\n List of unenriched reference rows with file paths\n \"\"\"\n if not prefixes:\n return []\n\n conds = build_prefix_like_conditions(prefixes)\n\n query = (\n sa.select(\n AssetReference.id,\n AssetReference.asset_id,\n AssetReference.file_path,\n AssetReference.enrichment_level,\n )\n .where(AssetReference.file_path.isnot(None))\n .where(AssetReference.deleted_at.is_(None))\n .where(sa.or_(*conds))\n .where(AssetReference.is_missing == False) # noqa: E712\n .where(AssetReference.enrichment_level <= max_level)\n .order_by(AssetReference.id.asc())\n .limit(limit)\n )\n\n rows = session.execute(query).all()\n return [\n UnenrichedReferenceRow(\n reference_id=row[0],\n asset_id=row[1],\n file_path=row[2],\n enrichment_level=row[3],\n )\n for row in rows\n ]\n\n\ndef bulk_update_enrichment_level(\n session: Session,\n reference_ids: list[str],\n level: int,\n) -> int:\n \"\"\"Update enrichment level for multiple references.\n\n Returns: Number of rows updated\n \"\"\"\n if not reference_ids:\n return 0\n result = session.execute(\n sa.update(AssetReference)\n .where(AssetReference.id.in_(reference_ids))\n .values(enrichment_level=level)\n )\n return result.rowcount\n\n\ndef bulk_insert_references_ignore_conflicts(\n session: Session,\n rows: list[dict],\n) -> None:\n \"\"\"Bulk insert reference rows with ON CONFLICT DO NOTHING on file_path.\n\n Each dict should have: id, asset_id, file_path, name, owner_id, mtime_ns, etc.\n The is_missing field is automatically set to False for new inserts.\n \"\"\"\n if not rows:\n return\n enriched_rows = [{**row, \"is_missing\": False} for row in rows]\n ins = sqlite.insert(AssetReference).on_conflict_do_nothing(\n index_elements=[AssetReference.file_path]\n )\n for chunk in iter_chunks(enriched_rows, calculate_rows_per_statement(14)):\n session.execute(ins, chunk)\n\n\ndef get_references_by_paths_and_asset_ids(\n session: Session,\n path_to_asset: dict[str, str],\n) -> set[str]:\n \"\"\"Query references to find paths where our asset_id won the insert.\n\n Args:\n path_to_asset: Mapping of file_path -> asset_id we tried to insert\n\n Returns:\n Set of file_paths where our asset_id is present\n \"\"\"\n if not path_to_asset:\n return set()\n\n pairs = list(path_to_asset.items())\n winners: set[str] = set()\n\n # Each pair uses 2 bind params, so chunk at MAX_BIND_PARAMS // 2\n for chunk in iter_chunks(pairs, MAX_BIND_PARAMS // 2):\n pairwise = sa.tuple_(AssetReference.file_path, AssetReference.asset_id).in_(\n chunk\n )\n result = session.execute(\n select(AssetReference.file_path).where(pairwise)\n )\n winners.update(result.scalars().all())\n\n return winners\n\n\ndef get_reference_ids_by_ids(\n session: Session,\n reference_ids: list[str],\n) -> set[str]:\n \"\"\"Query to find which reference IDs exist in the database.\"\"\"\n if not reference_ids:\n return set()\n\n found: set[str] = set()\n for chunk in iter_chunks(reference_ids, MAX_BIND_PARAMS):\n result = session.execute(\n select(AssetReference.id).where(AssetReference.id.in_(chunk))\n )\n found.update(result.scalars().all())\n return found\n" }, { "path": "app/assets/database/queries/common.py", "content": "\"\"\"Shared utilities for database query modules.\"\"\"\n\nimport os\nfrom decimal import Decimal\nfrom typing import Iterable, Sequence\n\nimport sqlalchemy as sa\nfrom sqlalchemy import exists\n\nfrom app.assets.database.models import AssetReference, AssetReferenceMeta, AssetReferenceTag\nfrom app.assets.helpers import escape_sql_like_string, normalize_tags\n\nMAX_BIND_PARAMS = 800\n\n\ndef calculate_rows_per_statement(cols: int) -> int:\n \"\"\"Calculate how many rows can fit in one statement given column count.\"\"\"\n return max(1, MAX_BIND_PARAMS // max(1, cols))\n\n\ndef iter_chunks(seq, n: int):\n \"\"\"Yield successive n-sized chunks from seq.\"\"\"\n for i in range(0, len(seq), n):\n yield seq[i : i + n]\n\n\ndef iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[list[dict]]:\n \"\"\"Yield chunks of rows sized to fit within bind param limits.\"\"\"\n if not rows:\n return\n yield from iter_chunks(rows, calculate_rows_per_statement(cols_per_row))\n\n\ndef build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:\n \"\"\"Build owner visibility predicate for reads.\n\n Owner-less rows are visible to everyone.\n \"\"\"\n owner_id = (owner_id or \"\").strip()\n if owner_id == \"\":\n return AssetReference.owner_id == \"\"\n return AssetReference.owner_id.in_([\"\", owner_id])\n\n\ndef build_prefix_like_conditions(\n prefixes: list[str],\n) -> list[sa.sql.ColumnElement]:\n \"\"\"Build LIKE conditions for matching file paths under directory prefixes.\"\"\"\n conds = []\n for p in prefixes:\n base = os.path.abspath(p)\n if not base.endswith(os.sep):\n base += os.sep\n escaped, esc = escape_sql_like_string(base)\n conds.append(AssetReference.file_path.like(escaped + \"%\", escape=esc))\n return conds\n\n\ndef apply_tag_filters(\n stmt: sa.sql.Select,\n include_tags: Sequence[str] | None = None,\n exclude_tags: Sequence[str] | None = None,\n) -> sa.sql.Select:\n \"\"\"include_tags: every tag must be present; exclude_tags: none may be present.\"\"\"\n include_tags = normalize_tags(include_tags)\n exclude_tags = normalize_tags(exclude_tags)\n\n if include_tags:\n for tag_name in include_tags:\n stmt = stmt.where(\n exists().where(\n (AssetReferenceTag.asset_reference_id == AssetReference.id)\n & (AssetReferenceTag.tag_name == tag_name)\n )\n )\n\n if exclude_tags:\n stmt = stmt.where(\n ~exists().where(\n (AssetReferenceTag.asset_reference_id == AssetReference.id)\n & (AssetReferenceTag.tag_name.in_(exclude_tags))\n )\n )\n return stmt\n\n\ndef apply_metadata_filter(\n stmt: sa.sql.Select,\n metadata_filter: dict | None = None,\n) -> sa.sql.Select:\n \"\"\"Apply filters using asset_reference_meta projection table.\"\"\"\n if not metadata_filter:\n return stmt\n\n def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:\n return sa.exists().where(\n AssetReferenceMeta.asset_reference_id == AssetReference.id,\n AssetReferenceMeta.key == key,\n *preds,\n )\n\n def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:\n if value is None:\n return sa.not_(\n sa.exists().where(\n AssetReferenceMeta.asset_reference_id == AssetReference.id,\n AssetReferenceMeta.key == key,\n )\n )\n\n if isinstance(value, bool):\n return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value))\n if isinstance(value, (int, float, Decimal)):\n num = value if isinstance(value, Decimal) else Decimal(str(value))\n return _exists_for_pred(key, AssetReferenceMeta.val_num == num)\n if isinstance(value, str):\n return _exists_for_pred(key, AssetReferenceMeta.val_str == value)\n return _exists_for_pred(key, AssetReferenceMeta.val_json == value)\n\n for k, v in metadata_filter.items():\n if isinstance(v, list):\n ors = [_exists_clause_for_value(k, elem) for elem in v]\n if ors:\n stmt = stmt.where(sa.or_(*ors))\n else:\n stmt = stmt.where(_exists_clause_for_value(k, v))\n return stmt\n" }, { "path": "app/assets/database/queries/tags.py", "content": "from dataclasses import dataclass\nfrom typing import Iterable, Sequence\n\nimport sqlalchemy as sa\nfrom sqlalchemy import delete, func, select\nfrom sqlalchemy.dialects import sqlite\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy.orm import Session\n\nfrom app.assets.database.models import (\n Asset,\n AssetReference,\n AssetReferenceMeta,\n AssetReferenceTag,\n Tag,\n)\nfrom app.assets.database.queries.common import (\n apply_metadata_filter,\n apply_tag_filters,\n build_visible_owner_clause,\n iter_row_chunks,\n)\nfrom app.assets.helpers import escape_sql_like_string, get_utc_now, normalize_tags\n\n\n@dataclass(frozen=True)\nclass AddTagsResult:\n added: list[str]\n already_present: list[str]\n total_tags: list[str]\n\n\n@dataclass(frozen=True)\nclass RemoveTagsResult:\n removed: list[str]\n not_present: list[str]\n total_tags: list[str]\n\n\n@dataclass(frozen=True)\nclass SetTagsResult:\n added: list[str]\n removed: list[str]\n total: list[str]\n\n\ndef validate_tags_exist(session: Session, tags: list[str]) -> None:\n \"\"\"Raise ValueError if any of the given tag names do not exist.\"\"\"\n existing_tag_names = set(\n name\n for (name,) in session.execute(select(Tag.name).where(Tag.name.in_(tags))).all()\n )\n missing = [t for t in tags if t not in existing_tag_names]\n if missing:\n raise ValueError(f\"Unknown tags: {missing}\")\n\n\ndef ensure_tags_exist(\n session: Session, names: Iterable[str], tag_type: str = \"user\"\n) -> None:\n wanted = normalize_tags(list(names))\n if not wanted:\n return\n rows = [{\"name\": n, \"tag_type\": tag_type} for n in list(dict.fromkeys(wanted))]\n ins = (\n sqlite.insert(Tag)\n .values(rows)\n .on_conflict_do_nothing(index_elements=[Tag.name])\n )\n session.execute(ins)\n\n\ndef get_reference_tags(session: Session, reference_id: str) -> list[str]:\n return [\n tag_name\n for (tag_name,) in (\n session.execute(\n select(AssetReferenceTag.tag_name)\n .where(AssetReferenceTag.asset_reference_id == reference_id)\n .order_by(AssetReferenceTag.tag_name.asc())\n )\n ).all()\n ]\n\n\ndef set_reference_tags(\n session: Session,\n reference_id: str,\n tags: Sequence[str],\n origin: str = \"manual\",\n) -> SetTagsResult:\n desired = normalize_tags(tags)\n\n current = set(get_reference_tags(session, reference_id))\n\n to_add = [t for t in desired if t not in current]\n to_remove = [t for t in current if t not in desired]\n\n if to_add:\n ensure_tags_exist(session, to_add, tag_type=\"user\")\n session.add_all(\n [\n AssetReferenceTag(\n asset_reference_id=reference_id,\n tag_name=t,\n origin=origin,\n added_at=get_utc_now(),\n )\n for t in to_add\n ]\n )\n session.flush()\n\n if to_remove:\n session.execute(\n delete(AssetReferenceTag).where(\n AssetReferenceTag.asset_reference_id == reference_id,\n AssetReferenceTag.tag_name.in_(to_remove),\n )\n )\n session.flush()\n\n return SetTagsResult(added=sorted(to_add), removed=sorted(to_remove), total=sorted(desired))\n\n\ndef add_tags_to_reference(\n session: Session,\n reference_id: str,\n tags: Sequence[str],\n origin: str = \"manual\",\n create_if_missing: bool = True,\n reference_row: AssetReference | None = None,\n) -> AddTagsResult:\n if not reference_row:\n ref = session.get(AssetReference, reference_id)\n if not ref:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n\n norm = normalize_tags(tags)\n if not norm:\n total = get_reference_tags(session, reference_id=reference_id)\n return AddTagsResult(added=[], already_present=[], total_tags=total)\n\n if create_if_missing:\n ensure_tags_exist(session, norm, tag_type=\"user\")\n\n current = set(get_reference_tags(session, reference_id))\n\n want = set(norm)\n to_add = sorted(want - current)\n\n if to_add:\n with session.begin_nested() as nested:\n try:\n session.add_all(\n [\n AssetReferenceTag(\n asset_reference_id=reference_id,\n tag_name=t,\n origin=origin,\n added_at=get_utc_now(),\n )\n for t in to_add\n ]\n )\n session.flush()\n except IntegrityError:\n nested.rollback()\n\n after = set(get_reference_tags(session, reference_id=reference_id))\n return AddTagsResult(\n added=sorted(((after - current) & want)),\n already_present=sorted(want & current),\n total_tags=sorted(after),\n )\n\n\ndef remove_tags_from_reference(\n session: Session,\n reference_id: str,\n tags: Sequence[str],\n) -> RemoveTagsResult:\n ref = session.get(AssetReference, reference_id)\n if not ref:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n\n norm = normalize_tags(tags)\n if not norm:\n total = get_reference_tags(session, reference_id=reference_id)\n return RemoveTagsResult(removed=[], not_present=[], total_tags=total)\n\n existing = set(get_reference_tags(session, reference_id))\n\n to_remove = sorted(set(t for t in norm if t in existing))\n not_present = sorted(set(t for t in norm if t not in existing))\n\n if to_remove:\n session.execute(\n delete(AssetReferenceTag).where(\n AssetReferenceTag.asset_reference_id == reference_id,\n AssetReferenceTag.tag_name.in_(to_remove),\n )\n )\n session.flush()\n\n total = get_reference_tags(session, reference_id=reference_id)\n return RemoveTagsResult(removed=to_remove, not_present=not_present, total_tags=total)\n\n\ndef add_missing_tag_for_asset_id(\n session: Session,\n asset_id: str,\n origin: str = \"automatic\",\n) -> None:\n select_rows = (\n sa.select(\n AssetReference.id.label(\"asset_reference_id\"),\n sa.literal(\"missing\").label(\"tag_name\"),\n sa.literal(origin).label(\"origin\"),\n sa.literal(get_utc_now()).label(\"added_at\"),\n )\n .where(AssetReference.asset_id == asset_id)\n .where(\n sa.not_(\n sa.exists().where(\n (AssetReferenceTag.asset_reference_id == AssetReference.id)\n & (AssetReferenceTag.tag_name == \"missing\")\n )\n )\n )\n )\n session.execute(\n sqlite.insert(AssetReferenceTag)\n .from_select(\n [\"asset_reference_id\", \"tag_name\", \"origin\", \"added_at\"],\n select_rows,\n )\n .on_conflict_do_nothing(\n index_elements=[\n AssetReferenceTag.asset_reference_id,\n AssetReferenceTag.tag_name,\n ]\n )\n )\n\n\ndef remove_missing_tag_for_asset_id(\n session: Session,\n asset_id: str,\n) -> None:\n session.execute(\n sa.delete(AssetReferenceTag).where(\n AssetReferenceTag.asset_reference_id.in_(\n sa.select(AssetReference.id).where(AssetReference.asset_id == asset_id)\n ),\n AssetReferenceTag.tag_name == \"missing\",\n )\n )\n\n\ndef list_tags_with_usage(\n session: Session,\n prefix: str | None = None,\n limit: int = 100,\n offset: int = 0,\n include_zero: bool = True,\n order: str = \"count_desc\",\n owner_id: str = \"\",\n) -> tuple[list[tuple[str, str, int]], int]:\n counts_sq = (\n select(\n AssetReferenceTag.tag_name.label(\"tag_name\"),\n func.count(AssetReferenceTag.asset_reference_id).label(\"cnt\"),\n )\n .select_from(AssetReferenceTag)\n .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)\n .where(build_visible_owner_clause(owner_id))\n .where(\n sa.or_(\n AssetReference.is_missing == False, # noqa: E712\n AssetReferenceTag.tag_name == \"missing\",\n )\n )\n .where(AssetReference.deleted_at.is_(None))\n .group_by(AssetReferenceTag.tag_name)\n .subquery()\n )\n\n q = (\n select(\n Tag.name,\n Tag.tag_type,\n func.coalesce(counts_sq.c.cnt, 0).label(\"count\"),\n )\n .select_from(Tag)\n .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)\n )\n\n if prefix:\n escaped, esc = escape_sql_like_string(prefix.strip().lower())\n q = q.where(Tag.name.like(escaped + \"%\", escape=esc))\n\n if not include_zero:\n q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)\n\n if order == \"name_asc\":\n q = q.order_by(Tag.name.asc())\n else:\n q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())\n\n total_q = select(func.count()).select_from(Tag)\n if prefix:\n escaped, esc = escape_sql_like_string(prefix.strip().lower())\n total_q = total_q.where(Tag.name.like(escaped + \"%\", escape=esc))\n if not include_zero:\n visible_tags_sq = (\n select(AssetReferenceTag.tag_name)\n .join(AssetReference, AssetReference.id == AssetReferenceTag.asset_reference_id)\n .where(build_visible_owner_clause(owner_id))\n .where(\n sa.or_(\n AssetReference.is_missing == False, # noqa: E712\n AssetReferenceTag.tag_name == \"missing\",\n )\n )\n .where(AssetReference.deleted_at.is_(None))\n .group_by(AssetReferenceTag.tag_name)\n )\n total_q = total_q.where(Tag.name.in_(visible_tags_sq))\n\n rows = (session.execute(q.limit(limit).offset(offset))).all()\n total = (session.execute(total_q)).scalar_one()\n\n rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]\n return rows_norm, int(total or 0)\n\n\ndef list_tag_counts_for_filtered_assets(\n session: Session,\n owner_id: str = \"\",\n include_tags: Sequence[str] | None = None,\n exclude_tags: Sequence[str] | None = None,\n name_contains: str | None = None,\n metadata_filter: dict | None = None,\n limit: int = 100,\n) -> dict[str, int]:\n \"\"\"Return tag counts for assets matching the given filters.\n\n Uses the same filtering logic as list_references_page but returns\n {tag_name: count} instead of paginated references.\n \"\"\"\n # Build a subquery of matching reference IDs\n ref_sq = (\n select(AssetReference.id)\n .join(Asset, Asset.id == AssetReference.asset_id)\n .where(build_visible_owner_clause(owner_id))\n .where(AssetReference.is_missing == False) # noqa: E712\n .where(AssetReference.deleted_at.is_(None))\n )\n\n if name_contains:\n escaped, esc = escape_sql_like_string(name_contains)\n ref_sq = ref_sq.where(AssetReference.name.ilike(f\"%{escaped}%\", escape=esc))\n\n ref_sq = apply_tag_filters(ref_sq, include_tags, exclude_tags)\n ref_sq = apply_metadata_filter(ref_sq, metadata_filter)\n ref_sq = ref_sq.subquery()\n\n # Count tags across those references\n q = (\n select(\n AssetReferenceTag.tag_name,\n func.count(AssetReferenceTag.asset_reference_id).label(\"cnt\"),\n )\n .where(AssetReferenceTag.asset_reference_id.in_(select(ref_sq.c.id)))\n .group_by(AssetReferenceTag.tag_name)\n .order_by(func.count(AssetReferenceTag.asset_reference_id).desc(), AssetReferenceTag.tag_name.asc())\n .limit(limit)\n )\n\n rows = session.execute(q).all()\n return {tag_name: int(cnt) for tag_name, cnt in rows}\n\n\ndef bulk_insert_tags_and_meta(\n session: Session,\n tag_rows: list[dict],\n meta_rows: list[dict],\n) -> None:\n \"\"\"Batch insert into asset_reference_tags and asset_reference_meta.\n\n Uses ON CONFLICT DO NOTHING.\n\n Args:\n session: Database session\n tag_rows: Dicts with: asset_reference_id, tag_name, origin, added_at\n meta_rows: Dicts with: asset_reference_id, key, ordinal, val_*\n \"\"\"\n if tag_rows:\n ins_tags = sqlite.insert(AssetReferenceTag).on_conflict_do_nothing(\n index_elements=[\n AssetReferenceTag.asset_reference_id,\n AssetReferenceTag.tag_name,\n ]\n )\n for chunk in iter_row_chunks(tag_rows, cols_per_row=4):\n session.execute(ins_tags, chunk)\n\n if meta_rows:\n ins_meta = sqlite.insert(AssetReferenceMeta).on_conflict_do_nothing(\n index_elements=[\n AssetReferenceMeta.asset_reference_id,\n AssetReferenceMeta.key,\n AssetReferenceMeta.ordinal,\n ]\n )\n for chunk in iter_row_chunks(meta_rows, cols_per_row=7):\n session.execute(ins_meta, chunk)\n" }, { "path": "app/assets/helpers.py", "content": "import os\nfrom datetime import datetime, timezone\nfrom typing import Sequence\n\n\ndef select_best_live_path(states: Sequence) -> str:\n \"\"\"\n Return the best on-disk path among cache states:\n 1) Prefer a path that exists with needs_verify == False (already verified).\n 2) Otherwise, pick the first path that exists.\n 3) Otherwise return empty string.\n \"\"\"\n alive = [\n s\n for s in states\n if getattr(s, \"file_path\", None) and os.path.isfile(s.file_path)\n ]\n if not alive:\n return \"\"\n for s in alive:\n if not getattr(s, \"needs_verify\", False):\n return s.file_path\n return alive[0].file_path\n\n\ndef escape_sql_like_string(s: str, escape: str = \"!\") -> tuple[str, str]:\n \"\"\"Escapes %, _ and the escape char in a LIKE prefix.\n\n Returns (escaped_prefix, escape_char).\n \"\"\"\n s = s.replace(escape, escape + escape) # escape the escape char first\n s = s.replace(\"%\", escape + \"%\").replace(\"_\", escape + \"_\") # escape LIKE wildcards\n return s, escape\n\n\ndef get_utc_now() -> datetime:\n \"\"\"Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC.\"\"\"\n return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\ndef normalize_tags(tags: list[str] | None) -> list[str]:\n \"\"\"\n Normalize a list of tags by:\n - Stripping whitespace and converting to lowercase.\n - Removing duplicates.\n \"\"\"\n return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or \"\").strip()))\n\n\ndef validate_blake3_hash(s: str) -> str:\n \"\"\"Validate and normalize a blake3 hash string.\n\n Returns canonical 'blake3:' or raises ValueError.\n \"\"\"\n s = s.strip().lower()\n if not s or \":\" not in s:\n raise ValueError(\"hash must be 'blake3:'\")\n algo, digest = s.split(\":\", 1)\n if (\n algo != \"blake3\"\n or len(digest) != 64\n or any(c for c in digest if c not in \"0123456789abcdef\")\n ):\n raise ValueError(\"hash must be 'blake3:'\")\n return f\"{algo}:{digest}\"\n" }, { "path": "app/assets/scanner.py", "content": "import logging\nimport os\nfrom pathlib import Path\nfrom typing import Callable, Literal, TypedDict\n\nimport folder_paths\nfrom app.assets.database.queries import (\n add_missing_tag_for_asset_id,\n bulk_update_enrichment_level,\n bulk_update_is_missing,\n bulk_update_needs_verify,\n delete_orphaned_seed_asset,\n delete_references_by_ids,\n ensure_tags_exist,\n get_asset_by_hash,\n get_references_for_prefixes,\n get_unenriched_references,\n mark_references_missing_outside_prefixes,\n reassign_asset_references,\n remove_missing_tag_for_asset_id,\n set_reference_system_metadata,\n update_asset_hash_and_mime,\n)\nfrom app.assets.services.bulk_ingest import (\n SeedAssetSpec,\n batch_insert_seed_assets,\n)\nfrom app.assets.services.file_utils import (\n get_mtime_ns,\n is_visible,\n list_files_recursively,\n verify_file_unchanged,\n)\nfrom app.assets.services.hashing import HashCheckpoint, compute_blake3_hash\nfrom app.assets.services.metadata_extract import extract_file_metadata\nfrom app.assets.services.path_utils import (\n compute_relative_filename,\n get_comfy_models_folders,\n get_name_and_tags_from_asset_path,\n)\nfrom app.database.db import create_session\n\n\nclass _RefInfo(TypedDict):\n ref_id: str\n file_path: str\n exists: bool\n stat_unchanged: bool\n needs_verify: bool\n\n\nclass _AssetAccumulator(TypedDict):\n hash: str | None\n size_db: int\n refs: list[_RefInfo]\n\n\nRootType = Literal[\"models\", \"input\", \"output\"]\n\n\ndef get_prefixes_for_root(root: RootType) -> list[str]:\n if root == \"models\":\n bases: list[str] = []\n for _bucket, paths in get_comfy_models_folders():\n bases.extend(paths)\n return [os.path.abspath(p) for p in bases]\n if root == \"input\":\n return [os.path.abspath(folder_paths.get_input_directory())]\n if root == \"output\":\n return [os.path.abspath(folder_paths.get_output_directory())]\n return []\n\n\ndef get_all_known_prefixes() -> list[str]:\n \"\"\"Get all known asset prefixes across all root types.\"\"\"\n all_roots: tuple[RootType, ...] = (\"models\", \"input\", \"output\")\n return [p for root in all_roots for p in get_prefixes_for_root(root)]\n\n\ndef collect_models_files() -> list[str]:\n out: list[str] = []\n for folder_name, bases in get_comfy_models_folders():\n rel_files = folder_paths.get_filename_list(folder_name) or []\n for rel_path in rel_files:\n if not all(is_visible(part) for part in Path(rel_path).parts):\n continue\n abs_path = folder_paths.get_full_path(folder_name, rel_path)\n if not abs_path:\n continue\n abs_path = os.path.abspath(abs_path)\n allowed = False\n abs_p = Path(abs_path)\n for b in bases:\n if abs_p.is_relative_to(os.path.abspath(b)):\n allowed = True\n break\n if allowed:\n out.append(abs_path)\n return out\n\n\ndef sync_references_with_filesystem(\n session,\n root: RootType,\n collect_existing_paths: bool = False,\n update_missing_tags: bool = False,\n) -> set[str] | None:\n \"\"\"Reconcile asset references with filesystem for a root.\n\n - Toggle needs_verify per reference using mtime/size stat check\n - For hashed assets with at least one stat-unchanged ref: delete stale missing refs\n - For seed assets with all refs missing: delete Asset and its references\n - Optionally add/remove 'missing' tags based on stat check in this root\n - Optionally return surviving absolute paths\n\n Args:\n session: Database session\n root: Root type to scan\n collect_existing_paths: If True, return set of surviving file paths\n update_missing_tags: If True, update 'missing' tags based on file status\n\n Returns:\n Set of surviving absolute paths if collect_existing_paths=True, else None\n \"\"\"\n prefixes = get_prefixes_for_root(root)\n if not prefixes:\n return set() if collect_existing_paths else None\n\n rows = get_references_for_prefixes(\n session, prefixes, include_missing=update_missing_tags\n )\n\n by_asset: dict[str, _AssetAccumulator] = {}\n for row in rows:\n acc = by_asset.get(row.asset_id)\n if acc is None:\n acc = {\"hash\": row.asset_hash, \"size_db\": row.size_bytes, \"refs\": []}\n by_asset[row.asset_id] = acc\n\n stat_unchanged = False\n try:\n exists = True\n stat_unchanged = verify_file_unchanged(\n mtime_db=row.mtime_ns,\n size_db=acc[\"size_db\"],\n stat_result=os.stat(row.file_path, follow_symlinks=True),\n )\n except FileNotFoundError:\n exists = False\n except PermissionError:\n exists = True\n logging.debug(\"Permission denied accessing %s\", row.file_path)\n except OSError as e:\n exists = False\n logging.debug(\"OSError checking %s: %s\", row.file_path, e)\n\n acc[\"refs\"].append(\n {\n \"ref_id\": row.reference_id,\n \"file_path\": row.file_path,\n \"exists\": exists,\n \"stat_unchanged\": stat_unchanged,\n \"needs_verify\": row.needs_verify,\n }\n )\n\n to_set_verify: list[str] = []\n to_clear_verify: list[str] = []\n stale_ref_ids: list[str] = []\n to_mark_missing: list[str] = []\n to_clear_missing: list[str] = []\n survivors: set[str] = set()\n\n for aid, acc in by_asset.items():\n a_hash = acc[\"hash\"]\n refs = acc[\"refs\"]\n any_unchanged = any(r[\"stat_unchanged\"] for r in refs)\n all_missing = all(not r[\"exists\"] for r in refs)\n\n for r in refs:\n if not r[\"exists\"]:\n to_mark_missing.append(r[\"ref_id\"])\n continue\n if r[\"stat_unchanged\"]:\n to_clear_missing.append(r[\"ref_id\"])\n if r[\"needs_verify\"]:\n to_clear_verify.append(r[\"ref_id\"])\n if not r[\"stat_unchanged\"] and not r[\"needs_verify\"]:\n to_set_verify.append(r[\"ref_id\"])\n\n if a_hash is None:\n if refs and all_missing:\n delete_orphaned_seed_asset(session, aid)\n else:\n for r in refs:\n if r[\"exists\"]:\n survivors.add(os.path.abspath(r[\"file_path\"]))\n continue\n\n if any_unchanged:\n for r in refs:\n if not r[\"exists\"]:\n stale_ref_ids.append(r[\"ref_id\"])\n if update_missing_tags:\n try:\n remove_missing_tag_for_asset_id(session, asset_id=aid)\n except Exception as e:\n logging.warning(\n \"Failed to remove missing tag for asset %s: %s\", aid, e\n )\n elif update_missing_tags:\n try:\n add_missing_tag_for_asset_id(session, asset_id=aid, origin=\"automatic\")\n except Exception as e:\n logging.warning(\"Failed to add missing tag for asset %s: %s\", aid, e)\n\n for r in refs:\n if r[\"exists\"]:\n survivors.add(os.path.abspath(r[\"file_path\"]))\n\n delete_references_by_ids(session, stale_ref_ids)\n stale_set = set(stale_ref_ids)\n to_mark_missing = [ref_id for ref_id in to_mark_missing if ref_id not in stale_set]\n bulk_update_is_missing(session, to_mark_missing, value=True)\n bulk_update_is_missing(session, to_clear_missing, value=False)\n bulk_update_needs_verify(session, to_set_verify, value=True)\n bulk_update_needs_verify(session, to_clear_verify, value=False)\n\n return survivors if collect_existing_paths else None\n\n\ndef sync_root_safely(root: RootType) -> set[str]:\n \"\"\"Sync a single root's references with the filesystem.\n\n Returns survivors (existing paths) or empty set on failure.\n \"\"\"\n try:\n with create_session() as sess:\n survivors = sync_references_with_filesystem(\n sess,\n root,\n collect_existing_paths=True,\n update_missing_tags=True,\n )\n sess.commit()\n return survivors or set()\n except Exception as e:\n logging.exception(\"fast DB scan failed for %s: %s\", root, e)\n return set()\n\n\ndef mark_missing_outside_prefixes_safely(prefixes: list[str]) -> int:\n \"\"\"Mark references as missing when outside the given prefixes.\n\n This is a non-destructive soft-delete. Returns count marked or 0 on failure.\n \"\"\"\n try:\n with create_session() as sess:\n count = mark_references_missing_outside_prefixes(sess, prefixes)\n sess.commit()\n return count\n except Exception as e:\n logging.exception(\"marking missing assets failed: %s\", e)\n return 0\n\n\ndef collect_paths_for_roots(roots: tuple[RootType, ...]) -> list[str]:\n \"\"\"Collect all file paths for the given roots.\"\"\"\n paths: list[str] = []\n if \"models\" in roots:\n paths.extend(collect_models_files())\n if \"input\" in roots:\n paths.extend(list_files_recursively(folder_paths.get_input_directory()))\n if \"output\" in roots:\n paths.extend(list_files_recursively(folder_paths.get_output_directory()))\n return paths\n\n\ndef build_asset_specs(\n paths: list[str],\n existing_paths: set[str],\n enable_metadata_extraction: bool = True,\n compute_hashes: bool = False,\n) -> tuple[list[SeedAssetSpec], set[str], int]:\n \"\"\"Build asset specs from paths, returning (specs, tag_pool, skipped_count).\n\n Args:\n paths: List of file paths to process\n existing_paths: Set of paths that already exist in the database\n enable_metadata_extraction: If True, extract tier 1 & 2 metadata\n compute_hashes: If True, compute blake3 hashes (slow for large files)\n \"\"\"\n specs: list[SeedAssetSpec] = []\n tag_pool: set[str] = set()\n skipped = 0\n\n for p in paths:\n abs_p = os.path.abspath(p)\n if abs_p in existing_paths:\n skipped += 1\n continue\n try:\n stat_p = os.stat(abs_p, follow_symlinks=True)\n except OSError:\n continue\n if not stat_p.st_size:\n continue\n name, tags = get_name_and_tags_from_asset_path(abs_p)\n rel_fname = compute_relative_filename(abs_p)\n\n # Extract metadata (tier 1: filesystem, tier 2: safetensors header)\n metadata = None\n if enable_metadata_extraction:\n metadata = extract_file_metadata(\n abs_p,\n stat_result=stat_p,\n relative_filename=rel_fname,\n )\n\n # Compute hash if requested\n asset_hash: str | None = None\n if compute_hashes:\n try:\n digest, _ = compute_blake3_hash(abs_p)\n asset_hash = \"blake3:\" + digest\n except Exception as e:\n logging.warning(\"Failed to hash %s: %s\", abs_p, e)\n\n mime_type = metadata.content_type if metadata else None\n specs.append(\n {\n \"abs_path\": abs_p,\n \"size_bytes\": stat_p.st_size,\n \"mtime_ns\": get_mtime_ns(stat_p),\n \"info_name\": name,\n \"tags\": tags,\n \"fname\": rel_fname,\n \"metadata\": metadata,\n \"hash\": asset_hash,\n \"mime_type\": mime_type,\n }\n )\n tag_pool.update(tags)\n\n return specs, tag_pool, skipped\n\n\n\ndef insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -> int:\n \"\"\"Insert asset specs into database, returning count of created refs.\"\"\"\n if not specs:\n return 0\n with create_session() as sess:\n if tag_pool:\n ensure_tags_exist(sess, tag_pool, tag_type=\"user\")\n result = batch_insert_seed_assets(sess, specs=specs, owner_id=\"\")\n sess.commit()\n return result.inserted_refs\n\n\n# Enrichment level constants\nENRICHMENT_STUB = 0 # Fast scan: path, size, mtime only\nENRICHMENT_METADATA = 1 # Metadata extracted (safetensors header, mime type)\nENRICHMENT_HASHED = 2 # Hash computed (blake3)\n\n\ndef get_unenriched_assets_for_roots(\n roots: tuple[RootType, ...],\n max_level: int = ENRICHMENT_STUB,\n limit: int = 1000,\n) -> list:\n \"\"\"Get assets that need enrichment for the given roots.\n\n Args:\n roots: Tuple of root types to scan\n max_level: Maximum enrichment level to include\n limit: Maximum number of rows to return\n\n Returns:\n List of UnenrichedReferenceRow\n \"\"\"\n prefixes: list[str] = []\n for root in roots:\n prefixes.extend(get_prefixes_for_root(root))\n\n if not prefixes:\n return []\n\n with create_session() as sess:\n return get_unenriched_references(\n sess, prefixes, max_level=max_level, limit=limit\n )\n\n\ndef enrich_asset(\n session,\n file_path: str,\n reference_id: str,\n asset_id: str,\n extract_metadata: bool = True,\n compute_hash: bool = False,\n interrupt_check: Callable[[], bool] | None = None,\n hash_checkpoints: dict[str, HashCheckpoint] | None = None,\n) -> int:\n \"\"\"Enrich a single asset with metadata and/or hash.\n\n Args:\n session: Database session (caller manages lifecycle)\n file_path: Absolute path to the file\n reference_id: ID of the reference to update\n asset_id: ID of the asset to update (for mime_type and hash)\n extract_metadata: If True, extract safetensors header and mime type\n compute_hash: If True, compute blake3 hash\n interrupt_check: Optional non-blocking callable that returns True if\n the operation should be interrupted (e.g. paused or cancelled)\n hash_checkpoints: Optional dict for saving/restoring hash progress\n across interruptions, keyed by file path\n\n Returns:\n New enrichment level achieved\n \"\"\"\n new_level = ENRICHMENT_STUB\n\n try:\n stat_p = os.stat(file_path, follow_symlinks=True)\n except OSError:\n return new_level\n\n rel_fname = compute_relative_filename(file_path)\n mime_type: str | None = None\n metadata = None\n\n if extract_metadata:\n metadata = extract_file_metadata(\n file_path,\n stat_result=stat_p,\n relative_filename=rel_fname,\n )\n if metadata:\n mime_type = metadata.content_type\n new_level = ENRICHMENT_METADATA\n\n full_hash: str | None = None\n if compute_hash:\n try:\n mtime_before = get_mtime_ns(stat_p)\n size_before = stat_p.st_size\n\n # Restore checkpoint if available and file unchanged\n checkpoint = None\n if hash_checkpoints is not None:\n checkpoint = hash_checkpoints.get(file_path)\n if checkpoint is not None:\n cur_stat = os.stat(file_path, follow_symlinks=True)\n if (checkpoint.mtime_ns != get_mtime_ns(cur_stat)\n or checkpoint.file_size != cur_stat.st_size):\n checkpoint = None\n hash_checkpoints.pop(file_path, None)\n else:\n mtime_before = get_mtime_ns(cur_stat)\n\n digest, new_checkpoint = compute_blake3_hash(\n file_path,\n interrupt_check=interrupt_check,\n checkpoint=checkpoint,\n )\n\n if digest is None:\n # Interrupted — save checkpoint for later resumption\n if hash_checkpoints is not None and new_checkpoint is not None:\n new_checkpoint.mtime_ns = mtime_before\n new_checkpoint.file_size = size_before\n hash_checkpoints[file_path] = new_checkpoint\n return new_level\n\n # Completed — clear any saved checkpoint\n if hash_checkpoints is not None:\n hash_checkpoints.pop(file_path, None)\n\n stat_after = os.stat(file_path, follow_symlinks=True)\n mtime_after = get_mtime_ns(stat_after)\n if mtime_before != mtime_after:\n logging.warning(\"File modified during hashing, discarding hash: %s\", file_path)\n else:\n full_hash = f\"blake3:{digest}\"\n metadata_ok = not extract_metadata or metadata is not None\n if metadata_ok:\n new_level = ENRICHMENT_HASHED\n except Exception as e:\n logging.warning(\"Failed to hash %s: %s\", file_path, e)\n\n if extract_metadata and metadata:\n system_metadata = metadata.to_user_metadata()\n set_reference_system_metadata(session, reference_id, system_metadata)\n\n if full_hash:\n existing = get_asset_by_hash(session, full_hash)\n if existing and existing.id != asset_id:\n reassign_asset_references(session, asset_id, existing.id, reference_id)\n delete_orphaned_seed_asset(session, asset_id)\n if mime_type:\n update_asset_hash_and_mime(session, existing.id, mime_type=mime_type)\n else:\n update_asset_hash_and_mime(session, asset_id, full_hash, mime_type)\n elif mime_type:\n update_asset_hash_and_mime(session, asset_id, mime_type=mime_type)\n\n bulk_update_enrichment_level(session, [reference_id], new_level)\n session.commit()\n\n return new_level\n\n\ndef enrich_assets_batch(\n rows: list,\n extract_metadata: bool = True,\n compute_hash: bool = False,\n interrupt_check: Callable[[], bool] | None = None,\n hash_checkpoints: dict[str, HashCheckpoint] | None = None,\n) -> tuple[int, list[str]]:\n \"\"\"Enrich a batch of assets.\n\n Uses a single DB session for the entire batch, committing after each\n individual asset to avoid long-held transactions while eliminating\n per-asset session creation overhead.\n\n Args:\n rows: List of UnenrichedReferenceRow from get_unenriched_assets_for_roots\n extract_metadata: If True, extract metadata for each asset\n compute_hash: If True, compute hash for each asset\n interrupt_check: Optional non-blocking callable that returns True if\n the operation should be interrupted (e.g. paused or cancelled)\n hash_checkpoints: Optional dict for saving/restoring hash progress\n across interruptions, keyed by file path\n\n Returns:\n Tuple of (enriched_count, failed_reference_ids)\n \"\"\"\n enriched = 0\n failed_ids: list[str] = []\n\n with create_session() as sess:\n for row in rows:\n if interrupt_check is not None and interrupt_check():\n break\n\n try:\n new_level = enrich_asset(\n sess,\n file_path=row.file_path,\n reference_id=row.reference_id,\n asset_id=row.asset_id,\n extract_metadata=extract_metadata,\n compute_hash=compute_hash,\n interrupt_check=interrupt_check,\n hash_checkpoints=hash_checkpoints,\n )\n if new_level > row.enrichment_level:\n enriched += 1\n else:\n failed_ids.append(row.reference_id)\n except Exception as e:\n logging.warning(\"Failed to enrich %s: %s\", row.file_path, e)\n sess.rollback()\n failed_ids.append(row.reference_id)\n\n return enriched, failed_ids\n" }, { "path": "app/assets/seeder.py", "content": "\"\"\"Background asset seeder with thread management and cancellation support.\"\"\"\n\nimport logging\nimport os\nimport threading\nimport time\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import Callable\n\nfrom app.assets.scanner import (\n ENRICHMENT_METADATA,\n ENRICHMENT_STUB,\n RootType,\n build_asset_specs,\n collect_paths_for_roots,\n enrich_assets_batch,\n get_all_known_prefixes,\n get_prefixes_for_root,\n get_unenriched_assets_for_roots,\n insert_asset_specs,\n mark_missing_outside_prefixes_safely,\n sync_root_safely,\n)\nfrom app.database.db import dependencies_available\n\n\nclass ScanInProgressError(Exception):\n \"\"\"Raised when an operation cannot proceed because a scan is running.\"\"\"\n\n\nclass State(Enum):\n \"\"\"Seeder state machine states.\"\"\"\n\n IDLE = \"IDLE\"\n RUNNING = \"RUNNING\"\n PAUSED = \"PAUSED\"\n CANCELLING = \"CANCELLING\"\n\n\nclass ScanPhase(Enum):\n \"\"\"Scan phase options.\"\"\"\n\n FAST = \"fast\" # Phase 1: filesystem only (stubs)\n ENRICH = \"enrich\" # Phase 2: metadata + hash\n FULL = \"full\" # Both phases sequentially\n\n\n@dataclass\nclass Progress:\n \"\"\"Progress information for a scan operation.\"\"\"\n\n scanned: int = 0\n total: int = 0\n created: int = 0\n skipped: int = 0\n\n\n@dataclass\nclass ScanStatus:\n \"\"\"Current status of the asset seeder.\"\"\"\n\n state: State\n progress: Progress | None\n errors: list[str] = field(default_factory=list)\n\n\nProgressCallback = Callable[[Progress], None]\n\n\nclass _AssetSeeder:\n \"\"\"Background asset scanning manager.\n\n Spawns ephemeral daemon threads for scanning.\n Each scan creates a new thread that exits when complete.\n Use the module-level ``asset_seeder`` instance.\n \"\"\"\n\n def __init__(self) -> None:\n self._lock = threading.Lock()\n self._state = State.IDLE\n self._progress: Progress | None = None\n self._last_progress: Progress | None = None\n self._errors: list[str] = []\n self._thread: threading.Thread | None = None\n self._cancel_event = threading.Event()\n self._run_gate = threading.Event()\n self._run_gate.set() # Start unpaused (set = running, clear = paused)\n self._roots: tuple[RootType, ...] = ()\n self._phase: ScanPhase = ScanPhase.FULL\n self._compute_hashes: bool = False\n self._prune_first: bool = False\n self._progress_callback: ProgressCallback | None = None\n self._disabled: bool = False\n\n def disable(self) -> None:\n \"\"\"Disable the asset seeder, preventing any scans from starting.\"\"\"\n self._disabled = True\n logging.info(\"Asset seeder disabled\")\n\n def is_disabled(self) -> bool:\n \"\"\"Check if the asset seeder is disabled.\"\"\"\n return self._disabled\n\n def start(\n self,\n roots: tuple[RootType, ...] = (\"models\", \"input\", \"output\"),\n phase: ScanPhase = ScanPhase.FULL,\n progress_callback: ProgressCallback | None = None,\n prune_first: bool = False,\n compute_hashes: bool = False,\n ) -> bool:\n \"\"\"Start a background scan for the given roots.\n\n Args:\n roots: Tuple of root types to scan (models, input, output)\n phase: Scan phase to run (FAST, ENRICH, or FULL for both)\n progress_callback: Optional callback called with progress updates\n prune_first: If True, prune orphaned assets before scanning\n compute_hashes: If True, compute blake3 hashes (slow)\n\n Returns:\n True if scan was started, False if already running\n \"\"\"\n if self._disabled:\n logging.debug(\"Asset seeder is disabled, skipping start\")\n return False\n logging.info(\"Seeder start (roots=%s, phase=%s)\", roots, phase.value)\n with self._lock:\n if self._state != State.IDLE:\n logging.info(\"Asset seeder already running, skipping start\")\n return False\n self._state = State.RUNNING\n self._progress = Progress()\n self._errors = []\n self._roots = roots\n self._phase = phase\n self._prune_first = prune_first\n self._compute_hashes = compute_hashes\n self._progress_callback = progress_callback\n self._cancel_event.clear()\n self._run_gate.set() # Ensure unpaused when starting\n self._thread = threading.Thread(\n target=self._run_scan,\n name=\"_AssetSeeder\",\n daemon=True,\n )\n self._thread.start()\n return True\n\n def start_fast(\n self,\n roots: tuple[RootType, ...] = (\"models\", \"input\", \"output\"),\n progress_callback: ProgressCallback | None = None,\n prune_first: bool = False,\n ) -> bool:\n \"\"\"Start a fast scan (phase 1 only) - creates stub records.\n\n Args:\n roots: Tuple of root types to scan\n progress_callback: Optional callback for progress updates\n prune_first: If True, prune orphaned assets before scanning\n\n Returns:\n True if scan was started, False if already running\n \"\"\"\n return self.start(\n roots=roots,\n phase=ScanPhase.FAST,\n progress_callback=progress_callback,\n prune_first=prune_first,\n compute_hashes=False,\n )\n\n def start_enrich(\n self,\n roots: tuple[RootType, ...] = (\"models\", \"input\", \"output\"),\n progress_callback: ProgressCallback | None = None,\n compute_hashes: bool = False,\n ) -> bool:\n \"\"\"Start an enrichment scan (phase 2 only) - extracts metadata and hashes.\n\n Args:\n roots: Tuple of root types to scan\n progress_callback: Optional callback for progress updates\n compute_hashes: If True, compute blake3 hashes\n\n Returns:\n True if scan was started, False if already running\n \"\"\"\n return self.start(\n roots=roots,\n phase=ScanPhase.ENRICH,\n progress_callback=progress_callback,\n prune_first=False,\n compute_hashes=compute_hashes,\n )\n\n def cancel(self) -> bool:\n \"\"\"Request cancellation of the current scan.\n\n Returns:\n True if cancellation was requested, False if not running or paused\n \"\"\"\n with self._lock:\n if self._state not in (State.RUNNING, State.PAUSED):\n return False\n logging.info(\"Asset seeder cancelling (was %s)\", self._state.value)\n self._state = State.CANCELLING\n self._cancel_event.set()\n self._run_gate.set() # Unblock if paused so thread can exit\n return True\n\n def stop(self) -> bool:\n \"\"\"Stop the current scan (alias for cancel).\n\n Returns:\n True if stop was requested, False if not running\n \"\"\"\n return self.cancel()\n\n def pause(self) -> bool:\n \"\"\"Pause the current scan.\n\n The scan will complete its current batch before pausing.\n\n Returns:\n True if pause was requested, False if not running\n \"\"\"\n with self._lock:\n if self._state != State.RUNNING:\n return False\n logging.info(\"Asset seeder pausing\")\n self._state = State.PAUSED\n self._run_gate.clear()\n return True\n\n def resume(self) -> bool:\n \"\"\"Resume a paused scan.\n\n This is a noop if the scan is not in the PAUSED state\n\n Returns:\n True if resumed, False if not paused\n \"\"\"\n with self._lock:\n if self._state != State.PAUSED:\n return False\n logging.info(\"Asset seeder resuming\")\n self._state = State.RUNNING\n self._run_gate.set()\n self._emit_event(\"assets.seed.resumed\", {})\n return True\n\n def restart(\n self,\n roots: tuple[RootType, ...] | None = None,\n phase: ScanPhase | None = None,\n progress_callback: ProgressCallback | None = None,\n prune_first: bool | None = None,\n compute_hashes: bool | None = None,\n timeout: float = 5.0,\n ) -> bool:\n \"\"\"Cancel any running scan and start a new one.\n\n Args:\n roots: Roots to scan (defaults to previous roots)\n phase: Scan phase (defaults to previous phase)\n progress_callback: Progress callback (defaults to previous)\n prune_first: Prune before scan (defaults to previous)\n compute_hashes: Compute hashes (defaults to previous)\n timeout: Max seconds to wait for current scan to stop\n\n Returns:\n True if new scan was started, False if failed to stop previous\n \"\"\"\n logging.info(\"Asset seeder restart requested\")\n with self._lock:\n prev_roots = self._roots\n prev_phase = self._phase\n prev_callback = self._progress_callback\n prev_prune = self._prune_first\n prev_hashes = self._compute_hashes\n\n self.cancel()\n if not self.wait(timeout=timeout):\n return False\n\n cb = progress_callback if progress_callback is not None else prev_callback\n return self.start(\n roots=roots if roots is not None else prev_roots,\n phase=phase if phase is not None else prev_phase,\n progress_callback=cb,\n prune_first=prune_first if prune_first is not None else prev_prune,\n compute_hashes=(\n compute_hashes if compute_hashes is not None else prev_hashes\n ),\n )\n\n def wait(self, timeout: float | None = None) -> bool:\n \"\"\"Wait for the current scan to complete.\n\n Args:\n timeout: Maximum seconds to wait, or None for no timeout\n\n Returns:\n True if scan completed, False if timeout expired or no scan running\n \"\"\"\n with self._lock:\n thread = self._thread\n if thread is None:\n return True\n thread.join(timeout=timeout)\n return not thread.is_alive()\n\n def get_status(self) -> ScanStatus:\n \"\"\"Get the current status and progress of the seeder.\"\"\"\n with self._lock:\n src = self._progress or self._last_progress\n return ScanStatus(\n state=self._state,\n progress=Progress(\n scanned=src.scanned,\n total=src.total,\n created=src.created,\n skipped=src.skipped,\n )\n if src\n else None,\n errors=list(self._errors),\n )\n\n def shutdown(self, timeout: float = 5.0) -> None:\n \"\"\"Gracefully shutdown: cancel any running scan and wait for thread.\n\n Args:\n timeout: Maximum seconds to wait for thread to exit\n \"\"\"\n self.cancel()\n self.wait(timeout=timeout)\n with self._lock:\n self._thread = None\n\n def mark_missing_outside_prefixes(self) -> int:\n \"\"\"Mark references as missing when outside all known root prefixes.\n\n This is a non-destructive soft-delete operation. Assets and their\n metadata are preserved, but references are flagged as missing.\n They can be restored if the file reappears in a future scan.\n\n This operation is decoupled from scanning to prevent partial scans\n from accidentally marking assets belonging to other roots.\n\n Should be called explicitly when cleanup is desired, typically after\n a full scan of all roots or during maintenance.\n\n Returns:\n Number of references marked as missing\n\n Raises:\n ScanInProgressError: If a scan is currently running\n \"\"\"\n with self._lock:\n if self._state != State.IDLE:\n raise ScanInProgressError(\n \"Cannot mark missing assets while scan is running\"\n )\n self._state = State.RUNNING\n\n try:\n if not dependencies_available():\n logging.warning(\n \"Database dependencies not available, skipping mark missing\"\n )\n return 0\n\n all_prefixes = get_all_known_prefixes()\n marked = mark_missing_outside_prefixes_safely(all_prefixes)\n if marked > 0:\n logging.info(\"Marked %d references as missing\", marked)\n return marked\n finally:\n with self._lock:\n self._last_progress = self._progress\n self._state = State.IDLE\n self._progress = None\n\n def _is_cancelled(self) -> bool:\n \"\"\"Check if cancellation has been requested.\"\"\"\n return self._cancel_event.is_set()\n\n def _is_paused_or_cancelled(self) -> bool:\n \"\"\"Non-blocking check: True if paused or cancelled.\n\n Use as interrupt_check for I/O-bound work (e.g. hashing) so that\n file handles are released immediately on pause rather than held\n open while blocked. The caller is responsible for blocking on\n _check_pause_and_cancel() afterward.\n \"\"\"\n return not self._run_gate.is_set() or self._cancel_event.is_set()\n\n def _check_pause_and_cancel(self) -> bool:\n \"\"\"Block while paused, then check if cancelled.\n\n Call this at checkpoint locations in scan loops. It will:\n 1. Block indefinitely while paused (until resume or cancel)\n 2. Return True if cancelled, False to continue\n\n Returns:\n True if scan should stop, False to continue\n \"\"\"\n if not self._run_gate.is_set():\n self._emit_event(\"assets.seed.paused\", {})\n self._run_gate.wait() # Blocks if paused\n return self._is_cancelled()\n\n def _emit_event(self, event_type: str, data: dict) -> None:\n \"\"\"Emit a WebSocket event if server is available.\"\"\"\n try:\n from server import PromptServer\n\n if hasattr(PromptServer, \"instance\") and PromptServer.instance:\n PromptServer.instance.send_sync(event_type, data)\n except Exception:\n pass\n\n def _update_progress(\n self,\n scanned: int | None = None,\n total: int | None = None,\n created: int | None = None,\n skipped: int | None = None,\n ) -> None:\n \"\"\"Update progress counters (thread-safe).\"\"\"\n callback: ProgressCallback | None = None\n progress: Progress | None = None\n\n with self._lock:\n if self._progress is None:\n return\n if scanned is not None:\n self._progress.scanned = scanned\n if total is not None:\n self._progress.total = total\n if created is not None:\n self._progress.created = created\n if skipped is not None:\n self._progress.skipped = skipped\n if self._progress_callback:\n callback = self._progress_callback\n progress = Progress(\n scanned=self._progress.scanned,\n total=self._progress.total,\n created=self._progress.created,\n skipped=self._progress.skipped,\n )\n\n if callback and progress:\n try:\n callback(progress)\n except Exception:\n pass\n\n _MAX_ERRORS = 200\n\n def _add_error(self, message: str) -> None:\n \"\"\"Add an error message (thread-safe), capped at _MAX_ERRORS.\"\"\"\n with self._lock:\n if len(self._errors) < self._MAX_ERRORS:\n self._errors.append(message)\n\n def _log_scan_config(self, roots: tuple[RootType, ...]) -> None:\n \"\"\"Log the directories that will be scanned.\"\"\"\n import folder_paths\n\n for root in roots:\n if root == \"models\":\n logging.info(\n \"Asset scan [models] directory: %s\",\n os.path.abspath(folder_paths.models_dir),\n )\n else:\n prefixes = get_prefixes_for_root(root)\n if prefixes:\n logging.info(\"Asset scan [%s] directories: %s\", root, prefixes)\n\n def _run_scan(self) -> None:\n \"\"\"Main scan loop running in background thread.\"\"\"\n t_start = time.perf_counter()\n roots = self._roots\n phase = self._phase\n cancelled = False\n total_created = 0\n total_enriched = 0\n skipped_existing = 0\n total_paths = 0\n\n try:\n if not dependencies_available():\n self._add_error(\"Database dependencies not available\")\n self._emit_event(\n \"assets.seed.error\",\n {\"message\": \"Database dependencies not available\"},\n )\n return\n\n if self._prune_first:\n all_prefixes = get_all_known_prefixes()\n marked = mark_missing_outside_prefixes_safely(all_prefixes)\n if marked > 0:\n logging.info(\"Marked %d refs as missing before scan\", marked)\n\n if self._check_pause_and_cancel():\n logging.info(\"Asset scan cancelled after pruning phase\")\n cancelled = True\n return\n\n self._log_scan_config(roots)\n\n # Phase 1: Fast scan (stub records)\n if phase in (ScanPhase.FAST, ScanPhase.FULL):\n created, skipped, paths = self._run_fast_phase(roots)\n total_created, skipped_existing, total_paths = created, skipped, paths\n\n if self._check_pause_and_cancel():\n cancelled = True\n return\n\n self._emit_event(\n \"assets.seed.fast_complete\",\n {\n \"roots\": list(roots),\n \"created\": total_created,\n \"skipped\": skipped_existing,\n \"total\": total_paths,\n },\n )\n\n # Phase 2: Enrichment scan (metadata + hashes)\n if phase in (ScanPhase.ENRICH, ScanPhase.FULL):\n if self._check_pause_and_cancel():\n cancelled = True\n return\n\n enrich_cancelled, total_enriched = self._run_enrich_phase(roots)\n\n if enrich_cancelled:\n cancelled = True\n return\n\n self._emit_event(\n \"assets.seed.enrich_complete\",\n {\n \"roots\": list(roots),\n \"enriched\": total_enriched,\n },\n )\n\n elapsed = time.perf_counter() - t_start\n logging.info(\n \"Scan(%s, %s) done %.3fs: created=%d enriched=%d skipped=%d\",\n roots,\n phase.value,\n elapsed,\n total_created,\n total_enriched,\n skipped_existing,\n )\n\n self._emit_event(\n \"assets.seed.completed\",\n {\n \"phase\": phase.value,\n \"total\": total_paths,\n \"created\": total_created,\n \"enriched\": total_enriched,\n \"skipped\": skipped_existing,\n \"elapsed\": round(elapsed, 3),\n },\n )\n\n except Exception as e:\n self._add_error(f\"Scan failed: {e}\")\n logging.exception(\"Asset scan failed\")\n self._emit_event(\"assets.seed.error\", {\"message\": str(e)})\n finally:\n if cancelled:\n self._emit_event(\n \"assets.seed.cancelled\",\n {\n \"scanned\": self._progress.scanned if self._progress else 0,\n \"total\": total_paths,\n \"created\": total_created,\n },\n )\n with self._lock:\n self._last_progress = self._progress\n self._state = State.IDLE\n self._progress = None\n\n def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, int, int]:\n \"\"\"Run phase 1: fast scan to create stub records.\n\n Returns:\n Tuple of (total_created, skipped_existing, total_paths)\n \"\"\"\n t_fast_start = time.perf_counter()\n total_created = 0\n skipped_existing = 0\n\n existing_paths: set[str] = set()\n t_sync = time.perf_counter()\n for r in roots:\n if self._check_pause_and_cancel():\n return total_created, skipped_existing, 0\n existing_paths.update(sync_root_safely(r))\n logging.debug(\n \"Fast scan: sync_root phase took %.3fs (%d existing paths)\",\n time.perf_counter() - t_sync,\n len(existing_paths),\n )\n\n if self._check_pause_and_cancel():\n return total_created, skipped_existing, 0\n\n t_collect = time.perf_counter()\n paths = collect_paths_for_roots(roots)\n logging.debug(\n \"Fast scan: collect_paths took %.3fs (%d paths found)\",\n time.perf_counter() - t_collect,\n len(paths),\n )\n total_paths = len(paths)\n self._update_progress(total=total_paths)\n\n self._emit_event(\n \"assets.seed.started\",\n {\"roots\": list(roots), \"total\": total_paths, \"phase\": \"fast\"},\n )\n\n # Use stub specs (no metadata extraction, no hashing)\n t_specs = time.perf_counter()\n specs, tag_pool, skipped_existing = build_asset_specs(\n paths,\n existing_paths,\n enable_metadata_extraction=False,\n compute_hashes=False,\n )\n logging.debug(\n \"Fast scan: build_asset_specs took %.3fs (%d specs, %d skipped)\",\n time.perf_counter() - t_specs,\n len(specs),\n skipped_existing,\n )\n self._update_progress(skipped=skipped_existing)\n\n if self._check_pause_and_cancel():\n return total_created, skipped_existing, total_paths\n\n batch_size = 500\n last_progress_time = time.perf_counter()\n progress_interval = 1.0\n\n for i in range(0, len(specs), batch_size):\n if self._check_pause_and_cancel():\n logging.info(\n \"Fast scan cancelled after %d/%d files (created=%d)\",\n i,\n len(specs),\n total_created,\n )\n return total_created, skipped_existing, total_paths\n\n batch = specs[i : i + batch_size]\n batch_tags = {t for spec in batch for t in spec[\"tags\"]}\n try:\n created = insert_asset_specs(batch, batch_tags)\n total_created += created\n except Exception as e:\n self._add_error(f\"Batch insert failed at offset {i}: {e}\")\n logging.exception(\"Batch insert failed at offset %d\", i)\n\n scanned = i + len(batch)\n now = time.perf_counter()\n self._update_progress(scanned=scanned, created=total_created)\n\n if now - last_progress_time >= progress_interval:\n self._emit_event(\n \"assets.seed.progress\",\n {\n \"phase\": \"fast\",\n \"scanned\": scanned,\n \"total\": len(specs),\n \"created\": total_created,\n },\n )\n last_progress_time = now\n\n self._update_progress(scanned=len(specs), created=total_created)\n logging.info(\n \"Fast scan complete: %.3fs total (created=%d, skipped=%d, total_paths=%d)\",\n time.perf_counter() - t_fast_start,\n total_created,\n skipped_existing,\n total_paths,\n )\n return total_created, skipped_existing, total_paths\n\n def _run_enrich_phase(self, roots: tuple[RootType, ...]) -> tuple[bool, int]:\n \"\"\"Run phase 2: enrich existing records with metadata and hashes.\n\n Returns:\n Tuple of (cancelled, total_enriched)\n \"\"\"\n total_enriched = 0\n batch_size = 100\n last_progress_time = time.perf_counter()\n progress_interval = 1.0\n\n # Get the target enrichment level based on compute_hashes\n if not self._compute_hashes:\n target_max_level = ENRICHMENT_STUB\n else:\n target_max_level = ENRICHMENT_METADATA\n\n self._emit_event(\n \"assets.seed.started\",\n {\"roots\": list(roots), \"phase\": \"enrich\"},\n )\n\n skip_ids: set[str] = set()\n consecutive_empty = 0\n max_consecutive_empty = 3\n\n # Hash checkpoints survive across batches so interrupted hashes\n # can be resumed without re-reading the entire file.\n hash_checkpoints: dict[str, object] = {}\n\n while True:\n if self._check_pause_and_cancel():\n logging.info(\"Enrich scan cancelled after %d assets\", total_enriched)\n return True, total_enriched\n\n # Fetch next batch of unenriched assets\n unenriched = get_unenriched_assets_for_roots(\n roots,\n max_level=target_max_level,\n limit=batch_size,\n )\n\n # Filter out previously failed references\n if skip_ids:\n unenriched = [r for r in unenriched if r.reference_id not in skip_ids]\n\n if not unenriched:\n break\n\n enriched, failed_ids = enrich_assets_batch(\n unenriched,\n extract_metadata=True,\n compute_hash=self._compute_hashes,\n interrupt_check=self._is_paused_or_cancelled,\n hash_checkpoints=hash_checkpoints,\n )\n total_enriched += enriched\n skip_ids.update(failed_ids)\n\n if enriched == 0:\n consecutive_empty += 1\n if consecutive_empty >= max_consecutive_empty:\n logging.warning(\n \"Enrich phase stopping: %d consecutive batches with no progress (%d skipped)\",\n consecutive_empty,\n len(skip_ids),\n )\n break\n else:\n consecutive_empty = 0\n\n now = time.perf_counter()\n if now - last_progress_time >= progress_interval:\n self._emit_event(\n \"assets.seed.progress\",\n {\n \"phase\": \"enrich\",\n \"enriched\": total_enriched,\n },\n )\n last_progress_time = now\n\n return False, total_enriched\n\n\nasset_seeder = _AssetSeeder()\n" }, { "path": "app/assets/services/__init__.py", "content": "from app.assets.services.asset_management import (\n asset_exists,\n delete_asset_reference,\n get_asset_by_hash,\n get_asset_detail,\n list_assets_page,\n resolve_asset_for_download,\n set_asset_preview,\n update_asset_metadata,\n)\nfrom app.assets.services.bulk_ingest import (\n BulkInsertResult,\n batch_insert_seed_assets,\n cleanup_unreferenced_assets,\n)\nfrom app.assets.services.file_utils import (\n get_mtime_ns,\n get_size_and_mtime_ns,\n list_files_recursively,\n verify_file_unchanged,\n)\nfrom app.assets.services.ingest import (\n DependencyMissingError,\n HashMismatchError,\n create_from_hash,\n upload_from_temp_path,\n)\nfrom app.assets.database.queries import (\n AddTagsResult,\n RemoveTagsResult,\n)\nfrom app.assets.services.schemas import (\n AssetData,\n AssetDetailResult,\n AssetSummaryData,\n DownloadResolutionResult,\n IngestResult,\n ListAssetsResult,\n ReferenceData,\n RegisterAssetResult,\n TagUsage,\n UploadResult,\n UserMetadata,\n)\nfrom app.assets.services.tagging import (\n apply_tags,\n list_tags,\n remove_tags,\n)\n\n__all__ = [\n \"AddTagsResult\",\n \"AssetData\",\n \"AssetDetailResult\",\n \"AssetSummaryData\",\n \"ReferenceData\",\n \"BulkInsertResult\",\n \"DependencyMissingError\",\n \"DownloadResolutionResult\",\n \"HashMismatchError\",\n \"IngestResult\",\n \"ListAssetsResult\",\n \"RegisterAssetResult\",\n \"RemoveTagsResult\",\n \"TagUsage\",\n \"UploadResult\",\n \"UserMetadata\",\n \"apply_tags\",\n \"asset_exists\",\n \"batch_insert_seed_assets\",\n \"create_from_hash\",\n \"delete_asset_reference\",\n \"get_asset_by_hash\",\n \"get_asset_detail\",\n \"get_mtime_ns\",\n \"get_size_and_mtime_ns\",\n \"list_assets_page\",\n \"list_files_recursively\",\n \"list_tags\",\n \"cleanup_unreferenced_assets\",\n \"remove_tags\",\n \"resolve_asset_for_download\",\n \"set_asset_preview\",\n \"update_asset_metadata\",\n \"upload_from_temp_path\",\n \"verify_file_unchanged\",\n]\n" }, { "path": "app/assets/services/asset_management.py", "content": "import contextlib\nimport mimetypes\nimport os\nfrom typing import Sequence\n\n\nfrom app.assets.database.models import Asset\nfrom app.assets.database.queries import (\n asset_exists_by_hash,\n reference_exists_for_asset_id,\n delete_reference_by_id,\n fetch_reference_and_asset,\n soft_delete_reference_by_id,\n fetch_reference_asset_and_tags,\n get_asset_by_hash as queries_get_asset_by_hash,\n get_reference_by_id,\n get_reference_with_owner_check,\n list_references_page,\n list_all_file_paths_by_asset_id,\n list_references_by_asset_id,\n set_reference_metadata,\n set_reference_preview,\n set_reference_tags,\n update_asset_hash_and_mime,\n update_reference_access_time,\n update_reference_name,\n update_reference_updated_at,\n)\nfrom app.assets.helpers import select_best_live_path\nfrom app.assets.services.path_utils import compute_relative_filename\nfrom app.assets.services.schemas import (\n AssetData,\n AssetDetailResult,\n AssetSummaryData,\n DownloadResolutionResult,\n ListAssetsResult,\n UserMetadata,\n extract_asset_data,\n extract_reference_data,\n)\nfrom app.database.db import create_session\n\n\ndef get_asset_detail(\n reference_id: str,\n owner_id: str = \"\",\n) -> AssetDetailResult | None:\n with create_session() as session:\n result = fetch_reference_asset_and_tags(\n session,\n reference_id=reference_id,\n owner_id=owner_id,\n )\n if not result:\n return None\n\n ref, asset, tags = result\n return AssetDetailResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tags,\n )\n\n\ndef update_asset_metadata(\n reference_id: str,\n name: str | None = None,\n tags: Sequence[str] | None = None,\n user_metadata: UserMetadata = None,\n tag_origin: str = \"manual\",\n owner_id: str = \"\",\n mime_type: str | None = None,\n preview_id: str | None = None,\n) -> AssetDetailResult:\n with create_session() as session:\n ref = get_reference_with_owner_check(session, reference_id, owner_id)\n\n touched = False\n if name is not None and name != ref.name:\n update_reference_name(session, reference_id=reference_id, name=name)\n touched = True\n\n computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None\n\n new_meta: dict | None = None\n if user_metadata is not None:\n new_meta = dict(user_metadata)\n elif computed_filename:\n current_meta = ref.user_metadata or {}\n if current_meta.get(\"filename\") != computed_filename:\n new_meta = dict(current_meta)\n\n if new_meta is not None:\n if computed_filename:\n new_meta[\"filename\"] = computed_filename\n set_reference_metadata(\n session, reference_id=reference_id, user_metadata=new_meta\n )\n touched = True\n\n if tags is not None:\n set_reference_tags(\n session,\n reference_id=reference_id,\n tags=tags,\n origin=tag_origin,\n )\n touched = True\n\n if mime_type is not None:\n updated = update_asset_hash_and_mime(\n session, asset_id=ref.asset_id, mime_type=mime_type\n )\n if updated:\n touched = True\n\n if preview_id is not None:\n set_reference_preview(\n session,\n reference_id=reference_id,\n preview_reference_id=preview_id,\n )\n touched = True\n\n if touched and user_metadata is None:\n update_reference_updated_at(session, reference_id=reference_id)\n\n result = fetch_reference_asset_and_tags(\n session,\n reference_id=reference_id,\n owner_id=owner_id,\n )\n if not result:\n raise RuntimeError(\"State changed during update\")\n\n ref, asset, tag_list = result\n detail = AssetDetailResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tag_list,\n )\n session.commit()\n\n return detail\n\n\ndef delete_asset_reference(\n reference_id: str,\n owner_id: str,\n delete_content_if_orphan: bool = True,\n) -> bool:\n with create_session() as session:\n if not delete_content_if_orphan:\n # Soft delete: mark the reference as deleted but keep everything\n deleted = soft_delete_reference_by_id(\n session, reference_id=reference_id, owner_id=owner_id\n )\n session.commit()\n return deleted\n\n ref_row = get_reference_by_id(session, reference_id=reference_id)\n asset_id = ref_row.asset_id if ref_row else None\n file_path = ref_row.file_path if ref_row else None\n\n deleted = delete_reference_by_id(\n session, reference_id=reference_id, owner_id=owner_id\n )\n if not deleted:\n session.commit()\n return False\n\n if not asset_id:\n session.commit()\n return True\n\n still_exists = reference_exists_for_asset_id(session, asset_id=asset_id)\n if still_exists:\n session.commit()\n return True\n\n # Orphaned asset - gather ALL file paths (including\n # soft-deleted / missing refs) so their on-disk files get cleaned up.\n file_paths = list_all_file_paths_by_asset_id(session, asset_id=asset_id)\n # Also include the just-deleted file path\n if file_path:\n file_paths.append(file_path)\n\n asset_row = session.get(Asset, asset_id)\n if asset_row is not None:\n session.delete(asset_row)\n\n session.commit()\n\n # Delete files after commit\n for p in file_paths:\n with contextlib.suppress(Exception):\n if p and os.path.isfile(p):\n os.remove(p)\n\n return True\n\n\ndef set_asset_preview(\n reference_id: str,\n preview_reference_id: str | None = None,\n owner_id: str = \"\",\n) -> AssetDetailResult:\n with create_session() as session:\n get_reference_with_owner_check(session, reference_id, owner_id)\n\n set_reference_preview(\n session,\n reference_id=reference_id,\n preview_reference_id=preview_reference_id,\n )\n\n result = fetch_reference_asset_and_tags(\n session, reference_id=reference_id, owner_id=owner_id\n )\n if not result:\n raise RuntimeError(\"State changed during preview update\")\n\n ref, asset, tags = result\n detail = AssetDetailResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tags,\n )\n session.commit()\n\n return detail\n\n\ndef asset_exists(asset_hash: str) -> bool:\n with create_session() as session:\n return asset_exists_by_hash(session, asset_hash=asset_hash)\n\n\ndef get_asset_by_hash(asset_hash: str) -> AssetData | None:\n with create_session() as session:\n asset = queries_get_asset_by_hash(session, asset_hash=asset_hash)\n return extract_asset_data(asset)\n\n\ndef list_assets_page(\n owner_id: str = \"\",\n include_tags: Sequence[str] | None = None,\n exclude_tags: Sequence[str] | None = None,\n name_contains: str | None = None,\n metadata_filter: dict | None = None,\n limit: int = 20,\n offset: int = 0,\n sort: str = \"created_at\",\n order: str = \"desc\",\n) -> ListAssetsResult:\n with create_session() as session:\n refs, tag_map, total = list_references_page(\n session,\n owner_id=owner_id,\n include_tags=include_tags,\n exclude_tags=exclude_tags,\n name_contains=name_contains,\n metadata_filter=metadata_filter,\n limit=limit,\n offset=offset,\n sort=sort,\n order=order,\n )\n\n items: list[AssetSummaryData] = []\n for ref in refs:\n items.append(\n AssetSummaryData(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(ref.asset),\n tags=tag_map.get(ref.id, []),\n )\n )\n\n return ListAssetsResult(items=items, total=total)\n\n\ndef resolve_hash_to_path(\n asset_hash: str,\n owner_id: str = \"\",\n) -> DownloadResolutionResult | None:\n \"\"\"Resolve a blake3 hash to an on-disk file path.\n\n Only references visible to *owner_id* are considered (owner-less\n references are always visible).\n\n Returns a DownloadResolutionResult with abs_path, content_type, and\n download_name, or None if no asset or live path is found.\n \"\"\"\n with create_session() as session:\n asset = queries_get_asset_by_hash(session, asset_hash)\n if not asset:\n return None\n refs = list_references_by_asset_id(session, asset_id=asset.id)\n visible = [\n r for r in refs\n if r.owner_id == \"\" or r.owner_id == owner_id\n ]\n abs_path = select_best_live_path(visible)\n if not abs_path:\n return None\n display_name = os.path.basename(abs_path)\n for ref in visible:\n if ref.file_path == abs_path and ref.name:\n display_name = ref.name\n break\n ctype = (\n asset.mime_type\n or mimetypes.guess_type(display_name)[0]\n or \"application/octet-stream\"\n )\n return DownloadResolutionResult(\n abs_path=abs_path,\n content_type=ctype,\n download_name=display_name,\n )\n\n\ndef resolve_asset_for_download(\n reference_id: str,\n owner_id: str = \"\",\n) -> DownloadResolutionResult:\n with create_session() as session:\n pair = fetch_reference_and_asset(\n session, reference_id=reference_id, owner_id=owner_id\n )\n if not pair:\n raise ValueError(f\"AssetReference {reference_id} not found\")\n\n ref, asset = pair\n\n # For references with file_path, use that directly\n if ref.file_path and os.path.isfile(ref.file_path):\n abs_path = ref.file_path\n else:\n # For API-created refs without file_path, find a path from other refs\n refs = list_references_by_asset_id(session, asset_id=asset.id)\n abs_path = select_best_live_path(refs)\n if not abs_path:\n raise FileNotFoundError(\n f\"No live path for AssetReference {reference_id} \"\n f\"(asset id={asset.id}, name={ref.name})\"\n )\n\n # Capture ORM attributes before commit (commit expires loaded objects)\n ref_name = ref.name\n asset_mime = asset.mime_type\n\n update_reference_access_time(session, reference_id=reference_id)\n session.commit()\n\n ctype = (\n asset_mime\n or mimetypes.guess_type(ref_name or abs_path)[0]\n or \"application/octet-stream\"\n )\n download_name = ref_name or os.path.basename(abs_path)\n return DownloadResolutionResult(\n abs_path=abs_path,\n content_type=ctype,\n download_name=download_name,\n )\n" }, { "path": "app/assets/services/bulk_ingest.py", "content": "from __future__ import annotations\n\nimport os\nimport uuid\nfrom dataclasses import dataclass\nfrom datetime import datetime\nfrom typing import TYPE_CHECKING, Any, TypedDict\n\nfrom sqlalchemy.orm import Session\n\nfrom app.assets.database.queries import (\n bulk_insert_assets,\n bulk_insert_references_ignore_conflicts,\n bulk_insert_tags_and_meta,\n delete_assets_by_ids,\n get_existing_asset_ids,\n get_reference_ids_by_ids,\n get_references_by_paths_and_asset_ids,\n get_unreferenced_unhashed_asset_ids,\n restore_references_by_paths,\n)\nfrom app.assets.helpers import get_utc_now\n\nif TYPE_CHECKING:\n from app.assets.services.metadata_extract import ExtractedMetadata\n\n\nclass SeedAssetSpec(TypedDict):\n \"\"\"Spec for seeding an asset from filesystem.\"\"\"\n\n abs_path: str\n size_bytes: int\n mtime_ns: int\n info_name: str\n tags: list[str]\n fname: str\n metadata: ExtractedMetadata | None\n hash: str | None\n mime_type: str | None\n\n\nclass AssetRow(TypedDict):\n \"\"\"Row data for inserting an Asset.\"\"\"\n\n id: str\n hash: str | None\n size_bytes: int\n mime_type: str | None\n created_at: datetime\n\n\nclass ReferenceRow(TypedDict):\n \"\"\"Row data for inserting an AssetReference.\"\"\"\n\n id: str\n asset_id: str\n file_path: str\n mtime_ns: int\n owner_id: str\n name: str\n preview_id: str | None\n user_metadata: dict[str, Any] | None\n created_at: datetime\n updated_at: datetime\n last_access_time: datetime\n\n\nclass TagRow(TypedDict):\n \"\"\"Row data for inserting a Tag.\"\"\"\n\n asset_reference_id: str\n tag_name: str\n origin: str\n added_at: datetime\n\n\nclass MetadataRow(TypedDict):\n \"\"\"Row data for inserting asset metadata.\"\"\"\n\n asset_reference_id: str\n key: str\n ordinal: int\n val_str: str | None\n val_num: float | None\n val_bool: bool | None\n val_json: dict[str, Any] | None\n\n\n@dataclass\nclass BulkInsertResult:\n \"\"\"Result of bulk asset insertion.\"\"\"\n\n inserted_refs: int\n won_paths: int\n lost_paths: int\n\n\ndef batch_insert_seed_assets(\n session: Session,\n specs: list[SeedAssetSpec],\n owner_id: str = \"\",\n) -> BulkInsertResult:\n \"\"\"Seed assets from filesystem specs in batch.\n\n Each spec is a dict with keys:\n - abs_path: str\n - size_bytes: int\n - mtime_ns: int\n - info_name: str\n - tags: list[str]\n - fname: Optional[str]\n\n This function orchestrates:\n 1. Insert seed Assets (hash=NULL)\n 2. Claim references with ON CONFLICT DO NOTHING on file_path\n 3. Query to find winners (paths where our asset_id was inserted)\n 4. Delete Assets for losers (path already claimed by another asset)\n 5. Insert tags and metadata for successfully inserted references\n\n Returns:\n BulkInsertResult with inserted_refs, won_paths, lost_paths\n \"\"\"\n if not specs:\n return BulkInsertResult(inserted_refs=0, won_paths=0, lost_paths=0)\n\n current_time = get_utc_now()\n asset_rows: list[AssetRow] = []\n reference_rows: list[ReferenceRow] = []\n path_to_asset_id: dict[str, str] = {}\n asset_id_to_ref_data: dict[str, dict] = {}\n absolute_path_list: list[str] = []\n\n for spec in specs:\n absolute_path = os.path.abspath(spec[\"abs_path\"])\n asset_id = str(uuid.uuid4())\n reference_id = str(uuid.uuid4())\n absolute_path_list.append(absolute_path)\n path_to_asset_id[absolute_path] = asset_id\n\n mime_type = spec.get(\"mime_type\")\n asset_rows.append(\n {\n \"id\": asset_id,\n \"hash\": spec.get(\"hash\"),\n \"size_bytes\": spec[\"size_bytes\"],\n \"mime_type\": mime_type,\n \"created_at\": current_time,\n }\n )\n\n # Build user_metadata from extracted metadata or fallback to filename\n extracted_metadata = spec.get(\"metadata\")\n if extracted_metadata:\n user_metadata: dict[str, Any] | None = extracted_metadata.to_user_metadata()\n elif spec[\"fname\"]:\n user_metadata = {\"filename\": spec[\"fname\"]}\n else:\n user_metadata = None\n\n reference_rows.append(\n {\n \"id\": reference_id,\n \"asset_id\": asset_id,\n \"file_path\": absolute_path,\n \"mtime_ns\": spec[\"mtime_ns\"],\n \"owner_id\": owner_id,\n \"name\": spec[\"info_name\"],\n \"preview_id\": None,\n \"user_metadata\": user_metadata,\n \"created_at\": current_time,\n \"updated_at\": current_time,\n \"last_access_time\": current_time,\n }\n )\n\n asset_id_to_ref_data[asset_id] = {\n \"reference_id\": reference_id,\n \"tags\": spec[\"tags\"],\n \"filename\": spec[\"fname\"],\n \"extracted_metadata\": extracted_metadata,\n }\n\n bulk_insert_assets(session, asset_rows)\n\n # Filter reference rows to only those whose assets were actually inserted\n # (assets with duplicate hashes are silently dropped by ON CONFLICT DO NOTHING)\n inserted_asset_ids = get_existing_asset_ids(\n session, [r[\"asset_id\"] for r in reference_rows]\n )\n reference_rows = [r for r in reference_rows if r[\"asset_id\"] in inserted_asset_ids]\n\n bulk_insert_references_ignore_conflicts(session, reference_rows)\n restore_references_by_paths(session, absolute_path_list)\n winning_paths = get_references_by_paths_and_asset_ids(session, path_to_asset_id)\n\n inserted_paths = {\n path\n for path in absolute_path_list\n if path_to_asset_id[path] in inserted_asset_ids\n }\n losing_paths = inserted_paths - winning_paths\n lost_asset_ids = [path_to_asset_id[path] for path in losing_paths]\n\n if lost_asset_ids:\n delete_assets_by_ids(session, lost_asset_ids)\n\n if not winning_paths:\n return BulkInsertResult(\n inserted_refs=0,\n won_paths=0,\n lost_paths=len(losing_paths),\n )\n\n # Get reference IDs for winners\n winning_ref_ids = [\n asset_id_to_ref_data[path_to_asset_id[path]][\"reference_id\"]\n for path in winning_paths\n ]\n inserted_ref_ids = get_reference_ids_by_ids(session, winning_ref_ids)\n\n tag_rows: list[TagRow] = []\n metadata_rows: list[MetadataRow] = []\n\n if inserted_ref_ids:\n for path in winning_paths:\n asset_id = path_to_asset_id[path]\n ref_data = asset_id_to_ref_data[asset_id]\n ref_id = ref_data[\"reference_id\"]\n\n if ref_id not in inserted_ref_ids:\n continue\n\n for tag in ref_data[\"tags\"]:\n tag_rows.append(\n {\n \"asset_reference_id\": ref_id,\n \"tag_name\": tag,\n \"origin\": \"automatic\",\n \"added_at\": current_time,\n }\n )\n\n # Use extracted metadata for meta rows if available\n extracted_metadata = ref_data.get(\"extracted_metadata\")\n if extracted_metadata:\n metadata_rows.extend(extracted_metadata.to_meta_rows(ref_id))\n elif ref_data[\"filename\"]:\n # Fallback: just store filename\n metadata_rows.append(\n {\n \"asset_reference_id\": ref_id,\n \"key\": \"filename\",\n \"ordinal\": 0,\n \"val_str\": ref_data[\"filename\"],\n \"val_num\": None,\n \"val_bool\": None,\n \"val_json\": None,\n }\n )\n\n bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=metadata_rows)\n\n return BulkInsertResult(\n inserted_refs=len(inserted_ref_ids),\n won_paths=len(winning_paths),\n lost_paths=len(losing_paths),\n )\n\n\ndef cleanup_unreferenced_assets(session: Session) -> int:\n \"\"\"Hard-delete unhashed assets with no active references.\n\n This is a destructive operation intended for explicit cleanup.\n Only deletes assets where hash=None and all references are missing.\n\n Returns:\n Number of assets deleted\n \"\"\"\n unreferenced_ids = get_unreferenced_unhashed_asset_ids(session)\n return delete_assets_by_ids(session, unreferenced_ids)\n" }, { "path": "app/assets/services/file_utils.py", "content": "import os\n\n\ndef get_mtime_ns(stat_result: os.stat_result) -> int:\n \"\"\"Extract mtime in nanoseconds from a stat result.\"\"\"\n return getattr(\n stat_result, \"st_mtime_ns\", int(stat_result.st_mtime * 1_000_000_000)\n )\n\n\ndef get_size_and_mtime_ns(path: str, follow_symlinks: bool = True) -> tuple[int, int]:\n \"\"\"Get file size in bytes and mtime in nanoseconds.\"\"\"\n st = os.stat(path, follow_symlinks=follow_symlinks)\n return st.st_size, get_mtime_ns(st)\n\n\ndef verify_file_unchanged(\n mtime_db: int | None,\n size_db: int | None,\n stat_result: os.stat_result,\n) -> bool:\n \"\"\"Check if a file is unchanged based on mtime and size.\n\n Returns True if the file's mtime and size match the database values.\n Returns False if mtime_db is None or values don't match.\n\n size_db=None means don't check size; 0 is a valid recorded size.\n \"\"\"\n if mtime_db is None:\n return False\n actual_mtime_ns = get_mtime_ns(stat_result)\n if int(mtime_db) != int(actual_mtime_ns):\n return False\n if size_db is not None:\n return int(stat_result.st_size) == int(size_db)\n return True\n\n\ndef is_visible(name: str) -> bool:\n \"\"\"Return True if a file or directory name is visible (not hidden).\"\"\"\n return not name.startswith(\".\")\n\n\ndef list_files_recursively(base_dir: str) -> list[str]:\n \"\"\"Recursively list all files in a directory, following symlinks.\"\"\"\n out: list[str] = []\n base_abs = os.path.abspath(base_dir)\n if not os.path.isdir(base_abs):\n return out\n # Track seen real directory identities to prevent circular symlink loops\n seen_dirs: set[tuple[int, int]] = set()\n for dirpath, subdirs, filenames in os.walk(\n base_abs, topdown=True, followlinks=True\n ):\n try:\n st = os.stat(dirpath)\n dir_id = (st.st_dev, st.st_ino)\n except OSError:\n subdirs.clear()\n continue\n if dir_id in seen_dirs:\n subdirs.clear()\n continue\n seen_dirs.add(dir_id)\n subdirs[:] = [d for d in subdirs if is_visible(d)]\n for name in filenames:\n if not is_visible(name):\n continue\n out.append(os.path.abspath(os.path.join(dirpath, name)))\n return out\n" }, { "path": "app/assets/services/hashing.py", "content": "import io\nimport os\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass\nfrom typing import IO, Any, Callable, Iterator\nimport logging\n\ntry:\n from blake3 import blake3\nexcept ModuleNotFoundError:\n logging.warning(\"WARNING: blake3 package not installed\")\n\nDEFAULT_CHUNK = 8 * 1024 * 1024\n\nInterruptCheck = Callable[[], bool]\n\n\n@dataclass\nclass HashCheckpoint:\n \"\"\"Saved state for resuming an interrupted hash computation.\"\"\"\n\n bytes_processed: int\n hasher: Any # blake3 hasher instance\n mtime_ns: int = 0\n file_size: int = 0\n\n\n@contextmanager\ndef _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], bool]]:\n \"\"\"Yield (file_object, is_path) with appropriate setup/teardown.\"\"\"\n if hasattr(fp, \"read\"):\n seekable = getattr(fp, \"seekable\", lambda: False)()\n orig_pos = None\n if seekable:\n try:\n orig_pos = fp.tell()\n if orig_pos != 0:\n fp.seek(0)\n except io.UnsupportedOperation:\n orig_pos = None\n try:\n yield fp, False\n finally:\n if orig_pos is not None:\n fp.seek(orig_pos)\n else:\n with open(os.fspath(fp), \"rb\") as f:\n yield f, True\n\n\ndef compute_blake3_hash(\n fp: str | IO[bytes],\n chunk_size: int = DEFAULT_CHUNK,\n interrupt_check: InterruptCheck | None = None,\n checkpoint: HashCheckpoint | None = None,\n) -> tuple[str | None, HashCheckpoint | None]:\n \"\"\"Compute BLAKE3 hash of a file, with optional checkpoint support.\n\n Args:\n fp: File path or file-like object\n chunk_size: Size of chunks to read at a time\n interrupt_check: Optional callable that returns True if the operation\n should be interrupted (e.g. paused or cancelled). Must be\n non-blocking so file handles are released immediately. Checked\n between chunk reads.\n checkpoint: Optional checkpoint to resume from (file paths only)\n\n Returns:\n Tuple of (hex_digest, None) on completion, or\n (None, checkpoint) on interruption (file paths only), or\n (None, None) on interruption of a file object\n \"\"\"\n if chunk_size <= 0:\n chunk_size = DEFAULT_CHUNK\n\n with _open_for_hashing(fp) as (f, is_path):\n if checkpoint is not None and is_path:\n f.seek(checkpoint.bytes_processed)\n h = checkpoint.hasher\n bytes_processed = checkpoint.bytes_processed\n else:\n h = blake3()\n bytes_processed = 0\n\n while True:\n if interrupt_check is not None and interrupt_check():\n if is_path:\n return None, HashCheckpoint(\n bytes_processed=bytes_processed,\n hasher=h,\n )\n return None, None\n chunk = f.read(chunk_size)\n if not chunk:\n break\n h.update(chunk)\n bytes_processed += len(chunk)\n\n return h.hexdigest(), None\n" }, { "path": "app/assets/services/ingest.py", "content": "import contextlib\nimport logging\nimport mimetypes\nimport os\nfrom typing import Any, Sequence\n\nfrom sqlalchemy.orm import Session\n\nimport app.assets.services.hashing as hashing\nfrom app.assets.database.queries import (\n add_tags_to_reference,\n fetch_reference_and_asset,\n get_asset_by_hash,\n get_reference_by_file_path,\n get_reference_tags,\n get_or_create_reference,\n reference_exists,\n remove_missing_tag_for_asset_id,\n set_reference_metadata,\n set_reference_tags,\n update_asset_hash_and_mime,\n upsert_asset,\n upsert_reference,\n validate_tags_exist,\n)\nfrom app.assets.helpers import normalize_tags\nfrom app.assets.services.file_utils import get_size_and_mtime_ns\nfrom app.assets.services.path_utils import (\n compute_relative_filename,\n get_name_and_tags_from_asset_path,\n resolve_destination_from_tags,\n validate_path_within_base,\n)\nfrom app.assets.services.schemas import (\n IngestResult,\n RegisterAssetResult,\n UploadResult,\n UserMetadata,\n extract_asset_data,\n extract_reference_data,\n)\nfrom app.database.db import create_session\n\n\ndef _ingest_file_from_path(\n abs_path: str,\n asset_hash: str,\n size_bytes: int,\n mtime_ns: int,\n mime_type: str | None = None,\n info_name: str | None = None,\n owner_id: str = \"\",\n preview_id: str | None = None,\n user_metadata: UserMetadata = None,\n tags: Sequence[str] = (),\n tag_origin: str = \"manual\",\n require_existing_tags: bool = False,\n) -> IngestResult:\n locator = os.path.abspath(abs_path)\n user_metadata = user_metadata or {}\n\n asset_created = False\n asset_updated = False\n ref_created = False\n ref_updated = False\n reference_id: str | None = None\n\n with create_session() as session:\n if preview_id:\n if not reference_exists(session, preview_id):\n preview_id = None\n\n asset, asset_created, asset_updated = upsert_asset(\n session,\n asset_hash=asset_hash,\n size_bytes=size_bytes,\n mime_type=mime_type,\n )\n\n ref_created, ref_updated = upsert_reference(\n session,\n asset_id=asset.id,\n file_path=locator,\n name=info_name or os.path.basename(locator),\n mtime_ns=mtime_ns,\n owner_id=owner_id,\n )\n\n # Get the reference we just created/updated\n ref = get_reference_by_file_path(session, locator)\n if ref:\n reference_id = ref.id\n\n if preview_id and ref.preview_id != preview_id:\n ref.preview_id = preview_id\n\n norm = normalize_tags(list(tags))\n if norm:\n if require_existing_tags:\n validate_tags_exist(session, norm)\n add_tags_to_reference(\n session,\n reference_id=reference_id,\n tags=norm,\n origin=tag_origin,\n create_if_missing=not require_existing_tags,\n )\n\n _update_metadata_with_filename(\n session,\n reference_id=reference_id,\n file_path=ref.file_path,\n current_metadata=ref.user_metadata,\n user_metadata=user_metadata,\n )\n\n try:\n remove_missing_tag_for_asset_id(session, asset_id=asset.id)\n except Exception:\n logging.exception(\"Failed to clear 'missing' tag for asset %s\", asset.id)\n\n session.commit()\n\n return IngestResult(\n asset_created=asset_created,\n asset_updated=asset_updated,\n ref_created=ref_created,\n ref_updated=ref_updated,\n reference_id=reference_id,\n )\n\n\ndef _register_existing_asset(\n asset_hash: str,\n name: str,\n user_metadata: UserMetadata = None,\n tags: list[str] | None = None,\n tag_origin: str = \"manual\",\n owner_id: str = \"\",\n mime_type: str | None = None,\n preview_id: str | None = None,\n) -> RegisterAssetResult:\n user_metadata = user_metadata or {}\n\n with create_session() as session:\n asset = get_asset_by_hash(session, asset_hash=asset_hash)\n if not asset:\n raise ValueError(f\"No asset with hash {asset_hash}\")\n\n if mime_type and not asset.mime_type:\n update_asset_hash_and_mime(session, asset_id=asset.id, mime_type=mime_type)\n\n if preview_id:\n if not reference_exists(session, preview_id):\n preview_id = None\n\n ref, ref_created = get_or_create_reference(\n session,\n asset_id=asset.id,\n owner_id=owner_id,\n name=name,\n preview_id=preview_id,\n )\n\n if not ref_created:\n if preview_id and ref.preview_id != preview_id:\n ref.preview_id = preview_id\n\n tag_names = get_reference_tags(session, reference_id=ref.id)\n result = RegisterAssetResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tag_names,\n created=False,\n )\n session.commit()\n return result\n\n new_meta = dict(user_metadata)\n computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None\n if computed_filename:\n new_meta[\"filename\"] = computed_filename\n\n if new_meta:\n set_reference_metadata(\n session,\n reference_id=ref.id,\n user_metadata=new_meta,\n )\n\n if tags is not None:\n set_reference_tags(\n session,\n reference_id=ref.id,\n tags=tags,\n origin=tag_origin,\n )\n\n tag_names = get_reference_tags(session, reference_id=ref.id)\n session.refresh(ref)\n result = RegisterAssetResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tag_names,\n created=True,\n )\n session.commit()\n\n return result\n\n\n\ndef _update_metadata_with_filename(\n session: Session,\n reference_id: str,\n file_path: str | None,\n current_metadata: dict | None,\n user_metadata: dict[str, Any],\n) -> None:\n computed_filename = compute_relative_filename(file_path) if file_path else None\n\n current_meta = current_metadata or {}\n new_meta = dict(current_meta)\n for k, v in user_metadata.items():\n new_meta[k] = v\n if computed_filename:\n new_meta[\"filename\"] = computed_filename\n\n if new_meta != current_meta:\n set_reference_metadata(\n session,\n reference_id=reference_id,\n user_metadata=new_meta,\n )\n\n\ndef _sanitize_filename(name: str | None, fallback: str) -> str:\n n = os.path.basename((name or \"\").strip() or fallback)\n return n if n else fallback\n\n\nclass HashMismatchError(Exception):\n pass\n\n\nclass DependencyMissingError(Exception):\n def __init__(self, message: str):\n self.message = message\n super().__init__(message)\n\n\ndef upload_from_temp_path(\n temp_path: str,\n name: str | None = None,\n tags: list[str] | None = None,\n user_metadata: dict | None = None,\n client_filename: str | None = None,\n owner_id: str = \"\",\n expected_hash: str | None = None,\n mime_type: str | None = None,\n preview_id: str | None = None,\n) -> UploadResult:\n try:\n digest, _ = hashing.compute_blake3_hash(temp_path)\n except ImportError as e:\n raise DependencyMissingError(str(e))\n except Exception as e:\n raise RuntimeError(f\"failed to hash uploaded file: {e}\")\n asset_hash = \"blake3:\" + digest\n\n if expected_hash and asset_hash != expected_hash.strip().lower():\n raise HashMismatchError(\"Uploaded file hash does not match provided hash.\")\n\n with create_session() as session:\n existing = get_asset_by_hash(session, asset_hash=asset_hash)\n\n if existing is not None:\n with contextlib.suppress(Exception):\n if temp_path and os.path.exists(temp_path):\n os.remove(temp_path)\n\n display_name = _sanitize_filename(name or client_filename, fallback=digest)\n result = _register_existing_asset(\n asset_hash=asset_hash,\n name=display_name,\n user_metadata=user_metadata or {},\n tags=tags or [],\n tag_origin=\"manual\",\n owner_id=owner_id,\n mime_type=mime_type,\n preview_id=preview_id,\n )\n return UploadResult(\n ref=result.ref,\n asset=result.asset,\n tags=result.tags,\n created_new=False,\n )\n\n if not tags:\n raise ValueError(\"tags are required for new asset uploads\")\n base_dir, subdirs = resolve_destination_from_tags(tags)\n dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir\n os.makedirs(dest_dir, exist_ok=True)\n\n src_for_ext = (client_filename or name or \"\").strip()\n _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else \"\"\n ext = _ext if 0 < len(_ext) <= 16 else \"\"\n hashed_basename = f\"{digest}{ext}\"\n dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))\n validate_path_within_base(dest_abs, base_dir)\n\n content_type = mime_type or (\n mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]\n or mimetypes.guess_type(hashed_basename, strict=False)[0]\n or \"application/octet-stream\"\n )\n\n try:\n os.replace(temp_path, dest_abs)\n except Exception as e:\n raise RuntimeError(f\"failed to move uploaded file into place: {e}\")\n\n try:\n size_bytes, mtime_ns = get_size_and_mtime_ns(dest_abs)\n except OSError as e:\n raise RuntimeError(f\"failed to stat destination file: {e}\")\n\n ingest_result = _ingest_file_from_path(\n asset_hash=asset_hash,\n abs_path=dest_abs,\n size_bytes=size_bytes,\n mtime_ns=mtime_ns,\n mime_type=content_type,\n info_name=_sanitize_filename(name or client_filename, fallback=digest),\n owner_id=owner_id,\n preview_id=preview_id,\n user_metadata=user_metadata or {},\n tags=tags,\n tag_origin=\"manual\",\n require_existing_tags=False,\n )\n reference_id = ingest_result.reference_id\n if not reference_id:\n raise RuntimeError(\"failed to create asset reference\")\n\n with create_session() as session:\n pair = fetch_reference_and_asset(\n session, reference_id=reference_id, owner_id=owner_id\n )\n if not pair:\n raise RuntimeError(\"inconsistent DB state after ingest\")\n ref, asset = pair\n tag_names = get_reference_tags(session, reference_id=ref.id)\n\n return UploadResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tag_names,\n created_new=ingest_result.asset_created,\n )\n\n\ndef register_file_in_place(\n abs_path: str,\n name: str,\n tags: list[str],\n owner_id: str = \"\",\n mime_type: str | None = None,\n) -> UploadResult:\n \"\"\"Register an already-saved file in the asset database without moving it.\n\n Tags are derived from the filesystem path (root category + subfolder names),\n merged with any caller-provided tags, matching the behavior of the scanner.\n If the path is not under a known root, only the caller-provided tags are used.\n \"\"\"\n try:\n _, path_tags = get_name_and_tags_from_asset_path(abs_path)\n except ValueError:\n path_tags = []\n merged_tags = normalize_tags([*path_tags, *tags])\n\n try:\n digest, _ = hashing.compute_blake3_hash(abs_path)\n except ImportError as e:\n raise DependencyMissingError(str(e))\n except Exception as e:\n raise RuntimeError(f\"failed to hash file: {e}\")\n asset_hash = \"blake3:\" + digest\n\n size_bytes, mtime_ns = get_size_and_mtime_ns(abs_path)\n content_type = mime_type or (\n mimetypes.guess_type(abs_path, strict=False)[0]\n or \"application/octet-stream\"\n )\n\n ingest_result = _ingest_file_from_path(\n abs_path=abs_path,\n asset_hash=asset_hash,\n size_bytes=size_bytes,\n mtime_ns=mtime_ns,\n mime_type=content_type,\n info_name=_sanitize_filename(name, fallback=digest),\n owner_id=owner_id,\n tags=merged_tags,\n tag_origin=\"upload\",\n require_existing_tags=False,\n )\n reference_id = ingest_result.reference_id\n if not reference_id:\n raise RuntimeError(\"failed to create asset reference\")\n\n with create_session() as session:\n pair = fetch_reference_and_asset(\n session, reference_id=reference_id, owner_id=owner_id\n )\n if not pair:\n raise RuntimeError(\"inconsistent DB state after ingest\")\n ref, asset = pair\n tag_names = get_reference_tags(session, reference_id=ref.id)\n\n return UploadResult(\n ref=extract_reference_data(ref),\n asset=extract_asset_data(asset),\n tags=tag_names,\n created_new=ingest_result.asset_created,\n )\n\n\ndef create_from_hash(\n hash_str: str,\n name: str,\n tags: list[str] | None = None,\n user_metadata: dict | None = None,\n owner_id: str = \"\",\n mime_type: str | None = None,\n preview_id: str | None = None,\n) -> UploadResult | None:\n canonical = hash_str.strip().lower()\n\n try:\n result = _register_existing_asset(\n asset_hash=canonical,\n name=_sanitize_filename(\n name, fallback=canonical.split(\":\", 1)[1] if \":\" in canonical else canonical\n ),\n user_metadata=user_metadata or {},\n tags=tags or [],\n tag_origin=\"manual\",\n owner_id=owner_id,\n mime_type=mime_type,\n preview_id=preview_id,\n )\n except ValueError:\n logging.warning(\"create_from_hash: no asset found for hash %s\", canonical)\n return None\n\n return UploadResult(\n ref=result.ref,\n asset=result.asset,\n tags=result.tags,\n created_new=False,\n )\n" }, { "path": "app/assets/services/metadata_extract.py", "content": "\"\"\"Metadata extraction for asset scanning.\n\nTier 1: Filesystem metadata (zero parsing)\nTier 2: Safetensors header metadata (fast JSON read only)\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport mimetypes\nimport os\nimport struct\nfrom dataclasses import dataclass\nfrom typing import Any\n\nfrom utils.mime_types import init_mime_types\n\ninit_mime_types()\n\n# Supported safetensors extensions\nSAFETENSORS_EXTENSIONS = frozenset({\".safetensors\", \".sft\"})\n\n# Maximum safetensors header size to read (8MB)\nMAX_SAFETENSORS_HEADER_SIZE = 8 * 1024 * 1024\n\n\n@dataclass\nclass ExtractedMetadata:\n \"\"\"Metadata extracted from a file during scanning.\"\"\"\n\n # Tier 1: Filesystem (always available)\n filename: str = \"\"\n file_path: str = \"\" # Full absolute path to the file\n content_length: int = 0\n content_type: str | None = None\n format: str = \"\" # file extension without dot\n\n # Tier 2: Safetensors header (if available)\n base_model: str | None = None\n trained_words: list[str] | None = None\n air: str | None = None # CivitAI AIR identifier\n has_preview_images: bool = False\n\n # Source provenance (populated if embedded in safetensors)\n source_url: str | None = None\n source_arn: str | None = None\n repo_url: str | None = None\n preview_url: str | None = None\n source_hash: str | None = None\n\n # HuggingFace specific\n repo_id: str | None = None\n revision: str | None = None\n filepath: str | None = None\n resolve_url: str | None = None\n\n def to_user_metadata(self) -> dict[str, Any]:\n \"\"\"Convert to user_metadata dict for AssetReference.user_metadata JSON field.\"\"\"\n data: dict[str, Any] = {\n \"filename\": self.filename,\n \"content_length\": self.content_length,\n \"format\": self.format,\n }\n if self.file_path:\n data[\"file_path\"] = self.file_path\n if self.content_type:\n data[\"content_type\"] = self.content_type\n\n # Tier 2 fields\n if self.base_model:\n data[\"base_model\"] = self.base_model\n if self.trained_words:\n data[\"trained_words\"] = self.trained_words\n if self.air:\n data[\"air\"] = self.air\n if self.has_preview_images:\n data[\"has_preview_images\"] = True\n\n # Source provenance\n if self.source_url:\n data[\"source_url\"] = self.source_url\n if self.source_arn:\n data[\"source_arn\"] = self.source_arn\n if self.repo_url:\n data[\"repo_url\"] = self.repo_url\n if self.preview_url:\n data[\"preview_url\"] = self.preview_url\n if self.source_hash:\n data[\"source_hash\"] = self.source_hash\n\n # HuggingFace\n if self.repo_id:\n data[\"repo_id\"] = self.repo_id\n if self.revision:\n data[\"revision\"] = self.revision\n if self.filepath:\n data[\"filepath\"] = self.filepath\n if self.resolve_url:\n data[\"resolve_url\"] = self.resolve_url\n\n return data\n\n def to_meta_rows(self, reference_id: str) -> list[dict]:\n \"\"\"Convert to asset_reference_meta rows for typed/indexed querying.\"\"\"\n rows: list[dict] = []\n\n def add_str(key: str, val: str | None, ordinal: int = 0) -> None:\n if val:\n rows.append({\n \"asset_reference_id\": reference_id,\n \"key\": key,\n \"ordinal\": ordinal,\n \"val_str\": val[:2048] if len(val) > 2048 else val,\n \"val_num\": None,\n \"val_bool\": None,\n \"val_json\": None,\n })\n\n def add_num(key: str, val: int | float | None) -> None:\n if val is not None:\n rows.append({\n \"asset_reference_id\": reference_id,\n \"key\": key,\n \"ordinal\": 0,\n \"val_str\": None,\n \"val_num\": val,\n \"val_bool\": None,\n \"val_json\": None,\n })\n\n def add_bool(key: str, val: bool | None) -> None:\n if val is not None:\n rows.append({\n \"asset_reference_id\": reference_id,\n \"key\": key,\n \"ordinal\": 0,\n \"val_str\": None,\n \"val_num\": None,\n \"val_bool\": val,\n \"val_json\": None,\n })\n\n # Tier 1\n add_str(\"filename\", self.filename)\n add_num(\"content_length\", self.content_length)\n add_str(\"content_type\", self.content_type)\n add_str(\"format\", self.format)\n\n # Tier 2\n add_str(\"base_model\", self.base_model)\n add_str(\"air\", self.air)\n has_previews = self.has_preview_images if self.has_preview_images else None\n add_bool(\"has_preview_images\", has_previews)\n\n # trained_words as multiple rows with ordinals\n if self.trained_words:\n for i, word in enumerate(self.trained_words[:100]): # limit to 100 words\n add_str(\"trained_words\", word, ordinal=i)\n\n # Source provenance\n add_str(\"source_url\", self.source_url)\n add_str(\"source_arn\", self.source_arn)\n add_str(\"repo_url\", self.repo_url)\n add_str(\"preview_url\", self.preview_url)\n add_str(\"source_hash\", self.source_hash)\n\n # HuggingFace\n add_str(\"repo_id\", self.repo_id)\n add_str(\"revision\", self.revision)\n add_str(\"filepath\", self.filepath)\n add_str(\"resolve_url\", self.resolve_url)\n\n return rows\n\n\ndef _read_safetensors_header(\n path: str, max_size: int = MAX_SAFETENSORS_HEADER_SIZE\n) -> dict[str, Any] | None:\n \"\"\"Read only the JSON header from a safetensors file.\n\n This is very fast - reads 8 bytes for header length, then the JSON header.\n No tensor data is loaded.\n\n Args:\n path: Absolute path to safetensors file\n max_size: Maximum header size to read (default 8MB)\n\n Returns:\n Parsed header dict or None if failed\n \"\"\"\n try:\n with open(path, \"rb\") as f:\n header_bytes = f.read(8)\n if len(header_bytes) < 8:\n return None\n length_of_header = struct.unpack(\" max_size:\n return None\n header_data = f.read(length_of_header)\n if len(header_data) < length_of_header:\n return None\n return json.loads(header_data.decode(\"utf-8\"))\n except (OSError, json.JSONDecodeError, UnicodeDecodeError, struct.error):\n return None\n\n\ndef _extract_safetensors_metadata(\n header: dict[str, Any], meta: ExtractedMetadata\n) -> None:\n \"\"\"Extract metadata from safetensors header __metadata__ section.\n\n Modifies meta in-place.\n \"\"\"\n st_meta = header.get(\"__metadata__\", {})\n if not isinstance(st_meta, dict):\n return\n\n # Common model metadata\n meta.base_model = (\n st_meta.get(\"ss_base_model_version\")\n or st_meta.get(\"modelspec.base_model\")\n or st_meta.get(\"base_model\")\n )\n\n # Trained words / trigger words\n trained_words = st_meta.get(\"ss_tag_frequency\")\n if trained_words and isinstance(trained_words, str):\n try:\n tag_freq = json.loads(trained_words)\n # Extract unique tags from all datasets\n all_tags: set[str] = set()\n for dataset_tags in tag_freq.values():\n if isinstance(dataset_tags, dict):\n all_tags.update(dataset_tags.keys())\n if all_tags:\n meta.trained_words = sorted(all_tags)[:100]\n except json.JSONDecodeError:\n pass\n\n # Direct trained_words field (some formats)\n if not meta.trained_words:\n tw = st_meta.get(\"trained_words\")\n if isinstance(tw, str):\n try:\n parsed = json.loads(tw)\n if isinstance(parsed, list):\n meta.trained_words = [str(x) for x in parsed]\n else:\n meta.trained_words = [w.strip() for w in tw.split(\",\") if w.strip()]\n except json.JSONDecodeError:\n meta.trained_words = [w.strip() for w in tw.split(\",\") if w.strip()]\n elif isinstance(tw, list):\n meta.trained_words = [str(x) for x in tw]\n\n # CivitAI AIR\n meta.air = st_meta.get(\"air\") or st_meta.get(\"modelspec.air\")\n\n # Preview images (ssmd_cover_images)\n cover_images = st_meta.get(\"ssmd_cover_images\")\n if cover_images:\n meta.has_preview_images = True\n\n # Source provenance fields\n meta.source_url = st_meta.get(\"source_url\")\n meta.source_arn = st_meta.get(\"source_arn\")\n meta.repo_url = st_meta.get(\"repo_url\")\n meta.preview_url = st_meta.get(\"preview_url\")\n meta.source_hash = st_meta.get(\"source_hash\") or st_meta.get(\"sshs_model_hash\")\n\n # HuggingFace fields\n meta.repo_id = st_meta.get(\"repo_id\") or st_meta.get(\"hf_repo_id\")\n meta.revision = st_meta.get(\"revision\") or st_meta.get(\"hf_revision\")\n meta.filepath = st_meta.get(\"filepath\") or st_meta.get(\"hf_filepath\")\n meta.resolve_url = st_meta.get(\"resolve_url\") or st_meta.get(\"hf_url\")\n\n\ndef extract_file_metadata(\n abs_path: str,\n stat_result: os.stat_result | None = None,\n relative_filename: str | None = None,\n) -> ExtractedMetadata:\n \"\"\"Extract metadata from a file using tier 1 and tier 2 methods.\n\n Tier 1: Filesystem metadata from path and stat\n Tier 2: Safetensors header parsing if applicable\n\n Args:\n abs_path: Absolute path to the file\n stat_result: Optional pre-fetched stat result (saves a syscall)\n relative_filename: Optional relative filename to use instead of basename\n (e.g., \"flux/123/model.safetensors\" for model paths)\n\n Returns:\n ExtractedMetadata with all available fields populated\n \"\"\"\n meta = ExtractedMetadata()\n\n # Tier 1: Filesystem metadata\n meta.filename = relative_filename or os.path.basename(abs_path)\n meta.file_path = abs_path\n _, ext = os.path.splitext(abs_path)\n meta.format = ext.lstrip(\".\").lower() if ext else \"\"\n\n mime_type, _ = mimetypes.guess_type(abs_path)\n meta.content_type = mime_type\n\n # Size from stat\n if stat_result is None:\n try:\n stat_result = os.stat(abs_path, follow_symlinks=True)\n except OSError:\n pass\n\n if stat_result:\n meta.content_length = stat_result.st_size\n\n # Tier 2: Safetensors header (if applicable and enabled)\n if ext.lower() in SAFETENSORS_EXTENSIONS:\n header = _read_safetensors_header(abs_path)\n if header:\n try:\n _extract_safetensors_metadata(header, meta)\n except Exception as e:\n logging.debug(\"Safetensors meta extract failed %s: %s\", abs_path, e)\n\n return meta\n" }, { "path": "app/assets/services/path_utils.py", "content": "import os\nfrom pathlib import Path\nfrom typing import Literal\n\nimport folder_paths\nfrom app.assets.helpers import normalize_tags\n\n\n_NON_MODEL_FOLDER_NAMES = frozenset({\"custom_nodes\"})\n\n\ndef get_comfy_models_folders() -> list[tuple[str, list[str]]]:\n \"\"\"Build list of (folder_name, base_paths[]) for all model locations.\n\n Includes every category registered in folder_names_and_paths,\n regardless of whether its paths are under the main models_dir,\n but excludes non-model entries like custom_nodes.\n \"\"\"\n targets: list[tuple[str, list[str]]] = []\n for name, values in folder_paths.folder_names_and_paths.items():\n if name in _NON_MODEL_FOLDER_NAMES:\n continue\n paths, _exts = values[0], values[1]\n if paths:\n targets.append((name, paths))\n return targets\n\n\ndef resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:\n \"\"\"Validates and maps tags -> (base_dir, subdirs_for_fs)\"\"\"\n if not tags:\n raise ValueError(\"tags must not be empty\")\n root = tags[0].lower()\n if root == \"models\":\n if len(tags) < 2:\n raise ValueError(\"at least two tags required for model asset\")\n try:\n bases = folder_paths.folder_names_and_paths[tags[1]][0]\n except KeyError:\n raise ValueError(f\"unknown model category '{tags[1]}'\")\n if not bases:\n raise ValueError(f\"no base path configured for category '{tags[1]}'\")\n base_dir = os.path.abspath(bases[0])\n raw_subdirs = tags[2:]\n elif root == \"input\":\n base_dir = os.path.abspath(folder_paths.get_input_directory())\n raw_subdirs = tags[1:]\n elif root == \"output\":\n base_dir = os.path.abspath(folder_paths.get_output_directory())\n raw_subdirs = tags[1:]\n else:\n raise ValueError(f\"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'\")\n _sep_chars = frozenset((\"/\", \"\\\\\", os.sep))\n for i in raw_subdirs:\n if i in (\".\", \"..\") or _sep_chars & set(i):\n raise ValueError(\"invalid path component in tags\")\n\n return base_dir, raw_subdirs if raw_subdirs else []\n\n\ndef validate_path_within_base(candidate: str, base: str) -> None:\n cand_abs = Path(os.path.abspath(candidate))\n base_abs = Path(os.path.abspath(base))\n if not cand_abs.is_relative_to(base_abs):\n raise ValueError(\"destination escapes base directory\")\n\n\ndef compute_relative_filename(file_path: str) -> str | None:\n \"\"\"\n Return the model's path relative to the last well-known folder (the model category),\n using forward slashes, eg:\n /.../models/checkpoints/flux/123/flux.safetensors -> \"flux/123/flux.safetensors\"\n /.../models/text_encoders/clip_g.safetensors -> \"clip_g.safetensors\"\n\n For non-model paths, returns None.\n \"\"\"\n try:\n root_category, rel_path = get_asset_category_and_relative_path(file_path)\n except ValueError:\n return None\n\n p = Path(rel_path)\n parts = [seg for seg in p.parts if seg not in (\".\", \"..\", p.anchor)]\n if not parts:\n return None\n\n if root_category == \"models\":\n # parts[0] is the category (\"checkpoints\", \"vae\", etc) – drop it\n inside = parts[1:] if len(parts) > 1 else [parts[0]]\n return \"/\".join(inside)\n return \"/\".join(parts) # input/output: keep all parts\n\n\ndef get_asset_category_and_relative_path(\n file_path: str,\n) -> tuple[Literal[\"input\", \"output\", \"models\"], str]:\n \"\"\"Determine which root category a file path belongs to.\n\n Categories:\n - 'input': under folder_paths.get_input_directory()\n - 'output': under folder_paths.get_output_directory()\n - 'models': under any base path from get_comfy_models_folders()\n\n Returns:\n (root_category, relative_path_inside_that_root)\n\n Raises:\n ValueError: path does not belong to any known root.\n \"\"\"\n fp_abs = os.path.abspath(file_path)\n\n def _check_is_within(child: str, parent: str) -> bool:\n return Path(child).is_relative_to(parent)\n\n def _compute_relative(child: str, parent: str) -> str:\n # Normalize relative path, stripping any leading \"..\" components\n # by anchoring to root (os.sep) then computing relpath back from it.\n return os.path.relpath(\n os.path.join(os.sep, os.path.relpath(child, parent)), os.sep\n )\n\n # 1) input\n input_base = os.path.abspath(folder_paths.get_input_directory())\n if _check_is_within(fp_abs, input_base):\n return \"input\", _compute_relative(fp_abs, input_base)\n\n # 2) output\n output_base = os.path.abspath(folder_paths.get_output_directory())\n if _check_is_within(fp_abs, output_base):\n return \"output\", _compute_relative(fp_abs, output_base)\n\n # 3) models (check deepest matching base to avoid ambiguity)\n best: tuple[int, str, str] | None = None # (base_len, bucket, rel_inside_bucket)\n for bucket, bases in get_comfy_models_folders():\n for b in bases:\n base_abs = os.path.abspath(b)\n if not _check_is_within(fp_abs, base_abs):\n continue\n cand = (len(base_abs), bucket, _compute_relative(fp_abs, base_abs))\n if best is None or cand[0] > best[0]:\n best = cand\n\n if best is not None:\n _, bucket, rel_inside = best\n combined = os.path.join(bucket, rel_inside)\n return \"models\", os.path.relpath(os.path.join(os.sep, combined), os.sep)\n\n raise ValueError(\n f\"Path is not within input, output, or configured model bases: {file_path}\"\n )\n\n\ndef get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:\n \"\"\"Return (name, tags) derived from a filesystem path.\n\n - name: base filename with extension\n - tags: [root_category] + parent folder names in order\n\n Raises:\n ValueError: path does not belong to any known root.\n \"\"\"\n root_category, some_path = get_asset_category_and_relative_path(file_path)\n p = Path(some_path)\n parent_parts = [\n part for part in p.parent.parts if part not in (\".\", \"..\", p.anchor)\n ]\n return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))\n" }, { "path": "app/assets/services/schemas.py", "content": "from dataclasses import dataclass\nfrom datetime import datetime\nfrom typing import Any, NamedTuple\n\nfrom app.assets.database.models import Asset, AssetReference\n\nUserMetadata = dict[str, Any] | None\n\n\n@dataclass(frozen=True)\nclass AssetData:\n hash: str | None\n size_bytes: int | None\n mime_type: str | None\n\n\n@dataclass(frozen=True)\nclass ReferenceData:\n \"\"\"Data transfer object for AssetReference.\"\"\"\n\n id: str\n name: str\n file_path: str | None\n user_metadata: UserMetadata\n preview_id: str | None\n created_at: datetime\n updated_at: datetime\n system_metadata: dict[str, Any] | None = None\n job_id: str | None = None\n last_access_time: datetime | None = None\n\n\n@dataclass(frozen=True)\nclass AssetDetailResult:\n ref: ReferenceData\n asset: AssetData | None\n tags: list[str]\n\n\n@dataclass(frozen=True)\nclass RegisterAssetResult:\n ref: ReferenceData\n asset: AssetData\n tags: list[str]\n created: bool\n\n\n@dataclass(frozen=True)\nclass IngestResult:\n asset_created: bool\n asset_updated: bool\n ref_created: bool\n ref_updated: bool\n reference_id: str | None\n\n\nclass TagUsage(NamedTuple):\n name: str\n tag_type: str\n count: int\n\n\n@dataclass(frozen=True)\nclass AssetSummaryData:\n ref: ReferenceData\n asset: AssetData | None\n tags: list[str]\n\n\n@dataclass(frozen=True)\nclass ListAssetsResult:\n items: list[AssetSummaryData]\n total: int\n\n\n@dataclass(frozen=True)\nclass DownloadResolutionResult:\n abs_path: str\n content_type: str\n download_name: str\n\n\n@dataclass(frozen=True)\nclass UploadResult:\n ref: ReferenceData\n asset: AssetData\n tags: list[str]\n created_new: bool\n\n\ndef extract_reference_data(ref: AssetReference) -> ReferenceData:\n return ReferenceData(\n id=ref.id,\n name=ref.name,\n file_path=ref.file_path,\n user_metadata=ref.user_metadata,\n preview_id=ref.preview_id,\n system_metadata=ref.system_metadata,\n job_id=ref.job_id,\n created_at=ref.created_at,\n updated_at=ref.updated_at,\n last_access_time=ref.last_access_time,\n )\n\n\ndef extract_asset_data(asset: Asset | None) -> AssetData | None:\n if asset is None:\n return None\n return AssetData(\n hash=asset.hash,\n size_bytes=asset.size_bytes,\n mime_type=asset.mime_type,\n )\n" }, { "path": "app/assets/services/tagging.py", "content": "from typing import Sequence\n\nfrom app.assets.database.queries import (\n AddTagsResult,\n RemoveTagsResult,\n add_tags_to_reference,\n get_reference_with_owner_check,\n list_tags_with_usage,\n remove_tags_from_reference,\n)\nfrom app.assets.database.queries.tags import list_tag_counts_for_filtered_assets\nfrom app.assets.services.schemas import TagUsage\nfrom app.database.db import create_session\n\n\ndef apply_tags(\n reference_id: str,\n tags: list[str],\n origin: str = \"manual\",\n owner_id: str = \"\",\n) -> AddTagsResult:\n with create_session() as session:\n ref_row = get_reference_with_owner_check(session, reference_id, owner_id)\n\n result = add_tags_to_reference(\n session,\n reference_id=reference_id,\n tags=tags,\n origin=origin,\n create_if_missing=True,\n reference_row=ref_row,\n )\n session.commit()\n\n return result\n\n\ndef remove_tags(\n reference_id: str,\n tags: list[str],\n owner_id: str = \"\",\n) -> RemoveTagsResult:\n with create_session() as session:\n get_reference_with_owner_check(session, reference_id, owner_id)\n\n result = remove_tags_from_reference(\n session,\n reference_id=reference_id,\n tags=tags,\n )\n session.commit()\n\n return result\n\n\ndef list_tags(\n prefix: str | None = None,\n limit: int = 100,\n offset: int = 0,\n order: str = \"count_desc\",\n include_zero: bool = True,\n owner_id: str = \"\",\n) -> tuple[list[TagUsage], int]:\n limit = max(1, min(1000, limit))\n offset = max(0, offset)\n\n with create_session() as session:\n rows, total = list_tags_with_usage(\n session,\n prefix=prefix,\n limit=limit,\n offset=offset,\n include_zero=include_zero,\n order=order,\n owner_id=owner_id,\n )\n\n return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total\n\n\ndef list_tag_histogram(\n owner_id: str = \"\",\n include_tags: Sequence[str] | None = None,\n exclude_tags: Sequence[str] | None = None,\n name_contains: str | None = None,\n metadata_filter: dict | None = None,\n limit: int = 100,\n) -> dict[str, int]:\n with create_session() as session:\n return list_tag_counts_for_filtered_assets(\n session,\n owner_id=owner_id,\n include_tags=include_tags,\n exclude_tags=exclude_tags,\n name_contains=name_contains,\n metadata_filter=metadata_filter,\n limit=limit,\n )\n" }, { "path": "app/custom_node_manager.py", "content": "from __future__ import annotations\n\nimport os\nimport folder_paths\nimport glob\nfrom aiohttp import web\nimport json\nimport logging\nfrom functools import lru_cache\n\nfrom utils.json_util import merge_json_recursive\n\n\n# Extra locale files to load into main.json\nEXTRA_LOCALE_FILES = [\n \"nodeDefs.json\",\n \"commands.json\",\n \"settings.json\",\n]\n\n\ndef safe_load_json_file(file_path: str) -> dict:\n if not os.path.exists(file_path):\n return {}\n\n try:\n with open(file_path, \"r\", encoding=\"utf-8\") as f:\n return json.load(f)\n except json.JSONDecodeError:\n logging.error(f\"Error loading {file_path}\")\n return {}\n\n\nclass CustomNodeManager:\n @lru_cache(maxsize=1)\n def build_translations(self):\n \"\"\"Load all custom nodes translations during initialization. Translations are\n expected to be loaded from `locales/` folder.\n\n The folder structure is expected to be the following:\n - custom_nodes/\n - custom_node_1/\n - locales/\n - en/\n - main.json\n - commands.json\n - settings.json\n\n returned translations are expected to be in the following format:\n {\n \"en\": {\n \"nodeDefs\": {...},\n \"commands\": {...},\n \"settings\": {...},\n ...{other main.json keys}\n }\n }\n \"\"\"\n\n translations = {}\n\n for folder in folder_paths.get_folder_paths(\"custom_nodes\"):\n # Sort glob results for deterministic ordering\n for custom_node_dir in sorted(glob.glob(os.path.join(folder, \"*/\"))):\n locales_dir = os.path.join(custom_node_dir, \"locales\")\n if not os.path.exists(locales_dir):\n continue\n\n for lang_dir in glob.glob(os.path.join(locales_dir, \"*/\")):\n lang_code = os.path.basename(os.path.dirname(lang_dir))\n\n if lang_code not in translations:\n translations[lang_code] = {}\n\n # Load main.json\n main_file = os.path.join(lang_dir, \"main.json\")\n node_translations = safe_load_json_file(main_file)\n\n # Load extra locale files\n for extra_file in EXTRA_LOCALE_FILES:\n extra_file_path = os.path.join(lang_dir, extra_file)\n key = extra_file.split(\".\")[0]\n json_data = safe_load_json_file(extra_file_path)\n if json_data:\n node_translations[key] = json_data\n\n if node_translations:\n translations[lang_code] = merge_json_recursive(\n translations[lang_code], node_translations\n )\n\n return translations\n\n def add_routes(self, routes, webapp, loadedModules):\n\n example_workflow_folder_names = [\"example_workflows\", \"example\", \"examples\", \"workflow\", \"workflows\"]\n\n @routes.get(\"/workflow_templates\")\n async def get_workflow_templates(request):\n \"\"\"Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.\"\"\"\n\n files = []\n\n for folder in folder_paths.get_folder_paths(\"custom_nodes\"):\n for folder_name in example_workflow_folder_names:\n pattern = os.path.join(folder, f\"*/{folder_name}/*.json\")\n matched_files = glob.glob(pattern)\n files.extend(matched_files)\n\n workflow_templates_dict = (\n {}\n ) # custom_nodes folder name -> example workflow names\n for file in files:\n custom_nodes_name = os.path.basename(\n os.path.dirname(os.path.dirname(file))\n )\n workflow_name = os.path.splitext(os.path.basename(file))[0]\n workflow_templates_dict.setdefault(custom_nodes_name, []).append(\n workflow_name\n )\n return web.json_response(workflow_templates_dict)\n\n # Serve workflow templates from custom nodes.\n for module_name, module_dir in loadedModules:\n for folder_name in example_workflow_folder_names:\n workflows_dir = os.path.join(module_dir, folder_name)\n\n if os.path.exists(workflows_dir):\n if folder_name != \"example_workflows\":\n logging.debug(\n \"Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'\",\n folder_name, module_name)\n\n webapp.add_routes(\n [\n web.static(\n \"/api/workflow_templates/\" + module_name, workflows_dir\n )\n ]\n )\n\n @routes.get(\"/i18n\")\n async def get_i18n(request):\n \"\"\"Returns translations from all custom nodes' locales folders.\"\"\"\n return web.json_response(self.build_translations())\n" }, { "path": "app/database/db.py", "content": "import logging\nimport os\nimport shutil\nfrom app.logger import log_startup_warning\nfrom utils.install_util import get_missing_requirements_message\nfrom filelock import FileLock, Timeout\nfrom comfy.cli_args import args\n\n_DB_AVAILABLE = False\nSession = None\n\n\ntry:\n from alembic import command\n from alembic.config import Config\n from alembic.runtime.migration import MigrationContext\n from alembic.script import ScriptDirectory\n from sqlalchemy import create_engine, event\n from sqlalchemy.orm import sessionmaker\n from sqlalchemy.pool import StaticPool\n\n from app.database.models import Base\n import app.assets.database.models # noqa: F401 — register models with Base.metadata\n\n _DB_AVAILABLE = True\nexcept ImportError as e:\n log_startup_warning(\n f\"\"\"\n------------------------------------------------------------------------\nError importing dependencies: {e}\n{get_missing_requirements_message()}\nThis error is happening because ComfyUI now uses a local sqlite database.\n------------------------------------------------------------------------\n\"\"\".strip()\n )\n\n\ndef dependencies_available():\n \"\"\"\n Temporary function to check if the dependencies are available\n \"\"\"\n return _DB_AVAILABLE\n\n\ndef can_create_session():\n \"\"\"\n Temporary function to check if the database is available to create a session\n During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created\n \"\"\"\n return dependencies_available() and Session is not None\n\n\ndef get_alembic_config():\n root_path = os.path.join(os.path.dirname(__file__), \"../..\")\n config_path = os.path.abspath(os.path.join(root_path, \"alembic.ini\"))\n scripts_path = os.path.abspath(os.path.join(root_path, \"alembic_db\"))\n\n config = Config(config_path)\n config.set_main_option(\"script_location\", scripts_path)\n config.set_main_option(\"sqlalchemy.url\", args.database_url)\n\n return config\n\n\ndef get_db_path():\n url = args.database_url\n if url.startswith(\"sqlite:///\"):\n return url.split(\"///\")[1]\n else:\n raise ValueError(f\"Unsupported database URL '{url}'.\")\n\n\n_db_lock = None\n\ndef _acquire_file_lock(db_path):\n \"\"\"Acquire an OS-level file lock to prevent multi-process access.\n\n Uses filelock for cross-platform support (macOS, Linux, Windows).\n The OS automatically releases the lock when the process exits, even on crashes.\n \"\"\"\n global _db_lock\n lock_path = db_path + \".lock\"\n _db_lock = FileLock(lock_path)\n try:\n _db_lock.acquire(timeout=0)\n except Timeout:\n raise RuntimeError(\n f\"Could not acquire lock on database '{db_path}'. \"\n \"Another ComfyUI process may already be using it. \"\n \"Use --database-url to specify a separate database file.\"\n )\n\n\ndef _is_memory_db(db_url):\n \"\"\"Check if the database URL refers to an in-memory SQLite database.\"\"\"\n return db_url in (\"sqlite:///:memory:\", \"sqlite://\")\n\n\ndef init_db():\n db_url = args.database_url\n logging.debug(f\"Database URL: {db_url}\")\n\n if _is_memory_db(db_url):\n _init_memory_db(db_url)\n else:\n _init_file_db(db_url)\n\n\ndef _init_memory_db(db_url):\n \"\"\"Initialize an in-memory SQLite database using metadata.create_all.\n\n Alembic migrations don't work with in-memory SQLite because each\n connection gets its own separate database — tables created by Alembic's\n internal connection are lost immediately.\n \"\"\"\n engine = create_engine(\n db_url,\n poolclass=StaticPool,\n connect_args={\"check_same_thread\": False},\n )\n\n @event.listens_for(engine, \"connect\")\n def set_sqlite_pragma(dbapi_connection, connection_record):\n cursor = dbapi_connection.cursor()\n cursor.execute(\"PRAGMA foreign_keys=ON\")\n cursor.close()\n\n Base.metadata.create_all(engine)\n\n global Session\n Session = sessionmaker(bind=engine)\n\n\ndef _init_file_db(db_url):\n \"\"\"Initialize a file-backed SQLite database using Alembic migrations.\"\"\"\n db_path = get_db_path()\n db_exists = os.path.exists(db_path)\n\n config = get_alembic_config()\n\n # Check if we need to upgrade\n engine = create_engine(db_url)\n\n # Enable foreign key enforcement for SQLite\n @event.listens_for(engine, \"connect\")\n def set_sqlite_pragma(dbapi_connection, connection_record):\n cursor = dbapi_connection.cursor()\n cursor.execute(\"PRAGMA foreign_keys=ON\")\n cursor.close()\n\n conn = engine.connect()\n\n context = MigrationContext.configure(conn)\n current_rev = context.get_current_revision()\n\n script = ScriptDirectory.from_config(config)\n target_rev = script.get_current_head()\n\n if target_rev is None:\n logging.warning(\"No target revision found.\")\n elif current_rev != target_rev:\n # Backup the database pre upgrade\n backup_path = db_path + \".bkp\"\n if db_exists:\n shutil.copy(db_path, backup_path)\n else:\n backup_path = None\n\n try:\n command.upgrade(config, target_rev)\n logging.info(f\"Database upgraded from {current_rev} to {target_rev}\")\n except Exception as e:\n if backup_path:\n # Restore the database from backup if upgrade fails\n shutil.copy(backup_path, db_path)\n os.remove(backup_path)\n logging.exception(\"Error upgrading database: \")\n raise e\n\n # Acquire an OS-level file lock after migrations are complete.\n # Alembic uses its own connection, so we must wait until it's done\n # before locking — otherwise our own lock blocks the migration.\n conn.close()\n _acquire_file_lock(db_path)\n\n global Session\n Session = sessionmaker(bind=engine)\n\n\ndef create_session():\n return Session()\n" }, { "path": "app/database/models.py", "content": "from typing import Any\nfrom datetime import datetime\nfrom sqlalchemy import MetaData\nfrom sqlalchemy.orm import DeclarativeBase\n\nNAMING_CONVENTION = {\n \"ix\": \"ix_%(table_name)s_%(column_0_N_name)s\",\n \"uq\": \"uq_%(table_name)s_%(column_0_N_name)s\",\n \"ck\": \"ck_%(table_name)s_%(constraint_name)s\",\n \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\",\n \"pk\": \"pk_%(table_name)s\",\n}\n\nclass Base(DeclarativeBase):\n metadata = MetaData(naming_convention=NAMING_CONVENTION)\n\ndef to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:\n fields = obj.__table__.columns.keys()\n out: dict[str, Any] = {}\n for field in fields:\n val = getattr(obj, field)\n if val is None and not include_none:\n continue\n if isinstance(val, datetime):\n out[field] = val.isoformat()\n else:\n out[field] = val\n return out\n\n# TODO: Define models here\n" }, { "path": "app/frontend_management.py", "content": "from __future__ import annotations\nimport argparse\nimport logging\nimport os\nimport re\nimport sys\nimport tempfile\nimport zipfile\nimport importlib\nfrom dataclasses import dataclass\nfrom functools import cached_property\nfrom pathlib import Path\nfrom typing import Dict, TypedDict, Optional\nfrom aiohttp import web\nfrom importlib.metadata import version\n\nimport requests\nfrom typing_extensions import NotRequired\n\nfrom utils.install_util import get_missing_requirements_message, get_required_packages_versions\n\nfrom comfy.cli_args import DEFAULT_VERSION_STRING\nimport app.logger\n\n\ndef frontend_install_warning_message():\n return f\"\"\"\n{get_missing_requirements_message()}\n\nThis error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.\n\"\"\".strip()\n\ndef parse_version(version: str) -> tuple[int, int, int]:\n return tuple(map(int, version.split(\".\")))\n\ndef is_valid_version(version: str) -> bool:\n \"\"\"Validate if a string is a valid semantic version (X.Y.Z format).\"\"\"\n pattern = r\"^(\\d+)\\.(\\d+)\\.(\\d+)$\"\n return bool(re.match(pattern, version))\n\ndef get_installed_frontend_version():\n \"\"\"Get the currently installed frontend package version.\"\"\"\n frontend_version_str = version(\"comfyui-frontend-package\")\n return frontend_version_str\n\n\ndef get_required_frontend_version():\n return get_required_packages_versions().get(\"comfyui-frontend-package\", None)\n\n\ndef check_frontend_version():\n \"\"\"Check if the frontend version is up to date.\"\"\"\n\n try:\n frontend_version_str = get_installed_frontend_version()\n frontend_version = parse_version(frontend_version_str)\n required_frontend_str = get_required_frontend_version()\n required_frontend = parse_version(required_frontend_str)\n if frontend_version < required_frontend:\n app.logger.log_startup_warning(\n f\"\"\"\n________________________________________________________________________\nWARNING WARNING WARNING WARNING WARNING\n\nInstalled frontend version {\".\".join(map(str, frontend_version))} is lower than the recommended version {\".\".join(map(str, required_frontend))}.\n\n{frontend_install_warning_message()}\n________________________________________________________________________\n\"\"\".strip()\n )\n else:\n logging.info(\"ComfyUI frontend version: {}\".format(frontend_version_str))\n except Exception as e:\n logging.error(f\"Failed to check frontend version: {e}\")\n\n\nREQUEST_TIMEOUT = 10 # seconds\n\n\nclass Asset(TypedDict):\n url: str\n\n\nclass Release(TypedDict):\n id: int\n tag_name: str\n name: str\n prerelease: bool\n created_at: str\n published_at: str\n body: str\n assets: NotRequired[list[Asset]]\n\n\n@dataclass\nclass FrontEndProvider:\n owner: str\n repo: str\n\n @property\n def folder_name(self) -> str:\n return f\"{self.owner}_{self.repo}\"\n\n @property\n def release_url(self) -> str:\n return f\"https://api.github.com/repos/{self.owner}/{self.repo}/releases\"\n\n @cached_property\n def all_releases(self) -> list[Release]:\n releases = []\n api_url = self.release_url\n while api_url:\n response = requests.get(api_url, timeout=REQUEST_TIMEOUT)\n response.raise_for_status() # Raises an HTTPError if the response was an error\n releases.extend(response.json())\n # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly.\n if \"next\" in response.links:\n api_url = response.links[\"next\"][\"url\"]\n else:\n api_url = None\n return releases\n\n @cached_property\n def latest_release(self) -> Release:\n latest_release_url = f\"{self.release_url}/latest\"\n response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT)\n response.raise_for_status() # Raises an HTTPError if the response was an error\n return response.json()\n\n @cached_property\n def latest_prerelease(self) -> Release:\n \"\"\"Get the latest pre-release version - even if it's older than the latest release\"\"\"\n release = [release for release in self.all_releases if release[\"prerelease\"]]\n\n if not release:\n raise ValueError(\"No pre-releases found\")\n\n # GitHub returns releases in reverse chronological order, so first is latest\n return release[0]\n\n def get_release(self, version: str) -> Release:\n if version == \"latest\":\n return self.latest_release\n elif version == \"prerelease\":\n return self.latest_prerelease\n else:\n for release in self.all_releases:\n if release[\"tag_name\"] in [version, f\"v{version}\"]:\n return release\n raise ValueError(f\"Version {version} not found in releases\")\n\n\ndef download_release_asset_zip(release: Release, destination_path: str) -> None:\n \"\"\"Download dist.zip from github release.\"\"\"\n asset_url = None\n for asset in release.get(\"assets\", []):\n if asset[\"name\"] == \"dist.zip\":\n asset_url = asset[\"url\"]\n break\n\n if not asset_url:\n raise ValueError(\"dist.zip not found in the release assets\")\n\n # Use a temporary file to download the zip content\n with tempfile.TemporaryFile() as tmp_file:\n headers = {\"Accept\": \"application/octet-stream\"}\n response = requests.get(\n asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT\n )\n response.raise_for_status() # Ensure we got a successful response\n\n # Write the content to the temporary file\n tmp_file.write(response.content)\n\n # Go back to the beginning of the temporary file\n tmp_file.seek(0)\n\n # Extract the zip file content to the destination path\n with zipfile.ZipFile(tmp_file, \"r\") as zip_ref:\n zip_ref.extractall(destination_path)\n\n\nclass FrontendManager:\n CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / \"web_custom_versions\")\n\n @classmethod\n def get_required_frontend_version(cls) -> str:\n \"\"\"Get the required frontend package version.\"\"\"\n return get_required_frontend_version()\n\n @classmethod\n def get_installed_templates_version(cls) -> str:\n \"\"\"Get the currently installed workflow templates package version.\"\"\"\n try:\n templates_version_str = version(\"comfyui-workflow-templates\")\n return templates_version_str\n except Exception:\n return None\n\n @classmethod\n def get_required_templates_version(cls) -> str:\n return get_required_packages_versions().get(\"comfyui-workflow-templates\", None)\n\n @classmethod\n def default_frontend_path(cls) -> str:\n try:\n import comfyui_frontend_package\n\n return str(importlib.resources.files(comfyui_frontend_package) / \"static\")\n except ImportError:\n logging.error(\n f\"\"\"\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed.\n\n{frontend_install_warning_message()}\n\n********** ERROR ***********\n\"\"\".strip()\n )\n sys.exit(-1)\n\n @classmethod\n def template_asset_map(cls) -> Optional[Dict[str, str]]:\n \"\"\"Return a mapping of template asset names to their absolute paths.\"\"\"\n try:\n from comfyui_workflow_templates import (\n get_asset_path,\n iter_templates,\n )\n except ImportError:\n logging.error(\n f\"\"\"\n********** ERROR ***********\n\ncomfyui-workflow-templates is not installed.\n\n{frontend_install_warning_message()}\n\n********** ERROR ***********\n\"\"\".strip()\n )\n return None\n\n try:\n template_entries = list(iter_templates())\n except Exception as exc:\n logging.error(f\"Failed to enumerate workflow templates: {exc}\")\n return None\n\n asset_map: Dict[str, str] = {}\n try:\n for entry in template_entries:\n for asset in entry.assets:\n asset_map[asset.filename] = get_asset_path(\n entry.template_id, asset.filename\n )\n except Exception as exc:\n logging.error(f\"Failed to resolve template asset paths: {exc}\")\n return None\n\n if not asset_map:\n logging.error(\"No workflow template assets found. Did the packages install correctly?\")\n return None\n\n return asset_map\n\n\n @classmethod\n def legacy_templates_path(cls) -> Optional[str]:\n \"\"\"Return the legacy templates directory shipped inside the meta package.\"\"\"\n try:\n import comfyui_workflow_templates\n\n return str(\n importlib.resources.files(comfyui_workflow_templates) / \"templates\"\n )\n except ImportError:\n logging.error(\n f\"\"\"\n********** ERROR ***********\n\ncomfyui-workflow-templates is not installed.\n\n{frontend_install_warning_message()}\n\n********** ERROR ***********\n\"\"\".strip()\n )\n return None\n\n @classmethod\n def embedded_docs_path(cls) -> str:\n \"\"\"Get the path to embedded documentation\"\"\"\n try:\n import comfyui_embedded_docs\n\n return str(\n importlib.resources.files(comfyui_embedded_docs) / \"docs\"\n )\n except ImportError:\n logging.info(\"comfyui-embedded-docs package not found\")\n return None\n\n @classmethod\n def parse_version_string(cls, value: str) -> tuple[str, str, str]:\n \"\"\"\n Args:\n value (str): The version string to parse.\n\n Returns:\n tuple[str, str]: A tuple containing provider name and version.\n\n Raises:\n argparse.ArgumentTypeError: If the version string is invalid.\n \"\"\"\n VERSION_PATTERN = r\"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\\d+\\.\\d+\\.\\d+[-._a-zA-Z0-9]*|latest|prerelease)$\"\n match_result = re.match(VERSION_PATTERN, value)\n if match_result is None:\n raise argparse.ArgumentTypeError(f\"Invalid version string: {value}\")\n\n return match_result.group(1), match_result.group(2), match_result.group(3)\n\n @classmethod\n def init_frontend_unsafe(\n cls, version_string: str, provider: Optional[FrontEndProvider] = None\n ) -> str:\n \"\"\"\n Initializes the frontend for the specified version.\n\n Args:\n version_string (str): The version string.\n provider (FrontEndProvider, optional): The provider to use. Defaults to None.\n\n Returns:\n str: The path to the initialized frontend.\n\n Raises:\n Exception: If there is an error during the initialization process.\n main error source might be request timeout or invalid URL.\n \"\"\"\n if version_string == DEFAULT_VERSION_STRING:\n check_frontend_version()\n return cls.default_frontend_path()\n\n repo_owner, repo_name, version = cls.parse_version_string(version_string)\n\n if version.startswith(\"v\"):\n expected_path = str(\n Path(cls.CUSTOM_FRONTENDS_ROOT)\n / f\"{repo_owner}_{repo_name}\"\n / version.lstrip(\"v\")\n )\n if os.path.exists(expected_path):\n logging.info(\n f\"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}\"\n )\n return expected_path\n\n logging.info(\n f\"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...\"\n )\n\n provider = provider or FrontEndProvider(repo_owner, repo_name)\n release = provider.get_release(version)\n\n semantic_version = release[\"tag_name\"].lstrip(\"v\")\n web_root = str(\n Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version\n )\n if not os.path.exists(web_root):\n try:\n os.makedirs(web_root, exist_ok=True)\n logging.info(\n \"Downloading frontend(%s) version(%s) to (%s)\",\n provider.folder_name,\n semantic_version,\n web_root,\n )\n logging.debug(release)\n download_release_asset_zip(release, destination_path=web_root)\n finally:\n # Clean up the directory if it is empty, i.e. the download failed\n if not os.listdir(web_root):\n os.rmdir(web_root)\n\n return web_root\n\n @classmethod\n def init_frontend(cls, version_string: str) -> str:\n \"\"\"\n Initializes the frontend with the specified version string.\n\n Args:\n version_string (str): The version string to initialize the frontend with.\n\n Returns:\n str: The path of the initialized frontend.\n \"\"\"\n try:\n return cls.init_frontend_unsafe(version_string)\n except Exception as e:\n logging.error(\"Failed to initialize frontend: %s\", e)\n logging.info(\"Falling back to the default frontend.\")\n check_frontend_version()\n return cls.default_frontend_path()\n @classmethod\n def template_asset_handler(cls):\n assets = cls.template_asset_map()\n if not assets:\n return None\n\n async def serve_template(request: web.Request) -> web.StreamResponse:\n rel_path = request.match_info.get(\"path\", \"\")\n target = assets.get(rel_path)\n if target is None:\n raise web.HTTPNotFound()\n return web.FileResponse(target)\n\n return serve_template\n" }, { "path": "app/logger.py", "content": "from collections import deque\nfrom datetime import datetime\nimport io\nimport logging\nimport sys\nimport threading\n\nlogs = None\nstdout_interceptor = None\nstderr_interceptor = None\n\n\nclass LogInterceptor(io.TextIOWrapper):\n def __init__(self, stream, *args, **kwargs):\n buffer = stream.buffer\n encoding = stream.encoding\n super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering)\n self._lock = threading.Lock()\n self._flush_callbacks = []\n self._logs_since_flush = []\n\n def write(self, data):\n entry = {\"t\": datetime.now().isoformat(), \"m\": data}\n with self._lock:\n self._logs_since_flush.append(entry)\n\n # Simple handling for cr to overwrite the last output if it isnt a full line\n # else logs just get full of progress messages\n if isinstance(data, str) and data.startswith(\"\\r\") and not logs[-1][\"m\"].endswith(\"\\n\"):\n logs.pop()\n logs.append(entry)\n super().write(data)\n\n def flush(self):\n super().flush()\n for cb in self._flush_callbacks:\n cb(self._logs_since_flush)\n self._logs_since_flush = []\n\n def on_flush(self, callback):\n self._flush_callbacks.append(callback)\n\n\ndef get_logs():\n return logs\n\n\ndef on_flush(callback):\n if stdout_interceptor is not None:\n stdout_interceptor.on_flush(callback)\n if stderr_interceptor is not None:\n stderr_interceptor.on_flush(callback)\n\ndef setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False):\n global logs\n if logs:\n return\n\n # Override output streams and log to buffer\n logs = deque(maxlen=capacity)\n\n global stdout_interceptor\n global stderr_interceptor\n stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout)\n stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr)\n\n # Setup default global logger\n logger = logging.getLogger()\n logger.setLevel(log_level)\n\n stream_handler = logging.StreamHandler()\n stream_handler.setFormatter(logging.Formatter(\"%(message)s\"))\n\n if use_stdout:\n # Only errors and critical to stderr\n stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR)\n\n # Lesser to stdout\n stdout_handler = logging.StreamHandler(sys.stdout)\n stdout_handler.setFormatter(logging.Formatter(\"%(message)s\"))\n stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR)\n logger.addHandler(stdout_handler)\n\n logger.addHandler(stream_handler)\n\n\nSTARTUP_WARNINGS = []\n\n\ndef log_startup_warning(msg):\n logging.warning(msg)\n STARTUP_WARNINGS.append(msg)\n\n\ndef print_startup_warnings():\n for s in STARTUP_WARNINGS:\n logging.warning(s)\n STARTUP_WARNINGS.clear()\n" }, { "path": "app/model_manager.py", "content": "from __future__ import annotations\n\nimport os\nimport base64\nimport json\nimport time\nimport logging\nimport folder_paths\nimport glob\nimport comfy.utils\nfrom aiohttp import web\nfrom PIL import Image\nfrom io import BytesIO\nfrom folder_paths import map_legacy, filter_files_extensions, filter_files_content_types\n\n\nclass ModelFileManager:\n def __init__(self) -> None:\n self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}\n\n def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None:\n return self.cache.get(key, default)\n\n def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]):\n self.cache[key] = value\n\n def clear_cache(self):\n self.cache.clear()\n\n def add_routes(self, routes):\n # NOTE: This is an experiment to replace `/models`\n @routes.get(\"/experiment/models\")\n async def get_model_folders(request):\n model_types = list(folder_paths.folder_names_and_paths.keys())\n folder_black_list = [\"configs\", \"custom_nodes\"]\n output_folders: list[dict] = []\n for folder in model_types:\n if folder in folder_black_list:\n continue\n output_folders.append({\"name\": folder, \"folders\": folder_paths.get_folder_paths(folder)})\n return web.json_response(output_folders)\n\n # NOTE: This is an experiment to replace `/models/{folder}`\n @routes.get(\"/experiment/models/{folder}\")\n async def get_all_models(request):\n folder = request.match_info.get(\"folder\", None)\n if folder not in folder_paths.folder_names_and_paths:\n return web.Response(status=404)\n files = self.get_model_file_list(folder)\n return web.json_response(files)\n\n @routes.get(\"/experiment/models/preview/{folder}/{path_index}/{filename:.*}\")\n async def get_model_preview(request):\n folder_name = request.match_info.get(\"folder\", None)\n path_index = int(request.match_info.get(\"path_index\", None))\n filename = request.match_info.get(\"filename\", None)\n\n if folder_name not in folder_paths.folder_names_and_paths:\n return web.Response(status=404)\n\n folders = folder_paths.folder_names_and_paths[folder_name]\n folder = folders[0][path_index]\n full_filename = os.path.join(folder, filename)\n\n previews = self.get_model_previews(full_filename)\n default_preview = previews[0] if len(previews) > 0 else None\n if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):\n return web.Response(status=404)\n\n try:\n with Image.open(default_preview) as img:\n img_bytes = BytesIO()\n img.save(img_bytes, format=\"WEBP\")\n img_bytes.seek(0)\n return web.Response(body=img_bytes.getvalue(), content_type=\"image/webp\")\n except:\n return web.Response(status=404)\n\n def get_model_file_list(self, folder_name: str):\n folder_name = map_legacy(folder_name)\n folders = folder_paths.folder_names_and_paths[folder_name]\n output_list: list[dict] = []\n\n for index, folder in enumerate(folders[0]):\n if not os.path.isdir(folder):\n continue\n out = self.cache_model_file_list_(folder)\n if out is None:\n out = self.recursive_search_models_(folder, index)\n self.set_cache(folder, out)\n output_list.extend(out[0])\n\n return output_list\n\n def cache_model_file_list_(self, folder: str):\n model_file_list_cache = self.get_cache(folder)\n\n if model_file_list_cache is None:\n return None\n if not os.path.isdir(folder):\n return None\n if os.path.getmtime(folder) != model_file_list_cache[1]:\n return None\n for x in model_file_list_cache[1]:\n time_modified = model_file_list_cache[1][x]\n folder = x\n if os.path.getmtime(folder) != time_modified:\n return None\n\n return model_file_list_cache\n\n def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]:\n if not os.path.isdir(directory):\n return [], {}, time.perf_counter()\n\n excluded_dir_names = [\".git\"]\n # TODO use settings\n include_hidden_files = False\n\n result: list[str] = []\n dirs: dict[str, float] = {}\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 if not include_hidden_files:\n subdirs[:] = [d for d in subdirs if not d.startswith(\".\")]\n filenames = [f for f in filenames if not f.startswith(\".\")]\n\n filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions)\n\n for file_name in filenames:\n try:\n full_path = os.path.join(dirpath, file_name)\n relative_path = os.path.relpath(full_path, directory)\n\n # Get file metadata\n file_info = {\n \"name\": relative_path,\n \"pathIndex\": pathIndex,\n \"modified\": os.path.getmtime(full_path), # Add modification time\n \"created\": os.path.getctime(full_path), # Add creation time\n \"size\": os.path.getsize(full_path) # Add file size\n }\n result.append(file_info)\n\n except Exception as e:\n logging.warning(f\"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.\")\n continue\n\n for d in subdirs:\n path: str = os.path.join(dirpath, d)\n try:\n dirs[path] = os.path.getmtime(path)\n except FileNotFoundError:\n logging.warning(f\"Warning: Unable to access {path}. Skipping this path.\")\n continue\n\n return result, dirs, time.perf_counter()\n\n def get_model_previews(self, filepath: str) -> list[str | BytesIO]:\n dirname = os.path.dirname(filepath)\n\n if not os.path.exists(dirname):\n return []\n\n basename = os.path.splitext(filepath)[0]\n match_files = glob.glob(f\"{basename}.*\", recursive=False)\n image_files = filter_files_content_types(match_files, \"image\")\n safetensors_file = next(filter(lambda x: x.endswith(\".safetensors\"), match_files), None)\n safetensors_metadata = {}\n\n result: list[str | BytesIO] = []\n\n for filename in image_files:\n _basename = os.path.splitext(filename)[0]\n if _basename == basename:\n result.append(filename)\n if _basename == f\"{basename}.preview\":\n result.append(filename)\n\n if safetensors_file:\n safetensors_filepath = os.path.join(dirname, safetensors_file)\n header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024)\n if header:\n safetensors_metadata = json.loads(header)\n safetensors_images = safetensors_metadata.get(\"__metadata__\", {}).get(\"ssmd_cover_images\", None)\n if safetensors_images:\n safetensors_images = json.loads(safetensors_images)\n for image in safetensors_images:\n result.append(BytesIO(base64.b64decode(image)))\n\n return result\n\n def __exit__(self, exc_type, exc_value, traceback):\n self.clear_cache()\n" }, { "path": "app/node_replace_manager.py", "content": "from __future__ import annotations\n\nfrom aiohttp import web\n\nfrom typing import TYPE_CHECKING, TypedDict\nif TYPE_CHECKING:\n from comfy_api.latest._io_public import NodeReplace\n\nfrom comfy_execution.graph_utils import is_link\nimport nodes\n\nclass NodeStruct(TypedDict):\n inputs: dict[str, str | int | float | bool | tuple[str, int]]\n class_type: str\n _meta: dict[str, str]\n\ndef copy_node_struct(node_struct: NodeStruct, empty_inputs: bool = False) -> NodeStruct:\n new_node_struct = node_struct.copy()\n if empty_inputs:\n new_node_struct[\"inputs\"] = {}\n else:\n new_node_struct[\"inputs\"] = node_struct[\"inputs\"].copy()\n new_node_struct[\"_meta\"] = node_struct[\"_meta\"].copy()\n return new_node_struct\n\n\nclass NodeReplaceManager:\n \"\"\"Manages node replacement registrations.\"\"\"\n\n def __init__(self):\n self._replacements: dict[str, list[NodeReplace]] = {}\n\n def register(self, node_replace: NodeReplace):\n \"\"\"Register a node replacement mapping.\"\"\"\n self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)\n\n def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:\n \"\"\"Get replacements for an old node ID.\"\"\"\n return self._replacements.get(old_node_id)\n\n def has_replacement(self, old_node_id: str) -> bool:\n \"\"\"Check if a replacement exists for an old node ID.\"\"\"\n return old_node_id in self._replacements\n\n def apply_replacements(self, prompt: dict[str, NodeStruct]):\n connections: dict[str, list[tuple[str, str, int]]] = {}\n need_replacement: set[str] = set()\n for node_number, node_struct in prompt.items():\n if \"class_type\" not in node_struct or \"inputs\" not in node_struct:\n continue\n class_type = node_struct[\"class_type\"]\n # need replacement if not in NODE_CLASS_MAPPINGS and has replacement\n if class_type not in nodes.NODE_CLASS_MAPPINGS.keys() and self.has_replacement(class_type):\n need_replacement.add(node_number)\n # keep track of connections\n for input_id, input_value in node_struct[\"inputs\"].items():\n if is_link(input_value):\n conn_number = input_value[0]\n connections.setdefault(conn_number, []).append((node_number, input_id, input_value[1]))\n for node_number in need_replacement:\n node_struct = prompt[node_number]\n class_type = node_struct[\"class_type\"]\n replacements = self.get_replacement(class_type)\n if replacements is None:\n continue\n # just use the first replacement\n replacement = replacements[0]\n new_node_id = replacement.new_node_id\n # if replacement is not a valid node, skip trying to replace it as will only cause confusion\n if new_node_id not in nodes.NODE_CLASS_MAPPINGS.keys():\n continue\n # first, replace node id (class_type)\n new_node_struct = copy_node_struct(node_struct, empty_inputs=True)\n new_node_struct[\"class_type\"] = new_node_id\n # TODO: consider replacing display_name in _meta as well for error reporting purposes; would need to query node schema\n # second, replace inputs\n if replacement.input_mapping is not None:\n for input_map in replacement.input_mapping:\n if \"set_value\" in input_map:\n new_node_struct[\"inputs\"][input_map[\"new_id\"]] = input_map[\"set_value\"]\n elif \"old_id\" in input_map:\n new_node_struct[\"inputs\"][input_map[\"new_id\"]] = node_struct[\"inputs\"][input_map[\"old_id\"]]\n # finalize input replacement\n prompt[node_number] = new_node_struct\n # third, replace outputs\n if replacement.output_mapping is not None:\n # re-mapping outputs requires changing the input values of nodes that receive connections from this one\n if node_number in connections:\n for conns in connections[node_number]:\n conn_node_number, conn_input_id, old_output_idx = conns\n for output_map in replacement.output_mapping:\n if output_map[\"old_idx\"] == old_output_idx:\n new_output_idx = output_map[\"new_idx\"]\n previous_input = prompt[conn_node_number][\"inputs\"][conn_input_id]\n previous_input[1] = new_output_idx\n\n def as_dict(self):\n \"\"\"Serialize all replacements to dict.\"\"\"\n return {\n k: [v.as_dict() for v in v_list]\n for k, v_list in self._replacements.items()\n }\n\n def add_routes(self, routes):\n @routes.get(\"/node_replacements\")\n async def get_node_replacements(request):\n return web.json_response(self.as_dict())\n" }, { "path": "app/subgraph_manager.py", "content": "from __future__ import annotations\n\nfrom typing import TypedDict\nimport os\nimport folder_paths\nimport glob\nfrom aiohttp import web\nimport hashlib\n\n\nclass Source:\n custom_node = \"custom_node\"\n templates = \"templates\"\n\nclass SubgraphEntry(TypedDict):\n source: str\n \"\"\"\n Source of subgraph - custom_nodes vs templates.\n \"\"\"\n path: str\n \"\"\"\n Relative path of the subgraph file.\n For custom nodes, will be the relative directory like /subgraphs/.json\n \"\"\"\n name: str\n \"\"\"\n Name of subgraph file.\n \"\"\"\n info: CustomNodeSubgraphEntryInfo\n \"\"\"\n Additional info about subgraph; in the case of custom_nodes, will contain nodepack name\n \"\"\"\n data: str\n\nclass CustomNodeSubgraphEntryInfo(TypedDict):\n node_pack: str\n \"\"\"Node pack name.\"\"\"\n\nclass SubgraphManager:\n def __init__(self):\n self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None\n self.cached_blueprint_subgraphs: dict[SubgraphEntry] | None = None\n\n def _create_entry(self, file: str, source: str, node_pack: str) -> tuple[str, SubgraphEntry]:\n \"\"\"Create a subgraph entry from a file path. Expects normalized path (forward slashes).\"\"\"\n entry_id = hashlib.sha256(f\"{source}{file}\".encode()).hexdigest()\n entry: SubgraphEntry = {\n \"source\": source,\n \"name\": os.path.splitext(os.path.basename(file))[0],\n \"path\": file,\n \"info\": {\"node_pack\": node_pack},\n }\n return entry_id, entry\n\n async def load_entry_data(self, entry: SubgraphEntry):\n with open(entry['path'], 'r', encoding='utf-8') as f:\n entry['data'] = f.read()\n return entry\n\n async def sanitize_entry(self, entry: SubgraphEntry | None, remove_data=False) -> SubgraphEntry | None:\n if entry is None:\n return None\n entry = entry.copy()\n entry.pop('path', None)\n if remove_data:\n entry.pop('data', None)\n return entry\n\n async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=False) -> dict[str, SubgraphEntry]:\n entries = entries.copy()\n for key in list(entries.keys()):\n entries[key] = await self.sanitize_entry(entries[key], remove_data)\n return entries\n\n async def get_custom_node_subgraphs(self, loadedModules, force_reload=False):\n \"\"\"Load subgraphs from custom nodes.\"\"\"\n if not force_reload and self.cached_custom_node_subgraphs is not None:\n return self.cached_custom_node_subgraphs\n\n subgraphs_dict: dict[SubgraphEntry] = {}\n for folder in folder_paths.get_folder_paths(\"custom_nodes\"):\n pattern = os.path.join(folder, \"*/subgraphs/*.json\")\n for file in glob.glob(pattern):\n file = file.replace('\\\\', '/')\n node_pack = \"custom_nodes.\" + file.split('/')[-3]\n entry_id, entry = self._create_entry(file, Source.custom_node, node_pack)\n subgraphs_dict[entry_id] = entry\n\n self.cached_custom_node_subgraphs = subgraphs_dict\n return subgraphs_dict\n\n async def get_blueprint_subgraphs(self, force_reload=False):\n \"\"\"Load subgraphs from the blueprints directory.\"\"\"\n if not force_reload and self.cached_blueprint_subgraphs is not None:\n return self.cached_blueprint_subgraphs\n\n subgraphs_dict: dict[SubgraphEntry] = {}\n blueprints_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'blueprints')\n\n if os.path.exists(blueprints_dir):\n for file in glob.glob(os.path.join(blueprints_dir, \"*.json\")):\n file = file.replace('\\\\', '/')\n entry_id, entry = self._create_entry(file, Source.templates, \"comfyui\")\n subgraphs_dict[entry_id] = entry\n\n self.cached_blueprint_subgraphs = subgraphs_dict\n return subgraphs_dict\n\n async def get_all_subgraphs(self, loadedModules, force_reload=False):\n \"\"\"Get all subgraphs from all sources (custom nodes and blueprints).\"\"\"\n custom_node_subgraphs = await self.get_custom_node_subgraphs(loadedModules, force_reload)\n blueprint_subgraphs = await self.get_blueprint_subgraphs(force_reload)\n return {**custom_node_subgraphs, **blueprint_subgraphs}\n\n async def get_subgraph(self, id: str, loadedModules):\n \"\"\"Get a specific subgraph by ID from any source.\"\"\"\n entry = (await self.get_all_subgraphs(loadedModules)).get(id)\n if entry is not None and entry.get('data') is None:\n await self.load_entry_data(entry)\n return entry\n\n def add_routes(self, routes, loadedModules):\n @routes.get(\"/global_subgraphs\")\n async def get_global_subgraphs(request):\n subgraphs_dict = await self.get_all_subgraphs(loadedModules)\n return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True))\n\n @routes.get(\"/global_subgraphs/{id}\")\n async def get_global_subgraph(request):\n id = request.match_info.get(\"id\", None)\n subgraph = await self.get_subgraph(id, loadedModules)\n return web.json_response(await self.sanitize_entry(subgraph))\n" }, { "path": "app/user_manager.py", "content": "from __future__ import annotations\nimport json\nimport os\nimport re\nimport uuid\nimport glob\nimport shutil\nimport logging\nimport tempfile\nfrom aiohttp import web\nfrom urllib import parse\nfrom comfy.cli_args import args\nimport folder_paths\nfrom .app_settings import AppSettings\nfrom typing import TypedDict\n\ndefault_user = \"default\"\n\n\nclass FileInfo(TypedDict):\n path: str\n size: int\n modified: int\n created: int\n\n\ndef get_file_info(path: str, relative_to: str) -> FileInfo:\n return {\n \"path\": os.path.relpath(path, relative_to).replace(os.sep, '/'),\n \"size\": os.path.getsize(path),\n \"modified\": os.path.getmtime(path),\n \"created\": os.path.getctime(path)\n }\n\n\nclass UserManager():\n def __init__(self):\n user_directory = folder_paths.get_user_directory()\n\n self.settings = AppSettings(self)\n if not os.path.exists(user_directory):\n os.makedirs(user_directory, exist_ok=True)\n if not args.multi_user:\n logging.warning(\"****** User settings have been changed to be stored on the server instead of browser storage. ******\")\n logging.warning(\"****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******\")\n\n if args.multi_user:\n if os.path.isfile(self.get_users_file()):\n with open(self.get_users_file()) as f:\n self.users = json.load(f)\n else:\n self.users = {}\n else:\n self.users = {\"default\": \"default\"}\n\n def get_users_file(self):\n return os.path.join(folder_paths.get_user_directory(), \"users.json\")\n\n def get_request_user_id(self, request):\n user = \"default\"\n if args.multi_user and \"comfy-user\" in request.headers:\n user = request.headers[\"comfy-user\"]\n # Block System Users (use same error message to prevent probing)\n if user.startswith(folder_paths.SYSTEM_USER_PREFIX):\n raise KeyError(\"Unknown user: \" + user)\n\n if user not in self.users:\n raise KeyError(\"Unknown user: \" + user)\n\n return user\n\n def get_request_user_filepath(self, request, file, type=\"userdata\", create_dir=True):\n if type == \"userdata\":\n root_dir = folder_paths.get_user_directory()\n else:\n raise KeyError(\"Unknown filepath type:\" + type)\n\n user = self.get_request_user_id(request)\n user_root = folder_paths.get_public_user_directory(user)\n if user_root is None:\n return None\n path = user_root\n\n # prevent leaving /{type}\n if os.path.commonpath((root_dir, user_root)) != root_dir:\n return None\n\n if file is not None:\n # Check if filename is url encoded\n if \"%\" in file:\n file = parse.unquote(file)\n\n # prevent leaving /{type}/{user}\n path = os.path.abspath(os.path.join(user_root, file))\n if os.path.commonpath((user_root, path)) != user_root:\n return None\n\n parent = os.path.split(path)[0]\n\n if create_dir and not os.path.exists(parent):\n os.makedirs(parent, exist_ok=True)\n\n return path\n\n def add_user(self, name):\n name = name.strip()\n if not name:\n raise ValueError(\"username not provided\")\n if name.startswith(folder_paths.SYSTEM_USER_PREFIX):\n raise ValueError(\"System User prefix not allowed\")\n user_id = re.sub(\"[^a-zA-Z0-9-_]+\", '-', name)\n if user_id.startswith(folder_paths.SYSTEM_USER_PREFIX):\n raise ValueError(\"System User prefix not allowed\")\n user_id = user_id + \"_\" + str(uuid.uuid4())\n\n self.users[user_id] = name\n\n with open(self.get_users_file(), \"w\") as f:\n json.dump(self.users, f)\n\n return user_id\n\n def add_routes(self, routes):\n self.settings.add_routes(routes)\n\n @routes.get(\"/users\")\n async def get_users(request):\n if args.multi_user:\n return web.json_response({\"storage\": \"server\", \"users\": self.users})\n else:\n user_dir = self.get_request_user_filepath(request, None, create_dir=False)\n return web.json_response({\n \"storage\": \"server\",\n \"migrated\": os.path.exists(user_dir)\n })\n\n @routes.post(\"/users\")\n async def post_users(request):\n body = await request.json()\n username = body[\"username\"]\n if username in self.users.values():\n return web.json_response({\"error\": \"Duplicate username.\"}, status=400)\n\n try:\n user_id = self.add_user(username)\n except ValueError as e:\n return web.json_response({\"error\": str(e)}, status=400)\n return web.json_response(user_id)\n\n @routes.get(\"/userdata\")\n async def listuserdata(request):\n \"\"\"\n List user data files in a specified directory.\n\n This endpoint allows listing files in a user's data directory, with options for recursion,\n full file information, and path splitting.\n\n Query Parameters:\n - dir (required): The directory to list files from.\n - recurse (optional): If \"true\", recursively list files in subdirectories.\n - full_info (optional): If \"true\", return detailed file information (path, size, modified time).\n - split (optional): If \"true\", split file paths into components (only applies when full_info is false).\n\n Returns:\n - 400: If 'dir' parameter is missing.\n - 403: If the requested path is not allowed.\n - 404: If the requested directory does not exist.\n - 200: JSON response with the list of files or file information.\n\n The response format depends on the query parameters:\n - Default: List of relative file paths.\n - full_info=true: List of dictionaries with file details.\n - split=true (and full_info=false): List of lists, each containing path components.\n \"\"\"\n directory = request.rel_url.query.get('dir', '')\n if not directory:\n return web.Response(status=400, text=\"Directory not provided\")\n\n path = self.get_request_user_filepath(request, directory)\n if not path:\n return web.Response(status=403, text=\"Invalid directory\")\n\n if not os.path.exists(path):\n return web.Response(status=404, text=\"Directory not found\")\n\n recurse = request.rel_url.query.get('recurse', '').lower() == \"true\"\n full_info = request.rel_url.query.get('full_info', '').lower() == \"true\"\n split_path = request.rel_url.query.get('split', '').lower() == \"true\"\n\n # Use different patterns based on whether we're recursing or not\n if recurse:\n pattern = os.path.join(glob.escape(path), '**', '*')\n else:\n pattern = os.path.join(glob.escape(path), '*')\n\n def process_full_path(full_path: str) -> FileInfo | str | list[str]:\n if full_info:\n return get_file_info(full_path, path)\n\n rel_path = os.path.relpath(full_path, path).replace(os.sep, '/')\n if split_path:\n return [rel_path] + rel_path.split('/')\n\n return rel_path\n\n results = [\n process_full_path(full_path)\n for full_path in glob.glob(pattern, recursive=recurse)\n if os.path.isfile(full_path)\n ]\n\n return web.json_response(results)\n\n @routes.get(\"/v2/userdata\")\n async def list_userdata_v2(request):\n \"\"\"\n List files and directories in a user's data directory.\n\n This endpoint provides a structured listing of contents within a specified\n subdirectory of the user's data storage.\n\n Query Parameters:\n - path (optional): The relative path within the user's data directory\n to list. Defaults to the root ('').\n\n Returns:\n - 400: If the requested path is invalid, outside the user's data directory, or is not a directory.\n - 404: If the requested path does not exist.\n - 403: If the user is invalid.\n - 500: If there is an error reading the directory contents.\n - 200: JSON response containing a list of file and directory objects.\n Each object includes:\n - name: The name of the file or directory.\n - type: 'file' or 'directory'.\n - path: The relative path from the user's data root.\n - size (for files): The size in bytes.\n - modified (for files): The last modified timestamp (Unix epoch).\n \"\"\"\n requested_rel_path = request.rel_url.query.get('path', '')\n\n # URL-decode the path parameter\n try:\n requested_rel_path = parse.unquote(requested_rel_path)\n except Exception as e:\n logging.warning(f\"Failed to decode path parameter: {requested_rel_path}, Error: {e}\")\n return web.Response(status=400, text=\"Invalid characters in path parameter\")\n\n\n # Check user validity and get the absolute path for the requested directory\n try:\n base_user_path = self.get_request_user_filepath(request, None, create_dir=False)\n\n if requested_rel_path:\n target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False)\n else:\n target_abs_path = base_user_path\n\n except KeyError as e:\n # Invalid user detected by get_request_user_id inside get_request_user_filepath\n logging.warning(f\"Access denied for user: {e}\")\n return web.Response(status=403, text=\"Invalid user specified in request\")\n\n\n if not target_abs_path:\n # Path traversal or other issue detected by get_request_user_filepath\n return web.Response(status=400, text=\"Invalid path requested\")\n\n # Handle cases where the user directory or target path doesn't exist\n if not os.path.exists(target_abs_path):\n # Check if it's the base user directory that's missing (new user case)\n if target_abs_path == base_user_path:\n # It's okay if the base user directory doesn't exist yet, return empty list\n return web.json_response([])\n else:\n # A specific subdirectory was requested but doesn't exist\n return web.Response(status=404, text=\"Requested path not found\")\n\n if not os.path.isdir(target_abs_path):\n return web.Response(status=400, text=\"Requested path is not a directory\")\n\n results = []\n try:\n for root, dirs, files in os.walk(target_abs_path, topdown=True):\n # Process directories\n for dir_name in dirs:\n dir_path = os.path.join(root, dir_name)\n rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/')\n results.append({\n \"name\": dir_name,\n \"path\": rel_path,\n \"type\": \"directory\"\n })\n\n # Process files\n for file_name in files:\n file_path = os.path.join(root, file_name)\n rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/')\n entry_info = {\n \"name\": file_name,\n \"path\": rel_path,\n \"type\": \"file\"\n }\n try:\n stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk\n entry_info[\"size\"] = stats.st_size\n entry_info[\"modified\"] = stats.st_mtime\n except OSError as stat_error:\n logging.warning(f\"Could not stat file {file_path}: {stat_error}\")\n pass # Include file with available info\n results.append(entry_info)\n except OSError as e:\n logging.error(f\"Error listing directory {target_abs_path}: {e}\")\n return web.Response(status=500, text=\"Error reading directory contents\")\n\n # Sort results alphabetically, directories first then files\n results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower()))\n\n return web.json_response(results)\n\n def get_user_data_path(request, check_exists = False, param = \"file\"):\n file = request.match_info.get(param, None)\n if not file:\n return web.Response(status=400)\n\n path = self.get_request_user_filepath(request, file)\n if not path:\n return web.Response(status=403)\n\n if check_exists and not os.path.exists(path):\n return web.Response(status=404)\n\n return path\n\n @routes.get(\"/userdata/{file}\")\n async def getuserdata(request):\n path = get_user_data_path(request, check_exists=True)\n if not isinstance(path, str):\n return path\n\n return web.FileResponse(path)\n\n @routes.post(\"/userdata/{file}\")\n async def post_userdata(request):\n \"\"\"\n Upload or update a user data file.\n\n This endpoint handles file uploads to a user's data directory, with options for\n controlling overwrite behavior and response format.\n\n Query Parameters:\n - overwrite (optional): If \"false\", prevents overwriting existing files. Defaults to \"true\".\n - full_info (optional): If \"true\", returns detailed file information (path, size, modified time).\n If \"false\", returns only the relative file path.\n\n Path Parameters:\n - file: The target file path (URL encoded if necessary).\n\n Returns:\n - 400: If 'file' parameter is missing.\n - 403: If the requested path is not allowed.\n - 409: If overwrite=false and the file already exists.\n - 200: JSON response with either:\n - Full file information (if full_info=true)\n - Relative file path (if full_info=false)\n\n The request body should contain the raw file content to be written.\n \"\"\"\n path = get_user_data_path(request)\n if not isinstance(path, str):\n return path\n\n overwrite = request.query.get(\"overwrite\", 'true') != \"false\"\n full_info = request.query.get('full_info', 'false').lower() == \"true\"\n\n if not overwrite and os.path.exists(path):\n return web.Response(status=409, text=\"File already exists\")\n\n try:\n body = await request.read()\n\n dir_name = os.path.dirname(path)\n fd, tmp_path = tempfile.mkstemp(dir=dir_name)\n try:\n with os.fdopen(fd, \"wb\") as f:\n f.write(body)\n os.replace(tmp_path, path)\n except:\n os.unlink(tmp_path)\n raise\n except OSError as e:\n logging.warning(f\"Error saving file '{path}': {e}\")\n return web.Response(\n status=400,\n reason=\"Invalid filename. Please avoid special characters like :\\\\/*?\\\"<>|\"\n )\n\n user_path = self.get_request_user_filepath(request, None)\n if full_info:\n resp = get_file_info(path, user_path)\n else:\n resp = os.path.relpath(path, user_path)\n\n return web.json_response(resp)\n\n @routes.delete(\"/userdata/{file}\")\n async def delete_userdata(request):\n path = get_user_data_path(request, check_exists=True)\n if not isinstance(path, str):\n return path\n\n os.remove(path)\n\n return web.Response(status=204)\n\n @routes.post(\"/userdata/{file}/move/{dest}\")\n async def move_userdata(request):\n \"\"\"\n Move or rename a user data file.\n\n This endpoint handles moving or renaming files within a user's data directory, with options for\n controlling overwrite behavior and response format.\n\n Path Parameters:\n - file: The source file path (URL encoded if necessary)\n - dest: The destination file path (URL encoded if necessary)\n\n Query Parameters:\n - overwrite (optional): If \"false\", prevents overwriting existing files. Defaults to \"true\".\n - full_info (optional): If \"true\", returns detailed file information (path, size, modified time).\n If \"false\", returns only the relative file path.\n\n Returns:\n - 400: If either 'file' or 'dest' parameter is missing\n - 403: If either requested path is not allowed\n - 404: If the source file does not exist\n - 409: If overwrite=false and the destination file already exists\n - 200: JSON response with either:\n - Full file information (if full_info=true)\n - Relative file path (if full_info=false)\n \"\"\"\n source = get_user_data_path(request, check_exists=True)\n if not isinstance(source, str):\n return source\n\n dest = get_user_data_path(request, check_exists=False, param=\"dest\")\n if not isinstance(dest, str):\n return dest\n\n overwrite = request.query.get(\"overwrite\", 'true') != \"false\"\n full_info = request.query.get('full_info', 'false').lower() == \"true\"\n\n if not overwrite and os.path.exists(dest):\n return web.Response(status=409, text=\"File already exists\")\n\n logging.info(f\"moving '{source}' -> '{dest}'\")\n shutil.move(source, dest)\n\n user_path = self.get_request_user_filepath(request, None)\n if full_info:\n resp = get_file_info(dest, user_path)\n else:\n resp = os.path.relpath(dest, user_path)\n\n return web.json_response(resp)\n" }, { "path": "blueprints/.glsl/Brightness_and_Contrast_1.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..100\nuniform float u_float1; // Contrast slider -100..100\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float MID_GRAY = 0.18; // 18% reflectance\n\n// sRGB gamma 2.2 approximation\nvec3 srgbToLinear(vec3 c) {\n return pow(max(c, 0.0), vec3(2.2));\n}\n\nvec3 linearToSrgb(vec3 c) {\n return pow(max(c, 0.0), vec3(1.0/2.2));\n}\n\nfloat mapBrightness(float b) {\n return clamp(b / 100.0, -1.0, 1.0);\n}\n\nfloat mapContrast(float c) {\n return clamp(c / 100.0 + 1.0, 0.0, 2.0);\n}\n\nvoid main() {\n vec4 orig = texture(u_image0, v_texCoord);\n\n float brightness = mapBrightness(u_float0);\n float contrast = mapContrast(u_float1);\n\n vec3 lin = srgbToLinear(orig.rgb);\n\n lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\n\n // Convert back to sRGB\n vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\n\n fragColor = vec4(result, orig.a);\n}\n" }, { "path": "blueprints/.glsl/Chromatic_Aberration_16.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0; // Mode\nuniform float u_float0; // Amount (0 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int MODE_LINEAR = 0;\nconst int MODE_RADIAL = 1;\nconst int MODE_BARREL = 2;\nconst int MODE_SWIRL = 3;\nconst int MODE_DIAGONAL = 4;\n\nconst float AMOUNT_SCALE = 0.0005;\nconst float RADIAL_MULT = 4.0;\nconst float BARREL_MULT = 8.0;\nconst float INV_SQRT2 = 0.70710678118;\n\nvoid main() {\n vec2 uv = v_texCoord;\n vec4 original = texture(u_image0, uv);\n\n float amount = u_float0 * AMOUNT_SCALE;\n\n if (amount < 0.000001) {\n fragColor = original;\n return;\n }\n\n // Aspect-corrected coordinates for circular effects\n float aspect = u_resolution.x / u_resolution.y;\n vec2 centered = uv - 0.5;\n vec2 corrected = vec2(centered.x * aspect, centered.y);\n float r = length(corrected);\n vec2 dir = r > 0.0001 ? corrected / r : vec2(0.0);\n vec2 offset = vec2(0.0);\n\n if (u_int0 == MODE_LINEAR) {\n // Horizontal shift (no aspect correction needed)\n offset = vec2(amount, 0.0);\n }\n else if (u_int0 == MODE_RADIAL) {\n // Outward from center, stronger at edges\n offset = dir * r * amount * RADIAL_MULT;\n offset.x /= aspect; // Convert back to UV space\n }\n else if (u_int0 == MODE_BARREL) {\n // Lens distortion simulation (r² falloff)\n offset = dir * r * r * amount * BARREL_MULT;\n offset.x /= aspect; // Convert back to UV space\n }\n else if (u_int0 == MODE_SWIRL) {\n // Perpendicular to radial (rotational aberration)\n vec2 perp = vec2(-dir.y, dir.x);\n offset = perp * r * amount * RADIAL_MULT;\n offset.x /= aspect; // Convert back to UV space\n }\n else if (u_int0 == MODE_DIAGONAL) {\n // 45° offset (no aspect correction needed)\n offset = vec2(amount, amount) * INV_SQRT2;\n }\n \n float red = texture(u_image0, uv + offset).r;\n float green = original.g;\n float blue = texture(u_image0, uv - offset).b;\n \n fragColor = vec4(red, green, blue, original.a);\n}" }, { "path": "blueprints/.glsl/Color_Adjustment_15.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // temperature (-100 to 100)\nuniform float u_float1; // tint (-100 to 100)\nuniform float u_float2; // vibrance (-100 to 100)\nuniform float u_float3; // saturation (-100 to 100)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst float INPUT_SCALE = 0.01;\nconst float TEMP_TINT_PRIMARY = 0.3;\nconst float TEMP_TINT_SECONDARY = 0.15;\nconst float VIBRANCE_BOOST = 2.0;\nconst float SATURATION_BOOST = 2.0;\nconst float SKIN_PROTECTION = 0.5;\nconst float EPSILON = 0.001;\nconst vec3 LUMA_WEIGHTS = vec3(0.299, 0.587, 0.114);\n\nvoid main() {\n vec4 tex = texture(u_image0, v_texCoord);\n vec3 color = tex.rgb;\n \n // Scale inputs: -100/100 → -1/1\n float temperature = u_float0 * INPUT_SCALE;\n float tint = u_float1 * INPUT_SCALE;\n float vibrance = u_float2 * INPUT_SCALE;\n float saturation = u_float3 * INPUT_SCALE;\n \n // Temperature (warm/cool): positive = warm, negative = cool\n color.r += temperature * TEMP_TINT_PRIMARY;\n color.b -= temperature * TEMP_TINT_PRIMARY;\n \n // Tint (green/magenta): positive = green, negative = magenta\n color.g += tint * TEMP_TINT_PRIMARY;\n color.r -= tint * TEMP_TINT_SECONDARY;\n color.b -= tint * TEMP_TINT_SECONDARY;\n \n // Single clamp after temperature/tint\n color = clamp(color, 0.0, 1.0);\n \n // Vibrance with skin protection\n if (vibrance != 0.0) {\n float maxC = max(color.r, max(color.g, color.b));\n float minC = min(color.r, min(color.g, color.b));\n float sat = maxC - minC;\n float gray = dot(color, LUMA_WEIGHTS);\n \n if (vibrance < 0.0) {\n // Desaturate: -100 → gray\n color = mix(vec3(gray), color, 1.0 + vibrance);\n } else {\n // Boost less saturated colors more\n float vibranceAmt = vibrance * (1.0 - sat);\n \n // Branchless skin tone protection\n float isWarmTone = step(color.b, color.g) * step(color.g, color.r);\n float warmth = (color.r - color.b) / max(maxC, EPSILON);\n float skinTone = isWarmTone * warmth * sat * (1.0 - sat);\n vibranceAmt *= (1.0 - skinTone * SKIN_PROTECTION);\n \n color = mix(vec3(gray), color, 1.0 + vibranceAmt * VIBRANCE_BOOST);\n }\n }\n \n // Saturation\n if (saturation != 0.0) {\n float gray = dot(color, LUMA_WEIGHTS);\n float satMix = saturation < 0.0\n ? 1.0 + saturation // -100 → gray\n : 1.0 + saturation * SATURATION_BOOST; // +100 → 3x boost\n color = mix(vec3(gray), color, satMix);\n }\n \n fragColor = vec4(clamp(color, 0.0, 1.0), tex.a);\n}" }, { "path": "blueprints/.glsl/Edge-Preserving_Blur_128.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Blur radius (0–20, default ~5)\nuniform float u_float1; // Edge threshold (0–100, default ~30)\nuniform int u_int0; // Step size (0/1 = every pixel, 2+ = skip pixels)\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int MAX_RADIUS = 20;\nconst float EPSILON = 0.0001;\n\n// Perceptual luminance\nfloat getLuminance(vec3 rgb) {\n return dot(rgb, vec3(0.299, 0.587, 0.114));\n}\n\nvec4 bilateralFilter(vec2 uv, vec2 texelSize, int radius,\n float sigmaSpatial, float sigmaColor)\n{\n vec4 center = texture(u_image0, uv);\n vec3 centerRGB = center.rgb;\n\n float invSpatial2 = -0.5 / (sigmaSpatial * sigmaSpatial);\n float invColor2 = -0.5 / (sigmaColor * sigmaColor + EPSILON);\n\n vec3 sumRGB = vec3(0.0);\n float sumWeight = 0.0;\n\n int step = max(u_int0, 1);\n float radius2 = float(radius * radius);\n\n for (int dy = -MAX_RADIUS; dy <= MAX_RADIUS; dy++) {\n if (dy < -radius || dy > radius) continue;\n if (abs(dy) % step != 0) continue;\n\n for (int dx = -MAX_RADIUS; dx <= MAX_RADIUS; dx++) {\n if (dx < -radius || dx > radius) continue;\n if (abs(dx) % step != 0) continue;\n\n vec2 offset = vec2(float(dx), float(dy));\n float dist2 = dot(offset, offset);\n if (dist2 > radius2) continue;\n\n vec3 sampleRGB = texture(u_image0, uv + offset * texelSize).rgb;\n\n // Spatial Gaussian\n float spatialWeight = exp(dist2 * invSpatial2);\n\n // Perceptual color distance (weighted RGB)\n vec3 diff = sampleRGB - centerRGB;\n float colorDist = dot(diff * diff, vec3(0.299, 0.587, 0.114));\n float colorWeight = exp(colorDist * invColor2);\n\n float w = spatialWeight * colorWeight;\n sumRGB += sampleRGB * w;\n sumWeight += w;\n }\n }\n\n vec3 resultRGB = sumRGB / max(sumWeight, EPSILON);\n return vec4(resultRGB, center.a); // preserve center alpha\n}\n\nvoid main() {\n vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n\n float radiusF = clamp(u_float0, 0.0, float(MAX_RADIUS));\n int radius = int(radiusF + 0.5);\n\n if (radius == 0) {\n fragColor = texture(u_image0, v_texCoord);\n return;\n }\n\n // Edge threshold → color sigma\n // Squared curve for better low-end control\n float t = clamp(u_float1, 0.0, 100.0) / 100.0;\n t *= t;\n float sigmaColor = mix(0.01, 0.5, t);\n\n // Spatial sigma tied to radius\n float sigmaSpatial = max(radiusF * 0.75, 0.5);\n\n fragColor = bilateralFilter(\n v_texCoord,\n texelSize,\n radius,\n sigmaSpatial,\n sigmaColor\n );\n}" }, { "path": "blueprints/.glsl/Film_Grain_15.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // grain amount [0.0 – 1.0] typical: 0.2–0.8\nuniform float u_float1; // grain size [0.3 – 3.0] lower = finer grain\nuniform float u_float2; // color amount [0.0 – 1.0] 0 = monochrome, 1 = RGB grain\nuniform float u_float3; // luminance bias [0.0 – 1.0] 0 = uniform, 1 = shadows only\nuniform int u_int0; // noise mode [0 or 1] 0 = smooth, 1 = grainy\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\n// High-quality integer hash (pcg-like)\nuint pcg(uint v) {\n uint state = v * 747796405u + 2891336453u;\n uint word = ((state >> ((state >> 28u) + 4u)) ^ state) * 277803737u;\n return (word >> 22u) ^ word;\n}\n\n// 2D -> 1D hash input\nuint hash2d(uvec2 p) {\n return pcg(p.x + pcg(p.y));\n}\n\n// Hash to float [0, 1]\nfloat hashf(uvec2 p) {\n return float(hash2d(p)) / float(0xffffffffu);\n}\n\n// Hash to float with offset (for RGB channels)\nfloat hashf(uvec2 p, uint offset) {\n return float(pcg(hash2d(p) + offset)) / float(0xffffffffu);\n}\n\n// Convert uniform [0,1] to roughly Gaussian distribution\n// Using simple approximation: average of multiple samples\nfloat toGaussian(uvec2 p) {\n float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);\n return (sum - 2.0) * 0.7; // Centered, scaled\n}\n\nfloat toGaussian(uvec2 p, uint offset) {\n float sum = hashf(p, offset) + hashf(p, offset + 1u) \n + hashf(p, offset + 2u) + hashf(p, offset + 3u);\n return (sum - 2.0) * 0.7;\n}\n\n// Smooth noise with better interpolation\nfloat smoothNoise(vec2 p) {\n vec2 i = floor(p);\n vec2 f = fract(p);\n \n // Quintic interpolation (less banding than cubic)\n f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n \n uvec2 ui = uvec2(i);\n float a = toGaussian(ui);\n float b = toGaussian(ui + uvec2(1u, 0u));\n float c = toGaussian(ui + uvec2(0u, 1u));\n float d = toGaussian(ui + uvec2(1u, 1u));\n \n return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nfloat smoothNoise(vec2 p, uint offset) {\n vec2 i = floor(p);\n vec2 f = fract(p);\n \n f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n \n uvec2 ui = uvec2(i);\n float a = toGaussian(ui, offset);\n float b = toGaussian(ui + uvec2(1u, 0u), offset);\n float c = toGaussian(ui + uvec2(0u, 1u), offset);\n float d = toGaussian(ui + uvec2(1u, 1u), offset);\n \n return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\n}\n\nvoid main() {\n vec4 color = texture(u_image0, v_texCoord);\n \n // Luminance (Rec.709)\n float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));\n \n // Grain UV (resolution-independent)\n vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);\n uvec2 grainPixel = uvec2(grainUV);\n \n float g;\n vec3 grainRGB;\n \n if (u_int0 == 1) {\n // Grainy mode: pure hash noise (no interpolation = no banding)\n g = toGaussian(grainPixel);\n grainRGB = vec3(\n toGaussian(grainPixel, 100u),\n toGaussian(grainPixel, 200u),\n toGaussian(grainPixel, 300u)\n );\n } else {\n // Smooth mode: interpolated with quintic curve\n g = smoothNoise(grainUV);\n grainRGB = vec3(\n smoothNoise(grainUV, 100u),\n smoothNoise(grainUV, 200u),\n smoothNoise(grainUV, 300u)\n );\n }\n \n // Luminance weighting (less grain in highlights)\n float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));\n \n // Strength\n float strength = u_float0 * 0.15;\n \n // Color vs monochrome grain\n vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));\n \n color.rgb += grainColor * strength * lumWeight;\n fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);\n}\n" }, { "path": "blueprints/.glsl/Glow_30.frag", "content": "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0; // Blend mode\nuniform int u_int1; // Color tint\nuniform float u_float0; // Intensity\nuniform float u_float1; // Radius\nuniform float u_float2; // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD = 0;\nconst int BLEND_SCREEN = 1;\nconst int BLEND_SOFT = 2;\nconst int BLEND_OVERLAY = 3;\nconst int BLEND_LIGHTEN = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n p = fract(p * vec2(123.34, 456.21));\n p += dot(p, p + 45.32);\n return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n return vec3(\n float((h >> 16) & 255),\n float((h >> 8) & 255),\n float(h & 255)\n ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n if (mode == BLEND_SCREEN) {\n return 1.0 - (1.0 - base) * (1.0 - glow);\n }\n if (mode == BLEND_SOFT) {\n return mix(\n base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n step(0.5, glow)\n );\n }\n if (mode == BLEND_OVERLAY) {\n return mix(\n 2.0 * base * glow,\n 1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n step(0.5, base)\n );\n }\n if (mode == BLEND_LIGHTEN) {\n return max(base, glow);\n }\n return base + glow;\n}\n\nvoid main() {\n vec4 original = texture(u_image0, v_texCoord);\n \n float intensity = u_float0 * 0.05;\n float radius = u_float1 * u_float1 * 0.012;\n \n if (intensity < 0.001 || radius < 0.1) {\n fragColor = original;\n return;\n }\n \n float threshold = 1.0 - u_float2 * 0.01;\n float t0 = threshold - 0.15;\n float t1 = threshold + 0.15;\n \n vec2 texelSize = 1.0 / u_resolution;\n float radius2 = radius * radius;\n \n float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n int samples = int(float(MAX_SAMPLES) * sampleScale);\n \n float noise = hash(gl_FragCoord.xy);\n float angleOffset = noise * GOLDEN_ANGLE;\n float radiusJitter = 0.85 + noise * 0.3;\n \n float ca = cos(GOLDEN_ANGLE);\n float sa = sin(GOLDEN_ANGLE);\n vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n \n vec3 glow = vec3(0.0);\n float totalWeight = 0.0;\n \n // Center tap\n float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n glow += original.rgb * centerMask * 2.0;\n totalWeight += 2.0;\n \n for (int i = 1; i < MAX_SAMPLES; i++) {\n if (i >= samples) break;\n \n float fi = float(i);\n float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n \n vec2 offset = dir * dist * texelSize;\n vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n float mask = smoothstep(t0, t1, dot(c, LUMA));\n \n float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n w = max(w, 0.0);\n w *= w;\n \n glow += c * mask * w;\n totalWeight += w;\n \n dir = vec2(\n dir.x * ca - dir.y * sa,\n dir.x * sa + dir.y * ca\n );\n }\n \n glow *= intensity / max(totalWeight, 0.001);\n \n if (u_int1 > 0) {\n glow *= hexToRgb(u_int1);\n }\n \n vec3 result = blend(original.rgb, glow, u_int0);\n result += (noise - 0.5) * (1.0 / 255.0);\n \n fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}" }, { "path": "blueprints/.glsl/Hue_and_Saturation_1.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform int u_int0; // Mode: 0=Master, 1=Reds, 2=Yellows, 3=Greens, 4=Cyans, 5=Blues, 6=Magentas, 7=Colorize\nuniform int u_int1; // Color Space: 0=HSL, 1=HSB/HSV\nuniform float u_float0; // Hue (-180 to 180)\nuniform float u_float1; // Saturation (-100 to 100)\nuniform float u_float2; // Lightness/Brightness (-100 to 100)\nuniform float u_float3; // Overlap (0 to 100) - feathering between adjacent color ranges\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\n// Color range modes\nconst int MODE_MASTER = 0;\nconst int MODE_RED = 1;\nconst int MODE_YELLOW = 2;\nconst int MODE_GREEN = 3;\nconst int MODE_CYAN = 4;\nconst int MODE_BLUE = 5;\nconst int MODE_MAGENTA = 6;\nconst int MODE_COLORIZE = 7;\n\n// Color space modes\nconst int COLORSPACE_HSL = 0;\nconst int COLORSPACE_HSB = 1;\n\nconst float EPSILON = 0.0001;\n\n//=============================================================================\n// RGB <-> HSL Conversions\n//=============================================================================\n\nvec3 rgb2hsl(vec3 c) {\n float maxC = max(max(c.r, c.g), c.b);\n float minC = min(min(c.r, c.g), c.b);\n float delta = maxC - minC;\n\n float h = 0.0;\n float s = 0.0;\n float l = (maxC + minC) * 0.5;\n\n if (delta > EPSILON) {\n s = l < 0.5\n ? delta / (maxC + minC)\n : delta / (2.0 - maxC - minC);\n\n if (maxC == c.r) {\n h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n } else if (maxC == c.g) {\n h = (c.b - c.r) / delta + 2.0;\n } else {\n h = (c.r - c.g) / delta + 4.0;\n }\n h /= 6.0;\n }\n\n return vec3(h, s, l);\n}\n\nfloat hue2rgb(float p, float q, float t) {\n t = fract(t);\n if (t < 1.0/6.0) return p + (q - p) * 6.0 * t;\n if (t < 0.5) return q;\n if (t < 2.0/3.0) return p + (q - p) * (2.0/3.0 - t) * 6.0;\n return p;\n}\n\nvec3 hsl2rgb(vec3 hsl) {\n if (hsl.y < EPSILON) return vec3(hsl.z);\n\n float q = hsl.z < 0.5\n ? hsl.z * (1.0 + hsl.y)\n : hsl.z + hsl.y - hsl.z * hsl.y;\n float p = 2.0 * hsl.z - q;\n\n return vec3(\n hue2rgb(p, q, hsl.x + 1.0/3.0),\n hue2rgb(p, q, hsl.x),\n hue2rgb(p, q, hsl.x - 1.0/3.0)\n );\n}\n\nvec3 rgb2hsb(vec3 c) {\n float maxC = max(max(c.r, c.g), c.b);\n float minC = min(min(c.r, c.g), c.b);\n float delta = maxC - minC;\n\n float h = 0.0;\n float s = (maxC > EPSILON) ? delta / maxC : 0.0;\n float b = maxC;\n\n if (delta > EPSILON) {\n if (maxC == c.r) {\n h = (c.g - c.b) / delta + (c.g < c.b ? 6.0 : 0.0);\n } else if (maxC == c.g) {\n h = (c.b - c.r) / delta + 2.0;\n } else {\n h = (c.r - c.g) / delta + 4.0;\n }\n h /= 6.0;\n }\n\n return vec3(h, s, b);\n}\n\nvec3 hsb2rgb(vec3 hsb) {\n vec3 rgb = clamp(abs(mod(hsb.x * 6.0 + vec3(0.0, 4.0, 2.0), 6.0) - 3.0) - 1.0, 0.0, 1.0);\n return hsb.z * mix(vec3(1.0), rgb, hsb.y);\n}\n\n//=============================================================================\n// Color Range Weight Calculation\n//=============================================================================\n\nfloat hueDistance(float a, float b) {\n float d = abs(a - b);\n return min(d, 1.0 - d);\n}\n\nfloat getHueWeight(float hue, float center, float overlap) {\n float baseWidth = 1.0 / 6.0;\n float feather = baseWidth * overlap;\n\n float d = hueDistance(hue, center);\n\n float inner = baseWidth * 0.5;\n float outer = inner + feather;\n\n return 1.0 - smoothstep(inner, outer, d);\n}\n\nfloat getModeWeight(float hue, int mode, float overlap) {\n if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;\n\n if (mode == MODE_RED) {\n return max(\n getHueWeight(hue, 0.0, overlap),\n getHueWeight(hue, 1.0, overlap)\n );\n }\n\n float center = float(mode - 1) / 6.0;\n return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n return amount > 0.0\n ? l + (1.0 - l) * amount\n : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n return amount > 0.0\n ? s + (1.0 - s) * amount\n : s + s * amount;\n}\n\nvec3 colorize(vec3 rgb, float hue, float sat, float light) {\n float lum = dot(rgb, vec3(0.299, 0.587, 0.114));\n float l = adjustLightness(lum, light);\n\n vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));\n return hsl2rgb(hsl);\n}\n\n//=============================================================================\n// Main\n//=============================================================================\n\nvoid main() {\n vec4 original = texture(u_image0, v_texCoord);\n\n float hueShift = u_float0 / 360.0; // -180..180 -> -0.5..0.5\n float satAmount = u_float1 / 100.0; // -100..100 -> -1..1\n float lightAmount= u_float2 / 100.0; // -100..100 -> -1..1\n float overlap = u_float3 / 100.0; // 0..100 -> 0..1\n\n vec3 result;\n\n if (u_int0 == MODE_COLORIZE) {\n result = colorize(original.rgb, hueShift, satAmount, lightAmount);\n fragColor = vec4(result, original.a);\n return;\n }\n\n vec3 hsx = (u_int1 == COLORSPACE_HSL)\n ? rgb2hsl(original.rgb)\n : rgb2hsb(original.rgb);\n\n float weight = getModeWeight(hsx.x, u_int0, overlap);\n\n if (u_int0 != MODE_MASTER && hsx.y < EPSILON) {\n weight = 0.0;\n }\n\n if (weight > EPSILON) {\n float h = fract(hsx.x + hueShift * weight);\n float s = clamp(adjustSaturation(hsx.y, satAmount * weight), 0.0, 1.0);\n float v = (u_int1 == COLORSPACE_HSL)\n ? clamp(adjustLightness(hsx.z, lightAmount * weight), 0.0, 1.0)\n : clamp(adjustBrightness(hsx.z, lightAmount * weight), 0.0, 1.0);\n\n vec3 adjusted = vec3(h, s, v);\n result = (u_int1 == COLORSPACE_HSL)\n ? hsl2rgb(adjusted)\n : hsb2rgb(adjusted);\n } else {\n result = original.rgb;\n }\n\n fragColor = vec4(result, original.a);\n}\n" }, { "path": "blueprints/.glsl/Image_Blur_1.frag", "content": "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0; // Blur radius/amount\nuniform int u_pass; // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n vec2 texelSize = 1.0 / u_resolution;\n float radius = max(u_float0, 0.0);\n\n // Radial (angular) blur - single pass, doesn't use separable\n if (u_int0 == BLUR_RADIAL) {\n // Only execute on first pass\n if (u_pass > 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec2 center = vec2(0.5);\n vec2 dir = v_texCoord - center;\n float dist = length(dir);\n\n if (dist < 1e-4) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec4 sum = vec4(0.0);\n float totalWeight = 0.0;\n float angleStep = radius * RADIAL_STRENGTH;\n\n dir /= dist;\n\n float cosStep = cos(angleStep);\n float sinStep = sin(angleStep);\n\n float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n vec2 rotDir = vec2(\n dir.x * cos(negAngle) - dir.y * sin(negAngle),\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\n );\n\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n vec2 uv = center + rotDir * dist;\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n sum += texture(u_image0, uv) * w;\n totalWeight += w;\n\n rotDir = vec2(\n rotDir.x * cosStep - rotDir.y * sinStep,\n rotDir.x * sinStep + rotDir.y * cosStep\n );\n }\n\n fragColor0 = sum / max(totalWeight, 0.001);\n return;\n }\n\n // Separable Gaussian / Box blur\n int samples = int(ceil(radius));\n\n if (samples == 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n // Direction: pass 0 = horizontal, pass 1 = vertical\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n vec4 color = vec4(0.0);\n float totalWeight = 0.0;\n float sigma = radius / 2.0;\n\n for (int i = -samples; i <= samples; i++) {\n vec2 offset = dir * float(i) * texelSize;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float weight;\n if (u_int0 == BLUR_GAUSSIAN) {\n weight = gaussian(float(i), sigma);\n } else {\n // BLUR_BOX\n weight = 1.0;\n }\n\n color += sample_color * weight;\n totalWeight += weight;\n }\n\n fragColor0 = color / totalWeight;\n}\n" }, { "path": "blueprints/.glsl/Image_Channels_23.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n vec4 color = texture(u_image0, v_texCoord);\n // Output each channel as grayscale to separate render targets\n fragColor0 = vec4(vec3(color.r), 1.0); // Red channel\n fragColor1 = vec4(vec3(color.g), 1.0); // Green channel\n fragColor2 = vec4(vec3(color.b), 1.0); // Blue channel\n fragColor3 = vec4(vec3(color.a), 1.0); // Alpha channel\n}\n" }, { "path": "blueprints/.glsl/Image_Levels_1.frag", "content": "#version 300 es\nprecision highp float;\n\n// Levels Adjustment\n// u_int0: channel (0=RGB, 1=R, 2=G, 3=B) default: 0\n// u_float0: input black (0-255) default: 0\n// u_float1: input white (0-255) default: 255\n// u_float2: gamma (0.01-9.99) default: 1.0\n// u_float3: output black (0-255) default: 0\n// u_float4: output white (0-255) default: 255\n\nuniform sampler2D u_image0;\nuniform int u_int0;\nuniform float u_float0;\nuniform float u_float1;\nuniform float u_float2;\nuniform float u_float3;\nuniform float u_float4;\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nvec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n float inRange = max(inWhite - inBlack, 0.0001);\n vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);\n result = pow(result, vec3(1.0 / gamma));\n result = mix(vec3(outBlack), vec3(outWhite), result);\n return result;\n}\n\nfloat applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n float inRange = max(inWhite - inBlack, 0.0001);\n float result = clamp((value - inBlack) / inRange, 0.0, 1.0);\n result = pow(result, 1.0 / gamma);\n result = mix(outBlack, outWhite, result);\n return result;\n}\n\nvoid main() {\n vec4 texColor = texture(u_image0, v_texCoord);\n vec3 color = texColor.rgb;\n \n float inBlack = u_float0 / 255.0;\n float inWhite = u_float1 / 255.0;\n float gamma = u_float2;\n float outBlack = u_float3 / 255.0;\n float outWhite = u_float4 / 255.0;\n \n vec3 result;\n \n if (u_int0 == 0) {\n result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else if (u_int0 == 1) {\n result = color;\n result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else if (u_int0 == 2) {\n result = color;\n result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else if (u_int0 == 3) {\n result = color;\n result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else {\n result = color;\n }\n \n fragColor = vec4(result, texColor.a);\n}" }, { "path": "blueprints/.glsl/README.md", "content": "# GLSL Shader Sources\n\nThis folder contains the GLSL fragment shaders extracted from blueprint JSON files for easier editing and version control.\n\n## File Naming Convention\n\n`{Blueprint_Name}_{node_id}.frag`\n\n- **Blueprint_Name**: The JSON filename with spaces/special chars replaced by underscores\n- **node_id**: The GLSLShader node ID within the subgraph\n\n## Usage\n\n```bash\n# Extract shaders from blueprint JSONs to this folder\npython update_blueprints.py extract\n\n# Patch edited shaders back into blueprint JSONs\npython update_blueprints.py patch\n```\n\n## Workflow\n\n1. Run `extract` to pull current shaders from JSONs\n2. Edit `.frag` files\n3. Run `patch` to update the blueprint JSONs\n4. Test\n5. Commit both `.frag` files and updated JSONs\n" }, { "path": "blueprints/.glsl/Sharpen_23.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n vec2 texel = 1.0 / u_resolution;\n \n // Sample center and neighbors\n vec4 center = texture(u_image0, v_texCoord);\n vec4 top = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0, texel.y));\n vec4 left = texture(u_image0, v_texCoord + vec2(-texel.x, 0.0));\n vec4 right = texture(u_image0, v_texCoord + vec2( texel.x, 0.0));\n \n // Edge enhancement (Laplacian)\n vec4 edges = center * 4.0 - top - bottom - left - right;\n \n // Add edges back scaled by strength\n vec4 sharpened = center + edges * u_float0;\n \n fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}" }, { "path": "blueprints/.glsl/Unsharp_Mask_26.frag", "content": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5\nuniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels\nuniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n vec2 texel = 1.0 / u_resolution;\n float radius = max(u_float1, 0.5);\n float amount = u_float0;\n float threshold = u_float2;\n\n vec4 original = texture(u_image0, v_texCoord);\n\n // Gaussian blur for the \"unsharp\" mask\n int samples = int(ceil(radius));\n float sigma = radius / 2.0;\n\n vec4 blurred = vec4(0.0);\n float totalWeight = 0.0;\n\n for (int x = -samples; x <= samples; x++) {\n for (int y = -samples; y <= samples; y++) {\n vec2 offset = vec2(float(x), float(y)) * texel;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float dist = length(vec2(float(x), float(y)));\n float weight = gaussian(dist, sigma);\n blurred += sample_color * weight;\n totalWeight += weight;\n }\n }\n blurred /= totalWeight;\n\n // Unsharp mask = original - blurred\n vec3 mask = original.rgb - blurred.rgb;\n\n // Luminance-based threshold with smooth falloff\n float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n mask *= thresholdScale;\n\n // Sharpen: original + mask * amount\n vec3 sharpened = original.rgb + mask * amount;\n\n fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n" }, { "path": "blueprints/.glsl/update_blueprints.py", "content": "#!/usr/bin/env python3\n\"\"\"\nShader Blueprint Updater\n\nSyncs GLSL shader files between this folder and blueprint JSON files.\n\nFile naming convention:\n {Blueprint Name}_{node_id}.frag\n\nUsage:\n python update_blueprints.py extract # Extract shaders from JSONs to here\n python update_blueprints.py patch # Patch shaders back into JSONs\n python update_blueprints.py # Same as patch (default)\n\"\"\"\n\nimport json\nimport logging\nimport sys\nimport re\nfrom pathlib import Path\n\nlogging.basicConfig(level=logging.INFO, format='%(message)s')\nlogger = logging.getLogger(__name__)\n\nGLSL_DIR = Path(__file__).parent\nBLUEPRINTS_DIR = GLSL_DIR.parent\n\n\ndef get_blueprint_files():\n \"\"\"Get all blueprint JSON files.\"\"\"\n return sorted(BLUEPRINTS_DIR.glob(\"*.json\"))\n\n\ndef sanitize_filename(name):\n \"\"\"Convert blueprint name to safe filename.\"\"\"\n return re.sub(r'[^\\w\\-]', '_', name)\n\n\ndef extract_shaders():\n \"\"\"Extract all shaders from blueprint JSONs to this folder.\"\"\"\n extracted = 0\n for json_path in get_blueprint_files():\n blueprint_name = json_path.stem\n\n try:\n with open(json_path, 'r') as f:\n data = json.load(f)\n except (json.JSONDecodeError, IOError) as e:\n logger.warning(\"Skipping %s: %s\", json_path.name, e)\n continue\n\n # Find GLSLShader nodes in subgraphs\n for subgraph in data.get('definitions', {}).get('subgraphs', []):\n for node in subgraph.get('nodes', []):\n if node.get('type') == 'GLSLShader':\n node_id = node.get('id')\n widgets = node.get('widgets_values', [])\n\n # Find shader code (first string that looks like GLSL)\n for widget in widgets:\n if isinstance(widget, str) and widget.startswith('#version'):\n safe_name = sanitize_filename(blueprint_name)\n frag_name = f\"{safe_name}_{node_id}.frag\"\n frag_path = GLSL_DIR / frag_name\n\n with open(frag_path, 'w') as f:\n f.write(widget)\n\n logger.info(\" Extracted: %s\", frag_name)\n extracted += 1\n break\n\n logger.info(\"\\nExtracted %d shader(s)\", extracted)\n\n\ndef patch_shaders():\n \"\"\"Patch shaders from this folder back into blueprint JSONs.\"\"\"\n # Build lookup: blueprint_name -> [(node_id, shader_code), ...]\n shader_updates = {}\n\n for frag_path in sorted(GLSL_DIR.glob(\"*.frag\")):\n # Parse filename: {blueprint_name}_{node_id}.frag\n parts = frag_path.stem.rsplit('_', 1)\n if len(parts) != 2:\n logger.warning(\"Skipping %s: invalid filename format\", frag_path.name)\n continue\n\n blueprint_name, node_id_str = parts\n\n try:\n node_id = int(node_id_str)\n except ValueError:\n logger.warning(\"Skipping %s: invalid node_id\", frag_path.name)\n continue\n\n with open(frag_path, 'r') as f:\n shader_code = f.read()\n\n if blueprint_name not in shader_updates:\n shader_updates[blueprint_name] = []\n shader_updates[blueprint_name].append((node_id, shader_code))\n\n # Apply updates to JSON files\n patched = 0\n for json_path in get_blueprint_files():\n blueprint_name = sanitize_filename(json_path.stem)\n\n if blueprint_name not in shader_updates:\n continue\n\n try:\n with open(json_path, 'r') as f:\n data = json.load(f)\n except (json.JSONDecodeError, IOError) as e:\n logger.error(\"Error reading %s: %s\", json_path.name, e)\n continue\n\n modified = False\n for node_id, shader_code in shader_updates[blueprint_name]:\n # Find the node and update\n for subgraph in data.get('definitions', {}).get('subgraphs', []):\n for node in subgraph.get('nodes', []):\n if node.get('id') == node_id and node.get('type') == 'GLSLShader':\n widgets = node.get('widgets_values', [])\n if len(widgets) > 0 and widgets[0] != shader_code:\n widgets[0] = shader_code\n modified = True\n logger.info(\" Patched: %s (node %d)\", json_path.name, node_id)\n patched += 1\n\n if modified:\n with open(json_path, 'w') as f:\n json.dump(data, f)\n\n if patched == 0:\n logger.info(\"No changes to apply.\")\n else:\n logger.info(\"\\nPatched %d shader(s)\", patched)\n\n\ndef main():\n if len(sys.argv) < 2:\n command = \"patch\"\n else:\n command = sys.argv[1].lower()\n\n if command == \"extract\":\n logger.info(\"Extracting shaders from blueprints...\")\n extract_shaders()\n elif command in (\"patch\", \"update\", \"apply\"):\n logger.info(\"Patching shaders into blueprints...\")\n patch_shaders()\n else:\n logger.info(__doc__)\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "blueprints/Brightness and Contrast.json", "content": "{\"revision\": 0, \"last_node_id\": 140, \"last_link_id\": 0, \"nodes\": [{\"id\": 140, \"type\": \"916dff42-6166-4d45-b028-04eaf69fbb35\", \"pos\": [500, 1440], \"size\": [250, 178], \"flags\": {}, \"order\": 2, \"mode\": 0, \"inputs\": [{\"label\": \"image\", \"localized_name\": \"images.image0\", \"name\": \"images.image0\", \"type\": \"IMAGE\", \"link\": null}], \"outputs\": [{\"label\": \"IMAGE\", \"localized_name\": \"IMAGE0\", \"name\": \"IMAGE0\", \"type\": \"IMAGE\", \"links\": []}], \"properties\": {\"proxyWidgets\": [[\"4\", \"value\"], [\"5\", \"value\"]]}, \"widgets_values\": [], \"title\": \"Brightness and Contrast\"}], \"links\": [], \"version\": 0.4, \"definitions\": {\"subgraphs\": [{\"id\": \"916dff42-6166-4d45-b028-04eaf69fbb35\", \"version\": 1, \"state\": {\"lastGroupId\": 0, \"lastNodeId\": 143, \"lastLinkId\": 118, \"lastRerouteId\": 0}, \"revision\": 0, \"config\": {}, \"name\": \"Brightness and Contrast\", \"inputNode\": {\"id\": -10, \"bounding\": [360, -176, 120, 60]}, \"outputNode\": {\"id\": -20, \"bounding\": [1410, -176, 120, 60]}, \"inputs\": [{\"id\": \"a5aae7ea-b511-4045-b5da-94101e269cd7\", \"name\": \"images.image0\", \"type\": \"IMAGE\", \"linkIds\": [117], \"localized_name\": \"images.image0\", \"label\": \"image\", \"pos\": [460, -156]}], \"outputs\": [{\"id\": \"30b72604-69b3-4944-b253-a9099bbd73a9\", \"name\": \"IMAGE0\", \"type\": \"IMAGE\", \"linkIds\": [118], \"localized_name\": \"IMAGE0\", \"label\": \"IMAGE\", \"pos\": [1430, -156]}], \"widgets\": [], \"nodes\": [{\"id\": 4, \"type\": \"PrimitiveFloat\", \"pos\": [540, -280], \"size\": [270, 58], \"flags\": {}, \"order\": 0, \"mode\": 0, \"inputs\": [{\"label\": \"brightness\", \"localized_name\": \"value\", \"name\": \"value\", \"type\": \"FLOAT\", \"widget\": {\"name\": \"value\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"FLOAT\", \"name\": \"FLOAT\", \"type\": \"FLOAT\", \"links\": [115]}], \"properties\": {\"Node name for S&R\": \"PrimitiveFloat\", \"min\": 0, \"max\": 100, \"precision\": 1, \"step\": 1, \"display\": \"gradientslider\", \"gradient_stops\": [{\"offset\": 0, \"color\": [0, 0, 0]}, {\"offset\": 1, \"color\": [255, 255, 255]}]}, \"widgets_values\": [50]}, {\"id\": 5, \"type\": \"PrimitiveFloat\", \"pos\": [540, -170], \"size\": [270, 58], \"flags\": {}, \"order\": 1, \"mode\": 0, \"inputs\": [{\"label\": \"contrast\", \"localized_name\": \"value\", \"name\": \"value\", \"type\": \"FLOAT\", \"widget\": {\"name\": \"value\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"FLOAT\", \"name\": \"FLOAT\", \"type\": \"FLOAT\", \"links\": [116]}], \"properties\": {\"Node name for S&R\": \"PrimitiveFloat\", \"min\": 0, \"max\": 100, \"precision\": 1, \"step\": 1, \"display\": \"gradientslider\", \"gradient_stops\": [{\"offset\": 0, \"color\": [136, 136, 136]}, {\"offset\": 0.4, \"color\": [68, 68, 68]}, {\"offset\": 0.6, \"color\": [187, 187, 187]}, {\"offset\": 0.8, \"color\": [0, 0, 0]}, {\"offset\": 1, \"color\": [255, 255, 255]}]}, \"widgets_values\": [0]}, {\"id\": 143, \"type\": \"GLSLShader\", \"pos\": [840, -280], \"size\": [400, 212], \"flags\": {}, \"order\": 2, \"mode\": 0, \"inputs\": [{\"label\": \"image0\", \"localized_name\": \"images.image0\", \"name\": \"images.image0\", \"type\": \"IMAGE\", \"link\": 117}, {\"label\": \"image1\", \"localized_name\": \"images.image1\", \"name\": \"images.image1\", \"shape\": 7, \"type\": \"IMAGE\", \"link\": null}, {\"label\": \"u_float0\", \"localized_name\": \"floats.u_float0\", \"name\": \"floats.u_float0\", \"shape\": 7, \"type\": \"FLOAT\", \"link\": 115}, {\"label\": \"u_float1\", \"localized_name\": \"floats.u_float1\", \"name\": \"floats.u_float1\", \"shape\": 7, \"type\": \"FLOAT\", \"link\": 116}, {\"label\": \"u_float2\", \"localized_name\": \"floats.u_float2\", \"name\": \"floats.u_float2\", \"shape\": 7, \"type\": \"FLOAT\", \"link\": null}, {\"label\": \"u_int0\", \"localized_name\": \"ints.u_int0\", \"name\": \"ints.u_int0\", \"shape\": 7, \"type\": \"INT\", \"link\": null}, {\"localized_name\": \"fragment_shader\", \"name\": \"fragment_shader\", \"type\": \"STRING\", \"widget\": {\"name\": \"fragment_shader\"}, \"link\": null}, {\"localized_name\": \"size_mode\", \"name\": \"size_mode\", \"type\": \"COMFY_DYNAMICCOMBO_V3\", \"widget\": {\"name\": \"size_mode\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"IMAGE0\", \"name\": \"IMAGE0\", \"type\": \"IMAGE\", \"links\": [118]}, {\"localized_name\": \"IMAGE1\", \"name\": \"IMAGE1\", \"type\": \"IMAGE\", \"links\": null}, {\"localized_name\": \"IMAGE2\", \"name\": \"IMAGE2\", \"type\": \"IMAGE\", \"links\": null}, {\"localized_name\": \"IMAGE3\", \"name\": \"IMAGE3\", \"type\": \"IMAGE\", \"links\": null}], \"properties\": {\"Node name for S&R\": \"GLSLShader\"}, \"widgets_values\": [\"#version 300 es\\nprecision highp float;\\n\\nuniform sampler2D u_image0;\\nuniform float u_float0; // Brightness slider -100..100\\nuniform float u_float1; // Contrast slider -100..100\\n\\nin vec2 v_texCoord;\\nout vec4 fragColor;\\n\\nconst float MID_GRAY = 0.18; // 18% reflectance\\n\\n// sRGB gamma 2.2 approximation\\nvec3 srgbToLinear(vec3 c) {\\n return pow(max(c, 0.0), vec3(2.2));\\n}\\n\\nvec3 linearToSrgb(vec3 c) {\\n return pow(max(c, 0.0), vec3(1.0/2.2));\\n}\\n\\nfloat mapBrightness(float b) {\\n return clamp(b / 100.0, -1.0, 1.0);\\n}\\n\\nfloat mapContrast(float c) {\\n return clamp(c / 100.0 + 1.0, 0.0, 2.0);\\n}\\n\\nvoid main() {\\n vec4 orig = texture(u_image0, v_texCoord);\\n\\n float brightness = mapBrightness(u_float0);\\n float contrast = mapContrast(u_float1);\\n\\n vec3 lin = srgbToLinear(orig.rgb);\\n\\n lin = (lin - MID_GRAY) * contrast + brightness + MID_GRAY;\\n\\n // Convert back to sRGB\\n vec3 result = linearToSrgb(clamp(lin, 0.0, 1.0));\\n\\n fragColor = vec4(result, orig.a);\\n}\\n\", \"from_input\"]}], \"groups\": [], \"links\": [{\"id\": 115, \"origin_id\": 4, \"origin_slot\": 0, \"target_id\": 143, \"target_slot\": 2, \"type\": \"FLOAT\"}, {\"id\": 116, \"origin_id\": 5, \"origin_slot\": 0, \"target_id\": 143, \"target_slot\": 3, \"type\": \"FLOAT\"}, {\"id\": 117, \"origin_id\": -10, \"origin_slot\": 0, \"target_id\": 143, \"target_slot\": 0, \"type\": \"IMAGE\"}, {\"id\": 118, \"origin_id\": 143, \"origin_slot\": 0, \"target_id\": -20, \"target_slot\": 0, \"type\": \"IMAGE\"}], \"extra\": {\"workflowRendererVersion\": \"LG\"}, \"category\": \"Image Tools/Color adjust\"}]}, \"extra\": {}}\n" }, { "path": "blueprints/Canny to Image (Z-Image-Turbo).json", "content": "{\"id\": \"e046dd74-e2a7-4f31-a75b-5e11a8c72d4e\", \"revision\": 0, \"last_node_id\": 18, \"last_link_id\": 32, \"nodes\": [{\"id\": 18, \"type\": \"c84f7959-3738-422b-ba6e-5808b5e90101\", \"pos\": [300, 3830], \"size\": [400, 460], \"flags\": {}, \"order\": 0, \"mode\": 0, \"inputs\": [{\"label\": \"control image\", \"name\": \"image\", \"type\": \"IMAGE\", \"link\": null}, {\"label\": \"prompt\", \"name\": \"text\", \"type\": \"STRING\", \"widget\": {\"name\": \"text\"}, \"link\": null}, {\"label\": \"canny low threshold\", \"name\": \"low_threshold\", \"type\": \"FLOAT\", \"widget\": {\"name\": \"low_threshold\"}, \"link\": null}, {\"label\": \"canny high threshold\", \"name\": \"high_threshold\", \"type\": \"FLOAT\", \"widget\": {\"name\": \"high_threshold\"}, \"link\": null}, {\"name\": \"unet_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"unet_name\"}, \"link\": null}, {\"name\": \"clip_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"clip_name\"}, \"link\": null}, {\"name\": \"vae_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"vae_name\"}, \"link\": null}, {\"name\": \"name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"name\"}, \"link\": null}], \"outputs\": [{\"name\": \"IMAGE\", \"type\": \"IMAGE\", \"links\": null}], \"properties\": {\"proxyWidgets\": [[\"-1\", \"text\"], [\"-1\", \"low_threshold\"], [\"-1\", \"high_threshold\"], [\"7\", \"seed\"], [\"7\", \"control_after_generate\"], [\"-1\", \"unet_name\"], [\"-1\", \"clip_name\"], [\"-1\", \"vae_name\"], [\"-1\", \"name\"]], \"cnr_id\": \"comfy-core\", \"ver\": \"0.11.0\"}, \"widgets_values\": [\"\", 0.3, 0.4, null, null, \"z_image_turbo_bf16.safetensors\", \"qwen_3_4b.safetensors\", \"ae.safetensors\", \"Z-Image-Turbo-Fun-Controlnet-Union.safetensors\"]}], \"links\": [], \"groups\": [], \"definitions\": {\"subgraphs\": [{\"id\": \"c84f7959-3738-422b-ba6e-5808b5e90101\", \"version\": 1, \"state\": {\"lastGroupId\": 3, \"lastNodeId\": 18, \"lastLinkId\": 32, \"lastRerouteId\": 0}, \"revision\": 0, \"config\": {}, \"name\": \"local-Canny to Image (Z-Image-Turbo)\", \"inputNode\": {\"id\": -10, \"bounding\": [-280, 4960, 158.880859375, 200]}, \"outputNode\": {\"id\": -20, \"bounding\": [1598.6038576146689, 4936.043696127976, 120, 60]}, \"inputs\": [{\"id\": \"29ca271b-8f63-4e7b-a4b8-c9b4192ada0b\", \"name\": \"image\", \"type\": \"IMAGE\", \"linkIds\": [26], \"label\": \"control image\", \"pos\": [-141.119140625, 4980]}, {\"id\": \"b6549f90-39ee-4b79-9e00-af4d9df969fe\", \"name\": \"text\", \"type\": \"STRING\", \"linkIds\": [16], \"label\": \"prompt\", \"pos\": [-141.119140625, 5000]}, {\"id\": \"6bd34d18-79f6-470f-94df-ca14c84ef3d8\", \"name\": \"low_threshold\", \"type\": \"FLOAT\", \"linkIds\": [24], \"label\": \"canny low threshold\", \"pos\": [-141.119140625, 5020]}, {\"id\": \"bbced993-057f-4d2d-909c-d791be73d1d2\", \"name\": \"high_threshold\", \"type\": \"FLOAT\", \"linkIds\": [25], \"label\": \"canny high threshold\", \"pos\": [-141.119140625, 5040]}, {\"id\": \"db7969bf-4b05-48a0-9598-87d3ac85b505\", \"name\": \"unet_name\", \"type\": \"COMBO\", \"linkIds\": [29], \"pos\": [-141.119140625, 5060]}, {\"id\": \"925b611c-5edf-406f-8dc5-7fec07d049a7\", \"name\": \"clip_name\", \"type\": \"COMBO\", \"linkIds\": [30], \"pos\": [-141.119140625, 5080]}, {\"id\": \"b4cf508b-4753-40d2-8c83-5a424237ee07\", \"name\": \"vae_name\", \"type\": \"COMBO\", \"linkIds\": [31], \"pos\": [-141.119140625, 5100]}, {\"id\": \"bd948f38-3a11-4091-99fc-bb2b3511bcd2\", \"name\": \"name\", \"type\": \"COMBO\", \"linkIds\": [32], \"pos\": [-141.119140625, 5120]}], \"outputs\": [{\"id\": \"47f9a22d-6619-4917-9447-a7d5d08dceb5\", \"name\": \"IMAGE\", \"type\": \"IMAGE\", \"linkIds\": [18], \"pos\": [1618.6038576146689, 4956.043696127976]}], \"widgets\": [], \"nodes\": [{\"id\": 1, \"type\": \"CLIPLoader\", \"pos\": [228.60376290329597, 4700.188357350136], \"size\": [270, 106], \"flags\": {}, \"order\": 0, \"mode\": 0, \"inputs\": [{\"localized_name\": \"clip_name\", \"name\": \"clip_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"clip_name\"}, \"link\": 30}, {\"localized_name\": \"type\", \"name\": \"type\", \"type\": \"COMBO\", \"widget\": {\"name\": \"type\"}, \"link\": null}, {\"localized_name\": \"device\", \"name\": \"device\", \"shape\": 7, \"type\": \"COMBO\", \"widget\": {\"name\": \"device\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"CLIP\", \"name\": \"CLIP\", \"type\": \"CLIP\", \"links\": [14]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.3.73\", \"Node name for S&R\": \"CLIPLoader\", \"models\": [{\"name\": \"qwen_3_4b.safetensors\", \"url\": \"https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors\", \"directory\": \"text_encoders\"}], \"enableTabs\": false, \"tabWidth\": 65, \"tabXOffset\": 10, \"hasSecondTab\": false, \"secondTabText\": \"Send Back\", \"secondTabOffset\": 80, \"secondTabWidth\": 65}, \"widgets_values\": [\"qwen_3_4b.safetensors\", \"lumina2\", \"default\"]}, {\"id\": 2, \"type\": \"UNETLoader\", \"pos\": [228.60376290329597, 4550.1883046176445], \"size\": [270, 82], \"flags\": {}, \"order\": 1, \"mode\": 0, \"inputs\": [{\"localized_name\": \"unet_name\", \"name\": \"unet_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"unet_name\"}, \"link\": 29}, {\"localized_name\": \"weight_dtype\", \"name\": \"weight_dtype\", \"type\": \"COMBO\", \"widget\": {\"name\": \"weight_dtype\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"MODEL\", \"name\": \"MODEL\", \"type\": \"MODEL\", \"links\": [9]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.3.73\", \"Node name for S&R\": \"UNETLoader\", \"models\": [{\"name\": \"z_image_turbo_bf16.safetensors\", \"url\": \"https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/diffusion_models/z_image_turbo_bf16.safetensors\", \"directory\": \"diffusion_models\"}], \"enableTabs\": false, \"tabWidth\": 65, 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toGaussian(ui + uvec2(1u, 0u));\\n float c = toGaussian(ui + uvec2(0u, 1u));\\n float d = toGaussian(ui + uvec2(1u, 1u));\\n \\n return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\\n}\\n\\nfloat smoothNoise(vec2 p, uint offset) {\\n vec2 i = floor(p);\\n vec2 f = fract(p);\\n \\n f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\\n \\n uvec2 ui = uvec2(i);\\n float a = toGaussian(ui, offset);\\n float b = toGaussian(ui + uvec2(1u, 0u), offset);\\n float c = toGaussian(ui + uvec2(0u, 1u), offset);\\n float d = toGaussian(ui + uvec2(1u, 1u), offset);\\n \\n return mix(mix(a, b, f.x), mix(c, d, f.x), f.y);\\n}\\n\\nvoid main() {\\n vec4 color = texture(u_image0, v_texCoord);\\n \\n // Luminance (Rec.709)\\n float luma = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));\\n \\n // Grain UV (resolution-independent)\\n vec2 grainUV = v_texCoord * u_resolution / max(u_float1, 0.01);\\n uvec2 grainPixel = uvec2(grainUV);\\n \\n float g;\\n vec3 grainRGB;\\n \\n if (u_int0 == 1) {\\n // Grainy mode: pure 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cos(negAngle) - dir.y * sin(negAngle),\\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\\n );\\n\\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\\n vec2 uv = center + rotDir * dist;\\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\\n sum += texture(u_image0, uv) * w;\\n totalWeight += w;\\n\\n rotDir = vec2(\\n rotDir.x * cosStep - rotDir.y * sinStep,\\n rotDir.x * sinStep + rotDir.y * cosStep\\n );\\n }\\n\\n fragColor0 = sum / max(totalWeight, 0.001);\\n return;\\n }\\n\\n // Separable Gaussian / Box blur\\n int samples = int(ceil(radius));\\n\\n if (samples == 0) {\\n fragColor0 = texture(u_image0, v_texCoord);\\n return;\\n }\\n\\n // Direction: pass 0 = horizontal, pass 1 = vertical\\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\\n\\n vec4 color = vec4(0.0);\\n float totalWeight = 0.0;\\n float sigma = radius / 2.0;\\n\\n for (int i = -samples; i <= samples; i++) {\\n vec2 offset = dir * float(i) * texelSize;\\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\\n\\n float weight;\\n if (u_int0 == BLUR_GAUSSIAN) {\\n weight = gaussian(float(i), sigma);\\n } else {\\n // BLUR_BOX\\n weight = 1.0;\\n }\\n\\n color += sample_color * weight;\\n totalWeight += weight;\\n }\\n\\n fragColor0 = color / totalWeight;\\n}\\n\", \"from_input\"]}], \"groups\": [], \"links\": [{\"id\": 10, \"origin_id\": 10, \"origin_slot\": 0, \"target_id\": 1, \"target_slot\": 2, \"type\": \"FLOAT\"}, {\"id\": 11, \"origin_id\": 12, \"origin_slot\": 1, \"target_id\": 1, \"target_slot\": 4, \"type\": \"INT\"}, {\"id\": 9, \"origin_id\": -10, \"origin_slot\": 0, \"target_id\": 1, \"target_slot\": 0, \"type\": \"IMAGE\"}, {\"id\": 3, \"origin_id\": 1, \"origin_slot\": 0, \"target_id\": -20, \"target_slot\": 0, \"type\": \"IMAGE\"}], \"extra\": {\"workflowRendererVersion\": \"LG\"}, \"category\": \"Image Tools/Blur\"}]}}\n" }, { "path": "blueprints/Image Captioning (gemini).json", "content": "{\"revision\": 0, \"last_node_id\": 231, \"last_link_id\": 0, \"nodes\": [{\"id\": 231, \"type\": \"e3e78497-720e-45a2-b4fb-c7bfdb80dd11\", \"pos\": [23.13283014087665, 1034.468391137315], \"size\": [280, 260], \"flags\": {}, \"order\": 8, \"mode\": 0, \"inputs\": [{\"localized_name\": \"images\", \"name\": \"images\", \"type\": \"IMAGE\", \"link\": null}, {\"name\": \"prompt\", \"type\": \"STRING\", \"widget\": {\"name\": \"prompt\"}, \"link\": null}, {\"name\": \"model\", \"type\": \"COMBO\", \"widget\": {\"name\": \"model\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"STRING\", \"name\": \"STRING\", \"type\": \"STRING\", \"links\": []}], \"properties\": {\"proxyWidgets\": [[\"-1\", \"prompt\"], [\"-1\", \"model\"], [\"1\", \"seed\"]], \"cnr_id\": \"comfy-core\", \"ver\": \"0.13.0\"}, \"widgets_values\": [\"Describe this image\", \"gemini-2.5-pro\"], \"title\": \"Image Captioning(Gemini)\"}], \"links\": [], \"version\": 0.4, \"definitions\": {\"subgraphs\": [{\"id\": \"e3e78497-720e-45a2-b4fb-c7bfdb80dd11\", 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\"name\": \"system_prompt\", \"shape\": 7, \"type\": \"STRING\", \"widget\": {\"name\": \"system_prompt\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"STRING\", \"name\": \"STRING\", \"type\": \"STRING\", \"links\": [6]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.5.1\", \"Node name for S&R\": \"GeminiNode\"}, \"widgets_values\": [\"Describe this image\", \"gemini-2.5-pro\", 511865409297955, \"randomize\", \"- Role: AI Image Analysis and Description Specialist\\n- Background: The user requires a prompt that enables AI to analyze images and generate detailed descriptions which can be used as drawing prompts to create similar images. This is essential for tasks like content creation, design inspiration, and artistic exploration.\\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. 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\"input_ue_unconnectable\": {}}}, \"widgets_values\": [\"disable\", 0, \"fixed\", 4, 1, \"euler\", \"simple\", 2, 4, \"disable\"]}, {\"id\": 67, \"type\": \"Note\", \"pos\": [510.0000345979581, 819.9999455547611], \"size\": [390, 88], \"flags\": {}, \"order\": 4, \"mode\": 0, \"inputs\": [], \"outputs\": [], \"title\": \"Video Size\", \"properties\": {\"ue_properties\": {\"widget_ue_connectable\": {}, \"version\": \"7.1\", \"input_ue_unconnectable\": {}}}, \"widgets_values\": [\"By default, we set the video to a smaller size for users with low VRAM. If you have enough VRAM, you can change the size\"], \"color\": \"#222\", \"bgcolor\": \"#000\"}, {\"id\": 105, \"type\": \"MarkdownNote\", \"pos\": [-469.9999795985529, 279.9998197772136], \"size\": [480, 170.65104166666669], \"flags\": {}, \"order\": 5, \"mode\": 0, \"inputs\": [], \"outputs\": [], \"title\": \"VRAM Usage\", \"properties\": {\"ue_properties\": {\"version\": \"7.1\", \"widget_ue_connectable\": {}, \"input_ue_unconnectable\": {}}}, \"widgets_values\": [\"## GPU:RTX4090D 24GB\\n\\n| Model | Size |VRAM Usage | 1st Generation | 2nd Generation |\\n|---------------------|-------|-----------|---------------|-----------------|\\n| fp8_scaled |640*640| 84% | ≈ 536s | ≈ 513s |\\n| fp8_scaled + 4steps LoRA | 640*640 | 83% | ≈ 97s | ≈ 71s |\"], \"color\": \"#222\", \"bgcolor\": \"#000\"}, {\"id\": 66, \"type\": \"MarkdownNote\", \"pos\": [-469.9999795985529, -320.00012452364496], \"size\": [480, 572.1354166666667], \"flags\": {}, \"order\": 6, \"mode\": 0, \"inputs\": [], \"outputs\": [], \"title\": \"Model Links\", \"properties\": {\"ue_properties\": {\"widget_ue_connectable\": {}, \"version\": \"7.1\", \"input_ue_unconnectable\": {}}}, \"widgets_values\": [\"[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\\n)\\n\\n**Diffusion Model**\\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\\n\\n**LoRA**\\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\\n\\n**VAE**\\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\\n\\n**Text Encoder** \\n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\\n\\n\\nFile save location\\n\\n```\\nComfyUI/\\n├───📂 models/\\n│ ├───📂 diffusion_models/\\n│ │ ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\\n│ │ └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\\n│ ├───📂 loras/\\n│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\\n│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\\n│ ├───📂 text_encoders/\\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \\n│ └───📂 vae/\\n│ └── wan_2.1_vae.safetensors\\n```\\n\"], \"color\": \"#222\", \"bgcolor\": \"#000\"}, {\"id\": 115, \"type\": \"Note\", \"pos\": [29.999978639114225, -470.00010361843204], \"size\": [360, 88], \"flags\": {}, \"order\": 7, \"mode\": 0, \"inputs\": [], \"outputs\": [], \"title\": \"About 4 Steps LoRA\", \"properties\": {\"ue_properties\": {\"widget_ue_connectable\": {}, \"version\": \"7.1\", \"input_ue_unconnectable\": {}}}, \"widgets_values\": [\"Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. 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[16]}, {\"id\": 112, \"type\": \"Note\", \"pos\": [30.33320002485607, -428.6666237736725], \"size\": [359.9869791666667, 52], \"flags\": {}, \"order\": 4, \"mode\": 0, \"inputs\": [], \"outputs\": [], \"title\": \"About 4 Steps LoRA\", \"properties\": {}, \"widgets_values\": [\"Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed.\"], \"color\": \"#222\", \"bgcolor\": \"#000\"}, {\"id\": 62, \"type\": \"MarkdownNote\", \"pos\": [-489.666771800538, -278.666700527147], \"size\": [479.9869791666667, 542.1354166666667], \"flags\": {}, \"order\": 5, \"mode\": 0, \"inputs\": [], \"outputs\": [], \"title\": \"Model Links\", \"properties\": {}, \"widgets_values\": [\"[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\\n)\\n\\n**Diffusion Model** \\n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\\n\\n**LoRA**\\n\\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)\\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)\\n\\n**VAE**\\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\\n\\n**Text Encoder** \\n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\\n\\n\\nFile save location\\n\\n```\\nComfyUI/\\n├───📂 models/\\n│ ├───📂 diffusion_models/\\n│ │ ├─── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors\\n│ │ └─── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors\\n│ ├───📂 loras/\\n│ │ ├───wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors\\n│ │ └───wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors\\n│ ├───📂 text_encoders/\\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \\n│ └───📂 vae/\\n│ └── wan_2.1_vae.safetensors\\n```\\n\"], \"color\": \"#222\", \"bgcolor\": \"#000\"}, {\"id\": 87, \"type\": \"VAEDecode\", \"pos\": [1020.3331497597994, 471.3333837135574], \"size\": [210, 46], \"flags\": {\"collapsed\": false}, \"order\": 14, \"mode\": 0, \"inputs\": [{\"localized_name\": \"samples\", \"name\": \"samples\", \"type\": \"LATENT\", \"link\": 157}, {\"localized_name\": \"vae\", \"name\": \"vae\", \"type\": \"VAE\", \"link\": 158}], \"outputs\": [{\"localized_name\": \"IMAGE\", \"name\": \"IMAGE\", \"type\": \"IMAGE\", \"slot_index\": 0, \"links\": [195]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.3.45\", \"Node name for S&R\": \"VAEDecode\"}, 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\"model\", \"name\": \"model\", \"type\": \"COMBO\", \"widget\": {\"name\": \"model\"}, \"link\": 16}, {\"localized_name\": \"seed\", \"name\": \"seed\", \"type\": \"INT\", \"widget\": {\"name\": \"seed\"}, \"link\": null}, {\"localized_name\": \"system_prompt\", \"name\": \"system_prompt\", \"shape\": 7, \"type\": \"STRING\", \"widget\": {\"name\": \"system_prompt\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"STRING\", \"name\": \"STRING\", \"type\": \"STRING\", \"links\": [6]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.5.1\", \"Node name for S&R\": \"GeminiNode\"}, \"widgets_values\": [\"Describe this video\", \"gemini-2.5-pro\", 511865409297955, \"randomize\", \"- Role: AI Video Analysis and Description Specialist\\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\\n- Constraints: \\n 1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\\n 2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\\n 3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\\n 4. The output must only contain the video generation prompt (no extra explanations).\\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\\n 1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\\n 2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\\n 3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\\n 4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\\n- Workflow:\\n 1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\\n 2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\\n 3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\\n 4. 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You can refer to this implementation idea to achieve video inpainting.\\n\\nYou can use KJNode’s Points Editor and Sam2Segmentation to create some dynamic mask functions.\\n\\nCustom node links:\\n- [ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes)\\n- [ComfyUI-segment-anything-2](https://github.com/kijai/ComfyUI-segment-anything-2)\"], \"color\": \"#432\", \"bgcolor\": \"#000\"}, {\"id\": 7, \"type\": \"CLIPTextEncode\", \"pos\": [-80, 390], \"size\": [425.27801513671875, 180.6060791015625], \"flags\": {}, \"order\": 8, \"mode\": 0, \"inputs\": [{\"localized_name\": \"clip\", \"name\": \"clip\", \"type\": \"CLIP\", \"link\": 75}, {\"localized_name\": \"text\", \"name\": \"text\", \"type\": \"STRING\", \"widget\": {\"name\": \"text\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"CONDITIONING\", \"name\": \"CONDITIONING\", \"type\": \"CONDITIONING\", \"slot_index\": 0, \"links\": [97]}], \"title\": \"CLIP Text Encode (Negative Prompt)\", \"properties\": {\"cnr_id\": 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\"origin_slot\": 5, \"target_id\": 79, \"target_slot\": 5, \"type\": \"COMBO\"}], \"extra\": {\"workflowRendererVersion\": \"LG\"}, \"category\": \"Video Tools/Stitch videos\"}]}}\n" }, { "path": "blueprints/Video Upscale(GAN x4).json", "content": "{\"revision\": 0, \"last_node_id\": 13, \"last_link_id\": 0, \"nodes\": [{\"id\": 13, \"type\": \"cf95b747-3e17-46cb-8097-cac60ff9b2e1\", \"pos\": [1120, 330], \"size\": [240, 58], \"flags\": {}, \"order\": 3, \"mode\": 0, \"inputs\": [{\"localized_name\": \"video\", \"name\": \"video\", \"type\": \"VIDEO\", \"link\": null}, {\"name\": \"model_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"model_name\"}, \"link\": null}], \"outputs\": [{\"localized_name\": \"VIDEO\", \"name\": \"VIDEO\", \"type\": \"VIDEO\", \"links\": []}], \"title\": \"Video Upscale(GAN x4)\", \"properties\": {\"proxyWidgets\": [[\"-1\", \"model_name\"]], \"cnr_id\": \"comfy-core\", \"ver\": \"0.14.1\"}, \"widgets_values\": [\"RealESRGAN_x4plus.safetensors\"]}], \"links\": [], \"version\": 0.4, \"definitions\": {\"subgraphs\": [{\"id\": \"cf95b747-3e17-46cb-8097-cac60ff9b2e1\", \"version\": 1, \"state\": {\"lastGroupId\": 0, \"lastNodeId\": 13, \"lastLinkId\": 19, \"lastRerouteId\": 0}, \"revision\": 0, \"config\": {}, \"name\": \"Video Upscale(GAN x4)\", \"inputNode\": {\"id\": -10, \"bounding\": [550, 460, 120, 80]}, \"outputNode\": {\"id\": -20, \"bounding\": [1490, 460, 120, 60]}, \"inputs\": [{\"id\": \"666d633e-93e7-42dc-8d11-2b7b99b0f2a6\", \"name\": \"video\", \"type\": \"VIDEO\", \"linkIds\": [10], \"localized_name\": \"video\", \"pos\": [650, 480]}, {\"id\": \"2e23a087-caa8-4d65-99e6-662761aa905a\", \"name\": \"model_name\", \"type\": \"COMBO\", \"linkIds\": [19], \"pos\": [650, 500]}], \"outputs\": [{\"id\": \"0c1768ea-3ec2-412f-9af6-8e0fa36dae70\", \"name\": \"VIDEO\", \"type\": \"VIDEO\", \"linkIds\": [15], \"localized_name\": \"VIDEO\", \"pos\": [1510, 480]}], \"widgets\": [], \"nodes\": [{\"id\": 2, \"type\": \"ImageUpscaleWithModel\", \"pos\": [1110, 450], \"size\": [320, 46], \"flags\": {}, \"order\": 1, \"mode\": 0, \"inputs\": [{\"localized_name\": \"upscale_model\", \"name\": \"upscale_model\", \"type\": \"UPSCALE_MODEL\", \"link\": 1}, {\"localized_name\": \"image\", \"name\": \"image\", \"type\": \"IMAGE\", \"link\": 14}], \"outputs\": [{\"localized_name\": \"IMAGE\", \"name\": \"IMAGE\", \"type\": \"IMAGE\", \"links\": [13]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.10.0\", \"Node name for S&R\": \"ImageUpscaleWithModel\"}}, {\"id\": 11, \"type\": \"CreateVideo\", \"pos\": [1110, 550], \"size\": [320, 78], \"flags\": {}, \"order\": 3, \"mode\": 0, \"inputs\": [{\"localized_name\": \"images\", \"name\": \"images\", \"type\": \"IMAGE\", \"link\": 13}, {\"localized_name\": \"audio\", \"name\": \"audio\", \"shape\": 7, \"type\": \"AUDIO\", \"link\": 16}, {\"localized_name\": \"fps\", \"name\": \"fps\", \"type\": \"FLOAT\", \"widget\": {\"name\": \"fps\"}, \"link\": 12}], \"outputs\": [{\"localized_name\": \"VIDEO\", \"name\": \"VIDEO\", \"type\": \"VIDEO\", \"links\": [15]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.10.0\", \"Node name for S&R\": \"CreateVideo\"}, \"widgets_values\": [30]}, {\"id\": 10, \"type\": \"GetVideoComponents\", \"pos\": [1110, 330], \"size\": [320, 70], \"flags\": {}, \"order\": 2, \"mode\": 0, \"inputs\": [{\"localized_name\": \"video\", \"name\": \"video\", \"type\": \"VIDEO\", \"link\": 10}], \"outputs\": [{\"localized_name\": \"images\", \"name\": \"images\", \"type\": \"IMAGE\", \"links\": [14]}, {\"localized_name\": \"audio\", \"name\": \"audio\", \"type\": \"AUDIO\", \"links\": [16]}, {\"localized_name\": \"fps\", \"name\": \"fps\", \"type\": \"FLOAT\", \"links\": [12]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.10.0\", \"Node name for S&R\": \"GetVideoComponents\"}}, {\"id\": 1, \"type\": \"UpscaleModelLoader\", \"pos\": [750, 450], \"size\": [280, 60], \"flags\": {}, \"order\": 0, \"mode\": 0, \"inputs\": [{\"localized_name\": \"model_name\", \"name\": \"model_name\", \"type\": \"COMBO\", \"widget\": {\"name\": \"model_name\"}, \"link\": 19}], \"outputs\": [{\"localized_name\": \"UPSCALE_MODEL\", \"name\": \"UPSCALE_MODEL\", \"type\": \"UPSCALE_MODEL\", \"links\": [1]}], \"properties\": {\"cnr_id\": \"comfy-core\", \"ver\": \"0.10.0\", \"Node name for S&R\": \"UpscaleModelLoader\", \"models\": [{\"name\": \"RealESRGAN_x4plus.safetensors\", \"url\": \"https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors\", \"directory\": \"upscale_models\"}]}, \"widgets_values\": [\"RealESRGAN_x4plus.safetensors\"]}], \"groups\": [], \"links\": [{\"id\": 1, \"origin_id\": 1, \"origin_slot\": 0, \"target_id\": 2, \"target_slot\": 0, \"type\": \"UPSCALE_MODEL\"}, {\"id\": 14, \"origin_id\": 10, \"origin_slot\": 0, \"target_id\": 2, \"target_slot\": 1, \"type\": \"IMAGE\"}, {\"id\": 13, \"origin_id\": 2, \"origin_slot\": 0, \"target_id\": 11, \"target_slot\": 0, \"type\": \"IMAGE\"}, {\"id\": 16, \"origin_id\": 10, \"origin_slot\": 1, \"target_id\": 11, \"target_slot\": 1, \"type\": \"AUDIO\"}, {\"id\": 12, \"origin_id\": 10, \"origin_slot\": 2, \"target_id\": 11, \"target_slot\": 2, \"type\": \"FLOAT\"}, {\"id\": 10, \"origin_id\": -10, \"origin_slot\": 0, \"target_id\": 10, \"target_slot\": 0, \"type\": \"VIDEO\"}, {\"id\": 15, \"origin_id\": 11, \"origin_slot\": 0, \"target_id\": -20, \"target_slot\": 0, \"type\": \"VIDEO\"}, {\"id\": 19, \"origin_id\": -10, \"origin_slot\": 1, \"target_id\": 1, \"target_slot\": 0, \"type\": \"COMBO\"}], \"extra\": {\"workflowRendererVersion\": \"LG\"}, \"category\": \"Video generation and editing/Enhance video\"}]}, \"extra\": {}}\n" }, { "path": "blueprints/put_blueprints_here", "content": "" }, { "path": "comfy/audio_encoders/audio_encoders.py", "content": "from .wav2vec2 import Wav2Vec2Model\nfrom .whisper import WhisperLargeV3\nimport comfy.model_management\nimport comfy.ops\nimport comfy.utils\nimport logging\nimport torchaudio\n\n\nclass AudioEncoderModel():\n def __init__(self, config):\n self.load_device = comfy.model_management.text_encoder_device()\n offload_device = comfy.model_management.text_encoder_offload_device()\n self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)\n model_type = config.pop(\"model_type\")\n model_config = dict(config)\n model_config.update({\n \"dtype\": self.dtype,\n \"device\": offload_device,\n \"operations\": comfy.ops.manual_cast\n })\n\n if model_type == \"wav2vec2\":\n self.model = Wav2Vec2Model(**model_config)\n elif model_type == \"whisper3\":\n self.model = WhisperLargeV3(**model_config)\n self.model.eval()\n self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)\n self.model_sample_rate = 16000\n comfy.model_management.archive_model_dtypes(self.model)\n\n def load_sd(self, sd):\n return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())\n\n def get_sd(self):\n return self.model.state_dict()\n\n def encode_audio(self, audio, sample_rate):\n comfy.model_management.load_model_gpu(self.patcher)\n audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate)\n out, all_layers = self.model(audio.to(self.load_device))\n outputs = {}\n outputs[\"encoded_audio\"] = out\n outputs[\"encoded_audio_all_layers\"] = all_layers\n outputs[\"audio_samples\"] = audio.shape[2]\n return outputs\n\n\ndef load_audio_encoder_from_sd(sd, prefix=\"\"):\n sd = comfy.utils.state_dict_prefix_replace(sd, {\"wav2vec2.\": \"\"})\n if \"encoder.layer_norm.bias\" in sd: #wav2vec2\n embed_dim = sd[\"encoder.layer_norm.bias\"].shape[0]\n if embed_dim == 1024:# large\n config = {\n \"model_type\": \"wav2vec2\",\n \"embed_dim\": 1024,\n \"num_heads\": 16,\n \"num_layers\": 24,\n \"conv_norm\": True,\n \"conv_bias\": True,\n \"do_normalize\": True,\n \"do_stable_layer_norm\": True\n }\n elif embed_dim == 768: # base\n config = {\n \"model_type\": \"wav2vec2\",\n \"embed_dim\": 768,\n \"num_heads\": 12,\n \"num_layers\": 12,\n \"conv_norm\": False,\n \"conv_bias\": False,\n \"do_normalize\": False, # chinese-wav2vec2-base has this False\n \"do_stable_layer_norm\": False\n }\n else:\n raise RuntimeError(\"ERROR: audio encoder file is invalid or unsupported embed_dim: {}\".format(embed_dim))\n elif \"model.encoder.embed_positions.weight\" in sd:\n sd = comfy.utils.state_dict_prefix_replace(sd, {\"model.\": \"\"})\n config = {\n \"model_type\": \"whisper3\",\n }\n else:\n raise RuntimeError(\"ERROR: audio encoder not supported.\")\n\n audio_encoder = AudioEncoderModel(config)\n m, u = audio_encoder.load_sd(sd)\n if len(m) > 0:\n logging.warning(\"missing audio encoder: {}\".format(m))\n if len(u) > 0:\n logging.warning(\"unexpected audio encoder: {}\".format(u))\n\n return audio_encoder\n" }, { "path": "comfy/audio_encoders/wav2vec2.py", "content": "import torch\nimport torch.nn as nn\nfrom comfy.ldm.modules.attention import optimized_attention_masked\n\n\nclass LayerNormConv(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):\n super().__init__()\n self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)\n self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype)\n\n def forward(self, x):\n x = self.conv(x)\n return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))\n\nclass LayerGroupNormConv(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):\n super().__init__()\n self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)\n self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype)\n\n def forward(self, x):\n x = self.conv(x)\n return torch.nn.functional.gelu(self.layer_norm(x))\n\nclass ConvNoNorm(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):\n super().__init__()\n self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)\n\n def forward(self, x):\n x = self.conv(x)\n return torch.nn.functional.gelu(x)\n\n\nclass ConvFeatureEncoder(nn.Module):\n def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):\n super().__init__()\n if conv_norm:\n self.conv_layers = nn.ModuleList([\n LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),\n LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ])\n else:\n self.conv_layers = nn.ModuleList([\n LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),\n ])\n\n def forward(self, x):\n x = x.unsqueeze(1)\n\n for conv in self.conv_layers:\n x = conv(x)\n\n return x.transpose(1, 2)\n\n\nclass FeatureProjection(nn.Module):\n def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None):\n super().__init__()\n self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype)\n self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype)\n\n def forward(self, x):\n x = self.layer_norm(x)\n x = self.projection(x)\n return x\n\n\nclass PositionalConvEmbedding(nn.Module):\n def __init__(self, embed_dim=768, kernel_size=128, groups=16):\n super().__init__()\n self.conv = nn.Conv1d(\n embed_dim,\n embed_dim,\n kernel_size=kernel_size,\n padding=kernel_size // 2,\n groups=groups,\n )\n self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name=\"weight\", dim=2)\n self.activation = nn.GELU()\n\n def forward(self, x):\n x = x.transpose(1, 2)\n x = self.conv(x)[:, :, :-1]\n x = self.activation(x)\n x = x.transpose(1, 2)\n return x\n\n\nclass TransformerEncoder(nn.Module):\n def __init__(\n self,\n embed_dim=768,\n num_heads=12,\n num_layers=12,\n mlp_ratio=4.0,\n do_stable_layer_norm=True,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim)\n self.layers = nn.ModuleList([\n TransformerEncoderLayer(\n embed_dim=embed_dim,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n do_stable_layer_norm=do_stable_layer_norm,\n device=device, dtype=dtype, operations=operations\n )\n for _ in range(num_layers)\n ])\n\n self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)\n self.do_stable_layer_norm = do_stable_layer_norm\n\n def forward(self, x, mask=None):\n x = x + self.pos_conv_embed(x)\n all_x = ()\n if not self.do_stable_layer_norm:\n x = self.layer_norm(x)\n for layer in self.layers:\n all_x += (x,)\n x = layer(x, mask)\n if self.do_stable_layer_norm:\n x = self.layer_norm(x)\n all_x += (x,)\n return x, all_x\n\n\nclass Attention(nn.Module):\n def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None):\n super().__init__()\n self.embed_dim = embed_dim\n self.num_heads = num_heads\n self.head_dim = embed_dim // num_heads\n\n self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)\n self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)\n self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)\n self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)\n\n def forward(self, x, mask=None):\n assert (mask is None) # TODO?\n q = self.q_proj(x)\n k = self.k_proj(x)\n v = self.v_proj(x)\n\n out = optimized_attention_masked(q, k, v, self.num_heads)\n return self.out_proj(out)\n\n\nclass FeedForward(nn.Module):\n def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None):\n super().__init__()\n self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype)\n self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype)\n\n def forward(self, x):\n x = self.intermediate_dense(x)\n x = torch.nn.functional.gelu(x)\n x = self.output_dense(x)\n return x\n\n\nclass TransformerEncoderLayer(nn.Module):\n def __init__(\n self,\n embed_dim=768,\n num_heads=12,\n mlp_ratio=4.0,\n do_stable_layer_norm=True,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations)\n\n self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)\n self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)\n self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)\n self.do_stable_layer_norm = do_stable_layer_norm\n\n def forward(self, x, mask=None):\n residual = x\n if self.do_stable_layer_norm:\n x = self.layer_norm(x)\n x = self.attention(x, mask=mask)\n x = residual + x\n if not self.do_stable_layer_norm:\n x = self.layer_norm(x)\n return self.final_layer_norm(x + self.feed_forward(x))\n else:\n return x + self.feed_forward(self.final_layer_norm(x))\n\n\nclass Wav2Vec2Model(nn.Module):\n \"\"\"Complete Wav2Vec 2.0 model.\"\"\"\n\n def __init__(\n self,\n embed_dim=1024,\n final_dim=256,\n num_heads=16,\n num_layers=24,\n conv_norm=True,\n conv_bias=True,\n do_normalize=True,\n do_stable_layer_norm=True,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n conv_dim = 512\n self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations)\n self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)\n\n self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))\n self.do_normalize = do_normalize\n\n self.encoder = TransformerEncoder(\n embed_dim=embed_dim,\n num_heads=num_heads,\n num_layers=num_layers,\n do_stable_layer_norm=do_stable_layer_norm,\n device=device, dtype=dtype, operations=operations\n )\n\n def forward(self, x, mask_time_indices=None, return_dict=False):\n x = torch.mean(x, dim=1)\n\n if self.do_normalize:\n x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)\n\n features = self.feature_extractor(x)\n features = self.feature_projection(features)\n batch_size, seq_len, _ = features.shape\n\n x, all_x = self.encoder(features)\n return x, all_x\n" }, { "path": "comfy/audio_encoders/whisper.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchaudio\nfrom typing import Optional\nfrom comfy.ldm.modules.attention import optimized_attention_masked\nimport comfy.ops\n\nclass WhisperFeatureExtractor(nn.Module):\n def __init__(self, n_mels=128, device=None):\n super().__init__()\n self.sample_rate = 16000\n self.n_fft = 400\n self.hop_length = 160\n self.n_mels = n_mels\n self.chunk_length = 30\n self.n_samples = 480000\n\n self.mel_spectrogram = torchaudio.transforms.MelSpectrogram(\n sample_rate=self.sample_rate,\n n_fft=self.n_fft,\n hop_length=self.hop_length,\n n_mels=self.n_mels,\n f_min=0,\n f_max=8000,\n norm=\"slaney\",\n mel_scale=\"slaney\",\n ).to(device)\n\n def __call__(self, audio):\n audio = torch.mean(audio, dim=1)\n batch_size = audio.shape[0]\n processed_audio = []\n\n for i in range(batch_size):\n aud = audio[i]\n if aud.shape[0] > self.n_samples:\n aud = aud[:self.n_samples]\n elif aud.shape[0] < self.n_samples:\n aud = F.pad(aud, (0, self.n_samples - aud.shape[0]))\n processed_audio.append(aud)\n\n audio = torch.stack(processed_audio)\n\n mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device)\n\n log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10()\n log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0)\n log_mel_spec = (log_mel_spec + 4.0) / 4.0\n\n return log_mel_spec\n\n\nclass MultiHeadAttention(nn.Module):\n def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None):\n super().__init__()\n assert d_model % n_heads == 0\n\n self.d_model = d_model\n self.n_heads = n_heads\n self.d_k = d_model // n_heads\n\n self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)\n self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device)\n self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)\n self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)\n\n def forward(\n self,\n query: torch.Tensor,\n key: torch.Tensor,\n value: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n batch_size, seq_len, _ = query.shape\n\n q = self.q_proj(query)\n k = self.k_proj(key)\n v = self.v_proj(value)\n\n attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask)\n attn_output = self.out_proj(attn_output)\n\n return attn_output\n\n\nclass EncoderLayer(nn.Module):\n def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None):\n super().__init__()\n\n self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations)\n self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)\n\n self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device)\n self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device)\n self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)\n\n def forward(\n self,\n x: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None\n ) -> torch.Tensor:\n residual = x\n x = self.self_attn_layer_norm(x)\n x = self.self_attn(x, x, x, attention_mask)\n x = residual + x\n\n residual = x\n x = self.final_layer_norm(x)\n x = self.fc1(x)\n x = F.gelu(x)\n x = self.fc2(x)\n x = residual + x\n\n return x\n\n\nclass AudioEncoder(nn.Module):\n def __init__(\n self,\n n_mels: int = 128,\n n_ctx: int = 1500,\n n_state: int = 1280,\n n_head: int = 20,\n n_layer: int = 32,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n\n self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device)\n self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device)\n\n self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device)\n\n self.layers = nn.ModuleList([\n EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations)\n for _ in range(n_layer)\n ])\n\n self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = F.gelu(self.conv1(x))\n x = F.gelu(self.conv2(x))\n\n x = x.transpose(1, 2)\n\n x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x)\n\n all_x = ()\n for layer in self.layers:\n all_x += (x,)\n x = layer(x)\n\n x = self.layer_norm(x)\n all_x += (x,)\n return x, all_x\n\n\nclass WhisperLargeV3(nn.Module):\n def __init__(\n self,\n n_mels: int = 128,\n n_audio_ctx: int = 1500,\n n_audio_state: int = 1280,\n n_audio_head: int = 20,\n n_audio_layer: int = 32,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n\n self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device)\n\n self.encoder = AudioEncoder(\n n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer,\n dtype=dtype, device=device, operations=operations\n )\n\n def forward(self, audio):\n mel = self.feature_extractor(audio)\n x, all_x = self.encoder(mel)\n return x, all_x\n" }, { "path": "comfy/cldm/cldm.py", "content": "#taken from: https://github.com/lllyasviel/ControlNet\n#and modified\n\nimport torch\nimport torch.nn as nn\n\nfrom ..ldm.modules.diffusionmodules.util import (\n timestep_embedding,\n)\n\nfrom ..ldm.modules.attention import SpatialTransformer\nfrom ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample\nfrom ..ldm.util import exists\nfrom .control_types import UNION_CONTROLNET_TYPES\nfrom collections import OrderedDict\nimport comfy.ops\nfrom comfy.ldm.modules.attention import optimized_attention\n\nclass OptimizedAttention(nn.Module):\n def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.heads = nhead\n self.c = c\n\n self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)\n self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)\n\n def forward(self, x):\n x = self.in_proj(x)\n q, k, v = x.split(self.c, dim=2)\n out = optimized_attention(q, k, v, self.heads)\n return self.out_proj(out)\n\nclass QuickGELU(nn.Module):\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\nclass ResBlockUnionControlnet(nn.Module):\n def __init__(self, dim, nhead, dtype=None, device=None, operations=None):\n super().__init__()\n self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)\n self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)\n self.mlp = nn.Sequential(\n OrderedDict([(\"c_fc\", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), (\"gelu\", QuickGELU()),\n (\"c_proj\", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))\n self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)\n\n def attention(self, x: torch.Tensor):\n return self.attn(x)\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\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 attn_precision=None,\n union_controlnet_num_control_type=None,\n device=None,\n operations=comfy.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 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, attn_precision=attn_precision, 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, attn_precision=attn_precision, 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 if union_controlnet_num_control_type is not None:\n self.num_control_type = union_controlnet_num_control_type\n num_trans_channel = 320\n num_trans_head = 8\n num_trans_layer = 1\n num_proj_channel = 320\n # task_scale_factor = num_trans_channel ** 0.5\n self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))\n\n self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])\n self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)\n #-----------------------------------------------------------------------------------------------------\n\n control_add_embed_dim = 256\n class ControlAddEmbedding(nn.Module):\n def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):\n super().__init__()\n self.num_control_type = num_control_type\n self.in_dim = in_dim\n self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)\n self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)\n def forward(self, control_type, dtype, device):\n c_type = torch.zeros((self.num_control_type,), device=device)\n c_type[control_type] = 1.0\n c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))\n return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))\n\n self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)\n else:\n self.task_embedding = None\n self.control_add_embedding = None\n\n def union_controlnet_merge(self, hint, control_type, emb, context):\n # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main\n inputs = []\n condition_list = []\n\n for idx in range(min(1, len(control_type))):\n controlnet_cond = self.input_hint_block(hint[idx], emb, context)\n feat_seq = torch.mean(controlnet_cond, dim=(2, 3))\n if idx < len(control_type):\n feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)\n\n inputs.append(feat_seq.unsqueeze(1))\n condition_list.append(controlnet_cond)\n\n x = torch.cat(inputs, dim=1)\n x = self.transformer_layes(x)\n controlnet_cond_fuser = None\n for idx in range(len(control_type)):\n alpha = self.spatial_ch_projs(x[:, idx])\n alpha = alpha.unsqueeze(-1).unsqueeze(-1)\n o = condition_list[idx] + alpha\n if controlnet_cond_fuser is None:\n controlnet_cond_fuser = o\n else:\n controlnet_cond_fuser += o\n return controlnet_cond_fuser\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 = None\n if self.control_add_embedding is not None: #Union Controlnet\n control_type = kwargs.get(\"control_type\", [])\n\n if any([c >= self.num_control_type for c in control_type]):\n max_type = max(control_type)\n max_type_name = {\n v: k for k, v in UNION_CONTROLNET_TYPES.items()\n }[max_type]\n raise ValueError(\n f\"Control type {max_type_name}({max_type}) is out of range for the number of control types\" +\n f\"({self.num_control_type}) supported.\\n\" +\n \"Please consider using the ProMax ControlNet Union model.\\n\" +\n \"https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main\"\n )\n\n emb += self.control_add_embedding(control_type, emb.dtype, emb.device)\n if len(control_type) > 0:\n if len(hint.shape) < 5:\n hint = hint.unsqueeze(dim=0)\n guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)\n\n if guided_hint is None:\n guided_hint = self.input_hint_block(hint, emb, context)\n\n out_output = []\n out_middle = []\n\n if self.num_classes is not None:\n if y is None:\n raise ValueError(\"y is None, did you try using a controlnet for SDXL on SD1?\")\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 out_output.append(zero_conv(h, emb, context))\n\n h = self.middle_block(h, emb, context)\n out_middle.append(self.middle_block_out(h, emb, context))\n\n return {\"middle\": out_middle, \"output\": out_output}\n\n" }, { "path": "comfy/cldm/control_types.py", "content": "UNION_CONTROLNET_TYPES = {\n \"openpose\": 0,\n \"depth\": 1,\n \"hed/pidi/scribble/ted\": 2,\n \"canny/lineart/anime_lineart/mlsd\": 3,\n \"normal\": 4,\n \"segment\": 5,\n \"tile\": 6,\n \"repaint\": 7,\n}\n" }, { "path": "comfy/cldm/dit_embedder.py", "content": "import math\nfrom typing import List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\n\nfrom comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch\n\n\nclass ControlNetEmbedder(nn.Module):\n\n def __init__(\n self,\n img_size: int,\n patch_size: int,\n in_chans: int,\n attention_head_dim: int,\n num_attention_heads: int,\n adm_in_channels: int,\n num_layers: int,\n main_model_double: int,\n double_y_emb: bool,\n device: torch.device,\n dtype: torch.dtype,\n pos_embed_max_size: Optional[int] = None,\n operations = None,\n ):\n super().__init__()\n self.main_model_double = main_model_double\n self.dtype = dtype\n self.hidden_size = num_attention_heads * attention_head_dim\n self.patch_size = patch_size\n self.x_embedder = PatchEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=in_chans,\n embed_dim=self.hidden_size,\n strict_img_size=pos_embed_max_size is None,\n device=device,\n dtype=dtype,\n operations=operations,\n )\n\n self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)\n\n self.double_y_emb = double_y_emb\n if self.double_y_emb:\n self.orig_y_embedder = VectorEmbedder(\n adm_in_channels, self.hidden_size, dtype, device, operations=operations\n )\n self.y_embedder = VectorEmbedder(\n self.hidden_size, self.hidden_size, dtype, device, operations=operations\n )\n else:\n self.y_embedder = VectorEmbedder(\n adm_in_channels, self.hidden_size, dtype, device, operations=operations\n )\n\n self.transformer_blocks = nn.ModuleList(\n DismantledBlock(\n hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(num_layers)\n )\n\n # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features\n # TODO double check this logic when 8b\n self.use_y_embedder = True\n\n self.controlnet_blocks = nn.ModuleList([])\n for _ in range(len(self.transformer_blocks)):\n controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)\n self.controlnet_blocks.append(controlnet_block)\n\n self.pos_embed_input = PatchEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=in_chans,\n embed_dim=self.hidden_size,\n strict_img_size=False,\n device=device,\n dtype=dtype,\n operations=operations,\n )\n\n def forward(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n y: Optional[torch.Tensor] = None,\n context: Optional[torch.Tensor] = None,\n hint = None,\n ) -> Tuple[Tensor, List[Tensor]]:\n x_shape = list(x.shape)\n x = self.x_embedder(x)\n if not self.double_y_emb:\n h = (x_shape[-2] + 1) // self.patch_size\n w = (x_shape[-1] + 1) // self.patch_size\n x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device)\n c = self.t_embedder(timesteps, dtype=x.dtype)\n if y is not None and self.y_embedder is not None:\n if self.double_y_emb:\n y = self.orig_y_embedder(y)\n y = self.y_embedder(y)\n c = c + y\n\n x = x + self.pos_embed_input(hint)\n\n block_out = ()\n\n repeat = math.ceil(self.main_model_double / len(self.transformer_blocks))\n for i in range(len(self.transformer_blocks)):\n out = self.transformer_blocks[i](x, c)\n if not self.double_y_emb:\n x = out\n block_out += (self.controlnet_blocks[i](out),) * repeat\n\n return {\"output\": block_out}\n" }, { "path": "comfy/cldm/mmdit.py", "content": "import torch\nfrom typing import Optional\nimport comfy.ldm.modules.diffusionmodules.mmdit\n\nclass ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):\n def __init__(\n self,\n num_blocks = None,\n control_latent_channels = None,\n dtype = None,\n device = None,\n operations = None,\n **kwargs,\n ):\n super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)\n # controlnet_blocks\n self.controlnet_blocks = torch.nn.ModuleList([])\n for _ in range(len(self.joint_blocks)):\n self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))\n\n if control_latent_channels is None:\n control_latent_channels = self.in_channels\n\n self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(\n None,\n self.patch_size,\n control_latent_channels,\n self.hidden_size,\n bias=True,\n strict_img_size=False,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n def forward(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n y: Optional[torch.Tensor] = None,\n context: Optional[torch.Tensor] = None,\n hint = None,\n ) -> torch.Tensor:\n\n #weird sd3 controlnet specific stuff\n y = torch.zeros_like(y)\n\n if self.context_processor is not None:\n context = self.context_processor(context)\n\n hw = x.shape[-2:]\n x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)\n x += self.pos_embed_input(hint)\n\n c = self.t_embedder(timesteps, dtype=x.dtype)\n if y is not None and self.y_embedder is not None:\n y = self.y_embedder(y)\n c = c + y\n\n if context is not None:\n context = self.context_embedder(context)\n\n output = []\n\n blocks = len(self.joint_blocks)\n for i in range(blocks):\n context, x = self.joint_blocks[i](\n context,\n x,\n c=c,\n use_checkpoint=self.use_checkpoint,\n )\n\n out = self.controlnet_blocks[i](x)\n count = self.depth // blocks\n if i == blocks - 1:\n count -= 1\n for j in range(count):\n output.append(out)\n\n return {\"output\": output}\n" }, { "path": "comfy/cli_args.py", "content": "import argparse\nimport enum\nimport os\nimport comfy.options\n\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,::\", help=\"Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)\")\nparser.add_argument(\"--port\", type=int, default=8188, help=\"Set the listen port.\")\nparser.add_argument(\"--tls-keyfile\", type=str, help=\"Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function\")\nparser.add_argument(\"--tls-certfile\", type=str, help=\"Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function\")\nparser.add_argument(\"--enable-cors-header\", type=str, default=None, metavar=\"ORIGIN\", nargs=\"?\", const=\"*\", help=\"Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.\")\nparser.add_argument(\"--max-upload-size\", type=float, default=100, help=\"Set the maximum upload size in MB.\")\n\nparser.add_argument(\"--base-directory\", type=str, default=None, help=\"Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.\")\nparser.add_argument(\"--extra-model-paths-config\", type=str, default=None, metavar=\"PATH\", nargs='+', action='append', help=\"Load one or more extra_model_paths.yaml files.\")\nparser.add_argument(\"--output-directory\", type=str, default=None, help=\"Set the ComfyUI output directory. Overrides --base-directory.\")\nparser.add_argument(\"--temp-directory\", type=str, default=None, help=\"Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.\")\nparser.add_argument(\"--input-directory\", type=str, default=None, help=\"Set the ComfyUI input directory. Overrides --base-directory.\")\nparser.add_argument(\"--auto-launch\", action=\"store_true\", help=\"Automatically launch ComfyUI in the default browser.\")\nparser.add_argument(\"--disable-auto-launch\", action=\"store_true\", help=\"Disable auto launching the browser.\")\nparser.add_argument(\"--cuda-device\", type=int, default=None, metavar=\"DEVICE_ID\", help=\"Set the id of the cuda device this instance will use. All other devices will not be visible.\")\nparser.add_argument(\"--default-device\", type=int, default=None, metavar=\"DEFAULT_DEVICE_ID\", help=\"Set the id of the default device, all other devices will stay visible.\")\ncm_group = parser.add_mutually_exclusive_group()\ncm_group.add_argument(\"--cuda-malloc\", action=\"store_true\", help=\"Enable cudaMallocAsync (enabled by default for torch 2.0 and up).\")\ncm_group.add_argument(\"--disable-cuda-malloc\", action=\"store_true\", help=\"Disable cudaMallocAsync.\")\n\n\nfp_group = parser.add_mutually_exclusive_group()\nfp_group.add_argument(\"--force-fp32\", action=\"store_true\", help=\"Force fp32 (If this makes your GPU work better please report it).\")\nfp_group.add_argument(\"--force-fp16\", action=\"store_true\", help=\"Force fp16.\")\n\nfpunet_group = parser.add_mutually_exclusive_group()\nfpunet_group.add_argument(\"--fp32-unet\", action=\"store_true\", help=\"Run the diffusion model in fp32.\")\nfpunet_group.add_argument(\"--fp64-unet\", action=\"store_true\", help=\"Run the diffusion model in fp64.\")\nfpunet_group.add_argument(\"--bf16-unet\", action=\"store_true\", help=\"Run the diffusion model in bf16.\")\nfpunet_group.add_argument(\"--fp16-unet\", action=\"store_true\", help=\"Run the diffusion model in fp16\")\nfpunet_group.add_argument(\"--fp8_e4m3fn-unet\", action=\"store_true\", help=\"Store unet weights in fp8_e4m3fn.\")\nfpunet_group.add_argument(\"--fp8_e5m2-unet\", action=\"store_true\", help=\"Store unet weights in fp8_e5m2.\")\nfpunet_group.add_argument(\"--fp8_e8m0fnu-unet\", action=\"store_true\", help=\"Store unet weights in fp8_e8m0fnu.\")\n\nfpvae_group = parser.add_mutually_exclusive_group()\nfpvae_group.add_argument(\"--fp16-vae\", action=\"store_true\", help=\"Run the VAE in fp16, might cause black images.\")\nfpvae_group.add_argument(\"--fp32-vae\", action=\"store_true\", help=\"Run the VAE in full precision fp32.\")\nfpvae_group.add_argument(\"--bf16-vae\", action=\"store_true\", help=\"Run the VAE in bf16.\")\n\nparser.add_argument(\"--cpu-vae\", action=\"store_true\", help=\"Run the VAE on the CPU.\")\n\nfpte_group = parser.add_mutually_exclusive_group()\nfpte_group.add_argument(\"--fp8_e4m3fn-text-enc\", action=\"store_true\", help=\"Store text encoder weights in fp8 (e4m3fn variant).\")\nfpte_group.add_argument(\"--fp8_e5m2-text-enc\", action=\"store_true\", help=\"Store text encoder weights in fp8 (e5m2 variant).\")\nfpte_group.add_argument(\"--fp16-text-enc\", action=\"store_true\", help=\"Store text encoder weights in fp16.\")\nfpte_group.add_argument(\"--fp32-text-enc\", action=\"store_true\", help=\"Store text encoder weights in fp32.\")\nfpte_group.add_argument(\"--bf16-text-enc\", action=\"store_true\", help=\"Store text encoder weights in bf16.\")\n\nparser.add_argument(\"--fp16-intermediates\", action=\"store_true\", help=\"Experimental: Use fp16 for intermediate tensors between nodes instead of fp32.\")\n\nparser.add_argument(\"--force-channels-last\", action=\"store_true\", help=\"Force channels last format when inferencing the models.\")\n\nparser.add_argument(\"--directml\", type=int, nargs=\"?\", metavar=\"DIRECTML_DEVICE\", const=-1, help=\"Use torch-directml.\")\n\nparser.add_argument(\"--oneapi-device-selector\", type=str, default=None, metavar=\"SELECTOR_STRING\", help=\"Sets the oneAPI device(s) this instance will use.\")\nparser.add_argument(\"--disable-ipex-optimize\", action=\"store_true\", help=\"Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.\")\nparser.add_argument(\"--supports-fp8-compute\", action=\"store_true\", help=\"ComfyUI will act like if the device supports fp8 compute.\")\n\nclass LatentPreviewMethod(enum.Enum):\n NoPreviews = \"none\"\n Auto = \"auto\"\n Latent2RGB = \"latent2rgb\"\n TAESD = \"taesd\"\n\n @classmethod\n def from_string(cls, value: str):\n for member in cls:\n if member.value == value:\n return member\n return None\n\nparser.add_argument(\"--preview-method\", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help=\"Default preview method for sampler nodes.\", action=EnumAction)\n\nparser.add_argument(\"--preview-size\", type=int, default=512, help=\"Sets the maximum preview size for sampler nodes.\")\n\ncache_group = parser.add_mutually_exclusive_group()\ncache_group.add_argument(\"--cache-classic\", action=\"store_true\", help=\"Use the old style (aggressive) caching.\")\ncache_group.add_argument(\"--cache-lru\", type=int, default=0, help=\"Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.\")\ncache_group.add_argument(\"--cache-none\", action=\"store_true\", help=\"Reduced RAM/VRAM usage at the expense of executing every node for each run.\")\ncache_group.add_argument(\"--cache-ram\", nargs='?', const=4.0, type=float, default=0, help=\"Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threhold the cache remove large items to free RAM. Default 4GB\")\n\nattn_group = parser.add_mutually_exclusive_group()\nattn_group.add_argument(\"--use-split-cross-attention\", action=\"store_true\", help=\"Use the split cross attention optimization. Ignored when xformers is used.\")\nattn_group.add_argument(\"--use-quad-cross-attention\", action=\"store_true\", help=\"Use the sub-quadratic cross attention optimization . Ignored when xformers is used.\")\nattn_group.add_argument(\"--use-pytorch-cross-attention\", action=\"store_true\", help=\"Use the new pytorch 2.0 cross attention function.\")\nattn_group.add_argument(\"--use-sage-attention\", action=\"store_true\", help=\"Use sage attention.\")\nattn_group.add_argument(\"--use-flash-attention\", action=\"store_true\", help=\"Use FlashAttention.\")\n\nparser.add_argument(\"--disable-xformers\", action=\"store_true\", help=\"Disable xformers.\")\n\nupcast = parser.add_mutually_exclusive_group()\nupcast.add_argument(\"--force-upcast-attention\", action=\"store_true\", help=\"Force enable attention upcasting, please report if it fixes black images.\")\nupcast.add_argument(\"--dont-upcast-attention\", action=\"store_true\", help=\"Disable all upcasting of attention. Should be unnecessary except for debugging.\")\n\n\nparser.add_argument(\"--enable-manager\", action=\"store_true\", help=\"Enable the ComfyUI-Manager feature.\")\nmanager_group = parser.add_mutually_exclusive_group()\nmanager_group.add_argument(\"--disable-manager-ui\", action=\"store_true\", help=\"Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.\")\nmanager_group.add_argument(\"--enable-manager-legacy-ui\", action=\"store_true\", help=\"Enables the legacy UI of ComfyUI-Manager\")\n\n\nvram_group = parser.add_mutually_exclusive_group()\nvram_group.add_argument(\"--gpu-only\", action=\"store_true\", help=\"Store and run everything (text encoders/CLIP models, etc... on the GPU).\")\nvram_group.add_argument(\"--highvram\", action=\"store_true\", help=\"By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.\")\nvram_group.add_argument(\"--normalvram\", action=\"store_true\", help=\"Used to force normal vram use if lowvram gets automatically enabled.\")\nvram_group.add_argument(\"--lowvram\", action=\"store_true\", help=\"Split the unet in parts to use less vram.\")\nvram_group.add_argument(\"--novram\", action=\"store_true\", help=\"When lowvram isn't enough.\")\nvram_group.add_argument(\"--cpu\", action=\"store_true\", help=\"To use the CPU for everything (slow).\")\n\nparser.add_argument(\"--reserve-vram\", type=float, default=None, help=\"Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.\")\n\nparser.add_argument(\"--async-offload\", nargs='?', const=2, type=int, default=None, metavar=\"NUM_STREAMS\", help=\"Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.\")\nparser.add_argument(\"--disable-async-offload\", action=\"store_true\", help=\"Disable async weight offloading.\")\nparser.add_argument(\"--disable-dynamic-vram\", action=\"store_true\", help=\"Disable dynamic VRAM and use estimate based model loading.\")\nparser.add_argument(\"--enable-dynamic-vram\", action=\"store_true\", help=\"Enable dynamic VRAM on systems where it's not enabled by default.\")\n\nparser.add_argument(\"--force-non-blocking\", action=\"store_true\", help=\"Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.\")\n\nparser.add_argument(\"--default-hashing-function\", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help=\"Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.\")\n\nparser.add_argument(\"--disable-smart-memory\", action=\"store_true\", help=\"Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.\")\nparser.add_argument(\"--deterministic\", action=\"store_true\", help=\"Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.\")\n\nclass PerformanceFeature(enum.Enum):\n Fp16Accumulation = \"fp16_accumulation\"\n Fp8MatrixMultiplication = \"fp8_matrix_mult\"\n CublasOps = \"cublas_ops\"\n AutoTune = \"autotune\"\n\nparser.add_argument(\"--fast\", nargs=\"*\", type=PerformanceFeature, help=\"Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}\".format(\" \".join(map(lambda c: c.value, PerformanceFeature))))\n\nparser.add_argument(\"--disable-pinned-memory\", action=\"store_true\", help=\"Disable pinned memory use.\")\n\nparser.add_argument(\"--mmap-torch-files\", action=\"store_true\", help=\"Use mmap when loading ckpt/pt files.\")\nparser.add_argument(\"--disable-mmap\", action=\"store_true\", help=\"Don't use mmap when loading safetensors.\")\n\nparser.add_argument(\"--dont-print-server\", action=\"store_true\", help=\"Don't print server output.\")\nparser.add_argument(\"--quick-test-for-ci\", action=\"store_true\", help=\"Quick test for CI.\")\nparser.add_argument(\"--windows-standalone-build\", action=\"store_true\", help=\"Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).\")\n\nparser.add_argument(\"--disable-metadata\", action=\"store_true\", help=\"Disable saving prompt metadata in files.\")\nparser.add_argument(\"--disable-all-custom-nodes\", action=\"store_true\", help=\"Disable loading all custom nodes.\")\nparser.add_argument(\"--whitelist-custom-nodes\", type=str, nargs='+', default=[], help=\"Specify custom node folders to load even when --disable-all-custom-nodes is enabled.\")\nparser.add_argument(\"--disable-api-nodes\", action=\"store_true\", help=\"Disable loading all api nodes. Also prevents the frontend from communicating with the internet.\")\n\nparser.add_argument(\"--multi-user\", action=\"store_true\", help=\"Enables per-user storage.\")\n\nparser.add_argument(\"--verbose\", default='INFO', const='DEBUG', nargs=\"?\", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')\nparser.add_argument(\"--log-stdout\", action=\"store_true\", help=\"Send normal process output to stdout instead of stderr (default).\")\n\n\n# The default built-in provider hosted under web/\nDEFAULT_VERSION_STRING = \"comfyanonymous/ComfyUI@latest\"\n\nparser.add_argument(\n \"--front-end-version\",\n type=str,\n default=DEFAULT_VERSION_STRING,\n help=\"\"\"\n Specifies the version of the frontend to be used. This command needs internet connectivity to query and\n download available frontend implementations from GitHub releases.\n\n The version string should be in the format of:\n [repoOwner]/[repoName]@[version]\n where version is one of: \"latest\" or a valid version number (e.g. \"1.0.0\")\n \"\"\",\n)\n\ndef is_valid_directory(path: str) -> str:\n \"\"\"Validate if the given path is a directory, and check permissions.\"\"\"\n if not os.path.exists(path):\n raise argparse.ArgumentTypeError(f\"The path '{path}' does not exist.\")\n if not os.path.isdir(path):\n raise argparse.ArgumentTypeError(f\"'{path}' is not a directory.\")\n if not os.access(path, os.R_OK):\n raise argparse.ArgumentTypeError(f\"You do not have read permissions for '{path}'.\")\n return path\n\nparser.add_argument(\n \"--front-end-root\",\n type=is_valid_directory,\n default=None,\n help=\"The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.\",\n)\n\nparser.add_argument(\"--user-directory\", type=is_valid_directory, default=None, help=\"Set the ComfyUI user directory with an absolute path. Overrides --base-directory.\")\n\nparser.add_argument(\"--enable-compress-response-body\", action=\"store_true\", help=\"Enable compressing response body.\")\n\nparser.add_argument(\n \"--comfy-api-base\",\n type=str,\n default=\"https://api.comfy.org\",\n help=\"Set the base URL for the ComfyUI API. (default: https://api.comfy.org)\",\n)\n\ndatabase_default_path = os.path.abspath(\n os.path.join(os.path.dirname(__file__), \"..\", \"user\", \"comfyui.db\")\n)\nparser.add_argument(\"--database-url\", type=str, default=f\"sqlite:///{database_default_path}\", help=\"Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.\")\nparser.add_argument(\"--enable-assets\", action=\"store_true\", help=\"Enable the assets system (API routes, database synchronization, and background scanning).\")\n\nif comfy.options.args_parsing:\n args = parser.parse_args()\nelse:\n args = parser.parse_args([])\n\nif args.windows_standalone_build:\n args.auto_launch = True\n\nif args.disable_auto_launch:\n args.auto_launch = False\n\nif args.force_fp16:\n args.fp16_unet = True\n\n\n# '--fast' is not provided, use an empty set\nif args.fast is None:\n args.fast = set()\n# '--fast' is provided with an empty list, enable all optimizations\nelif args.fast == []:\n args.fast = set(PerformanceFeature)\n# '--fast' is provided with a list of performance features, use that list\nelse:\n args.fast = set(args.fast)\n\ndef enables_dynamic_vram():\n if args.enable_dynamic_vram:\n return True\n return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu\n" }, { "path": "comfy/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\": 49407,\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": "comfy/clip_model.py", "content": "import torch\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\nimport comfy.ops\nimport math\n\ndef clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):\n image = image[:, :, :, :3] if image.shape[3] > 3 else image\n mean = torch.tensor(mean, device=image.device, dtype=image.dtype)\n std = torch.tensor(std, 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 if crop:\n scale = (size / min(image.shape[2], image.shape[3]))\n scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))\n else:\n scale_size = (size, size)\n\n image = torch.nn.functional.interpolate(image, size=scale_size, 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\ndef siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, eps=1e-5):\n def scale_dim(size, scale):\n scaled = math.ceil(size * scale / patch_size) * patch_size\n return max(patch_size, int(scaled))\n\n # Binary search for optimal scale\n lo, hi = eps / 10, 100.0\n while hi - lo >= eps:\n mid = (lo + hi) / 2\n h, w = scale_dim(oh, mid), scale_dim(ow, mid)\n if (h // patch_size) * (w // patch_size) <= max_num_patches:\n lo = mid\n else:\n hi = mid\n\n return scale_dim(oh, lo), scale_dim(ow, lo)\n\ndef siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True):\n if size > 0:\n return clip_preprocess(image, size=size, mean=mean, std=std, crop=crop)\n\n image = image[:, :, :, :3] if image.shape[3] > 3 else image\n mean = torch.tensor(mean, device=image.device, dtype=image.dtype)\n std = torch.tensor(std, device=image.device, dtype=image.dtype)\n image = image.movedim(-1, 1)\n\n b, c, h, w = image.shape\n h, w = siglip2_flex_calc_resolution(h, w, patch_size, num_patches)\n\n image = torch.nn.functional.interpolate(image, size=(h, w), mode=\"bilinear\", antialias=True)\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 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 \"gelu_pytorch_tanh\": lambda a: torch.nn.functional.gelu(a, approximate=\"tanh\"),\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 all_intermediate = None\n if intermediate_output is not None:\n if intermediate_output == \"all\":\n all_intermediate = []\n intermediate_output = None\n elif 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 if all_intermediate is not None:\n all_intermediate.append(x.unsqueeze(1).clone())\n\n if all_intermediate is not None:\n intermediate = torch.cat(all_intermediate, dim=1)\n\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, operations=None):\n super().__init__()\n self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)\n self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)\n\n def forward(self, input_tokens, dtype=torch.float32):\n return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)\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 num_positions = config_dict[\"max_position_embeddings\"]\n self.eos_token_id = config_dict[\"eos_token_id\"]\n\n super().__init__()\n self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations)\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=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]):\n if embeds is not None:\n x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device)\n else:\n x = self.embeddings(input_tokens, dtype=dtype)\n\n mask = None\n if attention_mask is not None:\n mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])\n mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)\n\n causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1)\n\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 if num_tokens is not None:\n pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))]\n else:\n pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().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 embed_dim = config_dict[\"hidden_size\"]\n self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)\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 x = self.text_model(*args, **kwargs)\n out = self.text_projection(x[2])\n return (x[0], x[1], out, x[2])\n\ndef siglip2_pos_embed(embed_weight, embeds, orig_shape):\n embed_weight_len = round(embed_weight.shape[0] ** 0.5)\n embed_weight = comfy.ops.cast_to_input(embed_weight, embeds).movedim(1, 0).reshape(1, -1, embed_weight_len, embed_weight_len)\n embed_weight = torch.nn.functional.interpolate(embed_weight, size=orig_shape, mode=\"bilinear\", align_corners=False, antialias=True)\n embed_weight = embed_weight.reshape(-1, embed_weight.shape[-2] * embed_weight.shape[-1]).movedim(0, 1)\n return embeds + embed_weight\n\nclass Siglip2Embeddings(torch.nn.Module):\n def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type=\"\", num_patches=None, dtype=None, device=None, operations=None):\n super().__init__()\n self.patch_embedding = operations.Linear(num_channels * patch_size * patch_size, embed_dim, dtype=dtype, device=device)\n self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)\n self.patch_size = patch_size\n\n def forward(self, pixel_values):\n b, c, h, w = pixel_values.shape\n img = pixel_values.movedim(1, -1).reshape(b, h // self.patch_size, self.patch_size, w // self.patch_size, self.patch_size, c)\n img = img.permute(0, 1, 3, 2, 4, 5)\n img = img.reshape(b, img.shape[1] * img.shape[2], -1)\n img = self.patch_embedding(img)\n return siglip2_pos_embed(self.position_embedding.weight, img, (h // self.patch_size, w // self.patch_size))\n\nclass CLIPVisionEmbeddings(torch.nn.Module):\n def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type=\"\", dtype=None, device=None, operations=None):\n super().__init__()\n\n num_patches = (image_size // patch_size) ** 2\n if model_type == \"siglip_vision_model\":\n self.class_embedding = None\n patch_bias = True\n else:\n num_patches = num_patches + 1\n self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))\n patch_bias = False\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=patch_bias,\n dtype=dtype,\n device=device\n )\n\n self.position_embedding = operations.Embedding(num_patches, 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 if self.class_embedding is not None:\n embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1)\n return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)\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 model_type = config_dict[\"model_type\"]\n\n if model_type in [\"siglip2_vision_model\"]:\n self.embeddings = Siglip2Embeddings(embed_dim, config_dict[\"num_channels\"], config_dict[\"patch_size\"], config_dict[\"image_size\"], model_type=model_type, num_patches=config_dict.get(\"num_patches\", None), dtype=dtype, device=device, operations=operations)\n else:\n self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict[\"num_channels\"], config_dict[\"patch_size\"], config_dict[\"image_size\"], model_type=model_type, dtype=dtype, device=device, operations=operations)\n if model_type in [\"siglip_vision_model\", \"siglip2_vision_model\"]:\n self.pre_layrnorm = lambda a: a\n self.output_layernorm = True\n else:\n self.pre_layrnorm = operations.LayerNorm(embed_dim)\n self.output_layernorm = False\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 if self.output_layernorm:\n x = self.post_layernorm(x)\n pooled_output = x\n else:\n pooled_output = self.post_layernorm(x[:, 0, :])\n return x, i, pooled_output\n\nclass LlavaProjector(torch.nn.Module):\n def __init__(self, in_dim, out_dim, dtype, device, operations):\n super().__init__()\n self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype)\n self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype)\n\n def forward(self, x):\n return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:])))\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 if \"projection_dim\" in config_dict:\n self.visual_projection = operations.Linear(config_dict[\"hidden_size\"], config_dict[\"projection_dim\"], bias=False)\n else:\n self.visual_projection = lambda a: a\n\n if \"llava3\" == config_dict.get(\"projector_type\", None):\n self.multi_modal_projector = LlavaProjector(config_dict[\"hidden_size\"], 4096, dtype, device, operations)\n else:\n self.multi_modal_projector = None\n\n def forward(self, *args, **kwargs):\n x = self.vision_model(*args, **kwargs)\n out = self.visual_projection(x[2])\n projected = None\n if self.multi_modal_projector is not None:\n projected = self.multi_modal_projector(x[1])\n\n return (x[0], x[1], out, projected)\n" }, { "path": "comfy/clip_vision.py", "content": "from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace\nimport os\nimport json\nimport logging\n\nimport comfy.ops\nimport comfy.model_patcher\nimport comfy.model_management\nimport comfy.utils\nimport comfy.clip_model\nimport comfy.image_encoders.dino2\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\nclip_preprocess = comfy.clip_model.clip_preprocess # Prevent some stuff from breaking, TODO: remove eventually\n\nIMAGE_ENCODERS = {\n \"clip_vision_model\": comfy.clip_model.CLIPVisionModelProjection,\n \"siglip_vision_model\": comfy.clip_model.CLIPVisionModelProjection,\n \"siglip2_vision_model\": comfy.clip_model.CLIPVisionModelProjection,\n \"dinov2\": comfy.image_encoders.dino2.Dinov2Model,\n}\n\nclass ClipVisionModel():\n def __init__(self, json_config):\n with open(json_config) as f:\n config = json.load(f)\n\n self.image_size = config.get(\"image_size\", 224)\n self.image_mean = config.get(\"image_mean\", [0.48145466, 0.4578275, 0.40821073])\n self.image_std = config.get(\"image_std\", [0.26862954, 0.26130258, 0.27577711])\n self.model_type = config.get(\"model_type\", \"clip_vision_model\")\n self.config = config.copy()\n model_class = IMAGE_ENCODERS.get(self.model_type)\n if self.model_type == \"siglip_vision_model\":\n self.return_all_hidden_states = True\n else:\n self.return_all_hidden_states = False\n\n self.load_device = comfy.model_management.text_encoder_device()\n offload_device = comfy.model_management.text_encoder_offload_device()\n self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)\n self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)\n self.model.eval()\n\n self.patcher = comfy.model_patcher.CoreModelPatcher(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, assign=self.patcher.is_dynamic())\n\n def get_sd(self):\n return self.model.state_dict()\n\n def encode_image(self, image, crop=True):\n comfy.model_management.load_model_gpu(self.patcher)\n if self.model_type == \"siglip2_vision_model\":\n pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get(\"patch_size\", 16), num_patches=self.config.get(\"num_patches\", 256), mean=self.image_mean, std=self.image_std, crop=crop).float()\n else:\n pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()\n out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)\n\n outputs = Output()\n outputs[\"last_hidden_state\"] = out[0].to(comfy.model_management.intermediate_device())\n outputs[\"image_embeds\"] = out[2].to(comfy.model_management.intermediate_device())\n outputs[\"image_sizes\"] = [pixel_values.shape[1:]] * pixel_values.shape[0]\n if self.return_all_hidden_states:\n all_hs = out[1].to(comfy.model_management.intermediate_device())\n outputs[\"penultimate_hidden_states\"] = all_hs[:, -2]\n outputs[\"all_hidden_states\"] = all_hs\n else:\n outputs[\"penultimate_hidden_states\"] = out[1].to(comfy.model_management.intermediate_device())\n\n outputs[\"mm_projected\"] = out[3]\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 embed_shape = sd[\"vision_model.embeddings.position_embedding.weight\"].shape[0]\n if sd[\"vision_model.encoder.layers.0.layer_norm1.weight\"].shape[0] == 1152:\n patch_embedding_shape = sd[\"vision_model.embeddings.patch_embedding.weight\"].shape\n if len(patch_embedding_shape) == 2:\n json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_siglip2_base_naflex.json\")\n else:\n if embed_shape == 729:\n json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_siglip_384.json\")\n elif embed_shape == 1024:\n json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_siglip_512.json\")\n elif embed_shape == 577:\n if \"multi_modal_projector.linear_1.bias\" in sd:\n json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_config_vitl_336_llava.json\")\n else:\n json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_config_vitl_336.json\")\n else:\n json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_config_vitl.json\")\n\n # Dinov2\n elif 'encoder.layer.39.layer_scale2.lambda1' in sd:\n json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), \"image_encoders\"), \"dino2_giant.json\")\n elif 'encoder.layer.23.layer_scale2.lambda1' in sd:\n json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), \"image_encoders\"), \"dino2_large.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 logging.warning(\"missing clip vision: {}\".format(m))\n u = set(u)\n keys = list(sd.keys())\n for k in keys:\n if k not in u:\n sd.pop(k)\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": "comfy/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": "comfy/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": "comfy/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": "comfy/clip_vision_config_vitl_336.json", "content": "{\n \"attention_dropout\": 0.0,\n \"dropout\": 0.0,\n \"hidden_act\": \"quick_gelu\",\n \"hidden_size\": 1024,\n \"image_size\": 336,\n \"initializer_factor\": 1.0,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 4096,\n \"layer_norm_eps\": 1e-5,\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": "comfy/clip_vision_config_vitl_336_llava.json", "content": "{\n \"attention_dropout\": 0.0,\n \"dropout\": 0.0,\n \"hidden_act\": \"quick_gelu\",\n \"hidden_size\": 1024,\n \"image_size\": 336,\n \"initializer_factor\": 1.0,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 4096,\n \"layer_norm_eps\": 1e-5,\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 \"projector_type\": \"llava3\",\n \"torch_dtype\": \"float32\"\n}\n" }, { "path": "comfy/clip_vision_siglip2_base_naflex.json", "content": "{\n \"num_channels\": 3,\n \"hidden_act\": \"gelu_pytorch_tanh\",\n \"hidden_size\": 1152,\n \"image_size\": -1,\n \"intermediate_size\": 4304,\n \"model_type\": \"siglip2_vision_model\",\n \"num_attention_heads\": 16,\n \"num_hidden_layers\": 27,\n \"patch_size\": 16,\n \"num_patches\": 256,\n \"image_mean\": [0.5, 0.5, 0.5],\n \"image_std\": [0.5, 0.5, 0.5]\n}\n" }, { "path": "comfy/clip_vision_siglip_384.json", "content": "{\n \"num_channels\": 3,\n \"hidden_act\": \"gelu_pytorch_tanh\",\n \"hidden_size\": 1152,\n \"image_size\": 384,\n \"intermediate_size\": 4304,\n \"model_type\": \"siglip_vision_model\",\n \"num_attention_heads\": 16,\n \"num_hidden_layers\": 27,\n \"patch_size\": 14,\n \"image_mean\": [0.5, 0.5, 0.5],\n \"image_std\": [0.5, 0.5, 0.5]\n}\n" }, { "path": "comfy/clip_vision_siglip_512.json", "content": "{\n \"num_channels\": 3,\n \"hidden_act\": \"gelu_pytorch_tanh\",\n \"hidden_size\": 1152,\n \"image_size\": 512,\n \"intermediate_size\": 4304,\n \"model_type\": \"siglip_vision_model\",\n \"num_attention_heads\": 16,\n \"num_hidden_layers\": 27,\n \"patch_size\": 16,\n \"image_mean\": [0.5, 0.5, 0.5],\n \"image_std\": [0.5, 0.5, 0.5]\n}\n" }, { "path": "comfy/comfy_types/README.md", "content": "# Comfy Typing\n## Type hinting for ComfyUI Node development\n\nThis module provides type hinting and concrete convenience types for node developers.\nIf cloned to the custom_nodes directory of ComfyUI, types can be imported using:\n\n```python\nfrom comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin\n\nclass ExampleNode(ComfyNodeABC):\n @classmethod\n def INPUT_TYPES(s) -> InputTypeDict:\n return {\"required\": {}}\n```\n\nFull example is in [examples/example_nodes.py](examples/example_nodes.py).\n\n# Types\nA few primary types are documented below. More complete information is available via the docstrings on each type.\n\n## `IO`\n\nA string enum of built-in and a few custom data types. Includes the following special types and their requisite plumbing:\n\n- `ANY`: `\"*\"`\n- `NUMBER`: `\"FLOAT,INT\"`\n- `PRIMITIVE`: `\"STRING,FLOAT,INT,BOOLEAN\"`\n\n## `ComfyNodeABC`\n\nAn abstract base class for nodes, offering type-hinting / autocomplete, and somewhat-alright docstrings.\n\n### Type hinting for `INPUT_TYPES`\n\n![INPUT_TYPES auto-completion in Visual Studio Code](examples/input_types.png)\n\n### `INPUT_TYPES` return dict\n\n![INPUT_TYPES return value type hinting in Visual Studio Code](examples/required_hint.png)\n\n### Options for individual inputs\n\n![INPUT_TYPES return value option auto-completion in Visual Studio Code](examples/input_options.png)\n" }, { "path": "comfy/comfy_types/__init__.py", "content": "import torch\nfrom typing import Callable, Protocol, TypedDict, Optional, List\nfrom .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator\n\n\nclass UnetApplyFunction(Protocol):\n \"\"\"Function signature protocol on comfy.model_base.BaseModel.apply_model\"\"\"\n\n def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:\n pass\n\n\nclass UnetApplyConds(TypedDict):\n \"\"\"Optional conditions for unet apply function.\"\"\"\n\n c_concat: Optional[torch.Tensor]\n c_crossattn: Optional[torch.Tensor]\n control: Optional[torch.Tensor]\n transformer_options: Optional[dict]\n\n\nclass UnetParams(TypedDict):\n # Tensor of shape [B, C, H, W]\n input: torch.Tensor\n # Tensor of shape [B]\n timestep: torch.Tensor\n c: UnetApplyConds\n # List of [0, 1], [0], [1], ...\n # 0 means conditional, 1 means conditional unconditional\n cond_or_uncond: List[int]\n\n\nUnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]\n\n\n__all__ = [\n \"UnetWrapperFunction\",\n UnetApplyConds.__name__,\n UnetParams.__name__,\n UnetApplyFunction.__name__,\n IO.__name__,\n InputTypeDict.__name__,\n ComfyNodeABC.__name__,\n CheckLazyMixin.__name__,\n FileLocator.__name__,\n]\n" }, { "path": "comfy/comfy_types/examples/example_nodes.py", "content": "from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict\nfrom inspect import cleandoc\n\n\nclass ExampleNode(ComfyNodeABC):\n \"\"\"An example node that just adds 1 to an input integer.\n\n * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc).\n * This node is intended as an example for developers only.\n \"\"\"\n\n DESCRIPTION = cleandoc(__doc__)\n CATEGORY = \"examples\"\n\n @classmethod\n def INPUT_TYPES(s) -> InputTypeDict:\n return {\n \"required\": {\n \"input_int\": (IO.INT, {\"defaultInput\": True}),\n }\n }\n\n RETURN_TYPES = (IO.INT,)\n RETURN_NAMES = (\"input_plus_one\",)\n FUNCTION = \"execute\"\n\n def execute(self, input_int: int):\n return (input_int + 1,)\n" }, { "path": "comfy/comfy_types/node_typing.py", "content": "\"\"\"Comfy-specific type hinting\"\"\"\n\nfrom __future__ import annotations\nfrom typing import Literal, TypedDict, Optional\nfrom typing_extensions import NotRequired\nfrom abc import ABC, abstractmethod\nfrom enum import Enum\n\n\nclass StrEnum(str, Enum):\n \"\"\"Base class for string enums. Python's StrEnum is not available until 3.11.\"\"\"\n\n def __str__(self) -> str:\n return self.value\n\n\nclass IO(StrEnum):\n \"\"\"Node input/output data types.\n\n Includes functionality for ``\"*\"`` (`ANY`) and ``\"MULTI,TYPES\"``.\n \"\"\"\n\n STRING = \"STRING\"\n IMAGE = \"IMAGE\"\n MASK = \"MASK\"\n LATENT = \"LATENT\"\n BOOLEAN = \"BOOLEAN\"\n INT = \"INT\"\n FLOAT = \"FLOAT\"\n COMBO = \"COMBO\"\n CONDITIONING = \"CONDITIONING\"\n SAMPLER = \"SAMPLER\"\n SIGMAS = \"SIGMAS\"\n GUIDER = \"GUIDER\"\n NOISE = \"NOISE\"\n CLIP = \"CLIP\"\n CONTROL_NET = \"CONTROL_NET\"\n VAE = \"VAE\"\n MODEL = \"MODEL\"\n LORA_MODEL = \"LORA_MODEL\"\n LOSS_MAP = \"LOSS_MAP\"\n CLIP_VISION = \"CLIP_VISION\"\n CLIP_VISION_OUTPUT = \"CLIP_VISION_OUTPUT\"\n STYLE_MODEL = \"STYLE_MODEL\"\n GLIGEN = \"GLIGEN\"\n UPSCALE_MODEL = \"UPSCALE_MODEL\"\n AUDIO = \"AUDIO\"\n WEBCAM = \"WEBCAM\"\n POINT = \"POINT\"\n FACE_ANALYSIS = \"FACE_ANALYSIS\"\n BBOX = \"BBOX\"\n SEGS = \"SEGS\"\n VIDEO = \"VIDEO\"\n\n ANY = \"*\"\n \"\"\"Always matches any type, but at a price.\n\n Causes some functionality issues (e.g. reroutes, link types), and should be avoided whenever possible.\n \"\"\"\n NUMBER = \"FLOAT,INT\"\n \"\"\"A float or an int - could be either\"\"\"\n PRIMITIVE = \"STRING,FLOAT,INT,BOOLEAN\"\n \"\"\"Could be any of: string, float, int, or bool\"\"\"\n\n def __ne__(self, value: object) -> bool:\n if self == \"*\" or value == \"*\":\n return False\n if not isinstance(value, str):\n return True\n a = frozenset(self.split(\",\"))\n b = frozenset(value.split(\",\"))\n return not (b.issubset(a) or a.issubset(b))\n\n\nclass RemoteInputOptions(TypedDict):\n route: str\n \"\"\"The route to the remote source.\"\"\"\n refresh_button: bool\n \"\"\"Specifies whether to show a refresh button in the UI below the widget.\"\"\"\n control_after_refresh: Literal[\"first\", \"last\"]\n \"\"\"Specifies the control after the refresh button is clicked. If \"first\", the first item will be automatically selected, and so on.\"\"\"\n timeout: int\n \"\"\"The maximum amount of time to wait for a response from the remote source in milliseconds.\"\"\"\n max_retries: int\n \"\"\"The maximum number of retries before aborting the request.\"\"\"\n refresh: int\n \"\"\"The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.\"\"\"\n\n\nclass MultiSelectOptions(TypedDict):\n placeholder: NotRequired[str]\n \"\"\"The placeholder text to display in the multi-select widget when no items are selected.\"\"\"\n chip: NotRequired[bool]\n \"\"\"Specifies whether to use chips instead of comma separated values for the multi-select widget.\"\"\"\n\n\nclass InputTypeOptions(TypedDict):\n \"\"\"Provides type hinting for the return type of the INPUT_TYPES node function.\n\n Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`).\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes\n \"\"\"\n\n default: NotRequired[bool | str | float | int | list | tuple]\n \"\"\"The default value of the widget\"\"\"\n defaultInput: NotRequired[bool]\n \"\"\"@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist.\n - defaultInput on required inputs should be dropped.\n - defaultInput on optional inputs should be replaced with forceInput.\n Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364\n \"\"\"\n forceInput: NotRequired[bool]\n \"\"\"Forces the input to be an input slot rather than a widget even a widget is available for the input type.\"\"\"\n lazy: NotRequired[bool]\n \"\"\"Declares that this input uses lazy evaluation\"\"\"\n rawLink: NotRequired[bool]\n \"\"\"When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `[\"nodeId\", ]`). Designed for node expansion.\"\"\"\n tooltip: NotRequired[str]\n \"\"\"Tooltip for the input (or widget), shown on pointer hover\"\"\"\n socketless: NotRequired[bool]\n \"\"\"All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created.\n Available from frontend v1.17.5\n Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548\n \"\"\"\n widgetType: NotRequired[str]\n \"\"\"Specifies a type to be used for widget initialization if different from the input type.\n Available from frontend v1.18.0\n https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550\"\"\"\n # class InputTypeNumber(InputTypeOptions):\n # default: float | int\n min: NotRequired[float]\n \"\"\"The minimum value of a number (``FLOAT`` | ``INT``)\"\"\"\n max: NotRequired[float]\n \"\"\"The maximum value of a number (``FLOAT`` | ``INT``)\"\"\"\n step: NotRequired[float]\n \"\"\"The amount to increment or decrement a widget by when stepping up/down (``FLOAT`` | ``INT``)\"\"\"\n round: NotRequired[float]\n \"\"\"Floats are rounded by this value (``FLOAT``)\"\"\"\n # class InputTypeBoolean(InputTypeOptions):\n # default: bool\n label_on: NotRequired[str]\n \"\"\"The label to use in the UI when the bool is True (``BOOLEAN``)\"\"\"\n label_off: NotRequired[str]\n \"\"\"The label to use in the UI when the bool is False (``BOOLEAN``)\"\"\"\n # class InputTypeString(InputTypeOptions):\n # default: str\n multiline: NotRequired[bool]\n \"\"\"Use a multiline text box (``STRING``)\"\"\"\n placeholder: NotRequired[str]\n \"\"\"Placeholder text to display in the UI when empty (``STRING``)\"\"\"\n # Deprecated:\n # defaultVal: str\n dynamicPrompts: NotRequired[bool]\n \"\"\"Causes the front-end to evaluate dynamic prompts (``STRING``)\"\"\"\n # class InputTypeCombo(InputTypeOptions):\n image_upload: NotRequired[bool]\n \"\"\"Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`.\"\"\"\n image_folder: NotRequired[Literal[\"input\", \"output\", \"temp\"]]\n \"\"\"Specifies which folder to get preview images from if the input has the ``image_upload`` flag.\n \"\"\"\n remote: NotRequired[RemoteInputOptions]\n \"\"\"Specifies the configuration for a remote input.\n Available after ComfyUI frontend v1.9.7\n https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422\"\"\"\n control_after_generate: NotRequired[bool]\n \"\"\"Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types.\"\"\"\n options: NotRequired[list[str | int | float]]\n \"\"\"COMBO type only. Specifies the selectable options for the combo widget.\n Prefer:\n [\"COMBO\", {\"options\": [\"Option 1\", \"Option 2\", \"Option 3\"]}]\n Over:\n [[\"Option 1\", \"Option 2\", \"Option 3\"]]\n \"\"\"\n multi_select: NotRequired[MultiSelectOptions]\n \"\"\"COMBO type only. Specifies the configuration for a multi-select widget.\n Available after ComfyUI frontend v1.13.4\n https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987\"\"\"\n gradient_stops: NotRequired[list[dict]]\n \"\"\"Gradient color stops for gradientslider display mode. Each stop is {\"offset\": float, \"color\": [r, g, b]}.\"\"\"\n\n\nclass HiddenInputTypeDict(TypedDict):\n \"\"\"Provides type hinting for the hidden entry of node INPUT_TYPES.\"\"\"\n\n node_id: NotRequired[Literal[\"UNIQUE_ID\"]]\n \"\"\"UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).\"\"\"\n unique_id: NotRequired[Literal[\"UNIQUE_ID\"]]\n \"\"\"UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).\"\"\"\n prompt: NotRequired[Literal[\"PROMPT\"]]\n \"\"\"PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.\"\"\"\n extra_pnginfo: NotRequired[Literal[\"EXTRA_PNGINFO\"]]\n \"\"\"EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).\"\"\"\n dynprompt: NotRequired[Literal[\"DYNPROMPT\"]]\n \"\"\"DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.\"\"\"\n\n\nclass InputTypeDict(TypedDict):\n \"\"\"Provides type hinting for node INPUT_TYPES.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs\n \"\"\"\n\n required: NotRequired[dict[str, tuple[IO, InputTypeOptions]]]\n \"\"\"Describes all inputs that must be connected for the node to execute.\"\"\"\n optional: NotRequired[dict[str, tuple[IO, InputTypeOptions]]]\n \"\"\"Describes inputs which do not need to be connected.\"\"\"\n hidden: NotRequired[HiddenInputTypeDict]\n \"\"\"Offers advanced functionality and server-client communication.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs\n \"\"\"\n\n\nclass ComfyNodeABC(ABC):\n \"\"\"Abstract base class for Comfy nodes. Includes the names and expected types of attributes.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview\n \"\"\"\n\n DESCRIPTION: str\n \"\"\"Node description, shown as a tooltip when hovering over the node.\n\n Usage::\n\n # Explicitly define the description\n DESCRIPTION = \"Example description here.\"\n\n # Use the docstring of the node class.\n DESCRIPTION = cleandoc(__doc__)\n \"\"\"\n CATEGORY: str\n \"\"\"The category of the node, as per the \"Add Node\" menu.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category\n \"\"\"\n EXPERIMENTAL: bool\n \"\"\"Flags a node as experimental, informing users that it may change or not work as expected.\"\"\"\n DEPRECATED: bool\n \"\"\"Flags a node as deprecated, indicating to users that they should find alternatives to this node.\"\"\"\n DEV_ONLY: bool\n \"\"\"Flags a node as dev-only, hiding it from search/menus unless dev mode is enabled.\"\"\"\n API_NODE: Optional[bool]\n \"\"\"Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.\"\"\"\n\n @classmethod\n @abstractmethod\n def INPUT_TYPES(s) -> InputTypeDict:\n \"\"\"Defines node inputs.\n\n * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute.\n * The ``optional`` key can be added to describe inputs which do not need to be connected.\n * The ``hidden`` key offers some advanced functionality. More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types\n \"\"\"\n return {\"required\": {}}\n\n OUTPUT_NODE: bool\n \"\"\"Flags this node as an output node, causing any inputs it requires to be executed.\n\n If a node is not connected to any output nodes, that node will not be executed. Usage::\n\n OUTPUT_NODE = True\n\n From the docs:\n\n By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node\n \"\"\"\n INPUT_IS_LIST: bool\n \"\"\"A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.\n\n All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in. This also affects ``check_lazy_status``.\n\n From the docs:\n\n A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing\n \"\"\"\n OUTPUT_IS_LIST: tuple[bool, ...]\n \"\"\"A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items.\n\n Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list.\n\n A ``tuple[bool]``, where the items match those in `RETURN_TYPES`::\n\n RETURN_TYPES = (IO.INT, IO.INT, IO.STRING)\n OUTPUT_IS_LIST = (True, True, False) # The string output will be handled normally\n\n From the docs:\n\n In order to tell Comfy that the list being returned should not be wrapped, but treated as a series of data for sequential processing,\n the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`,\n specifying which outputs which should be so treated.\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing\n \"\"\"\n\n RETURN_TYPES: tuple[IO, ...]\n \"\"\"A tuple representing the outputs of this node.\n\n Usage::\n\n RETURN_TYPES = (IO.INT, \"INT\", \"CUSTOM_TYPE\")\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types\n \"\"\"\n RETURN_NAMES: tuple[str, ...]\n \"\"\"The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = (\"count\", \"filter_string\")``\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names\n \"\"\"\n OUTPUT_TOOLTIPS: tuple[str, ...]\n \"\"\"A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`.\"\"\"\n FUNCTION: str\n \"\"\"The name of the function to execute as a literal string, e.g. `FUNCTION = \"execute\"`\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function\n \"\"\"\n\n\nclass CheckLazyMixin:\n \"\"\"Provides a basic check_lazy_status implementation and type hinting for nodes that use lazy inputs.\"\"\"\n\n def check_lazy_status(self, **kwargs) -> list[str]:\n \"\"\"Returns a list of input names that should be evaluated.\n\n This basic mixin impl. requires all inputs.\n\n :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \\\n When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``.\n\n Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).\n Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).\n\n Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status\n \"\"\"\n\n need = [name for name in kwargs if kwargs[name] is None]\n return need\n\n\nclass FileLocator(TypedDict):\n \"\"\"Provides type hinting for the file location\"\"\"\n\n filename: str\n \"\"\"The filename of the file.\"\"\"\n subfolder: str\n \"\"\"The subfolder of the file.\"\"\"\n type: Literal[\"input\", \"output\", \"temp\"]\n \"\"\"The root folder of the file.\"\"\"\n" }, { "path": "comfy/conds.py", "content": "import torch\nimport math\nimport comfy.utils\nimport logging\n\n\ndef is_equal(x, y):\n if torch.is_tensor(x) and torch.is_tensor(y):\n return torch.equal(x, y)\n elif isinstance(x, dict) and isinstance(y, dict):\n if x.keys() != y.keys():\n return False\n return all(is_equal(x[k], y[k]) for k in x)\n elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):\n if type(x) is not type(y) or len(x) != len(y):\n return False\n return all(is_equal(a, b) for a, b in zip(x, y))\n else:\n try:\n return x == y\n except Exception:\n logging.warning(\"comparison issue with COND\")\n return False\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, **kwargs):\n return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size))\n\n def can_concat(self, other):\n if self.cond.shape != other.cond.shape:\n return False\n if self.cond.device != other.cond.device:\n logging.warning(\"WARNING: conds not on same device, skipping concat.\")\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\n def size(self):\n return list(self.cond.size())\n\n\nclass CONDNoiseShape(CONDRegular):\n def process_cond(self, batch_size, area, **kwargs):\n data = self.cond\n if area is not None:\n dims = len(area) // 2\n for i in range(dims):\n data = data.narrow(i + 2, area[i + dims], area[i])\n\n return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size))\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 if self.cond.device != other.cond.device:\n logging.warning(\"WARNING: conds not on same device: skipping concat.\")\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\n\nclass CONDConstant(CONDRegular):\n def __init__(self, cond):\n self.cond = cond\n\n def process_cond(self, batch_size, **kwargs):\n return self._copy_with(self.cond)\n\n def can_concat(self, other):\n if not is_equal(self.cond, other.cond):\n return False\n return True\n\n def concat(self, others):\n return self.cond\n\n def size(self):\n return [1]\n\n\nclass CONDList(CONDRegular):\n def __init__(self, cond):\n self.cond = cond\n\n def process_cond(self, batch_size, **kwargs):\n out = []\n for c in self.cond:\n out.append(comfy.utils.repeat_to_batch_size(c, batch_size))\n\n return self._copy_with(out)\n\n def can_concat(self, other):\n if len(self.cond) != len(other.cond):\n return False\n for i in range(len(self.cond)):\n if self.cond[i].shape != other.cond[i].shape:\n return False\n\n return True\n\n def concat(self, others):\n out = []\n for i in range(len(self.cond)):\n o = [self.cond[i]]\n for x in others:\n o.append(x.cond[i])\n out.append(torch.cat(o))\n\n return out\n\n def size(self): # hackish implementation to make the mem estimation work\n o = 0\n c = 1\n for c in self.cond:\n size = c.size()\n o += math.prod(size)\n if len(size) > 1:\n c = size[1]\n\n return [1, c, o // c]\n" }, { "path": "comfy/context_windows.py", "content": "from __future__ import annotations\nfrom typing import TYPE_CHECKING, Callable\nimport torch\nimport numpy as np\nimport collections\nfrom dataclasses import dataclass\nfrom abc import ABC, abstractmethod\nimport logging\nimport comfy.model_management\nimport comfy.patcher_extension\nif TYPE_CHECKING:\n from comfy.model_base import BaseModel\n from comfy.model_patcher import ModelPatcher\n from comfy.controlnet import ControlBase\n\n\nclass ContextWindowABC(ABC):\n def __init__(self):\n ...\n\n @abstractmethod\n def get_tensor(self, full: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Get torch.Tensor applicable to current window.\n \"\"\"\n raise NotImplementedError(\"Not implemented.\")\n\n @abstractmethod\n def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Apply torch.Tensor of window to the full tensor, in place. Returns reference to updated full tensor, not a copy.\n \"\"\"\n raise NotImplementedError(\"Not implemented.\")\n\nclass ContextHandlerABC(ABC):\n def __init__(self):\n ...\n\n @abstractmethod\n def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:\n raise NotImplementedError(\"Not implemented.\")\n\n @abstractmethod\n def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: ContextWindowABC, device=None) -> list:\n raise NotImplementedError(\"Not implemented.\")\n\n @abstractmethod\n def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):\n raise NotImplementedError(\"Not implemented.\")\n\n\n\nclass IndexListContextWindow(ContextWindowABC):\n def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0):\n self.index_list = index_list\n self.context_length = len(index_list)\n self.dim = dim\n self.total_frames = total_frames\n self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)\n\n def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:\n if dim is None:\n dim = self.dim\n if dim == 0 and full.shape[dim] == 1:\n return full\n idx = tuple([slice(None)] * dim + [self.index_list])\n window = full[idx]\n if retain_index_list:\n idx = tuple([slice(None)] * dim + [retain_index_list])\n window[idx] = full[idx]\n return window.to(device)\n\n def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:\n if dim is None:\n dim = self.dim\n idx = tuple([slice(None)] * dim + [self.index_list])\n full[idx] += to_add\n return full\n\n def get_region_index(self, num_regions: int) -> int:\n region_idx = int(self.center_ratio * num_regions)\n return min(max(region_idx, 0), num_regions - 1)\n\n\nclass IndexListCallbacks:\n EVALUATE_CONTEXT_WINDOWS = \"evaluate_context_windows\"\n COMBINE_CONTEXT_WINDOW_RESULTS = \"combine_context_window_results\"\n EXECUTE_START = \"execute_start\"\n EXECUTE_CLEANUP = \"execute_cleanup\"\n RESIZE_COND_ITEM = \"resize_cond_item\"\n\n def init_callbacks(self):\n return {}\n\n\ndef slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, device, temporal_dim: int, temporal_scale: int=1, temporal_offset: int=0, retain_index_list: list[int]=[]):\n if not (hasattr(cond_value, \"cond\") and isinstance(cond_value.cond, torch.Tensor)):\n return None\n cond_tensor = cond_value.cond\n if temporal_dim >= cond_tensor.ndim:\n return None\n\n cond_size = cond_tensor.size(temporal_dim)\n\n if temporal_scale == 1:\n expected_size = x_in.size(window.dim) - temporal_offset\n if cond_size != expected_size:\n return None\n\n if temporal_offset == 0 and temporal_scale == 1:\n sliced = window.get_tensor(cond_tensor, device, dim=temporal_dim, retain_index_list=retain_index_list)\n return cond_value._copy_with(sliced)\n\n # skip leading latent positions that have no corresponding conditioning (e.g. reference frames)\n if temporal_offset > 0:\n indices = [i - temporal_offset for i in window.index_list[temporal_offset:]]\n indices = [i for i in indices if 0 <= i]\n else:\n indices = list(window.index_list)\n\n if not indices:\n return None\n\n if temporal_scale > 1:\n scaled = []\n for i in indices:\n for k in range(temporal_scale):\n si = i * temporal_scale + k\n if si < cond_size:\n scaled.append(si)\n indices = scaled\n if not indices:\n return None\n\n idx = tuple([slice(None)] * temporal_dim + [indices])\n sliced = cond_tensor[idx].to(device)\n return cond_value._copy_with(sliced)\n\n\n@dataclass\nclass ContextSchedule:\n name: str\n func: Callable\n\n@dataclass\nclass ContextFuseMethod:\n name: str\n func: Callable\n\nContextResults = collections.namedtuple(\"ContextResults\", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])\nclass IndexListContextHandler(ContextHandlerABC):\n def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,\n closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):\n self.context_schedule = context_schedule\n self.fuse_method = fuse_method\n self.context_length = context_length\n self.context_overlap = context_overlap\n self.context_stride = context_stride\n self.closed_loop = closed_loop\n self.dim = dim\n self._step = 0\n self.freenoise = freenoise\n self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(\",\")] if cond_retain_index_list else []\n self.split_conds_to_windows = split_conds_to_windows\n\n self.callbacks = {}\n\n def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:\n # for now, assume first dim is batch - should have stored on BaseModel in actual implementation\n if x_in.size(self.dim) > self.context_length:\n logging.info(f\"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.\")\n if self.cond_retain_index_list:\n logging.info(f\"Retaining original cond for indexes: {self.cond_retain_index_list}\")\n return True\n return False\n\n def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase:\n if control.previous_controlnet is not None:\n self.prepare_control_objects(control.previous_controlnet, device)\n return control\n\n def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list:\n if cond_in is None:\n return None\n # reuse or resize cond items to match context requirements\n resized_cond = []\n # if multiple conds, split based on primary region\n if self.split_conds_to_windows and len(cond_in) > 1:\n region = window.get_region_index(len(cond_in))\n logging.info(f\"Splitting conds to windows; using region {region} for window {window.index_list[0]}-{window.index_list[-1]} with center ratio {window.center_ratio:.3f}\")\n cond_in = [cond_in[region]]\n # cond object is a list containing a dict - outer list is irrelevant, so just loop through it\n for actual_cond in cond_in:\n resized_actual_cond = actual_cond.copy()\n # now we are in the inner dict - \"pooled_output\" is a tensor, \"control\" is a ControlBase object, \"model_conds\" is dictionary\n for key in actual_cond:\n try:\n cond_item = actual_cond[key]\n if isinstance(cond_item, torch.Tensor):\n # check that tensor is the expected length - x.size(0)\n if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim):\n # if so, it's subsetting time - tell controls the expected indeces so they can handle them\n actual_cond_item = window.get_tensor(cond_item)\n resized_actual_cond[key] = actual_cond_item.to(device)\n else:\n resized_actual_cond[key] = cond_item.to(device)\n # look for control\n elif key == \"control\":\n resized_actual_cond[key] = self.prepare_control_objects(cond_item, device)\n elif isinstance(cond_item, dict):\n new_cond_item = cond_item.copy()\n # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)\n for cond_key, cond_value in new_cond_item.items():\n # Allow callbacks to handle custom conditioning items\n handled = False\n for callback in comfy.patcher_extension.get_all_callbacks(\n IndexListCallbacks.RESIZE_COND_ITEM, self.callbacks\n ):\n result = callback(cond_key, cond_value, window, x_in, device, new_cond_item)\n if result is not None:\n new_cond_item[cond_key] = result\n handled = True\n break\n if not handled and self._model is not None:\n result = self._model.resize_cond_for_context_window(\n cond_key, cond_value, window, x_in, device,\n retain_index_list=self.cond_retain_index_list)\n if result is not None:\n new_cond_item[cond_key] = result\n handled = True\n if handled:\n continue\n if isinstance(cond_value, torch.Tensor):\n if (self.dim < cond_value.ndim and cond_value.size(self.dim) == x_in.size(self.dim)) or \\\n (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):\n new_cond_item[cond_key] = window.get_tensor(cond_value, device)\n # Handle audio_embed (temporal dim is 1)\n elif cond_key == \"audio_embed\" and hasattr(cond_value, \"cond\") and isinstance(cond_value.cond, torch.Tensor):\n audio_cond = cond_value.cond\n if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):\n new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))\n # Handle vace_context (temporal dim is 3)\n elif cond_key == \"vace_context\" and hasattr(cond_value, \"cond\") and isinstance(cond_value.cond, torch.Tensor):\n vace_cond = cond_value.cond\n if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):\n sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)\n new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)\n # if has cond that is a Tensor, check if needs to be subset\n elif hasattr(cond_value, \"cond\") and isinstance(cond_value.cond, torch.Tensor):\n if (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \\\n (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)):\n new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list))\n elif cond_key == \"num_video_frames\": # for SVD\n new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)\n new_cond_item[cond_key].cond = window.context_length\n resized_actual_cond[key] = new_cond_item\n else:\n resized_actual_cond[key] = cond_item\n finally:\n del cond_item # just in case to prevent VRAM issues\n resized_cond.append(resized_actual_cond)\n return resized_cond\n\n def set_step(self, timestep: torch.Tensor, model_options: dict[str]):\n mask = torch.isclose(model_options[\"transformer_options\"][\"sample_sigmas\"], timestep[0], rtol=0.0001)\n matches = torch.nonzero(mask)\n if torch.numel(matches) == 0:\n return # substep from multi-step sampler: keep self._step from the last full step\n self._step = int(matches[0].item())\n\n def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:\n full_length = x_in.size(self.dim) # TODO: choose dim based on model\n context_windows = self.context_schedule.func(full_length, self, model_options)\n context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows]\n return context_windows\n\n def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):\n self._model = model\n self.set_step(timestep, model_options)\n context_windows = self.get_context_windows(model, x_in, model_options)\n enumerated_context_windows = list(enumerate(context_windows))\n\n conds_final = [torch.zeros_like(x_in) for _ in conds]\n if self.fuse_method.name == ContextFuseMethods.RELATIVE:\n counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]\n else:\n counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]\n biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds]\n\n for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks):\n callback(self, model, x_in, conds, timestep, model_options)\n\n for enum_window in enumerated_context_windows:\n results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options)\n for result in results:\n self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep,\n conds_final, counts_final, biases_final)\n try:\n # finalize conds\n if self.fuse_method.name == ContextFuseMethods.RELATIVE:\n # relative is already normalized, so return as is\n del counts_final\n return conds_final\n else:\n # normalize conds via division by context usage counts\n for i in range(len(conds_final)):\n conds_final[i] /= counts_final[i]\n del counts_final\n return conds_final\n finally:\n for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks):\n callback(self, model, x_in, conds, timestep, model_options)\n\n def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]],\n model_options, device=None, first_device=None):\n results: list[ContextResults] = []\n for window_idx, window in enumerated_context_windows:\n # allow processing to end between context window executions for faster Cancel\n comfy.model_management.throw_exception_if_processing_interrupted()\n\n for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):\n callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)\n\n # update exposed params\n model_options[\"transformer_options\"][\"context_window\"] = window\n # get subsections of x, timestep, conds\n sub_x = window.get_tensor(x_in, device)\n sub_timestep = window.get_tensor(timestep, device, dim=0)\n sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds]\n\n sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options)\n if device is not None:\n for i in range(len(sub_conds_out)):\n sub_conds_out[i] = sub_conds_out[i].to(x_in.device)\n results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))\n return results\n\n\n def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor,\n conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]):\n if self.fuse_method.name == ContextFuseMethods.RELATIVE:\n for pos, idx in enumerate(window.index_list):\n # bias is the influence of a specific index in relation to the whole context window\n bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2)\n bias = max(1e-2, bias)\n # take weighted average relative to total bias of current idx\n for i in range(len(sub_conds_out)):\n bias_total = biases_final[i][idx]\n prev_weight = (bias_total / (bias_total + bias))\n new_weight = (bias / (bias_total + bias))\n # account for dims of tensors\n idx_window = tuple([slice(None)] * self.dim + [idx])\n pos_window = tuple([slice(None)] * self.dim + [pos])\n # apply new values\n conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight\n biases_final[i][idx] = bias_total + bias\n else:\n # add conds and counts based on weights of fuse method\n weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep)\n weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device)\n for i in range(len(sub_conds_out)):\n window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor)\n window.add_window(counts_final[i], weights_tensor)\n\n for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks):\n callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final)\n\n\ndef _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs):\n # limit noise_shape length to context_length for more accurate vram use estimation\n model_options = kwargs.get(\"model_options\", None)\n if model_options is None:\n raise Exception(\"model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.\")\n handler: IndexListContextHandler = model_options.get(\"context_handler\", None)\n if handler is not None:\n noise_shape = list(noise_shape)\n noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)\n return executor(model, noise_shape, *args, **kwargs)\n\n\ndef create_prepare_sampling_wrapper(model: ModelPatcher):\n model.add_wrapper_with_key(\n comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING,\n \"ContextWindows_prepare_sampling\",\n _prepare_sampling_wrapper\n )\n\n\ndef _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs):\n model_options = extra_args.get(\"model_options\", None)\n if model_options is None:\n raise Exception(\"model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.\")\n handler: IndexListContextHandler = model_options.get(\"context_handler\", None)\n if handler is None:\n raise Exception(\"context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.\")\n if not handler.freenoise:\n return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)\n noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args[\"seed\"])\n\n return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)\n\n\ndef create_sampler_sample_wrapper(model: ModelPatcher):\n model.add_wrapper_with_key(\n comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,\n \"ContextWindows_sampler_sample\",\n _sampler_sample_wrapper\n )\n\n\ndef match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:\n total_dims = len(x_in.shape)\n weights_tensor = torch.Tensor(weights).to(device=device)\n for _ in range(dim):\n weights_tensor = weights_tensor.unsqueeze(0)\n for _ in range(total_dims - dim - 1):\n weights_tensor = weights_tensor.unsqueeze(-1)\n return weights_tensor\n\ndef get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]:\n total_dims = len(x_in.shape)\n shape = []\n for _ in range(dim):\n shape.append(1)\n shape.append(x_in.shape[dim])\n for _ in range(total_dims - dim - 1):\n shape.append(1)\n return shape\n\nclass ContextSchedules:\n UNIFORM_LOOPED = \"looped_uniform\"\n UNIFORM_STANDARD = \"standard_uniform\"\n STATIC_STANDARD = \"standard_static\"\n BATCHED = \"batched\"\n\n\n# from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py\ndef create_windows_uniform_looped(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):\n windows = []\n if num_frames < handler.context_length:\n windows.append(list(range(num_frames)))\n return windows\n\n context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1)\n # obtain uniform windows as normal, looping and all\n for context_step in 1 << np.arange(context_stride):\n pad = int(round(num_frames * ordered_halving(handler._step)))\n for j in range(\n int(ordered_halving(handler._step) * context_step) + pad,\n num_frames + pad + (0 if handler.closed_loop else -handler.context_overlap),\n (handler.context_length * context_step - handler.context_overlap),\n ):\n windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)])\n\n return windows\n\ndef create_windows_uniform_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):\n # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning;\n # instead, they get shifted to the corresponding end of the frames.\n # in the case that a window (shifted or not) is identical to the previous one, it gets skipped.\n windows = []\n if num_frames <= handler.context_length:\n windows.append(list(range(num_frames)))\n return windows\n\n context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1)\n # first, obtain uniform windows as normal, looping and all\n for context_step in 1 << np.arange(context_stride):\n pad = int(round(num_frames * ordered_halving(handler._step)))\n for j in range(\n int(ordered_halving(handler._step) * context_step) + pad,\n num_frames + pad + (-handler.context_overlap),\n (handler.context_length * context_step - handler.context_overlap),\n ):\n windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)])\n\n # now that windows are created, shift any windows that loop, and delete duplicate windows\n delete_idxs = []\n win_i = 0\n while win_i < len(windows):\n # if window is rolls over itself, need to shift it\n is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames)\n if is_roll:\n roll_val = windows[win_i][roll_idx] # roll_val might not be 0 for windows of higher strides\n shift_window_to_end(windows[win_i], num_frames=num_frames)\n # check if next window (cyclical) is missing roll_val\n if roll_val not in windows[(win_i+1) % len(windows)]:\n # need to insert new window here - just insert window starting at roll_val\n windows.insert(win_i+1, list(range(roll_val, roll_val + handler.context_length)))\n # delete window if it's not unique\n for pre_i in range(0, win_i):\n if windows[win_i] == windows[pre_i]:\n delete_idxs.append(win_i)\n break\n win_i += 1\n\n # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation\n delete_idxs.reverse()\n for i in delete_idxs:\n windows.pop(i)\n\n return windows\n\n\ndef create_windows_static_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):\n windows = []\n if num_frames <= handler.context_length:\n windows.append(list(range(num_frames)))\n return windows\n # always return the same set of windows\n delta = handler.context_length - handler.context_overlap\n for start_idx in range(0, num_frames, delta):\n # if past the end of frames, move start_idx back to allow same context_length\n ending = start_idx + handler.context_length\n if ending >= num_frames:\n final_delta = ending - num_frames\n final_start_idx = start_idx - final_delta\n windows.append(list(range(final_start_idx, final_start_idx + handler.context_length)))\n break\n windows.append(list(range(start_idx, start_idx + handler.context_length)))\n return windows\n\n\ndef create_windows_batched(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):\n windows = []\n if num_frames <= handler.context_length:\n windows.append(list(range(num_frames)))\n return windows\n # always return the same set of windows;\n # no overlap, just cut up based on context_length;\n # last window size will be different if num_frames % opts.context_length != 0\n for start_idx in range(0, num_frames, handler.context_length):\n windows.append(list(range(start_idx, min(start_idx + handler.context_length, num_frames))))\n return windows\n\n\ndef create_windows_default(num_frames: int, handler: IndexListContextHandler):\n return [list(range(num_frames))]\n\n\nCONTEXT_MAPPING = {\n ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped,\n ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard,\n ContextSchedules.STATIC_STANDARD: create_windows_static_standard,\n ContextSchedules.BATCHED: create_windows_batched,\n}\n\n\ndef get_matching_context_schedule(context_schedule: str) -> ContextSchedule:\n func = CONTEXT_MAPPING.get(context_schedule, None)\n if func is None:\n raise ValueError(f\"Unknown context_schedule '{context_schedule}'.\")\n return ContextSchedule(context_schedule, func)\n\n\ndef get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None):\n return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs)\n\n\ndef create_weights_flat(length: int, **kwargs) -> list[float]:\n # weight is the same for all\n return [1.0] * length\n\ndef create_weights_pyramid(length: int, **kwargs) -> list[float]:\n # weight is based on the distance away from the edge of the context window;\n # based on weighted average concept in FreeNoise paper\n if length % 2 == 0:\n max_weight = length // 2\n weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1))\n else:\n max_weight = (length + 1) // 2\n weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))\n return weight_sequence\n\ndef create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs):\n # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302\n # only expected overlap is given different weights\n weights_torch = torch.ones((length))\n # blend left-side on all except first window\n if min(idxs) > 0:\n ramp_up = torch.linspace(1e-37, 1, handler.context_overlap)\n weights_torch[:handler.context_overlap] = ramp_up\n # blend right-side on all except last window\n if max(idxs) < full_length-1:\n ramp_down = torch.linspace(1, 1e-37, handler.context_overlap)\n weights_torch[-handler.context_overlap:] = ramp_down\n return weights_torch\n\nclass ContextFuseMethods:\n FLAT = \"flat\"\n PYRAMID = \"pyramid\"\n RELATIVE = \"relative\"\n OVERLAP_LINEAR = \"overlap-linear\"\n\n LIST = [PYRAMID, FLAT, OVERLAP_LINEAR]\n LIST_STATIC = [PYRAMID, RELATIVE, FLAT, OVERLAP_LINEAR]\n\n\nFUSE_MAPPING = {\n ContextFuseMethods.FLAT: create_weights_flat,\n ContextFuseMethods.PYRAMID: create_weights_pyramid,\n ContextFuseMethods.RELATIVE: create_weights_pyramid,\n ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear,\n}\n\ndef get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod:\n func = FUSE_MAPPING.get(fuse_method, None)\n if func is None:\n raise ValueError(f\"Unknown fuse_method '{fuse_method}'.\")\n return ContextFuseMethod(fuse_method, func)\n\n# Returns fraction that has denominator that is a power of 2\ndef ordered_halving(val):\n # get binary value, padded with 0s for 64 bits\n bin_str = f\"{val:064b}\"\n # flip binary value, padding included\n bin_flip = bin_str[::-1]\n # convert binary to int\n as_int = int(bin_flip, 2)\n # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616,\n # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's)\n return as_int / (1 << 64)\n\n\ndef get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]:\n all_indexes = list(range(num_frames))\n for w in windows:\n for val in w:\n try:\n all_indexes.remove(val)\n except ValueError:\n pass\n return all_indexes\n\n\ndef does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]:\n prev_val = -1\n for i, val in enumerate(window):\n val = val % num_frames\n if val < prev_val:\n return True, i\n prev_val = val\n return False, -1\n\n\ndef shift_window_to_start(window: list[int], num_frames: int):\n start_val = window[0]\n for i in range(len(window)):\n # 1) subtract each element by start_val to move vals relative to the start of all frames\n # 2) add num_frames and take modulus to get adjusted vals\n window[i] = ((window[i] - start_val) + num_frames) % num_frames\n\n\ndef shift_window_to_end(window: list[int], num_frames: int):\n # 1) shift window to start\n shift_window_to_start(window, num_frames)\n end_val = window[-1]\n end_delta = num_frames - end_val - 1\n for i in range(len(window)):\n # 2) add end_delta to each val to slide windows to end\n window[i] = window[i] + end_delta\n\n\n# https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465\ndef apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int):\n logging.info(\"Context windows: Applying FreeNoise\")\n generator = torch.Generator(device='cpu').manual_seed(seed)\n latent_video_length = noise.shape[dim]\n delta = context_length - context_overlap\n\n for start_idx in range(0, latent_video_length - context_length, delta):\n place_idx = start_idx + context_length\n\n actual_delta = min(delta, latent_video_length - place_idx)\n if actual_delta <= 0:\n break\n\n list_idx = torch.randperm(actual_delta, generator=generator, device='cpu') + start_idx\n\n source_slice = [slice(None)] * noise.ndim\n source_slice[dim] = list_idx\n target_slice = [slice(None)] * noise.ndim\n target_slice[dim] = slice(place_idx, place_idx + actual_delta)\n\n noise[tuple(target_slice)] = noise[tuple(source_slice)]\n\n return noise\n" }, { "path": "comfy/controlnet.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\n\nimport torch\nfrom enum import Enum\nimport math\nimport os\nimport logging\nimport comfy.utils\nimport comfy.model_management\nimport comfy.model_detection\nimport comfy.model_patcher\nimport comfy.ops\nimport comfy.latent_formats\nimport comfy.model_base\n\nimport comfy.cldm.cldm\nimport comfy.t2i_adapter.adapter\nimport comfy.ldm.cascade.controlnet\nimport comfy.cldm.mmdit\nimport comfy.ldm.hydit.controlnet\nimport comfy.ldm.flux.controlnet\nimport comfy.ldm.qwen_image.controlnet\nimport comfy.cldm.dit_embedder\nfrom typing import TYPE_CHECKING\nif TYPE_CHECKING:\n from comfy.hooks import HookGroup\n\n\ndef broadcast_image_to(tensor, target_batch_size, batched_number):\n current_batch_size = tensor.shape[0]\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 StrengthType(Enum):\n CONSTANT = 1\n LINEAR_UP = 2\n\nclass ControlBase:\n def __init__(self):\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.latent_format = None\n self.vae = None\n self.global_average_pooling = False\n self.timestep_range = None\n self.compression_ratio = 8\n self.upscale_algorithm = 'nearest-exact'\n self.extra_args = {}\n self.previous_controlnet = None\n self.extra_conds = []\n self.strength_type = StrengthType.CONSTANT\n self.concat_mask = False\n self.extra_concat_orig = []\n self.extra_concat = None\n self.extra_hooks: HookGroup = None\n self.preprocess_image = lambda a: a\n\n def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):\n self.cond_hint_original = cond_hint\n self.strength = strength\n self.timestep_percent_range = timestep_percent_range\n if self.latent_format is not None:\n if vae is None:\n logging.warning(\"WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.\")\n self.vae = vae\n self.extra_concat_orig = extra_concat.copy()\n if self.concat_mask and len(self.extra_concat_orig) == 0:\n self.extra_concat_orig.append(torch.tensor([[[[1.0]]]]))\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\n self.cond_hint = None\n self.extra_concat = 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 get_extra_hooks(self):\n out = []\n if self.extra_hooks is not None:\n out.append(self.extra_hooks)\n if self.previous_controlnet is not None:\n out += self.previous_controlnet.get_extra_hooks()\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 c.compression_ratio = self.compression_ratio\n c.upscale_algorithm = self.upscale_algorithm\n c.latent_format = self.latent_format\n c.extra_args = self.extra_args.copy()\n c.vae = self.vae\n c.extra_conds = self.extra_conds.copy()\n c.strength_type = self.strength_type\n c.concat_mask = self.concat_mask\n c.extra_concat_orig = self.extra_concat_orig.copy()\n c.extra_hooks = self.extra_hooks.clone() if self.extra_hooks else None\n c.preprocess_image = self.preprocess_image\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, control_prev, output_dtype):\n out = {'input':[], 'middle':[], 'output': []}\n\n for key in control:\n control_output = control[key]\n applied_to = set()\n for i in range(len(control_output)):\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 if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once\n applied_to.add(x)\n if self.strength_type == StrengthType.CONSTANT:\n x *= self.strength\n elif self.strength_type == StrengthType.LINEAR_UP:\n x *= (self.strength ** float(len(control_output) - i))\n\n if output_dtype is not None and x.dtype != output_dtype:\n x = x.to(output_dtype)\n\n out[key].append(x)\n\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 + o[i] #TODO: change back to inplace add if shared tensors stop being an issue\n return out\n\n def set_extra_arg(self, argument, value=None):\n self.extra_args[argument] = value\n\n\nclass ControlNet(ControlBase):\n def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=[\"y\"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a):\n super().__init__()\n self.control_model = control_model\n self.load_device = load_device\n if control_model is not None:\n self.control_model_wrapped = comfy.model_patcher.CoreModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())\n\n self.compression_ratio = compression_ratio\n self.global_average_pooling = global_average_pooling\n self.model_sampling_current = None\n self.manual_cast_dtype = manual_cast_dtype\n self.latent_format = latent_format\n self.extra_conds += extra_conds\n self.strength_type = strength_type\n self.concat_mask = concat_mask\n self.preprocess_image = preprocess_image\n\n def get_control(self, x_noisy, t, cond, batched_number, transformer_options):\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, transformer_options)\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 if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:\n if self.cond_hint is not None:\n del self.cond_hint\n self.cond_hint = None\n compression_ratio = self.compression_ratio\n if self.vae is not None:\n compression_ratio *= self.vae.spacial_compression_encode()\n else:\n if self.latent_format is not None:\n raise ValueError(\"This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.\")\n self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, \"center\")\n self.cond_hint = self.preprocess_image(self.cond_hint)\n if self.vae is not None:\n loaded_models = comfy.model_management.loaded_models(only_currently_used=True)\n self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))\n comfy.model_management.load_models_gpu(loaded_models)\n if self.latent_format is not None:\n self.cond_hint = self.latent_format.process_in(self.cond_hint)\n if len(self.extra_concat_orig) > 0:\n to_concat = []\n for c in self.extra_concat_orig:\n c = c.to(self.cond_hint.device)\n c = comfy.utils.common_upscale(c, self.cond_hint.shape[-1], self.cond_hint.shape[-2], self.upscale_algorithm, \"center\")\n if c.ndim < self.cond_hint.ndim:\n c = c.unsqueeze(2)\n c = comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[2], dim=2)\n to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))\n self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)\n\n self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype)\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.get('crossattn_controlnet', cond['c_crossattn'])\n extra = self.extra_args.copy()\n for c in self.extra_conds:\n temp = cond.get(c, None)\n if temp is not None:\n extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device)\n\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.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra)\n return self.control_merge(control, control_prev, output_dtype=None)\n\n def copy(self):\n c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)\n c.control_model = self.control_model\n c.control_model_wrapped = self.control_model_wrapped\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\n\nclass QwenFunControlNet(ControlNet):\n def get_control(self, x_noisy, t, cond, batched_number, transformer_options):\n # Fun checkpoints are more sensitive to high strengths in the generic\n # ControlNet merge path. Use a soft response curve so strength=1.0 stays\n # unchanged while >1 grows more gently.\n original_strength = self.strength\n self.strength = math.sqrt(max(self.strength, 0.0))\n try:\n return super().get_control(x_noisy, t, cond, batched_number, transformer_options)\n finally:\n self.strength = original_strength\n\n def pre_run(self, model, percent_to_timestep_function):\n super().pre_run(model, percent_to_timestep_function)\n self.set_extra_arg(\"base_model\", model.diffusion_model)\n\n def copy(self):\n c = QwenFunControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)\n c.control_model = self.control_model\n c.control_model_wrapped = self.control_model_wrapped\n self.copy_to(c)\n return c\n\nclass ControlLoraOps:\n class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):\n def __init__(self, in_features: int, out_features: int, bias: bool = True,\n device=None, dtype=None) -> None:\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, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)\n if self.up is not None:\n x = 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 x = torch.nn.functional.linear(input, weight, bias)\n comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):\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, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)\n if self.up is not None:\n x = 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 x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)\n comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\nclass ControlLora(ControlNet):\n def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options\n ControlBase.__init__(self)\n self.control_weights = control_weights\n self.global_average_pooling = global_average_pooling\n self.extra_conds += [\"y\"]\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, comfy.ops.disable_weight_init):\n pass\n else:\n class control_lora_ops(ControlLoraOps, comfy.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 = comfy.cldm.cldm.ControlNet(**controlnet_config)\n self.control_model.to(comfy.model_management.get_torch_device())\n diffusion_model = model.diffusion_model\n sd = diffusion_model.state_dict()\n\n for k in sd:\n weight = sd[k]\n try:\n comfy.utils.set_attr_param(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 if (k.endswith(\".up\") or k.endswith(\".down\") or k.endswith(\".weight\") or k.endswith(\".bias\")) and (\"__\" not in k):\n comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.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 comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)\n\ndef controlnet_config(sd, model_options={}):\n model_config = comfy.model_detection.model_config_from_unet(sd, \"\", True)\n\n unet_dtype = model_options.get(\"dtype\", None)\n if unet_dtype is None:\n weight_dtype = comfy.utils.weight_dtype(sd)\n\n supported_inference_dtypes = list(model_config.supported_inference_dtypes)\n unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype)\n\n load_device = comfy.model_management.get_torch_device()\n manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)\n\n operations = model_options.get(\"custom_operations\", None)\n if operations is None:\n operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)\n\n offload_device = comfy.model_management.unet_offload_device()\n return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device\n\ndef controlnet_load_state_dict(control_model, sd):\n missing, unexpected = control_model.load_state_dict(sd, strict=False)\n\n if len(missing) > 0:\n logging.warning(\"missing controlnet keys: {}\".format(missing))\n\n if len(unexpected) > 0:\n logging.debug(\"unexpected controlnet keys: {}\".format(unexpected))\n return control_model\n\n\ndef load_controlnet_mmdit(sd, model_options={}):\n new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, \"\")\n model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)\n num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')\n for k in sd:\n new_sd[k] = sd[k]\n\n concat_mask = False\n control_latent_channels = new_sd.get(\"pos_embed_input.proj.weight\").shape[1]\n if control_latent_channels == 17: #inpaint controlnet\n concat_mask = True\n\n control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)\n control_model = controlnet_load_state_dict(control_model, new_sd)\n\n latent_format = comfy.latent_formats.SD3()\n latent_format.shift_factor = 0 #SD3 controlnet weirdness\n control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype)\n return control\n\n\nclass ControlNetSD35(ControlNet):\n def pre_run(self, model, percent_to_timestep_function):\n if self.control_model.double_y_emb:\n missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False)\n else:\n missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False)\n super().pre_run(model, percent_to_timestep_function)\n\n def copy(self):\n c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)\n c.control_model = self.control_model\n c.control_model_wrapped = self.control_model_wrapped\n self.copy_to(c)\n return c\n\ndef load_controlnet_sd35(sd, model_options={}):\n control_type = -1\n if \"control_type\" in sd:\n control_type = round(sd.pop(\"control_type\").item())\n\n # blur_cnet = control_type == 0\n canny_cnet = control_type == 1\n depth_cnet = control_type == 2\n\n new_sd = {}\n for k in comfy.utils.MMDIT_MAP_BASIC:\n if k[1] in sd:\n new_sd[k[0]] = sd.pop(k[1])\n for k in sd:\n new_sd[k] = sd[k]\n sd = new_sd\n\n y_emb_shape = sd[\"y_embedder.mlp.0.weight\"].shape\n depth = y_emb_shape[0] // 64\n hidden_size = 64 * depth\n num_heads = depth\n head_dim = hidden_size // num_heads\n num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.')\n\n load_device = comfy.model_management.get_torch_device()\n offload_device = comfy.model_management.unet_offload_device()\n unet_dtype = comfy.model_management.unet_dtype(model_params=-1)\n\n manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)\n\n operations = model_options.get(\"custom_operations\", None)\n if operations is None:\n operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)\n\n control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None,\n patch_size=2,\n in_chans=16,\n num_layers=num_blocks,\n main_model_double=depth,\n double_y_emb=y_emb_shape[0] == y_emb_shape[1],\n attention_head_dim=head_dim,\n num_attention_heads=num_heads,\n adm_in_channels=2048,\n device=offload_device,\n dtype=unet_dtype,\n operations=operations)\n\n control_model = controlnet_load_state_dict(control_model, sd)\n\n latent_format = comfy.latent_formats.SD3()\n preprocess_image = lambda a: a\n if canny_cnet:\n preprocess_image = lambda a: (a * 255 * 0.5 + 0.5)\n elif depth_cnet:\n preprocess_image = lambda a: 1.0 - a\n\n control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image)\n return control\n\n\n\ndef load_controlnet_hunyuandit(controlnet_data, model_options={}):\n model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)\n\n control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)\n control_model = controlnet_load_state_dict(control_model, controlnet_data)\n\n latent_format = comfy.latent_formats.SDXL()\n extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img']\n control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)\n return control\n\ndef load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):\n model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)\n control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)\n sd = model_config.process_unet_state_dict(sd)\n control_model = controlnet_load_state_dict(control_model, sd)\n extra_conds = ['y', 'guidance']\n control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)\n return control\n\ndef load_controlnet_flux_instantx(sd, model_options={}):\n new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, \"\")\n model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)\n for k in sd:\n new_sd[k] = sd[k]\n\n num_union_modes = 0\n union_cnet = \"controlnet_mode_embedder.weight\"\n if union_cnet in new_sd:\n num_union_modes = new_sd[union_cnet].shape[0]\n\n control_latent_channels = new_sd.get(\"pos_embed_input.weight\").shape[1] // 4\n concat_mask = False\n if control_latent_channels == 17:\n concat_mask = True\n\n control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)\n control_model = controlnet_load_state_dict(control_model, new_sd)\n\n latent_format = comfy.latent_formats.Flux()\n extra_conds = ['y', 'guidance']\n control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)\n return control\n\ndef load_controlnet_qwen_instantx(sd, model_options={}):\n model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)\n control_latent_channels = sd.get(\"controlnet_x_embedder.weight\").shape[1]\n\n extra_condition_channels = 0\n concat_mask = False\n if control_latent_channels == 68: #inpaint controlnet\n extra_condition_channels = control_latent_channels - 64\n concat_mask = True\n control_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(extra_condition_channels=extra_condition_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)\n control_model = controlnet_load_state_dict(control_model, sd)\n latent_format = comfy.latent_formats.Wan21()\n extra_conds = []\n control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)\n return control\n\n\ndef load_controlnet_qwen_fun(sd, model_options={}):\n load_device = comfy.model_management.get_torch_device()\n weight_dtype = comfy.utils.weight_dtype(sd)\n unet_dtype = model_options.get(\"dtype\", weight_dtype)\n manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)\n\n operations = model_options.get(\"custom_operations\", None)\n if operations is None:\n operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)\n\n in_features = sd[\"control_img_in.weight\"].shape[1]\n inner_dim = sd[\"control_img_in.weight\"].shape[0]\n\n block_weight = sd[\"control_blocks.0.attn.to_q.weight\"]\n attention_head_dim = sd[\"control_blocks.0.attn.norm_q.weight\"].shape[0]\n num_attention_heads = max(1, block_weight.shape[0] // max(1, attention_head_dim))\n\n model = comfy.ldm.qwen_image.controlnet.QwenImageFunControlNetModel(\n control_in_features=in_features,\n inner_dim=inner_dim,\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n num_control_blocks=5,\n main_model_double=60,\n injection_layers=(0, 12, 24, 36, 48),\n operations=operations,\n device=comfy.model_management.unet_offload_device(),\n dtype=unet_dtype,\n )\n model = controlnet_load_state_dict(model, sd)\n\n latent_format = comfy.latent_formats.Wan21()\n control = QwenFunControlNet(\n model,\n compression_ratio=1,\n latent_format=latent_format,\n # Fun checkpoints already expect their own 33-channel context handling.\n # Enabling generic concat_mask injects an extra mask channel at apply-time\n # and breaks the intended fallback packing path.\n concat_mask=False,\n load_device=load_device,\n manual_cast_dtype=manual_cast_dtype,\n extra_conds=[],\n )\n return control\n\ndef convert_mistoline(sd):\n return comfy.utils.state_dict_prefix_replace(sd, {\"single_controlnet_blocks.\": \"controlnet_single_blocks.\"})\n\n\ndef load_controlnet_state_dict(state_dict, model=None, model_options={}):\n controlnet_data = state_dict\n if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT\n return load_controlnet_hunyuandit(controlnet_data, model_options=model_options)\n\n if \"lora_controlnet\" in controlnet_data:\n return ControlLora(controlnet_data, model_options=model_options)\n\n controlnet_config = None\n supported_inference_dtypes = None\n\n if \"controlnet_cond_embedding.conv_in.weight\" in controlnet_data: #diffusers format\n controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data)\n diffusers_keys = comfy.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 if \"control_add_embedding.linear_1.bias\" in controlnet_data: #Union Controlnet\n controlnet_config[\"union_controlnet_num_control_type\"] = controlnet_data[\"task_embedding\"].shape[0]\n for k in list(controlnet_data.keys()):\n new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')\n new_sd[new_k] = controlnet_data.pop(k)\n\n leftover_keys = controlnet_data.keys()\n if len(leftover_keys) > 0:\n logging.warning(\"leftover keys: {}\".format(leftover_keys))\n controlnet_data = new_sd\n elif \"controlnet_blocks.0.weight\" in controlnet_data:\n if \"double_blocks.0.img_attn.norm.key_norm.scale\" in controlnet_data:\n return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)\n elif \"pos_embed_input.proj.weight\" in controlnet_data:\n if \"transformer_blocks.0.adaLN_modulation.1.bias\" in controlnet_data:\n return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format\n else:\n return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet\n elif \"transformer_blocks.0.img_mlp.net.0.proj.weight\" in controlnet_data:\n return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options)\n elif \"controlnet_x_embedder.weight\" in controlnet_data:\n return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)\n elif \"control_blocks.0.after_proj.weight\" in controlnet_data and \"control_img_in.weight\" in controlnet_data:\n return load_controlnet_qwen_fun(controlnet_data, model_options=model_options)\n\n elif \"controlnet_blocks.0.linear.weight\" in controlnet_data: #mistoline flux\n return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)\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, model_options=model_options)\n if net is None:\n logging.error(\"error could not detect control model type.\")\n return net\n\n if controlnet_config is None:\n model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)\n supported_inference_dtypes = list(model_config.supported_inference_dtypes)\n controlnet_config = model_config.unet_config\n\n unet_dtype = model_options.get(\"dtype\", None)\n if unet_dtype is None:\n weight_dtype = comfy.utils.weight_dtype(controlnet_data)\n\n if supported_inference_dtypes is None:\n supported_inference_dtypes = [comfy.model_management.unet_dtype()]\n\n unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype)\n\n load_device = comfy.model_management.get_torch_device()\n\n manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)\n operations = model_options.get(\"custom_operations\", None)\n if operations is None:\n operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype)\n\n controlnet_config[\"operations\"] = operations\n controlnet_config[\"dtype\"] = unet_dtype\n controlnet_config[\"device\"] = comfy.model_management.unet_offload_device()\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 = comfy.cldm.cldm.ControlNet(**controlnet_config)\n\n if pth:\n if 'difference' in controlnet_data:\n if model is not None:\n comfy.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 logging.warning(\"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\n if len(missing) > 0:\n logging.warning(\"missing controlnet keys: {}\".format(missing))\n\n if len(unexpected) > 0:\n logging.debug(\"unexpected controlnet keys: {}\".format(unexpected))\n\n global_average_pooling = model_options.get(\"global_average_pooling\", False)\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\ndef load_controlnet(ckpt_path, model=None, model_options={}):\n model_options = model_options.copy()\n if \"global_average_pooling\" not in model_options:\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 model_options[\"global_average_pooling\"] = True\n\n cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options)\n if cnet is None:\n logging.error(\"error checkpoint does not contain controlnet or t2i adapter data {}\".format(ckpt_path))\n return cnet\n\nclass T2IAdapter(ControlBase):\n def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):\n super().__init__()\n self.t2i_model = t2i_model\n self.channels_in = channels_in\n self.control_input = None\n self.compression_ratio = compression_ratio\n self.upscale_algorithm = upscale_algorithm\n if device is None:\n device = comfy.model_management.get_torch_device()\n self.device = device\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, transformer_options):\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, transformer_options)\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] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != 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] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)\n self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, \"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 = {}\n for k in self.control_input:\n control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))\n\n return self.control_merge(control_input, control_prev, x_noisy.dtype)\n\n def copy(self):\n c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)\n self.copy_to(c)\n return c\n\ndef load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options\n compression_ratio = 8\n upscale_algorithm = 'nearest-exact'\n\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, )] = \"body.{}.\".format(i * 2)\n prefix_replace[\"adapter.\"] = \"\"\n t2i_data = comfy.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 = comfy.t2i_adapter.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 = comfy.t2i_adapter.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 elif \"backbone.0.0.weight\" in keys:\n model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])\n compression_ratio = 32\n upscale_algorithm = 'bilinear'\n elif \"backbone.10.blocks.0.weight\" in keys:\n model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode=\"large\", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])\n compression_ratio = 1\n upscale_algorithm = 'nearest-exact'\n else:\n return None\n\n missing, unexpected = model_ad.load_state_dict(t2i_data)\n if len(missing) > 0:\n logging.warning(\"t2i missing {}\".format(missing))\n\n if len(unexpected) > 0:\n logging.debug(\"t2i unexpected {}\".format(unexpected))\n\n return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)\n" }, { "path": "comfy/diffusers_convert.py", "content": "import re\nimport torch\nimport logging\n\n# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py\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, conv3d=False):\n # convert HF linear weights to SD conv2d weights\n if conv3d:\n return w.reshape(*w.shape, 1, 1, 1)\n else:\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 conv3d = False\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 if v.endswith(\".conv.weight\"):\n if not conv3d and vae_state_dict[k].ndim == 5:\n conv3d = True\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 logging.debug(f\"Reshaping {k} for SD format\")\n new_state_dict[k] = reshape_weight_for_sd(v, conv3d=conv3d)\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\n# This function exists because at the time of writing torch.cat can't do fp8 with cuda\ndef cat_tensors(tensors):\n x = 0\n for t in tensors:\n x += t.shape[0]\n\n shape = [x] + list(tensors[0].shape)[1:]\n out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)\n\n x = 0\n for t in tensors:\n out[x:x + t.shape[0]] = t\n x += t.shape[0]\n\n return out\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 text_proj = \"transformer.text_projection.weight\"\n if k.endswith(text_proj):\n new_state_dict[k.replace(text_proj, \"text_projection\")] = v.transpose(0, 1).contiguous()\n else:\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\"] = cat_tensors(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\"] = cat_tensors(tensors)\n\n return new_state_dict\n\n\ndef convert_text_enc_state_dict(text_enc_dict):\n return text_enc_dict\n" }, { "path": "comfy/diffusers_load.py", "content": "import os\n\nimport comfy.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 = comfy.sd.load_diffusion_model(unet_path)\n\n clip = None\n if output_clip:\n clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)\n\n vae = None\n if output_vae:\n sd = comfy.utils.load_torch_file(vae_path)\n vae = comfy.sd.VAE(sd=sd)\n\n return (unet, clip, vae)\n" }, { "path": "comfy/extra_samplers/uni_pc.py", "content": "#code taken from: https://github.com/wl-zhao/UniPC and modified\n\nimport torch\nimport math\nimport logging\n\nfrom tqdm.auto import trange\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 r\"\"\"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 ):\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\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 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 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 logging.info(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 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.tensordot(D1s, rhos_p, dims=([1], [0])) # 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.tensordot(D1s, rhos_c[:-1], dims=([1], [0])) # 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 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 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({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]})\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, sigmas, extra_args=None, callback=None, disable=False, 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 noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0)\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, variant=variant)\n x = uni_pc.sample(noise, 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\ndef sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):\n return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')\n" }, { "path": "comfy/float.py", "content": "import torch\n\ndef calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None):\n mantissa_scaled = torch.where(\n normal_mask,\n (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS),\n (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS)))\n )\n\n mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator)\n return mantissa_scaled.floor() / (2**MANTISSA_BITS)\n\n#Not 100% sure about this\ndef manual_stochastic_round_to_float8(x, dtype, generator=None):\n if dtype == torch.float8_e4m3fn:\n EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7\n elif dtype == torch.float8_e5m2:\n EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 5, 2, 15\n else:\n raise ValueError(\"Unsupported dtype\")\n\n x = x.half()\n sign = torch.sign(x)\n abs_x = x.abs()\n sign = torch.where(abs_x == 0, 0, sign)\n\n # Combine exponent calculation and clamping\n exponent = torch.clamp(\n torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS,\n 0, 2**EXPONENT_BITS - 1\n )\n\n # Combine mantissa calculation and rounding\n normal_mask = ~(exponent == 0)\n\n abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator)\n\n sign *= torch.where(\n normal_mask,\n (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x),\n (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x\n )\n\n inf = torch.finfo(dtype)\n torch.clamp(sign, min=inf.min, max=inf.max, out=sign)\n return sign\n\n\n\ndef stochastic_rounding(value, dtype, seed=0):\n if dtype == torch.float32:\n return value.to(dtype=torch.float32)\n if dtype == torch.float16:\n return value.to(dtype=torch.float16)\n if dtype == torch.bfloat16:\n return value.to(dtype=torch.bfloat16)\n if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:\n generator = torch.Generator(device=value.device)\n generator.manual_seed(seed)\n output = torch.empty_like(value, dtype=dtype)\n num_slices = max(1, (value.numel() / (4096 * 4096)))\n slice_size = max(1, round(value.shape[0] / num_slices))\n for i in range(0, value.shape[0], slice_size):\n output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator))\n return output\n\n return value.to(dtype=dtype)\n\n\n# TODO: improve this?\ndef stochastic_float_to_fp4_e2m1(x, generator):\n orig_shape = x.shape\n sign = torch.signbit(x).to(torch.uint8)\n\n exp = torch.floor(torch.log2(x.abs()) + 1.0).clamp(0, 3)\n x += (torch.rand(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) - 0.5) * (2 ** (exp - 2.0)) * 1.25\n\n x = x.abs()\n exp = torch.floor(torch.log2(x) + 1.1925).clamp(0, 3)\n\n mantissa = torch.where(\n exp > 0,\n (x / (2.0 ** (exp - 1)) - 1.0) * 2.0,\n (x * 2.0),\n out=x\n ).round().to(torch.uint8)\n del x\n\n exp = exp.to(torch.uint8)\n\n fp4 = (sign << 3) | (exp << 1) | mantissa\n del sign, exp, mantissa\n\n fp4_flat = fp4.view(-1)\n packed = (fp4_flat[0::2] << 4) | fp4_flat[1::2]\n return packed.reshape(list(orig_shape)[:-1] + [-1])\n\n\ndef to_blocked(input_matrix, flatten: bool = True) -> torch.Tensor:\n \"\"\"\n Rearrange a large matrix by breaking it into blocks and applying the rearrangement pattern.\n See:\n https://docs.nvidia.com/cuda/cublas/index.html#d-block-scaling-factors-layout\n\n Args:\n input_matrix: Input tensor of shape (H, W)\n Returns:\n Rearranged tensor of shape (32*ceil_div(H,128), 16*ceil_div(W,4))\n \"\"\"\n\n def ceil_div(a, b):\n return (a + b - 1) // b\n\n rows, cols = input_matrix.shape\n n_row_blocks = ceil_div(rows, 128)\n n_col_blocks = ceil_div(cols, 4)\n\n # Calculate the padded shape\n padded_rows = n_row_blocks * 128\n padded_cols = n_col_blocks * 4\n\n padded = input_matrix\n if (rows, cols) != (padded_rows, padded_cols):\n padded = torch.zeros(\n (padded_rows, padded_cols),\n device=input_matrix.device,\n dtype=input_matrix.dtype,\n )\n padded[:rows, :cols] = input_matrix\n\n # Rearrange the blocks\n blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3)\n rearranged = blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16)\n if flatten:\n return rearranged.flatten()\n\n return rearranged.reshape(padded_rows, padded_cols)\n\n\ndef stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator):\n F4_E2M1_MAX = 6.0\n F8_E4M3_MAX = 448.0\n\n orig_shape = x.shape\n\n block_size = 16\n\n x = x.reshape(orig_shape[0], -1, block_size)\n scaled_block_scales_fp8 = torch.clamp(((torch.amax(torch.abs(x), dim=-1)) / F4_E2M1_MAX) / per_tensor_scale.to(x.dtype), max=F8_E4M3_MAX).to(torch.float8_e4m3fn)\n x = x / (per_tensor_scale.to(x.dtype) * scaled_block_scales_fp8.to(x.dtype)).unsqueeze(-1)\n\n x = x.view(orig_shape).nan_to_num()\n data_lp = stochastic_float_to_fp4_e2m1(x, generator=generator)\n return data_lp, scaled_block_scales_fp8\n\n\ndef stochastic_round_quantize_nvfp4(x, per_tensor_scale, pad_16x, seed=0):\n def roundup(x: int, multiple: int) -> int:\n \"\"\"Round up x to the nearest multiple.\"\"\"\n return ((x + multiple - 1) // multiple) * multiple\n\n generator = torch.Generator(device=x.device)\n generator.manual_seed(seed)\n\n # Handle padding\n if pad_16x:\n rows, cols = x.shape\n padded_rows = roundup(rows, 16)\n padded_cols = roundup(cols, 16)\n if padded_rows != rows or padded_cols != cols:\n x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))\n\n x, blocked_scaled = stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator)\n return x, to_blocked(blocked_scaled, flatten=False)\n\n\ndef stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed=0, block_size=4096 * 4096):\n def roundup(x: int, multiple: int) -> int:\n \"\"\"Round up x to the nearest multiple.\"\"\"\n return ((x + multiple - 1) // multiple) * multiple\n\n orig_shape = x.shape\n\n # Handle padding\n if pad_16x:\n rows, cols = x.shape\n padded_rows = roundup(rows, 16)\n padded_cols = roundup(cols, 16)\n if padded_rows != rows or padded_cols != cols:\n x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))\n # Note: We update orig_shape because the output tensor logic below assumes x.shape matches\n # what we want to produce. If we pad here, we want the padded output.\n orig_shape = x.shape\n\n orig_shape = list(orig_shape)\n\n output_fp4 = torch.empty(orig_shape[:-1] + [orig_shape[-1] // 2], dtype=torch.uint8, device=x.device)\n output_block = torch.empty(orig_shape[:-1] + [orig_shape[-1] // 16], dtype=torch.float8_e4m3fn, device=x.device)\n\n generator = torch.Generator(device=x.device)\n generator.manual_seed(seed)\n\n num_slices = max(1, (x.numel() / block_size))\n slice_size = max(1, (round(x.shape[0] / num_slices)))\n\n for i in range(0, x.shape[0], slice_size):\n fp4, block = stochastic_round_quantize_nvfp4_block(x[i: i + slice_size], per_tensor_scale, generator=generator)\n output_fp4[i:i + slice_size].copy_(fp4)\n output_block[i:i + slice_size].copy_(block)\n\n return output_fp4, to_blocked(output_block, flatten=False)\n\n\ndef stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0):\n def roundup(x_val, multiple):\n return ((x_val + multiple - 1) // multiple) * multiple\n\n if pad_32x:\n rows, cols = x.shape\n padded_rows = roundup(rows, 32)\n padded_cols = roundup(cols, 32)\n if padded_rows != rows or padded_cols != cols:\n x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))\n\n F8_E4M3_MAX = 448.0\n E8M0_BIAS = 127\n BLOCK_SIZE = 32\n\n rows, cols = x.shape\n x_blocked = x.reshape(rows, -1, BLOCK_SIZE)\n max_abs = torch.amax(torch.abs(x_blocked), dim=-1)\n\n # E8M0 block scales (power-of-2 exponents)\n scale_needed = torch.clamp(max_abs.float() / F8_E4M3_MAX, min=2**(-127))\n exp_biased = torch.clamp(torch.ceil(torch.log2(scale_needed)).to(torch.int32) + E8M0_BIAS, 0, 254)\n block_scales_e8m0 = exp_biased.to(torch.uint8)\n\n zero_mask = (max_abs == 0)\n block_scales_f32 = (block_scales_e8m0.to(torch.int32) << 23).view(torch.float32)\n block_scales_f32 = torch.where(zero_mask, torch.ones_like(block_scales_f32), block_scales_f32)\n\n # Scale per-block then stochastic round\n data_scaled = (x_blocked.float() / block_scales_f32.unsqueeze(-1)).reshape(rows, cols)\n output_fp8 = stochastic_rounding(data_scaled, torch.float8_e4m3fn, seed=seed)\n\n block_scales_e8m0 = torch.where(zero_mask, torch.zeros_like(block_scales_e8m0), block_scales_e8m0)\n return output_fp8, to_blocked(block_scales_e8m0, flatten=False).view(torch.float8_e8m0fnu)\n" }, { "path": "comfy/gligen.py", "content": "import math\nimport torch\nfrom torch import nn\nfrom .ldm.modules.attention import CrossAttention, FeedForward\nimport comfy.ops\nops = comfy.ops.manual_cast\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 operations=ops)\n self.ff = FeedForward(query_dim, glu=True)\n\n self.norm1 = ops.LayerNorm(query_dim)\n self.norm2 = ops.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 = ops.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 operations=ops)\n self.ff = FeedForward(query_dim, glu=True)\n\n self.norm1 = ops.LayerNorm(query_dim)\n self.norm2 = ops.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 = ops.Linear(context_dim, query_dim)\n\n self.attn = CrossAttention(\n query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops)\n self.ff = FeedForward(query_dim, glu=True)\n\n self.norm1 = ops.LayerNorm(query_dim)\n self.norm2 = ops.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 ops.Linear(self.in_dim + self.position_dim, 512),\n nn.SiLU(),\n ops.Linear(512, 512),\n nn.SiLU(),\n ops.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 masks = masks.unsqueeze(-1)\n positive_embeddings = positive_embeddings\n\n # embedding position (it may includes padding as placeholder)\n xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C\n\n # learnable null embedding\n positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)\n xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).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.to(device=x.device, dtype=x.dtype))\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": "comfy/hooks.py", "content": "from __future__ import annotations\nfrom typing import TYPE_CHECKING, Callable\nimport enum\nimport math\nimport torch\nimport numpy as np\nimport itertools\nimport logging\n\nif TYPE_CHECKING:\n from comfy.model_patcher import ModelPatcher, PatcherInjection\n from comfy.model_base import BaseModel\n from comfy.sd import CLIP\nimport comfy.lora\nimport comfy.model_management\nimport comfy.patcher_extension\nfrom node_helpers import conditioning_set_values\n\n# #######################################################################################################\n# Hooks explanation\n# -------------------\n# The purpose of hooks is to allow conds to influence sampling without the need for ComfyUI core code to\n# make explicit special cases like it does for ControlNet and GLIGEN.\n#\n# This is necessary for nodes/features that are intended for use with masked or scheduled conds, or those\n# that should run special code when a 'marked' cond is used in sampling.\n# #######################################################################################################\n\nclass EnumHookMode(enum.Enum):\n '''\n Priority of hook memory optimization vs. speed, mostly related to WeightHooks.\n\n MinVram: No caching will occur for any operations related to hooks.\n MaxSpeed: Excess VRAM (and RAM, once VRAM is sufficiently depleted) will be used to cache hook weights when switching hook groups.\n '''\n MinVram = \"minvram\"\n MaxSpeed = \"maxspeed\"\n\nclass EnumHookType(enum.Enum):\n '''\n Hook types, each of which has different expected behavior.\n '''\n Weight = \"weight\"\n ObjectPatch = \"object_patch\"\n AdditionalModels = \"add_models\"\n TransformerOptions = \"transformer_options\"\n Injections = \"add_injections\"\n\nclass EnumWeightTarget(enum.Enum):\n Model = \"model\"\n Clip = \"clip\"\n\nclass EnumHookScope(enum.Enum):\n '''\n Determines if hook should be limited in its influence over sampling.\n\n AllConditioning: hook will affect all conds used in sampling.\n HookedOnly: hook will only affect the conds it was attached to.\n '''\n AllConditioning = \"all_conditioning\"\n HookedOnly = \"hooked_only\"\n\n\nclass _HookRef:\n pass\n\n\ndef default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n '''Example for how custom_should_register function can look like.'''\n return True\n\n\ndef create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[str]:\n '''Creates base dictionary for use with Hooks' target param.'''\n d = {}\n if target is not None:\n d['target'] = target\n d.update(kwargs)\n return d\n\n\nclass Hook:\n def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None, hook_id: str=None,\n hook_keyframe: HookKeyframeGroup=None, hook_scope=EnumHookScope.AllConditioning):\n self.hook_type = hook_type\n '''Enum identifying the general class of this hook.'''\n self.hook_ref = hook_ref if hook_ref else _HookRef()\n '''Reference shared between hook clones that have the same value. Should NOT be modified.'''\n self.hook_id = hook_id\n '''Optional string ID to identify hook; useful if need to consolidate duplicates at registration time.'''\n self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup()\n '''Keyframe storage that can be referenced to get strength for current sampling step.'''\n self.hook_scope = hook_scope\n '''Scope of where this hook should apply in terms of the conds used in sampling run.'''\n self.custom_should_register = default_should_register\n '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register'''\n\n @property\n def strength(self):\n return self.hook_keyframe.strength\n\n def initialize_timesteps(self, model: BaseModel):\n self.reset()\n self.hook_keyframe.initialize_timesteps(model)\n\n def reset(self):\n self.hook_keyframe.reset()\n\n def clone(self):\n c: Hook = self.__class__()\n c.hook_type = self.hook_type\n c.hook_ref = self.hook_ref\n c.hook_id = self.hook_id\n c.hook_keyframe = self.hook_keyframe\n c.hook_scope = self.hook_scope\n c.custom_should_register = self.custom_should_register\n return c\n\n def should_register(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n return self.custom_should_register(self, model, model_options, target_dict, registered)\n\n def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n raise NotImplementedError(\"add_hook_patches should be defined for Hook subclasses\")\n\n def __eq__(self, other: Hook):\n return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref\n\n def __hash__(self):\n return hash(self.hook_ref)\n\nclass WeightHook(Hook):\n '''\n Hook responsible for tracking weights to be applied to some model/clip.\n\n Note, value of hook_scope is ignored and is treated as HookedOnly.\n '''\n def __init__(self, strength_model=1.0, strength_clip=1.0):\n super().__init__(hook_type=EnumHookType.Weight, hook_scope=EnumHookScope.HookedOnly)\n self.weights: dict = None\n self.weights_clip: dict = None\n self.need_weight_init = True\n self._strength_model = strength_model\n self._strength_clip = strength_clip\n self.hook_scope = EnumHookScope.HookedOnly # this value does not matter for WeightHooks, just for docs\n\n @property\n def strength_model(self):\n return self._strength_model * self.strength\n\n @property\n def strength_clip(self):\n return self._strength_clip * self.strength\n\n def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n if not self.should_register(model, model_options, target_dict, registered):\n return False\n weights = None\n\n target = target_dict.get('target', None)\n if target == EnumWeightTarget.Clip:\n strength = self._strength_clip\n else:\n strength = self._strength_model\n\n if self.need_weight_init:\n key_map = {}\n if target == EnumWeightTarget.Clip:\n key_map = comfy.lora.model_lora_keys_clip(model.model, key_map)\n else:\n key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)\n weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False)\n else:\n if target == EnumWeightTarget.Clip:\n weights = self.weights_clip\n else:\n weights = self.weights\n model.add_hook_patches(hook=self, patches=weights, strength_patch=strength)\n registered.add(self)\n return True\n # TODO: add logs about any keys that were not applied\n\n def clone(self):\n c: WeightHook = super().clone()\n c.weights = self.weights\n c.weights_clip = self.weights_clip\n c.need_weight_init = self.need_weight_init\n c._strength_model = self._strength_model\n c._strength_clip = self._strength_clip\n return c\n\nclass ObjectPatchHook(Hook):\n def __init__(self, object_patches: dict[str]=None,\n hook_scope=EnumHookScope.AllConditioning):\n super().__init__(hook_type=EnumHookType.ObjectPatch)\n self.object_patches = object_patches\n self.hook_scope = hook_scope\n\n def clone(self):\n c: ObjectPatchHook = super().clone()\n c.object_patches = self.object_patches\n return c\n\n def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n raise NotImplementedError(\"ObjectPatchHook is not supported yet in ComfyUI.\")\n\nclass AdditionalModelsHook(Hook):\n '''\n Hook responsible for telling model management any additional models that should be loaded.\n\n Note, value of hook_scope is ignored and is treated as AllConditioning.\n '''\n def __init__(self, models: list[ModelPatcher]=None, key: str=None):\n super().__init__(hook_type=EnumHookType.AdditionalModels)\n self.models = models\n self.key = key\n\n def clone(self):\n c: AdditionalModelsHook = super().clone()\n c.models = self.models.copy() if self.models else self.models\n c.key = self.key\n return c\n\n def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n if not self.should_register(model, model_options, target_dict, registered):\n return False\n registered.add(self)\n return True\n\nclass TransformerOptionsHook(Hook):\n '''\n Hook responsible for adding wrappers, callbacks, patches, or anything else related to transformer_options.\n '''\n def __init__(self, transformers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None,\n hook_scope=EnumHookScope.AllConditioning):\n super().__init__(hook_type=EnumHookType.TransformerOptions)\n self.transformers_dict = transformers_dict\n self.hook_scope = hook_scope\n self._skip_adding = False\n '''Internal value used to avoid double load of transformer_options when hook_scope is AllConditioning.'''\n\n def clone(self):\n c: TransformerOptionsHook = super().clone()\n c.transformers_dict = self.transformers_dict\n c._skip_adding = self._skip_adding\n return c\n\n def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n if not self.should_register(model, model_options, target_dict, registered):\n return False\n # NOTE: to_load_options will be used to manually load patches/wrappers/callbacks from hooks\n self._skip_adding = False\n if self.hook_scope == EnumHookScope.AllConditioning:\n add_model_options = {\"transformer_options\": self.transformers_dict,\n \"to_load_options\": self.transformers_dict}\n # skip_adding if included in AllConditioning to avoid double loading\n self._skip_adding = True\n else:\n add_model_options = {\"to_load_options\": self.transformers_dict}\n registered.add(self)\n comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False)\n return True\n\n def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str]):\n if not self._skip_adding:\n comfy.patcher_extension.merge_nested_dicts(transformer_options, self.transformers_dict, copy_dict1=False)\n\nWrapperHook = TransformerOptionsHook\n'''Only here for backwards compatibility, WrapperHook is identical to TransformerOptionsHook.'''\n\nclass InjectionsHook(Hook):\n def __init__(self, key: str=None, injections: list[PatcherInjection]=None,\n hook_scope=EnumHookScope.AllConditioning):\n super().__init__(hook_type=EnumHookType.Injections)\n self.key = key\n self.injections = injections\n self.hook_scope = hook_scope\n\n def clone(self):\n c: InjectionsHook = super().clone()\n c.key = self.key\n c.injections = self.injections.copy() if self.injections else self.injections\n return c\n\n def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):\n raise NotImplementedError(\"InjectionsHook is not supported yet in ComfyUI.\")\n\nclass HookGroup:\n '''\n Stores groups of hooks, and allows them to be queried by type.\n\n To prevent breaking their functionality, never modify the underlying self.hooks or self._hook_dict vars directly;\n always use the provided functions on HookGroup.\n '''\n def __init__(self):\n self.hooks: list[Hook] = []\n self._hook_dict: dict[EnumHookType, list[Hook]] = {}\n\n def __len__(self):\n return len(self.hooks)\n\n def add(self, hook: Hook):\n if hook not in self.hooks:\n self.hooks.append(hook)\n self._hook_dict.setdefault(hook.hook_type, []).append(hook)\n\n def remove(self, hook: Hook):\n if hook in self.hooks:\n self.hooks.remove(hook)\n self._hook_dict[hook.hook_type].remove(hook)\n\n def get_type(self, hook_type: EnumHookType):\n return self._hook_dict.get(hook_type, [])\n\n def contains(self, hook: Hook):\n return hook in self.hooks\n\n def is_subset_of(self, other: HookGroup):\n self_hooks = set(self.hooks)\n other_hooks = set(other.hooks)\n return self_hooks.issubset(other_hooks)\n\n def new_with_common_hooks(self, other: HookGroup):\n c = HookGroup()\n for hook in self.hooks:\n if other.contains(hook):\n c.add(hook.clone())\n return c\n\n def clone(self):\n c = HookGroup()\n for hook in self.hooks:\n c.add(hook.clone())\n return c\n\n def clone_and_combine(self, other: HookGroup):\n c = self.clone()\n if other is not None:\n for hook in other.hooks:\n c.add(hook.clone())\n return c\n\n def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup):\n if hook_kf is None:\n hook_kf = HookKeyframeGroup()\n else:\n hook_kf = hook_kf.clone()\n for hook in self.hooks:\n hook.hook_keyframe = hook_kf\n\n def get_hooks_for_clip_schedule(self):\n scheduled_hooks: dict[WeightHook, list[tuple[tuple[float,float], HookKeyframe]]] = {}\n # only care about WeightHooks, for now\n for hook in self.get_type(EnumHookType.Weight):\n hook: WeightHook\n hook_schedule = []\n # if no hook keyframes, assign default value\n if len(hook.hook_keyframe.keyframes) == 0:\n hook_schedule.append(((0.0, 1.0), None))\n scheduled_hooks[hook] = hook_schedule\n continue\n # find ranges of values\n prev_keyframe = hook.hook_keyframe.keyframes[0]\n for keyframe in hook.hook_keyframe.keyframes:\n if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength):\n hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe))\n prev_keyframe = keyframe\n elif keyframe.start_percent == prev_keyframe.start_percent:\n prev_keyframe = keyframe\n # create final range, assuming last start_percent was not 1.0\n if not math.isclose(prev_keyframe.start_percent, 1.0):\n hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe))\n scheduled_hooks[hook] = hook_schedule\n # hooks should not have their schedules in a list of tuples\n all_ranges: list[tuple[float, float]] = []\n for range_kfs in scheduled_hooks.values():\n for t_range, keyframe in range_kfs:\n all_ranges.append(t_range)\n # turn list of ranges into boundaries\n boundaries_set = set(itertools.chain.from_iterable(all_ranges))\n boundaries_set.add(0.0)\n boundaries = sorted(boundaries_set)\n real_ranges = [(boundaries[i], boundaries[i + 1]) for i in range(len(boundaries) - 1)]\n # with real ranges defined, give appropriate hooks w/ keyframes for each range\n scheduled_keyframes: list[tuple[tuple[float,float], list[tuple[WeightHook, HookKeyframe]]]] = []\n for t_range in real_ranges:\n hooks_schedule = []\n for hook, val in scheduled_hooks.items():\n keyframe = None\n # check if is a keyframe that works for the current t_range\n for stored_range, stored_kf in val:\n # if stored start is less than current end, then fits - give it assigned keyframe\n if stored_range[0] < t_range[1] and stored_range[1] > t_range[0]:\n keyframe = stored_kf\n break\n hooks_schedule.append((hook, keyframe))\n scheduled_keyframes.append((t_range, hooks_schedule))\n return scheduled_keyframes\n\n def reset(self):\n for hook in self.hooks:\n hook.reset()\n\n @staticmethod\n def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) -> HookGroup:\n actual: list[HookGroup] = []\n for group in hooks_list:\n if group is not None:\n actual.append(group)\n if len(actual) < require_count:\n raise Exception(f\"Need at least {require_count} hooks to combine, but only had {len(actual)}.\")\n # if no hooks, then return None\n if len(actual) == 0:\n return None\n # if only 1 hook, just return itself without cloning\n elif len(actual) == 1:\n return actual[0]\n final_hook: HookGroup = None\n for hook in actual:\n if final_hook is None:\n final_hook = hook.clone()\n else:\n final_hook = final_hook.clone_and_combine(hook)\n return final_hook\n\n\nclass HookKeyframe:\n def __init__(self, strength: float, start_percent=0.0, guarantee_steps=1):\n self.strength = strength\n # scheduling\n self.start_percent = float(start_percent)\n self.start_t = 999999999.9\n self.guarantee_steps = guarantee_steps\n\n def get_effective_guarantee_steps(self, max_sigma: torch.Tensor):\n '''If keyframe starts before current sampling range (max_sigma), treat as 0.'''\n if self.start_t > max_sigma:\n return 0\n return self.guarantee_steps\n\n def clone(self):\n c = HookKeyframe(strength=self.strength,\n start_percent=self.start_percent, guarantee_steps=self.guarantee_steps)\n c.start_t = self.start_t\n return c\n\nclass HookKeyframeGroup:\n def __init__(self):\n self.keyframes: list[HookKeyframe] = []\n self._current_keyframe: HookKeyframe = None\n self._current_used_steps = 0\n self._current_index = 0\n self._current_strength = None\n self._curr_t = -1.\n\n # properties shadow those of HookWeightsKeyframe\n @property\n def strength(self):\n if self._current_keyframe is not None:\n return self._current_keyframe.strength\n return 1.0\n\n def reset(self):\n self._current_keyframe = None\n self._current_used_steps = 0\n self._current_index = 0\n self._current_strength = None\n self.curr_t = -1.\n self._set_first_as_current()\n\n def add(self, keyframe: HookKeyframe):\n # add to end of list, then sort\n self.keyframes.append(keyframe)\n self.keyframes = get_sorted_list_via_attr(self.keyframes, \"start_percent\")\n self._set_first_as_current()\n\n def _set_first_as_current(self):\n if len(self.keyframes) > 0:\n self._current_keyframe = self.keyframes[0]\n else:\n self._current_keyframe = None\n\n def has_guarantee_steps(self):\n for kf in self.keyframes:\n if kf.guarantee_steps > 0:\n return True\n return False\n\n def has_index(self, index: int):\n return index >= 0 and index < len(self.keyframes)\n\n def is_empty(self):\n return len(self.keyframes) == 0\n\n def clone(self):\n c = HookKeyframeGroup()\n for keyframe in self.keyframes:\n c.keyframes.append(keyframe.clone())\n c._set_first_as_current()\n return c\n\n def initialize_timesteps(self, model: BaseModel):\n for keyframe in self.keyframes:\n keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent)\n\n def prepare_current_keyframe(self, curr_t: float, transformer_options: dict[str, torch.Tensor]) -> bool:\n if self.is_empty():\n return False\n if curr_t == self._curr_t:\n return False\n max_sigma = torch.max(transformer_options[\"sample_sigmas\"])\n prev_index = self._current_index\n prev_strength = self._current_strength\n # if met guaranteed steps, look for next keyframe in case need to switch\n if self._current_used_steps >= self._current_keyframe.get_effective_guarantee_steps(max_sigma):\n # if has next index, loop through and see if need to switch\n if self.has_index(self._current_index+1):\n for i in range(self._current_index+1, len(self.keyframes)):\n eval_c = self.keyframes[i]\n # check if start_t is greater or equal to curr_t\n # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling\n if eval_c.start_t >= curr_t:\n self._current_index = i\n self._current_strength = eval_c.strength\n self._current_keyframe = eval_c\n self._current_used_steps = 0\n # if guarantee_steps greater than zero, stop searching for other keyframes\n if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:\n break\n # if eval_c is outside the percent range, stop looking further\n else:\n break\n # update steps current context is used\n self._current_used_steps += 1\n # update current timestep this was performed on\n self._curr_t = curr_t\n # return True if keyframe changed, False if no change\n return prev_index != self._current_index and prev_strength != self._current_strength\n\n\nclass InterpolationMethod:\n LINEAR = \"linear\"\n EASE_IN = \"ease_in\"\n EASE_OUT = \"ease_out\"\n EASE_IN_OUT = \"ease_in_out\"\n\n _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT]\n\n @classmethod\n def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False):\n diff = num_to - num_from\n if method == cls.LINEAR:\n weights = torch.linspace(num_from, num_to, length)\n elif method == cls.EASE_IN:\n index = torch.linspace(0, 1, length)\n weights = diff * np.power(index, 2) + num_from\n elif method == cls.EASE_OUT:\n index = torch.linspace(0, 1, length)\n weights = diff * (1 - np.power(1 - index, 2)) + num_from\n elif method == cls.EASE_IN_OUT:\n index = torch.linspace(0, 1, length)\n weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from\n else:\n raise ValueError(f\"Unrecognized interpolation method '{method}'.\")\n if reverse:\n weights = weights.flip(dims=(0,))\n return weights\n\ndef get_sorted_list_via_attr(objects: list, attr: str) -> list:\n if not objects:\n return objects\n elif len(objects) <= 1:\n return [x for x in objects]\n # now that we know we have to sort, do it following these rules:\n # a) if objects have same value of attribute, maintain their relative order\n # b) perform sorting of the groups of objects with same attributes\n unique_attrs = {}\n for o in objects:\n val_attr = getattr(o, attr)\n attr_list: list = unique_attrs.get(val_attr, list())\n attr_list.append(o)\n if val_attr not in unique_attrs:\n unique_attrs[val_attr] = attr_list\n # now that we have the unique attr values grouped together in relative order, sort them by key\n sorted_attrs = dict(sorted(unique_attrs.items()))\n # now flatten out the dict into a list to return\n sorted_list = []\n for object_list in sorted_attrs.values():\n sorted_list.extend(object_list)\n return sorted_list\n\ndef create_transformer_options_from_hooks(model: ModelPatcher, hooks: HookGroup, transformer_options: dict[str]=None):\n # if no hooks or is not a ModelPatcher for sampling, return empty dict\n if hooks is None or model.is_clip:\n return {}\n if transformer_options is None:\n transformer_options = {}\n for hook in hooks.get_type(EnumHookType.TransformerOptions):\n hook: TransformerOptionsHook\n hook.on_apply_hooks(model, transformer_options)\n return transformer_options\n\ndef create_hook_lora(lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float):\n hook_group = HookGroup()\n hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip)\n hook_group.add(hook)\n hook.weights = lora\n return hook_group\n\ndef create_hook_model_as_lora(weights_model, weights_clip, strength_model: float, strength_clip: float):\n hook_group = HookGroup()\n hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip)\n hook_group.add(hook)\n patches_model = None\n patches_clip = None\n if weights_model is not None:\n patches_model = {}\n for key in weights_model:\n patches_model[key] = (\"model_as_lora\", (weights_model[key],))\n if weights_clip is not None:\n patches_clip = {}\n for key in weights_clip:\n patches_clip[key] = (\"model_as_lora\", (weights_clip[key],))\n hook.weights = patches_model\n hook.weights_clip = patches_clip\n hook.need_weight_init = False\n return hook_group\n\ndef get_patch_weights_from_model(model: ModelPatcher, discard_model_sampling=True):\n if model is None:\n return None\n patches_model: dict[str, torch.Tensor] = model.model.state_dict()\n if discard_model_sampling:\n # do not include ANY model_sampling components of the model that should act as a patch\n for key in list(patches_model.keys()):\n if key.startswith(\"model_sampling\"):\n patches_model.pop(key, None)\n return patches_model\n\n# NOTE: this function shows how to register weight hooks directly on the ModelPatchers\ndef load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dict[str, torch.Tensor],\n strength_model: float, strength_clip: float):\n key_map = {}\n if model is not None:\n key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)\n if clip is not None:\n key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)\n\n hook_group = HookGroup()\n hook = WeightHook()\n hook_group.add(hook)\n loaded: dict[str] = comfy.lora.load_lora(lora, key_map)\n if model is not None:\n new_modelpatcher = model.clone()\n k = new_modelpatcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=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.patcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=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 logging.warning(f\"NOT LOADED {x}\")\n return (new_modelpatcher, new_clip, hook_group)\n\ndef _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, HookGroup], cache: dict[tuple[HookGroup, HookGroup], HookGroup]):\n hooks_key = 'hooks'\n # if hooks only exist in one dict, do what's needed so that it ends up in c_dict\n if hooks_key not in values:\n return\n if hooks_key not in c_dict:\n hooks_value = values.get(hooks_key, None)\n if hooks_value is not None:\n c_dict[hooks_key] = hooks_value\n return\n # otherwise, need to combine with minimum duplication via cache\n hooks_tuple = (c_dict[hooks_key], values[hooks_key])\n cached_hooks = cache.get(hooks_tuple, None)\n if cached_hooks is None:\n new_hooks = hooks_tuple[0].clone_and_combine(hooks_tuple[1])\n cache[hooks_tuple] = new_hooks\n c_dict[hooks_key] = new_hooks\n else:\n c_dict[hooks_key] = cache[hooks_tuple]\n\ndef conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True,\n cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None):\n c = []\n if cache is None:\n cache = {}\n for t in conditioning:\n n = [t[0], t[1].copy()]\n for k in values:\n if append_hooks and k == 'hooks':\n _combine_hooks_from_values(n[1], values, cache)\n else:\n n[1][k] = values[k]\n c.append(n)\n\n return c\n\ndef set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None):\n if hooks is None:\n return cond\n return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks, cache=cache)\n\ndef set_timesteps_for_conditioning(cond, timestep_range: tuple[float,float]):\n if timestep_range is None:\n return cond\n return conditioning_set_values(cond, {\"start_percent\": timestep_range[0],\n \"end_percent\": timestep_range[1]})\n\ndef set_mask_for_conditioning(cond, mask: torch.Tensor, set_cond_area: str, strength: float):\n if mask is None:\n return cond\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 return conditioning_set_values(cond, {'mask': mask,\n 'set_area_to_bounds': set_area_to_bounds,\n 'mask_strength': strength})\n\ndef combine_conditioning(conds: list):\n combined_conds = []\n for cond in conds:\n combined_conds.extend(cond)\n return combined_conds\n\ndef combine_with_new_conds(conds: list, new_conds: list):\n combined_conds = []\n for c, new_c in zip(conds, new_conds):\n combined_conds.append(combine_conditioning([c, new_c]))\n return combined_conds\n\ndef set_conds_props(conds: list, strength: float, set_cond_area: str,\n mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True):\n final_conds = []\n cache = {}\n for c in conds:\n # first, apply lora_hook to conditioning, if provided\n c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks, cache=cache)\n # next, apply mask to conditioning\n c = set_mask_for_conditioning(cond=c, mask=mask, strength=strength, set_cond_area=set_cond_area)\n # apply timesteps, if present\n c = set_timesteps_for_conditioning(cond=c, timestep_range=timesteps_range)\n # finally, apply mask to conditioning and store\n final_conds.append(c)\n return final_conds\n\ndef set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.0, set_cond_area: str=\"default\",\n mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True):\n combined_conds = []\n cache = {}\n for c, masked_c in zip(conds, new_conds):\n # first, apply lora_hook to new conditioning, if provided\n masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks, cache=cache)\n # next, apply mask to new conditioning, if provided\n masked_c = set_mask_for_conditioning(cond=masked_c, mask=mask, set_cond_area=set_cond_area, strength=strength)\n # apply timesteps, if present\n masked_c = set_timesteps_for_conditioning(cond=masked_c, timestep_range=timesteps_range)\n # finally, combine with existing conditioning and store\n combined_conds.append(combine_conditioning([c, masked_c]))\n return combined_conds\n\ndef set_default_conds_and_combine(conds: list, new_conds: list,\n hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True):\n combined_conds = []\n cache = {}\n for c, new_c in zip(conds, new_conds):\n # first, apply lora_hook to new conditioning, if provided\n new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks, cache=cache)\n # next, add default_cond key to cond so that during sampling, it can be identified\n new_c = conditioning_set_values(new_c, {'default': True})\n # apply timesteps, if present\n new_c = set_timesteps_for_conditioning(cond=new_c, timestep_range=timesteps_range)\n # finally, combine with existing conditioning and store\n combined_conds.append(combine_conditioning([c, new_c]))\n return combined_conds\n" }, { "path": "comfy/image_encoders/dino2.py", "content": "import torch\nfrom comfy.text_encoders.bert import BertAttention\nimport comfy.model_management\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\n\n\nclass Dino2AttentionOutput(torch.nn.Module):\n def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)\n\n def forward(self, x):\n return self.dense(x)\n\n\nclass Dino2AttentionBlock(torch.nn.Module):\n def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.attention = BertAttention(embed_dim, heads, dtype, device, operations)\n self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)\n\n def forward(self, x, mask, optimized_attention):\n return self.output(self.attention(x, mask, optimized_attention))\n\n\nclass LayerScale(torch.nn.Module):\n def __init__(self, dim, dtype, device, operations):\n super().__init__()\n self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype))\n\n def forward(self, x):\n return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype)\n\nclass Dinov2MLP(torch.nn.Module):\n def __init__(self, hidden_size: int, dtype, device, operations):\n super().__init__()\n\n mlp_ratio = 4\n hidden_features = int(hidden_size * mlp_ratio)\n self.fc1 = operations.Linear(hidden_size, hidden_features, bias = True, device=device, dtype=dtype)\n self.fc2 = operations.Linear(hidden_features, hidden_size, bias = True, device=device, dtype=dtype)\n\n def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:\n hidden_state = self.fc1(hidden_state)\n hidden_state = torch.nn.functional.gelu(hidden_state)\n hidden_state = self.fc2(hidden_state)\n return hidden_state\n\nclass SwiGLUFFN(torch.nn.Module):\n def __init__(self, dim, dtype, device, operations):\n super().__init__()\n in_features = out_features = dim\n hidden_features = int(dim * 4)\n hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8\n\n self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype)\n self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype)\n\n def forward(self, x):\n x = self.weights_in(x)\n x1, x2 = x.chunk(2, dim=-1)\n x = torch.nn.functional.silu(x1) * x2\n return self.weights_out(x)\n\n\nclass Dino2Block(torch.nn.Module):\n def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):\n super().__init__()\n self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)\n self.layer_scale1 = LayerScale(dim, dtype, device, operations)\n self.layer_scale2 = LayerScale(dim, dtype, device, operations)\n if use_swiglu_ffn:\n self.mlp = SwiGLUFFN(dim, dtype, device, operations)\n else:\n self.mlp = Dinov2MLP(dim, dtype, device, operations)\n self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)\n self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)\n\n def forward(self, x, optimized_attention):\n x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention))\n x = x + self.layer_scale2(self.mlp(self.norm2(x)))\n return x\n\n\nclass Dino2Encoder(torch.nn.Module):\n def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):\n super().__init__()\n self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)\n for _ in range(num_layers)])\n\n def forward(self, x, intermediate_output=None):\n optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)\n\n if intermediate_output is not None:\n if intermediate_output < 0:\n intermediate_output = len(self.layer) + intermediate_output\n\n intermediate = None\n for i, layer in enumerate(self.layer):\n x = layer(x, optimized_attention)\n if i == intermediate_output:\n intermediate = x.clone()\n return x, intermediate\n\n\nclass Dino2PatchEmbeddings(torch.nn.Module):\n def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None):\n super().__init__()\n self.projection = operations.Conv2d(\n in_channels=num_channels,\n out_channels=dim,\n kernel_size=patch_size,\n stride=patch_size,\n bias=True,\n dtype=dtype,\n device=device\n )\n\n def forward(self, pixel_values):\n return self.projection(pixel_values).flatten(2).transpose(1, 2)\n\n\nclass Dino2Embeddings(torch.nn.Module):\n def __init__(self, dim, dtype, device, operations):\n super().__init__()\n patch_size = 14\n image_size = 518\n\n self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)\n self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))\n self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device))\n self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))\n\n def forward(self, pixel_values):\n x = self.patch_embeddings(pixel_values)\n # TODO: mask_token?\n x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1)\n x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)\n return x\n\n\nclass Dinov2Model(torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n num_layers = config_dict[\"num_hidden_layers\"]\n dim = config_dict[\"hidden_size\"]\n heads = config_dict[\"num_attention_heads\"]\n layer_norm_eps = config_dict[\"layer_norm_eps\"]\n use_swiglu_ffn = config_dict[\"use_swiglu_ffn\"]\n\n self.embeddings = Dino2Embeddings(dim, dtype, device, operations)\n self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)\n self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)\n\n def forward(self, pixel_values, attention_mask=None, intermediate_output=None):\n x = self.embeddings(pixel_values)\n x, i = self.encoder(x, intermediate_output=intermediate_output)\n x = self.layernorm(x)\n pooled_output = x[:, 0, :]\n return x, i, pooled_output, None\n" }, { "path": "comfy/image_encoders/dino2_giant.json", "content": "{\n \"attention_probs_dropout_prob\": 0.0,\n \"drop_path_rate\": 0.0,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.0,\n \"hidden_size\": 1536,\n \"image_size\": 518,\n \"initializer_range\": 0.02,\n \"layer_norm_eps\": 1e-06,\n \"layerscale_value\": 1.0,\n \"mlp_ratio\": 4,\n \"model_type\": \"dinov2\",\n \"num_attention_heads\": 24,\n \"num_channels\": 3,\n \"num_hidden_layers\": 40,\n \"patch_size\": 14,\n \"qkv_bias\": true,\n \"use_swiglu_ffn\": true,\n \"image_mean\": [0.485, 0.456, 0.406],\n \"image_std\": [0.229, 0.224, 0.225]\n}\n" }, { "path": "comfy/image_encoders/dino2_large.json", "content": "{\n \"hidden_size\": 1024,\n \"use_mask_token\": true,\n \"patch_size\": 14,\n \"image_size\": 518,\n \"num_channels\": 3,\n \"num_attention_heads\": 16,\n \"initializer_range\": 0.02,\n \"attention_probs_dropout_prob\": 0.0,\n \"hidden_dropout_prob\": 0.0,\n \"hidden_act\": \"gelu\",\n \"mlp_ratio\": 4,\n \"model_type\": \"dinov2\",\n \"num_hidden_layers\": 24,\n \"layer_norm_eps\": 1e-6,\n \"qkv_bias\": true,\n \"use_swiglu_ffn\": false,\n \"layerscale_value\": 1.0,\n \"drop_path_rate\": 0.0,\n \"image_mean\": [0.485, 0.456, 0.406],\n \"image_std\": [0.229, 0.224, 0.225]\n}\n" }, { "path": "comfy/k_diffusion/deis.py", "content": "#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py\n#under Apache 2 license\nimport torch\nimport numpy as np\n\n# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis).\n#############################\n### Utils for DEIS solver ###\n#############################\n#----------------------------------------------------------------------------\n# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS.\n\ndef edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):\n vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d\n vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1)\n vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d\n t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu())\n return t_steps, vp_beta_min, vp_beta_d + vp_beta_min\n\n#----------------------------------------------------------------------------\n\ndef cal_poly(prev_t, j, taus):\n poly = 1\n for k in range(prev_t.shape[0]):\n if k == j:\n continue\n poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k])\n return poly\n\n#----------------------------------------------------------------------------\n# Transfer from t to alpha_t.\n\ndef t2alpha_fn(beta_0, beta_1, t):\n return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0)\n\n#----------------------------------------------------------------------------\n\ndef cal_intergrand(beta_0, beta_1, taus):\n with torch.inference_mode(mode=False):\n taus = taus.clone()\n beta_0 = beta_0.clone()\n beta_1 = beta_1.clone()\n with torch.enable_grad():\n taus.requires_grad_(True)\n alpha = t2alpha_fn(beta_0, beta_1, taus)\n log_alpha = alpha.log()\n log_alpha.sum().backward()\n d_log_alpha_dtau = taus.grad\n integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha))\n return integrand\n\n#----------------------------------------------------------------------------\n\ndef get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):\n \"\"\"\n Get the coefficient list for DEIS sampling.\n\n Args:\n t_steps: A pytorch tensor. The time steps for sampling.\n max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4\n N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'.\n deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS.\n Returns:\n A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True.\n \"\"\"\n if deis_mode == 'tab':\n t_steps, beta_0, beta_1 = edm2t(t_steps)\n C = []\n for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):\n order = min(i+1, max_order)\n if order == 1:\n C.append([])\n else:\n taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation\n dtau = (t_next - t_cur) / N\n prev_t = t_steps[[i - k for k in range(order)]]\n coeff_temp = []\n integrand = cal_intergrand(beta_0, beta_1, taus)\n for j in range(order):\n poly = cal_poly(prev_t, j, taus)\n coeff_temp.append(torch.sum(integrand * poly) * dtau)\n C.append(coeff_temp)\n\n elif deis_mode == 'rhoab':\n # Analytical solution, second order\n def get_def_intergral_2(a, b, start, end, c):\n coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b\n return coeff / ((c - a) * (c - b))\n\n # Analytical solution, third order\n def get_def_intergral_3(a, b, c, start, end, d):\n coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \\\n + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c\n return coeff / ((d - a) * (d - b) * (d - c))\n\n C = []\n for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):\n order = min(i, max_order)\n if order == 0:\n C.append([])\n else:\n prev_t = t_steps[[i - k for k in range(order+1)]]\n if order == 1:\n coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1]))\n coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur))\n coeff_temp = [coeff_cur, coeff_prev1]\n elif order == 2:\n coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur)\n coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1])\n coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2])\n coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2]\n elif order == 3:\n coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur)\n coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1])\n coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2])\n coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])\n coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]\n C.append(coeff_temp)\n return C\n\n" }, { "path": "comfy/k_diffusion/sa_solver.py", "content": "# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019)\n# Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf\n# Codebase ref: https://github.com/scxue/SA-Solver\n\nimport math\nfrom typing import Union, Callable\nimport torch\n\n\ndef compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor:\n \"\"\"Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts.\n\n Integral of exp((1 + tau^2) * x) * x^p dx\n = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx,\n with base case p=0 where integral equals product_terms[0].\n\n where\n product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2).\n\n Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1).\n Return coefficients used by the SA-Solver in data prediction mode.\n\n Args:\n s: Start time s.\n t: End time t.\n solver_order: Current order of the solver.\n tau_t: Stochastic strength parameter in the SDE.\n\n Returns:\n Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,).\n \"\"\"\n tau_mul = 1 + tau_t ** 2\n h = t - s\n p = torch.arange(solver_order, dtype=s.dtype, device=s.device)\n\n # product_terms after factoring out exp((1 + tau^2) * t)\n # Includes (1 + tau^2) factor from outside the integral\n product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp())\n\n # Lower triangular recursive coefficient matrix\n # Accumulates recursive coefficients based on p / (1 + tau^2)\n recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0)\n log_factorial = (p + 1).lgamma()\n recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0)\n if tau_t > 0:\n recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul))\n signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0)\n recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril()\n\n return recursive_coeff_mat @ product_terms_factored\n\n\ndef compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor:\n \"\"\"Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details).\"\"\"\n tau_mul = 1 + tau_t ** 2\n h = lambda_t - lambda_s\n alpha_t = sigma_next * lambda_t.exp()\n if is_corrector_step:\n # Simplified 1-step (order-2) corrector\n b_1 = alpha_t * (0.5 * tau_mul * h)\n b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1\n else:\n # Simplified 2-step predictor\n b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s)\n b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2\n return torch.stack([b_2, b_1])\n\n\ndef compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor:\n \"\"\"Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18).\n\n The solver order corresponds to the number of input lambdas (half-logSNR points).\n\n Args:\n sigma_next: Sigma at end time t.\n curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,).\n lambda_s: Lambda at start time s.\n lambda_t: Lambda at end time t.\n tau_t: Stochastic strength parameter in the SDE.\n simple_order_2: Whether to enable the simple order-2 scheme.\n is_corrector_step: Flag for corrector step in simple order-2 mode.\n\n Returns:\n b_i coefficients for the SA-Solver, shape (N,), where N is the solver order.\n \"\"\"\n num_timesteps = curr_lambdas.shape[0]\n\n if simple_order_2 and num_timesteps == 2:\n return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step)\n\n # Compute coefficients by solving a linear system from Lagrange basis interpolation\n exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t)\n vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T\n lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs)\n\n # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t)\n # = sigma_t * exp(lambda_t) = alpha_t\n # exp((1 + tau^2) * lambda_t) is extracted from the integral\n alpha_t = sigma_next * lambda_t.exp()\n return alpha_t * lagrange_integrals\n\n\ndef get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]:\n \"\"\"Return a function that controls the stochasticity of SA-Solver.\n\n When eta = 0, SA-Solver runs as ODE. The official approach uses\n time t to determine the SDE interval, while here we use sigma instead.\n\n See:\n https://github.com/scxue/SA-Solver/blob/main/README.md\n \"\"\"\n\n def tau_func(sigma: Union[torch.Tensor, float]) -> float:\n if eta <= 0:\n return 0.0 # ODE\n\n if isinstance(sigma, torch.Tensor):\n sigma = sigma.item()\n return eta if start_sigma >= sigma >= end_sigma else 0.0\n\n return tau_func\n" }, { "path": "comfy/k_diffusion/sampling.py", "content": "import math\nfrom functools import partial\n\nfrom scipy import integrate\nimport torch\nfrom torch import nn\nimport torchsde\nfrom tqdm.auto import tqdm\n\nfrom . import utils\nfrom . import deis\nfrom . import sa_solver\nimport comfy.model_patcher\nimport comfy.model_sampling\n\nimport comfy.memory_management\nfrom comfy.utils import model_trange as trange\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.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))\n return append_zero(sigmas)\n\n\ndef get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):\n \"\"\"Constructs the noise schedule proposed by Tiankai et al. (2024). \"\"\"\n epsilon = 1e-5 # avoid log(0)\n x = torch.linspace(0, 1, n, device=device)\n clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)\n lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)\n sigmas = clamp(torch.exp(lmb))\n return sigmas\n\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, seed=None):\n if seed is not None:\n if x.device == torch.device(\"cpu\"):\n seed += 1\n\n generator = torch.Generator(device=x.device)\n generator.manual_seed(seed)\n else:\n generator = None\n\n return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator)\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 = kwargs.pop(\"cpu\", True)\n t0, t1, self.sign = self.sort(t0, t1)\n w0 = kwargs.pop('w0', None)\n if w0 is None:\n w0 = torch.zeros_like(x)\n self.batched = False\n if seed is None:\n seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),)\n elif isinstance(seed, (tuple, list)):\n if len(seed) != x.shape[0]:\n raise ValueError(\"Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.\")\n self.batched = True\n w0 = w0[0]\n else:\n seed = (seed,)\n if self.cpu_tree:\n t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu()\n self.trees = tuple(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 device, dtype = t0.device, t0.dtype\n if self.cpu_tree:\n t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float()\n w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign)\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\ndef sigma_to_half_log_snr(sigma, model_sampling):\n \"\"\"Convert sigma to half-logSNR log(alpha_t / sigma_t).\"\"\"\n if isinstance(model_sampling, comfy.model_sampling.CONST):\n # log((1 - t) / t) = log((1 - sigma) / sigma)\n return sigma.logit().neg()\n return sigma.log().neg()\n\n\ndef half_log_snr_to_sigma(half_log_snr, model_sampling):\n \"\"\"Convert half-logSNR log(alpha_t / sigma_t) to sigma.\"\"\"\n if isinstance(model_sampling, comfy.model_sampling.CONST):\n # 1 / (1 + exp(half_log_snr))\n return half_log_snr.neg().sigmoid()\n return half_log_snr.neg().exp()\n\n\ndef offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):\n \"\"\"Adjust the first sigma to avoid invalid logSNR.\"\"\"\n if len(sigmas) <= 1:\n return sigmas\n if isinstance(model_sampling, comfy.model_sampling.CONST):\n if sigmas[0] >= 1:\n sigmas = sigmas.clone()\n sigmas[0] = model_sampling.percent_to_sigma(percent_offset)\n return sigmas\n\n\ndef ei_h_phi_1(h: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute the result of h*phi_1(h) in exponential integrator methods.\"\"\"\n return torch.expm1(h)\n\n\ndef ei_h_phi_2(h: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute the result of h*phi_2(h) in exponential integrator methods.\"\"\"\n return (torch.expm1(h) - h) / h\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 if s_churn > 0:\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 else:\n gamma = 0\n sigma_hat = sigmas[i]\n\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 if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):\n return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)\n \"\"\"Ancestral sampling with Euler method steps.\"\"\"\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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\n if sigma_down == 0:\n x = denoised\n else:\n d = to_d(x, sigmas[i], denoised)\n # Euler method\n dt = sigma_down - sigmas[i]\n x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up\n return x\n\n@torch.no_grad()\ndef sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, 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 seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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\n if sigmas[i + 1] == 0:\n x = denoised\n else:\n downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta\n sigma_down = sigmas[i + 1] * downstep_ratio\n alpha_ip1 = 1 - sigmas[i + 1]\n alpha_down = 1 - sigma_down\n renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5\n # Euler method\n sigma_down_i_ratio = sigma_down / sigmas[i]\n x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised\n if eta > 0:\n x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff\n return x\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 if s_churn > 0:\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 else:\n gamma = 0\n sigma_hat = sigmas[i]\n\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 if s_churn > 0:\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 else:\n gamma = 0\n sigma_hat = sigmas[i]\n\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 if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):\n return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)\n\n \"\"\"Ancestral sampling with DPM-Solver second-order steps.\"\"\"\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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@torch.no_grad()\ndef sample_dpm_2_ancestral_RF(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 seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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 downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta\n sigma_down = sigmas[i+1] * downstep_ratio\n alpha_ip1 = 1 - sigmas[i+1]\n alpha_down = 1 - sigma_down\n renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5\n\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 = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff\n return x\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 if sigmas[i + 1] == 0:\n # Denoising step\n x = denoised\n else:\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, seed=self.extra_args.get(\"seed\", None)) 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, seed=self.extra_args.get(\"seed\", None)) 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 if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):\n return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)\n\n \"\"\"Ancestral sampling with DPM-Solver++(2S) second-order steps.\"\"\"\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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_2s_ancestral_RF(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 seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n s_in = x.new_ones([x.shape[0]])\n sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1\n lambda_fn = lambda sigma: ((1-sigma)/sigma).log()\n\n # logged_x = x.unsqueeze(0)\n\n for i in trange(len(sigmas) - 1, disable=disable):\n denoised = model(x, sigmas[i] * s_in, **extra_args)\n downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta\n sigma_down = sigmas[i+1] * downstep_ratio\n alpha_ip1 = 1 - sigmas[i+1]\n alpha_down = 1 - sigma_down\n renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5\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 sigmas[i + 1] == 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 if sigmas[i] == 1.0:\n sigma_s = 0.9999\n else:\n t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down)\n r = 1 / 2\n h = t_down - t_i\n s = t_i + r * h\n sigma_s = sigma_fn(s)\n # sigma_s = sigmas[i+1]\n sigma_s_i_ratio = sigma_s / sigmas[i]\n u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised\n D_i = model(u, sigma_s * s_in, **extra_args)\n sigma_down_i_ratio = sigma_down / sigmas[i]\n x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i\n # print(\"sigma_i\", sigmas[i], \"sigma_ip1\", sigmas[i+1],\"sigma_down\", sigma_down, \"sigma_down_i_ratio\", sigma_down_i_ratio, \"sigma_s_i_ratio\", sigma_s_i_ratio, \"renoise_coeff\", renoise_coeff)\n # Noise addition\n if sigmas[i + 1] > 0 and eta > 0:\n x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff\n # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)\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 if len(sigmas) <= 1:\n return x\n\n extra_args = {} if extra_args is None else extra_args\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 s_in = x.new_ones([x.shape[0]])\n\n model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')\n sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)\n lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\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++\n lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])\n h = lambda_t - lambda_s\n lambda_s_1 = lambda_s + r * h\n fac = 1 / (2 * r)\n\n sigma_s_1 = sigma_fn(lambda_s_1)\n\n alpha_s = sigmas[i] * lambda_s.exp()\n alpha_s_1 = sigma_s_1 * lambda_s_1.exp()\n alpha_t = sigmas[i + 1] * lambda_t.exp()\n\n # Step 1\n sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta)\n lambda_s_1_ = sd.log().neg()\n h_ = lambda_s_1_ - lambda_s\n x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised\n if eta > 0 and s_noise > 0:\n x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su\n denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)\n\n # Step 2\n sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta)\n lambda_t_ = sd.log().neg()\n h_ = lambda_t_ - lambda_s\n denoised_d = (1 - fac) * denoised + fac * denoised_2\n x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d\n if eta > 0 and s_noise > 0:\n x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * 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\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 if len(sigmas) <= 1:\n return x\n\n if solver_type not in {'heun', 'midpoint'}:\n raise ValueError('solver_type must be \\'heun\\' or \\'midpoint\\'')\n\n extra_args = {} if extra_args is None else extra_args\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 s_in = x.new_ones([x.shape[0]])\n\n model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')\n lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\n\n old_denoised = None\n h, h_last = 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 # DPM-Solver++(2M) SDE\n lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])\n h = lambda_t - lambda_s\n h_eta = h * (eta + 1)\n\n alpha_t = sigmas[i + 1] * lambda_t.exp()\n\n x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised\n\n if old_denoised is not None:\n r = h_last / h\n if solver_type == 'heun':\n x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)\n elif solver_type == 'midpoint':\n x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)\n\n if eta > 0 and s_noise > 0:\n x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise\n\n old_denoised = denoised\n h_last = h\n return x\n\n\n@torch.no_grad()\ndef sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):\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\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 if len(sigmas) <= 1:\n return x\n\n extra_args = {} if extra_args is None else extra_args\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 s_in = x.new_ones([x.shape[0]])\n\n model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')\n lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\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 lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])\n h = lambda_t - lambda_s\n h_eta = h * (eta + 1)\n\n alpha_t = sigmas[i + 1] * lambda_t.exp()\n\n x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised\n\n if h_2 is not None:\n # DPM-Solver++(3M) SDE\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 + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2\n elif h_1 is not None:\n # DPM-Solver++(2M) SDE\n r = h_1 / h\n d = (denoised - denoised_1) / r\n phi_2 = h_eta.neg().expm1() / h_eta + 1\n x = x + (alpha_t * phi_2) * d\n\n if eta > 0 and s_noise > 0:\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\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 if len(sigmas) <= 1:\n return x\n extra_args = {} if extra_args is None else extra_args\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\n@torch.no_grad()\ndef sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):\n if len(sigmas) <= 1:\n return x\n extra_args = {} if extra_args is None else extra_args\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_heun(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\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 if len(sigmas) <= 1:\n return x\n extra_args = {} if extra_args is None else extra_args\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\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 if len(sigmas) <= 1:\n return x\n extra_args = {} if extra_args is None else extra_args\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 seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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 seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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 = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)\n return x\n\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#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py\n#under Apache 2 license\ndef sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):\n extra_args = {} if extra_args is None else extra_args\n s_in = x.new_ones([x.shape[0]])\n\n x_next = x\n\n buffer_model = []\n for i in trange(len(sigmas) - 1, disable=disable):\n t_cur = sigmas[i]\n t_next = sigmas[i + 1]\n\n x_cur = x_next\n\n denoised = model(x_cur, t_cur * 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 d_cur = (x_cur - denoised) / t_cur\n\n order = min(max_order, i+1)\n if t_next == 0: # Denoising step\n x_next = denoised\n elif order == 1: # First Euler step.\n x_next = x_cur + (t_next - t_cur) * d_cur\n elif order == 2: # Use one history point.\n x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2\n elif order == 3: # Use two history points.\n x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12\n elif order == 4: # Use three history points.\n x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24\n\n if len(buffer_model) == max_order - 1:\n for k in range(max_order - 2):\n buffer_model[k] = buffer_model[k+1]\n buffer_model[-1] = d_cur\n else:\n buffer_model.append(d_cur)\n\n return x_next\n\n\n#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py\n#under Apache 2 license\ndef sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):\n extra_args = {} if extra_args is None else extra_args\n s_in = x.new_ones([x.shape[0]])\n\n x_next = x\n t_steps = sigmas\n\n buffer_model = []\n for i in trange(len(sigmas) - 1, disable=disable):\n t_cur = sigmas[i]\n t_next = sigmas[i + 1]\n\n x_cur = x_next\n\n denoised = model(x_cur, t_cur * 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 d_cur = (x_cur - denoised) / t_cur\n\n order = min(max_order, i+1)\n if t_next == 0: # Denoising step\n x_next = denoised\n elif order == 1: # First Euler step.\n x_next = x_cur + (t_next - t_cur) * d_cur\n elif order == 2: # Use one history point.\n h_n = (t_next - t_cur)\n h_n_1 = (t_cur - t_steps[i-1])\n coeff1 = (2 + (h_n / h_n_1)) / 2\n coeff2 = -(h_n / h_n_1) / 2\n x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])\n elif order == 3: # Use two history points.\n h_n = (t_next - t_cur)\n h_n_1 = (t_cur - t_steps[i-1])\n h_n_2 = (t_steps[i-1] - t_steps[i-2])\n temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2\n coeff1 = (2 + (h_n / h_n_1)) / 2 + temp\n coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp\n coeff3 = temp * h_n_1 / h_n_2\n x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])\n elif order == 4: # Use three history points.\n h_n = (t_next - t_cur)\n h_n_1 = (t_cur - t_steps[i-1])\n h_n_2 = (t_steps[i-1] - t_steps[i-2])\n h_n_3 = (t_steps[i-2] - t_steps[i-3])\n temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2\n temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \\\n * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))\n coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2\n coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2\n coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2\n coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2\n x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])\n\n if len(buffer_model) == max_order - 1:\n for k in range(max_order - 2):\n buffer_model[k] = buffer_model[k+1]\n buffer_model[-1] = d_cur.detach()\n else:\n buffer_model.append(d_cur.detach())\n\n return x_next\n\n\n#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py\n#under Apache 2 license\n@torch.no_grad()\ndef sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):\n extra_args = {} if extra_args is None else extra_args\n s_in = x.new_ones([x.shape[0]])\n\n x_next = x\n t_steps = sigmas\n\n coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)\n\n buffer_model = []\n for i in trange(len(sigmas) - 1, disable=disable):\n t_cur = sigmas[i]\n t_next = sigmas[i + 1]\n\n x_cur = x_next\n\n denoised = model(x_cur, t_cur * 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 d_cur = (x_cur - denoised) / t_cur\n\n order = min(max_order, i+1)\n if t_next <= 0:\n order = 1\n\n if order == 1: # First Euler step.\n x_next = x_cur + (t_next - t_cur) * d_cur\n elif order == 2: # Use one history point.\n coeff_cur, coeff_prev1 = coeff_list[i]\n x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]\n elif order == 3: # Use two history points.\n coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]\n x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]\n elif order == 4: # Use three history points.\n coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]\n x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]\n\n if len(buffer_model) == max_order - 1:\n for k in range(max_order - 2):\n buffer_model[k] = buffer_model[k+1]\n buffer_model[-1] = d_cur.detach()\n else:\n buffer_model.append(d_cur.detach())\n\n return x_next\n\n\n@torch.no_grad()\ndef sample_euler_ancestral_cfg_pp(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 (CFG++).\"\"\"\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n\n model_sampling = model.inner_model.model_patcher.get_model_object(\"model_sampling\")\n lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)\n\n uncond_denoised = None\n\n def post_cfg_function(args):\n nonlocal uncond_denoised\n uncond_denoised = args[\"uncond_denoised\"]\n return args[\"denoised\"]\n\n model_options = extra_args.get(\"model_options\", {}).copy()\n extra_args[\"model_options\"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)\n\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 if sigmas[i + 1] == 0:\n # Denoising step\n x = denoised\n else:\n alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp()\n alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp()\n d = to_d(x, sigmas[i], alpha_s * uncond_denoised) # to noise\n\n # DDIM stochastic sampling\n sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta)\n sigma_down = alpha_t * sigma_down\n\n # Euler method\n x = alpha_t * denoised + sigma_down * d\n if eta > 0 and s_noise > 0:\n x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up\n return x\n\n\n@torch.no_grad()\ndef sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):\n \"\"\"Euler method steps (CFG++).\"\"\"\n return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None)\n\n\n@torch.no_grad()\ndef sample_dpmpp_2s_ancestral_cfg_pp(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 seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n\n temp = [0]\n def post_cfg_function(args):\n temp[0] = args[\"uncond_denoised\"]\n return args[\"denoised\"]\n\n model_options = extra_args.get(\"model_options\", {}).copy()\n extra_args[\"model_options\"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)\n\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], temp[0])\n x = denoised + d * sigma_down\n else:\n # DPM-Solver++(2S)\n t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)\n # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird\n r = 1 / 2\n h = t_next - t\n s = t + r * h\n x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-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 + (denoised - temp[0])) - (-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@torch.no_grad()\ndef sample_dpmpp_2m_cfg_pp(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 t_fn = lambda sigma: sigma.log().neg()\n\n old_uncond_denoised = None\n uncond_denoised = None\n def post_cfg_function(args):\n nonlocal uncond_denoised\n uncond_denoised = args[\"uncond_denoised\"]\n return args[\"denoised\"]\n\n model_options = extra_args.get(\"model_options\", {}).copy()\n extra_args[\"model_options\"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)\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_uncond_denoised is None or sigmas[i + 1] == 0:\n denoised_mix = -torch.exp(-h) * uncond_denoised\n else:\n h_last = t - t_fn(sigmas[i - 1])\n r = h_last / h\n denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)\n x = denoised + denoised_mix + torch.exp(-h) * x\n old_uncond_denoised = uncond_denoised\n return x\n\n@torch.no_grad()\ndef res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) 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 phi1_fn = lambda t: torch.expm1(t) / t\n phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t\n\n old_sigma_down = None\n old_denoised = None\n uncond_denoised = None\n def post_cfg_function(args):\n nonlocal uncond_denoised\n uncond_denoised = args[\"uncond_denoised\"]\n return args[\"denoised\"]\n\n if cfg_pp:\n model_options = extra_args.get(\"model_options\", {}).copy()\n extra_args[\"model_options\"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)\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 or old_denoised is None:\n # Euler method\n if cfg_pp:\n d = to_d(x, sigmas[i], uncond_denoised)\n x = denoised + d * sigma_down\n else:\n d = to_d(x, sigmas[i], denoised)\n dt = sigma_down - sigmas[i]\n x = x + d * dt\n else:\n # Second order multistep method in https://arxiv.org/pdf/2308.02157\n t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1])\n h = t_next - t\n c2 = (t_prev - t_old) / h\n\n phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)\n b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)\n b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)\n\n if cfg_pp:\n x = x + (denoised - uncond_denoised)\n x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)\n else:\n x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)\n\n # Noise addition\n if sigmas[i + 1] > 0:\n x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up\n\n if cfg_pp:\n old_denoised = uncond_denoised\n else:\n old_denoised = denoised\n old_sigma_down = sigma_down\n return x\n\n@torch.no_grad()\ndef sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):\n return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)\n\n@torch.no_grad()\ndef sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):\n return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)\n\n@torch.no_grad()\ndef sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)\n\n@torch.no_grad()\ndef sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)\n\n\n@torch.no_grad()\ndef sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):\n \"\"\"Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK\"\"\"\n extra_args = {} if extra_args is None else extra_args\n s_in = x.new_ones([x.shape[0]])\n old_d = None\n\n uncond_denoised = None\n def post_cfg_function(args):\n nonlocal uncond_denoised\n uncond_denoised = args[\"uncond_denoised\"]\n return args[\"denoised\"]\n\n if cfg_pp:\n model_options = extra_args.get(\"model_options\", {}).copy()\n extra_args[\"model_options\"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)\n\n for i in trange(len(sigmas) - 1, disable=disable):\n denoised = model(x, sigmas[i] * s_in, **extra_args)\n if cfg_pp:\n d = to_d(x, sigmas[i], uncond_denoised)\n else:\n d = to_d(x, sigmas[i], denoised)\n if callback is not None:\n callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n dt = sigmas[i + 1] - sigmas[i]\n if sigmas[i + 1] == 0:\n # Denoising step\n x = denoised\n else:\n # Euler method\n if cfg_pp:\n x = denoised + d * sigmas[i + 1]\n else:\n x = x + d * dt\n\n if i >= 1:\n # Gradient estimation\n d_bar = (ge_gamma - 1) * (d - old_d)\n x = x + d_bar * dt\n old_d = d\n return x\n\n\n@torch.no_grad()\ndef sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):\n return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True)\n\n\n@torch.no_grad()\ndef sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3):\n \"\"\"Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169.\n Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.\n \"\"\"\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n s_in = x.new_ones([x.shape[0]])\n\n def default_er_sde_noise_scaler(x):\n return x * ((x ** 0.3).exp() + 10.0)\n\n noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler\n num_integration_points = 200.0\n point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)\n\n model_sampling = model.inner_model.model_patcher.get_model_object(\"model_sampling\")\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\n half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling)\n er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t\n\n old_denoised = None\n old_denoised_d = 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 stage_used = min(max_stage, i + 1)\n if sigmas[i + 1] == 0:\n x = denoised\n else:\n er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1]\n alpha_s = sigmas[i] / er_lambda_s\n alpha_t = sigmas[i + 1] / er_lambda_t\n r_alpha = alpha_t / alpha_s\n r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s)\n\n # Stage 1 Euler\n x = r_alpha * r * x + alpha_t * (1 - r) * denoised\n\n if stage_used >= 2:\n dt = er_lambda_t - er_lambda_s\n lambda_step_size = -dt / num_integration_points\n lambda_pos = er_lambda_t + point_indice * lambda_step_size\n scaled_pos = noise_scaler(lambda_pos)\n\n # Stage 2\n s = torch.sum(1 / scaled_pos) * lambda_step_size\n denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1])\n x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d\n\n if stage_used >= 3:\n # Stage 3\n s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size\n denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2)\n x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u\n old_denoised_d = denoised_d\n\n if s_noise > 0:\n x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)\n old_denoised = denoised\n return x\n\n\n@torch.no_grad()\ndef sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5, solver_type=\"phi_1\"):\n \"\"\"SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.\n arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)\n \"\"\"\n if solver_type not in {\"phi_1\", \"phi_2\"}:\n raise ValueError(\"solver_type must be 'phi_1' or 'phi_2'\")\n\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n s_in = x.new_ones([x.shape[0]])\n inject_noise = eta > 0 and s_noise > 0\n\n model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')\n sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)\n lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\n\n fac = 1 / (2 * r)\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\n if sigmas[i + 1] == 0:\n x = denoised\n continue\n\n lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])\n h = lambda_t - lambda_s\n h_eta = h * (eta + 1)\n lambda_s_1 = torch.lerp(lambda_s, lambda_t, r)\n sigma_s_1 = sigma_fn(lambda_s_1)\n\n alpha_s_1 = sigma_s_1 * lambda_s_1.exp()\n alpha_t = sigmas[i + 1] * lambda_t.exp()\n\n # Step 1\n x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised\n if inject_noise:\n sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)\n x_2 = x_2 + sde_noise * sigma_s_1 * s_noise\n denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)\n\n # Step 2\n if solver_type == \"phi_1\":\n denoised_d = torch.lerp(denoised, denoised_2, fac)\n x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d\n elif solver_type == \"phi_2\":\n b2 = ei_h_phi_2(-h_eta) / r\n b1 = ei_h_phi_1(-h_eta) - b2\n x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b2 * denoised_2)\n\n if inject_noise:\n segment_factor = (r - 1) * h * eta\n sde_noise = sde_noise * segment_factor.exp()\n sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1])\n x = x + sde_noise * sigmas[i + 1] * s_noise\n return x\n\n@torch.no_grad()\ndef sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=None, disable=None, solver_type=\"phi_2\"):\n \"\"\"Deterministic exponential Heun second order method in data prediction (x0) and logSNR time.\"\"\"\n return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None, r=1.0, solver_type=solver_type)\n\n\n@torch.no_grad()\ndef sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type=\"phi_2\"):\n \"\"\"Stochastic exponential Heun second order method in data prediction (x0) and logSNR time.\"\"\"\n return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=1.0, solver_type=solver_type)\n\n\n@torch.no_grad()\ndef sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):\n \"\"\"SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3.\n arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)\n \"\"\"\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n s_in = x.new_ones([x.shape[0]])\n inject_noise = eta > 0 and s_noise > 0\n\n model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')\n sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)\n lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\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\n if sigmas[i + 1] == 0:\n x = denoised\n continue\n\n lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])\n h = lambda_t - lambda_s\n h_eta = h * (eta + 1)\n lambda_s_1 = torch.lerp(lambda_s, lambda_t, r_1)\n lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2)\n sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)\n\n alpha_s_1 = sigma_s_1 * lambda_s_1.exp()\n alpha_s_2 = sigma_s_2 * lambda_s_2.exp()\n alpha_t = sigmas[i + 1] * lambda_t.exp()\n\n # Step 1\n x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised\n if inject_noise:\n sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)\n x_2 = x_2 + sde_noise * sigma_s_1 * s_noise\n denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)\n\n # Step 2\n a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta)\n a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2\n x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2)\n if inject_noise:\n segment_factor = (r_1 - r_2) * h * eta\n sde_noise = sde_noise * segment_factor.exp()\n sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2)\n x_3 = x_3 + sde_noise * sigma_s_2 * s_noise\n denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)\n\n # Step 3\n b3 = ei_h_phi_2(-h_eta) / r_2\n b1 = ei_h_phi_1(-h_eta) - b3\n x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3)\n if inject_noise:\n segment_factor = (r_2 - 1) * h * eta\n sde_noise = sde_noise * segment_factor.exp()\n sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1])\n x = x + sde_noise * sigmas[i + 1] * s_noise\n return x\n\n\n@torch.no_grad()\ndef sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False):\n \"\"\"Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023).\"\"\"\n if len(sigmas) <= 1:\n return x\n extra_args = {} if extra_args is None else extra_args\n seed = extra_args.get(\"seed\", None)\n noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler\n s_in = x.new_ones([x.shape[0]])\n\n model_sampling = model.inner_model.model_patcher.get_model_object(\"model_sampling\")\n sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)\n lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)\n\n if tau_func is None:\n # Use default interval for stochastic sampling\n start_sigma = model_sampling.percent_to_sigma(0.2)\n end_sigma = model_sampling.percent_to_sigma(0.8)\n tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0)\n\n max_used_order = max(predictor_order, corrector_order)\n x_pred = x # x: current state, x_pred: predicted next state\n\n h = 0.0\n tau_t = 0.0\n noise = 0.0\n pred_list = []\n\n # Lower order near the end to improve stability\n lower_order_to_end = sigmas[-1].item() == 0\n\n for i in trange(len(sigmas) - 1, disable=disable):\n # Evaluation\n denoised = model(x_pred, sigmas[i] * s_in, **extra_args)\n if callback is not None:\n callback({\"x\": x_pred, \"i\": i, \"sigma\": sigmas[i], \"sigma_hat\": sigmas[i], \"denoised\": denoised})\n pred_list.append(denoised)\n pred_list = pred_list[-max_used_order:]\n\n predictor_order_used = min(predictor_order, len(pred_list))\n if i == 0 or (sigmas[i + 1] == 0 and not use_pece):\n corrector_order_used = 0\n else:\n corrector_order_used = min(corrector_order, len(pred_list))\n\n if lower_order_to_end:\n predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i)\n corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i)\n\n # Corrector\n if corrector_order_used == 0:\n # Update by the predicted state\n x = x_pred\n else:\n curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1]\n b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(\n sigmas[i],\n curr_lambdas,\n lambdas[i - 1],\n lambdas[i],\n tau_t,\n simple_order_2,\n is_corrector_step=True,\n )\n pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...)\n corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...)\n x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res\n\n if tau_t > 0 and s_noise > 0:\n # The noise from the previous predictor step\n x = x + noise\n\n if use_pece:\n # Evaluate the corrected state\n denoised = model(x, sigmas[i] * s_in, **extra_args)\n pred_list[-1] = denoised\n\n # Predictor\n if sigmas[i + 1] == 0:\n # Denoising step\n x_pred = denoised\n else:\n tau_t = tau_func(sigmas[i + 1])\n curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]\n b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(\n sigmas[i + 1],\n curr_lambdas,\n lambdas[i],\n lambdas[i + 1],\n tau_t,\n simple_order_2,\n is_corrector_step=False,\n )\n pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...)\n pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...)\n h = lambdas[i + 1] - lambdas[i]\n x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res\n\n if tau_t > 0 and s_noise > 0:\n noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise\n x_pred = x_pred + noise\n return x_pred\n\n\n@torch.no_grad()\ndef sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):\n \"\"\"Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023).\"\"\"\n return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)\n" }, { "path": "comfy/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": "comfy/latent_formats.py", "content": "import torch\n\nclass LatentFormat:\n scale_factor = 1.0\n latent_channels = 4\n latent_dimensions = 2\n latent_rgb_factors = None\n latent_rgb_factors_bias = None\n latent_rgb_factors_reshape = None\n taesd_decoder_name = None\n spacial_downscale_ratio = 8\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 scale_factor = 0.13025\n\n def __init__(self):\n self.latent_rgb_factors = [\n # R G B\n [ 0.3651, 0.4232, 0.4341],\n [-0.2533, -0.0042, 0.1068],\n [ 0.1076, 0.1111, -0.0362],\n [-0.3165, -0.2492, -0.2188]\n ]\n self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011]\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 latent_channels = 16\n spacial_downscale_ratio = 42\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 spacial_downscale_ratio = 4\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 ]\n\nclass SD3(LatentFormat):\n latent_channels = 16\n def __init__(self):\n self.scale_factor = 1.5305\n self.shift_factor = 0.0609\n self.latent_rgb_factors = [\n [-0.0922, -0.0175, 0.0749],\n [ 0.0311, 0.0633, 0.0954],\n [ 0.1994, 0.0927, 0.0458],\n [ 0.0856, 0.0339, 0.0902],\n [ 0.0587, 0.0272, -0.0496],\n [-0.0006, 0.1104, 0.0309],\n [ 0.0978, 0.0306, 0.0427],\n [-0.0042, 0.1038, 0.1358],\n [-0.0194, 0.0020, 0.0669],\n [-0.0488, 0.0130, -0.0268],\n [ 0.0922, 0.0988, 0.0951],\n [-0.0278, 0.0524, -0.0542],\n [ 0.0332, 0.0456, 0.0895],\n [-0.0069, -0.0030, -0.0810],\n [-0.0596, -0.0465, -0.0293],\n [-0.1448, -0.1463, -0.1189]\n ]\n self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925]\n self.taesd_decoder_name = \"taesd3_decoder\"\n\n def process_in(self, latent):\n return (latent - self.shift_factor) * self.scale_factor\n\n def process_out(self, latent):\n return (latent / self.scale_factor) + self.shift_factor\n\nclass StableAudio1(LatentFormat):\n latent_channels = 64\n latent_dimensions = 1\n\nclass Flux(SD3):\n latent_channels = 16\n def __init__(self):\n self.scale_factor = 0.3611\n self.shift_factor = 0.1159\n self.latent_rgb_factors =[\n [-0.0346, 0.0244, 0.0681],\n [ 0.0034, 0.0210, 0.0687],\n [ 0.0275, -0.0668, -0.0433],\n [-0.0174, 0.0160, 0.0617],\n [ 0.0859, 0.0721, 0.0329],\n [ 0.0004, 0.0383, 0.0115],\n [ 0.0405, 0.0861, 0.0915],\n [-0.0236, -0.0185, -0.0259],\n [-0.0245, 0.0250, 0.1180],\n [ 0.1008, 0.0755, -0.0421],\n [-0.0515, 0.0201, 0.0011],\n [ 0.0428, -0.0012, -0.0036],\n [ 0.0817, 0.0765, 0.0749],\n [-0.1264, -0.0522, -0.1103],\n [-0.0280, -0.0881, -0.0499],\n [-0.1262, -0.0982, -0.0778]\n ]\n self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]\n self.taesd_decoder_name = \"taef1_decoder\"\n\n def process_in(self, latent):\n return (latent - self.shift_factor) * self.scale_factor\n\n def process_out(self, latent):\n return (latent / self.scale_factor) + self.shift_factor\n\nclass Flux2(LatentFormat):\n latent_channels = 128\n spacial_downscale_ratio = 16\n\n def __init__(self):\n self.latent_rgb_factors =[\n [0.0058, 0.0113, 0.0073],\n [0.0495, 0.0443, 0.0836],\n [-0.0099, 0.0096, 0.0644],\n [0.2144, 0.3009, 0.3652],\n [0.0166, -0.0039, -0.0054],\n [0.0157, 0.0103, -0.0160],\n [-0.0398, 0.0902, -0.0235],\n [-0.0052, 0.0095, 0.0109],\n [-0.3527, -0.2712, -0.1666],\n [-0.0301, -0.0356, -0.0180],\n [-0.0107, 0.0078, 0.0013],\n [0.0746, 0.0090, -0.0941],\n [0.0156, 0.0169, 0.0070],\n [-0.0034, -0.0040, -0.0114],\n [0.0032, 0.0181, 0.0080],\n [-0.0939, -0.0008, 0.0186],\n [0.0018, 0.0043, 0.0104],\n [0.0284, 0.0056, -0.0127],\n [-0.0024, -0.0022, -0.0030],\n [0.1207, -0.0026, 0.0065],\n [0.0128, 0.0101, 0.0142],\n [0.0137, -0.0072, -0.0007],\n [0.0095, 0.0092, -0.0059],\n [0.0000, -0.0077, -0.0049],\n [-0.0465, -0.0204, -0.0312],\n [0.0095, 0.0012, -0.0066],\n [0.0290, -0.0034, 0.0025],\n [0.0220, 0.0169, -0.0048],\n [-0.0332, -0.0457, -0.0468],\n [-0.0085, 0.0389, 0.0609],\n [-0.0076, 0.0003, -0.0043],\n [-0.0111, -0.0460, -0.0614],\n ]\n\n self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]\n self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)\n\n def process_in(self, latent):\n return latent\n\n def process_out(self, latent):\n return latent\n\nclass Mochi(LatentFormat):\n latent_channels = 12\n latent_dimensions = 3\n\n def __init__(self):\n self.scale_factor = 1.0\n self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141,\n -0.05565264470995561, 0.012767231469026969, -0.04703542746246419,\n 0.043896967884726704, -0.09346305707025976, -0.09918314763016893,\n -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1)\n self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617,\n 0.959253732819592, 0.8244560132752793, 0.917259975397747,\n 0.9294154431013696, 1.3720942357788521, 0.881393668867029,\n 0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1)\n\n self.latent_rgb_factors =[\n [-0.0069, -0.0045, 0.0018],\n [ 0.0154, -0.0692, -0.0274],\n [ 0.0333, 0.0019, 0.0206],\n [-0.1390, 0.0628, 0.1678],\n [-0.0725, 0.0134, -0.1898],\n [ 0.0074, -0.0270, -0.0209],\n [-0.0176, -0.0277, -0.0221],\n [ 0.5294, 0.5204, 0.3852],\n [-0.0326, -0.0446, -0.0143],\n [-0.0659, 0.0153, -0.0153],\n [ 0.0185, -0.0217, 0.0014],\n [-0.0396, -0.0495, -0.0281]\n ]\n self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453]\n self.taesd_decoder_name = None #TODO\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\nclass LTXV(LatentFormat):\n latent_channels = 128\n latent_dimensions = 3\n spacial_downscale_ratio = 32\n\n def __init__(self):\n self.latent_rgb_factors = [\n [ 1.1202e-02, -6.3815e-04, -1.0021e-02],\n [ 8.6031e-02, 6.5813e-02, 9.5409e-04],\n [-1.2576e-02, -7.5734e-03, -4.0528e-03],\n [ 9.4063e-03, -2.1688e-03, 2.6093e-03],\n [ 3.7636e-03, 1.2765e-02, 9.1548e-03],\n [ 2.1024e-02, -5.2973e-03, 3.4373e-03],\n [-8.8896e-03, -1.9703e-02, -1.8761e-02],\n [-1.3160e-02, -1.0523e-02, 1.9709e-03],\n [-1.5152e-03, -6.9891e-03, -7.5810e-03],\n [-1.7247e-03, 4.6560e-04, -3.3839e-03],\n [ 1.3617e-02, 4.7077e-03, -2.0045e-03],\n [ 1.0256e-02, 7.7318e-03, 1.3948e-02],\n [-1.6108e-02, -6.2151e-03, 1.1561e-03],\n [ 7.3407e-03, 1.5628e-02, 4.4865e-04],\n [ 9.5357e-04, -2.9518e-03, -1.4760e-02],\n [ 1.9143e-02, 1.0868e-02, 1.2264e-02],\n [ 4.4575e-03, 3.6682e-05, -6.8508e-03],\n [-4.5681e-04, 3.2570e-03, 7.7929e-03],\n [ 3.3902e-02, 3.3405e-02, 3.7454e-02],\n [-2.3001e-02, -2.4877e-03, -3.1033e-03],\n [ 5.0265e-02, 3.8841e-02, 3.3539e-02],\n [-4.1018e-03, -1.1095e-03, 1.5859e-03],\n [-1.2689e-01, -1.3107e-01, -2.1005e-01],\n [ 2.6276e-02, 1.4189e-02, -3.5963e-03],\n [-4.8679e-03, 8.8486e-03, 7.8029e-03],\n [-1.6610e-03, -4.8597e-03, -5.2060e-03],\n [-2.1010e-03, 2.3610e-03, 9.3796e-03],\n [-2.2482e-02, -2.1305e-02, -1.5087e-02],\n [-1.5753e-02, -1.0646e-02, -6.5083e-03],\n [-4.6975e-03, 5.0288e-03, -6.7390e-03],\n [ 1.1951e-02, 2.0712e-02, 1.6191e-02],\n [-6.3704e-03, -8.4827e-03, -9.5483e-03],\n [ 7.2610e-03, -9.9326e-03, -2.2978e-02],\n [-9.1904e-04, 6.2882e-03, 9.5720e-03],\n [-3.7178e-02, -3.7123e-02, -5.6713e-02],\n [-1.3373e-01, -1.0720e-01, -5.3801e-02],\n [-5.3702e-03, 8.1256e-03, 8.8397e-03],\n [-1.5247e-01, -2.1437e-01, -2.1843e-01],\n [ 3.1441e-02, 7.0335e-03, -9.7541e-03],\n [ 2.1528e-03, -8.9817e-03, -2.1023e-02],\n [ 3.8461e-03, -5.8957e-03, -1.5014e-02],\n [-4.3470e-03, -1.2940e-02, -1.5972e-02],\n [-5.4781e-03, -1.0842e-02, -3.0204e-03],\n [-6.5347e-03, 3.0806e-03, -1.0163e-02],\n [-5.0414e-03, -7.1503e-03, -8.9686e-04],\n [-8.5851e-03, -2.4351e-03, 1.0674e-03],\n [-9.0016e-03, -9.6493e-03, 1.5692e-03],\n [ 5.0914e-03, 1.2099e-02, 1.9968e-02],\n [ 1.3758e-02, 1.1669e-02, 8.1958e-03],\n [-1.0518e-02, -1.1575e-02, -4.1307e-03],\n [-2.8410e-02, -3.1266e-02, -2.2149e-02],\n [ 2.9336e-03, 3.6511e-02, 1.8717e-02],\n [-1.6703e-02, -1.6696e-02, -4.4529e-03],\n [ 4.8818e-02, 4.0063e-02, 8.7410e-03],\n [-1.5066e-02, -5.7328e-04, 2.9785e-03],\n [-1.7613e-02, -8.1034e-03, 1.3086e-02],\n [-9.2633e-03, 1.0803e-02, -6.3489e-03],\n [ 3.0851e-03, 4.7750e-04, 1.2347e-02],\n [-2.2785e-02, -2.3043e-02, -2.6005e-02],\n [-2.4787e-02, -1.5389e-02, -2.2104e-02],\n [-2.3572e-02, 1.0544e-03, 1.2361e-02],\n [-7.8915e-03, -1.2271e-03, -6.0968e-03],\n [-1.1478e-02, -1.2543e-03, 6.2679e-03],\n [-5.4229e-02, 2.6644e-02, 6.3394e-03],\n [ 4.4216e-03, -7.3338e-03, -1.0464e-02],\n [-4.5013e-03, 1.6082e-03, 1.4420e-02],\n [ 1.3673e-02, 8.8877e-03, 4.1253e-03],\n [-1.0145e-02, 9.0072e-03, 1.5695e-02],\n [-5.6234e-03, 1.1847e-03, 8.1261e-03],\n [-3.7171e-03, -5.3538e-03, 1.2590e-03],\n [ 2.9476e-02, 2.1424e-02, 3.0424e-02],\n [-3.4925e-02, -2.4340e-02, -2.5316e-02],\n [-3.4127e-02, -2.2406e-02, -1.0589e-02],\n [-1.7342e-02, -1.3249e-02, -1.0719e-02],\n [-2.1478e-03, -8.6051e-03, -2.9878e-03],\n [ 1.2089e-03, -4.2391e-03, -6.8569e-03],\n [ 9.0411e-04, -6.6886e-03, -6.7547e-05],\n [ 1.6048e-02, -1.0057e-02, -2.8929e-02],\n [ 1.2290e-03, 1.0163e-02, 1.8861e-02],\n [ 1.7264e-02, 2.7257e-04, 1.3785e-02],\n [-1.3482e-02, -3.6427e-03, 6.7481e-04],\n [ 4.6782e-03, -5.2423e-03, 2.4467e-03],\n [-5.9113e-03, -6.2244e-03, -1.8162e-03],\n [ 1.5496e-02, 1.4582e-02, 1.9514e-03],\n [ 7.4958e-03, 1.5886e-03, -8.2305e-03],\n [ 1.9086e-02, 1.6360e-03, -3.9674e-03],\n [-5.7021e-03, -2.7307e-03, -4.1066e-03],\n [ 1.7450e-03, 1.4602e-02, 2.5794e-02],\n [-8.2788e-04, 2.2902e-03, 4.5161e-03],\n [ 1.1632e-02, 8.9193e-03, -7.2813e-03],\n [ 7.5721e-03, 2.6784e-03, 1.1393e-02],\n [ 5.1939e-03, 3.6903e-03, 1.4049e-02],\n [-1.8383e-02, -2.2529e-02, -2.4477e-02],\n [ 5.8842e-04, -5.7874e-03, -1.4770e-02],\n [-1.6125e-02, -8.6101e-03, -1.4533e-02],\n [ 2.0540e-02, 2.0729e-02, 6.4338e-03],\n [ 3.3587e-03, -1.1226e-02, -1.6444e-02],\n [-1.4742e-03, -1.0489e-02, 1.7097e-03],\n [ 2.8130e-02, 2.3546e-02, 3.2791e-02],\n [-1.8532e-02, -1.2842e-02, -8.7756e-03],\n [-8.0533e-03, -1.0771e-02, -1.7536e-02],\n [-3.9009e-03, 1.6150e-02, 3.3359e-02],\n [-7.4554e-03, -1.4154e-02, -6.1910e-03],\n [ 3.4734e-03, -1.1370e-02, -1.0581e-02],\n [ 1.1476e-02, 3.9281e-03, 2.8231e-03],\n [ 7.1639e-03, -1.4741e-03, -3.8066e-03],\n [ 2.2250e-03, -8.7552e-03, -9.5719e-03],\n [ 2.4146e-02, 2.1696e-02, 2.8056e-02],\n [-5.4365e-03, -2.4291e-02, -1.7802e-02],\n [ 7.4263e-03, 1.0510e-02, 1.2705e-02],\n [ 6.2669e-03, 6.2658e-03, 1.9211e-02],\n [ 1.6378e-02, 9.4933e-03, 6.6971e-03],\n [ 1.7173e-02, 2.3601e-02, 2.3296e-02],\n [-1.4568e-02, -9.8279e-03, -1.1556e-02],\n [ 1.4431e-02, 1.4430e-02, 6.6362e-03],\n [-6.8230e-03, 1.8863e-02, 1.4555e-02],\n [ 6.1156e-03, 3.4700e-03, -2.6662e-03],\n [-2.6983e-03, -5.9402e-03, -9.2276e-03],\n [ 1.0235e-02, 7.4173e-03, -7.6243e-03],\n [-1.3255e-02, 1.9322e-02, -9.2153e-04],\n [ 2.4222e-03, -4.8039e-03, -1.5759e-02],\n [ 2.6244e-02, 2.5951e-02, 2.0249e-02],\n [ 1.5711e-02, 1.8498e-02, 2.7407e-03],\n [-2.1714e-03, 4.7214e-03, -2.2443e-02],\n [-7.4747e-03, 7.4166e-03, 1.4430e-02],\n [-8.3906e-03, -7.9776e-03, 9.7927e-03],\n [ 3.8321e-02, 9.6622e-03, -1.9268e-02],\n [-1.4605e-02, -6.7032e-03, 3.9675e-03]\n ]\n\n self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]\n\nclass LTXAV(LTXV):\n def __init__(self):\n self.latent_rgb_factors = None\n self.latent_rgb_factors_bias = None\n\nclass HunyuanVideo(LatentFormat):\n latent_channels = 16\n latent_dimensions = 3\n scale_factor = 0.476986\n latent_rgb_factors = [\n [-0.0395, -0.0331, 0.0445],\n [ 0.0696, 0.0795, 0.0518],\n [ 0.0135, -0.0945, -0.0282],\n [ 0.0108, -0.0250, -0.0765],\n [-0.0209, 0.0032, 0.0224],\n [-0.0804, -0.0254, -0.0639],\n [-0.0991, 0.0271, -0.0669],\n [-0.0646, -0.0422, -0.0400],\n [-0.0696, -0.0595, -0.0894],\n [-0.0799, -0.0208, -0.0375],\n [ 0.1166, 0.1627, 0.0962],\n [ 0.1165, 0.0432, 0.0407],\n [-0.2315, -0.1920, -0.1355],\n [-0.0270, 0.0401, -0.0821],\n [-0.0616, -0.0997, -0.0727],\n [ 0.0249, -0.0469, -0.1703]\n ]\n\n latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]\n taesd_decoder_name = \"taehv\"\n\nclass Cosmos1CV8x8x8(LatentFormat):\n latent_channels = 16\n latent_dimensions = 3\n\n latent_rgb_factors = [\n [ 0.1817, 0.2284, 0.2423],\n [-0.0586, -0.0862, -0.3108],\n [-0.4703, -0.4255, -0.3995],\n [ 0.0803, 0.1963, 0.1001],\n [-0.0820, -0.1050, 0.0400],\n [ 0.2511, 0.3098, 0.2787],\n [-0.1830, -0.2117, -0.0040],\n [-0.0621, -0.2187, -0.0939],\n [ 0.3619, 0.1082, 0.1455],\n [ 0.3164, 0.3922, 0.2575],\n [ 0.1152, 0.0231, -0.0462],\n [-0.1434, -0.3609, -0.3665],\n [ 0.0635, 0.1471, 0.1680],\n [-0.3635, -0.1963, -0.3248],\n [-0.1865, 0.0365, 0.2346],\n [ 0.0447, 0.0994, 0.0881]\n ]\n\n latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976]\n\nclass Wan21(LatentFormat):\n latent_channels = 16\n latent_dimensions = 3\n\n latent_rgb_factors = [\n [-0.1299, -0.1692, 0.2932],\n [ 0.0671, 0.0406, 0.0442],\n [ 0.3568, 0.2548, 0.1747],\n [ 0.0372, 0.2344, 0.1420],\n [ 0.0313, 0.0189, -0.0328],\n [ 0.0296, -0.0956, -0.0665],\n [-0.3477, -0.4059, -0.2925],\n [ 0.0166, 0.1902, 0.1975],\n [-0.0412, 0.0267, -0.1364],\n [-0.1293, 0.0740, 0.1636],\n [ 0.0680, 0.3019, 0.1128],\n [ 0.0032, 0.0581, 0.0639],\n [-0.1251, 0.0927, 0.1699],\n [ 0.0060, -0.0633, 0.0005],\n [ 0.3477, 0.2275, 0.2950],\n [ 0.1984, 0.0913, 0.1861]\n ]\n\n latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360]\n\n def __init__(self):\n self.scale_factor = 1.0\n self.latents_mean = torch.tensor([\n -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,\n 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921\n ]).view(1, self.latent_channels, 1, 1, 1)\n self.latents_std = torch.tensor([\n 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,\n 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160\n ]).view(1, self.latent_channels, 1, 1, 1)\n\n\n self.taesd_decoder_name = \"lighttaew2_1\"\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\nclass Wan22(Wan21):\n latent_channels = 48\n latent_dimensions = 3\n spacial_downscale_ratio = 16\n\n latent_rgb_factors = [\n [ 0.0119, 0.0103, 0.0046],\n [-0.1062, -0.0504, 0.0165],\n [ 0.0140, 0.0409, 0.0491],\n [-0.0813, -0.0677, 0.0607],\n [ 0.0656, 0.0851, 0.0808],\n [ 0.0264, 0.0463, 0.0912],\n [ 0.0295, 0.0326, 0.0590],\n [-0.0244, -0.0270, 0.0025],\n [ 0.0443, -0.0102, 0.0288],\n [-0.0465, -0.0090, -0.0205],\n [ 0.0359, 0.0236, 0.0082],\n [-0.0776, 0.0854, 0.1048],\n [ 0.0564, 0.0264, 0.0561],\n [ 0.0006, 0.0594, 0.0418],\n [-0.0319, -0.0542, -0.0637],\n [-0.0268, 0.0024, 0.0260],\n [ 0.0539, 0.0265, 0.0358],\n [-0.0359, -0.0312, -0.0287],\n [-0.0285, -0.1032, -0.1237],\n [ 0.1041, 0.0537, 0.0622],\n [-0.0086, -0.0374, -0.0051],\n [ 0.0390, 0.0670, 0.2863],\n [ 0.0069, 0.0144, 0.0082],\n [ 0.0006, -0.0167, 0.0079],\n [ 0.0313, -0.0574, -0.0232],\n [-0.1454, -0.0902, -0.0481],\n [ 0.0714, 0.0827, 0.0447],\n [-0.0304, -0.0574, -0.0196],\n [ 0.0401, 0.0384, 0.0204],\n [-0.0758, -0.0297, -0.0014],\n [ 0.0568, 0.1307, 0.1372],\n [-0.0055, -0.0310, -0.0380],\n [ 0.0239, -0.0305, 0.0325],\n [-0.0663, -0.0673, -0.0140],\n [-0.0416, -0.0047, -0.0023],\n [ 0.0166, 0.0112, -0.0093],\n [-0.0211, 0.0011, 0.0331],\n [ 0.1833, 0.1466, 0.2250],\n [-0.0368, 0.0370, 0.0295],\n [-0.3441, -0.3543, -0.2008],\n [-0.0479, -0.0489, -0.0420],\n [-0.0660, -0.0153, 0.0800],\n [-0.0101, 0.0068, 0.0156],\n [-0.0690, -0.0452, -0.0927],\n [-0.0145, 0.0041, 0.0015],\n [ 0.0421, 0.0451, 0.0373],\n [ 0.0504, -0.0483, -0.0356],\n [-0.0837, 0.0168, 0.0055]\n ]\n\n latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388]\n\n def __init__(self):\n self.scale_factor = 1.0\n self.taesd_decoder_name = \"lighttaew2_2\"\n self.latents_mean = torch.tensor([\n -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,\n -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,\n -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502,\n -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230,\n -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748,\n 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667,\n ]).view(1, self.latent_channels, 1, 1, 1)\n self.latents_std = torch.tensor([\n 0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,\n 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,\n 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,\n 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,\n 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,\n 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744\n ]).view(1, self.latent_channels, 1, 1, 1)\n\nclass HunyuanImage21(LatentFormat):\n latent_channels = 64\n latent_dimensions = 2\n spacial_downscale_ratio = 32\n scale_factor = 0.75289\n\n latent_rgb_factors = [\n [-0.0154, -0.0397, -0.0521],\n [ 0.0005, 0.0093, 0.0006],\n [-0.0805, -0.0773, -0.0586],\n [-0.0494, -0.0487, -0.0498],\n [-0.0212, -0.0076, -0.0261],\n [-0.0179, -0.0417, -0.0505],\n [ 0.0158, 0.0310, 0.0239],\n [ 0.0409, 0.0516, 0.0201],\n [ 0.0350, 0.0553, 0.0036],\n [-0.0447, -0.0327, -0.0479],\n [-0.0038, -0.0221, -0.0365],\n [-0.0423, -0.0718, -0.0654],\n [ 0.0039, 0.0368, 0.0104],\n [ 0.0655, 0.0217, 0.0122],\n [ 0.0490, 0.1638, 0.2053],\n [ 0.0932, 0.0829, 0.0650],\n [-0.0186, -0.0209, -0.0135],\n [-0.0080, -0.0076, -0.0148],\n [-0.0284, -0.0201, 0.0011],\n [-0.0642, -0.0294, -0.0777],\n [-0.0035, 0.0076, -0.0140],\n [ 0.0519, 0.0731, 0.0887],\n [-0.0102, 0.0095, 0.0704],\n [ 0.0068, 0.0218, -0.0023],\n [-0.0726, -0.0486, -0.0519],\n [ 0.0260, 0.0295, 0.0263],\n [ 0.0250, 0.0333, 0.0341],\n [ 0.0168, -0.0120, -0.0174],\n [ 0.0226, 0.1037, 0.0114],\n [ 0.2577, 0.1906, 0.1604],\n [-0.0646, -0.0137, -0.0018],\n [-0.0112, 0.0309, 0.0358],\n [-0.0347, 0.0146, -0.0481],\n [ 0.0234, 0.0179, 0.0201],\n [ 0.0157, 0.0313, 0.0225],\n [ 0.0423, 0.0675, 0.0524],\n [-0.0031, 0.0027, -0.0255],\n [ 0.0447, 0.0555, 0.0330],\n [-0.0152, 0.0103, 0.0299],\n [-0.0755, -0.0489, -0.0635],\n [ 0.0853, 0.0788, 0.1017],\n [-0.0272, -0.0294, -0.0471],\n [ 0.0440, 0.0400, -0.0137],\n [ 0.0335, 0.0317, -0.0036],\n [-0.0344, -0.0621, -0.0984],\n [-0.0127, -0.0630, -0.0620],\n [-0.0648, 0.0360, 0.0924],\n [-0.0781, -0.0801, -0.0409],\n [ 0.0363, 0.0613, 0.0499],\n [ 0.0238, 0.0034, 0.0041],\n [-0.0135, 0.0258, 0.0310],\n [ 0.0614, 0.1086, 0.0589],\n [ 0.0428, 0.0350, 0.0205],\n [ 0.0153, 0.0173, -0.0018],\n [-0.0288, -0.0455, -0.0091],\n [ 0.0344, 0.0109, -0.0157],\n [-0.0205, -0.0247, -0.0187],\n [ 0.0487, 0.0126, 0.0064],\n [-0.0220, -0.0013, 0.0074],\n [-0.0203, -0.0094, -0.0048],\n [-0.0719, 0.0429, -0.0442],\n [ 0.1042, 0.0497, 0.0356],\n [-0.0659, -0.0578, -0.0280],\n [-0.0060, -0.0322, -0.0234]]\n\n latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206]\n\nclass HunyuanImage21Refiner(LatentFormat):\n latent_channels = 64\n latent_dimensions = 3\n scale_factor = 1.03682\n\n def process_in(self, latent):\n out = latent * self.scale_factor\n out = torch.cat((out[:, :, :1], out), dim=2)\n out = out.permute(0, 2, 1, 3, 4)\n b, f_times_2, c, h, w = out.shape\n out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)\n out = out.permute(0, 2, 1, 3, 4).contiguous()\n return out\n\n def process_out(self, latent):\n z = latent / self.scale_factor\n z = z.permute(0, 2, 1, 3, 4)\n b, f, c, h, w = z.shape\n z = z.reshape(b, f, 2, c // 2, h, w)\n z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)\n z = z.permute(0, 2, 1, 3, 4)\n z = z[:, :, 1:]\n return z\n\nclass HunyuanVideo15(LatentFormat):\n latent_rgb_factors = [\n [ 0.0568, -0.0521, -0.0131],\n [ 0.0014, 0.0735, 0.0326],\n [ 0.0186, 0.0531, -0.0138],\n [-0.0031, 0.0051, 0.0288],\n [ 0.0110, 0.0556, 0.0432],\n [-0.0041, -0.0023, -0.0485],\n [ 0.0530, 0.0413, 0.0253],\n [ 0.0283, 0.0251, 0.0339],\n [ 0.0277, -0.0372, -0.0093],\n [ 0.0393, 0.0944, 0.1131],\n [ 0.0020, 0.0251, 0.0037],\n [-0.0017, 0.0012, 0.0234],\n [ 0.0468, 0.0436, 0.0203],\n [ 0.0354, 0.0439, -0.0233],\n [ 0.0090, 0.0123, 0.0346],\n [ 0.0382, 0.0029, 0.0217],\n [ 0.0261, -0.0300, 0.0030],\n [-0.0088, -0.0220, -0.0283],\n [-0.0272, -0.0121, -0.0363],\n [-0.0664, -0.0622, 0.0144],\n [ 0.0414, 0.0479, 0.0529],\n [ 0.0355, 0.0612, -0.0247],\n [ 0.0147, 0.0264, 0.0174],\n [ 0.0438, 0.0038, 0.0542],\n [ 0.0431, -0.0573, -0.0033],\n [-0.0162, -0.0211, -0.0406],\n [-0.0487, -0.0295, -0.0393],\n [ 0.0005, -0.0109, 0.0253],\n [ 0.0296, 0.0591, 0.0353],\n [ 0.0119, 0.0181, -0.0306],\n [-0.0085, -0.0362, 0.0229],\n [ 0.0005, -0.0106, 0.0242]\n ]\n\n latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]\n latent_channels = 32\n latent_dimensions = 3\n spacial_downscale_ratio = 16\n scale_factor = 1.03682\n taesd_decoder_name = \"lighttaehy1_5\"\n\nclass Hunyuan3Dv2(LatentFormat):\n latent_channels = 64\n latent_dimensions = 1\n scale_factor = 0.9990943042622529\n\nclass Hunyuan3Dv2_1(LatentFormat):\n scale_factor = 1.0039506158752403\n latent_channels = 64\n latent_dimensions = 1\n\nclass Hunyuan3Dv2mini(LatentFormat):\n latent_channels = 64\n latent_dimensions = 1\n scale_factor = 1.0188137142395404\n\nclass ACEAudio(LatentFormat):\n latent_channels = 8\n latent_dimensions = 2\n\nclass ACEAudio15(LatentFormat):\n latent_channels = 64\n latent_dimensions = 1\n\nclass ChromaRadiance(LatentFormat):\n latent_channels = 3\n spacial_downscale_ratio = 1\n\n def __init__(self):\n self.latent_rgb_factors = [\n # R G B\n [ 1.0, 0.0, 0.0 ],\n [ 0.0, 1.0, 0.0 ],\n [ 0.0, 0.0, 1.0 ]\n ]\n\n def process_in(self, latent):\n return latent\n\n def process_out(self, latent):\n return latent\n\n\nclass ZImagePixelSpace(ChromaRadiance):\n \"\"\"Pixel-space latent format for ZImage DCT variant.\n No VAE encoding/decoding — the model operates directly on RGB pixels.\n \"\"\"\n pass\n" }, { "path": "comfy/ldm/ace/ace_step15.py", "content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport itertools\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.model_management\nfrom comfy.ldm.flux.layers import timestep_embedding\n\ndef get_silence_latent(length, device):\n head = torch.tensor([[[ 0.5707, 0.0982, 0.6909, -0.5658, 0.6266, 0.6996, -0.1365, -0.1291,\n -0.0776, -0.1171, -0.2743, -0.8422, -0.1168, 1.5539, -4.6936, 0.7436,\n -1.1846, -0.2637, 0.6933, -6.7266, 0.0966, -0.1187, -0.3501, -1.1736,\n 0.0587, -2.0517, -1.3651, 0.7508, -0.2490, -1.3548, -0.1290, -0.7261,\n 1.1132, -0.3249, 0.2337, 0.3004, 0.6605, -0.0298, -0.1989, -0.4041,\n 0.2843, -1.0963, -0.5519, 0.2639, -1.0436, -0.1183, 0.0640, 0.4460,\n -1.1001, -0.6172, -1.3241, 1.1379, 0.5623, -0.1507, -0.1963, -0.4742,\n -2.4697, 0.5302, 0.5381, 0.4636, -0.1782, -0.0687, 1.0333, 0.4202],\n [ 0.3040, -0.1367, 0.6200, 0.0665, -0.0642, 0.4655, -0.1187, -0.0440,\n 0.2941, -0.2753, 0.0173, -0.2421, -0.0147, 1.5603, -2.7025, 0.7907,\n -0.9736, -0.0682, 0.1294, -5.0707, -0.2167, 0.3302, -0.1513, -0.8100,\n -0.3894, -0.2884, -0.3149, 0.8660, -0.3817, -1.7061, 0.5824, -0.4840,\n 0.6938, 0.1859, 0.1753, 0.3081, 0.0195, 0.1403, -0.0754, -0.2091,\n 0.1251, -0.1578, -0.4968, -0.1052, -0.4554, -0.0320, 0.1284, 0.4974,\n -1.1889, -0.0344, -0.8313, 0.2953, 0.5445, -0.6249, -0.1595, -0.0682,\n -3.1412, 0.0484, 0.4153, 0.8260, -0.1526, -0.0625, 0.5366, 0.8473],\n [ 5.3524e-02, -1.7534e-01, 5.4443e-01, -4.3501e-01, -2.1317e-03,\n 3.7200e-01, -4.0143e-03, -1.5516e-01, -1.2968e-01, -1.5375e-01,\n -7.7107e-02, -2.0593e-01, -3.2780e-01, 1.5142e+00, -2.6101e+00,\n 5.8698e-01, -1.2716e+00, -2.4773e-01, -2.7933e-02, -5.0799e+00,\n 1.1601e-01, 4.0987e-01, -2.2030e-02, -6.6495e-01, -2.0995e-01,\n -6.3474e-01, -1.5893e-01, 8.2745e-01, -2.2992e-01, -1.6816e+00,\n 5.4440e-01, -4.9579e-01, 5.5128e-01, 3.0477e-01, 8.3052e-02,\n -6.1782e-02, 5.9036e-03, 2.9553e-01, -8.0645e-02, -1.0060e-01,\n 1.9144e-01, -3.8124e-01, -7.2949e-01, 2.4520e-02, -5.0814e-01,\n 2.3977e-01, 9.2943e-02, 3.9256e-01, -1.1993e+00, -3.2752e-01,\n -7.2707e-01, 2.9476e-01, 4.3542e-01, -8.8597e-01, -4.1686e-01,\n -8.5390e-02, -2.9018e+00, 6.4988e-02, 5.3945e-01, 9.1988e-01,\n 5.8762e-02, -7.0098e-02, 6.4772e-01, 8.9118e-01],\n [-3.2225e-02, -1.3195e-01, 5.6411e-01, -5.4766e-01, -5.2170e-03,\n 3.1425e-01, -5.4367e-02, -1.9419e-01, -1.3059e-01, -1.3660e-01,\n -9.0984e-02, -1.9540e-01, -2.5590e-01, 1.5440e+00, -2.6349e+00,\n 6.8273e-01, -1.2532e+00, -1.9810e-01, -2.2793e-02, -5.0506e+00,\n 1.8818e-01, 5.0109e-01, 7.3546e-03, -6.8771e-01, -3.0676e-01,\n -7.3257e-01, -1.6687e-01, 9.2232e-01, -1.8987e-01, -1.7267e+00,\n 5.3355e-01, -5.3179e-01, 4.4953e-01, 2.8820e-01, 1.3012e-01,\n -2.0943e-01, -1.1348e-01, 3.3929e-01, -1.5069e-01, -1.2919e-01,\n 1.8929e-01, -3.6166e-01, -8.0756e-01, 6.6387e-02, -5.8867e-01,\n 1.6978e-01, 1.0134e-01, 3.3877e-01, -1.2133e+00, -3.2492e-01,\n -8.1237e-01, 3.8101e-01, 4.3765e-01, -8.0596e-01, -4.4531e-01,\n -4.7513e-02, -2.9266e+00, 1.1741e-03, 4.5123e-01, 9.3075e-01,\n 5.3688e-02, -1.9621e-01, 6.4530e-01, 9.3870e-01]]], device=device).movedim(-1, 1)\n\n silence_latent = torch.tensor([[[-1.3672e-01, -1.5820e-01, 5.8594e-01, -5.7422e-01, 3.0273e-02,\n 2.7930e-01, -2.5940e-03, -2.0703e-01, -1.6113e-01, -1.4746e-01,\n -2.7710e-02, -1.8066e-01, -2.9688e-01, 1.6016e+00, -2.6719e+00,\n 7.7734e-01, -1.3516e+00, -1.9434e-01, -7.1289e-02, -5.0938e+00,\n 2.4316e-01, 4.7266e-01, 4.6387e-02, -6.6406e-01, -2.1973e-01,\n -6.7578e-01, -1.5723e-01, 9.5312e-01, -2.0020e-01, -1.7109e+00,\n 5.8984e-01, -5.7422e-01, 5.1562e-01, 2.8320e-01, 1.4551e-01,\n -1.8750e-01, -5.9814e-02, 3.6719e-01, -1.0059e-01, -1.5723e-01,\n 2.0605e-01, -4.3359e-01, -8.2812e-01, 4.5654e-02, -6.6016e-01,\n 1.4844e-01, 9.4727e-02, 3.8477e-01, -1.2578e+00, -3.3203e-01,\n -8.5547e-01, 4.3359e-01, 4.2383e-01, -8.9453e-01, -5.0391e-01,\n -5.6152e-02, -2.9219e+00, -2.4658e-02, 5.0391e-01, 9.8438e-01,\n 7.2754e-02, -2.1582e-01, 6.3672e-01, 1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, length)\n silence_latent[:, :, :head.shape[-1]] = head\n return silence_latent\n\n\ndef get_layer_class(operations, layer_name):\n if operations is not None and hasattr(operations, layer_name):\n return getattr(operations, layer_name)\n return getattr(nn, layer_name)\n\nclass RotaryEmbedding(nn.Module):\n def __init__(self, dim, max_position_embeddings=32768, base=1000000.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.dim = dim\n self.base = base\n self.max_position_embeddings = max_position_embeddings\n\n inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.float32, device=device) / self.dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n self._set_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.get_default_dtype() if dtype is None else dtype)\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)\n freqs = torch.outer(t, self.inv_freq)\n emb = torch.cat((freqs, freqs), dim=-1)\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n def forward(self, x, seq_len=None):\n if seq_len > self.max_seq_len_cached:\n self._set_cos_sin_cache(seq_len, x.device, x.dtype)\n return (\n self.cos_cached[:seq_len].to(dtype=x.dtype, device=x.device),\n self.sin_cached[:seq_len].to(dtype=x.dtype, device=x.device),\n )\n\ndef rotate_half(x):\n x1 = x[..., : x.shape[-1] // 2]\n x2 = x[..., x.shape[-1] // 2 :]\n return torch.cat((-x2, x1), dim=-1)\n\ndef apply_rotary_pos_emb(q, k, cos, sin):\n cos = cos.unsqueeze(0).unsqueeze(0)\n sin = sin.unsqueeze(0).unsqueeze(0)\n q_embed = (q * cos) + (rotate_half(q) * sin)\n k_embed = (k * cos) + (rotate_half(k) * sin)\n return q_embed, k_embed\n\nclass MLP(nn.Module):\n def __init__(self, hidden_size, intermediate_size, dtype=None, device=None, operations=None):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.gate_proj = Linear(hidden_size, intermediate_size, bias=False, dtype=dtype, device=device)\n self.up_proj = Linear(hidden_size, intermediate_size, bias=False, dtype=dtype, device=device)\n self.down_proj = Linear(intermediate_size, hidden_size, bias=False, dtype=dtype, device=device)\n self.act_fn = nn.SiLU()\n\n def forward(self, x):\n return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n\nclass TimestepEmbedding(nn.Module):\n def __init__(self, in_channels: int, time_embed_dim: int, scale: float = 1000, dtype=None, device=None, operations=None):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.linear_1 = Linear(in_channels, time_embed_dim, bias=True, dtype=dtype, device=device)\n self.act1 = nn.SiLU()\n self.linear_2 = Linear(time_embed_dim, time_embed_dim, bias=True, dtype=dtype, device=device)\n self.in_channels = in_channels\n self.act2 = nn.SiLU()\n self.time_proj = Linear(time_embed_dim, time_embed_dim * 6, dtype=dtype, device=device)\n self.scale = scale\n\n def forward(self, t, dtype=None):\n t_freq = timestep_embedding(t, self.in_channels, time_factor=self.scale)\n temb = self.linear_1(t_freq.to(dtype=dtype))\n temb = self.act1(temb)\n temb = self.linear_2(temb)\n timestep_proj = self.time_proj(self.act2(temb)).view(-1, 6, temb.shape[-1])\n return temb, timestep_proj\n\nclass AceStepAttention(nn.Module):\n def __init__(\n self,\n hidden_size,\n num_heads,\n num_kv_heads,\n head_dim,\n rms_norm_eps=1e-6,\n is_cross_attention=False,\n sliding_window=None,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.hidden_size = hidden_size\n self.num_heads = num_heads\n self.num_kv_heads = num_kv_heads\n self.head_dim = head_dim\n self.is_cross_attention = is_cross_attention\n self.sliding_window = sliding_window\n\n Linear = get_layer_class(operations, \"Linear\")\n\n self.q_proj = Linear(hidden_size, num_heads * head_dim, bias=False, dtype=dtype, device=device)\n self.k_proj = Linear(hidden_size, num_kv_heads * head_dim, bias=False, dtype=dtype, device=device)\n self.v_proj = Linear(hidden_size, num_kv_heads * head_dim, bias=False, dtype=dtype, device=device)\n self.o_proj = Linear(num_heads * head_dim, hidden_size, bias=False, dtype=dtype, device=device)\n\n self.q_norm = operations.RMSNorm(head_dim, eps=rms_norm_eps, dtype=dtype, device=device)\n self.k_norm = operations.RMSNorm(head_dim, eps=rms_norm_eps, dtype=dtype, device=device)\n\n def forward(\n self,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n position_embeddings=None,\n ):\n bsz, q_len, _ = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)\n query_states = self.q_norm(query_states)\n query_states = query_states.transpose(1, 2)\n\n if self.is_cross_attention and encoder_hidden_states is not None:\n bsz_enc, kv_len, _ = encoder_hidden_states.size()\n key_states = self.k_proj(encoder_hidden_states)\n value_states = self.v_proj(encoder_hidden_states)\n\n key_states = key_states.view(bsz_enc, kv_len, self.num_kv_heads, self.head_dim)\n key_states = self.k_norm(key_states)\n value_states = value_states.view(bsz_enc, kv_len, self.num_kv_heads, self.head_dim)\n\n key_states = key_states.transpose(1, 2)\n value_states = value_states.transpose(1, 2)\n else:\n kv_len = q_len\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n key_states = key_states.view(bsz, q_len, self.num_kv_heads, self.head_dim)\n key_states = self.k_norm(key_states)\n value_states = value_states.view(bsz, q_len, self.num_kv_heads, self.head_dim)\n\n key_states = key_states.transpose(1, 2)\n value_states = value_states.transpose(1, 2)\n\n if position_embeddings is not None:\n cos, sin = position_embeddings\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)\n\n n_rep = self.num_heads // self.num_kv_heads\n if n_rep > 1:\n key_states = key_states.repeat_interleave(n_rep, dim=1)\n value_states = value_states.repeat_interleave(n_rep, dim=1)\n\n attn_bias = None\n if self.sliding_window is not None and not self.is_cross_attention:\n indices = torch.arange(q_len, device=query_states.device)\n diff = indices.unsqueeze(1) - indices.unsqueeze(0)\n in_window = torch.abs(diff) <= self.sliding_window\n\n window_bias = torch.zeros((q_len, kv_len), device=query_states.device, dtype=query_states.dtype)\n min_value = torch.finfo(query_states.dtype).min\n window_bias.masked_fill_(~in_window, min_value)\n\n window_bias = window_bias.unsqueeze(0).unsqueeze(0)\n\n if attn_bias is not None:\n if attn_bias.dtype == torch.bool:\n base_bias = torch.zeros_like(window_bias)\n base_bias.masked_fill_(~attn_bias, min_value)\n attn_bias = base_bias + window_bias\n else:\n attn_bias = attn_bias + window_bias\n else:\n attn_bias = window_bias\n\n attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False)\n attn_output = self.o_proj(attn_output)\n\n return attn_output\n\nclass AceStepDiTLayer(nn.Module):\n def __init__(\n self,\n hidden_size,\n num_heads,\n num_kv_heads,\n head_dim,\n intermediate_size,\n rms_norm_eps=1e-6,\n layer_type=\"full_attention\",\n sliding_window=128,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n\n self_attn_window = sliding_window if layer_type == \"sliding_attention\" else None\n\n self.self_attn_norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.self_attn = AceStepAttention(\n hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps,\n is_cross_attention=False, sliding_window=self_attn_window,\n dtype=dtype, device=device, operations=operations\n )\n\n self.cross_attn_norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.cross_attn = AceStepAttention(\n hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps,\n is_cross_attention=True, dtype=dtype, device=device, operations=operations\n )\n\n self.mlp_norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.mlp = MLP(hidden_size, intermediate_size, dtype=dtype, device=device, operations=operations)\n\n self.scale_shift_table = nn.Parameter(torch.empty(1, 6, hidden_size, dtype=dtype, device=device))\n\n def forward(\n self,\n hidden_states,\n temb,\n encoder_hidden_states,\n position_embeddings,\n attention_mask=None,\n encoder_attention_mask=None\n ):\n modulation = comfy.model_management.cast_to(self.scale_shift_table, dtype=temb.dtype, device=temb.device) + temb\n shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = modulation.chunk(6, dim=1)\n\n norm_hidden = self.self_attn_norm(hidden_states)\n norm_hidden = norm_hidden * (1 + scale_msa) + shift_msa\n\n attn_out = self.self_attn(\n norm_hidden,\n position_embeddings=position_embeddings,\n attention_mask=attention_mask\n )\n hidden_states = hidden_states + attn_out * gate_msa\n\n norm_hidden = self.cross_attn_norm(hidden_states)\n attn_out = self.cross_attn(\n norm_hidden,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask\n )\n hidden_states = hidden_states + attn_out\n\n norm_hidden = self.mlp_norm(hidden_states)\n norm_hidden = norm_hidden * (1 + c_scale_msa) + c_shift_msa\n\n mlp_out = self.mlp(norm_hidden)\n hidden_states = hidden_states + mlp_out * c_gate_msa\n\n return hidden_states\n\nclass AceStepEncoderLayer(nn.Module):\n def __init__(\n self,\n hidden_size,\n num_heads,\n num_kv_heads,\n head_dim,\n intermediate_size,\n rms_norm_eps=1e-6,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.self_attn = AceStepAttention(\n hidden_size, num_heads, num_kv_heads, head_dim, rms_norm_eps,\n is_cross_attention=False, dtype=dtype, device=device, operations=operations\n )\n self.input_layernorm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.post_attention_layernorm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.mlp = MLP(hidden_size, intermediate_size, dtype=dtype, device=device, operations=operations)\n\n def forward(self, hidden_states, position_embeddings, attention_mask=None):\n residual = hidden_states\n hidden_states = self.input_layernorm(hidden_states)\n hidden_states = self.self_attn(\n hidden_states=hidden_states,\n position_embeddings=position_embeddings,\n attention_mask=attention_mask\n )\n hidden_states = residual + hidden_states\n\n residual = hidden_states\n hidden_states = self.post_attention_layernorm(hidden_states)\n hidden_states = self.mlp(hidden_states)\n hidden_states = residual + hidden_states\n return hidden_states\n\nclass AceStepLyricEncoder(nn.Module):\n def __init__(\n self,\n text_hidden_dim,\n hidden_size,\n num_layers,\n num_heads,\n num_kv_heads,\n head_dim,\n intermediate_size,\n rms_norm_eps=1e-6,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.embed_tokens = Linear(text_hidden_dim, hidden_size, dtype=dtype, device=device)\n self.norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n\n self.rotary_emb = RotaryEmbedding(\n head_dim,\n base=1000000.0,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n self.layers = nn.ModuleList([\n AceStepEncoderLayer(\n hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(num_layers)\n ])\n\n def forward(self, inputs_embeds, attention_mask=None):\n hidden_states = self.embed_tokens(inputs_embeds)\n seq_len = hidden_states.shape[1]\n cos, sin = self.rotary_emb(hidden_states, seq_len=seq_len)\n position_embeddings = (cos, sin)\n\n for layer in self.layers:\n hidden_states = layer(\n hidden_states,\n position_embeddings=position_embeddings,\n attention_mask=attention_mask\n )\n\n hidden_states = self.norm(hidden_states)\n return hidden_states\n\nclass AceStepTimbreEncoder(nn.Module):\n def __init__(\n self,\n timbre_hidden_dim,\n hidden_size,\n num_layers,\n num_heads,\n num_kv_heads,\n head_dim,\n intermediate_size,\n rms_norm_eps=1e-6,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.embed_tokens = Linear(timbre_hidden_dim, hidden_size, dtype=dtype, device=device)\n self.norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n\n self.rotary_emb = RotaryEmbedding(\n head_dim,\n base=1000000.0,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n self.layers = nn.ModuleList([\n AceStepEncoderLayer(\n hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(num_layers)\n ])\n self.special_token = nn.Parameter(torch.empty(1, 1, hidden_size, device=device, dtype=dtype))\n\n def unpack_timbre_embeddings(self, timbre_embs_packed, refer_audio_order_mask):\n N, d = timbre_embs_packed.shape\n device = timbre_embs_packed.device\n B = N\n counts = torch.bincount(refer_audio_order_mask, minlength=B)\n max_count = counts.max().item()\n\n sorted_indices = torch.argsort(\n refer_audio_order_mask * N + torch.arange(N, device=device),\n stable=True\n )\n sorted_batch_ids = refer_audio_order_mask[sorted_indices]\n\n positions = torch.arange(N, device=device)\n batch_starts = torch.cat([torch.tensor([0], device=device), torch.cumsum(counts, dim=0)[:-1]])\n positions_in_sorted = positions - batch_starts[sorted_batch_ids]\n\n inverse_indices = torch.empty_like(sorted_indices)\n inverse_indices[sorted_indices] = torch.arange(N, device=device)\n positions_in_batch = positions_in_sorted[inverse_indices]\n\n indices_2d = refer_audio_order_mask * max_count + positions_in_batch\n one_hot = F.one_hot(indices_2d, num_classes=B * max_count).to(timbre_embs_packed.dtype)\n\n timbre_embs_flat = one_hot.t() @ timbre_embs_packed\n timbre_embs_unpack = timbre_embs_flat.view(B, max_count, d)\n\n mask_flat = (one_hot.sum(dim=0) > 0).long()\n new_mask = mask_flat.view(B, max_count)\n return timbre_embs_unpack, new_mask\n\n def forward(self, refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask, attention_mask=None):\n hidden_states = self.embed_tokens(refer_audio_acoustic_hidden_states_packed)\n if hidden_states.dim() == 2:\n hidden_states = hidden_states.unsqueeze(0)\n\n seq_len = hidden_states.shape[1]\n cos, sin = self.rotary_emb(hidden_states, seq_len=seq_len)\n\n for layer in self.layers:\n hidden_states = layer(\n hidden_states,\n position_embeddings=(cos, sin),\n attention_mask=attention_mask\n )\n hidden_states = self.norm(hidden_states)\n\n flat_states = hidden_states[:, 0, :]\n unpacked_embs, unpacked_mask = self.unpack_timbre_embeddings(flat_states, refer_audio_order_mask)\n return unpacked_embs, unpacked_mask\n\n\ndef pack_sequences(hidden1, hidden2, mask1, mask2):\n hidden_cat = torch.cat([hidden1, hidden2], dim=1)\n B, L, D = hidden_cat.shape\n\n if mask1 is not None and mask2 is not None:\n mask_cat = torch.cat([mask1, mask2], dim=1)\n sort_idx = mask_cat.argsort(dim=1, descending=True, stable=True)\n gather_idx = sort_idx.unsqueeze(-1).expand(B, L, D)\n hidden_sorted = torch.gather(hidden_cat, 1, gather_idx)\n lengths = mask_cat.sum(dim=1)\n new_mask = (torch.arange(L, device=hidden_cat.device).unsqueeze(0) < lengths.unsqueeze(1))\n else:\n new_mask = None\n hidden_sorted = hidden_cat\n\n return hidden_sorted, new_mask\n\nclass AceStepConditionEncoder(nn.Module):\n def __init__(\n self,\n text_hidden_dim,\n timbre_hidden_dim,\n hidden_size,\n num_lyric_layers,\n num_timbre_layers,\n num_heads,\n num_kv_heads,\n head_dim,\n intermediate_size,\n rms_norm_eps=1e-6,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.text_projector = Linear(text_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device)\n\n self.lyric_encoder = AceStepLyricEncoder(\n text_hidden_dim=text_hidden_dim,\n hidden_size=hidden_size,\n num_layers=num_lyric_layers,\n num_heads=num_heads,\n num_kv_heads=num_kv_heads,\n head_dim=head_dim,\n intermediate_size=intermediate_size,\n rms_norm_eps=rms_norm_eps,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n self.timbre_encoder = AceStepTimbreEncoder(\n timbre_hidden_dim=timbre_hidden_dim,\n hidden_size=hidden_size,\n num_layers=num_timbre_layers,\n num_heads=num_heads,\n num_kv_heads=num_kv_heads,\n head_dim=head_dim,\n intermediate_size=intermediate_size,\n rms_norm_eps=rms_norm_eps,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n def forward(\n self,\n text_hidden_states=None,\n text_attention_mask=None,\n lyric_hidden_states=None,\n lyric_attention_mask=None,\n refer_audio_acoustic_hidden_states_packed=None,\n refer_audio_order_mask=None\n ):\n text_emb = self.text_projector(text_hidden_states)\n\n lyric_emb = self.lyric_encoder(\n inputs_embeds=lyric_hidden_states,\n attention_mask=lyric_attention_mask\n )\n\n timbre_emb, timbre_mask = self.timbre_encoder(\n refer_audio_acoustic_hidden_states_packed,\n refer_audio_order_mask\n )\n\n merged_emb, merged_mask = pack_sequences(lyric_emb, timbre_emb, lyric_attention_mask, timbre_mask)\n final_emb, final_mask = pack_sequences(merged_emb, text_emb, merged_mask, text_attention_mask)\n\n return final_emb, final_mask\n\n# --------------------------------------------------------------------------------\n# Main Diffusion Model (DiT)\n# --------------------------------------------------------------------------------\n\nclass AceStepDiTModel(nn.Module):\n def __init__(\n self,\n in_channels,\n hidden_size,\n num_layers,\n num_heads,\n num_kv_heads,\n head_dim,\n intermediate_size,\n patch_size,\n audio_acoustic_hidden_dim,\n layer_types=None,\n sliding_window=128,\n rms_norm_eps=1e-6,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.patch_size = patch_size\n self.rotary_emb = RotaryEmbedding(\n head_dim,\n base=1000000.0,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n Conv1d = get_layer_class(operations, \"Conv1d\")\n ConvTranspose1d = get_layer_class(operations, \"ConvTranspose1d\")\n Linear = get_layer_class(operations, \"Linear\")\n\n self.proj_in = nn.Sequential(\n nn.Identity(),\n Conv1d(\n in_channels, hidden_size, kernel_size=patch_size, stride=patch_size,\n dtype=dtype, device=device))\n\n self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)\n self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)\n self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)\n\n if layer_types is None:\n layer_types = [\"full_attention\"] * num_layers\n\n if len(layer_types) < num_layers:\n layer_types = list(itertools.islice(itertools.cycle(layer_types), num_layers))\n\n self.layers = nn.ModuleList([\n AceStepDiTLayer(\n hidden_size, num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,\n layer_type=layer_types[i],\n sliding_window=sliding_window,\n dtype=dtype, device=device, operations=operations\n ) for i in range(num_layers)\n ])\n\n self.norm_out = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.proj_out = nn.Sequential(\n nn.Identity(),\n ConvTranspose1d(hidden_size, audio_acoustic_hidden_dim, kernel_size=patch_size, stride=patch_size, dtype=dtype, device=device)\n )\n\n self.scale_shift_table = nn.Parameter(torch.empty(1, 2, hidden_size, dtype=dtype, device=device))\n\n def forward(\n self,\n hidden_states,\n timestep,\n timestep_r,\n attention_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n context_latents\n ):\n temb_t, proj_t = self.time_embed(timestep, dtype=hidden_states.dtype)\n temb_r, proj_r = self.time_embed_r(timestep - timestep_r, dtype=hidden_states.dtype)\n temb = temb_t + temb_r\n timestep_proj = proj_t + proj_r\n\n x = torch.cat([context_latents, hidden_states], dim=-1)\n original_seq_len = x.shape[1]\n\n pad_length = 0\n if x.shape[1] % self.patch_size != 0:\n pad_length = self.patch_size - (x.shape[1] % self.patch_size)\n x = F.pad(x, (0, 0, 0, pad_length), mode='constant', value=0)\n\n x = x.transpose(1, 2)\n x = self.proj_in(x)\n x = x.transpose(1, 2)\n\n encoder_hidden_states = self.condition_embedder(encoder_hidden_states)\n\n seq_len = x.shape[1]\n cos, sin = self.rotary_emb(x, seq_len=seq_len)\n\n for layer in self.layers:\n x = layer(\n hidden_states=x,\n temb=timestep_proj,\n encoder_hidden_states=encoder_hidden_states,\n position_embeddings=(cos, sin),\n attention_mask=None,\n encoder_attention_mask=None\n )\n\n shift, scale = (comfy.model_management.cast_to(self.scale_shift_table, dtype=temb.dtype, device=temb.device) + temb.unsqueeze(1)).chunk(2, dim=1)\n x = self.norm_out(x) * (1 + scale) + shift\n\n x = x.transpose(1, 2)\n x = self.proj_out(x)\n x = x.transpose(1, 2)\n\n x = x[:, :original_seq_len, :]\n return x\n\n\nclass AttentionPooler(nn.Module):\n def __init__(self, hidden_size, num_layers, head_dim, rms_norm_eps, dtype=None, device=None, operations=None):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.embed_tokens = Linear(hidden_size, hidden_size, dtype=dtype, device=device)\n self.norm = operations.RMSNorm(hidden_size, eps=rms_norm_eps, dtype=dtype, device=device)\n self.rotary_emb = RotaryEmbedding(head_dim, dtype=dtype, device=device, operations=operations)\n self.special_token = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))\n\n self.layers = nn.ModuleList([\n AceStepEncoderLayer(\n hidden_size, 16, 8, head_dim, hidden_size * 3, rms_norm_eps,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(num_layers)\n ])\n\n def forward(self, x):\n B, T, P, D = x.shape\n x = self.embed_tokens(x)\n special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1)\n x = torch.cat([special, x], dim=2)\n x = x.view(B * T, P + 1, D)\n\n cos, sin = self.rotary_emb(x, seq_len=P + 1)\n for layer in self.layers:\n x = layer(x, (cos, sin))\n\n x = self.norm(x)\n return x[:, 0, :].view(B, T, D)\n\n\nclass FSQ(nn.Module):\n def __init__(\n self,\n levels,\n dim=None,\n device=None,\n dtype=None,\n operations=None\n ):\n super().__init__()\n\n _levels = torch.tensor(levels, dtype=torch.int32, device=device)\n self.register_buffer('_levels', _levels, persistent=False)\n\n _basis = torch.cumprod(torch.tensor([1] + levels[:-1], dtype=torch.int32, device=device), dim=0)\n self.register_buffer('_basis', _basis, persistent=False)\n\n self.codebook_dim = len(levels)\n self.dim = dim if dim is not None else self.codebook_dim\n\n requires_projection = self.dim != self.codebook_dim\n if requires_projection:\n self.project_in = operations.Linear(self.dim, self.codebook_dim, device=device, dtype=dtype)\n self.project_out = operations.Linear(self.codebook_dim, self.dim, device=device, dtype=dtype)\n else:\n self.project_in = nn.Identity()\n self.project_out = nn.Identity()\n\n self.codebook_size = self._levels.prod().item()\n\n indices = torch.arange(self.codebook_size, device=device)\n implicit_codebook = self._indices_to_codes(indices)\n\n if dtype is not None:\n implicit_codebook = implicit_codebook.to(dtype)\n\n self.register_buffer('implicit_codebook', implicit_codebook, persistent=False)\n\n def bound(self, z):\n levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1)\n scale = 2. / levels_minus_1\n bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5\n\n zhat = bracket.floor()\n bracket_ste = bracket + (zhat - bracket).detach()\n\n return scale * bracket_ste - 1.\n\n def _indices_to_codes(self, indices):\n indices = indices.unsqueeze(-1)\n codes_non_centered = (indices // self._basis) % self._levels\n return codes_non_centered.float() * (2. / (self._levels.float() - 1)) - 1.\n\n def codes_to_indices(self, zhat):\n zhat_normalized = (zhat + 1.) / (2. / (comfy.model_management.cast_to(self._levels, device=zhat.device, dtype=zhat.dtype) - 1))\n return (zhat_normalized * comfy.model_management.cast_to(self._basis, device=zhat.device, dtype=zhat.dtype)).sum(dim=-1).round().to(torch.int32)\n\n def forward(self, z):\n orig_dtype = z.dtype\n z = self.project_in(z)\n\n codes = self.bound(z)\n indices = self.codes_to_indices(codes)\n\n out = self.project_out(codes)\n return out.to(orig_dtype), indices\n\n\nclass ResidualFSQ(nn.Module):\n def __init__(\n self,\n levels,\n num_quantizers,\n dim=None,\n bound_hard_clamp=True,\n device=None,\n dtype=None,\n operations=None,\n **kwargs\n ):\n super().__init__()\n\n codebook_dim = len(levels)\n dim = dim if dim is not None else codebook_dim\n\n requires_projection = codebook_dim != dim\n if requires_projection:\n self.project_in = operations.Linear(dim, codebook_dim, device=device, dtype=dtype)\n self.project_out = operations.Linear(codebook_dim, dim, device=device, dtype=dtype)\n else:\n self.project_in = nn.Identity()\n self.project_out = nn.Identity()\n\n self.layers = nn.ModuleList()\n levels_tensor = torch.tensor(levels, device=device)\n scales = []\n\n for ind in range(num_quantizers):\n scale_val = levels_tensor.float() ** -ind\n scales.append(scale_val)\n\n self.layers.append(FSQ(\n levels=levels,\n dim=codebook_dim,\n device=device,\n dtype=dtype,\n operations=operations\n ))\n\n scales_tensor = torch.stack(scales)\n if dtype is not None:\n scales_tensor = scales_tensor.to(dtype)\n self.register_buffer('scales', scales_tensor, persistent=False)\n\n if bound_hard_clamp:\n val = 1 + (1 / (levels_tensor.float() - 1))\n if dtype is not None:\n val = val.to(dtype)\n self.register_buffer('soft_clamp_input_value', val, persistent=False)\n\n def get_output_from_indices(self, indices, dtype=torch.float32):\n if indices.dim() == 2:\n indices = indices.unsqueeze(-1)\n\n all_codes = []\n for i, layer in enumerate(self.layers):\n idx = indices[..., i].long()\n codes = F.embedding(idx, comfy.model_management.cast_to(layer.implicit_codebook, device=idx.device, dtype=dtype))\n all_codes.append(codes * comfy.model_management.cast_to(self.scales[i], device=idx.device, dtype=dtype))\n\n codes_summed = torch.stack(all_codes).sum(dim=0)\n return self.project_out(codes_summed)\n\n def forward(self, x):\n x = self.project_in(x)\n\n if hasattr(self, 'soft_clamp_input_value'):\n sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype)\n x = (x / sc_val).tanh() * sc_val\n\n quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype)\n residual = x\n all_indices = []\n\n for layer, scale in zip(self.layers, self.scales):\n scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype)\n\n quantized, indices = layer(residual / scale)\n quantized = quantized * scale\n\n residual = residual - quantized.detach()\n quantized_out = quantized_out + quantized\n all_indices.append(indices)\n\n quantized_out = self.project_out(quantized_out)\n all_indices = torch.stack(all_indices, dim=-1)\n\n return quantized_out, all_indices\n\n\nclass AceStepAudioTokenizer(nn.Module):\n def __init__(\n self,\n audio_acoustic_hidden_dim,\n hidden_size,\n pool_window_size,\n fsq_dim,\n fsq_levels,\n fsq_input_num_quantizers,\n num_layers,\n head_dim,\n rms_norm_eps,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.audio_acoustic_proj = Linear(audio_acoustic_hidden_dim, hidden_size, dtype=dtype, device=device)\n self.attention_pooler = AttentionPooler(\n hidden_size, num_layers, head_dim, rms_norm_eps, dtype=dtype, device=device, operations=operations\n )\n self.pool_window_size = pool_window_size\n self.fsq_dim = fsq_dim\n self.quantizer = ResidualFSQ(\n dim=fsq_dim,\n levels=fsq_levels,\n num_quantizers=fsq_input_num_quantizers,\n bound_hard_clamp=True,\n dtype=dtype, device=device, operations=operations\n )\n\n def forward(self, hidden_states):\n hidden_states = self.audio_acoustic_proj(hidden_states)\n hidden_states = self.attention_pooler(hidden_states)\n quantized, indices = self.quantizer(hidden_states)\n return quantized, indices\n\n def tokenize(self, x):\n B, T, D = x.shape\n P = self.pool_window_size\n\n if T % P != 0:\n pad = P - (T % P)\n x = F.pad(x, (0, 0, 0, pad))\n T = x.shape[1]\n\n T_patch = T // P\n x = x.view(B, T_patch, P, D)\n\n quantized, indices = self.forward(x)\n return quantized, indices\n\n\nclass AudioTokenDetokenizer(nn.Module):\n def __init__(\n self,\n hidden_size,\n pool_window_size,\n audio_acoustic_hidden_dim,\n num_layers,\n head_dim,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n Linear = get_layer_class(operations, \"Linear\")\n self.pool_window_size = pool_window_size\n self.embed_tokens = Linear(hidden_size, hidden_size, dtype=dtype, device=device)\n self.special_tokens = nn.Parameter(torch.empty(1, pool_window_size, hidden_size, dtype=dtype, device=device))\n self.rotary_emb = RotaryEmbedding(head_dim, dtype=dtype, device=device, operations=operations)\n self.layers = nn.ModuleList([\n AceStepEncoderLayer(\n hidden_size, 16, 8, head_dim, hidden_size * 3, 1e-6,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(num_layers)\n ])\n self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device)\n self.proj_out = Linear(hidden_size, audio_acoustic_hidden_dim, dtype=dtype, device=device)\n\n def forward(self, x):\n B, T, D = x.shape\n x = self.embed_tokens(x)\n x = x.unsqueeze(2).repeat(1, 1, self.pool_window_size, 1)\n x = x + comfy.model_management.cast_to(self.special_tokens.expand(B, T, -1, -1), device=x.device, dtype=x.dtype)\n x = x.view(B * T, self.pool_window_size, D)\n\n cos, sin = self.rotary_emb(x, seq_len=self.pool_window_size)\n for layer in self.layers:\n x = layer(x, (cos, sin))\n\n x = self.norm(x)\n x = self.proj_out(x)\n return x.view(B, T * self.pool_window_size, -1)\n\n\nclass AceStepConditionGenerationModel(nn.Module):\n def __init__(\n self,\n in_channels=192,\n hidden_size=2048,\n text_hidden_dim=1024,\n timbre_hidden_dim=64,\n audio_acoustic_hidden_dim=64,\n num_dit_layers=24,\n num_lyric_layers=8,\n num_timbre_layers=4,\n num_tokenizer_layers=2,\n num_heads=16,\n num_kv_heads=8,\n head_dim=128,\n intermediate_size=6144,\n patch_size=2,\n pool_window_size=5,\n rms_norm_eps=1e-06,\n timestep_mu=-0.4,\n timestep_sigma=1.0,\n data_proportion=0.5,\n sliding_window=128,\n layer_types=None,\n fsq_dim=2048,\n fsq_levels=[8, 8, 8, 5, 5, 5],\n fsq_input_num_quantizers=1,\n audio_model=None,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.dtype = dtype\n self.timestep_mu = timestep_mu\n self.timestep_sigma = timestep_sigma\n self.data_proportion = data_proportion\n self.pool_window_size = pool_window_size\n\n if layer_types is None:\n layer_types = []\n for i in range(num_dit_layers):\n layer_types.append(\"sliding_attention\" if i % 2 == 0 else \"full_attention\")\n\n self.decoder = AceStepDiTModel(\n in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,\n intermediate_size, patch_size, audio_acoustic_hidden_dim,\n layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,\n dtype=dtype, device=device, operations=operations\n )\n self.encoder = AceStepConditionEncoder(\n text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,\n num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,\n dtype=dtype, device=device, operations=operations\n )\n self.tokenizer = AceStepAudioTokenizer(\n audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,\n dtype=dtype, device=device, operations=operations\n )\n self.detokenizer = AudioTokenDetokenizer(\n hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,\n dtype=dtype, device=device, operations=operations\n )\n self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))\n\n def prepare_condition(\n self,\n text_hidden_states, text_attention_mask,\n lyric_hidden_states, lyric_attention_mask,\n refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask,\n src_latents, chunk_masks, is_covers,\n precomputed_lm_hints_25Hz=None,\n audio_codes=None\n ):\n encoder_hidden, encoder_mask = self.encoder(\n text_hidden_states, text_attention_mask,\n lyric_hidden_states, lyric_attention_mask,\n refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask\n )\n\n if precomputed_lm_hints_25Hz is not None:\n lm_hints = precomputed_lm_hints_25Hz\n else:\n if audio_codes is not None:\n if audio_codes.shape[1] * 5 < src_latents.shape[1]:\n audio_codes = torch.nn.functional.pad(audio_codes, (0, math.ceil(src_latents.shape[1] / 5) - audio_codes.shape[1]), \"constant\", 35847)\n lm_hints_5Hz = self.tokenizer.quantizer.get_output_from_indices(audio_codes, dtype=text_hidden_states.dtype)\n else:\n lm_hints_5Hz, indices = self.tokenizer.tokenize(refer_audio_acoustic_hidden_states_packed)\n\n lm_hints = self.detokenizer(lm_hints_5Hz)\n\n lm_hints = lm_hints[:, :src_latents.shape[1], :]\n if is_covers is None or is_covers is True:\n src_latents = lm_hints\n elif is_covers is False:\n src_latents = refer_audio_acoustic_hidden_states_packed\n\n context_latents = torch.cat([src_latents, chunk_masks.to(src_latents.dtype)], dim=-1)\n\n return encoder_hidden, encoder_mask, context_latents\n\n def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs):\n text_attention_mask = None\n lyric_attention_mask = None\n refer_audio_order_mask = None\n attention_mask = None\n chunk_masks = None\n src_latents = None\n precomputed_lm_hints_25Hz = None\n lyric_hidden_states = lyric_embed\n text_hidden_states = context\n refer_audio_acoustic_hidden_states_packed = refer_audio.movedim(-1, -2)\n\n x = x.movedim(-1, -2)\n\n if refer_audio_order_mask is None:\n refer_audio_order_mask = torch.zeros((x.shape[0],), device=x.device, dtype=torch.long)\n\n if src_latents is None:\n src_latents = x\n\n if chunk_masks is None:\n chunk_masks = torch.ones_like(x)\n\n enc_hidden, enc_mask, context_latents = self.prepare_condition(\n text_hidden_states, text_attention_mask,\n lyric_hidden_states, lyric_attention_mask,\n refer_audio_acoustic_hidden_states_packed, refer_audio_order_mask,\n src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes\n )\n\n if replace_with_null_embeds:\n enc_hidden[:] = self.null_condition_emb.to(enc_hidden)\n\n out = self.decoder(hidden_states=x,\n timestep=timestep,\n timestep_r=timestep,\n attention_mask=attention_mask,\n encoder_hidden_states=enc_hidden,\n encoder_attention_mask=enc_mask,\n context_latents=context_latents\n )\n\n return out.movedim(-1, -2)\n" }, { "path": "comfy/ldm/ace/attention.py", "content": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py\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.\nfrom typing import Tuple, Union, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nimport comfy.model_management\nfrom comfy.ldm.modules.attention import optimized_attention\n\nclass Attention(nn.Module):\n def __init__(\n self,\n query_dim: int,\n cross_attention_dim: Optional[int] = None,\n heads: int = 8,\n kv_heads: Optional[int] = None,\n dim_head: int = 64,\n dropout: float = 0.0,\n bias: bool = False,\n qk_norm: Optional[str] = None,\n added_kv_proj_dim: Optional[int] = None,\n added_proj_bias: Optional[bool] = True,\n out_bias: bool = True,\n scale_qk: bool = True,\n only_cross_attention: bool = False,\n eps: float = 1e-5,\n rescale_output_factor: float = 1.0,\n residual_connection: bool = False,\n processor=None,\n out_dim: int = None,\n out_context_dim: int = None,\n context_pre_only=None,\n pre_only=False,\n elementwise_affine: bool = True,\n is_causal: bool = False,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n self.inner_dim = out_dim if out_dim is not None else dim_head * heads\n self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads\n self.query_dim = query_dim\n self.use_bias = bias\n self.is_cross_attention = cross_attention_dim is not None\n self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim\n self.rescale_output_factor = rescale_output_factor\n self.residual_connection = residual_connection\n self.dropout = dropout\n self.fused_projections = False\n self.out_dim = out_dim if out_dim is not None else query_dim\n self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim\n self.context_pre_only = context_pre_only\n self.pre_only = pre_only\n self.is_causal = is_causal\n\n self.scale_qk = scale_qk\n self.scale = dim_head**-0.5 if self.scale_qk else 1.0\n\n self.heads = out_dim // dim_head if out_dim is not None else heads\n # for slice_size > 0 the attention score computation\n # is split across the batch axis to save memory\n # You can set slice_size with `set_attention_slice`\n self.sliceable_head_dim = heads\n\n self.added_kv_proj_dim = added_kv_proj_dim\n self.only_cross_attention = only_cross_attention\n\n if self.added_kv_proj_dim is None and self.only_cross_attention:\n raise ValueError(\n \"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`.\"\n )\n\n self.group_norm = None\n self.spatial_norm = None\n\n self.norm_q = None\n self.norm_k = None\n\n self.norm_cross = None\n self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device)\n\n if not self.only_cross_attention:\n # only relevant for the `AddedKVProcessor` classes\n self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)\n self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)\n else:\n self.to_k = None\n self.to_v = None\n\n self.added_proj_bias = added_proj_bias\n if self.added_kv_proj_dim is not None:\n self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device)\n self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device)\n if self.context_pre_only is not None:\n self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device)\n else:\n self.add_q_proj = None\n self.add_k_proj = None\n self.add_v_proj = None\n\n if not self.pre_only:\n self.to_out = nn.ModuleList([])\n self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device))\n self.to_out.append(nn.Dropout(dropout))\n else:\n self.to_out = None\n\n if self.context_pre_only is not None and not self.context_pre_only:\n self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device)\n else:\n self.to_add_out = None\n\n self.norm_added_q = None\n self.norm_added_k = None\n self.processor = processor\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n transformer_options={},\n **cross_attention_kwargs,\n ) -> torch.Tensor:\n return self.processor(\n self,\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n transformer_options=transformer_options,\n **cross_attention_kwargs,\n )\n\n\nclass CustomLiteLAProcessor2_0:\n \"\"\"Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE\"\"\"\n\n def __init__(self):\n self.kernel_func = nn.ReLU(inplace=False)\n self.eps = 1e-15\n self.pad_val = 1.0\n\n def apply_rotary_emb(\n self,\n x: torch.Tensor,\n freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings\n to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are\n reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting\n tensors contain rotary embeddings and are returned as real tensors.\n\n Args:\n x (`torch.Tensor`):\n Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply\n freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.\n \"\"\"\n cos, sin = freqs_cis # [S, D]\n cos = cos[None, None]\n sin = sin[None, None]\n cos, sin = cos.to(x.device), sin.to(x.device)\n\n x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]\n x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)\n out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)\n\n return out\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: torch.FloatTensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n *args,\n **kwargs,\n ) -> torch.FloatTensor:\n hidden_states_len = hidden_states.shape[1]\n\n input_ndim = hidden_states.ndim\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n if encoder_hidden_states is not None:\n context_input_ndim = encoder_hidden_states.ndim\n if context_input_ndim == 4:\n batch_size, channel, height, width = encoder_hidden_states.shape\n encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size = hidden_states.shape[0]\n\n # `sample` projections.\n dtype = hidden_states.dtype\n query = attn.to_q(hidden_states)\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n\n # `context` projections.\n has_encoder_hidden_state_proj = hasattr(attn, \"add_q_proj\") and hasattr(attn, \"add_k_proj\") and hasattr(attn, \"add_v_proj\")\n if encoder_hidden_states is not None and has_encoder_hidden_state_proj:\n encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)\n encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)\n\n # attention\n if not attn.is_cross_attention:\n query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)\n key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)\n value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)\n else:\n query = hidden_states\n key = encoder_hidden_states\n value = encoder_hidden_states\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1)\n key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2)\n value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1)\n\n # RoPE需要 [B, H, S, D] 输入\n # 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE\n query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S])\n\n # Apply query and key normalization if needed\n if attn.norm_q is not None:\n query = attn.norm_q(query)\n if attn.norm_k is not None:\n key = attn.norm_k(key)\n\n # Apply RoPE if needed\n if rotary_freqs_cis is not None:\n query = self.apply_rotary_emb(query, rotary_freqs_cis)\n if not attn.is_cross_attention:\n key = self.apply_rotary_emb(key, rotary_freqs_cis)\n elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj:\n key = self.apply_rotary_emb(key, rotary_freqs_cis_cross)\n\n # 此时 query是 [B, H, S, D]，需要还原成 [B, H, D, S]\n query = query.permute(0, 1, 3, 2) # [B, H, D, S]\n\n if attention_mask is not None:\n # attention_mask: [B, S] -> [B, 1, S, 1]\n attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1]\n query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1]\n if not attn.is_cross_attention:\n key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘\n value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S]，那么需调整mask以匹配S维度\n\n if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj:\n encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1]\n # 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc]\n key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1]\n value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc]\n\n query = self.kernel_func(query)\n key = self.kernel_func(key)\n\n query, key, value = query.float(), key.float(), value.float()\n\n value = F.pad(value, (0, 0, 0, 1), mode=\"constant\", value=self.pad_val)\n\n vk = torch.matmul(value, key)\n\n hidden_states = torch.matmul(vk, query)\n\n if hidden_states.dtype in [torch.float16, torch.bfloat16]:\n hidden_states = hidden_states.float()\n\n hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps)\n\n hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1)\n\n hidden_states = hidden_states.to(dtype)\n if encoder_hidden_states is not None:\n encoder_hidden_states = encoder_hidden_states.to(dtype)\n\n # Split the attention outputs.\n if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj:\n hidden_states, encoder_hidden_states = (\n hidden_states[:, : hidden_states_len],\n hidden_states[:, hidden_states_len:],\n )\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, \"to_add_out\"):\n encoder_hidden_states = attn.to_add_out(encoder_hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n if encoder_hidden_states is not None and context_input_ndim == 4:\n encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if torch.get_autocast_gpu_dtype() == torch.float16:\n hidden_states = hidden_states.clip(-65504, 65504)\n if encoder_hidden_states is not None:\n encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)\n\n return hidden_states, encoder_hidden_states\n\n\nclass CustomerAttnProcessor2_0:\n r\"\"\"\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).\n \"\"\"\n\n def apply_rotary_emb(\n self,\n x: torch.Tensor,\n freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings\n to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are\n reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting\n tensors contain rotary embeddings and are returned as real tensors.\n\n Args:\n x (`torch.Tensor`):\n Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply\n freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.\n \"\"\"\n cos, sin = freqs_cis # [S, D]\n cos = cos[None, None]\n sin = sin[None, None]\n cos, sin = cos.to(x.device), sin.to(x.device)\n\n x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]\n x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)\n out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)\n\n return out\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: torch.FloatTensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n transformer_options={},\n *args,\n **kwargs,\n ) -> torch.Tensor:\n\n residual = hidden_states\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n has_encoder_hidden_state_proj = hasattr(attn, \"add_q_proj\") and hasattr(attn, \"add_k_proj\") and hasattr(attn, \"add_v_proj\")\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n if attn.norm_q is not None:\n query = attn.norm_q(query)\n if attn.norm_k is not None:\n key = attn.norm_k(key)\n\n # Apply RoPE if needed\n if rotary_freqs_cis is not None:\n query = self.apply_rotary_emb(query, rotary_freqs_cis)\n if not attn.is_cross_attention:\n key = self.apply_rotary_emb(key, rotary_freqs_cis)\n elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj:\n key = self.apply_rotary_emb(key, rotary_freqs_cis_cross)\n\n if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj:\n # attention_mask: N x S1\n # encoder_attention_mask: N x S2\n # cross attention 整合attention_mask和encoder_attention_mask\n combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :]\n attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf)\n attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype)\n\n elif not attn.is_cross_attention and attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n hidden_states = optimized_attention(\n query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options,\n ).to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\ndef val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore\n \"\"\"Repeat `val` for `repeat_time` times and return the list or val if list/tuple.\"\"\"\n if isinstance(x, (list, tuple)):\n return list(x)\n return [x for _ in range(repeat_time)]\n\n\ndef val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore\n \"\"\"Return tuple with min_len by repeating element at idx_repeat.\"\"\"\n # convert to list first\n x = val2list(x)\n\n # repeat elements if necessary\n if len(x) > 0:\n x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]\n\n return tuple(x)\n\n\ndef t2i_modulate(x, shift, scale):\n return x * (1 + scale) + shift\n\n\ndef get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]:\n if isinstance(kernel_size, tuple):\n return tuple([get_same_padding(ks) for ks in kernel_size])\n else:\n assert kernel_size % 2 > 0, f\"kernel size {kernel_size} should be odd number\"\n return kernel_size // 2\n\nclass ConvLayer(nn.Module):\n def __init__(\n self,\n in_dim: int,\n out_dim: int,\n kernel_size=3,\n stride=1,\n dilation=1,\n groups=1,\n padding: Union[int, None] = None,\n use_bias=False,\n norm=None,\n act=None,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n if padding is None:\n padding = get_same_padding(kernel_size)\n padding *= dilation\n\n self.in_dim = in_dim\n self.out_dim = out_dim\n self.kernel_size = kernel_size\n self.stride = stride\n self.dilation = dilation\n self.groups = groups\n self.padding = padding\n self.use_bias = use_bias\n\n self.conv = operations.Conv1d(\n in_dim,\n out_dim,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=use_bias,\n device=device,\n dtype=dtype\n )\n if norm is not None:\n self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device)\n else:\n self.norm = None\n if act is not None:\n self.act = nn.SiLU(inplace=True)\n else:\n self.act = None\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.conv(x)\n if self.norm:\n x = self.norm(x)\n if self.act:\n x = self.act(x)\n return x\n\n\nclass GLUMBConv(nn.Module):\n def __init__(\n self,\n in_features: int,\n hidden_features: int,\n out_feature=None,\n kernel_size=3,\n stride=1,\n padding: Union[int, None] = None,\n use_bias=False,\n norm=(None, None, None),\n act=(\"silu\", \"silu\", None),\n dilation=1,\n dtype=None, device=None, operations=None\n ):\n out_feature = out_feature or in_features\n super().__init__()\n use_bias = val2tuple(use_bias, 3)\n norm = val2tuple(norm, 3)\n act = val2tuple(act, 3)\n\n self.glu_act = nn.SiLU(inplace=False)\n self.inverted_conv = ConvLayer(\n in_features,\n hidden_features * 2,\n 1,\n use_bias=use_bias[0],\n norm=norm[0],\n act=act[0],\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.depth_conv = ConvLayer(\n hidden_features * 2,\n hidden_features * 2,\n kernel_size,\n stride=stride,\n groups=hidden_features * 2,\n padding=padding,\n use_bias=use_bias[1],\n norm=norm[1],\n act=None,\n dilation=dilation,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.point_conv = ConvLayer(\n hidden_features,\n out_feature,\n 1,\n use_bias=use_bias[2],\n norm=norm[2],\n act=act[2],\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = x.transpose(1, 2)\n x = self.inverted_conv(x)\n x = self.depth_conv(x)\n\n x, gate = torch.chunk(x, 2, dim=1)\n gate = self.glu_act(gate)\n x = x * gate\n\n x = self.point_conv(x)\n x = x.transpose(1, 2)\n\n return x\n\n\nclass LinearTransformerBlock(nn.Module):\n \"\"\"\n A Sana block with global shared adaptive layer norm (adaLN-single) conditioning.\n \"\"\"\n def __init__(\n self,\n dim,\n num_attention_heads,\n attention_head_dim,\n use_adaln_single=True,\n cross_attention_dim=None,\n added_kv_proj_dim=None,\n context_pre_only=False,\n mlp_ratio=4.0,\n add_cross_attention=False,\n add_cross_attention_dim=None,\n qk_norm=None,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6)\n self.attn = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim,\n added_kv_proj_dim=added_kv_proj_dim,\n dim_head=attention_head_dim,\n heads=num_attention_heads,\n out_dim=dim,\n bias=True,\n qk_norm=qk_norm,\n processor=CustomLiteLAProcessor2_0(),\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.add_cross_attention = add_cross_attention\n self.context_pre_only = context_pre_only\n\n if add_cross_attention and add_cross_attention_dim is not None:\n self.cross_attn = Attention(\n query_dim=dim,\n cross_attention_dim=add_cross_attention_dim,\n added_kv_proj_dim=add_cross_attention_dim,\n dim_head=attention_head_dim,\n heads=num_attention_heads,\n out_dim=dim,\n context_pre_only=context_pre_only,\n bias=True,\n qk_norm=qk_norm,\n processor=CustomerAttnProcessor2_0(),\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False)\n\n self.ff = GLUMBConv(\n in_features=dim,\n hidden_features=int(dim * mlp_ratio),\n use_bias=(True, True, False),\n norm=(None, None, None),\n act=(\"silu\", \"silu\", None),\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.use_adaln_single = use_adaln_single\n if use_adaln_single:\n self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device))\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: torch.FloatTensor = None,\n attention_mask: torch.FloatTensor = None,\n encoder_attention_mask: torch.FloatTensor = None,\n rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n temb: torch.FloatTensor = None,\n transformer_options={},\n ):\n\n N = hidden_states.shape[0]\n\n # step 1: AdaLN single\n if self.use_adaln_single:\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (\n comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1)\n ).chunk(6, dim=1)\n\n norm_hidden_states = self.norm1(hidden_states)\n if self.use_adaln_single:\n norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa\n\n # step 2: attention\n if not self.add_cross_attention:\n attn_output, encoder_hidden_states = self.attn(\n hidden_states=norm_hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n rotary_freqs_cis=rotary_freqs_cis,\n rotary_freqs_cis_cross=rotary_freqs_cis_cross,\n transformer_options=transformer_options,\n )\n else:\n attn_output, _ = self.attn(\n hidden_states=norm_hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n rotary_freqs_cis=rotary_freqs_cis,\n rotary_freqs_cis_cross=None,\n transformer_options=transformer_options,\n )\n\n if self.use_adaln_single:\n attn_output = gate_msa * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.add_cross_attention:\n attn_output = self.cross_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n rotary_freqs_cis=rotary_freqs_cis,\n rotary_freqs_cis_cross=rotary_freqs_cis_cross,\n transformer_options=transformer_options,\n )\n hidden_states = attn_output + hidden_states\n\n # step 3: add norm\n norm_hidden_states = self.norm2(hidden_states)\n if self.use_adaln_single:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp\n\n # step 4: feed forward\n ff_output = self.ff(norm_hidden_states)\n if self.use_adaln_single:\n ff_output = gate_mlp * ff_output\n\n hidden_states = hidden_states + ff_output\n\n return hidden_states\n" }, { "path": "comfy/ldm/ace/lyric_encoder.py", "content": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py\nfrom typing import Optional, Tuple, Union\nimport math\nimport torch\nfrom torch import nn\n\nimport comfy.model_management\n\nclass ConvolutionModule(nn.Module):\n \"\"\"ConvolutionModule in Conformer model.\"\"\"\n\n def __init__(self,\n channels: int,\n kernel_size: int = 15,\n activation: nn.Module = nn.ReLU(),\n norm: str = \"batch_norm\",\n causal: bool = False,\n bias: bool = True,\n dtype=None, device=None, operations=None):\n \"\"\"Construct an ConvolutionModule object.\n Args:\n channels (int): The number of channels of conv layers.\n kernel_size (int): Kernel size of conv layers.\n causal (int): Whether use causal convolution or not\n \"\"\"\n super().__init__()\n\n self.pointwise_conv1 = operations.Conv1d(\n channels,\n 2 * channels,\n kernel_size=1,\n stride=1,\n padding=0,\n bias=bias,\n dtype=dtype, device=device\n )\n # self.lorder is used to distinguish if it's a causal convolution,\n # if self.lorder > 0: it's a causal convolution, the input will be\n # padded with self.lorder frames on the left in forward.\n # else: it's a symmetrical convolution\n if causal:\n padding = 0\n self.lorder = kernel_size - 1\n else:\n # kernel_size should be an odd number for none causal convolution\n assert (kernel_size - 1) % 2 == 0\n padding = (kernel_size - 1) // 2\n self.lorder = 0\n self.depthwise_conv = operations.Conv1d(\n channels,\n channels,\n kernel_size,\n stride=1,\n padding=padding,\n groups=channels,\n bias=bias,\n dtype=dtype, device=device\n )\n\n assert norm in ['batch_norm', 'layer_norm']\n if norm == \"batch_norm\":\n self.use_layer_norm = False\n self.norm = nn.BatchNorm1d(channels)\n else:\n self.use_layer_norm = True\n self.norm = operations.LayerNorm(channels, dtype=dtype, device=device)\n\n self.pointwise_conv2 = operations.Conv1d(\n channels,\n channels,\n kernel_size=1,\n stride=1,\n padding=0,\n bias=bias,\n dtype=dtype, device=device\n )\n self.activation = activation\n\n def forward(\n self,\n x: torch.Tensor,\n mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),\n cache: torch.Tensor = torch.zeros((0, 0, 0)),\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Compute convolution module.\n Args:\n x (torch.Tensor): Input tensor (#batch, time, channels).\n mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),\n (0, 0, 0) means fake mask.\n cache (torch.Tensor): left context cache, it is only\n used in causal convolution (#batch, channels, cache_t),\n (0, 0, 0) meas fake cache.\n Returns:\n torch.Tensor: Output tensor (#batch, time, channels).\n \"\"\"\n # exchange the temporal dimension and the feature dimension\n x = x.transpose(1, 2) # (#batch, channels, time)\n\n # mask batch padding\n if mask_pad.size(2) > 0: # time > 0\n x.masked_fill_(~mask_pad, 0.0)\n\n if self.lorder > 0:\n if cache.size(2) == 0: # cache_t == 0\n x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)\n else:\n assert cache.size(0) == x.size(0) # equal batch\n assert cache.size(1) == x.size(1) # equal channel\n x = torch.cat((cache, x), dim=2)\n assert (x.size(2) > self.lorder)\n new_cache = x[:, :, -self.lorder:]\n else:\n # It's better we just return None if no cache is required,\n # However, for JIT export, here we just fake one tensor instead of\n # None.\n new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)\n\n # GLU mechanism\n x = self.pointwise_conv1(x) # (batch, 2*channel, dim)\n x = nn.functional.glu(x, dim=1) # (batch, channel, dim)\n\n # 1D Depthwise Conv\n x = self.depthwise_conv(x)\n if self.use_layer_norm:\n x = x.transpose(1, 2)\n x = self.activation(self.norm(x))\n if self.use_layer_norm:\n x = x.transpose(1, 2)\n x = self.pointwise_conv2(x)\n # mask batch padding\n if mask_pad.size(2) > 0: # time > 0\n x.masked_fill_(~mask_pad, 0.0)\n\n return x.transpose(1, 2), new_cache\n\nclass PositionwiseFeedForward(torch.nn.Module):\n \"\"\"Positionwise feed forward layer.\n\n FeedForward are appied on each position of the sequence.\n The output dim is same with the input dim.\n\n Args:\n idim (int): Input dimenstion.\n hidden_units (int): The number of hidden units.\n dropout_rate (float): Dropout rate.\n activation (torch.nn.Module): Activation function\n \"\"\"\n\n def __init__(\n self,\n idim: int,\n hidden_units: int,\n dropout_rate: float,\n activation: torch.nn.Module = torch.nn.ReLU(),\n dtype=None, device=None, operations=None\n ):\n \"\"\"Construct a PositionwiseFeedForward object.\"\"\"\n super(PositionwiseFeedForward, self).__init__()\n self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device)\n self.activation = activation\n self.dropout = torch.nn.Dropout(dropout_rate)\n self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device)\n\n def forward(self, xs: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function.\n\n Args:\n xs: input tensor (B, L, D)\n Returns:\n output tensor, (B, L, D)\n \"\"\"\n return self.w_2(self.dropout(self.activation(self.w_1(xs))))\n\nclass Swish(torch.nn.Module):\n \"\"\"Construct an Swish object.\"\"\"\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Return Swish activation function.\"\"\"\n return x * torch.sigmoid(x)\n\nclass MultiHeadedAttention(nn.Module):\n \"\"\"Multi-Head Attention layer.\n\n Args:\n n_head (int): The number of heads.\n n_feat (int): The number of features.\n dropout_rate (float): Dropout rate.\n\n \"\"\"\n\n def __init__(self,\n n_head: int,\n n_feat: int,\n dropout_rate: float,\n key_bias: bool = True,\n dtype=None, device=None, operations=None):\n \"\"\"Construct an MultiHeadedAttention object.\"\"\"\n super().__init__()\n assert n_feat % n_head == 0\n # We assume d_v always equals d_k\n self.d_k = n_feat // n_head\n self.h = n_head\n self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device)\n self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device)\n self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device)\n self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device)\n self.dropout = nn.Dropout(p=dropout_rate)\n\n def forward_qkv(\n self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Transform query, key and value.\n\n Args:\n query (torch.Tensor): Query tensor (#batch, time1, size).\n key (torch.Tensor): Key tensor (#batch, time2, size).\n value (torch.Tensor): Value tensor (#batch, time2, size).\n\n Returns:\n torch.Tensor: Transformed query tensor, size\n (#batch, n_head, time1, d_k).\n torch.Tensor: Transformed key tensor, size\n (#batch, n_head, time2, d_k).\n torch.Tensor: Transformed value tensor, size\n (#batch, n_head, time2, d_k).\n\n \"\"\"\n n_batch = query.size(0)\n q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)\n k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)\n v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)\n q = q.transpose(1, 2) # (batch, head, time1, d_k)\n k = k.transpose(1, 2) # (batch, head, time2, d_k)\n v = v.transpose(1, 2) # (batch, head, time2, d_k)\n return q, k, v\n\n def forward_attention(\n self,\n value: torch.Tensor,\n scores: torch.Tensor,\n mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)\n ) -> torch.Tensor:\n \"\"\"Compute attention context vector.\n\n Args:\n value (torch.Tensor): Transformed value, size\n (#batch, n_head, time2, d_k).\n scores (torch.Tensor): Attention score, size\n (#batch, n_head, time1, time2).\n mask (torch.Tensor): Mask, size (#batch, 1, time2) or\n (#batch, time1, time2), (0, 0, 0) means fake mask.\n\n Returns:\n torch.Tensor: Transformed value (#batch, time1, d_model)\n weighted by the attention score (#batch, time1, time2).\n\n \"\"\"\n n_batch = value.size(0)\n\n if mask is not None and mask.size(2) > 0: # time2 > 0\n mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)\n # For last chunk, time2 might be larger than scores.size(-1)\n mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2)\n scores = scores.masked_fill(mask, -float('inf'))\n attn = torch.softmax(scores, dim=-1).masked_fill(\n mask, 0.0) # (batch, head, time1, time2)\n\n else:\n attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)\n\n p_attn = self.dropout(attn)\n x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)\n x = (x.transpose(1, 2).contiguous().view(n_batch, -1,\n self.h * self.d_k)\n ) # (batch, time1, d_model)\n\n return self.linear_out(x) # (batch, time1, d_model)\n\n def forward(\n self,\n query: torch.Tensor,\n key: torch.Tensor,\n value: torch.Tensor,\n mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),\n pos_emb: torch.Tensor = torch.empty(0),\n cache: torch.Tensor = torch.zeros((0, 0, 0, 0))\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Compute scaled dot product attention.\n\n Args:\n query (torch.Tensor): Query tensor (#batch, time1, size).\n key (torch.Tensor): Key tensor (#batch, time2, size).\n value (torch.Tensor): Value tensor (#batch, time2, size).\n mask (torch.Tensor): Mask tensor (#batch, 1, time2) or\n (#batch, time1, time2).\n 1.When applying cross attention between decoder and encoder,\n the batch padding mask for input is in (#batch, 1, T) shape.\n 2.When applying self attention of encoder,\n the mask is in (#batch, T, T) shape.\n 3.When applying self attention of decoder,\n the mask is in (#batch, L, L) shape.\n 4.If the different position in decoder see different block\n of the encoder, such as Mocha, the passed in mask could be\n in (#batch, L, T) shape. But there is no such case in current\n CosyVoice.\n cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),\n where `cache_t == chunk_size * num_decoding_left_chunks`\n and `head * d_k == size`\n\n\n Returns:\n torch.Tensor: Output tensor (#batch, time1, d_model).\n torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)\n where `cache_t == chunk_size * num_decoding_left_chunks`\n and `head * d_k == size`\n\n \"\"\"\n q, k, v = self.forward_qkv(query, key, value)\n if cache.size(0) > 0:\n key_cache, value_cache = torch.split(cache,\n cache.size(-1) // 2,\n dim=-1)\n k = torch.cat([key_cache, k], dim=2)\n v = torch.cat([value_cache, v], dim=2)\n new_cache = torch.cat((k, v), dim=-1)\n\n scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)\n return self.forward_attention(v, scores, mask), new_cache\n\n\nclass RelPositionMultiHeadedAttention(MultiHeadedAttention):\n \"\"\"Multi-Head Attention layer with relative position encoding.\n Paper: https://arxiv.org/abs/1901.02860\n Args:\n n_head (int): The number of heads.\n n_feat (int): The number of features.\n dropout_rate (float): Dropout rate.\n \"\"\"\n\n def __init__(self,\n n_head: int,\n n_feat: int,\n dropout_rate: float,\n key_bias: bool = True,\n dtype=None, device=None, operations=None):\n \"\"\"Construct an RelPositionMultiHeadedAttention object.\"\"\"\n super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations)\n # linear transformation for positional encoding\n self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device)\n # these two learnable bias are used in matrix c and matrix d\n # as described in https://arxiv.org/abs/1901.02860 Section 3.3\n self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device))\n self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device))\n # torch.nn.init.xavier_uniform_(self.pos_bias_u)\n # torch.nn.init.xavier_uniform_(self.pos_bias_v)\n\n def rel_shift(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute relative positional encoding.\n\n Args:\n x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).\n time1 means the length of query vector.\n\n Returns:\n torch.Tensor: Output tensor.\n\n \"\"\"\n zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1),\n device=x.device,\n dtype=x.dtype)\n x_padded = torch.cat([zero_pad, x], dim=-1)\n\n x_padded = x_padded.view(x.size()[0],\n x.size()[1],\n x.size(3) + 1, x.size(2))\n x = x_padded[:, :, 1:].view_as(x)[\n :, :, :, : x.size(-1) // 2 + 1\n ] # only keep the positions from 0 to time2\n return x\n\n def forward(\n self,\n query: torch.Tensor,\n key: torch.Tensor,\n value: torch.Tensor,\n mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),\n pos_emb: torch.Tensor = torch.empty(0),\n cache: torch.Tensor = torch.zeros((0, 0, 0, 0))\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Compute 'Scaled Dot Product Attention' with rel. positional encoding.\n Args:\n query (torch.Tensor): Query tensor (#batch, time1, size).\n key (torch.Tensor): Key tensor (#batch, time2, size).\n value (torch.Tensor): Value tensor (#batch, time2, size).\n mask (torch.Tensor): Mask tensor (#batch, 1, time2) or\n (#batch, time1, time2), (0, 0, 0) means fake mask.\n pos_emb (torch.Tensor): Positional embedding tensor\n (#batch, time2, size).\n cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),\n where `cache_t == chunk_size * num_decoding_left_chunks`\n and `head * d_k == size`\n Returns:\n torch.Tensor: Output tensor (#batch, time1, d_model).\n torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)\n where `cache_t == chunk_size * num_decoding_left_chunks`\n and `head * d_k == size`\n \"\"\"\n q, k, v = self.forward_qkv(query, key, value)\n q = q.transpose(1, 2) # (batch, time1, head, d_k)\n\n if cache.size(0) > 0:\n key_cache, value_cache = torch.split(cache,\n cache.size(-1) // 2,\n dim=-1)\n k = torch.cat([key_cache, k], dim=2)\n v = torch.cat([value_cache, v], dim=2)\n # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's\n # non-trivial to calculate `next_cache_start` here.\n new_cache = torch.cat((k, v), dim=-1)\n\n n_batch_pos = pos_emb.size(0)\n p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)\n p = p.transpose(1, 2) # (batch, head, time1, d_k)\n\n # (batch, head, time1, d_k)\n q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2)\n # (batch, head, time1, d_k)\n q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2)\n\n # compute attention score\n # first compute matrix a and matrix c\n # as described in https://arxiv.org/abs/1901.02860 Section 3.3\n # (batch, head, time1, time2)\n matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))\n\n # compute matrix b and matrix d\n # (batch, head, time1, time2)\n matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))\n # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used\n if matrix_ac.shape != matrix_bd.shape:\n matrix_bd = self.rel_shift(matrix_bd)\n\n scores = (matrix_ac + matrix_bd) / math.sqrt(\n self.d_k) # (batch, head, time1, time2)\n\n return self.forward_attention(v, scores, mask), new_cache\n\n\n\ndef subsequent_mask(\n size: int,\n device: torch.device = torch.device(\"cpu\"),\n) -> torch.Tensor:\n \"\"\"Create mask for subsequent steps (size, size).\n\n This mask is used only in decoder which works in an auto-regressive mode.\n This means the current step could only do attention with its left steps.\n\n In encoder, fully attention is used when streaming is not necessary and\n the sequence is not long. In this case, no attention mask is needed.\n\n When streaming is need, chunk-based attention is used in encoder. See\n subsequent_chunk_mask for the chunk-based attention mask.\n\n Args:\n size (int): size of mask\n str device (str): \"cpu\" or \"cuda\" or torch.Tensor.device\n dtype (torch.device): result dtype\n\n Returns:\n torch.Tensor: mask\n\n Examples:\n >>> subsequent_mask(3)\n [[1, 0, 0],\n [1, 1, 0],\n [1, 1, 1]]\n \"\"\"\n arange = torch.arange(size, device=device)\n mask = arange.expand(size, size)\n arange = arange.unsqueeze(-1)\n mask = mask <= arange\n return mask\n\n\ndef subsequent_chunk_mask(\n size: int,\n chunk_size: int,\n num_left_chunks: int = -1,\n device: torch.device = torch.device(\"cpu\"),\n ) -> torch.Tensor:\n \"\"\"Create mask for subsequent steps (size, size) with chunk size,\n this is for streaming encoder\n\n Args:\n size (int): size of mask\n chunk_size (int): size of chunk\n num_left_chunks (int): number of left chunks\n <0: use full chunk\n >=0: use num_left_chunks\n device (torch.device): \"cpu\" or \"cuda\" or torch.Tensor.device\n\n Returns:\n torch.Tensor: mask\n\n Examples:\n >>> subsequent_chunk_mask(4, 2)\n [[1, 1, 0, 0],\n [1, 1, 0, 0],\n [1, 1, 1, 1],\n [1, 1, 1, 1]]\n \"\"\"\n ret = torch.zeros(size, size, device=device, dtype=torch.bool)\n for i in range(size):\n if num_left_chunks < 0:\n start = 0\n else:\n start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)\n ending = min((i // chunk_size + 1) * chunk_size, size)\n ret[i, start:ending] = True\n return ret\n\ndef add_optional_chunk_mask(xs: torch.Tensor,\n masks: torch.Tensor,\n use_dynamic_chunk: bool,\n use_dynamic_left_chunk: bool,\n decoding_chunk_size: int,\n static_chunk_size: int,\n num_decoding_left_chunks: int,\n enable_full_context: bool = True):\n \"\"\" Apply optional mask for encoder.\n\n Args:\n xs (torch.Tensor): padded input, (B, L, D), L for max length\n mask (torch.Tensor): mask for xs, (B, 1, L)\n use_dynamic_chunk (bool): whether to use dynamic chunk or not\n use_dynamic_left_chunk (bool): whether to use dynamic left chunk for\n training.\n decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's\n 0: default for training, use random dynamic chunk.\n <0: for decoding, use full chunk.\n >0: for decoding, use fixed chunk size as set.\n static_chunk_size (int): chunk size for static chunk training/decoding\n if it's greater than 0, if use_dynamic_chunk is true,\n this parameter will be ignored\n num_decoding_left_chunks: number of left chunks, this is for decoding,\n the chunk size is decoding_chunk_size.\n >=0: use num_decoding_left_chunks\n <0: use all left chunks\n enable_full_context (bool):\n True: chunk size is either [1, 25] or full context(max_len)\n False: chunk size ~ U[1, 25]\n\n Returns:\n torch.Tensor: chunk mask of the input xs.\n \"\"\"\n # Whether to use chunk mask or not\n if use_dynamic_chunk:\n max_len = xs.size(1)\n if decoding_chunk_size < 0:\n chunk_size = max_len\n num_left_chunks = -1\n elif decoding_chunk_size > 0:\n chunk_size = decoding_chunk_size\n num_left_chunks = num_decoding_left_chunks\n else:\n # chunk size is either [1, 25] or full context(max_len).\n # Since we use 4 times subsampling and allow up to 1s(100 frames)\n # delay, the maximum frame is 100 / 4 = 25.\n chunk_size = torch.randint(1, max_len, (1, )).item()\n num_left_chunks = -1\n if chunk_size > max_len // 2 and enable_full_context:\n chunk_size = max_len\n else:\n chunk_size = chunk_size % 25 + 1\n if use_dynamic_left_chunk:\n max_left_chunks = (max_len - 1) // chunk_size\n num_left_chunks = torch.randint(0, max_left_chunks,\n (1, )).item()\n chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size,\n num_left_chunks,\n xs.device) # (L, L)\n chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)\n chunk_masks = masks & chunk_masks # (B, L, L)\n elif static_chunk_size > 0:\n num_left_chunks = num_decoding_left_chunks\n chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size,\n num_left_chunks,\n xs.device) # (L, L)\n chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)\n chunk_masks = masks & chunk_masks # (B, L, L)\n else:\n chunk_masks = masks\n return chunk_masks\n\n\nclass ConformerEncoderLayer(nn.Module):\n \"\"\"Encoder layer module.\n Args:\n size (int): Input dimension.\n self_attn (torch.nn.Module): Self-attention module instance.\n `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`\n instance can be used as the argument.\n feed_forward (torch.nn.Module): Feed-forward module instance.\n `PositionwiseFeedForward` instance can be used as the argument.\n feed_forward_macaron (torch.nn.Module): Additional feed-forward module\n instance.\n `PositionwiseFeedForward` instance can be used as the argument.\n conv_module (torch.nn.Module): Convolution module instance.\n `ConvlutionModule` instance can be used as the argument.\n dropout_rate (float): Dropout rate.\n normalize_before (bool):\n True: use layer_norm before each sub-block.\n False: use layer_norm after each sub-block.\n \"\"\"\n\n def __init__(\n self,\n size: int,\n self_attn: torch.nn.Module,\n feed_forward: Optional[nn.Module] = None,\n feed_forward_macaron: Optional[nn.Module] = None,\n conv_module: Optional[nn.Module] = None,\n dropout_rate: float = 0.1,\n normalize_before: bool = True,\n dtype=None, device=None, operations=None\n ):\n \"\"\"Construct an EncoderLayer object.\"\"\"\n super().__init__()\n self.self_attn = self_attn\n self.feed_forward = feed_forward\n self.feed_forward_macaron = feed_forward_macaron\n self.conv_module = conv_module\n self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module\n self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module\n if feed_forward_macaron is not None:\n self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device)\n self.ff_scale = 0.5\n else:\n self.ff_scale = 1.0\n if self.conv_module is not None:\n self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module\n self.norm_final = operations.LayerNorm(\n size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block\n self.dropout = nn.Dropout(dropout_rate)\n self.size = size\n self.normalize_before = normalize_before\n\n def forward(\n self,\n x: torch.Tensor,\n mask: torch.Tensor,\n pos_emb: torch.Tensor,\n mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),\n att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),\n cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Compute encoded features.\n\n Args:\n x (torch.Tensor): (#batch, time, size)\n mask (torch.Tensor): Mask tensor for the input (#batch, time，time),\n (0, 0, 0) means fake mask.\n pos_emb (torch.Tensor): positional encoding, must not be None\n for ConformerEncoderLayer.\n mask_pad (torch.Tensor): batch padding mask used for conv module.\n (#batch, 1，time), (0, 0, 0) means fake mask.\n att_cache (torch.Tensor): Cache tensor of the KEY & VALUE\n (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.\n cnn_cache (torch.Tensor): Convolution cache in conformer layer\n (#batch=1, size, cache_t2)\n Returns:\n torch.Tensor: Output tensor (#batch, time, size).\n torch.Tensor: Mask tensor (#batch, time, time).\n torch.Tensor: att_cache tensor,\n (#batch=1, head, cache_t1 + time, d_k * 2).\n torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).\n \"\"\"\n\n # whether to use macaron style\n if self.feed_forward_macaron is not None:\n residual = x\n if self.normalize_before:\n x = self.norm_ff_macaron(x)\n x = residual + self.ff_scale * self.dropout(\n self.feed_forward_macaron(x))\n if not self.normalize_before:\n x = self.norm_ff_macaron(x)\n\n # multi-headed self-attention module\n residual = x\n if self.normalize_before:\n x = self.norm_mha(x)\n x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb,\n att_cache)\n x = residual + self.dropout(x_att)\n if not self.normalize_before:\n x = self.norm_mha(x)\n\n # convolution module\n # Fake new cnn cache here, and then change it in conv_module\n new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)\n if self.conv_module is not None:\n residual = x\n if self.normalize_before:\n x = self.norm_conv(x)\n x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)\n x = residual + self.dropout(x)\n\n if not self.normalize_before:\n x = self.norm_conv(x)\n\n # feed forward module\n residual = x\n if self.normalize_before:\n x = self.norm_ff(x)\n\n x = residual + self.ff_scale * self.dropout(self.feed_forward(x))\n if not self.normalize_before:\n x = self.norm_ff(x)\n\n if self.conv_module is not None:\n x = self.norm_final(x)\n\n return x, mask, new_att_cache, new_cnn_cache\n\n\n\nclass EspnetRelPositionalEncoding(torch.nn.Module):\n \"\"\"Relative positional encoding module (new implementation).\n\n Details can be found in https://github.com/espnet/espnet/pull/2816.\n\n See : Appendix B in https://arxiv.org/abs/1901.02860\n\n Args:\n d_model (int): Embedding dimension.\n dropout_rate (float): Dropout rate.\n max_len (int): Maximum input length.\n\n \"\"\"\n\n def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):\n \"\"\"Construct an PositionalEncoding object.\"\"\"\n super(EspnetRelPositionalEncoding, self).__init__()\n self.d_model = d_model\n self.xscale = math.sqrt(self.d_model)\n self.dropout = torch.nn.Dropout(p=dropout_rate)\n self.pe = None\n self.extend_pe(torch.tensor(0.0).expand(1, max_len))\n\n def extend_pe(self, x: torch.Tensor):\n \"\"\"Reset the positional encodings.\"\"\"\n if self.pe is not None:\n # self.pe contains both positive and negative parts\n # the length of self.pe is 2 * input_len - 1\n if self.pe.size(1) >= x.size(1) * 2 - 1:\n if self.pe.dtype != x.dtype or self.pe.device != x.device:\n self.pe = self.pe.to(dtype=x.dtype, device=x.device)\n return\n # Suppose `i` means to the position of query vecotr and `j` means the\n # position of key vector. We use position relative positions when keys\n # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Add positional encoding.\n\n Args:\n x (torch.Tensor): Input tensor (batch, time, `*`).\n\n Returns:\n torch.Tensor: Encoded tensor (batch, time, `*`).\n\n \"\"\"\n self.extend_pe(x)\n x = x * self.xscale\n pos_emb = self.position_encoding(size=x.size(1), offset=offset)\n return self.dropout(x), self.dropout(pos_emb)\n\n def position_encoding(self,\n offset: Union[int, torch.Tensor],\n size: int) -> torch.Tensor:\n \"\"\" For getting encoding in a streaming fashion\n\n Attention!!!!!\n we apply dropout only once at the whole utterance level in a none\n streaming way, but will call this function several times with\n increasing input size in a streaming scenario, so the dropout will\n be applied several times.\n\n Args:\n offset (int or torch.tensor): start offset\n size (int): required size of position encoding\n\n Returns:\n torch.Tensor: Corresponding encoding\n \"\"\"\n pos_emb = self.pe[\n :,\n self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size,\n ]\n return pos_emb\n\n\n\nclass LinearEmbed(torch.nn.Module):\n \"\"\"Linear transform the input without subsampling\n\n Args:\n idim (int): Input dimension.\n odim (int): Output dimension.\n dropout_rate (float): Dropout rate.\n\n \"\"\"\n\n def __init__(self, idim: int, odim: int, dropout_rate: float,\n pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None):\n \"\"\"Construct an linear object.\"\"\"\n super().__init__()\n self.out = torch.nn.Sequential(\n operations.Linear(idim, odim, dtype=dtype, device=device),\n operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device),\n torch.nn.Dropout(dropout_rate),\n )\n self.pos_enc = pos_enc_class #rel_pos_espnet\n\n def position_encoding(self, offset: Union[int, torch.Tensor],\n size: int) -> torch.Tensor:\n return self.pos_enc.position_encoding(offset, size)\n\n def forward(\n self,\n x: torch.Tensor,\n offset: Union[int, torch.Tensor] = 0\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Input x.\n\n Args:\n x (torch.Tensor): Input tensor (#batch, time, idim).\n x_mask (torch.Tensor): Input mask (#batch, 1, time).\n\n Returns:\n torch.Tensor: linear input tensor (#batch, time', odim),\n where time' = time .\n torch.Tensor: linear input mask (#batch, 1, time'),\n where time' = time .\n\n \"\"\"\n x = self.out(x)\n x, pos_emb = self.pos_enc(x, offset)\n return x, pos_emb\n\n\nATTENTION_CLASSES = {\n \"selfattn\": MultiHeadedAttention,\n \"rel_selfattn\": RelPositionMultiHeadedAttention,\n}\n\nACTIVATION_CLASSES = {\n \"hardtanh\": torch.nn.Hardtanh,\n \"tanh\": torch.nn.Tanh,\n \"relu\": torch.nn.ReLU,\n \"selu\": torch.nn.SELU,\n \"swish\": getattr(torch.nn, \"SiLU\", Swish),\n \"gelu\": torch.nn.GELU,\n}\n\n\ndef make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:\n \"\"\"Make mask tensor containing indices of padded part.\n\n See description of make_non_pad_mask.\n\n Args:\n lengths (torch.Tensor): Batch of lengths (B,).\n Returns:\n torch.Tensor: Mask tensor containing indices of padded part.\n\n Examples:\n >>> lengths = [5, 3, 2]\n >>> make_pad_mask(lengths)\n masks = [[0, 0, 0, 0 ,0],\n [0, 0, 0, 1, 1],\n [0, 0, 1, 1, 1]]\n \"\"\"\n batch_size = lengths.size(0)\n max_len = max_len if max_len > 0 else lengths.max().item()\n seq_range = torch.arange(0,\n max_len,\n dtype=torch.int64,\n device=lengths.device)\n seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)\n seq_length_expand = lengths.unsqueeze(-1)\n mask = seq_range_expand >= seq_length_expand\n return mask\n\n#https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml\nclass ConformerEncoder(torch.nn.Module):\n \"\"\"Conformer encoder module.\"\"\"\n\n def __init__(\n self,\n input_size: int,\n output_size: int = 1024,\n attention_heads: int = 16,\n linear_units: int = 4096,\n num_blocks: int = 6,\n dropout_rate: float = 0.1,\n positional_dropout_rate: float = 0.1,\n attention_dropout_rate: float = 0.0,\n input_layer: str = 'linear',\n pos_enc_layer_type: str = 'rel_pos_espnet',\n normalize_before: bool = True,\n static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask\n use_dynamic_chunk: bool = False,\n use_dynamic_left_chunk: bool = False,\n positionwise_conv_kernel_size: int = 1,\n macaron_style: bool =False,\n selfattention_layer_type: str = \"rel_selfattn\",\n activation_type: str = \"swish\",\n use_cnn_module: bool = False,\n cnn_module_kernel: int = 15,\n causal: bool = False,\n cnn_module_norm: str = \"batch_norm\",\n key_bias: bool = True,\n dtype=None, device=None, operations=None\n ):\n \"\"\"Construct ConformerEncoder\n\n Args:\n input_size to use_dynamic_chunk, see in BaseEncoder\n positionwise_conv_kernel_size (int): Kernel size of positionwise\n conv1d layer.\n macaron_style (bool): Whether to use macaron style for\n positionwise layer.\n selfattention_layer_type (str): Encoder attention layer type,\n the parameter has no effect now, it's just for configure\n compatibility. #'rel_selfattn'\n activation_type (str): Encoder activation function type.\n use_cnn_module (bool): Whether to use convolution module.\n cnn_module_kernel (int): Kernel size of convolution module.\n causal (bool): whether to use causal convolution or not.\n key_bias: whether use bias in attention.linear_k, False for whisper models.\n \"\"\"\n super().__init__()\n self.output_size = output_size\n self.embed = LinearEmbed(input_size, output_size, dropout_rate,\n EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations)\n self.normalize_before = normalize_before\n self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device)\n self.use_dynamic_chunk = use_dynamic_chunk\n\n self.static_chunk_size = static_chunk_size\n self.use_dynamic_chunk = use_dynamic_chunk\n self.use_dynamic_left_chunk = use_dynamic_left_chunk\n activation = ACTIVATION_CLASSES[activation_type]()\n\n # self-attention module definition\n encoder_selfattn_layer_args = (\n attention_heads,\n output_size,\n attention_dropout_rate,\n key_bias,\n )\n # feed-forward module definition\n positionwise_layer_args = (\n output_size,\n linear_units,\n dropout_rate,\n activation,\n )\n # convolution module definition\n convolution_layer_args = (output_size, cnn_module_kernel, activation,\n cnn_module_norm, causal)\n\n self.encoders = torch.nn.ModuleList([\n ConformerEncoderLayer(\n output_size,\n RelPositionMultiHeadedAttention(\n *encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations),\n PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations),\n PositionwiseFeedForward(\n *positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None,\n ConvolutionModule(\n *convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None,\n dropout_rate,\n normalize_before, dtype=dtype, device=device, operations=operations\n ) for _ in range(num_blocks)\n ])\n\n def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,\n pos_emb: torch.Tensor,\n mask_pad: torch.Tensor) -> torch.Tensor:\n for layer in self.encoders:\n xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)\n return xs\n\n def forward(\n self,\n xs: torch.Tensor,\n pad_mask: torch.Tensor,\n decoding_chunk_size: int = 0,\n num_decoding_left_chunks: int = -1,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Embed positions in tensor.\n\n Args:\n xs: padded input tensor (B, T, D)\n xs_lens: input length (B)\n decoding_chunk_size: decoding chunk size for dynamic chunk\n 0: default for training, use random dynamic chunk.\n <0: for decoding, use full chunk.\n >0: for decoding, use fixed chunk size as set.\n num_decoding_left_chunks: number of left chunks, this is for decoding,\n the chunk size is decoding_chunk_size.\n >=0: use num_decoding_left_chunks\n <0: use all left chunks\n Returns:\n encoder output tensor xs, and subsampled masks\n xs: padded output tensor (B, T' ~= T/subsample_rate, D)\n masks: torch.Tensor batch padding mask after subsample\n (B, 1, T' ~= T/subsample_rate)\n NOTE(xcsong):\n We pass the `__call__` method of the modules instead of `forward` to the\n checkpointing API because `__call__` attaches all the hooks of the module.\n https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2\n \"\"\"\n masks = None\n if pad_mask is not None:\n masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T)\n xs, pos_emb = self.embed(xs)\n mask_pad = masks # (B, 1, T/subsample_rate)\n chunk_masks = add_optional_chunk_mask(xs, masks,\n self.use_dynamic_chunk,\n self.use_dynamic_left_chunk,\n decoding_chunk_size,\n self.static_chunk_size,\n num_decoding_left_chunks)\n\n xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)\n if self.normalize_before:\n xs = self.after_norm(xs)\n # Here we assume the mask is not changed in encoder layers, so just\n # return the masks before encoder layers, and the masks will be used\n # for cross attention with decoder later\n return xs, masks\n\n" }, { "path": "comfy/ldm/ace/model.py", "content": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.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.\nfrom typing import Optional, List, Union\n\nimport torch\nfrom torch import nn\n\nimport comfy.model_management\nimport comfy.patcher_extension\n\nfrom comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps\nfrom .attention import LinearTransformerBlock, t2i_modulate\nfrom .lyric_encoder import ConformerEncoder as LyricEncoder\n\n\ndef cross_norm(hidden_states, controlnet_input):\n # input N x T x c\n mean_hidden_states, std_hidden_states = hidden_states.mean(dim=(1,2), keepdim=True), hidden_states.std(dim=(1,2), keepdim=True)\n mean_controlnet_input, std_controlnet_input = controlnet_input.mean(dim=(1,2), keepdim=True), controlnet_input.std(dim=(1,2), keepdim=True)\n controlnet_input = (controlnet_input - mean_controlnet_input) * (std_hidden_states / (std_controlnet_input + 1e-12)) + mean_hidden_states\n return controlnet_input\n\n\n# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2\nclass Qwen2RotaryEmbedding(nn.Module):\n def __init__(self, dim, max_position_embeddings=2048, base=10000, dtype=None, device=None):\n super().__init__()\n\n self.dim = dim\n self.max_position_embeddings = max_position_embeddings\n self.base = base\n inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n # Build here to make `torch.jit.trace` work.\n self._set_cos_sin_cache(\n seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32\n )\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)\n\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n def forward(self, x, seq_len=None):\n # x: [bs, num_attention_heads, seq_len, head_size]\n if seq_len > self.max_seq_len_cached:\n self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)\n\n return (\n self.cos_cached[:seq_len].to(dtype=x.dtype),\n self.sin_cached[:seq_len].to(dtype=x.dtype),\n )\n\n\nclass T2IFinalLayer(nn.Module):\n \"\"\"\n The final layer of Sana.\n \"\"\"\n\n def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True, dtype=dtype, device=device)\n self.scale_shift_table = nn.Parameter(torch.empty(2, hidden_size, dtype=dtype, device=device))\n self.out_channels = out_channels\n self.patch_size = patch_size\n\n def unpatchfy(\n self,\n hidden_states: torch.Tensor,\n width: int,\n ):\n # 4 unpatchify\n new_height, new_width = 1, hidden_states.size(1)\n hidden_states = hidden_states.reshape(\n shape=(hidden_states.shape[0], new_height, new_width, self.patch_size[0], self.patch_size[1], self.out_channels)\n ).contiguous()\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(hidden_states.shape[0], self.out_channels, new_height * self.patch_size[0], new_width * self.patch_size[1])\n ).contiguous()\n if width > new_width:\n output = torch.nn.functional.pad(output, (0, width - new_width, 0, 0), 'constant', 0)\n elif width < new_width:\n output = output[:, :, :, :width]\n return output\n\n def forward(self, x, t, output_length):\n shift, scale = (comfy.model_management.cast_to(self.scale_shift_table[None], device=t.device, dtype=t.dtype) + t[:, None]).chunk(2, dim=1)\n x = t2i_modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n # unpatchify\n output = self.unpatchfy(x, output_length)\n return output\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"2D Image to Patch Embedding\"\"\"\n\n def __init__(\n self,\n height=16,\n width=4096,\n patch_size=(16, 1),\n in_channels=8,\n embed_dim=1152,\n bias=True,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n patch_size_h, patch_size_w = patch_size\n self.early_conv_layers = nn.Sequential(\n operations.Conv2d(in_channels, in_channels*256, kernel_size=patch_size, stride=patch_size, padding=0, bias=bias, dtype=dtype, device=device),\n operations.GroupNorm(num_groups=32, num_channels=in_channels*256, eps=1e-6, affine=True, dtype=dtype, device=device),\n operations.Conv2d(in_channels*256, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device)\n )\n self.patch_size = patch_size\n self.height, self.width = height // patch_size_h, width // patch_size_w\n self.base_size = self.width\n\n def forward(self, latent):\n # early convolutions, N x C x H x W -> N x 256 * sqrt(patch_size) x H/patch_size x W/patch_size\n latent = self.early_conv_layers(latent)\n latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC\n return latent\n\n\nclass ACEStepTransformer2DModel(nn.Module):\n # _supports_gradient_checkpointing = True\n\n def __init__(\n self,\n in_channels: Optional[int] = 8,\n num_layers: int = 28,\n inner_dim: int = 1536,\n attention_head_dim: int = 64,\n num_attention_heads: int = 24,\n mlp_ratio: float = 4.0,\n out_channels: int = 8,\n max_position: int = 32768,\n rope_theta: float = 1000000.0,\n speaker_embedding_dim: int = 512,\n text_embedding_dim: int = 768,\n ssl_encoder_depths: List[int] = [9, 9],\n ssl_names: List[str] = [\"mert\", \"m-hubert\"],\n ssl_latent_dims: List[int] = [1024, 768],\n lyric_encoder_vocab_size: int = 6681,\n lyric_hidden_size: int = 1024,\n patch_size: List[int] = [16, 1],\n max_height: int = 16,\n max_width: int = 4096,\n audio_model=None,\n dtype=None, device=None, operations=None\n\n ):\n super().__init__()\n\n self.dtype = dtype\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n self.inner_dim = inner_dim\n self.out_channels = out_channels\n self.max_position = max_position\n self.patch_size = patch_size\n\n self.rope_theta = rope_theta\n\n self.rotary_emb = Qwen2RotaryEmbedding(\n dim=self.attention_head_dim,\n max_position_embeddings=self.max_position,\n base=self.rope_theta,\n dtype=dtype,\n device=device,\n )\n\n # 2. Define input layers\n self.in_channels = in_channels\n\n self.num_layers = num_layers\n # 3. Define transformers blocks\n self.transformer_blocks = nn.ModuleList(\n [\n LinearTransformerBlock(\n dim=self.inner_dim,\n num_attention_heads=self.num_attention_heads,\n attention_head_dim=attention_head_dim,\n mlp_ratio=mlp_ratio,\n add_cross_attention=True,\n add_cross_attention_dim=self.inner_dim,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n for i in range(self.num_layers)\n ]\n )\n\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim, dtype=dtype, device=device, operations=operations)\n self.t_block = nn.Sequential(nn.SiLU(), operations.Linear(self.inner_dim, 6 * self.inner_dim, bias=True, dtype=dtype, device=device))\n\n # speaker\n self.speaker_embedder = operations.Linear(speaker_embedding_dim, self.inner_dim, dtype=dtype, device=device)\n\n # genre\n self.genre_embedder = operations.Linear(text_embedding_dim, self.inner_dim, dtype=dtype, device=device)\n\n # lyric\n self.lyric_embs = operations.Embedding(lyric_encoder_vocab_size, lyric_hidden_size, dtype=dtype, device=device)\n self.lyric_encoder = LyricEncoder(input_size=lyric_hidden_size, static_chunk_size=0, dtype=dtype, device=device, operations=operations)\n self.lyric_proj = operations.Linear(lyric_hidden_size, self.inner_dim, dtype=dtype, device=device)\n\n projector_dim = 2 * self.inner_dim\n\n self.projectors = nn.ModuleList([\n nn.Sequential(\n operations.Linear(self.inner_dim, projector_dim, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(projector_dim, projector_dim, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(projector_dim, ssl_dim, dtype=dtype, device=device),\n ) for ssl_dim in ssl_latent_dims\n ])\n\n self.proj_in = PatchEmbed(\n height=max_height,\n width=max_width,\n patch_size=patch_size,\n embed_dim=self.inner_dim,\n bias=True,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.final_layer = T2IFinalLayer(self.inner_dim, patch_size=patch_size, out_channels=out_channels, dtype=dtype, device=device, operations=operations)\n\n def forward_lyric_encoder(\n self,\n lyric_token_idx: Optional[torch.LongTensor] = None,\n lyric_mask: Optional[torch.LongTensor] = None,\n out_dtype=None,\n ):\n # N x T x D\n lyric_embs = self.lyric_embs(lyric_token_idx, out_dtype=out_dtype)\n prompt_prenet_out, _mask = self.lyric_encoder(lyric_embs, lyric_mask, decoding_chunk_size=1, num_decoding_left_chunks=-1)\n prompt_prenet_out = self.lyric_proj(prompt_prenet_out)\n return prompt_prenet_out\n\n def encode(\n self,\n encoder_text_hidden_states: Optional[torch.Tensor] = None,\n text_attention_mask: Optional[torch.LongTensor] = None,\n speaker_embeds: Optional[torch.FloatTensor] = None,\n lyric_token_idx: Optional[torch.LongTensor] = None,\n lyric_mask: Optional[torch.LongTensor] = None,\n lyrics_strength=1.0,\n ):\n\n bs = encoder_text_hidden_states.shape[0]\n device = encoder_text_hidden_states.device\n\n # speaker embedding\n encoder_spk_hidden_states = self.speaker_embedder(speaker_embeds).unsqueeze(1)\n\n # genre embedding\n encoder_text_hidden_states = self.genre_embedder(encoder_text_hidden_states)\n\n # lyric\n encoder_lyric_hidden_states = self.forward_lyric_encoder(\n lyric_token_idx=lyric_token_idx,\n lyric_mask=lyric_mask,\n out_dtype=encoder_text_hidden_states.dtype,\n )\n\n encoder_lyric_hidden_states *= lyrics_strength\n\n encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1)\n\n encoder_hidden_mask = None\n if text_attention_mask is not None:\n speaker_mask = torch.ones(bs, 1, device=device)\n encoder_hidden_mask = torch.cat([speaker_mask, text_attention_mask, lyric_mask], dim=1)\n\n return encoder_hidden_states, encoder_hidden_mask\n\n def decode(\n self,\n hidden_states: torch.Tensor,\n attention_mask: torch.Tensor,\n encoder_hidden_states: torch.Tensor,\n encoder_hidden_mask: torch.Tensor,\n timestep: Optional[torch.Tensor],\n output_length: int = 0,\n block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,\n controlnet_scale: Union[float, torch.Tensor] = 1.0,\n transformer_options={},\n ):\n embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype))\n temb = self.t_block(embedded_timestep)\n\n hidden_states = self.proj_in(hidden_states)\n\n # controlnet logic\n if block_controlnet_hidden_states is not None:\n control_condi = cross_norm(hidden_states, block_controlnet_hidden_states)\n hidden_states = hidden_states + control_condi * controlnet_scale\n\n # inner_hidden_states = []\n\n rotary_freqs_cis = self.rotary_emb(hidden_states, seq_len=hidden_states.shape[1])\n encoder_rotary_freqs_cis = self.rotary_emb(encoder_hidden_states, seq_len=encoder_hidden_states.shape[1])\n\n for index_block, block in enumerate(self.transformer_blocks):\n hidden_states = block(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_hidden_mask,\n rotary_freqs_cis=rotary_freqs_cis,\n rotary_freqs_cis_cross=encoder_rotary_freqs_cis,\n temb=temb,\n transformer_options=transformer_options,\n )\n\n output = self.final_layer(hidden_states, embedded_timestep, output_length)\n return output\n\n def forward(self,\n x,\n timestep,\n attention_mask=None,\n context: Optional[torch.Tensor] = None,\n text_attention_mask: Optional[torch.LongTensor] = None,\n speaker_embeds: Optional[torch.FloatTensor] = None,\n lyric_token_idx: Optional[torch.LongTensor] = None,\n lyric_mask: Optional[torch.LongTensor] = None,\n block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,\n controlnet_scale: Union[float, torch.Tensor] = 1.0,\n lyrics_strength=1.0,\n **kwargs\n ):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get(\"transformer_options\", {}))\n ).execute(x, timestep, attention_mask, context, text_attention_mask, speaker_embeds, lyric_token_idx, lyric_mask, block_controlnet_hidden_states,\n controlnet_scale, lyrics_strength, **kwargs)\n\n def _forward(\n self,\n x,\n timestep,\n attention_mask=None,\n context: Optional[torch.Tensor] = None,\n text_attention_mask: Optional[torch.LongTensor] = None,\n speaker_embeds: Optional[torch.FloatTensor] = None,\n lyric_token_idx: Optional[torch.LongTensor] = None,\n lyric_mask: Optional[torch.LongTensor] = None,\n block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,\n controlnet_scale: Union[float, torch.Tensor] = 1.0,\n lyrics_strength=1.0,\n **kwargs\n ):\n hidden_states = x\n encoder_text_hidden_states = context\n encoder_hidden_states, encoder_hidden_mask = self.encode(\n encoder_text_hidden_states=encoder_text_hidden_states,\n text_attention_mask=text_attention_mask,\n speaker_embeds=speaker_embeds,\n lyric_token_idx=lyric_token_idx,\n lyric_mask=lyric_mask,\n lyrics_strength=lyrics_strength,\n )\n\n output_length = hidden_states.shape[-1]\n\n transformer_options = kwargs.get(\"transformer_options\", {})\n output = self.decode(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_hidden_mask=encoder_hidden_mask,\n timestep=timestep,\n output_length=output_length,\n block_controlnet_hidden_states=block_controlnet_hidden_states,\n controlnet_scale=controlnet_scale,\n transformer_options=transformer_options,\n )\n\n return output\n" }, { "path": "comfy/ldm/ace/vae/autoencoder_dc.py", "content": "# Rewritten from diffusers\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Tuple, Union\n\nimport comfy.model_management\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\n\nclass RMSNorm(ops.RMSNorm):\n def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False):\n super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine)\n if elementwise_affine:\n self.bias = nn.Parameter(torch.empty(dim)) if bias else None\n\n def forward(self, x):\n x = super().forward(x)\n if self.elementwise_affine:\n if self.bias is not None:\n x = x + comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device)\n return x\n\n\ndef get_normalization(norm_type, num_features, num_groups=32, eps=1e-5):\n if norm_type == \"batch_norm\":\n return nn.BatchNorm2d(num_features)\n elif norm_type == \"group_norm\":\n return ops.GroupNorm(num_groups, num_features)\n elif norm_type == \"layer_norm\":\n return ops.LayerNorm(num_features)\n elif norm_type == \"rms_norm\":\n return RMSNorm(num_features, eps=eps, elementwise_affine=True, bias=True)\n else:\n raise ValueError(f\"Unknown normalization type: {norm_type}\")\n\n\ndef get_activation(activation_type):\n if activation_type == \"relu\":\n return nn.ReLU()\n elif activation_type == \"relu6\":\n return nn.ReLU6()\n elif activation_type == \"silu\":\n return nn.SiLU()\n elif activation_type == \"leaky_relu\":\n return nn.LeakyReLU(0.2)\n else:\n raise ValueError(f\"Unknown activation type: {activation_type}\")\n\n\nclass ResBlock(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n norm_type: str = \"batch_norm\",\n act_fn: str = \"relu6\",\n ) -> None:\n super().__init__()\n\n self.norm_type = norm_type\n self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity()\n self.conv1 = ops.Conv2d(in_channels, in_channels, 3, 1, 1)\n self.conv2 = ops.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False)\n self.norm = get_normalization(norm_type, out_channels)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n residual = hidden_states\n hidden_states = self.conv1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.norm_type == \"rms_norm\":\n # move channel to the last dimension so we apply RMSnorm across channel dimension\n hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1)\n else:\n hidden_states = self.norm(hidden_states)\n\n return hidden_states + residual\n\nclass SanaMultiscaleAttentionProjection(nn.Module):\n def __init__(\n self,\n in_channels: int,\n num_attention_heads: int,\n kernel_size: int,\n ) -> None:\n super().__init__()\n\n channels = 3 * in_channels\n self.proj_in = ops.Conv2d(\n channels,\n channels,\n kernel_size,\n padding=kernel_size // 2,\n groups=channels,\n bias=False,\n )\n self.proj_out = ops.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = self.proj_in(hidden_states)\n hidden_states = self.proj_out(hidden_states)\n return hidden_states\n\nclass SanaMultiscaleLinearAttention(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_attention_heads: int = None,\n attention_head_dim: int = 8,\n mult: float = 1.0,\n norm_type: str = \"batch_norm\",\n kernel_sizes: tuple = (5,),\n eps: float = 1e-15,\n residual_connection: bool = False,\n ):\n super().__init__()\n\n self.eps = eps\n self.attention_head_dim = attention_head_dim\n self.norm_type = norm_type\n self.residual_connection = residual_connection\n\n num_attention_heads = (\n int(in_channels // attention_head_dim * mult)\n if num_attention_heads is None\n else num_attention_heads\n )\n inner_dim = num_attention_heads * attention_head_dim\n\n self.to_q = ops.Linear(in_channels, inner_dim, bias=False)\n self.to_k = ops.Linear(in_channels, inner_dim, bias=False)\n self.to_v = ops.Linear(in_channels, inner_dim, bias=False)\n\n self.to_qkv_multiscale = nn.ModuleList()\n for kernel_size in kernel_sizes:\n self.to_qkv_multiscale.append(\n SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size)\n )\n\n self.nonlinearity = nn.ReLU()\n self.to_out = ops.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False)\n self.norm_out = get_normalization(norm_type, out_channels)\n\n def apply_linear_attention(self, query, key, value):\n value = F.pad(value, (0, 0, 0, 1), mode=\"constant\", value=1)\n scores = torch.matmul(value, key.transpose(-1, -2))\n hidden_states = torch.matmul(scores, query)\n\n hidden_states = hidden_states.to(dtype=torch.float32)\n hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps)\n return hidden_states\n\n def apply_quadratic_attention(self, query, key, value):\n scores = torch.matmul(key.transpose(-1, -2), query)\n scores = scores.to(dtype=torch.float32)\n scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps)\n hidden_states = torch.matmul(value, scores.to(value.dtype))\n return hidden_states\n\n def forward(self, hidden_states):\n height, width = hidden_states.shape[-2:]\n if height * width > self.attention_head_dim:\n use_linear_attention = True\n else:\n use_linear_attention = False\n\n residual = hidden_states\n\n batch_size, _, height, width = list(hidden_states.size())\n original_dtype = hidden_states.dtype\n\n hidden_states = hidden_states.movedim(1, -1)\n query = self.to_q(hidden_states)\n key = self.to_k(hidden_states)\n value = self.to_v(hidden_states)\n hidden_states = torch.cat([query, key, value], dim=3)\n hidden_states = hidden_states.movedim(-1, 1)\n\n multi_scale_qkv = [hidden_states]\n for block in self.to_qkv_multiscale:\n multi_scale_qkv.append(block(hidden_states))\n\n hidden_states = torch.cat(multi_scale_qkv, dim=1)\n\n if use_linear_attention:\n # for linear attention upcast hidden_states to float32\n hidden_states = hidden_states.to(dtype=torch.float32)\n\n hidden_states = hidden_states.reshape(batch_size, -1, 3 * self.attention_head_dim, height * width)\n\n query, key, value = hidden_states.chunk(3, dim=2)\n query = self.nonlinearity(query)\n key = self.nonlinearity(key)\n\n if use_linear_attention:\n hidden_states = self.apply_linear_attention(query, key, value)\n hidden_states = hidden_states.to(dtype=original_dtype)\n else:\n hidden_states = self.apply_quadratic_attention(query, key, value)\n\n hidden_states = torch.reshape(hidden_states, (batch_size, -1, height, width))\n hidden_states = self.to_out(hidden_states.movedim(1, -1)).movedim(-1, 1)\n\n if self.norm_type == \"rms_norm\":\n hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1)\n else:\n hidden_states = self.norm_out(hidden_states)\n\n if self.residual_connection:\n hidden_states = hidden_states + residual\n\n return hidden_states\n\n\nclass EfficientViTBlock(nn.Module):\n def __init__(\n self,\n in_channels: int,\n mult: float = 1.0,\n attention_head_dim: int = 32,\n qkv_multiscales: tuple = (5,),\n norm_type: str = \"batch_norm\",\n ) -> None:\n super().__init__()\n\n self.attn = SanaMultiscaleLinearAttention(\n in_channels=in_channels,\n out_channels=in_channels,\n mult=mult,\n attention_head_dim=attention_head_dim,\n norm_type=norm_type,\n kernel_sizes=qkv_multiscales,\n residual_connection=True,\n )\n\n self.conv_out = GLUMBConv(\n in_channels=in_channels,\n out_channels=in_channels,\n norm_type=\"rms_norm\",\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.attn(x)\n x = self.conv_out(x)\n return x\n\n\nclass GLUMBConv(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n expand_ratio: float = 4,\n norm_type: str = None,\n residual_connection: bool = True,\n ) -> None:\n super().__init__()\n\n hidden_channels = int(expand_ratio * in_channels)\n self.norm_type = norm_type\n self.residual_connection = residual_connection\n\n self.nonlinearity = nn.SiLU()\n self.conv_inverted = ops.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0)\n self.conv_depth = ops.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2)\n self.conv_point = ops.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False)\n\n self.norm = None\n if norm_type == \"rms_norm\":\n self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n if self.residual_connection:\n residual = hidden_states\n\n hidden_states = self.conv_inverted(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.conv_depth(hidden_states)\n hidden_states, gate = torch.chunk(hidden_states, 2, dim=1)\n hidden_states = hidden_states * self.nonlinearity(gate)\n\n hidden_states = self.conv_point(hidden_states)\n\n if self.norm_type == \"rms_norm\":\n # move channel to the last dimension so we apply RMSnorm across channel dimension\n hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1)\n\n if self.residual_connection:\n hidden_states = hidden_states + residual\n\n return hidden_states\n\n\ndef get_block(\n block_type: str,\n in_channels: int,\n out_channels: int,\n attention_head_dim: int,\n norm_type: str,\n act_fn: str,\n qkv_mutliscales: tuple = (),\n):\n if block_type == \"ResBlock\":\n block = ResBlock(in_channels, out_channels, norm_type, act_fn)\n elif block_type == \"EfficientViTBlock\":\n block = EfficientViTBlock(\n in_channels,\n attention_head_dim=attention_head_dim,\n norm_type=norm_type,\n qkv_multiscales=qkv_mutliscales\n )\n else:\n raise ValueError(f\"Block with {block_type=} is not supported.\")\n\n return block\n\n\nclass DCDownBlock2d(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None:\n super().__init__()\n\n self.downsample = downsample\n self.factor = 2\n self.stride = 1 if downsample else 2\n self.group_size = in_channels * self.factor**2 // out_channels\n self.shortcut = shortcut\n\n out_ratio = self.factor**2\n if downsample:\n assert out_channels % out_ratio == 0\n out_channels = out_channels // out_ratio\n\n self.conv = ops.Conv2d(\n in_channels,\n out_channels,\n kernel_size=3,\n stride=self.stride,\n padding=1,\n )\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n x = self.conv(hidden_states)\n if self.downsample:\n x = F.pixel_unshuffle(x, self.factor)\n\n if self.shortcut:\n y = F.pixel_unshuffle(hidden_states, self.factor)\n y = y.unflatten(1, (-1, self.group_size))\n y = y.mean(dim=2)\n hidden_states = x + y\n else:\n hidden_states = x\n\n return hidden_states\n\n\nclass DCUpBlock2d(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n interpolate: bool = False,\n shortcut: bool = True,\n interpolation_mode: str = \"nearest\",\n ) -> None:\n super().__init__()\n\n self.interpolate = interpolate\n self.interpolation_mode = interpolation_mode\n self.shortcut = shortcut\n self.factor = 2\n self.repeats = out_channels * self.factor**2 // in_channels\n\n out_ratio = self.factor**2\n if not interpolate:\n out_channels = out_channels * out_ratio\n\n self.conv = ops.Conv2d(in_channels, out_channels, 3, 1, 1)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n if self.interpolate:\n x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode)\n x = self.conv(x)\n else:\n x = self.conv(hidden_states)\n x = F.pixel_shuffle(x, self.factor)\n\n if self.shortcut:\n y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats)\n y = F.pixel_shuffle(y, self.factor)\n hidden_states = x + y\n else:\n hidden_states = x\n\n return hidden_states\n\n\nclass Encoder(nn.Module):\n def __init__(\n self,\n in_channels: int,\n latent_channels: int,\n attention_head_dim: int = 32,\n block_type: str or tuple = \"ResBlock\",\n block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024),\n layers_per_block: tuple = (2, 2, 2, 2, 2, 2),\n qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)),\n downsample_block_type: str = \"pixel_unshuffle\",\n out_shortcut: bool = True,\n ):\n super().__init__()\n\n num_blocks = len(block_out_channels)\n\n if isinstance(block_type, str):\n block_type = (block_type,) * num_blocks\n\n if layers_per_block[0] > 0:\n self.conv_in = ops.Conv2d(\n in_channels,\n block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1],\n kernel_size=3,\n stride=1,\n padding=1,\n )\n else:\n self.conv_in = DCDownBlock2d(\n in_channels=in_channels,\n out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1],\n downsample=downsample_block_type == \"pixel_unshuffle\",\n shortcut=False,\n )\n\n down_blocks = []\n for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)):\n down_block_list = []\n\n for _ in range(num_layers):\n block = get_block(\n block_type[i],\n out_channel,\n out_channel,\n attention_head_dim=attention_head_dim,\n norm_type=\"rms_norm\",\n act_fn=\"silu\",\n qkv_mutliscales=qkv_multiscales[i],\n )\n down_block_list.append(block)\n\n if i < num_blocks - 1 and num_layers > 0:\n downsample_block = DCDownBlock2d(\n in_channels=out_channel,\n out_channels=block_out_channels[i + 1],\n downsample=downsample_block_type == \"pixel_unshuffle\",\n shortcut=True,\n )\n down_block_list.append(downsample_block)\n\n down_blocks.append(nn.Sequential(*down_block_list))\n\n self.down_blocks = nn.ModuleList(down_blocks)\n\n self.conv_out = ops.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1)\n\n self.out_shortcut = out_shortcut\n if out_shortcut:\n self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = self.conv_in(hidden_states)\n for down_block in self.down_blocks:\n hidden_states = down_block(hidden_states)\n\n if self.out_shortcut:\n x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size))\n x = x.mean(dim=2)\n hidden_states = self.conv_out(hidden_states) + x\n else:\n hidden_states = self.conv_out(hidden_states)\n\n return hidden_states\n\n\nclass Decoder(nn.Module):\n def __init__(\n self,\n in_channels: int,\n latent_channels: int,\n attention_head_dim: int = 32,\n block_type: str or tuple = \"ResBlock\",\n block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024),\n layers_per_block: tuple = (2, 2, 2, 2, 2, 2),\n qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)),\n norm_type: str or tuple = \"rms_norm\",\n act_fn: str or tuple = \"silu\",\n upsample_block_type: str = \"pixel_shuffle\",\n in_shortcut: bool = True,\n ):\n super().__init__()\n\n num_blocks = len(block_out_channels)\n\n if isinstance(block_type, str):\n block_type = (block_type,) * num_blocks\n if isinstance(norm_type, str):\n norm_type = (norm_type,) * num_blocks\n if isinstance(act_fn, str):\n act_fn = (act_fn,) * num_blocks\n\n self.conv_in = ops.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1)\n\n self.in_shortcut = in_shortcut\n if in_shortcut:\n self.in_shortcut_repeats = block_out_channels[-1] // latent_channels\n\n up_blocks = []\n for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))):\n up_block_list = []\n\n if i < num_blocks - 1 and num_layers > 0:\n upsample_block = DCUpBlock2d(\n block_out_channels[i + 1],\n out_channel,\n interpolate=upsample_block_type == \"interpolate\",\n shortcut=True,\n )\n up_block_list.append(upsample_block)\n\n for _ in range(num_layers):\n block = get_block(\n block_type[i],\n out_channel,\n out_channel,\n attention_head_dim=attention_head_dim,\n norm_type=norm_type[i],\n act_fn=act_fn[i],\n qkv_mutliscales=qkv_multiscales[i],\n )\n up_block_list.append(block)\n\n up_blocks.insert(0, nn.Sequential(*up_block_list))\n\n self.up_blocks = nn.ModuleList(up_blocks)\n\n channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1]\n\n self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True)\n self.conv_act = nn.ReLU()\n self.conv_out = None\n\n if layers_per_block[0] > 0:\n self.conv_out = ops.Conv2d(channels, in_channels, 3, 1, 1)\n else:\n self.conv_out = DCUpBlock2d(\n channels, in_channels, interpolate=upsample_block_type == \"interpolate\", shortcut=False\n )\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n if self.in_shortcut:\n x = hidden_states.repeat_interleave(\n self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats\n )\n hidden_states = self.conv_in(hidden_states) + x\n else:\n hidden_states = self.conv_in(hidden_states)\n\n for up_block in reversed(self.up_blocks):\n hidden_states = up_block(hidden_states)\n\n hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1)\n hidden_states = self.conv_act(hidden_states)\n hidden_states = self.conv_out(hidden_states)\n return hidden_states\n\n\nclass AutoencoderDC(nn.Module):\n def __init__(\n self,\n in_channels: int = 2,\n latent_channels: int = 8,\n attention_head_dim: int = 32,\n encoder_block_types: Union[str, Tuple[str]] = [\"ResBlock\", \"ResBlock\", \"ResBlock\", \"EfficientViTBlock\"],\n decoder_block_types: Union[str, Tuple[str]] = [\"ResBlock\", \"ResBlock\", \"ResBlock\", \"EfficientViTBlock\"],\n encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024),\n decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024),\n encoder_layers_per_block: Tuple[int] = (2, 2, 3, 3),\n decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3),\n encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)),\n decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)),\n upsample_block_type: str = \"interpolate\",\n downsample_block_type: str = \"Conv\",\n decoder_norm_types: Union[str, Tuple[str]] = \"rms_norm\",\n decoder_act_fns: Union[str, Tuple[str]] = \"silu\",\n scaling_factor: float = 0.41407,\n ) -> None:\n super().__init__()\n\n self.encoder = Encoder(\n in_channels=in_channels,\n latent_channels=latent_channels,\n attention_head_dim=attention_head_dim,\n block_type=encoder_block_types,\n block_out_channels=encoder_block_out_channels,\n layers_per_block=encoder_layers_per_block,\n qkv_multiscales=encoder_qkv_multiscales,\n downsample_block_type=downsample_block_type,\n )\n\n self.decoder = Decoder(\n in_channels=in_channels,\n latent_channels=latent_channels,\n attention_head_dim=attention_head_dim,\n block_type=decoder_block_types,\n block_out_channels=decoder_block_out_channels,\n layers_per_block=decoder_layers_per_block,\n qkv_multiscales=decoder_qkv_multiscales,\n norm_type=decoder_norm_types,\n act_fn=decoder_act_fns,\n upsample_block_type=upsample_block_type,\n )\n\n self.scaling_factor = scaling_factor\n self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1)\n\n def encode(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Internal encoding function.\"\"\"\n encoded = self.encoder(x)\n return encoded * self.scaling_factor\n\n def decode(self, z: torch.Tensor) -> torch.Tensor:\n # Scale the latents back\n z = z / self.scaling_factor\n decoded = self.decoder(z)\n return decoded\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n z = self.encode(x)\n return self.decode(z)\n\n" }, { "path": "comfy/ldm/ace/vae/music_dcae_pipeline.py", "content": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py\nimport torch\nfrom .autoencoder_dc import AutoencoderDC\nimport logging\ntry:\n import torchaudio\nexcept:\n logging.warning(\"torchaudio missing, ACE model will be broken\")\n\nimport torchvision.transforms as transforms\nfrom .music_vocoder import ADaMoSHiFiGANV1\n\n\nclass MusicDCAE(torch.nn.Module):\n def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}):\n super(MusicDCAE, self).__init__()\n\n self.dcae = AutoencoderDC(**dcae_config)\n self.vocoder = ADaMoSHiFiGANV1(**vocoder_config)\n\n if source_sample_rate is None:\n self.source_sample_rate = 48000\n else:\n self.source_sample_rate = source_sample_rate\n\n self.transform = transforms.Compose([\n transforms.Normalize(0.5, 0.5),\n ])\n self.min_mel_value = -11.0\n self.max_mel_value = 3.0\n self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000)))\n self.mel_chunk_size = 1024\n self.time_dimention_multiple = 8\n self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple\n self.scale_factor = 0.1786\n self.shift_factor = -1.9091\n\n def forward_mel(self, audios):\n mels = []\n for i in range(len(audios)):\n image = self.vocoder.mel_transform(audios[i])\n mels.append(image)\n mels = torch.stack(mels)\n return mels\n\n @torch.no_grad()\n def encode(self, audios, audio_lengths=None, sr=None):\n if audio_lengths is None:\n audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0])\n audio_lengths = audio_lengths.to(audios.device)\n\n if sr is None:\n sr = self.source_sample_rate\n\n if sr != 44100:\n audios = torchaudio.functional.resample(audios, sr, 44100)\n\n max_audio_len = audios.shape[-1]\n if max_audio_len % (8 * 512) != 0:\n audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512)))\n\n mels = self.forward_mel(audios)\n mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value)\n mels = self.transform(mels)\n latents = []\n for mel in mels:\n latent = self.dcae.encoder(mel.unsqueeze(0))\n latents.append(latent)\n latents = torch.cat(latents, dim=0)\n latents = (latents - self.shift_factor) * self.scale_factor\n return latents\n\n @torch.no_grad()\n def decode(self, latents, audio_lengths=None, sr=None):\n latents = latents / self.scale_factor + self.shift_factor\n\n pred_wavs = []\n\n for latent in latents:\n mels = self.dcae.decoder(latent.unsqueeze(0))\n mels = mels * 0.5 + 0.5\n mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value\n wav = self.vocoder.decode(mels[0]).squeeze(1)\n\n if sr is not None:\n wav = torchaudio.functional.resample(wav, 44100, sr)\n else:\n sr = 44100\n pred_wavs.append(wav)\n\n if audio_lengths is not None:\n pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]\n return torch.stack(pred_wavs)\n\n def forward(self, audios, audio_lengths=None, sr=None):\n latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)\n sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr)\n return sr, pred_wavs, latents, latent_lengths\n" }, { "path": "comfy/ldm/ace/vae/music_log_mel.py", "content": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nimport logging\ntry:\n from torchaudio.transforms import MelScale\nexcept:\n logging.warning(\"torchaudio missing, ACE model will be broken\")\n\nimport comfy.model_management\n\nclass LinearSpectrogram(nn.Module):\n def __init__(\n self,\n n_fft=2048,\n win_length=2048,\n hop_length=512,\n center=False,\n mode=\"pow2_sqrt\",\n ):\n super().__init__()\n\n self.n_fft = n_fft\n self.win_length = win_length\n self.hop_length = hop_length\n self.center = center\n self.mode = mode\n\n self.register_buffer(\"window\", torch.hann_window(win_length))\n\n def forward(self, y: Tensor) -> Tensor:\n if y.ndim == 3:\n y = y.squeeze(1)\n\n y = torch.nn.functional.pad(\n y.unsqueeze(1),\n (\n (self.win_length - self.hop_length) // 2,\n (self.win_length - self.hop_length + 1) // 2,\n ),\n mode=\"reflect\",\n ).squeeze(1)\n dtype = y.dtype\n spec = torch.stft(\n y.float(),\n self.n_fft,\n hop_length=self.hop_length,\n win_length=self.win_length,\n window=comfy.model_management.cast_to(self.window, dtype=torch.float32, device=y.device),\n center=self.center,\n pad_mode=\"reflect\",\n normalized=False,\n onesided=True,\n return_complex=True,\n )\n spec = torch.view_as_real(spec)\n\n if self.mode == \"pow2_sqrt\":\n spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)\n spec = spec.to(dtype)\n return spec\n\n\nclass LogMelSpectrogram(nn.Module):\n def __init__(\n self,\n sample_rate=44100,\n n_fft=2048,\n win_length=2048,\n hop_length=512,\n n_mels=128,\n center=False,\n f_min=0.0,\n f_max=None,\n ):\n super().__init__()\n\n self.sample_rate = sample_rate\n self.n_fft = n_fft\n self.win_length = win_length\n self.hop_length = hop_length\n self.center = center\n self.n_mels = n_mels\n self.f_min = f_min\n self.f_max = f_max or sample_rate // 2\n\n self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center)\n self.mel_scale = MelScale(\n self.n_mels,\n self.sample_rate,\n self.f_min,\n self.f_max,\n self.n_fft // 2 + 1,\n \"slaney\",\n \"slaney\",\n )\n\n def compress(self, x: Tensor) -> Tensor:\n return torch.log(torch.clamp(x, min=1e-5))\n\n def decompress(self, x: Tensor) -> Tensor:\n return torch.exp(x)\n\n def forward(self, x: Tensor, return_linear: bool = False) -> Tensor:\n linear = self.spectrogram(x)\n x = self.mel_scale(linear)\n x = self.compress(x)\n # print(x.shape)\n if return_linear:\n return x, self.compress(linear)\n\n return x\n" }, { "path": "comfy/ldm/ace/vae/music_vocoder.py", "content": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py\nimport torch\nfrom torch import nn\n\nfrom functools import partial\nfrom math import prod\nfrom typing import Callable, Tuple, List\n\nimport numpy as np\nimport torch.nn.functional as F\nfrom torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm\n\nfrom .music_log_mel import LogMelSpectrogram\n\nimport comfy.model_management\nimport comfy.ops\nops = comfy.ops.disable_weight_init\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 \"\"\" # noqa: E501\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).\"\"\" # noqa: E501\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\n\nclass LayerNorm(nn.Module):\n r\"\"\"LayerNorm that supports two data formats: channels_last (default) or channels_first.\n The ordering of the dimensions in the inputs. channels_last corresponds to inputs with\n shape (batch_size, height, width, channels) while channels_first corresponds to inputs\n with shape (batch_size, channels, height, width).\n \"\"\" # noqa: E501\n\n def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(normalized_shape))\n self.bias = nn.Parameter(torch.zeros(normalized_shape))\n self.eps = eps\n self.data_format = data_format\n if self.data_format not in [\"channels_last\", \"channels_first\"]:\n raise NotImplementedError\n self.normalized_shape = (normalized_shape,)\n\n def forward(self, x):\n if self.data_format == \"channels_last\":\n return F.layer_norm(\n x, self.normalized_shape, comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device), comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device), self.eps\n )\n elif self.data_format == \"channels_first\":\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = comfy.model_management.cast_to(self.weight[:, None], dtype=x.dtype, device=x.device) * x + comfy.model_management.cast_to(self.bias[:, None], dtype=x.dtype, device=x.device)\n return x\n\n\nclass ConvNeXtBlock(nn.Module):\n r\"\"\"ConvNeXt Block. There are two equivalent implementations:\n (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)\n (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back\n We use (2) as we find it slightly faster in PyTorch\n\n Args:\n dim (int): Number of input channels.\n drop_path (float): Stochastic depth rate. Default: 0.0\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.\n kernel_size (int): Kernel size for depthwise conv. Default: 7.\n dilation (int): Dilation for depthwise conv. Default: 1.\n \"\"\" # noqa: E501\n\n def __init__(\n self,\n dim: int,\n drop_path: float = 0.0,\n layer_scale_init_value: float = 1e-6,\n mlp_ratio: float = 4.0,\n kernel_size: int = 7,\n dilation: int = 1,\n ):\n super().__init__()\n\n self.dwconv = ops.Conv1d(\n dim,\n dim,\n kernel_size=kernel_size,\n padding=int(dilation * (kernel_size - 1) / 2),\n groups=dim,\n ) # depthwise conv\n self.norm = LayerNorm(dim, eps=1e-6)\n self.pwconv1 = ops.Linear(\n dim, int(mlp_ratio * dim)\n ) # pointwise/1x1 convs, implemented with linear layers\n self.act = nn.GELU()\n self.pwconv2 = ops.Linear(int(mlp_ratio * dim), dim)\n self.gamma = (\n nn.Parameter(torch.empty((dim)), requires_grad=False)\n if layer_scale_init_value > 0\n else None\n )\n self.drop_path = DropPath(\n drop_path) if drop_path > 0.0 else nn.Identity()\n\n def forward(self, x, apply_residual: bool = True):\n input = x\n\n x = self.dwconv(x)\n x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C)\n x = self.norm(x)\n x = self.pwconv1(x)\n x = self.act(x)\n x = self.pwconv2(x)\n\n if self.gamma is not None:\n x = comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) * x\n\n x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L)\n x = self.drop_path(x)\n\n if apply_residual:\n x = input + x\n\n return x\n\n\nclass ParallelConvNeXtBlock(nn.Module):\n def __init__(self, kernel_sizes: List[int], *args, **kwargs):\n super().__init__()\n self.blocks = nn.ModuleList(\n [\n ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs)\n for kernel_size in kernel_sizes\n ]\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return torch.stack(\n [block(x, apply_residual=False) for block in self.blocks] + [x],\n dim=1,\n ).sum(dim=1)\n\n\nclass ConvNeXtEncoder(nn.Module):\n def __init__(\n self,\n input_channels=3,\n depths=[3, 3, 9, 3],\n dims=[96, 192, 384, 768],\n drop_path_rate=0.0,\n layer_scale_init_value=1e-6,\n kernel_sizes: Tuple[int] = (7,),\n ):\n super().__init__()\n assert len(depths) == len(dims)\n\n self.channel_layers = nn.ModuleList()\n stem = nn.Sequential(\n ops.Conv1d(\n input_channels,\n dims[0],\n kernel_size=7,\n padding=3,\n padding_mode=\"replicate\",\n ),\n LayerNorm(dims[0], eps=1e-6, data_format=\"channels_first\"),\n )\n self.channel_layers.append(stem)\n\n for i in range(len(depths) - 1):\n mid_layer = nn.Sequential(\n LayerNorm(dims[i], eps=1e-6, data_format=\"channels_first\"),\n ops.Conv1d(dims[i], dims[i + 1], kernel_size=1),\n )\n self.channel_layers.append(mid_layer)\n\n block_fn = (\n partial(ConvNeXtBlock, kernel_size=kernel_sizes[0])\n if len(kernel_sizes) == 1\n else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes)\n )\n\n self.stages = nn.ModuleList()\n drop_path_rates = [\n x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n ]\n\n cur = 0\n for i in range(len(depths)):\n stage = nn.Sequential(\n *[\n block_fn(\n dim=dims[i],\n drop_path=drop_path_rates[cur + j],\n layer_scale_init_value=layer_scale_init_value,\n )\n for j in range(depths[i])\n ]\n )\n self.stages.append(stage)\n cur += depths[i]\n\n self.norm = LayerNorm(dims[-1], eps=1e-6, data_format=\"channels_first\")\n\n def forward(\n self,\n x: torch.Tensor,\n ) -> torch.Tensor:\n for channel_layer, stage in zip(self.channel_layers, self.stages):\n x = channel_layer(x)\n x = stage(x)\n\n return self.norm(x)\n\n\ndef get_padding(kernel_size, dilation=1):\n return (kernel_size * dilation - dilation) // 2\n\n\nclass ResBlock1(torch.nn.Module):\n def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):\n super().__init__()\n\n self.convs1 = nn.ModuleList(\n [\n torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[0],\n padding=get_padding(kernel_size, dilation[0]),\n )\n ),\n torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[1],\n padding=get_padding(kernel_size, dilation[1]),\n )\n ),\n torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[2],\n padding=get_padding(kernel_size, dilation[2]),\n )\n ),\n ]\n )\n\n self.convs2 = nn.ModuleList(\n [\n torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n )\n ),\n torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n )\n ),\n torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n )\n ),\n ]\n )\n\n def forward(self, x):\n for c1, c2 in zip(self.convs1, self.convs2):\n xt = F.silu(x)\n xt = c1(xt)\n xt = F.silu(xt)\n xt = c2(xt)\n x = xt + x\n return x\n\n def remove_weight_norm(self):\n for conv in self.convs1:\n remove_weight_norm(conv)\n for conv in self.convs2:\n remove_weight_norm(conv)\n\n\nclass HiFiGANGenerator(nn.Module):\n def __init__(\n self,\n *,\n hop_length: int = 512,\n upsample_rates: Tuple[int] = (8, 8, 2, 2, 2),\n upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2),\n resblock_kernel_sizes: Tuple[int] = (3, 7, 11),\n resblock_dilation_sizes: Tuple[Tuple[int]] = (\n (1, 3, 5), (1, 3, 5), (1, 3, 5)),\n num_mels: int = 128,\n upsample_initial_channel: int = 512,\n use_template: bool = True,\n pre_conv_kernel_size: int = 7,\n post_conv_kernel_size: int = 7,\n post_activation: Callable = partial(nn.SiLU, inplace=True),\n ):\n super().__init__()\n\n assert (\n prod(upsample_rates) == hop_length\n ), f\"hop_length must be {prod(upsample_rates)}\"\n\n self.conv_pre = torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n num_mels,\n upsample_initial_channel,\n pre_conv_kernel_size,\n 1,\n padding=get_padding(pre_conv_kernel_size),\n )\n )\n\n self.num_upsamples = len(upsample_rates)\n self.num_kernels = len(resblock_kernel_sizes)\n\n self.noise_convs = nn.ModuleList()\n self.use_template = use_template\n self.ups = nn.ModuleList()\n\n for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):\n c_cur = upsample_initial_channel // (2 ** (i + 1))\n self.ups.append(\n torch.nn.utils.parametrizations.weight_norm(\n ops.ConvTranspose1d(\n upsample_initial_channel // (2**i),\n upsample_initial_channel // (2 ** (i + 1)),\n k,\n u,\n padding=(k - u) // 2,\n )\n )\n )\n\n if not use_template:\n continue\n\n if i + 1 < len(upsample_rates):\n stride_f0 = np.prod(upsample_rates[i + 1:])\n self.noise_convs.append(\n ops.Conv1d(\n 1,\n c_cur,\n kernel_size=stride_f0 * 2,\n stride=stride_f0,\n padding=stride_f0 // 2,\n )\n )\n else:\n self.noise_convs.append(ops.Conv1d(1, c_cur, kernel_size=1))\n\n self.resblocks = nn.ModuleList()\n for i in range(len(self.ups)):\n ch = upsample_initial_channel // (2 ** (i + 1))\n for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):\n self.resblocks.append(ResBlock1(ch, k, d))\n\n self.activation_post = post_activation()\n self.conv_post = torch.nn.utils.parametrizations.weight_norm(\n ops.Conv1d(\n ch,\n 1,\n post_conv_kernel_size,\n 1,\n padding=get_padding(post_conv_kernel_size),\n )\n )\n\n def forward(self, x, template=None):\n x = self.conv_pre(x)\n\n for i in range(self.num_upsamples):\n x = F.silu(x, inplace=True)\n x = self.ups[i](x)\n\n if self.use_template:\n x = x + self.noise_convs[i](template)\n\n xs = None\n\n for j in range(self.num_kernels):\n if xs is None:\n xs = self.resblocks[i * self.num_kernels + j](x)\n else:\n xs += self.resblocks[i * self.num_kernels + j](x)\n\n x = xs / self.num_kernels\n\n x = self.activation_post(x)\n x = self.conv_post(x)\n x = torch.tanh(x)\n\n return x\n\n def remove_weight_norm(self):\n for up in self.ups:\n remove_weight_norm(up)\n for block in self.resblocks:\n block.remove_weight_norm()\n remove_weight_norm(self.conv_pre)\n remove_weight_norm(self.conv_post)\n\n\nclass ADaMoSHiFiGANV1(nn.Module):\n def __init__(\n self,\n input_channels: int = 128,\n depths: List[int] = [3, 3, 9, 3],\n dims: List[int] = [128, 256, 384, 512],\n drop_path_rate: float = 0.0,\n kernel_sizes: Tuple[int] = (7,),\n upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2),\n upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4),\n resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13),\n resblock_dilation_sizes: Tuple[Tuple[int]] = (\n (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)),\n num_mels: int = 512,\n upsample_initial_channel: int = 1024,\n use_template: bool = False,\n pre_conv_kernel_size: int = 13,\n post_conv_kernel_size: int = 13,\n sampling_rate: int = 44100,\n n_fft: int = 2048,\n win_length: int = 2048,\n hop_length: int = 512,\n f_min: int = 40,\n f_max: int = 16000,\n n_mels: int = 128,\n ):\n super().__init__()\n\n self.backbone = ConvNeXtEncoder(\n input_channels=input_channels,\n depths=depths,\n dims=dims,\n drop_path_rate=drop_path_rate,\n kernel_sizes=kernel_sizes,\n )\n\n self.head = HiFiGANGenerator(\n hop_length=hop_length,\n upsample_rates=upsample_rates,\n upsample_kernel_sizes=upsample_kernel_sizes,\n resblock_kernel_sizes=resblock_kernel_sizes,\n resblock_dilation_sizes=resblock_dilation_sizes,\n num_mels=num_mels,\n upsample_initial_channel=upsample_initial_channel,\n use_template=use_template,\n pre_conv_kernel_size=pre_conv_kernel_size,\n post_conv_kernel_size=post_conv_kernel_size,\n )\n self.sampling_rate = sampling_rate\n self.mel_transform = LogMelSpectrogram(\n sample_rate=sampling_rate,\n n_fft=n_fft,\n win_length=win_length,\n hop_length=hop_length,\n f_min=f_min,\n f_max=f_max,\n n_mels=n_mels,\n )\n self.eval()\n\n @torch.no_grad()\n def decode(self, mel):\n y = self.backbone(mel)\n y = self.head(y)\n return y\n\n @torch.no_grad()\n def encode(self, x):\n return self.mel_transform(x)\n\n def forward(self, mel):\n y = self.backbone(mel)\n y = self.head(y)\n return y\n" }, { "path": "comfy/ldm/anima/model.py", "content": "from comfy.ldm.cosmos.predict2 import MiniTrainDIT\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\ndef rotate_half(x):\n x1 = x[..., : x.shape[-1] // 2]\n x2 = x[..., x.shape[-1] // 2 :]\n return torch.cat((-x2, x1), dim=-1)\n\n\ndef apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1):\n cos = cos.unsqueeze(unsqueeze_dim)\n sin = sin.unsqueeze(unsqueeze_dim)\n x_embed = (x * cos) + (rotate_half(x) * sin)\n return x_embed\n\n\nclass RotaryEmbedding(nn.Module):\n def __init__(self, head_dim):\n super().__init__()\n self.rope_theta = 10000\n inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.int64).to(dtype=torch.float) / head_dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n @torch.no_grad()\n def forward(self, x, position_ids):\n inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)\n position_ids_expanded = position_ids[:, None, :].float()\n\n device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != \"mps\" else \"cpu\"\n with torch.autocast(device_type=device_type, enabled=False): # Force float32\n freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)\n emb = torch.cat((freqs, freqs), dim=-1)\n cos = emb.cos()\n sin = emb.sin()\n\n return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)\n\n\nclass Attention(nn.Module):\n def __init__(self, query_dim, context_dim, n_heads, head_dim, device=None, dtype=None, operations=None):\n super().__init__()\n\n inner_dim = head_dim * n_heads\n self.n_heads = n_heads\n self.head_dim = head_dim\n self.query_dim = query_dim\n self.context_dim = context_dim\n\n self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)\n self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)\n\n self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)\n self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)\n\n self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)\n\n self.o_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype)\n\n def forward(self, x, mask=None, context=None, position_embeddings=None, position_embeddings_context=None):\n context = x if context is None else context\n input_shape = x.shape[:-1]\n q_shape = (*input_shape, self.n_heads, self.head_dim)\n context_shape = context.shape[:-1]\n kv_shape = (*context_shape, self.n_heads, self.head_dim)\n\n query_states = self.q_norm(self.q_proj(x).view(q_shape)).transpose(1, 2)\n key_states = self.k_norm(self.k_proj(context).view(kv_shape)).transpose(1, 2)\n value_states = self.v_proj(context).view(kv_shape).transpose(1, 2)\n\n if position_embeddings is not None:\n assert position_embeddings_context is not None\n cos, sin = position_embeddings\n query_states = apply_rotary_pos_emb(query_states, cos, sin)\n cos, sin = position_embeddings_context\n key_states = apply_rotary_pos_emb(key_states, cos, sin)\n\n attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=mask)\n\n attn_output = attn_output.transpose(1, 2).reshape(*input_shape, -1).contiguous()\n attn_output = self.o_proj(attn_output)\n return attn_output\n\n def init_weights(self):\n torch.nn.init.zeros_(self.o_proj.weight)\n\n\nclass TransformerBlock(nn.Module):\n def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0, use_self_attn=False, layer_norm=False, device=None, dtype=None, operations=None):\n super().__init__()\n self.use_self_attn = use_self_attn\n\n if self.use_self_attn:\n self.norm_self_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)\n self.self_attn = Attention(\n query_dim=model_dim,\n context_dim=model_dim,\n n_heads=num_heads,\n head_dim=model_dim//num_heads,\n device=device,\n dtype=dtype,\n operations=operations,\n )\n\n self.norm_cross_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)\n self.cross_attn = Attention(\n query_dim=model_dim,\n context_dim=source_dim,\n n_heads=num_heads,\n head_dim=model_dim//num_heads,\n device=device,\n dtype=dtype,\n operations=operations,\n )\n\n self.norm_mlp = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)\n self.mlp = nn.Sequential(\n operations.Linear(model_dim, int(model_dim * mlp_ratio), device=device, dtype=dtype),\n nn.GELU(),\n operations.Linear(int(model_dim * mlp_ratio), model_dim, device=device, dtype=dtype)\n )\n\n def forward(self, x, context, target_attention_mask=None, source_attention_mask=None, position_embeddings=None, position_embeddings_context=None):\n if self.use_self_attn:\n normed = self.norm_self_attn(x)\n attn_out = self.self_attn(normed, mask=target_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings)\n x = x + attn_out\n\n normed = self.norm_cross_attn(x)\n attn_out = self.cross_attn(normed, mask=source_attention_mask, context=context, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context)\n x = x + attn_out\n\n x = x + self.mlp(self.norm_mlp(x))\n return x\n\n def init_weights(self):\n torch.nn.init.zeros_(self.mlp[2].weight)\n self.cross_attn.init_weights()\n\n\nclass LLMAdapter(nn.Module):\n def __init__(\n self,\n source_dim=1024,\n target_dim=1024,\n model_dim=1024,\n num_layers=6,\n num_heads=16,\n use_self_attn=True,\n layer_norm=False,\n device=None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n\n self.embed = operations.Embedding(32128, target_dim, device=device, dtype=dtype)\n if model_dim != target_dim:\n self.in_proj = operations.Linear(target_dim, model_dim, device=device, dtype=dtype)\n else:\n self.in_proj = nn.Identity()\n self.rotary_emb = RotaryEmbedding(model_dim//num_heads)\n self.blocks = nn.ModuleList([\n TransformerBlock(source_dim, model_dim, num_heads=num_heads, use_self_attn=use_self_attn, layer_norm=layer_norm, device=device, dtype=dtype, operations=operations) for _ in range(num_layers)\n ])\n self.out_proj = operations.Linear(model_dim, target_dim, device=device, dtype=dtype)\n self.norm = operations.RMSNorm(target_dim, eps=1e-6, device=device, dtype=dtype)\n\n def forward(self, source_hidden_states, target_input_ids, target_attention_mask=None, source_attention_mask=None):\n if target_attention_mask is not None:\n target_attention_mask = target_attention_mask.to(torch.bool)\n if target_attention_mask.ndim == 2:\n target_attention_mask = target_attention_mask.unsqueeze(1).unsqueeze(1)\n\n if source_attention_mask is not None:\n source_attention_mask = source_attention_mask.to(torch.bool)\n if source_attention_mask.ndim == 2:\n source_attention_mask = source_attention_mask.unsqueeze(1).unsqueeze(1)\n\n context = source_hidden_states\n x = self.in_proj(self.embed(target_input_ids, out_dtype=context.dtype))\n position_ids = torch.arange(x.shape[1], device=x.device).unsqueeze(0)\n position_ids_context = torch.arange(context.shape[1], device=x.device).unsqueeze(0)\n position_embeddings = self.rotary_emb(x, position_ids)\n position_embeddings_context = self.rotary_emb(x, position_ids_context)\n for block in self.blocks:\n x = block(x, context, target_attention_mask=target_attention_mask, source_attention_mask=source_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context)\n return self.norm(self.out_proj(x))\n\n\nclass Anima(MiniTrainDIT):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.llm_adapter = LLMAdapter(device=kwargs.get(\"device\"), dtype=kwargs.get(\"dtype\"), operations=kwargs.get(\"operations\"))\n\n def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=None):\n if text_ids is not None:\n out = self.llm_adapter(text_embeds, text_ids)\n if t5xxl_weights is not None:\n out = out * t5xxl_weights\n\n if out.shape[1] < 512:\n out = torch.nn.functional.pad(out, (0, 0, 0, 512 - out.shape[1]))\n return out\n else:\n return text_embeds\n\n def forward(self, x, timesteps, context, **kwargs):\n t5xxl_ids = kwargs.pop(\"t5xxl_ids\", None)\n if t5xxl_ids is not None:\n context = self.preprocess_text_embeds(context, t5xxl_ids, t5xxl_weights=kwargs.pop(\"t5xxl_weights\", None))\n return super().forward(x, timesteps, context, **kwargs)\n" }, { "path": "comfy/ldm/audio/autoencoder.py", "content": "# code adapted from: https://github.com/Stability-AI/stable-audio-tools\n\nimport torch\nfrom torch import nn\nfrom typing import Literal\nimport math\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\ndef vae_sample(mean, scale):\n stdev = nn.functional.softplus(scale) + 1e-4\n var = stdev * stdev\n logvar = torch.log(var)\n latents = torch.randn_like(mean) * stdev + mean\n\n kl = (mean * mean + var - logvar - 1).sum(1).mean()\n\n return latents, kl\n\nclass VAEBottleneck(nn.Module):\n def __init__(self):\n super().__init__()\n self.is_discrete = False\n\n def encode(self, x, return_info=False, **kwargs):\n info = {}\n\n mean, scale = x.chunk(2, dim=1)\n\n x, kl = vae_sample(mean, scale)\n\n info[\"kl\"] = kl\n\n if return_info:\n return x, info\n else:\n return x\n\n def decode(self, x):\n return x\n\n\ndef snake_beta(x, alpha, beta):\n return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)\n\n# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license\nclass SnakeBeta(nn.Module):\n\n def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):\n super(SnakeBeta, self).__init__()\n self.in_features = in_features\n\n # initialize alpha\n self.alpha_logscale = alpha_logscale\n if self.alpha_logscale: # log scale alphas initialized to zeros\n self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)\n self.beta = nn.Parameter(torch.zeros(in_features) * alpha)\n else: # linear scale alphas initialized to ones\n self.alpha = nn.Parameter(torch.ones(in_features) * alpha)\n self.beta = nn.Parameter(torch.ones(in_features) * alpha)\n\n # self.alpha.requires_grad = alpha_trainable\n # self.beta.requires_grad = alpha_trainable\n\n self.no_div_by_zero = 0.000000001\n\n def forward(self, x):\n alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T]\n beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device)\n if self.alpha_logscale:\n alpha = torch.exp(alpha)\n beta = torch.exp(beta)\n x = snake_beta(x, alpha, beta)\n\n return x\n\ndef WNConv1d(*args, **kwargs):\n return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))\n\ndef WNConvTranspose1d(*args, **kwargs):\n return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))\n\ndef get_activation(activation: Literal[\"elu\", \"snake\", \"none\"], antialias=False, channels=None) -> nn.Module:\n if activation == \"elu\":\n act = torch.nn.ELU()\n elif activation == \"snake\":\n act = SnakeBeta(channels)\n elif activation == \"none\":\n act = torch.nn.Identity()\n else:\n raise ValueError(f\"Unknown activation {activation}\")\n\n if antialias:\n act = Activation1d(act) # noqa: F821 Activation1d is not defined\n\n return act\n\n\nclass ResidualUnit(nn.Module):\n def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):\n super().__init__()\n\n self.dilation = dilation\n\n padding = (dilation * (7-1)) // 2\n\n self.layers = nn.Sequential(\n get_activation(\"snake\" if use_snake else \"elu\", antialias=antialias_activation, channels=out_channels),\n WNConv1d(in_channels=in_channels, out_channels=out_channels,\n kernel_size=7, dilation=dilation, padding=padding),\n get_activation(\"snake\" if use_snake else \"elu\", antialias=antialias_activation, channels=out_channels),\n WNConv1d(in_channels=out_channels, out_channels=out_channels,\n kernel_size=1)\n )\n\n def forward(self, x):\n res = x\n\n #x = checkpoint(self.layers, x)\n x = self.layers(x)\n\n return x + res\n\nclass EncoderBlock(nn.Module):\n def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):\n super().__init__()\n\n self.layers = nn.Sequential(\n ResidualUnit(in_channels=in_channels,\n out_channels=in_channels, dilation=1, use_snake=use_snake),\n ResidualUnit(in_channels=in_channels,\n out_channels=in_channels, dilation=3, use_snake=use_snake),\n ResidualUnit(in_channels=in_channels,\n out_channels=in_channels, dilation=9, use_snake=use_snake),\n get_activation(\"snake\" if use_snake else \"elu\", antialias=antialias_activation, channels=in_channels),\n WNConv1d(in_channels=in_channels, out_channels=out_channels,\n kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),\n )\n\n def forward(self, x):\n return self.layers(x)\n\nclass DecoderBlock(nn.Module):\n def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):\n super().__init__()\n\n if use_nearest_upsample:\n upsample_layer = nn.Sequential(\n nn.Upsample(scale_factor=stride, mode=\"nearest\"),\n WNConv1d(in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=2*stride,\n stride=1,\n bias=False,\n padding='same')\n )\n else:\n upsample_layer = WNConvTranspose1d(in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))\n\n self.layers = nn.Sequential(\n get_activation(\"snake\" if use_snake else \"elu\", antialias=antialias_activation, channels=in_channels),\n upsample_layer,\n ResidualUnit(in_channels=out_channels, out_channels=out_channels,\n dilation=1, use_snake=use_snake),\n ResidualUnit(in_channels=out_channels, out_channels=out_channels,\n dilation=3, use_snake=use_snake),\n ResidualUnit(in_channels=out_channels, out_channels=out_channels,\n dilation=9, use_snake=use_snake),\n )\n\n def forward(self, x):\n return self.layers(x)\n\nclass OobleckEncoder(nn.Module):\n def __init__(self,\n in_channels=2,\n channels=128,\n latent_dim=32,\n c_mults = [1, 2, 4, 8],\n strides = [2, 4, 8, 8],\n use_snake=False,\n antialias_activation=False\n ):\n super().__init__()\n\n c_mults = [1] + c_mults\n\n self.depth = len(c_mults)\n\n layers = [\n WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)\n ]\n\n for i in range(self.depth-1):\n layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]\n\n layers += [\n get_activation(\"snake\" if use_snake else \"elu\", antialias=antialias_activation, channels=c_mults[-1] * channels),\n WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)\n ]\n\n self.layers = nn.Sequential(*layers)\n\n def forward(self, x):\n return self.layers(x)\n\n\nclass OobleckDecoder(nn.Module):\n def __init__(self,\n out_channels=2,\n channels=128,\n latent_dim=32,\n c_mults = [1, 2, 4, 8],\n strides = [2, 4, 8, 8],\n use_snake=False,\n antialias_activation=False,\n use_nearest_upsample=False,\n final_tanh=True):\n super().__init__()\n\n c_mults = [1] + c_mults\n\n self.depth = len(c_mults)\n\n layers = [\n WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),\n ]\n\n for i in range(self.depth-1, 0, -1):\n layers += [DecoderBlock(\n in_channels=c_mults[i]*channels,\n out_channels=c_mults[i-1]*channels,\n stride=strides[i-1],\n use_snake=use_snake,\n antialias_activation=antialias_activation,\n use_nearest_upsample=use_nearest_upsample\n )\n ]\n\n layers += [\n get_activation(\"snake\" if use_snake else \"elu\", antialias=antialias_activation, channels=c_mults[0] * channels),\n WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),\n nn.Tanh() if final_tanh else nn.Identity()\n ]\n\n self.layers = nn.Sequential(*layers)\n\n def forward(self, x):\n return self.layers(x)\n\n\nclass AudioOobleckVAE(nn.Module):\n def __init__(self,\n in_channels=2,\n channels=128,\n latent_dim=64,\n c_mults = [1, 2, 4, 8, 16],\n strides = [2, 4, 4, 8, 8],\n use_snake=True,\n antialias_activation=False,\n use_nearest_upsample=False,\n final_tanh=False):\n super().__init__()\n self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation)\n self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation,\n use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh)\n self.bottleneck = VAEBottleneck()\n\n def encode(self, x):\n return self.bottleneck.encode(self.encoder(x))\n\n def decode(self, x):\n return self.decoder(self.bottleneck.decode(x))\n\n" }, { "path": "comfy/ldm/audio/dit.py", "content": "# code adapted from: https://github.com/Stability-AI/stable-audio-tools\n\nfrom comfy.ldm.modules.attention import optimized_attention\nimport typing as tp\n\nimport torch\n\nfrom einops import rearrange\nfrom torch import nn\nfrom torch.nn import functional as F\nimport math\nimport comfy.ops\n\nclass FourierFeatures(nn.Module):\n def __init__(self, in_features, out_features, std=1., dtype=None, device=None):\n super().__init__()\n assert out_features % 2 == 0\n self.weight = nn.Parameter(torch.empty(\n [out_features // 2, in_features], dtype=dtype, device=device))\n\n def forward(self, input):\n f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)\n return torch.cat([f.cos(), f.sin()], dim=-1)\n\n# norms\nclass LayerNorm(nn.Module):\n def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):\n \"\"\"\n bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less\n \"\"\"\n super().__init__()\n\n self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))\n\n if bias:\n self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))\n else:\n self.beta = None\n\n def forward(self, x):\n beta = self.beta\n if beta is not None:\n beta = comfy.ops.cast_to_input(beta, x)\n return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)\n\nclass GLU(nn.Module):\n def __init__(\n self,\n dim_in,\n dim_out,\n activation,\n use_conv = False,\n conv_kernel_size = 3,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.act = activation\n self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)\n self.use_conv = use_conv\n\n def forward(self, x):\n if self.use_conv:\n x = rearrange(x, 'b n d -> b d n')\n x = self.proj(x)\n x = rearrange(x, 'b d n -> b n d')\n else:\n x = self.proj(x)\n\n x, gate = x.chunk(2, dim = -1)\n return x * self.act(gate)\n\nclass AbsolutePositionalEmbedding(nn.Module):\n def __init__(self, dim, max_seq_len):\n super().__init__()\n self.scale = dim ** -0.5\n self.max_seq_len = max_seq_len\n self.emb = nn.Embedding(max_seq_len, dim)\n\n def forward(self, x, pos = None, seq_start_pos = None):\n seq_len, device = x.shape[1], x.device\n assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'\n\n if pos is None:\n pos = torch.arange(seq_len, device = device)\n\n if seq_start_pos is not None:\n pos = (pos - seq_start_pos[..., None]).clamp(min = 0)\n\n pos_emb = self.emb(pos)\n pos_emb = pos_emb * self.scale\n return pos_emb\n\nclass ScaledSinusoidalEmbedding(nn.Module):\n def __init__(self, dim, theta = 10000):\n super().__init__()\n assert (dim % 2) == 0, 'dimension must be divisible by 2'\n self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)\n\n half_dim = dim // 2\n freq_seq = torch.arange(half_dim).float() / half_dim\n inv_freq = theta ** -freq_seq\n self.register_buffer('inv_freq', inv_freq, persistent = False)\n\n def forward(self, x, pos = None, seq_start_pos = None):\n seq_len, device = x.shape[1], x.device\n\n if pos is None:\n pos = torch.arange(seq_len, device = device)\n\n if seq_start_pos is not None:\n pos = pos - seq_start_pos[..., None]\n\n emb = torch.einsum('i, j -> i j', pos, self.inv_freq)\n emb = torch.cat((emb.sin(), emb.cos()), dim = -1)\n return emb * self.scale\n\nclass RotaryEmbedding(nn.Module):\n def __init__(\n self,\n dim,\n use_xpos = False,\n scale_base = 512,\n interpolation_factor = 1.,\n base = 10000,\n base_rescale_factor = 1.,\n dtype=None,\n device=None,\n ):\n super().__init__()\n # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning\n # has some connection to NTK literature\n # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/\n base *= base_rescale_factor ** (dim / (dim - 2))\n\n # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))\n self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype))\n\n assert interpolation_factor >= 1.\n self.interpolation_factor = interpolation_factor\n\n if not use_xpos:\n self.register_buffer('scale', None)\n return\n\n scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)\n\n self.scale_base = scale_base\n self.register_buffer('scale', scale)\n\n def forward_from_seq_len(self, seq_len, device, dtype):\n # device = self.inv_freq.device\n\n t = torch.arange(seq_len, device=device, dtype=dtype)\n return self.forward(t)\n\n def forward(self, t):\n # device = self.inv_freq.device\n device = t.device\n\n # t = t.to(torch.float32)\n\n t = t / self.interpolation_factor\n\n freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t))\n freqs = torch.cat((freqs, freqs), dim = -1)\n\n if self.scale is None:\n return freqs, 1.\n\n power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base # noqa: F821 seq_len is not defined\n scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1')\n scale = torch.cat((scale, scale), dim = -1)\n\n return freqs, scale\n\ndef rotate_half(x):\n x = rearrange(x, '... (j d) -> ... j d', j = 2)\n x1, x2 = x.unbind(dim = -2)\n return torch.cat((-x2, x1), dim = -1)\n\ndef apply_rotary_pos_emb(t, freqs, scale = 1):\n out_dtype = t.dtype\n\n # cast to float32 if necessary for numerical stability\n dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))\n rot_dim, seq_len = freqs.shape[-1], t.shape[-2]\n freqs, t = freqs.to(dtype), t.to(dtype)\n freqs = freqs[-seq_len:, :]\n\n if t.ndim == 4 and freqs.ndim == 3:\n freqs = rearrange(freqs, 'b n d -> b 1 n d')\n\n # partial rotary embeddings, Wang et al. GPT-J\n t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]\n t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)\n\n t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)\n\n return torch.cat((t, t_unrotated), dim = -1)\n\nclass FeedForward(nn.Module):\n def __init__(\n self,\n dim,\n dim_out = None,\n mult = 4,\n no_bias = False,\n glu = True,\n use_conv = False,\n conv_kernel_size = 3,\n zero_init_output = True,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n inner_dim = int(dim * mult)\n\n # Default to SwiGLU\n\n activation = nn.SiLU()\n\n dim_out = dim if dim_out is None else dim_out\n\n if glu:\n linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)\n else:\n linear_in = nn.Sequential(\n rearrange('b n d -> b d n') if use_conv else nn.Identity(),\n operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),\n rearrange('b n d -> b d n') if use_conv else nn.Identity(),\n activation\n )\n\n linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)\n\n # # init last linear layer to 0\n # if zero_init_output:\n # nn.init.zeros_(linear_out.weight)\n # if not no_bias:\n # nn.init.zeros_(linear_out.bias)\n\n\n self.ff = nn.Sequential(\n linear_in,\n rearrange('b d n -> b n d') if use_conv else nn.Identity(),\n linear_out,\n rearrange('b n d -> b d n') if use_conv else nn.Identity(),\n )\n\n def forward(self, x):\n return self.ff(x)\n\nclass Attention(nn.Module):\n def __init__(\n self,\n dim,\n dim_heads = 64,\n dim_context = None,\n causal = False,\n zero_init_output=True,\n qk_norm = False,\n natten_kernel_size = None,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.dim = dim\n self.dim_heads = dim_heads\n self.causal = causal\n\n dim_kv = dim_context if dim_context is not None else dim\n\n self.num_heads = dim // dim_heads\n self.kv_heads = dim_kv // dim_heads\n\n if dim_context is not None:\n self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)\n else:\n self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)\n\n self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n\n # if zero_init_output:\n # nn.init.zeros_(self.to_out.weight)\n\n self.qk_norm = qk_norm\n\n\n def forward(\n self,\n x,\n context = None,\n mask = None,\n context_mask = None,\n rotary_pos_emb = None,\n causal = None,\n transformer_options={},\n ):\n h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None\n\n kv_input = context if has_context else x\n\n if hasattr(self, 'to_q'):\n # Use separate linear projections for q and k/v\n q = self.to_q(x)\n q = rearrange(q, 'b n (h d) -> b h n d', h = h)\n\n k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n\n k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))\n else:\n # Use fused linear projection\n q, k, v = self.to_qkv(x).chunk(3, dim=-1)\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))\n\n # Normalize q and k for cosine sim attention\n if self.qk_norm:\n q = F.normalize(q, dim=-1)\n k = F.normalize(k, dim=-1)\n\n if rotary_pos_emb is not None and not has_context:\n freqs, _ = rotary_pos_emb\n\n q_dtype = q.dtype\n k_dtype = k.dtype\n\n q = q.to(torch.float32)\n k = k.to(torch.float32)\n freqs = freqs.to(torch.float32)\n\n q = apply_rotary_pos_emb(q, freqs)\n k = apply_rotary_pos_emb(k, freqs)\n\n q = q.to(q_dtype)\n k = k.to(k_dtype)\n\n input_mask = context_mask\n\n if input_mask is None and not has_context:\n input_mask = mask\n\n # determine masking\n masks = []\n\n if input_mask is not None:\n input_mask = rearrange(input_mask, 'b j -> b 1 1 j')\n masks.append(~input_mask)\n\n # Other masks will be added here later\n n = q.shape[-2]\n\n causal = self.causal if causal is None else causal\n\n if n == 1 and causal:\n causal = False\n\n if h != kv_h:\n # Repeat interleave kv_heads to match q_heads\n heads_per_kv_head = h // kv_h\n k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))\n\n out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)\n out = self.to_out(out)\n\n if mask is not None:\n mask = rearrange(mask, 'b n -> b n 1')\n out = out.masked_fill(~mask, 0.)\n\n return out\n\nclass ConformerModule(nn.Module):\n def __init__(\n self,\n dim,\n norm_kwargs = {},\n ):\n\n super().__init__()\n\n self.dim = dim\n\n self.in_norm = LayerNorm(dim, **norm_kwargs)\n self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)\n self.glu = GLU(dim, dim, nn.SiLU())\n self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)\n self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm\n self.swish = nn.SiLU()\n self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)\n\n def forward(self, x):\n x = self.in_norm(x)\n x = rearrange(x, 'b n d -> b d n')\n x = self.pointwise_conv(x)\n x = rearrange(x, 'b d n -> b n d')\n x = self.glu(x)\n x = rearrange(x, 'b n d -> b d n')\n x = self.depthwise_conv(x)\n x = rearrange(x, 'b d n -> b n d')\n x = self.mid_norm(x)\n x = self.swish(x)\n x = rearrange(x, 'b n d -> b d n')\n x = self.pointwise_conv_2(x)\n x = rearrange(x, 'b d n -> b n d')\n\n return x\n\nclass TransformerBlock(nn.Module):\n def __init__(\n self,\n dim,\n dim_heads = 64,\n cross_attend = False,\n dim_context = None,\n global_cond_dim = None,\n causal = False,\n zero_init_branch_outputs = True,\n conformer = False,\n layer_ix = -1,\n remove_norms = False,\n attn_kwargs = {},\n ff_kwargs = {},\n norm_kwargs = {},\n dtype=None,\n device=None,\n operations=None,\n ):\n\n super().__init__()\n self.dim = dim\n self.dim_heads = dim_heads\n self.cross_attend = cross_attend\n self.dim_context = dim_context\n self.causal = causal\n\n self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()\n\n self.self_attn = Attention(\n dim,\n dim_heads = dim_heads,\n causal = causal,\n zero_init_output=zero_init_branch_outputs,\n dtype=dtype,\n device=device,\n operations=operations,\n **attn_kwargs\n )\n\n if cross_attend:\n self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()\n self.cross_attn = Attention(\n dim,\n dim_heads = dim_heads,\n dim_context=dim_context,\n causal = causal,\n zero_init_output=zero_init_branch_outputs,\n dtype=dtype,\n device=device,\n operations=operations,\n **attn_kwargs\n )\n\n self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()\n self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)\n\n self.layer_ix = layer_ix\n\n self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None\n\n self.global_cond_dim = global_cond_dim\n\n if global_cond_dim is not None:\n self.to_scale_shift_gate = nn.Sequential(\n nn.SiLU(),\n nn.Linear(global_cond_dim, dim * 6, bias=False)\n )\n\n nn.init.zeros_(self.to_scale_shift_gate[1].weight)\n #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)\n\n def forward(\n self,\n x,\n context = None,\n global_cond=None,\n mask = None,\n context_mask = None,\n rotary_pos_emb = None,\n transformer_options={}\n ):\n if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:\n\n scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)\n\n # self-attention with adaLN\n residual = x\n x = self.pre_norm(x)\n x = x * (1 + scale_self) + shift_self\n x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)\n x = x * torch.sigmoid(1 - gate_self)\n x = x + residual\n\n if context is not None:\n x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)\n\n if self.conformer is not None:\n x = x + self.conformer(x)\n\n # feedforward with adaLN\n residual = x\n x = self.ff_norm(x)\n x = x * (1 + scale_ff) + shift_ff\n x = self.ff(x)\n x = x * torch.sigmoid(1 - gate_ff)\n x = x + residual\n\n else:\n x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)\n\n if context is not None:\n x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)\n\n if self.conformer is not None:\n x = x + self.conformer(x)\n\n x = x + self.ff(self.ff_norm(x))\n\n return x\n\nclass ContinuousTransformer(nn.Module):\n def __init__(\n self,\n dim,\n depth,\n *,\n dim_in = None,\n dim_out = None,\n dim_heads = 64,\n cross_attend=False,\n cond_token_dim=None,\n global_cond_dim=None,\n causal=False,\n rotary_pos_emb=True,\n zero_init_branch_outputs=True,\n conformer=False,\n use_sinusoidal_emb=False,\n use_abs_pos_emb=False,\n abs_pos_emb_max_length=10000,\n dtype=None,\n device=None,\n operations=None,\n **kwargs\n ):\n\n super().__init__()\n\n self.dim = dim\n self.depth = depth\n self.causal = causal\n self.layers = nn.ModuleList([])\n\n self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()\n self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()\n\n if rotary_pos_emb:\n self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype)\n else:\n self.rotary_pos_emb = None\n\n self.use_sinusoidal_emb = use_sinusoidal_emb\n if use_sinusoidal_emb:\n self.pos_emb = ScaledSinusoidalEmbedding(dim)\n\n self.use_abs_pos_emb = use_abs_pos_emb\n if use_abs_pos_emb:\n self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)\n\n for i in range(depth):\n self.layers.append(\n TransformerBlock(\n dim,\n dim_heads = dim_heads,\n cross_attend = cross_attend,\n dim_context = cond_token_dim,\n global_cond_dim = global_cond_dim,\n causal = causal,\n zero_init_branch_outputs = zero_init_branch_outputs,\n conformer=conformer,\n layer_ix=i,\n dtype=dtype,\n device=device,\n operations=operations,\n **kwargs\n )\n )\n\n def forward(\n self,\n x,\n mask = None,\n prepend_embeds = None,\n prepend_mask = None,\n global_cond = None,\n return_info = False,\n **kwargs\n ):\n transformer_options = kwargs.get(\"transformer_options\", {})\n patches_replace = transformer_options.get(\"patches_replace\", {})\n batch, seq, device = *x.shape[:2], x.device\n context = kwargs[\"context\"]\n\n info = {\n \"hidden_states\": [],\n }\n\n x = self.project_in(x)\n\n if prepend_embeds is not None:\n prepend_length, prepend_dim = prepend_embeds.shape[1:]\n\n assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'\n\n x = torch.cat((prepend_embeds, x), dim = -2)\n\n if prepend_mask is not None or mask is not None:\n mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)\n prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)\n\n mask = torch.cat((prepend_mask, mask), dim = -1)\n\n # Attention layers\n\n if self.rotary_pos_emb is not None:\n rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)\n else:\n rotary_pos_emb = None\n\n if self.use_sinusoidal_emb or self.use_abs_pos_emb:\n x = x + self.pos_emb(x)\n\n blocks_replace = patches_replace.get(\"dit\", {})\n # Iterate over the transformer layers\n for i, layer in enumerate(self.layers):\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = layer(args[\"img\"], rotary_pos_emb=args[\"pe\"], global_cond=args[\"vec\"], context=args[\"txt\"], transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": global_cond, \"pe\": rotary_pos_emb, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)\n # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)\n\n if return_info:\n info[\"hidden_states\"].append(x)\n\n x = self.project_out(x)\n\n if return_info:\n return x, info\n\n return x\n\nclass AudioDiffusionTransformer(nn.Module):\n def __init__(self,\n io_channels=64,\n patch_size=1,\n embed_dim=1536,\n cond_token_dim=768,\n project_cond_tokens=False,\n global_cond_dim=1536,\n project_global_cond=True,\n input_concat_dim=0,\n prepend_cond_dim=0,\n depth=24,\n num_heads=24,\n transformer_type: tp.Literal[\"continuous_transformer\"] = \"continuous_transformer\",\n global_cond_type: tp.Literal[\"prepend\", \"adaLN\"] = \"prepend\",\n audio_model=\"\",\n dtype=None,\n device=None,\n operations=None,\n **kwargs):\n\n super().__init__()\n\n self.dtype = dtype\n self.cond_token_dim = cond_token_dim\n\n # Timestep embeddings\n timestep_features_dim = 256\n\n self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)\n\n self.to_timestep_embed = nn.Sequential(\n operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),\n )\n\n if cond_token_dim > 0:\n # Conditioning tokens\n\n cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim\n self.to_cond_embed = nn.Sequential(\n operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)\n )\n else:\n cond_embed_dim = 0\n\n if global_cond_dim > 0:\n # Global conditioning\n global_embed_dim = global_cond_dim if not project_global_cond else embed_dim\n self.to_global_embed = nn.Sequential(\n operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)\n )\n\n if prepend_cond_dim > 0:\n # Prepend conditioning\n self.to_prepend_embed = nn.Sequential(\n operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)\n )\n\n self.input_concat_dim = input_concat_dim\n\n dim_in = io_channels + self.input_concat_dim\n\n self.patch_size = patch_size\n\n # Transformer\n\n self.transformer_type = transformer_type\n\n self.global_cond_type = global_cond_type\n\n if self.transformer_type == \"continuous_transformer\":\n\n global_dim = None\n\n if self.global_cond_type == \"adaLN\":\n # The global conditioning is projected to the embed_dim already at this point\n global_dim = embed_dim\n\n self.transformer = ContinuousTransformer(\n dim=embed_dim,\n depth=depth,\n dim_heads=embed_dim // num_heads,\n dim_in=dim_in * patch_size,\n dim_out=io_channels * patch_size,\n cross_attend = cond_token_dim > 0,\n cond_token_dim = cond_embed_dim,\n global_cond_dim=global_dim,\n dtype=dtype,\n device=device,\n operations=operations,\n **kwargs\n )\n else:\n raise ValueError(f\"Unknown transformer type: {self.transformer_type}\")\n\n self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)\n self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)\n\n def _forward(\n self,\n x,\n t,\n mask=None,\n cross_attn_cond=None,\n cross_attn_cond_mask=None,\n input_concat_cond=None,\n global_embed=None,\n prepend_cond=None,\n prepend_cond_mask=None,\n return_info=False,\n **kwargs):\n\n if cross_attn_cond is not None:\n cross_attn_cond = self.to_cond_embed(cross_attn_cond)\n\n if global_embed is not None:\n # Project the global conditioning to the embedding dimension\n global_embed = self.to_global_embed(global_embed)\n\n prepend_inputs = None\n prepend_mask = None\n prepend_length = 0\n if prepend_cond is not None:\n # Project the prepend conditioning to the embedding dimension\n prepend_cond = self.to_prepend_embed(prepend_cond)\n\n prepend_inputs = prepend_cond\n if prepend_cond_mask is not None:\n prepend_mask = prepend_cond_mask\n\n if input_concat_cond is not None:\n\n # Interpolate input_concat_cond to the same length as x\n if input_concat_cond.shape[2] != x.shape[2]:\n input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')\n\n x = torch.cat([x, input_concat_cond], dim=1)\n\n # Get the batch of timestep embeddings\n timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)\n\n # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists\n if global_embed is not None:\n global_embed = global_embed + timestep_embed\n else:\n global_embed = timestep_embed\n\n # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer\n if self.global_cond_type == \"prepend\":\n if prepend_inputs is None:\n # Prepend inputs are just the global embed, and the mask is all ones\n prepend_inputs = global_embed.unsqueeze(1)\n prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)\n else:\n # Prepend inputs are the prepend conditioning + the global embed\n prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)\n prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)\n\n prepend_length = prepend_inputs.shape[1]\n\n x = self.preprocess_conv(x) + x\n\n x = rearrange(x, \"b c t -> b t c\")\n\n extra_args = {}\n\n if self.global_cond_type == \"adaLN\":\n extra_args[\"global_cond\"] = global_embed\n\n if self.patch_size > 1:\n x = rearrange(x, \"b (t p) c -> b t (c p)\", p=self.patch_size)\n\n if self.transformer_type == \"x-transformers\":\n output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)\n elif self.transformer_type == \"continuous_transformer\":\n output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)\n\n if return_info:\n output, info = output\n elif self.transformer_type == \"mm_transformer\":\n output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)\n\n output = rearrange(output, \"b t c -> b c t\")[:,:,prepend_length:]\n\n if self.patch_size > 1:\n output = rearrange(output, \"b (c p) t -> b c (t p)\", p=self.patch_size)\n\n output = self.postprocess_conv(output) + output\n\n if return_info:\n return output, info\n\n return output\n\n def forward(\n self,\n x,\n timestep,\n context=None,\n context_mask=None,\n input_concat_cond=None,\n global_embed=None,\n negative_global_embed=None,\n prepend_cond=None,\n prepend_cond_mask=None,\n mask=None,\n return_info=False,\n control=None,\n **kwargs):\n return self._forward(\n x,\n timestep,\n cross_attn_cond=context,\n cross_attn_cond_mask=context_mask,\n input_concat_cond=input_concat_cond,\n global_embed=global_embed,\n prepend_cond=prepend_cond,\n prepend_cond_mask=prepend_cond_mask,\n mask=mask,\n return_info=return_info,\n **kwargs\n )\n" }, { "path": "comfy/ldm/audio/embedders.py", "content": "# code adapted from: https://github.com/Stability-AI/stable-audio-tools\n\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nfrom typing import List, Union\nfrom einops import rearrange\nimport math\nimport comfy.ops\n\nclass LearnedPositionalEmbedding(nn.Module):\n \"\"\"Used for continuous time\"\"\"\n\n def __init__(self, dim: int):\n super().__init__()\n assert (dim % 2) == 0\n half_dim = dim // 2\n self.weights = nn.Parameter(torch.empty(half_dim))\n\n def forward(self, x: Tensor) -> Tensor:\n x = rearrange(x, \"b -> b 1\")\n freqs = x * rearrange(self.weights, \"d -> 1 d\") * 2 * math.pi\n fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)\n fouriered = torch.cat((x, fouriered), dim=-1)\n return fouriered\n\ndef TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:\n return nn.Sequential(\n LearnedPositionalEmbedding(dim),\n comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),\n )\n\n\nclass NumberEmbedder(nn.Module):\n def __init__(\n self,\n features: int,\n dim: int = 256,\n ):\n super().__init__()\n self.features = features\n self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)\n\n def forward(self, x: Union[List[float], Tensor]) -> Tensor:\n if not torch.is_tensor(x):\n device = next(self.embedding.parameters()).device\n x = torch.tensor(x, device=device)\n assert isinstance(x, Tensor)\n shape = x.shape\n x = rearrange(x, \"... -> (...)\")\n embedding = self.embedding(x)\n x = embedding.view(*shape, self.features)\n return x # type: ignore\n\n\nclass Conditioner(nn.Module):\n def __init__(\n self,\n dim: int,\n output_dim: int,\n project_out: bool = False\n ):\n\n super().__init__()\n\n self.dim = dim\n self.output_dim = output_dim\n self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()\n\n def forward(self, x):\n raise NotImplementedError()\n\nclass NumberConditioner(Conditioner):\n '''\n Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings\n '''\n def __init__(self,\n output_dim: int,\n min_val: float=0,\n max_val: float=1\n ):\n super().__init__(output_dim, output_dim)\n\n self.min_val = min_val\n self.max_val = max_val\n\n self.embedder = NumberEmbedder(features=output_dim)\n\n def forward(self, floats, device=None):\n # Cast the inputs to floats\n floats = [float(x) for x in floats]\n\n if device is None:\n device = next(self.embedder.parameters()).device\n\n floats = torch.tensor(floats).to(device)\n\n floats = floats.clamp(self.min_val, self.max_val)\n\n normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)\n\n # Cast floats to same type as embedder\n embedder_dtype = next(self.embedder.parameters()).dtype\n normalized_floats = normalized_floats.to(embedder_dtype)\n\n float_embeds = self.embedder(normalized_floats).unsqueeze(1)\n\n return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]\n" }, { "path": "comfy/ldm/aura/mmdit.py", "content": "#AuraFlow MMDiT\n#Originally written by the AuraFlow Authors\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.ops\nimport comfy.patcher_extension\nimport comfy.ldm.common_dit\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\ndef find_multiple(n: int, k: int) -> int:\n if n % k == 0:\n return n\n return n + k - (n % k)\n\n\nclass MLP(nn.Module):\n def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:\n super().__init__()\n if hidden_dim is None:\n hidden_dim = 4 * dim\n\n n_hidden = int(2 * hidden_dim / 3)\n n_hidden = find_multiple(n_hidden, 256)\n\n self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)\n self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)\n self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = F.silu(self.c_fc1(x)) * self.c_fc2(x)\n x = self.c_proj(x)\n return x\n\n\nclass MultiHeadLayerNorm(nn.Module):\n def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):\n # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78\n\n super().__init__()\n self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))\n self.variance_epsilon = eps\n\n def forward(self, hidden_states):\n input_dtype = hidden_states.dtype\n hidden_states = hidden_states.to(torch.float32)\n mean = hidden_states.mean(-1, keepdim=True)\n variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)\n hidden_states = (hidden_states - mean) * torch.rsqrt(\n variance + self.variance_epsilon\n )\n hidden_states = self.weight.to(torch.float32) * hidden_states\n return hidden_states.to(input_dtype)\n\nclass SingleAttention(nn.Module):\n def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):\n super().__init__()\n\n self.n_heads = n_heads\n self.head_dim = dim // n_heads\n\n # this is for cond\n self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n\n self.q_norm1 = (\n MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)\n if mh_qknorm\n else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)\n )\n self.k_norm1 = (\n MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)\n if mh_qknorm\n else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)\n )\n\n #@torch.compile()\n def forward(self, c, transformer_options={}):\n\n bsz, seqlen1, _ = c.shape\n\n q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)\n q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)\n k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)\n v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)\n q, k = self.q_norm1(q), self.k_norm1(k)\n\n output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)\n c = self.w1o(output)\n return c\n\n\n\nclass DoubleAttention(nn.Module):\n def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):\n super().__init__()\n\n self.n_heads = n_heads\n self.head_dim = dim // n_heads\n\n # this is for cond\n self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n\n # this is for x\n self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)\n\n self.q_norm1 = (\n MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)\n if mh_qknorm\n else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)\n )\n self.k_norm1 = (\n MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)\n if mh_qknorm\n else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)\n )\n\n self.q_norm2 = (\n MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)\n if mh_qknorm\n else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)\n )\n self.k_norm2 = (\n MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)\n if mh_qknorm\n else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)\n )\n\n\n #@torch.compile()\n def forward(self, c, x, transformer_options={}):\n\n bsz, seqlen1, _ = c.shape\n bsz, seqlen2, _ = x.shape\n\n cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)\n cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)\n ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)\n cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)\n cq, ck = self.q_norm1(cq), self.k_norm1(ck)\n\n xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)\n xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)\n xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)\n xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)\n xq, xk = self.q_norm2(xq), self.k_norm2(xk)\n\n # concat all\n q, k, v = (\n torch.cat([cq, xq], dim=1),\n torch.cat([ck, xk], dim=1),\n torch.cat([cv, xv], dim=1),\n )\n\n output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)\n\n c, x = output.split([seqlen1, seqlen2], dim=1)\n c = self.w1o(c)\n x = self.w2o(x)\n\n return c, x\n\n\nclass MMDiTBlock(nn.Module):\n def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):\n super().__init__()\n\n self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)\n self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)\n if not is_last:\n self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)\n self.modC = nn.Sequential(\n nn.SiLU(),\n operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),\n )\n else:\n self.modC = nn.Sequential(\n nn.SiLU(),\n operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),\n )\n\n self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)\n self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)\n self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)\n self.modX = nn.Sequential(\n nn.SiLU(),\n operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),\n )\n\n self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)\n self.is_last = is_last\n\n #@torch.compile()\n def forward(self, c, x, global_cond, transformer_options={}, **kwargs):\n\n cres, xres = c, x\n\n cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (\n self.modC(global_cond).chunk(6, dim=1)\n )\n\n c = modulate(self.normC1(c), cshift_msa, cscale_msa)\n\n # xpath\n xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (\n self.modX(global_cond).chunk(6, dim=1)\n )\n\n x = modulate(self.normX1(x), xshift_msa, xscale_msa)\n\n # attention\n c, x = self.attn(c, x, transformer_options=transformer_options)\n\n\n c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)\n c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))\n c = cres + c\n\n x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)\n x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))\n x = xres + x\n\n return c, x\n\nclass DiTBlock(nn.Module):\n # like MMDiTBlock, but it only has X\n def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):\n super().__init__()\n\n self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)\n self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)\n\n self.modCX = nn.Sequential(\n nn.SiLU(),\n operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),\n )\n\n self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)\n self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)\n\n #@torch.compile()\n def forward(self, cx, global_cond, transformer_options={}, **kwargs):\n cxres = cx\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(\n global_cond\n ).chunk(6, dim=1)\n cx = modulate(self.norm1(cx), shift_msa, scale_msa)\n cx = self.attn(cx, transformer_options=transformer_options)\n cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)\n mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))\n cx = gate_mlp.unsqueeze(1) * mlpout\n\n cx = cxres + cx\n\n return cx\n\n\n\nclass TimestepEmbedder(nn.Module):\n def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):\n super().__init__()\n self.mlp = nn.Sequential(\n operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),\n )\n self.frequency_embedding_size = frequency_embedding_size\n\n @staticmethod\n def timestep_embedding(t, dim, max_period=10000):\n half = dim // 2\n freqs = 1000 * torch.exp(\n -math.log(max_period) * torch.arange(start=0, end=half) / half\n ).to(t.device)\n args = t[:, None] * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat(\n [embedding, torch.zeros_like(embedding[:, :1])], dim=-1\n )\n return embedding\n\n #@torch.compile()\n def forward(self, t, dtype):\n t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)\n t_emb = self.mlp(t_freq)\n return t_emb\n\n\nclass MMDiT(nn.Module):\n def __init__(\n self,\n in_channels=4,\n out_channels=4,\n patch_size=2,\n dim=3072,\n n_layers=36,\n n_double_layers=4,\n n_heads=12,\n global_conddim=3072,\n cond_seq_dim=2048,\n max_seq=32 * 32,\n device=None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n self.dtype = dtype\n\n self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)\n\n self.cond_seq_linear = operations.Linear(\n cond_seq_dim, dim, bias=False, dtype=dtype, device=device\n ) # linear for something like text sequence.\n self.init_x_linear = operations.Linear(\n patch_size * patch_size * in_channels, dim, dtype=dtype, device=device\n ) # init linear for patchified image.\n\n self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))\n self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))\n\n self.double_layers = nn.ModuleList([])\n self.single_layers = nn.ModuleList([])\n\n\n for idx in range(n_double_layers):\n self.double_layers.append(\n MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)\n )\n\n for idx in range(n_double_layers, n_layers):\n self.single_layers.append(\n DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)\n )\n\n\n self.final_linear = operations.Linear(\n dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device\n )\n\n self.modF = nn.Sequential(\n nn.SiLU(),\n operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),\n )\n\n self.out_channels = out_channels\n self.patch_size = patch_size\n self.n_double_layers = n_double_layers\n self.n_layers = n_layers\n\n self.h_max = round(max_seq**0.5)\n self.w_max = round(max_seq**0.5)\n\n @torch.no_grad()\n def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):\n # extend pe\n pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]\n\n pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)\n\n # now we need to extend this to target_dim. for this we will use interpolation.\n # we will use torch.nn.functional.interpolate\n pe_as_2d = F.interpolate(\n pe_as_2d.unsqueeze(0), size=target_dim, mode=\"bilinear\"\n )\n pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)\n self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()\n self.h_max, self.w_max = target_dim\n\n def pe_selection_index_based_on_dim(self, h, w):\n h_p, w_p = h // self.patch_size, w // self.patch_size\n original_pe_indexes = torch.arange(self.positional_encoding.shape[1])\n original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)\n starth = self.h_max // 2 - h_p // 2\n endh =starth + h_p\n startw = self.w_max // 2 - w_p // 2\n endw = startw + w_p\n original_pe_indexes = original_pe_indexes[\n starth:endh, startw:endw\n ]\n return original_pe_indexes.flatten()\n\n def unpatchify(self, x, h, w):\n c = self.out_channels\n p = self.patch_size\n\n x = x.reshape(shape=(x.shape[0], h, w, p, p, c))\n x = torch.einsum(\"nhwpqc->nchpwq\", x)\n imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))\n return imgs\n\n def patchify(self, x):\n B, C, H, W = x.size()\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n x = x.view(\n B,\n C,\n (H + 1) // self.patch_size,\n self.patch_size,\n (W + 1) // self.patch_size,\n self.patch_size,\n )\n x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)\n return x\n\n def apply_pos_embeds(self, x, h, w):\n h = (h + 1) // self.patch_size\n w = (w + 1) // self.patch_size\n max_dim = max(h, w)\n\n cur_dim = self.h_max\n pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x)\n\n if max_dim > cur_dim:\n pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode=\"bilinear\").movedim(1, -1)\n cur_dim = max_dim\n\n from_h = (cur_dim - h) // 2\n from_w = (cur_dim - w) // 2\n pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]\n return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])\n\n def forward(self, x, timestep, context, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, transformer_options, **kwargs)\n\n def _forward(self, x, timestep, context, transformer_options={}, **kwargs):\n patches_replace = transformer_options.get(\"patches_replace\", {})\n # patchify x, add PE\n b, c, h, w = x.shape\n\n # pe_indexes = self.pe_selection_index_based_on_dim(h, w)\n # print(pe_indexes, pe_indexes.shape)\n\n x = self.init_x_linear(self.patchify(x)) # B, T_x, D\n x = self.apply_pos_embeds(x, h, w)\n # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)\n # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)\n\n # process conditions for MMDiT Blocks\n c_seq = context # B, T_c, D_c\n t = timestep\n\n c = self.cond_seq_linear(c_seq) # B, T_c, D\n c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1)\n\n global_cond = self.t_embedder(t, x.dtype) # B, D\n\n blocks_replace = patches_replace.get(\"dit\", {})\n if len(self.double_layers) > 0:\n for i, layer in enumerate(self.double_layers):\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"txt\"], out[\"img\"] = layer(args[\"txt\"],\n args[\"img\"],\n args[\"vec\"],\n transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": c, \"vec\": global_cond, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n c = out[\"txt\"]\n x = out[\"img\"]\n else:\n c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs)\n\n if len(self.single_layers) > 0:\n c_len = c.size(1)\n cx = torch.cat([c, x], dim=1)\n for i, layer in enumerate(self.single_layers):\n if (\"single_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = layer(args[\"img\"], args[\"vec\"], transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"single_block\", i)]({\"img\": cx, \"vec\": global_cond, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n cx = out[\"img\"]\n else:\n cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs)\n\n x = cx[:, c_len:]\n\n fshift, fscale = self.modF(global_cond).chunk(2, dim=1)\n\n x = modulate(x, fshift, fscale)\n x = self.final_linear(x)\n x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]\n return x\n" }, { "path": "comfy/ldm/cascade/common.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.ops\n\nclass OptimizedAttention(nn.Module):\n def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.heads = nhead\n\n self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device)\n self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device)\n self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device)\n\n self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)\n\n def forward(self, q, k, v, transformer_options={}):\n q = self.to_q(q)\n k = self.to_k(k)\n v = self.to_v(v)\n\n out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options)\n\n return self.out_proj(out)\n\nclass Attention2D(nn.Module):\n def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)\n # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)\n\n def forward(self, x, kv, self_attn=False, transformer_options={}):\n orig_shape = x.shape\n x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4\n if self_attn:\n kv = torch.cat([x, kv], dim=1)\n # x = self.attn(x, kv, kv, need_weights=False)[0]\n x = self.attn(x, kv, kv, transformer_options=transformer_options)\n x = x.permute(0, 2, 1).view(*orig_shape)\n return x\n\n\ndef LayerNorm2d_op(operations):\n class LayerNorm2d(operations.LayerNorm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def forward(self, x):\n return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n return LayerNorm2d\n\nclass GlobalResponseNorm(nn.Module):\n \"from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105\"\n def __init__(self, dim, dtype=None, device=None):\n super().__init__()\n self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))\n self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))\n\n def forward(self, x):\n Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)\n Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)\n return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x\n\n\nclass ResBlock(nn.Module):\n def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2):\n super().__init__()\n self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device)\n # self.depthwise = SAMBlock(c, num_heads, expansion)\n self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.channelwise = nn.Sequential(\n operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device),\n nn.GELU(),\n GlobalResponseNorm(c * 4, dtype=dtype, device=device),\n nn.Dropout(dropout),\n operations.Linear(c * 4, c, dtype=dtype, device=device)\n )\n\n def forward(self, x, x_skip=None):\n x_res = x\n x = self.norm(self.depthwise(x))\n if x_skip is not None:\n x = torch.cat([x, x_skip], dim=1)\n x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n return x + x_res\n\n\nclass AttnBlock(nn.Module):\n def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.self_attn = self_attn\n self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations)\n self.kv_mapper = nn.Sequential(\n nn.SiLU(),\n operations.Linear(c_cond, c, dtype=dtype, device=device)\n )\n\n def forward(self, x, kv, transformer_options={}):\n kv = self.kv_mapper(kv)\n x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options)\n return x\n\n\nclass FeedForwardBlock(nn.Module):\n def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.channelwise = nn.Sequential(\n operations.Linear(c, c * 4, dtype=dtype, device=device),\n nn.GELU(),\n GlobalResponseNorm(c * 4, dtype=dtype, device=device),\n nn.Dropout(dropout),\n operations.Linear(c * 4, c, dtype=dtype, device=device)\n )\n\n def forward(self, x):\n x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n return x\n\n\nclass TimestepBlock(nn.Module):\n def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None):\n super().__init__()\n self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)\n self.conds = conds\n for cname in conds:\n setattr(self, f\"mapper_{cname}\", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device))\n\n def forward(self, x, t):\n t = t.chunk(len(self.conds) + 1, dim=1)\n a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)\n for i, c in enumerate(self.conds):\n ac, bc = getattr(self, f\"mapper_{c}\")(t[i + 1])[:, :, None, None].chunk(2, dim=1)\n a, b = a + ac, b + bc\n return x * (1 + a) + b\n" }, { "path": "comfy/ldm/cascade/controlnet.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\n\nimport torchvision\nfrom torch import nn\nfrom .common import LayerNorm2d_op\n\n\nclass CNetResBlock(nn.Module):\n def __init__(self, c, dtype=None, device=None, operations=None):\n super().__init__()\n self.blocks = nn.Sequential(\n LayerNorm2d_op(operations)(c, dtype=dtype, device=device),\n nn.GELU(),\n operations.Conv2d(c, c, kernel_size=3, padding=1),\n LayerNorm2d_op(operations)(c, dtype=dtype, device=device),\n nn.GELU(),\n operations.Conv2d(c, c, kernel_size=3, padding=1),\n )\n\n def forward(self, x):\n return x + self.blocks(x)\n\n\nclass ControlNet(nn.Module):\n def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn):\n super().__init__()\n if bottleneck_mode is None:\n bottleneck_mode = 'effnet'\n self.proj_blocks = proj_blocks\n if bottleneck_mode == 'effnet':\n embd_channels = 1280\n self.backbone = torchvision.models.efficientnet_v2_s().features.eval()\n if c_in != 3:\n in_weights = self.backbone[0][0].weight.data\n self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device)\n if c_in > 3:\n # nn.init.constant_(self.backbone[0][0].weight, 0)\n self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone()\n else:\n self.backbone[0][0].weight.data = in_weights[:, :c_in].clone()\n elif bottleneck_mode == 'simple':\n embd_channels = c_in\n self.backbone = nn.Sequential(\n operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device),\n nn.LeakyReLU(0.2, inplace=True),\n operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device),\n )\n elif bottleneck_mode == 'large':\n self.backbone = nn.Sequential(\n operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device),\n nn.LeakyReLU(0.2, inplace=True),\n operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device),\n *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)],\n operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device),\n )\n embd_channels = 1280\n else:\n raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}')\n self.projections = nn.ModuleList()\n for _ in range(len(proj_blocks)):\n self.projections.append(nn.Sequential(\n operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device),\n nn.LeakyReLU(0.2, inplace=True),\n operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device),\n ))\n # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection\n self.xl = False\n self.input_channels = c_in\n self.unshuffle_amount = 8\n\n def forward(self, x):\n x = self.backbone(x)\n proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]\n for i, idx in enumerate(self.proj_blocks):\n proj_outputs[idx] = self.projections[i](x)\n return {\"input\": proj_outputs[::-1]}\n" }, { "path": "comfy/ldm/cascade/stage_a.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\n\nimport torch\nfrom torch import nn\nfrom torch.autograd import Function\nimport comfy.ops\n\nops = comfy.ops.disable_weight_init\n\n\nclass vector_quantize(Function):\n @staticmethod\n def forward(ctx, x, codebook):\n with torch.no_grad():\n codebook_sqr = torch.sum(codebook ** 2, dim=1)\n x_sqr = torch.sum(x ** 2, dim=1, keepdim=True)\n\n dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0)\n _, indices = dist.min(dim=1)\n\n ctx.save_for_backward(indices, codebook)\n ctx.mark_non_differentiable(indices)\n\n nn = torch.index_select(codebook, 0, indices)\n return nn, indices\n\n @staticmethod\n def backward(ctx, grad_output, grad_indices):\n grad_inputs, grad_codebook = None, None\n\n if ctx.needs_input_grad[0]:\n grad_inputs = grad_output.clone()\n if ctx.needs_input_grad[1]:\n # Gradient wrt. the codebook\n indices, codebook = ctx.saved_tensors\n\n grad_codebook = torch.zeros_like(codebook)\n grad_codebook.index_add_(0, indices, grad_output)\n\n return (grad_inputs, grad_codebook)\n\n\nclass VectorQuantize(nn.Module):\n def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False):\n \"\"\"\n Takes an input of variable size (as long as the last dimension matches the embedding size).\n Returns one tensor containing the nearest neigbour embeddings to each of the inputs,\n with the same size as the input, vq and commitment components for the loss as a touple\n in the second output and the indices of the quantized vectors in the third:\n quantized, (vq_loss, commit_loss), indices\n \"\"\"\n super(VectorQuantize, self).__init__()\n\n self.codebook = nn.Embedding(k, embedding_size)\n self.codebook.weight.data.uniform_(-1./k, 1./k)\n self.vq = vector_quantize.apply\n\n self.ema_decay = ema_decay\n self.ema_loss = ema_loss\n if ema_loss:\n self.register_buffer('ema_element_count', torch.ones(k))\n self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight))\n\n def _laplace_smoothing(self, x, epsilon):\n n = torch.sum(x)\n return ((x + epsilon) / (n + x.size(0) * epsilon) * n)\n\n def _updateEMA(self, z_e_x, indices):\n mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float()\n elem_count = mask.sum(dim=0)\n weight_sum = torch.mm(mask.t(), z_e_x)\n\n self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count)\n self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5)\n self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum)\n\n self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1)\n\n def idx2vq(self, idx, dim=-1):\n q_idx = self.codebook(idx)\n if dim != -1:\n q_idx = q_idx.movedim(-1, dim)\n return q_idx\n\n def forward(self, x, get_losses=True, dim=-1):\n if dim != -1:\n x = x.movedim(dim, -1)\n z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x\n z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach())\n vq_loss, commit_loss = None, None\n if self.ema_loss and self.training:\n self._updateEMA(z_e_x.detach(), indices.detach())\n # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss\n z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices)\n if get_losses:\n vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean()\n commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean()\n\n z_q_x = z_q_x.view(x.shape)\n if dim != -1:\n z_q_x = z_q_x.movedim(-1, dim)\n return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1])\n\n\nclass ResBlock(nn.Module):\n def __init__(self, c, c_hidden):\n super().__init__()\n # depthwise/attention\n self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.depthwise = nn.Sequential(\n nn.ReplicationPad2d(1),\n ops.Conv2d(c, c, kernel_size=3, groups=c)\n )\n\n # channelwise\n self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.channelwise = nn.Sequential(\n ops.Linear(c, c_hidden),\n nn.GELU(),\n ops.Linear(c_hidden, c),\n )\n\n self.gammas = nn.Parameter(torch.zeros(6), requires_grad=False)\n\n def _norm(self, x, norm):\n return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n\n def forward(self, x):\n mods = self.gammas\n\n x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1]\n try:\n x = x + self.depthwise(x_temp) * mods[2]\n except: #operation not implemented for bf16\n x_temp = self.depthwise[0](x_temp.float()).to(x.dtype)\n x = x + self.depthwise[1](x_temp) * mods[2]\n\n x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4]\n x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]\n\n return x\n\n\nclass StageA(nn.Module):\n def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192):\n super().__init__()\n self.c_latent = c_latent\n c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))]\n\n # Encoder blocks\n self.in_block = nn.Sequential(\n nn.PixelUnshuffle(2),\n ops.Conv2d(3 * 4, c_levels[0], kernel_size=1)\n )\n down_blocks = []\n for i in range(levels):\n if i > 0:\n down_blocks.append(ops.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))\n block = ResBlock(c_levels[i], c_levels[i] * 4)\n down_blocks.append(block)\n down_blocks.append(nn.Sequential(\n ops.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False),\n nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1\n ))\n self.down_blocks = nn.Sequential(*down_blocks)\n self.down_blocks[0]\n\n self.codebook_size = codebook_size\n self.vquantizer = VectorQuantize(c_latent, k=codebook_size)\n\n # Decoder blocks\n up_blocks = [nn.Sequential(\n ops.Conv2d(c_latent, c_levels[-1], kernel_size=1)\n )]\n for i in range(levels):\n for j in range(bottleneck_blocks if i == 0 else 1):\n block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)\n up_blocks.append(block)\n if i < levels - 1:\n up_blocks.append(\n ops.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2,\n padding=1))\n self.up_blocks = nn.Sequential(*up_blocks)\n self.out_block = nn.Sequential(\n ops.Conv2d(c_levels[0], 3 * 4, kernel_size=1),\n nn.PixelShuffle(2),\n )\n\n def encode(self, x, quantize=False):\n x = self.in_block(x)\n x = self.down_blocks(x)\n if quantize:\n qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1)\n return qe, x, indices, vq_loss + commit_loss * 0.25\n else:\n return x\n\n def decode(self, x):\n x = self.up_blocks(x)\n x = self.out_block(x)\n return x\n\n def forward(self, x, quantize=False):\n qe, x, _, vq_loss = self.encode(x, quantize)\n x = self.decode(qe)\n return x, vq_loss\n\n\nclass Discriminator(nn.Module):\n def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6):\n super().__init__()\n d = max(depth - 3, 3)\n layers = [\n nn.utils.spectral_norm(ops.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)),\n nn.LeakyReLU(0.2),\n ]\n for i in range(depth - 1):\n c_in = c_hidden // (2 ** max((d - i), 0))\n c_out = c_hidden // (2 ** max((d - 1 - i), 0))\n layers.append(nn.utils.spectral_norm(ops.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1)))\n layers.append(nn.InstanceNorm2d(c_out))\n layers.append(nn.LeakyReLU(0.2))\n self.encoder = nn.Sequential(*layers)\n self.shuffle = ops.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1)\n self.logits = nn.Sigmoid()\n\n def forward(self, x, cond=None):\n x = self.encoder(x)\n if cond is not None:\n cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1))\n x = torch.cat([x, cond], dim=1)\n x = self.shuffle(x)\n x = self.logits(x)\n return x\n" }, { "path": "comfy/ldm/cascade/stage_b.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\n\nimport math\nimport torch\nfrom torch import nn\nfrom .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock\n\nclass StageB(nn.Module):\n def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280],\n nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]],\n block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280,\n c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True,\n t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None):\n super().__init__()\n self.dtype = dtype\n self.c_r = c_r\n self.t_conds = t_conds\n self.c_clip_seq = c_clip_seq\n if not isinstance(dropout, list):\n dropout = [dropout] * len(c_hidden)\n if not isinstance(self_attn, list):\n self_attn = [self_attn] * len(c_hidden)\n\n # CONDITIONING\n self.effnet_mapper = nn.Sequential(\n operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),\n nn.GELU(),\n operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),\n LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n )\n self.pixels_mapper = nn.Sequential(\n operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),\n nn.GELU(),\n operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),\n LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n )\n self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device)\n self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n\n self.embedding = nn.Sequential(\n nn.PixelUnshuffle(patch_size),\n operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),\n LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n )\n\n def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):\n if block_type == 'C':\n return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)\n elif block_type == 'A':\n return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)\n elif block_type == 'F':\n return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)\n elif block_type == 'T':\n return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)\n else:\n raise Exception(f'Block type {block_type} not supported')\n\n # BLOCKS\n # -- down blocks\n self.down_blocks = nn.ModuleList()\n self.down_downscalers = nn.ModuleList()\n self.down_repeat_mappers = nn.ModuleList()\n for i in range(len(c_hidden)):\n if i > 0:\n self.down_downscalers.append(nn.Sequential(\n LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),\n operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device),\n ))\n else:\n self.down_downscalers.append(nn.Identity())\n down_block = nn.ModuleList()\n for _ in range(blocks[0][i]):\n for block_type in level_config[i]:\n block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])\n down_block.append(block)\n self.down_blocks.append(down_block)\n if block_repeat is not None:\n block_repeat_mappers = nn.ModuleList()\n for _ in range(block_repeat[0][i] - 1):\n block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))\n self.down_repeat_mappers.append(block_repeat_mappers)\n\n # -- up blocks\n self.up_blocks = nn.ModuleList()\n self.up_upscalers = nn.ModuleList()\n self.up_repeat_mappers = nn.ModuleList()\n for i in reversed(range(len(c_hidden))):\n if i > 0:\n self.up_upscalers.append(nn.Sequential(\n LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),\n operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device),\n ))\n else:\n self.up_upscalers.append(nn.Identity())\n up_block = nn.ModuleList()\n for j in range(blocks[1][::-1][i]):\n for k, block_type in enumerate(level_config[i]):\n c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0\n block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],\n self_attn=self_attn[i])\n up_block.append(block)\n self.up_blocks.append(up_block)\n if block_repeat is not None:\n block_repeat_mappers = nn.ModuleList()\n for _ in range(block_repeat[1][::-1][i] - 1):\n block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))\n self.up_repeat_mappers.append(block_repeat_mappers)\n\n # OUTPUT\n self.clf = nn.Sequential(\n LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),\n operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),\n nn.PixelShuffle(patch_size),\n )\n\n # --- WEIGHT INIT ---\n # self.apply(self._init_weights) # General init\n # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings\n # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings\n # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings\n # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings\n # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings\n # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs\n # nn.init.constant_(self.clf[1].weight, 0) # outputs\n #\n # # blocks\n # for level_block in self.down_blocks + self.up_blocks:\n # for block in level_block:\n # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):\n # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))\n # elif isinstance(block, TimestepBlock):\n # for layer in block.modules():\n # if isinstance(layer, nn.Linear):\n # nn.init.constant_(layer.weight, 0)\n #\n # def _init_weights(self, m):\n # if isinstance(m, (nn.Conv2d, nn.Linear)):\n # torch.nn.init.xavier_uniform_(m.weight)\n # if m.bias is not None:\n # nn.init.constant_(m.bias, 0)\n\n def gen_r_embedding(self, r, max_positions=10000):\n r = r * max_positions\n half_dim = self.c_r // 2\n emb = math.log(max_positions) / (half_dim - 1)\n emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()\n emb = r[:, None] * emb[None, :]\n emb = torch.cat([emb.sin(), emb.cos()], dim=1)\n if self.c_r % 2 == 1: # zero pad\n emb = nn.functional.pad(emb, (0, 1), mode='constant')\n return emb\n\n def gen_c_embeddings(self, clip):\n if len(clip.shape) == 2:\n clip = clip.unsqueeze(1)\n clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1)\n clip = self.clip_norm(clip)\n return clip\n\n def _down_encode(self, x, r_embed, clip, transformer_options={}):\n level_outputs = []\n block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)\n for down_block, downscaler, repmap in block_group:\n x = downscaler(x)\n for i in range(len(repmap) + 1):\n for block in down_block:\n if isinstance(block, ResBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n ResBlock)):\n x = block(x)\n elif isinstance(block, AttnBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n AttnBlock)):\n x = block(x, clip, transformer_options=transformer_options)\n elif isinstance(block, TimestepBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n TimestepBlock)):\n x = block(x, r_embed)\n else:\n x = block(x)\n if i < len(repmap):\n x = repmap[i](x)\n level_outputs.insert(0, x)\n return level_outputs\n\n def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}):\n x = level_outputs[0]\n block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)\n for i, (up_block, upscaler, repmap) in enumerate(block_group):\n for j in range(len(repmap) + 1):\n for k, block in enumerate(up_block):\n if isinstance(block, ResBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n ResBlock)):\n skip = level_outputs[i] if k == 0 and i > 0 else None\n if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):\n x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',\n align_corners=True)\n x = block(x, skip)\n elif isinstance(block, AttnBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n AttnBlock)):\n x = block(x, clip, transformer_options=transformer_options)\n elif isinstance(block, TimestepBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n TimestepBlock)):\n x = block(x, r_embed)\n else:\n x = block(x)\n if j < len(repmap):\n x = repmap[j](x)\n x = upscaler(x)\n return x\n\n def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs):\n if pixels is None:\n pixels = x.new_zeros(x.size(0), 3, 8, 8)\n\n # Process the conditioning embeddings\n r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)\n for c in self.t_conds:\n t_cond = kwargs.get(c, torch.zeros_like(r))\n r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)\n clip = self.gen_c_embeddings(clip)\n\n # Model Blocks\n x = self.embedding(x)\n x = x + self.effnet_mapper(\n nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))\n x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',\n align_corners=True)\n level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options)\n x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options)\n return self.clf(x)\n\n def update_weights_ema(self, src_model, beta=0.999):\n for self_params, src_params in zip(self.parameters(), src_model.parameters()):\n self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)\n for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):\n self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)\n" }, { "path": "comfy/ldm/cascade/stage_c.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\n\nimport torch\nfrom torch import nn\nimport math\nfrom .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock\n# from .controlnet import ControlNetDeliverer\n\nclass UpDownBlock2d(nn.Module):\n def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None):\n super().__init__()\n assert mode in ['up', 'down']\n interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear',\n align_corners=True) if enabled else nn.Identity()\n mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device)\n self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation])\n\n def forward(self, x):\n for block in self.blocks:\n x = block(x)\n return x\n\n\nclass StageC(nn.Module):\n def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32],\n blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'],\n c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3,\n dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None,\n dtype=None, device=None, operations=None):\n super().__init__()\n self.dtype = dtype\n self.c_r = c_r\n self.t_conds = t_conds\n self.c_clip_seq = c_clip_seq\n if not isinstance(dropout, list):\n dropout = [dropout] * len(c_hidden)\n if not isinstance(self_attn, list):\n self_attn = [self_attn] * len(c_hidden)\n\n # CONDITIONING\n self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device)\n self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device)\n self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device)\n self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n\n self.embedding = nn.Sequential(\n nn.PixelUnshuffle(patch_size),\n operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),\n LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6)\n )\n\n def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):\n if block_type == 'C':\n return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)\n elif block_type == 'A':\n return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)\n elif block_type == 'F':\n return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)\n elif block_type == 'T':\n return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)\n else:\n raise Exception(f'Block type {block_type} not supported')\n\n # BLOCKS\n # -- down blocks\n self.down_blocks = nn.ModuleList()\n self.down_downscalers = nn.ModuleList()\n self.down_repeat_mappers = nn.ModuleList()\n for i in range(len(c_hidden)):\n if i > 0:\n self.down_downscalers.append(nn.Sequential(\n LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6),\n UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)\n ))\n else:\n self.down_downscalers.append(nn.Identity())\n down_block = nn.ModuleList()\n for _ in range(blocks[0][i]):\n for block_type in level_config[i]:\n block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])\n down_block.append(block)\n self.down_blocks.append(down_block)\n if block_repeat is not None:\n block_repeat_mappers = nn.ModuleList()\n for _ in range(block_repeat[0][i] - 1):\n block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))\n self.down_repeat_mappers.append(block_repeat_mappers)\n\n # -- up blocks\n self.up_blocks = nn.ModuleList()\n self.up_upscalers = nn.ModuleList()\n self.up_repeat_mappers = nn.ModuleList()\n for i in reversed(range(len(c_hidden))):\n if i > 0:\n self.up_upscalers.append(nn.Sequential(\n LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6),\n UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)\n ))\n else:\n self.up_upscalers.append(nn.Identity())\n up_block = nn.ModuleList()\n for j in range(blocks[1][::-1][i]):\n for k, block_type in enumerate(level_config[i]):\n c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0\n block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],\n self_attn=self_attn[i])\n up_block.append(block)\n self.up_blocks.append(up_block)\n if block_repeat is not None:\n block_repeat_mappers = nn.ModuleList()\n for _ in range(block_repeat[1][::-1][i] - 1):\n block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))\n self.up_repeat_mappers.append(block_repeat_mappers)\n\n # OUTPUT\n self.clf = nn.Sequential(\n LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),\n operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),\n nn.PixelShuffle(patch_size),\n )\n\n # --- WEIGHT INIT ---\n # self.apply(self._init_weights) # General init\n # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings\n # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings\n # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings\n # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs\n # nn.init.constant_(self.clf[1].weight, 0) # outputs\n #\n # # blocks\n # for level_block in self.down_blocks + self.up_blocks:\n # for block in level_block:\n # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):\n # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))\n # elif isinstance(block, TimestepBlock):\n # for layer in block.modules():\n # if isinstance(layer, nn.Linear):\n # nn.init.constant_(layer.weight, 0)\n #\n # def _init_weights(self, m):\n # if isinstance(m, (nn.Conv2d, nn.Linear)):\n # torch.nn.init.xavier_uniform_(m.weight)\n # if m.bias is not None:\n # nn.init.constant_(m.bias, 0)\n\n def gen_r_embedding(self, r, max_positions=10000):\n r = r * max_positions\n half_dim = self.c_r // 2\n emb = math.log(max_positions) / (half_dim - 1)\n emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()\n emb = r[:, None] * emb[None, :]\n emb = torch.cat([emb.sin(), emb.cos()], dim=1)\n if self.c_r % 2 == 1: # zero pad\n emb = nn.functional.pad(emb, (0, 1), mode='constant')\n return emb\n\n def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):\n clip_txt = self.clip_txt_mapper(clip_txt)\n if len(clip_txt_pooled.shape) == 2:\n clip_txt_pooled = clip_txt_pooled.unsqueeze(1)\n if len(clip_img.shape) == 2:\n clip_img = clip_img.unsqueeze(1)\n clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1)\n clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1)\n clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)\n clip = self.clip_norm(clip)\n return clip\n\n def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}):\n level_outputs = []\n block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)\n for down_block, downscaler, repmap in block_group:\n x = downscaler(x)\n for i in range(len(repmap) + 1):\n for block in down_block:\n if isinstance(block, ResBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n ResBlock)):\n if cnet is not None:\n next_cnet = cnet.pop()\n if next_cnet is not None:\n x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',\n align_corners=True).to(x.dtype)\n x = block(x)\n elif isinstance(block, AttnBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n AttnBlock)):\n x = block(x, clip, transformer_options=transformer_options)\n elif isinstance(block, TimestepBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n TimestepBlock)):\n x = block(x, r_embed)\n else:\n x = block(x)\n if i < len(repmap):\n x = repmap[i](x)\n level_outputs.insert(0, x)\n return level_outputs\n\n def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}):\n x = level_outputs[0]\n block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)\n for i, (up_block, upscaler, repmap) in enumerate(block_group):\n for j in range(len(repmap) + 1):\n for k, block in enumerate(up_block):\n if isinstance(block, ResBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n ResBlock)):\n skip = level_outputs[i] if k == 0 and i > 0 else None\n if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):\n x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',\n align_corners=True)\n if cnet is not None:\n next_cnet = cnet.pop()\n if next_cnet is not None:\n x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',\n align_corners=True).to(x.dtype)\n x = block(x, skip)\n elif isinstance(block, AttnBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n AttnBlock)):\n x = block(x, clip, transformer_options=transformer_options)\n elif isinstance(block, TimestepBlock) or (\n hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,\n TimestepBlock)):\n x = block(x, r_embed)\n else:\n x = block(x)\n if j < len(repmap):\n x = repmap[j](x)\n x = upscaler(x)\n return x\n\n def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs):\n # Process the conditioning embeddings\n r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)\n for c in self.t_conds:\n t_cond = kwargs.get(c, torch.zeros_like(r))\n r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)\n clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img)\n\n if control is not None:\n cnet = control.get(\"input\")\n else:\n cnet = None\n\n # Model Blocks\n x = self.embedding(x)\n level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options)\n x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options)\n return self.clf(x)\n\n def update_weights_ema(self, src_model, beta=0.999):\n for self_params, src_params in zip(self.parameters(), src_model.parameters()):\n self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)\n for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):\n self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)\n" }, { "path": "comfy/ldm/cascade/stage_c_coder.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\nimport torch\nimport torchvision\nfrom torch import nn\n\nimport comfy.ops\n\nops = comfy.ops.disable_weight_init\n\n# EfficientNet\nclass EfficientNetEncoder(nn.Module):\n def __init__(self, c_latent=16):\n super().__init__()\n self.backbone = torchvision.models.efficientnet_v2_s().features.eval()\n self.mapper = nn.Sequential(\n ops.Conv2d(1280, c_latent, kernel_size=1, bias=False),\n nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1\n )\n self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]))\n self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225]))\n\n def forward(self, x):\n x = x * 0.5 + 0.5\n x = (x - self.mean.view([3,1,1]).to(device=x.device, dtype=x.dtype)) / self.std.view([3,1,1]).to(device=x.device, dtype=x.dtype)\n o = self.mapper(self.backbone(x))\n return o\n\n\n# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192\nclass Previewer(nn.Module):\n def __init__(self, c_in=16, c_hidden=512, c_out=3):\n super().__init__()\n self.blocks = nn.Sequential(\n ops.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels\n nn.GELU(),\n nn.BatchNorm2d(c_hidden),\n\n ops.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1),\n nn.GELU(),\n nn.BatchNorm2d(c_hidden),\n\n ops.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32\n nn.GELU(),\n nn.BatchNorm2d(c_hidden // 2),\n\n ops.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1),\n nn.GELU(),\n nn.BatchNorm2d(c_hidden // 2),\n\n ops.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64\n nn.GELU(),\n nn.BatchNorm2d(c_hidden // 4),\n\n ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),\n nn.GELU(),\n nn.BatchNorm2d(c_hidden // 4),\n\n ops.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128\n nn.GELU(),\n nn.BatchNorm2d(c_hidden // 4),\n\n ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),\n nn.GELU(),\n nn.BatchNorm2d(c_hidden // 4),\n\n ops.Conv2d(c_hidden // 4, c_out, kernel_size=1),\n )\n\n def forward(self, x):\n return (self.blocks(x) - 0.5) * 2.0\n\nclass StageC_coder(nn.Module):\n def __init__(self):\n super().__init__()\n self.previewer = Previewer()\n self.encoder = EfficientNetEncoder()\n\n def encode(self, x):\n return self.encoder(x)\n\n def decode(self, x):\n return self.previewer(x)\n" }, { "path": "comfy/ldm/chroma/layers.py", "content": "import torch\nfrom torch import Tensor, nn\n\nfrom comfy.ldm.flux.layers import (\n MLPEmbedder,\n ModulationOut,\n)\n\n# TODO: remove this in a few months\nSingleStreamBlock = None\nDoubleStreamBlock = None\n\n\nclass ChromaModulationOut(ModulationOut):\n @classmethod\n def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut:\n return cls(\n shift=tensor[:, offset : offset + 1, :],\n scale=tensor[:, offset + 1 : offset + 2, :],\n gate=tensor[:, offset + 2 : offset + 3, :],\n )\n\n\n\n\nclass Approximator(nn.Module):\n def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None):\n super().__init__()\n self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)\n self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])\n self.norms = nn.ModuleList([operations.RMSNorm(hidden_dim, dtype=dtype, device=device) for x in range( n_layers)])\n self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)\n\n @property\n def device(self):\n # Get the device of the module (assumes all parameters are on the same device)\n return next(self.parameters()).device\n\n def forward(self, x: Tensor) -> Tensor:\n x = self.in_proj(x)\n\n for layer, norms in zip(self.layers, self.norms):\n x = x + layer(norms(x))\n\n x = self.out_proj(x)\n\n return x\n\n\nclass LastLayer(nn.Module):\n def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device)\n\n def forward(self, x: Tensor, vec: Tensor) -> Tensor:\n shift, scale = vec\n shift = shift.squeeze(1)\n scale = scale.squeeze(1)\n x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))\n x = self.linear(x)\n return x\n" }, { "path": "comfy/ldm/chroma/model.py", "content": "#Original code can be found on: https://github.com/black-forest-labs/flux\n\nfrom dataclasses import dataclass\n\nimport torch\nfrom torch import Tensor, nn\nfrom einops import rearrange, repeat\nimport comfy.patcher_extension\nimport comfy.ldm.common_dit\n\nfrom comfy.ldm.flux.layers import (\n EmbedND,\n timestep_embedding,\n DoubleStreamBlock,\n SingleStreamBlock,\n)\n\nfrom .layers import (\n LastLayer,\n Approximator,\n ChromaModulationOut,\n)\n\n\n@dataclass\nclass ChromaParams:\n in_channels: int\n out_channels: int\n context_in_dim: int\n hidden_size: int\n mlp_ratio: float\n num_heads: int\n depth: int\n depth_single_blocks: int\n axes_dim: list\n theta: int\n patch_size: int\n qkv_bias: bool\n in_dim: int\n out_dim: int\n hidden_dim: int\n n_layers: int\n txt_ids_dims: list\n vec_in_dim: int\n\n\n\nclass Chroma(nn.Module):\n \"\"\"\n Transformer model for flow matching on sequences.\n \"\"\"\n\n def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):\n super().__init__()\n self.dtype = dtype\n params = ChromaParams(**kwargs)\n self.params = params\n self.patch_size = params.patch_size\n self.in_channels = params.in_channels\n self.out_channels = params.out_channels\n if params.hidden_size % params.num_heads != 0:\n raise ValueError(\n f\"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}\"\n )\n pe_dim = params.hidden_size // params.num_heads\n if sum(params.axes_dim) != pe_dim:\n raise ValueError(f\"Got {params.axes_dim} but expected positional dim {pe_dim}\")\n self.hidden_size = params.hidden_size\n self.num_heads = params.num_heads\n self.in_dim = params.in_dim\n self.out_dim = params.out_dim\n self.hidden_dim = params.hidden_dim\n self.n_layers = params.n_layers\n self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)\n self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)\n self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)\n # set as nn identity for now, will overwrite it later.\n self.distilled_guidance_layer = Approximator(\n in_dim=self.in_dim,\n hidden_dim=self.hidden_dim,\n out_dim=self.out_dim,\n n_layers=self.n_layers,\n dtype=dtype, device=device, operations=operations\n )\n\n\n self.double_blocks = nn.ModuleList(\n [\n DoubleStreamBlock(\n self.hidden_size,\n self.num_heads,\n mlp_ratio=params.mlp_ratio,\n qkv_bias=params.qkv_bias,\n modulation=False,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(params.depth)\n ]\n )\n\n self.single_blocks = nn.ModuleList(\n [\n SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations)\n for _ in range(params.depth_single_blocks)\n ]\n )\n\n if final_layer:\n self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)\n\n self.skip_mmdit = []\n self.skip_dit = []\n self.lite = False\n\n def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0):\n # This function slices up the modulations tensor which has the following layout:\n # single : num_single_blocks * 3 elements\n # double_img : num_double_blocks * 6 elements\n # double_txt : num_double_blocks * 6 elements\n # final : 2 elements\n if block_type == \"final\":\n return (tensor[:, -2:-1, :], tensor[:, -1:, :])\n single_block_count = self.params.depth_single_blocks\n double_block_count = self.params.depth\n offset = 3 * idx\n if block_type == \"single\":\n return ChromaModulationOut.from_offset(tensor, offset)\n # Double block modulations are 6 elements so we double 3 * idx.\n offset *= 2\n if block_type in {\"double_img\", \"double_txt\"}:\n # Advance past the single block modulations.\n offset += 3 * single_block_count\n if block_type == \"double_txt\":\n # Advance past the double block img modulations.\n offset += 6 * double_block_count\n return (\n ChromaModulationOut.from_offset(tensor, offset),\n ChromaModulationOut.from_offset(tensor, offset + 3),\n )\n raise ValueError(\"Bad block_type\")\n\n\n def forward_orig(\n self,\n img: Tensor,\n img_ids: Tensor,\n txt: Tensor,\n txt_ids: Tensor,\n timesteps: Tensor,\n guidance: Tensor = None,\n control = None,\n transformer_options={},\n attn_mask: Tensor = None,\n ) -> Tensor:\n transformer_options = transformer_options.copy()\n patches_replace = transformer_options.get(\"patches_replace\", {})\n\n # running on sequences img\n img = self.img_in(img)\n\n # distilled vector guidance\n mod_index_length = 344\n distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype)\n # guidance = guidance *\n distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)\n\n # get all modulation index\n modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)\n # we need to broadcast the modulation index here so each batch has all of the index\n modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)\n # and we need to broadcast timestep and guidance along too\n timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype)\n # then and only then we could concatenate it together\n input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype)\n\n mod_vectors = self.distilled_guidance_layer(input_vec)\n\n txt = self.txt_in(txt)\n\n ids = torch.cat((txt_ids, img_ids), dim=1)\n pe = self.pe_embedder(ids)\n\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.double_blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.double_blocks):\n transformer_options[\"block_index\"] = i\n if i not in self.skip_mmdit:\n double_mod = (\n self.get_modulations(mod_vectors, \"double_img\", idx=i),\n self.get_modulations(mod_vectors, \"double_txt\", idx=i),\n )\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"], out[\"txt\"] = block(img=args[\"img\"],\n txt=args[\"txt\"],\n vec=args[\"vec\"],\n pe=args[\"pe\"],\n attn_mask=args.get(\"attn_mask\"),\n transformer_options=args.get(\"transformer_options\"))\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": img,\n \"txt\": txt,\n \"vec\": double_mod,\n \"pe\": pe,\n \"attn_mask\": attn_mask,\n \"transformer_options\": transformer_options},\n {\"original_block\": block_wrap})\n txt = out[\"txt\"]\n img = out[\"img\"]\n else:\n img, txt = block(img=img,\n txt=txt,\n vec=double_mod,\n pe=pe,\n attn_mask=attn_mask,\n transformer_options=transformer_options)\n\n if control is not None: # Controlnet\n control_i = control.get(\"input\")\n if i < len(control_i):\n add = control_i[i]\n if add is not None:\n img += add\n\n img = torch.cat((txt, img), 1)\n\n transformer_options[\"total_blocks\"] = len(self.single_blocks)\n transformer_options[\"block_type\"] = \"single\"\n transformer_options[\"img_slice\"] = [txt.shape[1], img.shape[1]]\n for i, block in enumerate(self.single_blocks):\n transformer_options[\"block_index\"] = i\n if i not in self.skip_dit:\n single_mod = self.get_modulations(mod_vectors, \"single\", idx=i)\n if (\"single_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"],\n vec=args[\"vec\"],\n pe=args[\"pe\"],\n attn_mask=args.get(\"attn_mask\"),\n transformer_options=args.get(\"transformer_options\"))\n return out\n\n out = blocks_replace[(\"single_block\", i)]({\"img\": img,\n \"vec\": single_mod,\n \"pe\": pe,\n \"attn_mask\": attn_mask,\n \"transformer_options\": transformer_options},\n {\"original_block\": block_wrap})\n img = out[\"img\"]\n else:\n img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)\n\n if control is not None: # Controlnet\n control_o = control.get(\"output\")\n if i < len(control_o):\n add = control_o[i]\n if add is not None:\n img[:, txt.shape[1] :, ...] += add\n\n img = img[:, txt.shape[1] :, ...]\n if hasattr(self, \"final_layer\"):\n final_mod = self.get_modulations(mod_vectors, \"final\")\n img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels)\n return img\n\n def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs)\n\n def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):\n bs, c, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n\n img = rearrange(x, \"b c (h ph) (w pw) -> b (h w) (c ph pw)\", ph=self.patch_size, pw=self.patch_size)\n\n if img.ndim != 3 or context.ndim != 3:\n raise ValueError(\"Input img and txt tensors must have 3 dimensions.\")\n\n h_len = ((h + (self.patch_size // 2)) // self.patch_size)\n w_len = ((w + (self.patch_size // 2)) // self.patch_size)\n img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)\n img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)\n img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)\n img_ids = repeat(img_ids, \"h w c -> b (h w) c\", b=bs)\n\n txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)\n out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get(\"attention_mask\", None))\n return rearrange(out, \"b (h w) (c ph pw) -> b c (h ph) (w pw)\", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w]\n" }, { "path": "comfy/ldm/chroma_radiance/layers.py", "content": "# Adapted from https://github.com/lodestone-rock/flow\nfrom functools import lru_cache\n\nimport torch\nfrom torch import nn\n\n\nclass NerfEmbedder(nn.Module):\n \"\"\"\n An embedder module that combines input features with a 2D positional\n encoding that mimics the Discrete Cosine Transform (DCT).\n\n This module takes an input tensor of shape (B, P^2, C), where P is the\n patch size, and enriches it with positional information before projecting\n it to a new hidden size.\n \"\"\"\n def __init__(\n self,\n in_channels: int,\n hidden_size_input: int,\n max_freqs: int,\n dtype=None,\n device=None,\n operations=None,\n ):\n \"\"\"\n Initializes the NerfEmbedder.\n\n Args:\n in_channels (int): The number of channels in the input tensor.\n hidden_size_input (int): The desired dimension of the output embedding.\n max_freqs (int): The number of frequency components to use for both\n the x and y dimensions of the positional encoding.\n The total number of positional features will be max_freqs^2.\n \"\"\"\n super().__init__()\n self.dtype = dtype\n self.max_freqs = max_freqs\n self.hidden_size_input = hidden_size_input\n\n # A linear layer to project the concatenated input features and\n # positional encodings to the final output dimension.\n self.embedder = nn.Sequential(\n operations.Linear(in_channels + max_freqs**2, hidden_size_input, dtype=dtype, device=device)\n )\n\n @lru_cache(maxsize=4)\n def fetch_pos(self, patch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:\n \"\"\"\n Generates and caches 2D DCT-like positional embeddings for a given patch size.\n\n The LRU cache is a performance optimization that avoids recomputing the\n same positional grid on every forward pass.\n\n Args:\n patch_size (int): The side length of the square input patch.\n device: The torch device to create the tensors on.\n dtype: The torch dtype for the tensors.\n\n Returns:\n A tensor of shape (1, patch_size^2, max_freqs^2) containing the\n positional embeddings.\n \"\"\"\n # Create normalized 1D coordinate grids from 0 to 1.\n pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)\n pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)\n\n # Create a 2D meshgrid of coordinates.\n pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing=\"ij\")\n\n # Reshape positions to be broadcastable with frequencies.\n # Shape becomes (patch_size^2, 1, 1).\n pos_x = pos_x.reshape(-1, 1, 1)\n pos_y = pos_y.reshape(-1, 1, 1)\n\n # Create a 1D tensor of frequency values from 0 to max_freqs-1.\n freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device)\n\n # Reshape frequencies to be broadcastable for creating 2D basis functions.\n # freqs_x shape: (1, max_freqs, 1)\n # freqs_y shape: (1, 1, max_freqs)\n freqs_x = freqs[None, :, None]\n freqs_y = freqs[None, None, :]\n\n # A custom weighting coefficient, not part of standard DCT.\n # This seems to down-weight the contribution of higher-frequency interactions.\n coeffs = (1 + freqs_x * freqs_y) ** -1\n\n # Calculate the 1D cosine basis functions for x and y coordinates.\n # This is the core of the DCT formulation.\n dct_x = torch.cos(pos_x * freqs_x * torch.pi)\n dct_y = torch.cos(pos_y * freqs_y * torch.pi)\n\n # Combine the 1D basis functions to create 2D basis functions by element-wise\n # multiplication, and apply the custom coefficients. Broadcasting handles the\n # combination of all (pos_x, freqs_x) with all (pos_y, freqs_y).\n # The result is flattened into a feature vector for each position.\n dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2)\n\n return dct\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Forward pass for the embedder.\n\n Args:\n inputs (Tensor): The input tensor of shape (B, P^2, C).\n\n Returns:\n Tensor: The output tensor of shape (B, P^2, hidden_size_input).\n \"\"\"\n # Get the batch size, number of pixels, and number of channels.\n B, P2, C = inputs.shape\n\n # Infer the patch side length from the number of pixels (P^2).\n patch_size = int(P2 ** 0.5)\n\n input_dtype = inputs.dtype\n inputs = inputs.to(dtype=self.dtype)\n\n # Fetch the pre-computed or cached positional embeddings.\n dct = self.fetch_pos(patch_size, inputs.device, self.dtype)\n\n # Repeat the positional embeddings for each item in the batch.\n dct = dct.repeat(B, 1, 1)\n\n # Concatenate the original input features with the positional embeddings\n # along the feature dimension.\n inputs = torch.cat((inputs, dct), dim=-1)\n\n # Project the combined tensor to the target hidden size.\n return self.embedder(inputs).to(dtype=input_dtype)\n\n\nclass NerfGLUBlock(nn.Module):\n \"\"\"\n A NerfBlock using a Gated Linear Unit (GLU) like MLP.\n \"\"\"\n def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, dtype=None, device=None, operations=None):\n super().__init__()\n # The total number of parameters for the MLP is increased to accommodate\n # the gate, value, and output projection matrices.\n # We now need to generate parameters for 3 matrices.\n total_params = 3 * hidden_size_x**2 * mlp_ratio\n self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device)\n self.norm = operations.RMSNorm(hidden_size_x, dtype=dtype, device=device)\n self.mlp_ratio = mlp_ratio\n\n\n def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor:\n batch_size, num_x, hidden_size_x = x.shape\n mlp_params = self.param_generator(s)\n\n # Split the generated parameters into three parts for the gate, value, and output projection.\n fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1)\n\n # Reshape the parameters into matrices for batch matrix multiplication.\n fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)\n fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)\n fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x)\n\n # Normalize the generated weight matrices as in the original implementation.\n fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2)\n fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2)\n fc2 = torch.nn.functional.normalize(fc2, dim=-2)\n\n res_x = x\n x = self.norm(x)\n\n # Apply the final output projection.\n x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2)\n\n return x + res_x\n\n\nclass NerfFinalLayer(nn.Module):\n def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.\n # So we temporarily move the channel dimension to the end for the norm operation.\n return self.linear(self.norm(x.movedim(1, -1))).movedim(-1, 1)\n\n\nclass NerfFinalLayerConv(nn.Module):\n def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device)\n self.conv = operations.Conv2d(\n in_channels=hidden_size,\n out_channels=out_channels,\n kernel_size=3,\n padding=1,\n dtype=dtype,\n device=device,\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.\n # So we temporarily move the channel dimension to the end for the norm operation.\n return self.conv(self.norm(x.movedim(1, -1)).movedim(-1, 1))\n" }, { "path": "comfy/ldm/chroma_radiance/model.py", "content": "# Credits:\n# Original Flux code can be found on: https://github.com/black-forest-labs/flux\n# Chroma Radiance adaption referenced from https://github.com/lodestone-rock/flow\n\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nimport torch\nfrom torch import Tensor, nn\nfrom einops import repeat\nimport comfy.ldm.common_dit\n\nfrom comfy.ldm.flux.layers import EmbedND, DoubleStreamBlock, SingleStreamBlock\n\nfrom comfy.ldm.chroma.model import Chroma, ChromaParams\nfrom comfy.ldm.chroma.layers import (\n Approximator,\n)\nfrom .layers import (\n NerfEmbedder,\n NerfGLUBlock,\n NerfFinalLayer,\n NerfFinalLayerConv,\n)\n\n\n@dataclass\nclass ChromaRadianceParams(ChromaParams):\n patch_size: int\n nerf_hidden_size: int\n nerf_mlp_ratio: int\n nerf_depth: int\n nerf_max_freqs: int\n # Setting nerf_tile_size to 0 disables tiling.\n nerf_tile_size: int\n # Currently one of linear (legacy) or conv.\n nerf_final_head_type: str\n # None means use the same dtype as the model.\n nerf_embedder_dtype: Optional[torch.dtype]\n use_x0: bool\n\nclass ChromaRadiance(Chroma):\n \"\"\"\n Transformer model for flow matching on sequences.\n \"\"\"\n\n def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):\n if operations is None:\n raise RuntimeError(\"Attempt to create ChromaRadiance object without setting operations\")\n nn.Module.__init__(self)\n self.dtype = dtype\n params = ChromaRadianceParams(**kwargs)\n self.params = params\n self.patch_size = params.patch_size\n self.in_channels = params.in_channels\n self.out_channels = params.out_channels\n if params.hidden_size % params.num_heads != 0:\n raise ValueError(\n f\"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}\"\n )\n pe_dim = params.hidden_size // params.num_heads\n if sum(params.axes_dim) != pe_dim:\n raise ValueError(f\"Got {params.axes_dim} but expected positional dim {pe_dim}\")\n self.hidden_size = params.hidden_size\n self.num_heads = params.num_heads\n self.in_dim = params.in_dim\n self.out_dim = params.out_dim\n self.hidden_dim = params.hidden_dim\n self.n_layers = params.n_layers\n self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)\n self.img_in_patch = operations.Conv2d(\n params.in_channels,\n params.hidden_size,\n kernel_size=params.patch_size,\n stride=params.patch_size,\n bias=True,\n dtype=dtype,\n device=device,\n )\n self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)\n # set as nn identity for now, will overwrite it later.\n self.distilled_guidance_layer = Approximator(\n in_dim=self.in_dim,\n hidden_dim=self.hidden_dim,\n out_dim=self.out_dim,\n n_layers=self.n_layers,\n dtype=dtype, device=device, operations=operations\n )\n\n self.double_blocks = nn.ModuleList(\n [\n DoubleStreamBlock(\n self.hidden_size,\n self.num_heads,\n mlp_ratio=params.mlp_ratio,\n qkv_bias=params.qkv_bias,\n modulation=False,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(params.depth)\n ]\n )\n\n self.single_blocks = nn.ModuleList(\n [\n SingleStreamBlock(\n self.hidden_size,\n self.num_heads,\n mlp_ratio=params.mlp_ratio,\n modulation=False,\n dtype=dtype, device=device, operations=operations,\n )\n for _ in range(params.depth_single_blocks)\n ]\n )\n\n # pixel channel concat with DCT\n self.nerf_image_embedder = NerfEmbedder(\n in_channels=params.in_channels,\n hidden_size_input=params.nerf_hidden_size,\n max_freqs=params.nerf_max_freqs,\n dtype=params.nerf_embedder_dtype or dtype,\n device=device,\n operations=operations,\n )\n\n self.nerf_blocks = nn.ModuleList([\n NerfGLUBlock(\n hidden_size_s=params.hidden_size,\n hidden_size_x=params.nerf_hidden_size,\n mlp_ratio=params.nerf_mlp_ratio,\n dtype=dtype,\n device=device,\n operations=operations,\n ) for _ in range(params.nerf_depth)\n ])\n\n if params.nerf_final_head_type == \"linear\":\n self.nerf_final_layer = NerfFinalLayer(\n params.nerf_hidden_size,\n out_channels=params.in_channels,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n elif params.nerf_final_head_type == \"conv\":\n self.nerf_final_layer_conv = NerfFinalLayerConv(\n params.nerf_hidden_size,\n out_channels=params.in_channels,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n else:\n errstr = f\"Unsupported nerf_final_head_type {params.nerf_final_head_type}\"\n raise ValueError(errstr)\n\n self.skip_mmdit = []\n self.skip_dit = []\n self.lite = False\n\n if params.use_x0:\n self.register_buffer(\"__x0__\", torch.tensor([]))\n\n @property\n def _nerf_final_layer(self) -> nn.Module:\n if self.params.nerf_final_head_type == \"linear\":\n return self.nerf_final_layer\n if self.params.nerf_final_head_type == \"conv\":\n return self.nerf_final_layer_conv\n # Impossible to get here as we raise an error on unexpected types on initialization.\n raise NotImplementedError\n\n def img_in(self, img: Tensor) -> Tensor:\n img = self.img_in_patch(img) # -> [B, Hidden, H/P, W/P]\n # flatten into a sequence for the transformer.\n return img.flatten(2).transpose(1, 2) # -> [B, NumPatches, Hidden]\n\n def forward_nerf(\n self,\n img_orig: Tensor,\n img_out: Tensor,\n params: ChromaRadianceParams,\n ) -> Tensor:\n B, C, H, W = img_orig.shape\n num_patches = img_out.shape[1]\n patch_size = params.patch_size\n\n # Store the raw pixel values of each patch for the NeRF head later.\n # unfold creates patches: [B, C * P * P, NumPatches]\n nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size)\n nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P]\n\n # Reshape for per-patch processing\n nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)\n nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)\n\n if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size:\n # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than\n # the tile size.\n img_dct = self.forward_tiled_nerf(nerf_hidden, nerf_pixels, B, C, num_patches, patch_size, params)\n else:\n # Get DCT-encoded pixel embeddings [pixel-dct]\n img_dct = self.nerf_image_embedder(nerf_pixels)\n\n # Pass through the dynamic MLP blocks (the NeRF)\n for block in self.nerf_blocks:\n img_dct = block(img_dct, nerf_hidden)\n\n # Reassemble the patches into the final image.\n img_dct = img_dct.transpose(1, 2) # -> [B*NumPatches, C, P*P]\n # Reshape to combine with batch dimension for fold\n img_dct = img_dct.reshape(B, num_patches, -1) # -> [B, NumPatches, C*P*P]\n img_dct = img_dct.transpose(1, 2) # -> [B, C*P*P, NumPatches]\n img_dct = nn.functional.fold(\n img_dct,\n output_size=(H, W),\n kernel_size=patch_size,\n stride=patch_size,\n )\n return self._nerf_final_layer(img_dct)\n\n def forward_tiled_nerf(\n self,\n nerf_hidden: Tensor,\n nerf_pixels: Tensor,\n batch: int,\n channels: int,\n num_patches: int,\n patch_size: int,\n params: ChromaRadianceParams,\n ) -> Tensor:\n \"\"\"\n Processes the NeRF head in tiles to save memory.\n nerf_hidden has shape [B, L, D]\n nerf_pixels has shape [B, L, C * P * P]\n \"\"\"\n tile_size = params.nerf_tile_size\n output_tiles = []\n # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1.\n for i in range(0, num_patches, tile_size):\n end = min(i + tile_size, num_patches)\n\n # Slice the current tile from the input tensors\n nerf_hidden_tile = nerf_hidden[i * batch:end * batch]\n nerf_pixels_tile = nerf_pixels[i * batch:end * batch]\n\n # get DCT-encoded pixel embeddings [pixel-dct]\n img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)\n\n # pass through the dynamic MLP blocks (the NeRF)\n for block in self.nerf_blocks:\n img_dct_tile = block(img_dct_tile, nerf_hidden_tile)\n\n output_tiles.append(img_dct_tile)\n\n # Concatenate the processed tiles along the patch dimension\n return torch.cat(output_tiles, dim=0)\n\n def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams:\n params = self.params\n if not overrides:\n return params\n params_dict = {k: getattr(params, k) for k in params.__dataclass_fields__}\n nullable_keys = frozenset((\"nerf_embedder_dtype\",))\n bad_keys = tuple(k for k in overrides if k not in params_dict)\n if bad_keys:\n e = f\"Unknown key(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}\"\n raise ValueError(e)\n bad_keys = tuple(\n k\n for k, v in overrides.items()\n if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys)\n )\n if bad_keys:\n e = f\"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}\"\n raise ValueError(e)\n # At this point it's all valid keys and values so we can merge with the existing params.\n params_dict |= overrides\n return params.__class__(**params_dict)\n\n def _apply_x0_residual(self, predicted, noisy, timesteps):\n\n # non zero during training to prevent 0 div\n eps = 0.0\n return (noisy - predicted) / (timesteps.view(-1,1,1,1) + eps)\n\n def _forward(\n self,\n x: Tensor,\n timestep: Tensor,\n context: Tensor,\n guidance: Optional[Tensor],\n control: Optional[dict]=None,\n transformer_options: dict={},\n **kwargs: dict,\n ) -> Tensor:\n bs, c, h, w = x.shape\n img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n\n if img.ndim != 4:\n raise ValueError(\"Input img tensor must be in [B, C, H, W] format.\")\n if context.ndim != 3:\n raise ValueError(\"Input txt tensors must have 3 dimensions.\")\n\n params = self.radiance_get_override_params(transformer_options.get(\"chroma_radiance_options\", {}))\n\n h_len = (img.shape[-2] // self.patch_size)\n w_len = (img.shape[-1] // self.patch_size)\n\n img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)\n img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)\n img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)\n img_ids = repeat(img_ids, \"h w c -> b (h w) c\", b=bs)\n txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)\n\n img_out = self.forward_orig(\n img,\n img_ids,\n context,\n txt_ids,\n timestep,\n guidance,\n control,\n transformer_options,\n attn_mask=kwargs.get(\"attention_mask\", None),\n )\n\n out = self.forward_nerf(img, img_out, params)[:, :, :h, :w]\n\n # If x0 variant → v-pred, just return this instead\n if hasattr(self, \"__x0__\"):\n out = self._apply_x0_residual(out, img, timestep)\n return out\n\n" }, { "path": "comfy/ldm/common_dit.py", "content": "import torch\nimport comfy.rmsnorm\n\n\ndef pad_to_patch_size(img, patch_size=(2, 2), padding_mode=\"circular\"):\n if padding_mode == \"circular\" and (torch.jit.is_tracing() or torch.jit.is_scripting()):\n padding_mode = \"reflect\"\n\n pad = ()\n for i in range(img.ndim - 2):\n pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad\n\n return torch.nn.functional.pad(img, pad, mode=padding_mode)\n\n\nrms_norm = comfy.rmsnorm.rms_norm\n" }, { "path": "comfy/ldm/cosmos/blocks.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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 math\nfrom typing import Optional\nimport logging\n\nimport numpy as np\nimport torch\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom torch import nn\n\nfrom comfy.ldm.modules.attention import optimized_attention\n\n\ndef get_normalization(name: str, channels: int, weight_args={}, operations=None):\n if name == \"I\":\n return nn.Identity()\n elif name == \"R\":\n return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args)\n else:\n raise ValueError(f\"Normalization {name} not found\")\n\n\nclass BaseAttentionOp(nn.Module):\n def __init__(self):\n super().__init__()\n\n\nclass Attention(nn.Module):\n \"\"\"\n Generalized attention impl.\n\n Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided.\n If `context_dim` is None, self-attention is assumed.\n\n Parameters:\n query_dim (int): Dimension of each query vector.\n context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed.\n heads (int, optional): Number of attention heads. Defaults to 8.\n dim_head (int, optional): Dimension of each head. Defaults to 64.\n dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0.\n attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default.\n qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False.\n out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False.\n qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections.\n Defaults to \"SSI\".\n qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections.\n Defaults to 'per_head'. Only support 'per_head'.\n\n Examples:\n >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1)\n >>> query = torch.randn(10, 128) # Batch size of 10\n >>> context = torch.randn(10, 256) # Batch size of 10\n >>> output = attn(query, context) # Perform the attention operation\n\n Note:\n https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223\n \"\"\"\n\n def __init__(\n self,\n query_dim: int,\n context_dim=None,\n heads=8,\n dim_head=64,\n dropout=0.0,\n attn_op: Optional[BaseAttentionOp] = None,\n qkv_bias: bool = False,\n out_bias: bool = False,\n qkv_norm: str = \"SSI\",\n qkv_norm_mode: str = \"per_head\",\n backend: str = \"transformer_engine\",\n qkv_format: str = \"bshd\",\n weight_args={},\n operations=None,\n ) -> None:\n super().__init__()\n\n self.is_selfattn = context_dim is None # self attention\n\n inner_dim = dim_head * heads\n context_dim = query_dim if context_dim is None else context_dim\n\n self.heads = heads\n self.dim_head = dim_head\n self.qkv_norm_mode = qkv_norm_mode\n self.qkv_format = qkv_format\n\n if self.qkv_norm_mode == \"per_head\":\n norm_dim = dim_head\n else:\n raise ValueError(f\"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'\")\n\n self.backend = backend\n\n self.to_q = nn.Sequential(\n operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args),\n get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations),\n )\n self.to_k = nn.Sequential(\n operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args),\n get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations),\n )\n self.to_v = nn.Sequential(\n operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args),\n get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations),\n )\n\n self.to_out = nn.Sequential(\n operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args),\n nn.Dropout(dropout),\n )\n\n def cal_qkv(\n self, x, context=None, mask=None, rope_emb=None, **kwargs\n ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n del kwargs\n\n\n \"\"\"\n self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers.\n Before 07/24/2024, these modules normalize across all heads.\n After 07/24/2024, to support tensor parallelism and follow the common practice in the community,\n we support to normalize per head.\n To keep the checkpoint copatibility with the previous code,\n we keep the nn.Sequential but call the projection and the normalization layers separately.\n We use a flag `self.qkv_norm_mode` to control the normalization behavior.\n The default value of `self.qkv_norm_mode` is \"per_head\", which means we normalize per head.\n \"\"\"\n if self.qkv_norm_mode == \"per_head\":\n q = self.to_q[0](x)\n context = x if context is None else context\n k = self.to_k[0](context)\n v = self.to_v[0](context)\n q, k, v = map(\n lambda t: rearrange(t, \"s b (n c) -> b n s c\", n=self.heads, c=self.dim_head),\n (q, k, v),\n )\n else:\n raise ValueError(f\"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'\")\n\n q = self.to_q[1](q)\n k = self.to_k[1](k)\n v = self.to_v[1](v)\n if self.is_selfattn and rope_emb is not None: # only apply to self-attention!\n # apply_rotary_pos_emb inlined\n q_shape = q.shape\n q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)\n q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1]\n q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype)\n\n # apply_rotary_pos_emb inlined\n k_shape = k.shape\n k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)\n k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1]\n k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype)\n return q, k, v\n\n def forward(\n self,\n x,\n context=None,\n mask=None,\n rope_emb=None,\n transformer_options={},\n **kwargs,\n ):\n \"\"\"\n Args:\n x (Tensor): The query tensor of shape [B, Mq, K]\n context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None\n \"\"\"\n q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)\n out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)\n del q, k, v\n out = rearrange(out, \" b n s c -> s b (n c)\")\n return self.to_out(out)\n\n\nclass FeedForward(nn.Module):\n \"\"\"\n Transformer FFN with optional gating\n\n Parameters:\n d_model (int): Dimensionality of input features.\n d_ff (int): Dimensionality of the hidden layer.\n dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1.\n activation (callable, optional): The activation function applied after the first linear layer.\n Defaults to nn.ReLU().\n is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer.\n Defaults to False.\n bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True.\n\n Example:\n >>> ff = FeedForward(d_model=512, d_ff=2048)\n >>> x = torch.randn(64, 10, 512) # Example input tensor\n >>> output = ff(x)\n >>> print(output.shape) # Expected shape: (64, 10, 512)\n \"\"\"\n\n def __init__(\n self,\n d_model: int,\n d_ff: int,\n dropout: float = 0.1,\n activation=nn.ReLU(),\n is_gated: bool = False,\n bias: bool = False,\n weight_args={},\n operations=None,\n ) -> None:\n super().__init__()\n\n self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args)\n self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args)\n\n self.dropout = nn.Dropout(dropout)\n self.activation = activation\n self.is_gated = is_gated\n if is_gated:\n self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args)\n\n def forward(self, x: torch.Tensor):\n g = self.activation(self.layer1(x))\n if self.is_gated:\n x = g * self.linear_gate(x)\n else:\n x = g\n assert self.dropout.p == 0.0, \"we skip dropout\"\n return self.layer2(x)\n\n\nclass GPT2FeedForward(FeedForward):\n def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None):\n super().__init__(\n d_model=d_model,\n d_ff=d_ff,\n dropout=dropout,\n activation=nn.GELU(),\n is_gated=False,\n bias=bias,\n weight_args=weight_args,\n operations=operations,\n )\n\n def forward(self, x: torch.Tensor):\n assert self.dropout.p == 0.0, \"we skip dropout\"\n\n x = self.layer1(x)\n x = self.activation(x)\n x = self.layer2(x)\n\n return x\n\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\nclass Timesteps(nn.Module):\n def __init__(self, num_channels):\n super().__init__()\n self.num_channels = num_channels\n\n def forward(self, timesteps):\n half_dim = self.num_channels // 2\n exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device)\n exponent = exponent / (half_dim - 0.0)\n\n emb = torch.exp(exponent)\n emb = timesteps[:, None].float() * emb[None, :]\n\n sin_emb = torch.sin(emb)\n cos_emb = torch.cos(emb)\n emb = torch.cat([cos_emb, sin_emb], dim=-1)\n\n return emb\n\n\nclass TimestepEmbedding(nn.Module):\n def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None):\n super().__init__()\n logging.debug(\n f\"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility.\"\n )\n self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args)\n self.activation = nn.SiLU()\n self.use_adaln_lora = use_adaln_lora\n if use_adaln_lora:\n self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args)\n else:\n self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args)\n\n def forward(self, sample: torch.Tensor) -> torch.Tensor:\n emb = self.linear_1(sample)\n emb = self.activation(emb)\n emb = self.linear_2(emb)\n\n if self.use_adaln_lora:\n adaln_lora_B_3D = emb\n emb_B_D = sample\n else:\n emb_B_D = emb\n adaln_lora_B_3D = None\n\n return emb_B_D, adaln_lora_B_3D\n\n\nclass FourierFeatures(nn.Module):\n \"\"\"\n Implements a layer that generates Fourier features from input tensors, based on randomly sampled\n frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems.\n\n [B] -> [B, D]\n\n Parameters:\n num_channels (int): The number of Fourier features to generate.\n bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1.\n normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize\n the variance of the features. Defaults to False.\n\n Example:\n >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True)\n >>> x = torch.randn(10, 256) # Example input tensor\n >>> output = layer(x)\n >>> print(output.shape) # Expected shape: (10, 256)\n \"\"\"\n\n def __init__(self, num_channels, bandwidth=1, normalize=False):\n super().__init__()\n self.register_buffer(\"freqs\", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True)\n self.register_buffer(\"phases\", 2 * np.pi * torch.rand(num_channels), persistent=True)\n self.gain = np.sqrt(2) if normalize else 1\n\n def forward(self, x, gain: float = 1.0):\n \"\"\"\n Apply the Fourier feature transformation to the input tensor.\n\n Args:\n x (torch.Tensor): The input tensor.\n gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1.\n\n Returns:\n torch.Tensor: The transformed tensor, with Fourier features applied.\n \"\"\"\n in_dtype = x.dtype\n x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32))\n x = x.cos().mul(self.gain * gain).to(in_dtype)\n return x\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers,\n depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions,\n making it suitable for video and image processing tasks. It supports dividing the input into patches\n and embedding each patch into a vector of size `out_channels`.\n\n Parameters:\n - spatial_patch_size (int): The size of each spatial patch.\n - temporal_patch_size (int): The size of each temporal patch.\n - in_channels (int): Number of input channels. Default: 3.\n - out_channels (int): The dimension of the embedding vector for each patch. Default: 768.\n - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True.\n \"\"\"\n\n def __init__(\n self,\n spatial_patch_size,\n temporal_patch_size,\n in_channels=3,\n out_channels=768,\n bias=True,\n weight_args={},\n operations=None,\n ):\n super().__init__()\n self.spatial_patch_size = spatial_patch_size\n self.temporal_patch_size = temporal_patch_size\n\n self.proj = nn.Sequential(\n Rearrange(\n \"b c (t r) (h m) (w n) -> b t h w (c r m n)\",\n r=temporal_patch_size,\n m=spatial_patch_size,\n n=spatial_patch_size,\n ),\n operations.Linear(\n in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args\n ),\n )\n self.out = nn.Identity()\n\n def forward(self, x):\n \"\"\"\n Forward pass of the PatchEmbed module.\n\n Parameters:\n - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where\n B is the batch size,\n C is the number of channels,\n T is the temporal dimension,\n H is the height, and\n W is the width of the input.\n\n Returns:\n - torch.Tensor: The embedded patches as a tensor, with shape b t h w c.\n \"\"\"\n assert x.dim() == 5\n _, _, T, H, W = x.shape\n assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0\n assert T % self.temporal_patch_size == 0\n x = self.proj(x)\n return self.out(x)\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of video DiT.\n \"\"\"\n\n def __init__(\n self,\n hidden_size,\n spatial_patch_size,\n temporal_patch_size,\n out_channels,\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n weight_args={},\n operations=None,\n ):\n super().__init__()\n self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args)\n self.linear = operations.Linear(\n hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args\n )\n self.hidden_size = hidden_size\n self.n_adaln_chunks = 2\n self.use_adaln_lora = use_adaln_lora\n if use_adaln_lora:\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args),\n operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args),\n )\n else:\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args)\n )\n\n def forward(\n self,\n x_BT_HW_D,\n emb_B_D,\n adaln_lora_B_3D: Optional[torch.Tensor] = None,\n ):\n if self.use_adaln_lora:\n assert adaln_lora_B_3D is not None\n shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk(\n 2, dim=1\n )\n else:\n shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1)\n\n B = emb_B_D.shape[0]\n T = x_BT_HW_D.shape[0] // B\n shift_BT_D, scale_BT_D = repeat(shift_B_D, \"b d -> (b t) d\", t=T), repeat(scale_B_D, \"b d -> (b t) d\", t=T)\n x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D)\n\n x_BT_HW_D = self.linear(x_BT_HW_D)\n return x_BT_HW_D\n\n\nclass VideoAttn(nn.Module):\n \"\"\"\n Implements video attention with optional cross-attention capabilities.\n\n This module processes video features while maintaining their spatio-temporal structure. It can perform\n self-attention within the video features or cross-attention with external context features.\n\n Parameters:\n x_dim (int): Dimension of input feature vectors\n context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention\n num_heads (int): Number of attention heads\n bias (bool): Whether to include bias in attention projections. Default: False\n qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be \"per_head\". Default: \"per_head\"\n x_format (str): Format of input tensor. Must be \"BTHWD\". Default: \"BTHWD\"\n\n Input shape:\n - x: (T, H, W, B, D) video features\n - context (optional): (M, B, D) context features for cross-attention\n where:\n T: temporal dimension\n H: height\n W: width\n B: batch size\n D: feature dimension\n M: context sequence length\n \"\"\"\n\n def __init__(\n self,\n x_dim: int,\n context_dim: Optional[int],\n num_heads: int,\n bias: bool = False,\n qkv_norm_mode: str = \"per_head\",\n x_format: str = \"BTHWD\",\n weight_args={},\n operations=None,\n ) -> None:\n super().__init__()\n self.x_format = x_format\n\n self.attn = Attention(\n x_dim,\n context_dim,\n num_heads,\n x_dim // num_heads,\n qkv_bias=bias,\n qkv_norm=\"RRI\",\n out_bias=bias,\n qkv_norm_mode=qkv_norm_mode,\n qkv_format=\"sbhd\",\n weight_args=weight_args,\n operations=operations,\n )\n\n def forward(\n self,\n x: torch.Tensor,\n context: Optional[torch.Tensor] = None,\n crossattn_mask: Optional[torch.Tensor] = None,\n rope_emb_L_1_1_D: Optional[torch.Tensor] = None,\n transformer_options: Optional[dict] = {},\n ) -> torch.Tensor:\n \"\"\"\n Forward pass for video attention.\n\n Args:\n x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data.\n context (Tensor): Context tensor of shape (B, M, D) or (M, B, D),\n where M is the sequence length of the context.\n crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms.\n rope_emb_L_1_1_D (Optional[Tensor]):\n Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training.\n\n Returns:\n Tensor: The output tensor with applied attention, maintaining the input shape.\n \"\"\"\n\n x_T_H_W_B_D = x\n context_M_B_D = context\n T, H, W, B, D = x_T_H_W_B_D.shape\n x_THW_B_D = rearrange(x_T_H_W_B_D, \"t h w b d -> (t h w) b d\")\n x_THW_B_D = self.attn(\n x_THW_B_D,\n context_M_B_D,\n crossattn_mask,\n rope_emb=rope_emb_L_1_1_D,\n transformer_options=transformer_options,\n )\n x_T_H_W_B_D = rearrange(x_THW_B_D, \"(t h w) b d -> t h w b d\", h=H, w=W)\n return x_T_H_W_B_D\n\n\ndef adaln_norm_state(norm_state, x, scale, shift):\n normalized = norm_state(x)\n return normalized * (1 + scale) + shift\n\n\nclass DITBuildingBlock(nn.Module):\n \"\"\"\n A building block for the DiT (Diffusion Transformer) architecture that supports different types of\n attention and MLP operations with adaptive layer normalization.\n\n Parameters:\n block_type (str): Type of block - one of:\n - \"cross_attn\"/\"ca\": Cross-attention\n - \"full_attn\"/\"fa\": Full self-attention\n - \"mlp\"/\"ff\": MLP/feedforward block\n x_dim (int): Dimension of input features\n context_dim (Optional[int]): Dimension of context features for cross-attention\n num_heads (int): Number of attention heads\n mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0\n bias (bool): Whether to use bias in layers. Default: False\n mlp_dropout (float): Dropout rate for MLP. Default: 0.0\n qkv_norm_mode (str): QKV normalization mode. Default: \"per_head\"\n x_format (str): Input tensor format. Default: \"BTHWD\"\n use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False\n adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256\n \"\"\"\n\n def __init__(\n self,\n block_type: str,\n x_dim: int,\n context_dim: Optional[int],\n num_heads: int,\n mlp_ratio: float = 4.0,\n bias: bool = False,\n mlp_dropout: float = 0.0,\n qkv_norm_mode: str = \"per_head\",\n x_format: str = \"BTHWD\",\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n weight_args={},\n operations=None\n ) -> None:\n block_type = block_type.lower()\n\n super().__init__()\n self.x_format = x_format\n if block_type in [\"cross_attn\", \"ca\"]:\n self.block = VideoAttn(\n x_dim,\n context_dim,\n num_heads,\n bias=bias,\n qkv_norm_mode=qkv_norm_mode,\n x_format=self.x_format,\n weight_args=weight_args,\n operations=operations,\n )\n elif block_type in [\"full_attn\", \"fa\"]:\n self.block = VideoAttn(\n x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations\n )\n elif block_type in [\"mlp\", \"ff\"]:\n self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations)\n else:\n raise ValueError(f\"Unknown block type: {block_type}\")\n\n self.block_type = block_type\n self.use_adaln_lora = use_adaln_lora\n\n self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6)\n self.n_adaln_chunks = 3\n if use_adaln_lora:\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args),\n operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args),\n )\n else:\n self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args))\n\n def forward(\n self,\n x: torch.Tensor,\n emb_B_D: torch.Tensor,\n crossattn_emb: torch.Tensor,\n crossattn_mask: Optional[torch.Tensor] = None,\n rope_emb_L_1_1_D: Optional[torch.Tensor] = None,\n adaln_lora_B_3D: Optional[torch.Tensor] = None,\n transformer_options: Optional[dict] = {},\n ) -> torch.Tensor:\n \"\"\"\n Forward pass for dynamically configured blocks with adaptive normalization.\n\n Args:\n x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D).\n emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation.\n crossattn_emb (Tensor): Tensor for cross-attention blocks.\n crossattn_mask (Optional[Tensor]): Optional mask for cross-attention.\n rope_emb_L_1_1_D (Optional[Tensor]):\n Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training.\n\n Returns:\n Tensor: The output tensor after processing through the configured block and adaptive normalization.\n \"\"\"\n if self.use_adaln_lora:\n shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk(\n self.n_adaln_chunks, dim=1\n )\n else:\n shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1)\n\n shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = (\n shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),\n scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),\n gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),\n )\n\n if self.block_type in [\"mlp\", \"ff\"]:\n x = x + gate_1_1_1_B_D * self.block(\n adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),\n )\n elif self.block_type in [\"full_attn\", \"fa\"]:\n x = x + gate_1_1_1_B_D * self.block(\n adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),\n context=None,\n rope_emb_L_1_1_D=rope_emb_L_1_1_D,\n transformer_options=transformer_options,\n )\n elif self.block_type in [\"cross_attn\", \"ca\"]:\n x = x + gate_1_1_1_B_D * self.block(\n adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),\n context=crossattn_emb,\n crossattn_mask=crossattn_mask,\n rope_emb_L_1_1_D=rope_emb_L_1_1_D,\n transformer_options=transformer_options,\n )\n else:\n raise ValueError(f\"Unknown block type: {self.block_type}\")\n\n return x\n\n\nclass GeneralDITTransformerBlock(nn.Module):\n \"\"\"\n A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer.\n Each block in the sequence is specified by a block configuration string.\n\n Parameters:\n x_dim (int): Dimension of input features\n context_dim (int): Dimension of context features for cross-attention blocks\n num_heads (int): Number of attention heads\n block_config (str): String specifying block sequence (e.g. \"ca-fa-mlp\" for cross-attention,\n full-attention, then MLP)\n mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0\n x_format (str): Input tensor format. Default: \"BTHWD\"\n use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False\n adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256\n\n The block_config string uses \"-\" to separate block types:\n - \"ca\"/\"cross_attn\": Cross-attention block\n - \"fa\"/\"full_attn\": Full self-attention block\n - \"mlp\"/\"ff\": MLP/feedforward block\n\n Example:\n block_config = \"ca-fa-mlp\" creates a sequence of:\n 1. Cross-attention block\n 2. Full self-attention block\n 3. MLP block\n \"\"\"\n\n def __init__(\n self,\n x_dim: int,\n context_dim: int,\n num_heads: int,\n block_config: str,\n mlp_ratio: float = 4.0,\n x_format: str = \"BTHWD\",\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n weight_args={},\n operations=None\n ):\n super().__init__()\n self.blocks = nn.ModuleList()\n self.x_format = x_format\n for block_type in block_config.split(\"-\"):\n self.blocks.append(\n DITBuildingBlock(\n block_type,\n x_dim,\n context_dim,\n num_heads,\n mlp_ratio,\n x_format=self.x_format,\n use_adaln_lora=use_adaln_lora,\n adaln_lora_dim=adaln_lora_dim,\n weight_args=weight_args,\n operations=operations,\n )\n )\n\n def forward(\n self,\n x: torch.Tensor,\n emb_B_D: torch.Tensor,\n crossattn_emb: torch.Tensor,\n crossattn_mask: Optional[torch.Tensor] = None,\n rope_emb_L_1_1_D: Optional[torch.Tensor] = None,\n adaln_lora_B_3D: Optional[torch.Tensor] = None,\n transformer_options: Optional[dict] = {},\n ) -> torch.Tensor:\n for block in self.blocks:\n x = block(\n x,\n emb_B_D,\n crossattn_emb,\n crossattn_mask,\n rope_emb_L_1_1_D=rope_emb_L_1_1_D,\n adaln_lora_B_3D=adaln_lora_B_3D,\n transformer_options=transformer_options,\n )\n return x\n" }, { "path": "comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\"\"\"The model definition for 3D layers\n\nAdapted from: https://github.com/lucidrains/magvit2-pytorch/blob/\n9f49074179c912736e617d61b32be367eb5f993a/magvit2_pytorch/magvit2_pytorch.py#L889\n\n[MIT License Copyright (c) 2023 Phil Wang]\nhttps://github.com/lucidrains/magvit2-pytorch/blob/\n9f49074179c912736e617d61b32be367eb5f993a/LICENSE\n\"\"\"\nimport math\nfrom typing import Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport logging\n\nfrom comfy.ldm.modules.diffusionmodules.model import vae_attention\n\nfrom .patching import (\n Patcher,\n Patcher3D,\n UnPatcher,\n UnPatcher3D,\n)\nfrom .utils import (\n CausalNormalize,\n batch2space,\n batch2time,\n cast_tuple,\n is_odd,\n nonlinearity,\n replication_pad,\n space2batch,\n time2batch,\n)\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\n_LEGACY_NUM_GROUPS = 32\n\n\nclass CausalConv3d(nn.Module):\n def __init__(\n self,\n chan_in: int = 1,\n chan_out: int = 1,\n kernel_size: Union[int, Tuple[int, int, int]] = 3,\n pad_mode: str = \"constant\",\n **kwargs,\n ):\n super().__init__()\n kernel_size = cast_tuple(kernel_size, 3)\n\n time_kernel_size, height_kernel_size, width_kernel_size = kernel_size\n\n assert is_odd(height_kernel_size) and is_odd(width_kernel_size)\n\n dilation = kwargs.pop(\"dilation\", 1)\n stride = kwargs.pop(\"stride\", 1)\n time_stride = kwargs.pop(\"time_stride\", 1)\n time_dilation = kwargs.pop(\"time_dilation\", 1)\n padding = kwargs.pop(\"padding\", 1)\n\n self.pad_mode = pad_mode\n time_pad = time_dilation * (time_kernel_size - 1) + (1 - time_stride)\n self.time_pad = time_pad\n\n self.spatial_pad = (padding, padding, padding, padding)\n\n stride = (time_stride, stride, stride)\n dilation = (time_dilation, dilation, dilation)\n self.conv3d = ops.Conv3d(\n chan_in,\n chan_out,\n kernel_size,\n stride=stride,\n dilation=dilation,\n **kwargs,\n )\n\n def _replication_pad(self, x: torch.Tensor) -> torch.Tensor:\n x_prev = x[:, :, :1, ...].repeat(1, 1, self.time_pad, 1, 1)\n x = torch.cat([x_prev, x], dim=2)\n padding = self.spatial_pad + (0, 0)\n return F.pad(x, padding, mode=self.pad_mode, value=0.0)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self._replication_pad(x)\n return self.conv3d(x)\n\n\nclass CausalUpsample3d(nn.Module):\n def __init__(self, in_channels: int) -> None:\n super().__init__()\n self.conv = CausalConv3d(\n in_channels, in_channels, kernel_size=3, stride=1, padding=1\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4)\n time_factor = 1.0 + 1.0 * (x.shape[2] > 1)\n if isinstance(time_factor, torch.Tensor):\n time_factor = time_factor.item()\n x = x.repeat_interleave(int(time_factor), dim=2)\n # TODO(freda): Check if this causes temporal inconsistency.\n # Shoule reverse the order of the following two ops,\n # better perf and better temporal smoothness.\n x = self.conv(x)\n return x[..., int(time_factor - 1) :, :, :]\n\n\nclass CausalDownsample3d(nn.Module):\n def __init__(self, in_channels: int) -> None:\n super().__init__()\n self.conv = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=3,\n stride=2,\n time_stride=2,\n padding=0,\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n pad = (0, 1, 0, 1, 0, 0)\n x = F.pad(x, pad, mode=\"constant\", value=0)\n x = replication_pad(x)\n x = self.conv(x)\n return x\n\n\nclass CausalHybridUpsample3d(nn.Module):\n def __init__(\n self,\n in_channels: int,\n spatial_up: bool = True,\n temporal_up: bool = True,\n **kwargs,\n ) -> None:\n super().__init__()\n self.spatial_up = spatial_up\n self.temporal_up = temporal_up\n if not self.spatial_up and not self.temporal_up:\n return\n\n self.conv1 = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=(3, 1, 1),\n stride=1,\n time_stride=1,\n padding=0,\n )\n self.conv2 = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=(1, 3, 3),\n stride=1,\n time_stride=1,\n padding=1,\n )\n self.conv3 = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n time_stride=1,\n padding=0,\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n if not self.spatial_up and not self.temporal_up:\n return x\n\n # hybrid upsample temporally.\n if self.temporal_up:\n time_factor = 1.0 + 1.0 * (x.shape[2] > 1)\n if isinstance(time_factor, torch.Tensor):\n time_factor = time_factor.item()\n x = x.repeat_interleave(int(time_factor), dim=2)\n x = x[..., int(time_factor - 1) :, :, :]\n x = self.conv1(x) + x\n\n # hybrid upsample spatially.\n if self.spatial_up:\n x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4)\n x = self.conv2(x) + x\n\n # final 1x1x1 conv.\n x = self.conv3(x)\n return x\n\n\nclass CausalHybridDownsample3d(nn.Module):\n def __init__(\n self,\n in_channels: int,\n spatial_down: bool = True,\n temporal_down: bool = True,\n **kwargs,\n ) -> None:\n super().__init__()\n self.spatial_down = spatial_down\n self.temporal_down = temporal_down\n if not self.spatial_down and not self.temporal_down:\n return\n\n self.conv1 = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=(1, 3, 3),\n stride=2,\n time_stride=1,\n padding=0,\n )\n self.conv2 = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=(3, 1, 1),\n stride=1,\n time_stride=2,\n padding=0,\n )\n self.conv3 = CausalConv3d(\n in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n time_stride=1,\n padding=0,\n )\n\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n if not self.spatial_down and not self.temporal_down:\n return x\n\n # hybrid downsample spatially.\n if self.spatial_down:\n pad = (0, 1, 0, 1, 0, 0)\n x = F.pad(x, pad, mode=\"constant\", value=0)\n x1 = self.conv1(x)\n x2 = F.avg_pool3d(x, kernel_size=(1, 2, 2), stride=(1, 2, 2))\n x = x1 + x2\n\n # hybrid downsample temporally.\n if self.temporal_down:\n x = replication_pad(x)\n x1 = self.conv2(x)\n x2 = F.avg_pool3d(x, kernel_size=(2, 1, 1), stride=(2, 1, 1))\n x = x1 + x2\n\n # final 1x1x1 conv.\n x = self.conv3(x)\n return x\n\n\nclass CausalResnetBlock3d(nn.Module):\n def __init__(\n self,\n *,\n in_channels: int,\n out_channels: int = None,\n dropout: float,\n num_groups: int,\n ) -> None:\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n\n self.norm1 = CausalNormalize(in_channels, num_groups=num_groups)\n self.conv1 = CausalConv3d(\n in_channels, out_channels, kernel_size=3, stride=1, padding=1\n )\n self.norm2 = CausalNormalize(out_channels, num_groups=num_groups)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = CausalConv3d(\n out_channels, out_channels, kernel_size=3, stride=1, padding=1\n )\n self.nin_shortcut = (\n CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)\n if in_channels != out_channels\n else nn.Identity()\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n h = x\n h = self.norm1(h)\n h = nonlinearity(h)\n h = self.conv1(h)\n\n h = self.norm2(h)\n h = nonlinearity(h)\n h = self.dropout(h)\n h = self.conv2(h)\n x = self.nin_shortcut(x)\n\n return x + h\n\n\nclass CausalResnetBlockFactorized3d(nn.Module):\n def __init__(\n self,\n *,\n in_channels: int,\n out_channels: int = None,\n dropout: float,\n num_groups: int,\n ) -> None:\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n\n self.norm1 = CausalNormalize(in_channels, num_groups=1)\n self.conv1 = nn.Sequential(\n CausalConv3d(\n in_channels,\n out_channels,\n kernel_size=(1, 3, 3),\n stride=1,\n padding=1,\n ),\n CausalConv3d(\n out_channels,\n out_channels,\n kernel_size=(3, 1, 1),\n stride=1,\n padding=0,\n ),\n )\n self.norm2 = CausalNormalize(out_channels, num_groups=num_groups)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = nn.Sequential(\n CausalConv3d(\n out_channels,\n out_channels,\n kernel_size=(1, 3, 3),\n stride=1,\n padding=1,\n ),\n CausalConv3d(\n out_channels,\n out_channels,\n kernel_size=(3, 1, 1),\n stride=1,\n padding=0,\n ),\n )\n self.nin_shortcut = (\n CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)\n if in_channels != out_channels\n else nn.Identity()\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n h = x\n h = self.norm1(h)\n h = nonlinearity(h)\n h = self.conv1(h)\n\n h = self.norm2(h)\n h = nonlinearity(h)\n h = self.dropout(h)\n h = self.conv2(h)\n x = self.nin_shortcut(x)\n\n return x + h\n\n\nclass CausalAttnBlock(nn.Module):\n def __init__(self, in_channels: int, num_groups: int) -> None:\n super().__init__()\n\n self.norm = CausalNormalize(in_channels, num_groups=num_groups)\n self.q = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n self.k = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n self.v = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n self.proj_out = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n\n self.optimized_attention = vae_attention()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\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 q, batch_size = time2batch(q)\n k, batch_size = time2batch(k)\n v, batch_size = time2batch(v)\n\n b, c, h, w = q.shape\n h_ = self.optimized_attention(q, k, v)\n\n h_ = batch2time(h_, batch_size)\n h_ = self.proj_out(h_)\n return x + h_\n\n\nclass CausalTemporalAttnBlock(nn.Module):\n def __init__(self, in_channels: int, num_groups: int) -> None:\n super().__init__()\n\n self.norm = CausalNormalize(in_channels, num_groups=num_groups)\n self.q = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n self.k = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n self.v = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n self.proj_out = CausalConv3d(\n in_channels, in_channels, kernel_size=1, stride=1, padding=0\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\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 q, batch_size, height = space2batch(q)\n k, _, _ = space2batch(k)\n v, _, _ = space2batch(v)\n\n bhw, c, t = q.shape\n q = q.permute(0, 2, 1) # (bhw, t, c)\n k = k.permute(0, 2, 1) # (bhw, t, c)\n v = v.permute(0, 2, 1) # (bhw, t, c)\n\n w_ = torch.bmm(q, k.permute(0, 2, 1)) # (bhw, t, t)\n w_ = w_ * (int(c) ** (-0.5))\n\n # Apply causal mask\n mask = torch.tril(torch.ones_like(w_))\n w_ = w_.masked_fill(mask == 0, float(\"-inf\"))\n w_ = F.softmax(w_, dim=2)\n\n # attend to values\n h_ = torch.bmm(w_, v) # (bhw, t, c)\n h_ = h_.permute(0, 2, 1).reshape(bhw, c, t) # (bhw, c, t)\n\n h_ = batch2space(h_, batch_size, height)\n h_ = self.proj_out(h_)\n return x + h_\n\n\nclass EncoderBase(nn.Module):\n def __init__(\n self,\n in_channels: int,\n channels: int,\n channels_mult: list[int],\n num_res_blocks: int,\n attn_resolutions: list[int],\n dropout: float,\n resolution: int,\n z_channels: int,\n **ignore_kwargs,\n ) -> None:\n super().__init__()\n self.num_resolutions = len(channels_mult)\n self.num_res_blocks = num_res_blocks\n\n # Patcher.\n patch_size = ignore_kwargs.get(\"patch_size\", 1)\n self.patcher = Patcher(\n patch_size, ignore_kwargs.get(\"patch_method\", \"rearrange\")\n )\n in_channels = in_channels * patch_size * patch_size\n\n # downsampling\n self.conv_in = CausalConv3d(\n in_channels, channels, kernel_size=3, stride=1, padding=1\n )\n\n # num of groups for GroupNorm, num_groups=1 for LayerNorm.\n num_groups = ignore_kwargs.get(\"num_groups\", _LEGACY_NUM_GROUPS)\n curr_res = resolution // patch_size\n in_ch_mult = (1,) + tuple(channels_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 = channels * in_ch_mult[i_level]\n block_out = channels * channels_mult[i_level]\n for _ in range(self.num_res_blocks):\n block.append(\n CausalResnetBlock3d(\n in_channels=block_in,\n out_channels=block_out,\n dropout=dropout,\n num_groups=num_groups,\n )\n )\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(CausalAttnBlock(block_in, num_groups=num_groups))\n down = nn.Module()\n down.block = block\n down.attn = attn\n if i_level != self.num_resolutions - 1:\n down.downsample = CausalDownsample3d(block_in)\n curr_res = curr_res // 2\n self.down.append(down)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = CausalResnetBlock3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=num_groups,\n )\n self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups)\n self.mid.block_2 = CausalResnetBlock3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=num_groups,\n )\n\n # end\n self.norm_out = CausalNormalize(block_in, num_groups=num_groups)\n self.conv_out = CausalConv3d(\n block_in, z_channels, kernel_size=3, stride=1, padding=1\n )\n\n def patcher3d(self, x: torch.Tensor) -> torch.Tensor:\n x, batch_size = time2batch(x)\n x = self.patcher(x)\n x = batch2time(x, batch_size)\n return x\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.patcher3d(x)\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])\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 else:\n # temporal downsample (last level)\n time_factor = 1 + 1 * (hs[-1].shape[2] > 1)\n if isinstance(time_factor, torch.Tensor):\n time_factor = time_factor.item()\n hs[-1] = replication_pad(hs[-1])\n hs.append(\n F.avg_pool3d(\n hs[-1],\n kernel_size=[time_factor, 1, 1],\n stride=[2, 1, 1],\n )\n )\n\n # middle\n h = hs[-1]\n h = self.mid.block_1(h)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(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\nclass DecoderBase(nn.Module):\n def __init__(\n self,\n out_channels: int,\n channels: int,\n channels_mult: list[int],\n num_res_blocks: int,\n attn_resolutions: list[int],\n dropout: float,\n resolution: int,\n z_channels: int,\n **ignore_kwargs,\n ):\n super().__init__()\n self.num_resolutions = len(channels_mult)\n self.num_res_blocks = num_res_blocks\n\n # UnPatcher.\n patch_size = ignore_kwargs.get(\"patch_size\", 1)\n self.unpatcher = UnPatcher(\n patch_size, ignore_kwargs.get(\"patch_method\", \"rearrange\")\n )\n out_ch = out_channels * patch_size * patch_size\n\n block_in = channels * channels_mult[self.num_resolutions - 1]\n curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1)\n self.z_shape = (1, z_channels, curr_res, curr_res)\n logging.debug(\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 = CausalConv3d(\n z_channels, block_in, kernel_size=3, stride=1, padding=1\n )\n\n # num of groups for GroupNorm, num_groups=1 for LayerNorm.\n num_groups = ignore_kwargs.get(\"num_groups\", _LEGACY_NUM_GROUPS)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = CausalResnetBlock3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=num_groups,\n )\n self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups)\n self.mid.block_2 = CausalResnetBlock3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=num_groups,\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 = channels * channels_mult[i_level]\n for _ in range(self.num_res_blocks + 1):\n block.append(\n CausalResnetBlock3d(\n in_channels=block_in,\n out_channels=block_out,\n dropout=dropout,\n num_groups=num_groups,\n )\n )\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(CausalAttnBlock(block_in, num_groups=num_groups))\n up = nn.Module()\n up.block = block\n up.attn = attn\n if i_level != 0:\n up.upsample = CausalUpsample3d(block_in)\n curr_res = curr_res * 2\n self.up.insert(0, up) # prepend to get consistent order\n\n # end\n self.norm_out = CausalNormalize(block_in, num_groups=num_groups)\n self.conv_out = CausalConv3d(\n block_in, out_ch, kernel_size=3, stride=1, padding=1\n )\n\n def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor:\n x, batch_size = time2batch(x)\n x = self.unpatcher(x)\n x = batch2time(x, batch_size)\n\n return x\n\n def forward(self, z):\n h = self.conv_in(z)\n\n # middle block.\n h = self.mid.block_1(h)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h)\n\n # decoder blocks.\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)\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 else:\n # temporal upsample (last level)\n time_factor = 1.0 + 1.0 * (h.shape[2] > 1)\n if isinstance(time_factor, torch.Tensor):\n time_factor = time_factor.item()\n h = h.repeat_interleave(int(time_factor), dim=2)\n h = h[..., int(time_factor - 1) :, :, :]\n\n h = self.norm_out(h)\n h = nonlinearity(h)\n h = self.conv_out(h)\n h = self.unpatcher3d(h)\n return h\n\n\nclass EncoderFactorized(nn.Module):\n def __init__(\n self,\n in_channels: int,\n channels: int,\n channels_mult: list[int],\n num_res_blocks: int,\n attn_resolutions: list[int],\n dropout: float,\n resolution: int,\n z_channels: int,\n spatial_compression: int = 8,\n temporal_compression: int = 8,\n **ignore_kwargs,\n ) -> None:\n super().__init__()\n self.num_resolutions = len(channels_mult)\n self.num_res_blocks = num_res_blocks\n\n # Patcher.\n patch_size = ignore_kwargs.get(\"patch_size\", 1)\n self.patcher3d = Patcher3D(\n patch_size, ignore_kwargs.get(\"patch_method\", \"haar\")\n )\n in_channels = in_channels * patch_size * patch_size * patch_size\n\n # calculate the number of downsample operations\n self.num_spatial_downs = int(math.log2(spatial_compression)) - int(\n math.log2(patch_size)\n )\n assert (\n self.num_spatial_downs <= self.num_resolutions\n ), f\"Spatially downsample {self.num_resolutions} times at most\"\n\n self.num_temporal_downs = int(math.log2(temporal_compression)) - int(\n math.log2(patch_size)\n )\n assert (\n self.num_temporal_downs <= self.num_resolutions\n ), f\"Temporally downsample {self.num_resolutions} times at most\"\n\n # downsampling\n self.conv_in = nn.Sequential(\n CausalConv3d(\n in_channels,\n channels,\n kernel_size=(1, 3, 3),\n stride=1,\n padding=1,\n ),\n CausalConv3d(\n channels, channels, kernel_size=(3, 1, 1), stride=1, padding=0\n ),\n )\n\n curr_res = resolution // patch_size\n in_ch_mult = (1,) + tuple(channels_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 = channels * in_ch_mult[i_level]\n block_out = channels * channels_mult[i_level]\n for _ in range(self.num_res_blocks):\n block.append(\n CausalResnetBlockFactorized3d(\n in_channels=block_in,\n out_channels=block_out,\n dropout=dropout,\n num_groups=1,\n )\n )\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(\n nn.Sequential(\n CausalAttnBlock(block_in, num_groups=1),\n CausalTemporalAttnBlock(block_in, num_groups=1),\n )\n )\n down = nn.Module()\n down.block = block\n down.attn = attn\n if i_level != self.num_resolutions - 1:\n spatial_down = i_level < self.num_spatial_downs\n temporal_down = i_level < self.num_temporal_downs\n down.downsample = CausalHybridDownsample3d(\n block_in,\n spatial_down=spatial_down,\n temporal_down=temporal_down,\n )\n curr_res = curr_res // 2\n self.down.append(down)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = CausalResnetBlockFactorized3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=1,\n )\n self.mid.attn_1 = nn.Sequential(\n CausalAttnBlock(block_in, num_groups=1),\n CausalTemporalAttnBlock(block_in, num_groups=1),\n )\n self.mid.block_2 = CausalResnetBlockFactorized3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=1,\n )\n\n # end\n self.norm_out = CausalNormalize(block_in, num_groups=1)\n self.conv_out = nn.Sequential(\n CausalConv3d(\n block_in, z_channels, kernel_size=(1, 3, 3), stride=1, padding=1\n ),\n CausalConv3d(\n z_channels,\n z_channels,\n kernel_size=(3, 1, 1),\n stride=1,\n padding=0,\n ),\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.patcher3d(x)\n\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)\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)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(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\nclass DecoderFactorized(nn.Module):\n def __init__(\n self,\n out_channels: int,\n channels: int,\n channels_mult: list[int],\n num_res_blocks: int,\n attn_resolutions: list[int],\n dropout: float,\n resolution: int,\n z_channels: int,\n spatial_compression: int = 8,\n temporal_compression: int = 8,\n **ignore_kwargs,\n ):\n super().__init__()\n self.num_resolutions = len(channels_mult)\n self.num_res_blocks = num_res_blocks\n\n # UnPatcher.\n patch_size = ignore_kwargs.get(\"patch_size\", 1)\n self.unpatcher3d = UnPatcher3D(\n patch_size, ignore_kwargs.get(\"patch_method\", \"haar\")\n )\n out_ch = out_channels * patch_size * patch_size * patch_size\n\n # calculate the number of upsample operations\n self.num_spatial_ups = int(math.log2(spatial_compression)) - int(\n math.log2(patch_size)\n )\n assert (\n self.num_spatial_ups <= self.num_resolutions\n ), f\"Spatially upsample {self.num_resolutions} times at most\"\n self.num_temporal_ups = int(math.log2(temporal_compression)) - int(\n math.log2(patch_size)\n )\n assert (\n self.num_temporal_ups <= self.num_resolutions\n ), f\"Temporally upsample {self.num_resolutions} times at most\"\n\n block_in = channels * channels_mult[self.num_resolutions - 1]\n curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1)\n self.z_shape = (1, z_channels, curr_res, curr_res)\n logging.debug(\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 = nn.Sequential(\n CausalConv3d(\n z_channels, block_in, kernel_size=(1, 3, 3), stride=1, padding=1\n ),\n CausalConv3d(\n block_in, block_in, kernel_size=(3, 1, 1), stride=1, padding=0\n ),\n )\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = CausalResnetBlockFactorized3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=1,\n )\n self.mid.attn_1 = nn.Sequential(\n CausalAttnBlock(block_in, num_groups=1),\n CausalTemporalAttnBlock(block_in, num_groups=1),\n )\n self.mid.block_2 = CausalResnetBlockFactorized3d(\n in_channels=block_in,\n out_channels=block_in,\n dropout=dropout,\n num_groups=1,\n )\n\n legacy_mode = ignore_kwargs.get(\"legacy_mode\", False)\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 = channels * channels_mult[i_level]\n for _ in range(self.num_res_blocks + 1):\n block.append(\n CausalResnetBlockFactorized3d(\n in_channels=block_in,\n out_channels=block_out,\n dropout=dropout,\n num_groups=1,\n )\n )\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(\n nn.Sequential(\n CausalAttnBlock(block_in, num_groups=1),\n CausalTemporalAttnBlock(block_in, num_groups=1),\n )\n )\n up = nn.Module()\n up.block = block\n up.attn = attn\n if i_level != 0:\n # The layer index for temporal/spatial downsampling performed\n # in the encoder should correspond to the layer index in\n # reverse order where upsampling is performed in the decoder.\n # If you've a pre-trained model, you can simply finetune.\n i_level_reverse = self.num_resolutions - i_level - 1\n if legacy_mode:\n temporal_up = i_level_reverse < self.num_temporal_ups\n else:\n temporal_up = 0 < i_level_reverse < self.num_temporal_ups + 1\n spatial_up = temporal_up or (\n i_level_reverse < self.num_spatial_ups\n and self.num_spatial_ups > self.num_temporal_ups\n )\n up.upsample = CausalHybridUpsample3d(\n block_in, spatial_up=spatial_up, temporal_up=temporal_up\n )\n curr_res = curr_res * 2\n self.up.insert(0, up) # prepend to get consistent order\n\n # end\n self.norm_out = CausalNormalize(block_in, num_groups=1)\n self.conv_out = nn.Sequential(\n CausalConv3d(block_in, out_ch, kernel_size=(1, 3, 3), stride=1, padding=1),\n CausalConv3d(out_ch, out_ch, kernel_size=(3, 1, 1), stride=1, padding=0),\n )\n\n def forward(self, z):\n h = self.conv_in(z)\n\n # middle block.\n h = self.mid.block_1(h)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h)\n\n # decoder blocks.\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)\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 h = self.norm_out(h)\n h = nonlinearity(h)\n h = self.conv_out(h)\n h = self.unpatcher3d(h)\n return h\n" }, { "path": "comfy/ldm/cosmos/cosmos_tokenizer/patching.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\"\"\"The patcher and unpatcher implementation for 2D and 3D data.\n\nThe idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions.\nOne on the rows and one on the columns.\nFor example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2.\nWe can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component.\nFor H component, we can use a 1D convolution with kernel [1, -1] and stride 2.\nAlthough in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all\n as we need to support downsampling for more than 2x.\nFor example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be.\n [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64]\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom einops import rearrange\n\n_WAVELETS = {\n \"haar\": torch.tensor([0.7071067811865476, 0.7071067811865476]),\n \"rearrange\": torch.tensor([1.0, 1.0]),\n}\n_PERSISTENT = False\n\n\nclass Patcher(torch.nn.Module):\n \"\"\"A module to convert image tensors into patches using torch operations.\n\n The main difference from `class Patching` is that this module implements\n all operations using torch, rather than python or numpy, for efficiency purpose.\n\n It's bit-wise identical to the Patching module outputs, with the added\n benefit of being torch.jit scriptable.\n \"\"\"\n\n def __init__(self, patch_size=1, patch_method=\"haar\"):\n super().__init__()\n self.patch_size = patch_size\n self.patch_method = patch_method\n self.register_buffer(\n \"wavelets\", _WAVELETS[patch_method], persistent=_PERSISTENT\n )\n self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item()))\n self.register_buffer(\n \"_arange\",\n torch.arange(_WAVELETS[patch_method].shape[0]),\n persistent=_PERSISTENT,\n )\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, x):\n if self.patch_method == \"haar\":\n return self._haar(x)\n elif self.patch_method == \"rearrange\":\n return self._arrange(x)\n else:\n raise ValueError(\"Unknown patch method: \" + self.patch_method)\n\n def _dwt(self, x, mode=\"reflect\", rescale=False):\n dtype = x.dtype\n h = self.wavelets.to(device=x.device)\n\n n = h.shape[0]\n g = x.shape[1]\n hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)\n hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)\n hh = hh.to(dtype=dtype)\n hl = hl.to(dtype=dtype)\n\n x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype)\n xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2))\n xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2))\n xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1))\n xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1))\n xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1))\n xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1))\n\n out = torch.cat([xll, xlh, xhl, xhh], dim=1)\n if rescale:\n out = out / 2\n return out\n\n def _haar(self, x):\n for _ in self.range:\n x = self._dwt(x, rescale=True)\n return x\n\n def _arrange(self, x):\n x = rearrange(\n x,\n \"b c (h p1) (w p2) -> b (c p1 p2) h w\",\n p1=self.patch_size,\n p2=self.patch_size,\n ).contiguous()\n return x\n\n\nclass Patcher3D(Patcher):\n \"\"\"A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos.\"\"\"\n\n def __init__(self, patch_size=1, patch_method=\"haar\"):\n super().__init__(patch_method=patch_method, patch_size=patch_size)\n self.register_buffer(\n \"patch_size_buffer\",\n patch_size * torch.ones([1], dtype=torch.int32),\n persistent=_PERSISTENT,\n )\n\n def _dwt(self, x, wavelet, mode=\"reflect\", rescale=False):\n dtype = x.dtype\n h = self.wavelets.to(device=x.device)\n\n n = h.shape[0]\n g = x.shape[1]\n hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)\n hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)\n hh = hh.to(dtype=dtype)\n hl = hl.to(dtype=dtype)\n\n # Handles temporal axis.\n x = F.pad(\n x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode\n ).to(dtype)\n xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))\n xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))\n\n # Handles spatial axes.\n xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))\n xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))\n xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))\n xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))\n\n xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))\n\n out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1)\n if rescale:\n out = out / (2 * torch.sqrt(torch.tensor(2.0)))\n return out\n\n def _haar(self, x):\n xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)\n x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2)\n for _ in self.range:\n x = self._dwt(x, \"haar\", rescale=True)\n return x\n\n def _arrange(self, x):\n xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)\n x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2)\n x = rearrange(\n x,\n \"b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w\",\n p1=self.patch_size,\n p2=self.patch_size,\n p3=self.patch_size,\n ).contiguous()\n return x\n\n\nclass UnPatcher(torch.nn.Module):\n \"\"\"A module to convert patches into image tensorsusing torch operations.\n\n The main difference from `class Unpatching` is that this module implements\n all operations using torch, rather than python or numpy, for efficiency purpose.\n\n It's bit-wise identical to the Unpatching module outputs, with the added\n benefit of being torch.jit scriptable.\n \"\"\"\n\n def __init__(self, patch_size=1, patch_method=\"haar\"):\n super().__init__()\n self.patch_size = patch_size\n self.patch_method = patch_method\n self.register_buffer(\n \"wavelets\", _WAVELETS[patch_method], persistent=_PERSISTENT\n )\n self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item()))\n self.register_buffer(\n \"_arange\",\n torch.arange(_WAVELETS[patch_method].shape[0]),\n persistent=_PERSISTENT,\n )\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, x):\n if self.patch_method == \"haar\":\n return self._ihaar(x)\n elif self.patch_method == \"rearrange\":\n return self._iarrange(x)\n else:\n raise ValueError(\"Unknown patch method: \" + self.patch_method)\n\n def _idwt(self, x, wavelet=\"haar\", mode=\"reflect\", rescale=False):\n dtype = x.dtype\n h = self.wavelets.to(device=x.device)\n n = h.shape[0]\n\n g = x.shape[1] // 4\n hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])\n hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)\n hh = hh.to(dtype=dtype)\n hl = hl.to(dtype=dtype)\n\n xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1)\n\n # Inverse transform.\n yl = torch.nn.functional.conv_transpose2d(\n xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)\n )\n yl += torch.nn.functional.conv_transpose2d(\n xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)\n )\n yh = torch.nn.functional.conv_transpose2d(\n xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)\n )\n yh += torch.nn.functional.conv_transpose2d(\n xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)\n )\n y = torch.nn.functional.conv_transpose2d(\n yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)\n )\n y += torch.nn.functional.conv_transpose2d(\n yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)\n )\n\n if rescale:\n y = y * 2\n return y\n\n def _ihaar(self, x):\n for _ in self.range:\n x = self._idwt(x, \"haar\", rescale=True)\n return x\n\n def _iarrange(self, x):\n x = rearrange(\n x,\n \"b (c p1 p2) h w -> b c (h p1) (w p2)\",\n p1=self.patch_size,\n p2=self.patch_size,\n )\n return x\n\n\nclass UnPatcher3D(UnPatcher):\n \"\"\"A 3D inverse discrete wavelet transform for video wavelet decompositions.\"\"\"\n\n def __init__(self, patch_size=1, patch_method=\"haar\"):\n super().__init__(patch_method=patch_method, patch_size=patch_size)\n\n def _idwt(self, x, wavelet=\"haar\", mode=\"reflect\", rescale=False):\n dtype = x.dtype\n h = self.wavelets.to(device=x.device)\n\n g = x.shape[1] // 8 # split into 8 spatio-temporal filtered tesnors.\n hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])\n hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)\n hl = hl.to(dtype=dtype)\n hh = hh.to(dtype=dtype)\n\n xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1)\n del x\n\n # Height height transposed convolutions.\n xll = F.conv_transpose3d(\n xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xlll\n\n xll += F.conv_transpose3d(\n xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xllh\n\n xlh = F.conv_transpose3d(\n xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xlhl\n\n xlh += F.conv_transpose3d(\n xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xlhh\n\n xhl = F.conv_transpose3d(\n xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xhll\n\n xhl += F.conv_transpose3d(\n xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xhlh\n\n xhh = F.conv_transpose3d(\n xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xhhl\n\n xhh += F.conv_transpose3d(\n xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)\n )\n del xhhh\n\n # Handles width transposed convolutions.\n xl = F.conv_transpose3d(\n xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)\n )\n del xll\n\n xl += F.conv_transpose3d(\n xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)\n )\n del xlh\n\n xh = F.conv_transpose3d(\n xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)\n )\n del xhl\n\n xh += F.conv_transpose3d(\n xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)\n )\n del xhh\n\n # Handles time axis transposed convolutions.\n x = F.conv_transpose3d(\n xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)\n )\n del xl\n\n x += F.conv_transpose3d(\n xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)\n )\n\n if rescale:\n x = x * (2 * torch.sqrt(torch.tensor(2.0)))\n return x\n\n def _ihaar(self, x):\n for _ in self.range:\n x = self._idwt(x, \"haar\", rescale=True)\n x = x[:, :, self.patch_size - 1 :, ...]\n return x\n\n def _iarrange(self, x):\n x = rearrange(\n x,\n \"b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)\",\n p1=self.patch_size,\n p2=self.patch_size,\n p3=self.patch_size,\n )\n x = x[:, :, self.patch_size - 1 :, ...]\n return x\n" }, { "path": "comfy/ldm/cosmos/cosmos_tokenizer/utils.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\"\"\"Shared utilities for the networks module.\"\"\"\n\nfrom typing import Any\n\nimport torch\nfrom einops import rearrange\n\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\ndef time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:\n batch_size = x.shape[0]\n return rearrange(x, \"b c t h w -> (b t) c h w\"), batch_size\n\n\ndef batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor:\n return rearrange(x, \"(b t) c h w -> b c t h w\", b=batch_size)\n\n\ndef space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:\n batch_size, height = x.shape[0], x.shape[-2]\n return rearrange(x, \"b c t h w -> (b h w) c t\"), batch_size, height\n\n\ndef batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor:\n return rearrange(x, \"(b h w) c t -> b c t h w\", b=batch_size, h=height)\n\n\ndef cast_tuple(t: Any, length: int = 1) -> Any:\n return t if isinstance(t, tuple) else ((t,) * length)\n\n\ndef replication_pad(x):\n return torch.cat([x[:, :, :1, ...], x], dim=2)\n\n\ndef divisible_by(num: int, den: int) -> bool:\n return (num % den) == 0\n\n\ndef is_odd(n: int) -> bool:\n return not divisible_by(n, 2)\n\n\ndef nonlinearity(x):\n # x * sigmoid(x)\n return torch.nn.functional.silu(x)\n\n\ndef Normalize(in_channels, num_groups=32):\n return ops.GroupNorm(\n num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True\n )\n\n\nclass CausalNormalize(torch.nn.Module):\n def __init__(self, in_channels, num_groups=1):\n super().__init__()\n self.norm = ops.GroupNorm(\n num_groups=num_groups,\n num_channels=in_channels,\n eps=1e-6,\n affine=True,\n )\n self.num_groups = num_groups\n\n def forward(self, x):\n # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose.\n # All new models should use num_groups=1, otherwise causality is not guaranteed.\n if self.num_groups == 1:\n x, batch_size = time2batch(x)\n return batch2time(self.norm(x), batch_size)\n return self.norm(x)\n\n\ndef exists(v):\n return v is not None\n\n\ndef default(*args):\n for arg in args:\n if exists(arg):\n return arg\n return None\n\n\ndef round_ste(z: torch.Tensor) -> torch.Tensor:\n \"\"\"Round with straight through gradients.\"\"\"\n zhat = z.round()\n return z + (zhat - z).detach()\n\n\ndef log(t, eps=1e-5):\n return t.clamp(min=eps).log()\n\n\ndef entropy(prob):\n return (-prob * log(prob)).sum(dim=-1)\n" }, { "path": "comfy/ldm/cosmos/model.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\n\"\"\"\nA general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.\n\"\"\"\n\nfrom typing import Optional, Tuple\n\nimport torch\nfrom einops import rearrange\nfrom torch import nn\nfrom torchvision import transforms\n\nfrom enum import Enum\nimport logging\n\nimport comfy.patcher_extension\n\nfrom .blocks import (\n FinalLayer,\n GeneralDITTransformerBlock,\n PatchEmbed,\n TimestepEmbedding,\n Timesteps,\n)\n\nfrom .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb\n\n\nclass DataType(Enum):\n IMAGE = \"image\"\n VIDEO = \"video\"\n\n\nclass GeneralDIT(nn.Module):\n \"\"\"\n A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.\n\n Args:\n max_img_h (int): Maximum height of the input images.\n max_img_w (int): Maximum width of the input images.\n max_frames (int): Maximum number of frames in the video sequence.\n in_channels (int): Number of input channels (e.g., RGB channels for color images).\n out_channels (int): Number of output channels.\n patch_spatial (tuple): Spatial resolution of patches for input processing.\n patch_temporal (int): Temporal resolution of patches for input processing.\n concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.\n block_config (str): Configuration of the transformer block. See Notes for supported block types.\n model_channels (int): Base number of channels used throughout the model.\n num_blocks (int): Number of transformer blocks.\n num_heads (int): Number of heads in the multi-head attention layers.\n mlp_ratio (float): Expansion ratio for MLP blocks.\n block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD').\n crossattn_emb_channels (int): Number of embedding channels for cross-attention.\n use_cross_attn_mask (bool): Whether to use mask in cross-attention.\n pos_emb_cls (str): Type of positional embeddings.\n pos_emb_learnable (bool): Whether positional embeddings are learnable.\n pos_emb_interpolation (str): Method for interpolating positional embeddings.\n affline_emb_norm (bool): Whether to normalize affine embeddings.\n use_adaln_lora (bool): Whether to use AdaLN-LoRA.\n adaln_lora_dim (int): Dimension for AdaLN-LoRA.\n rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.\n rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.\n rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.\n extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.\n extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings.\n extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.\n extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.\n extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.\n\n Notes:\n Supported block types in block_config:\n * cross_attn, ca: Cross attention\n * full_attn: Full attention on all flattened tokens\n * mlp, ff: Feed forward block\n \"\"\"\n\n def __init__(\n self,\n max_img_h: int,\n max_img_w: int,\n max_frames: int,\n in_channels: int,\n out_channels: int,\n patch_spatial: tuple,\n patch_temporal: int,\n concat_padding_mask: bool = True,\n # attention settings\n block_config: str = \"FA-CA-MLP\",\n model_channels: int = 768,\n num_blocks: int = 10,\n num_heads: int = 16,\n mlp_ratio: float = 4.0,\n block_x_format: str = \"BTHWD\",\n # cross attention settings\n crossattn_emb_channels: int = 1024,\n use_cross_attn_mask: bool = False,\n # positional embedding settings\n pos_emb_cls: str = \"sincos\",\n pos_emb_learnable: bool = False,\n pos_emb_interpolation: str = \"crop\",\n affline_emb_norm: bool = False, # whether or not to normalize the affine embedding\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n rope_h_extrapolation_ratio: float = 1.0,\n rope_w_extrapolation_ratio: float = 1.0,\n rope_t_extrapolation_ratio: float = 1.0,\n extra_per_block_abs_pos_emb: bool = False,\n extra_per_block_abs_pos_emb_type: str = \"sincos\",\n extra_h_extrapolation_ratio: float = 1.0,\n extra_w_extrapolation_ratio: float = 1.0,\n extra_t_extrapolation_ratio: float = 1.0,\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n ) -> None:\n super().__init__()\n self.max_img_h = max_img_h\n self.max_img_w = max_img_w\n self.max_frames = max_frames\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.patch_spatial = patch_spatial\n self.patch_temporal = patch_temporal\n self.num_heads = num_heads\n self.num_blocks = num_blocks\n self.model_channels = model_channels\n self.use_cross_attn_mask = use_cross_attn_mask\n self.concat_padding_mask = concat_padding_mask\n # positional embedding settings\n self.pos_emb_cls = pos_emb_cls\n self.pos_emb_learnable = pos_emb_learnable\n self.pos_emb_interpolation = pos_emb_interpolation\n self.affline_emb_norm = affline_emb_norm\n self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio\n self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio\n self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio\n self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb\n self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower()\n self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio\n self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio\n self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio\n self.dtype = dtype\n weight_args = {\"device\": device, \"dtype\": dtype}\n\n in_channels = in_channels + 1 if concat_padding_mask else in_channels\n self.x_embedder = PatchEmbed(\n spatial_patch_size=patch_spatial,\n temporal_patch_size=patch_temporal,\n in_channels=in_channels,\n out_channels=model_channels,\n bias=False,\n weight_args=weight_args,\n operations=operations,\n )\n\n self.build_pos_embed(device=device, dtype=dtype)\n self.block_x_format = block_x_format\n self.use_adaln_lora = use_adaln_lora\n self.adaln_lora_dim = adaln_lora_dim\n self.t_embedder = nn.ModuleList(\n [Timesteps(model_channels),\n TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),]\n )\n\n self.blocks = nn.ModuleDict()\n\n for idx in range(num_blocks):\n self.blocks[f\"block{idx}\"] = GeneralDITTransformerBlock(\n x_dim=model_channels,\n context_dim=crossattn_emb_channels,\n num_heads=num_heads,\n block_config=block_config,\n mlp_ratio=mlp_ratio,\n x_format=self.block_x_format,\n use_adaln_lora=use_adaln_lora,\n adaln_lora_dim=adaln_lora_dim,\n weight_args=weight_args,\n operations=operations,\n )\n\n if self.affline_emb_norm:\n logging.debug(\"Building affine embedding normalization layer\")\n self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype)\n else:\n self.affline_norm = nn.Identity()\n\n self.final_layer = FinalLayer(\n hidden_size=self.model_channels,\n spatial_patch_size=self.patch_spatial,\n temporal_patch_size=self.patch_temporal,\n out_channels=self.out_channels,\n use_adaln_lora=self.use_adaln_lora,\n adaln_lora_dim=self.adaln_lora_dim,\n weight_args=weight_args,\n operations=operations,\n )\n\n def build_pos_embed(self, device=None, dtype=None):\n if self.pos_emb_cls == \"rope3d\":\n cls_type = VideoRopePosition3DEmb\n else:\n raise ValueError(f\"Unknown pos_emb_cls {self.pos_emb_cls}\")\n\n logging.debug(f\"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}\")\n kwargs = dict(\n model_channels=self.model_channels,\n len_h=self.max_img_h // self.patch_spatial,\n len_w=self.max_img_w // self.patch_spatial,\n len_t=self.max_frames // self.patch_temporal,\n is_learnable=self.pos_emb_learnable,\n interpolation=self.pos_emb_interpolation,\n head_dim=self.model_channels // self.num_heads,\n h_extrapolation_ratio=self.rope_h_extrapolation_ratio,\n w_extrapolation_ratio=self.rope_w_extrapolation_ratio,\n t_extrapolation_ratio=self.rope_t_extrapolation_ratio,\n device=device,\n )\n self.pos_embedder = cls_type(\n **kwargs,\n )\n\n if self.extra_per_block_abs_pos_emb:\n assert self.extra_per_block_abs_pos_emb_type in [\n \"learnable\",\n ], f\"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}\"\n kwargs[\"h_extrapolation_ratio\"] = self.extra_h_extrapolation_ratio\n kwargs[\"w_extrapolation_ratio\"] = self.extra_w_extrapolation_ratio\n kwargs[\"t_extrapolation_ratio\"] = self.extra_t_extrapolation_ratio\n kwargs[\"device\"] = device\n kwargs[\"dtype\"] = dtype\n self.extra_pos_embedder = LearnablePosEmbAxis(\n **kwargs,\n )\n\n def prepare_embedded_sequence(\n self,\n x_B_C_T_H_W: torch.Tensor,\n fps: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n latent_condition: Optional[torch.Tensor] = None,\n latent_condition_sigma: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"\n Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.\n\n Args:\n x_B_C_T_H_W (torch.Tensor): video\n fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.\n If None, a default value (`self.base_fps`) will be used.\n padding_mask (Optional[torch.Tensor]): current it is not used\n\n Returns:\n Tuple[torch.Tensor, Optional[torch.Tensor]]:\n - A tensor of shape (B, T, H, W, D) with the embedded sequence.\n - An optional positional embedding tensor, returned only if the positional embedding class\n (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.\n\n Notes:\n - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.\n - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.\n - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using\n the `self.pos_embedder` with the shape [T, H, W].\n - If \"fps_aware\" is in `self.pos_emb_cls`, the positional embeddings are generated using the\n `self.pos_embedder` with the fps tensor.\n - Otherwise, the positional embeddings are generated without considering fps.\n \"\"\"\n if self.concat_padding_mask:\n if padding_mask is not None:\n padding_mask = transforms.functional.resize(\n padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST\n )\n else:\n padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)\n\n x_B_C_T_H_W = torch.cat(\n [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1\n )\n x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)\n\n if self.extra_per_block_abs_pos_emb:\n extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)\n else:\n extra_pos_emb = None\n\n if \"rope\" in self.pos_emb_cls.lower():\n return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb\n\n if \"fps_aware\" in self.pos_emb_cls:\n x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device) # [B, T, H, W, D]\n else:\n x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D]\n\n return x_B_T_H_W_D, None, extra_pos_emb\n\n def decoder_head(\n self,\n x_B_T_H_W_D: torch.Tensor,\n emb_B_D: torch.Tensor,\n crossattn_emb: torch.Tensor,\n origin_shape: Tuple[int, int, int, int, int], # [B, C, T, H, W]\n crossattn_mask: Optional[torch.Tensor] = None,\n adaln_lora_B_3D: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n del crossattn_emb, crossattn_mask\n B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape\n x_BT_HW_D = rearrange(x_B_T_H_W_D, \"B T H W D -> (B T) (H W) D\")\n x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D)\n # This is to ensure x_BT_HW_D has the correct shape because\n # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D).\n x_BT_HW_D = x_BT_HW_D.view(\n B * T_before_patchify // self.patch_temporal,\n H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial,\n -1,\n )\n x_B_D_T_H_W = rearrange(\n x_BT_HW_D,\n \"(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)\",\n p1=self.patch_spatial,\n p2=self.patch_spatial,\n H=H_before_patchify // self.patch_spatial,\n W=W_before_patchify // self.patch_spatial,\n t=self.patch_temporal,\n B=B,\n )\n return x_B_D_T_H_W\n\n def forward_before_blocks(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n crossattn_emb: torch.Tensor,\n crossattn_mask: Optional[torch.Tensor] = None,\n fps: Optional[torch.Tensor] = None,\n image_size: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n scalar_feature: Optional[torch.Tensor] = None,\n data_type: Optional[DataType] = DataType.VIDEO,\n latent_condition: Optional[torch.Tensor] = None,\n latent_condition_sigma: Optional[torch.Tensor] = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n Args:\n x: (B, C, T, H, W) tensor of spatial-temp inputs\n timesteps: (B, ) tensor of timesteps\n crossattn_emb: (B, N, D) tensor of cross-attention embeddings\n crossattn_mask: (B, N) tensor of cross-attention masks\n \"\"\"\n del kwargs\n assert isinstance(\n data_type, DataType\n ), f\"Expected DataType, got {type(data_type)}. We need discuss this flag later.\"\n original_shape = x.shape\n x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(\n x,\n fps=fps,\n padding_mask=padding_mask,\n latent_condition=latent_condition,\n latent_condition_sigma=latent_condition_sigma,\n )\n # logging affline scale information\n affline_scale_log_info = {}\n\n timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype))\n affline_emb_B_D = timesteps_B_D\n affline_scale_log_info[\"timesteps_B_D\"] = timesteps_B_D.detach()\n\n if scalar_feature is not None:\n raise NotImplementedError(\"Scalar feature is not implemented yet.\")\n\n affline_scale_log_info[\"affline_emb_B_D\"] = affline_emb_B_D.detach()\n affline_emb_B_D = self.affline_norm(affline_emb_B_D)\n\n if self.use_cross_attn_mask:\n if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask):\n crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max\n crossattn_mask = crossattn_mask[:, None, None, :] # .to(dtype=torch.bool) # [B, 1, 1, length]\n else:\n crossattn_mask = None\n\n if self.blocks[\"block0\"].x_format == \"THWBD\":\n x = rearrange(x_B_T_H_W_D, \"B T H W D -> T H W B D\")\n if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:\n extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange(\n extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, \"B T H W D -> T H W B D\"\n )\n crossattn_emb = rearrange(crossattn_emb, \"B M D -> M B D\")\n\n if crossattn_mask:\n crossattn_mask = rearrange(crossattn_mask, \"B M -> M B\")\n\n elif self.blocks[\"block0\"].x_format == \"BTHWD\":\n x = x_B_T_H_W_D\n else:\n raise ValueError(f\"Unknown x_format {self.blocks[0].x_format}\")\n output = {\n \"x\": x,\n \"affline_emb_B_D\": affline_emb_B_D,\n \"crossattn_emb\": crossattn_emb,\n \"crossattn_mask\": crossattn_mask,\n \"rope_emb_L_1_1_D\": rope_emb_L_1_1_D,\n \"adaln_lora_B_3D\": adaln_lora_B_3D,\n \"original_shape\": original_shape,\n \"extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D\": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,\n }\n return output\n\n def forward(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n context: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n # crossattn_emb: torch.Tensor,\n # crossattn_mask: Optional[torch.Tensor] = None,\n fps: Optional[torch.Tensor] = None,\n image_size: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n scalar_feature: Optional[torch.Tensor] = None,\n data_type: Optional[DataType] = DataType.VIDEO,\n latent_condition: Optional[torch.Tensor] = None,\n latent_condition_sigma: Optional[torch.Tensor] = None,\n condition_video_augment_sigma: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get(\"transformer_options\", {}))\n ).execute(x,\n timesteps,\n context,\n attention_mask,\n fps,\n image_size,\n padding_mask,\n scalar_feature,\n data_type,\n latent_condition,\n latent_condition_sigma,\n condition_video_augment_sigma,\n **kwargs)\n\n def _forward(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n context: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n # crossattn_emb: torch.Tensor,\n # crossattn_mask: Optional[torch.Tensor] = None,\n fps: Optional[torch.Tensor] = None,\n image_size: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n scalar_feature: Optional[torch.Tensor] = None,\n data_type: Optional[DataType] = DataType.VIDEO,\n latent_condition: Optional[torch.Tensor] = None,\n latent_condition_sigma: Optional[torch.Tensor] = None,\n condition_video_augment_sigma: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n \"\"\"\n Args:\n x: (B, C, T, H, W) tensor of spatial-temp inputs\n timesteps: (B, ) tensor of timesteps\n crossattn_emb: (B, N, D) tensor of cross-attention embeddings\n crossattn_mask: (B, N) tensor of cross-attention masks\n condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to\n augment condition input, the lvg model will condition on the condition_video_augment_sigma value;\n we need forward_before_blocks pass to the forward_before_blocks function.\n \"\"\"\n\n crossattn_emb = context\n crossattn_mask = attention_mask\n\n inputs = self.forward_before_blocks(\n x=x,\n timesteps=timesteps,\n crossattn_emb=crossattn_emb,\n crossattn_mask=crossattn_mask,\n fps=fps,\n image_size=image_size,\n padding_mask=padding_mask,\n scalar_feature=scalar_feature,\n data_type=data_type,\n latent_condition=latent_condition,\n latent_condition_sigma=latent_condition_sigma,\n condition_video_augment_sigma=condition_video_augment_sigma,\n **kwargs,\n )\n x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = (\n inputs[\"x\"],\n inputs[\"affline_emb_B_D\"],\n inputs[\"crossattn_emb\"],\n inputs[\"crossattn_mask\"],\n inputs[\"rope_emb_L_1_1_D\"],\n inputs[\"adaln_lora_B_3D\"],\n inputs[\"original_shape\"],\n )\n extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs[\"extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D\"].to(x.dtype)\n del inputs\n\n if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:\n assert (\n x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape\n ), f\"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}\"\n\n transformer_options = kwargs.get(\"transformer_options\", {})\n for _, block in self.blocks.items():\n assert (\n self.blocks[\"block0\"].x_format == block.x_format\n ), f\"First block has x_format {self.blocks[0].x_format}, got {block.x_format}\"\n\n if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:\n x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D\n x = block(\n x,\n affline_emb_B_D,\n crossattn_emb,\n crossattn_mask,\n rope_emb_L_1_1_D=rope_emb_L_1_1_D,\n adaln_lora_B_3D=adaln_lora_B_3D,\n transformer_options=transformer_options,\n )\n\n x_B_T_H_W_D = rearrange(x, \"T H W B D -> B T H W D\")\n\n x_B_D_T_H_W = self.decoder_head(\n x_B_T_H_W_D=x_B_T_H_W_D,\n emb_B_D=affline_emb_B_D,\n crossattn_emb=None,\n origin_shape=original_shape,\n crossattn_mask=None,\n adaln_lora_B_3D=adaln_lora_B_3D,\n )\n\n return x_B_D_T_H_W\n" }, { "path": "comfy/ldm/cosmos/position_embedding.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\nfrom typing import List, Optional\n\nimport torch\nfrom einops import rearrange, repeat\nfrom torch import nn\nimport math\n\n\ndef normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0) -> torch.Tensor:\n \"\"\"\n Normalizes the input tensor along specified dimensions such that the average square norm of elements is adjusted.\n\n Args:\n x (torch.Tensor): The input tensor to normalize.\n dim (list, optional): The dimensions over which to normalize. If None, normalizes over all dimensions except the first.\n eps (float, optional): A small constant to ensure numerical stability during division.\n\n Returns:\n torch.Tensor: The normalized tensor.\n \"\"\"\n if dim is None:\n dim = list(range(1, x.ndim))\n norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)\n norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel()))\n return x / norm.to(x.dtype)\n\n\nclass VideoPositionEmb(nn.Module):\n def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:\n \"\"\"\n It delegates the embedding generation to generate_embeddings function.\n \"\"\"\n B_T_H_W_C = x_B_T_H_W_C.shape\n embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype)\n\n return embeddings\n\n def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None):\n raise NotImplementedError\n\n\nclass VideoRopePosition3DEmb(VideoPositionEmb):\n def __init__(\n self,\n *, # enforce keyword arguments\n head_dim: int,\n len_h: int,\n len_w: int,\n len_t: int,\n base_fps: int = 24,\n h_extrapolation_ratio: float = 1.0,\n w_extrapolation_ratio: float = 1.0,\n t_extrapolation_ratio: float = 1.0,\n enable_fps_modulation: bool = True,\n device=None,\n **kwargs, # used for compatibility with other positional embeddings; unused in this class\n ):\n del kwargs\n super().__init__()\n self.base_fps = base_fps\n self.max_h = len_h\n self.max_w = len_w\n self.enable_fps_modulation = enable_fps_modulation\n\n dim = head_dim\n dim_h = dim // 6 * 2\n dim_w = dim_h\n dim_t = dim - 2 * dim_h\n assert dim == dim_h + dim_w + dim_t, f\"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}\"\n self.register_buffer(\n \"dim_spatial_range\",\n torch.arange(0, dim_h, 2, device=device)[: (dim_h // 2)].float() / dim_h,\n persistent=False,\n )\n self.register_buffer(\n \"dim_temporal_range\",\n torch.arange(0, dim_t, 2, device=device)[: (dim_t // 2)].float() / dim_t,\n persistent=False,\n )\n\n self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2))\n self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2))\n self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2))\n\n def generate_embeddings(\n self,\n B_T_H_W_C: torch.Size,\n fps: Optional[torch.Tensor] = None,\n h_ntk_factor: Optional[float] = None,\n w_ntk_factor: Optional[float] = None,\n t_ntk_factor: Optional[float] = None,\n device=None,\n dtype=None,\n ):\n \"\"\"\n Generate embeddings for the given input size.\n\n Args:\n B_T_H_W_C (torch.Size): Input tensor size (Batch, Time, Height, Width, Channels).\n fps (Optional[torch.Tensor], optional): Frames per second. Defaults to None.\n h_ntk_factor (Optional[float], optional): Height NTK factor. If None, uses self.h_ntk_factor.\n w_ntk_factor (Optional[float], optional): Width NTK factor. If None, uses self.w_ntk_factor.\n t_ntk_factor (Optional[float], optional): Time NTK factor. If None, uses self.t_ntk_factor.\n\n Returns:\n Not specified in the original code snippet.\n \"\"\"\n h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor\n w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor\n t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor\n\n h_theta = 10000.0 * h_ntk_factor\n w_theta = 10000.0 * w_ntk_factor\n t_theta = 10000.0 * t_ntk_factor\n\n h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range.to(device=device))\n w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range.to(device=device))\n temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device))\n\n B, T, H, W, _ = B_T_H_W_C\n seq = torch.arange(max(H, W, T), dtype=torch.float, device=device)\n uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max())\n assert (\n uniform_fps or B == 1 or T == 1\n ), \"For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1\"\n half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs)\n half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs)\n\n # apply sequence scaling in temporal dimension\n if fps is None or self.enable_fps_modulation is False: # image case\n half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs)\n else:\n half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs)\n\n half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1)\n half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1)\n half_emb_t = torch.stack([torch.cos(half_emb_t), -torch.sin(half_emb_t), torch.sin(half_emb_t), torch.cos(half_emb_t)], dim=-1)\n\n em_T_H_W_D = torch.cat(\n [\n repeat(half_emb_t, \"t d x -> t h w d x\", h=H, w=W),\n repeat(half_emb_h, \"h d x -> t h w d x\", t=T, w=W),\n repeat(half_emb_w, \"w d x -> t h w d x\", t=T, h=H),\n ]\n , dim=-2,\n )\n\n return rearrange(em_T_H_W_D, \"t h w d (i j) -> (t h w) d i j\", i=2, j=2).float()\n\n\nclass LearnablePosEmbAxis(VideoPositionEmb):\n def __init__(\n self,\n *, # enforce keyword arguments\n interpolation: str,\n model_channels: int,\n len_h: int,\n len_w: int,\n len_t: int,\n device=None,\n dtype=None,\n **kwargs,\n ):\n \"\"\"\n Args:\n interpolation (str): we curretly only support \"crop\", ideally when we need extrapolation capacity, we should adjust frequency or other more advanced methods. they are not implemented yet.\n \"\"\"\n del kwargs # unused\n super().__init__()\n self.interpolation = interpolation\n assert self.interpolation in [\"crop\"], f\"Unknown interpolation method {self.interpolation}\"\n\n self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device, dtype=dtype))\n self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype))\n self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype))\n\n def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:\n B, T, H, W, _ = B_T_H_W_C\n if self.interpolation == \"crop\":\n emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype)\n emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype)\n emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype)\n emb = (\n repeat(emb_t_T, \"t d-> b t h w d\", b=B, h=H, w=W)\n + repeat(emb_h_H, \"h d-> b t h w d\", b=B, t=T, w=W)\n + repeat(emb_w_W, \"w d-> b t h w d\", b=B, t=T, h=H)\n )\n assert list(emb.shape)[:4] == [B, T, H, W], f\"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}\"\n else:\n raise ValueError(f\"Unknown interpolation method {self.interpolation}\")\n\n return normalize(emb, dim=-1, eps=1e-6)\n" }, { "path": "comfy/ldm/cosmos/predict2.py", "content": "# original code from: https://github.com/nvidia-cosmos/cosmos-predict2\n\nimport torch\nfrom torch import nn\nfrom einops import rearrange\nfrom einops.layers.torch import Rearrange\nimport logging\nfrom typing import Callable, Optional, Tuple\nimport math\n\nfrom .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis\nfrom torchvision import transforms\n\nimport comfy.patcher_extension\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.ldm.common_dit\n\ndef apply_rotary_pos_emb(\n t: torch.Tensor,\n freqs: torch.Tensor,\n) -> torch.Tensor:\n t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float()\n t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1]\n t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t)\n return t_out\n\n\n# ---------------------- Feed Forward Network -----------------------\nclass GPT2FeedForward(nn.Module):\n def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None:\n super().__init__()\n self.activation = nn.GELU()\n self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype)\n self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype)\n\n self._layer_id = None\n self._dim = d_model\n self._hidden_dim = d_ff\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.layer1(x)\n\n x = self.activation(x)\n x = self.layer2(x)\n return x\n\n\ndef torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:\n \"\"\"Computes multi-head attention using PyTorch's native implementation.\n\n This function provides a PyTorch backend alternative to Transformer Engine's attention operation.\n It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product\n attention, and rearranges the output back to the original format.\n\n The input tensor names use the following dimension conventions:\n\n - B: batch size\n - S: sequence length\n - H: number of attention heads\n - D: head dimension\n\n Args:\n q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim)\n k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim)\n v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim)\n\n Returns:\n Attention output tensor with shape (batch, seq_len, n_heads * head_dim)\n \"\"\"\n in_q_shape = q_B_S_H_D.shape\n in_k_shape = k_B_S_H_D.shape\n q_B_H_S_D = rearrange(q_B_S_H_D, \"b ... h k -> b h ... k\").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])\n k_B_H_S_D = rearrange(k_B_S_H_D, \"b ... h v -> b h ... v\").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])\n v_B_H_S_D = rearrange(v_B_S_H_D, \"b ... h v -> b h ... v\").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])\n return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options)\n\n\nclass Attention(nn.Module):\n \"\"\"\n A flexible attention module supporting both self-attention and cross-attention mechanisms.\n\n This module implements a multi-head attention layer that can operate in either self-attention\n or cross-attention mode. The mode is determined by whether a context dimension is provided.\n The implementation uses scaled dot-product attention and supports optional bias terms and\n dropout regularization.\n\n Args:\n query_dim (int): The dimensionality of the query vectors.\n context_dim (int, optional): The dimensionality of the context (key/value) vectors.\n If None, the module operates in self-attention mode using query_dim. Default: None\n n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8\n head_dim (int, optional): The dimension of each attention head. Default: 64\n dropout (float, optional): Dropout probability applied to the output. Default: 0.0\n qkv_format (str, optional): Format specification for QKV tensors. Default: \"bshd\"\n backend (str, optional): Backend to use for the attention operation. Default: \"transformer_engine\"\n\n Examples:\n >>> # Self-attention with 512 dimensions and 8 heads\n >>> self_attn = Attention(query_dim=512)\n >>> x = torch.randn(32, 16, 512) # (batch_size, seq_len, dim)\n >>> out = self_attn(x) # (32, 16, 512)\n\n >>> # Cross-attention\n >>> cross_attn = Attention(query_dim=512, context_dim=256)\n >>> query = torch.randn(32, 16, 512)\n >>> context = torch.randn(32, 8, 256)\n >>> out = cross_attn(query, context) # (32, 16, 512)\n \"\"\"\n\n def __init__(\n self,\n query_dim: int,\n context_dim: Optional[int] = None,\n n_heads: int = 8,\n head_dim: int = 64,\n dropout: float = 0.0,\n device=None,\n dtype=None,\n operations=None,\n ) -> None:\n super().__init__()\n logging.debug(\n f\"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using \"\n f\"{n_heads} heads with a dimension of {head_dim}.\"\n )\n self.is_selfattn = context_dim is None # self attention\n\n context_dim = query_dim if context_dim is None else context_dim\n inner_dim = head_dim * n_heads\n\n self.n_heads = n_heads\n self.head_dim = head_dim\n self.query_dim = query_dim\n self.context_dim = context_dim\n\n self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)\n self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)\n\n self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)\n self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)\n\n self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)\n self.v_norm = nn.Identity()\n\n self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype)\n self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity()\n\n self.attn_op = torch_attention_op\n\n self._query_dim = query_dim\n self._context_dim = context_dim\n self._inner_dim = inner_dim\n\n def compute_qkv(\n self,\n x: torch.Tensor,\n context: Optional[torch.Tensor] = None,\n rope_emb: Optional[torch.Tensor] = None,\n ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n q = self.q_proj(x)\n context = x if context is None else context\n k = self.k_proj(context)\n v = self.v_proj(context)\n q, k, v = map(\n lambda t: rearrange(t, \"b ... (h d) -> b ... h d\", h=self.n_heads, d=self.head_dim),\n (q, k, v),\n )\n\n def apply_norm_and_rotary_pos_emb(\n q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor]\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n q = self.q_norm(q)\n k = self.k_norm(k)\n v = self.v_norm(v)\n if self.is_selfattn and rope_emb is not None: # only apply to self-attention!\n q = apply_rotary_pos_emb(q, rope_emb)\n k = apply_rotary_pos_emb(k, rope_emb)\n return q, k, v\n\n q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb)\n\n return q, k, v\n\n def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:\n result = self.attn_op(q, k, v, transformer_options=transformer_options) # [B, S, H, D]\n return self.output_dropout(self.output_proj(result))\n\n def forward(\n self,\n x: torch.Tensor,\n context: Optional[torch.Tensor] = None,\n rope_emb: Optional[torch.Tensor] = None,\n transformer_options: Optional[dict] = {},\n ) -> torch.Tensor:\n \"\"\"\n Args:\n x (Tensor): The query tensor of shape [B, Mq, K]\n context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None\n \"\"\"\n q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)\n return self.compute_attention(q, k, v, transformer_options=transformer_options)\n\n\nclass Timesteps(nn.Module):\n def __init__(self, num_channels: int):\n super().__init__()\n self.num_channels = num_channels\n\n def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor:\n assert timesteps_B_T.ndim == 2, f\"Expected 2D input, got {timesteps_B_T.ndim}\"\n timesteps = timesteps_B_T.flatten().float()\n half_dim = self.num_channels // 2\n exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device)\n exponent = exponent / (half_dim - 0.0)\n\n emb = torch.exp(exponent)\n emb = timesteps[:, None].float() * emb[None, :]\n\n sin_emb = torch.sin(emb)\n cos_emb = torch.cos(emb)\n emb = torch.cat([cos_emb, sin_emb], dim=-1)\n\n return rearrange(emb, \"(b t) d -> b t d\", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1])\n\n\nclass TimestepEmbedding(nn.Module):\n def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None):\n super().__init__()\n logging.debug(\n f\"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility.\"\n )\n self.in_dim = in_features\n self.out_dim = out_features\n self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype)\n self.activation = nn.SiLU()\n self.use_adaln_lora = use_adaln_lora\n if use_adaln_lora:\n self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype)\n else:\n self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype)\n\n def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n emb = self.linear_1(sample)\n emb = self.activation(emb)\n emb = self.linear_2(emb)\n\n if self.use_adaln_lora:\n adaln_lora_B_T_3D = emb\n emb_B_T_D = sample\n else:\n adaln_lora_B_T_3D = None\n emb_B_T_D = emb\n\n return emb_B_T_D, adaln_lora_B_T_3D\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers,\n depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions,\n making it suitable for video and image processing tasks. It supports dividing the input into patches\n and embedding each patch into a vector of size `out_channels`.\n\n Parameters:\n - spatial_patch_size (int): The size of each spatial patch.\n - temporal_patch_size (int): The size of each temporal patch.\n - in_channels (int): Number of input channels. Default: 3.\n - out_channels (int): The dimension of the embedding vector for each patch. Default: 768.\n - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True.\n \"\"\"\n\n def __init__(\n self,\n spatial_patch_size: int,\n temporal_patch_size: int,\n in_channels: int = 3,\n out_channels: int = 768,\n device=None, dtype=None, operations=None\n ):\n super().__init__()\n self.spatial_patch_size = spatial_patch_size\n self.temporal_patch_size = temporal_patch_size\n\n self.proj = nn.Sequential(\n Rearrange(\n \"b c (t r) (h m) (w n) -> b t h w (c r m n)\",\n r=temporal_patch_size,\n m=spatial_patch_size,\n n=spatial_patch_size,\n ),\n operations.Linear(\n in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype\n ),\n )\n self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Forward pass of the PatchEmbed module.\n\n Parameters:\n - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where\n B is the batch size,\n C is the number of channels,\n T is the temporal dimension,\n H is the height, and\n W is the width of the input.\n\n Returns:\n - torch.Tensor: The embedded patches as a tensor, with shape b t h w c.\n \"\"\"\n assert x.dim() == 5\n _, _, T, H, W = x.shape\n assert (\n H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0\n ), f\"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}\"\n assert T % self.temporal_patch_size == 0\n x = self.proj(x)\n return x\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of video DiT.\n \"\"\"\n\n def __init__(\n self,\n hidden_size: int,\n spatial_patch_size: int,\n temporal_patch_size: int,\n out_channels: int,\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n device=None, dtype=None, operations=None\n ):\n super().__init__()\n self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)\n self.linear = operations.Linear(\n hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype\n )\n self.hidden_size = hidden_size\n self.n_adaln_chunks = 2\n self.use_adaln_lora = use_adaln_lora\n self.adaln_lora_dim = adaln_lora_dim\n if use_adaln_lora:\n self.adaln_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype),\n operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype),\n )\n else:\n self.adaln_modulation = nn.Sequential(\n nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype)\n )\n\n def forward(\n self,\n x_B_T_H_W_D: torch.Tensor,\n emb_B_T_D: torch.Tensor,\n adaln_lora_B_T_3D: Optional[torch.Tensor] = None,\n ):\n if self.use_adaln_lora:\n assert adaln_lora_B_T_3D is not None\n shift_B_T_D, scale_B_T_D = (\n self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size]\n ).chunk(2, dim=-1)\n else:\n shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1)\n\n shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, \"b t d -> b t 1 1 d\"), rearrange(\n scale_B_T_D, \"b t d -> b t 1 1 d\"\n )\n\n def _fn(\n _x_B_T_H_W_D: torch.Tensor,\n _norm_layer: nn.Module,\n _scale_B_T_1_1_D: torch.Tensor,\n _shift_B_T_1_1_D: torch.Tensor,\n ) -> torch.Tensor:\n return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D\n\n x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D)\n x_B_T_H_W_O = self.linear(x_B_T_H_W_D)\n return x_B_T_H_W_O\n\n\nclass Block(nn.Module):\n \"\"\"\n A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation.\n Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation.\n\n Parameters:\n x_dim (int): Dimension of input features\n context_dim (int): Dimension of context features for cross-attention\n num_heads (int): Number of attention heads\n mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0\n use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False\n adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256\n\n The block applies the following sequence:\n 1. Self-attention with AdaLN modulation\n 2. Cross-attention with AdaLN modulation\n 3. MLP with AdaLN modulation\n\n Each component uses skip connections and layer normalization.\n \"\"\"\n\n def __init__(\n self,\n x_dim: int,\n context_dim: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n device=None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n self.x_dim = x_dim\n self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)\n self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations)\n\n self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)\n self.cross_attn = Attention(\n x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations\n )\n\n self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)\n self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations)\n\n self.use_adaln_lora = use_adaln_lora\n if self.use_adaln_lora:\n self.adaln_modulation_self_attn = nn.Sequential(\n nn.SiLU(),\n operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),\n operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),\n )\n self.adaln_modulation_cross_attn = nn.Sequential(\n nn.SiLU(),\n operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),\n operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),\n )\n self.adaln_modulation_mlp = nn.Sequential(\n nn.SiLU(),\n operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),\n operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),\n )\n else:\n self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))\n self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))\n self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))\n\n def forward(\n self,\n x_B_T_H_W_D: torch.Tensor,\n emb_B_T_D: torch.Tensor,\n crossattn_emb: torch.Tensor,\n rope_emb_L_1_1_D: Optional[torch.Tensor] = None,\n adaln_lora_B_T_3D: Optional[torch.Tensor] = None,\n extra_per_block_pos_emb: Optional[torch.Tensor] = None,\n transformer_options: Optional[dict] = {},\n ) -> torch.Tensor:\n residual_dtype = x_B_T_H_W_D.dtype\n compute_dtype = emb_B_T_D.dtype\n if extra_per_block_pos_emb is not None:\n x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb\n\n if self.use_adaln_lora:\n shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = (\n self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D\n ).chunk(3, dim=-1)\n shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = (\n self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D\n ).chunk(3, dim=-1)\n shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = (\n self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D\n ).chunk(3, dim=-1)\n else:\n shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn(\n emb_B_T_D\n ).chunk(3, dim=-1)\n shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn(\n emb_B_T_D\n ).chunk(3, dim=-1)\n shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1)\n\n # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting\n shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, \"b t d -> b t 1 1 d\")\n scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, \"b t d -> b t 1 1 d\")\n gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, \"b t d -> b t 1 1 d\")\n\n shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, \"b t d -> b t 1 1 d\")\n scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, \"b t d -> b t 1 1 d\")\n gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, \"b t d -> b t 1 1 d\")\n\n shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, \"b t d -> b t 1 1 d\")\n scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, \"b t d -> b t 1 1 d\")\n gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, \"b t d -> b t 1 1 d\")\n\n B, T, H, W, D = x_B_T_H_W_D.shape\n\n def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D):\n return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D\n\n normalized_x_B_T_H_W_D = _fn(\n x_B_T_H_W_D,\n self.layer_norm_self_attn,\n scale_self_attn_B_T_1_1_D,\n shift_self_attn_B_T_1_1_D,\n )\n result_B_T_H_W_D = rearrange(\n self.self_attn(\n # normalized_x_B_T_HW_D,\n rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), \"b t h w d -> b (t h w) d\"),\n None,\n rope_emb=rope_emb_L_1_1_D,\n transformer_options=transformer_options,\n ),\n \"b (t h w) d -> b t h w d\",\n t=T,\n h=H,\n w=W,\n )\n x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)\n\n def _x_fn(\n _x_B_T_H_W_D: torch.Tensor,\n layer_norm_cross_attn: Callable,\n _scale_cross_attn_B_T_1_1_D: torch.Tensor,\n _shift_cross_attn_B_T_1_1_D: torch.Tensor,\n transformer_options: Optional[dict] = {},\n ) -> torch.Tensor:\n _normalized_x_B_T_H_W_D = _fn(\n _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D\n )\n _result_B_T_H_W_D = rearrange(\n self.cross_attn(\n rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), \"b t h w d -> b (t h w) d\"),\n crossattn_emb,\n rope_emb=rope_emb_L_1_1_D,\n transformer_options=transformer_options,\n ),\n \"b (t h w) d -> b t h w d\",\n t=T,\n h=H,\n w=W,\n )\n return _result_B_T_H_W_D\n\n result_B_T_H_W_D = _x_fn(\n x_B_T_H_W_D,\n self.layer_norm_cross_attn,\n scale_cross_attn_B_T_1_1_D,\n shift_cross_attn_B_T_1_1_D,\n transformer_options=transformer_options,\n )\n x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D\n\n normalized_x_B_T_H_W_D = _fn(\n x_B_T_H_W_D,\n self.layer_norm_mlp,\n scale_mlp_B_T_1_1_D,\n shift_mlp_B_T_1_1_D,\n )\n result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))\n x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)\n return x_B_T_H_W_D\n\n\nclass MiniTrainDIT(nn.Module):\n \"\"\"\n A clean impl of DIT that can load and reproduce the training results of the original DIT model in~(cosmos 1)\n A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.\n\n Args:\n max_img_h (int): Maximum height of the input images.\n max_img_w (int): Maximum width of the input images.\n max_frames (int): Maximum number of frames in the video sequence.\n in_channels (int): Number of input channels (e.g., RGB channels for color images).\n out_channels (int): Number of output channels.\n patch_spatial (tuple): Spatial resolution of patches for input processing.\n patch_temporal (int): Temporal resolution of patches for input processing.\n concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.\n model_channels (int): Base number of channels used throughout the model.\n num_blocks (int): Number of transformer blocks.\n num_heads (int): Number of heads in the multi-head attention layers.\n mlp_ratio (float): Expansion ratio for MLP blocks.\n crossattn_emb_channels (int): Number of embedding channels for cross-attention.\n pos_emb_cls (str): Type of positional embeddings.\n pos_emb_learnable (bool): Whether positional embeddings are learnable.\n pos_emb_interpolation (str): Method for interpolating positional embeddings.\n min_fps (int): Minimum frames per second.\n max_fps (int): Maximum frames per second.\n use_adaln_lora (bool): Whether to use AdaLN-LoRA.\n adaln_lora_dim (int): Dimension for AdaLN-LoRA.\n rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.\n rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.\n rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.\n extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.\n extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.\n extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.\n extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.\n \"\"\"\n\n def __init__(\n self,\n max_img_h: int,\n max_img_w: int,\n max_frames: int,\n in_channels: int,\n out_channels: int,\n patch_spatial: int, # tuple,\n patch_temporal: int,\n concat_padding_mask: bool = True,\n # attention settings\n model_channels: int = 768,\n num_blocks: int = 10,\n num_heads: int = 16,\n mlp_ratio: float = 4.0,\n # cross attention settings\n crossattn_emb_channels: int = 1024,\n # positional embedding settings\n pos_emb_cls: str = \"sincos\",\n pos_emb_learnable: bool = False,\n pos_emb_interpolation: str = \"crop\",\n min_fps: int = 1,\n max_fps: int = 30,\n use_adaln_lora: bool = False,\n adaln_lora_dim: int = 256,\n rope_h_extrapolation_ratio: float = 1.0,\n rope_w_extrapolation_ratio: float = 1.0,\n rope_t_extrapolation_ratio: float = 1.0,\n extra_per_block_abs_pos_emb: bool = False,\n extra_h_extrapolation_ratio: float = 1.0,\n extra_w_extrapolation_ratio: float = 1.0,\n extra_t_extrapolation_ratio: float = 1.0,\n rope_enable_fps_modulation: bool = True,\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n ) -> None:\n super().__init__()\n self.dtype = dtype\n self.max_img_h = max_img_h\n self.max_img_w = max_img_w\n self.max_frames = max_frames\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.patch_spatial = patch_spatial\n self.patch_temporal = patch_temporal\n self.num_heads = num_heads\n self.num_blocks = num_blocks\n self.model_channels = model_channels\n self.concat_padding_mask = concat_padding_mask\n # positional embedding settings\n self.pos_emb_cls = pos_emb_cls\n self.pos_emb_learnable = pos_emb_learnable\n self.pos_emb_interpolation = pos_emb_interpolation\n self.min_fps = min_fps\n self.max_fps = max_fps\n self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio\n self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio\n self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio\n self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb\n self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio\n self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio\n self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio\n self.rope_enable_fps_modulation = rope_enable_fps_modulation\n\n self.build_pos_embed(device=device, dtype=dtype)\n self.use_adaln_lora = use_adaln_lora\n self.adaln_lora_dim = adaln_lora_dim\n self.t_embedder = nn.Sequential(\n Timesteps(model_channels),\n TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,),\n )\n\n in_channels = in_channels + 1 if concat_padding_mask else in_channels\n self.x_embedder = PatchEmbed(\n spatial_patch_size=patch_spatial,\n temporal_patch_size=patch_temporal,\n in_channels=in_channels,\n out_channels=model_channels,\n device=device, dtype=dtype, operations=operations,\n )\n\n self.blocks = nn.ModuleList(\n [\n Block(\n x_dim=model_channels,\n context_dim=crossattn_emb_channels,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n use_adaln_lora=use_adaln_lora,\n adaln_lora_dim=adaln_lora_dim,\n device=device, dtype=dtype, operations=operations,\n )\n for _ in range(num_blocks)\n ]\n )\n\n self.final_layer = FinalLayer(\n hidden_size=self.model_channels,\n spatial_patch_size=self.patch_spatial,\n temporal_patch_size=self.patch_temporal,\n out_channels=self.out_channels,\n use_adaln_lora=self.use_adaln_lora,\n adaln_lora_dim=self.adaln_lora_dim,\n device=device, dtype=dtype, operations=operations,\n )\n\n self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype)\n\n def build_pos_embed(self, device=None, dtype=None) -> None:\n if self.pos_emb_cls == \"rope3d\":\n cls_type = VideoRopePosition3DEmb\n else:\n raise ValueError(f\"Unknown pos_emb_cls {self.pos_emb_cls}\")\n\n logging.debug(f\"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}\")\n kwargs = dict(\n model_channels=self.model_channels,\n len_h=self.max_img_h // self.patch_spatial,\n len_w=self.max_img_w // self.patch_spatial,\n len_t=self.max_frames // self.patch_temporal,\n max_fps=self.max_fps,\n min_fps=self.min_fps,\n is_learnable=self.pos_emb_learnable,\n interpolation=self.pos_emb_interpolation,\n head_dim=self.model_channels // self.num_heads,\n h_extrapolation_ratio=self.rope_h_extrapolation_ratio,\n w_extrapolation_ratio=self.rope_w_extrapolation_ratio,\n t_extrapolation_ratio=self.rope_t_extrapolation_ratio,\n enable_fps_modulation=self.rope_enable_fps_modulation,\n device=device,\n )\n self.pos_embedder = cls_type(\n **kwargs, # type: ignore\n )\n\n if self.extra_per_block_abs_pos_emb:\n kwargs[\"h_extrapolation_ratio\"] = self.extra_h_extrapolation_ratio\n kwargs[\"w_extrapolation_ratio\"] = self.extra_w_extrapolation_ratio\n kwargs[\"t_extrapolation_ratio\"] = self.extra_t_extrapolation_ratio\n kwargs[\"device\"] = device\n kwargs[\"dtype\"] = dtype\n self.extra_pos_embedder = LearnablePosEmbAxis(\n **kwargs, # type: ignore\n )\n\n def prepare_embedded_sequence(\n self,\n x_B_C_T_H_W: torch.Tensor,\n fps: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:\n \"\"\"\n Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.\n\n Args:\n x_B_C_T_H_W (torch.Tensor): video\n fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.\n If None, a default value (`self.base_fps`) will be used.\n padding_mask (Optional[torch.Tensor]): current it is not used\n\n Returns:\n Tuple[torch.Tensor, Optional[torch.Tensor]]:\n - A tensor of shape (B, T, H, W, D) with the embedded sequence.\n - An optional positional embedding tensor, returned only if the positional embedding class\n (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.\n\n Notes:\n - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.\n - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.\n - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using\n the `self.pos_embedder` with the shape [T, H, W].\n - If \"fps_aware\" is in `self.pos_emb_cls`, the positional embeddings are generated using the\n `self.pos_embedder` with the fps tensor.\n - Otherwise, the positional embeddings are generated without considering fps.\n \"\"\"\n if self.concat_padding_mask:\n if padding_mask is None:\n padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)\n else:\n padding_mask = transforms.functional.resize(\n padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST\n )\n x_B_C_T_H_W = torch.cat(\n [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1\n )\n x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)\n\n if self.extra_per_block_abs_pos_emb:\n extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)\n else:\n extra_pos_emb = None\n\n if \"rope\" in self.pos_emb_cls.lower():\n return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb\n x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D]\n\n return x_B_T_H_W_D, None, extra_pos_emb\n\n def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor:\n x_B_C_Tt_Hp_Wp = rearrange(\n x_B_T_H_W_M,\n \"B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)\",\n p1=self.patch_spatial,\n p2=self.patch_spatial,\n t=self.patch_temporal,\n )\n return x_B_C_Tt_Hp_Wp\n\n def forward(self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n context: torch.Tensor,\n fps: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get(\"transformer_options\", {}))\n ).execute(x, timesteps, context, fps, padding_mask, **kwargs)\n\n def _forward(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n context: torch.Tensor,\n fps: Optional[torch.Tensor] = None,\n padding_mask: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n orig_shape = list(x.shape)\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_temporal, self.patch_spatial, self.patch_spatial))\n x_B_C_T_H_W = x\n timesteps_B_T = timesteps\n crossattn_emb = context\n \"\"\"\n Args:\n x: (B, C, T, H, W) tensor of spatial-temp inputs\n timesteps: (B, ) tensor of timesteps\n crossattn_emb: (B, N, D) tensor of cross-attention embeddings\n \"\"\"\n x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(\n x_B_C_T_H_W,\n fps=fps,\n padding_mask=padding_mask,\n )\n\n if timesteps_B_T.ndim == 1:\n timesteps_B_T = timesteps_B_T.unsqueeze(1)\n t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype))\n t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D)\n\n # for logging purpose\n affline_scale_log_info = {}\n affline_scale_log_info[\"t_embedding_B_T_D\"] = t_embedding_B_T_D.detach()\n self.affline_scale_log_info = affline_scale_log_info\n self.affline_emb = t_embedding_B_T_D\n self.crossattn_emb = crossattn_emb\n\n if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:\n assert (\n x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape\n ), f\"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}\"\n\n block_kwargs = {\n \"rope_emb_L_1_1_D\": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),\n \"adaln_lora_B_T_3D\": adaln_lora_B_T_3D,\n \"extra_per_block_pos_emb\": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,\n \"transformer_options\": kwargs.get(\"transformer_options\", {}),\n }\n\n # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream\n # in fp32, but run attention and MLP modules in fp16.\n # An alternate method that clamps fp16 values \"works\" in the sense that it makes coherent images, but there is noticeable\n # quality degradation and visual artifacts.\n if x_B_T_H_W_D.dtype == torch.float16:\n x_B_T_H_W_D = x_B_T_H_W_D.float()\n\n for block in self.blocks:\n x_B_T_H_W_D = block(\n x_B_T_H_W_D,\n t_embedding_B_T_D,\n crossattn_emb,\n **block_kwargs,\n )\n\n x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)\n x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]\n return x_B_C_Tt_Hp_Wp\n" }, { "path": "comfy/ldm/cosmos/vae.py", "content": "# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\"\"\"The causal continuous video tokenizer with VAE or AE formulation for 3D data..\"\"\"\n\nimport logging\nimport torch\nfrom torch import nn\nfrom enum import Enum\nimport math\n\nfrom .cosmos_tokenizer.layers3d import (\n EncoderFactorized,\n DecoderFactorized,\n CausalConv3d,\n)\n\n\nclass IdentityDistribution(torch.nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, parameters):\n return parameters, (torch.tensor([0.0]), torch.tensor([0.0]))\n\n\nclass GaussianDistribution(torch.nn.Module):\n def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):\n super().__init__()\n self.min_logvar = min_logvar\n self.max_logvar = max_logvar\n\n def sample(self, mean, logvar):\n std = torch.exp(0.5 * logvar)\n return mean + std * torch.randn_like(mean)\n\n def forward(self, parameters):\n mean, logvar = torch.chunk(parameters, 2, dim=1)\n logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar)\n return self.sample(mean, logvar), (mean, logvar)\n\n\nclass ContinuousFormulation(Enum):\n VAE = GaussianDistribution\n AE = IdentityDistribution\n\n\nclass CausalContinuousVideoTokenizer(nn.Module):\n def __init__(\n self, z_channels: int, z_factor: int, latent_channels: int, **kwargs\n ) -> None:\n super().__init__()\n self.name = kwargs.get(\"name\", \"CausalContinuousVideoTokenizer\")\n self.latent_channels = latent_channels\n self.sigma_data = 0.5\n\n # encoder_name = kwargs.get(\"encoder\", Encoder3DType.BASE.name)\n self.encoder = EncoderFactorized(\n z_channels=z_factor * z_channels, **kwargs\n )\n if kwargs.get(\"temporal_compression\", 4) == 4:\n kwargs[\"channels_mult\"] = [2, 4]\n # decoder_name = kwargs.get(\"decoder\", Decoder3DType.BASE.name)\n self.decoder = DecoderFactorized(\n z_channels=z_channels, **kwargs\n )\n\n self.quant_conv = CausalConv3d(\n z_factor * z_channels,\n z_factor * latent_channels,\n kernel_size=1,\n padding=0,\n )\n self.post_quant_conv = CausalConv3d(\n latent_channels, z_channels, kernel_size=1, padding=0\n )\n\n # formulation_name = kwargs.get(\"formulation\", ContinuousFormulation.AE.name)\n self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value()\n\n num_parameters = sum(param.numel() for param in self.parameters())\n logging.debug(f\"model={self.name}, num_parameters={num_parameters:,}\")\n logging.debug(\n f\"z_channels={z_channels}, latent_channels={self.latent_channels}.\"\n )\n\n latent_temporal_chunk = 16\n self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32))\n self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32))\n\n\n def encode(self, x):\n h = self.encoder(x)\n moments = self.quant_conv(h)\n z, posteriors = self.distribution(moments)\n latent_ch = z.shape[1]\n latent_t = z.shape[2]\n in_dtype = z.dtype\n mean = self.latent_mean.view(latent_ch, -1)\n std = self.latent_std.view(latent_ch, -1)\n\n mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)\n std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)\n return ((z - mean) / std) * self.sigma_data\n\n def decode(self, z):\n in_dtype = z.dtype\n latent_ch = z.shape[1]\n latent_t = z.shape[2]\n mean = self.latent_mean.view(latent_ch, -1)\n std = self.latent_std.view(latent_ch, -1)\n\n mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)\n std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)\n\n z = z / self.sigma_data\n z = z * std + mean\n z = self.post_quant_conv(z)\n return self.decoder(z)\n\n" }, { "path": "comfy/ldm/flux/controlnet.py", "content": "#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py\n#modified to support different types of flux controlnets\n\nimport torch\nimport math\nfrom torch import Tensor, nn\nfrom einops import rearrange, repeat\n\nfrom .layers import (timestep_embedding)\n\nfrom .model import Flux\nimport comfy.ldm.common_dit\n\nclass MistolineCondDownsamplBlock(nn.Module):\n def __init__(self, dtype=None, device=None, operations=None):\n super().__init__()\n self.encoder = nn.Sequential(\n operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)\n )\n\n def forward(self, x):\n return self.encoder(x)\n\nclass MistolineControlnetBlock(nn.Module):\n def __init__(self, hidden_size, dtype=None, device=None, operations=None):\n super().__init__()\n self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)\n self.act = nn.SiLU()\n\n def forward(self, x):\n return self.act(self.linear(x))\n\n\nclass ControlNetFlux(Flux):\n def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):\n super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)\n\n self.main_model_double = 19\n self.main_model_single = 38\n\n self.mistoline = mistoline\n # add ControlNet blocks\n if self.mistoline:\n control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)\n else:\n control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)\n\n self.controlnet_blocks = nn.ModuleList([])\n for _ in range(self.params.depth):\n self.controlnet_blocks.append(control_block())\n\n self.controlnet_single_blocks = nn.ModuleList([])\n for _ in range(self.params.depth_single_blocks):\n self.controlnet_single_blocks.append(control_block())\n\n self.num_union_modes = num_union_modes\n self.controlnet_mode_embedder = None\n if self.num_union_modes > 0:\n self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device)\n\n self.gradient_checkpointing = False\n self.latent_input = latent_input\n if control_latent_channels is None:\n control_latent_channels = self.in_channels\n else:\n control_latent_channels *= 2 * 2 #patch size\n\n self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)\n if not self.latent_input:\n if self.mistoline:\n self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)\n else:\n self.input_hint_block = nn.Sequential(\n operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)\n )\n\n def forward_orig(\n self,\n img: Tensor,\n img_ids: Tensor,\n controlnet_cond: Tensor,\n txt: Tensor,\n txt_ids: Tensor,\n timesteps: Tensor,\n y: Tensor,\n guidance: Tensor = None,\n control_type: Tensor = None,\n ) -> Tensor:\n if img.ndim != 3 or txt.ndim != 3:\n raise ValueError(\"Input img and txt tensors must have 3 dimensions.\")\n\n if y is None:\n y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)\n else:\n y = y[:, :self.params.vec_in_dim]\n\n # running on sequences img\n img = self.img_in(img)\n\n controlnet_cond = self.pos_embed_input(controlnet_cond)\n img = img + controlnet_cond\n vec = self.time_in(timestep_embedding(timesteps, 256))\n if self.params.guidance_embed:\n vec = vec + self.guidance_in(timestep_embedding(guidance, 256))\n vec = vec + self.vector_in(y)\n txt = self.txt_in(txt)\n\n if self.controlnet_mode_embedder is not None and len(control_type) > 0:\n control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1))\n txt = torch.cat([control_cond, txt], dim=1)\n txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1)\n\n ids = torch.cat((txt_ids, img_ids), dim=1)\n pe = self.pe_embedder(ids)\n\n controlnet_double = ()\n\n for i in range(len(self.double_blocks)):\n img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)\n controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),)\n\n img = torch.cat((txt, img), 1)\n\n controlnet_single = ()\n\n for i in range(len(self.single_blocks)):\n img = self.single_blocks[i](img, vec=vec, pe=pe)\n controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),)\n\n repeat = math.ceil(self.main_model_double / len(controlnet_double))\n if self.latent_input:\n out_input = ()\n for x in controlnet_double:\n out_input += (x,) * repeat\n else:\n out_input = (controlnet_double * repeat)\n\n out = {\"input\": out_input[:self.main_model_double]}\n if len(controlnet_single) > 0:\n repeat = math.ceil(self.main_model_single / len(controlnet_single))\n out_output = ()\n if self.latent_input:\n for x in controlnet_single:\n out_output += (x,) * repeat\n else:\n out_output = (controlnet_single * repeat)\n out[\"output\"] = out_output[:self.main_model_single]\n return out\n\n def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs):\n patch_size = 2\n if self.latent_input:\n hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))\n elif self.mistoline:\n hint = hint * 2.0 - 1.0\n hint = self.input_cond_block(hint)\n else:\n hint = hint * 2.0 - 1.0\n hint = self.input_hint_block(hint)\n\n hint = rearrange(hint, \"b c (h ph) (w pw) -> b (h w) (c ph pw)\", ph=patch_size, pw=patch_size)\n\n bs, c, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))\n\n img = rearrange(x, \"b c (h ph) (w pw) -> b (h w) (c ph pw)\", ph=patch_size, pw=patch_size)\n\n h_len = ((h + (patch_size // 2)) // patch_size)\n w_len = ((w + (patch_size // 2)) // patch_size)\n img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)\n img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]\n img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]\n img_ids = repeat(img_ids, \"h w c -> b (h w) c\", b=bs)\n\n txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)\n return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get(\"control_type\", []))\n" }, { "path": "comfy/ldm/flux/layers.py", "content": "import math\nfrom dataclasses import dataclass\n\nimport torch\nfrom torch import Tensor, nn\n\nfrom .math import attention, rope\n\n# Fix import for some custom nodes, TODO: delete eventually.\nRMSNorm = None\n\nclass EmbedND(nn.Module):\n def __init__(self, dim: int, theta: int, axes_dim: list):\n super().__init__()\n self.dim = dim\n self.theta = theta\n self.axes_dim = axes_dim\n\n def forward(self, ids: Tensor) -> Tensor:\n n_axes = ids.shape[-1]\n emb = torch.cat(\n [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],\n dim=-3,\n )\n\n return emb.unsqueeze(1)\n\n\ndef timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param t: 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, D) Tensor of positional embeddings.\n \"\"\"\n t = time_factor * t\n half = dim // 2\n freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)\n\n args = t[:, 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 if torch.is_floating_point(t):\n embedding = embedding.to(t)\n return embedding\n\nclass MLPEmbedder(nn.Module):\n def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None, device=None, operations=None):\n super().__init__()\n self.in_layer = operations.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device)\n self.silu = nn.SiLU()\n self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=bias, dtype=dtype, device=device)\n\n def forward(self, x: Tensor) -> Tensor:\n return self.out_layer(self.silu(self.in_layer(x)))\n\nclass YakMLP(nn.Module):\n def __init__(self, hidden_size: int, intermediate_size: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.hidden_size = hidden_size\n self.intermediate_size = intermediate_size\n self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)\n self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)\n self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=True, dtype=dtype, device=device)\n self.act_fn = nn.SiLU()\n\n def forward(self, x: Tensor) -> Tensor:\n down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n return down_proj\n\ndef build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dtype=None, device=None, operations=None):\n if yak_mlp:\n return YakMLP(hidden_size, mlp_hidden_dim, dtype=dtype, device=device, operations=operations)\n if mlp_silu_act:\n return nn.Sequential(\n operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),\n SiLUActivation(),\n operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),\n )\n else:\n return nn.Sequential(\n operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),\n nn.GELU(approximate=\"tanh\"),\n operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),\n )\n\n\nclass QKNorm(torch.nn.Module):\n def __init__(self, dim: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.query_norm = operations.RMSNorm(dim, dtype=dtype, device=device)\n self.key_norm = operations.RMSNorm(dim, dtype=dtype, device=device)\n\n def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:\n q = self.query_norm(q)\n k = self.key_norm(k)\n return q.to(v), k.to(v)\n\n\nclass SelfAttention(nn.Module):\n def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, dtype=None, device=None, operations=None):\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n\n self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)\n self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)\n self.proj = operations.Linear(dim, dim, bias=proj_bias, dtype=dtype, device=device)\n\n\n@dataclass\nclass ModulationOut:\n shift: Tensor\n scale: Tensor\n gate: Tensor\n\n\nclass Modulation(nn.Module):\n def __init__(self, dim: int, double: bool, bias=True, dtype=None, device=None, operations=None):\n super().__init__()\n self.is_double = double\n self.multiplier = 6 if double else 3\n self.lin = operations.Linear(dim, self.multiplier * dim, bias=bias, dtype=dtype, device=device)\n\n def forward(self, vec: Tensor) -> tuple:\n if vec.ndim == 2:\n vec = vec[:, None, :]\n out = self.lin(nn.functional.silu(vec)).chunk(self.multiplier, dim=-1)\n\n return (\n ModulationOut(*out[:3]),\n ModulationOut(*out[3:]) if self.is_double else None,\n )\n\n\ndef apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):\n if modulation_dims is None:\n if m_add is not None:\n return torch.addcmul(m_add, tensor, m_mult)\n else:\n return tensor * m_mult\n else:\n for d in modulation_dims:\n tensor[:, d[0]:d[1]] *= m_mult[:, d[2]:d[2] + 1]\n if m_add is not None:\n tensor[:, d[0]:d[1]] += m_add[:, d[2]:d[2] + 1]\n return tensor\n\n\nclass SiLUActivation(nn.Module):\n def __init__(self):\n super().__init__()\n self.gate_fn = nn.SiLU()\n\n def forward(self, x: Tensor) -> Tensor:\n x1, x2 = x.chunk(2, dim=-1)\n return self.gate_fn(x1) * x2\n\n\nclass DoubleStreamBlock(nn.Module):\n def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):\n super().__init__()\n\n mlp_hidden_dim = int(hidden_size * mlp_ratio)\n self.num_heads = num_heads\n self.hidden_size = hidden_size\n self.modulation = modulation\n\n if self.modulation:\n self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)\n\n self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations)\n\n self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n\n self.img_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)\n\n if self.modulation:\n self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)\n\n self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, dtype=dtype, device=device, operations=operations)\n\n self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n\n self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)\n\n def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):\n if self.modulation:\n img_mod1, img_mod2 = self.img_mod(vec)\n txt_mod1, txt_mod2 = self.txt_mod(vec)\n else:\n (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec\n\n transformer_patches = transformer_options.get(\"patches\", {})\n extra_options = transformer_options.copy()\n\n # prepare image for attention\n img_modulated = self.img_norm1(img)\n img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img)\n img_qkv = self.img_attn.qkv(img_modulated)\n del img_modulated\n img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n del img_qkv\n img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)\n\n # prepare txt for attention\n txt_modulated = self.txt_norm1(txt)\n txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt)\n txt_qkv = self.txt_attn.qkv(txt_modulated)\n del txt_modulated\n txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n del txt_qkv\n txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)\n\n q = torch.cat((txt_q, img_q), dim=2)\n del txt_q, img_q\n k = torch.cat((txt_k, img_k), dim=2)\n del txt_k, img_k\n v = torch.cat((txt_v, img_v), dim=2)\n del txt_v, img_v\n\n extra_options[\"img_slice\"] = [txt.shape[1], q.shape[2]]\n if \"attn1_patch\" in transformer_patches:\n patch = transformer_patches[\"attn1_patch\"]\n for p in patch:\n out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)\n q, k, v, pe, attn_mask = out.get(\"q\", q), out.get(\"k\", k), out.get(\"v\", v), out.get(\"pe\", pe), out.get(\"attn_mask\", attn_mask)\n\n # run actual attention\n attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)\n del q, k, v\n\n if \"attn1_output_patch\" in transformer_patches:\n patch = transformer_patches[\"attn1_output_patch\"]\n for p in patch:\n attn = p(attn, extra_options)\n\n txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]\n\n # calculate the img bloks\n img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)\n del img_attn\n img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)\n\n # calculate the txt bloks\n txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt)\n del txt_attn\n txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt)\n\n if txt.dtype == torch.float16:\n txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)\n\n return img, txt\n\n\nclass SingleStreamBlock(nn.Module):\n \"\"\"\n A DiT block with parallel linear layers as described in\n https://arxiv.org/abs/2302.05442 and adapted modulation interface.\n \"\"\"\n\n def __init__(\n self,\n hidden_size: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n qk_scale: float = None,\n modulation=True,\n mlp_silu_act=False,\n bias=True,\n yak_mlp=False,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.hidden_dim = hidden_size\n self.num_heads = num_heads\n head_dim = hidden_size // num_heads\n self.scale = qk_scale or head_dim**-0.5\n\n self.mlp_hidden_dim = int(hidden_size * mlp_ratio)\n\n self.mlp_hidden_dim_first = self.mlp_hidden_dim\n self.yak_mlp = yak_mlp\n if mlp_silu_act:\n self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2)\n self.mlp_act = SiLUActivation()\n else:\n self.mlp_act = nn.GELU(approximate=\"tanh\")\n\n if self.yak_mlp:\n self.mlp_hidden_dim_first *= 2\n self.mlp_act = nn.SiLU()\n\n # qkv and mlp_in\n self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device)\n # proj and mlp_out\n self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, bias=bias, dtype=dtype, device=device)\n\n self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)\n\n self.hidden_size = hidden_size\n self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n\n if modulation:\n self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)\n else:\n self.modulation = None\n\n def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor:\n if self.modulation:\n mod, _ = self.modulation(vec)\n else:\n mod = vec\n\n transformer_patches = transformer_options.get(\"patches\", {})\n extra_options = transformer_options.copy()\n\n qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim_first], dim=-1)\n\n q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n del qkv\n q, k = self.norm(q, k, v)\n\n if \"attn1_patch\" in transformer_patches:\n patch = transformer_patches[\"attn1_patch\"]\n for p in patch:\n out = p(q, k, v, pe=pe, attn_mask=attn_mask, extra_options=extra_options)\n q, k, v, pe, attn_mask = out.get(\"q\", q), out.get(\"k\", k), out.get(\"v\", v), out.get(\"pe\", pe), out.get(\"attn_mask\", attn_mask)\n\n # compute attention\n attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)\n del q, k, v\n\n if \"attn1_output_patch\" in transformer_patches:\n patch = transformer_patches[\"attn1_output_patch\"]\n for p in patch:\n attn = p(attn, extra_options)\n\n # compute activation in mlp stream, cat again and run second linear layer\n if self.yak_mlp:\n mlp = self.mlp_act(mlp[..., self.mlp_hidden_dim_first // 2:]) * mlp[..., :self.mlp_hidden_dim_first // 2]\n else:\n mlp = self.mlp_act(mlp)\n output = self.linear2(torch.cat((attn, mlp), 2))\n x += apply_mod(output, mod.gate, None, modulation_dims)\n if x.dtype == torch.float16:\n x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)\n return x\n\n\nclass LastLayer(nn.Module):\n def __init__(self, hidden_size: int, patch_size: int, out_channels: int, bias=True, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=bias, dtype=dtype, device=device)\n self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=bias, dtype=dtype, device=device))\n\n def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor:\n if vec.ndim == 2:\n vec = vec[:, None, :]\n\n shift, scale = self.adaLN_modulation(vec).chunk(2, dim=-1)\n x = apply_mod(self.norm_final(x), (1 + scale), shift, modulation_dims)\n x = self.linear(x)\n return x\n" }, { "path": "comfy/ldm/flux/math.py", "content": "import torch\nfrom einops import rearrange\nfrom torch import Tensor\n\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.model_management\nimport logging\n\n\ndef attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:\n if pe is not None:\n q, k = apply_rope(q, k, pe)\n heads = q.shape[1]\n x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)\n return x\n\ndef rope(pos: Tensor, dim: int, theta: int) -> Tensor:\n assert dim % 2 == 0\n if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():\n device = torch.device(\"cpu\")\n else:\n device = pos.device\n\n scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)\n omega = 1.0 / (theta**scale)\n out = torch.einsum(\"...n,d->...nd\", pos.to(dtype=torch.float32, device=device), omega)\n out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)\n out = rearrange(out, \"b n d (i j) -> b n d i j\", i=2, j=2)\n return out.to(dtype=torch.float32, device=pos.device)\n\n\ndef _apply_rope1(x: Tensor, freqs_cis: Tensor):\n x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)\n if x_.shape[2] != 1 and freqs_cis.shape[2] != 1 and x_.shape[2] != freqs_cis.shape[2]:\n freqs_cis = freqs_cis[:, :, :x_.shape[2]]\n\n x_out = freqs_cis[..., 0] * x_[..., 0]\n x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])\n\n return x_out.reshape(*x.shape).type_as(x)\n\n\ndef _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):\n return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)\n\n\ntry:\n import comfy.quant_ops\n q_apply_rope = comfy.quant_ops.ck.apply_rope\n q_apply_rope1 = comfy.quant_ops.ck.apply_rope1\n def apply_rope(xq, xk, freqs_cis):\n if comfy.model_management.in_training:\n return _apply_rope(xq, xk, freqs_cis)\n else:\n return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)\n def apply_rope1(x, freqs_cis):\n if comfy.model_management.in_training:\n return _apply_rope1(x, freqs_cis)\n else:\n return q_apply_rope1(x, freqs_cis)\nexcept:\n logging.warning(\"No comfy kitchen, using old apply_rope functions.\")\n apply_rope = _apply_rope\n apply_rope1 = _apply_rope1\n" }, { "path": "comfy/ldm/flux/model.py", "content": "#Original code can be found on: https://github.com/black-forest-labs/flux\n\nfrom dataclasses import dataclass\n\nimport torch\nfrom torch import Tensor, nn\nfrom einops import rearrange, repeat\nimport comfy.ldm.common_dit\nimport comfy.patcher_extension\n\nfrom .layers import (\n DoubleStreamBlock,\n EmbedND,\n LastLayer,\n MLPEmbedder,\n SingleStreamBlock,\n timestep_embedding,\n Modulation,\n)\n\n@dataclass\nclass FluxParams:\n in_channels: int\n out_channels: int\n vec_in_dim: int\n context_in_dim: int\n hidden_size: int\n mlp_ratio: float\n num_heads: int\n depth: int\n depth_single_blocks: int\n axes_dim: list\n theta: int\n patch_size: int\n qkv_bias: bool\n guidance_embed: bool\n txt_ids_dims: list\n global_modulation: bool = False\n mlp_silu_act: bool = False\n ops_bias: bool = True\n default_ref_method: str = \"offset\"\n ref_index_scale: float = 1.0\n yak_mlp: bool = False\n txt_norm: bool = False\n\n\ndef invert_slices(slices, length):\n sorted_slices = sorted(slices)\n result = []\n current = 0\n\n for start, end in sorted_slices:\n if current < start:\n result.append((current, start))\n current = max(current, end)\n\n if current < length:\n result.append((current, length))\n\n return result\n\n\nclass Flux(nn.Module):\n \"\"\"\n Transformer model for flow matching on sequences.\n \"\"\"\n\n def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):\n super().__init__()\n self.dtype = dtype\n params = FluxParams(**kwargs)\n self.params = params\n self.patch_size = params.patch_size\n self.in_channels = params.in_channels * params.patch_size * params.patch_size\n self.out_channels = params.out_channels * params.patch_size * params.patch_size\n if params.hidden_size % params.num_heads != 0:\n raise ValueError(\n f\"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}\"\n )\n pe_dim = params.hidden_size // params.num_heads\n if sum(params.axes_dim) != pe_dim:\n raise ValueError(f\"Got {params.axes_dim} but expected positional dim {pe_dim}\")\n self.hidden_size = params.hidden_size\n self.num_heads = params.num_heads\n self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)\n self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)\n self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)\n if params.vec_in_dim is not None:\n self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)\n else:\n self.vector_in = None\n\n self.guidance_in = (\n MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()\n )\n self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)\n\n if params.txt_norm:\n self.txt_norm = operations.RMSNorm(params.context_in_dim, dtype=dtype, device=device)\n else:\n self.txt_norm = None\n\n self.double_blocks = nn.ModuleList(\n [\n DoubleStreamBlock(\n self.hidden_size,\n self.num_heads,\n mlp_ratio=params.mlp_ratio,\n qkv_bias=params.qkv_bias,\n modulation=params.global_modulation is False,\n mlp_silu_act=params.mlp_silu_act,\n proj_bias=params.ops_bias,\n yak_mlp=params.yak_mlp,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(params.depth)\n ]\n )\n\n self.single_blocks = nn.ModuleList(\n [\n SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations)\n for _ in range(params.depth_single_blocks)\n ]\n )\n\n if final_layer:\n self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)\n\n if params.global_modulation:\n self.double_stream_modulation_img = Modulation(\n self.hidden_size,\n double=True,\n bias=False,\n dtype=dtype, device=device, operations=operations\n )\n self.double_stream_modulation_txt = Modulation(\n self.hidden_size,\n double=True,\n bias=False,\n dtype=dtype, device=device, operations=operations\n )\n self.single_stream_modulation = Modulation(\n self.hidden_size, double=False, bias=False, dtype=dtype, device=device, operations=operations\n )\n\n def forward_orig(\n self,\n img: Tensor,\n img_ids: Tensor,\n txt: Tensor,\n txt_ids: Tensor,\n timesteps: Tensor,\n y: Tensor,\n guidance: Tensor = None,\n control = None,\n timestep_zero_index=None,\n transformer_options={},\n attn_mask: Tensor = None,\n ) -> Tensor:\n\n transformer_options = transformer_options.copy()\n patches = transformer_options.get(\"patches\", {})\n patches_replace = transformer_options.get(\"patches_replace\", {})\n if img.ndim != 3 or txt.ndim != 3:\n raise ValueError(\"Input img and txt tensors must have 3 dimensions.\")\n\n # running on sequences img\n img = self.img_in(img)\n vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))\n if self.params.guidance_embed:\n if guidance is not None:\n vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))\n\n if self.vector_in is not None:\n if y is None:\n y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)\n vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])\n\n if self.txt_norm is not None:\n txt = self.txt_norm(txt)\n txt = self.txt_in(txt)\n\n if \"post_input\" in patches:\n for p in patches[\"post_input\"]:\n out = p({\"img\": img, \"txt\": txt, \"img_ids\": img_ids, \"txt_ids\": txt_ids, \"transformer_options\": transformer_options})\n img = out[\"img\"]\n txt = out[\"txt\"]\n img_ids = out[\"img_ids\"]\n txt_ids = out[\"txt_ids\"]\n\n if img_ids is not None:\n ids = torch.cat((txt_ids, img_ids), dim=1)\n pe = self.pe_embedder(ids)\n else:\n pe = None\n\n vec_orig = vec\n txt_vec = vec\n extra_kwargs = {}\n if timestep_zero_index is not None:\n modulation_dims = []\n batch = vec.shape[0] // 2\n vec_orig = vec_orig.reshape(2, batch, vec.shape[1]).movedim(0, 1)\n invert = invert_slices(timestep_zero_index, img.shape[1])\n for s in invert:\n modulation_dims.append((s[0], s[1], 0))\n for s in timestep_zero_index:\n modulation_dims.append((s[0], s[1], 1))\n extra_kwargs[\"modulation_dims_img\"] = modulation_dims\n txt_vec = vec[:batch]\n\n if self.params.global_modulation:\n vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(txt_vec))\n\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.double_blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.double_blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"], out[\"txt\"] = block(img=args[\"img\"],\n txt=args[\"txt\"],\n vec=args[\"vec\"],\n pe=args[\"pe\"],\n attn_mask=args.get(\"attn_mask\"),\n transformer_options=args.get(\"transformer_options\"),\n **extra_kwargs)\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": img,\n \"txt\": txt,\n \"vec\": vec,\n \"pe\": pe,\n \"attn_mask\": attn_mask,\n \"transformer_options\": transformer_options},\n {\"original_block\": block_wrap})\n txt = out[\"txt\"]\n img = out[\"img\"]\n else:\n img, txt = block(img=img,\n txt=txt,\n vec=vec,\n pe=pe,\n attn_mask=attn_mask,\n transformer_options=transformer_options,\n **extra_kwargs)\n\n if control is not None: # Controlnet\n control_i = control.get(\"input\")\n if i < len(control_i):\n add = control_i[i]\n if add is not None:\n img[:, :add.shape[1]] += add\n\n if img.dtype == torch.float16:\n img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504)\n\n img = torch.cat((txt, img), 1)\n\n if self.params.global_modulation:\n vec, _ = self.single_stream_modulation(vec_orig)\n\n extra_kwargs = {}\n if timestep_zero_index is not None:\n lambda a: 0 if a == 0 else a + txt.shape[1]\n modulation_dims_combined = list(map(lambda x: (0 if x[0] == 0 else x[0] + txt.shape[1], x[1] + txt.shape[1], x[2]), modulation_dims))\n extra_kwargs[\"modulation_dims\"] = modulation_dims_combined\n\n transformer_options[\"total_blocks\"] = len(self.single_blocks)\n transformer_options[\"block_type\"] = \"single\"\n transformer_options[\"img_slice\"] = [txt.shape[1], img.shape[1]]\n for i, block in enumerate(self.single_blocks):\n transformer_options[\"block_index\"] = i\n if (\"single_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"],\n vec=args[\"vec\"],\n pe=args[\"pe\"],\n attn_mask=args.get(\"attn_mask\"),\n transformer_options=args.get(\"transformer_options\"),\n **extra_kwargs)\n return out\n\n out = blocks_replace[(\"single_block\", i)]({\"img\": img,\n \"vec\": vec,\n \"pe\": pe,\n \"attn_mask\": attn_mask,\n \"transformer_options\": transformer_options},\n {\"original_block\": block_wrap})\n img = out[\"img\"]\n else:\n img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs)\n\n if control is not None: # Controlnet\n control_o = control.get(\"output\")\n if i < len(control_o):\n add = control_o[i]\n if add is not None:\n img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add\n\n img = img[:, txt.shape[1] :, ...]\n\n extra_kwargs = {}\n if timestep_zero_index is not None:\n extra_kwargs[\"modulation_dims\"] = modulation_dims\n\n img = self.final_layer(img, vec_orig, **extra_kwargs) # (N, T, patch_size ** 2 * out_channels)\n return img\n\n def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):\n bs, c, h, w = x.shape\n patch_size = self.patch_size\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))\n\n img = rearrange(x, \"b c (h ph) (w pw) -> b (h w) (c ph pw)\", ph=patch_size, pw=patch_size)\n h_len = ((h + (patch_size // 2)) // patch_size)\n w_len = ((w + (patch_size // 2)) // patch_size)\n\n h_offset = ((h_offset + (patch_size // 2)) // patch_size)\n w_offset = ((w_offset + (patch_size // 2)) // patch_size)\n\n steps_h = h_len\n steps_w = w_len\n\n rope_options = transformer_options.get(\"rope_options\", None)\n if rope_options is not None:\n h_len = (h_len - 1.0) * rope_options.get(\"scale_y\", 1.0) + 1.0\n w_len = (w_len - 1.0) * rope_options.get(\"scale_x\", 1.0) + 1.0\n\n index += rope_options.get(\"shift_t\", 0.0)\n h_offset += rope_options.get(\"shift_y\", 0.0)\n w_offset += rope_options.get(\"shift_x\", 0.0)\n\n img_ids = torch.zeros((steps_h, steps_w, len(self.params.axes_dim)), device=x.device, dtype=torch.float32)\n img_ids[:, :, 0] = img_ids[:, :, 1] + index\n img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=torch.float32).unsqueeze(1)\n img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=torch.float32).unsqueeze(0)\n return img, repeat(img_ids, \"h w c -> b (h w) c\", b=bs)\n\n def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs)\n\n def _forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):\n bs, c, h_orig, w_orig = x.shape\n patch_size = self.patch_size\n\n h_len = ((h_orig + (patch_size // 2)) // patch_size)\n w_len = ((w_orig + (patch_size // 2)) // patch_size)\n img, img_ids = self.process_img(x, transformer_options=transformer_options)\n img_tokens = img.shape[1]\n timestep_zero_index = None\n if ref_latents is not None:\n ref_num_tokens = []\n h = 0\n w = 0\n index = 0\n ref_latents_method = kwargs.get(\"ref_latents_method\", self.params.default_ref_method)\n timestep_zero = ref_latents_method == \"index_timestep_zero\"\n for ref in ref_latents:\n if ref_latents_method in (\"index\", \"index_timestep_zero\"):\n index += self.params.ref_index_scale\n h_offset = 0\n w_offset = 0\n elif ref_latents_method == \"uxo\":\n index = 0\n h_offset = h_len * patch_size + h\n w_offset = w_len * patch_size + w\n h += ref.shape[-2]\n w += ref.shape[-1]\n else:\n index = 1\n h_offset = 0\n w_offset = 0\n if ref.shape[-2] + h > ref.shape[-1] + w:\n w_offset = w\n else:\n h_offset = h\n h = max(h, ref.shape[-2] + h_offset)\n w = max(w, ref.shape[-1] + w_offset)\n\n kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)\n img = torch.cat([img, kontext], dim=1)\n img_ids = torch.cat([img_ids, kontext_ids], dim=1)\n ref_num_tokens.append(kontext.shape[1])\n if timestep_zero:\n if index > 0:\n timestep = torch.cat([timestep, timestep * 0], dim=0)\n timestep_zero_index = [[img_tokens, img_ids.shape[1]]]\n transformer_options = transformer_options.copy()\n transformer_options[\"reference_image_num_tokens\"] = ref_num_tokens\n\n txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)\n\n if len(self.params.txt_ids_dims) > 0:\n for i in self.params.txt_ids_dims:\n txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)\n\n out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, attn_mask=kwargs.get(\"attention_mask\", None))\n out = out[:, :img_tokens]\n return rearrange(out, \"b (h w) (c ph pw) -> b c (h ph) (w pw)\", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]\n" }, { "path": "comfy/ldm/flux/redux.py", "content": "import torch\nimport comfy.ops\n\nops = comfy.ops.manual_cast\n\nclass ReduxImageEncoder(torch.nn.Module):\n def __init__(\n self,\n redux_dim: int = 1152,\n txt_in_features: int = 4096,\n device=None,\n dtype=None,\n ) -> None:\n super().__init__()\n\n self.redux_dim = redux_dim\n self.device = device\n self.dtype = dtype\n\n self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype)\n self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype)\n\n def forward(self, sigclip_embeds) -> torch.Tensor:\n projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds)))\n return projected_x\n" }, { "path": "comfy/ldm/genmo/joint_model/asymm_models_joint.py", "content": "#original code from https://github.com/genmoai/models under apache 2.0 license\n#adapted to ComfyUI\n\nfrom typing import Dict, List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n# from flash_attn import flash_attn_varlen_qkvpacked_func\nfrom comfy.ldm.modules.attention import optimized_attention\n\nfrom .layers import (\n FeedForward,\n PatchEmbed,\n TimestepEmbedder,\n)\n\nfrom .rope_mixed import (\n compute_mixed_rotation,\n create_position_matrix,\n)\nfrom .temporal_rope import apply_rotary_emb_qk_real\nfrom .utils import (\n AttentionPool,\n modulate,\n)\n\nimport comfy.ldm.common_dit\nimport comfy.ops\n\n\ndef modulated_rmsnorm(x, scale, eps=1e-6):\n # Normalize and modulate\n x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps)\n x_modulated = x_normed * (1 + scale.unsqueeze(1))\n\n return x_modulated\n\n\ndef residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6):\n # Apply tanh to gate\n tanh_gate = torch.tanh(gate).unsqueeze(1)\n\n # Normalize and apply gated scaling\n x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate\n\n # Apply residual connection\n output = x + x_normed\n\n return output\n\nclass AsymmetricAttention(nn.Module):\n def __init__(\n self,\n dim_x: int,\n dim_y: int,\n num_heads: int = 8,\n qkv_bias: bool = True,\n qk_norm: bool = False,\n attn_drop: float = 0.0,\n update_y: bool = True,\n out_bias: bool = True,\n attend_to_padding: bool = False,\n softmax_scale: Optional[float] = None,\n device: Optional[torch.device] = None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n self.dim_x = dim_x\n self.dim_y = dim_y\n self.num_heads = num_heads\n self.head_dim = dim_x // num_heads\n self.attn_drop = attn_drop\n self.update_y = update_y\n self.attend_to_padding = attend_to_padding\n self.softmax_scale = softmax_scale\n if dim_x % num_heads != 0:\n raise ValueError(\n f\"dim_x={dim_x} should be divisible by num_heads={num_heads}\"\n )\n\n # Input layers.\n self.qkv_bias = qkv_bias\n self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype)\n # Project text features to match visual features (dim_y -> dim_x)\n self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype)\n\n # Query and key normalization for stability.\n assert qk_norm\n self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)\n self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)\n self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)\n self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype)\n\n # Output layers. y features go back down from dim_x -> dim_y.\n self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype)\n self.proj_y = (\n operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype)\n if update_y\n else nn.Identity()\n )\n\n def forward(\n self,\n x: torch.Tensor, # (B, N, dim_x)\n y: torch.Tensor, # (B, L, dim_y)\n scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm.\n scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm.\n crop_y,\n transformer_options={},\n **rope_rotation,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n rope_cos = rope_rotation.get(\"rope_cos\")\n rope_sin = rope_rotation.get(\"rope_sin\")\n # Pre-norm for visual features\n x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size\n\n # Process visual features\n # qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x)\n # assert qkv_x.dtype == torch.bfloat16\n # qkv_x = all_to_all_collect_tokens(\n # qkv_x, self.num_heads\n # ) # (3, B, N, local_h, head_dim)\n\n # Process text features\n y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y)\n q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim)\n\n q_y = self.q_norm_y(q_y)\n k_y = self.k_norm_y(k_y)\n\n # Split qkv_x into q, k, v\n q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim)\n q_x = self.q_norm_x(q_x)\n q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin)\n k_x = self.k_norm_x(k_x)\n k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin)\n\n q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2)\n k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2)\n v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2)\n\n xy = optimized_attention(q,\n k,\n v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)\n\n x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)\n x = self.proj_x(x)\n o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype)\n o[:, :y.shape[1]] = y\n\n y = self.proj_y(o)\n # print(\"ox\", x)\n # print(\"oy\", y)\n return x, y\n\n\nclass AsymmetricJointBlock(nn.Module):\n def __init__(\n self,\n hidden_size_x: int,\n hidden_size_y: int,\n num_heads: int,\n *,\n mlp_ratio_x: float = 8.0, # Ratio of hidden size to d_model for MLP for visual tokens.\n mlp_ratio_y: float = 4.0, # Ratio of hidden size to d_model for MLP for text tokens.\n update_y: bool = True, # Whether to update text tokens in this block.\n device: Optional[torch.device] = None,\n dtype=None,\n operations=None,\n **block_kwargs,\n ):\n super().__init__()\n self.update_y = update_y\n self.hidden_size_x = hidden_size_x\n self.hidden_size_y = hidden_size_y\n self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype)\n if self.update_y:\n self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype)\n else:\n self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype)\n\n # Self-attention:\n self.attn = AsymmetricAttention(\n hidden_size_x,\n hidden_size_y,\n num_heads=num_heads,\n update_y=update_y,\n device=device,\n dtype=dtype,\n operations=operations,\n **block_kwargs,\n )\n\n # MLP.\n mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x)\n assert mlp_hidden_dim_x == int(1536 * 8)\n self.mlp_x = FeedForward(\n in_features=hidden_size_x,\n hidden_size=mlp_hidden_dim_x,\n multiple_of=256,\n ffn_dim_multiplier=None,\n device=device,\n dtype=dtype,\n operations=operations,\n )\n\n # MLP for text not needed in last block.\n if self.update_y:\n mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y)\n self.mlp_y = FeedForward(\n in_features=hidden_size_y,\n hidden_size=mlp_hidden_dim_y,\n multiple_of=256,\n ffn_dim_multiplier=None,\n device=device,\n dtype=dtype,\n operations=operations,\n )\n\n def forward(\n self,\n x: torch.Tensor,\n c: torch.Tensor,\n y: torch.Tensor,\n transformer_options={},\n **attn_kwargs,\n ):\n \"\"\"Forward pass of a block.\n\n Args:\n x: (B, N, dim) tensor of visual tokens\n c: (B, dim) tensor of conditioned features\n y: (B, L, dim) tensor of text tokens\n num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens\n\n Returns:\n x: (B, N, dim) tensor of visual tokens after block\n y: (B, L, dim) tensor of text tokens after block\n \"\"\"\n N = x.size(1)\n\n c = F.silu(c)\n mod_x = self.mod_x(c)\n scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1)\n\n mod_y = self.mod_y(c)\n if self.update_y:\n scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1)\n else:\n scale_msa_y = mod_y\n\n # Self-attention block.\n x_attn, y_attn = self.attn(\n x,\n y,\n scale_x=scale_msa_x,\n scale_y=scale_msa_y,\n transformer_options=transformer_options,\n **attn_kwargs,\n )\n\n assert x_attn.size(1) == N\n x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x)\n if self.update_y:\n y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y)\n\n # MLP block.\n x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x)\n if self.update_y:\n y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y)\n\n return x, y\n\n def ff_block_x(self, x, scale_x, gate_x):\n x_mod = modulated_rmsnorm(x, scale_x)\n x_res = self.mlp_x(x_mod)\n x = residual_tanh_gated_rmsnorm(x, x_res, gate_x) # Sandwich norm\n return x\n\n def ff_block_y(self, y, scale_y, gate_y):\n y_mod = modulated_rmsnorm(y, scale_y)\n y_res = self.mlp_y(y_mod)\n y = residual_tanh_gated_rmsnorm(y, y_res, gate_y) # Sandwich norm\n return y\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of DiT.\n \"\"\"\n\n def __init__(\n self,\n hidden_size,\n patch_size,\n out_channels,\n device: Optional[torch.device] = None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n self.norm_final = operations.LayerNorm(\n hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype\n )\n self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype)\n self.linear = operations.Linear(\n hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype\n )\n\n def forward(self, x, c):\n c = F.silu(c)\n shift, scale = self.mod(c).chunk(2, dim=1)\n x = modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n return x\n\n\nclass AsymmDiTJoint(nn.Module):\n \"\"\"\n Diffusion model with a Transformer backbone.\n\n Ingests text embeddings instead of a label.\n \"\"\"\n\n def __init__(\n self,\n *,\n patch_size=2,\n in_channels=4,\n hidden_size_x=1152,\n hidden_size_y=1152,\n depth=48,\n num_heads=16,\n mlp_ratio_x=8.0,\n mlp_ratio_y=4.0,\n use_t5: bool = False,\n t5_feat_dim: int = 4096,\n t5_token_length: int = 256,\n learn_sigma=True,\n patch_embed_bias: bool = True,\n timestep_mlp_bias: bool = True,\n attend_to_padding: bool = False,\n timestep_scale: Optional[float] = None,\n use_extended_posenc: bool = False,\n posenc_preserve_area: bool = False,\n rope_theta: float = 10000.0,\n image_model=None,\n device: Optional[torch.device] = None,\n dtype=None,\n operations=None,\n **block_kwargs,\n ):\n super().__init__()\n\n self.dtype = dtype\n self.learn_sigma = learn_sigma\n self.in_channels = in_channels\n self.out_channels = in_channels * 2 if learn_sigma else in_channels\n self.patch_size = patch_size\n self.num_heads = num_heads\n self.hidden_size_x = hidden_size_x\n self.hidden_size_y = hidden_size_y\n self.head_dim = (\n hidden_size_x // num_heads\n ) # Head dimension and count is determined by visual.\n self.attend_to_padding = attend_to_padding\n self.use_extended_posenc = use_extended_posenc\n self.posenc_preserve_area = posenc_preserve_area\n self.use_t5 = use_t5\n self.t5_token_length = t5_token_length\n self.t5_feat_dim = t5_feat_dim\n self.rope_theta = (\n rope_theta # Scaling factor for frequency computation for temporal RoPE.\n )\n\n self.x_embedder = PatchEmbed(\n patch_size=patch_size,\n in_chans=in_channels,\n embed_dim=hidden_size_x,\n bias=patch_embed_bias,\n dtype=dtype,\n device=device,\n operations=operations\n )\n # Conditionings\n # Timestep\n self.t_embedder = TimestepEmbedder(\n hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations\n )\n\n if self.use_t5:\n # Caption Pooling (T5)\n self.t5_y_embedder = AttentionPool(\n t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations\n )\n\n # Dense Embedding Projection (T5)\n self.t5_yproj = operations.Linear(\n t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device\n )\n\n # Initialize pos_frequencies as an empty parameter.\n self.pos_frequencies = nn.Parameter(\n torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device)\n )\n\n assert not self.attend_to_padding\n\n # for depth 48:\n # b = 0: AsymmetricJointBlock, update_y=True\n # b = 1: AsymmetricJointBlock, update_y=True\n # ...\n # b = 46: AsymmetricJointBlock, update_y=True\n # b = 47: AsymmetricJointBlock, update_y=False. No need to update text features.\n blocks = []\n for b in range(depth):\n # Joint multi-modal block\n update_y = b < depth - 1\n block = AsymmetricJointBlock(\n hidden_size_x,\n hidden_size_y,\n num_heads,\n mlp_ratio_x=mlp_ratio_x,\n mlp_ratio_y=mlp_ratio_y,\n update_y=update_y,\n attend_to_padding=attend_to_padding,\n device=device,\n dtype=dtype,\n operations=operations,\n **block_kwargs,\n )\n\n blocks.append(block)\n self.blocks = nn.ModuleList(blocks)\n\n self.final_layer = FinalLayer(\n hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations\n )\n\n def embed_x(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Args:\n x: (B, C=12, T, H, W) tensor of visual tokens\n\n Returns:\n x: (B, C=3072, N) tensor of visual tokens with positional embedding.\n \"\"\"\n return self.x_embedder(x) # Convert BcTHW to BCN\n\n def prepare(\n self,\n x: torch.Tensor,\n sigma: torch.Tensor,\n t5_feat: torch.Tensor,\n t5_mask: torch.Tensor,\n ):\n \"\"\"Prepare input and conditioning embeddings.\"\"\"\n # Visual patch embeddings with positional encoding.\n T, H, W = x.shape[-3:]\n pH, pW = H // self.patch_size, W // self.patch_size\n x = self.embed_x(x) # (B, N, D), where N = T * H * W / patch_size ** 2\n assert x.ndim == 3\n\n pH, pW = H // self.patch_size, W // self.patch_size\n N = T * pH * pW\n assert x.size(1) == N\n pos = create_position_matrix(\n T, pH=pH, pW=pW, device=x.device, dtype=torch.float32\n ) # (N, 3)\n rope_cos, rope_sin = compute_mixed_rotation(\n freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos\n ) # Each are (N, num_heads, dim // 2)\n\n c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype) # (B, D)\n\n t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask) # (B, D)\n\n c = c_t + t5_y_pool\n\n y_feat = self.t5_yproj(t5_feat) # (B, L, t5_feat_dim) --> (B, L, D)\n\n return x, c, y_feat, rope_cos, rope_sin\n\n def forward(\n self,\n x: torch.Tensor,\n timestep: torch.Tensor,\n context: List[torch.Tensor],\n attention_mask: List[torch.Tensor],\n num_tokens=256,\n packed_indices: Dict[str, torch.Tensor] = None,\n rope_cos: torch.Tensor = None,\n rope_sin: torch.Tensor = None,\n control=None, transformer_options={}, **kwargs\n ):\n patches_replace = transformer_options.get(\"patches_replace\", {})\n y_feat = context\n y_mask = attention_mask\n sigma = timestep\n \"\"\"Forward pass of DiT.\n\n Args:\n x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images)\n sigma: (B,) tensor of noise standard deviations\n y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048)\n y_mask: List((B, L) boolean tensor indicating which tokens are not padding)\n packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices.\n \"\"\"\n B, _, T, H, W = x.shape\n\n x, c, y_feat, rope_cos, rope_sin = self.prepare(\n x, sigma, y_feat, y_mask\n )\n del y_mask\n\n blocks_replace = patches_replace.get(\"dit\", {})\n for i, block in enumerate(self.blocks):\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"], out[\"txt\"] = block(\n args[\"img\"],\n args[\"vec\"],\n args[\"txt\"],\n rope_cos=args[\"rope_cos\"],\n rope_sin=args[\"rope_sin\"],\n crop_y=args[\"num_tokens\"],\n transformer_options=args[\"transformer_options\"]\n )\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": y_feat, \"vec\": c, \"rope_cos\": rope_cos, \"rope_sin\": rope_sin, \"num_tokens\": num_tokens, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n y_feat = out[\"txt\"]\n x = out[\"img\"]\n else:\n x, y_feat = block(\n x,\n c,\n y_feat,\n rope_cos=rope_cos,\n rope_sin=rope_sin,\n crop_y=num_tokens,\n transformer_options=transformer_options,\n ) # (B, M, D), (B, L, D)\n del y_feat # Final layers don't use dense text features.\n\n x = self.final_layer(x, c) # (B, M, patch_size ** 2 * out_channels)\n x = rearrange(\n x,\n \"B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)\",\n T=T,\n hp=H // self.patch_size,\n wp=W // self.patch_size,\n p1=self.patch_size,\n p2=self.patch_size,\n c=self.out_channels,\n )\n\n return -x\n" }, { "path": "comfy/ldm/genmo/joint_model/layers.py", "content": "#original code from https://github.com/genmoai/models under apache 2.0 license\n#adapted to ComfyUI\n\nimport collections.abc\nimport math\nfrom itertools import repeat\nfrom typing import Callable, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nimport comfy.ldm.common_dit\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 tuple(x)\n return tuple(repeat(x, n))\n\n return parse\n\n\nto_2tuple = _ntuple(2)\n\n\nclass TimestepEmbedder(nn.Module):\n def __init__(\n self,\n hidden_size: int,\n frequency_embedding_size: int = 256,\n *,\n bias: bool = True,\n timestep_scale: Optional[float] = None,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.mlp = nn.Sequential(\n operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device),\n )\n self.frequency_embedding_size = frequency_embedding_size\n self.timestep_scale = timestep_scale\n\n @staticmethod\n def timestep_embedding(t, dim, max_period=10000):\n half = dim // 2\n freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)\n freqs.mul_(-math.log(max_period) / half).exp_()\n args = t[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat(\n [embedding, torch.zeros_like(embedding[:, :1])], dim=-1\n )\n return embedding\n\n def forward(self, t, out_dtype):\n if self.timestep_scale is not None:\n t = t * self.timestep_scale\n t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype)\n t_emb = self.mlp(t_freq)\n return t_emb\n\n\nclass FeedForward(nn.Module):\n def __init__(\n self,\n in_features: int,\n hidden_size: int,\n multiple_of: int,\n ffn_dim_multiplier: Optional[float],\n device: Optional[torch.device] = None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n # keep parameter count and computation constant compared to standard FFN\n hidden_size = int(2 * hidden_size / 3)\n # custom dim factor multiplier\n if ffn_dim_multiplier is not None:\n hidden_size = int(ffn_dim_multiplier * hidden_size)\n hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of)\n\n self.hidden_dim = hidden_size\n self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype)\n self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype)\n\n def forward(self, x):\n x, gate = self.w1(x).chunk(2, dim=-1)\n x = self.w2(F.silu(x) * gate)\n return x\n\n\nclass PatchEmbed(nn.Module):\n def __init__(\n self,\n patch_size: int = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n norm_layer: Optional[Callable] = None,\n flatten: bool = True,\n bias: bool = True,\n dynamic_img_pad: bool = False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.patch_size = to_2tuple(patch_size)\n self.flatten = flatten\n self.dynamic_img_pad = dynamic_img_pad\n\n self.proj = operations.Conv2d(\n in_chans,\n embed_dim,\n kernel_size=patch_size,\n stride=patch_size,\n bias=bias,\n device=device,\n dtype=dtype,\n )\n assert norm_layer is None\n self.norm = (\n norm_layer(embed_dim, device=device) if norm_layer else nn.Identity()\n )\n\n def forward(self, x):\n B, _C, T, H, W = x.shape\n if not self.dynamic_img_pad:\n assert H % self.patch_size[0] == 0, f\"Input height ({H}) should be divisible by patch size ({self.patch_size[0]}).\"\n assert W % self.patch_size[1] == 0, f\"Input width ({W}) should be divisible by patch size ({self.patch_size[1]}).\"\n else:\n pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]\n pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]\n x = F.pad(x, (0, pad_w, 0, pad_h))\n\n x = rearrange(x, \"B C T H W -> (B T) C H W\", B=B, T=T)\n x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular')\n x = self.proj(x)\n\n # Flatten temporal and spatial dimensions.\n if not self.flatten:\n raise NotImplementedError(\"Must flatten output.\")\n x = rearrange(x, \"(B T) C H W -> B (T H W) C\", B=B, T=T)\n\n x = self.norm(x)\n return x\n" }, { "path": "comfy/ldm/genmo/joint_model/rope_mixed.py", "content": "#original code from https://github.com/genmoai/models under apache 2.0 license\n\n# import functools\nimport math\n\nimport torch\n\n\ndef centers(start: float, stop, num, dtype=None, device=None):\n \"\"\"linspace through bin centers.\n\n Args:\n start (float): Start of the range.\n stop (float): End of the range.\n num (int): Number of points.\n dtype (torch.dtype): Data type of the points.\n device (torch.device): Device of the points.\n\n Returns:\n centers (Tensor): Centers of the bins. Shape: (num,).\n \"\"\"\n edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device)\n return (edges[:-1] + edges[1:]) / 2\n\n\n# @functools.lru_cache(maxsize=1)\ndef create_position_matrix(\n T: int,\n pH: int,\n pW: int,\n device: torch.device,\n dtype: torch.dtype,\n *,\n target_area: float = 36864,\n):\n \"\"\"\n Args:\n T: int - Temporal dimension\n pH: int - Height dimension after patchify\n pW: int - Width dimension after patchify\n\n Returns:\n pos: [T * pH * pW, 3] - position matrix\n \"\"\"\n # Create 1D tensors for each dimension\n t = torch.arange(T, dtype=dtype)\n\n # Positionally interpolate to area 36864.\n # (3072x3072 frame with 16x16 patches = 192x192 latents).\n # This automatically scales rope positions when the resolution changes.\n # We use a large target area so the model is more sensitive\n # to changes in the learned pos_frequencies matrix.\n scale = math.sqrt(target_area / (pW * pH))\n w = centers(-pW * scale / 2, pW * scale / 2, pW)\n h = centers(-pH * scale / 2, pH * scale / 2, pH)\n\n # Use meshgrid to create 3D grids\n grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing=\"ij\")\n\n # Stack and reshape the grids.\n pos = torch.stack([grid_t, grid_h, grid_w], dim=-1) # [T, pH, pW, 3]\n pos = pos.view(-1, 3) # [T * pH * pW, 3]\n pos = pos.to(dtype=dtype, device=device)\n\n return pos\n\n\ndef compute_mixed_rotation(\n freqs: torch.Tensor,\n pos: torch.Tensor,\n):\n \"\"\"\n Project each 3-dim position into per-head, per-head-dim 1D frequencies.\n\n Args:\n freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position\n pos: [N, 3] - position of each token\n num_heads: int\n\n Returns:\n freqs_cos: [N, num_heads, num_freqs] - cosine components\n freqs_sin: [N, num_heads, num_freqs] - sine components\n \"\"\"\n assert freqs.ndim == 3\n freqs_sum = torch.einsum(\"Nd,dhf->Nhf\", pos.to(freqs), freqs)\n freqs_cos = torch.cos(freqs_sum)\n freqs_sin = torch.sin(freqs_sum)\n return freqs_cos, freqs_sin\n" }, { "path": "comfy/ldm/genmo/joint_model/temporal_rope.py", "content": "#original code from https://github.com/genmoai/models under apache 2.0 license\n\n# Based on Llama3 Implementation.\nimport torch\n\n\ndef apply_rotary_emb_qk_real(\n xqk: torch.Tensor,\n freqs_cos: torch.Tensor,\n freqs_sin: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"\n Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers.\n\n Args:\n xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D)\n Can be either just query or just key, or both stacked along some batch or * dim.\n freqs_cos (torch.Tensor): Precomputed cosine frequency tensor.\n freqs_sin (torch.Tensor): Precomputed sine frequency tensor.\n\n Returns:\n torch.Tensor: The input tensor with rotary embeddings applied.\n \"\"\"\n # Split the last dimension into even and odd parts\n xqk_even = xqk[..., 0::2]\n xqk_odd = xqk[..., 1::2]\n\n # Apply rotation\n cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk)\n sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk)\n\n # Interleave the results back into the original shape\n out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2)\n return out\n" }, { "path": "comfy/ldm/genmo/joint_model/utils.py", "content": "#original code from https://github.com/genmoai/models under apache 2.0 license\n#adapted to ComfyUI\n\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\ndef pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor:\n \"\"\"\n Pool tokens in x using mask.\n\n NOTE: We assume x does not require gradients.\n\n Args:\n x: (B, L, D) tensor of tokens.\n mask: (B, L) boolean tensor indicating which tokens are not padding.\n\n Returns:\n pooled: (B, D) tensor of pooled tokens.\n \"\"\"\n assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens.\n assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens.\n mask = mask[:, :, None].to(dtype=x.dtype)\n mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1)\n pooled = (x * mask).sum(dim=1, keepdim=keepdim)\n return pooled\n\n\nclass AttentionPool(nn.Module):\n def __init__(\n self,\n embed_dim: int,\n num_heads: int,\n output_dim: int = None,\n device: Optional[torch.device] = None,\n dtype=None,\n operations=None,\n ):\n \"\"\"\n Args:\n spatial_dim (int): Number of tokens in sequence length.\n embed_dim (int): Dimensionality of input tokens.\n num_heads (int): Number of attention heads.\n output_dim (int): Dimensionality of output tokens. Defaults to embed_dim.\n \"\"\"\n super().__init__()\n self.num_heads = num_heads\n self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype)\n self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)\n self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype)\n\n def forward(self, x, mask):\n \"\"\"\n Args:\n x (torch.Tensor): (B, L, D) tensor of input tokens.\n mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding.\n\n NOTE: We assume x does not require gradients.\n\n Returns:\n x (torch.Tensor): (B, D) tensor of pooled tokens.\n \"\"\"\n D = x.size(2)\n\n # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).\n attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L).\n attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L).\n\n # Average non-padding token features. These will be used as the query.\n x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D)\n\n # Concat pooled features to input sequence.\n x = torch.cat([x_pool, x], dim=1) # (B, L+1, D)\n\n # Compute queries, keys, values. Only the mean token is used to create a query.\n kv = self.to_kv(x) # (B, L+1, 2 * D)\n q = self.to_q(x[:, 0]) # (B, D)\n\n # Extract heads.\n head_dim = D // self.num_heads\n kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim)\n kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim)\n k, v = kv.unbind(2) # (B, H, 1+L, head_dim)\n q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim)\n q = q.unsqueeze(2) # (B, H, 1, head_dim)\n\n # Compute attention.\n x = F.scaled_dot_product_attention(\n q, k, v, attn_mask=attn_mask, dropout_p=0.0\n ) # (B, H, 1, head_dim)\n\n # Concatenate heads and run output.\n x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim)\n x = self.to_out(x)\n return x\n" }, { "path": "comfy/ldm/genmo/vae/model.py", "content": "#original code from https://github.com/genmoai/models under apache 2.0 license\n#adapted to ComfyUI\n\nfrom typing import List, Optional, Tuple, Union\nfrom functools import partial\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom comfy.ldm.modules.attention import optimized_attention\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\n# import mochi_preview.dit.joint_model.context_parallel as cp\n# from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames\n\n\ndef cast_tuple(t, length=1):\n return t if isinstance(t, tuple) else ((t,) * length)\n\n\nclass GroupNormSpatial(ops.GroupNorm):\n \"\"\"\n GroupNorm applied per-frame.\n \"\"\"\n\n def forward(self, x: torch.Tensor, *, chunk_size: int = 8):\n B, C, T, H, W = x.shape\n x = rearrange(x, \"B C T H W -> (B T) C H W\")\n # Run group norm in chunks.\n output = torch.empty_like(x)\n for b in range(0, B * T, chunk_size):\n output[b : b + chunk_size] = super().forward(x[b : b + chunk_size])\n return rearrange(output, \"(B T) C H W -> B C T H W\", B=B, T=T)\n\nclass PConv3d(ops.Conv3d):\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size: Union[int, Tuple[int, int, int]],\n stride: Union[int, Tuple[int, int, int]],\n causal: bool = True,\n context_parallel: bool = True,\n **kwargs,\n ):\n self.causal = causal\n self.context_parallel = context_parallel\n kernel_size = cast_tuple(kernel_size, 3)\n stride = cast_tuple(stride, 3)\n height_pad = (kernel_size[1] - 1) // 2\n width_pad = (kernel_size[2] - 1) // 2\n\n super().__init__(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n dilation=(1, 1, 1),\n padding=(0, height_pad, width_pad),\n **kwargs,\n )\n\n def forward(self, x: torch.Tensor):\n # Compute padding amounts.\n context_size = self.kernel_size[0] - 1\n if self.causal:\n pad_front = context_size\n pad_back = 0\n else:\n pad_front = context_size // 2\n pad_back = context_size - pad_front\n\n # Apply padding.\n assert self.padding_mode == \"replicate\" # DEBUG\n mode = \"constant\" if self.padding_mode == \"zeros\" else self.padding_mode\n x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode)\n return super().forward(x)\n\n\nclass Conv1x1(ops.Linear):\n \"\"\"*1x1 Conv implemented with a linear layer.\"\"\"\n\n def __init__(self, in_features: int, out_features: int, *args, **kwargs):\n super().__init__(in_features, out_features, *args, **kwargs)\n\n def forward(self, x: torch.Tensor):\n \"\"\"Forward pass.\n\n Args:\n x: Input tensor. Shape: [B, C, *] or [B, *, C].\n\n Returns:\n x: Output tensor. Shape: [B, C', *] or [B, *, C'].\n \"\"\"\n x = x.movedim(1, -1)\n x = super().forward(x)\n x = x.movedim(-1, 1)\n return x\n\n\nclass DepthToSpaceTime(nn.Module):\n def __init__(\n self,\n temporal_expansion: int,\n spatial_expansion: int,\n ):\n super().__init__()\n self.temporal_expansion = temporal_expansion\n self.spatial_expansion = spatial_expansion\n\n # When printed, this module should show the temporal and spatial expansion factors.\n def extra_repr(self):\n return f\"texp={self.temporal_expansion}, sexp={self.spatial_expansion}\"\n\n def forward(self, x: torch.Tensor):\n \"\"\"Forward pass.\n\n Args:\n x: Input tensor. Shape: [B, C, T, H, W].\n\n Returns:\n x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s].\n \"\"\"\n x = rearrange(\n x,\n \"B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)\",\n st=self.temporal_expansion,\n sh=self.spatial_expansion,\n sw=self.spatial_expansion,\n )\n\n # cp_rank, _ = cp.get_cp_rank_size()\n if self.temporal_expansion > 1: # and cp_rank == 0:\n # Drop the first self.temporal_expansion - 1 frames.\n # This is because we always want the 3x3x3 conv filter to only apply\n # to the first frame, and the first frame doesn't need to be repeated.\n assert all(x.shape)\n x = x[:, :, self.temporal_expansion - 1 :]\n assert all(x.shape)\n\n return x\n\n\ndef norm_fn(\n in_channels: int,\n affine: bool = True,\n):\n return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels)\n\n\nclass ResBlock(nn.Module):\n \"\"\"Residual block that preserves the spatial dimensions.\"\"\"\n\n def __init__(\n self,\n channels: int,\n *,\n affine: bool = True,\n attn_block: Optional[nn.Module] = None,\n causal: bool = True,\n prune_bottleneck: bool = False,\n padding_mode: str,\n bias: bool = True,\n ):\n super().__init__()\n self.channels = channels\n\n assert causal\n self.stack = nn.Sequential(\n norm_fn(channels, affine=affine),\n nn.SiLU(inplace=True),\n PConv3d(\n in_channels=channels,\n out_channels=channels // 2 if prune_bottleneck else channels,\n kernel_size=(3, 3, 3),\n stride=(1, 1, 1),\n padding_mode=padding_mode,\n bias=bias,\n causal=causal,\n ),\n norm_fn(channels, affine=affine),\n nn.SiLU(inplace=True),\n PConv3d(\n in_channels=channels // 2 if prune_bottleneck else channels,\n out_channels=channels,\n kernel_size=(3, 3, 3),\n stride=(1, 1, 1),\n padding_mode=padding_mode,\n bias=bias,\n causal=causal,\n ),\n )\n\n self.attn_block = attn_block if attn_block else nn.Identity()\n\n def forward(self, x: torch.Tensor):\n \"\"\"Forward pass.\n\n Args:\n x: Input tensor. Shape: [B, C, T, H, W].\n \"\"\"\n residual = x\n x = self.stack(x)\n x = x + residual\n del residual\n\n return self.attn_block(x)\n\n\nclass Attention(nn.Module):\n def __init__(\n self,\n dim: int,\n head_dim: int = 32,\n qkv_bias: bool = False,\n out_bias: bool = True,\n qk_norm: bool = True,\n ) -> None:\n super().__init__()\n self.head_dim = head_dim\n self.num_heads = dim // head_dim\n self.qk_norm = qk_norm\n\n self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)\n self.out = nn.Linear(dim, dim, bias=out_bias)\n\n def forward(\n self,\n x: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute temporal self-attention.\n\n Args:\n x: Input tensor. Shape: [B, C, T, H, W].\n chunk_size: Chunk size for large tensors.\n\n Returns:\n x: Output tensor. Shape: [B, C, T, H, W].\n \"\"\"\n B, _, T, H, W = x.shape\n\n if T == 1:\n # No attention for single frame.\n x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C]\n qkv = self.qkv(x)\n _, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys.\n x = self.out(x)\n return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W]\n\n # 1D temporal attention.\n x = rearrange(x, \"B C t h w -> (B h w) t C\")\n qkv = self.qkv(x)\n\n # Input: qkv with shape [B, t, 3 * num_heads * head_dim]\n # Output: x with shape [B, num_heads, t, head_dim]\n q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2)\n\n if self.qk_norm:\n q = F.normalize(q, p=2, dim=-1)\n k = F.normalize(k, p=2, dim=-1)\n\n x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)\n\n assert x.size(0) == q.size(0)\n\n x = self.out(x)\n x = rearrange(x, \"(B h w) t C -> B C t h w\", B=B, h=H, w=W)\n return x\n\n\nclass AttentionBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n **attn_kwargs,\n ) -> None:\n super().__init__()\n self.norm = norm_fn(dim)\n self.attn = Attention(dim, **attn_kwargs)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return x + self.attn(self.norm(x))\n\n\nclass CausalUpsampleBlock(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_res_blocks: int,\n *,\n temporal_expansion: int = 2,\n spatial_expansion: int = 2,\n **block_kwargs,\n ):\n super().__init__()\n\n blocks = []\n for _ in range(num_res_blocks):\n blocks.append(block_fn(in_channels, **block_kwargs))\n self.blocks = nn.Sequential(*blocks)\n\n self.temporal_expansion = temporal_expansion\n self.spatial_expansion = spatial_expansion\n\n # Change channels in the final convolution layer.\n self.proj = Conv1x1(\n in_channels,\n out_channels * temporal_expansion * (spatial_expansion**2),\n )\n\n self.d2st = DepthToSpaceTime(\n temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion\n )\n\n def forward(self, x):\n x = self.blocks(x)\n x = self.proj(x)\n x = self.d2st(x)\n return x\n\n\ndef block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs):\n attn_block = AttentionBlock(channels) if has_attention else None\n return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs)\n\n\nclass DownsampleBlock(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_res_blocks,\n *,\n temporal_reduction=2,\n spatial_reduction=2,\n **block_kwargs,\n ):\n \"\"\"\n Downsample block for the VAE encoder.\n\n Args:\n in_channels: Number of input channels.\n out_channels: Number of output channels.\n num_res_blocks: Number of residual blocks.\n temporal_reduction: Temporal reduction factor.\n spatial_reduction: Spatial reduction factor.\n \"\"\"\n super().__init__()\n layers = []\n\n # Change the channel count in the strided convolution.\n # This lets the ResBlock have uniform channel count,\n # as in ConvNeXt.\n assert in_channels != out_channels\n layers.append(\n PConv3d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),\n stride=(temporal_reduction, spatial_reduction, spatial_reduction),\n # First layer in each block always uses replicate padding\n padding_mode=\"replicate\",\n bias=block_kwargs[\"bias\"],\n )\n )\n\n for _ in range(num_res_blocks):\n layers.append(block_fn(out_channels, **block_kwargs))\n\n self.layers = nn.Sequential(*layers)\n\n def forward(self, x):\n return self.layers(x)\n\n\ndef add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1):\n num_freqs = (stop - start) // step\n assert inputs.ndim == 5\n C = inputs.size(1)\n\n # Create Base 2 Fourier features.\n freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device)\n assert num_freqs == len(freqs)\n w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs]\n C = inputs.shape[1]\n w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1]\n\n # Interleaved repeat of input channels to match w.\n h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W]\n # Scale channels by frequency.\n h = w * h\n\n return torch.cat(\n [\n inputs,\n torch.sin(h),\n torch.cos(h),\n ],\n dim=1,\n )\n\n\nclass FourierFeatures(nn.Module):\n def __init__(self, start: int = 6, stop: int = 8, step: int = 1):\n super().__init__()\n self.start = start\n self.stop = stop\n self.step = step\n\n def forward(self, inputs):\n \"\"\"Add Fourier features to inputs.\n\n Args:\n inputs: Input tensor. Shape: [B, C, T, H, W]\n\n Returns:\n h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W]\n \"\"\"\n return add_fourier_features(inputs, self.start, self.stop, self.step)\n\n\nclass Decoder(nn.Module):\n def __init__(\n self,\n *,\n out_channels: int = 3,\n latent_dim: int,\n base_channels: int,\n channel_multipliers: List[int],\n num_res_blocks: List[int],\n temporal_expansions: Optional[List[int]] = None,\n spatial_expansions: Optional[List[int]] = None,\n has_attention: List[bool],\n output_norm: bool = True,\n nonlinearity: str = \"silu\",\n output_nonlinearity: str = \"silu\",\n causal: bool = True,\n **block_kwargs,\n ):\n super().__init__()\n self.input_channels = latent_dim\n self.base_channels = base_channels\n self.channel_multipliers = channel_multipliers\n self.num_res_blocks = num_res_blocks\n self.output_nonlinearity = output_nonlinearity\n assert nonlinearity == \"silu\"\n assert causal\n\n ch = [mult * base_channels for mult in channel_multipliers]\n self.num_up_blocks = len(ch) - 1\n assert len(num_res_blocks) == self.num_up_blocks + 2\n\n blocks = []\n\n first_block = [\n ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))\n ] # Input layer.\n # First set of blocks preserve channel count.\n for _ in range(num_res_blocks[-1]):\n first_block.append(\n block_fn(\n ch[-1],\n has_attention=has_attention[-1],\n causal=causal,\n **block_kwargs,\n )\n )\n blocks.append(nn.Sequential(*first_block))\n\n assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks\n assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2\n\n upsample_block_fn = CausalUpsampleBlock\n\n for i in range(self.num_up_blocks):\n block = upsample_block_fn(\n ch[-i - 1],\n ch[-i - 2],\n num_res_blocks=num_res_blocks[-i - 2],\n has_attention=has_attention[-i - 2],\n temporal_expansion=temporal_expansions[-i - 1],\n spatial_expansion=spatial_expansions[-i - 1],\n causal=causal,\n **block_kwargs,\n )\n blocks.append(block)\n\n assert not output_norm\n\n # Last block. Preserve channel count.\n last_block = []\n for _ in range(num_res_blocks[0]):\n last_block.append(\n block_fn(\n ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs\n )\n )\n blocks.append(nn.Sequential(*last_block))\n\n self.blocks = nn.ModuleList(blocks)\n self.output_proj = Conv1x1(ch[0], out_channels)\n\n def forward(self, x):\n \"\"\"Forward pass.\n\n Args:\n x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1].\n\n Returns:\n x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1].\n T + 1 = (t - 1) * 4.\n H = h * 16, W = w * 16.\n \"\"\"\n for block in self.blocks:\n x = block(x)\n\n if self.output_nonlinearity == \"silu\":\n x = F.silu(x, inplace=not self.training)\n else:\n assert (\n not self.output_nonlinearity\n ) # StyleGAN3 omits the to-RGB nonlinearity.\n\n return self.output_proj(x).contiguous()\n\nclass LatentDistribution:\n def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):\n \"\"\"Initialize latent distribution.\n\n Args:\n mean: Mean of the distribution. Shape: [B, C, T, H, W].\n logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W].\n \"\"\"\n assert mean.shape == logvar.shape\n self.mean = mean\n self.logvar = logvar\n\n def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None):\n if temperature == 0.0:\n return self.mean\n\n if noise is None:\n noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator)\n else:\n assert noise.device == self.mean.device\n noise = noise.to(self.mean.dtype)\n\n if temperature != 1.0:\n raise NotImplementedError(f\"Temperature {temperature} is not supported.\")\n\n # Just Gaussian sample with no scaling of variance.\n return noise * torch.exp(self.logvar * 0.5) + self.mean\n\n def mode(self):\n return self.mean\n\nclass Encoder(nn.Module):\n def __init__(\n self,\n *,\n in_channels: int,\n base_channels: int,\n channel_multipliers: List[int],\n num_res_blocks: List[int],\n latent_dim: int,\n temporal_reductions: List[int],\n spatial_reductions: List[int],\n prune_bottlenecks: List[bool],\n has_attentions: List[bool],\n affine: bool = True,\n bias: bool = True,\n input_is_conv_1x1: bool = False,\n padding_mode: str,\n ):\n super().__init__()\n self.temporal_reductions = temporal_reductions\n self.spatial_reductions = spatial_reductions\n self.base_channels = base_channels\n self.channel_multipliers = channel_multipliers\n self.num_res_blocks = num_res_blocks\n self.latent_dim = latent_dim\n\n self.fourier_features = FourierFeatures()\n ch = [mult * base_channels for mult in channel_multipliers]\n num_down_blocks = len(ch) - 1\n assert len(num_res_blocks) == num_down_blocks + 2\n\n layers = (\n [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)]\n if not input_is_conv_1x1\n else [Conv1x1(in_channels, ch[0])]\n )\n\n assert len(prune_bottlenecks) == num_down_blocks + 2\n assert len(has_attentions) == num_down_blocks + 2\n block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias)\n\n for _ in range(num_res_blocks[0]):\n layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0]))\n prune_bottlenecks = prune_bottlenecks[1:]\n has_attentions = has_attentions[1:]\n\n assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1\n for i in range(num_down_blocks):\n layer = DownsampleBlock(\n ch[i],\n ch[i + 1],\n num_res_blocks=num_res_blocks[i + 1],\n temporal_reduction=temporal_reductions[i],\n spatial_reduction=spatial_reductions[i],\n prune_bottleneck=prune_bottlenecks[i],\n has_attention=has_attentions[i],\n affine=affine,\n bias=bias,\n padding_mode=padding_mode,\n )\n\n layers.append(layer)\n\n # Additional blocks.\n for _ in range(num_res_blocks[-1]):\n layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1]))\n\n self.layers = nn.Sequential(*layers)\n\n # Output layers.\n self.output_norm = norm_fn(ch[-1])\n self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False)\n\n @property\n def temporal_downsample(self):\n return math.prod(self.temporal_reductions)\n\n @property\n def spatial_downsample(self):\n return math.prod(self.spatial_reductions)\n\n def forward(self, x) -> LatentDistribution:\n \"\"\"Forward pass.\n\n Args:\n x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]\n\n Returns:\n means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1].\n h = H // 8, w = W // 8, t - 1 = (T - 1) // 6\n logvar: Shape: [B, latent_dim, t, h, w].\n \"\"\"\n assert x.ndim == 5, f\"Expected 5D input, got {x.shape}\"\n x = self.fourier_features(x)\n\n x = self.layers(x)\n\n x = self.output_norm(x)\n x = F.silu(x, inplace=True)\n x = self.output_proj(x)\n\n means, logvar = torch.chunk(x, 2, dim=1)\n\n assert means.ndim == 5\n assert logvar.shape == means.shape\n assert means.size(1) == self.latent_dim\n\n return LatentDistribution(means, logvar)\n\n\nclass VideoVAE(nn.Module):\n def __init__(self):\n super().__init__()\n self.encoder = Encoder(\n in_channels=15,\n base_channels=64,\n channel_multipliers=[1, 2, 4, 6],\n num_res_blocks=[3, 3, 4, 6, 3],\n latent_dim=12,\n temporal_reductions=[1, 2, 3],\n spatial_reductions=[2, 2, 2],\n prune_bottlenecks=[False, False, False, False, False],\n has_attentions=[False, True, True, True, True],\n affine=True,\n bias=True,\n input_is_conv_1x1=True,\n padding_mode=\"replicate\"\n )\n self.decoder = Decoder(\n out_channels=3,\n base_channels=128,\n channel_multipliers=[1, 2, 4, 6],\n temporal_expansions=[1, 2, 3],\n spatial_expansions=[2, 2, 2],\n num_res_blocks=[3, 3, 4, 6, 3],\n latent_dim=12,\n has_attention=[False, False, False, False, False],\n padding_mode=\"replicate\",\n output_norm=False,\n nonlinearity=\"silu\",\n output_nonlinearity=\"silu\",\n causal=True,\n )\n\n def encode(self, x):\n return self.encoder(x).mode()\n\n def decode(self, x):\n return self.decoder(x)\n" }, { "path": "comfy/ldm/hidream/model.py", "content": "from typing import Optional, Tuple, List\n\nimport torch\nimport torch.nn as nn\nimport einops\nfrom einops import repeat\n\nfrom comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps\nimport torch.nn.functional as F\n\nfrom comfy.ldm.flux.math import apply_rope, rope\nfrom comfy.ldm.flux.layers import LastLayer\n\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.model_management\nimport comfy.patcher_extension\nimport comfy.ldm.common_dit\n\n\n# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py\nclass EmbedND(nn.Module):\n def __init__(self, theta: int, axes_dim: List[int]):\n super().__init__()\n self.theta = theta\n self.axes_dim = axes_dim\n\n def forward(self, ids: torch.Tensor) -> torch.Tensor:\n n_axes = ids.shape[-1]\n emb = torch.cat(\n [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],\n dim=-3,\n )\n return emb.unsqueeze(2)\n\n\nclass PatchEmbed(nn.Module):\n def __init__(\n self,\n patch_size=2,\n in_channels=4,\n out_channels=1024,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n self.patch_size = patch_size\n self.out_channels = out_channels\n self.proj = operations.Linear(in_channels * patch_size * patch_size, out_channels, bias=True, dtype=dtype, device=device)\n\n def forward(self, latent):\n latent = self.proj(latent)\n return latent\n\n\nclass PooledEmbed(nn.Module):\n def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None, operations=None):\n super().__init__()\n self.pooled_embedder = TimestepEmbedding(in_channels=text_emb_dim, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations)\n\n def forward(self, pooled_embed):\n return self.pooled_embedder(pooled_embed)\n\n\nclass TimestepEmbed(nn.Module):\n def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):\n super().__init__()\n self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations)\n\n def forward(self, timesteps, wdtype):\n t_emb = self.time_proj(timesteps).to(dtype=wdtype)\n t_emb = self.timestep_embedder(t_emb)\n return t_emb\n\n\ndef attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}):\n return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options)\n\n\nclass HiDreamAttnProcessor_flashattn:\n \"\"\"Attention processor used typically in processing the SD3-like self-attention projections.\"\"\"\n\n def __call__(\n self,\n attn,\n image_tokens: torch.FloatTensor,\n image_tokens_masks: Optional[torch.FloatTensor] = None,\n text_tokens: Optional[torch.FloatTensor] = None,\n rope: torch.FloatTensor = None,\n transformer_options={},\n *args,\n **kwargs,\n ) -> torch.FloatTensor:\n dtype = image_tokens.dtype\n batch_size = image_tokens.shape[0]\n\n query_i = attn.q_rms_norm(attn.to_q(image_tokens)).to(dtype=dtype)\n key_i = attn.k_rms_norm(attn.to_k(image_tokens)).to(dtype=dtype)\n value_i = attn.to_v(image_tokens)\n\n inner_dim = key_i.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query_i = query_i.view(batch_size, -1, attn.heads, head_dim)\n key_i = key_i.view(batch_size, -1, attn.heads, head_dim)\n value_i = value_i.view(batch_size, -1, attn.heads, head_dim)\n if image_tokens_masks is not None:\n key_i = key_i * image_tokens_masks.view(batch_size, -1, 1, 1)\n\n if not attn.single:\n query_t = attn.q_rms_norm_t(attn.to_q_t(text_tokens)).to(dtype=dtype)\n key_t = attn.k_rms_norm_t(attn.to_k_t(text_tokens)).to(dtype=dtype)\n value_t = attn.to_v_t(text_tokens)\n\n query_t = query_t.view(batch_size, -1, attn.heads, head_dim)\n key_t = key_t.view(batch_size, -1, attn.heads, head_dim)\n value_t = value_t.view(batch_size, -1, attn.heads, head_dim)\n\n num_image_tokens = query_i.shape[1]\n num_text_tokens = query_t.shape[1]\n query = torch.cat([query_i, query_t], dim=1)\n key = torch.cat([key_i, key_t], dim=1)\n value = torch.cat([value_i, value_t], dim=1)\n else:\n query = query_i\n key = key_i\n value = value_i\n\n if query.shape[-1] == rope.shape[-3] * 2:\n query, key = apply_rope(query, key, rope)\n else:\n query_1, query_2 = query.chunk(2, dim=-1)\n key_1, key_2 = key.chunk(2, dim=-1)\n query_1, key_1 = apply_rope(query_1, key_1, rope)\n query = torch.cat([query_1, query_2], dim=-1)\n key = torch.cat([key_1, key_2], dim=-1)\n\n hidden_states = attention(query, key, value, transformer_options=transformer_options)\n\n if not attn.single:\n hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1)\n hidden_states_i = attn.to_out(hidden_states_i)\n hidden_states_t = attn.to_out_t(hidden_states_t)\n return hidden_states_i, hidden_states_t\n else:\n hidden_states = attn.to_out(hidden_states)\n return hidden_states\n\nclass HiDreamAttention(nn.Module):\n def __init__(\n self,\n query_dim: int,\n heads: int = 8,\n dim_head: int = 64,\n upcast_attention: bool = False,\n upcast_softmax: bool = False,\n scale_qk: bool = True,\n eps: float = 1e-5,\n processor = None,\n out_dim: int = None,\n single: bool = False,\n dtype=None, device=None, operations=None\n ):\n # super(Attention, self).__init__()\n super().__init__()\n self.inner_dim = out_dim if out_dim is not None else dim_head * heads\n self.query_dim = query_dim\n self.upcast_attention = upcast_attention\n self.upcast_softmax = upcast_softmax\n self.out_dim = out_dim if out_dim is not None else query_dim\n\n self.scale_qk = scale_qk\n self.scale = dim_head**-0.5 if self.scale_qk else 1.0\n\n self.heads = out_dim // dim_head if out_dim is not None else heads\n self.sliceable_head_dim = heads\n self.single = single\n\n linear_cls = operations.Linear\n self.linear_cls = linear_cls\n self.to_q = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device)\n self.to_k = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device)\n self.to_v = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device)\n self.to_out = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device)\n self.q_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device)\n self.k_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device)\n\n if not single:\n self.to_q_t = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device)\n self.to_k_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device)\n self.to_v_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device)\n self.to_out_t = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device)\n self.q_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device)\n self.k_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device)\n\n self.processor = processor\n\n def forward(\n self,\n norm_image_tokens: torch.FloatTensor,\n image_tokens_masks: torch.FloatTensor = None,\n norm_text_tokens: torch.FloatTensor = None,\n rope: torch.FloatTensor = None,\n transformer_options={},\n ) -> torch.Tensor:\n return self.processor(\n self,\n image_tokens = norm_image_tokens,\n image_tokens_masks = image_tokens_masks,\n text_tokens = norm_text_tokens,\n rope = rope,\n transformer_options=transformer_options,\n )\n\n\nclass FeedForwardSwiGLU(nn.Module):\n def __init__(\n self,\n dim: int,\n hidden_dim: int,\n multiple_of: int = 256,\n ffn_dim_multiplier: Optional[float] = None,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n hidden_dim = int(2 * hidden_dim / 3)\n # custom dim factor multiplier\n if ffn_dim_multiplier is not None:\n hidden_dim = int(ffn_dim_multiplier * hidden_dim)\n hidden_dim = multiple_of * (\n (hidden_dim + multiple_of - 1) // multiple_of\n )\n\n self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)\n self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device)\n self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)\n\n def forward(self, x):\n return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x))\n\n\n# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py\nclass MoEGate(nn.Module):\n def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01, dtype=None, device=None, operations=None):\n super().__init__()\n self.top_k = num_activated_experts\n self.n_routed_experts = num_routed_experts\n\n self.scoring_func = 'softmax'\n self.alpha = aux_loss_alpha\n self.seq_aux = False\n\n # topk selection algorithm\n self.norm_topk_prob = False\n self.gating_dim = embed_dim\n self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), dtype=dtype, device=device))\n self.reset_parameters()\n\n def reset_parameters(self) -> None:\n pass\n # import torch.nn.init as init\n # init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n\n def forward(self, hidden_states):\n bsz, seq_len, h = hidden_states.shape\n\n ### compute gating score\n hidden_states = hidden_states.view(-1, h)\n logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), None)\n if self.scoring_func == 'softmax':\n scores = logits.softmax(dim=-1)\n else:\n raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')\n\n ### select top-k experts\n topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)\n\n ### norm gate to sum 1\n if self.top_k > 1 and self.norm_topk_prob:\n denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20\n topk_weight = topk_weight / denominator\n\n aux_loss = None\n return topk_idx, topk_weight, aux_loss\n\n\n# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py\nclass MOEFeedForwardSwiGLU(nn.Module):\n def __init__(\n self,\n dim: int,\n hidden_dim: int,\n num_routed_experts: int,\n num_activated_experts: int,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n self.shared_experts = FeedForwardSwiGLU(dim, hidden_dim // 2, dtype=dtype, device=device, operations=operations)\n self.experts = nn.ModuleList([FeedForwardSwiGLU(dim, hidden_dim, dtype=dtype, device=device, operations=operations) for i in range(num_routed_experts)])\n self.gate = MoEGate(\n embed_dim = dim,\n num_routed_experts = num_routed_experts,\n num_activated_experts = num_activated_experts,\n dtype=dtype, device=device, operations=operations\n )\n self.num_activated_experts = num_activated_experts\n\n def forward(self, x):\n wtype = x.dtype\n identity = x\n orig_shape = x.shape\n topk_idx, topk_weight, aux_loss = self.gate(x)\n x = x.view(-1, x.shape[-1])\n flat_topk_idx = topk_idx.view(-1)\n if True: # self.training: # TODO: check which branch performs faster\n x = x.repeat_interleave(self.num_activated_experts, dim=0)\n y = torch.empty_like(x, dtype=wtype)\n for i, expert in enumerate(self.experts):\n y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(dtype=wtype)\n y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)\n y = y.view(*orig_shape).to(dtype=wtype)\n #y = AddAuxiliaryLoss.apply(y, aux_loss)\n else:\n y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)\n y = y + self.shared_experts(identity)\n return y\n\n @torch.no_grad()\n def moe_infer(self, x, flat_expert_indices, flat_expert_weights):\n expert_cache = torch.zeros_like(x)\n idxs = flat_expert_indices.argsort()\n tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)\n token_idxs = idxs // self.num_activated_experts\n for i, end_idx in enumerate(tokens_per_expert):\n start_idx = 0 if i == 0 else tokens_per_expert[i-1]\n if start_idx == end_idx:\n continue\n expert = self.experts[i]\n exp_token_idx = token_idxs[start_idx:end_idx]\n expert_tokens = x[exp_token_idx]\n expert_out = expert(expert_tokens)\n expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])\n\n # for fp16 and other dtype\n expert_cache = expert_cache.to(expert_out.dtype)\n expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum')\n return expert_cache\n\n\nclass TextProjection(nn.Module):\n def __init__(self, in_features, hidden_size, dtype=None, device=None, operations=None):\n super().__init__()\n self.linear = operations.Linear(in_features=in_features, out_features=hidden_size, bias=False, dtype=dtype, device=device)\n\n def forward(self, caption):\n hidden_states = self.linear(caption)\n return hidden_states\n\n\nclass BlockType:\n TransformerBlock = 1\n SingleTransformerBlock = 2\n\n\nclass HiDreamImageSingleTransformerBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n num_routed_experts: int = 4,\n num_activated_experts: int = 2,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n self.num_attention_heads = num_attention_heads\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device)\n )\n\n # 1. Attention\n self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device)\n self.attn1 = HiDreamAttention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n processor = HiDreamAttnProcessor_flashattn(),\n single = True,\n dtype=dtype, device=device, operations=operations\n )\n\n # 3. Feed-forward\n self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device)\n if num_routed_experts > 0:\n self.ff_i = MOEFeedForwardSwiGLU(\n dim = dim,\n hidden_dim = 4 * dim,\n num_routed_experts = num_routed_experts,\n num_activated_experts = num_activated_experts,\n dtype=dtype, device=device, operations=operations\n )\n else:\n self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations)\n\n def forward(\n self,\n image_tokens: torch.FloatTensor,\n image_tokens_masks: Optional[torch.FloatTensor] = None,\n text_tokens: Optional[torch.FloatTensor] = None,\n adaln_input: Optional[torch.FloatTensor] = None,\n rope: torch.FloatTensor = None,\n transformer_options={},\n ) -> torch.FloatTensor:\n wtype = image_tokens.dtype\n shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \\\n self.adaLN_modulation(adaln_input)[:,None].chunk(6, dim=-1)\n\n # 1. MM-Attention\n norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype)\n norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i\n attn_output_i = self.attn1(\n norm_image_tokens,\n image_tokens_masks,\n rope = rope,\n transformer_options=transformer_options,\n )\n image_tokens = gate_msa_i * attn_output_i + image_tokens\n\n # 2. Feed-forward\n norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype)\n norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i\n ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens.to(dtype=wtype))\n image_tokens = ff_output_i + image_tokens\n return image_tokens\n\n\nclass HiDreamImageTransformerBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n num_routed_experts: int = 4,\n num_activated_experts: int = 2,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n self.num_attention_heads = num_attention_heads\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(dim, 12 * dim, bias=True, dtype=dtype, device=device)\n )\n # nn.init.zeros_(self.adaLN_modulation[1].weight)\n # nn.init.zeros_(self.adaLN_modulation[1].bias)\n\n # 1. Attention\n self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device)\n self.norm1_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device)\n self.attn1 = HiDreamAttention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n processor = HiDreamAttnProcessor_flashattn(),\n single = False,\n dtype=dtype, device=device, operations=operations\n )\n\n # 3. Feed-forward\n self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device)\n if num_routed_experts > 0:\n self.ff_i = MOEFeedForwardSwiGLU(\n dim = dim,\n hidden_dim = 4 * dim,\n num_routed_experts = num_routed_experts,\n num_activated_experts = num_activated_experts,\n dtype=dtype, device=device, operations=operations\n )\n else:\n self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations)\n self.norm3_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False)\n self.ff_t = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations)\n\n def forward(\n self,\n image_tokens: torch.FloatTensor,\n image_tokens_masks: Optional[torch.FloatTensor] = None,\n text_tokens: Optional[torch.FloatTensor] = None,\n adaln_input: Optional[torch.FloatTensor] = None,\n rope: torch.FloatTensor = None,\n transformer_options={},\n ) -> torch.FloatTensor:\n wtype = image_tokens.dtype\n shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \\\n shift_msa_t, scale_msa_t, gate_msa_t, shift_mlp_t, scale_mlp_t, gate_mlp_t = \\\n self.adaLN_modulation(adaln_input)[:,None].chunk(12, dim=-1)\n\n # 1. MM-Attention\n norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype)\n norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i\n norm_text_tokens = self.norm1_t(text_tokens).to(dtype=wtype)\n norm_text_tokens = norm_text_tokens * (1 + scale_msa_t) + shift_msa_t\n\n attn_output_i, attn_output_t = self.attn1(\n norm_image_tokens,\n image_tokens_masks,\n norm_text_tokens,\n rope = rope,\n transformer_options=transformer_options,\n )\n\n image_tokens = gate_msa_i * attn_output_i + image_tokens\n text_tokens = gate_msa_t * attn_output_t + text_tokens\n\n # 2. Feed-forward\n norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype)\n norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i\n norm_text_tokens = self.norm3_t(text_tokens).to(dtype=wtype)\n norm_text_tokens = norm_text_tokens * (1 + scale_mlp_t) + shift_mlp_t\n\n ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens)\n ff_output_t = gate_mlp_t * self.ff_t(norm_text_tokens)\n image_tokens = ff_output_i + image_tokens\n text_tokens = ff_output_t + text_tokens\n return image_tokens, text_tokens\n\n\nclass HiDreamImageBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n num_routed_experts: int = 4,\n num_activated_experts: int = 2,\n block_type: BlockType = BlockType.TransformerBlock,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n block_classes = {\n BlockType.TransformerBlock: HiDreamImageTransformerBlock,\n BlockType.SingleTransformerBlock: HiDreamImageSingleTransformerBlock,\n }\n self.block = block_classes[block_type](\n dim,\n num_attention_heads,\n attention_head_dim,\n num_routed_experts,\n num_activated_experts,\n dtype=dtype, device=device, operations=operations\n )\n\n def forward(\n self,\n image_tokens: torch.FloatTensor,\n image_tokens_masks: Optional[torch.FloatTensor] = None,\n text_tokens: Optional[torch.FloatTensor] = None,\n adaln_input: torch.FloatTensor = None,\n rope: torch.FloatTensor = None,\n transformer_options={},\n ) -> torch.FloatTensor:\n return self.block(\n image_tokens,\n image_tokens_masks,\n text_tokens,\n adaln_input,\n rope,\n transformer_options=transformer_options,\n )\n\n\nclass HiDreamImageTransformer2DModel(nn.Module):\n def __init__(\n self,\n patch_size: Optional[int] = None,\n in_channels: int = 64,\n out_channels: Optional[int] = None,\n num_layers: int = 16,\n num_single_layers: int = 32,\n attention_head_dim: int = 128,\n num_attention_heads: int = 20,\n caption_channels: List[int] = None,\n text_emb_dim: int = 2048,\n num_routed_experts: int = 4,\n num_activated_experts: int = 2,\n axes_dims_rope: Tuple[int, int] = (32, 32),\n max_resolution: Tuple[int, int] = (128, 128),\n llama_layers: List[int] = None,\n image_model=None,\n dtype=None, device=None, operations=None\n ):\n self.patch_size = patch_size\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n self.num_layers = num_layers\n self.num_single_layers = num_single_layers\n\n self.gradient_checkpointing = False\n\n super().__init__()\n self.dtype = dtype\n self.out_channels = out_channels or in_channels\n self.inner_dim = self.num_attention_heads * self.attention_head_dim\n self.llama_layers = llama_layers\n\n self.t_embedder = TimestepEmbed(self.inner_dim, dtype=dtype, device=device, operations=operations)\n self.p_embedder = PooledEmbed(text_emb_dim, self.inner_dim, dtype=dtype, device=device, operations=operations)\n self.x_embedder = PatchEmbed(\n patch_size = patch_size,\n in_channels = in_channels,\n out_channels = self.inner_dim,\n dtype=dtype, device=device, operations=operations\n )\n self.pe_embedder = EmbedND(theta=10000, axes_dim=axes_dims_rope)\n\n self.double_stream_blocks = nn.ModuleList(\n [\n HiDreamImageBlock(\n dim = self.inner_dim,\n num_attention_heads = self.num_attention_heads,\n attention_head_dim = self.attention_head_dim,\n num_routed_experts = num_routed_experts,\n num_activated_experts = num_activated_experts,\n block_type = BlockType.TransformerBlock,\n dtype=dtype, device=device, operations=operations\n )\n for i in range(self.num_layers)\n ]\n )\n\n self.single_stream_blocks = nn.ModuleList(\n [\n HiDreamImageBlock(\n dim = self.inner_dim,\n num_attention_heads = self.num_attention_heads,\n attention_head_dim = self.attention_head_dim,\n num_routed_experts = num_routed_experts,\n num_activated_experts = num_activated_experts,\n block_type = BlockType.SingleTransformerBlock,\n dtype=dtype, device=device, operations=operations\n )\n for i in range(self.num_single_layers)\n ]\n )\n\n self.final_layer = LastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)\n\n caption_channels = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ]\n caption_projection = []\n for caption_channel in caption_channels:\n caption_projection.append(TextProjection(in_features=caption_channel, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations))\n self.caption_projection = nn.ModuleList(caption_projection)\n self.max_seq = max_resolution[0] * max_resolution[1] // (patch_size * patch_size)\n\n def expand_timesteps(self, timesteps, batch_size, device):\n if not torch.is_tensor(timesteps):\n is_mps = device.type == \"mps\"\n if isinstance(timesteps, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(device)\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(batch_size)\n return timesteps\n\n def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]) -> List[torch.Tensor]:\n x_arr = []\n for i, img_size in enumerate(img_sizes):\n pH, pW = img_size\n x_arr.append(\n einops.rearrange(x[i, :pH*pW].reshape(1, pH, pW, -1), 'B H W (p1 p2 C) -> B C (H p1) (W p2)',\n p1=self.patch_size, p2=self.patch_size)\n )\n x = torch.cat(x_arr, dim=0)\n return x\n\n def patchify(self, x, max_seq, img_sizes=None):\n pz2 = self.patch_size * self.patch_size\n if isinstance(x, torch.Tensor):\n B = x.shape[0]\n device = x.device\n dtype = x.dtype\n else:\n B = len(x)\n device = x[0].device\n dtype = x[0].dtype\n x_masks = torch.zeros((B, max_seq), dtype=dtype, device=device)\n\n if img_sizes is not None:\n for i, img_size in enumerate(img_sizes):\n x_masks[i, 0:img_size[0] * img_size[1]] = 1\n x = einops.rearrange(x, 'B C S p -> B S (p C)', p=pz2)\n elif isinstance(x, torch.Tensor):\n pH, pW = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size\n x = einops.rearrange(x, 'B C (H p1) (W p2) -> B (H W) (p1 p2 C)', p1=self.patch_size, p2=self.patch_size)\n img_sizes = [[pH, pW]] * B\n x_masks = None\n else:\n raise NotImplementedError\n return x, x_masks, img_sizes\n\n def forward(self,\n x: torch.Tensor,\n t: torch.Tensor,\n y: Optional[torch.Tensor] = None,\n context: Optional[torch.Tensor] = None,\n encoder_hidden_states_llama3=None,\n image_cond=None,\n control = None,\n transformer_options = {},\n ):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, t, y, context, encoder_hidden_states_llama3, image_cond, control, transformer_options)\n\n def _forward(\n self,\n x: torch.Tensor,\n t: torch.Tensor,\n y: Optional[torch.Tensor] = None,\n context: Optional[torch.Tensor] = None,\n encoder_hidden_states_llama3=None,\n image_cond=None,\n control = None,\n transformer_options = {},\n ) -> torch.Tensor:\n bs, c, h, w = x.shape\n if image_cond is not None:\n x = torch.cat([x, image_cond], dim=-1)\n hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n timesteps = t\n pooled_embeds = y\n T5_encoder_hidden_states = context\n\n img_sizes = None\n\n # spatial forward\n batch_size = hidden_states.shape[0]\n hidden_states_type = hidden_states.dtype\n\n # 0. time\n timesteps = self.expand_timesteps(timesteps, batch_size, hidden_states.device)\n timesteps = self.t_embedder(timesteps, hidden_states_type)\n p_embedder = self.p_embedder(pooled_embeds)\n adaln_input = timesteps + p_embedder\n\n hidden_states, image_tokens_masks, img_sizes = self.patchify(hidden_states, self.max_seq, img_sizes)\n if image_tokens_masks is None:\n pH, pW = img_sizes[0]\n img_ids = torch.zeros(pH, pW, 3, device=hidden_states.device)\n img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH, device=hidden_states.device)[:, None]\n img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW, device=hidden_states.device)[None, :]\n img_ids = repeat(img_ids, \"h w c -> b (h w) c\", b=batch_size)\n hidden_states = self.x_embedder(hidden_states)\n\n # T5_encoder_hidden_states = encoder_hidden_states[0]\n encoder_hidden_states = encoder_hidden_states_llama3.movedim(1, 0)\n encoder_hidden_states = [encoder_hidden_states[k] for k in self.llama_layers]\n\n if self.caption_projection is not None:\n new_encoder_hidden_states = []\n for i, enc_hidden_state in enumerate(encoder_hidden_states):\n enc_hidden_state = self.caption_projection[i](enc_hidden_state)\n enc_hidden_state = enc_hidden_state.view(batch_size, -1, hidden_states.shape[-1])\n new_encoder_hidden_states.append(enc_hidden_state)\n encoder_hidden_states = new_encoder_hidden_states\n T5_encoder_hidden_states = self.caption_projection[-1](T5_encoder_hidden_states)\n T5_encoder_hidden_states = T5_encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])\n encoder_hidden_states.append(T5_encoder_hidden_states)\n\n txt_ids = torch.zeros(\n batch_size,\n encoder_hidden_states[-1].shape[1] + encoder_hidden_states[-2].shape[1] + encoder_hidden_states[0].shape[1],\n 3,\n device=img_ids.device, dtype=img_ids.dtype\n )\n ids = torch.cat((img_ids, txt_ids), dim=1)\n rope = self.pe_embedder(ids)\n\n # 2. Blocks\n block_id = 0\n initial_encoder_hidden_states = torch.cat([encoder_hidden_states[-1], encoder_hidden_states[-2]], dim=1)\n initial_encoder_hidden_states_seq_len = initial_encoder_hidden_states.shape[1]\n for bid, block in enumerate(self.double_stream_blocks):\n cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id]\n cur_encoder_hidden_states = torch.cat([initial_encoder_hidden_states, cur_llama31_encoder_hidden_states], dim=1)\n hidden_states, initial_encoder_hidden_states = block(\n image_tokens = hidden_states,\n image_tokens_masks = image_tokens_masks,\n text_tokens = cur_encoder_hidden_states,\n adaln_input = adaln_input,\n rope = rope,\n transformer_options=transformer_options,\n )\n initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len]\n block_id += 1\n\n image_tokens_seq_len = hidden_states.shape[1]\n hidden_states = torch.cat([hidden_states, initial_encoder_hidden_states], dim=1)\n hidden_states_seq_len = hidden_states.shape[1]\n if image_tokens_masks is not None:\n encoder_attention_mask_ones = torch.ones(\n (batch_size, initial_encoder_hidden_states.shape[1] + cur_llama31_encoder_hidden_states.shape[1]),\n device=image_tokens_masks.device, dtype=image_tokens_masks.dtype\n )\n image_tokens_masks = torch.cat([image_tokens_masks, encoder_attention_mask_ones], dim=1)\n\n for bid, block in enumerate(self.single_stream_blocks):\n cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id]\n hidden_states = torch.cat([hidden_states, cur_llama31_encoder_hidden_states], dim=1)\n hidden_states = block(\n image_tokens=hidden_states,\n image_tokens_masks=image_tokens_masks,\n text_tokens=None,\n adaln_input=adaln_input,\n rope=rope,\n transformer_options=transformer_options,\n )\n hidden_states = hidden_states[:, :hidden_states_seq_len]\n block_id += 1\n\n hidden_states = hidden_states[:, :image_tokens_seq_len, ...]\n output = self.final_layer(hidden_states, adaln_input)\n output = self.unpatchify(output, img_sizes)\n return -output[:, :, :h, :w]\n" }, { "path": "comfy/ldm/hunyuan3d/model.py", "content": "import torch\nfrom torch import nn\nfrom comfy.ldm.flux.layers import (\n DoubleStreamBlock,\n LastLayer,\n MLPEmbedder,\n SingleStreamBlock,\n timestep_embedding,\n)\nimport comfy.patcher_extension\n\n\nclass Hunyuan3Dv2(nn.Module):\n def __init__(\n self,\n in_channels=64,\n context_in_dim=1536,\n hidden_size=1024,\n mlp_ratio=4.0,\n num_heads=16,\n depth=16,\n depth_single_blocks=32,\n qkv_bias=True,\n guidance_embed=False,\n image_model=None,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.dtype = dtype\n\n if hidden_size % num_heads != 0:\n raise ValueError(\n f\"Hidden size {hidden_size} must be divisible by num_heads {num_heads}\"\n )\n\n self.max_period = 1000 # While reimplementing the model I noticed that they messed up. This 1000 value was meant to be the time_factor but they set the max_period instead\n self.latent_in = operations.Linear(in_channels, hidden_size, bias=True, dtype=dtype, device=device)\n self.time_in = MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations)\n self.guidance_in = (\n MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) if guidance_embed else None\n )\n self.cond_in = operations.Linear(context_in_dim, hidden_size, dtype=dtype, device=device)\n self.double_blocks = nn.ModuleList(\n [\n DoubleStreamBlock(\n hidden_size,\n num_heads,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(depth)\n ]\n )\n self.single_blocks = nn.ModuleList(\n [\n SingleStreamBlock(\n hidden_size,\n num_heads,\n mlp_ratio=mlp_ratio,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(depth_single_blocks)\n ]\n )\n self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations)\n\n def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, guidance, transformer_options, **kwargs)\n\n def _forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs):\n x = x.movedim(-1, -2)\n timestep = 1.0 - timestep\n txt = context\n img = self.latent_in(x)\n\n vec = self.time_in(timestep_embedding(timestep, 256, self.max_period).to(dtype=img.dtype))\n if self.guidance_in is not None:\n if guidance is not None:\n vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.max_period).to(img.dtype))\n\n txt = self.cond_in(txt)\n pe = None\n attn_mask = None\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n for i, block in enumerate(self.double_blocks):\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"], out[\"txt\"] = block(img=args[\"img\"],\n txt=args[\"txt\"],\n vec=args[\"vec\"],\n pe=args[\"pe\"],\n attn_mask=args.get(\"attn_mask\"),\n transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": img,\n \"txt\": txt,\n \"vec\": vec,\n \"pe\": pe,\n \"attn_mask\": attn_mask,\n \"transformer_options\": transformer_options},\n {\"original_block\": block_wrap})\n txt = out[\"txt\"]\n img = out[\"img\"]\n else:\n img, txt = block(img=img,\n txt=txt,\n vec=vec,\n pe=pe,\n attn_mask=attn_mask,\n transformer_options=transformer_options)\n\n img = torch.cat((txt, img), 1)\n\n for i, block in enumerate(self.single_blocks):\n if (\"single_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"],\n vec=args[\"vec\"],\n pe=args[\"pe\"],\n attn_mask=args.get(\"attn_mask\"),\n transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"single_block\", i)]({\"img\": img,\n \"vec\": vec,\n \"pe\": pe,\n \"attn_mask\": attn_mask,\n \"transformer_options\": transformer_options},\n {\"original_block\": block_wrap})\n img = out[\"img\"]\n else:\n img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)\n\n img = img[:, txt.shape[1]:, ...]\n img = self.final_layer(img, vec)\n return img.movedim(-2, -1) * (-1.0)\n" }, { "path": "comfy/ldm/hunyuan3d/vae.py", "content": "# Original: https://github.com/Tencent/Hunyuan3D-2/blob/main/hy3dgen/shapegen/models/autoencoders/model.py\n# Since the header on their VAE source file was a bit confusing we asked for permission to use this code from tencent under the GPL license used in ComfyUI.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport math\nfrom tqdm import tqdm\n\nfrom typing import Optional\n\nimport logging\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\ndef fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True):\n\n # manually create the pointer vector\n assert src.size(0) == batch.numel()\n\n batch_size = int(batch.max()) + 1\n deg = src.new_zeros(batch_size, dtype = torch.long)\n\n deg.scatter_add_(0, batch, torch.ones_like(batch))\n\n ptr_vec = deg.new_zeros(batch_size + 1)\n torch.cumsum(deg, 0, out=ptr_vec[1:])\n\n #return fps_sampling(src, ptr_vec, ratio)\n sampled_indicies = []\n\n for b in range(batch_size):\n # start and the end of each batch\n start, end = ptr_vec[b].item(), ptr_vec[b + 1].item()\n # points from the point cloud\n points = src[start:end]\n\n num_points = points.size(0)\n num_samples = max(1, math.ceil(num_points * sampling_ratio))\n\n selected = torch.zeros(num_samples, device = src.device, dtype = torch.long)\n distances = torch.full((num_points,), float(\"inf\"), device = src.device)\n\n # select a random start point\n if start_random:\n farthest = torch.randint(0, num_points, (1,), device = src.device)\n else:\n farthest = torch.tensor([0], device = src.device, dtype = torch.long)\n\n for i in range(num_samples):\n selected[i] = farthest\n centroid = points[farthest].squeeze(0)\n dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance\n distances = torch.minimum(distances, dist)\n farthest = torch.argmax(distances)\n\n sampled_indicies.append(torch.arange(start, end)[selected])\n\n return torch.cat(sampled_indicies, dim = 0)\nclass PointCrossAttention(nn.Module):\n def __init__(self,\n num_latents: int,\n downsample_ratio: float,\n pc_size: int,\n pc_sharpedge_size: int,\n point_feats: int,\n width: int,\n heads: int,\n layers: int,\n fourier_embedder,\n normal_pe: bool = False,\n qkv_bias: bool = False,\n use_ln_post: bool = True,\n qk_norm: bool = True):\n\n super().__init__()\n\n self.fourier_embedder = fourier_embedder\n\n self.pc_size = pc_size\n self.normal_pe = normal_pe\n self.downsample_ratio = downsample_ratio\n self.pc_sharpedge_size = pc_sharpedge_size\n self.num_latents = num_latents\n self.point_feats = point_feats\n\n self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width)\n\n self.cross_attn = ResidualCrossAttentionBlock(\n width = width,\n heads = heads,\n qkv_bias = qkv_bias,\n qk_norm = qk_norm\n )\n\n self.self_attn = None\n if layers > 0:\n self.self_attn = Transformer(\n width = width,\n heads = heads,\n qkv_bias = qkv_bias,\n qk_norm = qk_norm,\n layers = layers\n )\n\n if use_ln_post:\n self.ln_post = nn.LayerNorm(width)\n else:\n self.ln_post = None\n\n def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor):\n\n \"\"\"\n Subsample points randomly from the point cloud (input_pc)\n Further sample the subsampled points to get query_pc\n take the fourier embeddings for both input and query pc\n\n Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc.\n Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc).\n More computationally efficient.\n\n Features are additional information for each point in the cloud\n \"\"\"\n\n B, _, D = point_cloud.shape\n\n num_latents = int(self.num_latents)\n\n num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents\n num_sharpedge_query = num_latents - num_random_query\n\n # Split random and sharpedge surface points\n random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1)\n\n # assert statements\n assert random_pc.shape[1] <= self.pc_size, \"Random surface points size must be less than or equal to pc_size\"\n assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, \"Sharpedge surface points size must be less than or equal to pc_sharpedge_size\"\n\n input_random_pc_size = int(num_random_query * self.downsample_ratio)\n random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \\\n self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size)\n\n input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio)\n\n if input_sharpedge_pc_size == 0:\n sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device)\n sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device)\n\n else:\n sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \\\n self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size)\n\n # concat the random and sharpedges\n query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1)\n input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1)\n\n query = self.fourier_embedder(query_pc)\n data = self.fourier_embedder(input_pc)\n\n if self.point_feats > 0:\n random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1)\n\n input_random_surface_features, query_random_features = \\\n self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B,\n input_pc_size = input_random_pc_size, idx_query = random_idx_query)\n\n if input_sharpedge_pc_size == 0:\n input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats,\n dtype = input_random_surface_features.dtype, device = point_cloud.device)\n\n query_sharpedge_features = torch.zeros(B, 0, self.point_feats,\n dtype = query_random_features.dtype, device = point_cloud.device)\n else:\n\n input_sharpedge_surface_features, query_sharpedge_features = \\\n self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features,\n batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size)\n\n query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1)\n input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1)\n\n if self.normal_pe:\n # apply the fourier embeddings on the first 3 dims (xyz)\n input_features_pe = self.fourier_embedder(input_features[..., :3])\n query_features_pe = self.fourier_embedder(query_features[..., :3])\n # replace the first 3 dims with the new PE ones\n input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1)\n query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1)\n\n # concat at the channels dim\n query = torch.cat([query, query_features], dim = -1)\n data = torch.cat([data, input_features], dim = -1)\n\n # don't return pc_info to avoid unnecessary memory usuage\n return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1])\n\n def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):\n\n query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features)\n\n # apply projections\n query = self.input_proj(query)\n data = self.input_proj(data)\n\n # apply cross attention between query and data\n latents = self.cross_attn(query, data)\n\n if self.self_attn is not None:\n latents = self.self_attn(latents)\n\n if self.ln_post is not None:\n latents = self.ln_post(latents)\n\n return latents\n\n\n def subsample(self, pc, num_query, input_pc_size: int):\n\n \"\"\"\n num_query: number of points to keep after FPS\n input_pc_size: number of points to select before FPS\n \"\"\"\n\n B, _, D = pc.shape\n query_ratio = num_query / input_pc_size\n\n # random subsampling of points inside the point cloud\n idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size]\n input_pc = pc[:, idx_pc, :]\n\n # flatten to allow applying fps across the whole batch\n flattent_input_pc = input_pc.view(B * input_pc_size, D)\n\n # construct a batch_down tensor to tell fps\n # which points belong to which batch\n N_down = int(flattent_input_pc.shape[0] / B)\n batch_down = torch.arange(B).to(pc.device)\n batch_down = torch.repeat_interleave(batch_down, N_down)\n\n idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio)\n query_pc = flattent_input_pc[idx_query].view(B, -1, D)\n\n return query_pc, input_pc, idx_pc, idx_query\n\n def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query):\n\n B = batch_size\n\n input_surface_features = features[:, idx_pc, :]\n flattent_input_features = input_surface_features.view(B * input_pc_size, -1)\n query_features = flattent_input_features[idx_query].view(B, -1,\n flattent_input_features.shape[-1])\n\n return input_surface_features, query_features\n\ndef normalize_mesh(mesh, scale = 0.9999):\n \"\"\"Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]\"\"\"\n\n bbox = mesh.bounds\n center = (bbox[1] + bbox[0]) / 2\n\n max_extent = (bbox[1] - bbox[0]).max()\n mesh.apply_translation(-center)\n mesh.apply_scale((2 * scale) / max_extent)\n\n return mesh\n\ndef sample_pointcloud(mesh, num = 200000):\n \"\"\" Uniformly sample points from the surface of the mesh \"\"\"\n\n points, face_idx = mesh.sample(num, return_index = True)\n normals = mesh.face_normals[face_idx]\n return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32))\n\ndef detect_sharp_edges(mesh, threshold=0.985):\n \"\"\"Return edge indices (a, b) that lie on sharp boundaries of the mesh.\"\"\"\n\n V, F = mesh.vertices, mesh.faces\n VN, FN = mesh.vertex_normals, mesh.face_normals\n\n sharp_mask = np.ones(V.shape[0])\n for i in range(3):\n indices = F[:, i]\n alignment = np.einsum('ij,ij->i', VN[indices], FN)\n dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1)\n sharp_mask[indices] = np.min(dot_stack, axis=-1)\n\n edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]])\n edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]])\n sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold)\n\n return edge_a[sharp_edges], edge_b[sharp_edges]\n\n\ndef sharp_sample_pointcloud(mesh, num = 16384):\n \"\"\" Sample points preferentially from sharp edges in the mesh. \"\"\"\n\n edge_a, edge_b = detect_sharp_edges(mesh)\n V, VN = mesh.vertices, mesh.vertex_normals\n\n va, vb = V[edge_a], V[edge_b]\n na, nb = VN[edge_a], VN[edge_b]\n\n edge_lengths = np.linalg.norm(vb - va, axis=-1)\n weights = edge_lengths / edge_lengths.sum()\n\n indices = np.searchsorted(np.cumsum(weights), np.random.rand(num))\n t = np.random.rand(num, 1)\n\n samples = t * va[indices] + (1 - t) * vb[indices]\n normals = t * na[indices] + (1 - t) * nb[indices]\n\n return samples.astype(np.float32), normals.astype(np.float32)\n\ndef load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = \"cuda\"):\n \"\"\"Load a surface with optional sharp-edge annotations from a trimesh mesh.\"\"\"\n\n import trimesh\n\n try:\n mesh_full = trimesh.util.concatenate(mesh.dump())\n except Exception:\n mesh_full = trimesh.util.concatenate(mesh)\n\n mesh_full = normalize_mesh(mesh_full)\n\n faces = mesh_full.faces\n vertices = mesh_full.vertices\n origin_face_count = faces.shape[0]\n\n mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count])\n mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:])\n\n area_surface = mesh_surface.area\n area_fill = mesh_fill.area\n total_area = area_surface + area_fill\n\n sample_num = 499712 // 2\n fill_ratio = area_fill / total_area if total_area > 0 else 0\n\n num_fill = int(sample_num * fill_ratio)\n num_surface = sample_num - num_fill\n\n surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface)\n fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill)\n\n sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num)\n\n def assemble_tensor(points, normals, label=None):\n\n data = torch.cat([points, normals], dim=1).half().to(device)\n\n if label is not None:\n label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device)\n data = torch.cat([data, label_tensor], dim=1)\n\n return data\n\n surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0),\n torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0),\n label = 0 if sharpedge_flag else None)\n\n sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals),\n label = 1 if sharpedge_flag else None)\n\n rng = np.random.default_rng()\n\n surface = surface[rng.choice(surface.shape[0], num_points, replace = False)]\n sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)]\n\n full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0)\n\n return full\n\nclass SharpEdgeSurfaceLoader:\n \"\"\" Load mesh surface and sharp edge samples. \"\"\"\n\n def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):\n\n self.num_uniform_points = num_uniform_points\n self.num_sharp_points = num_sharp_points\n self.total_points = num_uniform_points + num_sharp_points\n\n def __call__(self, mesh_input, device = \"cuda\"):\n mesh = self._load_mesh(mesh_input)\n return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device)\n\n @staticmethod\n def _load_mesh(mesh_input):\n import trimesh\n\n if isinstance(mesh_input, str):\n mesh = trimesh.load(mesh_input, force=\"mesh\", merge_primitives = True)\n else:\n mesh = mesh_input\n\n if isinstance(mesh, trimesh.Scene):\n combined = None\n for obj in mesh.geometry.values():\n combined = obj if combined is None else combined + obj\n return combined\n\n return mesh\n\nclass DiagonalGaussianDistribution:\n def __init__(self, params: torch.Tensor, feature_dim: int = -1):\n\n # divide quant channels (8) into mean and log variance\n self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim)\n\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n self.std = torch.exp(0.5 * self.logvar)\n\n def sample(self):\n\n eps = torch.randn_like(self.std)\n z = self.mean + eps * self.std\n\n return z\n\n################################################\n# Volume Decoder\n################################################\n\nclass VanillaVolumeDecoder():\n @torch.no_grad()\n def __call__(self, latents: torch.Tensor, geo_decoder: callable, octree_resolution: int, bounds = 1.01,\n num_chunks: int = 10_000, enable_pbar: bool = True, **kwargs):\n\n if isinstance(bounds, float):\n bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]\n\n bbox_min, bbox_max = torch.tensor(bounds[:3]), torch.tensor(bounds[3:])\n\n x = torch.linspace(bbox_min[0], bbox_max[0], int(octree_resolution) + 1, dtype = torch.float32)\n y = torch.linspace(bbox_min[1], bbox_max[1], int(octree_resolution) + 1, dtype = torch.float32)\n z = torch.linspace(bbox_min[2], bbox_max[2], int(octree_resolution) + 1, dtype = torch.float32)\n\n [xs, ys, zs] = torch.meshgrid(x, y, z, indexing = \"ij\")\n xyz = torch.stack((xs, ys, zs), axis=-1).to(latents.device, dtype = latents.dtype).contiguous().reshape(-1, 3)\n grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1]\n\n batch_logits = []\n for start in tqdm(range(0, xyz.shape[0], num_chunks), desc=\"Volume Decoding\",\n disable=not enable_pbar):\n\n chunk_queries = xyz[start: start + num_chunks, :]\n chunk_queries = chunk_queries.unsqueeze(0).repeat(latents.shape[0], 1, 1)\n logits = geo_decoder(queries = chunk_queries, latents = latents)\n batch_logits.append(logits)\n\n grid_logits = torch.cat(batch_logits, dim = 1)\n grid_logits = grid_logits.view((latents.shape[0], *grid_size)).float()\n\n return grid_logits\n\nclass FourierEmbedder(nn.Module):\n \"\"\"The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts\n each feature dimension of `x[..., i]` into:\n [\n sin(x[..., i]),\n sin(f_1*x[..., i]),\n sin(f_2*x[..., i]),\n ...\n sin(f_N * x[..., i]),\n cos(x[..., i]),\n cos(f_1*x[..., i]),\n cos(f_2*x[..., i]),\n ...\n cos(f_N * x[..., i]),\n x[..., i] # only present if include_input is True.\n ], here f_i is the frequency.\n\n Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs].\n If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...];\n Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)].\n\n Args:\n num_freqs (int): the number of frequencies, default is 6;\n logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],\n otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)];\n input_dim (int): the input dimension, default is 3;\n include_input (bool): include the input tensor or not, default is True.\n\n Attributes:\n frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],\n otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1);\n\n out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1),\n otherwise, it is input_dim * num_freqs * 2.\n\n \"\"\"\n\n def __init__(self,\n num_freqs: int = 6,\n logspace: bool = True,\n input_dim: int = 3,\n include_input: bool = True,\n include_pi: bool = True) -> None:\n\n \"\"\"The initialization\"\"\"\n\n super().__init__()\n\n if logspace:\n frequencies = 2.0 ** torch.arange(\n num_freqs,\n dtype=torch.float32\n )\n else:\n frequencies = torch.linspace(\n 1.0,\n 2.0 ** (num_freqs - 1),\n num_freqs,\n dtype=torch.float32\n )\n\n if include_pi:\n frequencies *= torch.pi\n\n self.register_buffer(\"frequencies\", frequencies, persistent=False)\n self.include_input = include_input\n self.num_freqs = num_freqs\n\n self.out_dim = self.get_dims(input_dim)\n\n def get_dims(self, input_dim):\n temp = 1 if self.include_input or self.num_freqs == 0 else 0\n out_dim = input_dim * (self.num_freqs * 2 + temp)\n\n return out_dim\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\" Forward process.\n\n Args:\n x: tensor of shape [..., dim]\n\n Returns:\n embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)]\n where temp is 1 if include_input is True and 0 otherwise.\n \"\"\"\n\n if self.num_freqs > 0:\n embed = (x[..., None].contiguous() * self.frequencies.to(device=x.device, dtype=x.dtype)).view(*x.shape[:-1], -1)\n if self.include_input:\n return torch.cat((x, embed.sin(), embed.cos()), dim=-1)\n else:\n return torch.cat((embed.sin(), embed.cos()), dim=-1)\n else:\n return x\n\nclass CrossAttentionProcessor:\n def __call__(self, attn, q, k, v):\n out = comfy.ops.scaled_dot_product_attention(q, k, v)\n return out\n\nclass DropPath(nn.Module):\n \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\n \"\"\"\n\n def __init__(self, drop_prob: float = 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 \"\"\"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 self.drop_prob == 0. or not self.training:\n return x\n keep_prob = 1 - self.drop_prob\n shape = (x.shape[0],) + (1,) * (x.ndim - 1) # 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 self.scale_by_keep:\n random_tensor.div_(keep_prob)\n return x * random_tensor\n\n def extra_repr(self):\n return f'drop_prob={round(self.drop_prob, 3):0.3f}'\n\n\nclass MLP(nn.Module):\n def __init__(\n self, *,\n width: int,\n expand_ratio: int = 4,\n output_width: int = None,\n drop_path_rate: float = 0.0\n ):\n super().__init__()\n self.width = width\n self.c_fc = ops.Linear(width, width * expand_ratio)\n self.c_proj = ops.Linear(width * expand_ratio, output_width if output_width is not None else width)\n self.gelu = nn.GELU()\n self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()\n\n def forward(self, x):\n return self.drop_path(self.c_proj(self.gelu(self.c_fc(x))))\n\nclass QKVMultiheadCrossAttention(nn.Module):\n def __init__(\n self,\n heads: int,\n n_data = None,\n width=None,\n qk_norm=False,\n norm_layer=ops.LayerNorm\n ):\n super().__init__()\n self.heads = heads\n self.n_data = n_data\n self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()\n self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()\n\n def forward(self, q, kv):\n\n _, n_ctx, _ = q.shape\n bs, n_data, width = kv.shape\n\n attn_ch = width // self.heads // 2\n q = q.view(bs, n_ctx, self.heads, -1)\n\n kv = kv.view(bs, n_data, self.heads, -1)\n k, v = torch.split(kv, attn_ch, dim=-1)\n\n q = self.q_norm(q)\n k = self.k_norm(k)\n\n q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]\n out = F.scaled_dot_product_attention(q, k, v)\n\n out = out.transpose(1, 2).reshape(bs, n_ctx, -1)\n\n return out\n\nclass MultiheadCrossAttention(nn.Module):\n def __init__(\n self,\n *,\n width: int,\n heads: int,\n qkv_bias: bool = True,\n data_width: Optional[int] = None,\n norm_layer=ops.LayerNorm,\n qk_norm: bool = False,\n kv_cache: bool = False,\n ):\n super().__init__()\n self.width = width\n self.heads = heads\n self.data_width = width if data_width is None else data_width\n self.c_q = ops.Linear(width, width, bias=qkv_bias)\n self.c_kv = ops.Linear(self.data_width, width * 2, bias=qkv_bias)\n self.c_proj = ops.Linear(width, width)\n self.attention = QKVMultiheadCrossAttention(\n heads=heads,\n width=width,\n norm_layer=norm_layer,\n qk_norm=qk_norm\n )\n self.kv_cache = kv_cache\n self.data = None\n\n def forward(self, x, data):\n x = self.c_q(x)\n if self.kv_cache:\n if self.data is None:\n self.data = self.c_kv(data)\n logging.info('Save kv cache,this should be called only once for one mesh')\n data = self.data\n else:\n data = self.c_kv(data)\n x = self.attention(x, data)\n x = self.c_proj(x)\n return x\n\nclass ResidualCrossAttentionBlock(nn.Module):\n def __init__(\n self,\n *,\n width: int,\n heads: int,\n mlp_expand_ratio: int = 4,\n data_width: Optional[int] = None,\n qkv_bias: bool = True,\n norm_layer=ops.LayerNorm,\n qk_norm: bool = False\n ):\n super().__init__()\n\n if data_width is None:\n data_width = width\n\n self.attn = MultiheadCrossAttention(\n width=width,\n heads=heads,\n data_width=data_width,\n qkv_bias=qkv_bias,\n norm_layer=norm_layer,\n qk_norm=qk_norm\n )\n self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6)\n self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6)\n self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6)\n self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio)\n\n def forward(self, x: torch.Tensor, data: torch.Tensor):\n x = x + self.attn(self.ln_1(x), self.ln_2(data))\n x = x + self.mlp(self.ln_3(x))\n return x\n\n\nclass QKVMultiheadAttention(nn.Module):\n def __init__(\n self,\n *,\n heads: int,\n width=None,\n qk_norm=False,\n norm_layer=ops.LayerNorm\n ):\n super().__init__()\n self.heads = heads\n self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()\n self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()\n\n def forward(self, qkv):\n bs, n_ctx, width = qkv.shape\n attn_ch = width // self.heads // 3\n qkv = qkv.view(bs, n_ctx, self.heads, -1)\n q, k, v = torch.split(qkv, attn_ch, dim=-1)\n\n q = self.q_norm(q)\n k = self.k_norm(k)\n\n q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]\n out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)\n return out\n\n\nclass MultiheadAttention(nn.Module):\n def __init__(\n self,\n *,\n width: int,\n heads: int,\n qkv_bias: bool,\n norm_layer=ops.LayerNorm,\n qk_norm: bool = False,\n drop_path_rate: float = 0.0\n ):\n super().__init__()\n\n self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias)\n self.c_proj = ops.Linear(width, width)\n self.attention = QKVMultiheadAttention(\n heads=heads,\n width=width,\n norm_layer=norm_layer,\n qk_norm=qk_norm\n )\n self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()\n\n def forward(self, x):\n x = self.c_qkv(x)\n x = self.attention(x)\n x = self.drop_path(self.c_proj(x))\n return x\n\n\nclass ResidualAttentionBlock(nn.Module):\n def __init__(\n self,\n *,\n width: int,\n heads: int,\n qkv_bias: bool = True,\n norm_layer=ops.LayerNorm,\n qk_norm: bool = False,\n drop_path_rate: float = 0.0,\n ):\n super().__init__()\n self.attn = MultiheadAttention(\n width=width,\n heads=heads,\n qkv_bias=qkv_bias,\n norm_layer=norm_layer,\n qk_norm=qk_norm,\n drop_path_rate=drop_path_rate\n )\n self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6)\n self.mlp = MLP(width=width, drop_path_rate=drop_path_rate)\n self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6)\n\n def forward(self, x: torch.Tensor):\n x = x + self.attn(self.ln_1(x))\n x = x + self.mlp(self.ln_2(x))\n return x\n\n\nclass Transformer(nn.Module):\n def __init__(\n self,\n *,\n width: int,\n layers: int,\n heads: int,\n qkv_bias: bool = True,\n norm_layer=ops.LayerNorm,\n qk_norm: bool = False,\n drop_path_rate: float = 0.0\n ):\n super().__init__()\n self.width = width\n self.layers = layers\n self.resblocks = nn.ModuleList(\n [\n ResidualAttentionBlock(\n width=width,\n heads=heads,\n qkv_bias=qkv_bias,\n norm_layer=norm_layer,\n qk_norm=qk_norm,\n drop_path_rate=drop_path_rate\n )\n for _ in range(layers)\n ]\n )\n\n def forward(self, x: torch.Tensor):\n for block in self.resblocks:\n x = block(x)\n return x\n\n\nclass CrossAttentionDecoder(nn.Module):\n\n def __init__(\n self,\n *,\n out_channels: int,\n fourier_embedder: FourierEmbedder,\n width: int,\n heads: int,\n mlp_expand_ratio: int = 4,\n downsample_ratio: int = 1,\n enable_ln_post: bool = True,\n qkv_bias: bool = True,\n qk_norm: bool = False,\n label_type: str = \"binary\"\n ):\n super().__init__()\n\n self.enable_ln_post = enable_ln_post\n self.fourier_embedder = fourier_embedder\n self.downsample_ratio = downsample_ratio\n self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width)\n if self.downsample_ratio != 1:\n self.latents_proj = ops.Linear(width * downsample_ratio, width)\n if not self.enable_ln_post:\n qk_norm = False\n self.cross_attn_decoder = ResidualCrossAttentionBlock(\n width=width,\n mlp_expand_ratio=mlp_expand_ratio,\n heads=heads,\n qkv_bias=qkv_bias,\n qk_norm=qk_norm\n )\n\n if self.enable_ln_post:\n self.ln_post = ops.LayerNorm(width)\n self.output_proj = ops.Linear(width, out_channels)\n self.label_type = label_type\n self.count = 0\n\n def forward(self, queries=None, query_embeddings=None, latents=None):\n if query_embeddings is None:\n query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype))\n self.count += query_embeddings.shape[1]\n if self.downsample_ratio != 1:\n latents = self.latents_proj(latents)\n x = self.cross_attn_decoder(query_embeddings, latents)\n if self.enable_ln_post:\n x = self.ln_post(x)\n occ = self.output_proj(x)\n return occ\n\n\nclass ShapeVAE(nn.Module):\n def __init__(\n self,\n *,\n num_latents: int = 4096,\n embed_dim: int = 64,\n width: int = 1024,\n heads: int = 16,\n num_decoder_layers: int = 16,\n num_encoder_layers: int = 8,\n pc_size: int = 81920,\n pc_sharpedge_size: int = 0,\n point_feats: int = 4,\n downsample_ratio: int = 20,\n geo_decoder_downsample_ratio: int = 1,\n geo_decoder_mlp_expand_ratio: int = 4,\n geo_decoder_ln_post: bool = True,\n num_freqs: int = 8,\n qkv_bias: bool = False,\n qk_norm: bool = True,\n drop_path_rate: float = 0.0,\n include_pi: bool = False,\n scale_factor: float = 1.0039506158752403,\n label_type: str = \"binary\",\n ):\n super().__init__()\n self.geo_decoder_ln_post = geo_decoder_ln_post\n\n self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)\n\n self.encoder = PointCrossAttention(layers = num_encoder_layers,\n num_latents = num_latents,\n downsample_ratio = downsample_ratio,\n heads = heads,\n pc_size = pc_size,\n width = width,\n point_feats = point_feats,\n fourier_embedder = self.fourier_embedder,\n pc_sharpedge_size = pc_sharpedge_size)\n\n self.post_kl = ops.Linear(embed_dim, width)\n\n self.transformer = Transformer(\n width=width,\n layers=num_decoder_layers,\n heads=heads,\n qkv_bias=qkv_bias,\n qk_norm=qk_norm,\n drop_path_rate=drop_path_rate\n )\n\n self.geo_decoder = CrossAttentionDecoder(\n fourier_embedder=self.fourier_embedder,\n out_channels=1,\n mlp_expand_ratio=geo_decoder_mlp_expand_ratio,\n downsample_ratio=geo_decoder_downsample_ratio,\n enable_ln_post=self.geo_decoder_ln_post,\n width=width // geo_decoder_downsample_ratio,\n heads=heads // geo_decoder_downsample_ratio,\n qkv_bias=qkv_bias,\n qk_norm=qk_norm,\n label_type=label_type,\n )\n\n self.volume_decoder = VanillaVolumeDecoder()\n self.scale_factor = scale_factor\n\n def decode(self, latents, **kwargs):\n latents = self.post_kl(latents.movedim(-2, -1))\n latents = self.transformer(latents)\n\n bounds = kwargs.get(\"bounds\", 1.01)\n num_chunks = kwargs.get(\"num_chunks\", 8000)\n octree_resolution = kwargs.get(\"octree_resolution\", 256)\n enable_pbar = kwargs.get(\"enable_pbar\", True)\n\n grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar)\n return grid_logits.movedim(-2, -1)\n\n def encode(self, surface):\n\n pc, feats = surface[:, :, :3], surface[:, :, 3:]\n latents = self.encoder(pc, feats)\n\n moments = self.pre_kl(latents)\n posterior = DiagonalGaussianDistribution(moments, feature_dim = -1)\n\n latents = posterior.sample()\n\n return latents\n" }, { "path": "comfy/ldm/hunyuan3dv2_1/hunyuandit.py", "content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.model_management\n\nclass GELU(nn.Module):\n\n def __init__(self, dim_in: int, dim_out: int, operations, device, dtype):\n super().__init__()\n self.proj = operations.Linear(dim_in, dim_out, device = device, dtype = dtype)\n\n def gelu(self, gate: torch.Tensor) -> torch.Tensor:\n\n if gate.device.type == \"mps\":\n return F.gelu(gate.to(dtype = torch.float32)).to(dtype = gate.dtype)\n\n return F.gelu(gate)\n\n def forward(self, hidden_states):\n\n hidden_states = self.proj(hidden_states)\n hidden_states = self.gelu(hidden_states)\n\n return hidden_states\n\nclass FeedForward(nn.Module):\n\n def __init__(self, dim: int, dim_out = None, mult: int = 4,\n dropout: float = 0.0, inner_dim = None, operations = None, device = None, dtype = None):\n\n super().__init__()\n if inner_dim is None:\n inner_dim = int(dim * mult)\n\n dim_out = dim_out if dim_out is not None else dim\n\n act_fn = GELU(dim, inner_dim, operations = operations, device = device, dtype = dtype)\n\n self.net = nn.ModuleList([])\n self.net.append(act_fn)\n\n self.net.append(nn.Dropout(dropout))\n self.net.append(operations.Linear(inner_dim, dim_out, device = device, dtype = dtype))\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n for module in self.net:\n hidden_states = module(hidden_states)\n return hidden_states\n\nclass AddAuxLoss(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, x, loss):\n # do nothing in forward (no computation)\n ctx.requires_aux_loss = loss.requires_grad\n ctx.dtype = loss.dtype\n\n return x\n\n @staticmethod\n def backward(ctx, grad_output):\n # add the aux loss gradients\n grad_loss = None\n # put the aux grad the same as the main grad loss\n # aux grad contributes equally\n if ctx.requires_aux_loss:\n grad_loss = torch.ones(1, dtype = ctx.dtype, device = grad_output.device)\n\n return grad_output, grad_loss\n\nclass MoEGate(nn.Module):\n\n def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01, device = None, dtype = None):\n\n super().__init__()\n self.top_k = num_experts_per_tok\n self.n_routed_experts = num_experts\n\n self.alpha = aux_loss_alpha\n\n self.gating_dim = embed_dim\n self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), device = device, dtype = dtype))\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n\n # flatten hidden states\n hidden_states = hidden_states.view(-1, hidden_states.size(-1))\n\n # get logits and pass it to softmax\n logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), bias = None)\n scores = logits.softmax(dim = -1)\n\n topk_weight, topk_idx = torch.topk(scores, k = self.top_k, dim = -1, sorted = False)\n\n if self.training and self.alpha > 0.0:\n scores_for_aux = scores\n\n # used bincount instead of one hot encoding\n counts = torch.bincount(topk_idx.view(-1), minlength = self.n_routed_experts).float()\n ce = counts / topk_idx.numel() # normalized expert usage\n\n # mean expert score\n Pi = scores_for_aux.mean(0)\n\n # expert balance loss\n aux_loss = (Pi * ce * self.n_routed_experts).sum() * self.alpha\n else:\n aux_loss = None\n\n return topk_idx, topk_weight, aux_loss\n\nclass MoEBlock(nn.Module):\n def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, dropout: float = 0.0,\n ff_inner_dim: int = None, operations = None, device = None, dtype = None):\n super().__init__()\n\n self.moe_top_k = moe_top_k\n self.num_experts = num_experts\n\n self.experts = nn.ModuleList([\n FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype)\n for _ in range(num_experts)\n ])\n\n self.gate = MoEGate(dim, num_experts = num_experts, num_experts_per_tok = moe_top_k, device = device, dtype = dtype)\n self.shared_experts = FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype)\n\n def forward(self, hidden_states) -> torch.Tensor:\n\n identity = hidden_states\n orig_shape = hidden_states.shape\n topk_idx, topk_weight, aux_loss = self.gate(hidden_states)\n\n hidden_states = hidden_states.view(-1, hidden_states.shape[-1])\n flat_topk_idx = topk_idx.view(-1)\n\n if self.training:\n\n hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim = 0)\n y = torch.empty_like(hidden_states, dtype = hidden_states.dtype)\n\n for i, expert in enumerate(self.experts):\n tmp = expert(hidden_states[flat_topk_idx == i])\n y[flat_topk_idx == i] = tmp.to(hidden_states.dtype)\n\n y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim = 1)\n y = y.view(*orig_shape)\n\n y = AddAuxLoss.apply(y, aux_loss)\n else:\n y = self.moe_infer(hidden_states, flat_expert_indices = flat_topk_idx,flat_expert_weights = topk_weight.view(-1, 1)).view(*orig_shape)\n\n y = y + self.shared_experts(identity)\n\n return y\n\n @torch.no_grad()\n def moe_infer(self, x, flat_expert_indices, flat_expert_weights):\n\n expert_cache = torch.zeros_like(x)\n idxs = flat_expert_indices.argsort()\n\n # no need for .numpy().cpu() here\n tokens_per_expert = flat_expert_indices.bincount().cumsum(0)\n token_idxs = idxs // self.moe_top_k\n\n for i, end_idx in enumerate(tokens_per_expert):\n\n start_idx = 0 if i == 0 else tokens_per_expert[i-1]\n\n if start_idx == end_idx:\n continue\n\n expert = self.experts[i]\n exp_token_idx = token_idxs[start_idx:end_idx]\n\n expert_tokens = x[exp_token_idx]\n expert_out = expert(expert_tokens)\n\n expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])\n\n # use index_add_ with a 1-D index tensor directly avoids building a large [N, D] index map and extra memcopy required by scatter_reduce_\n # + avoid dtype conversion\n expert_cache.index_add_(0, exp_token_idx, expert_out)\n\n return expert_cache\n\nclass Timesteps(nn.Module):\n def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0,\n scale: float = 1.0, max_period: int = 10000):\n super().__init__()\n\n self.num_channels = num_channels\n half_dim = num_channels // 2\n\n # precompute the “inv_freq” vector once\n exponent = -math.log(max_period) * torch.arange(\n half_dim, dtype=torch.float32\n ) / (half_dim - downscale_freq_shift)\n\n inv_freq = torch.exp(exponent)\n\n # pad\n if num_channels % 2 == 1:\n # we’ll pad a zero at the end of the cos-half\n inv_freq = torch.cat([inv_freq, inv_freq.new_zeros(1)])\n\n # register to buffer so it moves with the device\n self.register_buffer(\"inv_freq\", inv_freq, persistent = False)\n self.scale = scale\n\n def forward(self, timesteps: torch.Tensor):\n\n x = timesteps.float().unsqueeze(1) * self.inv_freq.to(timesteps.device).unsqueeze(0)\n\n\n # fused CUDA kernels for sin and cos\n sin_emb = x.sin()\n cos_emb = x.cos()\n\n emb = torch.cat([sin_emb, cos_emb], dim = 1)\n\n # scale factor\n if self.scale != 1.0:\n emb = emb * self.scale\n\n # If we padded inv_freq for odd, emb is already wide enough; otherwise:\n if emb.shape[1] > self.num_channels:\n emb = emb[:, :self.num_channels]\n\n return emb\n\nclass TimestepEmbedder(nn.Module):\n def __init__(self, hidden_size, frequency_embedding_size = 256, cond_proj_dim = None, operations = None, device = None, dtype = None):\n super().__init__()\n\n self.mlp = nn.Sequential(\n operations.Linear(hidden_size, frequency_embedding_size, bias=True, device = device, dtype = dtype),\n nn.GELU(),\n operations.Linear(frequency_embedding_size, hidden_size, bias=True, device = device, dtype = dtype),\n )\n self.frequency_embedding_size = frequency_embedding_size\n\n if cond_proj_dim is not None:\n self.cond_proj = operations.Linear(cond_proj_dim, frequency_embedding_size, bias=False, device = device, dtype = dtype)\n\n self.time_embed = Timesteps(hidden_size)\n\n def forward(self, timesteps, condition):\n\n timestep_embed = self.time_embed(timesteps).type(self.mlp[0].weight.dtype)\n\n if condition is not None:\n cond_embed = self.cond_proj(condition)\n timestep_embed = timestep_embed + cond_embed\n\n time_conditioned = self.mlp(timestep_embed)\n\n # for broadcasting with image tokens\n return time_conditioned.unsqueeze(1)\n\nclass MLP(nn.Module):\n def __init__(self, *, width: int, operations = None, device = None, dtype = None):\n super().__init__()\n self.width = width\n self.fc1 = operations.Linear(width, width * 4, device = device, dtype = dtype)\n self.fc2 = operations.Linear(width * 4, width, device = device, dtype = dtype)\n self.gelu = nn.GELU()\n\n def forward(self, x):\n return self.fc2(self.gelu(self.fc1(x)))\n\nclass CrossAttention(nn.Module):\n def __init__(\n self,\n qdim,\n kdim,\n num_heads,\n qkv_bias=True,\n qk_norm=False,\n norm_layer=nn.LayerNorm,\n use_fp16: bool = False,\n operations = None,\n dtype = None,\n device = None,\n **kwargs,\n ):\n super().__init__()\n self.qdim = qdim\n self.kdim = kdim\n\n self.num_heads = num_heads\n self.head_dim = self.qdim // num_heads\n\n self.scale = self.head_dim ** -0.5\n\n self.to_q = operations.Linear(qdim, qdim, bias=qkv_bias, device = device, dtype = dtype)\n self.to_k = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype)\n self.to_v = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype)\n\n if use_fp16:\n eps = 1.0 / 65504\n else:\n eps = 1e-6\n\n if norm_layer == nn.LayerNorm:\n norm_layer = operations.LayerNorm\n else:\n norm_layer = operations.RMSNorm\n\n self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()\n self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()\n self.out_proj = operations.Linear(qdim, qdim, bias=True, device = device, dtype = dtype)\n\n def forward(self, x, y):\n\n b, s1, _ = x.shape\n _, s2, _ = y.shape\n\n y = y.to(next(self.to_k.parameters()).dtype)\n\n q = self.to_q(x)\n k = self.to_k(y)\n v = self.to_v(y)\n\n kv = torch.cat((k, v), dim=-1)\n split_size = kv.shape[-1] // self.num_heads // 2\n\n kv = kv.view(1, -1, self.num_heads, split_size * 2)\n k, v = torch.split(kv, split_size, dim=-1)\n\n q = q.view(b, s1, self.num_heads, self.head_dim)\n k = k.view(b, s2, self.num_heads, self.head_dim)\n v = v.reshape(b, s2, self.num_heads * self.head_dim)\n\n q = self.q_norm(q)\n k = self.k_norm(k)\n\n x = optimized_attention(\n q.reshape(b, s1, self.num_heads * self.head_dim),\n k.reshape(b, s2, self.num_heads * self.head_dim),\n v,\n heads=self.num_heads,\n low_precision_attention=False,\n )\n\n out = self.out_proj(x)\n\n return out\n\nclass Attention(nn.Module):\n\n def __init__(\n self,\n dim,\n num_heads,\n qkv_bias = True,\n qk_norm = False,\n norm_layer = nn.LayerNorm,\n use_fp16: bool = False,\n operations = None,\n device = None,\n dtype = None\n ):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.head_dim = self.dim // num_heads\n self.scale = self.head_dim ** -0.5\n\n self.to_q = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)\n self.to_k = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)\n self.to_v = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)\n\n if use_fp16:\n eps = 1.0 / 65504\n else:\n eps = 1e-6\n\n if norm_layer == nn.LayerNorm:\n norm_layer = operations.LayerNorm\n else:\n norm_layer = operations.RMSNorm\n\n self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()\n self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()\n self.out_proj = operations.Linear(dim, dim, device = device, dtype = dtype)\n\n def forward(self, x):\n B, N, _ = x.shape\n\n query = self.to_q(x)\n key = self.to_k(x)\n value = self.to_v(x)\n\n qkv_combined = torch.cat((query, key, value), dim=-1)\n split_size = qkv_combined.shape[-1] // self.num_heads // 3\n\n qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3)\n query, key, value = torch.split(qkv, split_size, dim=-1)\n\n query = query.reshape(B, N, self.num_heads, self.head_dim)\n key = key.reshape(B, N, self.num_heads, self.head_dim)\n value = value.reshape(B, N, self.num_heads * self.head_dim)\n\n query = self.q_norm(query)\n key = self.k_norm(key)\n\n x = optimized_attention(\n query.reshape(B, N, self.num_heads * self.head_dim),\n key.reshape(B, N, self.num_heads * self.head_dim),\n value,\n heads=self.num_heads,\n low_precision_attention=False,\n )\n\n x = self.out_proj(x)\n return x\n\nclass HunYuanDiTBlock(nn.Module):\n def __init__(\n self,\n hidden_size,\n c_emb_size,\n num_heads,\n text_states_dim=1024,\n qk_norm=False,\n norm_layer=nn.LayerNorm,\n qk_norm_layer=True,\n qkv_bias=True,\n skip_connection=True,\n timested_modulate=False,\n use_moe: bool = False,\n num_experts: int = 8,\n moe_top_k: int = 2,\n use_fp16: bool = False,\n operations = None,\n device = None, dtype = None\n ):\n super().__init__()\n\n # eps can't be 1e-6 in fp16 mode because of numerical stability issues\n if use_fp16:\n eps = 1.0 / 65504\n else:\n eps = 1e-6\n\n self.norm1 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)\n\n self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm,\n norm_layer=qk_norm_layer, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations)\n\n self.norm2 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)\n\n self.timested_modulate = timested_modulate\n if self.timested_modulate:\n self.default_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(c_emb_size, hidden_size, bias=True, device = device, dtype = dtype)\n )\n\n self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=qkv_bias,\n qk_norm=qk_norm, norm_layer=qk_norm_layer, use_fp16 = use_fp16,\n device = device, dtype = dtype, operations = operations)\n\n self.norm3 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)\n\n if skip_connection:\n self.skip_norm = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)\n self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, device = device, dtype = dtype)\n else:\n self.skip_linear = None\n\n self.use_moe = use_moe\n\n if self.use_moe:\n self.moe = MoEBlock(\n hidden_size,\n num_experts = num_experts,\n moe_top_k = moe_top_k,\n dropout = 0.0,\n ff_inner_dim = int(hidden_size * 4.0),\n device = device, dtype = dtype,\n operations = operations\n )\n else:\n self.mlp = MLP(width=hidden_size, operations=operations, device = device, dtype = dtype)\n\n def forward(self, hidden_states, conditioning=None, text_states=None, skip_tensor=None):\n\n if self.skip_linear is not None:\n combined = torch.cat([skip_tensor, hidden_states], dim=-1)\n hidden_states = self.skip_linear(combined)\n hidden_states = self.skip_norm(hidden_states)\n\n # self attention\n if self.timested_modulate:\n modulation_shift = self.default_modulation(conditioning).unsqueeze(dim=1)\n hidden_states = hidden_states + modulation_shift\n\n self_attn_out = self.attn1(self.norm1(hidden_states))\n hidden_states = hidden_states + self_attn_out\n\n # cross attention\n hidden_states = hidden_states + self.attn2(self.norm2(hidden_states), text_states)\n\n # MLP Layer\n mlp_input = self.norm3(hidden_states)\n\n if self.use_moe:\n hidden_states = hidden_states + self.moe(mlp_input)\n else:\n hidden_states = hidden_states + self.mlp(mlp_input)\n\n return hidden_states\n\nclass FinalLayer(nn.Module):\n\n def __init__(self, final_hidden_size, out_channels, operations, use_fp16: bool = False, device = None, dtype = None):\n super().__init__()\n\n if use_fp16:\n eps = 1.0 / 65504\n else:\n eps = 1e-6\n\n self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)\n self.linear = operations.Linear(final_hidden_size, out_channels, bias = True, device = device, dtype = dtype)\n\n def forward(self, x):\n x = self.norm_final(x)\n x = x[:, 1:]\n x = self.linear(x)\n return x\n\nclass HunYuanDiTPlain(nn.Module):\n\n # init with the defaults values from https://huggingface.co/tencent/Hunyuan3D-2.1/blob/main/hunyuan3d-dit-v2-1/config.yaml\n def __init__(\n self,\n in_channels: int = 64,\n hidden_size: int = 2048,\n context_dim: int = 1024,\n depth: int = 21,\n num_heads: int = 16,\n qk_norm: bool = True,\n qkv_bias: bool = False,\n num_moe_layers: int = 6,\n guidance_cond_proj_dim = 2048,\n norm_type = 'layer',\n num_experts: int = 8,\n moe_top_k: int = 2,\n use_fp16: bool = False,\n dtype = None,\n device = None,\n operations = None,\n **kwargs\n ):\n\n self.dtype = dtype\n\n super().__init__()\n\n self.depth = depth\n\n self.in_channels = in_channels\n self.out_channels = in_channels\n\n self.num_heads = num_heads\n self.hidden_size = hidden_size\n\n norm = operations.LayerNorm if norm_type == 'layer' else operations.RMSNorm\n qk_norm = operations.RMSNorm\n\n self.context_dim = context_dim\n self.guidance_cond_proj_dim = guidance_cond_proj_dim\n\n self.x_embedder = operations.Linear(in_channels, hidden_size, bias = True, device = device, dtype = dtype)\n self.t_embedder = TimestepEmbedder(hidden_size, hidden_size * 4, cond_proj_dim = guidance_cond_proj_dim, device = device, dtype = dtype, operations = operations)\n\n\n # HUnYuanDiT Blocks\n self.blocks = nn.ModuleList([\n HunYuanDiTBlock(hidden_size=hidden_size,\n c_emb_size=hidden_size,\n num_heads=num_heads,\n text_states_dim=context_dim,\n qk_norm=qk_norm,\n norm_layer = norm,\n qk_norm_layer = qk_norm,\n skip_connection=layer > depth // 2,\n qkv_bias=qkv_bias,\n use_moe=True if depth - layer <= num_moe_layers else False,\n num_experts=num_experts,\n moe_top_k=moe_top_k,\n use_fp16 = use_fp16,\n device = device, dtype = dtype, operations = operations)\n for layer in range(depth)\n ])\n\n self.depth = depth\n\n self.final_layer = FinalLayer(hidden_size, self.out_channels, use_fp16 = use_fp16, operations = operations, device = device, dtype = dtype)\n\n def forward(self, x, t, context, transformer_options = {}, **kwargs):\n\n x = x.movedim(-1, -2)\n uncond_emb, cond_emb = context.chunk(2, dim = 0)\n\n context = torch.cat([cond_emb, uncond_emb], dim = 0)\n main_condition = context\n\n t = 1.0 - t\n\n time_embedded = self.t_embedder(t, condition = kwargs.get('guidance_cond'))\n\n x = x.to(dtype = next(self.x_embedder.parameters()).dtype)\n x_embedded = self.x_embedder(x)\n\n combined = torch.cat([time_embedded, x_embedded], dim=1)\n\n def block_wrap(args):\n return block(\n args[\"x\"],\n args[\"t\"],\n args[\"cond\"],\n skip_tensor=args.get(\"skip\"),)\n\n skip_stack = []\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n for idx, block in enumerate(self.blocks):\n if idx <= self.depth // 2:\n skip_input = None\n else:\n skip_input = skip_stack.pop()\n\n if (\"block\", idx) in blocks_replace:\n\n combined = blocks_replace[(\"block\", idx)](\n {\n \"x\": combined,\n \"t\": time_embedded,\n \"cond\": main_condition,\n \"skip\": skip_input,\n },\n {\"original_block\": block_wrap},\n )\n else:\n combined = block(combined, time_embedded, main_condition, skip_tensor=skip_input)\n\n if idx < self.depth // 2:\n skip_stack.append(combined)\n\n output = self.final_layer(combined)\n output = output.movedim(-2, -1) * (-1.0)\n\n cond_emb, uncond_emb = output.chunk(2, dim = 0)\n return torch.cat([uncond_emb, cond_emb])\n" }, { "path": "comfy/ldm/hunyuan_video/model.py", "content": "#Based on Flux code because of weird hunyuan video code license.\n\nimport torch\nimport comfy.patcher_extension\nimport comfy.ldm.flux.layers\nimport comfy.ldm.modules.diffusionmodules.mmdit\nfrom comfy.ldm.modules.attention import optimized_attention\n\nfrom dataclasses import dataclass\nfrom einops import repeat\n\nfrom torch import Tensor, nn\n\nfrom comfy.ldm.flux.layers import (\n DoubleStreamBlock,\n EmbedND,\n LastLayer,\n MLPEmbedder,\n SingleStreamBlock,\n timestep_embedding\n)\n\nimport comfy.ldm.common_dit\n\n\n@dataclass\nclass HunyuanVideoParams:\n in_channels: int\n out_channels: int\n vec_in_dim: int\n context_in_dim: int\n hidden_size: int\n mlp_ratio: float\n num_heads: int\n depth: int\n depth_single_blocks: int\n axes_dim: list\n theta: int\n patch_size: list\n qkv_bias: bool\n guidance_embed: bool\n byt5: bool\n meanflow: bool\n use_cond_type_embedding: bool\n vision_in_dim: int\n meanflow_sum: bool\n\n\nclass SelfAttentionRef(nn.Module):\n def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, device=None, operations=None):\n super().__init__()\n self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)\n self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)\n\n\nclass TokenRefinerBlock(nn.Module):\n def __init__(\n self,\n hidden_size,\n heads,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.heads = heads\n mlp_hidden_dim = hidden_size * 4\n\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device),\n )\n\n self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)\n self.self_attn = SelfAttentionRef(hidden_size, True, dtype=dtype, device=device, operations=operations)\n\n self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)\n\n self.mlp = nn.Sequential(\n operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),\n )\n\n def forward(self, x, c, mask, transformer_options={}):\n mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)\n\n norm_x = self.norm1(x)\n qkv = self.self_attn.qkv(norm_x)\n q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)\n attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)\n\n x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)\n x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)\n return x\n\n\nclass IndividualTokenRefiner(nn.Module):\n def __init__(\n self,\n hidden_size,\n heads,\n num_blocks,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.blocks = nn.ModuleList(\n [\n TokenRefinerBlock(\n hidden_size=hidden_size,\n heads=heads,\n dtype=dtype,\n device=device,\n operations=operations\n )\n for _ in range(num_blocks)\n ]\n )\n\n def forward(self, x, c, mask, transformer_options={}):\n m = None\n if mask is not None:\n m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)\n m = m + m.transpose(2, 3)\n\n for block in self.blocks:\n x = block(x, c, m, transformer_options=transformer_options)\n return x\n\n\n\nclass TokenRefiner(nn.Module):\n def __init__(\n self,\n text_dim,\n hidden_size,\n heads,\n num_blocks,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n\n self.input_embedder = operations.Linear(text_dim, hidden_size, bias=True, dtype=dtype, device=device)\n self.t_embedder = MLPEmbedder(256, hidden_size, dtype=dtype, device=device, operations=operations)\n self.c_embedder = MLPEmbedder(text_dim, hidden_size, dtype=dtype, device=device, operations=operations)\n self.individual_token_refiner = IndividualTokenRefiner(hidden_size, heads, num_blocks, dtype=dtype, device=device, operations=operations)\n\n def forward(\n self,\n x,\n timesteps,\n mask,\n transformer_options={},\n ):\n t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))\n # m = mask.float().unsqueeze(-1)\n # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise\n if x.dtype == torch.float16:\n c = x.float().sum(dim=1) / x.shape[1]\n else:\n c = x.sum(dim=1) / x.shape[1]\n\n c = t + self.c_embedder(c.to(x.dtype))\n x = self.input_embedder(x)\n x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)\n return x\n\n\nclass ByT5Mapper(nn.Module):\n def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=False, dtype=None, device=None, operations=None):\n super().__init__()\n self.layernorm = operations.LayerNorm(in_dim, dtype=dtype, device=device)\n self.fc1 = operations.Linear(in_dim, hidden_dim, dtype=dtype, device=device)\n self.fc2 = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)\n self.fc3 = operations.Linear(out_dim, out_dim1, dtype=dtype, device=device)\n self.use_res = use_res\n self.act_fn = nn.GELU()\n\n def forward(self, x):\n if self.use_res:\n res = x\n x = self.layernorm(x)\n x = self.fc1(x)\n x = self.act_fn(x)\n x = self.fc2(x)\n x2 = self.act_fn(x)\n x2 = self.fc3(x2)\n if self.use_res:\n x2 = x2 + res\n return x2\n\nclass HunyuanVideo(nn.Module):\n \"\"\"\n Transformer model for flow matching on sequences.\n \"\"\"\n\n def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):\n super().__init__()\n self.dtype = dtype\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n\n params = HunyuanVideoParams(**kwargs)\n self.params = params\n self.patch_size = params.patch_size\n self.in_channels = params.in_channels\n self.out_channels = params.out_channels\n self.use_cond_type_embedding = params.use_cond_type_embedding\n self.vision_in_dim = params.vision_in_dim\n if params.hidden_size % params.num_heads != 0:\n raise ValueError(\n f\"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}\"\n )\n pe_dim = params.hidden_size // params.num_heads\n if sum(params.axes_dim) != pe_dim:\n raise ValueError(f\"Got {params.axes_dim} but expected positional dim {pe_dim}\")\n self.hidden_size = params.hidden_size\n self.num_heads = params.num_heads\n self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)\n\n self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations)\n self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)\n if params.vec_in_dim is not None:\n self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)\n else:\n self.vector_in = None\n\n self.guidance_in = (\n MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()\n )\n\n self.txt_in = TokenRefiner(params.context_in_dim, self.hidden_size, self.num_heads, 2, dtype=dtype, device=device, operations=operations)\n\n self.double_blocks = nn.ModuleList(\n [\n DoubleStreamBlock(\n self.hidden_size,\n self.num_heads,\n mlp_ratio=params.mlp_ratio,\n qkv_bias=params.qkv_bias,\n dtype=dtype, device=device, operations=operations\n )\n for _ in range(params.depth)\n ]\n )\n\n self.single_blocks = nn.ModuleList(\n [\n SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)\n for _ in range(params.depth_single_blocks)\n ]\n )\n\n if params.byt5:\n self.byt5_in = ByT5Mapper(\n in_dim=1472,\n out_dim=2048,\n hidden_dim=2048,\n out_dim1=self.hidden_size,\n use_res=False,\n dtype=dtype, device=device, operations=operations\n )\n else:\n self.byt5_in = None\n\n if params.meanflow:\n self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)\n else:\n self.time_r_in = None\n\n if final_layer:\n self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)\n\n # HunyuanVideo 1.5 specific modules\n if self.vision_in_dim is not None:\n from comfy.ldm.wan.model import MLPProj\n self.vision_in = MLPProj(in_dim=self.vision_in_dim, out_dim=self.hidden_size, operation_settings=operation_settings)\n else:\n self.vision_in = None\n if self.use_cond_type_embedding:\n # 0: text_encoder feature 1: byt5 feature 2: vision_encoder feature\n self.cond_type_embedding = nn.Embedding(3, self.hidden_size)\n else:\n self.cond_type_embedding = None\n\n def forward_orig(\n self,\n img: Tensor,\n img_ids: Tensor,\n txt: Tensor,\n txt_ids: Tensor,\n txt_mask: Tensor,\n timesteps: Tensor,\n y: Tensor = None,\n txt_byt5=None,\n clip_fea=None,\n guidance: Tensor = None,\n guiding_frame_index=None,\n ref_latent=None,\n disable_time_r=False,\n control=None,\n transformer_options={},\n ) -> Tensor:\n transformer_options = transformer_options.copy()\n patches_replace = transformer_options.get(\"patches_replace\", {})\n\n initial_shape = list(img.shape)\n # running on sequences img\n img = self.img_in(img)\n vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))\n\n if (self.time_r_in is not None) and (not disable_time_r):\n w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0] # This most likely could be improved\n if len(w) > 0:\n timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]\n timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)\n vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))\n vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2\n\n if ref_latent is not None:\n ref_latent_ids = self.img_ids(ref_latent)\n ref_latent = self.img_in(ref_latent)\n img = torch.cat([ref_latent, img], dim=-2)\n ref_latent_ids[..., 0] = -1\n ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1])\n img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2)\n\n if guiding_frame_index is not None:\n token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0))\n if self.vector_in is not None:\n vec_ = self.vector_in(y[:, :self.params.vec_in_dim])\n vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)\n else:\n vec = torch.cat([(token_replace_vec).unsqueeze(1), (vec).unsqueeze(1)], dim=1)\n frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2])\n modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)]\n modulation_dims_txt = [(0, None, 1)]\n else:\n if self.vector_in is not None:\n vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])\n modulation_dims = None\n modulation_dims_txt = None\n\n if self.params.guidance_embed:\n if guidance is not None:\n vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))\n\n if txt_mask is not None and not torch.is_floating_point(txt_mask):\n txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max\n\n txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)\n\n if self.cond_type_embedding is not None:\n self.cond_type_embedding.to(txt.device)\n cond_emb = self.cond_type_embedding(torch.zeros_like(txt[:, :, 0], device=txt.device, dtype=torch.long))\n txt = txt + cond_emb.to(txt.dtype)\n\n if self.byt5_in is not None and txt_byt5 is not None:\n txt_byt5 = self.byt5_in(txt_byt5)\n if self.cond_type_embedding is not None:\n cond_emb = self.cond_type_embedding(torch.ones_like(txt_byt5[:, :, 0], device=txt_byt5.device, dtype=torch.long))\n txt_byt5 = txt_byt5 + cond_emb.to(txt_byt5.dtype)\n txt = torch.cat((txt_byt5, txt), dim=1) # byt5 first for HunyuanVideo1.5\n else:\n txt = torch.cat((txt, txt_byt5), dim=1)\n txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)\n txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1)\n\n if clip_fea is not None:\n txt_vision_states = self.vision_in(clip_fea)\n if self.cond_type_embedding is not None:\n cond_emb = self.cond_type_embedding(2 * torch.ones_like(txt_vision_states[:, :, 0], dtype=torch.long, device=txt_vision_states.device))\n txt_vision_states = txt_vision_states + cond_emb\n txt = torch.cat((txt_vision_states.to(txt.dtype), txt), dim=1)\n extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)\n txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1)\n\n ids = torch.cat((txt_ids, img_ids), dim=1)\n pe = self.pe_embedder(ids)\n\n img_len = img.shape[1]\n if txt_mask is not None:\n attn_mask_len = img_len + txt.shape[1]\n attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device)\n attn_mask[:, 0, :txt.shape[1]] = txt_mask\n else:\n attn_mask = None\n\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.double_blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.double_blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"], out[\"txt\"] = block(img=args[\"img\"], txt=args[\"txt\"], vec=args[\"vec\"], pe=args[\"pe\"], attn_mask=args[\"attention_mask\"], modulation_dims_img=args[\"modulation_dims_img\"], modulation_dims_txt=args[\"modulation_dims_txt\"], transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": img, \"txt\": txt, \"vec\": vec, \"pe\": pe, \"attention_mask\": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {\"original_block\": block_wrap})\n txt = out[\"txt\"]\n img = out[\"img\"]\n else:\n img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options)\n\n if control is not None: # Controlnet\n control_i = control.get(\"input\")\n if i < len(control_i):\n add = control_i[i]\n if add is not None:\n img += add\n\n img = torch.cat((txt, img), 1)\n\n transformer_options[\"total_blocks\"] = len(self.single_blocks)\n transformer_options[\"block_type\"] = \"single\"\n transformer_options[\"img_slice\"] = [txt.shape[1], img.shape[1]]\n for i, block in enumerate(self.single_blocks):\n transformer_options[\"block_index\"] = i\n if (\"single_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], vec=args[\"vec\"], pe=args[\"pe\"], attn_mask=args[\"attention_mask\"], modulation_dims=args[\"modulation_dims\"], transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"single_block\", i)]({\"img\": img, \"vec\": vec, \"pe\": pe, \"attention_mask\": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {\"original_block\": block_wrap})\n img = out[\"img\"]\n else:\n img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)\n\n if control is not None: # Controlnet\n control_o = control.get(\"output\")\n if i < len(control_o):\n add = control_o[i]\n if add is not None:\n img[:, txt.shape[1]: img_len + txt.shape[1]] += add\n\n img = img[:, txt.shape[1]: img_len + txt.shape[1]]\n if ref_latent is not None:\n img = img[:, ref_latent.shape[1]:]\n\n img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels)\n\n shape = initial_shape[-len(self.patch_size):]\n for i in range(len(shape)):\n shape[i] = shape[i] // self.patch_size[i]\n img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)\n if img.ndim == 8:\n img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)\n img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])\n else:\n img = img.permute(0, 3, 1, 4, 2, 5)\n img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3])\n return img\n\n def img_ids(self, x):\n bs, c, t, h, w = x.shape\n patch_size = self.patch_size\n t_len = ((t + (patch_size[0] // 2)) // patch_size[0])\n h_len = ((h + (patch_size[1] // 2)) // patch_size[1])\n w_len = ((w + (patch_size[2] // 2)) // patch_size[2])\n img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)\n img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)\n img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)\n img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)\n return repeat(img_ids, \"t h w c -> b (t h w) c\", b=bs)\n\n def img_ids_2d(self, x):\n bs, c, h, w = x.shape\n patch_size = self.patch_size\n h_len = ((h + (patch_size[0] // 2)) // patch_size[0])\n w_len = ((w + (patch_size[1] // 2)) // patch_size[1])\n img_ids = torch.zeros((h_len, w_len, 2), device=x.device, dtype=x.dtype)\n img_ids[:, :, 0] = img_ids[:, :, 0] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)\n img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)\n return repeat(img_ids, \"h w c -> b (h w) c\", b=bs)\n\n def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, y, txt_byt5, clip_fea, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)\n\n def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):\n bs = x.shape[0]\n if len(self.patch_size) == 3:\n img_ids = self.img_ids(x)\n txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)\n else:\n img_ids = self.img_ids_2d(x)\n txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype)\n out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, clip_fea, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)\n return out\n" }, { "path": "comfy/ldm/hunyuan_video/upsampler.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d\nfrom comfy.ldm.hunyuan_video.vae_refiner import RMS_norm\nimport comfy.model_management\nimport comfy.model_patcher\n\nclass SRResidualCausalBlock3D(nn.Module):\n def __init__(self, channels: int):\n super().__init__()\n self.block = nn.Sequential(\n VideoConv3d(channels, channels, kernel_size=3),\n nn.SiLU(inplace=True),\n VideoConv3d(channels, channels, kernel_size=3),\n nn.SiLU(inplace=True),\n VideoConv3d(channels, channels, kernel_size=3),\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return x + self.block(x)\n\nclass SRModel3DV2(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n hidden_channels: int = 64,\n num_blocks: int = 6,\n global_residual: bool = False,\n ):\n super().__init__()\n self.in_conv = VideoConv3d(in_channels, hidden_channels, kernel_size=3)\n self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)])\n self.out_conv = VideoConv3d(hidden_channels, out_channels, kernel_size=3)\n self.global_residual = bool(global_residual)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n residual = x\n y = self.in_conv(x)\n for blk in self.blocks:\n y = blk(y)\n y = self.out_conv(y)\n if self.global_residual and (y.shape == residual.shape):\n y = y + residual\n return y\n\n\nclass Upsampler(nn.Module):\n def __init__(\n self,\n z_channels: int,\n out_channels: int,\n block_out_channels: tuple[int, ...],\n num_res_blocks: int = 2,\n ):\n super().__init__()\n self.num_res_blocks = num_res_blocks\n self.block_out_channels = block_out_channels\n self.z_channels = z_channels\n\n ch = block_out_channels[0]\n self.conv_in = VideoConv3d(z_channels, ch, kernel_size=3)\n\n self.up = nn.ModuleList()\n\n for i, tgt in enumerate(block_out_channels):\n stage = nn.Module()\n stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,\n out_channels=tgt,\n temb_channels=0,\n conv_shortcut=False,\n conv_op=VideoConv3d, norm_op=RMS_norm)\n for j in range(num_res_blocks + 1)])\n ch = tgt\n self.up.append(stage)\n\n self.norm_out = RMS_norm(ch)\n self.conv_out = VideoConv3d(ch, out_channels, kernel_size=3)\n\n def forward(self, z):\n \"\"\"\n Args:\n z: (B, C, T, H, W)\n target_shape: (H, W)\n \"\"\"\n # z to block_in\n repeats = self.block_out_channels[0] // (self.z_channels)\n x = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)\n\n # upsampling\n for stage in self.up:\n for blk in stage.block:\n x = blk(x)\n\n out = self.conv_out(F.silu(self.norm_out(x)))\n return out\n\nUPSAMPLERS = {\n \"720p\": SRModel3DV2,\n \"1080p\": Upsampler,\n}\n\nclass HunyuanVideo15SRModel():\n def __init__(self, model_type, config):\n self.load_device = comfy.model_management.vae_device()\n offload_device = comfy.model_management.vae_offload_device()\n self.dtype = comfy.model_management.vae_dtype(self.load_device)\n self.model_class = UPSAMPLERS.get(model_type)\n self.model = self.model_class(**config).eval()\n\n self.patcher = comfy.model_patcher.CoreModelPatcher(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=True, assign=self.patcher.is_dynamic())\n\n def get_sd(self):\n return self.model.state_dict()\n\n def resample_latent(self, latent):\n comfy.model_management.load_model_gpu(self.patcher)\n return self.model(latent.to(self.load_device))\n" }, { "path": "comfy/ldm/hunyuan_video/vae.py", "content": "import torch.nn as nn\nimport torch.nn.functional as F\nfrom comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\n\nclass PixelShuffle2D(nn.Module):\n def __init__(self, in_dim, out_dim, op=ops.Conv2d):\n super().__init__()\n self.conv = op(in_dim, out_dim >> 2, 3, 1, 1)\n self.ratio = (in_dim << 2) // out_dim\n\n def forward(self, x):\n b, c, h, w = x.shape\n h2, w2 = h >> 1, w >> 1\n y = self.conv(x).view(b, -1, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, -1, h2, w2)\n r = x.view(b, c, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, c << 2, h2, w2)\n return y + r.view(b, y.shape[1], self.ratio, h2, w2).mean(2)\n\n\nclass PixelUnshuffle2D(nn.Module):\n def __init__(self, in_dim, out_dim, op=ops.Conv2d):\n super().__init__()\n self.conv = op(in_dim, out_dim << 2, 3, 1, 1)\n self.scale = (out_dim << 2) // in_dim\n\n def forward(self, x):\n b, c, h, w = x.shape\n h2, w2 = h << 1, w << 1\n y = self.conv(x).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2)\n r = x.repeat_interleave(self.scale, 1).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2)\n return y + r\n\n\nclass Encoder(nn.Module):\n def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,\n ffactor_spatial, downsample_match_channel=True, **_):\n super().__init__()\n self.z_channels = z_channels\n self.block_out_channels = block_out_channels\n self.num_res_blocks = num_res_blocks\n self.conv_in = ops.Conv2d(in_channels, block_out_channels[0], 3, 1, 1)\n\n self.down = nn.ModuleList()\n ch = block_out_channels[0]\n depth = (ffactor_spatial >> 1).bit_length()\n\n for i, tgt in enumerate(block_out_channels):\n stage = nn.Module()\n stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,\n out_channels=tgt,\n temb_channels=0,\n conv_op=ops.Conv2d)\n for j in range(num_res_blocks)])\n ch = tgt\n if i < depth:\n nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch\n stage.downsample = PixelShuffle2D(ch, nxt, ops.Conv2d)\n ch = nxt\n self.down.append(stage)\n\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)\n self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d)\n self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)\n\n self.norm_out = ops.GroupNorm(32, ch, 1e-6, True)\n self.conv_out = ops.Conv2d(ch, z_channels << 1, 3, 1, 1)\n\n def forward(self, x):\n x = self.conv_in(x)\n\n for stage in self.down:\n for blk in stage.block:\n x = blk(x)\n if hasattr(stage, 'downsample'):\n x = stage.downsample(x)\n\n x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))\n\n b, c, h, w = x.shape\n grp = c // (self.z_channels << 1)\n skip = x.view(b, c // grp, grp, h, w).mean(2)\n\n return self.conv_out(F.silu(self.norm_out(x))) + skip\n\n\nclass Decoder(nn.Module):\n def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,\n ffactor_spatial, upsample_match_channel=True, **_):\n super().__init__()\n block_out_channels = block_out_channels[::-1]\n self.z_channels = z_channels\n self.block_out_channels = block_out_channels\n self.num_res_blocks = num_res_blocks\n\n ch = block_out_channels[0]\n self.conv_in = ops.Conv2d(z_channels, ch, 3, 1, 1)\n\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)\n self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d)\n self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)\n\n self.up = nn.ModuleList()\n depth = (ffactor_spatial >> 1).bit_length()\n\n for i, tgt in enumerate(block_out_channels):\n stage = nn.Module()\n stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,\n out_channels=tgt,\n temb_channels=0,\n conv_op=ops.Conv2d)\n for j in range(num_res_blocks + 1)])\n ch = tgt\n if i < depth:\n nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch\n stage.upsample = PixelUnshuffle2D(ch, nxt, ops.Conv2d)\n ch = nxt\n self.up.append(stage)\n\n self.norm_out = ops.GroupNorm(32, ch, 1e-6, True)\n self.conv_out = ops.Conv2d(ch, out_channels, 3, 1, 1)\n\n def forward(self, z):\n x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)\n x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))\n\n for stage in self.up:\n for blk in stage.block:\n x = blk(x)\n if hasattr(stage, 'upsample'):\n x = stage.upsample(x)\n\n return self.conv_out(F.silu(self.norm_out(x)))\n" }, { "path": "comfy/ldm/hunyuan_video/vae_refiner.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed\nimport comfy.ops\nimport comfy.ldm.models.autoencoder\nimport comfy.model_management\nops = comfy.ops.disable_weight_init\n\n\nclass RMS_norm(nn.Module):\n def __init__(self, dim):\n super().__init__()\n shape = (dim, 1, 1, 1)\n self.scale = dim**0.5\n self.gamma = nn.Parameter(torch.empty(shape))\n\n def forward(self, x):\n return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)\n\nclass DnSmpl(nn.Module):\n def __init__(self, ic, oc, tds, refiner_vae, op):\n super().__init__()\n fct = 2 * 2 * 2 if tds else 1 * 2 * 2\n assert oc % fct == 0\n self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1)\n self.refiner_vae = refiner_vae\n\n self.tds = tds\n self.gs = fct * ic // oc\n\n def forward(self, x, conv_carry_in=None, conv_carry_out=None):\n r1 = 2 if self.tds else 1\n h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)\n\n if self.tds and self.refiner_vae and conv_carry_in is None:\n\n hf = h[:, :, :1, :, :]\n b, c, f, ht, wd = hf.shape\n hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)\n hf = hf.permute(0, 4, 6, 1, 2, 3, 5)\n hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)\n hf = torch.cat([hf, hf], dim=1)\n\n h = h[:, :, 1:, :, :]\n\n xf = x[:, :, :1, :, :]\n b, ci, f, ht, wd = xf.shape\n xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2)\n xf = xf.permute(0, 4, 6, 1, 2, 3, 5)\n xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2)\n B, C, T, H, W = xf.shape\n xf = xf.view(B, hf.shape[1], self.gs // 2, T, H, W).mean(dim=2)\n\n x = x[:, :, 1:, :, :]\n\n if h.shape[2] == 0:\n return hf + xf\n\n b, c, frms, ht, wd = h.shape\n nf = frms // r1\n h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)\n h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)\n h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)\n\n b, ci, frms, ht, wd = x.shape\n nf = frms // r1\n x = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)\n x = x.permute(0, 3, 5, 7, 1, 2, 4, 6)\n x = x.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)\n B, C, T, H, W = x.shape\n x = x.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)\n\n if self.tds and self.refiner_vae and conv_carry_in is None:\n h = torch.cat([hf, h], dim=2)\n x = torch.cat([xf, x], dim=2)\n\n return h + x\n\n\nclass UpSmpl(nn.Module):\n def __init__(self, ic, oc, tus, refiner_vae, op):\n super().__init__()\n fct = 2 * 2 * 2 if tus else 1 * 2 * 2\n self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)\n self.refiner_vae = refiner_vae\n\n self.tus = tus\n self.rp = fct * oc // ic\n\n def forward(self, x, conv_carry_in=None, conv_carry_out=None):\n r1 = 2 if self.tus else 1\n h = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)\n\n if self.tus and self.refiner_vae and conv_carry_in is None:\n hf = h[:, :, :1, :, :]\n b, c, f, ht, wd = hf.shape\n nc = c // (2 * 2)\n hf = hf.reshape(b, 2, 2, nc, f, ht, wd)\n hf = hf.permute(0, 3, 4, 5, 1, 6, 2)\n hf = hf.reshape(b, nc, f, ht * 2, wd * 2)\n hf = hf[:, : hf.shape[1] // 2]\n\n h = h[:, :, 1:, :, :]\n\n xf = x[:, :, :1, :, :]\n b, ci, f, ht, wd = xf.shape\n xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1)\n b, c, f, ht, wd = xf.shape\n nc = c // (2 * 2)\n xf = xf.reshape(b, 2, 2, nc, f, ht, wd)\n xf = xf.permute(0, 3, 4, 5, 1, 6, 2)\n xf = xf.reshape(b, nc, f, ht * 2, wd * 2)\n\n x = x[:, :, 1:, :, :]\n\n b, c, frms, ht, wd = h.shape\n nc = c // (r1 * 2 * 2)\n h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)\n h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)\n h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)\n\n x = x.repeat_interleave(repeats=self.rp, dim=1)\n b, c, frms, ht, wd = x.shape\n nc = c // (r1 * 2 * 2)\n x = x.reshape(b, r1, 2, 2, nc, frms, ht, wd)\n x = x.permute(0, 4, 5, 1, 6, 2, 7, 3)\n x = x.reshape(b, nc, frms * r1, ht * 2, wd * 2)\n\n if self.tus and self.refiner_vae and conv_carry_in is None:\n h = torch.cat([hf, h], dim=2)\n x = torch.cat([xf, x], dim=2)\n\n return h + x\n\nclass Encoder(nn.Module):\n def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,\n ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):\n super().__init__()\n self.z_channels = z_channels\n self.block_out_channels = block_out_channels\n self.num_res_blocks = num_res_blocks\n self.ffactor_temporal = ffactor_temporal\n\n self.refiner_vae = refiner_vae\n if self.refiner_vae:\n conv_op = CarriedConv3d\n norm_op = RMS_norm\n else:\n conv_op = ops.Conv3d\n norm_op = Normalize\n\n self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1)\n\n self.down = nn.ModuleList()\n ch = block_out_channels[0]\n depth = (ffactor_spatial >> 1).bit_length()\n depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length()\n\n for i, tgt in enumerate(block_out_channels):\n stage = nn.Module()\n stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,\n out_channels=tgt,\n temb_channels=0,\n conv_op=conv_op, norm_op=norm_op)\n for j in range(num_res_blocks)])\n ch = tgt\n if i < depth:\n nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch\n stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)\n ch = nxt\n self.down.append(stage)\n\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)\n self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)\n self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)\n\n self.norm_out = norm_op(ch)\n self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)\n\n self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer()\n\n def forward(self, x):\n if not self.refiner_vae and x.shape[2] == 1:\n x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)\n\n if self.refiner_vae:\n xl = [x[:, :, :1, :, :]]\n if x.shape[2] > self.ffactor_temporal:\n xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // self.ffactor_temporal) * self.ffactor_temporal, :, :], self.ffactor_temporal * 2, dim=2)\n x = xl\n else:\n x = [x]\n out = []\n\n conv_carry_in = None\n\n for i, x1 in enumerate(x):\n conv_carry_out = []\n if i == len(x) - 1:\n conv_carry_out = None\n\n x1 = [ x1 ]\n x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)\n\n for stage in self.down:\n for blk in stage.block:\n x1 = blk(x1, None, conv_carry_in, conv_carry_out)\n if hasattr(stage, 'downsample'):\n x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)\n\n out.append(x1)\n conv_carry_in = conv_carry_out\n\n out = torch_cat_if_needed(out, dim=2)\n\n x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))\n del out\n\n b, c, t, h, w = x.shape\n grp = c // (self.z_channels << 1)\n skip = x.view(b, c // grp, grp, t, h, w).mean(2)\n\n out = conv_carry_causal_3d([F.silu(self.norm_out(x))], self.conv_out) + skip\n\n if self.refiner_vae:\n out = self.regul(out)[0]\n\n return out\n\nclass Decoder(nn.Module):\n def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,\n ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_):\n super().__init__()\n block_out_channels = block_out_channels[::-1]\n self.z_channels = z_channels\n self.block_out_channels = block_out_channels\n self.num_res_blocks = num_res_blocks\n\n self.refiner_vae = refiner_vae\n if self.refiner_vae:\n conv_op = CarriedConv3d\n norm_op = RMS_norm\n else:\n conv_op = ops.Conv3d\n norm_op = Normalize\n\n ch = block_out_channels[0]\n self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)\n\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)\n self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)\n self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)\n\n self.up = nn.ModuleList()\n depth = (ffactor_spatial >> 1).bit_length()\n depth_temporal = (ffactor_temporal >> 1).bit_length()\n\n for i, tgt in enumerate(block_out_channels):\n stage = nn.Module()\n stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,\n out_channels=tgt,\n temb_channels=0,\n conv_op=conv_op, norm_op=norm_op)\n for j in range(num_res_blocks + 1)])\n ch = tgt\n if i < depth:\n nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch\n stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)\n ch = nxt\n self.up.append(stage)\n\n self.norm_out = norm_op(ch)\n self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)\n\n def forward(self, z):\n x = conv_carry_causal_3d([z], self.conv_in) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)\n x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))\n\n if self.refiner_vae:\n x = torch.split(x, 2, dim=2)\n else:\n x = [ x ]\n out = []\n\n conv_carry_in = None\n\n for i, x1 in enumerate(x):\n conv_carry_out = []\n if i == len(x) - 1:\n conv_carry_out = None\n for stage in self.up:\n for blk in stage.block:\n x1 = blk(x1, None, conv_carry_in, conv_carry_out)\n if hasattr(stage, 'upsample'):\n x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)\n\n x1 = [ F.silu(self.norm_out(x1)) ]\n x1 = conv_carry_causal_3d(x1, self.conv_out, conv_carry_in, conv_carry_out)\n out.append(x1)\n conv_carry_in = conv_carry_out\n del x\n\n out = torch_cat_if_needed(out, dim=2)\n\n if not self.refiner_vae:\n if z.shape[-3] == 1:\n out = out[:, :, -1:]\n\n return out\n\n" }, { "path": "comfy/ldm/hydit/attn_layers.py", "content": "import torch\nimport torch.nn as nn\nfrom typing import Tuple, Union, Optional\nfrom comfy.ldm.modules.attention import optimized_attention\n\n\ndef reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):\n \"\"\"\n Reshape frequency tensor for broadcasting it with another tensor.\n\n This function reshapes the frequency tensor to have the same shape as the target tensor 'x'\n for the purpose of broadcasting the frequency tensor during element-wise operations.\n\n Args:\n freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped.\n x (torch.Tensor): Target tensor for broadcasting compatibility.\n head_first (bool): head dimension first (except batch dim) or not.\n\n Returns:\n torch.Tensor: Reshaped frequency tensor.\n\n Raises:\n AssertionError: If the frequency tensor doesn't match the expected shape.\n AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.\n \"\"\"\n ndim = x.ndim\n assert 0 <= 1 < ndim\n\n if isinstance(freqs_cis, tuple):\n # freqs_cis: (cos, sin) in real space\n if head_first:\n assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'\n shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]\n else:\n assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'\n shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]\n return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)\n else:\n # freqs_cis: values in complex space\n if head_first:\n assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'\n shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]\n else:\n assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'\n shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]\n return freqs_cis.view(*shape)\n\n\ndef rotate_half(x):\n x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]\n return torch.stack([-x_imag, x_real], dim=-1).flatten(3)\n\n\ndef apply_rotary_emb(\n xq: torch.Tensor,\n xk: Optional[torch.Tensor],\n freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],\n head_first: bool = False,\n) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Apply rotary embeddings to input tensors using the given frequency tensor.\n\n This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided\n frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor\n is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are\n returned as real tensors.\n\n Args:\n xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D]\n xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D]\n freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials.\n head_first (bool): head dimension first (except batch dim) or not.\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.\n\n \"\"\"\n xk_out = None\n if isinstance(freqs_cis, tuple):\n cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D]\n xq_out = (xq * cos + rotate_half(xq) * sin)\n if xk is not None:\n xk_out = (xk * cos + rotate_half(xk) * sin)\n else:\n xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2]\n freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2]\n xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)\n if xk is not None:\n xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2]\n xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)\n\n return xq_out, xk_out\n\n\n\nclass CrossAttention(nn.Module):\n \"\"\"\n Use QK Normalization.\n \"\"\"\n def __init__(self,\n qdim,\n kdim,\n num_heads,\n qkv_bias=True,\n qk_norm=False,\n attn_drop=0.0,\n proj_drop=0.0,\n attn_precision=None,\n device=None,\n dtype=None,\n operations=None,\n ):\n factory_kwargs = {'device': device, 'dtype': dtype}\n super().__init__()\n self.attn_precision = attn_precision\n self.qdim = qdim\n self.kdim = kdim\n self.num_heads = num_heads\n assert self.qdim % num_heads == 0, \"self.qdim must be divisible by num_heads\"\n self.head_dim = self.qdim // num_heads\n assert self.head_dim % 8 == 0 and self.head_dim <= 128, \"Only support head_dim <= 128 and divisible by 8\"\n self.scale = self.head_dim ** -0.5\n\n self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)\n self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)\n\n # TODO: eps should be 1 / 65530 if using fp16\n self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()\n self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()\n self.attn_drop = nn.Dropout(attn_drop)\n self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, x, y, freqs_cis_img=None):\n \"\"\"\n Parameters\n ----------\n x: torch.Tensor\n (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim)\n y: torch.Tensor\n (batch, seqlen2, hidden_dim2)\n freqs_cis_img: torch.Tensor\n (batch, hidden_dim // 2), RoPE for image\n \"\"\"\n b, s1, c = x.shape # [b, s1, D]\n _, s2, c = y.shape # [b, s2, 1024]\n\n q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d]\n kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d]\n k, v = kv.unbind(dim=2) # [b, s, h, d]\n q = self.q_norm(q)\n k = self.k_norm(k)\n\n # Apply RoPE if needed\n if freqs_cis_img is not None:\n qq, _ = apply_rotary_emb(q, None, freqs_cis_img)\n assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'\n q = qq\n\n q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C\n k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2\n v = v.transpose(-2, -3).contiguous()\n\n context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)\n\n out = self.out_proj(context) # context.reshape - B, L1, -1\n out = self.proj_drop(out)\n\n out_tuple = (out,)\n\n return out_tuple\n\n\nclass Attention(nn.Module):\n \"\"\"\n We rename some layer names to align with flash attention\n \"\"\"\n def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None):\n super().__init__()\n self.attn_precision = attn_precision\n self.dim = dim\n self.num_heads = num_heads\n assert self.dim % num_heads == 0, 'dim should be divisible by num_heads'\n self.head_dim = self.dim // num_heads\n # This assertion is aligned with flash attention\n assert self.head_dim % 8 == 0 and self.head_dim <= 128, \"Only support head_dim <= 128 and divisible by 8\"\n self.scale = self.head_dim ** -0.5\n\n # qkv --> Wqkv\n self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)\n # TODO: eps should be 1 / 65530 if using fp16\n self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()\n self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()\n self.attn_drop = nn.Dropout(attn_drop)\n self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, x, freqs_cis_img=None):\n B, N, C = x.shape\n qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d]\n q, k, v = qkv.unbind(0) # [b, h, s, d]\n q = self.q_norm(q) # [b, h, s, d]\n k = self.k_norm(k) # [b, h, s, d]\n\n # Apply RoPE if needed\n if freqs_cis_img is not None:\n qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True)\n assert qq.shape == q.shape and kk.shape == k.shape, \\\n f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'\n q, k = qq, kk\n\n x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)\n x = self.out_proj(x)\n x = self.proj_drop(x)\n\n out_tuple = (x,)\n\n return out_tuple\n" }, { "path": "comfy/ldm/hydit/controlnet.py", "content": "\nimport torch\nimport torch.nn as nn\n\n\nfrom comfy.ldm.modules.diffusionmodules.mmdit import (\n TimestepEmbedder,\n PatchEmbed,\n)\nfrom .poolers import AttentionPool\n\nimport comfy.latent_formats\nfrom .models import HunYuanDiTBlock, calc_rope\n\n\n\nclass HunYuanControlNet(nn.Module):\n \"\"\"\n HunYuanDiT: Diffusion model with a Transformer backbone.\n\n Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.\n\n Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.\n\n Parameters\n ----------\n args: argparse.Namespace\n The arguments parsed by argparse.\n input_size: tuple\n The size of the input image.\n patch_size: int\n The size of the patch.\n in_channels: int\n The number of input channels.\n hidden_size: int\n The hidden size of the transformer backbone.\n depth: int\n The number of transformer blocks.\n num_heads: int\n The number of attention heads.\n mlp_ratio: float\n The ratio of the hidden size of the MLP in the transformer block.\n log_fn: callable\n The logging function.\n \"\"\"\n\n def __init__(\n self,\n input_size: tuple = 128,\n patch_size: int = 2,\n in_channels: int = 4,\n hidden_size: int = 1408,\n depth: int = 40,\n num_heads: int = 16,\n mlp_ratio: float = 4.3637,\n text_states_dim=1024,\n text_states_dim_t5=2048,\n text_len=77,\n text_len_t5=256,\n qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details.\n size_cond=False,\n use_style_cond=False,\n learn_sigma=True,\n norm=\"layer\",\n log_fn: callable = print,\n attn_precision=None,\n dtype=None,\n device=None,\n operations=None,\n **kwargs,\n ):\n super().__init__()\n self.log_fn = log_fn\n self.depth = depth\n self.learn_sigma = learn_sigma\n self.in_channels = in_channels\n self.out_channels = in_channels * 2 if learn_sigma else in_channels\n self.patch_size = patch_size\n self.num_heads = num_heads\n self.hidden_size = hidden_size\n self.text_states_dim = text_states_dim\n self.text_states_dim_t5 = text_states_dim_t5\n self.text_len = text_len\n self.text_len_t5 = text_len_t5\n self.size_cond = size_cond\n self.use_style_cond = use_style_cond\n self.norm = norm\n self.dtype = dtype\n self.latent_format = comfy.latent_formats.SDXL\n\n self.mlp_t5 = nn.Sequential(\n nn.Linear(\n self.text_states_dim_t5,\n self.text_states_dim_t5 * 4,\n bias=True,\n dtype=dtype,\n device=device,\n ),\n nn.SiLU(),\n nn.Linear(\n self.text_states_dim_t5 * 4,\n self.text_states_dim,\n bias=True,\n dtype=dtype,\n device=device,\n ),\n )\n # learnable replace\n self.text_embedding_padding = nn.Parameter(\n torch.randn(\n self.text_len + self.text_len_t5,\n self.text_states_dim,\n dtype=dtype,\n device=device,\n )\n )\n\n # Attention pooling\n pooler_out_dim = 1024\n self.pooler = AttentionPool(\n self.text_len_t5,\n self.text_states_dim_t5,\n num_heads=8,\n output_dim=pooler_out_dim,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n # Dimension of the extra input vectors\n self.extra_in_dim = pooler_out_dim\n\n if self.size_cond:\n # Image size and crop size conditions\n self.extra_in_dim += 6 * 256\n\n if self.use_style_cond:\n # Here we use a default learned embedder layer for future extension.\n self.style_embedder = nn.Embedding(\n 1, hidden_size, dtype=dtype, device=device\n )\n self.extra_in_dim += hidden_size\n\n # Text embedding for `add`\n self.x_embedder = PatchEmbed(\n input_size,\n patch_size,\n in_channels,\n hidden_size,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.t_embedder = TimestepEmbedder(\n hidden_size, dtype=dtype, device=device, operations=operations\n )\n self.extra_embedder = nn.Sequential(\n operations.Linear(\n self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device\n ),\n nn.SiLU(),\n operations.Linear(\n hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device\n ),\n )\n\n # HUnYuanDiT Blocks\n self.blocks = nn.ModuleList(\n [\n HunYuanDiTBlock(\n hidden_size=hidden_size,\n c_emb_size=hidden_size,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n text_states_dim=self.text_states_dim,\n qk_norm=qk_norm,\n norm_type=self.norm,\n skip=False,\n attn_precision=attn_precision,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n for _ in range(19)\n ]\n )\n\n # Input zero linear for the first block\n self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)\n\n\n # Output zero linear for the every block\n self.after_proj_list = nn.ModuleList(\n [\n\n operations.Linear(\n self.hidden_size, self.hidden_size, dtype=dtype, device=device\n )\n for _ in range(len(self.blocks))\n ]\n )\n\n def forward(\n self,\n x,\n hint,\n timesteps,\n context,#encoder_hidden_states=None,\n text_embedding_mask=None,\n encoder_hidden_states_t5=None,\n text_embedding_mask_t5=None,\n image_meta_size=None,\n style=None,\n return_dict=False,\n **kwarg,\n ):\n \"\"\"\n Forward pass of the encoder.\n\n Parameters\n ----------\n x: torch.Tensor\n (B, D, H, W)\n t: torch.Tensor\n (B)\n encoder_hidden_states: torch.Tensor\n CLIP text embedding, (B, L_clip, D)\n text_embedding_mask: torch.Tensor\n CLIP text embedding mask, (B, L_clip)\n encoder_hidden_states_t5: torch.Tensor\n T5 text embedding, (B, L_t5, D)\n text_embedding_mask_t5: torch.Tensor\n T5 text embedding mask, (B, L_t5)\n image_meta_size: torch.Tensor\n (B, 6)\n style: torch.Tensor\n (B)\n cos_cis_img: torch.Tensor\n sin_cis_img: torch.Tensor\n return_dict: bool\n Whether to return a dictionary.\n \"\"\"\n condition = hint\n if condition.shape[0] == 1:\n condition = torch.repeat_interleave(condition, x.shape[0], dim=0)\n\n text_states = context # 2,77,1024\n text_states_t5 = encoder_hidden_states_t5 # 2,256,2048\n text_states_mask = text_embedding_mask.bool() # 2,77\n text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256\n b_t5, l_t5, c_t5 = text_states_t5.shape\n text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)\n\n padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)\n\n text_states[:, -self.text_len :] = torch.where(\n text_states_mask[:, -self.text_len :].unsqueeze(2),\n text_states[:, -self.text_len :],\n padding[: self.text_len],\n )\n text_states_t5[:, -self.text_len_t5 :] = torch.where(\n text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2),\n text_states_t5[:, -self.text_len_t5 :],\n padding[self.text_len :],\n )\n\n text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024\n\n # _, _, oh, ow = x.shape\n # th, tw = oh // self.patch_size, ow // self.patch_size\n\n # Get image RoPE embedding according to `reso`lution.\n freqs_cis_img = calc_rope(\n x, self.patch_size, self.hidden_size // self.num_heads\n ) # (cos_cis_img, sin_cis_img)\n\n # ========================= Build time and image embedding =========================\n t = self.t_embedder(timesteps, dtype=self.dtype)\n x = self.x_embedder(x)\n\n # ========================= Concatenate all extra vectors =========================\n # Build text tokens with pooling\n extra_vec = self.pooler(encoder_hidden_states_t5)\n\n # Build image meta size tokens if applicable\n # if image_meta_size is not None:\n # image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256]\n # if image_meta_size.dtype != self.dtype:\n # image_meta_size = image_meta_size.half()\n # image_meta_size = image_meta_size.view(-1, 6 * 256)\n # extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256]\n\n # Build style tokens\n if style is not None:\n style_embedding = self.style_embedder(style)\n extra_vec = torch.cat([extra_vec, style_embedding], dim=1)\n\n # Concatenate all extra vectors\n c = t + self.extra_embedder(extra_vec) # [B, D]\n\n # ========================= Deal with Condition =========================\n condition = self.x_embedder(condition)\n\n # ========================= Forward pass through HunYuanDiT blocks =========================\n controls = []\n x = x + self.before_proj(condition) # add condition\n for layer, block in enumerate(self.blocks):\n x = block(x, c, text_states, freqs_cis_img)\n controls.append(self.after_proj_list[layer](x)) # zero linear for output\n\n return {\"output\": controls}\n" }, { "path": "comfy/ldm/hydit/models.py", "content": "\nimport torch\nimport torch.nn as nn\n\nimport comfy.ops\nfrom comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed\nfrom comfy.ldm.modules.diffusionmodules.util import timestep_embedding\nfrom torch.utils import checkpoint\n\nfrom .attn_layers import Attention, CrossAttention\nfrom .poolers import AttentionPool\nfrom .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop\n\ndef calc_rope(x, patch_size, head_size):\n th = (x.shape[2] + (patch_size // 2)) // patch_size\n tw = (x.shape[3] + (patch_size // 2)) // patch_size\n base_size = 512 // 8 // patch_size\n start, stop = get_fill_resize_and_crop((th, tw), base_size)\n sub_args = [start, stop, (th, tw)]\n # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']\n rope = get_2d_rotary_pos_embed(head_size, *sub_args)\n rope = (rope[0].to(x), rope[1].to(x))\n return rope\n\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\nclass HunYuanDiTBlock(nn.Module):\n \"\"\"\n A HunYuanDiT block with `add` conditioning.\n \"\"\"\n def __init__(self,\n hidden_size,\n c_emb_size,\n num_heads,\n mlp_ratio=4.0,\n text_states_dim=1024,\n qk_norm=False,\n norm_type=\"layer\",\n skip=False,\n attn_precision=None,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n use_ele_affine = True\n\n if norm_type == \"layer\":\n norm_layer = operations.LayerNorm\n elif norm_type == \"rms\":\n norm_layer = operations.RMSNorm\n else:\n raise ValueError(f\"Unknown norm_type: {norm_type}\")\n\n # ========================= Self-Attention =========================\n self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)\n self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)\n\n # ========================= FFN =========================\n self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)\n mlp_hidden_dim = int(hidden_size * mlp_ratio)\n approx_gelu = lambda: nn.GELU(approximate=\"tanh\")\n self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations)\n\n # ========================= Add =========================\n # Simply use add like SDXL.\n self.default_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device)\n )\n\n # ========================= Cross-Attention =========================\n self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,\n qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)\n self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)\n\n # ========================= Skip Connection =========================\n if skip:\n self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)\n self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device)\n else:\n self.skip_linear = None\n\n self.gradient_checkpointing = False\n\n def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):\n # Long Skip Connection\n if self.skip_linear is not None:\n cat = torch.cat([x, skip], dim=-1)\n if cat.dtype != x.dtype:\n cat = cat.to(x.dtype)\n cat = self.skip_norm(cat)\n x = self.skip_linear(cat)\n\n # Self-Attention\n shift_msa = self.default_modulation(c).unsqueeze(dim=1)\n attn_inputs = (\n self.norm1(x) + shift_msa, freq_cis_img,\n )\n x = x + self.attn1(*attn_inputs)[0]\n\n # Cross-Attention\n cross_inputs = (\n self.norm3(x), text_states, freq_cis_img\n )\n x = x + self.attn2(*cross_inputs)[0]\n\n # FFN Layer\n mlp_inputs = self.norm2(x)\n x = x + self.mlp(mlp_inputs)\n\n return x\n\n def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):\n if self.gradient_checkpointing and self.training:\n return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip)\n return self._forward(x, c, text_states, freq_cis_img, skip)\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of HunYuanDiT.\n \"\"\"\n def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)\n )\n\n def forward(self, x, c):\n shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)\n x = modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n return x\n\n\nclass HunYuanDiT(nn.Module):\n \"\"\"\n HunYuanDiT: Diffusion model with a Transformer backbone.\n\n Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.\n\n Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.\n\n Parameters\n ----------\n args: argparse.Namespace\n The arguments parsed by argparse.\n input_size: tuple\n The size of the input image.\n patch_size: int\n The size of the patch.\n in_channels: int\n The number of input channels.\n hidden_size: int\n The hidden size of the transformer backbone.\n depth: int\n The number of transformer blocks.\n num_heads: int\n The number of attention heads.\n mlp_ratio: float\n The ratio of the hidden size of the MLP in the transformer block.\n log_fn: callable\n The logging function.\n \"\"\"\n #@register_to_config\n def __init__(self,\n input_size: tuple = 32,\n patch_size: int = 2,\n in_channels: int = 4,\n hidden_size: int = 1152,\n depth: int = 28,\n num_heads: int = 16,\n mlp_ratio: float = 4.0,\n text_states_dim = 1024,\n text_states_dim_t5 = 2048,\n text_len = 77,\n text_len_t5 = 256,\n qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details.\n size_cond = False,\n use_style_cond = False,\n learn_sigma = True,\n norm = \"layer\",\n log_fn: callable = print,\n attn_precision=None,\n dtype=None,\n device=None,\n operations=None,\n **kwargs,\n ):\n super().__init__()\n self.log_fn = log_fn\n self.depth = depth\n self.learn_sigma = learn_sigma\n self.in_channels = in_channels\n self.out_channels = in_channels * 2 if learn_sigma else in_channels\n self.patch_size = patch_size\n self.num_heads = num_heads\n self.hidden_size = hidden_size\n self.text_states_dim = text_states_dim\n self.text_states_dim_t5 = text_states_dim_t5\n self.text_len = text_len\n self.text_len_t5 = text_len_t5\n self.size_cond = size_cond\n self.use_style_cond = use_style_cond\n self.norm = norm\n self.dtype = dtype\n #import pdb\n #pdb.set_trace()\n\n self.mlp_t5 = nn.Sequential(\n operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device),\n )\n # learnable replace\n self.text_embedding_padding = nn.Parameter(\n torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device))\n\n # Attention pooling\n pooler_out_dim = 1024\n self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations)\n\n # Dimension of the extra input vectors\n self.extra_in_dim = pooler_out_dim\n\n if self.size_cond:\n # Image size and crop size conditions\n self.extra_in_dim += 6 * 256\n\n if self.use_style_cond:\n # Here we use a default learned embedder layer for future extension.\n self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device)\n self.extra_in_dim += hidden_size\n\n # Text embedding for `add`\n self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations)\n self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)\n self.extra_embedder = nn.Sequential(\n operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device),\n )\n\n # HUnYuanDiT Blocks\n self.blocks = nn.ModuleList([\n HunYuanDiTBlock(hidden_size=hidden_size,\n c_emb_size=hidden_size,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n text_states_dim=self.text_states_dim,\n qk_norm=qk_norm,\n norm_type=self.norm,\n skip=layer > depth // 2,\n attn_precision=attn_precision,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n for layer in range(depth)\n ])\n\n self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)\n self.unpatchify_channels = self.out_channels\n\n\n\n def forward(self,\n x,\n t,\n context,#encoder_hidden_states=None,\n text_embedding_mask=None,\n encoder_hidden_states_t5=None,\n text_embedding_mask_t5=None,\n image_meta_size=None,\n style=None,\n return_dict=False,\n control=None,\n transformer_options={},\n ):\n \"\"\"\n Forward pass of the encoder.\n\n Parameters\n ----------\n x: torch.Tensor\n (B, D, H, W)\n t: torch.Tensor\n (B)\n encoder_hidden_states: torch.Tensor\n CLIP text embedding, (B, L_clip, D)\n text_embedding_mask: torch.Tensor\n CLIP text embedding mask, (B, L_clip)\n encoder_hidden_states_t5: torch.Tensor\n T5 text embedding, (B, L_t5, D)\n text_embedding_mask_t5: torch.Tensor\n T5 text embedding mask, (B, L_t5)\n image_meta_size: torch.Tensor\n (B, 6)\n style: torch.Tensor\n (B)\n cos_cis_img: torch.Tensor\n sin_cis_img: torch.Tensor\n return_dict: bool\n Whether to return a dictionary.\n \"\"\"\n patches_replace = transformer_options.get(\"patches_replace\", {})\n encoder_hidden_states = context\n text_states = encoder_hidden_states # 2,77,1024\n text_states_t5 = encoder_hidden_states_t5 # 2,256,2048\n text_states_mask = text_embedding_mask.bool() # 2,77\n text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256\n b_t5, l_t5, c_t5 = text_states_t5.shape\n text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)\n\n padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)\n\n text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len])\n text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:])\n\n text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024\n # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)\n\n _, _, oh, ow = x.shape\n th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size\n\n\n # Get image RoPE embedding according to `reso`lution.\n freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img)\n\n # ========================= Build time and image embedding =========================\n t = self.t_embedder(t, dtype=x.dtype)\n x = self.x_embedder(x)\n\n # ========================= Concatenate all extra vectors =========================\n # Build text tokens with pooling\n extra_vec = self.pooler(encoder_hidden_states_t5)\n\n # Build image meta size tokens if applicable\n if self.size_cond:\n image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256]\n image_meta_size = image_meta_size.view(-1, 6 * 256)\n extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256]\n\n # Build style tokens\n if self.use_style_cond:\n if style is None:\n style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)\n style_embedding = self.style_embedder(style, out_dtype=x.dtype)\n extra_vec = torch.cat([extra_vec, style_embedding], dim=1)\n\n # Concatenate all extra vectors\n c = t + self.extra_embedder(extra_vec) # [B, D]\n\n blocks_replace = patches_replace.get(\"dit\", {})\n\n controls = None\n if control:\n controls = control.get(\"output\", None)\n # ========================= Forward pass through HunYuanDiT blocks =========================\n skips = []\n for layer, block in enumerate(self.blocks):\n if layer > self.depth // 2:\n if controls is not None:\n skip = skips.pop() + controls.pop().to(dtype=x.dtype)\n else:\n skip = skips.pop()\n else:\n skip = None\n\n if (\"double_block\", layer) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], args[\"vec\"], args[\"txt\"], args[\"pe\"], args[\"skip\"])\n return out\n\n out = blocks_replace[(\"double_block\", layer)]({\"img\": x, \"txt\": text_states, \"vec\": c, \"pe\": freqs_cis_img, \"skip\": skip}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D)\n\n\n if layer < (self.depth // 2 - 1):\n skips.append(x)\n if controls is not None and len(controls) != 0:\n raise ValueError(\"The number of controls is not equal to the number of skip connections.\")\n\n # ========================= Final layer =========================\n x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels)\n x = self.unpatchify(x, th, tw) # (N, out_channels, H, W)\n\n if return_dict:\n return {'x': x}\n if self.learn_sigma:\n return x[:,:self.out_channels // 2,:oh,:ow]\n return x[:,:,:oh,:ow]\n\n def unpatchify(self, x, h, w):\n \"\"\"\n x: (N, T, patch_size**2 * C)\n imgs: (N, H, W, C)\n \"\"\"\n c = self.unpatchify_channels\n p = self.x_embedder.patch_size[0]\n # h = w = int(x.shape[1] ** 0.5)\n assert h * w == x.shape[1]\n\n x = x.reshape(shape=(x.shape[0], h, w, p, p, c))\n x = torch.einsum('nhwpqc->nchpwq', x)\n imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))\n return imgs\n" }, { "path": "comfy/ldm/hydit/poolers.py", "content": "import torch\nimport torch.nn as nn\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.ops\n\nclass AttentionPool(nn.Module):\n def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None):\n super().__init__()\n self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device))\n self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)\n self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)\n self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)\n self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device)\n self.num_heads = num_heads\n self.embed_dim = embed_dim\n\n def forward(self, x):\n x = x[:,:self.positional_embedding.shape[0] - 1]\n x = x.permute(1, 0, 2) # NLC -> LNC\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC\n x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC\n\n q = self.q_proj(x[:1])\n k = self.k_proj(x)\n v = self.v_proj(x)\n\n batch_size = q.shape[1]\n head_dim = self.embed_dim // self.num_heads\n q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)\n k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)\n v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)\n\n attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1)\n\n attn_output = self.c_proj(attn_output)\n return attn_output.squeeze(0)\n" }, { "path": "comfy/ldm/hydit/posemb_layers.py", "content": "import torch\nimport numpy as np\nfrom typing import Union\n\n\ndef _to_tuple(x):\n if isinstance(x, int):\n return x, x\n else:\n return x\n\n\ndef get_fill_resize_and_crop(src, tgt):\n th, tw = _to_tuple(tgt)\n h, w = _to_tuple(src)\n\n tr = th / tw # base resolution\n r = h / w # target resolution\n\n # resize\n if r > tr:\n resize_height = th\n resize_width = int(round(th / h * w))\n else:\n resize_width = tw\n resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution\n\n crop_top = int(round((th - resize_height) / 2.0))\n crop_left = int(round((tw - resize_width) / 2.0))\n\n return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)\n\n\ndef get_meshgrid(start, *args):\n if len(args) == 0:\n # start is grid_size\n num = _to_tuple(start)\n start = (0, 0)\n stop = num\n elif len(args) == 1:\n # start is start, args[0] is stop, step is 1\n start = _to_tuple(start)\n stop = _to_tuple(args[0])\n num = (stop[0] - start[0], stop[1] - start[1])\n elif len(args) == 2:\n # start is start, args[0] is stop, args[1] is num\n start = _to_tuple(start)\n stop = _to_tuple(args[0])\n num = _to_tuple(args[1])\n else:\n raise ValueError(f\"len(args) should be 0, 1 or 2, but got {len(args)}\")\n\n grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32)\n grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0) # [2, W, H]\n return grid\n\n#################################################################################\n# Sine/Cosine Positional Embedding Functions #\n#################################################################################\n# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py\n\ndef get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n grid = get_meshgrid(start, *args) # [2, H, w]\n # grid_h = np.arange(grid_size, dtype=np.float32)\n # grid_w = np.arange(grid_size, dtype=np.float32)\n # grid = np.meshgrid(grid_w, grid_h) # here w goes first\n # grid = np.stack(grid, axis=0) # [2, W, H]\n\n grid = grid.reshape([2, 1, *grid.shape[1:]])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token and extra_tokens > 0:\n pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (W,H)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float64)\n omega /= embed_dim / 2.\n omega = 1. / 10000**omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\n\n#################################################################################\n# Rotary Positional Embedding Functions #\n#################################################################################\n# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443\n\ndef get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):\n \"\"\"\n This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure.\n\n Parameters\n ----------\n embed_dim: int\n embedding dimension size\n start: int or tuple of int\n If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1;\n If len(args) == 2, start is start, args[0] is stop, args[1] is num.\n use_real: bool\n If True, return real part and imaginary part separately. Otherwise, return complex numbers.\n\n Returns\n -------\n pos_embed: torch.Tensor\n [HW, D/2]\n \"\"\"\n grid = get_meshgrid(start, *args) # [2, H, w]\n grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution\n pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)\n return pos_embed\n\n\ndef get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):\n assert embed_dim % 4 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4)\n emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4)\n\n if use_real:\n cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2)\n sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2)\n return cos, sin\n else:\n emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2)\n return emb\n\n\ndef get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):\n \"\"\"\n Precompute the frequency tensor for complex exponentials (cis) with given dimensions.\n\n This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'\n and the end index 'end'. The 'theta' parameter scales the frequencies.\n The returned tensor contains complex values in complex64 data type.\n\n Args:\n dim (int): Dimension of the frequency tensor.\n pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar\n theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.\n use_real (bool, optional): If True, return real part and imaginary part separately.\n Otherwise, return complex numbers.\n\n Returns:\n torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2]\n\n \"\"\"\n if isinstance(pos, int):\n pos = np.arange(pos)\n freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2]\n t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S]\n freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2]\n if use_real:\n freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D]\n freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D]\n return freqs_cos, freqs_sin\n else:\n freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2]\n return freqs_cis\n\n\n\ndef calc_sizes(rope_img, patch_size, th, tw):\n if rope_img == 'extend':\n # Expansion mode\n sub_args = [(th, tw)]\n elif rope_img.startswith('base'):\n # Based on the specified dimensions, other dimensions are obtained through interpolation.\n base_size = int(rope_img[4:]) // 8 // patch_size\n start, stop = get_fill_resize_and_crop((th, tw), base_size)\n sub_args = [start, stop, (th, tw)]\n else:\n raise ValueError(f\"Unknown rope_img: {rope_img}\")\n return sub_args\n\n\ndef init_image_posemb(rope_img,\n resolutions,\n patch_size,\n hidden_size,\n num_heads,\n log_fn,\n rope_real=True,\n ):\n freqs_cis_img = {}\n for reso in resolutions:\n th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size\n sub_args = calc_sizes(rope_img, patch_size, th, tw)\n freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real)\n log_fn(f\" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) \"\n f\"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}\")\n return freqs_cis_img\n" }, { "path": "comfy/ldm/kandinsky5/model.py", "content": "import torch\nfrom torch import nn\nimport math\n\nimport comfy.ldm.common_dit\nfrom comfy.ldm.modules.attention import optimized_attention\nfrom comfy.ldm.flux.math import apply_rope1\nfrom comfy.ldm.flux.layers import EmbedND\n\ndef attention(q, k, v, heads, transformer_options={}):\n return optimized_attention(\n q.transpose(1, 2),\n k.transpose(1, 2),\n v.transpose(1, 2),\n heads=heads,\n skip_reshape=True,\n transformer_options=transformer_options\n )\n\ndef apply_scale_shift_norm(norm, x, scale, shift):\n return torch.addcmul(shift, norm(x), scale + 1.0)\n\ndef apply_gate_sum(x, out, gate):\n return torch.addcmul(x, gate, out)\n\ndef get_shift_scale_gate(params):\n shift, scale, gate = torch.chunk(params, 3, dim=-1)\n return tuple(x.unsqueeze(1) for x in (shift, scale, gate))\n\ndef get_freqs(dim, max_period=10000.0):\n return torch.exp(-math.log(max_period) * torch.arange(start=0, end=dim, dtype=torch.float32) / dim)\n\n\nclass TimeEmbeddings(nn.Module):\n def __init__(self, model_dim, time_dim, max_period=10000.0, operation_settings=None):\n super().__init__()\n assert model_dim % 2 == 0\n self.model_dim = model_dim\n self.max_period = max_period\n self.register_buffer(\"freqs\", get_freqs(model_dim // 2, max_period), persistent=False)\n operations = operation_settings.get(\"operations\")\n self.in_layer = operations.Linear(model_dim, time_dim, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.activation = nn.SiLU()\n self.out_layer = operations.Linear(time_dim, time_dim, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, timestep, dtype):\n args = torch.outer(timestep, self.freqs.to(device=timestep.device))\n time_embed = torch.cat([torch.cos(args), torch.sin(args)], dim=-1).to(dtype)\n time_embed = self.out_layer(self.activation(self.in_layer(time_embed)))\n return time_embed\n\n\nclass TextEmbeddings(nn.Module):\n def __init__(self, text_dim, model_dim, operation_settings=None):\n super().__init__()\n operations = operation_settings.get(\"operations\")\n self.in_layer = operations.Linear(text_dim, model_dim, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.norm = operations.LayerNorm(model_dim, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, text_embed):\n text_embed = self.in_layer(text_embed)\n return self.norm(text_embed).type_as(text_embed)\n\n\nclass VisualEmbeddings(nn.Module):\n def __init__(self, visual_dim, model_dim, patch_size, operation_settings=None):\n super().__init__()\n self.patch_size = patch_size\n operations = operation_settings.get(\"operations\")\n self.in_layer = operations.Linear(visual_dim, model_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, x):\n x = x.movedim(1, -1) # B C T H W -> B T H W C\n B, T, H, W, dim = x.shape\n pt, ph, pw = self.patch_size\n\n x = x.view(\n B,\n T // pt, pt,\n H // ph, ph,\n W // pw, pw,\n dim,\n ).permute(0, 1, 3, 5, 2, 4, 6, 7).flatten(4, 7)\n\n return self.in_layer(x)\n\n\nclass Modulation(nn.Module):\n def __init__(self, time_dim, model_dim, num_params, operation_settings=None):\n super().__init__()\n self.activation = nn.SiLU()\n self.out_layer = operation_settings.get(\"operations\").Linear(time_dim, num_params * model_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, x):\n return self.out_layer(self.activation(x))\n\n\nclass SelfAttention(nn.Module):\n def __init__(self, num_channels, head_dim, operation_settings=None):\n super().__init__()\n assert num_channels % head_dim == 0\n self.num_heads = num_channels // head_dim\n self.head_dim = head_dim\n\n operations = operation_settings.get(\"operations\")\n self.to_query = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.to_key = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.to_value = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.query_norm = operations.RMSNorm(head_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.key_norm = operations.RMSNorm(head_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n self.out_layer = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.num_chunks = 2\n\n def _compute_qk(self, x, freqs, proj_fn, norm_fn):\n result = proj_fn(x).view(*x.shape[:-1], self.num_heads, -1)\n return apply_rope1(norm_fn(result), freqs)\n\n def _forward(self, x, freqs, transformer_options={}):\n q = self._compute_qk(x, freqs, self.to_query, self.query_norm)\n k = self._compute_qk(x, freqs, self.to_key, self.key_norm)\n v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)\n out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)\n return self.out_layer(out)\n\n def _forward_chunked(self, x, freqs, transformer_options={}):\n def process_chunks(proj_fn, norm_fn):\n x_chunks = torch.chunk(x, self.num_chunks, dim=1)\n freqs_chunks = torch.chunk(freqs, self.num_chunks, dim=1)\n chunks = []\n for x_chunk, freqs_chunk in zip(x_chunks, freqs_chunks):\n chunks.append(self._compute_qk(x_chunk, freqs_chunk, proj_fn, norm_fn))\n return torch.cat(chunks, dim=1)\n\n q = process_chunks(self.to_query, self.query_norm)\n k = process_chunks(self.to_key, self.key_norm)\n v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)\n out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)\n return self.out_layer(out)\n\n def forward(self, x, freqs, transformer_options={}):\n if x.shape[1] > 8192:\n return self._forward_chunked(x, freqs, transformer_options=transformer_options)\n else:\n return self._forward(x, freqs, transformer_options=transformer_options)\n\n\nclass CrossAttention(SelfAttention):\n def get_qkv(self, x, context):\n q = self.to_query(x).view(*x.shape[:-1], self.num_heads, -1)\n k = self.to_key(context).view(*context.shape[:-1], self.num_heads, -1)\n v = self.to_value(context).view(*context.shape[:-1], self.num_heads, -1)\n return q, k, v\n\n def forward(self, x, context, transformer_options={}):\n q, k, v = self.get_qkv(x, context)\n out = attention(self.query_norm(q), self.key_norm(k), v, self.num_heads, transformer_options=transformer_options)\n return self.out_layer(out)\n\n\nclass FeedForward(nn.Module):\n def __init__(self, dim, ff_dim, operation_settings=None):\n super().__init__()\n operations = operation_settings.get(\"operations\")\n self.in_layer = operations.Linear(dim, ff_dim, bias=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.activation = nn.GELU()\n self.out_layer = operations.Linear(ff_dim, dim, bias=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.num_chunks = 4\n\n def _forward(self, x):\n return self.out_layer(self.activation(self.in_layer(x)))\n\n def _forward_chunked(self, x):\n chunks = torch.chunk(x, self.num_chunks, dim=1)\n output_chunks = []\n for chunk in chunks:\n output_chunks.append(self._forward(chunk))\n return torch.cat(output_chunks, dim=1)\n\n def forward(self, x):\n if x.shape[1] > 8192:\n return self._forward_chunked(x)\n else:\n return self._forward(x)\n\n\nclass OutLayer(nn.Module):\n def __init__(self, model_dim, time_dim, visual_dim, patch_size, operation_settings=None):\n super().__init__()\n self.patch_size = patch_size\n self.modulation = Modulation(time_dim, model_dim, 2, operation_settings=operation_settings)\n operations = operation_settings.get(\"operations\")\n self.norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.out_layer = operations.Linear(model_dim, math.prod(patch_size) * visual_dim, bias=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, visual_embed, time_embed):\n B, T, H, W, _ = visual_embed.shape\n shift, scale = torch.chunk(self.modulation(time_embed), 2, dim=-1)\n scale = scale[:, None, None, None, :]\n shift = shift[:, None, None, None, :]\n visual_embed = apply_scale_shift_norm(self.norm, visual_embed, scale, shift)\n x = self.out_layer(visual_embed)\n\n out_dim = x.shape[-1] // (self.patch_size[0] * self.patch_size[1] * self.patch_size[2])\n x = x.view(\n B, T, H, W,\n out_dim,\n self.patch_size[0], self.patch_size[1], self.patch_size[2]\n )\n return x.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(2, 3).flatten(3, 4).flatten(4, 5)\n\n\nclass TransformerEncoderBlock(nn.Module):\n def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):\n super().__init__()\n self.text_modulation = Modulation(time_dim, model_dim, 6, operation_settings=operation_settings)\n operations = operation_settings.get(\"operations\")\n\n self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)\n\n self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)\n\n def forward(self, x, time_embed, freqs, transformer_options={}):\n self_attn_params, ff_params = torch.chunk(self.text_modulation(time_embed), 2, dim=-1)\n shift, scale, gate = get_shift_scale_gate(self_attn_params)\n out = apply_scale_shift_norm(self.self_attention_norm, x, scale, shift)\n out = self.self_attention(out, freqs, transformer_options=transformer_options)\n x = apply_gate_sum(x, out, gate)\n\n shift, scale, gate = get_shift_scale_gate(ff_params)\n out = apply_scale_shift_norm(self.feed_forward_norm, x, scale, shift)\n out = self.feed_forward(out)\n x = apply_gate_sum(x, out, gate)\n return x\n\n\nclass TransformerDecoderBlock(nn.Module):\n def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):\n super().__init__()\n self.visual_modulation = Modulation(time_dim, model_dim, 9, operation_settings=operation_settings)\n\n operations = operation_settings.get(\"operations\")\n self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)\n\n self.cross_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.cross_attention = CrossAttention(model_dim, head_dim, operation_settings=operation_settings)\n\n self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)\n\n def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options={}):\n self_attn_params, cross_attn_params, ff_params = torch.chunk(self.visual_modulation(time_embed), 3, dim=-1)\n # self attention\n shift, scale, gate = get_shift_scale_gate(self_attn_params)\n visual_out = apply_scale_shift_norm(self.self_attention_norm, visual_embed, scale, shift)\n visual_out = self.self_attention(visual_out, freqs, transformer_options=transformer_options)\n visual_embed = apply_gate_sum(visual_embed, visual_out, gate)\n # cross attention\n shift, scale, gate = get_shift_scale_gate(cross_attn_params)\n visual_out = apply_scale_shift_norm(self.cross_attention_norm, visual_embed, scale, shift)\n visual_out = self.cross_attention(visual_out, text_embed, transformer_options=transformer_options)\n visual_embed = apply_gate_sum(visual_embed, visual_out, gate)\n # feed forward\n shift, scale, gate = get_shift_scale_gate(ff_params)\n visual_out = apply_scale_shift_norm(self.feed_forward_norm, visual_embed, scale, shift)\n visual_out = self.feed_forward(visual_out)\n visual_embed = apply_gate_sum(visual_embed, visual_out, gate)\n return visual_embed\n\n\nclass Kandinsky5(nn.Module):\n def __init__(\n self,\n in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512,\n model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32,\n axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0),\n dtype=None, device=None, operations=None, **kwargs\n ):\n super().__init__()\n head_dim = sum(axes_dims)\n self.rope_scale_factor = rope_scale_factor\n self.in_visual_dim = in_visual_dim\n self.model_dim = model_dim\n self.patch_size = patch_size\n self.visual_embed_dim = visual_embed_dim\n self.dtype = dtype\n self.device = device\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n\n self.time_embeddings = TimeEmbeddings(model_dim, time_dim, operation_settings=operation_settings)\n self.text_embeddings = TextEmbeddings(in_text_dim, model_dim, operation_settings=operation_settings)\n self.pooled_text_embeddings = TextEmbeddings(in_text_dim2, time_dim, operation_settings=operation_settings)\n self.visual_embeddings = VisualEmbeddings(visual_embed_dim, model_dim, patch_size, operation_settings=operation_settings)\n\n self.text_transformer_blocks = nn.ModuleList(\n [TransformerEncoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_text_blocks)]\n )\n\n self.visual_transformer_blocks = nn.ModuleList(\n [TransformerDecoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_visual_blocks)]\n )\n\n self.out_layer = OutLayer(model_dim, time_dim, out_visual_dim, patch_size, operation_settings=operation_settings)\n\n self.rope_embedder_3d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=axes_dims)\n self.rope_embedder_1d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim])\n\n def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options={}):\n steps = seq_len if steps is None else steps\n seq_ids = torch.linspace(seq_start, seq_start + (seq_len - 1), steps=steps, device=device, dtype=dtype)\n seq_ids = seq_ids.reshape(-1, 1).unsqueeze(0) # Shape: (1, steps, 1)\n freqs = self.rope_embedder_1d(seq_ids).movedim(1, 2)\n return freqs\n\n def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):\n\n patch_size = self.patch_size\n t_len = ((t + (patch_size[0] // 2)) // patch_size[0])\n h_len = ((h + (patch_size[1] // 2)) // patch_size[1])\n w_len = ((w + (patch_size[2] // 2)) // patch_size[2])\n\n if steps_t is None:\n steps_t = t_len\n if steps_h is None:\n steps_h = h_len\n if steps_w is None:\n steps_w = w_len\n\n h_start = 0\n w_start = 0\n rope_options = transformer_options.get(\"rope_options\", None)\n if rope_options is not None:\n t_len = (t_len - 1.0) * rope_options.get(\"scale_t\", 1.0) + 1.0\n h_len = (h_len - 1.0) * rope_options.get(\"scale_y\", 1.0) + 1.0\n w_len = (w_len - 1.0) * rope_options.get(\"scale_x\", 1.0) + 1.0\n\n t_start += rope_options.get(\"shift_t\", 0.0)\n h_start += rope_options.get(\"shift_y\", 0.0)\n w_start += rope_options.get(\"shift_x\", 0.0)\n else:\n rope_scale_factor = self.rope_scale_factor\n if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions\n if h * w >= 14080:\n rope_scale_factor = (1.0, 3.16, 3.16)\n\n t_len = (t_len - 1.0) / rope_scale_factor[0] + 1.0\n h_len = (h_len - 1.0) / rope_scale_factor[1] + 1.0\n w_len = (w_len - 1.0) / rope_scale_factor[2] + 1.0\n\n img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)\n img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)\n img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)\n img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)\n img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])\n\n freqs = self.rope_embedder_3d(img_ids).movedim(1, 2)\n return freqs\n\n def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options={}, **kwargs):\n patches_replace = transformer_options.get(\"patches_replace\", {})\n context = self.text_embeddings(context)\n time_embed = self.time_embeddings(timestep, x.dtype) + self.pooled_text_embeddings(y)\n\n for block in self.text_transformer_blocks:\n context = block(context, time_embed, freqs_text, transformer_options=transformer_options)\n\n visual_embed = self.visual_embeddings(x)\n visual_shape = visual_embed.shape[:-1]\n visual_embed = visual_embed.flatten(1, -2)\n\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.visual_transformer_blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.visual_transformer_blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n return block(x=args[\"x\"], context=args[\"context\"], time_embed=args[\"time_embed\"], freqs=args[\"freqs\"], transformer_options=args.get(\"transformer_options\"))\n visual_embed = blocks_replace[(\"double_block\", i)]({\"x\": visual_embed, \"context\": context, \"time_embed\": time_embed, \"freqs\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})[\"x\"]\n else:\n visual_embed = block(visual_embed, context, time_embed, freqs=freqs, transformer_options=transformer_options)\n\n visual_embed = visual_embed.reshape(*visual_shape, -1)\n return self.out_layer(visual_embed, time_embed)\n\n def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):\n original_dims = x.ndim\n if original_dims == 4:\n x = x.unsqueeze(2)\n bs, c, t_len, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)\n\n if time_dim_replace is not None:\n time_dim_replace = comfy.ldm.common_dit.pad_to_patch_size(time_dim_replace, self.patch_size)\n x[:, :time_dim_replace.shape[1], :time_dim_replace.shape[2]] = time_dim_replace\n\n freqs = self.rope_encode_3d(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)\n freqs_text = self.rope_encode_1d(context.shape[1], device=x.device, dtype=x.dtype, transformer_options=transformer_options)\n\n out = self.forward_orig(x, timestep, context, y, freqs, freqs_text, transformer_options=transformer_options, **kwargs)\n if original_dims == 4:\n out = out.squeeze(2)\n return out\n\n def forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, y, time_dim_replace=time_dim_replace, transformer_options=transformer_options, **kwargs)\n" }, { "path": "comfy/ldm/lightricks/av_model.py", "content": "from typing import Tuple\nimport torch\nimport torch.nn as nn\nfrom comfy.ldm.lightricks.model import (\n ADALN_BASE_PARAMS_COUNT,\n ADALN_CROSS_ATTN_PARAMS_COUNT,\n CrossAttention,\n FeedForward,\n AdaLayerNormSingle,\n PixArtAlphaTextProjection,\n NormSingleLinearTextProjection,\n LTXVModel,\n apply_cross_attention_adaln,\n compute_prompt_timestep,\n)\nfrom comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier\nfrom comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector\nimport comfy.ldm.common_dit\n\nclass CompressedTimestep:\n \"\"\"Store video timestep embeddings in compressed form using per-frame indexing.\"\"\"\n __slots__ = ('data', 'batch_size', 'num_frames', 'patches_per_frame', 'feature_dim')\n\n def __init__(self, tensor: torch.Tensor, patches_per_frame: int):\n \"\"\"\n tensor: [batch_size, num_tokens, feature_dim] tensor where num_tokens = num_frames * patches_per_frame\n patches_per_frame: Number of spatial patches per frame (height * width in latent space), or None to disable compression\n \"\"\"\n self.batch_size, num_tokens, self.feature_dim = tensor.shape\n\n # Check if compression is valid (num_tokens must be divisible by patches_per_frame)\n if patches_per_frame is not None and num_tokens % patches_per_frame == 0 and num_tokens >= patches_per_frame:\n self.patches_per_frame = patches_per_frame\n self.num_frames = num_tokens // patches_per_frame\n\n # Reshape to [batch, frames, patches_per_frame, feature_dim] and store one value per frame\n # All patches in a frame are identical, so we only keep the first one\n reshaped = tensor.view(self.batch_size, self.num_frames, patches_per_frame, self.feature_dim)\n self.data = reshaped[:, :, 0, :].contiguous() # [batch, frames, feature_dim]\n else:\n # Not divisible or too small - store directly without compression\n self.patches_per_frame = 1\n self.num_frames = num_tokens\n self.data = tensor\n\n def expand(self):\n \"\"\"Expand back to original tensor.\"\"\"\n if self.patches_per_frame == 1:\n return self.data\n\n # [batch, frames, feature_dim] -> [batch, frames, patches_per_frame, feature_dim] -> [batch, tokens, feature_dim]\n expanded = self.data.unsqueeze(2).expand(self.batch_size, self.num_frames, self.patches_per_frame, self.feature_dim)\n return expanded.reshape(self.batch_size, -1, self.feature_dim)\n\n def expand_for_computation(self, scale_shift_table: torch.Tensor, batch_size: int, indices: slice = slice(None, None)):\n \"\"\"Compute ada values on compressed per-frame data, then expand spatially.\"\"\"\n num_ada_params = scale_shift_table.shape[0]\n\n # No compression - compute directly\n if self.patches_per_frame == 1:\n num_tokens = self.data.shape[1]\n dim_per_param = self.feature_dim // num_ada_params\n reshaped = self.data.reshape(batch_size, num_tokens, num_ada_params, dim_per_param)[:, :, indices, :]\n table_values = scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=self.data.device, dtype=self.data.dtype)\n ada_values = (table_values + reshaped).unbind(dim=2)\n return ada_values\n\n # Compressed: compute on per-frame data then expand spatially\n # Reshape: [batch, frames, feature_dim] -> [batch, frames, num_ada_params, dim_per_param]\n frame_reshaped = self.data.reshape(batch_size, self.num_frames, num_ada_params, -1)[:, :, indices, :]\n table_values = scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(\n device=self.data.device, dtype=self.data.dtype\n )\n frame_ada = (table_values + frame_reshaped).unbind(dim=2)\n\n # Expand each ada parameter spatially: [batch, frames, dim] -> [batch, frames, patches, dim] -> [batch, tokens, dim]\n return tuple(\n frame_val.unsqueeze(2).expand(batch_size, self.num_frames, self.patches_per_frame, -1)\n .reshape(batch_size, -1, frame_val.shape[-1])\n for frame_val in frame_ada\n )\n\nclass BasicAVTransformerBlock(nn.Module):\n def __init__(\n self,\n v_dim,\n a_dim,\n v_heads,\n a_heads,\n vd_head,\n ad_head,\n v_context_dim=None,\n a_context_dim=None,\n attn_precision=None,\n apply_gated_attention=False,\n cross_attention_adaln=False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n\n self.attn_precision = attn_precision\n self.cross_attention_adaln = cross_attention_adaln\n\n self.attn1 = CrossAttention(\n query_dim=v_dim,\n heads=v_heads,\n dim_head=vd_head,\n context_dim=None,\n attn_precision=self.attn_precision,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.audio_attn1 = CrossAttention(\n query_dim=a_dim,\n heads=a_heads,\n dim_head=ad_head,\n context_dim=None,\n attn_precision=self.attn_precision,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.attn2 = CrossAttention(\n query_dim=v_dim,\n context_dim=v_context_dim,\n heads=v_heads,\n dim_head=vd_head,\n attn_precision=self.attn_precision,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.audio_attn2 = CrossAttention(\n query_dim=a_dim,\n context_dim=a_context_dim,\n heads=a_heads,\n dim_head=ad_head,\n attn_precision=self.attn_precision,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n # Q: Video, K,V: Audio\n self.audio_to_video_attn = CrossAttention(\n query_dim=v_dim,\n context_dim=a_dim,\n heads=a_heads,\n dim_head=ad_head,\n attn_precision=self.attn_precision,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n # Q: Audio, K,V: Video\n self.video_to_audio_attn = CrossAttention(\n query_dim=a_dim,\n context_dim=v_dim,\n heads=a_heads,\n dim_head=ad_head,\n attn_precision=self.attn_precision,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.ff = FeedForward(\n v_dim, dim_out=v_dim, glu=True, dtype=dtype, device=device, operations=operations\n )\n self.audio_ff = FeedForward(\n a_dim, dim_out=a_dim, glu=True, dtype=dtype, device=device, operations=operations\n )\n\n num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT\n self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, v_dim, device=device, dtype=dtype))\n self.audio_scale_shift_table = nn.Parameter(\n torch.empty(num_ada_params, a_dim, device=device, dtype=dtype)\n )\n\n if cross_attention_adaln:\n self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, v_dim, device=device, dtype=dtype))\n self.audio_prompt_scale_shift_table = nn.Parameter(torch.empty(2, a_dim, device=device, dtype=dtype))\n\n self.scale_shift_table_a2v_ca_audio = nn.Parameter(\n torch.empty(5, a_dim, device=device, dtype=dtype)\n )\n self.scale_shift_table_a2v_ca_video = nn.Parameter(\n torch.empty(5, v_dim, device=device, dtype=dtype)\n )\n\n def get_ada_values(\n self, scale_shift_table: torch.Tensor, batch_size: int, timestep: torch.Tensor, indices: slice = slice(None, None)\n ):\n if isinstance(timestep, CompressedTimestep):\n return timestep.expand_for_computation(scale_shift_table, batch_size, indices)\n\n num_ada_params = scale_shift_table.shape[0]\n\n ada_values = (\n scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=timestep.device, dtype=timestep.dtype)\n + timestep.reshape(batch_size, timestep.shape[1], num_ada_params, -1)[:, :, indices, :]\n ).unbind(dim=2)\n return ada_values\n\n def get_av_ca_ada_values(\n self,\n scale_shift_table: torch.Tensor,\n batch_size: int,\n scale_shift_timestep: torch.Tensor,\n gate_timestep: torch.Tensor,\n num_scale_shift_values: int = 4,\n ):\n scale_shift_ada_values = self.get_ada_values(\n scale_shift_table[:num_scale_shift_values, :],\n batch_size,\n scale_shift_timestep,\n )\n gate_ada_values = self.get_ada_values(\n scale_shift_table[num_scale_shift_values:, :],\n batch_size,\n gate_timestep,\n )\n\n return (*scale_shift_ada_values, *gate_ada_values)\n\n def _apply_text_cross_attention(\n self, x, context, attn, scale_shift_table, prompt_scale_shift_table,\n timestep, prompt_timestep, attention_mask, transformer_options,\n ):\n \"\"\"Apply text cross-attention, with optional ADaLN modulation.\"\"\"\n if self.cross_attention_adaln:\n shift_q, scale_q, gate = self.get_ada_values(\n scale_shift_table, x.shape[0], timestep, slice(6, 9)\n )\n return apply_cross_attention_adaln(\n x, context, attn, shift_q, scale_q, gate,\n prompt_scale_shift_table, prompt_timestep,\n attention_mask, transformer_options,\n )\n return attn(\n comfy.ldm.common_dit.rms_norm(x), context=context,\n mask=attention_mask, transformer_options=transformer_options,\n )\n\n def forward(\n self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None,\n v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,\n v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None, self_attention_mask=None,\n v_prompt_timestep=None, a_prompt_timestep=None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n run_vx = transformer_options.get(\"run_vx\", True)\n run_ax = transformer_options.get(\"run_ax\", True)\n\n vx, ax = x\n run_ax = run_ax and ax.numel() > 0\n run_a2v = run_vx and transformer_options.get(\"a2v_cross_attn\", True) and ax.numel() > 0\n run_v2a = run_ax and transformer_options.get(\"v2a_cross_attn\", True)\n\n # video\n if run_vx:\n # video self-attention\n vshift_msa, vscale_msa = (self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 2)))\n norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa\n del vshift_msa, vscale_msa\n attn1_out = self.attn1(norm_vx, pe=v_pe, mask=self_attention_mask, transformer_options=transformer_options)\n del norm_vx\n # video cross-attention\n vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]\n vx.addcmul_(attn1_out, vgate_msa)\n del vgate_msa, attn1_out\n vx.add_(self._apply_text_cross_attention(\n vx, v_context, self.attn2, self.scale_shift_table,\n getattr(self, 'prompt_scale_shift_table', None),\n v_timestep, v_prompt_timestep, attention_mask, transformer_options,)\n )\n\n # audio\n if run_ax:\n # audio self-attention\n ashift_msa, ascale_msa = (self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 2)))\n norm_ax = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_msa) + ashift_msa\n del ashift_msa, ascale_msa\n attn1_out = self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)\n del norm_ax\n # audio cross-attention\n agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0]\n ax.addcmul_(attn1_out, agate_msa)\n del agate_msa, attn1_out\n ax.add_(self._apply_text_cross_attention(\n ax, a_context, self.audio_attn2, self.audio_scale_shift_table,\n getattr(self, 'audio_prompt_scale_shift_table', None),\n a_timestep, a_prompt_timestep, attention_mask, transformer_options,)\n )\n\n # video - audio cross attention.\n if run_a2v or run_v2a:\n vx_norm3 = comfy.ldm.common_dit.rms_norm(vx)\n ax_norm3 = comfy.ldm.common_dit.rms_norm(ax)\n\n # audio to video cross attention\n if run_a2v:\n scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v = self.get_ada_values(\n self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[:2]\n scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v = self.get_ada_values(\n self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[:2]\n\n vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_a2v_v) + shift_ca_video_hidden_states_a2v_v\n ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v) + shift_ca_audio_hidden_states_a2v\n del scale_ca_video_hidden_states_a2v_v, shift_ca_video_hidden_states_a2v_v, scale_ca_audio_hidden_states_a2v, shift_ca_audio_hidden_states_a2v\n\n a2v_out = self.audio_to_video_attn(vx_scaled, context=ax_scaled, pe=v_cross_pe, k_pe=a_cross_pe, transformer_options=transformer_options)\n del vx_scaled, ax_scaled\n\n gate_out_a2v = self.get_ada_values(self.scale_shift_table_a2v_ca_video[4:, :], vx.shape[0], v_cross_gate_timestep)[0]\n vx.addcmul_(a2v_out, gate_out_a2v)\n del gate_out_a2v, a2v_out\n\n # video to audio cross attention\n if run_v2a:\n scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a = self.get_ada_values(\n self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[2:4]\n scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a = self.get_ada_values(\n self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[2:4]\n\n ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a) + shift_ca_audio_hidden_states_v2a\n vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_v2a) + shift_ca_video_hidden_states_v2a\n del scale_ca_video_hidden_states_v2a, shift_ca_video_hidden_states_v2a, scale_ca_audio_hidden_states_v2a, shift_ca_audio_hidden_states_v2a\n\n v2a_out = self.video_to_audio_attn(ax_scaled, context=vx_scaled, pe=a_cross_pe, k_pe=v_cross_pe, transformer_options=transformer_options)\n del ax_scaled, vx_scaled\n\n gate_out_v2a = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[4:, :], ax.shape[0], a_cross_gate_timestep)[0]\n ax.addcmul_(v2a_out, gate_out_v2a)\n del gate_out_v2a, v2a_out\n\n del vx_norm3, ax_norm3\n\n # video feedforward\n if run_vx:\n vshift_mlp, vscale_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, 5))\n vx_scaled = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_mlp) + vshift_mlp\n del vshift_mlp, vscale_mlp\n\n ff_out = self.ff(vx_scaled)\n del vx_scaled\n\n vgate_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(5, 6))[0]\n vx.addcmul_(ff_out, vgate_mlp)\n del vgate_mlp, ff_out\n\n # audio feedforward\n if run_ax:\n ashift_mlp, ascale_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, 5))\n ax_scaled = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_mlp) + ashift_mlp\n del ashift_mlp, ascale_mlp\n\n ff_out = self.audio_ff(ax_scaled)\n del ax_scaled\n\n agate_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(5, 6))[0]\n ax.addcmul_(ff_out, agate_mlp)\n del agate_mlp, ff_out\n\n return vx, ax\n\n\nclass LTXAVModel(LTXVModel):\n \"\"\"LTXAV model for audio-video generation.\"\"\"\n\n def __init__(\n self,\n in_channels=128,\n audio_in_channels=128,\n cross_attention_dim=4096,\n audio_cross_attention_dim=2048,\n attention_head_dim=128,\n audio_attention_head_dim=64,\n num_attention_heads=32,\n audio_num_attention_heads=32,\n caption_channels=3840,\n num_layers=48,\n positional_embedding_theta=10000.0,\n positional_embedding_max_pos=[20, 2048, 2048],\n audio_positional_embedding_max_pos=[20],\n causal_temporal_positioning=False,\n vae_scale_factors=(8, 32, 32),\n use_middle_indices_grid=False,\n timestep_scale_multiplier=1000.0,\n av_ca_timestep_scale_multiplier=1.0,\n apply_gated_attention=False,\n caption_proj_before_connector=False,\n cross_attention_adaln=False,\n dtype=None,\n device=None,\n operations=None,\n **kwargs,\n ):\n # Store audio-specific parameters\n self.audio_in_channels = audio_in_channels\n self.audio_cross_attention_dim = audio_cross_attention_dim\n self.audio_attention_head_dim = audio_attention_head_dim\n self.audio_num_attention_heads = audio_num_attention_heads\n self.audio_positional_embedding_max_pos = audio_positional_embedding_max_pos\n self.apply_gated_attention = apply_gated_attention\n\n # Calculate audio dimensions\n self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim\n self.audio_out_channels = audio_in_channels\n\n # Audio-specific constants\n self.num_audio_channels = 8\n self.audio_frequency_bins = 16\n\n self.av_ca_timestep_scale_multiplier = av_ca_timestep_scale_multiplier\n\n super().__init__(\n in_channels=in_channels,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n num_attention_heads=num_attention_heads,\n caption_channels=caption_channels,\n num_layers=num_layers,\n positional_embedding_theta=positional_embedding_theta,\n positional_embedding_max_pos=positional_embedding_max_pos,\n causal_temporal_positioning=causal_temporal_positioning,\n vae_scale_factors=vae_scale_factors,\n use_middle_indices_grid=use_middle_indices_grid,\n timestep_scale_multiplier=timestep_scale_multiplier,\n caption_proj_before_connector=caption_proj_before_connector,\n cross_attention_adaln=cross_attention_adaln,\n dtype=dtype,\n device=device,\n operations=operations,\n **kwargs,\n )\n\n def _init_model_components(self, device, dtype, **kwargs):\n \"\"\"Initialize LTXAV-specific components.\"\"\"\n # Audio-specific projections\n self.audio_patchify_proj = self.operations.Linear(\n self.audio_in_channels, self.audio_inner_dim, bias=True, dtype=dtype, device=device\n )\n\n # Audio-specific AdaLN\n audio_embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT\n self.audio_adaln_single = AdaLayerNormSingle(\n self.audio_inner_dim,\n embedding_coefficient=audio_embedding_coefficient,\n use_additional_conditions=False,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n\n if self.cross_attention_adaln:\n self.audio_prompt_adaln_single = AdaLayerNormSingle(\n self.audio_inner_dim,\n embedding_coefficient=2,\n use_additional_conditions=False,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n else:\n self.audio_prompt_adaln_single = None\n\n num_scale_shift_values = 4\n self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle(\n self.inner_dim,\n use_additional_conditions=False,\n embedding_coefficient=num_scale_shift_values,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n self.av_ca_a2v_gate_adaln_single = AdaLayerNormSingle(\n self.inner_dim,\n use_additional_conditions=False,\n embedding_coefficient=1,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n self.av_ca_audio_scale_shift_adaln_single = AdaLayerNormSingle(\n self.audio_inner_dim,\n use_additional_conditions=False,\n embedding_coefficient=num_scale_shift_values,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n self.av_ca_v2a_gate_adaln_single = AdaLayerNormSingle(\n self.audio_inner_dim,\n use_additional_conditions=False,\n embedding_coefficient=1,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n\n # Audio caption projection\n if self.caption_proj_before_connector:\n if self.caption_projection_first_linear:\n self.audio_caption_projection = NormSingleLinearTextProjection(\n in_features=self.caption_channels,\n hidden_size=self.audio_inner_dim,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n else:\n self.audio_caption_projection = lambda a: a\n else:\n self.audio_caption_projection = PixArtAlphaTextProjection(\n in_features=self.caption_channels,\n hidden_size=self.audio_inner_dim,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n\n connector_split_rope = kwargs.get(\"rope_type\", \"split\") == \"split\"\n connector_gated_attention = kwargs.get(\"connector_apply_gated_attention\", False)\n attention_head_dim = kwargs.get(\"connector_attention_head_dim\", 128)\n num_attention_heads = kwargs.get(\"connector_num_attention_heads\", 30)\n num_layers = kwargs.get(\"connector_num_layers\", 2)\n\n self.audio_embeddings_connector = Embeddings1DConnector(\n attention_head_dim=kwargs.get(\"audio_connector_attention_head_dim\", attention_head_dim),\n num_attention_heads=kwargs.get(\"audio_connector_num_attention_heads\", num_attention_heads),\n num_layers=num_layers,\n split_rope=connector_split_rope,\n double_precision_rope=True,\n apply_gated_attention=connector_gated_attention,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n\n self.video_embeddings_connector = Embeddings1DConnector(\n attention_head_dim=attention_head_dim,\n num_attention_heads=num_attention_heads,\n num_layers=num_layers,\n split_rope=connector_split_rope,\n double_precision_rope=True,\n apply_gated_attention=connector_gated_attention,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n\n def preprocess_text_embeds(self, context, unprocessed=False):\n # LTXv2 fully processed context has dimension of self.caption_channels * 2\n # LTXv2.3 fully processed context has dimension of self.cross_attention_dim + self.audio_cross_attention_dim\n if not unprocessed:\n if context.shape[-1] in (self.cross_attention_dim + self.audio_cross_attention_dim, self.caption_channels * 2):\n return context\n if context.shape[-1] == self.cross_attention_dim + self.audio_cross_attention_dim:\n context_vid = context[:, :, :self.cross_attention_dim]\n context_audio = context[:, :, self.cross_attention_dim:]\n else:\n context_vid = context\n context_audio = context\n if self.caption_proj_before_connector:\n context_vid = self.caption_projection(context_vid)\n context_audio = self.audio_caption_projection(context_audio)\n out_vid = self.video_embeddings_connector(context_vid)[0]\n out_audio = self.audio_embeddings_connector(context_audio)[0]\n return torch.concat((out_vid, out_audio), dim=-1)\n\n def _init_transformer_blocks(self, device, dtype, **kwargs):\n \"\"\"Initialize transformer blocks for LTXAV.\"\"\"\n self.transformer_blocks = nn.ModuleList(\n [\n BasicAVTransformerBlock(\n v_dim=self.inner_dim,\n a_dim=self.audio_inner_dim,\n v_heads=self.num_attention_heads,\n a_heads=self.audio_num_attention_heads,\n vd_head=self.attention_head_dim,\n ad_head=self.audio_attention_head_dim,\n v_context_dim=self.cross_attention_dim,\n a_context_dim=self.audio_cross_attention_dim,\n apply_gated_attention=self.apply_gated_attention,\n cross_attention_adaln=self.cross_attention_adaln,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n for _ in range(self.num_layers)\n ]\n )\n\n def _init_output_components(self, device, dtype):\n \"\"\"Initialize output components for LTXAV.\"\"\"\n # Video output components\n super()._init_output_components(device, dtype)\n # Audio output components\n self.audio_scale_shift_table = nn.Parameter(\n torch.empty(2, self.audio_inner_dim, dtype=dtype, device=device)\n )\n self.audio_norm_out = self.operations.LayerNorm(\n self.audio_inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device\n )\n self.audio_proj_out = self.operations.Linear(\n self.audio_inner_dim, self.audio_out_channels, dtype=dtype, device=device\n )\n self.a_patchifier = AudioPatchifier(1, start_end=True)\n\n def separate_audio_and_video_latents(self, x, audio_length):\n \"\"\"Separate audio and video latents from combined input.\"\"\"\n # vx = x[:, : self.in_channels]\n # ax = x[:, self.in_channels :]\n #\n # ax = ax.reshape(ax.shape[0], -1)\n # ax = ax[:, : audio_length * self.num_audio_channels * self.audio_frequency_bins]\n #\n # ax = ax.reshape(\n # ax.shape[0], self.num_audio_channels, audio_length, self.audio_frequency_bins\n # )\n\n vx = x[0]\n ax = x[1] if len(x) > 1 else torch.zeros(\n (vx.shape[0], self.num_audio_channels, 0, self.audio_frequency_bins),\n device=vx.device, dtype=vx.dtype\n )\n return vx, ax\n\n def recombine_audio_and_video_latents(self, vx, ax, target_shape=None):\n if ax.numel() == 0:\n return vx\n else:\n return [vx, ax]\n \"\"\"Recombine audio and video latents for output.\"\"\"\n # if ax.device != vx.device or ax.dtype != vx.dtype:\n # logging.warning(\"Audio and video latents are on different devices or dtypes.\")\n # ax = ax.to(device=vx.device, dtype=vx.dtype)\n # logging.warning(f\"Audio audio latent moved to device: {ax.device}, dtype: {ax.dtype}\")\n #\n # ax = ax.reshape(ax.shape[0], -1)\n # # pad to f x h x w of the video latents\n # divisor = vx.shape[-1] * vx.shape[-2] * vx.shape[-3]\n # if target_shape is None:\n # repetitions = math.ceil(ax.shape[-1] / divisor)\n # else:\n # repetitions = target_shape[1] - vx.shape[1]\n # padded_len = repetitions * divisor\n # ax = F.pad(ax, (0, padded_len - ax.shape[-1]))\n # ax = ax.reshape(ax.shape[0], -1, vx.shape[-3], vx.shape[-2], vx.shape[-1])\n # return torch.cat([vx, ax], dim=1)\n\n def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):\n \"\"\"Process input for LTXAV - separate audio and video, then patchify.\"\"\"\n audio_length = kwargs.get(\"audio_length\", 0)\n # Separate audio and video latents\n vx, ax = self.separate_audio_and_video_latents(x, audio_length)\n\n has_spatial_mask = False\n if denoise_mask is not None:\n # check if any frame has spatial variation (inpainting)\n for frame_idx in range(denoise_mask.shape[2]):\n frame_mask = denoise_mask[0, 0, frame_idx]\n if frame_mask.numel() > 0 and frame_mask.min() != frame_mask.max():\n has_spatial_mask = True\n break\n\n [vx, v_pixel_coords, additional_args] = super()._process_input(\n vx, keyframe_idxs, denoise_mask, **kwargs\n )\n additional_args[\"has_spatial_mask\"] = has_spatial_mask\n\n ax, a_latent_coords = self.a_patchifier.patchify(ax)\n ax = self.audio_patchify_proj(ax)\n\n # additional_args.update({\"av_orig_shape\": list(x.shape)})\n return [vx, ax], [v_pixel_coords, a_latent_coords], additional_args\n\n def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):\n \"\"\"Prepare timestep embeddings.\"\"\"\n # TODO: some code reuse is needed here.\n grid_mask = kwargs.get(\"grid_mask\", None)\n if grid_mask is not None:\n timestep = timestep[:, grid_mask]\n\n timestep_scaled = timestep * self.timestep_scale_multiplier\n\n v_timestep, v_embedded_timestep = self.adaln_single(\n timestep_scaled.flatten(),\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n\n # Calculate patches_per_frame from orig_shape: [batch, channels, frames, height, width]\n # Video tokens are arranged as (frames * height * width), so patches_per_frame = height * width\n orig_shape = kwargs.get(\"orig_shape\")\n has_spatial_mask = kwargs.get(\"has_spatial_mask\", None)\n v_patches_per_frame = None\n if not has_spatial_mask and orig_shape is not None and len(orig_shape) == 5:\n # orig_shape[3] = height, orig_shape[4] = width (in latent space)\n v_patches_per_frame = orig_shape[3] * orig_shape[4]\n\n # Reshape to [batch_size, num_tokens, dim] and compress for storage\n v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame)\n v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame)\n\n v_prompt_timestep = compute_prompt_timestep(\n self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype\n )\n\n # Prepare audio timestep\n a_timestep = kwargs.get(\"a_timestep\")\n if a_timestep is not None:\n a_timestep_scaled = a_timestep * self.timestep_scale_multiplier\n a_timestep_flat = a_timestep_scaled.flatten()\n timestep_flat = timestep_scaled.flatten()\n av_ca_factor = self.av_ca_timestep_scale_multiplier / self.timestep_scale_multiplier\n\n # Cross-attention timesteps - compress these too\n av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single(\n timestep.max().expand_as(a_timestep_flat),\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n av_ca_video_scale_shift_timestep, _ = self.av_ca_video_scale_shift_adaln_single(\n a_timestep.max().expand_as(timestep_flat),\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n av_ca_a2v_gate_noise_timestep, _ = self.av_ca_a2v_gate_adaln_single(\n a_timestep.max().expand_as(timestep_flat) * av_ca_factor,\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n av_ca_v2a_gate_noise_timestep, _ = self.av_ca_v2a_gate_adaln_single(\n timestep.max().expand_as(a_timestep_flat) * av_ca_factor,\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n\n # Compress cross-attention timesteps (only video side, audio is too small to benefit)\n # v_patches_per_frame is None for spatial masks, set for temporal masks or no mask\n cross_av_timestep_ss = [\n av_ca_audio_scale_shift_timestep.view(batch_size, -1, av_ca_audio_scale_shift_timestep.shape[-1]),\n CompressedTimestep(av_ca_video_scale_shift_timestep.view(batch_size, -1, av_ca_video_scale_shift_timestep.shape[-1]), v_patches_per_frame), # video - compressed if possible\n CompressedTimestep(av_ca_a2v_gate_noise_timestep.view(batch_size, -1, av_ca_a2v_gate_noise_timestep.shape[-1]), v_patches_per_frame), # video - compressed if possible\n av_ca_v2a_gate_noise_timestep.view(batch_size, -1, av_ca_v2a_gate_noise_timestep.shape[-1]),\n ]\n\n a_timestep, a_embedded_timestep = self.audio_adaln_single(\n a_timestep_flat,\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n # Audio timesteps\n a_timestep = a_timestep.view(batch_size, -1, a_timestep.shape[-1])\n a_embedded_timestep = a_embedded_timestep.view(batch_size, -1, a_embedded_timestep.shape[-1])\n\n a_prompt_timestep = compute_prompt_timestep(\n self.audio_prompt_adaln_single, a_timestep_scaled, batch_size, hidden_dtype\n )\n else:\n a_timestep = timestep_scaled\n a_embedded_timestep = kwargs.get(\"embedded_timestep\")\n cross_av_timestep_ss = []\n a_prompt_timestep = None\n\n return [v_timestep, a_timestep, cross_av_timestep_ss, v_prompt_timestep, a_prompt_timestep], [\n v_embedded_timestep,\n a_embedded_timestep,\n ], None\n\n def _prepare_context(self, context, batch_size, x, attention_mask=None):\n vx = x[0]\n ax = x[1]\n video_dim = vx.shape[-1]\n audio_dim = ax.shape[-1]\n\n v_context_dim = self.caption_channels if self.caption_proj_before_connector is False else video_dim\n a_context_dim = self.caption_channels if self.caption_proj_before_connector is False else audio_dim\n\n v_context, a_context = torch.split(\n context, [v_context_dim, a_context_dim], len(context.shape) - 1\n )\n\n v_context, attention_mask = super()._prepare_context(\n v_context, batch_size, vx, attention_mask\n )\n if self.caption_proj_before_connector is False:\n a_context = self.audio_caption_projection(a_context)\n a_context = a_context.view(batch_size, -1, audio_dim)\n\n return [v_context, a_context], attention_mask\n\n def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):\n v_pixel_coords = pixel_coords[0]\n v_pe = super()._prepare_positional_embeddings(v_pixel_coords, frame_rate, x_dtype)\n\n a_latent_coords = pixel_coords[1]\n a_pe = self._precompute_freqs_cis(\n a_latent_coords,\n dim=self.audio_inner_dim,\n out_dtype=x_dtype,\n max_pos=self.audio_positional_embedding_max_pos,\n use_middle_indices_grid=self.use_middle_indices_grid,\n num_attention_heads=self.audio_num_attention_heads,\n )\n\n # calculate positional embeddings for the middle of the token duration, to use in av cross attention layers.\n max_pos = max(\n self.positional_embedding_max_pos[0], self.audio_positional_embedding_max_pos[0]\n )\n v_pixel_coords = v_pixel_coords.to(torch.float32)\n v_pixel_coords[:, 0] = v_pixel_coords[:, 0] * (1.0 / frame_rate)\n av_cross_video_freq_cis = self._precompute_freqs_cis(\n v_pixel_coords[:, 0:1, :],\n dim=self.audio_cross_attention_dim,\n out_dtype=x_dtype,\n max_pos=[max_pos],\n use_middle_indices_grid=True,\n num_attention_heads=self.audio_num_attention_heads,\n )\n av_cross_audio_freq_cis = self._precompute_freqs_cis(\n a_latent_coords[:, 0:1, :],\n dim=self.audio_cross_attention_dim,\n out_dtype=x_dtype,\n max_pos=[max_pos],\n use_middle_indices_grid=True,\n num_attention_heads=self.audio_num_attention_heads,\n )\n\n return [(v_pe, av_cross_video_freq_cis), (a_pe, av_cross_audio_freq_cis)]\n\n def _process_transformer_blocks(\n self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs\n ):\n vx = x[0]\n ax = x[1]\n v_context = context[0]\n a_context = context[1]\n v_timestep = timestep[0]\n a_timestep = timestep[1]\n v_pe, av_cross_video_freq_cis = pe[0]\n a_pe, av_cross_audio_freq_cis = pe[1]\n\n (\n av_ca_audio_scale_shift_timestep,\n av_ca_video_scale_shift_timestep,\n av_ca_a2v_gate_noise_timestep,\n av_ca_v2a_gate_noise_timestep,\n ) = timestep[2]\n\n v_prompt_timestep = timestep[3]\n a_prompt_timestep = timestep[4]\n\n \"\"\"Process transformer blocks for LTXAV.\"\"\"\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n\n # Process transformer blocks\n for i, block in enumerate(self.transformer_blocks):\n if (\"double_block\", i) in blocks_replace:\n\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(\n args[\"img\"],\n v_context=args[\"v_context\"],\n a_context=args[\"a_context\"],\n attention_mask=args[\"attention_mask\"],\n v_timestep=args[\"v_timestep\"],\n a_timestep=args[\"a_timestep\"],\n v_pe=args[\"v_pe\"],\n a_pe=args[\"a_pe\"],\n v_cross_pe=args[\"v_cross_pe\"],\n a_cross_pe=args[\"a_cross_pe\"],\n v_cross_scale_shift_timestep=args[\"v_cross_scale_shift_timestep\"],\n a_cross_scale_shift_timestep=args[\"a_cross_scale_shift_timestep\"],\n v_cross_gate_timestep=args[\"v_cross_gate_timestep\"],\n a_cross_gate_timestep=args[\"a_cross_gate_timestep\"],\n transformer_options=args[\"transformer_options\"],\n self_attention_mask=args.get(\"self_attention_mask\"),\n v_prompt_timestep=args.get(\"v_prompt_timestep\"),\n a_prompt_timestep=args.get(\"a_prompt_timestep\"),\n )\n return out\n\n out = blocks_replace[(\"double_block\", i)](\n {\n \"img\": (vx, ax),\n \"v_context\": v_context,\n \"a_context\": a_context,\n \"attention_mask\": attention_mask,\n \"v_timestep\": v_timestep,\n \"a_timestep\": a_timestep,\n \"v_pe\": v_pe,\n \"a_pe\": a_pe,\n \"v_cross_pe\": av_cross_video_freq_cis,\n \"a_cross_pe\": av_cross_audio_freq_cis,\n \"v_cross_scale_shift_timestep\": av_ca_video_scale_shift_timestep,\n \"a_cross_scale_shift_timestep\": av_ca_audio_scale_shift_timestep,\n \"v_cross_gate_timestep\": av_ca_a2v_gate_noise_timestep,\n \"a_cross_gate_timestep\": av_ca_v2a_gate_noise_timestep,\n \"transformer_options\": transformer_options,\n \"self_attention_mask\": self_attention_mask,\n \"v_prompt_timestep\": v_prompt_timestep,\n \"a_prompt_timestep\": a_prompt_timestep,\n },\n {\"original_block\": block_wrap},\n )\n vx, ax = out[\"img\"]\n else:\n vx, ax = block(\n (vx, ax),\n v_context=v_context,\n a_context=a_context,\n attention_mask=attention_mask,\n v_timestep=v_timestep,\n a_timestep=a_timestep,\n v_pe=v_pe,\n a_pe=a_pe,\n v_cross_pe=av_cross_video_freq_cis,\n a_cross_pe=av_cross_audio_freq_cis,\n v_cross_scale_shift_timestep=av_ca_video_scale_shift_timestep,\n a_cross_scale_shift_timestep=av_ca_audio_scale_shift_timestep,\n v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep,\n a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,\n transformer_options=transformer_options,\n self_attention_mask=self_attention_mask,\n v_prompt_timestep=v_prompt_timestep,\n a_prompt_timestep=a_prompt_timestep,\n )\n\n return [vx, ax]\n\n def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):\n vx = x[0]\n ax = x[1]\n v_embedded_timestep = embedded_timestep[0]\n a_embedded_timestep = embedded_timestep[1]\n\n # Expand compressed video timestep if needed\n if isinstance(v_embedded_timestep, CompressedTimestep):\n v_embedded_timestep = v_embedded_timestep.expand()\n\n vx = super()._process_output(vx, v_embedded_timestep, keyframe_idxs, **kwargs)\n\n # Process audio output\n a_scale_shift_values = (\n self.audio_scale_shift_table[None, None].to(device=a_embedded_timestep.device, dtype=a_embedded_timestep.dtype)\n + a_embedded_timestep[:, :, None]\n )\n a_shift, a_scale = a_scale_shift_values[:, :, 0], a_scale_shift_values[:, :, 1]\n\n ax = self.audio_norm_out(ax)\n ax = ax * (1 + a_scale) + a_shift\n ax = self.audio_proj_out(ax)\n\n # Unpatchify audio\n ax = self.a_patchifier.unpatchify(\n ax, channels=self.num_audio_channels, freq=self.audio_frequency_bins\n )\n\n # Recombine audio and video\n original_shape = kwargs.get(\"av_orig_shape\")\n return self.recombine_audio_and_video_latents(vx, ax, original_shape)\n\n def forward(\n self,\n x,\n timestep,\n context,\n attention_mask=None,\n frame_rate=25,\n transformer_options={},\n keyframe_idxs=None,\n **kwargs,\n ):\n \"\"\"\n Forward pass for LTXAV model.\n\n Args:\n x: Combined audio-video input tensor\n timestep: Tuple of (video_timestep, audio_timestep) or single timestep\n context: Context tensor (e.g., text embeddings)\n attention_mask: Attention mask tensor\n frame_rate: Frame rate for temporal processing\n transformer_options: Additional options for transformer blocks\n keyframe_idxs: Keyframe indices for temporal processing\n **kwargs: Additional keyword arguments including audio_length\n\n Returns:\n Combined audio-video output tensor\n \"\"\"\n # Handle timestep format\n if isinstance(timestep, (tuple, list)) and len(timestep) == 2:\n v_timestep, a_timestep = timestep\n kwargs[\"a_timestep\"] = a_timestep\n timestep = v_timestep\n else:\n kwargs[\"a_timestep\"] = timestep\n\n # Call parent forward method\n return super().forward(\n x,\n timestep,\n context,\n attention_mask,\n frame_rate,\n transformer_options,\n keyframe_idxs,\n **kwargs,\n )\n" }, { "path": "comfy/ldm/lightricks/embeddings_connector.py", "content": "import math\nfrom typing import Optional\n\nimport comfy.ldm.common_dit\nimport torch\nfrom comfy.ldm.lightricks.model import (\n CrossAttention,\n FeedForward,\n generate_freq_grid_np,\n interleaved_freqs_cis,\n split_freqs_cis,\n)\nfrom torch import nn\n\n\nclass BasicTransformerBlock1D(nn.Module):\n r\"\"\"\n A basic Transformer block.\n\n Parameters:\n\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n attention_bias (:\n obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the attention computation to float32. This is useful for mixed precision training.\n norm_elementwise_affine (`bool`, *optional*, defaults to `True`):\n Whether to use learnable elementwise affine parameters for normalization.\n standardization_norm (`str`, *optional*, defaults to `\"layer_norm\"`): The type of pre-normalization to use. Can be `\"layer_norm\"` or `\"rms_norm\"`.\n norm_eps (`float`, *optional*, defaults to 1e-5): Epsilon value for normalization layers.\n qk_norm (`str`, *optional*, defaults to None):\n Set to 'layer_norm' or `rms_norm` to perform query and key normalization.\n final_dropout (`bool` *optional*, defaults to False):\n Whether to apply a final dropout after the last feed-forward layer.\n ff_inner_dim (`int`, *optional*): Dimension of the inner feed-forward layer. If not provided, defaults to `dim * 4`.\n ff_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the feed-forward layer.\n attention_out_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the attention output layer.\n use_rope (`bool`, *optional*, defaults to `False`): Whether to use Rotary Position Embeddings (RoPE).\n ffn_dim_mult (`int`, *optional*, defaults to 4): Multiplier for the inner dimension of the feed-forward layer.\n \"\"\"\n\n def __init__(\n self,\n dim,\n n_heads,\n d_head,\n context_dim=None,\n attn_precision=None,\n apply_gated_attention=False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n\n # Define 3 blocks. Each block has its own normalization layer.\n # 1. Self-Attn\n self.attn1 = CrossAttention(\n query_dim=dim,\n heads=n_heads,\n dim_head=d_head,\n context_dim=None,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n # 3. Feed-forward\n self.ff = FeedForward(\n dim,\n dim_out=dim,\n glu=True,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n def forward(self, hidden_states, attention_mask=None, pe=None) -> torch.FloatTensor:\n\n # Notice that normalization is always applied before the real computation in the following blocks.\n\n # 1. Normalization Before Self-Attention\n norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)\n\n norm_hidden_states = norm_hidden_states.squeeze(1)\n\n # 2. Self-Attention\n attn_output = self.attn1(norm_hidden_states, mask=attention_mask, pe=pe)\n\n hidden_states = attn_output + hidden_states\n if hidden_states.ndim == 4:\n hidden_states = hidden_states.squeeze(1)\n\n # 3. Normalization before Feed-Forward\n norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)\n\n # 4. Feed-forward\n ff_output = self.ff(norm_hidden_states)\n\n hidden_states = ff_output + hidden_states\n if hidden_states.ndim == 4:\n hidden_states = hidden_states.squeeze(1)\n\n return hidden_states\n\n\nclass Embeddings1DConnector(nn.Module):\n _supports_gradient_checkpointing = True\n\n def __init__(\n self,\n in_channels=128,\n cross_attention_dim=2048,\n attention_head_dim=128,\n num_attention_heads=30,\n num_layers=2,\n positional_embedding_theta=10000.0,\n positional_embedding_max_pos=[4096],\n causal_temporal_positioning=False,\n num_learnable_registers: Optional[int] = 128,\n apply_gated_attention=False,\n dtype=None,\n device=None,\n operations=None,\n split_rope=False,\n double_precision_rope=False,\n **kwargs,\n ):\n super().__init__()\n self.dtype = dtype\n self.out_channels = in_channels\n self.num_attention_heads = num_attention_heads\n self.inner_dim = num_attention_heads * attention_head_dim\n self.causal_temporal_positioning = causal_temporal_positioning\n self.positional_embedding_theta = positional_embedding_theta\n self.positional_embedding_max_pos = positional_embedding_max_pos\n self.split_rope = split_rope\n self.double_precision_rope = double_precision_rope\n self.transformer_1d_blocks = nn.ModuleList(\n [\n BasicTransformerBlock1D(\n self.inner_dim,\n num_attention_heads,\n attention_head_dim,\n context_dim=cross_attention_dim,\n apply_gated_attention=apply_gated_attention,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n for _ in range(num_layers)\n ]\n )\n\n inner_dim = num_attention_heads * attention_head_dim\n self.num_learnable_registers = num_learnable_registers\n if self.num_learnable_registers:\n self.learnable_registers = nn.Parameter(\n torch.empty(\n self.num_learnable_registers, inner_dim, dtype=dtype, device=device\n )\n )\n\n def get_fractional_positions(self, indices_grid):\n fractional_positions = torch.stack(\n [\n indices_grid[:, i] / self.positional_embedding_max_pos[i]\n for i in range(1)\n ],\n dim=-1,\n )\n return fractional_positions\n\n def precompute_freqs(self, indices_grid, spacing):\n source_dtype = indices_grid.dtype\n dtype = (\n torch.float32\n if source_dtype in (torch.bfloat16, torch.float16)\n else source_dtype\n )\n\n fractional_positions = self.get_fractional_positions(indices_grid)\n indices = (\n generate_freq_grid_np(\n self.positional_embedding_theta,\n indices_grid.shape[1],\n self.inner_dim,\n )\n if self.double_precision_rope\n else self.generate_freq_grid(spacing, dtype, fractional_positions.device)\n ).to(device=fractional_positions.device)\n\n if spacing == \"exp_2\":\n freqs = (\n (indices * fractional_positions.unsqueeze(-1))\n .transpose(-1, -2)\n .flatten(2)\n )\n else:\n freqs = (\n (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))\n .transpose(-1, -2)\n .flatten(2)\n )\n return freqs\n\n def generate_freq_grid(self, spacing, dtype, device):\n dim = self.inner_dim\n theta = self.positional_embedding_theta\n n_pos_dims = 1\n n_elem = 2 * n_pos_dims # 2 for cos and sin e.g. x 3 = 6\n start = 1\n end = theta\n\n if spacing == \"exp\":\n indices = theta ** (torch.arange(0, dim, n_elem, device=\"cpu\", dtype=torch.float32) / (dim - n_elem))\n indices = indices.to(dtype=dtype, device=device)\n elif spacing == \"exp_2\":\n indices = 1.0 / theta ** (torch.arange(0, dim, n_elem, device=device) / dim)\n indices = indices.to(dtype=dtype)\n elif spacing == \"linear\":\n indices = torch.linspace(\n start, end, dim // n_elem, device=device, dtype=dtype\n )\n elif spacing == \"sqrt\":\n indices = torch.linspace(\n start**2, end**2, dim // n_elem, device=device, dtype=dtype\n ).sqrt()\n\n indices = indices * math.pi / 2\n\n return indices\n\n def precompute_freqs_cis(self, indices_grid, spacing=\"exp\", out_dtype=None):\n dim = self.inner_dim\n n_elem = 2 # 2 because of cos and sin\n freqs = self.precompute_freqs(indices_grid, spacing)\n if self.split_rope:\n expected_freqs = dim // 2\n current_freqs = freqs.shape[-1]\n pad_size = expected_freqs - current_freqs\n cos_freq, sin_freq = split_freqs_cis(\n freqs, pad_size, self.num_attention_heads\n )\n else:\n cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)\n return cos_freq.to(dtype=out_dtype), sin_freq.to(dtype=out_dtype), self.split_rope\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n ):\n \"\"\"\n The [`Transformer2DModel`] forward method.\n\n Args:\n hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):\n Input `hidden_states`.\n indices_grid (`torch.LongTensor` of shape `(batch size, 3, num latent pixels)`):\n attention_mask ( `torch.Tensor`, *optional*):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n Returns:\n If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a\n `tuple` where the first element is the sample tensor.\n \"\"\"\n # 1. Input\n\n if self.num_learnable_registers:\n num_registers_duplications = math.ceil(\n max(1024, hidden_states.shape[1]) / self.num_learnable_registers\n )\n learnable_registers = torch.tile(\n self.learnable_registers.to(hidden_states), (num_registers_duplications, 1)\n )\n\n hidden_states = torch.cat((hidden_states, learnable_registers[hidden_states.shape[1]:].unsqueeze(0).repeat(hidden_states.shape[0], 1, 1)), dim=1)\n\n if attention_mask is not None:\n attention_mask = torch.zeros([1, 1, 1, hidden_states.shape[1]], dtype=attention_mask.dtype, device=attention_mask.device)\n\n indices_grid = torch.arange(\n hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device\n )\n indices_grid = indices_grid[None, None, :]\n freqs_cis = self.precompute_freqs_cis(indices_grid, out_dtype=hidden_states.dtype)\n\n # 2. Blocks\n for block_idx, block in enumerate(self.transformer_1d_blocks):\n hidden_states = block(\n hidden_states, attention_mask=attention_mask, pe=freqs_cis\n )\n\n # 3. Output\n # if self.output_scale is not None:\n # hidden_states = hidden_states / self.output_scale\n\n hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)\n\n return hidden_states, attention_mask\n" }, { "path": "comfy/ldm/lightricks/latent_upsampler.py", "content": "from typing import Optional, Tuple\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\n\ndef _rational_for_scale(scale: float) -> Tuple[int, int]:\n mapping = {0.75: (3, 4), 1.5: (3, 2), 2.0: (2, 1), 4.0: (4, 1)}\n if float(scale) not in mapping:\n raise ValueError(\n f\"Unsupported spatial_scale {scale}. Choose from {list(mapping.keys())}\"\n )\n return mapping[float(scale)]\n\n\nclass PixelShuffleND(nn.Module):\n def __init__(self, dims, upscale_factors=(2, 2, 2)):\n super().__init__()\n assert dims in [1, 2, 3], \"dims must be 1, 2, or 3\"\n self.dims = dims\n self.upscale_factors = upscale_factors\n\n def forward(self, x):\n if self.dims == 3:\n return rearrange(\n x,\n \"b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)\",\n p1=self.upscale_factors[0],\n p2=self.upscale_factors[1],\n p3=self.upscale_factors[2],\n )\n elif self.dims == 2:\n return rearrange(\n x,\n \"b (c p1 p2) h w -> b c (h p1) (w p2)\",\n p1=self.upscale_factors[0],\n p2=self.upscale_factors[1],\n )\n elif self.dims == 1:\n return rearrange(\n x,\n \"b (c p1) f h w -> b c (f p1) h w\",\n p1=self.upscale_factors[0],\n )\n\n\nclass BlurDownsample(nn.Module):\n \"\"\"\n Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel.\n Applies only on H,W. Works for dims=2 or dims=3 (per-frame).\n \"\"\"\n\n def __init__(self, dims: int, stride: int):\n super().__init__()\n assert dims in (2, 3)\n assert stride >= 1 and isinstance(stride, int)\n self.dims = dims\n self.stride = stride\n\n # 5x5 separable binomial kernel [1,4,6,4,1] (outer product), normalized\n k = torch.tensor([1.0, 4.0, 6.0, 4.0, 1.0])\n k2d = k[:, None] @ k[None, :]\n k2d = (k2d / k2d.sum()).float() # shape (5,5)\n self.register_buffer(\"kernel\", k2d[None, None, :, :]) # (1,1,5,5)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n if self.stride == 1:\n return x\n\n def _apply_2d(x2d: torch.Tensor) -> torch.Tensor:\n # x2d: (B, C, H, W)\n B, C, H, W = x2d.shape\n weight = self.kernel.expand(C, 1, 5, 5) # depthwise\n x2d = F.conv2d(\n x2d, weight=weight, bias=None, stride=self.stride, padding=2, groups=C\n )\n return x2d\n\n if self.dims == 2:\n return _apply_2d(x)\n else:\n # dims == 3: apply per-frame on H,W\n b, c, f, h, w = x.shape\n x = rearrange(x, \"b c f h w -> (b f) c h w\")\n x = _apply_2d(x)\n h2, w2 = x.shape[-2:]\n x = rearrange(x, \"(b f) c h w -> b c f h w\", b=b, f=f, h=h2, w=w2)\n return x\n\n\nclass SpatialRationalResampler(nn.Module):\n \"\"\"\n Fully-learned rational spatial scaling: up by 'num' via PixelShuffle, then anti-aliased\n downsample by 'den' using fixed blur + stride. Operates on H,W only.\n\n For dims==3, work per-frame for spatial scaling (temporal axis untouched).\n \"\"\"\n\n def __init__(self, mid_channels: int, scale: float):\n super().__init__()\n self.scale = float(scale)\n self.num, self.den = _rational_for_scale(self.scale)\n self.conv = nn.Conv2d(\n mid_channels, (self.num**2) * mid_channels, kernel_size=3, padding=1\n )\n self.pixel_shuffle = PixelShuffleND(2, upscale_factors=(self.num, self.num))\n self.blur_down = BlurDownsample(dims=2, stride=self.den)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n b, c, f, h, w = x.shape\n x = rearrange(x, \"b c f h w -> (b f) c h w\")\n x = self.conv(x)\n x = self.pixel_shuffle(x)\n x = self.blur_down(x)\n x = rearrange(x, \"(b f) c h w -> b c f h w\", b=b, f=f)\n return x\n\n\nclass ResBlock(nn.Module):\n def __init__(\n self, channels: int, mid_channels: Optional[int] = None, dims: int = 3\n ):\n super().__init__()\n if mid_channels is None:\n mid_channels = channels\n\n Conv = nn.Conv2d if dims == 2 else nn.Conv3d\n\n self.conv1 = Conv(channels, mid_channels, kernel_size=3, padding=1)\n self.norm1 = nn.GroupNorm(32, mid_channels)\n self.conv2 = Conv(mid_channels, channels, kernel_size=3, padding=1)\n self.norm2 = nn.GroupNorm(32, channels)\n self.activation = nn.SiLU()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n residual = x\n x = self.conv1(x)\n x = self.norm1(x)\n x = self.activation(x)\n x = self.conv2(x)\n x = self.norm2(x)\n x = self.activation(x + residual)\n return x\n\n\nclass LatentUpsampler(nn.Module):\n \"\"\"\n Model to spatially upsample VAE latents.\n\n Args:\n in_channels (`int`): Number of channels in the input latent\n mid_channels (`int`): Number of channels in the middle layers\n num_blocks_per_stage (`int`): Number of ResBlocks to use in each stage (pre/post upsampling)\n dims (`int`): Number of dimensions for convolutions (2 or 3)\n spatial_upsample (`bool`): Whether to spatially upsample the latent\n temporal_upsample (`bool`): Whether to temporally upsample the latent\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 128,\n mid_channels: int = 512,\n num_blocks_per_stage: int = 4,\n dims: int = 3,\n spatial_upsample: bool = True,\n temporal_upsample: bool = False,\n spatial_scale: float = 2.0,\n rational_resampler: bool = False,\n ):\n super().__init__()\n\n self.in_channels = in_channels\n self.mid_channels = mid_channels\n self.num_blocks_per_stage = num_blocks_per_stage\n self.dims = dims\n self.spatial_upsample = spatial_upsample\n self.temporal_upsample = temporal_upsample\n self.spatial_scale = float(spatial_scale)\n self.rational_resampler = rational_resampler\n\n Conv = nn.Conv2d if dims == 2 else nn.Conv3d\n\n self.initial_conv = Conv(in_channels, mid_channels, kernel_size=3, padding=1)\n self.initial_norm = nn.GroupNorm(32, mid_channels)\n self.initial_activation = nn.SiLU()\n\n self.res_blocks = nn.ModuleList(\n [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]\n )\n\n if spatial_upsample and temporal_upsample:\n self.upsampler = nn.Sequential(\n nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),\n PixelShuffleND(3),\n )\n elif spatial_upsample:\n if rational_resampler:\n self.upsampler = SpatialRationalResampler(\n mid_channels=mid_channels, scale=self.spatial_scale\n )\n else:\n self.upsampler = nn.Sequential(\n nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),\n PixelShuffleND(2),\n )\n elif temporal_upsample:\n self.upsampler = nn.Sequential(\n nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),\n PixelShuffleND(1),\n )\n else:\n raise ValueError(\n \"Either spatial_upsample or temporal_upsample must be True\"\n )\n\n self.post_upsample_res_blocks = nn.ModuleList(\n [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]\n )\n\n self.final_conv = Conv(mid_channels, in_channels, kernel_size=3, padding=1)\n\n def forward(self, latent: torch.Tensor) -> torch.Tensor:\n b, c, f, h, w = latent.shape\n\n if self.dims == 2:\n x = rearrange(latent, \"b c f h w -> (b f) c h w\")\n x = self.initial_conv(x)\n x = self.initial_norm(x)\n x = self.initial_activation(x)\n\n for block in self.res_blocks:\n x = block(x)\n\n x = self.upsampler(x)\n\n for block in self.post_upsample_res_blocks:\n x = block(x)\n\n x = self.final_conv(x)\n x = rearrange(x, \"(b f) c h w -> b c f h w\", b=b, f=f)\n else:\n x = self.initial_conv(latent)\n x = self.initial_norm(x)\n x = self.initial_activation(x)\n\n for block in self.res_blocks:\n x = block(x)\n\n if self.temporal_upsample:\n x = self.upsampler(x)\n x = x[:, :, 1:, :, :]\n else:\n if isinstance(self.upsampler, SpatialRationalResampler):\n x = self.upsampler(x)\n else:\n x = rearrange(x, \"b c f h w -> (b f) c h w\")\n x = self.upsampler(x)\n x = rearrange(x, \"(b f) c h w -> b c f h w\", b=b, f=f)\n\n for block in self.post_upsample_res_blocks:\n x = block(x)\n\n x = self.final_conv(x)\n\n return x\n\n @classmethod\n def from_config(cls, config):\n return cls(\n in_channels=config.get(\"in_channels\", 4),\n mid_channels=config.get(\"mid_channels\", 128),\n num_blocks_per_stage=config.get(\"num_blocks_per_stage\", 4),\n dims=config.get(\"dims\", 2),\n spatial_upsample=config.get(\"spatial_upsample\", True),\n temporal_upsample=config.get(\"temporal_upsample\", False),\n spatial_scale=config.get(\"spatial_scale\", 2.0),\n rational_resampler=config.get(\"rational_resampler\", False),\n )\n\n def config(self):\n return {\n \"_class_name\": \"LatentUpsampler\",\n \"in_channels\": self.in_channels,\n \"mid_channels\": self.mid_channels,\n \"num_blocks_per_stage\": self.num_blocks_per_stage,\n \"dims\": self.dims,\n \"spatial_upsample\": self.spatial_upsample,\n \"temporal_upsample\": self.temporal_upsample,\n \"spatial_scale\": self.spatial_scale,\n \"rational_resampler\": self.rational_resampler,\n }\n" }, { "path": "comfy/ldm/lightricks/model.py", "content": "from abc import ABC, abstractmethod\nfrom enum import Enum\nimport functools\nimport logging\nimport math\nfrom typing import Dict, Optional, Tuple\n\nfrom einops import rearrange\nimport numpy as np\nimport torch\nfrom torch import nn\nimport comfy.patcher_extension\nimport comfy.ldm.modules.attention\nimport comfy.ldm.common_dit\n\nfrom .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords\n\nlogger = logging.getLogger(__name__)\n\ndef _log_base(x, base):\n return np.log(x) / np.log(base)\n\nclass LTXRopeType(str, Enum):\n INTERLEAVED = \"interleaved\"\n SPLIT = \"split\"\n\n KEY = \"rope_type\"\n\n @classmethod\n def from_dict(cls, kwargs, default=None):\n if default is None:\n default = cls.INTERLEAVED\n return cls(kwargs.get(cls.KEY, default))\n\n\nclass LTXFrequenciesPrecision(str, Enum):\n FLOAT32 = \"float32\"\n FLOAT64 = \"float64\"\n\n KEY = \"frequencies_precision\"\n\n @classmethod\n def from_dict(cls, kwargs, default=None):\n if default is None:\n default = cls.FLOAT32\n return cls(kwargs.get(cls.KEY, default))\n\n\ndef get_timestep_embedding(\n timesteps: torch.Tensor,\n embedding_dim: int,\n flip_sin_to_cos: bool = False,\n downscale_freq_shift: float = 1,\n scale: float = 1,\n max_period: int = 10000,\n):\n \"\"\"\n This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.\n\n Args\n timesteps (torch.Tensor):\n a 1-D Tensor of N indices, one per batch element. These may be fractional.\n embedding_dim (int):\n the dimension of the output.\n flip_sin_to_cos (bool):\n Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)\n downscale_freq_shift (float):\n Controls the delta between frequencies between dimensions\n scale (float):\n Scaling factor applied to the embeddings.\n max_period (int):\n Controls the maximum frequency of the embeddings\n Returns\n torch.Tensor: an [N x dim] Tensor of positional embeddings.\n \"\"\"\n assert len(timesteps.shape) == 1, \"Timesteps should be a 1d-array\"\n\n half_dim = embedding_dim // 2\n exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)\n exponent = exponent / (half_dim - downscale_freq_shift)\n\n emb = torch.exp(exponent)\n emb = timesteps[:, None].float() * emb[None, :]\n\n # scale embeddings\n emb = scale * emb\n\n # concat sine and cosine embeddings\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)\n\n # flip sine and cosine embeddings\n if flip_sin_to_cos:\n emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)\n\n # zero pad\n if embedding_dim % 2 == 1:\n emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))\n return emb\n\n\nclass TimestepEmbedding(nn.Module):\n def __init__(\n self,\n in_channels: int,\n time_embed_dim: int,\n act_fn: str = \"silu\",\n out_dim: int = None,\n post_act_fn: Optional[str] = None,\n cond_proj_dim=None,\n sample_proj_bias=True,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n\n self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device)\n\n if cond_proj_dim is not None:\n self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device)\n else:\n self.cond_proj = None\n\n self.act = nn.SiLU()\n\n if out_dim is not None:\n time_embed_dim_out = out_dim\n else:\n time_embed_dim_out = time_embed_dim\n self.linear_2 = operations.Linear(\n time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device\n )\n\n if post_act_fn is None:\n self.post_act = None\n # else:\n # self.post_act = get_activation(post_act_fn)\n\n def forward(self, sample, condition=None):\n if condition is not None:\n sample = sample + self.cond_proj(condition)\n sample = self.linear_1(sample)\n\n if self.act is not None:\n sample = self.act(sample)\n\n sample = self.linear_2(sample)\n\n if self.post_act is not None:\n sample = self.post_act(sample)\n return sample\n\n\nclass Timesteps(nn.Module):\n def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):\n super().__init__()\n self.num_channels = num_channels\n self.flip_sin_to_cos = flip_sin_to_cos\n self.downscale_freq_shift = downscale_freq_shift\n self.scale = scale\n\n def forward(self, timesteps):\n t_emb = get_timestep_embedding(\n timesteps,\n self.num_channels,\n flip_sin_to_cos=self.flip_sin_to_cos,\n downscale_freq_shift=self.downscale_freq_shift,\n scale=self.scale,\n )\n return t_emb\n\n\nclass PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):\n \"\"\"\n For PixArt-Alpha.\n\n Reference:\n https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29\n \"\"\"\n\n def __init__(\n self,\n embedding_dim,\n size_emb_dim,\n use_additional_conditions: bool = False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n\n self.outdim = size_emb_dim\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.timestep_embedder = TimestepEmbedding(\n in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations\n )\n\n def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):\n timesteps_proj = self.time_proj(timestep)\n timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)\n return timesteps_emb\n\n\nclass AdaLayerNormSingle(nn.Module):\n r\"\"\"\n Norm layer adaptive layer norm single (adaLN-single).\n\n As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).\n\n Parameters:\n embedding_dim (`int`): The size of each embedding vector.\n use_additional_conditions (`bool`): To use additional conditions for normalization or not.\n \"\"\"\n\n def __init__(\n self, embedding_dim: int, embedding_coefficient: int = 6, use_additional_conditions: bool = False, dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(\n embedding_dim,\n size_emb_dim=embedding_dim // 3,\n use_additional_conditions=use_additional_conditions,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.silu = nn.SiLU()\n self.linear = operations.Linear(embedding_dim, embedding_coefficient * embedding_dim, bias=True, dtype=dtype, device=device)\n\n def forward(\n self,\n timestep: torch.Tensor,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n batch_size: Optional[int] = None,\n hidden_dtype: Optional[torch.dtype] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n # No modulation happening here.\n added_cond_kwargs = added_cond_kwargs or {\"resolution\": None, \"aspect_ratio\": None}\n embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)\n return self.linear(self.silu(embedded_timestep)), embedded_timestep\n\n\nclass PixArtAlphaTextProjection(nn.Module):\n \"\"\"\n Projects caption embeddings. Also handles dropout for classifier-free guidance.\n\n Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py\n \"\"\"\n\n def __init__(\n self, in_features, hidden_size, out_features=None, act_fn=\"gelu_tanh\", dtype=None, device=None, operations=None\n ):\n super().__init__()\n if out_features is None:\n out_features = hidden_size\n self.linear_1 = operations.Linear(\n in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device\n )\n if act_fn == \"gelu_tanh\":\n self.act_1 = nn.GELU(approximate=\"tanh\")\n elif act_fn == \"silu\":\n self.act_1 = nn.SiLU()\n else:\n raise ValueError(f\"Unknown activation function: {act_fn}\")\n self.linear_2 = operations.Linear(\n in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device\n )\n\n def forward(self, caption):\n hidden_states = self.linear_1(caption)\n hidden_states = self.act_1(hidden_states)\n hidden_states = self.linear_2(hidden_states)\n return hidden_states\n\n\nclass NormSingleLinearTextProjection(nn.Module):\n \"\"\"Text projection for 20B models - single linear with RMSNorm (no activation).\"\"\"\n\n def __init__(\n self, in_features, hidden_size, dtype=None, device=None, operations=None\n ):\n super().__init__()\n if operations is None:\n operations = comfy.ops.disable_weight_init\n self.in_norm = operations.RMSNorm(\n in_features, eps=1e-6, elementwise_affine=False\n )\n self.linear_1 = operations.Linear(\n in_features, hidden_size, bias=True, dtype=dtype, device=device\n )\n self.hidden_size = hidden_size\n self.in_features = in_features\n\n def forward(self, caption):\n caption = self.in_norm(caption)\n caption = caption * (self.hidden_size / self.in_features) ** 0.5\n return self.linear_1(caption)\n\n\nclass GELU_approx(nn.Module):\n def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):\n super().__init__()\n self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device)\n\n def forward(self, x):\n return torch.nn.functional.gelu(self.proj(x), approximate=\"tanh\")\n\n\nclass FeedForward(nn.Module):\n def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0.0, dtype=None, device=None, operations=None):\n super().__init__()\n inner_dim = int(dim * mult)\n project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)\n\n self.net = nn.Sequential(\n project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)\n )\n\n def forward(self, x):\n return self.net(x)\n\ndef apply_rotary_emb(input_tensor, freqs_cis):\n cos_freqs, sin_freqs = freqs_cis[0], freqs_cis[1]\n split_pe = freqs_cis[2] if len(freqs_cis) > 2 else False\n return (\n apply_split_rotary_emb(input_tensor, cos_freqs, sin_freqs)\n if split_pe else\n apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs)\n )\n\ndef apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs): # TODO: remove duplicate funcs and pick the best/fastest one\n t_dup = rearrange(input_tensor, \"... (d r) -> ... d r\", r=2)\n t1, t2 = t_dup.unbind(dim=-1)\n t_dup = torch.stack((-t2, t1), dim=-1)\n input_tensor_rot = rearrange(t_dup, \"... d r -> ... (d r)\")\n\n out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs\n\n return out\n\ndef apply_split_rotary_emb(input_tensor, cos, sin):\n needs_reshape = False\n if input_tensor.ndim != 4 and cos.ndim == 4:\n B, H, T, _ = cos.shape\n input_tensor = input_tensor.reshape(B, T, H, -1).swapaxes(1, 2)\n needs_reshape = True\n split_input = rearrange(input_tensor, \"... (d r) -> ... d r\", d=2)\n first_half_input = split_input[..., :1, :]\n second_half_input = split_input[..., 1:, :]\n output = split_input * cos.unsqueeze(-2)\n first_half_output = output[..., :1, :]\n second_half_output = output[..., 1:, :]\n first_half_output.addcmul_(-sin.unsqueeze(-2), second_half_input)\n second_half_output.addcmul_(sin.unsqueeze(-2), first_half_input)\n output = rearrange(output, \"... d r -> ... (d r)\")\n return output.swapaxes(1, 2).reshape(B, T, -1) if needs_reshape else output\n\n\nclass CrossAttention(nn.Module):\n def __init__(\n self,\n query_dim,\n context_dim=None,\n heads=8,\n dim_head=64,\n dropout=0.0,\n attn_precision=None,\n apply_gated_attention=False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n inner_dim = dim_head * heads\n context_dim = query_dim if context_dim is None else context_dim\n self.attn_precision = attn_precision\n\n self.heads = heads\n self.dim_head = dim_head\n\n self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)\n self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)\n\n self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)\n self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)\n self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)\n\n # Optional per-head gating\n if apply_gated_attention:\n self.to_gate_logits = operations.Linear(query_dim, heads, bias=True, dtype=dtype, device=device)\n else:\n self.to_gate_logits = None\n\n self.to_out = nn.Sequential(\n operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)\n )\n\n def forward(self, x, context=None, mask=None, pe=None, k_pe=None, transformer_options={}):\n q = self.to_q(x)\n context = x if context is None else context\n k = self.to_k(context)\n v = self.to_v(context)\n\n q = self.q_norm(q)\n k = self.k_norm(k)\n\n if pe is not None:\n q = apply_rotary_emb(q, pe)\n k = apply_rotary_emb(k, pe if k_pe is None else k_pe)\n\n if mask is None:\n out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)\n else:\n out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)\n\n # Apply per-head gating if enabled\n if self.to_gate_logits is not None:\n gate_logits = self.to_gate_logits(x) # (B, T, H)\n b, t, _ = out.shape\n out = out.view(b, t, self.heads, self.dim_head)\n gates = 2.0 * torch.sigmoid(gate_logits) # zero-init -> identity\n out = out * gates.unsqueeze(-1)\n out = out.view(b, t, self.heads * self.dim_head)\n\n return self.to_out(out)\n\n# 6 base ADaLN params (shift/scale/gate for MSA + MLP), +3 for cross-attention Q (shift/scale/gate)\nADALN_BASE_PARAMS_COUNT = 6\nADALN_CROSS_ATTN_PARAMS_COUNT = 9\n\nclass BasicTransformerBlock(nn.Module):\n def __init__(\n self, dim, n_heads, d_head, context_dim=None, attn_precision=None, cross_attention_adaln=False, dtype=None, device=None, operations=None\n ):\n super().__init__()\n\n self.attn_precision = attn_precision\n self.cross_attention_adaln = cross_attention_adaln\n self.attn1 = CrossAttention(\n query_dim=dim,\n heads=n_heads,\n dim_head=d_head,\n context_dim=None,\n attn_precision=self.attn_precision,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)\n\n self.attn2 = CrossAttention(\n query_dim=dim,\n context_dim=context_dim,\n heads=n_heads,\n dim_head=d_head,\n attn_precision=self.attn_precision,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT\n self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, dim, device=device, dtype=dtype))\n\n if cross_attention_adaln:\n self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, dim, device=device, dtype=dtype))\n\n def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None, prompt_timestep=None):\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None, :6].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, :6, :]).unbind(dim=2)\n\n x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, mask=self_attention_mask, transformer_options=transformer_options) * gate_msa\n\n if self.cross_attention_adaln:\n shift_q_mca, scale_q_mca, gate_mca = (self.scale_shift_table[None, None, 6:9].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, 6:9, :]).unbind(dim=2)\n x += apply_cross_attention_adaln(\n x, context, self.attn2, shift_q_mca, scale_q_mca, gate_mca,\n self.prompt_scale_shift_table, prompt_timestep, attention_mask, transformer_options,\n )\n else:\n x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)\n\n y = comfy.ldm.common_dit.rms_norm(x)\n y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)\n x.addcmul_(self.ff(y), gate_mlp)\n\n return x\n\ndef compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, hidden_dtype):\n \"\"\"Compute a single global prompt timestep for cross-attention ADaLN.\n\n Uses the max across tokens (matching JAX max_per_segment) and broadcasts\n over text tokens. Returns None when *adaln_module* is None.\n \"\"\"\n if adaln_module is None:\n return None\n ts_input = (\n timestep_scaled.max(dim=1, keepdim=True).values.flatten()\n if timestep_scaled.dim() > 1\n else timestep_scaled.flatten()\n )\n prompt_ts, _ = adaln_module(\n ts_input,\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n return prompt_ts.view(batch_size, 1, prompt_ts.shape[-1])\n\n\ndef apply_cross_attention_adaln(\n x, context, attn, q_shift, q_scale, q_gate,\n prompt_scale_shift_table, prompt_timestep,\n attention_mask=None, transformer_options={},\n):\n \"\"\"Apply cross-attention with ADaLN modulation (shift/scale/gate on Q and KV).\n\n Q params (q_shift, q_scale, q_gate) are pre-extracted by the caller so\n that both regular tensors and CompressedTimestep are supported.\n \"\"\"\n batch_size = x.shape[0]\n shift_kv, scale_kv = (\n prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)\n + prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1)\n ).unbind(dim=2)\n attn_input = comfy.ldm.common_dit.rms_norm(x) * (1 + q_scale) + q_shift\n encoder_hidden_states = context * (1 + scale_kv) + shift_kv\n return attn(attn_input, context=encoder_hidden_states, mask=attention_mask, transformer_options=transformer_options) * q_gate\n\ndef get_fractional_positions(indices_grid, max_pos):\n n_pos_dims = indices_grid.shape[1]\n assert n_pos_dims == len(max_pos), f'Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})'\n fractional_positions = torch.stack(\n [indices_grid[:, i] / max_pos[i] for i in range(n_pos_dims)],\n axis=-1,\n )\n return fractional_positions\n\n\n@functools.lru_cache(maxsize=5)\ndef generate_freq_grid_np(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, _ = None):\n theta = positional_embedding_theta\n start = 1\n end = theta\n\n n_elem = 2 * positional_embedding_max_pos_count\n pow_indices = np.power(\n theta,\n np.linspace(\n _log_base(start, theta),\n _log_base(end, theta),\n inner_dim // n_elem,\n dtype=np.float64,\n ),\n )\n return torch.tensor(pow_indices * math.pi / 2, dtype=torch.float32)\n\ndef generate_freq_grid_pytorch(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, device):\n theta = positional_embedding_theta\n start = 1\n end = theta\n n_elem = 2 * positional_embedding_max_pos_count\n\n indices = theta ** (\n torch.linspace(\n math.log(start, theta),\n math.log(end, theta),\n inner_dim // n_elem,\n device=device,\n dtype=torch.float32,\n )\n )\n indices = indices.to(dtype=torch.float32)\n\n indices = indices * math.pi / 2\n\n return indices\n\ndef generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid):\n if use_middle_indices_grid:\n assert(len(indices_grid.shape) == 4 and indices_grid.shape[-1] ==2)\n indices_grid_start, indices_grid_end = indices_grid[..., 0], indices_grid[..., 1]\n indices_grid = (indices_grid_start + indices_grid_end) / 2.0\n elif len(indices_grid.shape) == 4:\n indices_grid = indices_grid[..., 0]\n\n # Get fractional positions and compute frequency indices\n fractional_positions = get_fractional_positions(indices_grid, max_pos)\n indices = indices.to(device=fractional_positions.device)\n\n freqs = (\n (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))\n .transpose(-1, -2)\n .flatten(2)\n )\n return freqs\n\ndef interleaved_freqs_cis(freqs, pad_size):\n cos_freq = freqs.cos().repeat_interleave(2, dim=-1)\n sin_freq = freqs.sin().repeat_interleave(2, dim=-1)\n if pad_size != 0:\n cos_padding = torch.ones_like(cos_freq[:, :, : pad_size])\n sin_padding = torch.zeros_like(cos_freq[:, :, : pad_size])\n cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)\n sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)\n return cos_freq, sin_freq\n\ndef split_freqs_cis(freqs, pad_size, num_attention_heads):\n cos_freq = freqs.cos()\n sin_freq = freqs.sin()\n\n if pad_size != 0:\n cos_padding = torch.ones_like(cos_freq[:, :, :pad_size])\n sin_padding = torch.zeros_like(sin_freq[:, :, :pad_size])\n\n cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)\n sin_freq = torch.concatenate([sin_padding, sin_freq], axis=-1)\n\n # Reshape freqs to be compatible with multi-head attention\n B , T, half_HD = cos_freq.shape\n\n cos_freq = cos_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)\n sin_freq = sin_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)\n\n cos_freq = torch.swapaxes(cos_freq, 1, 2) # (B,H,T,D//2)\n sin_freq = torch.swapaxes(sin_freq, 1, 2) # (B,H,T,D//2)\n return cos_freq, sin_freq\n\nclass LTXBaseModel(torch.nn.Module, ABC):\n \"\"\"\n Abstract base class for LTX models (Lightricks Transformer models).\n\n This class defines the common interface and shared functionality for all LTX models,\n including LTXV (video) and LTXAV (audio-video) variants.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n cross_attention_dim: int,\n attention_head_dim: int,\n num_attention_heads: int,\n caption_channels: int,\n num_layers: int,\n positional_embedding_theta: float = 10000.0,\n positional_embedding_max_pos: list = [20, 2048, 2048],\n causal_temporal_positioning: bool = False,\n vae_scale_factors: tuple = (8, 32, 32),\n use_middle_indices_grid=False,\n timestep_scale_multiplier = 1000.0,\n caption_proj_before_connector=False,\n cross_attention_adaln=False,\n caption_projection_first_linear=True,\n dtype=None,\n device=None,\n operations=None,\n **kwargs,\n ):\n super().__init__()\n self.generator = None\n self.vae_scale_factors = vae_scale_factors\n self.use_middle_indices_grid = use_middle_indices_grid\n self.dtype = dtype\n self.in_channels = in_channels\n self.cross_attention_dim = cross_attention_dim\n self.attention_head_dim = attention_head_dim\n self.num_attention_heads = num_attention_heads\n self.caption_channels = caption_channels\n self.num_layers = num_layers\n self.positional_embedding_theta = positional_embedding_theta\n self.positional_embedding_max_pos = positional_embedding_max_pos\n self.split_positional_embedding = LTXRopeType.from_dict(kwargs)\n self.freq_grid_generator = (\n generate_freq_grid_np if LTXFrequenciesPrecision.from_dict(kwargs) == LTXFrequenciesPrecision.FLOAT64\n else generate_freq_grid_pytorch\n )\n self.causal_temporal_positioning = causal_temporal_positioning\n self.operations = operations\n self.timestep_scale_multiplier = timestep_scale_multiplier\n self.caption_proj_before_connector = caption_proj_before_connector\n self.cross_attention_adaln = cross_attention_adaln\n self.caption_projection_first_linear = caption_projection_first_linear\n\n # Common dimensions\n self.inner_dim = num_attention_heads * attention_head_dim\n self.out_channels = in_channels\n\n # Initialize common components\n self._init_common_components(device, dtype)\n\n # Initialize model-specific components\n self._init_model_components(device, dtype, **kwargs)\n\n # Initialize transformer blocks\n self._init_transformer_blocks(device, dtype, **kwargs)\n\n # Initialize output components\n self._init_output_components(device, dtype)\n\n def _init_common_components(self, device, dtype):\n \"\"\"Initialize components common to all LTX models\n - patchify_proj: Linear projection for patchifying input\n - adaln_single: AdaLN layer for timestep embedding\n - caption_projection: Linear projection for caption embedding\n \"\"\"\n self.patchify_proj = self.operations.Linear(\n self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device\n )\n\n embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT\n self.adaln_single = AdaLayerNormSingle(\n self.inner_dim, embedding_coefficient=embedding_coefficient, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations\n )\n\n if self.cross_attention_adaln:\n self.prompt_adaln_single = AdaLayerNormSingle(\n self.inner_dim, embedding_coefficient=2, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations\n )\n else:\n self.prompt_adaln_single = None\n\n if self.caption_proj_before_connector:\n if self.caption_projection_first_linear:\n self.caption_projection = NormSingleLinearTextProjection(\n in_features=self.caption_channels,\n hidden_size=self.inner_dim,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n else:\n self.caption_projection = lambda a: a\n else:\n self.caption_projection = PixArtAlphaTextProjection(\n in_features=self.caption_channels,\n hidden_size=self.inner_dim,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n\n @abstractmethod\n def _init_model_components(self, device, dtype, **kwargs):\n \"\"\"Initialize model-specific components. Must be implemented by subclasses.\"\"\"\n pass\n\n @abstractmethod\n def _init_transformer_blocks(self, device, dtype, **kwargs):\n \"\"\"Initialize transformer blocks. Must be implemented by subclasses.\"\"\"\n pass\n\n @abstractmethod\n def _init_output_components(self, device, dtype):\n \"\"\"Initialize output components. Must be implemented by subclasses.\"\"\"\n pass\n\n @abstractmethod\n def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):\n \"\"\"Process input data. Must be implemented by subclasses.\"\"\"\n pass\n\n def _build_guide_self_attention_mask(self, x, transformer_options, merged_args):\n \"\"\"Build self-attention mask for per-guide attention attenuation.\n\n Base implementation returns None (no attenuation). Subclasses that\n support guide-based attention control should override this.\n \"\"\"\n return None\n\n @abstractmethod\n def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, self_attention_mask=None, **kwargs):\n \"\"\"Process transformer blocks. Must be implemented by subclasses.\"\"\"\n pass\n\n @abstractmethod\n def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):\n \"\"\"Process output data. Must be implemented by subclasses.\"\"\"\n pass\n\n def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):\n \"\"\"Prepare timestep embeddings.\"\"\"\n grid_mask = kwargs.get(\"grid_mask\", None)\n if grid_mask is not None:\n timestep = timestep[:, grid_mask]\n\n timestep_scaled = timestep * self.timestep_scale_multiplier\n timestep, embedded_timestep = self.adaln_single(\n timestep_scaled.flatten(),\n {\"resolution\": None, \"aspect_ratio\": None},\n batch_size=batch_size,\n hidden_dtype=hidden_dtype,\n )\n\n # Second dimension is 1 or number of tokens (if timestep_per_token)\n timestep = timestep.view(batch_size, -1, timestep.shape[-1])\n embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.shape[-1])\n\n prompt_timestep = compute_prompt_timestep(\n self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype\n )\n\n return timestep, embedded_timestep, prompt_timestep\n\n def _prepare_context(self, context, batch_size, x, attention_mask=None):\n \"\"\"Prepare context for transformer blocks.\"\"\"\n if self.caption_proj_before_connector is False:\n context = self.caption_projection(context)\n\n context = context.view(batch_size, -1, x.shape[-1])\n return context, attention_mask\n\n def _precompute_freqs_cis(\n self,\n indices_grid,\n dim,\n out_dtype,\n theta=10000.0,\n max_pos=[20, 2048, 2048],\n use_middle_indices_grid=False,\n num_attention_heads=32,\n ):\n split_mode = self.split_positional_embedding == LTXRopeType.SPLIT\n indices = self.freq_grid_generator(theta, indices_grid.shape[1], dim, indices_grid.device)\n freqs = generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid)\n\n if split_mode:\n expected_freqs = dim // 2\n current_freqs = freqs.shape[-1]\n pad_size = expected_freqs - current_freqs\n cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads)\n else:\n # 2 because of cos and sin by 3 for (t, x, y), 1 for temporal only\n n_elem = 2 * indices_grid.shape[1]\n cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)\n return cos_freq.to(out_dtype), sin_freq.to(out_dtype), split_mode\n\n def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):\n \"\"\"Prepare positional embeddings.\"\"\"\n fractional_coords = pixel_coords.to(torch.float32)\n fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)\n pe = self._precompute_freqs_cis(\n fractional_coords,\n dim=self.inner_dim,\n out_dtype=x_dtype,\n max_pos=self.positional_embedding_max_pos,\n use_middle_indices_grid=self.use_middle_indices_grid,\n num_attention_heads=self.num_attention_heads,\n )\n return pe\n\n def _prepare_attention_mask(self, attention_mask, x_dtype):\n \"\"\"Prepare attention mask.\"\"\"\n if attention_mask is not None and not torch.is_floating_point(attention_mask):\n attention_mask = (attention_mask - 1).to(x_dtype).reshape(\n (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])\n ) * torch.finfo(x_dtype).max\n return attention_mask\n\n def forward(\n self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs\n ):\n \"\"\"\n Forward pass for LTX models.\n\n Args:\n x: Input tensor\n timestep: Timestep tensor\n context: Context tensor (e.g., text embeddings)\n attention_mask: Attention mask tensor\n frame_rate: Frame rate for temporal processing\n transformer_options: Additional options for transformer blocks\n keyframe_idxs: Keyframe indices for temporal processing\n **kwargs: Additional keyword arguments\n\n Returns:\n Processed output tensor\n \"\"\"\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(\n comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options\n ),\n ).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs)\n\n def _forward(\n self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs\n ):\n \"\"\"\n Internal forward pass for LTX models.\n\n Args:\n x: Input tensor\n timestep: Timestep tensor\n context: Context tensor (e.g., text embeddings)\n attention_mask: Attention mask tensor\n frame_rate: Frame rate for temporal processing\n transformer_options: Additional options for transformer blocks\n keyframe_idxs: Keyframe indices for temporal processing\n **kwargs: Additional keyword arguments\n\n Returns:\n Processed output tensor\n \"\"\"\n if isinstance(x, list):\n input_dtype = x[0].dtype\n batch_size = x[0].shape[0]\n else:\n input_dtype = x.dtype\n batch_size = x.shape[0]\n # Process input\n merged_args = {**transformer_options, **kwargs}\n x, pixel_coords, additional_args = self._process_input(x, keyframe_idxs, denoise_mask, **merged_args)\n merged_args.update(additional_args)\n\n # Prepare timestep and context\n timestep, embedded_timestep, prompt_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)\n merged_args[\"prompt_timestep\"] = prompt_timestep\n context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask)\n\n # Prepare attention mask and positional embeddings\n attention_mask = self._prepare_attention_mask(attention_mask, input_dtype)\n pe = self._prepare_positional_embeddings(pixel_coords, frame_rate, input_dtype)\n\n # Build self-attention mask for per-guide attenuation\n self_attention_mask = self._build_guide_self_attention_mask(\n x, transformer_options, merged_args\n )\n\n # Process transformer blocks\n x = self._process_transformer_blocks(\n x, context, attention_mask, timestep, pe,\n transformer_options=transformer_options,\n self_attention_mask=self_attention_mask,\n **merged_args,\n )\n\n # Process output\n x = self._process_output(x, embedded_timestep, keyframe_idxs, **merged_args)\n return x\n\n\nclass LTXVModel(LTXBaseModel):\n \"\"\"LTXV model for video generation.\"\"\"\n\n def __init__(\n self,\n in_channels=128,\n cross_attention_dim=2048,\n attention_head_dim=64,\n num_attention_heads=32,\n caption_channels=4096,\n num_layers=28,\n positional_embedding_theta=10000.0,\n positional_embedding_max_pos=[20, 2048, 2048],\n causal_temporal_positioning=False,\n vae_scale_factors=(8, 32, 32),\n use_middle_indices_grid=False,\n timestep_scale_multiplier=1000.0,\n caption_proj_before_connector=False,\n cross_attention_adaln=False,\n dtype=None,\n device=None,\n operations=None,\n **kwargs,\n ):\n super().__init__(\n in_channels=in_channels,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n num_attention_heads=num_attention_heads,\n caption_channels=caption_channels,\n num_layers=num_layers,\n positional_embedding_theta=positional_embedding_theta,\n positional_embedding_max_pos=positional_embedding_max_pos,\n causal_temporal_positioning=causal_temporal_positioning,\n vae_scale_factors=vae_scale_factors,\n use_middle_indices_grid=use_middle_indices_grid,\n timestep_scale_multiplier=timestep_scale_multiplier,\n caption_proj_before_connector=caption_proj_before_connector,\n cross_attention_adaln=cross_attention_adaln,\n dtype=dtype,\n device=device,\n operations=operations,\n **kwargs,\n )\n\n def _init_model_components(self, device, dtype, **kwargs):\n \"\"\"Initialize LTXV-specific components.\"\"\"\n pass\n\n def _init_transformer_blocks(self, device, dtype, **kwargs):\n \"\"\"Initialize transformer blocks for LTXV.\"\"\"\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n self.inner_dim,\n self.num_attention_heads,\n self.attention_head_dim,\n context_dim=self.cross_attention_dim,\n cross_attention_adaln=self.cross_attention_adaln,\n dtype=dtype,\n device=device,\n operations=self.operations,\n )\n for _ in range(self.num_layers)\n ]\n )\n\n def _init_output_components(self, device, dtype):\n \"\"\"Initialize output components for LTXV.\"\"\"\n self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))\n self.norm_out = self.operations.LayerNorm(\n self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device\n )\n self.proj_out = self.operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)\n self.patchifier = SymmetricPatchifier(1, start_end=True)\n\n def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):\n \"\"\"Process input for LTXV.\"\"\"\n additional_args = {\"orig_shape\": list(x.shape)}\n x, latent_coords = self.patchifier.patchify(x)\n pixel_coords = latent_to_pixel_coords(\n latent_coords=latent_coords,\n scale_factors=self.vae_scale_factors,\n causal_fix=self.causal_temporal_positioning,\n )\n\n grid_mask = None\n if keyframe_idxs is not None:\n additional_args.update({ \"orig_patchified_shape\": list(x.shape)})\n denoise_mask = self.patchifier.patchify(denoise_mask)[0]\n grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0]\n additional_args.update({\"grid_mask\": grid_mask})\n x = x[:, grid_mask, :]\n pixel_coords = pixel_coords[:, :, grid_mask, ...]\n\n kf_grid_mask = grid_mask[-keyframe_idxs.shape[2]:]\n\n # Compute per-guide surviving token counts from guide_attention_entries.\n # Each entry tracks one guide reference; they are appended in order and\n # their pre_filter_counts partition the kf_grid_mask.\n guide_entries = kwargs.get(\"guide_attention_entries\", None)\n if guide_entries:\n total_pfc = sum(e[\"pre_filter_count\"] for e in guide_entries)\n if total_pfc != len(kf_grid_mask):\n raise ValueError(\n f\"guide pre_filter_counts ({total_pfc}) != \"\n f\"keyframe grid mask length ({len(kf_grid_mask)})\"\n )\n resolved_entries = []\n offset = 0\n for entry in guide_entries:\n pfc = entry[\"pre_filter_count\"]\n entry_mask = kf_grid_mask[offset:offset + pfc]\n surviving = int(entry_mask.sum().item())\n resolved_entries.append({\n **entry,\n \"surviving_count\": surviving,\n })\n offset += pfc\n additional_args[\"resolved_guide_entries\"] = resolved_entries\n\n keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :]\n pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs\n\n # Total surviving guide tokens (all guides)\n additional_args[\"num_guide_tokens\"] = keyframe_idxs.shape[2]\n\n x = self.patchify_proj(x)\n return x, pixel_coords, additional_args\n\n def _build_guide_self_attention_mask(self, x, transformer_options, merged_args):\n \"\"\"Build self-attention mask for per-guide attention attenuation.\n\n Reads resolved_guide_entries from merged_args (computed in _process_input)\n to build a log-space additive bias mask that attenuates noisy ↔ guide\n attention for each guide reference independently.\n\n Returns None if no attenuation is needed (all strengths == 1.0 and no\n spatial masks, or no guide tokens).\n \"\"\"\n if isinstance(x, list):\n # AV model: x = [vx, ax]; use vx for token count and device\n total_tokens = x[0].shape[1]\n device = x[0].device\n dtype = x[0].dtype\n else:\n total_tokens = x.shape[1]\n device = x.device\n dtype = x.dtype\n\n num_guide_tokens = merged_args.get(\"num_guide_tokens\", 0)\n if num_guide_tokens == 0:\n return None\n\n resolved_entries = merged_args.get(\"resolved_guide_entries\", None)\n if not resolved_entries:\n return None\n\n # Check if any attenuation is actually needed\n needs_attenuation = any(\n e[\"strength\"] < 1.0 or e.get(\"pixel_mask\") is not None\n for e in resolved_entries\n )\n if not needs_attenuation:\n return None\n\n # Build per-guide-token weights for all tracked guide tokens.\n # Guides are appended in order at the end of the sequence.\n guide_start = total_tokens - num_guide_tokens\n all_weights = []\n total_tracked = 0\n\n for entry in resolved_entries:\n surviving = entry[\"surviving_count\"]\n if surviving == 0:\n continue\n\n strength = entry[\"strength\"]\n pixel_mask = entry.get(\"pixel_mask\")\n latent_shape = entry.get(\"latent_shape\")\n\n if pixel_mask is not None and latent_shape is not None:\n f_lat, h_lat, w_lat = latent_shape\n per_token = self._downsample_mask_to_latent(\n pixel_mask.to(device=device, dtype=dtype),\n f_lat, h_lat, w_lat,\n )\n # per_token shape: (B, f_lat*h_lat*w_lat).\n # Collapse batch dim — the mask is assumed identical across the\n # batch; validate and take the first element to get (1, tokens).\n if per_token.shape[0] > 1:\n ref = per_token[0]\n for bi in range(1, per_token.shape[0]):\n if not torch.equal(ref, per_token[bi]):\n logger.warning(\n \"pixel_mask differs across batch elements; \"\n \"using first element only.\"\n )\n break\n per_token = per_token[:1]\n # `surviving` is the post-grid_mask token count.\n # Clamp to surviving to handle any mismatch safely.\n n_weights = min(per_token.shape[1], surviving)\n weights = per_token[:, :n_weights] * strength # (1, n_weights)\n else:\n weights = torch.full(\n (1, surviving), strength, device=device, dtype=dtype\n )\n\n all_weights.append(weights)\n total_tracked += weights.shape[1]\n\n if not all_weights:\n return None\n\n # Concatenate per-token weights for all tracked guides\n tracked_weights = torch.cat(all_weights, dim=1) # (1, total_tracked)\n\n # Check if any weight is actually < 1.0 (otherwise no attenuation needed)\n if (tracked_weights >= 1.0).all():\n return None\n\n # Build the mask: guide tokens are at the end of the sequence.\n # Tracked guides come first (in order), untracked follow.\n return self._build_self_attention_mask(\n total_tokens, num_guide_tokens, total_tracked,\n tracked_weights, guide_start, device, dtype,\n )\n\n @staticmethod\n def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat):\n \"\"\"Downsample a pixel-space mask to per-token latent weights.\n\n Args:\n mask: (B, 1, F_pix, H_pix, W_pix) pixel-space mask with values in [0, 1].\n f_lat: Number of latent frames (pre-dilation original count).\n h_lat: Latent height (pre-dilation original height).\n w_lat: Latent width (pre-dilation original width).\n\n Returns:\n (B, F_lat * H_lat * W_lat) flattened per-token weights.\n \"\"\"\n b = mask.shape[0]\n f_pix = mask.shape[2]\n\n # Spatial downsampling: area interpolation per frame\n spatial_down = torch.nn.functional.interpolate(\n rearrange(mask, \"b 1 f h w -> (b f) 1 h w\"),\n size=(h_lat, w_lat),\n mode=\"area\",\n )\n spatial_down = rearrange(spatial_down, \"(b f) 1 h w -> b 1 f h w\", b=b)\n\n # Temporal downsampling: first pixel frame maps to first latent frame,\n # remaining pixel frames are averaged in groups for causal temporal structure.\n first_frame = spatial_down[:, :, :1, :, :]\n if f_pix > 1 and f_lat > 1:\n remaining_pix = f_pix - 1\n remaining_lat = f_lat - 1\n t = remaining_pix // remaining_lat\n if t < 1:\n # Fewer pixel frames than latent frames — upsample by repeating\n # the available pixel frames via nearest interpolation.\n rest_flat = rearrange(\n spatial_down[:, :, 1:, :, :],\n \"b 1 f h w -> (b h w) 1 f\",\n )\n rest_up = torch.nn.functional.interpolate(\n rest_flat, size=remaining_lat, mode=\"nearest\",\n )\n rest = rearrange(\n rest_up, \"(b h w) 1 f -> b 1 f h w\",\n b=b, h=h_lat, w=w_lat,\n )\n else:\n # Trim trailing pixel frames that don't fill a complete group\n usable = remaining_lat * t\n rest = rearrange(\n spatial_down[:, :, 1:1 + usable, :, :],\n \"b 1 (f t) h w -> b 1 f t h w\",\n t=t,\n )\n rest = rest.mean(dim=3)\n latent_mask = torch.cat([first_frame, rest], dim=2)\n elif f_lat > 1:\n # Single pixel frame but multiple latent frames — repeat the\n # single frame across all latent frames.\n latent_mask = first_frame.expand(-1, -1, f_lat, -1, -1)\n else:\n latent_mask = first_frame\n\n return rearrange(latent_mask, \"b 1 f h w -> b (f h w)\")\n\n @staticmethod\n def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked_count,\n tracked_weights, guide_start, device, dtype):\n \"\"\"Build a log-space additive self-attention bias mask.\n\n Attenuates attention between noisy tokens and tracked guide tokens.\n Untracked guide tokens (at the end of the guide portion) keep full attention.\n\n Args:\n total_tokens: Total sequence length.\n num_guide_tokens: Total guide tokens (all guides) at end of sequence.\n tracked_count: Number of tracked guide tokens (first in the guide portion).\n tracked_weights: (1, tracked_count) tensor, values in [0, 1].\n guide_start: Index where guide tokens begin in the sequence.\n device: Target device.\n dtype: Target dtype.\n\n Returns:\n (1, 1, total_tokens, total_tokens) additive bias mask.\n 0.0 = full attention, negative = attenuated, finfo.min = effectively fully masked.\n \"\"\"\n finfo = torch.finfo(dtype)\n mask = torch.zeros((1, 1, total_tokens, total_tokens), device=device, dtype=dtype)\n tracked_end = guide_start + tracked_count\n\n # Convert weights to log-space bias\n w = tracked_weights.to(device=device, dtype=dtype) # (1, tracked_count)\n log_w = torch.full_like(w, finfo.min)\n positive_mask = w > 0\n if positive_mask.any():\n log_w[positive_mask] = torch.log(w[positive_mask].clamp(min=finfo.tiny))\n\n # noisy → tracked guides: each noisy row gets the same per-guide weight\n mask[:, :, :guide_start, guide_start:tracked_end] = log_w.view(1, 1, 1, -1)\n # tracked guides → noisy: each guide row broadcasts its weight across noisy cols\n mask[:, :, guide_start:tracked_end, :guide_start] = log_w.view(1, 1, -1, 1)\n\n return mask\n\n def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, self_attention_mask=None, **kwargs):\n \"\"\"Process transformer blocks for LTXV.\"\"\"\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n prompt_timestep = kwargs.get(\"prompt_timestep\", None)\n\n for i, block in enumerate(self.transformer_blocks):\n if (\"double_block\", i) in blocks_replace:\n\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], attention_mask=args[\"attention_mask\"], timestep=args[\"vec\"], pe=args[\"pe\"], transformer_options=args[\"transformer_options\"], self_attention_mask=args.get(\"self_attention_mask\"), prompt_timestep=args.get(\"prompt_timestep\"))\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"attention_mask\": attention_mask, \"vec\": timestep, \"pe\": pe, \"transformer_options\": transformer_options, \"self_attention_mask\": self_attention_mask, \"prompt_timestep\": prompt_timestep}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(\n x,\n context=context,\n attention_mask=attention_mask,\n timestep=timestep,\n pe=pe,\n transformer_options=transformer_options,\n self_attention_mask=self_attention_mask,\n prompt_timestep=prompt_timestep,\n )\n\n return x\n\n def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):\n \"\"\"Process output for LTXV.\"\"\"\n # Apply scale-shift modulation\n scale_shift_values = (\n self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]\n )\n shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]\n\n x = self.norm_out(x)\n x = x * (1 + scale) + shift\n x = self.proj_out(x)\n\n if keyframe_idxs is not None:\n grid_mask = kwargs[\"grid_mask\"]\n orig_patchified_shape = kwargs[\"orig_patchified_shape\"]\n full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device)\n full_x[:, grid_mask, :] = x\n x = full_x\n # Unpatchify to restore original dimensions\n orig_shape = kwargs[\"orig_shape\"]\n x = self.patchifier.unpatchify(\n latents=x,\n output_height=orig_shape[3],\n output_width=orig_shape[4],\n output_num_frames=orig_shape[2],\n out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),\n )\n\n return x\n" }, { "path": "comfy/ldm/lightricks/symmetric_patchifier.py", "content": "from abc import ABC, abstractmethod\nfrom typing import Tuple\n\nimport torch\nfrom einops import rearrange\nfrom torch import Tensor\n\n\ndef latent_to_pixel_coords(\n latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False\n) -> Tensor:\n \"\"\"\n Converts latent coordinates to pixel coordinates by scaling them according to the VAE's\n configuration.\n Args:\n latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents]\n containing the latent corner coordinates of each token.\n scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space.\n causal_fix (bool): Whether to take into account the different temporal scale\n of the first frame. Default = False for backwards compatibility.\n Returns:\n Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates.\n \"\"\"\n shape = [1] * latent_coords.ndim\n shape[1] = -1\n pixel_coords = (\n latent_coords\n * torch.tensor(scale_factors, device=latent_coords.device).view(*shape)\n )\n if causal_fix:\n # Fix temporal scale for first frame to 1 due to causality\n pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0)\n return pixel_coords\n\n\nclass Patchifier(ABC):\n def __init__(self, patch_size: int, start_end: bool=False):\n super().__init__()\n self._patch_size = (1, patch_size, patch_size)\n self.start_end = start_end\n\n @abstractmethod\n def patchify(\n self, latents: Tensor, frame_rates: Tensor, scale_grid: bool\n ) -> Tuple[Tensor, Tensor]:\n pass\n\n @abstractmethod\n def unpatchify(\n self,\n latents: Tensor,\n output_height: int,\n output_width: int,\n output_num_frames: int,\n out_channels: int,\n ) -> Tuple[Tensor, Tensor]:\n pass\n\n @property\n def patch_size(self):\n return self._patch_size\n\n def get_latent_coords(\n self, latent_num_frames, latent_height, latent_width, batch_size, device\n ):\n \"\"\"\n Return a tensor of shape [batch_size, 3, num_patches] containing the\n top-left corner latent coordinates of each latent patch.\n The tensor is repeated for each batch element.\n \"\"\"\n latent_sample_coords = torch.meshgrid(\n torch.arange(0, latent_num_frames, self._patch_size[0], device=device),\n torch.arange(0, latent_height, self._patch_size[1], device=device),\n torch.arange(0, latent_width, self._patch_size[2], device=device),\n indexing=\"ij\",\n )\n latent_sample_coords_start = torch.stack(latent_sample_coords, dim=0)\n delta = torch.tensor(self._patch_size, device=latent_sample_coords_start.device, dtype=latent_sample_coords_start.dtype)[:, None, None, None]\n latent_sample_coords_end = latent_sample_coords_start + delta\n\n latent_sample_coords_start = latent_sample_coords_start.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)\n latent_sample_coords_start = rearrange(\n latent_sample_coords_start, \"b c f h w -> b c (f h w)\", b=batch_size\n )\n if self.start_end:\n latent_sample_coords_end = latent_sample_coords_end.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)\n latent_sample_coords_end = rearrange(\n latent_sample_coords_end, \"b c f h w -> b c (f h w)\", b=batch_size\n )\n\n latent_coords = torch.stack((latent_sample_coords_start, latent_sample_coords_end), dim=-1)\n else:\n latent_coords = latent_sample_coords_start\n return latent_coords\n\n\nclass SymmetricPatchifier(Patchifier):\n def patchify(\n self,\n latents: Tensor,\n ) -> Tuple[Tensor, Tensor]:\n b, _, f, h, w = latents.shape\n latent_coords = self.get_latent_coords(f, h, w, b, latents.device)\n latents = rearrange(\n latents,\n \"b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)\",\n p1=self._patch_size[0],\n p2=self._patch_size[1],\n p3=self._patch_size[2],\n )\n return latents, latent_coords\n\n def unpatchify(\n self,\n latents: Tensor,\n output_height: int,\n output_width: int,\n output_num_frames: int,\n out_channels: int,\n ) -> Tuple[Tensor, Tensor]:\n output_height = output_height // self._patch_size[1]\n output_width = output_width // self._patch_size[2]\n latents = rearrange(\n latents,\n \"b (f h w) (c p q) -> b c f (h p) (w q) \",\n f=output_num_frames,\n h=output_height,\n w=output_width,\n p=self._patch_size[1],\n q=self._patch_size[2],\n )\n return latents\n\n\nclass AudioPatchifier(Patchifier):\n def __init__(self, patch_size: int,\n sample_rate=16000,\n hop_length=160,\n audio_latent_downsample_factor=4,\n is_causal=True,\n start_end=False,\n shift = 0\n ):\n super().__init__(patch_size, start_end=start_end)\n self.hop_length = hop_length\n self.sample_rate = sample_rate\n self.audio_latent_downsample_factor = audio_latent_downsample_factor\n self.is_causal = is_causal\n self.shift = shift\n\n def copy_with_shift(self, shift):\n return AudioPatchifier(\n self.patch_size, self.sample_rate, self.hop_length, self.audio_latent_downsample_factor,\n self.is_causal, self.start_end, shift\n )\n\n def _get_audio_latent_time_in_sec(self, start_latent, end_latent: int, dtype: torch.dtype, device=torch.device):\n audio_latent_frame = torch.arange(start_latent, end_latent, dtype=dtype, device=device)\n audio_mel_frame = audio_latent_frame * self.audio_latent_downsample_factor\n if self.is_causal:\n audio_mel_frame = (audio_mel_frame + 1 - self.audio_latent_downsample_factor).clip(min=0)\n return audio_mel_frame * self.hop_length / self.sample_rate\n\n\n def patchify(self, audio_latents: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n # audio_latents: (batch, channels, time, freq)\n b, _, t, _ = audio_latents.shape\n audio_latents = rearrange(\n audio_latents,\n \"b c t f -> b t (c f)\",\n )\n\n audio_latents_start_timings = self._get_audio_latent_time_in_sec(self.shift, t + self.shift, torch.float32, audio_latents.device)\n audio_latents_start_timings = audio_latents_start_timings.unsqueeze(0).expand(b, -1).unsqueeze(1)\n\n if self.start_end:\n audio_latents_end_timings = self._get_audio_latent_time_in_sec(self.shift + 1, t + self.shift + 1, torch.float32, audio_latents.device)\n audio_latents_end_timings = audio_latents_end_timings.unsqueeze(0).expand(b, -1).unsqueeze(1)\n\n audio_latents_timings = torch.stack([audio_latents_start_timings, audio_latents_end_timings], dim=-1)\n else:\n audio_latents_timings = audio_latents_start_timings\n return audio_latents, audio_latents_timings\n\n def unpatchify(self, audio_latents: torch.Tensor, channels: int, freq: int) -> torch.Tensor:\n # audio_latents: (batch, time, freq * channels)\n audio_latents = rearrange(\n audio_latents, \"b t (c f) -> b c t f\", c=channels, f=freq\n )\n return audio_latents\n" }, { "path": "comfy/ldm/lightricks/vae/audio_vae.py", "content": "import json\nfrom dataclasses import dataclass\nimport math\nimport torch\nimport torchaudio\n\nimport comfy.model_management\nimport comfy.model_patcher\nimport comfy.utils as utils\nfrom comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution\nfrom comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier\nfrom comfy.ldm.lightricks.vae.causal_audio_autoencoder import (\n CausalityAxis,\n CausalAudioAutoencoder,\n)\nfrom comfy.ldm.lightricks.vocoders.vocoder import Vocoder, VocoderWithBWE\n\nLATENT_DOWNSAMPLE_FACTOR = 4\n\n\n@dataclass(frozen=True)\nclass AudioVAEComponentConfig:\n \"\"\"Container for model component configuration extracted from metadata.\"\"\"\n\n autoencoder: dict\n vocoder: dict\n\n @classmethod\n def from_metadata(cls, metadata: dict) -> \"AudioVAEComponentConfig\":\n assert metadata is not None and \"config\" in metadata, \"Metadata is required for audio VAE\"\n\n raw_config = metadata[\"config\"]\n if isinstance(raw_config, str):\n parsed_config = json.loads(raw_config)\n else:\n parsed_config = raw_config\n\n audio_config = parsed_config.get(\"audio_vae\")\n vocoder_config = parsed_config.get(\"vocoder\")\n\n assert audio_config is not None, \"Audio VAE config is required for audio VAE\"\n assert vocoder_config is not None, \"Vocoder config is required for audio VAE\"\n\n return cls(autoencoder=audio_config, vocoder=vocoder_config)\n\n\nclass ModelDeviceManager:\n \"\"\"Manages device placement and GPU residency for the composed model.\"\"\"\n\n def __init__(self, module: torch.nn.Module):\n load_device = comfy.model_management.get_torch_device()\n offload_device = comfy.model_management.vae_offload_device()\n self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device)\n\n def ensure_model_loaded(self) -> None:\n comfy.model_management.free_memory(\n self.patcher.model_size(),\n self.patcher.load_device,\n )\n comfy.model_management.load_model_gpu(self.patcher)\n\n def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:\n return tensor.to(self.patcher.load_device)\n\n @property\n def load_device(self):\n return self.patcher.load_device\n\n\nclass AudioLatentNormalizer:\n \"\"\"Applies per-channel statistics in patch space and restores original layout.\"\"\"\n\n def __init__(self, patchfier: AudioPatchifier, statistics_processor: torch.nn.Module):\n self.patchifier = patchfier\n self.statistics = statistics_processor\n\n def normalize(self, latents: torch.Tensor) -> torch.Tensor:\n channels = latents.shape[1]\n freq = latents.shape[3]\n patched, _ = self.patchifier.patchify(latents)\n normalized = self.statistics.normalize(patched)\n return self.patchifier.unpatchify(normalized, channels=channels, freq=freq)\n\n def denormalize(self, latents: torch.Tensor) -> torch.Tensor:\n channels = latents.shape[1]\n freq = latents.shape[3]\n patched, _ = self.patchifier.patchify(latents)\n denormalized = self.statistics.un_normalize(patched)\n return self.patchifier.unpatchify(denormalized, channels=channels, freq=freq)\n\n\nclass AudioPreprocessor:\n \"\"\"Prepares raw waveforms for the autoencoder by matching training conditions.\"\"\"\n\n def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_length: int, n_fft: int):\n self.target_sample_rate = target_sample_rate\n self.mel_bins = mel_bins\n self.mel_hop_length = mel_hop_length\n self.n_fft = n_fft\n\n def resample(self, waveform: torch.Tensor, source_rate: int) -> torch.Tensor:\n if source_rate == self.target_sample_rate:\n return waveform\n return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate)\n\n def waveform_to_mel(\n self, waveform: torch.Tensor, waveform_sample_rate: int, device\n ) -> torch.Tensor:\n waveform = self.resample(waveform, waveform_sample_rate)\n\n mel_transform = torchaudio.transforms.MelSpectrogram(\n sample_rate=self.target_sample_rate,\n n_fft=self.n_fft,\n win_length=self.n_fft,\n hop_length=self.mel_hop_length,\n f_min=0.0,\n f_max=self.target_sample_rate / 2.0,\n n_mels=self.mel_bins,\n window_fn=torch.hann_window,\n center=True,\n pad_mode=\"reflect\",\n power=1.0,\n mel_scale=\"slaney\",\n norm=\"slaney\",\n ).to(device)\n\n mel = mel_transform(waveform)\n mel = torch.log(torch.clamp(mel, min=1e-5))\n return mel.permute(0, 1, 3, 2).contiguous()\n\n\nclass AudioVAE(torch.nn.Module):\n \"\"\"High-level Audio VAE wrapper exposing encode and decode entry points.\"\"\"\n\n def __init__(self, state_dict: dict, metadata: dict):\n super().__init__()\n\n component_config = AudioVAEComponentConfig.from_metadata(metadata)\n\n vae_sd = utils.state_dict_prefix_replace(state_dict, {\"audio_vae.\": \"\"}, filter_keys=True)\n vocoder_sd = utils.state_dict_prefix_replace(state_dict, {\"vocoder.\": \"\"}, filter_keys=True)\n\n self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)\n if \"bwe\" in component_config.vocoder:\n self.vocoder = VocoderWithBWE(config=component_config.vocoder)\n else:\n self.vocoder = Vocoder(config=component_config.vocoder)\n\n self.autoencoder.load_state_dict(vae_sd, strict=False)\n self.vocoder.load_state_dict(vocoder_sd, strict=False)\n\n autoencoder_config = self.autoencoder.get_config()\n self.normalizer = AudioLatentNormalizer(\n AudioPatchifier(\n patch_size=1,\n audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR,\n sample_rate=autoencoder_config[\"sampling_rate\"],\n hop_length=autoencoder_config[\"mel_hop_length\"],\n is_causal=autoencoder_config[\"is_causal\"],\n ),\n self.autoencoder.per_channel_statistics,\n )\n\n self.preprocessor = AudioPreprocessor(\n target_sample_rate=autoencoder_config[\"sampling_rate\"],\n mel_bins=autoencoder_config[\"mel_bins\"],\n mel_hop_length=autoencoder_config[\"mel_hop_length\"],\n n_fft=autoencoder_config[\"n_fft\"],\n )\n\n self.device_manager = ModelDeviceManager(self)\n\n def encode(self, audio: dict) -> torch.Tensor:\n \"\"\"Encode a waveform dictionary into normalized latent tensors.\"\"\"\n\n waveform = audio[\"waveform\"]\n waveform_sample_rate = audio[\"sample_rate\"]\n input_device = waveform.device\n # Ensure that Audio VAE is loaded on the correct device.\n self.device_manager.ensure_model_loaded()\n\n waveform = self.device_manager.move_to_load_device(waveform)\n expected_channels = self.autoencoder.encoder.in_channels\n if waveform.shape[1] != expected_channels:\n if waveform.shape[1] == 1:\n waveform = waveform.expand(-1, expected_channels, *waveform.shape[2:])\n else:\n raise ValueError(\n f\"Input audio must have {expected_channels} channels, got {waveform.shape[1]}\"\n )\n\n mel_spec = self.preprocessor.waveform_to_mel(\n waveform, waveform_sample_rate, device=self.device_manager.load_device\n )\n\n latents = self.autoencoder.encode(mel_spec)\n posterior = DiagonalGaussianDistribution(latents)\n latent_mode = posterior.mode()\n\n normalized = self.normalizer.normalize(latent_mode)\n return normalized.to(input_device)\n\n def decode(self, latents: torch.Tensor) -> torch.Tensor:\n \"\"\"Decode normalized latent tensors into an audio waveform.\"\"\"\n original_shape = latents.shape\n\n # Ensure that Audio VAE is loaded on the correct device.\n self.device_manager.ensure_model_loaded()\n\n latents = self.device_manager.move_to_load_device(latents)\n latents = self.normalizer.denormalize(latents)\n\n target_shape = self.target_shape_from_latents(original_shape)\n mel_spec = self.autoencoder.decode(latents, target_shape=target_shape)\n\n waveform = self.run_vocoder(mel_spec)\n return self.device_manager.move_to_load_device(waveform)\n\n def target_shape_from_latents(self, latents_shape):\n batch, _, time, _ = latents_shape\n target_length = time * LATENT_DOWNSAMPLE_FACTOR\n if self.autoencoder.causality_axis != CausalityAxis.NONE:\n target_length -= LATENT_DOWNSAMPLE_FACTOR - 1\n return (\n batch,\n self.autoencoder.decoder.out_ch,\n target_length,\n self.autoencoder.mel_bins,\n )\n\n def num_of_latents_from_frames(self, frames_number: int, frame_rate: int) -> int:\n return math.ceil((float(frames_number) / frame_rate) * self.latents_per_second)\n\n def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor:\n audio_channels = self.autoencoder.decoder.out_ch\n vocoder_input = mel_spec.transpose(2, 3)\n\n if audio_channels == 1:\n vocoder_input = vocoder_input.squeeze(1)\n elif audio_channels != 2:\n raise ValueError(f\"Unsupported audio_channels: {audio_channels}\")\n\n return self.vocoder(vocoder_input)\n\n @property\n def sample_rate(self) -> int:\n return int(self.autoencoder.sampling_rate)\n\n @property\n def mel_hop_length(self) -> int:\n return int(self.autoencoder.mel_hop_length)\n\n @property\n def mel_bins(self) -> int:\n return int(self.autoencoder.mel_bins)\n\n @property\n def latent_channels(self) -> int:\n return int(self.autoencoder.decoder.z_channels)\n\n @property\n def latent_frequency_bins(self) -> int:\n return int(self.mel_bins // LATENT_DOWNSAMPLE_FACTOR)\n\n @property\n def latents_per_second(self) -> float:\n return self.sample_rate / self.mel_hop_length / LATENT_DOWNSAMPLE_FACTOR\n\n @property\n def output_sample_rate(self) -> int:\n output_rate = getattr(self.vocoder, \"output_sample_rate\", None)\n if output_rate is not None:\n return int(output_rate)\n upsample_factor = getattr(self.vocoder, \"upsample_factor\", None)\n if upsample_factor is None:\n raise AttributeError(\n \"Vocoder is missing upsample_factor; cannot infer output sample rate\"\n )\n return int(self.sample_rate * upsample_factor / self.mel_hop_length)\n\n def memory_required(self, input_shape):\n return self.device_manager.patcher.model_size()\n" }, { "path": "comfy/ldm/lightricks/vae/causal_audio_autoencoder.py", "content": "from __future__ import annotations\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom typing import Optional\nfrom enum import Enum\nfrom .pixel_norm import PixelNorm\nimport comfy.ops\nimport logging\n\nops = comfy.ops.disable_weight_init\n\n\nclass StringConvertibleEnum(Enum):\n \"\"\"\n Base enum class that provides string-to-enum conversion functionality.\n\n This mixin adds a str_to_enum() class method that handles conversion from\n strings, None, or existing enum instances with case-insensitive matching.\n \"\"\"\n\n @classmethod\n def str_to_enum(cls, value):\n \"\"\"\n Convert a string, enum instance, or None to the appropriate enum member.\n\n Args:\n value: Can be an enum instance of this class, a string, or None\n\n Returns:\n Enum member of this class\n\n Raises:\n ValueError: If the value cannot be converted to a valid enum member\n \"\"\"\n # Already an enum instance of this class\n if isinstance(value, cls):\n return value\n\n # None maps to NONE member if it exists\n if value is None:\n if hasattr(cls, \"NONE\"):\n return cls.NONE\n raise ValueError(f\"{cls.__name__} does not have a NONE member to map None to\")\n\n # String conversion (case-insensitive)\n if isinstance(value, str):\n value_lower = value.lower()\n\n # Try to match against enum values\n for member in cls:\n # Handle members with None values\n if member.value is None:\n if value_lower == \"none\":\n return member\n # Handle members with string values\n elif isinstance(member.value, str) and member.value.lower() == value_lower:\n return member\n\n # Build helpful error message with valid values\n valid_values = []\n for member in cls:\n if member.value is None:\n valid_values.append(\"none\")\n elif isinstance(member.value, str):\n valid_values.append(member.value)\n\n raise ValueError(f\"Invalid {cls.__name__} string: '{value}'. \" f\"Valid values are: {valid_values}\")\n\n raise ValueError(\n f\"Cannot convert type {type(value).__name__} to {cls.__name__} enum. \"\n f\"Expected string, None, or {cls.__name__} instance.\"\n )\n\n\nclass AttentionType(StringConvertibleEnum):\n \"\"\"Enum for specifying the attention mechanism type.\"\"\"\n\n VANILLA = \"vanilla\"\n LINEAR = \"linear\"\n NONE = \"none\"\n\n\nclass CausalityAxis(StringConvertibleEnum):\n \"\"\"Enum for specifying the causality axis in causal convolutions.\"\"\"\n\n NONE = None\n WIDTH = \"width\"\n HEIGHT = \"height\"\n WIDTH_COMPATIBILITY = \"width-compatibility\"\n\n\ndef Normalize(in_channels, *, num_groups=32, normtype=\"group\"):\n if normtype == \"group\":\n return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)\n elif normtype == \"pixel\":\n return PixelNorm(dim=1, eps=1e-6)\n else:\n raise ValueError(f\"Invalid normalization type: {normtype}\")\n\n\nclass CausalConv2d(nn.Module):\n \"\"\"\n A causal 2D convolution.\n\n This layer ensures that the output at time `t` only depends on inputs\n at time `t` and earlier. It achieves this by applying asymmetric padding\n to the time dimension (width) before the convolution.\n \"\"\"\n\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size,\n stride=1,\n dilation=1,\n groups=1,\n bias=True,\n causality_axis: CausalityAxis = CausalityAxis.HEIGHT,\n ):\n super().__init__()\n\n self.causality_axis = causality_axis\n\n # Ensure kernel_size and dilation are tuples\n kernel_size = nn.modules.utils._pair(kernel_size)\n dilation = nn.modules.utils._pair(dilation)\n\n # Calculate padding dimensions\n pad_h = (kernel_size[0] - 1) * dilation[0]\n pad_w = (kernel_size[1] - 1) * dilation[1]\n\n # The padding tuple for F.pad is (pad_left, pad_right, pad_top, pad_bottom)\n match self.causality_axis:\n case CausalityAxis.NONE:\n self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)\n case CausalityAxis.WIDTH | CausalityAxis.WIDTH_COMPATIBILITY:\n self.padding = (pad_w, 0, pad_h // 2, pad_h - pad_h // 2)\n case CausalityAxis.HEIGHT:\n self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h, 0)\n case _:\n raise ValueError(f\"Invalid causality_axis: {causality_axis}\")\n\n # The internal convolution layer uses no padding, as we handle it manually\n self.conv = ops.Conv2d(\n in_channels,\n out_channels,\n kernel_size,\n stride=stride,\n padding=0,\n dilation=dilation,\n groups=groups,\n bias=bias,\n )\n\n def forward(self, x):\n # Apply causal padding before convolution\n x = F.pad(x, self.padding)\n return self.conv(x)\n\n\ndef make_conv2d(\n in_channels,\n out_channels,\n kernel_size,\n stride=1,\n padding=None,\n dilation=1,\n groups=1,\n bias=True,\n causality_axis: Optional[CausalityAxis] = None,\n):\n \"\"\"\n Create a 2D convolution layer that can be either causal or non-causal.\n\n Args:\n in_channels: Number of input channels\n out_channels: Number of output channels\n kernel_size: Size of the convolution kernel\n stride: Convolution stride\n padding: Padding (if None, will be calculated based on causal flag)\n dilation: Dilation rate\n groups: Number of groups for grouped convolution\n bias: Whether to use bias\n causality_axis: Dimension along which to apply causality.\n\n Returns:\n Either a regular Conv2d or CausalConv2d layer\n \"\"\"\n if causality_axis is not None:\n # For causal convolution, padding is handled internally by CausalConv2d\n return CausalConv2d(in_channels, out_channels, kernel_size, stride, dilation, groups, bias, causality_axis)\n else:\n # For non-causal convolution, use symmetric padding if not specified\n if padding is None:\n if isinstance(kernel_size, int):\n padding = kernel_size // 2\n else:\n padding = tuple(k // 2 for k in kernel_size)\n return ops.Conv2d(\n in_channels,\n out_channels,\n kernel_size,\n stride,\n padding,\n dilation,\n groups,\n bias,\n )\n\n\nclass Upsample(nn.Module):\n def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.HEIGHT):\n super().__init__()\n self.with_conv = with_conv\n self.causality_axis = causality_axis\n if self.with_conv:\n self.conv = make_conv2d(in_channels, in_channels, kernel_size=3, stride=1, causality_axis=causality_axis)\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 # Drop FIRST element in the causal axis to undo encoder's padding, while keeping the length 1 + 2 * n.\n # For example, if the input is [0, 1, 2], after interpolation, the output is [0, 0, 1, 1, 2, 2].\n # The causal convolution will pad the first element as [-, -, 0, 0, 1, 1, 2, 2],\n # So the output elements rely on the following windows:\n # 0: [-,-,0]\n # 1: [-,0,0]\n # 2: [0,0,1]\n # 3: [0,1,1]\n # 4: [1,1,2]\n # 5: [1,2,2]\n # Notice that the first and second elements in the output rely only on the first element in the input,\n # while all other elements rely on two elements in the input.\n # So we can drop the first element to undo the padding (rather than the last element).\n # This is a no-op for non-causal convolutions.\n match self.causality_axis:\n case CausalityAxis.NONE:\n pass # x remains unchanged\n case CausalityAxis.HEIGHT:\n x = x[:, :, 1:, :]\n case CausalityAxis.WIDTH:\n x = x[:, :, :, 1:]\n case CausalityAxis.WIDTH_COMPATIBILITY:\n pass # x remains unchanged\n case _:\n raise ValueError(f\"Invalid causality_axis: {self.causality_axis}\")\n\n return x\n\n\nclass Downsample(nn.Module):\n \"\"\"\n A downsampling layer that can use either a strided convolution\n or average pooling. Supports standard and causal padding for the\n convolutional mode.\n \"\"\"\n\n def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.WIDTH):\n super().__init__()\n self.with_conv = with_conv\n self.causality_axis = causality_axis\n\n if self.causality_axis != CausalityAxis.NONE and not self.with_conv:\n raise ValueError(\"causality is only supported when `with_conv=True`.\")\n\n if self.with_conv:\n # Do time downsampling here\n # no asymmetric padding in torch conv, must do it ourselves\n self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)\n\n def forward(self, x):\n if self.with_conv:\n # (pad_left, pad_right, pad_top, pad_bottom)\n match self.causality_axis:\n case CausalityAxis.NONE:\n pad = (0, 1, 0, 1)\n case CausalityAxis.WIDTH:\n pad = (2, 0, 0, 1)\n case CausalityAxis.HEIGHT:\n pad = (0, 1, 2, 0)\n case CausalityAxis.WIDTH_COMPATIBILITY:\n pad = (1, 0, 0, 1)\n case _:\n raise ValueError(f\"Invalid causality_axis: {self.causality_axis}\")\n\n x = torch.nn.functional.pad(x, pad, mode=\"constant\", value=0)\n x = self.conv(x)\n else:\n # This branch is only taken if with_conv=False, which implies causality_axis is NONE.\n x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)\n\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 norm_type=\"group\",\n causality_axis: CausalityAxis = CausalityAxis.HEIGHT,\n ):\n super().__init__()\n self.causality_axis = causality_axis\n\n if self.causality_axis != CausalityAxis.NONE and norm_type == \"group\":\n raise ValueError(\"Causal ResnetBlock with GroupNorm is not supported.\")\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, normtype=norm_type)\n self.non_linearity = nn.SiLU()\n self.conv1 = make_conv2d(in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis)\n if temb_channels > 0:\n self.temb_proj = ops.Linear(temb_channels, out_channels)\n self.norm2 = Normalize(out_channels, normtype=norm_type)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = make_conv2d(out_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis)\n if self.in_channels != self.out_channels:\n if self.use_conv_shortcut:\n self.conv_shortcut = make_conv2d(\n in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis\n )\n else:\n self.nin_shortcut = make_conv2d(\n in_channels, out_channels, kernel_size=1, stride=1, causality_axis=causality_axis\n )\n\n def forward(self, x, temb):\n h = x\n h = self.norm1(h)\n h = self.non_linearity(h)\n h = self.conv1(h)\n\n if temb is not None:\n h = h + self.temb_proj(self.non_linearity(temb))[:, :, None, None]\n\n h = self.norm2(h)\n h = self.non_linearity(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 AttnBlock(nn.Module):\n def __init__(self, in_channels, norm_type=\"group\"):\n super().__init__()\n self.in_channels = in_channels\n\n self.norm = Normalize(in_channels, normtype=norm_type)\n self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)\n self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)\n self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)\n self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)\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).contiguous()\n q = q.permute(0, 2, 1).contiguous() # b,hw,c\n k = k.reshape(b, c, h * w).contiguous() # b,c,hw\n w_ = torch.bmm(q, k).contiguous() # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]\n w_ = w_ * (int(c) ** (-0.5))\n w_ = torch.nn.functional.softmax(w_, dim=2)\n\n # attend to values\n v = v.reshape(b, c, h * w).contiguous()\n w_ = w_.permute(0, 2, 1).contiguous() # b,hw,hw (first hw of k, second of q)\n h_ = torch.bmm(v, w_).contiguous() # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]\n h_ = h_.reshape(b, c, h, w).contiguous()\n\n h_ = self.proj_out(h_)\n\n return x + h_\n\n\ndef make_attn(in_channels, attn_type=\"vanilla\", norm_type=\"group\"):\n # Convert string to enum if needed\n attn_type = AttentionType.str_to_enum(attn_type)\n\n if attn_type != AttentionType.NONE:\n logging.info(f\"making attention of type '{attn_type.value}' with {in_channels} in_channels\")\n else:\n logging.info(f\"making identity attention with {in_channels} in_channels\")\n\n match attn_type:\n case AttentionType.VANILLA:\n return AttnBlock(in_channels, norm_type=norm_type)\n case AttentionType.NONE:\n return nn.Identity(in_channels)\n case AttentionType.LINEAR:\n raise NotImplementedError(f\"Attention type {attn_type.value} is not supported yet.\")\n case _:\n raise ValueError(f\"Unknown attention type: {attn_type}\")\n\n\nclass Encoder(nn.Module):\n def __init__(\n self,\n *,\n ch,\n out_ch,\n ch_mult=(1, 2, 4, 8),\n num_res_blocks,\n attn_resolutions,\n dropout=0.0,\n resamp_with_conv=True,\n in_channels,\n resolution,\n z_channels,\n double_z=True,\n attn_type=\"vanilla\",\n mid_block_add_attention=True,\n norm_type=\"group\",\n causality_axis=CausalityAxis.WIDTH.value,\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.z_channels = z_channels\n self.double_z = double_z\n self.norm_type = norm_type\n # Convert string to enum if needed (for config loading)\n causality_axis = CausalityAxis.str_to_enum(causality_axis)\n self.attn_type = AttentionType.str_to_enum(attn_type)\n\n # downsampling\n self.conv_in = make_conv2d(\n in_channels,\n self.ch,\n kernel_size=3,\n stride=1,\n causality_axis=causality_axis,\n )\n\n self.non_linearity = nn.SiLU()\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\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\n for _ 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 norm_type=self.norm_type,\n causality_axis=causality_axis,\n )\n )\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type))\n\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, causality_axis=causality_axis)\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(\n in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout,\n norm_type=self.norm_type,\n causality_axis=causality_axis,\n )\n if mid_block_add_attention:\n self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)\n else:\n self.mid.attn_1 = nn.Identity()\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 norm_type=self.norm_type,\n causality_axis=causality_axis,\n )\n\n # end\n self.norm_out = Normalize(block_in, normtype=self.norm_type)\n self.conv_out = make_conv2d(\n block_in,\n 2 * z_channels if double_z else z_channels,\n kernel_size=3,\n stride=1,\n causality_axis=causality_axis,\n )\n\n def forward(self, x):\n \"\"\"\n Forward pass through the encoder.\n\n Args:\n x: Input tensor of shape [batch, channels, time, n_mels]\n\n Returns:\n Encoded latent representation\n \"\"\"\n feature_maps = [self.conv_in(x)]\n\n # Process each resolution level (from high to low resolution)\n for resolution_level in range(self.num_resolutions):\n # Apply residual blocks at current resolution level\n for block_idx in range(self.num_res_blocks):\n # Apply ResNet block with optional timestep embedding\n current_features = self.down[resolution_level].block[block_idx](feature_maps[-1], temb=None)\n\n # Apply attention if configured for this resolution level\n if len(self.down[resolution_level].attn) > 0:\n current_features = self.down[resolution_level].attn[block_idx](current_features)\n\n # Store processed features\n feature_maps.append(current_features)\n\n # Downsample spatial dimensions (except at the final resolution level)\n if resolution_level != self.num_resolutions - 1:\n downsampled_features = self.down[resolution_level].downsample(feature_maps[-1])\n feature_maps.append(downsampled_features)\n\n # === MIDDLE PROCESSING PHASE ===\n # Take the lowest resolution features for middle processing\n bottleneck_features = feature_maps[-1]\n\n # Apply first middle ResNet block\n bottleneck_features = self.mid.block_1(bottleneck_features, temb=None)\n\n # Apply middle attention block\n bottleneck_features = self.mid.attn_1(bottleneck_features)\n\n # Apply second middle ResNet block\n bottleneck_features = self.mid.block_2(bottleneck_features, temb=None)\n\n # === OUTPUT PHASE ===\n # Normalize the bottleneck features\n output_features = self.norm_out(bottleneck_features)\n\n # Apply non-linearity (SiLU activation)\n output_features = self.non_linearity(output_features)\n\n # Final convolution to produce latent representation\n # [batch, channels, time, n_mels] -> [batch, 2 * z_channels if double_z else z_channels, time, n_mels]\n return self.conv_out(output_features)\n\n\nclass Decoder(nn.Module):\n def __init__(\n self,\n *,\n ch,\n out_ch,\n ch_mult=(1, 2, 4, 8),\n num_res_blocks,\n attn_resolutions,\n dropout=0.0,\n resamp_with_conv=True,\n in_channels,\n resolution,\n z_channels,\n give_pre_end=False,\n tanh_out=False,\n attn_type=\"vanilla\",\n mid_block_add_attention=True,\n norm_type=\"group\",\n causality_axis=CausalityAxis.WIDTH.value,\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.out_ch = out_ch\n self.give_pre_end = give_pre_end\n self.tanh_out = tanh_out\n self.norm_type = norm_type\n self.z_channels = z_channels\n # Convert string to enum if needed (for config loading)\n causality_axis = CausalityAxis.str_to_enum(causality_axis)\n self.attn_type = AttentionType.str_to_enum(attn_type)\n\n # compute 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\n # z to block_in\n self.conv_in = make_conv2d(z_channels, block_in, kernel_size=3, stride=1, causality_axis=causality_axis)\n\n self.non_linearity = nn.SiLU()\n\n # middle\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 norm_type=self.norm_type,\n causality_axis=causality_axis,\n )\n if mid_block_add_attention:\n self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)\n else:\n self.mid.attn_1 = nn.Identity()\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 norm_type=self.norm_type,\n causality_axis=causality_axis,\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 _ 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 norm_type=self.norm_type,\n causality_axis=causality_axis,\n )\n )\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_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, causality_axis=causality_axis)\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, normtype=self.norm_type)\n self.conv_out = make_conv2d(block_in, out_ch, kernel_size=3, stride=1, causality_axis=causality_axis)\n\n def _adjust_output_shape(self, decoded_output, target_shape):\n \"\"\"\n Adjust output shape to match target dimensions for variable-length audio.\n\n This function handles the common case where decoded audio spectrograms need to be\n resized to match a specific target shape.\n\n Args:\n decoded_output: Tensor of shape (batch, channels, time, frequency)\n target_shape: Target shape tuple (batch, channels, time, frequency)\n\n Returns:\n Tensor adjusted to match target_shape exactly\n \"\"\"\n # Current output shape: (batch, channels, time, frequency)\n _, _, current_time, current_freq = decoded_output.shape\n _, target_channels, target_time, target_freq = target_shape\n\n # Step 1: Crop first to avoid exceeding target dimensions\n decoded_output = decoded_output[\n :, :target_channels, : min(current_time, target_time), : min(current_freq, target_freq)\n ]\n\n # Step 2: Calculate padding needed for time and frequency dimensions\n time_padding_needed = target_time - decoded_output.shape[2]\n freq_padding_needed = target_freq - decoded_output.shape[3]\n\n # Step 3: Apply padding if needed\n if time_padding_needed > 0 or freq_padding_needed > 0:\n # PyTorch padding format: (pad_left, pad_right, pad_top, pad_bottom)\n # For audio: pad_left/right = frequency, pad_top/bottom = time\n padding = (\n 0,\n max(freq_padding_needed, 0), # frequency padding (left, right)\n 0,\n max(time_padding_needed, 0), # time padding (top, bottom)\n )\n decoded_output = F.pad(decoded_output, padding)\n\n # Step 4: Final safety crop to ensure exact target shape\n decoded_output = decoded_output[:, :target_channels, :target_time, :target_freq]\n\n return decoded_output\n\n def get_config(self):\n return {\n \"ch\": self.ch,\n \"out_ch\": self.out_ch,\n \"ch_mult\": self.ch_mult,\n \"num_res_blocks\": self.num_res_blocks,\n \"in_channels\": self.in_channels,\n \"resolution\": self.resolution,\n \"z_channels\": self.z_channels,\n }\n\n def forward(self, latent_features, target_shape=None):\n \"\"\"\n Decode latent features back to audio spectrograms.\n\n Args:\n latent_features: Encoded latent representation of shape (batch, channels, height, width)\n target_shape: Optional target output shape (batch, channels, time, frequency)\n If provided, output will be cropped/padded to match this shape\n\n Returns:\n Reconstructed audio spectrogram of shape (batch, channels, time, frequency)\n \"\"\"\n assert target_shape is not None, \"Target shape is required for CausalAudioAutoencoder Decoder\"\n\n # Transform latent features to decoder's internal feature dimension\n hidden_features = self.conv_in(latent_features)\n\n # Middle processing\n hidden_features = self.mid.block_1(hidden_features, temb=None)\n hidden_features = self.mid.attn_1(hidden_features)\n hidden_features = self.mid.block_2(hidden_features, temb=None)\n\n # Upsampling\n # Progressively increase spatial resolution from lowest to highest\n for resolution_level in reversed(range(self.num_resolutions)):\n # Apply residual blocks at current resolution level\n for block_index in range(self.num_res_blocks + 1):\n hidden_features = self.up[resolution_level].block[block_index](hidden_features, temb=None)\n\n if len(self.up[resolution_level].attn) > 0:\n hidden_features = self.up[resolution_level].attn[block_index](hidden_features)\n\n if resolution_level != 0:\n hidden_features = self.up[resolution_level].upsample(hidden_features)\n\n # Output\n if self.give_pre_end:\n # Return intermediate features before final processing (for debugging/analysis)\n decoded_output = hidden_features\n else:\n # Standard output path: normalize, activate, and convert to output channels\n # Final normalization layer\n hidden_features = self.norm_out(hidden_features)\n\n # Apply SiLU (Swish) activation function\n hidden_features = self.non_linearity(hidden_features)\n\n # Final convolution to map to output channels (typically 2 for stereo audio)\n decoded_output = self.conv_out(hidden_features)\n\n # Optional tanh activation to bound output values to [-1, 1] range\n if self.tanh_out:\n decoded_output = torch.tanh(decoded_output)\n\n # Adjust shape for audio data\n if target_shape is not None:\n decoded_output = self._adjust_output_shape(decoded_output, target_shape)\n\n return decoded_output\n\n\nclass processor(nn.Module):\n def __init__(self):\n super().__init__()\n self.register_buffer(\"std-of-means\", torch.empty(128))\n self.register_buffer(\"mean-of-means\", torch.empty(128))\n\n def un_normalize(self, x):\n return (x * self.get_buffer(\"std-of-means\").to(x)) + self.get_buffer(\"mean-of-means\").to(x)\n\n def normalize(self, x):\n return (x - self.get_buffer(\"mean-of-means\").to(x)) / self.get_buffer(\"std-of-means\").to(x)\n\n\nclass CausalAudioAutoencoder(nn.Module):\n def __init__(self, config=None):\n super().__init__()\n\n if config is None:\n config = self.get_default_config()\n\n model_config = config.get(\"model\", {}).get(\"params\", {})\n\n self.sampling_rate = model_config.get(\n \"sampling_rate\", config.get(\"sampling_rate\", 16000)\n )\n encoder_config = model_config.get(\"encoder\", model_config.get(\"ddconfig\", {}))\n decoder_config = model_config.get(\"decoder\", encoder_config)\n\n # Load mel spectrogram parameters\n self.mel_bins = encoder_config.get(\"mel_bins\", 64)\n self.mel_hop_length = config.get(\"preprocessing\", {}).get(\"stft\", {}).get(\"hop_length\", 160)\n self.n_fft = config.get(\"preprocessing\", {}).get(\"stft\", {}).get(\"filter_length\", 1024)\n\n # Store causality configuration at VAE level (not just in encoder internals)\n causality_axis_value = encoder_config.get(\"causality_axis\", CausalityAxis.HEIGHT.value)\n self.causality_axis = CausalityAxis.str_to_enum(causality_axis_value)\n self.is_causal = self.causality_axis == CausalityAxis.HEIGHT\n\n self.encoder = Encoder(**encoder_config)\n self.decoder = Decoder(**decoder_config)\n\n self.per_channel_statistics = processor()\n\n def get_default_config(self):\n ddconfig = {\n \"double_z\": True,\n \"mel_bins\": 64,\n \"z_channels\": 8,\n \"resolution\": 256,\n \"downsample_time\": False,\n \"in_channels\": 2,\n \"out_ch\": 2,\n \"ch\": 128,\n \"ch_mult\": [1, 2, 4],\n \"num_res_blocks\": 2,\n \"attn_resolutions\": [],\n \"dropout\": 0.0,\n \"mid_block_add_attention\": False,\n \"norm_type\": \"pixel\",\n \"causality_axis\": \"height\",\n }\n\n config = {\n \"model\": {\n \"params\": {\n \"ddconfig\": ddconfig,\n \"sampling_rate\": 16000,\n }\n },\n \"preprocessing\": {\n \"stft\": {\n \"filter_length\": 1024,\n \"hop_length\": 160,\n },\n },\n }\n\n return config\n\n def get_config(self):\n return {\n \"sampling_rate\": self.sampling_rate,\n \"mel_bins\": self.mel_bins,\n \"mel_hop_length\": self.mel_hop_length,\n \"n_fft\": self.n_fft,\n \"causality_axis\": self.causality_axis.value,\n \"is_causal\": self.is_causal,\n }\n\n def encode(self, x):\n return self.encoder(x)\n\n def decode(self, x, target_shape=None):\n return self.decoder(x, target_shape=target_shape)\n" }, { "path": "comfy/ldm/lightricks/vae/causal_conv3d.py", "content": "from typing import Tuple, Union\n\nimport threading\nimport torch\nimport torch.nn as nn\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nclass CausalConv3d(nn.Module):\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size: int = 3,\n stride: Union[int, Tuple[int]] = 1,\n dilation: int = 1,\n groups: int = 1,\n spatial_padding_mode: str = \"zeros\",\n **kwargs,\n ):\n super().__init__()\n\n self.in_channels = in_channels\n self.out_channels = out_channels\n\n if isinstance(stride, int):\n self.time_stride = stride\n else:\n self.time_stride = stride[0]\n\n kernel_size = (kernel_size, kernel_size, kernel_size)\n self.time_kernel_size = kernel_size[0]\n\n dilation = (dilation, 1, 1)\n\n height_pad = kernel_size[1] // 2\n width_pad = kernel_size[2] // 2\n padding = (0, height_pad, width_pad)\n\n self.conv = ops.Conv3d(\n in_channels,\n out_channels,\n kernel_size,\n stride=stride,\n dilation=dilation,\n padding=padding,\n padding_mode=spatial_padding_mode,\n groups=groups,\n )\n self.temporal_cache_state={}\n\n def forward(self, x, causal: bool = True):\n tid = threading.get_ident()\n\n cached, is_end = self.temporal_cache_state.get(tid, (None, False))\n if cached is None:\n padding_length = self.time_kernel_size - 1\n if not causal:\n padding_length = padding_length // 2\n if x.shape[2] == 0:\n return x\n cached = x[:, :, :1, :, :].repeat((1, 1, padding_length, 1, 1))\n pieces = [ cached, x ]\n if is_end and not causal:\n pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1)))\n input_length = sum([piece.shape[2] for piece in pieces])\n cache_length = (self.time_kernel_size - self.time_stride) + ((input_length - self.time_kernel_size) % self.time_stride)\n\n needs_caching = not is_end\n if needs_caching and cache_length == 0:\n self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False)\n needs_caching = False\n if needs_caching and x.shape[2] >= cache_length:\n needs_caching = False\n self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)\n\n x = torch.cat(pieces, dim=2)\n del pieces\n del cached\n\n if needs_caching:\n self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)\n elif is_end:\n self.temporal_cache_state[tid] = (None, True)\n\n return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :]\n\n @property\n def weight(self):\n return self.conv.weight\n" }, { "path": "comfy/ldm/lightricks/vae/causal_video_autoencoder.py", "content": "from __future__ import annotations\nimport threading\nimport torch\nfrom torch import nn\nfrom functools import partial\nimport math\nfrom einops import rearrange\nfrom typing import List, Optional, Tuple, Union\nfrom .conv_nd_factory import make_conv_nd, make_linear_nd\nfrom .causal_conv3d import CausalConv3d\nfrom .pixel_norm import PixelNorm\nfrom ..model import PixArtAlphaCombinedTimestepSizeEmbeddings\nimport comfy.ops\nimport comfy.model_management\nfrom comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed\n\nops = comfy.ops.disable_weight_init\n\ndef in_meta_context():\n return torch.device(\"meta\") == torch.empty(0).device\n\ndef mark_conv3d_ended(module):\n tid = threading.get_ident()\n for _, m in module.named_modules():\n if isinstance(m, CausalConv3d):\n current = m.temporal_cache_state.get(tid, (None, False))\n m.temporal_cache_state[tid] = (current[0], True)\n\ndef split2(tensor, split_point, dim=2):\n return torch.split(tensor, [split_point, tensor.shape[dim] - split_point], dim=dim)\n\ndef add_exchange_cache(dest, cache_in, new_input, dim=2):\n if dest is not None:\n if cache_in is not None:\n cache_to_dest = min(dest.shape[dim], cache_in.shape[dim])\n lead_in_dest, dest = split2(dest, cache_to_dest, dim=dim)\n lead_in_source, cache_in = split2(cache_in, cache_to_dest, dim=dim)\n lead_in_dest.add_(lead_in_source)\n body, new_input = split2(new_input, dest.shape[dim], dim)\n dest.add_(body)\n return torch_cat_if_needed([cache_in, new_input], dim=dim)\n\nclass Encoder(nn.Module):\n r\"\"\"\n The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.\n\n Args:\n dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):\n The number of dimensions to use in convolutions.\n in_channels (`int`, *optional*, defaults to 3):\n The number of input channels.\n out_channels (`int`, *optional*, defaults to 3):\n The number of output channels.\n blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[(\"res_x\", 1)]`):\n The blocks to use. Each block is a tuple of the block name and the number of layers.\n base_channels (`int`, *optional*, defaults to 128):\n The number of output channels for the first convolutional layer.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups for normalization.\n patch_size (`int`, *optional*, defaults to 1):\n The patch size to use. Should be a power of 2.\n norm_layer (`str`, *optional*, defaults to `group_norm`):\n The normalization layer to use. Can be either `group_norm` or `pixel_norm`.\n latent_log_var (`str`, *optional*, defaults to `per_channel`):\n The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`.\n \"\"\"\n\n def __init__(\n self,\n dims: Union[int, Tuple[int, int]] = 3,\n in_channels: int = 3,\n out_channels: int = 3,\n blocks: List[Tuple[str, int | dict]] = [(\"res_x\", 1)],\n base_channels: int = 128,\n norm_num_groups: int = 32,\n patch_size: Union[int, Tuple[int]] = 1,\n norm_layer: str = \"group_norm\", # group_norm, pixel_norm\n latent_log_var: str = \"per_channel\",\n spatial_padding_mode: str = \"zeros\",\n ):\n super().__init__()\n self.patch_size = patch_size\n self.norm_layer = norm_layer\n self.latent_channels = out_channels\n self.latent_log_var = latent_log_var\n self.blocks_desc = blocks\n\n in_channels = in_channels * patch_size**2\n output_channel = base_channels\n\n self.conv_in = make_conv_nd(\n dims=dims,\n in_channels=in_channels,\n out_channels=output_channel,\n kernel_size=3,\n stride=1,\n padding=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n\n self.down_blocks = nn.ModuleList([])\n\n for block_name, block_params in blocks:\n input_channel = output_channel\n if isinstance(block_params, int):\n block_params = {\"num_layers\": block_params}\n\n if block_name == \"res_x\":\n block = UNetMidBlock3D(\n dims=dims,\n in_channels=input_channel,\n num_layers=block_params[\"num_layers\"],\n resnet_eps=1e-6,\n resnet_groups=norm_num_groups,\n norm_layer=norm_layer,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"res_x_y\":\n output_channel = block_params.get(\"multiplier\", 2) * output_channel\n block = ResnetBlock3D(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n eps=1e-6,\n groups=norm_num_groups,\n norm_layer=norm_layer,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_time\":\n block = make_conv_nd(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n kernel_size=3,\n stride=(2, 1, 1),\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_space\":\n block = make_conv_nd(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n kernel_size=3,\n stride=(1, 2, 2),\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_all\":\n block = make_conv_nd(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n kernel_size=3,\n stride=(2, 2, 2),\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_all_x_y\":\n output_channel = block_params.get(\"multiplier\", 2) * output_channel\n block = make_conv_nd(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n kernel_size=3,\n stride=(2, 2, 2),\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_all_res\":\n output_channel = block_params.get(\"multiplier\", 2) * output_channel\n block = SpaceToDepthDownsample(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n stride=(2, 2, 2),\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_space_res\":\n output_channel = block_params.get(\"multiplier\", 2) * output_channel\n block = SpaceToDepthDownsample(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n stride=(1, 2, 2),\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_time_res\":\n output_channel = block_params.get(\"multiplier\", 2) * output_channel\n block = SpaceToDepthDownsample(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n stride=(2, 1, 1),\n spatial_padding_mode=spatial_padding_mode,\n )\n else:\n raise ValueError(f\"unknown block: {block_name}\")\n\n self.down_blocks.append(block)\n\n # out\n if norm_layer == \"group_norm\":\n self.conv_norm_out = nn.GroupNorm(\n num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6\n )\n elif norm_layer == \"pixel_norm\":\n self.conv_norm_out = PixelNorm()\n elif norm_layer == \"layer_norm\":\n self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)\n\n self.conv_act = nn.SiLU()\n\n conv_out_channels = out_channels\n if latent_log_var == \"per_channel\":\n conv_out_channels *= 2\n elif latent_log_var == \"uniform\":\n conv_out_channels += 1\n elif latent_log_var == \"constant\":\n conv_out_channels += 1\n elif latent_log_var != \"none\":\n raise ValueError(f\"Invalid latent_log_var: {latent_log_var}\")\n self.conv_out = make_conv_nd(\n dims,\n output_channel,\n conv_out_channels,\n 3,\n padding=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n\n self.gradient_checkpointing = False\n\n def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch.FloatTensor]:\n sample = self.conv_in(sample)\n\n checkpoint_fn = (\n partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)\n if self.gradient_checkpointing and self.training\n else lambda x: x\n )\n\n for down_block in self.down_blocks:\n sample = checkpoint_fn(down_block)(sample)\n if sample is None or sample.shape[2] == 0:\n return None\n\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n if sample is None or sample.shape[2] == 0:\n return None\n\n if self.latent_log_var == \"uniform\":\n last_channel = sample[:, -1:, ...]\n num_dims = sample.dim()\n\n if num_dims == 4:\n # For shape (B, C, H, W)\n repeated_last_channel = last_channel.repeat(\n 1, sample.shape[1] - 2, 1, 1\n )\n sample = torch.cat([sample, repeated_last_channel], dim=1)\n elif num_dims == 5:\n # For shape (B, C, F, H, W)\n repeated_last_channel = last_channel.repeat(\n 1, sample.shape[1] - 2, 1, 1, 1\n )\n sample = torch.cat([sample, repeated_last_channel], dim=1)\n else:\n raise ValueError(f\"Invalid input shape: {sample.shape}\")\n elif self.latent_log_var == \"constant\":\n sample = sample[:, :-1, ...]\n approx_ln_0 = (\n -30\n ) # this is the minimal clamp value in DiagonalGaussianDistribution objects\n sample = torch.cat(\n [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0],\n dim=1,\n )\n\n return sample\n\n def forward_orig(self, sample: torch.FloatTensor, device=None) -> torch.FloatTensor:\n r\"\"\"The forward method of the `Encoder` class.\"\"\"\n\n max_chunk_size = get_max_chunk_size(sample.device if device is None else device) * 2 # encoder is more memory-efficient than decoder\n frame_size = sample[:, :, :1, :, :].numel() * sample.element_size()\n frame_size = int(frame_size * (self.conv_in.out_channels / self.conv_in.in_channels))\n\n outputs = []\n samples = [sample[:, :, :1, :, :]]\n if sample.shape[2] > 1:\n chunk_t = max(2, max_chunk_size // frame_size)\n if chunk_t < 4:\n chunk_t = 2\n elif chunk_t < 8:\n chunk_t = 4\n else:\n chunk_t = (chunk_t // 8) * 8\n samples += list(torch.split(sample[:, :, 1:, :, :], chunk_t, dim=2))\n for chunk_idx, chunk in enumerate(samples):\n if chunk_idx == len(samples) - 1:\n mark_conv3d_ended(self)\n chunk = patchify(chunk, patch_size_hw=self.patch_size, patch_size_t=1).to(device=device)\n output = self._forward_chunk(chunk)\n if output is not None:\n outputs.append(output)\n\n return torch_cat_if_needed(outputs, dim=2)\n\n def forward(self, *args, **kwargs):\n try:\n return self.forward_orig(*args, **kwargs)\n finally:\n tid = threading.get_ident()\n for _, module in self.named_modules():\n # ComfyUI doesn't thread this kind of stuff today, but just in case\n # we key on the thread to make it thread safe.\n tid = threading.get_ident()\n if hasattr(module, \"temporal_cache_state\"):\n module.temporal_cache_state.pop(tid, None)\n\n\nMIN_VRAM_FOR_CHUNK_SCALING = 6 * 1024 ** 3\nMAX_VRAM_FOR_CHUNK_SCALING = 24 * 1024 ** 3\nMIN_CHUNK_SIZE = 32 * 1024 ** 2\nMAX_CHUNK_SIZE = 128 * 1024 ** 2\n\ndef get_max_chunk_size(device: torch.device) -> int:\n total_memory = comfy.model_management.get_total_memory(dev=device)\n\n if total_memory <= MIN_VRAM_FOR_CHUNK_SCALING:\n return MIN_CHUNK_SIZE\n if total_memory >= MAX_VRAM_FOR_CHUNK_SCALING:\n return MAX_CHUNK_SIZE\n\n interp = (total_memory - MIN_VRAM_FOR_CHUNK_SCALING) / (\n MAX_VRAM_FOR_CHUNK_SCALING - MIN_VRAM_FOR_CHUNK_SCALING\n )\n return int(MIN_CHUNK_SIZE + interp * (MAX_CHUNK_SIZE - MIN_CHUNK_SIZE))\n\nclass Decoder(nn.Module):\n r\"\"\"\n The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.\n\n Args:\n dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):\n The number of dimensions to use in convolutions.\n in_channels (`int`, *optional*, defaults to 3):\n The number of input channels.\n out_channels (`int`, *optional*, defaults to 3):\n The number of output channels.\n blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[(\"res_x\", 1)]`):\n The blocks to use. Each block is a tuple of the block name and the number of layers.\n base_channels (`int`, *optional*, defaults to 128):\n The number of output channels for the first convolutional layer.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups for normalization.\n patch_size (`int`, *optional*, defaults to 1):\n The patch size to use. Should be a power of 2.\n norm_layer (`str`, *optional*, defaults to `group_norm`):\n The normalization layer to use. Can be either `group_norm` or `pixel_norm`.\n causal (`bool`, *optional*, defaults to `True`):\n Whether to use causal convolutions or not.\n \"\"\"\n\n def __init__(\n self,\n dims,\n in_channels: int = 3,\n out_channels: int = 3,\n blocks: List[Tuple[str, int | dict]] = [(\"res_x\", 1)],\n base_channels: int = 128,\n layers_per_block: int = 2,\n norm_num_groups: int = 32,\n patch_size: int = 1,\n norm_layer: str = \"group_norm\",\n causal: bool = True,\n timestep_conditioning: bool = False,\n spatial_padding_mode: str = \"zeros\",\n ):\n super().__init__()\n self.patch_size = patch_size\n self.layers_per_block = layers_per_block\n out_channels = out_channels * patch_size**2\n self.causal = causal\n self.blocks_desc = blocks\n\n # Compute output channel to be product of all channel-multiplier blocks\n output_channel = base_channels\n for block_name, block_params in list(reversed(blocks)):\n block_params = block_params if isinstance(block_params, dict) else {}\n if block_name == \"res_x_y\":\n output_channel = output_channel * block_params.get(\"multiplier\", 2)\n if block_name == \"compress_all\":\n output_channel = output_channel * block_params.get(\"multiplier\", 1)\n if block_name == \"compress_space\":\n output_channel = output_channel * block_params.get(\"multiplier\", 1)\n if block_name == \"compress_time\":\n output_channel = output_channel * block_params.get(\"multiplier\", 1)\n\n self.conv_in = make_conv_nd(\n dims,\n in_channels,\n output_channel,\n kernel_size=3,\n stride=1,\n padding=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n\n self.up_blocks = nn.ModuleList([])\n\n for block_name, block_params in list(reversed(blocks)):\n input_channel = output_channel\n if isinstance(block_params, int):\n block_params = {\"num_layers\": block_params}\n\n if block_name == \"res_x\":\n block = UNetMidBlock3D(\n dims=dims,\n in_channels=input_channel,\n num_layers=block_params[\"num_layers\"],\n resnet_eps=1e-6,\n resnet_groups=norm_num_groups,\n norm_layer=norm_layer,\n inject_noise=block_params.get(\"inject_noise\", False),\n timestep_conditioning=timestep_conditioning,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"res_x_y\":\n output_channel = output_channel // block_params.get(\"multiplier\", 2)\n block = ResnetBlock3D(\n dims=dims,\n in_channels=input_channel,\n out_channels=output_channel,\n eps=1e-6,\n groups=norm_num_groups,\n norm_layer=norm_layer,\n inject_noise=block_params.get(\"inject_noise\", False),\n timestep_conditioning=False,\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_time\":\n output_channel = output_channel // block_params.get(\"multiplier\", 1)\n block = DepthToSpaceUpsample(\n dims=dims,\n in_channels=input_channel,\n stride=(2, 1, 1),\n out_channels_reduction_factor=block_params.get(\"multiplier\", 1),\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_space\":\n output_channel = output_channel // block_params.get(\"multiplier\", 1)\n block = DepthToSpaceUpsample(\n dims=dims,\n in_channels=input_channel,\n stride=(1, 2, 2),\n out_channels_reduction_factor=block_params.get(\"multiplier\", 1),\n spatial_padding_mode=spatial_padding_mode,\n )\n elif block_name == \"compress_all\":\n output_channel = output_channel // block_params.get(\"multiplier\", 1)\n block = DepthToSpaceUpsample(\n dims=dims,\n in_channels=input_channel,\n stride=(2, 2, 2),\n residual=block_params.get(\"residual\", False),\n out_channels_reduction_factor=block_params.get(\"multiplier\", 1),\n spatial_padding_mode=spatial_padding_mode,\n )\n else:\n raise ValueError(f\"unknown layer: {block_name}\")\n\n self.up_blocks.append(block)\n\n if norm_layer == \"group_norm\":\n self.conv_norm_out = nn.GroupNorm(\n num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6\n )\n elif norm_layer == \"pixel_norm\":\n self.conv_norm_out = PixelNorm()\n elif norm_layer == \"layer_norm\":\n self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)\n\n self.conv_act = nn.SiLU()\n self.conv_out = make_conv_nd(\n dims,\n output_channel,\n out_channels,\n 3,\n padding=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n\n self.gradient_checkpointing = False\n\n # Precompute output scale factors: (channels, (t_scale, h_scale, w_scale), t_offset)\n ts, hs, ws, to = 1, 1, 1, 0\n for block in self.up_blocks:\n if isinstance(block, DepthToSpaceUpsample):\n ts *= block.stride[0]\n hs *= block.stride[1]\n ws *= block.stride[2]\n if block.stride[0] > 1:\n to = to * block.stride[0] + 1\n self._output_scale = (out_channels // (patch_size ** 2), (ts, hs * patch_size, ws * patch_size), to)\n\n self.timestep_conditioning = timestep_conditioning\n\n if timestep_conditioning:\n self.timestep_scale_multiplier = nn.Parameter(\n torch.tensor(1000.0, dtype=torch.float32)\n )\n self.last_time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings(\n output_channel * 2, 0, operations=ops,\n )\n self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel))\n else:\n self.register_buffer(\n \"last_scale_shift_table\",\n torch.tensor(\n [0.0, 0.0],\n device=\"cpu\" if in_meta_context() else None\n ).unsqueeze(1).expand(2, output_channel),\n persistent=False,\n )\n\n\n def decode_output_shape(self, input_shape):\n c, (ts, hs, ws), to = self._output_scale\n return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws)\n\n def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size):\n sample = sample_ref[0]\n sample_ref[0] = None\n if idx >= len(self.up_blocks):\n sample = self.conv_norm_out(sample)\n if timestep_shift_scale is not None:\n shift, scale = timestep_shift_scale\n sample = sample * (1 + scale) + shift\n sample = self.conv_act(sample)\n if ended:\n mark_conv3d_ended(self.conv_out)\n sample = self.conv_out(sample, causal=self.causal)\n if sample is not None and sample.shape[2] > 0:\n sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)\n t = sample.shape[2]\n output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample)\n output_offset[0] += t\n return\n\n up_block = self.up_blocks[idx]\n if ended:\n mark_conv3d_ended(up_block)\n if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):\n sample = checkpoint_fn(up_block)(\n sample, causal=self.causal, timestep=scaled_timestep\n )\n else:\n sample = checkpoint_fn(up_block)(sample, causal=self.causal)\n\n if sample is None or sample.shape[2] == 0:\n return\n\n total_bytes = sample.numel() * sample.element_size()\n num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size\n\n if num_chunks == 1:\n # when we are not chunking, detach our x so the callee can free it as soon as they are done\n next_sample_ref = [sample]\n del sample\n self.run_up(idx + 1, next_sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)\n return\n else:\n samples = torch.chunk(sample, chunks=num_chunks, dim=2)\n\n for chunk_idx, sample1 in enumerate(samples):\n self.run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)\n\n def forward_orig(\n self,\n sample: torch.FloatTensor,\n timestep: Optional[torch.Tensor] = None,\n output_buffer: Optional[torch.Tensor] = None,\n ) -> torch.FloatTensor:\n r\"\"\"The forward method of the `Decoder` class.\"\"\"\n batch_size = sample.shape[0]\n\n mark_conv3d_ended(self.conv_in)\n sample = self.conv_in(sample, causal=self.causal)\n\n checkpoint_fn = (\n partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)\n if self.gradient_checkpointing and self.training\n else lambda x: x\n )\n\n timestep_shift_scale = None\n scaled_timestep = None\n if self.timestep_conditioning:\n assert (\n timestep is not None\n ), \"should pass timestep with timestep_conditioning=True\"\n scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)\n embedded_timestep = self.last_time_embedder(\n timestep=scaled_timestep.flatten(),\n resolution=None,\n aspect_ratio=None,\n batch_size=sample.shape[0],\n hidden_dtype=sample.dtype,\n )\n embedded_timestep = embedded_timestep.view(\n batch_size, embedded_timestep.shape[-1], 1, 1, 1\n )\n ada_values = self.last_scale_shift_table[\n None, ..., None, None, None\n ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape(\n batch_size,\n 2,\n -1,\n embedded_timestep.shape[-3],\n embedded_timestep.shape[-2],\n embedded_timestep.shape[-1],\n )\n timestep_shift_scale = ada_values.unbind(dim=1)\n\n if output_buffer is None:\n output_buffer = torch.empty(\n self.decode_output_shape(sample.shape),\n dtype=sample.dtype, device=comfy.model_management.intermediate_device(),\n )\n output_offset = [0]\n\n max_chunk_size = get_max_chunk_size(sample.device)\n\n self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)\n\n return output_buffer\n\n def forward(self, *args, **kwargs):\n try:\n return self.forward_orig(*args, **kwargs)\n finally:\n for _, module in self.named_modules():\n #ComfyUI doesn't thread this kind of stuff today, but just incase\n #we key on the thread to make it thread safe.\n tid = threading.get_ident()\n if hasattr(module, \"temporal_cache_state\"):\n module.temporal_cache_state.pop(tid, None)\n\n\nclass UNetMidBlock3D(nn.Module):\n \"\"\"\n A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks.\n\n Args:\n in_channels (`int`): The number of input channels.\n dropout (`float`, *optional*, defaults to 0.0): The dropout rate.\n num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.\n resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.\n resnet_groups (`int`, *optional*, defaults to 32):\n The number of groups to use in the group normalization layers of the resnet blocks.\n norm_layer (`str`, *optional*, defaults to `group_norm`):\n The normalization layer to use. Can be either `group_norm` or `pixel_norm`.\n inject_noise (`bool`, *optional*, defaults to `False`):\n Whether to inject noise into the hidden states.\n timestep_conditioning (`bool`, *optional*, defaults to `False`):\n Whether to condition the hidden states on the timestep.\n\n Returns:\n `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,\n in_channels, height, width)`.\n\n \"\"\"\n\n def __init__(\n self,\n dims: Union[int, Tuple[int, int]],\n in_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_groups: int = 32,\n norm_layer: str = \"group_norm\",\n inject_noise: bool = False,\n timestep_conditioning: bool = False,\n spatial_padding_mode: str = \"zeros\",\n ):\n super().__init__()\n resnet_groups = (\n resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n )\n\n self.timestep_conditioning = timestep_conditioning\n\n if timestep_conditioning:\n self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings(\n in_channels * 4, 0, operations=ops,\n )\n\n self.res_blocks = nn.ModuleList(\n [\n ResnetBlock3D(\n dims=dims,\n in_channels=in_channels,\n out_channels=in_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n norm_layer=norm_layer,\n inject_noise=inject_noise,\n timestep_conditioning=timestep_conditioning,\n spatial_padding_mode=spatial_padding_mode,\n )\n for _ in range(num_layers)\n ]\n )\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n causal: bool = True,\n timestep: Optional[torch.Tensor] = None,\n ) -> torch.FloatTensor:\n timestep_embed = None\n if self.timestep_conditioning:\n assert (\n timestep is not None\n ), \"should pass timestep with timestep_conditioning=True\"\n batch_size = hidden_states.shape[0]\n timestep_embed = self.time_embedder(\n timestep=timestep.flatten(),\n resolution=None,\n aspect_ratio=None,\n batch_size=batch_size,\n hidden_dtype=hidden_states.dtype,\n )\n timestep_embed = timestep_embed.view(\n batch_size, timestep_embed.shape[-1], 1, 1, 1\n )\n\n for resnet in self.res_blocks:\n hidden_states = resnet(hidden_states, causal=causal, timestep=timestep_embed)\n\n return hidden_states\n\n\nclass SpaceToDepthDownsample(nn.Module):\n def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode):\n super().__init__()\n self.stride = stride\n self.group_size = in_channels * math.prod(stride) // out_channels\n self.conv = make_conv_nd(\n dims=dims,\n in_channels=in_channels,\n out_channels=out_channels // math.prod(stride),\n kernel_size=3,\n stride=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n self.temporal_cache_state = {}\n\n def forward(self, x, causal: bool = True):\n tid = threading.get_ident()\n cached, pad_first, cached_x, cached_input = self.temporal_cache_state.get(tid, (None, True, None, None))\n if cached_input is not None:\n x = torch_cat_if_needed([cached_input, x], dim=2)\n cached_input = None\n\n if self.stride[0] == 2 and pad_first:\n x = torch.cat(\n [x[:, :, :1, :, :], x], dim=2\n ) # duplicate first frames for padding\n pad_first = False\n\n if x.shape[2] < self.stride[0]:\n cached_input = x\n self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input)\n return None\n\n # skip connection\n x_in = rearrange(\n x,\n \"b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w\",\n p1=self.stride[0],\n p2=self.stride[1],\n p3=self.stride[2],\n )\n x_in = rearrange(x_in, \"b (c g) d h w -> b c g d h w\", g=self.group_size)\n x_in = x_in.mean(dim=2)\n\n # conv\n x = self.conv(x, causal=causal)\n if self.stride[0] == 2 and x.shape[2] == 1:\n if cached_x is not None:\n x = torch_cat_if_needed([cached_x, x], dim=2)\n cached_x = None\n else:\n cached_x = x\n x = None\n\n if x is not None:\n x = rearrange(\n x,\n \"b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w\",\n p1=self.stride[0],\n p2=self.stride[1],\n p3=self.stride[2],\n )\n\n cached = add_exchange_cache(x, cached, x_in, dim=2)\n\n self.temporal_cache_state[tid] = (cached, pad_first, cached_x, cached_input)\n\n return x\n\n\nclass DepthToSpaceUpsample(nn.Module):\n def __init__(\n self,\n dims,\n in_channels,\n stride,\n residual=False,\n out_channels_reduction_factor=1,\n spatial_padding_mode=\"zeros\",\n ):\n super().__init__()\n self.stride = stride\n self.out_channels = (\n math.prod(stride) * in_channels // out_channels_reduction_factor\n )\n self.conv = make_conv_nd(\n dims=dims,\n in_channels=in_channels,\n out_channels=self.out_channels,\n kernel_size=3,\n stride=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n self.residual = residual\n self.out_channels_reduction_factor = out_channels_reduction_factor\n self.temporal_cache_state = {}\n\n def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None):\n tid = threading.get_ident()\n cached, drop_first_conv, drop_first_res = self.temporal_cache_state.get(tid, (None, True, True))\n y = self.conv(x, causal=causal)\n y = rearrange(\n y,\n \"b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)\",\n p1=self.stride[0],\n p2=self.stride[1],\n p3=self.stride[2],\n )\n if self.stride[0] == 2 and y.shape[2] > 0 and drop_first_conv:\n y = y[:, :, 1:, :, :]\n drop_first_conv = False\n if self.residual:\n # Reshape and duplicate the input to match the output shape\n x_in = rearrange(\n x,\n \"b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)\",\n p1=self.stride[0],\n p2=self.stride[1],\n p3=self.stride[2],\n )\n num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor\n x_in = x_in.repeat(1, num_repeat, 1, 1, 1)\n if self.stride[0] == 2 and x_in.shape[2] > 0 and drop_first_res:\n x_in = x_in[:, :, 1:, :, :]\n drop_first_res = False\n\n if y.shape[2] == 0:\n y = None\n\n cached = add_exchange_cache(y, cached, x_in, dim=2)\n self.temporal_cache_state[tid] = (cached, drop_first_conv, drop_first_res)\n\n else:\n self.temporal_cache_state[tid] = (None, drop_first_conv, False)\n\n return y\n\nclass LayerNorm(nn.Module):\n def __init__(self, dim, eps, elementwise_affine=True) -> None:\n super().__init__()\n self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine)\n\n def forward(self, x):\n x = rearrange(x, \"b c d h w -> b d h w c\")\n x = self.norm(x)\n x = rearrange(x, \"b d h w c -> b c d h w\")\n return x\n\n\nclass ResnetBlock3D(nn.Module):\n r\"\"\"\n A Resnet block.\n\n Parameters:\n in_channels (`int`): The number of channels in the input.\n out_channels (`int`, *optional*, default to be `None`):\n The number of output channels for the first conv layer. If None, same as `in_channels`.\n dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.\n groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.\n eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.\n \"\"\"\n\n def __init__(\n self,\n dims: Union[int, Tuple[int, int]],\n in_channels: int,\n out_channels: Optional[int] = None,\n dropout: float = 0.0,\n groups: int = 32,\n eps: float = 1e-6,\n norm_layer: str = \"group_norm\",\n inject_noise: bool = False,\n timestep_conditioning: bool = False,\n spatial_padding_mode: str = \"zeros\",\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.inject_noise = inject_noise\n\n if norm_layer == \"group_norm\":\n self.norm1 = nn.GroupNorm(\n num_groups=groups, num_channels=in_channels, eps=eps, affine=True\n )\n elif norm_layer == \"pixel_norm\":\n self.norm1 = PixelNorm()\n elif norm_layer == \"layer_norm\":\n self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True)\n\n self.non_linearity = nn.SiLU()\n\n self.conv1 = make_conv_nd(\n dims,\n in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n\n if inject_noise:\n self.per_channel_scale1 = nn.Parameter(torch.zeros((in_channels, 1, 1)))\n\n if norm_layer == \"group_norm\":\n self.norm2 = nn.GroupNorm(\n num_groups=groups, num_channels=out_channels, eps=eps, affine=True\n )\n elif norm_layer == \"pixel_norm\":\n self.norm2 = PixelNorm()\n elif norm_layer == \"layer_norm\":\n self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True)\n\n self.dropout = torch.nn.Dropout(dropout)\n\n self.conv2 = make_conv_nd(\n dims,\n out_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1,\n causal=True,\n spatial_padding_mode=spatial_padding_mode,\n )\n\n if inject_noise:\n self.per_channel_scale2 = nn.Parameter(torch.zeros((in_channels, 1, 1)))\n\n self.conv_shortcut = (\n make_linear_nd(\n dims=dims, in_channels=in_channels, out_channels=out_channels\n )\n if in_channels != out_channels\n else nn.Identity()\n )\n\n self.norm3 = (\n LayerNorm(in_channels, eps=eps, elementwise_affine=True)\n if in_channels != out_channels\n else nn.Identity()\n )\n\n self.timestep_conditioning = timestep_conditioning\n\n if timestep_conditioning:\n self.scale_shift_table = nn.Parameter(\n torch.randn(4, in_channels) / in_channels**0.5\n )\n else:\n self.register_buffer(\n \"scale_shift_table\",\n torch.tensor(\n [0.0, 0.0, 0.0, 0.0],\n device=\"cpu\" if in_meta_context() else None\n ).unsqueeze(1).expand(4, in_channels),\n persistent=False,\n )\n\n self.temporal_cache_state={}\n\n def _feed_spatial_noise(\n self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor\n ) -> torch.FloatTensor:\n spatial_shape = hidden_states.shape[-2:]\n device = hidden_states.device\n dtype = hidden_states.dtype\n\n # similar to the \"explicit noise inputs\" method in style-gan\n spatial_noise = torch.randn(spatial_shape, device=device, dtype=dtype)[None]\n scaled_noise = (spatial_noise * per_channel_scale)[None, :, None, ...]\n hidden_states = hidden_states + scaled_noise\n\n return hidden_states\n\n def forward(\n self,\n input_tensor: torch.FloatTensor,\n causal: bool = True,\n timestep: Optional[torch.Tensor] = None,\n ) -> torch.FloatTensor:\n hidden_states = input_tensor\n batch_size = hidden_states.shape[0]\n\n hidden_states = self.norm1(hidden_states)\n if self.timestep_conditioning:\n assert (\n timestep is not None\n ), \"should pass timestep with timestep_conditioning=True\"\n ada_values = self.scale_shift_table[\n None, ..., None, None, None\n ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape(\n batch_size,\n 4,\n -1,\n timestep.shape[-3],\n timestep.shape[-2],\n timestep.shape[-1],\n )\n shift1, scale1, shift2, scale2 = ada_values.unbind(dim=1)\n\n hidden_states = hidden_states * (1 + scale1) + shift1\n\n hidden_states = self.non_linearity(hidden_states)\n\n hidden_states = self.conv1(hidden_states, causal=causal)\n\n if self.inject_noise:\n hidden_states = self._feed_spatial_noise(\n hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype)\n )\n\n hidden_states = self.norm2(hidden_states)\n\n if self.timestep_conditioning:\n hidden_states = hidden_states * (1 + scale2) + shift2\n\n hidden_states = self.non_linearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n\n hidden_states = self.conv2(hidden_states, causal=causal)\n\n if self.inject_noise:\n hidden_states = self._feed_spatial_noise(\n hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype)\n )\n\n input_tensor = self.norm3(input_tensor)\n\n batch_size = input_tensor.shape[0]\n\n input_tensor = self.conv_shortcut(input_tensor)\n\n tid = threading.get_ident()\n cached = self.temporal_cache_state.get(tid, None)\n cached = add_exchange_cache(hidden_states, cached, input_tensor, dim=2)\n self.temporal_cache_state[tid] = cached\n\n return hidden_states\n\n\ndef patchify(x, patch_size_hw, patch_size_t=1):\n if patch_size_hw == 1 and patch_size_t == 1:\n return x\n if x.dim() == 4:\n x = rearrange(\n x, \"b c (h q) (w r) -> b (c r q) h w\", q=patch_size_hw, r=patch_size_hw\n )\n elif x.dim() == 5:\n x = rearrange(\n x,\n \"b c (f p) (h q) (w r) -> b (c p r q) f h w\",\n p=patch_size_t,\n q=patch_size_hw,\n r=patch_size_hw,\n )\n else:\n raise ValueError(f\"Invalid input shape: {x.shape}\")\n\n return x\n\n\ndef unpatchify(x, patch_size_hw, patch_size_t=1):\n if patch_size_hw == 1 and patch_size_t == 1:\n return x\n\n if x.dim() == 4:\n x = rearrange(\n x, \"b (c r q) h w -> b c (h q) (w r)\", q=patch_size_hw, r=patch_size_hw\n )\n elif x.dim() == 5:\n x = rearrange(\n x,\n \"b (c p r q) f h w -> b c (f p) (h q) (w r)\",\n p=patch_size_t,\n q=patch_size_hw,\n r=patch_size_hw,\n )\n\n return x\n\nclass processor(nn.Module):\n def __init__(self):\n super().__init__()\n self.register_buffer(\"std-of-means\", torch.empty(128))\n self.register_buffer(\"mean-of-means\", torch.empty(128))\n\n def un_normalize(self, x):\n return (x * self.get_buffer(\"std-of-means\").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer(\"mean-of-means\").view(1, -1, 1, 1, 1).to(x)\n\n def normalize(self, x):\n return (x - self.get_buffer(\"mean-of-means\").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer(\"std-of-means\").view(1, -1, 1, 1, 1).to(x)\n\nclass VideoVAE(nn.Module):\n comfy_has_chunked_io = True\n\n def __init__(self, version=0, config=None):\n super().__init__()\n\n if config is None:\n config = self.get_default_config(version)\n\n self.config = config\n self.timestep_conditioning = config.get(\"timestep_conditioning\", False)\n self.decode_noise_scale = config.get(\"decode_noise_scale\", 0.025)\n self.decode_timestep = config.get(\"decode_timestep\", 0.05)\n double_z = config.get(\"double_z\", True)\n latent_log_var = config.get(\n \"latent_log_var\", \"per_channel\" if double_z else \"none\"\n )\n\n self.encoder = Encoder(\n dims=config[\"dims\"],\n in_channels=config.get(\"in_channels\", 3),\n out_channels=config[\"latent_channels\"],\n blocks=config.get(\"encoder_blocks\", config.get(\"encoder_blocks\", config.get(\"blocks\"))),\n patch_size=config.get(\"patch_size\", 1),\n latent_log_var=latent_log_var,\n norm_layer=config.get(\"norm_layer\", \"group_norm\"),\n spatial_padding_mode=config.get(\"spatial_padding_mode\", \"zeros\"),\n base_channels=config.get(\"encoder_base_channels\", 128),\n )\n\n self.decoder = Decoder(\n dims=config[\"dims\"],\n in_channels=config[\"latent_channels\"],\n out_channels=config.get(\"out_channels\", 3),\n blocks=config.get(\"decoder_blocks\", config.get(\"decoder_blocks\", config.get(\"blocks\"))),\n base_channels=config.get(\"decoder_base_channels\", 128),\n patch_size=config.get(\"patch_size\", 1),\n norm_layer=config.get(\"norm_layer\", \"group_norm\"),\n causal=config.get(\"causal_decoder\", False),\n timestep_conditioning=self.timestep_conditioning,\n spatial_padding_mode=config.get(\"spatial_padding_mode\", \"reflect\"),\n )\n\n self.per_channel_statistics = processor()\n\n def get_default_config(self, version):\n if version == 0:\n config = {\n \"_class_name\": \"CausalVideoAutoencoder\",\n \"dims\": 3,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"latent_channels\": 128,\n \"blocks\": [\n [\"res_x\", 4],\n [\"compress_all\", 1],\n [\"res_x_y\", 1],\n [\"res_x\", 3],\n [\"compress_all\", 1],\n [\"res_x_y\", 1],\n [\"res_x\", 3],\n [\"compress_all\", 1],\n [\"res_x\", 3],\n [\"res_x\", 4],\n ],\n \"scaling_factor\": 1.0,\n \"norm_layer\": \"pixel_norm\",\n \"patch_size\": 4,\n \"latent_log_var\": \"uniform\",\n \"use_quant_conv\": False,\n \"causal_decoder\": False,\n }\n elif version == 1:\n config = {\n \"_class_name\": \"CausalVideoAutoencoder\",\n \"dims\": 3,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"latent_channels\": 128,\n \"decoder_blocks\": [\n [\"res_x\", {\"num_layers\": 5, \"inject_noise\": True}],\n [\"compress_all\", {\"residual\": True, \"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 6, \"inject_noise\": True}],\n [\"compress_all\", {\"residual\": True, \"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 7, \"inject_noise\": True}],\n [\"compress_all\", {\"residual\": True, \"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 8, \"inject_noise\": False}]\n ],\n \"encoder_blocks\": [\n [\"res_x\", {\"num_layers\": 4}],\n [\"compress_all\", {}],\n [\"res_x_y\", 1],\n [\"res_x\", {\"num_layers\": 3}],\n [\"compress_all\", {}],\n [\"res_x_y\", 1],\n [\"res_x\", {\"num_layers\": 3}],\n [\"compress_all\", {}],\n [\"res_x\", {\"num_layers\": 3}],\n [\"res_x\", {\"num_layers\": 4}]\n ],\n \"scaling_factor\": 1.0,\n \"norm_layer\": \"pixel_norm\",\n \"patch_size\": 4,\n \"latent_log_var\": \"uniform\",\n \"use_quant_conv\": False,\n \"causal_decoder\": False,\n \"timestep_conditioning\": True,\n }\n else:\n config = {\n \"_class_name\": \"CausalVideoAutoencoder\",\n \"dims\": 3,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"latent_channels\": 128,\n \"encoder_blocks\": [\n [\"res_x\", {\"num_layers\": 4}],\n [\"compress_space_res\", {\"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 6}],\n [\"compress_time_res\", {\"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 6}],\n [\"compress_all_res\", {\"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 2}],\n [\"compress_all_res\", {\"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 2}]\n ],\n \"decoder_blocks\": [\n [\"res_x\", {\"num_layers\": 5, \"inject_noise\": False}],\n [\"compress_all\", {\"residual\": True, \"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 5, \"inject_noise\": False}],\n [\"compress_all\", {\"residual\": True, \"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 5, \"inject_noise\": False}],\n [\"compress_all\", {\"residual\": True, \"multiplier\": 2}],\n [\"res_x\", {\"num_layers\": 5, \"inject_noise\": False}]\n ],\n \"scaling_factor\": 1.0,\n \"norm_layer\": \"pixel_norm\",\n \"patch_size\": 4,\n \"latent_log_var\": \"uniform\",\n \"use_quant_conv\": False,\n \"causal_decoder\": False,\n \"timestep_conditioning\": True\n }\n return config\n\n def encode(self, x, device=None):\n x = x[:, :, :max(1, 1 + ((x.shape[2] - 1) // 8) * 8), :, :]\n means, logvar = torch.chunk(self.encoder(x, device=device), 2, dim=1)\n return self.per_channel_statistics.normalize(means)\n\n def decode_output_shape(self, input_shape):\n return self.decoder.decode_output_shape(input_shape)\n\n def decode(self, x, output_buffer=None):\n if self.timestep_conditioning: #TODO: seed\n x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x\n return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep, output_buffer=output_buffer)\n" }, { "path": "comfy/ldm/lightricks/vae/conv_nd_factory.py", "content": "from typing import Tuple, Union\n\n\nfrom .dual_conv3d import DualConv3d\nfrom .causal_conv3d import CausalConv3d\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\ndef make_conv_nd(\n dims: Union[int, Tuple[int, int]],\n in_channels: int,\n out_channels: int,\n kernel_size: int,\n stride=1,\n padding=0,\n dilation=1,\n groups=1,\n bias=True,\n causal=False,\n spatial_padding_mode=\"zeros\",\n temporal_padding_mode=\"zeros\",\n):\n if not (spatial_padding_mode == temporal_padding_mode or causal):\n raise NotImplementedError(\"spatial and temporal padding modes must be equal\")\n if dims == 2:\n return ops.Conv2d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=bias,\n padding_mode=spatial_padding_mode,\n )\n elif dims == 3:\n if causal:\n return CausalConv3d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=bias,\n spatial_padding_mode=spatial_padding_mode,\n )\n return ops.Conv3d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=bias,\n padding_mode=spatial_padding_mode,\n )\n elif dims == (2, 1):\n return DualConv3d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=bias,\n padding_mode=spatial_padding_mode,\n )\n else:\n raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\ndef make_linear_nd(\n dims: int,\n in_channels: int,\n out_channels: int,\n bias=True,\n):\n if dims == 2:\n return ops.Conv2d(\n in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias\n )\n elif dims == 3 or dims == (2, 1):\n return ops.Conv3d(\n in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias\n )\n else:\n raise ValueError(f\"unsupported dimensions: {dims}\")\n" }, { "path": "comfy/ldm/lightricks/vae/dual_conv3d.py", "content": "import math\nfrom typing import Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\n\nclass DualConv3d(nn.Module):\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size,\n stride: Union[int, Tuple[int, int, int]] = 1,\n padding: Union[int, Tuple[int, int, int]] = 0,\n dilation: Union[int, Tuple[int, int, int]] = 1,\n groups=1,\n bias=True,\n padding_mode=\"zeros\",\n ):\n super(DualConv3d, self).__init__()\n\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.padding_mode = padding_mode\n # Ensure kernel_size, stride, padding, and dilation are tuples of length 3\n if isinstance(kernel_size, int):\n kernel_size = (kernel_size, kernel_size, kernel_size)\n if kernel_size == (1, 1, 1):\n raise ValueError(\n \"kernel_size must be greater than 1. Use make_linear_nd instead.\"\n )\n if isinstance(stride, int):\n stride = (stride, stride, stride)\n if isinstance(padding, int):\n padding = (padding, padding, padding)\n if isinstance(dilation, int):\n dilation = (dilation, dilation, dilation)\n\n # Set parameters for convolutions\n self.groups = groups\n self.bias = bias\n\n # Define the size of the channels after the first convolution\n intermediate_channels = (\n out_channels if in_channels < out_channels else in_channels\n )\n\n # Define parameters for the first convolution\n self.weight1 = nn.Parameter(\n torch.Tensor(\n intermediate_channels,\n in_channels // groups,\n 1,\n kernel_size[1],\n kernel_size[2],\n )\n )\n self.stride1 = (1, stride[1], stride[2])\n self.padding1 = (0, padding[1], padding[2])\n self.dilation1 = (1, dilation[1], dilation[2])\n if bias:\n self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels))\n else:\n self.register_parameter(\"bias1\", None)\n\n # Define parameters for the second convolution\n self.weight2 = nn.Parameter(\n torch.Tensor(\n out_channels, intermediate_channels // groups, kernel_size[0], 1, 1\n )\n )\n self.stride2 = (stride[0], 1, 1)\n self.padding2 = (padding[0], 0, 0)\n self.dilation2 = (dilation[0], 1, 1)\n if bias:\n self.bias2 = nn.Parameter(torch.Tensor(out_channels))\n else:\n self.register_parameter(\"bias2\", None)\n\n # Initialize weights and biases\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5))\n nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5))\n if self.bias:\n fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1)\n bound1 = 1 / math.sqrt(fan_in1)\n nn.init.uniform_(self.bias1, -bound1, bound1)\n fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2)\n bound2 = 1 / math.sqrt(fan_in2)\n nn.init.uniform_(self.bias2, -bound2, bound2)\n\n def forward(self, x, use_conv3d=False, skip_time_conv=False):\n if use_conv3d:\n return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv)\n else:\n return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv)\n\n def forward_with_3d(self, x, skip_time_conv):\n # First convolution\n x = F.conv3d(\n x,\n self.weight1,\n self.bias1,\n self.stride1,\n self.padding1,\n self.dilation1,\n self.groups,\n padding_mode=self.padding_mode,\n )\n\n if skip_time_conv:\n return x\n\n # Second convolution\n x = F.conv3d(\n x,\n self.weight2,\n self.bias2,\n self.stride2,\n self.padding2,\n self.dilation2,\n self.groups,\n padding_mode=self.padding_mode,\n )\n\n return x\n\n def forward_with_2d(self, x, skip_time_conv):\n b, c, d, h, w = x.shape\n\n # First 2D convolution\n x = rearrange(x, \"b c d h w -> (b d) c h w\")\n # Squeeze the depth dimension out of weight1 since it's 1\n weight1 = self.weight1.squeeze(2)\n # Select stride, padding, and dilation for the 2D convolution\n stride1 = (self.stride1[1], self.stride1[2])\n padding1 = (self.padding1[1], self.padding1[2])\n dilation1 = (self.dilation1[1], self.dilation1[2])\n x = F.conv2d(\n x,\n weight1,\n self.bias1,\n stride1,\n padding1,\n dilation1,\n self.groups,\n padding_mode=self.padding_mode,\n )\n\n _, _, h, w = x.shape\n\n if skip_time_conv:\n x = rearrange(x, \"(b d) c h w -> b c d h w\", b=b)\n return x\n\n # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension\n x = rearrange(x, \"(b d) c h w -> (b h w) c d\", b=b)\n\n # Reshape weight2 to match the expected dimensions for conv1d\n weight2 = self.weight2.squeeze(-1).squeeze(-1)\n # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution\n stride2 = self.stride2[0]\n padding2 = self.padding2[0]\n dilation2 = self.dilation2[0]\n x = F.conv1d(\n x,\n weight2,\n self.bias2,\n stride2,\n padding2,\n dilation2,\n self.groups,\n padding_mode=self.padding_mode,\n )\n x = rearrange(x, \"(b h w) c d -> b c d h w\", b=b, h=h, w=w)\n\n return x\n\n @property\n def weight(self):\n return self.weight2\n\n\ndef test_dual_conv3d_consistency():\n # Initialize parameters\n in_channels = 3\n out_channels = 5\n kernel_size = (3, 3, 3)\n stride = (2, 2, 2)\n padding = (1, 1, 1)\n\n # Create an instance of the DualConv3d class\n dual_conv3d = DualConv3d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=True,\n )\n\n # Example input tensor\n test_input = torch.randn(1, 3, 10, 10, 10)\n\n # Perform forward passes with both 3D and 2D settings\n output_conv3d = dual_conv3d(test_input, use_conv3d=True)\n output_2d = dual_conv3d(test_input, use_conv3d=False)\n\n # Assert that the outputs from both methods are sufficiently close\n assert torch.allclose(\n output_conv3d, output_2d, atol=1e-6\n ), \"Outputs are not consistent between 3D and 2D convolutions.\"\n" }, { "path": "comfy/ldm/lightricks/vae/pixel_norm.py", "content": "import torch\nfrom torch import nn\n\n\nclass PixelNorm(nn.Module):\n def __init__(self, dim=1, eps=1e-8):\n super(PixelNorm, self).__init__()\n self.dim = dim\n self.eps = eps\n\n def forward(self, x):\n return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps)\n" }, { "path": "comfy/ldm/lightricks/vocoders/vocoder.py", "content": "import torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport comfy.ops\nimport comfy.model_management\nimport numpy as np\nimport math\n\nops = comfy.ops.disable_weight_init\n\nLRELU_SLOPE = 0.1\n\ndef get_padding(kernel_size, dilation=1):\n return int((kernel_size * dilation - dilation) / 2)\n\n\n# ---------------------------------------------------------------------------\n# Anti-aliased resampling helpers (kaiser-sinc filters) for BigVGAN v2\n# Adopted from https://github.com/NVIDIA/BigVGAN\n# ---------------------------------------------------------------------------\n\n\ndef _sinc(x: torch.Tensor):\n return torch.where(\n x == 0,\n torch.tensor(1.0, device=x.device, dtype=x.dtype),\n torch.sin(math.pi * x) / math.pi / x,\n )\n\n\ndef kaiser_sinc_filter1d(cutoff, half_width, kernel_size):\n even = kernel_size % 2 == 0\n half_size = kernel_size // 2\n delta_f = 4 * half_width\n A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95\n if A > 50.0:\n beta = 0.1102 * (A - 8.7)\n elif A >= 21.0:\n beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)\n else:\n beta = 0.0\n window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)\n if even:\n time = torch.arange(-half_size, half_size) + 0.5\n else:\n time = torch.arange(kernel_size) - half_size\n if cutoff == 0:\n filter_ = torch.zeros_like(time)\n else:\n filter_ = 2 * cutoff * window * _sinc(2 * cutoff * time)\n filter_ /= filter_.sum()\n filter = filter_.view(1, 1, kernel_size)\n return filter\n\n\nclass LowPassFilter1d(nn.Module):\n def __init__(\n self,\n cutoff=0.5,\n half_width=0.6,\n stride=1,\n padding=True,\n padding_mode=\"replicate\",\n kernel_size=12,\n ):\n super().__init__()\n if cutoff < -0.0:\n raise ValueError(\"Minimum cutoff must be larger than zero.\")\n if cutoff > 0.5:\n raise ValueError(\"A cutoff above 0.5 does not make sense.\")\n self.kernel_size = kernel_size\n self.even = kernel_size % 2 == 0\n self.pad_left = kernel_size // 2 - int(self.even)\n self.pad_right = kernel_size // 2\n self.stride = stride\n self.padding = padding\n self.padding_mode = padding_mode\n filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)\n self.register_buffer(\"filter\", filter)\n\n def forward(self, x):\n _, C, _ = x.shape\n if self.padding:\n x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)\n return F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)\n\n\nclass UpSample1d(nn.Module):\n def __init__(self, ratio=2, kernel_size=None, persistent=True, window_type=\"kaiser\"):\n super().__init__()\n self.ratio = ratio\n self.stride = ratio\n\n if window_type == \"hann\":\n # Hann-windowed sinc filter — identical to torchaudio.functional.resample\n # with its default parameters (rolloff=0.99, lowpass_filter_width=6).\n # Uses replicate boundary padding, matching the reference resampler exactly.\n rolloff = 0.99\n lowpass_filter_width = 6\n width = math.ceil(lowpass_filter_width / rolloff)\n self.kernel_size = 2 * width * ratio + 1\n self.pad = width\n self.pad_left = 2 * width * ratio\n self.pad_right = self.kernel_size - ratio\n t = (torch.arange(self.kernel_size) / ratio - width) * rolloff\n t_clamped = t.clamp(-lowpass_filter_width, lowpass_filter_width)\n window = torch.cos(t_clamped * math.pi / lowpass_filter_width / 2) ** 2\n filter = (torch.sinc(t) * window * rolloff / ratio).view(1, 1, -1)\n else:\n # Kaiser-windowed sinc filter (BigVGAN default).\n self.kernel_size = (\n int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size\n )\n self.pad = self.kernel_size // ratio - 1\n self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2\n self.pad_right = (\n self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2\n )\n filter = kaiser_sinc_filter1d(\n cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size\n )\n\n self.register_buffer(\"filter\", filter, persistent=persistent)\n\n def forward(self, x):\n _, C, _ = x.shape\n x = F.pad(x, (self.pad, self.pad), mode=\"replicate\")\n x = self.ratio * F.conv_transpose1d(\n x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C\n )\n x = x[..., self.pad_left : -self.pad_right]\n return x\n\n\nclass DownSample1d(nn.Module):\n def __init__(self, ratio=2, kernel_size=None):\n super().__init__()\n self.ratio = ratio\n self.kernel_size = (\n int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size\n )\n self.lowpass = LowPassFilter1d(\n cutoff=0.5 / ratio,\n half_width=0.6 / ratio,\n stride=ratio,\n kernel_size=self.kernel_size,\n )\n\n def forward(self, x):\n return self.lowpass(x)\n\n\nclass Activation1d(nn.Module):\n def __init__(\n self,\n activation,\n up_ratio=2,\n down_ratio=2,\n up_kernel_size=12,\n down_kernel_size=12,\n ):\n super().__init__()\n self.act = activation\n self.upsample = UpSample1d(up_ratio, up_kernel_size)\n self.downsample = DownSample1d(down_ratio, down_kernel_size)\n\n def forward(self, x):\n x = self.upsample(x)\n x = self.act(x)\n x = self.downsample(x)\n return x\n\n\n# ---------------------------------------------------------------------------\n# BigVGAN v2 activations (Snake / SnakeBeta)\n# ---------------------------------------------------------------------------\n\n\nclass Snake(nn.Module):\n def __init__(\n self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True\n ):\n super().__init__()\n self.alpha_logscale = alpha_logscale\n self.alpha = nn.Parameter(\n torch.zeros(in_features)\n if alpha_logscale\n else torch.ones(in_features) * alpha\n )\n self.alpha.requires_grad = alpha_trainable\n self.eps = 1e-9\n\n def forward(self, x):\n a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)\n if self.alpha_logscale:\n a = torch.exp(a)\n return x + (1.0 / (a + self.eps)) * torch.sin(x * a).pow(2)\n\n\nclass SnakeBeta(nn.Module):\n def __init__(\n self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True\n ):\n super().__init__()\n self.alpha_logscale = alpha_logscale\n self.alpha = nn.Parameter(\n torch.zeros(in_features)\n if alpha_logscale\n else torch.ones(in_features) * alpha\n )\n self.alpha.requires_grad = alpha_trainable\n self.beta = nn.Parameter(\n torch.zeros(in_features)\n if alpha_logscale\n else torch.ones(in_features) * alpha\n )\n self.beta.requires_grad = alpha_trainable\n self.eps = 1e-9\n\n def forward(self, x):\n a = comfy.model_management.cast_to(self.alpha.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)\n b = comfy.model_management.cast_to(self.beta.unsqueeze(0).unsqueeze(-1), dtype=x.dtype, device=x.device)\n if self.alpha_logscale:\n a = torch.exp(a)\n b = torch.exp(b)\n return x + (1.0 / (b + self.eps)) * torch.sin(x * a).pow(2)\n\n\n# ---------------------------------------------------------------------------\n# BigVGAN v2 AMPBlock (Anti-aliased Multi-Periodicity)\n# ---------------------------------------------------------------------------\n\n\nclass AMPBlock1(torch.nn.Module):\n def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation=\"snake\"):\n super().__init__()\n act_cls = SnakeBeta if activation == \"snakebeta\" else Snake\n self.convs1 = nn.ModuleList(\n [\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[0],\n padding=get_padding(kernel_size, dilation[0]),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[1],\n padding=get_padding(kernel_size, dilation[1]),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[2],\n padding=get_padding(kernel_size, dilation[2]),\n ),\n ]\n )\n\n self.convs2 = nn.ModuleList(\n [\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n ),\n ]\n )\n\n self.acts1 = nn.ModuleList(\n [Activation1d(act_cls(channels)) for _ in range(len(self.convs1))]\n )\n self.acts2 = nn.ModuleList(\n [Activation1d(act_cls(channels)) for _ in range(len(self.convs2))]\n )\n\n def forward(self, x):\n for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.acts1, self.acts2):\n xt = a1(x)\n xt = c1(xt)\n xt = a2(xt)\n xt = c2(xt)\n x = x + xt\n return x\n\n\n# ---------------------------------------------------------------------------\n# HiFi-GAN residual blocks\n# ---------------------------------------------------------------------------\n\n\nclass ResBlock1(torch.nn.Module):\n def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):\n super(ResBlock1, self).__init__()\n self.convs1 = nn.ModuleList(\n [\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[0],\n padding=get_padding(kernel_size, dilation[0]),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[1],\n padding=get_padding(kernel_size, dilation[1]),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[2],\n padding=get_padding(kernel_size, dilation[2]),\n ),\n ]\n )\n\n self.convs2 = nn.ModuleList(\n [\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1),\n ),\n ]\n )\n\n def forward(self, x):\n for c1, c2 in zip(self.convs1, self.convs2):\n xt = F.leaky_relu(x, LRELU_SLOPE)\n xt = c1(xt)\n xt = F.leaky_relu(xt, LRELU_SLOPE)\n xt = c2(xt)\n x = xt + x\n return x\n\n\nclass ResBlock2(torch.nn.Module):\n def __init__(self, channels, kernel_size=3, dilation=(1, 3)):\n super(ResBlock2, self).__init__()\n self.convs = nn.ModuleList(\n [\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[0],\n padding=get_padding(kernel_size, dilation[0]),\n ),\n ops.Conv1d(\n channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[1],\n padding=get_padding(kernel_size, dilation[1]),\n ),\n ]\n )\n\n def forward(self, x):\n for c in self.convs:\n xt = F.leaky_relu(x, LRELU_SLOPE)\n xt = c(xt)\n x = xt + x\n return x\n\n\nclass Vocoder(torch.nn.Module):\n \"\"\"\n Vocoder model for synthesizing audio from spectrograms, based on: https://github.com/jik876/hifi-gan.\n\n Supports both HiFi-GAN (resblock \"1\"/\"2\") and BigVGAN v2 (resblock \"AMP1\").\n \"\"\"\n\n def __init__(self, config=None):\n super(Vocoder, self).__init__()\n\n if config is None:\n config = self.get_default_config()\n\n resblock_kernel_sizes = config.get(\"resblock_kernel_sizes\", [3, 7, 11])\n upsample_rates = config.get(\"upsample_rates\", [5, 4, 2, 2, 2])\n upsample_kernel_sizes = config.get(\"upsample_kernel_sizes\", [16, 16, 8, 4, 4])\n resblock_dilation_sizes = config.get(\"resblock_dilation_sizes\", [[1, 3, 5], [1, 3, 5], [1, 3, 5]])\n upsample_initial_channel = config.get(\"upsample_initial_channel\", 1024)\n stereo = config.get(\"stereo\", True)\n activation = config.get(\"activation\", \"snake\")\n use_bias_at_final = config.get(\"use_bias_at_final\", True)\n\n\n # \"output_sample_rate\" is not present in recent checkpoint configs.\n # When absent (None), AudioVAE.output_sample_rate computes it as:\n # sample_rate * vocoder.upsample_factor / mel_hop_length\n # where upsample_factor = product of all upsample stride lengths,\n # and mel_hop_length is loaded from the autoencoder config at\n # preprocessing.stft.hop_length (see CausalAudioAutoencoder).\n self.output_sample_rate = config.get(\"output_sample_rate\")\n self.resblock = config.get(\"resblock\", \"1\")\n self.use_tanh_at_final = config.get(\"use_tanh_at_final\", True)\n self.apply_final_activation = config.get(\"apply_final_activation\", True)\n self.num_kernels = len(resblock_kernel_sizes)\n self.num_upsamples = len(upsample_rates)\n\n in_channels = 128 if stereo else 64\n self.conv_pre = ops.Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3)\n\n if self.resblock == \"1\":\n resblock_cls = ResBlock1\n elif self.resblock == \"2\":\n resblock_cls = ResBlock2\n elif self.resblock == \"AMP1\":\n resblock_cls = AMPBlock1\n else:\n raise ValueError(f\"Unknown resblock type: {self.resblock}\")\n\n self.ups = nn.ModuleList()\n for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):\n self.ups.append(\n ops.ConvTranspose1d(\n upsample_initial_channel // (2**i),\n upsample_initial_channel // (2 ** (i + 1)),\n k,\n u,\n padding=(k - u) // 2,\n )\n )\n\n self.resblocks = nn.ModuleList()\n for i in range(len(self.ups)):\n ch = upsample_initial_channel // (2 ** (i + 1))\n for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):\n if self.resblock == \"AMP1\":\n self.resblocks.append(resblock_cls(ch, k, d, activation=activation))\n else:\n self.resblocks.append(resblock_cls(ch, k, d))\n\n out_channels = 2 if stereo else 1\n if self.resblock == \"AMP1\":\n act_cls = SnakeBeta if activation == \"snakebeta\" else Snake\n self.act_post = Activation1d(act_cls(ch))\n else:\n self.act_post = nn.LeakyReLU()\n\n self.conv_post = ops.Conv1d(\n ch, out_channels, 7, 1, padding=3, bias=use_bias_at_final\n )\n\n self.upsample_factor = np.prod([self.ups[i].stride[0] for i in range(len(self.ups))])\n\n\n def get_default_config(self):\n \"\"\"Generate default configuration for the vocoder.\"\"\"\n\n config = {\n \"resblock_kernel_sizes\": [3, 7, 11],\n \"upsample_rates\": [5, 4, 2, 2, 2],\n \"upsample_kernel_sizes\": [16, 16, 8, 4, 4],\n \"resblock_dilation_sizes\": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],\n \"upsample_initial_channel\": 1024,\n \"stereo\": True,\n \"resblock\": \"1\",\n \"activation\": \"snake\",\n \"use_bias_at_final\": True,\n \"use_tanh_at_final\": True,\n }\n\n return config\n\n def forward(self, x):\n \"\"\"\n Forward pass of the vocoder.\n\n Args:\n x: Input spectrogram tensor. Can be:\n - 3D: (batch_size, channels, time_steps) for mono\n - 4D: (batch_size, 2, channels, time_steps) for stereo\n\n Returns:\n Audio tensor of shape (batch_size, out_channels, audio_length)\n \"\"\"\n if x.dim() == 4: # stereo\n assert x.shape[1] == 2, \"Input must have 2 channels for stereo\"\n x = torch.cat((x[:, 0, :, :], x[:, 1, :, :]), dim=1)\n x = self.conv_pre(x)\n\n for i in range(self.num_upsamples):\n if self.resblock != \"AMP1\":\n x = F.leaky_relu(x, LRELU_SLOPE)\n x = self.ups[i](x)\n xs = None\n for j in range(self.num_kernels):\n if xs is None:\n xs = self.resblocks[i * self.num_kernels + j](x)\n else:\n xs += self.resblocks[i * self.num_kernels + j](x)\n x = xs / self.num_kernels\n\n x = self.act_post(x)\n x = self.conv_post(x)\n\n if self.apply_final_activation:\n if self.use_tanh_at_final:\n x = torch.tanh(x)\n else:\n x = torch.clamp(x, -1, 1)\n\n return x\n\n\nclass _STFTFn(nn.Module):\n \"\"\"Implements STFT as a convolution with precomputed DFT × Hann-window bases.\n\n The DFT basis rows (real and imaginary parts interleaved) multiplied by the causal\n Hann window are stored as buffers and loaded from the checkpoint. Using the exact\n bfloat16 bases from training ensures the mel values fed to the BWE generator are\n bit-identical to what it was trained on.\n \"\"\"\n\n def __init__(self, filter_length: int, hop_length: int, win_length: int):\n super().__init__()\n self.hop_length = hop_length\n self.win_length = win_length\n n_freqs = filter_length // 2 + 1\n self.register_buffer(\"forward_basis\", torch.zeros(n_freqs * 2, 1, filter_length))\n self.register_buffer(\"inverse_basis\", torch.zeros(n_freqs * 2, 1, filter_length))\n\n def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Compute magnitude and phase spectrogram from a batch of waveforms.\n\n Applies causal (left-only) padding of win_length - hop_length samples so that\n each output frame depends only on past and present input — no lookahead.\n The STFT is computed by convolving the padded signal with forward_basis.\n\n Args:\n y: Waveform tensor of shape (B, T).\n\n Returns:\n magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames).\n phase: Phase spectrogram in radians, shape (B, n_freqs, T_frames).\n Computed in float32 for numerical stability, then cast back to\n the input dtype.\n \"\"\"\n if y.dim() == 2:\n y = y.unsqueeze(1) # (B, 1, T)\n left_pad = max(0, self.win_length - self.hop_length) # causal: left-only\n y = F.pad(y, (left_pad, 0))\n spec = F.conv1d(y, comfy.model_management.cast_to(self.forward_basis, dtype=y.dtype, device=y.device), stride=self.hop_length, padding=0)\n n_freqs = spec.shape[1] // 2\n real, imag = spec[:, :n_freqs], spec[:, n_freqs:]\n magnitude = torch.sqrt(real ** 2 + imag ** 2)\n phase = torch.atan2(imag.float(), real.float()).to(real.dtype)\n return magnitude, phase\n\n\nclass MelSTFT(nn.Module):\n \"\"\"Causal log-mel spectrogram module whose buffers are loaded from the checkpoint.\n\n Computes a log-mel spectrogram by running the causal STFT (_STFTFn) on the input\n waveform and projecting the linear magnitude spectrum onto the mel filterbank.\n\n The module's state dict layout matches the 'mel_stft.*' keys stored in the checkpoint\n (mel_basis, stft_fn.forward_basis, stft_fn.inverse_basis).\n \"\"\"\n\n def __init__(\n self,\n filter_length: int,\n hop_length: int,\n win_length: int,\n n_mel_channels: int,\n sampling_rate: int,\n mel_fmin: float,\n mel_fmax: float,\n ):\n super().__init__()\n self.stft_fn = _STFTFn(filter_length, hop_length, win_length)\n\n n_freqs = filter_length // 2 + 1\n self.register_buffer(\"mel_basis\", torch.zeros(n_mel_channels, n_freqs))\n\n def mel_spectrogram(\n self, y: torch.Tensor\n ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Compute log-mel spectrogram and auxiliary spectral quantities.\n\n Args:\n y: Waveform tensor of shape (B, T).\n\n Returns:\n log_mel: Log-compressed mel spectrogram, shape (B, n_mel_channels, T_frames).\n Computed as log(clamp(mel_basis @ magnitude, min=1e-5)).\n magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames).\n phase: Phase spectrogram in radians, shape (B, n_freqs, T_frames).\n energy: Per-frame energy (L2 norm over frequency), shape (B, T_frames).\n \"\"\"\n magnitude, phase = self.stft_fn(y)\n energy = torch.norm(magnitude, dim=1)\n mel = torch.matmul(comfy.model_management.cast_to(self.mel_basis, dtype=magnitude.dtype, device=y.device), magnitude)\n log_mel = torch.log(torch.clamp(mel, min=1e-5))\n return log_mel, magnitude, phase, energy\n\n\nclass VocoderWithBWE(torch.nn.Module):\n \"\"\"Vocoder with bandwidth extension (BWE) for higher sample rate output.\n\n Chains a base vocoder (mel → low-rate waveform) with a BWE stage that upsamples\n to a higher rate. The BWE computes a mel spectrogram from the low-rate waveform.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n vocoder_config = config[\"vocoder\"]\n bwe_config = config[\"bwe\"]\n\n self.vocoder = Vocoder(config=vocoder_config)\n self.bwe_generator = Vocoder(\n config={**bwe_config, \"apply_final_activation\": False}\n )\n\n self.input_sample_rate = bwe_config[\"input_sampling_rate\"]\n self.output_sample_rate = bwe_config[\"output_sampling_rate\"]\n self.hop_length = bwe_config[\"hop_length\"]\n\n self.mel_stft = MelSTFT(\n filter_length=bwe_config[\"n_fft\"],\n hop_length=bwe_config[\"hop_length\"],\n win_length=bwe_config[\"n_fft\"],\n n_mel_channels=bwe_config[\"num_mels\"],\n sampling_rate=bwe_config[\"input_sampling_rate\"],\n mel_fmin=0.0,\n mel_fmax=bwe_config[\"input_sampling_rate\"] / 2.0,\n )\n self.resampler = UpSample1d(\n ratio=bwe_config[\"output_sampling_rate\"] // bwe_config[\"input_sampling_rate\"],\n persistent=False,\n window_type=\"hann\",\n )\n\n def _compute_mel(self, audio):\n \"\"\"Compute log-mel spectrogram from waveform using causal STFT bases.\"\"\"\n B, C, T = audio.shape\n flat = audio.reshape(B * C, -1) # (B*C, T)\n mel, _, _, _ = self.mel_stft.mel_spectrogram(flat) # (B*C, n_mels, T_frames)\n return mel.reshape(B, C, mel.shape[1], mel.shape[2]) # (B, C, n_mels, T_frames)\n\n def forward(self, mel_spec):\n x = self.vocoder(mel_spec)\n _, _, T_low = x.shape\n T_out = T_low * self.output_sample_rate // self.input_sample_rate\n\n remainder = T_low % self.hop_length\n if remainder != 0:\n x = F.pad(x, (0, self.hop_length - remainder))\n\n mel = self._compute_mel(x)\n residual = self.bwe_generator(mel)\n skip = self.resampler(x)\n assert residual.shape == skip.shape, f\"residual {residual.shape} != skip {skip.shape}\"\n\n return torch.clamp(residual + skip, -1, 1)[..., :T_out]\n" }, { "path": "comfy/ldm/lumina/controlnet.py", "content": "import torch\nfrom torch import nn\n\nfrom .model import JointTransformerBlock\n\nclass ZImageControlTransformerBlock(JointTransformerBlock):\n def __init__(\n self,\n layer_id: int,\n dim: int,\n n_heads: int,\n n_kv_heads: int,\n multiple_of: int,\n ffn_dim_multiplier: float,\n norm_eps: float,\n qk_norm: bool,\n modulation=True,\n block_id=0,\n operation_settings=None,\n ):\n super().__init__(layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation, z_image_modulation=True, operation_settings=operation_settings)\n self.block_id = block_id\n if block_id == 0:\n self.before_proj = operation_settings.get(\"operations\").Linear(self.dim, self.dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.after_proj = operation_settings.get(\"operations\").Linear(self.dim, self.dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, c, x, **kwargs):\n if self.block_id == 0:\n c = self.before_proj(c) + x\n c = super().forward(c, **kwargs)\n c_skip = self.after_proj(c)\n return c_skip, c\n\nclass ZImage_Control(torch.nn.Module):\n def __init__(\n self,\n dim: int = 3840,\n n_heads: int = 30,\n n_kv_heads: int = 30,\n multiple_of: int = 256,\n ffn_dim_multiplier: float = (8.0 / 3.0),\n norm_eps: float = 1e-5,\n qk_norm: bool = True,\n n_control_layers=6,\n control_in_dim=16,\n additional_in_dim=0,\n broken=False,\n refiner_control=False,\n dtype=None,\n device=None,\n operations=None,\n **kwargs\n ):\n super().__init__()\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n\n self.broken = broken\n self.additional_in_dim = additional_in_dim\n self.control_in_dim = control_in_dim\n n_refiner_layers = 2\n self.n_control_layers = n_control_layers\n self.control_layers = nn.ModuleList(\n [\n ZImageControlTransformerBlock(\n i,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n block_id=i,\n operation_settings=operation_settings,\n )\n for i in range(self.n_control_layers)\n ]\n )\n\n all_x_embedder = {}\n patch_size = 2\n f_patch_size = 1\n x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * (self.control_in_dim + self.additional_in_dim), dim, bias=True, device=device, dtype=dtype)\n all_x_embedder[f\"{patch_size}-{f_patch_size}\"] = x_embedder\n\n self.refiner_control = refiner_control\n\n self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)\n if self.refiner_control:\n self.control_noise_refiner = nn.ModuleList(\n [\n ZImageControlTransformerBlock(\n layer_id,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n block_id=layer_id,\n operation_settings=operation_settings,\n )\n for layer_id in range(n_refiner_layers)\n ]\n )\n else:\n self.control_noise_refiner = nn.ModuleList(\n [\n JointTransformerBlock(\n layer_id,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n modulation=True,\n z_image_modulation=True,\n operation_settings=operation_settings,\n )\n for layer_id in range(n_refiner_layers)\n ]\n )\n\n def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):\n patch_size = 2\n f_patch_size = 1\n pH = pW = patch_size\n B, C, H, W = control_context.shape\n control_context = self.control_all_x_embedder[f\"{patch_size}-{f_patch_size}\"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))\n\n x_attn_mask = None\n if not self.refiner_control:\n for layer in self.control_noise_refiner:\n control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)\n\n return control_context\n\n def forward_noise_refiner_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):\n if self.refiner_control:\n if self.broken:\n if layer_id == 0:\n return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)\n if layer_id > 0:\n out = None\n for i in range(1, len(self.control_layers)):\n o, control_context = self.control_layers[i](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)\n if out is None:\n out = o\n\n return (out, control_context)\n else:\n return self.control_noise_refiner[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)\n else:\n return (None, control_context)\n\n def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):\n return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)\n" }, { "path": "comfy/ldm/lumina/model.py", "content": "# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py\nfrom __future__ import annotations\n\nfrom typing import List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport comfy.ldm.common_dit\n\nfrom comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder\nfrom comfy.ldm.modules.attention import optimized_attention_masked\nfrom comfy.ldm.flux.layers import EmbedND\nfrom comfy.ldm.flux.math import apply_rope\nimport comfy.patcher_extension\nimport comfy.utils\nfrom comfy.ldm.chroma_radiance.layers import NerfEmbedder\n\n\ndef invert_slices(slices, length):\n sorted_slices = sorted(slices)\n result = []\n current = 0\n\n for start, end in sorted_slices:\n if current < start:\n result.append((current, start))\n current = max(current, end)\n\n if current < length:\n result.append((current, length))\n\n return result\n\n\ndef modulate(x, scale, timestep_zero_index=None):\n if timestep_zero_index is None:\n return x * (1 + scale.unsqueeze(1))\n else:\n scale = (1 + scale.unsqueeze(1))\n actual_batch = scale.size(0) // 2\n slices = timestep_zero_index\n invert = invert_slices(timestep_zero_index, x.shape[1])\n for s in slices:\n x[:, s[0]:s[1]] *= scale[actual_batch:]\n for s in invert:\n x[:, s[0]:s[1]] *= scale[:actual_batch]\n return x\n\n\ndef apply_gate(gate, x, timestep_zero_index=None):\n if timestep_zero_index is None:\n return gate * x\n else:\n actual_batch = gate.size(0) // 2\n\n slices = timestep_zero_index\n invert = invert_slices(timestep_zero_index, x.shape[1])\n for s in slices:\n x[:, s[0]:s[1]] *= gate[actual_batch:]\n for s in invert:\n x[:, s[0]:s[1]] *= gate[:actual_batch]\n return x\n\n#############################################################################\n# Core NextDiT Model #\n#############################################################################\n\ndef clamp_fp16(x):\n if x.dtype == torch.float16:\n return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)\n return x\n\nclass JointAttention(nn.Module):\n \"\"\"Multi-head attention module.\"\"\"\n\n def __init__(\n self,\n dim: int,\n n_heads: int,\n n_kv_heads: Optional[int],\n qk_norm: bool,\n out_bias: bool = False,\n operation_settings={},\n ):\n \"\"\"\n Initialize the Attention module.\n\n Args:\n dim (int): Number of input dimensions.\n n_heads (int): Number of heads.\n n_kv_heads (Optional[int]): Number of kv heads, if using GQA.\n\n \"\"\"\n super().__init__()\n self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads\n self.n_local_heads = n_heads\n self.n_local_kv_heads = self.n_kv_heads\n self.n_rep = self.n_local_heads // self.n_local_kv_heads\n self.head_dim = dim // n_heads\n\n self.qkv = operation_settings.get(\"operations\").Linear(\n dim,\n (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,\n bias=False,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n self.out = operation_settings.get(\"operations\").Linear(\n n_heads * self.head_dim,\n dim,\n bias=out_bias,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n\n if qk_norm:\n self.q_norm = operation_settings.get(\"operations\").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.k_norm = operation_settings.get(\"operations\").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n else:\n self.q_norm = self.k_norm = nn.Identity()\n\n def forward(\n self,\n x: torch.Tensor,\n x_mask: torch.Tensor,\n freqs_cis: torch.Tensor,\n transformer_options={},\n ) -> torch.Tensor:\n \"\"\"\n\n Args:\n x:\n x_mask:\n freqs_cis:\n\n Returns:\n\n \"\"\"\n bsz, seqlen, _ = x.shape\n\n xq, xk, xv = torch.split(\n self.qkv(x),\n [\n self.n_local_heads * self.head_dim,\n self.n_local_kv_heads * self.head_dim,\n self.n_local_kv_heads * self.head_dim,\n ],\n dim=-1,\n )\n xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)\n xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)\n xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)\n\n xq = self.q_norm(xq)\n xk = self.k_norm(xk)\n\n xq, xk = apply_rope(xq, xk, freqs_cis)\n\n n_rep = self.n_local_heads // self.n_local_kv_heads\n if n_rep >= 1:\n xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)\n xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)\n output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)\n\n return self.out(output)\n\n\nclass FeedForward(nn.Module):\n def __init__(\n self,\n dim: int,\n hidden_dim: int,\n multiple_of: int,\n ffn_dim_multiplier: Optional[float],\n operation_settings={},\n ):\n \"\"\"\n Initialize the FeedForward module.\n\n Args:\n dim (int): Input dimension.\n hidden_dim (int): Hidden dimension of the feedforward layer.\n multiple_of (int): Value to ensure hidden dimension is a multiple\n of this value.\n ffn_dim_multiplier (float, optional): Custom multiplier for hidden\n dimension. Defaults to None.\n\n \"\"\"\n super().__init__()\n # custom dim factor multiplier\n if ffn_dim_multiplier is not None:\n hidden_dim = int(ffn_dim_multiplier * hidden_dim)\n hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)\n\n self.w1 = operation_settings.get(\"operations\").Linear(\n dim,\n hidden_dim,\n bias=False,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n self.w2 = operation_settings.get(\"operations\").Linear(\n hidden_dim,\n dim,\n bias=False,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n self.w3 = operation_settings.get(\"operations\").Linear(\n dim,\n hidden_dim,\n bias=False,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n\n # @torch.compile\n def _forward_silu_gating(self, x1, x3):\n return clamp_fp16(F.silu(x1) * x3)\n\n def forward(self, x):\n return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))\n\n\nclass JointTransformerBlock(nn.Module):\n def __init__(\n self,\n layer_id: int,\n dim: int,\n n_heads: int,\n n_kv_heads: int,\n multiple_of: int,\n ffn_dim_multiplier: float,\n norm_eps: float,\n qk_norm: bool,\n modulation=True,\n z_image_modulation=False,\n attn_out_bias=False,\n operation_settings={},\n ) -> None:\n \"\"\"\n Initialize a TransformerBlock.\n\n Args:\n layer_id (int): Identifier for the layer.\n dim (int): Embedding dimension of the input features.\n n_heads (int): Number of attention heads.\n n_kv_heads (Optional[int]): Number of attention heads in key and\n value features (if using GQA), or set to None for the same as\n query.\n multiple_of (int):\n ffn_dim_multiplier (float):\n norm_eps (float):\n\n \"\"\"\n super().__init__()\n self.dim = dim\n self.head_dim = dim // n_heads\n self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, out_bias=attn_out_bias, operation_settings=operation_settings)\n self.feed_forward = FeedForward(\n dim=dim,\n hidden_dim=dim,\n multiple_of=multiple_of,\n ffn_dim_multiplier=ffn_dim_multiplier,\n operation_settings=operation_settings,\n )\n self.layer_id = layer_id\n self.attention_norm1 = operation_settings.get(\"operations\").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.ffn_norm1 = operation_settings.get(\"operations\").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n self.attention_norm2 = operation_settings.get(\"operations\").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.ffn_norm2 = operation_settings.get(\"operations\").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n self.modulation = modulation\n if modulation:\n if z_image_modulation:\n self.adaLN_modulation = nn.Sequential(\n operation_settings.get(\"operations\").Linear(\n min(dim, 256),\n 4 * dim,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n else:\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operation_settings.get(\"operations\").Linear(\n min(dim, 1024),\n 4 * dim,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n\n def forward(\n self,\n x: torch.Tensor,\n x_mask: torch.Tensor,\n freqs_cis: torch.Tensor,\n adaln_input: Optional[torch.Tensor]=None,\n timestep_zero_index=None,\n transformer_options={},\n ):\n \"\"\"\n Perform a forward pass through the TransformerBlock.\n\n Args:\n x (torch.Tensor): Input tensor.\n freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.\n\n Returns:\n torch.Tensor: Output tensor after applying attention and\n feedforward layers.\n\n \"\"\"\n if self.modulation:\n assert adaln_input is not None\n scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)\n\n x = x + apply_gate(gate_msa.unsqueeze(1).tanh(), self.attention_norm2(\n clamp_fp16(self.attention(\n modulate(self.attention_norm1(x), scale_msa, timestep_zero_index=timestep_zero_index),\n x_mask,\n freqs_cis,\n transformer_options=transformer_options,\n ))), timestep_zero_index=timestep_zero_index\n )\n x = x + apply_gate(gate_mlp.unsqueeze(1).tanh(), self.ffn_norm2(\n clamp_fp16(self.feed_forward(\n modulate(self.ffn_norm1(x), scale_mlp, timestep_zero_index=timestep_zero_index),\n ))), timestep_zero_index=timestep_zero_index\n )\n else:\n assert adaln_input is None\n x = x + self.attention_norm2(\n clamp_fp16(self.attention(\n self.attention_norm1(x),\n x_mask,\n freqs_cis,\n transformer_options=transformer_options,\n ))\n )\n x = x + self.ffn_norm2(\n self.feed_forward(\n self.ffn_norm1(x),\n )\n )\n return x\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of NextDiT.\n \"\"\"\n\n def __init__(self, hidden_size, patch_size, out_channels, z_image_modulation=False, operation_settings={}):\n super().__init__()\n self.norm_final = operation_settings.get(\"operations\").LayerNorm(\n hidden_size,\n elementwise_affine=False,\n eps=1e-6,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n self.linear = operation_settings.get(\"operations\").Linear(\n hidden_size,\n patch_size * patch_size * out_channels,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n\n if z_image_modulation:\n min_mod = 256\n else:\n min_mod = 1024\n\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operation_settings.get(\"operations\").Linear(\n min(hidden_size, min_mod),\n hidden_size,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n\n def forward(self, x, c, timestep_zero_index=None):\n scale = self.adaLN_modulation(c)\n x = modulate(self.norm_final(x), scale, timestep_zero_index=timestep_zero_index)\n x = self.linear(x)\n return x\n\n\ndef pad_zimage(feats, pad_token, pad_tokens_multiple):\n pad_extra = (-feats.shape[1]) % pad_tokens_multiple\n return torch.cat((feats, pad_token.to(device=feats.device, dtype=feats.dtype, copy=True).unsqueeze(0).repeat(feats.shape[0], pad_extra, 1)), dim=1), pad_extra\n\n\ndef pos_ids_x(start_t, H_tokens, W_tokens, batch_size, device, transformer_options={}):\n rope_options = transformer_options.get(\"rope_options\", None)\n h_scale = 1.0\n w_scale = 1.0\n h_start = 0\n w_start = 0\n if rope_options is not None:\n h_scale = rope_options.get(\"scale_y\", 1.0)\n w_scale = rope_options.get(\"scale_x\", 1.0)\n\n h_start = rope_options.get(\"shift_y\", 0.0)\n w_start = rope_options.get(\"shift_x\", 0.0)\n x_pos_ids = torch.zeros((batch_size, H_tokens * W_tokens, 3), dtype=torch.float32, device=device)\n x_pos_ids[:, :, 0] = start_t\n x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()\n x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()\n return x_pos_ids\n\n\nclass NextDiT(nn.Module):\n \"\"\"\n Diffusion model with a Transformer backbone.\n \"\"\"\n\n def __init__(\n self,\n patch_size: int = 2,\n in_channels: int = 4,\n dim: int = 4096,\n n_layers: int = 32,\n n_refiner_layers: int = 2,\n n_heads: int = 32,\n n_kv_heads: Optional[int] = None,\n multiple_of: int = 256,\n ffn_dim_multiplier: float = 4.0,\n norm_eps: float = 1e-5,\n qk_norm: bool = False,\n cap_feat_dim: int = 5120,\n axes_dims: List[int] = (16, 56, 56),\n axes_lens: List[int] = (1, 512, 512),\n rope_theta=10000.0,\n z_image_modulation=False,\n time_scale=1.0,\n pad_tokens_multiple=None,\n clip_text_dim=None,\n siglip_feat_dim=None,\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n **kwargs,\n ) -> None:\n super().__init__()\n self.dtype = dtype\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n self.in_channels = in_channels\n self.out_channels = in_channels\n self.patch_size = patch_size\n self.time_scale = time_scale\n self.pad_tokens_multiple = pad_tokens_multiple\n\n self.x_embedder = operation_settings.get(\"operations\").Linear(\n in_features=patch_size * patch_size * in_channels,\n out_features=dim,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n )\n\n self.noise_refiner = nn.ModuleList(\n [\n JointTransformerBlock(\n layer_id,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n modulation=True,\n z_image_modulation=z_image_modulation,\n operation_settings=operation_settings,\n )\n for layer_id in range(n_refiner_layers)\n ]\n )\n self.context_refiner = nn.ModuleList(\n [\n JointTransformerBlock(\n layer_id,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n modulation=False,\n operation_settings=operation_settings,\n )\n for layer_id in range(n_refiner_layers)\n ]\n )\n\n self.t_embedder = TimestepEmbedder(min(dim, 1024), output_size=256 if z_image_modulation else None, **operation_settings)\n self.cap_embedder = nn.Sequential(\n operation_settings.get(\"operations\").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")),\n operation_settings.get(\"operations\").Linear(\n cap_feat_dim,\n dim,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n\n self.clip_text_pooled_proj = None\n\n if clip_text_dim is not None:\n self.clip_text_dim = clip_text_dim\n self.clip_text_pooled_proj = nn.Sequential(\n operation_settings.get(\"operations\").RMSNorm(clip_text_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")),\n operation_settings.get(\"operations\").Linear(\n clip_text_dim,\n clip_text_dim,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n self.time_text_embed = nn.Sequential(\n nn.SiLU(),\n operation_settings.get(\"operations\").Linear(\n min(dim, 1024) + clip_text_dim,\n min(dim, 1024),\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n\n self.layers = nn.ModuleList(\n [\n JointTransformerBlock(\n layer_id,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n z_image_modulation=z_image_modulation,\n attn_out_bias=False,\n operation_settings=operation_settings,\n )\n for layer_id in range(n_layers)\n ]\n )\n\n if siglip_feat_dim is not None:\n self.siglip_embedder = nn.Sequential(\n operation_settings.get(\"operations\").RMSNorm(siglip_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")),\n operation_settings.get(\"operations\").Linear(\n siglip_feat_dim,\n dim,\n bias=True,\n device=operation_settings.get(\"device\"),\n dtype=operation_settings.get(\"dtype\"),\n ),\n )\n self.siglip_refiner = nn.ModuleList(\n [\n JointTransformerBlock(\n layer_id,\n dim,\n n_heads,\n n_kv_heads,\n multiple_of,\n ffn_dim_multiplier,\n norm_eps,\n qk_norm,\n modulation=False,\n operation_settings=operation_settings,\n )\n for layer_id in range(n_refiner_layers)\n ]\n )\n self.siglip_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))\n else:\n self.siglip_embedder = None\n self.siglip_refiner = None\n self.siglip_pad_token = None\n\n # This norm final is in the lumina 2.0 code but isn't actually used for anything.\n # self.norm_final = operation_settings.get(\"operations\").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings)\n\n if self.pad_tokens_multiple is not None:\n self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))\n self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))\n\n assert (dim // n_heads) == sum(axes_dims)\n self.axes_dims = axes_dims\n self.axes_lens = axes_lens\n self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims)\n self.dim = dim\n self.n_heads = n_heads\n\n def unpatchify(\n self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False\n ) -> List[torch.Tensor]:\n \"\"\"\n x: (N, T, patch_size**2 * C)\n imgs: (N, H, W, C)\n \"\"\"\n pH = pW = self.patch_size\n imgs = []\n for i in range(x.size(0)):\n H, W = img_size[i]\n begin = cap_size[i]\n end = begin + (H // pH) * (W // pW)\n imgs.append(\n x[i][begin:end]\n .view(H // pH, W // pW, pH, pW, self.out_channels)\n .permute(4, 0, 2, 1, 3)\n .flatten(3, 4)\n .flatten(1, 2)\n )\n\n if return_tensor:\n imgs = torch.stack(imgs, dim=0)\n return imgs\n\n def embed_cap(self, cap_feats=None, offset=0, bsz=1, device=None, dtype=None):\n if cap_feats is not None:\n cap_feats = self.cap_embedder(cap_feats)\n cap_feats_len = cap_feats.shape[1]\n if self.pad_tokens_multiple is not None:\n cap_feats, _ = pad_zimage(cap_feats, self.cap_pad_token, self.pad_tokens_multiple)\n else:\n cap_feats_len = 0\n cap_feats = self.cap_pad_token.to(device=device, dtype=dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1)\n\n cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device)\n cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0 + offset\n embeds = (cap_feats,)\n freqs_cis = (self.rope_embedder(cap_pos_ids).movedim(1, 2),)\n return embeds, freqs_cis, cap_feats_len\n\n def embed_all(self, x, cap_feats=None, siglip_feats=None, offset=0, omni=False, transformer_options={}):\n bsz = 1\n pH = pW = self.patch_size\n device = x.device\n embeds, freqs_cis, cap_feats_len = self.embed_cap(cap_feats, offset=offset, bsz=bsz, device=device, dtype=x.dtype)\n\n if (not omni) or self.siglip_embedder is None:\n cap_feats_len = embeds[0].shape[1] + offset\n embeds += (None,)\n freqs_cis += (None,)\n else:\n cap_feats_len += offset\n if siglip_feats is not None:\n b, h, w, c = siglip_feats.shape\n siglip_feats = siglip_feats.permute(0, 3, 1, 2).reshape(b, h * w, c)\n siglip_feats = self.siglip_embedder(siglip_feats)\n siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device)\n siglip_pos_ids[:, :, 0] = cap_feats_len + 2\n siglip_pos_ids[:, :, 1] = (torch.linspace(0, h * 8 - 1, steps=h, dtype=torch.float32, device=device).floor()).view(-1, 1).repeat(1, w).flatten()\n siglip_pos_ids[:, :, 2] = (torch.linspace(0, w * 8 - 1, steps=w, dtype=torch.float32, device=device).floor()).view(1, -1).repeat(h, 1).flatten()\n if self.siglip_pad_token is not None:\n siglip_feats, pad_extra = pad_zimage(siglip_feats, self.siglip_pad_token, self.pad_tokens_multiple) # TODO: double check\n siglip_pos_ids = torch.nn.functional.pad(siglip_pos_ids, (0, 0, 0, pad_extra))\n else:\n if self.siglip_pad_token is not None:\n siglip_feats = self.siglip_pad_token.to(device=device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1)\n siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device)\n\n if siglip_feats is None:\n embeds += (None,)\n freqs_cis += (None,)\n else:\n embeds += (siglip_feats,)\n freqs_cis += (self.rope_embedder(siglip_pos_ids).movedim(1, 2),)\n\n B, C, H, W = x.shape\n x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))\n x_pos_ids = pos_ids_x(cap_feats_len + 1, H // pH, W // pW, bsz, device, transformer_options=transformer_options)\n if self.pad_tokens_multiple is not None:\n x, pad_extra = pad_zimage(x, self.x_pad_token, self.pad_tokens_multiple)\n x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))\n\n embeds += (x,)\n freqs_cis += (self.rope_embedder(x_pos_ids).movedim(1, 2),)\n return embeds, freqs_cis, cap_feats_len + len(freqs_cis) - 1\n\n\n def patchify_and_embed(\n self, x: torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}\n ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:\n bsz = x.shape[0]\n cap_mask = None # TODO?\n main_siglip = None\n orig_x = x\n\n embeds = ([], [], [])\n freqs_cis = ([], [], [])\n leftover_cap = []\n\n start_t = 0\n omni = len(ref_latents) > 0\n if omni:\n for i, ref in enumerate(ref_latents):\n if i < len(ref_contexts):\n ref_con = ref_contexts[i]\n else:\n ref_con = None\n if i < len(siglip_feats):\n sig_feat = siglip_feats[i]\n else:\n sig_feat = None\n\n out = self.embed_all(ref, ref_con, sig_feat, offset=start_t, omni=omni, transformer_options=transformer_options)\n for i, e in enumerate(out[0]):\n if e is not None:\n embeds[i].append(comfy.utils.repeat_to_batch_size(e, bsz))\n freqs_cis[i].append(out[1][i])\n start_t = out[2]\n leftover_cap = ref_contexts[len(ref_latents):]\n\n H, W = x.shape[-2], x.shape[-1]\n img_sizes = [(H, W)] * bsz\n out = self.embed_all(x, cap_feats, main_siglip, offset=start_t, omni=omni, transformer_options=transformer_options)\n img_len = out[0][-1].shape[1]\n cap_len = out[0][0].shape[1]\n for i, e in enumerate(out[0]):\n if e is not None:\n e = comfy.utils.repeat_to_batch_size(e, bsz)\n embeds[i].append(e)\n freqs_cis[i].append(out[1][i])\n start_t = out[2]\n\n for cap in leftover_cap:\n out = self.embed_cap(cap, offset=start_t, bsz=bsz, device=x.device, dtype=x.dtype)\n cap_len += out[0][0].shape[1]\n embeds[0].append(comfy.utils.repeat_to_batch_size(out[0][0], bsz))\n freqs_cis[0].append(out[1][0])\n start_t += out[2]\n\n patches = transformer_options.get(\"patches\", {})\n\n # refine context\n cap_feats = torch.cat(embeds[0], dim=1)\n cap_freqs_cis = torch.cat(freqs_cis[0], dim=1)\n for layer in self.context_refiner:\n cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)\n\n feats = (cap_feats,)\n fc = (cap_freqs_cis,)\n\n if omni and len(embeds[1]) > 0:\n siglip_mask = None\n siglip_feats_combined = torch.cat(embeds[1], dim=1)\n siglip_feats_freqs_cis = torch.cat(freqs_cis[1], dim=1)\n if self.siglip_refiner is not None:\n for layer in self.siglip_refiner:\n siglip_feats_combined = layer(siglip_feats_combined, siglip_mask, siglip_feats_freqs_cis, transformer_options=transformer_options)\n feats += (siglip_feats_combined,)\n fc += (siglip_feats_freqs_cis,)\n\n padded_img_mask = None\n x = torch.cat(embeds[-1], dim=1)\n fc_x = torch.cat(freqs_cis[-1], dim=1)\n if omni:\n timestep_zero_index = [(x.shape[1] - img_len, x.shape[1])]\n else:\n timestep_zero_index = None\n\n x_input = x\n for i, layer in enumerate(self.noise_refiner):\n x = layer(x, padded_img_mask, fc_x, t, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)\n if \"noise_refiner\" in patches:\n for p in patches[\"noise_refiner\"]:\n out = p({\"img\": x, \"img_input\": x_input, \"txt\": cap_feats, \"pe\": fc_x, \"vec\": t, \"x\": orig_x, \"block_index\": i, \"transformer_options\": transformer_options, \"block_type\": \"noise_refiner\"})\n if \"img\" in out:\n x = out[\"img\"]\n\n padded_full_embed = torch.cat(feats + (x,), dim=1)\n if timestep_zero_index is not None:\n ind = padded_full_embed.shape[1] - x.shape[1]\n timestep_zero_index = [(ind + x.shape[1] - img_len, ind + x.shape[1])]\n timestep_zero_index.append((feats[0].shape[1] - cap_len, feats[0].shape[1]))\n\n mask = None\n l_effective_cap_len = [padded_full_embed.shape[1] - img_len] * bsz\n return padded_full_embed, mask, img_sizes, l_effective_cap_len, torch.cat(fc + (fc_x,), dim=1), timestep_zero_index\n\n def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get(\"transformer_options\", {}))\n ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)\n\n # def forward(self, x, t, cap_feats, cap_mask):\n def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs):\n omni = len(ref_latents) > 0\n if omni:\n timesteps = torch.cat([timesteps * 0, timesteps], dim=0)\n\n t = 1.0 - timesteps\n cap_feats = context\n cap_mask = attention_mask\n bs, c, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n \"\"\"\n Forward pass of NextDiT.\n t: (N,) tensor of diffusion timesteps\n y: (N,) tensor of text tokens/features\n \"\"\"\n\n t = self.t_embedder(t * self.time_scale, dtype=x.dtype) # (N, D)\n adaln_input = t\n\n if self.clip_text_pooled_proj is not None:\n pooled = kwargs.get(\"clip_text_pooled\", None)\n if pooled is not None:\n pooled = self.clip_text_pooled_proj(pooled)\n else:\n pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)\n\n adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))\n\n patches = transformer_options.get(\"patches\", {})\n x_is_tensor = isinstance(x, torch.Tensor)\n img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, ref_latents=ref_latents, ref_contexts=ref_contexts, siglip_feats=siglip_feats, transformer_options=transformer_options)\n freqs_cis = freqs_cis.to(img.device)\n\n transformer_options[\"total_blocks\"] = len(self.layers)\n transformer_options[\"block_type\"] = \"double\"\n img_input = img\n for i, layer in enumerate(self.layers):\n transformer_options[\"block_index\"] = i\n img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)\n if \"double_block\" in patches:\n for p in patches[\"double_block\"]:\n out = p({\"img\": img[:, cap_size[0]:], \"img_input\": img_input[:, cap_size[0]:], \"txt\": img[:, :cap_size[0]], \"pe\": freqs_cis[:, cap_size[0]:], \"vec\": adaln_input, \"x\": x, \"block_index\": i, \"transformer_options\": transformer_options})\n if \"img\" in out:\n img[:, cap_size[0]:] = out[\"img\"]\n if \"txt\" in out:\n img[:, :cap_size[0]] = out[\"txt\"]\n\n img = self.final_layer(img, adaln_input, timestep_zero_index=timestep_zero_index)\n img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]\n return -img\n\n\n#############################################################################\n# Pixel Space Decoder Components #\n#############################################################################\n\ndef _modulate_shift_scale(x, shift, scale):\n return x * (1 + scale) + shift\n\n\nclass PixelResBlock(nn.Module):\n \"\"\"\n Residual block with AdaLN modulation, zero-initialised so it starts as\n an identity at the beginning of training.\n \"\"\"\n\n def __init__(self, channels: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.in_ln = operations.LayerNorm(channels, eps=1e-6, dtype=dtype, device=device)\n self.mlp = nn.Sequential(\n operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),\n )\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(channels, 3 * channels, bias=True, dtype=dtype, device=device),\n )\n\n def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n shift, scale, gate = self.adaLN_modulation(y).chunk(3, dim=-1)\n h = _modulate_shift_scale(self.in_ln(x), shift, scale)\n h = self.mlp(h)\n return x + gate * h\n\n\nclass DCTFinalLayer(nn.Module):\n \"\"\"Zero-initialised output projection (adopted from DiT).\"\"\"\n\n def __init__(self, model_channels: int, out_channels: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(model_channels, out_channels, bias=True, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return self.linear(self.norm_final(x))\n\n\nclass SimpleMLPAdaLN(nn.Module):\n \"\"\"\n Small MLP decoder head for the pixel-space variant.\n\n Takes per-patch pixel values and a per-patch conditioning vector from the\n transformer backbone and predicts the denoised pixel values.\n\n x : [B*N, P^2, C] – noisy pixel values per patch position\n c : [B*N, dim] – backbone hidden state per patch (conditioning)\n → [B*N, P^2, C]\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n model_channels: int,\n out_channels: int,\n z_channels: int,\n num_res_blocks: int,\n max_freqs: int = 8,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.dtype = dtype\n\n # Project backbone hidden state → per-patch conditioning\n self.cond_embed = operations.Linear(z_channels, model_channels, dtype=dtype, device=device)\n\n # Input projection with DCT positional encoding\n self.input_embedder = NerfEmbedder(\n in_channels=in_channels,\n hidden_size_input=model_channels,\n max_freqs=max_freqs,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n # Residual blocks\n self.res_blocks = nn.ModuleList([\n PixelResBlock(model_channels, dtype=dtype, device=device, operations=operations) for _ in range(num_res_blocks)\n ])\n\n # Output projection\n self.final_layer = DCTFinalLayer(model_channels, out_channels, dtype=dtype, device=device, operations=operations)\n\n def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:\n # x: [B*N, 1, P^2*C], c: [B*N, dim]\n original_dtype = x.dtype\n weight_dtype = self.cond_embed.weight.dtype if hasattr(self.cond_embed, \"weight\") and self.cond_embed.weight is not None else (self.dtype or x.dtype)\n x = self.input_embedder(x) # [B*N, 1, model_channels]\n y = self.cond_embed(c.to(weight_dtype)).unsqueeze(1) # [B*N, 1, model_channels]\n x = x.to(weight_dtype)\n for block in self.res_blocks:\n x = block(x, y)\n return self.final_layer(x).to(original_dtype) # [B*N, 1, P^2*C]\n\n\n#############################################################################\n# NextDiT – Pixel Space #\n#############################################################################\n\nclass NextDiTPixelSpace(NextDiT):\n \"\"\"\n Pixel-space variant of NextDiT.\n\n Identical transformer backbone to NextDiT, but the output head is replaced\n with a small MLP decoder (SimpleMLPAdaLN) that operates on raw pixel values\n per patch rather than a single affine projection.\n\n Key differences vs NextDiT:\n • ``final_layer`` is removed; ``dec_net`` (SimpleMLPAdaLN) is used instead.\n • ``_forward`` stores the raw patchified pixel values before the backbone\n embedding and feeds them to ``dec_net`` together with the per-patch\n backbone hidden states.\n • Supports optional x0 prediction via ``use_x0``.\n \"\"\"\n\n def __init__(\n self,\n # decoder-specific\n decoder_hidden_size: int = 3840,\n decoder_num_res_blocks: int = 4,\n decoder_max_freqs: int = 8,\n decoder_in_channels: int = None, # full flattened patch size (patch_size^2 * in_channels)\n use_x0: bool = False,\n # all NextDiT args forwarded unchanged\n **kwargs,\n ):\n super().__init__(**kwargs)\n\n # Remove the latent-space final layer – not used in pixel space\n del self.final_layer\n\n patch_size = kwargs.get(\"patch_size\", 2)\n in_channels = kwargs.get(\"in_channels\", 4)\n dim = kwargs.get(\"dim\", 4096)\n\n # decoder_in_channels is the full flattened patch: patch_size^2 * in_channels\n dec_in_ch = decoder_in_channels if decoder_in_channels is not None else patch_size ** 2 * in_channels\n\n self.dec_net = SimpleMLPAdaLN(\n in_channels=dec_in_ch,\n model_channels=decoder_hidden_size,\n out_channels=dec_in_ch,\n z_channels=dim,\n num_res_blocks=decoder_num_res_blocks,\n max_freqs=decoder_max_freqs,\n dtype=kwargs.get(\"dtype\"),\n device=kwargs.get(\"device\"),\n operations=kwargs.get(\"operations\"),\n )\n\n if use_x0:\n self.register_buffer(\"__x0__\", torch.tensor([]))\n\n # ------------------------------------------------------------------\n # Forward — mirrors NextDiT._forward exactly, replacing final_layer\n # with the pixel-space dec_net decoder.\n # ------------------------------------------------------------------\n def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs):\n omni = len(ref_latents) > 0\n if omni:\n timesteps = torch.cat([timesteps * 0, timesteps], dim=0)\n\n t = 1.0 - timesteps\n cap_feats = context\n cap_mask = attention_mask\n bs, c, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n\n t = self.t_embedder(t * self.time_scale, dtype=x.dtype)\n adaln_input = t\n\n if self.clip_text_pooled_proj is not None:\n pooled = kwargs.get(\"clip_text_pooled\", None)\n if pooled is not None:\n pooled = self.clip_text_pooled_proj(pooled)\n else:\n pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)\n adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))\n\n # ---- capture raw pixel patches before patchify_and_embed embeds them ----\n pH = pW = self.patch_size\n B, C, H, W = x.shape\n pixel_patches = (\n x.view(B, C, H // pH, pH, W // pW, pW)\n .permute(0, 2, 4, 3, 5, 1) # [B, Ht, Wt, pH, pW, C]\n .flatten(3) # [B, Ht, Wt, pH*pW*C]\n .flatten(1, 2) # [B, N, pH*pW*C]\n )\n N = pixel_patches.shape[1]\n # decoder sees one token per patch: [B*N, 1, P^2*C]\n pixel_values = pixel_patches.reshape(B * N, 1, pH * pW * C)\n\n patches = transformer_options.get(\"patches\", {})\n x_is_tensor = isinstance(x, torch.Tensor)\n img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(\n x, cap_feats, cap_mask, adaln_input, num_tokens,\n ref_latents=ref_latents, ref_contexts=ref_contexts,\n siglip_feats=siglip_feats, transformer_options=transformer_options\n )\n freqs_cis = freqs_cis.to(img.device)\n\n transformer_options[\"total_blocks\"] = len(self.layers)\n transformer_options[\"block_type\"] = \"double\"\n img_input = img\n for i, layer in enumerate(self.layers):\n transformer_options[\"block_index\"] = i\n img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)\n if \"double_block\" in patches:\n for p in patches[\"double_block\"]:\n out = p({\"img\": img[:, cap_size[0]:], \"img_input\": img_input[:, cap_size[0]:], \"txt\": img[:, :cap_size[0]], \"pe\": freqs_cis[:, cap_size[0]:], \"vec\": adaln_input, \"x\": x, \"block_index\": i, \"transformer_options\": transformer_options})\n if \"img\" in out:\n img[:, cap_size[0]:] = out[\"img\"]\n if \"txt\" in out:\n img[:, :cap_size[0]] = out[\"txt\"]\n\n # ---- pixel-space decoder (replaces final_layer + unpatchify) ----\n # img may have padding tokens beyond N; only the first N are real image patches\n img_hidden = img[:, cap_size[0]:cap_size[0] + N, :] # [B, N, dim]\n decoder_cond = img_hidden.reshape(B * N, self.dim) # [B*N, dim]\n\n output = self.dec_net(pixel_values, decoder_cond) # [B*N, 1, P^2*C]\n output = output.reshape(B, N, -1) # [B, N, P^2*C]\n\n # prepend zero cap placeholder so unpatchify indexing works unchanged\n cap_placeholder = torch.zeros(\n B, cap_size[0], output.shape[-1], device=output.device, dtype=output.dtype\n )\n img_out = self.unpatchify(\n torch.cat([cap_placeholder, output], dim=1),\n img_size, cap_size, return_tensor=x_is_tensor\n )[:, :, :h, :w]\n\n return -img_out\n\n def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):\n # _forward returns neg_x0 = -x0 (negated decoder output).\n #\n # Reference inference (working_inference_reference.py):\n # out = _forward(img, t) # = -x0\n # pred = (img - out) / t # = (img + x0) / t [_apply_x0_residual]\n # img += (t_prev - t_curr) * pred # Euler step\n #\n # ComfyUI's Euler sampler does the same:\n # x_next = x + (sigma_next - sigma) * model_output\n # So model_output must equal pred = (x - neg_x0) / t = (x - (-x0)) / t = (x + x0) / t\n neg_x0 = comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get(\"transformer_options\", {}))\n ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)\n\n return (x - neg_x0) / timesteps.view(-1, 1, 1, 1)\n" }, { "path": "comfy/ldm/mmaudio/vae/__init__.py", "content": "" }, { "path": "comfy/ldm/mmaudio/vae/activations.py", "content": "# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.\n# LICENSE is in incl_licenses directory.\n\nimport torch\nfrom torch import nn, sin, pow\nfrom torch.nn import Parameter\nimport comfy.model_management\n\nclass Snake(nn.Module):\n '''\n Implementation of a sine-based periodic activation function\n Shape:\n - Input: (B, C, T)\n - Output: (B, C, T), same shape as the input\n Parameters:\n - alpha - trainable parameter\n References:\n - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:\n https://arxiv.org/abs/2006.08195\n Examples:\n >>> a1 = snake(256)\n >>> x = torch.randn(256)\n >>> x = a1(x)\n '''\n def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):\n '''\n Initialization.\n INPUT:\n - in_features: shape of the input\n - alpha: trainable parameter\n alpha is initialized to 1 by default, higher values = higher-frequency.\n alpha will be trained along with the rest of your model.\n '''\n super(Snake, self).__init__()\n self.in_features = in_features\n\n # initialize alpha\n self.alpha_logscale = alpha_logscale\n if self.alpha_logscale:\n self.alpha = Parameter(torch.empty(in_features))\n else:\n self.alpha = Parameter(torch.empty(in_features))\n\n self.alpha.requires_grad = alpha_trainable\n\n self.no_div_by_zero = 0.000000001\n\n def forward(self, x):\n '''\n Forward pass of the function.\n Applies the function to the input elementwise.\n Snake ∶= x + 1/a * sin^2 (xa)\n '''\n alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]\n if self.alpha_logscale:\n alpha = torch.exp(alpha)\n x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)\n\n return x\n\n\nclass SnakeBeta(nn.Module):\n '''\n A modified Snake function which uses separate parameters for the magnitude of the periodic components\n Shape:\n - Input: (B, C, T)\n - Output: (B, C, T), same shape as the input\n Parameters:\n - alpha - trainable parameter that controls frequency\n - beta - trainable parameter that controls magnitude\n References:\n - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:\n https://arxiv.org/abs/2006.08195\n Examples:\n >>> a1 = snakebeta(256)\n >>> x = torch.randn(256)\n >>> x = a1(x)\n '''\n def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):\n '''\n Initialization.\n INPUT:\n - in_features: shape of the input\n - alpha - trainable parameter that controls frequency\n - beta - trainable parameter that controls magnitude\n alpha is initialized to 1 by default, higher values = higher-frequency.\n beta is initialized to 1 by default, higher values = higher-magnitude.\n alpha will be trained along with the rest of your model.\n '''\n super(SnakeBeta, self).__init__()\n self.in_features = in_features\n\n # initialize alpha\n self.alpha_logscale = alpha_logscale\n if self.alpha_logscale:\n self.alpha = Parameter(torch.empty(in_features))\n self.beta = Parameter(torch.empty(in_features))\n else:\n self.alpha = Parameter(torch.empty(in_features))\n self.beta = Parameter(torch.empty(in_features))\n\n self.alpha.requires_grad = alpha_trainable\n self.beta.requires_grad = alpha_trainable\n\n self.no_div_by_zero = 0.000000001\n\n def forward(self, x):\n '''\n Forward pass of the function.\n Applies the function to the input elementwise.\n SnakeBeta ∶= x + 1/b * sin^2 (xa)\n '''\n alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]\n beta = comfy.model_management.cast_to(self.beta, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1)\n if self.alpha_logscale:\n alpha = torch.exp(alpha)\n beta = torch.exp(beta)\n x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)\n\n return x\n" }, { "path": "comfy/ldm/mmaudio/vae/alias_free_torch.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nimport comfy.model_management\n\nif 'sinc' in dir(torch):\n sinc = torch.sinc\nelse:\n # This code is adopted from adefossez's julius.core.sinc under the MIT License\n # https://adefossez.github.io/julius/julius/core.html\n # LICENSE is in incl_licenses directory.\n def sinc(x: torch.Tensor):\n \"\"\"\n Implementation of sinc, i.e. sin(pi * x) / (pi * x)\n __Warning__: Different to julius.sinc, the input is multiplied by `pi`!\n \"\"\"\n return torch.where(x == 0,\n torch.tensor(1., device=x.device, dtype=x.dtype),\n torch.sin(math.pi * x) / math.pi / x)\n\n\n# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License\n# https://adefossez.github.io/julius/julius/lowpass.html\n# LICENSE is in incl_licenses directory.\ndef kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]\n even = (kernel_size % 2 == 0)\n half_size = kernel_size // 2\n\n #For kaiser window\n delta_f = 4 * half_width\n A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95\n if A > 50.:\n beta = 0.1102 * (A - 8.7)\n elif A >= 21.:\n beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)\n else:\n beta = 0.\n window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)\n\n # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio\n if even:\n time = (torch.arange(-half_size, half_size) + 0.5)\n else:\n time = torch.arange(kernel_size) - half_size\n if cutoff == 0:\n filter_ = torch.zeros_like(time)\n else:\n filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)\n # Normalize filter to have sum = 1, otherwise we will have a small leakage\n # of the constant component in the input signal.\n filter_ /= filter_.sum()\n filter = filter_.view(1, 1, kernel_size)\n\n return filter\n\n\nclass LowPassFilter1d(nn.Module):\n def __init__(self,\n cutoff=0.5,\n half_width=0.6,\n stride: int = 1,\n padding: bool = True,\n padding_mode: str = 'replicate',\n kernel_size: int = 12):\n # kernel_size should be even number for stylegan3 setup,\n # in this implementation, odd number is also possible.\n super().__init__()\n if cutoff < -0.:\n raise ValueError(\"Minimum cutoff must be larger than zero.\")\n if cutoff > 0.5:\n raise ValueError(\"A cutoff above 0.5 does not make sense.\")\n self.kernel_size = kernel_size\n self.even = (kernel_size % 2 == 0)\n self.pad_left = kernel_size // 2 - int(self.even)\n self.pad_right = kernel_size // 2\n self.stride = stride\n self.padding = padding\n self.padding_mode = padding_mode\n filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)\n self.register_buffer(\"filter\", filter)\n\n #input [B, C, T]\n def forward(self, x):\n _, C, _ = x.shape\n\n if self.padding:\n x = F.pad(x, (self.pad_left, self.pad_right),\n mode=self.padding_mode)\n out = F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device),\n stride=self.stride, groups=C)\n\n return out\n\n\nclass UpSample1d(nn.Module):\n def __init__(self, ratio=2, kernel_size=None):\n super().__init__()\n self.ratio = ratio\n self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size\n self.stride = ratio\n self.pad = self.kernel_size // ratio - 1\n self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2\n self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2\n filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,\n half_width=0.6 / ratio,\n kernel_size=self.kernel_size)\n self.register_buffer(\"filter\", filter)\n\n # x: [B, C, T]\n def forward(self, x):\n _, C, _ = x.shape\n\n x = F.pad(x, (self.pad, self.pad), mode='replicate')\n x = self.ratio * F.conv_transpose1d(\n x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)\n x = x[..., self.pad_left:-self.pad_right]\n\n return x\n\n\nclass DownSample1d(nn.Module):\n def __init__(self, ratio=2, kernel_size=None):\n super().__init__()\n self.ratio = ratio\n self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size\n self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,\n half_width=0.6 / ratio,\n stride=ratio,\n kernel_size=self.kernel_size)\n\n def forward(self, x):\n xx = self.lowpass(x)\n\n return xx\n\nclass Activation1d(nn.Module):\n def __init__(self,\n activation,\n up_ratio: int = 2,\n down_ratio: int = 2,\n up_kernel_size: int = 12,\n down_kernel_size: int = 12):\n super().__init__()\n self.up_ratio = up_ratio\n self.down_ratio = down_ratio\n self.act = activation\n self.upsample = UpSample1d(up_ratio, up_kernel_size)\n self.downsample = DownSample1d(down_ratio, down_kernel_size)\n\n # x: [B,C,T]\n def forward(self, x):\n x = self.upsample(x)\n x = self.act(x)\n x = self.downsample(x)\n\n return x\n" }, { "path": "comfy/ldm/mmaudio/vae/autoencoder.py", "content": "from typing import Literal\n\nimport torch\nimport torch.nn as nn\n\nfrom .distributions import DiagonalGaussianDistribution\nfrom .vae import VAE_16k\nfrom .bigvgan import BigVGANVocoder\nimport logging\n\ntry:\n import torchaudio\nexcept:\n logging.warning(\"torchaudio missing, MMAudio VAE model will be broken\")\n\ndef dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):\n return norm_fn(torch.clamp(x, min=clip_val) * C)\n\n\ndef spectral_normalize_torch(magnitudes, norm_fn):\n output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)\n return output\n\nclass MelConverter(nn.Module):\n\n def __init__(\n self,\n *,\n sampling_rate: float,\n n_fft: int,\n num_mels: int,\n hop_size: int,\n win_size: int,\n fmin: float,\n fmax: float,\n norm_fn,\n ):\n super().__init__()\n self.sampling_rate = sampling_rate\n self.n_fft = n_fft\n self.num_mels = num_mels\n self.hop_size = hop_size\n self.win_size = win_size\n self.fmin = fmin\n self.fmax = fmax\n self.norm_fn = norm_fn\n\n # mel = librosa_mel_fn(sr=self.sampling_rate,\n # n_fft=self.n_fft,\n # n_mels=self.num_mels,\n # fmin=self.fmin,\n # fmax=self.fmax)\n # mel_basis = torch.from_numpy(mel).float()\n mel_basis = torch.empty((num_mels, 1 + n_fft // 2))\n hann_window = torch.hann_window(self.win_size)\n\n self.register_buffer('mel_basis', mel_basis)\n self.register_buffer('hann_window', hann_window)\n\n @property\n def device(self):\n return self.mel_basis.device\n\n def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:\n waveform = waveform.clamp(min=-1., max=1.).to(self.device)\n\n waveform = torch.nn.functional.pad(\n waveform.unsqueeze(1),\n [int((self.n_fft - self.hop_size) / 2),\n int((self.n_fft - self.hop_size) / 2)],\n mode='reflect')\n waveform = waveform.squeeze(1)\n\n spec = torch.stft(waveform,\n self.n_fft,\n hop_length=self.hop_size,\n win_length=self.win_size,\n window=self.hann_window,\n center=center,\n pad_mode='reflect',\n normalized=False,\n onesided=True,\n return_complex=True)\n\n spec = torch.view_as_real(spec)\n spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))\n spec = torch.matmul(self.mel_basis, spec)\n spec = spectral_normalize_torch(spec, self.norm_fn)\n\n return spec\n\nclass AudioAutoencoder(nn.Module):\n\n def __init__(\n self,\n *,\n # ckpt_path: str,\n mode=Literal['16k', '44k'],\n need_vae_encoder: bool = True,\n ):\n super().__init__()\n\n assert mode == \"16k\", \"Only 16k mode is supported currently.\"\n self.mel_converter = MelConverter(sampling_rate=16_000,\n n_fft=1024,\n num_mels=80,\n hop_size=256,\n win_size=1024,\n fmin=0,\n fmax=8_000,\n norm_fn=torch.log10)\n\n self.vae = VAE_16k().eval()\n\n bigvgan_config = {\n \"resblock\": \"1\",\n \"num_mels\": 80,\n \"upsample_rates\": [4, 4, 2, 2, 2, 2],\n \"upsample_kernel_sizes\": [8, 8, 4, 4, 4, 4],\n \"upsample_initial_channel\": 1536,\n \"resblock_kernel_sizes\": [3, 7, 11],\n \"resblock_dilation_sizes\": [\n [1, 3, 5],\n [1, 3, 5],\n [1, 3, 5],\n ],\n \"activation\": \"snakebeta\",\n \"snake_logscale\": True,\n }\n\n self.vocoder = BigVGANVocoder(\n bigvgan_config\n ).eval()\n\n @torch.inference_mode()\n def encode_audio(self, x) -> DiagonalGaussianDistribution:\n # x: (B * L)\n mel = self.mel_converter(x)\n dist = self.vae.encode(mel)\n\n return dist\n\n @torch.no_grad()\n def decode(self, z):\n mel_decoded = self.vae.decode(z)\n audio = self.vocoder(mel_decoded)\n\n audio = torchaudio.functional.resample(audio, 16000, 44100)\n return audio\n\n @torch.no_grad()\n def encode(self, audio):\n audio = audio.mean(dim=1)\n audio = torchaudio.functional.resample(audio, 44100, 16000)\n dist = self.encode_audio(audio)\n return dist.mean\n" }, { "path": "comfy/ldm/mmaudio/vae/bigvgan.py", "content": "# Copyright (c) 2022 NVIDIA CORPORATION.\n# Licensed under the MIT license.\n\n# Adapted from https://github.com/jik876/hifi-gan under the MIT license.\n# LICENSE is in incl_licenses directory.\n\nimport torch\nimport torch.nn as nn\nfrom types import SimpleNamespace\nfrom . import activations\nfrom .alias_free_torch import Activation1d\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\ndef get_padding(kernel_size, dilation=1):\n return int((kernel_size * dilation - dilation) / 2)\n\nclass AMPBlock1(torch.nn.Module):\n\n def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):\n super(AMPBlock1, self).__init__()\n self.h = h\n\n self.convs1 = nn.ModuleList([\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[0],\n padding=get_padding(kernel_size, dilation[0])),\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[1],\n padding=get_padding(kernel_size, dilation[1])),\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[2],\n padding=get_padding(kernel_size, dilation[2]))\n ])\n\n self.convs2 = nn.ModuleList([\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1)),\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1)),\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=1,\n padding=get_padding(kernel_size, 1))\n ])\n\n self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers\n\n if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing\n self.activations = nn.ModuleList([\n Activation1d(\n activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))\n for _ in range(self.num_layers)\n ])\n elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing\n self.activations = nn.ModuleList([\n Activation1d(\n activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))\n for _ in range(self.num_layers)\n ])\n else:\n raise NotImplementedError(\n \"activation incorrectly specified. check the config file and look for 'activation'.\"\n )\n\n def forward(self, x):\n acts1, acts2 = self.activations[::2], self.activations[1::2]\n for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):\n xt = a1(x)\n xt = c1(xt)\n xt = a2(xt)\n xt = c2(xt)\n x = xt + x\n\n return x\n\n\nclass AMPBlock2(torch.nn.Module):\n\n def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):\n super(AMPBlock2, self).__init__()\n self.h = h\n\n self.convs = nn.ModuleList([\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[0],\n padding=get_padding(kernel_size, dilation[0])),\n ops.Conv1d(channels,\n channels,\n kernel_size,\n 1,\n dilation=dilation[1],\n padding=get_padding(kernel_size, dilation[1]))\n ])\n\n self.num_layers = len(self.convs) # total number of conv layers\n\n if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing\n self.activations = nn.ModuleList([\n Activation1d(\n activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))\n for _ in range(self.num_layers)\n ])\n elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing\n self.activations = nn.ModuleList([\n Activation1d(\n activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))\n for _ in range(self.num_layers)\n ])\n else:\n raise NotImplementedError(\n \"activation incorrectly specified. check the config file and look for 'activation'.\"\n )\n\n def forward(self, x):\n for c, a in zip(self.convs, self.activations):\n xt = a(x)\n xt = c(xt)\n x = xt + x\n\n return x\n\n\nclass BigVGANVocoder(torch.nn.Module):\n # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.\n def __init__(self, h):\n super().__init__()\n if isinstance(h, dict):\n h = SimpleNamespace(**h)\n self.h = h\n\n self.num_kernels = len(h.resblock_kernel_sizes)\n self.num_upsamples = len(h.upsample_rates)\n\n # pre conv\n self.conv_pre = ops.Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)\n\n # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default\n resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2\n\n # transposed conv-based upsamplers. does not apply anti-aliasing\n self.ups = nn.ModuleList()\n for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):\n self.ups.append(\n nn.ModuleList([\n ops.ConvTranspose1d(h.upsample_initial_channel // (2**i),\n h.upsample_initial_channel // (2**(i + 1)),\n k,\n u,\n padding=(k - u) // 2)\n ]))\n\n # residual blocks using anti-aliased multi-periodicity composition modules (AMP)\n self.resblocks = nn.ModuleList()\n for i in range(len(self.ups)):\n ch = h.upsample_initial_channel // (2**(i + 1))\n for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):\n self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))\n\n # post conv\n if h.activation == \"snake\": # periodic nonlinearity with snake function and anti-aliasing\n activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)\n self.activation_post = Activation1d(activation=activation_post)\n elif h.activation == \"snakebeta\": # periodic nonlinearity with snakebeta function and anti-aliasing\n activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)\n self.activation_post = Activation1d(activation=activation_post)\n else:\n raise NotImplementedError(\n \"activation incorrectly specified. check the config file and look for 'activation'.\"\n )\n\n self.conv_post = ops.Conv1d(ch, 1, 7, 1, padding=3)\n\n\n def forward(self, x):\n # pre conv\n x = self.conv_pre(x)\n\n for i in range(self.num_upsamples):\n # upsampling\n for i_up in range(len(self.ups[i])):\n x = self.ups[i][i_up](x)\n # AMP blocks\n xs = None\n for j in range(self.num_kernels):\n if xs is None:\n xs = self.resblocks[i * self.num_kernels + j](x)\n else:\n xs += self.resblocks[i * self.num_kernels + j](x)\n x = xs / self.num_kernels\n\n # post conv\n x = self.activation_post(x)\n x = self.conv_post(x)\n x = torch.tanh(x)\n\n return x\n" }, { "path": "comfy/ldm/mmaudio/vae/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, device=self.parameters.device)\n\n def sample(self):\n x = self.mean + self.std * torch.randn(self.mean.shape, 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": "comfy/ldm/mmaudio/vae/vae.py", "content": "import logging\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D,\n Upsample1D, nonlinearity)\nfrom .distributions import DiagonalGaussianDistribution\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nlog = logging.getLogger()\n\nDATA_MEAN_80D = [\n -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927,\n -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728,\n -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131,\n -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280,\n -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643,\n -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436,\n -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282,\n -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673\n]\n\nDATA_STD_80D = [\n 1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263,\n 0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194,\n 0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043,\n 0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973,\n 0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939,\n 0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604,\n 1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070\n]\n\nDATA_MEAN_128D = [\n -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597,\n -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033,\n -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157,\n -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782,\n -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647,\n -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795,\n -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121,\n -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960,\n -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712,\n -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120,\n -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663,\n -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628,\n -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861\n]\n\nDATA_STD_128D = [\n 2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659,\n 2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557,\n 2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182,\n 2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991,\n 2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900,\n 2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817,\n 2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609,\n 2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812,\n 2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451,\n 2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877,\n 2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164\n]\n\n\nclass VAE(nn.Module):\n\n def __init__(\n self,\n *,\n data_dim: int,\n embed_dim: int,\n hidden_dim: int,\n ):\n super().__init__()\n\n if data_dim == 80:\n self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32))\n self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32))\n elif data_dim == 128:\n self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32))\n self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32))\n\n self.data_mean = self.data_mean.view(1, -1, 1)\n self.data_std = self.data_std.view(1, -1, 1)\n\n self.encoder = Encoder1D(\n dim=hidden_dim,\n ch_mult=(1, 2, 4),\n num_res_blocks=2,\n attn_layers=[3],\n down_layers=[0],\n in_dim=data_dim,\n embed_dim=embed_dim,\n )\n self.decoder = Decoder1D(\n dim=hidden_dim,\n ch_mult=(1, 2, 4),\n num_res_blocks=2,\n attn_layers=[3],\n down_layers=[0],\n in_dim=data_dim,\n out_dim=data_dim,\n embed_dim=embed_dim,\n )\n\n self.embed_dim = embed_dim\n # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1)\n # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1)\n\n self.initialize_weights()\n\n def initialize_weights(self):\n pass\n\n def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution:\n if normalize:\n x = self.normalize(x)\n moments = self.encoder(x)\n posterior = DiagonalGaussianDistribution(moments)\n return posterior\n\n def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor:\n dec = self.decoder(z)\n if unnormalize:\n dec = self.unnormalize(dec)\n return dec\n\n def normalize(self, x: torch.Tensor) -> torch.Tensor:\n return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device)\n\n def unnormalize(self, x: torch.Tensor) -> torch.Tensor:\n return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)\n\n def forward(\n self,\n x: torch.Tensor,\n sample_posterior: bool = True,\n rng: Optional[torch.Generator] = None,\n normalize: bool = True,\n unnormalize: bool = True,\n ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]:\n\n posterior = self.encode(x, normalize=normalize)\n if sample_posterior:\n z = posterior.sample(rng)\n else:\n z = posterior.mode()\n dec = self.decode(z, unnormalize=unnormalize)\n return dec, posterior\n\n def load_weights(self, src_dict) -> None:\n self.load_state_dict(src_dict, strict=True)\n\n @property\n def device(self) -> torch.device:\n return next(self.parameters()).device\n\n def get_last_layer(self):\n return self.decoder.conv_out.weight\n\n def remove_weight_norm(self):\n return self\n\n\nclass Encoder1D(nn.Module):\n\n def __init__(self,\n *,\n dim: int,\n ch_mult: tuple[int] = (1, 2, 4, 8),\n num_res_blocks: int,\n attn_layers: list[int] = [],\n down_layers: list[int] = [],\n resamp_with_conv: bool = True,\n in_dim: int,\n embed_dim: int,\n double_z: bool = True,\n kernel_size: int = 3,\n clip_act: float = 256.0):\n super().__init__()\n self.dim = dim\n self.num_layers = len(ch_mult)\n self.num_res_blocks = num_res_blocks\n self.in_channels = in_dim\n self.clip_act = clip_act\n self.down_layers = down_layers\n self.attn_layers = attn_layers\n self.conv_in = ops.Conv1d(in_dim, self.dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n\n in_ch_mult = (1, ) + tuple(ch_mult)\n self.in_ch_mult = in_ch_mult\n # downsampling\n self.down = nn.ModuleList()\n for i_level in range(self.num_layers):\n block = nn.ModuleList()\n attn = nn.ModuleList()\n block_in = dim * in_ch_mult[i_level]\n block_out = dim * ch_mult[i_level]\n for i_block in range(self.num_res_blocks):\n block.append(\n ResnetBlock1D(in_dim=block_in,\n out_dim=block_out,\n kernel_size=kernel_size,\n use_norm=True))\n block_in = block_out\n if i_level in attn_layers:\n attn.append(AttnBlock1D(block_in))\n down = nn.Module()\n down.block = block\n down.attn = attn\n if i_level in down_layers:\n down.downsample = Downsample1D(block_in, resamp_with_conv)\n self.down.append(down)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock1D(in_dim=block_in,\n out_dim=block_in,\n kernel_size=kernel_size,\n use_norm=True)\n self.mid.attn_1 = AttnBlock1D(block_in)\n self.mid.block_2 = ResnetBlock1D(in_dim=block_in,\n out_dim=block_in,\n kernel_size=kernel_size,\n use_norm=True)\n\n # end\n self.conv_out = ops.Conv1d(block_in,\n 2 * embed_dim if double_z else embed_dim,\n kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n\n self.learnable_gain = nn.Parameter(torch.zeros([]))\n\n def forward(self, x):\n\n # downsampling\n h = self.conv_in(x)\n for i_level in range(self.num_layers):\n for i_block in range(self.num_res_blocks):\n h = self.down[i_level].block[i_block](h)\n if len(self.down[i_level].attn) > 0:\n h = self.down[i_level].attn[i_block](h)\n h = h.clamp(-self.clip_act, self.clip_act)\n if i_level in self.down_layers:\n h = self.down[i_level].downsample(h)\n\n # middle\n h = self.mid.block_1(h)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h)\n h = h.clamp(-self.clip_act, self.clip_act)\n\n # end\n h = nonlinearity(h)\n h = self.conv_out(h) * (self.learnable_gain + 1)\n return h\n\n\nclass Decoder1D(nn.Module):\n\n def __init__(self,\n *,\n dim: int,\n out_dim: int,\n ch_mult: tuple[int] = (1, 2, 4, 8),\n num_res_blocks: int,\n attn_layers: list[int] = [],\n down_layers: list[int] = [],\n kernel_size: int = 3,\n resamp_with_conv: bool = True,\n in_dim: int,\n embed_dim: int,\n clip_act: float = 256.0):\n super().__init__()\n self.ch = dim\n self.num_layers = len(ch_mult)\n self.num_res_blocks = num_res_blocks\n self.in_channels = in_dim\n self.clip_act = clip_act\n self.down_layers = [i + 1 for i in down_layers] # each downlayer add one\n\n # compute in_ch_mult, block_in and curr_res at lowest res\n block_in = dim * ch_mult[self.num_layers - 1]\n\n # z to block_in\n self.conv_in = ops.Conv1d(embed_dim, block_in, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)\n self.mid.attn_1 = AttnBlock1D(block_in)\n self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)\n\n # upsampling\n self.up = nn.ModuleList()\n for i_level in reversed(range(self.num_layers)):\n block = nn.ModuleList()\n attn = nn.ModuleList()\n block_out = dim * ch_mult[i_level]\n for i_block in range(self.num_res_blocks + 1):\n block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True))\n block_in = block_out\n if i_level in attn_layers:\n attn.append(AttnBlock1D(block_in))\n up = nn.Module()\n up.block = block\n up.attn = attn\n if i_level in self.down_layers:\n up.upsample = Upsample1D(block_in, resamp_with_conv)\n self.up.insert(0, up) # prepend to get consistent order\n\n # end\n self.conv_out = ops.Conv1d(block_in, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n self.learnable_gain = nn.Parameter(torch.zeros([]))\n\n def forward(self, z):\n # z to block_in\n h = self.conv_in(z)\n\n # middle\n h = self.mid.block_1(h)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h)\n h = h.clamp(-self.clip_act, self.clip_act)\n\n # upsampling\n for i_level in reversed(range(self.num_layers)):\n for i_block in range(self.num_res_blocks + 1):\n h = self.up[i_level].block[i_block](h)\n if len(self.up[i_level].attn) > 0:\n h = self.up[i_level].attn[i_block](h)\n h = h.clamp(-self.clip_act, self.clip_act)\n if i_level in self.down_layers:\n h = self.up[i_level].upsample(h)\n\n h = nonlinearity(h)\n h = self.conv_out(h) * (self.learnable_gain + 1)\n return h\n\n\ndef VAE_16k(**kwargs) -> VAE:\n return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs)\n\n\ndef VAE_44k(**kwargs) -> VAE:\n return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs)\n\n\ndef get_my_vae(name: str, **kwargs) -> VAE:\n if name == '16k':\n return VAE_16k(**kwargs)\n if name == '44k':\n return VAE_44k(**kwargs)\n raise ValueError(f'Unknown model: {name}')\n\n" }, { "path": "comfy/ldm/mmaudio/vae/vae_modules.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom comfy.ldm.modules.diffusionmodules.model import vae_attention\nimport math\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\ndef nonlinearity(x):\n # swish\n return torch.nn.functional.silu(x) / 0.596\n\ndef mp_sum(a, b, t=0.5):\n return a.lerp(b, t) / math.sqrt((1 - t)**2 + t**2)\n\ndef normalize(x, dim=None, eps=1e-4):\n if dim is None:\n dim = list(range(1, x.ndim))\n norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)\n norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel()))\n return x / norm.to(x.dtype)\n\nclass ResnetBlock1D(nn.Module):\n\n def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):\n super().__init__()\n self.in_dim = in_dim\n out_dim = in_dim if out_dim is None else out_dim\n self.out_dim = out_dim\n self.use_conv_shortcut = conv_shortcut\n self.use_norm = use_norm\n\n self.conv1 = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n self.conv2 = ops.Conv1d(out_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n if self.in_dim != self.out_dim:\n if self.use_conv_shortcut:\n self.conv_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)\n else:\n self.nin_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=1, padding=0, bias=False)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n\n # pixel norm\n if self.use_norm:\n x = normalize(x, dim=1)\n\n h = x\n h = nonlinearity(h)\n h = self.conv1(h)\n\n h = nonlinearity(h)\n h = self.conv2(h)\n\n if self.in_dim != self.out_dim:\n if self.use_conv_shortcut:\n x = self.conv_shortcut(x)\n else:\n x = self.nin_shortcut(x)\n\n return mp_sum(x, h, t=0.3)\n\n\nclass AttnBlock1D(nn.Module):\n\n def __init__(self, in_channels, num_heads=1):\n super().__init__()\n self.in_channels = in_channels\n\n self.num_heads = num_heads\n self.qkv = ops.Conv1d(in_channels, in_channels * 3, kernel_size=1, padding=0, bias=False)\n self.proj_out = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)\n self.optimized_attention = vae_attention()\n\n def forward(self, x):\n h = x\n y = self.qkv(h)\n y = y.reshape(y.shape[0], -1, 3, y.shape[-1])\n q, k, v = normalize(y, dim=1).unbind(2)\n\n h = self.optimized_attention(q, k, v)\n h = self.proj_out(h)\n\n return mp_sum(x, h, t=0.3)\n\n\nclass Upsample1D(nn.Module):\n\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.Conv1d(in_channels, in_channels, kernel_size=3, padding=1, bias=False)\n\n def forward(self, x):\n x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T)\n if self.with_conv:\n x = self.conv(x)\n return x\n\n\nclass Downsample1D(nn.Module):\n\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.conv1 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)\n self.conv2 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)\n\n def forward(self, x):\n\n if self.with_conv:\n x = self.conv1(x)\n\n x = F.avg_pool1d(x, kernel_size=2, stride=2)\n\n if self.with_conv:\n x = self.conv2(x)\n\n return x\n" }, { "path": "comfy/ldm/models/autoencoder.py", "content": "import logging\nimport math\nimport torch\nfrom contextlib import contextmanager\nfrom typing import Any, Dict, Tuple, Union\n\nfrom comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution\n\nfrom comfy.ldm.util import get_obj_from_str, instantiate_from_config\nfrom comfy.ldm.modules.ema import LitEma\nimport comfy.ops\nfrom einops import rearrange\nimport comfy.model_management\n\nclass DiagonalGaussianRegularizer(torch.nn.Module):\n def __init__(self, sample: bool = False):\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 posterior = DiagonalGaussianDistribution(z)\n if self.sample:\n z = posterior.sample()\n else:\n z = posterior.mode()\n return z, None\n\nclass EmptyRegularizer(torch.nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:\n return z, None\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 logging.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 logging.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 logging.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 logging.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 = 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\": \"comfy.ldm.modules.diffusionmodules.model.Encoder\",\n \"params\": ddconfig,\n },\n decoder_config={\n \"target\": \"comfy.ldm.modules.diffusionmodules.model.Decoder\",\n \"params\": ddconfig,\n },\n **kwargs,\n )\n\n if ddconfig.get(\"conv3d\", False):\n conv_op = comfy.ops.disable_weight_init.Conv3d\n else:\n conv_op = comfy.ops.disable_weight_init.Conv2d\n\n self.quant_conv = conv_op(\n (1 + ddconfig[\"double_z\"]) * ddconfig[\"z_channels\"],\n (1 + ddconfig[\"double_z\"]) * embed_dim,\n 1,\n )\n\n self.post_quant_conv = conv_op(embed_dim, ddconfig[\"z_channels\"], 1)\n self.embed_dim = embed_dim\n\n if ddconfig.get(\"batch_norm_latent\", False):\n self.bn_eps = 1e-4\n self.bn_momentum = 0.1\n self.ps = [2, 2]\n self.bn = torch.nn.BatchNorm2d(math.prod(self.ps) * ddconfig[\"z_channels\"],\n eps=self.bn_eps,\n momentum=self.bn_momentum,\n affine=False,\n track_running_stats=True,\n )\n self.bn.eval()\n else:\n self.bn = None\n\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\n if self.bn is not None:\n z = rearrange(z,\n \"... c (i pi) (j pj) -> ... (c pi pj) i j\",\n pi=self.ps[0],\n pj=self.ps[1],\n )\n\n z = torch.nn.functional.batch_norm(z,\n comfy.model_management.cast_to(self.bn.running_mean, dtype=z.dtype, device=z.device),\n comfy.model_management.cast_to(self.bn.running_var, dtype=z.dtype, device=z.device),\n momentum=self.bn_momentum,\n eps=self.bn_eps)\n\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.bn is not None:\n s = torch.sqrt(comfy.model_management.cast_to(self.bn.running_var.view(1, -1, 1, 1), dtype=z.dtype, device=z.device) + self.bn_eps)\n m = comfy.model_management.cast_to(self.bn.running_mean.view(1, -1, 1, 1), dtype=z.dtype, device=z.device)\n z = z * s + m\n z = rearrange(\n z,\n \"... (c pi pj) i j -> ... c (i pi) (j pj)\",\n pi=self.ps[0],\n pj=self.ps[1],\n )\n\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 \"comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer\"\n )\n },\n **kwargs,\n )\n" }, { "path": "comfy/ldm/modules/attention.py", "content": "import math\nimport sys\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, einsum\nfrom einops import rearrange, repeat\nfrom typing import Optional, Any, Callable, Union\nimport logging\nimport functools\n\nfrom .diffusionmodules.util import AlphaBlender, timestep_embedding\nfrom .sub_quadratic_attention import efficient_dot_product_attention\n\nfrom comfy import model_management\n\nif model_management.xformers_enabled():\n import xformers\n import xformers.ops\n\nSAGE_ATTENTION_IS_AVAILABLE = False\ntry:\n from sageattention import sageattn\n SAGE_ATTENTION_IS_AVAILABLE = True\nexcept ImportError as e:\n if model_management.sage_attention_enabled():\n if e.name == \"sageattention\":\n logging.error(f\"\\n\\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\\ncommand:\\n\\t{sys.executable} -m pip install sageattention\")\n else:\n raise e\n exit(-1)\n\nSAGE_ATTENTION3_IS_AVAILABLE = False\ntry:\n from sageattn3 import sageattn3_blackwell\n SAGE_ATTENTION3_IS_AVAILABLE = True\nexcept ImportError:\n pass\n\nFLASH_ATTENTION_IS_AVAILABLE = False\ntry:\n from flash_attn import flash_attn_func\n FLASH_ATTENTION_IS_AVAILABLE = True\nexcept ImportError:\n if model_management.flash_attention_enabled():\n logging.error(f\"\\n\\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\\ncommand:\\n\\t{sys.executable} -m pip install flash-attn\")\n exit(-1)\n\nREGISTERED_ATTENTION_FUNCTIONS = {}\ndef register_attention_function(name: str, func: Callable):\n # avoid replacing existing functions\n if name not in REGISTERED_ATTENTION_FUNCTIONS:\n REGISTERED_ATTENTION_FUNCTIONS[name] = func\n else:\n logging.warning(f\"Attention function {name} already registered, skipping registration.\")\n\ndef get_attention_function(name: str, default: Any=...) -> Union[Callable, None]:\n if name == \"optimized\":\n return optimized_attention\n elif name not in REGISTERED_ATTENTION_FUNCTIONS:\n if default is ...:\n raise KeyError(f\"Attention function {name} not found.\")\n else:\n return default\n return REGISTERED_ATTENTION_FUNCTIONS[name]\n\nfrom comfy.cli_args import args\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nFORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()\n\ndef get_attn_precision(attn_precision, current_dtype):\n if args.dont_upcast_attention:\n return None\n\n if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE:\n return FORCE_UPCAST_ATTENTION_DTYPE[current_dtype]\n return attn_precision\n\ndef exists(val):\n return val is not None\n\n\ndef default(val, d):\n if exists(val):\n return val\n return d\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\n\ndef wrap_attn(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n remove_attn_wrapper_key = False\n try:\n if \"_inside_attn_wrapper\" not in kwargs:\n transformer_options = kwargs.get(\"transformer_options\", None)\n remove_attn_wrapper_key = True\n kwargs[\"_inside_attn_wrapper\"] = True\n if transformer_options is not None:\n if \"optimized_attention_override\" in transformer_options:\n return transformer_options[\"optimized_attention_override\"](func, *args, **kwargs)\n return func(*args, **kwargs)\n finally:\n if remove_attn_wrapper_key:\n del kwargs[\"_inside_attn_wrapper\"]\n return wrapper\n\n@wrap_attn\ndef attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n attn_precision = get_attn_precision(attn_precision, q.dtype)\n\n if skip_reshape:\n b, _, _, dim_head = q.shape\n else:\n b, _, dim_head = q.shape\n dim_head //= heads\n\n scale = dim_head ** -0.5\n\n h = heads\n if skip_reshape:\n q, k, v = map(\n lambda t: t.reshape(b * heads, -1, dim_head),\n (q, k, v),\n )\n else:\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 == torch.float32:\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 if len(mask.shape) == 2:\n bs = 1\n else:\n bs = mask.shape[0]\n mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])\n sim.add_(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\n if skip_output_reshape:\n out = (\n out.unsqueeze(0)\n .reshape(b, heads, -1, dim_head)\n )\n else:\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@wrap_attn\ndef attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n attn_precision = get_attn_precision(attn_precision, query.dtype)\n\n if skip_reshape:\n b, _, _, dim_head = query.shape\n else:\n b, _, dim_head = query.shape\n dim_head //= heads\n\n if skip_reshape:\n query = query.reshape(b * heads, -1, dim_head)\n value = value.reshape(b * heads, -1, dim_head)\n key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)\n else:\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 key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)\n\n\n dtype = query.dtype\n upcast_attention = attn_precision == torch.float32 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\n mem_free_total, _ = 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 if mask is not None:\n if len(mask.shape) == 2:\n bs = 1\n else:\n bs = mask.shape[0]\n mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])\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 if skip_output_reshape:\n hidden_states = hidden_states.unflatten(0, (-1, heads))\n else:\n hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)\n return hidden_states\n\n@wrap_attn\ndef attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n attn_precision = get_attn_precision(attn_precision, q.dtype)\n\n if skip_reshape:\n b, _, _, dim_head = q.shape\n else:\n b, _, dim_head = q.shape\n dim_head //= heads\n\n scale = dim_head ** -0.5\n\n if skip_reshape:\n q, k, v = map(\n lambda t: t.reshape(b * heads, -1, dim_head),\n (q, k, v),\n )\n else:\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 == torch.float32:\n element_size = 4\n upcast = True\n else:\n element_size = q.element_size()\n upcast = False\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 if mask is not None:\n if len(mask.shape) == 2:\n bs = 1\n else:\n bs = mask.shape[0]\n mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])\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 upcast:\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 if mask.shape[1] == 1:\n s1 += mask\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 Exception as e:\n model_management.raise_non_oom(e)\n if first_op_done == False:\n model_management.soft_empty_cache(True)\n if cleared_cache == False:\n cleared_cache = True\n logging.warning(\"out of memory error, emptying cache and trying again\")\n continue\n steps *= 2\n if steps > 64:\n raise e\n logging.warning(\"out of memory error, increasing steps and trying again {}\".format(steps))\n else:\n raise e\n\n del q, k, v\n\n if skip_output_reshape:\n r1 = (\n r1.unsqueeze(0)\n .reshape(b, heads, -1, dim_head)\n )\n else:\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 # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error)\n BROKEN_XFORMERS = x_vers.startswith(\"0.0.2\") and not x_vers.startswith(\"0.0.20\")\nexcept:\n pass\n\n@wrap_attn\ndef attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n b = q.shape[0]\n dim_head = q.shape[-1]\n # check to make sure xformers isn't broken\n disabled_xformers = False\n\n if BROKEN_XFORMERS:\n if b * heads > 65535:\n disabled_xformers = True\n\n if not disabled_xformers:\n if torch.jit.is_tracing() or torch.jit.is_scripting():\n disabled_xformers = True\n\n if disabled_xformers:\n return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)\n\n if skip_reshape:\n # b h k d -> b k h d\n q, k, v = map(\n lambda t: t.permute(0, 2, 1, 3),\n (q, k, v),\n )\n # actually do the reshaping\n else:\n dim_head //= heads\n q, k, v = map(\n lambda t: t.reshape(b, -1, heads, dim_head),\n (q, k, v),\n )\n\n if mask is not None:\n # add a singleton batch dimension\n if mask.ndim == 2:\n mask = mask.unsqueeze(0)\n # add a singleton heads dimension\n if mask.ndim == 3:\n mask = mask.unsqueeze(1)\n # pad to a multiple of 8\n pad = 8 - mask.shape[-1] % 8\n # the xformers docs says that it's allowed to have a mask of shape (1, Nq, Nk)\n # but when using separated heads, the shape has to be (B, H, Nq, Nk)\n # in flux, this matrix ends up being over 1GB\n # here, we create a mask with the same batch/head size as the input mask (potentially singleton or full)\n mask_out = torch.empty([mask.shape[0], mask.shape[1], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device)\n\n mask_out[..., :mask.shape[-1]] = mask\n # doesn't this remove the padding again??\n mask = mask_out[..., :mask.shape[-1]]\n mask = mask.expand(b, heads, -1, -1)\n\n out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)\n\n if skip_output_reshape:\n out = out.permute(0, 2, 1, 3)\n else:\n out = (\n out.reshape(b, -1, heads * dim_head)\n )\n\n return out\n\nif model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.\n SDP_BATCH_LIMIT = 2**15\nelse:\n #TODO: other GPUs ?\n SDP_BATCH_LIMIT = 2**31\n\n@wrap_attn\ndef attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n if skip_reshape:\n b, _, _, dim_head = q.shape\n else:\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 if mask is not None:\n # add a batch dimension if there isn't already one\n if mask.ndim == 2:\n mask = mask.unsqueeze(0)\n # add a heads dimension if there isn't already one\n if mask.ndim == 3:\n mask = mask.unsqueeze(1)\n\n if SDP_BATCH_LIMIT >= b:\n out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)\n if not skip_output_reshape:\n out = (\n out.transpose(1, 2).reshape(b, -1, heads * dim_head)\n )\n else:\n out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)\n for i in range(0, b, SDP_BATCH_LIMIT):\n m = mask\n if mask is not None:\n if mask.shape[0] > 1:\n m = mask[i : i + SDP_BATCH_LIMIT]\n\n out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention(\n q[i : i + SDP_BATCH_LIMIT],\n k[i : i + SDP_BATCH_LIMIT],\n v[i : i + SDP_BATCH_LIMIT],\n attn_mask=m,\n dropout_p=0.0, is_causal=False\n ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)\n return out\n\n@wrap_attn\ndef attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n if kwargs.get(\"low_precision_attention\", True) is False:\n return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)\n\n exception_fallback = False\n if skip_reshape:\n b, _, _, dim_head = q.shape\n tensor_layout = \"HND\"\n else:\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),\n (q, k, v),\n )\n tensor_layout = \"NHD\"\n\n if mask is not None:\n # add a batch dimension if there isn't already one\n if mask.ndim == 2:\n mask = mask.unsqueeze(0)\n # add a heads dimension if there isn't already one\n if mask.ndim == 3:\n mask = mask.unsqueeze(1)\n\n try:\n out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)\n except Exception as e:\n logging.error(\"Error running sage attention: {}, using pytorch attention instead.\".format(e))\n exception_fallback = True\n if exception_fallback:\n if tensor_layout == \"NHD\":\n q, k, v = map(\n lambda t: t.transpose(1, 2),\n (q, k, v),\n )\n return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)\n\n if tensor_layout == \"HND\":\n if not skip_output_reshape:\n out = (\n out.transpose(1, 2).reshape(b, -1, heads * dim_head)\n )\n else:\n if skip_output_reshape:\n out = out.transpose(1, 2)\n else:\n out = out.reshape(b, -1, heads * dim_head)\n return out\n\n@wrap_attn\ndef attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n exception_fallback = False\n if (q.device.type != \"cuda\" or\n q.dtype not in (torch.float16, torch.bfloat16) or\n mask is not None):\n return attention_pytorch(\n q, k, v, heads,\n mask=mask,\n attn_precision=attn_precision,\n skip_reshape=skip_reshape,\n skip_output_reshape=skip_output_reshape,\n **kwargs\n )\n\n if skip_reshape:\n B, H, L, D = q.shape\n if H != heads:\n return attention_pytorch(\n q, k, v, heads,\n mask=mask,\n attn_precision=attn_precision,\n skip_reshape=True,\n skip_output_reshape=skip_output_reshape,\n **kwargs\n )\n q_s, k_s, v_s = q, k, v\n N = q.shape[2]\n dim_head = D\n else:\n B, N, inner_dim = q.shape\n if inner_dim % heads != 0:\n return attention_pytorch(\n q, k, v, heads,\n mask=mask,\n attn_precision=attn_precision,\n skip_reshape=False,\n skip_output_reshape=skip_output_reshape,\n **kwargs\n )\n dim_head = inner_dim // heads\n\n if dim_head >= 256 or N <= 1024:\n return attention_pytorch(\n q, k, v, heads,\n mask=mask,\n attn_precision=attn_precision,\n skip_reshape=skip_reshape,\n skip_output_reshape=skip_output_reshape,\n **kwargs\n )\n\n if not skip_reshape:\n q_s, k_s, v_s = map(\n lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(),\n (q, k, v),\n )\n B, H, L, D = q_s.shape\n\n try:\n out = sageattn3_blackwell(q_s, k_s, v_s, is_causal=False)\n except Exception as e:\n exception_fallback = True\n logging.error(\"Error running SageAttention3: %s, falling back to pytorch attention.\", e)\n\n if exception_fallback:\n if not skip_reshape:\n del q_s, k_s, v_s\n return attention_pytorch(\n q, k, v, heads,\n mask=mask,\n attn_precision=attn_precision,\n skip_reshape=False,\n skip_output_reshape=skip_output_reshape,\n **kwargs\n )\n\n if skip_reshape:\n if not skip_output_reshape:\n out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)\n else:\n if skip_output_reshape:\n pass\n else:\n out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)\n\n return out\n\ntry:\n @torch.library.custom_op(\"flash_attention::flash_attn\", mutates_args=())\n def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,\n dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:\n return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)\n\n\n @flash_attn_wrapper.register_fake\n def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):\n # Output shape is the same as q\n return q.new_empty(q.shape)\nexcept AttributeError as error:\n FLASH_ATTN_ERROR = error\n\n def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,\n dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:\n assert False, f\"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}\"\n\n@wrap_attn\ndef attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):\n if skip_reshape:\n b, _, _, dim_head = q.shape\n else:\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 if mask is not None:\n # add a batch dimension if there isn't already one\n if mask.ndim == 2:\n mask = mask.unsqueeze(0)\n # add a heads dimension if there isn't already one\n if mask.ndim == 3:\n mask = mask.unsqueeze(1)\n\n try:\n if mask is not None:\n raise RuntimeError(\"Mask must not be set for Flash attention\")\n out = flash_attn_wrapper(\n q.transpose(1, 2),\n k.transpose(1, 2),\n v.transpose(1, 2),\n dropout_p=0.0,\n causal=False,\n ).transpose(1, 2)\n except Exception as e:\n logging.warning(f\"Flash Attention failed, using default SDPA: {e}\")\n out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)\n if not skip_output_reshape:\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.sage_attention_enabled():\n logging.info(\"Using sage attention\")\n optimized_attention = attention_sage\nelif model_management.xformers_enabled():\n logging.info(\"Using xformers attention\")\n optimized_attention = attention_xformers\nelif model_management.flash_attention_enabled():\n logging.info(\"Using Flash Attention\")\n optimized_attention = attention_flash\nelif model_management.pytorch_attention_enabled():\n logging.info(\"Using pytorch attention\")\n optimized_attention = attention_pytorch\nelse:\n if args.use_split_cross_attention:\n logging.info(\"Using split optimization for attention\")\n optimized_attention = attention_split\n else:\n logging.info(\"Using sub quadratic optimization for attention, if you have memory or speed issues try using: --use-split-cross-attention\")\n optimized_attention = attention_sub_quad\n\noptimized_attention_masked = optimized_attention\n\n\n# register core-supported attention functions\nif SAGE_ATTENTION_IS_AVAILABLE:\n register_attention_function(\"sage\", attention_sage)\nif SAGE_ATTENTION3_IS_AVAILABLE:\n register_attention_function(\"sage3\", attention3_sage)\nif FLASH_ATTENTION_IS_AVAILABLE:\n register_attention_function(\"flash\", attention_flash)\nif model_management.xformers_enabled():\n register_attention_function(\"xformers\", attention_xformers)\nregister_attention_function(\"pytorch\", attention_pytorch)\nregister_attention_function(\"sub_quad\", attention_sub_quad)\nregister_attention_function(\"split\", attention_split)\n\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., attn_precision=None, dtype=None, device=None, operations=ops):\n super().__init__()\n inner_dim = dim_head * heads\n context_dim = default(context_dim, query_dim)\n self.attn_precision = attn_precision\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, transformer_options={}):\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, attn_precision=self.attn_precision, transformer_options=transformer_options)\n else:\n out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)\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, attn_precision=None, 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 self.attn_precision = attn_precision\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, attn_precision=self.attn_precision, 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, attn_precision=self.attn_precision, 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.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 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 extra_options[\"attn_precision\"] = self.attn_precision\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, transformer_options=transformer_options)\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 + x\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, transformer_options=transformer_options)\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 + x\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 + x\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, attn_precision=None, 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, attn_precision=attn_precision, 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 transformer_options[\"activations_shape\"] = list(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 = x.movedim(1, 3).flatten(1, 2).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 = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).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 attn_precision=None,\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 attn_precision=attn_precision,\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 attn_precision=attn_precision,\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 transformer_options[\"activations_shape\"] = list(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, transformer_options=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": "comfy/ldm/modules/diffusionmodules/__init__.py", "content": "" }, { "path": "comfy/ldm/modules/diffusionmodules/mmdit.py", "content": "from functools import partial\nfrom typing import Dict, Optional, List\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom ..attention import optimized_attention\nfrom einops import rearrange, repeat\nfrom .util import timestep_embedding\nimport comfy.ops\nimport comfy.ldm.common_dit\n\ndef default(x, y):\n if x is not None:\n return x\n return y\n\nclass Mlp(nn.Module):\n \"\"\" MLP as used in Vision Transformer, MLP-Mixer and related networks\n \"\"\"\n def __init__(\n self,\n in_features,\n hidden_features=None,\n out_features=None,\n act_layer=nn.GELU,\n norm_layer=None,\n bias=True,\n drop=0.,\n use_conv=False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n drop_probs = drop\n linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear\n\n self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device)\n self.act = act_layer()\n self.drop1 = nn.Dropout(drop_probs)\n self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()\n self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)\n self.drop2 = nn.Dropout(drop_probs)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop1(x)\n x = self.norm(x)\n x = self.fc2(x)\n x = self.drop2(x)\n return x\n\nclass PatchEmbed(nn.Module):\n \"\"\" 2D Image to Patch Embedding\n \"\"\"\n dynamic_img_pad: torch.jit.Final[bool]\n\n def __init__(\n self,\n img_size: Optional[int] = 224,\n patch_size: int = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n norm_layer = None,\n flatten: bool = True,\n bias: bool = True,\n strict_img_size: bool = True,\n dynamic_img_pad: bool = True,\n padding_mode='circular',\n conv3d=False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n try:\n len(patch_size)\n self.patch_size = patch_size\n except:\n if conv3d:\n self.patch_size = (patch_size, patch_size, patch_size)\n else:\n self.patch_size = (patch_size, patch_size)\n self.padding_mode = padding_mode\n\n # flatten spatial dim and transpose to channels last, kept for bwd compat\n self.flatten = flatten\n self.strict_img_size = strict_img_size\n self.dynamic_img_pad = dynamic_img_pad\n if conv3d:\n self.proj = operations.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)\n else:\n self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)\n self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()\n\n def forward(self, x):\n if self.dynamic_img_pad:\n x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)\n x = self.proj(x)\n if self.flatten:\n x = x.flatten(2).transpose(1, 2) # NCHW -> NLC\n x = self.norm(x)\n return x\n\ndef modulate(x, shift, scale):\n if shift is None:\n shift = torch.zeros_like(scale)\n return torch.addcmul(shift.unsqueeze(1), x, 1+ scale.unsqueeze(1))\n\n\n#################################################################################\n# Sine/Cosine Positional Embedding Functions #\n#################################################################################\n\n\ndef get_2d_sincos_pos_embed(\n embed_dim,\n grid_size,\n cls_token=False,\n extra_tokens=0,\n scaling_factor=None,\n offset=None,\n):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n grid_h = np.arange(grid_size, dtype=np.float32)\n grid_w = np.arange(grid_size, dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0)\n if scaling_factor is not None:\n grid = grid / scaling_factor\n if offset is not None:\n grid = grid - offset\n\n grid = grid.reshape([2, 1, grid_size, grid_size])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token and extra_tokens > 0:\n pos_embed = np.concatenate(\n [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0\n )\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (M,)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float64)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000**omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\ndef get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):\n omega = torch.arange(embed_dim // 2, device=device, dtype=dtype)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000**omega # (D/2,)\n pos = pos.reshape(-1) # (M,)\n out = torch.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n emb_sin = torch.sin(out) # (M, D/2)\n emb_cos = torch.cos(out) # (M, D/2)\n emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)\n return emb\n\ndef get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):\n small = min(h, w)\n val_h = (h / small) * val_magnitude\n val_w = (w / small) * val_magnitude\n grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')\n emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)\n emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)\n emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D)\n return emb\n\n\n#################################################################################\n# Embedding Layers for Timesteps and Class Labels #\n#################################################################################\n\n\nclass TimestepEmbedder(nn.Module):\n \"\"\"\n Embeds scalar timesteps into vector representations.\n \"\"\"\n\n def __init__(self, hidden_size, frequency_embedding_size=256, output_size=None, dtype=None, device=None, operations=None):\n super().__init__()\n if output_size is None:\n output_size = hidden_size\n self.mlp = nn.Sequential(\n operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(hidden_size, output_size, bias=True, dtype=dtype, device=device),\n )\n self.frequency_embedding_size = frequency_embedding_size\n\n def forward(self, t, dtype, **kwargs):\n t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype)\n t_emb = self.mlp(t_freq)\n return t_emb\n\n\nclass VectorEmbedder(nn.Module):\n \"\"\"\n Embeds a flat vector of dimension input_dim\n \"\"\"\n\n def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.mlp = nn.Sequential(\n operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n emb = self.mlp(x)\n return emb\n\n\n#################################################################################\n# Core DiT Model #\n#################################################################################\n\n\ndef split_qkv(qkv, head_dim):\n qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)\n return qkv[0], qkv[1], qkv[2]\n\n\nclass SelfAttention(nn.Module):\n ATTENTION_MODES = (\"xformers\", \"torch\", \"torch-hb\", \"math\", \"debug\")\n\n def __init__(\n self,\n dim: int,\n num_heads: int = 8,\n qkv_bias: bool = False,\n qk_scale: Optional[float] = None,\n proj_drop: float = 0.0,\n attn_mode: str = \"xformers\",\n pre_only: bool = False,\n qk_norm: Optional[str] = None,\n rmsnorm: bool = False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n\n self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)\n if not pre_only:\n self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)\n self.proj_drop = nn.Dropout(proj_drop)\n assert attn_mode in self.ATTENTION_MODES\n self.attn_mode = attn_mode\n self.pre_only = pre_only\n\n if qk_norm == \"rms\":\n self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)\n self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)\n elif qk_norm == \"ln\":\n self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)\n self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)\n elif qk_norm is None:\n self.ln_q = nn.Identity()\n self.ln_k = nn.Identity()\n else:\n raise ValueError(qk_norm)\n\n def pre_attention(self, x: torch.Tensor) -> torch.Tensor:\n B, L, C = x.shape\n qkv = self.qkv(x)\n q, k, v = split_qkv(qkv, self.head_dim)\n q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)\n k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)\n return (q, k, v)\n\n def post_attention(self, x: torch.Tensor) -> torch.Tensor:\n assert not self.pre_only\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n q, k, v = self.pre_attention(x)\n x = optimized_attention(\n q, k, v, heads=self.num_heads\n )\n x = self.post_attention(x)\n return x\n\n\nclass RMSNorm(torch.nn.Module):\n def __init__(\n self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs\n ):\n \"\"\"\n Initialize the RMSNorm normalization layer.\n Args:\n dim (int): The dimension of the input tensor.\n eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.\n Attributes:\n eps (float): A small value added to the denominator for numerical stability.\n weight (nn.Parameter): Learnable scaling parameter.\n \"\"\"\n super().__init__()\n self.eps = eps\n self.learnable_scale = elementwise_affine\n if self.learnable_scale:\n self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))\n else:\n self.register_parameter(\"weight\", None)\n\n def forward(self, x):\n return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)\n\n\n\nclass SwiGLUFeedForward(nn.Module):\n def __init__(\n self,\n dim: int,\n hidden_dim: int,\n multiple_of: int,\n ffn_dim_multiplier: Optional[float] = None,\n ):\n \"\"\"\n Initialize the FeedForward module.\n\n Args:\n dim (int): Input dimension.\n hidden_dim (int): Hidden dimension of the feedforward layer.\n multiple_of (int): Value to ensure hidden dimension is a multiple of this value.\n ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.\n\n Attributes:\n w1 (ColumnParallelLinear): Linear transformation for the first layer.\n w2 (RowParallelLinear): Linear transformation for the second layer.\n w3 (ColumnParallelLinear): Linear transformation for the third layer.\n\n \"\"\"\n super().__init__()\n hidden_dim = int(2 * hidden_dim / 3)\n # custom dim factor multiplier\n if ffn_dim_multiplier is not None:\n hidden_dim = int(ffn_dim_multiplier * hidden_dim)\n hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)\n\n self.w1 = nn.Linear(dim, hidden_dim, bias=False)\n self.w2 = nn.Linear(hidden_dim, dim, bias=False)\n self.w3 = nn.Linear(dim, hidden_dim, bias=False)\n\n def forward(self, x):\n return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))\n\n\nclass DismantledBlock(nn.Module):\n \"\"\"\n A DiT block with gated adaptive layer norm (adaLN) conditioning.\n \"\"\"\n\n ATTENTION_MODES = (\"xformers\", \"torch\", \"torch-hb\", \"math\", \"debug\")\n\n def __init__(\n self,\n hidden_size: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n attn_mode: str = \"xformers\",\n qkv_bias: bool = False,\n pre_only: bool = False,\n rmsnorm: bool = False,\n scale_mod_only: bool = False,\n swiglu: bool = False,\n qk_norm: Optional[str] = None,\n x_block_self_attn: bool = False,\n dtype=None,\n device=None,\n operations=None,\n **block_kwargs,\n ):\n super().__init__()\n assert attn_mode in self.ATTENTION_MODES\n if not rmsnorm:\n self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n else:\n self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)\n self.attn = SelfAttention(\n dim=hidden_size,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n attn_mode=attn_mode,\n pre_only=pre_only,\n qk_norm=qk_norm,\n rmsnorm=rmsnorm,\n dtype=dtype,\n device=device,\n operations=operations\n )\n if x_block_self_attn:\n assert not pre_only\n assert not scale_mod_only\n self.x_block_self_attn = True\n self.attn2 = SelfAttention(\n dim=hidden_size,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n attn_mode=attn_mode,\n pre_only=False,\n qk_norm=qk_norm,\n rmsnorm=rmsnorm,\n dtype=dtype,\n device=device,\n operations=operations\n )\n else:\n self.x_block_self_attn = False\n if not pre_only:\n if not rmsnorm:\n self.norm2 = operations.LayerNorm(\n hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device\n )\n else:\n self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)\n mlp_hidden_dim = int(hidden_size * mlp_ratio)\n if not pre_only:\n if not swiglu:\n self.mlp = Mlp(\n in_features=hidden_size,\n hidden_features=mlp_hidden_dim,\n act_layer=lambda: nn.GELU(approximate=\"tanh\"),\n drop=0,\n dtype=dtype,\n device=device,\n operations=operations\n )\n else:\n self.mlp = SwiGLUFeedForward(\n dim=hidden_size,\n hidden_dim=mlp_hidden_dim,\n multiple_of=256,\n )\n self.scale_mod_only = scale_mod_only\n if x_block_self_attn:\n assert not pre_only\n assert not scale_mod_only\n n_mods = 9\n elif not scale_mod_only:\n n_mods = 6 if not pre_only else 2\n else:\n n_mods = 4 if not pre_only else 1\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device)\n )\n self.pre_only = pre_only\n\n def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:\n if not self.pre_only:\n if not self.scale_mod_only:\n (\n shift_msa,\n scale_msa,\n gate_msa,\n shift_mlp,\n scale_mlp,\n gate_mlp,\n ) = self.adaLN_modulation(c).chunk(6, dim=1)\n else:\n shift_msa = None\n shift_mlp = None\n (\n scale_msa,\n gate_msa,\n scale_mlp,\n gate_mlp,\n ) = self.adaLN_modulation(\n c\n ).chunk(4, dim=1)\n qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))\n return qkv, (\n x,\n gate_msa,\n shift_mlp,\n scale_mlp,\n gate_mlp,\n )\n else:\n if not self.scale_mod_only:\n (\n shift_msa,\n scale_msa,\n ) = self.adaLN_modulation(\n c\n ).chunk(2, dim=1)\n else:\n shift_msa = None\n scale_msa = self.adaLN_modulation(c)\n qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))\n return qkv, None\n\n def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):\n assert not self.pre_only\n x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)\n x = x + gate_mlp.unsqueeze(1) * self.mlp(\n modulate(self.norm2(x), shift_mlp, scale_mlp)\n )\n return x\n\n def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:\n assert self.x_block_self_attn\n (\n shift_msa,\n scale_msa,\n gate_msa,\n shift_mlp,\n scale_mlp,\n gate_mlp,\n shift_msa2,\n scale_msa2,\n gate_msa2,\n ) = self.adaLN_modulation(c).chunk(9, dim=1)\n x_norm = self.norm1(x)\n qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa))\n qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2))\n return qkv, qkv2, (\n x,\n gate_msa,\n shift_mlp,\n scale_mlp,\n gate_mlp,\n gate_msa2,\n )\n\n def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2):\n assert not self.pre_only\n attn1 = self.attn.post_attention(attn)\n attn2 = self.attn2.post_attention(attn2)\n x = gate_cat(x, gate_msa, gate_msa2, attn1, attn2)\n x = x + gate_mlp.unsqueeze(1) * self.mlp(\n modulate(self.norm2(x), shift_mlp, scale_mlp)\n )\n return x\n\n def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:\n assert not self.pre_only\n if self.x_block_self_attn:\n qkv, qkv2, intermediates = self.pre_attention_x(x, c)\n attn, _ = optimized_attention(\n qkv[0], qkv[1], qkv[2],\n num_heads=self.attn.num_heads,\n )\n attn2, _ = optimized_attention(\n qkv2[0], qkv2[1], qkv2[2],\n num_heads=self.attn2.num_heads,\n )\n return self.post_attention_x(attn, attn2, *intermediates)\n else:\n qkv, intermediates = self.pre_attention(x, c)\n attn = optimized_attention(\n qkv[0], qkv[1], qkv[2],\n heads=self.attn.num_heads,\n )\n return self.post_attention(attn, *intermediates)\n\ndef gate_cat(x, gate_msa, gate_msa2, attn1, attn2):\n out1 = gate_msa.unsqueeze(1) * attn1\n out2 = gate_msa2.unsqueeze(1) * attn2\n x = torch.stack([x, out1, out2], dim=0).sum(dim=0)\n return x\n\ndef block_mixing(*args, use_checkpoint=True, **kwargs):\n if use_checkpoint:\n return torch.utils.checkpoint.checkpoint(\n _block_mixing, *args, use_reentrant=False, **kwargs\n )\n else:\n return _block_mixing(*args, **kwargs)\n\n\ndef _block_mixing(context, x, context_block, x_block, c, transformer_options={}):\n context_qkv, context_intermediates = context_block.pre_attention(context, c)\n\n if x_block.x_block_self_attn:\n x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c)\n else:\n x_qkv, x_intermediates = x_block.pre_attention(x, c)\n\n o = []\n for t in range(3):\n o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))\n qkv = tuple(o)\n\n attn = optimized_attention(\n qkv[0], qkv[1], qkv[2],\n heads=x_block.attn.num_heads,\n transformer_options=transformer_options,\n )\n context_attn, x_attn = (\n attn[:, : context_qkv[0].shape[1]],\n attn[:, context_qkv[0].shape[1] :],\n )\n\n if not context_block.pre_only:\n context = context_block.post_attention(context_attn, *context_intermediates)\n\n else:\n context = None\n if x_block.x_block_self_attn:\n attn2 = optimized_attention(\n x_qkv2[0], x_qkv2[1], x_qkv2[2],\n heads=x_block.attn2.num_heads,\n transformer_options=transformer_options,\n )\n x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)\n else:\n x = x_block.post_attention(x_attn, *x_intermediates)\n return context, x\n\n\nclass JointBlock(nn.Module):\n \"\"\"just a small wrapper to serve as a fsdp unit\"\"\"\n\n def __init__(\n self,\n *args,\n **kwargs,\n ):\n super().__init__()\n pre_only = kwargs.pop(\"pre_only\")\n qk_norm = kwargs.pop(\"qk_norm\", None)\n x_block_self_attn = kwargs.pop(\"x_block_self_attn\", False)\n self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)\n self.x_block = DismantledBlock(*args,\n pre_only=False,\n qk_norm=qk_norm,\n x_block_self_attn=x_block_self_attn,\n **kwargs)\n\n def forward(self, *args, **kwargs):\n return block_mixing(\n *args, context_block=self.context_block, x_block=self.x_block, **kwargs\n )\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of DiT.\n \"\"\"\n\n def __init__(\n self,\n hidden_size: int,\n patch_size: int,\n out_channels: int,\n total_out_channels: Optional[int] = None,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = (\n operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)\n if (total_out_channels is None)\n else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)\n )\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)\n )\n\n def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:\n shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)\n x = modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n return x\n\nclass SelfAttentionContext(nn.Module):\n def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):\n super().__init__()\n dim_head = dim // heads\n inner_dim = dim\n\n self.heads = heads\n self.dim_head = dim_head\n\n self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)\n\n self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device)\n\n def forward(self, x):\n qkv = self.qkv(x)\n q, k, v = split_qkv(qkv, self.dim_head)\n x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads)\n return self.proj(x)\n\nclass ContextProcessorBlock(nn.Module):\n def __init__(self, context_size, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations)\n self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate=\"tanh\"), drop=0, dtype=dtype, device=device, operations=operations)\n\n def forward(self, x):\n x += self.attn(self.norm1(x))\n x += self.mlp(self.norm2(x))\n return x\n\nclass ContextProcessor(nn.Module):\n def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):\n super().__init__()\n self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)])\n self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n\n def forward(self, x):\n for i, l in enumerate(self.layers):\n x = l(x)\n return self.norm(x)\n\nclass MMDiT(nn.Module):\n \"\"\"\n Diffusion model with a Transformer backbone.\n \"\"\"\n\n def __init__(\n self,\n input_size: int = 32,\n patch_size: int = 2,\n in_channels: int = 4,\n depth: int = 28,\n # hidden_size: Optional[int] = None,\n # num_heads: Optional[int] = None,\n mlp_ratio: float = 4.0,\n learn_sigma: bool = False,\n adm_in_channels: Optional[int] = None,\n context_embedder_config: Optional[Dict] = None,\n compile_core: bool = False,\n use_checkpoint: bool = False,\n register_length: int = 0,\n attn_mode: str = \"torch\",\n rmsnorm: bool = False,\n scale_mod_only: bool = False,\n swiglu: bool = False,\n out_channels: Optional[int] = None,\n pos_embed_scaling_factor: Optional[float] = None,\n pos_embed_offset: Optional[float] = None,\n pos_embed_max_size: Optional[int] = None,\n num_patches = None,\n qk_norm: Optional[str] = None,\n qkv_bias: bool = True,\n context_processor_layers = None,\n x_block_self_attn: bool = False,\n x_block_self_attn_layers: Optional[List[int]] = [],\n context_size = 4096,\n num_blocks = None,\n final_layer = True,\n skip_blocks = False,\n dtype = None, #TODO\n device = None,\n operations = None,\n ):\n super().__init__()\n self.dtype = dtype\n self.learn_sigma = learn_sigma\n self.in_channels = in_channels\n default_out_channels = in_channels * 2 if learn_sigma else in_channels\n self.out_channels = default(out_channels, default_out_channels)\n self.patch_size = patch_size\n self.pos_embed_scaling_factor = pos_embed_scaling_factor\n self.pos_embed_offset = pos_embed_offset\n self.pos_embed_max_size = pos_embed_max_size\n self.x_block_self_attn_layers = x_block_self_attn_layers\n\n # hidden_size = default(hidden_size, 64 * depth)\n # num_heads = default(num_heads, hidden_size // 64)\n\n # apply magic --> this defines a head_size of 64\n self.hidden_size = 64 * depth\n num_heads = depth\n if num_blocks is None:\n num_blocks = depth\n\n self.depth = depth\n self.num_heads = num_heads\n\n self.x_embedder = PatchEmbed(\n input_size,\n patch_size,\n in_channels,\n self.hidden_size,\n bias=True,\n strict_img_size=self.pos_embed_max_size is None,\n dtype=dtype,\n device=device,\n operations=operations\n )\n self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)\n\n self.y_embedder = None\n if adm_in_channels is not None:\n assert isinstance(adm_in_channels, int)\n self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations)\n\n if context_processor_layers is not None:\n self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations)\n else:\n self.context_processor = None\n\n self.context_embedder = nn.Identity()\n if context_embedder_config is not None:\n if context_embedder_config[\"target\"] == \"torch.nn.Linear\":\n self.context_embedder = operations.Linear(**context_embedder_config[\"params\"], dtype=dtype, device=device)\n\n self.register_length = register_length\n if self.register_length > 0:\n self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device))\n\n # num_patches = self.x_embedder.num_patches\n # Will use fixed sin-cos embedding:\n # just use a buffer already\n if num_patches is not None:\n self.register_buffer(\n \"pos_embed\",\n torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device),\n )\n else:\n self.pos_embed = None\n\n self.use_checkpoint = use_checkpoint\n if not skip_blocks:\n self.joint_blocks = nn.ModuleList(\n [\n JointBlock(\n self.hidden_size,\n num_heads,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n attn_mode=attn_mode,\n pre_only=(i == num_blocks - 1) and final_layer,\n rmsnorm=rmsnorm,\n scale_mod_only=scale_mod_only,\n swiglu=swiglu,\n qk_norm=qk_norm,\n x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n for i in range(num_blocks)\n ]\n )\n\n if final_layer:\n self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)\n\n if compile_core:\n assert False\n self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)\n\n def cropped_pos_embed(self, hw, device=None):\n p = self.x_embedder.patch_size[0]\n h, w = hw\n # patched size\n h = (h + 1) // p\n w = (w + 1) // p\n if self.pos_embed is None:\n return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)\n assert self.pos_embed_max_size is not None\n assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)\n assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)\n top = (self.pos_embed_max_size - h) // 2\n left = (self.pos_embed_max_size - w) // 2\n spatial_pos_embed = rearrange(\n self.pos_embed,\n \"1 (h w) c -> 1 h w c\",\n h=self.pos_embed_max_size,\n w=self.pos_embed_max_size,\n )\n spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]\n spatial_pos_embed = rearrange(spatial_pos_embed, \"1 h w c -> 1 (h w) c\")\n # print(spatial_pos_embed, top, left, h, w)\n # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res\n # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better\n # # print(t)\n # return t\n return spatial_pos_embed\n\n def unpatchify(self, x, hw=None):\n \"\"\"\n x: (N, T, patch_size**2 * C)\n imgs: (N, H, W, C)\n \"\"\"\n c = self.out_channels\n p = self.x_embedder.patch_size[0]\n if hw is None:\n h = w = int(x.shape[1] ** 0.5)\n else:\n h, w = hw\n h = (h + 1) // p\n w = (w + 1) // p\n assert h * w == x.shape[1]\n\n x = x.reshape(shape=(x.shape[0], h, w, p, p, c))\n x = torch.einsum(\"nhwpqc->nchpwq\", x)\n imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))\n return imgs\n\n def forward_core_with_concat(\n self,\n x: torch.Tensor,\n c_mod: torch.Tensor,\n context: Optional[torch.Tensor] = None,\n control = None,\n transformer_options = {},\n ) -> torch.Tensor:\n patches_replace = transformer_options.get(\"patches_replace\", {})\n if self.register_length > 0:\n context = torch.cat(\n (\n repeat(self.register, \"1 ... -> b ...\", b=x.shape[0]),\n default(context, torch.Tensor([]).type_as(x)),\n ),\n 1,\n )\n\n # context is B, L', D\n # x is B, L, D\n blocks_replace = patches_replace.get(\"dit\", {})\n blocks = len(self.joint_blocks)\n for i in range(blocks):\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"txt\"], out[\"img\"] = self.joint_blocks[i](args[\"txt\"], args[\"img\"], c=args[\"vec\"], transformer_options=args[\"transformer_options\"])\n return out\n\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": c_mod, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n context = out[\"txt\"]\n x = out[\"img\"]\n else:\n context, x = self.joint_blocks[i](\n context,\n x,\n c=c_mod,\n use_checkpoint=self.use_checkpoint,\n transformer_options=transformer_options,\n )\n if control is not None:\n control_o = control.get(\"output\")\n if i < len(control_o):\n add = control_o[i]\n if add is not None:\n x += add\n\n x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels)\n return x\n\n def forward(\n self,\n x: torch.Tensor,\n t: torch.Tensor,\n y: Optional[torch.Tensor] = None,\n context: Optional[torch.Tensor] = None,\n control = None,\n transformer_options = {},\n ) -> torch.Tensor:\n \"\"\"\n Forward pass of DiT.\n x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)\n t: (N,) tensor of diffusion timesteps\n y: (N,) tensor of class labels\n \"\"\"\n\n if self.context_processor is not None:\n context = self.context_processor(context)\n\n hw = x.shape[-2:]\n x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)\n c = self.t_embedder(t, dtype=x.dtype) # (N, D)\n if y is not None and self.y_embedder is not None:\n y = self.y_embedder(y) # (N, D)\n c = c + y # (N, D)\n\n if context is not None:\n context = self.context_embedder(context)\n\n x = self.forward_core_with_concat(x, c, context, control, transformer_options)\n\n x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W)\n return x[:,:,:hw[-2],:hw[-1]]\n\n\nclass OpenAISignatureMMDITWrapper(MMDiT):\n def forward(\n self,\n x: torch.Tensor,\n timesteps: torch.Tensor,\n context: Optional[torch.Tensor] = None,\n y: Optional[torch.Tensor] = None,\n control = None,\n transformer_options = {},\n **kwargs,\n ) -> torch.Tensor:\n return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options)\n\n" }, { "path": "comfy/ldm/modules/diffusionmodules/model.py", "content": "# pytorch_diffusion + derived encoder decoder\nimport math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport logging\n\nfrom comfy import model_management\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nif model_management.xformers_enabled_vae():\n import xformers\n import xformers.ops\n\ndef torch_cat_if_needed(xl, dim):\n xl = [x for x in xl if x is not None and x.shape[dim] > 0]\n if len(xl) > 1:\n return torch.cat(xl, dim)\n elif len(xl) == 1:\n return xl[0]\n else:\n return None\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 torch.nn.functional.silu(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 CarriedConv3d(nn.Module):\n def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):\n super().__init__()\n self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)\n\n def forward(self, x):\n return self.conv(x)\n\n\ndef conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):\n\n x = xl[0]\n xl.clear()\n\n if isinstance(op, CarriedConv3d):\n if conv_carry_in is None:\n x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')\n else:\n carry_len = conv_carry_in[0].shape[2]\n x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')\n x = torch.cat([conv_carry_in.pop(0), x], dim=2)\n\n if conv_carry_out is not None:\n to_push = x[:, :, -2:, :, :].clone()\n conv_carry_out.append(to_push)\n\n out = op(x)\n\n return out\n\n\nclass VideoConv3d(nn.Module):\n def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs):\n super().__init__()\n\n self.padding_mode = padding_mode\n if padding != 0:\n padding = (padding, padding, padding, padding, kernel_size - 1, 0)\n else:\n kwargs[\"padding\"] = padding\n\n self.padding = padding\n self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)\n\n def forward(self, x):\n if self.padding != 0:\n x = torch.nn.functional.pad(x, self.padding, mode=self.padding_mode)\n return self.conv(x)\n\ndef interpolate_up(x, scale_factor):\n return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode=\"nearest\")\n\nclass Upsample(nn.Module):\n def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_factor=2.0):\n super().__init__()\n self.with_conv = with_conv\n self.scale_factor = scale_factor\n\n if self.with_conv:\n self.conv = conv_op(in_channels,\n in_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, x, conv_carry_in=None, conv_carry_out=None):\n scale_factor = self.scale_factor\n if isinstance(scale_factor, (int, float)):\n scale_factor = (scale_factor,) * (x.ndim - 2)\n\n if x.ndim == 5 and scale_factor[0] > 1.0:\n results = []\n if conv_carry_in is None:\n first = x[:, :, :1, :, :]\n results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2))\n x = x[:, :, 1:, :, :]\n if x.shape[2] > 0:\n results.append(interpolate_up(x, scale_factor))\n x = torch_cat_if_needed(results, dim=2)\n else:\n x = interpolate_up(x, scale_factor)\n if self.with_conv:\n x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)\n return x\n\n\nclass Downsample(nn.Module):\n def __init__(self, in_channels, with_conv, stride=2, conv_op=ops.Conv2d):\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 = conv_op(in_channels,\n in_channels,\n kernel_size=3,\n stride=stride,\n padding=0)\n\n def forward(self, x, conv_carry_in=None, conv_carry_out=None):\n if self.with_conv:\n if isinstance(self.conv, CarriedConv3d):\n x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)\n elif x.ndim == 4:\n pad = (0, 1, 0, 1)\n mode = \"constant\"\n x = torch.nn.functional.pad(x, pad, mode=mode, value=0)\n x = self.conv(x)\n elif x.ndim == 5:\n pad = (1, 1, 1, 1, 2, 0)\n mode = \"replicate\"\n x = torch.nn.functional.pad(x, pad, mode=mode)\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=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize):\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 = norm_op(in_channels)\n self.conv1 = conv_op(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 = norm_op(out_channels)\n self.dropout = torch.nn.Dropout(dropout, inplace=True)\n self.conv2 = conv_op(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 = conv_op(in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n else:\n self.nin_shortcut = conv_op(in_channels,\n out_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n\n def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None):\n h = x\n h = self.norm1(h)\n h = [ self.swish(h) ]\n h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)\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 = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)\n\n if self.in_channels != self.out_channels:\n if self.use_conv_shortcut:\n x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)\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 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 Exception as e:\n model_management.raise_non_oom(e)\n model_management.soft_empty_cache(True)\n steps *= 2\n if steps > 128:\n raise e\n logging.warning(\"out of memory error, increasing steps and trying again {}\".format(steps))\n\n return r1\n\ndef normal_attention(q, k, v):\n # compute attention\n orig_shape = q.shape\n b = orig_shape[0]\n c = orig_shape[1]\n\n q = q.reshape(b, c, -1)\n q = q.permute(0, 2, 1) # b,hw,c\n k = k.reshape(b, c, -1) # b,c,hw\n v = v.reshape(b, c, -1)\n\n r1 = slice_attention(q, k, v)\n h_ = r1.reshape(orig_shape)\n del r1\n return h_\n\ndef xformers_attention(q, k, v):\n # compute attention\n orig_shape = q.shape\n B = orig_shape[0]\n C = orig_shape[1]\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(orig_shape)\n except NotImplementedError:\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(orig_shape)\n return out\n\ndef pytorch_attention(q, k, v):\n # compute attention\n orig_shape = q.shape\n B = orig_shape[0]\n C = orig_shape[1]\n oom_fallback = False\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 = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)\n out = out.transpose(2, 3).reshape(orig_shape)\n except Exception as e:\n model_management.raise_non_oom(e)\n logging.warning(\"scaled_dot_product_attention OOMed: switched to slice attention\")\n oom_fallback = True\n if oom_fallback:\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(orig_shape)\n return out\n\n\ndef vae_attention():\n if model_management.xformers_enabled_vae():\n logging.info(\"Using xformers attention in VAE\")\n return xformers_attention\n elif model_management.pytorch_attention_enabled_vae():\n logging.info(\"Using pytorch attention in VAE\")\n return pytorch_attention\n else:\n logging.info(\"Using split attention in VAE\")\n return normal_attention\n\nclass AttnBlock(nn.Module):\n def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize):\n super().__init__()\n self.in_channels = in_channels\n\n self.norm = norm_op(in_channels)\n self.q = conv_op(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.k = conv_op(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.v = conv_op(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.proj_out = conv_op(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n\n self.optimized_attention = vae_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, conv_op=ops.Conv2d):\n return AttnBlock(in_channels, conv_op=conv_op)\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:\n 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 conv3d=False, time_compress=None,\n **ignore_kwargs):\n super().__init__()\n if use_linear_attn:\n 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.carried = False\n\n if conv3d:\n if not attn_resolutions:\n conv_op = CarriedConv3d\n self.carried = True\n else:\n conv_op = VideoConv3d\n mid_attn_conv_op = ops.Conv3d\n else:\n conv_op = ops.Conv2d\n mid_attn_conv_op = ops.Conv2d\n\n # downsampling\n self.conv_in = conv_op(in_channels,\n self.ch,\n kernel_size=3,\n stride=1,\n padding=1)\n\n self.time_compress = 1\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 conv_op=conv_op))\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=attn_type, conv_op=conv_op))\n down = nn.Module()\n down.block = block\n down.attn = attn\n if i_level != self.num_resolutions-1:\n stride = 2\n if time_compress is not None:\n if (self.num_resolutions - 1 - i_level) > math.log2(time_compress):\n stride = (1, 2, 2)\n else:\n self.time_compress *= 2\n down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)\n curr_res = curr_res // 2\n self.down.append(down)\n\n if time_compress is not None:\n self.time_compress = time_compress\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 conv_op=conv_op)\n self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, conv_op=mid_attn_conv_op)\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 conv_op=conv_op)\n\n # end\n self.norm_out = Normalize(block_in)\n self.conv_out = conv_op(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\n if self.carried:\n xl = [x[:, :, :1, :, :]]\n if x.shape[2] > self.time_compress:\n tc = self.time_compress\n xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2)\n x = xl\n else:\n x = [x]\n out = []\n\n conv_carry_in = None\n\n for i, x1 in enumerate(x):\n conv_carry_out = []\n if i == len(x) - 1:\n conv_carry_out = None\n\n # downsampling\n x1 = [ x1 ]\n h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)\n\n for i_level in range(self.num_resolutions):\n for i_block in range(self.num_res_blocks):\n h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out)\n if len(self.down[i_level].attn) > 0:\n assert i == 0 #carried should not happen if attn exists\n h1 = self.down[i_level].attn[i_block](h1)\n if i_level != self.num_resolutions-1:\n h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out)\n\n out.append(h1)\n conv_carry_in = conv_carry_out\n\n h = torch_cat_if_needed(out, dim=2)\n del out\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 = conv_carry_causal_3d(h, self.conv_out)\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, tanh_out=False, use_linear_attn=False,\n conv_out_op=ops.Conv2d,\n resnet_op=ResnetBlock,\n attn_op=AttnBlock,\n conv3d=False,\n time_compress=None,\n **ignorekwargs):\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.tanh_out = tanh_out\n self.carried = False\n\n if conv3d:\n if not attn_resolutions and resnet_op == ResnetBlock:\n conv_op = CarriedConv3d\n conv_out_op = CarriedConv3d\n self.carried = True\n else:\n conv_op = VideoConv3d\n conv_out_op = VideoConv3d\n\n mid_attn_conv_op = ops.Conv3d\n else:\n conv_op = ops.Conv2d\n mid_attn_conv_op = ops.Conv2d\n\n # compute 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 logging.debug(\"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 = conv_op(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 conv_op=conv_op)\n self.mid.attn_1 = attn_op(block_in, conv_op=mid_attn_conv_op)\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 conv_op=conv_op)\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 conv_op=conv_op))\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(attn_op(block_in, conv_op=conv_op))\n up = nn.Module()\n up.block = block\n up.attn = attn\n if i_level != 0:\n scale_factor = 2.0\n if time_compress is not None:\n if i_level > math.log2(time_compress):\n scale_factor = (1.0, 2.0, 2.0)\n\n up.upsample = Upsample(block_in, resamp_with_conv, conv_op=conv_op, scale_factor=scale_factor)\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 # timestep embedding\n temb = None\n\n # z to block_in\n h = conv_carry_causal_3d([z], self.conv_in)\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 if self.carried:\n h = torch.split(h, 2, dim=2)\n else:\n h = [ h ]\n out = []\n\n conv_carry_in = None\n\n # upsampling\n for i, h1 in enumerate(h):\n conv_carry_out = []\n if i == len(h) - 1:\n conv_carry_out = None\n for i_level in reversed(range(self.num_resolutions)):\n for i_block in range(self.num_res_blocks+1):\n h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs)\n if len(self.up[i_level].attn) > 0:\n assert i == 0 #carried should not happen if attn exists\n h1 = self.up[i_level].attn[i_block](h1, **kwargs)\n if i_level != 0:\n h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out)\n\n h1 = self.norm_out(h1)\n h1 = [ nonlinearity(h1) ]\n h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out)\n if self.tanh_out:\n h1 = torch.tanh(h1)\n out.append(h1)\n conv_carry_in = conv_carry_out\n\n out = torch_cat_if_needed(out, dim=2)\n\n return out\n" }, { "path": "comfy/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\nimport logging\n\nfrom .util import (\n checkpoint,\n avg_pool_nd,\n timestep_embedding,\n AlphaBlender,\n)\nfrom ..attention import SpatialTransformer, SpatialVideoTransformer, default\nfrom comfy.ldm.util import exists\nimport comfy.patcher_extension\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nfrom ..sdpose import HeatmapHead\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 if \"patches\" in transformer_options and \"forward_timestep_embed_patch\" in transformer_options[\"patches\"]:\n found_patched = False\n for class_type, handler in transformer_options[\"patches\"][\"forward_timestep_embed_patch\"]:\n if isinstance(layer, class_type):\n x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator)\n found_patched = True\n break\n if found_patched:\n continue\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 = emb_out.movedim(1, 2)\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 logging.warning(\"warning control could not be applied {} {}\".format(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 attn_precision=None,\n heatmap_head=False,\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\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 logging.debug(\"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 attn_precision=attn_precision,\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, attn_precision=attn_precision, 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\n self.middle_block = None\n if transformer_depth_middle >= -1:\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 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 if heatmap_head:\n self.heatmap_head = HeatmapHead(device=device, dtype=self.dtype, operations=operations)\n\n def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timesteps, context, y, control, transformer_options, **kwargs)\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\", None)\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 \"emb_patch\" in transformer_patches:\n patch = transformer_patches[\"emb_patch\"]\n for p in patch:\n emb = p(emb, self.model_channels, transformer_options)\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 if self.middle_block is not None:\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": "comfy/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\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": "comfy/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 math\nimport logging\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom einops import repeat, rearrange\n\nfrom comfy.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, device) -> 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(device)\n elif self.merge_strategy == \"learned\":\n alpha = torch.sigmoid(self.mix_factor.to(device))\n # make shape compatible\n # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)\n elif self.merge_strategy == \"learned_with_images\":\n if image_only_indicator is None:\n alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), \"... -> ... 1\")\n else:\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, x_spatial.device)\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 = torch.clamp(betas, min=0, 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\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 logging.info(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 logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')\n logging.info(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": "comfy/ldm/modules/distributions/__init__.py", "content": "" }, { "path": "comfy/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, device=self.parameters.device)\n\n def sample(self):\n x = self.mean + self.std * torch.randn(self.mean.shape, 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": "comfy/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 key not 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 key not 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": "comfy/ldm/modules/encoders/__init__.py", "content": "" }, { "path": "comfy/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\")\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": "comfy/ldm/modules/sdpose.py", "content": "import torch\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter\n\nclass HeatmapHead(torch.nn.Module):\n def __init__(\n self,\n in_channels=640,\n out_channels=133,\n input_size=(768, 1024),\n heatmap_scale=4,\n deconv_out_channels=(640,),\n deconv_kernel_sizes=(4,),\n conv_out_channels=(640,),\n conv_kernel_sizes=(1,),\n final_layer_kernel_size=1,\n device=None, dtype=None, operations=None\n ):\n super().__init__()\n\n self.heatmap_size = (input_size[0] // heatmap_scale, input_size[1] // heatmap_scale)\n self.scale_factor = ((np.array(input_size) - 1) / (np.array(self.heatmap_size) - 1)).astype(np.float32)\n\n # Deconv layers\n if deconv_out_channels:\n deconv_layers = []\n for out_ch, kernel_size in zip(deconv_out_channels, deconv_kernel_sizes):\n if kernel_size == 4:\n padding, output_padding = 1, 0\n elif kernel_size == 3:\n padding, output_padding = 1, 1\n elif kernel_size == 2:\n padding, output_padding = 0, 0\n else:\n raise ValueError(f'Unsupported kernel size {kernel_size}')\n\n deconv_layers.extend([\n operations.ConvTranspose2d(in_channels, out_ch, kernel_size,\n stride=2, padding=padding, output_padding=output_padding, bias=False, device=device, dtype=dtype),\n torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype),\n torch.nn.SiLU(inplace=True)\n ])\n in_channels = out_ch\n self.deconv_layers = torch.nn.Sequential(*deconv_layers)\n else:\n self.deconv_layers = torch.nn.Identity()\n\n # Conv layers\n if conv_out_channels:\n conv_layers = []\n for out_ch, kernel_size in zip(conv_out_channels, conv_kernel_sizes):\n padding = (kernel_size - 1) // 2\n conv_layers.extend([\n operations.Conv2d(in_channels, out_ch, kernel_size,\n stride=1, padding=padding, device=device, dtype=dtype),\n torch.nn.InstanceNorm2d(out_ch, device=device, dtype=dtype),\n torch.nn.SiLU(inplace=True)\n ])\n in_channels = out_ch\n self.conv_layers = torch.nn.Sequential(*conv_layers)\n else:\n self.conv_layers = torch.nn.Identity()\n\n self.final_layer = operations.Conv2d(in_channels, out_channels, kernel_size=final_layer_kernel_size, padding=final_layer_kernel_size // 2, device=device, dtype=dtype)\n\n def forward(self, x): # Decode heatmaps to keypoints\n heatmaps = self.final_layer(self.conv_layers(self.deconv_layers(x)))\n heatmaps_np = heatmaps.float().cpu().numpy() # (B, K, H, W)\n B, K, H, W = heatmaps_np.shape\n\n batch_keypoints = []\n batch_scores = []\n\n for b in range(B):\n hm = heatmaps_np[b].copy() # (K, H, W)\n\n # --- vectorised argmax ---\n flat = hm.reshape(K, -1)\n idx = np.argmax(flat, axis=1)\n scores = flat[np.arange(K), idx].copy()\n y_locs, x_locs = np.unravel_index(idx, (H, W))\n keypoints = np.stack([x_locs, y_locs], axis=-1).astype(np.float32) # (K, 2) in heatmap space\n invalid = scores <= 0.\n keypoints[invalid] = -1\n\n # --- DARK sub-pixel refinement (UDP) ---\n # 1. Gaussian blur with max-preserving normalisation\n border = 5 # (kernel-1)//2 for kernel=11\n for k in range(K):\n origin_max = np.max(hm[k])\n dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32)\n dr[border:-border, border:-border] = hm[k].copy()\n dr = gaussian_filter(dr, sigma=2.0)\n hm[k] = dr[border:-border, border:-border].copy()\n cur_max = np.max(hm[k])\n if cur_max > 0:\n hm[k] *= origin_max / cur_max\n # 2. Log-space for Taylor expansion\n np.clip(hm, 1e-3, 50., hm)\n np.log(hm, hm)\n # 3. Hessian-based Newton step\n hm_pad = np.pad(hm, ((0, 0), (1, 1), (1, 1)), mode='edge').flatten()\n index = keypoints[:, 0] + 1 + (keypoints[:, 1] + 1) * (W + 2)\n index += (W + 2) * (H + 2) * np.arange(0, K)\n index = index.astype(int).reshape(-1, 1)\n i_ = hm_pad[index]\n ix1 = hm_pad[index + 1]\n iy1 = hm_pad[index + W + 2]\n ix1y1 = hm_pad[index + W + 3]\n ix1_y1_ = hm_pad[index - W - 3]\n ix1_ = hm_pad[index - 1]\n iy1_ = hm_pad[index - 2 - W]\n dx = 0.5 * (ix1 - ix1_)\n dy = 0.5 * (iy1 - iy1_)\n derivative = np.concatenate([dx, dy], axis=1).reshape(K, 2, 1)\n dxx = ix1 - 2 * i_ + ix1_\n dyy = iy1 - 2 * i_ + iy1_\n dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_)\n hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1).reshape(K, 2, 2)\n hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2))\n keypoints -= np.einsum('imn,ink->imk', hessian, derivative).squeeze(axis=-1)\n\n # --- restore to input image space ---\n keypoints = keypoints * self.scale_factor\n keypoints[invalid] = -1\n\n batch_keypoints.append(keypoints)\n batch_scores.append(scores)\n\n return batch_keypoints, batch_scores\n" }, { "path": "comfy/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\nimport logging\n\ntry:\n from typing import Optional, NamedTuple, List, Protocol\nexcept ImportError:\n from typing import Optional, NamedTuple, List\n from typing_extensions import Protocol\n\nfrom typing import List\n\nfrom comfy import model_management\n\ndef dynamic_slice(\n x: Tensor,\n starts: List[int],\n sizes: List[int],\n) -> Tensor:\n slicing = tuple(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 Exception as e:\n model_management.raise_non_oom(e)\n logging.warning(\"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 # noqa: F821 attn_scores is not defined\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 if mask.shape[1] == 1:\n return mask\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": "comfy/ldm/modules/temporal_ae.py", "content": "import functools\nfrom typing import Iterable, Union\n\nimport torch\nfrom einops import rearrange, repeat\n\nimport comfy.ops\nops = comfy.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 conv_op=ops.Conv2d,\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": "comfy/ldm/omnigen/omnigen2.py", "content": "# Original code: https://github.com/VectorSpaceLab/OmniGen2\n\nfrom typing import Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nfrom comfy.ldm.lightricks.model import Timesteps\nfrom comfy.ldm.flux.layers import EmbedND\nfrom comfy.ldm.modules.attention import optimized_attention_masked\nimport comfy.model_management\nimport comfy.ldm.common_dit\n\n\ndef apply_rotary_emb(x, freqs_cis):\n if x.shape[1] == 0:\n return x\n\n t_ = x.reshape(*x.shape[:-1], -1, 1, 2)\n t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]\n return t_out.reshape(*x.shape).to(dtype=x.dtype)\n\n\ndef swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n return F.silu(x) * y\n\n\nclass TimestepEmbedding(nn.Module):\n def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None):\n super().__init__()\n self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device)\n self.act = nn.SiLU()\n self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device)\n\n def forward(self, sample: torch.Tensor) -> torch.Tensor:\n sample = self.linear_1(sample)\n sample = self.act(sample)\n sample = self.linear_2(sample)\n return sample\n\n\nclass LuminaRMSNormZero(nn.Module):\n def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None):\n super().__init__()\n self.silu = nn.SiLU()\n self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device)\n self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n emb = self.linear(self.silu(emb))\n scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)\n x = self.norm(x) * (1 + scale_msa[:, None])\n return x, gate_msa, scale_mlp, gate_mlp\n\n\nclass LuminaLayerNormContinuous(nn.Module):\n def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None):\n super().__init__()\n self.silu = nn.SiLU()\n self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device)\n self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device)\n self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None\n\n def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:\n emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype))\n x = self.norm(x) * (1 + emb)[:, None, :]\n if self.linear_2 is not None:\n x = self.linear_2(x)\n return x\n\n\nclass LuminaFeedForward(nn.Module):\n def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None):\n super().__init__()\n inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)\n self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)\n self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device)\n self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n h1, h2 = self.linear_1(x), self.linear_3(x)\n return self.linear_2(swiglu(h1, h2))\n\n\nclass Lumina2CombinedTimestepCaptionEmbedding(nn.Module):\n def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None):\n super().__init__()\n self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale)\n self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations)\n self.caption_embedder = nn.Sequential(\n operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device),\n operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device),\n )\n\n def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:\n timestep_proj = self.time_proj(timestep).to(dtype=dtype)\n time_embed = self.timestep_embedder(timestep_proj)\n caption_embed = self.caption_embedder(text_hidden_states)\n return time_embed, caption_embed\n\n\nclass Attention(nn.Module):\n def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None):\n super().__init__()\n self.heads = heads\n self.kv_heads = kv_heads\n self.dim_head = dim_head\n self.scale = dim_head ** -0.5\n\n self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device)\n self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)\n self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)\n\n self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)\n self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)\n\n self.to_out = nn.Sequential(\n operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device),\n nn.Dropout(0.0)\n )\n\n def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:\n batch_size, sequence_length, _ = hidden_states.shape\n\n query = self.to_q(hidden_states)\n key = self.to_k(encoder_hidden_states)\n value = self.to_v(encoder_hidden_states)\n\n query = query.view(batch_size, -1, self.heads, self.dim_head)\n key = key.view(batch_size, -1, self.kv_heads, self.dim_head)\n value = value.view(batch_size, -1, self.kv_heads, self.dim_head)\n\n query = self.norm_q(query)\n key = self.norm_k(key)\n\n if image_rotary_emb is not None:\n query = apply_rotary_emb(query, image_rotary_emb)\n key = apply_rotary_emb(key, image_rotary_emb)\n\n query = query.transpose(1, 2)\n key = key.transpose(1, 2)\n value = value.transpose(1, 2)\n\n if self.kv_heads < self.heads:\n key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)\n value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)\n\n hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)\n hidden_states = self.to_out[0](hidden_states)\n return hidden_states\n\n\nclass OmniGen2TransformerBlock(nn.Module):\n def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None):\n super().__init__()\n self.modulation = modulation\n\n self.attn = Attention(\n query_dim=dim,\n dim_head=dim // num_attention_heads,\n heads=num_attention_heads,\n kv_heads=num_kv_heads,\n eps=1e-5,\n bias=False,\n dtype=dtype, device=device, operations=operations,\n )\n\n self.feed_forward = LuminaFeedForward(\n dim=dim,\n inner_dim=4 * dim,\n multiple_of=multiple_of,\n dtype=dtype, device=device, operations=operations\n )\n\n if modulation:\n self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)\n else:\n self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)\n\n self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)\n self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)\n self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)\n\n def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:\n if self.modulation:\n norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)\n attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)\n hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)\n mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))\n hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)\n else:\n norm_hidden_states = self.norm1(hidden_states)\n attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)\n hidden_states = hidden_states + self.norm2(attn_output)\n mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))\n hidden_states = hidden_states + self.ffn_norm2(mlp_output)\n return hidden_states\n\n\nclass OmniGen2RotaryPosEmbed(nn.Module):\n def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2):\n super().__init__()\n self.theta = theta\n self.axes_dim = axes_dim\n self.axes_lens = axes_lens\n self.patch_size = patch_size\n self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim)\n\n def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device):\n p = self.patch_size\n\n seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)]\n\n max_seq_len = max(seq_lengths)\n max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len])\n max_img_len = max(l_effective_img_len)\n\n position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device)\n\n for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):\n position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), \"l -> l 3\")\n\n pe_shift = cap_seq_len\n pe_shift_len = cap_seq_len\n\n if ref_img_sizes[i] is not None:\n for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]):\n H, W = ref_img_size\n ref_H_tokens, ref_W_tokens = H // p, W // p\n\n row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), \"h -> h w\", w=ref_W_tokens).flatten()\n col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), \"w -> h w\", h=ref_H_tokens).flatten()\n position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift\n position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids\n position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids\n\n pe_shift += max(ref_H_tokens, ref_W_tokens)\n pe_shift_len += ref_img_len\n\n H, W = img_sizes[i]\n H_tokens, W_tokens = H // p, W // p\n\n row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), \"h -> h w\", w=W_tokens).flatten()\n col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), \"w -> h w\", h=H_tokens).flatten()\n\n position_ids[i, pe_shift_len: seq_len, 0] = pe_shift\n position_ids[i, pe_shift_len: seq_len, 1] = row_ids\n position_ids[i, pe_shift_len: seq_len, 2] = col_ids\n\n freqs_cis = self.rope_embedder(position_ids).movedim(1, 2)\n\n cap_freqs_cis_shape = list(freqs_cis.shape)\n cap_freqs_cis_shape[1] = encoder_seq_len\n cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)\n\n ref_img_freqs_cis_shape = list(freqs_cis.shape)\n ref_img_freqs_cis_shape[1] = max_ref_img_len\n ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)\n\n img_freqs_cis_shape = list(freqs_cis.shape)\n img_freqs_cis_shape[1] = max_img_len\n img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)\n\n for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)):\n cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len]\n ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)]\n img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len]\n\n return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths\n\n\nclass OmniGen2Transformer2DModel(nn.Module):\n def __init__(\n self,\n patch_size: int = 2,\n in_channels: int = 16,\n out_channels: Optional[int] = None,\n hidden_size: int = 2304,\n num_layers: int = 26,\n num_refiner_layers: int = 2,\n num_attention_heads: int = 24,\n num_kv_heads: int = 8,\n multiple_of: int = 256,\n ffn_dim_multiplier: Optional[float] = None,\n norm_eps: float = 1e-5,\n axes_dim_rope: Tuple[int, int, int] = (32, 32, 32),\n axes_lens: Tuple[int, int, int] = (300, 512, 512),\n text_feat_dim: int = 1024,\n timestep_scale: float = 1.0,\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n\n self.patch_size = patch_size\n self.out_channels = out_channels or in_channels\n self.hidden_size = hidden_size\n self.dtype = dtype\n\n self.rope_embedder = OmniGen2RotaryPosEmbed(\n theta=10000,\n axes_dim=axes_dim_rope,\n axes_lens=axes_lens,\n patch_size=patch_size,\n )\n\n self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)\n self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)\n\n self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding(\n hidden_size=hidden_size,\n text_feat_dim=text_feat_dim,\n norm_eps=norm_eps,\n timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations\n )\n\n self.noise_refiner = nn.ModuleList([\n OmniGen2TransformerBlock(\n hidden_size, num_attention_heads, num_kv_heads,\n multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations\n ) for _ in range(num_refiner_layers)\n ])\n\n self.ref_image_refiner = nn.ModuleList([\n OmniGen2TransformerBlock(\n hidden_size, num_attention_heads, num_kv_heads,\n multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations\n ) for _ in range(num_refiner_layers)\n ])\n\n self.context_refiner = nn.ModuleList([\n OmniGen2TransformerBlock(\n hidden_size, num_attention_heads, num_kv_heads,\n multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations\n ) for _ in range(num_refiner_layers)\n ])\n\n self.layers = nn.ModuleList([\n OmniGen2TransformerBlock(\n hidden_size, num_attention_heads, num_kv_heads,\n multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations\n ) for _ in range(num_layers)\n ])\n\n self.norm_out = LuminaLayerNormContinuous(\n embedding_dim=hidden_size,\n conditioning_embedding_dim=min(hidden_size, 1024),\n elementwise_affine=False,\n eps=1e-6,\n out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations\n )\n\n self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype))\n\n def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states):\n batch_size = len(hidden_states)\n p = self.patch_size\n\n img_sizes = [(img.size(1), img.size(2)) for img in hidden_states]\n l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes]\n\n if ref_image_hidden_states is not None:\n ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states))\n ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size\n l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes]\n else:\n ref_img_sizes = [None for _ in range(batch_size)]\n l_effective_ref_img_len = [[0] for _ in range(batch_size)]\n\n flat_ref_img_hidden_states = None\n if ref_image_hidden_states is not None:\n imgs = []\n for ref_img in ref_image_hidden_states:\n B, C, H, W = ref_img.size()\n ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)\n imgs.append(ref_img)\n flat_ref_img_hidden_states = torch.cat(imgs, dim=1)\n\n img = hidden_states\n B, C, H, W = img.size()\n flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)\n\n return (\n flat_hidden_states, flat_ref_img_hidden_states,\n None, None,\n l_effective_ref_img_len, l_effective_img_len,\n ref_img_sizes, img_sizes,\n )\n\n def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}):\n batch_size = len(hidden_states)\n\n hidden_states = self.x_embedder(hidden_states)\n if ref_image_hidden_states is not None:\n ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states)\n image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device)\n\n for i in range(batch_size):\n shift = 0\n for j, ref_img_len in enumerate(l_effective_ref_img_len[i]):\n ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j]\n shift += ref_img_len\n\n for layer in self.noise_refiner:\n hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options)\n\n if ref_image_hidden_states is not None:\n for layer in self.ref_image_refiner:\n ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options)\n\n hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)\n\n return hidden_states\n\n def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs):\n B, C, H, W = x.shape\n hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))\n _, _, H_padded, W_padded = hidden_states.shape\n timestep = 1.0 - timesteps\n text_hidden_states = context\n text_attention_mask = attention_mask\n ref_image_hidden_states = ref_latents\n device = hidden_states.device\n\n temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype)\n\n (\n hidden_states, ref_image_hidden_states,\n img_mask, ref_img_mask,\n l_effective_ref_img_len, l_effective_img_len,\n ref_img_sizes, img_sizes,\n ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states)\n\n (\n context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb,\n rotary_emb, encoder_seq_lengths, seq_lengths,\n ) = self.rope_embedder(\n hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0],\n l_effective_ref_img_len, l_effective_img_len,\n ref_img_sizes, img_sizes, device,\n )\n\n for layer in self.context_refiner:\n text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options)\n\n img_len = hidden_states.shape[1]\n combined_img_hidden_states = self.img_patch_embed_and_refine(\n hidden_states, ref_image_hidden_states,\n img_mask, ref_img_mask,\n noise_rotary_emb, ref_img_rotary_emb,\n l_effective_ref_img_len, l_effective_img_len,\n temb,\n transformer_options=transformer_options,\n )\n\n hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)\n attention_mask = None\n\n for layer in self.layers:\n hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options)\n\n hidden_states = self.norm_out(hidden_states, temb)\n\n p = self.patch_size\n output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W]\n\n return -output\n" }, { "path": "comfy/ldm/pixart/blocks.py", "content": "# Based on:\n# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]\n# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, Mlp, timestep_embedding\nfrom comfy.ldm.modules.attention import optimized_attention\n\n# if model_management.xformers_enabled():\n# import xformers.ops\n# if int((xformers.__version__).split(\".\")[2].split(\"+\")[0]) >= 28:\n# block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens\n# else:\n# block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens\n\ndef modulate(x, shift, scale):\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\ndef t2i_modulate(x, shift, scale):\n return x * (1 + scale) + shift\n\nclass MultiHeadCrossAttention(nn.Module):\n def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **kwargs):\n super(MultiHeadCrossAttention, self).__init__()\n assert d_model % num_heads == 0, \"d_model must be divisible by num_heads\"\n\n self.d_model = d_model\n self.num_heads = num_heads\n self.head_dim = d_model // num_heads\n\n self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device)\n self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, x, cond, mask=None):\n # query/value: img tokens; key: condition; mask: if padding tokens\n B, N, C = x.shape\n\n q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim)\n kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)\n k, v = kv.unbind(2)\n\n assert mask is None # TODO?\n # # TODO: xformers needs separate mask logic here\n # if model_management.xformers_enabled():\n # attn_bias = None\n # if mask is not None:\n # attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask)\n # x = xformers.ops.memory_efficient_attention(q, k, v, p=0, attn_bias=attn_bias)\n # else:\n # q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),)\n # attn_mask = None\n # mask = torch.ones(())\n # if mask is not None and len(mask) > 1:\n # # Create equivalent of xformer diagonal block mask, still only correct for square masks\n # # But depth doesn't matter as tensors can expand in that dimension\n # attn_mask_template = torch.ones(\n # [q.shape[2] // B, mask[0]],\n # dtype=torch.bool,\n # device=q.device\n # )\n # attn_mask = torch.block_diag(attn_mask_template)\n #\n # # create a mask on the diagonal for each mask in the batch\n # for _ in range(B - 1):\n # attn_mask = torch.block_diag(attn_mask, attn_mask_template)\n # x = optimized_attention(q, k, v, self.num_heads, mask=attn_mask, skip_reshape=True)\n\n x = optimized_attention(q.view(B, -1, C), k.view(B, -1, C), v.view(B, -1, C), self.num_heads, mask=None)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass AttentionKVCompress(nn.Module):\n \"\"\"Multi-head Attention block with KV token compression and qk norm.\"\"\"\n def __init__(self, dim, num_heads=8, qkv_bias=True, sampling='conv', sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **kwargs):\n \"\"\"\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n qkv_bias (bool: If True, add a learnable bias to query, key, value.\n \"\"\"\n super().__init__()\n assert dim % num_heads == 0, 'dim should be divisible by num_heads'\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n self.scale = self.head_dim ** -0.5\n\n self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)\n self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)\n\n self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every']\n self.sr_ratio = sr_ratio\n if sr_ratio > 1 and sampling == 'conv':\n # Avg Conv Init.\n self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device)\n # self.sr.weight.data.fill_(1/sr_ratio**2)\n # self.sr.bias.data.zero_()\n self.norm = operations.LayerNorm(dim, dtype=dtype, device=device)\n if qk_norm:\n self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device)\n self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device)\n else:\n self.q_norm = nn.Identity()\n self.k_norm = nn.Identity()\n\n def downsample_2d(self, tensor, H, W, scale_factor, sampling=None):\n if sampling is None or scale_factor == 1:\n return tensor\n B, N, C = tensor.shape\n\n if sampling == 'uniform_every':\n return tensor[:, ::scale_factor], int(N // scale_factor)\n\n tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2)\n new_H, new_W = int(H / scale_factor), int(W / scale_factor)\n new_N = new_H * new_W\n\n if sampling == 'ave':\n tensor = F.interpolate(\n tensor, scale_factor=1 / scale_factor, mode='nearest'\n ).permute(0, 2, 3, 1)\n elif sampling == 'uniform':\n tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1)\n elif sampling == 'conv':\n tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1)\n tensor = self.norm(tensor)\n else:\n raise ValueError\n\n return tensor.reshape(B, new_N, C).contiguous(), new_N\n\n def forward(self, x, mask=None, HW=None, block_id=None):\n B, N, C = x.shape # 2 4096 1152\n new_N = N\n if HW is None:\n H = W = int(N ** 0.5)\n else:\n H, W = HW\n qkv = self.qkv(x).reshape(B, N, 3, C)\n\n q, k, v = qkv.unbind(2)\n q = self.q_norm(q)\n k = self.k_norm(k)\n\n # KV compression\n if self.sr_ratio > 1:\n k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling)\n v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling)\n\n q = q.reshape(B, N, self.num_heads, C // self.num_heads)\n k = k.reshape(B, new_N, self.num_heads, C // self.num_heads)\n v = v.reshape(B, new_N, self.num_heads, C // self.num_heads)\n\n if mask is not None:\n raise NotImplementedError(\"Attn mask logic not added for self attention\")\n\n # This is never called at the moment\n # attn_bias = None\n # if mask is not None:\n # attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device)\n # attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf'))\n\n # attention 2\n q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),)\n x = optimized_attention(q, k, v, self.num_heads, mask=None, skip_reshape=True)\n\n x = x.view(B, N, C)\n x = self.proj(x)\n return x\n\n\nclass FinalLayer(nn.Module):\n \"\"\"\n The final layer of PixArt.\n \"\"\"\n def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)\n )\n\n def forward(self, x, c):\n shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)\n x = modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n return x\n\nclass T2IFinalLayer(nn.Module):\n \"\"\"\n The final layer of PixArt.\n \"\"\"\n def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)\n self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)\n self.out_channels = out_channels\n\n def forward(self, x, t):\n shift, scale = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t[:, None]).chunk(2, dim=1)\n x = t2i_modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n return x\n\n\nclass MaskFinalLayer(nn.Module):\n \"\"\"\n The final layer of PixArt.\n \"\"\"\n def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)\n )\n def forward(self, x, t):\n shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)\n x = modulate(self.norm_final(x), shift, scale)\n x = self.linear(x)\n return x\n\n\nclass DecoderLayer(nn.Module):\n \"\"\"\n The final layer of PixArt.\n \"\"\"\n def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None):\n super().__init__()\n self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(),\n operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)\n )\n def forward(self, x, t):\n shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)\n x = modulate(self.norm_decoder(x), shift, scale)\n x = self.linear(x)\n return x\n\n\nclass SizeEmbedder(TimestepEmbedder):\n \"\"\"\n Embeds scalar timesteps into vector representations.\n \"\"\"\n def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):\n super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations)\n self.mlp = nn.Sequential(\n operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),\n nn.SiLU(),\n operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),\n )\n self.frequency_embedding_size = frequency_embedding_size\n self.outdim = hidden_size\n\n def forward(self, s, bs):\n if s.ndim == 1:\n s = s[:, None]\n assert s.ndim == 2\n if s.shape[0] != bs:\n s = s.repeat(bs//s.shape[0], 1)\n assert s.shape[0] == bs\n b, dims = s.shape[0], s.shape[1]\n s = rearrange(s, \"b d -> (b d)\")\n s_freq = timestep_embedding(s, self.frequency_embedding_size)\n s_emb = self.mlp(s_freq.to(s.dtype))\n s_emb = rearrange(s_emb, \"(b d) d2 -> b (d d2)\", b=b, d=dims, d2=self.outdim)\n return s_emb\n\n\nclass LabelEmbedder(nn.Module):\n \"\"\"\n Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.\n \"\"\"\n def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None):\n super().__init__()\n use_cfg_embedding = dropout_prob > 0\n self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device),\n self.num_classes = num_classes\n self.dropout_prob = dropout_prob\n\n def token_drop(self, labels, force_drop_ids=None):\n \"\"\"\n Drops labels to enable classifier-free guidance.\n \"\"\"\n if force_drop_ids is None:\n drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob\n else:\n drop_ids = force_drop_ids == 1\n labels = torch.where(drop_ids, self.num_classes, labels)\n return labels\n\n def forward(self, labels, train, force_drop_ids=None):\n use_dropout = self.dropout_prob > 0\n if (train and use_dropout) or (force_drop_ids is not None):\n labels = self.token_drop(labels, force_drop_ids)\n embeddings = self.embedding_table(labels)\n return embeddings\n\n\nclass CaptionEmbedder(nn.Module):\n \"\"\"\n Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.\n \"\"\"\n def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):\n super().__init__()\n self.y_proj = Mlp(\n in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,\n dtype=dtype, device=device, operations=operations,\n )\n self.register_buffer(\"y_embedding\", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5))\n self.uncond_prob = uncond_prob\n\n def token_drop(self, caption, force_drop_ids=None):\n \"\"\"\n Drops labels to enable classifier-free guidance.\n \"\"\"\n if force_drop_ids is None:\n drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob\n else:\n drop_ids = force_drop_ids == 1\n caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)\n return caption\n\n def forward(self, caption, train, force_drop_ids=None):\n if train:\n assert caption.shape[2:] == self.y_embedding.shape\n use_dropout = self.uncond_prob > 0\n if (train and use_dropout) or (force_drop_ids is not None):\n caption = self.token_drop(caption, force_drop_ids)\n caption = self.y_proj(caption)\n return caption\n\n\nclass CaptionEmbedderDoubleBr(nn.Module):\n \"\"\"\n Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.\n \"\"\"\n def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):\n super().__init__()\n self.proj = Mlp(\n in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,\n dtype=dtype, device=device, operations=operations,\n )\n self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5)\n self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5)\n self.uncond_prob = uncond_prob\n\n def token_drop(self, global_caption, caption, force_drop_ids=None):\n \"\"\"\n Drops labels to enable classifier-free guidance.\n \"\"\"\n if force_drop_ids is None:\n drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob\n else:\n drop_ids = force_drop_ids == 1\n global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)\n caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)\n return global_caption, caption\n\n def forward(self, caption, train, force_drop_ids=None):\n assert caption.shape[2: ] == self.y_embedding.shape\n global_caption = caption.mean(dim=2).squeeze()\n use_dropout = self.uncond_prob > 0\n if (train and use_dropout) or (force_drop_ids is not None):\n global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids)\n y_embed = self.proj(global_caption)\n return y_embed, caption\n" }, { "path": "comfy/ldm/pixart/pixartms.py", "content": "# Based on:\n# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]\n# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]\nimport torch\nimport torch.nn as nn\n\nfrom .blocks import (\n t2i_modulate,\n CaptionEmbedder,\n AttentionKVCompress,\n MultiHeadCrossAttention,\n T2IFinalLayer,\n SizeEmbedder,\n)\nfrom comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch\n\n\ndef get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):\n grid_h, grid_w = torch.meshgrid(\n torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,\n torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,\n indexing='ij'\n )\n emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)\n emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)\n emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D)\n return emb\n\nclass PixArtMSBlock(nn.Module):\n \"\"\"\n A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.\n \"\"\"\n def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,\n sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):\n super().__init__()\n self.hidden_size = hidden_size\n self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n self.attn = AttentionKVCompress(\n hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,\n qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs\n )\n self.cross_attn = MultiHeadCrossAttention(\n hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs\n )\n self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)\n # to be compatible with lower version pytorch\n approx_gelu = lambda: nn.GELU(approximate=\"tanh\")\n self.mlp = Mlp(\n in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,\n dtype=dtype, device=device, operations=operations\n )\n self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)\n\n def forward(self, x, y, t, mask=None, HW=None, **kwargs):\n B, N, C = x.shape\n\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)\n x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))\n x = x + self.cross_attn(x, y, mask)\n x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))\n\n return x\n\n\n### Core PixArt Model ###\nclass PixArtMS(nn.Module):\n \"\"\"\n Diffusion model with a Transformer backbone.\n \"\"\"\n def __init__(\n self,\n input_size=32,\n patch_size=2,\n in_channels=4,\n hidden_size=1152,\n depth=28,\n num_heads=16,\n mlp_ratio=4.0,\n class_dropout_prob=0.1,\n learn_sigma=True,\n pred_sigma=True,\n drop_path: float = 0.,\n caption_channels=4096,\n pe_interpolation=None,\n pe_precision=None,\n config=None,\n model_max_length=120,\n micro_condition=True,\n qk_norm=False,\n kv_compress_config=None,\n dtype=None,\n device=None,\n operations=None,\n **kwargs,\n ):\n nn.Module.__init__(self)\n self.dtype = dtype\n self.pred_sigma = pred_sigma\n self.in_channels = in_channels\n self.out_channels = in_channels * 2 if pred_sigma else in_channels\n self.patch_size = patch_size\n self.num_heads = num_heads\n self.pe_interpolation = pe_interpolation\n self.pe_precision = pe_precision\n self.hidden_size = hidden_size\n self.depth = depth\n\n approx_gelu = lambda: nn.GELU(approximate=\"tanh\")\n self.t_block = nn.Sequential(\n nn.SiLU(),\n operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)\n )\n self.x_embedder = PatchEmbed(\n patch_size=patch_size,\n in_chans=in_channels,\n embed_dim=hidden_size,\n bias=True,\n dtype=dtype,\n device=device,\n operations=operations\n )\n self.t_embedder = TimestepEmbedder(\n hidden_size, dtype=dtype, device=device, operations=operations,\n )\n self.y_embedder = CaptionEmbedder(\n in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,\n act_layer=approx_gelu, token_num=model_max_length,\n dtype=dtype, device=device, operations=operations,\n )\n\n self.micro_conditioning = micro_condition\n if self.micro_conditioning:\n self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)\n self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)\n\n # For fixed sin-cos embedding:\n # num_patches = (input_size // patch_size) * (input_size // patch_size)\n # self.base_size = input_size // self.patch_size\n # self.register_buffer(\"pos_embed\", torch.zeros(1, num_patches, hidden_size))\n\n drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule\n if kv_compress_config is None:\n kv_compress_config = {\n 'sampling': None,\n 'scale_factor': 1,\n 'kv_compress_layer': [],\n }\n self.blocks = nn.ModuleList([\n PixArtMSBlock(\n hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],\n sampling=kv_compress_config['sampling'],\n sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,\n qk_norm=qk_norm,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n for i in range(depth)\n ])\n self.final_layer = T2IFinalLayer(\n hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations\n )\n\n def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):\n \"\"\"\n Original forward pass of PixArt.\n x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)\n t: (N,) tensor of diffusion timesteps\n y: (N, 1, 120, C) conditioning\n ar: (N, 1): aspect ratio\n cs: (N ,2) size conditioning for height/width\n \"\"\"\n B, C, H, W = x.shape\n c_res = (H + W) // 2\n pe_interpolation = self.pe_interpolation\n if pe_interpolation is None or self.pe_precision is not None:\n # calculate pe_interpolation on-the-fly\n pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0)\n\n pos_embed = get_2d_sincos_pos_embed_torch(\n self.hidden_size,\n h=(H // self.patch_size),\n w=(W // self.patch_size),\n pe_interpolation=pe_interpolation,\n base_size=((round(c_res / 64) * 64) // self.patch_size),\n device=x.device,\n dtype=x.dtype,\n ).unsqueeze(0)\n\n x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2\n t = self.t_embedder(timestep, x.dtype) # (N, D)\n\n if self.micro_conditioning and (c_size is not None and c_ar is not None):\n bs = x.shape[0]\n c_size = self.csize_embedder(c_size, bs) # (N, D)\n c_ar = self.ar_embedder(c_ar, bs) # (N, D)\n t = t + torch.cat([c_size, c_ar], dim=1)\n\n t0 = self.t_block(t)\n y = self.y_embedder(y, self.training) # (N, D)\n\n if mask is not None:\n if mask.shape[0] != y.shape[0]:\n mask = mask.repeat(y.shape[0] // mask.shape[0], 1)\n mask = mask.squeeze(1).squeeze(1)\n y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])\n y_lens = mask.sum(dim=1).tolist()\n else:\n y_lens = None\n y = y.squeeze(1).view(1, -1, x.shape[-1])\n for block in self.blocks:\n x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D)\n\n x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)\n x = self.unpatchify(x, H, W) # (N, out_channels, H, W)\n\n return x\n\n def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):\n B, C, H, W = x.shape\n\n # Fallback for missing microconds\n if self.micro_conditioning:\n if c_size is None:\n c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)\n\n if c_ar is None:\n c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)\n\n ## Still accepts the input w/o that dim but returns garbage\n if len(context.shape) == 3:\n context = context.unsqueeze(1)\n\n ## run original forward pass\n out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)\n\n ## only return EPS\n if self.pred_sigma:\n return out[:, :self.in_channels]\n return out\n\n def unpatchify(self, x, h, w):\n \"\"\"\n x: (N, T, patch_size**2 * C)\n imgs: (N, H, W, C)\n \"\"\"\n c = self.out_channels\n p = self.x_embedder.patch_size[0]\n h = h // self.patch_size\n w = w // self.patch_size\n assert h * w == x.shape[1]\n\n x = x.reshape(shape=(x.shape[0], h, w, p, p, c))\n x = torch.einsum('nhwpqc->nchpwq', x)\n imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))\n return imgs\n" }, { "path": "comfy/ldm/qwen_image/controlnet.py", "content": "import torch\nimport math\n\nfrom .model import QwenImageTransformer2DModel\nfrom .model import QwenImageTransformerBlock\n\n\nclass QwenImageFunControlBlock(QwenImageTransformerBlock):\n def __init__(self, dim, num_attention_heads, attention_head_dim, has_before_proj=False, dtype=None, device=None, operations=None):\n super().__init__(\n dim=dim,\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.has_before_proj = has_before_proj\n if has_before_proj:\n self.before_proj = operations.Linear(dim, dim, device=device, dtype=dtype)\n self.after_proj = operations.Linear(dim, dim, device=device, dtype=dtype)\n\n\nclass QwenImageFunControlNetModel(torch.nn.Module):\n def __init__(\n self,\n control_in_features=132,\n inner_dim=3072,\n num_attention_heads=24,\n attention_head_dim=128,\n num_control_blocks=5,\n main_model_double=60,\n injection_layers=(0, 12, 24, 36, 48),\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.dtype = dtype\n self.main_model_double = main_model_double\n self.injection_layers = tuple(injection_layers)\n # Keep base hint scaling at 1.0 so user-facing strength behaves similarly\n # to the reference Gen2/VideoX implementation around strength=1.\n self.hint_scale = 1.0\n self.control_img_in = operations.Linear(control_in_features, inner_dim, device=device, dtype=dtype)\n\n self.control_blocks = torch.nn.ModuleList([])\n for i in range(num_control_blocks):\n self.control_blocks.append(\n QwenImageFunControlBlock(\n dim=inner_dim,\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n has_before_proj=(i == 0),\n dtype=dtype,\n device=device,\n operations=operations,\n )\n )\n\n def _process_hint_tokens(self, hint):\n if hint is None:\n return None\n if hint.ndim == 4:\n hint = hint.unsqueeze(2)\n\n # Fun checkpoints are trained with 33 latent channels before 2x2 packing:\n # [control_latent(16), mask(1), inpaint_latent(16)] -> 132 features.\n # Default behavior (no inpaint input in stock Apply ControlNet) should use\n # zeros for mask/inpaint branches, matching VideoX fallback semantics.\n expected_c = self.control_img_in.weight.shape[1] // 4\n if hint.shape[1] == 16 and expected_c == 33:\n zeros_mask = torch.zeros_like(hint[:, :1])\n zeros_inpaint = torch.zeros_like(hint)\n hint = torch.cat([hint, zeros_mask, zeros_inpaint], dim=1)\n\n bs, c, t, h, w = hint.shape\n hidden_states = torch.nn.functional.pad(hint, (0, w % 2, 0, h % 2))\n orig_shape = hidden_states.shape\n hidden_states = hidden_states.view(\n orig_shape[0],\n orig_shape[1],\n orig_shape[-3],\n orig_shape[-2] // 2,\n 2,\n orig_shape[-1] // 2,\n 2,\n )\n hidden_states = hidden_states.permute(0, 2, 3, 5, 1, 4, 6)\n hidden_states = hidden_states.reshape(\n bs,\n t * ((h + 1) // 2) * ((w + 1) // 2),\n c * 4,\n )\n\n expected_in = self.control_img_in.weight.shape[1]\n cur_in = hidden_states.shape[-1]\n if cur_in < expected_in:\n pad = torch.zeros(\n (hidden_states.shape[0], hidden_states.shape[1], expected_in - cur_in),\n device=hidden_states.device,\n dtype=hidden_states.dtype,\n )\n hidden_states = torch.cat([hidden_states, pad], dim=-1)\n elif cur_in > expected_in:\n hidden_states = hidden_states[:, :, :expected_in]\n\n return hidden_states\n\n def forward(\n self,\n x,\n timesteps,\n context,\n attention_mask=None,\n guidance: torch.Tensor = None,\n hint=None,\n transformer_options={},\n base_model=None,\n **kwargs,\n ):\n if base_model is None:\n raise RuntimeError(\"Qwen Fun ControlNet requires a QwenImage base model at runtime.\")\n\n encoder_hidden_states_mask = attention_mask\n # Keep attention mask disabled inside Fun control blocks to mirror\n # VideoX behavior (they rely on seq lengths for RoPE, not masked attention).\n encoder_hidden_states_mask = None\n\n hidden_states, img_ids, _ = base_model.process_img(x)\n hint_tokens = self._process_hint_tokens(hint)\n if hint_tokens is None:\n raise RuntimeError(\"Qwen Fun ControlNet requires a control hint image.\")\n\n if hint_tokens.shape[1] != hidden_states.shape[1]:\n max_tokens = min(hint_tokens.shape[1], hidden_states.shape[1])\n hint_tokens = hint_tokens[:, :max_tokens]\n hidden_states = hidden_states[:, :max_tokens]\n img_ids = img_ids[:, :max_tokens]\n\n txt_start = round(\n max(\n ((x.shape[-1] + (base_model.patch_size // 2)) // base_model.patch_size) // 2,\n ((x.shape[-2] + (base_model.patch_size // 2)) // base_model.patch_size) // 2,\n )\n )\n txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)\n ids = torch.cat((txt_ids, img_ids), dim=1)\n image_rotary_emb = base_model.pe_embedder(ids).to(x.dtype).contiguous()\n\n hidden_states = base_model.img_in(hidden_states)\n encoder_hidden_states = base_model.txt_norm(context)\n encoder_hidden_states = base_model.txt_in(encoder_hidden_states)\n\n if guidance is not None:\n guidance = guidance * 1000\n\n temb = (\n base_model.time_text_embed(timesteps, hidden_states)\n if guidance is None\n else base_model.time_text_embed(timesteps, guidance, hidden_states)\n )\n\n c = self.control_img_in(hint_tokens)\n\n for i, block in enumerate(self.control_blocks):\n if i == 0:\n c_in = block.before_proj(c) + hidden_states\n all_c = []\n else:\n all_c = list(torch.unbind(c, dim=0))\n c_in = all_c.pop(-1)\n\n encoder_hidden_states, c_out = block(\n hidden_states=c_in,\n encoder_hidden_states=encoder_hidden_states,\n encoder_hidden_states_mask=encoder_hidden_states_mask,\n temb=temb,\n image_rotary_emb=image_rotary_emb,\n transformer_options=transformer_options,\n )\n\n c_skip = block.after_proj(c_out) * self.hint_scale\n all_c += [c_skip, c_out]\n c = torch.stack(all_c, dim=0)\n\n hints = torch.unbind(c, dim=0)[:-1]\n\n controlnet_block_samples = [None] * self.main_model_double\n for local_idx, base_idx in enumerate(self.injection_layers):\n if local_idx < len(hints) and base_idx < len(controlnet_block_samples):\n controlnet_block_samples[base_idx] = hints[local_idx]\n\n return {\"input\": controlnet_block_samples}\n\n\nclass QwenImageControlNetModel(QwenImageTransformer2DModel):\n def __init__(\n self,\n extra_condition_channels=0,\n dtype=None,\n device=None,\n operations=None,\n **kwargs\n ):\n super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)\n self.main_model_double = 60\n\n # controlnet_blocks\n self.controlnet_blocks = torch.nn.ModuleList([])\n for _ in range(len(self.transformer_blocks)):\n self.controlnet_blocks.append(operations.Linear(self.inner_dim, self.inner_dim, device=device, dtype=dtype))\n self.controlnet_x_embedder = operations.Linear(self.in_channels + extra_condition_channels, self.inner_dim, device=device, dtype=dtype)\n\n def forward(\n self,\n x,\n timesteps,\n context,\n attention_mask=None,\n guidance: torch.Tensor = None,\n ref_latents=None,\n hint=None,\n transformer_options={},\n **kwargs\n ):\n timestep = timesteps\n encoder_hidden_states = context\n encoder_hidden_states_mask = attention_mask\n\n hidden_states, img_ids, orig_shape = self.process_img(x)\n hint, _, _ = self.process_img(hint)\n\n txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))\n txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)\n ids = torch.cat((txt_ids, img_ids), dim=1)\n image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()\n del ids, txt_ids, img_ids\n\n hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint)\n encoder_hidden_states = self.txt_norm(encoder_hidden_states)\n encoder_hidden_states = self.txt_in(encoder_hidden_states)\n\n if guidance is not None:\n guidance = guidance * 1000\n\n temb = (\n self.time_text_embed(timestep, hidden_states)\n if guidance is None\n else self.time_text_embed(timestep, guidance, hidden_states)\n )\n\n repeat = math.ceil(self.main_model_double / len(self.controlnet_blocks))\n\n controlnet_block_samples = ()\n for i, block in enumerate(self.transformer_blocks):\n encoder_hidden_states, hidden_states = block(\n hidden_states=hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n encoder_hidden_states_mask=encoder_hidden_states_mask,\n temb=temb,\n image_rotary_emb=image_rotary_emb,\n )\n\n controlnet_block_samples = controlnet_block_samples + (self.controlnet_blocks[i](hidden_states),) * repeat\n\n return {\"input\": controlnet_block_samples[:self.main_model_double]}\n" }, { "path": "comfy/ldm/qwen_image/model.py", "content": "# https://github.com/QwenLM/Qwen-Image (Apache 2.0)\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Optional, Tuple\nfrom einops import repeat\n\nfrom comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps\nfrom comfy.ldm.modules.attention import optimized_attention_masked\nfrom comfy.ldm.flux.layers import EmbedND\nimport comfy.ldm.common_dit\nimport comfy.patcher_extension\nfrom comfy.ldm.flux.math import apply_rope1\n\nclass GELU(nn.Module):\n def __init__(self, dim_in: int, dim_out: int, approximate: str = \"none\", bias: bool = True, dtype=None, device=None, operations=None):\n super().__init__()\n self.proj = operations.Linear(dim_in, dim_out, bias=bias, dtype=dtype, device=device)\n self.approximate = approximate\n\n def forward(self, hidden_states):\n hidden_states = self.proj(hidden_states)\n hidden_states = F.gelu(hidden_states, approximate=self.approximate)\n return hidden_states\n\n\nclass FeedForward(nn.Module):\n def __init__(\n self,\n dim: int,\n dim_out: Optional[int] = None,\n mult: int = 4,\n dropout: float = 0.0,\n inner_dim=None,\n bias: bool = True,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n if inner_dim is None:\n inner_dim = int(dim * mult)\n dim_out = dim_out if dim_out is not None else dim\n\n self.net = nn.ModuleList([])\n self.net.append(GELU(dim, inner_dim, approximate=\"tanh\", bias=bias, dtype=dtype, device=device, operations=operations))\n self.net.append(nn.Dropout(dropout))\n self.net.append(operations.Linear(inner_dim, dim_out, bias=bias, dtype=dtype, device=device))\n\n def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n for module in self.net:\n hidden_states = module(hidden_states)\n return hidden_states\n\n\ndef apply_rotary_emb(x, freqs_cis):\n if x.shape[1] == 0:\n return x\n\n t_ = x.reshape(*x.shape[:-1], -1, 1, 2)\n t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]\n return t_out.reshape(*x.shape)\n\n\nclass QwenTimestepProjEmbeddings(nn.Module):\n def __init__(self, embedding_dim, pooled_projection_dim, use_additional_t_cond=False, dtype=None, device=None, operations=None):\n super().__init__()\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000)\n self.timestep_embedder = TimestepEmbedding(\n in_channels=256,\n time_embed_dim=embedding_dim,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n self.use_additional_t_cond = use_additional_t_cond\n if self.use_additional_t_cond:\n self.addition_t_embedding = operations.Embedding(2, embedding_dim, device=device, dtype=dtype)\n\n def forward(self, timestep, hidden_states, addition_t_cond=None):\n timesteps_proj = self.time_proj(timestep)\n timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype))\n\n if self.use_additional_t_cond:\n if addition_t_cond is None:\n addition_t_cond = torch.zeros((timesteps_emb.shape[0]), device=timesteps_emb.device, dtype=torch.long)\n timesteps_emb += self.addition_t_embedding(addition_t_cond, out_dtype=timesteps_emb.dtype)\n\n return timesteps_emb\n\n\nclass Attention(nn.Module):\n def __init__(\n self,\n query_dim: int,\n dim_head: int = 64,\n heads: int = 8,\n dropout: float = 0.0,\n bias: bool = False,\n eps: float = 1e-5,\n out_bias: bool = True,\n out_dim: int = None,\n out_context_dim: int = None,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.inner_dim = out_dim if out_dim is not None else dim_head * heads\n self.inner_kv_dim = self.inner_dim\n self.heads = heads\n self.dim_head = dim_head\n self.out_dim = out_dim if out_dim is not None else query_dim\n self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim\n self.dropout = dropout\n\n # Q/K normalization\n self.norm_q = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device)\n self.norm_k = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device)\n self.norm_added_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)\n self.norm_added_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)\n\n # Image stream projections\n self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device)\n self.to_k = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)\n self.to_v = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)\n\n # Text stream projections\n self.add_q_proj = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device)\n self.add_k_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)\n self.add_v_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)\n\n # Output projections\n self.to_out = nn.ModuleList([\n operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device),\n nn.Dropout(dropout)\n ])\n self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device)\n\n def forward(\n self,\n hidden_states: torch.FloatTensor, # Image stream\n encoder_hidden_states: torch.FloatTensor = None, # Text stream\n encoder_hidden_states_mask: torch.FloatTensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n image_rotary_emb: Optional[torch.Tensor] = None,\n transformer_options={},\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n batch_size = hidden_states.shape[0]\n seq_img = hidden_states.shape[1]\n seq_txt = encoder_hidden_states.shape[1]\n\n transformer_patches = transformer_options.get(\"patches\", {})\n extra_options = transformer_options.copy()\n\n # Project and reshape to BHND format (batch, heads, seq, dim)\n img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()\n img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()\n img_value = self.to_v(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2)\n\n txt_query = self.add_q_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()\n txt_key = self.add_k_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()\n txt_value = self.add_v_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2)\n\n img_query = self.norm_q(img_query)\n img_key = self.norm_k(img_key)\n txt_query = self.norm_added_q(txt_query)\n txt_key = self.norm_added_k(txt_key)\n\n joint_query = torch.cat([txt_query, img_query], dim=2)\n joint_key = torch.cat([txt_key, img_key], dim=2)\n joint_value = torch.cat([txt_value, img_value], dim=2)\n\n if encoder_hidden_states_mask is not None:\n attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device)\n attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask\n else:\n attn_mask = None\n\n extra_options[\"img_slice\"] = [txt_query.shape[2], joint_query.shape[2]]\n if \"attn1_patch\" in transformer_patches:\n patch = transformer_patches[\"attn1_patch\"]\n for p in patch:\n out = p(joint_query, joint_key, joint_value, pe=image_rotary_emb, attn_mask=encoder_hidden_states_mask, extra_options=extra_options)\n joint_query, joint_key, joint_value, image_rotary_emb, encoder_hidden_states_mask = out.get(\"q\", joint_query), out.get(\"k\", joint_key), out.get(\"v\", joint_value), out.get(\"pe\", image_rotary_emb), out.get(\"attn_mask\", encoder_hidden_states_mask)\n\n joint_query = apply_rope1(joint_query, image_rotary_emb)\n joint_key = apply_rope1(joint_key, image_rotary_emb)\n\n joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,\n attn_mask, transformer_options=transformer_options,\n skip_reshape=True)\n\n txt_attn_output = joint_hidden_states[:, :seq_txt, :]\n img_attn_output = joint_hidden_states[:, seq_txt:, :]\n\n img_attn_output = self.to_out[0](img_attn_output)\n img_attn_output = self.to_out[1](img_attn_output)\n txt_attn_output = self.to_add_out(txt_attn_output)\n\n return img_attn_output, txt_attn_output\n\n\nclass QwenImageTransformerBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n eps: float = 1e-6,\n dtype=None,\n device=None,\n operations=None\n ):\n super().__init__()\n self.dim = dim\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n\n self.img_mod = nn.Sequential(\n nn.SiLU(),\n operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device),\n )\n self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)\n self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)\n self.img_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations)\n\n self.txt_mod = nn.Sequential(\n nn.SiLU(),\n operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device),\n )\n self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)\n self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)\n self.txt_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations)\n\n self.attn = Attention(\n query_dim=dim,\n dim_head=attention_head_dim,\n heads=num_attention_heads,\n out_dim=dim,\n bias=True,\n eps=eps,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n def _apply_gate(self, x, y, gate, timestep_zero_index=None):\n if timestep_zero_index is not None:\n return y + torch.cat((x[:, :timestep_zero_index] * gate[0], x[:, timestep_zero_index:] * gate[1]), dim=1)\n else:\n return torch.addcmul(y, gate, x)\n\n def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor, timestep_zero_index=None) -> Tuple[torch.Tensor, torch.Tensor]:\n shift, scale, gate = torch.chunk(mod_params, 3, dim=-1)\n if timestep_zero_index is not None:\n actual_batch = shift.size(0) // 2\n shift, shift_0 = shift[:actual_batch], shift[actual_batch:]\n scale, scale_0 = scale[:actual_batch], scale[actual_batch:]\n gate, gate_0 = gate[:actual_batch], gate[actual_batch:]\n reg = torch.addcmul(shift.unsqueeze(1), x[:, :timestep_zero_index], 1 + scale.unsqueeze(1))\n zero = torch.addcmul(shift_0.unsqueeze(1), x[:, timestep_zero_index:], 1 + scale_0.unsqueeze(1))\n return torch.cat((reg, zero), dim=1), (gate.unsqueeze(1), gate_0.unsqueeze(1))\n else:\n return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: torch.Tensor,\n encoder_hidden_states_mask: torch.Tensor,\n temb: torch.Tensor,\n image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,\n timestep_zero_index=None,\n transformer_options={},\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n img_mod_params = self.img_mod(temb)\n\n if timestep_zero_index is not None:\n temb = temb.chunk(2, dim=0)[0]\n\n txt_mod_params = self.txt_mod(temb)\n img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)\n txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)\n\n img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1, timestep_zero_index)\n del img_mod1\n txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1)\n del txt_mod1\n\n img_attn_output, txt_attn_output = self.attn(\n hidden_states=img_modulated,\n encoder_hidden_states=txt_modulated,\n encoder_hidden_states_mask=encoder_hidden_states_mask,\n image_rotary_emb=image_rotary_emb,\n transformer_options=transformer_options,\n )\n del img_modulated\n del txt_modulated\n\n hidden_states = self._apply_gate(img_attn_output, hidden_states, img_gate1, timestep_zero_index)\n encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output\n del img_attn_output\n del txt_attn_output\n del img_gate1\n del txt_gate1\n\n img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2, timestep_zero_index)\n hidden_states = self._apply_gate(self.img_mlp(img_modulated2), hidden_states, img_gate2, timestep_zero_index)\n\n txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2)\n encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2))\n\n return encoder_hidden_states, hidden_states\n\n\nclass LastLayer(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n conditioning_embedding_dim: int,\n elementwise_affine=False,\n eps=1e-6,\n bias=True,\n dtype=None, device=None, operations=None\n ):\n super().__init__()\n self.silu = nn.SiLU()\n self.linear = operations.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias, dtype=dtype, device=device)\n self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine=False, bias=bias, dtype=dtype, device=device)\n\n def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:\n emb = self.linear(self.silu(conditioning_embedding))\n scale, shift = torch.chunk(emb, 2, dim=1)\n x = torch.addcmul(shift[:, None, :], self.norm(x), (1 + scale)[:, None, :])\n return x\n\n\nclass QwenImageTransformer2DModel(nn.Module):\n def __init__(\n self,\n patch_size: int = 2,\n in_channels: int = 64,\n out_channels: Optional[int] = 16,\n num_layers: int = 60,\n attention_head_dim: int = 128,\n num_attention_heads: int = 24,\n joint_attention_dim: int = 3584,\n pooled_projection_dim: int = 768,\n axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),\n default_ref_method=\"index\",\n image_model=None,\n final_layer=True,\n use_additional_t_cond=False,\n dtype=None,\n device=None,\n operations=None,\n ):\n super().__init__()\n self.dtype = dtype\n self.patch_size = patch_size\n self.in_channels = in_channels\n self.out_channels = out_channels or in_channels\n self.inner_dim = num_attention_heads * attention_head_dim\n self.default_ref_method = default_ref_method\n\n self.pe_embedder = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope))\n\n self.time_text_embed = QwenTimestepProjEmbeddings(\n embedding_dim=self.inner_dim,\n pooled_projection_dim=pooled_projection_dim,\n use_additional_t_cond=use_additional_t_cond,\n dtype=dtype,\n device=device,\n operations=operations\n )\n\n self.txt_norm = operations.RMSNorm(joint_attention_dim, eps=1e-6, dtype=dtype, device=device)\n self.img_in = operations.Linear(in_channels, self.inner_dim, dtype=dtype, device=device)\n self.txt_in = operations.Linear(joint_attention_dim, self.inner_dim, dtype=dtype, device=device)\n\n self.transformer_blocks = nn.ModuleList([\n QwenImageTransformerBlock(\n dim=self.inner_dim,\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n dtype=dtype,\n device=device,\n operations=operations\n )\n for _ in range(num_layers)\n ])\n\n if self.default_ref_method == \"index_timestep_zero\":\n self.register_buffer(\"__index_timestep_zero__\", torch.tensor([]))\n\n if final_layer:\n self.norm_out = LastLayer(self.inner_dim, self.inner_dim, dtype=dtype, device=device, operations=operations)\n self.proj_out = operations.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True, dtype=dtype, device=device)\n\n def process_img(self, x, index=0, h_offset=0, w_offset=0):\n bs, c, t, h, w = x.shape\n patch_size = self.patch_size\n hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size))\n orig_shape = hidden_states.shape\n hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-3], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2)\n hidden_states = hidden_states.permute(0, 2, 3, 5, 1, 4, 6)\n hidden_states = hidden_states.reshape(orig_shape[0], orig_shape[-3] * (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4)\n t_len = t\n h_len = ((h + (patch_size // 2)) // patch_size)\n w_len = ((w + (patch_size // 2)) // patch_size)\n\n h_offset = ((h_offset + (patch_size // 2)) // patch_size)\n w_offset = ((w_offset + (patch_size // 2)) // patch_size)\n\n img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device)\n\n if t_len > 1:\n img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).unsqueeze(1).unsqueeze(1)\n else:\n img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + index\n\n img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1).unsqueeze(0) - (h_len // 2)\n img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0).unsqueeze(0) - (w_len // 2)\n return hidden_states, repeat(img_ids, \"t h w c -> b (t h w) c\", b=bs), orig_shape\n\n def forward(self, x, timestep, context, attention_mask=None, ref_latents=None, additional_t_cond=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, attention_mask, ref_latents, additional_t_cond, transformer_options, **kwargs)\n\n def _forward(\n self,\n x,\n timesteps,\n context,\n attention_mask=None,\n ref_latents=None,\n additional_t_cond=None,\n transformer_options={},\n control=None,\n **kwargs\n ):\n timestep = timesteps\n encoder_hidden_states = context\n encoder_hidden_states_mask = attention_mask\n\n if encoder_hidden_states_mask is not None and not torch.is_floating_point(encoder_hidden_states_mask):\n encoder_hidden_states_mask = (encoder_hidden_states_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max\n\n hidden_states, img_ids, orig_shape = self.process_img(x)\n num_embeds = hidden_states.shape[1]\n\n timestep_zero_index = None\n if ref_latents is not None:\n ref_num_tokens = []\n h = 0\n w = 0\n index = 0\n ref_method = kwargs.get(\"ref_latents_method\", self.default_ref_method)\n index_ref_method = (ref_method == \"index\") or (ref_method == \"index_timestep_zero\")\n negative_ref_method = ref_method == \"negative_index\"\n timestep_zero = ref_method == \"index_timestep_zero\"\n for ref in ref_latents:\n if index_ref_method:\n index += 1\n h_offset = 0\n w_offset = 0\n elif negative_ref_method:\n index -= 1\n h_offset = 0\n w_offset = 0\n else:\n index = 1\n h_offset = 0\n w_offset = 0\n if ref.shape[-2] + h > ref.shape[-1] + w:\n w_offset = w\n else:\n h_offset = h\n h = max(h, ref.shape[-2] + h_offset)\n w = max(w, ref.shape[-1] + w_offset)\n\n kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)\n hidden_states = torch.cat([hidden_states, kontext], dim=1)\n img_ids = torch.cat([img_ids, kontext_ids], dim=1)\n ref_num_tokens.append(kontext.shape[1])\n if timestep_zero:\n if index > 0:\n timestep = torch.cat([timestep, timestep * 0], dim=0)\n timestep_zero_index = num_embeds\n transformer_options = transformer_options.copy()\n transformer_options[\"reference_image_num_tokens\"] = ref_num_tokens\n\n txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))\n txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)\n\n hidden_states = self.img_in(hidden_states)\n encoder_hidden_states = self.txt_norm(encoder_hidden_states)\n encoder_hidden_states = self.txt_in(encoder_hidden_states)\n\n temb = self.time_text_embed(timestep, hidden_states, additional_t_cond)\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n patches = transformer_options.get(\"patches\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n\n if \"post_input\" in patches:\n for p in patches[\"post_input\"]:\n out = p({\"img\": hidden_states, \"txt\": encoder_hidden_states, \"img_ids\": img_ids, \"txt_ids\": txt_ids, \"transformer_options\": transformer_options})\n hidden_states = out[\"img\"]\n encoder_hidden_states = out[\"txt\"]\n img_ids = out[\"img_ids\"]\n txt_ids = out[\"txt_ids\"]\n\n ids = torch.cat((txt_ids, img_ids), dim=1)\n image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()\n del ids, txt_ids, img_ids\n\n transformer_options[\"total_blocks\"] = len(self.transformer_blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.transformer_blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"txt\"], out[\"img\"] = block(hidden_states=args[\"img\"], encoder_hidden_states=args[\"txt\"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args[\"vec\"], image_rotary_emb=args[\"pe\"], timestep_zero_index=timestep_zero_index, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": hidden_states, \"txt\": encoder_hidden_states, \"vec\": temb, \"pe\": image_rotary_emb, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n hidden_states = out[\"img\"]\n encoder_hidden_states = out[\"txt\"]\n else:\n encoder_hidden_states, hidden_states = block(\n hidden_states=hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n encoder_hidden_states_mask=encoder_hidden_states_mask,\n temb=temb,\n image_rotary_emb=image_rotary_emb,\n timestep_zero_index=timestep_zero_index,\n transformer_options=transformer_options,\n )\n\n if \"double_block\" in patches:\n for p in patches[\"double_block\"]:\n out = p({\"img\": hidden_states, \"txt\": encoder_hidden_states, \"x\": x, \"block_index\": i, \"transformer_options\": transformer_options})\n hidden_states = out[\"img\"]\n encoder_hidden_states = out[\"txt\"]\n\n if control is not None: # Controlnet\n control_i = control.get(\"input\")\n if i < len(control_i):\n add = control_i[i]\n if add is not None:\n hidden_states[:, :add.shape[1]] += add\n\n if timestep_zero_index is not None:\n temb = temb.chunk(2, dim=0)[0]\n\n hidden_states = self.norm_out(hidden_states, temb)\n hidden_states = self.proj_out(hidden_states)\n\n hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-3], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2)\n hidden_states = hidden_states.permute(0, 4, 1, 2, 5, 3, 6)\n return hidden_states.reshape(orig_shape)[:, :, :, :x.shape[-2], :x.shape[-1]]\n" }, { "path": "comfy/ldm/util.py", "content": "import importlib\nimport logging\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 logging.warning(\"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 logging.info(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 \"target\" not 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 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\n" }, { "path": "comfy/ldm/wan/model.py", "content": "# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py\n# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.\nimport math\n\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange\n\nfrom comfy.ldm.modules.attention import optimized_attention\nfrom comfy.ldm.flux.layers import EmbedND\nfrom comfy.ldm.flux.math import apply_rope1\nimport comfy.ldm.common_dit\nimport comfy.model_management\nimport comfy.patcher_extension\n\n\ndef sinusoidal_embedding_1d(dim, position):\n # preprocess\n assert dim % 2 == 0\n half = dim // 2\n position = position.type(torch.float32)\n\n # calculation\n sinusoid = torch.outer(\n position, torch.pow(10000, -torch.arange(half).to(position).div(half)))\n x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)\n return x\n\n\nclass WanSelfAttention(nn.Module):\n\n def __init__(self,\n dim,\n num_heads,\n window_size=(-1, -1),\n qk_norm=True,\n eps=1e-6,\n kv_dim=None,\n operation_settings={}):\n assert dim % num_heads == 0\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n self.window_size = window_size\n self.qk_norm = qk_norm\n self.eps = eps\n if kv_dim is None:\n kv_dim = dim\n\n # layers\n self.q = operation_settings.get(\"operations\").Linear(dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.k = operation_settings.get(\"operations\").Linear(kv_dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.v = operation_settings.get(\"operations\").Linear(kv_dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.o = operation_settings.get(\"operations\").Linear(dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.norm_q = operation_settings.get(\"operations\").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")) if qk_norm else nn.Identity()\n self.norm_k = operation_settings.get(\"operations\").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")) if qk_norm else nn.Identity()\n\n def forward(self, x, freqs, transformer_options={}):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L, num_heads, C / num_heads]\n freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]\n \"\"\"\n patches = transformer_options.get(\"patches\", {})\n\n b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim\n\n def qkv_fn_q(x):\n q = self.norm_q(self.q(x)).view(b, s, n, d)\n return apply_rope1(q, freqs)\n\n def qkv_fn_k(x):\n k = self.norm_k(self.k(x)).view(b, s, n, d)\n return apply_rope1(k, freqs)\n\n #These two are VRAM hogs, so we want to do all of q computation and\n #have pytorch garbage collect the intermediates on the sub function\n #return before we touch k\n q = qkv_fn_q(x)\n k = qkv_fn_k(x)\n\n x = optimized_attention(\n q.view(b, s, n * d),\n k.view(b, s, n * d),\n self.v(x).view(b, s, n * d),\n heads=self.num_heads,\n transformer_options=transformer_options,\n )\n\n if \"attn1_patch\" in patches:\n for p in patches[\"attn1_patch\"]:\n x = p({\"x\": x, \"q\": q, \"k\": k, \"transformer_options\": transformer_options})\n\n x = self.o(x)\n return x\n\n\nclass WanT2VCrossAttention(WanSelfAttention):\n\n def forward(self, x, context, transformer_options={}, **kwargs):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L1, C]\n context(Tensor): Shape [B, L2, C]\n \"\"\"\n # compute query, key, value\n q = self.norm_q(self.q(x))\n k = self.norm_k(self.k(context))\n v = self.v(context)\n\n # compute attention\n x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)\n\n x = self.o(x)\n return x\n\n\nclass WanI2VCrossAttention(WanSelfAttention):\n\n def __init__(self,\n dim,\n num_heads,\n window_size=(-1, -1),\n qk_norm=True,\n eps=1e-6, operation_settings={}):\n super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings)\n\n self.k_img = operation_settings.get(\"operations\").Linear(dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.v_img = operation_settings.get(\"operations\").Linear(dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n # self.alpha = nn.Parameter(torch.zeros((1, )))\n self.norm_k_img = operation_settings.get(\"operations\").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")) if qk_norm else nn.Identity()\n\n def forward(self, x, context, context_img_len, transformer_options={}):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L1, C]\n context(Tensor): Shape [B, L2, C]\n \"\"\"\n context_img = context[:, :context_img_len]\n context = context[:, context_img_len:]\n\n # compute query, key, value\n q = self.norm_q(self.q(x))\n k = self.norm_k(self.k(context))\n v = self.v(context)\n k_img = self.norm_k_img(self.k_img(context_img))\n v_img = self.v_img(context_img)\n img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options)\n # compute attention\n x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)\n\n # output\n x = x + img_x\n x = self.o(x)\n return x\n\n\nWAN_CROSSATTENTION_CLASSES = {\n 't2v_cross_attn': WanT2VCrossAttention,\n 'i2v_cross_attn': WanI2VCrossAttention,\n}\n\n\ndef repeat_e(e, x):\n repeats = 1\n if e.size(1) > 1:\n repeats = x.size(1) // e.size(1)\n if repeats == 1:\n return e\n if repeats * e.size(1) == x.size(1):\n return torch.repeat_interleave(e, repeats, dim=1)\n else:\n return torch.repeat_interleave(e, repeats + 1, dim=1)[:, :x.size(1)]\n\n\nclass WanAttentionBlock(nn.Module):\n\n def __init__(self,\n cross_attn_type,\n dim,\n ffn_dim,\n num_heads,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=False,\n eps=1e-6, operation_settings={}):\n super().__init__()\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.qk_norm = qk_norm\n self.cross_attn_norm = cross_attn_norm\n self.eps = eps\n\n # layers\n self.norm1 = operation_settings.get(\"operations\").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,\n eps, operation_settings=operation_settings)\n self.norm3 = operation_settings.get(\"operations\").LayerNorm(\n dim, eps,\n elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")) if cross_attn_norm else nn.Identity()\n self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,\n num_heads,\n (-1, -1),\n qk_norm,\n eps, operation_settings=operation_settings)\n self.norm2 = operation_settings.get(\"operations\").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.ffn = nn.Sequential(\n operation_settings.get(\"operations\").Linear(dim, ffn_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")), nn.GELU(approximate='tanh'),\n operation_settings.get(\"operations\").Linear(ffn_dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n\n # modulation\n self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n\n def forward(\n self,\n x,\n e,\n freqs,\n context,\n context_img_len=257,\n transformer_options={},\n ):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L, C]\n e(Tensor): Shape [B, 6, C]\n freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]\n \"\"\"\n # assert e.dtype == torch.float32\n\n patches = transformer_options.get(\"patches\", {})\n\n if e.ndim < 4:\n e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)\n else:\n e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)\n # assert e[0].dtype == torch.float32\n\n # self-attention\n x = x.contiguous() # otherwise implicit in LayerNorm\n y = self.self_attn(\n torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),\n freqs, transformer_options=transformer_options)\n\n x = torch.addcmul(x, y, repeat_e(e[2], x))\n del y\n\n # cross-attention & ffn\n x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)\n\n if \"attn2_patch\" in patches:\n for p in patches[\"attn2_patch\"]:\n x = p({\"x\": x, \"transformer_options\": transformer_options})\n\n y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))\n x = torch.addcmul(x, y, repeat_e(e[5], x))\n return x\n\n\nclass VaceWanAttentionBlock(WanAttentionBlock):\n def __init__(\n self,\n cross_attn_type,\n dim,\n ffn_dim,\n num_heads,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=False,\n eps=1e-6,\n block_id=0,\n operation_settings={}\n ):\n super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)\n self.block_id = block_id\n if block_id == 0:\n self.before_proj = operation_settings.get(\"operations\").Linear(self.dim, self.dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.after_proj = operation_settings.get(\"operations\").Linear(self.dim, self.dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, c, x, **kwargs):\n if self.block_id == 0:\n c = self.before_proj(c) + x\n c = super().forward(c, **kwargs)\n c_skip = self.after_proj(c)\n return c_skip, c\n\n\nclass WanCamAdapter(nn.Module):\n def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}):\n super(WanCamAdapter, self).__init__()\n\n # Pixel Unshuffle: reduce spatial dimensions by a factor of 8\n self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)\n\n # Convolution: reduce spatial dimensions by a factor\n # of 2 (without overlap)\n self.conv = operation_settings.get(\"operations\").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n # Residual blocks for feature extraction\n self.residual_blocks = nn.Sequential(\n *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)]\n )\n\n def forward(self, x):\n # Reshape to merge the frame dimension into batch\n bs, c, f, h, w = x.size()\n x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)\n\n # Pixel Unshuffle operation\n x_unshuffled = self.pixel_unshuffle(x)\n\n # Convolution operation\n x_conv = self.conv(x_unshuffled)\n\n # Feature extraction with residual blocks\n out = self.residual_blocks(x_conv)\n\n # Reshape to restore original bf dimension\n out = out.view(bs, f, out.size(1), out.size(2), out.size(3))\n\n # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames\n out = out.permute(0, 2, 1, 3, 4)\n\n return out\n\n\nclass WanCamResidualBlock(nn.Module):\n def __init__(self, dim, operation_settings={}):\n super(WanCamResidualBlock, self).__init__()\n self.conv1 = operation_settings.get(\"operations\").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = operation_settings.get(\"operations\").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, x):\n residual = x\n out = self.relu(self.conv1(x))\n out = self.conv2(out)\n out += residual\n return out\n\n\nclass Head(nn.Module):\n\n def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):\n super().__init__()\n self.dim = dim\n self.out_dim = out_dim\n self.patch_size = patch_size\n self.eps = eps\n\n # layers\n out_dim = math.prod(patch_size) * out_dim\n self.norm = operation_settings.get(\"operations\").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n self.head = operation_settings.get(\"operations\").Linear(dim, out_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n # modulation\n self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n\n def forward(self, x, e):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L1, C]\n e(Tensor): Shape [B, C]\n \"\"\"\n # assert e.dtype == torch.float32\n if e.ndim < 3:\n e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)\n else:\n e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2)\n\n x = (self.head(torch.addcmul(repeat_e(e[0], x), self.norm(x), 1 + repeat_e(e[1], x))))\n return x\n\n\nclass MLPProj(torch.nn.Module):\n\n def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}):\n super().__init__()\n\n self.proj = torch.nn.Sequential(\n operation_settings.get(\"operations\").LayerNorm(in_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")), operation_settings.get(\"operations\").Linear(in_dim, in_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")),\n torch.nn.GELU(), operation_settings.get(\"operations\").Linear(in_dim, out_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")),\n operation_settings.get(\"operations\").LayerNorm(out_dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n\n if flf_pos_embed_token_number is not None:\n self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n else:\n self.emb_pos = None\n\n def forward(self, image_embeds):\n if self.emb_pos is not None:\n image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device)\n\n clip_extra_context_tokens = self.proj(image_embeds)\n return clip_extra_context_tokens\n\n\nclass WanModel(torch.nn.Module):\n r\"\"\"\n Wan diffusion backbone supporting both text-to-video and image-to-video.\n \"\"\"\n\n def __init__(self,\n model_type='t2v',\n patch_size=(1, 2, 2),\n text_len=512,\n in_dim=16,\n dim=2048,\n ffn_dim=8192,\n freq_dim=256,\n text_dim=4096,\n out_dim=16,\n num_heads=16,\n num_layers=32,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=True,\n eps=1e-6,\n flf_pos_embed_token_number=None,\n in_dim_ref_conv=None,\n wan_attn_block_class=WanAttentionBlock,\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n ):\n r\"\"\"\n Initialize the diffusion model backbone.\n\n Args:\n model_type (`str`, *optional*, defaults to 't2v'):\n Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)\n patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):\n 3D patch dimensions for video embedding (t_patch, h_patch, w_patch)\n text_len (`int`, *optional*, defaults to 512):\n Fixed length for text embeddings\n in_dim (`int`, *optional*, defaults to 16):\n Input video channels (C_in)\n dim (`int`, *optional*, defaults to 2048):\n Hidden dimension of the transformer\n ffn_dim (`int`, *optional*, defaults to 8192):\n Intermediate dimension in feed-forward network\n freq_dim (`int`, *optional*, defaults to 256):\n Dimension for sinusoidal time embeddings\n text_dim (`int`, *optional*, defaults to 4096):\n Input dimension for text embeddings\n out_dim (`int`, *optional*, defaults to 16):\n Output video channels (C_out)\n num_heads (`int`, *optional*, defaults to 16):\n Number of attention heads\n num_layers (`int`, *optional*, defaults to 32):\n Number of transformer blocks\n window_size (`tuple`, *optional*, defaults to (-1, -1)):\n Window size for local attention (-1 indicates global attention)\n qk_norm (`bool`, *optional*, defaults to True):\n Enable query/key normalization\n cross_attn_norm (`bool`, *optional*, defaults to False):\n Enable cross-attention normalization\n eps (`float`, *optional*, defaults to 1e-6):\n Epsilon value for normalization layers\n \"\"\"\n\n super().__init__()\n self.dtype = dtype\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n\n assert model_type in ['t2v', 'i2v']\n self.model_type = model_type\n\n self.patch_size = patch_size\n self.text_len = text_len\n self.in_dim = in_dim\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.freq_dim = freq_dim\n self.text_dim = text_dim\n self.out_dim = out_dim\n self.num_heads = num_heads\n self.num_layers = num_layers\n self.window_size = window_size\n self.qk_norm = qk_norm\n self.cross_attn_norm = cross_attn_norm\n self.eps = eps\n\n # embeddings\n self.patch_embedding = operations.Conv3d(\n in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get(\"device\"), dtype=torch.float32)\n self.text_embedding = nn.Sequential(\n operations.Linear(text_dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")), nn.GELU(approximate='tanh'),\n operations.Linear(dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n\n self.time_embedding = nn.Sequential(\n operations.Linear(freq_dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\")))\n\n # blocks\n cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'\n self.blocks = nn.ModuleList([\n wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads,\n window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)\n for i in range(num_layers)\n ])\n\n # head\n self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)\n\n d = dim // num_heads\n self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])\n\n if model_type == 'i2v':\n self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings)\n else:\n self.img_emb = None\n\n if in_dim_ref_conv is not None:\n self.ref_conv = operations.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:], device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n else:\n self.ref_conv = None\n\n def forward_orig(\n self,\n x,\n t,\n context,\n clip_fea=None,\n freqs=None,\n transformer_options={},\n **kwargs,\n ):\n r\"\"\"\n Forward pass through the diffusion model\n\n Args:\n x (Tensor):\n List of input video tensors with shape [B, C_in, F, H, W]\n t (Tensor):\n Diffusion timesteps tensor of shape [B]\n context (List[Tensor]):\n List of text embeddings each with shape [B, L, C]\n seq_len (`int`):\n Maximum sequence length for positional encoding\n clip_fea (Tensor, *optional*):\n CLIP image features for image-to-video mode\n y (List[Tensor], *optional*):\n Conditional video inputs for image-to-video mode, same shape as x\n\n Returns:\n List[Tensor]:\n List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]\n \"\"\"\n # embeddings\n x = self.patch_embedding(x.float()).to(x.dtype)\n grid_sizes = x.shape[2:]\n transformer_options[\"grid_sizes\"] = grid_sizes\n x = x.flatten(2).transpose(1, 2)\n\n # time embeddings\n e = self.time_embedding(\n sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))\n e = e.reshape(t.shape[0], -1, e.shape[-1])\n e0 = self.time_projection(e).unflatten(2, (6, self.dim))\n\n full_ref = None\n if self.ref_conv is not None:\n full_ref = kwargs.get(\"reference_latent\", None)\n if full_ref is not None:\n full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)\n x = torch.concat((full_ref, x), dim=1)\n\n # context\n context = self.text_embedding(context)\n\n context_img_len = None\n if clip_fea is not None:\n if self.img_emb is not None:\n context_clip = self.img_emb(clip_fea) # bs x 257 x dim\n context = torch.concat([context_clip, context], dim=1)\n context_img_len = clip_fea.shape[-2]\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], context_img_len=context_img_len, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)\n\n # head\n x = self.head(x, e)\n\n if full_ref is not None:\n x = x[:, full_ref.shape[1]:]\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n return x\n\n def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):\n patch_size = self.patch_size\n t_len = ((t + (patch_size[0] // 2)) // patch_size[0])\n h_len = ((h + (patch_size[1] // 2)) // patch_size[1])\n w_len = ((w + (patch_size[2] // 2)) // patch_size[2])\n\n if steps_t is None:\n steps_t = t_len\n if steps_h is None:\n steps_h = h_len\n if steps_w is None:\n steps_w = w_len\n\n h_start = 0\n w_start = 0\n rope_options = transformer_options.get(\"rope_options\", None)\n if rope_options is not None:\n t_len = (t_len - 1.0) * rope_options.get(\"scale_t\", 1.0) + 1.0\n h_len = (h_len - 1.0) * rope_options.get(\"scale_y\", 1.0) + 1.0\n w_len = (w_len - 1.0) * rope_options.get(\"scale_x\", 1.0) + 1.0\n\n t_start += rope_options.get(\"shift_t\", 0.0)\n h_start += rope_options.get(\"shift_y\", 0.0)\n w_start += rope_options.get(\"shift_x\", 0.0)\n\n img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)\n img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)\n img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)\n img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)\n img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])\n\n freqs = self.rope_embedder(img_ids).movedim(1, 2)\n return freqs\n\n def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._forward,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)\n ).execute(x, timestep, context, clip_fea, time_dim_concat, transformer_options, **kwargs)\n\n def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):\n bs, c, t, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)\n\n t_len = t\n if time_dim_concat is not None:\n time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)\n x = torch.cat([x, time_dim_concat], dim=2)\n t_len = x.shape[2]\n\n if self.ref_conv is not None and \"reference_latent\" in kwargs:\n t_len += 1\n\n freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)\n return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]\n\n def unpatchify(self, x, grid_sizes):\n r\"\"\"\n Reconstruct video tensors from patch embeddings.\n\n Args:\n x (List[Tensor]):\n List of patchified features, each with shape [L, C_out * prod(patch_size)]\n grid_sizes (Tensor):\n Original spatial-temporal grid dimensions before patching,\n shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)\n\n Returns:\n List[Tensor]:\n Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8]\n \"\"\"\n\n c = self.out_dim\n u = x\n b = u.shape[0]\n u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c)\n u = torch.einsum('bfhwpqrc->bcfphqwr', u)\n u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])\n return u\n\n\nclass VaceWanModel(WanModel):\n r\"\"\"\n Wan diffusion backbone supporting both text-to-video and image-to-video.\n \"\"\"\n\n def __init__(self,\n model_type='vace',\n patch_size=(1, 2, 2),\n text_len=512,\n in_dim=16,\n dim=2048,\n ffn_dim=8192,\n freq_dim=256,\n text_dim=4096,\n out_dim=16,\n num_heads=16,\n num_layers=32,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=True,\n eps=1e-6,\n flf_pos_embed_token_number=None,\n image_model=None,\n vace_layers=None,\n vace_in_dim=None,\n device=None,\n dtype=None,\n operations=None,\n ):\n\n super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n\n # Vace\n if vace_layers is not None:\n self.vace_layers = vace_layers\n self.vace_in_dim = vace_in_dim\n # vace blocks\n self.vace_blocks = nn.ModuleList([\n VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=i, operation_settings=operation_settings)\n for i in range(self.vace_layers)\n ])\n\n self.vace_layers_mapping = {i: n for n, i in enumerate(range(0, self.num_layers, self.num_layers // self.vace_layers))}\n # vace patch embeddings\n self.vace_patch_embedding = operations.Conv3d(\n self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size, device=device, dtype=torch.float32\n )\n\n def forward_orig(\n self,\n x,\n t,\n context,\n vace_context,\n vace_strength,\n clip_fea=None,\n freqs=None,\n transformer_options={},\n **kwargs,\n ):\n # embeddings\n x = self.patch_embedding(x.float()).to(x.dtype)\n grid_sizes = x.shape[2:]\n transformer_options[\"grid_sizes\"] = grid_sizes\n x = x.flatten(2).transpose(1, 2)\n\n # time embeddings\n e = self.time_embedding(\n sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))\n e0 = self.time_projection(e).unflatten(1, (6, self.dim))\n\n # context\n context = self.text_embedding(context)\n\n context_img_len = None\n if clip_fea is not None:\n if self.img_emb is not None:\n context_clip = self.img_emb(clip_fea) # bs x 257 x dim\n context = torch.concat([context_clip, context], dim=1)\n context_img_len = clip_fea.shape[-2]\n\n orig_shape = list(vace_context.shape)\n vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:])\n c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype)\n c = c.flatten(2).transpose(1, 2)\n c = list(c.split(orig_shape[0], dim=0))\n\n # arguments\n x_orig = x\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], context_img_len=context_img_len, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)\n\n ii = self.vace_layers_mapping.get(i, None)\n if ii is not None:\n for iii in range(len(c)):\n c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)\n x += c_skip * vace_strength[iii]\n del c_skip\n # head\n x = self.head(x, e)\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n return x\n\nclass CameraWanModel(WanModel):\n r\"\"\"\n Wan diffusion backbone supporting both text-to-video and image-to-video.\n \"\"\"\n\n def __init__(self,\n model_type='camera',\n patch_size=(1, 2, 2),\n text_len=512,\n in_dim=16,\n dim=2048,\n ffn_dim=8192,\n freq_dim=256,\n text_dim=4096,\n out_dim=16,\n num_heads=16,\n num_layers=32,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=True,\n eps=1e-6,\n flf_pos_embed_token_number=None,\n image_model=None,\n in_dim_control_adapter=24,\n device=None,\n dtype=None,\n operations=None,\n ):\n\n if model_type == 'camera':\n model_type = 'i2v'\n else:\n model_type = 't2v'\n\n super().__init__(model_type=model_type, patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)\n operation_settings = {\"operations\": operations, \"device\": device, \"dtype\": dtype}\n\n self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings)\n\n\n def forward_orig(\n self,\n x,\n t,\n context,\n clip_fea=None,\n freqs=None,\n camera_conditions = None,\n transformer_options={},\n **kwargs,\n ):\n # embeddings\n x = self.patch_embedding(x.float()).to(x.dtype)\n if self.control_adapter is not None and camera_conditions is not None:\n x = x + self.control_adapter(camera_conditions).to(x.dtype)\n grid_sizes = x.shape[2:]\n x = x.flatten(2).transpose(1, 2)\n\n # time embeddings\n e = self.time_embedding(\n sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))\n e0 = self.time_projection(e).unflatten(1, (6, self.dim))\n\n # context\n context = self.text_embedding(context)\n\n context_img_len = None\n if clip_fea is not None:\n if self.img_emb is not None:\n context_clip = self.img_emb(clip_fea) # bs x 257 x dim\n context = torch.concat([context_clip, context], dim=1)\n context_img_len = clip_fea.shape[-2]\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], context_img_len=context_img_len, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)\n\n # head\n x = self.head(x, e)\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n return x\n\n\nclass CausalConv1d(nn.Module):\n\n def __init__(self,\n chan_in,\n chan_out,\n kernel_size=3,\n stride=1,\n dilation=1,\n pad_mode='replicate',\n operations=None,\n **kwargs):\n super().__init__()\n\n self.pad_mode = pad_mode\n padding = (kernel_size - 1, 0) # T\n self.time_causal_padding = padding\n\n self.conv = operations.Conv1d(\n chan_in,\n chan_out,\n kernel_size,\n stride=stride,\n dilation=dilation,\n **kwargs)\n\n def forward(self, x):\n x = torch.nn.functional.pad(x, self.time_causal_padding, mode=self.pad_mode)\n return self.conv(x)\n\n\nclass MotionEncoder_tc(nn.Module):\n\n def __init__(self,\n in_dim: int,\n hidden_dim: int,\n num_heads: int,\n need_global=True,\n dtype=None,\n device=None,\n operations=None,):\n factory_kwargs = {\"dtype\": dtype, \"device\": device}\n super().__init__()\n\n self.num_heads = num_heads\n self.need_global = need_global\n self.conv1_local = CausalConv1d(in_dim, hidden_dim // 4 * num_heads, 3, stride=1, operations=operations, **factory_kwargs)\n if need_global:\n self.conv1_global = CausalConv1d(\n in_dim, hidden_dim // 4, 3, stride=1, operations=operations, **factory_kwargs)\n self.norm1 = operations.LayerNorm(\n hidden_dim // 4,\n elementwise_affine=False,\n eps=1e-6,\n **factory_kwargs)\n self.act = nn.SiLU()\n self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2, operations=operations, **factory_kwargs)\n self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2, operations=operations, **factory_kwargs)\n\n if need_global:\n self.final_linear = operations.Linear(hidden_dim, hidden_dim, **factory_kwargs)\n\n self.norm1 = operations.LayerNorm(\n hidden_dim // 4,\n elementwise_affine=False,\n eps=1e-6,\n **factory_kwargs)\n\n self.norm2 = operations.LayerNorm(\n hidden_dim // 2,\n elementwise_affine=False,\n eps=1e-6,\n **factory_kwargs)\n\n self.norm3 = operations.LayerNorm(\n hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n\n self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs))\n\n def forward(self, x):\n x = rearrange(x, 'b t c -> b c t')\n x_ori = x.clone()\n b, c, t = x.shape\n x = self.conv1_local(x)\n x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads)\n x = self.norm1(x)\n x = self.act(x)\n x = rearrange(x, 'b t c -> b c t')\n x = self.conv2(x)\n x = rearrange(x, 'b c t -> b t c')\n x = self.norm2(x)\n x = self.act(x)\n x = rearrange(x, 'b t c -> b c t')\n x = self.conv3(x)\n x = rearrange(x, 'b c t -> b t c')\n x = self.norm3(x)\n x = self.act(x)\n x = rearrange(x, '(b n) t c -> b t n c', b=b)\n padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1)\n x = torch.cat([x, padding], dim=-2)\n x_local = x.clone()\n\n if not self.need_global:\n return x_local\n\n x = self.conv1_global(x_ori)\n x = rearrange(x, 'b c t -> b t c')\n x = self.norm1(x)\n x = self.act(x)\n x = rearrange(x, 'b t c -> b c t')\n x = self.conv2(x)\n x = rearrange(x, 'b c t -> b t c')\n x = self.norm2(x)\n x = self.act(x)\n x = rearrange(x, 'b t c -> b c t')\n x = self.conv3(x)\n x = rearrange(x, 'b c t -> b t c')\n x = self.norm3(x)\n x = self.act(x)\n x = self.final_linear(x)\n x = rearrange(x, '(b n) t c -> b t n c', b=b)\n\n return x, x_local\n\n\nclass CausalAudioEncoder(nn.Module):\n\n def __init__(self,\n dim=5120,\n num_layers=25,\n out_dim=2048,\n video_rate=8,\n num_token=4,\n need_global=False,\n dtype=None,\n device=None,\n operations=None):\n super().__init__()\n self.encoder = MotionEncoder_tc(\n in_dim=dim,\n hidden_dim=out_dim,\n num_heads=num_token,\n need_global=need_global, dtype=dtype, device=device, operations=operations)\n weight = torch.empty((1, num_layers, 1, 1), dtype=dtype, device=device)\n\n self.weights = torch.nn.Parameter(weight)\n self.act = torch.nn.SiLU()\n\n def forward(self, features):\n # features B * num_layers * dim * video_length\n weights = self.act(comfy.model_management.cast_to(self.weights, dtype=features.dtype, device=features.device))\n weights_sum = weights.sum(dim=1, keepdims=True)\n weighted_feat = ((features * weights) / weights_sum).sum(\n dim=1) # b dim f\n weighted_feat = weighted_feat.permute(0, 2, 1) # b f dim\n res = self.encoder(weighted_feat) # b f n dim\n return res # b f n dim\n\n\nclass AdaLayerNorm(nn.Module):\n def __init__(self, embedding_dim, output_dim=None, norm_elementwise_affine=False, norm_eps=1e-5, dtype=None, device=None, operations=None):\n super().__init__()\n\n output_dim = output_dim or embedding_dim * 2\n\n self.silu = nn.SiLU()\n self.linear = operations.Linear(embedding_dim, output_dim, dtype=dtype, device=device)\n self.norm = operations.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine, dtype=dtype, device=device)\n\n def forward(self, x, temb):\n temb = self.linear(self.silu(temb))\n shift, scale = temb.chunk(2, dim=1)\n shift = shift[:, None, :]\n scale = scale[:, None, :]\n x = self.norm(x) * (1 + scale) + shift\n return x\n\n\nclass AudioInjector_WAN(nn.Module):\n\n def __init__(self,\n dim=2048,\n num_heads=32,\n inject_layer=[0, 27],\n root_net=None,\n enable_adain=False,\n adain_dim=2048,\n adain_mode=None,\n dtype=None,\n device=None,\n operations=None):\n super().__init__()\n self.enable_adain = enable_adain\n self.adain_mode = adain_mode\n self.injected_block_id = {}\n audio_injector_id = 0\n for inject_id in inject_layer:\n self.injected_block_id[inject_id] = audio_injector_id\n audio_injector_id += 1\n\n self.injector = nn.ModuleList([\n WanT2VCrossAttention(\n dim=dim,\n num_heads=num_heads,\n qk_norm=True, operation_settings={\"operations\": operations, \"device\": device, \"dtype\": dtype}\n ) for _ in range(audio_injector_id)\n ])\n self.injector_pre_norm_feat = nn.ModuleList([\n operations.LayerNorm(\n dim,\n elementwise_affine=False,\n eps=1e-6, dtype=dtype, device=device\n ) for _ in range(audio_injector_id)\n ])\n self.injector_pre_norm_vec = nn.ModuleList([\n operations.LayerNorm(\n dim,\n elementwise_affine=False,\n eps=1e-6, dtype=dtype, device=device\n ) for _ in range(audio_injector_id)\n ])\n if enable_adain:\n self.injector_adain_layers = nn.ModuleList([\n AdaLayerNorm(\n output_dim=dim * 2, embedding_dim=adain_dim, dtype=dtype, device=device, operations=operations)\n for _ in range(audio_injector_id)\n ])\n if adain_mode != \"attn_norm\":\n self.injector_adain_output_layers = nn.ModuleList(\n [operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)])\n\n def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len):\n audio_attn_id = self.injected_block_id.get(block_id, None)\n if audio_attn_id is None:\n return x\n\n num_frames = audio_emb.shape[1]\n input_hidden_states = rearrange(x[:, :seq_len], \"b (t n) c -> (b t) n c\", t=num_frames)\n if self.enable_adain and self.adain_mode == \"attn_norm\":\n audio_emb_global = rearrange(audio_emb_global, \"b t n c -> (b t) n c\")\n adain_hidden_states = self.injector_adain_layers[audio_attn_id](input_hidden_states, temb=audio_emb_global[:, 0])\n attn_hidden_states = adain_hidden_states\n else:\n attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states)\n audio_emb = rearrange(audio_emb, \"b t n c -> (b t) n c\", t=num_frames)\n attn_audio_emb = audio_emb\n residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb)\n residual_out = rearrange(\n residual_out, \"(b t) n c -> b (t n) c\", t=num_frames)\n x[:, :seq_len] = x[:, :seq_len] + residual_out\n return x\n\n\nclass FramePackMotioner(nn.Module):\n def __init__(\n self,\n inner_dim=1024,\n num_heads=16, # Used to indicate the number of heads in the backbone network; unrelated to this module's design\n zip_frame_buckets=[\n 1, 2, 16\n ], # Three numbers representing the number of frames sampled for patch operations from the nearest to the farthest frames\n drop_mode=\"drop\", # If not \"drop\", it will use \"padd\", meaning padding instead of deletion\n dtype=None,\n device=None,\n operations=None):\n super().__init__()\n self.proj = operations.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2), dtype=dtype, device=device)\n self.proj_2x = operations.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4), dtype=dtype, device=device)\n self.proj_4x = operations.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8), dtype=dtype, device=device)\n self.zip_frame_buckets = zip_frame_buckets\n\n self.inner_dim = inner_dim\n self.num_heads = num_heads\n\n self.drop_mode = drop_mode\n\n def forward(self, motion_latents, rope_embedder, add_last_motion=2):\n lat_height, lat_width = motion_latents.shape[3], motion_latents.shape[4]\n padd_lat = torch.zeros(motion_latents.shape[0], 16, sum(self.zip_frame_buckets), lat_height, lat_width).to(device=motion_latents.device, dtype=motion_latents.dtype)\n overlap_frame = min(padd_lat.shape[2], motion_latents.shape[2])\n if overlap_frame > 0:\n padd_lat[:, :, -overlap_frame:] = motion_latents[:, :, -overlap_frame:]\n\n if add_last_motion < 2 and self.drop_mode != \"drop\":\n zero_end_frame = sum(self.zip_frame_buckets[:len(self.zip_frame_buckets) - add_last_motion - 1])\n padd_lat[:, :, -zero_end_frame:] = 0\n\n clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -sum(self.zip_frame_buckets):, :, :].split(self.zip_frame_buckets[::-1], dim=2) # 16, 2 ,1\n\n # patchfy\n clean_latents_post = self.proj(clean_latents_post).flatten(2).transpose(1, 2)\n clean_latents_2x = self.proj_2x(clean_latents_2x)\n l_2x_shape = clean_latents_2x.shape\n clean_latents_2x = clean_latents_2x.flatten(2).transpose(1, 2)\n clean_latents_4x = self.proj_4x(clean_latents_4x)\n l_4x_shape = clean_latents_4x.shape\n clean_latents_4x = clean_latents_4x.flatten(2).transpose(1, 2)\n\n if add_last_motion < 2 and self.drop_mode == \"drop\":\n clean_latents_post = clean_latents_post[:, :\n 0] if add_last_motion < 2 else clean_latents_post\n clean_latents_2x = clean_latents_2x[:, :\n 0] if add_last_motion < 1 else clean_latents_2x\n\n motion_lat = torch.cat([clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1)\n\n rope_post = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-1, device=motion_latents.device, dtype=motion_latents.dtype)\n rope_2x = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-3, steps_h=l_2x_shape[-2], steps_w=l_2x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype)\n rope_4x = rope_embedder.rope_encode(4, lat_height, lat_width, t_start=-19, steps_h=l_4x_shape[-2], steps_w=l_4x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype)\n\n rope = torch.cat([rope_post, rope_2x, rope_4x], dim=1)\n return motion_lat, rope\n\n\nclass WanModel_S2V(WanModel):\n def __init__(self,\n model_type='s2v',\n patch_size=(1, 2, 2),\n text_len=512,\n in_dim=16,\n dim=2048,\n ffn_dim=8192,\n freq_dim=256,\n text_dim=4096,\n out_dim=16,\n num_heads=16,\n num_layers=32,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=True,\n eps=1e-6,\n audio_dim=1024,\n num_audio_token=4,\n enable_adain=True,\n cond_dim=16,\n audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39],\n adain_mode=\"attn_norm\",\n framepack_drop_mode=\"padd\",\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n ):\n\n super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, device=device, dtype=dtype, operations=operations)\n\n self.trainable_cond_mask = operations.Embedding(3, self.dim, device=device, dtype=dtype)\n\n self.casual_audio_encoder = CausalAudioEncoder(\n dim=audio_dim,\n out_dim=self.dim,\n num_token=num_audio_token,\n need_global=enable_adain, dtype=dtype, device=device, operations=operations)\n\n if cond_dim > 0:\n self.cond_encoder = operations.Conv3d(\n cond_dim,\n self.dim,\n kernel_size=self.patch_size,\n stride=self.patch_size, device=device, dtype=dtype)\n\n self.audio_injector = AudioInjector_WAN(\n dim=self.dim,\n num_heads=self.num_heads,\n inject_layer=audio_inject_layers,\n root_net=self,\n enable_adain=enable_adain,\n adain_dim=self.dim,\n adain_mode=adain_mode,\n dtype=dtype, device=device, operations=operations\n )\n\n self.frame_packer = FramePackMotioner(\n inner_dim=self.dim,\n num_heads=self.num_heads,\n zip_frame_buckets=[1, 2, 16],\n drop_mode=framepack_drop_mode,\n dtype=dtype, device=device, operations=operations)\n\n def forward_orig(\n self,\n x,\n t,\n context,\n audio_embed=None,\n reference_latent=None,\n control_video=None,\n reference_motion=None,\n clip_fea=None,\n freqs=None,\n transformer_options={},\n **kwargs,\n ):\n if audio_embed is not None:\n num_embeds = x.shape[-3] * 4\n audio_emb_global, audio_emb = self.casual_audio_encoder(audio_embed[:, :, :, :num_embeds])\n else:\n audio_emb = None\n\n # embeddings\n bs, _, time, height, width = x.shape\n x = self.patch_embedding(x.float()).to(x.dtype)\n if control_video is not None:\n x = x + self.cond_encoder(control_video)\n\n if t.ndim == 1:\n t = t.unsqueeze(1).repeat(1, x.shape[2])\n\n grid_sizes = x.shape[2:]\n x = x.flatten(2).transpose(1, 2)\n seq_len = x.size(1)\n\n cond_mask_weight = comfy.model_management.cast_to(self.trainable_cond_mask.weight, dtype=x.dtype, device=x.device).unsqueeze(1).unsqueeze(1)\n x = x + cond_mask_weight[0]\n\n if reference_latent is not None:\n ref = self.patch_embedding(reference_latent.float()).to(x.dtype)\n ref = ref.flatten(2).transpose(1, 2)\n freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=max(30, time + 9), device=x.device, dtype=x.dtype)\n ref = ref + cond_mask_weight[1]\n x = torch.cat([x, ref], dim=1)\n freqs = torch.cat([freqs, freqs_ref], dim=1)\n t = torch.cat([t, torch.zeros((t.shape[0], reference_latent.shape[-3]), device=t.device, dtype=t.dtype)], dim=1)\n del ref, freqs_ref\n\n if reference_motion is not None:\n motion_encoded, freqs_motion = self.frame_packer(reference_motion, self)\n motion_encoded = motion_encoded + cond_mask_weight[2]\n x = torch.cat([x, motion_encoded], dim=1)\n freqs = torch.cat([freqs, freqs_motion], dim=1)\n\n t = torch.repeat_interleave(t, 2, dim=1)\n t = torch.cat([t, torch.zeros((t.shape[0], 3), device=t.device, dtype=t.dtype)], dim=1)\n del motion_encoded, freqs_motion\n\n # time embeddings\n e = self.time_embedding(\n sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))\n e = e.reshape(t.shape[0], -1, e.shape[-1])\n e0 = self.time_projection(e).unflatten(2, (6, self.dim))\n\n # context\n context = self.text_embedding(context)\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, transformer_options=transformer_options)\n if audio_emb is not None:\n x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len)\n # head\n x = self.head(x, e)\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n return x\n\n\nclass WanT2VCrossAttentionGather(WanSelfAttention):\n\n def forward(self, x, context, transformer_options={}, **kwargs):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L1, C] - video tokens\n context(Tensor): Shape [B, L2, C] - audio tokens with shape [B, frames*16, 1536]\n \"\"\"\n b, n, d = x.size(0), self.num_heads, self.head_dim\n\n q = self.norm_q(self.q(x))\n k = self.norm_k(self.k(context))\n v = self.v(context)\n\n # Handle audio temporal structure (16 tokens per frame)\n k = k.reshape(-1, 16, n, d).transpose(1, 2)\n v = v.reshape(-1, 16, n, d).transpose(1, 2)\n\n # Handle video spatial structure\n q = q.reshape(k.shape[0], -1, n, d).transpose(1, 2)\n\n x = optimized_attention(q, k, v, heads=self.num_heads, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)\n\n x = x.transpose(1, 2).reshape(b, -1, n * d)\n x = self.o(x)\n return x\n\n\nclass AudioCrossAttentionWrapper(nn.Module):\n def __init__(self, dim, kv_dim, num_heads, qk_norm=True, eps=1e-6, operation_settings={}):\n super().__init__()\n\n self.audio_cross_attn = WanT2VCrossAttentionGather(dim, num_heads, qk_norm=qk_norm, kv_dim=kv_dim, eps=eps, operation_settings=operation_settings)\n self.norm1_audio = operation_settings.get(\"operations\").LayerNorm(dim, eps, elementwise_affine=True, device=operation_settings.get(\"device\"), dtype=operation_settings.get(\"dtype\"))\n\n def forward(self, x, audio, transformer_options={}):\n x = x + self.audio_cross_attn(self.norm1_audio(x), audio, transformer_options=transformer_options)\n return x\n\n\nclass WanAttentionBlockAudio(WanAttentionBlock):\n\n def __init__(self,\n cross_attn_type,\n dim,\n ffn_dim,\n num_heads,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=False,\n eps=1e-6, operation_settings={}):\n super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings)\n self.audio_cross_attn_wrapper = AudioCrossAttentionWrapper(dim, 1536, num_heads, qk_norm, eps, operation_settings=operation_settings)\n\n def forward(\n self,\n x,\n e,\n freqs,\n context,\n context_img_len=257,\n audio=None,\n transformer_options={},\n ):\n r\"\"\"\n Args:\n x(Tensor): Shape [B, L, C]\n e(Tensor): Shape [B, 6, C]\n freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]\n \"\"\"\n # assert e.dtype == torch.float32\n\n if e.ndim < 4:\n e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)\n else:\n e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)\n # assert e[0].dtype == torch.float32\n\n # self-attention\n y = self.self_attn(\n torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),\n freqs, transformer_options=transformer_options)\n\n x = torch.addcmul(x, y, repeat_e(e[2], x))\n\n # cross-attention & ffn\n x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)\n if audio is not None:\n x = self.audio_cross_attn_wrapper(x, audio, transformer_options=transformer_options)\n y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))\n x = torch.addcmul(x, y, repeat_e(e[5], x))\n return x\n\nclass DummyAdapterLayer(nn.Module):\n def __init__(self, layer):\n super().__init__()\n self.layer = layer\n\n def forward(self, *args, **kwargs):\n return self.layer(*args, **kwargs)\n\n\nclass AudioProjModel(nn.Module):\n def __init__(\n self,\n seq_len=5,\n blocks=13, # add a new parameter blocks\n channels=768, # add a new parameter channels\n intermediate_dim=512,\n output_dim=1536,\n context_tokens=16,\n device=None,\n dtype=None,\n operations=None,\n ):\n super().__init__()\n\n self.seq_len = seq_len\n self.blocks = blocks\n self.channels = channels\n self.input_dim = seq_len * blocks * channels # update input_dim to be the product of blocks and channels.\n self.intermediate_dim = intermediate_dim\n self.context_tokens = context_tokens\n self.output_dim = output_dim\n\n # define multiple linear layers\n self.audio_proj_glob_1 = DummyAdapterLayer(operations.Linear(self.input_dim, intermediate_dim, dtype=dtype, device=device))\n self.audio_proj_glob_2 = DummyAdapterLayer(operations.Linear(intermediate_dim, intermediate_dim, dtype=dtype, device=device))\n self.audio_proj_glob_3 = DummyAdapterLayer(operations.Linear(intermediate_dim, context_tokens * output_dim, dtype=dtype, device=device))\n\n self.audio_proj_glob_norm = DummyAdapterLayer(operations.LayerNorm(output_dim, dtype=dtype, device=device))\n\n def forward(self, audio_embeds):\n video_length = audio_embeds.shape[1]\n audio_embeds = rearrange(audio_embeds, \"bz f w b c -> (bz f) w b c\")\n batch_size, window_size, blocks, channels = audio_embeds.shape\n audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)\n\n audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds))\n audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds))\n\n context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim)\n\n context_tokens = self.audio_proj_glob_norm(context_tokens)\n context_tokens = rearrange(context_tokens, \"(bz f) m c -> bz f m c\", f=video_length)\n\n return context_tokens\n\n\nclass HumoWanModel(WanModel):\n r\"\"\"\n Wan diffusion backbone supporting both text-to-video and image-to-video.\n \"\"\"\n\n def __init__(self,\n model_type='humo',\n patch_size=(1, 2, 2),\n text_len=512,\n in_dim=16,\n dim=2048,\n ffn_dim=8192,\n freq_dim=256,\n text_dim=4096,\n out_dim=16,\n num_heads=16,\n num_layers=32,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=True,\n eps=1e-6,\n flf_pos_embed_token_number=None,\n image_model=None,\n audio_token_num=16,\n device=None,\n dtype=None,\n operations=None,\n ):\n\n super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, wan_attn_block_class=WanAttentionBlockAudio, image_model=image_model, device=device, dtype=dtype, operations=operations)\n\n self.audio_proj = AudioProjModel(seq_len=8, blocks=5, channels=1280, intermediate_dim=512, output_dim=1536, context_tokens=audio_token_num, dtype=dtype, device=device, operations=operations)\n\n def forward_orig(\n self,\n x,\n t,\n context,\n freqs=None,\n audio_embed=None,\n reference_latent=None,\n transformer_options={},\n **kwargs,\n ):\n bs, _, time, height, width = x.shape\n\n # embeddings\n x = self.patch_embedding(x.float()).to(x.dtype)\n grid_sizes = x.shape[2:]\n x = x.flatten(2).transpose(1, 2)\n\n # time embeddings\n e = self.time_embedding(\n sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))\n e = e.reshape(t.shape[0], -1, e.shape[-1])\n e0 = self.time_projection(e).unflatten(2, (6, self.dim))\n\n if reference_latent is not None:\n ref = self.patch_embedding(reference_latent.float()).to(x.dtype)\n ref = ref.flatten(2).transpose(1, 2)\n freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=time, device=x.device, dtype=x.dtype)\n x = torch.cat([x, ref], dim=1)\n freqs = torch.cat([freqs, freqs_ref], dim=1)\n del ref, freqs_ref\n\n # context\n context = self.text_embedding(context)\n context_img_len = None\n\n if audio_embed is not None:\n if reference_latent is not None:\n zero_audio_pad = torch.zeros(audio_embed.shape[0], reference_latent.shape[-3], *audio_embed.shape[2:], device=audio_embed.device, dtype=audio_embed.dtype)\n audio_embed = torch.cat([audio_embed, zero_audio_pad], dim=1)\n audio = self.audio_proj(audio_embed).permute(0, 3, 1, 2).flatten(2).transpose(1, 2)\n else:\n audio = None\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], context_img_len=context_img_len, audio=audio, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, audio=audio, transformer_options=transformer_options)\n\n # head\n x = self.head(x, e)\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n return x\n\nclass SCAILWanModel(WanModel):\n def __init__(self, model_type=\"scail\", patch_size=(1, 2, 2), in_dim=20, dim=5120, operations=None, device=None, dtype=None, **kwargs):\n super().__init__(model_type='i2v', patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs)\n\n self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)\n\n def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs):\n\n if reference_latent is not None:\n x = torch.cat((reference_latent, x), dim=2)\n\n # embeddings\n x = self.patch_embedding(x.float()).to(x.dtype)\n grid_sizes = x.shape[2:]\n transformer_options[\"grid_sizes\"] = grid_sizes\n x = x.flatten(2).transpose(1, 2)\n\n scail_pose_seq_len = 0\n if pose_latents is not None:\n scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype)\n scail_x = scail_x.flatten(2).transpose(1, 2)\n scail_pose_seq_len = scail_x.shape[1]\n x = torch.cat([x, scail_x], dim=1)\n del scail_x\n\n # time embeddings\n e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))\n e = e.reshape(t.shape[0], -1, e.shape[-1])\n e0 = self.time_projection(e).unflatten(2, (6, self.dim))\n\n # context\n context = self.text_embedding(context)\n\n context_img_len = None\n if clip_fea is not None:\n if self.img_emb is not None:\n context_clip = self.img_emb(clip_fea) # bs x 257 x dim\n context = torch.cat([context_clip, context], dim=1)\n context_img_len = clip_fea.shape[-2]\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], context_img_len=context_img_len, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)\n\n # head\n x = self.head(x, e)\n\n if scail_pose_seq_len > 0:\n x = x[:, :-scail_pose_seq_len]\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n\n if reference_latent is not None:\n x = x[:, :, reference_latent.shape[2]:]\n\n return x\n\n def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}):\n main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options)\n\n if pose_latents is None:\n return main_freqs\n\n ref_t_patches = 0\n if reference_latent is not None:\n ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]\n\n F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]\n\n # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames\n h_scale = h / H_pose\n w_scale = w / W_pose\n\n # 120 w-offset and shift 0.5 to place positions at midpoints (0.5, 2.5, ...) to match the original code\n h_shift = (h_scale - 1) / 2\n w_shift = (w_scale - 1) / 2\n pose_transformer_options = {\"rope_options\": {\"shift_y\": h_shift, \"shift_x\": 120.0 + w_shift, \"scale_y\": h_scale, \"scale_x\": w_scale}}\n pose_freqs = super().rope_encode(F_pose, H_pose, W_pose, t_start=t_start+ref_t_patches, device=device, dtype=dtype, transformer_options=pose_transformer_options)\n\n return torch.cat([main_freqs, pose_freqs], dim=1)\n\n def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs):\n bs, c, t, h, w = x.shape\n x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)\n\n if pose_latents is not None:\n pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size)\n\n t_len = t\n if time_dim_concat is not None:\n time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)\n x = torch.cat([x, time_dim_concat], dim=2)\n t_len = x.shape[2]\n\n reference_latent = None\n if \"reference_latent\" in kwargs:\n reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop(\"reference_latent\"), self.patch_size)\n t_len += reference_latent.shape[2]\n\n freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent)\n return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w]\n" }, { "path": "comfy/ldm/wan/model_animate.py", "content": "from torch import nn\nimport torch\nfrom typing import Tuple, Optional\nfrom einops import rearrange\nimport torch.nn.functional as F\nimport math\nfrom .model import WanModel, sinusoidal_embedding_1d\nfrom comfy.ldm.modules.attention import optimized_attention\nimport comfy.model_management\n\nclass CausalConv1d(nn.Module):\n\n def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode=\"replicate\", operations=None, **kwargs):\n super().__init__()\n\n self.pad_mode = pad_mode\n padding = (kernel_size - 1, 0) # T\n self.time_causal_padding = padding\n\n self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)\n\n def forward(self, x):\n x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)\n return self.conv(x)\n\n\nclass FaceEncoder(nn.Module):\n def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None):\n factory_kwargs = {\"dtype\": dtype, \"device\": device}\n super().__init__()\n\n self.num_heads = num_heads\n self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs)\n self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n self.act = nn.SiLU()\n self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)\n self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)\n\n self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs)\n self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n\n self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n\n self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n\n self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs))\n\n def forward(self, x):\n\n x = rearrange(x, \"b t c -> b c t\")\n b, c, t = x.shape\n\n x = self.conv1_local(x)\n x = rearrange(x, \"b (n c) t -> (b n) t c\", n=self.num_heads)\n\n x = self.norm1(x)\n x = self.act(x)\n x = rearrange(x, \"b t c -> b c t\")\n x = self.conv2(x)\n x = rearrange(x, \"b c t -> b t c\")\n x = self.norm2(x)\n x = self.act(x)\n x = rearrange(x, \"b t c -> b c t\")\n x = self.conv3(x)\n x = rearrange(x, \"b c t -> b t c\")\n x = self.norm3(x)\n x = self.act(x)\n x = self.out_proj(x)\n x = rearrange(x, \"(b n) t c -> b t n c\", b=b)\n padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1)\n x = torch.cat([x, padding], dim=-2)\n x_local = x.clone()\n\n return x_local\n\n\ndef get_norm_layer(norm_layer, operations=None):\n \"\"\"\n Get the normalization layer.\n\n Args:\n norm_layer (str): The type of normalization layer.\n\n Returns:\n norm_layer (nn.Module): The normalization layer.\n \"\"\"\n if norm_layer == \"layer\":\n return operations.LayerNorm\n elif norm_layer == \"rms\":\n return operations.RMSNorm\n else:\n raise NotImplementedError(f\"Norm layer {norm_layer} is not implemented\")\n\n\nclass FaceAdapter(nn.Module):\n def __init__(\n self,\n hidden_dim: int,\n heads_num: int,\n qk_norm: bool = True,\n qk_norm_type: str = \"rms\",\n num_adapter_layers: int = 1,\n dtype=None, device=None, operations=None\n ):\n\n factory_kwargs = {\"dtype\": dtype, \"device\": device}\n super().__init__()\n self.hidden_size = hidden_dim\n self.heads_num = heads_num\n self.fuser_blocks = nn.ModuleList(\n [\n FaceBlock(\n self.hidden_size,\n self.heads_num,\n qk_norm=qk_norm,\n qk_norm_type=qk_norm_type,\n operations=operations,\n **factory_kwargs,\n )\n for _ in range(num_adapter_layers)\n ]\n )\n\n def forward(\n self,\n x: torch.Tensor,\n motion_embed: torch.Tensor,\n idx: int,\n freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,\n freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,\n ) -> torch.Tensor:\n\n return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)\n\n\n\nclass FaceBlock(nn.Module):\n def __init__(\n self,\n hidden_size: int,\n heads_num: int,\n qk_norm: bool = True,\n qk_norm_type: str = \"rms\",\n qk_scale: float = None,\n dtype: Optional[torch.dtype] = None,\n device: Optional[torch.device] = None,\n operations=None\n ):\n factory_kwargs = {\"device\": device, \"dtype\": dtype}\n super().__init__()\n\n self.deterministic = False\n self.hidden_size = hidden_size\n self.heads_num = heads_num\n head_dim = hidden_size // heads_num\n self.scale = qk_scale or head_dim**-0.5\n\n self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs)\n self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs)\n\n self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs)\n\n qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations)\n self.q_norm = (\n qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()\n )\n self.k_norm = (\n qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()\n )\n\n self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n\n self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)\n\n def forward(\n self,\n x: torch.Tensor,\n motion_vec: torch.Tensor,\n motion_mask: Optional[torch.Tensor] = None,\n # use_context_parallel=False,\n ) -> torch.Tensor:\n\n B, T, N, C = motion_vec.shape\n T_comp = T\n\n x_motion = self.pre_norm_motion(motion_vec)\n x_feat = self.pre_norm_feat(x)\n\n kv = self.linear1_kv(x_motion)\n q = self.linear1_q(x_feat)\n\n k, v = rearrange(kv, \"B L N (K H D) -> K B L N H D\", K=2, H=self.heads_num)\n q = rearrange(q, \"B S (H D) -> B S H D\", H=self.heads_num)\n\n # Apply QK-Norm if needed.\n q = self.q_norm(q).to(v)\n k = self.k_norm(k).to(v)\n\n k = rearrange(k, \"B L N H D -> (B L) N H D\")\n v = rearrange(v, \"B L N H D -> (B L) N H D\")\n\n q = rearrange(q, \"B (L S) H D -> (B L) S (H D)\", L=T_comp)\n\n attn = optimized_attention(q, k, v, heads=self.heads_num)\n\n attn = rearrange(attn, \"(B L) S C -> B (L S) C\", L=T_comp)\n\n output = self.linear2(attn)\n\n if motion_mask is not None:\n output = output * rearrange(motion_mask, \"B T H W -> B (T H W)\").unsqueeze(-1)\n\n return output\n\n# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162\ndef upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):\n _, minor, in_h, in_w = input.shape\n kernel_h, kernel_w = kernel.shape\n\n out = input.view(-1, minor, in_h, 1, in_w, 1)\n out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])\n out = out.view(-1, minor, in_h * up_y, in_w * up_x)\n\n out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])\n out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)]\n\n out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])\n w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)\n out = F.conv2d(out, w)\n out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)\n return out[:, :, ::down_y, ::down_x]\n\ndef upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):\n return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])\n\n# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81\nclass FusedLeakyReLU(torch.nn.Module):\n def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None):\n super().__init__()\n self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device))\n self.negative_slope = negative_slope\n self.scale = scale\n\n def forward(self, input):\n return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale)\n\ndef fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):\n return F.leaky_relu(input + bias, negative_slope) * scale\n\nclass Blur(torch.nn.Module):\n def __init__(self, kernel, pad, dtype=None, device=None):\n super().__init__()\n kernel = torch.tensor(kernel, dtype=dtype, device=device)\n kernel = kernel[None, :] * kernel[:, None]\n kernel = kernel / kernel.sum()\n self.register_buffer('kernel', kernel)\n self.pad = pad\n\n def forward(self, input):\n return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad)\n\n#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590\nclass ScaledLeakyReLU(torch.nn.Module):\n def __init__(self, negative_slope=0.2):\n super().__init__()\n self.negative_slope = negative_slope\n\n def forward(self, input):\n return F.leaky_relu(input, negative_slope=self.negative_slope)\n\n# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605\nclass EqualConv2d(torch.nn.Module):\n def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None):\n super().__init__()\n self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype))\n self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)\n self.stride = stride\n self.padding = padding\n self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None\n\n def forward(self, input):\n if self.bias is None:\n bias = None\n else:\n bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype)\n\n return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding)\n\n# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134\nclass EqualLinear(torch.nn.Module):\n def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None):\n super().__init__()\n self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype))\n self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None\n self.activation = activation\n self.scale = (1 / math.sqrt(in_dim)) * lr_mul\n self.lr_mul = lr_mul\n\n def forward(self, input):\n if self.bias is None:\n bias = None\n else:\n bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul\n\n if self.activation:\n out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale)\n return fused_leaky_relu(out, bias)\n return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias)\n\n# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654\nclass ConvLayer(torch.nn.Sequential):\n def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None):\n layers = []\n\n if downsample:\n factor = 2\n p = (len(blur_kernel) - factor) + (kernel_size - 1)\n layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2)))\n stride, padding = 2, 0\n else:\n stride, padding = 1, kernel_size // 2\n\n layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations))\n\n if activate:\n layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2))\n\n super().__init__(*layers)\n\n# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704\nclass ResBlock(torch.nn.Module):\n def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None):\n super().__init__()\n self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations)\n self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations)\n self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations)\n\n def forward(self, input):\n out = self.conv2(self.conv1(input))\n skip = self.skip(input)\n return (out + skip) / math.sqrt(2)\n\n\nclass EncoderApp(torch.nn.Module):\n def __init__(self, w_dim=512, dtype=None, device=None, operations=None):\n super().__init__()\n kwargs = {\"device\": device, \"dtype\": dtype, \"operations\": operations}\n\n self.convs = torch.nn.ModuleList([\n ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs),\n ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs),\n ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs),\n ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs),\n EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs)\n ])\n\n def forward(self, x):\n h = x\n for conv in self.convs:\n h = conv(h)\n return h.squeeze(-1).squeeze(-1)\n\nclass Encoder(torch.nn.Module):\n def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None):\n super().__init__()\n self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations)\n self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)])\n\n def encode_motion(self, x):\n return self.fc(self.net_app(x))\n\nclass Direction(torch.nn.Module):\n def __init__(self, motion_dim, dtype=None, device=None, operations=None):\n super().__init__()\n self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype))\n self.motion_dim = motion_dim\n\n def forward(self, input):\n stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype)\n Q, _ = torch.linalg.qr(stabilized_weight.float())\n if input is None:\n return Q\n return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1)\n\nclass Synthesis(torch.nn.Module):\n def __init__(self, motion_dim, dtype=None, device=None, operations=None):\n super().__init__()\n self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations)\n\nclass Generator(torch.nn.Module):\n def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None):\n super().__init__()\n self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations)\n self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations)\n\n def get_motion(self, img):\n motion_feat = self.enc.encode_motion(img)\n return self.dec.direction(motion_feat)\n\nclass AnimateWanModel(WanModel):\n r\"\"\"\n Wan diffusion backbone supporting both text-to-video and image-to-video.\n \"\"\"\n\n def __init__(self,\n model_type='animate',\n patch_size=(1, 2, 2),\n text_len=512,\n in_dim=16,\n dim=2048,\n ffn_dim=8192,\n freq_dim=256,\n text_dim=4096,\n out_dim=16,\n num_heads=16,\n num_layers=32,\n window_size=(-1, -1),\n qk_norm=True,\n cross_attn_norm=True,\n eps=1e-6,\n flf_pos_embed_token_number=None,\n motion_encoder_dim=512,\n image_model=None,\n device=None,\n dtype=None,\n operations=None,\n ):\n\n super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)\n\n self.pose_patch_embedding = operations.Conv3d(\n 16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype\n )\n\n self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations)\n\n self.face_adapter = FaceAdapter(\n heads_num=self.num_heads,\n hidden_dim=self.dim,\n num_adapter_layers=self.num_layers // 5,\n device=device, dtype=dtype, operations=operations\n )\n\n self.face_encoder = FaceEncoder(\n in_dim=motion_encoder_dim,\n hidden_dim=self.dim,\n num_heads=4,\n device=device, dtype=dtype, operations=operations\n )\n\n def after_patch_embedding(self, x, pose_latents, face_pixel_values):\n if pose_latents is not None:\n pose_latents = self.pose_patch_embedding(pose_latents)\n x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1]\n\n if face_pixel_values is None:\n return x, None\n\n b, c, T, h, w = face_pixel_values.shape\n face_pixel_values = rearrange(face_pixel_values, \"b c t h w -> (b t) c h w\")\n encode_bs = 8\n face_pixel_values_tmp = []\n for i in range(math.ceil(face_pixel_values.shape[0] / encode_bs)):\n face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i * encode_bs: (i + 1) * encode_bs]))\n\n motion_vec = torch.cat(face_pixel_values_tmp)\n\n motion_vec = rearrange(motion_vec, \"(b t) c -> b t c\", t=T)\n motion_vec = self.face_encoder(motion_vec)\n\n B, L, H, C = motion_vec.shape\n pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)\n motion_vec = torch.cat([pad_face, motion_vec], dim=1)\n\n if motion_vec.shape[1] < x.shape[2]:\n B, L, H, C = motion_vec.shape\n pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec)\n motion_vec = torch.cat([motion_vec, pad], dim=1)\n else:\n motion_vec = motion_vec[:, :x.shape[2]]\n return x, motion_vec\n\n def forward_orig(\n self,\n x,\n t,\n context,\n clip_fea=None,\n pose_latents=None,\n face_pixel_values=None,\n freqs=None,\n transformer_options={},\n **kwargs,\n ):\n # embeddings\n x = self.patch_embedding(x.float()).to(x.dtype)\n x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)\n grid_sizes = x.shape[2:]\n x = x.flatten(2).transpose(1, 2)\n\n # time embeddings\n e = self.time_embedding(\n sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))\n e = e.reshape(t.shape[0], -1, e.shape[-1])\n e0 = self.time_projection(e).unflatten(2, (6, self.dim))\n\n full_ref = None\n if self.ref_conv is not None:\n full_ref = kwargs.get(\"reference_latent\", None)\n if full_ref is not None:\n full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)\n x = torch.concat((full_ref, x), dim=1)\n\n # context\n context = self.text_embedding(context)\n\n context_img_len = None\n if clip_fea is not None:\n if self.img_emb is not None:\n context_clip = self.img_emb(clip_fea) # bs x 257 x dim\n context = torch.concat([context_clip, context], dim=1)\n context_img_len = clip_fea.shape[-2]\n\n patches_replace = transformer_options.get(\"patches_replace\", {})\n blocks_replace = patches_replace.get(\"dit\", {})\n transformer_options[\"total_blocks\"] = len(self.blocks)\n transformer_options[\"block_type\"] = \"double\"\n for i, block in enumerate(self.blocks):\n transformer_options[\"block_index\"] = i\n if (\"double_block\", i) in blocks_replace:\n def block_wrap(args):\n out = {}\n out[\"img\"] = block(args[\"img\"], context=args[\"txt\"], e=args[\"vec\"], freqs=args[\"pe\"], context_img_len=context_img_len, transformer_options=args[\"transformer_options\"])\n return out\n out = blocks_replace[(\"double_block\", i)]({\"img\": x, \"txt\": context, \"vec\": e0, \"pe\": freqs, \"transformer_options\": transformer_options}, {\"original_block\": block_wrap})\n x = out[\"img\"]\n else:\n x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)\n\n if i % 5 == 0 and motion_vec is not None:\n x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec)\n\n # head\n x = self.head(x, e)\n\n if full_ref is not None:\n x = x[:, full_ref.shape[1]:]\n\n # unpatchify\n x = self.unpatchify(x, grid_sizes)\n return x\n" }, { "path": "comfy/ldm/wan/model_multitalk.py", "content": "import torch\nfrom einops import rearrange, repeat\nimport comfy\nfrom comfy.ldm.modules.attention import optimized_attention\n\n\ndef calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, split_num=8):\n scale = 1.0 / visual_q.shape[-1] ** 0.5\n visual_q = visual_q.transpose(1, 2) * scale\n\n B, H, x_seqlens, K = visual_q.shape\n\n x_ref_attn_maps = []\n for class_idx, ref_target_mask in enumerate(ref_target_masks):\n ref_target_mask = ref_target_mask.view(1, 1, 1, -1)\n\n x_ref_attnmap = torch.zeros(B, H, x_seqlens, device=visual_q.device, dtype=visual_q.dtype)\n chunk_size = min(max(x_seqlens // split_num, 1), x_seqlens)\n\n for i in range(0, x_seqlens, chunk_size):\n end_i = min(i + chunk_size, x_seqlens)\n\n attn_chunk = visual_q[:, :, i:end_i] @ ref_k.permute(0, 2, 3, 1) # B, H, chunk, ref_seqlens\n\n # Apply softmax\n attn_max = attn_chunk.max(dim=-1, keepdim=True).values\n attn_chunk = (attn_chunk - attn_max).exp()\n attn_sum = attn_chunk.sum(dim=-1, keepdim=True)\n attn_chunk = attn_chunk / (attn_sum + 1e-8)\n\n # Apply mask and sum\n masked_attn = attn_chunk * ref_target_mask\n x_ref_attnmap[:, :, i:end_i] = masked_attn.sum(-1) / (ref_target_mask.sum() + 1e-8)\n\n del attn_chunk, masked_attn\n\n # Average across heads\n x_ref_attnmap = x_ref_attnmap.mean(dim=1) # B, x_seqlens\n x_ref_attn_maps.append(x_ref_attnmap)\n\n del visual_q, ref_k\n\n return torch.cat(x_ref_attn_maps, dim=0)\n\ndef get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=2):\n \"\"\"Args:\n query (torch.tensor): B M H K\n key (torch.tensor): B M H K\n shape (tuple): (N_t, N_h, N_w)\n ref_target_masks: [B, N_h * N_w]\n \"\"\"\n\n N_t, N_h, N_w = shape\n\n x_seqlens = N_h * N_w\n ref_k = ref_k[:, :x_seqlens]\n _, seq_lens, heads, _ = visual_q.shape\n class_num, _ = ref_target_masks.shape\n x_ref_attn_maps = torch.zeros(class_num, seq_lens).to(visual_q)\n\n split_chunk = heads // split_num\n\n for i in range(split_num):\n x_ref_attn_maps_perhead = calculate_x_ref_attn_map(\n visual_q[:, :, i*split_chunk:(i+1)*split_chunk, :],\n ref_k[:, :, i*split_chunk:(i+1)*split_chunk, :],\n ref_target_masks\n )\n x_ref_attn_maps += x_ref_attn_maps_perhead\n\n return x_ref_attn_maps / split_num\n\n\ndef normalize_and_scale(column, source_range, target_range, epsilon=1e-8):\n source_min, source_max = source_range\n new_min, new_max = target_range\n normalized = (column - source_min) / (source_max - source_min + epsilon)\n scaled = normalized * (new_max - new_min) + new_min\n return scaled\n\n\ndef rotate_half(x):\n x = rearrange(x, \"... (d r) -> ... d r\", r=2)\n x1, x2 = x.unbind(dim=-1)\n x = torch.stack((-x2, x1), dim=-1)\n return rearrange(x, \"... d r -> ... (d r)\")\n\n\ndef get_audio_embeds(encoded_audio, audio_start, audio_end):\n audio_embs = []\n human_num = len(encoded_audio)\n audio_frames = encoded_audio[0].shape[0]\n\n indices = (torch.arange(4 + 1) - 2) * 1\n\n for human_idx in range(human_num):\n if audio_end > audio_frames: # in case of not enough audio for current window, pad with first audio frame as that's most likely silence\n pad_len = audio_end - audio_frames\n pad_shape = list(encoded_audio[human_idx].shape)\n pad_shape[0] = pad_len\n pad_tensor = encoded_audio[human_idx][:1].repeat(pad_len, *([1] * (encoded_audio[human_idx].dim() - 1)))\n encoded_audio_in = torch.cat([encoded_audio[human_idx], pad_tensor], dim=0)\n else:\n encoded_audio_in = encoded_audio[human_idx]\n center_indices = torch.arange(audio_start, audio_end, 1).unsqueeze(1) + indices.unsqueeze(0)\n center_indices = torch.clamp(center_indices, min=0, max=encoded_audio_in.shape[0] - 1)\n audio_emb = encoded_audio_in[center_indices].unsqueeze(0)\n audio_embs.append(audio_emb)\n\n return torch.cat(audio_embs, dim=0)\n\n\ndef project_audio_features(audio_proj, encoded_audio, audio_start, audio_end):\n audio_embs = get_audio_embeds(encoded_audio, audio_start, audio_end)\n\n first_frame_audio_emb_s = audio_embs[:, :1, ...]\n latter_frame_audio_emb = audio_embs[:, 1:, ...]\n latter_frame_audio_emb = rearrange(latter_frame_audio_emb, \"b (n_t n) w s c -> b n_t n w s c\", n=4)\n\n middle_index = audio_proj.seq_len // 2\n\n latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index+1, ...]\n latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, \"b n_t n w s c -> b n_t (n w) s c\")\n latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...]\n latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, \"b n_t n w s c -> b n_t (n w) s c\")\n latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index+1, ...]\n latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, \"b n_t n w s c -> b n_t (n w) s c\")\n latter_frame_audio_emb_s = torch.cat([latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2)\n\n audio_emb = audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s)\n audio_emb = torch.cat(audio_emb.split(1), dim=2)\n\n return audio_emb\n\n\nclass RotaryPositionalEmbedding1D(torch.nn.Module):\n def __init__(self,\n head_dim,\n ):\n super().__init__()\n self.head_dim = head_dim\n self.base = 10000\n\n def precompute_freqs_cis_1d(self, pos_indices):\n freqs = 1.0 / (self.base ** (torch.arange(0, self.head_dim, 2)[: (self.head_dim // 2)].float() / self.head_dim))\n freqs = freqs.to(pos_indices.device)\n freqs = torch.einsum(\"..., f -> ... f\", pos_indices.float(), freqs)\n freqs = repeat(freqs, \"... n -> ... (n r)\", r=2)\n return freqs\n\n def forward(self, x, pos_indices):\n freqs_cis = self.precompute_freqs_cis_1d(pos_indices)\n\n x_ = x.float()\n\n freqs_cis = freqs_cis.float().to(x.device)\n cos, sin = freqs_cis.cos(), freqs_cis.sin()\n cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d')\n x_ = (x_ * cos) + (rotate_half(x_) * sin)\n\n return x_.type_as(x)\n\nclass SingleStreamAttention(torch.nn.Module):\n def __init__(\n self,\n dim: int,\n encoder_hidden_states_dim: int,\n num_heads: int,\n qkv_bias: bool,\n device=None, dtype=None, operations=None\n ) -> None:\n super().__init__()\n self.dim = dim\n self.encoder_hidden_states_dim = encoder_hidden_states_dim\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n\n self.q_linear = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype)\n self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)\n self.kv_linear = operations.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype)\n\n def forward(self, x: torch.Tensor, encoder_hidden_states: torch.Tensor, shape=None) -> torch.Tensor:\n N_t, N_h, N_w = shape\n\n expected_tokens = N_t * N_h * N_w\n actual_tokens = x.shape[1]\n x_extra = None\n\n if actual_tokens != expected_tokens:\n x_extra = x[:, -N_h * N_w:, :]\n x = x[:, :-N_h * N_w, :]\n N_t = N_t - 1\n\n B = x.shape[0]\n S = N_h * N_w\n x = x.view(B * N_t, S, self.dim)\n\n # get q for hidden_state\n q = self.q_linear(x).view(B * N_t, S, self.num_heads, self.head_dim)\n\n # get kv from encoder_hidden_states # shape: (B, N, num_heads, head_dim)\n kv = self.kv_linear(encoder_hidden_states)\n encoder_k, encoder_v = kv.view(B * N_t, encoder_hidden_states.shape[1], 2, self.num_heads, self.head_dim).unbind(2)\n\n #print(\"q.shape\", q.shape) #torch.Size([21, 1024, 40, 128])\n x = optimized_attention(\n q.transpose(1, 2),\n encoder_k.transpose(1, 2),\n encoder_v.transpose(1, 2),\n heads=self.num_heads, skip_reshape=True, skip_output_reshape=True).transpose(1, 2)\n\n # linear transform\n x = self.proj(x.reshape(B * N_t, S, self.dim))\n x = x.view(B, N_t * S, self.dim)\n\n if x_extra is not None:\n x = torch.cat([x, torch.zeros_like(x_extra)], dim=1)\n\n return x\n\nclass SingleStreamMultiAttention(SingleStreamAttention):\n def __init__(\n self,\n dim: int,\n encoder_hidden_states_dim: int,\n num_heads: int,\n qkv_bias: bool,\n class_range: int = 24,\n class_interval: int = 4,\n device=None, dtype=None, operations=None\n ) -> None:\n super().__init__(\n dim=dim,\n encoder_hidden_states_dim=encoder_hidden_states_dim,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n device=device,\n dtype=dtype,\n operations=operations\n )\n\n # Rotary-embedding layout parameters\n self.class_interval = class_interval\n self.class_range = class_range\n self.max_humans = self.class_range // self.class_interval\n\n # Constant bucket used for background tokens\n self.rope_bak = int(self.class_range // 2)\n\n self.rope_1d = RotaryPositionalEmbedding1D(self.head_dim)\n\n def forward(\n self,\n x: torch.Tensor,\n encoder_hidden_states: torch.Tensor,\n shape=None,\n x_ref_attn_map=None\n ) -> torch.Tensor:\n encoder_hidden_states = encoder_hidden_states.squeeze(0).to(x.device)\n human_num = x_ref_attn_map.shape[0] if x_ref_attn_map is not None else 1\n # Single-speaker fall-through\n if human_num <= 1:\n return super().forward(x, encoder_hidden_states, shape)\n\n N_t, N_h, N_w = shape\n\n x_extra = None\n if x.shape[0] * N_t != encoder_hidden_states.shape[0]:\n x_extra = x[:, -N_h * N_w:, :]\n x = x[:, :-N_h * N_w, :]\n N_t = N_t - 1\n x = rearrange(x, \"B (N_t S) C -> (B N_t) S C\", N_t=N_t)\n\n # Query projection\n B, N, C = x.shape\n q = self.q_linear(x)\n q = q.view(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)\n\n # Use `class_range` logic for 2 speakers\n rope_h1 = (0, self.class_interval)\n rope_h2 = (self.class_range - self.class_interval, self.class_range)\n rope_bak = int(self.class_range // 2)\n\n # Normalize and scale attention maps for each speaker\n max_values = x_ref_attn_map.max(1).values[:, None, None]\n min_values = x_ref_attn_map.min(1).values[:, None, None]\n max_min_values = torch.cat([max_values, min_values], dim=2)\n\n human1_max_value, human1_min_value = max_min_values[0, :, 0].max(), max_min_values[0, :, 1].min()\n human2_max_value, human2_min_value = max_min_values[1, :, 0].max(), max_min_values[1, :, 1].min()\n\n human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), rope_h1)\n human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), rope_h2)\n back = torch.full((x_ref_attn_map.size(1),), rope_bak, dtype=human1.dtype, device=human1.device)\n\n # Token-wise speaker dominance\n max_indices = x_ref_attn_map.argmax(dim=0)\n normalized_map = torch.stack([human1, human2, back], dim=1)\n normalized_pos = normalized_map[torch.arange(x_ref_attn_map.size(1)), max_indices]\n\n # Apply rotary to Q\n q = rearrange(q, \"(B N_t) H S C -> B H (N_t S) C\", N_t=N_t)\n q = self.rope_1d(q, normalized_pos)\n q = rearrange(q, \"B H (N_t S) C -> (B N_t) H S C\", N_t=N_t)\n\n # Keys / Values\n _, N_a, _ = encoder_hidden_states.shape\n encoder_kv = self.kv_linear(encoder_hidden_states)\n encoder_kv = encoder_kv.view(B, N_a, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)\n encoder_k, encoder_v = encoder_kv.unbind(0)\n\n # Rotary for keys – assign centre of each speaker bucket to its context tokens\n per_frame = torch.zeros(N_a, dtype=encoder_k.dtype, device=encoder_k.device)\n per_frame[: per_frame.size(0) // 2] = (rope_h1[0] + rope_h1[1]) / 2\n per_frame[per_frame.size(0) // 2 :] = (rope_h2[0] + rope_h2[1]) / 2\n encoder_pos = torch.cat([per_frame] * N_t, dim=0)\n\n encoder_k = rearrange(encoder_k, \"(B N_t) H S C -> B H (N_t S) C\", N_t=N_t)\n encoder_k = self.rope_1d(encoder_k, encoder_pos)\n encoder_k = rearrange(encoder_k, \"B H (N_t S) C -> (B N_t) H S C\", N_t=N_t)\n\n # Final attention\n q = rearrange(q, \"B H M K -> B M H K\")\n encoder_k = rearrange(encoder_k, \"B H M K -> B M H K\")\n encoder_v = rearrange(encoder_v, \"B H M K -> B M H K\")\n\n x = optimized_attention(\n q.transpose(1, 2),\n encoder_k.transpose(1, 2),\n encoder_v.transpose(1, 2),\n heads=self.num_heads, skip_reshape=True, skip_output_reshape=True).transpose(1, 2)\n\n # Linear projection\n x = x.reshape(B, N, C)\n x = self.proj(x)\n\n # Restore original layout\n x = rearrange(x, \"(B N_t) S C -> B (N_t S) C\", N_t=N_t)\n if x_extra is not None:\n x = torch.cat([x, torch.zeros_like(x_extra)], dim=1)\n\n return x\n\n\nclass MultiTalkAudioProjModel(torch.nn.Module):\n def __init__(\n self,\n seq_len: int = 5,\n seq_len_vf: int = 12,\n blocks: int = 12,\n channels: int = 768,\n intermediate_dim: int = 512,\n out_dim: int = 768,\n context_tokens: int = 32,\n device=None, dtype=None, operations=None\n ):\n super().__init__()\n\n self.seq_len = seq_len\n self.blocks = blocks\n self.channels = channels\n self.input_dim = seq_len * blocks * channels\n self.input_dim_vf = seq_len_vf * blocks * channels\n self.intermediate_dim = intermediate_dim\n self.context_tokens = context_tokens\n self.out_dim = out_dim\n\n # define multiple linear layers\n self.proj1 = operations.Linear(self.input_dim, intermediate_dim, device=device, dtype=dtype)\n self.proj1_vf = operations.Linear(self.input_dim_vf, intermediate_dim, device=device, dtype=dtype)\n self.proj2 = operations.Linear(intermediate_dim, intermediate_dim, device=device, dtype=dtype)\n self.proj3 = operations.Linear(intermediate_dim, context_tokens * out_dim, device=device, dtype=dtype)\n self.norm = operations.LayerNorm(out_dim, device=device, dtype=dtype)\n\n def forward(self, audio_embeds, audio_embeds_vf):\n video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]\n B, _, _, S, C = audio_embeds.shape\n\n # process audio of first frame\n audio_embeds = rearrange(audio_embeds, \"bz f w b c -> (bz f) w b c\")\n batch_size, window_size, blocks, channels = audio_embeds.shape\n audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)\n\n # process audio of latter frame\n audio_embeds_vf = rearrange(audio_embeds_vf, \"bz f w b c -> (bz f) w b c\")\n batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape\n audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf)\n\n # first projection\n audio_embeds = torch.relu(self.proj1(audio_embeds))\n audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf))\n audio_embeds = rearrange(audio_embeds, \"(bz f) c -> bz f c\", bz=B)\n audio_embeds_vf = rearrange(audio_embeds_vf, \"(bz f) c -> bz f c\", bz=B)\n audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1)\n batch_size_c, N_t, C_a = audio_embeds_c.shape\n audio_embeds_c = audio_embeds_c.view(batch_size_c*N_t, C_a)\n\n # second projection\n audio_embeds_c = torch.relu(self.proj2(audio_embeds_c))\n\n context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c*N_t, self.context_tokens, self.out_dim)\n\n # normalization and reshape\n context_tokens = self.norm(context_tokens)\n context_tokens = rearrange(context_tokens, \"(bz f) m c -> bz f m c\", f=video_length)\n\n return context_tokens\n\n\nclass WanMultiTalkAttentionBlock(torch.nn.Module):\n def __init__(self, in_dim=5120, out_dim=768, device=None, dtype=None, operations=None):\n super().__init__()\n self.audio_cross_attn = SingleStreamMultiAttention(in_dim, out_dim, num_heads=40, qkv_bias=True, device=device, dtype=dtype, operations=operations)\n self.norm_x = operations.LayerNorm(in_dim, device=device, dtype=dtype, elementwise_affine=True)\n\n\nclass MultiTalkGetAttnMapPatch:\n def __init__(self, ref_target_masks=None):\n self.ref_target_masks = ref_target_masks\n\n def __call__(self, kwargs):\n transformer_options = kwargs.get(\"transformer_options\", {})\n x = kwargs[\"x\"]\n\n if self.ref_target_masks is not None:\n x_ref_attn_map = get_attn_map_with_target(kwargs[\"q\"], kwargs[\"k\"], transformer_options[\"grid_sizes\"], ref_target_masks=self.ref_target_masks.to(x.device))\n transformer_options[\"x_ref_attn_map\"] = x_ref_attn_map\n return x\n\n\nclass MultiTalkCrossAttnPatch:\n def __init__(self, model_patch, audio_scale=1.0, ref_target_masks=None):\n self.model_patch = model_patch\n self.audio_scale = audio_scale\n self.ref_target_masks = ref_target_masks\n\n def __call__(self, kwargs):\n transformer_options = kwargs.get(\"transformer_options\", {})\n block_idx = transformer_options.get(\"block_index\", None)\n x = kwargs[\"x\"]\n if block_idx is None:\n return torch.zeros_like(x)\n\n audio_embeds = transformer_options.get(\"audio_embeds\")\n x_ref_attn_map = transformer_options.pop(\"x_ref_attn_map\", None)\n\n norm_x = self.model_patch.model.blocks[block_idx].norm_x(x)\n x_audio = self.model_patch.model.blocks[block_idx].audio_cross_attn(\n norm_x, audio_embeds.to(x.dtype),\n shape=transformer_options[\"grid_sizes\"],\n x_ref_attn_map=x_ref_attn_map\n )\n x = x + x_audio * self.audio_scale\n return x\n\n def models(self):\n return [self.model_patch]\n\nclass MultiTalkApplyModelWrapper:\n def __init__(self, init_latents):\n self.init_latents = init_latents\n\n def __call__(self, executor, x, *args, **kwargs):\n x[:, :, :self.init_latents.shape[2]] = self.init_latents.to(x)\n samples = executor(x, *args, **kwargs)\n return samples\n\n\nclass InfiniteTalkOuterSampleWrapper:\n def __init__(self, motion_frames_latent, model_patch, is_extend=False):\n self.motion_frames_latent = motion_frames_latent\n self.model_patch = model_patch\n self.is_extend = is_extend\n\n def __call__(self, executor, *args, **kwargs):\n model_patcher = executor.class_obj.model_patcher\n model_options = executor.class_obj.model_options\n process_latent_in = model_patcher.model.process_latent_in\n\n # for InfiniteTalk, model input first latent(s) need to always be replaced on every step\n if self.motion_frames_latent is not None:\n wrappers = model_options[\"transformer_options\"][\"wrappers\"]\n w = wrappers.setdefault(comfy.patcher_extension.WrappersMP.APPLY_MODEL, {})\n w[\"MultiTalk_apply_model\"] = [MultiTalkApplyModelWrapper(process_latent_in(self.motion_frames_latent))]\n\n # run the sampling process\n result = executor(*args, **kwargs)\n\n # insert motion frames before decoding\n if self.is_extend:\n overlap = self.motion_frames_latent.shape[2]\n result = torch.cat([self.motion_frames_latent.to(result), result[:, :, overlap:]], dim=2)\n\n return result\n\n def to(self, device_or_dtype):\n if isinstance(device_or_dtype, torch.device):\n if self.motion_frames_latent is not None:\n self.motion_frames_latent = self.motion_frames_latent.to(device_or_dtype)\n return self\n" }, { "path": "comfy/ldm/wan/vae.py", "content": "# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py\n# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom comfy.ldm.modules.diffusionmodules.model import vae_attention, torch_cat_if_needed\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nCACHE_T = 2\n\n\nclass CausalConv3d(ops.Conv3d):\n \"\"\"\n Causal 3d convolusion.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._padding = 2 * self.padding[0]\n self.padding = (0, self.padding[1], self.padding[2])\n\n def forward(self, x, cache_x=None, cache_list=None, cache_idx=None):\n if cache_list is not None:\n cache_x = cache_list[cache_idx]\n cache_list[cache_idx] = None\n\n if cache_x is None and x.shape[2] == 1:\n #Fast path - the op will pad for use by truncating the weight\n #and save math on a pile of zeros.\n return super().forward(x, autopad=\"causal_zero\")\n\n if self._padding > 0:\n padding_needed = self._padding\n if cache_x is not None:\n cache_x = cache_x.to(x.device)\n padding_needed = max(0, padding_needed - cache_x.shape[2])\n padding_shape = list(x.shape)\n padding_shape[2] = padding_needed\n padding = torch.zeros(padding_shape, device=x.device, dtype=x.dtype)\n x = torch_cat_if_needed([padding, cache_x, x], dim=2)\n del cache_x\n\n return super().forward(x)\n\n\nclass RMS_norm(nn.Module):\n\n def __init__(self, dim, channel_first=True, images=True, bias=False):\n super().__init__()\n broadcastable_dims = (1, 1, 1) if not images else (1, 1)\n shape = (dim, *broadcastable_dims) if channel_first else (dim,)\n\n self.channel_first = channel_first\n self.scale = dim**0.5\n self.gamma = nn.Parameter(torch.ones(shape))\n self.bias = nn.Parameter(torch.zeros(shape)) if bias else None\n\n def forward(self, x):\n return F.normalize(\n x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0)\n\n\nclass Resample(nn.Module):\n\n def __init__(self, dim, mode):\n assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',\n 'downsample3d')\n super().__init__()\n self.dim = dim\n self.mode = mode\n\n # layers\n if mode == 'upsample2d':\n self.resample = nn.Sequential(\n nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'),\n ops.Conv2d(dim, dim // 2, 3, padding=1))\n elif mode == 'upsample3d':\n self.resample = nn.Sequential(\n nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'),\n ops.Conv2d(dim, dim // 2, 3, padding=1))\n self.time_conv = CausalConv3d(\n dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))\n\n elif mode == 'downsample2d':\n self.resample = nn.Sequential(\n nn.ZeroPad2d((0, 1, 0, 1)),\n ops.Conv2d(dim, dim, 3, stride=(2, 2)))\n elif mode == 'downsample3d':\n self.resample = nn.Sequential(\n nn.ZeroPad2d((0, 1, 0, 1)),\n ops.Conv2d(dim, dim, 3, stride=(2, 2)))\n self.time_conv = CausalConv3d(\n dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))\n\n else:\n self.resample = nn.Identity()\n\n def forward(self, x, feat_cache=None, feat_idx=[0], final=False):\n b, c, t, h, w = x.size()\n if self.mode == 'upsample3d':\n if feat_cache is not None:\n idx = feat_idx[0]\n if feat_cache[idx] is None:\n feat_cache[idx] = 'Rep'\n feat_idx[0] += 1\n else:\n\n cache_x = x[:, :, -CACHE_T:, :, :]\n if feat_cache[idx] == 'Rep':\n x = self.time_conv(x)\n else:\n x = self.time_conv(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n\n x = x.reshape(b, 2, c, t, h, w)\n x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),\n 3)\n x = x.reshape(b, c, t * 2, h, w)\n t = x.shape[2]\n x = rearrange(x, 'b c t h w -> (b t) c h w')\n x = self.resample(x)\n x = rearrange(x, '(b t) c h w -> b c t h w', t=t)\n\n if self.mode == 'downsample3d':\n if feat_cache is not None:\n idx = feat_idx[0]\n if feat_cache[idx] is None:\n feat_cache[idx] = x\n else:\n\n cache_x = x[:, :, -1:, :, :]\n x = self.time_conv(\n torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))\n feat_cache[idx] = cache_x\n\n deferred_x = feat_cache[idx + 1]\n if deferred_x is not None:\n x = torch.cat([deferred_x, x], 2)\n feat_cache[idx + 1] = None\n\n if x.shape[2] == 1 and not final:\n feat_cache[idx + 1] = x\n x = None\n\n feat_idx[0] += 2\n return x\n\n\nclass ResidualBlock(nn.Module):\n\n def __init__(self, in_dim, out_dim, dropout=0.0):\n super().__init__()\n self.in_dim = in_dim\n self.out_dim = out_dim\n\n # layers\n self.residual = nn.Sequential(\n RMS_norm(in_dim, images=False), nn.SiLU(),\n CausalConv3d(in_dim, out_dim, 3, padding=1),\n RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout),\n CausalConv3d(out_dim, out_dim, 3, padding=1))\n self.shortcut = CausalConv3d(in_dim, out_dim, 1) \\\n if in_dim != out_dim else nn.Identity()\n\n def forward(self, x, feat_cache=None, feat_idx=[0], final=False):\n old_x = x\n for layer in self.residual:\n if isinstance(layer, CausalConv3d) and feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :]\n x = layer(x, cache_list=feat_cache, cache_idx=idx)\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = layer(x)\n return x + self.shortcut(old_x)\n\n\nclass AttentionBlock(nn.Module):\n \"\"\"\n Causal self-attention with a single head.\n \"\"\"\n\n def __init__(self, dim):\n super().__init__()\n self.dim = dim\n\n # layers\n self.norm = RMS_norm(dim)\n self.to_qkv = ops.Conv2d(dim, dim * 3, 1)\n self.proj = ops.Conv2d(dim, dim, 1)\n self.optimized_attention = vae_attention()\n\n def forward(self, x, feat_cache=None, feat_idx=[0], final=False):\n identity = x\n b, c, t, h, w = x.size()\n x = rearrange(x, 'b c t h w -> (b t) c h w')\n x = self.norm(x)\n # compute query, key, value\n\n q, k, v = self.to_qkv(x).chunk(3, dim=1)\n x = self.optimized_attention(q, k, v)\n\n # output\n x = self.proj(x)\n x = rearrange(x, '(b t) c h w-> b c t h w', t=t)\n return x + identity\n\n\nclass Encoder3d(nn.Module):\n\n def __init__(self,\n dim=128,\n z_dim=4,\n input_channels=3,\n dim_mult=[1, 2, 4, 4],\n num_res_blocks=2,\n attn_scales=[],\n temperal_downsample=[True, True, False],\n dropout=0.0):\n super().__init__()\n self.dim = dim\n self.z_dim = z_dim\n self.dim_mult = dim_mult\n self.num_res_blocks = num_res_blocks\n self.attn_scales = attn_scales\n self.temperal_downsample = temperal_downsample\n\n # dimensions\n dims = [dim * u for u in [1] + dim_mult]\n scale = 1.0\n\n # init block\n self.conv1 = CausalConv3d(input_channels, dims[0], 3, padding=1)\n\n # downsample blocks\n downsamples = []\n for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):\n # residual (+attention) blocks\n for _ in range(num_res_blocks):\n downsamples.append(ResidualBlock(in_dim, out_dim, dropout))\n if scale in attn_scales:\n downsamples.append(AttentionBlock(out_dim))\n in_dim = out_dim\n\n # downsample block\n if i != len(dim_mult) - 1:\n mode = 'downsample3d' if temperal_downsample[\n i] else 'downsample2d'\n downsamples.append(Resample(out_dim, mode=mode))\n scale /= 2.0\n self.downsamples = nn.Sequential(*downsamples)\n\n # middle blocks\n self.middle = nn.Sequential(\n ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim),\n ResidualBlock(out_dim, out_dim, dropout))\n\n # output blocks\n self.head = nn.Sequential(\n RMS_norm(out_dim, images=False), nn.SiLU(),\n CausalConv3d(out_dim, z_dim, 3, padding=1))\n\n def forward(self, x, feat_cache=None, feat_idx=[0], final=False):\n if feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :]\n x = self.conv1(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = self.conv1(x)\n\n ## downsamples\n for layer in self.downsamples:\n if feat_cache is not None:\n x = layer(x, feat_cache, feat_idx, final=final)\n if x is None:\n return None\n else:\n x = layer(x)\n\n ## middle\n for layer in self.middle:\n if feat_cache is not None:\n x = layer(x, feat_cache, feat_idx, final=final)\n else:\n x = layer(x)\n\n ## head\n for layer in self.head:\n if isinstance(layer, CausalConv3d) and feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :]\n x = layer(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = layer(x)\n return x\n\n\nclass Decoder3d(nn.Module):\n\n def __init__(self,\n dim=128,\n z_dim=4,\n output_channels=3,\n dim_mult=[1, 2, 4, 4],\n num_res_blocks=2,\n attn_scales=[],\n temperal_upsample=[False, True, True],\n dropout=0.0):\n super().__init__()\n self.dim = dim\n self.z_dim = z_dim\n self.dim_mult = dim_mult\n self.num_res_blocks = num_res_blocks\n self.attn_scales = attn_scales\n self.temperal_upsample = temperal_upsample\n\n # dimensions\n dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]\n scale = 1.0 / 2**(len(dim_mult) - 2)\n\n # init block\n self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)\n\n # middle blocks\n self.middle = nn.Sequential(\n ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]),\n ResidualBlock(dims[0], dims[0], dropout))\n\n # upsample blocks\n upsamples = []\n for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):\n # residual (+attention) blocks\n if i == 1 or i == 2 or i == 3:\n in_dim = in_dim // 2\n for _ in range(num_res_blocks + 1):\n upsamples.append(ResidualBlock(in_dim, out_dim, dropout))\n if scale in attn_scales:\n upsamples.append(AttentionBlock(out_dim))\n in_dim = out_dim\n\n # upsample block\n if i != len(dim_mult) - 1:\n mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'\n upsamples.append(Resample(out_dim, mode=mode))\n scale *= 2.0\n self.upsamples = nn.Sequential(*upsamples)\n\n # output blocks\n self.head = nn.Sequential(\n RMS_norm(out_dim, images=False), nn.SiLU(),\n CausalConv3d(out_dim, output_channels, 3, padding=1))\n\n def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks):\n x = x_ref[0]\n x_ref[0] = None\n if layer_idx >= len(self.upsamples):\n for layer in self.head:\n if isinstance(layer, CausalConv3d) and feat_cache is not None:\n cache_x = x[:, :, -CACHE_T:, :, :]\n x = layer(x, feat_cache[feat_idx[0]])\n feat_cache[feat_idx[0]] = cache_x\n feat_idx[0] += 1\n else:\n x = layer(x)\n out_chunks.append(x)\n return\n\n layer = self.upsamples[layer_idx]\n if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:\n for frame_idx in range(x.shape[2]):\n self.run_up(\n layer_idx,\n [x[:, :, frame_idx:frame_idx + 1, :, :]],\n feat_cache,\n feat_idx.copy(),\n out_chunks,\n )\n del x\n return\n\n if feat_cache is not None:\n x = layer(x, feat_cache, feat_idx)\n else:\n x = layer(x)\n\n next_x_ref = [x]\n del x\n self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks)\n\n def forward(self, x, feat_cache=None, feat_idx=[0]):\n ## conv1\n if feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :]\n x = self.conv1(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = self.conv1(x)\n\n ## middle\n for layer in self.middle:\n if feat_cache is not None:\n x = layer(x, feat_cache, feat_idx)\n else:\n x = layer(x)\n\n out_chunks = []\n\n self.run_up(0, [x], feat_cache, feat_idx, out_chunks)\n return out_chunks\n\n\ndef count_cache_layers(model):\n count = 0\n for m in model.modules():\n if isinstance(m, CausalConv3d) or (isinstance(m, Resample) and m.mode == 'downsample3d'):\n count += 1\n return count\n\n\nclass WanVAE(nn.Module):\n\n def __init__(self,\n dim=128,\n z_dim=4,\n dim_mult=[1, 2, 4, 4],\n num_res_blocks=2,\n attn_scales=[],\n temperal_downsample=[True, True, False],\n image_channels=3,\n conv_out_channels=3,\n dropout=0.0):\n super().__init__()\n self.dim = dim\n self.z_dim = z_dim\n self.dim_mult = dim_mult\n self.num_res_blocks = num_res_blocks\n self.attn_scales = attn_scales\n self.temperal_downsample = temperal_downsample\n self.temperal_upsample = temperal_downsample[::-1]\n\n # modules\n self.encoder = Encoder3d(dim, z_dim * 2, image_channels, dim_mult, num_res_blocks,\n attn_scales, self.temperal_downsample, dropout)\n self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)\n self.conv2 = CausalConv3d(z_dim, z_dim, 1)\n self.decoder = Decoder3d(dim, z_dim, conv_out_channels, dim_mult, num_res_blocks,\n attn_scales, self.temperal_upsample, dropout)\n\n def encode(self, x):\n conv_idx = [0]\n ## cache\n t = x.shape[2]\n t = 1 + ((t - 1) // 4) * 4\n iter_ = 1 + (t - 1) // 2\n feat_map = None\n if iter_ > 1:\n feat_map = [None] * count_cache_layers(self.encoder)\n ## 对encode输入的x，按时间拆分为1、2、2、2....(总帧数先按4N+1向下取整)\n for i in range(iter_):\n conv_idx = [0]\n if i == 0:\n out = self.encoder(\n x[:, :, :1, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx)\n else:\n out_ = self.encoder(\n x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx,\n final=(i == (iter_ - 1)))\n if out_ is None:\n continue\n out = torch.cat([out, out_], 2)\n\n mu, log_var = self.conv1(out).chunk(2, dim=1)\n return mu\n\n def decode(self, z):\n # z: [b,c,t,h,w]\n iter_ = 1 + z.shape[2] // 2\n feat_map = None\n if iter_ > 1:\n feat_map = [None] * count_cache_layers(self.decoder)\n x = self.conv2(z)\n for i in range(iter_):\n conv_idx = [0]\n if i == 0:\n out = self.decoder(\n x[:, :, i:i + 1, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx)\n else:\n out_ = self.decoder(\n x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx)\n out += out_\n return torch.cat(out, 2)\n" }, { "path": "comfy/ldm/wan/vae2_2.py", "content": "# original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py\n# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom .vae import AttentionBlock, CausalConv3d, RMS_norm\n\nimport comfy.ops\nops = comfy.ops.disable_weight_init\n\nCACHE_T = 2\n\n\nclass Resample(nn.Module):\n\n def __init__(self, dim, mode):\n assert mode in (\n \"none\",\n \"upsample2d\",\n \"upsample3d\",\n \"downsample2d\",\n \"downsample3d\",\n )\n super().__init__()\n self.dim = dim\n self.mode = mode\n\n # layers\n if mode == \"upsample2d\":\n self.resample = nn.Sequential(\n nn.Upsample(scale_factor=(2.0, 2.0), mode=\"nearest-exact\"),\n ops.Conv2d(dim, dim, 3, padding=1),\n )\n elif mode == \"upsample3d\":\n self.resample = nn.Sequential(\n nn.Upsample(scale_factor=(2.0, 2.0), mode=\"nearest-exact\"),\n ops.Conv2d(dim, dim, 3, padding=1),\n # ops.Conv2d(dim, dim//2, 3, padding=1)\n )\n self.time_conv = CausalConv3d(\n dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))\n elif mode == \"downsample2d\":\n self.resample = nn.Sequential(\n nn.ZeroPad2d((0, 1, 0, 1)),\n ops.Conv2d(dim, dim, 3, stride=(2, 2)))\n elif mode == \"downsample3d\":\n self.resample = nn.Sequential(\n nn.ZeroPad2d((0, 1, 0, 1)),\n ops.Conv2d(dim, dim, 3, stride=(2, 2)))\n self.time_conv = CausalConv3d(\n dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))\n else:\n self.resample = nn.Identity()\n\n def forward(self, x, feat_cache=None, feat_idx=[0]):\n b, c, t, h, w = x.size()\n if self.mode == \"upsample3d\":\n if feat_cache is not None:\n idx = feat_idx[0]\n if feat_cache[idx] is None:\n feat_cache[idx] = \"Rep\"\n feat_idx[0] += 1\n else:\n cache_x = x[:, :, -CACHE_T:, :, :].clone()\n if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and\n feat_cache[idx] != \"Rep\"):\n # cache last frame of last two chunk\n cache_x = torch.cat(\n [\n feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n cache_x.device),\n cache_x,\n ],\n dim=2,\n )\n if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and\n feat_cache[idx] == \"Rep\"):\n cache_x = torch.cat(\n [\n torch.zeros_like(cache_x).to(cache_x.device),\n cache_x\n ],\n dim=2,\n )\n if feat_cache[idx] == \"Rep\":\n x = self.time_conv(x)\n else:\n x = self.time_conv(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n x = x.reshape(b, 2, c, t, h, w)\n x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),\n 3)\n x = x.reshape(b, c, t * 2, h, w)\n t = x.shape[2]\n x = rearrange(x, \"b c t h w -> (b t) c h w\")\n x = self.resample(x)\n x = rearrange(x, \"(b t) c h w -> b c t h w\", t=t)\n\n if self.mode == \"downsample3d\":\n if feat_cache is not None:\n idx = feat_idx[0]\n if feat_cache[idx] is None:\n feat_cache[idx] = x.clone()\n feat_idx[0] += 1\n else:\n cache_x = x[:, :, -1:, :, :].clone()\n x = self.time_conv(\n torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n return x\n\n\nclass ResidualBlock(nn.Module):\n\n def __init__(self, in_dim, out_dim, dropout=0.0):\n super().__init__()\n self.in_dim = in_dim\n self.out_dim = out_dim\n\n # layers\n self.residual = nn.Sequential(\n RMS_norm(in_dim, images=False),\n nn.SiLU(),\n CausalConv3d(in_dim, out_dim, 3, padding=1),\n RMS_norm(out_dim, images=False),\n nn.SiLU(),\n nn.Dropout(dropout),\n CausalConv3d(out_dim, out_dim, 3, padding=1),\n )\n self.shortcut = (\n CausalConv3d(in_dim, out_dim, 1)\n if in_dim != out_dim else nn.Identity())\n\n def forward(self, x, feat_cache=None, feat_idx=[0]):\n old_x = x\n for layer in self.residual:\n if isinstance(layer, CausalConv3d) and feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :].clone()\n if cache_x.shape[2] < 2 and feat_cache[idx] is not None:\n # cache last frame of last two chunk\n cache_x = torch.cat(\n [\n feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n cache_x.device),\n cache_x,\n ],\n dim=2,\n )\n x = layer(x, cache_list=feat_cache, cache_idx=idx)\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = layer(x)\n return x + self.shortcut(old_x)\n\n\ndef patchify(x, patch_size):\n if patch_size == 1:\n return x\n if x.dim() == 4:\n x = rearrange(\n x, \"b c (h q) (w r) -> b (c r q) h w\", q=patch_size, r=patch_size)\n elif x.dim() == 5:\n x = rearrange(\n x,\n \"b c f (h q) (w r) -> b (c r q) f h w\",\n q=patch_size,\n r=patch_size,\n )\n else:\n raise ValueError(f\"Invalid input shape: {x.shape}\")\n\n return x\n\n\ndef unpatchify(x, patch_size):\n if patch_size == 1:\n return x\n\n if x.dim() == 4:\n x = rearrange(\n x, \"b (c r q) h w -> b c (h q) (w r)\", q=patch_size, r=patch_size)\n elif x.dim() == 5:\n x = rearrange(\n x,\n \"b (c r q) f h w -> b c f (h q) (w r)\",\n q=patch_size,\n r=patch_size,\n )\n return x\n\n\nclass AvgDown3D(nn.Module):\n\n def __init__(\n self,\n in_channels,\n out_channels,\n factor_t,\n factor_s=1,\n ):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.factor_t = factor_t\n self.factor_s = factor_s\n self.factor = self.factor_t * self.factor_s * self.factor_s\n\n assert in_channels * self.factor % out_channels == 0\n self.group_size = in_channels * self.factor // out_channels\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t\n pad = (0, 0, 0, 0, pad_t, 0)\n x = F.pad(x, pad)\n B, C, T, H, W = x.shape\n x = x.view(\n B,\n C,\n T // self.factor_t,\n self.factor_t,\n H // self.factor_s,\n self.factor_s,\n W // self.factor_s,\n self.factor_s,\n )\n x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()\n x = x.view(\n B,\n C * self.factor,\n T // self.factor_t,\n H // self.factor_s,\n W // self.factor_s,\n )\n x = x.view(\n B,\n self.out_channels,\n self.group_size,\n T // self.factor_t,\n H // self.factor_s,\n W // self.factor_s,\n )\n x = x.mean(dim=2)\n return x\n\n\nclass DupUp3D(nn.Module):\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n factor_t,\n factor_s=1,\n ):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n\n self.factor_t = factor_t\n self.factor_s = factor_s\n self.factor = self.factor_t * self.factor_s * self.factor_s\n\n assert out_channels * self.factor % in_channels == 0\n self.repeats = out_channels * self.factor // in_channels\n\n def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:\n x = x.repeat_interleave(self.repeats, dim=1)\n x = x.view(\n x.size(0),\n self.out_channels,\n self.factor_t,\n self.factor_s,\n self.factor_s,\n x.size(2),\n x.size(3),\n x.size(4),\n )\n x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()\n x = x.view(\n x.size(0),\n self.out_channels,\n x.size(2) * self.factor_t,\n x.size(4) * self.factor_s,\n x.size(6) * self.factor_s,\n )\n if first_chunk:\n x = x[:, :, self.factor_t - 1:, :, :]\n return x\n\n\nclass Down_ResidualBlock(nn.Module):\n\n def __init__(self,\n in_dim,\n out_dim,\n dropout,\n mult,\n temperal_downsample=False,\n down_flag=False):\n super().__init__()\n\n # Shortcut path with downsample\n self.avg_shortcut = AvgDown3D(\n in_dim,\n out_dim,\n factor_t=2 if temperal_downsample else 1,\n factor_s=2 if down_flag else 1,\n )\n\n # Main path with residual blocks and downsample\n downsamples = []\n for _ in range(mult):\n downsamples.append(ResidualBlock(in_dim, out_dim, dropout))\n in_dim = out_dim\n\n # Add the final downsample block\n if down_flag:\n mode = \"downsample3d\" if temperal_downsample else \"downsample2d\"\n downsamples.append(Resample(out_dim, mode=mode))\n\n self.downsamples = nn.Sequential(*downsamples)\n\n def forward(self, x, feat_cache=None, feat_idx=[0]):\n x_copy = x\n for module in self.downsamples:\n x = module(x, feat_cache, feat_idx)\n\n return x + self.avg_shortcut(x_copy)\n\n\nclass Up_ResidualBlock(nn.Module):\n\n def __init__(self,\n in_dim,\n out_dim,\n dropout,\n mult,\n temperal_upsample=False,\n up_flag=False):\n super().__init__()\n # Shortcut path with upsample\n if up_flag:\n self.avg_shortcut = DupUp3D(\n in_dim,\n out_dim,\n factor_t=2 if temperal_upsample else 1,\n factor_s=2 if up_flag else 1,\n )\n else:\n self.avg_shortcut = None\n\n # Main path with residual blocks and upsample\n upsamples = []\n for _ in range(mult):\n upsamples.append(ResidualBlock(in_dim, out_dim, dropout))\n in_dim = out_dim\n\n # Add the final upsample block\n if up_flag:\n mode = \"upsample3d\" if temperal_upsample else \"upsample2d\"\n upsamples.append(Resample(out_dim, mode=mode))\n\n self.upsamples = nn.Sequential(*upsamples)\n\n def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):\n x_main = x\n for module in self.upsamples:\n x_main = module(x_main, feat_cache, feat_idx)\n if self.avg_shortcut is not None:\n x_shortcut = self.avg_shortcut(x, first_chunk)\n return x_main + x_shortcut\n else:\n return x_main\n\n\nclass Encoder3d(nn.Module):\n\n def __init__(\n self,\n dim=128,\n z_dim=4,\n dim_mult=[1, 2, 4, 4],\n num_res_blocks=2,\n attn_scales=[],\n temperal_downsample=[True, True, False],\n dropout=0.0,\n ):\n super().__init__()\n self.dim = dim\n self.z_dim = z_dim\n self.dim_mult = dim_mult\n self.num_res_blocks = num_res_blocks\n self.attn_scales = attn_scales\n self.temperal_downsample = temperal_downsample\n\n # dimensions\n dims = [dim * u for u in [1] + dim_mult]\n scale = 1.0\n\n # init block\n self.conv1 = CausalConv3d(12, dims[0], 3, padding=1)\n\n # downsample blocks\n downsamples = []\n for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):\n t_down_flag = (\n temperal_downsample[i]\n if i < len(temperal_downsample) else False)\n downsamples.append(\n Down_ResidualBlock(\n in_dim=in_dim,\n out_dim=out_dim,\n dropout=dropout,\n mult=num_res_blocks,\n temperal_downsample=t_down_flag,\n down_flag=i != len(dim_mult) - 1,\n ))\n scale /= 2.0\n self.downsamples = nn.Sequential(*downsamples)\n\n # middle blocks\n self.middle = nn.Sequential(\n ResidualBlock(out_dim, out_dim, dropout),\n AttentionBlock(out_dim),\n ResidualBlock(out_dim, out_dim, dropout),\n )\n\n # # output blocks\n self.head = nn.Sequential(\n RMS_norm(out_dim, images=False),\n nn.SiLU(),\n CausalConv3d(out_dim, z_dim, 3, padding=1),\n )\n\n def forward(self, x, feat_cache=None, feat_idx=[0]):\n\n if feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :].clone()\n if cache_x.shape[2] < 2 and feat_cache[idx] is not None:\n cache_x = torch.cat(\n [\n feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n cache_x.device),\n cache_x,\n ],\n dim=2,\n )\n x = self.conv1(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = self.conv1(x)\n\n ## downsamples\n for layer in self.downsamples:\n if feat_cache is not None:\n x = layer(x, feat_cache, feat_idx)\n else:\n x = layer(x)\n\n ## middle\n for layer in self.middle:\n if isinstance(layer, ResidualBlock) and feat_cache is not None:\n x = layer(x, feat_cache, feat_idx)\n else:\n x = layer(x)\n\n ## head\n for layer in self.head:\n if isinstance(layer, CausalConv3d) and feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :].clone()\n if cache_x.shape[2] < 2 and feat_cache[idx] is not None:\n cache_x = torch.cat(\n [\n feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n cache_x.device),\n cache_x,\n ],\n dim=2,\n )\n x = layer(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = layer(x)\n\n return x\n\n\nclass Decoder3d(nn.Module):\n\n def __init__(\n self,\n dim=128,\n z_dim=4,\n dim_mult=[1, 2, 4, 4],\n num_res_blocks=2,\n attn_scales=[],\n temperal_upsample=[False, True, True],\n dropout=0.0,\n ):\n super().__init__()\n self.dim = dim\n self.z_dim = z_dim\n self.dim_mult = dim_mult\n self.num_res_blocks = num_res_blocks\n self.attn_scales = attn_scales\n self.temperal_upsample = temperal_upsample\n\n # dimensions\n dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]\n # init block\n self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)\n\n # middle blocks\n self.middle = nn.Sequential(\n ResidualBlock(dims[0], dims[0], dropout),\n AttentionBlock(dims[0]),\n ResidualBlock(dims[0], dims[0], dropout),\n )\n\n # upsample blocks\n upsamples = []\n for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):\n t_up_flag = temperal_upsample[i] if i < len(\n temperal_upsample) else False\n upsamples.append(\n Up_ResidualBlock(\n in_dim=in_dim,\n out_dim=out_dim,\n dropout=dropout,\n mult=num_res_blocks + 1,\n temperal_upsample=t_up_flag,\n up_flag=i != len(dim_mult) - 1,\n ))\n self.upsamples = nn.Sequential(*upsamples)\n\n # output blocks\n self.head = nn.Sequential(\n RMS_norm(out_dim, images=False),\n nn.SiLU(),\n CausalConv3d(out_dim, 12, 3, padding=1),\n )\n\n def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):\n if feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :].clone()\n if cache_x.shape[2] < 2 and feat_cache[idx] is not None:\n cache_x = torch.cat(\n [\n feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n cache_x.device),\n cache_x,\n ],\n dim=2,\n )\n x = self.conv1(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = self.conv1(x)\n\n for layer in self.middle:\n if isinstance(layer, ResidualBlock) and feat_cache is not None:\n x = layer(x, feat_cache, feat_idx)\n else:\n x = layer(x)\n\n ## upsamples\n for layer in self.upsamples:\n if feat_cache is not None:\n x = layer(x, feat_cache, feat_idx, first_chunk)\n else:\n x = layer(x)\n\n ## head\n for layer in self.head:\n if isinstance(layer, CausalConv3d) and feat_cache is not None:\n idx = feat_idx[0]\n cache_x = x[:, :, -CACHE_T:, :, :].clone()\n if cache_x.shape[2] < 2 and feat_cache[idx] is not None:\n cache_x = torch.cat(\n [\n feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(\n cache_x.device),\n cache_x,\n ],\n dim=2,\n )\n x = layer(x, feat_cache[idx])\n feat_cache[idx] = cache_x\n feat_idx[0] += 1\n else:\n x = layer(x)\n return x\n\n\ndef count_conv3d(model):\n count = 0\n for m in model.modules():\n if isinstance(m, CausalConv3d):\n count += 1\n return count\n\n\nclass WanVAE(nn.Module):\n\n def __init__(\n self,\n dim=160,\n dec_dim=256,\n z_dim=16,\n dim_mult=[1, 2, 4, 4],\n num_res_blocks=2,\n attn_scales=[],\n temperal_downsample=[True, True, False],\n dropout=0.0,\n ):\n super().__init__()\n self.dim = dim\n self.z_dim = z_dim\n self.dim_mult = dim_mult\n self.num_res_blocks = num_res_blocks\n self.attn_scales = attn_scales\n self.temperal_downsample = temperal_downsample\n self.temperal_upsample = temperal_downsample[::-1]\n\n # modules\n self.encoder = Encoder3d(\n dim,\n z_dim * 2,\n dim_mult,\n num_res_blocks,\n attn_scales,\n self.temperal_downsample,\n dropout,\n )\n self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)\n self.conv2 = CausalConv3d(z_dim, z_dim, 1)\n self.decoder = Decoder3d(\n dec_dim,\n z_dim,\n dim_mult,\n num_res_blocks,\n attn_scales,\n self.temperal_upsample,\n dropout,\n )\n\n def encode(self, x):\n conv_idx = [0]\n feat_map = [None] * count_conv3d(self.encoder)\n x = patchify(x, patch_size=2)\n t = x.shape[2]\n iter_ = 1 + (t - 1) // 4\n for i in range(iter_):\n conv_idx = [0]\n if i == 0:\n out = self.encoder(\n x[:, :, :1, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx,\n )\n else:\n out_ = self.encoder(\n x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx,\n )\n out = torch.cat([out, out_], 2)\n mu, log_var = self.conv1(out).chunk(2, dim=1)\n return mu\n\n def decode(self, z):\n conv_idx = [0]\n feat_map = [None] * count_conv3d(self.decoder)\n iter_ = z.shape[2]\n x = self.conv2(z)\n for i in range(iter_):\n conv_idx = [0]\n if i == 0:\n out = self.decoder(\n x[:, :, i:i + 1, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx,\n first_chunk=True,\n )\n else:\n out_ = self.decoder(\n x[:, :, i:i + 1, :, :],\n feat_cache=feat_map,\n feat_idx=conv_idx,\n )\n out = torch.cat([out, out_], 2)\n out = unpatchify(out, patch_size=2)\n return out\n\n def reparameterize(self, mu, log_var):\n std = torch.exp(0.5 * log_var)\n eps = torch.randn_like(std)\n return eps * std + mu\n\n def sample(self, imgs, deterministic=False):\n mu, log_var = self.encode(imgs)\n if deterministic:\n return mu\n std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))\n return mu + std * torch.randn_like(std)\n" }, { "path": "comfy/lora.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\nfrom __future__ import annotations\nimport comfy.utils\nimport comfy.model_management\nimport comfy.model_base\nimport comfy.weight_adapter as weight_adapter\nimport logging\nimport torch\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, log_missing=True):\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 dora_scale_name = \"{}.dora_scale\".format(x)\n dora_scale = None\n if dora_scale_name in lora.keys():\n dora_scale = lora[dora_scale_name]\n loaded_keys.add(dora_scale_name)\n\n for adapter_cls in weight_adapter.adapters:\n adapter = adapter_cls.load(x, lora, alpha, dora_scale, loaded_keys)\n if adapter is not None:\n patch_dict[to_load[x]] = adapter\n loaded_keys.update(adapter.loaded_keys)\n continue\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 set_weight_name = \"{}.set_weight\".format(x)\n set_weight = lora.get(set_weight_name, None)\n if set_weight is not None:\n patch_dict[to_load[x]] = (\"set\", (set_weight,))\n loaded_keys.add(set_weight_name)\n\n if log_missing:\n for x in lora.keys():\n if x not in loaded_keys:\n logging.warning(\"lora key not loaded: {}\".format(x))\n\n return patch_dict\n\ndef model_lora_keys_clip(model, key_map={}):\n sdk = model.state_dict().keys()\n for k in sdk:\n if k.endswith(\".weight\"):\n key_map[\"text_encoders.{}\".format(k[:-len(\".weight\")])] = k #generic lora format without any weird key names\n tp = k.find(\".transformer.\") #also map without wrapper prefix for composite text encoder models\n if tp > 0 and not k.startswith(\"clip_\"):\n key_map[\"text_encoders.{}\".format(k[tp + 1:-len(\".weight\")])] = k\n\n text_model_lora_key = \"lora_te_text_model_encoder_layers_{}_{}\"\n clip_l_present = False\n clip_g_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 clip_g_present = True\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 lora_key = \"lora_prior_te_text_model_encoder_layers_{}_{}\".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config\n key_map[lora_key] = k\n\n for k in sdk:\n if k.endswith(\".weight\"):\n if k.startswith(\"t5xxl.transformer.\"):#OneTrainer SD3 and Flux lora\n l_key = k[len(\"t5xxl.transformer.\"):-len(\".weight\")]\n t5_index = 1\n if clip_g_present:\n t5_index += 1\n if clip_l_present:\n t5_index += 1\n if t5_index == 2:\n key_map[\"lora_te{}_{}\".format(t5_index, l_key.replace(\".\", \"_\"))] = k #OneTrainer Flux\n t5_index += 1\n\n key_map[\"lora_te{}_{}\".format(t5_index, l_key.replace(\".\", \"_\"))] = k\n elif k.startswith(\"hydit_clip.transformer.bert.\"): #HunyuanDiT Lora\n l_key = k[len(\"hydit_clip.transformer.bert.\"):-len(\".weight\")]\n lora_key = \"lora_te1_{}\".format(l_key.replace(\".\", \"_\"))\n key_map[lora_key] = k\n\n\n k = \"clip_g.transformer.text_projection.weight\"\n if k in sdk:\n key_map[\"lora_prior_te_text_projection\"] = k #cascade lora?\n # key_map[\"text_encoder.text_projection\"] = k #TODO: check if other lora have the text_projection too\n key_map[\"lora_te2_text_projection\"] = k #OneTrainer SD3 lora\n\n k = \"clip_l.transformer.text_projection.weight\"\n if k in sdk:\n key_map[\"lora_te1_text_projection\"] = k #OneTrainer SD3 lora, not necessary but omits warning\n\n return key_map\n\ndef model_lora_keys_unet(model, key_map={}):\n sd = model.state_dict()\n sdk = sd.keys()\n\n for k in sdk:\n if k.startswith(\"diffusion_model.\"):\n if k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")].replace(\".\", \"_\")\n key_map[\"lora_unet_{}\".format(key_lora)] = k\n key_map[\"{}\".format(k[:-len(\".weight\")])] = k #generic lora format without any weird key names\n else:\n key_map[\"{}\".format(k)] = k #generic lora format for not .weight without any weird key names\n\n diffusers_keys = comfy.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 key_map[\"lycoris_{}\".format(key_lora)] = unet_key #simpletuner lycoris format\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\n if isinstance(model, comfy.model_base.StableCascade_C):\n for k in sdk:\n if k.startswith(\"diffusion_model.\"):\n if k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")].replace(\".\", \"_\")\n key_map[\"lora_prior_unet_{}\".format(key_lora)] = k\n\n if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3\n diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix=\"diffusion_model.\")\n for k in diffusers_keys:\n if k.endswith(\".weight\"):\n to = diffusers_keys[k]\n key_lora = \"transformer.{}\".format(k[:-len(\".weight\")]) #regular diffusers sd3 lora format\n key_map[key_lora] = to\n\n key_lora = \"base_model.model.{}\".format(k[:-len(\".weight\")]) #format for flash-sd3 lora and others?\n key_map[key_lora] = to\n\n key_lora = \"lora_transformer_{}\".format(k[:-len(\".weight\")].replace(\".\", \"_\")) #OneTrainer lora\n key_map[key_lora] = to\n\n key_lora = \"lycoris_{}\".format(k[:-len(\".weight\")].replace(\".\", \"_\")) #simpletuner lycoris format\n key_map[key_lora] = to\n\n if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow\n diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix=\"diffusion_model.\")\n for k in diffusers_keys:\n if k.endswith(\".weight\"):\n to = diffusers_keys[k]\n key_lora = \"transformer.{}\".format(k[:-len(\".weight\")]) #simpletrainer and probably regular diffusers lora format\n key_map[key_lora] = to\n\n if isinstance(model, comfy.model_base.PixArt):\n diffusers_keys = comfy.utils.pixart_to_diffusers(model.model_config.unet_config, output_prefix=\"diffusion_model.\")\n for k in diffusers_keys:\n if k.endswith(\".weight\"):\n to = diffusers_keys[k]\n key_lora = \"transformer.{}\".format(k[:-len(\".weight\")]) #default format\n key_map[key_lora] = to\n\n key_lora = \"base_model.model.{}\".format(k[:-len(\".weight\")]) #diffusers training script\n key_map[key_lora] = to\n\n key_lora = \"unet.base_model.model.{}\".format(k[:-len(\".weight\")]) #old reference peft script\n key_map[key_lora] = to\n\n if isinstance(model, comfy.model_base.HunyuanDiT):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"base_model.model.{}\".format(key_lora)] = k #official hunyuan lora format\n\n if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux\n diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix=\"diffusion_model.\")\n for k in diffusers_keys:\n if k.endswith(\".weight\"):\n to = diffusers_keys[k]\n key_map[\"transformer.{}\".format(k[:-len(\".weight\")])] = to #simpletrainer and probably regular diffusers flux lora format\n key_map[\"lycoris_{}\".format(k[:-len(\".weight\")].replace(\".\", \"_\"))] = to #simpletrainer lycoris\n key_map[\"lora_transformer_{}\".format(k[:-len(\".weight\")].replace(\".\", \"_\"))] = to #onetrainer\n key_map[k[:-len(\".weight\")]] = to #DiffSynth lora format\n for k in sdk:\n hidden_size = model.model_config.unet_config.get(\"hidden_size\", 0)\n if k.endswith(\".weight\") and \".linear1.\" in k:\n key_map[\"{}\".format(k.replace(\".linear1.weight\", \".linear1_qkv\"))] = (k, (0, 0, hidden_size * 3))\n\n if isinstance(model, comfy.model_base.GenmoMochi):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"): #Official Mochi lora format\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"{}\".format(key_lora)] = k\n\n if isinstance(model, comfy.model_base.HunyuanVideo):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"):\n # diffusion-pipe lora format\n key_lora = k\n key_lora = key_lora.replace(\"_mod.lin.\", \"_mod.linear.\").replace(\"_attn.qkv.\", \"_attn_qkv.\").replace(\"_attn.proj.\", \"_attn_proj.\")\n key_lora = key_lora.replace(\"mlp.0.\", \"mlp.fc1.\").replace(\"mlp.2.\", \"mlp.fc2.\")\n key_lora = key_lora.replace(\".modulation.lin.\", \".modulation.linear.\")\n key_lora = key_lora[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"transformer.{}\".format(key_lora)] = k\n key_map[\"diffusion_model.{}\".format(key_lora)] = k # Old loras\n\n if isinstance(model, comfy.model_base.HiDream):\n for k in sdk:\n if k.startswith(\"diffusion_model.\"):\n if k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"lycoris_{}\".format(key_lora.replace(\".\", \"_\"))] = k #SimpleTuner lycoris format\n key_map[\"transformer.{}\".format(key_lora)] = k #SimpleTuner regular format\n\n if isinstance(model, comfy.model_base.ACEStep):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"): #Official ACE step lora format\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"{}\".format(key_lora)] = k\n\n if isinstance(model, comfy.model_base.Omnigen2):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"{}\".format(key_lora)] = k\n\n if isinstance(model, comfy.model_base.QwenImage):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"): #QwenImage lora format\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n # Direct mapping for transformer_blocks format (QwenImage LoRA format)\n key_map[\"{}\".format(key_lora)] = k\n # Support transformer prefix format\n key_map[\"transformer.{}\".format(key_lora)] = k\n key_map[\"lycoris_{}\".format(key_lora.replace(\".\", \"_\"))] = k #SimpleTuner lycoris format\n\n if isinstance(model, comfy.model_base.Lumina2):\n diffusers_keys = comfy.utils.z_image_to_diffusers(model.model_config.unet_config, output_prefix=\"diffusion_model.\")\n for k in diffusers_keys:\n if k.endswith(\".weight\"):\n to = diffusers_keys[k]\n key_lora = k[:-len(\".weight\")]\n key_map[\"diffusion_model.{}\".format(key_lora)] = to\n key_map[\"transformer.{}\".format(key_lora)] = to\n key_map[\"lycoris_{}\".format(key_lora.replace(\".\", \"_\"))] = to\n key_map[key_lora] = to\n\n if isinstance(model, comfy.model_base.Kandinsky5):\n for k in sdk:\n if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")]\n key_map[\"{}\".format(key_lora)] = k\n key_map[\"transformer.{}\".format(key_lora)] = k\n\n if isinstance(model, comfy.model_base.ACEStep15):\n for k in sdk:\n if k.startswith(\"diffusion_model.decoder.\") and k.endswith(\".weight\"):\n key_lora = k[len(\"diffusion_model.decoder.\"):-len(\".weight\")]\n key_map[\"base_model.model.{}\".format(key_lora)] = k # Official base model loras\n key_map[\"lycoris_{}\".format(key_lora.replace(\".\", \"_\"))] = k # LyCORIS/LoKR format\n\n return key_map\n\n\ndef pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor:\n \"\"\"\n Pad a tensor to a new shape with zeros.\n\n Args:\n tensor (torch.Tensor): The original tensor to be padded.\n new_shape (List[int]): The desired shape of the padded tensor.\n\n Returns:\n torch.Tensor: A new tensor padded with zeros to the specified shape.\n\n Note:\n If the new shape is smaller than the original tensor in any dimension,\n the original tensor will be truncated in that dimension.\n \"\"\"\n if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]):\n raise ValueError(\"The new shape must be larger than the original tensor in all dimensions\")\n\n if len(new_shape) != len(tensor.shape):\n raise ValueError(\"The new shape must have the same number of dimensions as the original tensor\")\n\n # Create a new tensor filled with zeros\n padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device)\n\n # Create slicing tuples for both tensors\n orig_slices = tuple(slice(0, dim) for dim in tensor.shape)\n new_slices = tuple(slice(0, dim) for dim in tensor.shape)\n\n # Copy the original tensor into the new tensor\n padded_tensor[new_slices] = tensor[orig_slices]\n\n return padded_tensor\n\ndef calculate_shape(patches, weight, key, original_weights=None):\n current_shape = weight.shape\n\n for p in patches:\n v = p[1]\n offset = p[3]\n\n # Offsets restore the old shape; lists force a diff without metadata\n if offset is not None or isinstance(v, list):\n continue\n\n if isinstance(v, weight_adapter.WeightAdapterBase):\n adapter_shape = v.calculate_shape(key)\n if adapter_shape is not None:\n current_shape = adapter_shape\n continue\n\n # Standard diff logic with padding\n if len(v) == 2:\n patch_type, patch_data = v[0], v[1]\n if patch_type == \"diff\" and len(patch_data) > 1 and patch_data[1]['pad_weight']:\n current_shape = patch_data[0].shape\n\n return current_shape\n\ndef calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, original_weights=None):\n for p in patches:\n strength = p[0]\n v = p[1]\n strength_model = p[2]\n offset = p[3]\n function = p[4]\n if function is None:\n function = lambda a: a\n\n old_weight = None\n if offset is not None:\n old_weight = weight\n weight = weight.narrow(offset[0], offset[1], offset[2])\n\n if strength_model != 1.0:\n weight *= strength_model\n\n if isinstance(v, list):\n v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), )\n\n if isinstance(v, weight_adapter.WeightAdapterBase):\n output = v.calculate_weight(weight, key, strength, strength_model, offset, function, intermediate_dtype, original_weights)\n if output is None:\n logging.warning(\"Calculate Weight Failed: {} {}\".format(v.name, key))\n else:\n weight = output\n if old_weight is not None:\n weight = old_weight\n continue\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 diff: torch.Tensor = v[0]\n # An extra flag to pad the weight if the diff's shape is larger than the weight\n do_pad_weight = len(v) > 1 and v[1]['pad_weight']\n if do_pad_weight and diff.shape != weight.shape:\n logging.info(\"Pad weight {} from {} to shape: {}\".format(key, weight.shape, diff.shape))\n weight = pad_tensor_to_shape(weight, diff.shape)\n\n if strength != 0.0:\n if diff.shape != weight.shape:\n logging.warning(\"WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}\".format(key, diff.shape, weight.shape))\n else:\n weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype))\n elif patch_type == \"set\":\n weight.copy_(v[0])\n elif patch_type == \"model_as_lora\":\n target_weight: torch.Tensor = v[0]\n diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \\\n comfy.model_management.cast_to_device(original_weights[key][0][0], weight.device, intermediate_dtype)\n weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype))\n else:\n logging.warning(\"patch type not recognized {} {}\".format(patch_type, key))\n\n if old_weight is not None:\n weight = old_weight\n\n return weight\n" }, { "path": "comfy/lora_convert.py", "content": "import torch\nimport comfy.utils\n\n\ndef convert_lora_bfl_control(sd): #BFL loras for Flux\n sd_out = {}\n for k in sd:\n k_to = \"diffusion_model.{}\".format(k.replace(\".lora_B.bias\", \".diff_b\").replace(\"_norm.scale\", \"_norm.set_weight\"))\n sd_out[k_to] = sd[k]\n\n sd_out[\"diffusion_model.img_in.reshape_weight\"] = torch.tensor([sd[\"img_in.lora_B.weight\"].shape[0], sd[\"img_in.lora_A.weight\"].shape[1]])\n return sd_out\n\n\ndef convert_lora_wan_fun(sd): #Wan Fun loras\n return comfy.utils.state_dict_prefix_replace(sd, {\"lora_unet__\": \"lora_unet_\"})\n\ndef convert_uso_lora(sd):\n sd_out = {}\n for k in sd:\n tensor = sd[k]\n k_to = \"diffusion_model.{}\".format(k.replace(\".down.weight\", \".lora_down.weight\")\n .replace(\".up.weight\", \".lora_up.weight\")\n .replace(\".qkv_lora2.\", \".txt_attn.qkv.\")\n .replace(\".qkv_lora1.\", \".img_attn.qkv.\")\n .replace(\".proj_lora1.\", \".img_attn.proj.\")\n .replace(\".proj_lora2.\", \".txt_attn.proj.\")\n .replace(\".qkv_lora.\", \".linear1_qkv.\")\n .replace(\".proj_lora.\", \".linear2.\")\n .replace(\".processor.\", \".\")\n )\n sd_out[k_to] = tensor\n return sd_out\n\n\ndef convert_lora(sd):\n if \"img_in.lora_A.weight\" in sd and \"single_blocks.0.norm.key_norm.scale\" in sd:\n return convert_lora_bfl_control(sd)\n if \"lora_unet__blocks_0_cross_attn_k.lora_down.weight\" in sd:\n return convert_lora_wan_fun(sd)\n if \"single_blocks.37.processor.qkv_lora.up.weight\" in sd and \"double_blocks.18.processor.qkv_lora2.up.weight\" in sd:\n return convert_uso_lora(sd)\n return sd\n" }, { "path": "comfy/memory_management.py", "content": "import math\nimport ctypes\nimport threading\nimport dataclasses\nimport torch\nfrom typing import NamedTuple\n\nfrom comfy.quant_ops import QuantizedTensor\n\n\nclass TensorFileSlice(NamedTuple):\n file_ref: object\n thread_id: int\n offset: int\n size: int\n\n\ndef read_tensor_file_slice_into(tensor, destination):\n\n if isinstance(tensor, QuantizedTensor):\n if not isinstance(destination, QuantizedTensor):\n return False\n if tensor._layout_cls != destination._layout_cls:\n return False\n\n if not read_tensor_file_slice_into(tensor._qdata, destination._qdata):\n return False\n\n dst_orig_dtype = destination._params.orig_dtype\n destination._params.copy_from(tensor._params, non_blocking=False)\n destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype)\n return True\n\n info = getattr(tensor.untyped_storage(), \"_comfy_tensor_file_slice\", None)\n if info is None:\n return False\n\n file_obj = info.file_ref\n if (destination.device.type != \"cpu\"\n or file_obj is None\n or threading.get_ident() != info.thread_id\n or destination.numel() * destination.element_size() < info.size\n or tensor.numel() * tensor.element_size() != info.size\n or tensor.storage_offset() != 0\n or not tensor.is_contiguous()):\n return False\n\n if info.size == 0:\n return True\n\n buf_type = ctypes.c_ubyte * info.size\n view = memoryview(buf_type.from_address(destination.data_ptr()))\n\n try:\n file_obj.seek(info.offset)\n done = 0\n while done < info.size:\n try:\n n = file_obj.readinto(view[done:])\n except OSError:\n return False\n if n <= 0:\n return False\n done += n\n return True\n finally:\n view.release()\n\nclass TensorGeometry(NamedTuple):\n shape: any\n dtype: torch.dtype\n\n def element_size(self):\n info = torch.finfo(self.dtype) if self.dtype.is_floating_point else torch.iinfo(self.dtype)\n return info.bits // 8\n\n def numel(self):\n return math.prod(self.shape)\n\ndef tensors_to_geometries(tensors, dtype=None):\n geometries = []\n for t in tensors:\n if t is None or isinstance(t, QuantizedTensor):\n geometries.append(t)\n continue\n tdtype = t.dtype\n if hasattr(t, \"_model_dtype\"):\n tdtype = t._model_dtype\n if dtype is not None:\n tdtype = dtype\n geometries.append(TensorGeometry(shape=t.shape, dtype=tdtype))\n return geometries\n\ndef vram_aligned_size(tensor):\n if isinstance(tensor, list):\n return sum([vram_aligned_size(t) for t in tensor])\n\n if isinstance(tensor, QuantizedTensor):\n inner_tensors, _ = tensor.__tensor_flatten__()\n return vram_aligned_size([ getattr(tensor, attr) for attr in inner_tensors ])\n\n if tensor is None:\n return 0\n\n size = tensor.numel() * tensor.element_size()\n aligment_req = 1024\n return (size + aligment_req - 1) // aligment_req * aligment_req\n\ndef interpret_gathered_like(tensors, gathered):\n offset = 0\n dest_views = []\n\n if gathered.dim() != 1 or gathered.element_size() != 1:\n raise ValueError(f\"Buffer must be 1D and single-byte (got {gathered.dim()}D {gathered.dtype})\")\n\n for tensor in tensors:\n\n if tensor is None:\n dest_views.append(None)\n continue\n\n if isinstance(tensor, QuantizedTensor):\n inner_tensors, qt_ctx = tensor.__tensor_flatten__()\n templates = { attr: getattr(tensor, attr) for attr in inner_tensors }\n else:\n templates = { \"data\": tensor }\n\n actuals = {}\n for attr, template in templates.items():\n size = template.numel() * template.element_size()\n if offset + size > gathered.numel():\n raise ValueError(f\"Buffer too small: needs {offset + size} bytes, but only has {gathered.numel()}. \")\n actuals[attr] = gathered[offset:offset+size].view(dtype=template.dtype).view(template.shape)\n offset += vram_aligned_size(template)\n\n if isinstance(tensor, QuantizedTensor):\n dest_views.append(QuantizedTensor.__tensor_unflatten__(actuals, qt_ctx, 0, 0))\n else:\n dest_views.append(actuals[\"data\"])\n\n return dest_views\n\naimdo_enabled = False\n" }, { "path": "comfy/model_base.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\nimport comfy.ldm.hunyuan3dv2_1\nimport comfy.ldm.hunyuan3dv2_1.hunyuandit\nimport torch\nimport logging\nimport comfy.ldm.lightricks.av_model\nfrom comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep\nfrom comfy.ldm.cascade.stage_c import StageC\nfrom comfy.ldm.cascade.stage_b import StageB\nfrom comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation\nfrom comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation\nfrom comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper\nimport comfy.ldm.genmo.joint_model.asymm_models_joint\nimport comfy.ldm.aura.mmdit\nimport comfy.ldm.pixart.pixartms\nimport comfy.ldm.hydit.models\nimport comfy.ldm.audio.dit\nimport comfy.ldm.audio.embedders\nimport comfy.ldm.flux.model\nimport comfy.ldm.lightricks.model\nimport comfy.ldm.hunyuan_video.model\nimport comfy.ldm.cosmos.model\nimport comfy.ldm.cosmos.predict2\nimport comfy.ldm.lumina.model\nimport comfy.ldm.wan.model\nimport comfy.ldm.wan.model_animate\nimport comfy.ldm.hunyuan3d.model\nimport comfy.ldm.hidream.model\nimport comfy.ldm.chroma.model\nimport comfy.ldm.chroma_radiance.model\nimport comfy.ldm.ace.model\nimport comfy.ldm.omnigen.omnigen2\nimport comfy.ldm.qwen_image.model\nimport comfy.ldm.kandinsky5.model\nimport comfy.ldm.anima.model\nimport comfy.ldm.ace.ace_step15\n\nimport comfy.model_management\nimport comfy.patcher_extension\nimport comfy.conds\nimport comfy.ops\nfrom enum import Enum\nfrom . import utils\nimport comfy.latent_formats\nimport comfy.model_sampling\nimport math\nfrom typing import TYPE_CHECKING\nif TYPE_CHECKING:\n from comfy.model_patcher import ModelPatcher\n\nclass ModelType(Enum):\n EPS = 1\n V_PREDICTION = 2\n V_PREDICTION_EDM = 3\n STABLE_CASCADE = 4\n EDM = 5\n FLOW = 6\n V_PREDICTION_CONTINUOUS = 7\n FLUX = 8\n IMG_TO_IMG = 9\n FLOW_COSMOS = 10\n IMG_TO_IMG_FLOW = 11\n\n\ndef model_sampling(model_config, model_type):\n s = comfy.model_sampling.ModelSamplingDiscrete\n\n if model_type == ModelType.EPS:\n c = comfy.model_sampling.EPS\n elif model_type == ModelType.V_PREDICTION:\n c = comfy.model_sampling.V_PREDICTION\n elif model_type == ModelType.V_PREDICTION_EDM:\n c = comfy.model_sampling.V_PREDICTION\n s = comfy.model_sampling.ModelSamplingContinuousEDM\n elif model_type == ModelType.FLOW:\n c = comfy.model_sampling.CONST\n s = comfy.model_sampling.ModelSamplingDiscreteFlow\n elif model_type == ModelType.STABLE_CASCADE:\n c = comfy.model_sampling.EPS\n s = comfy.model_sampling.StableCascadeSampling\n elif model_type == ModelType.EDM:\n c = comfy.model_sampling.EDM\n s = comfy.model_sampling.ModelSamplingContinuousEDM\n elif model_type == ModelType.V_PREDICTION_CONTINUOUS:\n c = comfy.model_sampling.V_PREDICTION\n s = comfy.model_sampling.ModelSamplingContinuousV\n elif model_type == ModelType.FLUX:\n c = comfy.model_sampling.CONST\n s = comfy.model_sampling.ModelSamplingFlux\n elif model_type == ModelType.IMG_TO_IMG:\n c = comfy.model_sampling.IMG_TO_IMG\n elif model_type == ModelType.FLOW_COSMOS:\n c = comfy.model_sampling.COSMOS_RFLOW\n s = comfy.model_sampling.ModelSamplingCosmosRFlow\n elif model_type == ModelType.IMG_TO_IMG_FLOW:\n c = comfy.model_sampling.IMG_TO_IMG_FLOW\n\n class ModelSampling(s, c):\n pass\n\n return ModelSampling(model_config)\n\n\ndef convert_tensor(extra, dtype, device):\n if hasattr(extra, \"dtype\"):\n if extra.dtype != torch.int and extra.dtype != torch.long:\n extra = comfy.model_management.cast_to_device(extra, device, dtype)\n else:\n extra = comfy.model_management.cast_to_device(extra, device, None)\n return extra\n\n\nclass BaseModel(torch.nn.Module):\n def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):\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 self.device = device\n self.current_patcher: 'ModelPatcher' = None\n\n if not unet_config.get(\"disable_unet_model_creation\", False):\n if model_config.custom_operations is None:\n fp8 = model_config.optimizations.get(\"fp8\", False)\n operations = comfy.ops.pick_operations(unet_config.get(\"dtype\", None), self.manual_cast_dtype, fp8_optimizations=fp8, model_config=model_config)\n else:\n operations = model_config.custom_operations\n self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)\n self.diffusion_model.eval()\n if comfy.model_management.force_channels_last():\n self.diffusion_model.to(memory_format=torch.channels_last)\n logging.debug(\"using channels last mode for diffusion model\")\n logging.info(\"model weight dtype {}, manual cast: {}\".format(self.get_dtype(), self.manual_cast_dtype))\n comfy.model_management.archive_model_dtypes(self.diffusion_model)\n\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\n self.concat_keys = ()\n logging.info(\"model_type {}\".format(model_type.name))\n logging.debug(\"adm {}\".format(self.adm_channels))\n self.memory_usage_factor = model_config.memory_usage_factor\n self.memory_usage_factor_conds = ()\n self.memory_usage_shape_process = {}\n\n def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self._apply_model,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, transformer_options)\n ).execute(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)\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\n if c_concat is not None:\n xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1)\n\n context = c_crossattn\n dtype = self.get_dtype_inference()\n\n xc = xc.to(dtype)\n device = xc.device\n t = self.model_sampling.timestep(t).float()\n if context is not None:\n context = comfy.model_management.cast_to_device(context, device, dtype)\n\n extra_conds = {}\n for o in kwargs:\n extra = kwargs[o]\n\n if hasattr(extra, \"dtype\"):\n extra = convert_tensor(extra, dtype, device)\n elif isinstance(extra, list):\n ex = []\n for ext in extra:\n ex.append(convert_tensor(ext, dtype, device))\n extra = ex\n extra_conds[o] = extra\n\n t = self.process_timestep(t, x=x, **extra_conds)\n if \"latent_shapes\" in extra_conds:\n xc = utils.unpack_latents(xc, extra_conds.pop(\"latent_shapes\"))\n\n model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds)\n if len(model_output) > 1 and not torch.is_tensor(model_output):\n model_output, _ = utils.pack_latents(model_output)\n\n return self.model_sampling.calculate_denoised(sigma, model_output.float(), x)\n\n def process_timestep(self, timestep, **kwargs):\n return timestep\n\n def get_dtype(self):\n return self.diffusion_model.dtype\n\n def get_dtype_inference(self):\n dtype = self.get_dtype()\n\n if self.manual_cast_dtype is not None:\n dtype = self.manual_cast_dtype\n return dtype\n\n def encode_adm(self, **kwargs):\n return None\n\n def concat_cond(self, **kwargs):\n if len(self.concat_keys) > 0:\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 if noise.ndim == 5:\n if concat_latent_image.shape[-3] < noise.shape[-3]:\n concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), \"constant\", 0)\n else:\n concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]]\n\n concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])\n\n if denoise_mask is not None:\n if len(denoise_mask.shape) == len(noise.shape):\n denoise_mask = denoise_mask[:, :1]\n\n num_dim = noise.ndim - 2\n denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:]))\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 for ck in self.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 elif ck == \"mask_inverted\":\n cond_concat.append(1.0 - denoise_mask.to(device))\n else:\n if ck == \"mask\":\n cond_concat.append(torch.ones_like(noise)[:, :1])\n elif ck == \"masked_image\":\n cond_concat.append(self.blank_inpaint_image_like(noise))\n elif ck == \"mask_inverted\":\n cond_concat.append(torch.zeros_like(noise)[:, :1])\n if ck == \"concat_image\":\n if concat_latent_image is not None:\n cond_concat.append(concat_latent_image.to(device))\n else:\n cond_concat.append(torch.zeros_like(noise))\n data = torch.cat(cond_concat, dim=1)\n return data\n return None\n\n def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):\n \"\"\"Override in subclasses to handle model-specific cond slicing for context windows.\n Return a sliced cond object, or None to fall through to default handling.\n Use comfy.context_windows.slice_cond() for common cases.\"\"\"\n return None\n\n def extra_conds(self, **kwargs):\n out = {}\n concat_cond = self.concat_cond(**kwargs)\n if concat_cond is not None:\n out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond)\n\n adm = self.encode_adm(**kwargs)\n if adm is not None:\n out['y'] = comfy.conds.CONDRegular(adm)\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)\n\n cross_attn_cnet = kwargs.get(\"cross_attn_controlnet\", None)\n if cross_attn_cnet is not None:\n out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet)\n\n c_concat = kwargs.get(\"noise_concat\", None)\n if c_concat is not None:\n out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat)\n\n return out\n\n def load_model_weights(self, sd, unet_prefix=\"\", assign=False):\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, assign=assign)\n if len(m) > 0:\n logging.warning(\"unet missing: {}\".format(m))\n\n if len(u) > 0:\n logging.warning(\"unet unexpected: {}\".format(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, unet_state_dict, 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 unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)\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.concat_keys = (\"mask\", \"masked_image\")\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 self.blank_inpaint_image_like = blank_inpaint_image_like\n\n def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):\n return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)\n\n def memory_required(self, input_shape, cond_shapes={}):\n input_shapes = [input_shape]\n for c in self.memory_usage_factor_conds:\n shape = cond_shapes.get(c, None)\n if shape is not None:\n if c in self.memory_usage_shape_process:\n out = []\n for s in shape:\n out.append(self.memory_usage_shape_process[c](s))\n shape = out\n\n if len(shape) > 0:\n input_shapes += shape\n\n if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():\n dtype = self.get_dtype_inference()\n #TODO: this needs to be tweaked\n area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))\n return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (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 = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))\n return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)\n\n def extra_conds_shapes(self, **kwargs):\n return {}\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), device=device)\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\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'] = comfy.conds.CONDRegular(adm)\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'] = comfy.conds.CONDNoiseShape(latent_image)\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)\n\n if \"time_conditioning\" in kwargs:\n out[\"time_context\"] = comfy.conds.CONDCrossAttn(kwargs[\"time_conditioning\"])\n\n out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0])\n return out\n\nclass SV3D_u(SVD_img2vid):\n def encode_adm(self, **kwargs):\n augmentation = kwargs.get(\"augmentation_level\", 0)\n\n out = []\n out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))\n\n flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)\n return flat\n\nclass SV3D_p(SVD_img2vid):\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_512 = Timestep(512)\n\n def encode_adm(self, **kwargs):\n augmentation = kwargs.get(\"augmentation_level\", 0)\n elevation = kwargs.get(\"elevation\", 0) #elevation and azimuth are in degrees here\n azimuth = kwargs.get(\"azimuth\", 0)\n noise = kwargs.get(\"noise\", None)\n\n out = []\n out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))\n out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0))))\n out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0))))\n\n out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out))\n return torch.cat(out, dim=1)\n\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 = comfy.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'] = comfy.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'] = comfy.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'] = comfy.conds.CONDNoiseShape(image)\n out['y'] = comfy.conds.CONDRegular(noise_level)\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)\n return out\n\nclass IP2P:\n def concat_cond(self, **kwargs):\n image = kwargs.get(\"concat_latent_image\", None)\n noise = kwargs.get(\"noise\", None)\n device = kwargs[\"device\"]\n\n if image is None:\n image = torch.zeros_like(noise)\n else:\n image = image.to(device=device)\n\n if image.shape[1:] != noise.shape[1:]:\n image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n\n image = utils.resize_to_batch_size(image, noise.shape[0])\n return self.process_ip2p_image_in(image)\n\n\nclass SD15_instructpix2pix(IP2P, BaseModel):\n def __init__(self, model_config, model_type=ModelType.EPS, device=None):\n super().__init__(model_config, model_type, device=device)\n self.process_ip2p_image_in = lambda image: image\n\n\nclass SDXL_instructpix2pix(IP2P, SDXL):\n def __init__(self, model_config, model_type=ModelType.EPS, device=None):\n super().__init__(model_config, model_type, device=device)\n if model_type == ModelType.V_PREDICTION_EDM:\n self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p\n else:\n self.process_ip2p_image_in = lambda image: image #diffusers ip2p\n\nclass Lotus(BaseModel):\n def extra_conds(self, **kwargs):\n out = {}\n cross_attn = kwargs.get(\"cross_attn\", None)\n out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)\n device = kwargs[\"device\"]\n task_emb = torch.tensor([1, 0]).float().to(device)\n task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)]).unsqueeze(0)\n out['y'] = comfy.conds.CONDRegular(task_emb)\n return out\n\n def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, device=None):\n super().__init__(model_config, model_type, device=device)\n\nclass StableCascade_C(BaseModel):\n def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=StageC)\n\n def extra_conds(self, **kwargs):\n out = {}\n clip_text_pooled = kwargs[\"pooled_output\"]\n if clip_text_pooled is not None:\n out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)\n\n if \"unclip_conditioning\" in kwargs:\n embeds = []\n for unclip_cond in kwargs[\"unclip_conditioning\"]:\n weight = unclip_cond[\"strength\"]\n embeds.append(unclip_cond[\"clip_vision_output\"].image_embeds.unsqueeze(0) * weight)\n clip_img = torch.cat(embeds, dim=1)\n else:\n clip_img = torch.zeros((1, 1, 768))\n out[\"clip_img\"] = comfy.conds.CONDRegular(clip_img)\n out[\"sca\"] = comfy.conds.CONDRegular(torch.zeros((1,)))\n out[\"crp\"] = comfy.conds.CONDRegular(torch.zeros((1,)))\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn)\n return out\n\n\nclass StableCascade_B(BaseModel):\n def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=StageB)\n\n def extra_conds(self, **kwargs):\n out = {}\n noise = kwargs.get(\"noise\", None)\n\n clip_text_pooled = kwargs[\"pooled_output\"]\n if clip_text_pooled is not None:\n out['clip'] = comfy.conds.CONDRegular(clip_text_pooled)\n\n #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched\n prior = kwargs.get(\"stable_cascade_prior\", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))\n\n out[\"effnet\"] = comfy.conds.CONDRegular(prior.to(device=noise.device))\n out[\"sca\"] = comfy.conds.CONDRegular(torch.zeros((1,)))\n return out\n\n\nclass SD3(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper)\n\n def encode_adm(self, **kwargs):\n return kwargs[\"pooled_output\"]\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n return out\n\n\nclass AuraFlow(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n return out\n\n\nclass StableAudio1(BaseModel):\n def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)\n self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)\n self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)\n self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)\n self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)\n\n def extra_conds(self, **kwargs):\n out = {}\n\n noise = kwargs.get(\"noise\", None)\n device = kwargs[\"device\"]\n\n seconds_start = kwargs.get(\"seconds_start\", 0)\n seconds_total = kwargs.get(\"seconds_total\", int(noise.shape[-1] / 21.53))\n\n seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device)\n seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)\n\n global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))\n out['global_embed'] = comfy.conds.CONDRegular(global_embed)\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n return out\n\n def state_dict_for_saving(self, unet_state_dict, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):\n sd = super().state_dict_for_saving(unet_state_dict, clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict)\n d = {\"conditioner.conditioners.seconds_start.\": self.seconds_start_embedder.state_dict(), \"conditioner.conditioners.seconds_total.\": self.seconds_total_embedder.state_dict()}\n for k in d:\n s = d[k]\n for l in s:\n sd[\"{}{}\".format(k, l)] = s[l]\n return sd\n\n\nclass HunyuanDiT(BaseModel):\n def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask)\n\n conditioning_mt5xl = kwargs.get(\"conditioning_mt5xl\", None)\n if conditioning_mt5xl is not None:\n out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl)\n\n attention_mask_mt5xl = kwargs.get(\"attention_mask_mt5xl\", None)\n if attention_mask_mt5xl is not None:\n out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl)\n\n width = kwargs.get(\"width\", 768)\n height = kwargs.get(\"height\", 768)\n target_width = kwargs.get(\"target_width\", width)\n target_height = kwargs.get(\"target_height\", height)\n\n out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))\n return out\n\nclass PixArt(BaseModel):\n def __init__(self, model_config, model_type=ModelType.EPS, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n width = kwargs.get(\"width\", None)\n height = kwargs.get(\"height\", None)\n if width is not None and height is not None:\n out[\"c_size\"] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width]]))\n out[\"c_ar\"] = comfy.conds.CONDRegular(torch.FloatTensor([[kwargs.get(\"aspect_ratio\", height/width)]]))\n\n return out\n\nclass Flux(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux):\n super().__init__(model_config, model_type, device=device, unet_model=unet_model)\n self.memory_usage_factor_conds = (\"ref_latents\",)\n\n def concat_cond(self, **kwargs):\n try:\n #Handle Flux control loras dynamically changing the img_in weight.\n num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size)\n except:\n #Some cases like tensorrt might not have the weights accessible\n num_channels = self.model_config.unet_config[\"in_channels\"]\n\n out_channels = self.model_config.unet_config[\"out_channels\"]\n\n if num_channels <= out_channels:\n return None\n\n image = kwargs.get(\"concat_latent_image\", None)\n noise = kwargs.get(\"noise\", None)\n device = kwargs[\"device\"]\n\n if image is None:\n image = torch.zeros_like(noise)\n\n image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n image = utils.resize_to_batch_size(image, noise.shape[0])\n image = self.process_latent_in(image)\n if num_channels <= out_channels * 2:\n return image\n\n #inpaint model\n mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n if mask is None:\n mask = torch.ones_like(noise)[:, :1]\n\n mask = torch.mean(mask, dim=1, keepdim=True)\n mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, \"bilinear\", \"center\")\n mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)\n mask = utils.resize_to_batch_size(mask, noise.shape[0])\n return torch.cat((image, mask), dim=1)\n\n def encode_adm(self, **kwargs):\n return kwargs[\"pooled_output\"]\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n # upscale the attention mask, since now we\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n shape = kwargs[\"noise\"].shape\n mask_ref_size = kwargs.get(\"attention_mask_img_shape\", None)\n if mask_ref_size is not None:\n # the model will pad to the patch size, and then divide\n # essentially dividing and rounding up\n (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))\n attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n\n guidance = kwargs.get(\"guidance\", 3.5)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n latents = []\n for lat in ref_latents:\n latents.append(self.process_latent_in(lat))\n out['ref_latents'] = comfy.conds.CONDList(latents)\n\n ref_latents_method = kwargs.get(\"reference_latents_method\", None)\n if ref_latents_method is not None:\n out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method)\n return out\n\n def extra_conds_shapes(self, **kwargs):\n out = {}\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])\n return out\n\nclass LongCatImage(Flux):\n def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):\n transformer_options = transformer_options.copy()\n rope_opts = transformer_options.get(\"rope_options\", {})\n rope_opts = dict(rope_opts)\n rope_opts.setdefault(\"shift_t\", 1.0)\n rope_opts.setdefault(\"shift_y\", 512.0)\n rope_opts.setdefault(\"shift_x\", 512.0)\n transformer_options[\"rope_options\"] = rope_opts\n return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)\n\n def encode_adm(self, **kwargs):\n return None\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n out.pop('guidance', None)\n return out\n\nclass Flux2(Flux):\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n target_text_len = 512\n if cross_attn.shape[1] < target_text_len:\n cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, target_text_len - cross_attn.shape[1], 0))\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n return out\n\nclass GenmoMochi(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n return out\n\nclass LTXV(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLUX, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get(\"frame_rate\", 25))\n\n denoise_mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n if denoise_mask is not None:\n out[\"denoise_mask\"] = comfy.conds.CONDRegular(denoise_mask)\n\n keyframe_idxs = kwargs.get(\"keyframe_idxs\", None)\n if keyframe_idxs is not None:\n out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)\n\n guide_attention_entries = kwargs.get(\"guide_attention_entries\", None)\n if guide_attention_entries is not None:\n out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)\n\n return out\n\n def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):\n if denoise_mask is None:\n return timestep\n return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0]\n\n def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):\n return latent_image\n\nclass LTXAV(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLUX, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.av_model.LTXAVModel) #TODO\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n device = kwargs[\"device\"]\n\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n if hasattr(self.diffusion_model, \"preprocess_text_embeds\"):\n cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()), unprocessed=kwargs.get(\"unprocessed_ltxav_embeds\", False))\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get(\"frame_rate\", 25))\n\n denoise_mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n\n audio_denoise_mask = None\n if denoise_mask is not None and \"latent_shapes\" in kwargs:\n denoise_mask = utils.unpack_latents(denoise_mask, kwargs[\"latent_shapes\"])\n if len(denoise_mask) > 1:\n audio_denoise_mask = denoise_mask[1]\n denoise_mask = denoise_mask[0]\n\n if denoise_mask is not None:\n out[\"denoise_mask\"] = comfy.conds.CONDRegular(denoise_mask)\n\n if audio_denoise_mask is not None:\n out[\"audio_denoise_mask\"] = comfy.conds.CONDRegular(audio_denoise_mask)\n\n keyframe_idxs = kwargs.get(\"keyframe_idxs\", None)\n if keyframe_idxs is not None:\n out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)\n\n latent_shapes = kwargs.get(\"latent_shapes\", None)\n if latent_shapes is not None:\n out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes)\n\n guide_attention_entries = kwargs.get(\"guide_attention_entries\", None)\n if guide_attention_entries is not None:\n out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)\n\n return out\n\n def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):\n v_timestep = timestep\n a_timestep = timestep\n\n if denoise_mask is not None:\n v_timestep = self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0]\n if audio_denoise_mask is not None:\n a_timestep = self.diffusion_model.a_patchifier.patchify(((audio_denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (audio_denoise_mask.ndim - 1)))[:, :1, :, :1])[0]\n\n return v_timestep, a_timestep\n\n def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):\n return latent_image\n\nclass HunyuanVideo(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)\n\n def encode_adm(self, **kwargs):\n return kwargs[\"pooled_output\"]\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n guidance = kwargs.get(\"guidance\", 6.0)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n\n guiding_frame_index = kwargs.get(\"guiding_frame_index\", None)\n if guiding_frame_index is not None:\n out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index]))\n\n ref_latent = kwargs.get(\"ref_latent\", None)\n if ref_latent is not None:\n out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent))\n\n return out\n\n def scale_latent_inpaint(self, latent_image, **kwargs):\n return latent_image\n\nclass HunyuanVideoI2V(HunyuanVideo):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device)\n self.concat_keys = (\"concat_image\", \"mask_inverted\")\n\n def scale_latent_inpaint(self, latent_image, **kwargs):\n return super().scale_latent_inpaint(latent_image=latent_image, **kwargs)\n\nclass HunyuanVideoSkyreelsI2V(HunyuanVideo):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device)\n self.concat_keys = (\"concat_image\",)\n\n def scale_latent_inpaint(self, latent_image, **kwargs):\n return super().scale_latent_inpaint(latent_image=latent_image, **kwargs)\n\nclass CosmosVideo(BaseModel):\n def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT)\n self.image_to_video = image_to_video\n if self.image_to_video:\n self.concat_keys = (\"mask_inverted\",)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n out['fps'] = comfy.conds.CONDConstant(kwargs.get(\"frame_rate\", None))\n return out\n\n def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):\n sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1))\n sigma_noise_augmentation = 0 #TODO\n if sigma_noise_augmentation != 0:\n latent_image = latent_image + noise\n latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)\n return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)\n\nclass CosmosPredict2(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, image_to_video=False, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.predict2.MiniTrainDIT)\n self.image_to_video = image_to_video\n if self.image_to_video:\n self.concat_keys = (\"mask_inverted\",)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n denoise_mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n if denoise_mask is not None:\n out[\"denoise_mask\"] = comfy.conds.CONDRegular(denoise_mask)\n\n out['fps'] = comfy.conds.CONDConstant(kwargs.get(\"frame_rate\", None))\n return out\n\n def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):\n if denoise_mask is None:\n return timestep\n if denoise_mask.ndim <= 4:\n return timestep\n condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True)\n c_noise_B_1_T_1_1 = 0.0 * (1.0 - condition_video_mask_B_1_T_1_1) + timestep.reshape(timestep.shape[0], 1, 1, 1, 1) * condition_video_mask_B_1_T_1_1\n out = c_noise_B_1_T_1_1.squeeze(dim=[1, 3, 4])\n return out\n\n def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):\n sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1))\n sigma_noise_augmentation = 0 #TODO\n if sigma_noise_augmentation != 0:\n latent_image = latent_image + noise\n latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)\n sigma = (sigma / (sigma + 1))\n return latent_image / (1.0 - sigma)\n\nclass Anima(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.anima.model.Anima)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n t5xxl_ids = kwargs.get(\"t5xxl_ids\", None)\n t5xxl_weights = kwargs.get(\"t5xxl_weights\", None)\n device = kwargs[\"device\"]\n if cross_attn is not None:\n if t5xxl_ids is not None:\n if t5xxl_weights is not None:\n t5xxl_weights = t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn)\n t5xxl_ids = t5xxl_ids.unsqueeze(0)\n\n if torch.is_inference_mode_enabled(): # if not we are training\n cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()), t5xxl_ids.to(device=device), t5xxl_weights=t5xxl_weights.to(device=device, dtype=self.get_dtype_inference()))\n else:\n out['t5xxl_ids'] = comfy.conds.CONDRegular(t5xxl_ids)\n out['t5xxl_weights'] = comfy.conds.CONDRegular(t5xxl_weights)\n\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n return out\n\nclass Lumina2(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT)\n self.memory_usage_factor_conds = (\"ref_latents\",)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n if torch.numel(attention_mask) != attention_mask.sum():\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n if 'num_tokens' not in out:\n out['num_tokens'] = comfy.conds.CONDConstant(cross_attn.shape[1])\n\n clip_text_pooled = kwargs.get(\"pooled_output\", None) # NewBie\n if clip_text_pooled is not None:\n out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)\n\n clip_vision_outputs = kwargs.get(\"clip_vision_outputs\", list(map(lambda a: a.get(\"clip_vision_output\"), kwargs.get(\"unclip_conditioning\", [{}])))) # Z Image omni\n if clip_vision_outputs is not None and len(clip_vision_outputs) > 0:\n sigfeats = []\n for clip_vision_output in clip_vision_outputs:\n if clip_vision_output is not None:\n image_size = clip_vision_output.image_sizes[0]\n shape = clip_vision_output.last_hidden_state.shape\n sigfeats.append(clip_vision_output.last_hidden_state.reshape(shape[0], image_size[1] // 16, image_size[2] // 16, shape[-1]))\n if len(sigfeats) > 0:\n out['siglip_feats'] = comfy.conds.CONDList(sigfeats)\n\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n latents = []\n for lat in ref_latents:\n latents.append(self.process_latent_in(lat))\n out['ref_latents'] = comfy.conds.CONDList(latents)\n\n ref_contexts = kwargs.get(\"reference_latents_text_embeds\", None)\n if ref_contexts is not None:\n out['ref_contexts'] = comfy.conds.CONDList(ref_contexts)\n\n return out\n\n def extra_conds_shapes(self, **kwargs):\n out = {}\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])\n return out\n\nclass ZImagePixelSpace(Lumina2):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n BaseModel.__init__(self, model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace)\n self.memory_usage_factor_conds = (\"ref_latents\",)\n\nclass WAN21(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)\n self.image_to_video = image_to_video\n\n def concat_cond(self, **kwargs):\n noise = kwargs.get(\"noise\", None)\n extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1]\n if extra_channels == 0:\n return None\n\n image = kwargs.get(\"concat_latent_image\", None)\n device = kwargs[\"device\"]\n\n if image is None:\n shape_image = list(noise.shape)\n shape_image[1] = extra_channels\n image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)\n else:\n latent_dim = self.latent_format.latent_channels\n image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n for i in range(0, image.shape[1], latent_dim):\n image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim])\n image = utils.resize_to_batch_size(image, noise.shape[0])\n\n if extra_channels != image.shape[1] + 4:\n if not self.image_to_video or extra_channels == image.shape[1]:\n return image\n\n if image.shape[1] > (extra_channels - 4):\n image = image[:, :(extra_channels - 4)]\n\n mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n if mask is None:\n mask = torch.zeros_like(noise)[:, :4]\n else:\n if mask.shape[1] != 4:\n mask = torch.mean(mask, dim=1, keepdim=True)\n mask = 1.0 - mask\n mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n if mask.shape[-3] < noise.shape[-3]:\n mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)\n if mask.shape[1] == 1:\n mask = mask.repeat(1, 4, 1, 1, 1)\n mask = utils.resize_to_batch_size(mask, noise.shape[0])\n\n concat_mask_index = kwargs.get(\"concat_mask_index\", 0)\n if concat_mask_index != 0:\n return torch.cat((image[:, :concat_mask_index], mask, image[:, concat_mask_index:]), dim=1)\n else:\n return torch.cat((mask, image), dim=1)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n clip_vision_output = kwargs.get(\"clip_vision_output\", None)\n if clip_vision_output is not None:\n out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states)\n\n time_dim_concat = kwargs.get(\"time_dim_concat\", None)\n if time_dim_concat is not None:\n out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat))\n\n reference_latents = kwargs.get(\"reference_latents\", None)\n if reference_latents is not None:\n out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0])\n\n return out\n\n\nclass WAN21_Vace(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel)\n self.image_to_video = image_to_video\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n noise = kwargs.get(\"noise\", None)\n noise_shape = list(noise.shape)\n vace_frames = kwargs.get(\"vace_frames\", None)\n if vace_frames is None:\n noise_shape[1] = 32\n vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)]\n\n mask = kwargs.get(\"vace_mask\", None)\n if mask is None:\n noise_shape[1] = 64\n mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames)\n\n vace_frames_out = []\n for j in range(len(vace_frames)):\n vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True)\n for i in range(0, vf.shape[1], 16):\n vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16])\n vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1)\n vace_frames_out.append(vf)\n\n vace_frames = torch.stack(vace_frames_out, dim=1)\n out['vace_context'] = comfy.conds.CONDRegular(vace_frames)\n\n vace_strength = kwargs.get(\"vace_strength\", [1.0] * len(vace_frames_out))\n out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)\n return out\n\n def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):\n if cond_key == \"vace_context\":\n import comfy.context_windows\n return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=3, retain_index_list=retain_index_list)\n return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)\n\nclass WAN21_Camera(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel)\n self.image_to_video = image_to_video\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n camera_conditions = kwargs.get(\"camera_conditions\", None)\n if camera_conditions is not None:\n out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)\n return out\n\nclass WAN21_HuMo(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.HumoWanModel)\n self.image_to_video = image_to_video\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n noise = kwargs.get(\"noise\", None)\n\n audio_embed = kwargs.get(\"audio_embed\", None)\n if audio_embed is not None:\n out['audio_embed'] = comfy.conds.CONDRegular(audio_embed)\n\n if \"c_concat\" not in out: # 1.7B model\n reference_latents = kwargs.get(\"reference_latents\", None)\n if reference_latents is not None:\n out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1]))\n else:\n noise_shape = list(noise.shape)\n noise_shape[1] += 4\n concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)\n zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1)\n zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1)\n zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1)\n concat_latent[:, 4:] = zero_vae_values\n concat_latent[:, 4:, :1] = zero_vae_values_first\n concat_latent[:, 4:, 1:2] = zero_vae_values_second\n out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent)\n reference_latents = kwargs.get(\"reference_latents\", None)\n if reference_latents is not None:\n ref_latent = self.process_latent_in(reference_latents[-1])\n ref_latent_shape = list(ref_latent.shape)\n ref_latent_shape[1] += 4 + ref_latent_shape[1]\n ref_latent_full = torch.zeros(ref_latent_shape, device=ref_latent.device, dtype=ref_latent.dtype)\n ref_latent_full[:, 20:] = ref_latent\n ref_latent_full[:, 16:20] = 1.0\n out['reference_latent'] = comfy.conds.CONDRegular(ref_latent_full)\n\n return out\n\n def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):\n if cond_key == \"audio_embed\":\n import comfy.context_windows\n return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1)\n return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)\n\nclass WAN22_Animate(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel)\n self.image_to_video = image_to_video\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n\n face_video_pixels = kwargs.get(\"face_video_pixels\", None)\n if face_video_pixels is not None:\n out['face_pixel_values'] = comfy.conds.CONDRegular(face_video_pixels)\n\n pose_latents = kwargs.get(\"pose_video_latent\", None)\n if pose_latents is not None:\n out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents))\n return out\n\n def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):\n import comfy.context_windows\n if cond_key == \"face_pixel_values\":\n return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_scale=4, temporal_offset=1)\n if cond_key == \"pose_latents\":\n return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1)\n return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)\n\nclass WAN22_S2V(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)\n self.memory_usage_factor_conds = (\"reference_latent\", \"reference_motion\")\n self.memory_usage_shape_process = {\"reference_motion\": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]}\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n audio_embed = kwargs.get(\"audio_embed\", None)\n if audio_embed is not None:\n out['audio_embed'] = comfy.conds.CONDRegular(audio_embed)\n\n reference_latents = kwargs.get(\"reference_latents\", None)\n if reference_latents is not None:\n out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1]))\n\n reference_motion = kwargs.get(\"reference_motion\", None)\n if reference_motion is not None:\n out['reference_motion'] = comfy.conds.CONDRegular(self.process_latent_in(reference_motion))\n\n control_video = kwargs.get(\"control_video\", None)\n if control_video is not None:\n out['control_video'] = comfy.conds.CONDRegular(self.process_latent_in(control_video))\n return out\n\n def extra_conds_shapes(self, **kwargs):\n out = {}\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n out['reference_latent'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])\n\n reference_motion = kwargs.get(\"reference_motion\", None)\n if reference_motion is not None:\n out['reference_motion'] = reference_motion.shape\n return out\n\n def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):\n if cond_key == \"audio_embed\":\n import comfy.context_windows\n return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=1)\n return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)\n\nclass WAN22(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)\n self.image_to_video = image_to_video\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n denoise_mask = kwargs.get(\"denoise_mask\", None)\n if denoise_mask is not None:\n out[\"denoise_mask\"] = comfy.conds.CONDRegular(denoise_mask)\n return out\n\n def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):\n if denoise_mask is None:\n return timestep\n temp_ts = (torch.mean(denoise_mask[:, :, :, :, :], dim=(1, 3, 4), keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)\n return temp_ts\n\n def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):\n return latent_image\n\nclass WAN21_FlowRVS(WAN21):\n def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG_FLOW, image_to_video=False, device=None):\n model_config.unet_config[\"model_type\"] = \"t2v\"\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)\n self.image_to_video = image_to_video\n\nclass WAN21_SCAIL(WAN21):\n def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):\n super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAILWanModel)\n self.memory_usage_factor_conds = (\"reference_latent\", \"pose_latents\")\n self.memory_usage_shape_process = {\"pose_latents\": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]}\n self.image_to_video = image_to_video\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n\n reference_latents = kwargs.get(\"reference_latents\", None)\n if reference_latents is not None:\n ref_latent = self.process_latent_in(reference_latents[-1])\n ref_mask = torch.ones_like(ref_latent[:, :4])\n ref_latent = torch.cat([ref_latent, ref_mask], dim=1)\n out['reference_latent'] = comfy.conds.CONDRegular(ref_latent)\n\n pose_latents = kwargs.get(\"pose_video_latent\", None)\n if pose_latents is not None:\n pose_latents = self.process_latent_in(pose_latents)\n pose_mask = torch.ones_like(pose_latents[:, :4])\n pose_latents = torch.cat([pose_latents, pose_mask], dim=1)\n out['pose_latents'] = comfy.conds.CONDRegular(pose_latents)\n\n return out\n\n def extra_conds_shapes(self, **kwargs):\n out = {}\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n out['reference_latent'] = list([1, 20, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])\n\n pose_latents = kwargs.get(\"pose_video_latent\", None)\n if pose_latents is not None:\n out['pose_latents'] = [pose_latents.shape[0], 20, *pose_latents.shape[2:]]\n\n return out\n\nclass Hunyuan3Dv2(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n guidance = kwargs.get(\"guidance\", 5.0)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n return out\n\nclass Hunyuan3Dv2_1(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3dv2_1.hunyuandit.HunYuanDiTPlain)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n guidance = kwargs.get(\"guidance\", 5.0)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n return out\n\nclass HiDream(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel)\n\n def encode_adm(self, **kwargs):\n return kwargs[\"pooled_output\"]\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n conditioning_llama3 = kwargs.get(\"conditioning_llama3\", None)\n if conditioning_llama3 is not None:\n out['encoder_hidden_states_llama3'] = comfy.conds.CONDRegular(conditioning_llama3)\n image_cond = kwargs.get(\"concat_latent_image\", None)\n if image_cond is not None:\n out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond))\n return out\n\nclass Chroma(Flux):\n def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma):\n super().__init__(model_config, model_type, device=device, unet_model=unet_model)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n\n guidance = kwargs.get(\"guidance\", 0)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n return out\n\nclass ChromaRadiance(Chroma):\n def __init__(self, model_config, model_type=ModelType.FLUX, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma_radiance.model.ChromaRadiance)\n\nclass ACEStep(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n noise = kwargs.get(\"noise\", None)\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n conditioning_lyrics = kwargs.get(\"conditioning_lyrics\", None)\n if cross_attn is not None:\n out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics)\n out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype))\n out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get(\"lyrics_strength\", 1.0))\n return out\n\nclass ACEStep15(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.ace_step15.AceStepConditionGenerationModel)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n device = kwargs[\"device\"]\n noise = kwargs[\"noise\"]\n\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n if torch.count_nonzero(cross_attn) == 0:\n out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True)\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n conditioning_lyrics = kwargs.get(\"conditioning_lyrics\", None)\n if cross_attn is not None:\n out['lyric_embed'] = comfy.conds.CONDRegular(conditioning_lyrics)\n\n refer_audio = kwargs.get(\"reference_audio_timbre_latents\", None)\n if refer_audio is None or len(refer_audio) == 0:\n refer_audio = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)\n pass_audio_codes = True\n else:\n refer_audio = refer_audio[-1][:, :, :noise.shape[2]]\n out['is_covers'] = comfy.conds.CONDConstant(True)\n pass_audio_codes = False\n\n if pass_audio_codes:\n audio_codes = kwargs.get(\"audio_codes\", None)\n if audio_codes is not None:\n out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))\n refer_audio = refer_audio[:, :, :750]\n else:\n out['is_covers'] = comfy.conds.CONDConstant(False)\n\n if refer_audio.shape[2] < noise.shape[2]:\n pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)\n refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2)\n\n out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)\n return out\n\nclass Omnigen2(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel)\n self.memory_usage_factor_conds = (\"ref_latents\",)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n if torch.numel(attention_mask) != attention_mask.sum():\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n latents = []\n for lat in ref_latents:\n latents.append(self.process_latent_in(lat))\n out['ref_latents'] = comfy.conds.CONDList(latents)\n return out\n\n def extra_conds_shapes(self, **kwargs):\n out = {}\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])\n return out\n\nclass QwenImage(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLUX, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel)\n self.memory_usage_factor_conds = (\"ref_latents\",)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n latents = []\n for lat in ref_latents:\n latents.append(self.process_latent_in(lat))\n out['ref_latents'] = comfy.conds.CONDList(latents)\n\n ref_latents_method = kwargs.get(\"reference_latents_method\", None)\n if ref_latents_method is not None:\n out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method)\n return out\n\n def extra_conds_shapes(self, **kwargs):\n out = {}\n ref_latents = kwargs.get(\"reference_latents\", None)\n if ref_latents is not None:\n out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])\n return out\n\nclass HunyuanImage21(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n if torch.numel(attention_mask) != attention_mask.sum():\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n conditioning_byt5small = kwargs.get(\"conditioning_byt5small\", None)\n if conditioning_byt5small is not None:\n out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small)\n\n guidance = kwargs.get(\"guidance\", 6.0)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n\n return out\n\nclass HunyuanImage21Refiner(HunyuanImage21):\n def concat_cond(self, **kwargs):\n noise = kwargs.get(\"noise\", None)\n image = kwargs.get(\"concat_latent_image\", None)\n noise_augmentation = kwargs.get(\"noise_augmentation\", 0.0)\n device = kwargs[\"device\"]\n\n if image is None:\n shape_image = list(noise.shape)\n image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)\n else:\n image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n image = self.process_latent_in(image)\n image = utils.resize_to_batch_size(image, noise.shape[0])\n if noise_augmentation > 0:\n generator = torch.Generator(device=\"cpu\")\n generator.manual_seed(kwargs.get(\"seed\", 0) - 10)\n noise = torch.randn(image.shape, generator=generator, dtype=image.dtype, device=\"cpu\").to(image.device)\n image = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image\n else:\n image = 0.75 * image\n return image\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n out['disable_time_r'] = comfy.conds.CONDConstant(True)\n return out\n\nclass HunyuanVideo15(HunyuanVideo):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device)\n\n def concat_cond(self, **kwargs):\n noise = kwargs.get(\"noise\", None)\n extra_channels = self.diffusion_model.img_in.proj.weight.shape[1] - noise.shape[1] - 1 #noise 32 img cond 32 + mask 1\n if extra_channels == 0:\n return None\n\n image = kwargs.get(\"concat_latent_image\", None)\n device = kwargs[\"device\"]\n\n if image is None:\n shape_image = list(noise.shape)\n shape_image[1] = extra_channels\n image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)\n else:\n latent_dim = self.latent_format.latent_channels\n image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n for i in range(0, image.shape[1], latent_dim):\n image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim])\n image = utils.resize_to_batch_size(image, noise.shape[0])\n\n mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n if mask is None:\n mask = torch.zeros_like(noise)[:, :1]\n else:\n mask = 1.0 - mask\n mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n if mask.shape[-3] < noise.shape[-3]:\n mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)\n mask = utils.resize_to_batch_size(mask, noise.shape[0])\n\n return torch.cat((image, mask), dim=1)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n if torch.numel(attention_mask) != attention_mask.sum():\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n conditioning_byt5small = kwargs.get(\"conditioning_byt5small\", None)\n if conditioning_byt5small is not None:\n out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small)\n\n guidance = kwargs.get(\"guidance\", 6.0)\n if guidance is not None:\n out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))\n\n clip_vision_output = kwargs.get(\"clip_vision_output\", None)\n if clip_vision_output is not None:\n out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.last_hidden_state)\n\n return out\n\nclass HunyuanVideo15_SR_Distilled(HunyuanVideo15):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device)\n\n def concat_cond(self, **kwargs):\n noise = kwargs.get(\"noise\", None)\n image = kwargs.get(\"concat_latent_image\", None)\n noise_augmentation = kwargs.get(\"noise_augmentation\", 0.0)\n device = kwargs[\"device\"]\n\n if image is None:\n image = torch.zeros([noise.shape[0], noise.shape[1] * 2 + 2, noise.shape[-3], noise.shape[-2], noise.shape[-1]], device=comfy.model_management.intermediate_device())\n else:\n image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n #image = self.process_latent_in(image) # scaling wasn't applied in reference code\n image = utils.resize_to_batch_size(image, noise.shape[0])\n lq_image_slice = slice(noise.shape[1] + 1, 2 * noise.shape[1] + 1)\n if noise_augmentation > 0:\n generator = torch.Generator(device=\"cpu\")\n generator.manual_seed(kwargs.get(\"seed\", 0) - 10)\n noise = torch.randn(image[:, lq_image_slice].shape, generator=generator, dtype=image.dtype, device=\"cpu\").to(image.device)\n image[:, lq_image_slice] = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image[:, lq_image_slice]\n else:\n image[:, lq_image_slice] = 0.75 * image[:, lq_image_slice]\n return image\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n out['disable_time_r'] = comfy.conds.CONDConstant(False)\n return out\n\nclass Kandinsky5(BaseModel):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.kandinsky5.model.Kandinsky5)\n\n def encode_adm(self, **kwargs):\n return kwargs[\"pooled_output\"]\n\n def concat_cond(self, **kwargs):\n noise = kwargs.get(\"noise\", None)\n device = kwargs[\"device\"]\n image = torch.zeros_like(noise)\n\n mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n if mask is None:\n mask = torch.zeros_like(noise)[:, :1]\n else:\n mask = 1.0 - mask\n mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n if mask.shape[-3] < noise.shape[-3]:\n mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)\n mask = utils.resize_to_batch_size(mask, noise.shape[0])\n\n return torch.cat((image, mask), dim=1)\n\n def extra_conds(self, **kwargs):\n out = super().extra_conds(**kwargs)\n attention_mask = kwargs.get(\"attention_mask\", None)\n if attention_mask is not None:\n out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)\n cross_attn = kwargs.get(\"cross_attn\", None)\n if cross_attn is not None:\n out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)\n\n time_dim_replace = kwargs.get(\"time_dim_replace\", None)\n if time_dim_replace is not None:\n out['time_dim_replace'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_replace))\n\n return out\n\nclass Kandinsky5Image(Kandinsky5):\n def __init__(self, model_config, model_type=ModelType.FLOW, device=None):\n super().__init__(model_config, model_type, device=device)\n\n def concat_cond(self, **kwargs):\n return None\n" }, { "path": "comfy/model_detection.py", "content": "import json\nimport comfy.memory_management\nimport comfy.supported_models\nimport comfy.supported_models_base\nimport comfy.utils\nimport math\nimport logging\nimport torch\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 any_suffix_in(keys, prefix, main, suffix_list=[]):\n for x in suffix_list:\n if \"{}{}{}\".format(prefix, main, x) in keys:\n return True\n return False\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 time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict\n return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross\n return None\n\ndef detect_unet_config(state_dict, key_prefix, metadata=None):\n state_dict_keys = list(state_dict.keys())\n\n if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model\n unet_config = {}\n unet_config[\"in_channels\"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]\n patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]\n unet_config[\"patch_size\"] = patch_size\n final_layer = '{}final_layer.linear.weight'.format(key_prefix)\n if final_layer in state_dict:\n unet_config[\"out_channels\"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)\n\n unet_config[\"depth\"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64\n unet_config[\"input_size\"] = None\n y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)\n if y_key in state_dict_keys:\n unet_config[\"adm_in_channels\"] = state_dict[y_key].shape[1]\n\n context_key = '{}context_embedder.weight'.format(key_prefix)\n if context_key in state_dict_keys:\n in_features = state_dict[context_key].shape[1]\n out_features = state_dict[context_key].shape[0]\n unet_config[\"context_embedder_config\"] = {\"target\": \"torch.nn.Linear\", \"params\": {\"in_features\": in_features, \"out_features\": out_features}}\n num_patches_key = '{}pos_embed'.format(key_prefix)\n if num_patches_key in state_dict_keys:\n num_patches = state_dict[num_patches_key].shape[1]\n unet_config[\"num_patches\"] = num_patches\n unet_config[\"pos_embed_max_size\"] = round(math.sqrt(num_patches))\n\n rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)\n if rms_qk in state_dict_keys:\n unet_config[\"qk_norm\"] = \"rms\"\n\n unet_config[\"pos_embed_scaling_factor\"] = None #unused for inference\n context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)\n if context_processor in state_dict_keys:\n unet_config[\"context_processor_layers\"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')\n unet_config[\"x_block_self_attn_layers\"] = []\n for key in state_dict_keys:\n if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'):\n layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')]\n unet_config[\"x_block_self_attn_layers\"].append(int(layer))\n return unet_config\n\n if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade\n unet_config = {}\n text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)\n if text_mapper_name in state_dict_keys:\n unet_config['stable_cascade_stage'] = 'c'\n w = state_dict[text_mapper_name]\n if w.shape[0] == 1536: #stage c lite\n unet_config['c_cond'] = 1536\n unet_config['c_hidden'] = [1536, 1536]\n unet_config['nhead'] = [24, 24]\n unet_config['blocks'] = [[4, 12], [12, 4]]\n elif w.shape[0] == 2048: #stage c full\n unet_config['c_cond'] = 2048\n elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:\n unet_config['stable_cascade_stage'] = 'b'\n w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]\n if w.shape[-1] == 640:\n unet_config['c_hidden'] = [320, 640, 1280, 1280]\n unet_config['nhead'] = [-1, -1, 20, 20]\n unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]\n unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]\n elif w.shape[-1] == 576: #stage b lite\n unet_config['c_hidden'] = [320, 576, 1152, 1152]\n unet_config['nhead'] = [-1, 9, 18, 18]\n unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]\n unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]\n return unet_config\n\n if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit\n unet_config = {}\n unet_config[\"audio_model\"] = \"dit1.0\"\n return unet_config\n\n if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit\n unet_config = {}\n unet_config[\"max_seq\"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]\n unet_config[\"cond_seq_dim\"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]\n double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')\n single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')\n unet_config[\"n_double_layers\"] = double_layers\n unet_config[\"n_layers\"] = double_layers + single_layers\n return unet_config\n\n if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT\n unet_config = {}\n unet_config[\"image_model\"] = \"hydit\"\n unet_config[\"depth\"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')\n unet_config[\"hidden_size\"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]\n if unet_config[\"hidden_size\"] == 1408 and unet_config[\"depth\"] == 40: #DiT-g/2\n unet_config[\"mlp_ratio\"] = 4.3637\n if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:\n unet_config[\"size_cond\"] = True\n unet_config[\"use_style_cond\"] = True\n unet_config[\"image_model\"] = \"hydit1\"\n return unet_config\n\n if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video\n dit_config = {}\n in_w = state_dict['{}img_in.proj.weight'.format(key_prefix)]\n out_w = state_dict['{}final_layer.linear.weight'.format(key_prefix)]\n dit_config[\"image_model\"] = \"hunyuan_video\"\n dit_config[\"in_channels\"] = in_w.shape[1] #SkyReels img2video has 32 input channels\n dit_config[\"patch_size\"] = list(in_w.shape[2:])\n dit_config[\"out_channels\"] = out_w.shape[0] // math.prod(dit_config[\"patch_size\"])\n if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys):\n dit_config[\"vec_in_dim\"] = 768\n else:\n dit_config[\"vec_in_dim\"] = None\n\n if len(dit_config[\"patch_size\"]) == 2:\n dit_config[\"axes_dim\"] = [64, 64]\n else:\n dit_config[\"axes_dim\"] = [16, 56, 56]\n\n if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys):\n dit_config[\"meanflow\"] = True\n else:\n dit_config[\"meanflow\"] = False\n\n dit_config[\"context_in_dim\"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1]\n dit_config[\"hidden_size\"] = in_w.shape[0]\n dit_config[\"mlp_ratio\"] = 4.0\n dit_config[\"num_heads\"] = in_w.shape[0] // 128\n dit_config[\"depth\"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')\n dit_config[\"depth_single_blocks\"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')\n dit_config[\"theta\"] = 256\n dit_config[\"qkv_bias\"] = True\n if '{}byt5_in.fc1.weight'.format(key_prefix) in state_dict:\n dit_config[\"byt5\"] = True\n else:\n dit_config[\"byt5\"] = False\n\n guidance_keys = list(filter(lambda a: a.startswith(\"{}guidance_in.\".format(key_prefix)), state_dict_keys))\n dit_config[\"guidance_embed\"] = len(guidance_keys) > 0\n\n # HunyuanVideo 1.5\n if '{}cond_type_embedding.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"use_cond_type_embedding\"] = True\n else:\n dit_config[\"use_cond_type_embedding\"] = False\n if '{}vision_in.proj.0.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"vision_in_dim\"] = state_dict['{}vision_in.proj.0.weight'.format(key_prefix)].shape[0]\n dit_config[\"meanflow_sum\"] = True\n else:\n dit_config[\"vision_in_dim\"] = None\n dit_config[\"meanflow_sum\"] = False\n return dit_config\n\n if any_suffix_in(state_dict_keys, key_prefix, 'double_blocks.0.img_attn.norm.key_norm.', [\"weight\", \"scale\"]) and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', [\"weight\", \"scale\"])): #Flux, Chroma or Chroma Radiance (has no img_in.weight)\n dit_config = {}\n if '{}double_stream_modulation_img.lin.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"image_model\"] = \"flux2\"\n dit_config[\"axes_dim\"] = [32, 32, 32, 32]\n dit_config[\"num_heads\"] = 48\n dit_config[\"mlp_ratio\"] = 3.0\n dit_config[\"theta\"] = 2000\n dit_config[\"out_channels\"] = 128\n dit_config[\"global_modulation\"] = True\n dit_config[\"mlp_silu_act\"] = True\n dit_config[\"qkv_bias\"] = False\n dit_config[\"ops_bias\"] = False\n dit_config[\"default_ref_method\"] = \"index\"\n dit_config[\"ref_index_scale\"] = 10.0\n dit_config[\"txt_ids_dims\"] = [3]\n patch_size = 1\n else:\n dit_config[\"image_model\"] = \"flux\"\n dit_config[\"axes_dim\"] = [16, 56, 56]\n dit_config[\"num_heads\"] = 24\n dit_config[\"mlp_ratio\"] = 4.0\n dit_config[\"theta\"] = 10000\n dit_config[\"out_channels\"] = 16\n dit_config[\"qkv_bias\"] = True\n dit_config[\"txt_ids_dims\"] = []\n patch_size = 2\n\n dit_config[\"in_channels\"] = 16\n dit_config[\"hidden_size\"] = 3072\n dit_config[\"context_in_dim\"] = 4096\n\n dit_config[\"patch_size\"] = patch_size\n in_key = \"{}img_in.weight\".format(key_prefix)\n if in_key in state_dict_keys:\n w = state_dict[in_key]\n dit_config[\"in_channels\"] = w.shape[1] // (patch_size * patch_size)\n dit_config[\"hidden_size\"] = w.shape[0]\n\n txt_in_key = \"{}txt_in.weight\".format(key_prefix)\n if txt_in_key in state_dict_keys:\n w = state_dict[txt_in_key]\n dit_config[\"context_in_dim\"] = w.shape[1]\n dit_config[\"hidden_size\"] = w.shape[0]\n\n vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix)\n if vec_in_key in state_dict_keys:\n dit_config[\"vec_in_dim\"] = state_dict[vec_in_key].shape[1]\n else:\n dit_config[\"vec_in_dim\"] = None\n\n dit_config[\"num_heads\"] = dit_config[\"hidden_size\"] // sum(dit_config[\"axes_dim\"])\n\n dit_config[\"depth\"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')\n dit_config[\"depth_single_blocks\"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')\n\n if any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.0.norms.0.', [\"weight\", \"scale\"]) or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', [\"weight\", \"scale\"]): #Chroma\n dit_config[\"image_model\"] = \"chroma\"\n dit_config[\"in_channels\"] = 64\n dit_config[\"out_channels\"] = 64\n dit_config[\"in_dim\"] = 64\n dit_config[\"out_dim\"] = 3072\n dit_config[\"hidden_dim\"] = 5120\n dit_config[\"n_layers\"] = 5\n\n if any_suffix_in(state_dict_keys, key_prefix, 'nerf_blocks.0.norm.', [\"weight\", \"scale\"]): #Chroma Radiance\n dit_config[\"image_model\"] = \"chroma_radiance\"\n dit_config[\"in_channels\"] = 3\n dit_config[\"out_channels\"] = 3\n dit_config[\"patch_size\"] = state_dict.get('{}img_in_patch.weight'.format(key_prefix)).size(dim=-1)\n dit_config[\"nerf_hidden_size\"] = 64\n dit_config[\"nerf_mlp_ratio\"] = 4\n dit_config[\"nerf_depth\"] = 4\n dit_config[\"nerf_max_freqs\"] = 8\n dit_config[\"nerf_tile_size\"] = 512\n dit_config[\"nerf_final_head_type\"] = \"conv\" if any_suffix_in(state_dict_keys, key_prefix, 'nerf_final_layer_conv.norm.', [\"weight\", \"scale\"]) else \"linear\"\n dit_config[\"nerf_embedder_dtype\"] = torch.float32\n if \"{}__x0__\".format(key_prefix) in state_dict_keys: # x0 pred\n dit_config[\"use_x0\"] = True\n else:\n dit_config[\"use_x0\"] = False\n else:\n dit_config[\"guidance_embed\"] = \"{}guidance_in.in_layer.weight\".format(key_prefix) in state_dict_keys\n dit_config[\"yak_mlp\"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys\n dit_config[\"txt_norm\"] = any_suffix_in(state_dict_keys, key_prefix, 'txt_norm.', [\"weight\", \"scale\"])\n if dit_config[\"yak_mlp\"] and dit_config[\"txt_norm\"]: # Ovis model\n dit_config[\"txt_ids_dims\"] = [1, 2]\n if dit_config.get(\"context_in_dim\") == 3584 and dit_config[\"vec_in_dim\"] is None: # LongCat-Image\n dit_config[\"txt_ids_dims\"] = [1, 2]\n\n return dit_config\n\n if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview\n dit_config = {}\n dit_config[\"image_model\"] = \"mochi_preview\"\n dit_config[\"depth\"] = 48\n dit_config[\"patch_size\"] = 2\n dit_config[\"num_heads\"] = 24\n dit_config[\"hidden_size_x\"] = 3072\n dit_config[\"hidden_size_y\"] = 1536\n dit_config[\"mlp_ratio_x\"] = 4.0\n dit_config[\"mlp_ratio_y\"] = 4.0\n dit_config[\"learn_sigma\"] = False\n dit_config[\"in_channels\"] = 12\n dit_config[\"qk_norm\"] = True\n dit_config[\"qkv_bias\"] = False\n dit_config[\"out_bias\"] = True\n dit_config[\"attn_drop\"] = 0.0\n dit_config[\"patch_embed_bias\"] = True\n dit_config[\"posenc_preserve_area\"] = True\n dit_config[\"timestep_mlp_bias\"] = True\n dit_config[\"attend_to_padding\"] = False\n dit_config[\"timestep_scale\"] = 1000.0\n dit_config[\"use_t5\"] = True\n dit_config[\"t5_feat_dim\"] = 4096\n dit_config[\"t5_token_length\"] = 256\n dit_config[\"rope_theta\"] = 10000.0\n return dit_config\n\n if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys:\n # PixArt diffusers\n return None\n\n if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv\n dit_config = {}\n dit_config[\"image_model\"] = \"ltxav\" if f'{key_prefix}audio_adaln_single.linear.weight' in state_dict_keys else \"ltxv\"\n dit_config[\"num_layers\"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')\n shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape\n dit_config[\"attention_head_dim\"] = shape[0] // 32\n dit_config[\"cross_attention_dim\"] = shape[1]\n if metadata is not None and \"config\" in metadata:\n dit_config.update(json.loads(metadata[\"config\"]).get(\"transformer\", {}))\n return dit_config\n\n if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model\n dit_config = {}\n dit_config[\"audio_model\"] = \"ace\"\n dit_config[\"attention_head_dim\"] = 128\n dit_config[\"in_channels\"] = 8\n dit_config[\"inner_dim\"] = 2560\n dit_config[\"max_height\"] = 16\n dit_config[\"max_position\"] = 32768\n dit_config[\"max_width\"] = 32768\n dit_config[\"mlp_ratio\"] = 2.5\n dit_config[\"num_attention_heads\"] = 20\n dit_config[\"num_layers\"] = 24\n dit_config[\"out_channels\"] = 8\n dit_config[\"patch_size\"] = [16, 1]\n dit_config[\"rope_theta\"] = 1000000.0\n dit_config[\"speaker_embedding_dim\"] = 512\n dit_config[\"text_embedding_dim\"] = 768\n\n dit_config[\"ssl_encoder_depths\"] = [8, 8]\n dit_config[\"ssl_latent_dims\"] = [1024, 768]\n dit_config[\"ssl_names\"] = [\"mert\", \"m-hubert\"]\n dit_config[\"lyric_encoder_vocab_size\"] = 6693\n dit_config[\"lyric_hidden_size\"] = 1024\n return dit_config\n\n if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt\n patch_size = 2\n dit_config = {}\n dit_config[\"num_heads\"] = 16\n dit_config[\"patch_size\"] = patch_size\n dit_config[\"hidden_size\"] = 1152\n dit_config[\"in_channels\"] = 4\n dit_config[\"depth\"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')\n\n y_key = \"{}y_embedder.y_embedding\".format(key_prefix)\n if y_key in state_dict_keys:\n dit_config[\"model_max_length\"] = state_dict[y_key].shape[0]\n\n pe_key = \"{}pos_embed\".format(key_prefix)\n if pe_key in state_dict_keys:\n dit_config[\"input_size\"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size\n dit_config[\"pe_interpolation\"] = dit_config[\"input_size\"] // (512//8) # guess\n\n ar_key = \"{}ar_embedder.mlp.0.weight\".format(key_prefix)\n if ar_key in state_dict_keys:\n dit_config[\"image_model\"] = \"pixart_alpha\"\n dit_config[\"micro_condition\"] = True\n else:\n dit_config[\"image_model\"] = \"pixart_sigma\"\n dit_config[\"micro_condition\"] = False\n return dit_config\n\n if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos\n dit_config = {}\n dit_config[\"image_model\"] = \"cosmos\"\n dit_config[\"max_img_h\"] = 240\n dit_config[\"max_img_w\"] = 240\n dit_config[\"max_frames\"] = 128\n concat_padding_mask = True\n dit_config[\"in_channels\"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)\n dit_config[\"out_channels\"] = 16\n dit_config[\"patch_spatial\"] = 2\n dit_config[\"patch_temporal\"] = 1\n dit_config[\"model_channels\"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0]\n dit_config[\"block_config\"] = \"FA-CA-MLP\"\n dit_config[\"concat_padding_mask\"] = concat_padding_mask\n dit_config[\"pos_emb_cls\"] = \"rope3d\"\n dit_config[\"pos_emb_learnable\"] = False\n dit_config[\"pos_emb_interpolation\"] = \"crop\"\n dit_config[\"block_x_format\"] = \"THWBD\"\n dit_config[\"affline_emb_norm\"] = True\n dit_config[\"use_adaln_lora\"] = True\n dit_config[\"adaln_lora_dim\"] = 256\n\n if dit_config[\"model_channels\"] == 4096:\n # 7B\n dit_config[\"num_blocks\"] = 28\n dit_config[\"num_heads\"] = 32\n dit_config[\"extra_per_block_abs_pos_emb\"] = True\n dit_config[\"rope_h_extrapolation_ratio\"] = 1.0\n dit_config[\"rope_w_extrapolation_ratio\"] = 1.0\n dit_config[\"rope_t_extrapolation_ratio\"] = 2.0\n dit_config[\"extra_per_block_abs_pos_emb_type\"] = \"learnable\"\n else: # 5120\n # 14B\n dit_config[\"num_blocks\"] = 36\n dit_config[\"num_heads\"] = 40\n dit_config[\"extra_per_block_abs_pos_emb\"] = True\n dit_config[\"rope_h_extrapolation_ratio\"] = 2.0\n dit_config[\"rope_w_extrapolation_ratio\"] = 2.0\n dit_config[\"rope_t_extrapolation_ratio\"] = 2.0\n dit_config[\"extra_h_extrapolation_ratio\"] = 2.0\n dit_config[\"extra_w_extrapolation_ratio\"] = 2.0\n dit_config[\"extra_t_extrapolation_ratio\"] = 2.0\n dit_config[\"extra_per_block_abs_pos_emb_type\"] = \"learnable\"\n return dit_config\n\n if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys: # Lumina 2\n dit_config = {}\n dit_config[\"image_model\"] = \"lumina2\"\n dit_config[\"patch_size\"] = 2\n dit_config[\"in_channels\"] = 16\n w = state_dict['{}cap_embedder.1.weight'.format(key_prefix)]\n dit_config[\"dim\"] = w.shape[0]\n dit_config[\"cap_feat_dim\"] = w.shape[1]\n dit_config[\"n_layers\"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')\n dit_config[\"qk_norm\"] = True\n\n if dit_config[\"dim\"] == 2304: # Original Lumina 2\n dit_config[\"n_heads\"] = 24\n dit_config[\"n_kv_heads\"] = 8\n dit_config[\"axes_dims\"] = [32, 32, 32]\n dit_config[\"axes_lens\"] = [300, 512, 512]\n dit_config[\"rope_theta\"] = 10000.0\n dit_config[\"ffn_dim_multiplier\"] = 4.0\n ctd_weight = state_dict.get('{}clip_text_pooled_proj.0.weight'.format(key_prefix), None)\n if ctd_weight is not None: # NewBie\n dit_config[\"clip_text_dim\"] = ctd_weight.shape[0]\n # NewBie also sets axes_lens = [1024, 512, 512] but it's not used in ComfyUI\n elif dit_config[\"dim\"] == 3840: # Z image\n dit_config[\"n_heads\"] = 30\n dit_config[\"n_kv_heads\"] = 30\n dit_config[\"axes_dims\"] = [32, 48, 48]\n dit_config[\"axes_lens\"] = [1536, 512, 512]\n dit_config[\"rope_theta\"] = 256.0\n dit_config[\"ffn_dim_multiplier\"] = (8.0 / 3.0)\n dit_config[\"z_image_modulation\"] = True\n dit_config[\"time_scale\"] = 1000.0\n try:\n dit_config[\"allow_fp16\"] = torch.std(state_dict['{}layers.{}.ffn_norm1.weight'.format(key_prefix, dit_config[\"n_layers\"] - 2)], unbiased=False).item() < 0.42\n except Exception:\n pass\n if '{}cap_pad_token'.format(key_prefix) in state_dict_keys:\n dit_config[\"pad_tokens_multiple\"] = 32\n sig_weight = state_dict.get('{}siglip_embedder.0.weight'.format(key_prefix), None)\n if sig_weight is not None:\n dit_config[\"siglip_feat_dim\"] = sig_weight.shape[0]\n\n dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix)\n if dec_cond_key in state_dict_keys: # pixel-space variant\n dit_config[\"image_model\"] = \"zimage_pixel\"\n # patch_size and in_channels are derived from x_embedder:\n # x_embedder: Linear(patch_size * patch_size * in_channels, dim)\n # The decoder also receives the full flat patch, so decoder_in_channels = x_embedder input dim.\n x_emb_in = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1]\n dec_out = state_dict['{}dec_net.final_layer.linear.weight'.format(key_prefix)].shape[0]\n # patch_size: infer from decoder final layer output matching x_embedder input\n # in_channels: infer from dec_net input_embedder (in_features = dec_in_ch + max_freqs^2)\n embedder_w = state_dict['{}dec_net.input_embedder.embedder.0.weight'.format(key_prefix)]\n dec_in_ch = dec_out # decoder in == decoder out (same pixel space)\n dit_config[\"patch_size\"] = round((x_emb_in / 3) ** 0.5) # assume RGB (in_channels=3)\n dit_config[\"in_channels\"] = 3\n dit_config[\"decoder_in_channels\"] = dec_in_ch\n dit_config[\"decoder_hidden_size\"] = state_dict[dec_cond_key].shape[0]\n dit_config[\"decoder_num_res_blocks\"] = count_blocks(\n state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.'\n )\n dit_config[\"decoder_max_freqs\"] = int((embedder_w.shape[1] - dec_in_ch) ** 0.5)\n if '{}__x0__'.format(key_prefix) in state_dict_keys:\n dit_config[\"use_x0\"] = True\n\n return dit_config\n\n if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1\n dit_config = {}\n dit_config[\"image_model\"] = \"wan2.1\"\n dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]\n out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4\n dit_config[\"dim\"] = dim\n dit_config[\"out_dim\"] = out_dim\n dit_config[\"num_heads\"] = dim // 128\n dit_config[\"ffn_dim\"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]\n dit_config[\"num_layers\"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')\n dit_config[\"patch_size\"] = (1, 2, 2)\n dit_config[\"freq_dim\"] = 256\n dit_config[\"window_size\"] = (-1, -1)\n dit_config[\"qk_norm\"] = True\n dit_config[\"cross_attn_norm\"] = True\n dit_config[\"eps\"] = 1e-6\n dit_config[\"in_dim\"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1]\n if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"vace\"\n dit_config[\"vace_in_dim\"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1]\n dit_config[\"vace_layers\"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.')\n elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys:\n if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"camera\"\n else:\n dit_config[\"model_type\"] = \"camera_2.2\"\n elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"s2v\"\n elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"humo\"\n elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"animate\"\n elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"scail\"\n else:\n if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:\n dit_config[\"model_type\"] = \"i2v\"\n else:\n dit_config[\"model_type\"] = \"t2v\"\n flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix))\n if flf_weight is not None:\n dit_config[\"flf_pos_embed_token_number\"] = flf_weight.shape[1]\n\n ref_conv_weight = state_dict.get('{}ref_conv.weight'.format(key_prefix))\n if ref_conv_weight is not None:\n dit_config[\"in_dim_ref_conv\"] = ref_conv_weight.shape[1]\n\n if metadata is not None and \"config\" in metadata:\n dit_config.update(json.loads(metadata[\"config\"]).get(\"transformer\", {}))\n\n return dit_config\n\n if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D\n in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape\n dit_config = {}\n dit_config[\"image_model\"] = \"hunyuan3d2\"\n dit_config[\"in_channels\"] = in_shape[1]\n dit_config[\"context_in_dim\"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1]\n dit_config[\"hidden_size\"] = in_shape[0]\n dit_config[\"mlp_ratio\"] = 4.0\n dit_config[\"num_heads\"] = 16\n dit_config[\"depth\"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')\n dit_config[\"depth_single_blocks\"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')\n dit_config[\"qkv_bias\"] = True\n dit_config[\"guidance_embed\"] = \"{}guidance_in.in_layer.weight\".format(key_prefix) in state_dict_keys\n return dit_config\n\n if f\"{key_prefix}t_embedder.mlp.2.weight\" in state_dict_keys and f\"{key_prefix}blocks.0.attn1.k_norm.weight\" in state_dict_keys: # Hunyuan 3D 2.1\n dit_config = {}\n dit_config[\"image_model\"] = \"hunyuan3d2_1\"\n dit_config[\"in_channels\"] = state_dict[f\"{key_prefix}x_embedder.weight\"].shape[1]\n dit_config[\"context_dim\"] = 1024\n dit_config[\"hidden_size\"] = state_dict[f\"{key_prefix}x_embedder.weight\"].shape[0]\n dit_config[\"mlp_ratio\"] = 4.0\n dit_config[\"num_heads\"] = 16\n dit_config[\"depth\"] = count_blocks(state_dict_keys, f\"{key_prefix}blocks.{{}}\")\n dit_config[\"qkv_bias\"] = False\n dit_config[\"guidance_cond_proj_dim\"] = None#f\"{key_prefix}t_embedder.cond_proj.weight\" in state_dict_keys\n return dit_config\n\n if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream\n dit_config = {}\n dit_config[\"image_model\"] = \"hidream\"\n dit_config[\"attention_head_dim\"] = 128\n dit_config[\"axes_dims_rope\"] = [64, 32, 32]\n dit_config[\"caption_channels\"] = [4096, 4096]\n dit_config[\"max_resolution\"] = [128, 128]\n dit_config[\"in_channels\"] = 16\n dit_config[\"llama_layers\"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]\n dit_config[\"num_attention_heads\"] = 20\n dit_config[\"num_routed_experts\"] = 4\n dit_config[\"num_activated_experts\"] = 2\n dit_config[\"num_layers\"] = 16\n dit_config[\"num_single_layers\"] = 32\n dit_config[\"out_channels\"] = 16\n dit_config[\"patch_size\"] = 2\n dit_config[\"text_emb_dim\"] = 2048\n return dit_config\n\n if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2\n dit_config = {}\n dit_config[\"image_model\"] = \"cosmos_predict2\"\n if \"{}llm_adapter.blocks.0.cross_attn.q_proj.weight\".format(key_prefix) in state_dict_keys:\n dit_config[\"image_model\"] = \"anima\"\n dit_config[\"max_img_h\"] = 240\n dit_config[\"max_img_w\"] = 240\n dit_config[\"max_frames\"] = 128\n concat_padding_mask = True\n dit_config[\"in_channels\"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)\n dit_config[\"out_channels\"] = 16\n dit_config[\"patch_spatial\"] = 2\n dit_config[\"patch_temporal\"] = 1\n dit_config[\"model_channels\"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0]\n dit_config[\"concat_padding_mask\"] = concat_padding_mask\n dit_config[\"crossattn_emb_channels\"] = 1024\n dit_config[\"pos_emb_cls\"] = \"rope3d\"\n dit_config[\"pos_emb_learnable\"] = True\n dit_config[\"pos_emb_interpolation\"] = \"crop\"\n dit_config[\"min_fps\"] = 1\n dit_config[\"max_fps\"] = 30\n\n dit_config[\"use_adaln_lora\"] = True\n dit_config[\"adaln_lora_dim\"] = 256\n if dit_config[\"model_channels\"] == 2048:\n dit_config[\"num_blocks\"] = 28\n dit_config[\"num_heads\"] = 16\n elif dit_config[\"model_channels\"] == 5120:\n dit_config[\"num_blocks\"] = 36\n dit_config[\"num_heads\"] = 40\n\n if dit_config[\"in_channels\"] == 16:\n dit_config[\"extra_per_block_abs_pos_emb\"] = False\n dit_config[\"rope_h_extrapolation_ratio\"] = 4.0\n dit_config[\"rope_w_extrapolation_ratio\"] = 4.0\n dit_config[\"rope_t_extrapolation_ratio\"] = 1.0\n elif dit_config[\"in_channels\"] == 17: # img to video\n if dit_config[\"model_channels\"] == 2048:\n dit_config[\"extra_per_block_abs_pos_emb\"] = False\n dit_config[\"rope_h_extrapolation_ratio\"] = 3.0\n dit_config[\"rope_w_extrapolation_ratio\"] = 3.0\n dit_config[\"rope_t_extrapolation_ratio\"] = 1.0\n elif dit_config[\"model_channels\"] == 5120:\n dit_config[\"rope_h_extrapolation_ratio\"] = 2.0\n dit_config[\"rope_w_extrapolation_ratio\"] = 2.0\n dit_config[\"rope_t_extrapolation_ratio\"] = 0.8333333333333334\n\n dit_config[\"extra_h_extrapolation_ratio\"] = 1.0\n dit_config[\"extra_w_extrapolation_ratio\"] = 1.0\n dit_config[\"extra_t_extrapolation_ratio\"] = 1.0\n dit_config[\"rope_enable_fps_modulation\"] = False\n\n return dit_config\n\n if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2\n dit_config = {}\n dit_config[\"image_model\"] = \"omnigen2\"\n dit_config[\"axes_dim_rope\"] = [40, 40, 40]\n dit_config[\"axes_lens\"] = [1024, 1664, 1664]\n dit_config[\"ffn_dim_multiplier\"] = None\n dit_config[\"hidden_size\"] = 2520\n dit_config[\"in_channels\"] = 16\n dit_config[\"multiple_of\"] = 256\n dit_config[\"norm_eps\"] = 1e-05\n dit_config[\"num_attention_heads\"] = 21\n dit_config[\"num_kv_heads\"] = 7\n dit_config[\"num_layers\"] = 32\n dit_config[\"num_refiner_layers\"] = 2\n dit_config[\"out_channels\"] = None\n dit_config[\"patch_size\"] = 2\n dit_config[\"text_feat_dim\"] = 2048\n dit_config[\"timestep_scale\"] = 1000.0\n return dit_config\n\n if '{}txt_norm.weight'.format(key_prefix) in state_dict_keys: # Qwen Image\n dit_config = {}\n dit_config[\"image_model\"] = \"qwen_image\"\n dit_config[\"in_channels\"] = state_dict['{}img_in.weight'.format(key_prefix)].shape[1]\n dit_config[\"num_layers\"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')\n if \"{}__index_timestep_zero__\".format(key_prefix) in state_dict_keys: # 2511\n dit_config[\"default_ref_method\"] = \"index_timestep_zero\"\n if \"{}time_text_embed.addition_t_embedding.weight\".format(key_prefix) in state_dict_keys: # Layered\n dit_config[\"use_additional_t_cond\"] = True\n dit_config[\"default_ref_method\"] = \"negative_index\"\n return dit_config\n\n if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5\n dit_config = {}\n model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]\n dit_config[\"model_dim\"] = model_dim\n if model_dim in [4096, 2560]: # pro video and lite image\n dit_config[\"axes_dims\"] = (32, 48, 48)\n if model_dim == 2560: # lite image\n dit_config[\"rope_scale_factor\"] = (1.0, 1.0, 1.0)\n elif model_dim == 1792: # lite video\n dit_config[\"axes_dims\"] = (16, 24, 24)\n dit_config[\"time_dim\"] = state_dict['{}time_embeddings.in_layer.bias'.format(key_prefix)].shape[0]\n dit_config[\"image_model\"] = \"kandinsky5\"\n dit_config[\"ff_dim\"] = state_dict['{}visual_transformer_blocks.0.feed_forward.in_layer.weight'.format(key_prefix)].shape[0]\n dit_config[\"visual_embed_dim\"] = state_dict['{}visual_embeddings.in_layer.weight'.format(key_prefix)].shape[1]\n dit_config[\"num_text_blocks\"] = count_blocks(state_dict_keys, '{}text_transformer_blocks.'.format(key_prefix) + '{}.')\n dit_config[\"num_visual_blocks\"] = count_blocks(state_dict_keys, '{}visual_transformer_blocks.'.format(key_prefix) + '{}.')\n return dit_config\n\n if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:\n dit_config = {}\n dit_config[\"audio_model\"] = \"ace1.5\"\n return dit_config\n\n if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:\n return None\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 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 transformer_depth = []\n transformer_depth_output = []\n context_dim = None\n use_linear_in_transformer = False\n\n video_model = False\n video_model_cross = 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 video_model_cross = out[4]\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 elif \"{}middle_block.0.in_layers.0.weight\".format(key_prefix) in state_dict_keys:\n transformer_depth_middle = -1\n else:\n transformer_depth_middle = -2\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 unet_config[\"disable_temporal_crossattention\"] = not video_model_cross\n else:\n unet_config[\"use_temporal_resblock\"] = False\n unet_config[\"use_temporal_attention\"] = False\n\n heatmap_key = '{}heatmap_head.conv_layers.0.weight'.format(key_prefix)\n if heatmap_key in state_dict_keys:\n unet_config[\"heatmap_head\"] = True\n\n return unet_config\n\ndef model_config_from_unet_config(unet_config, state_dict=None):\n for model_config in comfy.supported_models.models:\n if model_config.matches(unet_config, state_dict):\n return model_config(unet_config)\n\n logging.error(\"no match {}\".format(unet_config))\n return None\n\ndef model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None):\n unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)\n if unet_config is None:\n return None\n model_config = model_config_from_unet_config(unet_config, state_dict)\n if model_config is None and use_base_if_no_match:\n model_config = comfy.supported_models_base.BASE(unet_config)\n\n # Detect per-layer quantization (mixed precision)\n quant_config = comfy.utils.detect_layer_quantization(state_dict, unet_key_prefix)\n if quant_config:\n model_config.quant_config = quant_config\n logging.info(\"Detected mixed precision quantization\")\n\n return model_config\n\ndef unet_prefix_from_state_dict(state_dict):\n candidates = [\"model.diffusion_model.\", #ldm/sgm models\n \"model.model.\", #audio models\n \"net.\", #cosmos\n ]\n counts = {k: 0 for k in candidates}\n for k in state_dict:\n for c in candidates:\n if k.startswith(c):\n counts[c] += 1\n break\n\n top = max(counts, key=counts.get)\n if counts[top] > 5:\n return top\n else:\n return \"model.\" #aura flow and others\n\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 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=None):\n if \"conv_in.weight\" not in state_dict:\n return None\n\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 res_blocks = count_blocks(state_dict, \"down_blocks.{}.resnets.\".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 for i in range(res_blocks):\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 SDXL_diffusers_ip2p = {'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': 8, '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 KOALA_700M = {'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': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],\n 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,\n 'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n KOALA_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': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],\n 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,\n 'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n SD09_XS = {'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': [1, 1, 1],\n 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,\n 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],\n 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}\n\n SD_XS = {'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': [1, 1, 1],\n 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,\n 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],\n 'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,\n 'dtype': dtype, 'in_channels': 9, '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 LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4,\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': True, 'context_dim': 1024, 'num_head_channels': 64,\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 supported_models = [LotusD, SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]\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):\n unet_config = unet_config_from_diffusers_unet(state_dict)\n if unet_config is not None:\n return model_config_from_unet_config(unet_config)\n return None\n\ndef convert_diffusers_mmdit(state_dict, output_prefix=\"\"):\n out_sd = {}\n\n if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow\n num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')\n num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')\n sd_map = comfy.utils.auraflow_to_diffusers({\"n_double_layers\": num_joint, \"n_layers\": num_joint + num_single}, output_prefix=output_prefix)\n elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt\n num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')\n sd_map = comfy.utils.pixart_to_diffusers({\"depth\": num_blocks}, output_prefix=output_prefix)\n elif 'noise_refiner.0.attention.norm_k.weight' in state_dict:\n n_layers = count_blocks(state_dict, 'layers.{}.')\n dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0]\n sd_map = comfy.utils.z_image_to_diffusers({\"n_layers\": n_layers, \"dim\": dim}, output_prefix=output_prefix)\n for k in state_dict: # For zeta chroma\n if k not in sd_map:\n sd_map[k] = k\n elif 'x_embedder.weight' in state_dict: #Flux\n depth = count_blocks(state_dict, 'transformer_blocks.{}.')\n depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')\n hidden_size = state_dict[\"x_embedder.bias\"].shape[0]\n sd_map = comfy.utils.flux_to_diffusers({\"depth\": depth, \"depth_single_blocks\": depth_single_blocks, \"hidden_size\": hidden_size}, output_prefix=output_prefix)\n elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict and 'pos_embed.proj.weight' in state_dict: #SD3\n num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')\n depth = state_dict[\"pos_embed.proj.weight\"].shape[0] // 64\n sd_map = comfy.utils.mmdit_to_diffusers({\"depth\": depth, \"num_blocks\": num_blocks}, output_prefix=output_prefix)\n else:\n return None\n\n for k in sd_map:\n weight = state_dict.get(k, None)\n if weight is not None:\n t = sd_map[k]\n\n if not isinstance(t, str):\n if len(t) > 2:\n fun = t[2]\n else:\n fun = lambda a: a\n offset = t[1]\n if offset is not None:\n old_weight = out_sd.get(t[0], None)\n if old_weight is None:\n old_weight = torch.empty_like(weight)\n if old_weight.shape[offset[0]] < offset[1] + offset[2]:\n exp = list(weight.shape)\n exp[offset[0]] = offset[1] + offset[2]\n new = torch.empty(exp, device=weight.device, dtype=weight.dtype)\n new[:old_weight.shape[0]] = old_weight\n old_weight = new\n\n if old_weight is out_sd.get(t[0], None) and comfy.memory_management.aimdo_enabled:\n old_weight = old_weight.clone()\n\n w = old_weight.narrow(offset[0], offset[1], offset[2])\n else:\n if comfy.memory_management.aimdo_enabled:\n weight = weight.clone()\n old_weight = weight\n w = weight\n w[:] = fun(weight)\n t = t[0]\n out_sd[t] = old_weight\n else:\n out_sd[t] = weight\n state_dict.pop(k)\n\n return out_sd\n" }, { "path": "comfy/model_management.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\nimport psutil\nimport logging\nfrom enum import Enum\nfrom comfy.cli_args import args, PerformanceFeature\nimport threading\nimport torch\nimport sys\nimport platform\nimport weakref\nimport gc\nimport os\nfrom contextlib import nullcontext\nimport comfy.memory_management\nimport comfy.utils\nimport comfy.quant_ops\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\n\n# Training Related State\nin_training = False\n\n\ndef get_supported_float8_types():\n float8_types = []\n try:\n float8_types.append(torch.float8_e4m3fn)\n except:\n pass\n try:\n float8_types.append(torch.float8_e4m3fnuz)\n except:\n pass\n try:\n float8_types.append(torch.float8_e5m2)\n except:\n pass\n try:\n float8_types.append(torch.float8_e5m2fnuz)\n except:\n pass\n try:\n float8_types.append(torch.float8_e8m0fnu)\n except:\n pass\n return float8_types\n\nFLOAT8_TYPES = get_supported_float8_types()\n\nxpu_available = False\ntorch_version = \"\"\ntry:\n torch_version = torch.version.__version__\n temp = torch_version.split(\".\")\n torch_version_numeric = (int(temp[0]), int(temp[1]))\nexcept:\n pass\n\nlowvram_available = True\nif args.deterministic:\n logging.info(\"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 logging.warning(\"WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.\")\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 logging.info(\"Using directml with device: {}\".format(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 # noqa: F401\nexcept:\n pass\n\ntry:\n _ = torch.xpu.device_count()\n xpu_available = torch.xpu.is_available()\nexcept:\n xpu_available = False\n\ntry:\n if torch.backends.mps.is_available():\n cpu_state = CPUState.MPS\n import torch.mps\nexcept:\n pass\n\ntry:\n import torch_npu # noqa: F401\n _ = torch.npu.device_count()\n npu_available = torch.npu.is_available()\nexcept:\n npu_available = False\n\ntry:\n import torch_mlu # noqa: F401\n _ = torch.mlu.device_count()\n mlu_available = torch.mlu.is_available()\nexcept:\n mlu_available = False\n\ntry:\n ixuca_available = hasattr(torch, \"corex\")\nexcept:\n ixuca_available = False\n\nif args.cpu:\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 is_ascend_npu():\n global npu_available\n if npu_available:\n return True\n return False\n\ndef is_mlu():\n global mlu_available\n if mlu_available:\n return True\n return False\n\ndef is_ixuca():\n global ixuca_available\n if ixuca_available:\n return True\n return False\n\ndef is_wsl():\n version = platform.uname().release\n if version.endswith(\"-Microsoft\"):\n return True\n elif version.endswith(\"microsoft-standard-WSL2\"):\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\", torch.xpu.current_device())\n elif is_ascend_npu():\n return torch.device(\"npu\", torch.npu.current_device())\n elif is_mlu():\n return torch.device(\"mlu\", torch.mlu.current_device())\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_xpu = torch.xpu.get_device_properties(dev).total_memory\n mem_total_torch = mem_reserved\n mem_total = mem_total_xpu\n elif is_ascend_npu():\n stats = torch.npu.memory_stats(dev)\n mem_reserved = stats['reserved_bytes.all.current']\n _, mem_total_npu = torch.npu.mem_get_info(dev)\n mem_total_torch = mem_reserved\n mem_total = mem_total_npu\n elif is_mlu():\n stats = torch.mlu.memory_stats(dev)\n mem_reserved = stats['reserved_bytes.all.current']\n _, mem_total_mlu = torch.mlu.mem_get_info(dev)\n mem_total_torch = mem_reserved\n mem_total = mem_total_mlu\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\ndef mac_version():\n try:\n return tuple(int(n) for n in platform.mac_ver()[0].split(\".\"))\n except:\n return None\n\ntotal_vram = get_total_memory(get_torch_device()) / (1024 * 1024)\ntotal_ram = psutil.virtual_memory().total / (1024 * 1024)\nlogging.info(\"Total VRAM {:0.0f} MB, total RAM {:0.0f} MB\".format(total_vram, total_ram))\n\ntry:\n logging.info(\"pytorch version: {}\".format(torch_version))\n mac_ver = mac_version()\n if mac_ver is not None:\n logging.info(\"Mac Version {}\".format(mac_ver))\nexcept:\n pass\n\ntry:\n OOM_EXCEPTION = torch.cuda.OutOfMemoryError\nexcept:\n OOM_EXCEPTION = Exception\n\ntry:\n ACCELERATOR_ERROR = torch.AcceleratorError\nexcept AttributeError:\n ACCELERATOR_ERROR = RuntimeError\n\ndef is_oom(e):\n if isinstance(e, OOM_EXCEPTION):\n return True\n if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or \"out of memory\" in str(e).lower()):\n discard_cuda_async_error()\n return True\n return False\n\ndef raise_non_oom(e):\n if not is_oom(e):\n raise e\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 logging.info(\"xformers version: {}\".format(XFORMERS_VERSION))\n if XFORMERS_VERSION.startswith(\"0.0.18\"):\n logging.warning(\"\\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.\")\n logging.warning(\"Please downgrade or upgrade xformers to a different version.\\n\")\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\ndef is_amd():\n global cpu_state\n if cpu_state == CPUState.GPU:\n if torch.version.hip:\n return True\n return False\n\ndef amd_min_version(device=None, min_rdna_version=0):\n if not is_amd():\n return False\n\n if is_device_cpu(device):\n return False\n\n arch = torch.cuda.get_device_properties(device).gcnArchName\n if arch.startswith('gfx') and len(arch) == 7:\n try:\n cmp_rdna_version = int(arch[4]) + 2\n except:\n cmp_rdna_version = 0\n if cmp_rdna_version >= min_rdna_version:\n return True\n\n return False\n\nMIN_WEIGHT_MEMORY_RATIO = 0.4\nif is_nvidia():\n MIN_WEIGHT_MEMORY_RATIO = 0.0\n\nENABLE_PYTORCH_ATTENTION = False\nif args.use_pytorch_cross_attention:\n ENABLE_PYTORCH_ATTENTION = True\n XFORMERS_IS_AVAILABLE = False\n\ntry:\n if is_nvidia():\n if torch_version_numeric[0] >= 2:\n if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:\n ENABLE_PYTORCH_ATTENTION = True\n if is_intel_xpu() or is_ascend_npu() or is_mlu() or is_ixuca():\n if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:\n ENABLE_PYTORCH_ATTENTION = True\nexcept:\n pass\n\n\nSUPPORT_FP8_OPS = args.supports_fp8_compute\n\nAMD_RDNA2_AND_OLDER_ARCH = [\"gfx1030\", \"gfx1031\", \"gfx1010\", \"gfx1011\", \"gfx1012\", \"gfx906\", \"gfx900\", \"gfx803\"]\nAMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'\n\ntry:\n if is_amd():\n arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName.split(':')[0]\n if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):\n if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1':\n torch.backends.cudnn.enabled = False # Seems to improve things a lot on AMD\n logging.info(\"Set: torch.backends.cudnn.enabled = False for better AMD performance.\")\n\n try:\n rocm_version = tuple(map(int, str(torch.version.hip).split(\".\")[:2]))\n except:\n rocm_version = (6, -1)\n\n def aotriton_supported(gpu_arch):\n path = torch.__path__[0]\n path = os.path.join(os.path.join(path, \"lib\"), \"aotriton.images\")\n gfx = set(map(lambda a: a[4:], filter(lambda a: a.startswith(\"amd-gfx\"), os.listdir(path))))\n if gpu_arch in gfx:\n return True\n if \"{}x\".format(gpu_arch[:-1]) in gfx:\n return True\n if \"{}xx\".format(gpu_arch[:-2]) in gfx:\n return True\n return False\n\n logging.info(\"AMD arch: {}\".format(arch))\n logging.info(\"ROCm version: {}\".format(rocm_version))\n if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:\n if aotriton_supported(arch): # AMD efficient attention implementation depends on aotriton.\n if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much\n if any((a in arch) for a in [\"gfx90a\", \"gfx942\", \"gfx950\", \"gfx1100\", \"gfx1101\", \"gfx1150\", \"gfx1151\"]): # TODO: more arches, TODO: gfx950\n ENABLE_PYTORCH_ATTENTION = True\n if rocm_version >= (7, 0):\n if any((a in arch) for a in [\"gfx1200\", \"gfx1201\"]):\n ENABLE_PYTORCH_ATTENTION = True\n if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):\n if any((a in arch) for a in [\"gfx1200\", \"gfx1201\", \"gfx950\"]): # TODO: more arches, \"gfx942\" gives error on pytorch nightly 2.10 1013 rocm7.0\n SUPPORT_FP8_OPS = True\n\nexcept:\n pass\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\n\nPRIORITIZE_FP16 = False # TODO: remove and replace with something that shows exactly which dtype is faster than the other\ntry:\n if (is_nvidia() or is_amd()) and PerformanceFeature.Fp16Accumulation in args.fast:\n torch.backends.cuda.matmul.allow_fp16_accumulation = True\n PRIORITIZE_FP16 = True # TODO: limit to cards where it actually boosts performance\n logging.info(\"Enabled fp16 accumulation.\")\nexcept:\n pass\n\nif torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:\n torch.backends.cudnn.benchmark = True\n\ntry:\n if torch_version_numeric >= (2, 5):\n torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)\nexcept:\n logging.warning(\"Warning, could not set allow_fp16_bf16_reduction_math_sdp\")\n\nif args.lowvram:\n set_vram_to = VRAMState.LOW_VRAM\n lowvram_available = True\nelif args.novram:\n set_vram_to = VRAMState.NO_VRAM\nelif args.highvram or args.gpu_only:\n vram_state = VRAMState.HIGH_VRAM\n\nFORCE_FP32 = False\nif args.force_fp32:\n logging.info(\"Forcing FP32, if this improves things please report it.\")\n FORCE_FP32 = 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\nlogging.info(f\"Set vram state to: {vram_state.name}\")\n\nDISABLE_SMART_MEMORY = args.disable_smart_memory\n\nif DISABLE_SMART_MEMORY:\n logging.info(\"Disabling smart memory management\")\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 elif device.type == \"xpu\":\n return \"{} {}\".format(device, torch.xpu.get_device_name(device))\n else:\n return \"{}\".format(device.type)\n elif is_intel_xpu():\n return \"{} {}\".format(device, torch.xpu.get_device_name(device))\n elif is_ascend_npu():\n return \"{} {}\".format(device, torch.npu.get_device_name(device))\n elif is_mlu():\n return \"{} {}\".format(device, torch.mlu.get_device_name(device))\n else:\n return \"CUDA {}: {}\".format(device, torch.cuda.get_device_name(device))\n\ntry:\n logging.info(\"Device: {}\".format(get_torch_device_name(get_torch_device())))\nexcept:\n logging.warning(\"Could not pick default device.\")\n\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.nbytes\n return module_mem\n\ndef module_mmap_residency(module, free=False):\n mmap_touched_mem = 0\n module_mem = 0\n bounced_mmaps = set()\n sd = module.state_dict()\n for k in sd:\n t = sd[k]\n module_mem += t.nbytes\n storage = t._qdata.untyped_storage() if isinstance(t, comfy.quant_ops.QuantizedTensor) else t.untyped_storage()\n if not getattr(storage, \"_comfy_tensor_mmap_touched\", False):\n continue\n mmap_touched_mem += t.nbytes\n if not free:\n continue\n storage._comfy_tensor_mmap_touched = False\n mmap_obj = storage._comfy_tensor_mmap_refs[0]\n if mmap_obj in bounced_mmaps:\n continue\n mmap_obj.bounce()\n bounced_mmaps.add(mmap_obj)\n return mmap_touched_mem, module_mem\n\nclass LoadedModel:\n def __init__(self, model):\n self._set_model(model)\n self.device = model.load_device\n self.real_model = None\n self.currently_used = True\n self.model_finalizer = None\n self._patcher_finalizer = None\n\n def _set_model(self, model):\n self._model = weakref.ref(model)\n if model.parent is not None:\n self._parent_model = weakref.ref(model.parent)\n self._patcher_finalizer = weakref.finalize(model, self._switch_parent)\n self._patcher_finalizer.atexit = False\n\n def _switch_parent(self):\n model = self._parent_model()\n if model is not None:\n self._set_model(model)\n\n @property\n def model(self):\n return self._model()\n\n def model_memory(self):\n return self.model.model_size()\n\n def model_mmap_residency(self, free=False):\n return self.model.model_mmap_residency(free=free)\n\n def model_loaded_memory(self):\n return self.model.loaded_size()\n\n def model_offloaded_memory(self):\n return self.model.model_size() - self.model.loaded_size()\n\n def model_memory_required(self, device):\n if device == self.model.current_loaded_device():\n return self.model_offloaded_memory()\n else:\n return self.model_memory()\n\n def model_load(self, lowvram_model_memory=0, force_patch_weights=False):\n self.model.model_patches_to(self.device)\n self.model.model_patches_to(self.model.model_dtype())\n\n # if self.model.loaded_size() > 0:\n use_more_vram = lowvram_model_memory\n if use_more_vram == 0:\n use_more_vram = 1e32\n self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights)\n\n real_model = self.model.model\n\n if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None:\n with torch.no_grad():\n real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)\n\n self.real_model = weakref.ref(real_model)\n self.model_finalizer = weakref.finalize(real_model, cleanup_models)\n self.model_finalizer.atexit = False\n return real_model\n\n def should_reload_model(self, force_patch_weights=False):\n if force_patch_weights and self.model.lowvram_patch_counter() > 0:\n return True\n return False\n\n def model_unload(self, memory_to_free=None, unpatch_weights=True):\n if memory_to_free is not None:\n if memory_to_free < self.model.loaded_size():\n freed = self.model.partially_unload(self.model.offload_device, memory_to_free)\n if freed >= memory_to_free:\n return False\n self.model.detach(unpatch_weights)\n self.model_finalizer.detach()\n self.model_finalizer = None\n self.real_model = None\n return True\n\n def model_use_more_vram(self, extra_memory, force_patch_weights=False):\n return self.model.partially_load(self.device, extra_memory, force_patch_weights=force_patch_weights)\n\n def __eq__(self, other):\n return self.model is other.model\n\n def __del__(self):\n if self._patcher_finalizer is not None:\n self._patcher_finalizer.detach()\n\n def is_dead(self):\n return self.real_model() is not None and self.model is None\n\n\ndef use_more_memory(extra_memory, loaded_models, device):\n for m in loaded_models:\n if m.device == device:\n extra_memory -= m.model_use_more_vram(extra_memory)\n if extra_memory <= 0:\n break\n\ndef offloaded_memory(loaded_models, device):\n offloaded_mem = 0\n for m in loaded_models:\n if m.device == device:\n offloaded_mem += m.model_offloaded_memory()\n return offloaded_mem\n\nWINDOWS = any(platform.win32_ver())\n\nEXTRA_RESERVED_VRAM = 400 * 1024 * 1024\nif WINDOWS:\n import comfy.windows\n EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue\n if total_vram > (15 * 1024): # more extra reserved vram on 16GB+ cards\n EXTRA_RESERVED_VRAM += 100 * 1024 * 1024\n def get_free_ram():\n return comfy.windows.get_free_ram()\nelse:\n def get_free_ram():\n return psutil.virtual_memory().available\n\nif args.reserve_vram is not None:\n EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024\n logging.debug(\"Reserving {}MB vram for other applications.\".format(EXTRA_RESERVED_VRAM / (1024 * 1024)))\n\ndef extra_reserved_memory():\n return EXTRA_RESERVED_VRAM\n\ndef minimum_inference_memory():\n return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()\n\ndef free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):\n cleanup_models_gc()\n unloaded_model = []\n can_unload = []\n unloaded_models = []\n\n for i in range(len(current_loaded_models) -1, -1, -1):\n shift_model = current_loaded_models[i]\n if shift_model.device == device:\n if shift_model not in keep_loaded and not shift_model.is_dead():\n can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))\n shift_model.currently_used = False\n\n can_unload_sorted = sorted(can_unload)\n for x in can_unload_sorted:\n i = x[-1]\n memory_to_free = 1e32\n pins_to_free = 1e32\n if not DISABLE_SMART_MEMORY:\n memory_to_free = memory_required - get_free_memory(device)\n pins_to_free = pins_required - get_free_ram()\n if current_loaded_models[i].model.is_dynamic() and for_dynamic:\n #don't actually unload dynamic models for the sake of other dynamic models\n #as that works on-demand.\n memory_required -= current_loaded_models[i].model.loaded_size()\n memory_to_free = 0\n if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free):\n logging.debug(f\"Unloading {current_loaded_models[i].model.model.__class__.__name__}\")\n unloaded_model.append(i)\n if pins_to_free > 0:\n logging.debug(f\"PIN Unloading {current_loaded_models[i].model.model.__class__.__name__}\")\n current_loaded_models[i].model.partially_unload_ram(pins_to_free)\n\n for x in can_unload_sorted:\n i = x[-1]\n ram_to_free = ram_required - psutil.virtual_memory().available\n if ram_to_free <= 0 and i not in unloaded_model:\n continue\n resident_memory, _ = current_loaded_models[i].model_mmap_residency(free=True)\n if resident_memory > 0:\n logging.debug(f\"RAM Unloading {current_loaded_models[i].model.model.__class__.__name__}\")\n\n for i in sorted(unloaded_model, reverse=True):\n unloaded_models.append(current_loaded_models.pop(i))\n\n if len(unloaded_model) > 0:\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 return unloaded_models\n\ndef load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):\n cleanup_models_gc()\n global vram_state\n\n inference_memory = minimum_inference_memory()\n extra_mem = max(inference_memory, memory_required + extra_reserved_memory())\n if minimum_memory_required is None:\n minimum_memory_required = extra_mem\n else:\n minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())\n\n models_temp = set()\n for m in models:\n models_temp.add(m)\n for mm in m.model_patches_models():\n models_temp.add(mm)\n\n models = models_temp\n\n models_to_load = []\n\n free_for_dynamic=True\n for x in models:\n if not x.is_dynamic():\n free_for_dynamic = False\n loaded_model = LoadedModel(x)\n try:\n loaded_model_index = current_loaded_models.index(loaded_model)\n except:\n loaded_model_index = None\n\n if loaded_model_index is not None:\n loaded = current_loaded_models[loaded_model_index]\n loaded.currently_used = True\n models_to_load.append(loaded)\n else:\n if hasattr(x, \"model\"):\n logging.info(f\"Requested to load {x.model.__class__.__name__}\")\n models_to_load.append(loaded_model)\n\n for loaded_model in models_to_load:\n to_unload = []\n for i in range(len(current_loaded_models)):\n if loaded_model.model.is_clone(current_loaded_models[i].model):\n to_unload = [i] + to_unload\n for i in to_unload:\n model_to_unload = current_loaded_models.pop(i)\n model_to_unload.model.detach(unpatch_all=False)\n model_to_unload.model_finalizer.detach()\n\n\n total_memory_required = {}\n total_pins_required = {}\n total_ram_required = {}\n for loaded_model in models_to_load:\n device = loaded_model.device\n total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device)\n resident_memory, model_memory = loaded_model.model_mmap_residency()\n pinned_memory = loaded_model.model.pinned_memory_size()\n #FIXME: This can over-free the pins as it budgets to pin the entire model. We should\n #make this JIT to keep as much pinned as possible.\n pins_required = model_memory - pinned_memory\n ram_required = model_memory - resident_memory\n total_pins_required[device] = total_pins_required.get(device, 0) + pins_required\n total_ram_required[device] = total_ram_required.get(device, 0) + ram_required\n\n for device in total_memory_required:\n if device != torch.device(\"cpu\"):\n free_memory(total_memory_required[device] * 1.1 + extra_mem,\n device,\n for_dynamic=free_for_dynamic,\n pins_required=total_pins_required[device],\n ram_required=total_ram_required[device])\n\n for device in total_memory_required:\n if device != torch.device(\"cpu\"):\n free_mem = get_free_memory(device)\n if free_mem < minimum_memory_required:\n models_l = free_memory(minimum_memory_required, device, for_dynamic=free_for_dynamic)\n logging.info(\"{} models unloaded.\".format(len(models_l)))\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) and not force_full_load:\n loaded_memory = loaded_model.model_loaded_memory()\n current_free_mem = get_free_memory(torch_dev) + loaded_memory\n\n lowvram_model_memory = max(0, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))\n lowvram_model_memory = lowvram_model_memory - loaded_memory\n\n if lowvram_model_memory == 0:\n lowvram_model_memory = 0.1\n\n if vram_set_state == VRAMState.NO_VRAM:\n lowvram_model_memory = 0.1\n\n loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)\n current_loaded_models.insert(0, loaded_model)\n return\n\ndef load_model_gpu(model):\n return load_models_gpu([model])\n\ndef loaded_models(only_currently_used=False):\n output = []\n for m in current_loaded_models:\n if only_currently_used:\n if not m.currently_used:\n continue\n\n output.append(m.model)\n return output\n\n\ndef cleanup_models_gc():\n do_gc = False\n\n reset_cast_buffers()\n\n for i in range(len(current_loaded_models)):\n cur = current_loaded_models[i]\n if cur.is_dead():\n logging.info(\"Potential memory leak detected with model {}, doing a full garbage collect, for maximum performance avoid circular references in the model code.\".format(cur.real_model().__class__.__name__))\n do_gc = True\n break\n\n if do_gc:\n gc.collect()\n soft_empty_cache()\n\n for i in range(len(current_loaded_models)):\n cur = current_loaded_models[i]\n if cur.is_dead():\n logging.warning(\"WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.\".format(cur.real_model().__class__.__name__))\n\n\ndef archive_model_dtypes(model):\n for name, module in model.named_modules():\n for param_name, param in module.named_parameters(recurse=False):\n setattr(module, f\"{param_name}_comfy_model_dtype\", param.dtype)\n for buf_name, buf in module.named_buffers(recurse=False):\n setattr(module, f\"{buf_name}_comfy_model_dtype\", buf.dtype)\n\n\ndef cleanup_models():\n to_delete = []\n for i in range(len(current_loaded_models)):\n if current_loaded_models[i].real_model() is None:\n to_delete = [i] + to_delete\n\n for i in to_delete:\n x = current_loaded_models.pop(i)\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 cpu_dev = torch.device(\"cpu\")\n if comfy.memory_management.aimdo_enabled:\n return cpu_dev\n\n torch_dev = get_torch_device()\n if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:\n return torch_dev\n\n if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM:\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 maximum_vram_for_weights(device=None):\n return (get_total_memory(device) * 0.88 - minimum_inference_memory())\n\ndef unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None):\n if model_params < 0:\n model_params = 1000000000000000000000\n if args.fp32_unet:\n return torch.float32\n if args.fp64_unet:\n return torch.float64\n if args.bf16_unet:\n return torch.bfloat16\n if args.fp16_unet:\n return torch.float16\n if args.fp8_e4m3fn_unet:\n return torch.float8_e4m3fn\n if args.fp8_e5m2_unet:\n return torch.float8_e5m2\n if args.fp8_e8m0fnu_unet:\n return torch.float8_e8m0fnu\n\n fp8_dtype = None\n if weight_dtype in FLOAT8_TYPES:\n fp8_dtype = weight_dtype\n\n if fp8_dtype is not None:\n if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive\n return fp8_dtype\n\n free_model_memory = maximum_vram_for_weights(device)\n if model_params * 2 > free_model_memory:\n return fp8_dtype\n\n if PRIORITIZE_FP16 or weight_dtype == torch.float16:\n if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params):\n return torch.float16\n\n for dt in supported_dtypes:\n if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params):\n if torch.float16 in supported_dtypes:\n return torch.float16\n if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params):\n if torch.bfloat16 in supported_dtypes:\n return torch.bfloat16\n\n for dt in supported_dtypes:\n if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True):\n if torch.float16 in supported_dtypes:\n return torch.float16\n if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True):\n if torch.bfloat16 in supported_dtypes:\n return torch.bfloat16\n\n return torch.float32\n\n# None means no manual cast\ndef unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):\n if weight_dtype == torch.float32 or weight_dtype == torch.float64:\n return None\n\n fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)\n if fp16_supported and weight_dtype == torch.float16:\n return None\n\n bf16_supported = should_use_bf16(inference_device)\n if bf16_supported and weight_dtype == torch.bfloat16:\n return None\n\n fp16_supported = should_use_fp16(inference_device, prioritize_performance=True)\n if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes:\n return torch.float16\n\n for dt in supported_dtypes:\n if dt == torch.float16 and fp16_supported:\n return torch.float16\n if dt == torch.bfloat16 and bf16_supported:\n return torch.bfloat16\n\n return torch.float32\n\ndef text_encoder_offload_device():\n if args.gpu_only:\n return get_torch_device()\n else:\n return torch.device(\"cpu\")\n\ndef text_encoder_device():\n if args.gpu_only:\n return get_torch_device()\n elif vram_state in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:\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_initial_device(load_device, offload_device, model_size=0):\n if comfy.memory_management.aimdo_enabled:\n return offload_device\n\n if load_device == offload_device or model_size <= 1024 * 1024 * 1024:\n return offload_device\n\n if is_device_mps(load_device):\n return load_device\n\n mem_l = get_free_memory(load_device)\n mem_o = get_free_memory(offload_device)\n if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l:\n return load_device\n else:\n return offload_device\n\ndef text_encoder_dtype(device=None):\n if args.fp8_e4m3fn_text_enc:\n return torch.float8_e4m3fn\n elif args.fp8_e5m2_text_enc:\n return torch.float8_e5m2\n elif args.fp16_text_enc:\n return torch.float16\n elif args.bf16_text_enc:\n return torch.bfloat16\n elif args.fp32_text_enc:\n return torch.float32\n\n if is_device_cpu(device):\n return torch.float16\n\n return torch.float16\n\n\ndef intermediate_device():\n if args.gpu_only:\n return get_torch_device()\n else:\n return torch.device(\"cpu\")\n\ndef intermediate_dtype():\n if args.fp16_intermediates:\n return torch.float16\n else:\n return torch.float32\n\ndef vae_device():\n if args.cpu_vae:\n return torch.device(\"cpu\")\n return get_torch_device()\n\ndef vae_offload_device():\n if args.gpu_only:\n return get_torch_device()\n else:\n return torch.device(\"cpu\")\n\ndef vae_dtype(device=None, allowed_dtypes=[]):\n if args.fp16_vae:\n return torch.float16\n elif args.bf16_vae:\n return torch.bfloat16\n elif args.fp32_vae:\n return torch.float32\n\n for d in allowed_dtypes:\n if d == torch.float16 and should_use_fp16(device):\n return d\n\n if d == torch.bfloat16 and should_use_bf16(device):\n return d\n\n return torch.float32\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 supports_cast(device, dtype): #TODO\n if dtype == torch.float32:\n return True\n if dtype == torch.float16:\n return True\n if directml_enabled: #TODO: test this\n return False\n if dtype == torch.bfloat16:\n return True\n if is_device_mps(device):\n return False\n if dtype == torch.float8_e4m3fn:\n return True\n if dtype == torch.float8_e5m2:\n return True\n return False\n\ndef pick_weight_dtype(dtype, fallback_dtype, device=None):\n if dtype is None:\n dtype = fallback_dtype\n elif dtype_size(dtype) > dtype_size(fallback_dtype):\n dtype = fallback_dtype\n\n if not supports_cast(device, dtype):\n dtype = fallback_dtype\n\n return dtype\n\ndef device_supports_non_blocking(device):\n if args.force_non_blocking:\n return True\n if is_device_mps(device):\n return False #pytorch bug? mps doesn't support non blocking\n if is_intel_xpu(): #xpu does support non blocking but it is slower on iGPUs for some reason so disable by default until situation changes\n return False\n if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)\n return False\n if directml_enabled:\n return False\n return True\n\ndef force_channels_last():\n if args.force_channels_last:\n return True\n\n #TODO\n return False\n\n\nSTREAMS = {}\nNUM_STREAMS = 0\nif args.async_offload is not None:\n NUM_STREAMS = args.async_offload\nelse:\n # Enable by default on Nvidia and AMD\n if is_nvidia() or is_amd():\n NUM_STREAMS = 2\n\nif args.disable_async_offload:\n NUM_STREAMS = 0\n\nif NUM_STREAMS > 0:\n logging.info(\"Using async weight offloading with {} streams\".format(NUM_STREAMS))\n\ndef current_stream(device):\n if device is None:\n return None\n if is_device_cuda(device):\n return torch.cuda.current_stream()\n elif is_device_xpu(device):\n return torch.xpu.current_stream()\n else:\n return None\n\nstream_counters = {}\n\nSTREAM_CAST_BUFFERS = {}\nLARGEST_CASTED_WEIGHT = (None, 0)\n\ndef get_cast_buffer(offload_stream, device, size, ref):\n global LARGEST_CASTED_WEIGHT\n\n if offload_stream is not None:\n wf_context = offload_stream\n if hasattr(wf_context, \"as_context\"):\n wf_context = wf_context.as_context(offload_stream)\n else:\n wf_context = nullcontext()\n\n cast_buffer = STREAM_CAST_BUFFERS.get(offload_stream, None)\n if cast_buffer is None or cast_buffer.numel() < size:\n if ref is LARGEST_CASTED_WEIGHT[0]:\n #If there is one giant weight we do not want both streams to\n #allocate a buffer for it. It's up to the caster to get the other\n #offload stream in this corner case\n return None\n if cast_buffer is not None and cast_buffer.numel() > 50 * (1024 ** 2):\n #I want my wrongly sized 50MB+ of VRAM back from the caching allocator right now\n synchronize()\n del STREAM_CAST_BUFFERS[offload_stream]\n del cast_buffer\n soft_empty_cache()\n with wf_context:\n cast_buffer = torch.empty((size), dtype=torch.int8, device=device)\n STREAM_CAST_BUFFERS[offload_stream] = cast_buffer\n\n if size > LARGEST_CASTED_WEIGHT[1]:\n LARGEST_CASTED_WEIGHT = (ref, size)\n\n return cast_buffer\n\ndef reset_cast_buffers():\n global LARGEST_CASTED_WEIGHT\n LARGEST_CASTED_WEIGHT = (None, 0)\n for offload_stream in STREAM_CAST_BUFFERS:\n offload_stream.synchronize()\n synchronize()\n STREAM_CAST_BUFFERS.clear()\n soft_empty_cache()\n\ndef get_offload_stream(device):\n stream_counter = stream_counters.get(device, 0)\n if NUM_STREAMS == 0:\n return None\n\n if torch.compiler.is_compiling():\n return None\n\n if device in STREAMS:\n ss = STREAMS[device]\n #Sync the oldest stream in the queue with the current\n ss[stream_counter].wait_stream(current_stream(device))\n stream_counter = (stream_counter + 1) % len(ss)\n stream_counters[device] = stream_counter\n return ss[stream_counter]\n elif is_device_cuda(device):\n ss = []\n for k in range(NUM_STREAMS):\n s1 = torch.cuda.Stream(device=device, priority=0)\n s1.as_context = torch.cuda.stream\n ss.append(s1)\n STREAMS[device] = ss\n s = ss[stream_counter]\n stream_counters[device] = stream_counter\n return s\n elif is_device_xpu(device):\n ss = []\n for k in range(NUM_STREAMS):\n s1 = torch.xpu.Stream(device=device, priority=0)\n s1.as_context = torch.xpu.stream\n ss.append(s1)\n STREAMS[device] = ss\n s = ss[stream_counter]\n stream_counters[device] = stream_counter\n return s\n return None\n\ndef sync_stream(device, stream):\n if stream is None or current_stream(device) is None:\n return\n current_stream(device).wait_stream(stream)\n\n\ndef cast_to_gathered(tensors, r, non_blocking=False, stream=None):\n wf_context = nullcontext()\n if stream is not None:\n wf_context = stream\n if hasattr(wf_context, \"as_context\"):\n wf_context = wf_context.as_context(stream)\n\n dest_views = comfy.memory_management.interpret_gathered_like(tensors, r)\n with wf_context:\n for tensor in tensors:\n dest_view = dest_views.pop(0)\n if tensor is None:\n continue\n if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view):\n continue\n storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage()\n if hasattr(storage, \"_comfy_tensor_mmap_touched\"):\n storage._comfy_tensor_mmap_touched = True\n dest_view.copy_(tensor, non_blocking=non_blocking)\n\n\ndef cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=None):\n if device is None or weight.device == device:\n if not copy:\n if dtype is None or weight.dtype == dtype:\n return weight\n if stream is not None:\n wf_context = stream\n if hasattr(wf_context, \"as_context\"):\n wf_context = wf_context.as_context(stream)\n with wf_context:\n return weight.to(dtype=dtype, copy=copy)\n return weight.to(dtype=dtype, copy=copy)\n\n\n if stream is not None:\n wf_context = stream\n if hasattr(wf_context, \"as_context\"):\n wf_context = wf_context.as_context(stream)\n with wf_context:\n if r is None:\n r = torch.empty_like(weight, dtype=dtype, device=device)\n r.copy_(weight, non_blocking=non_blocking)\n else:\n if r is None:\n r = torch.empty_like(weight, dtype=dtype, device=device)\n r.copy_(weight, non_blocking=non_blocking)\n return r\n\ndef cast_to_device(tensor, device, dtype, copy=False):\n non_blocking = device_supports_non_blocking(device)\n return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)\n\n\nPINNED_MEMORY = {}\nTOTAL_PINNED_MEMORY = 0\nMAX_PINNED_MEMORY = -1\nif not args.disable_pinned_memory:\n if is_nvidia() or is_amd():\n if WINDOWS:\n MAX_PINNED_MEMORY = get_total_memory(torch.device(\"cpu\")) * 0.45 # Windows limit is apparently 50%\n else:\n MAX_PINNED_MEMORY = get_total_memory(torch.device(\"cpu\")) * 0.95\n logging.info(\"Enabled pinned memory {}\".format(MAX_PINNED_MEMORY // (1024 * 1024)))\n\nPINNING_ALLOWED_TYPES = set([\"Tensor\", \"Parameter\", \"QuantizedTensor\"])\n\ndef discard_cuda_async_error():\n try:\n a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())\n b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())\n _ = a + b\n synchronize()\n except RuntimeError:\n #Dump it! We already know about it from the synchronous return\n pass\n\ndef pin_memory(tensor):\n global TOTAL_PINNED_MEMORY\n if MAX_PINNED_MEMORY <= 0:\n return False\n\n if type(tensor).__name__ not in PINNING_ALLOWED_TYPES:\n return False\n\n if not is_device_cpu(tensor.device):\n return False\n\n if tensor.is_pinned():\n #NOTE: Cuda does detect when a tensor is already pinned and would\n #error below, but there are proven cases where this also queues an error\n #on the GPU async. So dont trust the CUDA API and guard here\n return False\n\n if not tensor.is_contiguous():\n return False\n\n size = tensor.nbytes\n if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:\n return False\n\n ptr = tensor.data_ptr()\n if ptr == 0:\n return False\n\n if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0:\n PINNED_MEMORY[ptr] = size\n TOTAL_PINNED_MEMORY += size\n return True\n else:\n logging.warning(\"Pin error.\")\n discard_cuda_async_error()\n\n return False\n\ndef unpin_memory(tensor):\n global TOTAL_PINNED_MEMORY\n if MAX_PINNED_MEMORY <= 0:\n return False\n\n if not is_device_cpu(tensor.device):\n return False\n\n ptr = tensor.data_ptr()\n size = tensor.nbytes\n\n size_stored = PINNED_MEMORY.get(ptr, None)\n if size_stored is None:\n logging.warning(\"Tried to unpin tensor not pinned by ComfyUI\")\n return False\n\n if size != size_stored:\n logging.warning(\"Size of pinned tensor changed\")\n return False\n\n if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:\n TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)\n if len(PINNED_MEMORY) == 0:\n TOTAL_PINNED_MEMORY = 0\n return True\n else:\n logging.warning(\"Unpin error.\")\n discard_cuda_async_error()\n\n return False\n\ndef sage_attention_enabled():\n return args.use_sage_attention\n\ndef flash_attention_enabled():\n return args.use_flash_attention\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 is_ascend_npu():\n return False\n if is_mlu():\n return False\n if is_ixuca():\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_enabled_vae():\n if is_amd():\n return False # enabling pytorch attention on AMD currently causes crash when doing high res\n return pytorch_attention_enabled()\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():\n return True\n if is_intel_xpu():\n return True\n if is_ascend_npu():\n return True\n if is_mlu():\n return True\n if is_amd():\n return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention\n if is_ixuca():\n return True\n return False\n\ndef force_upcast_attention_dtype():\n upcast = args.force_upcast_attention\n\n macos_version = mac_version()\n if macos_version is not None and ((14, 5) <= macos_version): # black image bug on recent versions of macOS, I don't think it's ever getting fixed\n upcast = True\n\n if upcast:\n return {torch.float16: torch.float32}\n else:\n return None\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_reserved = stats['reserved_bytes.all.current']\n mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved\n mem_free_torch = mem_reserved - mem_active\n mem_free_total = mem_free_xpu + mem_free_torch\n elif is_ascend_npu():\n stats = torch.npu.memory_stats(dev)\n mem_active = stats['active_bytes.all.current']\n mem_reserved = stats['reserved_bytes.all.current']\n mem_free_npu, _ = torch.npu.mem_get_info(dev)\n mem_free_torch = mem_reserved - mem_active\n mem_free_total = mem_free_npu + mem_free_torch\n elif is_mlu():\n stats = torch.mlu.memory_stats(dev)\n mem_active = stats['active_bytes.all.current']\n mem_reserved = stats['reserved_bytes.all.current']\n mem_free_mlu, _ = torch.mlu.mem_get_info(dev)\n mem_free_torch = mem_reserved - mem_active\n mem_free_total = mem_free_mlu + mem_free_torch\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_type(device, type):\n if hasattr(device, 'type'):\n if (device.type == type):\n return True\n return False\n\ndef is_device_cpu(device):\n return is_device_type(device, 'cpu')\n\ndef is_device_mps(device):\n return is_device_type(device, 'mps')\n\ndef is_device_xpu(device):\n return is_device_type(device, 'xpu')\n\ndef is_device_cuda(device):\n return is_device_type(device, 'cuda')\n\ndef is_directml_enabled():\n global directml_enabled\n if directml_enabled:\n return True\n\n return False\n\ndef should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):\n if device is not None:\n if is_device_cpu(device):\n return False\n\n if args.force_fp16:\n return True\n\n if FORCE_FP32:\n return False\n\n if is_directml_enabled():\n return True\n\n if (device is not None and is_device_mps(device)) or mps_mode():\n return True\n\n if cpu_mode():\n return False\n\n if is_intel_xpu():\n if torch_version_numeric < (2, 3):\n return True\n else:\n return torch.xpu.get_device_properties(device).has_fp16\n\n if is_ascend_npu():\n return True\n\n if is_mlu():\n return True\n\n if is_ixuca():\n return True\n\n if torch.version.hip:\n return True\n\n props = torch.cuda.get_device_properties(device)\n if props.major >= 8:\n return True\n\n if props.major < 6:\n return False\n\n #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32\n nvidia_10_series = [\"1080\", \"1070\", \"titan x\", \"p3000\", \"p3200\", \"p4000\", \"p4200\", \"p5000\", \"p5200\", \"p6000\", \"1060\", \"1050\", \"p40\", \"p100\", \"p6\", \"p4\"]\n for x in nvidia_10_series:\n if x in props.name.lower():\n if WINDOWS or manual_cast:\n return True\n else:\n return False #weird linux behavior where fp32 is faster\n\n if manual_cast:\n free_model_memory = maximum_vram_for_weights(device)\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 should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):\n if device is not None:\n if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow\n return False\n\n if FORCE_FP32:\n return False\n\n if directml_enabled:\n return False\n\n if (device is not None and is_device_mps(device)) or mps_mode():\n if mac_version() < (14,):\n return False\n return True\n\n if cpu_mode():\n return False\n\n if is_intel_xpu():\n if torch_version_numeric < (2, 3):\n return True\n else:\n return torch.xpu.is_bf16_supported()\n\n if is_ascend_npu():\n return True\n\n if is_ixuca():\n return True\n\n if is_amd():\n arch = torch.cuda.get_device_properties(device).gcnArchName\n if any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH): # RDNA2 and older don't support bf16\n if manual_cast:\n return True\n return False\n\n props = torch.cuda.get_device_properties(device)\n\n if is_mlu():\n if props.major > 3:\n return True\n\n if props.major >= 8:\n return True\n\n bf16_works = torch.cuda.is_bf16_supported()\n\n if bf16_works and manual_cast:\n free_model_memory = maximum_vram_for_weights(device)\n if (not prioritize_performance) or model_params * 4 > free_model_memory:\n return True\n\n return False\n\ndef supports_fp8_compute(device=None):\n if SUPPORT_FP8_OPS:\n return True\n\n if not is_nvidia():\n return False\n\n props = torch.cuda.get_device_properties(device)\n if props.major >= 9:\n return True\n if props.major < 8:\n return False\n if props.minor < 9:\n return False\n\n if torch_version_numeric < (2, 3):\n return False\n\n if WINDOWS:\n if torch_version_numeric < (2, 4):\n return False\n\n return True\n\ndef supports_nvfp4_compute(device=None):\n if not is_nvidia():\n return False\n\n props = torch.cuda.get_device_properties(device)\n if props.major < 10:\n return False\n\n return True\n\ndef supports_mxfp8_compute(device=None):\n if not is_nvidia():\n return False\n\n if torch_version_numeric < (2, 10):\n return False\n\n props = torch.cuda.get_device_properties(device)\n if props.major < 10:\n return False\n\n return True\n\ndef extended_fp16_support():\n # TODO: check why some models work with fp16 on newer torch versions but not on older\n if torch_version_numeric < (2, 7):\n return False\n\n return True\n\nLORA_COMPUTE_DTYPES = {}\ndef lora_compute_dtype(device):\n dtype = LORA_COMPUTE_DTYPES.get(device, None)\n if dtype is not None:\n return dtype\n\n if should_use_fp16(device):\n dtype = torch.float16\n else:\n dtype = torch.float32\n\n LORA_COMPUTE_DTYPES[device] = dtype\n return dtype\n\ndef synchronize():\n if cpu_mode():\n return\n if is_intel_xpu():\n torch.xpu.synchronize()\n elif torch.cuda.is_available():\n torch.cuda.synchronize()\n\ndef soft_empty_cache(force=False):\n if cpu_mode():\n return\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 is_ascend_npu():\n torch.npu.empty_cache()\n elif is_mlu():\n torch.mlu.empty_cache()\n elif torch.cuda.is_available():\n torch.cuda.synchronize()\n torch.cuda.empty_cache()\n torch.cuda.ipc_collect()\n\ndef unload_all_models():\n free_memory(1e30, get_torch_device())\n\ndef debug_memory_summary():\n if is_amd() or is_nvidia():\n return torch.cuda.memory.memory_summary()\n return \"\"\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": "comfy/model_patcher.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\nfrom __future__ import annotations\n\nimport collections\nimport inspect\nimport logging\nimport math\nimport uuid\nfrom typing import Callable, Optional\n\nimport torch\n\nimport comfy.float\nimport comfy.hooks\nimport comfy.lora\nimport comfy.model_management\nimport comfy.patcher_extension\nimport comfy.utils\nfrom comfy.comfy_types import UnetWrapperFunction\nfrom comfy.quant_ops import QuantizedTensor\nfrom comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP\n\nimport comfy_aimdo.model_vbar\n\ndef set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):\n to = model_options[\"transformer_options\"].copy()\n\n if \"patches_replace\" not in to:\n to[\"patches_replace\"] = {}\n else:\n to[\"patches_replace\"] = to[\"patches_replace\"].copy()\n\n if name not in to[\"patches_replace\"]:\n to[\"patches_replace\"][name] = {}\n else:\n to[\"patches_replace\"][name] = to[\"patches_replace\"][name].copy()\n\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 model_options[\"transformer_options\"] = to\n return model_options\n\ndef set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False):\n model_options[\"sampler_post_cfg_function\"] = model_options.get(\"sampler_post_cfg_function\", []) + [post_cfg_function]\n if disable_cfg1_optimization:\n model_options[\"disable_cfg1_optimization\"] = True\n return model_options\n\ndef set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False):\n model_options[\"sampler_pre_cfg_function\"] = model_options.get(\"sampler_pre_cfg_function\", []) + [pre_cfg_function]\n if disable_cfg1_optimization:\n model_options[\"disable_cfg1_optimization\"] = True\n return model_options\n\ndef create_model_options_clone(orig_model_options: dict):\n return comfy.patcher_extension.copy_nested_dicts(orig_model_options)\n\ndef create_hook_patches_clone(orig_hook_patches):\n new_hook_patches = {}\n for hook_ref in orig_hook_patches:\n new_hook_patches[hook_ref] = {}\n for k in orig_hook_patches[hook_ref]:\n new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:]\n return new_hook_patches\n\ndef wipe_lowvram_weight(m):\n if hasattr(m, \"prev_comfy_cast_weights\"):\n m.comfy_cast_weights = m.prev_comfy_cast_weights\n del m.prev_comfy_cast_weights\n\n if hasattr(m, \"weight_function\"):\n m.weight_function = []\n\n if hasattr(m, \"bias_function\"):\n m.bias_function = []\n\ndef move_weight_functions(m, device):\n if device is None:\n return 0\n\n memory = 0\n if hasattr(m, \"weight_function\"):\n for f in m.weight_function:\n if hasattr(f, \"move_to\"):\n memory += f.move_to(device=device)\n\n if hasattr(m, \"bias_function\"):\n for f in m.bias_function:\n if hasattr(f, \"move_to\"):\n memory += f.move_to(device=device)\n return memory\n\ndef string_to_seed(data):\n logging.warning(\"WARNING: string_to_seed has moved from comfy.model_patcher to comfy.utils\")\n return comfy.utils.string_to_seed(data)\n\nclass LowVramPatch:\n def __init__(self, key, patches, convert_func=None, set_func=None):\n self.key = key\n self.patches = patches\n self.convert_func = convert_func # TODO: remove\n self.set_func = set_func\n\n def __call__(self, weight):\n return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)\n\nLOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2\n\ndef low_vram_patch_estimate_vram(model, key):\n weight, set_func, convert_func = get_key_weight(model, key)\n if weight is None:\n return 0\n model_dtype = getattr(model, \"manual_cast_dtype\", torch.float32)\n if model_dtype is None:\n model_dtype = weight.dtype\n\n return weight.numel() * model_dtype.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR\n\ndef get_key_weight(model, key):\n set_func = None\n convert_func = None\n op_keys = key.rsplit('.', 1)\n if len(op_keys) < 2:\n weight = comfy.utils.get_attr(model, key)\n else:\n op = comfy.utils.get_attr(model, op_keys[0])\n try:\n set_func = getattr(op, \"set_{}\".format(op_keys[1]))\n except AttributeError:\n pass\n\n try:\n convert_func = getattr(op, \"convert_{}\".format(op_keys[1]))\n except AttributeError:\n pass\n\n weight = getattr(op, op_keys[1])\n if convert_func is not None:\n weight = comfy.utils.get_attr(model, key)\n\n return weight, set_func, convert_func\n\ndef key_param_name_to_key(key, param):\n if len(key) == 0:\n return param\n return \"{}.{}\".format(key, param)\n\nclass AutoPatcherEjector:\n def __init__(self, model: 'ModelPatcher', skip_and_inject_on_exit_only=False):\n self.model = model\n self.was_injected = False\n self.prev_skip_injection = False\n self.skip_and_inject_on_exit_only = skip_and_inject_on_exit_only\n\n def __enter__(self):\n self.was_injected = False\n self.prev_skip_injection = self.model.skip_injection\n if self.skip_and_inject_on_exit_only:\n self.model.skip_injection = True\n if self.model.is_injected:\n self.model.eject_model()\n self.was_injected = True\n\n def __exit__(self, *args):\n if self.skip_and_inject_on_exit_only:\n self.model.skip_injection = self.prev_skip_injection\n self.model.inject_model()\n if self.was_injected and not self.model.skip_injection:\n self.model.inject_model()\n self.model.skip_injection = self.prev_skip_injection\n\nclass MemoryCounter:\n def __init__(self, initial: int, minimum=0):\n self.value = initial\n self.minimum = minimum\n # TODO: add a safe limit besides 0\n\n def use(self, weight: torch.Tensor):\n weight_size = weight.nelement() * weight.element_size()\n if self.is_useable(weight_size):\n self.decrement(weight_size)\n return True\n return False\n\n def is_useable(self, used: int):\n return self.value - used > self.minimum\n\n def decrement(self, used: int):\n self.value -= used\n\nCustomTorchDevice = collections.namedtuple(\"FakeDevice\", [\"type\", \"index\"])(\"comfy-lazy-caster\", 0)\n\nclass LazyCastingParam(torch.nn.Parameter):\n def __new__(cls, model, key, tensor):\n return super().__new__(cls, tensor)\n\n def __init__(self, model, key, tensor):\n self.model = model\n self.key = key\n\n @property\n def device(self):\n return CustomTorchDevice\n\n #safetensors will .to() us to the cpu which we catch here to cast on demand. The returned tensor is\n #then just a short lived thing in the safetensors serialization logic inside its big for loop over\n #all weights getting garbage collected per-weight\n def to(self, *args, **kwargs):\n return self.model.patch_weight_to_device(self.key, device_to=self.model.load_device, return_weight=True).to(\"cpu\")\n\n\nclass ModelPatcher:\n def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):\n self.size = size\n self.model = model\n if not hasattr(self.model, 'device'):\n logging.debug(\"Model doesn't have a device attribute.\")\n self.model.device = offload_device\n elif self.model.device is None:\n self.model.device = offload_device\n\n self.patches = {}\n self.backup = {}\n self.backup_buffers = {}\n self.object_patches = {}\n self.object_patches_backup = {}\n self.weight_wrapper_patches = {}\n self.model_options = {\"transformer_options\":{}}\n self.load_device = load_device\n self.offload_device = offload_device\n self.weight_inplace_update = weight_inplace_update\n self.force_cast_weights = False\n self.patches_uuid = uuid.uuid4()\n self.parent = None\n self.pinned = set()\n\n self.attachments: dict[str] = {}\n self.additional_models: dict[str, list[ModelPatcher]] = {}\n self.callbacks: dict[str, dict[str, list[Callable]]] = CallbacksMP.init_callbacks()\n self.wrappers: dict[str, dict[str, list[Callable]]] = WrappersMP.init_wrappers()\n\n self.is_injected = False\n self.skip_injection = False\n self.injections: dict[str, list[PatcherInjection]] = {}\n\n self.hook_patches: dict[comfy.hooks._HookRef] = {}\n self.hook_patches_backup: dict[comfy.hooks._HookRef] = None\n self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {}\n self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {}\n self.current_hooks: Optional[comfy.hooks.HookGroup] = None\n self.forced_hooks: Optional[comfy.hooks.HookGroup] = None # NOTE: only used for CLIP at this time\n self.is_clip = False\n self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed\n\n self.cached_patcher_init: tuple[Callable, tuple] | None = None\n if not hasattr(self.model, 'model_loaded_weight_memory'):\n self.model.model_loaded_weight_memory = 0\n\n if not hasattr(self.model, 'lowvram_patch_counter'):\n self.model.lowvram_patch_counter = 0\n\n if not hasattr(self.model, 'model_lowvram'):\n self.model.model_lowvram = False\n\n if not hasattr(self.model, 'current_weight_patches_uuid'):\n self.model.current_weight_patches_uuid = None\n\n if not hasattr(self.model, 'model_offload_buffer_memory'):\n self.model.model_offload_buffer_memory = 0\n\n def is_dynamic(self):\n return False\n\n def model_size(self):\n if self.size > 0:\n return self.size\n self.size = comfy.model_management.module_size(self.model)\n return self.size\n\n def model_mmap_residency(self, free=False):\n return comfy.model_management.module_mmap_residency(self.model, free=free)\n\n def get_ram_usage(self):\n return self.model_size()\n\n def loaded_size(self):\n return self.model.model_loaded_weight_memory\n\n def lowvram_patch_counter(self):\n return self.model.lowvram_patch_counter\n\n def get_free_memory(self, device):\n #Prioritize batching (incl. CFG/conds etc) over keeping the model resident. In\n #the vast majority of setups a little bit of offloading on the giant model more\n #than pays for CFG. So return everything both torch and Aimdo could give us\n aimdo_mem = 0\n if comfy.memory_management.aimdo_enabled:\n aimdo_mem = comfy_aimdo.model_vbar.vbars_analyze()\n return comfy.model_management.get_free_memory(device) + aimdo_mem\n\n def get_clone_model_override(self):\n return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned)\n\n def clone(self, disable_dynamic=False, model_override=None):\n class_ = self.__class__\n if self.is_dynamic() and disable_dynamic:\n class_ = ModelPatcher\n if model_override is None:\n if self.cached_patcher_init is None:\n raise RuntimeError(\"Cannot create non-dynamic delegate: cached_patcher_init is not initialized.\")\n temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)\n model_override = temp_model_patcher.get_clone_model_override()\n if model_override is None:\n model_override = self.get_clone_model_override()\n\n n = class_(model_override[0], self.load_device, self.offload_device, self.model_size(), 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.patches_uuid = self.patches_uuid\n\n n.object_patches = self.object_patches.copy()\n n.weight_wrapper_patches = self.weight_wrapper_patches.copy()\n n.model_options = comfy.utils.deepcopy_list_dict(self.model_options)\n n.parent = self\n\n n.force_cast_weights = self.force_cast_weights\n\n n.backup, n.backup_buffers, n.object_patches_backup, n.pinned = model_override[1]\n\n # attachments\n n.attachments = {}\n for k in self.attachments:\n if hasattr(self.attachments[k], \"on_model_patcher_clone\"):\n n.attachments[k] = self.attachments[k].on_model_patcher_clone()\n else:\n n.attachments[k] = self.attachments[k]\n # additional models\n for k, c in self.additional_models.items():\n n.additional_models[k] = [x.clone() for x in c]\n # callbacks\n for k, c in self.callbacks.items():\n n.callbacks[k] = {}\n for k1, c1 in c.items():\n n.callbacks[k][k1] = c1.copy()\n # sample wrappers\n for k, w in self.wrappers.items():\n n.wrappers[k] = {}\n for k1, w1 in w.items():\n n.wrappers[k][k1] = w1.copy()\n # injection\n n.is_injected = self.is_injected\n n.skip_injection = self.skip_injection\n for k, i in self.injections.items():\n n.injections[k] = i.copy()\n # hooks\n n.hook_patches = create_hook_patches_clone(self.hook_patches)\n n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup\n for group in self.cached_hook_patches:\n n.cached_hook_patches[group] = {}\n for k in self.cached_hook_patches[group]:\n n.cached_hook_patches[group][k] = self.cached_hook_patches[group][k]\n n.hook_backup = self.hook_backup\n n.current_hooks = self.current_hooks.clone() if self.current_hooks else self.current_hooks\n n.forced_hooks = self.forced_hooks.clone() if self.forced_hooks else self.forced_hooks\n n.is_clip = self.is_clip\n n.hook_mode = self.hook_mode\n\n n.cached_patcher_init = self.cached_patcher_init\n\n for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):\n callback(self, n)\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 clone_has_same_weights(self, clone: 'ModelPatcher'):\n if not self.is_clone(clone):\n return False\n\n if self.current_hooks != clone.current_hooks:\n return False\n if self.forced_hooks != clone.forced_hooks:\n return False\n if self.hook_patches.keys() != clone.hook_patches.keys():\n return False\n if self.attachments.keys() != clone.attachments.keys():\n return False\n if self.additional_models.keys() != clone.additional_models.keys():\n return False\n for key in self.callbacks:\n if len(self.callbacks[key]) != len(clone.callbacks[key]):\n return False\n for key in self.wrappers:\n if len(self.wrappers[key]) != len(clone.wrappers[key]):\n return False\n if self.injections.keys() != clone.injections.keys():\n return False\n\n if len(self.patches) == 0 and len(clone.patches) == 0:\n return True\n\n if self.patches_uuid == clone.patches_uuid:\n if len(self.patches) != len(clone.patches):\n logging.warning(\"WARNING: something went wrong, same patch uuid but different length of patches.\")\n else:\n return True\n\n def memory_required(self, input_shape):\n return self.model.memory_required(input_shape=input_shape)\n\n def disable_model_cfg1_optimization(self):\n self.model_options[\"disable_cfg1_optimization\"] = True\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.disable_model_cfg1_optimization()\n\n def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):\n self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)\n\n def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False):\n self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization)\n\n def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond_batch_function):\n self.model_options[\"sampler_calc_cond_batch_function\"] = sampler_calc_cond_batch_function\n\n def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):\n self.model_options[\"model_function_wrapper\"] = unet_wrapper_function\n\n def set_model_denoise_mask_function(self, denoise_mask_function):\n self.model_options[\"denoise_mask_function\"] = denoise_mask_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 self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index)\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 set_model_emb_patch(self, patch):\n self.set_model_patch(patch, \"emb_patch\")\n\n def set_model_forward_timestep_embed_patch(self, patch):\n self.set_model_patch(patch, \"forward_timestep_embed_patch\")\n\n def set_model_double_block_patch(self, patch):\n self.set_model_patch(patch, \"double_block\")\n\n def set_model_post_input_patch(self, patch):\n self.set_model_patch(patch, \"post_input\")\n\n def set_model_noise_refiner_patch(self, patch):\n self.set_model_patch(patch, \"noise_refiner\")\n\n def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs):\n rope_options = self.model_options[\"transformer_options\"].get(\"rope_options\", {})\n rope_options[\"scale_x\"] = scale_x\n rope_options[\"scale_y\"] = scale_y\n rope_options[\"scale_t\"] = scale_t\n\n rope_options[\"shift_x\"] = shift_x\n rope_options[\"shift_y\"] = shift_y\n rope_options[\"shift_t\"] = shift_t\n\n self.model_options[\"transformer_options\"][\"rope_options\"] = rope_options\n\n\n def add_object_patch(self, name, obj):\n self.object_patches[name] = obj\n\n def set_model_compute_dtype(self, dtype):\n self.add_object_patch(\"manual_cast_dtype\", dtype)\n if dtype is not None:\n self.force_cast_weights = True\n self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this\n\n def add_weight_wrapper(self, name, function):\n self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function]\n self.patches_uuid = uuid.uuid4()\n\n def get_model_object(self, name: str) -> torch.nn.Module:\n \"\"\"Retrieves a nested attribute from an object using dot notation considering\n object patches.\n\n Args:\n name (str): The attribute path using dot notation (e.g. \"model.layer.weight\")\n\n Returns:\n The value of the requested attribute\n\n Example:\n patcher = ModelPatcher()\n weight = patcher.get_model_object(\"layer1.conv.weight\")\n \"\"\"\n if name in self.object_patches:\n return self.object_patches[name]\n else:\n if name in self.object_patches_backup:\n return self.object_patches_backup[name]\n else:\n return comfy.utils.get_attr(self.model, name)\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_patches_models(self):\n to = self.model_options[\"transformer_options\"]\n models = []\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], \"models\"):\n models += patch_list[i].models()\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], \"models\"):\n models += patch_list[k].models()\n if \"model_function_wrapper\" in self.model_options:\n wrap_func = self.model_options[\"model_function_wrapper\"]\n if hasattr(wrap_func, \"models\"):\n models += wrap_func.models()\n\n return models\n\n def model_patches_call_function(self, function_name=\"cleanup\", arguments={}):\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], function_name):\n getattr(patch_list[i], function_name)(**arguments)\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], function_name):\n getattr(patch_list[k], function_name)(**arguments)\n if \"model_function_wrapper\" in self.model_options:\n wrap_func = self.model_options[\"model_function_wrapper\"]\n if hasattr(wrap_func, function_name):\n getattr(wrap_func, function_name)(**arguments)\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 with self.use_ejected():\n p = set()\n model_sd = self.model.state_dict()\n for k in patches:\n offset = None\n function = None\n if isinstance(k, str):\n key = k\n else:\n offset = k[1]\n key = k[0]\n if len(k) > 2:\n function = k[2]\n\n if key in model_sd:\n p.add(k)\n current_patches = self.patches.get(key, [])\n current_patches.append((strength_patch, patches[k], strength_model, offset, function))\n self.patches[key] = current_patches\n\n self.patches_uuid = uuid.uuid4()\n return list(p)\n\n def get_key_patches(self, filter_prefix=None):\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 bk = self.backup.get(k, None)\n hbk = self.hook_backup.get(k, None)\n weight, set_func, convert_func = get_key_weight(self.model, k)\n if bk is not None:\n weight = bk.weight\n if hbk is not None:\n weight = hbk[0]\n if convert_func is None:\n convert_func = lambda a, **kwargs: a\n\n if k in self.patches:\n p[k] = [(weight, convert_func)] + self.patches[k]\n else:\n p[k] = [(weight, convert_func)]\n return p\n\n def model_state_dict(self, filter_prefix=None):\n with self.use_ejected():\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_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False):\n weight, set_func, convert_func = get_key_weight(self.model, key)\n if key not in self.patches:\n return weight\n\n inplace_update = self.weight_inplace_update or inplace_update\n\n if key not in self.backup and not return_weight:\n self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)\n\n temp_dtype = comfy.model_management.lora_compute_dtype(device_to)\n if device_to is not None:\n temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)\n else:\n temp_weight = weight.to(temp_dtype, copy=True)\n if convert_func is not None:\n temp_weight = convert_func(temp_weight, inplace=True)\n\n out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)\n if set_func is None:\n out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))\n if return_weight:\n return out_weight\n elif inplace_update:\n comfy.utils.copy_to_param(self.model, key, out_weight)\n else:\n comfy.utils.set_attr_param(self.model, key, out_weight)\n else:\n return set_func(out_weight, inplace_update=inplace_update, seed=comfy.utils.string_to_seed(key), return_weight=return_weight)\n\n def pin_weight_to_device(self, key):\n weight, set_func, convert_func = get_key_weight(self.model, key)\n if comfy.model_management.pin_memory(weight):\n self.pinned.add(key)\n\n def unpin_weight(self, key):\n if key in self.pinned:\n weight, set_func, convert_func = get_key_weight(self.model, key)\n comfy.model_management.unpin_memory(weight)\n self.pinned.remove(key)\n\n def unpin_all_weights(self):\n for key in list(self.pinned):\n self.unpin_weight(key)\n\n def _load_list(self, for_dynamic=False, default_device=None):\n loading = []\n for n, m in self.model.named_modules():\n default = False\n params = { name: param for name, param in m.named_parameters(recurse=False) }\n for name, param in m.named_parameters(recurse=True):\n if name not in params:\n default = True # default random weights in non leaf modules\n break\n if default and default_device is not None:\n for param_name, param in params.items():\n param.data = param.data.to(device=default_device, dtype=getattr(m, param_name + \"_comfy_model_dtype\", None))\n if not default and (hasattr(m, \"comfy_cast_weights\") or len(params) > 0):\n module_mem = comfy.model_management.module_size(m)\n module_offload_mem = module_mem\n if hasattr(m, \"comfy_cast_weights\"):\n def check_module_offload_mem(key):\n if key in self.patches:\n return low_vram_patch_estimate_vram(self.model, key)\n model_dtype = getattr(self.model, \"manual_cast_dtype\", None)\n weight, _, _ = get_key_weight(self.model, key)\n if model_dtype is None or weight is None:\n return 0\n if (weight.dtype != model_dtype or isinstance(weight, QuantizedTensor)):\n return weight.numel() * model_dtype.itemsize\n return 0\n module_offload_mem += check_module_offload_mem(\"{}.weight\".format(n))\n module_offload_mem += check_module_offload_mem(\"{}.bias\".format(n))\n # Dynamic: small weights (<64KB) first, then larger weights prioritized by size.\n # Non-dynamic: prioritize by module offload cost.\n if for_dynamic:\n sort_criteria = (module_offload_mem >= 64 * 1024, -module_offload_mem)\n else:\n sort_criteria = (module_offload_mem,)\n loading.append(sort_criteria + (module_mem, n, m, params))\n return loading\n\n def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):\n with self.use_ejected():\n self.unpatch_hooks()\n mem_counter = 0\n patch_counter = 0\n lowvram_counter = 0\n lowvram_mem_counter = 0\n loading = self._load_list()\n\n load_completely = []\n offloaded = []\n offload_buffer = 0\n loading.sort(reverse=True)\n for i, x in enumerate(loading):\n module_offload_mem, module_mem, n, m, params = x\n\n lowvram_weight = False\n\n potential_offload = max(offload_buffer, module_offload_mem + sum([ x1[1] for x1 in loading[i+1:i+1+comfy.model_management.NUM_STREAMS]]))\n lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory\n\n weight_key = \"{}.weight\".format(n)\n bias_key = \"{}.bias\".format(n)\n\n if not full_load and hasattr(m, \"comfy_cast_weights\"):\n if not lowvram_fits:\n offload_buffer = potential_offload\n lowvram_weight = True\n lowvram_counter += 1\n lowvram_mem_counter += module_mem\n if hasattr(m, \"prev_comfy_cast_weights\"): #Already lowvramed\n continue\n\n cast_weight = self.force_cast_weights\n m.comfy_force_cast_weights = self.force_cast_weights\n if lowvram_weight:\n if hasattr(m, \"comfy_cast_weights\"):\n m.weight_function = []\n m.bias_function = []\n\n if weight_key in self.patches:\n if force_patch_weights:\n self.patch_weight_to_device(weight_key)\n else:\n _, set_func, convert_func = get_key_weight(self.model, weight_key)\n m.weight_function = [LowVramPatch(weight_key, self.patches, convert_func, set_func)]\n patch_counter += 1\n if bias_key in self.patches:\n if force_patch_weights:\n self.patch_weight_to_device(bias_key)\n else:\n _, set_func, convert_func = get_key_weight(self.model, bias_key)\n m.bias_function = [LowVramPatch(bias_key, self.patches, convert_func, set_func)]\n patch_counter += 1\n\n cast_weight = True\n offloaded.append((module_mem, n, m, params))\n else:\n if hasattr(m, \"comfy_cast_weights\"):\n wipe_lowvram_weight(m)\n\n if full_load or lowvram_fits:\n mem_counter += module_mem\n load_completely.append((module_mem, n, m, params))\n else:\n offload_buffer = potential_offload\n\n if cast_weight and hasattr(m, \"comfy_cast_weights\"):\n m.prev_comfy_cast_weights = m.comfy_cast_weights\n m.comfy_cast_weights = True\n\n if weight_key in self.weight_wrapper_patches:\n m.weight_function.extend(self.weight_wrapper_patches[weight_key])\n\n if bias_key in self.weight_wrapper_patches:\n m.bias_function.extend(self.weight_wrapper_patches[bias_key])\n\n mem_counter += move_weight_functions(m, device_to)\n\n load_completely.sort(reverse=True)\n for x in load_completely:\n n = x[1]\n m = x[2]\n params = x[3]\n if hasattr(m, \"comfy_patched_weights\"):\n if m.comfy_patched_weights == True:\n continue\n\n for param in params:\n key = key_param_name_to_key(n, param)\n self.unpin_weight(key)\n self.patch_weight_to_device(key, device_to=device_to)\n if comfy.model_management.is_device_cuda(device_to):\n torch.cuda.synchronize()\n\n logging.debug(\"lowvram: loaded module regularly {} {}\".format(n, m))\n m.comfy_patched_weights = True\n\n for x in load_completely:\n x[2].to(device_to)\n\n for x in offloaded:\n n = x[1]\n params = x[3]\n for param in params:\n self.pin_weight_to_device(key_param_name_to_key(n, param))\n\n usable_stat = \"{:.2f} MB usable,\".format(lowvram_model_memory / (1024 * 1024)) if lowvram_model_memory < 1e32 else \"\"\n if lowvram_counter > 0:\n logging.info(\"loaded partially; {} {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}\".format(usable_stat, mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))\n self.model.model_lowvram = True\n else:\n logging.info(\"loaded completely; {} {:.2f} MB loaded, full load: {}\".format(usable_stat, mem_counter / (1024 * 1024), full_load))\n self.model.model_lowvram = False\n if full_load:\n self.model.to(device_to)\n mem_counter = self.model_size()\n\n self.model.lowvram_patch_counter += patch_counter\n self.model.device = device_to\n self.model.model_loaded_weight_memory = mem_counter\n self.model.model_offload_buffer_memory = offload_buffer\n self.model.current_weight_patches_uuid = self.patches_uuid\n\n for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD):\n callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load)\n\n self.apply_hooks(self.forced_hooks, force_apply=True)\n\n def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):\n with self.use_ejected():\n for k in self.object_patches:\n old = comfy.utils.set_attr(self.model, k, self.object_patches[k])\n if k not in self.object_patches_backup:\n self.object_patches_backup[k] = old\n\n if lowvram_model_memory == 0:\n full_load = True\n else:\n full_load = False\n\n if load_weights:\n self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)\n self.inject_model()\n return self.model\n\n def unpatch_model(self, device_to=None, unpatch_weights=True):\n self.eject_model()\n if unpatch_weights:\n self.unpatch_hooks()\n self.unpin_all_weights()\n if self.model.model_lowvram:\n for m in self.model.modules():\n move_weight_functions(m, device_to)\n wipe_lowvram_weight(m)\n\n self.model.model_lowvram = False\n self.model.lowvram_patch_counter = 0\n\n keys = list(self.backup.keys())\n\n for k in keys:\n bk = self.backup[k]\n if bk.inplace_update:\n comfy.utils.copy_to_param(self.model, k, bk.weight)\n else:\n comfy.utils.set_attr_param(self.model, k, bk.weight)\n\n self.model.current_weight_patches_uuid = None\n self.backup.clear()\n\n if device_to is not None:\n self.model.to(device_to)\n self.model.device = device_to\n self.model.model_loaded_weight_memory = 0\n self.model.model_offload_buffer_memory = 0\n\n for m in self.model.modules():\n if hasattr(m, \"comfy_patched_weights\"):\n del m.comfy_patched_weights\n\n keys = list(self.object_patches_backup.keys())\n for k in keys:\n comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])\n\n self.object_patches_backup.clear()\n\n def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False):\n with self.use_ejected():\n hooks_unpatched = False\n memory_freed = 0\n patch_counter = 0\n unload_list = self._load_list()\n unload_list.sort()\n\n offload_buffer = self.model.model_offload_buffer_memory\n if len(unload_list) > 0:\n NS = comfy.model_management.NUM_STREAMS\n offload_weight_factor = [ min(offload_buffer / (NS + 1), unload_list[0][1]) ] * NS\n\n for unload in unload_list:\n if memory_to_free + offload_buffer - self.model.model_offload_buffer_memory < memory_freed:\n break\n module_offload_mem, module_mem, n, m, params = unload\n\n potential_offload = module_offload_mem + sum(offload_weight_factor)\n\n lowvram_possible = hasattr(m, \"comfy_cast_weights\")\n if hasattr(m, \"comfy_patched_weights\") and m.comfy_patched_weights == True:\n move_weight = True\n for param in params:\n key = key_param_name_to_key(n, param)\n bk = self.backup.get(key, None)\n if bk is not None:\n if not lowvram_possible:\n move_weight = False\n break\n\n if not hooks_unpatched:\n self.unpatch_hooks()\n hooks_unpatched = True\n\n if bk.inplace_update:\n comfy.utils.copy_to_param(self.model, key, bk.weight)\n else:\n comfy.utils.set_attr_param(self.model, key, bk.weight)\n self.backup.pop(key)\n\n weight_key = \"{}.weight\".format(n)\n bias_key = \"{}.bias\".format(n)\n if move_weight:\n cast_weight = self.force_cast_weights\n m.to(device_to)\n module_mem += move_weight_functions(m, device_to)\n if lowvram_possible:\n if weight_key in self.patches:\n if force_patch_weights:\n self.patch_weight_to_device(weight_key)\n else:\n _, set_func, convert_func = get_key_weight(self.model, weight_key)\n m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func))\n patch_counter += 1\n if bias_key in self.patches:\n if force_patch_weights:\n self.patch_weight_to_device(bias_key)\n else:\n _, set_func, convert_func = get_key_weight(self.model, bias_key)\n m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func))\n patch_counter += 1\n cast_weight = True\n\n if cast_weight and hasattr(m, \"comfy_cast_weights\"):\n m.prev_comfy_cast_weights = m.comfy_cast_weights\n m.comfy_cast_weights = True\n m.comfy_patched_weights = False\n memory_freed += module_mem\n offload_buffer = max(offload_buffer, potential_offload)\n offload_weight_factor.append(module_mem)\n offload_weight_factor.pop(0)\n logging.debug(\"freed {}\".format(n))\n\n for param in params:\n self.pin_weight_to_device(key_param_name_to_key(n, param))\n\n\n self.model.model_lowvram = True\n self.model.lowvram_patch_counter += patch_counter\n self.model.model_loaded_weight_memory -= memory_freed\n self.model.model_offload_buffer_memory = offload_buffer\n logging.info(\"Unloaded partially: {:.2f} MB freed, {:.2f} MB remains loaded, {:.2f} MB buffer reserved, lowvram patches: {}\".format(memory_freed / (1024 * 1024), self.model.model_loaded_weight_memory / (1024 * 1024), offload_buffer / (1024 * 1024), self.model.lowvram_patch_counter))\n return memory_freed\n\n def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):\n with self.use_ejected(skip_and_inject_on_exit_only=True):\n unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights)\n # TODO: force_patch_weights should not unload + reload full model\n used = self.model.model_loaded_weight_memory\n self.unpatch_model(self.offload_device, unpatch_weights=unpatch_weights)\n if unpatch_weights:\n extra_memory += (used - self.model.model_loaded_weight_memory)\n\n self.patch_model(load_weights=False)\n if extra_memory < 0 and not unpatch_weights:\n self.partially_unload(self.offload_device, -extra_memory, force_patch_weights=force_patch_weights)\n return 0\n full_load = False\n if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0:\n self.apply_hooks(self.forced_hooks, force_apply=True)\n return 0\n if self.model.model_loaded_weight_memory + extra_memory > self.model_size():\n full_load = True\n current_used = self.model.model_loaded_weight_memory\n try:\n self.load(device_to, lowvram_model_memory=current_used + extra_memory, force_patch_weights=force_patch_weights, full_load=full_load)\n except Exception as e:\n self.detach()\n raise e\n\n return self.model.model_loaded_weight_memory - current_used\n\n def pinned_memory_size(self):\n # Pinned memory pressure tracking is only implemented for DynamicVram loading\n return 0\n\n def partially_unload_ram(self, ram_to_unload):\n pass\n\n def detach(self, unpatch_all=True):\n self.eject_model()\n self.model_patches_to(self.offload_device)\n if unpatch_all:\n self.unpatch_model(self.offload_device, unpatch_weights=unpatch_all)\n for callback in self.get_all_callbacks(CallbacksMP.ON_DETACH):\n callback(self, unpatch_all)\n return self.model\n\n def current_loaded_device(self):\n return self.model.device\n\n def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):\n logging.warning(\"The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead\")\n return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)\n\n def cleanup(self):\n self.model_patches_call_function(function_name=\"cleanup\")\n self.clean_hooks()\n if hasattr(self.model, \"current_patcher\"):\n self.model.current_patcher = None\n for callback in self.get_all_callbacks(CallbacksMP.ON_CLEANUP):\n callback(self)\n\n def add_callback(self, call_type: str, callback: Callable):\n self.add_callback_with_key(call_type, None, callback)\n\n def add_callback_with_key(self, call_type: str, key: str, callback: Callable):\n c = self.callbacks.setdefault(call_type, {}).setdefault(key, [])\n c.append(callback)\n\n def remove_callbacks_with_key(self, call_type: str, key: str):\n c = self.callbacks.get(call_type, {})\n if key in c:\n c.pop(key)\n\n def get_callbacks(self, call_type: str, key: str):\n return self.callbacks.get(call_type, {}).get(key, [])\n\n def get_all_callbacks(self, call_type: str):\n c_list = []\n for c in self.callbacks.get(call_type, {}).values():\n c_list.extend(c)\n return c_list\n\n def add_wrapper(self, wrapper_type: str, wrapper: Callable):\n self.add_wrapper_with_key(wrapper_type, None, wrapper)\n\n def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable):\n w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, [])\n w.append(wrapper)\n\n def remove_wrappers_with_key(self, wrapper_type: str, key: str):\n w = self.wrappers.get(wrapper_type, {})\n if key in w:\n w.pop(key)\n\n def get_wrappers(self, wrapper_type: str, key: str):\n return self.wrappers.get(wrapper_type, {}).get(key, [])\n\n def get_all_wrappers(self, wrapper_type: str):\n w_list = []\n for w in self.wrappers.get(wrapper_type, {}).values():\n w_list.extend(w)\n return w_list\n\n def set_attachments(self, key: str, attachment):\n self.attachments[key] = attachment\n\n def remove_attachments(self, key: str):\n if key in self.attachments:\n self.attachments.pop(key)\n\n def get_attachment(self, key: str):\n return self.attachments.get(key, None)\n\n def set_injections(self, key: str, injections: list[PatcherInjection]):\n self.injections[key] = injections\n\n def remove_injections(self, key: str):\n if key in self.injections:\n self.injections.pop(key)\n\n def get_injections(self, key: str):\n return self.injections.get(key, None)\n\n def set_additional_models(self, key: str, models: list['ModelPatcher']):\n self.additional_models[key] = models\n\n def remove_additional_models(self, key: str):\n if key in self.additional_models:\n self.additional_models.pop(key)\n\n def get_additional_models_with_key(self, key: str):\n return self.additional_models.get(key, [])\n\n def get_additional_models(self):\n all_models = []\n for models in self.additional_models.values():\n all_models.extend(models)\n return all_models\n\n def get_nested_additional_models(self):\n def _evaluate_sub_additional_models(prev_models: list[ModelPatcher], cache_set: set[ModelPatcher]):\n '''Make sure circular references do not cause infinite recursion.'''\n next_models = []\n for model in prev_models:\n candidates = model.get_additional_models()\n for c in candidates:\n if c not in cache_set:\n next_models.append(c)\n cache_set.add(c)\n if len(next_models) == 0:\n return prev_models\n return prev_models + _evaluate_sub_additional_models(next_models, cache_set)\n\n all_models = self.get_additional_models()\n models_set = set(all_models)\n real_all_models = _evaluate_sub_additional_models(prev_models=all_models, cache_set=models_set)\n return real_all_models\n\n def use_ejected(self, skip_and_inject_on_exit_only=False):\n return AutoPatcherEjector(self, skip_and_inject_on_exit_only=skip_and_inject_on_exit_only)\n\n def inject_model(self):\n if self.is_injected or self.skip_injection:\n return\n for injections in self.injections.values():\n for inj in injections:\n inj.inject(self)\n self.is_injected = True\n if self.is_injected:\n for callback in self.get_all_callbacks(CallbacksMP.ON_INJECT_MODEL):\n callback(self)\n\n def eject_model(self):\n if not self.is_injected:\n return\n for injections in self.injections.values():\n for inj in injections:\n inj.eject(self)\n self.is_injected = False\n for callback in self.get_all_callbacks(CallbacksMP.ON_EJECT_MODEL):\n callback(self)\n\n def pre_run(self):\n if hasattr(self.model, \"current_patcher\"):\n self.model.current_patcher = self\n for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN):\n callback(self)\n\n def prepare_state(self, timestep):\n for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE):\n callback(self, timestep)\n\n def restore_hook_patches(self):\n if self.hook_patches_backup is not None:\n self.hook_patches = self.hook_patches_backup\n self.hook_patches_backup = None\n\n def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode):\n self.hook_mode = hook_mode\n\n def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]):\n curr_t = t[0]\n reset_current_hooks = False\n transformer_options = model_options.get(\"transformer_options\", {})\n for hook in hook_group.hooks:\n changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options)\n # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref;\n # this will cause the weights to be recalculated when sampling\n if changed:\n # reset current_hooks if contains hook that changed\n if self.current_hooks is not None:\n for current_hook in self.current_hooks.hooks:\n if current_hook == hook:\n reset_current_hooks = True\n break\n for cached_group in list(self.cached_hook_patches.keys()):\n if cached_group.contains(hook):\n self.cached_hook_patches.pop(cached_group)\n if reset_current_hooks:\n self.patch_hooks(None)\n\n def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None,\n registered: comfy.hooks.HookGroup = None):\n self.restore_hook_patches()\n if registered is None:\n registered = comfy.hooks.HookGroup()\n # handle WeightHooks\n weight_hooks_to_register: list[comfy.hooks.WeightHook] = []\n for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight):\n if hook.hook_ref not in self.hook_patches:\n weight_hooks_to_register.append(hook)\n else:\n registered.add(hook)\n if len(weight_hooks_to_register) > 0:\n # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state\n self.hook_patches_backup = create_hook_patches_clone(self.hook_patches)\n for hook in weight_hooks_to_register:\n hook.add_hook_patches(self, model_options, target_dict, registered)\n for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES):\n callback(self, hooks, target_dict, model_options, registered)\n return registered\n\n def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0):\n with self.use_ejected():\n # NOTE: this mirrors behavior of add_patches func\n current_hook_patches: dict[str,list] = self.hook_patches.get(hook.hook_ref, {})\n p = set()\n model_sd = self.model.state_dict()\n for k in patches:\n offset = None\n function = None\n if isinstance(k, str):\n key = k\n else:\n offset = k[1]\n key = k[0]\n if len(k) > 2:\n function = k[2]\n\n if key in model_sd:\n p.add(k)\n current_patches: list[tuple] = current_hook_patches.get(key, [])\n current_patches.append((strength_patch, patches[k], strength_model, offset, function))\n current_hook_patches[key] = current_patches\n self.hook_patches[hook.hook_ref] = current_hook_patches\n # since should care about these patches too to determine if same model, reroll patches_uuid\n self.patches_uuid = uuid.uuid4()\n return list(p)\n\n def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup):\n # combined_patches will contain weights of all relevant hooks, per key\n combined_patches = {}\n if hooks is not None:\n for hook in hooks.hooks:\n hook_patches: dict = self.hook_patches.get(hook.hook_ref, {})\n for key in hook_patches.keys():\n current_patches: list[tuple] = combined_patches.get(key, [])\n if math.isclose(hook.strength, 1.0):\n current_patches.extend(hook_patches[key])\n else:\n # patches are stored as tuples: (strength_patch, (tuple_with_weights,), strength_model)\n for patch in hook_patches[key]:\n new_patch = list(patch)\n new_patch[0] *= hook.strength\n current_patches.append(tuple(new_patch))\n combined_patches[key] = current_patches\n return combined_patches\n\n def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False):\n # TODO: return transformer_options dict with any additions from hooks\n if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)):\n return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options)\n self.patch_hooks(hooks=hooks)\n for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS):\n callback(self, hooks)\n return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options)\n\n def patch_hooks(self, hooks: comfy.hooks.HookGroup):\n with self.use_ejected():\n if hooks is not None:\n model_sd_keys = list(self.model_state_dict().keys())\n memory_counter = None\n if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:\n # TODO: minimum_counter should have a minimum that conforms to loaded model requirements\n memory_counter = MemoryCounter(initial=comfy.model_management.get_free_memory(self.load_device),\n minimum=comfy.model_management.minimum_inference_memory()*2)\n # if have cached weights for hooks, use it\n cached_weights = self.cached_hook_patches.get(hooks, None)\n if cached_weights is not None:\n model_sd_keys_set = set(model_sd_keys)\n for key in cached_weights:\n if key not in model_sd_keys:\n logging.warning(f\"Cached hook could not patch. Key does not exist in model: {key}\")\n continue\n self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter)\n model_sd_keys_set.remove(key)\n self.unpatch_hooks(model_sd_keys_set)\n else:\n self.unpatch_hooks()\n relevant_patches = self.get_combined_hook_patches(hooks=hooks)\n original_weights = None\n if len(relevant_patches) > 0:\n original_weights = self.get_key_patches()\n for key in relevant_patches:\n if key not in model_sd_keys:\n logging.warning(f\"Cached hook would not patch. Key does not exist in model: {key}\")\n continue\n self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights,\n memory_counter=memory_counter)\n else:\n self.unpatch_hooks()\n self.current_hooks = hooks\n\n def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter):\n if key not in self.hook_backup:\n weight: torch.Tensor = comfy.utils.get_attr(self.model, key)\n target_device = self.offload_device\n if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:\n used = memory_counter.use(weight)\n if used:\n target_device = weight.device\n self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device)\n comfy.utils.copy_to_param(self.model, key, cached_weights[key][0].to(device=cached_weights[key][1]))\n\n def clear_cached_hook_weights(self):\n self.cached_hook_patches.clear()\n self.patch_hooks(None)\n\n def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter):\n if key not in combined_patches:\n return\n\n weight, set_func, convert_func = get_key_weight(self.model, key)\n weight: torch.Tensor\n if key not in self.hook_backup:\n target_device = self.offload_device\n if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:\n used = memory_counter.use(weight)\n if used:\n target_device = weight.device\n self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device)\n # TODO: properly handle LowVramPatch, if it ends up an issue\n temp_weight = comfy.model_management.cast_to_device(weight, weight.device, torch.float32, copy=True)\n if convert_func is not None:\n temp_weight = convert_func(temp_weight, inplace=True)\n\n out_weight = comfy.lora.calculate_weight(combined_patches[key],\n temp_weight,\n key, original_weights=original_weights)\n del original_weights[key]\n if set_func is None:\n out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))\n comfy.utils.copy_to_param(self.model, key, out_weight)\n else:\n set_func(out_weight, inplace_update=True, seed=comfy.utils.string_to_seed(key))\n if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:\n # TODO: disable caching if not enough system RAM to do so\n target_device = self.offload_device\n used = memory_counter.use(weight)\n if used:\n target_device = weight.device\n self.cached_hook_patches.setdefault(hooks, {})\n self.cached_hook_patches[hooks][key] = (out_weight.to(device=target_device, copy=False), weight.device)\n del temp_weight\n del out_weight\n del weight\n\n def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:\n with self.use_ejected():\n if len(self.hook_backup) == 0:\n self.current_hooks = None\n return\n keys = list(self.hook_backup.keys())\n if whitelist_keys_set:\n for k in keys:\n if k in whitelist_keys_set:\n comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))\n self.hook_backup.pop(k)\n else:\n for k in keys:\n comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))\n\n self.hook_backup.clear()\n self.current_hooks = None\n\n def clean_hooks(self):\n self.unpatch_hooks()\n self.clear_cached_hook_weights()\n\n def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):\n unet_state_dict = self.model.diffusion_model.state_dict()\n for k, v in unet_state_dict.items():\n op_keys = k.rsplit('.', 1)\n if (len(op_keys) < 2) or op_keys[1] not in [\"weight\", \"bias\"]:\n continue\n try:\n op = comfy.utils.get_attr(self.model.diffusion_model, op_keys[0])\n except:\n continue\n if not op or not hasattr(op, \"comfy_cast_weights\") or \\\n (hasattr(op, \"comfy_patched_weights\") and op.comfy_patched_weights == True):\n continue\n key = \"diffusion_model.\" + k\n unet_state_dict[k] = LazyCastingParam(self, key, comfy.utils.get_attr(self.model, key))\n return self.model.state_dict_for_saving(unet_state_dict, clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict)\n\n def __del__(self):\n self.unpin_all_weights()\n self.detach(unpatch_all=False)\n\nclass ModelPatcherDynamic(ModelPatcher):\n\n def __new__(cls, model=None, load_device=None, offload_device=None, size=0, weight_inplace_update=False):\n if load_device is not None and comfy.model_management.is_device_cpu(load_device):\n #reroute to default MP for CPUs\n return ModelPatcher(model, load_device, offload_device, size, weight_inplace_update)\n return super().__new__(cls)\n\n def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):\n super().__init__(model, load_device, offload_device, size, weight_inplace_update)\n if not hasattr(self.model, \"dynamic_vbars\"):\n self.model.dynamic_vbars = {}\n self.non_dynamic_delegate_model = None\n assert load_device is not None\n\n def is_dynamic(self):\n return True\n\n def _vbar_get(self, create=False):\n if self.load_device == torch.device(\"cpu\"):\n return None\n vbar = self.model.dynamic_vbars.get(self.load_device, None)\n if create and vbar is None:\n # x10. We dont know what model defined type casts we have in the vbar, but virtual address\n # space is pretty free. This will cover someone casting an entire model from FP4 to FP32\n # with some left over.\n vbar = comfy_aimdo.model_vbar.ModelVBAR(self.model_size() * 10, self.load_device.index)\n self.model.dynamic_vbars[self.load_device] = vbar\n return vbar\n\n def loaded_size(self):\n vbar = self._vbar_get()\n return (vbar.loaded_size() if vbar is not None else 0) + self.model.model_loaded_weight_memory\n\n #Pinning is deferred to ops time. Assert against this API to avoid pin leaks.\n\n def pin_weight_to_device(self, key):\n raise RuntimeError(\"pin_weight_to_device invalid for dymamic weight loading\")\n\n def unpin_weight(self, key):\n raise RuntimeError(\"unpin_weight invalid for dymamic weight loading\")\n\n def unpin_all_weights(self):\n self.partially_unload_ram(1e32)\n\n def memory_required(self, input_shape):\n #Pad this significantly. We are trying to get away from precise estimates. This\n #estimate is only used when using the ModelPatcherDynamic after ModelPatcher. If you\n #use all ModelPatcherDynamic this is ignored and its all done dynamically.\n return super().memory_required(input_shape=input_shape) * 1.3 + (1024 ** 3)\n\n\n def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False, dirty=False):\n\n #Force patching doesn't make sense in Dynamic loading, as you dont know what does and\n #doesn't need to be forced at this stage. The only thing you could do would be patch\n #it all on CPU which consumes huge RAM.\n assert not force_patch_weights\n\n #Full load doesn't make sense as we dont actually have any loader capability here and\n #now.\n assert not full_load\n\n assert device_to == self.load_device\n\n num_patches = 0\n allocated_size = 0\n self.model.model_loaded_weight_memory = 0\n\n with self.use_ejected():\n self.unpatch_hooks()\n\n vbar = self._vbar_get(create=True)\n if vbar is not None:\n vbar.prioritize()\n\n loading = self._load_list(for_dynamic=True, default_device=device_to)\n loading.sort()\n\n for x in loading:\n *_, module_mem, n, m, params = x\n\n def set_dirty(item, dirty):\n if dirty or not hasattr(item, \"_v_signature\"):\n item._v_signature = None\n\n def setup_param(self, m, n, param_key):\n nonlocal num_patches\n key = key_param_name_to_key(n, param_key)\n\n weight_function = []\n\n weight, _, _ = get_key_weight(self.model, key)\n if weight is None:\n return (False, 0)\n if key in self.patches:\n if comfy.lora.calculate_shape(self.patches[key], weight, key) != weight.shape:\n return (True, 0)\n setattr(m, param_key + \"_lowvram_function\", LowVramPatch(key, self.patches))\n num_patches += 1\n else:\n setattr(m, param_key + \"_lowvram_function\", None)\n\n if key in self.weight_wrapper_patches:\n weight_function.extend(self.weight_wrapper_patches[key])\n setattr(m, param_key + \"_function\", weight_function)\n geometry = weight\n if not isinstance(weight, QuantizedTensor):\n model_dtype = getattr(m, param_key + \"_comfy_model_dtype\", None) or weight.dtype\n weight._model_dtype = model_dtype\n geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype)\n return (False, comfy.memory_management.vram_aligned_size(geometry))\n\n def force_load_param(self, param_key, device_to):\n key = key_param_name_to_key(n, param_key)\n if key in self.backup:\n comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)\n self.patch_weight_to_device(key, device_to=device_to)\n weight, _, _ = get_key_weight(self.model, key)\n if weight is not None:\n self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()\n\n if hasattr(m, \"comfy_cast_weights\"):\n m.comfy_cast_weights = True\n m.pin_failed = False\n m.seed_key = n\n set_dirty(m, dirty)\n\n force_load, v_weight_size = setup_param(self, m, n, \"weight\")\n force_load_bias, v_weight_bias = setup_param(self, m, n, \"bias\")\n force_load = force_load or force_load_bias\n v_weight_size += v_weight_bias\n\n if force_load:\n logging.info(f\"Module {n} has resizing Lora - force loading\")\n force_load_param(self, \"weight\", device_to)\n force_load_param(self, \"bias\", device_to)\n else:\n if vbar is not None and not hasattr(m, \"_v\"):\n m._v = vbar.alloc(v_weight_size)\n allocated_size += v_weight_size\n\n else:\n for param in params:\n key = key_param_name_to_key(n, param)\n weight, _, _ = get_key_weight(self.model, key)\n if key not in self.backup:\n self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight, False)\n model_dtype = getattr(m, param + \"_comfy_model_dtype\", None)\n casted_weight = weight.to(dtype=model_dtype, device=device_to)\n comfy.utils.set_attr_param(self.model, key, casted_weight)\n self.model.model_loaded_weight_memory += casted_weight.numel() * casted_weight.element_size()\n\n move_weight_functions(m, device_to)\n\n for key, buf in self.model.named_buffers(recurse=True):\n if key not in self.backup_buffers:\n self.backup_buffers[key] = buf\n module, buf_name = comfy.utils.resolve_attr(self.model, key)\n model_dtype = getattr(module, buf_name + \"_comfy_model_dtype\", None)\n casted_buf = buf.to(dtype=model_dtype, device=device_to)\n comfy.utils.set_attr_buffer(self.model, key, casted_buf)\n self.model.model_loaded_weight_memory += casted_buf.numel() * casted_buf.element_size()\n\n force_load_stat = f\" Force pre-loaded {len(self.backup)} weights: {self.model.model_loaded_weight_memory // 1024} KB.\" if len(self.backup) > 0 else \"\"\n logging.info(f\"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}\")\n\n self.model.device = device_to\n self.model.current_weight_patches_uuid = self.patches_uuid\n\n for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD):\n #These are all super dangerous. Who knows what the custom nodes actually do here...\n callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load)\n\n self.apply_hooks(self.forced_hooks, force_apply=True)\n\n def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False):\n assert not force_patch_weights #See above\n assert self.load_device != torch.device(\"cpu\")\n\n vbar = self._vbar_get()\n freed = 0 if vbar is None else vbar.free_memory(memory_to_free)\n\n if freed < memory_to_free:\n for key in list(self.backup.keys()):\n bk = self.backup.pop(key)\n comfy.utils.set_attr_param(self.model, key, bk.weight)\n for key in list(self.backup_buffers.keys()):\n comfy.utils.set_attr_buffer(self.model, key, self.backup_buffers.pop(key))\n freed += self.model.model_loaded_weight_memory\n self.model.model_loaded_weight_memory = 0\n\n return freed\n\n def pinned_memory_size(self):\n total = 0\n loading = self._load_list(for_dynamic=True)\n for x in loading:\n _, _, _, _, m, _ = x\n pin = comfy.pinned_memory.get_pin(m)\n if pin is not None:\n total += pin.numel() * pin.element_size()\n return total\n\n def partially_unload_ram(self, ram_to_unload):\n loading = self._load_list(for_dynamic=True, default_device=self.offload_device)\n for x in loading:\n *_, m, _ = x\n ram_to_unload -= comfy.pinned_memory.unpin_memory(m)\n if ram_to_unload <= 0:\n return\n\n def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):\n #This isn't used by the core at all and can only be to load a model out of\n #the control of proper model_managment. If you are a custom node author reading\n #this, the correct pattern is to call load_models_gpu() to get a proper\n #managed load of your model.\n assert not load_weights\n return super().patch_model(load_weights=load_weights, force_patch_weights=force_patch_weights)\n\n def unpatch_model(self, device_to=None, unpatch_weights=True):\n super().unpatch_model(device_to=None, unpatch_weights=False)\n\n if unpatch_weights:\n self.partially_unload_ram(1e32)\n self.partially_unload(None, 1e32)\n for m in self.model.modules():\n move_weight_functions(m, device_to)\n\n def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):\n assert not force_patch_weights #See above\n with self.use_ejected(skip_and_inject_on_exit_only=True):\n dirty = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid)\n\n self.unpatch_model(self.offload_device, unpatch_weights=False)\n self.patch_model(load_weights=False)\n\n try:\n self.load(device_to, dirty=dirty)\n except Exception as e:\n self.detach()\n raise e\n #ModelPatcher::partially_load returns a number on what got loaded but\n #nothing in core uses this and we have no data in the Dynamic world. Hit\n #the custom node devs with a None rather than a 0 that would mislead any\n #logic they might have.\n return None\n\n def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter):\n assert False #Should be unreachable - we dont ever cache in the new implementation\n\n def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter):\n if key not in combined_patches:\n return\n\n raise RuntimeError(\"Hooks not implemented in ModelPatcherDynamic. Please remove --fast arguments form ComfyUI startup\")\n\n def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:\n pass\n\n def get_non_dynamic_delegate(self):\n model_patcher = self.clone(disable_dynamic=True, model_override=self.non_dynamic_delegate_model)\n self.non_dynamic_delegate_model = model_patcher.get_clone_model_override()\n return model_patcher\n\n\nCoreModelPatcher = ModelPatcher\n" }, { "path": "comfy/model_sampling.py", "content": "import torch\nfrom comfy.ldm.modules.diffusionmodules.util import make_beta_schedule\nimport math\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\ndef reshape_sigma(sigma, noise_dim):\n if sigma.nelement() == 1:\n return sigma.view(())\n else:\n return sigma.view(sigma.shape[:1] + (1,) * (noise_dim - 1))\n\nclass EPS:\n def calculate_input(self, sigma, noise):\n sigma = reshape_sigma(sigma, noise.ndim)\n return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5\n\n def calculate_denoised(self, sigma, model_output, model_input):\n sigma = reshape_sigma(sigma, model_output.ndim)\n return model_input - model_output * sigma\n\n def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):\n sigma = reshape_sigma(sigma, noise.ndim)\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 = reshape_sigma(sigma, model_output.ndim)\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 = reshape_sigma(sigma, model_output.ndim)\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 CONST:\n def calculate_input(self, sigma, noise):\n return noise\n\n def calculate_denoised(self, sigma, model_output, model_input):\n sigma = reshape_sigma(sigma, model_output.ndim)\n return model_input - model_output * sigma\n\n def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):\n sigma = reshape_sigma(sigma, noise.ndim)\n return sigma * noise + (1.0 - sigma) * latent_image\n\n def inverse_noise_scaling(self, sigma, latent):\n sigma = reshape_sigma(sigma, latent.ndim)\n return latent / (1.0 - sigma)\n\nclass X0(EPS):\n def calculate_denoised(self, sigma, model_output, model_input):\n return model_output\n\nclass IMG_TO_IMG(X0):\n def calculate_input(self, sigma, noise):\n return noise\n\nclass IMG_TO_IMG_FLOW(CONST):\n def calculate_denoised(self, sigma, model_output, model_input):\n return model_output\n\n def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):\n return latent_image\n\n def inverse_noise_scaling(self, sigma, latent):\n return 1.0 - latent\n\nclass COSMOS_RFLOW:\n def calculate_input(self, sigma, noise):\n sigma = (sigma / (sigma + 1))\n sigma = reshape_sigma(sigma, noise.ndim)\n return noise * (1.0 - sigma)\n\n def calculate_denoised(self, sigma, model_output, model_input):\n sigma = (sigma / (sigma + 1))\n sigma = reshape_sigma(sigma, model_output.ndim)\n return model_input * (1.0 - sigma) - model_output * sigma\n\n def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):\n sigma = reshape_sigma(sigma, noise.ndim)\n noise = noise * sigma\n noise += latent_image\n return noise\n\n def inverse_noise_scaling(self, sigma, latent):\n return latent\n\nclass ModelSamplingDiscrete(torch.nn.Module):\n def __init__(self, model_config=None, zsnr=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 timesteps = sampling_settings.get(\"timesteps\", 1000)\n\n if zsnr is None:\n zsnr = sampling_settings.get(\"zsnr\", False)\n\n self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr)\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, zsnr=False):\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 self.zsnr = zsnr\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 if self.zsnr:\n sigmas = rescale_zero_terminal_snr_sigmas(sigmas)\n\n self.set_sigmas(sigmas)\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 @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\nclass ModelSamplingDiscreteEDM(ModelSamplingDiscrete):\n def timestep(self, sigma):\n return 0.25 * sigma.log()\n\n def sigma(self, timestep):\n return (timestep / 0.25).exp()\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\n\nclass ModelSamplingContinuousV(ModelSamplingContinuousEDM):\n def timestep(self, sigma):\n return sigma.atan() / math.pi * 2\n\n def sigma(self, timestep):\n return (timestep * math.pi / 2).tan()\n\n\ndef time_snr_shift(alpha, t):\n if alpha == 1.0:\n return t\n return alpha * t / (1 + (alpha - 1) * t)\n\nclass ModelSamplingDiscreteFlow(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 self.set_parameters(shift=sampling_settings.get(\"shift\", 1.0), multiplier=sampling_settings.get(\"multiplier\", 1000))\n\n def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):\n self.shift = shift\n self.multiplier = multiplier\n ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier)\n self.register_buffer('sigmas', ts)\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 sigma * self.multiplier\n\n def sigma(self, timestep):\n return time_snr_shift(self.shift, timestep / self.multiplier)\n\n def percent_to_sigma(self, percent):\n if percent <= 0.0:\n return 1.0\n if percent >= 1.0:\n return 0.0\n return time_snr_shift(self.shift, 1.0 - percent)\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))\n\n\ndef flux_time_shift(mu: float, sigma: float, t):\n return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)\n\nclass ModelSamplingFlux(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 self.set_parameters(shift=sampling_settings.get(\"shift\", 1.15))\n\n def set_parameters(self, shift=1.15, timesteps=10000):\n self.shift = shift\n ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps))\n self.register_buffer('sigmas', ts)\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 sigma\n\n def sigma(self, timestep):\n return flux_time_shift(self.shift, 1.0, timestep)\n\n def percent_to_sigma(self, percent):\n if percent <= 0.0:\n return 1.0\n if percent >= 1.0:\n return 0.0\n return flux_time_shift(self.shift, 1.0, 1.0 - percent)\n\n\nclass ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM):\n def timestep(self, sigma):\n return sigma / (sigma + 1)\n\n def sigma(self, timestep):\n sigma_max = self.sigma_max\n if timestep >= (sigma_max / (sigma_max + 1)):\n return sigma_max\n\n return timestep / (1 - timestep)\n" }, { "path": "comfy/nested_tensor.py", "content": "import torch\n\nclass NestedTensor:\n def __init__(self, tensors):\n self.tensors = list(tensors)\n self.is_nested = True\n\n def _copy(self):\n return NestedTensor(self.tensors)\n\n def apply_operation(self, other, operation):\n o = self._copy()\n if isinstance(other, NestedTensor):\n for i, t in enumerate(o.tensors):\n o.tensors[i] = operation(t, other.tensors[i])\n else:\n for i, t in enumerate(o.tensors):\n o.tensors[i] = operation(t, other)\n return o\n\n def __add__(self, b):\n return self.apply_operation(b, lambda x, y: x + y)\n\n def __sub__(self, b):\n return self.apply_operation(b, lambda x, y: x - y)\n\n def __mul__(self, b):\n return self.apply_operation(b, lambda x, y: x * y)\n\n # def __itruediv__(self, b):\n # return self.apply_operation(b, lambda x, y: x / y)\n\n def __truediv__(self, b):\n return self.apply_operation(b, lambda x, y: x / y)\n\n def __getitem__(self, *args, **kwargs):\n return self.apply_operation(None, lambda x, y: x.__getitem__(*args, **kwargs))\n\n def unbind(self):\n return self.tensors\n\n def to(self, *args, **kwargs):\n o = self._copy()\n for i, t in enumerate(o.tensors):\n o.tensors[i] = t.to(*args, **kwargs)\n return o\n\n def new_ones(self, *args, **kwargs):\n return self.tensors[0].new_ones(*args, **kwargs)\n\n def float(self):\n return self.to(dtype=torch.float)\n\n def chunk(self, *args, **kwargs):\n return self.apply_operation(None, lambda x, y: x.chunk(*args, **kwargs))\n\n def size(self):\n return self.tensors[0].size()\n\n @property\n def shape(self):\n return self.tensors[0].shape\n\n @property\n def ndim(self):\n dims = 0\n for t in self.tensors:\n dims = max(t.ndim, dims)\n return dims\n\n @property\n def device(self):\n return self.tensors[0].device\n\n @property\n def dtype(self):\n return self.tensors[0].dtype\n\n @property\n def layout(self):\n return self.tensors[0].layout\n\n\ndef cat_nested(tensors, *args, **kwargs):\n cated_tensors = []\n for i in range(len(tensors[0].tensors)):\n tens = []\n for j in range(len(tensors)):\n tens.append(tensors[j].tensors[i])\n cated_tensors.append(torch.cat(tens, *args, **kwargs))\n return NestedTensor(cated_tensors)\n" }, { "path": "comfy/ops.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Stability AI\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 .\n\"\"\"\n\nimport torch\nimport logging\nimport comfy.model_management\nfrom comfy.cli_args import args, PerformanceFeature\nimport comfy.float\nimport json\nimport comfy.memory_management\nimport comfy.pinned_memory\nimport comfy.utils\n\nimport comfy_aimdo.model_vbar\nimport comfy_aimdo.torch\n\ndef run_every_op():\n if torch.compiler.is_compiling():\n return\n\n comfy.model_management.throw_exception_if_processing_interrupted()\n\ndef scaled_dot_product_attention(q, k, v, *args, **kwargs):\n return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)\n\n\ntry:\n if torch.cuda.is_available() and comfy.model_management.WINDOWS:\n from torch.nn.attention import SDPBackend, sdpa_kernel\n import inspect\n if \"set_priority\" in inspect.signature(sdpa_kernel).parameters:\n SDPA_BACKEND_PRIORITY = [\n SDPBackend.FLASH_ATTENTION,\n SDPBackend.EFFICIENT_ATTENTION,\n SDPBackend.MATH,\n ]\n\n SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION)\n\n def scaled_dot_product_attention(q, k, v, *args, **kwargs):\n if q.nelement() < 1024 * 128: # arbitrary number, for small inputs cudnn attention seems slower\n return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)\n with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True):\n return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)\n else:\n logging.warning(\"Torch version too old to set sdpa backend priority.\")\nexcept (ModuleNotFoundError, TypeError):\n logging.warning(\"Could not set sdpa backend priority.\")\n\nNVIDIA_MEMORY_CONV_BUG_WORKAROUND = False\ntry:\n if comfy.model_management.is_nvidia():\n cudnn_version = torch.backends.cudnn.version()\n if (cudnn_version >= 91002 and cudnn_version < 91500) and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):\n #TODO: change upper bound version once it's fixed'\n NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True\n logging.info(\"working around nvidia conv3d memory bug.\")\nexcept:\n pass\n\ncast_to = comfy.model_management.cast_to #TODO: remove once no more references\n\ndef cast_to_input(weight, input, non_blocking=False, copy=True):\n return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)\n\n\ndef cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant):\n\n #vbar doesn't support CPU weights, but some custom nodes have weird paths\n #that might switch the layer to the CPU and expect it to work. We have to take\n #a clone conservatively as we are mmapped and some SFT files are packed misaligned\n #If you are a custom node author reading this, please move your layer to the GPU\n #or declare your ModelPatcher as CPU in the first place.\n if comfy.model_management.is_device_cpu(device):\n weight = s.weight.to(dtype=dtype, copy=True)\n if isinstance(weight, QuantizedTensor):\n weight = weight.dequantize()\n bias = None\n if s.bias is not None:\n bias = s.bias.to(dtype=bias_dtype, copy=True)\n return weight, bias, (None, None, None)\n\n offload_stream = None\n xfer_dest = None\n\n signature = comfy_aimdo.model_vbar.vbar_fault(s._v)\n resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)\n if signature is not None:\n if resident:\n weight = s._v_weight\n bias = s._v_bias\n else:\n xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device)\n\n if not resident:\n cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ])\n cast_dest = None\n\n xfer_source = [ s.weight, s.bias ]\n\n pin = comfy.pinned_memory.get_pin(s)\n if pin is not None:\n xfer_source = [ pin ]\n\n for data, geometry in zip([ s.weight, s.bias ], cast_geometry):\n if data is None:\n continue\n if data.dtype != geometry.dtype:\n cast_dest = xfer_dest\n if cast_dest is None:\n cast_dest = torch.empty((comfy.memory_management.vram_aligned_size(cast_geometry),), dtype=torch.uint8, device=device)\n xfer_dest = None\n break\n\n dest_size = comfy.memory_management.vram_aligned_size(xfer_source)\n offload_stream = comfy.model_management.get_offload_stream(device)\n if xfer_dest is None and offload_stream is not None:\n xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s)\n if xfer_dest is None:\n offload_stream = comfy.model_management.get_offload_stream(device)\n xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s)\n if xfer_dest is None:\n xfer_dest = torch.empty((dest_size,), dtype=torch.uint8, device=device)\n offload_stream = None\n\n if signature is None and pin is None:\n comfy.pinned_memory.pin_memory(s)\n pin = comfy.pinned_memory.get_pin(s)\n else:\n pin = None\n\n if pin is not None:\n comfy.model_management.cast_to_gathered(xfer_source, pin)\n xfer_source = [ pin ]\n #send it over\n comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=offload_stream)\n comfy.model_management.sync_stream(device, offload_stream)\n\n if cast_dest is not None:\n for pre_cast, post_cast in zip(comfy.memory_management.interpret_gathered_like([s.weight, s.bias ], xfer_dest),\n comfy.memory_management.interpret_gathered_like(cast_geometry, cast_dest)):\n if post_cast is not None:\n post_cast.copy_(pre_cast)\n xfer_dest = cast_dest\n\n params = comfy.memory_management.interpret_gathered_like(cast_geometry, xfer_dest)\n weight = params[0]\n bias = params[1]\n if signature is not None:\n s._v_weight = weight\n s._v_bias = bias\n s._v_signature=signature\n\n def post_cast(s, param_key, x, dtype, resident, update_weight):\n lowvram_fn = getattr(s, param_key + \"_lowvram_function\", None)\n fns = getattr(s, param_key + \"_function\", [])\n\n orig = x\n\n def to_dequant(tensor, dtype):\n tensor = tensor.to(dtype=dtype)\n if isinstance(tensor, QuantizedTensor):\n tensor = tensor.dequantize()\n return tensor\n\n if orig.dtype != dtype or len(fns) > 0:\n x = to_dequant(x, dtype)\n if not resident and lowvram_fn is not None:\n x = to_dequant(x, dtype if compute_dtype is None else compute_dtype)\n x = lowvram_fn(x)\n if (want_requant and len(fns) == 0 or update_weight):\n seed = comfy.utils.string_to_seed(s.seed_key)\n if isinstance(orig, QuantizedTensor):\n y = QuantizedTensor.from_float(x, s.layout_type, scale=\"recalculate\", stochastic_rounding=seed)\n else:\n y = comfy.float.stochastic_rounding(x, orig.dtype, seed=seed)\n if want_requant and len(fns) == 0:\n x = y\n if update_weight:\n orig.copy_(y)\n for f in fns:\n x = f(x)\n return x\n\n update_weight = signature is not None\n\n weight = post_cast(s, \"weight\", weight, dtype, resident, update_weight)\n if s.bias is not None:\n bias = post_cast(s, \"bias\", bias, bias_dtype, resident, update_weight)\n\n #FIXME: weird offload return protocol\n return weight, bias, (offload_stream, device if signature is not None else None, None)\n\n\ndef cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None, want_requant=False):\n # NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass\n # offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This\n # will add async-offload support to your cast and improve performance.\n if input is not None:\n if dtype is None:\n if isinstance(input, QuantizedTensor):\n dtype = input.params.orig_dtype\n else:\n dtype = input.dtype\n if bias_dtype is None:\n bias_dtype = dtype\n if device is None:\n device = input.device\n\n non_blocking = comfy.model_management.device_supports_non_blocking(device)\n\n if hasattr(s, \"_v\"):\n return cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant)\n\n if offloadable and (device != s.weight.device or\n (s.bias is not None and device != s.bias.device)):\n offload_stream = comfy.model_management.get_offload_stream(device)\n else:\n offload_stream = None\n\n bias = None\n weight = None\n\n if offload_stream is not None and not args.cuda_malloc:\n cast_buffer_size = comfy.memory_management.vram_aligned_size([ s.weight, s.bias ])\n cast_buffer = comfy.model_management.get_cast_buffer(offload_stream, device, cast_buffer_size, s)\n #The streams can be uneven in buffer capability and reject us. Retry to get the other stream\n if cast_buffer is None:\n offload_stream = comfy.model_management.get_offload_stream(device)\n cast_buffer = comfy.model_management.get_cast_buffer(offload_stream, device, cast_buffer_size, s)\n params = comfy.memory_management.interpret_gathered_like([ s.weight, s.bias ], cast_buffer)\n weight = params[0]\n bias = params[1]\n\n weight_has_function = len(s.weight_function) > 0\n bias_has_function = len(s.bias_function) > 0\n\n weight = comfy.model_management.cast_to(s.weight, None, device, non_blocking=non_blocking, copy=weight_has_function, stream=offload_stream, r=weight)\n\n if s.bias is not None:\n bias = comfy.model_management.cast_to(s.bias, None, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream, r=bias)\n\n comfy.model_management.sync_stream(device, offload_stream)\n\n bias_a = bias\n weight_a = weight\n\n if s.bias is not None:\n bias = bias.to(dtype=bias_dtype)\n for f in s.bias_function:\n bias = f(bias)\n\n if weight_has_function or weight.dtype != dtype:\n weight = weight.to(dtype=dtype)\n if isinstance(weight, QuantizedTensor):\n weight = weight.dequantize()\n for f in s.weight_function:\n weight = f(weight)\n\n if offloadable:\n return weight, bias, (offload_stream, weight_a, bias_a)\n else:\n #Legacy function signature\n return weight, bias\n\n\ndef uncast_bias_weight(s, weight, bias, offload_stream):\n if offload_stream is None:\n return\n os, weight_a, bias_a = offload_stream\n device=None\n #FIXME: This is really bad RTTI\n if weight_a is not None and not isinstance(weight_a, torch.Tensor):\n comfy_aimdo.model_vbar.vbar_unpin(s._v)\n device = weight_a\n if os is None:\n return\n if device is None:\n if weight_a is not None:\n device = weight_a.device\n else:\n if bias_a is None:\n return\n device = bias_a.device\n os.wait_stream(comfy.model_management.current_stream(device))\n\n\nclass CastWeightBiasOp:\n comfy_cast_weights = False\n weight_function = []\n bias_function = []\n\nclass disable_weight_init:\n @staticmethod\n def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata,\n missing_keys, unexpected_keys, weight_shape,\n bias_shape=None):\n assign_to_params_buffers = local_metadata.get(\"assign_to_params_buffers\", False)\n prefix_len = len(prefix)\n for k, v in state_dict.items():\n key = k[prefix_len:]\n if key == \"weight\":\n if not assign_to_params_buffers:\n v = v.clone()\n module.weight = torch.nn.Parameter(v, requires_grad=False)\n elif bias_shape is not None and key == \"bias\" and v is not None:\n if not assign_to_params_buffers:\n v = v.clone()\n module.bias = torch.nn.Parameter(v, requires_grad=False)\n else:\n unexpected_keys.append(k)\n\n if module.weight is None:\n module.weight = torch.nn.Parameter(torch.zeros(weight_shape), requires_grad=False)\n missing_keys.append(prefix + \"weight\")\n\n if bias_shape is not None and module.bias is None and getattr(module, \"comfy_need_lazy_init_bias\", False):\n module.bias = torch.nn.Parameter(torch.zeros(bias_shape), requires_grad=False)\n missing_keys.append(prefix + \"bias\")\n\n class Linear(torch.nn.Linear, CastWeightBiasOp):\n\n def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):\n # don't trust subclasses that BYO state dict loader to call us.\n if (not comfy.model_management.WINDOWS\n or not comfy.memory_management.aimdo_enabled\n or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):\n super().__init__(in_features, out_features, bias, device, dtype)\n return\n\n # Issue is with `torch.empty` still reserving the full memory for the layer.\n # Windows doesn't over-commit memory so without this, We are momentarily commit\n # charged for the weight even though we might zero-copy it when we load the\n # state dict. If the commit charge exceeds the ceiling we can destabilize the\n # system.\n torch.nn.Module.__init__(self)\n self.in_features = in_features\n self.out_features = out_features\n self.weight = None\n self.bias = None\n self.comfy_need_lazy_init_bias=bias\n self.weight_comfy_model_dtype = dtype\n self.bias_comfy_model_dtype = dtype\n\n def _load_from_state_dict(self, state_dict, prefix, local_metadata,\n strict, missing_keys, unexpected_keys, error_msgs):\n\n if (not comfy.model_management.WINDOWS\n or not comfy.memory_management.aimdo_enabled\n or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):\n return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs)\n disable_weight_init._lazy_load_from_state_dict(\n self,\n state_dict,\n prefix,\n local_metadata,\n missing_keys,\n unexpected_keys,\n weight_shape=(self.in_features, self.out_features),\n bias_shape=(self.out_features,),\n )\n\n\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = torch.nn.functional.linear(input, weight, bias)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = self._conv_forward(input, weight, bias)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = self._conv_forward(input, weight, bias)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def _conv_forward(self, input, weight, bias, autopad=None, *args, **kwargs):\n if autopad == \"causal_zero\":\n weight = weight[:, :, -input.shape[2]:, :, :]\n if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):\n out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)\n if bias is not None:\n out += bias.reshape((1, -1) + (1,) * (out.ndim - 2))\n return out\n else:\n return super()._conv_forward(input, weight, bias, *args, **kwargs)\n\n def forward_comfy_cast_weights(self, input, autopad=None):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = self._conv_forward(input, weight, bias, autopad=autopad)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0 or \"autopad\" in kwargs:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input):\n if self.weight is not None:\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n else:\n weight = None\n bias = None\n offload_stream = None\n x = torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class RMSNorm(torch.nn.RMSNorm, CastWeightBiasOp):\n def reset_parameters(self):\n self.bias = None\n return None\n\n def forward_comfy_cast_weights(self, input):\n if self.weight is not None:\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n else:\n weight = None\n bias = None\n offload_stream = None\n x = torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input, output_size=None):\n num_spatial_dims = 2\n output_padding = self._output_padding(\n input, output_size, self.stride, self.padding, self.kernel_size,\n num_spatial_dims, self.dilation)\n\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = torch.nn.functional.conv_transpose2d(\n input, weight, bias, self.stride, self.padding,\n output_padding, self.groups, self.dilation)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input, output_size=None):\n num_spatial_dims = 1\n output_padding = self._output_padding(\n input, output_size, self.stride, self.padding, self.kernel_size,\n num_spatial_dims, self.dilation)\n\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = torch.nn.functional.conv_transpose1d(\n input, weight, bias, self.stride, self.padding,\n output_padding, self.groups, self.dilation)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n class Embedding(torch.nn.Embedding, CastWeightBiasOp):\n def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,\n norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,\n _freeze=False, device=None, dtype=None):\n # don't trust subclasses that BYO state dict loader to call us.\n if (not comfy.model_management.WINDOWS\n or not comfy.memory_management.aimdo_enabled\n or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):\n super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,\n norm_type, scale_grad_by_freq, sparse, _weight,\n _freeze, device, dtype)\n return\n\n torch.nn.Module.__init__(self)\n self.num_embeddings = num_embeddings\n self.embedding_dim = embedding_dim\n self.padding_idx = padding_idx\n self.max_norm = max_norm\n self.norm_type = norm_type\n self.scale_grad_by_freq = scale_grad_by_freq\n self.sparse = sparse\n # Keep shape/dtype visible for module introspection without reserving storage.\n embedding_dtype = dtype if dtype is not None else torch.get_default_dtype()\n self.weight = torch.nn.Parameter(\n torch.empty((num_embeddings, embedding_dim), device=\"meta\", dtype=embedding_dtype),\n requires_grad=False,\n )\n self.bias = None\n self.weight_comfy_model_dtype = dtype\n\n def _load_from_state_dict(self, state_dict, prefix, local_metadata,\n strict, missing_keys, unexpected_keys, error_msgs):\n\n if (not comfy.model_management.WINDOWS\n or not comfy.memory_management.aimdo_enabled\n or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):\n return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs)\n disable_weight_init._lazy_load_from_state_dict(\n self,\n state_dict,\n prefix,\n local_metadata,\n missing_keys,\n unexpected_keys,\n weight_shape=(self.num_embeddings, self.embedding_dim),\n )\n\n def reset_parameters(self):\n self.bias = None\n return None\n\n def forward_comfy_cast_weights(self, input, out_dtype=None):\n output_dtype = out_dtype\n if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:\n out_dtype = None\n weight, bias, offload_stream = cast_bias_weight(self, device=input.device, dtype=out_dtype, offloadable=True)\n x = torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n if \"out_dtype\" in kwargs:\n kwargs.pop(\"out_dtype\")\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 comfy_cast_weights = True\n\n class Conv1d(disable_weight_init.Conv1d):\n comfy_cast_weights = True\n\n class Conv2d(disable_weight_init.Conv2d):\n comfy_cast_weights = True\n\n class Conv3d(disable_weight_init.Conv3d):\n comfy_cast_weights = True\n\n class GroupNorm(disable_weight_init.GroupNorm):\n comfy_cast_weights = True\n\n class LayerNorm(disable_weight_init.LayerNorm):\n comfy_cast_weights = True\n\n class ConvTranspose2d(disable_weight_init.ConvTranspose2d):\n comfy_cast_weights = True\n\n class ConvTranspose1d(disable_weight_init.ConvTranspose1d):\n comfy_cast_weights = True\n\n class RMSNorm(disable_weight_init.RMSNorm):\n comfy_cast_weights = True\n\n class Embedding(disable_weight_init.Embedding):\n comfy_cast_weights = True\n\n\ndef fp8_linear(self, input):\n \"\"\"\n Legacy FP8 linear function for backward compatibility.\n Uses QuantizedTensor subclass for dispatch.\n \"\"\"\n dtype = self.weight.dtype\n if dtype not in [torch.float8_e4m3fn]:\n return None\n\n input_dtype = input.dtype\n input_shape = input.shape\n tensor_3d = input.ndim == 3\n\n if tensor_3d:\n input = input.reshape(-1, input_shape[2])\n\n if input.ndim != 2:\n return None\n lora_compute_dtype=comfy.model_management.lora_compute_dtype(input.device)\n w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True, compute_dtype=lora_compute_dtype, want_requant=True)\n scale_weight = torch.ones((), device=input.device, dtype=torch.float32)\n\n scale_input = torch.ones((), device=input.device, dtype=torch.float32)\n input = torch.clamp(input, min=-448, max=448, out=input)\n input_fp8 = input.to(dtype).contiguous()\n layout_params_input = TensorCoreFP8Layout.Params(scale=scale_input, orig_dtype=input_dtype, orig_shape=tuple(input_fp8.shape))\n quantized_input = QuantizedTensor(input_fp8, \"TensorCoreFP8Layout\", layout_params_input)\n\n # Wrap weight in QuantizedTensor - this enables unified dispatch\n # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!\n layout_params_weight = TensorCoreFP8Layout.Params(scale=scale_weight, orig_dtype=input_dtype, orig_shape=tuple(w.shape))\n quantized_weight = QuantizedTensor(w, \"TensorCoreFP8Layout\", layout_params_weight)\n o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)\n\n uncast_bias_weight(self, w, bias, offload_stream)\n if tensor_3d:\n o = o.reshape((input_shape[0], input_shape[1], w.shape[0]))\n\n return o\n\nclass fp8_ops(manual_cast):\n class Linear(manual_cast.Linear):\n def reset_parameters(self):\n self.scale_weight = None\n self.scale_input = None\n return None\n\n def forward_comfy_cast_weights(self, input):\n if len(self.weight_function) == 0 and len(self.bias_function) == 0:\n try:\n out = fp8_linear(self, input)\n if out is not None:\n return out\n except Exception as e:\n logging.info(\"Exception during fp8 op: {}\".format(e))\n\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = torch.nn.functional.linear(input, weight, bias)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\nCUBLAS_IS_AVAILABLE = False\ntry:\n from cublas_ops import CublasLinear, cublas_half_matmul\n CUBLAS_IS_AVAILABLE = True\nexcept ImportError:\n pass\n\nif CUBLAS_IS_AVAILABLE:\n class cublas_ops(manual_cast):\n class Linear(CublasLinear, manual_cast.Linear):\n def reset_parameters(self):\n return None\n\n def forward_comfy_cast_weights(self, input):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)\n x = cublas_half_matmul(input, weight, bias, self._epilogue_str, self.has_bias)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, *args, **kwargs):\n run_every_op()\n if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:\n return self.forward_comfy_cast_weights(*args, **kwargs)\n else:\n return super().forward(*args, **kwargs)\n\n# ==============================================================================\n# Mixed Precision Operations\n# ==============================================================================\nfrom .quant_ops import (\n QuantizedTensor,\n QUANT_ALGOS,\n TensorCoreFP8Layout,\n get_layout_class,\n)\n\n\nclass QuantLinearFunc(torch.autograd.Function):\n \"\"\"Custom autograd function for quantized linear: quantized forward, compute_dtype backward.\n Handles any input rank by flattening to 2D for matmul and restoring shape after.\n \"\"\"\n\n @staticmethod\n def forward(ctx, input_float, weight, bias, layout_type, input_scale, compute_dtype):\n input_shape = input_float.shape\n inp = input_float.detach().flatten(0, -2) # zero-cost view to 2D\n\n # Quantize input (same as inference path)\n if layout_type is not None:\n q_input = QuantizedTensor.from_float(inp, layout_type, scale=input_scale)\n else:\n q_input = inp\n\n w = weight.detach() if weight.requires_grad else weight\n b = bias.detach() if bias is not None and bias.requires_grad else bias\n\n output = torch.nn.functional.linear(q_input, w, b)\n\n # Restore original input shape\n if len(input_shape) > 2:\n output = output.unflatten(0, input_shape[:-1])\n\n ctx.save_for_backward(input_float, weight)\n ctx.input_shape = input_shape\n ctx.has_bias = bias is not None\n ctx.compute_dtype = compute_dtype\n ctx.weight_requires_grad = weight.requires_grad\n\n return output\n\n @staticmethod\n @torch.autograd.function.once_differentiable\n def backward(ctx, grad_output):\n input_float, weight = ctx.saved_tensors\n compute_dtype = ctx.compute_dtype\n grad_2d = grad_output.flatten(0, -2).to(compute_dtype)\n\n # Dequantize weight to compute dtype for backward matmul\n if isinstance(weight, QuantizedTensor):\n weight_f = weight.dequantize().to(compute_dtype)\n else:\n weight_f = weight.to(compute_dtype)\n\n # grad_input = grad_output @ weight\n grad_input = torch.mm(grad_2d, weight_f)\n if len(ctx.input_shape) > 2:\n grad_input = grad_input.unflatten(0, ctx.input_shape[:-1])\n\n # grad_weight (only if weight requires grad, typically frozen for quantized training)\n grad_weight = None\n if ctx.weight_requires_grad:\n input_f = input_float.flatten(0, -2).to(compute_dtype)\n grad_weight = torch.mm(grad_2d.t(), input_f)\n\n # grad_bias\n grad_bias = None\n if ctx.has_bias:\n grad_bias = grad_2d.sum(dim=0)\n\n return grad_input, grad_weight, grad_bias, None, None, None\n\n\ndef mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):\n class MixedPrecisionOps(manual_cast):\n _quant_config = quant_config\n _compute_dtype = compute_dtype\n _full_precision_mm = full_precision_mm\n _disabled = disabled\n\n class Linear(torch.nn.Module, CastWeightBiasOp):\n def __init__(\n self,\n in_features: int,\n out_features: int,\n bias: bool = True,\n device=None,\n dtype=None,\n ) -> None:\n super().__init__()\n\n self.factory_kwargs = {\"device\": device, \"dtype\": MixedPrecisionOps._compute_dtype}\n # self.factory_kwargs = {\"device\": device, \"dtype\": dtype}\n\n self.in_features = in_features\n self.out_features = out_features\n if bias:\n self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))\n else:\n self.register_parameter(\"bias\", None)\n\n self.tensor_class = None\n self._full_precision_mm = MixedPrecisionOps._full_precision_mm\n self._full_precision_mm_config = False\n\n def reset_parameters(self):\n return None\n\n def _load_scale_param(self, state_dict, prefix, param_name, device, manually_loaded_keys, dtype=None):\n key = f\"{prefix}{param_name}\"\n value = state_dict.pop(key, None)\n if value is not None:\n value = value.to(device=device)\n if dtype is not None:\n value = value.view(dtype=dtype)\n manually_loaded_keys.append(key)\n return value\n\n def _load_from_state_dict(self, state_dict, prefix, local_metadata,\n strict, missing_keys, unexpected_keys, error_msgs):\n\n device = self.factory_kwargs[\"device\"]\n layer_name = prefix.rstrip('.')\n weight_key = f\"{prefix}weight\"\n weight = state_dict.pop(weight_key, None)\n if weight is None:\n logging.warning(f\"Missing weight for layer {layer_name}\")\n return\n\n manually_loaded_keys = [weight_key]\n\n layer_conf = state_dict.pop(f\"{prefix}comfy_quant\", None)\n if layer_conf is not None:\n layer_conf = json.loads(layer_conf.numpy().tobytes())\n\n if layer_conf is None:\n self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)\n else:\n self.quant_format = layer_conf.get(\"format\", None)\n self._full_precision_mm_config = layer_conf.get(\"full_precision_matrix_mult\", False)\n if not self._full_precision_mm:\n self._full_precision_mm = self._full_precision_mm_config\n\n if self.quant_format in MixedPrecisionOps._disabled:\n self._full_precision_mm = True\n\n if self.quant_format is None:\n raise ValueError(f\"Unknown quantization format for layer {layer_name}\")\n\n qconfig = QUANT_ALGOS[self.quant_format]\n self.layout_type = qconfig[\"comfy_tensor_layout\"]\n layout_cls = get_layout_class(self.layout_type)\n\n # Load format-specific parameters\n if self.quant_format in [\"float8_e4m3fn\", \"float8_e5m2\"]:\n # FP8: single tensor scale\n scale = self._load_scale_param(state_dict, prefix, \"weight_scale\", device, manually_loaded_keys)\n\n params = layout_cls.Params(\n scale=scale,\n orig_dtype=MixedPrecisionOps._compute_dtype,\n orig_shape=(self.out_features, self.in_features),\n )\n\n elif self.quant_format == \"mxfp8\":\n # MXFP8: E8M0 block scales stored as uint8 in safetensors\n block_scale = self._load_scale_param(state_dict, prefix, \"weight_scale\", device, manually_loaded_keys,\n dtype=torch.uint8)\n\n if block_scale is None:\n raise ValueError(f\"Missing MXFP8 block scales for layer {layer_name}\")\n\n block_scale = block_scale.view(torch.float8_e8m0fnu)\n\n params = layout_cls.Params(\n scale=block_scale,\n orig_dtype=MixedPrecisionOps._compute_dtype,\n orig_shape=(self.out_features, self.in_features),\n )\n\n elif self.quant_format == \"nvfp4\":\n # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale)\n tensor_scale = self._load_scale_param(state_dict, prefix, \"weight_scale_2\", device, manually_loaded_keys)\n block_scale = self._load_scale_param(state_dict, prefix, \"weight_scale\", device, manually_loaded_keys,\n dtype=torch.float8_e4m3fn)\n\n if tensor_scale is None or block_scale is None:\n raise ValueError(f\"Missing NVFP4 scales for layer {layer_name}\")\n\n params = layout_cls.Params(\n scale=tensor_scale,\n block_scale=block_scale,\n orig_dtype=MixedPrecisionOps._compute_dtype,\n orig_shape=(self.out_features, self.in_features),\n )\n else:\n raise ValueError(f\"Unsupported quantization format: {self.quant_format}\")\n\n self.weight = torch.nn.Parameter(\n QuantizedTensor(weight.to(device=device, dtype=qconfig[\"storage_t\"]), self.layout_type, params),\n requires_grad=False\n )\n\n for param_name in qconfig[\"parameters\"]:\n if param_name in {\"weight_scale\", \"weight_scale_2\"}:\n continue # Already handled above\n\n param_key = f\"{prefix}{param_name}\"\n _v = state_dict.pop(param_key, None)\n if _v is None:\n continue\n self.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))\n manually_loaded_keys.append(param_key)\n\n super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)\n\n for key in manually_loaded_keys:\n if key in missing_keys:\n missing_keys.remove(key)\n\n def state_dict(self, *args, destination=None, prefix=\"\", **kwargs):\n if destination is not None:\n sd = destination\n else:\n sd = {}\n\n if not hasattr(self, 'weight'):\n logging.warning(\"Warning: state dict on uninitialized op {}\".format(prefix))\n return sd\n\n if self.bias is not None:\n sd[\"{}bias\".format(prefix)] = self.bias\n\n if isinstance(self.weight, QuantizedTensor):\n sd_out = self.weight.state_dict(\"{}weight\".format(prefix))\n for k in sd_out:\n sd[k] = sd_out[k]\n\n quant_conf = {\"format\": self.quant_format}\n if self._full_precision_mm_config:\n quant_conf[\"full_precision_matrix_mult\"] = True\n sd[\"{}comfy_quant\".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)\n\n input_scale = getattr(self, 'input_scale', None)\n if input_scale is not None:\n sd[\"{}input_scale\".format(prefix)] = input_scale\n else:\n sd[\"{}weight\".format(prefix)] = self.weight\n return sd\n\n def _forward(self, input, weight, bias):\n return torch.nn.functional.linear(input, weight, bias)\n\n def forward_comfy_cast_weights(self, input, compute_dtype=None, want_requant=False):\n weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True, compute_dtype=compute_dtype, want_requant=want_requant)\n x = self._forward(input, weight, bias)\n uncast_bias_weight(self, weight, bias, offload_stream)\n return x\n\n def forward(self, input, *args, **kwargs):\n run_every_op()\n\n input_shape = input.shape\n reshaped_3d = False\n #If cast needs to apply lora, it should be done in the compute dtype\n compute_dtype = input.dtype\n\n _use_quantized = (\n getattr(self, 'layout_type', None) is not None and\n not isinstance(input, QuantizedTensor) and not self._full_precision_mm and\n not getattr(self, 'comfy_force_cast_weights', False) and\n len(self.weight_function) == 0 and len(self.bias_function) == 0\n )\n\n # Training path: quantized forward with compute_dtype backward via autograd function\n if (input.requires_grad and _use_quantized):\n\n weight, bias, offload_stream = cast_bias_weight(\n self,\n input,\n offloadable=True,\n compute_dtype=compute_dtype,\n want_requant=True\n )\n\n scale = getattr(self, 'input_scale', None)\n if scale is not None:\n scale = comfy.model_management.cast_to_device(scale, input.device, None)\n\n output = QuantLinearFunc.apply(\n input, weight, bias, self.layout_type, scale, compute_dtype\n )\n\n uncast_bias_weight(self, weight, bias, offload_stream)\n return output\n\n # Inference path (unchanged)\n if _use_quantized:\n\n # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)\n input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input\n\n # Fall back to non-quantized for non-2D tensors\n if input_reshaped.ndim == 2:\n reshaped_3d = input.ndim == 3\n # dtype is now implicit in the layout class\n scale = getattr(self, 'input_scale', None)\n if scale is not None:\n scale = comfy.model_management.cast_to_device(scale, input.device, None)\n input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)\n\n output = self.forward_comfy_cast_weights(input, compute_dtype, want_requant=isinstance(input, QuantizedTensor))\n\n # Reshape output back to 3D if input was 3D\n if reshaped_3d:\n output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))\n\n return output\n\n def convert_weight(self, weight, inplace=False, **kwargs):\n if isinstance(weight, QuantizedTensor):\n return weight.dequantize()\n else:\n return weight\n\n def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):\n if getattr(self, 'layout_type', None) is not None:\n # dtype is now implicit in the layout class\n weight = QuantizedTensor.from_float(weight, self.layout_type, scale=\"recalculate\", stochastic_rounding=seed, inplace_ops=True).to(self.weight.dtype)\n else:\n weight = weight.to(self.weight.dtype)\n if return_weight:\n return weight\n\n assert inplace_update is False # TODO: eventually remove the inplace_update stuff\n self.weight = torch.nn.Parameter(weight, requires_grad=False)\n\n def _apply(self, fn, recurse=True): # This is to get torch.compile + moving weights to another device working\n if recurse:\n for module in self.children():\n module._apply(fn)\n\n for key, param in self._parameters.items():\n if param is None:\n continue\n p = fn(param)\n if p.is_inference():\n p = p.clone()\n self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))\n for key, buf in self._buffers.items():\n if buf is not None:\n self._buffers[key] = fn(buf)\n return self\n\n return MixedPrecisionOps\n\ndef pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):\n fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular\n nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device)\n mxfp8_compute = comfy.model_management.supports_mxfp8_compute(load_device)\n\n if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:\n logging.info(\"Using mixed precision operations\")\n disabled = set()\n if not nvfp4_compute:\n disabled.add(\"nvfp4\")\n if not mxfp8_compute:\n disabled.add(\"mxfp8\")\n if not fp8_compute:\n disabled.add(\"float8_e4m3fn\")\n disabled.add(\"float8_e5m2\")\n return mixed_precision_ops(model_config.quant_config, compute_dtype, disabled=disabled)\n\n if (\n fp8_compute and\n (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and\n not disable_fast_fp8\n ):\n return fp8_ops\n\n if (\n PerformanceFeature.CublasOps in args.fast and\n CUBLAS_IS_AVAILABLE and\n weight_dtype == torch.float16 and\n (compute_dtype == torch.float16 or compute_dtype is None)\n ):\n logging.info(\"Using cublas ops\")\n return cublas_ops\n\n if compute_dtype is None or weight_dtype == compute_dtype:\n return disable_weight_init\n\n return manual_cast\n" }, { "path": "comfy/options.py", "content": "\nargs_parsing = False\n\ndef enable_args_parsing(enable=True):\n global args_parsing\n args_parsing = enable\n" }, { "path": "comfy/patcher_extension.py", "content": "from __future__ import annotations\nfrom typing import Callable\n\nclass CallbacksMP:\n ON_CLONE = \"on_clone\"\n ON_LOAD = \"on_load_after\"\n ON_DETACH = \"on_detach_after\"\n ON_CLEANUP = \"on_cleanup\"\n ON_PRE_RUN = \"on_pre_run\"\n ON_PREPARE_STATE = \"on_prepare_state\"\n ON_APPLY_HOOKS = \"on_apply_hooks\"\n ON_REGISTER_ALL_HOOK_PATCHES = \"on_register_all_hook_patches\"\n ON_INJECT_MODEL = \"on_inject_model\"\n ON_EJECT_MODEL = \"on_eject_model\"\n\n # callbacks dict is in the format:\n # {\"call_type\": {\"key\": [Callable1, Callable2, ...]} }\n @classmethod\n def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]:\n return {}\n\ndef add_callback(call_type: str, callback: Callable, transformer_options: dict, is_model_options=False):\n add_callback_with_key(call_type, None, callback, transformer_options, is_model_options)\n\ndef add_callback_with_key(call_type: str, key: str, callback: Callable, transformer_options: dict, is_model_options=False):\n if is_model_options:\n transformer_options = transformer_options.setdefault(\"transformer_options\", {})\n callbacks: dict[str, dict[str, list]] = transformer_options.setdefault(\"callbacks\", {})\n c = callbacks.setdefault(call_type, {}).setdefault(key, [])\n c.append(callback)\n\ndef get_callbacks_with_key(call_type: str, key: str, transformer_options: dict, is_model_options=False):\n if is_model_options:\n transformer_options = transformer_options.get(\"transformer_options\", {})\n c_list = []\n callbacks: dict[str, list] = transformer_options.get(\"callbacks\", {})\n c_list.extend(callbacks.get(call_type, {}).get(key, []))\n return c_list\n\ndef get_all_callbacks(call_type: str, transformer_options: dict, is_model_options=False):\n if is_model_options:\n transformer_options = transformer_options.get(\"transformer_options\", {})\n c_list = []\n callbacks: dict[str, list] = transformer_options.get(\"callbacks\", {})\n for c in callbacks.get(call_type, {}).values():\n c_list.extend(c)\n return c_list\n\nclass WrappersMP:\n OUTER_SAMPLE = \"outer_sample\"\n PREPARE_SAMPLING = \"prepare_sampling\"\n SAMPLER_SAMPLE = \"sampler_sample\"\n PREDICT_NOISE = \"predict_noise\"\n CALC_COND_BATCH = \"calc_cond_batch\"\n APPLY_MODEL = \"apply_model\"\n DIFFUSION_MODEL = \"diffusion_model\"\n\n # wrappers dict is in the format:\n # {\"wrapper_type\": {\"key\": [Callable1, Callable2, ...]} }\n @classmethod\n def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]:\n return {}\n\ndef add_wrapper(wrapper_type: str, wrapper: Callable, transformer_options: dict, is_model_options=False):\n add_wrapper_with_key(wrapper_type, None, wrapper, transformer_options, is_model_options)\n\ndef add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable, transformer_options: dict, is_model_options=False):\n if is_model_options:\n transformer_options = transformer_options.setdefault(\"transformer_options\", {})\n wrappers: dict[str, dict[str, list]] = transformer_options.setdefault(\"wrappers\", {})\n w = wrappers.setdefault(wrapper_type, {}).setdefault(key, [])\n w.append(wrapper)\n\ndef get_wrappers_with_key(wrapper_type: str, key: str, transformer_options: dict, is_model_options=False):\n if is_model_options:\n transformer_options = transformer_options.get(\"transformer_options\", {})\n w_list = []\n wrappers: dict[str, list] = transformer_options.get(\"wrappers\", {})\n w_list.extend(wrappers.get(wrapper_type, {}).get(key, []))\n return w_list\n\ndef get_all_wrappers(wrapper_type: str, transformer_options: dict, is_model_options=False):\n if is_model_options:\n transformer_options = transformer_options.get(\"transformer_options\", {})\n w_list = []\n wrappers: dict[str, list] = transformer_options.get(\"wrappers\", {})\n for w in wrappers.get(wrapper_type, {}).values():\n w_list.extend(w)\n return w_list\n\nclass WrapperExecutor:\n \"\"\"Handles call stack of wrappers around a function in an ordered manner.\"\"\"\n def __init__(self, original: Callable, class_obj: object, wrappers: list[Callable], idx: int):\n # NOTE: class_obj exists so that wrappers surrounding a class method can access\n # the class instance at runtime via executor.class_obj\n self.original = original\n self.class_obj = class_obj\n self.wrappers = wrappers.copy()\n self.idx = idx\n self.is_last = idx == len(wrappers)\n\n def __call__(self, *args, **kwargs):\n \"\"\"Calls the next wrapper or original function, whichever is appropriate.\"\"\"\n new_executor = self._create_next_executor()\n return new_executor.execute(*args, **kwargs)\n\n def execute(self, *args, **kwargs):\n \"\"\"Used to initiate executor internally - DO NOT use this if you received executor in wrapper.\"\"\"\n args = list(args)\n kwargs = dict(kwargs)\n if self.is_last:\n return self.original(*args, **kwargs)\n return self.wrappers[self.idx](self, *args, **kwargs)\n\n def _create_next_executor(self) -> 'WrapperExecutor':\n new_idx = self.idx + 1\n if new_idx > len(self.wrappers):\n raise Exception(\"Wrapper idx exceeded available wrappers; something went very wrong.\")\n if self.class_obj is None:\n return WrapperExecutor.new_executor(self.original, self.wrappers, new_idx)\n return WrapperExecutor.new_class_executor(self.original, self.class_obj, self.wrappers, new_idx)\n\n @classmethod\n def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0):\n return cls(original, class_obj=None, wrappers=wrappers, idx=idx)\n\n @classmethod\n def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0):\n return cls(original, class_obj, wrappers, idx=idx)\n\nclass PatcherInjection:\n def __init__(self, inject: Callable, eject: Callable):\n self.inject = inject\n self.eject = eject\n\ndef copy_nested_dicts(input_dict: dict):\n new_dict = input_dict.copy()\n for key, value in input_dict.items():\n if isinstance(value, dict):\n new_dict[key] = copy_nested_dicts(value)\n elif isinstance(value, list):\n new_dict[key] = value.copy()\n return new_dict\n\ndef merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True):\n if copy_dict1:\n merged_dict = copy_nested_dicts(dict1)\n else:\n merged_dict = dict1\n for key, value in dict2.items():\n if isinstance(value, dict):\n curr_value = merged_dict.setdefault(key, {})\n merged_dict[key] = merge_nested_dicts(curr_value, value)\n elif isinstance(value, list):\n merged_dict.setdefault(key, []).extend(value)\n else:\n merged_dict[key] = value\n return merged_dict\n" }, { "path": "comfy/pinned_memory.py", "content": "import comfy.model_management\nimport comfy.memory_management\nimport comfy_aimdo.host_buffer\nimport comfy_aimdo.torch\n\nfrom comfy.cli_args import args\n\ndef get_pin(module):\n return getattr(module, \"_pin\", None)\n\ndef pin_memory(module):\n if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None:\n return\n #FIXME: This is a RAM cache trigger event\n size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])\n\n if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:\n module.pin_failed = True\n return False\n\n try:\n hostbuf = comfy_aimdo.host_buffer.HostBuffer(size)\n except RuntimeError:\n module.pin_failed = True\n return False\n\n module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)\n module._pin_hostbuf = hostbuf\n comfy.model_management.TOTAL_PINNED_MEMORY += size\n return True\n\ndef unpin_memory(module):\n if get_pin(module) is None:\n return 0\n size = module._pin.numel() * module._pin.element_size()\n\n comfy.model_management.TOTAL_PINNED_MEMORY -= size\n if comfy.model_management.TOTAL_PINNED_MEMORY < 0:\n comfy.model_management.TOTAL_PINNED_MEMORY = 0\n\n del module._pin\n del module._pin_hostbuf\n return size\n" }, { "path": "comfy/pixel_space_convert.py", "content": "import torch\n\n\n# \"Fake\" VAE that converts from IMAGE B, H, W, C and values on the scale of 0..1\n# to LATENT B, C, H, W and values on the scale of -1..1.\nclass PixelspaceConversionVAE(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.pixel_space_vae = torch.nn.Parameter(torch.tensor(1.0))\n\n def encode(self, pixels: torch.Tensor, *_args, **_kwargs) -> torch.Tensor:\n return pixels\n\n def decode(self, samples: torch.Tensor, *_args, **_kwargs) -> torch.Tensor:\n return samples\n\n" }, { "path": "comfy/quant_ops.py", "content": "import torch\nimport logging\n\ntry:\n import comfy_kitchen as ck\n from comfy_kitchen.tensor import (\n QuantizedTensor,\n QuantizedLayout,\n TensorCoreFP8Layout as _CKFp8Layout,\n TensorCoreNVFP4Layout as _CKNvfp4Layout,\n register_layout_op,\n register_layout_class,\n get_layout_class,\n )\n _CK_AVAILABLE = True\n if torch.version.cuda is None:\n ck.registry.disable(\"cuda\")\n else:\n cuda_version = tuple(map(int, str(torch.version.cuda).split('.')))\n if cuda_version < (13,):\n ck.registry.disable(\"cuda\")\n logging.warning(\"WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.\")\n\n ck.registry.disable(\"triton\")\n for k, v in ck.list_backends().items():\n logging.info(f\"Found comfy_kitchen backend {k}: {v}\")\nexcept ImportError as e:\n logging.error(f\"Failed to import comfy_kitchen, Error: {e}, fp8 and fp4 support will not be available.\")\n _CK_AVAILABLE = False\n\n class QuantizedTensor:\n pass\n\n class _CKFp8Layout:\n pass\n\n class _CKNvfp4Layout:\n pass\n\n def register_layout_class(name, cls):\n pass\n\n def get_layout_class(name):\n return None\n\n_CK_MXFP8_AVAILABLE = False\nif _CK_AVAILABLE:\n try:\n from comfy_kitchen.tensor import TensorCoreMXFP8Layout as _CKMxfp8Layout\n _CK_MXFP8_AVAILABLE = True\n except ImportError:\n logging.warning(\"comfy_kitchen does not support MXFP8, please update comfy_kitchen.\")\n\nif not _CK_MXFP8_AVAILABLE:\n class _CKMxfp8Layout:\n pass\n\nimport comfy.float\n\n# ==============================================================================\n# FP8 Layouts with Comfy-Specific Extensions\n# ==============================================================================\n\nclass _TensorCoreFP8LayoutBase(_CKFp8Layout):\n FP8_DTYPE = None # Must be overridden in subclass\n\n @classmethod\n def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):\n if cls.FP8_DTYPE is None:\n raise NotImplementedError(f\"{cls.__name__} must define FP8_DTYPE\")\n\n orig_dtype = tensor.dtype\n orig_shape = tuple(tensor.shape)\n\n if isinstance(scale, str) and scale == \"recalculate\":\n scale = torch.amax(tensor.abs()).to(dtype=torch.float32) / torch.finfo(cls.FP8_DTYPE).max\n if tensor.dtype not in [torch.float32, torch.bfloat16]: # Prevent scale from being too small\n tensor_info = torch.finfo(tensor.dtype)\n scale = (1.0 / torch.clamp((1.0 / scale), min=tensor_info.min, max=tensor_info.max))\n\n if scale is None:\n scale = torch.ones((), device=tensor.device, dtype=torch.float32)\n if not isinstance(scale, torch.Tensor):\n scale = torch.tensor(scale, device=tensor.device, dtype=torch.float32)\n\n if stochastic_rounding > 0:\n if inplace_ops:\n tensor *= (1.0 / scale).to(tensor.dtype)\n else:\n tensor = tensor * (1.0 / scale).to(tensor.dtype)\n qdata = comfy.float.stochastic_rounding(tensor, dtype=cls.FP8_DTYPE, seed=stochastic_rounding)\n else:\n qdata = ck.quantize_per_tensor_fp8(tensor, scale, cls.FP8_DTYPE)\n\n params = cls.Params(scale=scale.float(), orig_dtype=orig_dtype, orig_shape=orig_shape)\n return qdata, params\n\n\nclass TensorCoreMXFP8Layout(_CKMxfp8Layout):\n @classmethod\n def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):\n if tensor.dim() != 2:\n raise ValueError(f\"MXFP8 requires 2D tensor, got {tensor.dim()}D\")\n\n orig_dtype = tensor.dtype\n orig_shape = tuple(tensor.shape)\n\n padded_shape = cls.get_padded_shape(orig_shape)\n needs_padding = padded_shape != orig_shape\n\n if stochastic_rounding > 0:\n qdata, block_scale = comfy.float.stochastic_round_quantize_mxfp8_by_block(tensor, pad_32x=needs_padding, seed=stochastic_rounding)\n else:\n qdata, block_scale = ck.quantize_mxfp8(tensor, pad_32x=needs_padding)\n\n params = cls.Params(\n scale=block_scale,\n orig_dtype=orig_dtype,\n orig_shape=orig_shape,\n )\n return qdata, params\n\n\nclass TensorCoreNVFP4Layout(_CKNvfp4Layout):\n @classmethod\n def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):\n if tensor.dim() != 2:\n raise ValueError(f\"NVFP4 requires 2D tensor, got {tensor.dim()}D\")\n\n orig_dtype = tensor.dtype\n orig_shape = tuple(tensor.shape)\n\n if scale is None or (isinstance(scale, str) and scale == \"recalculate\"):\n scale = torch.amax(tensor.abs()) / (ck.float_utils.F8_E4M3_MAX * ck.float_utils.F4_E2M1_MAX)\n\n if not isinstance(scale, torch.Tensor):\n scale = torch.tensor(scale)\n scale = scale.to(device=tensor.device, dtype=torch.float32)\n\n padded_shape = cls.get_padded_shape(orig_shape)\n needs_padding = padded_shape != orig_shape\n\n if stochastic_rounding > 0:\n qdata, block_scale = comfy.float.stochastic_round_quantize_nvfp4_by_block(tensor, scale, pad_16x=needs_padding, seed=stochastic_rounding)\n else:\n qdata, block_scale = ck.quantize_nvfp4(tensor, scale, pad_16x=needs_padding)\n\n params = cls.Params(\n scale=scale,\n orig_dtype=orig_dtype,\n orig_shape=orig_shape,\n block_scale=block_scale,\n )\n return qdata, params\n\n\nclass TensorCoreFP8E4M3Layout(_TensorCoreFP8LayoutBase):\n FP8_DTYPE = torch.float8_e4m3fn\n\n\nclass TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):\n FP8_DTYPE = torch.float8_e5m2\n\n\n# Backward compatibility alias - default to E4M3\nTensorCoreFP8Layout = TensorCoreFP8E4M3Layout\n\n\n# ==============================================================================\n# Registry\n# ==============================================================================\n\nregister_layout_class(\"TensorCoreFP8Layout\", TensorCoreFP8Layout)\nregister_layout_class(\"TensorCoreFP8E4M3Layout\", TensorCoreFP8E4M3Layout)\nregister_layout_class(\"TensorCoreFP8E5M2Layout\", TensorCoreFP8E5M2Layout)\nregister_layout_class(\"TensorCoreNVFP4Layout\", TensorCoreNVFP4Layout)\nif _CK_MXFP8_AVAILABLE:\n register_layout_class(\"TensorCoreMXFP8Layout\", TensorCoreMXFP8Layout)\n\nQUANT_ALGOS = {\n \"float8_e4m3fn\": {\n \"storage_t\": torch.float8_e4m3fn,\n \"parameters\": {\"weight_scale\", \"input_scale\"},\n \"comfy_tensor_layout\": \"TensorCoreFP8E4M3Layout\",\n },\n \"float8_e5m2\": {\n \"storage_t\": torch.float8_e5m2,\n \"parameters\": {\"weight_scale\", \"input_scale\"},\n \"comfy_tensor_layout\": \"TensorCoreFP8E5M2Layout\",\n },\n \"nvfp4\": {\n \"storage_t\": torch.uint8,\n \"parameters\": {\"weight_scale\", \"weight_scale_2\", \"input_scale\"},\n \"comfy_tensor_layout\": \"TensorCoreNVFP4Layout\",\n \"group_size\": 16,\n },\n}\n\nif _CK_MXFP8_AVAILABLE:\n QUANT_ALGOS[\"mxfp8\"] = {\n \"storage_t\": torch.float8_e4m3fn,\n \"parameters\": {\"weight_scale\", \"input_scale\"},\n \"comfy_tensor_layout\": \"TensorCoreMXFP8Layout\",\n \"group_size\": 32,\n }\n\n\n# ==============================================================================\n# Re-exports for backward compatibility\n# ==============================================================================\n\n__all__ = [\n \"QuantizedTensor\",\n \"QuantizedLayout\",\n \"TensorCoreFP8Layout\",\n \"TensorCoreFP8E4M3Layout\",\n \"TensorCoreFP8E5M2Layout\",\n \"TensorCoreNVFP4Layout\",\n \"QUANT_ALGOS\",\n \"register_layout_op\",\n]\n" }, { "path": "comfy/rmsnorm.py", "content": "import torch\nimport comfy.model_management\n\nRMSNorm = torch.nn.RMSNorm\n\ndef rms_norm(x, weight=None, eps=1e-6):\n if weight is None:\n return torch.nn.functional.rms_norm(x, (x.shape[-1],), eps=eps)\n else:\n return torch.nn.functional.rms_norm(x, weight.shape, weight=comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps)\n" }, { "path": "comfy/sample.py", "content": "import torch\nimport comfy.model_management\nimport comfy.samplers\nimport comfy.utils\nimport numpy as np\nimport logging\nimport comfy.nested_tensor\n\ndef prepare_noise_inner(latent_image, generator, noise_inds=None):\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 return torch.cat(noises, axis=0)\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\n if latent_image.is_nested:\n tensors = latent_image.unbind()\n noises = []\n for t in tensors:\n noises.append(prepare_noise_inner(t, generator, noise_inds))\n noises = comfy.nested_tensor.NestedTensor(noises)\n else:\n noises = prepare_noise_inner(latent_image, generator, noise_inds)\n\n return noises\n\ndef fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None):\n if latent_image.is_nested:\n return latent_image\n latent_format = model.get_model_object(\"latent_format\") #Resize the empty latent image so it has the right number of channels\n if torch.count_nonzero(latent_image) == 0:\n if latent_format.latent_channels != latent_image.shape[1]:\n latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)\n if downscale_ratio_spacial is not None:\n if downscale_ratio_spacial != latent_format.spacial_downscale_ratio:\n ratio = downscale_ratio_spacial / latent_format.spacial_downscale_ratio\n latent_image = comfy.utils.common_upscale(latent_image, round(latent_image.shape[-1] * ratio), round(latent_image.shape[-2] * ratio), \"nearest-exact\", crop=\"disabled\")\n\n if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:\n latent_image = latent_image.unsqueeze(2)\n return latent_image\n\ndef prepare_sampling(model, noise_shape, positive, negative, noise_mask):\n logging.warning(\"Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed\")\n return model, positive, negative, noise_mask, []\n\ndef cleanup_additional_models(models):\n logging.warning(\"Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed\")\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 sampler = comfy.samplers.KSampler(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, negative, 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(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())\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 samples = comfy.samplers.sample(model, noise, positive, negative, 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(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())\n return samples\n" }, { "path": "comfy/sampler_helpers.py", "content": "from __future__ import annotations\nimport uuid\nimport math\nimport collections\nimport comfy.model_management\nimport comfy.conds\nimport comfy.utils\nimport comfy.hooks\nimport comfy.patcher_extension\nfrom typing import TYPE_CHECKING\nif TYPE_CHECKING:\n from comfy.model_patcher import ModelPatcher\n from comfy.model_base import BaseModel\n from comfy.controlnet import ControlBase\n\ndef prepare_mask(noise_mask, shape, device):\n return comfy.utils.reshape_mask(noise_mask, shape).to(device)\n\ndef get_models_from_cond(cond, model_type):\n models = []\n for c in cond:\n if model_type in c:\n if isinstance(c[model_type], list):\n models += c[model_type]\n else:\n models += [c[model_type]]\n return models\n\ndef get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup):\n # get hooks from conds, and collect cnets so they can be checked for extra_hooks\n cnets: list[ControlBase] = []\n for c in cond:\n if 'hooks' in c:\n for hook in c['hooks'].hooks:\n full_hooks.add(hook)\n if 'control' in c:\n cnets.append(c['control'])\n\n def get_extra_hooks_from_cnet(cnet: ControlBase, _list: list):\n if cnet.extra_hooks is not None:\n _list.append(cnet.extra_hooks)\n if cnet.previous_controlnet is None:\n return _list\n return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list)\n\n hooks_list = []\n cnets = set(cnets)\n for base_cnet in cnets:\n get_extra_hooks_from_cnet(base_cnet, hooks_list)\n extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list)\n if extra_hooks is not None:\n for hook in extra_hooks.hooks:\n full_hooks.add(hook)\n\n return full_hooks\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 temp[\"cross_attn\"] = c[0]\n temp[\"model_conds\"] = model_conds\n temp[\"uuid\"] = uuid.uuid4()\n out.append(temp)\n return out\n\ndef cond_has_hooks(cond):\n for c in cond:\n temp = c[1]\n if \"hooks\" in temp:\n return True\n if \"control\" in temp:\n control = temp[\"control\"]\n extra_hooks = control.get_extra_hooks()\n if len(extra_hooks) > 0:\n return True\n return False\n\ndef get_additional_models(conds, dtype):\n \"\"\"loads additional models in conditioning\"\"\"\n cnets: list[ControlBase] = []\n gligen = []\n add_models = []\n\n for k in conds:\n cnets += get_models_from_cond(conds[k], \"control\")\n gligen += get_models_from_cond(conds[k], \"gligen\")\n add_models += get_models_from_cond(conds[k], \"additional_models\")\n\n control_nets = set(cnets)\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 = [x[1] for x in gligen]\n models = control_models + gligen + add_models\n\n return models, inference_memory\n\ndef get_additional_models_from_model_options(model_options: dict[str]=None):\n \"\"\"loads additional models from registered AddModels hooks\"\"\"\n models = []\n if model_options is not None and \"registered_hooks\" in model_options:\n registered: comfy.hooks.HookGroup = model_options[\"registered_hooks\"]\n for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels):\n hook: comfy.hooks.AdditionalModelsHook\n models.extend(hook.models)\n return models\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 estimate_memory(model, noise_shape, conds):\n cond_shapes = collections.defaultdict(list)\n cond_shapes_min = {}\n for _, cs in conds.items():\n for cond in cs:\n for k, v in model.model.extra_conds_shapes(**cond).items():\n cond_shapes[k].append(v)\n if cond_shapes_min.get(k, None) is None:\n cond_shapes_min[k] = [v]\n elif math.prod(v) > math.prod(cond_shapes_min[k][0]):\n cond_shapes_min[k] = [v]\n\n memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes)\n minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)\n return memory_required, minimum_memory_required\n\ndef prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):\n executor = comfy.patcher_extension.WrapperExecutor.new_executor(\n _prepare_sampling,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)\n )\n return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load, force_offload=force_offload)\n\ndef _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):\n real_model: BaseModel = None\n models, inference_memory = get_additional_models(conds, model.model_dtype())\n models += get_additional_models_from_model_options(model_options)\n models += model.get_nested_additional_models() # TODO: does this require inference_memory update?\n if force_offload: # In training + offload enabled, we want to force prepare sampling to trigger partial load\n memory_required = 1e20\n minimum_memory_required = None\n else:\n memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)\n memory_required += inference_memory\n minimum_memory_required += inference_memory\n comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)\n real_model = model.model\n\n return real_model, conds, models\n\ndef cleanup_models(conds, models):\n cleanup_additional_models(models)\n\n control_cleanup = []\n for k in conds:\n control_cleanup += get_models_from_cond(conds[k], \"control\")\n\n cleanup_additional_models(set(control_cleanup))\n\ndef prepare_model_patcher(model: ModelPatcher, conds, model_options: dict):\n '''\n Registers hooks from conds.\n '''\n # check for hooks in conds - if not registered, see if can be applied\n hooks = comfy.hooks.HookGroup()\n for k in conds:\n get_hooks_from_cond(conds[k], hooks)\n # add wrappers and callbacks from ModelPatcher to transformer_options\n comfy.patcher_extension.merge_nested_dicts(model_options[\"transformer_options\"].setdefault(\"wrappers\", {}), model.wrappers, copy_dict1=False)\n comfy.patcher_extension.merge_nested_dicts(model_options[\"transformer_options\"].setdefault(\"callbacks\", {}), model.callbacks, copy_dict1=False)\n # begin registering hooks\n registered = comfy.hooks.HookGroup()\n target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model)\n # handle all TransformerOptionsHooks\n for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions):\n hook: comfy.hooks.TransformerOptionsHook\n hook.add_hook_patches(model, model_options, target_dict, registered)\n # handle all AddModelsHooks\n for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels):\n hook: comfy.hooks.AdditionalModelsHook\n hook.add_hook_patches(model, model_options, target_dict, registered)\n # handle all WeightHooks by registering on ModelPatcher\n model.register_all_hook_patches(hooks, target_dict, model_options, registered)\n # add registered_hooks onto model_options for further reference\n if len(registered) > 0:\n model_options[\"registered_hooks\"] = registered\n # merge original wrappers and callbacks with hooked wrappers and callbacks\n to_load_options: dict[str] = model_options.setdefault(\"to_load_options\", {})\n for wc_name in [\"wrappers\", \"callbacks\"]:\n comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options[\"transformer_options\"][wc_name],\n copy_dict1=False)\n return to_load_options\n" }, { "path": "comfy/samplers.py", "content": "from __future__ import annotations\nfrom .k_diffusion import sampling as k_diffusion_sampling\nfrom .extra_samplers import uni_pc\nfrom typing import TYPE_CHECKING, Callable, NamedTuple\nif TYPE_CHECKING:\n from comfy.model_patcher import ModelPatcher\n from comfy.model_base import BaseModel\n from comfy.controlnet import ControlBase\nimport torch\nfrom functools import partial\nimport collections\nimport math\nimport logging\nimport comfy.sampler_helpers\nimport comfy.model_patcher\nimport comfy.patcher_extension\nimport comfy.hooks\nimport comfy.context_windows\nimport comfy.utils\nimport scipy.stats\nimport numpy\n\n\ndef add_area_dims(area, num_dims):\n while (len(area) // 2) < num_dims:\n area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:]\n return area\n\ndef get_area_and_mult(conds, x_in, timestep_in):\n dims = tuple(x_in.shape[2:])\n area = None\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 = list(conds['area'])\n area = add_area_dims(area, len(dims))\n if (len(area) // 2) > len(dims):\n area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)]\n\n if 'strength' in conds:\n strength = conds['strength']\n\n input_x = x_in\n if area is not None:\n for i in range(len(dims)):\n area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])\n input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])\n\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\n mask = mask[:input_x.shape[0]]\n if area is not None:\n for i in range(len(dims)):\n mask = mask.narrow(i + 1, area[len(dims) + i], area[i])\n\n mask = mask * mask_strength\n mask = mask.unsqueeze(1).repeat((input_x.shape[0] // mask.shape[0], input_x.shape[1]) + (1, ) * (mask.ndim - 1))\n else:\n mask = torch.ones_like(input_x)\n mult = mask * strength\n\n if 'mask' not in conds and area is not None:\n fuzz = 8\n for i in range(len(dims)):\n rr = min(fuzz, mult.shape[2 + i] // 4)\n if area[len(dims) + i] != 0:\n for t in range(rr):\n m = mult.narrow(i + 2, t, 1)\n m *= ((1.0 / rr) * (t + 1))\n if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:\n for t in range(rr):\n m = mult.narrow(i + 2, area[i] - 1 - t, 1)\n m *= ((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], area=area)\n\n hooks = conds.get('hooks', None)\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', 'uuid', 'hooks'])\n return cond_obj(input_x, mult, conditioning, area, control, patches, conds['uuid'], hooks)\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 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 finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep, model_options):\n # need to figure out remaining unmasked area for conds\n default_mults = []\n for _ in default_conds:\n default_mults.append(torch.ones_like(x_in))\n # look through each finalized cond in hooked_to_run for 'mult' and subtract it from each cond\n for lora_hooks, to_run in hooked_to_run.items():\n for cond_obj, i in to_run:\n # if no default_cond for cond_type, do nothing\n if len(default_conds[i]) == 0:\n continue\n area: list[int] = cond_obj.area\n if area is not None:\n curr_default_mult: torch.Tensor = default_mults[i]\n dims = len(area) // 2\n for i in range(dims):\n curr_default_mult = curr_default_mult.narrow(i + 2, area[i + dims], area[i])\n curr_default_mult -= cond_obj.mult\n else:\n default_mults[i] -= cond_obj.mult\n # for each default_mult, ReLU to make negatives=0, and then check for any nonzeros\n for i, mult in enumerate(default_mults):\n # if no default_cond for cond type, do nothing\n if len(default_conds[i]) == 0:\n continue\n torch.nn.functional.relu(mult, inplace=True)\n # if mult is all zeros, then don't add default_cond\n if torch.max(mult) == 0.0:\n continue\n\n cond = default_conds[i]\n for x in cond:\n # do get_area_and_mult to get all the expected values\n p = get_area_and_mult(x, x_in, timestep)\n if p is None:\n continue\n # replace p's mult with calculated mult\n p = p._replace(mult=mult)\n if p.hooks is not None:\n model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)\n hooked_to_run.setdefault(p.hooks, list())\n hooked_to_run[p.hooks] += [(p, i)]\n\ndef calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options: dict[str]):\n handler: comfy.context_windows.ContextHandlerABC = model_options.get(\"context_handler\", None)\n if handler is None or not handler.should_use_context(model, conds, x_in, timestep, model_options):\n return _calc_cond_batch_outer(model, conds, x_in, timestep, model_options)\n return handler.execute(_calc_cond_batch_outer, model, conds, x_in, timestep, model_options)\n\ndef _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):\n executor = comfy.patcher_extension.WrapperExecutor.new_executor(\n _calc_cond_batch,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True)\n )\n return executor.execute(model, conds, x_in, timestep, model_options)\n\ndef _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):\n out_conds = []\n out_counts = []\n # separate conds by matching hooks\n hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {}\n default_conds = []\n has_default_conds = False\n\n for i in range(len(conds)):\n out_conds.append(torch.zeros_like(x_in))\n out_counts.append(torch.ones_like(x_in) * 1e-37)\n\n cond = conds[i]\n default_c = []\n if cond is not None:\n for x in cond:\n if 'default' in x:\n default_c.append(x)\n has_default_conds = True\n continue\n p = get_area_and_mult(x, x_in, timestep)\n if p is None:\n continue\n if p.hooks is not None:\n model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)\n hooked_to_run.setdefault(p.hooks, list())\n hooked_to_run[p.hooks] += [(p, i)]\n default_conds.append(default_c)\n\n if has_default_conds:\n finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)\n\n model.current_patcher.prepare_state(timestep)\n\n # run every hooked_to_run separately\n for hooks, to_run in hooked_to_run.items():\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.current_patcher.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 cond_shapes = collections.defaultdict(list)\n for tt in batch_amount:\n cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()}\n for k, v in to_run[tt][0].conditioning.items():\n cond_shapes[k].append(v.size())\n\n if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory:\n to_batch = batch_amount\n break\n\n input_x = []\n mult = []\n c = []\n cond_or_uncond = []\n uuids = []\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 uuids.append(p.uuid)\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 transformer_options = model.current_patcher.apply_hooks(hooks=hooks)\n if 'transformer_options' in model_options:\n transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options,\n model_options['transformer_options'],\n copy_dict1=False)\n\n if patches is not None:\n transformer_options[\"patches\"] = comfy.patcher_extension.merge_nested_dicts(\n transformer_options.get(\"patches\", {}),\n patches\n )\n\n transformer_options[\"cond_or_uncond\"] = cond_or_uncond[:]\n transformer_options[\"uuids\"] = uuids[:]\n transformer_options[\"sigmas\"] = timestep\n\n c['transformer_options'] = transformer_options\n\n if control is not None:\n c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond), 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\n for o in range(batch_chunks):\n cond_index = cond_or_uncond[o]\n a = area[o]\n if a is None:\n out_conds[cond_index] += output[o] * mult[o]\n out_counts[cond_index] += mult[o]\n else:\n out_c = out_conds[cond_index]\n out_cts = out_counts[cond_index]\n dims = len(a) // 2\n for i in range(dims):\n out_c = out_c.narrow(i + 2, a[i + dims], a[i])\n out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])\n out_c += output[o] * mult[o]\n out_cts += mult[o]\n\n for i in range(len(out_conds)):\n out_conds[i] /= out_counts[i]\n\n return out_conds\n\ndef calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove\n logging.warning(\"WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.\")\n return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))\n\ndef cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):\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, \"input_cond\": cond, \"input_uncond\": uncond}\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, \"cond_scale\": cond_scale, \"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\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 conds = [cond, uncond_]\n if \"sampler_calc_cond_batch_function\" in model_options:\n args = {\"conds\": conds, \"input\": x, \"sigma\": timestep, \"model\": model, \"model_options\": model_options}\n out = model_options[\"sampler_calc_cond_batch_function\"](args)\n else:\n out = calc_cond_batch(model, conds, x, timestep, model_options)\n\n for fn in model_options.get(\"sampler_pre_cfg_function\", []):\n args = {\"conds\":conds, \"conds_out\": out, \"cond_scale\": cond_scale, \"timestep\": timestep,\n \"input\": x, \"sigma\": timestep, \"model\": model, \"model_options\": model_options}\n out = fn(args)\n\n return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)\n\n\nclass KSamplerX0Inpaint:\n def __init__(self, model, sigmas):\n self.inner_model = model\n self.sigmas = sigmas\n def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):\n if denoise_mask is not None:\n if \"denoise_mask_function\" in model_options:\n denoise_mask = model_options[\"denoise_mask_function\"](sigma, denoise_mask, extra_options={\"model\": self.inner_model, \"sigmas\": self.sigmas})\n latent_mask = 1. - denoise_mask\n x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask\n out = self.inner_model(x, sigma, 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_sampling, steps):\n s = 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_sampling, steps):\n s = model_sampling\n sigs = []\n x = 1\n if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001):\n steps += 1\n sigs = []\n else:\n sigs = [0.0]\n\n ss = max(len(s.sigmas) // steps, 1)\n while x < len(s.sigmas):\n sigs += [float(s.sigmas[x])]\n x += ss\n sigs = sigs[::-1]\n return torch.FloatTensor(sigs)\n\ndef normal_scheduler(model_sampling, steps, sgm=False, floor=False):\n s = model_sampling\n start = s.timestep(s.sigma_max)\n end = s.timestep(s.sigma_min)\n\n append_zero = True\n if sgm:\n timesteps = torch.linspace(start, end, steps + 1)[:-1]\n else:\n if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001):\n steps += 1\n append_zero = False\n timesteps = torch.linspace(start, end, steps)\n\n sigs = []\n for x in range(len(timesteps)):\n ts = timesteps[x]\n sigs.append(float(s.sigma(ts)))\n\n if append_zero:\n sigs += [0.0]\n\n return torch.FloatTensor(sigs)\n\n# Implemented based on: https://arxiv.org/abs/2407.12173\ndef beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):\n total_timesteps = (len(model_sampling.sigmas) - 1)\n ts = 1 - numpy.linspace(0, 1, steps, endpoint=False)\n ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)\n\n sigs = []\n last_t = -1\n for t in ts:\n if t != last_t:\n sigs += [float(model_sampling.sigmas[int(t)])]\n last_t = t\n sigs += [0.0]\n return torch.FloatTensor(sigs)\n\n# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41\ndef linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None):\n if steps == 1:\n sigma_schedule = [1.0, 0.0]\n else:\n if linear_steps is None:\n linear_steps = steps // 2\n linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]\n threshold_noise_step_diff = linear_steps - threshold_noise * steps\n quadratic_steps = steps - linear_steps\n quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2)\n linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2)\n const = quadratic_coef * (linear_steps ** 2)\n quadratic_sigma_schedule = [\n quadratic_coef * (i ** 2) + linear_coef * i + const\n for i in range(linear_steps, steps)\n ]\n sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]\n sigma_schedule = [1.0 - x for x in sigma_schedule]\n return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu()\n\n# Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608\ndef kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor:\n adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1)\n sigmas = adj_idxs.new_zeros(n + 1)\n sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_()\n return sigmas\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_multidim(conditions, dims, 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 a = area[1:]\n a_len = len(a) // 2\n area = ()\n for d in range(len(dims)):\n area += (max(1, round(a[d] * dims[d])),)\n for d in range(len(dims)):\n area += (round(a[d + a_len] * dims[d]),)\n\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) == len(dims):\n mask = mask.unsqueeze(0)\n if mask.shape[1:] != dims:\n if mask.ndim < 4:\n mask = comfy.utils.common_upscale(mask.unsqueeze(1), dims[-1], dims[-2], 'bilinear', 'none').squeeze(1)\n else:\n mask = comfy.utils.common_upscale(mask, dims[-1], dims[-2], 'bilinear', 'none')\n\n if modified.get(\"set_area_to_bounds\", False): #TODO: handle dim != 2\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 resolve_areas_and_cond_masks(conditions, h, w, device):\n logging.warning(\"WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.\")\n return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)\n\ndef create_cond_with_same_area_if_none(conds, c):\n if 'area' not in c:\n return\n\n def area_inside(a, area_cmp):\n a = add_area_dims(a, len(area_cmp) // 2)\n area_cmp = add_area_dims(area_cmp, len(a) // 2)\n\n a_l = len(a) // 2\n area_cmp_l = len(area_cmp) // 2\n for i in range(min(a_l, area_cmp_l)):\n if a[a_l + i] < area_cmp[area_cmp_l + i]:\n return False\n for i in range(min(a_l, area_cmp_l)):\n if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]):\n return False\n return True\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 area_inside(c_area, a):\n if smallest is None:\n smallest = x\n elif 'area' not in smallest:\n smallest = x\n else:\n if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]):\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 # handle clip hook schedule, if needed\n if 'clip_start_percent' in x:\n timestep_start = s.percent_to_sigma(max(x['clip_start_percent'], x.get('start_percent', 0.0)))\n timestep_end = s.percent_to_sigma(min(x['clip_end_percent'], x.get('end_percent', 1.0)))\n else:\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 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 default_width = None\n if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model\n default_width = noise.shape[3] * 8\n params[\"width\"] = params.get(\"width\", default_width)\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\nKSAMPLER_NAMES = [\"euler\", \"euler_cfg_pp\", \"euler_ancestral\", \"euler_ancestral_cfg_pp\", \"heun\", \"heunpp2\", \"exp_heun_2_x0\", \"exp_heun_2_x0_sde\", \"dpm_2\", \"dpm_2_ancestral\",\n \"lms\", \"dpm_fast\", \"dpm_adaptive\", \"dpmpp_2s_ancestral\", \"dpmpp_2s_ancestral_cfg_pp\", \"dpmpp_sde\", \"dpmpp_sde_gpu\",\n \"dpmpp_2m\", \"dpmpp_2m_cfg_pp\", \"dpmpp_2m_sde\", \"dpmpp_2m_sde_gpu\", \"dpmpp_2m_sde_heun\", \"dpmpp_2m_sde_heun_gpu\", \"dpmpp_3m_sde\", \"dpmpp_3m_sde_gpu\", \"ddpm\", \"lcm\",\n \"ipndm\", \"ipndm_v\", \"deis\", \"res_multistep\", \"res_multistep_cfg_pp\", \"res_multistep_ancestral\", \"res_multistep_ancestral_cfg_pp\",\n \"gradient_estimation\", \"gradient_estimation_cfg_pp\", \"er_sde\", \"seeds_2\", \"seeds_3\", \"sa_solver\", \"sa_solver_pece\"]\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, sigmas)\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 noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))\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 samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)\n samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)\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 if len(sigmas) <= 1:\n return noise\n\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, **extra_options):\n if len(sigmas) <= 1:\n return noise\n\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, **extra_options)\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\n\ndef process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None, latent_shapes=None):\n for k in conds:\n conds[k] = conds[k][:]\n resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)\n\n for k in conds:\n calculate_start_end_timesteps(model, conds[k])\n\n if hasattr(model, 'extra_conds'):\n for k in conds:\n conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed, latent_shapes=latent_shapes)\n\n #make sure each cond area has an opposite one with the same area\n for k in conds:\n for c in conds[k]:\n for kk in conds:\n if k != kk:\n create_cond_with_same_area_if_none(conds[kk], c)\n\n for k in conds:\n for c in conds[k]:\n if 'hooks' in c:\n for hook in c['hooks'].hooks:\n hook.initialize_timesteps(model)\n\n for k in conds:\n pre_run_control(model, conds[k])\n\n if \"positive\" in conds:\n positive = conds[\"positive\"]\n for k in conds:\n if k != \"positive\":\n apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x])\n apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x])\n\n return conds\n\n\ndef preprocess_conds_hooks(conds: dict[str, list[dict[str]]]):\n # determine which ControlNets have extra_hooks that should be combined with normal hooks\n hook_replacement: dict[tuple[ControlBase, comfy.hooks.HookGroup], list[dict]] = {}\n for k in conds:\n for kk in conds[k]:\n if 'control' in kk:\n control: 'ControlBase' = kk['control']\n extra_hooks = control.get_extra_hooks()\n if len(extra_hooks) > 0:\n hooks: comfy.hooks.HookGroup = kk.get('hooks', None)\n to_replace = hook_replacement.setdefault((control, hooks), [])\n to_replace.append(kk)\n # if nothing to replace, do nothing\n if len(hook_replacement) == 0:\n return\n\n # for optimal sampling performance, common ControlNets + hook combos should have identical hooks\n # on the cond dicts\n for key, conds_to_modify in hook_replacement.items():\n control = key[0]\n hooks = key[1]\n hooks = comfy.hooks.HookGroup.combine_all_hooks(control.get_extra_hooks() + [hooks])\n # if combined hooks are not None, set as new hooks for all relevant conds\n if hooks is not None:\n for cond in conds_to_modify:\n cond['hooks'] = hooks\n\ndef filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]):\n '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for\n HookGroups that have the same reference.'''\n registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None)\n # if None were registered, make sure all hooks are cleaned from conds\n if registered is None:\n for k in conds:\n for kk in conds[k]:\n kk.pop('hooks', None)\n return\n # find conds that contain hooks to be replaced - group by common HookGroup refs\n hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {}\n for k in conds:\n for kk in conds[k]:\n hooks: comfy.hooks.HookGroup = kk.get('hooks', None)\n if hooks is not None:\n if not hooks.is_subset_of(registered):\n to_replace = hook_replacement.setdefault(hooks, [])\n to_replace.append(kk)\n # for each hook to replace, create a new proper HookGroup and assign to all common conds\n for hooks, conds_to_modify in hook_replacement.items():\n new_hooks = hooks.new_with_common_hooks(registered)\n if len(new_hooks) == 0:\n new_hooks = None\n for kk in conds_to_modify:\n kk['hooks'] = new_hooks\n\n\ndef get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]):\n hooks_set = set()\n for k in conds:\n for kk in conds[k]:\n hooks_set.add(kk.get('hooks', None))\n return len(hooks_set)\n\n\ndef cast_to_load_options(model_options: dict[str], device=None, dtype=None):\n '''\n If any patches from hooks, wrappers, or callbacks have .to to be called, call it.\n '''\n if model_options is None:\n return\n to_load_options = model_options.get(\"to_load_options\", None)\n if to_load_options is None:\n return\n\n casts = []\n if device is not None:\n casts.append(device)\n if dtype is not None:\n casts.append(dtype)\n # if nothing to apply, do nothing\n if len(casts) == 0:\n return\n\n # try to call .to on patches\n if \"patches\" in to_load_options:\n patches = to_load_options[\"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 for cast in casts:\n patch_list[i] = patch_list[i].to(cast)\n if \"patches_replace\" in to_load_options:\n patches = to_load_options[\"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 for cast in casts:\n patch_list[k] = patch_list[k].to(cast)\n # try to call .to on any wrappers/callbacks\n wrappers_and_callbacks = [\"wrappers\", \"callbacks\"]\n for wc_name in wrappers_and_callbacks:\n if wc_name in to_load_options:\n wc: dict[str, list] = to_load_options[wc_name]\n for wc_dict in wc.values():\n for wc_list in wc_dict.values():\n for i in range(len(wc_list)):\n if hasattr(wc_list[i], \"to\"):\n for cast in casts:\n wc_list[i] = wc_list[i].to(cast)\n\n\nclass CFGGuider:\n def __init__(self, model_patcher: ModelPatcher):\n self.model_patcher = model_patcher\n self.model_options = model_patcher.model_options\n self.original_conds = {}\n self.cfg = 1.0\n\n def set_conds(self, positive, negative):\n self.inner_set_conds({\"positive\": positive, \"negative\": negative})\n\n def set_cfg(self, cfg):\n self.cfg = cfg\n\n def inner_set_conds(self, conds):\n for k in conds:\n if self.model_patcher.is_dynamic() and comfy.sampler_helpers.cond_has_hooks(conds[k]):\n self.model_patcher = self.model_patcher.get_non_dynamic_delegate()\n self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])\n\n def __call__(self, *args, **kwargs):\n return self.outer_predict_noise(*args, **kwargs)\n\n def outer_predict_noise(self, x, timestep, model_options={}, seed=None):\n return comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self.predict_noise,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, self.model_options, is_model_options=True)\n ).execute(x, timestep, model_options, seed)\n\n def predict_noise(self, x, timestep, model_options={}, seed=None):\n return sampling_function(self.inner_model, x, timestep, self.conds.get(\"negative\", None), self.conds.get(\"positive\", None), self.cfg, model_options=model_options, seed=seed)\n\n def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None):\n if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.\n latent_image = self.inner_model.process_latent_in(latent_image)\n\n self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed, latent_shapes=latent_shapes)\n\n extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options)\n extra_model_options.setdefault(\"transformer_options\", {})[\"sample_sigmas\"] = sigmas\n extra_args = {\"model_options\": extra_model_options, \"seed\": seed}\n\n executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(\n sampler.sample,\n sampler,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, extra_args[\"model_options\"], is_model_options=True)\n )\n samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)\n return self.inner_model.process_latent_out(samples.to(torch.float32))\n\n def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None):\n self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)\n device = self.model_patcher.load_device\n\n noise = noise.to(device)\n latent_image = latent_image.to(device)\n sigmas = sigmas.to(device)\n cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())\n\n try:\n self.model_patcher.pre_run()\n output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)\n finally:\n self.model_patcher.cleanup()\n\n comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)\n del self.inner_model\n del self.loaded_models\n return output\n\n def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):\n if sigmas.shape[-1] == 0:\n return latent_image\n\n if latent_image.is_nested:\n latent_image, latent_shapes = comfy.utils.pack_latents(latent_image.unbind())\n noise, _ = comfy.utils.pack_latents(noise.unbind())\n else:\n latent_shapes = [latent_image.shape]\n\n if denoise_mask is not None:\n if denoise_mask.is_nested:\n denoise_masks = denoise_mask.unbind()\n denoise_masks = denoise_masks[:len(latent_shapes)]\n else:\n denoise_masks = [denoise_mask]\n\n for i in range(len(denoise_masks), len(latent_shapes)):\n denoise_masks.append(torch.ones(latent_shapes[i]))\n\n for i in range(len(denoise_masks)):\n denoise_masks[i] = comfy.sampler_helpers.prepare_mask(denoise_masks[i], latent_shapes[i], self.model_patcher.load_device)\n\n if len(denoise_masks) > 1:\n denoise_mask, _ = comfy.utils.pack_latents(denoise_masks)\n else:\n denoise_mask = denoise_masks[0]\n\n self.conds = {}\n for k in self.original_conds:\n self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))\n preprocess_conds_hooks(self.conds)\n\n try:\n orig_model_options = self.model_options\n self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options)\n # if one hook type (or just None), then don't bother caching weights for hooks (will never change after first step)\n orig_hook_mode = self.model_patcher.hook_mode\n if get_total_hook_groups_in_conds(self.conds) <= 1:\n self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram\n comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options)\n filter_registered_hooks_on_conds(self.conds, self.model_options)\n executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(\n self.outer_sample,\n self,\n comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True)\n )\n output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)\n finally:\n cast_to_load_options(self.model_options, device=self.model_patcher.offload_device)\n self.model_options = orig_model_options\n self.model_patcher.hook_mode = orig_hook_mode\n self.model_patcher.restore_hook_patches()\n\n del self.conds\n\n if len(latent_shapes) > 1:\n output = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(output, latent_shapes))\n return output\n\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 cfg_guider = CFGGuider(model)\n cfg_guider.set_conds(positive, negative)\n cfg_guider.set_cfg(cfg)\n return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)\n\n\nSAMPLER_NAMES = KSAMPLER_NAMES + [\"ddim\", \"uni_pc\", \"uni_pc_bh2\"]\n\nclass SchedulerHandler(NamedTuple):\n handler: Callable[..., torch.Tensor]\n # Boolean indicates whether to call the handler like:\n # scheduler_function(model_sampling, steps) or\n # scheduler_function(n, sigma_min: float, sigma_max: float)\n use_ms: bool = True\n\nSCHEDULER_HANDLERS = {\n \"simple\": SchedulerHandler(simple_scheduler),\n \"sgm_uniform\": SchedulerHandler(partial(normal_scheduler, sgm=True)),\n \"karras\": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False),\n \"exponential\": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False),\n \"ddim_uniform\": SchedulerHandler(ddim_scheduler),\n \"beta\": SchedulerHandler(beta_scheduler),\n \"normal\": SchedulerHandler(normal_scheduler),\n \"linear_quadratic\": SchedulerHandler(linear_quadratic_schedule),\n \"kl_optimal\": SchedulerHandler(kl_optimal_scheduler, use_ms=False),\n}\nSCHEDULER_NAMES = list(SCHEDULER_HANDLERS)\n\ndef calculate_sigmas(model_sampling: object, scheduler_name: str, steps: int) -> torch.Tensor:\n handler = SCHEDULER_HANDLERS.get(scheduler_name)\n if handler is None:\n err = f\"error invalid scheduler {scheduler_name}\"\n logging.error(err)\n raise ValueError(err)\n if handler.use_ms:\n return handler.handler(model_sampling, steps)\n return handler.handler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))\n\ndef sampler_object(name):\n if name == \"uni_pc\":\n sampler = KSAMPLER(uni_pc.sample_unipc)\n elif name == \"uni_pc_bh2\":\n sampler = KSAMPLER(uni_pc.sample_unipc_bh2)\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 DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2'))\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 self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS:\n steps += 1\n discard_penultimate_sigma = True\n\n sigmas = calculate_sigmas(self.model.get_model_object(\"model_sampling\"), 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 if denoise <= 0.0:\n self.sigmas = torch.FloatTensor([])\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": "comfy/sd.py", "content": "from __future__ import annotations\nimport json\nimport torch\nfrom enum import Enum\nimport logging\n\nfrom comfy import model_management\nfrom comfy.utils import ProgressBar\nfrom .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine\nfrom .ldm.cascade.stage_a import StageA\nfrom .ldm.cascade.stage_c_coder import StageC_coder\nfrom .ldm.audio.autoencoder import AudioOobleckVAE\nimport comfy.ldm.genmo.vae.model\nimport comfy.ldm.lightricks.vae.causal_video_autoencoder\nimport comfy.ldm.cosmos.vae\nimport comfy.ldm.wan.vae\nimport comfy.ldm.wan.vae2_2\nimport comfy.ldm.hunyuan3d.vae\nimport comfy.ldm.ace.vae.music_dcae_pipeline\nimport comfy.ldm.hunyuan_video.vae\nimport comfy.ldm.mmaudio.vae.autoencoder\nimport comfy.pixel_space_convert\nimport comfy.weight_adapter\nimport yaml\nimport math\nimport os\n\nimport comfy.utils\n\nfrom . import clip_vision\nfrom . import gligen\nfrom . import diffusers_convert\nfrom . import model_detection\n\nfrom . import sd1_clip\nfrom . import sdxl_clip\nimport comfy.text_encoders.sd2_clip\nimport comfy.text_encoders.sd3_clip\nimport comfy.text_encoders.sa_t5\nimport comfy.text_encoders.aura_t5\nimport comfy.text_encoders.pixart_t5\nimport comfy.text_encoders.hydit\nimport comfy.text_encoders.flux\nimport comfy.text_encoders.long_clipl\nimport comfy.text_encoders.genmo\nimport comfy.text_encoders.lt\nimport comfy.text_encoders.hunyuan_video\nimport comfy.text_encoders.cosmos\nimport comfy.text_encoders.lumina2\nimport comfy.text_encoders.wan\nimport comfy.text_encoders.hidream\nimport comfy.text_encoders.ace\nimport comfy.text_encoders.omnigen2\nimport comfy.text_encoders.qwen_image\nimport comfy.text_encoders.hunyuan_image\nimport comfy.text_encoders.z_image\nimport comfy.text_encoders.ovis\nimport comfy.text_encoders.kandinsky5\nimport comfy.text_encoders.jina_clip_2\nimport comfy.text_encoders.newbie\nimport comfy.text_encoders.anima\nimport comfy.text_encoders.ace15\nimport comfy.text_encoders.longcat_image\n\nimport comfy.model_patcher\nimport comfy.lora\nimport comfy.lora_convert\nimport comfy.hooks\nimport comfy.t2i_adapter.adapter\nimport comfy.taesd.taesd\nimport comfy.taesd.taehv\nimport comfy.latent_formats\n\nimport comfy.ldm.flux.redux\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 = comfy.lora.model_lora_keys_unet(model.model, key_map)\n if clip is not None:\n key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)\n\n lora = comfy.lora_convert.convert_lora(lora)\n loaded = comfy.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 logging.warning(\"NOT LOADED {}\".format(x))\n\n return (new_modelpatcher, new_clip)\n\n\ndef load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip):\n \"\"\"\n Load LoRA in bypass mode without modifying base model weights.\n\n Instead of patching weights, this injects the LoRA computation into the\n forward pass: output = base_forward(x) + lora_path(x)\n\n Non-adapter patches (bias diff, weight diff, etc.) are applied as regular patches.\n\n This is useful for training and when model weights are offloaded.\n \"\"\"\n key_map = {}\n if model is not None:\n key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)\n if clip is not None:\n key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)\n\n logging.debug(f\"[BypassLoRA] key_map has {len(key_map)} entries\")\n\n lora = comfy.lora_convert.convert_lora(lora)\n loaded = comfy.lora.load_lora(lora, key_map)\n\n logging.debug(f\"[BypassLoRA] loaded has {len(loaded)} entries\")\n\n # Separate adapters (for bypass) from other patches (for regular patching)\n bypass_patches = {} # WeightAdapterBase instances -> bypass mode\n regular_patches = {} # diff, set, bias patches -> regular weight patching\n\n for key, patch_data in loaded.items():\n if isinstance(patch_data, comfy.weight_adapter.WeightAdapterBase):\n bypass_patches[key] = patch_data\n else:\n regular_patches[key] = patch_data\n\n logging.debug(f\"[BypassLoRA] {len(bypass_patches)} bypass adapters, {len(regular_patches)} regular patches\")\n\n k = set()\n k1 = set()\n\n if model is not None:\n new_modelpatcher = model.clone()\n\n # Apply regular patches (bias diff, weight diff, etc.) via normal patching\n if regular_patches:\n patched_keys = new_modelpatcher.add_patches(regular_patches, strength_model)\n k.update(patched_keys)\n\n # Apply adapter patches via bypass injection\n manager = comfy.weight_adapter.BypassInjectionManager()\n model_sd_keys = set(new_modelpatcher.model.state_dict().keys())\n\n for key, adapter in bypass_patches.items():\n if key in model_sd_keys:\n manager.add_adapter(key, adapter, strength=strength_model)\n k.add(key)\n else:\n logging.warning(f\"[BypassLoRA] Adapter key not in model state_dict: {key}\")\n\n injections = manager.create_injections(new_modelpatcher.model)\n\n if manager.get_hook_count() > 0:\n new_modelpatcher.set_injections(\"bypass_lora\", injections)\n else:\n new_modelpatcher = None\n\n if clip is not None:\n new_clip = clip.clone()\n\n # Apply regular patches to clip\n if regular_patches:\n patched_keys = new_clip.add_patches(regular_patches, strength_clip)\n k1.update(patched_keys)\n\n # Apply adapter patches via bypass injection\n clip_manager = comfy.weight_adapter.BypassInjectionManager()\n clip_sd_keys = set(new_clip.cond_stage_model.state_dict().keys())\n\n for key, adapter in bypass_patches.items():\n if key in clip_sd_keys:\n clip_manager.add_adapter(key, adapter, strength=strength_clip)\n k1.add(key)\n\n clip_injections = clip_manager.create_injections(new_clip.cond_stage_model)\n if clip_manager.get_hook_count() > 0:\n new_clip.patcher.set_injections(\"bypass_lora\", clip_injections)\n else:\n new_clip = None\n\n for x in loaded:\n if (x not in k) and (x not in k1):\n patch_data = loaded[x]\n patch_type = type(patch_data).__name__\n if isinstance(patch_data, tuple):\n patch_type = f\"tuple({patch_data[0]})\"\n logging.warning(f\"NOT LOADED: {x} (type={patch_type})\")\n\n return (new_modelpatcher, new_clip)\n\n\nclass CLIP:\n def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}, disable_dynamic=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_options.get(\"load_device\", model_management.text_encoder_device())\n offload_device = model_options.get(\"offload_device\", model_management.text_encoder_offload_device())\n dtype = model_options.get(\"dtype\", None)\n if dtype is None:\n dtype = model_management.text_encoder_dtype(load_device)\n\n params['dtype'] = dtype\n params['device'] = model_options.get(\"initial_device\", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype)))\n params['model_options'] = model_options\n\n self.cond_stage_model = clip(**(params))\n\n for dt in self.cond_stage_model.dtypes:\n if not model_management.supports_cast(load_device, dt):\n load_device = offload_device\n if params['device'] != offload_device:\n self.cond_stage_model.to(offload_device)\n logging.warning(\"Had to shift TE back.\")\n\n model_management.archive_model_dtypes(self.cond_stage_model)\n\n self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher\n self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)\n #Match torch.float32 hardcode upcast in TE implemention\n self.patcher.set_model_compute_dtype(torch.float32)\n self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram\n self.patcher.is_clip = True\n self.apply_hooks_to_conds = None\n if len(state_dict) > 0:\n if isinstance(state_dict, list):\n for c in state_dict:\n m, u = self.load_sd(c)\n if len(m) > 0:\n logging.warning(\"clip missing: {}\".format(m))\n\n if len(u) > 0:\n logging.debug(\"clip unexpected: {}\".format(u))\n else:\n m, u = self.load_sd(state_dict, full_model=True)\n if len(m) > 0:\n m_filter = list(filter(lambda a: \".logit_scale\" not in a and \".transformer.text_projection.weight\" not in a, m))\n if len(m_filter) > 0:\n logging.warning(\"clip missing: {}\".format(m))\n else:\n logging.debug(\"clip missing: {}\".format(m))\n\n if len(u) > 0:\n logging.debug(\"clip unexpected {}:\".format(u))\n\n if params['device'] == load_device:\n model_management.load_models_gpu([self.patcher], force_full_load=True)\n self.layer_idx = None\n self.use_clip_schedule = False\n logging.info(\"CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}\".format(load_device, offload_device, params['device'], dtype))\n self.tokenizer_options = {}\n\n def clone(self, disable_dynamic=False):\n n = CLIP(no_init=True)\n n.patcher = self.patcher.clone(disable_dynamic=disable_dynamic)\n n.cond_stage_model = self.cond_stage_model\n n.tokenizer = self.tokenizer\n n.layer_idx = self.layer_idx\n n.tokenizer_options = self.tokenizer_options.copy()\n n.use_clip_schedule = self.use_clip_schedule\n n.apply_hooks_to_conds = self.apply_hooks_to_conds\n return n\n\n def get_ram_usage(self):\n return self.patcher.get_ram_usage()\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 set_tokenizer_option(self, option_name, value):\n self.tokenizer_options[option_name] = value\n\n def clip_layer(self, layer_idx):\n self.layer_idx = layer_idx\n\n def tokenize(self, text, return_word_ids=False, **kwargs):\n tokenizer_options = kwargs.get(\"tokenizer_options\", {})\n if len(self.tokenizer_options) > 0:\n tokenizer_options = {**self.tokenizer_options, **tokenizer_options}\n if len(tokenizer_options) > 0:\n kwargs[\"tokenizer_options\"] = tokenizer_options\n return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs)\n\n def add_hooks_to_dict(self, pooled_dict: dict[str]):\n if self.apply_hooks_to_conds:\n pooled_dict[\"hooks\"] = self.apply_hooks_to_conds\n return pooled_dict\n\n def encode_from_tokens_scheduled(self, tokens, unprojected=False, add_dict: dict[str]={}, show_pbar=True):\n all_cond_pooled: list[tuple[torch.Tensor, dict[str]]] = []\n all_hooks = self.patcher.forced_hooks\n if all_hooks is None or not self.use_clip_schedule:\n # if no hooks or shouldn't use clip schedule, do unscheduled encode_from_tokens and perform add_dict\n return_pooled = \"unprojected\" if unprojected else True\n pooled_dict = self.encode_from_tokens(tokens, return_pooled=return_pooled, return_dict=True)\n cond = pooled_dict.pop(\"cond\")\n # add/update any keys with the provided add_dict\n pooled_dict.update(add_dict)\n all_cond_pooled.append([cond, pooled_dict])\n else:\n scheduled_keyframes = all_hooks.get_hooks_for_clip_schedule()\n\n self.cond_stage_model.reset_clip_options()\n if self.layer_idx is not None:\n self.cond_stage_model.set_clip_options({\"layer\": self.layer_idx})\n if unprojected:\n self.cond_stage_model.set_clip_options({\"projected_pooled\": False})\n\n self.load_model(tokens)\n self.cond_stage_model.set_clip_options({\"execution_device\": self.patcher.load_device})\n all_hooks.reset()\n self.patcher.patch_hooks(None)\n if show_pbar:\n pbar = ProgressBar(len(scheduled_keyframes))\n\n for scheduled_opts in scheduled_keyframes:\n t_range = scheduled_opts[0]\n # don't bother encoding any conds outside of start_percent and end_percent bounds\n if \"start_percent\" in add_dict:\n if t_range[1] < add_dict[\"start_percent\"]:\n continue\n if \"end_percent\" in add_dict:\n if t_range[0] > add_dict[\"end_percent\"]:\n continue\n hooks_keyframes = scheduled_opts[1]\n for hook, keyframe in hooks_keyframes:\n hook.hook_keyframe._current_keyframe = keyframe\n # apply appropriate hooks with values that match new hook_keyframe\n self.patcher.patch_hooks(all_hooks)\n # perform encoding as normal\n o = self.cond_stage_model.encode_token_weights(tokens)\n cond, pooled = o[:2]\n pooled_dict = {\"pooled_output\": pooled}\n # add clip_start_percent and clip_end_percent in pooled\n pooled_dict[\"clip_start_percent\"] = t_range[0]\n pooled_dict[\"clip_end_percent\"] = t_range[1]\n # add/update any keys with the provided add_dict\n pooled_dict.update(add_dict)\n # add hooks stored on clip\n self.add_hooks_to_dict(pooled_dict)\n all_cond_pooled.append([cond, pooled_dict])\n if show_pbar:\n pbar.update(1)\n model_management.throw_exception_if_processing_interrupted()\n all_hooks.reset()\n return all_cond_pooled\n\n def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False):\n self.cond_stage_model.reset_clip_options()\n\n if self.layer_idx is not None:\n self.cond_stage_model.set_clip_options({\"layer\": self.layer_idx})\n\n if return_pooled == \"unprojected\":\n self.cond_stage_model.set_clip_options({\"projected_pooled\": False})\n\n self.load_model(tokens)\n self.cond_stage_model.set_clip_options({\"execution_device\": self.patcher.load_device})\n o = self.cond_stage_model.encode_token_weights(tokens)\n cond, pooled = o[:2]\n if return_dict:\n out = {\"cond\": cond, \"pooled_output\": pooled}\n if len(o) > 2:\n for k in o[2]:\n out[k] = o[2][k]\n self.add_hooks_to_dict(out)\n return out\n\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, full_model=False):\n if full_model:\n return self.cond_stage_model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())\n else:\n can_assign = self.patcher.is_dynamic()\n self.cond_stage_model.can_assign_sd = can_assign\n\n # The CLIP models are a pretty complex web of wrappers and its\n # a bit of an API change to plumb this all the way through.\n # So spray paint the model with this flag that the loading\n # nn.Module can then inspect for itself.\n for m in self.cond_stage_model.modules():\n m.can_assign_sd = can_assign\n\n return self.cond_stage_model.load_sd(sd)\n\n def get_sd(self):\n sd_clip = self.cond_stage_model.state_dict()\n sd_tokenizer = self.tokenizer.state_dict()\n for k in sd_tokenizer:\n sd_clip[k] = sd_tokenizer[k]\n return sd_clip\n\n def load_model(self, tokens={}):\n memory_used = 0\n if hasattr(self.cond_stage_model, \"memory_estimation_function\"):\n memory_used = self.cond_stage_model.memory_estimation_function(tokens, device=self.patcher.load_device)\n model_management.load_models_gpu([self.patcher], memory_required=memory_used)\n return self.patcher\n\n def get_key_patches(self):\n return self.patcher.get_key_patches()\n\n def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):\n self.cond_stage_model.reset_clip_options()\n\n self.load_model(tokens)\n self.cond_stage_model.set_clip_options({\"layer\": None})\n self.cond_stage_model.set_clip_options({\"execution_device\": self.patcher.load_device})\n return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)\n\n def decode(self, token_ids, skip_special_tokens=True):\n return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)\n\nclass VAE:\n def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=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 if model_management.is_amd():\n VAE_KL_MEM_RATIO = 2.73\n else:\n VAE_KL_MEM_RATIO = 1.0\n\n self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #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) * VAE_KL_MEM_RATIO\n self.downscale_ratio = 8\n self.upscale_ratio = 8\n self.latent_channels = 4\n self.latent_dim = 2\n self.output_channels = 3\n self.pad_channel_value = None\n self.process_input = lambda image: image * 2.0 - 1.0\n self.process_output = lambda image: image.add_(1.0).div_(2.0).clamp_(0.0, 1.0)\n self.working_dtypes = [torch.bfloat16, torch.float32]\n self.disable_offload = False\n self.not_video = False\n self.size = None\n\n self.downscale_index_formula = None\n self.upscale_index_formula = None\n self.extra_1d_channel = None\n self.crop_input = True\n\n self.audio_sample_rate = 44100\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': \"comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer\"},\n encoder_config={'target': \"comfy.ldm.modules.diffusionmodules.model.Encoder\", 'params': encoder_config},\n decoder_config={'target': \"comfy.ldm.modules.temporal_ae.VideoDecoder\", 'params': decoder_config})\n elif \"taesd_decoder.1.weight\" in sd:\n self.latent_channels = sd[\"taesd_decoder.1.weight\"].shape[1]\n self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)\n elif \"vquantizer.codebook.weight\" in sd: #VQGan: stage a of stable cascade\n self.first_stage_model = StageA()\n self.downscale_ratio = 4\n self.upscale_ratio = 4\n #TODO\n #self.memory_used_encode\n #self.memory_used_decode\n self.process_input = lambda image: image\n self.process_output = lambda image: image\n elif \"backbone.1.0.block.0.1.num_batches_tracked\" in sd: #effnet: encoder for stage c latent of stable cascade\n self.first_stage_model = StageC_coder()\n self.downscale_ratio = 32\n self.latent_channels = 16\n new_sd = {}\n for k in sd:\n new_sd[\"encoder.{}\".format(k)] = sd[k]\n sd = new_sd\n elif \"blocks.11.num_batches_tracked\" in sd: #previewer: decoder for stage c latent of stable cascade\n self.first_stage_model = StageC_coder()\n self.latent_channels = 16\n new_sd = {}\n for k in sd:\n new_sd[\"previewer.{}\".format(k)] = sd[k]\n sd = new_sd\n elif \"encoder.backbone.1.0.block.0.1.num_batches_tracked\" in sd: #combined effnet and previewer for stable cascade\n self.first_stage_model = StageC_coder()\n self.downscale_ratio = 32\n self.latent_channels = 16\n elif \"decoder.conv_in.weight\" in sd:\n if sd['decoder.conv_in.weight'].shape[1] == 64:\n ddconfig = {\"block_out_channels\": [128, 256, 512, 512, 1024, 1024], \"in_channels\": 3, \"out_channels\": 3, \"num_res_blocks\": 2, \"ffactor_spatial\": 32, \"downsample_match_channel\": True, \"upsample_match_channel\": True}\n self.latent_channels = ddconfig['z_channels'] = sd[\"decoder.conv_in.weight\"].shape[1]\n self.downscale_ratio = 32\n self.upscale_ratio = 32\n self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n self.first_stage_model = AutoencodingEngine(regularizer_config={'target': \"comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer\"},\n encoder_config={'target': \"comfy.ldm.hunyuan_video.vae.Encoder\", 'params': ddconfig},\n decoder_config={'target': \"comfy.ldm.hunyuan_video.vae.Decoder\", 'params': ddconfig})\n\n self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype)\n elif sd['decoder.conv_in.weight'].shape[1] == 32 and sd['decoder.conv_in.weight'].ndim == 5:\n ddconfig = {\"block_out_channels\": [128, 256, 512, 1024, 1024], \"in_channels\": 3, \"out_channels\": 3, \"num_res_blocks\": 2, \"ffactor_spatial\": 16, \"ffactor_temporal\": 4, \"downsample_match_channel\": True, \"upsample_match_channel\": True, \"refiner_vae\": False}\n self.latent_channels = ddconfig['z_channels'] = sd[\"decoder.conv_in.weight\"].shape[1]\n self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)\n self.upscale_index_formula = (4, 16, 16)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)\n self.downscale_index_formula = (4, 16, 16)\n self.latent_dim = 3\n self.not_video = True\n self.first_stage_model = AutoencodingEngine(regularizer_config={'target': \"comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer\"},\n encoder_config={'target': \"comfy.ldm.hunyuan_video.vae_refiner.Encoder\", 'params': ddconfig},\n decoder_config={'target': \"comfy.ldm.hunyuan_video.vae_refiner.Decoder\", 'params': ddconfig})\n\n self.memory_used_encode = lambda shape, dtype: (2800 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (2800 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)\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 and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE\n ddconfig['ch_mult'] = [1, 2, 4]\n self.downscale_ratio = 4\n self.upscale_ratio = 4\n\n self.latent_channels = ddconfig['z_channels'] = sd[\"decoder.conv_in.weight\"].shape[1]\n if 'decoder.post_quant_conv.weight' in sd:\n sd = comfy.utils.state_dict_prefix_replace(sd, {\"decoder.post_quant_conv.\": \"post_quant_conv.\", \"encoder.quant_conv.\": \"quant_conv.\"})\n\n if 'bn.running_mean' in sd:\n ddconfig[\"batch_norm_latent\"] = True\n self.downscale_ratio *= 2\n self.upscale_ratio *= 2\n self.latent_channels *= 4\n old_memory_used_decode = self.memory_used_decode\n self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0\n\n if 'post_quant_conv.weight' in sd:\n self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])\n else:\n self.first_stage_model = AutoencodingEngine(regularizer_config={'target': \"comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer\"},\n encoder_config={'target': \"comfy.ldm.modules.diffusionmodules.model.Encoder\", 'params': ddconfig},\n decoder_config={'target': \"comfy.ldm.modules.diffusionmodules.model.Decoder\", 'params': ddconfig})\n elif \"decoder.layers.1.layers.0.beta\" in sd:\n config = {}\n param_key = None\n self.upscale_ratio = 2048\n self.downscale_ratio = 2048\n if \"decoder.layers.2.layers.1.weight_v\" in sd:\n param_key = \"decoder.layers.2.layers.1.weight_v\"\n if \"decoder.layers.2.layers.1.parametrizations.weight.original1\" in sd:\n param_key = \"decoder.layers.2.layers.1.parametrizations.weight.original1\"\n if param_key is not None:\n if sd[param_key].shape[-1] == 12:\n config[\"strides\"] = [2, 4, 4, 6, 10]\n self.audio_sample_rate = 48000\n self.upscale_ratio = 1920\n self.downscale_ratio = 1920\n\n self.first_stage_model = AudioOobleckVAE(**config)\n self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)\n self.latent_channels = 64\n self.output_channels = 2\n self.pad_channel_value = \"replicate\"\n self.latent_dim = 1\n self.process_output = lambda audio: audio\n self.process_input = lambda audio: audio\n self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n self.disable_offload = True\n elif \"blocks.2.blocks.3.stack.5.weight\" in sd or \"decoder.blocks.2.blocks.3.stack.5.weight\" in sd or \"layers.4.layers.1.attn_block.attn.qkv.weight\" in sd or \"encoder.layers.4.layers.1.attn_block.attn.qkv.weight\" in sd: #genmo mochi vae\n if \"blocks.2.blocks.3.stack.5.weight\" in sd:\n sd = comfy.utils.state_dict_prefix_replace(sd, {\"\": \"decoder.\"})\n if \"layers.4.layers.1.attn_block.attn.qkv.weight\" in sd:\n sd = comfy.utils.state_dict_prefix_replace(sd, {\"\": \"encoder.\"})\n self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()\n self.latent_channels = 12\n self.latent_dim = 3\n self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)\n self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)\n self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)\n self.upscale_index_formula = (6, 8, 8)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8)\n self.downscale_index_formula = (6, 8, 8)\n self.working_dtypes = [torch.float16, torch.float32]\n elif \"decoder.up_blocks.0.res_blocks.0.conv1.conv.weight\" in sd: #lightricks ltxv\n tensor_conv1 = sd[\"decoder.up_blocks.0.res_blocks.0.conv1.conv.weight\"]\n version = 0\n if tensor_conv1.shape[0] == 512:\n version = 0\n elif tensor_conv1.shape[0] == 1024:\n version = 1\n if \"encoder.down_blocks.1.conv.conv.bias\" in sd:\n version = 2\n vae_config = None\n if metadata is not None and \"config\" in metadata:\n vae_config = json.loads(metadata[\"config\"]).get(\"vae\", None)\n self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config)\n self.latent_channels = 128\n self.latent_dim = 3\n self.memory_used_decode = lambda shape, dtype: (1200 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)\n self.memory_used_encode = lambda shape, dtype: (80 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)\n self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)\n self.upscale_index_formula = (8, 32, 32)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)\n self.downscale_index_formula = (8, 32, 32)\n self.working_dtypes = [torch.bfloat16, torch.float32]\n elif \"decoder.conv_in.conv.weight\" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32:\n ddconfig = {\"block_out_channels\": [128, 256, 512, 1024, 1024], \"in_channels\": 3, \"out_channels\": 3, \"num_res_blocks\": 2, \"ffactor_spatial\": 16, \"ffactor_temporal\": 4, \"downsample_match_channel\": True, \"upsample_match_channel\": True}\n ddconfig['z_channels'] = sd[\"decoder.conv_in.conv.weight\"].shape[1]\n self.latent_channels = 32\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)\n self.upscale_index_formula = (4, 16, 16)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)\n self.downscale_index_formula = (4, 16, 16)\n self.latent_dim = 3\n self.not_video = False\n self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n self.first_stage_model = AutoencodingEngine(regularizer_config={'target': \"comfy.ldm.models.autoencoder.EmptyRegularizer\"},\n encoder_config={'target': \"comfy.ldm.hunyuan_video.vae_refiner.Encoder\", 'params': ddconfig},\n decoder_config={'target': \"comfy.ldm.hunyuan_video.vae_refiner.Decoder\", 'params': ddconfig})\n\n self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (3600 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)\n elif \"decoder.conv_in.conv.weight\" in sd:\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 ddconfig[\"conv3d\"] = True\n ddconfig[\"time_compress\"] = 4\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)\n self.upscale_index_formula = (4, 8, 8)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)\n self.downscale_index_formula = (4, 8, 8)\n self.latent_dim = 3\n self.latent_channels = ddconfig['z_channels'] = sd[\"decoder.conv_in.conv.weight\"].shape[1]\n self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])\n #This is likely to significantly over-estimate with single image or low frame counts as the\n #implementation is able to completely skip caching. Rework if used as an image only VAE\n self.memory_used_decode = lambda shape, dtype: (2800 * min(8, ((shape[2] - 1) * 4) + 1) * shape[3] * shape[4] * (8 * 8)) * model_management.dtype_size(dtype)\n self.memory_used_encode = lambda shape, dtype: (1400 * min(9, shape[2]) * shape[3] * shape[4]) * model_management.dtype_size(dtype)\n self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n elif \"decoder.unpatcher3d.wavelets\" in sd:\n self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8)\n self.upscale_index_formula = (8, 8, 8)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8)\n self.downscale_index_formula = (8, 8, 8)\n self.latent_dim = 3\n self.latent_channels = 16\n ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8}\n self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig)\n #TODO: these values are a bit off because this is not a standard VAE\n self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)\n self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)\n self.working_dtypes = [torch.bfloat16, torch.float32]\n elif \"decoder.middle.0.residual.0.gamma\" in sd:\n if \"decoder.upsamples.0.upsamples.0.residual.2.weight\" in sd: # Wan 2.2 VAE\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)\n self.upscale_index_formula = (4, 16, 16)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)\n self.downscale_index_formula = (4, 16, 16)\n self.latent_dim = 3\n self.latent_channels = 48\n ddconfig = {\"dim\": 160, \"z_dim\": self.latent_channels, \"dim_mult\": [1, 2, 4, 4], \"num_res_blocks\": 2, \"attn_scales\": [], \"temperal_downsample\": [False, True, True], \"dropout\": 0.0}\n self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig)\n self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)\n else: # Wan 2.1 VAE\n dim = sd[\"decoder.head.0.gamma\"].shape[0]\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)\n self.upscale_index_formula = (4, 8, 8)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)\n self.downscale_index_formula = (4, 8, 8)\n self.latent_dim = 3\n self.latent_channels = 16\n self.output_channels = sd[\"encoder.conv1.weight\"].shape[1]\n self.conv_out_channels = sd[\"decoder.head.2.weight\"].shape[0]\n self.pad_channel_value = 1.0\n ddconfig = {\"dim\": dim, \"z_dim\": self.latent_channels, \"dim_mult\": [1, 2, 4, 4], \"num_res_blocks\": 2, \"attn_scales\": [], \"temperal_downsample\": [False, True, True], \"image_channels\": self.output_channels, \"conv_out_channels\": self.conv_out_channels, \"dropout\": 0.0}\n self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)\n self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n self.memory_used_encode = lambda shape, dtype: (1500 if shape[2]<=4 else 6000) * shape[3] * shape[4] * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (2200 if shape[2]<=4 else 7000) * shape[3] * shape[4] * (8*8) * model_management.dtype_size(dtype)\n\n\n # Hunyuan 3d v2 2.0 & 2.1\n elif \"geo_decoder.cross_attn_decoder.ln_1.bias\" in sd:\n\n self.latent_dim = 1\n\n def estimate_memory(shape, dtype, num_layers = 16, kv_cache_multiplier = 2):\n batch, num_tokens, hidden_dim = shape\n dtype_size = model_management.dtype_size(dtype)\n\n total_mem = batch * num_tokens * hidden_dim * dtype_size * (1 + kv_cache_multiplier * num_layers)\n return total_mem\n\n # better memory estimations\n self.memory_used_encode = lambda shape, dtype, num_layers = 8, kv_cache_multiplier = 0:\\\n estimate_memory(shape, dtype, num_layers, kv_cache_multiplier)\n\n self.memory_used_decode = lambda shape, dtype, num_layers = 16, kv_cache_multiplier = 2: \\\n estimate_memory(shape, dtype, num_layers, kv_cache_multiplier)\n\n self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE()\n self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n\n\n elif \"vocoder.backbone.channel_layers.0.0.bias\" in sd: #Ace Step Audio\n self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100)\n self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype)\n self.latent_channels = 8\n self.output_channels = 2\n self.pad_channel_value = \"replicate\"\n self.upscale_ratio = 4096\n self.downscale_ratio = 4096\n self.latent_dim = 2\n self.process_output = lambda audio: audio\n self.process_input = lambda audio: audio\n self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n self.disable_offload = True\n self.extra_1d_channel = 16\n elif \"pixel_space_vae\" in sd:\n self.first_stage_model = comfy.pixel_space_convert.PixelspaceConversionVAE()\n self.memory_used_encode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype)\n self.downscale_ratio = 1\n self.upscale_ratio = 1\n self.latent_channels = 3\n self.latent_dim = 2\n self.output_channels = 3\n elif \"vocoder.activation_post.downsample.lowpass.filter\" in sd: #MMAudio VAE\n sample_rate = 16000\n if sample_rate == 16000:\n mode = '16k'\n else:\n mode = '44k'\n\n self.first_stage_model = comfy.ldm.mmaudio.vae.autoencoder.AudioAutoencoder(mode=mode)\n self.memory_used_encode = lambda shape, dtype: (30 * shape[2]) * model_management.dtype_size(dtype)\n self.memory_used_decode = lambda shape, dtype: (90 * shape[2] * 1411.2) * model_management.dtype_size(dtype)\n self.latent_channels = 20\n self.output_channels = 2\n self.upscale_ratio = 512 * (44100 / sample_rate)\n self.downscale_ratio = 512 * (44100 / sample_rate)\n self.latent_dim = 1\n self.process_output = lambda audio: audio\n self.process_input = lambda audio: audio\n self.working_dtypes = [torch.float32]\n self.crop_input = False\n elif \"decoder.22.bias\" in sd: # taehv, taew and lighttae\n self.latent_channels = sd[\"decoder.1.weight\"].shape[1]\n self.latent_dim = 3\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)\n self.upscale_index_formula = (4, 16, 16)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)\n self.downscale_index_formula = (4, 16, 16)\n if self.latent_channels in [48, 128]: # Wan 2.2 and LTX2\n self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=None) # taehv doesn't need scaling\n self.process_input = self.process_output = lambda image: image\n self.process_output = lambda image: image\n self.memory_used_decode = lambda shape, dtype: (1800 * (max(1, (shape[-3] ** 0.7 * 0.1)) * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype))\n elif self.latent_channels == 32 and sd[\"decoder.22.bias\"].shape[0] == 12: # lighttae_hv15\n self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=comfy.latent_formats.HunyuanVideo15)\n self.memory_used_decode = lambda shape, dtype: (1200 * (max(1, (shape[-3] ** 0.7 * 0.05)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))\n else:\n if sd[\"decoder.1.weight\"].dtype == torch.float16: # taehv currently only available in float16, so assume it's not lighttaew2_1 as otherwise state dicts are identical\n latent_format=comfy.latent_formats.HunyuanVideo\n else:\n latent_format=None # lighttaew2_1 doesn't need scaling\n self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=latent_format)\n self.process_input = self.process_output = lambda image: image\n self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)\n self.upscale_index_formula = (4, 8, 8)\n self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)\n self.downscale_index_formula = (4, 8, 8)\n self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype))\n self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))\n else:\n logging.warning(\"WARNING: No VAE weights detected, VAE not initalized.\")\n self.first_stage_model = None\n return\n else:\n self.first_stage_model = AutoencoderKL(**(config['params']))\n self.first_stage_model = self.first_stage_model.eval()\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(self.device, self.working_dtypes)\n self.vae_dtype = dtype\n self.first_stage_model.to(self.vae_dtype)\n model_management.archive_model_dtypes(self.first_stage_model)\n self.output_device = model_management.intermediate_device()\n\n mp = comfy.model_patcher.CoreModelPatcher\n if self.disable_offload:\n mp = comfy.model_patcher.ModelPatcher\n self.patcher = mp(self.first_stage_model, load_device=self.device, offload_device=offload_device)\n\n m, u = self.first_stage_model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())\n if len(m) > 0:\n logging.warning(\"Missing VAE keys {}\".format(m))\n\n if len(u) > 0:\n logging.debug(\"Leftover VAE keys {}\".format(u))\n\n logging.info(\"VAE load device: {}, offload device: {}, dtype: {}\".format(self.device, offload_device, self.vae_dtype))\n self.model_size()\n\n def model_size(self):\n if self.size is not None:\n return self.size\n self.size = comfy.model_management.module_size(self.first_stage_model)\n return self.size\n\n def get_ram_usage(self):\n return self.model_size()\n\n def throw_exception_if_invalid(self):\n if self.first_stage_model is None:\n raise RuntimeError(\"ERROR: VAE is invalid: None\\n\\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.\")\n\n def vae_encode_crop_pixels(self, pixels):\n if self.crop_input:\n downscale_ratio = self.spacial_compression_encode()\n\n dims = pixels.shape[1:-1]\n for d in range(len(dims)):\n x = (dims[d] // downscale_ratio) * downscale_ratio\n x_offset = (dims[d] % downscale_ratio) // 2\n if x != dims[d]:\n pixels = pixels.narrow(d + 1, x_offset, x)\n\n if pixels.shape[-1] > self.output_channels:\n pixels = pixels[..., :self.output_channels]\n elif pixels.shape[-1] < self.output_channels:\n if self.pad_channel_value is not None:\n if isinstance(self.pad_channel_value, str):\n mode = self.pad_channel_value\n value = None\n else:\n mode = \"constant\"\n value = self.pad_channel_value\n\n pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value)\n return pixels\n\n def vae_output_dtype(self):\n return model_management.intermediate_dtype()\n\n def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):\n steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)\n steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)\n steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)\n pbar = comfy.utils.ProgressBar(steps)\n\n decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n output = self.process_output(\n (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +\n comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +\n comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))\n / 3.0)\n return output\n\n def decode_tiled_1d(self, samples, tile_x=256, overlap=32):\n if samples.ndim == 3:\n decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n else:\n og_shape = samples.shape\n samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1))\n decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n\n return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))\n\n def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):\n decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))\n\n def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):\n steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)\n steps += pixel_samples.shape[0] * comfy.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] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)\n pbar = comfy.utils.ProgressBar(steps)\n\n encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n samples = comfy.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 += comfy.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 += comfy.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 encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048):\n if self.latent_dim == 1:\n encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n out_channels = self.latent_channels\n upscale_amount = 1 / self.downscale_ratio\n else:\n extra_channel_size = self.extra_1d_channel\n out_channels = self.latent_channels * extra_channel_size\n tile_x = tile_x // extra_channel_size\n overlap = overlap // extra_channel_size\n upscale_amount = 1 / self.downscale_ratio\n encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).to(dtype=self.vae_output_dtype())\n\n out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device)\n if self.latent_dim == 1:\n return out\n else:\n return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1)\n\n def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):\n encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())\n return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)\n\n def decode(self, samples_in, vae_options={}):\n self.throw_exception_if_invalid()\n pixel_samples = None\n do_tile = False\n if self.latent_dim == 2 and samples_in.ndim == 5:\n samples_in = samples_in[:, :, 0]\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, force_full_load=self.disable_offload)\n free_memory = self.patcher.get_free_memory(self.device)\n batch_number = int(free_memory / memory_used)\n batch_number = max(1, batch_number)\n\n # Pre-allocate output for VAEs that support direct buffer writes\n preallocated = False\n if getattr(self.first_stage_model, 'comfy_has_chunked_io', False):\n pixel_samples = torch.empty(self.first_stage_model.decode_output_shape(samples_in.shape), device=self.output_device, dtype=self.vae_output_dtype())\n preallocated = True\n\n for x in range(0, samples_in.shape[0], batch_number):\n samples = samples_in[x:x + batch_number].to(device=self.device, dtype=self.vae_dtype)\n if preallocated:\n self.first_stage_model.decode(samples, output_buffer=pixel_samples[x:x+batch_number], **vae_options)\n else:\n out = self.first_stage_model.decode(samples, **vae_options).to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True)\n if pixel_samples is None:\n pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())\n pixel_samples[x:x+batch_number].copy_(out)\n del out\n self.process_output(pixel_samples[x:x+batch_number])\n except Exception as e:\n model_management.raise_non_oom(e)\n logging.warning(\"Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.\")\n #NOTE: We don't know what tensors were allocated to stack variables at the time of the\n #exception and the exception itself refs them all until we get out of this except block.\n #So we just set a flag for tiler fallback so that tensor gc can happen once the\n #exception is fully off the books.\n do_tile = True\n\n if do_tile:\n comfy.model_management.soft_empty_cache()\n dims = samples_in.ndim - 2\n if dims == 1 or self.extra_1d_channel is not None:\n pixel_samples = self.decode_tiled_1d(samples_in)\n elif dims == 2:\n pixel_samples = self.decode_tiled_(samples_in)\n elif dims == 3:\n tile = 256 // self.spacial_compression_decode()\n overlap = tile // 4\n pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))\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=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):\n self.throw_exception_if_invalid()\n memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile\n model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)\n dims = samples.ndim - 2\n args = {}\n if tile_x is not None:\n args[\"tile_x\"] = tile_x\n if tile_y is not None:\n args[\"tile_y\"] = tile_y\n if overlap is not None:\n args[\"overlap\"] = overlap\n\n if dims == 1 or self.extra_1d_channel is not None:\n args.pop(\"tile_y\")\n output = self.decode_tiled_1d(samples, **args)\n elif dims == 2:\n output = self.decode_tiled_(samples, **args)\n elif dims == 3:\n if overlap_t is None:\n args[\"overlap\"] = (1, overlap, overlap)\n else:\n args[\"overlap\"] = (max(1, overlap_t), overlap, overlap)\n if tile_t is not None:\n args[\"tile_t\"] = max(2, tile_t)\n\n output = self.decode_tiled_3d(samples, **args)\n return output.movedim(1, -1)\n\n def encode(self, pixel_samples):\n self.throw_exception_if_invalid()\n pixel_samples = self.vae_encode_crop_pixels(pixel_samples)\n pixel_samples = pixel_samples.movedim(-1, 1)\n do_tile = False\n if self.latent_dim == 3 and pixel_samples.ndim < 5:\n if not self.not_video:\n pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)\n else:\n pixel_samples = pixel_samples.unsqueeze(2)\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, force_full_load=self.disable_offload)\n free_memory = self.patcher.get_free_memory(self.device)\n batch_number = int(free_memory / max(1, memory_used))\n batch_number = max(1, batch_number)\n samples = None\n for x in range(0, pixel_samples.shape[0], batch_number):\n pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype)\n if getattr(self.first_stage_model, 'comfy_has_chunked_io', False):\n out = self.first_stage_model.encode(pixels_in, device=self.device)\n else:\n pixels_in = pixels_in.to(self.device)\n out = self.first_stage_model.encode(pixels_in)\n out = out.to(self.output_device).to(dtype=self.vae_output_dtype())\n if samples is None:\n samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())\n samples[x:x + batch_number] = out\n\n except Exception as e:\n model_management.raise_non_oom(e)\n logging.warning(\"Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.\")\n #NOTE: We don't know what tensors were allocated to stack variables at the time of the\n #exception and the exception itself refs them all until we get out of this except block.\n #So we just set a flag for tiler fallback so that tensor gc can happen once the\n #exception is fully off the books.\n do_tile = True\n\n if do_tile:\n comfy.model_management.soft_empty_cache()\n if self.latent_dim == 3:\n tile = 256\n overlap = tile // 4\n samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))\n elif self.latent_dim == 1 or self.extra_1d_channel is not None:\n samples = self.encode_tiled_1d(pixel_samples)\n else:\n samples = self.encode_tiled_(pixel_samples)\n\n return samples\n\n def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):\n self.throw_exception_if_invalid()\n pixel_samples = self.vae_encode_crop_pixels(pixel_samples)\n dims = self.latent_dim\n pixel_samples = pixel_samples.movedim(-1, 1)\n if dims == 3:\n if not self.not_video:\n pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)\n else:\n pixel_samples = pixel_samples.unsqueeze(2)\n\n memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) # TODO: calculate mem required for tile\n model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)\n\n args = {}\n if tile_x is not None:\n args[\"tile_x\"] = tile_x\n if tile_y is not None:\n args[\"tile_y\"] = tile_y\n if overlap is not None:\n args[\"overlap\"] = overlap\n\n if dims == 1:\n args.pop(\"tile_y\")\n samples = self.encode_tiled_1d(pixel_samples, **args)\n elif dims == 2:\n samples = self.encode_tiled_(pixel_samples, **args)\n elif dims == 3:\n if tile_t is not None:\n tile_t_latent = max(2, self.downscale_ratio[0](tile_t))\n else:\n tile_t_latent = 9999\n args[\"tile_t\"] = self.upscale_ratio[0](tile_t_latent)\n\n if overlap_t is None:\n args[\"overlap\"] = (1, overlap, overlap)\n else:\n args[\"overlap\"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)\n maximum = pixel_samples.shape[2]\n maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))\n\n samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)\n\n return samples\n\n def get_sd(self):\n return self.first_stage_model.state_dict()\n\n def spacial_compression_decode(self):\n try:\n return self.upscale_ratio[-1]\n except:\n return self.upscale_ratio\n\n def spacial_compression_encode(self):\n try:\n return self.downscale_ratio[-1]\n except:\n return self.downscale_ratio\n\n def temporal_compression_decode(self):\n try:\n return round(self.upscale_ratio[0](8192) / 8192)\n except:\n return None\n\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 = comfy.utils.load_torch_file(ckpt_path, safe_load=True)\n keys = model_data.keys()\n if \"style_embedding\" in keys:\n model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)\n elif \"redux_down.weight\" in keys:\n model = comfy.ldm.flux.redux.ReduxImageEncoder()\n else:\n raise Exception(\"invalid style model {}\".format(ckpt_path))\n model.load_state_dict(model_data)\n return StyleModel(model)\n\nclass CLIPType(Enum):\n STABLE_DIFFUSION = 1\n STABLE_CASCADE = 2\n SD3 = 3\n STABLE_AUDIO = 4\n HUNYUAN_DIT = 5\n FLUX = 6\n MOCHI = 7\n LTXV = 8\n HUNYUAN_VIDEO = 9\n PIXART = 10\n COSMOS = 11\n LUMINA2 = 12\n WAN = 13\n HIDREAM = 14\n CHROMA = 15\n ACE = 16\n OMNIGEN2 = 17\n QWEN_IMAGE = 18\n HUNYUAN_IMAGE = 19\n HUNYUAN_VIDEO_15 = 20\n OVIS = 21\n KANDINSKY5 = 22\n KANDINSKY5_IMAGE = 23\n NEWBIE = 24\n FLUX2 = 25\n LONGCAT_IMAGE = 26\n\n\n\ndef load_clip_model_patcher(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):\n clip = load_clip(ckpt_paths, embedding_directory, clip_type, model_options, disable_dynamic)\n return clip.patcher\n\ndef load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):\n clip_data = []\n for p in ckpt_paths:\n sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True)\n if model_options.get(\"custom_operations\", None) is None:\n sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix=\"\", metadata=metadata)\n clip_data.append(sd)\n clip = load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options, disable_dynamic=disable_dynamic)\n clip.patcher.cached_patcher_init = (load_clip_model_patcher, (ckpt_paths, embedding_directory, clip_type, model_options))\n return clip\n\n\nclass TEModel(Enum):\n CLIP_L = 1\n CLIP_H = 2\n CLIP_G = 3\n T5_XXL = 4\n T5_XL = 5\n T5_BASE = 6\n LLAMA3_8 = 7\n T5_XXL_OLD = 8\n GEMMA_2_2B = 9\n QWEN25_3B = 10\n QWEN25_7B = 11\n BYT5_SMALL_GLYPH = 12\n GEMMA_3_4B = 13\n MISTRAL3_24B = 14\n MISTRAL3_24B_PRUNED_FLUX2 = 15\n QWEN3_4B = 16\n QWEN3_2B = 17\n GEMMA_3_12B = 18\n JINA_CLIP_2 = 19\n QWEN3_8B = 20\n QWEN3_06B = 21\n GEMMA_3_4B_VISION = 22\n\n\ndef detect_te_model(sd):\n if \"text_model.encoder.layers.30.mlp.fc1.weight\" in sd:\n return TEModel.CLIP_G\n if \"text_model.encoder.layers.22.mlp.fc1.weight\" in sd:\n return TEModel.CLIP_H\n if \"text_model.encoder.layers.0.mlp.fc1.weight\" in sd:\n return TEModel.CLIP_L\n if \"model.encoder.layers.0.mixer.Wqkv.weight\" in sd:\n return TEModel.JINA_CLIP_2\n if \"encoder.block.23.layer.1.DenseReluDense.wi_1.weight\" in sd:\n weight = sd[\"encoder.block.23.layer.1.DenseReluDense.wi_1.weight\"]\n if weight.shape[0] == 10240:\n return TEModel.T5_XXL\n elif weight.shape[0] == 5120:\n return TEModel.T5_XL\n if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd:\n return TEModel.T5_XXL_OLD\n if \"encoder.block.0.layer.0.SelfAttention.k.weight\" in sd:\n weight = sd['encoder.block.0.layer.0.SelfAttention.k.weight']\n if weight.shape[0] == 384:\n return TEModel.BYT5_SMALL_GLYPH\n return TEModel.T5_BASE\n if 'model.layers.0.post_feedforward_layernorm.weight' in sd:\n if 'model.layers.47.self_attn.q_norm.weight' in sd:\n return TEModel.GEMMA_3_12B\n if 'model.layers.0.self_attn.q_norm.weight' in sd:\n if 'vision_model.embeddings.patch_embedding.weight' in sd:\n return TEModel.GEMMA_3_4B_VISION\n else:\n return TEModel.GEMMA_3_4B\n return TEModel.GEMMA_2_2B\n if 'model.layers.0.self_attn.k_proj.bias' in sd:\n weight = sd['model.layers.0.self_attn.k_proj.bias']\n if weight.shape[0] == 256:\n return TEModel.QWEN25_3B\n if weight.shape[0] == 512:\n return TEModel.QWEN25_7B\n if \"model.layers.0.post_attention_layernorm.weight\" in sd:\n weight = sd['model.layers.0.post_attention_layernorm.weight']\n if 'model.layers.0.self_attn.q_norm.weight' in sd:\n if weight.shape[0] == 2560:\n return TEModel.QWEN3_4B\n elif weight.shape[0] == 2048:\n return TEModel.QWEN3_2B\n elif weight.shape[0] == 4096:\n return TEModel.QWEN3_8B\n elif weight.shape[0] == 1024:\n return TEModel.QWEN3_06B\n if weight.shape[0] == 5120:\n if \"model.layers.39.post_attention_layernorm.weight\" in sd:\n return TEModel.MISTRAL3_24B\n else:\n return TEModel.MISTRAL3_24B_PRUNED_FLUX2\n\n return TEModel.LLAMA3_8\n return None\n\n\ndef t5xxl_detect(clip_data):\n weight_name = \"encoder.block.23.layer.1.DenseReluDense.wi_1.weight\"\n weight_name_old = \"encoder.block.23.layer.1.DenseReluDense.wi.weight\"\n\n for sd in clip_data:\n if weight_name in sd or weight_name_old in sd:\n return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd)\n\n return {}\n\ndef llama_detect(clip_data):\n weight_name = \"model.layers.0.self_attn.k_proj.weight\"\n\n for sd in clip_data:\n if weight_name in sd:\n return comfy.text_encoders.hunyuan_video.llama_detect(sd)\n\n return {}\n\ndef load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):\n clip_data = state_dicts\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] = comfy.utils.clip_text_transformers_convert(clip_data[i], \"\", \"\")\n else:\n if \"text_projection\" in clip_data[i]:\n clip_data[i][\"text_projection.weight\"] = clip_data[i][\"text_projection\"].transpose(0, 1) #old models saved with the CLIPSave node\n if \"lm_head.weight\" in clip_data[i]:\n clip_data[i][\"model.lm_head.weight\"] = clip_data[i].pop(\"lm_head.weight\") # prefix missing in some models\n\n tokenizer_data = {}\n clip_target = EmptyClass()\n clip_target.params = {}\n if len(clip_data) == 1:\n te_model = detect_te_model(clip_data[0])\n if te_model == TEModel.CLIP_G:\n if clip_type == CLIPType.STABLE_CASCADE:\n clip_target.clip = sdxl_clip.StableCascadeClipModel\n clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer\n elif clip_type == CLIPType.SD3:\n clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)\n clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer\n elif clip_type == CLIPType.HIDREAM:\n clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None)\n clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer\n else:\n clip_target.clip = sdxl_clip.SDXLRefinerClipModel\n clip_target.tokenizer = sdxl_clip.SDXLTokenizer\n elif te_model == TEModel.CLIP_H:\n clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel\n clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer\n elif te_model == TEModel.T5_XXL:\n if clip_type == CLIPType.SD3:\n clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer\n elif clip_type == CLIPType.LTXV:\n clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer\n elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA:\n clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer\n elif clip_type == CLIPType.WAN:\n clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n elif clip_type == CLIPType.HIDREAM:\n clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),\n clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None)\n clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer\n else: #CLIPType.MOCHI\n clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer\n elif te_model == TEModel.T5_XXL_OLD:\n clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer\n elif te_model == TEModel.T5_XL:\n clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model\n clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer\n elif te_model == TEModel.T5_BASE:\n if clip_type == CLIPType.ACE or \"spiece_model\" in clip_data[0]:\n clip_target.clip = comfy.text_encoders.ace.AceT5Model\n clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n else:\n clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model\n clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer\n elif te_model == TEModel.GEMMA_2_2B:\n clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n elif te_model == TEModel.GEMMA_3_4B:\n clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type=\"gemma3_4b\")\n clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n elif te_model == TEModel.GEMMA_3_4B_VISION:\n clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type=\"gemma3_4b_vision\")\n clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n elif te_model == TEModel.GEMMA_3_12B:\n clip_target.clip = comfy.text_encoders.lt.gemma3_te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.lt.Gemma3_12BTokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n elif te_model == TEModel.LLAMA3_8:\n clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),\n clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)\n clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer\n elif te_model == TEModel.QWEN25_3B:\n clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer\n elif te_model == TEModel.QWEN25_7B:\n if clip_type == CLIPType.HUNYUAN_IMAGE:\n clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, **llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer\n elif clip_type == CLIPType.LONGCAT_IMAGE:\n clip_target.clip = comfy.text_encoders.longcat_image.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.longcat_image.LongCatImageTokenizer\n else:\n clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer\n elif te_model == TEModel.MISTRAL3_24B or te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2:\n clip_target.clip = comfy.text_encoders.flux.flux2_te(**llama_detect(clip_data), pruned=te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2)\n clip_target.tokenizer = comfy.text_encoders.flux.Flux2Tokenizer\n tokenizer_data[\"tekken_model\"] = clip_data[0].get(\"tekken_model\", None)\n elif te_model == TEModel.QWEN3_4B:\n if clip_type == CLIPType.FLUX or clip_type == CLIPType.FLUX2:\n clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=\"qwen3_4b\")\n clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer\n else:\n clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer\n elif te_model == TEModel.QWEN3_2B:\n clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer\n elif te_model == TEModel.QWEN3_8B:\n clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=\"qwen3_8b\")\n clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B\n elif te_model == TEModel.JINA_CLIP_2:\n clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper\n clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper\n elif te_model == TEModel.QWEN3_06B:\n clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer\n else:\n # clip_l\n if clip_type == CLIPType.SD3:\n clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)\n clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer\n elif clip_type == CLIPType.HIDREAM:\n clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None)\n clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer\n else:\n clip_target.clip = sd1_clip.SD1ClipModel\n clip_target.tokenizer = sd1_clip.SD1Tokenizer\n elif len(clip_data) == 2:\n if clip_type == CLIPType.SD3:\n te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]\n clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer\n elif clip_type == CLIPType.HUNYUAN_DIT:\n clip_target.clip = comfy.text_encoders.hydit.HyditModel\n clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer\n elif clip_type == CLIPType.FLUX:\n clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer\n elif clip_type == CLIPType.HUNYUAN_VIDEO:\n clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer\n elif clip_type == CLIPType.HIDREAM:\n # Detect\n hidream_dualclip_classes = []\n for hidream_te in clip_data:\n te_model = detect_te_model(hidream_te)\n hidream_dualclip_classes.append(te_model)\n\n clip_l = TEModel.CLIP_L in hidream_dualclip_classes\n clip_g = TEModel.CLIP_G in hidream_dualclip_classes\n t5 = TEModel.T5_XXL in hidream_dualclip_classes\n llama = TEModel.LLAMA3_8 in hidream_dualclip_classes\n\n # Initialize t5xxl_detect and llama_detect kwargs if needed\n t5_kwargs = t5xxl_detect(clip_data) if t5 else {}\n llama_kwargs = llama_detect(clip_data) if llama else {}\n\n clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs)\n clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer\n elif clip_type == CLIPType.HUNYUAN_IMAGE:\n clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer\n elif clip_type == CLIPType.HUNYUAN_VIDEO_15:\n clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer\n elif clip_type == CLIPType.KANDINSKY5:\n clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer\n elif clip_type == CLIPType.KANDINSKY5_IMAGE:\n clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage\n elif clip_type == CLIPType.LTXV:\n clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data), **comfy.text_encoders.lt.sd_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.lt.LTXAVGemmaTokenizer\n tokenizer_data[\"spiece_model\"] = clip_data[0].get(\"spiece_model\", None)\n elif clip_type == CLIPType.NEWBIE:\n clip_target.clip = comfy.text_encoders.newbie.te(**llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.newbie.NewBieTokenizer\n if \"model.layers.0.self_attn.q_norm.weight\" in clip_data[0]:\n clip_data_gemma = clip_data[0]\n clip_data_jina = clip_data[1]\n else:\n clip_data_gemma = clip_data[1]\n clip_data_jina = clip_data[0]\n tokenizer_data[\"gemma_spiece_model\"] = clip_data_gemma.get(\"spiece_model\", None)\n tokenizer_data[\"jina_spiece_model\"] = clip_data_jina.get(\"spiece_model\", None)\n elif clip_type == CLIPType.ACE:\n te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]\n if TEModel.QWEN3_4B in te_models:\n model_type = \"qwen3_4b\"\n else:\n model_type = \"qwen3_2b\"\n clip_target.clip = comfy.text_encoders.ace15.te(lm_model=model_type, **llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.ace15.ACE15Tokenizer\n else:\n clip_target.clip = sdxl_clip.SDXLClipModel\n clip_target.tokenizer = sdxl_clip.SDXLTokenizer\n elif len(clip_data) == 3:\n clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer\n elif len(clip_data) == 4:\n clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), **llama_detect(clip_data))\n clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer\n\n parameters = 0\n for c in clip_data:\n parameters += comfy.utils.calculate_parameters(c)\n tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)\n\n clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options, disable_dynamic=disable_dynamic)\n return clip\n\ndef load_gligen(ckpt_path):\n data = comfy.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 comfy.model_patcher.CoreModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())\n\ndef model_detection_error_hint(path, state_dict):\n filename = os.path.basename(path)\n if 'lora' in filename.lower():\n return \"\\nHINT: This seems to be a Lora file and Lora files should be put in the lora folder and loaded with a lora loader node..\"\n return \"\"\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 logging.warning(\"Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.\")\n model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)\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\n if \"parameterization\" in model_config_params:\n if model_config_params[\"parameterization\"] == \"v\":\n m = model.clone()\n class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION):\n pass\n m.add_object_patch(\"model_sampling\", ModelSamplingAdvanced(model.model.model_config))\n model = m\n\n layer_idx = clip_config.get(\"params\", {}).get(\"layer_idx\", None)\n if layer_idx is not None:\n clip.clip_layer(layer_idx)\n\n return (model, 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, model_options={}, te_model_options={}, disable_dynamic=False):\n sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)\n out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)\n if out is None:\n raise RuntimeError(\"ERROR: Could not detect model type of: {}\\n{}\".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))\n if output_model and out[0] is not None:\n out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options))\n if output_clip and out[1] is not None:\n out[1].patcher.cached_patcher_init = (load_checkpoint_guess_config_clip_only, (ckpt_path, embedding_directory, model_options, te_model_options))\n return out\n\ndef load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):\n model, *_ = load_checkpoint_guess_config(ckpt_path, False, False, False,\n embedding_directory=embedding_directory,\n model_options=model_options,\n te_model_options=te_model_options,\n disable_dynamic=disable_dynamic)\n return model\n\ndef load_checkpoint_guess_config_clip_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):\n _, clip, *_ = load_checkpoint_guess_config(ckpt_path, False, True, False,\n embedding_directory=embedding_directory, output_model=False,\n model_options=model_options,\n te_model_options=te_model_options,\n disable_dynamic=disable_dynamic)\n return clip.patcher\n\ndef load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None, disable_dynamic=False):\n clip = None\n clipvision = None\n vae = None\n model = None\n model_patcher = None\n\n diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)\n parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)\n weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)\n load_device = model_management.get_torch_device()\n\n custom_operations = model_options.get(\"custom_operations\", None)\n if custom_operations is None:\n sd, metadata = comfy.utils.convert_old_quants(sd, diffusion_model_prefix, metadata=metadata)\n\n model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)\n if model_config is None:\n logging.warning(\"Warning, This is not a checkpoint file, trying to load it as a diffusion model only.\")\n diffusion_model = load_diffusion_model_state_dict(sd, model_options={})\n if diffusion_model is None:\n return None\n return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used'\n\n unet_weight_dtype = list(model_config.supported_inference_dtypes)\n if model_config.quant_config is not None:\n weight_dtype = None\n\n if custom_operations is not None:\n model_config.custom_operations = custom_operations\n\n unet_dtype = model_options.get(\"dtype\", model_options.get(\"weight_dtype\", None))\n\n if unet_dtype is None:\n unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)\n\n if model_config.quant_config is not None:\n manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)\n else:\n manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)\n model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)\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 model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)\n ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher\n model_patcher = ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())\n model.load_model_weights(sd, diffusion_model_prefix, assign=model_patcher.is_dynamic())\n\n if output_vae:\n vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: \"\" for k in model_config.vae_key_prefix}, filter_keys=True)\n vae_sd = model_config.process_vae_state_dict(vae_sd)\n vae = VAE(sd=vae_sd, metadata=metadata)\n\n if output_clip:\n if te_model_options.get(\"custom_operations\", None) is None:\n scaled_fp8_list = []\n for k in list(sd.keys()): # Convert scaled fp8 to mixed ops\n if k.endswith(\".scaled_fp8\"):\n scaled_fp8_list.append(k[:-len(\"scaled_fp8\")])\n\n if len(scaled_fp8_list) > 0:\n out_sd = {}\n for k in sd:\n skip = False\n for pref in scaled_fp8_list:\n skip = skip or k.startswith(pref)\n if not skip:\n out_sd[k] = sd[k]\n\n for pref in scaled_fp8_list:\n quant_sd, qmetadata = comfy.utils.convert_old_quants(sd, pref, metadata={})\n for k in quant_sd:\n out_sd[k] = quant_sd[k]\n sd = out_sd\n\n clip_target = model_config.clip_target(state_dict=sd)\n if clip_target is not None:\n clip_sd = model_config.process_clip_state_dict(sd)\n if len(clip_sd) > 0:\n parameters = comfy.utils.calculate_parameters(clip_sd)\n clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options, disable_dynamic=disable_dynamic)\n else:\n logging.warning(\"no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.\")\n\n left_over = sd.keys()\n if len(left_over) > 0:\n logging.debug(\"left over keys: {}\".format(left_over))\n\n if output_model:\n if inital_load_device != torch.device(\"cpu\"):\n logging.info(\"loaded diffusion model directly to GPU\")\n model_management.load_models_gpu([model_patcher], force_full_load=True)\n\n return (model_patcher, clip, vae, clipvision)\n\n\ndef load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable_dynamic=False):\n \"\"\"\n Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats.\n\n Args:\n sd (dict): State dictionary containing model weights and configuration\n model_options (dict, optional): Additional options for model loading. Supports:\n - dtype: Override model data type\n - custom_operations: Custom model operations\n - fp8_optimizations: Enable FP8 optimizations\n\n Returns:\n ModelPatcher: A wrapped model instance that handles device management and weight loading.\n Returns None if the model configuration cannot be detected.\n\n The function:\n 1. Detects and handles different model formats (regular, diffusers, mmdit)\n 2. Configures model dtype based on parameters and device capabilities\n 3. Handles weight conversion and device placement\n 4. Manages model optimization settings\n 5. Loads weights and returns a device-managed model instance\n \"\"\"\n dtype = model_options.get(\"dtype\", None)\n\n #Allow loading unets from checkpoint files\n diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)\n temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: \"\"}, filter_keys=True)\n if len(temp_sd) > 0:\n sd = temp_sd\n\n custom_operations = model_options.get(\"custom_operations\", None)\n if custom_operations is None:\n sd, metadata = comfy.utils.convert_old_quants(sd, \"\", metadata=metadata)\n parameters = comfy.utils.calculate_parameters(sd)\n weight_dtype = comfy.utils.weight_dtype(sd)\n\n load_device = model_management.get_torch_device()\n model_config = model_detection.model_config_from_unet(sd, \"\", metadata=metadata)\n\n if model_config is not None:\n new_sd = sd\n else:\n new_sd = model_detection.convert_diffusers_mmdit(sd, \"\")\n if new_sd is not None: #diffusers mmdit\n model_config = model_detection.model_config_from_unet(new_sd, \"\")\n if model_config is None:\n return None\n else: #diffusers unet\n model_config = model_detection.model_config_from_diffusers_unet(sd)\n if model_config is None:\n return None\n\n diffusers_keys = comfy.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 logging.warning(\"{} {}\".format(diffusers_keys[k], k))\n\n offload_device = model_management.unet_offload_device()\n unet_weight_dtype = list(model_config.supported_inference_dtypes)\n if model_config.quant_config is not None:\n weight_dtype = None\n\n if dtype is None:\n unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)\n else:\n unet_dtype = dtype\n\n if model_config.quant_config is not None:\n manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)\n else:\n manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)\n model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)\n\n if custom_operations is not None:\n model_config.custom_operations = custom_operations\n\n if model_options.get(\"fp8_optimizations\", False):\n model_config.optimizations[\"fp8\"] = True\n\n model = model_config.get_model(new_sd, \"\")\n ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher\n model_patcher = ModelPatcher(model, load_device=load_device, offload_device=offload_device)\n if not model_management.is_device_cpu(offload_device):\n model.to(offload_device)\n model.load_model_weights(new_sd, \"\", assign=model_patcher.is_dynamic())\n left_over = sd.keys()\n if len(left_over) > 0:\n logging.info(\"left over keys in diffusion model: {}\".format(left_over))\n return model_patcher\n\ndef load_diffusion_model(unet_path, model_options={}, disable_dynamic=False):\n sd, metadata = comfy.utils.load_torch_file(unet_path, return_metadata=True)\n model = load_diffusion_model_state_dict(sd, model_options=model_options, metadata=metadata, disable_dynamic=disable_dynamic)\n if model is None:\n logging.error(\"ERROR UNSUPPORTED DIFFUSION MODEL {}\".format(unet_path))\n raise RuntimeError(\"ERROR: Could not detect model type of: {}\\n{}\".format(unet_path, model_detection_error_hint(unet_path, sd)))\n model.cached_patcher_init = (load_diffusion_model, (unet_path, model_options))\n return model\n\ndef load_unet(unet_path, dtype=None):\n logging.warning(\"The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model\")\n return load_diffusion_model(unet_path, model_options={\"dtype\": dtype})\n\ndef load_unet_state_dict(sd, dtype=None):\n logging.warning(\"The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict\")\n return load_diffusion_model_state_dict(sd, model_options={\"dtype\": dtype})\n\ndef save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):\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 vae_sd = None\n if vae is not None:\n vae_sd = vae.get_sd()\n\n if metadata is None:\n metadata = {}\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.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)\n for k in extra_keys:\n sd[k] = extra_keys[k]\n\n for k in sd:\n t = sd[k]\n if not t.is_contiguous():\n sd[k] = t.contiguous()\n\n comfy.utils.save_torch_file(sd, output_path, metadata=metadata)\n" }, { "path": "comfy/sd1_clip.py", "content": "import os\n\nfrom transformers import CLIPTokenizer\nimport comfy.ops\nimport torch\nimport traceback\nimport zipfile\nfrom . import model_management\nimport comfy.clip_model\nimport json\nimport logging\nimport numbers\nimport re\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 if hasattr(self, \"gen_empty_tokens\"):\n to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len))\n else:\n to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))\n\n o = self.encode(to_encode)\n out, pooled = o[:2]\n\n if pooled is not None:\n first_pooled = pooled[0:1].to(device=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 r = (out[-1:].to(device=model_management.intermediate_device()), first_pooled)\n else:\n r = (torch.cat(output, dim=-2).to(device=model_management.intermediate_device()), first_pooled)\n\n if len(o) > 2:\n extra = {}\n for k in o[2]:\n v = o[2][k]\n if k == \"attention_mask\":\n v = v[:sections].flatten().unsqueeze(dim=0).to(device=model_management.intermediate_device())\n extra[k] = v\n\n r = r + (extra,)\n return r\n\nclass SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):\n LAYERS = [\n \"last\",\n \"pooled\",\n \"hidden\",\n \"all\"\n ]\n def __init__(self, device=\"cpu\", max_length=77,\n freeze=True, layer=\"last\", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,\n special_tokens={\"start\": 49406, \"end\": 49407, \"pad\": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,\n return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32\n super().__init__()\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 if \"model_name\" not in model_options:\n model_options = {**model_options, \"model_name\": \"clip_l\"}\n\n if isinstance(textmodel_json_config, dict):\n config = textmodel_json_config\n else:\n with open(textmodel_json_config) as f:\n config = json.load(f)\n\n te_model_options = model_options.get(\"{}_model_config\".format(model_options.get(\"model_name\", \"\")), {})\n for k, v in te_model_options.items():\n config[k] = v\n\n operations = model_options.get(\"custom_operations\", None)\n quant_config = model_options.get(\"quantization_metadata\", None)\n\n if operations is None:\n if quant_config is not None:\n operations = comfy.ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)\n logging.info(\"Using MixedPrecisionOps for text encoder\")\n else:\n operations = comfy.ops.manual_cast\n\n self.operations = operations\n self.transformer = model_class(config, dtype, device, self.operations)\n\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\n self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))\n self.enable_attention_masks = enable_attention_masks\n self.zero_out_masked = zero_out_masked\n\n self.layer_norm_hidden_state = layer_norm_hidden_state\n self.return_projected_pooled = return_projected_pooled\n self.return_attention_masks = return_attention_masks\n self.execution_device = None\n\n if layer == \"hidden\":\n assert layer_idx is not None\n assert abs(layer_idx) < self.num_layers\n self.set_clip_options({\"layer\": layer_idx})\n self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)\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 set_clip_options(self, options):\n layer_idx = options.get(\"layer\", self.layer_idx)\n self.return_projected_pooled = options.get(\"projected_pooled\", self.return_projected_pooled)\n self.execution_device = options.get(\"execution_device\", self.execution_device)\n if isinstance(self.layer, list) or self.layer == \"all\":\n pass\n elif isinstance(layer_idx, list):\n self.layer = layer_idx\n elif layer_idx is None or 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_options(self):\n self.layer = self.options_default[0]\n self.layer_idx = self.options_default[1]\n self.return_projected_pooled = self.options_default[2]\n self.execution_device = None\n\n def process_tokens(self, tokens, device):\n end_token = self.special_tokens.get(\"end\", None)\n pad_token = self.special_tokens.get(\"pad\", -1)\n if end_token is None:\n cmp_token = pad_token\n else:\n cmp_token = end_token\n\n embeds_out = []\n attention_masks = []\n num_tokens = []\n\n for x in tokens:\n attention_mask = []\n tokens_temp = []\n other_embeds = []\n eos = False\n index = 0\n left_pad = False\n for y in x:\n if isinstance(y, numbers.Integral):\n token = int(y)\n if index == 0 and token == pad_token:\n left_pad = True\n\n if eos or (left_pad and token == pad_token):\n attention_mask.append(0)\n else:\n attention_mask.append(1)\n left_pad = False\n\n tokens_temp += [token]\n if not eos and token == cmp_token and not left_pad:\n if end_token is None:\n attention_mask[-1] = 0\n eos = True\n else:\n other_embeds.append((index, y))\n index += 1\n\n tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long)\n tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32)\n index = 0\n pad_extra = 0\n embeds_info = []\n for o in other_embeds:\n emb = o[1]\n if torch.is_tensor(emb):\n emb = {\"type\": \"embedding\", \"data\": emb}\n\n extra = None\n emb_type = emb.get(\"type\", None)\n if emb_type == \"embedding\":\n emb = emb.get(\"data\", None)\n else:\n if hasattr(self.transformer, \"preprocess_embed\"):\n emb, extra = self.transformer.preprocess_embed(emb, device=device)\n else:\n emb = None\n\n if emb is None:\n index += -1\n continue\n\n ind = index + o[0]\n emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32)\n emb_shape = emb.shape[1]\n if emb.shape[-1] == tokens_embed.shape[-1]:\n tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1)\n attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:]\n index += emb_shape - 1\n embeds_info.append({\"type\": emb_type, \"index\": ind, \"size\": emb_shape, \"extra\": extra})\n else:\n index += -1\n pad_extra += emb_shape\n logging.warning(\"WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}\".format(emb.shape[-1], tokens_embed.shape[-1]))\n\n if pad_extra > 0:\n padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens[\"pad\"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32)\n tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1)\n attention_mask = attention_mask + [0] * pad_extra\n\n embeds_out.append(tokens_embed)\n attention_masks.append(attention_mask)\n num_tokens.append(sum(attention_mask))\n\n return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info\n\n def forward(self, tokens):\n if self.execution_device is None:\n device = self.transformer.get_input_embeddings().weight.device\n else:\n device = self.execution_device\n\n embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)\n\n attention_mask_model = None\n if self.enable_attention_masks:\n attention_mask_model = attention_mask\n\n if isinstance(self.layer, list):\n intermediate_output = self.layer\n elif self.layer == \"all\":\n intermediate_output = \"all\"\n else:\n intermediate_output = self.layer_idx\n\n outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info)\n\n if self.layer == \"last\":\n z = outputs[0].float()\n else:\n z = outputs[1].float()\n\n if self.zero_out_masked:\n z *= attention_mask.unsqueeze(-1).float()\n\n pooled_output = None\n if len(outputs) >= 3:\n if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:\n pooled_output = outputs[3].float()\n elif outputs[2] is not None:\n pooled_output = outputs[2].float()\n\n extra = {}\n if self.return_attention_masks:\n extra[\"attention_mask\"] = attention_mask\n\n if len(extra) > 0:\n return z, pooled_output, extra\n\n return z, pooled_output\n\n def encode(self, tokens):\n return self(tokens)\n\n def load_sd(self, sd):\n return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, \"can_assign_sd\", False))\n\n def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):\n if isinstance(tokens, dict):\n tokens_only = next(iter(tokens.values())) # todo: get this better?\n else:\n tokens_only = tokens\n tokens_only = [[t[0] for t in b] for b in tokens_only]\n embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]\n return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)\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 bundled_embed(embed, prefix, suffix): #bundled embedding in lora format\n out_list = []\n for k in embed:\n if k.startswith(prefix) and k.endswith(suffix):\n out_list.append(embed[k])\n if len(out_list) == 0:\n return None\n\n return torch.cat(out_list, dim=0)\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 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 except Exception:\n logging.warning(\"{}\\n\\nerror loading embedding, skipping loading: {}\".format(traceback.format_exc(), 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 embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*')\n if embed_out is None:\n embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key))\n if embed_out is None:\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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, start_token=None, min_padding=None, pad_left=False, disable_weights=False, tokenizer_data={}, tokenizer_args={}):\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, **tokenizer_args)\n self.max_length = tokenizer_data.get(\"{}_max_length\".format(embedding_key), max_length)\n self.min_length = tokenizer_data.get(\"{}_min_length\".format(embedding_key), min_length)\n self.end_token = None\n self.min_padding = min_padding\n self.pad_left = pad_left\n\n empty = self.tokenizer('')[\"input_ids\"]\n self.tokenizer_adds_end_token = has_end_token\n if has_start_token:\n if len(empty) > 0:\n self.tokens_start = 1\n self.start_token = empty[0]\n else:\n self.tokens_start = 0\n self.start_token = start_token\n if start_token is None:\n logging.warning(\"WARNING: There's something wrong with your tokenizers.'\")\n\n if end_token is not None:\n self.end_token = end_token\n else:\n if has_end_token:\n self.end_token = empty[1]\n else:\n self.tokens_start = 0\n self.start_token = start_token\n if end_token is not None:\n self.end_token = end_token\n else:\n if has_end_token:\n self.end_token = empty[0]\n\n if pad_token is not None:\n self.pad_token = pad_token\n elif pad_with_end:\n self.pad_token = self.end_token\n else:\n self.pad_token = 0\n\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 self.disable_weights = disable_weights\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 split_embed = embedding_name.split()\n embedding_name = split_embed[0]\n leftover = ' '.join(split_embed[1:])\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, \"{} {}\".format(embedding_name[len(stripped):], leftover))\n return (embed, leftover)\n\n def pad_tokens(self, tokens, amount):\n if self.pad_left:\n for i in range(amount):\n tokens.insert(0, (self.pad_token, 1.0, 0))\n else:\n tokens.extend([(self.pad_token, 1.0, 0)] * amount)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs):\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 min_length = tokenizer_options.get(\"{}_min_length\".format(self.embedding_key), self.min_length)\n min_padding = tokenizer_options.get(\"{}_min_padding\".format(self.embedding_key), self.min_padding)\n\n min_length = kwargs.get(\"min_length\", min_length)\n\n text = escape_important(text)\n if kwargs.get(\"disable_weights\", self.disable_weights):\n parsed_weights = [(text, 1.0)]\n else:\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)\n split = re.split(' {0}|\\n{0}'.format(self.embedding_identifier), to_tokenize)\n to_tokenize = [split[0]]\n for i in range(1, len(split)):\n to_tokenize.append(\"{}{}\".format(self.embedding_identifier, split[i]))\n\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 logging.warning(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 end = 999999999999\n if self.tokenizer_adds_end_token:\n end = -1\n #parse word\n tokens.append([(t, weight) for t in self.tokenizer(word)[\"input_ids\"][self.tokens_start:end]])\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 if self.end_token is not None:\n has_end_token = 1\n else:\n has_end_token = 0\n\n while len(t_group) > 0:\n if len(t_group) + len(batch) > self.max_length - has_end_token:\n remaining_length = self.max_length - len(batch) - has_end_token\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 if self.end_token is not None:\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 if self.end_token is not None:\n batch.append((self.end_token, 1.0, 0))\n if self.pad_to_max_length:\n self.pad_tokens(batch, 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 if self.end_token is not None:\n batch.append((self.end_token, 1.0, 0))\n if min_padding is not None:\n self.pad_tokens(batch, min_padding)\n if self.pad_to_max_length and len(batch) < self.max_length:\n self.pad_tokens(batch, self.max_length - len(batch))\n if min_length is not None and len(batch) < min_length:\n self.pad_tokens(batch, min_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 def state_dict(self):\n return {}\n\n def decode(self, token_ids, skip_special_tokens=True):\n return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)\n\nclass SD1Tokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name=\"l\", tokenizer=SDTokenizer, name=None):\n if name is not None:\n self.clip_name = name\n self.clip = \"{}\".format(self.clip_name)\n else:\n self.clip_name = clip_name\n self.clip = \"clip_{}\".format(self.clip_name)\n\n tokenizer = tokenizer_data.get(\"{}_tokenizer_class\".format(self.clip), tokenizer)\n setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return getattr(self, self.clip).untokenize(token_weight_pair)\n\n def state_dict(self):\n return getattr(self, self.clip).state_dict()\n\n def decode(self, token_ids, skip_special_tokens=True):\n return getattr(self, self.clip).decode(token_ids, skip_special_tokens=skip_special_tokens)\n\nclass SD1CheckpointClipModel(SDClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)\n\nclass SD1ClipModel(torch.nn.Module):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, clip_name=\"l\", clip_model=SD1CheckpointClipModel, name=None, **kwargs):\n super().__init__()\n\n if name is not None:\n self.clip_name = name\n self.clip = \"{}\".format(self.clip_name)\n else:\n self.clip_name = clip_name\n self.clip = \"clip_{}\".format(self.clip_name)\n\n clip_model = model_options.get(\"{}_class\".format(self.clip), clip_model)\n model_options = {**model_options, \"model_name\": self.clip}\n setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))\n\n self.dtypes = set()\n if dtype is not None:\n self.dtypes.add(dtype)\n\n def set_clip_options(self, options):\n getattr(self, self.clip).set_clip_options(options)\n\n def reset_clip_options(self):\n getattr(self, self.clip).reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs = token_weight_pairs[self.clip_name]\n out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)\n return out\n\n def load_sd(self, sd):\n return getattr(self, self.clip).load_sd(sd)\n\n def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):\n return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)\n" }, { "path": "comfy/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\": 49407,\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": "comfy/sd1_tokenizer/merges.txt", "content": "#version: 0.2\ni n\nt h\na n\nr e\na r\ne r\nth e\nin g\no u\no n\ns t\no r\ne n\no n\na l\na t\ne r\ni t\ni n\nt o\nr o\ni s\nl e\ni c\na t\nan d\ne d\no f\nc h\no r\ne s\ni l\ne l\ns t\na c\no m\na m\nl o\na n\na y\ns h\nr i\nl i\nt i\nf or\nn e\nð Ł\nr a\nh a\nd e\no l\nv e\ns i\nu r\na l\ns e\n' s\nu n\nd i\nb e\nl a\nw h\no o\nd ay\ne n\nm a\nn o\nl e\nt o\nou r\ni r\ng h\nw it\ni t\ny o\na s\ns p\nth is\nt s\nat i\nyo u\nwit h\na d\ni s\na b\nl y\nw e\nth e\nt e\na s\na g\nv i\np p\ns u\nh o\nm y\n. .\nb u\nc om\ns e\ner s\nm e\nm e\nal l\nc on\nm o\nk e\ng e\nou t\nen t\nc o\nf e\nv er\na r\nf ro\na u\np o\nc e\ngh t\nar e\ns s\nfro m\nc h\nt r\nou n\non e\nb y\nd o\nt h\nw or\ner e\nk e\np ro\nf or\nd s\nb o\nt a\nw e\ng o\nh e\nt er\nin g\nd e\nb e\nati on\nm or\na y\ne x\nil l\np e\nk s\ns c\nl u\nf u\nq u\nv er\nðŁ ĺ\nj u\nm u\nat e\nan d\nv e\nk ing\nm ar\no p\nh i\n.. .\np re\na d\nr u\nth at\nj o\no f\nc e\nne w\na m\na p\ng re\ns s\nd u\nno w\ny e\nt ing\ny our\nit y\nn i\nc i\np ar\ng u\nf i\na f\np er\nt er\nu p\ns o\ng i\non s\ng r\ng e\nb r\np l\n' t\nm i\nin e\nwe e\nb i\nu s\nsh o\nha ve\nto day\na v\nm an\nen t\nac k\nur e\nou r\nâ Ģ\nc u\nl d\nlo o\ni m\nic e\ns om\nf in\nre d\nre n\noo d\nw as\nti on\np i\ni r\nth er\nt y\np h\nar d\ne c\n! !\nm on\nmor e\nw ill\nt ra\nc an\nc ol\np u\nt e\nw n\nm b\ns o\nit i\nju st\nn ing\nh ere\nt u\np a\np r\nbu t\nwh at\nal ly\nf ir\nm in\nc a\nan t\ns a\nt ed\ne v\nm ent\nf a\nge t\nam e\nab out\ng ra\nno t\nha pp\nay s\nm an\nh is\nti me\nli ke\ng h\nha s\nth an\nlo ve\nar t\nst e\nd ing\nh e\nc re\nw s\nw at\nd er\nit e\ns er\nac e\nag e\nen d\nst r\na w\nst or\nr e\nc ar\nel l\nal l\np s\nf ri\np ho\np or\nd o\na k\nw i\nf re\nwh o\nsh i\nb oo\ns on\nel l\nwh en\nil l\nho w\ngre at\nw in\ne l\nb l\ns si\nal i\nsom e\nðŁ Ĵ\nt on\nd er\nle s\np la\nï ¸\ne d\ns ch\nh u\non g\nd on\nk i\ns h\nan n\nc or\n. .\noun d\na z\nin e\nar y\nfu l\nst u\nou ld\nst i\ng o\nse e\nab le\nar s\nl l\nm is\nb er\nc k\nw a\nen ts\nn o\nsi g\nf e\nfir st\ne t\nsp e\nac k\ni f\nou s\n' m\nst er\na pp\nan g\nan ce\nan s\ng ood\nb re\ne ver\nthe y\nt ic\ncom e\nof f\nb ack\nas e\ning s\nol d\ni ght\nf o\nh er\nhapp y\np ic\nit s\nv ing\nu s\nm at\nh om\nd y\ne m\ns k\ny ing\nthe ir\nle d\nr y\nu l\nh ar\nc k\nt on\non al\nh el\nr ic\nb ir\nvi e\nw ay\nt ri\nd a\np le\nb ro\nst o\noo l\nni ght\ntr u\nb a\nre ad\nre s\nye ar\nf r\nt or\nal s\nc oun\nc la\nt ure\nv el\nat ed\nle c\nen d\nth ing\nv o\nic i\nbe st\nc an\nwor k\nla st\naf ter\nen ce\np ri\np e\ne s\ni l\nâĢ ¦\nd re\ny s\no ver\ni es\nðŁ ij\ncom m\nt w\nin k\ns un\nc l\nli fe\nt t\na ch\nl and\ns y\nt re\nt al\np ol\ns m\ndu c\ns al\nf t\n' re\nch e\nw ar\nt ur\nati ons\nac h\nm s\nil e\np m\nou gh\nat e\nst ar\nwee k\n! !!\nc lu\nth ere\nn er\nt om\ns el\nï¸ ı\nwor ld\nv es\nc am\ngo t\nin ter\nof f\nu m\nton ight\no ther\nh ou\nloo k\nj e\ni d\nsi on\nbe au\nat t\nel i\nor t\nre c\nf f\nst er\nsu pp\ng en\nbe en\nil y\nte am\nm m\ni c\npe op\nit t\nat s\non ly\nmb er\nen g\nb ri\nm p\nk now\nb ur\nb ar\nin s\nlo w\nsh e\nro w\nâ Ŀ\nt ro\npeop le\nvi a\nlo w\nag a\nbe t\nx t\nf ac\nch ar\ne ar\nw al\ns en\nf am\nb le\nn ati\nis h\nn or\ng ame\nli ve\ns co\nle y\nd on\nic k\nb all\nver y\nthe se\np an\ni a\nat ing\nc r\na re\ng ir\nma ke\nst re\nsho w\n. \"\nf l\nu p\nd r\nthan ks\nil li\nw om\nst s\ni g\ns ur\never y\nc ur\nvie w\nle t\nin to\nmo st\nn a\nin di\ng ar\nha d\ns ou\nv ed\nan t\niti on\nma de\nf ol\nun i\nit ed\nðŁ ı\nic al\nth r\nread y\nch ec\nd ra\nk es\nboo k\ne p\nsi c\nmor ning\nne ws\nc au\nc t\nw ell\nan c\npho to\nth an\nor s\nbir th\ng g\nou t\nne xt\nsom e\nen ing\nstor y\nch ri\ndo wn\nhom e\nf fe\nfre e\nd a\nb or\nf il\nci al\nthan k\nsi de\nle ar\nqu e\nl ine\nt en\nat es\nye ars\nm y\npho to\nbeau ti\nri ght\nn u\nfor m\nshi p\nb an\nth er\nd ays\ng am\nas on\ng y\nðŁ İ\nbirth day\nse t\nic k\ne t\nst ill\ncom ing\nta ke\nðŁ ĩ\nb b\ns ol\ns on\nd en\ne p\nmu sic\nthe m\nde n\nwh y\nf oo\nc ra\nam az\nw n\nh ol\nt ting\nw r\nu e\nma g\nc ro\nl an\nc lo\nb ra\na k\ns ing\nc al\nre ad\n' ve\njo h\nb ab\nd ri\nb lo\nbi g\ner ic\nin t\nt or\ntr y\nl a\nle g\nhou se\nm ic\nv al\nbeauti ful\nl itt\nchec k\nne w\nver s\ns w\nar i\npla y\nh er\nâĢ ĵ\nw in\nm a\ncon gr\nsch ool\nf un\n. @\nhe al\nic h\nd el\nwh ere\nl on\nke t\ntw o\nmu ch\nwat ch\nv en\nd ed\na st\nk ed\nb as\ngo ing\nm p\ne ver\nw ays\nro o\nde sig\nl y\ns ed\nto p\nl in\nch an\nto o\nit ing\nd ent\ngh ts\nt y\nsp o\nne ed\nb lu\nin st\nbe ing\nâĿ ¤\nw el\nl s\nhi m\nm ay\nst ing\nn a\nel y\nlitt le\ng a\nn at\ntom or\nm c\nh on\nw ant\na ir\npi c\nam eric\np er\nle ss\nwee k\nve l\na h\nc ap\nch am\ng er\nti m\ntomor row\nne ss\nst ate\nh al\nser v\nz e\no s\np at\nv is\nex c\ns in\nf f\nc ity\nc en\nan y\nb el\nsu mm\nt in\nw ould\nloo king\nk o\nce le\nfam ily\nm er\npo w\nhel p\nbu s\nc o\nc le\nsel f\nen s\nic s\nth o\nan i\nch o\nle ad\nb s\nt wee\nth ink\nfor e\nch il\nvi de\ndi d\nal e\nch i\nv il\nen ds\nw ing\np as\n' ll\nv ol\ns a\ng s\nman y\nj ec\nbe fore\ngra ph\nn y\nur ing\nw il\nd d\nbu il\nf av\nst ed\ntr an\nl ing\nou d\nd ge\nfi el\nnati onal\nst a\nc er\nw ere\nin a\nse ason\nc ou\nn ed\namaz ing\nti ons\ncele br\nn s\na th\nhe ad\ns day\nd ar\nlo c\nv in\nan other\ng oo\ns at\nn y\njo in\npre s\ns es\ns ing\nan a\nin ing\n.. ..\nc our\nï¸ ı\nac t\ncau se\nli ght\nam s\nt a\nb al\nf c\nhi gh\noff ici\nt t\nchri st\nd ic\nd ay\nra l\nh or\n: )\nvi si\nn am\no b\nma s\ngh t\nre ally\nt un\nfin d\nthr ough\npor t\nu t\nti ve\nst y\nn e\nor e\nðŁĺ Ĥ\nsupp ort\nne ver\nev en\nðŁ Ķ\nh a\ny a\nl d\nu k\nr an\nj am\nwi th\nme di\nd es\nne y\nch ing\nal e\nh y\nk in\n! !\nd y\npl ace\nal so\nb le\nwh ich\nbl ack\nb li\ns ay\npar k\npl ay\nir e\nvide o\nweek end\na il\nke y\np t\nw ard\nfri day\nd in\nine ss\ng ro\nb en\nal ways\nt ball\nag o\nm il\nc y\npro duc\ndi sc\nun der\nple ase\nsp or\nfu ll\ne y\nðŁ Ļ\nis e\niti es\nc at\nk no\nu se\nfo re\nk er\nar t\nhi gh\nop en\ns an\ne f\nour s\nsh ed\nst ri\nd ro\naga in\ni m\nðŁ ĵ\nen jo\nfu n\nge tting\np en\ng er\nc li\nan y\never y\ne u\nwom en\nâ ľ\ne st\nc ould\nr y\n\" @\nth ou\nsh a\ncomm un\nb er\nd ents\ndi s\nwh ile\naw ay\ndi o\nh am\ng la\nd ate\nk a\nmis s\nun ch\nw on\nin f\nroo m\ng a\nre al\nex per\ndi rec\nsh ould\nsp r\ng ol\nl ong\nbet ter\nor i\ne y\ni ence\nil s\nz z\nh an\nf ound\nv s\nâ Ļ\npo st\nti c\npar t\nm en\nren ce\nce ss\nv ic\ns il\nsho p\nðŁĺ Ĥ\nf ood\nv al\nsti c\ny ou\ns ays\ne lec\nst ar\no c\nl and\ni d\nc tion\nfiel d\ns of\nst art\nwat er\nfri ends\non es\nðŁ Į\nf la\nf ar\nwh ite\npar ty\nin st\ngr ou\nt v\nevery one\nm ent\nj a\nch a\npr in\nan ts\nd uring\nl at\nl ar\nwe st\nth en\nk a\ny oun\nin sp\nin te\nwe en\nvisi t\naga inst\nre le\nhe ad\nc es\nto wn\nloo ks\nth re\nre gi\nren t\npro jec\ngir l\nse ar\nw o\nm om\nc ar\nh un\npu bli\nd i\np le\nc all\nc ri\nu m\nfor d\nper fe\nfri end\nh ard\nssi on\nte st\npla ying\nar ound\nbe cause\nke ts\nme et\nsat ur\nar ti\nwor k\nj un\nv en\nr un\nme mber\npor t\nsu per\nt wit\ns am\nel s\nt ly\nad v\nati ve\nat h\ns ure\nav ail\nla r\ns qu\nar ds\nev ent\nm en\nl l\no ver\nlo gy\nit al\ntim es\nm al\nb ack\nc oo\nma king\nst ru\nâ ģ\nit u\nsh ar\ng an\nc as\ns n\nsumm er\npic ture\nf an\nh in\nchrist mas\nc y\npr oud\ncham pi\ndesig n\npp ing\nho pe\nc a\navail able\nma y\nwe d\nphoto graph\nspe cial\nsal e\nsto p\ner y\na we\nal ity\nhi story\nam a\npre si\nb ru\nwor king\nd one\nd r\nk en\nfe at\nw ood\nate st\nsun day\nmo vi\nvel y\ns le\nf ace\nsp ec\nstu dents\nb y\nha m\nsp on\nbus iness\nd at\ni e\ni p\nso ci\ng lo\nh and\nre cor\nr s\nme e\nke ep\np ur\nheal th\nsh e\ncom ple\ngo d\nda vi\ncol lec\nli st\nr a\nclu b\nt ers\nin clu\nth ings\npl an\nâ ĺ\njoh n\nsh ing\nat ul\nso on\nblu e\ng or\nsatur day\nw on\ncongr atul\nse e\nâĿ¤ ï¸ı\ntho se\nðŁĺ į\nfin al\nd ou\nit h\no wn\nro ad\nt our\na st\nindi a\nti l\nn d\nf er\nfav or\nsu l\nlear n\nfir e\nju st\ngrou p\na h\nr ac\nbo dy\nu r\nc are\nà ¸\np lo\no h\npo s\ngi ve\nte ch\nsu b\nc ent\ner ing\ny m\nil ity\nf ic\nlon don\nv ir\ngu ys\nb a\nðŁ ¤\nbab y\nsc re\nðŁĺ į\ntru mp\nun der\nchan ge\ni an\ncol le\nss es\nl er\nss ed\nn ice\nann oun\npow er\ns ar\na king\nmin i\ns li\ns wee\nk ar\nfu l\nc ru\nac tion\na ther\n) .\nst and\nde vel\na a\ng an\nle ft\nlo l\nre l\ntran s\nm ents\nin t\ne f\nman ag\ndi g\ngen er\ndo wn\np au\nti v\nk u\nth ur\nk en\nst on\nf ans\ntal k\ntwee t\nt oo\nsty le\npro te\nse con\nfr on\nawe some\ng l\np al\nne t\ns or\nla u\ng on\nsin ce\nt ty\nser ies\nme mor\nb eli\nfil m\ndi d\ndi es\no t\ncongratul ations\np ra\ne ve\nw oo\noffici al\nsu c\nin cre\nb on\npar t\npp ed\ncla ss\nsi ve\nbo y\ncu l\nperfe ct\nt ou\nd am\nwel come\nfoo tball\nh i\np ap\nwa it\nad a\ncongr ats\nyoun g\nexc ited\nre ce\nj an\nv a\nre d\nst ra\nmedi a\n' d\ndo es\nle t\nmu l\nill s\ngre en\nm el\nto ge\nfu ture\nye ster\nvers ity\nfor m\nta in\ni de\nch es\nki ds\nqu i\nha ha\nde ta\nbi g\nfavor ite\ngir ls\ncon tin\ndo m\nsear ch\nu al\na ir\nd ers\nmon th\nc er\nyester day\ncommun ity\nad e\ndo g\nvil le\nic es\nd eli\nsy ste\nru n\nis m\nhe art\nc up\nen ti\nfe w\npresi dent\ne ds\nun til\nfe sti\no k\nf lo\nsa id\nol e\nme d\ntra vel\n £\nph one\ntoge ther\nfa st\nlo t\ngam es\nsh ir\nbet ween\ny es\nth ers\ndo ing\nm ac\nat or\nb and\nfol low\nprojec t\ndevel op\ndi ffe\ncon fe\nspe ci\nca st\ny s\nbo ard\nr d\ni al\nsh oo\nr am\nha ving\nsh are\nfol low\non e\nn ame\nm r\npu t\ndisc u\nor y\nc ame\nou s\ns ite\ntwit ter\nt b\nt it\nfin ally\nz ed\nsu per\ncom pan\nus ing\nall s\nli st\nr is\nsho t\ng al\nt ar\nde l\njoh n\nâĢ Ķ\nsome thing\nra m\ninte re\nwh e\nb it\nðŁ į\nstre et\noun d\na i\ntic kets\nmovi e\nre al\nk y\nta king\no pp\nc c\nl am\nm oun\nin ve\nbl ack\nus ed\non line\ny or\nloc al\ngu e\nc ks\no w\nge st\nbo ys\nilli on\ncon t\nre ci\nin ed\neu ro\nno w\nse en\np h\nte ach\nde f\nsou th\nsu ch\naw ard\nmu st\nis su\nca re\nfe el\np lu\nl atest\nspor ts\nwe b\nte x\ne ment\ns k\nfi c\nw an\nte ch\no t\nbo x\nn er\nfre e\nt al\na sh\nc ase\nho t\nwon der\nmee ting\ner a\nch all\nðŁ IJ\njo b\nil i\nc ool\nj our\nth s\nm o\nf el\ndi e\nmic ha\ne le\nte am\nserv ice\nst and\nma kes\np ing\near ly\ncom es\ne k\nho li\nv ers\nag ue\ns au\nthre e\nmon day\nfa shi\nsome one\nth ro\nse a\nb ad\nsupp or\ntur n\nur y\nm ing\nphotograph y\nn ic\nmar k\npre tty\nss ing\nwat ching\nme mb\nar ri\ncoun ty\nbe ach\nfr an\ncen ter\npol ice\nb at\npubli c\nt an\npre ss\ns af\ns y\nge ts\nro y\nn ers\ny our\nbu y\nst ers\nsho w\nas ed\nchil dre\naf ric\nin es\nsp ace\nsc ri\nh all\npa in\nar ing\nhom e\nm ur\nheal th\nch ed\ns and\nrece i\ngu y\ne a\nameric an\nre si\nchildre n\n- -\ni ri\ning ton\ncoun try\nro ss\nle n\nann a\nboo ks\nb c\ne ce\nd om\nlo vely\nk h\npe t\ng y\ng ri\nst age\noff ice\nro ck\nm on\nb ay\nt able\nsu n\nm ed\nth in\nl or\nf low\n( @\nuni versity\nstor e\nfron t\ngoo d\nz a\nvo te\nnor th\nhe y\nan im\nor der\nmi d\nwith out\na de\nre member\nmar ket\n? ?\nmu s\ntra ining\ne duc\nbu t\nco ver\nst an\nsc en\nb la\nbre ak\nl ou\ns ame\ng old\na in\no s\nbo th\nl it\nver n\na i\nal bu\np a\nenjo y\nbe g\nell ing\nthur sday\ninf o\ns an\nameric a\nha ir\nte l\nmar ch\ncon cer\ncolle ge\nconfe rence\nap p\nh our\nch ang\nâ ļ\ns our\nol s\nwe ather\nw ar\np hi\nfesti val\nsecon d\ncu te\npr ac\nen er\nstr y\nle a\npol it\ns av\nse n\no w\nm i\nne ar\nou ght\nz e\nco ffe\nw illi\nd an\nse y\ndavi d\ne se\nf an\nde ci\nthe at\nno v\nati on\ntr ac\nsc i\nre view\nc el\ne m\nu n\nju ly\nor ig\nti on\nd ru\nform er\nst ay\naf ter\nin v\ntoo k\ndat a\nb al\ntu es\nd an\nev ening\nðŁĺĤ ðŁĺĤ\nd ol\nu res\npro vi\nt s\ne st\nsig n\nj ac\nu k\ns ong\nye t\nbo w\nin du\nj ap\nh oo\npo int\nany one\nz y\ni st\nh ur\nit al\nbuil ding\nwom an\nch ur\nj er\nper for\nco ach\nle ague\nce ss\nne t\ni mag\nnati on\nbr it\nqu e\naw ards\nag es\nwor ks\nc ed\nman ce\nl ate\nig n\nmon ey\ntru e\ni i\nt ell\npl ac\np ac\nas y\nwor ld\nbe hin\nim port\nread ing\ngra m\ngi ving\nme t\nh it\nfor ward\nst om\npres ent\njun e\nso cial\nno on\nmar t\nhal f\ns we\ngo vern\nk er\ndeta ils\nli sh\n_ _\nac y\nsi a\nber t\nf all\n! !!!\n) ,\nth i\nd iti\nsp ort\nk ing\nf it\nst af\nc at\nmu se\ncen tr\ny er\ncon tro\nb loo\nwal k\nac tu\ndid n\nli m\nlear ning\nre search\nwed ne\nau th\nh ours\nk y\nf ar\nh en\n.. ..\nit ch\nri l\nstr ong\nsk y\nque sti\njam es\nr on\nd g\nf ur\nc in\ndo es\napp ro\nmar ke\ntu res\nful ly\nch at\nbehin d\nte m\nfin i\nmis sion\nb att\nfe el\nhe av\nevery thing\nb ar\nw ish\npre mi\ni ma\nexper ience\ne ach\nre port\nswee t\ntic s\nspr ing\nre spon\nsyste m\nvic tor\nl in\nsa w\nal ready\ngh ter\nf le\nã ĥ\nbr ing\nalbu m\n- -\nell s\nst an\nto m\ninter national\nw ent\nan ni\nmat ch\npp er\nst one\nsm all\nra in\nfashi on\nare a\nv an\nag ram\nk o\nthou ght\nwor th\nv an\nm er\ncoffe e\nit es\ng n\narti st\nc on\nar ch\nc ir\nse cre\ngr ound\nis o\nh and\nco m\nbri dge\nh s\nx i\nl ink\npu l\nsp l\nr ace\nf li\nri ver\ng as\ndi sco\nd al\nplay er\nf it\nphoto s\nit y\no k\nj or\ntr a\nap ril\nad s\na di\nsol u\nbeau ty\ndo or\nme ss\nup date\nali a\nsch o\nen ed\nmom ent\nsco t\nsc ience\ni or\nti es\nac ross\nous ly\nsh es\ndoes n\np age\nwat er\nm illion\ncla ssi\nl ic\nca st\nform ation\nmicha el\nell o\ns mo\nin ts\nvi sion\nop ening\nld n\nau str\ntues day\nwin ner\npo ssi\nr ound\nshir t\ndi t\nb o\nu es\nil led\nal ong\ntri p\nstar ting\nim pro\nk an\nper son\nno t\nre co\nne eds\nc le\nli e\nre st\nr ing\nwin ter\nsi mp\nmo m\nbe er\nfac e\ntor s\nus a\ncollec tion\nge or\nse ssion\ntr ying\nla s\nla ke\nj en\norig in\nstu dent\nse cur\nv in\npic s\nex pe\ncom p\ngon na\ne qu\nb ad\nle y\na u\nmemb ers\nbre ak\nw all\ngi c\ndin ner\nbu l\ninsp ir\nr i\nmin d\nic a\nwin ning\ntal king\nt ren\ns is\nt en\nwonder ful\ns now\nhe ar\nth om\nno thing\ngu i\nst in\nblo g\nfe st\nb un\nle e\nwar ds\nch ance\ndre ss\nre n\npau l\np es\ntech no\nru ssi\nc ard\ne ast\nmar i\nw ine\nt i\nla w\nstr ic\nk i\nap e\nau gu\npro fe\nas h\ncour se\nma il\nren tly\nd un\nm un\nlo ve\nis land\ndri ve\ns l\nend ed\nma in\nlo st\nnat ure\nâĿ¤ ï¸ı\nch ic\nre por\np in\npr o\nst ation\nce p\nta kes\ncompan y\ngo es\non d\nma ch\nra dio\nd ad\nro ck\nj a\np ay\nchampi on\ne e\nin de\ntt a\nati c\nt ab\nbeli eve\nener gy\nz i\nt at\nwor d\non ce\nre sul\ny l\nand re\nan o\ninst agram\nclo se\nt am\ncu stom\nw a\ncon om\nsho ws\nli fe\nk in\nro b\nt age\nn ation\nal most\nlist en\nsa ve\nre li\nac e\nmar y\ntre e\nfor get\nj ack\nwa iting\ndirec tor\nh ill\nbor n\nte mp\nf l\nst e\non a\nsing le\nwedne sday\nun ited\nin o\n@ _\nne l\ncelebr ate\nen ding\nde al\nj i\ncan ada\nhu ge\ntr ack\nâĢ ¢\nf y\nfan ta\nan g\nyor k\nrele ase\np un\nep iso\nwor ds\nt our\np ack\ni gh\nclassi c\nperfor mance\nke t\nafter noon\nrecor d\nwin s\npro ble\nâĿ ¤\nf our\nb ed\nban k\nd ance\ns la\ncal led\nmi ght\na p\npa st\nðŁ ļ\ndiffe rent\nit e\ngi ft\nssi ve\nchur ch\nc us\npro gram\nho tel\nic e\nma d\nsecur ity\nen ge\nd c\nen ough\nst a\ne ty\nde ad\ng un\nhe ar\nm ir\nhu man\ngre ss\noun ds\npi ece\nbre aking\ngar den\nfi ght\nvie ws\nf ish\nstar ted\nrun ning\ngre en\nser i\ns m\nas k\nd or\nde ath\ne conom\ner i\nir d\ns er\nl unch\nâģ ¦\nbo x\nnat u\nba se\nb an\nf al\nglo bal\nwil d\nwo w\nout side\nmo ve\nle ad\nan al\nmuse um\non g\nha w\npow er\nthan k\nb ac\nchar ac\ncam pa\ndig ital\nr o\nop er\nde v\nw ol\np ati\nf a\nm ale\npap er\nill ing\nc s\nâ ĥ\neduc ation\nta ken\ne ffe\nm ou\ns ad\n\" .\nbas ed\nstaf f\ninclu ding\nli ving\na c\nch ina\nmo b\nstor m\nlu ck\nph il\no o\ny n\ntra vel\nk el\nti al\npr ice\nboo k\nimport ant\nbi o\np ool\nny c\nf ab\nlo ad\n? !\nchall enge\ncr y\nser ve\nwe ar\nbu s\nta in\nnu mber\nro r\nk at\ni z\nth ough\nho sp\nm m\nfa ir\nut es\nho t\npo p\nfi ed\ncam p\ndevelop ment\nli br\nc ali\nem s\nâģ¦ @\nb ol\nis ed\nstand ing\nmo del\nit a\ng le\nbro wn\nima ge\nve red\nfor ce\no il\npar tic\nsh u\nda ily\nla w\nse c\ncla ss\ncam p\nholi day\ncl in\nk ers\npres ent\ngam e\nincre di\ner ship\ninter view\nb ill\ndu e\nand y\nab o\nin nov\nke y\nac ade\np il\nmo der\nst ars\nbr and\nf er\nwee ks\ncon si\npr e\nsa fe\nwr it\ndi um\nla unch\nmarke ting\nann ual\nas si\ncour t\nla dy\nc ted\nand a\nin side\nchil d\nopp or\nsm ith\ncentr e\ngu e\nâģ ©\nf ren\nst y\nfor t\nent ly\nis n\nke ep\nto ber\non y\nbo y\nal d\ncol la\nde mo\nle vel\ncom pet\nad o\nb our\nfanta stic\nm ate\ns u\nsou th\noppor tun\nvers ary\nlat er\nbu d\nface book\nla un\nster n\np it\n! \"\nma j\ngr am\ntb t\nfi re\nhapp y\na ks\nwh ole\nactu ally\nill er\nell a\nlo ts\nal ex\nan ge\nlan ds\nðŁĺ Ń\nen ter\nr ou\nepiso de\np ed\nin ten\nsh ire\nwh o\npl an\nh o\nca ke\nwe st\nmag az\nfre sh\nc c\nn ar\nch ris\nwr iting\nw er\nn om\nl o\nmi dd\ndre am\no l\nti onal\nde b\n> >\nbe come\ns i\ngr and\nall ing\nhi stor\nri de\ni red\nsaf e\nque en\nci l\nin tro\nvi l\nd ani\n.. .\nar tic\nst at\nsh ort\nor ing\nsel fi\nmis si\ndo c\nb it\ng all\nb om\ni re\nse lec\nd ition\nðŁĶ ¥\nfri end\nbe at\ngh ting\nðŁĺ Ĭ\npe ace\nex hi\nant a\nab ility\nil lu\nj on\nqu ality\ntri bu\nm es\nplay ers\nfa ir\ncu t\nc ab\nsuc cess\nb i\nsu s\npro mo\nsch e\nan ge\nic o\ncomm it\ncat ch\nill a\nkin d\nfeel ing\nqu o\ns ay\nanni versary\nspo t\nmo ther\nan e\np end\nyour self\nop s\napp le\nmin utes\np o\ngr and\nri es\nha ha\ncare er\ned ition\nde c\nric k\nam i\nconcer t\niti ve\nge ous\nd ly\nt te\nadv ent\ni g\nli ghts\nak er\nsk y\nâĥ £\nr ay\nfini shed\nw ay\ns d\nac coun\nðŁĴ ķ\nck y\nch el\nlit er\npain ting\nlo s\nst un\ntechno logy\nn as\nma r\nb il\nafric a\nki e\ney es\ngol f\nplu s\nni a\nit ec\nserv ices\nwed ding\nkno wn\nte le\n.. ...\nstar ts\npa ren\nw ants\nati onal\nmon ths\nwin do\nfav our\ner t\nmagaz ine\nex clu\nre ve\nb c\norigin al\ne ss\nn al\nan ti\nst ro\nt ice\nstu dy\nà ¤\nv ac\nnation al\nfi ve\nra in\nve ment\nu te\nver se\nem er\nar my\npossi ble\ngue ss\nval ley\nther n\ncro w\nm r\ncol or\non to\npic k\ncle ar\ndar k\nt ac\nwan ted\nit ting\ncan cer\ngovern ment\ndi e\nri se\nz ing\ncol d\nf oun\nstu dio\nstr ation\nbro ther\na head\nsh el\nmic ro\nic ally\nd au\nsig ned\nvi ol\na x\nas se\ni o\nw re\nspl ay\nch ick\naugu st\npl at\nti ps\nsp i\nhu man\ne asy\nlo gi\nmi ke\ngro w\nag re\nw w\nsh ad\nmo tiv\nwi de\ntur ns\nom g\nv ar\nde fin\nsu g\nj im\nðŁĶ ¥\nt d\ncampa ign\nnam ed\nre tweet\nco p\nt v\nle av\nk is\ndou ble\ns mar\nissu e\nvil la\nin formation\nli es\nsto ck\nn t\ndi stric\nsh or\nmi x\ner o\nse p\nme x\nsee ing\nli ve\nre min\nco de\ng ur\ns c\nwil d\nl un\nh ood\nspo t\nfa ther\nfore ver\nup d\ntra f\nf ly\nne ed\ngra du\ntra in\nma ke\ns ab\nbe y\nsi ze\nlead er\ntal ks\ne u\nlo g\nfo x\ngor geous\nle ss\nle ts\nsur pri\nmy self\nno te\nli ves\nf ru\nlo ved\nse ver\nde m\nj i\nso c\nh old\ndo gs\nn i\nâ ŀ\nlea ve\nair port\nben ef\nex pl\nshi ps\ncomple te\nach i\ngre at\nvin tage\nj ack\nro c\nwoo d\npri v\noff er\ney e\nver sion\nte a\nco ach\noff ic\nw ell\ng en\ns at\nh h\nyou th\no x\n? \"\nm t\nmi x\ng g\nd le\nnatu ral\nbuil d\nbreak fast\nthin king\ntheat re\nmo on\nber g\ngo als\ngeor ge\nen e\nexc ell\nil ing\ntun e\ny ed\ng ate\nm it\nnet work\njo e\nh ello\nf b\ntu be\nwe aring\nath le\nstru c\nhar d\ngla ss\ng ers\nthro w\ng es\nb t\nindu stry\nmanag ement\nali st\ngo al\nstre am\ny el\na vi\nici ous\no thers\ns ki\nchri sti\nbir d\ne sc\nm in\ntr o\nl t\nj an\nim p\nri ghts\nsh a\nor gan\ncent ral\nar a\nro ll\nfavour ite\nche ster\nel se\np ay\ncar s\nm ine\nste p\nprac tice\nmaj or\nh ang\nðŁĺ ĺ\nn on\nv ari\neng ine\nvol un\ndi a\ni led\narch itec\np ink\nd s\nth y\nwa sh\nweb site\nba g\ncontro l\nel li\nf ra\nan sw\nd ence\ny u\nr on\nol a\ng in\ndr in\nli c\ncou ple\nsp ar\ng on\ncre ate\nc t\ncelebr ating\nde ep\ne at\nte e\nvo ice\ndro p\nvis it\nat ors\nsta dium\nf t\nw is\nro l\ngra de\nfam il\npo ints\nre pre\nw as\ntraf fic\njap an\nor g\nhon or\ntex as\nman u\nâĻ ¥\nsafe ty\nre r\nb ag\nem plo\nrele ased\nre gu\nak a\nn av\nro le\nsen ior\nspec t\ncro ss\nlin es\nbe st\np ack\ns in\nti e\nmis sing\nsun set\nli ber\nis ing\nj ay\nsk i\nchampion ship\nac tiv\nla dies\nplay ed\ny y\npu bl\nal o\npri de\ns r\npa ki\nlu x\nsur vi\nck ed\ne ts\ncho col\naustr alia\npar is\nmi les\nh at\nment al\nal a\nme an\nmob ile\nen a\nin si\nf ound\nchi ef\nt ag\nincredi ble\nre turn\nà ©\ngoo gle\nfren ch\ncre w\nhal lo\nali an\nj az\nch er\nsil ver\nnor th\neng lish\nbase ball\nc af\nlim ited\nfollow ing\napp reci\near th\nk ir\nve mber\nw ed\np tion\ng ed\noc tober\nfl ori\nc r\nen cy\nga ve\nlor d\nstu ff\nber ry\npo st\nsm ile\nbro ad\nst ate\ngg er\nme ans\nic y\ngu n\ny o\nma ster\nbur g\nhan ds\nni e\n/ /\nuni on\nbrit ish\nbig gest\ndistric t\nam ing\nh il\no ce\nper son\npas s\nen vir\nscho ols\narri ved\nanc es\ninsp ired\nex pla\nbe n\nlibr ary\nbo tt\nam p\nste ph\ncont act\nb ang\nm s\ncali for\nt old\nbatt le\nb b\nchic ago\nâľ ¨\nstr ate\nsh i\nde ce\n- )\nad d\nla b\nj ones\nleg end\ncast le\ning er\nst ance\nbe l\nur a\nre fu\nlead ers\npo t\nse x\nh ic\nartic le\nki d\nfr ance\nx x\nex e\ngui de\nvolun te\npr int\nal i\nce o\ntwee ts\nw x\nscen e\nvol u\nant i\nh an\nas soci\nshar ing\nro se\nmini ster\nsh er\nin ste\ncle an\ndemo cr\npo ster\nsk in\np sy\npro per\ncra zy\ni am\no re\nin i\nany thing\npo d\nmo ving\ncl ick\nex plo\ncom b\ncra ft\nf i\nbloo d\nis ra\npubl ic\nd ent\nol ym\neng land\na si\nch er\nfac t\nenvir on\nhar ry\ng one\nme dic\nenjo ying\njust ice\nj r\nindi an\nwi fe\ns ound\nt es\ndra wing\np al\nide a\ncr it\nju li\nil er\nwar m\ncl ar\nthou ghts\ndef en\ncoun cil\nintro duc\ndi ed\njan u\nan i\ns end\nli er\nm l\nintere sting\ntra de\nwin d\nb ay\ns ac\nanc y\nsour ce\nb es\norg ani\nar ly\nlar ge\nff ici\nta g\nu t\nde sp\no es\ntit le\nsy m\npic tures\nop en\nwom en\nsho wing\nri a\nle ast\nlead ership\ncur rent\nelec tr\nval ent\nlist ening\nc key\ngener al\nde ser\ndu ce\n; )\nc ent\nðŁĺį ðŁĺį\nsco tt\npo or\nselfi e\nev ents\ni on\nwr ong\nde v\nh ill\nsep te\ncul ture\nl ine\nsor ry\ns ent\nsi ster\nce pt\nk ri\nno vember\nar i\nannoun ce\nz ation\nbr an\ng ent\nd u\nl en\nper s\nf m\nmart in\no p\ne mb\nom e\nmidd le\nsuc cess\npe ter\njanu ary\nf lu\nrac ing\nd av\nbi ke\nðŁı »\npe t\nshoo t\nprofe ssi\nfeat uring\nsepte mber\nnow playing\nsta ur\nz a\non ic\nqu ick\nbas ke\nspe aking\nmil it\nz er\nchick en\nb ell\ns ad\nco ast\nlo ving\ny ers\nd j\npan el\nver age\ns wit\nic ks\nb ou\ncalifor nia\ns am\nparen ts\ner o\nk illed\nph ys\njo bs\nmi gr\nan th\ne mo\nhallo ween\nand er\nc m\ncompet ition\ne ag\ns ket\nsp ir\nmay be\nexclu sive\napp e\njour ney\nscre en\nfor d\ni o\nh ate\nu g\nsou l\nher o\nsoci ety\nsy n\ngu it\nn h\nd j\nas es\nim pre\nti me\nsal es\nd d\nf ts\nsumm it\nstun ning\nom s\ntur ned\ncle an\nsof t\nbe at\nre staur\nde red\nen ces\nma gic\ndi o\nsh ine\ngu est\nhealth y\nexhi b\nstor ies\npo pu\nn is\nel a\nbel ow\nfun ny\nresul ts\ns ne\ncur rently\nar d\ndown load\nf light\nm al\nf ine\np ad\nch u\nent ed\nh at\nðŁij ı\nste ve\nj o\nmar k\nr at\nb all\np c\np on\nb by\no li\nar ts\nas ure\nbow l\natt ack\nmi c\nde ar\nran ge\nen ter\nchocol ate\nbr illi\nac cess\n, \"\n? ??\nch ap\ncon st\nt n\nmat ter\nblu e\ngall ery\nem p\nwork shop\nlead ing\ny ours\nbaske tball\nw anna\nth u\n_ _\nmar ri\nsle ep\nbi a\nch e\nma d\nimp act\no wn\nsi r\nchan nel\neuro pe\ne sp\nk itch\nhosp ital\nw ra\nroy al\nf s\nne u\nqu ar\nne y\nac ks\nch ase\npp y\nst al\nat ely\nti m\ndece mber\nr are\nper form\ncre am\nwe ight\nch oo\nni ght\nha ven\nfr anc\nkh an\nbuil t\nhel ping\ntru st\nty pe\ngol den\nta x\ns now\ns wi\ndi sa\nquesti ons\nve y\nli ght\nc n\ncl oud\nthom as\nag ed\nsh ou\nte ams\ngr an\nre ason\na a\nyou tube\nv p\npi zz\nmanag er\nbur y\ncre dit\ntre at\nma x\ni k\nma in\ng ing\nde ad\npro bab\nye ah\nã Ĥ\nbr and\nso li\npl ant\nta yl\ngir l\nðŁĺ Ń\nnam ent\nau to\nmess age\nko re\nn ur\nter r\nag u\nma p\nsen ting\nlo ves\ngi ves\ng ab\nz en\nro bert\ncon fir\nw ars\no m\nsta in\ncam era\nand er\nwon der\na b\nca p\ns old\nsu it\nwal king\ncontin ue\neffe c\ndau ghter\nd anc\ncha in\nmul ti\nki d\ny an\nchampi on\nv o\nta ins\nho st\nmin i\nmis sed\nre sc\nly n\nfin ish\ndel icious\ns as\ntayl or\ni b\npro mis\nproduc ts\nmoun tain\nflori da\nregi ster\ntre at\nrec ent\nfe male\nboo th\nmat t\nve hic\ns op\nmo tor\nsuppor ting\nphi c\nex tre\ndr ink\nlan e\nth ird\np s\ncon stru\nce re\nfar m\nðŁİ ī\ntu red\nðŁij ī\nc ats\na j\ngi e\nshoo ting\nas ked\npaki stan\nam e\nm b\ng il\nleg al\nsqu are\nin vol\ndra w\noo oo\n!! !!\nopportun ity\np y\ne i\nb ts\nteach er\ncharac ter\njohn son\nbr on\nly wood\nch ine\nc ing\nc ine\nd ge\ngam ing\nrussi a\nci a\nquo te\nric h\ngo v\nflow ers\nsp iri\nst in\ngrow th\nðŁı ¼\ncomm er\nj uni\nmu m\nr an\ns na\na ren\nc b\nac tor\ncol or\nsi t\npa ir\nch i\nbo w\nacade my\nhel d\nr ang\nme tal\ny l\nac tive\nprobab ly\nt ch\nneed ed\nspe e\ncho ice\nital y\nry an\nðŁĩ º\nflow er\nv it\nm n\nfound ation\nb ak\nsi ons\nne igh\nf loo\nhe ard\nre mo\nfre sh\ning ing\nre f\nto wn\ncl ou\nje sus\nspiri t\ncou ldn\nz es\nðŁĴ Ļ\nwilli ams\npro ce\nmoder n\npro cess\nsho es\ncre ated\ntri c\nissu es\nann e\natt en\nde but\nh r\nn it\nsti g\na po\ne ps\nz u\nã Ģ\nsi x\ncar ds\nlan gu\nfam ous\ntour nament\nse l\ne bay\ny n\nst on\nk ick\nannoun ced\nk am\nvo c\nbrilli ant\nhou se\nche ese\nwar ri\nmus ic\nho ckey\nðŁĺĤ ðŁĺĤ\nsk ills\nau tom\nsmar t\nmed ical\nmon y\ne x\ngu ar\ngi ve\npers onal\nven tion\nal li\npre ss\nflo or\nm c\nvictor y\nhi m\nsimp le\nth or\nðŁĩº ðŁĩ\nta il\nlu cky\nale x\nqu ite\nbo t\nssi ons\nchall eng\nc ann\namaz on\nh ell\nb ought\n) :\ned y\nsecre t\nproduc tion\ninde pend\nde fe\nad ded\np r\np ag\nbe d\ngre atest\nwith in\nj ay\nðŁ ¥\nire land\nre ly\ns d\nte xt\ndri ving\npro gram\nspe ed\ncol um\nstr on\nà ©\nfore st\nâ ĸ\nmach ine\nco in\nsc ar\noun t\nbi e\n¡ ï¸ı\npor tra\ncomm on\nwre st\nrecei ved\nkno w\ninve st\npl ans\nac cor\nad op\nter y\nre ali\np p\nk al\nart work\nme an\ngo d\ninste ad\nan ci\nmotiv ation\nas ing\ninspir ation\nup coming\npolit ical\neuro pe\nm ers\nheav y\nðŁij į\nfe bru\nscot land\nou gh\nb t\nbo ss\nsche du\nspe ak\nn ick\nu red\nin o\ne k\nri sk\ntor y\npres ents\nb on\nru g\nst ates\nexhib ition\nil o\nm ill\nbr ought\n: -)\ntou ri\ncom e\noffici ally\nchampi ons\ndo ors\nre p\npo se\nex tra\nk ings\nsoc cer\nsqu ad\napp lic\nat a\nsome times\nt ari\nexcell ent\nðŁĺ ĺ\nstra ight\ncar ol\nri p\nâĢ į\ngra phic\nm ol\nelec tion\nfebru ary\nas ons\nl i\ndi r\nm t\nn ick\nu su\nm rs\ncom ics\ninst itu\ncor por\nv i\nðŁĻ ı\ntu ral\ndi se\nac ci\nwe are\nam ong\nsho pping\nt ill\nwh at\ncha ir\nsp an\nchine se\ninnov ation\njo y\nk it\ncent ury\nob ama\nph ili\nf c\nre ach\nc iti\nul ous\nn on\nd ang\nhapp ening\nbur n\np el\nor ange\nd v\nk ick\ncla im\ning ham\nph y\nno v\npod cast\nwh i\nni ghts\near lier\nbe ar\nla h\nexc iting\nor a\ngi ven\ns lo\nmemor ies\ncontin ues\nproduc t\ngh o\nc d\nkno ws\nðŁİ ī\npubli shed\ndiscu ss\ny ard\ni phone\ntri es\nw all\nfe b\nare n\ntru th\nwin ners\ntu re\nditi onal\nmilit ary\nproble m\nm and\ndo g\nlo ss\nc ric\ncan adi\nve ter\nvilla ge\n\" ,\ny r\nun g\ndon ald\nag ing\nbir ds\nsci enti\nle s\nth is\nregi on\ntic al\nitt en\nil a\nðŁĺ İ\nd ad\ndi am\nabo ve\nst ren\nli t\np ir\nla b\nfo cus\nbus y\nd ur\napp ly\ns ma\nauth or\nac i\nexe cu\ndom in\nre la\njack son\nat o\nwash ington\nðŁĻ Į\nk ill\npopu lar\nce ment\nro ad\ne ating\nloc ation\nv ent\nar re\nn an\ncu sto\nadvent ure\nor din\nspor t\nul t\nlo ck\nquesti on\ndri ver\nland sc\non i\nk ins\np d\njor dan\nte red\nk k\na f\nchil d\ns p\njust in\nen i\ns elling\nz o\nwh it\nbo ston\npartic ip\nsig ning\nhapp ened\nhe at\nm am\ndre ams\nlo ws\ngra ph\nthe day\nhead ing\nbr o\nble ssed\nvi c\nve gas\nh d\nin ning\nro man\nand ro\nden ti\nu se\nc it\npro gress\nwrit er\nbo b\nff s\ngro wing\nb ly\naw are\nex am\nsp ent\nbe t\nsc ore\nbey ond\ndo cu\nad el\ns f\ncou ra\ncolla bor\nin c\npriv ate\nbo at\n* *\nz one\np ha\nb ill\nto tal\nplan ning\nto wards\nplac es\npre view\ncre ative\ndam n\nide as\nse ems\npo ten\nsay ing\ndi splay\ns w\na qu\nlou is\nby e\nli l\ne mail\nwe stern\nger many\nell er\nre s\nf ant\nment ary\nde als\nric hard\njer sey\nstren g\nra d\npizz a\nmon d\nw are\nl ac\ng i\nar chi\nc d\nyel low\nrec ently\nre ach\nà ¹\nkitch en\ndesig ned\ntr y\ng al\nrestaur ant\nat ure\nw w\nj as\nl ma\nðŁij Į\npa in\nav o\nmin ute\nsch ol\nther ap\ntic ket\nd ry\njap an\nditi ons\nter ri\nsel ves\nhapp en\nt up\nma g\ncop y\nsh er\nfree dom\nf ile\nspeci ally\ntor onto\nlo ad\ng ary\nre y\nansw er\nlo y\ncau ght\npri ze\nu ne\nfic ation\nni ger\nsy d\ntou ch\nfeat ure\njaz z\nrecor ds\nhim self\ndi sh\nro ber\nspot ted\nma ster\nwa ve\nfin als\nbu ll\nfor um\nal d\nre comm\nch a\na e\nd oo\ninst ru\ntru ly\nl g\nin k\nbro thers\nde st\nj im\nm it\nclo sed\nis on\ntri ed\ns anta\naf fe\nw an\nhor se\ng row\ncamp us\nrel ation\nnati ve\njour n\ngo v\no ct\nk it\nb ound\npart ner\nre ma\ncrow d\n! )\nc alls\nra il\nqu ali\nsolu tion\ncon test\ncon vers\nsn ap\nb ase\nin iti\nta x\ny e\nent repre\nit or\nconstru ction\nfoo d\npresent ed\nn ings\ncli mate\nk m\nmo del\nb j\nblo ck\npresent ation\ndre am\nfi x\nc alling\nbus ine\ncon gress\nunder stand\nwe b\nval ue\nï¸ı âĥ£\nmex ico\nit ely\nki m\nchar ity\nref lec\nbl an\nfl ying\nanal y\nfamil ies\nb and\nreci pe\ncelebr ation\nac cep\nar y\nto t\ng b\nintere sted\ncap tain\nâĻ ¥\nti p\nab sol\nbra z\ninve stig\no logy\nde c\ntru ck\nver ing\nc lear\ndon t\ngo tta\nad vis\nbeg ins\nma ss\nde scri\nblo ck\nk im\ndavi d\nson gs\nmemor ial\nfeat ures\nsu stain\n' .\ngra b\njo se\nv a\ncon serv\nse ts\nman chester\nfi ghting\nde gre\nag a\nin d\nsle ep\npos ition\nha ir\nsig ns\npol icy\nit o\nal ert\nst am\nsp end\nw y\nabsol ut\nd m\nanim al\nmy ster\nsuccess ful\nproble ms\nro bo\nk ay\ngar den\np d\nmay or\nd ale\nt ol\noff ers\nvis iting\nfriend ly\ntre es\noffic er\naccoun t\nke vin\nðŁij į\ngi ant\ncontin u\ncon su\ntr act\nn fl\nðŁĺ Ĭ\nh q\nb ility\na ar\ndis ney\nte en\non ed\nwh ite\ntra iler\nde dic\nal one\nabsolut ely\ndig ital\nwilli am\nin ation\ns wa\ne e\nenti re\nger man\nro ll\nh its\nco st\nst ay\nth a\nali ve\naccor ding\nco t\nliter ally\nher it\nre ti\nhaha ha\nexper i\nli kes\ng t\nste el\n__ __\nch air\nchristi an\nto wer\ndiffe rence\nm d\ntre ss\nmi d\nprin ce\nafric an\nfe der\nfoo t\ncar ri\nser ved\nr ice\nsh all\nfeat ured\nck er\nrec ru\npo e\nsen se\nni fic\ncom edy\ncont ent\nf at\npo sted\ncon tribu\ntim ate\nli ver\nmb le\ninter net\nag e\neurope an\ncl ing\ngla d\nff ic\nsc o\nak es\nel le\nter min\nton y\np ale\ncol our\nseri ous\npat ri\nmovi es\nb m\nprofessi onal\nad o\nal u\nbr inging\nf alls\nisra el\nter m\nlangu age\nbro ok\nman n\ncommun ic\ncan not\nac ti\np he\ny an\nentrepre ne\ntur key\nlog ical\nlon g\nar m\nur s\nwork ers\ning ly\ngg s\nri c\ntu al\nrecei ve\nop ens\nge ar\nsoci al\nfe et\nc king\nad ver\nfin an\nfe els\nsp la\nh r\nea ster\nbra in\nã ģ\nfi g\nle dge\nne arly\nprote ct\nma ssive\ne th\naw a\nðŁĺ ģ\ny rs\naware ness\ndefin itely\nk n\nimag ine\nk u\nsyste ms\nðŁij ı\nf as\nli k\nprovi de\nam o\ndisco ver\ninf lu\nma ker\ng az\nfit ness\nstre et\ner s\nte d\nw c\nys is\npos itive\nhel ped\nque st\nandre w\nbra d\nb in\nhang ing\nl ing\nbri ght\nse ction\nma ss\nðŁĻ Į\nfollow ers\nho sting\ntem por\nfla g\na ve\nlet ter\nk ur\nre qui\nof ten\ncry p\nsu ff\nâļ ½\nrussi an\ntreat ment\nal le\nha y\nl an\nkeep ing\nhol y\npower ful\npre dic\nfun d\ne specially\nwindo w\nje wel\nil y\nðŁĴ ľ\ngener ation\napp a\nseri ously\no d\nðŁĺĤðŁĺĤ ðŁĺĤ\ncer ti\niri sh\nðŁij Į\nmi ami\nbe th\nv ity\nse cu\nche f\ncri me\ngraph y\nma x\narti sts\nre volu\ngu ard\nspee ch\nu c\nupd ates\nfac es\nst ant\nchang ed\nrepor ts\nlow er\npe ar\nn c\nk il\nloo ked\nspe aker\ns f\nre spect\nok ay\noce an\ns itting\narchitec ture\ntra il\nse at\ni ra\nle g\njapan ese\nd am\nu lar\nsw im\npolit ics\nfinan cial\nol d\nmou th\nat temp\nde stin\nfi shing\natten tion\nme m\nchang es\ndeci ded\nreli gi\ng in\nc av\nz z\nad am\nma c\nwr ite\nbeg in\nsc ul\nal ter\nis s\nath on\nimag es\nm oo\njo ined\nðŁĺ ī\nâŀ ¡ï¸ı\npas sed\nmu sli\nh ir\nlar gest\ncam er\ncom ic\ngh ted\nrug by\nbur gh\ngg ing\nte sting\npre par\nlau gh\nal ed\nimpro ve\nbeli ev\nadv ice\nsha res\nhe art\ntur ning\ns b\nt el\ncaf e\nn es\ndani el\npat ter\nt z\nse tt\npar k\nc and\nst ick\nhapp ens\nbri an\nne west\ne pic\nad or\nki es\nwar ning\nanim als\ncusto m\nar c\ndi an\ngol d\ncor e\nt f\nc ity\npan ts\nre ality\ncon fi\nin ju\nfo x\ngu il\nk new\nâĺ º\ncor rec\nitu de\nd den\n. #\nre duc\npas s\nf on\ny a\now ner\nre turns\nn c\ne ast\nap ol\nin sur\nth o\nsi m\njuni or\nbe e\nang el\natt le\nelec tric\nhor ror\ncra sh\ne ye\npat h\nsou thern\nemplo ye\nge o\nt an\nha z\nr ally\nðŁı »\nproper ty\nwas n\nenjo yed\ngre y\ng as\nbre w\nnor thern\nhol ding\ng p\nta ke\nch art\nly n\ndr ama\nz o\npa id\nthrow back\ncu p\ndiscu ssion\ndown town\nw ill\nle w\nb is\nt ary\nbre ad\nup on\nr ate\nteach ers\nit ation\nanc ed\ncy cle\nchoo se\nd c\nir an\nco w\nda ve\nra ise\nprin cess\nfa ith\n- >\nindu stri\nsp ain\nguit ar\nfac ts\nm n\nsp en\ncour te\ngo tt\nprojec ts\nau di\no sc\npe ter\ns and\nintere st\nhapp iness\nven ue\nsol di\nsurpri se\npoten tial\nper io\ncustom er\ni i\ng ni\nmanu fac\ne co\nbro ken\nsing er\nvel s\nwal es\nhu s\nin j\nf our\ntal ent\nd ying\nmat the\nfil m\njo ining\ns ell\nj ar\nlma o\nsur ger\nbb c\nsour ces\nau stin\nni k\nchar les\nf am\nprin ci\nange l\ncas h\nlo t\no red\npla ys\npl ate\ndon e\nmemor y\nbr ings\nn ba\nsolu tions\nteach ing\ngr ace\ncir cu\nhel ps\nfoun der\nmar y\nexpl ore\nde cor\npar ts\nch o\ninte gr\nha u\nis es\npu tting\nin er\nr it\nv y\nmic hel\nblu es\nevery day\nfor ms\nbi o\nye ar\np in\nt ter\nspr ing\n) )\npo t\nal ing\nperform ing\nsh an\nplan et\nmus ical\nhead s\nit alian\nstru gg\nâĢį âĻ\nw ings\npu mp\nh h\ntr ou\na id\npri me\near th\npa int\nmon t\nam y\nbb c\nfab ulous\nfru it\nandro id\nbour ne\ncere mony\nenti al\n? ?\ndeb ate\non ing\ndra ft\nsol ar\nt x\nj am\ncor n\n!! !!!\nbro o\nmil k\npo sed\no hi\nmo vement\nb ren\npart ner\np g\net te\nar ies\nsh out\nn g\nleav ing\nt ells\nsen s\nta ste\nkel ly\nwor l\ngy m\nric h\ne gy\npi d\nma s\nâ Ĥ\ncourte sy\nfran k\nincre ase\nwr itten\npp ers\nre l\nha i\ns as\ns ound\ntt i\nw ich\nri ver\n.. .\"\na g\nfel low\nro me\nsm all\ngen cy\nic an\nlux ury\npro of\nme t\nwild life\nmom ents\nra ther\ncor ner\ncom pe\ncanadi an\nlik ely\ntherap y\nli am\neconom ic\nindi e\nrou te\nfi ght\nho pe\nse tting\nant ly\ncro ss\nfant asy\nde e\nsket ch\ncomp li\nym i\nru les\nengine ering\nfig ure\nro w\n. ,\nf w\nsyd ney\nw ou\nt ation\ndre w\nus es\nthe re\nsp read\nstruc ture\npat rick\nappa rently\nro s\nh ills\nw we\nann y\ncom mission\ndi v\nf ying\ncon sul\nanal ysis\nex i\nten nis\nvehic le\nðŁĺŃ ðŁĺŃ\nas s\nhigh ly\nop ened\nb ann\nðŁĴ Ļ\nmp h\nwi shing\nv or\nfi f\ngive away\nr r\nra y\nje ss\ng at\nic ymi\nx it\nhigh est\nyor k\npi e\ninvol ved\nhigh er\nri e\nmal ay\nint elli\ndesp ite\nche e\nsar ah\nbe an\nreco gni\nar sen\ntal ented\npas sion\nic h\nab c\nlead s\ndise ase\nv is\nse c\npre senting\nm illi\nhol e\nsho ts\nde part\nsurger y\ngov t\nb in\ndu al\ne vi\nlon ger\nev ol\nscre en\nportra it\net c\nlo se\nch at\np en\np i\nom a\ns ick\ner c\ncompan ies\nen try\nplan e\ngr y\nven e\nliver pool\npremi ere\nsha red\na red\nfil ms\nir a\nholi days\ncric ket\nici an\nv ing\n. )\nul timate\ndi vision\ncon duc\nse pt\nfor ces\nmon t\ns mart\ndisa pp\nsun shine\nin d\nb less\nma de\ncol ors\nfran k\nir on\nbott le\ns go\nm ood\nj ason\ner ic\nbir th\nte en\nrespon se\ntar get\nstate ment\nfe ar\nth el\nal um\nar ab\nbl in\ndirec tion\nste ps\ner ial\nwor ked\nat l\nðŁĴ ķ\nfel t\npol i\nscen es\nhom es\nb ell\ne at\nate ful\nt in\nl ace\nfol ks\np se\nan n\nwis dom\nfa v\nbut ter\ns r\nare as\nsm oo\nbi z\ndg es\napp o\nmo re\nthe m\neffe ct\nwindo ws\nsun ny\ncap ital\ntot ally\nc ities\ngr ant\nmb ers\ns low\nau tu\nil ities\nw ro\nri sing\nst ics\nviol ence\ni gh\nqu ot\nh it\nt c\nherit age\nbu ff\nne s\nz ar\nden tial\nex ac\ned ge\nde ep\naren a\nbe came\nbenef its\nmar ks\nmb er\na z\nam es\npre ci\ndra gon\nre g\nd ings\ndo s\nðŁĴ ª\nn el\ns ity\nme al\ndi st\nleg end\npur chase\npic al\nst ick\nf at\ndu ba\nprofe ss\ncar to\npro f\ncoun tries\nrespon si\nse qu\nfa b\ntribu te\nhon ored\nprac tic\npur ple\nan ton\npa red\nt ough\nsumm er\nenviron ment\ns ons\nðŁĻ ı\nm ps\ngi es\nher oes\nt elling\nhen ry\nf en\nknow ledge\nĢ ï¸ı\nf r\nne g\nu re\nac king\nhear ts\ns oo\nhol lywood\nju mp\nsau ce\nschedu le\ntur n\nyo ga\ncre ating\nc ket\ncre ek\nâ Ń\ncustom ers\nma dri\ngu l\nasse mb\nmoun t\nc ell\nto p\nst al\ndav is\nt wi\nsig n\npremi er\niti ons\nhe aring\nun k\npati ents\napp ear\nheav en\nal ty\ndoc tor\na e\nplat form\nje ff\nðŁĵ ·\nregi onal\nbi d\nbox ing\nex ten\nor ity\na w\nw ise\nil le\nsever al\nbi e\ns itu\nsy ria\nâľ ħ\nremin der\nenter tain\nli on\npart ners\nin n\nph ar\nf au\npl s\nexpe cted\nsug ar\ndeci sion\ns b\nch ron\nassoci ation\nleav es\nvis ited\nsh ap\nðŁĴ ĸ\nfur ther\nh ann\nw i\nrun s\nl er\nfun ding\nfil led\n.. ....\ntin y\nhan g\nor g\nco ol\nse min\nðŁı Ĩ\nspon s\nnav y\nsa int\ndru g\nd al\nr oun\nco vered\ntra ditional\ninvest ment\nde te\nal ism\nf low\nn is\nsun rise\nfe at\nf ted\nwe ird\nje re\nve gan\nmedic ine\nan o\nac cu\ndeli very\ntemp le\nchang ing\nwil son\nphili pp\nre fe\nn d\nis er\ng ay\nr and\nati ves\nt ely\np and\nintelli g\ng are\nam bas\nde mon\ncommit tee\nstrate gy\nrefu ge\nbud get\nprote c\npi er\nex press\nnom in\neconom y\nal low\nic on\ngal ax\no h\nindi vi\ndem and\nvir gin\nlu ke\nali sts\nman i\ns mi\nju dge\nent y\nmic hi\nresul t\nam ed\nspe aks\n' ,\nhou ston\nsh in\nb ing\nfl y\nch em\nau to\nv as\nge t\nar m\nthank s\nd in\ngan g\nx x\nsi on\nloc ated\np l\njo sh\nin fo\njo ins\nadver ti\not d\nel d\nsi e\nre asons\nv ent\nðŁĩºðŁĩ ¸\nâ ł\nconvers ation\nstu di\nðŁĶ¥ ðŁĶ¥\ngo s\ns ounds\nun it\nmu sc\nge l\nack ed\npac i\nco s\nde re\nu u\na o\nla m\ninspir ing\nar ms\ntw are\nmat ters\nad dic\ndu de\nex t\ncri sis\nb ath\nme et\nsing h\nexpe ct\ndel hi\nresc ue\nwor st\nau g\nshi pping\nser ving\nst o\ndar k\nac es\nhistor ic\nlandsc ape\ndesig ner\nb illion\ngr ateful\nwa ke\ne ve\nm iller\nhou sing\ndy nam\nis co\nbe ha\nsh op\npr ou\ne as\na sia\ne ding\nk on\ndepart ment\naw ar\nmar ine\nin ci\nphotograph er\nta pe\nlo go\nr ings\nd it\n-- --\nvin yl\nw c\nvo ting\nse ven\nambas sad\ndal las\nt u\ncom ment\nk ra\nb les\nw ag\nu d\nau dio\nstri ke\noffici al\no ts\nme tho\nto ols\nra di\nal an\nhun t\nwat ched\na ke\nfa ke\ndrin king\nmer ry\nm l\nb day\nri o\nni ke\nc ant\nre pe\nco stu\nmur der\nak ers\nch ers\nou ts\nbeg inning\nso s\nad es\nn in\nnot es\nwro te\nsol o\nc i\nli ghting\nur ban\nbre xit\natt end\nshir ts\npla yo\nac tress\npl ic\nstand ard\nquot es\npar ade\nanci ent\n ©\ntur ing\nre e\npri mary\nfla sh\nciti z\nmat es\nste in\nz i\nclin ton\nsk in\ngen e\nhu m\ng ar\nt le\ny i\nfo cu\nde an\npl ants\ncy ber\nb u\nom e\nho p\nad dress\nti x\ngi fts\nrelation ship\nsub scri\nfe ed\nexac tly\nhaw ks\nex o\nstre ss\ns n\narre sted\nan e\nsof tware\nz ero\nthe me\nmu mb\nim migr\nmi a\nmake up\nple asure\nuni vers\nhar b\neng ine\nap er\nr in\nbr a\ninstitu te\nle ather\nal th\nsing ing\nco s\ngh ty\nme as\nst ic\nsi de\ninsur ance\nco t\npit ch\nmoun tains\ncri min\nsu pre\nvalent ine\nat er\nwou ldn\nsc ale\nrel ated\nre gar\nstar tup\npack ed\nmi ke\nweek ly\np ts\ncoun t\nha r\ngott en\nmin d\nber lin\ncon ditions\nswit ch\ncor n\nsa ve\ng li\nemer gency\ntun ed\nsto ck\ndiscu ssing\nevery body\ns day\nwhe ther\nwrest ling\nec es\ngen der\nch en\nðŁij Ģ\nmadri d\nmar athon\ne gg\ni er\nth x\nas king\nkore a\nwol f\nay a\ng m\ng au\nat ory\nv r\ngra ss\nk illing\nb ble\nur o\nun i\ne th\nsh ore\nth en\nre ale\nbot tom\nex erc\nk ar\nor ies\nad ri\nsan ds\nse x\n. '\nvolunte ers\nper form\npar liam\ninclu de\ndeli ghted\nexecu tive\nfu el\nkis s\nã ħ\nchar ge\nh u\nca kes\nve t\ng lu\nagre e\npr ices\nn au\nh l\ng ru\nra j\nstreng th\nb ic\nsp ending\nal es\nav en\nb last\n: (\nyo f\nnor mal\nsi x\nqu ick\nse a\nd aw\nmee ts\nlo vers\nupd ated\npo tat\ncomple ted\ncoo k\nopportun ities\np ure\norgan ic\ntem per\nc am\navo id\npar king\nduba i\nand o\ndi stri\nto y\ncomple tely\ndon ald\ntri al\nbas s\nb oun\nback ground\nv as\nmar vel\nlu m\nru s\nt ool\ncom missi\nthrow back\nfin ding\nis lam\n! ?\nst op\ne vil\nor al\nresi dents\ni denti\no ak\nðŁİ ¶\nl il\nspan ish\nchap ter\nsto pped\ndirec t\nho sted\npic ked\nlab our\nlew is\ndefen se\nà ®\nhealth care\nwh is\nmat h\npe ak\nra ised\nfi x\nbu ll\nth ir\nchel sea\nfol k\ntr e\ncan di\npau l\nei ther\nad am\npoe try\njewel ry\nðŁ ¦\npr ay\nØ §\ng c\no z\nwi shes\nfore ign\nsun g\nlear ned\nen e\nn ing\nmicha el\nillu stration\nlegend ary\nw av\nb au\nðŁļ ¨\ncal end\nstre ets\nâ Ĩ\nmon ster\nbu ck\ng r\nscho ol\nba th\nwa ste\nne ck\nha wa\nbe ach\nre plac\njec t\non er\nfac tory\ncoun t\nðŁĵ ¸\nmor gan\nder ing\nse an\nsteph en\nde p\nno vel\nvide os\nic al\npress ure\narsen al\nex pre\nir s\ntren ding\nss a\nfla sh\nre sear\nthr ough\nprofess or\nscul p\nto s\ngg ed\nmm a\nbe e\na pe\nhun ter\nam i\nhe i\npla stic\nbu cks\nuni verse\nle gen\nniger ia\nple ased\nri s\nthin ks\nautu mn\ni ds\nd is\nanth ony\nðŁı ½\nak ed\ngla sses\nfin ance\nz er\nk as\ncon tract\nnu mbers\nsh aw\npartner ship\nt il\nlaun ched\ns al\nvictor ia\ntheat er\nusu al\nnam es\nperio d\neli za\ni th\nbar cel\nro cks\nbag s\nmat e\ndistri bu\nj on\ndi ffic\nali zed\ncur ren\nsco red\nb ha\ndu blin\nro se\nin ted\nsoli d\nbeha vi\nwal ker\nsimp ly\ngarden s\nhead ed\nin i\nohi o\nwe ap\nf o\ngl en\ne state\nran dom\nth under\nthr u\nk ill\njac ket\nit i\nentertain ment\nthanks giving\nent al\nen coura\nel o\na ther\ntan k\nhigh lights\nf ting\nru le\nmodel s\nbor der\nbj p\nhus band\nin done\nken ya\nbe ars\nal o\nn inten\npi x\nstr o\nor ders\nsal ad\nro ads\nn or\nl ation\nsop hi\nðŁı ¼\npi eces\nb one\nmin s\ninclu des\nnu tr\nphi l\ns ent\nfun dra\nga in\nbor ough\nn ad\nmon day\nactiv ity\nit ems\nbe coming\nken ne\nde tro\ncar di\ngue sts\nu x\nworld wide\nsever e\nnew s\nthank ful\nfic tion\nve ge\nm all\nsi an\ner al\ninj ury\nle e\nmen u\ndanc ing\nscot ti\nexam ple\n( #\nna i\nstudi os\nba i\nðŁĴ Ľ\nj av\ndiam ond\nvin ce\nric k\nprote ction\nlin col\ncham ps\nappro ach\nd ar\nm ile\nclou ds\nje ff\nin fin\nl ers\np les\npe ace\ngo p\nâĻ ¡\ntech n\nstr a\na verage\nef fort\nintroduc ing\ndi versity\naustr alian\nam p\nboo st\ns ke\npati ent\nappreci ate\nici ans\npu r\nf ell\nwoo ds\nillu str\nðŁ ĸ\nag ency\nac tions\nbrit ain\nunder way\nse attle\nel and\nag o\nf ill\nstre aming\npro test\nchalleng es\nky o\net sy\ncoo king\nexper t\nru ss\nrain bow\ncommer cial\nsp in\nbe ats\nc ry\nval u\nel i\nth row\ngr ams\nle vels\nmichi gan\nc ad\nador able\nconst itu\nw s\npu b\nmid night\nth at\nnet fli\nbraz il\ndie go\nregu lar\njo y\nâĤ ¬\nli qu\nea stern\nk ni\nfl at\nn p\nbro wn\nw er\nse y\ntt ers\nac ting\nv anc\ncy cling\nprogram me\nra w\ncomple x\ntat too\nthrowback thursday\nse ssions\nro oms\nsi ght\nspeci es\nbom b\nlau gh\nke eps\nmo on\noffic ers\ncon ver\nt r\nha sh\nt ack\nri ous\nad ap\na j\nreco gn\nex po\nsug ge\nconfir med\nrol ling\ndre ssing\nic t\nfri day\nph ones\nri dge\ncon cept\nro y\nke ys\nef for\nc ate\nk ne\nev en\nl ay\ncommun ities\nmo d\nn az\nevery where\nal ab\nbit coin\nban ks\nout door\nfeder al\nsto res\nh p\nc al\nm ely\nsig nific\nbe ar\nre public\nclo ser\nal lah\npic k\nx d\npal ace\nch ill\nb am\ner ous\nun a\nal len\nout standing\nolym pic\nsupp ly\nfi gu\nv au\nl p\nchar lie\nun es\n> >>\nlegen ds\nici al\nco ast\nbenef it\nmul ti\nf its\nfar mers\nam ount\nsi sters\nhar ve\nhon ey\nque en\nb ers\npl ann\nâŃ IJ\nm u\nbarcel ona\nal ber\nstat us\nre main\nex tra\nc andy\nvi ous\nâľ Į\no v\nwarri ors\n-- >\nju mp\nam ar\nx mas\nstu dies\ni ors\nk or\ndon ate\npre p\nfi sh\nim a\npain ted\nad mini\nco splay\nspor ts\ndro ps\nfi ghter\nevi dence\nðŁĴ ª\nla ke\nro b\ncine ma\npro file\nà ±\nstan ds\nleg acy\nsh ape\nro of\nci vil\ni ans\nsy l\nsh am\nvo ted\nre tail\nph illi\nli sted\ndu ty\nn b\nth es\nf are\nau ction\nffici al\nstor ms\nd p\nl oun\nsh ops\nal y\nani me\nmulti ple\nðŁĺį ðŁĺį\npsy cho\nje an\nap art\ncandi date\ngg y\ncon f\njose ph\nw ick\nme at\nfr ame\nc l\nfor got\nph y\nf ing\nli ed\nre p\nse ed\nf all\nu fc\nnu t\nlin d\nmo de\nfiel ds\nen ce\ns ley\nðŁ¤ Ķ\nch ill\nfollow ed\nannoun ces\ncor ru\ntro phy\nthem selves\nac le\nal du\nk ong\nl on\ns v\nbro ke\nander son\nta i\nstor y\ntempor ary\nactiv ities\nk ati\nari z\ncry stal\nspo ke\nextre mely\ntra ding\nðŁĴ ļ\nà ¼\nin ch\ned in\nout fit\nequ ip\nma di\nform ed\nbe ef\npo p\nti ger\nthis day\nti red\nneigh b\nre tro\nis a\nun t\nt as\nkan sas\nde st\nsecon ds\nta y\nhur ric\no u\ngalax y\ndad dy\nbro w\nbur ger\nen ced\nde sk\nac cur\nsecre tary\nel ite\nk ab\nch in\ntouri sm\nbud dy\nici de\ndre ssed\nu d\nvac ation\nche ers\ncom for\ncharac ters\nj et\nbu ying\nl ins\nn ap\nreale state\nli e\naf c\ni ii\nf ame\nn r\nb at\nag ent\nma kers\nâĢ ¼\nsec tor\nop ti\nle on\ndi et\npra yer\nhi p\nmi r\nle x\nbr y\nan a\npas sing\nw en\nreco very\nak i\npo pul\nres ort\nmar ia\nstu ck\nread s\nti er\nperfe c\nnetfli x\np oo\ncham p\no c\nre duce\nwe red\ncomm ents\ncla im\nacci dent\ns ag\nh ack\nsal t\nkin da\nk iller\ni os\nz y\nex change\nlec ture\neng er\nic king\nt au\nreve als\npri son\nz om\ngh an\nu l\njour nal\ni ot\ntr in\njon a\ngovern or\ncap e\nquar ter\nspec tive\nimpre ssive\nbab ies\nt x\nm ill\no y\nhar ri\njo int\nsu e\ncollabor ation\ntren d\nrevolu tion\nre new\nalum ni\nge tt\nsh ell\nsun day\nent u\nni c\ndonald trump\nblock chain\npaci fic\nexpla ins\nsp y\nad voc\npar adi\nto f\nstar ring\np av\nfe ed\nbr ac\nsmo ke\nham p\ny am\nto kyo\nsi mon\nd h\ne ffici\nphys ical\nn j\nell i\ns low\ngradu ate\nameric ans\nti fy\nf red\nap ore\nfin ds\nrob in\nwe t\nnot ice\nse mi\nun ve\nk om\npil ot\nscre ening\nda ily\nðŁĴ Ĺ\nroy al\nsp a\nvo tes\nn ag\nwh ate\natt ending\nexper im\nad dition\nk ate\nsto l\nm ali\nfoo t\nchri st\nch an\nde e\nlic en\nglo bal\nmo ore\nti a\nbri gh\nmyster y\ny ay\nâĿ¤ï¸ı âĿ¤ï¸ı\ncre ati\nme chan\nclo ck\ndi c\nâĢ Ķ\npp er\nal ph\nthrough out\nal low\nre sources\nselec tion\nham il\nbb q\naa aa\nvirgin ia\ndis ney\nen g\nso red\ndrin ks\nf ancy\nconsi der\nend a\njan e\nhand made\ndu l\non tari\ni us\ns ville\ncolor ado\nwhate ver\nwhe el\npromis e\nne ver\ndesig ns\nab ly\nsex ual\nvanc ou\nat i\ncon vention\ncul tural\nsing apore\npro mo\nload ed\ngla sgo\npp l\nn oo\nke e\nste m\nmen tion\ni do\ncru ise\nri ding\nbe comes\nbe y\nâļ½ ï¸ı\ntw in\ndedic ated\nna sh\nde si\nwork out\njen ni\ni v\ngrou ps\nrela x\npho eni\nli ft\nmix ed\nm ck\np c\nmu st\nme tro\nci es\ny ar\na im\nang er\ni e\nrec y\nmarri ed\ndro pped\neng ag\nle st\nambassad or\nop h\nde s\nw ick\nassi stant\nnat ur\nfa il\nl td\nshor t\nk ap\nsha w\nbi gger\nrema ins\ncrit ical\nsur vey\nco verage\ner son\nwin d\nn b\nbil ly\nlet es\nac ts\njim my\nat lan\nal and\nt c\nimport ance\ndam age\nf g\nstor age\ntw t\nbon d\nbal ance\ncr ying\npu ppy\nvo te\npu sh\nðŁĴ ľ\npol y\nme l\nlon don\nterr ori\neffec tive\ncorpor ate\natl anta\njac o\nnas a\ngre ek\nsen ate\ni sh\nev a\nintellig ence\neffor ts\nal co\nk un\nh all\ndi ag\nclaim s\nfir st\nh b\nba e\nv ul\npu ll\n °\nse par\nspe ed\nvic ti\non thisday\naudi ence\nr ates\nte ach\nfil ming\nbu sh\nson g\ny um\nbr un\nra ine\naw a\npar ks\nð Ŀ\nra bb\nra ch\nra id\nreach ed\nra il\nmo ves\nselec ted\nfr i\nra ising\nom y\nst ones\nsu k\nfranc isco\ncas es\ncap it\ncon fu\nw tf\npo ke\nequip ment\ngre g\ness ential\noff ering\nne x\npi es\nbe c\ncre ation\nchair man\ncro wn\nw al\njohn ny\nshi ft\nne ck\nban g\nbir d\nðŁĺ ı\ndu ck\nre serve\nde pu\nma sters\nover all\nno tic\nju ice\nsne ak\nche er\ncla sses\neag les\nn ca\ncar pet\nci vil\ncoach es\nhar ris\nu ps\nb alls\ndec or\nmar tin\nro s\nv ice\nannoun cement\nwho se\nti gers\nste red\nc ts\ndr am\nste el\nyoun g\ninst all\nsupp o\nrecor ding\nde ck\nse ats\nl der\nang le\nbo t\nsty les\nelec tions\nfor tun\nn ab\nbut ter\nari an\nka sh\nin ner\nou red\nbe ast\nwe i\nic onic\nexper ts\nne cess\nb eng\njam es\nli a\ngre ece\nðŁĵ ·\nðŁĺ ģ\ngood bye\nm itch\ntw ice\nmumb ai\nste am\nru sh\nmed al\nne tt\nfashi on\nt ar\nr s\nsav ing\nric ul\nl m\nsleep ing\nbrook lyn\nmis s\nsen ding\ndisco vered\nsp here\nof theday\nk icks\nmissi ons\nw right\ner n\nght ly\ni ous\nmel bourne\nstar tu\nmo ved\ncar ry\nd ak\nag ues\nbel gi\ne ma\nway ne\ndo t\ner ie\npe l\nit unes\nmatthe w\nno body\nest ab\ncal m\nwin ds\nlu c\nprep are\ntren ds\nexerc ise\nadv ant\nðŁĴ ¯\nathle tics\napp s\nc tions\nadv ance\nlaun ches\nlitt le\nreal donaldtrump\neliza beth\ncarol ina\nhu b\nhi dden\nn w\nus er\npol l\ngreat er\nmo st\nf ed\np at\nlife style\ns ati\nsco res\nmarri age\nl r\naven ue\nde serve\nri f\nðŁ Ĺ\nwat ch\nchampion ships\ngr ay\nen ni\ncot ton\ng om\nwhe re\npack age\nsu m\nab solu\nnew ly\nfoo ds\nty ler\nassemb ly\nmusli m\nban k\nre memb\nop tions\nproduc er\nland o\nfun ds\nu pper\nshad ow\npro gre\nco p\ning e\nleg s\ndetro it\nhill ary\njo se\ngi ants\nsou p\nsustain able\nt us\nclo thes\nroc king\nn z\nmin ne\nmat eri\nbru ce\near t\nca sting\nindepend ent\nthou sands\nta h\nde cl\nveter ans\nli ons\nwra p\nâĢ ¦\nde ss\nbl ing\nst ine\ne ggs\no on\nclo sing\nz ay\nat t\nbac on\nfa il\nariz ona\nde pre\ngho st\nnew sp\nw ers\nvi p\nli ked\nid ent\nvolunte er\nad ult\npu pp\ncir cle\nmat erial\ndegre e\ngro wn\nboo m\ncalend ar\nsu r\nvie wing\nath letes\nch and\nre ll\nasi an\nen tr\nvol ley\nvicti ms\nbo dy\nm ama\ntrans fer\nge ek\nin dic\nsav ed\nma i\ng ent\nit s\nloun ge\nk ol\nthe ory\nsitu ation\nis lands\nar th\nz oo\nfloo d\nvi ously\nshow ed\nparliam ent\nch ev\nel ine\nat trac\nab ad\nta il\nh rs\nlu s\npor tu\ngor y\nprovi des\nto ys\nde ath\nin fe\nan ce\ng le\nli am\nlo ver\nhu d\ndv d\nreve aled\ng w\nre ment\nca the\nl ying\nra dio\nder by\nstor s\nche mi\nhosp it\nâľ ¨\n' :\nilo ve\nle mon\nre public\ns ni\nne ss\ndo or\nre action\npre gn\nfla v\nschol ar\nspo tify\nis ation\nvis ual\naw are\nspon sored\njo ke\nless ons\nleg is\nlo ck\nsi mil\nðŁĺ ĭ\nkin d\nla y\nma h\nho ping\nvancou ver\nas er\nclean ing\ngal a\nthre at\nla p\nach e\nro mance\nex pen\nre post\nz am\ne pi\nmir ror\no ak\nad ul\nbat man\ns lu\nl c\nvie wed\nre views\nd ates\nindone sia\nacti vi\noff en\nlea f\ni si\nag ricul\ncostu me\ns ites\nspir itu\nappear ance\nir y\nst air\napplic ation\nspec tac\nic ity\nski es\nhand le\npun k\nparadi se\nt n\nde al\nprovi ding\ndo c\nrecei ving\nbre w\nmicro soft\nà ¶\nfer r\nme tro\nth ail\ny um\ncar ter\nà ¡\ngent le\nbre aks\ncoo per\nshow case\ncu tting\negy pt\nbab y\nsemin ar\ngl ori\nss on\nfa ve\nre hear\nlo tte\nla dy\nal as\npre p\ndeli vered\nnu clear\nir o\nengag ement\nat ta\ncon ven\nz an\ngl ory\nhol ds\nbusine sses\nstr ange\nsch e\nit self\ngra d\nmar kets\nf alling\nst ats\nge on\nbu dd\nli s\nshe et\nthi si\nco lo\ndeser t\nregi stration\nig n\nexpla in\ninter ior\nla ws\nwrit ers\nspr ings\nk r\nfri ed\nblo om\ninf ra\na o\ncre d\npa st\nline up\nbo o\nbre a\nboo ts\ncelebr ity\natt acks\nbro ok\nev es\nex cu\ncher ry\noo p\nfas cin\nboy friend\nse as\nn ine\neffec ts\npo wered\nk ha\nðŁĺ Ģ\nsh out\ncon dition\ni j\nher o\nenter pri\nwin ter\napplic ations\nsho e\ng el\nbatt le\npro grams\nw art\nðŁĴ ¥\nra p\nho l\ndang erous\ndi a\ncoun ter\nric s\ni or\nk night\nco at\nemo tional\nat ures\nd as\nwhe el\nfore cast\ntran sport\nglasgo w\nking dom\nprepar ing\nim medi\nff in\nawar ded\nprin ting\nro man\nfight ers\nany more\nbel t\np ine\nwin e\nx i\nemploye es\nlogi es\nal led\nde mo\nbirth day\nange les\nlo g\ndri vers\nneck lace\nk ath\ns it\nathle te\nef s\ns burg\npur pose\nresi stance\nrele ases\nt is\nvari ous\ndeli ver\nch al\ns anc\nopp o\ncra w\nneu ro\ndr a\nsuppor ters\nsna p\ndiffic ult\nswe ar\nlogi st\npa th\nattemp t\nà ¥\nswim ming\nste ve\nhur t\ninclu ded\nb ap\nwa re\nðŁĴ ĭ\nend ers\nja ke\nle eds\ncli mb\nl b\nim ple\nli sa\nclo thing\nðŁĺ İ\nd t\ncom pla\nsw ing\nstra w\nv als\nk le\nus ers\nstor m\ncu ts\nontari o\np an\nhand some\ni ow\nar gu\nchec king\nscotti sh\nĶ ï¸ı\nsi er\nem ma\npo d\npatter n\nde sh\nen h\ned ward\nt ing\nk h\nhal f\nlincol n\nmo ther\nal leg\nr c\nvolley ball\nd n\ng ay\nall y\nle ton\ngro ve\nl oud\nadv anced\nre spec\ncli ent\nsupre me\nthail and\nho w\ngi g\nto i\ndo t\ndol lar\nðŁij ĩ\np it\nr b\nh n\nproduc ed\ngg ers\nâĨ Ĵ\nml b\ncan vas\nfin eart\nus d\nin the\np son\nactu al\ns l\nt b\nip ad\nen sure\nu mb\nw d\nsk a\nmar s\nk end\nf eli\nth ing\ncount down\nabsolu te\nr out\ndra l\np y\ninju red\nmin t\nhun ting\nmm er\ns age\nli gh\nac ity\nex pan\nmur ray\nar o\nsec ure\nfour th\neag le\nreli ef\nst akes\nindustri al\nclar k\nunder standing\nsee m\npl enty\nsil ver\ncla u\nthre at\nsa il\npro duce\nab str\nis is\nb r\neng ers\nwor ry\nbie ber\ns j\njust in\nreali ze\nky le\nesp n\nfil ter\ns ch\nty pes\ngame dev\nd ing\ntwit ter\nsoldi ers\np om\ncar bon\ny ards\nchild hood\nri ed\nke l\nele ph\nt ons\nkey note\nqui et\nwi re\npo sting\nis sa\nrepre senting\nbac ks\nalex ander\ncelebr ates\nta ining\n| |\nch or\nesc ape\npe ek\nti ves\nfiel d\nssi e\nim pac\nspons or\nr c\nwe dd\ncann ab\nsi des\ntrac ks\ncom par\ncon trac\ntechn ical\nbi ble\nexpl oring\nsh are\ntra v\nn ate\nill o\nsc ru\nm ingham\ngun s\nof the\nsh ame\nse es\nca tho\nac cess\nce l\nrepor ted\n »\nmari o\np ad\nhope fully\nou se\ny on\ndisapp o\nol o\np itt\npa c\nga p\ncru sh\ns g\nk le\nge m\nemp ire\ndir ty\na is\navi ation\nze aland\nfac ing\nhigh way\nd anny\nspi der\not ta\nðŁĺ Ħ\nw y\ncol ours\nin fl\nco sts\nolym pics\nau s\nh m\nho ward\npas ses\nlau ren\nmu sh\nop in\nr ho\ndisc ount\noper ation\nem ily\nmm m\ncham ber\nd il\nto yo\nshi p\nsam u\npic tured\nun ic\npo l\nkeep er\ncarto on\nst en\nig nor\nn ations\nn l\nta sting\ndeta il\noffici als\nmo tor\nfranc is\ned itor\nðŁij ĩ\npe ts\nrang ers\nt g\nr n\nw ri\nnic hol\ni se\nspo ts\nani e\nchec k\ntri ple\nku mar\nspe akers\nic ing\npre pared\nab use\nfriend ship\nmon th\nswi m\nair e\nsc ent\nhamil ton\nindi an\nj es\nyum my\nte ars\nda wn\ni zed\nworl ds\nðŁ ķ\nb illi\nst one\nn hs\nba sic\np or\nst le\nir on\nol der\ncle vel\ne ing\nðŁĺįðŁĺį ðŁĺį\nprin ts\nfir m\nair craft\nfin est\ndevel op\naar on\nt z\ngra ham\nown ers\nfo li\nless on\nqu es\nbab e\ncra ft\nph en\nju n\nbir mingham\nv ine\nll er\ni an\nfineart america\nevol u\nst ab\nim per\nwar d\ncom ic\nwi z\ninv ited\ndu ke\nmat ch\npor ts\nro ger\ndiag no\nke pt\nte st\nvis u\nr hy\nso c\nto x\nb aker\nsur face\nco vers\nman s\nb its\nx box\nff le\nn an\ngar d\nh art\nwat ers\nv illa\nre tro\nlight ning\ncatho lic\ndemocr acy\nneigh bor\npen n\ncr an\njona than\nla ura\nvi bes\nsu b\ncoach ing\nclear ly\nuk raine\nbra ve\ncommit ment\nt all\nmar t\nra p\nmo di\nsco tt\nbro s\nshow er\nðŁı ¾\nâĺº ï¸ı\ncou sin\nappro ach\nbr e\ncom pos\nhil ari\nphil ly\ng ad\nquick ly\nri an\nt m\nvir tual\nhou ses\nk t\nphoeni x\nw ire\nff y\nb unch\nanc ing\ntal e\nsnap chat\nstar ter\nh t\nk icking\nap art\nth y\n) !\nblo gger\nit z\ncom fort\nang els\nw ash\n\" :\nar gent\nre quest\nhon est\nmi ghty\nbo bby\nk g\nro l\nthou se\nex po\nh c\ntab les\nmag ical\npo sts\nde m\nn w\nor lando\nab er\n* **\nðŁĺ ľ\nenviron mental\ntrans formation\nmi le\nw ic\nhir ing\nma ine\nbo ar\nr ying\nti s\nnit ure\ntwee ted\nanton io\nopin ion\nfin ale\ndi y\nf is\nth in\ntrou ble\nle go\nfi les\nqu art\nsp a\ncurren cy\ncli mate\nfan art\nrail way\nsp ace\nban ds\ndani el\nmo tion\nl eng\nhol der\noc cu\nmar ie\ncathe dral\nbu zz\nbi es\nnas car\nbm w\nbat tery\nchar lotte\ndoc tor\nzz le\nse ven\nin san\nd dy\nst en\nlab or\nthr illed\nse ren\ndocu mentary\nwav es\ncer tain\ncan did\nallow ed\nninten do\nstar wars\nta p\nhome made\nd les\nther ing\nbre e\nemp ty\npi ano\npos iti\ncoun try\npor k\npu ts\nper ry\nm atic\nspot light\nti st\nor ities\nwe alth\nc p\nbar bar\ncommit ted\nas sau\npro fit\ne ight\nhu l\nfini shing\nrun ner\nss o\ninsp ec\nchar ged\nchrist op\nlo sing\nco al\nho o\nele v\nde le\nmo ham\ndon ation\nc able\nclin ic\nj in\nmanag ed\nter ing\nâ ¬\nur ban\ndepu ty\nbb er\nbur n\nacade mic\no tt\nsta ke\nit er\nsto wn\nack er\nadvent ures\nad ams\ngre g\npro m\nvo l\nac qu\ncon gre\npa int\ncitiz ens\nc all\naf ford\nv c\nas ks\nthe tic\nindepend ence\nâ Ľ\nh itting\nbl on\nfu ture\nâ ı\nin no\ngen e\nbo ards\ndi stance\nse t\nre mem\nth al\npre vent\nl ang\nob jec\nsu sp\nmat t\nin duc\nbor o\npi one\nre di\nvir tu\nprin ted\nsco pe\nshar k\nsuc ce\na stron\nil legal\nj ag\nc ting\nine e\nat o\nrob in\nnutr ition\nb f\ndu tch\nb n\nfur niture\nfor gotten\nat ar\nru p\nhy per\nbran ch\ncommunic ation\ndegre es\non ia\nun cle\npromo te\nor che\nwi i\nj s\nbut ton\nma jor\nc bs\nbri stol\npremi um\nordin ary\ne dit\nm g\nwe ed\nst even\n: '\ngu s\nte s\ncap tured\ndru gs\ndo w\nwr ites\nbi shop\nwhe els\nali zation\ndisco very\nw r\nrach el\nne il\nhy dr\ncu test\nentreprene ur\nkore an\nore gon\nul ty\nperfec tly\nsuppor ted\nhistor ical\nt wins\nell y\nwe l\nde vil\nin come\nscienti sts\nde leg\nh en\non i\nic ed\ngi o\ncur ry\nreve al\ne g\nbuff alo\nn ol\nop era\ncamer on\nhaha haha\nj ab\ngradu ation\ncra ig\nr al\ni f\norgani zation\nle ge\ng ang\nsu d\nedin burgh\nl ack\nfli es\ng ate\nthr ones\nq b\nthe real\ne leg\npp in\nc les\njam ie\ntn am\ncryp to\nou l\np ages\na se\nroo ts\nstu pid\na did\nboo t\nprote in\ns ap\nsi um\nsu s\nend or\nfun ction\ndon t\nen na\nch y\nsqu e\nwor ker\nm tv\ne a\nk an\nðŁĴ ļ\nmu s\nprofessi on\nt to\noper ations\nal lo\nc tor\ninv ite\nsc and\nou th\nz im\nlin ks\ncli ents\nsam sung\ndiscu sses\nn ell\nul tra\nsome where\nste wart\nine t\nde z\nb out\nfac tor\nti an\ntr ans\njere my\nd b\nðŁĩ ¬\nor n\ndevelop ing\nspo l\ncoo per\nma u\nrememb ering\ntre k\nfamil y\nsen iors\nfo ster\natt ended\nw ing\ntrans form\nele mentary\nhor iz\nli sting\nmalay sia\nit ch\nwarri or\nphilipp ines\nruss ell\nm end\niniti ative\ncre ep\nto ps\nbr iti\na ur\nshar p\nadverti sing\nug ly\nachi ev\nmateri als\nbu g\ndev ice\nbon us\nfac ility\ncol e\nnh l\ny as\nplann ed\npol e\nexcell ence\ntr ick\ncon fl\nr p\nachi eve\nlo an\nswa g\njess ica\nho we\np our\nsc u\nz oo\nr ated\ndre sses\nre bel\nmex ican\nco ordin\nme ss\natlan tic\nt l\nosc ar\nwal ks\nphar mac\ninvestig ation\n... #\ncc i\neas ily\nmonday motivation\ny ment\nau ti\nfor ced\nar med\ncolle agues\npap ers\npro per\nsha ke\nbu c\nle an\nexhi bit\ne vement\nco tt\nbi z\nsp er\nk ent\nsw an\n/ @\ngirl friend\nhaw k\nâĺ Ģï¸ı\nmon o\nðŁĴ Ľ\nstat ue\nðŁĺ ³\nra s\nte eth\npreci ous\nt ile\np am\nswi ft\nv ali\nno se\ndr unk\nexperi ences\ncome back\ngen ius\nwor se\nsh ef\nra d\ned it\nhon our\nau spol\nlar ry\nh ire\ngor don\nachi evement\n.... ....\nsu icide\nalter native\nsu p\nsur roun\nsha ke\nke ith\npe pper\ntur k\ncrimin al\nbe ck\nsu m\nw alls\ncn n\nan tic\nof fe\ncol li\nwin es\nhigh light\nhawa ii\nemb ar\nl fc\nðŁĩ ®\nm v\n> >\nat mo\nwor d\ncar l\nshout out\nbre wing\nì Ŀ\ndo f\ns ic\nhot test\ncol on\nhh h\nshu t\nlow ing\nvolu me\napart ment\nagre ement\nde stro\nwe e\nreligi ous\niow a\nro d\nland ing\nre present\nðŁĵ· :\nla s\nusu ally\nh l\nc ac\nsal v\nal ong\nlaugh ing\nbe ans\nremin ds\npha se\nsome body\nma sk\nran ked\ndest roy\nsc i\nâĢ¼ ï¸ı\ngab ri\nle o\nro a\nfa iled\nsi l\nrefuge es\nre vi\nr ing\nber ries\ncoo kies\ny y\nconserv ation\nsh ab\nhuman s\nde termin\na in\nni all\nas su\nmb a\nfro m\nextre me\nvic es\ncommer ce\nght ful\nor dered\nsuppor ts\nre cap\nv or\ndro pping\ncorrec t\npay ing\nmean ing\nn j\nqui z\n\" #\nbusine ss\nðŁĩ® ðŁĩ\nindi gen\ndu st\nbox es\nbl ind\nx xx\nzz y\nðŁĩ¬ ðŁĩ\nss els\ns ant\ndd le\nhilari ous\ndesig n\nwonder ing\nvehic les\nk re\nju d\nrece ption\npar ker\nà Ń\npri vi\nhy dro\nsof tball\npol lu\nlo cked\nba h\ne ar\nscri pt\ndi vi\nbr ace\ngeor ge\nthe ast\nbel o\nj al\ntion ary\ndent al\nroc ket\npur ch\nsh ak\nmanufac turing\ne z\nit is\ncon cep\ntb all\nch s\ndirec ted\npra yers\noo k\nphil os\nvari ety\nche ss\nser ver\ng and\nbal ti\nðŁĵ ¸\nsel y\ncru z\nspectac ular\nbur ning\nre present\ni z\nt one\nmer ce\nh ell\nbed room\nestab li\nbo l\ncom mon\nãĥ »\nab or\nkit ty\nhei ghts\nre pair\nwilli am\nqu ake\nalab ama\npopul ation\nre v\nre tt\ni sts\nn ite\nle m\na ha\nclevel and\nr m\npo ver\nob se\nmon tre\nman ia\n ®\ncon ne\ncar ni\nsh ah\nf y\nu a\nsc or\nstrugg le\nbo b\n' '\nappro pri\ndeci de\nff ed\nca ster\ns ort\nhun gry\ndra g\nا Ù\ngr ounds\nd w\nsli ghtly\ncar din\ndead line\nbron ze\nweb in\nbar ry\nsil ence\ne uro\nop tion\near n\nðŁĴ ĸ\nhowe ver\nna ren\nna ils\nbath room\nv ine\nph d\nmin ing\ngar age\n( )\nshou lder\ndefe at\ndi r\no v\nliber ty\nple as\nx on\ncom pre\na v\nj in\nab les\nsil ent\nfam ili\nvis its\ndi pl\nha bit\nmilli ons\nregar ding\ninnov ative\nsen ator\nr ts\nv on\nk l\nwh il\nrequi red\nâĿ Ħ\nlu v\npresi dential\npo cket\nhun dre\nsho wn\nfro zen\nto ward\nfa st\nconfi dence\nr ough\nindivi dual\nqu et\nðŁı ½\ndom e\nfi fa\nengine er\nz en\nre mix\nðŁĺ ĥ\npl ant\nmin or\nrobin son\nas y\npul led\ncer tain\npotat o\n( :\npre s\noc ca\nw it\nit em\nsi e\nd ating\nthom pson\nown ed\nan u\nvi e\nte dly\ngood night\nex cept\nðŁĮ Ł\nira q\nki e\nren ces\nli p\nsimil ar\nsau di\nvi g\narth ur\npic ks\nmil an\nhon da\nma xi\no g\nste st\nar ch\nanaly tics\nba sti\npear l\nter ry\nhor se\nast ro\nac ce\nlaun ching\ninter national\ns no\nta sty\nden ver\nir l\npe te\ntor n\nadvant age\nvar sity\n\" \"\nsol e\ng c\nlan g\ndemon str\nol ds\nun ity\nne ts\ninsp ire\ncre te\nnash ville\nnel son\ne ter\nwal k\nhy un\nm ack\ntre as\nsee king\nra ge\nbru sh\nab and\nwhil st\nco con\nh ong\nshel ter\ni p\npossi bly\nso o\nit ed\nâ Ħ\nrac es\nwar ming\nqu in\ntele vision\nmat ches\nra pi\nment al\npal m\njenni fer\nrol ls\nindi ana\nb ars\ncat ching\nresc u\ncandid ates\nfa re\nâł Ģ\nse o\nvie tnam\nalph a\nmichel le\nvisi ble\nre gre\nwn ed\napp le\nli p\nf fe\nli z\nyork shire\nha il\nse asons\nbe gan\nm d\nk c\nla p\nfascin ating\nhel p\nur y\nu ms\nnu ts\nse m\nalong side\nbri dge\nori al\no ve\nworld cup\nbriti sh\ncomfor table\ni ve\nhot els\nfair s\nhor ri\nso x\nd ining\nstre am\nbar ri\nss y\nw im\nter ms\nv u\npe re\nl ens\nwal ked\nr or\nl ars\nshi eld\ndou bt\npro to\ncro ssing\nme ant\nmedi um\nad ding\ne b\nche ap\nfun c\npap er\nbran ds\nry an\nfeed back\ncol lins\nun known\ntro pical\nsand wich\nfal len\nfor mu\nselec t\nlo ads\nansw ers\nor i\nmag a\nd or\ndu o\nali e\ndru m\nur i\nde er\nsou l\nsh ut\nâĺ º\nsto len\ndon ated\nbu zz\npatri ots\nha l\nna sty\nnomin ated\nmon te\nki a\nth ri\ning u\nte sts\npe tro\nðŁij ij\nho sts\nne st\nto pic\npat ch\nm my\nhu gh\nab ilities\nma the\ns miles\ng b\nag enda\ninsi ghts\nchi p\nph an\nfail ure\ndg ers\nha i\nsignific ant\nsho ck\nru ral\ngl am\nfigu res\npot us\no ta\nmini stry\nappe ars\nfe ar\nr h\nameric an\nh att\nson y\nfi res\ne di\nn ou\ne qui\nwh en\nunivers al\nmad ness\ni x\nsculp ture\nb ach\nt to\nswe den\net a\nen to\ndevelop ed\nmonth ly\nma ps\nra h\nle d\ndel ta\nsa ints\nis lam\nben ch\nfif th\nv ard\nso cks\nwel coming\nj e\ntur ner\nv b\nad i\nnor way\nad y\nhurric ane\npor sche\ntra dition\nex am\nnewsp aper\nlu ci\na ver\nide al\nd na\nmadi son\nðŁ §\nwit ness\nac ou\ninsi ght\nsi mon\nrobo t\nsna ke\nn bc\nac o\nro ss\nsh ment\nreligi on\nch ann\nin su\ncamp bell\ninst alled\nwe ather\nhor ses\nol i\nrober t\nk az\nðŁı Ģ\nveter an\nth read\nquar ter\nea sier\ncap ture\nhi pho\nlaw rence\nroman tic\npas sion\ncl ay\nox ford\nth ai\nstu dying\nfi a\nelec ted\nmost ly\nc b\ntu mb\nâĢįâĻ Ĥ\nx l\nsh an\nfa ster\nev ans\nsli de\nsh ri\nsee k\nmi es\nchemi stry\npump kin\ntu m\n, ,\nro om\nfi red\nli ps\npres ence\naf f\nbrew ery\narri ve\nsw ag\nphoto graph\npen gu\nchi ps\nat tor\nval ues\naccur ate\ncon temporary\nprinci pal\ncannab is\nari o\nany where\ngi a\ndemocr ats\nbuil dings\nli ved\nap s\nneg ative\nm are\nbal lo\nli on\ndiam on\nloo k\nre form\ntom my\nil la\ntre ats\nhundre ds\nport land\nwor thy\nex cep\nar ia\nido l\nbe er\ncd n\ny u\naw k\nðŁĩ ¨\nc ells\nà ³\nident ity\ndra wn\nde vil\nf inger\nth am\nðŁij Ĭ\near ned\nfin tech\ndol ph\ntwee ting\nevolu tion\nðŁĵ į\nest im\nm vp\nn one\nðŁĩºðŁĩ ¸\ntoyo ta\nau x\nmar in\nb old\nl bs\nste ak\nmur phy\nit able\nlou is\nsol ve\npi a\nsk ir\nill ino\nwebin ar\nban ana\nlo v\nth on\nvo ters\nafford able\ndefe ated\nlm fa\nair lines\nsuper b\nany way\ndeb t\nbo red\nver si\nme tal\nresponsi ble\nm k\ns se\nf ay\ncau sed\nf p\nrecomm end\npla za\nspor ting\nalli ance\nau stri\nn n\nt ours\nsurpri sed\narti f\nth under\nsur ve\nwor e\nbri ef\nnecess ary\nz ie\nash ley\ndra ke\nr t\nkni fe\nim mun\nchar ges\na the\nbri de\nrep ly\ng av\nbroad cast\npu er\nbrace let\ncap acity\nharve st\nid k\nperfor man\nd ding\nil ers\npar a\njam a\npro vince\nch in\nid ers\nhar i\nte aser\nch en\nre stor\nr at\nfl at\ncol om\nðŁĴ ŀ\nðŁĩ¨ ðŁĩ\nsmoo th\nr t\np itch\nstay ing\nisra eli\nt cot\nper spective\ndo ck\nopen er\nlo vel\nx o\nclass room\nl ington\ngo al\nkenne dy\nsh am\nsp aces\nmitch ell\nhome coming\nuk i\nclaim ed\nrecru it\ning o\nmu fc\nmon it\ng roo\nresi dent\nper cent\nper man\notta wa\nint ment\nan xi\nstand ards\nwor ship\nsche me\nf x\npot ter\nbi an\nathle tic\naf gh\ns se\nsat ell\npar ties\nâĿ¤ âĿ¤\ninfra structure\nrela x\nmo du\nwor n\nsmo king\ny ach\npractic es\nwc w\nam b\ndome stic\ntay lor\nk entu\nprovi ded\nmo di\nve g\n\" ...\nob serv\nðŁĺ ©\nbe ard\nm our\nan gry\nðŁĺ ±\nstartu ps\nwoo den\ndi ve\nna il\nanti que\nro ses\ntorn ado\nm at\n^ ^\nsu spect\nfar m\nde vices\nme ga\ntu l\nscholar ship\nge e\ndisa ster\narri val\npo in\nmar c\nkati e\nbb ed\nfal se\ndeser ves\nric hard\nju ana\nfre y\ntion ed\nhy bri\nr w\nsar ah\nach i\nc ure\no le\nmor ris\nch ic\nbroad way\nla bel\npa k\npover ty\ngol f\ne red\nf u\ner ies\nbe es\nalo gue\nst el\nwire less\nje wish\nti de\nblo cked\nlife time\nb har\nsp lit\nam ster\nth i\njo shu\nbr unch\nha ps\ns for\noo ps\nka poor\nhi king\nsuppo sed\nro of\nre as\ntra in\nti ght\ntru mp\nbas ically\nr r\nea red\nsee ds\nentr ance\nc p\nwi e\nson ic\nvic tim\nhe re\ne h\near rings\nsal mon\narc tic\nan ne\ndou gla\ncorru ption\nhann ah\nha sn\nvo ices\ncon ce\natt a\nfle et\nclin ical\ndemocr atic\nton y\nst ood\nle f\ntwit ch\na il\nhonest ly\nincre ased\ndro me\ndon na\naccep ted\nvisit ors\nap ar\nad or\np ar\njer ry\nra i\nbrand on\nab u\n!! !!!!\nme me\nin gh\nglori ous\nb hu\npu mp\nj ol\nli ke\nfi sher\nma z\nag an\ndestin ation\nplay list\nle tters\ngen u\nbr ace\ncelebr ated\nbann er\nr he\ndra gon\nðŁĺ ħ\nsig nature\ngre y\nâľ Ķï¸ı\nal ice\nbe red\nph er\nber n\nca th\nga thering\nsc oring\ninflu ence\nsm iling\nde pt\nlo cal\na x\nac u\nreti rement\nhon or\nher self\nchem ical\nasse ss\ny all\nfre qu\nappreci ation\nac a\ncho ir\ncu z\nso il\nc il\nrepor ting\nu h\nenterpri se\ngr at\njaco b\nru m\nfe e\nj ak\nsp in\nbi kes\nphi a\nste re\np is\nbloo d\nt att\nra ft\nwar ren\nsh eri\nback stage\nmar sh\nhash tag\nther ine\nre in\ngame day\nguar an\nreci pes\nmin ds\nstron ger\nissu ed\nbic y\nn ak\nment ed\nsc ary\nu x\npre vious\ntt le\nth ats\nac tors\nu ma\ntin a\nbun ny\npromo tion\nu ss\noli ver\nmontre al\nwhat s\nappreci ated\nla kes\nexcu se\nkno wing\npri zes\nmusc le\nshad es\nsco t\ning redi\nelectr onic\nju an\ncomb at\ns ri\ne h\nturk ish\nl om\nstri kes\npri son\nre e\npo pe\nvi d\nol dest\ndol l\nsw iss\ncerti fied\ncli p\nre turning\nlat or\nle igh\ntt es\nwat son\nheal ing\nel im\nper haps\nha ss\nk au\nd der\nmou se\nnew castle\nindigen ous\nwel comes\nco le\ntau ght\nno ise\nappe ar\njo e\ncan on\nwedne sday\nu tah\nc tive\ndri ven\ni v\nc ell\nstri p\nac c\nfocu sed\nar rest\nsto cks\nwo o\nâ Ĺ\nnotic ed\nshad o\ndi spla\nter ror\nbor ne\nsecon d\nque ens\nwo ke\nja il\nno tt\ncam bridge\nhar t\nse af\nfa x\nac cept\nâĺ ħ\ngoo ds\nk at\nt win\nh s\nthou sand\ns ins\nsu ite\namp ton\nar n\nrele v\nric har\nhoo ps\nn bc\nclass ic\np ab\nsoldi er\nde plo\nle ans\ninstall ation\ncla sh\nle ban\nee e\nti re\nbelo ved\nfu sion\ntravel ing\nne i\ncoo kie\nglo be\nphys ics\ns q\nco l\nwol ves\nd l\nex it\n\" -\nfoo tball\nle af\nster ling\nhi de\nminne so\nfresh man\nnatu re\nindi e\nsupp lies\nbri s\niri sh\nink tober\ndoo dle\nic op\nmess ages\nadul ts\nrecor ded\nfix ed\nar do\noffe red\nunder ground\ndr one\np ine\nma inten\nand re\nham mer\ns x\nr ound\nhi ke\nbra d\nro me\nfu ll\non ey\nro ws\ncolum bia\narchi ves\nappro ved\nbat ch\nillino is\nrecogn ition\nshou ldn\nfo g\nnca a\nke vin\nhuman ity\nal though\npow ers\np ou\ns ar\npe st\nalco hol\ncon sci\nphil adel\nen o\nt m\nok la\ncate gory\nparticip ate\naccu sed\nbri ef\npo em\nclu bs\nconsul t\nja b\nbig data\namster dam\nac ing\ncerti fic\nn u\nd at\nimpro ved\nand y\ncampa ig\npale stin\np ace\nmo bi\nfeel ings\nwol f\nbra in\npro pos\ninter active\nprin ce\ninde x\nc is\ncha e\npeace ful\nco vering\nac o\ncour ses\nmon key\nre place\nb l\nbloo dy\ntal es\nbrigh ton\nneighbor hood\ng ates\nspiritu al\naf raid\nbre ast\nb ones\nðŁij ī\nvide o\nw au\ntou ch\ninju ries\ncar l\nri x\nune x\nâĢ ¢\nfre d\nconsi dered\nthu si\nan ch\non y\nu sa\ngraph ics\nac re\nðŁĺ ©\ncom memor\ncom mod\ngo ti\nguar dian\nstar bucks\npre vention\nhaha haha\nadmini stration\nportu gal\nfac ulty\nbet a\nul a\nal bert\nbre ath\ner i\nle tting\ntr ic\nment ation\nincredi bly\nten nes\nv d\nðŁĻ Ī\ned die\nbr ick\ngr ill\nbt w\nwat ches\nresear chers\nt ney\nni e\np as\na ster\nvi br\npoke mon\nch rome\ngo at\npitt s\nil ly\nfesti ve\ny d\ncan al\nðŁ Ĩ\nfi es\ncar los\nre que\npartic i\ntra ins\nsam ple\ntemper ature\nsym ph\npic king\nin door\nz ers\nplayo ffs\n____ ____\nap es\nly rics\nislam ic\nperforman ces\nd ick\nspar k\nse as\nhom a\ngr ound\ndisc i\nemploye e\ncom mu\nalas ka\nal an\nfe ast\ndg ing\nban king\nmanu el\nslow ly\ntru cks\nmc car\noo o\nsc rat\norche stra\nindivi du\nm x\nbre ath\nstair s\nequ ality\nbla ke\nloc ations\ncocon ut\nbalti more\naa a\nl c\nðŁı Ĩ\nhar vey\nresi st\nimmigr ation\nadid as\nfil i\nre f\nlg bt\nmo s\npp i\nken ny\nterr or\nban e\napol is\ns g\nsocial media\nka i\nhon est\nas sas\nbol lywood\nâĢįâĻ Ģï¸ı\nferr ari\nhor n\ncryp to\nbo om\nmainten ance\ni di\ns man\nw l\next ended\nin sul\nve s\ngo sp\ntr i\npi g\ntar ge\ncel er\nst ati\nsm h\nri dic\nappe al\n? )\ncon clu\ncos me\nshe ep\nchristop her\nen thusi\npo lish\nme ts\noun ded\nsustain ability\ncreati vity\ncon crete\nra i\nali en\nble ss\nte es\nclu b\nro t\nbo s\nex ist\nperfe ction\nlu ck\nrock y\nexpen sive\nmean while\nhappy birthday\npre t\nthr iller\nca ve\nplayo ff\nsom er\nl u\nle x\ndef ence\nam writing\nhome less\npro phe\nch et\npast or\nðŁ¤ £\nland er\nww w\nĢ ï¸ı\ntic a\n! #\no tic\nrad ar\npo sters\npow der\npo li\nha un\ntra p\nbl in\nassau lt\nshor ts\nre y\nsh y\nsqu ir\nrac ist\ngar lic\nfu r\nremo te\nsm ell\nimpre ssed\nfing ers\nâł Ģ\ndin o\nle ment\ns nu\npromo ting\nstr ing\nproduc tive\nb age\nma son\nra z\ndirec tly\nj k\nev al\nðŁij Ĭ\ndoc tors\nco w\nri der\nst v\nre move\nw u\nna than\nro d\nn r\n= >\naffe cted\ninve st\nmp tion\ng inger\no d\nagricul ture\ns que\nmu g\ncoun ting\nke e\nmag nific\ncoo k\nani stan\nroo t\nplac ed\nsym po\ngh ana\nun d\nche er\nthro wing\nsecre ts\nf illing\nopti mi\nbutter fly\nbu bb\nðŁĺ ī\nterri ble\nd g\nsil k\nobse ssed\nlo u\nai de\nsal ute\nmon u\nphiladel phia\nscienti fic\ni st\nu ae\ndess ert\nbott les\ncan yon\nðŁĺ Ī\ncar ib\no ther\nw ich\nre source\nguil ty\nun d\nle on\ne ss\nkan e\nel e\ntra iner\nhe im\nan te\nman age\nroo kie\ntre ated\npo ses\nrs vp\ncau ses\naw ak\nje well\nle tt\non ics\ntit les\ncardi ff\ng aga\nbu mp\nuse ful\n? !\nloo se\nbb ing\n: :\nargent ina\nde bu\ncy cl\nwh el\ndis gu\nj el\nk ills\nbio logy\nex ter\ntra sh\nbo dies\ntr am\ncircu it\nexpe ct\nla ds\nw ells\nsho t\nge e\nnaren dr\nfa stest\nb ent\nb ills\nmar shall\nh ats\nintro duce\nciti zen\nim possible\ngi b\naz z\nnet working\nr ant\nthin k\nin dy\nst ops\nf theday\nbri an\n* *\namo di\ndom e\ncoura ge\npac king\naf fairs\ng n\nsi zed\nent ary\npol and\nswit zer\nafgh anistan\nw u\nten der\nsubscri be\nmo sco\natt end\nrepublic an\nhon ey\nâĢ ĭ\nsi mul\nwe ster\nfoo die\nor o\nmidd le\nab t\nco pies\nma je\nnarendr amodi\nty pical\ninspir ational\nvit am\nwis con\ncu bs\ntiv ity\nh ali\ne ars\nk ay\nd are\nmari juana\ncu rious\nan ia\ntom ato\nre mind\nðŁĩ ·\nsc ared\ncou p\npo et\nland ed\nri d\nwra pped\nmor ri\nclimb ing\ne ws\nfe eding\ncon tra\ntho logy\ngri d\nti vely\nread er\nla ser\ndi ving\ndi g\nlat in\nti ed\nshake spe\no ci\nad m\nshow ers\nchu ck\nmar cus\noo s\nkne e\no live\now l\ndy lan\nan no\ng ym\ndeci sions\nwell ness\narri ves\nsati s\nchri s\nthur s\nðŁ¤ £\ninter views\nthank you\nswitzer land\nover night\njourn alist\nser ves\nvol can\n.... ...\nplo t\nnic ol\ncar rying\nmag ne\ntre asure\nex p\nbe ver\nðŁĺ ¢\nmar ty\nmo le\ndon ations\nrecogni zed\nb h\ndu s\nsh ann\nal do\nsuccess fully\nent e\nðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤ\ncab inet\ncu is\ntit led\nd as\nso l\nstrate gies\ndeli vering\nad ds\nani an\nne ther\nðŁĴ ĥ\ncon tain\nsu its\npa irs\nto dd\nrel la\nro pe\nci o\ncro p\npaint ings\nsu z\nre jec\nbu st\nd h\nfra ud\nm h\ncontro l\nje al\ndestroy ed\nal lows\nwo ol\nminneso ta\nom en\nj u\nsympo sium\nd af\nlim it\naccoun ts\nload ing\ninter n\nre solution\nhol land\nqu al\nmeet ings\ngra ve\ncam ping\nv am\nre nov\nliber al\nam ber\ngre e\nhu mb\nfe ver\nel ing\nbroo ks\nà ²\nbe th\nad ed\nal t\nro e\nperform ed\njo sh\nfrank lin\nnic ole\nde ss\nbb s\nm g\nnet works\nmin im\nal t\nweap ons\ngu y\njas on\ng ha\nharb our\nat on\npra ise\nkentu cky\nbel fast\nst icks\nblo ss\nho pes\nan thro\nfamili ar\nwa it\nch ile\ndepre ssion\nla x\nje ts\nle ice\nrecei ves\nsi er\nan k\nde x\ninde ed\nfle xi\nfab ric\nlam b\nhel icop\nam anda\nâĢĶ âĢĶ\ncompe te\nsn ack\ntechno logies\nsy rian\nmom s\nmu ham\ncho sen\nan at\ndev on\nshar ks\nre t\nfundra iser\nselfi es\nst ations\ncommunic ations\ntennes see\ntu tor\nro t\nvalu able\ndynam ic\nnur se\ni ed\nearth quake\ndeser ved\na ve\nsar a\nstre tch\ndougla s\nne pal\nà §\nob viously\nd ame\nra pe\nany body\nk w\npat rol\nhol ders\nh anna\ninfo graphic\nec o\nbe ating\nstan ley\nbo ats\nri bb\ne z\nwit ch\ninv a\nac id\nboar ding\n- @\ngi l\nda ve\ncare ers\nopp os\nl loy\nin ter\ndo pe\nre su\nj agu\nsh ade\nin dy\non ist\nrel ations\nag en\nab le\ninci dent\nme ter\nshar ma\nid r\npro ve\nimmedi ately\ntro ops\nam an\ng low\ngaz a\nblo cks\nperson al\nchron ic\nall er\nsi d\nsh r\nwhats app\nlu cy\nar chae\nho u\njourn alism\nour selves\ngo t\nthe med\nshap ed\nwe ak\ncas ual\nleng th\nsla m\nab bey\ne v\ncoun ter\nest a\nreci pi\ncha pel\nexpan sion\nsel f\nsuff ering\nsp ice\nn z\nsp art\ndesp er\nboo king\nquart ers\ny on\nðŁĴ Ĺ\np k\ncontinu ed\n- #\nman hatt\ntal ked\nsh en\ncom bo\nhybri d\nje ans\nliqu id\nse al\nre tweets\nac celer\ncollec tive\nt as\n: ))\nprofession als\nra w\no tt\nsu san\nir ing\nokla homa\nre ven\nsurvi val\ncre ator\ntran sit\nst ac\nsur f\ni k\ned iting\nch illing\nbai ley\nste al\nra ble\npa rent\nhun ger\nsn app\ncollec t\nphilos oph\ndedic ation\nc f\nc m\nle ep\nrepe at\nre ha\nun fortun\na er\na ero\nabstr act\nmon itor\nag ents\nbu l\nsci ence\nharb or\ndrag ons\nfloo ding\nac compli\nd ash\njuli a\nthe red\ntues day\ncy ber\nb low\nta ined\nle m\nrefe rence\npp o\nne goti\nchar le\ncon nor\nau lt\naccess ories\ncommissi oner\nrain y\nre ar\nadvis ory\nluc as\nma id\nco al\nk av\npol o\nðŁı ¾\ntran sport\nmar gare\nstraw berry\nbur ns\ngre ens\nne v\npartici pants\ncol in\nbelgi um\ncol our\nin form\nd ell\nbr on\ncal y\nkick off\nstrate gic\nre union\nhon ors\nli b\negy p\nâŃIJ ï¸ı\nhy po\nsi zes\nregi stered\nbet es\nrelax ing\nbloo m\ninten se\nvalent ines\ninsan e\nw wii\np x\ntri o\nbla de\nwiscon sin\ncon e\nplat in\nali ze\nra ven\nincre asing\nindi ans\nil ian\nbl u\nrabb it\nexten sion\nje f\nau di\nfer ry\ns ell\na day\nus b\nswe at\ncham pag\nmetho d\nmem ph\nassi st\ns by\nca pe\nremo ved\nmag n\nv t\nr ams\nf bi\ntack le\nphe w\nh on\nmotor cycle\nsu spec\neleph ant\nsub ject\nlet te\nda iry\nwhe at\nawk ward\nac t\ntro l\nmit ted\nzay n\nsheri ff\nene my\ncon s\nke tt\nbul ls\nev alu\nbt c\nsatell ite\nho lo\npor ter\ndia betes\nbet ter\nrele asing\nsur f\n: -\nse basti\ncollec ting\nen cing\ne thi\ngo ds\nal ley\nhealth y\nm ills\nsma sh\nco pper\ncr ack\nread ers\nsp ac\nlicen se\nbas ket\nbang la\nen tic\nom i\nm ere\nsi vely\nanim ation\nlan es\ndent ally\nchill in\nfi e\nk aren\ndep th\nli pse\nn g\nri p\nmel o\nsand y\nðŁijı ðŁijı\nvin cent\nnu t\nhu g\nwho le\ncre ates\n? ???\nâĿ¤ï¸ı âĿ¤ï¸ı\nbak ed\nup grade\nrober ts\nhar a\ncarib bean\nauth entic\nmb s\nmosco w\nattor ney\nwi ki\nch lo\nhu ll\ncor k\n\" !\nsty lish\nðŁĵ¸ :\ndi ary\nimpro ving\nex pand\nbri ght\npollu tion\nk nights\nperson ality\nchec ked\nfac ilities\nz el\nbow ling\ngu er\nðŁİ Ĥ\non going\nun its\nhoo k\nbe ck\nconfl ict\nto dd\nfar ming\neduc ational\nk ak\ncla y\nstro ke\nbel ly\nexplo re\nmill enni\nth m\nloo p\nsm s\nconsi st\ncir ca\nbr yan\nd ab\nyoun ger\nsoli dar\npp a\nexperi enced\nb ella\nbo ard\nshef field\nsteph en\nconsu mer\nsub mit\nspon sor\nt ang\nag gre\ncomb ined\ntrac king\nsand ers\nb az\nsurvi ve\nfer red\nequ al\nse p\nre ed\nstr ong\npriv acy\nst ap\nun g\nac ry\npa sta\npir ates\nag er\nfair y\ndu p\nintroduc ed\nwi p\nlet s\nspr ay\nðŁĵ º\ngre w\na sts\npitts burgh\nnew york\njo ey\nlau ren\ntra de\nch op\npi pe\ncla ire\nbehavi or\nv ap\ncre ws\nlap top\nðŁ¤ Ĺ\nche ster\ndisci pl\nd f\nout doors\nk s\ngo ver\nsuper star\ncas ino\nfar mer\n; -)\nre turned\nðŁı Ī\nma il\nroa sted\nco sta\nv ill\npe z\ngard ening\ndistribu tion\nsh ining\ninve stors\nra sp\ndec ades\nreali zed\nbar n\np ti\nst able\nut d\npan thers\nm ens\nb n\nca de\nbu cket\nyn n\nwhen ever\nwa ke\nda is\nber nie\nlo dge\nju lie\natmo sphere\nðŁĺĺ ðŁĺĺ\nmajor ity\npar ti\nexc it\ncu t\nme h\nmusli ms\nbe gun\nfli ghts\nvene ss\nce me\npo sing\nso le\ng ou\ndark ness\npe ach\ncel tic\nauth ority\ngrand ma\nful ness\nsmi th\nspeci fic\ngar cia\nco ins\ngood ness\naldu b\nrecru iting\nden nis\ngar y\nsle eve\nweap on\npl z\ndisco ver\nharri son\nrecruit ment\nja i\nch im\ncom pared\ntom s\nmo thers\nam y\narchi ve\nt ask\nben jam\nse g\nlaw yer\nal um\ninve sting\nmi e\nche z\nj p\na ke\nfl am\nwall paper\nâĻ¥ ï¸ı\nt ton\nche st\nfavor ites\nwe igh\ncoo lest\nr ating\nrelev ant\nlo gan\nma ple\nrun ners\npri or\npeop le\nma ur\nterrori st\nte sted\ncarni val\nsu spen\nme asure\nm v\ncyber security\napp ren\nterror ism\no z\nv ital\nni es\ngon z\nfun ded\ntwi st\nassess ment\ndie sel\nen for\ncolum n\nad dressing\nca sts\npay ment\nx ton\nfi er\n, '\nla st\nne e\nun less\nclo se\nsk ill\ncuis ine\nfun eral\nti les\na un\nk ru\nrelation ships\nðŁĴ ¯\nev ent\nâĢįâĻĤ ï¸ı\nkind ness\npro posed\nacou stic\na es\ndefen der\ndan ce\nh tt\nw at\nvo y\nðŁ¤ ĺ\nau s\ncli ff\nsear ching\nbeauti fully\nin qu\nat l\nspeci alist\nðŁIJ ¶\nda i\ntra ils\nclass ics\ninst ant\nv ous\nre venue\nmar ch\nkir k\nfr inge\nfire works\ntri via\nâĺ ħ\ntr action\nwal ter\nmo to\nl ily\natt itude\ncli mb\nsc an\nsav ings\nc w\nfa ith\ncred its\nab led\ngra ff\nauto graph\nhe he\nran ch\nha d\nro gers\nðŁĮ ¹\nf in\nre qu\nfol k\nad ditional\nlyn n\nu ber\ndol lars\nlo gic\nwor th\nso m\nthe sis\np ound\nbi c\nst ur\ncer am\nspen cer\nen tered\nv amp\norgani zed\nâľ Ī\npp s\ntr on\nmerce des\nno ti\ncompet itive\ndo w\nous ness\nvic tor\ngr illed\nna i\npu tin\nab ra\nbl ame\nalex and\nanim al\ndec ent\np ent\ninter ior\n:' )\nbut ler\nbal let\nðŁĴ Ķ\nalbu ms\ndown s\nla d\nsi r\npla in\np ers\nblon de\ndis c\npaki stan\nse ment\nga a\nw age\nch as\nman i\nco ps\nterr it\nlo l\nlau ghter\nri vers\nmagnific ent\nlam p\nw b\nnew sle\nchar ts\nble ssing\np unch\nlon gest\nfl oral\ncu tie\nfare well\nsto pping\nmb b\nbu d\nchee se\nde cla\nsi m\nmc donald\nde ter\nyou th\nt ch\nfre der\nkin dle\nfer n\nat or\nas leep\np ond\nspr int\np ounds\nla zy\ngh e\nfundra ising\ndead ly\ngran de\ndou g\nhe y\nlin da\nconsi dering\ni um\ngol den\nvi k\nauth ors\ndi ss\nu ally\nappropri ate\nmor ning\ny le\nhon oring\nfoli o\nbe c\nre bec\nfin land\nformu la\ncorn wall\nsh ay\ncau sing\nbl end\nsig nal\nt ent\nkash mir\nnation als\nhar mony\nsc out\nacce ssi\nhe ight\nmedi eval\nimpro vement\nke es\nprac tical\ncar d\nde par\nhu n\nom ing\ncal gary\nste l\nbu bble\ngur u\nma h\nunex pe\nn h\ned a\nme at\ni ge\nsi o\ngod dess\nin ches\ntun es\nbr itt\nsti on\nra j\nâĻ «\nmer cy\nðŁĴ ĺ\nsen ds\ni est\npol ici\nval e\nreduc ed\nas ap\nvi jay\ndefen sive\ncelebr ations\nri ders\nmed itation\nhar mon\ng ing\n ¡\nprogram ming\nin au\nsud den\nm h\nreplac ement\nsk u\nj ar\ngra des\nta st\nk itt\nbrand ing\nk aw\nboo t\nf ought\np ays\ng f\niz ation\nho p\nk k\nactivi st\nv end\ncoast al\ncha os\nðŁĶ ´\nse me\nbill board\nli fting\ncu mb\nsc al\nðŁĸ ¤\nstru ck\nl v\nindie dev\nbeat en\njun gle\nal right\ndestin y\nm ing\nk c\nch ances\nom an\nq atar\ncra f\ntra ined\npri x\nchar m\no tive\ns mu\ne c\nand ers\nhand ed\nal ban\ncertain ly\narri ving\ni ze\nsa i\ntr ack\npain ter\nhu mble\nappo intment\nhead line\nmanag ing\nmo d\nas pe\nandre a\nà ¤\nethi op\nun ited\nexi st\nbal i\nk ad\nn t\nd red\nre x\nrecogni ze\ntam pa\nbe ers\nati a\nhe els\nno te\ntransport ation\ntur tle\nre de\nhipho p\nsp icy\nsp urs\n⬠ĩ\ncor p\nther n\nto ast\nhur ry\nproper ties\nma ge\nmar co\nele ments\nbou ti\nsyn drome\nms g\ndevelop er\ngra ders\nhe im\nre sil\noff ices\ndel ay\ndi men\nvin tag\nbarbar a\nðŁĺ ±\nvene zu\ncu lar\nfac ed\nbar n\nðŁĺ Ĩ\nsurvi vor\nwor m\nconfu sed\npassion ate\nØ ±\nidenti fy\nelectr icity\nsou ls\nbrad ley\nrepor tedly\nlun ch\nshel f\neli a\nswee t\nsmoo th\nemplo yment\nam el\nmanhatt an\nste am\noun ts\nye p\nli ving\nun e\ndescri be\nca res\nman ila\nsha wn\nac ted\nbas h\nst even\nre st\npet ition\ndiv ine\nwel sh\nrac e\nplatin um\nðŁĮ ¸\np b\nextra ordinary\nsolidar ity\nm all\non ion\nschedu led\ngame of\nfer gu\nde ms\nnor m\np k\ntri als\npolici es\npubli shing\nst ole\nfron t\ncharac ter\nvan ia\nex ce\nsti e\nsc a\nresi dential\nsa iling\nðŁĶ¥ðŁĶ¥ ðŁĶ¥\nspons ors\nth ick\nchampag ne\nshe pher\ncontinu ing\nven ice\nper th\nna p\na ster\ny ak\nun limited\ncho ices\nne o\nhi v\nrepor ter\nbru ssels\nf old\ndy s\nse mi\nla wn\nit alia\nwi fi\nas k\nem ed\nfr ame\nmonit oring\nste ad\ni da\ngr in\nis a\nfli p\nre stric\noffen sive\natta ched\ndi sh\nwh y\nphilli ps\ngre et\np als\nmix tape\nv ou\nfiel der\nspar k\nalber ta\ng len\nca sh\ns ri\nu ri\nro dri\nentreprene urs\nclimate change\np sy\nd le\nem ents\nlin ked\nnether lands\nacci dentally\noppos ition\nvel vet\nra ys\nc w\nom o\nm f\nlmfa o\nnewsle tter\n: )\ntoi let\nliter ature\ndi sp\nphili p\nuni form\nsudden ly\nhead er\ncool er\n-- -\nprou d\nbri g\nnis san\nscienti st\nj ah\ncon centr\npac ks\nappo inted\nso ap\neng age\ncho se\nâĻ ¡\nse tup\njeal ous\nhar ry\ng ation\ntun nel\nte mp\nosc ars\ndec ade\nrecomm ended\nchild ren\nab a\nanxi ety\nve ments\nsal on\npho too\norgani z\nmach ines\nab s\nvil le\nhy pe\nti ff\nemer ging\nav geek\n[ #\ncontribu tion\nbra dy\nre sto\ng mail\nfit z\nphoto shoot\nhel met\nh t\neleg ant\nug anda\nnur sing\nor leans\npen n\nna h\nfoo tage\nem a\nw o\nw ad\nconcer ns\nve re\nre mark\nwho ever\nstr ang\np t\nqu it\nsh ang\nhistor y\ns ick\nperman ent\nill ness\ncol d\nvisi on\nhe m\nar row\ncon vic\npin k\noc cup\nbal d\nex hau\nu of\nam o\non t\nãĥ »\nadop t\nla id\nsmo ked\ninter pre\ness enti\nassoci ated\nb d\nbb y\nfi er\ninst all\ndipl om\ncon diti\nc f\nw ak\nany a\ngr aci\nfi sher\ns ss\nap r\nil it\nmus ician\nsymph ony\ncor d\nh ack\nle gi\nl v\nbless ings\nhum or\nsc ra\ne ti\nmin ster\ntrav elling\nbu sh\njewell ery\nli me\n!! !\npregn ant\npe e\nlo b\ncap ital\nip a\npen cil\nla bor\nduc ks\nprou dly\nwedd ing\ndere k\nm w\npe g\nvalent ine\nan gu\nre treat\npro spect\ndang er\nvul ner\nup set\n, #\nsr k\nx im\nthur sday\nn fl\nkis ses\nre ds\ncr ack\nre ward\nc u\nko k\nme te\naband oned\nit t\nme als\nsp ell\nstan bul\ndel ays\nru m\nle op\ngu m\nno va\nsuper man\nch ick\nm is\ndram atic\ninno cent\nr ounds\nre c\nauti sm\nbangla desh\nmor al\nmo vie\nsp oo\nk la\nâĥ £\nou ting\nmess i\nab road\nloo kin\na im\nq i\nst ack\ncolla ge\nà ¯\nhud son\nsc an\nho e\nch au\noc cur\ncomm ander\nho les\nðŁİ Ħ\nbi as\nv on\nstick er\nma k\nresponsi bility\ncolum bus\nsa int\ned mon\nrac ism\nfar ms\nw en\ngul f\nmay o\n!!!! !!!!\ncorpor ation\nba chel\nel a\ninter nal\nje ep\nfol lows\ndi alogue\nde rer\nsmart phone\nhe len\nrich mond\nequ ity\ns land\nb g\nne ar\nav i\nmemph is\nwe ir\ndiscu ssed\nbad ge\np up\nmi stake\nphen omen\nun ite\nðŁ Ľ\nde pic\nri des\nin augu\nn at\nsof twitter\ncomb ination\ngosp el\nâļ ¾\nad mission\nretro gaming\nðŁIJ ¾\nsch u\nmb o\njun ction\nal arm\nà ¦\ngr ac\nkh ali\nk ul\nm ale\ncap tion\nwi sh\nte re\ncor ps\nru bber\nplay station\ner in\neffici ent\nl or\njo kes\nin ary\nnor man\nlu is\ninaugu ral\nch ed\nâļ½ ï¸ı\ndi p\nto e\nstr at\naa c\nam u\npi er\nco tt\ncomm and\ntt en\nsn oo\ncu be\nclo ses\nclass ical\ns word\nexpre ssion\nreach ing\nn app\nco st\naffe ct\nric o\ngi f\nbrea the\ntri be\nor tho\nh ay\nl g\nfri es\nn m\nhi ding\nrichar ds\nen de\nmic ro\ncapit ol\ncop y\nro m\nregi me\nmary land\ntax i\ndi al\nembar ra\nun believ\nch t\nv s\nelim in\no dd\npen ny\nsound track\nl ings\ntrans ition\nrema ining\na is\nmali k\n? !?\nrand om\ndef end\nul tra\ntru m\ndanc er\nst ol\ndri ve\na ver\nro ast\ndefin ition\nse an\nexcit ement\npartic ul\nsu rely\nsh av\nber y\ndi shes\ncom m\nis ol\ni am\nob li\ngho st\nhugh es\nchi efs\nb as\nconserv ative\nspeci al\nfe min\nsh ri\nn ancy\ninte l\ntu ne\nðŁĩ ª\njo el\ngg le\nmo to\nðŁĺ Ķ\nbu ck\nd ag\nantic ip\nmont ana\ngu id\nfro g\nec raft\nop e\ndri ves\nnu mer\nx y\ncolor ful\nwednesday wisdom\nillu min\nbey on\ninau gur\ndeep ly\npre fer\nfor tune\ncoo ked\nti ble\nâĺ ķ\nswe ater\nit ter\ntt y\nu i\ngi e\ncom plic\n~ ~\ntax es\ncu ps\ndi verse\nsam anth\nâłĢ âłĢ\nba king\nsy mp\nwa i\nbe half\nmer cur\ntravel s\nðŁİī ðŁİ\nor ia\neng aged\njump ing\nreti red\nn aked\np uni\nspeed way\nsci ences\nrehear sal\non ym\ndy ou\npl ates\nr ati\nkri sh\njaz z\ncar ol\nra f\npen alty\ntim eline\nru by\nengine ers\nra f\nbel le\ndo se\nche on\nesc ap\nme g\nran k\nor d\nme gan\nmer ch\nec lipse\nâĺº ï¸ı\nple dge\nkir k\nper si\nleice ster\nsa k\nw k\nsaf ely\nyy y\nje t\npromis ed\nj c\nen ne\nno ah\nre no\nre a\nðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤ\ntra il\nðŁij Ģ\nf d\nsoo o\nri min\nw k\nภ²\ni al\nx ox\nbis cu\nd ale\nfan dom\nparticip ating\nfla g\nprivi lege\npe ach\nmach ine\nbo ston\ngro ss\no g\nmir acle\nadop tion\nu ss\nmon sters\nbe ij\nclar ke\npu shing\npra ying\nar o\nd n\nell is\napol lo\nod ds\nrefuge e\nto w\nb p\nðŁĩ¬ðŁĩ §\nh end\napp eared\nmemb ership\npe an\ndu m\nviol ent\nv y\npotat oes\naw w\ngreet ings\nt ts\nac on\nsh ane\nphotograph ed\ncra b\ntemper atures\ncu ba\nc fc\nwel com\nhe l\nin nings\nm k\nco de\nkno ck\ngra ss\nswe dish\np ta\nick y\nv at\nlin ing\ns q\nsa p\nar c\nannoun cing\nsk ins\ncit yof\nbr ing\nco x\ngam er\nit arian\ni da\nh d\nros se\nsad ly\nge o\nâļ ¡ï¸ı\ntag s\nfa ther\nchan ge\nl ance\nwhis key\nadel aide\nte c\nstick ers\nmarke t\nclass y\nbad ass\nflo rence\nlin er\nfro st\nk ate\nac on\nscand al\nes sex\nðŁĺ ı\nvi vi\ndr ill\nblo ggers\nrecomm end\nd ha\nac res\nro ma\nbu y\ngro cer\ner ia\nma har\nff er\npatter ns\nver i\ncom pu\nst ev\nang a\nment or\ndo o\nit ali\ncdn poli\non ly\nconduc t\nelec tro\nde f\nwh ale\nprepar ation\nbicy cle\nvi ral\nturn out\nbra ss\nqu ad\nhospit ality\npack aging\nden cy\nceme tery\nabo ard\ndre aming\npic ture\nt all\ninv ent\nad mi\no e\ntem ps\nqu an\nfun dam\npro mp\nresi dence\nmu d\nsour i\nâĦ ¢\ngraff iti\ngi f\nd nd\ncom p\ns war\npe eps\npale stine\ndevil s\nsan g\nassi stance\nbi ke\nmissi ssi\ninter viewed\nne phew\ndru ms\nv and\ngentle men\nn sw\ninst a\nleban on\nee ee\noli via\nver y\nrou gh\nindustri es\nm ation\nðŁĺ Ĵ\nbar rel\nn ay\npo ps\nmoder n\nill y\nare st\non ents\nprotec ting\nv ans\ne o\nvi kings\nrestaur ants\nre ck\njac kie\nandre w\nw illing\nhe ath\ncitiz en\ndisc rimin\n๠Ī\nstu art\nm ys\nhi p\ntran sp\n\" ?\nte x\nsu shi\nke d\ncro ssed\ndist ur\npe dia\nf ate\nsome how\nmo th\nproce ssing\nis s\nr in\nu ts\nyy c\nver t\nlg bt\nre id\non to\narab ia\nhabit at\n= =\nstre ak\nsimp son\naddic tion\nwim ble\ndeli vers\nchalleng ing\nðŁİ ¶\nfran ch\ne du\ns me\nai ds\nhur st\nth am\ntari an\nremem bered\npalestin ian\nfe es\ntru m\nsket ch\nur u\nfit ting\njes se\nðŁĶ¥ ðŁĶ¥\n---- ----\nba ch\nici a\ncolo red\nda h\nassoci ate\nint el\ns eller\np u\nstu ffed\nac s\nb s\nsh in\ncooper ation\ncertific ate\nab u\ningredi ents\nre v\nin ge\nel der\nchristi an\nbun dle\nth ic\ndir t\nbeij ing\ncomm it\nted dy\ned u\nto day\ns field\nw yn\nconfir ms\nlo o\nj v\nene ss\nal pha\nvir us\nari um\ngr ind\nbri dges\nintroduc tion\npol ls\nbac ter\nz ach\ntermin al\nra iders\nfla vor\nzom bie\nvo d\nsp reading\ngameof thrones\neffici ency\nlat ely\nale m\ntwee t\ncri mes\ncl er\nde y\ndg ed\nhy un\npay ments\ncir cus\nðŁĺŃ ðŁĺŃ\nmis souri\nlu b\nepiso des\nc age\npo s\nmat ching\ntumb lr\nlin ed\nge st\nam bi\nnar r\ning ton\nregu l\nblo wn\nis le\nco co\non don\njoshu a\ntour ing\nsm a\nsau sage\nbest friend\nbo eing\ndesi re\nsav age\nra pper\nde vo\nte ar\ntake over\ncow boys\npo ker\npar ag\npp e\nh int\nwe ars\nse th\nro les\nl anc\nman ga\nform at\nfl yer\nc ay\nmo or\nba ke\nspla sh\nv ad\nker ala\nproce eds\nsil ly\nreflec tion\ndi str\nwi d\nsu it\nci vic\nyan kees\nby n\nmigr ation\ndi stin\nor ch\nfe mini\nquali fying\ntu ri\no be\nhun dred\ncra p\nwan g\nmathe mat\nbu re\nexpo sure\nfergu son\nseme ster\nre serv\npl ym\na hu\nfac ial\nwa x\nwor ried\nca b\nvi o\nas a\nco d\nto pics\np cs\nhal o\nrescu ed\nhoriz on\nar k\nâļ ª\nhol ly\nel f\nul ti\npu p\nquali fied\nattend ance\nati vely\ndestro y\ny c\nfor th\nphotoo ftheday\nc ents\nic eland\nmeas ures\nde sk\nport folio\nartic les\ndirec tors\ndat ab\ne w\ncreep y\noun ding\nhon oured\nmi st\nj it\nmen tioned\nport able\niti c\nd ann\nfriday feeling\nam id\nti ger\nscri p\nhelicop ter\nhard ware\nexpl or\nwork place\naustri a\nbeat les\nber nar\nspi der\ndisc o\ncul t\nlim its\nshor tly\nfin al\nnin ja\nlu ke\nle bron\nwal mart\no il\nvan illa\nshi re\nye g\nak y\nc s\nbl er\ncollec ted\nt g\nrol led\nspeci als\nb ff\npier re\nsh im\nvi er\nflash back\nrestor ation\nindividu als\npro d\nfre aking\ntu rer\no a\nre fre\nmor oc\ngre et\nre yn\ncare ful\nour ing\nu sh\nis d\ng ill\nvie w\nthunder storm\nb led\npic nic\nguar di\npi g\nar k\nsyl vania\nbann ed\nu cl\nvi jay\nori um\nav engers\nbeliev es\neu r\nmonu ment\nconcer ned\nla bs\nber g\na ap\nvi sh\nsing les\ncan cel\nz el\nar ab\nru th\ntoo th\nar ta\nsh af\nchair s\nr ack\ndise ases\ncrow d\ncl y\nfle x\nchrist ma\nartif icial\ntom at\nfin e\ndra ws\nadvoc ate\nfran ce\nÙ Ĭ\nðŁĺ ³\nheav y\ns our\ncompre hen\nno ble\naa p\nhin du\ncor al\ng ars\now en\nn l\nst all\nyel low\nmar ina\nin ver\nsuppor t\ntou gh\npromis es\npi e\nmaster piece\nsco re\nfor ce\nmor tg\ncrypto currency\no x\nr ors\nrock in\npro vin\nho g\nno stal\noak land\npat rick\ninclu sion\ntra ffic\nah med\na ha\nlux ury\ncon secu\nde mon\nâĸ º\nb lowing\nst ag\n: \"\nencoura ge\nben e\nsku ll\ndo dge\nbu ster\nkin son\nwit ne\ner ror\nlo west\nfel low\nà °\nsh re\nbl ur\nvir gin\ncompos er\nsli p\nmor nings\nga ins\ntab le\ngra in\nari st\nbraz ilian\nw we\ntu es\nribb on\nan ag\ndi st\nsac rif\nem brace\nentreprene ur\naf fili\nde o\nt ali\ntouri st\nfat al\nì Ĭ\nautom atic\nðŁĩ µ\nwe ak\nwel fare\nconfir m\nbenjam in\nfi ghts\nalleg ed\nme ad\nstrugg ling\npro secu\nche f\nà ¨\npropos al\ner n\nðŁĺ Ħ\ndy k\non gs\nhon g\nm ack\nmel on\non ent\nru sh\nd ap\ntol er\npro pag\nc ze\ntrans lation\nwal let\ncott age\nsa il\nconstitu tion\nðŁĴ Ģ\nmun ici\nfav or\nstorm hour\ni h\nðŁĺ Į\napproach ing\npin ned\nj ed\nniger ian\nn ach\nsh at\nparticul arly\nmc don\ncamer as\nanni e\nadmini str\nhe at\nelectr ical\nchar ming\ngib son\nbouti que\nex posed\nac tor\npil low\nbeach es\ngenu ine\nmargare t\nben nett\nlou isi\npos itions\nel y\nshin y\nten tion\narchitec t\nren tal\nac qui\ngoo gle\nsub way\nmom ent\nðŁļ ¨\nri m\nmetho ds\ncy cli\nnor folk\nÙ Ī\nover whel\nra pid\nwe ar\nhappy birthday\nprogre ssive\nðŁĴ ¥\nco gn\npap a\nf ool\nphilosoph y\npol ar\njim my\nwi g\nðŁĴ ĭ\noper ating\nreduc tion\nph i\nfla gs\nto the\no di\na res\nk oo\nk ang\nar kansas\nash ton\nwimble don\nsci fi\nattrac tive\nmississi ppi\nlogi sts\nral ph\nla bel\ngradu ates\nma ha\nhome town\nâľĮ ï¸ı\nfoun ded\non the\nli z\ntrans l\nmini mum\npre sti\nta m\ngener ations\nre bel\njourn alists\npar am\nmc m\nacry lic\ndeath s\ntes la\nw t\nbry ant\njer us\ni stanbul\nmuham mad\nri ley\nk ris\nwork shops\nis o\ncoun ts\nstre t\nprote cted\ntrin ity\nman ual\nr hin\nr il\npleas ant\nle mon\nner d\nhar der\ndar ren\nbur y\nra h\nbas is\nmi gu\nocca sion\nli sts\nâĿ¤ï¸ıâĿ¤ï¸ı âĿ¤ï¸ı\ne b\nde cre\nhamp ton\nìĿ ´\ntra vis\ntrans form\npuer to\nnh l\nav oc\ntri ps\nunexpe cted\nve t\ndi dyou\nbar ber\nst ages\nm son\nre presented\nfor t\nl al\npp le\nnic ely\nignor e\nqu il\nqu inn\nh k\ncarri er\nremin ded\nam ong\npass enger\nel len\ngue z\nsc ape\nmu ral\nyoun gest\nma sh\nd ill\nrout ine\nstain less\njack son\ngand hi\nth al\non ers\nedit orial\nconvers ations\nsd ale\nautom ation\ni ke\nา à¸\nðŁĩ ª\nhau l\nla ying\nmen tions\nam en\nabor tion\ni bi\ncoun ties\nca therine\nman ds\njam e\nroll er\nau t\nn am\no logical\ncep tion\nran king\ntox ic\nsn acks\nvictor ian\nbang kok\npsycho logy\nre g\nang ela\nrespon d\nsty le\nsophi e\ndak ota\nachiev ed\nmar ked\nimper ial\nin as\nglo ves\nsli m\nconfi dent\natt acked\ngg er\nlon ely\nvalentine sday\nre b\ncraft beer\norig in\nzim bab\nce iling\nte ens\nother wise\nw b\nf ers\nday sof\nadvis or\ny ah\nâĻ ª\nen der\nrepublic ans\nav a\nskir t\npi pel\nchi e\njan e\nja x\nðŁĺ ĭ\nâľ Ĭ\nj ays\nbre tt\nbal o\ncru cial\nd har\nas is\nde au\nlloy d\nchat ting\nâĿĦ ï¸ı\nrel ay\nremark able\nn s\nwe t\nbris bane\nðŁĶ ´\ntion ally\nf k\nla yer\nhouse hold\nconsecu tive\nes is\npend ant\nst ir\ncrit ic\nsu gar\nphoto shop\npa res\narti stic\ndo dgers\nc un\ncra fted\nam end\nbo at\nâŃIJ ï¸ı\negyp tian\nsa w\ntra ge\nsmall er\nox y\npa ired\nnex t\ni res\ntac o\no y\nu c\nst i\na erial\n: //\ndr o\ndot com\ngg ins\nr pg\nay e\nle an\nstri ker\nlo bby\nprote sts\npri ority\ncongre ss\nam ate\ninv it\nr ington\nmom my\nth us\nallow ing\npione er\nenfor cement\ng ori\ntal k\ndra g\ndu mb\nbul let\nsan ge\ner y\ntar gets\nðŁĩ ¦\nhe ather\nconsi der\nseaf ood\nve st\nris ks\n% .\np g\nsac red\nhe ating\nkick ed\ntto t\n. -\nchan di\nco ven\npo ol\npul se\ni a\nro ster\nshakespe are\nes a\ncar go\npean ut\ntro op\nac tion\ntab let\nhome work\ncast le\nstru ction\nmus icians\nfree zing\nbu tt\njustin bieber\nj j\nbah rain\nan them\nau dit\ndidyou know\nna vig\nguid ance\nâĸ ¶\ntur f\nn un\nfic ations\nye men\nchar ging\nx c\nbron cos\nsu bur\np ale\nbor ing\namong st\nfor the\nem per\nom fg\np j\nexpe cting\nðŁĴ «\nst l\nad min\nexpect ations\nsw an\nshoo t\noooo o\nmin ent\nãĢ IJ\nwall ace\nstan g\nsatur day\nadop ted\ndou bles\nhom ie\nome z\nd han\nvent ure\nsurroun ding\nfi le\nmob ility\nde es\nw ski\nbroo ke\nemb ro\nre members\nkar a\ntest im\nbo tan\nm tv\nsacrif ice\njerus alem\nd l\n ´\nproper ly\nili on\nas i\nleg it\nco pe\nm cla\nrecy cling\nlar ger\nðŁĴ ĵ\npat ric\ngener ous\nja red\np f\nmol ly\nthom as\nju dges\nh b\nsor ts\nbl vd\no ven\nenter ing\nplan es\nbe et\nintegr ation\nboo ked\nfre ed\nver n\nash es\nto pped\nde pot\nwelcom ed\nren a\nm ick\nd and\nsee ks\ngam er\nran kings\nren e\nmu t\nwhis ky\nfire fighters\ngu es\nga ther\ntour ney\nde men\ny ang\nnew ton\nautom otive\nback yard\ndeta iled\nmi st\nto bac\nfi ber\nun usual\ngrat itude\nsp are\nne ys\n: *\nper i\nflo ating\nfin alist\ndon ating\ndre ss\nbro ad\nbe the\neconom ics\ntai wan\ned wards\nplu g\npra iri\nval en\nbab a\nf ad\nan as\nhar per\ndis order\napp lied\np att\nbi kin\nli ver\ncu ri\ncarol ine\nann er\njuli an\nwal king\nmal col\nscreen shot\nco ding\nskin care\nactivi sts\nmyster ious\nex act\nblo cking\nmercur y\nbat ter\ndu mp\nâľ Į\nen se\nli sh\nridic ulous\nprote sters\nðŁĻ Ī\nlu st\nswe at\nas s\nali ke\nco dy\nre ments\nwin ds\nas pir\nvi enna\npra y\n.. .@\nbo i\ncand le\nassi sts\nte e\nder son\np ony\nf ence\ncon spir\nâĺħ âĺħ\noo th\ne pic\nba rely\na unt\nb am\ndiamon ds\nend less\nscre ens\ncan cer\ngr o\np st\npro spec\nmo sque\nhelp ful\nou ri\nbro ther\ngu jar\ncri sti\nine z\nto wers\nad dresses\ngra y\nbur ton\nre tweeted\nðŁ¤ Ķ\nn ity\ndu ck\nsuper vis\njo an\nkin der\nsanc tu\npi ed\nâı °\nł ï¸ı\nm ati\nreven ge\nce ster\neli fe\ndesig ners\nback ed\nbo li\nwei ght\ncou ch\nsu res\ns its\nshri mp\nla gos\nauth orities\nos ity\nhol ly\ncompu ting\nfac tors\nab e\npan els\nram ad\nsent ence\nmissi on\nhol m\nr b\nd ads\nshang hai\nmon ey\nshe ets\nsk ate\nthre w\ncup cakes\ninfin ite\nl is\npractic ing\ness ay\nka i\nas ci\nmo b\nu gh\nhol mes\nre gg\nik h\nmo ck\ncollec tions\npe p\no va\nsal t\nnan dez\nco y\nthre ats\ntex ts\ncin nam\npregn ancy\npen ding\nstam p\nflow er\ng is\nagre ed\npay ne\nro ver\nph ra\nsof t\nf fin\nfa thers\npass engers\naw ays\nal a\nh es\nli van\nin s\nsamu el\ningu i\nh of\nj j\nchen nai\ncat al\nom ic\nhe ath\nni ece\npump ed\nintegr ated\nare l\nno m\nproduc tivity\nwan ting\nvis a\ndi ana\ntw il\nit v\ncam ps\nro wing\nd ley\nblack and\ngu ards\nb ells\nre verse\nvi be\nric ky\nmo ss\nny t\nâĺ Ģï¸ı\nel le\ntro y\ncu dd\nev an\nwomen s\nfo to\nmi stakes\nwick ed\nmi l\nc led\nme mes\nco smo\nschol ar\nren o\nðŁĺ Ģ\nv ents\n# âĢ¦\nterrori sts\nca sey\ncardin als\nðŁĺĬ ðŁĺĬ\nvenezu ela\nbol a\nliter acy\nt w\nen o\ncon tains\nau stin\nfin anci\nev an\nhar vard\norigin ally\nchev ro\nher ald\nnott ingham\nmanag ers\nâŀ ¡\naccep ting\nwal sh\ntutor ial\nentrepreneur ship\nyach t\nrequi rements\nglen n\npe de\nunfortun ately\nach ing\ndais y\ngi an\nnight mare\nâĿ Ĺ\nr ina\nb art\nema ils\noppo site\nwho m\nsa ke\npu zzle\nda shi\npar ty\nblan ket\nbus es\nlo re\nbeau ty\nreas on\npun jab\nwinds or\nfunc tional\nexi sting\nhel lo\ngli mp\ncon vin\nla k\nscre aming\nrebec ca\nbli ss\nnorth west\ninfin ity\ncosme tics\npul ling\ncoffe e\npl ing\nop ho\ncolom bia\ninterior design\n( +\nemo tions\nsa c\nsun glasses\nsav es\nd f\nsix th\nal y\nðŁĺ »\nde en\ndev ast\npolit icians\nlac rosse\ng u\npe i\njav a\ncomb ine\ncoal ition\ner ts\nsurvi v\nch ad\nstri an\nn n\nde vi\ncoun c\nconcer n\ncontro ller\nbre ast\nj ury\ntu m\nintroduc es\nla di\nmobi le\nal z\nste ady\nnur ses\nh acking\non line\noce an\nðŁİ Ħ\na am\nju ven\nic c\nlouisi ana\nar te\nstreet art\nis on\nwn s\nfr m\np anda\nno ir\nmain tain\ndel ay\nsymp toms\nthor n\nge ome\nter n\ncarri ed\np ru\npan or\nas sy\nper u\nclou d\nsp ra\npe di\ne ste\ntag ged\nðŁĺ Ŀ\nshado ws\nnaz i\nا٠Ħ\ncor ri\nâĻ¥ âĻ¥\nj ad\nðŁĩ «\nform al\nspo ken\nðŁĮ ŀ\nenjo y\nlo pez\nout look\nin ho\nw ander\nÙ ħ\nma ya\npe e\nd ine\nãĢ ij\nbrief ing\nsuppor ter\nar ily\nght ers\nnatur ally\ndoctor who\nj en\nv ar\nnew year\nre se\nsi mm\nre x\ncon sequ\ntomat oes\nbur st\nbra vo\nbur gers\ncr acking\nnor theast\nbi om\nmush room\nmar que\ndou ble\nni er\nv ag\ntw enty\nkey board\nwin ni\njama ica\npar ish\n: -\nmental health\nali zing\nren der\nwa king\nðŁİ Ĥ\ng ly\nna than\nwa shing\nmel issa\njun g\nloy al\nchil i\nsong writer\nguit arist\nbo wie\nneighb ors\nonym ous\nas set\nta i\nhead quarters\nðŁĮ Ī\ni hear\nci gare\nsur g\n) \"\nre pl\ndar ling\nðŁĻ Ħ\nz ak\nsa re\nãħ ĭ\nmic key\nware house\nmass age\nine es\ndid nt\ni w\nhur ts\neng aging\nmag ic\nwomen in\nk itten\nmor s\nc art\ntit ans\ncolle ague\ncompe ting\ner an\nk hal\nmar ble\ndem and\ndel ight\net ary\nbli zz\nlou ise\nm ls\nfini shes\nexperim ent\nconduc ted\nelectr onics\nitt ers\ncar ing\nwh ats\nsym bol\njun g\ne cu\npi x\ncon text\nchar ger\nðŁĺ ĩ\nre ig\nfra g\në ĭ\nch ad\ntru e\nker ry\ndef ending\na int\nau ton\ncheck out\nbar nes\nless ly\nd t\nm me\nclou dy\nsecond ary\nare z\n_ :\napp a\nconst ant\n\" )\nve ts\njo b\ni ent\nðŁĺŃðŁĺŃ ðŁĺŃ\nm j\nfren ch\ndi ver\ndavi es\nhh hh\ne book\n๠ī\nmar iti\nbree ze\nsusp ended\nmat o\nvi et\nra hu\nse i\nbol t\nen ary\nle is\nkar l\nfr amed\nexpla ining\nab c\nde aling\nnat o\nja ke\nexp and\nleon ard\nestabli shed\ndu b\nar men\nel led\nvoc al\nnichol as\nori ent\nk yo\nillustr ated\nah h\ndanc ers\nmilli on\nge ta\npo pp\nas u\nmur dered\ngi ble\nsto ked\ngri ffin\nmaxi mum\nadri an\nen counter\nther o\ndavid son\nðŁį »\nholi day\nev o\nasse ts\ncar son\nmemor able\nâļ ½\nob am\nrepresent ative\ncb d\ntr icks\nvo gue\nvo ice\nmm mm\nsebasti an\ncli f\nath y\npar alle\nðŁ¤ ·\npa k\nev acu\ne ats\nا Ø\ntou ched\norgan ised\nspir its\ncan ad\ngui ded\nframe work\nðŁĮ Ł\npe d\nnatur al\nag ar\nreplac ed\nanch or\nti t\nsha h\norgan is\nsuper ior\nr n\nch ro\neric a\nst ill\ncor on\nchu ck\nloc ks\nor gan\nro sen\nsc am\nben ed\n/ #\nke en\ntre vor\nvamp ire\nsor ted\n! '\naf ford\nin tro\ngr ace\nðŁĺ ľ\nsau r\nkick starter\ninflu en\nv u\ny up\npo c\nðŁİ ¥\na ar\ns ang\ntre k\net sy\ntb h\nscre am\nchevro let\npix el\nshepher d\nan or\ngabri el\ntw ood\nsd cc\nme ters\ndevelop ers\nclo sure\nv w\ntwit ch\nì Ĺ\nse oul\npr ice\nho g\nn ish\nhill ary\nscrat ch\nin cen\nwag on\ndis ability\npan ther\nch ats\ng d\nwit z\nsus sex\nl ate\nden mark\nger ald\ncancel led\nnet te\ni x\nnav al\nbap tist\nte t\ny ad\nma th\nho y\nr andy\npo int\nintel lec\nfru its\nw ool\ngu in\npr on\nthe ft\ncon dem\nmar ry\nn ola\narchitec ts\ncin cin\nroc kets\ngentle man\nex plan\nt ate\ndo e\nra ises\nwild life\nw l\ninsi der\nblan c\nw p\nfor sale\nny c\npo well\nunbeliev able\npen s\ngoo dies\nmu stang\np ens\nst ays\nsqu ash\nxox o\nnear by\never ton\nco co\nle agu\nk han\nstu d\nsouth west\ncon struc\ns worth\ncro atia\nle a\nsu ms\naim s\ne an\nvan ess\niti ous\npa thy\narc ade\nb end\nsugge sts\nsac ram\nroy als\nri er\nem ir\nin cl\nan k\nclar k\nri ght\nvac c\nठ¾\ntan e\nli b\nu sc\nsal es\nhu h\ns ally\nver a\np ga\ngro ws\ndru m\ntre e\neth ics\nsug gest\nis ab\nse aled\npre viously\nanim ated\nab du\nri ses\nglo b\npre dat\nscar f\ndel ic\nom ar\nll i\nsx sw\npy thon\nne bra\nfun k\nreflec t\npav ilion\ntic ally\nch asing\nbak ery\ninva sion\nko h\nbeliev ed\nco hen\ncon qu\ncra fts\nnat i\ncle ver\ngovern ance\nsam ples\nfa ils\nâ Ķ\nti mo\nr itu\nstri king\ninclu sive\nsho cking\ncan t\nrequi res\ndra wings\nภŃ\npurch ased\ndu m\nz ach\nwar ner\ncon sole\nman sion\nfoun tain\ncircu m\ne sh\nis land\nmil k\npro fits\nhali fax\nri val\nâľĪ ï¸ı\njen ny\nsand ra\nny e\nk elly\ny al\nqu ad\nno s\ninste in\nfin alists\nmid fielder\ncu e\nexcep tional\na an\nsa pp\ngett in\nsa a\nf ati\nsl ice\nvol k\ns wal\nla sting\nsum mary\nit as\nsm o\ns z\nâĺ Ĩ\nip l\nfl ames\nene ws\nha v\nhoo die\npitch er\nwin dy\nre vol\ncentr al\nton ite\nðŁİī ðŁİī\nsol ved\nmil wau\norganiz ations\nwee ts\nre fin\ns th\nãĥ ¼\nel in\nton a\ncinnam on\nðŁİ ¨\nðŁİ ģ\nron aldo\npen insu\nome ga\nel ds\ndesig ning\ne igh\nblu et\nben z\nnu g\nash a\nrobo ts\nsu dan\nchoo sing\nen do\nser ge\nclo sely\nhand y\nfing er\nbe ing\nar te\nsurvi ved\nfl ame\nmile stone\ngu t\nd war\nfu tures\né e\nel o\nfri dge\neli c\nou ch\nu b\np v\ntit an\ncol lar\nst ation\nnev ada\naur ora\nr d\ndun can\nâģ ł\nbri en\nmar sh\nÐ ¾\nto tal\nch ry\ns ers\nsu ffe\nra chel\ncolle ge\nto days\ncour ts\nch it\nre united\ngym na\ngen esis\nbe side\nre presentation\nch ant\ncollec tor\nra k\nath ens\nni gh\nmun ich\nlangu ages\nfl u\nparticip ation\n__ _\nc v\nspec trum\nso da\nco ver\nrefe ren\nab bo\nap a\npublic ation\ned m\nmon ica\nar my\nðŁļ Ģ\ndiv or\ndr y\nstre ams\nrobo tics\nci der\nbull ying\nappro val\nsto ke\nplat forms\nsier ra\nex tin\ni b\nha yes\nsucce ed\nsuff er\nat ically\nda i\nlyn ch\nh ound\ndel ines\nack now\nd ated\nexclu sively\nhe res\nfac ilit\ndam aged\nchar ter\nla kers\nfal con\nunve iled\nwel ove\ne ase\npati ence\nl one\ngent le\ngene tic\nproduc ing\ng our\nshann on\nbil ities\nzimbab we\np int\ndau ghters\nliter ary\nbel le\ncl am\nsurroun ded\nk any\nne il\npir ate\nrang er\nhb d\nnat alie\nbel ong\nolym pi\nemb assy\nsc ol\nen er\nak in\nlo ren\nb h\n: /\ndi va\nden im\nhi pp\nðŁĩµ ðŁĩ\narn old\n? '\nwe ren\nem power\ndis abled\nman or\nrasp berry\nb af\naw ful\ndru mmer\nkar dashi\nn ash\nmachine learning\nch u\nrebel s\ntim ing\nmon roe\nton gue\nran ge\npup ils\nre ss\namaz on\nb z\nhar ley\npal mer\nballo on\ns ings\nic ec\nj b\nc ers\ng ps\nwhi st\nri se\nl t\noo oo\nc attle\nshoo ter\nvod ka\nuc l\nmt g\nle sli\njon as\ndi spo\nat ric\nste in\nvintag e\nfir ms\nflo yd\ncow boy\nsoo oo\nis aac\nwar craft\ndisney land\nbeauti ful\nbe am\nfranch ise\nbu n\nk ag\nan on\ntur bo\nswee p\nmade in\nkar achi\ndete ctive\npenn sylvania\ncontro versi\nvitam in\na side\nchron ic\ndescri bes\nremo val\nha h\nap er\nten ed\nu to\nbad ly\nmir ac\nf ry\nye a\nin jec\nther mal\ncomp act\nth or\nte ed\nur gent\nl ite\ng illi\nsop hom\nic o\nche m\np m\nfor k\nfre ak\nch ak\nrecipi ent\ni y\nni k\nmodel ing\nc ans\nðŁı Ģ\ndel ux\nse am\nsurviv ors\nrad ical\ninvestig ating\nreli able\nf m\ntur t\nligh thouse\nto ol\ngo wn\n) )\nbo ts\nauto graph\na id\nbu ffe\nh mm\nhorri ble\nssi onal\nann i\n๠Ģ\nk its\nsch i\neter nal\nhu ss\nsens itive\nr u\ntast es\nchec ks\nim o\npor tion\nsk ate\ne den\nhalf time\nfri ed\nri hanna\nti se\nfl ick\nca in\ns gt\nâľ Ķ\nsh au\nsta ined\nra ffle\ndro ve\nsal man\nprinci ples\nsh o\nar u\nje ss\ngu ine\ngar bage\nmy an\njel ly\ndis ru\nz ia\nq ld\nent ries\nla v\nfle w\nad mit\nobjec ts\ncomp are\nny times\ncann es\np n\nsuff ol\nro c\nd ana\ne gg\nhi st\ncoun sel\n' !\nphy si\nimag ination\nad just\nexplo sion\nplym outh\nhor ror\nelli ott\nbour ne\nde x\nbre ed\nau dio\nlob ster\ndisappo inted\nnation wide\n( (\nincre ases\naustr ali\nce dar\nstar ing\nrac ial\ne is\ng mt\nvisi ons\nstay ed\ndiscu ssions\nde an\ncur tis\nmai den\nstel lar\nhapp iest\nh wy\npre season\ncar av\nmon days\nhospit als\nglimp se\nschol ars\nja i\nter race\nann a\ngoo se\ngra ded\nlot us\nhun g\ngrocer y\nstam ps\nemper or\nsc oop\nin ser\nc as\nexist ence\nhe al\nfal cons\nmar vel\nreduc ing\nterri fic\nmagne tic\nperfor ms\nbar re\np us\ntre ating\nic on\nw h\ndecla red\ntra uma\ndo d\ncome dian\nnik on\nbu gs\nas m\nmont gom\nibi za\ncomprehen sive\nha s\nsan ti\nfellow ship\nda sh\np sal\nlouis ville\nsp y\nfau lt\nd the\nfi led\nvi sta\nde sc\nfe ars\nyou tu\nsp s\nes p\nri g\ncri me\nber ger\nwonder land\nk ent\nin formed\nstev ens\nmy th\nast on\nir i\nvisit or\nat ri\nproduc ers\nal la\nperson ally\nsepar ate\nagen cies\naf ri\nil an\nspo ke\nn ina\nsqu ad\ndi ves\nde pend\nli v\nfier ce\nenter taining\ncha in\nsc at\nbor ders\npal ette\nsp ro\nos is\nder by\ntobac co\nzi o\nwilli e\nju vent\nzoo m\nhol y\nenti rely\naf e\nmart inez\nbe ds\npe a\nbull dogs\nðŁĩª ðŁĩ\nib m\nne on\nethiop ia\nteam mates\nplan ting\ntw er\nany time\nfor bes\nó n\nrun way\nner vous\nro ger\np ile\nch anc\napo caly\nu w\no i\ndr ought\nterrit ory\nbr ick\ncre atures\ngo in\nw aff\ngre n\nsou theast\nje an\nam bul\ned ited\nstra p\nc v\naar on\nãĥ» ãĥ»\nt su\ndescri ption\nkin dly\nclu tch\nim mer\nen or\nwomen sday\nor ange\nra g\nob vious\nhy der\nchann els\nman go\nme yer\nra ining\nge tty\npil gri\ncoordin ator\nup load\nninten do\ndon uts\nsan chez\napp arel\nj r\nzz i\n, @\njeff erson\naccessi ble\ngreat ly\ne id\niniti al\nbudd ha\npar is\nma scot\nâ¬ĩ ï¸ı\nsch war\nsi ri\nsp inning\nmortg age\ne cho\nend ange\nge dly\nchlo e\nenh ance\nkar nat\nk ry\nexplo res\nðŁĴ ģ\naf fair\nic als\nall a\ndar t\ndolph ins\ndiffe rences\nsquir rel\nau gh\ndr ones\nell en\nre store\npa w\nun for\npi ke\nhil ton\ncolla b\nconsu mers\nco inci\nout comes\npp p\na q\ncoup on\nli est\nsi ms\nk ho\nav es\nspo on\npu dding\ncor byn\nhat ers\nex ams\nsla ve\n. !\np sa\napp les\ntam il\nse d\nco ke\nzz o\nlo sange\ncar bon\ncla ir\n... )\nk hu\ncra ig\nexplor ation\nsanctu ary\nsu e\nal way\ndemen tia\nwon ders\nsuper hero\npakistan i\nbrown s\nbluet ooth\nlo cker\nmar c\nev entu\ndelux e\nrodri guez\nâĿ¤ âĿ¤\nro bb\nðŁĴ ¦\nlin ux\nten s\nintellig ent\nse ed\nvo ter\ns ler\npe aks\ninter n\nteen age\npeninsu la\nhand ling\nti e\ncou sins\nwen dy\nme e\nà¹Ģ à¸\ndin o\nðŁĴ °\nðŁĺ ĥ\nze e\ns bury\ntrage dy\nb k\nbo re\nz in\nwar ns\nidi ot\ntou ching\ncontin ental\ntac os\nsaf ari\nwa shed\npo dium\nmorri son\nfore sts\nc bc\nal on\npartic ular\nbe ads\ninv ented\nlo ch\nli ghter\nwhere ver\ni de\ndocu ments\na we\nk r\nno where\nmin er\nst it\nro x\ncontribu te\nhar dy\ncl an\nob ject\nca it\nðŁĴķ ðŁĴķ\nhapp ier\nvege tables\nt art\ng ag\nnom inee\nheav ily\npan ic\nj d\nthere sa\nat m\nu ph\ns fc\nsu ri\ndrin k\nn al\nre vel\nk l\navoc ado\nnom ination\nma donna\nshar on\nmalcol m\ncontrol led\nsh ers\nrevi val\nlegis lation\nshoo ts\nn in\ncomm entary\npro s\nhuman rights\nstr anger\nmit ch\npipel ine\nleg ally\nth u\ngil bert\ntol l\ngran ted\ngh s\nir anian\nrefre shing\ndu k\nab i\npri me\njose ph\nmo sa\nstati stics\nproduc tions\nmer ry\npat el\nsa x\nhuman itarian\nstruc tures\ne missions\ntown s\nfre el\nster ing\nrat ings\nalle gedly\ncab in\nst l\nw ade\nfl yers\ntri m\npromis ing\nz u\nbal lot\ncompar ison\nfree ze\nou ter\ngreat ness\nas sign\nsnow y\nr ale\ntor ies\nmed iter\nkno ck\nconsult ant\ncincin nati\nanaly st\nsc oo\nje ws\nappro xim\npu re\nportra its\ncy rus\nation al\nlo ans\nacqu is\nel u\naccep table\nuni on\nwater color\nru st\nbatt les\nper fu\nseas onal\nser ial\nmind set\nri ot\nfel d\nenni al\nclo set\npri est\ntan ks\nint l\nscre w\nbu m\nab dul\nou x\nexpla ined\nric a\nimag ing\nlaw yers\nbu ried\nãĥ»ãĥ» ãĥ»\near l\nâĢ ķ\nl ton\nresto red\nstri pes\nfo ss\nde mands\nste aling\nalex is\nmun d\nak er\nur us\nwar dro\nhu gs\ngen re\ne go\nÙ Ħ\nparticip ated\nbab es\nban quet\nti ous\nhe mi\nds b\nlo st\nmilwau kee\njen ner\nge m\nou tra\nlo ses\nid i\nre ps\nðŁİ §\nregu lation\nfla w\nf ang\nvibr ant\nram p\nra ins\nwell being\nso viet\nvie wers\nde po\nlibr aries\nbi go\nser y\ng ill\nde struction\nco z\nc x\nbri dal\nal ds\nplan ted\namate ur\nlu d\nche ering\nshow cas\npro file\ni u\nver tical\npack ers\nwiz ard\nski p\ns light\nbe au\nair ways\nmu ch\nre ra\nðŁĮ Ĭ\nab sor\npati o\npack ages\ns ells\nment ally\nðŁĺ ¢\nreyn olds\nk are\ntri bun\nwal t\nkn it\nta ste\nsur rey\nboun ce\ncre ature\nb are\nbet ting\nsu re\nmi ley\nlaugh s\nal ore\ncy n\nt l\narti st\nann ah\nwar mer\ndynam ics\nlunch time\nmariti me\nvulner able\nðŁĴ ĥ\nwol ver\ndur ham\nconst antly\nam in\nsi bl\n: @\nbul let\nk ach\nangel o\nwil der\ndoo m\ndesk top\nlaw suit\nk ca\nhen derson\ninv iting\nbet ty\nta wards\nra fa\nle aked\nand i\nge ms\naf l\nvel o\nmediter ran\npro be\nto tten\nsteph anie\nsn ation\ncom be\nq s\nover come\nassas sin\nra v\nfil ip\nwinni peg\nsh il\ndetermin ed\nk as\nou tre\nregre t\ngui des\naa a\nðŁĺ Ī\nwi ves\nmani fe\ner ly\nsm y\nsh ima\nx ing\npix el\njac ob\nac commod\nto y\non o\npo o\nti er\nan swe\nðŁĴ ģ\nro sa\nle ase\nbel ongs\nth ar\neventu ally\nnei ther\ngo a\nski ing\nat ra\nag h\nbroad casting\nf ury\npy ram\nd ice\nvolk swag\nwom ens\nprovi der\nbom bs\nmiss ile\nwhi p\nd ick\nnor we\nback up\nel der\nmat ure\nconcer ts\ngi ous\nsque e\ngood morning\nbra ves\n^ _\nau ssie\nlun a\nmal es\nhe ck\nfor tn\nrome o\nsteel ers\np n\npe er\nre presents\n «\nkat y\nmigu el\nrequ ire\ncha ins\nl ur\nimmedi ate\nti mber\nâĸ¶ ï¸ı\nadvoc acy\nex port\nan z\ntiff any\nauth or\nðŁİ Ī\ndu des\nchil ly\nhi d\nhar m\nbu g\nmon ster\nterri er\ntu c\nstory telling\nta k\nin ti\nimmigr ants\nb is\nreach es\ncom passion\njohn ny\ncontribu tions\nðŁIJ ¶\nmechan ical\nimpre ssion\nran ks\nko be\nmen ting\nbloss om\npab lo\nbuil der\nbom bing\ntw el\nsul livan\nom o\npe te\nde mi\nku dos\nw bb\nt gif\nmass ach\nneighb or\nche fs\neng ines\npun e\nga ined\nphan tom\ns days\next end\ngr an\ncent ers\njac qu\ndat asci\nsleep y\nel vis\nanswe red\ns lot\ncon y\nflexi ble\nti ally\nle tics\n% ,\nandre ws\nsi ble\nmom ma\nvin o\ndo x\ninvit ational\ntwil ight\nj ade\nill ery\njoh ns\nf ou\np v\n-- ->\nbreak down\nbilli on\nprin ter\nmon d\nc bc\nmag gie\nlegi on\ndu b\nkur t\npo or\nparen ting\nregi ons\nbikin i\nbe ware\nsi onal\nau burn\nkid ding\namp les\nsp an\ncon tempor\nc ic\nha bits\nak o\npre fe\nbud dies\nit z\nem ily\nperson nel\nmoun tain\nver sus\nðŁĺ ¬\near ning\ns ink\ndar i\nu u\ns win\ni ster\nbru tal\nn ac\nkat a\nclo th\nam and\nðŁĶ Ĺ\nne o\nalu min\nweek ends\nnebra ska\nco des\ndelay ed\nbrun o\npro ven\nin c\ni ght\nfl an\nor o\nlam bert\nregu lat\nw f\nmassach use\nkardashi an\nbern ard\nfi esta\nvolcan o\ngrand pa\nanc a\nd re\nst itu\nmean ing\nfo am\nau ck\nat ed\nr l\nhot el\npers ons\ndy nasty\nell or\nma i\nam ne\nsty ling\navi er\ne g\nvege tarian\n, âĢ¦\nfoun ders\nsta in\ng d\ncy cles\nsky line\ntrac tor\nexi sts\ntra l\nkid ney\nmar il\ninst ag\nse tte\naddic t\ntri angle\nflash back\ncontroversi al\nz on\np ins\ni as\ntr ay\ntown ship\ndeleg ates\nsp am\nh ms\ncr ane\npeop les\no lo\nfac tion\nbut es\non ica\ndeleg ation\nnew profile\neli er\nmc a\nw and\ng ely\nlosange les\nber ke\nti ve\ndis rup\nzz a\ncas a\njor dan\nford shire\nga thered\nic hi\natten dees\nà¸Ń à¸\npe ppers\nco in\nbour bon\nern ity\nro tary\nbehavi our\njere my\nteam work\ncompli ance\ntre mend\nðŁĩ §\nbu hari\ncam bo\nbu yers\nha gen\nbu ds\nbay ern\nmon te\nsm ells\nan za\nath lon\ndescri bed\nwork force\ngi ving\nap i\ninvest ments\nda il\nsel ena\ndatab ase\nth um\nmor tal\nstu dent\nbu yer\ndo ver\ngar ten\natt le\nloy alty\ngen oci\nholo cau\ntheat ers\nru ling\nven us\npat ent\nch un\nab by\nawa ke\nmass acre\nbang alore\nbreak ing\nsimm ons\nju sti\nhal e\ned chat\ngg les\nhaw k\nmar king\nhead lines\nstro m\nco ve\nbreath taking\nmed als\nhair cut\nchrist ine\ntele graph\ngujar at\nju ra\ncan e\nsho re\npropag anda\nmu eller\n.... ....\nsa vi\nstom ach\nthro ws\nta b\nwar m\nj ong\nreno wned\nhi r\nra is\nmush rooms\nguaran teed\nbo a\nm j\nrevolu tionary\ncerti fication\nbru ins\njo in\nw es\npas sport\nc g\nsex u\ncap able\nw v\nton es\njac kets\nac compan\nspin ach\nfore ver\nbla ir\nwat ts\ng l\ncou ples\nprairi e\nnewprofile pic\nlogi stics\nmassachuse tts\njagu ar\no id\nwe al\nunder water\nmo z\ny i\nma ths\nmyan mar\npre ps\nsuffe red\ntr ace\nwal i\nah hh\nbor g\nst itch\ncu lin\nreal ise\ninfe ction\ndiscrimin ation\nsh ame\nan kle\nhu mid\ny t\nbrac ket\ntru ck\ntri u\nea ster\ncommun ity\npost card\ninvol ving\nty ler\ncar amel\nover view\nex amples\nintegr ity\nbase ment\ninstru ments\nani um\nat us\ngh er\nlaun dry\nachi eve\ngen eva\npr icing\nhyder abad\nbeli ef\nme ta\nj aw\naccoun ting\nlead er\ncristi ano\ncou ture\ncy p\nvis ed\n, ,,\nk nu\nh ick\nbreak er\nbr am\nra b\nmo or\nham as\ngradu ating\npupp ies\nak h\nta h\nach es\nri e\nop ini\ng ta\nre ign\ntra gic\nre ver\np ill\npine apple\ntou ches\nda re\nle ys\nil o\ninter iors\nsc outs\nbar t\nen zie\ndon o\nbro ck\nchristi ans\nense mble\n ·\ncine mas\nnew port\nair line\nwin ston\nle igh\ncont ents\npre scri\nur ge\ntr out\nfic ally\nil ia\nsub si\nare r\nâļ¾ ï¸ı\nw ounded\nðŁĻ Ĥ\npe pper\nðŁĴ ŀ\nfit ted\naf f\nre sur\nthursday thoughts\nz ero\narchae ology\ndi v\nje e\ni on\nawa iting\nco zy\nbeauti es\nbal d\ndat a\ngri zz\nstal k\nkin ds\ncle ared\njess ic\nregu lar\nali ens\nplac e\nbo s\nbi zar\nthisi s\nðŁĴ Ģ\ntotten ham\nma fia\ns lam\nari ana\ncar roll\nback pack\ncare y\nuni v\nr g\npe p\ndig it\ntatt oos\nag on\nvolunte ering\ndiffe ren\nconsu mption\nka thr\nhead phones\nt shirt\no b\nele ment\nre tail\nsh ru\nal gori\ncontain er\nconsci ous\nfi l\ncom ing\nra sh\nu rope\ndef ine\ngi or\nfemini st\nflow ing\nrout es\ngl aci\nfer t\nsomer set\nant es\ntwee ps\n$ $\nh our\nendange red\nyear sof\nro h\npo pped\nbac king\nba sil\nbra ke\nmon aco\nlgbt q\npra gue\nut ility\ncas si\ngate way\nhaun ted\nsch ul\nðŁİ µ\nshou ld\nwalking dead\ncomple ting\ndann y\nmontgom ery\npengu in\nss i\nmer chandi\nðŁij ij\nchur ch\nh ates\ncap tain\nbrea thing\nce t\nfair ly\napproach es\ncompan ion\nsurpri sing\nkany e\npe y\nhin di\ntarge ted\nlor ds\nde ut\ndi gging\nger man\nru t\nener gy\nclose st\ny un\napo logi\nภ±\ns ack\nru p\ndd y\nport al\nd ough\nb ats\nðŁĵ °\nat ur\ngraph er\npi res\nmo tors\nðŁĮ ¹\nj c\ndan g\ntu k\nclu e\nus c\npag e\nd less\nbro ws\nju s\nad ing\nre marks\noo m\ncar dio\nste fan\narm strong\nâĢ¢ âĢ¢\nni est\nbelgi an\nbi op\nso y\nlo f\ní ĥ\nq t\nflashback friday\nce e\nģ à¸\nwre ck\nmar ines\namend ment\nwardro be\nvo y\nbur ned\nguit ars\nra inf\nli fel\nssi l\noun ce\nexter nal\nc key\nme sh\nshe ikh\ninv itation\nsugge sti\npop corn\nphenomen al\nan onymous\ntun a\nchic ago\no val\ndel y\nloc als\n( &\npro f\nno vel\nfin der\nspar ks\nla ven\nin fu\nnic ks\nqu ant\nra e\nexe c\ndist ingui\nst ances\nmu tual\nsh al\nunve ils\nedmon ton\nzan ia\na dio\nvie wer\nbrad ford\naudit orium\nqu is\nre act\nhtt p\nl ero\nchee ky\nimpac ts\nta k\ned t\ndesper ate\nt ay\nì Ħ\nsett le\nbar gain\nresu me\nun ite\nthro wn\nke st\nse ys\nmar ching\nam it\ndecl ine\nsch ar\nme tr\nstan ford\nlin ke\nber ra\ndol ls\nrug by\njam i\nb or\nroad trip\ndino saur\nmi k\nsun der\nre m\nb k\nover seas\nnau ghty\nimple mentation\niam srk\nlun cheon\nfir ing\nmi ami\npere z\nthe e\nz on\ngi fted\ncon version\nceram ic\n¡ ï¸ı\npe dro\nì Ĩ\nv ick\n! @\nhe ed\nsi d\nb w\ndocu ment\npl un\ngr ants\nfant asy\npredic tions\nvali d\ncar ved\ngradu ated\nðŁijį ðŁı»\nnation ally\nch y\naf l\nre sso\nblan k\nri vals\nj ig\ne ties\nom ics\nune mp\nb ound\nsk o\ninspec tion\npar al\nhigh s\ncri sp\nb ans\nob a\n[ @\nco spla\ncostu mes\nrec all\nmou th\nni gel\nb ts\nter a\nko v\ndo cs\nwest minster\ndic t\ngra vity\nkar i\nro gue\nt ted\nwar k\nida ho\nw end\naw i\nqueen sland\nproce sses\ncli ffe\nm ick\ncom pens\nop ol\nthe y\ncl ari\nwiki pedia\nsalman khan\nhaz ard\npre ston\nswee test\npd f\nche es\ntr ilo\nsouth africa\nbur nt\n( $\ncon tain\nt p\nsub mitted\nsound cloud\nat u\nre z\nword press\ncorru pt\nn f\nma ker\ní ķ\npar as\nadv ent\nri al\nca fe\nfo ssil\n!!!! !!!\nco ws\nc j\nsp ur\ninstitu tions\nland mark\nent it\nre ut\nh is\nalz heim\nwe mb\nregg ae\nmo squ\nst at\nidenti fied\ndeal er\nre am\nre land\nten sion\nðŁĩ ©\nwra pping\ndeep er\nfr at\nred dit\nar is\nmoroc co\n.. \"\nb low\nma pping\npri orities\ning a\nswa p\nre wards\nconspir acy\ncreati ve\nc j\ncongre ssional\nvau lt\nple x\nsophom ore\nshad ow\nele ss\nðŁĺ ħ\ndar ts\naldu b\nanno ying\npro ps\nn as\nalumin um\nh bo\noffen se\nj ill\noni ons\nla ur\nta e\nhar dest\nsh ro\nga ining\nmeas ure\ned tech\ncyp rus\ntar a\nang eli\ncar lo\ngo on\nall i\nim plic\nju pit\nresil ience\nha il\nbal anced\n) ...\njoy ce\ngr a\nth eli\ndefin ed\nshi pped\nmain ly\nmin a\nl m\nsac ri\no ber\np im\nclaim ing\nent ers\nco rey\nbo k\ncri ed\ncool ing\ndani elle\npharmac y\nthor ough\nca ke\nk lo\noutre ach\nz ens\ndigital marketing\nval ent\nsn p\nher b\nmr w\ncaf é\ncap tures\nno tre\ntriu mph\npan cakes\ncu mber\nspi ke\nd ation\nbi gg\nsp er\ncrit ical\nam al\ntoo th\nfoun ding\na stro\n' #\nquan tum\nth ames\nun c\npri de\nair bus\nkno cked\nun defeated\nmediterran ean\ncal cu\nclo wn\nsens or\nham mer\nfor give\ncu shi\nber ry\nmaje stic\nelec t\npolit an\ng ta\nk ari\nbur ke\nsea hawks\nvolkswag en\nre i\nlandsc apes\ncas u\ngrand father\nlist ened\n/ /\nstar trek\nrainf all\nfur ry\nvi er\nstar k\nrif le\nff a\nleg es\nhillary clinton\nmin us\ncorrec tly\narchitec tural\npre ce\nup side\nbox er\nðŁĻĮ ðŁı¼\nis ai\nde t\npro vo\ntis sue\nspoo ky\nve led\nre con\nprospec ts\nque bec\nâļ «\nig no\nanat omy\nshap es\nw p\np interest\nhor e\nan es\npick up\nti p\npra desh\nhu gh\nco e\npo k\ngram my\nwell ington\nsti gate\nri gh\nlea p\nking ston\nscen ic\ngo sh\nv ani\nau g\ns ary\nzi er\nbure au\nlin son\ncon te\nfra gr\nall an\ng aw\nlan a\ncolli sion\nsurve ill\nren ais\nar range\ns ali\ndo in\nbr ance\nbren dan\nour se\nin coming\nsuspen sion\nà ´\nl la\neduc ators\nin tri\nda e\nbio graphy\nbul gar\nvilla in\ngo thic\nrw anda\ne w\nmay or\nmeet up\ndemocr at\nmor gan\nsu dden\nte sco\ncar rot\nbom ber\nmck in\nre ne\nfun day\nagricul tural\nhaha h\nshow time\nform ing\ncol a\nscor pi\nquo te\npo ppy\ns life\nd az\ntu b\nne n\nmo t\nðŁĺ »\ns ore\nelder ly\no ve\nskin ny\num i\nanc o\nman ship\nwe re\ng v\nk ah\nfol ding\nne at\nsamanth a\ndan ish\nuk rain\nhumid ity\nnu tri\njak arta\ncand les\noooo oooo\nat ile\nstreng th\ni bra\nbap ti\ncharle ston\nfr ames\ngirl s\nclear ing\nglu ten\n# #\nsuper natural\nju bi\nph one\nhe in\ndr un\nle ak\ninvest or\ny er\ndom ain\nball room\nmi sh\napp li\noff shore\nbla ze\ndor o\nâĺķ ï¸ı\nwin ery\nshar if\nad ore\nn ir\nsaf er\nsi gh\nas cri\nstrong ly\ntrac y\nck er\nol l\nfaith ful\ney ed\ndeli ghtful\nvis m\nkarnat aka\ntit an\nwh ar\njer seys\nre fur\nheav en\ngri p\npan ama\npre li\nglu ten\no dd\ncont ent\npon ti\ntion ing\ne commerce\nfeder ation\nflaw less\nge ar\nti res\nby r\npol ice\ncu ban\ntri butes\ntic ul\nchur ches\nnur sery\ndi aries\nmuse ums\nsnapp ed\ni van\nwi ght\ntouri sts\nramad an\nt rent\nprophe t\nwon dered\nfocu sing\nhi d\nic ons\ni q\nambul ance\npi st\nfun niest\ntime less\nsr ilan\nbu ys\nki ds\ncolour ful\na shi\nch ir\nmu m\nðŁĵ ļ\nlet ter\nx en\nreut ers\npre serve\nin ting\nste p\nfu ji\nuni ver\ni u\nshow down\npo ems\nsurveill ance\nsuspec ted\nta e\nsol ving\ntom b\nmother sday\ncar pen\nrecru it\npil ots\nbro c\nmix ing\nfri days\nty r\nrepresent atives\ntra pped\nabdu l\nfree style\nclu ster\nâļ łï¸ı\nk d\nsk ill\npit t\nex o\ncommer ci\nmuse um\nloc ally\ng ina\nno bel\nimmun e\nfr ac\ncap su\nmain ed\nattemp ts\nbull dog\nbe spoke\nsing ers\nsp elling\nseg ment\nnat ures\ntic k\nlip stick\nclean er\ngett able\npreci sion\nâĢ¼ ï¸ı\nth ood\nre ef\nno pe\nbill y\ndi gi\nmu si\nri val\nfigu red\ntal ity\nsun ny\nber k\naw ww\nawa its\nun real\nco pen\nasy lum\nex otic\nbu en\nmo ck\nen able\narch y\nfr a\npla stic\nal mond\namp li\ndispla ys\nabbo tt\ns me\nx p\nðŁĻ ĥ\ngraph ic\ni ved\nmar a\ncau tion\nlea ks\nen berg\nul u\nunic orn\ncann on\nappren tic\nðŁĺĺ ðŁĺĺ\nb ball\nwil low\nat ics\nam as\nmanufac turer\ncampaig ns\nport ers\nflo ors\nl su\nty pe\nke j\nhonor ary\nit im\nto le\nmin ecraft\nd x\nma sh\nri o\nconsequ ences\nron ald\ngo ssi\nsuffol k\nmu se\nr bi\nlive music\ni van\nðŁİ ¤\nle u\npatri ot\nman it\nlan ca\nhome decor\nde ar\nsig ma\nti de\nstr ings\nv ita\nsequ el\ntry na\ninve stigate\nbor is\nve gan\nbarri er\nmind fulness\nweb b\nhu stle\nin da\ntan zania\nstr ay\ntex as\nc ag\ndiagno sis\nwom an\ng w\nob session\nl ative\nnu fc\nfl ynn\nmoment um\nsof a\nwal d\nvege table\ntu cker\nsupp er\nse ab\nar ro\nse ag\nven ting\ncounc ill\nsp lat\ncal cul\n.. #\ncom fy\nodi sha\nsto pp\nwar fare\nca es\nà ¨\nco y\nprice less\nin sec\nðŁĺ Ľ\ncontro ls\nempower ment\ndatasci ence\nper pe\ngen ic\ne res\ntru deau\nman o\nsla very\nexpand ing\nma he\nfa iling\ns aga\nphotograph s\ncre st\nre on\nsurf ing\nhi e\nðŁį Ģ\nja e\nfel lows\nsouth ampton\nsol om\nce ster\ntab ility\nhor n\nse ct\nhe e\ncole man\nat las\nexplo rer\nconsul tation\ncopy right\norgani zing\nden ied\nmon keys\nnoo dles\nbr is\nfl or\ndou gh\nbon ds\nsho cked\neco system\ncare fully\nw m\napart ments\ncur ve\nsan diego\nmust ard\ncomm en\ncere mon\ne ch\nru th\nðŁĻĮ ðŁı»\nhawa i\nfil med\nte ar\nas ingly\nca ir\nwat t\ninstru ment\nou tta\nye ol\nriver side\në °\n. :\nnor wich\nalo g\nmigr ants\nnew man\nri de\nspr ink\ntarge ting\nbeli eve\ntor ch\nreflec ts\nper mission\nff man\nene mies\nbas ics\nse ized\nsun days\nle i\nhass an\nen do\nh c\nst ad\nle ments\nkk kk\nnan o\nshar k\nman a\non ic\ntreat ments\near ly\ncollabor ative\nshu ttle\nbran ches\nmis ses\nmained cm\nap ers\nky le\ncarri e\nleis ure\nsh et\nbir ding\nadv ances\nðŁĵ Ŀ\npopu lar\ndi ane\na be\nre war\nneigh bour\nk pop\nremem brance\nplay ground\nru b\nkrish na\ne bola\ninqu iry\nep a\nlu min\norgan isation\nabra ham\nnorm ally\npre ten\njan et\nw t\nðŁĴ İ\nencoura ging\na stic\nbu mp\nsyd ney\ns z\nss ss\ngar rett\nðŁĵ »\nconsul ting\nroman ia\nspo tting\nchanc ellor\nar ma\npresti gious\nðĿ IJ\nt ad\ncry st\ncompe tit\nrati o\ncat aly\nbro w\nj ur\nvi king\ncommu te\ny day\nla yers\ndu mb\nesc al\ngenoci de\nf ill\ngu pta\nste pping\nse i\nfo to\nwild cats\ncol i\nprojec t\near nings\nst r\nge ons\ncomple tion\nb m\ndecor ated\ncraw ford\naf ghan\nsc are\nvisi bility\nhi b\ndirec tion\nstro ll\nchrist ina\nalter nate\ncl are\nsty list\nbe hold\ns ance\nleop ard\nacqui red\nnarr ative\nash i\nthe a\n?? ??\npe as\nat ch\nsli des\nle en\nrenew able\neng lish\nqu ir\nco aster\nr x\nfo ols\nmatch day\nmis m\namaz ing\nz ig\nke ting\nwon t\nto wel\ndi ab\nsta ke\nn m\nmel t\ne than\ngra pe\npolit ician\nsm en\ní ĺ\nre o\nwedd ings\ncat cher\nor acle\nme mo\nðŁĮ ´\nec k\nrob bie\nnorwe gian\noper ator\nam or\nse wing\nju l\nx ie\nu v\nfif ty\nme ga\ntatt oo\nliber als\nu pri\ntraffic king\nrichard son\nsu v\nki p\nmess y\ntremend ous\ngl ou\ncour tney\nla d\nstere o\nmy ers\ni dio\n^_ ^\nman ning\ndy e\nw d\nthr one\njun k\nas u\nprovin cial\nk ook\nwr c\nfine art\nhamp shire\nrenais sance\nb red\nfall out\ns j\nsn l\nal am\ntor ture\nfy i\nsh ines\npa w\nch ar\nhen ry\nc row\naci ous\ndi an\npa ige\nba re\nstock holm\nscen ery\nðŁĩ ·\njef frey\npu sh\ndecor ation\nne d\ncu te\nbrig ade\nlaven der\ninv ites\ne sports\nvo ir\ndri ed\ntran spl\nsur geon\nno vels\npul ls\nson y\nlun ar\nman e\ni vy\nfru str\ndor set\nsa i\ntor res\nssi on\nshut down\nsuggesti ons\nwrit ing\ne o\nbattle field\nu ga\nðŁIJ ¾\nvac u\nspl ac\ng it\nu g\nhigh land\n% )\nmer maid\nsacram ento\nta ils\np w\nka h\nt ell\nenh anced\nì ķ\nauck land\ncru el\nðŁ¤ ©\nau dre\nsail or\ngram mar\ng love\nde on\ninfl am\nfresh ly\nk ell\nzi p\nchristi e\nmil d\ndi xon\ninstru ctor\ng ence\nãħ ł\nsub jec\nconstitu tional\ncrow ds\nin visible\nru ins\nda k\nsi p\npla que\np ouring\ncomple x\nz ine\nste ad\nf let\ntrans mission\nlo way\nar un\nincre asingly\nau d\ntransp aren\ncro wned\nsc oun\nblizz ard\nlux u\nfi ers\nachieve ments\nhun ters\nrock ed\nbas in\nvio let\npro ves\nachiev ing\npro sper\nse ga\nflo at\nvi an\nxi v\npol ic\ntur a\napproxim ately\nwander lust\nkeep ers\ngeta way\nco d\npol is\nbr yan\ncol ts\ntal ents\nyo gur\ngluten free\nwri st\ngr y\ncze ch\nðŁİ Ī\nev ille\nðŁı Ī\nto x\ndani els\nam er\nbi ds\nweare one\nme tab\ng t\nboy z\npd x\npos session\npu shed\nshr ine\nreali stic\ntri gger\nna vi\nru mors\nn af\njen kins\ntr un\ncomm uni\nà Ĺ\ngam ers\narm or\nmoham med\nbal cony\ny ah\nstron gest\nrhy thm\nunfor gettable\nk p\nho bb\ncusto dy\ngreg or\nr ita\naes thetic\nil ation\nsponsor ing\nn ay\nkid napp\nsh s\nra jas\nme g\nsignific antly\nbutt ons\nla c\nver sions\nessenti als\nopini ons\nk ro\nd printing\nwi dely\nd k\nur an\ny al\nreque sted\nc n\ncur ric\nplu m\ngr un\nv m\ndev on\nm yo\nrel ation\njuvent us\nrou ge\nmin ority\nmin es\njupit er\nn ine\noxy gen\nfran kie\nune sco\nfab ric\ndisgu sting\nsal man\ndete ction\nlan ka\nd ac\nðŁĩ« ðŁĩ·\nargu ment\nshel ves\ncel tics\nrober to\npi gs\nhe dge\nfau l\npow ering\nbutter flies\nfi r\nre make\natt i\ncom o\nemp ha\nkend all\npoke mon\nse ating\nd ans\nbald win\nðŁij »\nlesli e\none direction\nti mber\nim an\nfon t\ne der\ndi on\nste ph\nfor mat\ngre gory\npro p\nhe x\nru in\nsor y\ninf er\nn aw\nbar ak\nsd gs\nkar ao\nlu sh\nv ander\nend ent\ng is\na fro\nsoc cer\nay an\nt uni\nlun g\nda yof\nalex a\nmar ath\naddic ted\nag ile\nhy gi\nlight weight\nì §\nmand ela\njo ey\nanc y\nhu m\nbi r\nmemor ial\njim in\nging er\nv ak\njav ascri\ncro ps\norig ins\nd ari\npi per\nim port\naggre ssive\npredic tion\nre pairs\ncr acker\nvoy age\nni ke\nmu mmy\nlinke din\ncountry side\nbor der\ngla ss\nper t\ns als\nsho e\nautograph ed\nwal nut\ncolle gi\nsal ary\npa iring\nðŁĮ ¸\ncath ol\nswee the\ndefe ats\nstreng then\nroof top\nimpro vements\nbarri ers\nur u\nt ally\nru led\nðŁĨ ļ\nnai ja\nemo ji\nper cent\ngi o\npro bs\non ce\nadm its\npa ths\nli ar\nday tona\npe ters\ncal i\ncal li\nmu g\no sa\nap h\nab y\nhy de\neth nic\npla ins\nol f\nhaha hahaha\nholi c\n?! ?!\nsu bli\nbl acks\nmo t\ngh ton\nlo vin\nb rent\nbar u\nl ati\nde w\nate au\nq a\npain ful\nbu sters\nst atic\nðŁĩ¨ðŁĩ ¦\nnote book\nout fits\nsi es\nr f\nfloo ds\nÑ Ģ\nthro at\nsu ici\nro vers\nbeng al\npre pares\nblo g\nmini ature\nØ ¨\nam phi\ncom b\nr sp\nin timate\ngreen e\nÌ ĩ\nal tar\nsurg ical\nves sel\n... ?\ngav in\ng ator\nthreat ened\nz ar\nrob bery\ndi er\npromo ted\ny g\nx s\nsu bs\ninter viewing\nthreat ening\ndo zen\nme ado\nwater fall\nnintendo switch\ncal um\nmini sters\ndro p\nunivers ities\nwar ned\ntac tics\nðŁĩ ²\nrefu se\nad ju\nv ast\nðŁĺ ´\nmc fc\nlib ya\nno filter\ndistribu ted\nre ser\nron nie\nde co\njavascri pt\nmon k\nintere sts\nfle x\nmar tha\nsti es\noo d\nðŁ¤£ ðŁ¤£\ne un\nb ali\ng omez\nsti mul\nmoder ate\nd ity\nir is\nstra w\nconsist ent\ndirec tions\nadop t\nsal sa\ncro o\nreco vered\nblack friday\nlan caster\naccep t\nweareone exo\nbuil ds\nfree man\nair plane\nditi on\nbel ong\njam ie\npit ching\nli f\nom in\ncri spy\npre pping\nve g\nchan g\naccompli shed\ngraci as\ndolph in\nelec tor\nculin ary\nsuper bowl\nwal a\npur suit\nblack berry\nbe an\ncardin al\npro ved\nimmigr ant\nstric tly\nholocau st\npass age\nha us\ncou p\npur se\nhar ass\n< <\nle ed\nado be\nst ad\nlegis lat\npar ked\npri yan\nsil va\nkri st\ns the\nfun ky\nig a\nsett lement\nph s\nt mrw\nstre ssed\nhun t\nho ckey\ntreas ures\ncham bers\nol u\nhu t\nmar ley\ntex ture\nwilder ness\nmm ing\npoten tially\nom aha\nju dy\nto es\nspo iler\ndistingui shed\nfeli x\nah u\nrecommend ations\nzom bies\nhit ler\ntri ple\ncolla pse\nmotiv ated\nulti mat\ngg ling\nso y\nci gar\nfo ren\nvine yard\ngl itter\nfin dings\ncolon ial\nhun ter\neri k\nden s\nbeet le\nlot te\nsub tle\ns matter\ntru sted\nexperim ental\nnam ents\nðŁĺ Ĩ\nregi on\nacquis ition\nbre eding\nquarter back\nam reading\noo td\nru de\niniti atives\nst out\nhy ung\nout come\nal fred\nmic s\nexper tise\nbacter ia\npengu ins\njump er\nvalen cia\nbar k\ning day\nsell ers\ncontrac ts\nhou ston\ncommissi oned\nadap tation\nswan sea\nsanti ago\ncommon wealth\nju dging\nsub mission\nsco rer\ntom my\nñ o\nex quis\nfil ing\nexplan ation\nalli son\nwemb ley\nri dge\nchev y\nsan tos\nown ership\ncogn itive\nfavour ites\nsh ed\nphil anthro\ndele ted\ngo dd\ns nor\ngui delines\nff ing\nje ep\ncli ps\nsw amp\nan or\nguil d\nbol ton\nspring field\nmunici pal\ngoal keeper\nye on\nðŁĺįðŁĺį ðŁĺįðŁĺį\nãħĭ ãħĭ\nwater front\ngra ve\ncontempor ary\nar ity\nÃŃ a\nsle eps\nsy rup\nal am\npi re\nco yo\nmoto gp\nty son\nkej ri\ncir cul\nsing ly\ncr unch\ncomplic ated\nnostal gia\nk op\nmo ve\nk ale\nmac ro\nmid west\nh ans\ntri bal\nnu de\n௠į\nbey once\ncongratul ate\ncat er\nleagu e\nðŁĻ Ĭ\nla dder\ncra shed\ntech nic\nkarao ke\nharass ment\nro ts\nexperi encing\nkri sten\nðŁĩ ³\nðŁ¤ Ĺ\nreflec tions\nguin ness\nillustr ator\nðŁĻı ðŁı»\ncen ter\nnar row\ncomm ons\nregul ations\nÙ Ĩ\nhar m\ncro ft\ncu ssion\nhong kong\nst ical\nintern ship\nzo e\ncho p\nhoo ds\nestim ated\nbatter ies\nberke ley\nsmooth ie\nshau n\ncro s\n~ ~\ncam pe\nhu mp\nb g\nproto type\ncl ick\nshaw n\nre viewed\ntem pl\np f\njed i\nblo gs\nray mond\nas th\nba h\nav ail\nscot ch\nleaf s\nnik ki\nto k\nhol low\nur ges\nof t\nun like\nlat in\nu e\ncat ering\nmil i\nalter nati\nma ver\nÐ ¸\nag le\npre order\nlu x\ncu cu\nðŁijı ðŁijı\nt art\nâĿ¤âĿ¤ âĿ¤\narab ic\nrapi dly\nar rang\nall en\ntravel tuesday\npa ws\nflo ws\nst ability\nflu id\nca pp\ncan berra\nuu uu\nsp ani\ndemon stration\nm la\nplac ement\nm w\npresi dents\nawe som\nbever ly\nani st\nne al\nfather sday\nreferen dum\nla hore\no aks\ndeb bie\nhalf way\ngho sts\nde bor\nmatthe ws\nfi at\nt fw\npre sen\nrob i\nde d\nbro ck\nlaugh ed\nam ounts\nbam boo\nkinder garten\neat en\nmtv hottest\nbreak out\nu sic\nfra ser\nlegis lative\np ang\nmodu le\nsam my\ngo ver\near ns\nexpe dition\ngar h\nconcep ts\nchar lie\nla va\nbachel or\nveg gies\ndeter mine\nel lie\nun locked\nfru it\ndal la\ncou pe\nwash ington\ndepo sit\niv ory\npau la\nchic ag\ngu cci\nðŁİ ĥ\ncul tiv\npier ce\nli fted\nstu mb\nre cover\nmusc les\nconduc ting\ncb s\nmcla ren\nsophi a\ncel lu\noce ans\nup loaded\ngame play\nmal dives\nkim ber\navo i\nrac er\nca ine\ncav s\nh ana\nli ga\nra ven\ninter vention\ninaugur ation\noo h\nat traction\nmerchandi se\ntune in\nli king\njuni ors\nint ended\natt acking\naqu arium\ni wd\ncomp onents\nsur ing\ncent u\nyogur t\nðŁı ĥ\nshow room\nop tical\nty our\nju dge\nyi eld\nan to\npl c\ntransparen cy\nrecy cled\nchi ef\nar om\nambassad ors\nplan et\nâĿĦ ï¸ı\nom ed\nvaness a\ncour t\nmar gar\nhal ey\nv r\nreg ina\npd ates\nhi span\nlive stream\nâģ £\nya hoo\ngal la\nsecu red\nw ir\nbene ath\noff l\nn il\nam b\nye g\nout let\nu te\npe ep\nlind say\nbent ley\n... !\nhe el\ntrilo gy\nvo s\nty re\nthere fore\ntor onto\nab i\nsimp li\nja e\nexten sive\neleph ants\ns or\norient ation\nim peach\nre play\nconstru cted\npeter son\npa is\npor ted\ncustom s\ncolla p\nad u\nhigh lands\nsal em\nshel by\nko vic\nstra in\nro sie\nsen ators\nsnap s\nbo bb\nsuz uki\nbla des\nk p\nlo lo\ngener ate\nsi ght\nma e\nstruc tural\npredic t\njump ed\nah mad\nsun g\njust ice\ngla m\nvol vo\njubi lee\nde tention\nlo sses\npu ri\nevery time\nÐ °\nra o\ned ge\nli mer\nrese mb\nhar old\nre tri\nsacri fic\nsurpri ses\nam c\nsrilan ka\nbar bie\nmen s\nfin n\nag s\nukrain ian\nem brac\nî IJ\nflav ors\nhom er\nlau re\nou th\npr iced\nver de\nfir m\nah s\ncu b\ntre y\npar anor\npro fit\nin dv\nwho a\nhar sh\nal ot\ncrit ics\nhu bby\nfi gur\ngi ra\nca stro\nchan el\nin put\norigin als\nten ant\nyy yy\nture rs\nlincol n\nco on\nlear n\nch ou\nac are\no les\ndin er\nhy p\nbizar re\nmc r\nlet sgo\ndecor ating\nðŁĮ İ\nal ison\nar vin\nf d\nreha b\nmccar thy\nlot tery\nda h\nminne apolis\neli gible\ndiagno sed\nemer ald\ndestin ations\ns ans\nor y\nbla zers\nn v\nba il\ndigital art\nno c\nmal ta\nsol ar\npi pes\nalleg ations\nno ck\npo pe\nbri d\npremi er\nn x\npresent ations\nef a\nbo ws\nval ve\nopp onent\nĮ ë\nvisu al\ning le\ncate gor\ne ter\npo is\ndan i\nat tract\nneu tral\nth ene\ncra shes\nfred die\nut ili\nc st\nawak ening\nslo ven\nquali fy\npro of\nfair y\nle v\nfre ight\nenjo ys\ncup cake\nflav our\nâ ķ\nprotec tive\nðŁijı ðŁı»\nis u\nad mir\nh mmm\ncontinu ous\nai res\nrap tors\nshowcas ing\ny uk\npa ste\nfollow er\ninstru ctions\nsp ru\n@ __\nthe o\ndebu ts\nve tte\nsto w\nes of\nach ed\nsul tan\nsand wich\nsom alia\nfranc o\ncar ne\nflu ffy\nal pine\njas mine\nhe ated\nviol in\nple ss\ndivor ce\nper former\nphi es\nport sm\ndar a\nkir by\nlo p\nchill i\nfor th\nsky pe\nðŁĩ®ðŁĩ ¹\ncelebr ities\ned y\nve e\npo ison\ney el\ngra bs\nssi c\nun o\nwester n\nrail road\nam er\nnumer ous\ns v\nfo w\nfi st\nâĢ ĭ\nreque sts\nmar tial\nem my\naccept ance\nlau ra\nภ´\ner up\nhyun dai\nout lander\nu tt\nwrest le\nesp resso\ndemand ing\ng dp\ngeo graphy\nsas kat\ntro ll\nconfe der\nsu es\nse m\nbe ts\nt ful\nto sh\nteach es\ncol oured\ngal way\nmac y\ndis orders\nbb cra\nat em\nfen der\nlit ter\ne sh\nprovi ders\nrenov ation\nnomin ate\nps g\nnomin ations\njen na\nshar p\nsome day\nz ur\nbra ins\nche shire\npre y\nhu go\n ¿\nto ken\nr v\ncar r\ntac tical\nzel da\nkay la\nfern ando\nphotograph ers\nj our\numb rella\nwoo dy\ncongress man\ndu mp\nle vy\nju an\nd azz\nsign als\nla in\nan u\nmic hel\npor ch\nal den\nsibl ings\ny ale\npe el\nsw ick\ngg in\nll c\nk ale\ns con\nil d\npat reon\nre el\nqu in\nwit t\nmar ty\nmoo dy\nton i\nder y\ng ators\nspeci fically\ndd in\nly on\ntr ick\nmeado ws\np j\nbor gh\nvi k\ntu r\nbron x\npu ff\nlan tern\nðŁ¤ ¦\ng ently\nbe stie\nfac t\nrefu sed\nfas ci\nmp y\nðŁĶ µ\ncross over\nmead ow\nindian apolis\nduc ation\nsle y\nloo m\nmix er\nnew music\nfilm maker\nprosper ity\nli m\nweek end\ncre amy\nneu tr\nlu ther\nh v\nnor thern\ntw o\nh ra\ncat ches\nappear ances\nha bit\nkitt ens\nn v\nilla c\ninf an\nregar dless\nliz ard\ndun k\ncur tain\nac om\nin tu\nve z\ne min\nfl ats\ncalend ars\nem power\nru ined\nhun gary\nvi d\nwe x\nu lum\naber deen\no sa\nk t\nma ssi\nse emed\ns den\n' ?\ntele phone\nde fi\ninsp ires\nme ow\nz ones\nbl ind\npl y\ntuc son\nadvent ure\nge d\noy ster\nðŁijıðŁijı ðŁijı\nout put\ntt t\nmetal lic\nsma sh\nucl a\nsco ts\nperfe ct\nlu cy\nregular ly\nsp ic\nrel ative\nath ers\nmis e\nbatt ling\ndeci des\nmat a\noccu pied\nrandom ly\ncat softwitter\ngi an\nball y\nal ties\nal lies\nim men\nsy rac\nðŁĴľ ðŁĴľ\nl lan\nau r\nk ut\nlam ar\naffe cts\nn ra\nstar war\nðŁ¤ ĺ\nsc ram\nen chan\npro cess\nluxu rious\nar ray\nsher lock\ncomp ati\ndor f\nstre ss\nm su\ns with\nsal a\nsof instagram\nfo il\nunder stood\nqu ay\nr p\nc ade\nja w\nen ab\nen coun\nðŁİī :\ndo ck\nsatur n\nmu ll\nlay out\nra rely\nhapp ily\nfix ture\nor ph\nover looking\nher bs\nm itt\npil lar\nnol an\npe tty\nstr y\nu i\nmu k\no res\no vers\ná µ\nre creation\nwe sley\nri t\nkejri wal\nsto cking\ng v\nsubscri bers\nmoo se\nma e\nber t\nopp re\nassign ment\nu ro\nhigh lighting\ncal vin\nwe igh\ncambo dia\nav on\nke m\ndis abilities\nread y\nchar gers\np ads\niz ing\nilli an\ntru ste\ncol leges\nassoci ates\nalban y\nmil ton\ncr on\nbu r\nhar dly\nsi ghts\nanti ques\ne cho\nsurpri singly\nha iti\ncap t\nph p\nop io\nine quality\nequ al\nken y\nsch mid\nautograph s\nren t\nqu er\ncit rus\nchalleng ed\nte c\nepi de\nfe st\nz hou\nli me\ncitizen ship\ncry stal\nconvin ced\nmess enger\ncopen hagen\nâĿĹ ï¸ı\nwar ran\ndevelop ments\nï¸ı âĥ£\nfore x\nhi ro\nsne akers\nxi de\nvi va\nstere o\nbat ting\nss el\nho st\nbeng al\ncritic ism\nq c\ncr un\nattemp ted\nry e\ndetermin ation\ncre ations\nd read\nlabel s\npos se\nanc er\njoh an\nsi ster\npartner ships\nles bian\nk st\nguaran tee\nbar o\nfix ing\nma son\nm ous\nchem icals\nt less\nbio diversity\npar o\nbhar at\nac ol\nrefu ge\nen te\nt iti\ndys sey\nrespon ds\nlef to\nin er\nse vel\nrahu l\nol ine\nfrank fur\ncho reo\nenjoy able\nc to\nstrugg les\nwood land\nheavy weight\ngen s\nrece p\nac cred\nðŁĺ ¡\ntrans formed\nlist en\nat op\nn k\nsur ge\nbe re\ngover nor\nprison ers\nclau de\nt ill\nmu lator\nemo tion\nwater loo\nstar t\nðŁĩ º\nclean ed\ngrand mother\nfear less\nafric an\nastron omy\nðŁı ģ\nภĻ\nthe world\nsu itable\nanth ony\nk and\ntt en\nmeaning ful\ndisc lo\njaco bs\nà ¸\ntom linson\nghe tti\nty pho\nsub stan\nas co\nte k\nnag ar\nmu d\nam on\nvacc ine\nf ty\nfle sh\nno el\ninfl ation\nportu gue\nglam our\ntra m\nv re\nte qu\nroun dup\nw yn\nrejec ted\nmosa ic\nsi ghting\ncal f\no ta\ncom position\ngo pro\ngonz ale\ne ed\nb ard\ntu e\neffec tively\nwe en\nal to\nri bs\nrel ate\nthir sty\nfu rious\ndi m\nch ard\nperfu me\ns ny\nchur chill\nk of\nmaster class\nwa ve\nðŁĶ µ\ner in\nown s\nto be\nsk illed\nte m\ngo f\nen i\ntor i\ncra zy\nl ick\nresi stant\nici al\nag ar\n! :\ng ali\ndel aware\nbl itz\nkoh li\npu ck\navail ability\nhi malay\ninflu ential\ncro chet\nvictor i\nread ing\nho bby\nvie t\nj as\nen gra\nsk ul\nðŁĩ² ðŁĩ\neduc ate\ntech no\ndistric ts\nblu es\nse tt\nseven th\nlear ns\nee ee\napocaly pse\nhang out\ncru el\nmu tu\nbru h\nhel en\nshe er\nc tion\nkle in\ntex ans\nce real\nsh ine\nne red\ngra s\nam bro\nf ella\nhin du\nmatthe w\nli ma\nmir anda\nje wel\nso ho\neuro vision\nneighb ours\nchand ler\nbe sides\nðŁ¥ °\nast ros\nthu mbs\nren ault\nra ve\nhi red\nðŁĸ ¤\nit ary\nz or\nbla zer\nk ine\nea u\nkat y\ndc comics\npe c\nro dgers\nwater proof\nkill ers\nsuper int\npre serv\nas so\nbrew ers\npromo tional\nsc am\nvilla ges\nsket ches\nju icy\nfor life\nau dit\nso lo\nfundam ental\nlen e\nphilipp ine\nt end\nconserv atives\nsponsor ship\ndd le\na ine\nh tc\nos i\nhul k\nw af\nภĻ\nevalu ation\nant ine\nsle e\nrobert son\nroo sevel\nag i\nsophi stic\nemplo yers\nbubb les\nko wski\ninter action\nsh u\nbou le\nic an\nj are\nhan k\nleg itim\nk nicks\nkar ma\nrecei ver\nper ks\nu h\nsta ir\nsun i\nlabor atory\ngra ves\nvoc als\noo t\nc ture\nthri ve\ntic o\nãĥ ³\nb w\ncarto ons\nmcdon alds\ndra w\ny ung\npl er\nli d\neth ical\ngroo ve\nent a\ninternational womensday\npat ron\nwor ries\nðŁİ ħ\nðŁij ĭ\nka therine\ndi az\ntor i\nbach chan\ntru st\nmin eral\nic om\nbuil ders\nbor n\ncol oring\nlat te\nca se\nrevolu tion\ntra der\nox id\nchi pot\ninst antly\nsou thern\nse hun\npro b\nher nandez\nlis bon\nhu awe\np ong\nme a\nro oney\nwheel chair\nke en\nbe tt\ncor in\nregulat ory\ndi splac\nka ren\nsch em\nsun sets\nwh ales\nremin is\nhe p\nhi de\nmar cel\npand ora\ndo yle\nth fc\not to\nno kia\ntrans gender\nko v\nhawai ian\nsha ve\nso vere\nexc er\nnick i\npu g\nst or\nro th\nwee t\nleg al\ndig nity\npo w\nhom age\nðŁĩ³ ðŁĩ\ns re\ncan on\nla x\nwo ah\nquart z\nñ a\ngree ting\nflick r\nnai robi\nadvoc ates\nan c\nvi i\neu gene\nth ra\nc re\nel an\npen sion\nth letics\nton i\nre agan\nx v\nsto re\nben ch\nhar lem\ntodd ler\nsent enced\nâĻ¥ ï¸ı\nglob ally\nche aper\nu f\nma m\nnic o\nik u\ntho u\nni st\ndam i\nth ala\nrho des\nsal e\nbow ls\nâ Ī\nlas vegas\nsanc tions\nadm ire\nmat ched\nun able\ntravel er\nele ven\nstraw berries\nâĢĶâĢĶ âĢĶâĢĶ\nstu dio\njac ques\nim s\nvalu ed\ns no\ncheese cake\nn xt\ne os\ns x\nf x\nton ic\nhat ch\nchic ks\ngra ds\nhand ic\nr ory\nas p\nri pped\ndenti st\nn en\nlu fc\nâľ Ĭ\ndi ge\nhop kins\nsher man\nf da\nfor all\nash ley\nstr and\nh y\nliqu or\nbuffe t\ness ence\nphar ma\nsuri ya\nðŁĴĻ ðŁĴĻ\nfesti vals\nz an\nre fresh\npur ple\nuni forms\nkenne th\n= )\nas an\nhel sin\ntransform ers\nk ali\nperson alized\nchal k\nbo bby\nâ Į\nthe mes\ndepar ture\nprin t\nillustr ations\nqui et\nagre es\ngri ff\nØ ³\nm iti\ntoge ther\nconven ience\nab ar\ncar lo\nturt les\ninfo sec\nsome what\nar lington\nscholar ships\nemir ates\nmu ms\nst ella\nauton om\nfe ather\ng ore\nnom inees\nfragr ance\nÑ Ĥ\nw ong\nthea stern\ngr e\nz illa\nis i\nbump er\ngo o\ndo zens\nab duc\nâļª ï¸ı\no ils\ndon ors\nsil icon\ni pod\nfortn ite\nðŁĴ ¨\ntor o\nspark ling\nconsci ousness\npal a\nnu m\nmoun ted\nffin s\nthi eves\nteam mate\npra b\nom er\nta pes\nbo d\nmit su\nste w\ne re\np bs\ntu sc\nlo we\nra de\nparliam entary\nh m\ned gar\nðŁijĩ ðŁijĩ\nto a\na gh\nhon i\ns late\nge ek\nap t\nhard t\nta p\nhoriz on\ngrow th\nmake over\nhi l\npaper back\nid an\nreha bil\ngi u\npossi bilities\nlet tu\nfran co\nbo ss\nach er\ndoes nt\nmo e\nta ker\nhuss ain\nml k\ndi l\nth ia\nham a\nreal ised\nraven s\ncurric ulum\nm ith\nk night\nted x\nr v\nisai ah\ncumb ria\nbirth days\nf ing\npre z\nmu barak\nexquis ite\nclear ance\ny en\npar i\nev o\nà º\nmodi fied\napp lying\nimple ment\ndisco vering\nchap man\nindie game\ndis k\ncrowd funding\nmach in\nli vel\nsty led\nâĿ Į\nma king\nrehear sals\nnutr iti\nsubscri ption\nand ro\ncre ators\ncar ries\nky lie\ncam den\nappren tice\ntax pay\nc ca\ntuesday thoughts\npis sed\ner man\ndete c\nfreed om\nmer i\n.. !\npsal m\nsun light\nper spec\nbe ings\nbook store\nrock star\nfun ctions\np ence\nfav es\nz n\nobam acare\nsp ill\ncoven try\npi geon\npi vo\nba it\nkol kata\nav al\ndon or\nwa h\nprivi leg\ntra ditions\nrajas than\nten ess\nportugue se\nyn es\ntack les\nde fic\ntor n\npol ling\nthor ne\nin a\nbened ict\nbar ry\ncal ories\nver dict\nsave the\nnor ton\noff ice\nmain stream\nimpro ves\nfr on\nrespon ding\nreal tor\nscotti sh\nde clar\nr l\nshi v\nsupp lier\nre sting\nswee ts\nqu i\n. âĢ¦\nwhit ney\nstartu p\nthank you\nteach er\nh alls\nha ve\nhand made\npro ving\nquar tet\nro chester\nli an\nvirtu al\nmend es\nof icial\nmid lands\nx box\nmeas uring\no vo\naccommod ation\nbri des\ncollegi ate\nintellec tual\nin car\nni ag\nðŁį ·\nsf w\ncoco a\nco ats\ncivil ians\npresi dency\nmat rix\nsweethe art\ntri athlon\nwag ner\nra dic\nplann er\nthe o\nexecu tion\nk um\nthe walkingdead\nsc ar\nro tation\nblo gging\nbom b\nre son\nbb les\nst are\nassi sted\ne do\nbrand ed\nwar nings\nthor pe\nacknow le\nsatis fied\nsho res\nri d\ndor a\nphys ically\nbi gh\nappro ves\nha h\nric al\nvers atile\npret end\nlu m\nab hi\nye e\nsp it\nãĢ Į\ndj s\nash tra\nj t\nven ues\ngram mys\ncy clo\ntr acker\nover watch\nrepl ica\nel yn\nnr l\nlind sey\nhom o\nballo ons\nkitch en\nsi s\nam os\nende av\nðŁĴ »\na rec\nthu g\nhoo ked\nhr c\nnew york\nbur gh\nameric as\npatric ia\nug u\nap athy\nha st\npsy chi\ncor k\npetro l\nðŁİ ¬\nak u\npo pping\npsycho logical\nau x\ng ma\ncad illac\nwa ste\nauth ent\nbri stol\nnam e\nque er\nto ber\njer ry\ncom in\nch ant\nprivileg ed\nop ar\nlo ser\ntex t\nmar ker\nstri es\nequ ally\nak i\nchrist mas\ngare th\nble w\nem ma\nimag in\nse als\nche at\nconditi oning\nj ana\nren s\ndar ies\no asis\ndisc ounts\ncoun cil\ni ka\nshir ley\nvou cher\nal ps\nw x\nq r\ndri ft\nattemp ting\nut c\nØ ª\ngonzale z\nm f\njo ker\nparalle l\npa re\naspe cts\nproce du\nn p\nam a\nrale igh\nbright en\ngu ire\nradi ation\ncre scent\nho b\nil le\nstr and\nv ore\nn ard\nche st\ndi wali\nav atar\nal der\nd ling\npa thetic\nðŁĴ ĺ\nspir it\njor ge\nfilm making\nðŁĻı ðŁĻı\nchalleng er\nb j\ndown town\nht ml\nade qu\ntwi sted\nin ely\n( '\nwra ps\noper ational\ny ne\nn us\nmag net\nmarket place\nhealth ier\nsnap shot\ndam on\ninter ven\nfe derer\now ls\nbiscu its\nj p\nro deo\nblue berry\nlec tion\nfron tier\nsumm ers\nre yes\npede strian\ngo l\ncaf fe\nrefur bi\nbou lder\nme ghan\nspeci alty\nla ss\ne i\nsuspec ts\nappro x\nrr r\nra th\nst im\ncru shed\nhe d\nwh un\nlo af\ncr ore\nriver a\ngene tics\nso ck\nwa sted\nny pd\nansw ering\ndo ve\nbel la\nol in\ndu n\nfi ji\npre tty\nspar kle\ny un\nj d\neuro pa\nli fts\nam ber\nmu r\nte k\nboy d\nroy alty\nin do\nri b\ngo tham\nti est\ninst alling\nke mp\nthe photo\ncos mic\n) ))\nwhole sale\nloy ment\neas y\nsu ing\nsett led\naf p\npro ver\nsuppor tive\nre es\nne ath\ndeli ber\nc é\nwel come\npic oftheday\nnew born\npat ty\nsun s\nsi est\nfl int\ndiffe rently\nspo ilers\ntroop er\ng ins\ncor y\nlook out\nequi pped\nta pe\nto by\nresear cher\nu sh\nke yes\nal ma\ninduc tion\nk w\nk har\nsl ick\nbri de\ne ur\ncra ving\nbook ings\nch es\ntr unk\nvern on\nsp her\ncryst als\nrel atively\npom pe\nuni ons\nval ley\npar a\nw ant\nok c\nde af\nser gio\nlen non\nsh ay\ncr a\nv at\nhe e\nt we\nliqu id\npol y\nðŁİ ģ\nb ent\nbe aring\nmotor sport\nbar be\nte sti\nhan i\nfin ancing\nastron aut\nwater colour\nri sh\ncomic con\ngar t\nwr ong\nber n\nit an\nste pped\nfil ters\nc low\nme x\ndem ons\nall o\nexpand ed\ncomm and\net ers\ngo ats\nsi ri\ny r\npot tery\nmari on\ni le\nel an\nsan to\nperson a\ndu ke\nhom eless\nli ghted\nwheel er\nchang er\ncab bage\nsur real\nham burg\nsma shed\nstr an\nk not\ni art\nob i\nbe dro\ndi al\nth ick\nb ingo\nfu s\nvacu um\ncon ve\nati ve\naccur acy\naccoun t\nre fer\nri z\nspider man\nban a\nr ite\nu b\nab s\nmedic al\nlin k\nsi em\n> >>>\nbe tra\ng lowing\nre actions\npupp et\nspa ghetti\nang s\nre medi\npray for\nroy ce\nchar lotte\n£ ï¸ı\ngh et\naffe cting\nro de\nsoci alist\nmo ses\naz i\no it\nre porters\ncd t\nap ing\ns nat\nminim al\nwa ist\nsie ge\n>> >>\nri g\nschmid t\nh are\nec a\nthor n\nhe mp\nes the\ncly de\nth a\ndon ut\nmoham ed\nling erie\nle gg\ncarpen ter\nperform ers\nde a\nimag ined\ncur se\nla sh\nct r\nagu a\nro ar\ngr i\nro le\nj fk\nresur rec\nroosevel t\nmaril yn\nsm alle\nwill is\nwa ited\nchar ities\nthe res\nli k\norigin al\ncar i\nc ough\ncru ci\nla gun\ncontra st\nk ou\narm our\nre moving\nt ent\nmaz da\nbri ghter\nthi ef\ncor ner\ntequ ila\nbuzz ing\nal bi\np am\naz ure\ndisc oun\npixel art\npossi bility\nham ont\ntra des\nbu da\nhi ve\nvers y\nfin ch\ntran spa\nem i\nterri fying\nin qui\ng ba\nsub stitu\ncollec ti\nplac ing\ncin dy\nk ann\npa tho\ndiamon d\nmour inho\nguine a\nanthro po\nair s\npu mps\nì ļ\npas o\ncur ling\nan ita\nresi dency\nne wh\njo on\ncigare tte\nque ue\nex trac\ngam es\nspl en\nex press\npublic ly\nbon nie\ntribun e\nba ek\nreason able\nc or\ntimo thy\nshe eran\nÄ ±\nf dn\nsu tton\nconcentr ation\ncarav an\nx avier\nal ger\ncy lin\nfreder ick\nner ve\npe ak\nlettu ce\nj ail\npre game\nkav an\nup graded\neco logy\nsquad ron\ngra pes\ngoo g\npa stry\nðŁĹ £\nãĥ¼ ãĥ\nmil ano\nawa z\npresen ter\nðŁĮ ¿\nher d\nking s\ntem plate\nfl our\nh v\nk ley\ni ya\nspe c\nat er\nfrankfur t\nco ch\ntex ting\ndel i\ncommuni st\nregi ment\nele anor\nanticip ated\nðŁijĮ ðŁı»\nthephoto hour\nran o\nsurvi ving\nsimul ation\ndaw son\nar in\naqu a\nm or\nâĢ¦ .\ncin o\nira qi\nsh az\ndun dee\nwe s\ndra u\nhann ah\ns news\noccup ation\nste en\nx m\nang les\nsett ings\ngur u\nkno x\nor ca\nshap ing\nw ent\ndr illing\nzz ie\nbr i\nkis sing\nfin d\nma ine\nâŃIJï¸ı âŃIJï¸ı\nðŁĮ į\nlar ry\nbu sted\nta vern\nacti vely\n- \"\nreplac ing\nno d\nun lock\n. \"\nâŀ ¤\naffili ate\nto w\nl n\nhappy newyear\ndi f\nj m\ngreen wich\ncontro versy\ndaw g\ncon dol\nsav annah\ncompens ation\ntouch down\nte o\namb itious\nembro i\nconvic ted\niart g\nbar ack\ntr ance\ntestim ony\nau dition\nthum b\nmy ths\nbe x\nque z\norch id\nden y\nentit led\nhoo d\ngr ant\nin box\nblue jays\nr illa\nsmalle st\nbur den\nin famous\ndivi ded\nboun daries\nt ter\nel t\nwy oming\nbe verage\nme sm\none ws\nbudd hist\ny ana\nas sad\nis ms\nbar rett\npredic ted\nback to\ntw it\ne there\ncap tains\nescap ed\nay o\nlam borgh\ngard ner\nla ps\nk al\nadverti sement\ninsec ts\nna po\nam en\nac y\nr and\ng k\nte h\nk athle\ntri dge\npan cake\nat ro\npyram id\nbu la\nparal ym\ngau ge\nen cies\ntom y\nbiscu it\nbut cher\nquali fier\ncoun ty\nke i\npo ols\ndar ker\nshould ers\nðŁĩºðŁĩ¸ ðŁĩºðŁĩ¸\nsp re\n( \"\nwrit ers\ng m\nðŁİ ĵ\nk nit\nhu ff\nmt b\nphilli es\no st\nden is\ng art\nlicen sed\ninter face\nex cel\nd well\nfrom the\nco fficial\naz zi\nappear ing\nfore st\nn ana\nke ith\nmanufac turers\nbeck ham\n) ?\ne se\ncol ony\ndelic ate\nut ter\nmc in\ntranspl ant\npre ferred\npar d\nari e\nhu b\npo ds\nperspec tives\npic t\ndel u\napp er\nbe than\np mo\ncrimin als\nfemin ism\nsh ack\ncircum stances\nfel las\nprote sting\nwa x\nsugge sted\nt ator\ndre w\nom ni\nfa ke\nkath y\nre b\ndel ine\nber ni\nmi sty\nðŁij ©\ner able\nbreak through\nmen swear\nmillenni als\nchan yeol\nla z\ninser t\nrep lies\nphra se\nn x\nihear tawards\naudre y\ngran ite\nrac ec\nori e\nter ra\ninnov ations\nbritt any\nat eral\npe ar\nbio logical\nsh ments\ninstitu tion\nm sn\nfrequ ency\nd man\nneg lec\nt f\nste fan\nfox news\nty po\ncomm s\nsequ ence\ncar men\nwh ites\neconom ist\nexe ter\nse um\nre sorts\ncas ually\nbun de\ndivi de\nØ ¹\nga g\ncre ed\nreti re\ncau cus\nrapi ds\nwrestle mania\ntul sa\nsunder land\nfundam ent\no di\nyam aha\nv ary\nintri gu\nel se\nbe acon\nan gie\ntra ded\ntran sm\ng ents\nkn itting\ngal ac\nðĿ Ĺ\nu to\nsea side\nhol t\nre rs\nfar go\ntrain ers\nmon soon\nb ale\nsou ght\nmad die\nh w\nco li\nfr an\nfav s\nðŁĴ Ķ\nint ent\nr ally\ns bs\nlemon ade\nbarack obama\nbre ad\nstick y\nexplo sive\nchel ten\nt j\nas soc\nram en\nhom ies\nv log\nmi ster\nlor d\nâĢįâĻ Ģï¸ı\naly ssa\nsketch book\nru mble\ncat ch\nmigr ant\ndiscipl ine\nun likely\nchronic les\nfl ora\nsl ams\nam id\ns boro\ncoo p\nju mps\ntran qu\nmel is\nsof ia\nen ri\ngab e\nsy ri\nnicol as\ncha i\nw v\nbe cky\nfoo ty\nta o\nsuppo se\nðŁĺįðŁĺį ðŁĺįðŁĺį\nplu sh\nri sh\nðŁ¤ ĵ\nk ha\nsatur days\nac cent\nhe c\nlim it\ncarl ton\nwi red\ntaylor swift\nðŁĺ ij\nsq l\nhar ro\nrecipi ents\ng at\ngo p\nth of\namaz ed\ngh an\nðŁıĨ ðŁıĨ\npor to\ncla re\ndi stant\nna c\nohi o\nðŁĻı ðŁı¼\nmt n\nanti bio\ndino sa\nme sa\npar tial\nb v\nlear nt\nlov ato\nquesti on\nex tract\ngossi p\ngi bb\nniag ara\nðŁij ¨\ndispla yed\nso oner\nste vie\nnug gets\nml n\nbro m\ntur b\ngive aways\nstu pi\nbl ink\nc ili\nconven ient\nmo h\nvi ve\nf ric\ncau se\ncham ber\ncu les\nne arest\nis se\nsmall biz\nt j\ncanadi ans\nsmar ter\nbra sil\nra re\nque tte\nw ha\ncand le\nat omic\nðŁijį ðŁijį\nwarri or\nrelax ed\nstri ps\nne ur\nk ka\nr fc\njen sen\nreco vering\nrespon ses\nsal am\northo dox\nacti ve\nell ers\nn it\nâŃ IJ\nmetro politan\ncentu ries\nvi da\ngra ding\ntranspa rent\nsim ple\ndo ts\nsuperint endent\nelev ator\nautom ated\nred skins\nima m\nsummer time\njona than\nge aring\nmichel le\nconfl ic\nm ice\nto te\npubli sh\npa x\n) -\nna iled\ná ´\ntele scope\nser bia\nba b\nape u\nst ically\nsen ti\nr ats\nisol ated\ngrou p\nhat red\nparanor mal\nstan ley\nali on\nsafe ty\nl s\nठ°\nnex us\nalexand ra\nmas ks\n+ +\ntr on\nau k\nbrother hood\nbrow se\nmix es\nsim one\nmu sk\nappro ve\nlo la\nex p\nper th\nfu turi\nun seen\nd m\nchel se\nsc outing\no we\nportsm outh\nk ram\nmi ze\ndi spen\nsu p\nd lc\nadver t\ntere sa\nis le\ncy cle\nmet all\nshi elds\nmarin ers\nra z\ning en\nfun d\nan go\njon es\no ka\nmad den\nbroc coli\ndomin ic\nsitu ations\nmer o\ncric ke\npuni shment\nd b\nsha king\nðŁĺ ļ\nm q\nari ans\nle h\ncla w\nwe ds\nd ure\nni el\nj elly\ngour met\ntra ders\nle vi\nw ages\nkne es\nwi se\nheaven ly\navi d\nmelo dy\nz ack\nban anas\napprentic e\npro p\nfun ny\no de\nrespec ted\nme gan\nfe wer\ndra fted\nmed it\ngra pe\nus army\ncru sad\nvo cali\nprepar ations\nnon sense\nus age\nth r\nro th\nwiz ards\ninsi de\npromo tions\nmon a\nred sox\nsi g\neleg ance\nch ia\nuniver sal\nãĢ į\nra ja\nun ga\npol lin\nfilip ino\nak a\nt sun\nik on\nbi king\ndecor ations\nz ac\ncade ts\nhum our\nag m\nre ppin\nvac cin\nelo ve\nu w\ndia be\ngalla gher\naz er\ndo l\na while\npro minent\nwel sh\nt ann\n' )\nbi en\nwa g\nin al\nc wc\nwic ket\nur st\nq anon\nx e\nout door\ndun n\nstar r\nco logy\nric ky\nu efa\nreb ounds\ns music\ninf ant\nðŁĻ ĭ\nso p\nu mber\nhand ing\nbeg in\nsor ting\nha sh\nsp ati\nre k\nbuda pest\nblack hawks\ndele te\nro m\ncan did\nauth ori\nde bris\nspe cul\ninter section\nmarri ott\nim ran\nðŁĺģ ðŁĺģ\ncru ises\nram sey\nrafa el\naware ness\nvas cular\nbeyon cé\nru g\nðŁĺ Į\nfesti v\nar am\ns able\nbas il\np ill\nflo oring\nun beaten\nimplic ations\nu f\nw ound\nfor ge\npoin ting\npo ts\npopular ity\nðŁijı ðŁı»\nmani pul\ns lots\ndeb ates\nabs ence\nver mont\nnever forget\nwri st\ngl oria\nren ce\nhu sk\nmel ting\nðŁİ Ł\nbr aces\ntim ely\ntransform ing\nam ps\nma k\npo e\nah an\ngener ally\nnd p\nale ppo\nunic ef\npro fs\nnor d\nma sk\njackson ville\nv v\nsh ells\nbloom ing\noper ators\nchar coal\nne ville\nma gi\nchi p\nsam a\nir an\nre forms\naccu mul\nru e\næ ľ\nweb sites\nga on\ndevast ating\nsto s\nglaci er\nra pp\nchipot le\npr a\nor ous\nrom ney\nseas on\ndecor ative\nc isco\ndit ch\ncompla in\nll o\nassu me\nðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤðŁĺĤ\nn els\ncent ric\nft w\ncar rots\ntat a\ncan ter\nper ience\nli ers\ndemo s\nbl unt\noper ate\nreserv ations\nle ah\nsub stance\ndi son\nan te\nelec tion\nv ue\nsqu are\nnon profit\nca a\nf su\ny am\nãĤ ¤\nv ladi\ncomple tes\nmar i\nphilli p\nne ill\ner as\nka it\nmen do\nmahar ashtra\ng p\ndan e\nprovi dence\nther apeu\njuven ile\nme mo\nin corpor\naa aa\nseven teen\nteen ager\nà £\nor ns\nwi de\ncu teness\ntw d\nff les\nbar a\ncom edy\nover time\ny az\nbar on\nunemp loyment\nðŁij ĭ\nexter ior\nden se\ncent res\nmatch up\nhistory month\nartif icial\nqu it\ne sk\nwar n\ncr itic\nj af\nðŁĵ ²\ninform ative\nfu els\nrecy cle\nnam ing\nstri pe\nsol ic\nmole cular\ndee pi\ncon vo\ns sel\nna e\nde scent\nti z\naccoun tability\nter ry\nr ito\nsl ay\nem o\ndem ol\nsens ation\nco v\ntor e\nround table\ny ol\nexcu ses\nॠį\ntur quo\nhh hh\npod casts\ncele b\nme ssi\nli o\nman n\ncontribu ted\nu z\ngener ator\nele ts\nveg gie\nindu l\nen suring\ndetro it\npun jab\ntran spor\ninstru ction\nad d\npor cel\npan eli\ncir cles\npersi st\nclay ton\nsp n\ndog softwitter\nis nt\nsp r\nretail ers\np w\nhun gar\nel ena\nmon aster\ngu atem\nje ssie\nan z\nra shi\nfle e\ncar ving\nfau x\nl al\nhen ri\nd jo\ndu ll\ns ana\nlar a\nglo be\ncri mson\ncom pass\npau se\nna b\nlion el\nba ths\nu fo\ninvent ory\nsin gh\nsat an\nðŁĩ ¸\nce ments\nin form\ngener ated\nbi den\nav g\ntas ks\nde er\nsa u\nja iled\npa stel\nsc c\nna il\nsteel e\nper is\nlamborgh ini\npur sue\nmar gin\nu ch\nbo sch\ndra in\ncl ara\nbo m\nlat ino\nweb ster\nrose mary\nr ha\ns oun\nbillion aire\nnot ch\npercent age\ncon or\n' \"\nhom es\nearth day\nh ort\nbig gest\ndi sin\nwal ton\nedit ors\nim ma\nom ar\nequi valent\npharmac eu\nah med\ncam eo\nhan ni\nunder rated\nge ment\nmicro bi\nv oo\nhonor able\nobe sity\nâļ ¡ï¸ı\nlimer ick\ninvol vement\nst agram\nboule vard\nbur g\nblackand white\nliber ation\nfi ve\ninter im\nsm m\nrival ry\ncap abilities\nstat ements\nthu mb\nve d\nsw ans\nbar ber\ne que\nseren a\nhel m\nnoo dle\nsam pling\nn awaz\nsing le\nthunder storms\nsh on\nin ev\në ¯\nto pp\norch ard\nbi an\nðŁĺ Ķ\ndoor step\nsalv ation\nmarke ting\nr ons\ncle mson\nra vi\nin take\nstand with\nsin a\nha iku\nple y\nelector al\nph illy\nla ys\nelectr ic\ncap turing\nu pp\ner gy\nbeliev ing\ncul tures\nes day\ninva sive\ned ed\nspee ch\nend ur\nviet nam\nboy cott\npe de\ndeli ver\nðŁĴĸ ðŁĴĸ\nmer chant\nst ir\nden ies\npoc kets\no ti\ncu ddle\nro land\nmm ed\nden ed\nlear ners\nhoo p\nsour cing\nh acked\ndi m\nenviron ments\nben son\njud icial\nwor cester\npear ls\ngovern ments\narri vals\ncor ners\ntun ing\nla bour\ny m\nor dering\nle wi\ni fe\nhygi ene\nthou ghtful\nindone sian\ncampaig ning\nprinci ple\nassau l\nru bb\nat v\nwil ly\nen tre\nil i\nph on\ndu ties\nâĻ¥ âĻ¥\nsn akes\nlo op\nam ar\nconver tible\nbon ding\nment oring\nmax well\nethere um\ndestro ying\nax is\nca iro\nfin nish\nsho ck\nðŁĺ IJ\ncal eb\ncom a\npe dal\nco re\ncontin ent\nel son\ntemp o\nhelsin ki\nac p\ntack ling\nst ated\nbl a\ndou b\nsma shing\na ja\ncamer on\ndisru ption\nwarm th\nbeing salmankhan\nbullet in\no de\nsyrac use\nar an\nmc gregor\nbul k\nan ton\nconfir mation\nsp ine\nim ran\ninstru c\njac ks\nchi o\npal m\nstr e\nembarra ssing\nun t\nelimin ate\nto ss\nc ise\na ws\noni sts\nsh inee\njo s\nho se\nli vely\nopp onents\nmo vements\nrecogni zing\nsandwich es\nsh akes\nexerc ises\nse at\nprofe ssion\nmerry christmas\nlu gg\nadopt dont\nmar vin\nbyr ne\nun le\nhe t\nku wait\nrah man\naspe ct\nhumb led\ngen es\nf and\nlong time\n) ;\ncam pu\nan gus\nðŁijį ðŁı¼\nq uran\nsle eves\ns lic\n¸ ë\ntwel ve\nyour e\ni ke\ngo gh\nb st\ndic tionary\nreflec ting\nto on\nyar n\nem bed\nðŁı ´\nre serves\nfloo ded\nver iz\ndu sk\nestab lish\npro li\nau d\nritu al\nor bit\ndeclar ation\nrecor dings\ncam o\ncas sette\ngood luck\ncu tter\nbo p\nb ho\nche ating\npaci fic\nma res\ntim er\ncol t\ntr ous\ntomor row\nhan sen\nci e\nw ang\nban i\ncircu lar\nac ute\nfar mer\nco ys\np se\nir ving\nw j\nhaw kins\nb ison\nur day\ncru ising\no te\nk ath\nwhi stle\nyour selves\nant is\nsla sh\nthorough ly\nke sh\nser ie\nex em\nen ig\nguil d\nsh red\nho gan\nap o\nä ¸\npu zz\nne tball\nau ssi\npanor ama\nws j\nav is\nar ming\nhum ph\nbrow ser\ncri es\nfo ggy\nmat te\nðŁĮ »\nit er\ntal lest\nby ron\ncap tiv\nje su\nany ways\nflag ship\np ton\nwe y\nfay ette\nfinanci al\nf oul\nsolom on\njenni fer\ncucu mber\nar gue\ntex tile\nwrest ler\njohn ston\npa stor\nðŁĺŃðŁĺŃ ðŁĺŃðŁĺŃ\ncac tus\nedi ble\nre served\nric hie\nmet res\ningredi ent\nh ella\nun to\nch ol\ncele bs\npo ets\ngra ham\nhay den\ncoinci dence\nb aw\ncommunic ate\nflet cher\n/ -\ntole do\necu ador\ncoun sel\ns laughter\nline ar\nat p\nos u\njo el\nev ed\nconqu er\nru stic\nplic ity\nrecogn ise\nroom mate\ncr acked\njas per\nph er\nðŁĮ º\nwo ven\nmo ist\nff c\nste ering\nni sh\nstand ings\nfrequ ent\nar di\nhaz el\nas msg\nbau m\nd art\nsi dd\nnat h\nch ero\ncard board\nc ss\nn sfw\npa ir\nðŁĺį ðŁĺĺ\noccur red\nhomeless ness\nmal one\nph e\nxi a\npad dy\ndecl are\ntheat re\nb f\nper sian\nta d\nax e\nsusp icious\nlam b\nmu cho\nsen ior\nst as\nk ite\nst ing\ngra d\nk af\nwat ering\nØ ¯\nspi ral\nth ms\neduc ator\njer ome\nof c\nclo ck\nsu l\npe mb\n.... .....\npark way\nde aux\nrestric tions\nm ons\nneed le\ne j\nle agues\nwater melon\nam an\npl enary\nmax im\nw ab\ncoming soon\nbry ce\nvi gil\nsuper market\nfortun ate\nturquo ise\npresi dent\nli v\ninter ns\nfeel in\nfix tures\nstun t\nst aged\npremi eres\nlo k\nprac titi\nshor tage\nlog ne\nve c\ncon cor\nroc ke\nli g\ncom posed\nsyn thetic\ndi p\ncam ila\nch is\nj ou\nsu san\neye brows\nsupp lement\nsatis faction\nmoham mad\nti bet\nhouse of\npu n\nas sam\nshado whun\npsy ched\nse duc\nmand atory\nher bert\nsc allo\nstream ers\nproto col\nblock buster\nproduc es\nsch nei\nlau rel\ntri be\ntime hop\npl a\nmod elling\ntv time\nmtv stars\nwi dow\nme tric\nch am\ncon do\nflow ering\nale c\nd ms\ninten sity\n ¨\nmccar tney\nislam abad\nk b\nf fi\nph al\nanal og\nf ond\nh acks\npositi vity\ntreat y\nsub marine\nconne ct\nsel en\ncategor ies\ncu b\norgani ze\nsi k\nquote oftheday\nremin ding\nam or\nloc king\nðŁijı ðŁı¼\ncomp ound\net te\nb out\nrec ur\nfe rence\nmi zz\ntren d\nhip ster\nfor tress\nforth coming\npreli min\no dyssey\nang p\ndel ici\neven ings\nðŁĶ ¹\ni q\nd w\nda ir\nkathr yn\nchristian ity\nmoon light\nha b\nwh oo\nf bf\nse th\ngenu inely\npa x\nchar ity\ndeplo yed\nb nb\nbu cs\nju dg\ncon ge\nplant ation\nim press\ncar a\nsc lub\nsco py\nland ers\ncompla ints\nb ama\nre build\nx y\nreal ism\nsh our\nle in\nbrac elets\nmer a\nassas sin\nan chor\nðŁijĮ ðŁı¼\nlin en\ncon fron\nchronic le\ncomm ent\ncat alog\nil les\ngor ge\nme try\njung kook\nlove my\nsent in\nse em\nfit ness\nalli ed\nts man\ndigital transformation\npr an\nlo ft\nmin ton\nalden richards\nen vel\ncher ish\ncertain ty\nzz z\nrhin o\nper kins\nen rich\ncape town\nome ter\nsec tions\nske leton\ndef enders\nðŁĺ Ŀ\npen c\nbri t\nja h\ncapital ism\nðŁ¥ ĩ\nbaz aar\nre me\nex t\nkk k\nconver t\nstor my\nb ye\nkar an\nchry sler\nad os\npre ssed\nsyn c\nation day\ndang er\nbad ges\nrefu ses\nem powering\nly m\nex ports\nadoptdont shop\nðŁĩ ¯\nth c\nawa ited\nfocu ses\nfin ed\no at\nhaha hah\nâģ ©\nn family\nfi ona\nluck ily\nthr illing\nty ping\nout break\ndi es\nhe u\ncraw l\nne sses\no ath\nscri pts\ngee ks\nðŁIJ Ŀ\np b\nmathemat ics\nal is\n________ ________\ngymna stics\nacti vism\nrecommend ation\ngre n\nwa in\ncour ty\nn apol\ncau li\nhor nets\ng als\njo ckey\ndir ty\nat ar\nenor mous\npe st\ngreg ation\nan os\nii ii\ndef ends\nblack historymonth\nat x\nmb c\nlugg age\nwit ch\nco b\nla sts\ncu m\ngg g\nba thing\nn ar\nce bu\nðŁį ĥ\nnavig ation\nmin e\nre jo\nðŁİ Ģ\ngif tide\nre ta\nuse less\npu ll\ndefic it\nal lu\nati me\nit v\ntr illion\npu e\nac ies\nproce dure\nl ori\njen ny\nc ad\nul ously\ndr ac\npromo tes\ning the\ncan u\nwoo hoo\nna omi\nzar dari\nts u\nbe ir\nsd g\nle ver\nwe ber\nab ud\nlun d\ncrow ded\ndeplo yment\nter rain\nken ny\nho f\nwitne ssed\nlo ch\nj k\nbul ly\nw ren\npoe try\ndo ff\nww i\nmo red\ndin i\ncul ture\npromp t\n ¥\nmaur ice\nto pps\nr m\ncor respon\nab out\njewel s\ngi br\neag le\nðŁĺĺ ðŁĺĺðŁĺĺ\nl ending\nsou ven\nç Ķ\ncontemporary art\nestabli shment\nj ong\nâĢ¦ \"\ngat or\npatri otic\nmc coy\nv ape\nhuman e\nfeli z\ncoach ella\nre posting\nste als\nfu ller\nn ering\nat ra\n( -\nbla ke\nhe ather\nwor ms\ndiscipl inary\nrede mption\ny ard\nam in\n\" @_\nd nc\nt ds\nk appa\nne wark\ncomm its\nspe ars\nj ams\nt and\nmsn bc\ninter medi\naim ed\nat ic\nteen th\nobserv ation\nkash mir\nkavan augh\nou l\nsan francisco\nre u\nbel ated\ncho w\npass word\nst ills\ndeta ined\nsar i\nday ton\ndar ren\nitali an\nar th\namu sic\nar bit\nw m\nv m\nhe m\ndou g\nmy r\na sho\npre v\nvin d\nbra h\nsta g\nภµ\npre views\ngu k\ncon taining\nleon ardo\nsad dle\nru shing\nst av\nlon gh\ngam bling\nve gas\nreserv ation\nend ale\nbal a\nfl a\nvari ant\nhe dge\nbulgar ia\nnat ali\nwe aver\nsol st\nencoura ged\nap c\nas parag\nne st\ncycli sts\nfe l\nìĬ ¤\noverwhel ming\npey ton\nj it\na post\nmb le\nble eding\nneighbour hood\na very\nexpre ssions\nmac donald\ngi gs\nmon ds\nillu sion\nn ct\ncam ero\nover head\nmy th\nol y\nvi o\net v\nlau rie\nunve iling\npri or\ncon n\niron man\ndi ff\nday in\ncrit ici\ncon go\nre vision\nwal e\ndirec tor\np ines\nblack pink\ngar ner\ncur ated\nmanit oba\nh ac\ncommon ly\nbar ton\n.... #\nmor tality\nlive smatter\nphilos op\nshor ter\ncon vince\nfre ak\nvend ors\ninsi ghtful\nel ly\nsens ors\ne led\ns berg\nweight loss\nu kip\nsp ur\npriv ate\nqu a\nss c\n, ...\nsupervis or\nadvis er\namaz ingly\nless er\nat es\nmah on\noooo oo\nsar as\npmo india\nwaff le\nun ders\ntoler ance\nsculp tures\nher sh\nkno cking\nsmo ke\ncathol ic\ngri m\ntra veled\nfli p\nge off\ndinosa urs\nsle pt\nscar let\nok i\ncompla int\nob sc\nnam i\nla g\ncross fit\nu fc\nmc cain\nrefe ree\nsad ness\npen ny\nli eu\nmo de\nki er\nvol s\nw is\nel on\nshe a\nba o\nson ia\ncla ire\nem manuel\nmoist ure\ndi gest\nvi ii\nt eller\nch on\naccess ory\nnight club\nfoss il\naw an\nhu sky\nab original\nbrand on\nffici ent\ncou gars\nste d\nad mitted\nigno red\ncontent marketing\nag as\nv ase\nexecu ted\nnegoti ations\nshe ad\nn and\ntab lets\ngo th\nts al\nd fw\non ep\nprotec tor\nsp ho\ngaz ette\nandre as\nss er\ncomp ilation\nha v\ncontain ers\nbro ker\nsoc al\nporcel ain\nhy uk\nair ing\nðŁĴ °\npubli sher\nscen ario\nspart ans\nre viewing\nitu des\ned el\npear son\nba sh\nmau i\na ad\nðŁĮ Ĭ\nli u\nul ate\nprogram mes\nfav our\nweb design\nreal ty\nmotiv ational\ncro sses\n' ...\nbus ch\nadjust able\nar jun\nmist ak\ndimen sion\npi stol\nweigh s\nen y\nunve il\nindy car\ngor don\nf ade\nfran ken\nqual ities\nbet t\nloc ate\nker r\nsp c\nconfu sion\nne e\nluck y\nbas es\ndep ends\nfire fighter\nol a\nre t\nmar oon\nðŁĶ Ĭ\nw am\ndefin ing\nwhe at\nbi l\né s\nb hai\npsy ch\nta u\nic ans\nthi k\nob ile\ninspec tor\nìĨ Įë\nill on\ngo s\nev angel\nfa i\nsi st\nvoc ation\nbur ge\nchi stan\nrenew ed\nenthusi asm\nen ting\nag ri\nike a\nm sc\naero space\nsens iti\nmemo ir\nhosp ice\nco caine\nder ry\nmechan ics\nĦ à¸\ntin o\nreduc es\ncollec tors\nin justice\nsupp re\nv ana\nab un\nnap a\nsu sa\nos lo\ne ff\nen core\nlic ence\nched dar\nz al\nmoun t\nðŁĴ IJ\nthreat ens\n!! \"\narchi e\nfu tsal\nscu ba\njo s\ngn on\nse xi\ns official\ncompar ing\ndomin ant\ntof theday\nfa it\npropos als\ngi ft\ny as\ncn c\nl r\nha b\nreser voir\nbeli efs\ngener al\nmar ti\nt d\nest e\nì ł\nwi l\nðŁij ¯\nðŁĶ «\nsp x\net work\nexcer pt\ne instein\nhir o\nsil hou\nteam ed\nper ception\ncorri dor\nmental health\nhin ts\nben ny\ninduc ted\nsw x\nwi desp\nspe ak\ncher yl\ndru g\nðŁĺ ķ\nh f\nasparag us\nmyster ies\nfitz gerald\noff er\ntherap ist\ncare er\ndam aging\nts d\nper u\nwei bo\ny ay\nphoeni x\ndisc re\nmac book\nbar ker\nstig ma\nsp read\nroc kies\nkang ar\nbri dg\npa i\nbi shop\nta iled\ncapsu le\nðŁĴ ĵ\nge of\nroy ale\nshort listed\no ste\nash amed\nch app\nkey e\ncl a\nscreen shot\naustri an\nnati ve\nen ight\njuli et\nmichel e\nðŁĮ ´\ntravel ers\npi l\nfootball er\nwin chester\nðŁĻ Ħ\nazer bai\ngold eng\norganis ations\ninterpre tation\npredat or\nofthe week\nlo gan\npok é\nmari e\ncal la\nt nt\ncin de\nge tic\nfit fam\ngra v\now ens\nðŁĮ ±\nshoot out\nsal is\ncommissi ons\nco he\np tic\nni xon\nhi a\namb ition\nmar ine\ncruel ty\nt k\ncru de\nsal ty\njim a\nmon go\nir ony\non wards\narre sts\nstrang ers\nig er\ncycli st\nra g\nexten ds\ntra dio\nbour g\nmo i\nel la\ne able\nlex us\nau l\nder a\nhistor ian\nmor ton\nti ff\nman ner\nko t\nd k\npo inted\nmar qu\na an\nen ey\ndu blin\non poli\nem ili\nsecre t\nfl o\nâļ ¡\nba j\nste ep\naccompan ied\nrum ours\ndev i\npurch asing\nfi g\npu b\nsch oo\nautonom ous\ngo alie\nx ia\nautom atically\nre vers\nter o\nfu ku\ntitan ic\nshoo k\nsand als\nsee kers\nexc av\nnor dic\nbigo live\nba ke\nr att\nz ak\nne p\nðŁĺ ¤\ncand y\nbilli ons\nbook worm\npp et\nà ³\nsur faces\nsc ars\nphil ip\ndo gg\nci gars\nco te\ntransl ated\ncur ator\nsin dh\nhan gover\nbre wer\non es\nel ton\nðŁĴª ðŁı¼\nmar cu\nelli ot\nrigh te\ndi oce\nru ss\nrail ways\ngrand son\nas cen\napo logy\nawa it\nmob ili\nre spir\nparti san\noli vi\nstri ke\nyo o\nwhite house\nexpre ssed\npu ps\nbed ford\ncul tur\nfro gs\nfly ing\ncav ali\nc ds\nfri ger\nstreet photography\nre solve\ntali ban\nkan g\ncru shing\nju m\nðŁĺ Ĵ\nwilliam son\ntan g\ncur ly\nt man\nveter an\nfa ire\nartificial intelligence\nun anim\npre n\nback drop\nfr ances\noc cer\ndoro thy\nwork ing\nar thr\nconver ted\nday light\nserv ant\npad dle\ncompla ining\nthir ty\nnad al\nak u\nibra him\nad dressed\np iss\ngreen house\nbatt alion\nsi mulator\nout lets\nembroi dery\nðŁĵ ±\nfis cal\nger ard\nsas sy\nðŁİī ðŁİīðŁİī\nvent ures\nmer it\npublic ity\nðŁij Ī\nsophistic ated\nc tu\nconven tional\ncondol ences\nisra el\ntra dition\nar an\nte ss\ngla d\nðŁĺĬ ðŁĺĬ\ncorrec tion\nge on\nam d\nor ship\nbe ast\nch ment\nì ŀ\nnic o\nwk nd\nwel s\ncushi on\nbeli e\nvo c\nidio ts\nunder neath\npu ma\ncorn ell\nen ation\nlu l\nswa ch\nab ig\nu rer\nmi e\nform erly\nca f\ner nal\nchor us\njuli us\nsen ator\nâľ į\nwh ir\nsalv ador\nph d\nuni fied\nboo ster\ngraph ical\nw rec\nson ny\nmi z\ndere rs\ns all\nven s\ntusc any\nwi d\ny ong\nkur ds\nw az\ntrol ls\nmac ro\ncat urday\npre ssing\nsa sha\ncent ennial\ngu sts\nem c\nbe fore\nden ise\ncu st\nðŁĵ ¢\nlo oo\nbase l\neng land\ny olo\nar du\nmanife sto\ndo ha\nì ľ\nkni ves\nbourne mouth\nbi bl\nbar b\nal icia\nØ ©\ncom er\ncycl one\ng it\nane ws\ncharacter i\nvent ura\nin tra\nsf giants\nhu t\nbe a\ndar win\nell er\nal v\nre ese\nbl y\nkar an\nconclu sion\nman ny\nfla kes\nunite blue\nnad u\nco pp\ned ges\nlanca shire\ni als\no tta\nphilipp e\nl ent\nche e\nment ors\nfesti val\nan ism\ncompli mentary\nr j\npu g\nd ine\nwe i\ncli ffs\nsar my\nti veness\ntreas ury\nil and\nafter math\nrabb i\nou n\nbou quet\nherit age\nzi on\nsur render\nshen an\nin ks\nkar l\ngh ty\npol icing\nexam ination\nce y\nper su\nmeasure ment\nhydro gen\nlu han\nâłĢâłĢ âłĢâłĢ\nwar i\nо Ð\nj y\nfow ler\nmis h\nal fre\nâĺ ij\nbb naija\ncat alogue\nrecogn ised\nsa ver\nhu skies\ncol in\nmun do\nsi va\np ng\ndiscoun ted\nman utd\nfre sno\nde vin\nprelimin ary\ntro phies\npla stics\ndu g\npro cu\nindi go\ng ard\ndy lan\npit ches\nground breaking\nin son\nbl ac\nan thology\nf h\nexpl ic\nr ard\nadmi ral\nso chi\nla shes\nsplen did\nen vy\nad v\nsex y\nfestiv ities\nstic king\nbi b\nthr ill\nop p\nari el\nbotan ical\nendur ance\nfe males\nbr icks\nvat ican\nblack pool\nber mu\nbr ough\nroll er\nbi d\nsue de\nsloven ia\nmm ing\nml b\nmed alist\ndi ans\nrehabil itation\nne on\ns go\nli thu\nram os\nz ed\npi anist\ninten sive\nbroad band\nstu dy\npeter sburg\nlu ca\nah hhh\nphys ician\ndill on\ntele com\ngri ef\nmu n\nac ro\nsi ded\ns ly\nblo ws\nclassic cars\ntri um\nar gy\n? :\nh ri\nmarsh mal\nâĢ ĵ\nto pping\nwar saw\ntran sc\npreserv ation\nb av\nre friger\nexperim ents\nä º\ngl it\nsli ga\ng age\nfac tor\nflav ours\nbr ony\nsp o\ncook book\ncarri age\naw ay\nny fw\non ian\nw g\nsimp sons\nro lex\nðŁı ¿\ncro sby\nãħ ¤\ncre di\nsyn dic\npu bs\nali fe\npoor ly\nmac ed\nðŁĺ ŀ\nbehin dthe\nw enger\nn ats\nðŁİ Ł\nrubb ish\nprocedu res\ntypho on\nopho bia\ner do\nfu el\nvi era\nbu mps\nmillenni um\nnew zealand\nlec tures\nit on\nmil ky\nrespon ded\nê °\nlandsc ape\n.. @\nbo ther\nâĸ ¶\nz hang\nhuawe i\ntu ition\ns worn\nin u\ny or\npa olo\nau ditions\nab il\nmalay sian\nho ps\nfe athers\nmp le\nau ts\nã o\nboun ty\nic he\nì ĺ\nsh q\npin ot\nge ars\ndisapp ear\nvideo games\nt na\nalzheim er\nðŁĮ ŀ\na ji\nunder wear\nswit ching\nsign age\no scar\nec on\ndro w\ncl int\npl ated\ngun dy\nemb lem\nho es\nici st\nnel ly\njuni or\nroad show\nminer als\nat le\nalexand ria\nac claimed\nv ell\nshi va\nad he\nen ne\namne sty\nh ounds\ncouncill or\nðŁĴ ¦\naes the\npart nering\ninflu enced\nmag no\nfl are\nextin ction\ncivil ian\nmaje sty\nva il\nlaw makers\nrac ks\nmc c\nori an\nsp ices\ner rors\nmay er\nco ca\npa i\ns ooooo\nreti ring\nba thro\nðŁĻĮ ðŁĻĮ\nâĸ ª\nsu f\nendor sement\nbuil ding\nbroo ch\npal la\narvin d\nag ent\nkar ate\nr hi\nc tv\nta ine\num m\nba x\nreig ns\nuni of\nenterpri ses\nadel e\nfla ke\nat tire\nbru ce\nba hamas\ngra vy\nsa in\nche ek\ntri vi\nlo v\ne en\nbb lo\nlady gaga\nitt a\n. \"-\ndu stin\nobserv atory\neigh th\nbloom berg\nkh s\nf cc\ngi st\ncommemor ate\nve er\nsexu ality\ned c\nnic ole\nvac ancy\nu ser\nson a\n:' (\ndipl oma\nt end\nup grades\nÅ Ł\njura ssic\ncardi ac\ndr s\nwidesp read\nà ł\ndail ies\nvend or\nsim plicity\nwi der\nlen ses\nsupp lements\nde pos\nob served\nvin es\nparti ally\nrenew al\ncollabor ate\nali g\nfin ity\nph u\nzz y\npe tit\nðŁĵ ħ\nz in\ni gu\nsm ack\nfall on\nðŁĵ £\nback wards\ncomp onent\no so\ncompati ble\nbin ding\nzur ich\nthom e\nw ounds\nly ric\nfresh men\nsne aky\nfi bro\ndi et\nemplo yer\nin sect\nh ated\nsch er\nraz or\nn sw\nboo ker\ncalifor ni\nav fc\n °\npreten ding\npep si\nal is\nun titled\nk art\ngrand parents\ne the\no ck\nlux emb\nvisu als\nsmall business\nabdul lah\nmin ho\nsu baru\nh ra\nreve aling\nheart breaking\nclar ity\nam g\nsl r\n** **\nâŀ ĸ\nrecor d\nici ary\nmin ded\nye h\nexce ssive\nknu ck\nicec ream\ntru th\nev ic\nta stic\nant arc\nren dering\n, ,\nmit t\nloren zo\nst patrick\nbound ary\nzi g\nvo cab\nosa ka\nfur n\ntu n\ngu l\ns ounding\nblo gger\nutter ly\ng af\nadv ancing\nl cd\nmar gin\nlifel ong\nsolst ice\nsh ra\nwa its\nple ar\nbre ach\nen ligh\nad er\nitt le\nc ation\nho on\nstu died\n?? ???\nk ash\nev angeli\nps l\nwei ghts\nmet als\nty res\ntur no\nwi e\ncar b\ng ale\nse al\nsun ite\nam ic\npatter son\ná n\neu ph\nup stairs\nquali fiers\nkhali fa\napple music\nìĨĮë ħ\nvau ghan\nal ter\ncru iser\nmu a\nt ana\nkat rina\nid ols\nspo iled\nsecre tly\nfi bre\npart nered\num es\ngi ov\ncom et\nscreenshot saturday\nk eller\nfil tr\nfe t\ncon way\npe u\nbad minton\ngi d\nm ound\ndon key\nbu ff\nlea ther\nlar gely\nbro ch\nint ments\nam use\nr k\nsto ve\nimpac ted\ncon t\ncr acks\nprison er\nbar i\ncontrac tor\nori oles\ndomin ate\npol ar\nam elia\ndr c\nðŁijĮ ðŁijĮ\nvi st\nsu arez\ninjec tion\nblo oms\nðŁļ¨ ðŁļ¨\nsti ff\npay pal\nsno wing\nthur sdays\ngoo se\nwe dge\neduc ated\nweak ness\nde cker\nabud ha\nbree zy\nÛ Į\nhope ful\no bi\nrai der\ngh am\nde u\nse ve\npar tly\nfu t\ninfu sed\nmer ri\nthan e\nsome time\nhu e\nme in\ncre dit\nsli ding\nran de\ncher ry\ndead pool\nsh ol\nar am\nunder wood\nsky e\ndistur bing\nm nt\npoli shed\nguardi ans\nha dn\npic asso\nari us\nak shay\nir ri\nj h\nhapp en\nla kh\ndal ton\nat the\ns well\nmar sha\nre h\ncour s\nj kt\ntop us\nserv ice\nr ink\nhack ers\ndono van\nhor o\ntc m\nmay hem\ncha se\ndev ops\nken sing\nsc up\nsh ere\nquali fication\nc live\nton g\nn ancy\nmar is\nder dale\nber man\ncinde rella\njol ly\nci c\nloo t\ncollecti bles\nhom icide\ng ge\nepide mic\nsu ites\nmu ddy\ngi mme\ne rec\n- *\ntal la\nlis le\nembro ide\nðŁĩ© ðŁĩª\nveriz on\nve ctor\nbe anie\narti san\nga in\nflo res\nvi gil\nu so\nðŁĻı ðŁı½\ngrin ding\ngh er\nair ports\nrespon sive\nshaf t\ncan cel\nceremon ies\ne me\nat ari\nbru shes\neag er\nbo hemi\nchildren s\nyan kee\nma a\nsuspen se\nmor an\nmac ar\nsun flower\ncre w\nvo id\nke ar\nfashi oned\njen nings\nsunday funday\nsub missions\nme ad\nher man\nwa i\ncrit ically\nle um\nbaek hyun\nfor cing\nco bra\nãģ ®\nacqu ire\nal k\nge ology\npri mar\nimport antly\nire z\nbunde sliga\ncuri osity\nsen a\nstric t\ncon soli\nwin ters\nven om\nchelten ham\nðŁį º\ncen a\nt at\nba in\nglo ver\nunder cover\nas ses\ncar n\nmemorial day\nam eli\ni rene\nch on\nsyn thesis\nspe edy\nmitsu bi\nsla yer\ncompos ite\nunder stands\npe w\ninter rup\nhen ri\nmor row\nan om\nthof july\ng lee\nthre e\nðŁĺ ®\nand hi\nch att\nrenew ables\nye s\ntrans fers\n!!!! !!!!\nbab u\ndu ter\nlo ops\npe ers\no ilers\npau lo\nic ation\nh mu\nwar a\nmer cer\nhom eland\nfu ji\nale y\nyear book\nre m\nre en\nab sur\nbo is\n] :\ncaes ar\nshot gun\nkur dish\no ren\nra e\nanci es\nty pic\nf h\ndef ault\nre plic\nlu k\ntrans actions\nr ys\ninfan try\nðŁį ¾\ncho w\nchick ens\nba gh\nwy att\nay e\ngg i\nbre ws\ned itions\nmi ra\ncommen cement\npre su\nperis cope\nic hi\nguatem ala\nzam bia\npain ts\nwit ches\nwan i\nun dere\ncro y\nvo ws\nus mc\nhear ted\ntheat res\nshu ffle\nle vel\nmul tic\nsquee ze\nfer n\napp et\npost al\nmal t\non board\nld nt\nco o\ns sc\nk ac\nðŁĺ ĩ\nsc rap\nmar cos\ndeal ers\nann u\nmill er\nco ve\nul ary\nvladi mir\nbe ef\nth ur\npick led\nse same\nbengal uru\nmo tt\nkathle en\nhi st\nno tor\ndr ank\ndu chess\nsnow fall\ne ff\ntin y\nj n\nsy our\nspeci alists\nscot us\nbay lor\neve rest\nmali bu\npre m\nharm ful\nl ali\nb ates\ng ye\ndifferen ti\nand ra\ngeome try\nel over\nblack out\n== ==\nko ta\ninter act\nasi an\nla yo\nsamu rai\nfi del\nexhau sted\ngla di\npd t\nspher ic\nanti qu\nguit ar\nstu ri\nho pper\nang le\nf ills\nsla p\nmi th\nrod ney\nong i\nin som\npre venting\ncassi dy\nap ho\nore gon\nlo in\nham mond\ncontribu ting\nf n\ngar ri\nori on\ncomp elling\nescap ing\naim ing\nplu mb\nbi stro\nbe asts\nconcer ning\nbo e\ndo pp\nshop local\nstumb led\nâĤ ¹\nnaz is\nâĢįâĻĤ ï¸ı\ngest ure\nwar ts\nus open\nhi ggins\nchar li\nhang s\nbom bers\n° :\nfe eds\nc ch\nst il\nnic ola\nðŁĵ º\nclam ation\ntro pic\naf ro\nou k\nexpen ses\nder rick\nal ine\nfa w\nreg ard\nim er\nsat in\nthi um\nry der\npear l\nte ss\nmm mmm\nsen ses\nðŁĩ ¹\npositi ve\nexhau st\noccu r\nnor ris\nlil ly\nis les\ndirec ting\nyo fficial\ncount less\nsam ar\non stage\nflo ck\nmir rors\narch er\nmo i\nk d\nvi v\nin os\nsi kh\nle i\nsen sory\nbr its\nkno x\nchest nut\nop y\ncoli seum\nz af\ndi vin\nadap ter\n:) ))\ntem ple\nku n\nhel mets\nt df\ngu ide\nm old\no ids\nlu ther\nhe is\nmonaster y\nsp ree\nk lu\nbrit ney\njagu ars\ngre ats\nc cc\nky rie\nmachin ery\ncric ket\nre ro\nab o\naspir ing\nsemi finals\nale ss\nsig natures\nvar d\nme th\nher bal\nhol den\nking dom\nap or\nreg gie\nore o\npalestin ians\nem mys\nsec tional\nro i\nney mar\nqu el\ncu ll\nl ka\nhaz el\nestim ate\nul ties\ngo w\nbe a\npurch ases\nbel ts\nprotec ts\nm é\ngue ssing\nbb o\nclau dia\nfr acking\njon ny\nel k\ncel tic\nal mighty\nra je\ncourty ard\nig i\ncan es\nðŁĴª ðŁı»\nbank rup\nle thal\nâľĮ ï¸ı\ngraphic design\nvad er\npenc ils\nrough ly\ndan te\nm fg\nconst ell\ncam el\nj b\nbloss oms\nen to\nbalo chistan\ncine mato\nill ard\njer sey\ncon sent\ndent ed\ncon templ\nsch er\nhol i\nlou gh\nst our\na yo\nbegin ners\ncur b\nv hs\na jax\ndu ff\nav eng\ndom est\ncommit ting\nai red\ncha p\nhedge hog\ndisappo inting\nfreel ance\nin land\nchar ms\nðŁĺį âĿ¤ï¸ı\nai sh\nm x\nbuck le\nti dal\nper mit\nbo ating\nra cha\nkend rick\nb ello\nb hi\nple a\nestim ates\nl b\napo logies\njay a\nbb l\nast oni\ninter state\nmain taining\nel bow\nmu p\nep it\nðŁĺ ¡\nviol ations\ndef end\nbe h\nsl c\nam ir\npur i\nti um\nfi fa\nblur ry\nscri m\nðŁĻı ðŁı¾\nma ple\nrel atives\nâĺ Ŀ\ncho c\ncon nor\n⾨ ⾨\nwhi sp\nlist ings\nma ze\nthan king\nri dd\ngrass roots\nshi fting\ndesper ately\ngor illa\nden i\nju les\nstra th\ng ley\nja in\nbu ick\nt anner\nðŁĴ Ŀ\nga e\npri m\nit ors\nn ano\nsepar ation\narmen ia\nbor deaux\nðŁ ħ\npj net\nbu rial\ne bon\nglo ss\nre new\ngri er\nspe eds\ncomic books\nsym boli\npur poses\nãħł ãħł\nspati al\nno table\nci on\nn ps\nho ffman\nnor man\nrt g\ndu sty\nsitu ated\ntr an\nk fc\nem en\nnic kel\nhast ings\nsett ling\ngr it\nl ena\nw aw\nart s\ngu m\nca regi\nle wis\nsapp hire\nrememb er\nembed ded\nt lc\nbl at\nserge ant\nel sa\nboot camp\nbow man\nphoto graphic\npill ars\ndirection ers\nclassi fied\nno is\nve er\nbarre ls\nwh oop\nðŁĺ± ðŁĺ±\nfe male\npetro leum\nmedi a\ne fc\npoké mon\nठķ\nenthusi astic\nvar un\npro files\npedi atric\nacci dents\ncon rad\njan g\njo jo\nac or\nob server\nl f\nlive stock\nfor gi\nfo s\nel m\nan and\ngo e\nc ere\navoi ding\ngri t\nom an\nthank fully\nscat tered\nnick y\ncylin der\nchees y\ndi ver\nmahe sh\ncav es\near liest\nqu inte\nsubjec ts\nb end\ngul f\nvocali st\nglu e\npat ches\nun stopp\nsny der\ndemonstr ating\npi o\nhor ns\nwic kets\nand the\nr ama\nyo on\nstra ight\nbed time\nor ang\nbul lets\nsa urus\nmin ers\ninci dents\n! ...\nðŁİ ¸\nag ers\nhand les\nstat es\nin ity\nd ons\nincredi ble\nemin em\navi v\nru dy\nmoz art\nfolk lore\nappli ances\nmt l\nfre y\ndi as\nhu a\npage ant\nstri ve\nim prison\nbul lish\nr ana\nal erts\nbb mas\nhy per\nderby shire\nre cre\nre dd\ndebor ah\ncosmo s\nlaw son\nmel anie\npsy cho\nho or\ndoo dles\nsni per\nshad y\nman tle\ncanadi an\nnew year\ninter actions\nsepar ated\ncor ds\nspiritu ality\nap u\nit o\np ct\npel osi\nrebel lion\nse iz\nwor cester\nsec tors\nul i\nsan ta\nÐ µ\nðŁĩªðŁĩ ¸\nbi ased\nclass ical\ngam ma\ndee plear\nemer ge\nback er\nsur ance\nhand crafted\nðŁİ ¥\nfranc is\nmill an\nic i\ncro wn\nwo w\nstri ped\nun fair\nrelax ation\n³ ï¸ı\nembrac ing\nshe alth\npale o\nmartin i\ndist illery\nwr ink\nor k\nna th\nhay ley\ncour thouse\nsi ber\nsa di\nquiet ly\nmel t\nm sm\nme h\nsmart phones\nrel ent\npp ing\nwar wick\nco logne\ngli a\ncot ton\npro g\nlon e\nip sw\nstar ters\nexpan ds\nu mp\nsu ed\nski pper\ninfe ctions\ning le\nà ¡\ncler k\ndemonstr ate\nac ar\nðŁĺĤðŁĺĤ ðŁĺĤ\nti bet\nbun s\nalo m\ndemol ition\nssi a\ng st\n[ ]\nso ar\nâĺ Ģ\nðŁĺ ª\nðŁĵ Ĭ\ndee pest\nbeyon d\nare t\natt ends\nactiv ated\ndi mit\nâļª ï¸ı\nhigh lighted\nmagaz ines\nrum or\naz za\nsteph ens\ndol ph\nsho ckey\nmat s\nwe av\nmel an\nserv ers\ntra um\nku sh\næ Ĺ\nbab ys\npa z\na al\nla use\nbreak ers\ncanter bury\nul ture\nmi ri\neuro s\ntane ous\nimpre ssions\ndu tch\nil d\ngh i\npur due\nadequ ate\nl p\nsy ner\nang ler\ndu rable\ngal ore\nro wn\nmg mt\nðŁĵ Į\nlu cia\nâĺij ï¸ı\nzay n\nbor row\n. (\nnorth umber\ncru sh\neng a\nsu sh\nextra vag\nt out\nma hal\nali stic\nther mo\ngall eries\nes se\nchi bi\nattrac tions\nlex ington\nlegislat ure\ndocu mented\nresi den\nbrow nies\nw f\nst ool\nplan ets\nsho ppers\nconduc tor\nms p\ntr icky\nfru ity\nend ra\nfeel the\nwhi pped\nhair style\nre fer\noo k\noc topus\naudi ences\nku mar\nafter no\nop tim\nc fl\nni p\ngen i\nalpha bet\nann ab\nlam in\naccep ts\nl ng\nðŁĺ «\nt ine\nac om\ncheer leaders\nt k\ngr on\nv g\nk ung\nja x\ndha bi\nr ss\nmack enzie\nbeir ut\nclean up\ngy psy\nst ell\nbur ger\nhurric anes\neduc ation\nst ina\nâĻ¡ âĻ¡\nunfortun ate\njere mi\nbad ger\nat ers\n: âĢ¦\nter ra\nsubli me\nstu d\ny mca\nmr u\nduter te\nbren nan\nbul b\nmel o\nyl on\nhack er\nc red\ngu d\nas an\npad illa\nembroide red\nvietnam ese\npione ers\nprojec tion\nre boot\nid c\nan ey\npri mer\nsuff ers\nwin ding\np on\nsto day\nmor n\nu ch\nall in\nadid as\neliza beth\ntu ck\no graphy\nðŁļ Ģ\nbe g\nos borne\nghet to\nr h\ncn n\nir ma\nma kin\ncab les\nmur ders\noc ks\ninst a\nal as\nsi k\ncu ff\nla re\nfoo dies\no vic\nat om\ngeome tric\nem pathy\nภµ\ncent enary\nnewsp apers\nadministr ative\nðŁİ Ĭ\nsti ve\ncontrac tors\nle tt\ntas mania\nawesom eness\nden sity\nve en\nprince ton\nfrequ ently\nre ject\ngh i\nmodu lar\nceram ics\nsh ag\nki wi\ncan vas\nsweat shirt\nan j\nti mm\nnapol i\nil er\nappe als\nhamil ton\nma yo\nwe ave\narrang ed\nwhar f\noccu py\nb vb\nas aki\not ter\nnor m\nvi es\nde tox\ntion al\ndere k\nid ad\nad missions\nconstitu ency\nu pper\nwoo t\nallo y\nse ve\nlu b\nun comfortable\ned win\nab re\nd wight\nar che\nvirtu ally\nsp ol\npri e\nai i\ner r\nswit ch\nbar ack\nse ok\ncou l\nwn t\npou l\no live\ncaffe ine\ncardi ff\nnotor ious\nde mp\nex cess\nbar r\nt ford\na jay\nbump ed\nmy thology\nshel ley\nfal con\nshakespe are\nmust angs\nno ted\nbon e\ncivil ization\nsy d\npar sons\nun official\nhy ped\nsp ends\noppo sed\nv ings\nspace x\nnoti fication\ndeci ding\nbio tech\nout si\nsal ah\n! .\nfe d\nss y\nc ms\nbad gers\ncr o\nela ine\nn ba\ndy our\nn ant\nhoney moon\nclimb ed\nconom y\nath a\nm ell\nne bula\nnature photography\njuli e\nbm x\ninve sted\nmon o\nlieu tenant\nwat kins\ntechn ician\no se\nka e\nì Ľ\nmc queen\npre ach\ntrav eller\nflexi bility\nze bra\nreta iler\np ant\nben der\nbrand t\nsqu id\nwar rant\nveri fied\ncas s\npier cing\nhon ours\nt ying\nmor ris\nkis sed\nop rah\npanor amic\nme i\nsplat oon\nwich ita\nari as\ngal li\nindy ref\ngood times\nathe ist\nconfe ssion\now ski\nre pping\nad ditions\nmechan ism\nz im\nj ans\nsu f\ncho pped\nbeg innings\nvitam ins\nãħ¤ ãħ¤\nor th\npo les\nru b\nantarc tica\nindie film\nweb cam\nket ch\nbre tt\ncle ment\nher on\ndefe ating\nhydr o\nbuc ket\nwand ering\nsid ney\nfuture of\nb inge\non ies\nknock out\nadministr ator\nsyn the\nl ent\njan i\nbar ley\npremier league\nner ds\ncr m\nbra s\nbot any\nevol ved\nrot ter\nro wed\ntum or\nweal thy\n Ń\nmon arch\nli shed\nda hl\nðŁİ ĥ\nbu ch\nken yan\nØ §\nred ness\nassemb led\nse mit\nhud der\nshro p\nran i\nlear ning\nmor y\niti a\ngeo graphic\nworl dof\nf b\npho sp\nboo gie\nam ped\n? ...\nche w\ndwar f\nar us\ns sen\nru sty\nrecru its\nh k\ngar de\napp lause\nvol umes\ninvol ves\nta c\nhand bag\ntrans late\nffe l\nse ym\naqu atic\ntrans fer\nzo di\nand r\nacade mia\ncr ater\nte z\nar se\nadap t\ncol oni\nsnow man\nmal i\nhang in\ndi schar\noy sters\npho e\ncolon el\nw ba\nhispan ic\nthri ving\nsh y\nag les\nsales force\ncre me\nso les\nla fayette\nâ ī\nter ia\nach a\nsp erson\ngo go\ncar ly\nthe ore\nam ore\nvo x\naf t\nãĤ ¹\nstap le\nmu ffin\ndi agram\nino x\nsu stained\nav ent\nme ta\narbit r\ndec ay\nado le\nÐ ½\nec ol\nph o\nn k\no cu\ngr anny\nç a\nluxemb our\nstad t\nalber to\nle vit\nam as\nd x\nor phan\nco bb\nas c\nlo gy\nimmen se\nchan ts\noff line\np ent\nbre x\nw inger\nplan e\ni el\nnichol s\nca thy\nnar uto\nlow ed\n/ //\nignor ance\ncat astro\nyou ts\nsch en\nbuil d\nhaz i\ns ine\ncritical role\ndu g\ndete ct\nlo gs\nen amel\nstpatrick sday\ned die\nco pa\ncigare ttes\nho ff\nkay a\nla goon\nra pha\nair borne\nchoo se\npuer tor\nke v\ngui ding\nfro sty\nbor ough\nmir a\nðŁİ Ĭ\ncade t\nanu sh\nyo gi\ne ger\nfl ing\nslo pe\nnin th\nwe ston\nfoot wear\nf n\nmay weather\na am\npla in\nstair case\nwitne sses\nwork outs\nro bust\ndex ter\nco hort\nðŁļ Ĺ\nsp ell\nha ze\no om\norgan ising\nwild fire\ncont acts\nav on\nmin o\nupd ating\nðŁį »\nli thium\ning ual\nk is\nau ga\nlo com\nde duc\nu da\nth ak\nboy le\nmp er\nhot tie\neri k\nre vised\nis la\ntravel photography\noo za\nen qui\nconfe rences\nclo ver\ng room\ncur ves\nlive on\nper f\ndisplac ed\nbo log\nxx xx\nðŁĺ© ðŁĺ©\nte al\nve ssels\nrain forest\ncal ci\npan ther\ngira ffe\nta sted\nimag ery\npad res\nday time\nbas s\nri pe\nopio id\nnu e\nvin yl\ninvent or\nsen s\nprocess or\nmu t\ngad gets\nbibl ical\nshann on\njacqu eline\ncar y\nthe resistance\nali en\nn vi\nco sy\nbi har\nfo ley\nren d\nmu gs\nfa ken\ncl one\nni allo\ngra bbed\nchi hu\npower house\nn tt\nchero kee\nspon ge\nimple menting\nrh ine\nle one\nðŁį Ģ\npret tiest\ninfra red\nimpro v\nswit ched\ntu bes\ncon tr\nbl k\nprojec ted\nbe aver\nyo t\nbbcra dio\nthi gh\nper secu\napologi ze\nw ack\npo ster\noli ver\naz a\nlou d\n( ?)\nf the\nwomen shi\nspar row\nblu sh\nus able\nsc ales\nit ative\npeu ge\nne eding\nlegg ings\nglam orous\nmat ur\nc z\nwat t\nda b\ntam ar\net sym\nbau er\nheart felt\nh n\nelse where\nbir ch\nalu mini\nhu ck\ne me\nj l\ntraf ford\nd z\npor tions\nana sta\narthr itis\nesp n\nber gen\nviol ation\nyo shi\nc z\nnorthumber land\nclo sures\nðŁĩ¯ ðŁĩ\nsmi ley\nr w\ntel ugu\ninten si\ngre gg\nve ga\ndun geon\nsouth bound\nba il\ndomin ican\nsemi final\nchap ters\nh itch\nvan ity\ntrans iti\nrecomm ends\nsati sf\nbar ca\nqueen s\n( (\nde struc\nstra it\nra vi\ndess erts\nin tru\nhar am\nk os\nfo e\nfat ty\npais ley\nmagn itude\ndri dge\ncom ey\nschem es\nvision ary\nour t\ndown loaded\nðŁĻĮ ðŁı½\ngd pr\nlan i\np wc\ngu ad\nnic est\nstake holders\nre ferred\ngeorge town\narvind kejriwal\nschnei der\nin doors\nall star\nstrand ed\ngen der\nze pp\nma sses\nðŁIJ ±\npati ently\nbl dg\nz ab\nwe arab\nvi vid\nhe ck\nd ella\nsy mb\nje opar\nla ger\nà ª\ncomb ines\nne c\nbr ay\nflo p\ntx wx\njo ys\npon t\npro found\nsur round\nmad hu\nma ble\nay r\nte as\nn sa\nopen ly\ner nest\nãĥ ©\nto po\ng na\nanti oxid\nti an\ne tr\nc ello\nma thi\ngener osity\nb iting\nman ic\nkel sey\nchee ks\nten der\nw th\npron oun\nultimat ely\ngu sta\nari anag\nger ry\nble ed\nred dy\nmic h\nmitsubi shi\noper ated\nsex ually\nma u\ncl lr\nvi ds\nco c\nmel ted\nðŁĮ Ī\nq ld\nite ch\ninstru mental\nend game\nðŁĵ ĸ\nener gi\nbrow nie\ntam il\nat in\ndomin ated\npra ises\nfire place\nsens ational\nmen a\nk arti\nun prece\nru pt\nori ental\nmc cor\ntour naments\nscen ter\nre eves\nprescri ption\nsam e\nfra u\ntru ffle\nem bo\nroman s\nbla sts\ntechno logical\npr at\nb sb\ny ar\ntren dy\nac l\nal ad\nðŁį ģ\no hh\nbankrup t\ntho ven\nregar ds\nis er\nwar wick\nvine yards\nreal m\nniallo fficial\ndo ta\nge mini\nto do\nv able\n¨ ¨\nla u\nwre ath\nju ve\nnat asha\nle ver\nlor i\nhor ser\ncc tv\nair bnb\nes anders\nsin clair\nema biggest\nhigh school\ncon test\noptimi stic\nt te\nðŁĴķ ðŁĴķ\nss d\nye e\nhel ena\ncon sen\nric ks\njes se\nan ic\nðŁİ ¯\nre acts\nro be\nindepend ence\nvol tage\nm ington\ns ant\nà¸Ļ à¸\n-------- --------\nsentin el\nke tt\nrehear sing\naaaa aaaa\nsof the\nstir ling\nsear ch\nwi gan\nstand out\nsna il\npent agon\nÄ ģ\nch lor\ncru st\nnet any\nchemi st\ndisapp eared\nric ardo\nsp iders\nbo se\nwar ren\nme ssing\nbann ers\ngu el\npar ach\nma id\ncoun ted\nepi le\nbon fire\nspeech less\nse tter\nmeas ured\nrejec ts\nnik ki\nle ster\nforen sic\nfab rics\nalo ha\npre served\nwat ford\ndeta iling\ndar th\nbo u\ncar ly\n... '\ntail gate\nnoti fications\nå ¤\npas sive\ntrous ers\nbalo ch\nro ther\ntypic ally\nà ¥\nsp it\nwi z\nsic ily\ntechnic ally\nex pose\nst age\nhu bb\ncre am\ncap s\npo ke\nsle ek\nju ne\ntempor arily\nde z\nawak ens\nl ame\n_ -\nji ha\ntues days\nadvis ed\nadvis ors\nexi sted\ndis agree\nnews room\nlo sers\nworld tour\ndr ying\nal di\nhar ness\nfoot print\nhobb it\np mln\ni ro\nque red\nasse ss\ngaz e\nsa b\nth ian\ní Ĭ\nti f\nob serve\nev il\ndra wer\nswee p\ncor y\nco dy\nkyo to\ncal lum\nn inj\nlau rent\nbe i\nsket ching\ncustom ized\ndu r\nregre ts\nknox ville\nìķ Ħ\nmess aging\ngrac ie\nabun dance\nbi dding\nbre wed\nfl ouri\ntherapeu tic\nalt itude\nho gs\nbur ner\nelec tro\nwonder fully\nhe ater\npost pon\nli very\nr all\nad as\na ac\nsau l\nbrook lyn\nplay house\nâĻ¥âĻ¥ âĻ¥\nchar itable\nin y\nz ah\ncompet itions\nbe av\nplu gged\no is\ndo om\nastron om\nspeci alized\nmax i\nta ps\ncellu lar\ndepre ssed\nfolklore thursday\ncri b\ne mul\në° ©\nfi gh\nru z\ncar lisle\nspe ar\nside walk\nde i\ndepend ent\nlac es\nnh s\nðŁĮ Ļ\nreali zing\nnet work\nric he\nre gin\nre fresh\nst ral\npa thology\npla id\npsyched elic\nhin d\nu ka\nalgori thm\nlin king\nprogre ssi\nfe y\nd ade\nhydr ated\nb ant\nfam ed\ncot sw\nbo ise\nas c\nrac ing\nja vier\nww en\nmar lins\npoo p\nswe pt\ntoni ghts\nwe f\nani me\nslo vak\nâŀĸ âŀĸ\ncla us\nlem me\ncli ppers\nre ls\narianag rande\nr te\nko t\nthal apathy\nhungar ian\nzu ma\ny von\nis u\njour neys\nclin ics\nbe be\nww f\nn ws\nsuper heroes\ner it\nsle ague\nidenti fication\nmo tto\nba i\nsour ced\nill er\nap i\npri se\nunprece dented\ndam as\ntuni sia\ndra in\nundere stim\ne ther\nquarter ly\nrewar ding\nal ham\nwolver ine\ncab ine\nhyp no\nnad ine\nhav ana\nda e\nðŁĵ Ī\ndr on\nread ings\nb ati\npic o\nmer ci\niti an\nwal kers\nel ope\nmi key\ngod zilla\nbur lington\nabu ja\nsocial ism\nat ility\nsh ell\nharry potter\ng no\nab ur\nre leg\nfel ici\nro gen\nneuro science\ninst in\nath am\nvou chers\nj arre\nfu se\ndef ici\nmonte rey\nde port\nmid day\npp ard\nfre ed\name ter\nwil t\nn ingham\npr att\nliber ty\nslo gan\no to\npr i\nco ated\nc pd\nne tt\nil las\nmal awi\nevol ve\naccessi bility\nðŁĶ¥ðŁĶ¥ ðŁĶ¥ðŁĶ¥\nor nament\nb p\nel is\nson line\nchi ro\nfl ick\nib m\nar ak\nen ables\ngar land\nsan e\ncu ties\ntri p\nrotter dam\nn ys\nlam ps\nlu cas\nbo g\nra ils\ntravel led\nhic ks\nen u\nsab ha\nscru b\nhi er\nhart ford\nfo o\nfer nandez\ntre vor\nmat tress\nappo intments\nale j\nfe i\no logist\nsaf ar\noc ta\nsr c\nsha un\nambi ent\ndri c\nbi ker\nshe e\nmust ache\nh ta\nbo one\nher ty\ncar dio\nbra kes\nrec ital\nconsi sts\noverwhel med\ncau l\nrobb ins\nim it\nal th\nur l\nbi bli\non ne\nblack livesmatter\ndiffic ulties\ntel ang\ntall er\nðŁĵ Ĩ\ndeb ating\nbur rito\nmo vember\nstrength ening\nbo e\nte stam\nmirac les\nbase ball\nre nee\nðŁijī ðŁı»\nal fa\nâĺ ĺ\nunstopp able\nec s\ng mo\ngiftide as\npath way\nfen cing\nðŁİ ¤\nb ham\nra s\nsk o\nd led\nthel ast\nmagn um\nbin ary\nwil de\nwil der\nwh ati\nbarbe cue\nh ism\ncan oe\nkur di\neli ve\nadvant ages\nmad ame\nbi er\nmis sing\nenter tain\nair force\ny ama\nc is\nhash tags\nj is\nve il\ndream y\nten se\nmay ward\nch ateau\nhunt ington\nâļ ĵ\nv all\nup on\nbl ouse\ndun es\nðŁĺ ´\nfert ility\nm ole\ncurren cies\nst u\nber lin\ntoa sted\ndiv as\nwal t\nlar k\npor a\nhit ter\num er\nchil led\nbal ancing\nfa is\ny in\nor tiz\neast enders\nh ate\nur al\nap ril\ntim el\nà ±\nper o\nsto cked\nrespec ts\nth t\nbest friends\ngiving tuesday\nbe ad\ninv ent\nim i\nnap les\ncomb ining\ntok ens\nthir st\nma sc\npar rot\nsp u\ndent on\n* -*\nt res\nsubur ban\nwid th\nsi ve\ncon tender\nsiri us\nlo k\ntroop ers\noutra ge\ntur bo\nfrag ile\nme ssed\ndo h\ndisc ord\nnetany ahu\nre sign\nforgi veness\nmo han\nmun ch\ncam ou\nidenti fying\nenab ling\nhot ter\nthorn ton\njai pur\nar ya\nðŁı» âĢįâĻĢï¸ı\nmu staf\nmaj ors\no ke\ndu ffy\nroh ing\ntil t\nðŁĩ®ðŁĩ ³\nrock star\nshe ep\nhend rix\nra v\nin vention\ndo u\nlagun a\ngru mpy\nsw is\nim pe\n) '\nyou ths\nbun ker\nst ache\noppo se\nindi es\nacceler ate\nml p\ned en\nw ann\nk ail\nakshay kumar\nsu pt\npol ym\nmidd leton\nextra ordin\nwil son\naustrali an\nalumini um\nway ne\nalum nus\nmat ics\ngri m\ner nie\nopp a\ncompetit ors\nrand all\nh ence\ndecla res\npre aching\nsha he\ncan e\nsustain able\nstap les\nle dge\nad ena\ndoctor al\nbur gundy\ndecor ate\nren dered\nri sen\npr ank\ndi or\nbee thoven\nflo or\nac com\nto t\nho dg\ntouri sm\nsay in\nobjec tive\nmar kers\npremi ership\nen abled\ncamou fla\ngi ant\nÑ ģ\nsmo key\nric ket\npan g\nde pending\ns ation\nevol ving\ninter cep\ncen sus\ntof the\nre en\nmendo za\ntrum pet\nmarke ters\nan it\nðŁĻ Ĭ\nnorth western\nv la\nfoto gra\nblackand white\nche wan\nwi g\ntro om\nginger bread\nk n\nro mero\nn fc\nor chi\nfun ko\nsour ce\nf s\nra ped\no st\ntar ot\nann ually\nðŁĺ ¬\nr ill\ndel av\n.. !!\nse s\ncan n\nmedic are\nph el\nape x\nguardi an\nrema ined\nr pm\na ñ\nstory month\ninstag ood\nneighb our\np ing\nsem ite\nmy stic\nas cot\nmat er\nhand ful\ndang ers\nti d\nana heim\nopol y\nsh allow\nnami bia\ntor ia\nprocu rement\nbig bang\nannoun cements\nprosecu tor\nbeng als\nsal le\nen roll\nga stro\nsugge stion\nba k\nha ul\nbudd hism\nberni esanders\nflu te\nfati gue\ncyn thia\ncho i\nir win\ngu a\nstr ous\nh p\nba p\nsatisf ying\nplay a\nðŁİ ¼\ninst ap\nal ice\nt p\nirri gation\nðŁĩ¬ðŁĩ §\nin tric\nclu es\nple x\nsa x\nhe pat\ndump ed\nsignific ance\nby u\nmedic ation\npro v\ntough est\ncorn ish\nâŀ ľ\nkel ley\nu v\nsi zz\nsi bling\nme st\ndi stor\ndiplom atic\naun tie\nb hat\nson ic\nbren da\npump kins\nro ch\nblack burn\nur ged\nshi a\narrange ments\nfloo d\nsa unders\nlec turer\nnou ri\npopul ations\ndiplom acy\nconsist ently\nðŁ¤ Ļ\nt mund\ncauli flower\nl ily\nvocab ulary\nvari eties\ncoo ker\nup town\nqu ent\nmo sa\nre inde\nvelo city\nspru ce\nsocial medi\ni ber\nvolun tary\nproce ssed\nbal tic\ny ang\nleban ese\nd p\ndol ly\narrange ment\ny uri\ncran berry\nkal yan\nelev ation\ncli ff\npu shes\nìĬ ¤\nsil ic\nco wx\neter nity\nsla ves\nvine gar\nglou cester\ncon tained\nbreaking news\naga inst\nrenov ated\nnorm andy\nhero in\nys m\nmo ds\ngre ek\nun di\ntren ch\nv h\nencoura ges\nhead ache\ngr ange\n: '\never green\nÙ Ĭ\nreck on\nab used\nth ru\ncho ice\nti dy\ncol der\nscho ice\nha in\nbru m\nli ars\nbre it\nyor ker\nsh ack\nhe idi\nmicha els\nsco pic\nfasci st\nplay ful\nca c\nyas ss\nsh ad\n.. ?\nqu en\nram irez\nclif ton\npr s\nbest fan\nâģ ł\ngener ating\nhead set\ndisappo intment\nabstr act\nbo iled\nparen thood\nazerbai jan\nexhib iting\nbom bay\noli vier\nko so\nun lea\nmat ernity\niz er\nsi ves\nr hu\ncol l\nsaskat chewan\nfre akin\nde k\nna g\nstab ili\nðŁį ķ\norgani zer\nbo sses\nar u\nu va\nat able\nta un\nafter wards\nfert ili\nver ge\naz i\nmor ph\n๠ģà¸\njer k\ncosme tic\nko w\nstru st\nap ache\npost cards\nfor mul\nì ĭ\nspin al\njack pot\nelec tri\nà Ń\nlo y\ngra der\ndiab lo\nar di\nhe sit\nf w\narch ery\npa sh\nthe ories\nrepe al\nre live\nper cy\nâĺ Ĩ\nim in\nsyn chron\nsham poo\ncoup ons\no to\nla i\nthou ght\nluxembour g\nmo v\nðŁĺ ¥\nge mma\nse ated\nm ga\nstrat ford\nun certainty\nshi fts\nest o\nfo ol\nfire arms\ncor rie\nki ki\nappa rent\np ills\nolym pia\nfi d\nelev ated\nde cks\nignor ing\nav alan\nro v\nwhist le\np tsd\nmilit ants\nrobo tic\npac ers\nquil t\nbankrupt cy\nlic h\nper cussion\ncelebr ity\nal s\n( ;\nsu t\npokemon go\nh g\noff s\ngibr altar\nscre ams\nbilli e\ngen ome\nmar in\nbe ams\narch bishop\nem in\nbedro oms\ng ated\nol ly\nwarran ty\nat own\ncudd les\ngun na\nk ic\nvi ve\ncy mru\nnar row\npro b\nle o\nrefe rences\nmanufac tured\ncho pper\nbrun swick\nsem is\ndon ia\nr ye\nman o\nhur ting\n? #\nhol li\ninvestig ations\nc els\nðŁĵ ŀ\nle ster\ntemp les\nsto rey\nmc mahon\ntoi lets\nwo of\nï¸ İ\nle verage\nat om\nnight mares\nvictor ious\nhaun ting\ncustom er\nag i\nyo ongi\nmon ty\nver onica\nw ur\ninti mid\nblan kets\nvolu tion\nj m\nâĺ İ\nam on\njud ith\nðŁĺİ ðŁĺİ\ndistr acted\ndri p\nhurric ane\nand es\nrevel ation\ntro op\nab leg\ncol lin\ntibet an\nwor rying\ninter nationally\neat er\ncamero on\nbrad or\ny uk\nðŁĴĹ ðŁĴĹ\ntra k\nslo pes\nci er\nne a\nol er\nta ka\nalbi on\nvolcan ic\nam n\na fi\nob stac\nface time\nger ing\nn pr\nmetall ica\norgan ic\nðŁĴ ¡\nki dd\nd ances\npemb ro\nwash er\nm its\nom er\nemo tionally\ntan go\nip o\ndo cks\nscan ning\nspec s\ntho m\nthe ology\nemer gen\nom i\ng pa\nselec tions\nun necessary\nima ge\nter s\ninduc ed\ngi gan\nrent als\nsupp lied\nm fa\nshan kar\nlat er\npa jam\ncla ve\nÙ ģ\nma hin\ncarl son\navi an\nano va\nkati e\naj ith\ndesign ated\nchocol ates\ninvestig ators\ngla zed\nprin cess\ner ry\nra gn\nou rable\nhr u\nsun dance\npeuge ot\nsteam punk\ngh lin\ngre ase\nhi res\nz ap\nper ce\nj ill\ntom e\nhe hehe\njoy ful\nmae stro\nni shed\ngene alo\nv ich\np its\nfox es\ngood man\nemer son\nlo bes\ncon verse\no ats\nthom son\nra him\nmal ware\nah i\nman kind\nre sin\nim g\nsw ood\nkin der\nsc roll\nar a\nsak ura\nro bbed\nxi on\nny a\nc ism\nce dar\nbe in\nmour ning\ntor to\nheath row\ndone gal\nbar b\nhydr ation\nk or\nelim ination\nsu pdates\nhill s\nappe ti\nstar red\nko m\ngw en\ndd d\ncra y\nsc anner\npersonal ised\nseren ity\nre design\nmeta ph\nbox ed\njudg ment\nno se\në ¹\ner ad\nac ne\nsupp liers\nener getic\nv om\nas ap\nðŁĶ ¸\nir vine\nhat ch\nla ss\nad ren\nwaff les\naccur ately\nici o\nitt le\nse un\noccup y\nweb cam\nthene w\nent es\nga i\nj w\naccoun table\nvis or\nir rit\nlicen sing\nhudder sfield\ngen ie\nðŁİ ¾\natmo spheric\nten sions\nspart an\nclif ford\nol an\nnorth bound\name en\ncen sor\nu el\nster y\n$ $\nfar rell\nhy ster\ncl t\nse dan\nrep lied\ndescri bing\nmicro wave\nsla b\npro sp\nassi sting\nru bio\ne than\nhh hhh\ngu ay\nz man\nra ise\nroll ing\no e\nn ile\nambro se\nscar borough\nhero ic\ncoo ks\nmor t\nchop ra\nðŁĮ ·\nto b\nshav ing\nstac ey\ndor m\nmotor sports\nwi ki\nfol ds\nsp iced\nstress ful\nliter al\nfu dge\npe ggy\nwa ite\ntre sses\nse sh\npr ic\nðŁİ ħ\nfri ght\nr va\nmumb ai\npo m\ntt v\ncel lar\ntom e\nandro id\ndor is\ntsun ami\ntin der\no ec\nm wc\ndor tmund\nno thin\nl iti\nso u\nbelieve in\nat u\nkno cks\nmag ni\nss sss\nro hit\nine ws\nang i\nm andy\nke ttle\nintermedi ate\nav ant\ncur l\nendor sed\nori o\nur t\nconsider ation\nwi res\nshel ters\nb ino\nvik ram\nimple mented\nly dia\nbu k\nparo dy\nc news\nunder graduate\ncanu cks\nsam i\npolit ically\nro tten\ngh z\ntex tiles\nover load\nmoder ni\nrecre ational\nfli r\nbat on\ntypo graphy\nov ation\nintrigu ing\npilgri mage\nal ge\nad ays\ntcm party\nsp elled\ncur ls\nboo ze\nste m\nann es\nir ls\nspon ge\nsho pper\nsig nation\nbra ss\nmi stress\nle ah\nbeg inner\nlau derdale\naugu st\npre school\nta ping\ntai pei\nexecu tives\nb d\nrhe tor\nesc or\nimmun o\ndeeplear ning\nstat ues\nit us\nmanu script\nly ric\ncor vette\nmol ly\nla ge\nde p\ncn bc\nle st\nje ssi\nfi fe\ngriff ith\noppo sing\nran g\ndr ills\nrespec tful\np ity\nd ell\nhar ding\nplay boy\nblo ke\nshut out\nk ili\no sp\nse attle\nbc poli\nmis es\njourn als\nteam ing\nes ther\nfre ddy\nĶ ï¸ı\nmetr ics\nno tre\ngar ry\nfor ty\nnavi gate\nperio ds\nbened ic\nj id\nda w\nance stors\nrestor ing\ncon g\naller gy\ntit anium\nc ence\nlean ing\nab bas\nv ast\nuc f\nroof ing\ne man\nseve rely\nvo gue\nve au\nin bound\nd z\ntane ously\nstret ching\nman chester\ndr yer\ndav is\nkan th\nthe game\nit ted\nre tain\nel les\nconge stion\nfrat ernity\nol lie\nlo ki\nfre ely\ncho o\npon y\nsc ep\ntab ly\nbal t\nrock n\ndi me\nlo gging\nðŁį ·\nad u\nha voc\nwater ford\nchar is\nswee tie\nrun ning\nner d\nerdo gan\nz ara\nweigh ing\nfif ty\npre cise\nlow ell\nkurdi stan\nr yo\nor th\nsyn th\nlin ers\nphenomen on\nart illery\nil legally\nconstru ct\nnostal gic\ngar th\nal ta\nshel ton\na sean\nw ander\ndur ban\ndi versi\nbon o\ncl on\nle man\nsh un\nobstac les\nappet ite\nfe eder\nrespir atory\ndi xie\nformu la\nan to\nso ber\nextin ct\nau c\ning les\nlegitim ate\n; ;\nmin nie\nipsw ich\ndram atically\nðŁijı ðŁı¼\ningh am\nmilit ary\nmon et\nus navy\nfor k\ndun no\nplay er\nq otd\nst oo\nex or\nethiop ian\nfilm fest\npe red\nc ate\nsau di\nin ner\nsin cere\ntion ality\nale e\nde eds\ncooper ative\nir onic\ncro cod\nbr ary\npost season\ncam per\ncan ary\ne in\nexten sions\nnb d\nsher wood\nspo kane\nhu mp\njit su\nê ¹\ndar yl\np si\nstab bed\noffer ings\nexpe cts\ncav al\nbody building\nfr aming\nf ca\nye arly\nbom bed\nsk il\nresear ching\njud iciary\ngree ted\ntu dor\nmil o\ninnov ate\nðŁĺ Ľ\nr hs\nru by\ncontribu tor\nfam er\nsoci ally\nm lin\nfi ery\nut ter\nbeau t\nit os\nde voted\nrain bow\nbar ney\npe ren\nar jun\nr na\ngab by\nut i\nhann ity\npick le\nser v\nqu akes\npp e\nfe m\nwh itec\nj n\nvictor ies\nðŁ§ ¡\ngol fer\ncongratul ates\nresul ting\nmechan ic\nur ve\ncen tered\nkie v\nan s\nin cub\n< <\nc mo\nbestfan army\ndap h\nen ham\non cology\nku sh\nt xt\nori ented\nfashion able\nc sr\nsa hara\nr ack\npd p\nhan son\nภĩ\nti ers\nra r\npan am\nin sky\nsa hi\ntestam ent\nasth ma\nin her\nfisher ies\nor der\nho we\ngall on\nep is\nsuz anne\ndrow ning\npaneli sts\nðŁĺ ²\në ¦\nal ach\ncommemor ative\nat tribu\nðŁij »\nmo o\nvisi onal\nweek sary\ngu st\nak in\npoin te\nee e\ndi spar\nni pp\ndent al\nst all\npi an\nbor e\nul ster\ntic k\nir r\ntae hyung\nmicro phone\nbermu da\nga ard\nel er\nplumb ing\nhu gely\nâļ« ï¸ı\nrace way\ncam bridge\nmar cel\nburn ley\nto ast\nholly wood\nfa sting\nme red\nhib ition\nca pped\nbenef icial\now ning\ncont amin\narab ian\nto on\ncap ac\nhul u\nsm ir\nnutri ents\nse in\ngraph s\ncon ditional\nðŁij ħ\nor ac\nplay in\nnor the\ntor nad\nmar ian\nju mbo\nlex i\nincredible india\nroad to\nuk one\nconfu sing\nsp h\nshan k\npi ed\nmq m\npositi vely\nsher ry\npath ways\nconsi ders\ntof u\nargu ments\nresil ient\nche tt\nwith dra\nter o\nated ly\nsw ana\nhe b\nfli ght\nhar ley\ndecre ase\nkind le\nbook shop\n³ ï¸ı\nmarty rs\nsm ur\nmc cl\nconcer to\nsti me\nrejo ice\napp lau\ncle ment\nmer kel\njai me\nim mortal\nisle of\nmar co\nyoutu ber\nstal king\nme too\nst ack\nsp ouse\nu st\nlu v\nâļ¾ ï¸ı\neque strian\nev ing\nfl in\nnick name\nthe big\nas ar\nst acks\nwal ker\nbor a\nkidnapp ed\nhur ling\nhumb old\nrec alls\nco pper\nann is\nse o\nmer ger\nmu ir\nad dy\nðŁĴª ðŁĴª\nbe x\ncr acy\ncon an\ncongratul ation\nmid st\nâĻ ¬\nfor bi\nop tic\ncr ate\ncrocod ile\nmad agas\nsecur ing\nast on\no gue\nsavi or\nsalis bury\nlove it\nfuji film\ncast les\nas st\nar rows\nsp acious\ntr s\npoly vore\nprogre ssion\nm ri\nnel son\nbi m\nindic ator\no da\npe pe\nre signation\ngu t\nsne aker\nlog ically\naz y\nare lla\nte aring\njo shi\nssion ism\nq pr\nmari ah\np x\nble ed\nmi an\nmed ley\nwe iss\nker ry\ngat ory\nat al\nmadi son\nav enger\nnab y\npl and\ngi les\nfresh water\nd ington\nta j\ndemonstr ates\nn tv\nbul bs\nsunday morning\npe ake\nsouven ir\nwa h\nton nes\nm kt\ncomplex ity\ncon den\nross i\nb ing\ny ds\nsu k\nn go\nmid land\nol y\nlife is\nri pple\nmo reno\ndd ers\ntu s\ná ĥ\nbou l\nx a\nhol dings\nwn y\nshadowhun ters\nke i\nasp ire\nm ous\now en\nso ak\nskir ts\nmoun taine\nstor ming\nch rome\nri ots\nsar ato\namaz e\nless ness\nnav ar\ncrit eria\nra fa\nindul ge\nay er\npor to\nnam o\n........ ........\nyi elds\nval le\nj h\nmac ron\nsa ins\ndur ant\ntra ilers\nwo t\nconfeder ate\nsh rin\nid ol\nform ally\nten e\nmotor cycles\nthan g\nno de\nbang er\ndal y\np ats\nenroll ment\nau ctions\nat al\nar bor\nlo gos\nde arest\ntrans action\ndom ingo\nfle a\nser mon\nde ck\nsin cere\nquesti oning\njuli o\nwas p\npre tz\narmen ian\nk ham\ninflam mation\npicture sque\nacci dental\nfilm makers\nðŁĺ ļ\nðŁĴ į\nca sey\nso b\nyee zy\ngood will\nparag ra\nss ly\nfe ather\ndy ed\nassassin ation\nna de\nb cs\napp lies\nfemin ine\nfe u\next ent\ndepu ties\nl ack\npsy chic\ngo i\nkill ings\npse u\nðŁ¤ ª\nun c\nmar l\ntan e\nmck enna\nsur fer\ninflu ences\nfree way\nhack ney\nmal aria\nel and\nte au\nrema stered\nØ ±\nraz or\ngg y\ncor ro\nlak sh\nfla ir\nhonest y\nhoor ay\nde pp\nam c\nwedne sdays\nq a\ned its\n- $\nse villa\ndou bled\nhuman ities\nc cot\nsom os\nr ine\naf a\nsi oux\nre construction\nwel ding\nth reads\nam ish\nencoura gement\npo der\nbo ck\nbal m\np tions\nstand up\naccompli shments\nguar ding\nconvic tion\nac ion\nnapo leon\ndepic ting\natt ack\nsu i\nwear able\nâĸª ï¸ı\npot ter\nesc ort\nvis e\nto ts\nbo on\nevent profs\nangu lar\nwomenshi storymonth\nbar row\nsch i\nac comp\nti k\nl end\nkensing ton\nwol fe\nst acked\ncra shing\nexhi bit\nwing ed\nsab rina\nma sa\nk ms\nalway s\net t\npla sma\ncounsel ing\npick les\nnfl draft\nmr s\ninev itable\ncoura geous\nstaf ford\nwriters life\nho s\ne j\ngh yun\ntrade mark\nadri an\ninfluen cer\ncoron ation\nra ging\nexplo red\nusa f\nexcep tion\neu x\ntan ker\nsw ami\npac ket\nðŁij¨ âĢį\nf en\nshe en\na ero\nj l\nre gal\nnw t\nau ster\nmeh ta\nchar ge\na ste\nb ate\ninf eld\nracec ourse\ncollap sed\nfle ece\nz il\nal lie\nalternati ves\ngeor ges\nðŁĵ į\nquir ky\nfc b\nnat geo\nphilanthro py\nbra i\nevery day\nðŁIJ °\nach ers\nja an\nfin es\nq i\nfisher man\ndistin ct\ngri mes\nnation alist\ncomm ence\nro wn\nâĢ ³\nz ing\nf ter\nhr w\nbaro que\nbl ender\nkitt y\nhoo ks\nc ited\nw anda\nconsen sus\nreinde er\nan and\nsupp ly\nme ds\nv n\nol ph\nrat chet\nshel don\nsecur ities\në°© íĥ\ncro m\nmosqu ito\nj eric\nim mac\ndimen sions\nâ ¤\ndi ssi\nsponge bob\ndami en\nsteven son\njo anne\ndel ish\nyi kes\nthan x\nsurve ys\npostpon ed\nalco holic\nal ised\nðŁĻı ðŁı»\ndo ch\nsen tim\nmered ith\ncom pares\nb ago\nhappy days\nmo ss\nãħ ĭ\nne c\ngn ment\nfrustr ated\ncomb in\nri v\nec lec\ncol lo\ncompli ment\nactor slife\nct to\nnic ar\nop hon\napar the\nman t\nja de\ntrol ley\noptimi zation\neye on\neco logical\nqui st\nep he\nॠĩ\ncin co\nappo ints\nold school\nc pr\nbehavi oral\nmin aj\n:- (\ntag ging\nev al\njo aqu\nðŁĺ «\nha k\nde me\njama ican\nso s\nhy att\nhand book\nlibr arian\nhanni bal\npump ing\nch om\nf man\nga i\nhu ll\nrespon ders\ngreen ville\nn us\nvau gh\nðŁİī ðŁİī\nta xi\ngold berg\nman tra\nte ase\nforbi dden\nmetho dist\nati vity\n* ***\nec t\nmc gr\nĦ ëĭ\nse b\namid st\ndisapp ear\nthy ro\nphili ps\ner ina\nv icious\nstream er\nmillion aire\nma p\nstr ick\nhack athon\ngh a\ned ic\nmi ka\npe ck\nill i\nanto ine\nar ca\nop tic\nma ure\nðŁĩ¦ ðŁĩº\ncla shes\nman ly\nâĺ ģ\nal var\nand res\nme i\nel m\nww ww\nal tered\nl te\nê¹ Ģ\nmo jo\nfor rest\nthal ai\nnon t\nspee ches\nacknow ledge\nign ite\nx factor\nðŁ¥ Ĥ\nmead ow\ndisru pt\ndebu ted\nscrim mage\npharmaceu tical\nfi dd\nfound ations\nphilosop her\net al\npubli shers\nbo ys\nc ke\nru gged\nopti mism\nre be\nphil harmon\nnar cis\nral lies\nlu is\ngo blue\nfol ded\nun acceptable\noptim al\nli sa\npol aro\n+ .\nen za\nâĿ £ï¸ı\nmon opoly\ngrace ful\ndair y\ndu a\ndiffic ulty\njudge ment\no si\nmer sey\nflu x\nnew found\nter ns\ndimen sional\nin vic\nal ba\nam it\nabudha bi\nalger ia\nautom obile\nthe ad\nlo tion\nacceler ator\nvac ant\niti on\nlu f\nal ic\npl l\nbla zing\nba z\nsen e\nðŁij ¼\nvilla ins\ndirec tory\neis en\nto ck\nbroch ure\nri pp\nhb d\nzayn malik\nnic he\nlo lol\ncertific ates\nmor se\nfac up\nx ham\nun wanted\nim ports\ncarne gie\nfan sign\nmo u\nr alph\ndestroy er\nsw ing\ntrek king\ncili ation\npit bull\ng aps\nho well\ndefin itive\nmc le\nf ps\net z\nbol ly\nlyn n\ngan o\nat ure\nfur suit\nco il\nna v\nbut ts\ntro jans\neu re\nen ko\nsch umer\nhorri fic\ninstall ment\nbr b\nsubur bs\na bel\nvi r\nde sh\ncun ningham\nðŁIJ »\nspan n\nsch we\nke mp\ntr u\nste alth\nqu es\nle w\ndeli ghts\nko ch\nhu mili\ncr iti\nil t\nsp ells\nmi ley\ncar ic\nðŁį ´\nlc fc\nsubstitu te\noun g\n? !!\naf fir\npredic table\nclass of\ner r\ncy press\nchand ra\nage ing\n__ __\nther land\ndon caster\nel in\nyo shi\nsail ors\nhar ris\njo anna\nniger ians\nh ers\npla gue\npro cra\nk no\ncan ton\nbusine s\nun h\npra kash\nc in\nbow en\nco ating\nm als\nbe gging\nsmith son\nponti ac\nsp ies\ndam ian\npl ine\nund ant\nal ta\none ss\nshame less\nda q\nbb m\nwal es\nstam pede\nser um\nÙ Ĩ\ncataly st\nx n\nab sc\nfree zer\nch un\nari os\nmc cre\nfore head\nhe ars\ndamas cus\ntac oma\nardu ino\nencoun ters\nstan ton\nlg b\nab as\n\" ..\nke te\ndrac ula\nele m\ng ne\nzepp elin\nla brador\npul p\nop tional\nor n\nrussi ans\nsan itation\nhil ary\netsym ntt\npen alties\nau st\nig ans\nolympi an\nmedic aid\nvers ace\nva pe\nre stra\npe ep\nsexi est\nst alls\ndi le\nthe a\npunjab i\npupp y\ntuesday motivation\nðŁĵ ļ\nthe flash\nroc ket\nmo dest\nchihu ahu\non na\nk sa\nhur dles\nca ve\nfail ures\nsp lit\nbo ho\ngur l\ndisappo int\nho ward\nnug get\nfran z\nstal ert\nkaz akh\nfor getting\nsch ri\nag ate\nam at\neve rett\ndu et\nveter inary\njuli an\nch ills\nbra ve\nghost busters\nlan do\ngre ets\nprofit able\nd é\nti r\nze e\nom en\npd x\ngray son\nhar i\nfix es\nstab bing\nswim mer\nsymb ols\ncompli ments\npo se\nfunc tioning\nth nx\ngi r\ncorpor ations\nbar low\nlo e\noff season\ndistin ctive\nmarvel ous\nnik on\nenri que\nky u\nja ws\namo to\nlom bar\ntravel blogger\nfa h\nouri sm\ntri stan\nso e\nce ase\nðŁı ħ\nz ac\nmck enzie\ntaxpay ers\nswim suit\nbl o\nles ley\nkan sas\nw ks\nki el\nprovo king\nmy les\nstr ing\nkangar oo\ngalac tic\nfif th\ns ke\nwe ir\nll is\nmat ory\nðŁĩ ¿\nun ci\nre productive\nroo ting\nti des\ngad get\n.... ......\nalex ander\nbow ler\nscre w\napo log\neri ka\nwal ters\nshet ty\nlan e\nban ter\nas ant\nme so\nv ain\n\" \"\"\nus i\nfer din\naccomp lish\nman sfield\nbom bar\ncollabor ating\ncla p\nit ure\ns da\nsmo ky\nna k\nim person\ncar la\ncom ra\nbur gl\nlo co\nti es\nin hi\ntrac ey\nse is\ndiss er\nrr rr\ndra y\nprote ct\ncor ona\nhun ger\nck en\nc eli\ntrou bled\npredat ors\nfic tional\nshav ed\nriche st\nmetab oli\nful ham\ngro oming\nmono chrome\nwa sting\nas co\nast e\nti sta\nremedi es\nung soo\nsouth end\nperman ently\nbu mble\nprocra stin\nident ical\npractic ally\nma scul\nsu ke\nassu red\nval erie\ndevi ant\ngrizz lies\nthi er\npur a\nne pal\nnot ts\nbil ateral\nspo il\ncar mel\ncine matic\nph l\nni fty\nma o\nhypo cri\nla ser\npan try\nmathemat ical\nel isa\ncoordin ation\nbel mont\na it\nradi ant\nbo iler\nman g\nf ag\ncr c\nh ams\nbr in\nâ¬ĩ ï¸ı\nfamil ia\nâĿ £\nsab er\nru pert\ngg an\nrit z\nmic h\nsal ford\nle vi\ngra l\nðŁĴ ¤\nn ino\nce d\nbusiness man\nul tr\nsim ply\ncompre ssion\npa ins\nhal t\në°©íĥ Ħ\nlandsc aping\nn f\ncroo ked\ner d\nitt in\nddle ston\nsur passed\nino a\nda g\nbl en\nexten ding\nat ing\nal gae\nball er\nu mar\nsnoo ker\ncol lu\nflo wn\nthu b\nridic ulously\nki sh\nop le\ndi re\nas ser\nari sto\nsc iss\nh ating\ntrou ble\nsyl via\nsuc cul\nplo ts\nsincere ly\nal er\nlaure ate\nbr ack\natt n\nrif les\nme to\ncollec tible\ncu omo\nconte stant\nconsist ency\nant z\nrang es\nabig ail\nde b\nmini ster\ngrow ers\nan oo\nhoo ver\ndream er\nnu cle\nresear ch\nmi y\nsha hid\nma v\nd honi\ncin i\ndo j\nhin dus\npart ying\ndal i\nalon so\ninform al\nclark son\nit ton\nki an\ncit yo\nmor i\nla sted\nas pen\nlibr ary\nsusp ici\nqu at\nden ial\nfol der\nch ori\nswee ping\neni x\nðŁį Ĥ\nØ Ń\nnas car\nhandmade hour\nmou l\nheat wave\nem er\nexam ine\nib n\ngr ind\npo v\ntion ist\nm bo\nshe ila\nintegr ate\nom es\ntake away\ncer v\ncon nie\ntic ket\nce led\nbi en\nvisu ally\nmadagas car\nsor ry\ngu i\npark run\ntra its\nla be\npois oning\nॠĢ\nvi able\nbohemi an\ndenti stry\nbad os\nspr outs\nmask ed\nte ddy\nðŁĺ ·\nsa f\nsa as\nji ang\nti ght\nspe aker\nwithdra wal\nbc n\nas signed\nclass rooms\nfle ming\nðŁĴ «\nsuper girl\ntot als\ntable top\ne books\nhorizon tal\ncra z\nflu sh\nj ard\nc dc\ner son\nãħ ł\ngreen wood\nni h\nco x\nad a\nlit re\ngo ing\nv icky\ncur ved\nlou ie\ngra ins\nhy e\nlon ge\nreme dy\ntra inee\nsan jay\nsuper stars\nma ser\nman u\ns age\nwh l\nðŁĺĤ ðŁĺŃ\nðŁijį ðŁı»\nm sd\nen z\nrab hu\nj oo\ngh u\nac er\ne po\nresurrec tion\njustice for\nbl ended\nmo da\navalan che\nfrance sco\nre spective\ng s\nye ast\nwel ch\ndevo tion\nge tin\nathe ism\nam ic\ncarol yn\nlo c\nld nont\nave c\nus da\nle gged\nbra very\nb lower\ncow boy\nhe h\nsti ble\nbuff al\nchann el\nrun chat\nâĺķ ï¸ı\nide ology\nbest seller\ny oo\npe anu\nbon ne\nfel ic\nedi son\nfr actu\nnaren dra\npp ets\nseym our\nri viera\nhe ctor\nnecess arily\nbi anca\nsoci eties\nthe best\nw g\nsent ences\nwin k\nvacc ines\npal ooza\njam ming\nas f\nmp us\nagre ements\nec k\nba c\nhon ore\ncom pul\nwild cat\nim posed\nyo ga\nhud son\ncan celed\nl ich\nfu zzy\nes que\nch uk\nw vu\nse k\nfli pping\nr hon\nwi shed\nwh a\ncap ability\nlen ovo\nìĨĮëħ Ħëĭ\nvi vo\ntv d\nnor a\nsil k\npas adena\nyo semite\nvalu ation\nclo cks\nu ber\nmr c\ndar kest\nau bre\nss o\nbell y\nwrest lers\nkill in\nlou der\nbuck ley\nge el\nad on\nun s\nappe aling\nðŁij ¯\nsemit ism\nlist ens\nfit z\nãĥ³ ãĥ\nny lon\nar ty\nseem ingly\nhal a\nsu ited\net y\nshe ds\nmu ffins\nap ric\num ents\nu ta\njam mu\nchelse afc\nstar z\nyo ko\nroo t\nclean sing\ndi ar\npione ering\nihear tradio\ndig iti\nfin dyour\ncan o\nðŁĴ İ\nz ol\nspac ecraft\nsix ers\nmoi sturi\nb ile\nti sts\nhor ton\nrang ing\ncolum bi\nmete oro\nsenti ment\nep l\nfoo th\ntext book\ndrain age\nr ly\nsc ue\nimran khan\nðŁĴ ¸\nmargar ita\ned dy\npredic ts\ngamer gate\nadvis e\ngrowth hacking\nlove you\nug and\nv f\nbeng hazi\ns later\nne wor\nch el\nindependence day\np np\ncul len\nhoo dies\nnum bered\nbrit t\nt sa\nkl tu\ns ages\nmom o\nonep lus\ncol l\ngu ts\nw ta\nmesm eri\nenh ancing\nchiro prac\nj is\nteen agers\nm one\nconstell ation\nsweep stakes\ne ze\nslovak ia\nla ye\npear ce\nwa ver\npo gba\nk ron\nsur geons\nmar x\nti d\ngg a\ndesc end\np ours\nupri sing\nwal la\nsab bath\nbachel ore\nmack in\nk am\npeter borough\nhor a\nðŁĮŁ ðŁĮŁ\nthink big\nr j\nhy drau\nsp al\nunivers it\nðŁı ī\nmail online\nleague of\nten ants\nw ally\nlan ce\nheav ens\ndd r\nbol ts\nam ir\ni phone\nci gar\nen du\nre i\nel abor\nr inging\njohn son\ncharacteri stics\nsal oon\nalgori thms\ntal kin\nm tn\ndi ve\nregion als\nff ice\nhat i\ndeviant art\nso tto\nshir o\nl ama\nk we\nf aded\npor ting\ntu mmy\nest ates\nbuen os\nðŁ¦ ģ\nbeli ever\npen etr\ndar n\nsp ite\ncan opy\nfashi oni\nt illa\npet als\neli jah\nbra wl\nmarty r\në°©íĥĦ ìĨĮëħĦëĭ\nmid town\neric h\nd apper\nsm town\nme gam\nww w\nle le\non s\ncat fish\nfir th\nfossil friday\nball park\nth aw\npot ent\nilli e\ncre ep\ncar p\nso ap\ngun dam\ninfe c\nyy yyy\nठ¨\nz ag\nrit t\ncalcu lator\nbo ca\nok o\nto ad\nthreat en\nrefin ed\nolym pic\naccompli shment\nbacter ial\na ji\ntat um\nfeli z\nshe ed\nj at\nth ic\njam al\nðĿ ĺ\nlin a\nðŁIJ ¯\njo king\nyot po\npin ch\nak ron\nher b\nmotiv ation\nli a\nho stage\ncre ek\ngam ble\nruss ell\npatt i\nfo tos\nc pc\nbro ken\nback the\ncla ys\nu mm\nstock ton\nmat ernal\nü r\nla kel\ncent ury\nbe k\ninfe cted\nภ¡\nsmack down\nman ned\nta hoe\nsm es\nbas a\nsu la\naugu sta\n. *\nrohing ya\ngre ed\ncounsel or\nsilhou ette\ngra vit\ncla use\n' -\nbo bc\nocca sions\nnow adays\ndic tat\nbe ard\nn ally\nbrigh test\nkab ul\ninc india\ndhan ush\narchae ological\nche ape\nmizz ou\nd hi\nov ski\nbax ter\nasse mble\nà ¢\ngi gi\nac am\nwis ely\nhaz ard\nnorth ampton\nâľĪ ï¸ı\nme th\nbla sting\nre unite\nmu lus\nali zes\nt read\nmil a\ned ward\nko va\npe sto\nðŁij ¶\nvit z\nhydrau lic\nrefurbi shed\nmo tel\nisab ella\nhom me\nsever ance\nuph ol\nmis erable\nf ari\nlat ter\nef er\ncrack ers\nes l\nac io\nyy j\nin an\nec b\nz ind\npan as\ntru cking\nre ed\nsh aker\nburge ss\nem pire\nag nes\nn ington\nart works\nfr s\nti le\nbi ome\neu n\nch ong\nameric ana\ngod father\ngo blin\ni shi\n! ).\ntemp ted\ngen omics\nmand ate\nck y\nðŁĴĻ ðŁĴĽ\nsom ali\nbr andy\nin ven\nspoke sperson\npc b\nyu an\nh g\nfa z\nstarwar s\nro wan\nblue grass\ndon g\nd day\ntrin idad\ner ton\nban ning\nre tention\ncu red\ntober fest\nre set\nwe is\ndeta ched\nbehindthe scenes\nimmun ity\nph a\nbra y\nðŁij ½\nran cho\nram say\nest onia\nnd tv\n] .\ncab aret\ntar o\nd v\nshow cases\nplu m\nðŁij ¸\nson oma\npre pa\nmemor ab\ne stu\ndrive way\nu les\nmagn us\nx r\nnn n\nmuch as\nen ge\nstre amed\nfore stry\naudio book\ntro y\nreck less\nkil om\nru ler\nra k\nproce ssion\ni ons\npo ole\nnoc tur\nwh s\nfarm house\nper a\npar me\nhypocri sy\ns ics\nv ant\ncas k\nholi stic\nau st\nÐ ¿\nin do\nðŁij© âĢį\ndi so\ndisp atch\nol sen\nmake it\nen nis\ncent re\nar range\nðŁĮ ¼\nsal ted\nea siest\nf ate\nreg atta\nmo zz\nac an\nsin i\ng ically\nch ops\nchick en\nwork in\nha gg\ninvol ve\nwee ds\nbook day\nwake up\nky r\nmichel in\nfu ss\nre juven\nvac ancies\nincar cer\nm st\nsc ents\nsovere ign\nkick er\nà §\nbo d\nâĢĶ >\nsa h\nmob il\nshrop shire\noph one\ndress er\nmis suni\nhep burn\ni mo\nfoli age\ndiagno stic\nas san\ncycl ing\nguil t\nc sa\npuertor ico\nwin elover\nwake field\ndo ggy\nk he\npa pp\nco g\nal lot\ncu ck\npoe tic\nmi o\nre vit\nmag ician\nç ¥\nant enna\nwest wood\nmber g\nlux e\noat meal\nØ ¬\nte at\nffe e\nsear ches\nl ly\nplu to\nel on\nlet tering\ninno cence\nfa i\nann on\ntelang ana\nma it\nneu ral\ncan ni\nar oma\na stor\nfe x\nco cac\nmon etary\nf ent\nun sure\n' @\nindi rec\nteh ran\nisol ation\nli bs\nmake up\nmerce des\nff y\nhe tero\nde o\nsco m\ncur sed\nveteran sday\nfranken stein\nshre ws\nde co\nge ese\nlefto ver\nha did\nvari able\nacade mics\ncarol in\nunder going\nvari ation\nna h\nssi er\ngamer sunite\npur suing\nemer ged\nll ers\ncontrol ling\nro aring\nmete or\nvol t\ndaw gs\nbe aver\nis life\nbathro oms\naci onal\npre vent\nlake district\nin als\ny ani\ngra bbing\nsac ks\nle z\nsw ay\nk ool\ntime s\nklo pp\nla de\ncon cord\nresul ted\nrevi ve\nrecon ciliation\nol and\naz z\ngir o\nmand arin\nde en\nnutriti onal\nis coming\nvan i\naw www\nder ived\nlove your\nstop the\nshou ting\nnov ak\nðŁĻĮ ðŁı¾\nlo af\ndispla ying\nsunday with\nma guire\nch eri\nðŁı Ł\nre match\nqu ic\nÚ ©\ny in\nðŁĺ ¹\nili ve\nz ip\nour ke\ndown loads\nsw at\nmissi ss\ncare rs\nt ment\nproper ty\nhahahaha haha\ngi bbs\nsur rey\nar ise\ntic ism\nsti a\nir ling\nfro g\nco se\nbas sist\nfore ig\nlea u\npil lows\nhol la\neli e\ndisclo sure\npeanu ts\ninte ch\nww c\nplun ge\ntrium ph\ncor i\nsli ppers\nðŁĻı ðŁĻı\nneutr ality\nma re\nhair y\ngang ster\nhu mming\ncust ard\nmer lin\nale a\ns by\ndam p\nmo han\nver bal\nj st\ngu tted\nb jor\nun finished\nðŁĩ¯ðŁĩ µ\nun happy\nâļ« ï¸ı\nby pass\nat su\nfis cher\nsa v\nafric ans\nre use\nmid way\ndemo lished\nger rard\nher cules\nÄ Ł\nmedic ines\ncl icking\nsur round\njo ong\nwav ing\ntri bes\nwet lands\noffici el\nargu ing\nl le\ndo va\nsu zy\nclub house\nne gro\nob tain\nga o\ngl ance\nassi st\nch os\nãĤ ¢\nâĺ ķ\nadri d\noccur s\nst ans\npar don\nlivel i\nemplo yed\nre visit\nff xiv\nbb le\nne aring\nmin er\nðŁĺ ¹\ngiov anni\nup to\nmar vell\nmar se\nto wels\ncb n\nengine ered\ny elling\nspart an\nsi ans\nðŁĻĮ ðŁı¼\nse v\ncoyo te\nsta di\nt cm\napp en\nshenan igans\nopen access\nso aked\nma squ\nle vine\nstro kes\nl k\naparthe id\nhipho p\nchar don\nmay may\nha asan\nstri pped\nfr o\nscri ption\nf ton\nh f\npri sons\nmarsh al\nķ ãĤ\nan cho\ncom promise\nclassi fication\nbuzz feed\nbblo ggers\ndeser ving\n) /\ns way\nob o\ncamp ers\npoder nfamily\np oured\nbri e\nsquir rels\nse ize\n: #\nle k\nti mb\nst acy\nnas daq\nrepe atedly\nbr at\nmi ghty\ncompetit or\nmah one\nde si\no ke\nbm w\nshi e\nf cb\ncheape st\nminim alist\npar amount\nn ate\nhar as\ninsan ity\nlat eral\nment ality\nmo zam\nta pped\nyad av\nu sp\nb way\nthe od\nbil t\nra ids\nem press\nadap ted\npat ron\nnut shell\nag ra\nbe aded\nsundaywith marsha\nvi king\nproce ed\nmain tained\nthinkbig sundaywithmarsha\nsn es\nmus ica\nto wer\nch ab\nbo k\nsm t\ninsul t\nharve sting\nwindo w\nru ther\nbe ige\ndec al\nindic ate\nma iling\nri ft\npo le\nander son\nch oral\nsp ride\nl ili\nev elyn\nimrankhan pti\n.... \"\nke red\nun dp\nwater falls\nse ars\nle mans\nworld series\nri el\nani e\napp ar\nscore rs\nlam p\na than\nphys icians\nqu inoa\nrefu sing\nvu itton\nunle ash\ns la\npat i\nshou ts\ninten tions\nfo amed\neurope an\nneighbor hoods\nme er\nman son\ndu h\nbr at\ncon es\nbow l\nkazakh stan\nठ¿\nin appropriate\ndel hi\nketch up\nful ton\ns ys\nconsul t\ngar field\nto go\nf ml\nf led\nb ds\nfacilit ate\nree bok\nselfi e\nelev ate\nactiv ate\nbi ble\nca wx\nb ys\ncam ille\nsy ou\nsk ool\nher t\nw bc\nple dges\nrecor der\npo sh\nac re\nso aking\nmat il\nv sco\nshoot ings\npla r\ne con\nðŁĻĮ ðŁı»\nrashi d\nu bi\nðŁ¤ ¤\nsw inging\nwi pe\nrap tor\nm su\nmusic video\ndur ham\nat tic\napar ty\nfe tus\nactiv ation\naa z\nmotiv ate\nðŁĴķ ðŁĴķðŁĴķ\nj al\nठ®\nag on\nsche er\nstal ker\nfo ster\naz zo\ntele gram\nvi gor\ns laugh\nscreen shots\nentrepre neu\nkri stin\ninten tion\nch illi\nfr action\ndon a\nge a\ntc u\ns ite\nla k\nem il\nd nt\nbor o\nwil kinson\nre cu\nato day\nt anya\nbl anco\ncd n\nbrilli antly\ng cc\nac c\nevacu ated\nther ine\nden ny\ncait lin\nshe pard\npou ch\nhand held\nsou theastern\nha a\nà ´\nre solutions\nled ger\nsr in\nr ar\nshat tered\nchim ney\nim with\nmete or\nhand led\nra ke\ntown send\nen han\nshi py\nduc t\ntw x\ninflam matory\nwar hammer\ntheat rical\ngro s\nsk ar\nsco tty\nni el\ntit o\ntin i\nconne ction\n_ .\ngoldeng lobes\nsha q\nðŁı ³ï¸ı\nhall way\nfron ts\neffec tiveness\ngla ston\nd hs\nex pi\nto h\nc pl\nsc s\nre o\nha g\nresemb lance\nhor an\nabu sive\nqu er\nvirtu e\ncho lester\na q\nshan e\nm ce\ncarri ers\ndi stress\nre wind\n ¡\nvoo doo\nint act\nann o\nðŁĺ ¤\npi led\nadi a\nãĥ ³\nen ow\ndi gs\nlight ly\ngoo fy\nturb ine\ngovernor s\ncon te\nre open\npa h\ni ve\ncra fting\nswee ps\njo di\nan de\nzu cker\nkaw aii\no ko\nv ai\nout line\nkri sti\nts n\ninsp o\nqu int\nfil thy\nlyn ne\nlisten ers\ndepar ting\nor d\nt weed\n, &\nale k\nsel fish\nnor ther\nrecogni zes\ni ps\nbe s\na ed\nw ills\npe at\nsurround ings\nmon uments\nais le\nbe cker\nla v\nquant ity\nv ah\nhelicop ters\ntu cked\nalv arez\nsha pe\no bey\nad diti\nroad side\nm ite\nbl ers\nep age\nj au\nignor ant\nb ins\nlu lu\nx o\nc fo\nee eee\napprentice ship\nshef fiel\nto i\nho k\nfaken ews\ndeplo y\naid an\nhusk ers\nãĢ İ\nwest brook\nmi ster\nconfi gur\ncar r\nfic a\nproceed ings\nha w\nste ak\nmur derer\npay day\na jo\np vc\ndon ates\nbi af\nnom nom\nbe it\nk ali\nx rp\nahmed abad\nse mic\nche y\nx tra\nan twer\nhead lining\nsqu ares\nroun ded\nflu ore\nbol d\ndisa sters\nam oo\ngener ic\ncran es\nbrief ly\ngi g\nauster ity\nanticip ation\nfor ti\ntreas urer\ncann y\nce cil\ndete cted\ncheck list\nภ§\npam ela\nbar bados\nan field\nhear ty\ntx lege\nperen ni\narro g\ning ram\nâĹ ı\nty ne\nspo on\nr ation\nam ba\nm be\ncam el\nh hs\nyork shire\nreflec tive\nfre aks\nto k\nju do\npartic les\ndu bs\nban jo\naccred itation\nprover bs\nover dose\ninte gral\ngu ang\nmc s\nsuper car\naf b\nal vin\nail s\nx tre\nst aging\ntw ent\nrabb its\nmar o\ninste m\ndol l\ncr ay\nsant ana\nble ach\nmini ons\nche ap\nman t\ndi vers\ncatal onia\nlo is\nmat ri\ncou gar\nkay ak\ne gre\np so\na ia\nå ®\nchar lton\ntr acked\nsc ari\npe tt\nf wd\nx in\ngra vel\nbr ic\nbigg boss\nar den\nhu gging\npal ms\nst v\nli mb\nthe movie\nhandic ap\nri me\nz ai\nstu b\nindi a\nlithu ania\nrhy th\np ita\nmaced onia\nhigh ered\nbrid get\nschwar z\nske let\nhi kes\nant arctic\nc ps\nmash up\nÐ °\nn ell\nchand ra\nhe ir\nan us\nsher idan\nmi mi\nmuse u\nbec ca\nan ir\nbar rie\ndioce se\ncompar able\nðŁı³ï¸ı âĢį\nyuk on\nme p\nhor mon\nmer ic\nal f\ncon quered\nchrist church\nðŁĴĻ ðŁĴĻ\nhazard ous\npoo h\ncont ing\nretro spective\npar ame\nna ir\ncon sor\nho tra\nastoni shing\ncater pillar\nu man\nti sm\nt vs\nserv ic\ncroy don\nmor ales\nc g\ncu m\nte ur\nscan ada\ns all\nmagno lia\nel ise\nth our\nà® ¿\nag omez\nphel ps\në°©íĥĦìĨĮëħĦëĭ ¨\nwh os\nweav ing\nsi sd\npro poses\ncro ws\npre sale\neconom ies\nbernar do\nsha hid\nair show\nmc cann\nhor ticul\nnr l\ndu el\nmongo lia\ntou lou\nrequi rement\nstruc tured\ned i\no lives\nhe a\ncu ter\nÐ º\nenthusi ast\nharri et\ndomin ion\nsub mer\nðŁį ĥ\nsa ab\nnes burg\nmo ff\ndef ended\nbur t\nrewar ded\ngold man\nop tics\nkhali d\nhouse holds\nbuc kets\nce cil\nche ss\nsubstan tial\nef l\noper ation\nevalu ate\nst n\nrece ssion\nl ll\ntom as\ntru ths\nak bar\ns words\np act\nembarra ss\nha o\nay urve\nscrip ture\nny cc\nop t\ndi ameter\nsc ented\norgani zers\nre lat\nha e\ndream ers\nde se\nðŁĮ »\nrestric ted\nn ale\nr hp\ndol an\nmun ster\nha ired\nconsult ants\njo ints\nhu mil\nd ill\nrelent less\nt é\naf il\nut ilities\njapan ese\ncondem n\npet ite\ncolli de\nq f\npeach es\ncou rier\nl ore\nâĺİ ï¸ı\nreli ability\nch uk\nðŁĻ ĥ\nstu res\nge ther\nho stel\nbi er\n- _-\nâ ĩ\ne ze\nta ilo\ndi ent\nblu ff\nchu ffed\npil ip\nmon arch\ne em\nbu chan\nb ick\nop au\nku ps\nภ¢\npist ons\nsp ins\nm and\nce st\nbur ne\nv ile\ncher ries\nbec kett\nneed les\npan ch\në Ĥ\nhaha h\ntrou bles\ninsi sts\ndo you\ng mc\nmor tar\ndeleg ate\nin n\ng anda\nsin atra\nठ¤\nspee ding\npu pil\npre mises\nali gnment\npi kach\nas us\nj alan\nØ µ\nlime stone\nfol kl\nparme san\nce il\nmo y\nshawn mendes\nac up\nhu st\not es\nmed ina\nma di\ngta v\ncensor ship\nar g\nswe eney\nsy kes\ncol o\nfoot steps\ncann ed\nadv ance\ngta online\nhealthy living\nðŁį ¾\na ig\np ality\noc s\nhe brew\nim minent\nberk shire\njeremi ah\nout going\nbak er\nentr ata\nma ids\ngro ves\nbo c\na del\nm fw\ncon science\narm ys\nnut ella\nconte stalert\nnovel ist\nla h\nban ker\nmarque z\nðŁı ¡\nto ff\nout age\ngr p\nðŁĺŃðŁĺŃ ðŁĺŃðŁĺŃ\nmusc le\ndu dley\nnvi dia\nmi di\nm uni\ness ays\ndat ac\ncar ter\nภ£\nt ans\ni ves\npublic ations\nal er\nok wx\nil u\ncu tt\nhar p\nout law\nluther an\nbr ill\nbo lic\ndo well\ngreen land\nbe sties\npath i\npay ton\ngue st\nhar den\nðŁ¤ ©\nann ed\nevacu ation\npo ised\nmc der\nb han\no i\nenvel ope\nci d\nca vi\nta pas\nbook review\ngrey hound\nâĻ ª\nfe ud\nlun gs\nfor te\nrai der\nff er\noni x\ndep end\nyn wa\nrel ating\nde vs\nðŁĴ IJ\nacqui res\nd ha\nj yo\npriv ati\ncan ine\nk b\ncra b\nsar din\nimag ining\nk j\nem por\ndown hill\nne z\nta eyeon\nnick imin\ngb p\nà µ\nw ap\nsec co\nma shed\nðŁĴ¥ ðŁĴ¥\naugu stine\ndiss ol\ndic tator\nâ ĵ\nvi per\ned fringe\nvau x\nhard work\nbook let\nno x\nchi ff\nðŁĴ ¨\nobserv ations\nxbox one\nu sher\nke er\nlu p\ndal las\ncal gary\nma dra\ndi ous\nk bs\nwood ward\nhero ine\nlu mber\nsea world\no ws\nmc ke\nmaver ick\ngu la\ncross roads\nfan g\ns ade\nnik ol\nchee tah\nme c\npp g\ner ick\nðŁİ µ\ntox ic\nbj j\nviol a\nsp ire\nch ino\ntra vis\ninstitu tional\nha as\nlow ry\nw ac\nea e\nhu mid\nmp ton\nru ck\nje w\nc ine\nzim mer\nse f\nbhar at\nfre es\naam ir\nðŁĴ ħ\nz inc\nwan e\nmulti player\nroyal wedding\ne el\npreci pit\nqu ery\nkimber ly\nisa bel\nful fill\nig an\nvau l\npan e\nsc y\ndig it\ngun n\nu tah\ndog day\nfi on\nxia omi\nda c\nel ast\ncha vez\nro blo\ng ine\nten th\nab h\nke to\nhur dle\nna dia\nmemorab ilia\nha bs\nqu an\nh w\nhv ac\npix ar\nec cle\nkram er\naccu ses\nðŁĴļ ðŁĴļ\nper se\nmean time\nwa hl\natle tico\nâĢ¢âĢ¢ âĢ¢âĢ¢\nott oman\nno vo\nk us\nconne cted\ntru sts\nd mv\nspen cer\nrahu lg\ndo ve\nsto kes\nbolog na\nenthusi asts\nà ª\nrockstar games\nted cruz\ndu ras\ns acked\nlate x\nimmer sive\ncer t\nlu cin\nprinci pals\nfa res\nsa ils\nfar n\nam ent\nsaf fron\nquent in\ncheck point\nfer ris\nex cur\nðŁijī ðŁı¼\nbai ley\nse h\nter re\nmad am\ns band\nwan derers\ncumber batch\nyy c\ndigit ally\nblackandwhite photography\nroll in\nmoroc can\nðŁĮ ħ\ndin ner\nd well\nto om\nm ye\nez ra\ncp fc\nwar hol\nme er\njon ah\nno aa\ns gate\nso on\nsecu lar\ng ating\nti o\ndri ver\nsi ssy\nassan ge\nta th\ned mund\nbobc ats\nra ji\npo stage\nstu ds\nm gm\nkat o\nedin burgh\nmeet the\nshir t\nfa a\nmens fashion\nsp reads\nwi m\ncar ts\nphoe be\nj ars\nbot swana\nÙ Ĥ\ned war\nsk ar\nri ve\ngu sty\nc tv\nferdin and\nsu therland\nnickimin aj\nk v\nsi us\nbee ch\nre z\ndesi res\non ial\ncamp o\nquar ry\nlor raine\ngil more\nig gy\nµ ï¸ı\nho pping\navi z\nðŁĮ º\nuni sex\ndedic ate\natt itudes\nste er\njun kie\nrail way\ny b\nwhi sper\nkey an\nk us\nju g\ndi x\na ins\nsum mon\nov ich\nsy ed\nher ald\nma ison\nme ded\nwild flower\nmain land\nri sky\nru kh\nover looked\nki c\ndestro ys\nnam an\nki p\nz ano\nchampion sleague\nban dit\nquin cy\nsmi le\ncal vin\nopen ings\nta pp\nol ulu\nspec tro\naccred ited\nap k\npra ised\nbar nett\npol len\npremi ered\nselen agomez\ntou red\nscreen ings\nuu u\nmis o\nen se\nadam lambert\nguel ph\nhar yana\nhu tto\nle ar\nl tc\npo ached\nbrex it\næ Ŀ\ntt c\npa vement\nmon gers\nro e\nad ers\nling ton\nparticip ant\nca red\nga il\ny ates\nlan tic\ndash board\njo o\nfeli pe\nssi onist\nbu m\ns end\na eri\nthu gs\nluci fer\na he\ndete ctor\nfil ly\ngas oline\nham per\nhump day\nthe ta\nthe band\nfore casts\no hhh\nlo bb\nhol l\ncp u\naz u\nad ar\nhai ley\nbu b\ncar t\nquo ted\nan archy\npan cre\ntwit art\nal den\nst ash\nthe less\nor ni\nbelie bers\nmor mon\npartic le\navi ation\n⬠Ĩ\nwebcam toy\nsad dened\ncru is\nham let\nn ct\nroll ins\nmarque e\nsaw yer\nreli ance\na ura\ndi ec\nsoo thing\nsig nings\nak is\nà ³\nat kins\naer op\nðŁĮ ¿\ny ab\nsh ari\ncon nol\ndu bbed\nmanufac ture\nconvin cing\nfeelthe bern\nra u\npu lit\non ec\ngem stone\nur ging\nbag u\nga h\naci ds\nfi anc\nzodi ac\nsn oop\nher rera\niniti ated\nven ge\nprofess ors\npro di\nstron ger\ne mission\nbb a\nhal le\nta pp\nhaw an\nwh im\ncompe ted\nmyr tle\nir port\ncold play\nach e\nske p\nm son\nss ic\ncalli graphy\nswim mers\nme y\npp c\nthri ft\npo c\nre places\ncommu ter\nâģ¦ âģ¦@\ngo ers\nlo gue\npara dig\nbas kets\nsensiti vity\njoh an\natl antis\n& &\nsuit case\nanxi ous\nl h\nstr i\ngal loway\nstre ad\nwar den\ngr ounded\nffici ency\nli feat\nreli c\ndisgu ise\nisland ers\nf cofficial\nclassical music\nb mc\nen field\nbi que\noak ley\nbat man\nsla ying\nner ves\nmul tit\ncalci um\nprojec tor\nscott sdale\nant ino\ngri ps\nkim mel\ndes mond\nprote stors\nhi atus\nmetaboli sm\nconclu ded\npress er\nti pping\nsli de\ne to\nhun ting\naus open\nri k\npp ery\ninnov ators\npitch ers\nag ger\nfun gi\nz ad\nproli fic\nrockn roll\nbl ames\nct ar\nstam ford\nq ad\nmozz arella\ninsan ely\nden ver\nph ouse\nnom ad\nï ¿\ns ris\npro du\nhen ley\npag an\nam trak\nru bi\nin cl\ntu tor\nsco tia\nwo es\nsing apo\nfun nel\nturn bull\nknow ledge\ngri mm\nreal madrid\nwe are\nmissi les\ncon sol\nemo jis\nsne ak\nsmi ths\nru iz\nbr ou\ni el\nha ver\nðŁĮ ļ\nkin gof\nbasil ica\ncircul ation\nprin ters\nta pping\nri dley\ndra gged\nha j\nwrit er\nfundament als\npersonal ities\nme tre\nstereo types\nbur le\nbest of\nn ffc\nha th\nmini stries\na ali\ntrac ing\npav ed\nł ï¸ı\ng ic\ninsp ire\ntu g\nha re\nrepe ated\nex pon\nlol li\nrho de\npre cin\ninstall ations\ninstag ram\naz ar\ni es\nsole ly\ndu kes\nmission ary\nvan guard\nfursuit friday\non d\npol ari\nma st\nhar an\njos é\njack ed\nec oun\nal ities\nne ph\nra vel\nmoder ated\nsco w\ns fb\nuru guay\nas o\nni g\nau du\np ints\nlat ina\nben z\nm itting\nchar ted\nmat ology\ncit ro\nbiop ic\nðŁij Ń\ndjo kovic\nfox y\nagu il\nso to\nan ada\nsin king\nsc rap\nhair s\nbethan y\nfact friday\nðŁIJ IJ\nunlea shed\n) (\ncontra dic\nram on\ncoast line\ny ong\nsn sd\nli gan\np ome\nmit age\nge tt\nwat i\nri sk\nso aring\nbru sh\nf pl\nav an\nå Ĩ\nlar son\nsh ear\nmul til\nblu r\nmulti media\nchun ky\npar i\nn ani\nweir d\ncholester ol\nchar les\ndream ed\ntan ning\npuzz les\nfr am\nhand ball\nch ag\nbeli ze\nal u\nbang s\nÑ Ħ\ndetec tives\nmc g\nish q\nbo thered\nsaf c\nmp ing\nten eri\ng ays\nsail or\nan gi\nmul ticul\ngue ssed\nros é\nhigh ways\nbro om\nchatt anoo\n- '\nsee ker\non ed\nat f\nlu c\n> <\nbar i\nper cep\njewel ry\nas ph\nsor row\nsl ing\nmam moth\njac kie\në §\nwilt shire\nsa o\ncan cell\nim paired\ntor ial\nbre ed\nguy en\njud ice\ntit le\npro spective\napplic ants\nðŁį Ĭ\nepis cop\ne id\nb yo\nstock ings\nðŁĴĥ ðŁĴĥ\nll p\nsna g\nkeep it\nl ough\nol son\nmatur ity\n!! !\"\ncop ter\ni sha\nbl i\nwil mington\ntr youts\nth ai\nðŁ¥ ³\npe bble\nkra ft\nf p\n º\nssi vely\nli vin\ncontest ants\ntex tures\njo an\nh dr\nfilm festival\nprov ence\nwi do\nop end\nc si\nsto wn\ncro ati\nad just\nhost ile\nanaly sts\nil an\ncu ppa\nbru m\nnewfound land\ngood win\nme tt\nmall orca\nplu gs\nbu k\nbb hutto\nwrest le\nsa ire\nsho pped\nfor za\nle head\nvi vo\nba st\nro xy\nreg is\nhard working\nhon olulu\ndesp air\nyoung sters\nni g\nimpro mp\nroll tide\nde emed\ntre ason\nru shed\nfor ged\nff f\npikach u\nbri ggs\ndo it\nac cent\nla us\ngla ze\ncompet ent\na ho\nphoto g\nmid field\nle go\nhar vard\nmin orities\nre illy\nslic ed\nonce upon\niniti ally\nfinanci ally\nlandscape photography\nhar dro\nqu o\nmm ers\npar kinson\nsmu gg\nread iness\nbru tally\nglou cester\nmp ed\nbbhutto zardari\nmur der\nye d\ndat aviz\nsr t\ndow ning\nbi ans\nm ü\nfle ck\nfli pped\ns ly\nbrilli ance\nri m\nk um\nbubb a\nko i\nknit ted\nsor g\nma is\nðŁĮ ²\nti ss\nsu stain\nsen su\nak han\nzi est\nexam ines\nchardon nay\nuser name\nshort list\nre bs\non o\ndar ing\nhard wood\nche que\nrighte ous\nlight ening\ndir k\nshra dd\ndu ra\ndown stairs\nsh al\nami gos\nru ff\ns law\nri es\nred nation\nman us\nðŁĩ§ ðŁĩ·\ndistin ction\nu bun\ndur an\nmi gra\nthi ans\nla ver\ndomest ic\nk x\njaz zy\njusti fy\nbelong ing\ninsul ation\ncolor stv\ndrun ken\nchann eling\nqu and\nxi ii\nenligh ten\nkan o\nfati ma\nteen choice\nterri fied\np ba\nas ley\nmet museum\ndun e\npack er\nki o\nðŁĴľ ðŁĴľ\nbo iler\nfas cism\nar mored\nback grounds\nin mates\nembarra ssed\ndefin es\nth d\nwe go\nsilic one\nlo on\nel ding\nbor rowed\nhe mp\nak sh\nkaw asaki\nbr y\nde af\nkill er\ndispo sal\nðŁĩ °\nglaston bury\nun covered\no xide\npo ff\nd ant\nk j\nku ro\ndri zzle\npeop les\nfe e\npro pri\ndd lovato\npi ggy\not is\naller gies\nu bis\npengu in\nser a\nvi z\nprosp erous\nici des\ntornad oes\nsene gal\nweb cast\nsto red\nenchan ted\nbb cone\nbay area\nentrepreneu rial\nrednation rising\nexperim enting\nang an\nlot to\nthey re\npor e\ner p\nseren e\neast wood\nbro kers\nbar ge\nstal lion\ntimber lake\ntailo red\ndy stop\nb ate\nlat ors\ndi xit\nbran son\ndynam o\nky lie\nshame ful\nbt wn\nspring time\nmix ture\ns ounded\nlu ton\ndad es\nmal a\nop ra\nen ic\nrahulg andhi\nse wer\n~~ ~~\nky u\nnor theastern\nca er\nbc u\nnir vana\nkitch ens\nous y\nal m\nriver dale\nhid den\nfl int\nsp d\npat rons\nkaty perry\nau gh\nexhib itions\nsm c\nshu ts\nat ore\nda in\nsome thing\nber th\nbo g\npor ter\ngen to\ncon cussion\nang lic\nro we\ngr illing\nscar lett\nmaster ing\nmor nin\ncomm ented\nsi me\nsi zing\nchrist y\nce os\nst m\nat ry\ntari ffs\nvac ation\npre judice\np su\nparen tal\nfar age\ncan a\ncap com\nkoso vo\nyou re\nmen stru\nstal in\ngrape fruit\nbr an\nche sa\ndav en\nexc el\n!! )\n๠Į\ndistribu tor\nce a\nbride sma\nmillenni al\nwa in\nob serving\nmis ery\nplan etary\nexpo sing\nbra ised\ncomp ton\ndon gha\nq l\nspring steen\nth ul\nsyl ve\ncab o\npal ad\nniel sen\ngaz ing\nba ja\nr oud\norchi ds\njohan nesburg\nse man\nd ji\noper ative\naffe ction\neclec tic\nat c\nmut ant\naw x\nnic e\nmel bourne\nindu lg\ntu lip\ndias pora\nwel p\nbig gie\nmississ auga\nretri ever\nor an\ntam my\nc ta\nhipp o\nseas oned\nger mans\neng v\nmarvell ous\nim f\nrela ys\nmon tan\nmaur iti\nme ister\nas surance\nreig ning\nsu fficient\nhan e\nno thing\npos se\nnav y\nin love\nbrigh ton\nen qu\nch ung\nsweat y\nes c\ncal ed\nman s\nnicar agua\nsl ices\nmo cha\nwashington post\nbb n\ndam ned\ngrow ing\nen burg\nlo an\nme s\nwh oops\nbeliev ers\nspi el\nvo daf\nl at\ns led\ncricke ter\nbrown e\ngolf ers\nbar ra\nwat chers\nlu igi\nsw amy\nmom s\npit ched\nsan tor\ncr s\nsi re\nsc amp\nbo de\nste war\njon ny\nent ity\npac qui\nmind ful\nmin india\nbear ded\ntemp t\nscorpi on\neat on\nauthori zed\nar to\ns vp\nop athy\ncch ini\nhouse music\ndisney world\nâĢĶ @\npro pose\ndi y\nexpen se\nten g\npupp ets\nsm el\nd aca\nper ry\nfin n\nboo sting\nlefto vers\ncou gs\nsatell ites\nman y\naz e\ng ong\nfi e\nmetho do\nfer ries\nðŁ¤Ķ ðŁ¤Ķ\nexplore rs\nload er\nattrac ted\nil ton\ngodd amn\npi azza\ndoc tr\nsav ing\nparagra ph\nvisu alization\nmay ors\nwork flow\nack les\nðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\nठ¸\ntwer k\nclu t\nlo ver\nte ases\nsi an\no te\ndeter ior\naccor d\nl fw\nswar ovski\nnat al\ntra ps\nk ina\nanaly ze\nlaye red\nbever ages\nun it\nran som\npe shaw\ndest ined\nastro logy\nsi pping\nmiley cyrus\ncam ino\nmarshmal low\nbli ss\nout back\nfa q\nint oler\nhumil ity\npo ppin\nhallo ween\nmon tene\nop hy\nnu n\ntattoo ed\na as\nðŁĮ ³\ndale y\nqual ity\ndu sa\nfisher men\nswi f\nter rac\nst au\nle in\ntrol ling\nship ment\ngarden er\nmarch madness\nhead band\ngr t\nbur nett\nw and\n!!!! !!!!!\ngh e\ndu x\nhu d\nwar ner\nðŁĩ ¦\nex ile\nrescu e\nrat a\nd han\nduc ati\ndro wn\nbl ends\nspi e\nalli gator\nsimul taneously\nbroo ke\nu ke\nk har\ncomm union\nri ka\nford fc\nchin atown\nyou rown\nme y\ncan al\nsyste matic\nde pri\nox ford\nan il\nw ut\nequ ation\nbe z\nfle ur\nthe good\nlang ley\nad ity\ned ith\nal fie\nо ÑĤ\nen cry\nbr ill\nex emp\nce sar\nmb ling\nab ri\nsc icom\nj ing\nschool ing\nmi ka\nmechan isms\nimpromp tu\nrhe a\nmoo re\ncrime a\nbe sto\nwri ght\nel ders\nro ds\nkam al\nfolkl ore\nbe et\nmini on\nreli eve\nthr o\nteam usa\npas cal\nmade with\nboli via\nitt i\nfree bies\ndesi red\nbest selling\nl iness\nla den\nke ane\nmi sts\nhipp ie\natta chment\n@ /\nse w\nflan agan\nâĿĹ ï¸ı\nsupre mac\nstl cards\nsi as\nq u\nrh ys\nste ep\nval leys\nv w\npav ing\ndisp at\nal ison\npor te\nid u\nnew sc\nsoc ket\nmo s\nco star\nre vo\nprote ins\nstanley cup\nm cal\near ring\nse cs\nmc lean\ncap ric\nnick elo\nad en\nv c\nshou se\nadap tive\nmaxi mize\nentertain er\npro se\ngri ffi\nsix teen\nlam ar\nmi rage\nsaudi arabia\nawe ather\nru st\nin filtr\nfashion week\nðŁĺĬðŁĺĬ ðŁĺĬ\nselec tive\nbubb le\na den\nfen nel\ndeci sive\nm ta\nmock ing\nmb les\nst amp\nmu le\nbernar do\ngr in\npo tt\nj ingle\nvet tel\ncolom bian\ncam o\nmotivation monday\nba han\np ly\ndh ary\nk ami\nx men\nsleep er\ngar a\nmy sti\nconfi dential\nconflic ts\np neu\nce s\ninsur tech\nclean se\nme rely\nva is\ntu x\nthe great\nshar on\nma j\nhol a\neco systems\naj ay\naa j\nhu sh\nhar mon\nbackto school\nwiki leaks\nreflec ted\nðŁĺ ĵ\ncommemor ating\nac et\nbuck ingham\nmessi ah\ntu ous\nhor net\nto be\nd q\nhe ine\nmi g\npl ate\nnichol son\nsp ie\ncumber land\nnor mal\npho bia\nhappy halloween\ncity fc\nmc el\ngilli an\nke to\nlu de\nde mise\nsu ga\nstr ate\nmcgr ath\nvisit scotland\nfoo led\ncb r\ngc se\ncol ori\npo td\nmissuni verse\nfin ances\nma poli\nfor ks\nØ ´\ncann on\nmedic inal\nðŁĹ ĵ\nkh o\nwre ck\npan to\nbag el\ngu ll\nsyndic ate\nic y\npr c\nki en\nzi ka\nti sh\npe ta\nc co\nli za\nch ut\nex traction\nel g\ngl i\nfu eled\npos it\nrespec tively\nleice ster\nbr ink\nvulner ability\nim ported\ne sha\nðŁ¦ ħ\nr ural\nre ll\ngam ing\natlan tic\naband on\nno ah\nre solved\npro state\naller gic\nps d\nâĺ ¹\ndun geon\nfang irl\nillumin ated\nm hs\nwhite sox\nd ently\nck o\nendor se\nover ly\ndazz ling\nprior iti\nnight life\nut il\nbe have\nflam en\neast bound\nðŁĴ Ł\nilove you\ngov uk\nmozam bique\nalle gi\ndr i\ntestim onial\nath s\nì§ Ģ\nmm y\nshab by\npro secco\nfriend ships\ncal am\ndam ages\noff set\njura ssic\njun o\narre ll\nðŁĴ ©\ninterven tions\ndare devil\ncar ver\nrun away\nran e\ntruste es\nha ute\ndep ths\nðŁİ Ń\nme in\nsacrific es\ncon cier\nne sting\ni zzy\nme tam\nilove my\nur ine\ndu lu\nmal hotra\nve ins\nnight ly\nco at\nan di\nhe witt\nlon el\nci ble\nwr ite\njen nie\nsant ac\nĸ ï¸ı\nstr ato\nsingapo re\nsop rano\nkri sten\ncheer ful\nflee twood\nfa iri\nm eli\nwa st\ntur nt\nsfor sale\nsc rolling\nangel ina\nren dition\njeric ho\nnick y\nor b\nfla vo\npatri ot\nash eville\nsick ness\nre fund\naggre ssion\nb pl\nãĥ ĥ\nelu sive\nthi story\nhang er\nbu ffs\nvil las\nat kinson\nsp h\nja it\ndecl ined\nwo k\nsupre macy\noo tball\ney ang\nðŁİ ĵ\ns ford\nath i\nconsu me\nroad ster\ne so\nu pro\nreci pe\nau f\nuc i\nar on\noo oh\ncs go\nre ich\nmc d\nmin ute\nladi es\npun k\nrut gers\nmee k\nariz on\nta j\nland lord\nde gra\nautu mn\nlyn x\nus f\nb hi\nfairy tale\ndongha e\nbet sy\nexplo ded\nchen nai\nop a\npro tag\nbr ant\nðŁĵ °:\ng f\npal li\nðŁı¼ âĢįâĻĢï¸ı\nsu t\nill ini\ncolum nist\nshir tless\nde centr\nsear ched\nec or\nbu ggy\ns ack\nðŁĺĤ ðŁĺŃ\nde t\nther i\nor naments\nbring back\nto v\nquarter finals\nic he\ncon stra\ngi er\nbuchan an\nvi x\nkay aking\nmu stread\nswal low\nmel b\nsc af\nop al\nmay oral\nhar at\nðŁ¦ ĭ\nschedu les\nid f\nha gue\nro z\na ah\nd mc\ndu plic\nca che\norph an\nfrac ture\nrec on\nch av\nbun nies\nal ain\nmustaf a\nðŁİ Ļ\nvac ations\ndynam ite\ntex ted\nbroad caster\nðŁĴ £\nste amed\nrock er\ndi etary\nluxury travel\ninaugur ated\nsa wards\nvaugh n\nlincoln shire\nclick ed\nkra ja\nf anc\nremo ves\nlayo ffs\nmc far\nbre eds\nwin nie\njon ghyun\nincen tive\nvari ations\npat ton\natur day\npersist ent\npr un\npi ers\ndal es\næ ĸ\nbreast feeding\nr ance\nta wa\nĤ âĸ\nmur doch\ncap tive\nthi stle\nnic a\ncommod ity\ncou ldnt\nboard walk\ngraci ous\npractiti oners\nn gc\nscru m\nner o\ncamoufla ge\ncol on\nhe i\nphys icist\nsaturday morning\nten er\nsi won\ncolum ns\nbru ne\ny vr\nba ir\nreti res\nhal am\ncab er\nshaz am\nmin u\ncas cade\nmilk shake\ngri d\nd ren\nvin cent\nso dium\nplat ter\ncheer leader\nchen ko\ny ak\nelimin ated\nty po\ny man\nre think\nâĿ Ĺ\nts ville\nbernardo kath\nex tr\nðŁĺģ ðŁĺģðŁĺģ\nta o\nre per\nmo ths\nem powered\nc iting\ntranspor ted\nmon ks\nsan at\ncle ars\nbachelore tte\ncamp bell\nracha el\nhar le\nhand ler\nclimb s\ninter ference\nrele ase\nsh and\nr bs\nhr h\nãģ ª\nval le\nr é\nsli me\nw akes\nchu bby\nslo an\nel ves\nath en\nattor neys\nmicro scope\nston er\nsc aling\no be\nc out\nse man\nmid week\nbal sam\nðŁĺį âĿ¤\nti ful\nv ish\nlo tta\nri pping\nre mn\nti re\nle ap\nha vent\nla by\nhi mach\nwhisp ers\nwe in\nðŁİ ¸\nwild flowers\nse le\nu cc\nli ability\naz ine\nsw ings\nk ya\nta ir\nre main\ne do\nflo ps\npoc ket\ngrand ad\nexam iner\ngr is\nffe ct\nðŁijĬ ðŁı»\nstud ded\nheart beat\nde acon\nfirm ly\ninfec tious\nste f\nout lines\nle asing\ncla ws\nsen se\ntab s\nhoo t\nmo sul\nspa wn\nco a\nhog warts\nve in\nalban ia\nmanu el\nb ino\nvaux hall\nscot land\ngo bucks\nmat ty\nphy sio\ntor ino\nconst able\ninvestig ated\ns lower\nmistak en\nbay er\nwild fires\nvo ic\nx on\ntime to\nchas sis\nbar ric\npi on\nbald head\nwoo k\nregi str\ndra fts\nb hs\nli gue\nl ick\nstaf fordshire\nbaf ta\ndar ry\nje anne\nven ding\ncor p\n⼠³ï¸ı\nkid dos\nfen way\nca o\nwest bound\nðŁĺ Ļ\ndv r\nquick er\nbla h\ngoo die\nðŁĴĭ ðŁĴĭ\nvo x\nesp er\nfac ade\ncor relation\nred bull\nrou p\ndecl ining\nchi ve\nmc gee\ntur o\nin der\nf eller\nfu g\nil ysm\nmar di\npeshaw ar\nki eran\nine ma\nmeat balls\npe ck\ndepre ssing\nsen sing\ngi z\ndd ington\nspring watch\nro aming\nyellow stone\nhorse shoe\nam man\nweek day\nol or\nðŁ¥ °\nboo sts\nspr int\nscar ves\nje e\nbee tro\ncl an\nall the\nìĦ ¸ë\nenlighten ment\nado be\nre generation\n? @\ncont ag\nyach ts\nto u\nmor a\nen voy\nr ani\ngo li\ndhanush kraja\nwood working\nstreng ths\nse di\ndisc s\nar ina\nsc on\nlit e\nano ther\nðŁ¥ Ĭ\nye men\ngu ern\nsav vy\nlo yed\nbiom ed\nheart break\ncomra des\nmilli e\npat ch\nun f\njar vis\nbl aming\ncommemor ation\nge y\nå ¥\ncardio vascular\nalig ned\ndocu ment\n. ?\naesthe tics\nem u\nthe irs\nle h\nps ic\nsi f\npl ateau\nex pend\ndomin ating\nrob es\nmauriti us\nexcep tionally\nhom er\ndiscover ies\nbra un\nten nant\ninsul in\nðŁİ ®\ncar bs\nte as\n? !\"\nzi e\nfranco is\nbrow sing\nth ol\ncla rence\nhel per\nob tained\ncas sie\nle es\n! ,\npome gran\nhu bs\npresti ge\n] [\nmach er\nbott led\npun ch\npi pe\no ch\ngall ons\ndeliver ies\nu ra\nun day\nmon de\ndepic ts\nre gency\noutra geous\nkhal ed\ncar o\nhe arti\nza g\ndevelop mental\nover coming\nstati stical\nflavo red\nfor ds\ncre atives\nlau rence\ndi as\nsun screen\nin ked\npre acher\nn ul\nimpac ting\nauti stic\nâļ Ķï¸ı\no ss\npel icans\ncele ste\nv b\nru mp\nmc gra\nfair fax\nhu mor\nbbc news\nrow ling\ncal der\nseam less\nag ne\np ti\nmix ed\nt shirts\nmer ci\nb tob\nwomen instem\ngenealo gy\npre ven\nl our\ncra dle\ngi use\nÐ ¾\nchron o\nfair ness\nchocol ate\ntor y\nas da\npre scott\nstret ched\nal man\nu il\nre charge\nin tre\nob st\nhosp ital\nhay ward\nteneri fe\nfried man\nvap ing\nconfe ssions\nye ah\nbal li\nluck now\ncor pse\nsculp tor\namp ton\nt pp\nindic ates\nsur plus\ntru man\nðĿ Ļ\nsin ha\nin vo\nsovere ign\nke v\nestabli shing\nengra ved\nassu ming\nðŁı ģ\nsou za\nfab i\nton ed\noun ge\ndel oit\ndow ney\nno ble\nom or\ncar tridge\nðŁı IJ\nu hur\nhol loway\nsucce sses\nr sa\nâĦ ¢\nma zz\ntw d\ndisc ourse\n. <\ny at\nsatis fy\ncom pri\nठ¹\ngraph ite\ndisser tation\nar ter\ní Ķ\nb ally\nzom bi\nly ons\na ic\nu bc\npra da\ne il\nda x\ncla i\ngrand daughter\nextravag anza\nchall enge\nðŁ¤ ŀ\npo ver\nprimar ily\ndad dy\nman a\nbi kers\ninqui ries\nda un\nfel ine\ngener ative\nhe f\nbenef iting\nlind sey\npol ka\ndemonstr ated\nal le\nrand y\no su\nlow key\nweir dest\nred bull\nour y\nn ous\nwood stock\ncre denti\nnic er\ng ado\naly ss\nap h\nprepa redness\nstation ary\nincorpor ated\ndy er\nsarato ga\ncele sti\n: \"\nantibio tics\nor gs\ninde fin\nap ron\nи Ð\nfif teen\nno f\nðŁĶ Ŀ\nph x\nte ga\nm z\norganiz ational\non air\nband ung\npleas ures\nmor i\nsecre tari\nrac coon\nca shi\npil ates\nk on\ngeof frey\nla o\nkam p\ndepart ments\nback packing\nan am\nà «\ncrack down\naun ty\non do\nli zzie\nph ers\ncu n\nðŁĩ ±\nk pop\npu t\ninten tional\nconnol ly\nbar clays\nhs fb\nswin don\nu ku\ns ally\na int\nâľ ħ\npen ang\nup lifting\nepile psy\ninter ro\nbun gal\ngo ku\nblue berries\nठ¦\nu ssia\nsil ky\nmou red\ni stic\nbri efs\nme ats\ngo b\nch aser\nstate wide\npra sad\ngl itch\nar in\nban ff\nmemb er\nðŁĺŃ âĿ¤ï¸ı\nlo ving\nhall a\nภ¡\nsmo kers\nyak u\nscicom m\nphysi o\nsw ol\nlem ons\ngel ato\nch ool\ncapit als\nki stan\nti ghts\nspi kes\ntrav ellers\nik lan\ncommissi oning\nar ine\nemabiggest fans\nempha sis\nfront line\npad dock\ndestruc tive\nba ha\nl inger\nje wish\nshet land\nmc gin\nmon key\nko z\ns one\nraj ini\nte h\ny en\nc vs\nmasqu er\ngir ly\nwe sle\nwas nt\nbro dy\ntermin ator\ngil le\nmag gi\nbir die\njeopar dy\ncu bic\nvm ware\nintric ate\nan up\nto pia\neast on\nsab res\ninvestig ates\nbu sting\nbil ingual\nvalent ino\nin format\nfer re\nadvent ur\nhydr ate\nfor sy\naz iz\nsan to\ne de\nwhist ler\ncontinu ously\nd ham\nun used\nji had\naddic tive\nvi dy\ndo b\ni do\nfi ed\nni versary\nn one\nfu er\nðŁĺį ðŁĺĺ\ncoven ant\nprin table\nimmac ulate\no em\ncl t\nserv ants\nconsu med\nun released\nsc um\npack aged\nme re\nìĦ¸ë ¸\nto by\nta f\nspo ons\nme al\nf ball\nfair field\njan et\nsilver stone\ndart mouth\nfollow me\nvoy ager\nkom bat\nanni ver\nene w\nmag dal\nho ve\nsa th\ngrizz ly\ncar di\ngart ner\nsand y\nkan ye\npost ure\npo ign\nim pulse\nradio logy\nhoriz ons\nsi am\naish war\n= =>\nno che\ntr is\nel yn\ncom me\ndu i\nce c\ncouncill ors\ncudd ling\ncreep ing\nloc ke\nmanag es\ntrans ferred\nne cks\ndi er\ndan o\nv ick\nlun ches\nd he\nen sures\ncri ss\nul ster\nbann on\ncont enders\nsp am\nsweet ness\nmed al\nhon duras\narc tic\nultra sound\nin fr\ndisco vers\nei ffel\nca sters\nru ben\ndu st\nawe ed\natri um\nlest we\nse ared\nðŁĵº :\nty ne\nex changes\nlittle mix\nl le\nastron auts\nhersh ey\nwork day\nkno b\nso v\nre signs\ntoday show\nder man\nan th\naf c\nta ster\nsw oo\nsa eed\nper ing\nnarrow ly\nrn li\nbest buy\npanas onic\nobst acle\nfarmer s\nðŁİ Ļ\npa wan\nki est\nang ers\nabsur d\noh my\nsin o\npist achi\nsp ice\ngiu li\nprime time\nko w\nk ens\nex agger\n! ?!\nu ba\nmidd les\nju dd\ne jec\nslam med\npen sions\nof a\nre create\nb hp\nxx l\nliver pool\nthre sh\npur ity\nni eu\nhol ics\nwr ath\nra do\ngli o\nam ma\ndile mma\ncr u\nlets go\n.... @\nâĿ ĵ\nsugge sting\ntru mps\nhor us\nf v\nic om\nrefer ring\npredic tive\ntar ts\nge tte\nso ck\nglo ssy\npin ky\nal ec\nthy me\nou ra\nthero ad\npe tr\ncr am\np fi\ndv n\nme ier\nincen tives\ntun nels\nmobi l\nrec ap\nextra s\nupri ght\nrev amp\nper severance\n, -\not p\nmir ror\nar wx\nger ry\nma her\ng or\nhom epage\nam is\nag ra\nmade le\nbest friend\nsirius xm\nbun dles\nadmir ing\nt dsb\nðŁį ģ\nch as\nslow ing\nro h\nwall papers\nâĢ¦ /\ntek ken\ngang s\ntal a\nlind say\nshou l\nline backer\ntool kit\nur anium\ncaly p\nab rams\nmat thi\nðŁı ¿\nhon ourable\nda yo\nver sail\ntan k\nst c\nfr itz\nspl end\npat ag\nanno yed\non day\ndevast ated\nchattanoo ga\nnational ism\nmas sey\njen n\ntail or\ndev gn\norg ans\nzu cchini\non fox\nsat ire\nwex ford\ndis grace\nno to\nvol ta\nâĿ¤ï¸ıâĿ¤ï¸ı âĿ¤ï¸ıâĿ¤ï¸ı\nà ¶\nhome owners\npoin ter\nm cr\nau sten\nday sto\nmo ons\npal ma\ngra zing\ne so\ninfluen cers\nshahid kapoor\ncompli ant\nmeasure ments\ndevelop s\ny d\npar l\np vt\nrand olph\ntor tured\nger ald\neli as\ndeepi kap\nwar mup\nhick ory\ng ap\nco ffin\nam our\nre neg\nmoun ting\nseven s\nig le\nhi er\ndec ad\ntri ght\nesc apes\nwer ner\nt fl\nful filled\nni ger\nsour dough\nre aper\nchoo ses\nspin ner\nweek nd\nfil tered\nsh uk\nkat i\nold ham\nopen source\nkh anna\nat elier\nconne c\nopho bic\ngla s\ncomplic ations\nar son\ncounc ils\nsm ol\nas sy\nlur king\nling ui\nhan ks\ne in\nÙ ħ\nru gs\nn guyen\nnou veau\nmen ace\nle v\nalad din\nru ining\nround about\nk m\ncon or\nshoo ps\nmay day\ntraum atic\nprab has\nka iser\nk ita\nrou ter\npe dro\nre tar\nstun ner\nspani sh\ndistur bed\nacade my\ne learning\nwit ty\nsen g\nfer al\nav y\nsta b\nke aton\nur du\nko to\nhu i\ncoo ke\nari an\nthe personal\nu ma\nse ap\na sting\nrhetor ic\nhand writing\nmunici pality\nconsor tium\nðŁIJ Ł\nglasgo w\nra ya\neli za\npolym er\nbro th\nprac ti\ncorrespon dent\naddic ts\ngay le\nail ing\no fe\np li\nhear tw\nst itch\nsight ings\nprie sts\nsam o\nslo th\ngood wood\nroc co\nsab c\nsumm it\nl ace\npres ley\nitt en\ncin cy\nthepersonal network\ns week\npe gas\naf con\nregi stry\nci m\nle th\ndic ap\ncand ice\nflu ent\nsm ack\npede stri\nal oud\ncar ac\npriyan kach\np gh\nir ons\ndol ce\nlat via\ndece ased\nthero ck\ncla p\ncen e\nfo am\nmorris sey\ngre t\nessenti ally\ncom cast\nbe agle\nargu es\ning ed\n- âĢ¦\nsa g\nha san\nðŁĻ Ĩ\nðŁį °\nnh ra\nkann ada\nindic ators\non er\nbri xton\nat as\nscreen play\nsor ority\nsha heed\nhe em\nclass mates\ntain ment\nes i\nbreast cancer\nzucker berg\naur or\nen cia\nref ers\nkae per\nvor tex\ncom part\nlym ph\nphotograph ing\nste ff\nrest ling\npar sley\nmom ento\nth man\nlac king\ndu tt\nocu lus\nfin o\nfren zy\nra sc\nder n\ndis missed\nnoo k\nmet gala\nsh ill\nrapha el\nmaver icks\nexhib its\neag erly\nc pa\namen ities\n. âłĢ\nexo dus\nern st\nlit a\ndeal t\nwomens march\ni ain\nscore board\ncampe ones\nc en\nti ki\ngarri son\nfidel ity\nbra g\nroad map\npsy chop\nlo e\nble u\nðŁijĬ ðŁı¼\nsau vi\nspr inger\ntemp tation\nru dolph\nac ura\nwic z\nparach ute\nstro l\nlen ny\nzi k\ndom s\nnb af\nal pac\nvivi an\nro ve\npre et\nperpe tu\nsna ke\nair soft\ninfl atable\nprin ces\nati e\nffe y\npati ent\nm ire\nchel le\nsl ack\ngroo vy\n# :\nup loading\n!!!!!!!! !!!!!!!!\nsiem ens\nprovi sion\nv fx\nneed y\nf ats\nto poli\nbhu tto\nsa thletics\nalu ms\nt winning\nsouth western\nadop ting\nlast night\nman ne\nla ga\ntw ell\nac ia\n-- --\neye wear\nhur ley\nfle e\nsa ch\npe cker\ncost ly\nis k\ncr ates\npolic y\nero sion\nin go\nwer k\nðŁIJ į\ntorto ise\ntherap ies\ninter net\nchihuahu a\nri ps\nfre i\ned or\ntai ji\nt fc\ndo d\ndemp sey\nchrist in\nchen g\nhi ps\ngra eme\ncom passionate\ncavali ers\nhistor ic\nsoul ful\ncrimin al\nja c\nvin ci\nexpi red\nsur at\nturi smo\nk ona\nse aweed\nber ts\nle ica\nexpre ssing\na al\nwor t\nbreak fast\nher ring\nam used\nrhu barb\nmar tian\ncospla yer\ny ash\nstri al\nra ul\nrefer ral\ndw ts\nj w\nad ler\ncur tains\ngu r\nval ence\ntyr one\nsw fc\ncoach ed\nre born\ndiabe tic\ncho ke\nnor folk\ninvestig ative\nðŁĴ¯ ðŁĴ¯\nz id\nv mas\nphi e\nobjec tives\nâľ ĭ\nover due\ndi vers\nmat su\nðŁİŁ ï¸ı\ncasu alties\nภ§\nal k\nstand ardi\nre alist\narti facts\npand or\nke x\nin vin\n( !)\nine y\npar aly\nmr t\nfay e\nthe voice\non ga\nde ed\nskin ner\naz wx\nspeci men\npriyankach opra\nnu evo\nbar kley\ntoulou se\nresu mes\nfootball ers\ncit i\nfe tch\nè re\nlestwe forget\nðŁĻ ĭ\nch unk\ndri fting\nmanipul ation\nequ als\npu tt\nky ungsoo\nâĿ¤ï¸ı #\nela stic\npar ano\nfo y\ndo ping\ncin cy\nss ler\ninterrup ted\nal ay\nado res\name thy\ncon voy\nãĢ ı\nĬ ãģ\nblack list\ngener als\nsa chin\nbru shed\noun ces\nnon stop\nilli ams\nbt sarmy\nu av\nru ff\nbur ma\nbi k\ndefen ce\nschul tz\nbo asts\nlonel iness\ngo re\ntrans forms\nalum na\n@ @\nra ppers\nne hru\ncar o\nhimalay an\nwearab les\nge h\npepper mint\nre development\nflam ingo\ncos by\nbig baldhead\nag ri\nbare foot\nsco pes\nre gram\ngh ana\nðŁİ «\ni heart\nsa die\ncarri e\nmicrobi al\nku ala\nsk ater\nquer que\nâĻ ©\ngen res\nreas oning\nch ased\nas o\nsli pped\nen can\nvam os\nker s\nad verse\nmo il\ncommod ities\nwith you\nsil ent\nhy pe\nan de\nam ination\nwhi spe\nlit z\nâļ½ï¸ı âļ½ï¸ı\nri ff\npp y\nlam bs\ngan esh\nab sent\nregu lator\nmarse ille\nen roll\npar cel\nwa p\nby rd\nðŁĩ Ń\ntu ber\ncountry music\npar l\ncontro llers\nresponsi bilities\nwe y\nch ate\nmontene gro\nchic o\nmil an\nl ms\ntra inees\nappropri ately\nun certain\npopp ies\ned sheeran\nnutr itious\ngar o\ndeut sch\nawe some\nãĥ ¼\ncomfor tably\nland marks\net i\nre usable\ndaniel le\nro sal\nco les\njust ic\nc cs\nf anny\nni m\nmc u\nclin ch\nat ene\nmer ge\nim db\nang lo\nuc cino\npan ini\nan not\nbur berry\nfeat ure\npredic ting\nfashioni sta\ns ask\nimag inary\nmm o\nsouth sudan\nspe ar\nhu bble\njo inthe\ncoyo tes\nsli go\nko dak\nsit com\npolaro id\nroo ted\ncorru p\nðŁĻĮ ðŁĻĮ\nbris ban\nat z\nah l\nre my\ntal ent\naval on\nra da\npau line\nlocom otive\ngo ons\nne mo\nmaser ati\nic u\nstu tt\nhistor ically\nsm b\npres by\navo id\nso oners\nrhine stone\nw ad\nri sing\ntro t\nmo des\nreg ent\noptimi ze\nre ece\nsm u\nver ti\nnewyork city\ncor tez\nra c\nin case\nsin c\nfiel ding\ne tta\ntiff any\nal monds\nsad dle\nk rat\nmat ter\ng low\nstar ving\ngl o\ncra ppy\nsl ur\nst d\nmonit ors\nrecei pt\nmaymay entrata\nmc il\nun is\nrain bows\ncal dwell\npacqui ao\nj op\na fe\nhoo k\nes sen\nwiz ard\nmedi an\nfla ws\ncom s\nâĿ Ħ\ning h\nha ynes\nanton io\ntem plates\nou ter\nna w\ncardi gan\nbel grade\nðŁĴ ī\nhom o\na ise\nro pes\nno ve\nwhat you\ntri gge\nconcep tion\nad ukone\nna di\nfri ars\nsw er\nadju sted\nhot line\nsan ity\nkau r\ndown loading\nc gi\nten or\neth nic\napp alach\nภ¸\npa g\ngol ds\non set\ninvestig ator\ncar tel\npeace fully\njarre tt\ncat alan\npoli o\nn um\nfru stration\ndhar ma\nmy life\nâľĮ ðŁı»\naber deen\nmu sa\nbin der\nspark ly\nfle eing\ninstin ct\nco ping\ndomin ance\nill ers\ner a\nu conn\nlo oms\nliving ston\ngal i\nhe s\nc ma\nbel a\nse ley\nmon k\nla ch\nmar x\n ´\nm erica\nwoman in\nes sex\nra ina\njim i\nnep tune\nz ack\nchine se\nmart ins\nchand elier\nher n\nwith us\near l\nasph alt\nmodu les\nst p\nul la\npsychi atric\nmile age\ncaptiv ating\nsi der\nmen to\nmor t\ntran ce\ntal bot\nab by\nì ĥ\nâľĮ ðŁı¼\nj ak\ndaw n\nturn up\nscre wed\nfe ds\nblue print\nðŁĴĸ ðŁĴĸ\nhar sh\ner os\ninsom nia\nban kers\nta emin\nmis conduct\nhu mber\ngi di\nedu ardo\ncon a\nmusc ular\nconsu ming\nra sh\ndon nie\ndi pped\ncol lie\nsamu el\nmelt down\nðŁĺįðŁĺį ðŁĺį\nme z\nexam ining\nschwar tz\npri stine\nðŁIJ Ŀ\nve it\nful filling\nan esthe\ngue sses\ndra ft\nsom me\nsoli d\npati onal\nho ped\nevolu tionary\nall er\nenter tained\nsli ps\nlud wig\nconclu des\nsen sible\nbon net\ncra ze\ntra s\nhaz ards\nconst antine\ned ics\nstar trek\nto c\noccu pational\nin cheon\ndeepikap adukone\npizz as\nnew comer\nde part\noppre ssion\nebon y\nfoss ils\ntro jan\nel en\nste aks\nk hou\npositi oning\nug by\nred cross\nak h\ndol ce\nus mnt\npp en\ndil ig\nma vs\ncall er\ncost ello\n⼠Ħ\ndy n\nthing s\nrhin os\na xi\nsar kar\ncon vocation\natt ers\nss ss\nfun gus\neu gen\nruss o\nsqu at\nw sb\neli on\nwilliam sburg\ns off\ndefici ency\nbe arer\no kin\nkey stone\nt wain\ncal ming\nbreak able\nwa res\nhorser acing\ncom bs\nbun ting\nu it\nt land\nðŁĴĻðŁĴĻ ðŁĴĻ\nga stron\nsab ot\nick ers\ncommissi oners\nsen ate\nii ot\nath ena\nnit rogen\nan tony\nero tic\ndi alo\nmis sou\nhypo cr\nâľ Ī\nkaeper nick\ncan v\nd roo\nclevel and\no sh\nmon sta\nstefan o\n^ )\nsh ul\npo ison\nha e\ncommerci als\nma ul\nnit ro\nco worker\nalo e\nvap or\nt ents\nrussi an\nqu id\nquestion able\nmid get\npo ker\ngirl friends\nsin the\nerit rea\nten ure\ndepos its\nbuc keyes\nspot ter\ntheod ore\ntrin ity\njoaqu in\nu cci\nfollow the\ncaf c\nmp a\nðŁIJ »\nplo tting\ndom ino\nta ek\nsion ally\ndicap rio\npa p\ncar mel\nig er\nbt cc\nbeth le\nwww bigbaldhead\nfoo die\nbagh dad\nmason ry\noff ended\nà ·\nภģ\nsc ro\nvers es\nori ent\nar ches\npi yu\nknow your\ngre e\nta kers\ngu ard\ndish on\nbucket list\nbha fc\nwar dly\nðŁİīðŁİ Ĭ\nleigh ton\npe w\nstra y\nassaul ted\nin hal\nly fe\namar keting\nl x\nkat z\nubun tu\nme o\ncarto onist\nturno ver\nmi z\ndis like\nmul len\nmo f\nbl and\nhi des\nemer ges\nchori zo\ntruste e\nma hog\nlan sing\nparalym pic\nfa int\nfa una\nch al\nsn ar\ncat h\nbent on\ncast illo\nsli ppery\napric ot\noec d\nbar o\nl z\nhe ming\nclow ns\nco workers\nperu vian\ncommu ters\ny ell\nðŁļ ´\nunder ing\nv j\ntt p\nfli pk\nw ana\nsoc ent\nĤâĸ Ĥâĸ\nठĤ\noo sa\njag ger\ndi sm\ne less\nd ham\ncali f\na official\nec lip\nharro gate\ngra pp\ncom rade\nn tr\nconcentr ate\nthi ghs\nbit coin\nbel arus\në ĵ\nend uring\nnow watching\nindustri al\npi p\nar on\nar at\n ®\nwhit by\noooo ooo\nsa ree\ntic als\nmis leading\nyo on\nyear s\nsle igh\nroman ian\nsciss ors\nvam pires\nac up\nab ba\nth weeksary\ncent ri\nfl ye\nu o\nc bi\nbu ena\nsin d\nmar ino\nbur r\nre building\nठ²\nanniver saire\nac ca\nðŁĴĢ ðŁĴĢ\ngett ing\ntu lips\nwolf pack\nâľį ï¸ı\nmore than\nta kin\nðŁ¤ĺ ðŁı»\nu be\nmon ic\ndou bts\nmo wer\nco balt\ndon ne\nspecul ation\nargu ably\nkak u\nhtt ps\nprosecu tion\ndin ah\nstam atic\ndisclo sed\nbever ly\nfl wx\ncra bs\nextraordin aire\nwar mest\nimper i\no logists\ntrac es\npar c\nlake side\nam r\nter i\nhour ly\ndomin ation\nar row\nshrews bury\nance stry\nwr angler\ntrigge red\npen sac\nroo ster\nsurvi ves\na on\nbo ko\nval or\nlove is\nla g\npe y\nfo cal\nout laws\nbl anc\nartic ho\nwit s\nmarsh all\ndie go\nsupport small\nu ca\nsa h\nje et\nsyn ago\ngover ning\nðŁĴ ¬\nsal ads\ncre ate\nmiri am\ncen sored\nami de\nno u\nz eta\nallegi ance\n* )\nbl m\nric an\npa stors\noly mpus\nblo c\nwhir l\nstar ry\npr one\ny k\np ne\ncongratul ating\nbe v\nso ber\nlove island\nsa ir\nan ing\ntutor ials\nq e\nlun d\nin ist\ncle ver\ntaxpay er\nali z\nwren ch\ndd ling\ncap ri\nh pa\nðŁı» âĢįâĻĤï¸ı\nna j\no j\nfuturi stic\njelly fish\nðŁĶ¥ðŁĶ¥ ðŁĶ¥ðŁĶ¥\ncel ery\nplan k\nfil a\nne me\nun healthy\nlec tions\nðŁ§ ¡\nrit chie\nn ws\nmi kha\nwonder woman\nâĢ İ\nhip stamatic\nka g\nðŁĴľðŁĴľ ðŁĴľ\npoul try\nmo w\nwor ds\nlo ff\nðŁ¤£ ðŁ¤£\nrelat able\nre mixes\nkeny atta\nke m\nre signed\nfo d\nstra igh\nj lo\nhu tch\nbox ers\ncolle en\nmag s\ninstruc tional\nko l\nattrac ts\npra g\naccount ant\ngo ggles\nbr u\nth ole\nmar row\nleu ke\noc to\npon ds\nbubb ly\nhe ist\nìĹ ij\nim p\na har\nha unt\nhall mark\npsy ch\nkkkk kkkk\ncol umb\njump suit\ncost co\nsi delines\nag gies\nover turned\nni b\nkey chain\nfu k\nf af\nmi am\nassist ants\ncy cled\nri der\ndam mit\nred wings\nmag es\nkin s\nì Ĥ\nho d\nson t\ncarol ine\n\" '\ncu le\nbra id\nfel ony\nar ities\nruther ford\ndepic tion\nisab elle\nro ach\nk day\nfifth harmony\nem y\nli gam\nbari sta\nalbu querque\ngro ss\nðŁį º\noo ks\nðŁij ¼\ndun can\ntry in\njag s\ng ould\nli tho\nâģ £\nа Ð\nsam my\ntun g\ncas ser\napo lo\naaaa a\nman g\nas ics\nsh en\np ye\ntur bul\nss p\nsaint sfc\non lin\nn anny\nhe ster\ndo z\nภĶ\nth read\nren ts\nkh and\nðŁĴª ðŁı½\nun conditional\nrob son\ncar re\nph on\nsacrific ed\n £\nauto s\npar ker\noc a\nlog in\nkee gan\nhard cover\ndough nuts\nðŁĮ İ\nspit fire\nrefresh ments\nsaskat oon\ncommod ore\nj f\nrub ber\nhalam adrid\nchild care\nstra da\nio m\nri k\ndak ar\nther mom\ncro pped\ngar u\nali k\nven i\ni ft\nsi ka\nritu als\nz ul\ne ch\n ©\nsu dan\nl land\ni me\ndo cker\nì ¤\nfe ared\nfa o\nwal ter\nno g\nmutu als\nl h\nali gn\nmon ia\nconcep tart\nðŁĻı ðŁı¼\nsco e\ncompet ence\nsw ine\nly me\nlaun ch\ngreen er\nabstract art\ninqu is\ngran ada\nga elic\nflu ff\nd backs\ngrave yard\nba be\nacade mic\nadventur ous\njoh ann\n~ !\nbi bi\n| #\npl ings\ngett y\nas b\nâĿ¤ï¸ı @\nstaf f\nreligi ons\nbang or\nworld bookday\nme gh\nde vin\nash ore\nmeri dian\ngi thub\nqui z\nall stars\nbe stest\nir resi\nack er\ndo te\nwar rington\npol ly\nnewor leans\ncr ou\nwi gs\nche y\nsmithson ian\nla sag\nde tour\nbor is\nstra ps\nmari ah\ninten tionally\nko h\nðŁį ¸\nssi an\nmar issa\ncor al\nepiscop al\ncasu alty\ntom o\nsupply chain\nsam p\non go\nro o\ncavi ar\np fw\nclau dio\nbuff alo\ns ations\nmat ty\nsnap back\nl ds\nal arms\nmat te\nâĺ Ķï¸ı\nconditi oner\nd ors\nhe x\nfi zz\na stri\nsus sex\nsecur ity\nqa eda\nall star\ncocac ola\nas one\ncl icks\nsc ans\nmu te\nhe avier\nðŁİ §\nâĺ ŀ\nlv l\nbook boost\nyoutu be\nfla shes\nf jor\nc su\nexplo de\ndo dge\ncair n\ngonz ales\nth ill\npel le\nhart ley\nrenew able\nre tin\ne stre\ncostar ica\nshipy ard\nnc fc\npri ya\na ghan\nan ath\nplu gin\nco rey\nre bound\nor u\nkat rin\nhor mone\ngi m\nmahin dra\ns sus\npark land\nhar per\nfanta stic\ninfer no\nep ilo\nwrest ling\nfe ct\nc it\nac oun\nto ssed\nmonu mental\nchar tered\nbu st\npe tra\nâĮ ļ\nwildflower hour\nsweat ers\n* .\nbl er\nate ch\ngo wan\ndemo graphic\nbra l\nsuici de\nrenov ations\nvu el\nsin ister\nar mani\nmiso gy\nph arrell\nnap s\nun iting\ncrusad ers\ncor gi\ninsu red\nthan i\nno or\ng q\nd ada\nbicy cles\nsnu ggle\nsch an\nten berg\nss al\nfe mme\nbo il\n½ ï¸ı\nre ap\noccur ring\nhus sein\ndivi d\nsto ke\nsh alom\nna ia\no lic\nfrustr ating\nÙ ĩ\nig s\ngro ver\nscen arios\nn ds\nbru tality\nmed alli\nbu on\nsas s\nskate boarding\nony x\nlor ry\nny u\ngau tam\nmm ings\ngu g\nend i\nlo thian\ncomm ando\nchal k\nph ora\nasse ssing\nti gh\ncrun chy\nad ay\nis l\nci ara\npilgri ms\nkam al\np to\nbrit anni\nt ani\nsm c\nl ure\napp store\nab y\ngolf ing\ncl c\nfa u\nan as\nshu tting\nregul ated\ncarn age\nscow boys\nall enge\nc ma\nhumbold t\nrel le\nku mb\nher i\nrefin ery\nsound check\nd wayne\nbos nia\ni sp\nthe alth\nanni v\nrelev ance\nmy a\nbag gage\ndre ad\ns bc\nth ed\nbu h\nhi jab\nlo id\nke w\nc te\nrespec t\nlovel ies\ncu bes\ncelebr ate\ndir t\nsav ers\n_ ,\ngar ment\npulit zer\nmas jid\nbeat port\nal arts\nencry ption\ns ner\nple ads\nfound ry\nsym metry\nru mi\nbirth place\nscallo ps\nsupp le\npivo tal\nt ati\nno de\nso d\npro xim\ntr ics\ncol dest\nbren t\nmand u\ncla ir\ne ach\nand alu\nhi ddleston\nðŁIJ º\nmel ts\nv ance\npin n\nse ments\nscre ened\nsa chs\no bl\nic ha\nâĺĺ ï¸ı\nschool ers\nheal ed\nlo gged\nðŁ¤ĺ ðŁı¼\nic us\nbore dom\nb ish\nb ffs\ntal king\nsure sh\nhoo kem\nde on\nde fl\nei leen\nðŁį ķ\nwomen intech\nri sotto\nrang er\nadverti se\nภģà¸\ntel ly\nla go\ndart moor\nd ong\nsk ates\nlo go\nun ner\nmail box\nma sala\nlo oooo\namethy st\nche wing\nc bb\naustrali ans\nrc mp\ngame art\n# ...\nkor n\nextre mism\nfruit ful\nanci ent\npu bg\npol ite\nwh it\nmur als\nm gr\nline man\ndav ao\nste ms\nten nis\nav age\ntu pac\ngigan tic\nhs bc\nauto biography\nup the\nี à¹Ī\nre gal\nfig uring\nku l\nmis sy\nhoo p\ngra s\nfor ums\nback lash\nabduc ted\np nw\nmin ic\nbu tt\nbott oms\nat on\nven g\nðŁĮ ı\ndel aney\nprab hu\nfan club\nover haul\nhealth ye\nsy no\naa f\nren amed\nkim i\nun cle\nman city\nse u\nqu anti\neste em\num in\nen zo\nmel vin\nunder go\nj har\nfar ah\ncoast ers\nhumph rey\nmh z\nchildren s\n^ .\nd hi\ndisrup tive\nintegr ating\nr nb\nover sized\na ide\nne au\ndocu mentation\nðŁijĢ ðŁijĢ\npal o\nhear th\nri yad\npun ctu\nabc news\nsecu res\nboy band\nbir ch\nju co\ntra ff\nlegislat ors\nbay a\nãĤ ¯\nno ises\ncollec ts\ns warm\nk ner\nbi shops\nstur geon\nsnapp ing\nmo l\nfre aky\nchair person\ntro p\nlyn ch\ncar cin\nart sy\ne sto\ncha i\nfl ur\ninv ali\nsau sages\nim el\nj or\nfun fact\nwit ter\npuni shed\nac ons\nh ya\nre versi\nem c\ndif fu\nz x\nsp aw\ncla d\nd mit\nhol land\nfre sco\npay roll\nab undant\nstu ffing\nmor o\nc ny\nboy cott\nwend y\nele ven\npro voc\npil ot\ntr x\nbe ad\nclimate action\nri on\nassi e\nì ĸ\no sm\nislam ic\nho ar\ngood reads\nal ici\nafterno ons\nspoke sman\njo lie\nit as\nmasc ara\nâĻ© âĻ«\npre vail\nbeetro ot\nlu jah\nk li\ndod ger\n »\nru le\nl n\nscre am\nho bart\ncol bert\nr tc\ner m\npat ro\nquo ting\ns live\nque st\nnon fiction\nsemin ary\nprosecu tors\nve st\nexpress way\ng ge\nnau tical\net f\nðŁİīðŁİ Ĭ\ndur ation\ncha ired\nthe film\nfab io\nshe h\ncan o\nðŁĴª ðŁı»\nwith draw\n! :)\ncor pus\nphen om\nyel p\nla wn\nent om\nsnapp er\nbut te\npin ball\npro xy\nlibr e\nalle vi\nn ada\ngabri el\nfo wl\neure ka\ndaph ne\ntu nes\npun ched\nwh ore\njo g\nren tial\nman ners\no pe\nwh ufc\ngu th\nrevol t\nsne aker\nphilharmon ic\nho ste\nsovereign ty\nðŁĻıðŁĻı ðŁĻı\nfish ing\nsci art\nfe ta\ni pp\ndump ing\nkel own\ngir i\ndig its\nsal u\nsan jay\ntwee ters\nsp as\ncol chester\nsc ab\nma dd\n๠Ħà¸\nÄ ĩ\nged don\nmarch for\ndo p\nmaure en\nun plugged\ndi do\nfashion blogger\nup a\nmex ic\ntar y\npol ye\njame son\nv t\ngrin der\nmad dy\nconsult ancy\n¬ ë\nleagueof legends\nac cents\num ni\njane iro\ntu ss\nh ens\nampli fier\nto shi\npret tier\npre vents\nnew town\nred wood\nvant age\nball ard\nar tof\na she\na sion\nlac ey\nap at\ngro ve\nภĦ\nrw and\nreal tors\ntra itor\nbed ding\nö r\nzi on\nfla shing\ncam pan\nboom er\nsecretari at\nab ol\nliti gation\ncont amination\nse dly\nshred ded\nin for\ndo herty\nbench mark\nro che\nskate board\nsho vel\ni zz\nto pper\no ster\nlaby rin\nautu m\nk ong\nhum mus\nvi z\ntech news\nkla us\nam using\nsocialmedi amarketing\ni des\ncast ell\nste e\nunderestim ate\ncal ab\npa ign\nb illing\nunanim ously\ng mb\nfly fishing\nhath away\ncommerci al\ncolour ing\nskul ls\npivo t\nte p\ntb c\nmotor way\nx press\nconstruc tive\npu k\nunder lying\nkir sten\nmani ac\ncha o\nse ma\nchiff on\nðŁijĮ ðŁı»\nver ona\nkom o\nstan doff\nwi ped\nc ated\nbla ir\nwor kin\nm sc\nbethle hem\nswi pe\nunexpe c\npe es\npe tri\norig ami\nðŁij ħ\nmex ico\nflav or\nru dd\ncannab is\nmar u\nri ddle\nwor shi\nsil on\nsch at\nap se\ntang er\nbi ous\ne er\nquesti oned\no zar\ndan k\nangle sey\nchar an\nbak u\ncompe ten\nre pri\nbat ter\nsa xon\ncal ves\nleng ths\n$ $$\nâŀ ¡ï¸ı\nimmer sion\nga unt\ncar ry\ncy to\nb anda\nshu tt\nexperi ence\nel gin\nmous se\nta z\nê µ\nin correct\nen z\nb ham\nmor on\nso ver\nar un\nti pped\nla ble\nde arly\nbau tista\ní Ļ\nmor tal\nwoo p\ndt la\nsho cks\ndav os\nðŁĵ Ŀ\nswim wear\nher man\nðŁijĩ ðŁijĩ\nz ir\nneglec ted\ngrac ed\ncampu ses\nav s\nar ora\nswach hb\nlive pd\nac cra\nenqui ries\nshoo ters\nkur t\nvancou ver\nbrad ley\ngar da\ng ü\nol la\nattrac ting\nup ton\nne win\nlu mia\nfurn ace\nev ers\ne on\nsw a\nroo kies\na oc\nv ss\nbris ket\ntor ch\nyo da\nheart land\ntac o\nph ony\nfood bank\nab bey\nbab ylon\nu y\ngre ate\nexpre sses\nd andy\nsc apes\nsurvi vor\nron d\ne ci\nha vin\nab el\nchil dish\ntor que\nwav y\nur self\nkanye west\nyear of\nale stine\no brien\nal fon\nsk ag\nkore an\nanchor age\nval eri\nde w\nðŁİ ¨\nland slide\ncar ole\nchrist en\ngo phers\naf i\npriyan ka\nq q\npower of\nit te\npc so\ntw ol\npr y\nintellec tu\nguer rero\npi les\nwish list\nw ren\ntime table\në ı\nprodi gy\ngibb ons\n. /\nne ur\nanz ac\nmur ray\nvie st\npla ster\nla ir\nart gallery\ninter continental\ng br\nbell ator\nnam joon\nmam mals\nam el\ny aw\nsaras ota\ncam ar\nbud ding\nsum mari\naco sta\nla sh\ney ou\npost graduate\ninstruc tors\nti g\nconst ant\nwere wolf\nic os\ncla s\nglen n\nbud ge\nðŁĻ Ĥ\ner ta\nsta ins\npersecu tion\ncumb ri\no ch\nsyner gy\nhu ang\nscand in\nmid terms\ncomment ator\nregar ded\nperpe tual\nbo iling\nal p\nlan ge\nsch le\nfac eli\ntwee ta\nri dden\nok toberfest\ncharlotte sville\nik lan\njo u\nch atham\nb sc\nðŁį ¦\nstra uss\nmel low\nxx xx\nhappy hour\nre actor\nww er\ndistr action\nat orial\nðŁĴª ðŁı¼\ntwin peaks\nfay ette\na or\nko k\nbro om\nsy fy\nou se\nam ag\nØ ·\nubis oft\nlu lu\nhall mark\nstu art\nit ya\nsi deline\nvenge ance\nre lu\nsex ism\nboun cing\nun ites\ngu stav\nte ssa\nstu mp\npro clamation\nima x\ndivid end\ncol by\nðŁį İ\nplay wright\nun safe\nco smo\nðŁĩ²ðŁĩ ½\ncup board\nconstitu ents\nang lia\nram page\nðŁĺįðŁĺį ðŁĺįðŁĺįðŁĺį\nthan ked\ntake aways\nshro ff\nde bat\nkh ur\nconduc ts\nformat s\nà ©\nport age\ngraph ers\nu ten\npre m\nmo ines\ncondem ns\ns ous\nl ps\nf cs\ndeal ership\nleuke mia\nbure au\nski d\nguardi ola\nca ster\nthir d\navoi ded\nen cyclo\nc sr\nvi xx\nanaly zing\nshe ar\ndulu th\nshap iro\nchan ting\nstre sses\nas be\nmil itia\nãĥ ª\ncol lin\narsen e\nsure sh\nteach ings\nyi xing\nsh ill\nnu des\nsv u\nclear water\nwar ped\npro life\nartist son\nit u\nversail les\ngalax y\nax el\nspring st\ncal a\nhu hu\nsc u\ncommit ments\nexe ter\npoign ant\nmo tion\nconserv atory\nrow dy\nrec alled\nmu sk\nemb elli\nso the\nâĺ Ģ\nsto pper\nsch ild\nto pe\nel mo\nzi el\nj om\nbarn sley\nsnow den\non tour\njour ney\nhills borough\npar ole\nw ts\nmo ving\nag ility\ntiv o\nff ers\nkindle unlimited\ng wen\nann an\nah mad\ntex tured\nhepat itis\ndra m\ninsi ders\ntis sues\nãĥ Ħ\nfc barcelona\ncr atic\nna acp\npe can\nf gm\ncustom ize\nconcer t\ng sm\npe g\np one\njustin trudeau\nsuper cars\nhappy holidays\nbu lar\nado x\nlap tops\ndigital health\ndestin ation\ngradu ally\náĥ ¦\npopp y\nss l\ninhi bit\nstar light\nof fro\nglo omy\nx per\nhal der\nim plants\nle to\nhass el\na as\nun told\nen ci\nliber ia\nor an\ncon tests\nil ah\nsma g\nsc out\nmari anne\ncr yo\nschedu ling\nlo s\nkan e\nstutt gart\nne se\nlaw rence\nda in\npho tom\ncar ou\nภ£\ng wy\nnational dogday\nroa sting\nband camp\nkentu cky\nstret ches\nke rel\nca she\nãĤ ¸\nsta x\ntran si\ndog gie\nat ric\nhal le\nci vic\nbrow ning\nlein ster\ncat day\nhigh land\njoy ous\nin cumb\nor lando\nro mo\ncol ton\ndel ta\ncar ab\nro tc\naster oid\ngoose bumps\nmo logy\nyo ko\nan ds\ntomor rows\nred carpet\nsm p\nca sio\nðŁ¤£ðŁ¤£ ðŁ¤£\nse au\nrejec tion\nrot ating\nbi partisan\nth un\nmat i\nbon i\nol l\nener gye\ndo it\nl j\nmother hood\nlou ise\nneck laces\nel ite\nni x\nl cs\nen v\ngl u\nle sh\ncran k\nsu sie\nm clau\nso tu\ncrow ley\nrat ri\nuse d\nbre ton\nalfre do\nye o\ntravel pics\nti pp\nelli son\nsax ophone\nme red\nheu ghan\nta ine\nf es\nvi ro\nsuppo sedly\ni as\ndige stive\ny le\nli zzy\nwildlife photography\nbri anna\nwest field\nra ined\nam her\nðŁĺĦ ðŁĺĦ\ndistribu te\nbott om\npre serving\noil and\ncraf ty\nde scen\ncol ling\nshakespeare sunday\nr wc\nang led\nci an\nt ations\nmon tage\nme yers\nfrance sca\nðŁĮ ·\nwi ggins\nsan ford\nvolunte er\ncar ra\nbar k\nvari ed\npl in\nam u\nkap il\nrock ers\nqu ind\nbr ane\nin mate\nent al\nimpro vis\nmichi gan\nre tweeting\nprogre ssing\nmercedes benz\nsmo ker\nphysi ology\ndor ado\nwatt pad\nh wa\nsr bachchan\nw ga\nvol atility\nhi re\nac ap\nwn ba\nhein z\nstit ches\nkidnapp ing\nbur ys\nlim b\nf itters\nthumb nail\nton e\nmir and\ndesi rable\nad dison\ntar an\ntamil nadu\nspec tator\nsoci ology\namit shah\nremo tely\nâĻ ¦\nham id\nr ds\ng lee\nsmooth ly\nsch ro\ner c\nlali ga\nhe als\nus f\nni shi\nd hu\nun il\nh le\ntro mb\nbhu tan\npilip inas\nse ung\nwhit man\nte y\nmin ce\nsnow boarding\nre au\nk ker\nav o\nzach ary\nran veer\nti k\ngover n\nqu al\nbeck y\nanthropo logy\natt en\ngrocer ies\nde bit\nwar p\nsil icon\nhawa ii\nðŁĴ ħ\npomegran ate\npe er\norang es\npeople schoice\nend ure\nðŁĴĽ ðŁĴĽ\nãĤ¹ ãĥ\nac ial\na haha\nstu k\nimper ial\nbl ond\npow der\nkno ts\nvin ce\nwood lands\nden a\nwatch in\nmat cha\nma hat\ngalax ies\nmiddles brough\nk ö\nstre e\nresc ues\nwal do\nlero y\ndesp ic\nreal ities\ntm nt\nha q\nun o\npe c\nbolly wood\nblin ds\ndesign thinking\nhe ms\nand hra\nab sen\nfan s\nste ch\nshire hour\nbla ine\nshak ti\npu rely\nðŁı ı\ntra fal\nke ynes\ngr ate\nto bias\nspon taneous\nsatur ated\ncaval ry\npri sc\nðŁĺ ij\nwh t\npas si\n~~ ~\nvir at\npatt inson\nla o\nweir do\nsym pathy\nju da\nocca sionally\ncred ited\nstat u\nes co\nhil ly\nesc ape\ndischar ge\nse er\nmay nard\nsud bury\nz lat\nor al\nwe er\nencoun tered\nsm elling\nover sight\nê ¸\nthat cher\nmack ay\nyou can\nfre ep\nfreed oms\nprophe cy\nho e\nishq ba\ndra ke\nqu its\npel led\ntur k\no vi\nwesle yan\nnew music\nleg g\nch eng\nh illi\nay y\npan ties\nad versity\nad jac\nvaccin ation\nju ke\nga c\nexce ed\ntime sof\nsta ining\nep cot\nv ital\nup ward\nbethe sda\napar k\nma hi\ncamp fire\nenchan ting\nrha pso\nh z\nna ver\nfa x\nvali dation\nac ad\nny r\nas ym\ncoordin ated\ndepar ted\nall ery\nvar ies\nspr ite\nchap lin\nss occer\ns wat\nbre t\nrelu ct\ntunes app\nsuper star\nreminis cing\no co\nhome grown\ndough nut\nun canny\nla pd\nthyro id\n! âĿ¤ï¸ı\nbotan ic\nbre s\nsp ade\ni ste\necho es\ndu lil\nbur sting\nqui ero\nðŁij İ\nloy ola\namuse ment\nha ils\nsleep y\nburgl ary\nâľ ı\nro gue\ncot land\nmo ors\nlow er\nwic ked\nðŁĶ Ĭ\ncompet iti\nargent ine\nyvon ne\nkarti keyan\nili ary\ngat sby\nprecin ct\nsix ty\nna ji\ncam s\npractiti oner\nðŁĺ³ ðŁĺ³\npu ne\nneg li\njuli en\ninv aded\ncali br\ncla m\nduba i\nmu k\nlan tic\nproduc t\nfe dex\nï¸ı :\neu ra\ndari us\ns ling\nvirtual reality\nhome stead\nðŁı³ï¸ıâĢį ðŁĮĪ\npac ed\nin ha\npul mon\nla zy\npremi ering\nma stered\nin he\ncon gregation\nba jo\nsport ing\nnew jersey\nhor ny\nlma oo\nleng thy\ndu t\nyo gh\nswe aring\nphilosoph ical\npap ua\nin ski\nknow les\ndy ke\nâĢ ²\nto ken\nmc guire\nri ot\nprobab ility\nmc con\ngro s\nsu mat\nc ite\nda a\non da\nmad dow\nche w\nboard games\nspar ked\nre claimed\nad hd\nny se\nimwith her\nequ inox\nboo ths\nbalsam ic\nha zy\ndor chester\nag os\nse aw\nmoder ator\nseri ea\nander sen\npilgri m\nâŃIJ âŃIJ\nitch en\nhal li\nx ton\nnathan iel\nmun ition\ncelesti al\nga f\nzo om\nmark le\npen thouse\ncal e\ns fa\nbar king\ntu cket\nem ery\ncal orie\nli que\nad ar\nmc nam\ntor tilla\nwood pecker\nmo town\nbad ger\nayr shire\nscram ble\ndd ay\ncra ziest\nper rie\ncho co\ncast e\ni ot\nwre cked\nselec ting\nuss r\ngra ft\npun t\nlab ou\nir st\nba ek\nÛ Į\nsu ki\nque u\nach at\nte ster\naug mented\nwc vb\nsin ks\nðŁĵ »\nra ke\ninter ne\nbe cause\nbelle vue\nune arth\nlight en\nðŁĺ £\nturn around\nlabe led\nunemp loyed\ntwitter kurds\nle ia\nh ye\ngreat er\nðŁIJ İ\ntim ed\ni red\ne tt\nlimit ations\ncab e\ns out\nbee ch\nanni hil\nre trac\nyo ona\nang er\nden nis\nsupp lying\ndi z\n\" (\nsc ur\ngun man\nsu ho\nsauvi gnon\nภ¥\nwi ley\nland on\nchoreo graphy\npre historic\nðŁı ĥ\nvar gas\nassess ments\npinn acle\ndi i\nchamber lain\nì Ī\nv p\npresent ers\ndeut sche\nsun shine\nsal utes\nr one\nbu siest\n- .-\nmotor ists\nhemi sphere\nal wx\nps p\now a\nden ying\ncho c\ngu tier\nhan uk\nmus kete\njait ley\nse wage\nt ame\nthin kers\nshi m\nse quo\npap ar\nmiddle east\nk wa\nke g\npatag onia\nno y\nbar ça\ntake off\nhe a\nà ¬\nn sc\ng dc\nðŁij Ī\nmou stache\nmel ania\nthr a\nâ¬Ĩ ï¸ı\npier ced\nze us\nfon ts\nber a\nit iner\nq atar\ncontr ary\nire land\ni fy\nou los\ncommun al\nfin s\nun paid\npa a\nðŁijĩ ðŁı»\nri os\nou p\nf iller\ncafe teria\nภŃ\nkas i\ncali ber\nz ulu\nv sco\nts ford\ndragon fly\nsmo kin\npi st\npsycho logist\ndiplom at\nwe bs\nbuc cane\nà® ¾\nmotiv ational\ndu ne\nba e\nc fs\nwith out\ner on\ni ac\nate e\npen sion\nfra zier\nen sis\nsk is\npar ting\nger y\nterrit ories\nnach os\neni ght\never lasting\nmsd honi\ntel e\nsp un\npo di\nsab ah\nenviron mentally\nce ase\nbeau mont\nmar ta\nkel vin\nho ff\nsun il\nn da\nco b\nsh ale\nree dus\nun boxing\nu bio\nre opened\nn all\ncapsu les\nmar r\nhimalay as\nswee ter\nja z\nf mr\ntwee ter\ndha ka\nna u\nde mi\nd fs\nta urus\nfad ing\nit utes\nci p\nover flow\njef frey\ndon ny\ncar tunesapp\nðŁį ij\nprefe cture\ndanc ed\nc pt\nple asing\nital k\nearth quakes\nul ation\nhi o\nãĢ ĭ\nant an\nnutri ent\nde ere\nselec ts\nenrich ment\nr iti\ntram pol\nbl amed\nj ia\ncontribu tors\nchesa peake\npi geons\ntribun al\nmad uro\nw su\nilo ve\neffici ently\ndar cy\nwar ms\nar ra\nec u\nho wer\nstrugg led\nrajini kanth\nðŁĺ¢ ðŁĺ¢\nhou sing\nstr at\neli x\ndisp ro\nraf fic\nthi erry\nna sty\nc fb\nstaf fing\nal ma\nback ers\nhen son\nsky walker\nreale state\nroo s\nness y\nchan ce\ncair ns\nc ci\npe dal\nly ft\ncross word\nwait er\nonly in\nkru ger\nk ir\nalej andro\ncar tier\ncar rera\nre paired\nou at\nun clear\nun breakable\ntoday in\nqu eries\njo dy\ngen ital\nwin ner\nto l\nkelown a\nfascin ated\nãĥ ¬\nsris ri\nsqu ared\nspr ung\nnegoti ate\npriv ately\nav en\n>> >>>\ng ical\ngav in\nchester field\nzu mba\nor r\nnat alia\nimpeach ment\nmn l\ncar at\ncriti que\ncredi ble\ntrac y\ntan i\nmusi k\njig saw\ngam bia\ntol kien\nfe u\nas per\nsav ory\nfo xx\nf itt\nmar lon\nl rt\nv ell\np br\nimprison ed\ni om\nchu l\nwind shield\nkay e\nba a\nchor d\ns art\nal gon\nminister ial\nnat geo\nla zio\nnor ms\nðŁijį ðŁijį\nlic king\nfut bol\nun sung\ndalla scowboys\nsh red\ndistur b\ndev ine\nbe ards\nch f\nb day\nro sso\nig or\nay i\nsi ren\nk air\nsti les\nro f\nmag nets\nun cover\nmou se\nbang ing\nsi ghted\nspe ople\nimpac t\nrow land\nkir a\nenviron ment\nlove the\np sis\nmish ra\ngl endale\nca jun\no che\nde ception\nsex ist\nstra ws\ns ga\nbuff er\napost le\nsp l\npop up\nðŁļ Ĺ\nr g\nup er\nball in\ni dy\nocca sional\nnational park\nðŁı Ĭ\nu an\ninnov ation\nภ«\nte aparty\nre tte\ncounter fe\nb ha\nrec s\nig en\nðŁĮ IJ\nhumming bird\ncu r\nha ven\nla zar\npue blo\n: :\nzi onist\nop ath\ninver ness\npromo ter\ncarto on\ncabine ts\nmahog any\nsurve ying\nr ational\nfeel ing\ntesti fy\nso w\noc on\nภ¢\nne el\nmar is\nsol itary\nche mo\nrad cliffe\nsim ons\nros ary\nnew er\njo die\nre tali\npra wn\npad dy\nhen ge\nk ala\nim plant\nat y\nbren twood\npar adox\nene z\nre designed\np our\nwy d\nal de\n௠ģ\nsol d\nbiomed ical\n๠Ĥ\ntt tt\nmat teo\nys er\nnew ton\nde bun\nner dy\nloo l\nwo on\nelisa beth\nec c\nwh i\nach o\nsalv age\nsal aries\nqu ity\nnavig ating\noph thal\ncon soles\nre built\no pec\nast ers\nsho red\nset list\nkathr yn\nrhy mes\nre visiting\nash ish\nli ft\nre post\nsole il\nâı ±\nweal th\nsa at\nwe c\nking james\nflipk art\nfield work\nse gu\nmo dal\nbu b\nare rs\nðŁį Ĵ\nclo oney\npad dington\nnecess ity\nguth rie\npen te\nli mo\njo sie\nar tin\nen c\nl hs\nbetra yal\ninfo graphics\ni er\nmo a\nhear ings\nbon jour\nsym bolic\nag ro\nwed ges\nkrist ina\nwild flower\nathle tic\nphotograph y\npe sh\nca hill\nchi lean\ngou l\nfi oren\nðŁij ¶\nz il\nsk im\nbad oo\ndeli a\ntre ble\nn cc\nðŁĩ¦ ðŁĩ\na house\nbul lock\nsol itude\nا٠Ĩ\ncan cers\nfutureof work\nhu tch\nwater shed\nwar mongers\nsp illed\ncolom bo\nmo th\nassoci ations\nweigh ed\nglobal goals\nnot just\nchrist i\ntor g\nswe ating\nman eu\nclu sters\nâĢ¼ï¸ı âĢ¼ï¸ı\nta ped\nul y\ntru sting\nyu suf\nte in\nra b\n, ,,,\nsin ai\naudi ble\nexplic it\ncro wns\nsch iz\nat least\nðŁĹ £\nde bra\nje suit\nene gger\nz hen\none sie\ni it\nss f\ngur gaon\nchak ra\nbear cats\nk ran\nk awa\nreque sting\nhan over\ng end\nsor os\nmer cy\nlovel y\ndo omed\ntim my\nku z\nul l\nab ram\nsa ison\nãĥ «\nclean ers\nre mo\ncircu its\nbar red\no th\nmo ist\nmadele ine\ngall o\nu j\nper mits\nhea viest\ncar ols\naz te\ngior gio\nflo ats\ndecl aring\nus rc\nmin at\ncraf ts\npri ma\nconven i\nnickelo deon\ndanc ing\nceremon ial\nblo gg\ntw p\nanglic an\nshe k\nk nick\n( ((\nhubb ard\nharve y\nhit man\nfen g\nwe some\nfor za\ns word\nop us\nbro m\ngi bility\nz al\nm unch\ndance hall\ngre edy\nhd mi\nre birth\nðŁĺĭ ðŁĺĭ\ns world\nfigur ine\ncom post\nk f\nengra ving\ngior no\nst ana\nk man\nham ster\ncompos ers\naj e\nfunc tionality\npol k\nis ons\nair planes\nte se\nhor rors\nmusc at\ngi ven\nsp ence\nðŁĩ¸ ðŁĩ\neli ot\nach illes\nfre ck\ncrypto currencies\nsou ther\nhal o\nbor neo\npolit ic\nhahahaha h\nup state\nsi ena\nobsc ure\nhau sen\nlloy d\nhappy friday\nmotor bike\nbon a\nameric as\nhol s\n- (\nspor ty\nun aware\nreven ues\nchristop her\nbank sy\nav an\nev apor\ncom press\neyel iner\nto dos\nbuff y\nrenewable energy\nly rical\nar chan\nrapi st\nfair trade\nlma ooo\nbeat z\npro active\nla pse\nir ical\nrevers al\npo de\nmcin tyre\nmac au\nãĥ ķãĤ\nnash grier\nf sa\ng all\nçĶ Ł\nperpe tr\nil ya\nconfigur ation\n% ;\nstr ange\nrac i\nภĩ\npic kups\nkov sky\nmam mal\nw ps\ng able\ncompar ative\nz h\nsave our\nda vey\non etsy\nmu ssels\nmis er\ncri stina\nelectr on\ncra ve\nlo ren\nprecipit ation\nm z\nðŁį «\nvin cen\nsnow board\nno ida\nah n\nmarin ated\ng tr\ntown hall\nmin is\nbethe l\nadv an\nsu ra\nshi el\nfur ry\nðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤ\nlyn d\nso il\nsc ence\nsen eca\nshar jah\ndick ens\ncredenti als\nav ar\nper k\nrequ iring\npre fer\nj ian\nde ca\nr ach\ning for\ndel e\nbe ep\nðŁĴ »\ncis ely\nhu ddle\ngreen sboro\nhaw king\nho ax\nhang ar\nç ľ\nmis o\nlo vin\ngre ta\nab ad\nlogi e\nat an\nsnow flake\nmahe sh\nfear the\nal kal\nbobb lehead\nba hn\nju dged\nfu tu\nfeli x\nðŁį ĵ\npi ke\nder iv\nnotic es\nau er\ndis super\nor da\nwi pes\nam ino\nstri kers\nfoo tb\ndram as\npun ching\nscore less\nheming way\nbi h\nbal lad\nchat ter\nam mo\nkle in\nfabric ation\nkari m\nz end\nhi sto\nvol ta\nrock y\nmarke ter\nxtre me\nsequ encing\nparadig m\ncle ats\nboom ing\nâģł âģł\nblock ade\npromp ts\nyogh urt\npur pose\nnu r\nregu late\nnois y\ning rid\nbird watching\nbar tender\nÙ ĥ\nwor dof\ncha otic\nshor ty\nel dest\nz app\nonceupon atime\nfl yo\nrit os\nmike quind\nðŁIJ ´\nregi stering\n. ]\nad ol\ngg gg\npur ge\nkid lit\nar bor\nval ves\nsynago gue\no th\nunanim ous\nveri fication\ndar rell\nãģ Ħ\nvander bilt\ntape stry\npro sper\ndid dy\ndra fting\nde cep\nmarqu is\nst int\nmichael jackson\npee led\nmen us\nbb b\nsc are\nema il\nwri gley\nit is\nf ell\nsome thin\nbar ra\ned gar\ndi pping\npu ddle\nsla de\nlear ner\njal en\nðŁ§ IJ\nthe daily\nmikequind azzi\nju x\niq bal\nmckin ney\nra iser\nef an\ndr one\ncat o\npic ket\ncro we\nl att\nuk o\ngiuse ppe\nhin i\nsynthe si\nponti fex\nsong writing\nto d\nswit ches\ndin ners\nh q\ngabri elle\npensac ola\ncir cle\nexpo ses\nev s\nriyad h\npro men\no ck\nsa j\ncit ation\nbrew co\njo si\nep aper\ndri f\npoint less\ntang led\ncri pp\nline ups\nfairi es\ndaz e\nmour n\nbla dder\nsal z\nbur undi\nbook mark\nthe people\nsub sequ\nprinci pal\nsk er\ncourt ney\na oki\nrac ers\nad m\nmom a\ncritical role\nhou n\nshed ding\nsa ka\nace ous\nmck ay\nhus bands\n ½\nme da\naccu sations\nro sel\nnc is\nwitne ssing\nor ama\ngo ds\nhil ton\nel man\nÃŃ n\nmeg ap\ncra ven\nannoun cer\ncrit eri\nsheffiel dissuper\nmilit ant\nconsu l\nhoo ded\naby ss\nb x\nma dam\nlo cu\nmary am\nmanic ure\ngrat is\nac tresses\nros ario\nthis dayin\nking ly\ngn ome\ncel ine\nr ous\nhe el\nlil ac\nvish al\nab h\nthor ns\ns ls\nne al\nconstruc ting\nbe ren\ns lang\nma ins\nfar ra\nsar ko\npai ge\ngu iller\nl ala\nice berg\nnou n\nplann ers\nu mmm\nou ses\nill ary\nma an\nbox ing\nzi pper\nsrin agar\nmigu el\no str\nmp o\nresponsi bly\nlan terns\nappli ance\nx b\ngren ade\nneglec t\ndy sle\nham mock\nne ctar\nwit cher\nr gv\ndi ence\nser bian\nseed ed\ncru z\nbi sh\nsp he\ne q\nsky rim\nalge bra\nphil ately\nbungal ow\nge off\ny ves\ndemand ed\nconsider ations\nthe vamp\npawan kalyan\nco ded\ngrit ty\nerup tion\nse infeld\nuni denti\nëĭ Ī\nwor m\nac us\nse ung\ndun g\nro land\nsu d\ndi visions\nab lanc\nshor test\nj f\np oun\nplant based\nbe to\ntough er\nmc o\ndon et\nmark us\nv fl\nðŁı ł\nopen ing\nco ward\ncaber net\no xi\nburle sque\nsand ra\nsu mo\nconsi st\ntho t\ncay man\nmotor ola\ngutier rez\nd slr\ny w\nno bel\nnov ice\nmoms demand\ngrun ge\nsp or\nd cc\npre sses\nsli st\nallot ment\nvoc ational\nft c\npu ja\nlo ven\nutt arak\ntan dem\nsh ep\ncome dians\nanat om\ncant wait\nhealthye ating\nwest side\nmar gins\nchi ang\nasbe stos\nstupi dity\nproble matic\nfit bit\n: $\nceil ings\nshu a\nprotec tions\nbio tic\nbeng ali\nre sts\nbien nale\ntim o\ncul min\ne minent\naffe ction\nunbeliev ably\nindividu ally\ncanvas sing\nwh itt\nnov asco\nchin son\nh pe\ngo w\ngloucester shire\npa o\nthresh old\nchev ron\ns ine\nwe ther\npp ie\naqu ino\nantwer p\nâĸ ¬\npo on\ninst af\nequ ine\ncinemato graphy\nnbaf inals\nvali ant\nkil kenny\nte rence\nsyste mic\nsr l\np ound\nmade ira\npl ough\ntre cht\nmat ed\nmp d\nransom ware\nph in\nli qui\nbb ce\nboom er\ni standwith\ncon ju\nr te\nnar a\nfoo lish\nda shing\nvier nes\nbr ite\nda u\njuni per\nai da\nyou now\nra zer\nde i\nrepe ating\ncomfor ting\nadjac ent\ne to\nca sted\nchat ur\nmu er\nsyn th\nsan itary\nmac le\nindepend ent\nlaw ful\ne erie\nh or\nðŁĴ Ń\nam rit\nvel o\nstation ery\nmu f\nmay may\ncontempl ating\nelabor ate\ngre gor\ndri es\nac col\nภļ\nschwarz enegger\nill nesses\nday break\nfollow back\ncollu sion\nelectr onic\njo vi\nhiro shima\nta w\nhom ec\nmic ah\nqu itting\nfro sting\nben fica\nhel i\ns ical\npic cad\ncorpor ate\nment orship\nyou are\nsing er\nshi va\nru ne\ning er\nri um\nplay able\ndoo p\nwil low\nter re\nni p\nat d\nwar bler\nprofession ally\ner ase\nproce ed\npedestri ans\nmis chief\nben ding\nalas kan\nc kett\nmo p\ndd les\nshut ter\nge ared\natene o\nma deline\ng ations\no sha\nder ick\nsw ild\nan gry\npat ents\nhun k\ndecre ased\nfr y\nðŁĴĸðŁĴĸ ðŁĴĸ\nsal on\nquant ities\nd ario\nni gel\nku ma\njen n\nhapp ye\nxx x\nrex perience\npro s\nau sch\nrele ssly\nham burger\nfuku shima\ner ne\nstat ec\nren d\nmay field\nj one\nlef ty\nbern stein\nsm il\ngener ates\nfore station\nband its\nta yo\nr ca\nac ci\nrodri go\nkn app\nelo vers\nvege tation\nu ral\nle ft\nħ ï¸ı\nworl dre\nsur i\nembar k\nw son\nba you\nmu ller\nmo vers\nðŁķ º\npresby ter\nl f\ncre e\nbat b\nsal am\ndemonstr ations\nan ec\nn pc\nit ics\nto graphy\nre inst\nthur st\ntal e\noff ences\nsmart city\nbro tha\nofthe year\nin valuable\near n\nðŁijı ðŁı½\nkre mlin\ngra dy\ntown fc\nguern sey\nma ha\ncontag ious\ndre x\nbe en\n( £\nnati vity\nk tm\nsomer halder\ncomp ounds\níķ ĺ\n\" âĢ¦\naf g\nott news\nh ound\nfire fly\ncil an\ndonet sk\nvolunte ered\nak ira\nè ª\nsing ul\nst h\ndro wned\nmand o\nhe ir\nðŁİīðŁİ Ī\ntax is\ny uki\nvel d\nk ans\nel k\nran ts\nhash tag\nt eng\nro g\na at\ngru b\ne ber\nin india\ncolo ssus\nsig ni\nso ever\nmile stones\nder o\ndifferen tial\nphu ket\nmaster mind\nan gh\nmel ani\nbro ker\nactor vijay\nstun ned\ncontinu ity\naf fl\nvo cal\nperenni al\nfianc é\nin complete\nhun ts\nre issue\ndomin ates\ntur meric\nro am\nri on\nbag ged\nnas sau\nfu t\nx ox\nnational trust\njo ye\nsan o\nhearth stone\ndis respect\nle es\nh se\nsiber ian\noffe e\nre stock\nwolf gang\nre gan\nplan o\nun wind\nre par\nmil le\n] ,\nskul l\nfat ally\nconcep tual\nðŁĮ ²\nf é\nber to\nb ms\nu a\nmag na\nnotre dame\nle te\nla undering\nheartw arming\nbuffe tt\ngo at\npe abo\nwind mill\nv ac\ncontinu ally\naz alea\nmem brane\ncan cels\nmake yourown\nathe red\np to\ntor pe\nðŁĺ ł\nðŁĴ §\nsc ares\nle aking\nz et\npix els\nac i\nkh il\nmarath i\nðŁĻı ðŁı½\nu la\ntam u\nchandi garh\nz agre\naa b\npronoun ced\naubre y\nsand er\npun ta\nhar low\nic elan\ncelebr atory\nso t\nunci ation\nstru ly\nmc dowell\ndeepi ka\nremin ders\nmy stical\nct c\nchat ted\ns ica\nbar gains\nch hat\nru bin\nm net\noiland gas\npel ican\no at\nmor ality\nk our\ni h\nnu clear\ngc u\nric her\nvene zia\nm ma\nle ith\nac company\nrich mond\nsports net\nba ahu\nsmu ggling\nmm i\nðŁĩ®ðŁĩ ª\ntwi sts\nsahi b\n.... .\namb itions\nil lo\nhistor ical\nfo rec\nshow biz\npon ies\nchas ers\nremo del\nwill ing\nprince sses\nam ple\ncushi ons\nac les\nlot r\nda ch\nan the\nin corporate\nnew bury\nki ri\nfried rich\nab v\nball ers\nalber t\nðŁij Ń\nlet i\nnan op\nci de\nanal o\nn sf\n)) ))\ngriffi ths\nvalen ci\nro ano\nfun run\nbabys itting\nca day\nent re\nu ck\nslu g\ntic al\nthe sims\nro ar\ncar ney\ng am\nsto we\nfi d\nbun ny\nsham rock\npe cu\nmol ina\ngo cougs\ncon tributes\ntransform ation\nmo y\nv aj\nsever y\nantioxid ants\nthir teen\nsight seeing\nl j\nreversi ble\nodd ly\nhoo kah\nnou vel\nhal al\nfe i\nstab les\nmul t\nho pped\nbra ids\ninter change\nghana ian\nww ww\neth no\ncon junction\nago v\nye ti\nearth and\nts p\ncon serve\nheir loom\nmetaph or\nwoo f\ntor io\nself less\nn wa\nem ilia\nyl ene\ny xe\ngi ar\nmoder ating\npro bz\nb fi\nne er\ndu mmy\nhanuk kah\nwe bber\nk v\neye brow\ndag ger\nsu mp\nra ges\nork ney\ntb o\nhal sey\nassign ments\ntr onic\nscri b\nco on\nan war\n# âĢİ\njal ape\nflori da\nqu aid\nhaw keyes\nâĻ¡ âĻ¡\nstreet car\nro g\ndat lantic\ngran ola\nun changed\nexpect ation\nÙ ĩ\nmar lin\ngu mmy\nðŁĻı ðŁı¾\nawareness month\noil painting\nmu th\nper ch\njun to\nvilla gers\nmor g\nche ated\nweb comic\nthe future\nd ps\nla kings\nmen tioning\nvo or\nident ities\naccor d\nmc gu\nl pga\nrum our\nmassi vely\nm pls\nheal y\nd ate\nsp oli\nre visited\non t\nal and\nscru tiny\nlakel and\nbl ending\n< /\nan kara\njami edor\nmetab olic\nf ences\nann y\nå ħ\nsemic on\noo tt\nspace ship\nwack y\nle ta\nap ac\nshe e\nin herit\ndo res\nðŁĩ¨ðŁĩ ¦\ngent e\ntw ick\nri ms\ngal ve\nde ville\nking fisher\nscorpi o\now l\nal ar\nvari an\nðŁĹ ĵ\nvene tian\nstar dust\nthen orth\nq ing\nhar rington\nconsul ate\nspectac le\nho bbs\ntur ks\ngre er\nmat ing\nðŁİ Ģ\nðŁĮ Ģ\ndirec ts\ní ĭ\npompe o\nvo iced\nla os\ntz u\npro me\npri sm\nmer c\nfortun ately\nbc fc\nmcdon nell\nnot sorry\nsmi led\nt ba\nfor war\nmid term\ndar by\nwe instein\nup grading\nwol ff\nbron co\ncab ello\nðŁ¥ ĩ\nfi able\nshar pe\nbat tered\nsat o\nmyth ical\ninstap ic\npre pped\neni um\ne spo\ndi aper\nexplan ations\nwho pping\nragn ar\npe el\nantibio tic\nl acks\nharri son\nli sm\nau l\nqu ail\nmartin a\nsent encing\nsc ams\ndi di\ntr onics\nãħł ãħł\ngo ff\nza in\nparam ore\ncha ined\nclin ton\nli ff\ncott ages\nem on\nreve rend\nconsu mer\nce an\nt any\nlum pur\ne bay\nsto ol\nðŁĺ» ðŁĺ»\nta pro\nh ath\nmodern art\njust ine\nprover b\napp y\ntra x\nmani fest\nam bu\nnai k\npe pp\nr sd\nmer chants\nkitch ener\nshi fted\nli zz\nâĺħâĺħ âĺħâĺħ\nâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ\nuto pia\ntom o\nou ted\ncom ers\nchiroprac tic\nbook club\ncin dy\npro hibition\nse uss\në¯ ¼\nthin kin\nrr rr\ngo fund\nt ack\nom b\ncatastro phic\nling u\nguild ford\nbo td\nॠĭ\nplan ter\n^ ^\nwin k\nkath mandu\nsto ppers\nsmooth ies\nre efs\nhin d\nbell amy\nĦ ë\nwaste water\nvo or\nnat l\n! ]\nre el\ny ap\nscoo by\nwork space\ncorin thians\nbl un\nobli gation\ng bbo\ndy son\ncra vings\nell ington\ndap l\nwre xham\nearthand clouds\nuk runchat\npositi oned\nkal b\nfour square\njo ck\nim pending\neven ing\nath y\npro claimed\nc ites\nann apolis\nsan i\nmar th\nir l\naccom mo\nka a\nfin a\ny aa\ndi sper\nec ar\nbha k\nwill y\nðŁĺĢ ðŁĺĢ\nmcder mott\nmo j\ngener ational\nu said\ntrain ing\nlon ely\nlo res\nimpe cc\nâĢ IJ\nbeav ers\nma ki\nhe b\naap l\nå ı\nwolver hampton\nleader board\nme u\nc fa\neaster n\nhu r\ncivil war\nou rage\nhor ned\nle high\nawar ds\nevi dent\ngi gab\nr ous\nma del\nro byn\nur gently\nk ors\nen as\nheis man\nbam bam\nfab ian\nf om\nevalu ating\nassemb ly\nout sourcing\nhun tsville\nðŁĶ ª\njusti fied\ncashi er\nsp aper\nbuc keye\nanaly tical\nillumin ati\nau tho\no j\nsha de\ngeel ong\nwh ey\nhe aton\nterri bly\nele k\nun charted\nsd live\nmoto cross\nher mes\ndar shan\ndar lington\ncash mere\ngri pping\ncilan tro\npun ish\n... :\nðŁĴ Ħ\ninst ance\nder i\nlo bal\nmuk her\nsp ar\nthin ker\nfre mont\ncom piled\ncolor ado\nvig ne\nsm d\nwhe ad\nvilla ge\nle ek\nformula e\nta res\npersist ence\n?? ????\nped ago\nhe z\nalzheim ers\nvul ture\noff ence\nis great\nsuff ra\nkick in\nh mmmm\nbroad way\nï¸ı @\nart i\nalli son\nendor ses\nry u\nlolli pop\nsoy bean\nkend all\ncer a\ninv ade\n( ðŁĵ·:\nconver ter\ncar pets\nho bo\nfr it\npe ac\nes qu\nern an\nou f\nan il\ndi ffer\nch ing\nbre cht\nsp g\ndaven port\nstra va\nsever n\nn gos\nstor ians\nfe te\nparame dic\nj hb\nal amo\nsne aking\ngold coast\nroof s\nisi l\ndepic ted\nprojec tions\nnu mb\no ss\nep i\nglu cose\nzid ane\ninfin iti\níĺ Ħ\nran som\nton ics\nfal k\ng ler\nou tw\nre ss\nweek ly\nthe on\nn ole\nðŁĩªðŁĩ º\nvol ley\nsum mar\nneg ativity\nsam son\nye w\naus votes\nju l\nju dy\nf art\npra yed\npal ate\nmulticul tural\ndouble header\ncycl ones\npier re\nãģ ¨\nâĺ łï¸ı\nrt w\nconver ting\nwir ral\nl ari\nir relevant\naustin mahone\nan che\nya an\nsd f\n$ .\nexplo ding\nulti mate\nprof ici\ngofund me\ncell ence\nep stein\nbul lied\nsep tic\nà® ¤\nlu mber\ncu ff\nvsco cam\npl or\nภ¥\nse ok\nro to\nvenezu elan\nsor ta\nspir ited\ndaniel padilla\nteam sisd\nradio active\nicelan dic\nðŁĴ ¤\nver e\naccommo date\nshi pp\not ter\nol ina\ne go\nsu la\nsan antonio\nde as\nsimil arities\nâļ ¾\ny om\nbro ward\nå °\ncan cun\nveri fy\non te\ncandle light\nìł ķ\ninf ants\naz am\nðŁĺ °\nle ven\nun stable\nbloom ington\nx ford\ncon tour\ny p\ninnov ator\nhistor ies\npo y\nlolo lol\nex pires\ncat alo\nbill boards\nan ab\nel ic\nnovasco tia\nfa ire\nìĿ ´\nrock well\ngr ille\naz tec\njoh or\nur struly\nfi ren\ndun lop\nid le\nport man\njo es\ntx hsfb\nhol m\ncham ele\nunder world\nlo ss\nti em\ntherap ists\npast ure\npa ste\ning now\nvul can\nra gon\nlar kin\no shi\nho co\nchild hood\numb rel\nsuccess or\nkath y\niz en\n° ï¸ı\nshare holders\nol ga\nai b\nhe ap\nfl aming\nro u\nair tel\nrat t\nz ane\nvo w\nthor ough\nsn ag\npar th\nun conscious\nve y\nnew release\ngh ee\ncroati an\nfacilit ating\nswan son\nastor ia\nto logy\nmaster y\nðŁ¤ ij\nbil bao\ntrou pe\nthe ori\nchey enne\nro tt\nshore line\ngra sso\nmaster chef\n+ )\nvi x\nellen show\nas g\nan ak\nku ya\nsafar ilive\ndebu ting\nblu m\nlist ener\nv ins\nbook shelf\nsmart cities\nmakeyourown lane\n; ;\nðŁIJ ¯\nri zz\non ward\nbull dog\nbear ish\nvir uses\nfri gh\nlin den\nwe iser\nsn t\ngon a\ndre sden\nfl anders\ncu k\nwheel ing\nba u\natu esday\nsurf ers\nswi ft\nmc call\narbitr ation\naw d\nmon c\nb ine\nat x\nre fr\nmi ro\npo sey\nn are\nrit ter\nâģ ¦\nplay book\nblow out\nsports manship\ns oooooo\nmalay alam\ngri ms\nbur bank\ninfin ity\nsar gent\noit nb\njoseph ine\nski pping\npar kin\nexcur sion\nsemin ars\njo har\npar tridge\npost game\nll ll\nblan che\ntemp ting\nm na\nlu ka\nis ers\nto ffee\nbar ron\nhe mmings\nsa e\ngo hawks\ncu pid\nli mbs\ncon se\nun common\nz ada\nhead shot\nso ils\npione er\nmam ma\nsem itic\npan dey\njamiedor nan\nspl its\nvel a\nson i\nra ff\nt mobile\nâŀ ĸ\npra wns\nlit er\nenjo yment\negg plant\ntu b\ncultur al\nus ic\nsuspici on\nsy cam\nsumm ed\nma du\nho ck\nup wards\neye ing\nri ve\nassas sins\nâĤ ¬\nout fy\nchi ves\nt ner\nla is\npor ridge\nsad dest\nw cc\nvick i\nsna ils\nbiz italk\nmill an\nðŁĮ į\nsam oa\nj ing\nmi key\ngu j\nchel ms\neli gibility\narma da\nthro p\nsurger ies\nãĤ ¿\nmo hawk\nex its\nme m\nis lington\nc me\nland fill\nkait lyn\nðŁİ ¼\ncombin ations\ntomorrow land\nver b\ncor a\npre cisely\nna om\nðŁĨ ķ\nshr ink\nsof tly\nmerce de\nmand el\npoo dle\nball erina\nsop h\njux ta\ny at\nary an\nhesit ate\nlo wered\ngu lar\ndungeon sand\nron an\nmy ri\nsp f\nmen opau\ngra sp\npa thi\nfe asi\nfla w\nshi story\nste ward\ngg le\nfay re\ncli que\ncredi bility\nyo g\nsec tion\nmu sko\nse ville\nno tt\ncal m\nmate o\nindic ted\nfi ba\nby l\nlin o\nu kin\n!! #\nenig ma\nsiri us\nbu sc\nðŁį Ĭ\nmac kerel\npsal ms\na at\ntomorrow spaper\nðŁĺ ĸ\np fc\n........ ...\nshre k\nmul let\no sh\ndanger ously\nimmen sely\nam ur\nðŁį Ĥ\npro por\nsy a\nlondon marathon\nabo ve\nobli gatory\npro v\nra cha\nalex is\npri mary\nsh h\nether net\nd stv\ncou gar\nun lucky\nni l\nsteak house\nmel a\nfc bayern\ncause way\nca therine\nfluore scent\nnx t\nto kyo\nau sp\nreleg ation\nqui zz\nshored itch\nproud tobe\npromo s\ninter acting\nhome brew\nda esh\nw pg\nstead ily\nprovin ces\nbal lots\ni ah\nal to\n< <<\nyou u\nri ley\nprefe rence\ntra verse\nincen se\nam munition\nho dges\n# @\nhail state\ntart an\nwitch craft\nvent ilation\nliber tarian\n! âĢ¦\now es\n% !\nong chang\nbru shing\nle ic\nfi ber\nunder attack\ndown load\nex pir\nhy o\npompe y\nmc bride\ny ag\nstre e\ncom bat\nten ding\nai ra\ngug gen\nab ra\nin na\nfli ps\naw al\nm ach\ndol lar\ninspir ations\nz um\no du\nit ty\nvideo game\naqu aman\nhar u\nbel fast\nje b\nbut ch\nus gs\ncalcu lus\ngo yal\nmor gen\nx finity\nstand up\ncontrac ep\nsab re\nna be\nin secure\ngener ously\nepit ome\nl w\nt ca\nnarr atives\ndon nell\npand as\nber gh\ntu t\nker al\nfel icity\nbr ampton\nquinte t\nnom ore\nðŁĶ ij\nlo i\nalham dulil\nðŁĶ¥ ðŁĶĹ\nston er\nshaw l\nclin ical\nbren dan\ngon e\nfla wed\ntri ppy\nj g\nal location\npo aching\nve vo\nmo cks\nlef tist\nbon uses\ncondem ned\nabil ity\nst ating\nmicrobi ome\nbio logist\nfor you\nwahl berg\nss or\nift ar\nw ul\nÑĦ оÑĤ\npom er\nme me\nver te\ntre ll\ntra it\nin let\nhormon es\ndeliber ately\nvill ar\nbattle ship\np bl\ntw enti\nho kies\ndal ail\nsay a\nmay fair\nhan s\ndie ts\n⾨ ⾨\nod in\nhot spur\npap i\nk ana\nk amp\nfin na\nflo tus\nti ans\nunic orns\ntribe ca\nchang ers\nfore ground\nout a\ninv aders\ngett ys\ntomorrowspaper stoday\nmac millan\nhand written\nw fp\nu de\nstate of\nbase d\nâĺģ ï¸ı\ncas m\npsy ched\nhistor ians\nfol d\nd da\nag grav\np ans\ngreen way\nau sv\nðŁĺ ¶\nshradd ha\ninde x\nbe sti\nzim mer\nt ness\neye shadow\not te\ngo ts\ndistribu ting\npro min\nyo l\nace a\ntram rahim\nhoo per\nsupre me\njam min\nintu itive\nquali fications\nsli m\nsid di\njay ne\ntri pping\ng tx\npun s\ne manuel\nom g\nmid summer\nin to\nsuccul ent\nri en\nnew mexico\no or\nhoo king\nin f\nðŁ¤ Ŀ\nflir ting\nna hi\ng friend\nt ps\nhel ix\nz s\non ie\nct f\nkri s\nirresi stible\nfla p\nðŁijıðŁı» ðŁijıðŁı»\nus wnt\nru d\nram ps\npin oy\not w\nlol z\nlow ering\nfavor ite\nt mc\nphra ses\nher mi\naver aging\nem br\nben o\nestu ary\nsle eve\nribb ons\nta sh\nภ¹\nx f\naw gs\nsun ited\nbrew eries\nanir ud\npun ches\nol die\nip ads\nwi fey\nland lords\nd ji\ngun ner\níķ ´\ntex an\nex op\ncas sandra\ns off\nðŁļ «\nigh ton\nbak ers\nawareness week\nv all\near p\nbts bbmas\napologi zes\nâļĵ ï¸ı\nwas ps\nstates man\nsnat ch\nwatch dog\nra fi\nafter party\nspi ke\nj er\nperi ph\nr nc\nmu ll\nle en\nshi es\nli eu\nurstruly mahesh\nmer ton\nde sai\nshi f\nðŁĮ ±\npe dic\ngos ling\narrang ing\nww g\ngen y\nyou uu\nnetfli x\ne ttes\nk wi\nbernar dino\nam iga\nØ ¨\nkashmir i\nt ings\nemer itus\nde cat\nab domin\ndc i\npha ses\nd jan\nbe am\nop ry\ni shed\nthe ellenshow\nthe st\nhabit ats\nto ons\nmclau ghlin\nri pper\nmicro biology\ntal aga\nclu eless\nss u\ncro che\nbro mance\nlonge vity\nzagre b\nprev ented\ntra ve\nspo ilt\ndarry l\nmigra ine\nal cat\ndd dd\nvi v\nser pent\nmat tel\njam a\ncon quest\nî Ħ\nsam sung\npresbyter ian\nket ch\nfire fox\nmo tif\nle c\ncho pping\ncher no\nj ann\nðŁIJ °\npro lon\nwake up\nconver gence\nmersey side\nheart broken\nlo oming\nhal lucin\nmai ze\ncommun ism\nmo h\ntwitter storians\nserge y\nres eller\nfavor able\ned gy\nre iter\nmal aga\nlive me\nka hn\npul sion\nbig g\nkim kardashian\nati o\ntyr anny\nru ption\nq ant\npro ven\nby z\npu shaw\nkri stin\ne er\ntar dis\nri z\nawak en\nmi ko\nun documented\npath finder\nindirec t\nresemb les\nh ler\nconce aled\nscand al\nre im\nd nb\ncr itters\nattend ant\napprentice ships\naa u\nscre amed\nl su\nfa h\nhar bour\ned d\nbat sman\nli ss\nmi sha\nspani el\nit f\nadvan cement\nfa c\nclose up\ncecil ia\nmedi c\nnarcis si\nlav ish\ngi ac\nma ys\nle it\nwine wednesday\npushaw ard\nlet to\ncurren ts\nbug atti\nout ine\nw j\nun do\nler osis\ndevo tional\nðŁij «\non na\nfais al\nsa una\nhimach al\nam ii\nà® ®\ndi zzy\nscreen writing\nph x\nsp n\nick i\nag irl\nfi shes\nwb z\npi m\nbo ar\nac id\n! ..\nrocke feller\nn ga\ndra stically\nsimpli fy\ndru mming\nautum nal\ngur mee\nlor de\njo ann\ngive up\nb our\nam ura\nder land\nsim pler\nwat son\ntri dent\nconcor dia\nbel lum\nbre k\ndum plings\nvi on\ndungeonsand dragons\nsp ri\nascen sion\nwil datlantic\nu st\nrob ins\nlegi on\ninsi st\njar o\ngue ss\nso b\nbigh it\npool side\nnegoti ating\nmc gill\nbil d\ntechn icians\nmiti gation\najay devgn\nb to\nant en\ncosmo politan\nðŁĺĬðŁĺĬ ðŁĺĬðŁĺĬ\npatri oti\ntemp er\npromen ade\nnav ajo\nnam m\nwrink les\ndc fc\nle ach\nbru nette\nr f\ncout inho\nal ti\ntradition ally\nop tome\nna z\naccord ingly\nrec ard\nde ets\nsw ell\npo sure\nwhit ening\nstrang er\nilli on\nhere ford\nu wu\nro bber\ncotsw olds\ncl en\ngor ge\nnam aste\nre lish\ngri ff\nadren aline\nbla sio\nval e\nê ²\ntoler ate\nrail minindia\njen sen\nho ven\nel lu\nob sole\neisen hower\nunidenti fied\nthan niversary\nbody guard\nØ ¯\ni dge\nsch al\nstock port\nsn i\nre taining\npo po\npix ie\noli thic\nki er\nha jj\nsa z\ncor bin\n!!!! !!!!!!\nv it\nme gat\nde h\ncircu it\naf fleck\ntheore tical\nhope less\nu ab\nslu mp\nb ice\njam med\nlet stalk\ncan i\nside ways\nlabyrin th\nre fs\nha hn\njare d\nðŁį ¹\njam bo\nph yl\nenhan cement\nc tr\nful lest\nse ye\ndo ba\ncho ic\nyo s\ncb j\nandr é\nre watch\npri ma\ndoctr ine\nfor gets\nu hm\nar ound\nu le\nart lovers\nshi raz\nhar th\nex tor\nÅ ¡\nunexpec tedly\neli us\ny x\nem my\nse ac\nðŁijĩðŁijĩ ðŁijĩ\ncorrec ted\ncom bu\nwom anc\ncou gh\nwhat son\npubli shes\ndivers ity\nback bone\nlock down\nmesmeri zing\nnor te\nma b\ndesig ner\ní ģ\nra gh\nmole cules\nget outside\nthe beatles\nsemicon duc\nnach o\nlun es\nham mers\nsul tan\no on\nfe ren\natt ach\nar qu\nuttarak hand\ns ash\n; -\ntre ad\ni ko\nar thur\nscandin avian\nr ation\nga el\ncharge able\nfish y\nv ma\nhand bags\nchar a\nay ne\nde fam\nsett lers\nqad ri\npal ais\nin wx\napocaly ptic\npoo ja\na es\nat ories\nproof ing\nn lp\nts la\nv ina\nli do\ndee phouse\ninformat ics\nv v\npp ings\ndi ss\nà ¯\nuhur u\nst ony\nbetra yed\nb aff\nmy ra\nas pen\nallow ance\ntam ara\nci f\ncor bett\nser ge\ndi go\nambi gu\npain ters\np cr\np ca\nnom s\nlo ft\nve e\nopend ata\nðŁIJ ±\nalex andre\nidenti fies\nfantasy football\nre production\nbrom ley\nware agle\nmm er\np ss\ncu es\nay at\nhut chinson\nsar ac\njack man\nira h\nap ink\ncol s\naussi es\nex ecs\nday ton\nðŁĻ Ĩ\nim v\nhar am\nchuck le\nauthent icity\nar do\nincub ator\nภª\nphoto shopped\nembrac ed\nfight for\ngor man\nzz zz\nschol astic\ncri sps\nte apo\nmid night\nga ine\ncol lier\ns ate\nde tte\nå Ń\nimag ine\ni ff\ntw ili\ni fication\nteat ro\nnor ma\nes ur\nemergen cies\nrise up\nr inger\nhass le\ncait lyn\ntranqu il\nvers a\nse b\nover look\ngin i\nbo go\nse re\nmay ne\nhenri k\ncontamin ated\nrhapso dy\npro portion\nwildatlantic way\nâģ© .\norganis ers\ntran e\nstand ard\nsper m\nlaun cher\nric ci\nher ts\npaper work\nshowcas ed\nmer yl\npen a\np imp\ndisa strous\n^. ^\nphar a\nx is\nfron tal\nsw irl\nsp ills\nswag ger\nsmart watch\nsizz ling\nsavi our\ncat ar\nbb cr\nrefurbi shment\ndr is\ncitro en\nabsor b\npatrioti sm\nil leg\nchro mo\nfresh ers\nru s\nlim iting\nef ish\ndown ed\nman dir\nhazel nut\np all\nmac on\ndisappear ing\nquali fies\nbo on\nbar racks\nam ine\ngen dere\nðŁļ ĺ\nj es\nãĥ Ń\nqu ito\nmiddle weight\nsch au\nquad ru\naci ones\nlimit less\nðŁijĮ ðŁı½\nch man\nar av\nregulat ors\nit up\nbatter sea\nmil ford\ng z\ntic king\ngh ou\ncru shes\ntu tu\ndread ful\nfam ine\nfor change\ndalail ama\nðŁĴ į\nwhit aker\nhash mi\nh us\nvo d\nbet te\naa ah\niso o\nðŁ¥ Ī\nha ar\nla ine\nb v\nall day\nspr out\nindie games\nfree bie\ngree ks\nbut ler\nill in\nha al\nware ness\nsi ma\npublic health\ngam a\nwa a\noun g\ngoo oo\nokin awa\noff enders\nim pose\nho c\nyoung ster\nstory teller\nsc ap\nfigh ter\n+ ,\nwhit es\nmusic monday\nre za\ngo ducks\nbri a\nmi um\ncas per\ncru mbs\na ad\nmarti alarts\nch p\nri gged\ntn g\nharve sted\nsa k\ndo jo\nmill wall\nb nw\noc d\nhistor yof\nt mr\nsi rens\nfan ci\ncaregi vers\nvir a\nson i\nrecur ring\nacknowle dged\nðŁı Ł\noph ile\nbu cky\nstre ssing\nroo k\ndi gger\nvi val\nsan do\nfle et\nsi ers\nsel caday\nrefre shed\nanti fa\na que\npo lo\ndisappear ance\nde mb\nâĮļ ï¸ı\nren ted\nber ger\ng mb\ncu la\nss al\ngoo dy\nu hh\nmarcel o\nw anna\nsoft ware\nshop small\nturt le\ntom as\nfri sco\nðŁĺį ðŁĴķ\njim enez\nc su\nday z\nan do\nwyn ne\nchoreo grapher\ncerv ical\ntrail blazers\ned g\nzend aya\ntravel blog\nel s\nwhole some\nco g\nlab out\nar ney\ndel le\nsu isse\nma si\nine se\nom be\nfi ddle\nre claim\npa u\nwat cher\nsla in\nber ty\nopti mum\nel ites\nmin is\ntur key\npatro ls\nger ard\nau reli\nwild ly\nwal tz\nbr gy\nw ob\ncre st\n+ ++\nve z\nfro sted\ndavi do\nthe x\nparam edics\np into\nhan k\ndu pont\nur g\nfo stering\nmicro poetry\nspec tre\n---- >\nne uro\nfri da\nmusic al\ngalve ston\ne ffic\nsc ape\npal azzo\nth all\npro visional\np js\nau re\nðŁĶ ľ\nmam amoo\nkit ties\ncre e\nwa k\nlo ool\nlu pus\ncn blue\nà º\nðŁİ ¬\nrac ed\ntro se\nom as\nstri de\nco ors\n⤠µï¸ı\nin comparable\ncy ril\nbroad er\narec lipse\nðŁį Ķ\ninter val\nti ru\nco working\nw aco\na ham\na bee\nflouri sh\nthe times\nol ini\nkick boxing\nlu cer\nat la\nas un\ncasser ole\nmi aw\nlobb ying\njan ice\ncir que\nre flex\nle ary\nsanat omy\ntem pest\nse mb\nmur dering\nus av\nro bo\non et\np cc\nnati ves\nlife of\nsa ha\nruth less\nrel ates\nappeti zer\npye ongchang\nnor d\ner u\na thing\nug ly\npl ying\nbran ce\norgan ise\nkend ra\ndat o\nchees es\npar ma\nburn out\na stra\npre toria\nadjust ment\nuk u\nsl o\nli ken\nfav ors\ncli ve\nbe ets\nsnow donia\ngo tv\nsy n\nopen house\npan i\nportra yed\nsl ated\nme cca\nren al\nsupportsmall streamers\nstaf fs\nda o\nbi ker\nvik tor\ntit us\nadmi red\nðŁĵ ±\nhurric an\nhe ats\ngl ory\nphoto genic\nmer i\nde por\nburn ham\nor angu\ndj ing\nimpre ssionism\nign ition\nca i\nw ynn\nde pe\ncove ted\ncolla gen\nsau s\nor nam\nadministr ators\nss on\nnh politics\nhahahaha hahahaha\naspir ations\nr gb\nswol len\nso we\nsc r\ndiver gent\nhou ghton\nhan oi\nd ory\nni ki\nland ry\nb cci\nðŁijĮ ðŁijĮ\nis mail\ntri pod\nher d\nbhat t\ndress age\ntab by\ningu ish\nhur on\nà³ į\nà ł\nto das\nevangel ical\nchor ds\nst john\nslo ppy\nmarty r\nface book\nali ght\nsen sei\nkath niel\nr ites\nzi one\nu o\nrevel ations\nweight lifting\npan o\nnc wx\nac ton\nà® ķ\nØ ²\nsom a\nภĹ\nrespec ting\nmar che\nfore man\nbe tty\nki k\nshi bu\npo on\nargy le\nk swx\net z\nmar bella\nbrac kets\nstand by\nfire side\ndefi ance\nv ex\nbritanni a\nin habit\nappo int\npiyu sh\nle ash\nsci ento\nfla sk\nsen na\n> :\nat roc\nsand erson\nid lib\ndhan ush\nðŁĺ Ļ\nen thr\nhit ch\nde dly\nal ley\ndor k\nmon do\ncudd ly\nmis sin\nye sss\nnight ing\nj pn\nw ary\nump ire\nma z\nê ³\nbab s\nĭ ãģ\nstan ford\nposse ssed\nexce eded\nðŁĶ ¶\nwall art\ntra p\nj il\nhi bis\nsp ying\nscri be\nkhali l\ntrans lator\nlu mb\ndi zed\nch c\nsuper vision\nshut ter\nja g\n_ *\nyester days\nms f\nhi hi\ngonz aga\ngille spie\nvive k\nec static\nthis morning\nch us\ned es\nston ed\nbe es\nðŁĩ¹ ðŁĩ\ntur in\nho ver\nat rics\nster n\nsam heughan\nauti sm\nmi ya\neye witness\nwrit ings\ntravel tips\nchut ney\npx rtg\nkeny ans\nmy stic\nk rit\n/ $\nred head\nworld ly\nam us\nop la\nle ve\ngab bana\nse en\no clock\ngang a\nkeen an\nsc ent\nol dies\ngo green\ncorner stone\ncomp ly\ncon cours\nðŁİ¶ ðŁİ¶\nha an\ncon fis\naw son\ncle op\nî Ģ\nsu zu\nsau té\nal gar\nsubscri ber\neste emed\nãĤ¤ ãĥ\nworth while\nmel rose\nflo ck\nbri ghtly\nviol inist\np ere\nsli pping\nand co\nsi gh\nha van\ncu lo\nm sa\nfibro sis\nmatil da\nra fting\naw ard\në ª\nmm mm\nge aux\nste iner\nsin n\nhelp ers\nbeet les\nai mee\ntai wan\npistachi o\nmac beth\nm zan\ndescend ants\non sale\nin r\nil m\ngrou se\nsa ig\nmo w\nbi gre\nadjust ments\ntu la\nmathe w\ntransl ates\nmu h\nbol lah\nðŁĴĽ ðŁĴĻ\namo res\nab outs\nbomb shell\nbla ster\nx avi\ns ns\nk roger\nga ther\nerad ic\ndaf t\nchem o\nben ches\nðŁĩ© ðŁĩ\nut v\nour a\nn ko\ngator ade\nbiaf ra\nok state\nim danielpadilla\ndom ains\nopen ingday\nkid do\ndo i\nric e\nday care\nmac millan\nba thurst\ncheer leading\nðŁ¦ ģ\ncash back\nk won\nhob bies\nexem pl\nries ling\nâļ ª\nag les\nny s\nevery thing\nnav is\nad di\nmagne sium\nfaceli ft\nark ham\ngrand es\nextre mist\ndon at\nvit ality\npump kin\nbe tta\nsl td\narti san\nli by\npe aked\nah hhhh\nmary am\nassi m\nun sc\nment e\nal aya\nlow ers\nar as\ngri ev\nle ip\ngr ati\ncri ses\nspr ints\nexe cute\nw to\nms d\nmag ical\nre viewer\nspark les\njuke box\nðŁĺĤ âĿ¤ï¸ı\npay back\nlicen ses\ndun kin\nbel t\nlake wood\nh ateful\nbud gets\nrev amped\nph erson\nky iv\nwent worth\nro sen\ncru ise\ngi ggle\ndef star\nassassin scre\nym outh\nwin kle\nw fc\nband wagon\nb kk\nw iring\nkear ney\nsouth side\npe tit\n! ðŁĺį\nnor dic\nmir za\nmu gabe\nv l\nscon es\nk tv\nsand al\ndu c\nm alls\nðŁĴŀ ðŁĴŀ\nit c\nal ay\nim pair\nun rest\nflo ss\nc é\nab ou\nvar ying\nmuse o\nser ver\ndi ya\nhibis cus\nero y\nmer ritt\nfin dom\nf pp\nun usually\ngo tt\nconting ent\nali aa\nball on\njo l\nhi ked\nzy me\nay r\nag n\nga z\nperio dic\nspar ty\npracti sing\nlin ton\ntal is\ncy pri\nwomanin biz\nradio disney\nðŁĮ ¼\njump ers\nendo cr\nðŁļ¨ ðŁļ¨\nand on\nshar apo\nmi er\nma sonic\nfac tories\nvi en\nbb ers\nìĽ IJ\nhol d\nke bab\nbe ak\napproach ed\nac milan\nmun ro\nko sher\nexcell ency\nnegoti ation\nwalt disneyworld\ncr ouch\nte asing\nsuppre ssion\nen ya\nb ce\ntransformation tuesday\ncal lie\nvis was\np gat\nic ted\nend ings\nesc u\nrecru ited\nit fc\ncollabor ations\ng ino\nsnu ck\nausch witz\ni fc\nx ii\nke sha\nger vais\nclo ak\nx l\nsa ad\nprob ation\npre cau\nmac in\nanasta si\nle k\ne azy\ndaysof code\nmariah carey\nyo g\nstit ched\nboy friends\nsh ar\nph ile\nag u\ntwin kle\nphi shing\nweek ender\nic ton\ngurmee tramrahim\nal ton\nl eness\nall an\npen ultimate\nkry stal\ngo u\nlan de\ndis mant\nab using\nnor se\npat erson\ned mun\nap an\nxi umin\nsk el\ncat walk\nre act\nwal led\nt angle\nbr yn\nve to\nsuper moon\ncas ablanc\nappreci ates\nski d\nbo th\ncatal ina\nele ague\ncyber monday\ncau tious\nðŁ¤ ĵ\nnov o\nhamp ton\nha ye\njose f\nvar an\nlo bos\nroano ke\norph ans\ntt in\nsqu ads\nishqba aaz\nblack panther\ne tu\nk sh\ncru mble\ncess na\nreli eved\nscul ly\npollin ators\nexplore canada\nki es\nkam loops\nkir an\npri mal\nsett lements\nhot spot\nbrain storming\nce dric\nbi ennial\nsh ant\nâĻ¡âĻ¡ âĻ¡\ndo on\nhear n\nwalk way\nfe m\nve al\ndeport ation\ntox ins\nelimin ating\ndescen ding\nby the\nbla sphe\nha sta\ncomple ment\nas cent\nri ga\nprovo st\nâĸ ª\nwee ping\nanti semitism\nemploye e\nunearth ed\npin o\nnatali e\nbla d\nang ola\nlock heed\nin ian\nag r\nni ster\nim pala\nm ke\nfan atic\nâĺħ âĺħ\nðŁij ¸\nlu ch\nsimpli fied\ngall ery\neconom ic\ncy borg\ncon i\nsel ma\nin ception\nko ala\ndv ds\ncre sted\nm mor\nvisi ble\nn sd\nðŁĻĮ ðŁı½\nw under\nrefriger ator\nre opening\ne era\ncarou sel\nas p\nballi stic\nvictor y\nmo tive\ntre y\nsharapo va\nsi i\nmon ter\nint end\nwest chester\nsp e\ncy mb\nvi dal\nll ama\nuni v\nfin er\ncrafts manship\njazz fest\nb ch\nag gio\nn cc\nlamb da\ntranqu ility\ncis co\nba den\nso bbing\nof i\ngo ta\nru mored\nwar med\nore an\nac ton\nmar ci\ngh ani\nâľ ĵ\nas sorted\npembro ke\npen elope\nda f\nat ty\naim o\npretz el\ncarni val\nthan os\nko chi\nmer sal\nham radio\nar twit\ncas c\nguer rilla\nkush ner\nk app\nal ise\ntodd lers\nsteward ship\no tti\nter ri\ntem pe\nrest less\nvit o\nzay ed\nrsp b\npi on\nhi ppo\nhaw thorne\nin as\nam ily\nnut cracker\nlo p\nd ali\ntro pic\nðŁ¤ ł\nul o\njare dle\npy rene\npale o\nusa ir\nm ould\nit ated\ngene tically\nbiom ass\nðŁĩ³ðŁĩ ±\ndo dd\npractic ed\nmonarch s\nun manned\nm buhari\nam al\nphoto gra\nko ol\nbren don\nju ices\ncu re\nworld bank\npoin ters\nðŁĴ Ŀ\ntur f\nle ds\nbor ussia\nbapti sm\nwarwick shire\nmoun ts\ngay o\nbe gg\nco pied\nasi ans\nk g\nmoder nist\ngi d\nfront man\nconcentr ated\ny t\nsc avenger\niron ically\nadi c\nps n\nðŁ¥ ī\ncultur ally\nyu v\nmac arthur\nfertili zer\nbe withyou\nri gor\nmin ors\nz oning\nâĸ ł\nri r\nadole scent\nvin ny\nren g\nsand stone\ngu et\nwe sth\nple dged\nlac ed\nsp ide\nv ai\nty coon\nseiz ure\ndu p\nappalach ian\nro k\ncathol ics\nsey chel\nposse ss\nla ger\njo di\ncham p\nstra s\nd ina\ncent uri\ncal der\nblur ay\nðŁĩ¨ðŁĩ ³\nmo do\nan nette\nyoutu bers\nchap s\nang ling\nlabel ing\na qui\npk wy\nly le\nbi sexual\nlit ur\ndug out\nli bby\ngrey sanatomy\nsub stances\naugust us\nrall ying\nfi del\ning ue\näº º\nhallmark channel\ntooth brush\nm á\nadi rond\nag gi\nðŁĵį :\ncru sade\ntax ation\nk z\ni ver\ndou bling\nroom ie\nwa b\nen rolled\naz on\na ju\ngrand children\nas df\nðŁ¥ º\nmat ic\nough ton\nutili ze\nðŁĴ £\npon der\nrais in\ndys function\nco bain\nbutter nut\ne man\nsu red\ndri an\nand friends\nwith the\non omy\nheine ken\nbri dal\nleader ship\npyram ids\ndeutsch land\njo cel\nbo wel\ny qr\nhorse power\nbe acon\ning eni\ngra dient\nfer mented\nmo om\nthing y\npot assi\nwrist band\nbor d\nbo died\nðŁĺŃ ðŁĺį\nma pp\nka u\ncyber punk\nph ish\nloo king\nco ates\nap ur\nam ie\nuk labour\nat in\ng la\nadop table\nshel by\nv illi\nri ya\nm ingly\ncli mber\nbumble bee\nðŁĺ ¸\nc sd\nâĿ ¥\nhospit alized\nc ki\nhat er\nch r\nre tina\nit a\nfan base\nbeat rice\ngwy ne\ngo ss\nfo s\nfavor ited\nswachhb harat\nmal ade\nmon mouth\n\" [\nsi van\nsh hh\ncommand ing\nsains burys\nwee d\ng man\nss w\nrep tile\niv y\ntro pics\nroll ers\nover cast\nex position\nmasquer ade\nman crush\nwa ist\nspr inter\nsle et\nle vin\nj pg\n_ (\no pel\nexplo it\nap a\npo we\nwrec king\njong in\nor b\ner ick\nbo sco\npra ising\nber tr\nto wing\nin security\nku t\nresto cked\nrr p\nprescri bed\ntrafal gar\nper t\ng ases\napp rais\ng har\nmusic als\nâĸ¬ âĸ¬\nmc fad\nag ony\nconditi on\nequi p\nshi k\natra vel\nðŁĩ¿ ðŁĩ¦\nke h\nabduc tion\npe oria\nwil kins\ng ms\nas d\nev i\nðŁĴĹ ðŁĴĹðŁĴĹ\nu z\nmo c\nhalle lujah\nguad alu\nlou vre\ndra wing\ngo ve\nph ant\nfri e\nweb dev\nprogram mer\nz able\ngames com\nclari fy\nli th\nkin ky\nâĿ £\nlabour doorstep\nson ata\nju ris\nmai den\nvi adu\nbuch arest\nconditi oned\ncapit alist\nu de\nps b\nsp ca\nlul la\nfooth ills\nkay o\nbon d\nwom b\nroun der\nce sar\nbur sts\nap ra\nsw oon\nsab rin\nfra grant\ncle arer\nku brick\ncli max\njour no\nag le\nðŁı½ âĢįâĻĢï¸ı\npoo ch\nhal e\nsol it\nsal mon\norganis ms\nbron son\nart en\nhodg son\nalo ve\nvent ure\nbb i\nae a\nðŁIJ ¢\nld n\nd nr\no zone\nel las\nman ny\nazz ur\nun beat\ntru ffles\nth ong\nma ñ\nlas ers\nley e\ngettys burg\nback packs\nor is\nma ison\ncraw ling\nla bra\ncl ing\ndra gging\nste al\ndou bt\nde van\nck ers\nagent sof\nphoto bomb\nelon musk\nabo y\ndist ances\nstory line\nsp i\nnor than\neurope ans\nwh ale\nser pent\nðŁļ ²\nfi or\ntr it\nox o\nawar ding\nclass mate\nsu fc\nsmar test\nrich es\npr k\nbig foot\nar mb\nbi polar\ndw elling\nom ars\nk wan\ngri me\nm eng\nfreder ick\nnavar ro\nsorry notsorry\njaredle to\npa ve\nsl ack\nbarn sley\natt ar\nevic tion\naccumul ation\no ir\ncat chy\nwel ter\nvik as\nhas see\nnik ita\nmo yes\nmathe ws\nshi v\ngat wick\npro filing\ncompan ions\nmar rake\nan tics\nðŁĻĮðŁĻĮ ðŁĻĮ\nse se\nbo i\nbart lett\npoison ous\nab uses\nym m\nkam pala\nguggen heim\nimv kohli\ndol om\nbre e\nthro ttle\ngare th\nfitz patrick\nun ya\npar ad\nmar got\nj nr\nwe a\npotassi um\np nc\ndisgu ised\ncra sh\nren ergy\nill ic\ncoup led\nni els\nci ones\næĹ ¥\nim ent\ndespic able\nd ye\nwhat cha\nconne ctions\nparalym pics\ngaunt let\nwait rose\nsuici dal\nstar ship\nvap or\nst ou\nlaw maker\ncoo led\nsi mo\nthen o\noffro ad\nja den\nbas que\nvick y\nlu kaku\ncentr o\ntri sh\nstrate gist\nmedic ations\nhor st\nb fc\ngra il\nsharp ly\nad itya\ntom b\nkau fman\ntri pad\nsam ba\npastor al\nbrit ney\nsag an\nhill side\nmas ons\nsar a\nz one\nx u\nto tes\nrob bie\napp en\nmon tag\nder o\nshort film\ncharis matic\ntat ors\nki ba\nand ri\nal arming\nsplit ting\nic ar\nth ug\nscari est\nsylve ster\nan an\nu trecht\na difference\nme ade\nbu ster\nair strikes\ncu ffs\naccount ants\nðŁĺ¡ ðŁĺ¡\nnew t\nbo tt\nissu ing\ncl ancy\nwwen etwork\nkyu hyun\nrese mble\npajam as\nsin k\nkin ney\nsul ph\nor k\nli es\nla gh\nor ton\nra hul\nd sc\nwe will\nre am\ncollo qui\nshar ia\nhec tic\nsar casm\nland er\ntm z\nendor f\nro z\nham mered\nfri s\nw adi\npope francis\nhe it\nflash light\nun born\nop es\nhol iness\nðŁIJ ¦\nnach t\nim sa\ngr acing\nbj p\nver ts\nc sc\nhome owner\na que\nbigo try\nanni e\nbag h\nâĿ¤ï¸ı ðŁĺį\ncar i\nthom p\ndispo sable\ncardio logy\npat ented\nhh hhhh\nld r\nstephen son\ncro res\nfan ning\ncli mat\nðŁijį ðŁijįðŁijį\nðŁijį ðŁı¼\naer on\npiccad illy\nbank rupt\nsil via\nemplo y\ndon ny\ncommen ting\nscreen writer\nio ta\nce an\nanc ers\ntu an\nstreet wear\nठ¯\nsk ine\nesp a\nasi f\nos ce\nshe ppard\nmore cam\nbott le\nder s\norac le\ngoogle play\naver aged\nedmon ton\nsteph an\nsister hood\ncru sted\nstag gering\nmethodo logy\ncongress woman\nc abo\ntri ggers\nmil ky\ngli de\ntooth paste\nroom mates\nnu ff\ngu am\nsprink les\nalternati ve\nwat fordfc\nuof t\nhal ey\ncont acted\nbun dy\npro stitu\ngh ar\npre ston\non site\nhil ar\ng ts\nc att\nhamp stead\n? ?!\nðŁĩ§ ðŁĩ\nbbc qt\naless andro\nresi st\nma idan\nt ko\nshad ing\npin up\ngal lo\nsin u\nat ec\nfun k\nac lu\nstri des\nrhy me\nwet land\nbbc springwatch\nt ins\nwild card\nst our\nflamen co\npau la\nonto logy\ngang sta\nam ade\nãĤ «\nt bs\nskelet al\nrun ner\njard in\nharri er\nhun ted\nz hen\nbelievein film\nde mean\nau diti\nre start\nchon dri\nâĿ¤ï¸ı ðŁĴĻ\nmcla ren\nga b\nsh um\nau sa\nlewi sham\ny pg\nk jv\nfur nished\ndor o\nbon ded\nmor ty\nlat itude\n_ )\nlo va\nwater ways\nvin ai\nshor th\ndrun k\nc ay\nay ana\nkap lan\ncapp uccino\nspr o\nlife boat\nhas bro\nspol ice\ntor on\ndo ing\ndam n\nsh ree\nfoun tains\nent ation\nmar u\nboar der\nto pless\nj ada\nchan ning\nul ls\nen closure\ngib son\nfractu red\nbrit ton\nà ¶\nt ous\npor th\ndra f\ntra iling\nmar gate\neli fe\ndown ward\nlin n\ngla des\ngirl power\nak rish\nu ki\nron da\nts c\nappreci ationday\nvis ing\nlo om\nðŁį ³\nmex ican\nar gos\ny ya\njad ine\nsouth port\nd end\nsi sta\nrede em\nmen g\nbra xton\nantioxid ant\ns key\nmp g\nfin ding\nvibr ation\nce u\nkh art\ndi mini\ncl ine\nshel ly\nhin es\nī ï¸ı\nto pical\nno ver\nma xx\nprim itive\nillustr ate\nb ounds\ntren ton\njoin tly\nbreed ers\nu chi\nwakeup america\nb ada\nðŁĹ £ï¸ı\ngu acam\nsp heres\npere gr\nyouth ful\nlo lo\nbir min\nt ly\njeremy corbyn\ndefe cts\nco sm\na rent\nv aa\nbag els\nmedi ac\ncori ander\nic ago\ng haz\nab bas\nre model\nstruc turing\npu m\nout law\nad ani\nr bc\ngul ls\nn li\nconfu se\nðŁijĩ ðŁı¼\nvil a\nmcnam ara\ncorrec tions\nmug hal\nser i\nre gain\nss b\nlea ve\nhaha hah\ngran de\ndi stressed\nre chargeable\nho a\nhou sed\nsti l\nattribu ted\nopath ic\ndi ps\npri t\nhead phone\nconclu de\npil o\nhe t\nut sa\nnit in\nje m\nsni ppet\ntutor ing\nop er\nsun k\nen sla\ncha u\nac orn\nquinte ss\nran kin\naffili ated\nour lives\ncl int\nse ater\nisa ac\nba shing\nsme ar\nnur se\ndoo dling\n\" ;\nsa ku\natroc ities\nim am\ng fs\nviol ating\ncomm end\nbrad shaw\ner ville\nb illed\nb be\nthul hu\ni phones\nmoo se\ndi os\nre w\nme thane\nstrang ely\nwhis ky\nti ghtly\nspiel berg\nradi us\nnotic ing\nwi f\nig nati\ni fa\nap is\nw ali\nha itian\nbu shes\ny z\nv l\nex ited\nasse l\ntru ec\ndom en\nash er\nin king\nnewyear seve\nhend ricks\nbat i\nìĿ´ ì\nrich ter\nmon santo\ncon line\nagre at\nðŁ¤ ¯\nmaster pieces\nar n\nrough s\ncle ve\nse v\nfashi ons\nto ya\nsh ail\ncop eland\naqu ari\ndec als\nare you\ny aya\na str\nfon t\nml m\nar ca\npp or\npol lock\nxper ia\nconserv ation\nchain saw\nag gie\n?! ?!?\nsi le\nsh on\nìĹ IJ\nnote books\nmarque tte\nde us\nbb led\nspic er\nmc cabe\nnor wich\nmodi fication\nboo sted\nstru m\nsales man\nbang le\nnis san\nhez bollah\nbrea sts\na af\nanth us\nsk er\now ed\nher os\ngi fs\nfo sters\neat ers\ndu es\n_ /\nlymph oma\nsf am\nme gal\nafri di\nag ic\np amp\njeal ousy\nðŁijĮ ðŁı¼\ncalcul ate\nnapp ing\ng ale\nðŁ¦ Ħ\nlub bock\nassu med\nren ting\níĥ ľ\nsubur b\nãĤ ·\ntech nic\nu cla\nin front\ngar net\nster oids\nstri ving\nho war\nmo ver\nle ton\nbull do\nis in\nci ao\nsn z\nfore front\nd ams\nmid wife\nma wards\ncla pton\nwe in\nsubsi dies\nspr oud\nrother ham\nphan tom\nar ach\nspi el\nrac ket\nsel amat\nno on\nl bc\nenti ally\nðŁĴ ¸\nsil ve\nm oud\nkine tic\ny asi\nðŁİ ©\no ol\nmi ku\ni za\nfer a\nflo ren\nbarber shop\ngroo t\nz est\nne ars\nstan is\nz and\npolice man\njuris dic\nform ations\nappar atus\nsp d\narti fact\nto sc\nmotiv ating\nwomanc rush\nre dro\ndiagno stics\nra za\nout fitters\nel xn\ndod gy\nry n\nsh d\northo don\nol de\njay anti\nbal ances\nquic kest\ncan ton\nfriday reads\n! *\nna a\na ak\nðŁĶ ·\nbehavi ors\nrasp berries\nä »\npolit ical\ncam il\nå ľ\ndi k\nast ounding\nlie be\nnovel ty\ntur moil\nsul ly\nspring break\nhon ouring\ncc g\nðŁı Ĵ\nmy little\nky c\npro ms\nðŁķ Ĭ\nà ¨\nbi ge\nav ril\nðŁĩµðŁĩ °\nmari on\nas ants\nsur ya\noc tag\nluf than\nac ron\nfayette ville\nti que\nlove s\nen ca\nde kalb\nta ver\nde vote\naux iliary\njoh annes\ntread mill\nay an\nqu r\ndonald son\ncher yl\n\" ....\ns ven\nkir sty\ngun ners\nra dish\no ahu\nv sky\ni ble\ncon course\nb ps\nelo qu\nash ford\nte bow\nroblo x\nma da\ndri ving\nth day\nspro ject\nm ms\nband ed\n. !!\nlibr arians\nflan nel\nintoler ance\nher al\nç µ\nneme sis\nlist a\ntar ak\ncry pt\nstar plus\nvish nu\nsc ale\ncr is\n% ),\nj illian\nregg ae\npegas us\nol in\nip ment\nman ic\nl fc\ngodd ard\nite am\nparl our\nanch ors\nlee minho\ntalla hassee\nant it\nd ho\nkid ney\ny ash\nbatt led\naz ad\ngar is\nfaul kner\nsni ff\npapar azzi\ned m\nphy llis\ncon tested\naa ay\nse ca\nk ton\nvel ve\nrain ier\nfor um\ntam pab\nho sp\ntrac tors\nox fordshire\nno tion\nguang zhou\nðŁĺ ¯\nref ill\nwednesday motivation\nsli der\nmukher jee\npr att\nfon taine\nalph on\naf ar\nts i\npest icides\nfi ends\nmo cking\nbra w\ntran sat\ndo ses\nco res\nhom ophobia\ndocu menting\nzlat an\ncon doms\ns é\nsun set\nkun st\nton ga\nภª\nv ation\nsp ray\nchow der\nra ps\npalla dium\nnor wood\nmusic history\nhoo ker\nsi si\nosp rey\nph ys\nconce ded\nbob cat\nar mad\nze it\nÙ Ħ\nðŁĺģ ðŁĺģ\nmer idi\nðŁĩ· ðŁĩº\ncorn wall\n! ),\ntouch downs\nze it\nchal et\nmm m\nal che\ngor illa\nfo ss\nati ku\nlumin ous\nivan ka\nbe ek\nsta res\nsw iss\nâĿ¤âĿ¤ âĿ¤âĿ¤\nscru bs\nme ath\ngusta v\njo gging\nconfe tti\nas os\ners fc\nbreit bart\napplic able\nautho red\nya ho\nh in\ndisplac ement\nj v\nðŁĮ¹ ðŁĮ¹\not c\nnon profits\ndiec ast\ngu sto\ninte stin\nc ages\nme en\nlu kas\nmoon ey\nðŁĺ ·\nvery day\ntor ah\nis sion\nwa c\nlever aging\nish able\ncu se\nle wood\nmay an\nturn table\nju ice\ntru sty\ntu p\neti quette\nsupervis ors\nstu n\ngu zman\nconfe ren\nric o\nfe ast\nback ward\npol aris\nmic he\njo g\nh ing\nfield house\nvel ing\nsho cker\nesc ence\nठ¾\nvi be\nanasta sia\nmar ched\nkill ing\nĶ ë\nfe tt\nexop lan\n... (\nsnow day\nlo h\nir ani\nla khs\ndel a\npo caly\nboom ers\ndictat orship\nac er\ntur keys\nquarter final\nmuskete ers\nðŁĴĽ ðŁĴļ\nsf x\nmuseum week\nsc ala\nri sis\n( ðŁĵ·\nãĢ Ĥ\nz ies\nbo eh\nhu es\nlu sci\ndol a\nimpeach trump\nroo d\ndon caster\ntor re\nhero es\nfo yer\ntar i\nblur red\nke w\nfrank ly\ndro id\nap al\nÐ ¼\ny af\nbre t\npar agu\ncac ao\nðŁĻĮ ðŁı¾\nru e\nhead aches\nshaw ty\nchar ley\npal er\ngo wns\ncorrec tional\nðŁĺ© ðŁĺ©\nbreaking bad\nol ing\nda p\nendeav our\ncit adel\ntra d\nincumb ent\nmedit ate\nfoo ted\nðŁĴ µ\nshab bat\ndayof the\nwil lem\ngal way\nto red\nmarri age\nf illion\nsleeve less\naud itor\njin young\ninvin cible\nkad una\na and\nvolcan oes\nmon eti\nindie gogo\nbuccane ers\nðŁijī ðŁı½\nãĢ Ĥ\nlay ton\ncuck oo\nhu mber\nbuzz er\nÏ ī\nto re\nstra ins\nsto m\npa ine\ns we\ndu ff\nz ou\nsi mi\nli pp\nur n\nse agu\nðŁĶ ®\nsun dae\nhi c\nðŁĺ ¨\nbull pen\nu per\nflyo ver\nal dridge\nglo bes\nali es\nken zie\nge es\ny cle\nsp lin\nmag enta\nj ha\nbal u\ngh orn\nti pper\nwick er\ntaste of\ncon clave\nch ale\ninv asi\ncat er\ndio xide\nme gab\nwin n\nat p\ntransform ative\nnest led\nhi g\nbri dging\nlil ies\nchee red\nbad dest\nsc rolls\nreal is\ndipl o\nðŁĶ «\nconce ssion\nprefe rences\nexplo des\ner gon\nintroduc tory\nine au\nch af\nsom es\nland rover\nspir ation\nsex y\nsco recard\nillustr ates\nsoul mate\nwi en\ninter disciplinary\nfore casting\nent ities\nglu ed\nen lar\ncur t\npercep tions\nboot leg\nmi re\nasho k\nv az\nhor ne\ncal le\nac ulture\nther oy\nnight time\noc al\ncharacter design\nar mist\nðŁĺı ðŁĺı\nyah oo\nac eae\nto se\neven to\nsou t\nnay anth\nwh om\nv are\nri gging\ngen us\nhi ve\ncom mands\nsti e\nday a\nethan ol\nen f\nhi fi\nflu ence\ncle mson\nre invent\nthermom eter\nhumor ous\nemer ging\naci ón\nðŁĺĺ ðŁĺį\ns ity\nhaw ke\naccompan ying\nt ility\nðŁĺ ª\nre cess\nprotag onist\nl ery\ndun dal\nint l\nbritt any\nq bs\noff the\nmarri ages\nhow to\nviol ated\nadel aide\nwit t\nlanc er\npak v\nhu me\nst ade\nbra gging\nou tright\nad c\nsuper st\nreal time\ncu res\ngarden ers\nero ck\ndale jr\nver o\nbar tol\nmo ti\nmc fly\nv pn\nst ink\nover rated\nguer ra\ne tis\nath ome\ntwd family\nth ab\ntn x\nrafa el\nfamily travel\nx ley\nsat anic\nequ ations\nru dy\nwal dorf\nstan i\ntu be\nmeas les\nzimmer man\nobli gations\ni ously\nbow ser\ntrans former\nsho ppe\nshak en\ngh ouse\nto d\nke tball\nshare holder\nmar ca\nkp mg\nak an\ngiven chy\ncoast al\nau th\nroller coaster\nmar ches\ncoordin ate\ncine ma\napprentic es\npar lor\nmit o\nmen on\nconsider able\nbar re\nglo ss\nenh ances\njaz eera\nfal mouth\nthra sh\nstat en\nk zn\neng el\nsamanth ap\nflo ppy\nsal om\nðŁıĨ ðŁıĨ\nw ack\ndeliber ate\nosc ill\nherit ag\ndu sted\norni thology\npad dle\nfer ns\nbar un\ncl ans\nanticip ate\na ay\nmat ically\né ĩ\ntu mble\npost man\nunic ef\ntro tter\nop d\nleaf let\nge ist\ncease fire\nscre ws\ncre ation\nwal nuts\nlongh orns\nunder statement\nab b\nproxim ity\nna x\nun ity\nturn pike\norda ined\ndub step\nchak ra\nme ch\nlove her\nlook alike\ndonne in\nvir on\nÙ Ī\nbang ers\nvari ants\nout dated\nin ta\ncri sto\nsp elt\nfood and\nf on\nstefan i\nmargin al\nhu tton\nti ara\ntel ford\nqu en\nfair grounds\nque tta\nmikha il\nheal er\nv ball\nty re\nunder grad\ngl end\nhom ers\nscri bed\nmain tains\npo che\nmis sal\nmar ko\nu as\ná n\nsh p\ncon vey\npad re\nsab a\npu glia\nmadhu ri\npa xton\nchap lain\nn ago\nca si\n... !!!\nfli rt\nsal eh\nk are\ndi re\nstam ped\nextre me\nðŁĺĥ ðŁĺĥ\nho ppy\nguadalu pe\nadvant aged\neu char\np low\nun n\nmac qu\nport land\ncla sh\npe s\nlou bout\ny p\nkeep ing\narca dia\nfran kie\nfi u\nde th\nencyclo pedia\nsi ze\ninve sts\nðŁį ©\ngeo logical\nfran ç\ncon front\nðŁĺ ¥\nd ys\naf m\ntex an\ngraph ene\nrepost app\nac f\nur sula\ngaz a\ndd led\nfu m\nwsb tv\nm be\nfron tiers\nchrono graph\nke s\ninter faith\ntab oo\nspar ta\nwon do\nflori st\nem braces\nca w\nno el\narch ers\nðŁIJ ·\nroman o\nban an\nsh akers\nmelo dies\ngeo thermal\nse phora\nìļ °\nоР´\npro c\nhand shake\npan de\npopul ated\nslow down\nhor tons\nregistr ations\nun deni\nlan ts\npas sover\nthak ur\nli ef\nadhe sive\npe tal\nmicro scopy\nmemph is\nconfir ming\nair drop\nmesm er\nperce ived\nming le\nlifel ine\ngh j\nworcester shire\npas sions\nach er\nel lar\nah o\nfiren ze\nbar ang\nletter man\nhat field\nlu cha\nje ter\ne shop\nwilliam s\nhoro scope\npre de\neast bourne\ndur ga\ndi version\nal trin\nseis mic\npremi osm\nnar co\nti r\nori g\nor m\nland fall\nci ous\nlin do\nmax ine\nx ico\ntra y\nos wald\nc ba\nric otta\nn cr\nmar au\nภ²\ngladi ator\nch ery\nlun g\nu me\npo psic\nlon ging\ncan als\nta ya\ndecentr alized\nsho pp\npres sures\nmahar aj\neti had\nwal greens\nsucce ssion\nsign aling\nli g\nstaf fer\nnorth korea\ndef ying\nas ma\nde g\nperi meter\noak ville\nm sk\nbalti more\nrece ip\nde ple\nðŁĺŃ ðŁĺĤ\njambo ree\n> .<\nrsp b\npuni sher\nconsider ably\nin tothe\npari sian\nacceler ated\npolye ster\nlow es\nfr ying\nsauté ed\nmou ths\nseychel les\nra x\ngo dis\ndak ota\nhouse wives\nthe me\nmat inee\nblack bird\nye sung\npre fers\npelle gr\nin ated\ntrun ks\nstronger together\nre pet\nre pairing\nped als\ntoler ant\nher r\ndun ne\nindic ation\ndecat ur\nb tv\nexhibit ors\nik on\nfriday motivation\nbra gg\nlive tweet\nal ves\nwomens art\nforeig ners\nwal lets\nmin dy\nlan ey\nbb in\ntv miaw\nlif ter\ntar get\ntam e\ndr ou\nastro photography\nmp c\ng pu\nnord strom\nfric tion\nrun off\nlov able\nsp nfamily\next ingui\nbloo dy\nsch el\narti stry\nsw ish\nscar ce\nph ils\nmax im\npos sum\ncom promised\nsty li\nsc fc\nis sa\nbirmin gham\nsket ched\nangel ica\nordin ance\nje ts\nconqu er\nðŁĺ IJ\nonline shopping\ns ori\nreason ably\nnue stro\nar turo\nch l\nbenef ici\nspho to\nwel t\nni kk\nðŁ¤ ŀ\ndan ao\nfor mid\nas se\naf irst\nâľ Ĥ\ngil lette\nas sor\nan onym\nsel ca\nfe mi\nbear able\ny and\nar mory\ncre pe\nceltic fc\nbra vo\nin expensive\nde lec\nge cko\nnew market\nsnow flakes\nkab ir\ncon tra\ncan ning\nmor pho\ngar wal\nðŁĴĥ ðŁı»\nfight ing\nmu tation\nwoo dy\nju gg\ngr aces\npremiosm tvmiaw\nkenne dy\ngu p\nsa e\nop ha\noff spring\nfini sher\nbet ts\nspan ning\nmar j\nh one\nsh ing\ncontin ents\nsamanthap rabhu\nun related\nl acy\nexplo sions\nbenjam in\nsophi e\nno ting\nmicro soft\nas sen\na hoy\ni ker\nho fer\nmo e\nah madi\nyan n\nan ak\nma hi\nbe u\naha h\ncreep er\nbaahu bali\nam at\npri ory\nhaw keye\ndeloit te\nsko da\nprint making\nassemb ling\nmirac ulous\nno ch\nsw o\nleg a\noper ates\nborder lands\neli e\nstron gh\nrep tiles\npir ate\nun fold\n ¯\nqual comm\nun predictable\not r\nrose wood\ndirec tional\ncounsel ors\ncorn ell\nliber ated\nj ad\nir regular\nbulgar ian\nhigh ness\nvodaf one\nsw ild\nmini mize\ngra zie\n๠ĩ\nr stats\nstre ep\nome tric\nhumb le\nlu mp\nl ille\nb ü\nhome depot\ntripad visor\nki wan\na via\ner z\nex ico\ndu f\nblu men\nmi zing\nar ma\nin im\ncon stan\nsor a\nju al\nau n\ntw ell\ntren ches\nher a\nr k\npo plar\nrecipe oftheday\nll an\nbhu ban\nshort ages\ning don\nbridge water\nðŁIJ ĺ\nfortn ite\ncam den\nun cture\npro w\ncolon ies\nt ks\nn go\nb hm\nlive pd\nspl ace\nsli ke\nhappye aster\nter rence\nrevol ver\nj ed\nyy yy\noffice of\nm ts\nexist ential\nr ourke\nexplore bc\nsse d\npri est\nvix en\nsi ding\nk pa\na har\nju ic\nob struc\nforen sics\nuk mfg\ncancell ation\nwe ary\nab q\nele c\npri zed\ndeb ts\nme zz\nsalv atore\nm dc\ngre tte\nc gc\nth on\nsnow storm\nts ch\ncook ery\nå ¹\nwa xing\nn acional\nmur s\nra ve\ncap es\nger main\ndri pping\nsub mitting\nome lette\niter ation\naj es\nshim mer\nfu eling\nðŁĩ§ ðŁĩª\nli po\nbo bble\nun follow\nislam ist\nhi ber\ncat s\nagentsof shield\nsen si\n____ _\nster ia\ninst al\nausp icious\nhar row\nover land\nfemini sts\ninst ant\nchar iot\nblind ness\nsp ed\nsc arec\nnu it\nmini atures\nho seok\nglo ck\nfifa worldcup\ne te\ndis m\nwe iner\nex foli\near ts\nภĶ\nmy art\nman il\niss ant\nform a\nin cu\nbuffal ob\nin tim\nmc cul\nanj ali\npo po\nun doub\nhil a\nfun gal\nthank ful\nfu tur\nen dish\nren ds\nth ar\nshe ff\nring o\nnichol ls\nio wa\npo tom\ncl ams\nãģ Ħ\nacon f\nstadi ums\ndi mp\ndi k\nresiden ces\ndo v\ncaric ature\nseagu ll\nkl m\nconfe ss\nsla pped\ncele b\nturb ines\npp v\nnur ture\nel ab\n.... .#\ntu ff\nde press\nal far\namii bo\ndi spon\ne wing\nque er\nfriend s\nfor re\nâĺ ¼\nsw t\naqu arius\nhead liner\ncur d\nfi gs\no tters\nlove fl\nkare em\ngo vegan\nfri yay\nconsol ation\nat ri\nì§ Ħ\nâĺĿ ï¸ı\npoly ne\ngu ed\no ya\nla us\nintestin al\ncam illa\nscal p\npi r\nleed s\nhorri fying\nbore tum\ndand elion\nfer rer\nell ic\nas x\nso ren\nre loaded\nale ague\nnavig ator\nine tte\nadd ams\nal chemist\nak shay\ndystop ian\nawe c\nn aya\nal isa\nai led\nag or\navi ator\nali zer\nsmo bile\nfindyour park\ncop ying\nto ddy\nsh ti\nmon ger\ncal houn\nnap kin\nbreak up\ny atra\nse thu\nric hi\neras mus\nfer ry\nam ore\nprac tise\nbo bo\npower point\noo se\nli ffe\nchin a\nsh ka\nfad navis\ndu ane\nwar on\nfal se\nðŁļ Ĥ\nwa shes\ndisc ip\n==== ====\ng k\nab b\nstub born\nmedi eval\np ci\nðŁį ª\nmaril yn\nh yo\nman di\ncr i\nprede cess\ncontinu ation\nom usic\ns lat\nwh al\nmall ory\nbon n\nshen zhen\nca i\nâĺ ĥ\nsa fest\nfor wards\ndra wers\nbla sted\nsle e\nmor phe\nmb ta\ndumb ass\nÑĦоÑĤ о\nalhamdulil lah\nec lub\nal beit\nheal ey\nayurve da\nadverti sed\ncro cs\nitt les\nbry son\nbe i\nnj pw\nhonore e\nfu sed\nðŁĶ ĺ\nmul tin\nn aga\nde parts\nko p\nkin o\njhar khand\ned na\nax le\nmil ton\nsupremac ist\nmarrake ch\ndomin ic\ntran script\n] [#\n: ).\nwo c\nsur rounds\no gil\nleaf lets\nco well\nwhe w\ntru de\nproli fer\nsucce s\nsports man\ncon dom\npo che\nk up\nimprison ment\n{ }\nscram bled\nå Ľ\nka ine\ncell phone\nmetam or\ncon i\nremn ants\nee z\ndown pour\nafterno on\nexerc ising\nber ser\narchitec ture\nwick low\nm ns\nis p\nbo c\nn iss\nmn wild\nstu mble\nr si\nlu ffy\nsil en\ndd ad\nbul lies\nhaw ker\nbb cc\nscu ba\ne pp\nque ts\nfor aging\npal let\nha di\ncinemato grapher\ncat chers\nto aster\nk hi\nlite coin\nkid lit\namher st\nmaur icio\nip ad\nmar malade\nfe y\ndon nelly\ng to\nest as\ncere bral\nant grasso\nzz led\nvir gil\nswa pped\nðŁĺħ ðŁĺħ\nno dapl\ngreate st\nnhl bruins\nfra ser\nb mo\nane w\n. âĿ¤ï¸ı\nse gregation\nremark ably\nmccor mick\nlo gger\ner as\ncontrac ting\nâłĢ âłĢ\nyor ks\nuku lele\ntouch screen\nde cked\nben n\nsouth wark\nra vin\nnu mis\nðŁ¤ Ļ\nru t\ngre co\neth ic\nred neck\nar r\nt cs\nih ri\nðŁĩ« ðŁĩ·\nl k\ninher ited\nzy k\nviadu ct\nmarty red\nhi gu\nss n\nbe in\nstreet style\nfer gie\nbank of\næĹ ¥\nstake holder\nexempl ary\ncre ss\ness a\nero tica\nintre pid\ngom es\nbra un\nbethan y\nbang tan\npulmon ary\nm illing\ndoctor ate\ntrump russia\nठ°\ns ani\nbl att\npla u\ndepri ved\nt le\nful ly\nbour n\nst ak\nlufthan sa\nkio sk\nfar oo\ndef y\nbad an\nðŁĺĺ âĿ¤ï¸ı\nrit z\ntri sha\nran ds\nmiddle sex\narab s\npro j\nsport scenter\nrepe ats\niv f\nbleed blue\nas sure\no bs\nterrit orial\nele n\nbever ley\nann ah\nâĿ¤ï¸ıâĿ¤ï¸ı âĿ¤ï¸ıâĿ¤ï¸ı\nz l\nfor good\nscience fiction\ngla u\nson ya\npri th\nst weets\nmix ers\nmari o\nant elope\nwriting community\nwent z\nden ham\nbe di\nsf o\nharley davidson\nlook book\nimmuno therapy\nor phe\nes ville\ned ged\ntas k\nsb ball\ncorro sion\nkilom eters\nco sting\nplay back\nke ke\ndi visi\nu ter\nre location\nyel led\npen g\nup beat\nser ve\nâļ ł\nhal en\nstir ring\nreh man\nen v\nschu macher\nfrag ment\nalkal ine\nsb k\nresil i\nshare point\nrol lover\ntra sh\ncounter part\nâĻ «\nob itu\nà ½\nãĤ ¹\nmul berry\nðŁİ Ĩ\nauton omy\nspra ying\nnat l\nlove you\nfran ki\nnu k\nesc ar\ncan teen\nali baba\nde plor\nmole cule\npu d\nfort night\nblon die\nsp hin\nportra yal\nta che\nbu te\nconsi sting\nfreep alestine\nc sp\nim mort\nd ns\nðŁĴ¥ ðŁĴ¥\ntour de\ncoo king\narchi val\nga thers\nbit t\nb anc\npre mature\nsnow ball\npoetry day\nlou dly\nfug itive\ned ay\nem ra\nðŁĩ¸ ðŁĩª\nsci en\nnode js\njur gen\nje ong\nband ana\nun is\nfox sports\nv andy\npro visions\nwee p\ntu k\ni ko\nh oun\nzig gy\nz r\nfil let\nbat a\ntin k\ncon e\nwe want\nk ilo\nhor ace\nsl t\nsc t\nstay tuned\nvictor ia\numb ria\natt acker\ningham shire\nfright ening\nno ir\nfr at\ncon tempt\nlia ison\nho i\nbr ink\ntr ill\nni agar\nkick ass\ndun das\nnot my\nrho de\nbu mble\nno xi\nfa g\nspec tators\nmancrush monday\njin ping\ndistr act\ndais y\nwal den\nportra it\nar thistory\nvol tron\nev el\nis c\nac m\nr ite\nna o\nde ported\nswe ats\nru fus\nlo bo\nlabor day\ngam o\nihri thik\nbl it\nabdomin al\nãħ¤ãħ¤ ãħ¤ãħ¤\ni it\ne q\nbu sy\nallu arjun\nun disclosed\nde ton\npro create\nki l\nðŁİĤ ðŁİĤ\nmitch ell\nki i\ninherit ance\nal p\njo burg\npat rolling\ncompul sory\nun signed\nni am\nl ga\neshop suk\ntr illi\nma w\nappreci ating\nrock ab\nmañ ana\nan tal\nmal vern\nroy o\ngrand prix\nsut ton\ngo ftheday\ndig i\nãħĭãħĭ ãħĭãħĭ\nt les\nvaran asi\nerec ted\ndiscip les\ncont act\nðŁĺ µ\nli d\n⬠ĩ\nscen tre\nradi ator\ning tips\ntrans itions\nthursday motivation\nchem ical\nsepar ati\nsal is\nmi m\ngeo graphical\nbook fest\n/ .\nâľ ĭ\nv ae\ncur rie\nag garwal\nacceler ation\nthe ses\nlg m\nu mass\npro portions\nnat a\nani ans\nku ch\nbe acons\nap r\n@ #\nðŁĴª ðŁı¾\nnu ke\nsher aton\nki o\nma kati\npolit ico\nmor ale\nì Ļ\neconom ically\ngg ly\nss en\npa stries\nintern ships\nvic ente\nfanta ken\naveng ers\naccu se\nslee pover\nindic ated\nthe dream\nster one\nren ders\nfro st\nou i\ngre gg\nd ore\n⾨ ⾨⾨\npu gs\nsat y\nnu mb\nhems worth\ntam i\nla ssic\nschi ff\nigle sias\nag awa\n] \"\nre shi\ngame stop\ndivor ced\ntheat er\nclau di\nun conventional\nprophe ts\nac in\ntwel f\ntow ering\nt ml\nsc lerosis\nk wan\nge ts\ndistur b\nna ira\nener g\npir acy\npru itt\nnoti fied\nhen na\nbra m\nground water\nbl s\nopti mis\n$ )\nluci e\nbiz hour\nfang irling\ngr ills\nor l\nver se\nc ina\nlaw less\nartistson twitter\ntele vised\nmarshmal lows\nradio head\nbar r\nm fc\nbre vi\nmmor pg\ng aya\nâĸ «\nsub titles\nj t\ndisney land\nto bago\nnh m\ngroo ve\nfi awec\n\" /\nba o\nscra bble\nom ni\nff l\num c\nsi mba\nali er\nter rell\nplu me\nmi di\ndig nit\nco c\nbru t\nad ata\nalche my\nd sm\nðŁĺĨ ðŁĺĨ\nwin try\nspa res\ncu er\nconclu sions\nto ys\nod or\nfl ann\ngar vey\nscrip tions\ninspec tions\ncat ap\nang lo\nst louis\nheim er\nat ay\ntr ich\nen yc\nchil ds\nvent il\nmont p\nguiller mo\ncircu lare\nz ell\nmode led\ncraf tsman\nal ina\nstimul ation\ncashe w\nju das\nbest of\nto ire\nsusp ends\nscol lege\nreal ising\nby tes\nbloo ds\nas si\nðŁĴ ¿\no hs\nðŁį ĭ\nscallo p\nठµ\ngi fting\ncamo gie\nwil kes\no zzy\nðŁ¤ ¤\nver onic\nsav oy\ndeme tri\nbaby girl\nðŁĺį ðŁĺŃ\nso x\ncly de\ninduc tee\ncount down\nself care\nठľ\nvi ka\ntor re\nphd chat\npe ars\naw h\nsuff rage\nle sn\nadmir ation\nmp p\nshark week\nschul z\nsantor ini\nclo ver\n( *\nstras bourg\nex iting\nso yu\nfinger print\nche a\nãĢ ľ\nvin dic\nsong writers\nso a\nprou der\nnam a\n= ))\nsimple st\ndelici ously\ngil les\nu q\nmn wx\nep p\nsh un\nken nel\nfall on\nðŁIJ £\nsin d\ntra gically\nout es\nmodern ism\nco ke\ngy n\nspi on\nâĺ¹ ï¸ı\nle am\ncompress or\napolog ise\ntwent yon\nfan atics\nâĻ »\nsco tsman\nsa wa\nko u\nas er\nภļ\nwelter weight\nphen om\ntwick enham\nstri a\np out\nka z\ngi am\ncd p\nho y\nemplo y\nred mond\nภĦà¸\nsm ere\ntrance family\nproto cols\npie ce\nlu iz\niter acy\ncarl s\nunited states\nhar med\nphd life\nch aw\nfoot prints\nl é\ncho ker\nz ana\nsli pper\neric sson\ninsul ting\narticho ke\nadvis ing\nacquis itions\nop or\nmut ations\nre ar\nॠģ\npod cast\nwi ther\nkun g\níĺ ¸\nwin slow\ndi apers\nðŁĵ¸ @\nec ker\ncol lar\nhu ey\ngi ro\nmono gram\nkas ich\nsi veness\nmalay si\narom atic\ngre s\ngali leo\nu ji\nrob b\ndr m\nnone theless\nas a\n: >\nlo a\nl np\nat work\nag t\nlaksh mi\npipel ines\nid al\nstre l\nre all\nchain z\nstone wall\nsan sk\nðŁı ´\npied mont\nhoste ss\nci u\nt é\nanaly ses\nwil helm\nscott y\nrw by\nmosqu it\nuse mb\nqu ins\nðŁij İ\ntu cker\ns conf\nspeci fications\npsychi atry\nbroo kes\ns ils\nol af\nde to\nco di\ncli p\nfil th\nwomancrush wednesday\ngo to\nang erous\nbe ale\nw tc\npaneli st\nne x\nlar sen\nemili o\ntab leau\nh itters\nconce ived\nameric ani\nor tega\nmar di\nÑ ĥ\npain tball\nthir sty\nnew yorker\netis ation\ngo ss\nwe aker\nu gh\ntro ll\nhar ga\ndu al\nght ning\nat ine\nðŁĺİ ðŁĺİðŁĺİ\ncook out\npyrene es\npo ss\nauthent ication\nsports wear\nyun ho\nkir o\narchi pel\nshen ko\nren der\nnov ation\ndivin ity\nðŁij £\nsu fi\nhumb ling\nge opol\ndevote es\nwait ress\ntr ough\npy ro\ni ba\nbl ing\ngra f\nepilo ts\nbt r\nof tball\nbas king\ndomin os\nso om\nr ath\nsher yl\nqu el\nastronom ical\nwel d\ntrack list\nsig nee\nslee pless\ncom man\nch ron\nsumm on\npure michigan\ncri spr\nsli p\nla gi\nra q\num u\nthal ap\nchar med\nscru mp\nquad copter\nski p\npeter sen\nmun i\nðŁĮ ¾\nmon aghan\ntra ys\nick ed\ncanad aday\nte gr\nï¿ ½\nhot ness\nheavy metal\nab ar\ngop debate\naz ul\nspider man\nsun flowers\nľ ë\nweb comics\nbar d\nÐ ²\nnichol as\nslu sh\nram an\nmark ham\nffici al\nff ler\níĬ ¸\nple ss\nanush ka\nto to\nsk aters\npro wrestling\ncompet es\nay ala\nmyster y\nthr ills\nmp g\nindepend ently\ny ul\nimper ative\nformid able\ntire less\nst acking\nton gues\nmal tese\npot ts\nmat ti\nchar ting\nchill out\nsuper nova\nome o\nsky sports\nnu tty\nðŁĹĵ ï¸ı\nro han\ninsp ired\nconcier ge\nser ra\nma kk\ngal at\nchi pp\nye v\nì £\nreim bur\nop ul\nkimber ley\ni eee\nbre men\nch itec\nor in\nnak u\nbon kers\nfoo ty\nemer gence\nðŁĨ ĺ\nsti p\nserge i\nzo ey\nai me\nwou ld\ndy es\ndestin y\nvinai grette\ndri er\ncirculare conomy\nan archi\nss r\nsch el\ncin er\ngro om\ndetermin ing\ngar min\ncal ais\nincarcer ation\nbu kit\nno i\nchelms ford\nmckin ley\nchi pped\nbelong ed\ntu mors\nstr oud\nmi i\ninfluen za\nwwen xt\ntun dra\ntele communications\ncat sofinstagram\nt ages\nbeat ty\no du\nml kday\noo per\ndang le\nak ley\ncru mb\nanti gua\nti mbers\nrou hani\nðŁĴª ðŁĴªðŁĴª\nha fi\n... !!\nw cs\ncoo p\nsn c\nlit res\nãĢ Ĭ\nha z\nco z\nk ant\ngreen field\ncur ti\ny ale\nflye agles\nwhat soever\nwor thing\nrou lette\nflyeagles fly\nun da\na inted\nstand ing\nlusci ous\nh pc\neffic acy\nash land\nme ghan\nky wx\nn pr\nbath tub\nac os\nh ani\nmar cor\nman tis\nda isi\nbo ba\nab bie\nmu til\nvi al\nspy der\npo z\ng ti\nel fie\nnigh tw\nmetro id\nanton i\nmad die\ndh ry\ndar lings\nten ds\ntaek wondo\natlan ta\nme ow\nchlo e\nãĥ İ\nym es\nsiber ia\nk con\ngu es\nmar iner\nfac il\nazz le\n[ ...\nhan nover\nbav aria\nvir go\nte uk\nu sps\n) #\nwall a\nsam pson\nneed less\nver bally\nhay ley\nbow led\npi us\nlam pard\nham string\nvol vo\nroad safety\ncho king\nsor bet\na hem\nhealthy food\nbrai ded\nhorticul ture\ncr ative\nche ek\nad do\nthe force\nko ko\nschiz oph\nj ie\nw ada\ntwentyon epilots\nh bcu\npro ton\npau ls\nlou isa\nlat am\nkyr gy\ncom pac\nsd k\nsap i\n?? ?\nliber alism\nep silon\nai den\nw usa\nspra yed\nbaske tball\nkim ono\nblue wave\nali as\në§ Ī\nmug shot\nce c\ndo gre\nad ora\nðŁĵ· @\nkra kow\nintrigu ed\nexhau sting\nastron omer\nven ison\nlady bug\nci v\nbra e\nus m\nbri be\nacup uncture\npembro ke\nke ating\nchi e\ny ad\nt si\nsm i\nsee ding\ngate shead\nlis boa\ngy p\ncanv ass\nðŁĶ´ âļªï¸ı\nop i\nni r\nsoci etal\nly te\nati es\nc sm\nar tery\nal in\naka poor\nabstr acts\nâĢ¦ âĢ¦\nteen wolf\nne we\ntravel gram\nsentim ental\nper ched\nhan del\nho ek\nf ay\ncoordin ating\nanim ate\nman ian\neffor t\njer ky\nf ck\nadri enne\nma bly\ntra ding\nmy el\nspi ro\nsol a\nstor ing\nover drive\nmonday morning\ndream team\npul se\nbon di\nber nie\npgat our\ntri poli\nson am\nplat t\nâļ ¡\nag roup\nîIJ Ĵ\ninv ading\nv cu\nk ell\nñ os\nun dead\npod casting\nmercede sam\nmana fort\ncor tex\nque so\nimpecc able\npal mer\nwil doz\nsport sc\nguacam ole\ndispen ser\ncate gori\nstun ts\nper il\ninvit ations\ndune din\nxi e\nachi eves\nsaf er\npre ds\nph an\nknuck les\nk ak\nigno res\nlovemy job\naru ba\nound ation\ndatac enter\nco vert\ngr ing\ncou ple\nا ر\nvol i\nmc cle\narti sans\nlu do\nkal am\narom a\nunder taker\nhu la\nwiz kid\ngu mb\ngod frey\nbakers field\nker n\nengine er\ncar ve\npal in\nguaran tees\npe bbles\nb ays\nzi eg\nfin k\nâ¬ĩï¸ı â¬ĩï¸ı\ndown pours\nro chelle\nrasp berry\nðŁĺ ®\ngra phies\nstom p\ncaf es\nari zed\nutt ar\ncal vary\ndri e\ncrusad er\nbus an\ntux edo\nsi u\nseam us\ncul tured\nblan chard\ntown house\nge red\nbutter milk\nflu ctu\nroger federer\nhel i\nðŁ¦ ĥ\nu ous\nram esh\nmu ppets\nemail marketing\nye ss\nbr ice\nri zio\npel o\ndonnein arte\nu rable\ninve stin\nbump ing\nraji v\nsav a\nthro wer\nfore x\no hhhh\nth rust\npull man\nr fid\nsep sis\nle ed\nfri ght\nroun ding\nne b\nph ins\nai sha\nutili zing\nsqu ats\ngold smith\nj ic\nbo ks\nvau s\ni po\nexclu sion\ntari ff\npo kes\nmin al\nland s\nen force\nwashington dc\nor char\ng x\nmar ys\ney our\naussi e\nbak ers\nun popular\nlatin os\nlar ge\npu tnam\nbol o\nwa de\npel o\ndi zz\nob struction\nfla ppy\nweare the\ndepend ence\npajam a\ne te\ny ann\ne wan\ndisc la\na ay\nkar ina\ne ic\nan trim\nw soc\nneg atively\nkai do\nfotogra fia\ndh ru\ncolo ssal\nmcle od\nk wang\nmani pu\nex hilar\nus atoday\nsummer slam\nco les\ntapro om\nunbeat able\nde ma\ntic ks\nk ling\nfil s\ncampaig ners\nภķ\nbrew ster\naudu bon\nqu ay\nch s\nki gali\nd ler\nstrength ens\nsom al\nsign ingday\ngol ds\npig ment\norche stral\ng q\nlin kin\nðŁı ĩ\nta w\nalgar ve\nho v\near le\ngold fish\nam ig\nex er\nben in\ndru id\nðŁIJ ¸\nshe m\nquat tro\nmer cen\nmen te\nincorpor ating\nbon anza\nstate fair\nen de\nconcep tions\ne es\nâĻ¥ï¸ı âĻ¥ï¸ı\nd son\nfire arm\norb ital\nwe h\nmulti p\nfo b\nrequi em\np light\nthou se\nsa id\noc re\nremem brance\nn old\nchi pping\nbe v\ner t\nca thy\nsy m\nri ggs\nm ley\ndialo gues\nsl ender\nhow l\ngau teng\nwd w\nto bi\nsmo kes\nim plo\nb pm\nad n\nmom basa\ncap sul\nbloom field\nartic ul\ncle o\ngoog led\nflu ffy\nl ard\nen zyme\nve sti\nibra hi\nfl ame\ne mea\nout ages\ndispro por\nble ak\nan sel\nick er\nst louis\nstock market\ngood friday\nsau lt\nstal led\npro m\nep som\nb é\nthe se\nsau ces\nme w\nlit fest\npre d\nre u\nkar ak\nsi enna\nell in\nbio technology\nï¸ıâĥ£ -\ntac tic\nsa in\npor k\nmon za\nka j\nlu sh\ncompart ment\nchang ing\nshraddha kapoor\nfo al\nar tem\ncu ando\ncan ola\nori ente\nme sse\nd ited\nbr c\nbox er\nbbc two\ns st\nment day\nem ing\nde wey\nkof i\nâŀĸâŀĸ âŀĸâŀĸ\nreali zation\nsmo l\ntw ood\nsan je\nflag staff\nber wick\ncor set\ncan ary\nwhistle blower\net ched\ncom posing\nsquee zed\nbow er\nauto desk\nne h\nmathi eu\nba ja\nÅ Ĥ\nhy dra\nda im\nam eri\ninsi sted\nmer lot\ngar ros\nheart news\ngaine sville\ncut ler\nbo de\nðŁĺī ðŁĺī\nlew es\nscoun try\ng sa\nus u\ncc m\ngod awgs\nphara oh\ncra e\nmor ley\nhyp noti\nf ades\nneur ons\nfu zz\ning co\nhigh landers\nstar k\nvig ne\npac kets\namar illo\nreu ben\ninsul ts\nbas ic\nvec tor\nn me\nac ruz\ntro s\ntransm itter\nðŁĺ ŀ\ninterpre t\nðŁĺ ²\npre quel\nmc gowan\ndis semin\nðŁĴĺ ðŁĴĺ\nmascul inity\nindie gamedev\nali ve\nte t\npe tal\nema iled\nar med\nko o\nhe er\nba ird\nsuper junior\nmetro polis\ndelav in\ndecl ines\nstit utes\nÛ ģ\np tbo\ng lan\ncho res\ne aling\nchri ssy\nste mc\nvi an\nassassin ated\npron ounce\nilleg als\ndiscover y\ncav ill\nfri fotos\nf al\nso i\nsabot age\nt int\np dc\nðŁİīðŁİ Ī\nãĤ Ĭãģ\nji o\nendeav or\nin sig\ncommit tees\nshe arer\nme tz\nmar rying\nh dd\ng by\nfre t\ntri sh\npu l\nscrip ted\nsa ki\nl w\nke ye\nshim i\nnan aimo\nca h\nà «\ntem pered\nici an\ndu gg\ndish washer\nair field\ns rugby\ngr inch\ny st\nr ms\nmahat ma\nlan kan\ndisc ar\ndige stion\nno des\nl ls\nom ic\ngu tter\ntis garh\nfeder ico\nelection day\nbo he\nmaster card\nfire ball\nâľ Ķï¸ı\noy ster\np ong\ndo k\nen route\nm vc\nbeat the\nali stair\nshu b\nsh aming\ncherno byl\nghi bli\nthe s\npin ion\nd bs\nsal ts\nic tion\nepi ph\nnc pol\nin convenience\nwhit ley\ninspec ting\nwood ley\nwi ener\nskil let\nno les\nm ca\nh ina\na sha\nwilling ness\nwell ness\ntam ed\nshow time\ndis advantaged\nber nat\nus n\nmission aries\ncoun selling\narrog ant\nquant itative\nleg alization\nho dge\nenergye fficiency\ncameron dallas\npos sessions\np bb\nharris burg\nv g\nhindu ism\nhappy thanksgiving\nfi b\nre acting\ntweeta picture\npol iti\nmu ppet\nhur rah\npac e\ncoast guard\nguar ded\nas am\npar ry\nfore very\nx q\noom f\nke anu\nj ind\nri st\ncustomer service\nsac red\nðŁĺ º\nton er\noccur rence\nmat u\nval dez\nred d\nis ak\npower rangers\npe asant\nraj ini\nabra ham\ne mil\ncar do\ntr il\nhair styles\nobsole te\nsam pler\ndirec tive\ndelavin kisses\nver ton\nglo s\nsp ay\npaler mo\ncom ets\nman ziel\nchicag of\nski pped\npic torial\nh ant\nb mi\na ol\nre opens\npad dling\ndevo s\nfra ud\nbas eline\nque ues\nsp ired\nsn are\neu ve\ndescri ptions\ndaisi es\nca ching\ngall eria\ntri mmed\nstin o\nrecy cla\nic ular\nbir ken\nraw lings\nfli x\nchic as\nb gt\nlik eli\nargy ll\nthel ove\nga ston\nbl anca\nha k\nf one\nsailor moon\nh aci\nima c\nfl yn\nde can\nbel les\nap ic\nzo g\ntaun ton\ncon stance\nlasag na\nker nel\nin ka\nhar bor\ncollec tively\ncalcul ated\nav ille\nshil pa\npur du\ngi mm\nfun er\na est\npembroke shire\nnighting ale\nn unes\nhyper tension\nhu bert\nsli ders\ninfer tility\ncomm ended\ntransat lantic\nmetr ical\n!! @\nÅ Ł\nss g\nbac ca\ninver ted\nfun factfriday\nit ans\nalbu m\nacqu ainted\nri er\nwhel an\nsar ab\nmu e\nsnoo ze\npi ff\nagre eing\nsp itting\njer maine\nn ye\nâľı ï¸ı\nam bush\nze ph\ncon greg\nunivers ity\ns app\nwann abe\npat rice\nib d\ndo glo\nfri dges\nsun d\nking ston\nar gon\nkam en\nhardro ck\nds ley\ndo lores\nì °\nota ku\npi ping\nbe having\nâŃIJï¸ıâŃIJï¸ı âŃIJï¸ı\nblue bird\nan sari\nteapo t\nfire work\ncro p\nlog ans\nty ped\nthick ness\nig ers\nc fp\ndys functional\ncontra sting\net ty\naston martin\ntx st\ndra grace\nat tributes\nmarath on\nmanu scripts\njohn stone\nðŁĺ± ðŁĺ±\nbo er\nay u\naru gula\npoo rest\ncon du\nassu mption\nanag h\nno h\ndelav in\nsit ter\ng ö\nmor ow\nkick start\ncom i\ngl acial\nghe ad\nba in\nker shaw\nen dof\nfre ud\nom at\ni af\nhu g\nsign up\neach other\ndefin ite\ntu bing\nshak ira\nðŁijı ðŁı½\nuu uu\nsw in\nsham bles\nol as\nsk ell\nbrit ain\nkn w\nclu tter\nom y\nj ens\nhang ed\ncity scape\nscra ps\nun locking\ndead liest\ner no\nbreast cancer\na it\ninspec t\nfu ri\nðŁĴ Į\nku d\nju le\nor ah\nmi ds\nm dt\nbur gring\nr attle\npu sa\nstal k\ncle ans\niss ance\nz ek\nworth it\nnam eis\nmusko ka\ncouncil man\nurban art\nbar rac\nun solved\ntu l\ng ita\nwhite board\nsoy beans\nem ent\ncont i\nsaturday motivation\nconveni ently\ndoc king\nt ado\nâı ©\nsp ino\npuppy love\npo f\nfabric ated\nrobb ers\nadop ts\nti fied\nkk r\nindulg ence\nnotic eable\nmacqu arie\nchap el\nsensu al\nki ko\nmelan oma\nlore tta\nli ance\nab en\nsp lus\nga al\nac ele\nlib dems\ncompar isons\nðŁĮ µ\nrhy thms\nmer y\nen capsul\nnap ier\nðŁijĮ ðŁijĮðŁijĮ\nðŁij IJ\nplat z\nfre sno\nre formed\nran bir\nel it\nthe best\nbhu shan\nvin nie\nimpro vised\ns ittin\nre created\ne ba\nec ker\nac rob\npon te\ncor d\ngi ddy\neur usd\nfe ver\nintu ition\ngar i\ndum mies\nbud weiser\namend ments\nte tra\nsch nit\nay as\nmar ys\nci st\nk ani\nker mit\nðŁĺ±ðŁĺ± ðŁĺ±\ntin ker\nstrol ling\ndi visional\nniger i\nomin ous\nmenstru al\nkar ab\nk hy\nbw fc\npan handle\nl illi\nwell er\nstra pped\nson the\ntransfer ring\nethe real\nsne aks\nru dol\ngab les\njac king\ncin code\nfor tune\ncanadi ens\ncon for\nab normal\nfrank lin\ntit a\nmu la\npersi st\ncu ties\nki el\nðŁĩ± ðŁĩ\nher mann\naw k\nfi asco\nko to\nwe ta\nhi ker\nbudd y\npreven tive\nmcgra w\ngame boy\nforsy th\ntop shop\nsi ob\nsad h\nin tram\nfollow art\nso aps\ndragon ball\nou x\nmorri son\n๠ĥ\nlu bric\nadul thood\nmorri sons\nâļ łï¸ı\nher mo\nta ka\nstall one\nmis use\nteam gb\nra gha\ncon fined\nat y\nhom ophobic\nnw o\nsky news\nho ya\nac rosse\nwi iu\npur ée\njed dah\nðŁ¤ §\nadvis ers\nph ine\nan is\nscrump tious\në° ķ\nc ke\nvin y\nter m\ns dc\no do\nhome school\nvas c\nleop ards\ndebor ah\nillic it\ncur ran\nas roma\nnau ght\nmar ig\nbrand i\nem p\nðŁĺį ðŁijĮ\nî Į\nsu spend\nlu z\niniti ation\nsch aft\njensen ackles\ncraw ler\npost doc\ndes ks\ntrail blazer\nden omin\ntri x\nno ise\npo et\n± ï¸ı\ns mug\nvol atile\nproof s\npharmac ist\nsardin ia\nmash able\nkim chi\nco ed\nschal ke\ndoo dled\nc sw\nsh ur\nro x\ndo k\nchris brown\nmathemat ician\nab ound\nang elic\nrock ford\nd ole\nyor kers\nms n\ng man\nxavi er\nbor rowing\nmark ings\nlongh orn\nk ja\ndiver ted\nmm it\neuph oria\nay yy\nte a\npa h\nck i\nun cut\nli ven\nky ung\nfan art\nmer ing\nred ding\namo vie\ngri di\nc thulhu\nschol arly\nju dah\nth bewithyou\neu calyp\nðŁIJ ķ\nhert fordshire\ncour troom\nby u\nauc tioned\nple ase\nmar cia\nê° ĵ\nsucce eded\nel as\narvin d\nt lot\nsaig on\nre tt\nra kesh\nfd ny\nas en\nse bring\ngladi ators\nyou know\nv lad\ngol a\npar ap\nÑĢ и\nsab cnews\none team\noh l\nsun e\nri j\ncd c\nstar gate\nrun down\nplat o\nph c\nchat ter\nra viol\nmn f\nmand ala\nli et\nภķ\nmari a\nhun gover\nconsoli dation\nfer rell\ntradition al\nilove art\ngal ap\nðŁı Į\nque zon\nespa ña\nðŁĩ¨ðŁĩ Ń\nho bby\nsteam boat\nmali gn\nguil lau\npro hi\nits me\níĥ Ģ\nin scription\nal z\nmari an\nk ade\nmm on\nadju sting\nne sts\nintern ally\nci r\nvik ram\nmal ala\nk ph\nfel icia\nthe real\ncap tivity\nat is\nmarcor ubio\nkale ido\nche v\nmano j\nle more\ngent ri\nvi ps\ntro pe\n\" âĢĶ\npair ings\nmal nutrition\nfr ay\ndesig nation\nbrun omars\naz e\ntor rential\npan zer\nga il\nunder the\nthe ological\nschizoph re\ndazz le\nfreder ic\nmo par\nad illa\nso ggy\nra un\nmedi ocre\ncolo rec\ni fe\np inst\nblu ef\n ²\nworld water\ngir oud\nclar inet\nad olf\ntar antino\nreceip ts\nassu mp\nðŁij Ł\ncoffe es\nâľĬ ðŁı¾\ndu plex\ns of\nr x\nlin o\ntimber wolves\npan dit\nmo tm\ne ga\nay ama\nach s\noutsi der\nll en\nco er\ntil ly\ncheese burger\nma ds\nple dis\nemp ty\nnational parks\naz iz\np mi\njun kies\nf ener\nsq n\nè s\ngener ation\ncleop atra\nbhuban es\nmosqu es\nty free\npopp ins\ntw c\nor well\nn age\nka whi\nhol low\ndal ai\n¨¨ ¨¨\nou ro\nm health\ngi on\naz o\nvis as\nreneg ade\nre ic\nw sop\nðŁĴļ ðŁĴĽ\ne chel\ntox icity\nmü n\nbun k\nstimul ating\nasth our\n\\ '\nep h\nende mic\ncn bc\nshrin king\npeabo dy\nmichel angelo\ncan yon\nwal e\nsu mi\nsi ders\ninu it\n? .\nprofession alism\ndr acing\nplat oon\np ons\nout bound\nmaple leafs\nde sol\ncen cy\na than\nver ma\nru bbing\nok an\nðŁij ł\nmull ins\nauthent ic\nÅ į\nalman ac\nga ia\nbb q\non imo\nke h\nty a\ntou ts\ny av\nre posit\n, .\nwi ght\nse eyou\ncal lof\ndone sia\nbar gaining\ngr anth\nsd su\namphi theater\np su\nre watching\nwine tasting\npeak district\ndete cting\nthur man\nphe e\nèª ķ\nu mich\nre r\nsculp ted\ngo le\nname sake\nðŁĶ ģ\nserv icing\nbau gh\npu gh\npen cil\ndar th\nmunch kin\nat orium\nten ers\nsun y\nrolling stones\nmag ing\nstar rer\ni dris\nfe instein\nag ron\nâĺºï¸ı âĺºï¸ı\nsupervis ed\nchamele on\naggre gate\nsucce ssive\nmo gul\ninst yle\npol dark\ncustom e\nohio state\nha ya\nci des\nbroker age\nangel ou\nfifa wwc\nde forestation\nal ton\npam ph\nhu gged\nho bo\nchange able\nku ber\nbur roughs\ndemon etisation\ncape cod\nvers atility\nor ice\nle ila\nwomenin science\ntu a\nhe dges\nembarrass ment\nali fe\nso ars\nni ghter\nhy mn\ngi pp\nchas u\ntech s\nni all\nk illa\nhi ka\ncam els\nvalu e\n ¢\nsc oops\nmah moud\nclu sive\nadri ana\npac o\noz il\nun as\ntransl ations\nwhispe rer\ns bi\nbu xton\nbio tics\nindi ffe\nken ney\nk lar\net ching\nbarra best\ninst ability\nse ine\nvo tel\nblo gged\nwhis key\nmy space\nt ant\nlan dia\ngive back\nillu s\naw ak\nac ab\nf bloggers\ncloud computing\nblat ant\nsyri ans\nband ra\nsty n\nan em\nke ted\nkar thik\nbarun sob\npin ot\ngu bernat\ngay e\narti ste\ni fied\nconven tions\nhu an\ngeni uses\neeee ee\nfol ly\nsomer ville\npride month\nðŁĩºðŁĩ¸ ðŁĩºðŁĩ¸\nchemo therapy\npaul s\nbak ar\nìĦ¸ë¸ IJ\ntaiwan ese\nfol lo\nc ss\nre ign\nnn nn\nfla un\ncatastro phe\niti es\nfrag ments\nextre mists\nym oun\ncar men\neze kiel\nconne cting\nse h\nman ta\nremodel ing\nwe ymouth\nat oms\nce m\nne well\nlu mi\nthe open\nmo c\nmili band\ng land\nz shq\nmag gie\nmani acs\nm sp\nad y\ncre ams\nle anne\ne sta\npy g\naf finity\npray er\ndun bar\nligh troom\nac adi\nwyn onna\nroman tic\nstate dept\nsick le\nwh os\nlam o\net our\nfin ity\nshru b\nshar pen\npun dit\ned on\naf ore\nmar s\njeff ery\nter ps\nmedal list\nkath arine\naccu sing\nta z\nroy d\nfrom home\nconfron tation\nalle gh\nðŁijī ðŁijī\nrefresh er\nran veer\nnever land\njo jo\nlu crative\nen am\nca ver\npa edi\nman jaro\nflu ids\nthe ssal\noppre ssed\nmu ss\njoh anna\nØ ®\ncn g\nbuil dthe\nsett les\ns ith\nfu ego\ncl amp\nar ag\npay er\nted x\nmand y\ninter stellar\nfr c\nch and\nb cc\nmo lo\nlen til\njohan sson\ngrims by\nnature lovers\nðŁļ¨ ðŁļ¨ðŁļ¨\nshin de\nx in\ninternational dayof\ntransiti onal\nsat a\ncad dy\nwo d\nif u\nha ys\nholl yo\nj ang\nir c\nco im\ngrad able\n\" \"\nðŁį ´\nঠ¾\na el\nn yo\nwest lake\ntime out\nsof i\nphenom ena\ncultiv ation\nag no\nun armed\nso t\ncon j\ngen o\nroyal navy\nnutriti on\nfair mont\nti relessly\nsn g\nre ty\nmic a\nlu cent\nslo ane\ndroo l\nriz al\nod ell\ncritici zed\n. '\"\nla ze\ndeser ted\nco der\npra s\nl illian\nitiner ary\ndav y\nan ap\nwhi pping\nhobo ken\nkare ena\nçľ Ł\nvi us\nter n\nnan tucket\nmis understood\nbu laga\nst ant\nchin ook\nz am\nreli es\nd ss\ned mond\nsket chy\nm ell\nfe x\nrec tor\ndist ill\nday dream\nwine maker\nri pley\nbillion aires\nhel ene\nati f\ncul prit\nbertr and\nwou ldnt\nma pped\nv ak\ngla dly\nparliam ent\nkidlit art\nware ness\ngoli ath\nâĨ ĵ\nview point\ntat ted\nfu ls\ndor sey\nang lers\nli ds\nki ya\nbow les\nbe h\nb ite\ncompati bility\nance stral\npro x\nbeha ved\ngubernat orial\nch field\nsab an\nz h\nteen y\nshibu ya\nholli day\npan cy\nâĿĦï¸ı âĿĦï¸ı\nseun gri\n? ,\nðŁĩ¦ ðŁĩ·\nim itation\nimpac tful\nany i\ngene vie\nañ os\nbate man\ngli der\naf ar\nra sheed\neffor tless\nsh war\ndach sh\ner un\nat os\nkin i\nch d\nkha ki\nk lin\nfelici dades\nbel o\nas l\nto ppers\nfin ley\nstac ey\nrigor ous\nkar ting\nle ppard\ncar michael\nbe ret\nc se\nak hi\nmer ingue\nab an\nha ke\nger i\ner jee\nre sto\ncomm anders\npr it\nfl or\nad ven\nex termin\nremain der\nå IJ\nes g\nmartin o\nlulla by\n| @\nmi gn\nin store\nbig bang\ncor di\ncau ley\nante bellum\ndg ate\ncro ck\nspan dex\nscaf folding\nore os\nê°ĵ ìĦ¸ë¸IJ\npom ona\nma uro\nuni versi\nre mi\naf ootball\nt ant\nsm alls\nne h\nworl do\ntropic al\nmor ph\njav elin\ngla r\narqu itec\nreminis cent\ntu bs\nspide y\nmake u\nsyl la\nprogressi ves\nblo t\nshor ten\nkeep in\nch ak\nang st\nsuper food\ndecad ent\nston y\nneuro logical\nar boretum\nann ak\nfe ma\nper cu\ndis respectful\nsmall biz\nlo x\nco om\nc sc\nbs bi\npre valence\nhim ss\nesp an\nmo ga\nfr ampton\nsky map\nmas se\nlevi athan\n( ).\nnoctur nal\ncar ameli\nang or\namne sia\noutsi ders\nshe alth\nrhin o\nant ag\nag io\nðŁĴ° ðŁĴ°\ntake me\nkab addi\nc si\nm sh\ncoch rane\nthessal oni\nsil a\nha us\ndu sting\nobe se\nmack lemore\nmani sh\nlen in\nm dc\ngro wn\nshef field\ns rs\nke le\ncar son\nch um\ndah lia\ncan tore\nopp o\nhow ling\ncyber crime\nsur realism\nsc ran\nfa iz\nthre n\nrac ists\nr out\npk not\nse mana\nsin i\nmc cull\nma chi\nalfon so\ny b\nsar dar\nkend rick\nden g\nreci pro\non f\ndoom sday\nbri bery\ncustom iz\nart is\nc pi\nðŁĻĪ ðŁĻĪ\nsla va\nlet te\nen s\nâĿ¤ï¸ı ðŁĺĺ\ncra yon\nad an\ntr c\nmigr ate\nsimp son\nrow ers\nking sley\nfarmers market\nshee han\nne phe\nbor non\ncar ton\nmic key\nall ure\nu lu\nsli pknot\nheb do\ngui do\ndog celebration\nonline marketing\nacceler ating\n) ..\norigin ated\nmacar oni\ned tech\nout field\nmit z\ndisc us\nadverti ser\nman or\nha shi\ndescri p\ncap ita\nful bright\nrecep tor\ncon n\ncon ey\nspion age\nr attle\npre st\nu li\nblog post\nacker ay\n) âĢ¦\nred velvet\nmat th\ninspir ing\nb sd\nker ri\npo con\nmil lar\nre pur\naccent ure\nä ¹\nram bo\nragnar ok\ndele ting\nbritish museum\npat ory\nleip zig\nflori an\nsci fi\nin ers\nbr ate\nyo y\nmelis sa\nab er\nma sa\npo te\nmosquit oes\ntranspl ant\nr pa\n; ))\nbast ille\nyl an\njoye ux\nmelo dic\ncap tions\natri st\nroch dale\ngott i\npew die\ncuties aturday\nwho is\naqu aculture\ntiv a\nsp el\nhe ss\nha ji\nfred die\nco per\nbrand o\nv k\nphoto book\n* ,\nmy dayin\nmicha ela\nbrune i\nsr ini\nin te\nÄ ±\nde ol\nd fc\nsepar ately\nbun d\nve sts\nto c\nme ck\nrein forced\nconstra ints\ncar roll\nsq ft\nre ver\ncam per\nbird man\nin action\ngener ators\ntriumph ant\npe sts\no vo\ngy pt\nal amo\nsc aled\nsuresh pp\nsd n\nis mo\ngi os\n) @\njustic eleague\nrestaur ant\ngab i\nden gue\nnext gen\nexemp li\nap ex\ninspir ational\ndown side\nkid z\nu pl\net na\nalvar o\nfel dman\nbar net\nm ha\nes ch\nbloo ded\n>>>> >>>>\nkan i\nho fficial\ncasablanc a\nbir ds\nty ga\nsw amp\no day\nnew castle\nnb ap\nci sion\ncho ols\naf lo\nne p\nmon ton\nak b\nsuper model\ndown time\nth os\nsc wx\nsnoo py\nag greg\nyo ke\nnor cal\nwe tt\nprolon ged\nme tast\nbeat er\nf ta\nt lap\ndisgu sted\ny h\nvoice over\nitch y\nip c\nðŁİ ¾\nphe asant\nstra its\nram pant\nj g\nfer til\nassu res\nfortun es\nsal inas\nliz ards\nkett le\ni bs\ncyn thi\nhe g\nmc cr\nsoccer oos\nhappen ings\ncor den\nðŁĺĤ ðŁijĮ\nt ches\negre t\nwolver ines\ncongratul ated\nho gg\nbott ling\nwr i\nfer ri\nbo sch\naf ire\nog den\ns jo\nj dm\nsv t\ncon tex\ntol lywood\nmin k\nme se\nsuper sonic\nop oulos\nå ¸\nâĶ ģ\nknuck le\ngu ise\ngam i\nchu cky\nz inger\nradi al\ncompla ined\nbo da\nfe tal\ndiscipl ines\ncor ro\nðŁĩ®ðŁĩ ¹\nop ted\nfiltr ation\nad nan\nem cee\nmi stre\ninsom ni\nfer gus\ntra jec\non don\nmed tech\ntanger ine\nmadra s\ngru e\ncab s\nz hu\nsureshpp rabhu\ninsul ated\nday swild\npp m\nband ai\nv day\ns ff\nsqu id\nlo thing\nnot dead\nexpre ssive\ncu ll\nala stair\nx u\nup front\nfish ers\nen es\num d\ndis missal\nsti er\nsel s\nlu st\nre active\nprote ster\neyel ashes\nal im\ngoo de\ngre eng\nda ir\ncom pen\nanush ka\nproto typing\nma pu\nbear ings\nðŁIJ Ł\nfor me\nbsbi botany\ntimo thy\nout skirts\nam bed\nare tha\nwend ell\nstre aks\nni m\nk pk\nsne e\nfit ter\nquo ta\np ate\nwin ning\nðŁį Ń\nsho pping\nma inst\ncul ver\nste vie\nmcfad den\ncounter parts\ngren fell\nfol som\ndor set\ntech crunch\n⬠ħï¸ı\ntip tuesday\nus l\ntre x\ngeor gie\nranveer official\nlic ks\nse wn\nk f\n' âĢ¦\njap s\np ate\north op\nfe sta\nstra s\nmon tal\nhammer smith\nfore most\nwido ws\nmad re\nite z\nmito chondri\nlig ans\nz ona\ncari bou\nm ss\nandre i\nweather channel\ngh c\n: ...\nta ft\nawe ather\nal isation\nbru tal\nbliss ful\nnik ola\nmal icious\nq m\nmpg vip\nbro die\nbl itz\napplau d\ndri bb\nv ague\ndog go\ntransl ating\ninterpre ted\nhat ched\nge tyour\nbenefici aries\nspar ring\ncaes ars\naw illiams\nla hat\nbro ke\nti mp\nvirtu es\nrel ying\npie tro\nk tn\nici sts\npab lo\nlou i\na ag\npn pp\ncha st\npul ses\nfini sh\nusair force\ntype writer\nthomp son\ndog s\nut to\nãģ į\nsand al\nnew ly\ndo ge\nz w\nwan kers\nne gr\nmu cha\ndetermin es\nblack fish\nsk unk\nmu ps\ninstru ment\nphy to\ndaysto go\nskin ned\nhai der\ncon ten\nðŁIJ¾ ðŁIJ¾\nwe iler\nundoub tedly\nchair ing\nwall is\nsh ard\nzind abad\nadul t\nabsor ption\npre sto\ndeplo ying\ndrum mond\nbattle front\nseag ulls\nhow dy\njuda ism\ndes de\npart ition\nâľ Ŀ\nno logy\nnational bestfriend\nlesn ar\nfilm fare\nco asts\nchristen sen\nac an\nmb u\nco pped\nru bble\nsw c\nfun nier\nfar ther\nwhere as\nnano technology\nwith stand\npil low\nbow ers\nto pe\nit ly\ncon fit\nma kar\ncomfor ts\nbo sh\ncli pper\nbal la\nsti k\nmil b\nsafe guard\nmusi que\neas port\nya z\npad ded\nbad er\nfore ign\nchop in\narchi ve\no ka\ntran sporting\ntml talk\naj it\nconsequ ence\nsc roo\nff o\ncollabor ated\npug chat\nye mi\njav ed\nau burn\no of\nma w\nsau cer\nmiti gate\ni les\nevangeli st\nter ie\nre cl\nindic tment\ncat a\nbright ness\nmay the\nwhim sical\nun lv\nkey word\ncu min\nmed way\nwest world\ntra w\nim posing\nform ity\ncoul ter\nab z\nny pd\ngrass i\nkel sey\nqld pol\nclock work\nf dr\ndi anne\nâĺ ij\nad h\np ann\nbra vely\nae ge\nun lawful\nver di\npocaly pse\nphar o\nkar la\nreson ance\nma stiff\nla dak\nbu u\nma iled\nhi i\ncraw ley\ntor rent\nmach ado\nliby an\neffort lessly\nfal sely\nq vist\nke ef\ncraf thour\ncheri shed\nval kyrie\ns ari\nkal amaz\nbe he\nðŁĮ Ļ\nth im\nro ddy\ncol trane\nbut chers\nach im\nwk end\nawk ward\ncab rera\n:) )))\nfran c\ndecl an\ncon dos\na ja\npandor amusic\nchar ter\nph ill\nmon trose\nhatch back\nhandic app\ngre aves\neucalyp tus\nut most\nt son\nbur ton\nmid wives\nin cur\nðŁĺį #\nmoo d\ncompre ssed\ntom a\nmust ang\nmo g\nas ana\nte stic\nsho tel\nin sol\ncor sair\nnh q\nben ny\nsm ma\nkap ur\nin con\njon as\nener gies\ndon al\nas ad\nse z\nn pa\narchi ved\nstimul ate\ndo p\nhy d\ngri eving\nãĥ Ī\nron a\nwhy te\ntree house\nss ell\nsand ro\nko bo\nther most\nse clu\nhi ya\nge ez\nmam as\nprisc illa\nflav oured\nfas s\nw old\nmaker space\ncospla y\np tv\nhappy valentinesday\nsequo ia\nlove craft\ngu an\nd tm\nci i\nyoko hama\npos thum\nre q\nðŁĶµ âļªï¸ı\ngalat asar\ndol by\nhamp tons\ndisturb ance\nstone henge\nok c\ndisrup ting\nmonth sary\njun gle\nhead lights\ndu stin\nmicro sof\nhappy mothersday\nko ko\ngra zi\nte sto\nna idu\nmal ay\nari al\nru mb\nab oo\nhar man\ntra pe\nspo ils\nje ho\ngo dly\nlock screen\nz un\npi ous\nma gento\nl enders\nprob able\ncorpor al\nm our\naw al\nsu a\ncall me\nton ne\ngo vin\ndevast ation\nx j\ngear box\nwar lock\nper me\nit ate\ngaza underattack\ndu val\nparas ite\nclement e\nle th\ni va\nfro zen\ntho les\nto bin\ncair n\ns ill\nluc kiest\nconver ts\nst ale\npan cra\neuro pale\nwis dom\nsch ur\nì ¶\nverti go\nbi j\nu bc\nnu re\nrighte ousness\nmt c\nfactor y\nver st\nrevers ed\nhur i\nhee chul\nfab er\nar r\nul ous\nven om\nph at\ngreen ery\nbra dy\nà ¦\n: ((\nnever giveup\ndi sha\nmo ta\nhealth care\ndun ham\ndex po\nden zel\nbb ins\nf ics\nwh am\nmc g\neli an\nwat a\nstr alia\ntel lu\npe sky\nspin off\nar moured\nre acted\ndo fficial\nte du\nsag ar\nmor ally\nparalle led\nfi os\ndow ner\ndau gh\nre do\nworld cup\ntari q\nbar ne\nglaci ers\noc cult\nbarbar ian\nher mosa\n!! !)\ny ur\ninter nation\np ss\nsit u\np int\namerican air\nsw am\ndopp ler\nðŁĴĻ ðŁĴľ\ncincode mayo\nle van\nhell enic\nmc ne\nju di\nyu h\nst x\nqu are\nðŁĺĤ .\nsti g\ng els\nmot ley\nhard work\neuro zone\ne ad\nç¥ Ń\nseab ir\nci us\nla id\nalpac a\npresu mably\npewdie pie\nboo ted\nam ari\ntam ine\nsol ace\nbar row\nacade mies\nx ian\nom ination\ndun geons\nb ma\nde ity\nai k\nstab il\nhir a\naffection ate\nving ne\nnew port\nãħĭ ãħĭ\nthir ds\nre tains\naroma therapy\nski er\nni ma\ndo pe\ncr inge\ncon domin\nto or\nanim ator\nsar aj\nseas cape\nminim alism\nlake shore\ncalla way\nberg man\nठĹ\nwhisp ering\nstupi d\nri ghtful\nrequ is\nir n\nse va\nut pol\ntuber culo\nsqu ish\nde but\ngovern mental\nchrist ine\nall man\nweap on\ns ito\nbur i\nlo lita\nleaf y\nfu ch\ntin ted\nmck en\na hahaha\nðŁĩµðŁĩ ¹\nrepe al\nne gan\nðŁķ Ĭ\ntail gating\ngame insight\nðŁıŁ ï¸ı\nyaku za\nz t\nti ring\npro posing\nbow lers\ntra itors\nak shi\ncler gy\ncit o\nup sets\ntu scal\nsymph onic\nsil ently\nshu ff\nblack well\nðŁĺĤ )\nko be\nrober to\nri dg\ndc u\nmer ino\nft p\neast side\n. ~\nnb l\nmn leg\nts for\nfrau dul\nca pping\nin my\ngymna st\nston es\nss in\ntwe aks\nshag gy\noak land\ndem sin\nsang ria\nmm va\nhen nessy\ndown ton\nri ghtly\nin it\naga ve\nob last\nnorthe ast\nfriend ship\ndal a\ntro phy\nðŁij ½\nmag in\nmargar itas\nê ·\nww fc\nfa sh\ndi ke\ncu d\nchar t\nðŁij ®\nrefuge es\njop lin\nn cs\nimp y\nfirm ware\npas cu\nflam in\nhealth tech\nbell letstalk\nw aka\nol ls\nla go\nco wan\nbombar dier\nsh ome\nðŁĻ ħ\nmc master\nna ve\nwell s\nu ta\ntell ers\nmis fits\nkap il\nface off\naf firm\na pro\nwhit epaper\nsuper yacht\nspeci mens\nal located\n... ,\n- __\nka w\ndachsh und\ndjo ker\ns work\nqui ere\nor um\nðŁIJ ł\nsom m\nc mt\ningh our\nskin ny\nlgb ti\ngi ggles\nbreak away\nresear ched\npar ity\nmy al\nms l\nre tained\nsi vity\nmake inindia\nsol ves\ndefam ation\nwal tham\nsri racha\nroad way\nconcep tu\nal in\niw ant\nå Ī\ndel ft\ntender loin\nga ins\nfaul ts\nsw ire\nst ellen\npol lo\ndy ne\nbornon thisday\nasdf ghj\nsq l\nsali m\nadvis es\nvo ip\nìĹij ìĨ\nun touched\nshe il\nontari o\nuph ill\nso bre\nde shi\nnov ella\ndu tton\ncraw fish\nا٠Ĩ\nma a\ntw ine\nkal in\nðŁĩµðŁĩ Ń\nye ss\nbrook s\nhoo siers\nton ka\numbrel las\nay ers\nate am\nacqu iring\nsu ction\nä n\nwi es\ntari ans\nsoci o\nmat tb\nshepher ds\no so\ncharity tuesday\ns logans\nninj as\nal bat\nby te\nbash ir\ntrampol ine\nmydayin la\ni ja\nbas el\nror y\ngol die\nfi rec\nun noticed\npecu liar\nsch a\nker son\nmour ns\nliquid ity\nqu ipment\nhi bs\nar s\naeron au\nslide show\nsla bs\ndelici ousness\nsk itchen\nhta fc\nfull erton\ncre ighton\naer ob\nprocrastin ation\naz ores\nwhite hall\nuss occer\nmedi ation\ndjoker nole\nand me\num en\nnoxi ous\njo ss\nili fe\nanni vers\nsudan ese\net res\nunder mine\nwhole foods\ndiso be\nkor i\nade le\neli z\ncan ti\nal on\ngymna sium\nsarko die\nmeteoro logist\nyl de\nste en\nstamp collecting\nnas al\nlo tt\nfran ks\nex ol\nack i\ngood year\nanimal rights\ny les\nvio lets\nmm es\ns thel\nra pping\ntu scan\nwai ver\ntur ner\neat local\nnorthe asthour\nanim ations\ntom morow\nt sh\nff ame\nbra e\npe tron\nglam our\nbr yn\nd cs\nbal es\nðŁĶ ¶\nbro v\nbre v\nb ons\nphysi que\ncar ne\nx e\nelix ir\nvol ved\nl oma\nìľ ł\næ ĺ\nvan u\nri gs\nbal ance\nva res\nbon ita\nsprink le\nperfec to\ndi on\nle ak\ncalcu tta\no ba\nd ma\nc mon\ntun er\npneu monia\nbo gus\napolo ge\ncl ough\nbor ne\n)) ))\nrevi ved\no varian\nner f\nc legg\nfan fest\ncho u\nreali zes\nmc n\nli gu\nleg alize\njust saying\nfor ster\nbo sni\nk hi\nin dom\nhei del\nen cryp\nsi ss\ned di\nmar bles\nbrisban e\ny ing\npre paid\nwal sall\ncooper ate\norche str\nmar isa\nho wie\nche wy\nbren ner\nandro meda\ne gan\nsto cki\ncav endish\nag an\nban o\nde ir\ngo g\nbl k\nre thinking\nch ig\nrhe u\nsni p\np eng\nsemin ole\nm swx\nan nex\nlyn da\nlewisham ilton\ncu mul\ntb l\ndolph in\nagu ero\n........ ....\npre lude\nat our\ngr anger\ntoo ting\nro tun\ndis ar\nhome items\nda res\n**** ****\nðŁij Ĩ\ncompre h\njin x\nas well\niri e\ncircul ating\nðŁIJ ¥\nover board\ncultiv ate\nrhe tt\noriente ering\nca k\nbal kans\ns itt\njas min\nbritney spears\nro tor\nse aling\ng bc\noc ci\nf as\neman cip\ncom er\nwar time\ntic kle\nson ny\npac es\nlog g\nat rix\nsr p\ng win\ndo bbs\nuz be\nthe wanted\ndru sh\nex tru\nm icky\nhonore es\ndar win\nre dux\nmm j\nram i\njalape ño\nio c\ndo ver\nju ju\nwhit ney\ns eng\nen ly\nau ch\narchipel ago\nvigil ant\nman gal\nwil dest\nparano id\nhal i\nbb ly\nsanc tioned\nreal ms\ncon co\nu ddin\nc sk\nplay time\nlibr a\nsav ag\noc tane\nrec tan\nre turn\npar rish\nmor rha\ncc p\nc mu\nsa iled\nse vent\nro sie\npil ing\nhe w\nboar ded\nseg ments\nneph ro\n( .\ncr ats\nbak es\nðŁį ¸\nback tothe\nsibl ing\nkirk land\nke o\ngu wa\nbre ads\nðŁĺľ ðŁĺľ\nt q\nharas sed\nga u\nwil bur\nj isoo\nep er\nli sam\ntri ppin\nsh ino\nru kh\nbeast mode\ncho a\ninst aweather\nrich land\ngar i\nfe z\ncowboy snation\nfur suit\nk run\na en\nsycam ore\nse gun\nent ennial\ndi h\no ax\ndemsin philly\nðŁĻ Ģ\nsn hl\npen nies\npass words\nma kin\nty e\nd eng\nkni gh\njeep life\nhel pline\na for\nzz zz\nste amy\npic ker\niter ate\nhappen ingnow\nki b\nbloom berg\nmartyr dom\nbul ly\nassor tment\na hora\nzo e\nno i\nillu stri\nagar wal\np sc\nelectr onica\nrecruit er\ngar diner\nrad ha\nnaf ta\ndot net\npi ero\ngeor g\nbel s\nðŁĺĤ ðŁĺį\ntuberculo sis\nrun nin\nmor is\nhaul ing\nev oc\nbre thren\nsha ir\nframe works\na stu\nri gid\nku ma\nkre me\njin nah\ninsu rers\nny u\nf ere\nnol lywood\ngood vibes\n- ...\ntoi le\nsk ril\ninstaweather pro\ncze ch\npa vel\none piece\nnike plus\nfi let\ncav ity\nðŁı½ âĢįâĻĤï¸ı\nðŁİ £\ndra stic\ndail ys\nsiam ese\nre bu\noste o\nlar k\nf re\nsh elling\np é\nglad ys\nðŁıĢ ðŁıĢ\ngusta ve\nsubmer ged\ngrand stand\natt u\nwon t\nf pv\nb ley\njon i\nang ames\nweigh ted\nal ou\nठ¶\nles bians\nf j\nanni es\nam l\ndor ia\ndav in\nbe ta\ncan c\nmadewith unity\nha j\nbad lands\nmu l\nblu ec\npa wn\ncov ington\nneuro logy\nhtt weets\ndysle xia\nthel ove\nne at\nfork lift\nautom ate\nune ven\nmonte ss\nhe in\nha g\nrel ics\ncompetiti veness\ncan elo\nmar tens\nbullet proof\nsk ittles\ng ya\npri mo\nameric afirst\nwoo o\nabor tions\n?? !!\nma che\nld ers\nrl ly\npreli ms\ndirec t\ncour se\nswa in\nsuper cell\nec centric\nsting ray\nple ts\nwil cox\nwest in\nokan agan\nkir an\ncar bo\nbomb ings\nra rest\nbo h\ngaw d\ndi gg\nmo ana\nenti rety\nen closed\ndodge ball\npar ton\nmilky way\nat r\nthorough bred\nre ally\nqant as\nepiph any\nine e\naero smith\nspi eth\nar thro\nell ini\ndu bu\nbra ving\nâļ½ âļ½\nre structuring\nillumin ate\nequ ili\nmp i\nash ton\npony tail\nma scots\nflat tering\ncru m\nast a\nà® °\nstranger things\nbar nab\nر ÙĬ\nmake shift\ngot cha\nwill am\ncho irs\nkilom etres\ngho sh\neu than\ndol ly\nun ning\nthe ar\ncre we\nw sw\nj ace\ndis miss\nke an\nho ta\nkh at\n~ >\nthir u\nren dez\nhart man\ntee ssi\ncas ca\nz ah\nhydr ange\nfo d\naw p\nmzan si\nthick er\nnago ya\nne va\nsti que\ncast el\ndam ian\nthere by\nji ang\nale k\nmusic islife\nra q\ncalla han\ngou ache\nsomal iland\nsean hannity\nra heem\nlo se\nelo ve\nwhar ton\nrectan gular\nillustr ating\nhar ne\nauti sma\nscra pped\nell and\ndecre e\nnag pur\nki pp\nso re\nn md\nma as\ngun a\ngart ner\nbel li\nthen ight\nje on\ngendere quality\ngi ver\na el\ngar ments\nne u\nmardi gras\nmar sden\nro wer\npollu ted\ncamer aman\nvin od\nbe asley\ncro c\nji u\nhollyo aks\nanesthe sia\nal les\nste ward\nlati mes\nðŁĩºðŁĩ¸ðŁĩºðŁĩ¸ ðŁĩºðŁĩ¸\ntic ian\ngor ia\ncome dic\nðŁ¤Ķ ðŁ¤ĶðŁ¤Ķ\nnai ve\nsli ons\nł Ī\nbur glar\nðŁĺŃðŁĺŃ ðŁĺŃðŁĺŃðŁĺŃ\nyork shi\nse ñ\nfan boy\nlau rel\ninci dence\npotom ac\nrober ta\npresi den\npr yor\nos bourne\nw ku\nte me\npal ae\nðŁ¥ º\nre boun\nitu de\nred dish\nk hand\ncoloni alism\nnorth carolina\nðĿ Ĵ\nmanne quin\nlady bird\nta sty\nknowledge able\ng shore\nðŁĮ Į\nà® ©\nqu aker\nsalz burg\nmed alists\nchy na\nbridesma id\nma ori\nro p\noutra ged\nin adequate\ntruck ers\nal ana\nìĿ ¼\nri x\noooo oooo\ncommand ments\nlam beth\naa j\neco friendly\nbla z\nmorecam be\nboun cy\nrou x\nrai ded\nmi zed\nsh c\ngaw x\nlabor atories\nru bs\nrest room\nconsult ations\nca jun\nvirgin i\nso ir\nrev ue\nple in\nwag er\nç ¹\nwe do\ngrowing up\n! ðŁĺĬ\nface ted\nsin ners\nho vering\nti ene\nseas oning\nan ja\nleg go\nil is\nfla x\ndev o\nash ram\nmati sse\nker i\ngo wer\nbo tox\nmar shes\nunh cr\nts m\nopti mus\ndun i\nstu ffs\nso k\norder ly\nn bad\nislam ophobia\nraviol i\nfab er\ncre ds\nwon ka\nin fusion\nover weight\ndaily news\nassi mil\nacol lege\nmedalli on\nkili manjaro\nsti ff\ntham es\nsun ken\nth ard\nmy dubai\nhilari ously\nhan nel\nplu mber\nfair view\nsepar ating\nrasc al\nqui en\nnecess ities\nconfeder ation\nll ll\n: ]\nweak nesses\nbron co\nra ffles\nel ot\nãĤ¸ ãĥ\nadvent calendar\nðŁİ ¹\nstra vel\ntun ic\nk su\nim peach\ne spionage\n! -\ndi ment\ncur rant\nbio de\ncommu ting\nby ron\nðŁĴĵ ðŁĴĵ\nshad ed\ntr uro\ncray ons\nar ne\nh sc\nfre aked\ndram ati\nfle ek\nu cd\nmarl borough\n^ -\ncross ings\nmal o\nblack ops\nbin ance\ncho ked\nchen ey\npl o\nge stures\nval edic\nryan air\nrem ington\nv cs\nmc kee\nec z\nbe gs\nnail art\nmayor of\nhappy fathersday\nwar t\npet itions\nn ingly\nclean energy\nbro x\nsl alom\nexist ent\nab ay\nug liest\ntom p\nstom a\nsel by\ngoal scorer\nben ji\noverwhel mingly\nlan s\nsemiconduc tor\nsouth korea\nre scheduled\nsk yl\nen listed\ndow ski\nsi del\nrosen berg\nnas ser\nwhite head\npri us\nhar are\nen n\nry der\ní Ĥ\nmon g\nclas ico\ntranspor ter\npo tty\nis me\n** ***\nvic e\nsk it\node ssa\nl mp\nher n\nraci ally\npin oy\nparagu ay\nobitu ary\ngo es\nbu cha\nside walks\nangu lar\nun constitutional\ntransiti oning\ni bu\ngu ys\nun packing\noooo oo\nblack girl\nber gs\n ¯\nwordof theday\ntrump train\nthunder bolt\nm si\nfasci sts\nठ¬\nt sk\ncollap ses\nraje sh\nloveis love\nmigr ating\nset back\nðŁĺĬ âĿ¤ï¸ı\nt els\nsafety first\nnar rated\njae joong\nun answered\nlique ur\nen nes\ndal go\nbill ings\nsalt water\nmer maids\nlon gs\nclap ham\nwe arec\npic collage\nn ach\nh ace\npois oned\nlo th\nag na\nadel rey\nguar dia\npoli shing\npeace keeping\nd all\np isa\nla pland\nprocess ors\nde andre\nso bs\np once\ndra ins\nc be\nðŁİ¥ :\nspla sh\nmeat ball\nfon tana\nworcester shirehour\nne v\nbri sk\nb int\nac r\npo x\ncay enne\nskril lex\nj fc\nhahahaha hahaha\ngla s\nen gul\ntempor al\noni zed\ncon cre\ncom pose\nvibr ations\nplant ers\nfer t\ncriticalrole fanart\nt bli\nsch allenge\nhuck abee\nmunici pal\niam bic\nradi os\nne vis\ndura bility\nmc cla\nhorse back\ninst itutes\nful fill\natta ch\nate ur\nak an\nresi sting\nillumin ation\nhand le\nhair care\nom ent\nmacle od\nka iser\ng no\nbear down\nly f\ngl omer\ndistor tion\nz m\nsan k\nroo sters\nis now\nas ports\nag en\nwo ken\nst george\nro mper\nmy le\neconom ists\nru to\nt will\nhealth and\nd ito\nws l\ntair p\npra kash\nmic heal\nh ts\nw rights\nkat su\nfioren tina\ndefen seman\nd itch\nvar sity\ntexan scheer\nba ham\nsc anned\nwe il\nseduc tive\nðŁijį ðŁı½\nfu e\ner win\ndav ison\nter ran\nmoo ds\nwool f\nre source\n@ .\ncu sh\nðŁį °\nregre ssion\ncur led\nla zer\njo anne\nab bott\nmo z\ndown ers\nmm mmmm\nvalent ina\nk hair\ndream t\ncro ok\nche k\nste aming\nnephe ws\ncl eric\nas ober\nindefin itely\nw ye\nus news\njoy ce\nflu shing\nwynonna earp\nron do\nkis s\nhot dog\nbar ns\nsax ophon\nfar ley\ngas p\ndecre asing\nal way\npe x\nl sd\nshi ft\np outine\nra zz\nrescu ing\nni ko\nho ch\ncc l\nu aap\nn ts\nm car\nil wx\nconqu ering\nket tering\nstur dy\ndelay ing\nsto k\nvani shed\ncath ar\nbin gham\nin v\nic hiro\nhe mo\nbudge ting\n[... ]\nbe ss\nsebasti an\nslow ed\nðĿ ij\nmusli m\nstun s\nacton climate\nve a\nse ton\nrose tta\noun t\nhard in\nflu id\nca w\nðŁ¥ Ĥ\nyach t\nun l\nsp hy\nprovoc ative\nor ic\nis back\n__ _\nnicol as\ngy an\nloo se\nfl in\nreb ate\n: ::\n! \"@\ncom icon\nshe ff\ndown stream\nchic hester\nbeach life\nmom life\ndiabe te\nar ra\nvan e\nok u\nye o\nman go\ntry out\napp ell\nhe irs\narjun a\ndd u\nna veen\nmovi c\nsoci alists\ns back\ncriteri on\nsoyu z\nk her\nda z\nyol anda\nwine oclock\nre ina\none w\nleon ard\nen dez\nu bs\nsupport local\nfacilit ated\ncarameli zed\nb pa\nvuel ta\nmy tho\nm ami\nspe are\nnbap layoffs\nfe vre\nnick jonas\nim print\nc so\ncraig slist\nla salle\ngi deon\nha doop\ndis regard\nw ud\ntu c\nma gee\nacou stics\nta a\nqui e\npol a\ncr t\ndw yer\ndis sec\ncapit ol\nmen tion\nkn oll\nhe igh\nfin ders\nplac ements\nl se\nindi ra\ngur i\nmadhuri dixit\nkingdom s\niambic pent\ngeor gina\nje ky\nconflic ting\nbay an\naga tha\nuph old\ndr on\nvic ar\nex pat\nperiph eral\npe ssi\nfa f\nance stor\n? ..\nwid get\npun c\ncomm enced\nbeav s\nair waves\nad dis\npo a\nde sses\nco den\nvu e\nru pee\nkar in\nspo ck\nm sy\nภ°\npr ick\nfill more\nti fication\nthing sto\nsar de\nem ile\npere ira\nn ad\nbright ening\narre sting\nwo king\nusc g\nsp ill\nraspberry pi\nhu go\nite c\nis ma\ncuff links\noptimi zed\noc c\nmi wx\nen ka\nel ited\nafford able\nsa kh\ncoron ado\nho h\nat ul\nai oli\njim cantore\naccoun ted\nvin ay\nher mit\ngroo ves\nran ch\nr illa\nwe tter\nou tof\nveter in\nni kov\nki an\nfair banks\nram apho\nn iti\nk ko\nru sty\nne stle\ntv xq\nshahe er\nâĿ¤âĿ¤ âĿ¤âĿ¤\npenn ant\ngem stones\ndem debate\nðŁIJ Ĭ\nauton ews\nsupport indiefilm\nmach o\nve x\nnew sat\nne ti\nconce ssions\ncan died\nyof the\nmac au\nden ds\ncricke ters\nsan iti\nmari ano\ngh at\nar toftheday\n¡ ľ\ne gos\ngen oa\nchat bots\nbri er\nal labout\nmon ty\nspi ed\nr tr\ncomfor t\nsni ppets\nreal time\ngra in\nexam ined\nen lightening\ntt u\ngod bless\nrelease the\nsing ular\nki ans\nha ka\nsor ren\ndefe ct\nmar g\nequ ities\nd orian\nsu ka\nper l\naishwar ya\npul lover\npreci sion\nfair way\nne ve\nrive ting\nvill anova\nen com\nak o\npassion ately\neuropale ague\nsiem pre\nx vi\nenligh tened\nc fr\nâĺħâĺħ âĺħâĺħ\nwast eland\nis f\nnew comers\nemergen cy\namphi theatre\n- .\ntext books\nfigur ative\ntre mb\npe sc\nab hin\nab bot\nac acia\nhar ds\npor sche\nkau ai\nel isa\ncar rick\nabo u\nelli er\nbe ch\nneu tron\ngalap agos\nru ben\nin nis\nhow to\nnun s\nsab ine\ni ac\nclin ched\nno tori\nfi ves\ncairn gor\nper i\ngr c\nðŁĴ¯ ðŁĴ¯\nmal m\ntwelf th\ndi ff\nrout ines\nmarty n\nlin den\nsynthesi zer\nnu mber\ngame cube\nfal kirk\nbyz antine\nqueu ing\ngr ill\nscal able\nchar red\nrou ting\nher bali\ngri zz\nðŁĺŃðŁĺŃ ðŁĺŃ\ntol l\ntermin als\nl pc\nab d\nwar mups\nremo vable\n¯ \\\nvi go\npap aya\nne ve\nlov ingly\njo kers\nib les\nsse tt\npoten ti\npel e\ngi gi\nsadi q\nleg acy\nson o\nru pees\nretar ded\nele e\npar r\nfi ance\ney re\nsay ers\npend ants\nmak nae\nal bans\nadap ting\np ff\npu berty\nji u\ning rad\nhypocr ite\ndiplom ats\nphys ical\nrob by\nbon sai\nãģ ·\nf att\ncatal unya\nâľ ĸï¸ı\nro ma\nmore land\nso e\nconver sions\nstl blues\nshol m\ngra ssy\npra do\non u\nassaul ting\n> _\nsett es\ndis graceful\naph ra\nâļ½ï¸ı âļ½ï¸ı\nठª\nkil n\ngoal tender\ns ru\nphilanthro pist\nb als\nth n\nstu den\nsando val\ndogre scue\neli ons\nasse ssed\nlar go\nhec tares\nsh rm\nsa if\ncle avage\nno ches\nn ene\nfat alities\ncur ing\nclean ser\nal es\np vp\nsouth bank\npizz eria\nmarsh als\nkni fe\nan dover\ntbli ghtning\nsr sly\nou te\ndigi mon\ntimesof india\nprome the\nle bo\nf su\nwit z\nrever e\nman as\nmam ba\nch ica\ngu an\nexhibit or\ncsr racing\nd ere\nxx xxx\ngu sta\nstory time\nston ey\norgan ics\nand u\nse am\nmin ogue\nanushka sharma\nab a\nðŁİĻ ï¸ı\nugand an\nchro matic\nas sn\ndocument aries\nsh t\nru paul\nloy d\nk ats\ne us\nite ch\nme dusa\npan ty\nkel logg\net to\ntalla de\nsha a\ndo st\np ms\nmari ana\nje ster\ncroo ks\nðŁĶ ¬\nmin danao\nind hoven\nðŁ¤ ª\nle xi\ntv n\njan is\nco te\nãģ Ĩ\nser rano\niw m\nðŁIJ ¬\nk ke\ndistribu tors\ncap u\ncounterfe it\ncamp site\nag gie\nðŁĺ ¼\nchhat tisgarh\n~ @\nstate u\nsan di\nprevent able\ncl s\ncan ne\nmm c\ni ver\nsa haran\npal is\nnight out\ndo s\nap ia\nabsc bn\nmanag erial\naro se\nmo wx\naro sa\nðŁĮ ³\nunder dog\nremo ver\nastronom ers\nlent ils\nsu scep\nsmoo ther\npend leton\nfau cet\ne mory\ndal mati\naf cb\ntic us\nexem pt\nen rol\nd heim\nðŁIJ º\nrestric tion\nstar fish\nsto w\nsnor kel\nthunder birds\nshe ad\nhomo sexual\ndy n\nas li\nandre tti\ndou che\ndom o\ntar mac\nslu mber\npr onto\nfirst dayof\nmini ature\nmari achi\nargu s\nrecomm ending\nmobi les\nin ce\nillustri ous\nor c\nadver ts\ngr its\nwea sel\npag oda\nover pass\ngre ys\nmaxi mus\narma gh\nwood land\nsun ni\nðŁĴ ī\në Ŀ\nti one\nsoci o\nho s\nðŁ¤Ĺ ðŁ¤Ĺ\nwind sor\nsubsequ ent\nmunch ies\nid h\nexclu ding\ne mi\ncu th\nz ai\nweek days\nlaw suits\nbarn ard\nØ ª\npe tting\nnet es\nmul ligan\npharmac ists\nra quel\ne ton\ncran ston\ngil ded\ncle ary\nce ph\nra a\npam per\nlombar di\nas in\nsher ry\npro d\nfor te\nari anism\nbuffalob ills\næľ ¬\nðŁĶ¥ #\nuu u\njust ices\ncar ina\nnat in\nmas low\ndro oling\ncog nac\ncam ber\nel ong\nr dr\nin en\nconvic tions\nam use\ntro ck\nharm less\nvisit ation\ngen omic\nbl and\nbeno it\nchim p\ntuscal oosa\ngre asy\nx po\ngil t\nse q\nper mitted\nchristma seve\nbook s\nmu e\nold school\nhuman right\nbe ati\nðŁĶ Ŀ\nsh at\nsculp ting\nh wan\nfern andes\nsci utto\nfu entes\nendeav ors\nmaid stone\nun paralleled\nshou ted\nqueen of\nmer c\nband ic\nve da\nsel angor\npi le\nja han\nintimid ating\ndisapp ears\ncl ich\nza ha\nw urst\nhi v\nfod ils\ncor dless\naaaa aa\nhy dra\nbel inda\ne els\nbu f\nsu staining\nrugby league\nno c\nbrig itte\n( ðŁĵ¸:\ntromb one\nsoo the\nsmo g\nad p\nstab le\ning ley\ndiagno se\nms g\nwe ss\ntic keting\none e\nnsw pol\ne up\nauto psy\nadity anath\nsun down\nriver front\nsi ya\np is\nhier archy\ndur ango\ndi jk\nren shaw\nhe aps\nepide mi\ndavid bowie\ninterne tof\ndd i\nnation ality\nmb ar\nair y\nwin der\nw alia\nelli ott\nc x\nbav arian\npl att\nan tw\nwi wx\nsof ter\nne ha\nh eller\nth and\ndani ela\nbo ast\ndegra dation\nðŁĴ¦ ðŁĴ¦\ntransform ing\nman e\nav ut\nðŁĺĪ ðŁĺĪ\nvo ter\nthe e\nt ate\npu ff\nin door\nsop roud\nboy ce\nboris johnson\nwait in\nimmun ology\nðŁıĨðŁıĨ ðŁıĨ\nâĿ Į\nstreet food\nliz asober\ncavali er\nc elia\nneed le\nmotor ing\ng ato\n, )\nra de\nharve st\nt ms\njar pad\non ey\nair men\nv re\nimpair ment\nabhi shek\nsnoo p\nl ant\nfam ously\nbl ou\ns ze\ng ander\nun touch\ntu f\ndee jay\ncol lateral\nb ind\nðŁļ ©\npin ning\nic n\n' ;\nthe economist\nul tram\nworldwater day\nti poff\nthe i\nfeed ers\ncampa ign\nsc umb\nday weekend\nyo m\npe dic\nh ough\nps v\npl in\non de\nboston marathon\naz zy\n* _*\ncon ley\nthi ago\nhoo o\ngal erie\nluci d\nje tt\ngl itz\nfinal fantasy\nachiev ers\ny ung\nperegr ine\nop hi\ndam es\nbiom ar\nâĺĢï¸ı âĺĢï¸ı\nsk c\nl ics\nfl ank\nar rahman\nho of\nuphol stery\nt ats\nwo z\n ¿\nsnor ing\nra er\nl ju\nap d\npl ating\nkan u\nim ation\nfragr ances\nm ra\nmor ay\nmo tt\nim muni\nhearti es\nbho pal\ntim ers\ng ata\ncolor way\ncar nation\nwin get\nsi ghs\ns ville\noptimi st\nchate au\nolympi ans\nci o\nsinger songwriter\nny o\nfi bers\nbur ch\nag ro\nmil ne\nig bo\ncr amer\nation als\ndan ube\npad ma\nnor mani\nen forced\nbre ck\nboeh ner\nar den\nsur rendered\npros thetic\nom a\nha iled\ncalcul ations\nw fa\nbi b\nfcb live\nfon da\nwest coast\nque sts\nfriend ly\nto wie\nfit ch\nbal ot\nstar dom\nscrat ching\nho sa\nthi ka\no ven\nstro ke\nout post\npharmaceu ticals\nhi kari\nmu y\naf d\nfallon tonight\nsqu at\nor u\ndra ined\nchocol at\në¯ ¼\nwor ths\nri b\nmu j\nthat s\nresiden te\nit el\nboo st\nmi gos\nmul led\nla a\netsy shop\ndon keys\nme k\np tc\nflin ders\ne hs\nro hit\nmu ir\ng ad\ncompos itions\nåĨ Ļ\ncombu stion\ni kh\nyemen i\nwav ed\ngar ci\nak os\noo ds\nfu sion\nse que\ns lan\npl ur\nkic chasu\nshenan do\ns ams\nworl den\nhoro witz\nwith me\nmic robes\nk ki\nðŁĴĶ ðŁĴĶ\nw su\npatch work\nfre er\ny aki\nthe art\nsymboli sm\nmil er\nbt n\nma bu\nside kick\nmotiv ates\nsag itt\nnatur als\nserv iced\nps ori\npa ola\nqu ig\ni badan\ngi ggs\në ³\nsciento logy\nsi oux\nsalam at\nd res\ncad bury\nd hawan\nci ón\n_ '\nswa pping\nmaris ka\njames bond\nexplo sives\nay les\naf er\ns agu\ncen sor\ntom a\njeff erson\nring ed\npar tist\nir responsible\naguil ar\nvac ay\nequ itable\naltrin cham\nac ur\nman ish\nger min\nschoo led\npu tter\ned ad\nnav al\ntoast y\nsol areclipse\ndish u\ncoy ne\nac co\nmu ck\nmar an\nel os\nlen der\ncro ix\nworth less\nha ber\ngun men\nðŁį ĵ\nzen ith\nt enders\nhur st\nhol tz\nitali ans\ncar low\nu cd\ncharacteri stic\nbun g\nav l\nu th\nsa sia\nrs l\nred man\nneighbor ing\ngreen peace\nsti ps\nfollow party\ny gk\nen os\nomni bus\nna issance\nchri ssy\nsecu re\ncall back\nji hoon\nmemor y\nblock er\nl anta\ndaf fodils\nbil t\nffer ty\nfau st\nie c\nnipp les\nso g\nm nd\njagu ar\nbol dly\nab poli\npro position\ngun sense\nevan sville\ncu tters\nwe go\ndou n\ndo x\nstal lions\nka j\nshi ppers\nj awa\nvol o\nle ven\npap rika\nkov ich\njor di\ninduc tees\napp alling\ndial ysis\nallevi ate\nâĢĶ âĢĶ\npie ter\nmid wi\nq tr\njuli ette\ninter mission\nhaw ks\nact ment\none ill\nk lin\nvam ps\nfam ous\ncou ld\nautom obi\nda an\nwest end\nelli p\nnh c\nmel anch\nweb series\nton gue\nsnat ched\nsmy th\ntan gible\nsl i\ne asing\nbar stool\nover lay\nafford ability\nting ed\nter as\nay ush\nwanna one\nrh ine\ndan a\nsh ana\nkend al\nfer tile\nw ir\nrepl eni\nlar vae\nis ro\ncon vos\nab brevi\nu cc\nhun gry\nbur rows\nag er\nnav i\nmat in\ndu per\ncer n\nma don\nķ ï¸ı\né ģ\ntu ps\nhy att\nsh ep\nfriday night\nwis er\nhei di\nhat ton\np gh\nfoun tain\nwrist bands\nahmadi yya\naeri al\nsubscri bed\nso los\nm ace\nsla yed\nfor fe\ndul ce\nchrist mass\narun jaitley\nviol ate\nob stru\nni eces\nw vu\nidy l\nfa ze\npre serves\ninfr inge\npremi ers\ninter vals\nagen cy\n( ©\nstand alone\ndi mes\nbo er\nparam eters\nge tit\nðŁĺĺðŁĺĺ ðŁĺĺðŁĺĺ\ntu lane\nfor given\nscol l\nmb ps\nsmash bros\nrob bi\nprima vera\nali st\nghost ly\nay at\nye ats\nimpre ssionist\near phones\ncaul field\nwai kiki\nsal ute\nsc ou\nmu ay\nlouis vuitton\nbak hta\nado g\ninven tions\nhur d\nforec lo\nstream line\nthalai var\nch snews\nwill ard\nt sn\neuro parl\ncru sher\nmy sore\ngro wer\nra ping\npat ti\ng den\nsm w\nmuf ti\nkid man\nab r\nsoun ders\nskep tical\nðŁĶ İ\nsun dar\ni me\nfer g\nfeather weight\nar lington\npas qu\nag azine\nwearab le\nnati c\nmccl ure\ninter mitt\nhor de\nsix ties\ncar te\nbha v\nze al\nexperi ential\nador ned\nsom mer\neno te\nhypo thesis\nstin ky\npro to\ndead lines\nvo gel\nmus ings\nmonc ton\ngu ter\nf le\naci on\nvoice of\nta sha\ninhabit ants\ntype face\ns ba\nbts x\nðŁĶ Ĵ\nwor x\nu hc\njo ko\ncell ars\ngor o\ncontinu um\n... &\nweather cee\nha p\nsr k\nris ers\nlonely planet\nun named\nco eur\nðŁį Į\nthe world\nili ke\nfa sten\nami go\nri ba\nramapho sa\nstaf fers\nhad ley\n? ?\"\nfi ore\nsal ut\nhu ff\nbez os\nÑ ĭ\nra der\nkam ala\nin line\nfill ers\num atic\nall in\nshat ter\nre in\no ku\nch ases\nfla gged\nbaby metal\nwater stones\nts b\ncut out\nop hel\naam a\nrockab illy\nsto lic\njet blue\nich ick\ndown ton\nuzbe kistan\npat na\nla q\ngr ange\n) _/\nsubsi di\nsc p\nnewsc ast\nit sa\ntwee tyour\ne mor\narchae ologists\nuni fication\npor ta\nq x\nprotec tors\npro hib\ncharis ma\ncar tag\nren fre\nscul pt\nguwa hati\nde ma\nboo p\nunf pa\ndex ter\nlay la\nalleg es\nsou ps\nnever again\nl ys\ncal c\nbar oness\nvisu alize\nger ber\nabsor bed\ni ers\na han\nfon tein\ndetec tors\nverst appen\nsv c\nformul ated\nac dc\nli x\nin competent\nbh k\nlour des\nwater house\nsnow ed\nappreci ative\nsig ma\nlizasober ano\npen ned\npay check\ntall inn\nfanc afe\npar isi\nav alley\nvi g\nru fc\nhard ship\nso cute\npo ise\nì ¹\nroth schild\nk ly\n???? ????\nl hp\nil ay\nf hs\nam ad\nide als\nbrad bury\nbal boa\nnic ot\nkid nap\nwol ve\ntas manian\nop t\nmatthi as\nãĥ³ ãĤ\nsuper markets\nmylittle pony\nme lee\nli ster\ngr oun\nfe dora\nkind ness\nen en\nbra hms\n¯\\ _(\nros well\nmar lene\nic u\nre formation\nor ail\nhe brides\ndispar ities\nterrac otta\nswal lows\nre id\ninflu encing\nflu or\nden e\ntum our\nblon des\nthunder bird\nsh eva\nmoga dishu\nka b\ncre eps\ni ving\nene ed\nanno y\nâĶ Ģ\nintri gue\nenqu iry\nar aj\ntur al\nkuber netes\nend lessly\ndivi dends\ntor a\nti sh\ncommemor ates\nun ra\ntri b\npon ty\nne m\ndiss ent\nbrew ingco\nðŁĺ ½\nnor mali\nbi of\n( ...\nchil len\nì£ ¼\nmell on\nav is\nmccor mack\ning ra\nenrich ed\ncustome rexperience\ntesto sterone\nsnu g\nsett i\nger onimo\ninqui rer\nbre aches\nvery thing\nbloom ing\nmu ra\ndispo s\nbi de\nde va\nshade sof\nin trin\nsh ev\ns ven\nnayanth ara\ngan esha\nc ws\nber ta\nlabel led\nuse um\nnick named\nma han\ncar uso\nap ur\nðŁij Ĩ\nw q\norphan age\ndiscar ded\nmag nu\nlu e\nje on\nbridge port\npac ing\nmercur y\n( ðŁĵ¸\nmarx ist\namphi bious\ntransplant ation\nstit ching\nthen burg\ngradu al\nãĤ Į\nro ft\nma ils\nine c\nguy ana\ndopp elg\nver o\nre write\nhead less\nharb augh\ngate way\ncar sforsale\nsw i\nst is\nmach t\nun de\nsura baya\nstap leton\nnur turing\nmil ner\nya o\nlma oooo\nko sh\narsen al\nk ame\ner ry\nar royo\ndis misses\nru bbed\nrc b\nlew d\ndil u\nand or\nvi de\nur in\ninter sec\nha ar\nal b\nyear swith\napp leton\né al\nul livan\nsuc cu\nmonter rey\nd mx\nartem is\nron nie\nfarm land\ns football\ngro tto\nanth i\nãĢ ģ\nà® Ł\nvid ya\njimmy fallon\nൠį\nt zer\ngravit ational\nw thr\nu hhh\ne hr\ntin ker\nti juana\nscran ton\nram charan\nbar clay\nre van\nm si\nka p\nwr s\nwe thenorth\ntor al\nsat u\ngro m\nfac ep\nerick son\nz yn\nse dge\noo dle\nspur sofficial\nds p\nsic ilian\nsoli hull\nrecei vers\nladak h\nhend rick\nther i\npresi ding\nmc guinness\nlitt ers\ngun nar\ngh oul\nwi b\nn tv\nkar o\nfro ck\nb lau\nampli fy\nall is\nul lah\nmemo irs\nkh loe\nintercep tions\npet day\nlo oney\ncon fin\nch ay\npiyush goyal\nfrequ encies\nut z\nevent ual\nwarm ly\nobli vion\nan ka\nta it\nâĿ¤ï¸ı .\ndirector ial\nru lers\nprince s\nmu ck\nstur ridge\ndeu ce\nabri dged\nbagu ette\nun cles\npen du\nmin ding\nforre ster\nav ila\nwall er\nwall street\nment or\nhin o\nhigh way\ncrom well\nfanart friday\nmb i\nco yle\na hi\ntro ve\nspie gel\npay tm\nmcin tosh\njan sen\nnit i\nnash ville\nlen o\nleicester shire\nle gos\ndic t\nðŁĵ ½\nsp ad\nbeverly hills\nsy rah\nsepar ates\nz ain\nun fit\ndra gs\ntan ia\nover flowing\nhri thik\nhaw thorn\nz ani\nmac far\nfi de\nto tem\npe ds\nfundament ally\ncal ico\nsin ner\nj ä\nhil de\nds d\nten ay\nta hit\nmil f\nlie b\ninform ing\nup lift\nra el\nmortg ages\nlec t\nii ii\nguillau me\ncompos ites\nold smobile\nl end\ngar th\ncom mish\nbapti zed\nscorpi ons\nru cker\nbringback our\nalli ance\nthalap athy\ntal i\nsp ans\neri dge\nwither spoon\nlin da\nsky lar\nkor n\nhom s\nÄ į\nsil enced\ncaf fe\nar ty\ndist inguish\nto wed\npun g\njessic a\near nest\nbeau fort\nt ama\nstudy abroad\nsi khs\nnew bie\nnav ratri\nmar ble\nloun ging\nlit ter\ndal it\nso sa\niz es\ngra de\ncom promising\ntr iton\nde tta\nv j\nchau ffe\nspec tral\npowe red\nmontess ori\nartic ulate\nhal ton\nal co\nye y\nmn twins\nacoun ty\nðŁijı ðŁı¾\nâī Ī\nmad men\nkal a\ngru m\nchi k\nati s\nsu me\nakh tar\njob search\nhigh lighter\nbo ath\nâĦ ¹\ntar zan\nlam bo\nâĽĦ ï¸ı\nox fam\ndump ster\npretz els\nmac os\nincl ined\nfac tual\nadverti sers\nshu i\npu ree\nml pfi\nanti dote\ncap o\npa str\nmerc ado\nbut ton\nar min\nag g\nlol la\nhorri bly\ner rands\nchristop he\ntime snow\nmonday motiv\nli ss\nscand als\nmc i\ndispropor tion\nâĺ İ\nsur pass\nsamar itan\nso tho\npu rest\nfl att\ntrivi atuesday\ndelec table\nleop old\nhermi one\nchou dhary\nen rich\n¡ ¡\nsubsi diary\nine qualities\nbachel or\nauto immune\nla kota\ni hop\nad jec\nthe simpsons\nsh es\nse k\ngret chen\nup stream\nhin akhan\ncoper nic\nx tina\nlu g\ntough ness\ne ad\ncli pped\nbi us\nsl v\nfah ren\ndee pak\nca u\nx an\nim mature\ndig ni\nbo bs\nshred ding\nbut tery\naccommod ations\nde ven\nchun ks\nsuper league\nsky bet\nkil dare\nje et\në į\nce k\nwrec ks\npro pane\noh l\ntb d\nquo i\ntrum pp\nmi mo\nreluct ant\nver ne\no ic\nma gh\nar nau\nse ver\nli dge\nstair way\nkicchasu deep\nðŁĶ º\nmach ining\naama admi\not i\nc da\nal it\npan y\ninst alls\nac ct\ne shop\ndi em\nhard well\nfulfill ment\nsc afe\nqu ack\nextrac ts\nswee tened\nfi ghton\nf di\nd inger\nwal tham\nus ur\nrefe rees\nseok jin\ngran n\naf rin\nth n\nsch af\npar cels\nbet is\namar ine\nnom an\nkh tar\nmor itz\ncou pling\nbar ons\nðŁIJ ¸\nà ¸\nsl p\nsad ler\nx ander\ntri ad\nmc millan\nkh z\ndivi ding\nìĹijìĨ Į\ndar yl\nzed d\nle ys\npla ques\nflu ori\ntipper ary\non nell\ndi dier\nlang ford\nim c\nthe sun\nbir dies\nar cha\nye ssss\nt di\ndar ia\ncand ace\nal tam\npal aces\nch it\nsant am\nevent ful\nbook of\nad b\nmon stax\ncre ole\nco el\nâĸ ½\nwe aren\nsten nis\nshe ath\nati sm\ngron ingen\nmlpfi m\nle pre\nwrong ly\nrsp ca\nrendez vous\nacknowle dging\npel vic\nsolic itor\nsla ys\nnue stra\nlo d\nis lander\nfer oci\nfashion show\nra ss\ndge on\nadole scents\nsma shes\nnegli gence\ngrate ful\nved ere\nsw oop\ning l\napol ice\nvand alism\ngan n\njo ao\ndi supdates\nzimbab we\nunder age\nradi ance\nw of\nbour geo\npla s\ncr ani\ngh ue\nwrec kem\nwarran ts\nre form\njim mie\nat wood\nys l\nneil himself\nl bj\ni man\ntan to\nnois se\nver bs\nequip o\nal together\nmam ent\nl ice\ndou glass\ntier ney\npri med\nj hal\nfurn itu\nbraz ili\nv ill\npast els\nn ison\nu ff\nparal ysis\njay e\nim po\nðŁij ģ\nstrate gically\npakistan is\nwas sup\nsuper bike\nthank u\ntru elove\nsha ikh\nisrael is\nvi p\nto g\nli en\nla ker\ngrey hounds\ncul ars\nbian chi\nbalot elli\nar ran\nloo s\nstr ates\nhe bron\nar vo\nsunder land\nthe al\ntomb stone\nsand man\nc pac\nthanks giving\nlove him\nlat ino\nan in\naka if\nĭ ãĤ\ntor quay\ndi est\nalli anz\nðŁĺ ķ\ngolf club\ncl lr\nwal cott\nsch nau\npromp ted\nnomin ating\nlen nox\nval et\nmon ro\nmay ward\ne ph\nðŁĶ Ķ\ninter oper\nr da\nre flex\narm chair\nê° ķ\nstri pper\npor ti\nph arm\nham za\nni reland\nne ue\nh pv\nport foli\nsun burn\nfris bee\nbe al\nbapti ste\nx h\nty m\npr ati\no vers\nhaz rat\ndeser t\nder ry\nus ky\nem mett\nach arya\n)_/ ¯\nshu d\nmay a\nham ill\nra im\nnr c\nfitt ings\ncur vy\nðŁı ĩ\nster ling\nॠĢ\nwal kin\nshort cuts\nmil ly\nast ur\nalpha be\npl i\npe z\nmiss you\nrad ford\nml g\nta eyang\nnotjust lakes\ndu mps\nseren dip\nle ur\nra ving\ne ster\nde priv\nabsc bn\nðŁijĩ ðŁı»\nscar city\no cr\nmean ings\ncap t\nda hl\nfer mentation\nbri oche\nto win\nout lander\nmassi mo\nen cro\nðŁ¥ ³\nbuil t\npo tam\nkir i\ntm w\nmonit ored\nk ites\npeoples vote\ngray son\níģ ¬\nafri ka\na dies\ni vote\ngy ne\ng annon\ndi x\nc mc\nou ral\nfox andfriends\nbel i\nig ne\ngl an\nkatrin akaif\nco politics\nqual itative\np si\nlu cci\ndisc oura\nâĺ ®\nkel li\ngau tam\ncarac as\nreale st\npu la\nin us\nhill top\nmake aw\natten borough\ntw y\nr arity\npeck ham\nma hon\ncorn elius\nclin icians\nton line\ntb i\nparadi se\nka si\ninev it\nfresh ness\ncolling wood\nlun atic\ndefen se\ncop d\nin fra\nwain wright\nsains bury\nalab am\nte ma\nlac o\nchec ker\nreleg ated\ntren t\nstal ks\nhuff post\nbhubanes war\nast ral\nshare your\nprim rose\nhi me\ncat an\nend ment\nen dow\ncle mens\nmal oney\nhil ary\ngame time\nden ise\ncollabor ators\nb wo\nradic als\ngue tta\nici on\nau a\nsnap matic\nsat chel\nexcav ation\nbase man\ns ão\ngn ation\nfel d\nsurve y\nshah zad\nma st\nanirud hofficial\ntru cker\not ago\ngeo graph\nethe l\nâļ¡ï¸ı âļ¡ï¸ı\ns ver\nmu tt\ninternetof things\nancho red\nwh ouse\nbang la\nbal main\nç¹ ĭãģ\nbreak fa\ná Ģ\ntwi ster\nte tris\nca v\nstag s\ng z\nau b\nstor med\nhel ens\nyar mouth\nst asy\ngustav o\nco sc\nvin son\nup p\nsc ricket\nassump tions\napp e\nnu h\nu er\npre mise\nn aga\ne amon\ncoron ary\nna f\nnorth side\nel mer\nro tar\nout lining\nel f\nre surg\nkat elyn\nin can\nhyster ia\nce e\nam bani\npro lly\nĮ ãĤĬãģ\nax es\nsan jose\nrem brandt\nmag pie\neven ly\nscor sese\nqu aint\nf g\nb buk\nindian football\nweare all\nspd wy\npis ces\nec g\nâĺħâĺħâĺħâĺħ âĺħ\npre orders\n: |\nni pple\nsal azar\nju me\njail break\nmin n\nbas sett\nze tta\njef free\nad jun\ntic on\nsan diego\ndrink local\nchol era\nsolic itors\no bo\ncom post\nni an\nwr a\ntre ach\nic ic\nprofession al\ndel ve\nleg ate\nhistor ia\ncro issant\ncon noisse\nnam o\npalli ative\nchem trails\ni ority\nglobal warming\ncomic art\nbehavi oural\nre sted\nli as\ncli mates\nŁ ãģĦ\nrut land\nnou rish\nmenopau se\nhot ties\ndemen ti\nve spa\nmel ville\nanal ogue\ntz man\nstr ung\nim perfect\ngl are\ncir cling\nros berg\nrec o\noc ity\nlo ire\nem be\ndo ssier\nne el\nnan do\nme a\ngal vani\nfin esse\nag p\nberke ley\nasi m\nâĺº âĺº\nquil ted\nish ere\nun matched\npo tion\nfor z\nat re\nselfi es\njuli ana\nðŁļ ¶\nâĸ º\nmel ton\nâłĢâłĢâłĢâłĢ âłĢâłĢâłĢâłĢ\nspin rilla\npur cell\ned p\nat leti\ntony awards\nra ja\npro gno\nmol ten\nstu ff\np ally\nnobel prize\nâĻ» ï¸ı\nspiritu al\nspe ake\nsa sha\nbri um\ntru ss\ncritici ze\nassassinscre ed\nyor uba\nu lo\nfire man\nworkin progress\nef cc\nfla res\nro bot\nhi kers\ncl l\nshado wing\npat sy\nleh man\nc ns\nå ±\nguad al\nà± į\nra pe\nr honda\nparalle ls\nson ja\nlangu age\nland ings\nz ola\ncr amps\nbur ning\napprais al\njol la\nham m\nkas a\ngul ly\nf go\nuly sses\nri be\nðŁĴ Ħ\nib u\neti enne\nbri ar\nfin ely\ncomb ating\ny ql\ngo tham\nwe chat\nto paz\nprimar ies\nl se\niz z\nhel e\ndispon ible\ncy stic\nbel ichick\nth rush\nkansas city\nge om\nsoli di\nred bubble\nby stand\ncambridge shire\npar fait\nast le\now o\nind ore\nstom ping\nsm elly\nðŁ¤ ĸ\nlocom o\nadm itting\nhol me\nclock wise\nmin sk\nmc co\nfor get\nev p\ncam ra\nab ella\nyo tes\nuniversit yof\nmé xico\nsilver ado\nric ket\ncrom bie\npu j\neradic ate\ndeli ght\ny go\nglam ping\nvic a\ndu ggan\ncoun ters\ncf d\nsc our\nreact js\npu ram\nparas ites\nin ki\nvill en\nstel la\nli mbo\nang as\nk cr\nðŁĴļðŁĴļ ðŁĴļ\nvap ori\nmum ford\noli gar\nà ¼\nal oo\nboo ties\nad r\nk elli\ndru mmers\nav ici\nnature uk\nron al\nin trac\nun splash\nle che\ng oma\nel ine\nenvir o\nbi onic\nbu eno\nmi k\nav in\nstar ling\nem powers\ncake day\nboy cot\nðŁĴļ ðŁĴļ\nðŁĮ¸ ðŁĮ¸\nv ach\nm ci\nfractu res\nger i\nsk ing\nexclu ded\nlu ce\nja ve\nig gy\nevi den\naki stan\na wn\nmor als\nluci fer\nha ban\ntumb ling\nsunday motivation\nmo sley\ncaptain america\nsch icago\nthe one\nmo td\nd ts\nðŁIJ ¼\nrep ell\nii i\nlocu st\ngeo spatial\nmer sey\nimmer se\ndesc end\nber nade\nj s\nboat sales\nwin der\ncran k\nsing leton\ncandid acy\nben a\nðŁı» âĢį\nhigh lander\nol t\nk prs\nhealthy lifestyle\nfour teen\nend the\nith aca\ncircul ated\nr ans\npre valent\nha vas\nsplend or\nroo ster\nkalamaz oo\njewell ers\nenne dy\nrou sey\nes y\ncann ons\nornam ental\n// //\nren don\nwin ne\nmol ding\neid mubarak\ncoun tess\nsimon a\nha wa\nfo es\ndu ster\nsb u\npor tray\nmar ries\ngoo dday\ncho co\nachi ever\nðŁĺ¹ ðŁĺ¹\npre neur\ntr amp\ntom i\nn bat\ngarden chat\nfarra khan\never glades\nab ru\nsou sa\nse ce\nhomes wee\nterre strial\nbar it\nsri devi\nol u\nmel inda\nf rick\ncan dies\nðŁĺŃ ðŁĴķ\nqu reshi\nfamily fun\nexor cist\ncardin al\nny t\ndies el\ncu mulus\ncapric orn\nsi ology\nlor na\ndou gie\nan die\nsuper sport\nc fl\nп ÑĢи\nsay ang\npe ek\nภĬ\nlo be\nj em\ning lis\ngg led\nc sn\namne sty\nchu ps\nba es\nsau er\nðŁı IJ\nmongo lian\nen et\nback street\ndr illed\nacce ssing\nce o\nb se\nai ken\npur r\nwor sen\nwhe res\nwar k\ntesti fying\nbu ri\nbla st\naw g\nðŁĵ ĭ\nre defining\nhear ing\nu ci\nc mp\nbon i\ntail oring\nta ji\nnoc chi\nem t\nstephen king\nne et\ncompla ins\ncampaig ner\nluci ano\ntwili ght\nti esto\npas sports\nflo yd\ncathe dr\nna ked\ncaregi ver\nb coz\nade cides\nku ri\nly k\nbr aries\ndren ched\ndisc lose\nðŁĴª ðŁı½\nle blanc\nje tty\ngar ty\nchip mun\nb su\nrhyth mic\nic z\nfri d\nanne x\name x\nsolo ist\nlanc ers\narro whead\nspeci fication\nsimul ated\nna is\ninver te\nbo wing\nwor ship\nf z\nabo ss\nsha q\nì¶ ķ\nchalleng ers\nan arch\naamaadmi party\nãħĭãħĭ ãħĭ\nsuffol k\nso corro\nsn ell\ncla dding\nabsor bing\nshaw a\nparticip ates\nðŁį Ķ\nbook stores\nbak u\nseap ort\nko jima\ngab y\npack ard\nelectr ician\nlet it\nmo wing\nfa wad\nyoung jae\nhot mail\nmen ing\nu rie\nintim acy\ncon ti\n: \")\nlifeis good\nin ciner\ni dri\ncraz iness\njour nos\nfran chi\nbott len\nal da\nff es\nk x\nsouth we\nair a\nclay ton\nsco ti\nf j\nbri ga\nðŁ¤ĺ ðŁı»\ndemonstr ators\ny z\nstor k\nna q\ncasc ades\ntravel chat\nplat a\npad ma\nfran ci\nat tain\nbat girl\nlom bard\nhoo s\nd dos\nneon atal\ndiscla imer\nr ss\nr ant\ndi sen\ntex aste\nso cal\nfrac tal\ncam ry\nstri fe\nsn acking\nmu h\nsant ander\nmor ons\ngra f\npar ades\nhu ston\ndru pal\nmi ento\nkir stel\nhy de\nvom it\nforti fied\nsphin x\nda v\nbir yani\nwin nings\ns baseball\nmer ged\nlovel ondon\nling ering\ndream big\ncar leton\nliveli hood\ndjan go\nastri d\ngri ds\ndown e\nbru ised\ns ne\nscarec row\nhel ium\nf nc\nbi ggs\nan ter\nrestor ative\nem pires\nab del\nlife style\nkiwan is\ncolloqui um\nme en\npr ick\nanti que\nze b\nmi mic\nedmon ds\nðŁijĬ ðŁijĬ\nq ing\npp el\nmc gill\ninterpre ting\nâŀ ķ\nrash ad\ndo ka\nnarr ator\nelectro magnetic\nash by\nsau ra\niran deal\nâģ īï¸ı\nkrish nan\nin di\nff en\nbre a\nos man\nmultin ational\nchi ppe\nrecruit ers\naus biz\np ounding\nre gen\ncur sor\nrefu sal\nmac s\nin ak\nax ial\nwa ifu\nup cycled\nhindu stan\ncas sini\ncarly le\nscrat ches\nre ef\nman atee\neat ery\nðŁĵ ¢\nun condition\nsen pai\non ther\ncomic book\npro sciutto\nde mar\nmi se\nma ge\nfre ec\naye sha\nal der\nandroid games\nley ton\nho ck\ndoor way\nchicagof ire\naali yah\nsw elling\nbi x\n. ðŁĺĤ\nevan kirstel\ntorpe do\nkon stant\ngenevie ve\nma ia\nha user\ndo torg\nhide ous\nfi k\nsp raw\ne ek\nz appa\nwan dered\n' '\nra jan\nbam bi\n( $)\nwid ening\ntool box\nsa ir\nillumin ating\npra ys\nout patient\ni w\nday o\nlo b\nsw fl\nsha des\ngu ms\ncoo kin\nko di\ngri ffin\ntraum ati\nste a\nslaugh tered\ngod bless\nair time\npseu do\nb sa\nhau led\nar if\nà¸Ńภĩ\nle l\nwc po\nmil iti\nchar ters\nworl da\nru k\nk gs\ndigital india\nis able\nidyl lic\nesp ino\nmarie tta\ne bo\nteam canada\nab our\nwil ton\nrock stars\nfav ored\nphys ic\nwrink le\ntb r\nd print\nball arat\nad al\nz ey\nðŁĺį ðŁĶ¥\ntom lin\nmt r\npal sy\nfener bah\ntight en\nphil ia\nir oning\nry u\nb ant\nenqu ire\nca ir\nabur ger\ntru n\ngreen berg\nchau han\nir ina\nsh ani\ntrend setter\npre tt\nzaf ar\nalo ve\nv ici\npan ic\nno o\nlu stre\ndisrup ted\nbal lis\nson sof\nmon si\ninst ac\nake st\nëĭ ¤\nkw ame\nhorror movies\ndistric t\nsau cy\nmb an\nar mies\nwith drawn\nmed ics\nloft us\ner oom\nbe kind\nar ns\nall on\nun ison\ndavi ds\ncr at\nnicot ine\nso or\nsm x\non co\ncospla ying\nzombi es\nhar ms\ne ger\nro sy\nmoon shine\nfe in\nce tt\ndu brov\nreg ents\nben itez\nðŁijıðŁı¼ ðŁijıðŁı¼\nste c\nm alia\nprioriti ze\nic eland\nft se\nv amo\nlam ont\nhomo sexuality\nbre es\nregu i\ncb p\nte j\nsky sports\ndeter gent\nsha sta\nde rel\nconserv ancy\ncolori zed\naccol ades\nvis o\nshow your\nnan ow\nbice ps\nus ability\nbi m\ndailys ketch\npearl jam\nstran gest\nmega deth\nbroad casts\nbar ren\nar ton\nchri ss\nconfi gu\nlu res\nis the\ne ul\nrailway ana\nglobal health\ngi anni\nu aap\ns lum\nconsci ously\nab re\nn up\nbud get\nv ada\ne sch\nreal ness\ner ased\nth unt\nbe z\narmist ice\nðŁij ¹\nsh run\no led\ndriver less\nðŁ¤· ðŁı»âĢįâĻĢï¸ı\nwon dr\nsk an\nsal aam\nmother land\nh wang\ngen o\ngang nam\ntw right\nendor sing\nen ic\nador ation\npau sed\npatric ks\ndo cked\nplat te\nff xv\nethnic ity\nauto show\nside show\nafter life\nre located\norphan ed\nfood network\ndare to\nand ra\nsla ps\nv live\nswim s\nre imagined\nmist le\nre vise\nreal ity\nbhar ti\nðŁĴĻ ðŁĴĽ\nlate st\nprou dest\ngra sses\nlan yard\nfresh est\ncarcin oma\nanom aly\nzieg ler\nsum ner\nly rix\ngor g\nis d\nav el\nswild life\nme squ\njohn cena\neuro league\nsab er\nmaster ful\nyar ra\ncogn ition\njacob son\nabo lic\nsir loin\nshuk la\nmoj ito\nsu pere\nst weet\nme z\ne sa\nrudol f\ngur a\nwhere you\ntt m\nwin s\ntrust worthy\nny k\nbra den\ntable top\ngood food\nes on\nbe k\nlingui stic\ngra ys\nch ath\nh cs\nmon i\nde ans\ncu ssions\nch ell\nslo ws\nhe mi\nd app\nshar pie\nboo sters\na os\nstr ack\nse dona\nmu eller\nhard wick\nor nate\nthor a\nsal ud\no twol\nch um\nmi ho\nfor age\nthel ittle\ntear ful\nones elf\nmin dy\nsm g\ngmb h\nemer ald\nðŁĶ´ âļªï¸ı\ntu tti\nrecep tions\nre vising\ni brox\ntope ka\nsal ami\nexpan se\ni books\ndob son\ncli o\nat s\nðŁļ Į\nmo ha\nis ance\nshu tters\nmoo t\njan ine\nmarvel comics\njor dani\npos er\nkenne th\nhy ung\nde ja\nase ball\nspeci ality\neu ston\nclassic car\nhad ith\nðŁIJ ī\nchas ing\niz o\ngros ven\nag lia\nthisdayin history\nt row\nom ile\nhu ar\nby n\nsal ine\ndiv ine\ndemon ic\nty ran\nhan dover\nrevit alization\npa ella\ncryp tic\nse dg\nm end\ndun kirk\nbre d\nwal d\nsport scar\na ard\nwhe aton\nda ener\nk lan\nbr t\nbakhta war\nspi res\nschu bert\nro ti\npoli sh\no se\nag ame\nwonder con\nprote stant\nbo sa\nðŁĺ Ł\nd ü\njoy ride\nger trude\nâĿ Ŀ\ngil a\nv h\ntw a\ntra v\nswal lowed\nstar ve\nla in\nent ren\nrei ki\nsu kh\ncra ic\naz u\nweb page\nkee fe\nhypo the\nhir sch\nhel le\ncamp ground\nw amy\ntra vi\nsha hi\nsan deep\nru i\nhan uman\ndw p\nreposit ory\nno or\nno ff\nun real\np ell\nblack history\nhar vick\nma scar\npay ee\npa sha\ngastron omy\nd ÃŃ\nai g\nrosen thal\nopen day\nembelli shed\nt tip\nsun bathing\ngo pack\nend ome\nï¸ı #\ninvali d\nfinal four\nst fu\nsquish y\nra sta\nmo sch\njam esc\ndie trich\nsel a\nmel b\nel vi\nt dp\nsun i\nsli t\nj ha\nbi za\nspi ked\nl li\nl illard\nvam pi\nsyno psis\naz har\nkendrick lamar\nĮãĤĬãģ ŁãģĦ\nheart less\ncountry file\nair play\narrog ance\npre e\nvirtu oso\nãħłãħł ãħłãħł\nraj u\nle bu\nfor ward\ntu g\ndro s\nmondaymotiv aton\nconcep cion\nthel o\npad i\nlooo ol\nÑĢ од\nit ss\neth ical\nend uro\n__ :\nexpend iture\nmon ste\nmas king\nterri ers\nib is\ne mber\ncu mple\npunctu ation\npi per\nir vin\nade e\nyy yyyy\nflash backs\ncel sius\ndon nie\nbo gota\nben evol\nthe script\nshil pa\npro se\nfin dia\nze ke\nne ko\ndo ves\nblues lyrix\nfro sh\nsowe to\nmp lo\nal ai\nsab i\nraq qa\nwf tv\nstro ller\nian somerhalder\nðŁĶ ª\nan on\nmo seley\n! ?!?\nsta king\nmol y\ncar tri\nc sg\nast or\ntransc end\nma er\nde ux\ncow girl\nsas k\npun ter\nma ken\no ates\nlove tt\ngrow ler\nsag in\nv n\nssi ble\nofficeof rg\ny mc\nsab ar\nfaul ty\nap ha\nak on\nðŁij «\nsnow don\nae w\nraise the\nðĿ ĵ\ngrue some\nclement ine\nsp ing\nlat a\nworlden viron\nmi mic\ncan aria\nbakhtawar bz\nao a\nfal a\nãĤ Ń\navi va\nyou uuu\nthi gh\nla dders\ngu mbo\ntz ky\nfu zz\nplastic pollution\nest ate\nstrength ened\nk ant\ndr in\ncal vert\ntransform ational\nfrigh tened\nmac lean\nelited angerous\near thy\nt son\nto da\nj nu\n.. ,\nmic hal\ni ban\nje ong\nis real\nsim coe\nexclu sives\nblue bells\nben e\nte u\npil sner\npens ke\nathe ists\nm pu\ncartag ena\nðŁĴĹ ðŁĴĹ\nmillion aires\nkk kk\nit ar\nsubscri ptions\nremo te\nma fi\nhin ton\nw cc\nho k\nds b\nab leton\nsevent y\npun ks\ne indhoven\nsh one\nmcfar lane\nlim popo\nempha si\nà ¼\nsin fo\npe tre\nman grove\nch ino\nber tie\nplay lists\npush awards\np af\ndeb bie\nc do\nr ino\nðŁı¾ âĢįâĻĤï¸ı\nfol ke\nbon nar\nth ine\nsl an\nhal ter\nevi e\naw some\nvul tures\nspar ky\nseiz ures\nâľ Ķ\nram one\nine ffe\nal n\npro ctor\nast ra\nthe voice\ngro te\nsci on\ndead line\nam aya\ntain ted\npatter ned\nexce eding\ncross fit\nkay lee\ndrop box\nru shes\ntack led\nmo by\nretro gamer\nn cbd\nbenef itting\nshay kh\nguild hall\ngen try\ndream cast\ndread ed\nbun dled\nth aw\nrevol ving\nn pt\nkylie jenner\nimagin ative\nron i\nover came\nfamily time\nds burg\ncar naval\nrelation ship\nrecogni zable\ncor oner\nho le\nfan fic\nemir ates\nbur ritos\nanaly se\nthin ner\nne es\ngalli poli\nbl r\ncat woman\n-- >>\nau lt\nada ily\nnau ghty\nili o\nsolit aire\nmtv br\njocel yn\narun ach\nrep ent\nsouth gate\nhy acin\nessenti al\nfent on\nand um\nit or\ngo pal\nsl inger\npo sei\naw il\nwi elding\nra ila\neli as\na sto\nà ¤\ntend ency\nstr ata\nker t\n< -\nim acele\nda es\nsti mulus\nhan ley\nfit nes\nec stasy\nlim ous\nha iling\nðŁ¤ Ń\nchis wick\ntar ies\nsla v\npul i\nmoderni zation\nblack mail\nb ingham\nh fx\n+ +\nðŁĩ®ðŁĩ ³\nni v\nwe a\nprofess or\nk off\nbol ster\nsu ave\nsequ ences\npepper oni\nnot te\ndre n\nãģ¨ ç¹ĭãģ\nhs v\no ga\nap tly\nz ad\nexcel si\nrin ka\nmol dova\nmin n\nma bel\nconferen cing\nbas ing\nof er\nob si\nhamill himself\ncare less\nbrief ed\ninhe rent\npar ish\ndub nation\ntown sville\nsar awak\ngee ky\ndoncaster isgreat\nwas abi\ngu p\nphen o\ndra inthe\ncarrie underwood\nble eds\nbbc world\nane w\nalta f\ndul wich\nani ston\nw ti\nsumat ra\ngra fton\nbl n\nme ster\nbode ga\nre go\nes q\nan jo\nsump tuous\nmai sie\nï¿ ½\nwil t\njak ob\nel vis\nse pul\nmu ster\nair pollution\npresident e\nhappy monday\nexten sively\nfl ondon\nt ls\nplay ing\npe ed\ndin ho\nvar dy\npi ka\nn iro\nau cus\nðŁį ¦\nnu ll\nel ondon\njuvent us\nimag ines\ndis ab\nlit o\nd ura\nwork places\npromo te\nmc caf\nwood work\nwaw x\nà® ª\ntt ino\nshar i\nsem per\nbetter together\nðŁijĬ ðŁı»\nze bra\npon dering\nen chil\nho m\ncosm ic\ntan z\nmo cked\nec cc\nath ed\nabo lish\nprop eller\nparis agreement\nassemb lies\nindu stry\nfraudul ent\npe sa\nchang min\nax x\nðŁĴ µ\nirr ational\ncu sa\nramad han\nocta via\non elove\njac ki\nbar ak\ntaxi der\nseri ous\nnathan fillion\nmc en\nch k\npo part\ngrav ity\ncopp ola\nreading fc\nillu sions\nj ig\nww x\nre sh\nex porting\nbuzz ard\nâĻ ¤\np cm\nlan apar\nko s\narom as\nantal ya\nww dc\nven a\nphil a\nball in\nðŁij Ħ\nquin ta\nma o\nf ery\neigh ty\nsentim ents\nsafe guarding\nr wa\npu ffs\nluc ille\nde cath\nsl u\nnu gent\nde ter\nbraz il\nze iss\nsuper bowl\nsubsi dy\nalter n\nhi dalgo\nenz ymes\nä ½\ntag ne\nhair dresser\nadri en\nwalk out\noppo ses\ncan tina\nbed side\naf an\nðŁĶ Ĺ\nprophe tic\ndan es\nun successful\nsuper charged\npk k\nexem ption\nhart le\nsecu lar\ncli pping\nbr s\nunited way\nc net\npat chy\nha gan\ne en\nâļ ľ\nvar a\nsym pathi\nnever trump\naffir mation\nom f\nny cfc\nma ja\nsur ro\nkeer th\nup scale\nsandal wood\nmon archy\nkno bs\nå ĭ\npo tholes\nhunger games\nter races\nna sir\ncoun sell\nwelcome to\nwa q\nse aman\nm ita\nstun ningly\non theroad\nin ability\n) !!\nbon go\nant v\nsp ut\nworldenviron mentday\nresu sc\ny td\nfi m\neun hyuk\nsa chin\nrose anne\ncler mont\nape c\nam ina\nv ening\nn antes\nal most\nsin us\nex as\nty l\nti en\nple ad\nlanc s\nbur naby\nre k\njo om\nobserv ers\ndisco graphy\ncl g\nâĻ ¦\nsn ack\nr ti\no ily\ncrystal li\nbru te\nweb development\ntopp ings\nla f\nan is\nad der\nreli ving\ncar lin\nbattle of\nwe g\nsyri an\npon t\nn dc\nlagh ate\nyu ma\nsp p\np iti\nro bbing\nmart ing\nrey kja\nraj put\nnc ds\nkie wicz\nâĢ¢ âĢ¢\nvam pire\nsubstan tially\nopio ids\nnepal i\nk line\nar oo\nunder stand\nlit t\nu it\nthro mbo\nsar ies\nqu ot\nb alling\nt tr\ns gh\nphilip p\nbr ant\nac l\nm ello\nwhit taker\n. ;\ndefi ant\nb gc\nrepl ying\nmir ren\nmetamor pho\nsch wab\nbul ge\nutili zed\npick ering\npar don\nd sa\nภĪ\ndoo ley\ncumul ative\nÐ »\nur gency\ne mir\n+ /-\n¦ Ī\not as\nâı ³\nstation ed\ngrape vine\nar ac\nkaran johar\nf ancy\nsau l\ncoo gs\nlgbt q\nا٠ħ\njav i\nu mmer\npl l\nden is\ndai pur\npu ffin\nlewi sham\nfand om\nco pe\nves matter\ns ve\nhel pless\ndeo dor\nostr ich\nkaz an\nfriday the\ncon dor\nv x\nsophom ores\nrob les\ncu tt\ncli mbers\në¦ ¬\nsle g\nsn f\nmac ys\nhydr ating\ngrou pe\npo yn\nmou lin\nhg tv\nlmfa ooo\nsulph ur\nasdfghj kl\nannab elle\nhump back\nbra ved\nviswas am\nmulti purpose\nhu midi\nescor ted\nbarb ican\nf ad\ncor sa\nðŁ¤ «\npi ppa\nhere to\ncan y\nser gi\nor cas\no vie\ned ou\ns any\nglob alization\nman cini\nfood truck\nf is\ndefi brill\nsch re\nsma fia\nlove wins\nla ut\nk aka\nhol lande\ngame on\nresurg ence\nout side\nolympi ad\nint an\nabstr action\nrapi d\npal om\ncal le\njas min\nattack ers\nswag g\nmit ra\nky lo\nà® ²\nher mitage\ngor do\ne ira\nso sfam\nroll out\nexc ite\nsy nod\nmer rill\nc als\nas sa\nliveli hoods\nju ve\nthe black\ngopack go\nant lers\nalban ian\nwool ly\nqu iche\npuri fication\nare th\nsmar thome\nne k\nall blacks\nmex icans\nis m\nger ms\ncomple xion\nmar ck\nu shi\nðŁIJ IJ\nchar l\nca stic\ntill erson\ngiuli ani\nbiode gradable\nmal bec\nbo is\nju bil\nim es\nr ame\ngene tic\nesp nu\nch ley\nso ho\ngo pher\ng sc\nbuu ren\ncu be\nbridesma ids\nwebin ars\nto e\nmani pur\nviol ently\nnotic ias\nex changing\nchi ev\nreplac eable\nmuay thai\nbu ss\nsp il\ninstal ment\ndiv ya\ncait lin\no lim\nfil tering\nwhirl wind\nsta red\nprior it\npr am\npompe ii\nmono logue\nk ite\nbu ka\nâĢ¦ ..\nvac cine\nbre ro\nwoz ni\nsol ent\nre ferr\nmy rt\ngridi ron\ngalatasar ay\nfro ze\nclare mont\nðŁ¥ ĥ\nvictori as\nssel dorf\npa stures\nnet neutrality\nch or\nðŁij ģ\nಠ¿\nwe ho\nsymp tom\njo sel\nin ous\ndragon con\npower ball\np te\nfour thofjuly\nec la\near buds\nwhere abouts\nsalt life\ndepriv ation\nch ter\nwi ggle\nsyste m\nps st\nch az\nd any\nri mo\noax aca\nlanapar rilla\nbarcel on\nmelanch oly\nway back\nho tro\nn si\nl illy\nkur o\nja han\nintellec t\nboard game\nðŁı Ĭ\nsneak peek\nk prc\njail s\ncand el\nzan zi\nmor timer\nstar ch\nra gs\np fa\nlong live\nk art\ngir ona\ncro cker\nchristop h\nprecau tions\nwar ship\nper m\nparen t\nvan gogh\ngif ford\nallegh eny\nra yn\nut m\nsten cil\nrec alling\npen ney\nz azzle\nìĥ Ŀ\nhin ds\naren as\nnu ev\nlaw ler\ngu in\ndo this\nðŁij ķ\nì¶ķ íķĺ\nwe g\nti b\nri din\ncomplex es\nturbul ent\npe sos\nde marcus\nvall arta\nsam sun\nkis ses\nhein rich\ndeport es\nwil ms\nur d\nthen ext\ninki gayo\nho wi\nfir sts\ncarri age\nclean liness\nmas war\nis ch\nax el\nsi zzle\nroad house\nfr ans\nent ourage\nco bble\nboo th\nbenedic t\ntal on\nfc u\nyear ofthe\nray on\nraider nation\nfo yle\nko val\npi anos\nl pg\nbur mese\nman ure\ngeo caching\ncosc ino\nb np\nfer ra\nstro phy\nmar ais\nce es\nlegen dof\nkat niss\neno ch\nav ed\nyou know\nd prk\nðŁĺ¢ ðŁĺ¢\nsp un\npro st\nsor rows\ncent red\nke a\ngal icia\n? ðŁ¤Ķ\nÑĢод а\nbou chard\nðŁĴĻ ðŁĴľ\nyu i\nseed lings\njon ah\nreco vers\nny rd\nboard room\nsu ma\nmy japs\ntun g\nsha i\nir gc\neli o\nwag ons\nka shi\npolic emen\njohn nie\nale coscino\nshop ify\ndot ted\nde tri\nva w\nto fficial\nin your\nchal mers\ntrac ed\nno vi\nby es\nari el\nnipp on\nla pel\ngri ez\nb gs\nfool ing\nd ita\nvijay sethu\nnm wx\nas ot\nkr anti\nhel m\nve di\nsic kest\nmo chi\nk abo\nshru bs\nhe red\nb sp\nsq m\nham r\ndul kar\nanth a\nnr f\navoid ance\nat en\npubli x\nbe arers\nnas i\nha p\nh ells\nðŁĸ ¥\nภ·\nthelast jedi\noh wx\nðŁį «\nwa hoo\nthere se\nrec aps\nss nhq\nbird photography\nv ay\npet ti\npau lo\nbel vedere\n( *\ngr l\ndu vet\nc pec\nsa it\npor sch\nmeas urable\navi ators\nfre mantle\nbre en\non om\nme and\nlife saving\neu ref\nen don\nembar as\naira sia\nel is\ndun kin\nstar magic\ns ill\nporto bello\nki efer\nex e\nmu ted\nãģ ¦\nwe thepeople\nlogi a\nliber al\ntheforce awakens\nmin ed\nhaun ts\nfreck les\ncare taker\ns india\nâķ IJ\ndev lin\nlist on\ndirection er\noh n\nfi garo\nem manuel\ndu bois\ncl ones\nbru ise\nðŁİĪ ðŁİī\ndisin fe\nder matology\nas r\ns watch\ndis comfort\ntam anna\npi day\nmack en\nk atic\ndelu sional\nshaw nee\ngu d\nal bino\np ali\ndin gh\ncucu mbers\ncoffe y\nanticip ating\ntreas ured\nweb summit\nshel tered\nsav or\npedago gy\nm gs\nsh ma\ns bu\nden ali\ncam pos\nbubble gum\no ir\nle aps\ny ler\nr one\nsansk rit\nmin t\nmeat less\nfuturi st\ndu de\na vel\nprote sted\nsqu ire\nz aki\nsz n\nhar court\ncycl one\nbour dain\ngather ings\nd ant\nadvent urer\nparag on\nalt man\ndd ing\nban erjee\nsnorkel ing\nmother well\nmis sy\nen der\nglo ws\nki wis\nchick pea\npor o\ne fron\napp t\nu y\nspeci fied\ngab by\ne strada\ncom bos\nbour bon\nvin i\nvar un\nsteph ani\nkey words\ncar vings\namit abh\nwr ought\ntw al\nre els\nclu bbing\nubi quit\ncri t\nambed kar\næ Ļ\nprun ing\nvaccin ated\nboe ing\ns ks\nlo ona\nhypno sis\nedel man\npho l\nhe w\ncolo sse\nmckin sey\nu on\nto te\nsacrific ing\nox i\nn ang\ne mu\nпÑĢи ÑĢода\nm th\nkers wednesday\nargu ed\ntimel apse\nris king\nregul ating\nni gh\nlikeli hood\ncu bic\nau ction\nrein for\npi stor\nno ses\nye l\nsnu ggles\npe i\njean ette\nta ku\nri th\nguy z\nภŀ\ny te\nver ted\npay soff\njau regui\nhoo ligans\nprocedu ral\nmi b\nhar dy\nel eng\nchec kers\nall ine\nthe met\nprou dof\nkeerth yofficial\ncollabor ator\nni u\ninfl icted\nadv ani\nre twee\nmemor iam\nf icial\nti ghter\nsal em\nre viewers\nbr ics\nben digo\nam ell\ntur kish\nsush maswar\npaul son\npal awan\nmol lie\nstitch er\ns burgh\nir u\nhay dn\nen ers\naro a\nu zzi\nsaraj evo\nhel a\napol lo\nnine ty\nvac a\nsp on\nvent u\njel ena\nhei fer\navo ids\nsp ine\npri ze\nmar ist\nre creating\nme de\nwoo den\nfind lay\nro fl\nn di\ncompreh end\nyu go\ny ü\nto work\nu fos\nson ar\npi ston\nrecor ding\ntent ative\nart forsale\npel lets\nfre do\nÙĪ ر\nmu ses\ncustom ization\npro found\nis ner\nide ally\nsi am\nplan kton\ncm dr\nman ger\nfran ken\ncustomiz able\nठ®\nwalk away\nswi vel\nvast ly\nno ton\nlex a\nex moor\nz as\ntan te\nreduc tions\nlol ly\nhip sters\nbenef ited\në ²\nww www\nmascul ine\nfi ji\ndre y\nph ill\nane ous\nnic ol\nmen dez\ndisapp ro\nch ner\nthrough s\nshen mue\neast man\nðŁIJ İ\nyu ck\nunder tale\nre ys\ngo beavs\neng en\nc na\nmer r\nbir k\nãģ¨ç¹ĭãģ ĮãĤĬãģŁãģĦ\nâĥ£ @\nyn na\nste ed\noffen der\nat um\nvani shing\npresi denti\nlove them\ng nocchi\nfri ggin\nper il\nmad hya\nag ne\ndee jay\nmar nock\nm tb\nfold able\n@ ___\nstand re\nbron x\nbow ski\nfin ite\ncro ckett\nb sf\nge tit\nseren awilliams\nmir o\nignati us\nsla y\nrin se\nfon due\nsel dom\ns more\ngan i\ndy ce\ndmit ry\ncru mb\nlate post\npri mark\noh ana\nflor als\ndo a\nremembrance day\nd ds\nazi one\ntoon ami\nair port\næĿ ±\nth ad\nfi st\ndine sh\ndr who\nad words\nadmi rer\npro je\nkyrgy z\nà «\nmanife station\nle wan\nj ic\nthi bau\nle ased\nvan ity\nnouri shed\nnever theless\naug mente\nfu elled\nche ad\nwil shere\nru di\np z\nmy co\nmor ro\nherbali fe\nhardro ck\nde man\ndre ality\nsp ades\nce vic\nbha i\nbar on\nultimat efan\nhou news\nto bi\nstru t\nke el\naffili ation\nthe masters\nsm al\nhu e\neste ban\ncon v\nom nic\ndatab ases\nco v\nter ti\nst g\nsnoop dogg\nmetab ol\nleth bridge\nðŁı» âĢįâĻĢï¸ı\nyear ling\nresidente vil\nnws l\niy aki\ngriez mann\nc ous\nðŁĵĿ :\ntor ian\nsam i\nðŁĶ¥ðŁĶ¥ ðŁĶ¥ðŁĶ¥ðŁĶ¥\ng are\nalli ances\nwhit field\nwe ther\nrefin ing\ncoy i\nkra ken\nðŁĺĺ âĿ¤\nsingul arity\nlil i\nh ns\nbol dand\nwaw rinka\nmisogy ny\nlo vers\nc q\nb dg\nad ona\ngar ter\nwomen of\nsc d\nrecogn ising\nmun a\nstr ou\nsign alling\nlare do\nhell boy\nalek sand\nun available\npedi atric\nas in\nmer ia\nri shi\nfuturi sm\nw ye\npolari zed\ne we\npro pel\nin forms\ncre ase\n~ \"\narti ston\nlike for\nheidel berg\ner ra\nlife in\nlen ny\ninter rupt\ncohe rent\nca z\nvick ers\nle veled\nf bs\ncab ins\nbu mmed\napost les\nwe h\nten don\nsouven irs\ninfu ri\npier ce\nasse t\nm las\ngo th\ndi ggin\nann as\nyl or\nth waite\nsw el\npan era\nmur derers\ncroo ked\nbs go\nac u\na on\nre an\none of\nko hl\nbloo dh\npest icide\nlost dog\nfle xing\nëĤ ĺ\nsu pra\neter nally\nðŁļ Ļ\npa olo\nol an\nmom o\nis elle\ncaptain marvel\ns lou\nmistak enly\nakhi lesh\nmer t\nil inan\nbu on\nbal kan\nmir ro\nmill en\nder ail\ndam on\ntit i\nbi os\nre don\npic ard\npar te\nðŁ¤ Ł\nØ º\nson ics\nfir sth\ndd c\nveg ans\ntur ban\nni gan\nlot tie\nlyn don\nstar buck\npink floyd\nlife styles\nam ara\na she\nr sc\nval a\nsm er\ncw gc\ncli ent\nbuen as\njag an\ncoo ps\nðŁijij ðŁijij\nspeci alizes\nsnag ged\ng lar\nben net\nwildlife wednesday\nbow den\npi k\nart in\nempor ium\nar l\nre ba\npas ser\ndisappo ints\nadditi ve\nâľĬ ðŁı½\nbay er\nmissou la\nha skell\ncomm ences\nni x\nne man\nexplo ited\nplastic surgery\ncc d\naso cial\nvo t\nsie gel\nfro ome\nkap am\nfar a\ne ha\npro bes\nmw f\nmeet ing\np bb\nak ins\nmistle toe\nkingdom hearts\nfor kids\nec r\nbal e\nescor ts\nadidas originals\nk wa\nk ts\nhallo ffame\nðŁĺį .\nwag s\npot ted\no wing\nhoney comb\nhe fty\nuro logy\nmer le\nb pd\nstri pping\nre ich\nk state\ngu ay\nyon ge\nshak ti\ng loom\nbat t\nson om\nn ery\nel ba\nblan ks\nhel le\ntriple ts\nbom bay\nak arta\nab ia\ntransm itted\nrol f\nja is\nangular js\nfi erc\nm ss\ntrac e\nॠĩ\ntom bs\nold man\nkom bucha\nfo l\ne health\ncere als\nare lli\nin ari\nðŁĴ ©\nwo l\nliber ties\nfa wn\naf firm\nnun avut\nhyster ical\nk drama\nart es\nâĢ¢âĢ¢âĢ¢âĢ¢ âĢ¢âĢ¢âĢ¢âĢ¢\nvalent in\nman slaughter\ngal es\neo in\nenergi zed\ndel s\nwith draws\nst les\nsar castic\nram esh\nincredi bles\nlock hart\nya wn\nultimatefan live\noooooooo oooooooo\nmu en\nguru dev\nte er\npe eling\nnew snow\nlingui stics\ndirec tv\nag end\nuni lever\nru ger\nhan dedly\nero se\nli mel\nthe c\nroyal ties\nfini shers\nnr g\nm gt\nfid get\ncom ps\nbac on\naggre ssively\nab it\nch â\ntar de\nslu gger\nq anda\ngre ening\nd ats\nensla ved\nspec tor\no ye\nfre ef\nb hand\nstop brexit\nmis conceptions\ncav a\nðŁĺįðŁĺįðŁĺįðŁĺį ðŁĺįðŁĺįðŁĺįðŁĺį\nmultit asking\nhou sel\nferre ira\ncen time\nank les\njo dh\nhel ly\nfro me\nout tuesday\nnar nia\nbal aji\nl bloggers\njyo ti\nðŁį ĩ\nlan cia\ncap ri\ny ap\nnat ash\ndown fall\n.\" âĢĶ\nà ®\nligam ent\ncoat ings\nai ded\nhi ko\nfall ing\nencryp ted\nyeg food\ninfringe ment\ncu di\nce p\nðŁĺį ðŁĺĤ\ntra 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ite\nmacfar lane\ncur few\nbou do\nw gn\nsti x\nsla p\nscrat ched\nphilli p\njour ne\nex pelled\nwa z\nu ke\ntati ana\nou e\nho pp\ndimit ri\nðŁĵ £\nmato logist\nelectri fying\nblu ffs\nbill smafia\naz cardinals\ny aa\nx mas\nshar a\nr ith\ng ills\ndre s\nbar ton\nauthori zation\nimperi alism\nhome of\nto do\nfoot path\nband width\nvisit spain\nmoh sin\nerup ted\nmi ki\ninsig nia\nmike l\nss h\nger a\nbank holiday\naw an\nt weak\nstar craft\ne al\nconstruc tion\nskelet ons\nle ep\nine m\nbar clay\nship wreck\nmonsi eur\nyo h\nron t\nform ative\nser o\nle p\nhorse man\nhoo sier\nhaz mat\ncylin ders\ncen ti\nðŁĴ¥ðŁĴ¥ ðŁĴ¥\nre em\nna ire\nmus ically\ngras shopper\nest onian\ntermin ology\nro main\nblogger rt\ntox in\nstan ce\ncultiv ated\nan ast\nðŁIJ į\nshi mano\ngo pher\nene i\nrecycla ble\ngam ification\nfight for\nc q\navoc ados\nke ys\neli ke\ngly cer\nshak ur\nmobili zation\ngal ley\nexpla in\nex changed\npe th\nobe dience\nilla ge\nen nis\nãĥ ŀ\nwi v\nwalla bies\nma ar\nig ers\nfin tech\nfin alized\nwo j\nmeaning less\nin field\nonna ise\ne et\nbron te\npass ages\nðŁij §\nstrick land\nnorthern lights\nlom ond\nh tc\nwr ay\nshi fter\ndi alog\nðŁį į\n>> >>>>\nte atime\nste ch\nsic huan\nqu ill\nfran ca\ncomple mentary\nbar rington\nmarcu s\nmal am\ngoo oo\nfor sa\nelec tra\naf s\nâĹ Ĩ\ntri fe\nsn azzy\nfo lia\nand olan\nafter dark\nwood son\nstra de\nlitt lest\no gun\ncon wy\nco wards\nðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤðŁĺĤ\níĬ ¸\nse ul\nmur phy\ndun ks\nkapil shar\njo achim\nwom ack\nequal ity\naver ages\na ine\nðŁ¦ Ī\ntac ular\ndis ability\nu ked\nmid century\nbar thol\nteas ers\ntab ern\nnj caa\nsp out\nop i\nku bball\nbl om\nso ar\npopu lism\nmeth yl\nðŁijĬ ðŁı¼\no spre\nalo ils\nðŁĵ ĸ\nðŁĮ ļ\nx er\nsp illing\npubl ica\ncar dam\nadi sh\nsa cha\np kg\nbu da\nlyric ist\ni bc\ngru mp\nho ver\nhal ep\nanti body\nanem one\nâĻ¥âĻ¥ âĻ¥âĻ¥\nm cl\nlitho graph\ncc u\ns fest\npath ic\ncalli ster\notta wa\ngun sn\nrut ger\nhali but\nen vision\ndifferenti ate\nðŁļĢ ðŁļĢ\npir an\nlat el\nuc n\ntrou bad\nra ine\nfierc ely\nlearn english\nlea se\nwex mondays\nem it\ndray ton\nbur rell\nscuba diving\nhol ler\ndr u\nclo cked\nw ral\nap ro\ntrans lucent\nw bo\npatri arch\nmo ja\nlan nister\nfish ery\nne derland\nmil dly\nmi rai\nma ko\nja p\nðŁĺ©ðŁĺ© ðŁĺ©\npro statec\np anna\nar ama\nunder taking\ntomp kins\nne op\nsoli ds\nsav oury\ne ames\ncut lery\nwood bridge\nsteam er\nri zzo\nwild cat\nrat na\nlamin ated\nkin eni\njal ap\nai des\nacknowle dges\n?! ?!?!\n! ðŁİī\nw afc\nmag gio\nha ves\ndar je\nof i\ngr il\nv asi\nbru x\nmo hd\nfake speare\narn old\nr mb\nfor be\nwal leye\nro di\ntherapeu tics\nstrate gi\nob ste\nmu dder\ndownload able\ndd ings\nd ca\nasi angames\ncampe on\nappropri ation\nth century\nram atta\ndra ped\nbul lion\nmu c\none x\nse greg\nophel ia\nbod ily\nâĿ¤ ðŁĺį\nwi zar\nte ased\nade my\nto id\nsur a\nlazar us\nsn ickers\nma se\nlo h\nbow ed\nbibli o\nx change\nhar lan\ngho shal\nflavor ful\nbha gat\nalle z\nwhiche ver\nten stein\ndisc er\norgan iser\nmt g\ndream liner\nt se\nhok kaido\nmo k\nindulg ent\nhick man\nblin ded\nal yn\naaa ah\nsp ool\nlough borough\ninter pret\net v\naristo tle\noptimi zing\navici i\nmadu rai\nju li\nnaw az\nmat chups\nab ide\npaint ing\nw elling\nvel i\noctag on\nin scribed\npo king\nplac er\nlife cycle\nkili g\ng sp\neli ves\ncle ments\nna sheed\nme sut\nincarcer ated\ndist illed\nwal ang\ndelic acy\ndel gado\nche z\nch ita\nad ero\ntu x\npati l\no do\nabh cosmetics\ntv c\np bc\nin accurate\nhardwork paysoff\nball er\nquot ation\nmerchandi sing\nga stri\ndefen ses\ndro gba\nbex hill\nban kno\nwin ona\nsi eg\np gs\nhahah ha\nagu chi\nsu bram\nmirac le\nde sch\nli bre\nba cher\nent ine\nbbcra di\nlou dest\nr ps\npi erc\nfr yer\nstorm trooper\nrafael nadal\npas co\nexhau stion\nepic onetsy\nrc tid\nkel lie\nga ines\nd bz\nsm riti\ns bridge\nlim ited\ncla w\ntechnic al\nbio graphical\nado red\nภ°\nexclu de\nac adia\nkey boards\nfur man\nso ca\nsur u\nni ps\nsw aps\nserver less\nrun e\npu ffy\nnorth ampton\nnish ings\nhen der\ncartri dges\ngun shot\nðŁĵ ¹\nfil ament\nrespon dents\npey ton\nmountaine er\nmer ging\nlife span\nintimid ation\np afc\nnl wx\nexpan sive\npur r\nf ck\nca e\nat ti\ntele thon\nso hn\nmend el\nlo pes\ndor i\nun broken\nte red\ntast ings\nin active\ndisin tegr\nt assel\nshare the\npi ano\nis lay\nair space\nz awa\nricci ardo\nming ton\nfresh er\ncur ry\nre vs\npharo ah\nh mv\nexhilar ating\nwh oo\nlin kin\nkri spy\ncompeten cy\nste wards\nne bu\nkat su\nad mins\nbaz ar\nas ar\ngiving back\ns summit\nsong z\nlin us\nraj kumar\nfarm ington\nfanta sia\nðŁĺ´ ðŁĺ´\nso bri\nlis se\nbarry more\npri sm\nblo b\nsen ew\nmono xide\nexp ire\neigh teen\ndi pper\nxi ao\nkil t\nhin ch\nbbc sport\nbam boo\np ter\nex al\nðŁ¦ ĭ\nham lin\nexpe ditions\nstar gazing\nfood security\nwy lie\nul f\nst ingly\non storm\nlo eb\nbro ome\nbn ha\npancre atic\neli ve\n!!!!!!!! !!!\nther apper\northo pedic\navengers endgame\nantit rust\nìļ °\ngo te\nom d\noff side\ngy llen\nwin eries\nwhite water\nad l\nlu pita\nexce eds\nconsi sted\nchew bacca\nash leigh\nnhl jets\nis san\nsh ld\nhay at\ncran berries\nðŁ¤ĺ ðŁı½\nrock the\nspring training\nfall out\ndairy free\nwa j\nun decided\nso wn\nrc n\nnorth wales\nhtt r\nfu mble\nd its\ncomp elled\npopu list\nmin ted\nblan chett\n. ''\npro pulsion\nm illa\nau berg\nher tz\nh ta\nu daipur\nserendip ity\nazte cs\nals ace\nðŁIJ ij\nlu n\nsho es\nchar li\ngar za\nðŁĴ Ł\npro biotics\nfox tv\nol is\nmi ff\nloc alized\ndiffu ser\nsi gue\nfun ko\nrend ous\nðŁĴ ij\njeky ll\n" }, { "path": "comfy/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": "comfy/sd1_tokenizer/tokenizer_config.json", "content": "{\n \"add_prefix_space\": false,\n \"bos_token\": {\n \"__type\": \"AddedToken\",\n \"content\": \"<|startoftext|>\",\n 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\"ðŁĺĤðŁĺĤðŁĺĤ\": 22233,\n \"ðŁĺĤðŁĺĤðŁĺĤ\": 4887,\n \"ðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\": 9936,\n \"ðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\": 11522,\n \"ðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\": 19295,\n \"ðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\": 33415,\n \"ðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\": 48973,\n \"ðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\": 28504,\n \"ðŁĺĤðŁĺį\": 43128,\n \"ðŁĺĤðŁĺŃ\": 28965,\n \"ðŁĺĤðŁĺŃ\": 25802,\n \"ðŁĺĥ\": 14079,\n \"ðŁĺĥ\": 8520,\n \"ðŁĺĥðŁĺĥ\": 38358,\n \"ðŁĺĦ\": 12141,\n \"ðŁĺĦ\": 7624,\n \"ðŁĺĦðŁĺĦ\": 32312,\n \"ðŁĺħ\": 15245,\n \"ðŁĺħ\": 9188,\n \"ðŁĺħðŁĺħ\": 39078,\n \"ðŁĺĨ\": 16541,\n \"ðŁĺĨ\": 10943,\n \"ðŁĺĨðŁĺĨ\": 39503,\n \"ðŁĺĩ\": 21694,\n \"ðŁĺĩ\": 13091,\n \"ðŁĺĪ\": 14377,\n \"ðŁĺĪ\": 9756,\n \"ðŁĺĪðŁĺĪ\": 44473,\n \"ðŁĺī\": 9740,\n \"ðŁĺī\": 4955,\n \"ðŁĺīðŁĺī\": 40430,\n \"ðŁĺĬ\": 4692,\n \"ðŁĺĬ\": 3020,\n \"ðŁĺĬâĿ¤ï¸ı\": 43606,\n \"ðŁĺĬðŁĺĬ\": 12838,\n \"ðŁĺĬðŁĺĬ\": 20842,\n \"ðŁĺĬðŁĺĬðŁĺĬ\": 28685,\n \"ðŁĺĬðŁĺĬðŁĺĬðŁĺĬ\": 35519,\n \"ðŁĺĭ\": 12391,\n \"ðŁĺĭ\": 7203,\n \"ðŁĺĭðŁĺĭ\": 33304,\n \"ðŁĺĮ\": 19221,\n \"ðŁĺĮ\": 12163,\n \"ðŁĺį\": 1796,\n \"ðŁĺį#\": 42357,\n \"ðŁĺį.\": 48579,\n \"ðŁĺį\": 1754,\n \"ðŁĺįâĿ¤\": 29122,\n \"ðŁĺįâĿ¤ï¸ı\": 21945,\n \"ðŁĺįðŁijĮ\": 41005,\n \"ðŁĺįðŁĴķ\": 35946,\n \"ðŁĺįðŁĶ¥\": 46648,\n \"ðŁĺįðŁĺĤ\": 48715,\n \"ðŁĺįðŁĺį\": 3663,\n \"ðŁĺįðŁĺį\": 6471,\n \"ðŁĺįðŁĺįðŁĺį\": 30614,\n \"ðŁĺįðŁĺįðŁĺį\": 7703,\n \"ðŁĺįðŁĺįðŁĺįðŁĺį\": 16603,\n \"ðŁĺįðŁĺįðŁĺįðŁĺį\": 18925,\n \"ðŁĺįðŁĺįðŁĺįðŁĺįðŁĺį\": 32078,\n \"ðŁĺįðŁĺįðŁĺįðŁĺįðŁĺįðŁĺįðŁĺįðŁĺį\": 48683,\n \"ðŁĺįðŁĺĺ\": 29646,\n \"ðŁĺįðŁĺĺ\": 19849,\n \"ðŁĺįðŁĺŃ\": 39555,\n \"ðŁĺİ\": 7426,\n \"ðŁĺİ\": 4345,\n \"ðŁĺİðŁĺİ\": 24048,\n \"ðŁĺİðŁĺİðŁĺİ\": 39742,\n \"ðŁĺı\": 11624,\n \"ðŁĺı\": 6909,\n \"ðŁĺıðŁĺı\": 38151,\n \"ðŁĺIJ\": 38586,\n \"ðŁĺIJ\": 19618,\n \"ðŁĺij\": 32469,\n \"ðŁĺij\": 18937,\n \"ðŁĺĴ\": 20792,\n \"ðŁĺĴ\": 11702,\n \"ðŁĺĵ\": 28733,\n \"ðŁĺĶ\": 19532,\n \"ðŁĺĶ\": 11432,\n \"ðŁĺķ\": 45741,\n \"ðŁĺķ\": 20602,\n \"ðŁĺĸ\": 35006,\n \"ðŁĺĺ\": 4240,\n \"ðŁĺĺ\": 3352,\n \"ðŁĺĺâĿ¤\": 48409,\n \"ðŁĺĺâĿ¤ï¸ı\": 39150,\n \"ðŁĺĺðŁĺį\": 38176,\n \"ðŁĺĺðŁĺĺ\": 15663,\n \"ðŁĺĺðŁĺĺ\": 10507,\n \"ðŁĺĺðŁĺĺðŁĺĺ\": 20208,\n \"ðŁĺĺðŁĺĺðŁĺĺðŁĺĺ\": 44892,\n \"ðŁĺĻ\": 36201,\n \"ðŁĺĻ\": 29209,\n \"ðŁĺļ\": 24897,\n \"ðŁĺļ\": 19102,\n \"ðŁĺĽ\": 24550,\n \"ðŁĺĽ\": 15745,\n \"ðŁĺľ\": 13226,\n \"ðŁĺľ\": 7830,\n \"ðŁĺľðŁĺľ\": 43065,\n \"ðŁĺĿ\": 20064,\n \"ðŁĺĿ\": 12970,\n \"ðŁĺŀ\": 40458,\n \"ðŁĺŀ\": 21103,\n \"ðŁĺŁ\": 46947,\n \"ðŁĺł\": 34094,\n \"ðŁĺŃ\": 2962,\n \"ðŁĺŃ\": 3915,\n \"ðŁĺŃâĿ¤ï¸ı\": 29567,\n \"ðŁĺŃðŁĴķ\": 46306,\n \"ðŁĺŃðŁĺĤ\": 38505,\n \"ðŁĺŃðŁĺį\": 36893,\n \"ðŁĺŃðŁĺŃ\": 5300,\n \"ðŁĺŃðŁĺŃ\": 11834,\n \"ðŁĺŃðŁĺŃðŁĺŃ\": 44089,\n \"ðŁĺŃðŁĺŃðŁĺŃ\": 13116,\n \"ðŁĺŃðŁĺŃðŁĺŃðŁĺŃ\": 19793,\n \"ðŁĺŃðŁĺŃðŁĺŃðŁĺŃ\": 27322,\n \"ðŁĺŃðŁĺŃðŁĺŃðŁĺŃðŁĺŃ\": 43366,\n \"ðŁĻ\": 1478,\n \"ðŁĻĢ\": 43092,\n \"ðŁĻĤ\": 32006,\n \"ðŁĻĤ\": 14860,\n \"ðŁĻĥ\": 27222,\n \"ðŁĻĥ\": 15652,\n \"ðŁĻĦ\": 20648,\n \"ðŁĻĦ\": 13049,\n \"ðŁĻħ\": 42702,\n \"ðŁĻĨ\": 30050,\n \"ðŁĻĨ\": 35730,\n \"ðŁĻĪ\": 12661,\n \"ðŁĻĪ\": 9516,\n \"ðŁĻĪðŁĻĪ\": 41796,\n \"ðŁĻĬ\": 23684,\n \"ðŁĻĬ\": 16636,\n \"ðŁĻĭ\": 19193,\n \"ðŁĻĭ\": 30274,\n \"ðŁĻĮ\": 4366,\n \"ðŁĻĮ\": 4855,\n \"ðŁĻĮðŁı»\": 26756,\n \"ðŁĻĮðŁı»\": 15799,\n \"ðŁĻĮðŁı¼\": 26584,\n \"ðŁĻĮðŁı¼\": 15364,\n \"ðŁĻĮðŁı½\": 36660,\n \"ðŁĻĮðŁı½\": 22962,\n \"ðŁĻĮðŁı¾\": 38023,\n \"ðŁĻĮðŁı¾\": 26466,\n \"ðŁĻĮðŁĻĮ\": 21202,\n \"ðŁĻĮðŁĻĮ\": 30430,\n \"ðŁĻĮðŁĻĮðŁĻĮ\": 37127,\n \"ðŁĻı\": 4260,\n \"ðŁĻı\": 5503,\n \"ðŁĻıðŁı»\": 25100,\n \"ðŁĻıðŁı»\": 16650,\n \"ðŁĻıðŁı¼\": 31163,\n \"ðŁĻıðŁı¼\": 18952,\n \"ðŁĻıðŁı½\": 34103,\n \"ðŁĻıðŁı½\": 21540,\n \"ðŁĻıðŁı¾\": 34277,\n \"ðŁĻıðŁı¾\": 21979,\n \"ðŁĻıðŁĻı\": 18227,\n \"ðŁĻıðŁĻı\": 26510,\n \"ðŁĻıðŁĻıðŁĻı\": 31702,\n \"ðŁļ\": 2730,\n \"ðŁļ¨\": 12198,\n \"ðŁļ¨\": 6056,\n \"ðŁļ¨ðŁļ¨\": 36487,\n \"ðŁļ¨ðŁļ¨\": 21440,\n \"ðŁļ¨ðŁļ¨ðŁļ¨\": 41515,\n \"ðŁļ©\": 44514,\n \"ðŁļ«\": 35291,\n \"ðŁļ²\": 37085,\n \"ðŁļ´\": 30825,\n \"ðŁļ¶\": 46060,\n \"ðŁļĢ\": 22400,\n \"ðŁļĢ\": 13542,\n \"ðŁļĢðŁļĢ\": 49033,\n \"ðŁļĤ\": 38949,\n \"ðŁļĮ\": 46891,\n \"ðŁļĹ\": 33054,\n \"ðŁļĹ\": 22783,\n \"ðŁļĺ\": 35825,\n \"ðŁļĻ\": 48487,\n \"ðŁĽ\": 11306,\n \"ñ\": 173,\n \"ñ\": 429,\n \"ò\": 174,\n \"ò\": 430,\n \"ó\": 175,\n \"ó\": 431,\n \"ô\": 176,\n \"ô\": 432,\n \"õ\": 177,\n \"õ\": 433,\n \"ö\": 178,\n \"ö\": 434,\n \"÷\": 179,\n \"÷\": 435,\n \"ø\": 180,\n \"ø\": 436,\n \"ù\": 181,\n \"ù\": 437,\n \"ú\": 182,\n \"ú\": 438,\n \"û\": 183,\n \"û\": 439,\n \"ü\": 184,\n \"ü\": 440,\n \"ý\": 185,\n \"ý\": 441,\n \"þ\": 186,\n \"þ\": 442,\n \"ÿ\": 187,\n \"ÿ\": 443,\n \"Ā\": 188,\n \"Ā\": 444,\n \"ā\": 189,\n \"ā\": 445,\n \"Ă\": 190,\n \"Ă\": 446,\n \"ă\": 191,\n \"ă\": 447,\n \"Ą\": 192,\n \"Ą\": 448,\n \"ą\": 193,\n \"ą\": 449,\n \"Ć\": 194,\n \"Ć\": 450,\n \"ć\": 195,\n \"ć\": 451,\n \"Ĉ\": 196,\n \"Ĉ\": 452,\n \"ĉ\": 197,\n \"ĉ\": 453,\n \"Ċ\": 198,\n \"Ċ\": 454,\n \"ċ\": 199,\n \"ċ\": 455,\n \"Č\": 200,\n \"Č\": 456,\n \"č\": 201,\n \"č\": 457,\n \"Ď\": 202,\n \"Ď\": 458,\n \"ď\": 203,\n \"ď\": 459,\n \"Đ\": 204,\n \"Đ\": 460,\n \"đ\": 205,\n \"đ\": 461,\n \"Ē\": 206,\n \"Ē\": 462,\n \"ē\": 207,\n \"ē\": 463,\n \"Ĕ\": 208,\n \"Ĕ\": 464,\n \"ĕ\": 209,\n \"ĕ\": 465,\n \"Ė\": 210,\n \"Ė\": 466,\n \"ė\": 211,\n \"ė\": 467,\n \"Ę\": 212,\n \"Ę\": 468,\n \"ę\": 213,\n \"ę\": 469,\n \"Ě\": 214,\n \"Ě\": 470,\n \"ě\": 215,\n \"ě\": 471,\n \"Ĝ\": 216,\n \"Ĝ\": 472,\n \"ĝ\": 217,\n \"ĝ\": 473,\n \"Ğ\": 218,\n \"Ğ\": 474,\n \"ğ\": 219,\n \"ğ\": 475,\n \"Ġ\": 220,\n \"Ġ\": 476,\n \"ġ\": 221,\n \"ġ\": 477,\n \"Ģ\": 222,\n \"Ģ\": 478,\n \"Ģï¸ı\": 9668,\n \"Ģï¸ı\": 5511,\n \"ģ\": 223,\n \"ģ\": 479,\n \"ģà¸\": 15016,\n \"Ĥ\": 224,\n \"Ĥ\": 480,\n \"Ĥâĸ\": 29036,\n \"ĤâĸĤâĸ\": 30832,\n \"ĥ\": 225,\n \"ĥ\": 481,\n \"Ħ\": 226,\n \"Ħ\": 482,\n \"Ħà¸\": 20537,\n \"Ħë\": 34462,\n \"Ħëĭ\": 25170,\n \"ħ\": 227,\n \"ħ\": 483,\n \"ħï¸ı\": 33950,\n \"Ĩ\": 228,\n \"Ĩ\": 484,\n \"ĩ\": 229,\n \"ĩ\": 485,\n \"Ī\": 230,\n \"Ī\": 486,\n \"ī\": 231,\n \"ī\": 487,\n \"īï¸ı\": 37463,\n \"Ĭ\": 232,\n \"Ĭ\": 488,\n \"Ĭãģ\": 30294,\n \"ĭ\": 233,\n \"ĭ\": 489,\n \"ĭãģ\": 36218,\n \"ĭãĤ\": 45737,\n \"Į\": 234,\n \"Į\": 490,\n \"ĮãĤĬãģ\": 45969,\n \"ĮãĤĬãģŁãģĦ\": 47021,\n \"Įë\": 17003,\n \"į\": 235,\n \"į\": 491,\n \"İ\": 236,\n \"İ\": 492,\n \"ı\": 237,\n \"ı\": 493,\n \"IJ\": 238,\n \"IJ\": 494,\n \"ij\": 239,\n \"ij\": 495,\n \"Ĵ\": 240,\n \"Ĵ\": 496,\n \"ĵ\": 241,\n \"ĵ\": 497,\n \"Ķ\": 242,\n \"Ķ\": 498,\n \"Ķë\": 37978,\n \"Ķï¸ı\": 24395,\n \"Ķï¸ı\": 7443,\n \"ķ\": 243,\n \"ķ\": 499,\n \"ķãĤ\": 26609,\n \"ķï¸ı\": 44853,\n \"ĸ\": 244,\n \"ĸ\": 500,\n \"ĸï¸ı\": 28877,\n \"Ĺ\": 245,\n \"Ĺ\": 501,\n \"ĺ\": 246,\n \"ĺ\": 502,\n \"Ļ\": 247,\n \"Ļ\": 503,\n \"ļ\": 248,\n \"ļ\": 504,\n \"Ľ\": 249,\n \"Ľ\": 505,\n \"ľ\": 250,\n \"ľ\": 506,\n \"ľë\": 39810,\n \"Ŀ\": 251,\n \"Ŀ\": 507,\n \"ŀ\": 252,\n \"ŀ\": 508,\n \"Ł\": 253,\n \"Ł\": 509,\n \"ŁãģĦ\": 46023,\n \"ł\": 254,\n \"ł\": 510,\n \"łï¸ı\": 27899,\n \"łï¸ı\": 12715,\n \"łĪ\": 43364,\n \"Ń\": 255,\n \"Ń\": 511\n}\n" }, { "path": "comfy/sdxl_clip.py", "content": "from comfy 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, model_options={}):\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 model_options = {**model_options, \"model_name\": \"clip_g\"}\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, return_projected_pooled=True, model_options=model_options)\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, tokenizer_data={}):\n super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data)\n\n\nclass SDXLTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"g\"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.clip_g.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {}\n\nclass SDXLClipModel(torch.nn.Module):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n self.clip_l = sd1_clip.SDClipModel(layer=\"hidden\", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)\n self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options)\n self.dtypes = set([dtype])\n\n def set_clip_options(self, options):\n self.clip_l.set_clip_options(options)\n self.clip_g.set_clip_options(options)\n\n def reset_clip_options(self):\n self.clip_g.reset_clip_options()\n self.clip_l.reset_clip_options()\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 cut_to = min(l_out.shape[1], g_out.shape[1])\n return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], 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, model_options={}):\n super().__init__(device=device, dtype=dtype, clip_name=\"g\", clip_model=SDXLClipG, model_options=model_options)\n\n\nclass StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):\n super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data)\n\nclass StableCascadeTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"g\", tokenizer=StableCascadeClipGTokenizer)\n\nclass StableCascadeClipG(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", max_length=77, freeze=True, layer=\"hidden\", layer_idx=-1, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_config_bigg.json\")\n model_options = {**model_options, \"model_name\": \"clip_g\"}\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\": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options)\n\n def load_sd(self, sd):\n return super().load_sd(sd)\n\nclass StableCascadeClipModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, clip_name=\"g\", clip_model=StableCascadeClipG, model_options=model_options)\n" }, { "path": "comfy/supported_models.py", "content": "import torch\nfrom . import model_base\nfrom . import utils\n\nfrom . import sd1_clip\nfrom . import sdxl_clip\nimport comfy.text_encoders.sd2_clip\nimport comfy.text_encoders.sd3_clip\nimport comfy.text_encoders.sa_t5\nimport comfy.text_encoders.aura_t5\nimport comfy.text_encoders.pixart_t5\nimport comfy.text_encoders.hydit\nimport comfy.text_encoders.flux\nimport comfy.text_encoders.genmo\nimport comfy.text_encoders.lt\nimport comfy.text_encoders.hunyuan_video\nimport comfy.text_encoders.cosmos\nimport comfy.text_encoders.lumina2\nimport comfy.text_encoders.wan\nimport comfy.text_encoders.ace\nimport comfy.text_encoders.omnigen2\nimport comfy.text_encoders.qwen_image\nimport comfy.text_encoders.hunyuan_image\nimport comfy.text_encoders.kandinsky5\nimport comfy.text_encoders.z_image\nimport comfy.text_encoders.anima\nimport comfy.text_encoders.ace15\nimport comfy.text_encoders.longcat_image\n\nfrom . import supported_models_base\nfrom . import latent_formats\n\nfrom . import diffusers_convert\nimport comfy.model_management\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 memory_usage_factor = 1.0\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.\"] = \"clip_l.\"\n state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)\n return state_dict\n\n def process_clip_state_dict_for_saving(self, state_dict):\n pop_keys = [\"clip_l.transformer.text_projection.weight\", \"clip_l.logit_scale\"]\n for p in pop_keys:\n if p in state_dict:\n state_dict.pop(p)\n\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, state_dict={}):\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 unet_extra_config = {\n \"num_heads\": -1,\n \"num_head_channels\": 64,\n \"attn_precision\": torch.float32,\n }\n\n latent_format = latent_formats.SD15\n memory_usage_factor = 1.0\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.get(k, None)\n if out is not None and 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.\"] = \"clip_h.\" #SD2 in sgm format\n replace_prefix[\"cond_stage_model.model.\"] = \"clip_h.\"\n state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)\n state_dict = utils.clip_text_transformers_convert(state_dict, \"clip_h.\", \"clip_h.transformer.\")\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, state_dict={}):\n return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.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 memory_usage_factor = 1.0\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 replace_prefix[\"conditioner.embedders.0.model.\"] = \"clip_g.\"\n state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)\n\n state_dict = utils.clip_text_transformers_convert(state_dict, \"clip_g.\", \"clip_g.transformer.\")\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, state_dict={}):\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 memory_usage_factor = 0.8\n\n def model_type(self, state_dict, prefix=\"\"):\n if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5\n self.latent_format = latent_formats.SDXL_Playground_2_5()\n self.sampling_settings[\"sigma_data\"] = 0.5\n self.sampling_settings[\"sigma_max\"] = 80.0\n self.sampling_settings[\"sigma_min\"] = 0.002\n return model_base.ModelType.EDM\n elif \"edm_vpred.sigma_max\" in state_dict:\n self.sampling_settings[\"sigma_max\"] = float(state_dict[\"edm_vpred.sigma_max\"].item())\n if \"edm_vpred.sigma_min\" in state_dict:\n self.sampling_settings[\"sigma_min\"] = float(state_dict[\"edm_vpred.sigma_min\"].item())\n return model_base.ModelType.V_PREDICTION_EDM\n elif \"v_pred\" in state_dict:\n if \"ztsnr\" in state_dict: #Some zsnr anime checkpoints\n self.sampling_settings[\"zsnr\"] = True\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\"] = \"clip_l.transformer.text_model\"\n replace_prefix[\"conditioner.embedders.1.model.\"] = \"clip_g.\"\n state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)\n\n state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)\n state_dict = utils.clip_text_transformers_convert(state_dict, \"clip_g.\", \"clip_g.transformer.\")\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 for k in state_dict:\n if k.startswith(\"clip_l\"):\n state_dict_g[k] = state_dict[k]\n\n state_dict_g[\"clip_l.transformer.text_model.embeddings.position_ids\"] = torch.arange(77).expand((1, -1))\n pop_keys = [\"clip_l.transformer.text_projection.weight\", \"clip_l.logit_scale\"]\n for p in pop_keys:\n if p in state_dict_g:\n state_dict_g.pop(p)\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, state_dict={}):\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 KOALA_700M(SDXL):\n unet_config = {\n \"model_channels\": 320,\n \"use_linear_in_transformer\": True,\n \"transformer_depth\": [0, 2, 5],\n \"context_dim\": 2048,\n \"adm_in_channels\": 2816,\n \"use_temporal_attention\": False,\n }\n\nclass KOALA_1B(SDXL):\n unet_config = {\n \"model_channels\": 320,\n \"use_linear_in_transformer\": True,\n \"transformer_depth\": [0, 2, 6],\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 unet_extra_config = {\n \"num_heads\": -1,\n \"num_head_channels\": 64,\n \"attn_precision\": torch.float32,\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, state_dict={}):\n return None\n\nclass SV3D_u(SVD_img2vid):\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\": 256,\n \"use_temporal_attention\": True,\n \"use_temporal_resblock\": True\n }\n\n vae_key_prefix = [\"conditioner.embedders.1.encoder.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.SV3D_u(self, device=device)\n return out\n\nclass SV3D_p(SV3D_u):\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\": 1280,\n \"use_temporal_attention\": True,\n \"use_temporal_resblock\": True\n }\n\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.SV3D_p(self, device=device)\n return out\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 required_keys = {\n \"cc_projection.weight\": None,\n \"cc_projection.bias\": None,\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, state_dict={}):\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\nclass Stable_Cascade_C(supported_models_base.BASE):\n unet_config = {\n \"stable_cascade_stage\": 'c',\n }\n\n unet_extra_config = {}\n\n latent_format = latent_formats.SC_Prior\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n sampling_settings = {\n \"shift\": 2.0,\n }\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoder.\"]\n clip_vision_prefix = \"clip_l_vision.\"\n\n def process_unet_state_dict(self, state_dict):\n key_list = list(state_dict.keys())\n for y in [\"weight\", \"bias\"]:\n suffix = \"in_proj_{}\".format(y)\n keys = filter(lambda a: a.endswith(suffix), key_list)\n for k_from in keys:\n weights = state_dict.pop(k_from)\n prefix = k_from[:-(len(suffix) + 1)]\n shape_from = weights.shape[0] // 3\n for x in range(3):\n p = [\"to_q\", \"to_k\", \"to_v\"]\n k_to = \"{}.{}.{}\".format(prefix, p[x], y)\n state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]\n return state_dict\n\n def process_clip_state_dict(self, state_dict):\n state_dict = utils.state_dict_prefix_replace(state_dict, {k: \"\" for k in self.text_encoder_key_prefix}, filter_keys=True)\n if \"clip_g.text_projection\" in state_dict:\n state_dict[\"clip_g.transformer.text_projection.weight\"] = state_dict.pop(\"clip_g.text_projection\").transpose(0, 1)\n return state_dict\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.StableCascade_C(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)\n\nclass Stable_Cascade_B(Stable_Cascade_C):\n unet_config = {\n \"stable_cascade_stage\": 'b',\n }\n\n unet_extra_config = {}\n\n latent_format = latent_formats.SC_B\n supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n\n sampling_settings = {\n \"shift\": 1.0,\n }\n\n clip_vision_prefix = None\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.StableCascade_B(self, device=device)\n return out\n\nclass SD15_instructpix2pix(SD15):\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 def get_model(self, state_dict, prefix=\"\", device=None):\n return model_base.SD15_instructpix2pix(self, device=device)\n\nclass SDXL_instructpix2pix(SDXL):\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 \"in_channels\": 8,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)\n\nclass LotusD(SD20):\n unet_config = {\n \"model_channels\": 320,\n \"use_linear_in_transformer\": True,\n \"use_temporal_attention\": False,\n \"adm_in_channels\": 4,\n \"in_channels\": 4,\n }\n\n unet_extra_config = {\n \"num_classes\": 'sequential',\n \"num_head_channels\": 64,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n return model_base.Lotus(self, device=device)\n\nclass SD3(supported_models_base.BASE):\n unet_config = {\n \"in_channels\": 16,\n \"pos_embed_scaling_factor\": None,\n }\n\n sampling_settings = {\n \"shift\": 3.0,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.SD3\n\n memory_usage_factor = 1.6\n\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.SD3(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n clip_l = False\n clip_g = False\n t5 = False\n pref = self.text_encoder_key_prefix[0]\n if \"{}clip_l.transformer.text_model.final_layer_norm.weight\".format(pref) in state_dict:\n clip_l = True\n if \"{}clip_g.transformer.text_model.final_layer_norm.weight\".format(pref) in state_dict:\n clip_g = True\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n if \"dtype_t5\" in t5_detect:\n t5 = True\n\n return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect))\n\nclass StableAudio(supported_models_base.BASE):\n unet_config = {\n \"audio_model\": \"dit1.0\",\n }\n\n sampling_settings = {\"sigma_max\": 500.0, \"sigma_min\": 0.03}\n\n unet_extra_config = {}\n latent_format = latent_formats.StableAudio1\n\n text_encoder_key_prefix = [\"text_encoders.\"]\n vae_key_prefix = [\"pretransform.model.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {\"conditioner.conditioners.seconds_start.\": \"\"}, filter_keys=True)\n seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {\"conditioner.conditioners.seconds_total.\": \"\"}, filter_keys=True)\n return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)\n\n def process_unet_state_dict(self, state_dict):\n for k in list(state_dict.keys()):\n if k.endswith(\".cross_attend_norm.beta\") or k.endswith(\".ff_norm.beta\") or k.endswith(\".pre_norm.beta\"): #These weights are all zero\n state_dict.pop(k)\n return state_dict\n\n def process_unet_state_dict_for_saving(self, state_dict):\n replace_prefix = {\"\": \"model.model.\"}\n return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n def clip_target(self, state_dict={}):\n return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)\n\nclass AuraFlow(supported_models_base.BASE):\n unet_config = {\n \"cond_seq_dim\": 2048,\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 1.73,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.SDXL\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.AuraFlow(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)\n\nclass PixArtAlpha(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"pixart_alpha\",\n }\n\n sampling_settings = {\n \"beta_schedule\" : \"sqrt_linear\",\n \"linear_start\" : 0.0001,\n \"linear_end\" : 0.02,\n \"timesteps\" : 1000,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.SD15\n\n memory_usage_factor = 0.5\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.PixArt(self, device=device)\n return out.eval()\n\n def clip_target(self, state_dict={}):\n return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.PixArtT5XXL)\n\nclass PixArtSigma(PixArtAlpha):\n unet_config = {\n \"image_model\": \"pixart_sigma\",\n }\n latent_format = latent_formats.SDXL\n\nclass HunyuanDiT(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"hydit\",\n }\n\n unet_extra_config = {\n \"attn_precision\": torch.float32,\n }\n\n sampling_settings = {\n \"linear_start\": 0.00085,\n \"linear_end\": 0.018,\n }\n\n latent_format = latent_formats.SDXL\n\n memory_usage_factor = 1.3\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanDiT(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)\n\nclass HunyuanDiT1(HunyuanDiT):\n unet_config = {\n \"image_model\": \"hydit1\",\n }\n\n unet_extra_config = {}\n\n sampling_settings = {\n \"linear_start\" : 0.00085,\n \"linear_end\" : 0.03,\n }\n\nclass Flux(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"flux\",\n \"guidance_embed\": True,\n }\n\n sampling_settings = {\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Flux\n\n memory_usage_factor = 3.1 # TODO: debug why flux mem usage is so weird on windows.\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n\n def process_unet_state_dict(self, state_dict):\n out_sd = {}\n for k in list(state_dict.keys()):\n key_out = k\n if key_out.endswith(\"_norm.scale\"):\n key_out = \"{}.weight\".format(key_out[:-len(\".scale\")])\n out_sd[key_out] = state_dict[k]\n return out_sd\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Flux(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))\n\nclass FluxInpaint(Flux):\n unet_config = {\n \"image_model\": \"flux\",\n \"guidance_embed\": True,\n \"in_channels\": 96,\n }\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\nclass FluxSchnell(Flux):\n unet_config = {\n \"image_model\": \"flux\",\n \"guidance_embed\": False,\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 1.0,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)\n return out\n\nclass Flux2(Flux):\n unet_config = {\n \"image_model\": \"flux2\",\n }\n\n sampling_settings = {\n \"shift\": 2.02,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Flux2\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n self.memory_usage_factor = self.memory_usage_factor * (2.0 * 2.0) * (unet_config['hidden_size'] / 2604)\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Flux2(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen3_4b.transformer.\".format(pref))\n if len(detect) > 0:\n detect[\"model_type\"] = \"qwen3_4b\"\n return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer, comfy.text_encoders.flux.klein_te(**detect))\n\n detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen3_8b.transformer.\".format(pref))\n if len(detect) > 0:\n detect[\"model_type\"] = \"qwen3_8b\"\n return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer8B, comfy.text_encoders.flux.klein_te(**detect))\n\n detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}mistral3_24b.transformer.\".format(pref))\n if len(detect) > 0:\n if \"{}mistral3_24b.transformer.model.layers.39.post_attention_layernorm.weight\".format(pref) not in state_dict:\n detect[\"pruned\"] = True\n return supported_models_base.ClipTarget(comfy.text_encoders.flux.Flux2Tokenizer, comfy.text_encoders.flux.flux2_te(**detect))\n\n return None\n\nclass GenmoMochi(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"mochi_preview\",\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 6.0,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Mochi\n\n memory_usage_factor = 2.0 #TODO\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.GenmoMochi(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect))\n\nclass LTXV(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"ltxv\",\n }\n\n sampling_settings = {\n \"shift\": 2.37,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.LTXV\n\n memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n self.memory_usage_factor = (unet_config.get(\"cross_attention_dim\", 2048) / 2048) * 5.5\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.LTXV(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))\n\nclass LTXAV(LTXV):\n unet_config = {\n \"image_model\": \"ltxav\",\n }\n\n latent_format = latent_formats.LTXAV\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n self.memory_usage_factor = 0.077 # TODO\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.LTXAV(self, device=device)\n return out\n\nclass HunyuanVideo(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n }\n\n sampling_settings = {\n \"shift\": 7.0,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.HunyuanVideo\n\n memory_usage_factor = 1.8 #TODO\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanVideo(self, device=device)\n return out\n\n def process_unet_state_dict(self, state_dict):\n out_sd = {}\n for k in list(state_dict.keys()):\n key_out = k\n key_out = key_out.replace(\"txt_in.t_embedder.mlp.0.\", \"txt_in.t_embedder.in_layer.\").replace(\"txt_in.t_embedder.mlp.2.\", \"txt_in.t_embedder.out_layer.\")\n key_out = key_out.replace(\"txt_in.c_embedder.linear_1.\", \"txt_in.c_embedder.in_layer.\").replace(\"txt_in.c_embedder.linear_2.\", \"txt_in.c_embedder.out_layer.\")\n key_out = key_out.replace(\"_mod.linear.\", \"_mod.lin.\").replace(\"_attn_qkv.\", \"_attn.qkv.\")\n key_out = key_out.replace(\"mlp.fc1.\", \"mlp.0.\").replace(\"mlp.fc2.\", \"mlp.2.\")\n key_out = key_out.replace(\"_attn_q_norm.weight\", \"_attn.norm.query_norm.weight\").replace(\"_attn_k_norm.weight\", \"_attn.norm.key_norm.weight\")\n key_out = key_out.replace(\".q_norm.weight\", \".norm.query_norm.weight\").replace(\".k_norm.weight\", \".norm.key_norm.weight\")\n key_out = key_out.replace(\"_attn_proj.\", \"_attn.proj.\")\n key_out = key_out.replace(\".modulation.linear.\", \".modulation.lin.\")\n key_out = key_out.replace(\"_in.mlp.2.\", \"_in.out_layer.\").replace(\"_in.mlp.0.\", \"_in.in_layer.\")\n if key_out.endswith(\".scale\"):\n key_out = \"{}.weight\".format(key_out[:-len(\".scale\")])\n out_sd[key_out] = state_dict[k]\n return out_sd\n\n def process_unet_state_dict_for_saving(self, state_dict):\n replace_prefix = {\"\": \"model.model.\"}\n return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}llama.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect))\n\nclass HunyuanVideoI2V(HunyuanVideo):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n \"in_channels\": 33,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanVideoI2V(self, device=device)\n return out\n\nclass HunyuanVideoSkyreelsI2V(HunyuanVideo):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n \"in_channels\": 32,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanVideoSkyreelsI2V(self, device=device)\n return out\n\nclass CosmosT2V(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"cosmos\",\n \"in_channels\": 16,\n }\n\n sampling_settings = {\n \"sigma_data\": 0.5,\n \"sigma_max\": 80.0,\n \"sigma_min\": 0.002,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Cosmos1CV8x8x8\n\n memory_usage_factor = 1.6 #TODO\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.CosmosVideo(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))\n\nclass CosmosI2V(CosmosT2V):\n unet_config = {\n \"image_model\": \"cosmos\",\n \"in_channels\": 17,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.CosmosVideo(self, image_to_video=True, device=device)\n return out\n\nclass CosmosT2IPredict2(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"cosmos_predict2\",\n \"in_channels\": 16,\n }\n\n sampling_settings = {\n \"sigma_data\": 1.0,\n \"sigma_max\": 80.0,\n \"sigma_min\": 0.002,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Wan21\n\n memory_usage_factor = 1.0\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n self.memory_usage_factor = (unet_config.get(\"model_channels\", 2048) / 2048) * 0.95\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.CosmosPredict2(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))\n\nclass Anima(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"anima\",\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 3.0,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Wan21\n\n memory_usage_factor = 1.0\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Anima(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen3_06b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect))\n\n def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs):\n self.memory_usage_factor = (self.unet_config.get(\"model_channels\", 2048) / 2048) * 0.95\n if dtype is torch.float16:\n self.memory_usage_factor *= 1.4\n return super().set_inference_dtype(dtype, manual_cast_dtype, **kwargs)\n\nclass CosmosI2VPredict2(CosmosT2IPredict2):\n unet_config = {\n \"image_model\": \"cosmos_predict2\",\n \"in_channels\": 17,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.CosmosPredict2(self, image_to_video=True, device=device)\n return out\n\nclass Lumina2(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"lumina2\",\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 6.0,\n }\n\n memory_usage_factor = 1.4\n\n unet_extra_config = {}\n latent_format = latent_formats.Flux\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Lumina2(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}gemma2_2b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect))\n\nclass ZImage(Lumina2):\n unet_config = {\n \"image_model\": \"lumina2\",\n \"dim\": 3840,\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 3.0,\n }\n\n memory_usage_factor = 2.8\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n if comfy.model_management.extended_fp16_support() and unet_config.get(\"allow_fp16\", False):\n self.supported_inference_dtypes = self.supported_inference_dtypes.copy()\n self.supported_inference_dtypes.insert(1, torch.float16)\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen3_4b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect))\n\nclass ZImagePixelSpace(ZImage):\n unet_config = {\n \"image_model\": \"zimage_pixel\",\n }\n\n # Pixel-space model: no spatial compression, operates on raw RGB patches.\n latent_format = latent_formats.ZImagePixelSpace\n\n # Much lower memory than latent-space models (no VAE, small patches).\n memory_usage_factor = 0.03 # TODO: figure out the optimal value for this.\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n return model_base.ZImagePixelSpace(self, device=device)\n\nclass WAN21_T2V(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"t2v\",\n }\n\n sampling_settings = {\n \"shift\": 8.0,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Wan21\n\n memory_usage_factor = 0.9\n\n supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n self.memory_usage_factor = self.unet_config.get(\"dim\", 2000) / 2222\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}umt5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect))\n\nclass WAN21_I2V(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"i2v\",\n \"in_dim\": 36,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21(self, image_to_video=True, device=device)\n return out\n\nclass WAN21_FunControl2V(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"i2v\",\n \"in_dim\": 48,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21(self, image_to_video=False, device=device)\n return out\n\nclass WAN21_Camera(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"camera\",\n \"in_dim\": 32,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21_Camera(self, image_to_video=False, device=device)\n return out\n\nclass WAN22_Camera(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"camera_2.2\",\n \"in_dim\": 36,\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21_Camera(self, image_to_video=False, device=device)\n return out\n\nclass WAN21_Vace(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"vace\",\n }\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n self.memory_usage_factor = 1.2 * self.memory_usage_factor\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21_Vace(self, image_to_video=False, device=device)\n return out\n\nclass WAN21_HuMo(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"humo\",\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21_HuMo(self, image_to_video=False, device=device)\n return out\n\nclass WAN22_S2V(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"s2v\",\n }\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN22_S2V(self, device=device)\n return out\n\nclass WAN22_Animate(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"animate\",\n }\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN22_Animate(self, device=device)\n return out\n\nclass WAN22_T2V(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"t2v\",\n \"out_dim\": 48,\n }\n\n latent_format = latent_formats.Wan22\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN22(self, image_to_video=True, device=device)\n return out\n\nclass WAN21_FlowRVS(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"flow_rvs\",\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21_FlowRVS(self, image_to_video=True, device=device)\n return out\n\nclass WAN21_SCAIL(WAN21_T2V):\n unet_config = {\n \"image_model\": \"wan2.1\",\n \"model_type\": \"scail\",\n }\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device)\n return out\n\nclass Hunyuan3Dv2(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"hunyuan3d2\",\n }\n\n unet_extra_config = {}\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 1.0,\n }\n\n memory_usage_factor = 3.5\n\n clip_vision_prefix = \"conditioner.main_image_encoder.model.\"\n vae_key_prefix = [\"vae.\"]\n\n latent_format = latent_formats.Hunyuan3Dv2\n\n def process_unet_state_dict(self, state_dict):\n out_sd = {}\n for k in list(state_dict.keys()):\n key_out = k\n if key_out.endswith(\".scale\"):\n key_out = \"{}.weight\".format(key_out[:-len(\".scale\")])\n out_sd[key_out] = state_dict[k]\n return out_sd\n\n def process_unet_state_dict_for_saving(self, state_dict):\n replace_prefix = {\"\": \"model.\"}\n return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Hunyuan3Dv2(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n return None\n\nclass Hunyuan3Dv2_1(Hunyuan3Dv2):\n unet_config = {\n \"image_model\": \"hunyuan3d2_1\",\n }\n\n latent_format = latent_formats.Hunyuan3Dv2_1\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Hunyuan3Dv2_1(self, device = device)\n return out\n\nclass Hunyuan3Dv2mini(Hunyuan3Dv2):\n unet_config = {\n \"image_model\": \"hunyuan3d2\",\n \"depth\": 8,\n }\n\n latent_format = latent_formats.Hunyuan3Dv2mini\n\nclass HiDream(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"hidream\",\n }\n\n sampling_settings = {\n \"shift\": 3.0,\n }\n\n sampling_settings = {\n }\n\n # memory_usage_factor = 1.2 # TODO\n\n unet_extra_config = {}\n latent_format = latent_formats.Flux\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HiDream(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n return None # TODO\n\nclass Chroma(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"chroma\",\n }\n\n unet_extra_config = {\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n }\n\n latent_format = comfy.latent_formats.Flux\n\n memory_usage_factor = 3.2\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n\n def process_unet_state_dict(self, state_dict):\n out_sd = {}\n for k in list(state_dict.keys()):\n key_out = k\n if key_out.endswith(\".scale\"):\n key_out = \"{}.weight\".format(key_out[:-len(\".scale\")])\n out_sd[key_out] = state_dict[k]\n return out_sd\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Chroma(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, \"{}t5xxl.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))\n\nclass ChromaRadiance(Chroma):\n unet_config = {\n \"image_model\": \"chroma_radiance\",\n }\n\n latent_format = comfy.latent_formats.ChromaRadiance\n\n # Pixel-space model, no spatial compression for model input.\n memory_usage_factor = 0.044\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n return model_base.ChromaRadiance(self, device=device)\n\nclass ACEStep(supported_models_base.BASE):\n unet_config = {\n \"audio_model\": \"ace\",\n }\n\n unet_extra_config = {\n }\n\n sampling_settings = {\n \"shift\": 3.0,\n }\n\n latent_format = comfy.latent_formats.ACEAudio\n\n memory_usage_factor = 0.5\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.ACEStep(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model)\n\nclass Omnigen2(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"omnigen2\",\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 2.6,\n }\n\n memory_usage_factor = 1.95 #TODO\n\n unet_extra_config = {}\n latent_format = latent_formats.Flux\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def __init__(self, unet_config):\n super().__init__(unet_config)\n if comfy.model_management.extended_fp16_support():\n self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Omnigen2(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_3b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))\n\nclass QwenImage(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"qwen_image\",\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 1.15,\n }\n\n memory_usage_factor = 1.8 #TODO\n\n unet_extra_config = {}\n latent_format = latent_formats.Wan21\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.QwenImage(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.qwen_image.QwenImageTokenizer, comfy.text_encoders.qwen_image.te(**hunyuan_detect))\n\nclass HunyuanImage21(HunyuanVideo):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n \"vec_in_dim\": None,\n }\n\n sampling_settings = {\n \"shift\": 5.0,\n }\n\n latent_format = latent_formats.HunyuanImage21\n\n memory_usage_factor = 8.7\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanImage21(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))\n\nclass HunyuanImage21Refiner(HunyuanVideo):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n \"patch_size\": [1, 1, 1],\n \"vec_in_dim\": None,\n }\n\n sampling_settings = {\n \"shift\": 4.0,\n }\n\n latent_format = latent_formats.HunyuanImage21Refiner\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanImage21Refiner(self, device=device)\n return out\n\nclass HunyuanVideo15(HunyuanVideo):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n \"vision_in_dim\": 1152,\n }\n\n sampling_settings = {\n \"shift\": 7.0,\n }\n memory_usage_factor = 4.0 #TODO\n supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n\n latent_format = latent_formats.HunyuanVideo15\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanVideo15(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))\n\n\nclass HunyuanVideo15_SR_Distilled(HunyuanVideo):\n unet_config = {\n \"image_model\": \"hunyuan_video\",\n \"vision_in_dim\": 1152,\n \"in_channels\": 98,\n }\n\n sampling_settings = {\n \"shift\": 2.0,\n }\n memory_usage_factor = 4.0 #TODO\n supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n\n latent_format = latent_formats.HunyuanVideo15\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.HunyuanVideo15_SR_Distilled(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))\n\n\nclass Kandinsky5(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"kandinsky5\",\n }\n\n sampling_settings = {\n \"shift\": 10.0,\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.HunyuanVideo\n\n memory_usage_factor = 1.25 #TODO\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Kandinsky5(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))\n\n\nclass Kandinsky5Image(Kandinsky5):\n unet_config = {\n \"image_model\": \"kandinsky5\",\n \"model_dim\": 2560,\n \"visual_embed_dim\": 64,\n }\n\n sampling_settings = {\n \"shift\": 3.0,\n }\n\n latent_format = latent_formats.Flux\n memory_usage_factor = 1.25 #TODO\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.Kandinsky5Image(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))\n\n\nclass ACEStep15(supported_models_base.BASE):\n unet_config = {\n \"audio_model\": \"ace1.5\",\n }\n\n unet_extra_config = {\n }\n\n sampling_settings = {\n \"multiplier\": 1.0,\n \"shift\": 3.0,\n }\n\n latent_format = comfy.latent_formats.ACEAudio15\n\n memory_usage_factor = 4.7\n\n supported_inference_dtypes = [torch.bfloat16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.ACEStep15(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n detect_2b = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen3_2b.transformer.\".format(pref))\n detect_4b = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen3_4b.transformer.\".format(pref))\n if \"dtype_llama\" in detect_2b:\n detect = detect_2b\n detect[\"lm_model\"] = \"qwen3_2b\"\n elif \"dtype_llama\" in detect_4b:\n detect = detect_4b\n detect[\"lm_model\"] = \"qwen3_4b\"\n\n return supported_models_base.ClipTarget(comfy.text_encoders.ace15.ACE15Tokenizer, comfy.text_encoders.ace15.te(**detect))\n\n\nclass LongCatImage(supported_models_base.BASE):\n unet_config = {\n \"image_model\": \"flux\",\n \"guidance_embed\": False,\n \"vec_in_dim\": None,\n \"context_in_dim\": 3584,\n \"txt_ids_dims\": [1, 2],\n }\n\n sampling_settings = {\n }\n\n unet_extra_config = {}\n latent_format = latent_formats.Flux\n\n memory_usage_factor = 2.5\n\n supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]\n\n vae_key_prefix = [\"vae.\"]\n text_encoder_key_prefix = [\"text_encoders.\"]\n\n def get_model(self, state_dict, prefix=\"\", device=None):\n out = model_base.LongCatImage(self, device=device)\n return out\n\n def clip_target(self, state_dict={}):\n pref = self.text_encoder_key_prefix[0]\n hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, \"{}qwen25_7b.transformer.\".format(pref))\n return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))\n\nmodels = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]\n\nmodels += [SVD_img2vid]\n" }, { "path": "comfy/supported_models_base.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\nimport torch\nimport logging\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 required_keys = {}\n\n clip_prefix = []\n clip_vision_prefix = None\n noise_aug_config = None\n sampling_settings = {}\n latent_format = latent_formats.LatentFormat\n vae_key_prefix = [\"first_stage_model.\"]\n text_encoder_key_prefix = [\"cond_stage_model.\"]\n supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]\n\n memory_usage_factor = 2.0\n\n manual_cast_dtype = None\n custom_operations = None\n quant_config = None # quantization configuration for mixed precision\n optimizations = {\"fp8\": False}\n\n @classmethod\n def matches(s, unet_config, state_dict=None):\n for k in s.unet_config:\n if k not in unet_config or s.unet_config[k] != unet_config[k]:\n return False\n if state_dict is not None:\n for k in s.required_keys:\n if k not in state_dict:\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.copy()\n self.sampling_settings = self.sampling_settings.copy()\n self.latent_format = self.latent_format()\n self.optimizations = self.optimizations.copy()\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 state_dict = utils.state_dict_prefix_replace(state_dict, {k: \"\" for k in self.text_encoder_key_prefix}, filter_keys=True)\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 = {\"\": self.text_encoder_key_prefix[0]}\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 = {\"\": self.vae_key_prefix[0]}\n return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n def set_inference_dtype(self, dtype, manual_cast_dtype):\n self.unet_config['dtype'] = dtype\n self.manual_cast_dtype = manual_cast_dtype\n\n def __getattr__(self, name):\n logging.warning(\"\\nWARNING, you accessed {} from the model config object which doesn't exist. Please fix your code.\\n\".format(name))\n return None\n" }, { "path": "comfy/t2i_adapter/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 features = features[::-1]\n\n if self.xl:\n return {\"input\": features[1:], \"middle\": features[:1]}\n else:\n return {\"input\": features}\n\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 {\"input\": features[::-1]}\n" }, { "path": "comfy/taesd/taehv.py", "content": "# Tiny AutoEncoder for HunyuanVideo and WanVideo https://github.com/madebyollin/taehv\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom tqdm.auto import tqdm\nfrom collections import namedtuple, deque\n\nimport comfy.ops\noperations=comfy.ops.disable_weight_init\n\nDecoderResult = namedtuple(\"DecoderResult\", (\"frame\", \"memory\"))\nTWorkItem = namedtuple(\"TWorkItem\", (\"input_tensor\", \"block_index\"))\n\ndef conv(n_in, n_out, **kwargs):\n return operations.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 MemBlock(nn.Module):\n def __init__(self, n_in, n_out, act_func):\n super().__init__()\n self.conv = nn.Sequential(conv(n_in * 2, n_out), act_func, conv(n_out, n_out), act_func, conv(n_out, n_out))\n self.skip = operations.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()\n self.act = act_func\n def forward(self, x, past):\n return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x))\n\nclass TPool(nn.Module):\n def __init__(self, n_f, stride):\n super().__init__()\n self.stride = stride\n self.conv = operations.Conv2d(n_f*stride,n_f, 1, bias=False)\n def forward(self, x):\n _NT, C, H, W = x.shape\n return self.conv(x.reshape(-1, self.stride * C, H, W))\n\nclass TGrow(nn.Module):\n def __init__(self, n_f, stride):\n super().__init__()\n self.stride = stride\n self.conv = operations.Conv2d(n_f, n_f*stride, 1, bias=False)\n def forward(self, x):\n _NT, C, H, W = x.shape\n x = self.conv(x)\n return x.reshape(-1, C, H, W)\n\ndef apply_model_with_memblocks(model, x, parallel, show_progress_bar):\n\n B, T, C, H, W = x.shape\n if parallel:\n x = x.reshape(B*T, C, H, W)\n # parallel over input timesteps, iterate over blocks\n for b in tqdm(model, disable=not show_progress_bar):\n if isinstance(b, MemBlock):\n BT, C, H, W = x.shape\n T = BT // B\n _x = x.reshape(B, T, C, H, W)\n mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape)\n x = b(x, mem)\n else:\n x = b(x)\n BT, C, H, W = x.shape\n T = BT // B\n x = x.view(B, T, C, H, W)\n else:\n out = []\n work_queue = deque([TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(B, T * C, H, W).chunk(T, dim=1))])\n progress_bar = tqdm(range(T), disable=not show_progress_bar)\n mem = [None] * len(model)\n while work_queue:\n xt, i = work_queue.popleft()\n if i == 0:\n progress_bar.update(1)\n if i == len(model):\n out.append(xt)\n del xt\n else:\n b = model[i]\n if isinstance(b, MemBlock):\n if mem[i] is None:\n xt_new = b(xt, xt * 0)\n mem[i] = xt.detach().clone()\n else:\n xt_new = b(xt, mem[i])\n mem[i] = xt.detach().clone()\n del xt\n work_queue.appendleft(TWorkItem(xt_new, i+1))\n elif isinstance(b, TPool):\n if mem[i] is None:\n mem[i] = []\n mem[i].append(xt.detach().clone())\n if len(mem[i]) == b.stride:\n B, C, H, W = xt.shape\n xt = b(torch.cat(mem[i], 1).view(B*b.stride, C, H, W))\n mem[i] = []\n work_queue.appendleft(TWorkItem(xt, i+1))\n elif isinstance(b, TGrow):\n xt = b(xt)\n NT, C, H, W = xt.shape\n for xt_next in reversed(xt.view(B, b.stride*C, H, W).chunk(b.stride, 1)):\n work_queue.appendleft(TWorkItem(xt_next, i+1))\n del xt\n else:\n xt = b(xt)\n work_queue.appendleft(TWorkItem(xt, i+1))\n progress_bar.close()\n x = torch.stack(out, 1)\n return x\n\n\nclass TAEHV(nn.Module):\n def __init__(self, latent_channels, parallel=False, encoder_time_downscale=(True, True, False), decoder_time_upscale=(False, True, True), decoder_space_upscale=(True, True, True),\n latent_format=None, show_progress_bar=False):\n super().__init__()\n self.image_channels = 3\n self.patch_size = 1\n self.latent_channels = latent_channels\n self.parallel = parallel\n self.latent_format = latent_format\n self.show_progress_bar = show_progress_bar\n self.process_in = latent_format().process_in if latent_format is not None else (lambda x: x)\n self.process_out = latent_format().process_out if latent_format is not None else (lambda x: x)\n if self.latent_channels in [48, 32]: # Wan 2.2 and HunyuanVideo1.5\n self.patch_size = 2\n elif self.latent_channels == 128: # LTX2\n self.patch_size, self.latent_channels, encoder_time_downscale, decoder_time_upscale = 4, 128, (True, True, True), (True, True, True)\n\n if self.latent_channels == 32: # HunyuanVideo1.5\n act_func = nn.LeakyReLU(0.2, inplace=True)\n else: # HunyuanVideo, Wan 2.1\n act_func = nn.ReLU(inplace=True)\n\n self.encoder = nn.Sequential(\n conv(self.image_channels*self.patch_size**2, 64), act_func,\n TPool(64, 2 if encoder_time_downscale[0] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),\n TPool(64, 2 if encoder_time_downscale[1] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),\n TPool(64, 2 if encoder_time_downscale[2] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),\n conv(64, self.latent_channels),\n )\n n_f = [256, 128, 64, 64]\n\n self.decoder = nn.Sequential(\n Clamp(), conv(self.latent_channels, n_f[0]), act_func,\n MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 2 if decoder_time_upscale[0] else 1), conv(n_f[0], n_f[1], bias=False),\n MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[1] else 1), conv(n_f[1], n_f[2], bias=False),\n MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[2] else 1), conv(n_f[2], n_f[3], bias=False),\n act_func, conv(n_f[3], self.image_channels*self.patch_size**2),\n )\n\n self.t_downscale = 2**sum(t.stride == 2 for t in self.encoder if isinstance(t, TPool))\n self.t_upscale = 2**sum(t.stride == 2 for t in self.decoder if isinstance(t, TGrow))\n self.frames_to_trim = self.t_upscale - 1\n self._show_progress_bar = show_progress_bar\n\n @property\n def show_progress_bar(self):\n return self._show_progress_bar\n\n @show_progress_bar.setter\n def show_progress_bar(self, value):\n self._show_progress_bar = value\n\n def encode(self, x, **kwargs):\n x = x.movedim(2, 1) # [B, C, T, H, W] -> [B, T, C, H, W]\n if self.patch_size > 1:\n B, T, C, H, W = x.shape\n x = x.reshape(B * T, C, H, W)\n x = F.pixel_unshuffle(x, self.patch_size)\n x = x.reshape(B, T, C * self.patch_size ** 2, H // self.patch_size, W // self.patch_size)\n if x.shape[1] % self.t_downscale != 0:\n # pad at end to multiple of t_downscale\n n_pad = self.t_downscale - x.shape[1] % self.t_downscale\n padding = x[:, -1:].repeat_interleave(n_pad, dim=1)\n x = torch.cat([x, padding], 1)\n x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar).movedim(2, 1)\n return self.process_out(x)\n\n def decode(self, x, **kwargs):\n x = x.unsqueeze(0) if x.ndim == 4 else x # [T, C, H, W] -> [1, T, C, H, W]\n x = x.movedim(1, 2) if x.shape[1] != self.latent_channels else x # [B, T, C, H, W] or [B, C, T, H, W]\n x = self.process_in(x).movedim(2, 1) # [B, C, T, H, W] -> [B, T, C, H, W]\n x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)\n if self.patch_size > 1:\n x = F.pixel_shuffle(x, self.patch_size)\n return x[:, self.frames_to_trim:].movedim(2, 1)\n" }, { "path": "comfy/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 comfy.utils\nimport comfy.ops\n\ndef conv(n_in, n_out, **kwargs):\n return comfy.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 = comfy.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(latent_channels=4):\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, latent_channels),\n )\n\n\ndef Decoder(latent_channels=4):\n return nn.Sequential(\n Clamp(), conv(latent_channels, 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, latent_channels=4):\n \"\"\"Initialize pretrained TAESD on the given device from the given checkpoints.\"\"\"\n super().__init__()\n self.taesd_encoder = Encoder(latent_channels=latent_channels)\n self.taesd_decoder = Decoder(latent_channels=latent_channels)\n self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))\n self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))\n if encoder_path is not None:\n self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))\n if decoder_path is not None:\n self.taesd_decoder.load_state_dict(comfy.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_shift) * 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) + self.vae_shift\n" }, { "path": "comfy/text_encoders/ace.py", "content": "from comfy import sd1_clip\nfrom .spiece_tokenizer import SPieceTokenizer\nimport comfy.text_encoders.t5\nimport os\nimport re\nimport torch\nimport logging\n\nfrom tokenizers import Tokenizer\nfrom .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji\n\nSUPPORT_LANGUAGES = {\n \"en\": 259, \"de\": 260, \"fr\": 262, \"es\": 284, \"it\": 285,\n \"pt\": 286, \"pl\": 294, \"tr\": 295, \"ru\": 267, \"cs\": 293,\n \"nl\": 297, \"ar\": 5022, \"zh\": 5023, \"ja\": 5412, \"hu\": 5753,\n \"ko\": 6152, \"hi\": 6680\n}\n\nstructure_pattern = re.compile(r\"\\[.*?\\]\")\n\nDEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), \"ace_lyrics_tokenizer\"), \"vocab.json\")\n\n\nclass VoiceBpeTokenizer:\n def __init__(self, vocab_file=DEFAULT_VOCAB_FILE):\n self.tokenizer = None\n if vocab_file is not None:\n self.tokenizer = Tokenizer.from_file(vocab_file)\n\n def preprocess_text(self, txt, lang):\n txt = multilingual_cleaners(txt, lang)\n return txt\n\n def encode(self, txt, lang='en'):\n # lang = lang.split(\"-\")[0] # remove the region\n # self.check_input_length(txt, lang)\n txt = self.preprocess_text(txt, lang)\n lang = \"zh-cn\" if lang == \"zh\" else lang\n txt = f\"[{lang}]{txt}\"\n txt = txt.replace(\" \", \"[SPACE]\")\n return self.tokenizer.encode(txt).ids\n\n def get_lang(self, line):\n if line.startswith(\"[\") and line[3:4] == ']':\n lang = line[1:3].lower()\n if lang in SUPPORT_LANGUAGES:\n return lang, line[4:]\n return \"en\", line\n\n def __call__(self, string):\n lines = string.split(\"\\n\")\n lyric_token_idx = [261]\n for line in lines:\n line = line.strip()\n if not line:\n lyric_token_idx += [2]\n continue\n\n lang, line = self.get_lang(line)\n\n if lang not in SUPPORT_LANGUAGES:\n lang = \"en\"\n if \"zh\" in lang:\n lang = \"zh\"\n if \"spa\" in lang:\n lang = \"es\"\n\n try:\n line_out = japanese_to_romaji(line)\n if line_out != line:\n lang = \"ja\"\n line = line_out\n except:\n pass\n\n try:\n if structure_pattern.match(line):\n token_idx = self.encode(line, \"en\")\n else:\n token_idx = self.encode(line, lang)\n lyric_token_idx = lyric_token_idx + token_idx + [2]\n except Exception as e:\n logging.warning(\"tokenize error {} for line {} major_language {}\".format(e, line, lang))\n return {\"input_ids\": lyric_token_idx}\n\n @staticmethod\n def from_pretrained(path, **kwargs):\n return VoiceBpeTokenizer(path, **kwargs)\n\n def get_vocab(self):\n return {}\n\n\nclass UMT5BaseModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"umt5_config_base.json\")\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options)\n\nclass UMT5BaseTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data)\n\n def state_dict(self):\n return {\"spiece_model\": self.tokenizer.serialize_model()}\n\nclass LyricsTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), \"ace_lyrics_tokenizer\"), \"vocab.json\")\n super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data)\n\nclass AceT5Tokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"lyrics\"] = self.voicebpe.tokenize_with_weights(kwargs.get(\"lyrics\", \"\"), return_word_ids, **kwargs)\n out[\"umt5base\"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.umt5base.untokenize(token_weight_pair)\n\n def state_dict(self):\n return self.umt5base.state_dict()\n\nclass AceT5Model(torch.nn.Module):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, **kwargs):\n super().__init__()\n self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options)\n self.dtypes = set()\n if dtype is not None:\n self.dtypes.add(dtype)\n\n def set_clip_options(self, options):\n self.umt5base.set_clip_options(options)\n\n def reset_clip_options(self):\n self.umt5base.reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_umt5base = token_weight_pairs[\"umt5base\"]\n token_weight_pairs_lyrics = token_weight_pairs[\"lyrics\"]\n\n t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base)\n\n lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0)\n return t5_out, None, {\"conditioning_lyrics\": lyrics_embeds}\n\n def load_sd(self, sd):\n return self.umt5base.load_sd(sd)\n" }, { "path": "comfy/text_encoders/ace15.py", "content": "from .anima import Qwen3Tokenizer\nimport comfy.text_encoders.llama\nfrom comfy import sd1_clip\nimport torch\nimport math\nimport yaml\nimport comfy.utils\n\n\ndef sample_manual_loop_no_classes(\n model,\n ids=None,\n execution_dtype=None,\n cfg_scale: float = 2.0,\n temperature: float = 0.85,\n top_p: float = 0.9,\n top_k: int = None,\n min_p: float = 0.000,\n seed: int = 1,\n min_tokens: int = 1,\n max_new_tokens: int = 2048,\n audio_start_id: int = 151669, # The cutoff ID for audio codes\n audio_end_id: int = 215669,\n eos_token_id: int = 151645,\n):\n if ids is None:\n return []\n device = model.execution_device\n\n if execution_dtype is None:\n if comfy.model_management.should_use_bf16(device):\n execution_dtype = torch.bfloat16\n else:\n execution_dtype = torch.float32\n\n embeds, attention_mask, num_tokens, embeds_info = model.process_tokens(ids, device)\n embeds_batch = embeds.shape[0]\n\n output_audio_codes = []\n past_key_values = []\n generator = torch.Generator(device=device)\n generator.manual_seed(seed)\n model_config = model.transformer.model.config\n past_kv_shape = [embeds_batch, model_config.num_key_value_heads, embeds.shape[1] + min_tokens, model_config.head_dim]\n\n for x in range(model_config.num_hidden_layers):\n past_key_values.append((torch.empty(past_kv_shape, device=device, dtype=execution_dtype), torch.empty(past_kv_shape, device=device, dtype=execution_dtype), 0))\n\n progress_bar = comfy.utils.ProgressBar(max_new_tokens)\n\n for step in comfy.utils.model_trange(max_new_tokens, desc=\"LM sampling\"):\n outputs = model.transformer(None, attention_mask, embeds=embeds.to(execution_dtype), num_tokens=num_tokens, intermediate_output=None, dtype=execution_dtype, embeds_info=embeds_info, past_key_values=past_key_values)\n next_token_logits = model.transformer.logits(outputs[0])[:, -1]\n past_key_values = outputs[2]\n\n if cfg_scale != 1.0:\n cond_logits = next_token_logits[0:1]\n uncond_logits = next_token_logits[1:2]\n cfg_logits = uncond_logits + cfg_scale * (cond_logits - uncond_logits)\n else:\n cfg_logits = next_token_logits[0:1]\n\n use_eos_score = eos_token_id is not None and eos_token_id < audio_start_id and min_tokens < step\n if use_eos_score:\n eos_score = cfg_logits[:, eos_token_id].clone()\n\n remove_logit_value = torch.finfo(cfg_logits.dtype).min\n # Only generate audio tokens\n cfg_logits[:, :audio_start_id] = remove_logit_value\n cfg_logits[:, audio_end_id:] = remove_logit_value\n\n if use_eos_score:\n cfg_logits[:, eos_token_id] = eos_score\n\n if top_k is not None and top_k > 0:\n top_k_vals, _ = torch.topk(cfg_logits, top_k)\n min_val = top_k_vals[..., -1, None]\n cfg_logits[cfg_logits < min_val] = remove_logit_value\n\n if min_p is not None and min_p > 0:\n probs = torch.softmax(cfg_logits, dim=-1)\n p_max = probs.max(dim=-1, keepdim=True).values\n indices_to_remove = probs < (min_p * p_max)\n cfg_logits[indices_to_remove] = remove_logit_value\n\n if top_p is not None and top_p < 1.0:\n sorted_logits, sorted_indices = torch.sort(cfg_logits, descending=True)\n cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)\n sorted_indices_to_remove = cumulative_probs > top_p\n sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()\n sorted_indices_to_remove[..., 0] = 0\n indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)\n cfg_logits[indices_to_remove] = remove_logit_value\n\n if temperature > 0:\n cfg_logits = cfg_logits / temperature\n next_token = torch.multinomial(torch.softmax(cfg_logits, dim=-1), num_samples=1, generator=generator).squeeze(1)\n else:\n next_token = torch.argmax(cfg_logits, dim=-1)\n\n token = next_token.item()\n\n if token == eos_token_id:\n break\n\n embed, _, _, _ = model.process_tokens([[token]], device)\n embeds = embed.repeat(embeds_batch, 1, 1)\n attention_mask = torch.cat([attention_mask, torch.ones((embeds_batch, 1), device=device, dtype=attention_mask.dtype)], dim=1)\n\n output_audio_codes.append(token - audio_start_id)\n progress_bar.update_absolute(step)\n\n return output_audio_codes\n\n\ndef generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0, min_p=0.000):\n positive = [[token for token, _ in inner_list] for inner_list in positive]\n positive = positive[0]\n\n if cfg_scale != 1.0:\n negative = [[token for token, _ in inner_list] for inner_list in negative]\n negative = negative[0]\n\n neg_pad = 0\n if len(negative) < len(positive):\n neg_pad = (len(positive) - len(negative))\n negative = [model.special_tokens[\"pad\"]] * neg_pad + negative\n\n pos_pad = 0\n if len(negative) > len(positive):\n pos_pad = (len(negative) - len(positive))\n positive = [model.special_tokens[\"pad\"]] * pos_pad + positive\n\n ids = [positive, negative]\n else:\n ids = [positive]\n\n return sample_manual_loop_no_classes(model, ids, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)\n\n\nclass ACE15Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"qwen3_06b\", tokenizer=Qwen3Tokenizer)\n\n def _metas_to_cot(self, *, return_yaml: bool = False, **kwargs) -> str:\n user_metas = {\n k: kwargs.pop(k)\n for k in (\"bpm\", \"duration\", \"keyscale\", \"timesignature\")\n if k in kwargs\n }\n timesignature = user_metas.get(\"timesignature\")\n if isinstance(timesignature, str) and timesignature.endswith(\"/4\"):\n user_metas[\"timesignature\"] = timesignature[:-2]\n user_metas = {\n k: v if not isinstance(v, str) or not v.isdigit() else int(v)\n for k, v in user_metas.items()\n if v not in {\"unspecified\", None}\n }\n if len(user_metas):\n meta_yaml = yaml.dump(user_metas, allow_unicode=True, sort_keys=True).strip()\n else:\n meta_yaml = \"\"\n return f\"\\n{meta_yaml}\\n\" if not return_yaml else meta_yaml\n\n def _metas_to_cap(self, **kwargs) -> str:\n use_keys = (\"bpm\", \"timesignature\", \"keyscale\", \"duration\")\n user_metas = { k: kwargs.pop(k, \"N/A\") for k in use_keys }\n timesignature = user_metas.get(\"timesignature\")\n if isinstance(timesignature, str) and timesignature.endswith(\"/4\"):\n user_metas[\"timesignature\"] = timesignature[:-2]\n duration = user_metas[\"duration\"]\n if duration == \"N/A\":\n user_metas[\"duration\"] = \"30 seconds\"\n elif isinstance(duration, (str, int, float)):\n user_metas[\"duration\"] = f\"{math.ceil(float(duration))} seconds\"\n else:\n raise TypeError(\"Unexpected type for duration key, must be str, int or float\")\n return \"\\n\".join(f\"- {k}: {user_metas[k]}\" for k in use_keys)\n\n def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):\n text = text.strip()\n text_negative = kwargs.get(\"caption_negative\", text).strip()\n lyrics = kwargs.get(\"lyrics\", \"\")\n lyrics_negative = kwargs.get(\"lyrics_negative\", lyrics)\n duration = kwargs.get(\"duration\", 120)\n if isinstance(duration, str):\n duration = float(duration.split(None, 1)[0])\n language = kwargs.get(\"language\")\n seed = kwargs.get(\"seed\", 0)\n\n generate_audio_codes = kwargs.get(\"generate_audio_codes\", True)\n cfg_scale = kwargs.get(\"cfg_scale\", 2.0)\n temperature = kwargs.get(\"temperature\", 0.85)\n top_p = kwargs.get(\"top_p\", 0.9)\n top_k = kwargs.get(\"top_k\", 0.0)\n min_p = kwargs.get(\"min_p\", 0.000)\n\n duration = math.ceil(duration)\n kwargs[\"duration\"] = duration\n tokens_duration = duration * 5\n min_tokens = int(kwargs.get(\"min_tokens\", tokens_duration))\n max_tokens = int(kwargs.get(\"max_tokens\", tokens_duration))\n\n metas_negative = {\n k.rsplit(\"_\", 1)[0]: kwargs.pop(k)\n for k in (\"bpm_negative\", \"duration_negative\", \"keyscale_negative\", \"timesignature_negative\", \"language_negative\", \"caption_negative\")\n if k in kwargs\n }\n if not kwargs.get(\"use_negative_caption\"):\n _ = metas_negative.pop(\"caption\", None)\n\n cot_text = self._metas_to_cot(caption=text, **kwargs)\n cot_text_negative = \"\\n\\n\" if not metas_negative else self._metas_to_cot(**metas_negative)\n meta_cap = self._metas_to_cap(**kwargs)\n\n lm_template = \"<|im_start|>system\\n# Instruction\\nGenerate audio semantic tokens based on the given conditions:\\n\\n<|im_end|>\\n<|im_start|>user\\n# Caption\\n{}\\n\\n# Lyric\\n{}\\n<|im_end|>\\n<|im_start|>assistant\\n{}\\n\\n<|im_end|>\\n\"\n lyrics_template = \"# Languages\\n{}\\n\\n# Lyric\\n{}<|endoftext|><|endoftext|>\"\n qwen3_06b_template = \"# Instruction\\nGenerate audio semantic tokens based on the given conditions:\\n\\n# Caption\\n{}\\n\\n# Metas\\n{}\\n<|endoftext|>\\n<|endoftext|>\"\n\n llm_prompts = {\n \"lm_prompt\": lm_template.format(text, lyrics.strip(), cot_text),\n \"lm_prompt_negative\": lm_template.format(text_negative, lyrics_negative.strip(), cot_text_negative),\n \"lyrics\": lyrics_template.format(language if language is not None else \"\", lyrics),\n \"qwen3_06b\": qwen3_06b_template.format(text, meta_cap),\n }\n\n out = {\n prompt_key: self.qwen3_06b.tokenize_with_weights(\n prompt,\n prompt_key == \"qwen3_06b\" and return_word_ids,\n disable_weights = True,\n **kwargs,\n )\n for prompt_key, prompt in llm_prompts.items()\n }\n out[\"lm_metadata\"] = {\"min_tokens\": min_tokens,\n \"max_tokens\": max_tokens,\n \"seed\": seed,\n \"generate_audio_codes\": generate_audio_codes,\n \"cfg_scale\": cfg_scale,\n \"temperature\": temperature,\n \"top_p\": top_p,\n \"top_k\": top_k,\n \"min_p\": min_p,\n }\n return out\n\n\nclass Qwen3_06BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_06B_ACE15, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass Qwen3_2B_ACE15(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_2B_ACE15_lm, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass Qwen3_4B_ACE15(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B_ACE15_lm, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass ACE15TEModel(torch.nn.Module):\n def __init__(self, device=\"cpu\", dtype=None, dtype_llama=None, lm_model=None, model_options={}):\n super().__init__()\n if dtype_llama is None:\n dtype_llama = dtype\n\n model = None\n self.constant = 0.4375\n if lm_model == \"qwen3_4b\":\n model = Qwen3_4B_ACE15\n self.constant = 0.5625\n elif lm_model == \"qwen3_2b\":\n model = Qwen3_2B_ACE15\n\n self.lm_model = lm_model\n self.qwen3_06b = Qwen3_06BModel(device=device, dtype=dtype, model_options=model_options)\n if model is not None:\n setattr(self, self.lm_model, model(device=device, dtype=dtype_llama, model_options=model_options))\n\n self.dtypes = set([dtype, dtype_llama])\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_base = token_weight_pairs[\"qwen3_06b\"]\n token_weight_pairs_lyrics = token_weight_pairs[\"lyrics\"]\n\n self.qwen3_06b.set_clip_options({\"layer\": None})\n base_out, _, extra = self.qwen3_06b.encode_token_weights(token_weight_pairs_base)\n self.qwen3_06b.set_clip_options({\"layer\": [0]})\n lyrics_embeds, _, extra_l = self.qwen3_06b.encode_token_weights(token_weight_pairs_lyrics)\n\n out = {\"conditioning_lyrics\": lyrics_embeds[:, 0]}\n\n lm_metadata = token_weight_pairs[\"lm_metadata\"]\n if lm_metadata[\"generate_audio_codes\"]:\n audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs[\"lm_prompt\"], token_weight_pairs[\"lm_prompt_negative\"], min_tokens=lm_metadata[\"min_tokens\"], max_tokens=lm_metadata[\"min_tokens\"], seed=lm_metadata[\"seed\"], cfg_scale=lm_metadata[\"cfg_scale\"], temperature=lm_metadata[\"temperature\"], top_p=lm_metadata[\"top_p\"], top_k=lm_metadata[\"top_k\"], min_p=lm_metadata[\"min_p\"])\n out[\"audio_codes\"] = [audio_codes]\n\n return base_out, None, out\n\n def set_clip_options(self, options):\n self.qwen3_06b.set_clip_options(options)\n lm_model = getattr(self, self.lm_model, None)\n if lm_model is not None:\n lm_model.set_clip_options(options)\n\n def reset_clip_options(self):\n self.qwen3_06b.reset_clip_options()\n lm_model = getattr(self, self.lm_model, None)\n if lm_model is not None:\n lm_model.reset_clip_options()\n\n def load_sd(self, sd):\n if \"model.layers.0.post_attention_layernorm.weight\" in sd:\n shape = sd[\"model.layers.0.post_attention_layernorm.weight\"].shape\n if shape[0] == 1024:\n return self.qwen3_06b.load_sd(sd)\n else:\n return getattr(self, self.lm_model).load_sd(sd)\n\n def memory_estimation_function(self, token_weight_pairs, device=None):\n lm_metadata = token_weight_pairs.get(\"lm_metadata\", {})\n constant = self.constant\n if comfy.model_management.should_use_bf16(device):\n constant *= 0.5\n\n token_weight_pairs = token_weight_pairs.get(\"lm_prompt\", [])\n num_tokens = sum(map(lambda a: len(a), token_weight_pairs))\n num_tokens += lm_metadata.get(\"min_tokens\", 0)\n return num_tokens * constant * 1024 * 1024\n\ndef te(dtype_llama=None, llama_quantization_metadata=None, lm_model=\"qwen3_2b\"):\n class ACE15TEModel_(ACE15TEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n super().__init__(device=device, dtype_llama=dtype_llama, lm_model=lm_model, dtype=dtype, model_options=model_options)\n return ACE15TEModel_\n" }, { "path": "comfy/text_encoders/ace_lyrics_tokenizer/vocab.json", "content": "{\n \"version\": \"1.0\",\n \"truncation\": null,\n \"padding\": null,\n \"added_tokens\": [\n {\n \"id\": 0,\n \"special\": true,\n \"content\": \"[STOP]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 1,\n \"special\": true,\n \"content\": \"[UNK]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 2,\n \"special\": true,\n \"content\": \"[SPACE]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 259,\n \"special\": true,\n \"content\": \"[en]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 260,\n \"special\": true,\n \"content\": \"[de]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 261,\n \"special\": true,\n \"content\": \"[START]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 262,\n \"special\": true,\n \"content\": \"[fr]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 284,\n \"special\": true,\n \"content\": \"[es]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 285,\n \"special\": true,\n \"content\": \"[it]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 286,\n \"special\": true,\n \"content\": \"[pt]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 294,\n \"special\": true,\n \"content\": \"[pl]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 295,\n \"special\": true,\n \"content\": \"[tr]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 267,\n \"special\": true,\n \"content\": \"[ru]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 293,\n \"special\": true,\n \"content\": \"[cs]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 297,\n \"special\": true,\n \"content\": \"[nl]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 5022,\n \"special\": true,\n \"content\": \"[ar]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 5023,\n \"special\": true,\n \"content\": \"[zh-cn]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 5412,\n \"special\": true,\n \"content\": \"[ja]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 5753,\n \"special\": true,\n \"content\": \"[hu]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6152,\n \"special\": true,\n \"content\": \"[ko]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6680,\n \"special\": true,\n \"content\": \"[hi]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6681,\n \"special\": true,\n \"content\": \"[start]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6682,\n \"special\": true,\n \"content\": \"[intro]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6683,\n \"special\": true,\n \"content\": \"[verse]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6684,\n \"special\": true,\n \"content\": \"[chorus]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6685,\n \"special\": true,\n \"content\": \"[bridge]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6686,\n \"special\": true,\n \"content\": \"[outro]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6687,\n \"special\": true,\n \"content\": \"[end]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6688,\n \"special\": true,\n \"content\": \"[inst]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6689,\n \"special\": true,\n \"content\": \"[solo]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6690,\n \"special\": true,\n \"content\": \"[hook]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6691,\n \"special\": true,\n \"content\": \"[pre-chorus]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n },\n {\n \"id\": 6692,\n \"special\": true,\n \"content\": \"[break]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false\n }\n ],\n \"normalizer\": null,\n \"pre_tokenizer\": {\n \"type\": \"Whitespace\"\n },\n \"post_processor\": null,\n \"decoder\": null,\n \"model\": {\n \"type\": \"BPE\",\n \"dropout\": null,\n \"unk_token\": \"[UNK]\",\n \"continuing_subword_prefix\": null,\n \"end_of_word_suffix\": null,\n \"fuse_unk\": false,\n \"vocab\": {\n \"[STOP]\": 0,\n \"[UNK]\": 1,\n \"[SPACE]\": 2,\n \"!\": 3,\n \"'\": 4,\n \"(\": 5,\n \")\": 6,\n \",\": 7,\n \"-\": 8,\n \".\": 9,\n \"/\": 10,\n \":\": 11,\n \";\": 12,\n \"?\": 13,\n \"a\": 14,\n \"b\": 15,\n \"c\": 16,\n \"d\": 17,\n \"e\": 18,\n \"f\": 19,\n \"g\": 20,\n \"h\": 21,\n \"i\": 22,\n \"j\": 23,\n \"k\": 24,\n \"l\": 25,\n \"m\": 26,\n \"n\": 27,\n \"o\": 28,\n \"p\": 29,\n \"q\": 30,\n \"r\": 31,\n \"s\": 32,\n \"t\": 33,\n \"u\": 34,\n \"v\": 35,\n \"w\": 36,\n \"x\": 37,\n \"y\": 38,\n \"z\": 39,\n \"th\": 40,\n \"in\": 41,\n \"the\": 42,\n \"an\": 43,\n \"er\": 44,\n \"ou\": 45,\n \"re\": 46,\n \"on\": 47,\n \"at\": 48,\n \"ed\": 49,\n \"en\": 50,\n \"to\": 51,\n \"ing\": 52,\n \"and\": 53,\n \"is\": 54,\n \"as\": 55,\n \"al\": 56,\n \"or\": 57,\n \"of\": 58,\n \"ar\": 59,\n \"it\": 60,\n \"es\": 61,\n \"he\": 62,\n \"st\": 63,\n \"le\": 64,\n \"om\": 65,\n \"se\": 66,\n \"be\": 67,\n \"ad\": 68,\n \"ow\": 69,\n \"ly\": 70,\n \"ch\": 71,\n \"wh\": 72,\n \"that\": 73,\n \"you\": 74,\n \"li\": 75,\n \"ve\": 76,\n \"ac\": 77,\n \"ti\": 78,\n \"ld\": 79,\n \"me\": 80,\n \"was\": 81,\n \"gh\": 82,\n \"id\": 83,\n \"ll\": 84,\n \"wi\": 85,\n \"ent\": 86,\n \"for\": 87,\n \"ay\": 88,\n \"ro\": 89,\n \"ver\": 90,\n \"ic\": 91,\n \"her\": 92,\n \"ke\": 93,\n \"his\": 94,\n \"no\": 95,\n \"ut\": 96,\n \"un\": 97,\n \"ir\": 98,\n \"lo\": 99,\n \"we\": 100,\n \"ri\": 101,\n \"ha\": 102,\n \"with\": 103,\n \"ght\": 104,\n \"out\": 105,\n \"im\": 106,\n \"ion\": 107,\n \"all\": 108,\n \"ab\": 109,\n \"one\": 110,\n \"ne\": 111,\n \"ge\": 112,\n \"ould\": 113,\n \"ter\": 114,\n \"mo\": 115,\n \"had\": 116,\n \"ce\": 117,\n \"she\": 118,\n \"go\": 119,\n \"sh\": 120,\n \"ur\": 121,\n \"am\": 122,\n \"so\": 123,\n \"pe\": 124,\n \"my\": 125,\n \"de\": 126,\n \"are\": 127,\n \"but\": 128,\n \"ome\": 129,\n \"fr\": 130,\n \"ther\": 131,\n \"fe\": 132,\n \"su\": 133,\n \"do\": 134,\n \"con\": 135,\n \"te\": 136,\n \"ain\": 137,\n \"ere\": 138,\n \"po\": 139,\n \"if\": 140,\n \"they\": 141,\n \"us\": 142,\n \"ag\": 143,\n \"tr\": 144,\n \"now\": 145,\n \"oun\": 146,\n \"this\": 147,\n \"have\": 148,\n \"not\": 149,\n \"sa\": 150,\n \"il\": 151,\n \"up\": 152,\n \"thing\": 153,\n \"from\": 154,\n \"ap\": 155,\n \"him\": 156,\n \"ack\": 157,\n \"ation\": 158,\n \"ant\": 159,\n \"our\": 160,\n \"op\": 161,\n \"like\": 162,\n \"ust\": 163,\n \"ess\": 164,\n \"bo\": 165,\n \"ok\": 166,\n \"ul\": 167,\n \"ind\": 168,\n \"ex\": 169,\n \"com\": 170,\n \"some\": 171,\n \"there\": 172,\n \"ers\": 173,\n \"co\": 174,\n \"res\": 175,\n \"man\": 176,\n \"ard\": 177,\n \"pl\": 178,\n \"wor\": 179,\n \"way\": 180,\n \"tion\": 181,\n \"fo\": 182,\n \"ca\": 183,\n \"were\": 184,\n \"by\": 185,\n \"ate\": 186,\n \"pro\": 187,\n \"ted\": 188,\n \"ound\": 189,\n \"own\": 190,\n \"would\": 191,\n \"ts\": 192,\n \"what\": 193,\n \"qu\": 194,\n \"ally\": 195,\n \"ight\": 196,\n \"ck\": 197,\n \"gr\": 198,\n \"when\": 199,\n \"ven\": 200,\n \"can\": 201,\n \"ough\": 202,\n \"ine\": 203,\n \"end\": 204,\n \"per\": 205,\n \"ous\": 206,\n \"od\": 207,\n \"ide\": 208,\n \"know\": 209,\n \"ty\": 210,\n \"very\": 211,\n \"si\": 212,\n \"ak\": 213,\n \"who\": 214,\n \"about\": 215,\n \"ill\": 216,\n \"them\": 217,\n \"est\": 218,\n \"red\": 219,\n \"ye\": 220,\n \"could\": 221,\n \"ong\": 222,\n \"your\": 223,\n \"their\": 224,\n \"em\": 225,\n \"just\": 226,\n \"other\": 227,\n \"into\": 228,\n \"any\": 229,\n \"whi\": 230,\n \"um\": 231,\n \"tw\": 232,\n \"ast\": 233,\n \"der\": 234,\n \"did\": 235,\n \"ie\": 236,\n \"been\": 237,\n \"ace\": 238,\n \"ink\": 239,\n \"ity\": 240,\n \"back\": 241,\n \"ting\": 242,\n \"br\": 243,\n \"more\": 244,\n \"ake\": 245,\n \"pp\": 246,\n \"then\": 247,\n \"sp\": 248,\n \"el\": 249,\n \"use\": 250,\n \"bl\": 251,\n \"said\": 252,\n \"over\": 253,\n \"get\": 254,\n \"ß\": 255,\n \"ä\": 256,\n \"ö\": 257,\n \"ü\": 258,\n \"[en]\": 259,\n \"[de]\": 260,\n \"[START]\": 261,\n \"[fr]\": 262,\n \"œ\": 263,\n \"ï\": 264,\n \"ê\": 265,\n \"â\": 266,\n \"[ru]\": 267,\n \"ÿ\": 268,\n \"è\": 269,\n \"à\": 270,\n \"ë\": 271,\n \"ù\": 272,\n \"î\": 273,\n \"ç\": 274,\n \"æ\": 275,\n \"ô\": 276,\n \"û\": 277,\n \"á\": 278,\n \"é\": 279,\n \"í\": 280,\n \"ó\": 281,\n \"ú\": 282,\n \"ñ\": 283,\n \"[es]\": 284,\n \"[it]\": 285,\n \"[pt]\": 286,\n \"ń\": 287,\n \"ś\": 288,\n \"ę\": 289,\n \"ą\": 290,\n \"ż\": 291,\n \"ć\": 292,\n \"[cs]\": 293,\n \"[pl]\": 294,\n \"[tr]\": 295,\n \"ã\": 296,\n \"[nl]\": 297,\n \"ş\": 298,\n \"ğ\": 299,\n \"ı\": 300,\n \"ò\": 301,\n \"ì\": 302,\n \"¿\": 303,\n \"…\": 304,\n \"i̇\": 305,\n \"õ\": 306,\n \"\\\"\": 307,\n \"´\": 308,\n \"ø\": 309,\n \"č\": 310,\n \"ō\": 311,\n \"š\": 312,\n \"ž\": 313,\n \"̇\": 314,\n \"ei\": 315,\n \"ich\": 316,\n \"ein\": 317,\n \"au\": 318,\n \"sch\": 319,\n \"und\": 320,\n \"die\": 321,\n \"da\": 322,\n \"den\": 323,\n \"gen\": 324,\n \"zu\": 325,\n \"hr\": 326,\n \"ten\": 327,\n \"mi\": 328,\n \"sie\": 329,\n \"das\": 330,\n \"eine\": 331,\n \"icht\": 332,\n \"ber\": 333,\n \"ach\": 334,\n \"auf\": 335,\n \"lich\": 336,\n \"nicht\": 337,\n \"mm\": 338,\n \"ben\": 339,\n \"war\": 340,\n \"mit\": 341,\n \"sich\": 342,\n \"ig\": 343,\n \"aus\": 344,\n \"ist\": 345,\n \"wie\": 346,\n \"och\": 347,\n \"ung\": 348,\n \"ann\": 349,\n \"ür\": 350,\n \"hn\": 351,\n \"ihr\": 352,\n \"sen\": 353,\n \"tz\": 354,\n \"dem\": 355,\n \"eit\": 356,\n \"hat\": 357,\n \"wir\": 358,\n \"von\": 359,\n \"wei\": 360,\n \"ier\": 361,\n \"ra\": 362,\n \"einen\": 363,\n \"vor\": 364,\n \"als\": 365,\n \"wo\": 366,\n \"rei\": 367,\n \"ste\": 368,\n \"lie\": 369,\n \"auch\": 370,\n \"du\": 371,\n \"des\": 372,\n \"ko\": 373,\n \"über\": 374,\n \"bei\": 375,\n \"hen\": 376,\n \"hm\": 377,\n \"lei\": 378,\n \"aber\": 379,\n \"wen\": 380,\n \"hl\": 381,\n \"ger\": 382,\n \"nach\": 383,\n \"ft\": 384,\n \"imm\": 385,\n \"je\": 386,\n \"schen\": 387,\n \"wer\": 388,\n \"ser\": 389,\n \"än\": 390,\n \"sein\": 391,\n \"ol\": 392,\n \"cht\": 393,\n \"für\": 394,\n \"kl\": 395,\n \"ff\": 396,\n \"einem\": 397,\n \"nen\": 398,\n \"ja\": 399,\n \"noch\": 400,\n \"hatte\": 401,\n \"pf\": 402,\n \"hin\": 403,\n \"di\": 404,\n \"chen\": 405,\n \"rü\": 406,\n \"iel\": 407,\n \"sel\": 408,\n \"dass\": 409,\n \"ihn\": 410,\n \"mir\": 411,\n \"schl\": 412,\n \"ön\": 413,\n \"gan\": 414,\n \"gt\": 415,\n \"einer\": 416,\n \"sten\": 417,\n \"mich\": 418,\n \"wenn\": 419,\n \"ell\": 420,\n \"gte\": 421,\n \"mal\": 422,\n \"gel\": 423,\n \"ken\": 424,\n \"nur\": 425,\n \"mmen\": 426,\n \"fü\": 427,\n \"ern\": 428,\n \"ör\": 429,\n \"unter\": 430,\n \"ander\": 431,\n \"dur\": 432,\n \"uch\": 433,\n \"ta\": 434,\n \"men\": 435,\n \"mach\": 436,\n \"doch\": 437,\n \"durch\": 438,\n \"os\": 439,\n \"gl\": 440,\n \"hal\": 441,\n \"ihre\": 442,\n \"wä\": 443,\n \"immer\": 444,\n \"ihm\": 445,\n \"kann\": 446,\n \"ort\": 447,\n \"dann\": 448,\n \"lan\": 449,\n \"tzt\": 450,\n \"oder\": 451,\n \"hren\": 452,\n \"et\": 453,\n \"kön\": 454,\n \"ick\": 455,\n \"fa\": 456,\n \"wieder\": 457,\n \"daß\": 458,\n \"mein\": 459,\n \"fen\": 460,\n \"ganz\": 461,\n \"diese\": 462,\n \"ster\": 463,\n \"dar\": 464,\n \"wa\": 465,\n \"ges\": 466,\n \"na\": 467,\n \"fl\": 468,\n \"igen\": 469,\n \"sche\": 470,\n \"ungen\": 471,\n \"mehr\": 472,\n \"ßen\": 473,\n \"ot\": 474,\n \"kon\": 475,\n \"gew\": 476,\n \"haben\": 477,\n \"geh\": 478,\n \"ät\": 479,\n \"sind\": 480,\n \"dr\": 481,\n \"wel\": 482,\n \"uns\": 483,\n \"vo\": 484,\n \"ma\": 485,\n \"ute\": 486,\n \"schon\": 487,\n \"bes\": 488,\n \"gesch\": 489,\n \"bt\": 490,\n \"che\": 491,\n \"son\": 492,\n \"ob\": 493,\n \"la\": 494,\n \"rück\": 495,\n \"seine\": 496,\n \"kr\": 497,\n \"fre\": 498,\n \"eil\": 499,\n \"zum\": 500,\n \"hier\": 501,\n \"kt\": 502,\n \"ige\": 503,\n \"spr\": 504,\n \"leben\": 505,\n \"bst\": 506,\n \"zeit\": 507,\n \"gro\": 508,\n \"denn\": 509,\n \"ho\": 510,\n \"scha\": 511,\n \"bar\": 512,\n \"alle\": 513,\n \"gegen\": 514,\n \"wür\": 515,\n \"mü\": 516,\n \"ze\": 517,\n \"werden\": 518,\n \"jetzt\": 519,\n \"kommen\": 520,\n \"nie\": 521,\n \"sei\": 522,\n \"heit\": 523,\n \"soll\": 524,\n \"glei\": 525,\n \"meine\": 526,\n \"woll\": 527,\n \"ner\": 528,\n \"habe\": 529,\n \"wur\": 530,\n \"lichen\": 531,\n \"assen\": 532,\n \"nte\": 533,\n \"sehen\": 534,\n \"wird\": 535,\n \"bis\": 536,\n \"gar\": 537,\n \"ien\": 538,\n \"mus\": 539,\n \"uß\": 540,\n \"är\": 541,\n \"stell\": 542,\n \"keit\": 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\"ett em\",\n \"ul t\",\n \"h ány\",\n \"ás t\",\n \"a hol\",\n \"ők et\",\n \"h ár\",\n \"k el\",\n \"n ő\",\n \"cs i\",\n \"tal ál\",\n \"el te\",\n \"lá tt\",\n \"tör t\",\n \"ha gy\",\n \"e sz\",\n \"s en\",\n \"n él\",\n \"p ar\",\n \"v ál\",\n \"k ut\",\n \"l ány\",\n \"ami t\",\n \"s ő\",\n \"ell en\",\n \"mag át\",\n \"in k\",\n \"u gyan\",\n \"kül ön\",\n \"a sz\",\n \"mind ig\",\n \"l ép\",\n \"tal án\",\n \"u n\",\n \"sz or\",\n \"k e\",\n \"il lan\",\n \"n incs\",\n \"z et\",\n \"vagy ok\",\n \"tel en\",\n \"is mer\",\n \"s or\",\n \"is ten\",\n \"ít ott\",\n \"j obb\",\n \"v es\",\n \"dul t\",\n \"j uk\",\n \"sz en\",\n \"r o\",\n \"ö m\",\n \"l ett\",\n \"k ar\",\n \"egy ik\",\n \"b ár\",\n \"sz i\",\n \"sz ív\",\n \"az on\",\n \"e szt\",\n \"föl d\",\n \"kut y\",\n \"p illan\",\n \"f ér\",\n \"k om\",\n \"t ől\",\n \"t ű\",\n \"é be\",\n \"t ött\",\n \"bar át\",\n \"í g\",\n \"a hogy\",\n \"e h\",\n \"e p\",\n \"s o\",\n \"v en\",\n \"jel ent\",\n \"t at\",\n \"sz 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eo\",\n \"eon g\",\n \"y o\",\n \"i da\",\n \"t t\",\n \"k k\",\n \"j eo\",\n \"d eul\",\n \"w a\",\n \"eu m\",\n \"g e\",\n \"o n\",\n \"o g\",\n \"s al\",\n \"m an\",\n \"yeon g\",\n \"geo s\",\n \"h ag\",\n \"an eun\",\n \"j a\",\n \"g i\",\n \"s u\",\n \"i ss\",\n \"o l\",\n \"d ae\",\n \"eo b\",\n \"h a\",\n \"j u\",\n \"eo l\",\n \"g eu\",\n \"j eong\",\n \"s ae\",\n \"do e\",\n \"g eul\",\n \"s eu\",\n \"s in\",\n \"eul o\",\n \"b n\",\n \"s ang\",\n \"bn ida\",\n \"h al\",\n \"b o\",\n \"han eun\",\n \"m al\",\n \"i m\",\n \"m o\",\n \"b u\",\n \"jeo g\",\n \"sae ng\",\n \"in eun\",\n \"an h\",\n \"m a\",\n \"sal am\",\n \"j o\",\n \"s a\",\n \"eo m\",\n \"n ae\",\n \"w i\",\n \"l o\",\n \"g wa\",\n \"yeo l\",\n \"n a\",\n \"e seo\",\n \"y e\",\n \"m yeon\",\n \"tt ae\",\n \"h w\",\n \"j e\",\n \"eob s\",\n \"j ang\",\n \"g u\",\n \"g w\",\n \"il eul\",\n \"yeo g\",\n \"j eon\",\n \"si g\",\n \"j ag\",\n \"j in\",\n \"y u\",\n \"o e\",\n \"s e\",\n \"hag o\",\n \"d eun\",\n \"y a\",\n \"m un\",\n \"s eong\",\n \"g ag\",\n \"h am\",\n \"d ang\",\n \"b a\",\n \"l eul\",\n \"s il\",\n \"do ng\",\n \"kk a\",\n \"b al\",\n \"da l\",\n \"han da\",\n \"eo ssda\",\n \"ae g\",\n \"l i\",\n \"ha ji\",\n \"s eon\",\n \"o ng\",\n \"hae ssda\",\n \"d e\",\n \"i ssda\",\n \"e ge\",\n \"b un\",\n \"m ul\",\n \"ju ng\",\n \"ji g\",\n \"m u\",\n \"iss neun\",\n \"b i\",\n \"g eun\",\n \"seu bnida\",\n \"w on\",\n \"p p\",\n \"d aneun\",\n \"eo h\",\n \"d eo\",\n \"ga m\",\n \"j al\",\n \"hae ng\",\n \"ag o\",\n \"y ang\",\n \"b ul\",\n \"b ang\",\n \"u m\",\n \"s o\",\n \"h i\",\n \"j ae\",\n \"si m\",\n \"saeng gag\",\n \"hag e\",\n \"s og\",\n \"eo ss\",\n \"d an\",\n \"ja sin\",\n \"j il\",\n \"eo g\",\n \"g yeong\",\n \"doe n\",\n \"go ng\",\n \"m i\",\n \"ch i\",\n \"d eu\",\n \"d eon\",\n \"hae ss\",\n \"d u\",\n \"n am\",\n \"eun g\",\n \"jo h\",\n \"n al\",\n \"m yeong\",\n \"w o\",\n \"eon a\",\n \"i go\",\n \"g yeol\",\n \"y ag\",\n \"gw an\",\n \"ul i\",\n \"yo ng\",\n \"n o\",\n \"l yeo\",\n \"j og\",\n \"eoh ge\",\n \"ga t\",\n \"b og\",\n \"mo s\",\n \"t ong\",\n \"ch a\",\n \"man h\",\n \"jeo l\",\n \"geo l\",\n \"h oe\",\n \"ag a\",\n \"n aneun\",\n \"g an\",\n \"un eun\",\n \"ch eol\",\n \"ch e\",\n \"do l\",\n \"b on\",\n \"b an\",\n \"ba d\",\n \"ch u\",\n \"ham yeon\",\n \"yeo ssda\",\n \"i bnida\",\n \"g ye\",\n \"eo s\",\n \"hw al\",\n \"salam deul\",\n \"ji man\",\n \"dang sin\",\n \"ji b\",\n \"ttae mun\",\n \"m ae\",\n \"i b\",\n \"e neun\",\n \"eu g\",\n \"jeo m\",\n \"geul eon\",\n \"h wa\",\n \"a ssda\",\n \"b eob\",\n \"bu t\",\n \"b ae\",\n \"yeo ss\",\n \"ch in\",\n \"ch aeg\",\n \"g eon\",\n \"g ae\",\n \"nae ga\",\n \"i ga\",\n \"m og\",\n \"sig an\",\n \"g il\",\n \"h yeon\",\n \"l yeog\",\n \"gu g\",\n \"p yeon\",\n \"s an\",\n \"w ae\",\n \"j ul\",\n \"s eul\",\n \"deun g\",\n \"haji man\",\n \"eum yeon\",\n \"p il\",\n \"m ol\",\n \"n eu\",\n \"a ss\",\n \"n yeon\",\n \"t ae\",\n \"h u\",\n \"p yo\",\n \"s ul\",\n \"g ang\",\n \"j ineun\",\n \"b eon\",\n \"ha da\",\n \"seo l\",\n \"si p\",\n \"dal eun\",\n \"a p\",\n \"sal m\",\n \"g yo\",\n \"ch eon\",\n \"hag i\",\n \"in a\",\n \"cheol eom\",\n \"g al\",\n \"il a\",\n \"kka ji\",\n \"anh neun\",\n \"ha bnida\",\n \"tt eon\",\n \"n u\",\n \"hae seo\",\n \"doen da\",\n \"s ol\",\n \"tt al\",\n \"l a\",\n \"il o\",\n \"seu b\",\n \"b yeon\",\n \"m yeo\",\n \"b eol\",\n \"s on\",\n \"n un\",\n \"j un\",\n \"j am\",\n \"j eung\",\n \"tt o\",\n \"e n\",\n \"mo m\",\n \"h o\",\n \"ch im\",\n \"hw ang\",\n \"eun eun\",\n \"jo ng\",\n \"bo da\",\n \"n ol\",\n \"n eom\",\n \"but eo\",\n \"jig eum\",\n \"eobs da\",\n \"dae lo\",\n \"i g\",\n \"y ul\",\n \"p yeong\",\n \"seon eun\",\n \"sal ang\",\n \"seu t\",\n \"h im\",\n \"n an\",\n \"h eom\",\n \"h yang\",\n \"p i\",\n \"gw ang\",\n \"eobs neun\",\n \"hw ag\",\n \"ge ss\",\n \"jag i\",\n \"il eon\",\n \"wi hae\",\n \"dae han\",\n \"ga ji\",\n \"m eog\",\n \"j yeo\",\n \"cha j\",\n \"b yeong\",\n \"eo d\",\n \"g yeo\",\n \"do n\",\n \"eo ji\",\n \"g ul\",\n \"mo deun\",\n \"j on\",\n \"in saeng\",\n \"geul ae\",\n \"h ang\",\n \"sa sil\",\n \"si b\",\n \"ch al\",\n \"il ago\",\n \"doe l\",\n \"g eum\",\n \"doe neun\",\n \"b ol\",\n \"ga jang\",\n \"geul igo\",\n \"e l\",\n \"h yeong\",\n \"haeng bog\",\n \"ch ul\",\n \"h on\",\n \"ch ae\",\n \"s am\",\n \"m ang\",\n \"in da\",\n \"da m\",\n \"w ol\",\n \"ch oe\",\n \"d ul\",\n \"si jag\",\n \"ch eong\",\n \"il aneun\",\n \"ul ineun\",\n \"ae n\",\n \"kk e\",\n \"mun je\",\n \"a do\",\n \"t eu\",\n \"g un\",\n \"geun eun\",\n \"b ge\",\n \"ch eo\",\n \"b aeg\",\n \"ju g\",\n \"t a\",\n \"sang dae\",\n \"geu geos\",\n \"do g\",\n \"eu s\",\n \"deu s\",\n \"ja b\",\n \"h yeo\",\n \"tt eohge\",\n \"u g\",\n \"ma j\",\n \"ch il\",\n \"s wi\",\n \"j ileul\",\n \"ch ang\",\n \"g aneun\",\n \"m ag\",\n \"i ji\",\n \"da go\",\n \"m in\",\n \"yo han\",\n \"t eug\",\n \"pp un\",\n \"al eul\",\n \"haeng dong\",\n \"p o\",\n \"m il\",\n \"ch am\",\n \"se sang\",\n \"e do\",\n \"p an\",\n \"man deul\",\n \"am yeon\",\n \"a b\",\n \"kk ae\",\n \"b ag\",\n \"i deul\",\n \"p um\",\n \"m eol\",\n \"s un\",\n \"n eul\",\n \"ham kke\",\n \"chu ng\",\n \"da b\",\n \"yu g\",\n \"s ag\",\n \"gwang ye\",\n \"il eohge\",\n \"bal o\",\n \"neun de\",\n \"ham yeo\",\n \"go s\",\n \"geul eoh\",\n \"an ila\",\n \"bang beob\",\n \"da si\",\n \"b yeol\",\n \"g yeon\",\n \"gam jeong\",\n \"on eul\",\n \"j aneun\",\n \"yeo m\",\n \"l ago\",\n \"i gi\",\n \"hw an\",\n \"t eul\",\n \"eo seo\",\n \"si k\",\n \"ch o\",\n \"jag a\",\n \"geul eom\",\n \"geul eona\",\n \"jeong do\",\n \"g yeog\",\n \"geul eohge\",\n \"geu deul\",\n \"eu t\",\n \"im yeon\",\n \"j jae\",\n \"k eun\",\n \"i sang\",\n \"mal haessda\",\n \"eu ge\",\n \"no p\",\n \"in gan\",\n \"bo myeon\",\n \"t aeg\",\n \"seu s\",\n \"d wi\",\n \"s aneun\",\n \"w an\",\n \"anh go\",\n \"t an\",\n \"nu gu\",\n \"su ng\",\n \"da myeon\",\n \"a deul\",\n \"p eul\",\n \"ttal a\",\n \"d i\",\n \"geos do\",\n \"a ji\",\n \"m eon\",\n \"eum yeo\",\n \"dol og\",\n \"neun g\",\n \"mo du\",\n \"क े\",\n \"ह ै\",\n \"े ं\",\n \"् र\",\n \"ा र\",\n \"न े\",\n \"य ा\",\n \"म ें\",\n \"स े\",\n \"क ी\",\n \"क ा\",\n \"ो ं\",\n \"त ा\",\n \"क र\",\n \"स ्\",\n \"क ि\",\n \"क ो\",\n \"र ्\",\n \"न ा\",\n \"क ्\",\n \"ह ी\",\n \"औ र\",\n \"प र\",\n \"त े\",\n \"ह ो\",\n \"प ्र\",\n \"ा न\",\n \"् य\",\n \"ल ा\",\n \"व ा\",\n \"ल े\",\n \"स ा\",\n \"है ं\",\n \"ल ि\",\n \"ज ा\",\n \"ह ा\",\n \"भ ी\",\n \"व ि\",\n \"इ स\",\n \"त ी\",\n \"न ्\",\n \"र ा\",\n \"म ा\",\n \"द े\",\n \"द ि\",\n \"ब ा\",\n \"त ि\",\n \"थ ा\",\n \"न ि\",\n \"क ार\",\n \"ए क\",\n \"ही ं\",\n \"ह ु\",\n \"ं ग\",\n \"ै ं\",\n \"न ी\",\n \"स ी\",\n \"अ प\",\n \"त ्\",\n \"न हीं\",\n \"र ी\",\n \"म े\",\n \"म ु\",\n \"ि त\",\n \"त ो\",\n \"प ा\",\n \"ल ी\",\n \"लि ए\",\n \"ग ा\",\n \"ल ्\",\n \"र ह\",\n \"र े\",\n \"क् ष\",\n \"म ैं\",\n \"स म\",\n \"उ स\",\n \"ज ि\",\n \"त ्र\",\n \"म ि\",\n \"च ा\",\n \"ो ग\",\n \"स ं\",\n \"द ्\",\n \"स ि\",\n \"आ प\",\n \"त ु\",\n \"द ा\",\n \"क ु\",\n \"य ों\",\n \"व े\",\n \"ज ी\",\n \"् या\",\n \"उ न\",\n \"ि क\",\n \"य े\",\n \"भ ा\",\n \"् ट\",\n \"ह म\",\n \"स् ट\",\n \"श ा\",\n \"ड ़\",\n \"ं द\",\n \"ख ा\",\n \"म ्\",\n \"श ्\",\n \"य ह\",\n \"स क\",\n \"प ू\",\n \"कि या\",\n \"अप ने\",\n \"र ू\",\n \"स ु\",\n \"म ी\",\n \"ह ि\",\n \"ज ो\",\n \"थ े\",\n \"र ि\",\n \"द ी\",\n \"थ ी\",\n \"ग ी\",\n \"ल ोग\",\n \"ग या\",\n \"त र\",\n \"न् ह\",\n \"च ्\",\n \"व ार\",\n \"ब ी\",\n \"प ्\",\n \"द ो\",\n \"ट ी\",\n \"श ि\",\n \"कर ने\",\n \"ग े\",\n \"ै से\",\n \"इ न\",\n \"ं ड\",\n \"सा थ\",\n \"प ु\",\n \"ब े\",\n \"ब ार\",\n \"व ी\",\n \"अ न\",\n \"ह र\",\n \"उ न्ह\",\n \"हो ता\",\n \"ज ब\",\n \"कु छ\",\n \"म ान\",\n \"क ्र\",\n \"ब ि\",\n \"प ह\",\n \"फ ि\",\n \"स र\",\n \"ार ी\",\n \"र ो\",\n \"द ू\",\n \"क हा\",\n \"त क\",\n \"श न\",\n \"ब ्\",\n \"स् थ\",\n \"व ह\",\n \"बा द\",\n \"ओ ं\",\n \"ग ु\",\n \"ज ्\",\n \"्र े\",\n \"ग र\",\n \"रह े\",\n \"व र्\",\n \"ह ू\",\n \"ार ्\",\n \"प ी\",\n \"ब हु\",\n \"मु झ\",\n \"्र ा\",\n \"दि या\",\n \"स ब\",\n \"कर ते\",\n \"अप नी\",\n \"बहु त\",\n \"क ह\",\n \"ट े\",\n \"हु ए\",\n \"कि सी\",\n \"र हा\",\n \"ष ्ट\",\n \"ज ़\",\n \"ब ना\",\n \"स ो\",\n \"ड ि\",\n \"को ई\",\n \"व ्य\",\n \"बा त\",\n \"र ु\",\n \"व ो\",\n \"मुझ े\",\n \"द् ध\",\n \"च ार\",\n \"मे रे\",\n \"व र\",\n \"्र ी\",\n \"जा ता\",\n \"न ों\",\n \"प्र ा\",\n \"दे ख\",\n \"ट ा\",\n \"क् या\",\n \"अ ध\",\n \"ल ग\",\n \"ल ो\",\n \"प ि\",\n \"य ु\",\n \"च े\",\n \"जि स\",\n \"ं त\",\n \"ान ी\",\n \"प ै\",\n \"ज न\",\n \"ार े\",\n \"च ी\",\n \"मि ल\",\n \"द ु\",\n \"दे श\",\n \"च् छ\",\n \"ष ्\",\n \"स ू\",\n \"ख े\",\n \"च ु\",\n \"ि या\",\n \"ल गा\",\n \"ब ु\",\n \"उन के\",\n \"ज् ञ\",\n \"क्ष ा\",\n \"त रह\",\n \"्या दा\",\n \"वा ले\",\n \"पू र्\",\n \"मैं ने\",\n \"का म\",\n \"रू प\",\n \"हो ती\",\n \"उ प\",\n \"ज ान\",\n \"प्र कार\",\n \"भ ार\",\n \"म न\",\n \"हु आ\",\n \"ट र\",\n \"हू ँ\",\n \"पर ि\",\n \"पा स\",\n \"अन ु\",\n \"रा ज\",\n \"लोग ों\",\n \"अ ब\",\n \"सम झ\",\n \"ड ी\",\n \"म ौ\",\n \"श ु\",\n \"च ि\",\n \"प े\",\n \"क ृ\",\n \"सक ते\",\n \"म ह\",\n \"य ोग\",\n \"द र्\",\n \"उ से\",\n \"ं ध\",\n \"ड ा\",\n \"जा ए\",\n \"ब ो\",\n \"ू ल\",\n \"म ो\",\n \"ों ने\",\n \"ं स\",\n \"तु म\",\n \"पह ले\",\n \"ब ता\",\n \"त था\",\n \"य ो\",\n \"ग ई\",\n \"उ त्\",\n \"सक ता\",\n \"क म\",\n \"ज ्यादा\",\n \"र ख\",\n \"सम य\",\n \"ार ा\",\n \"अ गर\",\n \"स् त\",\n \"च ल\",\n \"फि र\",\n \"वार ा\",\n \"कर ना\",\n \"श ी\",\n \"ग ए\",\n \"ब न\",\n \"ौ र\",\n \"हो ने\",\n \"चा ह\",\n \"ख ु\",\n \"हा ँ\",\n \"उन्ह ें\",\n \"उन्ह ोंने\",\n \"छ ो\",\n \"म् ह\",\n \"प्र ति\",\n \"नि क\",\n \"व न\",\n \"्य ू\",\n \"र ही\",\n \"तु म्ह\",\n \"ज ैसे\",\n \"ि यों\",\n \"क् यों\",\n \"ल ों\",\n \"फ ़\",\n \"ं त्र\",\n \"हो ते\",\n \"क् ति\",\n \"त ्य\",\n \"कर ्\",\n \"क ई\",\n \"व ं\",\n \"कि न\",\n \"प ो\",\n \"कार ण\",\n \"ड़ ी\",\n \"भ ि\",\n \"इस के\",\n \"ब र\",\n \"उस के\",\n \"द् वारा\",\n \"श े\",\n \"क ॉ\",\n \"दि न\",\n \"न् न\",\n \"ड़ ा\",\n \"स् व\",\n \"नि र्\",\n \"मु ख\",\n \"लि या\",\n \"ट ि\",\n \"ज्ञ ान\",\n \"क् त\",\n \"द ्र\",\n \"ग ्\",\n \"क् स\",\n \"म ै\",\n \"ग ो\",\n \"ज े\",\n \"ट ्र\",\n \"म ार\",\n \"त् व\",\n \"ध ार\",\n \"भा व\",\n \"कर ता\",\n \"ख ि\",\n \"क ं\",\n \"चा हि\",\n \"य र\",\n \"प् त\",\n \"क ों\",\n \"ं च\",\n \"ज ु\",\n \"म त\",\n \"अ च्छ\",\n \"हु ई\",\n \"क भी\",\n \"ले किन\",\n \"भ ू\",\n \"अप ना\",\n \"दू स\",\n \"चाहि ए\",\n \"य ू\",\n \"घ र\",\n \"सब से\",\n \"मे री\",\n \"ना म\",\n \"ढ ़\",\n \"ं ट\",\n \"ें गे\",\n \"ब ै\",\n \"फ ा\",\n \"ए वं\",\n \"य ी\",\n \"ग ्र\",\n \"क्ष े\",\n \"आ ज\",\n \"आप को\",\n \"भा ग\",\n \"ठ ा\",\n \"क ै\",\n \"भार त\",\n \"उन की\",\n \"प हु\",\n \"स भी\",\n \"ध ा\",\n \"ण ा\",\n \"स ान\",\n \"हो गा\",\n \"त ब\",\n \"स ंग\",\n \"प र्\",\n \"अ व\",\n \"त ना\",\n \"ग ि\",\n \"य न\",\n \"स् था\",\n \"च ित\",\n \"ट ्\",\n \"छ ा\",\n \"जा ने\",\n \"क्षे त्र\",\n \"वा ली\",\n \"पूर् ण\",\n \"स मा\",\n \"कार ी\"\n ]\n }\n}" }, { "path": "comfy/text_encoders/ace_text_cleaners.py", "content": "# basic text cleaners for the ACE step model\n# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies\n# TODO: more languages than english?\n\nimport re\n\ndef japanese_to_romaji(japanese_text):\n \"\"\"\n Convert Japanese hiragana and katakana to romaji (Latin alphabet representation).\n\n Args:\n japanese_text (str): Text containing hiragana and/or katakana characters\n\n Returns:\n str: The romaji (Latin alphabet) equivalent\n \"\"\"\n # Dictionary mapping kana characters to their romaji equivalents\n kana_map = {\n # Katakana characters\n 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o',\n 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko',\n 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so',\n 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to',\n 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no',\n 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho',\n 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo',\n 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo',\n 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro',\n 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n',\n\n # Katakana voiced consonants\n 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go',\n 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo',\n 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do',\n 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo',\n 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po',\n\n # Katakana combinations\n 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo',\n 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho',\n 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho',\n 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo',\n 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo',\n 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo',\n 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo',\n 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo',\n 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo',\n 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo',\n 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo',\n\n # Katakana small characters and special cases\n 'ッ': '', # Small tsu (doubles the following consonant)\n 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo',\n\n # Katakana extras\n 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo',\n 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo',\n\n # Hiragana characters\n 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o',\n 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko',\n 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so',\n 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to',\n 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no',\n 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho',\n 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo',\n 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo',\n 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro',\n 'わ': 'wa', 'を': 'wo', 'ん': 'n',\n\n # Hiragana voiced consonants\n 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go',\n 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo',\n 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do',\n 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo',\n 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po',\n\n # Hiragana combinations\n 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo',\n 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho',\n 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho',\n 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo',\n 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo',\n 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo',\n 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo',\n 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo',\n 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo',\n 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo',\n 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo',\n\n # Hiragana small characters and special cases\n 'っ': '', # Small tsu (doubles the following consonant)\n 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo',\n\n # Common punctuation and spaces\n '　': ' ', # Japanese space\n '、': ', ', '。': '. ',\n }\n\n result = []\n i = 0\n\n while i < len(japanese_text):\n # Check for small tsu (doubling the following consonant)\n if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'):\n if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map:\n next_romaji = kana_map[japanese_text[i+1]]\n if next_romaji and next_romaji[0] not in 'aiueon':\n result.append(next_romaji[0]) # Double the consonant\n i += 1\n continue\n\n # Check for combinations with small ya, yu, yo\n if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'):\n combo = japanese_text[i:i+2]\n if combo in kana_map:\n result.append(kana_map[combo])\n i += 2\n continue\n\n # Regular character\n if japanese_text[i] in kana_map:\n result.append(kana_map[japanese_text[i]])\n else:\n # If it's not in our map, keep it as is (might be kanji, romaji, etc.)\n result.append(japanese_text[i])\n\n i += 1\n\n return ''.join(result)\n\ndef number_to_text(num, ordinal=False):\n \"\"\"\n Convert a number (int or float) to its text representation.\n\n Args:\n num: The number to convert\n\n Returns:\n str: Text representation of the number\n \"\"\"\n\n if not isinstance(num, (int, float)):\n return \"Input must be a number\"\n\n # Handle special case of zero\n if num == 0:\n return \"zero\"\n\n # Handle negative numbers\n negative = num < 0\n num = abs(num)\n\n # Handle floats\n if isinstance(num, float):\n # Split into integer and decimal parts\n int_part = int(num)\n\n # Convert both parts\n int_text = _int_to_text(int_part)\n\n # Handle decimal part (convert to string and remove '0.')\n decimal_str = str(num).split('.')[1]\n decimal_text = \" point \" + \" \".join(_digit_to_text(int(digit)) for digit in decimal_str)\n\n result = int_text + decimal_text\n else:\n # Handle integers\n result = _int_to_text(num)\n\n # Add 'negative' prefix for negative numbers\n if negative:\n result = \"negative \" + result\n\n return result\n\n\ndef _int_to_text(num):\n \"\"\"Helper function to convert an integer to text\"\"\"\n\n ones = [\"\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\",\n \"ten\", \"eleven\", \"twelve\", \"thirteen\", \"fourteen\", \"fifteen\", \"sixteen\",\n \"seventeen\", \"eighteen\", \"nineteen\"]\n\n tens = [\"\", \"\", \"twenty\", \"thirty\", \"forty\", \"fifty\", \"sixty\", \"seventy\", \"eighty\", \"ninety\"]\n\n if num < 20:\n return ones[num]\n\n if num < 100:\n return tens[num // 10] + (\" \" + ones[num % 10] if num % 10 != 0 else \"\")\n\n if num < 1000:\n return ones[num // 100] + \" hundred\" + (\" \" + _int_to_text(num % 100) if num % 100 != 0 else \"\")\n\n if num < 1000000:\n return _int_to_text(num // 1000) + \" thousand\" + (\" \" + _int_to_text(num % 1000) if num % 1000 != 0 else \"\")\n\n if num < 1000000000:\n return _int_to_text(num // 1000000) + \" million\" + (\" \" + _int_to_text(num % 1000000) if num % 1000000 != 0 else \"\")\n\n return _int_to_text(num // 1000000000) + \" billion\" + (\" \" + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else \"\")\n\n\ndef _digit_to_text(digit):\n \"\"\"Convert a single digit to text\"\"\"\n digits = [\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\"]\n return digits[digit]\n\n\n_whitespace_re = re.compile(r\"\\s+\")\n\n\n# List of (regular expression, replacement) pairs for abbreviations:\n_abbreviations = {\n \"en\": [\n (re.compile(\"\\\\b%s\\\\.\" % x[0], re.IGNORECASE), x[1])\n for x in [\n (\"mrs\", \"misess\"),\n (\"mr\", \"mister\"),\n (\"dr\", \"doctor\"),\n (\"st\", \"saint\"),\n (\"co\", \"company\"),\n (\"jr\", \"junior\"),\n (\"maj\", \"major\"),\n (\"gen\", \"general\"),\n (\"drs\", \"doctors\"),\n (\"rev\", \"reverend\"),\n (\"lt\", \"lieutenant\"),\n (\"hon\", \"honorable\"),\n (\"sgt\", \"sergeant\"),\n (\"capt\", \"captain\"),\n (\"esq\", \"esquire\"),\n (\"ltd\", \"limited\"),\n (\"col\", \"colonel\"),\n (\"ft\", \"fort\"),\n ]\n ],\n}\n\n\ndef expand_abbreviations_multilingual(text, lang=\"en\"):\n for regex, replacement in _abbreviations[lang]:\n text = re.sub(regex, replacement, text)\n return text\n\n\n_symbols_multilingual = {\n \"en\": [\n (re.compile(r\"%s\" % re.escape(x[0]), re.IGNORECASE), x[1])\n for x in [\n (\"&\", \" and \"),\n (\"@\", \" at \"),\n (\"%\", \" percent \"),\n (\"#\", \" hash \"),\n (\"$\", \" dollar \"),\n (\"£\", \" pound \"),\n (\"°\", \" degree \"),\n ]\n ],\n}\n\n\ndef expand_symbols_multilingual(text, lang=\"en\"):\n for regex, replacement in _symbols_multilingual[lang]:\n text = re.sub(regex, replacement, text)\n text = text.replace(\" \", \" \") # Ensure there are no double spaces\n return text.strip()\n\n\n_ordinal_re = {\n \"en\": re.compile(r\"([0-9]+)(st|nd|rd|th)\"),\n}\n_number_re = re.compile(r\"[0-9]+\")\n_currency_re = {\n \"USD\": re.compile(r\"((\\$[0-9\\.\\,]*[0-9]+)|([0-9\\.\\,]*[0-9]+\\$))\"),\n \"GBP\": re.compile(r\"((£[0-9\\.\\,]*[0-9]+)|([0-9\\.\\,]*[0-9]+£))\"),\n \"EUR\": re.compile(r\"(([0-9\\.\\,]*[0-9]+€)|((€[0-9\\.\\,]*[0-9]+)))\"),\n}\n\n_comma_number_re = re.compile(r\"\\b\\d{1,3}(,\\d{3})*(\\.\\d+)?\\b\")\n_dot_number_re = re.compile(r\"\\b\\d{1,3}(.\\d{3})*(\\,\\d+)?\\b\")\n_decimal_number_re = re.compile(r\"([0-9]+[.,][0-9]+)\")\n\n\ndef _remove_commas(m):\n text = m.group(0)\n if \",\" in text:\n text = text.replace(\",\", \"\")\n return text\n\n\ndef _remove_dots(m):\n text = m.group(0)\n if \".\" in text:\n text = text.replace(\".\", \"\")\n return text\n\n\ndef _expand_decimal_point(m, lang=\"en\"):\n amount = m.group(1).replace(\",\", \".\")\n return number_to_text(float(amount))\n\n\ndef _expand_currency(m, lang=\"en\", currency=\"USD\"):\n amount = float((re.sub(r\"[^\\d.]\", \"\", m.group(0).replace(\",\", \".\"))))\n full_amount = number_to_text(amount)\n\n and_equivalents = {\n \"en\": \", \",\n \"es\": \" con \",\n \"fr\": \" et \",\n \"de\": \" und \",\n \"pt\": \" e \",\n \"it\": \" e \",\n \"pl\": \", \",\n \"cs\": \", \",\n \"ru\": \", \",\n \"nl\": \", \",\n \"ar\": \", \",\n \"tr\": \", \",\n \"hu\": \", \",\n \"ko\": \", \",\n }\n\n if amount.is_integer():\n last_and = full_amount.rfind(and_equivalents[lang])\n if last_and != -1:\n full_amount = full_amount[:last_and]\n\n return full_amount\n\n\ndef _expand_ordinal(m, lang=\"en\"):\n return number_to_text(int(m.group(1)), ordinal=True)\n\n\ndef _expand_number(m, lang=\"en\"):\n return number_to_text(int(m.group(0)))\n\n\ndef expand_numbers_multilingual(text, lang=\"en\"):\n if lang in [\"en\", \"ru\"]:\n text = re.sub(_comma_number_re, _remove_commas, text)\n else:\n text = re.sub(_dot_number_re, _remove_dots, text)\n try:\n text = re.sub(_currency_re[\"GBP\"], lambda m: _expand_currency(m, lang, \"GBP\"), text)\n text = re.sub(_currency_re[\"USD\"], lambda m: _expand_currency(m, lang, \"USD\"), text)\n text = re.sub(_currency_re[\"EUR\"], lambda m: _expand_currency(m, lang, \"EUR\"), text)\n except:\n pass\n\n text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text)\n text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text)\n text = re.sub(_number_re, lambda m: _expand_number(m, lang), text)\n return text\n\n\ndef lowercase(text):\n return text.lower()\n\n\ndef collapse_whitespace(text):\n return re.sub(_whitespace_re, \" \", text)\n\n\ndef multilingual_cleaners(text, lang):\n text = text.replace('\"', \"\")\n if lang == \"tr\":\n text = text.replace(\"İ\", \"i\")\n text = text.replace(\"Ö\", \"ö\")\n text = text.replace(\"Ü\", \"ü\")\n text = lowercase(text)\n try:\n text = expand_numbers_multilingual(text, lang)\n except:\n pass\n try:\n text = expand_abbreviations_multilingual(text, lang)\n except:\n pass\n try:\n text = expand_symbols_multilingual(text, lang=lang)\n except:\n pass\n text = collapse_whitespace(text)\n return text\n\n\ndef basic_cleaners(text):\n \"\"\"Basic pipeline that lowercases and collapses whitespace without transliteration.\"\"\"\n text = lowercase(text)\n text = collapse_whitespace(text)\n return text\n" }, { "path": "comfy/text_encoders/anima.py", "content": "from transformers import Qwen2Tokenizer, T5TokenizerFast\nimport comfy.text_encoders.llama\nfrom comfy import sd1_clip\nimport os\nimport torch\n\n\nclass Qwen3Tokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"qwen25_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='qwen3_06b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data)\n\nclass AnimaTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.qwen3_06b = Qwen3Tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"qwen3_06b\"] = [[(k[0], 1.0, k[2]) if return_word_ids else (k[0], 1.0) for k in inner_list] for inner_list in qwen_ids] # Set weights to 1.0\n out[\"t5xxl\"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.t5xxl.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {}\n\n def decode(self, token_ids, **kwargs):\n return self.qwen3_06b.decode(token_ids, **kwargs)\n\nclass Qwen3_06BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_06B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\nclass AnimaTEModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"qwen3_06b\", clip_model=Qwen3_06BModel, model_options=model_options)\n\n def encode_token_weights(self, token_weight_pairs):\n out = super().encode_token_weights(token_weight_pairs)\n out[2][\"t5xxl_ids\"] = torch.tensor(list(map(lambda a: a[0], token_weight_pairs[\"t5xxl\"][0])), dtype=torch.int)\n out[2][\"t5xxl_weights\"] = torch.tensor(list(map(lambda a: a[1], token_weight_pairs[\"t5xxl\"][0])))\n return out\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class AnimaTEModel_(AnimaTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if dtype_llama is not None:\n dtype = dtype_llama\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return AnimaTEModel_\n" }, { "path": "comfy/text_encoders/aura_t5.py", "content": "from comfy import sd1_clip\nfrom .spiece_tokenizer import SPieceTokenizer\nimport comfy.text_encoders.t5\nimport os\n\nclass PT5XlModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_pile_config_xl.json\")\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"end\": 2, \"pad\": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options)\n\nclass PT5XlTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_pile_tokenizer\"), \"tokenizer.model\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1, tokenizer_data=tokenizer_data)\n\nclass AuraT5Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"pile_t5xl\", tokenizer=PT5XlTokenizer)\n\nclass AuraT5Model(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, **kwargs):\n super().__init__(device=device, dtype=dtype, model_options=model_options, name=\"pile_t5xl\", clip_model=PT5XlModel, **kwargs)\n" }, { "path": "comfy/text_encoders/bert.py", "content": "import torch\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\nimport comfy.ops\n\nclass BertAttention(torch.nn.Module):\n def __init__(self, embed_dim, heads, dtype, device, operations):\n super().__init__()\n\n self.heads = heads\n self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n\n\n def forward(self, x, mask=None, optimized_attention=None):\n q = self.query(x)\n k = self.key(x)\n v = self.value(x)\n\n out = optimized_attention(q, k, v, self.heads, mask)\n return out\n\nclass BertOutput(torch.nn.Module):\n def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)\n self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device)\n # self.dropout = nn.Dropout(0.0)\n\n def forward(self, x, y):\n x = self.dense(x)\n # hidden_states = self.dropout(hidden_states)\n x = self.LayerNorm(x + y)\n return x\n\nclass BertAttentionBlock(torch.nn.Module):\n def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.self = BertAttention(embed_dim, heads, dtype, device, operations)\n self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)\n\n def forward(self, x, mask, optimized_attention):\n y = self.self(x, mask, optimized_attention)\n return self.output(y, x)\n\nclass BertIntermediate(torch.nn.Module):\n def __init__(self, embed_dim, intermediate_dim, dtype, device, operations):\n super().__init__()\n self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device)\n\n def forward(self, x):\n x = self.dense(x)\n return torch.nn.functional.gelu(x)\n\n\nclass BertBlock(torch.nn.Module):\n def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations)\n self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations)\n self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations)\n\n def forward(self, x, mask, optimized_attention):\n x = self.attention(x, mask, optimized_attention)\n y = self.intermediate(x)\n return self.output(y, x)\n\nclass BertEncoder(torch.nn.Module):\n def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, 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.layer) + intermediate_output\n\n intermediate = None\n for i, l in enumerate(self.layer):\n x = l(x, mask, optimized_attention)\n if i == intermediate_output:\n intermediate = x.clone()\n return x, intermediate\n\nclass BertEmbeddings(torch.nn.Module):\n def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):\n super().__init__()\n self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)\n self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)\n self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)\n\n self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)\n\n def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None):\n if embeds is not None:\n x = embeds\n else:\n x = self.word_embeddings(input_tokens, out_dtype=dtype)\n x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x)\n if token_type_ids is not None:\n x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype)\n else:\n x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)\n x = self.LayerNorm(x)\n return x\n\n\nclass BertModel_(torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n embed_dim = config_dict[\"hidden_size\"]\n layer_norm_eps = config_dict[\"layer_norm_eps\"]\n\n self.embeddings = BertEmbeddings(config_dict[\"vocab_size\"], config_dict[\"max_position_embeddings\"], config_dict[\"type_vocab_size\"], config_dict[\"pad_token_id\"], embed_dim, layer_norm_eps, dtype, device, operations)\n self.encoder = BertEncoder(config_dict[\"num_hidden_layers\"], embed_dim, config_dict[\"intermediate_size\"], config_dict[\"num_attention_heads\"], layer_norm_eps, dtype, device, operations)\n\n def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):\n x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype)\n mask = None\n if attention_mask is not None:\n mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])\n mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)\n\n x, i = self.encoder(x, mask, intermediate_output)\n return x, i\n\n\nclass BertModel(torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n self.bert = BertModel_(config_dict, dtype, device, operations)\n self.num_layers = config_dict[\"num_hidden_layers\"]\n\n def get_input_embeddings(self):\n return self.bert.embeddings.word_embeddings\n\n def set_input_embeddings(self, embeddings):\n self.bert.embeddings.word_embeddings = embeddings\n\n def forward(self, *args, **kwargs):\n return self.bert(*args, **kwargs)\n" }, { "path": "comfy/text_encoders/byt5_config_small_glyph.json", "content": "{\n \"d_ff\": 3584,\n \"d_kv\": 64,\n \"d_model\": 1472,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 1,\n \"dense_act_fn\": \"gelu_pytorch_tanh\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": true,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"t5\",\n \"num_decoder_layers\": 4,\n \"num_heads\": 6,\n \"num_layers\": 12,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 1510\n}\n" }, { "path": "comfy/text_encoders/byt5_tokenizer/added_tokens.json", "content": "{\n \"\": 259,\n \"\": 359,\n \"\": 360,\n \"\": 361,\n \"\": 362,\n \"\": 363,\n \"\": 364,\n \"\": 365,\n \"\": 366,\n \"\": 367,\n \"\": 368,\n \"\": 269,\n \"\": 369,\n \"\": 370,\n \"\": 371,\n \"\": 372,\n \"\": 373,\n \"\": 374,\n \"\": 375,\n \"\": 376,\n \"\": 377,\n \"\": 378,\n \"\": 270,\n \"\": 379,\n \"\": 380,\n \"\": 381,\n \"\": 382,\n \"\": 383,\n \"\": 271,\n \"\": 272,\n \"\": 273,\n \"\": 274,\n \"\": 275,\n \"\": 276,\n \"\": 277,\n \"\": 278,\n \"\": 260,\n \"\": 279,\n \"\": 280,\n \"\": 281,\n \"\": 282,\n \"\": 283,\n \"\": 284,\n \"\": 285,\n \"\": 286,\n \"\": 287,\n \"\": 288,\n \"\": 261,\n \"\": 289,\n \"\": 290,\n \"\": 291,\n \"\": 292,\n \"\": 293,\n \"\": 294,\n \"\": 295,\n \"\": 296,\n \"\": 297,\n \"\": 298,\n \"\": 262,\n \"\": 299,\n \"\": 300,\n \"\": 301,\n \"\": 302,\n \"\": 303,\n \"\": 304,\n \"\": 305,\n \"\": 306,\n \"\": 307,\n \"\": 308,\n \"\": 263,\n \"\": 309,\n \"\": 310,\n \"\": 311,\n \"\": 312,\n \"\": 313,\n \"\": 314,\n \"\": 315,\n \"\": 316,\n \"\": 317,\n \"\": 318,\n \"\": 264,\n \"\": 319,\n \"\": 320,\n \"\": 321,\n \"\": 322,\n \"\": 323,\n \"\": 324,\n \"\": 325,\n \"\": 326,\n \"\": 327,\n \"\": 328,\n \"\": 265,\n \"\": 329,\n \"\": 330,\n \"\": 331,\n \"\": 332,\n \"\": 333,\n \"\": 334,\n \"\": 335,\n \"\": 336,\n \"\": 337,\n \"\": 338,\n \"\": 266,\n \"\": 339,\n \"\": 340,\n \"\": 341,\n \"\": 342,\n \"\": 343,\n \"\": 344,\n \"\": 345,\n \"\": 346,\n \"\": 347,\n \"\": 348,\n \"\": 267,\n \"\": 349,\n \"\": 350,\n \"\": 351,\n \"\": 352,\n \"\": 353,\n \"\": 354,\n \"\": 355,\n \"\": 356,\n \"\": 357,\n \"\": 358,\n \"\": 268\n}\n" }, { "path": "comfy/text_encoders/byt5_tokenizer/special_tokens_map.json", "content": "{\n \"additional_special_tokens\": [\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\"\n ],\n \"eos_token\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": true,\n \"rstrip\": false,\n \"single_word\": false\n },\n \"pad_token\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": true,\n \"rstrip\": false,\n \"single_word\": false\n },\n \"unk_token\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": true,\n \"rstrip\": false,\n \"single_word\": false\n }\n}\n" }, { "path": "comfy/text_encoders/byt5_tokenizer/tokenizer_config.json", "content": "{\n \"added_tokens_decoder\": {\n \"0\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": true,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"1\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": true,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"2\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": true,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"259\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"260\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"261\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"262\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"263\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"264\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"265\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"266\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"267\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"268\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"269\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"270\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"271\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"272\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"273\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"274\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"275\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"276\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"277\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"278\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"279\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"280\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"281\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"282\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"283\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"284\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"285\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"286\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"287\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"288\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"289\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"290\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"291\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"292\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"293\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"294\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"295\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"296\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"297\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"298\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"299\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"300\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"301\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"302\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"303\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"304\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"305\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"306\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"307\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"308\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"309\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"310\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"311\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"312\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"313\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"314\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"315\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"316\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"317\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"318\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"319\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"320\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"321\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"322\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"323\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"324\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"325\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"326\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"327\": {\n \"content\": \"\",\n 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true\n },\n \"347\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"348\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"349\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"350\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"351\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"352\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"353\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": 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\"360\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"361\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"362\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"363\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"364\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"365\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"366\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"367\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"368\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"369\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"370\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"371\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"372\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"373\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"374\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"375\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"376\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"377\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"378\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"379\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"380\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"381\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"382\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"383\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n }\n },\n \"additional_special_tokens\": [\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\"\n ],\n \"clean_up_tokenization_spaces\": false,\n \"eos_token\": \"\",\n \"extra_ids\": 0,\n \"extra_special_tokens\": {},\n \"model_max_length\": 1000000000000000019884624838656,\n \"pad_token\": \"\",\n \"tokenizer_class\": \"ByT5Tokenizer\",\n \"unk_token\": \"\"\n}\n" }, { "path": "comfy/text_encoders/cosmos.py", "content": "from comfy import sd1_clip\nimport comfy.text_encoders.t5\nimport os\nfrom transformers import T5TokenizerFast\n\n\nclass T5XXLModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_old_config_xxl.json\")\n t5xxl_quantization_metadata = model_options.get(\"t5xxl_quantization_metadata\", None)\n if t5xxl_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = t5xxl_quantization_metadata\n\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options)\n\nclass CosmosT5XXL(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"t5xxl\", clip_model=T5XXLModel, model_options=model_options)\n\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data)\n\n\nclass CosmosT5Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"t5xxl\", tokenizer=T5XXLTokenizer)\n\n\ndef te(dtype_t5=None, t5_quantization_metadata=None):\n class CosmosTEModel_(CosmosT5XXL):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"t5xxl_quantization_metadata\"] = t5_quantization_metadata\n if dtype_t5 is not None:\n dtype = dtype_t5\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return CosmosTEModel_\n" }, { "path": "comfy/text_encoders/flux.py", "content": "from comfy import sd1_clip\nimport comfy.text_encoders.t5\nimport comfy.text_encoders.sd3_clip\nimport comfy.text_encoders.llama\nimport comfy.model_management\nfrom transformers import T5TokenizerFast, LlamaTokenizerFast, Qwen2Tokenizer\nimport torch\nimport os\nimport json\nimport base64\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data)\n\n\nclass FluxTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"t5xxl\"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.clip_l.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {}\n\n\nclass FluxClipModel(torch.nn.Module):\n def __init__(self, dtype_t5=None, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)\n self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)\n self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)\n self.dtypes = set([dtype, dtype_t5])\n\n def set_clip_options(self, options):\n self.clip_l.set_clip_options(options)\n self.t5xxl.set_clip_options(options)\n\n def reset_clip_options(self):\n self.clip_l.reset_clip_options()\n self.t5xxl.reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_l = token_weight_pairs[\"l\"]\n token_weight_pairs_t5 = token_weight_pairs[\"t5xxl\"]\n\n t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)\n l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)\n return t5_out, l_pooled\n\n def load_sd(self, sd):\n if \"text_model.encoder.layers.1.mlp.fc1.weight\" in sd:\n return self.clip_l.load_sd(sd)\n else:\n return self.t5xxl.load_sd(sd)\n\ndef flux_clip(dtype_t5=None, t5_quantization_metadata=None):\n class FluxClipModel_(FluxClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"t5xxl_quantization_metadata\"] = t5_quantization_metadata\n super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)\n return FluxClipModel_\n\ndef load_mistral_tokenizer(data):\n if torch.is_tensor(data):\n data = data.numpy().tobytes()\n\n try:\n from transformers.integrations.mistral import MistralConverter\n except ModuleNotFoundError:\n from transformers.models.pixtral.convert_pixtral_weights_to_hf import MistralConverter\n\n mistral_vocab = json.loads(data)\n\n special_tokens = {}\n vocab = {}\n\n max_vocab = mistral_vocab[\"config\"][\"default_vocab_size\"]\n max_vocab -= len(mistral_vocab[\"special_tokens\"])\n\n for w in mistral_vocab[\"vocab\"]:\n r = w[\"rank\"]\n if r >= max_vocab:\n continue\n\n vocab[base64.b64decode(w[\"token_bytes\"])] = r\n\n for w in mistral_vocab[\"special_tokens\"]:\n if \"token_bytes\" in w:\n special_tokens[base64.b64decode(w[\"token_bytes\"])] = w[\"rank\"]\n else:\n special_tokens[w[\"token_str\"]] = w[\"rank\"]\n\n all_special = []\n for v in special_tokens:\n all_special.append(v)\n\n special_tokens.update(vocab)\n vocab = special_tokens\n return {\"tokenizer_object\": MistralConverter(vocab=vocab, additional_special_tokens=all_special).converted(), \"legacy\": False}\n\nclass MistralTokenizerClass:\n @staticmethod\n def from_pretrained(path, **kwargs):\n return LlamaTokenizerFast(**kwargs)\n\nclass Mistral3Tokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.tekken_data = tokenizer_data.get(\"tekken_model\", None)\n super().__init__(\"\", pad_with_end=False, embedding_directory=embedding_directory, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_class=MistralTokenizerClass, has_end_token=False, pad_to_max_length=False, pad_token=11, start_token=1, max_length=99999999, min_length=1, pad_left=True, tokenizer_args=load_mistral_tokenizer(self.tekken_data), tokenizer_data=tokenizer_data)\n\n def state_dict(self):\n return {\"tekken_model\": self.tekken_data}\n\nclass Flux2Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"mistral3_24b\", tokenizer=Mistral3Tokenizer)\n self.llama_template = '[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]{}[/INST]'\n\n def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):\n if llama_template is None:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n\n tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)\n return tokens\n\nclass Mistral3_24BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=[10, 20, 30], layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n textmodel_json_config = {}\n num_layers = model_options.get(\"num_layers\", None)\n if num_layers is not None:\n textmodel_json_config[\"num_hidden_layers\"] = num_layers\n if num_layers < 40:\n textmodel_json_config[\"final_norm\"] = False\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"start\": 1, \"pad\": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Mistral3Small24B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass Flux2TEModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, name=\"mistral3_24b\", clip_model=Mistral3_24BModel):\n super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)\n\n def encode_token_weights(self, token_weight_pairs):\n out, pooled, extra = super().encode_token_weights(token_weight_pairs)\n\n out = torch.stack((out[:, 0], out[:, 1], out[:, 2]), dim=1)\n out = out.movedim(1, 2)\n out = out.reshape(out.shape[0], out.shape[1], -1)\n return out, pooled, extra\n\ndef flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=False):\n class Flux2TEModel_(Flux2TEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if dtype_llama is not None:\n dtype = dtype_llama\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n if pruned:\n model_options = model_options.copy()\n model_options[\"num_layers\"] = 30\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return Flux2TEModel_\n\nclass Qwen3Tokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"qwen25_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=151643, tokenizer_data=tokenizer_data)\n\nclass Qwen3Tokenizer8B(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"qwen25_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=4096, embedding_key='qwen3_8b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=151643, tokenizer_data=tokenizer_data)\n\nclass KleinTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}, name=\"qwen3_4b\"):\n if name == \"qwen3_4b\":\n tokenizer = Qwen3Tokenizer\n elif name == \"qwen3_8b\":\n tokenizer = Qwen3Tokenizer8B\n\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=name, tokenizer=tokenizer)\n self.llama_template = \"<|im_start|>user\\n{}<|im_end|>\\n<|im_start|>assistant\\n\\n\\n\\n\\n\"\n\n def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):\n if llama_template is None:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n\n tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)\n return tokens\n\nclass KleinTokenizer8B(KleinTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}, name=\"qwen3_8b\"):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=name)\n\nclass Qwen3_4BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=[9, 18, 27], layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass Qwen3_8BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=[9, 18, 27], layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_8B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\ndef klein_te(dtype_llama=None, llama_quantization_metadata=None, model_type=\"qwen3_4b\"):\n if model_type == \"qwen3_4b\":\n model = Qwen3_4BModel\n elif model_type == \"qwen3_8b\":\n model = Qwen3_8BModel\n\n class Flux2TEModel_(Flux2TEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)\n return Flux2TEModel_\n" }, { "path": "comfy/text_encoders/genmo.py", "content": "from comfy import sd1_clip\nimport comfy.text_encoders.sd3_clip\nimport os\nfrom transformers import T5TokenizerFast\n\n\nclass T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):\n def __init__(self, **kwargs):\n kwargs[\"attention_mask\"] = True\n super().__init__(**kwargs)\n\n\nclass MochiT5XXL(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"t5xxl\", clip_model=T5XXLModel, model_options=model_options)\n\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data)\n\n\nclass MochiT5Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"t5xxl\", tokenizer=T5XXLTokenizer)\n\n\ndef mochi_te(dtype_t5=None, t5_quantization_metadata=None):\n class MochiTEModel_(MochiT5XXL):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"t5xxl_quantization_metadata\"] = t5_quantization_metadata\n if dtype_t5 is not None:\n dtype = dtype_t5\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return MochiTEModel_\n" }, { "path": "comfy/text_encoders/hidream.py", "content": "from . import hunyuan_video\nfrom . import sd3_clip\nfrom comfy import sd1_clip\nfrom comfy import sdxl_clip\nimport comfy.model_management\nimport torch\nimport logging\n\n\nclass HiDreamTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data)\n self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"g\"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)\n t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"t5xxl\"] = [t5xxl[0]] # Use only first 128 tokens\n out[\"llama\"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.clip_g.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {}\n\n\nclass HiDreamTEModel(torch.nn.Module):\n def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n self.dtypes = set()\n if clip_l:\n self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=True, model_options=model_options)\n self.dtypes.add(dtype)\n else:\n self.clip_l = None\n\n if clip_g:\n self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)\n self.dtypes.add(dtype)\n else:\n self.clip_g = None\n\n if t5:\n dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)\n self.t5xxl = sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=True)\n self.dtypes.add(dtype_t5)\n else:\n self.t5xxl = None\n\n if llama:\n dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device)\n if \"vocab_size\" not in model_options:\n model_options[\"vocab_size\"] = 128256\n self.llama = hunyuan_video.LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options, layer=\"all\", layer_idx=None, special_tokens={\"start\": 128000, \"pad\": 128009})\n self.dtypes.add(dtype_llama)\n else:\n self.llama = None\n\n logging.debug(\"Created HiDream text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}, llama {}:{}\".format(clip_l, clip_g, t5, dtype_t5, llama, dtype_llama))\n\n def set_clip_options(self, options):\n if self.clip_l is not None:\n self.clip_l.set_clip_options(options)\n if self.clip_g is not None:\n self.clip_g.set_clip_options(options)\n if self.t5xxl is not None:\n self.t5xxl.set_clip_options(options)\n if self.llama is not None:\n self.llama.set_clip_options(options)\n\n def reset_clip_options(self):\n if self.clip_l is not None:\n self.clip_l.reset_clip_options()\n if self.clip_g is not None:\n self.clip_g.reset_clip_options()\n if self.t5xxl is not None:\n self.t5xxl.reset_clip_options()\n if self.llama is not None:\n self.llama.reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_l = token_weight_pairs[\"l\"]\n token_weight_pairs_g = token_weight_pairs[\"g\"]\n token_weight_pairs_t5 = token_weight_pairs[\"t5xxl\"]\n token_weight_pairs_llama = token_weight_pairs[\"llama\"]\n lg_out = None\n pooled = None\n extra = {}\n\n if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:\n if self.clip_l is not None:\n lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)\n else:\n l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())\n\n if self.clip_g is not None:\n g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)\n else:\n g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())\n\n pooled = torch.cat((l_pooled, g_pooled), dim=-1)\n\n if self.t5xxl is not None:\n t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)\n t5_out, t5_pooled = t5_output[:2]\n else:\n t5_out = None\n\n if self.llama is not None:\n ll_output = self.llama.encode_token_weights(token_weight_pairs_llama)\n ll_out, ll_pooled = ll_output[:2]\n ll_out = ll_out[:, 1:]\n else:\n ll_out = None\n\n if t5_out is None:\n t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device())\n\n if ll_out is None:\n ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device())\n\n if pooled is None:\n pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())\n\n extra[\"conditioning_llama3\"] = ll_out\n return t5_out, pooled, extra\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 elif \"text_model.encoder.layers.1.mlp.fc1.weight\" in sd:\n return self.clip_l.load_sd(sd)\n elif \"encoder.block.23.layer.1.DenseReluDense.wi_1.weight\" in sd:\n return self.t5xxl.load_sd(sd)\n else:\n return self.llama.load_sd(sd)\n\n\ndef hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5_quantization_metadata=None, llama_quantization_metadata=None):\n class HiDreamTEModel_(HiDreamTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"t5xxl_quantization_metadata\"] = t5_quantization_metadata\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)\n return HiDreamTEModel_\n" }, { "path": "comfy/text_encoders/hunyuan_image.py", "content": "from comfy import sd1_clip\nimport comfy.text_encoders.llama\nfrom .qwen_image import QwenImageTokenizer, QwenImageTEModel\nfrom transformers import ByT5Tokenizer\nimport os\nimport re\n\nclass ByT5SmallTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"byt5_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1472, embedding_key='byt5_small', tokenizer_class=ByT5Tokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data)\n\nclass HunyuanImageTokenizer(QwenImageTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.llama_template = \"<|im_start|>system\\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\\n<|im_start|>user\\n{}<|im_end|>\"\n # self.llama_template_images = \"{}\"\n self.byt5 = ByT5SmallTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = super().tokenize_with_weights(text, return_word_ids, **kwargs)\n\n # ByT5 processing for HunyuanImage\n text_prompt_texts = []\n pattern_quote_double = r'\\\"(.*?)\\\"'\n pattern_quote_chinese_single = r'‘(.*?)’'\n pattern_quote_chinese_double = r'“(.*?)”'\n\n matches_quote_double = re.findall(pattern_quote_double, text)\n matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text)\n matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text)\n\n text_prompt_texts.extend(matches_quote_double)\n text_prompt_texts.extend(matches_quote_chinese_single)\n text_prompt_texts.extend(matches_quote_chinese_double)\n\n if len(text_prompt_texts) > 0:\n out['byt5'] = self.byt5.tokenize_with_weights(''.join(map(lambda a: 'Text \"{}\". '.format(a), text_prompt_texts)), return_word_ids, **kwargs)\n return out\n\nclass Qwen25_7BVLIModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"hidden\", layer_idx=-3, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\nclass ByT5SmallModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"byt5_config_small_glyph.json\")\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)\n\n\nclass HunyuanImageTEModel(QwenImageTEModel):\n def __init__(self, byt5=True, device=\"cpu\", dtype=None, model_options={}):\n super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name=\"qwen25_7b\", clip_model=Qwen25_7BVLIModel, model_options=model_options)\n\n if byt5:\n self.byt5_small = ByT5SmallModel(device=device, dtype=dtype, model_options=model_options)\n else:\n self.byt5_small = None\n\n def encode_token_weights(self, token_weight_pairs):\n tok_pairs = token_weight_pairs[\"qwen25_7b\"][0]\n template_end = -1\n if tok_pairs[0][0] == 27:\n if len(tok_pairs) > 36: # refiner prompt uses a fixed 36 template_end\n template_end = 36\n\n cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=template_end)\n if self.byt5_small is not None and \"byt5\" in token_weight_pairs:\n out = self.byt5_small.encode_token_weights(token_weight_pairs[\"byt5\"])\n extra[\"conditioning_byt5small\"] = out[0]\n return cond, p, extra\n\n def set_clip_options(self, options):\n super().set_clip_options(options)\n if self.byt5_small is not None:\n self.byt5_small.set_clip_options(options)\n\n def reset_clip_options(self):\n super().reset_clip_options()\n if self.byt5_small is not None:\n self.byt5_small.reset_clip_options()\n\n def load_sd(self, sd):\n if \"encoder.block.0.layer.0.SelfAttention.o.weight\" in sd:\n return self.byt5_small.load_sd(sd)\n else:\n return super().load_sd(sd)\n\ndef te(byt5=True, dtype_llama=None, llama_quantization_metadata=None):\n class QwenImageTEModel_(HunyuanImageTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options)\n return QwenImageTEModel_\n" }, { "path": "comfy/text_encoders/hunyuan_video.py", "content": "from comfy import sd1_clip\nimport comfy.model_management\nimport comfy.text_encoders.llama\nfrom .hunyuan_image import HunyuanImageTokenizer\nfrom transformers import LlamaTokenizerFast\nimport torch\nimport os\nimport numbers\nimport comfy.utils\n\ndef llama_detect(state_dict, prefix=\"\"):\n out = {}\n norm_keys = [\"{}model.norm.weight\".format(prefix), \"{}model.layers.0.input_layernorm.weight\".format(prefix)]\n for norm_key in norm_keys:\n if norm_key in state_dict:\n out[\"dtype_llama\"] = state_dict[norm_key].dtype\n break\n\n quant = comfy.utils.detect_layer_quantization(state_dict, prefix)\n if quant is not None:\n out[\"llama_quantization_metadata\"] = quant\n\n return out\n\n\nclass LLAMA3Tokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256, pad_token=128258):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"llama_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=pad_token, min_length=min_length, tokenizer_data=tokenizer_data)\n\nclass LLAMAModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"hidden\", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={\"start\": 128000, \"pad\": 128258}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n\n textmodel_json_config = {}\n vocab_size = model_options.get(\"vocab_size\", None)\n if vocab_size is not None:\n textmodel_json_config[\"vocab_size\"] = vocab_size\n\n model_options = {**model_options, \"model_name\": \"llama\"}\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\nclass HunyuanVideoTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.llama_template = \"\"\"<|start_header_id|>system<|end_header_id|>\\n\\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\n{}<|eot_id|>\"\"\" # 95 tokens\n self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs):\n out = {}\n out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)\n\n if llama_template is None:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs)\n embed_count = 0\n for r in llama_text_tokens:\n for i in range(len(r)):\n if r[i][0] == 128257:\n if image_embeds is not None and embed_count < image_embeds.shape[0]:\n r[i] = ({\"type\": \"embedding\", \"data\": image_embeds[embed_count], \"original_type\": \"image\", \"image_interleave\": image_interleave},) + r[i][1:]\n embed_count += 1\n out[\"llama\"] = llama_text_tokens\n return out\n\n def untokenize(self, token_weight_pair):\n return self.clip_l.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {}\n\n\nclass HunyuanVideo15Tokenizer(HunyuanImageTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.llama_template = \"<|im_start|>system\\nYou are a helpful assistant. Describe the video by detailing the following aspects:\\n1. The main content and theme of the video.\\n2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.\\n3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.\\n4. background environment, light, style and atmosphere.\\n5. camera angles, movements, and transitions used in the video.<|im_end|>\\n<|im_start|>user\\n{}<|im_end|>\\n<|im_start|>assistant\\n\"\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n return super().tokenize_with_weights(text, return_word_ids, prevent_empty_text=True, **kwargs)\n\nclass HunyuanVideoClipModel(torch.nn.Module):\n def __init__(self, dtype_llama=None, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device)\n self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)\n self.llama = LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options)\n self.dtypes = set([dtype, dtype_llama])\n\n def set_clip_options(self, options):\n self.clip_l.set_clip_options(options)\n self.llama.set_clip_options(options)\n\n def reset_clip_options(self):\n self.clip_l.reset_clip_options()\n self.llama.reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_l = token_weight_pairs[\"l\"]\n token_weight_pairs_llama = token_weight_pairs[\"llama\"]\n\n llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama)\n\n template_end = 0\n extra_template_end = 0\n extra_sizes = 0\n user_end = 9999999999999\n images = []\n\n tok_pairs = token_weight_pairs_llama[0]\n for i, v in enumerate(tok_pairs):\n elem = v[0]\n if not torch.is_tensor(elem):\n if isinstance(elem, numbers.Integral):\n if elem == 128006:\n if tok_pairs[i + 1][0] == 882:\n if tok_pairs[i + 2][0] == 128007:\n template_end = i + 2\n user_end = -1\n if elem == 128009 and user_end == -1:\n user_end = i + 1\n else:\n if elem.get(\"original_type\") == \"image\":\n elem_size = elem.get(\"data\").shape[0]\n if template_end > 0:\n if user_end == -1:\n extra_template_end += elem_size - 1\n else:\n image_start = i + extra_sizes\n image_end = i + elem_size + extra_sizes\n images.append((image_start, image_end, elem.get(\"image_interleave\", 1)))\n extra_sizes += elem_size - 1\n\n if llama_out.shape[1] > (template_end + 2):\n if tok_pairs[template_end + 1][0] == 271:\n template_end += 2\n llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]\n llama_extra_out[\"attention_mask\"] = llama_extra_out[\"attention_mask\"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]\n if llama_extra_out[\"attention_mask\"].sum() == torch.numel(llama_extra_out[\"attention_mask\"]):\n llama_extra_out.pop(\"attention_mask\") # attention mask is useless if no masked elements\n\n if len(images) > 0:\n out = []\n for i in images:\n out.append(llama_out[:, i[0]: i[1]: i[2]])\n llama_output = torch.cat(out + [llama_output], dim=1)\n\n l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)\n return llama_output, l_pooled, llama_extra_out\n\n def load_sd(self, sd):\n if \"text_model.encoder.layers.1.mlp.fc1.weight\" in sd:\n return self.clip_l.load_sd(sd)\n else:\n return self.llama.load_sd(sd)\n\n\ndef hunyuan_video_clip(dtype_llama=None, llama_quantization_metadata=None):\n class HunyuanVideoClipModel_(HunyuanVideoClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)\n return HunyuanVideoClipModel_\n" }, { "path": "comfy/text_encoders/hydit.py", "content": "from comfy import sd1_clip\nfrom transformers import BertTokenizer\nfrom .spiece_tokenizer import SPieceTokenizer\nfrom .bert import BertModel\nimport comfy.text_encoders.t5\nimport os\nimport torch\n\nclass HyditBertModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"hydit_clip.json\")\n model_options = {**model_options, \"model_name\": \"hydit_clip\"}\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"start\": 101, \"end\": 102, \"pad\": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)\n\nclass HyditBertTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"hydit_clip_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77, tokenizer_data=tokenizer_data)\n\n\nclass MT5XLModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"mt5_config_xl.json\")\n model_options = {**model_options, \"model_name\": \"mt5xl\"}\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)\n\nclass MT5XLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), \"mt5_tokenizer\"), \"spiece.model\")\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data)\n\n def state_dict(self):\n return {\"spiece_model\": self.tokenizer.serialize_model()}\n\nclass HyditTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n mt5_tokenizer_data = tokenizer_data.get(\"mt5xl.spiece_model\", None)\n self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory)\n self.mt5xl = MT5XLTokenizer(tokenizer_data={**tokenizer_data, \"spiece_model\": mt5_tokenizer_data}, embedding_directory=embedding_directory)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"hydit_clip\"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"mt5xl\"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.hydit_clip.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {\"mt5xl.spiece_model\": self.mt5xl.state_dict()[\"spiece_model\"]}\n\nclass HyditModel(torch.nn.Module):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options)\n self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options)\n\n self.dtypes = set()\n if dtype is not None:\n self.dtypes.add(dtype)\n\n def encode_token_weights(self, token_weight_pairs):\n hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs[\"hydit_clip\"])\n mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs[\"mt5xl\"])\n return hydit_out[0], hydit_out[1], {\"attention_mask\": hydit_out[2][\"attention_mask\"], \"conditioning_mt5xl\": mt5_out[0], \"attention_mask_mt5xl\": mt5_out[2][\"attention_mask\"]}\n\n def load_sd(self, sd):\n if \"bert.encoder.layer.0.attention.self.query.weight\" in sd:\n return self.hydit_clip.load_sd(sd)\n else:\n return self.mt5xl.load_sd(sd)\n\n def set_clip_options(self, options):\n self.hydit_clip.set_clip_options(options)\n self.mt5xl.set_clip_options(options)\n\n def reset_clip_options(self):\n self.hydit_clip.reset_clip_options()\n self.mt5xl.reset_clip_options()\n" }, { "path": "comfy/text_encoders/hydit_clip.json", "content": "{\n \"_name_or_path\": \"hfl/chinese-roberta-wwm-ext-large\",\n \"architectures\": [\n \"BertModel\"\n ],\n \"attention_probs_dropout_prob\": 0.1,\n \"bos_token_id\": 0,\n \"classifier_dropout\": null,\n \"directionality\": \"bidi\",\n \"eos_token_id\": 2,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.1,\n \"hidden_size\": 1024,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 4096,\n \"layer_norm_eps\": 1e-12,\n \"max_position_embeddings\": 512,\n \"model_type\": \"bert\",\n \"num_attention_heads\": 16,\n \"num_hidden_layers\": 24,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"pooler_fc_size\": 768,\n \"pooler_num_attention_heads\": 12,\n \"pooler_num_fc_layers\": 3,\n \"pooler_size_per_head\": 128,\n \"pooler_type\": \"first_token_transform\",\n \"position_embedding_type\": \"absolute\",\n \"torch_dtype\": \"float32\",\n \"transformers_version\": \"4.22.1\",\n \"type_vocab_size\": 2,\n \"use_cache\": true,\n \"vocab_size\": 47020\n}\n\n" }, { "path": "comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json", "content": "{\n \"cls_token\": \"[CLS]\",\n \"mask_token\": \"[MASK]\",\n \"pad_token\": \"[PAD]\",\n \"sep_token\": \"[SEP]\",\n \"unk_token\": \"[UNK]\"\n}\n" }, { "path": "comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json", "content": "{\n \"cls_token\": \"[CLS]\",\n \"do_basic_tokenize\": true,\n \"do_lower_case\": true,\n \"mask_token\": \"[MASK]\",\n \"name_or_path\": \"hfl/chinese-roberta-wwm-ext\",\n \"never_split\": null,\n \"pad_token\": \"[PAD]\",\n \"sep_token\": \"[SEP]\",\n \"special_tokens_map_file\": \"/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json\",\n \"strip_accents\": null,\n \"tokenize_chinese_chars\": true,\n \"tokenizer_class\": \"BertTokenizer\",\n \"unk_token\": \"[UNK]\",\n \"model_max_length\": 77\n}\n" }, { "path": "comfy/text_encoders/hydit_clip_tokenizer/vocab.txt", "content": "[PAD]\n[unused1]\n[unused2]\n[unused3]\n[unused4]\n[unused5]\n[unused6]\n[unused7]\n[unused8]\n[unused9]\n[unused10]\n[unused11]\n[unused12]\n[unused13]\n[unused14]\n[unused15]\n[unused16]\n[unused17]\n[unused18]\n[unused19]\n[unused20]\n[unused21]\n[unused22]\n[unused23]\n[unused24]\n[unused25]\n[unused26]\n[unused27]\n[unused28]\n[unused29]\n[unused30]\n[unused31]\n[unused32]\n[unused33]\n[unused34]\n[unused35]\n[unused36]\n[unused37]\n[unused38]\n[unused39]\n[unused40]\n[unused41]\n[unused42]\n[unused43]\n[unused44]\n[unused45]\n[unused46]\n[unused47]\n[unused48]\n[unused49]\n[unused50]\n[unused51]\n[unused52]\n[unused53]\n[unused54]\n[unused55]\n[unused56]\n[unused57]\n[unused58]\n[unused59]\n[unused60]\n[unused61]\n[unused62]\n[unused63]\n[unused64]\n[unused65]\n[unused66]\n[unused67]\n[unused68]\n[unused69]\n[unused70]\n[unused71]\n[unused72]\n[unused73]\n[unused74]\n[unused75]\n[unused76]\n[unused77]\n[unused78]\n[unused79]\n[unused80]\n[unused81]\n[unused82]\n[unused83]\n[unused84]\n[unused85]\n[unused86]\n[unused87]\n[unused88]\n[unused89]\n[unused90]\n[unused91]\n[unused92]\n[unused93]\n[unused94]\n[unused95]\n[unused96]\n[unused97]\n[unused98]\n[unused99]\n[UNK]\n[CLS]\n[SEP]\n[MASK]\n\n\n!\n\"\n#\n$\n%\n&\n'\n(\n)\n*\n+\n,\n-\n.\n/\n0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n:\n;\n<\n=\n>\n?\n@\n[\n\\\n]\n^\n_\na\nb\nc\nd\ne\nf\ng\nh\ni\nj\nk\nl\nm\nn\no\np\nq\nr\ns\nt\nu\nv\nw\nx\ny\nz\n{\n|\n}\n~\n£\n¤\n¥\n§\n©\n«\n®\n°\n±\n²\n³\nµ\n·\n¹\nº\n»\n¼\n×\nß\næ\n÷\nø\nđ\nŋ\nɔ\nə\nɡ\nʰ\nˇ\nˈ\nˊ\nˋ\nˍ\nː\n˙\n˚\nˢ\nα\nβ\nγ\nδ\nε\nη\nθ\nι\nκ\nλ\nμ\nν\nο\nπ\nρ\nς\nσ\nτ\nυ\nφ\nχ\nψ\nω\nа\nб\nв\nг\nд\nе\nж\nз\nи\nк\nл\nм\nн\nо\nп\nр\nс\nт\nу\nф\nх\nц\nч\nш\nы\nь\nя\nі\nا\nب\nة\nت\nد\nر\nس\nع\nل\nم\nن\nه\nو\nي\n۩\nก\nง\nน\nม\nย\nร\nอ\nา\nเ\n๑\n་\nღ\nᄀ\nᄁ\nᄂ\nᄃ\nᄅ\nᄆ\nᄇ\nᄈ\nᄉ\nᄋ\nᄌ\nᄎ\nᄏ\nᄐ\nᄑ\nᄒ\nᅡ\nᅢ\nᅣ\nᅥ\nᅦ\nᅧ\nᅨ\nᅩ\nᅪ\nᅬ\nᅭ\nᅮ\nᅯ\nᅲ\nᅳ\nᅴ\nᅵ\nᆨ\nᆫ\nᆯ\nᆷ\nᆸ\nᆺ\nᆻ\nᆼ\nᗜ\nᵃ\nᵉ\nᵍ\nᵏ\nᵐ\nᵒ\nᵘ\n‖\n„\n†\n•\n‥\n‧\n
\n‰\n′\n″\n‹\n›\n※\n‿\n⁄\nⁱ\n⁺\nⁿ\n₁\n₂\n₃\n₄\n€\n℃\n№\n™\nⅰ\nⅱ\nⅲ\nⅳ\nⅴ\n←\n↑\n→\n↓\n↔\n↗\n↘\n⇒\n∀\n−\n∕\n∙\n√\n∞\n∟\n∠\n∣\n∥\n∩\n∮\n∶\n∼\n∽\n≈\n≒\n≡\n≤\n≥\n≦\n≧\n≪\n≫\n⊙\n⋅\n⋈\n⋯\n⌒\n①\n②\n③\n④\n⑤\n⑥\n⑦\n⑧\n⑨\n⑩\n⑴\n⑵\n⑶\n⑷\n⑸\n⒈\n⒉\n⒊\n⒋\nⓒ\nⓔ\nⓘ\n─\n━\n│\n┃\n┅\n┆\n┊\n┌\n└\n├\n┣\n═\n║\n╚\n╞\n╠\n╭\n╮\n╯\n╰\n╱\n╳\n▂\n▃\n▅\n▇\n█\n▉\n▋\n▌\n▍\n▎\n■\n□\n▪\n▫\n▬\n▲\n△\n▶\n►\n▼\n▽\n◆\n◇\n○\n◎\n●\n◕\n◠\n◢\n◤\n☀\n★\n☆\n☕\n☞\n☺\n☼\n♀\n♂\n♠\n♡\n♣\n♥\n♦\n♪\n♫\n♬\n✈\n✔\n✕\n✖\n✦\n✨\n✪\n✰\n✿\n❀\n❤\n➜\n➤\n⦿\n、\n。\n〃\n々\n〇\n〈\n〉\n《\n》\n「\n」\n『\n』\n【\n】\n〓\n〔\n〕\n〖\n〗\n〜\n〝\n〞\nぁ\nあ\nぃ\nい\nう\nぇ\nえ\nお\nか\nき\nく\nけ\nこ\nさ\nし\nす\nせ\nそ\nた\nち\nっ\nつ\nて\nと\nな\nに\nぬ\nね\nの\nは\nひ\nふ\nへ\nほ\nま\nみ\nむ\nめ\nも\nゃ\nや\nゅ\nゆ\nょ\nよ\nら\nり\nる\nれ\nろ\nわ\nを\nん\n゜\nゝ\nァ\nア\nィ\nイ\nゥ\nウ\nェ\nエ\nォ\nオ\nカ\nキ\nク\nケ\nコ\nサ\nシ\nス\nセ\nソ\nタ\nチ\nッ\nツ\nテ\nト\nナ\nニ\nヌ\nネ\nノ\nハ\nヒ\nフ\nヘ\nホ\nマ\nミ\nム\nメ\nモ\nャ\nヤ\nュ\nユ\nョ\nヨ\nラ\nリ\nル\nレ\nロ\nワ\nヲ\nン\nヶ\n・\nー\nヽ\nㄅ\nㄆ\nㄇ\nㄉ\nㄋ\nㄌ\nㄍ\nㄎ\nㄏ\nㄒ\nㄚ\nㄛ\nㄞ\nㄟ\nㄢ\nㄤ\nㄥ\nㄧ\nㄨ\nㆍ\n㈦\n㊣\n㎡\n㗎\n一\n丁\n七\n万\n丈\n三\n上\n下\n不\n与\n丐\n丑\n专\n且\n丕\n世\n丘\n丙\n业\n丛\n东\n丝\n丞\n丟\n両\n丢\n两\n严\n並\n丧\n丨\n个\n丫\n中\n丰\n串\n临\n丶\n丸\n丹\n为\n主\n丼\n丽\n举\n丿\n乂\n乃\n久\n么\n义\n之\n乌\n乍\n乎\n乏\n乐\n乒\n乓\n乔\n乖\n乗\n乘\n乙\n乜\n九\n乞\n也\n习\n乡\n书\n乩\n买\n乱\n乳\n乾\n亀\n亂\n了\n予\n争\n事\n二\n于\n亏\n云\n互\n五\n井\n亘\n亙\n亚\n些\n亜\n亞\n亟\n亡\n亢\n交\n亥\n亦\n产\n亨\n亩\n享\n京\n亭\n亮\n亲\n亳\n亵\n人\n亿\n什\n仁\n仃\n仄\n仅\n仆\n仇\n今\n介\n仍\n从\n仏\n仑\n仓\n仔\n仕\n他\n仗\n付\n仙\n仝\n仞\n仟\n代\n令\n以\n仨\n仪\n们\n仮\n仰\n仲\n件\n价\n任\n份\n仿\n企\n伉\n伊\n伍\n伎\n伏\n伐\n休\n伕\n众\n优\n伙\n会\n伝\n伞\n伟\n传\n伢\n伤\n伦\n伪\n伫\n伯\n估\n伴\n伶\n伸\n伺\n似\n伽\n佃\n但\n佇\n佈\n位\n低\n住\n佐\n佑\n体\n佔\n何\n佗\n佘\n余\n佚\n佛\n作\n佝\n佞\n佟\n你\n佢\n佣\n佤\n佥\n佩\n佬\n佯\n佰\n佳\n併\n佶\n佻\n佼\n使\n侃\n侄\n來\n侈\n例\n侍\n侏\n侑\n侖\n侗\n供\n依\n侠\n価\n侣\n侥\n侦\n侧\n侨\n侬\n侮\n侯\n侵\n侶\n侷\n便\n係\n促\n俄\n俊\n俎\n俏\n俐\n俑\n俗\n俘\n俚\n保\n俞\n俟\n俠\n信\n俨\n俩\n俪\n俬\n俭\n修\n俯\n俱\n俳\n俸\n俺\n俾\n倆\n倉\n個\n倌\n倍\n倏\n們\n倒\n倔\n倖\n倘\n候\n倚\n倜\n借\n倡\n値\n倦\n倩\n倪\n倫\n倬\n倭\n倶\n债\n值\n倾\n偃\n假\n偈\n偉\n偌\n偎\n偏\n偕\n做\n停\n健\n側\n偵\n偶\n偷\n偻\n偽\n偿\n傀\n傅\n傍\n傑\n傘\n備\n傚\n傢\n傣\n傥\n储\n傩\n催\n傭\n傲\n傳\n債\n傷\n傻\n傾\n僅\n働\n像\n僑\n僕\n僖\n僚\n僥\n僧\n僭\n僮\n僱\n僵\n價\n僻\n儀\n儂\n億\n儆\n儉\n儋\n儒\n儕\n儘\n償\n儡\n優\n儲\n儷\n儼\n儿\n兀\n允\n元\n兄\n充\n兆\n兇\n先\n光\n克\n兌\n免\n児\n兑\n兒\n兔\n兖\n党\n兜\n兢\n入\n內\n全\n兩\n八\n公\n六\n兮\n兰\n共\n兲\n关\n兴\n兵\n其\n具\n典\n兹\n养\n兼\n兽\n冀\n内\n円\n冇\n冈\n冉\n冊\n册\n再\n冏\n冒\n冕\n冗\n写\n军\n农\n冠\n冢\n冤\n冥\n冨\n冪\n冬\n冯\n冰\n冲\n决\n况\n冶\n冷\n冻\n冼\n冽\n冾\n净\n凄\n准\n凇\n凈\n凉\n凋\n凌\n凍\n减\n凑\n凛\n凜\n凝\n几\n凡\n凤\n処\n凪\n凭\n凯\n凰\n凱\n凳\n凶\n凸\n凹\n出\n击\n函\n凿\n刀\n刁\n刃\n分\n切\n刈\n刊\n刍\n刎\n刑\n划\n列\n刘\n则\n刚\n创\n初\n删\n判\n別\n刨\n利\n刪\n别\n刮\n到\n制\n刷\n券\n刹\n刺\n刻\n刽\n剁\n剂\n剃\n則\n剉\n削\n剋\n剌\n前\n剎\n剐\n剑\n剔\n剖\n剛\n剜\n剝\n剣\n剤\n剥\n剧\n剩\n剪\n副\n割\n創\n剷\n剽\n剿\n劃\n劇\n劈\n劉\n劊\n劍\n劏\n劑\n力\n劝\n办\n功\n加\n务\n劣\n动\n助\n努\n劫\n劭\n励\n劲\n劳\n労\n劵\n効\n劾\n势\n勁\n勃\n勇\n勉\n勋\n勐\n勒\n動\n勖\n勘\n務\n勛\n勝\n勞\n募\n勢\n勤\n勧\n勳\n勵\n勸\n勺\n勻\n勾\n勿\n匀\n包\n匆\n匈\n匍\n匐\n匕\n化\n北\n匙\n匝\n匠\n匡\n匣\n匪\n匮\n匯\n匱\n匹\n区\n医\n匾\n匿\n區\n十\n千\n卅\n升\n午\n卉\n半\n卍\n华\n协\n卑\n卒\n卓\n協\n单\n卖\n南\n単\n博\n卜\n卞\n卟\n占\n卡\n卢\n卤\n卦\n卧\n卫\n卮\n卯\n印\n危\n即\n却\n卵\n卷\n卸\n卻\n卿\n厂\n厄\n厅\n历\n厉\n压\n厌\n厕\n厘\n厚\n厝\n原\n厢\n厥\n厦\n厨\n厩\n厭\n厮\n厲\n厳\n去\n县\n叁\n参\n參\n又\n叉\n及\n友\n双\n反\n収\n发\n叔\n取\n受\n变\n叙\n叛\n叟\n叠\n叡\n叢\n口\n古\n句\n另\n叨\n叩\n只\n叫\n召\n叭\n叮\n可\n台\n叱\n史\n右\n叵\n叶\n号\n司\n叹\n叻\n叼\n叽\n吁\n吃\n各\n吆\n合\n吉\n吊\n吋\n同\n名\n后\n吏\n吐\n向\n吒\n吓\n吕\n吖\n吗\n君\n吝\n吞\n吟\n吠\n吡\n否\n吧\n吨\n吩\n含\n听\n吭\n吮\n启\n吱\n吳\n吴\n吵\n吶\n吸\n吹\n吻\n吼\n吽\n吾\n呀\n呂\n呃\n呆\n呈\n告\n呋\n呎\n呐\n呓\n呕\n呗\n员\n呛\n呜\n呢\n呤\n呦\n周\n呱\n呲\n味\n呵\n呷\n呸\n呻\n呼\n命\n咀\n咁\n咂\n咄\n咆\n咋\n和\n咎\n咏\n咐\n咒\n咔\n咕\n咖\n咗\n咘\n咙\n咚\n咛\n咣\n咤\n咦\n咧\n咨\n咩\n咪\n咫\n咬\n咭\n咯\n咱\n咲\n咳\n咸\n咻\n咽\n咿\n哀\n品\n哂\n哄\n哆\n哇\n哈\n哉\n哋\n哌\n响\n哎\n哏\n哐\n哑\n哒\n哔\n哗\n哟\n員\n哥\n哦\n哧\n哨\n哩\n哪\n哭\n哮\n哲\n哺\n哼\n哽\n唁\n唄\n唆\n唇\n唉\n唏\n唐\n唑\n唔\n唠\n唤\n唧\n唬\n售\n唯\n唰\n唱\n唳\n唷\n唸\n唾\n啃\n啄\n商\n啉\n啊\n問\n啓\n啕\n啖\n啜\n啞\n啟\n啡\n啤\n啥\n啦\n啧\n啪\n啫\n啬\n啮\n啰\n啱\n啲\n啵\n啶\n啷\n啸\n啻\n啼\n啾\n喀\n喂\n喃\n善\n喆\n喇\n喉\n喊\n喋\n喎\n喏\n喔\n喘\n喙\n喚\n喜\n喝\n喟\n喧\n喪\n喫\n喬\n單\n喰\n喱\n喲\n喳\n喵\n営\n喷\n喹\n喺\n喻\n喽\n嗅\n嗆\n嗇\n嗎\n嗑\n嗒\n嗓\n嗔\n嗖\n嗚\n嗜\n嗝\n嗟\n嗡\n嗣\n嗤\n嗦\n嗨\n嗪\n嗬\n嗯\n嗰\n嗲\n嗳\n嗶\n嗷\n嗽\n嘀\n嘅\n嘆\n嘈\n嘉\n嘌\n嘍\n嘎\n嘔\n嘖\n嘗\n嘘\n嘚\n嘛\n嘜\n嘞\n嘟\n嘢\n嘣\n嘤\n嘧\n嘩\n嘭\n嘮\n嘯\n嘰\n嘱\n嘲\n嘴\n嘶\n嘸\n嘹\n嘻\n嘿\n噁\n噌\n噎\n噓\n噔\n噗\n噙\n噜\n噠\n噢\n噤\n器\n噩\n噪\n噬\n噱\n噴\n噶\n噸\n噹\n噻\n噼\n嚀\n嚇\n嚎\n嚏\n嚐\n嚓\n嚕\n嚟\n嚣\n嚥\n嚨\n嚮\n嚴\n嚷\n嚼\n囂\n囉\n囊\n囍\n囑\n囔\n囗\n囚\n四\n囝\n回\n囟\n因\n囡\n团\n団\n囤\n囧\n囪\n囫\n园\n困\n囱\n囲\n図\n围\n囹\n固\n国\n图\n囿\n圃\n圄\n圆\n圈\n國\n圍\n圏\n園\n圓\n圖\n團\n圜\n土\n圣\n圧\n在\n圩\n圭\n地\n圳\n场\n圻\n圾\n址\n坂\n均\n坊\n坍\n坎\n坏\n坐\n坑\n块\n坚\n坛\n坝\n坞\n坟\n坠\n坡\n坤\n坦\n坨\n坪\n坯\n坳\n坵\n坷\n垂\n垃\n垄\n型\n垒\n垚\n垛\n垠\n垢\n垣\n垦\n垩\n垫\n垭\n垮\n垵\n埂\n埃\n埋\n城\n埔\n埕\n埗\n域\n埠\n埤\n埵\n執\n埸\n培\n基\n埼\n堀\n堂\n堃\n堅\n堆\n堇\n堑\n堕\n堙\n堡\n堤\n堪\n堯\n堰\n報\n場\n堵\n堺\n堿\n塊\n塌\n塑\n塔\n塗\n塘\n塚\n塞\n塢\n塩\n填\n塬\n塭\n塵\n塾\n墀\n境\n墅\n墉\n墊\n墒\n墓\n増\n墘\n墙\n墜\n增\n墟\n墨\n墩\n墮\n墳\n墻\n墾\n壁\n壅\n壆\n壇\n壊\n壑\n壓\n壕\n壘\n壞\n壟\n壢\n壤\n壩\n士\n壬\n壮\n壯\n声\n売\n壳\n壶\n壹\n壺\n壽\n处\n备\n変\n复\n夏\n夔\n夕\n外\n夙\n多\n夜\n够\n夠\n夢\n夥\n大\n天\n太\n夫\n夭\n央\n夯\n失\n头\n夷\n夸\n夹\n夺\n夾\n奂\n奄\n奇\n奈\n奉\n奋\n奎\n奏\n奐\n契\n奔\n奕\n奖\n套\n奘\n奚\n奠\n奢\n奥\n奧\n奪\n奬\n奮\n女\n奴\n奶\n奸\n她\n好\n如\n妃\n妄\n妆\n妇\n妈\n妊\n妍\n妒\n妓\n妖\n妘\n妙\n妝\n妞\n妣\n妤\n妥\n妨\n妩\n妪\n妮\n妲\n妳\n妹\n妻\n妾\n姆\n姉\n姊\n始\n姍\n姐\n姑\n姒\n姓\n委\n姗\n姚\n姜\n姝\n姣\n姥\n姦\n姨\n姪\n姫\n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հ\nմ\nյ\nն\nո\nպ\nս\nվ\nտ\nր\nւ\nք\n־\nא\nב\nג\nד\nה\nו\nז\nח\nט\nי\nך\nכ\nל\nם\nמ\nן\nנ\nס\nע\nף\nפ\nץ\nצ\nק\nר\nש\nת\n،\nء\nث\nج\nح\nخ\nذ\nز\nش\nص\nض\nط\nظ\nغ\nـ\nف\nق\nك\nى\nٹ\nپ\nچ\nک\nگ\nں\nھ\nہ\nی\nے\nअ\nआ\nउ\nए\nक\nख\nग\nच\nज\nट\nड\nण\nत\nथ\nद\nध\nन\nप\nब\nभ\nम\nय\nर\nल\nव\nश\nष\nस\nह\nा\nि\nी\nो\n।\n॥\nং\nঅ\nআ\nই\nউ\nএ\nও\nক\nখ\nগ\nচ\nছ\nজ\nট\nড\nণ\nত\nথ\nদ\nধ\nন\nপ\nব\nভ\nম\nয\nর\nল\nশ\nষ\nস\nহ\nা\nি\nী\nে\nக\nச\nட\nத\nந\nன\nப\nம\nய\nர\nல\nள\nவ\nா\nி\nு\nே\nை\nನ\nರ\nಾ\nක\nය\nර\nල\nව\nා\nต\nท\nพ\nล\nว\nส\n།\nག\nང\nད\nན\nཔ\nབ\nམ\nའ\nར\nལ\nས\nမ\nა\nბ\nგ\nდ\nე\nვ\nთ\nი\nკ\nლ\nმ\nნ\nო\nრ\nს\nტ\nუ\nᄊ\nᴬ\nᴮ\nᴰ\nᴵ\nᴺ\nᵀ\nᵇ\nᵈ\nᵖ\nᵗ\nᵢ\nᵣ\nᵤ\nᵥ\nᶜ\nᶠ\n‐\n‑\n‒\n–\n—\n―\n‘\n’\n‚\n“\n”\n‡\n…\n⁰\n⁴\n⁵\n⁶\n⁷\n⁸\n⁹\n⁻\n₀\n₅\n₆\n₇\n₈\n₉\n₊\n₍\n₎\nₐ\nₑ\nₒ\nₓ\nₕ\nₖ\nₗ\nₘ\nₙ\nₚ\nₛ\nₜ\n₤\n₩\n₱\n₹\nℓ\nℝ\n⅓\n⅔\n↦\n⇄\n⇌\n∂\n∅\n∆\n∇\n∈\n∗\n∘\n∧\n∨\n∪\n⊂\n⊆\n⊕\n⊗\n☉\n♭\n♯\n⟨\n⟩\nⱼ\n⺩\n⺼\n⽥\n亻\n宀\n彳\n忄\n扌\n氵\n疒\n糹\n訁\n辶\n阝\n龸\nfi\nfl\nhad\nwere\nwhich\nhim\ntheir\nbeen\nwould\nthen\nthem\ncould\nduring\nthrough\nbetween\nwhile\nlater\naround\ndid\nsuch\nbeing\nused\nagainst\nmany\nboth\nthese\nknown\nuntil\neven\ndidn\nbecause\nborn\nsince\nstill\nbecame\nany\nincluding\ntook\nsame\neach\ncalled\nmuch\nhowever\nfour\nanother\nfound\nwon\ngoing\naway\nhand\nseveral\nfollowing\nreleased\nplayed\nbegan\ndistrict\nthose\nheld\nown\nearly\nleague\ngovernment\ncame\nbased\nthought\nlooked\nalong\nwent\nfew\nfather\nformer\nlocated\ngot\nthough\nevery\ncentury\nwithout\nwithin\nbuilding\nlarge\nnamed\nstarted\nonce\nshould\nbuilt\nbritish\ndeath\nmoved\ndoor\nneed\npresident\nwasn\nalthough\ndue\nmajor\ndied\nthird\nknew\nasked\nturned\nwanted\ntogether\nreceived\nson\nserved\ndifferent\nbehind\nhimself\nfelt\nmembers\nfootball\nnear\nhaving\nsaw\nmother\narmy\nfront\nlate\nhands\nput\ndivision\nacross\ntold\noften\never\nfrench\nsix\ninclude\ntell\namong\nspecies\nreally\naccording\nhalf\noriginal\ngave\nmaking\nenough\nopened\nmust\nincluded\ngiven\ngerman\nwoman\ncommunity\nmight\nmillion\ncourt\nshort\nround\nseen\nalways\nbecome\nsure\nalmost\ndirector\ncouncil\ncareer\nthings\nusing\ncouldn\nbetter\nstudents\nmarried\nnothing\nworked\nothers\nrecord\nanything\ncontinued\ngive\nmilitary\nestablished\nreturned\ndoes\nwritten\nthing\nfeet\nfar\nalready\nchampionship\nwestern\ndepartment\nrole\nvarious\nproduction\ntelevision\nproduced\nworking\nregion\npresent\nperiod\nlooking\nleast\ntotal\nengland\nwife\nper\nbrother\nsoon\npolitical\ntaken\ncreated\nfurther\nable\nreached\njoined\nupon\ndone\nimportant\neither\nappeared\nposition\nground\nlead\nelection\narms\npolice\ninstead\nwords\nmoment\nsomeone\nannounced\nless\nwrote\npast\nfollowed\nfounded\nfinally\nindia\ntaking\nrecords\nconsidered\nnorthern\ntoward\neuropean\noutside\ndescribed\ntrack\nplaying\nheard\nprofessional\naustralia\nmiles\nyet\ntrying\nblood\nsouthern\nmaybe\neverything\nmouth\nrace\nrecorded\nabove\ndaughter\npoints\nmiddle\nmove\ntried\nelected\nclosed\nten\nminister\nchief\nperson\nsimilar\nbrought\nrest\nformed\nfloor\ndoing\nkilled\ntraining\nneeded\nturn\nfinished\nrailway\nrather\nsent\nexample\nran\nterm\ncoming\ncurrently\nforces\ndespite\nareas\nfact\ndead\noriginally\ngermany\nprobably\ndeveloped\npulled\nstood\nsigned\nsongs\nchild\neventually\nmet\naverage\nteams\nminutes\ncurrent\nkind\ndecided\nusually\neastern\nseemed\nepisode\nbed\nadded\nindian\nroute\navailable\nthroughout\naddition\nappointed\neight\nconstruction\nmean\nremained\nschools\nsometimes\nevents\npossible\naustralian\nforward\ndebut\nseat\nperformance\ncommittee\nfeatures\ncharacter\nherself\nlot\nrussian\nrange\nhours\nsold\nquickly\ndirected\nguitar\nperformed\nplayers\nsmile\nmyself\nplaced\nprovince\ntowards\nwouldn\nleading\nwhole\ndesigned\ncensus\neurope\nattack\njapanese\ngetting\nalone\nlower\nwide\nhospital\nbelieve\nchanged\nsister\ngone\nhadn\nship\nstudies\nacademy\nshot\nbelow\ninvolved\nkept\nlargest\nespecially\nbeginning\nmovement\nsection\nfemale\nprofessor\nlord\nlonger\nwalked\nactually\ncivil\nfamilies\nthus\naircraft\ncompleted\nincludes\ncaptain\nfight\nvocals\nfeatured\nfourth\nofficer\nhear\nmeans\nmedical\ngroups\nlips\ncompetition\nentire\nlived\nleaving\nfederal\ntournament\npassed\nindependent\nkingdom\nspent\nfine\ndoesn\nreported\nfall\nraised\nitself\nreplaced\nleader\ntheatre\nwhose\nparents\nspanish\ncanadian\ndegree\nwriting\nawarded\nhigher\ncoast\nprovided\nsenior\norganization\nstopped\nonto\ncountries\nparts\nconference\ninterest\nsaying\nallowed\nearlier\nmatter\nwinning\ntry\nhappened\nmoving\nlos\nbreath\nnearly\nmid\ncertain\nitalian\nafrican\nstanding\nfell\nartist\nshows\ndeal\nmine\nindustry\neveryone\nrepublic\nprovide\nstudent\nprimary\nowned\nolder\nheavy\n1st\nmakes\nattention\nanyone\nafrica\nstated\nlength\nended\nfingers\ncommand\nstaff\nforeign\nopening\ngovernor\nokay\nmedal\nkill\nintroduced\nchest\nhell\nfeeling\nsuccess\nmeet\nreason\nmeeting\nnovel\ntrade\nbuildings\nguy\ngoal\nnative\nhusband\npreviously\nentered\n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}, { "path": "comfy/text_encoders/jina_clip_2.py", "content": "# Jina CLIP v2 and Jina Embeddings v3 both use their modified XLM-RoBERTa architecture. Reference implementation:\n# Jina CLIP v2 (both text and vision): https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/modeling_clip.py\n# Jina XLM-RoBERTa (text only): http://huggingface.co/jinaai/xlm-roberta-flash-implementation/blob/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_xlm_roberta.py\n\nfrom dataclasses import dataclass\n\nimport torch\nfrom torch import nn as nn\nfrom torch.nn import functional as F\n\nimport comfy.model_management\nimport comfy.ops\nfrom comfy import sd1_clip\nfrom .spiece_tokenizer import SPieceTokenizer\n\nclass JinaClip2Tokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n # The official NewBie uses max_length=8000, but Jina Embeddings v3 actually supports 8192\n super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='jina_clip_2', tokenizer_class=SPieceTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=False, max_length=8192, min_length=1, pad_token=1, end_token=2, tokenizer_args={\"add_bos\": True, \"add_eos\": True}, tokenizer_data=tokenizer_data)\n\n def state_dict(self):\n return {\"spiece_model\": self.tokenizer.serialize_model()}\n\nclass JinaClip2TokenizerWrapper(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=JinaClip2Tokenizer, name=\"jina_clip_2\")\n\n# https://huggingface.co/jinaai/jina-embeddings-v3/blob/343dbf534c76fe845f304fa5c2d1fd87e1e78918/config.json\n@dataclass\nclass XLMRobertaConfig:\n vocab_size: int = 250002\n type_vocab_size: int = 1\n hidden_size: int = 1024\n num_hidden_layers: int = 24\n num_attention_heads: int = 16\n rotary_emb_base: float = 20000.0\n intermediate_size: int = 4096\n hidden_act: str = \"gelu\"\n hidden_dropout_prob: float = 0.1\n attention_probs_dropout_prob: float = 0.1\n layer_norm_eps: float = 1e-05\n bos_token_id: int = 0\n eos_token_id: int = 2\n pad_token_id: int = 1\n\nclass XLMRobertaEmbeddings(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n embed_dim = config.hidden_size\n self.word_embeddings = ops.Embedding(config.vocab_size, embed_dim, padding_idx=config.pad_token_id, device=device, dtype=dtype)\n self.token_type_embeddings = ops.Embedding(config.type_vocab_size, embed_dim, device=device, dtype=dtype)\n\n def forward(self, input_ids=None, embeddings=None):\n if input_ids is not None and embeddings is None:\n embeddings = self.word_embeddings(input_ids)\n\n if embeddings is not None:\n token_type_ids = torch.zeros(embeddings.shape[1], device=embeddings.device, dtype=torch.int32)\n token_type_embeddings = self.token_type_embeddings(token_type_ids)\n embeddings = embeddings + token_type_embeddings\n return embeddings\n\nclass RotaryEmbedding(nn.Module):\n def __init__(self, dim, base, device=None):\n super().__init__()\n inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n self._seq_len_cached = 0\n self._cos_cached = None\n self._sin_cached = None\n\n def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):\n if seqlen > self._seq_len_cached or self._cos_cached is None or self._cos_cached.device != device or self._cos_cached.dtype != dtype:\n self._seq_len_cached = seqlen\n t = torch.arange(seqlen, device=device, dtype=torch.float32)\n freqs = torch.outer(t, self.inv_freq.to(device=t.device))\n emb = torch.cat((freqs, freqs), dim=-1)\n self._cos_cached = emb.cos().to(dtype)\n self._sin_cached = emb.sin().to(dtype)\n\n def forward(self, q, k):\n batch, seqlen, heads, head_dim = q.shape\n self._update_cos_sin_cache(seqlen, device=q.device, dtype=q.dtype)\n\n cos = self._cos_cached[:seqlen].view(1, seqlen, 1, head_dim)\n sin = self._sin_cached[:seqlen].view(1, seqlen, 1, head_dim)\n\n def rotate_half(x):\n size = x.shape[-1] // 2\n x1, x2 = x[..., :size], x[..., size:]\n return torch.cat((-x2, x1), dim=-1)\n\n q_embed = (q * cos) + (rotate_half(q) * sin)\n k_embed = (k * cos) + (rotate_half(k) * sin)\n return q_embed, k_embed\n\nclass MHA(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n embed_dim = config.hidden_size\n self.num_heads = config.num_attention_heads\n self.head_dim = embed_dim // config.num_attention_heads\n\n self.rotary_emb = RotaryEmbedding(self.head_dim, config.rotary_emb_base, device=device)\n self.Wqkv = ops.Linear(embed_dim, 3 * embed_dim, device=device, dtype=dtype)\n self.out_proj = ops.Linear(embed_dim, embed_dim, device=device, dtype=dtype)\n\n def forward(self, x, mask=None, optimized_attention=None):\n qkv = self.Wqkv(x)\n batch_size, seq_len, _ = qkv.shape\n qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim)\n q, k, v = qkv.unbind(2)\n\n q, k = self.rotary_emb(q, k)\n\n # NHD -> HND\n q = q.transpose(1, 2)\n k = k.transpose(1, 2)\n v = v.transpose(1, 2)\n\n out = optimized_attention(q, k, v, heads=self.num_heads, mask=mask, skip_reshape=True)\n return self.out_proj(out)\n\nclass MLP(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n self.fc1 = ops.Linear(config.hidden_size, config.intermediate_size, device=device, dtype=dtype)\n self.activation = F.gelu\n self.fc2 = ops.Linear(config.intermediate_size, config.hidden_size, device=device, dtype=dtype)\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 Block(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n self.mixer = MHA(config, device=device, dtype=dtype, ops=ops)\n self.dropout1 = nn.Dropout(config.hidden_dropout_prob)\n self.norm1 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)\n self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)\n self.dropout2 = nn.Dropout(config.hidden_dropout_prob)\n self.norm2 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)\n\n def forward(self, hidden_states, mask=None, optimized_attention=None):\n mixer_out = self.mixer(hidden_states, mask=mask, optimized_attention=optimized_attention)\n hidden_states = self.norm1(self.dropout1(mixer_out) + hidden_states)\n mlp_out = self.mlp(hidden_states)\n hidden_states = self.norm2(self.dropout2(mlp_out) + hidden_states)\n return hidden_states\n\nclass XLMRobertaEncoder(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n self.layers = nn.ModuleList([Block(config, device=device, dtype=dtype, ops=ops) for _ in range(config.num_hidden_layers)])\n\n def forward(self, hidden_states, attention_mask=None):\n optimized_attention = comfy.ldm.modules.attention.optimized_attention_for_device(hidden_states.device, mask=attention_mask is not None, small_input=True)\n for layer in self.layers:\n hidden_states = layer(hidden_states, mask=attention_mask, optimized_attention=optimized_attention)\n return hidden_states\n\nclass XLMRobertaModel_(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n self.embeddings = XLMRobertaEmbeddings(config, device=device, dtype=dtype, ops=ops)\n self.emb_ln = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)\n self.emb_drop = nn.Dropout(config.hidden_dropout_prob)\n self.encoder = XLMRobertaEncoder(config, device=device, dtype=dtype, ops=ops)\n\n def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):\n x = self.embeddings(input_ids=input_ids, embeddings=embeds)\n x = self.emb_ln(x)\n x = self.emb_drop(x)\n\n mask = None\n if attention_mask is not None:\n mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, 1, attention_mask.shape[-1]))\n mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)\n\n sequence_output = self.encoder(x, attention_mask=mask)\n\n # Mean pool, see https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/hf_model.py\n pooled_output = None\n if attention_mask is None:\n pooled_output = sequence_output.mean(dim=1)\n else:\n attention_mask = attention_mask.to(sequence_output.dtype)\n pooled_output = (sequence_output * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=-1, keepdim=True)\n\n # Intermediate output is not yet implemented, use None for placeholder\n return sequence_output, None, pooled_output\n\nclass XLMRobertaModel(nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n self.config = XLMRobertaConfig(**config_dict)\n self.model = XLMRobertaModel_(self.config, device=device, dtype=dtype, ops=operations)\n self.num_layers = self.config.num_hidden_layers\n\n def get_input_embeddings(self):\n return self.model.embeddings.word_embeddings\n\n def set_input_embeddings(self, embeddings):\n self.model.embeddings.word_embeddings = embeddings\n\n def forward(self, *args, **kwargs):\n return self.model(*args, **kwargs)\n\nclass JinaClip2TextModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, textmodel_json_config={}, model_class=XLMRobertaModel, special_tokens={\"start\": 0, \"end\": 2, \"pad\": 1}, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)\n\nclass JinaClip2TextModelWrapper(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, clip_model=JinaClip2TextModel, name=\"jina_clip_2\", model_options=model_options)\n" }, { "path": "comfy/text_encoders/kandinsky5.py", "content": "from comfy import sd1_clip\nfrom .qwen_image import QwenImageTokenizer, QwenImageTEModel\nfrom .llama import Qwen25_7BVLI\n\n\nclass Kandinsky5Tokenizer(QwenImageTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.llama_template = \"<|im_start|>system\\nYou are a prompt engineer. Describe the video in detail.\\nDescribe how the camera moves or shakes, describe the zoom and view angle, whether it follows the objects.\\nDescribe the location of the video, main characters or objects and their action.\\nDescribe the dynamism of the video and presented actions.\\nName the visual style of the video: whether it is a professional footage, user generated content, some kind of animation, video game or screen content.\\nDescribe the visual effects, postprocessing and transitions if they are presented in the video.\\nPay attention to the order of key actions shown in the scene.<|im_end|>\\n<|im_start|>user\\n{}<|im_end|>\"\n self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = super().tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)\n\n return out\n\n\nclass Kandinsky5TokenizerImage(Kandinsky5Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.llama_template = \"<|im_start|>system\\nYou are a promt engineer. Describe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\\n<|im_start|>user\\n{}<|im_end|>\"\n\n\nclass Qwen25_7BVLIModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"hidden\", layer_idx=-1, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\nclass Kandinsky5TEModel(QwenImageTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name=\"qwen25_7b\", clip_model=Qwen25_7BVLIModel, model_options=model_options)\n self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)\n\n def encode_token_weights(self, token_weight_pairs):\n cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=-1)\n l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs[\"l\"])\n\n return cond, l_pooled, extra\n\n def set_clip_options(self, options):\n super().set_clip_options(options)\n self.clip_l.set_clip_options(options)\n\n def reset_clip_options(self):\n super().reset_clip_options()\n self.clip_l.reset_clip_options()\n\n def load_sd(self, sd):\n if \"text_model.encoder.layers.1.mlp.fc1.weight\" in sd:\n return self.clip_l.load_sd(sd)\n else:\n return super().load_sd(sd)\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class Kandinsky5TEModel_(Kandinsky5TEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return Kandinsky5TEModel_\n" }, { "path": "comfy/text_encoders/llama.py", "content": "import torch\nimport torch.nn as nn\nfrom dataclasses import dataclass\nfrom typing import Optional, Any, Tuple\nimport math\nfrom tqdm import tqdm\nimport comfy.utils\n\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\nimport comfy.model_management\nimport comfy.ops\nimport comfy.ldm.common_dit\nimport comfy.clip_model\n\nfrom . import qwen_vl\n\n@dataclass\nclass Llama2Config:\n vocab_size: int = 128320\n hidden_size: int = 4096\n intermediate_size: int = 14336\n num_hidden_layers: int = 32\n num_attention_heads: int = 32\n num_key_value_heads: int = 8\n max_position_embeddings: int = 8192\n rms_norm_eps: float = 1e-5\n rope_theta: float = 500000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = None\n k_norm = None\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n\n@dataclass\nclass Mistral3Small24BConfig:\n vocab_size: int = 131072\n hidden_size: int = 5120\n intermediate_size: int = 32768\n num_hidden_layers: int = 40\n num_attention_heads: int = 32\n num_key_value_heads: int = 8\n max_position_embeddings: int = 8192\n rms_norm_eps: float = 1e-5\n rope_theta: float = 1000000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = None\n k_norm = None\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n\n@dataclass\nclass Qwen25_3BConfig:\n vocab_size: int = 151936\n hidden_size: int = 2048\n intermediate_size: int = 11008\n num_hidden_layers: int = 36\n num_attention_heads: int = 16\n num_key_value_heads: int = 2\n max_position_embeddings: int = 128000\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = True\n rope_dims = None\n q_norm = None\n k_norm = None\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n\n@dataclass\nclass Qwen3_06BConfig:\n vocab_size: int = 151936\n hidden_size: int = 1024\n intermediate_size: int = 3072\n num_hidden_layers: int = 28\n num_attention_heads: int = 16\n num_key_value_heads: int = 8\n max_position_embeddings: int = 32768\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [151643, 151645]\n\n@dataclass\nclass Qwen3_06B_ACE15_Config:\n vocab_size: int = 151669\n hidden_size: int = 1024\n intermediate_size: int = 3072\n num_hidden_layers: int = 28\n num_attention_heads: int = 16\n num_key_value_heads: int = 8\n max_position_embeddings: int = 32768\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [151643, 151645]\n\n@dataclass\nclass Qwen3_2B_ACE15_lm_Config:\n vocab_size: int = 217204\n hidden_size: int = 2048\n intermediate_size: int = 6144\n num_hidden_layers: int = 28\n num_attention_heads: int = 16\n num_key_value_heads: int = 8\n max_position_embeddings: int = 40960\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [151643, 151645]\n\n@dataclass\nclass Qwen3_4B_ACE15_lm_Config:\n vocab_size: int = 217204\n hidden_size: int = 2560\n intermediate_size: int = 9728\n num_hidden_layers: int = 36\n num_attention_heads: int = 32\n num_key_value_heads: int = 8\n max_position_embeddings: int = 40960\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [151643, 151645]\n\n@dataclass\nclass Qwen3_4BConfig:\n vocab_size: int = 151936\n hidden_size: int = 2560\n intermediate_size: int = 9728\n num_hidden_layers: int = 36\n num_attention_heads: int = 32\n num_key_value_heads: int = 8\n max_position_embeddings: int = 40960\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [151643, 151645]\n\n@dataclass\nclass Qwen3_8BConfig:\n vocab_size: int = 151936\n hidden_size: int = 4096\n intermediate_size: int = 12288\n num_hidden_layers: int = 36\n num_attention_heads: int = 32\n num_key_value_heads: int = 8\n max_position_embeddings: int = 40960\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [151643, 151645]\n\n@dataclass\nclass Ovis25_2BConfig:\n vocab_size: int = 151936\n hidden_size: int = 2048\n intermediate_size: int = 6144\n num_hidden_layers: int = 28\n num_attention_heads: int = 16\n num_key_value_heads: int = 8\n max_position_embeddings: int = 40960\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n\n@dataclass\nclass Qwen25_7BVLI_Config:\n vocab_size: int = 152064\n hidden_size: int = 3584\n intermediate_size: int = 18944\n num_hidden_layers: int = 28\n num_attention_heads: int = 28\n num_key_value_heads: int = 4\n max_position_embeddings: int = 128000\n rms_norm_eps: float = 1e-6\n rope_theta: float = 1000000.0\n transformer_type: str = \"llama\"\n head_dim = 128\n rms_norm_add = False\n mlp_activation = \"silu\"\n qkv_bias = True\n rope_dims = [16, 24, 24]\n q_norm = None\n k_norm = None\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n\n@dataclass\nclass Gemma2_2B_Config:\n vocab_size: int = 256000\n hidden_size: int = 2304\n intermediate_size: int = 9216\n num_hidden_layers: int = 26\n num_attention_heads: int = 8\n num_key_value_heads: int = 4\n max_position_embeddings: int = 8192\n rms_norm_eps: float = 1e-6\n rope_theta: float = 10000.0\n transformer_type: str = \"gemma2\"\n head_dim = 256\n rms_norm_add = True\n mlp_activation = \"gelu_pytorch_tanh\"\n qkv_bias = False\n rope_dims = None\n q_norm = None\n k_norm = None\n sliding_attention = None\n rope_scale = None\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [1]\n\n@dataclass\nclass Gemma3_4B_Config:\n vocab_size: int = 262208\n hidden_size: int = 2560\n intermediate_size: int = 10240\n num_hidden_layers: int = 34\n num_attention_heads: int = 8\n num_key_value_heads: int = 4\n max_position_embeddings: int = 131072\n rms_norm_eps: float = 1e-6\n rope_theta = [1000000.0, 10000.0]\n transformer_type: str = \"gemma3\"\n head_dim = 256\n rms_norm_add = True\n mlp_activation = \"gelu_pytorch_tanh\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n sliding_attention = [1024, 1024, 1024, 1024, 1024, False]\n rope_scale = [8.0, 1.0]\n final_norm: bool = True\n lm_head: bool = False\n stop_tokens = [1, 106]\n\nGEMMA3_VISION_CONFIG = {\"num_channels\": 3, \"hidden_act\": \"gelu_pytorch_tanh\", \"hidden_size\": 1152, \"image_size\": 896, \"intermediate_size\": 4304, \"model_type\": \"siglip_vision_model\", \"num_attention_heads\": 16, \"num_hidden_layers\": 27, \"patch_size\": 14}\n\n@dataclass\nclass Gemma3_4B_Vision_Config(Gemma3_4B_Config):\n vision_config = GEMMA3_VISION_CONFIG\n mm_tokens_per_image = 256\n\n@dataclass\nclass Gemma3_12B_Config:\n vocab_size: int = 262208\n hidden_size: int = 3840\n intermediate_size: int = 15360\n num_hidden_layers: int = 48\n num_attention_heads: int = 16\n num_key_value_heads: int = 8\n max_position_embeddings: int = 131072\n rms_norm_eps: float = 1e-6\n rope_theta = [1000000.0, 10000.0]\n transformer_type: str = \"gemma3\"\n head_dim = 256\n rms_norm_add = True\n mlp_activation = \"gelu_pytorch_tanh\"\n qkv_bias = False\n rope_dims = None\n q_norm = \"gemma3\"\n k_norm = \"gemma3\"\n sliding_attention = [1024, 1024, 1024, 1024, 1024, False]\n rope_scale = [8.0, 1.0]\n final_norm: bool = True\n lm_head: bool = False\n vision_config = GEMMA3_VISION_CONFIG\n mm_tokens_per_image = 256\n stop_tokens = [1, 106]\n\nclass RMSNorm(nn.Module):\n def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):\n super().__init__()\n self.eps = eps\n self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))\n self.add = add\n\n def forward(self, x: torch.Tensor):\n w = self.weight\n if self.add:\n w = w + 1.0\n\n return comfy.ldm.common_dit.rms_norm(x, w, self.eps)\n\n\n\ndef precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None):\n if not isinstance(theta, list):\n theta = [theta]\n\n out = []\n for index, t in enumerate(theta):\n theta_numerator = torch.arange(0, head_dim, 2, device=device).float()\n inv_freq = 1.0 / (t ** (theta_numerator / head_dim))\n\n if rope_scale is not None:\n if isinstance(rope_scale, list):\n inv_freq /= rope_scale[index]\n else:\n inv_freq /= rope_scale\n\n inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)\n position_ids_expanded = position_ids[:, None, :].float()\n freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)\n emb = torch.cat((freqs, freqs), dim=-1)\n cos = emb.cos()\n sin = emb.sin()\n if rope_dims is not None and position_ids.shape[0] > 1:\n mrope_section = rope_dims * 2\n cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)\n sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)\n else:\n cos = cos.unsqueeze(1)\n sin = sin.unsqueeze(1)\n sin_split = sin.shape[-1] // 2\n out.append((cos, sin[..., : sin_split], -sin[..., sin_split :]))\n\n if len(out) == 1:\n return out[0]\n\n return out\n\ndef apply_rope(xq, xk, freqs_cis):\n org_dtype = xq.dtype\n cos = freqs_cis[0]\n sin = freqs_cis[1]\n nsin = freqs_cis[2]\n\n q_embed = (xq * cos)\n q_split = q_embed.shape[-1] // 2\n q_embed[..., : q_split].addcmul_(xq[..., q_split :], nsin)\n q_embed[..., q_split :].addcmul_(xq[..., : q_split], sin)\n\n k_embed = (xk * cos)\n k_split = k_embed.shape[-1] // 2\n k_embed[..., : k_split].addcmul_(xk[..., k_split :], nsin)\n k_embed[..., k_split :].addcmul_(xk[..., : k_split], sin)\n\n return q_embed.to(org_dtype), k_embed.to(org_dtype)\n\n\nclass Attention(nn.Module):\n def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):\n super().__init__()\n self.num_heads = config.num_attention_heads\n self.num_kv_heads = config.num_key_value_heads\n self.hidden_size = config.hidden_size\n\n self.head_dim = config.head_dim\n self.inner_size = self.num_heads * self.head_dim\n\n ops = ops or nn\n self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype)\n self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)\n self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)\n self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)\n\n self.q_norm = None\n self.k_norm = None\n\n if config.q_norm == \"gemma3\":\n self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n if config.k_norm == \"gemma3\":\n self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n freqs_cis: Optional[torch.Tensor] = None,\n optimized_attention=None,\n past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,\n sliding_window: Optional[int] = None,\n ):\n batch_size, seq_length, _ = hidden_states.shape\n\n xq = self.q_proj(hidden_states)\n xk = self.k_proj(hidden_states)\n xv = self.v_proj(hidden_states)\n\n xq = xq.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)\n xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)\n xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)\n\n if self.q_norm is not None:\n xq = self.q_norm(xq)\n if self.k_norm is not None:\n xk = self.k_norm(xk)\n\n xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)\n\n present_key_value = None\n if past_key_value is not None:\n index = 0\n num_tokens = xk.shape[2]\n if len(past_key_value) > 0:\n past_key, past_value, index = past_key_value\n if past_key.shape[2] >= (index + num_tokens):\n past_key[:, :, index:index + xk.shape[2]] = xk\n past_value[:, :, index:index + xv.shape[2]] = xv\n xk = past_key[:, :, :index + xk.shape[2]]\n xv = past_value[:, :, :index + xv.shape[2]]\n present_key_value = (past_key, past_value, index + num_tokens)\n else:\n xk = torch.cat((past_key[:, :, :index], xk), dim=2)\n xv = torch.cat((past_value[:, :, :index], xv), dim=2)\n present_key_value = (xk, xv, index + num_tokens)\n else:\n present_key_value = (xk, xv, index + num_tokens)\n\n if sliding_window is not None and xk.shape[2] > sliding_window:\n xk = xk[:, :, -sliding_window:]\n xv = xv[:, :, -sliding_window:]\n attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None\n\n xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)\n xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)\n\n output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)\n return self.o_proj(output), present_key_value\n\nclass MLP(nn.Module):\n def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):\n super().__init__()\n ops = ops or nn\n self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)\n self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)\n self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype)\n if config.mlp_activation == \"silu\":\n self.activation = torch.nn.functional.silu\n elif config.mlp_activation == \"gelu_pytorch_tanh\":\n self.activation = lambda a: torch.nn.functional.gelu(a, approximate=\"tanh\")\n\n def forward(self, x):\n return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))\n\nclass TransformerBlock(nn.Module):\n def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):\n super().__init__()\n self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)\n self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)\n self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)\n self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)\n\n def forward(\n self,\n x: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n freqs_cis: Optional[torch.Tensor] = None,\n optimized_attention=None,\n past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,\n ):\n # Self Attention\n residual = x\n x = self.input_layernorm(x)\n x, present_key_value = self.self_attn(\n hidden_states=x,\n attention_mask=attention_mask,\n freqs_cis=freqs_cis,\n optimized_attention=optimized_attention,\n past_key_value=past_key_value,\n )\n x = residual + x\n\n # MLP\n residual = x\n x = self.post_attention_layernorm(x)\n x = self.mlp(x)\n x = residual + x\n\n return x, present_key_value\n\nclass TransformerBlockGemma2(nn.Module):\n def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):\n super().__init__()\n self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)\n self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)\n self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n\n if config.sliding_attention is not None:\n self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)]\n else:\n self.sliding_attention = False\n\n self.transformer_type = config.transformer_type\n\n def forward(\n self,\n x: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n freqs_cis: Optional[torch.Tensor] = None,\n optimized_attention=None,\n past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,\n ):\n sliding_window = None\n if self.transformer_type == 'gemma3':\n if self.sliding_attention:\n sliding_window = self.sliding_attention\n if x.shape[1] > self.sliding_attention:\n sliding_mask = torch.full((x.shape[1], x.shape[1]), torch.finfo(x.dtype).min, device=x.device, dtype=x.dtype)\n sliding_mask.tril_(diagonal=-self.sliding_attention)\n if attention_mask is not None:\n attention_mask = attention_mask + sliding_mask\n else:\n attention_mask = sliding_mask\n freqs_cis = freqs_cis[1]\n else:\n freqs_cis = freqs_cis[0]\n\n # Self Attention\n residual = x\n x = self.input_layernorm(x)\n x, present_key_value = self.self_attn(\n hidden_states=x,\n attention_mask=attention_mask,\n freqs_cis=freqs_cis,\n optimized_attention=optimized_attention,\n past_key_value=past_key_value,\n sliding_window=sliding_window,\n )\n\n x = self.post_attention_layernorm(x)\n x = residual + x\n\n # MLP\n residual = x\n x = self.pre_feedforward_layernorm(x)\n x = self.mlp(x)\n x = self.post_feedforward_layernorm(x)\n x = residual + x\n\n return x, present_key_value\n\nclass Llama2_(nn.Module):\n def __init__(self, config, device=None, dtype=None, ops=None):\n super().__init__()\n self.config = config\n self.vocab_size = config.vocab_size\n\n self.embed_tokens = ops.Embedding(\n config.vocab_size,\n config.hidden_size,\n device=device,\n dtype=dtype\n )\n if self.config.transformer_type == \"gemma2\" or self.config.transformer_type == \"gemma3\":\n transformer = TransformerBlockGemma2\n self.normalize_in = True\n else:\n transformer = TransformerBlock\n self.normalize_in = False\n\n self.layers = nn.ModuleList([\n transformer(config, index=i, device=device, dtype=dtype, ops=ops)\n for i in range(config.num_hidden_layers)\n ])\n\n if config.final_norm:\n self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n else:\n self.norm = None\n\n if config.lm_head:\n self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)\n\n def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None):\n if embeds is not None:\n x = embeds\n else:\n x = self.embed_tokens(x, out_dtype=dtype)\n\n if self.normalize_in:\n x *= self.config.hidden_size ** 0.5\n\n seq_len = x.shape[1]\n past_len = 0\n if past_key_values is not None and len(past_key_values) > 0:\n past_len = past_key_values[0][2]\n\n if position_ids is None:\n position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0)\n\n freqs_cis = precompute_freqs_cis(self.config.head_dim,\n position_ids,\n self.config.rope_theta,\n self.config.rope_scale,\n self.config.rope_dims,\n device=x.device)\n\n mask = None\n if attention_mask is not None:\n mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1])\n mask = mask.masked_fill(mask.to(torch.bool), torch.finfo(x.dtype).min / 4)\n\n if seq_len > 1:\n causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(torch.finfo(x.dtype).min / 4).triu_(1)\n if mask is not None:\n mask += causal_mask\n else:\n mask = causal_mask\n\n optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)\n\n intermediate = None\n all_intermediate = None\n only_layers = None\n if intermediate_output is not None:\n if isinstance(intermediate_output, list):\n all_intermediate = []\n only_layers = set(intermediate_output)\n elif intermediate_output == \"all\":\n all_intermediate = []\n intermediate_output = None\n elif intermediate_output < 0:\n intermediate_output = len(self.layers) + intermediate_output\n\n next_key_values = []\n for i, layer in enumerate(self.layers):\n if all_intermediate is not None:\n if only_layers is None or (i in only_layers):\n all_intermediate.append(x.unsqueeze(1).clone())\n\n past_kv = None\n if past_key_values is not None:\n past_kv = past_key_values[i] if len(past_key_values) > 0 else []\n\n x, current_kv = layer(\n x=x,\n attention_mask=mask,\n freqs_cis=freqs_cis,\n optimized_attention=optimized_attention,\n past_key_value=past_kv,\n )\n\n if current_kv is not None:\n next_key_values.append(current_kv)\n\n if i == intermediate_output:\n intermediate = x.clone()\n\n if self.norm is not None:\n x = self.norm(x)\n\n if all_intermediate is not None:\n if only_layers is None or ((i + 1) in only_layers):\n all_intermediate.append(x.unsqueeze(1).clone())\n\n if all_intermediate is not None:\n intermediate = torch.cat(all_intermediate, dim=1)\n\n if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:\n intermediate = self.norm(intermediate)\n\n if len(next_key_values) > 0:\n return x, intermediate, next_key_values\n else:\n return x, intermediate\n\n\nclass Gemma3MultiModalProjector(torch.nn.Module):\n def __init__(self, config, dtype, device, operations):\n super().__init__()\n\n self.mm_input_projection_weight = nn.Parameter(\n torch.empty(config.vision_config[\"hidden_size\"], config.hidden_size, device=device, dtype=dtype)\n )\n\n self.mm_soft_emb_norm = RMSNorm(config.vision_config[\"hidden_size\"], eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)\n\n self.patches_per_image = int(config.vision_config[\"image_size\"] // config.vision_config[\"patch_size\"])\n self.tokens_per_side = int(config.mm_tokens_per_image**0.5)\n self.kernel_size = self.patches_per_image // self.tokens_per_side\n self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size)\n\n def forward(self, vision_outputs: torch.Tensor):\n batch_size, _, seq_length = vision_outputs.shape\n\n reshaped_vision_outputs = vision_outputs.transpose(1, 2)\n reshaped_vision_outputs = reshaped_vision_outputs.reshape(\n batch_size, seq_length, self.patches_per_image, self.patches_per_image\n )\n reshaped_vision_outputs = reshaped_vision_outputs.contiguous()\n\n pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)\n pooled_vision_outputs = pooled_vision_outputs.flatten(2)\n pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)\n\n normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)\n\n projected_vision_outputs = torch.matmul(normed_vision_outputs, comfy.model_management.cast_to_device(self.mm_input_projection_weight, device=normed_vision_outputs.device, dtype=normed_vision_outputs.dtype))\n return projected_vision_outputs.type_as(vision_outputs)\n\n\nclass BaseLlama:\n def get_input_embeddings(self):\n return self.model.embed_tokens\n\n def set_input_embeddings(self, embeddings):\n self.model.embed_tokens = embeddings\n\n def forward(self, input_ids, *args, **kwargs):\n return self.model(input_ids, *args, **kwargs)\n\nclass BaseGenerate:\n def logits(self, x):\n input = x[:, -1:]\n if hasattr(self.model, \"lm_head\"):\n module = self.model.lm_head\n else:\n module = self.model.embed_tokens\n\n offload_stream = None\n if module.comfy_cast_weights:\n weight, _, offload_stream = comfy.ops.cast_bias_weight(module, input, offloadable=True)\n else:\n weight = self.model.embed_tokens.weight.to(x)\n\n x = torch.nn.functional.linear(input, weight, None)\n\n comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)\n return x\n\n def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0):\n device = embeds.device\n model_config = self.model.config\n\n if stop_tokens is None:\n stop_tokens = self.model.config.stop_tokens\n\n if execution_dtype is None:\n if comfy.model_management.should_use_bf16(device):\n execution_dtype = torch.bfloat16\n else:\n execution_dtype = torch.float32\n embeds = embeds.to(execution_dtype)\n\n if embeds.ndim == 2:\n embeds = embeds.unsqueeze(0)\n\n past_key_values = [] #kv_cache init\n max_cache_len = embeds.shape[1] + max_length\n for x in range(model_config.num_hidden_layers):\n past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),\n torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))\n\n generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None\n\n generated_token_ids = []\n pbar = comfy.utils.ProgressBar(max_length)\n\n # Generation loop\n for step in tqdm(range(max_length), desc=\"Generating tokens\"):\n x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values)\n logits = self.logits(x)[:, -1]\n next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample)\n token_id = next_token[0].item()\n generated_token_ids.append(token_id)\n\n embeds = self.model.embed_tokens(next_token).to(execution_dtype)\n pbar.update(1)\n\n if token_id in stop_tokens:\n break\n\n return generated_token_ids\n\n def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True):\n\n if not do_sample or temperature == 0.0:\n return torch.argmax(logits, dim=-1, keepdim=True)\n\n # Sampling mode\n if repetition_penalty != 1.0:\n for i in range(logits.shape[0]):\n for token_id in set(token_history):\n logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty\n\n if temperature != 1.0:\n logits = logits / temperature\n\n if top_k > 0:\n indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]\n logits[indices_to_remove] = torch.finfo(logits.dtype).min\n\n if min_p > 0.0:\n probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)\n top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)\n min_threshold = min_p * top_probs\n indices_to_remove = probs_before_filter < min_threshold\n logits[indices_to_remove] = torch.finfo(logits.dtype).min\n\n if top_p < 1.0:\n sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)\n sorted_indices_to_remove = cumulative_probs > top_p\n sorted_indices_to_remove[..., 0] = False\n indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)\n indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)\n logits[indices_to_remove] = torch.finfo(logits.dtype).min\n\n probs = torch.nn.functional.softmax(logits, dim=-1)\n\n return torch.multinomial(probs, num_samples=1, generator=generator)\n\nclass BaseQwen3:\n def logits(self, x):\n input = x[:, -1:]\n module = self.model.embed_tokens\n\n offload_stream = None\n if module.comfy_cast_weights:\n weight, _, offload_stream = comfy.ops.cast_bias_weight(module, input, offloadable=True)\n else:\n weight = self.model.embed_tokens.weight.to(x)\n\n x = torch.nn.functional.linear(input, weight, None)\n\n comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)\n return x\n\nclass Llama2(BaseLlama, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Llama2Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Mistral3Small24B(BaseLlama, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Mistral3Small24BConfig(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen25_3B(BaseLlama, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen25_3BConfig(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen3_06B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen3_06BConfig(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen3_06B_ACE15(BaseLlama, BaseQwen3, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen3_06B_ACE15_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen3_2B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen3_2B_ACE15_lm_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen3_4B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen3_4BConfig(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen3_4B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen3_4B_ACE15_lm_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen3_8B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen3_8BConfig(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Ovis25_2B(BaseLlama, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Ovis25_2BConfig(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Qwen25_7BVLI_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.visual = qwen_vl.Qwen2VLVisionTransformer(hidden_size=1280, output_hidden_size=config.hidden_size, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\n # todo: should this be tied or not?\n #self.lm_head = operations.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)\n\n def preprocess_embed(self, embed, device):\n if embed[\"type\"] == \"image\":\n image, grid = qwen_vl.process_qwen2vl_images(embed[\"data\"])\n return self.visual(image.to(device, dtype=torch.float32), grid), grid\n return None, None\n\n def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):\n grid = None\n position_ids = None\n offset = 0\n for e in embeds_info:\n if e.get(\"type\") == \"image\":\n grid = e.get(\"extra\", None)\n start = e.get(\"index\")\n if position_ids is None:\n position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)\n position_ids[:, :start] = torch.arange(0, start, device=embeds.device)\n end = e.get(\"size\") + start\n len_max = int(grid.max()) // 2\n start_next = len_max + start\n position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)\n position_ids[0, start:end] = start + offset\n max_d = int(grid[0][1]) // 2\n position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]\n max_d = int(grid[0][2]) // 2\n position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]\n offset += len_max - (end - start)\n\n if grid is None:\n position_ids = None\n\n return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids)\n\nclass Gemma2_2B(BaseLlama, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Gemma2_2B_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Gemma3_4B(BaseLlama, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Gemma3_4B_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n\nclass Gemma3_4B_Vision(BaseLlama, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Gemma3_4B_Vision_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.dtype = dtype\n self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations)\n self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations)\n self.image_size = config.vision_config[\"image_size\"]\n\n def preprocess_embed(self, embed, device):\n if embed[\"type\"] == \"image\":\n image = comfy.clip_model.clip_preprocess(embed[\"data\"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True)\n return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None\n return None, None\n\nclass Gemma3_12B(BaseLlama, BaseGenerate, torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n config = Gemma3_12B_Config(**config_dict)\n self.num_layers = config.num_hidden_layers\n\n self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)\n self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations)\n self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations)\n self.dtype = dtype\n self.image_size = config.vision_config[\"image_size\"]\n\n def preprocess_embed(self, embed, device):\n if embed[\"type\"] == \"image\":\n image = comfy.clip_model.clip_preprocess(embed[\"data\"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True)\n return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None\n return None, None\n" }, { "path": "comfy/text_encoders/llama_tokenizer/tokenizer.json", "content": "{\n \"version\": \"1.0\",\n \"truncation\": null,\n \"padding\": null,\n \"added_tokens\": [\n {\n \"id\": 128000,\n \"content\": \"<|begin_of_text|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128001,\n \"content\": \"<|end_of_text|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128002,\n \"content\": \"<|reserved_special_token_0|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128003,\n \"content\": \"<|reserved_special_token_1|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128004,\n \"content\": \"<|reserved_special_token_2|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128005,\n \"content\": \"<|reserved_special_token_3|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128006,\n \"content\": \"<|start_header_id|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128007,\n \"content\": \"<|end_header_id|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128008,\n \"content\": \"<|reserved_special_token_4|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128009,\n \"content\": \"<|eot_id|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128010,\n \"content\": \"<|reserved_special_token_5|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128011,\n \"content\": \"<|reserved_special_token_6|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128012,\n \"content\": \"<|reserved_special_token_7|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128013,\n \"content\": \"<|reserved_special_token_8|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128014,\n \"content\": \"<|reserved_special_token_9|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128015,\n \"content\": \"<|reserved_special_token_10|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128016,\n \"content\": \"<|reserved_special_token_11|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128017,\n \"content\": \"<|reserved_special_token_12|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128018,\n \"content\": \"<|reserved_special_token_13|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128019,\n \"content\": \"<|reserved_special_token_14|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128020,\n \"content\": \"<|reserved_special_token_15|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128021,\n \"content\": \"<|reserved_special_token_16|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128022,\n \"content\": \"<|reserved_special_token_17|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128023,\n \"content\": \"<|reserved_special_token_18|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128024,\n \"content\": \"<|reserved_special_token_19|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128025,\n \"content\": \"<|reserved_special_token_20|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128026,\n \"content\": \"<|reserved_special_token_21|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128027,\n \"content\": \"<|reserved_special_token_22|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128028,\n \"content\": \"<|reserved_special_token_23|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128029,\n \"content\": \"<|reserved_special_token_24|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128030,\n \"content\": \"<|reserved_special_token_25|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128031,\n \"content\": \"<|reserved_special_token_26|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128032,\n \"content\": \"<|reserved_special_token_27|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128033,\n \"content\": \"<|reserved_special_token_28|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128034,\n \"content\": \"<|reserved_special_token_29|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128035,\n \"content\": \"<|reserved_special_token_30|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128036,\n \"content\": \"<|reserved_special_token_31|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128037,\n \"content\": \"<|reserved_special_token_32|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128038,\n \"content\": \"<|reserved_special_token_33|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128039,\n \"content\": \"<|reserved_special_token_34|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128040,\n \"content\": \"<|reserved_special_token_35|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128041,\n \"content\": \"<|reserved_special_token_36|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128042,\n \"content\": \"<|reserved_special_token_37|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128043,\n \"content\": \"<|reserved_special_token_38|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128044,\n \"content\": \"<|reserved_special_token_39|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128045,\n \"content\": \"<|reserved_special_token_40|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128046,\n \"content\": \"<|reserved_special_token_41|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128047,\n \"content\": \"<|reserved_special_token_42|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128048,\n \"content\": \"<|reserved_special_token_43|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128049,\n \"content\": \"<|reserved_special_token_44|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128050,\n \"content\": \"<|reserved_special_token_45|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128051,\n \"content\": \"<|reserved_special_token_46|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128052,\n \"content\": \"<|reserved_special_token_47|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128053,\n \"content\": \"<|reserved_special_token_48|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128054,\n \"content\": \"<|reserved_special_token_49|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128055,\n \"content\": \"<|reserved_special_token_50|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128056,\n \"content\": \"<|reserved_special_token_51|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128057,\n \"content\": \"<|reserved_special_token_52|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128058,\n \"content\": \"<|reserved_special_token_53|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128059,\n \"content\": \"<|reserved_special_token_54|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128060,\n \"content\": \"<|reserved_special_token_55|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128061,\n \"content\": \"<|reserved_special_token_56|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128062,\n \"content\": \"<|reserved_special_token_57|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128063,\n \"content\": \"<|reserved_special_token_58|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128064,\n \"content\": \"<|reserved_special_token_59|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128065,\n \"content\": \"<|reserved_special_token_60|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128066,\n \"content\": \"<|reserved_special_token_61|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128067,\n \"content\": \"<|reserved_special_token_62|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128068,\n \"content\": \"<|reserved_special_token_63|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128069,\n \"content\": \"<|reserved_special_token_64|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128070,\n \"content\": \"<|reserved_special_token_65|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128071,\n \"content\": \"<|reserved_special_token_66|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128072,\n \"content\": \"<|reserved_special_token_67|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128073,\n \"content\": \"<|reserved_special_token_68|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128074,\n \"content\": \"<|reserved_special_token_69|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128075,\n \"content\": \"<|reserved_special_token_70|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128076,\n \"content\": \"<|reserved_special_token_71|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128077,\n \"content\": \"<|reserved_special_token_72|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128078,\n \"content\": \"<|reserved_special_token_73|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128079,\n \"content\": \"<|reserved_special_token_74|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128080,\n \"content\": \"<|reserved_special_token_75|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128081,\n \"content\": \"<|reserved_special_token_76|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128082,\n \"content\": \"<|reserved_special_token_77|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128083,\n \"content\": \"<|reserved_special_token_78|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128084,\n \"content\": \"<|reserved_special_token_79|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128085,\n \"content\": \"<|reserved_special_token_80|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128086,\n \"content\": \"<|reserved_special_token_81|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128087,\n \"content\": \"<|reserved_special_token_82|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128088,\n \"content\": \"<|reserved_special_token_83|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128089,\n \"content\": \"<|reserved_special_token_84|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128090,\n \"content\": \"<|reserved_special_token_85|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128091,\n \"content\": \"<|reserved_special_token_86|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128092,\n \"content\": \"<|reserved_special_token_87|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128093,\n \"content\": \"<|reserved_special_token_88|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128094,\n \"content\": \"<|reserved_special_token_89|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128095,\n \"content\": \"<|reserved_special_token_90|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128096,\n \"content\": \"<|reserved_special_token_91|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128097,\n \"content\": \"<|reserved_special_token_92|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128098,\n \"content\": \"<|reserved_special_token_93|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128099,\n \"content\": \"<|reserved_special_token_94|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128100,\n \"content\": \"<|reserved_special_token_95|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128101,\n \"content\": \"<|reserved_special_token_96|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128102,\n \"content\": \"<|reserved_special_token_97|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128103,\n \"content\": \"<|reserved_special_token_98|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128104,\n \"content\": \"<|reserved_special_token_99|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128105,\n \"content\": \"<|reserved_special_token_100|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128106,\n \"content\": \"<|reserved_special_token_101|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128107,\n \"content\": \"<|reserved_special_token_102|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128108,\n \"content\": \"<|reserved_special_token_103|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128109,\n \"content\": \"<|reserved_special_token_104|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128110,\n \"content\": \"<|reserved_special_token_105|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128111,\n \"content\": \"<|reserved_special_token_106|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128112,\n \"content\": \"<|reserved_special_token_107|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128113,\n \"content\": \"<|reserved_special_token_108|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128114,\n \"content\": \"<|reserved_special_token_109|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128115,\n \"content\": \"<|reserved_special_token_110|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128116,\n \"content\": \"<|reserved_special_token_111|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128117,\n \"content\": \"<|reserved_special_token_112|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128118,\n \"content\": \"<|reserved_special_token_113|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128119,\n \"content\": \"<|reserved_special_token_114|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128120,\n \"content\": \"<|reserved_special_token_115|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128121,\n \"content\": \"<|reserved_special_token_116|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128122,\n \"content\": \"<|reserved_special_token_117|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128123,\n \"content\": \"<|reserved_special_token_118|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128124,\n \"content\": \"<|reserved_special_token_119|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128125,\n \"content\": \"<|reserved_special_token_120|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128126,\n \"content\": \"<|reserved_special_token_121|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128127,\n \"content\": \"<|reserved_special_token_122|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128128,\n \"content\": \"<|reserved_special_token_123|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128129,\n \"content\": \"<|reserved_special_token_124|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128130,\n \"content\": \"<|reserved_special_token_125|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128131,\n \"content\": \"<|reserved_special_token_126|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128132,\n \"content\": \"<|reserved_special_token_127|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128133,\n \"content\": \"<|reserved_special_token_128|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128134,\n \"content\": \"<|reserved_special_token_129|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128135,\n \"content\": \"<|reserved_special_token_130|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128136,\n \"content\": \"<|reserved_special_token_131|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128137,\n \"content\": \"<|reserved_special_token_132|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128138,\n \"content\": \"<|reserved_special_token_133|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128139,\n \"content\": \"<|reserved_special_token_134|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128140,\n \"content\": \"<|reserved_special_token_135|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128141,\n \"content\": \"<|reserved_special_token_136|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128142,\n \"content\": \"<|reserved_special_token_137|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128143,\n \"content\": \"<|reserved_special_token_138|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128144,\n \"content\": \"<|reserved_special_token_139|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128145,\n \"content\": \"<|reserved_special_token_140|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128146,\n \"content\": \"<|reserved_special_token_141|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128147,\n \"content\": \"<|reserved_special_token_142|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128148,\n \"content\": \"<|reserved_special_token_143|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128149,\n \"content\": \"<|reserved_special_token_144|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128150,\n \"content\": \"<|reserved_special_token_145|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128151,\n \"content\": \"<|reserved_special_token_146|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128152,\n \"content\": \"<|reserved_special_token_147|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128153,\n \"content\": \"<|reserved_special_token_148|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128154,\n \"content\": \"<|reserved_special_token_149|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128155,\n \"content\": \"<|reserved_special_token_150|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128156,\n \"content\": \"<|reserved_special_token_151|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128157,\n \"content\": \"<|reserved_special_token_152|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128158,\n \"content\": \"<|reserved_special_token_153|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128159,\n \"content\": \"<|reserved_special_token_154|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128160,\n \"content\": \"<|reserved_special_token_155|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128161,\n \"content\": \"<|reserved_special_token_156|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128162,\n \"content\": \"<|reserved_special_token_157|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128163,\n \"content\": \"<|reserved_special_token_158|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128164,\n \"content\": \"<|reserved_special_token_159|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128165,\n \"content\": \"<|reserved_special_token_160|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128166,\n \"content\": \"<|reserved_special_token_161|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128167,\n \"content\": \"<|reserved_special_token_162|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128168,\n \"content\": \"<|reserved_special_token_163|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128169,\n \"content\": \"<|reserved_special_token_164|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128170,\n \"content\": \"<|reserved_special_token_165|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128171,\n \"content\": \"<|reserved_special_token_166|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128172,\n \"content\": \"<|reserved_special_token_167|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128173,\n \"content\": \"<|reserved_special_token_168|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128174,\n \"content\": \"<|reserved_special_token_169|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128175,\n \"content\": \"<|reserved_special_token_170|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128176,\n \"content\": \"<|reserved_special_token_171|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128177,\n \"content\": \"<|reserved_special_token_172|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128178,\n \"content\": \"<|reserved_special_token_173|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128179,\n \"content\": \"<|reserved_special_token_174|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128180,\n \"content\": \"<|reserved_special_token_175|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128181,\n \"content\": \"<|reserved_special_token_176|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128182,\n \"content\": \"<|reserved_special_token_177|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128183,\n \"content\": \"<|reserved_special_token_178|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128184,\n \"content\": \"<|reserved_special_token_179|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128185,\n \"content\": \"<|reserved_special_token_180|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128186,\n \"content\": \"<|reserved_special_token_181|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128187,\n \"content\": \"<|reserved_special_token_182|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128188,\n \"content\": \"<|reserved_special_token_183|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128189,\n \"content\": \"<|reserved_special_token_184|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128190,\n \"content\": \"<|reserved_special_token_185|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128191,\n \"content\": \"<|reserved_special_token_186|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128192,\n \"content\": \"<|reserved_special_token_187|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128193,\n \"content\": \"<|reserved_special_token_188|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128194,\n \"content\": \"<|reserved_special_token_189|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128195,\n \"content\": \"<|reserved_special_token_190|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128196,\n \"content\": \"<|reserved_special_token_191|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128197,\n \"content\": \"<|reserved_special_token_192|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128198,\n \"content\": \"<|reserved_special_token_193|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128199,\n \"content\": \"<|reserved_special_token_194|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128200,\n \"content\": \"<|reserved_special_token_195|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128201,\n \"content\": \"<|reserved_special_token_196|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128202,\n \"content\": \"<|reserved_special_token_197|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128203,\n \"content\": \"<|reserved_special_token_198|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128204,\n \"content\": \"<|reserved_special_token_199|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128205,\n \"content\": \"<|reserved_special_token_200|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128206,\n \"content\": \"<|reserved_special_token_201|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128207,\n \"content\": \"<|reserved_special_token_202|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128208,\n \"content\": \"<|reserved_special_token_203|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128209,\n \"content\": \"<|reserved_special_token_204|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128210,\n \"content\": \"<|reserved_special_token_205|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128211,\n \"content\": \"<|reserved_special_token_206|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128212,\n \"content\": \"<|reserved_special_token_207|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128213,\n \"content\": \"<|reserved_special_token_208|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128214,\n \"content\": \"<|reserved_special_token_209|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128215,\n \"content\": \"<|reserved_special_token_210|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128216,\n \"content\": \"<|reserved_special_token_211|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128217,\n \"content\": \"<|reserved_special_token_212|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128218,\n \"content\": \"<|reserved_special_token_213|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128219,\n \"content\": \"<|reserved_special_token_214|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128220,\n \"content\": \"<|reserved_special_token_215|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128221,\n \"content\": \"<|reserved_special_token_216|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128222,\n \"content\": \"<|reserved_special_token_217|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128223,\n \"content\": \"<|reserved_special_token_218|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128224,\n \"content\": \"<|reserved_special_token_219|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128225,\n \"content\": \"<|reserved_special_token_220|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128226,\n \"content\": \"<|reserved_special_token_221|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128227,\n \"content\": \"<|reserved_special_token_222|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128228,\n \"content\": \"<|reserved_special_token_223|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128229,\n \"content\": \"<|reserved_special_token_224|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128230,\n \"content\": \"<|reserved_special_token_225|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128231,\n \"content\": \"<|reserved_special_token_226|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128232,\n \"content\": \"<|reserved_special_token_227|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128233,\n \"content\": \"<|reserved_special_token_228|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128234,\n \"content\": \"<|reserved_special_token_229|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128235,\n \"content\": \"<|reserved_special_token_230|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128236,\n \"content\": \"<|reserved_special_token_231|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128237,\n \"content\": \"<|reserved_special_token_232|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128238,\n \"content\": \"<|reserved_special_token_233|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128239,\n \"content\": \"<|reserved_special_token_234|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128240,\n \"content\": \"<|reserved_special_token_235|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128241,\n \"content\": \"<|reserved_special_token_236|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128242,\n \"content\": \"<|reserved_special_token_237|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128243,\n \"content\": \"<|reserved_special_token_238|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128244,\n \"content\": \"<|reserved_special_token_239|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128245,\n \"content\": \"<|reserved_special_token_240|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128246,\n \"content\": \"<|reserved_special_token_241|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128247,\n \"content\": \"<|reserved_special_token_242|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128248,\n \"content\": \"<|reserved_special_token_243|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128249,\n \"content\": \"<|reserved_special_token_244|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128250,\n \"content\": \"<|reserved_special_token_245|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128251,\n \"content\": \"<|reserved_special_token_246|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128252,\n \"content\": \"<|reserved_special_token_247|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128253,\n \"content\": \"<|reserved_special_token_248|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128254,\n \"content\": \"<|reserved_special_token_249|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128255,\n \"content\": \"<|reserved_special_token_250|>\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128256,\n \"content\": \"\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128257,\n \"content\": \"\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 128258,\n \"content\": \"\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n }\n ],\n \"normalizer\": null,\n \"pre_tokenizer\": {\n \"type\": \"Sequence\",\n \"pretokenizers\": [\n {\n \"type\": \"Split\",\n \"pattern\": {\n \"Regex\": \"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\\\r\\\\n\\\\p{L}\\\\p{N}]?\\\\p{L}+|\\\\p{N}{1,3}| ?[^\\\\s\\\\p{L}\\\\p{N}]+[\\\\r\\\\n]*|\\\\s*[\\\\r\\\\n]+|\\\\s+(?!\\\\S)|\\\\s+\"\n },\n \"behavior\": \"Isolated\",\n \"invert\": false\n },\n {\n \"type\": \"ByteLevel\",\n \"add_prefix_space\": false,\n \"trim_offsets\": true,\n \"use_regex\": false\n }\n ]\n },\n \"post_processor\": {\n \"type\": \"TemplateProcessing\",\n \"single\": [\n {\n \"SpecialToken\": {\n \"id\": \"<|begin_of_text|>\",\n \"type_id\": 0\n }\n },\n {\n \"Sequence\": {\n \"id\": \"A\",\n \"type_id\": 0\n }\n }\n ],\n \"pair\": [\n {\n \"SpecialToken\": {\n \"id\": \"<|begin_of_text|>\",\n \"type_id\": 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\"aan\": 42802,\n \".sol\": 42803,\n \"_Adjust\": 42804,\n \"Ġrighteous\": 42805,\n \"ĠLogging\": 42806,\n \".filters\": 42807,\n \"_TAB\": 42808,\n \"ĉsys\": 42809,\n \"rophic\": 42810,\n \"otherapy\": 42811,\n \"ĠBrowse\": 42812,\n \"keyboard\": 42813,\n \"RON\": 42814,\n \"+\\\\\": 42815,\n \"ropped\": 42816,\n \"Ġextensively\": 42817,\n \"fk\": 42818,\n \"Ġlime\": 42819,\n \"years\": 42820,\n \"Exc\": 42821,\n \"Ġsph\": 42822,\n \"Ġcheating\": 42823,\n \"andro\": 42824,\n \"ÃŃo\": 42825,\n \"Ġprince\": 42826,\n \"oire\": 42827,\n \"ĠDestination\": 42828,\n \"ĠConverts\": 42829,\n \"Ġupstream\": 42830,\n \"oled\": 42831,\n \"Ġservants\": 42832,\n \"Ġsemantic\": 42833,\n \"Ġcrunch\": 42834,\n \"Ġeventual\": 42835,\n \"runner\": 42836,\n \"/error\": 42837,\n \"Spin\": 42838,\n \"Ġsecretly\": 42839,\n \"Ġassemble\": 42840,\n \".Person\": 42841,\n \"enderror\": 42842,\n \"_<\": 42843,\n \"Ġpendant\": 42844,\n \"Sleep\": 42845,\n \"ĠChemistry\": 42846,\n \"Ġbosses\": 42847,\n \"lk\": 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\"ाथ\": 101029,\n \"ĠÙħÙĤ\": 101030,\n \"ĠÎĶE\": 101031,\n \"ÑİÑĤÑĮ\": 101032,\n \"á»Ļc\": 101033,\n \"Ġη\": 101034,\n \"sob\": 101035,\n \"Ġtheo\": 101036,\n \"åŀ\": 101037,\n \"ĠاÙĦØ´\": 101038,\n \"à¹Ģà¸ŀ\": 101039,\n \"ÎŃÏĤ\": 101040,\n \"à¹Ģà¸Ĥ\": 101041,\n \"åĻ\": 101042,\n \"िश\": 101043,\n \"Ġباز\": 101044,\n \"ÑĢоб\": 101045,\n \"Ġγια\": 101046,\n \"με\": 101047,\n \"Ġباش\": 101048,\n \"ाà¤ĩ\": 101049,\n \"Ġquy\": 101050,\n \"λε\": 101051,\n \"اÙĥ\": 101052,\n \"ĠÑĢок\": 101053,\n \"ĠTürk\": 101054,\n \"ĠÐ¥\": 101055,\n \"ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ\": 101056,\n \"æ©\": 101057,\n \"Ġphải\": 101058,\n \"à¸Ħวาม\": 101059,\n \":::\": 101060,\n \"lÃŃ\": 101061,\n \"Ġjsou\": 101062,\n \"ÛĮÙĦ\": 101063,\n \"álnÃŃ\": 101064,\n \"ĶĶ\": 101065,\n \"íĸī\": 101066,\n \"æĥ³\": 101067,\n \"lá\": 101068,\n \"ĠÏĥÏħ\": 101069,\n \"Ñĭва\": 101070,\n \"Ġnhất\": 101071,\n \"à¸Ńม\": 101072,\n \"Û¸\": 101073,\n \"ecek\": 101074,\n \"ÑĸÑĢ\": 101075,\n \"ÙĪØ´\": 101076,\n \"λα\": 101077,\n \"ĠÎĴ\": 101078,\n \"оÑĢа\": 101079,\n \"Ùģت\": 101080,\n \"edir\": 101081,\n \"ÑĥÑħ\": 101082,\n \"ä¸ĸ\": 101083,\n \"ĠУкÑĢаÑĹ\": 101084,\n \"ĠíĶ\": 101085,\n \"άν\": 101086,\n \"Ġشر\": 101087,\n \"ĠاÙĦج\": 101088,\n \"еÑĢед\": 101089,\n \"ìĺģ\": 101090,\n \"ĠhÃłnh\": 101091,\n \"￣￣\": 101092,\n \"ме\": 101093,\n \"ÑİÑĤÑģÑı\": 101094,\n \"ĠØ¥ÙĦÙī\": 101095,\n \"ìĹħ\": 101096,\n \"Ġتر\": 101097,\n \"ком\": 101098,\n \"Ġشد\": 101099,\n \"ĠاÙĦÙĥ\": 101100,\n \"ĠÏĥÏĦο\": 101101,\n \"à¥įद\": 101102,\n \"볤\": 101103,\n \"ÑĥваннÑı\": 101104,\n \"Ġthì\": 101105,\n \"ê´Ģ\": 101106,\n \"κε\": 101107,\n \"سب\": 101108,\n \"íĥĢ\": 101109,\n \"Ġï¼ı\": 101110,\n \"Ġà¹ģละ\": 101111,\n \"ĠÏĮ\": 101112,\n \"ниÑĨ\": 101113,\n \"ĠÐĿа\": 101114,\n \"Ñıв\": 101115,\n \"lü\": 101116,\n \"ιο\": 101117,\n \"ÙĨدÙĩ\": 101118,\n \"ÙĦÙĥ\": 101119,\n \"ĠngÃły\": 101120,\n \"Ġnhân\": 101121,\n \"Ġ^{\": 101122,\n \"à¥ĥ\": 101123,\n \"Ġgerek\": 101124,\n \"ارÙĩ\": 101125,\n \"ĠcÆ¡\": 101126,\n \"Ġà¸ķ\": 101127,\n \"æĤ\": 101128,\n \"çĶ°\": 101129,\n \"à¥Īà¤Ĥ।\": 101130,\n \"ัว\": 101131,\n \"vÄĽ\": 101132,\n \"öz\": 101133,\n \"или\": 101134,\n \"Ġpháp\": 101135,\n \"ê¸Ī\": 101136,\n \"ĠÎŁ\": 101137,\n \"ĠpÅĻi\": 101138,\n \"Ġìĸ´\": 101139,\n \"Ġдол\": 101140,\n \"ÙĪرد\": 101141,\n \"à¹Ģม\": 101142,\n \"Ïĥε\": 101143,\n \"าà¸Ĺ\": 101144,\n \"oÃłi\": 101145,\n \"รม\": 101146,\n \"Û¶\": 101147,\n \"Ġà¸ļ\": 101148,\n \"iyet\": 101149,\n \"ÏĦαι\": 101150,\n \"ìĦł\": 101151,\n \"ĠεÏĢ\": 101152,\n \"िव\": 101153,\n \"ê¹Į\": 101154,\n \"га\": 101155,\n \"ĠÑģлÑĥ\": 101156,\n \"Ġhình\": 101157,\n \"ĠداÙĨ\": 101158,\n \"Ġà¤Ĺय\": 101159,\n \"ÙĬا\": 101160,\n \"èij\": 101161,\n \"à¤Ĥत\": 101162,\n \"ĠساÙĦ\": 101163,\n \"ëłĪ\": 101164,\n \"lerin\": 101165,\n \"à¥ĩत\": 101166,\n \".:.:.:.:\": 101167,\n \"Ġëħ\": 101168,\n \"ĠاÙĦØ¥\": 101169,\n \"ảng\": 101170,\n \"èĦ\": 101171,\n \"ολ\": 101172,\n \"пов\": 101173,\n \"Ġθ\": 101174,\n \"Û·\": 101175,\n \"Ġnó\": 101176,\n \"ĠdÃ¼ÅŁ\": 101177,\n \"Ġtiế\": 101178,\n \"ÙĪج\": 101179,\n \"Ġjsem\": 101180,\n \"ạng\": 101181,\n \"ãģĤãĤĭ\": 101182,\n \"à¸Ńà¸ļ\": 101183,\n \"ÙĪÙĬ\": 101184,\n \"à¤ķर\": 101185,\n \"Ġде\": 101186,\n \"¯¼\": 101187,\n \"Ġно\": 101188,\n \"ÑĨÑĸй\": 101189,\n \"ÏĥÏĦ\": 101190,\n \"кие\": 101191,\n \"Ïĥει\": 101192,\n \"ìķĪ\": 101193,\n \"ĠhÆ¡n\": 101194,\n \"Ġà¤ķह\": 101195,\n \"اض\": 101196,\n \"ì¸\": 101197,\n \"ãĥŁ\": 101198,\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\": 101199,\n \"ãĤĪãģĨ\": 101200,\n \"ा,\": 101201,\n \"еÑĢи\": 101202,\n \"ë©°\": 101203,\n \"íĶĦ\": 101204,\n \"ĠпоÑģÑĤ\": 101205,\n \"خر\": 101206,\n \"à¥ĭत\": 101207,\n \"âu\": 101208,\n \"кой\": 101209,\n \"daki\": 101210,\n \"íħ\": 101211,\n \"::::::::::::::::\": 101212,\n \"Ġöz\": 101213,\n \"ÑĢаж\": 101214,\n \"nÃŃho\": 101215,\n \"หล\": 101216,\n \"ĠÏĥÏĦη\": 101217,\n \"ĠÄijá»ģ\": 101218,\n \"Ġká»\": 101219,\n \"iá»ĥn\": 101220,\n \"ÅĻi\": 101221,\n \"Ġkteré\": 101222,\n \"¢ħ\": 101223,\n \"üç\": 101224,\n \"ÙĬÙģ\": 101225,\n \"Ġlý\": 101226,\n \"Ġthá»Ŀi\": 101227,\n \"ĠìĨĮ\": 101228,\n \"нÑĮ\": 101229,\n \"ÐĨ\": 101230,\n \"ÑĤÑĢ\": 101231,\n \"à¸ĩาà¸Ļ\": 101232,\n \"коÑĹ\": 101233,\n \"μο\": 101234,\n \"Ġsür\": 101235,\n \"uyá»ģn\": 101236,\n \"ĠÙħا\": 101237,\n \"à¤Ĥà¤Ĺ\": 101238,\n \"ĠÄijá»ĵng\": 101239,\n \"òn\": 101240,\n \"à¥ģल\": 101241,\n \"à¥įप\": 101242,\n \"λη\": 101243,\n \"Ùħر\": 101244,\n \"пÑĢи\": 101245,\n \"iyle\": 101246,\n \"ाप\": 101247,\n \"Ġà¤ħन\": 101248,\n \"ĠÑĶ\": 101249,\n \"Ġyön\": 101250,\n \"ÙĦÙģ\": 101251,\n \"adır\": 101252,\n \"á½\": 101253,\n \"Ġê³ł\": 101254,\n \"خص\": 101255,\n \"imiz\": 101256,\n \"åľĭ\": 101257,\n \"Ġнад\": 101258,\n \"ĠÅĻ\": 101259,\n \"ноÑģÑĤÑĸ\": 101260,\n \"ĠاÙģ\": 101261,\n \"анÑĸ\": 101262,\n \"à¥ĩà¤Ł\": 101263,\n \"Ġë§IJ\": 101264,\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\": 101265,\n \"ĠìĬ¤\": 101266,\n \"ิà¸ķ\": 101267,\n \"åĦ\": 101268,\n \"ÛĮÙĩ\": 101269,\n \"оÑĪ\": 101270,\n \"žit\": 101271,\n \"ìĭ¤\": 101272,\n \"à¥Ģन\": 101273,\n \"Ġî\": 101274,\n \"æ¥Ń\": 101275,\n \"à¥ĩन\": 101276,\n \"ĠØ°\": 101277,\n \"Ġloại\": 101278,\n \"à¹Ħà¸Ľ\": 101279,\n \"Ñĸк\": 101280,\n \"ĠкÑĢа\": 101281,\n \"à¥ĭर\": 101282,\n \"ุà¸Ķ\": 101283,\n \"ĠاÙĦر\": 101284,\n \"ĠÑģоб\": 101285,\n \"าà¸Ĭ\": 101286,\n \"Ġसà¤ķत\": 101287,\n \"ĠÎĿ\": 101288,\n \"اÙħÙĩ\": 101289,\n \"à¹īาà¸Ļ\": 101290,\n \"Ġtrình\": 101291,\n \"ĠاÙĦÙģ\": 101292,\n \"ĠاÙĦد\": 101293,\n \"unun\": 101294,\n \"оÑĤов\": 101295,\n \"Æ°á»Ł\": 101296,\n \"ĠÑģво\": 101297,\n \"ίαÏĤ\": 101298,\n \"ấn\": 101299,\n \"огда\": 101300,\n \"à¸Ĺย\": 101301,\n \"Ġbyl\": 101302,\n \"ØŃد\": 101303,\n \"à¸ģล\": 101304,\n \"بÙĩ\": 101305,\n \"ĠvÄĽ\": 101306,\n \"被\": 101307,\n \"ĠØ¢Ùħ\": 101308,\n \"ĠÄijiá»ģu\": 101309,\n \"å¨\": 101310,\n \"Ġkdy\": 101311,\n \"ĠبÙĪ\": 101312,\n \"ẫn\": 101313,\n \"ìľł\": 101314,\n \"ाà¤ķ\": 101315,\n \"ků\": 101316,\n \"ĠtrÆ°á»Ŀng\": 101317,\n \"ické\": 101318,\n \"ниÑı\": 101319,\n \"ĠÏĢοÏħ\": 101320,\n \"Æ°á»Łng\": 101321,\n \"номÑĥ\": 101322,\n \"à¹Īà¸Ļ\": 101323,\n \"ูà¹Ī\": 101324,\n \"Ġkết\": 101325,\n \"Ġï¼¼\": 101326,\n \"Ġìĭł\": 101327,\n \"iç\": 101328,\n \"ĠnÄĥng\": 101329,\n \"ÄįÃŃ\": 101330,\n \"ÑĤÑı\": 101331,\n \"ÑĢеб\": 101332,\n \"Ùĭا\": 101333,\n \"Ú¯ÛĮ\": 101334,\n \"ãĥİ\": 101335,\n \"ĠkarÅŁ\": 101336,\n \"вÑĸ\": 101337,\n \"Ġphần\": 101338,\n \"à¸Īะ\": 101339,\n \"ắt\": 101340,\n \"رة\": 101341,\n \"ิà¸ĩ\": 101342,\n \"ิà¹Ī\": 101343,\n \"ाà¤Ī\": 101344,\n \"าà¸ŀ\": 101345,\n \"ÙĨÛĮ\": 101346,\n \"ìĹ°\": 101347,\n \"bÄĽ\": 101348,\n \"ĠاÙĦص\": 101349,\n \"íĹ\": 101350,\n \"Ġسر\": 101351,\n \"lara\": 101352,\n \"ëĭ¨\": 101353,\n \"ĠÙĤر\": 101354,\n \"èİ\": 101355,\n \"بد\": 101356,\n \"Ġйого\": 101357,\n \"à¥įह\": 101358,\n \"Ġcách\": 101359,\n \"íķĺê³ł\": 101360,\n \"ĠÏĢÏģο\": 101361,\n \"Ġتع\": 101362,\n \"ĴĪ\": 101363,\n \"Ġвод\": 101364,\n \"ç¥ŀ\": 101365,\n \"ким\": 101366,\n \"Ġdá»±\": 101367,\n \"à¹Ģห\": 101368,\n \"ана\": 101369,\n \"Ġï½\": 101370,\n \"ĠbaÄŁ\": 101371,\n \"Ġपह\": 101372,\n \"Ġcao\": 101373,\n \"ÏģÏĮ\": 101374,\n \"ÙĨج\": 101375,\n \"ाà¤ı\": 101376,\n \"Ġå¹´\": 101377,\n \"Ġnghiá»ĩp\": 101378,\n \"Û²Û°\": 101379,\n \"каÑı\": 101380,\n \"Ïģί\": 101381,\n \"Ġбол\": 101382,\n \"Ġgiá\": 101383,\n \"Ġзд\": 101384,\n \"à¥ĩल\": 101385,\n \"Ġcấp\": 101386,\n \"à¹Ģส\": 101387,\n \"Ïģγ\": 101388,\n \"ĠìĤ\": 101389,\n \"dÄĽ\": 101390,\n \"à¥ģन\": 101391,\n \"ìĪ\": 101392,\n \"ılan\": 101393,\n \"лаÑģ\": 101394,\n \"Ġว\": 101395,\n \"ĠÏĥε\": 101396,\n \"ĠØ«\": 101397,\n \"ĠЦ\": 101398,\n \"çĤº\": 101399,\n \"Ġbüy\": 101400,\n \"еÑĨ\": 101401,\n \"太\": 101402,\n \"Ġबन\": 101403,\n \"огÑĢа\": 101404,\n \"ĠпÑĢоÑĤ\": 101405,\n \"Ġlượng\": 101406,\n \"Ġdön\": 101407,\n \"รà¸ĩ\": 101408,\n \"ало\": 101409,\n \"ĠجÙħ\": 101410,\n \"à¥Ī,\": 101411,\n \"Ġ미\": 101412,\n \"Ġê¹\": 101413,\n \"ÙĪت\": 101414,\n \"à¥Ģय\": 101415,\n \"à¸Īาà¸ģ\": 101416,\n \"Ġchất\": 101417,\n \"Ω\": 101418,\n \"Ġkhác\": 101419,\n \"Ġtháng\": 101420,\n \"jÅ¡ÃŃ\": 101421,\n \"ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł\": 101422,\n \"á»ijt\": 101423,\n \"หร\": 101424,\n \"Ñĸл\": 101425,\n \"åħī\": 101426,\n \"åĤ\": 101427,\n \"ÙĦØ©\": 101428,\n \"Ġê±°\": 101429,\n \"овоÑĢ\": 101430,\n \"iá»ĥu\": 101431,\n \"ĠмеÑĤ\": 101432,\n \"аÑĶ\": 101433,\n \"ĠÑĩаÑģ\": 101434,\n \"Ïģε\": 101435,\n \"ì¹´\": 101436,\n \"âĢĮØ´\": 101437,\n \"물\": 101438,\n \"úc\": 101439,\n \"âĢĮÙĩا\": 101440,\n \"iá»ģn\": 101441,\n \"stav\": 101442,\n \"íŀ\": 101443,\n \"ĠÙĨظ\": 101444,\n \"ĨĴ\": 101445,\n \"ĠÏĦα\": 101446,\n \"Ġзаб\": 101447,\n \"ÙĥØ©\": 101448,\n \"ĠгÑĢÑĥ\": 101449,\n \"во\": 101450,\n \"ĠÙħج\": 101451,\n \"Ġsah\": 101452,\n \"بÙĦ\": 101453,\n \"عة\": 101454,\n \"ÑĥÑĪ\": 101455,\n \"ĠÑĤем\": 101456,\n \"íĭ\": 101457,\n \"eck\": 101458,\n \"ÏīÏĤ\": 101459,\n \"ÙĬت\": 101460,\n \"ìĹĪ\": 101461,\n \"çĭ\": 101462,\n \"ذا\": 101463,\n \"ìłĢ\": 101464,\n \"ĠнаÑģ\": 101465,\n \"ĠпоÑĩ\": 101466,\n \"æł¡\": 101467,\n \"ÏĪ\": 101468,\n \"Ñģкой\": 101469,\n \"üc\": 101470,\n \"ÙĤÙĦ\": 101471,\n \"Ġпоз\": 101472,\n \"ĠоÑģоб\": 101473,\n \"าล\": 101474,\n \"нÑĭми\": 101475,\n \"олод\": 101476,\n \"è¼\": 101477,\n \"ĠدÛĮ\": 101478,\n \"ĠÑĥÑģÑĤ\": 101479,\n \"Ġ무\": 101480,\n \"ÙĬس\": 101481,\n \"ë°©\": 101482,\n \"à¥įà¤ļ\": 101483,\n \"ила\": 101484,\n \"Ġnên\": 101485,\n \"ние\": 101486,\n \"ιν\": 101487,\n \"larını\": 101488,\n \"à¹Ģà¸Ļ\": 101489,\n \"ÙĨت\": 101490,\n \"aģı\": 101491,\n \"ımız\": 101492,\n \"ĠاÙĦØ®\": 101493,\n \"à¹Ģว\": 101494,\n \"à¥įन\": 101495,\n \"ĠÏħ\": 101496,\n \"ĠíĨ\": 101497,\n \"ẻ\": 101498,\n \"ิà¹Ĥ\": 101499,\n \"αÏĤ\": 101500,\n \"меÑĤ\": 101501,\n \"Ġzp\": 101502,\n \"Ġjeho\": 101503,\n \"ียà¸Ļ\": 101504,\n \"ÑĦоÑĢ\": 101505,\n \"ınız\": 101506,\n \"klad\": 101507,\n \"íĮĮ\": 101508,\n \"uyá»ĩ\": 101509,\n \"ιά\": 101510,\n \"ĠãĢģ\": 101511,\n \"شر\": 101512,\n \"æ©Ł\": 101513,\n \"Ġتا\": 101514,\n \"Ġзна\": 101515,\n \"ستاÙĨ\": 101516,\n \"à¥ĩर\": 101517,\n \"매\": 101518,\n \"çĥ\": 101519,\n \"Ġже\": 101520,\n \"าà¸Ķ\": 101521,\n \"Ġض\": 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101573,\n \"ìĺĢ\": 101574,\n \"Ġан\": 101575,\n \"çĹ\": 101576,\n \"ìĹŃ\": 101577,\n \"нÑĸÑģÑĤÑĮ\": 101578,\n \"Åŀ\": 101579,\n \"Ġphát\": 101580,\n \"ÙĤØ©\": 101581,\n \"Ġthế\": 101582,\n \"Ġï¾\": 101583,\n \"ì²ľ\": 101584,\n \"ĠìĦł\": 101585,\n \"à¹ĥà¸Ĭ\": 101586,\n \"iêu\": 101587,\n \"ÄŁini\": 101588,\n \"ÙĤد\": 101589,\n \"Ġkterý\": 101590,\n \"Ñģкий\": 101591,\n \"à¥įड\": 101592,\n \"tadır\": 101593,\n \"ĠÑģм\": 101594,\n \"ÙĪÙģ\": 101595,\n \"ارÙĬ\": 101596,\n \"å¾·\": 101597,\n \"ิม\": 101598,\n \"خت\": 101599,\n \"å¾Ī\": 101600,\n \"ĠгоÑĢ\": 101601,\n \"ï¼ĮæĪij\": 101602,\n \"Ġìĺģ\": 101603,\n \"ĠëıĻ\": 101604,\n \"Ñģа\": 101605,\n \"à¹Ģà¸Ħ\": 101606,\n \"민\": 101607,\n \"ึà¹Ī\": 101608,\n \"Ġliên\": 101609,\n \"ĠÙĩا\": 101610,\n \"lerini\": 101611,\n \"ĠÑĨе\": 101612,\n \"اÙĦÛĮ\": 101613,\n \"Ġमह\": 101614,\n \"Ġvụ\": 101615,\n \"Ġxuất\": 101616,\n \"ิà¸ģ\": 101617,\n \"ĠпÑĢоÑĨ\": 101618,\n \"Ġαν\": 101619,\n \"ÑĢим\": 101620,\n \"Ġcần\": 101621,\n \"ĠиÑħ\": 101622,\n 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103334,\n \"ĠÑĢод\": 103335,\n \"ĠÙĪÛĮ\": 103336,\n \"ický\": 103337,\n \"림\": 103338,\n \"ï½²\": 103339,\n \"æĢİ\": 103340,\n \"ĠÙĩذا\": 103341,\n \"ĠÑĩаÑģÑĤи\": 103342,\n \"ÃŃr\": 103343,\n \"á»ĩnh\": 103344,\n \"ĠíĹ\": 103345,\n \"ê»\": 103346,\n \"luž\": 103347,\n \"ÃŃl\": 103348,\n \"cÃŃch\": 103349,\n \"å®Ł\": 103350,\n \"ãģłãģ£ãģŁ\": 103351,\n \"ÙĬرة\": 103352,\n \"ĠvÄĥn\": 103353,\n \"港\": 103354,\n \"ĠÏĦιÏĤ\": 103355,\n \"ارت\": 103356,\n \"Ġvấn\": 103357,\n \"âĶģâĶģâĶģâĶģ\": 103358,\n \"対\": 103359,\n \"ÏģÎŃ\": 103360,\n \"ĠгодÑĥ\": 103361,\n \"Ġسب\": 103362,\n \"ارات\": 103363,\n \"елей\": 103364,\n \"ĠзаÑħ\": 103365,\n \"Ġваж\": 103366,\n \"Ġtá»īnh\": 103367,\n \"ابع\": 103368,\n \"Ġà¤ľà¤¬\": 103369,\n \"Ġà¤IJस\": 103370,\n \"ĠдÑĥ\": 103371,\n \"Ġé«ĺ\": 103372,\n \"ê²ł\": 103373,\n \"нее\": 103374,\n \"ï½Į\": 103375,\n \"Ġмал\": 103376,\n \"è¾¹\": 103377,\n \"ãģłãģij\": 103378,\n \"à¹īร\": 103379,\n \"ÙĤØ·\": 103380,\n \"Ġbên\": 103381,\n \"Ġseb\": 103382,\n \"ĠØ®ÙĪاÙĩ\": 103383,\n \"siz\": 103384,\n \"Ġolur\": 103385,\n \"ĠëĶ°\": 103386,\n \"Ġì¢ĭ\": 103387,\n \"ĠsvÄĽt\": 103388,\n \"ická\": 103389,\n \"ỹ\": 103390,\n \"Ġquản\": 103391,\n \"ĠиÑģ\": 103392,\n \"ĠzaÄį\": 103393,\n \"ืà¸Ńà¸Ļ\": 103394,\n \"ÑĶÑİ\": 103395,\n \"िष\": 103396,\n \"çĬ¶\": 103397,\n \"Ïĥμ\": 103398,\n \"ัส\": 103399,\n \"óc\": 103400,\n \"ĠбеÑĢ\": 103401,\n \"ĠíĿ\": 103402,\n \";:;:\": 103403,\n \"Ġپس\": 103404,\n \"ĠëijIJ\": 103405,\n \"ниÑĩ\": 103406,\n \"ĠоÑĩенÑĮ\": 103407,\n \"ĠìķĦìĿ´ì½ĺ\": 103408,\n \"Ġθα\": 103409,\n \"ĠвÑģÑĤ\": 103410,\n \"ادة\": 103411,\n \"Ġdevam\": 103412,\n \"ืà¸Ńà¸ĩ\": 103413,\n \"ĠлÑİди\": 103414,\n \"ìĺĪ\": 103415,\n \"á»±a\": 103416,\n \"ÑıÑħ\": 103417,\n \"âĢĮاÛĮ\": 103418,\n \"ĠسÙĪ\": 103419,\n \"å°¼\": 103420,\n \"Ġthứ\": 103421,\n \"meye\": 103422,\n \"Ġèµ\": 103423,\n \"èī¯\": 103424,\n \"ĠdeÄŁiÅŁ\": 103425,\n \"ÑĪÑĸ\": 103426,\n \"Ġtrợ\": 103427,\n \"ĠâĢİ#\": 103428,\n \"çĹħ\": 103429,\n \"ìĽĮ\": 103430,\n \"Ġkde\": 103431,\n \"Χ\": 103432,\n \"æ¤\": 103433,\n \"ĠÑħаÑĢакÑĤ\": 103434,\n \"æĩ\": 103435,\n \"Ġbiến\": 103436,\n \"ÙĤع\": 103437,\n \"åŁŁ\": 103438,\n \"Ġнеп\": 103439,\n \"Ġdů\": 103440,\n \"ĠпиÑĤ\": 103441,\n \"ĠÑĤÑĢеб\": 103442,\n \"ازÛĮ\": 103443,\n \"Ġطر\": 103444,\n \"ĠÙħÙĦ\": 103445,\n \"Ġtham\": 103446,\n \"ĠÙĪجÙĪد\": 103447,\n \"Ġsvé\": 103448,\n \"é§ħ\": 103449,\n \"اÛĮÙĨ\": 103450,\n \"Ġtiên\": 103451,\n \"stru\": 103452,\n \"ĠváºŃy\": 103453,\n \"üne\": 103454,\n \"Ġà¹Ģม\": 103455,\n \"Ġrằng\": 103456,\n \"аÑĤÑĥ\": 103457,\n \"äºij\": 103458,\n \"ниÑĤ\": 103459,\n \"ä¼Ĭ\": 103460,\n \"ÙĪص\": 103461,\n \"ĠéĿ\": 103462,\n \"ĠпÑĢоблем\": 103463,\n \"deki\": 103464,\n \"**************\": 103465,\n \"òa\": 103466,\n \"ĠÄijá»ģu\": 103467,\n \"ãĤĮãģŁ\": 103468,\n \"ارس\": 103469,\n \"ãģªãģı\": 103470,\n \"اÙĤع\": 103471,\n \"è»į\": 103472,\n \"ÙĥÙħ\": 103473,\n \"Äįas\": 103474,\n \"Ġkỳ\": 103475,\n \"Ø´Ùħ\": 103476,\n \"à¥ĩड\": 103477,\n \"éĺ¿\": 103478,\n \"ĠjejÃŃ\": 103479,\n \"ĠæĻ\": 103480,\n \"ĠÄ°ÅŁ\": 103481,\n \"ardım\": 103482,\n \"Ġसमय\": 103483,\n \"ĠÐĿо\": 103484,\n \"ilerin\": 103485,\n \"Ġعبد\": 103486,\n \"nÃŃk\": 103487,\n \"ĠØ´Ú©ÙĨ\": 103488,\n \"ิà¸Ĺย\": 103489,\n \"á»ħ\": 103490,\n \"ÑĢез\": 103491,\n \"Ġchứng\": 103492,\n \"Ġ:.\": 103493,\n \"Ġपत\": 103494,\n \"Ġživot\": 103495,\n \"å¢ĥ\": 103496,\n \"«a\": 103497,\n \"Ġtrung\": 103498,\n \"никÑĸв\": 103499,\n \"ĠاÙĦÙħÙĨ\": 103500,\n \"ĠÑĢаÑģÑģ\": 103501,\n \"Ġжив\": 103502,\n \"Ġзакон\": 103503,\n \"Ġ목\": 103504,\n \"Ġzáv\": 103505,\n \"Ġhakk\": 103506,\n \"令\": 103507,\n \"ĠÑıкий\": 103508,\n \"ĠبÙĬ\": 103509,\n \"λÎŃ\": 103510,\n \"ocuk\": 103511,\n \"ĠÑİ\": 103512,\n \"à¸ģว\": 103513,\n \"ĠاÙĨÚ¯\": 103514,\n \"à¥ģà¤Ĥ\": 103515,\n \"Ġnám\": 103516,\n \"á»ķng\": 103517,\n \"Ġжел\": 103518,\n \"ĠÄijặc\": 103519,\n \"Äįit\": 103520,\n \"Ġê±´\": 103521,\n \"ĠبÛĮØ´\": 103522,\n \"кÑĢаÑĹн\": 103523,\n \"ĠÙĪÙĩ\": 103524,\n \"неннÑı\": 103525,\n \"Ġà¹Ģà¸ŀ\": 103526,\n \"омен\": 103527,\n \"Ġlần\": 103528,\n \"ĠعÙħÙĦ\": 103529,\n \"Ġîģµ\": 103530,\n \"Äŀ\": 103531,\n \"ÑĸÑģлÑı\": 103532,\n \"Æ°ng\": 103533,\n \"ाफ\": 103534,\n \"à¸Ĺà¸ĺ\": 103535,\n \"ден\": 103536,\n \"ĠÑīоб\": 103537,\n \"Ñĩив\": 103538,\n \"ılır\": 103539,\n \"اعات\": 103540,\n \"jÃŃcÃŃ\": 103541,\n \"벨\": 103542,\n \"ÚĨÙĩ\": 103543,\n \"ارج\": 103544,\n \"ĠپرÙĪ\": 103545,\n \"Ġодин\": 103546,\n \"лин\": 103547,\n \"бÑĥ\": 103548,\n \"Ġसरà¤ķ\": 103549,\n \"åĢĻ\": 103550,\n \"ë¶ĢíĦ°\": 103551,\n \"à¥Īà¤Ĥ,\": 103552,\n \"å´\": 103553,\n \"à¹Ĥล\": 103554,\n \"ĠvÅ¡ak\": 103555,\n \"ĠопÑĢед\": 103556,\n \"ì±\": 103557,\n \"æ½\": 103558,\n \"Ġdá»±ng\": 103559,\n \"práv\": 103560,\n \"ิส\": 103561,\n \"Ġnhiá»ĩm\": 103562,\n \"ĠiliÅŁ\": 103563,\n \"ĠеÑīе\": 103564,\n \"ĠjeÅ¡tÄĽ\": 103565,\n \"ĠÑĢаÑģÑĤ\": 103566,\n \"ฮ\": 103567,\n \"à¤Ĥà¤Ł\": 103568,\n \"âĢĮÚ©\": 103569,\n \"ĠبÛĮÙĨ\": 103570,\n \"ovou\": 103571,\n \"æĻ®\": 103572,\n \"ίεÏĤ\": 103573,\n \"оÑĢоÑĪ\": 103574,\n \"Ġolmak\": 103575,\n \"Ġstát\": 103576,\n \"diÄŁi\": 103577,\n \"Ġtình\": 103578,\n \"ĠdÄĽ\": 103579,\n \"ĠگرÙģ\": 103580,\n \"Ïĥο\": 103581,\n \"ĠÑĥÑĤ\": 103582,\n \"íķĻêµIJ\": 103583,\n \"ัà¸IJ\": 103584,\n \"าà¸Ń\": 103585,\n \"ĠÄijặt\": 103586,\n \"ĠмогÑĥÑĤ\": 103587,\n \"ë°°\": 103588,\n \"tik\": 103589,\n \"ª½\": 103590,\n \"liÄŁ\": 103591,\n \"ÏĢε\": 103592,\n \"ĠèĢ\": 103593,\n \"kü\": 103594,\n \"adece\": 103595,\n \"κÏĮ\": 103596,\n \"ĠдÑĸ\": 103597,\n \"ầm\": 103598,\n \"çĦ¡ãģĹ\": 103599,\n \"Û²Û°Û±\": 103600,\n \"èµĽ\": 103601,\n \"оÑģÑĥд\": 103602,\n \"ĠìķĪëĤ´\": 103603,\n \"ĠÐĶж\": 103604,\n \"座\": 103605,\n \"ických\": 103606,\n \"Ġìłģ\": 103607,\n \"à¥ĩ,\": 103608,\n \"ového\": 103609,\n \"Ġvẫn\": 103610,\n \"Ġbirlikte\": 103611,\n \"Ġरà¤ĸ\": 103612,\n \"ĠÙĨÙĩ\": 103613,\n \"ÙĤر\": 103614,\n \"पर\": 103615,\n \"etÃŃ\": 103616,\n \"ĠÑĤÑĭ\": 103617,\n \"ĢìĿ´\": 103618,\n \"Ġà¤ħल\": 103619,\n \"Ġможе\": 103620,\n \"ãĤ´\": 103621,\n \"Ġstran\": 103622,\n \"طر\": 103623,\n \"è¿Ļ个\": 103624,\n \"Ġبع\": 103625,\n \"åĨĽ\": 103626,\n \"ektir\": 103627,\n \"ĠhÆ°á»Ľng\": 103628,\n \"ÙĨاÙĨ\": 103629,\n \"Ġà¤ij\": 103630,\n \"ÏĮÏĦη\": 103631,\n \"оÑģк\": 103632,\n \"åįĥ\": 103633,\n \"asına\": 103634,\n \"ĠØ´Ùĩ\": 103635,\n \"ĠдеÑĢ\": 103636,\n \"ĠÙħخت\": 103637,\n \"ĠØŃÙĤ\": 103638,\n \"ãĥ¾\": 103639,\n \"ساÙĨ\": 103640,\n \"Ġcung\": 103641,\n \"коÑĢиÑģÑĤ\": 103642,\n \"ÏĦικά\": 103643,\n \"Ġвона\": 103644,\n \"با\": 103645,\n \"ãģķãĤĮãģŁ\": 103646,\n \"nout\": 103647,\n \"Ġı\": 103648,\n \"è§ī\": 103649,\n \"ĠÃ¶ÄŁren\": 103650,\n \"Ġì½Ķ\": 103651,\n \"带\": 103652,\n \"Ñģлов\": 103653,\n \"ĠεÏĢι\": 103654,\n \"ê°IJ\": 103655,\n \"ĠÙħرب\": 103656,\n \"ĠÙģÛĮÙĦÙħ\": 103657,\n \"ĠкÑĢов\": 103658,\n \"Ġëį°\": 103659,\n \"ाण\": 103660,\n \"Ġelekt\": 103661,\n \"ĠнаÑĢод\": 103662,\n \"ÛĮدÙĩ\": 103663,\n \"ç´Ħ\": 103664,\n \"ĠпÑĢоÑĦ\": 103665,\n \"ÏģοÏĤ\": 103666,\n \"Ġãħ\": 103667,\n \"ä¸įæĺ¯\": 103668,\n \"Ġà¤ľà¤¨\": 103669,\n \"ัล\": 103670,\n \"ĠصÙĪرت\": 103671,\n \"ãĥľ\": 103672,\n \"Ġà¤Ĺà¤Ī\": 103673,\n \"ÄŁitim\": 103674,\n \"ÑģÑĮкиÑħ\": 103675,\n \"Ġлег\": 103676,\n \"ĠتÙĪÙĦ\": 103677,\n \"Ġìļ´\": 103678,\n \"عر\": 103679,\n \"ĠmÃłu\": 103680,\n \"гов\": 103681,\n \"æ³¢\": 103682,\n \"indeki\": 103683,\n \"ìłģìĿ¸\": 103684,\n \"ấm\": 103685,\n \"ĠíĻķ\": 103686,\n \"ĠباÛĮد\": 103687,\n \"à¹Įà¸Ĺ\": 103688,\n \"Ġkendi\": 103689,\n \"ีว\": 103690,\n \"ิà¸ģาร\": 103691,\n \"ĠکردÙĩ\": 103692,\n \"å·´\": 103693,\n \"à¤ģ\": 103694,\n \"ราà¸Ĭ\": 103695,\n \"à¥įश\": 103696,\n \"ĠÐĶлÑı\": 103697,\n \"å¥ĩ\": 103698,\n \"ĠÑĥÑģÑĤанов\": 103699,\n \"йÑĤе\": 103700,\n \"ãĤĩ\": 103701,\n \"άÏģ\": 103702,\n \"ĠЮ\": 103703,\n \"ĠluáºŃt\": 103704,\n \"ãĢī\": 103705,\n \"è´¨\": 103706,\n \"دا\": 103707,\n \"Ġdüzen\": 103708,\n \"สà¸Ļ\": 103709,\n \"ÑĢон\": 103710,\n \"dıģı\": 103711,\n \"âĢĻda\": 103712,\n \"Ġfarklı\": 103713,\n \"Ñħов\": 103714,\n \"lán\": 103715,\n \"ÑĩаÑģ\": 103716,\n \"Ñĩин\": 103717,\n \"Ġì°¸\": 103718,\n \"ì´Ī\": 103719,\n \"ÑĨип\": 103720,\n \"ç¹\": 103721,\n \"éĸĢ\": 103722,\n \"жа\": 103723,\n \"ÑĢован\": 103724,\n \"à¸ĵะ\": 103725,\n \"ÙĦÙĬزÙĬØ©\": 103726,\n \"Ïĩει\": 103727,\n \"à¥Ī.\": 103728,\n \"кÑģп\": 103729,\n \"اÙĪر\": 103730,\n \"Ġnguyên\": 103731,\n \"ãģ«ãĤĪ\": 103732,\n \"à¥ĩम\": 103733,\n \"ÏĥÏĦε\": 103734,\n \"تÙĪ\": 103735,\n \"Äįek\": 103736,\n \"ÑĨÑĭ\": 103737,\n \"Ġ물\": 103738,\n \"ÑįÑĤ\": 103739,\n \"Ġkazan\": 103740,\n \"Ùģس\": 103741,\n \"ehir\": 103742,\n \"вÑĸÑĤ\": 103743,\n \"ĠدÙĪÙĦ\": 103744,\n \"Ġëĵľ\": 103745,\n \"Ġà¤ļल\": 103746,\n \"еÑģÑĤва\": 103747,\n \"δα\": 103748,\n \"ĠбÑĥв\": 103749,\n \"ĠÐĿе\": 103750,\n \"ØŃر\": 103751,\n \"огÑĢаÑĦ\": 103752,\n \"Ġrozhod\": 103753,\n \"ĠвикоÑĢиÑģÑĤ\": 103754,\n \"Ġyêu\": 103755,\n \"λοÏĤ\": 103756,\n \"کس\": 103757,\n \"Ġشب\": 103758,\n \"ิษ\": 103759,\n \"æ¯į\": 103760,\n \"ĠдоÑĢ\": 103761,\n \"Ġnghá»ĩ\": 103762,\n \"Ġtrang\": 103763,\n \"à¥ĩद\": 103764,\n \"Ġtìm\": 103765,\n \"Ñĩно\": 103766,\n \"ĠاÙħا\": 103767,\n \"éģĭ\": 103768,\n \"کر\": 103769,\n \"ké\": 103770,\n \"ĠvÄĽt\": 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\"íķĻìĥĿ\": 111291,\n \"Ġìī\": 111292,\n \"rnek\": 111293,\n \"ĠاستخداÙħ\": 111294,\n \"ãĢĢĠãĢĢĠãĢĢĠãĢĢ\": 111295,\n \"ĠвÑģем\": 111296,\n \"ĠìłķëıĦ\": 111297,\n \"Ġvyj\": 111298,\n \"éĢ±\": 111299,\n \"алÑĮное\": 111300,\n \"Ġchuyá»ĩn\": 111301,\n \"ì§ĢìĽIJ\": 111302,\n \"ilerine\": 111303,\n \"ĠìķĦ무\": 111304,\n \"Ġоколо\": 111305,\n \"ावन\": 111306,\n \"à¸Ļา\": 111307,\n \"опÑĢи\": 111308,\n \"drž\": 111309,\n \"ĠÑģÑĥÑģпÑĸлÑĮ\": 111310,\n \"ĠبÙĥ\": 111311,\n \"uky\": 111312,\n \"ĠÏĩÏī\": 111313,\n \"Ġtuần\": 111314,\n \"nictvÃŃ\": 111315,\n \"ĠÙĩدÙģ\": 111316,\n \"Ġchiá»ģu\": 111317,\n \"ÎĹÎĿ\": 111318,\n \"å°ıå§IJ\": 111319,\n \"íķĺìĺĢ\": 111320,\n \"Ġklas\": 111321,\n \"á»Ļn\": 111322,\n \"ĠìĿ´íĽĦ\": 111323,\n \"ÙĨاÙħج\": 111324,\n \"Äįast\": 111325,\n \"ĠاÙĦخاص\": 111326,\n \"lÄ±ÅŁ\": 111327,\n \"ĠعÙħر\": 111328,\n \"ãĢįĊ\": 111329,\n \"иболее\": 111330,\n \"ãĤĬãģ®\": 111331,\n \"ãħł\": 111332,\n \"ä¹Łä¸į\": 111333,\n \"кÑĢеÑĤ\": 111334,\n \"ĠìĶ\": 111335,\n \"ÏĦια\": 111336,\n 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\"ÏĥÏĦημα\": 119976,\n \"ï¼ĮçĶ¨\": 119977,\n \"ìĭŃìĭľìĺ¤\": 119978,\n \"ĠÙħثاÙĦ\": 119979,\n \"-Ðij\": 119980,\n \"ÑĨÑĸйнÑĸ\": 119981,\n \"ĠدستÙĩ\": 119982,\n \"à¥īस\": 119983,\n \"ÑĢÑĸп\": 119984,\n \"ĠpÅĻipom\": 119985,\n \"ĠÙĪÙĦÙĬ\": 119986,\n \"ĠÙĪزÙĨ\": 119987,\n \"Ġelektrik\": 119988,\n \"ĠQuân\": 119989,\n \"ivé\": 119990,\n \"Ġlẽ\": 119991,\n \"ç®Ģåįķ\": 119992,\n \"Ġonlara\": 119993,\n \"оÑģлав\": 119994,\n \"ìĭľíĤ¤\": 119995,\n \"몬\": 119996,\n \"ĠÙħÙĤدار\": 119997,\n \"ĠOrta\": 119998,\n \"ĠSeç\": 119999,\n \"ĠÙĨÙĪÙģÙħبر\": 120000,\n \"ุà¸Ļายà¸Ļ\": 120001,\n \"ĠÑĥмови\": 120002,\n \"Ġपरम\": 120003,\n \"Ġstrom\": 120004,\n \"ĠкÑĢаÑīе\": 120005,\n \"秦\": 120006,\n \"缸æīĭ\": 120007,\n \"鼻è¦ĸ\": 120008,\n \"Ġuygulama\": 120009,\n \"ĠÑĢиз\": 120010,\n \"æĪ²\": 120011,\n \"यर\": 120012,\n \"ĠHlav\": 120013,\n \"Ġìĭ¸\": 120014,\n \"ĠлипнÑı\": 120015,\n \"ÅĪujÃŃ\": 120016,\n \"ÑĢиз\": 120017,\n \"é«ĺéĢŁ\": 120018,\n \"缸å½ĵ\": 120019,\n \"kenin\": 120020,\n \"ĠоÑģÑĤанов\": 120021,\n \"Ġbitk\": 120022,\n \"ovaného\": 120023,\n \"ĠÐľÐ°ÑĢи\": 120024,\n \"赶\": 120025,\n \"콩\": 120026,\n \"Ġölçü\": 120027,\n \"ĠСеÑĢед\": 120028,\n \"ĠThá»Ŀi\": 120029,\n \"Ïīνα\": 120030,\n \"ÙĪبة\": 120031,\n \"Ġchụp\": 120032,\n \"âĢĮد\": 120033,\n \"Ġcháy\": 120034,\n \"ĠÐĴели\": 120035,\n \"ĠобÑģÑĤ\": 120036,\n \"Ġìĭľì¦Į\": 120037,\n \"دÙħØ©\": 120038,\n \"под\": 120039,\n \"lue\": 120040,\n \"ĠдÑĸлÑıн\": 120041,\n \"ĠÙ¾ÙĪست\": 120042,\n \"ĠاÙĦÙĨس\": 120043,\n \"èĤĮ\": 120044,\n \"ìĪĺ를\": 120045,\n \"Ġúrov\": 120046,\n \"ĠÙħØ´Ú©ÙĦ\": 120047,\n \"éĩįè¤ĩéĩįè¤ĩ\": 120048,\n \"нез\": 120049,\n \"ĠdoporuÄį\": 120050,\n \"Ġtasarım\": 120051,\n \"íģ¬ê¸°\": 120052,\n \"ìĿ´ìħĺ\": 120053,\n \"Ġdeset\": 120054,\n \"ĠÙħرتبط\": 120055,\n \"ัà¸Ĵà¸Ļา\": 120056,\n \"'ı\": 120057,\n \"Ñĩки\": 120058,\n \"ĠìŀĪëįĺ\": 120059,\n \"ÑĪка\": 120060,\n \"nám\": 120061,\n \"ÑģÑĤÑĢов\": 120062,\n \"à¥įसर\": 120063,\n \"нÑĥлаÑģÑĮ\": 120064,\n \"ãģ¡ãĤĩãģ£ãģ¨\": 120065,\n \"Ġå¦\": 120066,\n \"γÏĮ\": 120067,\n \"Ġé»ij\": 120068,\n \"Xem\": 120069,\n \"Ġtá»ĩ\": 120070,\n \"ĠëĮĢíĨµëł¹\": 120071,\n \"기ê´Ģ\": 120072,\n \"æīįèĥ½\": 120073,\n \"è¯Ńè¨Ģ\": 120074,\n \"edeyse\": 120075,\n \"ĠТÑĭ\": 120076,\n \"ĠÑģоедин\": 120077,\n \"ĠìĹĨìĬµëĭĪëĭ¤\": 120078,\n \"ÑıÑİÑĤ\": 120079,\n \"à¹ģหล\": 120080,\n \"Ġì§Ģë°©\": 120081,\n \"ĠosobnÃŃ\": 120082,\n \"ÛĮÙĦÛĮ\": 120083,\n \"ĠавгÑĥÑģÑĤа\": 120084,\n \"Ñīик\": 120085,\n \"Ġvýše\": 120086,\n \"gth\": 120087,\n \"ĠÏĢαν\": 120088,\n \"جار\": 120089,\n \"Ġвидов\": 120090,\n \"ìĿ´ìĬĪ\": 120091,\n \"ĠÐijаÑĢ\": 120092,\n \"ĠÏĮÏĢοÏħ\": 120093,\n \"æ¤ħ\": 120094,\n \"ĠعاÙĦÛĮ\": 120095,\n \"ĠQuyết\": 120096,\n \"ÃľM\": 120097,\n \"ãĥĿãĤ¤ãĥ³ãĥĪ\": 120098,\n \"Ġê¹Į\": 120099,\n \"Ġканди\": 120100,\n \"kového\": 120101,\n \"ĠMerkez\": 120102,\n \"Ġyiy\": 120103,\n \"ĠpÅĻÃŃspÄĽ\": 120104,\n \"ĠÑĤемпеÑĢаÑĤÑĥÑĢÑĭ\": 120105,\n \"ĠÙ¾ÙĬ\": 120106,\n \"ฤศà¸Ī\": 120107,\n \"è°ĥçĶ¨\": 120108,\n \"ĠÑģÑĤоÑĢонÑĥ\": 120109,\n \"à¹īà¸Ĭ\": 120110,\n \"好ãģį\": 120111,\n \".Åŀ\": 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\"ĠÑĢÑıдом\": 120204,\n \"ĠÑĩеÑĢвнÑı\": 120205,\n \"à¸ģระà¸Ĺ\": 120206,\n \"人人\": 120207,\n \"æ°ĹæĮģãģ¡\": 120208,\n \"undaki\": 120209,\n \"åľĭ家\": 120210,\n \"εÏģμαν\": 120211,\n \"ĠлÑĮ\": 120212,\n \"ĠNüfus\": 120213,\n \"ĠмеÑĢе\": 120214,\n \"براÙĬر\": 120215,\n \"наннÑı\": 120216,\n \"ĠнаÑĢ\": 120217,\n \"Ġtấm\": 120218,\n \"æĸ½å·¥\": 120219,\n \"顯\": 120220,\n \"Ġhè\": 120221,\n \"æĺİçĻ½\": 120222,\n \"Ġдогов\": 120223,\n \"ĠÙģرÙħ\": 120224,\n \"èĢĹ\": 120225,\n \"ìĬ¤ìĿĺ\": 120226,\n \"ìĦ¸ëĮĢ\": 120227,\n \"è¯ļ\": 120228,\n \"ĠнеболÑĮ\": 120229,\n \"Ġà¸Ľà¸£à¸°à¸ģ\": 120230,\n \"Ġì¹¼\": 120231,\n \"Ġovliv\": 120232,\n \"ĠNGC\": 120233,\n \"ãĢĤä¸į\": 120234,\n \"اÙĦÙī\": 120235,\n \"æī£\": 120236,\n \".ÐIJ\": 120237,\n \"ÑĢаÑģÑĤа\": 120238,\n \"ĠÃĩev\": 120239,\n \"ãģ£ãģ¡\": 120240,\n \"ï¼Įéĥ½\": 120241,\n \"ĠrovnÄĽÅ¾\": 120242,\n \"ĠÏĩÏģÏĮνια\": 120243,\n \"Ġì¡°ìĦł\": 120244,\n \"Ġآباد\": 120245,\n \"ĠÐľÐ°Ñģ\": 120246,\n \"çĻ¼å±ķ\": 120247,\n \"ä»Ķ\": 120248,\n \"Ġkendisini\": 120249,\n \"à¹Īà¸Ńà¸ĩà¹Ģà¸Ĺ\": 120250,\n \"ĠVÄĽ\": 120251,\n \"Ġrượu\": 120252,\n \"Ġmáme\": 120253,\n \"ĠоÑĩеÑĢедÑĮ\": 120254,\n \"ĠسبتÙħبر\": 120255,\n \"Ġбок\": 120256,\n \"ì§ĢìĹŃ\": 120257,\n \"ĠتاثÛĮر\": 120258,\n \"Ġlisans\": 120259,\n \"Ġgerektir\": 120260,\n \"Ġsizi\": 120261,\n \"Ñĸно\": 120262,\n \"ĠMÃ¼ÅŁ\": 120263,\n \"ãģıãĤīãģĦ\": 120264,\n \"ĠзаклÑİÑĩ\": 120265,\n \"ãģĵãģ¨ãģ«\": 120266,\n \"è¨ĢãģĦ\": 120267,\n \"ãĢģå°ı\": 120268,\n \"Ġetmektedir\": 120269,\n \"åł±åijĬ\": 120270,\n \"ĠkarÄ±ÅŁ\": 120271,\n \"Ġоблад\": 120272,\n \"å¥ij\": 120273,\n \"racat\": 120274,\n \"ĠارتÙģاع\": 120275,\n \"μαι\": 120276,\n \"íĶĪ\": 120277,\n \"ĠÙĪÙĦÙħ\": 120278,\n \"ëĬĶì§Ģ\": 120279,\n \"lomou\": 120280,\n \"ĠлиÑĨа\": 120281,\n \"ĠìĿĮìķħ\": 120282,\n \"ĠhodnÄĽ\": 120283,\n \"èĭ±æĸĩ\": 120284,\n \"ÂĦ\": 120285,\n \"à¹īาà¸Ĥà¸Ńà¸ĩ\": 120286,\n \"Ġê³Ħìķ½\": 120287,\n \"åIJĦç§į\": 120288,\n \"ĠÙħرگ\": 120289,\n \"éĶģ\": 120290,\n \"Ġनद\": 120291,\n \"ãĥĭãĥ¡\": 120292,\n \"Ġем\": 120293,\n \"ĠeleÅŁtir\": 120294,\n \"ĠíĬ¹ë³Ħ\": 120295,\n \"ĠÎ¥ÏĢο\": 120296,\n \"Å¡ker\": 120297,\n \"LERÄ°\": 120298,\n \"æ²Ī\": 120299,\n \"likleri\": 120300,\n \"ĠÙħÙĩÙĨدسÛĮ\": 120301,\n \"Ġbaģır\": 120302,\n \"dıģını\": 120303,\n \"ĠاÙĦتد\": 120304,\n \"à¸¸à¸Ľà¸ģรà¸ĵ\": 120305,\n \"ĠÑģледÑĥÑİÑīие\": 120306,\n \"Ġì§ģìłij\": 120307,\n \"å°¤\": 120308,\n \"ĠоÑģновÑĸ\": 120309,\n \"ĠtÄĽla\": 120310,\n \"Ġпак\": 120311,\n \"izace\": 120312,\n \"Ġnárod\": 120313,\n \"aný\": 120314,\n \"ĠÑįп\": 120315,\n \"Ġüçüncü\": 120316,\n \"ΥΡ\": 120317,\n \"éĨ´éĨ´\": 120318,\n \"à¹Ģà¸ģà¸Ńร\": 120319,\n \"âĢĮاÙĨبار\": 120320,\n \"ç¶Ļ\": 120321,\n \"ÎijÎł\": 120322,\n \"ılıģı\": 120323,\n \"ĠÃľrün\": 120324,\n \"Ġдозвол\": 120325,\n \"ĠíĥĪ\": 120326,\n \"Ġà¤ĵवर\": 120327,\n \"諸\": 120328,\n \"èĺĩ\": 120329,\n \"ĠпÑĢоÑģÑĤÑĢан\": 120330,\n \"éĿĴå¹´\": 120331,\n \"ãģ®æĸ¹\": 120332,\n \"ĠÚĨÚ¯ÙĪÙĨÙĩ\": 120333,\n \"ÙĦØ·\": 120334,\n \"âĢľæĪij\": 120335,\n \"Ġëĭ¤ìļ´ë°Ľ\": 120336,\n \"ा.Ċ\": 120337,\n \"Ġmücadele\": 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ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"t h\",\n \") Ċ\",\n \"Ġ {Ċ\",\n \"Ġ{ Ċ\",\n \"Ġ g\",\n \"i g\",\n \"i v\",\n \", Ċ\",\n \"c e\",\n \"o d\",\n \"Ġ v\",\n \"a te\",\n \"at e\",\n \"Ġ T\",\n \"a g\",\n \"a y\",\n \"Ġ *\",\n \"o t\",\n \"u s\",\n \"Ġ C\",\n \"Ġ st\",\n \"Ġs t\",\n \"Ġ I\",\n \"u n\",\n \"u l\",\n \"u e\",\n \"Ġ A\",\n \"o w\",\n \"Ġ '\",\n \"e w\",\n \"Ġ <\",\n \"a tion\",\n \"at ion\",\n \"atio n\",\n \"ati on\",\n \"( )\",\n \"Ġ for\",\n \"Ġf or\",\n \"Ġfo r\",\n \"a b\",\n \"o rt\",\n \"or t\",\n \"u m\",\n \"a me\",\n \"am e\",\n \"Ġ is\",\n \"Ġi s\",\n \"p e\",\n \"t r\",\n \"c k\",\n \"â Ģ\",\n \"Ġ y\",\n \"i st\",\n \"is t\",\n \"- ---\",\n \"-- --\",\n \"--- -\",\n \". ĊĊ\",\n \".Ċ Ċ\",\n \"h e\",\n \"Ġ e\",\n \"l o\",\n \"Ġ M\",\n \"Ġ be\",\n \"Ġb e\",\n \"e rs\",\n \"er s\",\n \"Ġ on\",\n \"Ġo n\",\n \"Ġ con\",\n \"Ġc on\",\n \"Ġco n\",\n \"a p\",\n \"u b\",\n \"Ġ P\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"a ss\",\n \"as s\",\n \"i nt\",\n \"in t\",\n \"> Ċ\",\n \"l y\",\n \"u rn\",\n \"ur n\",\n \"Ġ $\",\n \"; ĊĊ\",\n \";Ċ Ċ\",\n \"a v\",\n \"p ort\",\n \"por t\",\n \"po rt\",\n \"i r\",\n \"- >\",\n \"n t\",\n \"c tion\",\n \"ct ion\",\n \"e nd\",\n \"en d\",\n \"Ġ de\",\n \"Ġd e\",\n \"0 0\",\n \"i th\",\n \"it h\",\n \"o ut\",\n \"ou t\",\n \"t urn\",\n \"tu rn\",\n \"tur n\",\n \"o ur\",\n \"ou r\",\n \"Ġ ĠĠĠĠ\",\n \"ĠĠ ĠĠĠ\",\n \"ĠĠĠĠ Ġ\",\n \"ĠĠĠ ĠĠ\",\n \"l ic\",\n \"li c\",\n \"r es\",\n \"re s\",\n \"p t\",\n \"= =\",\n \"Ġ this\",\n \"Ġt his\",\n \"Ġth is\",\n \"Ġthi s\",\n \"Ġ wh\",\n \"Ġw h\",\n \"Ġ if\",\n \"Ġi f\",\n \"Ġ D\",\n \"v er\",\n \"ve r\",\n \"a ge\",\n \"ag e\",\n \"Ġ B\",\n \"h t\",\n \"e xt\",\n \"ex t\",\n \"= \\\"\",\n \"Ġ that\",\n \"Ġt hat\",\n \"Ġth at\",\n \"Ġtha t\",\n \"* ***\",\n \"** **\",\n \"*** *\",\n \"Ġ R\",\n \"Ġ it\",\n \"Ġi t\",\n \"e ss\",\n \"es s\",\n \"Ġ F\",\n \"Ġ r\",\n \"o s\",\n \"a nd\",\n \"an d\",\n \"Ġ as\",\n \"Ġa s\",\n \"e ct\",\n \"ec t\",\n \"k e\",\n \"r om\",\n \"ro m\",\n \"Ġ //\",\n \"Ġ/ /\",\n \"c on\",\n \"co n\",\n \"Ġ L\",\n \"( \\\"\",\n \"q u\",\n \"l ass\",\n \"la ss\",\n \"las s\",\n \"Ġ with\",\n \"Ġw ith\",\n \"Ġwi th\",\n \"Ġwit h\",\n \"i z\",\n \"d e\",\n \"Ġ N\",\n \"Ġ al\",\n \"Ġa l\",\n \"o p\",\n \"u p\",\n \"g et\",\n \"ge t\",\n \"Ġ }Ċ\",\n \"Ġ} Ċ\",\n \"i le\",\n \"il e\",\n \"Ġ an\",\n \"Ġa n\",\n \"a ta\",\n \"at a\",\n \"o re\",\n \"or e\",\n \"r i\",\n \"Ġ pro\",\n \"Ġp ro\",\n \"Ġpr o\",\n \"; čĊ\",\n \"ĉ ĉĉĉ\",\n \"ĉĉ ĉĉ\",\n \"ĉĉĉ ĉ\",\n \"t er\",\n \"te r\",\n \"a in\",\n \"ai n\",\n \"Ġ W\",\n \"Ġ E\",\n \"Ġ com\",\n \"Ġc om\",\n \"Ġco m\",\n \"Ġ return\",\n \"Ġre turn\",\n \"Ġr eturn\",\n \"Ġret urn\",\n \"a rt\",\n \"ar t\",\n \"Ġ H\",\n \"a ck\",\n \"ac k\",\n \"im port\",\n \"imp ort\",\n \"ub lic\",\n \"ubl ic\",\n \"Ġ or\",\n \"Ġo r\",\n \"e st\",\n \"es t\",\n \"m ent\",\n \"me nt\",\n \"men t\",\n \"Ġ G\",\n \"a ble\",\n \"ab le\",\n \"abl e\",\n \"Ġ -\",\n \"i ne\",\n \"in e\",\n \"i ll\",\n \"il l\",\n \"i nd\",\n \"in d\",\n \"e re\",\n \"er e\",\n \": :\",\n \"i ty\",\n \"it y\",\n \"Ġ +\",\n \"Ġ tr\",\n \"Ġt r\",\n \"e lf\",\n \"el f\",\n \"i ght\",\n \"ig ht\",\n \"igh t\",\n \"( '\",\n \"o rm\",\n \"or m\",\n \"u lt\",\n \"ul t\",\n \"s tr\",\n \"st r\",\n \". .\",\n \"\\\" ,\",\n \"Ġ you\",\n \"Ġy ou\",\n \"Ġyo u\",\n \"y pe\",\n \"yp e\",\n \"p l\",\n \"Ġ new\",\n \"Ġn ew\",\n \"Ġne w\",\n \"Ġ j\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġ from\",\n \"Ġf rom\",\n \"Ġfr om\",\n \"Ġfro m\",\n \"Ġ ex\",\n \"Ġe x\",\n \"Ġ O\",\n \"2 0\",\n \"l d\",\n \"Ġ [\",\n \"o c\",\n \": Ċ\",\n \"Ġ se\",\n \"Ġs e\",\n \"Ġ le\",\n \"Ġl e\",\n \"- -------\",\n \"-- ------\",\n \"---- ----\",\n \"--- -----\",\n \"----- ---\",\n \"------ --\",\n \"------- -\",\n \". s\",\n \"{ Ċ\",\n \"' ,\",\n \"a nt\",\n \"an t\",\n \"Ġ at\",\n \"Ġa t\",\n \"a se\",\n \"as e\",\n \". c\",\n \"Ġ ch\",\n \"Ġc h\",\n \"< /\",\n \"a ve\",\n \"av e\",\n \"a ng\",\n \"an g\",\n \"Ġ are\",\n \"Ġa re\",\n \"Ġar e\",\n \"Ġ int\",\n \"Ġin t\",\n \"Ġi nt\",\n \"âĢ Ļ\",\n \"_ t\",\n \"e rt\",\n \"er t\",\n \"i al\",\n \"ia l\",\n \"a ct\",\n \"ac t\",\n \"} Ċ\",\n \"i ve\",\n \"iv e\",\n \"o de\",\n \"od e\",\n \"o st\",\n \"os t\",\n \"Ġ class\",\n \"Ġc lass\",\n \"Ġcl ass\",\n \"Ġclas s\",\n \"Ġcla ss\",\n \"Ġ not\",\n \"Ġn ot\",\n \"Ġno t\",\n \"o g\",\n \"o rd\",\n \"or d\",\n \"a lue\",\n \"al ue\",\n \"alu e\",\n \"a ll\",\n \"al l\",\n \"f f\",\n \"( );Ċ\",\n \"() ;Ċ\",\n \"(); Ċ\",\n \"o nt\",\n \"on t\",\n \"i me\",\n \"im e\",\n \"a re\",\n \"ar e\",\n \"Ġ U\",\n \"Ġ pr\",\n \"Ġp r\",\n \"Ġ :\",\n \"i es\",\n \"ie s\",\n \"i ze\",\n \"iz e\",\n \"u re\",\n \"ur e\",\n \"Ġ by\",\n \"Ġb y\",\n \"i re\",\n \"ir e\",\n \"Ġ }ĊĊ\",\n \"Ġ} ĊĊ\",\n \"Ġ}Ċ Ċ\",\n \". p\",\n \"Ġ sh\",\n \"Ġs h\",\n \"i ce\",\n \"ic e\",\n \"a st\",\n \"as t\",\n \"p tion\",\n \"pt ion\",\n \"t ring\",\n \"tr ing\",\n \"tri ng\",\n \"o k\",\n \"_ _\",\n \"c l\",\n \"# #\",\n \"Ġ he\",\n \"Ġh e\",\n \"a rd\",\n \"ar d\",\n \") .\",\n \"Ġ @\",\n \"i ew\",\n \"ie w\",\n \"ĉ ĉĉ\",\n \"ĉĉ ĉ\",\n \"Ġ was\",\n \"Ġw as\",\n \"Ġwa s\",\n \"i p\",\n \"t his\",\n \"th is\",\n \"Ġ u\",\n \"Ġ The\",\n \"ĠT he\",\n \"ĠTh e\",\n \"i de\",\n \"id e\",\n \"a ce\",\n \"ac e\",\n \"i b\",\n \"a c\",\n \"r ou\",\n \"ro u\",\n \"Ġ we\",\n \"Ġw e\",\n \"j ect\",\n \"je ct\",\n \"jec t\",\n \"Ġ public\",\n \"Ġp ublic\",\n \"Ġpub lic\",\n \"Ġpubli c\",\n \"a k\",\n \"v e\",\n \"a th\",\n \"at h\",\n \"o id\",\n \"oi d\",\n \"Ġ =>\",\n \"Ġ= >\",\n \"u st\",\n \"us t\",\n \"q ue\",\n \"qu e\",\n \"Ġ res\",\n \"Ġre s\",\n \"Ġr es\",\n \") )\",\n \"' s\",\n \"Ġ k\",\n \"a ns\",\n \"an s\",\n \"y st\",\n \"ys t\",\n \"un ction\",\n \"unc tion\",\n \"unct ion\",\n \"* *******\",\n \"** ******\",\n \"**** ****\",\n \"****** **\",\n \"*** *****\",\n \"***** ***\",\n \"******* *\",\n \"Ġ i\",\n \"Ġ us\",\n \"Ġu s\",\n \"p p\",\n \"1 0\",\n \"o ne\",\n \"on e\",\n \"a il\",\n \"ai l\",\n \"= ===\",\n \"== ==\",\n \"=== =\",\n \"n ame\",\n \"na me\",\n \"nam e\",\n \"Ġ str\",\n \"Ġs tr\",\n \"Ġst r\",\n \"Ġ /\",\n \"Ġ &\",\n \"a ch\",\n \"ac h\",\n \"d iv\",\n \"di v\",\n \"y stem\",\n \"yst em\",\n \"ys tem\",\n \"e ll\",\n \"el l\",\n \"Ġ have\",\n \"Ġh ave\",\n \"Ġha ve\",\n \"Ġhav e\",\n \"e rr\",\n \"er r\",\n \"o uld\",\n \"ou ld\",\n \"oul d\",\n \"u ll\",\n \"ul l\",\n \"p on\",\n \"po n\",\n \"Ġ J\",\n \"_ p\",\n \"Ġ ==\",\n \"Ġ= =\",\n \"i gn\",\n \"ig n\",\n \"S t\",\n \". Ċ\",\n \"Ġ pl\",\n \"Ġp l\",\n \") ;ĊĊ\",\n \");Ċ Ċ\",\n \"); ĊĊ\",\n \"f orm\",\n \"fo rm\",\n \"for m\",\n \"p ut\",\n \"pu t\",\n \"o unt\",\n \"ou nt\",\n \"oun t\",\n \"} ĊĊ\",\n \"}Ċ Ċ\",\n \"d d\",\n \"i te\",\n \"it e\",\n \"Ġ get\",\n \"Ġg et\",\n \"Ġge t\",\n \"r r\",\n \"o me\",\n \"om e\",\n \"Ġ âĢ\",\n \"Ġâ Ģ\",\n \"a ram\",\n \"ar am\",\n \"ara m\",\n \"c c\",\n \"Ġ */\",\n \"Ġ* /\",\n \"E R\",\n \"I n\",\n \"l es\",\n \"le s\",\n \"_ s\",\n \"o ng\",\n \"on g\",\n \"i e\",\n \"Ġ can\",\n \"Ġc an\",\n \"Ġca n\",\n \"Ġ V\",\n \"e rv\",\n \"er v\",\n \"p r\",\n \"Ġ un\",\n \"Ġu n\",\n \"r ow\",\n \"ro w\",\n \"b er\",\n \"be r\",\n \"Ġ do\",\n \"Ġd o\",\n \"l l\",\n \"Ġ el\",\n \"Ġe l\",\n \"Ġ self\",\n \"Ġs elf\",\n \"Ġse lf\",\n \"Ġsel f\",\n \"a ted\",\n \"at ed\",\n \"ate d\",\n \"a ry\",\n \"ar y\",\n \"Ġ .\",\n \"' ]\",\n \"u d\",\n \"Ġ en\",\n \"Ġe n\",\n \"Ġ Th\",\n \"ĠT h\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"t e\",\n \"_ c\",\n \"u ct\",\n \"uc t\",\n \"Ġ ab\",\n \"Ġa b\",\n \"o rk\",\n \"or k\",\n \". get\",\n \".g et\",\n \".ge t\",\n \"Ġ #\",\n \"a w\",\n \"r ess\",\n \"re ss\",\n \"res s\",\n \"o b\",\n \"N ame\",\n \"Na me\",\n \"Nam e\",\n \"2 01\",\n \"20 1\",\n \"a pp\",\n \"ap p\",\n \"[ '\",\n \"Ġ all\",\n \"Ġa ll\",\n \"Ġal l\",\n \"o ry\",\n \"or y\",\n \"i tion\",\n \"it ion\",\n \"iti on\",\n \"a nce\",\n \"an ce\",\n \"anc e\",\n \"e ar\",\n \"ea r\",\n \"Ġ cont\",\n \"Ġc ont\",\n \"Ġcon t\",\n \"Ġco nt\",\n \"v ent\",\n \"ve nt\",\n \"ven t\",\n \"i a\",\n \"Ġ will\",\n \"Ġw ill\",\n \"Ġwi ll\",\n \"Ġwil l\",\n \"I N\",\n \"Ġ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠ\",\n \"r eturn\",\n \"re turn\",\n \"ret urn\",\n \"Ġ \",\n \"\\\" ,Ċ\",\n \"\\\", Ċ\",\n \"e c\",\n \"Ġ In\",\n \"ĠI n\",\n \"p h\",\n \"Ġ |\",\n \"_ f\",\n \"Ġ var\",\n \"Ġv ar\",\n \"Ġva r\",\n \"e nce\",\n \"en ce\",\n \"enc e\",\n \"I d\",\n \"r ee\",\n \"re e\",\n \"i nk\",\n \"in k\",\n \"l ect\",\n \"le ct\",\n \"lec t\",\n \"u g\",\n \"e th\",\n \"et h\",\n \"Ġ else\",\n \"Ġe lse\",\n \"Ġel se\",\n \"Ġels e\",\n \"- ---------------\",\n \"-- --------------\",\n \"---- ------------\",\n \"-------- --------\",\n \"--- -------------\",\n \"------------ ----\",\n \"----- -----------\",\n \"---------- ------\",\n \"------ ----------\",\n \"----------- -----\",\n \"------------- ---\",\n \"------- ---------\",\n \"--------- -------\",\n \"--------------- -\",\n \"-------------- --\",\n \"1 9\",\n \"c ont\",\n \"con t\",\n \"co nt\",\n \"Ġ so\",\n \"Ġs o\",\n \"a tic\",\n \"at ic\",\n \"ati c\",\n \"Ġ lo\",\n \"Ġl o\",\n \"p ro\",\n \"pr o\",\n \"t on\",\n \"to n\",\n \"s s\",\n \"o wn\",\n \"ow n\",\n \"a bel\",\n \"ab el\",\n \"abe l\",\n \"o int\",\n \"oin t\",\n \"oi nt\",\n \"o us\",\n \"ou s\",\n \"e ld\",\n \"el d\",\n \"S T\",\n \"T he\",\n \"Th e\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"R E\",\n \"\\\" :\",\n \"o lor\",\n \"ol or\",\n \"olo r\",\n \"t p\",\n \"e g\",\n \"k ey\",\n \"ke y\",\n \"u de\",\n \"ud e\",\n \"Ġ St\",\n \"ĠS t\",\n \"o und\",\n \"ou nd\",\n \"oun d\",\n \"Ġ ar\",\n \"Ġa r\",\n \"\\\" );Ċ\",\n \"\\\") ;Ċ\",\n \"\\\"); Ċ\",\n \"e ner\",\n \"en er\",\n \"ene r\",\n \"s er\",\n \"se r\",\n \"1 1\",\n \"b ject\",\n \"bj ect\",\n \"es sage\",\n \"ess age\",\n \"essa ge\",\n \"f er\",\n \"fe r\",\n \"Ġ more\",\n \"Ġm ore\",\n \"Ġmor e\",\n \"Ġmo re\",\n \"at ions\",\n \"ation s\",\n \"atio ns\",\n \"ati ons\",\n \"e nts\",\n \"en ts\",\n \"ent s\",\n \"Ġ his\",\n \"Ġh is\",\n \"Ġhi s\",\n \"Ġ they\",\n \"Ġt hey\",\n \"Ġth ey\",\n \"Ġthe y\",\n \". S\",\n \"Ġ Y\",\n \"u se\",\n \"us e\",\n \"n e\",\n \"i sh\",\n \"is h\",\n \"o ld\",\n \"ol d\",\n \"_ d\",\n \"i o\",\n \"i eld\",\n \"ie ld\",\n \"iel d\",\n \"Ġ per\",\n \"Ġp er\",\n \"Ġpe r\",\n \"C ont\",\n \"Con t\",\n \"Co nt\",\n \"in gs\",\n \"ing s\",\n \"# ###\",\n \"## ##\",\n \"### #\",\n \"Ġ data\",\n \"Ġd ata\",\n \"Ġda ta\",\n \"Ġdat a\",\n \"Ġ sa\",\n \"Ġs a\",\n \"e f\",\n \"f o\",\n \"Ġ one\",\n \"Ġo ne\",\n \"Ġon e\",\n \"e ng\",\n \"en g\",\n \"Ġ dis\",\n \"Ġd is\",\n \"Ġdi s\",\n \"A T\",\n \"Ġ name\",\n \"Ġn ame\",\n \"Ġna me\",\n \"Ġnam e\",\n \"Ġ true\",\n \"Ġtr ue\",\n \"v al\",\n \"va l\",\n \"l ed\",\n \"le d\",\n \". f\",\n \"Ġ ne\",\n \"Ġn e\",\n \"Ġ end\",\n \"Ġe nd\",\n \"Ġen d\",\n \"3 2\",\n \". T\",\n \"1 6\",\n \"c re\",\n \"cr e\",\n \"a rk\",\n \"ar k\",\n \"l og\",\n \"lo g\",\n \"E x\",\n \"e rror\",\n \"er ror\",\n \"err or\",\n \"erro r\",\n \"_ id\",\n \"_i d\",\n \"ur re\",\n \"urr e\",\n \"a nge\",\n \"an ge\",\n \"ang e\",\n \"Ġ null\",\n \"Ġn ull\",\n \"Ġnu ll\",\n \"r ray\",\n \"rr ay\",\n \"Ġ my\",\n \"Ġm y\",\n \"p an\",\n \"pa n\",\n \"i ct\",\n \"ic t\",\n \"a tor\",\n \"at or\",\n \"ato r\",\n \"V iew\",\n \"Vi ew\",\n \"L ist\",\n \"Li st\",\n \"ĉ return\",\n \"ĉr eturn\",\n \"ĉret urn\",\n \"ĉre turn\",\n \"âĢ Ŀ\",\n \"Ġ pre\",\n \"Ġp re\",\n \"Ġpr e\",\n \"Ġ x\",\n \"c lude\",\n \"cl ude\",\n \"clud e\",\n \"a rg\",\n \"ar g\",\n \"1 5\",\n \"o v\",\n \". h\",\n \"Ġ >\",\n \"Ġ their\",\n \"Ġthe ir\",\n \"' )\",\n \"i rst\",\n \"ir st\",\n \"irs t\",\n \"i ck\",\n \"ic k\",\n \"g h\",\n \"L E\",\n \"O R\",\n \"Ġ private\",\n \"Ġpr ivate\",\n \"Ġpriv ate\",\n \"Ġprivat e\",\n \"t em\",\n \"te m\",\n \"čĊ čĊ\",\n \"u ser\",\n \"us er\",\n \"use r\",\n \"Ġ )\",\n \"c om\",\n \"co m\",\n \". A\",\n \"\\\" ;Ċ\",\n \"\\\"; Ċ\",\n \"Ġ id\",\n \"Ġi d\",\n \"r ead\",\n \"re ad\",\n \"rea d\",\n \"Ġ who\",\n \"Ġw ho\",\n \"Ġwh o\",\n \"_ b\",\n \"\\\" >Ċ\",\n \"\\\"> Ċ\",\n \"Ġ time\",\n \"Ġt ime\",\n \"Ġtim e\",\n \"Ġti me\",\n \"Ġ man\",\n \"Ġm an\",\n \"Ġma n\",\n \"r y\",\n \"= =======\",\n \"== ======\",\n \"==== ====\",\n \"=== =====\",\n \"====== ==\",\n \"===== ===\",\n \"======= =\",\n \"r oup\",\n \"ro up\",\n \"rou p\",\n \"r op\",\n \"ro p\",\n \"p ublic\",\n \"pub lic\",\n \"v el\",\n \"ve l\",\n \"um ber\",\n \"umb er\",\n \"b le\",\n \"bl e\",\n \"Ġ which\",\n \"Ġwh ich\",\n \"** **************\",\n \"**** ************\",\n \"******** ********\",\n \"************ ****\",\n \"************** **\",\n \"Ġ any\",\n \"Ġa ny\",\n \"Ġan y\",\n \"Ġ false\",\n \"Ġf alse\",\n \"Ġfa lse\",\n \"Ġfal se\",\n \"Ġfals e\",\n \"w e\",\n \"Ġ value\",\n \"Ġv alue\",\n \"Ġval ue\",\n \"Ġva lue\",\n \"Ġvalu e\",\n \"Ġ li\",\n \"Ġl i\",\n \"\\\" )\",\n \"n der\",\n \"nd er\",\n \"nde r\",\n \"g r\",\n \"Ġ no\",\n \"Ġn o\",\n \"p aram\",\n \"par am\",\n \"pa ram\",\n \"para m\",\n \"2 5\",\n \"f ig\",\n \"fi g\",\n \". com\",\n \".c om\",\n \".co m\",\n \"Ġ app\",\n \"Ġa pp\",\n \"Ġap p\",\n \"_ l\",\n \"i ons\",\n \"ion s\",\n \"io ns\",\n \". D\",\n \"Ġ Ch\",\n \"ĠC h\",\n \"Ġ about\",\n \"Ġa bout\",\n \"Ġab out\",\n \"Ġ add\",\n \"Ġa dd\",\n \"Ġad d\",\n \"Ġ su\",\n \"Ġs u\",\n \"Ġ string\",\n \"Ġs tring\",\n \"Ġst ring\",\n \"Ġstr ing\",\n \"Ġstri ng\",\n \"I D\",\n \"Ġ over\",\n \"Ġo ver\",\n \"Ġov er\",\n \"s tring\",\n \"st ring\",\n \"str ing\",\n \"stri ng\",\n \". l\",\n \"our ce\",\n \"0 00\",\n \"00 0\",\n \"_ C\",\n \"] Ċ\",\n \"Ġ qu\",\n \"Ġq u\",\n \"Ġ String\",\n \"ĠS tring\",\n \"ĠSt ring\",\n \"ĠStr ing\",\n \"c a\",\n \"S E\",\n \"Ġ ro\",\n \"Ġr o\",\n \"s h\",\n \"u al\",\n \"ua l\",\n \"T ype\",\n \"Typ e\",\n \"Ty pe\",\n \"s on\",\n \"so n\",\n \"n ew\",\n \"ne w\",\n \"e rn\",\n \"er n\",\n \"Ġ ag\",\n \"Ġa g\",\n \"A R\",\n \"] ;Ċ\",\n \"]; Ċ\",\n \"] .\",\n \"Ġ ?\",\n \"i cal\",\n \"ic al\",\n \"ica l\",\n \"Ġ des\",\n \"Ġd es\",\n \"Ġde s\",\n \"u th\",\n \"ut h\",\n \"i x\",\n \"a ys\",\n \"ay s\",\n \"Ġ type\",\n \"Ġt ype\",\n \"Ġtyp e\",\n \"Ġty pe\",\n \"' t\",\n \"a ult\",\n \"au lt\",\n \"aul t\",\n \"Ġ inter\",\n \"Ġin ter\",\n \"Ġint er\",\n \"Ġinte r\",\n \"v ar\",\n \"va r\",\n \". b\",\n \"Ġ part\",\n \"Ġp art\",\n \"Ġpar t\",\n \"Ġpa rt\",\n \". d\",\n \"ur rent\",\n \"urre nt\",\n \"urr ent\",\n \"I T\",\n \"E N\",\n \"3 0\",\n \"e nc\",\n \"en c\",\n \"( f\",\n \"r a\",\n \"v alue\",\n \"val ue\",\n \"va lue\",\n \"valu e\",\n \"c ho\",\n \"ch o\",\n \"1 8\",\n \"ut ton\",\n \"utt on\",\n \"utto n\",\n \"o se\",\n \"os e\",\n \"1 4\",\n \"Ġ !=\",\n \"Ġ! =\",\n \"a ter\",\n \"at er\",\n \"ate r\",\n \"à ©\",\n \"re ate\",\n \"reat e\",\n \"rea te\",\n \"o ll\",\n \"ol l\",\n \"p os\",\n \"po s\",\n \"y le\",\n \"yl e\",\n \"n g\",\n \"A L\",\n \"u sing\",\n \"us ing\",\n \"usi ng\",\n \"a mes\",\n \"am es\",\n \"ame s\",\n \"Ġ {čĊ\",\n \"Ġ{ čĊ\",\n \"a tes\",\n \"at es\",\n \"ate s\",\n \"e ly\",\n \"el y\",\n \"Ġ work\",\n \"Ġw ork\",\n \"Ġwor k\",\n \"Ġwo rk\",\n \"Ġ em\",\n \"Ġe m\",\n \"i nal\",\n \"in al\",\n \"ina l\",\n \"Ġ sp\",\n \"Ġs p\",\n \"Ġ when\",\n \"Ġw hen\",\n \"Ġwh en\",\n \"Ġwhe n\",\n \". set\",\n \".s et\",\n \".se t\",\n \"Ġ ĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠ\",\n \"ĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠ Ġ\",\n \") :Ċ\",\n \"): Ċ\",\n \"t o\",\n \"q uire\",\n \"qu ire\",\n \"quir e\",\n \"qui re\",\n \"ind ow\",\n \"indo w\",\n \"l ement\",\n \"le ment\",\n \"lem ent\",\n \"lemen t\",\n \"leme nt\",\n \"p ect\",\n \"pe ct\",\n \"pec t\",\n \"a sh\",\n \"as h\",\n \"[ i\",\n \"Ġ use\",\n \"Ġu se\",\n \"Ġus e\",\n \". F\",\n \"p ec\",\n \"pe c\",\n \"Ġ ad\",\n \"Ġa d\",\n \"o ve\",\n \"ov e\",\n \"ce ption\",\n \"cept ion\",\n \"cep tion\",\n \"e ngth\",\n \"en gth\",\n \"eng th\",\n \"in clude\",\n \"inc lude\",\n \"incl ude\",\n \"inclu de\",\n \"a der\",\n \"ad er\",\n \"ade r\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"at us\",\n \"atu s\",\n \"T h\",\n \"i tle\",\n \"it le\",\n \"r it\",\n \"ri t\",\n \"v oid\",\n \"vo id\",\n \"( ).\",\n \"() .\",\n \"( Ċ\",\n \"Ġ off\",\n \"Ġo ff\",\n \"Ġof f\",\n \"Ġ other\",\n \"Ġo ther\",\n \"Ġot her\",\n \"Ġ &&\",\n \"Ġ& &\",\n \"' ;Ċ\",\n \"'; Ċ\",\n \"m s\",\n \"Ġ been\",\n \"Ġb een\",\n \"Ġbe en\",\n \"Ġbee n\",\n \"Ġ te\",\n \"Ġt e\",\n \"m l\",\n \"c o\",\n \"n c\",\n \"1 3\",\n \"er vice\",\n \"erv ice\",\n \"Ġ %\",\n \"* *Ċ\",\n \"** Ċ\",\n \"a nn\",\n \"an n\",\n \"a de\",\n \"ad e\",\n \"Ċ ĊĊĊ\",\n \"ĊĊ ĊĊ\",\n \"ĊĊĊ Ċ\",\n \"l ock\",\n \"lo ck\",\n \"loc k\",\n \"con st\",\n \"co nst\",\n \"cons t\",\n \"1 00\",\n \"10 0\",\n \"p onse\",\n \"pon se\",\n \"pons e\",\n \"Ġ sup\",\n \"Ġs up\",\n \"Ġsu p\",\n \"+ +\",\n \"d ate\",\n \"da te\",\n \"dat e\",\n \"Ġ acc\",\n \"Ġa cc\",\n \"Ġac c\",\n \"Ġ had\",\n \"Ġh ad\",\n \"Ġha d\",\n \"Ġ bu\",\n \"Ġb u\",\n \"2 00\",\n \"20 0\",\n \"Ġ Re\",\n \"ĠR e\",\n \"Ġ were\",\n \"Ġw ere\",\n \"Ġwe re\",\n \"Ġwer e\",\n \"Ġ file\",\n \"Ġf ile\",\n \"Ġfil e\",\n \"Ġfi le\",\n \"Ġ would\",\n \"Ġw ould\",\n \"Ġwo uld\",\n \"Ġ âĢľ\",\n \"ĠâĢ ľ\",\n \"v en\",\n \"ve n\",\n \"i ss\",\n \"is s\",\n \"Ġ our\",\n \"Ġo ur\",\n \"Ġou r\",\n \"c lass\",\n \"cl ass\",\n \"cla ss\",\n \"clas s\",\n \"r aw\",\n \"ra w\",\n \"Ġ year\",\n \"Ġy ear\",\n \"Ġye ar\",\n \"D ata\",\n \"Da ta\",\n \"Dat a\",\n \"Ġ val\",\n \"Ġv al\",\n \"Ġva l\",\n \"Ġ some\",\n \"Ġs ome\",\n \"Ġso me\",\n \"Ġsom e\",\n \"f ter\",\n \"ft er\",\n \"fte r\",\n \"y s\",\n \"Ġ ///\",\n \"Ġ// /\",\n \"Ġ/ //\",\n \"r ound\",\n \"ro und\",\n \"rou nd\",\n \"v iew\",\n \"vi ew\",\n \"vie w\",\n \"Ġ pe\",\n \"Ġp e\",\n \"Ġ there\",\n \"Ġt here\",\n \"Ġth ere\",\n \"Ġthe re\",\n \"Ġther e\",\n \"Ġ said\",\n \"Ġs aid\",\n \"Ġsa id\",\n \"Ġsai d\",\n \"d u\",\n \"o f\",\n \"l ine\",\n \"li ne\",\n \"lin e\",\n \"/ *\",\n \"d uct\",\n \"du ct\",\n \"duc t\",\n \"Ġ her\",\n \"Ġh er\",\n \"Ġhe r\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"R es\",\n \"Re s\",\n \"Ġ co\",\n \"Ġc o\",\n \"Ġ comm\",\n \"Ġc omm\",\n \"Ġcom m\",\n \"Ġco mm\",\n \"i se\",\n \"is e\",\n \"m in\",\n \"mi n\",\n \"Ġ ĠĠĠĊ\",\n \"ĠĠ ĠĠĊ\",\n \"ĠĠĠĠ Ċ\",\n \"ĠĠĠ ĠĊ\",\n \"# include\",\n \"et hod\",\n \"eth od\",\n \". P\",\n \"u te\",\n \"ut e\",\n \"Ġ ass\",\n \"Ġa ss\",\n \"Ġas s\",\n \"I nt\",\n \"In t\",\n \"a sk\",\n \"as k\",\n \"l oc\",\n \"lo c\",\n \"Ġ like\",\n \"Ġl ike\",\n \"Ġli ke\",\n \"Ġlik e\",\n \"o dy\",\n \"od y\",\n \"Ġ let\",\n \"Ġl et\",\n \"Ġle t\",\n \"l oad\",\n \"lo ad\",\n \"Ġ am\",\n \"Ġa m\",\n \"r ol\",\n \"ro l\",\n \"Ġ gr\",\n \"Ġg r\",\n \"y p\",\n \"Ġ also\",\n \"Ġal so\",\n \"Ġals o\",\n \"Ġ It\",\n \"ĠI t\",\n \"u rl\",\n \"ur l\",\n \"i fic\",\n \"if ic\",\n \"ifi c\",\n \"o rs\",\n \"or s\",\n \"_ P\",\n \"_ n\",\n \"i gh\",\n \"ig h\",\n \"Ġ than\",\n \"Ġt han\",\n \"Ġth an\",\n \"Ġtha n\",\n \"C om\",\n \"Co m\",\n \"A N\",\n \"U L\",\n \"a ting\",\n \"at ing\",\n \"ati ng\",\n \"atin g\",\n \"1 7\",\n \"Ġ This\",\n \"ĠT his\",\n \"ĠTh is\",\n \"ĠThi s\",\n \"r ef\",\n \"re f\",\n \"_ S\",\n \"Ġ static\",\n \"Ġst atic\",\n \"Ġstat ic\",\n \"Ġsta tic\",\n \"Ġstati c\",\n \"r oll\",\n \"ro ll\",\n \"rol l\",\n \"Ġ just\",\n \"Ġj ust\",\n \"Ġju st\",\n \"Ġjus t\",\n \"Ġ result\",\n \"Ġres ult\",\n \"i an\",\n \"ia n\",\n \"id th\",\n \"Ġ them\",\n \"Ġt hem\",\n \"Ġth em\",\n \"Ġthe m\",\n \") );Ċ\",\n \")) ;Ċ\",\n \")); Ċ\",\n \"d er\",\n \"de r\",\n \"re ak\",\n \"rea k\",\n \"C on\",\n \"Co n\",\n \": //\",\n \":/ /\",\n \"u le\",\n \"ul e\",\n \". ..\",\n \".. .\",\n \"a rch\",\n \"ar ch\",\n \"arc h\",\n \"e ment\",\n \"em ent\",\n \"eme nt\",\n \"emen t\",\n \"Ġ <<\",\n \"Ġ< <\",\n \"5 0\",\n \"u sh\",\n \"us h\",\n \"en se\",\n \"ens e\",\n \"a rr\",\n \"ar r\",\n \"Ġ into\",\n \"Ġin to\",\n \"Ġint o\",\n \"c ess\",\n \"ce ss\",\n \"ces s\",\n \"a mp\",\n \"am p\",\n \"i ed\",\n \"ie d\",\n \"u ment\",\n \"um ent\",\n \"ume nt\",\n \"umen t\",\n \"Ġ \\\\\",\n \"] ,\",\n \"w o\",\n \"a ls\",\n \"al s\",\n \"Ġ what\",\n \"Ġw hat\",\n \"Ġwh at\",\n \"a nc\",\n \"an c\",\n \"V alue\",\n \"Val ue\",\n \"Va lue\",\n \"= '\",\n \"o lum\",\n \"ol um\",\n \"olu m\",\n \"Ġ pos\",\n \"Ġp os\",\n \"Ġpo s\",\n \"a ges\",\n \"ag es\",\n \"age s\",\n \"a yer\",\n \"ay er\",\n \"aye r\",\n \"Ġ sc\",\n \"Ġs c\",\n \"u es\",\n \"ue s\",\n \"\\\" )Ċ\",\n \"\\\") Ċ\",\n \"_ T\",\n \"Ġ list\",\n \"Ġl ist\",\n \"Ġli st\",\n \"Ġlis t\",\n \"( s\",\n \"Ġ case\",\n \"Ġc ase\",\n \"Ġca se\",\n \"Ġcas e\",\n \"C h\",\n \"ĉ ĉĉĉĉ\",\n \"ĉĉ ĉĉĉ\",\n \"ĉĉĉĉ ĉ\",\n \"ĉĉĉ ĉĉ\",\n \"//// ////\",\n \"/// /////\",\n \"///// ///\",\n \"p onent\",\n \"pon ent\",\n \"pone nt\",\n \"Ġ z\",\n \"Ġ kn\",\n \"Ġk n\",\n \"l et\",\n \"le t\",\n \"D E\",\n \"r ed\",\n \"re d\",\n \"Ġ fe\",\n \"Ġf e\",\n \"Ġ },Ċ\",\n \"Ġ} ,Ċ\",\n \"Ġ}, Ċ\",\n \"Ġ ,\",\n \"( t\",\n \"Ġ first\",\n \"Ġf irst\",\n \"Ġfi rst\",\n \"Ġfir st\",\n \"' );Ċ\",\n \"') ;Ċ\",\n \"'); Ċ\",\n \"w ord\",\n \"wo rd\",\n \"wor d\",\n \"Ġ import\",\n \"Ġim port\",\n \"Ġimp ort\",\n \"Ġ act\",\n \"Ġa ct\",\n \"Ġac t\",\n \"Ġ char\",\n \"Ġc har\",\n \"Ġch ar\",\n \"Ġcha r\",\n \"C T\",\n \"Ġ Tr\",\n \"ĠT r\",\n \"o ple\",\n \"op le\",\n \"opl e\",\n \"= {\",\n \"ĉ f\",\n \"2 4\",\n \"i ent\",\n \"ie nt\",\n \"ien t\",\n \"c ent\",\n \"ce nt\",\n \"cen t\",\n \". j\",\n \"l ection\",\n \"le ction\",\n \"lect ion\",\n \"lec tion\",\n \") )Ċ\",\n \")) Ċ\",\n \"Ġ only\",\n \"Ġon ly\",\n \"Ġ print\",\n \"Ġp rint\",\n \"Ġpr int\",\n \"Ġpri nt\",\n \"Ġprin t\",\n \"m er\",\n \"me r\",\n \". W\",\n \"o ck\",\n \"oc k\",\n \"Ġ --\",\n \"Ġ- -\",\n \"T ext\",\n \"Te xt\",\n \"Tex t\",\n \"Ġ op\",\n \"Ġo p\",\n \"a nk\",\n \"an k\",\n \"Ġ its\",\n \"Ġit s\",\n \"Ġi ts\",\n \"Ġ back\",\n \"Ġb ack\",\n \"Ġba ck\",\n \"Ġbac k\",\n \"[ \\\"\",\n \"Ġ need\",\n \"Ġn eed\",\n \"Ġne ed\",\n \"Ġ cl\",\n \"Ġc l\",\n \"Ġ sub\",\n \"Ġs ub\",\n \"Ġsu b\",\n \"Ġ la\",\n \"Ġl a\",\n \"( (\",\n \". \\\"\",\n \"O bject\",\n \"Ob ject\",\n \"Obj ect\",\n \"Ġ start\",\n \"Ġst art\",\n \"Ġstar t\",\n \"Ġsta rt\",\n \"f ile\",\n \"fi le\",\n \"fil e\",\n \"( self\",\n \"(s elf\",\n \"(se lf\",\n \"(sel f\",\n \"n er\",\n \"ne r\",\n \"e y\",\n \"Ġ user\",\n \"Ġu ser\",\n \"Ġus er\",\n \"Ġuse r\",\n \"Ġ ent\",\n \"Ġe nt\",\n \"Ġen t\",\n \"Ġ Com\",\n \"ĠC om\",\n \"ĠCo m\",\n \"i ts\",\n \"it s\",\n \"Ġ Con\",\n \"ĠC on\",\n \"ĠCo n\",\n \"o uble\",\n \"ou ble\",\n \"oub le\",\n \"o wer\",\n \"ow er\",\n \"owe r\",\n \"i tem\",\n \"it em\",\n \"ite m\",\n \"v ery\",\n \"ver y\",\n \"ve ry\",\n \"Ġ We\",\n \"ĠW e\",\n \"6 4\",\n \"l ick\",\n \"lic k\",\n \"li ck\",\n \"Ġ Q\",\n \"p hp\",\n \"ph p\",\n \"t tp\",\n \"tt p\",\n \"' :\",\n \"i cs\",\n \"ic s\",\n \"Ġ under\",\n \"Ġu nder\",\n \"Ġun der\",\n \"Ġund er\",\n \"Ġunde r\",\n \"Ġ *Ċ\",\n \"Ġ* Ċ\",\n \". L\",\n \") ;\",\n \"i ces\",\n \"ic es\",\n \"ice s\",\n \"Ġ reg\",\n \"Ġre g\",\n \"Ġr eg\",\n \") čĊ\",\n \"ĉ public\",\n \"ĉp ublic\",\n \"ĉpub lic\",\n \"S S\",\n \"Ġ then\",\n \"Ġt hen\",\n \"Ġth en\",\n \"Ġthe n\",\n \"r eat\",\n \"re at\",\n \"rea t\",\n \"i ous\",\n \"io us\",\n \"iou s\",\n \". G\",\n \"e k\",\n \"i rect\",\n \"ir ect\",\n \"ire ct\",\n \"h eck\",\n \"he ck\",\n \"hec k\",\n \"cri pt\",\n \"cr ipt\",\n \"n ing\",\n \"ni ng\",\n \"nin g\",\n \"Ġ Un\",\n \"ĠU n\",\n \"Ġ may\",\n \"Ġm ay\",\n \"Ġma y\",\n \"Ġ Wh\",\n \"ĠW h\",\n \"B o\",\n \"I tem\",\n \"It em\",\n \"str uct\",\n \"stru ct\",\n \". st\",\n \".s t\",\n \"r eam\",\n \"re am\",\n \"rea m\",\n \"i ble\",\n \"ib le\",\n \"lo at\",\n \"Ġ org\",\n \"Ġo rg\",\n \"Ġor g\",\n \"u nd\",\n \"un d\",\n \"s um\",\n \"su m\",\n \"_ in\",\n \"_i n\",\n \". ./\",\n \".. /\",\n \"_ M\",\n \"Ġ how\",\n \"Ġh ow\",\n \"Ġho w\",\n \"r ite\",\n \"ri te\",\n \"rit e\",\n \"' Ċ\",\n \"T o\",\n \"4 0\",\n \"w w\",\n \"Ġ people\",\n \"Ġpe ople\",\n \"in dex\",\n \"ind ex\",\n \"inde x\",\n \". n\",\n \"h ttp\",\n \"ht tp\",\n \"htt p\",\n \"( m\",\n \"e ctor\",\n \"ect or\",\n \"ec tor\",\n \"Ġ ind\",\n \"Ġin d\",\n \"Ġi nd\",\n \"Ġ jav\",\n \"Ġj av\",\n \"Ġja v\",\n \"] ,Ċ\",\n \"], Ċ\",\n \"Ġ He\",\n \"ĠH e\",\n \"_ st\",\n \"_s t\",\n \"f ul\",\n \"fu l\",\n \"o le\",\n \"ol e\",\n \") {Ċ\",\n \"){ Ċ\",\n \"Ġ should\",\n \"Ġsh ould\",\n \"Ġsho uld\",\n \"o py\",\n \"op y\",\n \"e lp\",\n \"el p\",\n \"i er\",\n \"ie r\",\n \"_ name\",\n \"_n ame\",\n \"_na me\",\n \"er son\",\n \"ers on\",\n \"I ON\",\n \"IO N\",\n \"o te\",\n \"ot e\",\n \"Ġ test\",\n \"Ġt est\",\n \"Ġte st\",\n \"Ġtes t\",\n \"Ġ bet\",\n \"Ġb et\",\n \"Ġbe t\",\n \"r ror\",\n \"rr or\",\n \"u lar\",\n \"ul ar\",\n \"ula r\",\n \"ã Ģ\",\n \"Ġ Ð\",\n \"b s\",\n \"t ing\",\n \"ti ng\",\n \"tin g\",\n \"Ġ make\",\n \"Ġm ake\",\n \"Ġma ke\",\n \"Ġmak e\",\n \"T r\",\n \"Ġ after\",\n \"Ġa fter\",\n \"Ġaf ter\",\n \"Ġaft er\",\n \"ar get\",\n \"arg et\",\n \"arge t\",\n \"R O\",\n \"ol umn\",\n \"olum n\",\n \"olu mn\",\n \"r c\",\n \"_ re\",\n \"_r e\",\n \"de fine\",\n \"def ine\",\n \"2 2\",\n \"Ġ right\",\n \"Ġr ight\",\n \"Ġrig ht\",\n \"Ġri ght\",\n \"r ight\",\n \"ri ght\",\n \"rig ht\",\n \"d ay\",\n \"da y\",\n \"Ġ long\",\n \"Ġl ong\",\n \"Ġlo ng\",\n \"Ġlon g\",\n \"[ ]\",\n \"( p\",\n \"t d\",\n \"c ond\",\n \"con d\",\n \"co nd\",\n \"Ġ Pro\",\n \"ĠP ro\",\n \"ĠPr o\",\n \"Ġ rem\",\n \"Ġre m\",\n \"Ġr em\",\n \"pt ions\",\n \"ption s\",\n \"v id\",\n \"vi d\",\n \". g\",\n \"Ġ ext\",\n \"Ġe xt\",\n \"Ġex t\",\n \"Ġ __\",\n \"Ġ_ _\",\n \"' )Ċ\",\n \"') Ċ\",\n \"p ace\",\n \"pa ce\",\n \"pac e\",\n \"m p\",\n \"Ġ min\",\n \"Ġm in\",\n \"Ġmi n\",\n \"st ance\",\n \"sta nce\",\n \"stan ce\",\n \"a ir\",\n \"ai r\",\n \"a ction\",\n \"act ion\",\n \"ac tion\",\n \"w h\",\n \"t ype\",\n \"ty pe\",\n \"typ e\",\n \"u til\",\n \"ut il\",\n \"uti l\",\n \"a it\",\n \"ai t\",\n \"< ?\",\n \"I C\",\n \"t ext\",\n \"te xt\",\n \"tex t\",\n \"Ġ ph\",\n \"Ġp h\",\n \"Ġ fl\",\n \"Ġf l\",\n \". M\",\n \"c cess\",\n \"cc ess\",\n \"cce ss\",\n \"b r\",\n \"f ore\",\n \"fo re\",\n \"for e\",\n \"ers ion\",\n \") ,Ċ\",\n \"), Ċ\",\n \". re\",\n \".r e\",\n \"a teg\",\n \"at eg\",\n \"ate g\",\n \"Ġ loc\",\n \"Ġl oc\",\n \"Ġlo c\",\n \"i ns\",\n \"in s\",\n \"- s\",\n \"t rib\",\n \"tr ib\",\n \"tri b\",\n \"Ġ Int\",\n \"ĠI nt\",\n \"ĠIn t\",\n \"Ġ array\",\n \"Ġa rray\",\n \"Ġar ray\",\n \"Ġarr ay\",\n \", \\\"\",\n \"P ro\",\n \"Pr o\",\n \"( c\",\n \"ess ion\",\n \"> ĊĊ\",\n \">Ċ Ċ\",\n \"Ġ she\",\n \"Ġs he\",\n \"Ġsh e\",\n \"\\\" ]\",\n \"a ph\",\n \"ap h\",\n \"Ġ exp\",\n \"Ġe xp\",\n \"Ġex p\",\n \"er ty\",\n \"ert y\",\n \"Ġ Se\",\n \"ĠS e\",\n \"Ġ par\",\n \"Ġp ar\",\n \"Ġpa r\",\n \"u nc\",\n \"un c\",\n \"E T\",\n \"Ġ read\",\n \"Ġre ad\",\n \"Ġr ead\",\n \"p rint\",\n \"pr int\",\n \"pri nt\",\n \"Ġ rel\",\n \"Ġre l\",\n \"Ġr el\",\n \"Ġ form\",\n \"Ġf orm\",\n \"Ġfor m\",\n \"Ġfo rm\",\n \"Ġ dr\",\n \"Ġd r\",\n \"Ex ception\",\n \"Except ion\",\n \"in put\",\n \"inp ut\",\n \"Ġ trans\",\n \"Ġt rans\",\n \"Ġtr ans\",\n \"Ġtra ns\",\n \"Ġtran s\",\n \"# #######\",\n \"## ######\",\n \"#### ####\",\n \"### #####\",\n \"##### ###\",\n \"###### ##\",\n \"####### #\",\n \"or der\",\n \"ord er\",\n \"orde r\",\n \"B y\",\n \"Ġ aw\",\n \"Ġa w\",\n \"i ties\",\n \"it ies\",\n \"iti es\",\n \"u ff\",\n \"uf f\",\n \"p lay\",\n \"pl ay\",\n \"pla y\",\n \". add\",\n \".a dd\",\n \".ad d\",\n \"Ġ âĢĵ\",\n \"ĠâĢ ĵ\",\n \"Ġ want\",\n \"Ġw ant\",\n \"Ġwa nt\",\n \"Ġwan t\",\n \"Ġ comp\",\n \"Ġc omp\",\n \"Ġcom p\",\n \"Ġco mp\",\n \"m ents\",\n \"ment s\",\n \"me nts\",\n \"men ts\",\n \"Ġ ||\",\n \"Ġ| |\",\n \"a z\",\n \"b e\",\n \"Ġ number\",\n \"Ġn umber\",\n \"Ġnum ber\",\n \"Ġnumb er\",\n \"Ġ require\",\n \"Ġre quire\",\n \"Ġreq uire\",\n \"Ġrequ ire\",\n \"Ġ Ex\",\n \"ĠE x\",\n \"6 0\",\n \"Ġ col\",\n \"Ġc ol\",\n \"Ġco l\",\n \"Ġ key\",\n \"Ġk ey\",\n \"Ġke y\",\n \"em ber\",\n \"emb er\",\n \"Ġ two\",\n \"Ġt wo\",\n \"Ġtw o\",\n \"Ġ size\",\n \"Ġs ize\",\n \"Ġsi ze\",\n \"Ġsiz e\",\n \"Ġ where\",\n \"Ġw here\",\n \"Ġwh ere\",\n \"Ġwhe re\",\n \"U T\",\n \"res ult\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"o ugh\",\n \"ou gh\",\n \"oug h\",\n \"or ld\",\n \"o od\",\n \"oo d\",\n \"u ch\",\n \"uc h\",\n \"at ive\",\n \"ati ve\",\n \"ativ e\",\n \"g er\",\n \"ge r\",\n \"a rent\",\n \"ar ent\",\n \"are nt\",\n \"aren t\",\n \"Ġ /*\",\n \"Ġ/ *\",\n \"Ġ arg\",\n \"Ġa rg\",\n \"Ġar g\",\n \"Ġ while\",\n \"Ġwh ile\",\n \"2 3\",\n \"( this\",\n \"(t his\",\n \"(th is\",\n \"Ġ rec\",\n \"Ġre c\",\n \"Ġr ec\",\n \"Ġ dif\",\n \"Ġd if\",\n \"Ġdi f\",\n \"St ate\",\n \"Stat e\",\n \"Ġ spec\",\n \"Ġs pec\",\n \"Ġsp ec\",\n \"Ġspe c\",\n \"r ide\",\n \"ri de\",\n \"rid e\",\n \"_ F\",\n \"Ġ look\",\n \"Ġl ook\",\n \"Ġlo ok\",\n \"A M\",\n \"il ity\",\n \"ilit y\",\n \"ili ty\",\n \"e ter\",\n \"et er\",\n \"ete r\",\n \"âĢĻ t\",\n \"Ċ ĊĊ\",\n \"ĊĊ Ċ\",\n \"ay out\",\n \"ayo ut\",\n \"-- ------------------------------\",\n \"---- ----------------------------\",\n \"---------------- ----------------\",\n \"------------ --------------------\",\n \"---------------------------- ----\",\n \"------------------------------ --\",\n \"-------------------- ------------\",\n \"a ger\",\n \"ag er\",\n \"age r\",\n \"Ġ could\",\n \"Ġc ould\",\n \"Ġco uld\",\n \"Ġcou ld\",\n \"Ġ br\",\n \"Ġb r\",\n \"e nds\",\n \"en ds\",\n \"end s\",\n \"u res\",\n \"ur es\",\n \"ure s\",\n \"Ġ know\",\n \"Ġk now\",\n \"Ġkn ow\",\n \"e ts\",\n \"et s\",\n \"Ġ If\",\n \"ĠI f\",\n \"Ġ Sh\",\n \"ĠS h\",\n \". w\",\n \"b ack\",\n \"ba ck\",\n \"bac k\",\n \"Ġ ser\",\n \"Ġs er\",\n \"Ġse r\",\n \"Ġ +=\",\n \"Ġ+ =\",\n \"Ġ fr\",\n \"Ġf r\",\n \"( ));Ċ\",\n \"() );Ċ\",\n \"()) ;Ċ\",\n \"()); Ċ\",\n \"Ġ hand\",\n \"Ġh and\",\n \"Ġha nd\",\n \"Ġhan d\",\n \"I nd\",\n \"In d\",\n \"U LL\",\n \"UL L\",\n \"I m\",\n \"( );ĊĊ\",\n \"() ;ĊĊ\",\n \"();Ċ Ċ\",\n \"(); ĊĊ\",\n \"Ġ most\",\n \"Ġm ost\",\n \"Ġmo st\",\n \"Ġmos t\",\n \"Ġ try\",\n \"Ġt ry\",\n \"Ġtr y\",\n \"Ġ now\",\n \"Ġn ow\",\n \"Ġno w\",\n \"r ough\",\n \"ro ugh\",\n \"rou gh\",\n \"> čĊ\",\n \"ack age\",\n \"Ġ him\",\n \"Ġh im\",\n \"Ġhi m\",\n \". _\",\n \"i fy\",\n \"if y\",\n \"Ġ break\",\n \"Ġb reak\",\n \"Ġbre ak\",\n \"Ġ );Ċ\",\n \"Ġ) ;Ċ\",\n \"Ġ); Ċ\",\n \"r en\",\n \"re n\",\n \"# define\",\n \"i tt\",\n \"it t\",\n \"Ġ ap\",\n \"Ġa p\",\n \"ĉ c\",\n \"( n\",\n \"Ġ You\",\n \"ĠY ou\",\n \"ĠYo u\",\n \": ĊĊ\",\n \":Ċ Ċ\",\n \"- m\",\n \"Ġ every\",\n \"Ġe very\",\n \"Ġever y\",\n \"Ġev ery\",\n \"Ġeve ry\",\n \"us tom\",\n \"ust om\",\n \"usto m\",\n \"l ient\",\n \"li ent\",\n \"lie nt\",\n \"lien t\",\n \"oc ument\",\n \"ocu ment\",\n \"cri ption\",\n \"cript ion\",\n \"E rror\",\n \"Err or\",\n \"Er ror\",\n \"Erro r\",\n \"- b\",\n \"Ð ¾\",\n \"] [\",\n \"9 9\",\n \"t rans\",\n \"tr ans\",\n \"tra ns\",\n \"tran s\",\n \"Ġ point\",\n \"Ġp oint\",\n \"Ġpo int\",\n \"Ġpoi nt\",\n \"Ġ std\",\n \"Ġs td\",\n \"Ġst d\",\n \"Ġ fil\",\n \"Ġf il\",\n \"Ġfi l\",\n \"T ime\",\n \"Tim e\",\n \"Ti me\",\n \"8 0\",\n \"Ġ mod\",\n \"Ġm od\",\n \"Ġmo d\",\n \"Ġ ->\",\n \"Ġ- >\",\n \"Ġ error\",\n \"Ġe rror\",\n \"Ġerr or\",\n \"Ġer ror\",\n \"Ġerro r\",\n \"a h\",\n \"Ġ text\",\n \"Ġt ext\",\n \"Ġte xt\",\n \"Ġtex t\",\n \"r oller\",\n \"ro ller\",\n \"rol ler\",\n \"roll er\",\n \"l ose\",\n \"lo se\",\n \"los e\",\n \"q l\",\n \"Ġ pol\",\n \"Ġp ol\",\n \"Ġpo l\",\n \"> < /\",\n \"Ġ show\",\n \"Ġs how\",\n \"Ġsh ow\",\n \"Ġsho w\",\n \"U ser\",\n \"Us er\",\n \"Use r\",\n \"a sed\",\n \"as ed\",\n \"ase d\",\n \"Ġ {ĊĊ\",\n \"Ġ{ ĊĊ\",\n \"Ġ{Ċ Ċ\",\n \"Ġ find\",\n \"Ġf ind\",\n \"Ġfin d\",\n \"Ġfi nd\",\n \"Ð °\",\n \"E D\",\n \"s pan\",\n \"sp an\",\n \"spa n\",\n \"e nu\",\n \"en u\",\n \"Ġ current\",\n \"Ġc urrent\",\n \"Ġcur rent\",\n \"Ġcurr ent\",\n \"Ġ used\",\n \"Ġu sed\",\n \"Ġus ed\",\n \"Ġuse d\",\n \"c ept\",\n \"ce pt\",\n \"cep t\",\n \"cl ud\",\n \"Ġ play\",\n \"Ġp lay\",\n \"Ġpl ay\",\n \"Ġpla y\",\n \"Ġ log\",\n \"Ġl og\",\n \"Ġlo g\",\n \"u tion\",\n \"ut ion\",\n \"uti on\",\n \"f l\",\n \"Ġ see\",\n \"Ġs ee\",\n \"Ġse e\",\n \"ind ows\",\n \"indow s\",\n \"indo ws\",\n \"Ġ help\",\n \"Ġh elp\",\n \"Ġhe lp\",\n \"Ġhel p\",\n \"Ġ these\",\n \"Ġth ese\",\n \"Ġthe se\",\n \"Ġ pass\",\n \"Ġp ass\",\n \"Ġpas s\",\n \"Ġpa ss\",\n \"Ġ down\",\n \"Ġd own\",\n \"Ġdo wn\",\n \"Ġdow n\",\n \"Ġ even\",\n \"Ġe ven\",\n \"Ġev en\",\n \"Ġeve n\",\n \"a son\",\n \"as on\",\n \"aso n\",\n \"u ild\",\n \"ui ld\",\n \"uil d\",\n \"f rom\",\n \"fr om\",\n \"( d\",\n \"Ġ bl\",\n \"Ġb l\",\n \"l abel\",\n \"la bel\",\n \"lab el\",\n \"e lse\",\n \"el se\",\n \"els e\",\n \"Ð µ\",\n \"Ġ (!\",\n \"Ġ( !\",\n \"i zed\",\n \"iz ed\",\n \"ize d\",\n \"( ),\",\n \"() ,\",\n \"Ġ ob\",\n \"Ġo b\",\n \"Ġ item\",\n \"Ġit em\",\n \"Ġi tem\",\n \"u mp\",\n \"um p\",\n \"U R\",\n \"o rn\",\n 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\"**************************** ****\",\n \"************ ********************\",\n \"p s\",\n \"E lement\",\n \"El ement\",\n \"Elem ent\",\n \"Ele ment\",\n \"a lk\",\n \"al k\",\n \"l ication\",\n \"lic ation\",\n \"li cation\",\n \"lica tion\",\n \"b y\",\n \"S ize\",\n \"Si ze\",\n \"ra wing\",\n \"raw ing\",\n \"Ġ person\",\n \"Ġp erson\",\n \"Ġper son\",\n \"Ġpers on\",\n \"Ġperso n\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"\\\\ n\",\n \"o bject\",\n \"ob ject\",\n \"obj ect\",\n \"i nce\",\n \"in ce\",\n \"inc e\",\n \"E n\",\n \"F ile\",\n \"Fi le\",\n \"Fil e\",\n \"u f\",\n \"f fect\",\n \"ff ect\",\n \"ffe ct\",\n \"A C\",\n \"Ġ style\",\n \"Ġst yle\",\n \"Ġsty le\",\n \"Ġstyl e\",\n \"sum mary\",\n \"summ ary\",\n \"Ġ que\",\n \"Ġqu e\",\n \"Ġq ue\",\n \"_ r\",\n \"Ġ ($\",\n \"Ġ( $\",\n \"M odel\",\n \"Mode l\",\n \"Mod el\",\n \"Mo del\",\n \"i dent\",\n \"id ent\",\n \"ide nt\",\n \"iden t\",\n \"Ġ method\",\n \"Ġm ethod\",\n \"Ġmet hod\",\n \"Ġmeth od\",\n \"I L\",\n \"o tt\",\n \"ot t\",\n \"l ess\",\n \"le ss\",\n \"les s\",\n \"I NG\",\n \"IN G\",\n \"Ġ ()\",\n \"Ġ( )\",\n \"Ġ expect\",\n \"Ġex pect\",\n \"Ġexp ect\",\n \"y nc\",\n \"yn c\",\n \"p ackage\",\n \"pack age\",\n \"3 5\",\n \"u rs\",\n \"ur s\",\n \"Ġ prot\",\n \"Ġp rot\",\n \"Ġpro t\",\n \"Ġpr ot\",\n \". /\",\n \"p re\",\n \"pr e\",\n \"Ġ )Ċ\",\n \"Ġ) Ċ\",\n \"m a\",\n \"Ġ sur\",\n \"Ġs ur\",\n \"Ġsu r\",\n \"Ġ found\",\n \"Ġf ound\",\n \"Ġfo und\",\n \"Ġfou nd\",\n \"In fo\",\n \"Inf o\",\n \"p ar\",\n \"pa r\",\n \"i mes\",\n \"im es\",\n \"ime s\",\n \". e\",\n \"a ins\",\n \"ain s\",\n \"ai ns\",\n \"Ġ 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B\",\n \"- c\",\n \"Ġ open\",\n \"Ġo pen\",\n \"Ġop en\",\n \"Ġ est\",\n \"Ġe st\",\n \"Ġes t\",\n \"Ġ ĠĠĠĠĠĠĠĊ\",\n \"ĠĠ ĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠ ĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠ Ċ\",\n \"ĠĠĠ ĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠ ĠĊ\",\n \"ĠĠĠĠĠ ĠĠĠĊ\",\n \"ĠĠĠĠĠĠ ĠĠĊ\",\n \"Ġ next\",\n \"Ġn ext\",\n \"Ġne xt\",\n \"Ġnex t\",\n \"I M\",\n \"Ñ Ĥ\",\n \"O T\",\n \"à ³\",\n \"Ġ follow\",\n \"Ġf ollow\",\n \"Ġfol low\",\n \"Ġfoll ow\",\n \"c ontent\",\n \"con tent\",\n \"cont ent\",\n \"conte nt\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"Ġin clud\",\n \"Ġincl ud\",\n \"Ġinclu d\",\n \"H E\",\n \"Ġ Res\",\n \"ĠR es\",\n \"ĠRe s\",\n \"Ġ href\",\n \"Ġh ref\",\n \"Ġhr ef\",\n \"Ð ¸\",\n \"Ġ car\",\n \"Ġc ar\",\n \"Ġca r\",\n \"y pes\",\n \"ype s\",\n \"yp es\",\n \"i mage\",\n \"im age\",\n \"ima ge\",\n \"imag e\",\n \"U n\",\n \"Ġ bool\",\n \"Ġb ool\",\n \"Ġbo ol\",\n \"Ġboo l\",\n \"A D\",\n \"Ġ game\",\n \"Ġg ame\",\n \"Ġgam e\",\n \"Ġga me\",\n \". Form\",\n \".F orm\",\n \".For m\",\n \"r ows\",\n \"ro ws\",\n \"row s\",\n \"* /\",\n \"v elop\",\n \"ve lop\",\n \"vel op\",\n \". Drawing\",\n \".D rawing\",\n \".Draw ing\",\n \"Ġ path\",\n \"Ġp ath\",\n \"Ġpat h\",\n \"Ġpa th\",\n \"is ion\",\n \"isi on\",\n \"Ġ each\",\n \"Ġe ach\",\n \"Ġea ch\",\n \"Ġ Pl\",\n \"ĠP l\",\n \"_ type\",\n \"_t ype\",\n \"_typ e\",\n \"_ty pe\",\n \"P ath\",\n \"Pa th\",\n \"Pat h\",\n \"n ection\",\n \"ne ction\",\n \"nect ion\",\n \"Ġ av\",\n \"Ġa v\",\n \"' ).\",\n \"') .\",\n \"Ġ support\",\n \"Ġs upport\",\n \"Ġsup port\",\n \"Ġsupp ort\",\n \"E NT\",\n \"EN T\",\n \"r em\",\n \"re m\",\n \"\\\" ).\",\n \"\\\") .\",\n \"Ġ own\",\n \"Ġo wn\",\n \"Ġow n\",\n \"Ġ cor\",\n \"Ġc or\",\n \"Ġco r\",\n \"c ount\",\n \"co unt\",\n \"cou nt\",\n \"m iss\",\n \"mi ss\",\n \"mis s\",\n \"u ally\",\n \"ual ly\",\n \"Ġ mem\",\n \"Ġm em\",\n \"Ġme m\",\n \"s td\",\n \"st d\",\n \"i ence\",\n \"ie nce\",\n \"ien ce\",\n \"s earch\",\n \"se arch\",\n \"sea rch\",\n \"\\\" ĊĊ\",\n \"\\\"Ċ Ċ\",\n \"F orm\",\n \"For m\",\n \"Fo rm\",\n \"Ġ sex\",\n \"Ġs ex\",\n \"Ġse x\",\n \"e name\",\n \"en ame\",\n \"ena me\",\n \"Ġ sign\",\n \"Ġs ign\",\n \"Ġsi gn\",\n \"Ġsig n\",\n \"Ġ et\",\n \"Ġe t\",\n \"Ġ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠ\",\n \"' ,'\",\n \"', '\",\n \"Ġ App\",\n \"ĠA pp\",\n \"ĠAp p\",\n \"Ġ those\",\n \"Ġth ose\",\n \"Ġtho se\",\n \"o ff\",\n \"of f\",\n \"Ġ err\",\n \"Ġe rr\",\n \"Ġer r\",\n \"Ġ system\",\n \"Ġs ystem\",\n \"Ġsys tem\",\n \"Ġsy stem\",\n \"Ġsyst em\",\n \"Ġ best\",\n \"Ġb est\",\n \"Ġbe st\",\n \"Ġbes t\",\n \"c ode\",\n \"co de\",\n \"cod e\",\n \"Ġ same\",\n \"Ġs ame\",\n \"Ġsa me\",\n \"Ġsam e\",\n \"Ġ di\",\n \"Ġd i\",\n \"u ss\",\n \"us s\",\n \"Ġ create\",\n \"Ġc reate\",\n \"Ġcre ate\",\n \"Ġcreat e\",\n \"Ġcrea te\",\n \"a ther\",\n \"at her\",\n \"ath er\",\n \"athe r\",\n \"A rray\",\n \"Ar ray\",\n \"Arr ay\",\n \". in\",\n \".i n\",\n \"f e\",\n \"S ervice\",\n \"Ser vice\",\n \"Serv ice\",\n \"U N\",\n \"a ts\",\n \"at s\",\n \"Ġ Z\",\n \"al th\",\n \"alt h\",\n \"Ġ made\",\n \"Ġm ade\",\n \"Ġma de\",\n \"Ġmad e\",\n \"tr ue\",\n \"tru e\",\n \"A B\",\n \"Ġ mark\",\n \"Ġm ark\",\n \"Ġmar k\",\n \"Ġma rk\",\n \"r id\",\n \"ri d\",\n \"if ied\",\n \"ifi ed\",\n \"ifie d\",\n \", čĊ\",\n \"y n\",\n \"p ress\",\n \"pr ess\",\n \"pre ss\",\n \"pres s\",\n \"Ġ group\",\n \"Ġg roup\",\n \"Ġgr oup\",\n \"Ġgro up\",\n \"Ġgrou p\",\n \"Ġ fin\",\n \"Ġf in\",\n \"Ġfi n\",\n \"Ġ License\",\n \"ĠL icense\",\n \"ĠLic ense\",\n \"F ield\",\n \"Fi eld\",\n \"e ger\",\n \"eg er\",\n \"Ġ world\",\n \"Ġw orld\",\n \"Ġwor ld\",\n \"i ness\",\n \"in ess\",\n \"ine ss\",\n \"ines s\",\n \"t y\",\n \"Ġ process\",\n \"Ġp rocess\",\n \"Ġpro cess\",\n \"Ġproc ess\",\n \"Ġproces s\",\n \"( b\",\n \"Ġ cre\",\n \"Ġc re\",\n \"Ġcr e\",\n \"a rn\",\n \"ar n\",\n \"i ves\",\n \"iv es\",\n \"ive s\",\n \"Ġ main\",\n \"Ġm ain\",\n \"Ġma in\",\n \"Ġmai n\",\n \"i deo\",\n \"id eo\",\n \"ide o\",\n \"3 6\",\n \"_ g\",\n \"A G\",\n \"val id\",\n \"va lid\",\n \"i mg\",\n \"im g\",\n \"P I\",\n \"Ġ color\",\n \"Ġc olor\",\n \"Ġco lor\",\n \"Ġcol or\",\n \"Ġ report\",\n \"Ġre port\",\n \"Ġrep ort\",\n \"Ġrepo rt\",\n \"Ġ take\",\n \"Ġt ake\",\n \"Ġta ke\",\n \"Ġtak e\",\n \"r ib\",\n \"ri b\",\n \"O M\",\n \"Ġ day\",\n \"Ġd ay\",\n \"Ġda y\",\n \"Re quest\",\n \"Req uest\",\n \"Ġ sk\",\n \"Ġs k\",\n \"b ers\",\n \"ber s\",\n \"be rs\",\n \"ĉ s\",\n \". Add\",\n \".A dd\",\n \".Ad d\",\n \"o ot\",\n \"oo t\",\n \"I mage\",\n \"Im age\",\n \"Ġcom ple\",\n \"Ġcomp le\",\n \"Ġcompl e\",\n \"ol lection\",\n \"oll ection\",\n \"ollect ion\",\n \"olle ction\",\n \"Ġ top\",\n \"Ġt op\",\n \"Ġto p\",\n \"Ġ free\",\n \"Ġf ree\",\n \"Ġfr ee\",\n \"Ġfre e\",\n \"A S\",\n \"D e\",\n \"Ġ On\",\n \"ĠO n\",\n \"I G\",\n \"9 0\",\n \"e ta\",\n \"et a\",\n \"D ate\",\n \"Da te\",\n \"Dat e\",\n \"Ġ action\",\n \"Ġa ction\",\n \"Ġact ion\",\n \"Ġac tion\",\n \"3 4\",\n \"O ver\",\n \"i tor\",\n \"it or\",\n \"ito r\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"n ot\",\n \"no t\",\n \"Ġ index\",\n \"Ġin dex\",\n \"Ġind ex\",\n \"Ġinde x\",\n \"h er\",\n \"he r\",\n \"i con\",\n \"ic on\",\n \"ico n\",\n \"O n\",\n \"; čĊčĊ\",\n \";čĊ čĊ\",\n \"iv ity\",\n \"ivi ty\",\n \"m and\",\n \"man d\",\n \"ma nd\",\n \". Windows\",\n \".W indows\",\n \".Window s\",\n \"O L\",\n \"Ġ real\",\n \"Ġre al\",\n \"Ġ max\",\n \"Ġm ax\",\n \"Ġma x\",\n \"l and\",\n \"la nd\",\n \"lan d\",\n \". ...\",\n \".. ..\",\n \"... .\",\n \"r aph\",\n \"ra ph\",\n \"rap h\",\n \"Ġ build\",\n \"Ġb uild\",\n \"Ġbu ild\",\n \"l eg\",\n \"le g\",\n \"as sword\",\n \"ass word\",\n \"? ĊĊ\",\n \"?Ċ Ċ\",\n \"âĢ ¦\",\n \"o ok\",\n \"oo k\",\n \"u ck\",\n \"uc k\",\n \"Ġ message\",\n \"Ġm essage\",\n \"Ġmess age\",\n \"Ġmes sage\",\n \"t est\",\n \"te st\",\n \"tes t\",\n \"i vers\",\n \"iv ers\",\n \"ive rs\",\n \"iver s\",\n \"3 8\",\n \"Ġ input\",\n \"Ġin put\",\n \"Ġinp ut\",\n \"Ġ art\",\n \"Ġa rt\",\n \"Ġar t\",\n \"Ġ between\",\n \"Ġb etween\",\n \"Ġbet ween\",\n \"G et\",\n \"Ge t\",\n \"en ter\",\n \"ent er\",\n \"ente r\",\n \"g round\",\n \"gr ound\",\n \"gro und\",\n \"e ne\",\n \"en e\",\n \"à ¡\",\n \". length\",\n \".l ength\",\n \".len gth\",\n \".le ngth\",\n \"N ode\",\n \"No de\",\n \"( i\",\n \"C lass\",\n \"Cl ass\",\n \"Cla ss\",\n \"f or\",\n \"fo r\",\n \"Ġ âĢĶ\",\n \"ĠâĢ Ķ\",\n \"t en\",\n \"te n\",\n \"o in\",\n \"oi n\",\n \"Ġ ke\",\n \"Ġk e\",\n \"u i\",\n \"Ġ IN\",\n \"ĠI N\",\n \"Ġ table\",\n \"Ġt able\",\n \"Ġtab le\",\n \"Ġta ble\",\n \"s ub\",\n \"su b\",\n \"Ġ Le\",\n \"ĠL e\",\n \"Ġ head\",\n \"Ġh ead\",\n \"Ġhe ad\",\n \"Ġ must\",\n \"Ġm ust\",\n \"Ġmus t\",\n \"Ġmu st\",\n \"//// ////////////\",\n \"//////// ////////\",\n \"//////////// ////\",\n \". util\",\n \".u til\",\n \".ut il\",\n \"Cont ext\",\n \"Con text\",\n \"Ġ order\",\n \"Ġor der\",\n \"Ġord er\",\n \"Ġ mov\",\n \"Ġm ov\",\n \"Ġmo v\",\n \"o ver\",\n \"ov er\",\n \"ove r\",\n \"Ġ contin\",\n \"Ġcon tin\",\n \"Ġcont in\",\n \"Ġ say\",\n \"Ġs ay\",\n \"Ġsa y\",\n \"st atic\",\n \"stat ic\",\n \"sta tic\",\n \". Text\",\n \".T ext\",\n \".Te xt\",\n \"Ġ className\",\n \"Ġclass Name\",\n \"p any\",\n \"pan y\",\n \"pa ny\",\n \"Ġ ter\",\n \"Ġt er\",\n \"Ġte r\",\n \"h ead\",\n \"he ad\",\n \"hea d\",\n \"r g\",\n \"Ġ product\",\n \"Ġpro duct\",\n \"Ġprodu ct\",\n \"Ġprod uct\",\n \"T his\",\n \"Th is\",\n \". âĢĿ\",\n \"Ġ But\",\n \"ĠB ut\",\n \"ĠBu t\",\n \"7 0\",\n \"l oy\",\n \"lo y\",\n \"Ġ double\",\n \"Ġd ouble\",\n \"Ġdo uble\",\n \"Ġdoub le\",\n \"Ġdou ble\",\n \"s g\",\n \"Ġ place\",\n \"Ġp lace\",\n \"Ġpl ace\",\n \"Ġplac e\",\n \"Ġpla ce\",\n \". x\",\n \"m essage\",\n \"mes sage\",\n \"mess age\",\n \"Ġ information\",\n \"Ġin formation\",\n \"Ġinform ation\",\n \"Ġinformat ion\",\n \"pr ivate\",\n \"priv ate\",\n \"Ġ oper\",\n \"Ġo per\",\n \"Ġop er\",\n \"c ed\",\n \"ce d\",\n \"d b\",\n \"\\\" > < /\",\n \"P aram\",\n \"Par am\",\n \"Pa ram\",\n \"Para m\",\n \"i cle\",\n \"ic le\",\n \"icl e\",\n \"Ġ week\",\n \"Ġwe ek\",\n \"Ġwee k\",\n \"Ġ prop\",\n \"Ġp rop\",\n \"Ġpro p\",\n \"Ġpr op\",\n \"t able\",\n \"ta ble\",\n \"tab le\",\n \"tabl e\",\n \"id get\",\n \"idge t\",\n \"p lace\",\n \"pl ace\",\n \"pla ce\",\n \"P rop\",\n \"Pro p\",\n \"Pr op\",\n \"Ġ All\",\n \"ĠA ll\",\n \"ĠAl l\",\n \"e ls\",\n \"el s\",\n \"b ox\",\n \"bo x\",\n \". ĊĊĊĊ\",\n \".ĊĊ ĊĊ\",\n \".Ċ ĊĊĊ\",\n \".ĊĊĊ Ċ\",\n \". R\",\n \"Ġ To\",\n \"ĠT o\",\n \"i ter\",\n \"it er\",\n \"ite r\",\n \"S h\",\n \"u ration\",\n \"ur ation\",\n \"ura tion\",\n \"urat ion\",\n \"o lder\",\n \"ol der\",\n \"old er\",\n \"_ list\",\n \"_l ist\",\n \"_li st\",\n \"c ome\",\n \"com e\",\n \"co me\",\n \"Ġ sw\",\n \"Ġs w\",\n \"iz ation\",\n \"iza tion\",\n \"ĉ for\",\n \"ĉf or\",\n \"b l\",\n \"Ġ program\",\n \"Ġp rogram\",\n \"Ġpro gram\",\n \"Ġpr ogram\",\n \"Ġprog ram\",\n \"( e\",\n \"a pe\",\n \"ap e\",\n \"c heck\",\n \"ch eck\",\n \"che ck\",\n \". 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ĊĊ\",\n \"Ġw om\",\n \"Ġwo m\",\n \"l eft\",\n \"le ft\",\n \"lef t\",\n \"our ces\",\n \"ource s\",\n \"a tri\",\n \"at ri\",\n \"atr i\",\n \"Ġ Im\",\n \"ĠI m\",\n \"Ġ Man\",\n \"ĠM an\",\n \"ĠMa n\",\n \"or th\",\n \"ort h\",\n \"$ {\",\n \"8 8\",\n \"qu als\",\n \"qual s\",\n \"qua ls\",\n \"e se\",\n \"es e\",\n \"_ size\",\n \"_s ize\",\n \"_si ze\",\n \"Ġ iss\",\n \"Ġis s\",\n \"Ġi ss\",\n \"o tal\",\n \"ot al\",\n \"ota l\",\n \"- g\",\n \"i que\",\n \"iqu e\",\n \"iq ue\",\n \"r ame\",\n \"ra me\",\n \"ram e\",\n \"Ġ width\",\n \"Ġw idth\",\n \"Ġwid th\",\n \"e rg\",\n \"er g\",\n \") (\",\n \"it tle\",\n \"itt le\",\n \"T R\",\n \"Ġ They\",\n \"ĠT hey\",\n \"ĠThe y\",\n \"ĠTh ey\",\n \"e nces\",\n \"en ces\",\n \"ence s\",\n \"enc es\",\n \"0 2\",\n \"r l\",\n \"o ns\",\n \"on s\",\n \"Ġ label\",\n \"Ġl abel\",\n \"Ġla bel\",\n \"Ġlab el\",\n \". y\",\n \"- t\",\n \"up date\",\n \"upd ate\",\n \"a nel\",\n \"an el\",\n \"ane l\",\n \"s c\",\n \". to\",\n \".t o\",\n \"Ġ project\",\n \"Ġpro ject\",\n \"Ġproj ect\",\n \"Ġproje ct\",\n \"à ¼\",\n \"Ġ element\",\n \"Ġe lement\",\n \"Ġel ement\",\n \"Ġele ment\",\n \"Ġelem ent\",\n \"Ġ success\",\n \"Ġs uccess\",\n \"Ġsu ccess\",\n \"Ġsuc cess\",\n \"Ġsucc ess\",\n \"Ġsucces s\",\n \"ĉ ĉĊ\",\n \"ĉĉ Ċ\",\n \". sh\",\n \".s h\",\n \"r am\",\n \"ra m\",\n \"c hed\",\n \"ch ed\",\n \"che d\",\n \"( ))Ċ\",\n \"() )Ċ\",\n \"()) Ċ\",\n \"Ġ (Ċ\",\n \"Ġ( Ċ\",\n \"Ġ date\",\n \"Ġd ate\",\n \"Ġda te\",\n \"Ġdat e\",\n \"Ġ tot\",\n \"Ġt ot\",\n \"Ġto t\",\n \"_ ST\",\n \"_S T\",\n \"A ll\",\n \"Al l\",\n \"if ication\",\n \"ific ation\",\n \"ifi cation\",\n \"ifica tion\",\n \"ĉ var\",\n \"ĉv ar\",\n \"ĉva r\",\n \"Ġ tri\",\n \"Ġt ri\",\n \"Ġtr i\",\n \"c hem\",\n \"ch em\",\n \"che m\",\n \"m y\",\n \"Ġ big\",\n \"Ġb ig\",\n \"Ġbi g\",\n \"Ġ Ad\",\n \"ĠA d\",\n \"Ġ At\",\n \"ĠA t\",\n \"o ts\",\n \"ot s\",\n \"n um\",\n \"nu m\",\n \"A ct\",\n \"Ac t\",\n \"Ġ map\",\n \"Ġm ap\",\n \"Ġma p\",\n \"e ra\",\n \"er a\",\n \"c ope\",\n \"co pe\",\n \"cop e\",\n \". $\",\n \", âĢĿ\",\n \"Ġ pop\",\n \"Ġp op\",\n \"Ġpo p\",\n \"Ġ few\",\n \"Ġf ew\",\n \"Ġfe w\",\n \"Ġ len\",\n \"Ġl en\",\n \"Ġle n\",\n \"u id\",\n \"ui d\",\n \"e ters\",\n \"et ers\",\n \"eter s\",\n \"ete rs\",\n \"u les\",\n \"ul es\",\n \"ule s\",\n \"à Ń\",\n \"s ource\",\n \"ht tps\",\n \"http s\",\n \"htt ps\",\n \"Ġ dem\",\n \"Ġd em\",\n \"Ġde m\",\n \"Ġ ear\",\n \"Ġe ar\",\n \"Ġea r\",\n \"#### ############\",\n \"######## ########\",\n \"############ ####\",\n \"Ġ match\",\n \"Ġm atch\",\n \"Ġmat ch\",\n \"o ries\",\n \"or ies\",\n \"ori es\",\n \"orie s\",\n \"4 9\",\n \"a ces\",\n \"ace s\",\n \"ac es\",\n \"Ġ Cl\",\n \"ĠC l\",\n \"Ġ node\",\n \"Ġn ode\",\n \"Ġno de\",\n \"Ġnod e\",\n \"7 8\",\n \"i rc\",\n \"ir c\",\n \"l ocal\",\n \"lo cal\",\n \"loc al\",\n \"un ity\",\n \"unit y\",\n \"uni ty\",\n \"} ;Ċ\",\n \"}; Ċ\",\n \"Ġ another\",\n \"Ġan other\",\n \"Ġano ther\",\n \"< <\",\n \"o gle\",\n \"og le\",\n \"ogl e\",\n \"Ġ sit\",\n \"Ġs it\",\n \"Ġsi t\",\n \"e work\",\n \"ew ork\",\n \"T E\",\n \". I\",\n \"N S\",\n \"o logy\",\n \"ol ogy\",\n \"olog y\",\n \"olo gy\",\n \"o ught\",\n \"ou ght\",\n \"ough t\",\n \"oug ht\",\n \". Cont\",\n \".C ont\",\n \".Con t\",\n \".Co nt\",\n \"> >\",\n \"Ġ care\",\n \"Ġc are\",\n \"Ġcar e\",\n \"Ġca re\",\n \"st ate\",\n \"stat e\",\n \"sta te\",\n \"ĉ private\",\n \"ĉpr ivate\",\n \"ĉpriv ate\",\n \"Ġ effect\",\n \"Ġe ffect\",\n \"Ġeff ect\",\n \"Ġef fect\",\n \"+ +)\",\n \"++ )\",\n \"_ file\",\n \"_f ile\",\n \"_fil e\",\n \"en ding\",\n \"end ing\",\n \"endi ng\",\n \"L ine\",\n \"Li ne\",\n \"Lin e\",\n \"F or\",\n \"Fo r\",\n \"i or\",\n \"io r\",\n \"Ġ Sc\",\n \"ĠS c\",\n \"Ġ fun\",\n \"Ġf un\",\n \"Ġfu n\",\n \". Size\",\n \".S ize\",\n \"ĉ else\",\n \"ĉe lse\",\n \"ĉel se\",\n \"] )\",\n \"st art\",\n \"star t\",\n \"sta rt\",\n \"v ious\",\n \"vi ous\",\n \"vio us\",\n \"Ġ },\",\n \"Ġ} ,\",\n \"o urs\",\n \"ou rs\",\n \"our s\",\n \"Ġ leg\",\n \"Ġl eg\",\n \"Ġle g\",\n \"Ġ service\",\n \"Ġs ervice\",\n \"Ġser vice\",\n \"Ġserv ice\",\n \"Ġservi ce\",\n \"Ġservic e\",\n \"Ġ since\",\n \"Ġs ince\",\n \"Ġsi nce\",\n \"Ġsin ce\",\n \"Ġsinc e\",\n \"i ron\",\n \"ir on\",\n \"iro n\",\n \"L abel\",\n \"La bel\",\n \"Lab el\",\n \"Ġ non\",\n \"Ġn on\",\n \"Ġno n\",\n \"Ġ los\",\n \"Ġl os\",\n \"Ġlo s\",\n \"i ction\",\n \"ic tion\",\n \"ict ion\",\n \"Ġ full\",\n \"Ġf ull\",\n \"Ġful l\",\n \"Ġfu ll\",\n \"a cter\",\n \"act er\",\n \"ac ter\",\n \"b oard\",\n \"bo ard\",\n \"boa rd\",\n \"g ress\",\n \"gr ess\",\n \"gre ss\",\n \"gres s\",\n \"Ġ turn\",\n \"Ġt urn\",\n \"Ġtu rn\",\n \"Ġtur n\",\n \"i ther\",\n \"it her\",\n \"ith er\",\n \"ithe r\",\n \"0 9\",\n \". size\",\n \".s ize\",\n \".si ze\",\n \"Ġ body\",\n \"Ġb ody\",\n \"Ġbo dy\",\n \"Ġbod y\",\n \"r esh\",\n \"re sh\",\n \"res h\",\n \"e turn\",\n \"et urn\",\n \"etur n\",\n \"etu rn\",\n \"1 99\",\n \"19 9\",\n \"( _\",\n \"y les\",\n \"yle s\",\n \"yl es\",\n \"or mal\",\n \"orm al\",\n \"p i\",\n \"Ġ something\",\n \"Ġs omething\",\n \"Ġsome thing\",\n \"Ġsom ething\",\n \"! --\",\n \"u int\",\n \"ui nt\",\n \"uin t\",\n \"Ġ produ\",\n \"Ġp rodu\",\n \"Ġpro du\",\n \"Ġpr odu\",\n \"Ġprod u\",\n \"Ġ stand\",\n \"Ġst and\",\n \"Ġsta nd\",\n \"Ġstan d\",\n \"Ġpro ble\",\n \"Ġpr oble\",\n \"Ġprob le\",\n \"Ġprobl e\",\n \"Ġ available\",\n \"Ġa vailable\",\n \"Ġav ailable\",\n \"Ġavail able\",\n \"m t\",\n \"Ġ Bl\",\n \"ĠB l\",\n \"Ġ ...\",\n \"Ġ. ..\",\n \"Ġ.. .\",\n \"Ġ block\",\n \"Ġb lock\",\n \"Ġbl ock\",\n \"Ġblo ck\",\n \"Ġbloc k\",\n \"In put\",\n \"Ġ keep\",\n \"Ġke ep\",\n \"C ount\",\n \"Co unt\",\n \"Cou nt\",\n \"o pen\",\n \"op en\",\n \"ope n\",\n \"Ġ ['\",\n \"Ġ[ '\",\n \"Ġ throw\",\n \"Ġth row\",\n \"Ġthr ow\",\n \"Ġthro w\",\n \"u ilder\",\n \"uild er\",\n \"ui lder\",\n \"uil der\",\n \"A ction\",\n \"Act ion\",\n \"Ac tion\",\n \"Ġ things\",\n \"Ġth ings\",\n \"Ġthing s\",\n \"Ġthin gs\",\n \"Tr ue\",\n \"Ġ url\",\n \"Ġu rl\",\n \"Ġur l\",\n \"Ġ Bo\",\n \"ĠB o\",\n \"print f\",\n \"Ġ red\",\n \"Ġre d\",\n \"Ġr ed\",\n \"j s\",\n \". create\",\n \".c reate\",\n \"Ġ Or\",\n \"ĠO r\",\n \"S tatus\",\n \"St atus\",\n \"Stat us\",\n \"In stance\",\n \"Inst ance\",\n \"Ġ control\",\n \"Ġc ontrol\",\n \"Ġcont rol\",\n \"Ġcontr ol\",\n \"Ġcontro l\",\n \"Ġ come\",\n \"Ġc ome\",\n \"Ġcom e\",\n \"Ġco me\",\n \"Ġ custom\",\n \"Ġc ustom\",\n \"Ġcust om\",\n \"Ġcus tom\",\n \"l ocation\",\n \"lo cation\",\n \"loc ation\",\n \"0 7\",\n \"m odel\",\n \"mod el\",\n \"mo del\",\n \"mode l\",\n \"Ġ čĊ\",\n \"Ġč Ċ\",\n \"Ġ source\",\n \"Ġs ource\",\n \"Ġsour ce\",\n \"Ġe as\",\n \"Ġea s\",\n \". out\",\n \".o ut\",\n \"] ĊĊ\",\n \"]Ċ Ċ\",\n \"o ney\",\n \"on ey\",\n \"one y\",\n \"Ġ await\",\n \"Ġa wait\",\n \"Ġaw ait\",\n \"Ġp artic\",\n \"Ġpart ic\",\n \"Ġpar tic\",\n \"Ġparti c\",\n \"A P\",\n \"ub lish\",\n \"ubl ish\",\n \"o des\",\n \"od es\",\n \"ode s\",\n \"_ pro\",\n \"_p ro\",\n \"_pr o\",\n \"p ly\",\n \"pl y\",\n \"r iter\",\n \"ri ter\",\n \"rit er\",\n \"rite r\",\n \"Ġ prov\",\n \"Ġp rov\",\n \"Ġpro v\",\n \"Ġpr ov\",\n \"Ġ mill\",\n \"Ġm ill\",\n \"Ġmil l\",\n \"Ġmi ll\",\n \"H T\",\n \"] )Ċ\",\n \"]) Ċ\",\n \"Ġ chang\",\n \"Ġc hang\",\n \"Ġch ang\",\n \"Ġcha ng\",\n \"Ġchan g\",\n \"Ġ ask\",\n \"Ġa sk\",\n \"Ġas k\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"Ġ output\",\n \"Ġout put\",\n \"Ġ email\",\n \"Ġe mail\",\n \"Ġem ail\",\n \"6 8\",\n \". push\",\n \".p ush\",\n \"Ġ }čĊčĊ\",\n \"Ġ} čĊčĊ\",\n \"Ġ}čĊ čĊ\",\n \"i nation\",\n \"in ation\",\n \"ina tion\",\n \"inati on\",\n \"4 7\",\n \"at rix\",\n \"atri x\",\n \"atr ix\",\n \"T able\",\n \"Tab le\",\n \"Ta ble\",\n \"u ccess\",\n \"uc cess\",\n \"ucc ess\",\n \"] );Ċ\",\n \"]) ;Ċ\",\n \"]); Ċ\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġ disc\",\n \"Ġd isc\",\n \"Ġdis c\",\n \"Ġdi sc\",\n \"( [\",\n \"Ġ business\",\n \"Ġb usiness\",\n \"Ġbus iness\",\n \"h eight\",\n \"he ight\",\n \"hei ght\",\n \". html\",\n \".h tml\",\n \".ht ml\",\n \".htm l\",\n \"t a\",\n \"f ield\",\n \"fi eld\",\n \"Ġ required\",\n \"Ġre quired\",\n \"Ġrequire d\",\n \"Ġrequ ired\",\n \"_ R\",\n \"Ġg overn\",\n \"Ġgo vern\",\n \"Ġgover n\",\n \"Ġgov ern\",\n \"} čĊčĊ\",\n \"}čĊ čĊ\",\n \"l ex\",\n \"le x\",\n \"5 00\",\n \"50 0\",\n \". ,\",\n \"Ġ Set\",\n \"ĠS et\",\n \"ĠSe t\",\n \"u rch\",\n \"ur ch\",\n \"/ //\",\n \"// /\",\n \"t s\",\n \"a f\",\n \"Ġ might\",\n \"Ġm ight\",\n \"Ġmi ght\",\n \"Ġmig ht\",\n \"i story\",\n \"ist ory\",\n \"istor y\",\n \"isto ry\",\n \"S tr\",\n \"St r\",\n \"Ġ never\",\n \"Ġn ever\",\n \"Ġne ver\",\n \"Ġnev er\",\n \"Res ponse\",\n \"Resp onse\",\n \"Respons e\",\n \"ar se\",\n \"ars e\",\n \"a da\",\n \"ad a\",\n \"Ġ How\",\n \"ĠH ow\",\n \"ĠHo w\",\n \"Ġ *)\",\n \"Ġ* )\",\n \"Ġ ;\",\n \"Ġ hard\",\n \"Ġh ard\",\n \"Ġhar d\",\n \"Ġha rd\",\n \"A d\",\n \"Ġ intern\",\n \"Ġin tern\",\n \"Ġint ern\",\n \"Ġinter n\",\n \"Ġinte rn\",\n \"u sed\",\n \"us ed\",\n \"use d\",\n \"( data\",\n \"(d ata\",\n \"(dat a\",\n \"(da ta\",\n \"m od\",\n \"mo d\",\n \"an nel\",\n \"ann el\",\n \"anne l\",\n \"Ġ np\",\n \"Ġn p\",\n \"u gg\",\n \"ug g\",\n \"Ġ />Ċ\",\n \"Ġ/ >Ċ\",\n \"Ġ/> Ċ\",\n \"Ġ called\",\n \"Ġc alled\",\n \"Ġcall ed\",\n \"Ġcal led\",\n \"Ġcalle d\",\n \"b ody\",\n \"bo dy\",\n \"Ġ cho\",\n \"Ġc ho\",\n \"Ġch o\",\n \"( r\",\n \"_ set\",\n \"_s et\",\n \"_se t\",\n \"i rd\",\n \"ir d\",\n \"Ġ >=\",\n \"Ġ> =\",\n \"Ġ };Ċ\",\n \"Ġ} ;Ċ\",\n \"Ġ}; Ċ\",\n \"Ġ options\",\n \"Ġo ptions\",\n \"Ġoption s\",\n \"Ġopt ions\",\n \"Ġ Gener\",\n \"ĠG ener\",\n \"ĠGe ner\",\n \"ĠGen er\",\n \"ĠGene r\",\n \"Ġ height\",\n \"Ġh eight\",\n \"Ġhe ight\",\n \"Ġhei ght\",\n \"P oint\",\n \"Po int\",\n \"Y ou\",\n \"Yo u\",\n \"e ty\",\n \"et y\",\n \"C lick\",\n \"Cl ick\",\n \"Cli ck\",\n \"Ġ small\",\n \"Ġs mall\",\n \"Ġsm all\",\n \"Ġ ide\",\n \"Ġi de\",\n \"Ġid e\",\n \"Ġ access\",\n \"Ġa ccess\",\n \"Ġacc ess\",\n \"Ġac cess\",\n \"Ġacces s\",\n \"angu age\",\n \"Ġ protected\",\n \"Ġprot ected\",\n \"Ġprotect ed\",\n \"Ġ job\",\n \"Ġj ob\",\n \"Ġjo b\",\n \"Ġ There\",\n \"ĠT here\",\n \"ĠThe re\",\n \"ĠTh ere\",\n \"ĠTher e\",\n \"D ef\",\n \"De f\",\n \"Ġ address\",\n \"Ġadd ress\",\n \"Ġad dress\",\n \"Ġaddr ess\",\n \"Ġ uint\",\n \"Ġu int\",\n \"Ġui nt\",\n \"N ot\",\n \"No t\",\n \"o o\",\n \"a ps\",\n \"ap s\",\n \"< div\",\n \"\",\n \"() ->\",\n \"()- >\",\n \"ĉ ĠĠĠ\",\n \"ĉĠ ĠĠ\",\n \"ĉĠĠ Ġ\",\n \"\\\" ))\",\n \"\\\") )\",\n \"C ontent\",\n \"Cont ent\",\n \"Con tent\",\n \"_ W\",\n \"p lement\",\n \"pl ement\",\n \"ple ment\",\n \"Ġ won\",\n \"Ġw on\",\n \"Ġwo n\",\n \"Ġ video\",\n \"Ġv ideo\",\n \"Ġvi deo\",\n \"Ġvid eo\",\n \"Ġvide o\",\n \"a di\",\n \"ad i\",\n \"p oint\",\n \"po int\",\n \"poi nt\",\n \"% %\",\n \"0 3\",\n \"Ġ gl\",\n \"Ġg l\",\n \"er ved\",\n \"erv ed\",\n \"erve d\",\n \"v iron\",\n \"vi ron\",\n \"vir on\",\n \"I F\",\n \"u ted\",\n \"ut ed\",\n \"ute d\",\n \"ã ĥ\",\n \"' m\",\n \"Ġ cert\",\n \"Ġc ert\",\n \"Ġce rt\",\n \"Ġcer t\",\n \"Ġ prof\",\n \"Ġp rof\",\n \"Ġpro f\",\n \"Ġpr of\",\n \"Ġ cell\",\n \"Ġc ell\",\n \"Ġce ll\",\n \"Ġcel l\",\n \"a ri\",\n \"ar i\",\n \"Ġ player\",\n \"Ġp layer\",\n \"Ġpl ayer\",\n \"Ġplay er\",\n \"Ġpla yer\",\n \"a is\",\n \"ai s\",\n \"Ġ cost\",\n \"Ġc ost\",\n \"Ġco st\",\n \"Ġcos t\",\n \"Ġ hum\",\n \"Ġh um\",\n \"Ġhu m\",\n \"( R\",\n \"Ġo ffic\",\n \"Ġof fic\",\n \"Ġoff ic\",\n \"k s\",\n \". text\",\n \".t ext\",\n \".te xt\",\n \".tex t\",\n \"a tures\",\n \"at ures\",\n \"ature s\",\n \"atur es\",\n \"atu res\",\n \"Ġ total\",\n \"Ġt otal\",\n \"Ġto tal\",\n \"Ġtot al\",\n \"Ġ */ĊĊ\",\n \"Ġ* /ĊĊ\",\n \"Ġ*/ ĊĊ\",\n \"Ġ*/Ċ Ċ\",\n \"o pe\",\n \"op e\",\n \"Ġ stat\",\n \"Ġs tat\",\n \"Ġst at\",\n \"Ġsta t\",\n \"U M\",\n \"Ġ load\",\n \"Ġl oad\",\n \"Ġlo ad\",\n \"Ġloa d\",\n \"ight s\",\n \"igh ts\",\n \"Ġ clear\",\n \"Ġc lear\",\n \"Ġcl ear\",\n \"Ġcle ar\",\n \"u ro\",\n \"ur o\",\n \"Ġ techn\",\n \"Ġt echn\",\n \"Ġte chn\",\n \"Ġtech n\",\n \"Ġtec hn\",\n \"up port\",\n \"upp ort\",\n \"I R\",\n \"Ġ row\",\n \"Ġr ow\",\n \"Ġro w\",\n \"Ġse em\",\n \"Ġsee m\",\n \"Ġ q\",\n \"Ġ short\",\n \"Ġs hort\",\n \"Ġsh ort\",\n \"Ġsho rt\",\n \"Ġ Not\",\n \"ĠN ot\",\n \"ĠNo t\",\n \"i pp\",\n \"ip p\",\n \"G roup\",\n \"Gr oup\",\n \"Gro up\",\n \"s ection\",\n \"se ction\",\n \"sec tion\",\n \"sect ion\",\n \"m ax\",\n \"ma x\",\n \"i rl\",\n \"ir l\",\n \"Ġ override\",\n \"Ġover ride\",\n \"Ġ company\",\n \"Ġcom pany\",\n \"Ġcomp any\",\n \"Ġcompan y\",\n \"Ġ done\",\n \"Ġd one\",\n \"Ġdo ne\",\n \"Ġdon e\",\n \"\\\" );čĊ\",\n \"\\\") ;čĊ\",\n \"\\\"); čĊ\",\n \"Ġ gre\",\n \"Ġg re\",\n \"Ġgr e\",\n \". Re\",\n \".R e\",\n \"Ġ belie\",\n \"Ġbe lie\",\n \"Ġbel ie\",\n \"r ist\",\n \"ri st\",\n \"ris t\",\n \"Ġ health\",\n \"Ġhe alth\",\n \"Ġheal th\",\n \"A NT\",\n \"AN T\",\n \"( )ĊĊ\",\n \"() ĊĊ\",\n \"()Ċ Ċ\",\n \"Ġ Be\",\n \"ĠB e\",\n \". value\",\n \".v alue\",\n \".val ue\",\n \".va lue\",\n \"Ġ Gr\",\n \"ĠG r\",\n \"ot tom\",\n \"ott om\",\n \"otto m\",\n \"Ġ args\",\n \"Ġar gs\",\n \"Ġarg s\",\n \"P T\",\n \"s tatus\",\n \"st atus\",\n \"stat us\",\n \"f unc\",\n \"fun c\",\n \"fu nc\",\n \"u ments\",\n \"um ents\",\n \"ument s\",\n \"ume nts\",\n \"umen ts\",\n \"- h\",\n \"N umber\",\n \"Num ber\",\n \": čĊ\",\n \"Ġ Log\",\n \"ĠL og\",\n \"ĠLo g\",\n \"er ver\",\n \"erv er\",\n \"erve r\",\n \"Ġ ),Ċ\",\n \"Ġ) ,Ċ\",\n \"Ġ), Ċ\",\n \"a ment\",\n \"am ent\",\n \"ame nt\",\n \"amen t\",\n \"Ġ obj\",\n \"Ġo bj\",\n \"Ġob j\",\n \"i nc\",\n \"in c\",\n \"Ġ children\",\n \"Ġch ildren\",\n \"Ġchild ren\",\n \"i cy\",\n \"ic y\",\n \"I Z\",\n \"a nds\",\n \"an ds\",\n \"and s\",\n \"ab ly\",\n \"abl y\",\n \"Ġd istrib\",\n \"Ġdis trib\",\n \"Ġdist rib\",\n \"Ġdistr ib\",\n \"Ġ cur\",\n \"Ġc ur\",\n \"Ġcu r\",\n \"e rial\",\n \"er ial\",\n \"eri al\",\n \"eria l\",\n \"Ġ days\",\n \"Ġd ays\",\n \"Ġday s\",\n \"Ġda ys\",\n \"r eated\",\n \"re ated\",\n \"reate d\",\n \"reat ed\",\n \"rea ted\",\n \"r ect\",\n \"re ct\",\n \"rec t\",\n \"- l\",\n \"i rm\",\n \"ir m\",\n \"i dden\",\n \"id den\",\n \"idd en\",\n \"o mb\",\n \"om b\",\n \"Ġ initial\",\n \"Ġin itial\",\n \"Ġinit ial\",\n \"Ġiniti al\",\n \". js\",\n \".j s\",\n \"Ġ â\",\n \"Qu ery\",\n \"Que ry\",\n \"Ġ online\",\n \"Ġon line\",\n \"i mal\",\n \"im al\",\n \"ima l\",\n \". con\",\n \".c on\",\n \".co n\",\n \"a u\",\n \"U rl\",\n \"Ur l\",\n \"c ontrol\",\n \"cont rol\",\n \"contr ol\",\n \"contro l\",\n \"i rection\",\n \"ir ection\",\n \"ire ction\",\n \"irect ion\",\n \"Ġ instance\",\n \"Ġin stance\",\n \"Ġinst ance\",\n \"O RT\",\n \"OR T\",\n \"Ġ Fr\",\n \"ĠF r\",\n \"w here\",\n \"wh ere\",\n \"Ġ javax\",\n \"Ġj avax\",\n \"Ġjav ax\",\n \"Ġjava x\",\n \"Ġ organ\",\n \"Ġo rgan\",\n \"Ġor gan\",\n \"Ġorg an\",\n \"a pter\",\n \"ap ter\",\n \"apt er\",\n \"Ġ reason\",\n \"Ġre ason\",\n \"o ptions\",\n \"option s\",\n \"opt ions\",\n \"5 9\",\n \"Ġ Mar\",\n \"ĠM ar\",\n \"ĠMa r\",\n \"( a\",\n \"Ġ within\",\n \"Ġwith in\",\n \"Ġwi thin\",\n \"Ġwit hin\",\n \". âĢĿĊĊ\",\n \".âĢĿ ĊĊ\",\n \".âĢĿĊ Ċ\",\n \"O DE\",\n \"OD E\",\n \"_ DE\",\n \"_D E\",\n \"ad min\",\n \"adm in\",\n \"en ded\",\n \"end ed\",\n \"ende d\",\n \"Ġ design\",\n \"Ġd esign\",\n \"Ġde sign\",\n \"Ġdes ign\",\n \"Ġdesi gn\",\n \"Ġ Data\",\n \"ĠD ata\",\n \"ĠDa ta\",\n \"ĠDat a\",\n \"u ne\",\n \"un e\",\n \"Ġ File\",\n \"ĠF ile\",\n \"ĠFil e\",\n \"ĠFi le\",\n \"r oot\",\n \"ro ot\",\n \"Ġ cent\",\n \"Ġc ent\",\n \"Ġce nt\",\n \"Ġcen t\",\n \"Ġ arr\",\n \"Ġa rr\",\n \"Ġar r\",\n \"_ add\",\n \"_a dd\",\n \"_ad d\",\n \"l en\",\n \"le n\",\n \"p age\",\n \"pa ge\",\n \"pag e\",\n \", '\",\n \"_ str\",\n \"_s tr\",\n \"_st r\",\n \"Ġ bro\",\n \"Ġb ro\",\n \"Ġbr o\",\n \"ab ility\",\n \"abil ity\",\n \"abilit y\",\n \"o uth\",\n \"ou th\",\n \"out h\",\n \"5 8\",\n \"/ c\",\n \"p ose\",\n \"pos e\",\n \"po se\",\n \"ir tual\",\n \"irt ual\",\n \"e arch\",\n \"ear ch\",\n \"ea rch\",\n \"_ url\",\n \"_u rl\",\n \"_ur l\",\n \"ar gin\",\n \"arg in\",\n \"H ttp\",\n \"Ġ school\",\n \"Ġs chool\",\n \"Ġsch ool\",\n \"Ġscho ol\",\n \"a va\",\n \"av a\",\n \"Ġ consider\",\n \"Ġcons ider\",\n \"Ġconsid er\",\n \". label\",\n \".l abel\",\n \".lab el\",\n \"Ġ Array\",\n \"ĠA rray\",\n \"ĠAr ray\",\n \"ĠArr ay\",\n \"4 2\",\n \"w eb\",\n \"we b\",\n \"o pt\",\n \"op t\",\n \". println\",\n \".print ln\",\n \"u lation\",\n \"ul ation\",\n \"ula tion\",\n \"Ġ func\",\n \"Ġf unc\",\n \"Ġfun c\",\n \"Ġfu nc\",\n \"P L\",\n \"Ġ \\\"\\\\\",\n \"Ġ\\\" \\\\\",\n \"Ġ Text\",\n \"ĠT ext\",\n \"ĠTe xt\",\n \"ĠTex t\",\n \"act ory\",\n \"actor y\",\n \"( function\",\n \"(f unction\",\n \"(func tion\",\n \"(fun ction\",\n \"n ull\",\n \"nu ll\",\n \"nul l\",\n \"Ġ eng\",\n \"Ġe ng\",\n \"Ġen g\",\n \"d own\",\n \"do wn\",\n \"Ġ include\",\n \"Ġin clude\",\n \"Ġinclud e\",\n \"Ġinc lude\",\n \"Ġincl ude\",\n \"Ġinclu de\",\n \"Ġ En\",\n \"ĠE n\",\n \"Ġ Dr\",\n \"ĠD r\",\n \"Ġ db\",\n \"Ġd b\",\n \"! 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\"Ġf ail\",\n \"Ġfa il\",\n \"Ġ black\",\n \"Ġb lack\",\n \"Ġbl ack\",\n \"Ġbla ck\",\n \"Ġ component\",\n \"Ġcom ponent\",\n \"Ġcomp onent\",\n \"Ġ American\",\n \"ĠA merican\",\n \"ĠAmeric an\",\n \"ĠAmerica n\",\n \"ĠAmer ican\",\n \"Ġ added\",\n \"Ġadd ed\",\n \"Ġad ded\",\n \"Ġ buy\",\n \"Ġb uy\",\n \"Ġbu y\",\n \"s tit\",\n \"st it\",\n \"sti t\",\n \"Ġ came\",\n \"Ġc ame\",\n \"Ġca me\",\n \"Ġcam e\",\n \"Ġ delete\",\n \"Ġde lete\",\n \"Ġdel ete\",\n \"Ġdelet e\",\n \"Ġdele te\",\n \"p roperty\",\n \"pro perty\",\n \"prop erty\",\n \"proper ty\",\n \"o ding\",\n \"od ing\",\n \"odi ng\",\n \"Ġ card\",\n \"Ġc ard\",\n \"Ġcar d\",\n \"Ġca rd\",\n \"r ops\",\n \"ro ps\",\n \"rop s\",\n \"Ġ https\",\n \"Ġhttp s\",\n \"Ġht tps\",\n \"Ġhtt ps\",\n \"Ġ root\",\n \"Ġr oot\",\n \"Ġro ot\",\n \"Ġ handle\",\n \"Ġh andle\",\n \"Ġhand le\",\n \"Ġhan dle\",\n \"C C\",\n \"B ack\",\n \"Ba ck\",\n \"em plate\",\n \"emp late\",\n \"empl ate\",\n \"Ġ getting\",\n \"Ġg etting\",\n \"Ġget ting\",\n \"_ by\",\n \"_b y\",\n \"m ail\",\n \"ma il\",\n \"mai l\",\n \"_ sh\",\n \"_s h\",\n \". assert\",\n \".as sert\",\n \"Ġ Dec\",\n \"ĠD ec\",\n \"ĠDe c\",\n \"( true\",\n \"(tr ue\",\n \"Ġ comput\",\n \"Ġcom put\",\n \"Ġcomp ut\",\n \"Ġ claim\",\n \"Ġcl aim\",\n \"Ġcla im\",\n \"' =>\",\n \"'= >\",\n \"Ġ Sub\",\n \"ĠS ub\",\n \"ĠSu b\",\n \"Ġ air\",\n \"Ġa ir\",\n \"Ġai r\",\n \"o ps\",\n \"op s\",\n \"n av\",\n \"na v\",\n \"e ments\",\n \"em ents\",\n \"ement s\",\n \"eme nts\",\n \"emen ts\",\n \"( id\",\n \"(i d\",\n \"Ġ enter\",\n \"Ġen ter\",\n \"Ġent er\",\n \"an ged\",\n \"ang ed\",\n \"ange d\",\n \"E nd\",\n \"En d\",\n \"Ġ location\",\n \"Ġl ocation\",\n \"Ġlo cation\",\n \"Ġloc ation\",\n \"Ġ night\",\n \"Ġn ight\",\n \"Ġni ght\",\n \"Ġnig ht\",\n \"Ġ doing\",\n \"Ġdo ing\",\n \"Ġdoi ng\",\n \"Ġ Red\",\n \"ĠR ed\",\n \"ĠRe d\",\n \"l in\",\n \"li n\",\n \"} ĊĊĊ\",\n \"}Ċ ĊĊ\",\n \"}ĊĊ Ċ\",\n \"v ider\",\n \"vid er\",\n \"vi der\",\n \"vide r\",\n \"Ġ pick\",\n \"Ġp ick\",\n \"Ġpi ck\",\n \"Ġpic k\",\n \"Ġ watch\",\n \"Ġw atch\",\n \"Ġwat ch\",\n \"ess ages\",\n \"essage s\",\n \"essa ges\",\n \"Ġ human\",\n \"Ġh uman\",\n \"Ġhum an\",\n \"Ġhu man\",\n \"Ġ dam\",\n \"Ġd am\",\n \"Ġda m\",\n \"p end\",\n \"pe nd\",\n \"pen d\",\n \"d ir\",\n \"di r\",\n \"Ġ tax\",\n \"Ġt ax\",\n \"Ġta x\",\n \"Ġ girl\",\n \"Ġg irl\",\n \"Ġgi rl\",\n \"Ġgir l\",\n \"re et\",\n \"ree t\",\n \"Ġ box\",\n \"Ġb ox\",\n \"Ġbo x\",\n \"Ġ strong\",\n \"Ġst rong\",\n \"Ġstr ong\",\n \"Ġstro ng\",\n \"Ġstron g\",\n \"( v\",\n \"r el\",\n \"re l\",\n \"Ġ interface\",\n \"Ġinter face\",\n \"Ġinterf ace\",\n \"Ġ msg\",\n \"Ġm sg\",\n \"Ġms g\",\n \"f ect\",\n \"fe ct\",\n \"fec t\",\n \"_ at\",\n \"_a t\",\n \"Ġ house\",\n \"Ġh ouse\",\n \"Ġhous e\",\n \"Ġho use\",\n \"Ġ track\",\n \"Ġt rack\",\n \"Ġtr ack\",\n \"Ġtra ck\",\n \"' );ĊĊ\",\n \"') ;ĊĊ\",\n \"');Ċ Ċ\",\n \"'); ĊĊ\",\n \"j e\",\n \"Ġ John\",\n \"ĠJ ohn\",\n \"ĠJo hn\",\n \"ĠJoh n\",\n \"i str\",\n \"is tr\",\n \"ist r\",\n \"( S\",\n \"u be\",\n \"ub e\",\n \"Ġ ce\",\n \"Ġc e\",\n \"it ted\",\n \"itt ed\",\n \"itte d\",\n \"V ER\",\n \"VE R\",\n \"* )\",\n \"p arent\",\n \"par ent\",\n \"pare nt\",\n \"pa rent\",\n \"paren t\",\n \"Ġ application\",\n \"Ġapp lication\",\n \"Ġap plication\",\n \"Ġapplic ation\",\n \"Ġappl ication\",\n \"a ny\",\n \"an y\",\n \". swing\",\n \".s wing\",\n \".sw ing\",\n \"Ġ pack\",\n \"Ġp ack\",\n \"Ġpa ck\",\n \"Ġpac k\",\n \"\\\\ u\",\n \"Ġp ract\",\n \"Ġpr act\",\n \"Ġpra ct\",\n \"Ġprac t\",\n \"Ġ section\",\n \"Ġs ection\",\n \"Ġse ction\",\n \"Ġsec tion\",\n \"Ġsect ion\",\n \"c tx\",\n \"ct x\",\n \"Ġ unsigned\",\n \"Ġun signed\",\n \"Ġuns igned\",\n \". Point\",\n \".P oint\",\n \"Ġ One\",\n \"ĠO ne\",\n \"ĠOn e\",\n \"Ä ±\",\n \"i ple\",\n \"ip le\",\n \"ipl e\",\n \"a id\",\n \"ai d\",\n \"Ñ ĥ\",\n \"V ector\",\n \"Vec tor\",\n \"Ve ctor\",\n \"Vect or\",\n \"b yte\",\n \"by te\",\n \"byt e\",\n \"Ġ wait\",\n \"Ġw ait\",\n \"Ġwa it\",\n \"Ġ Ãł\",\n \"Ġà ł\",\n \"à ¥\",\n \"Ġto gether\",\n \"Ġtog ether\",\n \"Ġ throws\",\n \"Ġth rows\",\n \"Ġthrow s\",\n \"Ġthr ows\",\n \"Ġthro ws\",\n \"F O\",\n \"' ))\",\n \"') )\",\n \"h ost\",\n \"ho st\",\n \"hos t\",\n \"i sing\",\n \"is ing\",\n \"isi ng\",\n \"isin g\",\n \". view\",\n \".v iew\",\n \"Ġ terms\",\n \"Ġte rms\",\n \"Ġter ms\",\n \"Ġterm s\",\n \"f ramework\",\n \"fr amework\",\n \"frame work\",\n \"fram ework\",\n \"- r\",\n \"Ġ apply\",\n \"Ġapp ly\",\n \"Ġap ply\",\n \"Ġappl y\",\n \"Ġ session\",\n \"Ġs ession\",\n \"Ġsess ion\",\n \"O ptions\",\n \"Option s\",\n \"Opt ions\",\n \"ug gest\",\n \"ugg est\",\n \"Ġ others\",\n \"Ġo thers\",\n \"Ġother s\",\n \"w itter\",\n \"wit ter\",\n \"Ġ fund\",\n \"Ġf und\",\n \"Ġfun d\",\n \"Ġfu nd\",\n \"I nit\",\n \"In it\",\n \"Ini t\",\n \"_ _(\",\n \"__ (\",\n \"ens or\",\n \"enso r\",\n \"G ET\",\n \"GE T\",\n \"Ġse veral\",\n \"Ġsever al\",\n \"Ġsev eral\",\n \"i i\",\n \"[ j\",\n \"I O\",\n \"Ġ template\",\n \"Ġt emplate\",\n \"Ġtem plate\",\n \"Ġtemp late\",\n \"Ġtempl ate\",\n \"P osition\",\n \"Pos ition\",\n \"Ġe con\",\n \"Ġec on\",\n \"Ġeco n\",\n \"a chine\",\n \"ach ine\",\n \"achi ne\",\n \"Ġ il\",\n \"Ġi l\",\n \". spring\",\n \".s pring\",\n \".sp ring\",\n \"m ain\",\n \"ma in\",\n \"mai n\",\n \"e lt\",\n \"el t\",\n \"i ment\",\n \"im ent\",\n \"ime nt\",\n \"imen t\",\n \"R ec\",\n \"Re c\",\n \"m m\",\n \"Ġ University\",\n \"ĠUn iversity\",\n \"ĠUnivers ity\",\n \"urs or\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"G L\",\n \"ic ture\",\n \"ict ure\",\n \"it hub\",\n \"ith ub\",\n \"c er\",\n \"ce r\",\n \"c ast\",\n \"ca st\",\n \"cas t\",\n \"F rom\",\n \"Fr om\",\n \"a les\",\n \"al es\",\n \"ale s\",\n \"Ġ subject\",\n \"Ġsu bject\",\n \"Ġsub ject\",\n \"Ġsubj ect\",\n \"p assword\",\n \"pass word\",\n \"pas sword\",\n \"n y\",\n \"Ġ esc\",\n \"Ġe sc\",\n \"Ġes c\",\n \". write\",\n \".w rite\",\n \".writ e\",\n \".wr ite\",\n \"ï¼ Į\",\n \"W hat\",\n \"Wh at\",\n \". H\",\n \"Ġ history\",\n \"Ġh istory\",\n \"Ġhistor y\",\n \"Ġhist ory\",\n \"Ġhi story\",\n \"Ġhisto ry\",\n \"Ġ Fe\",\n \"ĠF e\",\n \"Ġ individual\",\n \"Ġind ividual\",\n \"Ġindiv idual\",\n \"Ġindivid ual\",\n \"u nit\",\n \"un it\",\n \"uni t\",\n \"Ġ -->\",\n \"Ġ- ->\",\n \"Ġ-- >\",\n \"Ġ du\",\n \"Ġd u\",\n \"I ST\",\n \"IS T\",\n \"Ġ users\",\n \"Ġus ers\",\n \"Ġuse rs\",\n \"Ġuser s\",\n \"f s\",\n \"f alse\",\n \"fa lse\",\n \"fal se\",\n \"u nt\",\n \"un t\",\n \"T itle\",\n \"Tit le\",\n \"Ti tle\",\n \"Ġ mot\",\n \"Ġm ot\",\n \"Ġmo t\",\n \"Ġ future\",\n \"Ġf uture\",\n \"Ġfut ure\",\n \"Ġfu ture\",\n \"a ched\",\n \"ac hed\",\n \"ach ed\",\n \"ache d\",\n \"Ġ started\",\n \"Ġstart ed\",\n \"Ġstar ted\",\n \"Ġ mode\",\n \"Ġm ode\",\n \"Ġmod e\",\n \"Ġmo de\",\n \"Ġ '<\",\n \"Ġ' <\",\n \"_ array\",\n \"_a rray\",\n \"_arr ay\",\n \"_ar ray\",\n \"Ġ ax\",\n \"Ġa x\",\n \"' ];Ċ\",\n \"'] ;Ċ\",\n \"']; Ċ\",\n \"i res\",\n \"ir es\",\n \"ire s\",\n \"T here\",\n \"The re\",\n \"Th ere\",\n \"u ght\",\n \"ug ht\",\n \"ugh t\",\n \"t ml\",\n \"tm l\",\n \"p osed\",\n \"pos ed\",\n \"pose d\",\n \"po sed\",\n \"i cult\",\n \"ic ult\",\n \"Ġ took\",\n \"Ġt ook\",\n \"Ġto ok\",\n \"Ġtoo k\",\n \"Ġ games\",\n \"Ġg ames\",\n \"Ġgame s\",\n \"Ġgam es\",\n \"Ġga mes\",\n \"Ġ }}\",\n \"Ġ} }\",\n \"Ġ ?>Ċ\",\n \"Ġ? >Ċ\",\n \"Ġ?> Ċ\",\n \"Ġ products\",\n \"Ġproduct s\",\n \"Ġprodu cts\",\n \"I s\",\n \"Ġ bad\",\n \"Ġb ad\",\n \"Ġba d\",\n \"Ġ Des\",\n \"ĠD es\",\n \"ĠDe s\",\n \". path\",\n \".p ath\",\n \".pa th\",\n \".pat h\",\n \"' ĊĊ\",\n \"'Ċ Ċ\",\n \"Ġ Post\",\n \"ĠP ost\",\n \"ĠPo st\",\n \"ĠPos t\",\n \"a vel\",\n \"av el\",\n \"ave l\",\n \"( :\",\n \"1 50\",\n \"15 0\",\n \"Ġ needs\",\n \"Ġne eds\",\n \"Ġneed s\",\n \"Ġ known\",\n \"Ġk nown\",\n \"Ġkn own\",\n \"Ġknow n\",\n \"F l\",\n \"Ġ exec\",\n \"Ġe xec\",\n \"Ġex ec\",\n \"Ġexe c\",\n \"Ġ seen\",\n \"Ġs een\",\n \"Ġse en\",\n \"Ġsee n\",\n \"5 1\",\n \"u me\",\n \"um e\",\n \"Ġ border\",\n \"Ġb order\",\n \"Ġbor der\",\n \"Ġbord er\",\n \"Ġ live\",\n \"Ġl ive\",\n \"Ġli ve\",\n \"Ġliv e\",\n \"t emp\",\n \"te mp\",\n \"tem p\",\n \"P er\",\n \"Pe r\",\n \"Ġ variable\",\n \"Ġvar iable\",\n \"Ġvari able\",\n \"i et\",\n \"ie t\",\n \"Ġ Def\",\n \"ĠD ef\",\n \"ĠDe f\",\n \"Ġ ge\",\n \"Ġg e\",\n \"e me\",\n \"em e\",\n \"_ back\",\n \"_b ack\",\n \"f irst\",\n \"fi rst\",\n \"fir st\",\n \"Ġ provided\",\n \"Ġpro vided\",\n \"Ġprovid ed\",\n \"Ġprov ided\",\n \"Ġprovide d\",\n \"//////////////// ////////////////\",\n \"Ġ filename\",\n \"Ġf ilename\",\n \"Ġfile name\",\n \"Ġfil ename\",\n \"Ġfi lename\",\n \"Ġ hope\",\n \"Ġh ope\",\n \"Ġhop e\",\n \"Ġho pe\",\n \"u ly\",\n \"ul y\",\n \"a uto\",\n \"aut o\",\n \"au to\",\n \"f ind\",\n \"fin d\",\n \"fi nd\",\n \"_ string\",\n \"_s tring\",\n \"_st ring\",\n \"_str ing\",\n \"b tn\",\n \"bt n\",\n \"it ude\",\n \"itud e\",\n \"itu de\",\n \"At tribute\",\n \"Attrib ute\",\n \"Ġ young\",\n \"Ġyou ng\",\n \"Ġyo ung\",\n \". txt\",\n \".t xt\",\n \".tx t\",\n \"Ġ website\",\n \"Ġwe bsite\",\n \"Ġweb site\",\n \"Ġwebs ite\",\n \"Ġ Prop\",\n \"ĠP rop\",\n \"ĠPro p\",\n \"ĠPr op\",\n \"Ġ ey\",\n \"Ġe y\",\n \"> ();Ċ\",\n \">( );Ċ\",\n \">() ;Ċ\",\n \">(); Ċ\",\n \"i onal\",\n \"ion al\",\n \"io nal\",\n \"iona l\",\n \"A RR\",\n \"AR R\",\n \"iction ary\",\n \"ur ther\",\n \"urt her\",\n \". \",\n \")- >\",\n \"t x\",\n \"Ġ pur\",\n \"Ġp ur\",\n \"Ġpu r\",\n \"u el\",\n \"ue l\",\n \"ym bol\",\n \"ymb ol\",\n \"u ation\",\n \"ua tion\",\n \"uat ion\",\n \"a nger\",\n \"an ger\",\n \"ang er\",\n \"ange r\",\n \"Ġ background\",\n \"Ġback ground\",\n \"e cess\",\n \"ec ess\",\n \"ece ss\",\n \"ef ined\",\n \". .......\",\n \".. ......\",\n \"... .....\",\n \".... ....\",\n \"..... ...\",\n \"...... ..\",\n \"....... .\",\n \"Ġ description\",\n \"Ġd escription\",\n \"Ġde scription\",\n \"Ġdes cription\",\n \"Ġdescri ption\",\n \"Ġ represent\",\n \"Ġre present\",\n \"Ġrep resent\",\n \"\\\" ));Ċ\",\n \"\\\") );Ċ\",\n \"\\\")) ;Ċ\",\n \"\\\")); Ċ\",\n \"p ression\",\n \"pr ession\",\n \"press ion\",\n \"row ser\",\n \"rows er\",\n \"rowse r\",\n \"Ġ series\",\n \"Ġs eries\",\n \"Ġse ries\",\n \"Ġser ies\",\n \"Ġserie s\",\n \"Ġseri es\",\n \"w ards\",\n \"ward s\",\n \"war ds\",\n \"5 2\",\n \"( $_\",\n \"($ _\",\n \"a ise\",\n \"ai se\",\n \"ais e\",\n \"Ġ hot\",\n \"Ġh ot\",\n \"Ġho t\",\n \"a city\",\n \"ac ity\",\n \"aci ty\",\n \"r ies\",\n \"ri es\",\n \"rie s\",\n \"a ctions\",\n \"act ions\",\n \"action s\",\n \"C reate\",\n \"Cre ate\",\n \"Creat e\",\n \"a dio\",\n \"ad io\",\n \"adi o\",\n \"am ples\",\n \"amp les\",\n \"ample s\",\n \"Ġ original\",\n \"Ġor iginal\",\n \"Ġorig inal\",\n \"Ġorigin al\",\n \"ens ive\",\n \"ensi ve\",\n \"f ont\",\n \"fo nt\",\n \"fon t\",\n \"st ream\",\n \"str eam\",\n \"stre am\",\n \" using\",\n \".spring framework\",\n \"0 01\",\n \"00 1\",\n \"s erver\",\n \"ser ver\",\n \"serve r\",\n \"serv er\",\n \"Ġ bill\",\n \"Ġb ill\",\n \"Ġbi ll\",\n \"Ġbil l\",\n \"A CK\",\n \"AC K\",\n \"i lename\",\n \"il ename\",\n \"ile name\",\n \"ilen ame\",\n \"Ġ frame\",\n \"Ġf rame\",\n \"Ġfr ame\",\n \"Ġfra me\",\n \"Ġfram e\",\n \"Ġ =Ċ\",\n \"Ġ= Ċ\",\n \"E dit\",\n \"Ed it\",\n \"ad ius\",\n \"adi us\",\n \"Ġ draw\",\n \"Ġd raw\",\n \"Ġdr aw\",\n \"Ġdra w\",\n \"an ks\",\n \"ank s\",\n \"Ġd eter\",\n \"Ġde ter\",\n \"Ġdet er\",\n \"Ġ comes\",\n \"Ġc omes\",\n \"Ġcom es\",\n \"Ġco mes\",\n \"Ġcome s\",\n \"_ int\",\n \"_in t\",\n \"_i nt\",\n \"Ġ foreach\",\n \"Ġf oreach\",\n \"Ġfor each\",\n \"Ġfore ach\",\n \"Ġfo reach\",\n \"a ngle\",\n \"an gle\",\n \"ang le\",\n \"angl e\",\n \"Ġ elect\",\n \"Ġe lect\",\n \"Ġel ect\",\n \"Ġele ct\",\n \"p ected\",\n \"pect ed\",\n \"pec ted\",\n \"He ader\",\n \"Head er\",\n \"i stration\",\n \"ist ration\",\n \"istr ation\",\n \"istra tion\",\n \"istrat ion\",\n \"F alse\",\n \"Fa lse\",\n \"Fal se\",\n \"Ġ Game\",\n \"ĠG ame\",\n \"ĠGa me\",\n \"ĠGam e\",\n \"Ġ filter\",\n \"Ġf ilter\",\n \"Ġfil ter\",\n \"Ġfilt er\",\n \"Act ivity\",\n \"Activ ity\",\n \"Ġ larg\",\n \"Ġl arg\",\n \"Ġla rg\",\n \"Ġlar g\",\n \"in ition\",\n \"init ion\",\n \"ini tion\",\n \"Ġ \\\"<\",\n \"Ġ\\\" <\",\n \"2 56\",\n \"25 6\",\n \"i sed\",\n \"is ed\",\n \"ise d\",\n \"Ġ remove\",\n \"Ġre move\",\n \"Ġrem ove\",\n \"Ġ Trans\",\n \"ĠT rans\",\n \"ĠTr ans\",\n \"ĠTra ns\",\n \"ĠTran s\",\n \"m et\",\n \"me t\",\n \"s ee\",\n \"se e\",\n \"Form at\",\n \"For mat\",\n \"Com mand\",\n \"Comm and\",\n \"Ġ EX\",\n \"ĠE X\",\n \"N one\",\n \"No ne\",\n \"Non e\",\n \"Ġ front\",\n \"Ġf ront\",\n \"Ġfr ont\",\n \"Ġfro nt\",\n \"Ġfron t\",\n \"A SE\",\n \"AS E\",\n \"Ġ Rec\",\n \"ĠR ec\",\n \"ĠRe c\",\n \"ound ation\",\n \"Ġ vo\",\n \"Ġv o\",\n \"9 6\",\n \"= \\\\\\\"\",\n \"=\\\\ \\\"\",\n \"( *\",\n \"Ch ange\",\n \"Chan ge\",\n \"Cha nge\",\n \". 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ode\",\n \"_co de\",\n \"_cod e\",\n \"Ġ ANY\",\n \"ĠA NY\",\n \"ĠAN Y\",\n \"_ ch\",\n \"_c h\",\n \"Ġ download\",\n \"Ġd ownload\",\n \"Ġdown load\",\n \"( T\",\n \"a ved\",\n \"av ed\",\n \"ave d\",\n \"âĢ ĵ\",\n \"Ġ students\",\n \"Ġst udents\",\n \"Ġstud ents\",\n \"Ġstudent s\",\n \"Ġ fig\",\n \"Ġf ig\",\n \"Ġfi g\",\n \"l ight\",\n \"li ght\",\n \"lig ht\",\n \"x x\",\n \"Ġ buffer\",\n \"Ġb uffer\",\n \"Ġbu ffer\",\n \"Ġbuf fer\",\n \"Ġbuff er\",\n \"Ġ Dep\",\n \"ĠD ep\",\n \"ĠDe p\",\n \"Ġ Math\",\n \"ĠM ath\",\n \"ĠMat h\",\n \"ĠMa th\",\n \"I TH\",\n \"IT H\",\n \"Ġ vari\",\n \"Ġv ari\",\n \"Ġvar i\",\n \"Ġva ri\",\n \"Ġ due\",\n \"Ġd ue\",\n \"Ġdu e\",\n \"F actory\",\n \"Fact ory\",\n \"Factor y\",\n \"Ġ por\",\n \"Ġp or\",\n \"Ġpo r\",\n \"Ġ ep\",\n \"Ġe p\",\n \"o type\",\n \"ot ype\",\n \"otyp e\",\n \"oty pe\",\n \"Ġ cannot\",\n \"Ġc annot\",\n \"Ġcan not\",\n \"Ġcann ot\",\n \"Ġ white\",\n \"Ġwh ite\",\n \"Ġwhit e\",\n \"< int\",\n \"čĊ\",\n \"\\\"> čĊ\",\n \". annot\",\n \".an not\",\n \"Ġ collection\",\n \"Ġc ollection\",\n \"Ġcol lection\",\n \"Ġcoll ection\",\n \"Ġcollect ion\",\n \"Ġcolle ction\",\n \"' .\",\n \"Ġ similar\",\n \"Ġs imilar\",\n \"Ġsim ilar\",\n \"Ġsimil ar\",\n \"Ġ taken\",\n \"Ġt aken\",\n \"Ġtake n\",\n \"Ġta ken\",\n \"Ġtak en\",\n \"( \\\"%\",\n \"(\\\" %\",\n \"Or der\",\n \"Ord er\",\n \"' ]Ċ\",\n \"'] Ċ\",\n \"- md\",\n \"-m d\",\n \"Ġ TH\",\n \"ĠT H\",\n \"a ced\",\n \"ace d\",\n \"ac ed\",\n \"Ġis n\",\n \"Ġi sn\",\n \"/ j\",\n \"Ġ son\",\n \"Ġs on\",\n \"Ġso n\",\n \"g raph\",\n \"gr aph\",\n \"gra ph\",\n \"Ġ Integer\",\n \"ĠInt eger\",\n \"Ġn ecess\",\n \"Ġne cess\",\n \"Ġnec ess\",\n \"Ġneces s\",\n \"r een\",\n \"re en\",\n \"ree n\",\n \"Ġ um\",\n \"Ġu m\",\n \"Ġ \\\\<\",\n \"Ġ\\\\ <\",\n \"Ġ moment\",\n \"Ġm oment\",\n \"Ġmom ent\",\n \"Ġmo ment\",\n \"Ġ bring\",\n \"Ġb ring\",\n \"Ġbr ing\",\n \"Ġbri ng\",\n \"Ġ indic\",\n \"Ġin dic\",\n \"Ġind ic\",\n \"y sis\",\n \"ys is\",\n \"ysi s\",\n \"Le vel\",\n \"v erse\",\n \"ver se\",\n \"vers e\",\n \"ur renc\",\n \"urre nc\",\n \"urr enc\",\n \"_ test\",\n \"_t est\",\n \"_te st\",\n \"Ġent ire\",\n \"D own\",\n \"Do wn\",\n \"Ġ }ĊĊĊ\",\n \"Ġ} ĊĊĊ\",\n \"Ġ}Ċ ĊĊ\",\n \"Ġ}ĊĊ Ċ\",\n \"( result\",\n \"(res ult\",\n \"Ġ Read\",\n \"ĠR ead\",\n \"ĠRe ad\",\n \"à ¨\",\n \"M od\",\n \"Mo d\",\n \"Ġ trying\",\n \"Ġt rying\",\n \"Ġtr ying\",\n \"Ġtry ing\",\n \"\\\" ),Ċ\",\n \"\\\") ,Ċ\",\n \"\\\"), Ċ\",\n \"Ġ member\",\n \"Ġm ember\",\n \"Ġmem ber\",\n \"Ġmemb er\",\n \"Ġ Cor\",\n \"ĠC or\",\n \"ĠCo r\",\n \"O DO\",\n \"OD O\",\n \"- control\",\n \"-c ontrol\",\n \"-cont rol\",\n \"un time\",\n \"unt ime\",\n \"Ġ Sim\",\n \"ĠS im\",\n \"ĠSi m\",\n \"D ialog\",\n \"Di alog\",\n \"Dia log\",\n \"p lot\",\n \"pl ot\",\n \"_ on\",\n \"_o n\",\n \"Ġ phys\",\n \"Ġph ys\",\n \"Ġphy s\",\n \"} /\",\n \"Ġ namespace\",\n \"Ġn amespace\",\n \"Ġname space\",\n \"Ġnames pace\",\n \"ĉ čĊ\",\n \"a cc\",\n \"ac c\",\n \"P layer\",\n \"Pl ayer\",\n \"Play er\",\n \"A RE\",\n \"AR E\",\n \"8 9\",\n \"Ġ foot\",\n \"Ġf oot\",\n \"Ġfo ot\",\n \"Ġfoo t\",\n \"Ġ board\",\n \"Ġb oard\",\n \"Ġbo ard\",\n \"Ġboa rd\",\n \"p art\",\n \"par t\",\n \"pa rt\",\n \"Ġ sus\",\n \"Ġs us\",\n \"Ġsu s\",\n \"w ise\",\n \"wi se\",\n \"wis e\",\n \"Ġ Mc\",\n \"ĠM c\",\n \"Ġ push\",\n \"Ġp ush\",\n \"Ġpu sh\",\n \"Ġpus h\",\n \"A TA\",\n \"AT A\",\n \"Ġ please\",\n \"Ġp lease\",\n \"Ġpl ease\",\n \"Ġple ase\",\n \"Ġplea se\",\n \"Ġpleas e\",\n \"r ied\",\n \"ri ed\",\n \"rie d\",\n \"we et\",\n \"b it\",\n \"bi t\",\n \"i ded\",\n \"id ed\",\n \"ide d\",\n \"V E\",\n \"Ġ Sw\",\n \"ĠS w\",\n \"U B\",\n \"Ġ types\",\n \"Ġt ypes\",\n \"Ġtype s\",\n \"Ġtyp es\",\n \"Ġty pes\",\n \"e dia\",\n \"ed ia\",\n \"edi a\",\n \"Ġc los\",\n \"Ġcl os\",\n \"Ġclo s\",\n \"ace book\",\n \"W hen\",\n \"Wh en\",\n \"Ġ edit\",\n \"Ġe dit\",\n \"Ġed it\",\n \"Ġedi t\",\n \"i gger\",\n \"ig ger\",\n \"igg er\",\n \"Ġe nerg\",\n \"Ġen erg\",\n \"Ġener g\",\n \"Cont ainer\",\n \"Contain er\",\n \"Conta iner\",\n \"Ġ phot\",\n \"Ġp hot\",\n \"Ġph ot\",\n \"Ġ Count\",\n \"ĠC ount\",\n \"ĠCo unt\",\n \"ĠCou nt\",\n \"ĠCoun t\",\n \"Ġ Europe\",\n \"ĠE urope\",\n \"ĠEuro pe\",\n \"ĠEurop e\",\n \"ĠEur ope\",\n \". Is\",\n \".I s\",\n \"Ġ Russ\",\n \"ĠR uss\",\n \"ĠRu ss\",\n \"ĠRus s\",\n \"p eed\",\n \"pe ed\",\n \"pee d\",\n \"Ġ Str\",\n \"ĠS tr\",\n \"ĠSt r\",\n \"Ġ py\",\n \"Ġp y\",\n \"Ġ cult\",\n \"Ġc ult\",\n \"Ġcu lt\",\n \"Ġcul t\",\n \"Ġ defined\",\n \"Ġd efined\",\n \"Ġdef ined\",\n \"Ġdefine d\",\n \"Ġdefin ed\",\n \"c count\",\n \"cc ount\",\n \"cco unt\",\n \"Ġo bt\",\n \"Ġob t\",\n \". Location\",\n \".L ocation\",\n \".Lo cation\",\n \"Ġ thread\",\n \"Ġt hread\",\n \"Ġth read\",\n \"Ġthr ead\",\n \"i lle\",\n \"il le\",\n \"ill e\",\n \"Ġ instead\",\n \"Ġin stead\",\n \"Ġinst ead\",\n \"st rong\",\n \"str ong\",\n \"stro ng\",\n \"Ġ Sec\",\n \"ĠS ec\",\n \"ĠSe c\",\n \"U RE\",\n \"UR E\",\n \"Ġ idea\",\n \"Ġi dea\",\n \"Ġid ea\",\n \"Ġide a\",\n \". se\",\n \".s e\",\n \"e my\",\n \"em y\",\n \"se lected\",\n \"select ed\",\n \"sel ected\",\n \"Con nection\",\n \"Conn ection\",\n \"Connect ion\",\n \"a cing\",\n \"ac ing\",\n \"aci ng\",\n \"acin g\",\n \"t hread\",\n \"th read\",\n \"thr ead\",\n \". next\",\n \".n ext\",\n \".ne xt\",\n \"Ġ coll\",\n \"Ġc oll\",\n \"Ġco ll\",\n \"Ġcol l\",\n \"Ġ film\",\n \"Ġf ilm\",\n \"Ġfil m\",\n \"Ġfi lm\",\n \"is tic\",\n \"ist ic\",\n \"isti c\",\n \"Ġ compet\",\n \"Ġcom pet\",\n \"Ġcomp et\",\n \"Ġ conn\",\n \"Ġc onn\",\n \"Ġcon n\",\n \"Ġco nn\",\n \"th ough\",\n \"Ġ compan\",\n \"Ġcom pan\",\n \"Ġcomp an\",\n \"o cket\",\n \"oc ket\",\n \"ock et\",\n \"Ġt each\",\n \"Ġte ach\",\n \"Ġtea ch\",\n \"= (\",\n \"Ġ phone\",\n \"Ġp hone\",\n \"Ġph one\",\n \"Ġphon e\",\n \"Ġ active\",\n \"Ġact ive\",\n \"Ġactiv e\",\n \"7 9\",\n \"de lete\",\n \"del ete\",\n \"1 01\",\n \"10 1\",\n \"t ries\",\n \"tr ies\",\n \"trie s\",\n \"tri es\",\n \"Ġ mo\",\n \"Ġm o\",\n \"Ġ death\",\n \"Ġde ath\",\n \"} );ĊĊ\",\n \"});Ċ Ċ\",\n \"}) ;ĊĊ\",\n \"}); ĊĊ\",\n \"o col\",\n \"oc ol\",\n \"oco l\",\n \"W idget\",\n \"Ġ article\",\n \"Ġart icle\",\n \"Ġartic le\",\n \"r odu\",\n \"ro du\",\n \"rod u\",\n \"an did\",\n \"and id\",\n \"andi d\",\n \"Ñ ĭ\",\n \"Ġ Cr\",\n \"ĠC r\",\n \"k a\",\n \"( ):\",\n \"() :\",\n \"l ood\",\n \"lo od\",\n \"loo d\",\n \"ĉ ĉĉĊ\",\n \"ĉĉ ĉĊ\",\n \"ĉĉĉ Ċ\",\n \"Ġ almost\",\n \"Ġal most\",\n \"Ġalm ost\",\n \"Ġ sell\",\n \"Ġs ell\",\n \"Ġse ll\",\n \"Ġsel l\",\n \"erv let\",\n \"r ip\",\n \"ri p\",\n \"U nit\",\n \"Un it\",\n \"Uni t\",\n \"Ġapp lic\",\n \"Ġappl ic\",\n \"Ġ connect\",\n \"Ġcon nect\",\n \"Ġconn ect\",\n \"Ġ feature\",\n \"Ġf eature\",\n \"Ġfe ature\",\n \"Ġfeat ure\",\n \"Ġ via\",\n \"Ġv ia\",\n \"Ġvi a\",\n \"' ),\",\n \"') ,\",\n \"Ġ lim\",\n \"Ġl im\",\n \"Ġli m\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġ Gu\",\n \"ĠG u\",\n \"E ngine\",\n \"Eng ine\",\n \"Ġ ens\",\n \"Ġe ns\",\n \"Ġen s\",\n \"Ġ environment\",\n \"Ġen vironment\",\n \"Ġenviron ment\",\n \"b lock\",\n \"bl ock\",\n \"blo ck\",\n \"H ERE\",\n \"HE RE\",\n \"HER E\",\n \"N ULL\",\n \"NU LL\",\n \"g y\",\n \"t ag\",\n \"ta g\",\n \") ).\",\n \")) .\",\n \"e xp\",\n \"ex p\",\n \"Ġcom pl\",\n \"Ġco mpl\",\n \"Ġcomp l\",\n \"Ġ install\",\n \"Ġinst all\",\n \"Ġinstal l\",\n \"Ġ complete\",\n \"Ġcom plete\",\n \"Ġcomp lete\",\n \"Ġcomple te\",\n \"Ġcomplet e\",\n \"Ġcompl ete\",\n \"q ueue\",\n \"que ue\",\n \"at ural\",\n \"atur al\",\n \"atura l\",\n \"atu ral\",\n \"Ġ general\",\n \"Ġg eneral\",\n \"Ġgener al\",\n \"Ġgen eral\",\n \"Ġgene ral\",\n \"Ġgenera l\",\n \"t hon\",\n \"th on\",\n \"Ġas ked\",\n \"Ġask ed\",\n \"o res\",\n \"or es\",\n \"ore s\",\n \"( res\",\n \"(r es\",\n \"(re s\",\n \"Ġ reserved\",\n \"Ġres erved\",\n \"Ġreserve d\",\n \"Ġreserv ed\",\n \"S P\",\n \"Ġ âĢ¦\",\n \"ĠâĢ ¦\",\n \"Å Ĥ\",\n \"Ġsign ific\",\n \"O ff\",\n \"Of f\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġ Ag\",\n \"ĠA g\",\n \"Ġ Just\",\n \"ĠJ ust\",\n \"ĠJu st\",\n \"Ġ Error\",\n \"ĠE rror\",\n \"ĠEr ror\",\n \"ĠErr or\",\n \"Ġin fl\",\n \"Ġinf l\",\n \"a data\",\n \"ad ata\",\n \"ada ta\",\n \"Ġ icon\",\n \"Ġi con\",\n \"Ġic on\",\n \"as ks\",\n \"ask s\",\n \"' '\",\n \"_ LO\",\n \"_L O\",\n \"? .\",\n \"a ccount\",\n \"ac count\",\n \"acc ount\",\n \"acco unt\",\n \"Ġ (*\",\n \"Ġ( *\",\n \"' )ĊĊ\",\n \"') ĊĊ\",\n \"')Ċ Ċ\",\n \"r ap\",\n \"ra p\",\n \"_ var\",\n \"_v ar\",\n \"_va r\",\n \"Ġ FOR\",\n \"ĠF OR\",\n \"ĠFO R\",\n \"Ġ party\",\n \"Ġp arty\",\n \"Ġpart y\",\n \"Ġpar ty\",\n \"Ġ Your\",\n \"ĠY our\",\n \"ĠYou r\",\n \"ĠYo ur\",\n \"c at\",\n \"ca t\",\n \"s try\",\n \"st ry\",\n \"str y\",\n \". new\",\n \".n ew\",\n \".ne w\",\n \"b oot\",\n \"bo ot\",\n \"boo t\",\n \"Ġ Nov\",\n \"ĠN ov\",\n \"ĠNo v\",\n \"Ġ vector\",\n \"Ġv ector\",\n \"Ġve ctor\",\n \"Ġvec tor\",\n \"Ġvect or\",\n \"Ġ normal\",\n \"Ġn ormal\",\n \"Ġnor mal\",\n \"Ġnorm al\",\n \"Ġf urther\",\n \"Ġfur ther\",\n \"Re pository\",\n \"8 00\",\n \"80 0\",\n \"Ġ database\",\n \"Ġd atabase\",\n \"Ġdata base\",\n \"Ġdat abase\",\n \"Ġdatab ase\",\n \"at tle\",\n \"att le\",\n \"Ġ music\",\n \"Ġm usic\",\n \"Ġmus ic\",\n \"Ġmu sic\",\n \"Ġ speed\",\n \"Ġs peed\",\n \"Ġsp eed\",\n \"Ġspe ed\",\n \"Ġ doc\",\n \"Ġd oc\",\n \"Ġdo c\",\n \"p rocess\",\n \"pro cess\",\n \"proc ess\",\n \"IG HT\",\n \"IGH T\",\n \". parse\",\n \".p arse\",\n \".par se\",\n \"Ġ taking\",\n \"Ġt aking\",\n \"Ġta king\",\n \"Ġtak ing\",\n \"Ġ viol\",\n \"Ġv iol\",\n \"Ġvi ol\",\n \"c eed\",\n \"ce ed\",\n \"cee d\",\n \"Ġ After\",\n \"ĠA fter\",\n \"ĠAf ter\",\n \"Ġ forward\",\n \"Ġfor ward\",\n \"Ġ crit\",\n \"Ġc rit\",\n \"Ġcr it\",\n \"Ġcri t\",\n \"\\\" />Ċ\",\n \"\\\"/ >Ċ\",\n \"\\\"/> Ċ\",\n \"r ot\",\n \"ro t\",\n \"Ġ failed\",\n \"Ġf ailed\",\n \"Ġfa iled\",\n \"Ġfail ed\",\n \"e fore\",\n \"ef ore\",\n \"Ġcon cern\",\n \"Ġconc ern\",\n \"Ġconce rn\",\n \"o e\",\n \"b a\",\n \"Ġ sender\",\n \"Ġs ender\",\n \"Ġse nder\",\n \"Ġsend er\",\n \"Ġsen der\",\n \"Ġ term\",\n \"Ġt erm\",\n \"Ġte rm\",\n \"Ġter m\",\n \"h as\",\n \"ha s\",\n \"= \\\"#\",\n \"=\\\" #\",\n \"Ġ potential\",\n \"Ġpot ential\",\n \"Ġpotent ial\",\n \"N um\",\n \"Nu m\",\n \"Ġ published\",\n \"Ġp ublished\",\n \"Ġpublish ed\",\n \"Ġpubli shed\",\n \". close\",\n \".c lose\",\n \".cl ose\",\n \"Ġ Image\",\n \"ĠI mage\",\n \"ĠIm age\",\n \"ĠImag e\",\n \"str aint\",\n \"stra int\",\n \"strain t\",\n \"U D\",\n \"Ġ Ob\",\n \"ĠO b\",\n \"Ġ probably\",\n \"Ġprob ably\",\n \"l im\",\n \"li m\",\n \"\\\" :Ċ\",\n \"\\\": Ċ\",\n \"ol ume\",\n \"olum e\",\n \"olu me\",\n \"Ġ consum\",\n \"Ġcon sum\",\n \"Ġcons um\",\n \"7 6\",\n \"a gue\",\n \"ag ue\",\n \"agu e\",\n \"ens ions\",\n \"ension s\",\n \"ensi ons\",\n \"Ġinvest ig\",\n \"- year\",\n \"-y ear\",\n \"' );\",\n \"') ;\",\n \"- sm\",\n \"-s m\",\n \"Ġen joy\",\n \"Ġenjo y\",\n \"o rig\",\n \"or ig\",\n \"ori g\",\n \"e ring\",\n \"er ing\",\n \"eri ng\",\n \"erin g\",\n \"c p\",\n \"le ased\",\n \"lease d\",\n \"lea sed\",\n \"p lements\",\n \"pl ements\",\n \"ple ments\",\n \"plement s\",\n \"Ġ returns\",\n \"Ġreturn s\",\n \"p at\",\n \"pa t\",\n \"B O\",\n \"Ġ House\",\n \"ĠH ouse\",\n \"ĠHo use\",\n \"ĠHou se\",\n \". Label\",\n \".L abel\",\n \"Ġ weight\",\n \"Ġw eight\",\n \"Ġwe ight\",\n \"Ġweigh t\",\n \"Ġwei ght\",\n \"ig hb\",\n \"igh b\",\n \"Ġ conditions\",\n \"Ġcondition s\",\n \"Ġcond itions\",\n \"Ġ exception\",\n \"Ġex ception\",\n \"Ġexcept ion\",\n \"Ġexce ption\",\n \"d escription\",\n \"de scription\",\n \"des cription\",\n \"Ġ trad\",\n \"Ġt rad\",\n \"Ġtr ad\",\n \"Ġtra d\",\n \"- to\",\n \"-t o\",\n \"Ġ {}\",\n \"Ġ{ }\",\n \"Ġ module\",\n \"Ġm odule\",\n \"Ġmod ule\",\n \"E ND\",\n \"EN D\",\n \". ap\",\n \".a p\",\n \". props\",\n \".p rops\",\n \".pro ps\",\n \".pr ops\",\n \".prop s\",\n \"Ġ constructor\",\n \"Ġcon structor\",\n \"Ġconstruct or\",\n \"Ġconstr uctor\",\n \"a ves\",\n \"av es\",\n \"ave s\",\n \"Ġ favor\",\n \"Ġf avor\",\n \"Ġfa vor\",\n \"Ġfav or\",\n \"Ġ Now\",\n \"ĠN ow\",\n \"ĠNo w\",\n \"; i\",\n \"Ġ Main\",\n \"ĠM ain\",\n \"ĠMa in\",\n \"ĠMai n\",\n \"_ k\",\n \"e ries\",\n \"er ies\",\n \"erie s\",\n \"eri es\",\n \"âĢĻ ll\",\n \"âĢĻl l\",\n \"trans form\",\n \"ime stamp\",\n \"imest amp\",\n \"P re\",\n \"Pr e\",\n \"Ġ mer\",\n \"Ġm er\",\n \"Ġme r\",\n \". res\",\n \".re s\",\n \".r es\",\n \"s tant\",\n \"st ant\",\n \"sta nt\",\n \"stan t\",\n \"L ocation\",\n \"Lo cation\",\n \"Loc ation\",\n \"_ NAME\",\n \"_N AME\",\n \"Ġ loss\",\n \"Ġl oss\",\n \"Ġlo ss\",\n \"Ġlos s\",\n \"Ġ ĊĊ\",\n \"ĠĊ Ċ\",\n \"n et\",\n \"ne t\",\n \"Ġ engine\",\n \"Ġe ngine\",\n \"Ġeng ine\",\n \"B lock\",\n \"Bl ock\",\n \"Bloc k\",\n \"Blo ck\",\n \"Ġ issues\",\n \"Ġiss ues\",\n \"Ġissue s\",\n \"Ġissu es\",\n \"Ġ parse\",\n \"Ġp arse\",\n \"Ġpar se\",\n \"Ġpars e\",\n \"Ġ Bar\",\n \"ĠB ar\",\n \"ĠBa r\",\n \"Ġ stay\",\n \"Ġst ay\",\n \"Ġsta y\",\n \"Ġ JSON\",\n \"ĠJ SON\",\n \"ĠJS ON\",\n \"Ġ dom\",\n \"Ġd om\",\n \"Ġdo m\",\n \"a irs\",\n \"air s\",\n \"ai rs\",\n \"w ner\",\n \"wn er\",\n \"Ġ lower\",\n \"Ġl ower\",\n \"Ġlo wer\",\n \"Ġlow er\",\n \"\\\" ,čĊ\",\n \"\\\", čĊ\",\n \"Ġ Dem\",\n \"ĠD em\",\n \"ĠDe m\",\n \"u fact\",\n \"uf act\",\n \"ufac t\",\n \"Ġ ps\",\n \"Ġp s\",\n \"Ġ perfect\",\n \"Ġper fect\",\n \"Ġperf ect\",\n \"R L\",\n \"Ġ educ\",\n \"Ġe duc\",\n \"Ġed uc\",\n \"Ġedu c\",\n \"l s\",\n \"em ory\",\n \"emo ry\",\n \"ARR ANT\",\n \"u ge\",\n \"ug e\",\n \"Ġ exact\",\n \"Ġex act\",\n \". key\",\n \".k ey\",\n \".ke y\",\n \"al led\",\n \"all ed\",\n \"alle d\",\n \"e ch\",\n \"ec h\",\n \"i ef\",\n \"ie f\",\n \"\\\\ /\",\n \"o ke\",\n \"ok e\",\n \"Ġ former\",\n \"Ġfor mer\",\n \"Ġform er\",\n \"Ġforme r\",\n \"al loc\",\n \"all oc\",\n \"allo c\",\n \"Ġ six\",\n \"Ġs ix\",\n \"Ġsi x\",\n \"i da\",\n \"id a\",\n \"Ġ margin\",\n \"Ġm argin\",\n \"Ġmar gin\",\n \"Ġmarg in\",\n \"Ġ heart\",\n \"Ġhe art\",\n \"Ġhear t\",\n \"a ld\",\n \"al d\",\n \"p ack\",\n \"pa ck\",\n \"pac k\",\n \".getElement ById\",\n \"ĠW ARRANT\",\n \"Ġ rather\",\n \"Ġr ather\",\n \"Ġrat her\",\n \"Ġra ther\",\n \"Ġ building\",\n \"Ġbuild ing\",\n \"er man\",\n \"erm an\",\n \"l ice\",\n \"lic e\",\n \"li ce\",\n \"Ġ questions\",\n \"Ġquest ions\",\n \"Ġquestion s\",\n \"Ġquesti ons\",\n \"i zes\",\n \"iz es\",\n \"ize s\",\n \"le ge\",\n \"leg e\",\n \"irect ory\",\n \"irector y\",\n \"Ġ je\",\n \"Ġj e\",\n \"Ġ cas\",\n \"Ġc as\",\n \"Ġca s\",\n \"p rops\",\n \"pr ops\",\n \"pro ps\",\n \"prop s\",\n \"u tf\",\n \"ut f\",\n \"Ġ security\",\n \"Ġs ecurity\",\n \"Ġse curity\",\n \"Ġsec urity\",\n \"Ġ however\",\n \"Ġhow ever\",\n \"w eight\",\n \"we ight\",\n \"wei ght\",\n \"weigh t\",\n \"Ġ inside\",\n \"Ġin side\",\n \"Ġins ide\",\n \"Ġp resident\",\n \"Ġpres ident\",\n \"C har\",\n \"Ch ar\",\n \"Cha r\",\n \"Ġ WITH\",\n \"ĠW ITH\",\n \"ĠWI TH\",\n \". map\",\n \".m ap\",\n \".ma p\",\n \"Ġ graph\",\n \"Ġg raph\",\n \"Ġgr aph\",\n \"Ġgra ph\",\n \"Ġgrap h\",\n \"Ġ tag\",\n \"Ġt ag\",\n \"Ġta g\",\n \"_ status\",\n \"_s tatus\",\n \"_st atus\",\n \"_stat us\",\n \"Ġ attempt\",\n \"Ġat tempt\",\n \"Ġatt empt\",\n \"o pp\",\n \"op p\",\n \"u ses\",\n \"us es\",\n \"use s\",\n \"ĉ const\",\n \"ĉcon st\",\n \"Ġ round\",\n \"Ġr ound\",\n \"Ġro und\",\n \"Ġrou nd\",\n \", $\",\n \"Ġ friends\",\n \"Ġf riends\",\n \"Ġfri ends\",\n \"Ġfriend s\",\n \"E mail\",\n \"Em ail\",\n \"? >\",\n \"Re source\",\n \"Res ource\",\n \"K EY\",\n \"KE Y\",\n \"o sp\",\n \"os p\",\n \". query\",\n \".qu ery\",\n \"Ġ North\",\n \"ĠN orth\",\n \"ĠNor th\",\n \"a bles\",\n \"ab les\",\n \"able s\",\n \"abl es\",\n \"is trib\",\n \"ist rib\",\n \"istr ib\",\n \"_ class\",\n \"_c lass\",\n \"_cl ass\",\n \"el lo\",\n \"ell o\",\n \"T hat\",\n \"Th at\",\n \"Ð º\",\n \"pec ially\",\n \"pecial ly\",\n \"Ġ President\",\n \"ĠP resident\",\n \"ĠPres ident\",\n \"Ġ campaign\",\n \"Ġc ampaign\",\n \"Ġcamp aign\",\n \"Ġ alt\",\n \"Ġa lt\",\n \"Ġal t\",\n \"a rea\",\n \"ar ea\",\n \"are a\",\n \"Ġc hall\",\n \"Ġch all\",\n \"Ġcha ll\",\n \"Ġop port\",\n \"Ġopp ort\",\n \". Con\",\n \".C on\",\n \".Co n\",\n \"Ġ energy\",\n \"Ġe nergy\",\n \"Ġen ergy\",\n \"Ġenerg y\",\n \"Ġener gy\",\n \"l ike\",\n \"li ke\",\n \"lik e\",\n \". string\",\n \".s tring\",\n \".st ring\",\n \".str ing\",\n \"ing ton\",\n \"ingt on\",\n \") *\",\n \"y y\",\n \"Ġ profession\",\n \"Ġprof ession\",\n \"Ġprofess ion\",\n \"ir th\",\n \"irt h\",\n \"Ġ seg\",\n \"Ġs eg\",\n \"Ġse g\",\n \"æ ľ\",\n \"Ġ hor\",\n \"Ġh or\",\n \"Ġho r\",\n \"i ers\",\n \"ie rs\",\n \"ier s\",\n \"c an\",\n \"ca n\",\n \"Ġbe hind\",\n \"Ġbeh ind\",\n \"Pro duct\",\n \"Produ ct\",\n \"Prod uct\",\n \"f g\",\n \"Ġ Sk\",\n \"ĠS k\",\n \". jpg\",\n \".j pg\",\n \".jp g\",\n \"? :\",\n \"] ;ĊĊ\",\n \"];Ċ Ċ\",\n \"]; ĊĊ\",\n \"Ġ callback\",\n \"Ġc allback\",\n \"Ġcall back\",\n \"Ġ Http\",\n \"ĠH ttp\",\n \"Ñ Į\",\n \"l ong\",\n \"lo ng\",\n \"lon g\",\n \"M S\",\n \"A TH\",\n \"AT H\",\n \"Ġ raise\",\n \"Ġr aise\",\n \"Ġrais e\",\n \"Ġra ise\",\n \"Ġ wanted\",\n \"Ġw anted\",\n \"Ġwant ed\",\n \"Ġwan ted\",\n \"r own\",\n \"ro wn\",\n \"row n\",\n \"u tor\",\n \"ut or\",\n \"uto r\",\n \"l t\",\n \"] =\",\n \"e line\",\n \"el ine\",\n \"eli ne\",\n \"elin e\",\n \"M A\",\n \"Ġs epar\",\n \"Ġse par\",\n \"Ġsep ar\",\n \"c s\",\n \"s emb\",\n \"se mb\",\n \"sem b\",\n \"D is\",\n \"Di s\",\n \"b serv\",\n \"bs erv\",\n \"Ġ Will\",\n \"ĠW ill\",\n \"ĠWil l\",\n \"ĠWi ll\",\n \"Ġ policy\",\n \"Ġp olicy\",\n \"Ġpol icy\",\n \"Ġpolic y\",\n \"Ġ third\",\n \"Ġth ird\",\n \"Ġthi rd\",\n \"p hone\",\n \"ph one\",\n \"phon e\",\n \"Ġ bed\",\n \"Ġb ed\",\n \"Ġbe d\",\n \"/ g\",\n \". __\",\n \"._ _\",\n \"Ġ Inc\",\n \"ĠI nc\",\n \"ĠIn c\",\n \"i zing\",\n \"iz ing\",\n \"izi ng\",\n \"izin g\",\n \". remove\",\n \".re move\",\n \".rem ove\",\n \"in stance\",\n \"inst ance\",\n \"instanc e\",\n \". type\",\n \".t ype\",\n \".typ e\",\n \"Ġ serv\",\n \"Ġs erv\",\n \"Ġse rv\",\n \"Ġser v\",\n \"E ach\",\n \"Ġ har\",\n \"Ġh ar\",\n \"Ġha r\",\n \"Ġ Message\",\n \"ĠM essage\",\n \"ĠMess age\",\n \"ĠMes sage\",\n \"( key\",\n \"(k ey\",\n \"SE LECT\",\n \"SEL ECT\",\n \"P os\",\n \"Po s\",\n \") );čĊ\",\n \")) ;čĊ\",\n \")); čĊ\",\n \"Ġre comm\",\n \"Ġrec omm\",\n \"Ġrecom m\",\n \"Ġreco mm\",\n \"Ġ training\",\n \"Ġtr aining\",\n \"Ġtrain ing\",\n \"Ġtra ining\",\n \"Ġtrai ning\",\n \"Ġ Ent\",\n \"ĠE nt\",\n \"ĠEn t\",\n \"Ġ Char\",\n \"ĠC har\",\n \"ĠCh ar\",\n \"ĠCha r\",\n \"i cht\",\n \"ic ht\",\n \"ich t\",\n \"( file\",\n \"(f ile\",\n \"(fi le\",\n \"(fil e\",\n \"Ġ prior\",\n \"Ġp rior\",\n \"Ġpr ior\",\n \"Ġpri or\",\n \"Ġprio r\",\n \"G ame\",\n \"Gam e\",\n \"Ga me\",\n \"Ġ exit\",\n \"Ġe xit\",\n \"Ġex it\",\n \"Param s\",\n \"Par ams\",\n \"Pa rams\",\n \"Para ms\",\n \". core\",\n \".c ore\",\n \".co re\",\n \".cor e\",\n \"P C\",\n \"n es\",\n \"ne s\",\n \"an ced\",\n \"ance d\",\n \"anc ed\",\n \"( request\",\n \"(re quest\",\n \"(req uest\",\n \"P assword\",\n \"Pass word\",\n \"Pas sword\",\n \"} >Ċ\",\n \"}> Ċ\",\n \"Ġ mag\",\n \"Ġm ag\",\n \"Ġma g\",\n \"Ġ release\",\n \"Ġre lease\",\n \"Ġr elease\",\n \"Ġrel ease\",\n \"Ġrele ase\",\n \"Ġ shall\",\n \"Ġs hall\",\n \"Ġsh all\",\n \"Ġsha ll\",\n \"u dent\",\n \"ud ent\",\n \"ude nt\",\n \"uden t\",\n \"Ġ South\",\n \"ĠS outh\",\n \"ĠSo uth\",\n \"ĠSou th\",\n \"a ndo\",\n \"an do\",\n \"and o\",\n \": '\",\n \". TabIndex\",\n \".Tab Index\",\n \"s k\",\n \"an ner\",\n \"ann er\",\n \"anne r\",\n \"is set\",\n \"iss et\",\n \"isse t\",\n \"Ġ outside\",\n \"Ġout side\",\n \"Ġouts ide\",\n \"l edge\",\n \"le dge\",\n \"led ge\",\n \"Ġ å\",\n \"Ġ Rob\",\n \"ĠR ob\",\n \"ĠRo b\",\n \"Ġ imm\",\n \"Ġi mm\",\n \"Ġim m\",\n \"! Ċ\",\n \"Ġ Web\",\n \"ĠW eb\",\n \"ĠWe b\",\n \"D es\",\n \"De s\",\n \"B C\",\n \"an cial\",\n \"anc ial\",\n \"ancia l\",\n \"R oute\",\n \"Ro ute\",\n \"D ec\",\n \"De c\",\n \"fer ences\",\n \"ference s\",\n \"Ġp urch\",\n \"Ġpur ch\",\n \"Ġpu rch\",\n \"Ġ Model\",\n \"ĠM odel\",\n \"ĠMod el\",\n \"ĠMo del\",\n \"ĠMode l\",\n \"c tor\",\n \"ct or\",\n \"g n\",\n \"_ start\",\n \"_st art\",\n \"_star t\",\n \"_sta rt\",\n \"_ un\",\n \"_u n\",\n \". *\",\n \"i ses\",\n \"is es\",\n \"ise s\",\n \"Ġ ground\",\n \"Ġg round\",\n \"Ġgr ound\",\n \"Ġgro und\",\n \"Ġgrou nd\",\n \"Ġ unique\",\n \"Ġun ique\",\n \"Ġuniqu e\",\n \"Ġuni que\",\n \"Ġuniq ue\",\n \"Ġbe aut\",\n \"Ġbeau t\",\n \"{ \\\"\",\n \"Ġ pour\",\n \"Ġp our\",\n \"Ġpo ur\",\n \"Ġpou r\",\n \"Ġ Oct\",\n \"ĠO ct\",\n \"ĠOc t\",\n \"Ġ tree\",\n \"Ġt ree\",\n \"Ġtr ee\",\n \"Ġtre e\",\n \"s ets\",\n \"se ts\",\n \"set s\",\n \"_ res\",\n \"_re s\",\n \"_r es\",\n \"' )->\",\n \"') ->\",\n \"_ reg\",\n \"_re g\",\n \"_r eg\",\n \"( \\\"\\\\\",\n \"(\\\" \\\\\",\n \"Ġ byte\",\n \"Ġb yte\",\n \"Ġby te\",\n \"Ġbyt e\",\n \"B l\",\n \"Ġ dating\",\n \"Ġd ating\",\n \"Ġda ting\",\n \"Ġdat ing\",\n \"Ġdati ng\",\n \"Ġ matter\",\n \"Ġm atter\",\n \"Ġmat ter\",\n \"Ġmatt er\",\n \"Ġmatte r\",\n \"Ġ Rem\",\n \"ĠR em\",\n \"ĠRe m\",\n \"Ġ' ../\",\n \"Ġ'. ./\",\n \"Ġ'.. /\",\n \"Ġ Aug\",\n \"ĠA ug\",\n \"ĠAu g\",\n \"Ġ La\",\n \"ĠL a\",\n \"Ġ $(\",\n \"Ġ$ (\",\n \"o urnal\",\n \"our nal\",\n \"ourn al\",\n \"1 11\",\n \"11 1\",\n \"i am\",\n \"ia m\",\n \"Ġ shows\",\n \"Ġsh ows\",\n \"Ġshow s\",\n \"Ġsho ws\",\n \"w rite\",\n \"wr ite\",\n \"Ġ ball\",\n \"Ġb all\",\n \"Ġbal l\",\n \"Ġba ll\",\n \"Ġsim ply\",\n \"Ġsimp ly\",\n \"Ġsimpl y\",\n \"Ġ fast\",\n \"Ġf ast\",\n \"Ġfa st\",\n \"Ġfas t\",\n \"Ġ memory\",\n \"Ġm emory\",\n \"Ġmem ory\",\n \"Ġmemor y\",\n \"Ġmemo ry\",\n \"A SS\",\n \"AS S\",\n \"Ġ Of\",\n \"ĠO f\",\n \"o ved\",\n \"ov ed\",\n \"ove d\",\n \"a nte\",\n \"an te\",\n \"ant e\",\n \"a ul\",\n \"au l\",\n \"i stry\",\n \"is try\",\n \"ist ry\",\n \"istr y\",\n \") ));Ċ\",\n \")) );Ċ\",\n \"))) ;Ċ\",\n \"))); Ċ\",\n \"Ġ fit\",\n \"Ġf it\",\n \"Ġfi t\",\n \"< string\",\n \"_\",\n \"-> _\",\n \"\\\" )ĊĊ\",\n \"\\\") ĊĊ\",\n \"\\\")Ċ Ċ\",\n \"o x\",\n \"ap plication\",\n \"app lication\",\n \"appl ication\",\n \"Ġ ]Ċ\",\n \"Ġ] Ċ\",\n \"Ċ ĊĊĊĊĊ\",\n \"ĊĊ ĊĊĊĊ\",\n \"ĊĊĊĊ ĊĊ\",\n \"ĊĊĊ ĊĊĊ\",\n \"ĊĊĊĊĊ Ċ\",\n \"1 80\",\n \"18 0\",\n \"Ġ soon\",\n \"Ġs oon\",\n \"Ġso on\",\n \"Ġsoo n\",\n \"ct ions\",\n \"ction s\",\n \"i nger\",\n \"in ger\",\n \"ing er\",\n \"inge r\",\n \"Ġ join\",\n \"Ġj oin\",\n \"Ġjo in\",\n \"Ġ Pe\",\n \"ĠP e\",\n \"Ġ ë\",\n \"Ġ las\",\n \"Ġl as\",\n \"Ġla s\",\n \". E\",\n \"c ss\",\n \"cs s\",\n \"/ or\",\n \"/o r\",\n \"Ġ Start\",\n \"ĠSt art\",\n \"ĠStar t\",\n \"ĠSta rt\",\n \"Ġ TO\",\n \"ĠT O\",\n \"Ġ subs\",\n \"Ġs ubs\",\n \"Ġsu bs\",\n \"Ġsub s\",\n \"c onn\",\n \"con n\",\n \"co nn\",\n \"com ponents\",\n \"comp onents\",\n \"component s\",\n \"DE BUG\",\n \"qu are\",\n \"qua re\",\n \"F unction\",\n \"Func tion\",\n \"Fun ction\",\n \"en dar\",\n \"end ar\",\n \"enda r\",\n \". index\",\n \".in dex\",\n \".ind ex\",\n \"Ġ fill\",\n \"Ġf ill\",\n \"Ġfil l\",\n \"Ġfi ll\",\n \"Ä Ļ\",\n \"Ġ choose\",\n \"Ġch oose\",\n \"Ġcho ose\",\n \"h ow\",\n \"ho w\",\n \"Ġ America\",\n \"ĠAmeric a\",\n \"ĠAmer ica\",\n \"as sets\",\n \"ass ets\",\n \"asset s\",\n \"asse ts\",\n \"- -----------\",\n \"-- ----------\",\n \"---- --------\",\n \"-------- ----\",\n \"--- ---------\",\n \"----- -------\",\n \"---------- --\",\n \"------ ------\",\n \"----------- -\",\n \"------- -----\",\n \"--------- ---\",\n \"Ġ Value\",\n \"ĠV alue\",\n \"ĠVal ue\",\n \"ĠVa lue\",\n \"Ġ office\",\n \"Ġoff ice\",\n \"Ġoffic e\",\n \"Ġ veh\",\n \"Ġv eh\",\n \"Ġve h\",\n \"Ġ transform\",\n \"Ġtrans form\",\n \"Ġtransf orm\",\n \"Ġ Art\",\n \"ĠA rt\",\n \"ĠAr t\",\n \"Ġ inde\",\n \"Ġin de\",\n \"Ġi nde\",\n \"Ġind e\",\n \"Ġ fn\",\n \"Ġf n\",\n \"Ġ implements\",\n \"Ġim plements\",\n \"Ġimp lements\",\n \"Ġimplement s\",\n \"Ġimpl ements\",\n \"a ngo\",\n \"an go\",\n \"ang o\",\n \"p lete\",\n \"pl ete\",\n \"ple te\",\n \"plet e\",\n \"+ \\\"\",\n \"t mp\",\n \"tm p\",\n \"am ily\",\n \"ami ly\",\n \"amil y\",\n \"Ġ hash\",\n \"Ġh ash\",\n \"Ġhas h\",\n \"Ġha sh\",\n \"m issions\",\n \"miss ions\",\n \"mission s\",\n \"E ST\",\n \"ES T\",\n \"g t\",\n \"Pro vider\",\n \"Provid er\",\n \"Provide r\",\n \"Prov ider\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"Ġ flag\",\n \"Ġf lag\",\n \"Ġfl ag\",\n \"Ġfla g\",\n \"Ġpart icip\",\n \"Ġpartic ip\",\n \"Ġparti cip\",\n \"d en\",\n \"de n\",\n \"Ġ Returns\",\n \"ĠReturn s\",\n \"Ġ note\",\n \"Ġn ote\",\n \"Ġnot e\",\n \"Ġno te\",\n \"ü r\",\n \"p m\",\n \"id eos\",\n \"ide os\",\n \"ideo s\",\n \"Ġ specified\",\n \"Ġs pecified\",\n \"Ġspec ified\",\n \"Ġ EN\",\n \"ĠE N\",\n \"e ster\",\n \"es ter\",\n \"est er\",\n \"este r\",\n \"o lid\",\n \"ol id\",\n \"oli d\",\n \"Ġ upon\",\n \"Ġu pon\",\n \"Ġup on\",\n \"( std\",\n \"(s td\",\n \"(st d\",\n \"ĉ v\",\n \"Ġ '\\\\\",\n \"Ġ' \\\\\",\n \"u z\",\n \"Ġ vert\",\n \"Ġv ert\",\n \"Ġver t\",\n \"Ġve rt\",\n \"Ġv ict\",\n \"Ġvi ct\",\n \"Ġvic t\",\n \"ĉ self\",\n \"ĉs elf\",\n \"ĉse lf\",\n \"Ġ \\\"$\",\n \"Ġ\\\" $\",\n \"8 5\",\n \". k\",\n \"Ġ groups\",\n \"Ġg roups\",\n \"Ġgroup s\",\n \"Ġgro ups\",\n \"Ġgrou ps\",\n \"g ithub\",\n \"git hub\",\n \"l ang\",\n \"la ng\",\n \"lan g\",\n \"Ġ mut\",\n \"Ġm ut\",\n \"Ġmu t\",\n \"T O\",\n \"Ġ ve\",\n \"Ġv e\",\n \"Ġ Please\",\n \"ĠP lease\",\n \"ĠPl ease\",\n \"ĠPle ase\",\n \"; ĊĊĊ\",\n \";Ċ ĊĊ\",\n \";ĊĊ Ċ\",\n \"a ccess\",\n \"ac cess\",\n \"acc ess\",\n \"Ġ {\\\"\",\n \"Ġ{ \\\"\",\n \"r ea\",\n \"re a\",\n \"Ġ risk\",\n \"Ġr isk\",\n \"Ġris k\",\n \"Ġri sk\",\n \"i cker\",\n \"ic ker\",\n \"ick er\",\n \"og gle\",\n \"ogg le\",\n \"ĉ while\",\n \"A NG\",\n \"AN G\",\n \". send\",\n \".s end\",\n \".se nd\",\n \"7 2\",\n \"Ġ woman\",\n \"Ġw oman\",\n \"Ġwom an\",\n \"Ġwo man\",\n \"Ġ gets\",\n \"Ġg ets\",\n \"Ġget s\",\n \"Ġge ts\",\n \"Ġ ign\",\n \"Ġi gn\",\n \"Ġig n\",\n \"Ġ Id\",\n \"ĠI d\",\n \"_ log\",\n \"_l og\",\n \"_lo g\",\n \"O NE\",\n \"ON E\",\n \"Ġe vid\",\n \"Ġev id\",\n \"Ġ Har\",\n \"ĠH ar\",\n \"ĠHa r\",\n \"_ sub\",\n \"_s ub\",\n \"_su b\",\n \"Ġ endl\",\n \"Ġe ndl\",\n \"Ġen dl\",\n \"Ġend l\",\n \"Ġ included\",\n \"Ġin cluded\",\n \"Ġinclud ed\",\n \"Ġinclude d\",\n \"Ġincl uded\",\n \"Ġinclu ded\",\n \"( ));ĊĊ\",\n \"() );ĊĊ\",\n \"());Ċ Ċ\",\n \"()) ;ĊĊ\",\n \"()); ĊĊ\",\n \"Ġ Ap\",\n \"ĠA p\",\n \"i gr\",\n \"ig r\",\n \"Ġ sem\",\n \"Ġs em\",\n \"Ġse m\",\n \"Ġ Black\",\n \"ĠB lack\",\n \"ĠBl ack\",\n \"d oc\",\n \"do c\",\n \"_ table\",\n \"_t able\",\n \"_tab le\",\n \"_ta ble\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"- up\",\n \"-u p\",\n \"Ġ cause\",\n \"Ġc ause\",\n \"Ġca use\",\n \"Ġcaus e\",\n \"Ġ ..\",\n \"Ġ. .\",\n \"Ġ van\",\n \"Ġv an\",\n \"Ġva n\",\n \"_ dict\",\n \"_d ict\",\n \"_di ct\",\n \"_dic t\",\n \"Ġ focus\",\n \"Ġf ocus\",\n \"Ġfoc us\",\n \"Ġfo cus\",\n \"I ND\",\n \"IN D\",\n \"C ESS\",\n \"CE SS\",\n \"CES S\",\n \". Log\",\n \".L og\",\n \".Lo g\",\n \"Ġ multiple\",\n \"Ġm ultiple\",\n \"Ġmult iple\",\n \"Ġmulti ple\",\n \"Ġmultip le\",\n \"i do\",\n \"id o\",\n \"Ġre gard\",\n \"Ġreg ard\",\n \"- M\",\n \"and ler\",\n \"andle r\",\n \"o urse\",\n \"our se\",\n \"ours e\",\n \"Ġ deg\",\n \"Ġd eg\",\n \"Ġde g\",\n \". U\",\n \"Ġadd ition\",\n \"Ġad dition\",\n \"Ġv arious\",\n \"Ġvar ious\",\n \"Ġvari ous\",\n \"Ġva rious\",\n \"Ġ receive\",\n \"Ġre ceive\",\n \"Ġrece ive\",\n \"е н\",\n \"еР½\",\n \"Ġ HT\",\n \"ĠH T\",\n \"O bj\",\n \"Ob j\",\n \"D F\",\n \"Ġ increase\",\n \"Ġin crease\",\n \"Ġincre ase\",\n \"Ġincr ease\",\n \"Ġ Open\",\n \"ĠO pen\",\n \"ĠOp en\",\n \"] ;\",\n \"Ġ commit\",\n \"Ġcom mit\",\n \"Ġcomm it\",\n \"? Ċ\",\n \"ateg ories\",\n \"ategor ies\",\n \"ategori es\",\n \"ategorie s\",\n \"atego ries\",\n \"at ory\",\n \"ator y\",\n \"ato ry\",\n \"s hip\",\n \"sh ip\",\n \"shi p\",\n \"Ġ Mich\",\n \"ĠM ich\",\n \"ĠMi ch\",\n \"ĠMic h\",\n \"Ġ html\",\n \"Ġh tml\",\n \"Ġht ml\",\n \"ro mise\",\n \"rom ise\",\n \"Ġ leave\",\n \"Ġle ave\",\n \"Ġstr ateg\",\n \"Ġstrat eg\",\n \"Ġstra teg\",\n \"Ġstrate g\",\n \"a ven\",\n \"av en\",\n \"ave n\",\n \"Ġ Console\",\n \"ĠCon sole\",\n \"ĠCons ole\",\n \"k nown\",\n \"kn own\",\n \"know n\",\n \"- n\",\n \"_ LE\",\n \"_L E\",\n \". component\",\n \".com ponent\",\n \".comp onent\",\n \"Ġ bre\",\n \"Ġb re\",\n \"Ġbr e\",\n \"S ession\",\n \"i ance\",\n \"ia nce\",\n \"ian ce\",\n \"Ġ align\",\n \"Ġal ign\",\n \"Ġali gn\",\n \"type def\",\n \"typ edef\",\n \"typed ef\",\n \"_ result\",\n \"_res ult\",\n \"Ġ WHERE\",\n \"ĠW HERE\",\n \"ĠWH ERE\",\n \". split\",\n \".s plit\",\n \".sp lit\",\n \"Ġ reading\",\n \"Ġre ading\",\n \"Ġread ing\",\n \"FA ULT\",\n \"Ġ clo\",\n \"Ġc lo\",\n \"Ġcl o\",\n \"Ġ notice\",\n \"Ġnot ice\",\n \"_ pr\",\n \"_p r\",\n \"ar ter\",\n \"art er\",\n \"arte r\",\n \"Ġ lock\",\n \"Ġl ock\",\n \"Ġlo ck\",\n \"Ġloc k\",\n \"Ġ standard\",\n \"Ġst andard\",\n \"Ġstand ard\",\n \"e tic\",\n \"et ic\",\n \"eti c\",\n \"el low\",\n \"ell ow\",\n \"ello w\",\n \"Ġ padding\",\n \"Ġp adding\",\n \"Ġpad ding\",\n \"Ġpadd ing\",\n \"Ġ His\",\n \"ĠH is\",\n \"ĠHi s\",\n \"Ġ states\",\n \"Ġst ates\",\n \"Ġstate s\",\n \"Ġstat es\",\n \"Ġsta tes\",\n \"_ cast\",\n \"_c ast\",\n \"_ca st\",\n \"( P\",\n \"a a\",\n \"Ġ internal\",\n \"Ġin ternal\",\n \"Ġint ernal\",\n \"Ġinter nal\",\n \"Ġintern al\",\n \"e an\",\n \"ea n\",\n \"Ġ PRO\",\n \"ĠP RO\",\n \"ĠPR O\",\n \"Ġ Key\",\n \"ĠK ey\",\n \"ĠKe y\",\n \"Ġ especially\",\n \"Ġes pecially\",\n \"Ġespecial ly\",\n \"Ġespec ially\",\n \"m ing\",\n \"min g\",\n \"mi ng\",\n \"Ġ cross\",\n \"Ġc ross\",\n \"Ġcr oss\",\n \"Ġcro ss\",\n \"Ġ national\",\n \"Ġn ational\",\n \"Ġnation al\",\n \"Ġnat ional\",\n \"_ object\",\n \"_obj ect\",\n \"_o bject\",\n \"_ob ject\",\n \"f ilter\",\n \"fil ter\",\n \"filt er\",\n \"Ġ script\",\n \"Ġs cript\",\n \"Ġscr ipt\",\n \"Ġscri pt\",\n \". update\",\n \".up date\",\n \"_ i\",\n \"Ġ Assert\",\n \"ĠAs sert\",\n \"ĠAss ert\",\n \"/ core\",\n \"/c ore\",\n \"/co re\",\n \"% %%%\",\n \"%% %%\",\n \"%%% %\",\n \"Ġ problems\",\n \"Ġpro blems\",\n \"Ġproble ms\",\n \"Ġproblem s\",\n \"Ġprob lems\",\n \"Ġprobl ems\",\n \"i stor\",\n \"is tor\",\n \"ist or\",\n \"isto r\",\n \"Ġ .=\",\n \"Ġ. =\",\n \"Ġ arch\",\n \"Ġa rch\",\n \"Ġar ch\",\n \"Ġarc h\",\n \"Ġ written\",\n \"Ġw ritten\",\n \"Ġwrit ten\",\n \"Ġwr itten\",\n \"Ġm ilit\",\n \"Ġmil it\",\n \"Ġmi lit\",\n \"Ġmili t\",\n \"M ENT\",\n \"ME NT\",\n \". ch\",\n \".c h\",\n \"c ape\",\n \"ca pe\",\n \"cap e\",\n \"Ġ Mus\",\n \"ĠM us\",\n \"ĠMu s\",\n \"_ config\",\n \"_con fig\",\n \"_conf ig\",\n \"Ġ API\",\n \"ĠA PI\",\n \"ĠAP I\",\n \"f oot\",\n \"fo ot\",\n \"foo t\",\n \"Ġ images\",\n \"Ġim ages\",\n \"Ġimage s\",\n \"Ġimag es\",\n \"Ġima ges\",\n \"e ndl\",\n \"en dl\",\n \"end l\",\n \". In\",\n \".I n\",\n \"F irst\",\n \"Fi rst\",\n \"Ġ platform\",\n \"Ġpl atform\",\n \"Ġplat form\",\n \". prot\",\n \".p rot\",\n \".pro t\",\n \".pr ot\",\n \"O ption\",\n \"Op tion\",\n \"Opt ion\",\n \"s te\",\n \"st e\",\n \"Ġ TODO\",\n \"ĠT ODO\",\n \"ĠTO DO\",\n \"ĠTOD O\",\n \"Ġ force\",\n \"Ġf orce\",\n \"Ġfor ce\",\n \"Ġforc e\",\n \". cont\",\n \".c ont\",\n \".con t\",\n \".co nt\",\n \"ĉ echo\",\n \"ĉe cho\",\n \"ĠD av\",\n \"ĠDa v\",\n \"P tr\",\n \"Pt r\",\n \"( B\",\n \"R T\",\n \"Ġ Base\",\n \"ĠB ase\",\n \"ĠBa se\",\n \"ĠBas e\",\n \"] ['\",\n \"][ '\",\n \"Ġann ounc\",\n \"Ġanno unc\",\n \"con sole\",\n \"cons ole\",\n \"Ġ Py\",\n \"ĠP y\",\n \"d s\",\n \". as\",\n \".a s\",\n \"Ġ prevent\",\n \"Ġpr event\",\n \"Ġpre vent\",\n \"Ġprev ent\",\n \"a pan\",\n \"ap an\",\n \"apa n\",\n \"Ġ {'\",\n \"Ġ{ '\",\n \"} '\",\n \"Ġ dead\",\n \"Ġd ead\",\n \"Ġde ad\",\n \"V AL\",\n \"VA L\",\n \"Q UE\",\n \"QU E\",\n \"******** ****************************************************************\",\n \"**************** ********************************************************\",\n \"******************************** ****************************************\",\n \"**************************************************************** ********\",\n \"************************ ************************************************\",\n \"**************************************** ********************************\",\n \"************************************************ ************************\",\n \"******************************************************** ****************\",\n \"Ġ charg\",\n \"Ġch arg\",\n \"Ġchar g\",\n \"Ġcha rg\",\n \"R eturn\",\n \"Re turn\",\n \"Ret urn\",\n \"Ġ ful\",\n \"Ġf ul\",\n \"Ġfu l\",\n \"d om\",\n \"do m\",\n \"Ġ rules\",\n \"Ġr ules\",\n \"Ġrule s\",\n \"Ġru les\",\n \"Ġ modify\",\n \"Ġmod ify\",\n \"Ġ eval\",\n \"Ġe val\",\n \"Ġev al\",\n \"h am\",\n \"ha m\",\n \"a tement\",\n \"at ement\",\n \"ate ment\",\n \"atem ent\",\n \"\\\\ <\",\n \"u la\",\n \"ul a\",\n \"= False\",\n \"=F alse\",\n \"R A\",\n \"Ġ contains\",\n \"Ġcon tains\",\n \"Ġcont ains\",\n \"Ġcontain s\",\n \"Ġconta ins\",\n \"7 4\",\n \"Ġ stack\",\n \"Ġst ack\",\n \"Ġsta ck\",\n \"m ar\",\n \"ma r\",\n \"Ġ {}Ċ\",\n \"Ġ{ }Ċ\",\n \"Ġ{} Ċ\",\n \"Ġ undefined\",\n \"Ġun defined\",\n \"Ġund efined\",\n \"Ġundef ined\",\n \"A ss\",\n \"As s\",\n \"Ġ China\",\n \"ĠCh ina\",\n \"ĠChi na\",\n \"ĠChin a\",\n \"v ey\",\n \"ve y\",\n \"* Ċ\",\n \"Ġ playing\",\n \"Ġpl aying\",\n \"Ġplay ing\",\n \"Ġpla ying\",\n \") /\",\n \"a ctor\",\n \"act or\",\n \"ac tor\",\n \"Ġ bottom\",\n \"Ġb ottom\",\n \"Ġbot tom\",\n \"Ġbott om\",\n \"l ier\",\n \"li er\",\n \"lie r\",\n \"Ġ Number\",\n \"ĠN umber\",\n \"ĠNum ber\",\n \"Ġc ouple\",\n \"Ġco uple\",\n \"Ġcou ple\",\n \"Ġcoup le\",\n \"D C\",\n \"Ġ SO\",\n \"ĠS O\",\n \"g or\",\n \"go r\",\n \". setText\",\n \".set Text\",\n \"s uccess\",\n \"su ccess\",\n \"succ ess\",\n \"com mand\",\n \"comm and\",\n \"comma nd\",\n \"F ilter\",\n \"Fil ter\",\n \"Ġ Our\",\n \"ĠO ur\",\n \"ĠOu r\",\n \"_ item\",\n \"_i tem\",\n \"_it em\",\n \"Ġ ctx\",\n \"Ġc tx\",\n \"Ġct x\",\n \"Ġ road\",\n \"Ġr oad\",\n \"Ġro ad\",\n \"V ersion\",\n \"Vers ion\",\n \"c ase\",\n \"ca se\",\n \"cas e\",\n \"u rt\",\n \"ur t\",\n \"av ior\",\n \"avi or\",\n \"y ch\",\n \"yc h\",\n \"semb ly\",\n \"sembl y\",\n \"Ġ Product\",\n \"ĠPro duct\",\n \"ĠProdu ct\",\n \"ĠProd uct\",\n \"Ġ held\",\n \"Ġh eld\",\n \"Ġhe ld\",\n \"Ġhel d\",\n \"a fe\",\n \"af e\",\n \"Ġ includes\",\n \"Ġin cludes\",\n \"Ġinclud es\",\n \"Ġinclude s\",\n \"Ġincl udes\",\n \"Ġinclu des\",\n \"< quote\",\n \"Ġ avoid\",\n \"Ġa void\",\n \"Ġav oid\",\n \"Ġ Fin\",\n \"ĠF in\",\n \"ĠFi n\",\n \"Ġ Mod\",\n \"ĠM od\",\n \"ĠMo d\",\n \"Ġ tab\",\n \"Ġt ab\",\n \"Ġta b\",\n \"a no\",\n \"an o\",\n \"à ±\",\n \"i pping\",\n \"ip ping\",\n \"ipp ing\",\n \"ippi ng\",\n \"- e\",\n \"Ġ insert\",\n \"Ġin sert\",\n \"Ġins ert\",\n \"Ġinser t\",\n \"Ġinse rt\",\n \"t arget\",\n \"tar get\",\n \"c han\",\n \"ch an\",\n \"cha n\",\n \". Model\",\n \".M odel\",\n \".Mod el\",\n \".Mode l\",\n \"I ME\",\n \"IM E\",\n \"\\\\ Ċ\",\n \"Ġ machine\",\n \"Ġm achine\",\n \"Ġma chine\",\n \"Ġmach ine\",\n \"a vy\",\n \"av y\",\n \"Ġ NO\",\n \"ĠN O\",\n \"Ġ Inter\",\n \"ĠIn ter\",\n \"ĠInt er\",\n \"Ġ operation\",\n \"Ġo peration\",\n \"Ġop eration\",\n \"Ġoper ation\",\n \"Ġopera tion\",\n \"m odal\",\n \"mod al\",\n \"mo dal\",\n \"T ag\",\n \"Ta g\",\n \"] :\",\n \"Ġ production\",\n \"Ġp roduction\",\n \"Ġpro duction\",\n \"Ġproduct ion\",\n \"Ġprodu ction\",\n \"Ġprod uction\",\n \"Ġ areas\",\n \"Ġa reas\",\n \"Ġare as\",\n \"Ġarea s\",\n \"Ġ ren\",\n \"Ġre n\",\n \"Ġr en\",\n \"_ from\",\n \"_f rom\",\n \"_fr om\",\n \"n bsp\",\n \"nb sp\",\n \"Ġ operator\",\n \"Ġo perator\",\n \"Ġop erator\",\n \"Ġoper ator\",\n \"Ġopera tor\",\n \"m en\",\n \"me n\",\n \"a pped\",\n \"ap ped\",\n \"app ed\",\n \"appe d\",\n \"_ per\",\n \"_p er\",\n \"_pe r\",\n \"z en\",\n \"ze n\",\n \"( \\\".\",\n \"(\\\" .\",\n \". save\",\n \".s ave\",\n \".sa ve\",\n \".sav e\",\n \"=\\\" {{\",\n \"=\\\"{ {\",\n \"Ġ tor\",\n \"Ġt or\",\n \"Ġto r\",\n \"( response\",\n \"(res ponse\",\n \"(resp onse\",\n \"Ġc andid\",\n \"Ġcan did\",\n \"Ġcand id\",\n \"Ġ conv\",\n \"Ġcon v\",\n \"Ġco nv\",\n \"a iled\",\n \"ail ed\",\n \"ai led\",\n \"Ġ Lib\",\n \"ĠL ib\",\n \"ĠLi b\",\n \"c omp\",\n \"com p\",\n \"co mp\",\n \"u ra\",\n \"ur a\",\n \"ï¿ ½\",\n \"Ġ Here\",\n \"ĠH ere\",\n \"ĠHe re\",\n \"ĠHer e\",\n \"Ġ argument\",\n \"Ġarg ument\",\n \"h ood\",\n \"ho od\",\n \"hoo d\",\n \"Ġ establish\",\n \"Ġest ablish\",\n \"ograph y\",\n \"ogra phy\",\n \"Ġ onClick\",\n \"Ġon Click\",\n \"amb da\",\n \"Ġ sch\",\n \"Ġs ch\",\n \"Ġsc h\",\n \"Ġ movie\",\n \"Ġm ovie\",\n \"Ġmov ie\",\n \"Ġmo vie\",\n \"Ġ sec\",\n \"Ġs ec\",\n \"Ġse c\",\n \"Ġ activity\",\n \"Ġact ivity\",\n \"Ġactiv ity\",\n \"Ø §\",\n \"Ġ sql\",\n \"Ġs ql\",\n \"Ġsq l\",\n \"_ all\",\n \"_a ll\",\n \"_al l\",\n \"in cip\",\n \"inc ip\",\n \"inci p\",\n \"Ġpro vides\",\n \"Ġprovid es\",\n \"Ġprov ides\",\n \"Ġprovide s\",\n \"Ġ sys\",\n \"Ġs ys\",\n \"Ġsy s\",\n \"a cket\",\n \"ack et\",\n \"ac ket\",\n \"Ġw asn\",\n \"Ġwas n\",\n \"Ġwa sn\",\n \"Ġ uses\",\n \"Ġu ses\",\n \"Ġus es\",\n \"Ġuse s\",\n \"Ġ Function\",\n \"ĠF unction\",\n \"ĠFun ction\",\n \"ĠFunc tion\",\n \". google\",\n \".g oogle\",\n \".go ogle\",\n \"Ġ Result\",\n \"ĠRes ult\",\n \"8 4\",\n \"V isible\",\n \"Vis ible\",\n \"ag ma\",\n \"el come\",\n \"Ġ Sy\",\n \"ĠS y\",\n \"Ġ Cent\",\n \"ĠC ent\",\n \"ĠCe nt\",\n \"AL SE\",\n \"ALS E\",\n \"a ción\",\n \"ac ión\",\n \"aci ón\",\n \"E XT\",\n \"EX T\",\n \"Ġ license\",\n \"Ġl icense\",\n \"Ġlic ense\",\n \"Ġlicens e\",\n \"Ġ Long\",\n \"ĠL ong\",\n \"ĠLo ng\",\n \"ĠLon g\",\n \"Ġ accom\",\n \"Ġacc om\",\n \"Ġac com\",\n \"Ġ ability\",\n \"Ġab ility\",\n \". height\",\n \".h eight\",\n \".he ight\",\n \"Act ive\",\n \"Activ e\",\n \"o logical\",\n \"olog ical\",\n \"ologic al\",\n \"ologi cal\",\n \"o ly\",\n \"ol y\",\n \") ),\",\n \")) ,\",\n \". 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čĊ\",\n \"m ine\",\n \"min e\",\n \"mi ne\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"( k\",\n \"b on\",\n \"bo n\",\n \"a sic\",\n \"as ic\",\n \"asi c\",\n \"p ite\",\n \"pi te\",\n \"pit e\",\n \"_ __\",\n \"__ _\",\n \"M ax\",\n \"Ma x\",\n \"Ġ errors\",\n \"Ġerror s\",\n \"Ġerr ors\",\n \"Ġer rors\",\n \"Ġerro rs\",\n \"Ġ While\",\n \"ĠWh ile\",\n \"Ġ arguments\",\n \"Ġarg uments\",\n \"Ġargument s\",\n \"Ġ ensure\",\n \"Ġen sure\",\n \"Ġens ure\",\n \"R ight\",\n \"- based\",\n \"-b ased\",\n \"-base d\",\n \"W eb\",\n \"We b\",\n \"Ġ -=\",\n \"Ġ- =\",\n \"Ġint rodu\",\n \"Ġintr odu\",\n \"Ġintro du\",\n \"Ġ Inst\",\n \"ĠI nst\",\n \"ĠIn st\",\n \"ĠIns t\",\n \"ĠW ash\",\n \"ĠWas h\",\n \"ĠWa sh\",\n \"or din\",\n \"ord in\",\n \"j oin\",\n \"jo in\",\n \"D atabase\",\n \"Data base\",\n \"Dat abase\",\n \"Ġ grad\",\n \"Ġg rad\",\n \"Ġgr ad\",\n \"Ġgra d\",\n \"Ġ usually\",\n \"Ġus ually\",\n \"Ġusual ly\",\n \"Ġusu ally\",\n \"I TE\",\n \"IT E\",\n \"P rops\",\n \"Pro ps\",\n \"Prop s\",\n \"Pr ops\",\n \"? >Ċ\",\n \"?> Ċ\",\n \"Ġ Go\",\n \"ĠG o\",\n \"@ Override\",\n \"R EF\",\n \"RE F\",\n \"Ġ ip\",\n \"Ġi p\",\n \"ĠA ustral\",\n \"ĠAust ral\",\n \"ĠAu stral\",\n \"ĠAustr al\",\n \"Ġ ist\",\n \"Ġis t\",\n \"Ġi st\",\n \"View ById\",\n \"Ġ serious\",\n \"Ġse rious\",\n \"Ġser ious\",\n \"Ġseri ous\",\n \"Ġ customer\",\n \"Ġc ustomer\",\n \"Ġcustom er\",\n \"Ġcust omer\",\n \". prototype\",\n \".prot otype\",\n \".proto type\",\n \"o do\",\n \"od o\",\n \"c or\",\n \"co r\",\n \"Ġ door\",\n \"Ġd oor\",\n \"Ġdo or\",\n \"Ġ WITHOUT\",\n \"ĠWITH OUT\",\n \"Ġ plant\",\n \"Ġp lant\",\n \"Ġpl ant\",\n \"Ġplan t\",\n \"Ġpla nt\",\n \"Ġb egan\",\n \"Ġbe gan\",\n \"Ġbeg an\",\n \"Ġ distance\",\n \"Ġd istance\",\n \"Ġdist ance\",\n \"Ġdi stance\",\n \"( )).\",\n \"() ).\",\n \"()) .\",\n \"Ġch ance\",\n \"Ġcha nce\",\n \"Ġchan ce\",\n \"Ġ ord\",\n \"Ġo rd\",\n \"Ġor d\",\n \"c ame\",\n \"ca me\",\n \"cam e\",\n \"pr agma\",\n \"Ġ protect\",\n \"Ġprot ect\",\n \"Ġprote ct\",\n \"r agment\",\n \"ra gment\",\n \"rag ment\",\n \"Ġ Node\",\n \"ĠN ode\",\n \"ĠNo de\",\n \"e ning\",\n \"en ing\",\n \"eni ng\",\n \"enin g\",\n \"Ñ ĩ\",\n \"Ġ route\",\n \"Ġr oute\",\n \"Ġro ute\",\n \"Ġrout e\",\n \"Ġrou te\",\n \"Ġ School\",\n \"ĠS chool\",\n \"ĠSch ool\",\n \"ĠScho ol\",\n \"h i\",\n \"Ġne ighb\",\n \"Ġneigh b\",\n \"A fter\",\n \"Af ter\",\n \"l icit\",\n \"lic it\",\n \"li cit\",\n \"Ġ contr\",\n \"Ġcon tr\",\n \"Ġcont r\",\n \"Ġ primary\",\n \"Ġpr imary\",\n \"Ġprim ary\",\n \"Ġpri mary\",\n \"Ġprima ry\",\n \"A A\",\n \".Write Line\",\n \"ut ils\",\n \"util s\",\n \"uti ls\",\n \"Ġ bi\",\n \"Ġb i\",\n \"R ed\",\n \"Re d\",\n \".L inq\",\n \". object\",\n \".o bject\",\n \".obj ect\",\n \".ob ject\",\n \"Ġ leaders\",\n \"Ġle aders\",\n \"Ġlead ers\",\n \"Ġleader s\",\n \"un ities\",\n \"unit ies\",\n \"uni ties\",\n \"Ġ gun\",\n \"Ġg un\",\n \"Ġgu n\",\n \"o nth\",\n \"on th\",\n \"ont h\",\n \"Ġ Dev\",\n \"ĠD ev\",\n \"ĠDe v\",\n \"F ILE\",\n \"FI LE\",\n \"Ġ comments\",\n \"Ġcom ments\",\n \"Ġcomm ents\",\n \"Ġcomment s\",\n \"Ġcomme nts\",\n \"_ len\",\n \"_l en\",\n \"_le n\",\n \"ar row\",\n \"arr ow\",\n \"arro w\",\n \"a mount\",\n \"am ount\",\n \"amo unt\",\n \"R ange\",\n \"Ra nge\",\n \"s ert\",\n \"se rt\",\n \"ser t\",\n \"Grid View\",\n \"Ġ updated\",\n \"Ġup dated\",\n \"Ġupdate d\",\n \"Ġupd ated\",\n \"Ġ Mo\",\n \"ĠM o\",\n \"Ġ inform\",\n \"Ġin form\",\n \"Ġinfo rm\",\n \"Ġinf orm\",\n \"oci ety\",\n \"a la\",\n \"al a\",\n \"A ccess\",\n \"Ac cess\",\n \"Acc ess\",\n \"Ġ hab\",\n \"Ġh ab\",\n \"Ġha b\",\n \"Ġ creat\",\n \"Ġc reat\",\n \"Ġcr eat\",\n \"Ġcre at\",\n \"Ġcrea t\",\n \"_ arg\",\n \"_a rg\",\n \"_ar g\",\n \"Ġ January\",\n \"ĠJan uary\",\n \"Ġ Day\",\n \"ĠD ay\",\n \"ĠDa y\",\n \"\\\" )čĊ\",\n \"\\\") čĊ\",\n \"u ple\",\n \"up le\",\n \"d ocument\",\n \"doc ument\",\n \"gor ith\",\n \"gorit h\",\n \"m enu\",\n \"me nu\",\n \"men u\",\n \"Ġ Over\",\n \"ĠO ver\",\n \"ĠOv er\",\n \"b b\",\n \". title\",\n \".t itle\",\n \"_ out\",\n \"_o ut\",\n \"Ġ led\",\n \"Ġl ed\",\n \"Ġle d\",\n \"u ri\",\n \"ur i\",\n \"Ġ ?> < /\",\n \"g l\",\n \"Ġ bank\",\n \"Ġb ank\",\n \"Ġban k\",\n \"Ġba nk\",\n \"ay ment\",\n \"ĉ printf\",\n \"ĉprint f\",\n \"M D\",\n \"Ġ sample\",\n \"Ġs ample\",\n \"Ġsam ple\",\n \"Ġsamp le\",\n \"Ġ hands\",\n \"Ġh ands\",\n \"Ġhand s\",\n \"Ġha nds\",\n \"Ġhan ds\",\n \"Ġ Version\",\n \"ĠV ersion\",\n \"ĠVers ion\",\n \"u ario\",\n \"ua rio\",\n \"uar io\",\n \"Ġ offers\",\n \"Ġof fers\",\n \"Ġoff ers\",\n \"Ġoffer s\",\n \"ity Engine\",\n \"Ġ shape\",\n \"Ġs hape\",\n \"Ġsh ape\",\n \"Ġsha pe\",\n \"Ġ sleep\",\n \"Ġs leep\",\n \"Ġsle ep\",\n \"Ġslee p\",\n \"_ point\",\n \"_p oint\",\n \"_po int\",\n \"S ettings\",\n \"Set tings\",\n \"Setting s\",\n \"Ġ achie\",\n \"Ġa chie\",\n \"Ġach ie\",\n \"Ġ sold\",\n \"Ġs old\",\n \"Ġso ld\",\n \"Ġsol d\",\n \"o ta\",\n \"ot a\",\n \". bind\",\n \".b ind\",\n \".bin d\",\n \".bi nd\",\n \"A m\",\n \"Ġ safe\",\n \"Ġs afe\",\n \"Ġsa fe\",\n \"Ġsaf e\",\n \"St ore\",\n \"Ġ shared\",\n \"Ġsh ared\",\n \"Ġshare d\",\n \"Ġsha red\",\n \"Ġshar ed\",\n \"Ġ priv\",\n \"Ġp riv\",\n \"Ġpr iv\",\n \"Ġpri v\",\n \"_ VAL\",\n \"_V AL\",\n \"Ġs ens\",\n \"Ġse ns\",\n \"Ġsen s\",\n \") {\",\n \"Ġ remember\",\n \"Ġre member\",\n \"Ġrem ember\",\n \"sh ared\",\n \"sha red\",\n \"share d\",\n \"e lement\",\n \"el ement\",\n \"ele ment\",\n \"elem ent\",\n \"Ġ shoot\",\n \"Ġs hoot\",\n \"Ġsh oot\",\n \"Ġsho ot\",\n \"V ert\",\n \"Ver t\",\n \"Ve rt\",\n \"c out\",\n \"co ut\",\n \"cou t\",\n \"Ġ env\",\n \"Ġe nv\",\n \"Ġen v\",\n \"_ label\",\n \"_l abel\",\n \"_lab el\",\n \"_la bel\",\n \"Ġ >Ċ\",\n \"Ġ> Ċ\",\n \"r un\",\n \"ru n\",\n \"Ġ scene\",\n \"Ġs cene\",\n \"Ġsc ene\",\n \"Ġscen e\",\n \"Ġsce ne\",\n \"( array\",\n \"(a rray\",\n \"(arr ay\",\n \"(ar ray\",\n \"d evice\",\n \"de vice\",\n \"dev ice\",\n \"_ title\",\n \"_t itle\",\n \"_ti tle\",\n \"a gon\",\n \"ag on\",\n \"ago n\",\n \"] čĊ\",\n \"a by\",\n \"ab y\",\n \"Ġbe came\",\n \"Ġbec ame\",\n \"bo olean\",\n \"bool ean\",\n \"boo lean\",\n \"Ġ park\",\n \"Ġp ark\",\n \"Ġpar k\",\n \"Ġpa rk\",\n \"Ġ Code\",\n \"ĠC ode\",\n \"ĠCo de\",\n \"ĠCod e\",\n \"up load\",\n \"r iday\",\n \"ri day\",\n \"rid ay\",\n \"Ġ September\",\n \"ĠSept ember\",\n \"ĠSep tember\",\n \"F e\",\n \"Ġ sen\",\n \"Ġs en\",\n \"Ġse n\",\n \"c ing\",\n \"ci ng\",\n \"cin g\",\n \"F L\",\n \"C ol\",\n \"Co l\",\n \"u ts\",\n \"ut s\",\n \"_ page\",\n \"_p age\",\n \"_pag e\",\n \"_pa ge\",\n \"i nn\",\n \"in n\",\n \"Ġim plied\",\n \"Ġimp lied\",\n \"Ġimpl ied\",\n \"a ling\",\n \"al ing\",\n \"ali ng\",\n \"alin g\",\n \"Ġyour self\",\n \"Ġyours elf\",\n \". Count\",\n \".C ount\",\n \".Co unt\",\n \"con f\",\n \"co nf\",\n \"Ġ aud\",\n \"Ġa ud\",\n \"Ġau d\",\n \"_ init\",\n \"_in it\",\n \"_i nit\",\n \"_ini t\",\n \". )\",\n \"Ġw rote\",\n \"Ġwr ote\",\n \"0 03\",\n \"00 3\",\n \"N G\",\n \". Error\",\n \".E rror\",\n \".Err or\",\n \"ä »\",\n \". for\",\n \".f or\",\n \"Ġ equal\",\n \"Ġe qual\",\n \"Ġequ al\",\n \"Ġeq ual\",\n \"Ġ Request\",\n \"ĠRe quest\",\n \"ĠReq uest\",\n \"Ġ serial\",\n \"Ġs erial\",\n \"Ġse rial\",\n \"Ġser ial\",\n \"Ġseria l\",\n \"Ġseri al\",\n \"Ġ allows\",\n \"Ġall ows\",\n \"Ġallow s\",\n \"Ġallo ws\",\n \"X X\",\n \"Ġ middle\",\n \"Ġm iddle\",\n \"Ġmid dle\",\n \"Ġmidd le\",\n \"c hor\",\n \"ch or\",\n \"cho r\",\n \"1 95\",\n \"19 5\",\n \"9 4\",\n \"à ¸\",\n \"er val\",\n \"erv al\",\n \"erva l\",\n \". Column\",\n \".C olumn\",\n \".Col umn\",\n \"re ading\",\n \"read ing\",\n \"rea ding\",\n \"Ġ escort\",\n \"Ġesc ort\",\n \"Ġ August\",\n \"ĠAug ust\",\n \"Ġquick ly\",\n \"Ġwe ap\",\n \"Ġ CG\",\n \"ĠC G\",\n \"ro pri\",\n \"rop ri\",\n \"h o\",\n \"Ġ cop\",\n \"Ġc op\",\n \"Ġco p\",\n \"( struct\",\n \"(str uct\",\n \"Ġ Big\",\n \"ĠB ig\",\n \"ĠBi g\",\n \"Ġ vs\",\n \"Ġv s\",\n \"Ġf requ\",\n \"Ġfr equ\",\n \"Ġfre qu\",\n \"Ġfreq u\",\n \". Value\",\n \".V alue\",\n \".Val ue\",\n \"Ġ actions\",\n \"Ġa ctions\",\n \"Ġact ions\",\n \"Ġaction s\",\n \"Ġ proper\",\n \"Ġpro per\",\n \"Ġpr oper\",\n \"Ġprop er\",\n \"Ġ inn\",\n \"Ġin n\",\n \"Ġi nn\",\n \"Ġ objects\",\n \"Ġobject s\",\n \"Ġobj ects\",\n \"Ġ matrix\",\n \"Ġm atrix\",\n \"Ġmat rix\",\n \"av ascript\",\n \"ava script\",\n \"Ġ ones\",\n \"Ġo nes\",\n \"Ġon es\",\n \"Ġone s\",\n \". group\",\n \".g roup\",\n \".gr oup\",\n \"Ġ green\",\n \"Ġg reen\",\n \"Ġgr een\",\n \"Ġgre en\",\n \"Ġ paint\",\n \"Ġp aint\",\n \"Ġpain t\",\n \"Ġpa int\",\n \"Ġpai nt\",\n \"o ols\",\n \"ool s\",\n \"oo ls\",\n \"y cl\",\n \"yc l\",\n \"en code\",\n \"enc ode\",\n \"enco de\",\n \"o lt\",\n \"ol t\",\n \"com ment\",\n \"comm ent\",\n \". api\",\n \".ap i\",\n \".a pi\",\n \"D ir\",\n \"Di r\",\n \"Ġ une\",\n \"Ġu ne\",\n \"Ġun e\",\n \"iz ont\",\n \"izon t\",\n \"izo nt\",\n \". position\",\n \".p osition\",\n \".pos ition\",\n \"Ġde signed\",\n \"Ġdes igned\",\n \"Ġdesign ed\",\n \"_ val\",\n \"_v al\",\n \"_va l\",\n \"a vi\",\n \"av i\",\n \"i ring\",\n \"ir ing\",\n \"iri ng\",\n \"t ab\",\n \"ta b\",\n \"Ġ layer\",\n \"Ġl ayer\",\n \"Ġla yer\",\n \"Ġlay er\",\n \"Ġ views\",\n \"Ġview s\",\n \"Ġvi ews\",\n \"Ġvie ws\",\n \"Ġ reve\",\n \"Ġre ve\",\n \"Ġr eve\",\n \"Ġrev e\",\n \"r ael\",\n \"ra el\",\n \"Ġ ON\",\n \"ĠO N\",\n \"r ics\",\n \"ri cs\",\n \"ric s\",\n \"1 60\",\n \"16 0\",\n \"n p\",\n \"Ġ core\",\n \"Ġc ore\",\n \"Ġco re\",\n \"Ġcor e\",\n \"( ));čĊ\",\n \"() );čĊ\",\n \"()) ;čĊ\",\n \"()); čĊ\",\n \"M ain\",\n \"Ma in\",\n \"Ġ expert\",\n \"Ġex pert\",\n \"Ġexp ert\",\n \"Ġexper t\",\n \"ĉ ĉčĊ\",\n \"ĉĉ čĊ\",\n \"_ en\",\n \"_e n\",\n \"Ġ />\",\n \"Ġ/ >\",\n \"ut ter\",\n \"utt er\",\n \"I AL\",\n \"IA L\",\n \"a ils\",\n \"ail s\",\n \"ai ls\",\n \"Ġ King\",\n \"ĠK ing\",\n \"ĠKi ng\",\n \"ĠKin g\",\n \"* /ĊĊ\",\n \"*/ ĊĊ\",\n \"*/Ċ Ċ\",\n \"Ġ Met\",\n \"ĠM et\",\n \"ĠMe t\",\n \"_ end\",\n \"_e nd\",\n \"_en d\",\n \"ad dr\",\n \"add r\",\n \"o ra\",\n \"or a\",\n \"Ġ ir\",\n \"Ġi r\",\n \"M in\",\n \"Mi n\",\n \"Ġsur pr\",\n \"Ġre pe\",\n \"Ġrep e\",\n \"Ġ directory\",\n \"Ġd irectory\",\n \"Ġdirect ory\",\n \"Ġdirector y\",\n \"P UT\",\n \"PU T\",\n \"- S\",\n \"Ġ election\",\n \"Ġe lection\",\n \"Ġel ection\",\n \"Ġelect ion\",\n \"Ġele ction\",\n \"h aps\",\n \"ha ps\",\n \"hap s\",\n \". pre\",\n \".p re\",\n \".pr e\",\n \"c m\",\n \"Value s\",\n \"Val ues\",\n \"Ġ \\\"Ċ\",\n \"Ġ\\\" Ċ\",\n \"c olumn\",\n \"col umn\",\n \"i vil\",\n \"iv il\",\n \"ivi l\",\n \"Log in\",\n \"Lo gin\",\n \"in ue\",\n \"inu e\",\n \"9 3\",\n \"Ġ beautiful\",\n \"Ġbe autiful\",\n \"Ġbeaut iful\",\n \"Ġ secret\",\n \"Ġs ecret\",\n \"Ġse cret\",\n \"Ġsec ret\",\n \"Ġsecre t\",\n \"( event\",\n \"(e vent\",\n \"(ev ent\",\n \"Ġ chat\",\n \"Ġc hat\",\n \"Ġch at\",\n \"Ġcha t\",\n \"u ms\",\n \"um s\",\n \"Ġ origin\",\n \"Ġor igin\",\n \"Ġorig in\",\n \"Ġori gin\",\n \"Ġ effects\",\n \"Ġe ffects\",\n \"Ġeffect s\",\n \"Ġeff ects\",\n \"Ġ management\",\n \"Ġman agement\",\n \"Ġmanage ment\",\n \"Ġmana gement\",\n \"i lla\",\n \"il la\",\n \"ill a\",\n \"t k\",\n \"Ġ setting\",\n \"Ġs etting\",\n \"Ġset ting\",\n \"Ġsett ing\",\n \"Ġ Cour\",\n \"ĠC our\",\n \"ĠCo ur\",\n \"ĠCou r\",\n \"Ġ massage\",\n \"Ġm assage\",\n \"Ġmass age\",\n \"Ġmas sage\",\n \"Ġmassa ge\",\n \"ĉ end\",\n \"ĉe nd\",\n \"ĉen d\",\n \"Ġ happy\",\n \"Ġh appy\",\n \"Ġhapp y\",\n \"Ġha ppy\",\n \"Ġhap py\",\n \"Ġ finish\",\n \"Ġf inish\",\n \"Ġfin ish\",\n \"Ġfi nish\",\n \"Ġ camera\",\n \"Ġc amera\",\n \"Ġcame ra\",\n \"Ġcam era\",\n \"Ġcamer a\",\n \"Ġ Ver\",\n \"ĠV er\",\n \"ĠVe r\",\n \"ĠDem ocr\",\n \"ĠDemo cr\",\n \"Ġ Her\",\n \"ĠH er\",\n \"ĠHe r\",\n \"( Q\",\n \"c ons\",\n \"con s\",\n \"co ns\",\n \"i ta\",\n \"it a\",\n \"Ġ '.\",\n \"Ġ' .\",\n \"{ }\",\n \"ĉ C\",\n \"Ġ stuff\",\n \"Ġst uff\",\n \"Ġstu ff\",\n \"1 94\",\n \"19 4\",\n \"Ġ :Ċ\",\n \"Ġ: Ċ\",\n \"Ġ AR\",\n \"ĠA R\",\n \"T ask\",\n \"Ta sk\",\n \"h idden\",\n \"hi dden\",\n \"hid den\",\n \"e ros\",\n \"er os\",\n \"ero s\",\n \"I GN\",\n \"IG N\",\n \"at io\",\n \"ati o\",\n \"Ġ Health\",\n \"ĠHe alth\",\n \"ĠHeal th\",\n \"ol ute\",\n \"olut e\",\n \"olu te\",\n \"En ter\",\n \"Ent er\",\n \"' >\",\n \"Ġ Twitter\",\n \"ĠT witter\",\n \"ĠTw itter\",\n \"Ġ County\",\n \"ĠC ounty\",\n \"ĠCount y\",\n \"ĠCou nty\",\n \"ĠCoun ty\",\n \"s cribe\",\n \"scri be\",\n \"scr ibe\",\n \"Ġ= >Ċ\",\n \"Ġ=> Ċ\",\n \"Ġ hy\",\n \"Ġh y\",\n \"f it\",\n \"fi t\",\n \"Ġm ilitary\",\n \"Ġmilit ary\",\n \"Ġmil itary\",\n \"Ġmilitar y\",\n \"Ġ sale\",\n \"Ġs ale\",\n \"Ġsa le\",\n \"Ġsal e\",\n \"re quired\",\n \"require d\",\n \"requ ired\",\n \"n on\",\n \"no n\",\n \"boot strap\",\n \"h old\",\n \"ho ld\",\n \"hol d\",\n \"r im\",\n \"ri m\",\n \"- old\",\n \"-o ld\",\n \"Ġ Down\",\n \"ĠD own\",\n \"ĠDo wn\",\n \"ĠDow n\",\n \"Ġ mention\",\n \"Ġm ention\",\n \"Ġmen tion\",\n \"Ġment ion\",\n \"cont act\",\n \"_ group\",\n \"_g roup\",\n \"_gr oup\",\n \"o day\",\n \"od ay\",\n \"oda y\",\n \"Ġ town\",\n \"Ġt own\",\n \"Ġto wn\",\n \"Ġtow n\",\n \"Ġ solution\",\n \"Ġs olution\",\n \"Ġsol ution\",\n \"u ate\",\n \"ua te\",\n \"uat e\",\n \"el ling\",\n \"ell ing\",\n \"elli ng\",\n \"] ->\",\n \"]- >\",\n \"o tes\",\n \"ot es\",\n \"ote s\",\n \"en tal\",\n \"ent al\",\n \"enta l\",\n \"o men\",\n \"om en\",\n \"ome n\",\n \"osp ital\",\n \"Ġ Sup\",\n \"ĠS up\",\n \"ĠSu p\",\n \"_ EN\",\n \"_E N\",\n \"Ġ slow\",\n \"Ġs low\",\n \"Ġsl ow\",\n \"Ġslo w\",\n \"SE SSION\",\n \"SES SION\",\n \"Ġ blue\",\n \"Ġb lue\",\n \"Ġbl ue\",\n \"a go\",\n \"ag o\",\n \"Ġl ives\",\n \"Ġli ves\",\n \"Ġlive s\",\n \"Ġliv es\",\n \"Ġ ^\",\n \". un\",\n \".u n\",\n \"i nst\",\n \"in st\",\n \"ins t\",\n \"e nge\",\n \"en ge\",\n \"eng e\",\n \"Ġ customers\",\n \"Ġcustom ers\",\n \"Ġcustomer s\",\n \"Ġcust omers\",\n \"Ġ cast\",\n \"Ġc ast\",\n \"Ġca st\",\n \"Ġcas t\",\n \"ud get\",\n \"udge t\",\n \"ï¼ ģ\",\n \"ic ens\",\n \"ice ns\",\n \"Ġd etermin\",\n \"Ġde termin\",\n \"Ġdeter min\",\n \"Ġdeterm in\",\n \"Se lected\",\n \"Select ed\",\n \"Sel ected\",\n \"_ pl\",\n \"_p l\",\n \"ue ue\",\n \"Ġ dark\",\n \"Ġd ark\",\n \"Ġda rk\",\n \"Ġdar k\",\n \"/ /ĊĊ\",\n \"// ĊĊ\",\n \"//Ċ Ċ\",\n \"s i\",\n \"th ern\",\n \"ther n\",\n \"the rn\",\n \"Ġ Japan\",\n \"ĠJ apan\",\n \"ĠJa pan\",\n \"ĠJap an\",\n \"/ w\",\n \"P U\",\n \"Ġ East\",\n \"ĠE ast\",\n \"ĠEa st\",\n \"o vie\",\n \"ov ie\",\n \"ovi e\",\n \"Ġ package\",\n \"Ġp ackage\",\n \"Ġpack age\",\n \"Ġ nor\",\n \"Ġn or\",\n \"Ġno r\",\n \"Ġ api\",\n \"Ġa pi\",\n \"Ġap i\",\n \"b ot\",\n \"bo t\",\n \"\\\" ];Ċ\",\n \"\\\"] ;Ċ\",\n \"\\\"]; Ċ\",\n \"_ post\",\n \"_p ost\",\n \"_pos t\",\n \"_po st\",\n \"u late\",\n \"ul ate\",\n \"ula te\",\n \"Ġ club\",\n \"Ġc lub\",\n \"Ġcl ub\",\n \"' ));Ċ\",\n \"') );Ċ\",\n \"')) ;Ċ\",\n \"')); Ċ\",\n \"Ġ loop\",\n \"Ġl oop\",\n \"Ġlo op\",\n \"P IO\",\n \"PI O\",\n \"i one\",\n \"ion e\",\n \"io ne\",\n \"s hot\",\n \"sh ot\",\n \"In itial\",\n \"Init ial\",\n \"Ġ played\",\n \"Ġpl ayed\",\n \"Ġplay ed\",\n \"reg ister\",\n \"regist er\",\n \"r ought\",\n \"ro ught\",\n \"rou ght\",\n \"rough t\",\n \"_ max\",\n \"_m ax\",\n \"_ma x\",\n \"ace ment\",\n \"ac ement\",\n \"m atch\",\n \"mat ch\",\n \"raph ics\",\n \"raphic s\",\n \"A ST\",\n \"AS T\",\n \"Ġ existing\",\n \"Ġex isting\",\n \"Ġexist ing\",\n \"Ġ complex\",\n \"Ġcom plex\",\n \"Ġcomp lex\",\n \"Ġcomple x\",\n \"Ġcompl ex\",\n \"D A\",\n \". Ch\",\n \".C h\",\n \". common\",\n \".com mon\",\n \".comm on\",\n \"m o\",\n \"Ġ' ../../\",\n \"Ġ'../ ../\",\n \"Ġ'.. /../\",\n \"i to\",\n \"it o\",\n \"Ġ analysis\",\n \"Ġan alysis\",\n \"Ġanal ysis\",\n \"Ġanaly sis\",\n \"Ġanalys is\",\n \"Ġ deliver\",\n \"Ġdel iver\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ\",\n \"i dx\",\n \"id x\",\n \"à ł\",\n \"o ngo\",\n \"on go\",\n \"ong o\",\n \"Ġ English\",\n \"ĠEng lish\",\n \"ĠEngl ish\",\n \"< !--\",\n \" \\\";Ċ\",\n \">\\\" ;Ċ\",\n \">\\\"; Ċ\",\n \"_ UN\",\n \"_U N\",\n \"ĉ std\",\n \"ĉs td\",\n \"ĉst d\",\n \"o ded\",\n \"od ed\",\n \"ode d\",\n \"Ġ calls\",\n \"Ġc alls\",\n \"Ġcall s\",\n \"Ġcal ls\",\n \"h ere\",\n \"he re\",\n \"her e\",\n \"R el\",\n \"Re l\",\n \"Ġ brand\",\n \"Ġb rand\",\n \"Ġbr and\",\n \"Ġbra nd\",\n \"Ġbran d\",\n \"back ground\",\n \"g a\",\n \"_ address\",\n \"_add ress\",\n \"_addr ess\",\n \"_ad dress\",\n \"_ params\",\n \"_param s\",\n \"_par ams\",\n \"_para ms\",\n \"_pa rams\",\n \"C ategory\",\n \"1 03\",\n \"10 3\",\n \"Ġ India\",\n \"ĠIn dia\",\n \"ĠInd ia\",\n \"ĠIndi a\",\n \"_ event\",\n \"_e vent\",\n \"_ev ent\",\n \"_even t\",\n \"Ġ ing\",\n \"Ġin g\",\n \"Ġi ng\",\n \"R ender\",\n \"Re nder\",\n \"Ren der\",\n \". cl\",\n \".c l\",\n \"um py\",\n \"ump y\",\n \"Ġ pet\",\n \"Ġp et\",\n \"Ġpe t\",\n \"F C\",\n \"Ġ Ant\",\n \"ĠA nt\",\n \"ĠAn t\",\n \"E xt\",\n \"Ex t\",\n \"Ġ charge\",\n \"Ġch arge\",\n \"Ġchar ge\",\n \"Ġcharg e\",\n \"e ned\",\n \"en ed\",\n \"ene d\",\n \"g rad\",\n \"gr ad\",\n \"gra d\",\n \"E O\",\n \"Ġ depend\",\n \"Ġd epend\",\n \"Ġde pend\",\n \"Ġdep end\",\n \"Ġ .ĊĊ\",\n \"Ġ. ĊĊ\",\n \"Ġ.Ċ Ċ\",\n \"f rame\",\n \"fr ame\",\n \"fra me\",\n \"fram e\",\n \"Ġ df\",\n \"Ġd f\",\n \"Ġ huge\",\n \"Ġh uge\",\n \"Ġhug e\",\n \"Ġhu ge\",\n \"Ġ PART\",\n \"ĠP ART\",\n \"ĠPA RT\",\n \"ĠPAR T\",\n \"e ds\",\n \"ed s\",\n \"; ;\",\n \"Ġ AM\",\n \"ĠA M\",\n \"Ġ basic\",\n \"Ġb asic\",\n \"Ġbas ic\",\n \"Ġba sic\",\n \"Ġ Let\",\n \"ĠL et\",\n \"ĠLe t\",\n \"l ich\",\n \"lic h\",\n \"li ch\",\n \"Ġ arm\",\n \"Ġa rm\",\n \"Ġar m\",\n \"Ġ star\",\n \"Ġs tar\",\n \"Ġst ar\",\n \"Ġsta r\",\n \"Ġf ederal\",\n \"Ġfed eral\",\n \"Ġfeder al\",\n \"W ork\",\n \"Wo rk\",\n \"Ġ carry\",\n \"Ġc arry\",\n \"Ġcar ry\",\n \"Ġcarr y\",\n \"Ġ Israel\",\n \"ĠIs rael\",\n \"( obj\",\n \"(o bj\",\n \"(ob j\",\n \"= {{\",\n \"={ {\",\n \"Ġ saved\",\n \"Ġs aved\",\n \"Ġsa ved\",\n \"Ġsave d\",\n \"Ġsav ed\",\n \"Ġ syn\",\n \"Ġs yn\",\n \"Ġsy n\",\n \"Ġ constant\",\n \"Ġcon stant\",\n \"Ġconst ant\",\n \"Ġcons tant\",\n \"V ENT\",\n \"VE NT\",\n \"VEN T\",\n \"Ġ positive\",\n \"Ġpos itive\",\n \"Ġposit ive\",\n \"Ġ conduct\",\n \"Ġcon duct\",\n \"Ġcond uct\",\n \"Ġconduc t\",\n \"Ġcondu ct\",\n \"Ġ skin\",\n \"Ġs kin\",\n \"Ġsk in\",\n \"Ġski n\",\n \"Ġear lier\",\n \"Ġ layout\",\n \"Ġl ayout\",\n \"Ġlay out\",\n \"Ġ IP\",\n \"ĠI P\",\n \"O UR\",\n \"OU R\",\n \"Ġ tim\",\n \"Ġt im\",\n \"Ġti m\",\n \"style sheet\",\n \"styles heet\",\n \"_ cl\",\n \"_c l\",\n \"Ġ Card\",\n \"ĠC ard\",\n \"ĠCar d\",\n \"ĠCa rd\",\n \"++ ){Ċ\",\n \"++) {Ċ\",\n \"++){ Ċ\",\n \"Ġtem per\",\n \"Ġtemp er\",\n \"Ġ David\",\n \"ĠD avid\",\n \"ĠDav id\",\n \"ĠDa vid\",\n \"ĉ try\",\n \"ĉt ry\",\n \"ĉtr y\",\n \". dart\",\n \".d art\",\n \".da rt\",\n \"Ġw ants\",\n \"Ġwant s\",\n \"Ġwa nts\",\n \"Ġwan ts\",\n \"Ġ picture\",\n \"Ġp icture\",\n \"Ġpic ture\",\n \"Ġpict ure\",\n \"Ġ videos\",\n \"Ġv ideos\",\n \"Ġvideo s\",\n \"Ġvid eos\",\n \"Ġvide os\",\n \"Ġ Comm\",\n \"ĠC omm\",\n \"ĠCom m\",\n \"ĠCo mm\",\n \"is ions\",\n \"ision s\",\n \"isi ons\",\n \"_ MAX\",\n \"_M AX\",\n \"_MA X\",\n \"M apping\",\n \"Map ping\",\n \"Ma pping\",\n \"- content\",\n \"-c ontent\",\n \"-con tent\",\n \"-cont ent\",\n \"Ġ Ear\",\n \"ĠE ar\",\n \"ĠEa r\",\n \"- de\",\n \"-d e\",\n \"Ġp rem\",\n \"Ġpr em\",\n \"Ġpre m\",\n \"b ruary\",\n \"br uary\",\n \"bru ary\",\n \"bruar y\",\n \"Ġ components\",\n \"Ġcom ponents\",\n \"Ġcomp onents\",\n \"Ġcomponent s\",\n \"Ġthrough out\",\n \"Ġ pull\",\n \"Ġp ull\",\n \"Ġpul l\",\n \"Ġpu ll\",\n \"Ġ pages\",\n \"Ġp ages\",\n \"Ġpage s\",\n \"Ġpa ges\",\n \"Ġpag es\",\n \"e nte\",\n \"en te\",\n \"ent e\",\n \"res pond\",\n \"resp ond\",\n \"Ġ gas\",\n \"Ġg as\",\n \"Ġga s\",\n \"cript or\",\n \"Ġ edge\",\n \"Ġe dge\",\n \"Ġed ge\",\n \"Ġ bound\",\n \"Ġb ound\",\n \"Ġbo und\",\n \"Ġbou nd\",\n \"A CT\",\n \"AC T\",\n \"* *****\",\n \"** ****\",\n \"**** **\",\n \"*** ***\",\n \"***** *\",\n \"Ġ creating\",\n \"Ġc reating\",\n \"Ġcr eating\",\n \"Ġcre ating\",\n \"Ġcreat ing\",\n \"Ġcrea ting\",\n \"Ġ CH\",\n \"ĠC H\",\n \"Ġ nullptr\",\n \"Ġnull ptr\",\n \"B r\",\n \"+ '\",\n \". co\",\n \".c o\",\n \"> ::\",\n \">: :\",\n \"Ġ learning\",\n \"Ġl earning\",\n \"Ġle arning\",\n \"Ġlearn ing\",\n \"Ġlear ning\",\n \". Length\",\n \".L ength\",\n \".Le ngth\",\n \".Len gth\",\n \"_ SH\",\n \"_S H\",\n \"Ġ patients\",\n \"Ġpat ients\",\n \"Ġpatient s\",\n \"A IN\",\n \"AI N\",\n \"Ġ kids\",\n \"Ġk ids\",\n \"Ġkid s\",\n \"Ġki ds\",\n \"Ġ comfort\",\n \"Ġcom fort\",\n \"Ġ shown\",\n \"Ġsh own\",\n \"Ġshow n\",\n \"Ġsho wn\",\n \"ug ins\",\n \"ugin s\",\n \"ugi ns\",\n \"Ġ Back\",\n \"ĠB ack\",\n \"ĠBa ck\",\n \"ĠBac k\",\n \"e lla\",\n \"el la\",\n \"ell a\",\n \"_ CL\",\n \"_C L\",\n \"Ġ lat\",\n \"Ġl at\",\n \"Ġla t\",\n \"Ġ dispatch\",\n \"Ġdis patch\",\n \"Ġdisp atch\",\n \"Ġ classes\",\n \"Ġc lasses\",\n \"Ġclass es\",\n \"Ġcl asses\",\n \"Ġclasse s\",\n \"Ġclas ses\",\n \". at\",\n \".a t\",\n \". begin\",\n \".b egin\",\n \".be gin\",\n \"Ġ successful\",\n \"Ġsuccess ful\",\n \"b an\",\n \"ba n\",\n \"Ġob tain\",\n \"Ġobt ain\",\n \"Ġ Sl\",\n \"ĠS l\",\n \"Ġ lack\",\n \"Ġl ack\",\n \"Ġla ck\",\n \"Ġlac k\",\n \"it erator\",\n \"iter ator\",\n \"T hread\",\n \"Th read\",\n \"Thr ead\",\n \"( size\",\n \"(s ize\",\n \"(si ze\",\n \"Ġ none\",\n \"Ġn one\",\n \"Ġno ne\",\n \"Ġnon e\",\n \". has\",\n \".h as\",\n \"_ X\",\n \"s ort\",\n \"so rt\",\n \"n ap\",\n \"na p\",\n \"p et\",\n \"pe t\",\n \"b in\",\n \"bi n\",\n \"7 00\",\n \"70 0\",\n \"Ġ Canada\",\n \"ĠCan ada\",\n \"T hey\",\n \"The y\",\n \"Th ey\",\n \"Ġd ans\",\n \"Ġda ns\",\n \"Ġdan s\",\n \"Ġ Mat\",\n \"ĠM at\",\n \"ĠMa t\",\n \"< td\",\n \"'\",\n \"'=> '\",\n \"'= >'\",\n \"Ġ Paul\",\n \"ĠP aul\",\n \"ĠPa ul\",\n \"m as\",\n \"ma s\",\n \"ĉ print\",\n \"ĉp rint\",\n \"ĉpr int\",\n \"( len\",\n \"(l en\",\n \"(le n\",\n \"f d\",\n \"Ġ );\",\n \"Ġ) ;\",\n \". Event\",\n \".E vent\",\n \"q li\",\n \"ql i\",\n \"i rit\",\n \"ir it\",\n \"iri t\",\n \"ie lds\",\n \"ield s\",\n \"iel ds\",\n \"o man\",\n \"om an\",\n \"oma n\",\n \"Ġ Top\",\n \"ĠT op\",\n \"ĠTo p\",\n \"Ġ vote\",\n \"Ġv ote\",\n \"Ġvo te\",\n \"Ġvot e\",\n \"Ġ mask\",\n \"Ġm ask\",\n \"Ġma sk\",\n \"Ġmas k\",\n \"Ġ theme\",\n \"Ġth eme\",\n \"Ġthe me\",\n \"Ġthem e\",\n \"- Ċ\",\n \"Ġ props\",\n \"Ġp rops\",\n \"Ġpro ps\",\n \"Ġpr ops\",\n \"Ġprop s\",\n \"Ġ fine\",\n \"Ġf ine\",\n \"Ġfin e\",\n \"Ġfi ne\",\n \"Ġ writer\",\n \"Ġw riter\",\n \"Ġwrit er\",\n \"Ġwr iter\",\n \"Ġwrite r\",\n \"_ offset\",\n \"_off set\",\n \"_o ffset\",\n \"c ar\",\n \"ca r\",\n \"Ġ altern\",\n \"Ġal tern\",\n \"Ġalt ern\",\n \"Ġalter n\",\n \"Ġalte rn\",\n \"Ġ copyright\",\n \"Ġc opyright\",\n \"Ġcopy right\",\n \"Ġ destroy\",\n \"Ġd estroy\",\n \"Ġde stroy\",\n \"Ġdest roy\",\n \"p per\",\n \"pp er\",\n \"ppe r\",\n \"Ġ generate\",\n \"Ġg enerate\",\n \"Ġgener ate\",\n \"Ġgen erate\",\n \"Ġgene rate\",\n \"Ġgenera te\",\n \"p ped\",\n \"pp ed\",\n \"ppe d\",\n \"âĢĻ d\",\n \"Ġ ĠĠĠĠĠĊ\",\n \"ĠĠ ĠĠĠĠĊ\",\n \"ĠĠĠĠ ĠĠĊ\",\n \"ĠĠĠ ĠĠĠĊ\",\n \"ĠĠĠĠĠ ĠĊ\",\n \"ĠĠĠĠĠĠ Ċ\",\n \"m ake\",\n \"ma ke\",\n \"mak e\",\n \"Ġ Show\",\n \"ĠS how\",\n \"ĠSh ow\",\n \"ĠSho w\",\n \"Ġ browser\",\n \"Ġb rowser\",\n \"Ġbrows er\",\n \"Ġbrowse r\",\n \"Ġbrow ser\",\n \"Ġ favorite\",\n \"Ġf avorite\",\n \"Ġfavor ite\",\n \"Ġ career\",\n \"Ġcar eer\",\n \"Ġcare er\",\n \"Ġhapp ened\",\n \"Ġhappen ed\",\n \"( char\",\n \"(c har\",\n \"(ch ar\",\n \"Ġ recommend\",\n \"Ġre commend\",\n \"Ġrecomm end\",\n \"Ġ liter\",\n \"Ġl iter\",\n \"Ġli ter\",\n \"Ġlit er\",\n \"Ġlite r\",\n \". filter\",\n \".f ilter\",\n \".fil ter\",\n \"g rade\",\n \"gr ade\",\n \"grad e\",\n \"gra de\",\n \"Ġ £\",\n \"Ġ £\",\n \"P hone\",\n \"Ph one\",\n \"o ms\",\n \"om s\",\n \"Ġ named\",\n \"Ġn amed\",\n \"Ġname d\",\n \"Ġna med\",\n \"Ġnam ed\",\n \"- label\",\n \"-l abel\",\n \"-la bel\",\n \"i po\",\n \"ip o\",\n \"Ġ Other\",\n \"ĠO ther\",\n \"ĠOt her\",\n \"Ġ panel\",\n \"Ġp anel\",\n \"Ġpa nel\",\n \"Ġpan el\",\n \"Ġpane l\",\n \"Ġ rock\",\n \"Ġr ock\",\n \"Ġro ck\",\n \"Ġroc k\",\n \"S cale\",\n \"Sc ale\",\n \"ĉ assert\",\n \"ĉas sert\",\n \"Ð ´\",\n \"Ġ trust\",\n \"Ġt rust\",\n \"Ġtr ust\",\n \"f ront\",\n \"fr ont\",\n \"Ġd emon\",\n \"Ġde mon\",\n \"Ġdem on\",\n \"Ġdemo n\",\n \"A r\",\n \"N et\",\n \"Ne t\",\n \"Ġ economic\",\n \"Ġe conomic\",\n \"Ġecon omic\",\n \"Ġeconom ic\",\n \"Ġec onomic\",\n \"f ooter\",\n \"fo oter\",\n \"foot er\",\n \"foo ter\",\n \"Ġ race\",\n \"Ġr ace\",\n \"Ġrac e\",\n \"Ġra ce\",\n \"( node\",\n \"(n ode\",\n \"(no de\",\n \"Ġ Option\",\n \"ĠO ption\",\n \"ĠOp tion\",\n \"ĠOpt ion\",\n \"s plit\",\n \"sp lit\",\n \"spl it\",\n \"Ġ physical\",\n \"Ġph ysical\",\n \"Ġphys ical\",\n \"Ġphysic al\",\n \"i fest\",\n \"if est\",\n \"ife st\",\n \"ifes t\",\n \"Ġ removed\",\n \"Ġre moved\",\n \"Ġrem oved\",\n \"Ġremove d\",\n \". http\",\n \".h ttp\",\n \".ht tp\",\n \") ),Ċ\",\n \")) ,Ċ\",\n \")), Ċ\",\n \"Ġlo oked\",\n \"Ġlook ed\",\n \"' ;\",\n \"d ing\",\n \"di ng\",\n \"din g\",\n \"g est\",\n \"ge st\",\n \"ges t\",\n \"atur day\",\n \"/ licenses\",\n \"/lic enses\",\n \"/license s\",\n \"P rice\",\n \"Pr ice\",\n \"Pri ce\",\n \"Ġ dro\",\n \"Ġd ro\",\n \"Ġdr o\",\n \"Ġt owards\",\n \"Ġto wards\",\n \"Ġtoward s\",\n \"Ġtow ards\",\n \"Ġ uns\",\n \"Ġu ns\",\n \"Ġun s\",\n \"Ġ CL\",\n \"ĠC L\",\n \"ĉ static\",\n \"ĉst atic\",\n \"ĉstat ic\",\n \"Ġ rows\",\n \"Ġr ows\",\n \"Ġro ws\",\n \"Ġrow s\",\n \"Ġ define\",\n \"Ġde fine\",\n \"Ġdef ine\",\n \"Ġdefin e\",\n \". replace\",\n \".re place\",\n \".rep lace\",\n \"Ġ father\",\n \"Ġf ather\",\n \"Ġfa ther\",\n \"Ġfat her\",\n \"Ġ Design\",\n \"ĠD esign\",\n \"ĠDe sign\",\n \"ĠDes ign\",\n \"as sign\",\n \"ass ign\",\n \"assi gn\",\n \"m ut\",\n \"mu t\",\n \"D evice\",\n \"De vice\",\n \"Dev ice\",\n \"D id\",\n \"Di d\",\n \"' ))Ċ\",\n \"') )Ċ\",\n \"')) Ċ\",\n \"o metry\",\n \"om etry\",\n \"ome try\",\n \"omet ry\",\n \"ometr y\",\n \"ay load\",\n \"Ġ histor\",\n \"Ġh istor\",\n \"Ġhis tor\",\n \"Ġhist or\",\n \"Ġhi stor\",\n \"Ġhisto r\",\n \"Ġ Param\",\n \"ĠP aram\",\n \"ĠPar am\",\n \"ĠPa ram\",\n \"ĠPara m\",\n \"Ġ Boolean\",\n \"ĠBo olean\",\n \"ĠBool ean\",\n \"ĠBoo lean\",\n \"Ġ nature\",\n \"Ġn ature\",\n \"Ġna ture\",\n \"Ġnatur e\",\n \"Ġnat ure\",\n \"Ġ js\",\n \"Ġj s\",\n \"Ġ nation\",\n \"Ġn ation\",\n \"Ġna tion\",\n \"Ġnat ion\",\n \"i h\",\n \"Ġ discover\",\n \"Ġdis cover\",\n \"Ġdisc over\",\n \"Ġdisco ver\",\n \"s em\",\n \"se m\",\n \"H andle\",\n \"Hand le\",\n \"Han dle\",\n \"ĉ r\",\n \"Ġ Techn\",\n \"ĠT echn\",\n \"ĠTe chn\",\n \"ĠTech n\",\n \"ĠTec hn\",\n \"Ġ wall\",\n \"Ġw all\",\n \"Ġwa ll\",\n \"Ġwal l\",\n \"{ $\",\n \"@ property\",\n \"Ġ\\\" ../\",\n \"Ġ\\\". ./\",\n \"Ġ\\\".. /\",\n \"Ġ exam\",\n \"Ġex am\",\n \". draw\",\n \".d raw\",\n \".dr aw\",\n \"o pping\",\n \"op ping\",\n \"opp ing\",\n \"Ġn early\",\n \"Ġnear ly\",\n \"Ġ cool\",\n \"Ġc ool\",\n \"Ġco ol\",\n \"Ġin depend\",\n \"Ġind epend\",\n \"Ġinde pend\",\n \"R ES\",\n \"RE S\",\n \"Ġ handler\",\n \"Ġh andler\",\n \"Ġhand ler\",\n \"Ġhandle r\",\n \"Ġ Monday\",\n \"ĠMon day\",\n \"ĠMond ay\",\n \"Ġ sun\",\n \"Ġs un\",\n \"Ġsu n\",\n \"St yles\",\n \"Style s\",\n \"ous ly\",\n \"Ġ ĉ\",\n \"v est\",\n \"ve st\",\n \"ves t\",\n \"D isplay\",\n \"Dis play\",\n \"Disp lay\",\n \"( y\",\n \"at ically\",\n \"atic ally\",\n \"atical ly\",\n \"Ġ predict\",\n \"Ġp redict\",\n \"Ġpre dict\",\n \"Ġpred ict\",\n \"Ġpredic t\",\n \"y ing\",\n \"yi ng\",\n \"Ġ sometimes\",\n \"Ġs ometimes\",\n \"Ġsome times\",\n \"Ġsom etimes\",\n \"Ġsometime s\",\n \"\\\" ]Ċ\",\n \"\\\"] Ċ\",\n \"Ġ drink\",\n \"Ġd rink\",\n \"Ġdr ink\",\n \"Ġdri nk\",\n \"Ġ bul\",\n \"Ġb ul\",\n \"Ġbu l\",\n \"if ications\",\n \"ific ations\",\n \"ification s\",\n \". insert\",\n \".in sert\",\n \".ins ert\",\n \". reg\",\n \".re g\",\n \".r eg\",\n \"Ġ tests\",\n \"Ġt ests\",\n \"Ġte sts\",\n \"Ġtest s\",\n \"Ġtes ts\",\n \"Al ignment\",\n \"Align ment\",\n \"Ġal leg\",\n \"Ġall eg\",\n \"Ġalle g\",\n \"Ġ attribute\",\n \"Ġat tribute\",\n \"Ġattrib ute\",\n \"Ġ Note\",\n \"ĠN ote\",\n \"ĠNo te\",\n \"ĠNot e\",\n \"Ġmy self\",\n \"Ġmys elf\",\n \"a rts\",\n \"ar ts\",\n \"art s\",\n \"N ow\",\n \"No w\",\n \"Ġ interesting\",\n \"Ġinter esting\",\n \"Ġinterest ing\",\n \"Ġinteres ting\",\n \"l ients\",\n \"li ents\",\n \"lient s\",\n \"lie nts\",\n \"lien ts\",\n \"Ġ population\",\n \"Ġpop ulation\",\n \"Ġpopul ation\",\n \"Ġ California\",\n \"ĠCal ifornia\",\n \"ĠCaliforn ia\",\n \"\\\" I\",\n \"å ¹\",\n \"Ġ greater\",\n \"Ġg reater\",\n \"Ġgreat er\",\n \"Ġgre ater\",\n \"ues day\",\n \"Ġth ous\",\n \"Ġthou s\",\n \"Ġtho us\",\n \"Ġco sts\",\n \"Ġcost s\",\n \"Ġcos ts\",\n \"Ġ launch\",\n \"Ġl aunch\",\n \"Ġla unch\",\n \"\\\\ Http\",\n \"k er\",\n \"ke r\",\n \"b and\",\n \"ba nd\",\n \"ban d\",\n \"Ġ Play\",\n \"ĠP lay\",\n \"ĠPl ay\",\n \"ĠPla y\",\n \"Ġ band\",\n \"Ġb and\",\n \"Ġban d\",\n \"Ġba nd\",\n \". shape\",\n \".s hape\",\n \".sh ape\",\n \".sha pe\",\n \"e some\",\n \"es ome\",\n \"eso me\",\n \"art icle\",\n \"arti cle\",\n \"artic le\",\n \". rf\",\n \".r f\",\n \"Ġ wer\",\n \"Ġw er\",\n \"Ġwe r\",\n \"á s\",\n \"em bers\",\n \"ember s\",\n \"emb ers\",\n \"u sr\",\n \"us r\",\n \"B A\",\n \"i can\",\n \"ic an\",\n \"ica n\",\n \"e tt\",\n \"et t\",\n \"valid ate\",\n \"ul ti\",\n \"ult i\",\n \"Ġim mediately\",\n \"Ġimmedi ately\",\n \"Ġimmediate ly\",\n \"z er\",\n \"ze r\",\n \"Ġ figure\",\n \"Ġf igure\",\n \"Ġfig ure\",\n \"Ġfigur e\",\n \"o es\",\n \"oe s\",\n \"e ller\",\n \"el ler\",\n \"ell er\",\n \"elle r\",\n \"ir cle\",\n \"irc le\",\n \"Ġ Sign\",\n \"ĠS ign\",\n \"ĠSi gn\",\n \"ĠSig n\",\n \". db\",\n \".d b\",\n \"Ġ rank\",\n \"Ġr ank\",\n \"Ġran k\",\n \"Ġra nk\",\n \"By tes\",\n \"Byte s\",\n \"Ġ projects\",\n \"Ġproject s\",\n \"Ġproj ects\",\n \"Ġproje cts\",\n \"_ rec\",\n \"_re c\",\n \"_r ec\",\n \"U LAR\",\n \"UL AR\",\n \"ULA R\",\n \"A PI\",\n \"AP I\",\n \"Ġ Line\",\n \"ĠL ine\",\n \"ĠLin e\",\n \"ĠLi ne\",\n \"P ort\",\n \"Por t\",\n \"Po rt\",\n \"Ġ poll\",\n \"Ġp oll\",\n \"Ġpol l\",\n \"Ġpo ll\",\n \"Ġg iving\",\n \"Ġgi ving\",\n \"id ence\",\n \"ide nce\",\n \"iden ce\",\n \"- -Ċ\",\n \"-- Ċ\",\n \"Ġ plot\",\n \"Ġp lot\",\n \"Ġpl ot\",\n \"Ġplo t\",\n \"i cial\",\n \"ic ial\",\n \"ici al\",\n \"icia l\",\n \"Ġw arrant\",\n \"Ġwar rant\",\n \"I TION\",\n \"IT ION\",\n \"Ġ Double\",\n \"ĠD ouble\",\n \"ĠDo uble\",\n \"ĠDou ble\",\n \"ĠDoub le\",\n \"Ġb illion\",\n \"Ġbill ion\",\n \"Ġbil lion\",\n \"gorith m\",\n \"gorit hm\",\n \"Ġ equipment\",\n \"Ġe quipment\",\n \"Ġequ ipment\",\n \"Ġequip ment\",\n \"D ATE\",\n \"DA TE\",\n \"DAT E\",\n \"Ġ @\\\"\",\n \"Ġ@ \\\"\",\n \"E E\",\n \"Ġ ple\",\n \"Ġp le\",\n \"Ġpl e\",\n \"i ation\",\n \"ia tion\",\n \"iat ion\",\n \"Ġ headers\",\n \"Ġhe aders\",\n \"Ġhead ers\",\n \"Ġheader s\",\n \"Ġpro ced\",\n \"Ġproc ed\",\n \".Component 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\"o lar\",\n \"ol ar\",\n \"ola r\",\n \"Ġ Even\",\n \"ĠE ven\",\n \"ĠEv en\",\n \"ĠEve n\",\n \"Ġ accur\",\n \"Ġacc ur\",\n \"Ġac cur\",\n \"Ġ Power\",\n \"ĠP ower\",\n \"ĠPo wer\",\n \"ĠPow er\",\n \"Ġ Gold\",\n \"ĠG old\",\n \"ĠGo ld\",\n \"ĠGol d\",\n \"c lear\",\n \"cl ear\",\n \"cle ar\",\n \"P rocess\",\n \"Pro cess\",\n \"Proc ess\",\n \"Ġ records\",\n \"Ġrec ords\",\n \"Ġrecord s\",\n \"Ġk illed\",\n \"Ġkill ed\",\n \"Ġkil led\",\n \". clear\",\n \".c lear\",\n \".cl ear\",\n \"ĠWARRANT IES\",\n \"Ġ purpose\",\n \"Ġp urpose\",\n \"Ġpur pose\",\n \"Ġpurpos e\",\n \"p anel\",\n \"pan el\",\n \"pa nel\",\n \"pane l\",\n \"J ECT\",\n \"JE CT\",\n \"ÃŃ a\",\n \"Ġex erc\",\n \"Ġexe rc\",\n \"W S\",\n \"/ L\",\n \". exports\",\n \".ex ports\",\n \".exp orts\",\n \".export s\",\n \"Ġ ___\",\n \"Ġ_ __\",\n \"Ġ__ _\",\n \"Ġ sin\",\n \"Ġs in\",\n \"Ġsi n\",\n \"S ervlet\",\n \"Serv let\",\n \"Ġd é\",\n \". delete\",\n \".de lete\",\n \".del ete\",\n \"r oke\",\n \"ro ke\",\n \"rok e\",\n 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NT\",\n \"al let\",\n \"all et\",\n \"alle t\",\n \"Ġ norm\",\n \"Ġn orm\",\n \"Ġno rm\",\n \"Ġnor m\",\n \"I RE\",\n \"IR E\",\n \"ex as\",\n \"Ġ programs\",\n \"Ġpro grams\",\n \"Ġpr ograms\",\n \"Ġprogram s\",\n \"Ġprog rams\",\n \"Ġ weak\",\n \"Ġwe ak\",\n \"' .$\",\n \"'. $\",\n \"u ing\",\n \"ui ng\",\n \"uin g\",\n \"ĉ ĠĠĠĠĠĠĠ\",\n \"ĉĠĠĠ ĠĠĠĠ\",\n \"ĉĠ ĠĠĠĠĠĠ\",\n \"ĉĠĠ ĠĠĠĠĠ\",\n \"ĉĠĠĠĠĠ ĠĠ\",\n \"ĉĠĠĠĠ ĠĠĠ\",\n \"ĉĠĠĠĠĠĠ Ġ\",\n \"Ġ mil\",\n \"Ġm il\",\n \"Ġmi l\",\n \"Ġ firm\",\n \"Ġf irm\",\n \"Ġfi rm\",\n \"Ġfir m\",\n \"init ely\",\n \"inite ly\",\n \"_ VALUE\",\n \"_VAL UE\",\n \"ap se\",\n \"aps e\",\n \"atis f\",\n \"ati sf\",\n \"Ġ demand\",\n \"Ġd emand\",\n \"Ġde mand\",\n \"Ġdem and\",\n \"_ mod\",\n \"_m od\",\n \"_mo d\",\n \"Ġde scribed\",\n \"Ġdes cribed\",\n \"Ġdescri bed\",\n \"Ġdescribe d\",\n \"Ġ places\",\n \"Ġp laces\",\n \"Ġpl aces\",\n \"Ġplace s\",\n \"Ġplac es\",\n \"Ġpla ces\",\n \"V ID\",\n \"VI D\",\n \"Ġ alone\",\n \"Ġal one\",\n \"Ġalo ne\",\n \"Ġ export\",\n \"Ġex port\",\n \"Ġexp ort\",\n \"Ġexpo rt\",\n \"Ġ vec\",\n \"Ġv ec\",\n \"Ġve c\",\n \"Ġ Max\",\n \"ĠM ax\",\n \"ĠMa x\",\n \"Ġ activities\",\n \"Ġact ivities\",\n \"Ġactiv ities\",\n \"ic tures\",\n \"ict ures\",\n \"icture s\",\n \"g ener\",\n \"ge ner\",\n \"gen er\",\n \"gene r\",\n \"Ġ ma\",\n \"Ġm a\",\n \"Ĥ ¬\",\n \"Ġ expression\",\n \"Ġex pression\",\n \"Ġexp ression\",\n \"Ġexpress ion\",\n \"Ġexpr ession\",\n \"C allback\",\n \"Call back\",\n \"_ content\",\n \"_c ontent\",\n \"_con tent\",\n \"_cont ent\",\n \"Ġ Most\",\n \"ĠM ost\",\n \"ĠMo st\",\n \"ĠMos t\",\n \"Ġ testing\",\n \"Ġt esting\",\n \"Ġtest ing\",\n \"Ġtes ting\",\n \"E C\",\n \"CH ANT\",\n \"CHA NT\",\n \"CHAN T\",\n \"Ġ adjust\",\n \"Ġad just\",\n \"Ġadj ust\",\n \".Th reading\",\n \".Thread ing\",\n \"( ctx\",\n \"(c tx\",\n \"(ct x\",\n \"Ġ agree\",\n \"Ġa gree\",\n \"Ġag ree\",\n \"Ġagre e\",\n \"Ġagr ee\",\n \"i ghest\",\n \"ig hest\",\n \"igh est\",\n \"Ġ ui\",\n \"Ġu i\",\n \"Ġ Law\",\n \"ĠL aw\",\n \"ĠLa w\",\n \". Y\",\n \"> < ?\",\n \"Ġ pod\",\n \"Ġp od\",\n \"Ġpo d\",\n \"- lg\",\n \"-l g\",\n \"âĢĿ ĊĊ\",\n \"âĢĿĊ Ċ\",\n \"Ġ describe\",\n \"Ġde scribe\",\n \"Ġdes cribe\",\n \"Ġdescri be\",\n \"Ġdescr ibe\",\n \"Ġ European\",\n \"ĠE uropean\",\n \"ĠEurope an\",\n \"ĠEurop ean\",\n \"- sh\",\n \"-s h\",\n \"ĠPUR POSE\",\n \"O RY\",\n \"OR Y\",\n \"Ġcon vers\",\n \"Ġconv ers\",\n \"Ġconver s\",\n \"Ġ Illuminate\",\n \"ĠI lluminate\",\n \"ĠIllum inate\",\n \"Ġ Av\",\n \"ĠA v\",\n \"( ch\",\n \"(c h\",\n \"? \\\"\",\n \"c hen\",\n \"ch en\",\n \"che n\",\n \"i ma\",\n \"im a\",\n \"D ocument\",\n \"Doc ument\",\n \"Ġ operations\",\n \"Ġoper ations\",\n \"Ġoperation s\",\n \"w in\",\n \"wi n\",\n \"ĉ function\",\n \"ĉf unction\",\n \"ĉfunc tion\",\n \"ĉfun ction\",\n \". Image\",\n \".I mage\",\n \".Im age\",\n \"Ġs cen\",\n \"Ġsc en\",\n \"Ġsce n\",\n \"/ h\",\n \"Ġ SC\",\n \"ĠS C\",\n \"Ġ explo\",\n \"Ġexp lo\",\n \"Ġexpl o\",\n \": %\",\n \"/ **čĊ\",\n \"/* *čĊ\",\n \"/** čĊ\",\n \"N AME\",\n \"NA ME\",\n \"æ Ī\",\n \"( var\",\n \"(v ar\",\n \"(va r\",\n \"Ġ director\",\n \"Ġd irector\",\n \"Ġdirect or\",\n \"Ġdir ector\",\n \"Ġdire ctor\",\n \"O NG\",\n \"ON G\",\n \"Ġ yield\",\n \"Ġy ield\",\n \"Ġyi eld\",\n \"Ġfe et\",\n \"Ġfee t\",\n \"Ġ Search\",\n \"ĠS earch\",\n \"ĠSe arch\",\n \"ĠSea rch\",\n \"Ġ Il\",\n \"ĠI l\",\n \"Ġrest aur\",\n \"Ġresta ur\",\n \"Ġrestau r\",\n \"d uc\",\n \"du c\",\n \"Ġ integer\",\n \"Ġint eger\",\n \"Ġinteg er\",\n \"Ġinte ger\",\n \"1 07\",\n \"10 7\",\n \"Ġ' ';Ċ\",\n \"Ġ'' ;Ċ\",\n \"Ġ''; Ċ\",\n \"Ġhigh ly\",\n \"check ed\",\n \"ĠPART IC\",\n \"ER CHANT\",\n \"ï¼ ī\",\n \"Ġ optim\",\n \"Ġop tim\",\n \"Ġopt im\",\n \"Q ueue\",\n \"Que ue\",\n \"Ġ LI\",\n \"ĠL I\",\n \"it ation\",\n \"ita tion\",\n \"itat ion\",\n \"Ġ transport\",\n \"Ġtrans port\",\n \"Ġtran sport\",\n \"iss ion\",\n \"f ill\",\n \"fi ll\",\n \"fil l\",\n \"us ion\",\n \"usi on\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĉ bool\",\n \"ĉb ool\",\n \"- th\",\n \"-t h\",\n \"u pt\",\n \"up t\",\n \"Ġ essential\",\n \"Ġess ential\",\n \"an ted\",\n \"ant ed\",\n \"ante d\",\n \"Ġbenef its\",\n \"Ġbenefit s\",\n \"Ġbene fits\",\n \"ĉ S\",\n \"' ;čĊ\",\n \"'; čĊ\",\n \"i ki\",\n \"ik i\",\n \"Ġ girls\",\n \"Ġgirl s\",\n \"Ġgir ls\",\n \"i ced\",\n \"ic ed\",\n \"ice d\",\n \"b uffer\",\n \"buf fer\",\n \"bu ffer\",\n \"buff er\",\n \"] +\",\n \"Ġ socket\",\n \"Ġs ocket\",\n \"Ġso cket\",\n \"Ġsock et\",\n \"Ġsoc ket\",\n \"Ġ prices\",\n \"Ġp rices\",\n \"Ġpr ices\",\n \"Ġprice s\",\n \"Ġpri ces\",\n \"Ġ Fre\",\n \"ĠF re\",\n \"ĠFr e\",\n \"Ġ sat\",\n \"Ġs at\",\n \"Ġsa t\",\n \"Ġ wood\",\n \"Ġw ood\",\n \"Ġwo od\",\n \"Ġwoo d\",\n \"Menu Item\",\n \"A RG\",\n \"AR G\",\n \"Ġ Admin\",\n \"ĠAd min\",\n \"O WN\",\n \"OW N\",\n \"d k\",\n \"Ġ reset\",\n \"Ġre set\",\n \"Ġres et\",\n \"Ġ forms\",\n \"Ġfor ms\",\n \"Ġform s\",\n \"Ġfo rms\",\n \"Ġ и\",\n \"ĠÐ ¸\",\n \"æ ĸ\",\n \"Ġ Tuesday\",\n \"ĠT uesday\",\n \"ĠTues day\",\n \"1 09\",\n \"10 9\",\n \"Ġ Initialized\",\n \"ĠInitial ized\",\n \"ĠInitialize d\",\n \"ĠInit ialized\",\n \"_ train\",\n \"_t rain\",\n \"_tr ain\",\n \"_tra in\",\n \"o rary\",\n \"or ary\",\n \"ora ry\",\n \"ate gor\",\n \"ateg or\",\n \"atego r\",\n \"Ġ dt\",\n \"Ġd t\",\n \"T otal\",\n \"To tal\",\n \"Tot al\",\n \"con struct\",\n \"i lies\",\n \"il ies\",\n \"ili es\",\n \"Ġgu ys\",\n \"Ġguy s\",\n \"е ÑĢ\",\n \"Ġ instruction\",\n \"Ġin struction\",\n \"Ġinstr uction\",\n \"Ġinstruct ion\",\n \"0 10\",\n \"01 0\",\n \"y led\",\n \"yle d\",\n \"yl ed\",\n \"Ġ internet\",\n \"Ġin ternet\",\n \"Ġint ernet\",\n \"Ġinter net\",\n \"Ġintern et\",\n \"et adata\",\n \"eta data\",\n \"a dy\",\n \"ad y\",\n \"f aces\",\n \"face s\",\n \"fa ces\",\n \"fac es\",\n \"j ection\",\n \"ject ion\",\n \"je ction\",\n \"jec tion\",\n \"Ġ Jack\",\n \"ĠJ ack\",\n \"ĠJac k\",\n \"ĠJa ck\",\n \"Ġ rect\",\n \"Ġre ct\",\n \"Ġr ect\",\n \"Ġrec t\",\n \"[ -\",\n \"Ġ Leg\",\n \"ĠL eg\",\n \"ĠLe g\",\n \"Ġ devices\",\n \"Ġdev ices\",\n \"Ġdevice s\",\n \"O C\",\n \"Ġ *čĊ\",\n \"Ġ* čĊ\",\n \"o ration\",\n \"or ation\",\n \"ora tion\",\n \"er tain\",\n \"ert ain\",\n \"erta in\",\n \"Ġ guard\",\n \"Ġg uard\",\n \"Ġgu ard\",\n \"Ġguar d\",\n \"o stream\",\n \"ost ream\",\n \"Ġ enum\",\n \"Ġe num\",\n \"Ġen um\",\n \". layout\",\n \".l ayout\",\n \"Ġ \\\";Ċ\",\n \"Ġ\\\" ;Ċ\",\n \"Ġ\\\"; Ċ\",\n \"v oke\",\n \"vo ke\",\n \"Ġ Ok\",\n \"ĠO k\",\n \"H ome\",\n \"Ho me\",\n \"Hom e\",\n \"( tr\",\n \"(t r\",\n \"E TH\",\n \"ET H\",\n \"Ġ delay\",\n \"Ġd elay\",\n \"Ġde lay\",\n \"Ġdel ay\",\n \"Ġdela y\",\n \"Ġ purchase\",\n \"Ġp urchase\",\n \"Ġpurch ase\",\n \"d c\",\n \"Ġ aren\",\n \"Ġa ren\",\n \"Ġare n\",\n \"Ġar en\",\n \"_ once\",\n \"_on ce\",\n \"_o nce\",\n \"ĉ ĉĉĉĊ\",\n \"ĉĉ ĉĉĊ\",\n \"ĉĉĉĉ Ċ\",\n \"ĉĉĉ ĉĊ\",\n \"r or\",\n \"ro r\",\n \"d raw\",\n \"dr aw\",\n \"dra w\",\n \". run\",\n \".r un\",\n \".ru n\",\n \"( model\",\n \"(m odel\",\n \"(mod el\",\n \"(mode l\",\n \"Time out\",\n \"l ik\",\n \"li k\",\n \"Ġ Arg\",\n \"ĠA rg\",\n \"ĠAr g\",\n \". en\",\n \".e n\",\n \"Ġ fish\",\n \"Ġf ish\",\n \"Ġfi sh\",\n \"Ġfis h\",\n \"c py\",\n \"cp y\",\n \"_ fe\",\n \"_f e\",\n \"ERCHANT ABILITY\",\n \"( X\",\n \"_ output\",\n \"_out put\",\n \"? ?\",\n \"Ġ jo\",\n \"Ġj o\",\n \"and ard\",\n \"anda rd\",\n \"Ġ doll\",\n \"Ġd oll\",\n \"Ġdo ll\",\n \"Ġdol l\",\n \"er rors\",\n \"err ors\",\n \"error s\",\n \"erro rs\",\n \"_ base\",\n \"_b ase\",\n \"ĠPARTIC ULAR\",\n \"Ġ leader\",\n \"Ġle ader\",\n \"Ġlead er\",\n \"Ġcom par\",\n \"Ġco mpar\",\n \"Ġcomp ar\",\n \"Ġd oub\",\n \"Ġdo ub\",\n \"Ġdou b\",\n \"Ġ Vis\",\n \"ĠV is\",\n \"ĠVi s\",\n \"Stack Trace\",\n \"- C\",\n \"ĠSt ud\",\n \"stit ute\",\n \"M ore\",\n \"Mo re\",\n \"Mor e\",\n \"Ġ Description\",\n \"ĠD escription\",\n \"ĠDe scription\",\n \"ĠDes cription\",\n \"W ARE\",\n \"WA RE\",\n \"WAR E\",\n \"a ds\",\n \"ad s\",\n \"Ġ к\",\n \"ĠÐ º\",\n \"b ind\",\n \"bin d\",\n \"bi nd\",\n \"= self\",\n \"=s elf\",\n \"e mploy\",\n \"em ploy\",\n \"emp loy\",\n \"empl oy\",\n \"emplo y\",\n \"[ n\",\n \". all\",\n \".a ll\",\n \".al l\",\n \"- B\",\n \"& &\",\n \"a lm\",\n \"al m\",\n \"Ġ culture\",\n \"Ġc ulture\",\n \"Ġcult ure\",\n \"Ġcul ture\",\n \"h ouse\",\n \"ho use\",\n \"hou se\",\n \"Ġs uffer\",\n \"Ġsu ffer\",\n \"Ġsuff er\",\n \"Ġsuf fer\",\n \"Ġ '%\",\n \"Ġ' %\",\n \"Ġ straight\",\n \"Ġstr aight\",\n \"Ġstra ight\",\n \"Ġ Star\",\n \"ĠS tar\",\n \"ĠSt ar\",\n \"ĠSta r\",\n \"u do\",\n \"ud o\",\n \"Ġ ded\",\n \"Ġd ed\",\n \"Ġde d\",\n \"Ġ COM\",\n \"ĠC OM\",\n \"ĠCO M\",\n \"Ġ confirm\",\n \"Ġcon firm\",\n \"Ġconf irm\",\n \"Ġ Good\",\n \"ĠG ood\",\n \"ĠGo od\",\n \". sc\",\n \".s c\",\n \"____ ____________\",\n \"________ ________\",\n \"____________ ____\",\n \"D R\",\n \"Config uration\",\n \"Date Time\",\n \"Ġ advert\",\n \"Ġad vert\",\n \"Ġadv ert\",\n \"Ġ couldn\",\n \"Ġcould n\",\n \"a sync\",\n \"as ync\",\n \"asy nc\",\n \"st ack\",\n \"sta ck\",\n \"' )čĊ\",\n \"') čĊ\",\n \"K it\",\n \"Ki t\",\n \"Ġh ous\",\n \"Ġho us\",\n \"Ġm echan\",\n \"Ġme chan\",\n \"Ġmec han\",\n \"Ġmech an\",\n \"r ate\",\n \"ra te\",\n \"rat e\",\n \"2 04\",\n \"20 4\",\n \"Ġ audio\",\n \"Ġa udio\",\n \"Ġaud io\",\n \"Ġau dio\",\n \"Ġaudi o\",\n \"ĉ cout\",\n \"ĉc out\",\n \"c ores\",\n \"co res\",\n \"core s\",\n \"cor es\",\n \"Ġ spot\",\n \"Ġs pot\",\n \"Ġsp ot\",\n \"Ġspo t\",\n \"Ġin creasing\",\n \"Ġincre asing\",\n \"Ġ ##\",\n \"Ġ# #\",\n \") ))\",\n \")) )\",\n \"p oints\",\n \"point s\",\n \"po ints\",\n \"poi nts\",\n \"Ġcom pared\",\n \"Ġcomp ared\",\n \"Ġcompar ed\",\n \"Ġcompare d\",\n \"l ig\",\n \"li g\",\n \"Ġ behavior\",\n \"Ġbeh avior\",\n \"Ġ BY\",\n \"ĠB Y\",\n \"Ġ Att\",\n \"ĠA tt\",\n \"ĠAt t\",\n \"c raft\",\n \"cr aft\",\n \"he aders\",\n \"head ers\",\n \"header s\",\n \"hea ders\",\n \"e te\",\n \"et e\",\n \"end region\",\n \"Ġ detail\",\n \"Ġd etail\",\n \"Ġde tail\",\n \"Ġdet ail\",\n \"U LE\",\n \"UL E\",\n \"Ġ Common\",\n \"ĠCom mon\",\n \"ĠComm on\",\n \"ĉ protected\",\n \"s ton\",\n \"st on\",\n \"sto n\",\n \"ĠFIT NESS\",\n \"Ġ fresh\",\n \"Ġf resh\",\n \"Ġfr esh\",\n \"Ġfre sh\",\n \"Ġfres h\",\n \"\\\" >ĊĊ\",\n \"\\\"> ĊĊ\",\n \"\\\">Ċ Ċ\",\n \". example\",\n \".ex ample\",\n \".exam ple\",\n \"b erg\",\n \"ber g\",\n \"be rg\",\n \"Ġ moved\",\n \"Ġm oved\",\n \"Ġmov ed\",\n \"Ġmove d\",\n \"Ġmo ved\",\n \"ĉ e\",\n \"Ġ Saturday\",\n \"ĠS aturday\",\n \"Ġ payload\",\n \"Ġp ayload\",\n \"Ġpay load\",\n \"Ä ĩ\",\n \") :ĊĊ\",\n \"):Ċ Ċ\",\n \"): ĊĊ\",\n \"Ġb ey\",\n \"Ġbe y\",\n \"u rer\",\n \"ur er\",\n \"ure r\",\n \"< script\",\n \" ,\",\n \"\\\" > < ?\",\n \"( num\",\n \"(n um\",\n \"ĉ inline\",\n \"ĉin line\",\n \"Trans action\",\n \". On\",\n \".O n\",\n \"Ġ mail\",\n \"Ġm ail\",\n \"Ġma il\",\n \"Ġmai l\",\n \"r ey\",\n \"re y\",\n \"res ults\",\n \"result s\",\n \"Ġ nav\",\n \"Ġn av\",\n \"Ġna v\",\n \"I MIT\",\n \"IM IT\",\n \"_ ids\",\n \"_id s\",\n \"_i ds\",\n \"M ake\",\n \"Ma ke\",\n \"å Ĭ\",\n \"M odal\",\n \"Mod al\",\n \"Mo dal\",\n \"Ġ LOG\",\n \"ĠL OG\",\n \"ĠLO G\",\n \"Ġ Sur\",\n \"ĠS ur\",\n \"ĠSu r\",\n \"Ġinstance of\",\n \"Ġ overall\",\n \"Ġover all\",\n \"Ġ Information\",\n \"ĠIn formation\",\n \"ĠInform ation\",\n \"Ġ construction\",\n \"Ġcon struction\",\n \"Ġconstruct ion\",\n \"Ġconstr uction\",\n \"_ FILE\",\n \"_F ILE\",\n \"b ut\",\n \"bu t\",\n \"Ġm edic\",\n \"Ġme dic\",\n \"Ġmed ic\",\n \"Ġmedi c\",\n \"Ġ duration\",\n \"Ġd uration\",\n \"Ġdu ration\",\n \"Ġdur ation\",\n \"it ness\",\n \"a gent\",\n \"ag ent\",\n \"age nt\",\n \"agen t\",\n \"A V\",\n \"Ġ seven\",\n \"Ġs even\",\n \"Ġse ven\",\n \"Ġsev en\",\n \"o lf\",\n \"ol f\",\n \"Ġ }}Ċ\",\n \"Ġ} }Ċ\",\n \"Ġ}} Ċ\",\n \"\\\" ],Ċ\",\n \"\\\"] ,Ċ\",\n \"\\\"], Ċ\",\n \"1 70\",\n \"17 0\",\n \"1 22\",\n \"12 2\",\n \"Ġ calling\",\n \"Ġc alling\",\n \"Ġcall ing\",\n \"Ġcal ling\",\n \"Ġ ans\",\n \"Ġa ns\",\n \"Ġan s\",\n \"th rows\",\n \"throw s\",\n \"thr ows\",\n \"thro ws\",\n \"or izontal\",\n \"Ġ useState\",\n \"Ġuse State\",\n \". fl\",\n \".f l\",\n \"Ġ Status\",\n \"ĠS tatus\",\n \"ĠSt atus\",\n \"ĠStat us\",\n \"Ġ Online\",\n \"ĠOn line\",\n \"R R\",\n \"Ġ Rich\",\n \"ĠR ich\",\n \"ĠRic h\",\n \"ĠRi ch\",\n \"ĠH ill\",\n \"ĠHi ll\",\n \"ĠHil l\",\n \"Ġ brain\",\n \"Ġb rain\",\n \"Ġbr ain\",\n \"Ġbra in\",\n \"Ġfollow ed\",\n \"Ġfoll owed\",\n \"2 40\",\n \"24 0\",\n \"e mic\",\n \"em ic\",\n \"emi c\",\n \"Ġs light\",\n \"Ġsl ight\",\n \"Ġ insurance\",\n \"Ġins urance\",\n \". Array\",\n \".A rray\",\n \".Ar ray\",\n \"Ġ abstract\",\n \"Ġa bstract\",\n \"Ġab stract\",\n \"Ġabs tract\",\n \"Ġabst ract\",\n \"Ġ Sum\",\n \"ĠS um\",\n \"ĠSu m\",\n \"re direct\",\n \"red irect\",\n \"redi rect\",\n \"o wner\",\n \"ow ner\",\n \"own er\",\n \"( msg\",\n \"(m sg\",\n \"(ms g\",\n \"Ġ Clinton\",\n \"ĠCl inton\",\n \"ĠClin ton\",\n \"ĠClint on\",\n \"ĠCli nton\",\n \"N on\",\n \"No n\",\n \"ĉ ex\",\n \"ĉe x\",\n \"Ġ volume\",\n \"Ġv olume\",\n \"Ġvol ume\",\n \"Ġvolum e\",\n \"Ġ EventArgs\",\n \"ĠEvent Args\",\n \"- L\",\n \"Ġ Dim\",\n \"ĠD im\",\n \"ĠDi m\",\n \"Ġ Mart\",\n \"ĠM art\",\n \"ĠMar t\",\n \"ĠMa rt\",\n \"Ġ cursor\",\n \"Ġc ursor\",\n \"Ġcurs or\",\n \"Ġcurso r\",\n \"Ġ implementation\",\n \"Ġim plementation\",\n \"Ġimplement ation\",\n \"ur red\",\n \"urre d\",\n \"urr ed\",\n \"Ġl arger\",\n \"Ġlarge r\",\n \"Ġlarg er\",\n \"Ġlar ger\",\n \") ;ĊĊĊ\",\n \");Ċ ĊĊ\",\n \");ĊĊ Ċ\",\n \"); ĊĊĊ\",\n \"' +\",\n \". transform\",\n \".trans form\",\n \"Ġ upload\",\n \"Ġup load\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"D raw\",\n \"Dr aw\",\n \"n el\",\n \"ne l\",\n \"ĉ float\",\n \"ĉf loat\",\n \"q rt\",\n \"qr t\",\n \"Ġ Network\",\n \"ĠN etwork\",\n \"ĠNet work\",\n \"Ġ tit\",\n \"Ġt it\",\n \"Ġti t\",\n \"A xis\",\n \"Ax is\",\n \". android\",\n \".and roid\",\n \"Ġ completed\",\n \"Ġcom pleted\",\n \"Ġcomp leted\",\n \"Ġcomple ted\",\n \"Ġcomplet ed\",\n \"Ġcompl eted\",\n \"Ġcomplete d\",\n \"Ġ mur\",\n \"Ġm ur\",\n \"Ġmu r\",\n \"Ġ columns\",\n \"Ġcolumn s\",\n \"Ġcolum ns\",\n \"x c\",\n \"Ġ supply\",\n \"Ġs upply\",\n \"Ġsup ply\",\n \"Ġsuppl y\",\n \"Ġsupp ly\",\n \"im inal\",\n \"imi nal\",\n \"imin al\",\n \"Ġ spr\",\n \"Ġs pr\",\n \"Ġsp r\",\n \"================ ================================================\",\n \"================================ ================================\",\n \"================================================ ================\",\n \"Ġ units\",\n \"Ġun its\",\n \"Ġunit s\",\n \"Ġuni ts\",\n \"( u\",\n \"m i\",\n \"re place\",\n \"rep lace\",\n \"[ key\",\n \"[k ey\",\n \"à ¹\",\n \"an tic\",\n \"ant ic\",\n \"anti c\",\n \"Ġ payment\",\n \"Ġp ayment\",\n \"Ġpay ment\",\n \", B\",\n \"Ġ Apple\",\n \"ĠApp le\",\n \"ĠAp ple\",\n \"ĠAppl e\",\n \"g in\",\n \"gi n\",\n \"Re quired\",\n \"Require d\",\n \"# +\",\n \"l ands\",\n \"land s\",\n \"la nds\",\n \"lan ds\",\n \"Ġ squ\",\n \"Ġs qu\",\n \"Ġsq u\",\n \"Ġ factor\",\n \"Ġf actor\",\n \"Ġfact or\",\n \"Ġfa ctor\",\n \"Ġfac tor\",\n \"Ġfacto r\",\n \"d ec\",\n \"de c\",\n \"Ġ strength\",\n \"Ġst rength\",\n \"Ġstr ength\",\n \"Ġstre ngth\",\n \"Ġstren gth\",\n \"Ġ boy\",\n \"Ġb oy\",\n \"Ġbo y\",\n \"Ġ balance\",\n \"Ġb alance\",\n \"Ġbal ance\",\n \"Ġ sources\",\n \"Ġs ources\",\n \"Ġsource s\",\n \"Ġsour ces\",\n \"s creen\",\n \"sc reen\",\n \"scr een\",\n \"- top\",\n \"-t op\",\n \"-to p\",\n \"Ġ Amazon\",\n \"ĠA mazon\",\n \"ĠAm azon\",\n \"ĠAma zon\",\n \"Ġ hidden\",\n \"Ġh idden\",\n \"Ġhi dden\",\n \"Ġhid den\",\n \"е ÑĤ\",\n \"_ client\",\n \"_c lient\",\n \"_cl ient\",\n \"_cli ent\",\n \"Ġ eat\",\n \"Ġe at\",\n \"Ġea t\",\n \". display\",\n \".d isplay\",\n \".dis play\",\n \"Ġ »\",\n \"Ġ »\",\n \"Ġ trigger\",\n \"Ġtr igger\",\n \"Ġtri gger\",\n \"Ġtrig ger\",\n \"an ager\",\n \"ana ger\",\n \"Ġ tro\",\n \"Ġt ro\",\n \"Ġtr o\",\n \"Ġ claims\",\n \"Ġcl aims\",\n \"Ġclaim s\",\n \"Ġcla ims\",\n \"f ord\",\n \"fo rd\",\n \"for d\",\n \"Ġ Company\",\n \"ĠCom pany\",\n \"ĠComp any\",\n \"Ġ gift\",\n \"Ġg ift\",\n \"Ġgi ft\",\n \"Ġgif t\",\n \", :\",\n \"_ app\",\n \"_a pp\",\n \"_ap p\",\n \"h andle\",\n \"han dle\",\n \"hand le\",\n \"Ġ produce\",\n \"Ġp roduce\",\n \"Ġpro duce\",\n \"Ġprodu ce\",\n \"Ġprod uce\",\n \"/ lib\",\n \"/l ib\",\n \"/li b\",\n \"5 12\",\n \"51 2\",\n \"Ġ -*\",\n \"Ġ- *\",\n \"ĉ set\",\n \"ĉs et\",\n \"ĉse t\",\n \"' ];\",\n \"'] ;\",\n \"a rc\",\n \"ar c\",\n \"a nder\",\n \"an der\",\n \"and er\",\n \"ande r\",\n \"Ġ Engine\",\n \"ĠE ngine\",\n \"ĠEng ine\",\n \"Ġ attributes\",\n \"Ġat tributes\",\n \"Ġattribute s\",\n \"Ġattrib utes\",\n \"t ask\",\n \"ta sk\",\n \"tas k\",\n \"< =\",\n \"( N\",\n \"Ġ warm\",\n \"Ġw arm\",\n \"Ġwar m\",\n \"Ġwa rm\",\n \"wh ich\",\n \"Ġ Fore\",\n \"ĠF ore\",\n \"ĠFor e\",\n \"ĠFo re\",\n \"ag nost\",\n \"agn ost\",\n \"m ys\",\n \"my s\",\n \"Ġ tal\",\n \"Ġt al\",\n \"Ġta l\",\n \"Ġ Sal\",\n \"ĠS al\",\n \"ĠSa l\",\n \"g i\",\n \"Ġ Print\",\n \"ĠP rint\",\n \"ĠPr int\",\n \"ĠPri nt\",\n \"Ġ TRUE\",\n \"ĠTR UE\",\n \"Ġ о\",\n \"ĠÐ ¾\",\n \". UI\",\n \".U I\",\n \"Ġ flash\",\n \"Ġf lash\",\n \"Ġfl ash\",\n \"Ġfla sh\",\n \"ro perty\",\n \"rop erty\",\n \". location\",\n \".l ocation\",\n \".loc ation\",\n \".lo cation\",\n \"Ġ Mill\",\n \"ĠM ill\",\n \"ĠMil l\",\n \"ĠMi ll\",\n \"b i\",\n \"con tr\",\n \"cont r\",\n \". request\",\n \".re quest\",\n \".req uest\",\n \"Ġ Sam\",\n \"ĠS am\",\n \"ĠSa m\",\n \"Ġ negative\",\n \"Ġn egative\",\n \"Ġneg ative\",\n \"k it\",\n \"ki t\",\n \"Ġ sett\",\n \"Ġs ett\",\n \"Ġse tt\",\n \"Ġset t\",\n \".print StackTrace\",\n \"a be\",\n \"ab e\",\n \"ĉ i\",\n \"Ġ burn\",\n \"Ġb urn\",\n \"Ġbu rn\",\n \"Ġbur n\",\n \"Ġs ociety\",\n \"Ġsoci ety\",\n \"C ache\",\n \"Ca che\",\n \"Ġ Security\",\n \"ĠS ecurity\",\n \"ĠSe curity\",\n \"ĠSec urity\",\n \". models\",\n \".model s\",\n \".mod els\",\n \".mode ls\",\n \"ĠWARRANT Y\",\n \"_ up\",\n \"_u p\",\n \"ce ive\",\n \"Ġ clients\",\n \"Ġc lients\",\n \"Ġcl ients\",\n \"Ġclient s\",\n \"Ġcli ents\",\n \". Tr\",\n \".T r\",\n \"Ġprovid ing\",\n \"Ġprov iding\",\n \"Ġr out\",\n \"Ġro ut\",\n \"Ġrou t\",\n \"m aterial\",\n \"mat erial\",\n \"mate rial\",\n \"Ġ ||Ċ\",\n \"Ġ| |Ċ\",\n \"Ġ|| Ċ\",\n \"Ġ Ser\",\n \"ĠS er\",\n \"ĠSe r\",\n \"Ġ Office\",\n \"ĠOff ice\",\n \"FT WARE\",\n \"Ġ '$\",\n \"Ġ' $\",\n \"Ġf oc\",\n \"Ġfo c\",\n \"Ġex cell\",\n \"Ġexc ell\",\n \"Ġexcel l\",\n \"Ġexce ll\",\n \"Ġ cat\",\n \"Ġc at\",\n \"Ġca t\",\n \"n ormal\",\n \"norm al\",\n \"nor mal\",\n \"Ġd etermine\",\n \"Ġdeter mine\",\n \"Ġdetermin e\",\n \"Ġdeterm ine\",\n \"ĉ uint\",\n \"ĉu int\",\n \"ĉui nt\",\n \"P ane\",\n \"Pa ne\",\n \"Pan e\",\n \"Ġ employees\",\n \"Ġemploy ees\",\n \"Ġemployee s\",\n \"Ġ Texas\",\n \"ĠT exas\",\n \"ĠTex as\",\n \"Ġtr aff\",\n \"Ġtra ff\",\n \"Ġtraf f\",\n \"Ġ Report\",\n \"ĠRe port\",\n \"ĠRep ort\",\n \"ĠRepo rt\",\n \"an ta\",\n \"ant a\",\n \"Ġ Box\",\n \"ĠB ox\",\n \"ĠBo x\",\n \"Ġ django\",\n \"Ġd jango\",\n \"Ġdj ango\",\n \"Ġ partner\",\n \"Ġp artner\",\n \"Ġpart ner\",\n \"E B\",\n \"L INE\",\n \"LI NE\",\n \"LIN E\",\n \"Ġfe eling\",\n \"Ġfeel ing\",\n \"Ġfee ling\",\n \"Ġ civil\",\n \"Ġc ivil\",\n \"Ġci vil\",\n \"Ġciv il\",\n \"( float\",\n \"(f loat\",\n \"S ql\",\n \"Sq l\",\n \"Ġwould n\",\n \". init\",\n \".in it\",\n \".i nit\",\n \".ini t\",\n \". left\",\n \".l eft\",\n \".le ft\",\n \"- v\",\n \"_ level\",\n \"_le vel\",\n \"' }\",\n \"A F\",\n \"Ġ loading\",\n \"Ġlo ading\",\n \"Ġload ing\",\n \"Ġloa ding\",\n \"Ġ Only\",\n \"ĠOn ly\",\n \"Ġ cookies\",\n \"Ġc ookies\",\n \"Ġco okies\",\n \"Ġcook ies\",\n \"Ġcookie s\",\n \"Ġ Gl\",\n \"ĠG l\",\n \"C O\",\n \"Ġ strategy\",\n \"Ġstr ategy\",\n \"Ġstrateg y\",\n \"Ġstrate gy\",\n \"(' ./\",\n \"('. /\",\n \"Ġ ship\",\n \"Ġs hip\",\n \"Ġsh ip\",\n \"p oses\",\n \"pos es\",\n \"pose s\",\n \"po ses\",\n \"Ġ signal\",\n \"Ġs ignal\",\n \"Ġsign al\",\n \"Ġsig nal\",\n \"Ġ alpha\",\n \"Ġal pha\",\n \"Ġalph a\",\n \". pop\",\n \".p op\",\n \".po p\",\n \"R adius\",\n \"Rad ius\",\n \"Radi us\",\n \"Ġ replace\",\n \"Ġre place\",\n \"Ġrep lace\",\n \"Ġrepl ace\",\n \"_ DIR\",\n \"_D IR\",\n \"_DI R\",\n \"c ounter\",\n \"co unter\",\n \"count er\",\n \"bserv able\",\n \"e la\",\n \"el a\",\n \"W eight\",\n \"We ight\",\n \"Wei ght\",\n \"h ash\",\n \"ha sh\",\n \"has h\",\n \"b ose\",\n \"bo se\",\n \"bos e\",\n \"f x\",\n \"Ġ Email\",\n \"ĠE mail\",\n \"ĠEm ail\",\n \"Ġ refer\",\n \"Ġre fer\",\n \"Ġref er\",\n \"local host\",\n \"_ RO\",\n \"_R O\",\n \"i ques\",\n \"ique s\",\n \"iqu es\",\n \"iq ues\",\n \"S tep\",\n \"St ep\",\n \"Ste p\",\n \"Ġ ahead\",\n \"Ġa head\",\n \"Ġah ead\",\n \"( View\",\n \"(V iew\",\n \"Ġ Services\",\n \"ĠS ervices\",\n \"ĠService s\",\n \"ĠServ ices\",\n \"Ġ Json\",\n \"ĠJ son\",\n \"ĠJs on\",\n \"ess or\",\n \"esso r\",\n \"Ġ pun\",\n \"Ġp un\",\n \"Ġpu n\",\n \"Ġ appropriate\",\n \"Ġapp ropriate\",\n \"Ġappropri ate\",\n \"a kers\",\n \"ak ers\",\n \"ake rs\",\n \"aker s\",\n \"o sen\",\n \"os en\",\n \"ose n\",\n \"p osing\",\n \"pos ing\",\n \"po sing\",\n \"Ġ agent\",\n \"Ġa gent\",\n \"Ġag ent\",\n \"Ġage nt\",\n \"f c\",\n \"Ġ transfer\",\n \"Ġtrans fer\",\n \"Ġtransf er\",\n \"Ġ invalid\",\n \"Ġin valid\",\n \"Ġinval id\",\n \"Ġ Research\",\n \"ĠRe search\",\n \"ĠRes earch\",\n \"Vert ex\",\n \"Ver tex\",\n \"Ġ gay\",\n \"Ġg ay\",\n \"Ġga y\",\n \"Ġ journal\",\n \"Ġj ournal\",\n \"Ġjo urnal\",\n \"Ġjour nal\",\n \"[ x\",\n \"Ġ \\\"\\\",Ċ\",\n \"Ġ\\\" \\\",Ċ\",\n \"Ġ\\\"\\\" ,Ċ\",\n \"Ġ\\\"\\\", Ċ\",\n \"Ġ Well\",\n \"ĠW ell\",\n \"ĠWe ll\",\n \"ĠWel l\",\n \". Tasks\",\n \".T asks\",\n \".Task s\",\n \"S pec\",\n \"Sp ec\",\n \"Spe c\",\n \"Ġ ol\",\n \"Ġo l\",\n \"Ġs pend\",\n \"Ġsp end\",\n \"Ġspe nd\",\n \"Ġ Australia\",\n \"ĠA ustralia\",\n \"ĠAustral ia\",\n \"ĠAustr alia\",\n \"M atch\",\n \"Mat ch\",\n \".j unit\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġ MAX\",\n \"ĠM AX\",\n \"ĠMA X\",\n \"iz able\",\n \"iza ble\",\n \"cl usive\",\n \"clus ive\",\n \"_ valid\",\n \"_val id\",\n \"_va lid\",\n \"Ġ quarter\",\n \"Ġqu arter\",\n \"Ġquar ter\",\n \"Ġquart er\",\n \"y an\",\n \"ya n\",\n \"0 05\",\n \"00 5\",\n \"Ġ Edit\",\n \"ĠE dit\",\n \"ĠEd it\",\n \"ar den\",\n \"ard en\",\n \"arde n\",\n \"= new\",\n \"=n ew\",\n \"Ġ frag\",\n \"Ġf rag\",\n \"Ġfr ag\",\n \"Ġfra g\",\n \"B it\",\n \"Bi t\",\n \"z i\",\n \"a ine\",\n \"ain e\",\n \"ai ne\",\n \"u dd\",\n \"ud d\",\n \". Object\",\n \".O bject\",\n \".Obj ect\",\n \"de bug\",\n \"deb ug\",\n \"Ġ cash\",\n \"Ġc ash\",\n \"Ġca sh\",\n \"Ġcas h\",\n \"_ IM\",\n \"_I M\",\n \"Ġ een\",\n \"Ġe en\",\n \"Ġee n\",\n \"Ġ commercial\",\n \"Ġcom mercial\",\n \"Ġcomm ercial\",\n \"Ġcommerc ial\",\n \"Ġ Video\",\n \"ĠV ideo\",\n \"ĠVi deo\",\n \"ĠVid eo\",\n \"ĠVide o\",\n \"l oader\",\n \"lo ader\",\n \"load er\",\n \"Ġ fixed\",\n \"Ġf ixed\",\n \"Ġfix ed\",\n \"Ġfi xed\",\n \"Ġ applications\",\n \"Ġapp lications\",\n \"Ġapplication s\",\n \"Ġapplic ations\",\n \"Ġappl ications\",\n \"Ġ _,\",\n \"Ġ_ ,\",\n \"Ġ Russia\",\n \"ĠR ussia\",\n \"ĠRuss ia\",\n \"it ect\",\n \"ite ct\",\n \"_ (\",\n \"Ġ Block\",\n \"ĠB lock\",\n \"ĠBl ock\",\n \"ĠBlo ck\",\n \"ĠBloc k\",\n \"Ġ san\",\n \"Ġs an\",\n \"Ġsa n\",\n \"Ġ Tom\",\n \"ĠT om\",\n \"ĠTo m\",\n \"Ġ perhaps\",\n \"Ġper haps\",\n \"Ġ sig\",\n \"Ġs ig\",\n \"Ġsi g\",\n \"le vant\",\n \"lev ant\",\n \"Ġc orpor\",\n \"Ġcor por\",\n \"Ġcorp or\",\n \"Ġcorpo r\",\n \"at aset\",\n \"ata set\",\n \"atas et\",\n \"r onic\",\n \"ro nic\",\n \"ron ic\",\n \"x e\",\n \"Ġ eth\",\n \"Ġe th\",\n \"Ġet h\",\n \"S ome\",\n \"So me\",\n \"Som e\",\n \"p op\",\n \"po p\",\n \"_ OK\",\n \"_O K\",\n \"Ġt end\",\n \"Ġte nd\",\n \"Ġten d\",\n \". Res\",\n \".R es\",\n \".Re s\",\n \"_ and\",\n \"_a nd\",\n \"_an d\",\n \"Ġ reviews\",\n \"Ġre views\",\n \"Ġreview s\",\n \"Ġ wild\",\n \"Ġw ild\",\n \"Ġwi ld\",\n \"Ġwil d\",\n \"1 17\",\n \"11 7\",\n \"Ġ degree\",\n \"Ġd egree\",\n \"Ġde gree\",\n \"Ġdeg ree\",\n \". O\",\n \". objects\",\n \".object s\",\n \".obj ects\",\n \"_ args\",\n \"_arg s\",\n \"_ar gs\",\n \"n il\",\n \"ni l\",\n \"Ġ disabled\",\n \"Ġdis abled\",\n \"Ġdisable d\",\n \"P arent\",\n \"Par ent\",\n \"Pa rent\",\n \"Paren t\",\n \"Ġ notes\",\n \"Ġn otes\",\n \"Ġnot es\",\n \"Ġno tes\",\n \"Ġnote s\",\n \"Ġ \\\"\\\"Ċ\",\n \"Ġ\\\" \\\"Ċ\",\n \"Ġ\\\"\\\" Ċ\",\n \"( state\",\n \"(st ate\",\n \"(stat e\",\n \"i strict\",\n \"istr ict\",\n \"Ġ logging\",\n \"Ġlog ging\",\n \". 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Ċ\",\n \"Ġ thanks\",\n \"Ġth anks\",\n \"Ġthan ks\",\n \"Ġthank s\",\n \"( props\",\n \"(p rops\",\n \"(pro ps\",\n \"(pr ops\",\n \"(prop s\",\n \"Ġ tick\",\n \"Ġt ick\",\n \"Ġti ck\",\n \"Ġtic k\",\n \"Ġ experiment\",\n \"Ġex periment\",\n \"Ġexper iment\",\n \"Ġexperi ment\",\n \"Ġpr ison\",\n \"Ġpri son\",\n \"Ġpris on\",\n \"t ree\",\n \"tr ee\",\n \"tre e\",\n \"- text\",\n \"-t ext\",\n \"-te xt\",\n \"Ġ IOException\",\n \"ĠIO Exception\",\n \"- width\",\n \"-w idth\",\n \"_ STATUS\",\n \"_ST ATUS\",\n \"_STAT US\",\n \"f ast\",\n \"fa st\",\n \"fas t\",\n \"- body\",\n \"-b ody\",\n \"-bo dy\",\n \"- header\",\n \"-head er\",\n \"-he ader\",\n \"Ġg uar\",\n \"Ġgu ar\",\n \"c rete\",\n \"cre te\",\n \"cret e\",\n \"cr ete\",\n \"Ġ Tim\",\n \"ĠT im\",\n \"ĠTi m\",\n \"Ġcl early\",\n \"Ġclear ly\",\n \"Ġ Republican\",\n \"ĠRepublic an\",\n \"Ġ justify\",\n \"Ġjust ify\",\n \"и ÑĤ\",\n \"ĉ ĠĠĠĠ\",\n \"ĉĠĠĠ Ġ\",\n \"ĉĠ ĠĠĠ\",\n \"ĉĠĠ ĠĠ\",\n \"c ache\",\n \"ca che\",\n \"cac he\",\n \"; //\",\n \";/ /\",\n \"Ġ presence\",\n \"Ġpres ence\",\n \"Ġf actors\",\n \"Ġfact ors\",\n \"Ġfa ctors\",\n \"Ġfac tors\",\n \"Ġfactor s\",\n \"Ġfacto rs\",\n \"Ġ employee\",\n \"Ġe mployee\",\n \"Ġemploy ee\",\n \"] ))\",\n \"]) )\",\n \"M ember\",\n \"Mem ber\",\n \"Ġ selector\",\n \"Ġse lector\",\n \"Ġselect or\",\n \"Ġsel ector\",\n \"Ġsele ctor\",\n \"b or\",\n \"bo r\",\n \"Ġ Mex\",\n \"ĠM ex\",\n \"ĠMe x\",\n \"çļ Ħ\",\n \"u tex\",\n \"ut ex\",\n \"ute x\",\n \"_ tag\",\n \"_t ag\",\n \"_ta g\",\n \"ail ure\",\n \"Ġ Net\",\n \"ĠN et\",\n \"ĠNe t\",\n \"Ġre li\",\n \"Ġr eli\",\n \"Ġrel i\",\n \"E G\",\n \"Ġ fprintf\",\n \"Ġf printf\",\n \"Ġ teen\",\n \"Ġt een\",\n \"Ġte en\",\n \"Ġtee n\",\n \"l oss\",\n \"lo ss\",\n \"los s\",\n \"Ġle aving\",\n \"1 34\",\n \"13 4\",\n \"De legate\",\n \"Ġ beat\",\n \"Ġb eat\",\n \"Ġbe at\",\n \"Ġ minute\",\n \"Ġmin ute\",\n \"Ġminut e\",\n \"sub scribe\",\n \"subs cribe\",\n \"Ġred istribute\",\n \"Ġredistrib ute\",\n \"Ġredis tribute\",\n \"Con stants\",\n \"Constant s\",\n \"Const ants\",\n \"Ġc ancer\",\n \"Ġcan cer\",\n \"Ġcanc er\",\n \"/ {\",\n \"B L\",\n \"Ġ span\",\n \"Ġs pan\",\n \"Ġsp an\",\n \"Ġspa n\",\n \"Ġ Child\",\n \"ĠCh ild\",\n \"ĠChi ld\",\n \"C enter\",\n \"Cent er\",\n \"Ġ earth\",\n \"Ġe arth\",\n \"Ġear th\",\n \"Y S\",\n \"Ġ Level\",\n \"ĠLe vel\",\n \"ĠLev el\",\n \"Ġ sea\",\n \"Ġs ea\",\n \"Ġse a\",\n \". support\",\n \".s upport\",\n \".sup port\",\n \". inner\",\n \".in ner\",\n \". Item\",\n \".I tem\",\n \".It em\",\n \"il ling\",\n \"ill ing\",\n \"illi ng\",\n \"illin g\",\n \"Ġ ĠĠĠĊĠĠĠĠĊ\",\n \"ĠĠ ĠĠĊĠĠĠĠĊ\",\n \"ĠĠĠĠ ĊĠĠĠĠĊ\",\n \"ĠĠĠ ĠĊĠĠĠĠĊ\",\n \"ĠĠĠĠĊ ĠĠĠĠĊ\",\n \"Ġ Label\",\n \"ĠL abel\",\n \"ĠLa bel\",\n \"ĠLab el\",\n \"3 20\",\n \"32 0\",\n \"Ġ Est\",\n \"ĠE st\",\n \"ĠEs t\",\n \"( arg\",\n \"(a rg\",\n \"(ar g\",\n \"1 45\",\n \"14 5\",\n \"bo Box\",\n \"ĉ foreach\",\n \"ĉf oreach\",\n \"ĉfor each\",\n \"c os\",\n \"co s\",\n \"F ailed\",\n \"Fail ed\",\n \"Fa iled\",\n \"s wers\",\n \"sw ers\",\n \"swer s\",\n \"E ditor\",\n \"Ed itor\",\n \"Edit or\",\n \"r ont\",\n \"ro nt\",\n \"ron t\",\n \"Ġ MP\",\n \"ĠM P\",\n \"ex pr\",\n \"exp r\",\n \"Ġ Life\",\n \"ĠL ife\",\n \"ĠLi fe\",\n \"ĠLif e\",\n \"Ġ ??\",\n \"Ġ? ?\",\n \"ö r\",\n \"Ġ attend\",\n \"Ġatt end\",\n \"Ġatte nd\",\n \"Ġ Que\",\n \"ĠQ ue\",\n \"ĠQu e\",\n \"Ġ species\",\n \"Ġs pecies\",\n \"Ġsp ecies\",\n \"Ġspec ies\",\n \"Ġspe cies\",\n \"Ġspeci es\",\n \"- D\",\n \"Ġ aus\",\n \"Ġa us\",\n \"Ġau s\",\n \"Str uct\",\n \"Ġadv antage\",\n \"Ġadvant age\",\n \"o ston\",\n \"os ton\",\n \"ost on\",\n \"osto n\",\n \"- block\",\n \"-b lock\",\n \"-bl ock\",\n \"in itial\",\n \"init ial\",\n \"C RE\",\n \"CR E\",\n \"Ġtr uly\",\n \"Ġ compare\",\n \"Ġcom pare\",\n \"Ġcomp are\",\n \"Ġcompar e\",\n \"or ney\",\n \"orn ey\",\n \"orne y\",\n \"Ġ spect\",\n \"Ġs pect\",\n \"Ġsp ect\",\n \"Ġspec t\",\n \"Ġspe ct\",\n \"F ull\",\n \"Fu ll\",\n \"b es\",\n \"be s\",\n \"Ġ visible\",\n \"Ġv isible\",\n \"Ġvis ible\",\n \"Ġ mess\",\n \"Ġm ess\",\n \"Ġme ss\",\n \"Ġmes s\",\n \"st ances\",\n \"stance s\",\n \"sta nces\",\n \"stan ces\",\n \"Ġ cloud\",\n \"Ġc loud\",\n \"Ġcl oud\",\n \"Ġclo ud\",\n \"_ version\",\n \"_v ersion\",\n \"Ġf urn\",\n \"Ġfur n\",\n \"Ġfu rn\",\n \"ic ago\",\n \"ica go\",\n \"L OW\",\n \"LO W\",\n \"Ġ traffic\",\n \"Ġtraff ic\",\n \"Ġtra ffic\",\n \"Ġtraf fic\",\n \"Ġ fol\",\n \"Ġf ol\",\n \"Ġfo l\",\n \"ry pto\",\n \"rypt o\",\n \"Ġ declar\",\n \"Ġde clar\",\n \"Ġdec lar\",\n \"Ġdecl ar\",\n \"Ġ slot\",\n \"Ġs lot\",\n \"Ġsl ot\",\n \"Ġslo t\",\n \"Ġ Ext\",\n \"ĠE xt\",\n \"ĠEx t\",\n \"Ġ England\",\n \"ĠEng land\",\n \"ĠEngl and\",\n \"Ġ Under\",\n \"ĠU nder\",\n \"ĠUn der\",\n \"ĠUnd er\",\n \"Ġ ta\",\n \"Ġt a\",\n \"l etter\",\n \"let ter\",\n \"lette r\",\n \"lett er\",\n \"2 03\",\n \"20 3\",\n \"Ġoff icer\",\n \"Ġoffic er\",\n \"Ġoffice r\",\n \"Ġ Donald\",\n \"ĠD onald\",\n \"ĠDon ald\",\n \"Y es\",\n \"Ye s\",\n \"_ json\",\n \"_j son\",\n \"_js on\",\n \"I TableView\",\n \"IT ableView\",\n \"Ġ USE\",\n \"ĠU SE\",\n \"ĠUS E\",\n \"mploy ee\",\n \"Ġop inion\",\n \"Ġopin ion\",\n \"Ġ Aut\",\n \"ĠA ut\",\n \"ĠAu t\",\n \"b order\",\n \"bor der\",\n \"Ġad vice\",\n \"Ġadv ice\",\n \"Ġautom atically\",\n \"Ġautomatic ally\",\n \"Ġautomat ically\",\n \"is co\",\n \"isc o\",\n \"Ġ mm\",\n \"Ġm m\",\n \". vis\",\n \".v is\",\n \"a ml\",\n \"am l\",\n \"Ġ initialize\",\n \"Ġinitial ize\",\n \"Ġ ({\",\n \"Ġ( {\",\n \"Ġ ;ĊĊ\",\n \"Ġ; ĊĊ\",\n \"Ġ;Ċ Ċ\",\n \"Ġ generation\",\n \"Ġg eneration\",\n \"Ġgener ation\",\n \"Ġgen eration\",\n \"Ġgene ration\",\n \"Ġgenera tion\",\n \"Ġ bits\",\n \"Ġb its\",\n \"Ġbit s\",\n \"Ġbi ts\",\n \"cl ipse\",\n \"clip se\",\n \"Ġu nf\",\n \"Ġun f\",\n \"u tors\",\n \"ut ors\",\n \"uto rs\",\n \"utor s\",\n \"p lt\",\n \"pl t\",\n \"Ġ delta\",\n \"Ġd elta\",\n \"Ġdel ta\",\n \"Ġdelt a\",\n \"e stroy\",\n \"est roy\",\n \"estr oy\",\n \"estro y\",\n \"i sis\",\n \"is is\",\n \"isi s\",\n \"< br\",\n \"Ċ\",\n \"'> Ċ\",\n \"a pers\",\n \"ap ers\",\n \"ape rs\",\n \"aper s\",\n \"] (\",\n \"cont inue\",\n \"contin ue\",\n \"s pec\",\n \"sp ec\",\n \"spe c\",\n \"Ġ Road\",\n \"ĠR oad\",\n \"ĠRo ad\",\n \"A SH\",\n \"AS H\",\n \"il iar\",\n \"ili ar\",\n \"ilia r\",\n \"Ġcontin ues\",\n \"Ġcontinue s\",\n \"Ġcontinu es\",\n \"Ġ appoint\",\n \"Ġapp oint\",\n \"Ġap point\",\n \"Ġ #Ċ\",\n \"Ġ# Ċ\",\n \"Ġ Vir\",\n \"ĠV ir\",\n \"ĠVi r\",\n \"Ġ ?>\\\"\",\n \"Ġ? >\\\"\",\n \"Ġ?> \\\"\",\n \"Ġ bin\",\n \"Ġb in\",\n \"Ġbi n\",\n \"} \\\",\",\n \"}\\\" ,\",\n \"go ing\",\n \"e ach\",\n \"ea ch\",\n \"B D\",\n \"1 85\",\n \"18 5\",\n \"Ġ Access\",\n \"ĠA ccess\",\n \"ĠAc cess\",\n \"ĠAcc ess\",\n \"D oc\",\n \"Do c\",\n \"Ġ Management\",\n \"ĠMan agement\",\n \"ĠManage ment\",\n \"ĠMana gement\",\n \"B ER\",\n \"BE R\",\n \"as ket\",\n \"ask et\",\n \". getInstance\",\n \".get Instance\",\n \".getIn stance\",\n \"1 29\",\n \"12 9\",\n \"Ġestablish ed\",\n \"s ocket\",\n \"so cket\",\n \"sock et\",\n \"soc ket\",\n \"I NS\",\n \"IN S\",\n \"ĉ virtual\",\n \"ĉv irtual\",\n \"ĉ result\",\n \"ĉres ult\",\n \"RE AD\",\n \"REA D\",\n \"_ height\",\n \"_h eight\",\n \"_he ight\",\n \"1 52\",\n \"15 2\",\n \"Ġ Font\",\n \"ĠF ont\",\n \"ĠFo nt\",\n \"ĠFon t\",\n \"Ġ ();Ċ\",\n \"Ġ( );Ċ\",\n \"Ġ() ;Ċ\",\n \"Ġ(); Ċ\",\n \"_ html\",\n \"_h tml\",\n \"_ht ml\",\n \"Ġ neighbor\",\n \"Ġne ighbor\",\n \"Ġneighb or\",\n \"Ġneigh bor\",\n \"l or\",\n \"lo r\",\n \"Ġ gather\",\n \"Ġg ather\",\n \"Ġga ther\",\n \"Ġ })ĊĊ\",\n \"Ġ} )ĊĊ\",\n \"Ġ})Ċ Ċ\",\n \"Ġ}) ĊĊ\",\n \"Ġ identity\",\n \"Ġid entity\",\n \"Ġide ntity\",\n \"Ġident ity\",\n \"Ġ fab\",\n \"Ġf ab\",\n \"Ġfa b\",\n \"p adding\",\n \"pad ding\",\n \"Ġ Route\",\n \"ĠR oute\",\n \"ĠRo ute\",\n \"ĠRou te\",\n \"ĠRout e\",\n \"Enumer able\",\n \"Enum erable\",\n \"à ´\",\n \"Ġ forced\",\n \"Ġfor ced\",\n \"Ġforce d\",\n \"Ġforc ed\",\n \"/ jquery\",\n \"/j query\",\n \". ĊĊĊĊĊĊ\",\n \".ĊĊ ĊĊĊĊ\",\n \".Ċ ĊĊĊĊĊ\",\n \".ĊĊĊĊ ĊĊ\",\n \".ĊĊĊ ĊĊĊ\",\n \".ĊĊĊĊĊ Ċ\",\n \"res ents\",\n \"resent s\",\n \"rese nts\",\n \"_ left\",\n \"_l eft\",\n \"_le ft\",\n \". Param\",\n \".P aram\",\n \".Par am\",\n \"ĉ throw\",\n \"ĉth row\",\n \"Ġ Ham\",\n \"ĠH am\",\n \"ĠHa m\",\n \"Ġevent ually\",\n \"Ġeventual ly\",\n \"a cer\",\n \"ace r\",\n \"ac er\",\n \"p ub\",\n \"pu b\",\n \"Ġ tra\",\n \"Ġt ra\",\n \"Ġtr a\",\n \"un ique\",\n \"uni que\",\n \"uniq ue\",\n \"d el\",\n \"de l\",\n \"Ġ Florida\",\n \"ĠFl orida\",\n \"ĠFlor ida\",\n \"Ġ Clean\",\n \"ĠC lean\",\n \"ĠCl ean\",\n \"ĠCle an\",\n \"x a\",\n \"Ġ ·\",\n \"Ġ ·\",\n \"Ġ validate\",\n \"Ġvalid ate\",\n \"Ġvalida te\",\n \"Vis ual\",\n \"Ex pression\",\n \"Exp ression\",\n \"Express ion\",\n \"Expr ession\",\n \"_ func\",\n \"_f unc\",\n \"_fun c\",\n \"_fu nc\",\n \"m ember\",\n \"mem ber\",\n \"ĉ h\",\n \"t rl\",\n \"tr l\",\n \"1 36\",\n \"13 6\",\n \"ĉ G\",\n \"nap shot\",\n \"Ġ PropTypes\",\n \"ĠProp Types\",\n \"v in\",\n \"vi n\",\n \"1 53\",\n \"15 3\",\n \"] )ĊĊ\",\n \"]) ĊĊ\",\n \"])Ċ Ċ\",\n \"o wl\",\n \"ow l\",\n \"if ies\",\n \"ifi es\",\n \"ifie s\",\n \"Ġ $('.\",\n \"Ġ$ ('.\",\n \"Ġ$( '.\",\n \"Ġ$(' .\",\n \"Ġ Context\",\n \"ĠCon text\",\n \"ĠCont ext\",\n \"ĠConte xt\",\n \"Ġ Toast\",\n \"ĠTo ast\",\n \". Key\",\n \".K ey\",\n \"Ġoff icers\",\n \"Ġoffic ers\",\n \"Ġoffice rs\",\n \"Ġofficer s\",\n \"/ n\",\n \"s n\",\n \"un defined\",\n \"und efined\",\n \"undef ined\",\n \". items\",\n \".i tems\",\n \".item s\",\n \".it ems\",\n \"ut ow\",\n \"uto w\",\n \"a mage\",\n \"am age\",\n \"ama ge\",\n \"Ġ accounts\",\n \"Ġac counts\",\n \"Ġaccount s\",\n \"o okie\",\n \"ook ie\",\n \"oo kie\",\n \"S ection\",\n \"Se ction\",\n \"Sec tion\",\n \"ic ians\",\n \"ici ans\",\n \"ician s\",\n \"icia ns\",\n \"Ġad vis\",\n \"Ġadv is\",\n \"( is\",\n \"(i s\",\n \"[ :,\",\n \"[: ,\",\n \"Ġ France\",\n \"ĠF rance\",\n \"ĠFr ance\",\n \"ĠFranc e\",\n \"ĠFra nce\",\n \"ĠFran ce\",\n \"F unc\",\n \"Fun c\",\n \"Fu nc\",\n \"ic ious\",\n \"ici ous\",\n \"icio us\",\n \"Ġ tok\",\n \"Ġt ok\",\n \"Ġto k\",\n \"Ch annel\",\n \"Chan nel\",\n \"Ġ AD\",\n \"ĠA D\",\n \"_ NUM\",\n \"_N UM\",\n \"Ġ timeout\",\n \"Ġtime out\",\n \"l emma\",\n \"le mma\",\n \"lem ma\",\n \"r eme\",\n \"re me\",\n \"rem e\",\n \"u j\",\n \". Al\",\n \".A l\",\n \"u clear\",\n \"uc lear\",\n \"ucle ar\",\n \"( os\",\n \"(o s\",\n \"( \\\"<\",\n \"(\\\" <\",\n \"[ Ċ\",\n \"f etch\",\n \"fet ch\",\n \"Ġ bal\",\n \"Ġb al\",\n \"Ġba l\",\n \"Ġ guid\",\n \"Ġg uid\",\n \"Ġgu id\",\n \"Ġgui d\",\n \"- align\",\n \"-al ign\",\n \"Ġ Write\",\n \"ĠW rite\",\n \"ĠWr ite\",\n \"Ġ Once\",\n \"ĠO nce\",\n \"ĠOn ce\",\n \"ĠOnc e\",\n \"utow ired\",\n \"OD ULE\",\n \"Ġ pitch\",\n \"Ġp itch\",\n \"Ġpit ch\",\n \"C F\",\n \"by tes\",\n \"byte s\",\n \"byt es\",\n \"Ġ Commission\",\n \"ĠCom mission\",\n \"ĠComm ission\",\n \"Ġin cred\",\n \"Ġincre d\",\n \"Ġinc red\",\n \"Ġincr ed\",\n \"P ER\",\n \"PE R\",\n \"_ response\",\n \"_res ponse\",\n \"_resp onse\",\n \"Ġ Los\",\n \"ĠL os\",\n \"ĠLo s\",\n \"p arser\",\n \"par ser\",\n \"parse r\",\n \"pars er\",\n \"Ġ assume\",\n \"Ġas sume\",\n \"Ġass ume\",\n \"Ġassum e\",\n \". Request\",\n \".Re quest\",\n \"Ġ Token\",\n \"ĠT oken\",\n \"ĠTo ken\",\n \"ĠTok en\",\n \"_ position\",\n \"_p osition\",\n \"_pos ition\",\n \"Ġ nom\",\n \"Ġn om\",\n \"Ġno m\",\n \"- term\",\n \"-t erm\",\n \"-te rm\",\n \"Ġ remaining\",\n \"Ġrem aining\",\n \"Ġremain ing\",\n \"i ostream\",\n \"io stream\",\n \"Ġ pieces\",\n \"Ġp ieces\",\n \"Ġpie ces\",\n \"Ġpiece s\",\n \"a py\",\n \"ap y\",\n \"Ġ Less\",\n \"ĠL ess\",\n \"ĠLe ss\",\n \"ĠLes s\",\n \"r ange\",\n \"ra nge\",\n \"ran ge\",\n \"rang e\",\n \"um bn\",\n \"umb n\",\n \"p rise\",\n \"pr ise\",\n \"pri se\",\n \"_ option\",\n \"_op tion\",\n \"_o ption\",\n \"_opt ion\",\n \"2 30\",\n \"23 0\",\n \"I mpl\",\n \"Im pl\",\n \"Imp l\",\n \"k wargs\",\n \"kw args\",\n \"Ġbusiness es\",\n \"Al ert\",\n \"Ale rt\",\n \"Ġpart ies\",\n \"Ġpar ties\",\n \"Ġpartie s\",\n \"Ġparti es\",\n \"Ġ Container\",\n \"ĠCont ainer\",\n \"Ġ Private\",\n \"ĠPr ivate\",\n \"ĠPriv ate\",\n \"Ġ Plan\",\n \"ĠP lan\",\n \"ĠPl an\",\n \"ĠPla n\",\n \"Ġ registered\",\n \"Ġregister ed\",\n \"Ġregist ered\",\n \"Ġ jour\",\n \"Ġj our\",\n \"Ġjo ur\",\n \"Ġjou r\",\n \"a cker\",\n \"ack er\",\n \"ac ker\",\n \"е ни\",\n \"ен и\",\n \"/ >\",\n \"c hat\",\n \"ch at\",\n \"cha t\",\n \"s ect\",\n \"se ct\",\n \"sec t\",\n \"Ġ creation\",\n \"Ġc reation\",\n \"Ġcre ation\",\n \"Ġcreat ion\",\n \"Ġcrea tion\",\n \"ol utely\",\n \"olute ly\",\n \"olut ely\",\n \"Ġ instant\",\n \"Ġin stant\",\n \"Ġins tant\",\n \"Ġinst ant\",\n \"Ġ delivery\",\n \"Ġd elivery\",\n \"Ġdel ivery\",\n \"Ġdeliver y\",\n \"i cken\",\n \"ic ken\",\n \"ick en\",\n \"y es\",\n \"ye s\",\n \"1 63\",\n \"16 3\",\n \"Ġ Franc\",\n \"ĠFr anc\",\n \"ĠFra nc\",\n \"ĠFran c\",\n \"b ling\",\n \"bl ing\",\n \"e nda\",\n \"en da\",\n \"end a\",\n \"[ (\",\n \"_ range\",\n \"_r ange\",\n \"_ra nge\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"Ġ schedule\",\n \"Ġs chedule\",\n \"Ġsched ule\",\n \"C onn\",\n \"Con n\",\n \"Co nn\",\n \"Ġ thank\",\n \"Ġth ank\",\n \"Ġthan k\",\n \"Ġtha nk\",\n \"x d\",\n \"Ġ hook\",\n \"Ġh ook\",\n \"Ġho ok\",\n \"Ġ documentation\",\n \"Ġdocument ation\",\n \"Param eters\",\n \"Parameter s\",\n \"H ello\",\n \"Hel lo\",\n \"Hell o\",\n \"v t\",\n \"Ġ articles\",\n \"Ġart icles\",\n \"Ġarticle s\",\n \"Ġartic les\",\n \"Ġ west\",\n \"Ġw est\",\n \"Ġwe st\",\n \"Ġwes t\",\n \"d efined\",\n \"def ined\",\n \"define d\",\n \". select\",\n \".s elect\",\n \".se lect\",\n \".sel ect\",\n \"ok ens\",\n \"oken s\",\n \"oke ns\",\n \"Ġ VAL\",\n \"ĠV AL\",\n \"ĠVA L\",\n \". file\",\n \".f ile\",\n \".fi le\",\n \".fil e\",\n \"re set\",\n \"res et\",\n \"rese t\",\n \"Ġ mys\",\n \"Ġm ys\",\n \"Ġmy s\",\n \"Ġ MA\",\n \"ĠM A\",\n \"] ),\",\n \"]) ,\",\n \"Ġ cities\",\n \"Ġc ities\",\n \"Ġcit ies\",\n \"Ġci ties\",\n \"re lated\",\n \"rel ated\",\n \"å Ľ\",\n \"Ġ appeared\",\n \"Ġappe ared\",\n \"Ġappear ed\",\n \"Ġ wid\",\n \"Ġw id\",\n \"Ġwi d\",\n \". panel\",\n \".p anel\",\n \".pa nel\",\n \".pan el\",\n \"Ġ Ins\",\n \"ĠI ns\",\n \"ĠIn s\",\n \". entity\",\n \".e ntity\",\n \".ent ity\",\n \"Ġde cre\",\n \"Ġdec re\",\n \"Ġ Lou\",\n \"ĠL ou\",\n \"ĠLo u\",\n \"( time\",\n \"(t ime\",\n \"(ti me\",\n \"Ġ Thank\",\n \"ĠTh ank\",\n \"ĠThan k\",\n \". createElement\",\n \".create Element\",\n \"Ġ mentioned\",\n \"Ġmention ed\",\n \"Ġment ioned\",\n \"o unce\",\n \"ou nce\",\n \"oun ce\",\n \"ounc e\",\n \"Ġ Try\",\n \"ĠT ry\",\n \"ĠTr y\",\n \"Ġ Wall\",\n \"ĠW all\",\n \"ĠWal l\",\n \"ĠWa ll\",\n \"/ images\",\n \"/image s\",\n \"/im ages\",\n \"Ġ Menu\",\n \"ĠM enu\",\n \"ĠMe nu\",\n \"ĠMen u\",\n \"' čĊ\",\n \"Ġ Er\",\n \"ĠE r\",\n \"Ġc ritic\",\n \"Ġcr itic\",\n \"Ġcrit ic\",\n \"Ġcri tic\",\n \"Ġ Year\",\n \"ĠY ear\",\n \"ĠYe ar\",\n \"( param\",\n \"(p aram\",\n \"(par am\",\n \"(pa ram\",\n \"(para m\",\n \"Ġ flo\",\n \"Ġf lo\",\n \"Ġfl o\",\n \"N N\",\n \"o oter\",\n \"oot er\",\n \"oo ter\",\n \"Ġ ];Ċ\",\n \"Ġ] ;Ċ\",\n \"Ġ]; Ċ\",\n \"Ġ Aff\",\n \"ĠA ff\",\n \"ĠAf f\",\n \"\\\" github\",\n \"\\\"g ithub\",\n \"ro oms\",\n \"room s\",\n \"Ġ hyp\",\n \"Ġh yp\",\n \"Ġhy p\",\n \"g lobal\",\n \"glob al\",\n \"Ġ avec\",\n \"Ġa vec\",\n \"Ġav ec\",\n \"Ġave c\",\n \"æľ Ī\",\n \"Ġ completion\",\n \"Ġcom pletion\",\n \"Ġcomp letion\",\n \"Ġcomple tion\",\n \"Ġcomplet ion\",\n \"Ġ cond\",\n \"Ġc ond\",\n \"Ġcon d\",\n \"Ġco nd\",\n \"on ymous\",\n \"onym ous\",\n \"( temp\",\n \"(t emp\",\n \"(te mp\",\n \"Ġ stars\",\n \"Ġst ars\",\n \"Ġstar s\",\n \"Ġsta rs\",\n \"Ġ relevant\",\n \"Ġre levant\",\n \"Ġrele vant\",\n \"Ġrelev ant\",\n \"Ġ covered\",\n \"Ġcover ed\",\n \"Ġcov ered\",\n \"Ġ elim\",\n \"Ġe lim\",\n \"Ġel im\",\n \"_ types\",\n \"_t ypes\",\n \"_type s\",\n \"_typ es\",\n \"_ty pes\",\n \"( bool\",\n \"(b ool\",\n \"Ġ tu\",\n \"Ġt u\",\n \"_ exists\",\n \"_ex ists\",\n \"_exist s\",\n \"Ġ secure\",\n \"Ġs ecure\",\n \"Ġsec ure\",\n \"Ġ stored\",\n \"Ġst ored\",\n \"Ġstore d\",\n \"Ġstor ed\",\n \"Ġsto red\",\n \"] /\",\n \"x F\",\n \"Ġ Controller\",\n \"ĠCont roller\",\n \"ĠControl ler\",\n \"ĠContr oller\",\n \"Ġm igr\",\n \"Ġmi gr\",\n \"Ġmig r\",\n \"M I\",\n \"Ġ Den\",\n \"ĠD en\",\n \"ĠDe n\",\n \"Ġ annual\",\n \"Ġann ual\",\n \"U IL\",\n \"UI L\",\n \"- and\",\n \"-a nd\",\n \"-an d\",\n \"Ġ crime\",\n \"Ġcr ime\",\n \"Ġcri me\",\n \"Ġcrim e\",\n \"b el\",\n \"be l\",\n \"Ġk itchen\",\n \"Ġkit chen\",\n \"@ g\",\n \"_ ph\",\n \"_p h\",\n \"ourn ament\",\n \"Ġ Social\",\n \"ĠS ocial\",\n \"ĠSo cial\",\n \"ĠSoc ial\",\n \"ĠSoci al\",\n \"Ġ Special\",\n \"ĠS pecial\",\n \"ĠSp ecial\",\n \"ĠSpec ial\",\n \"ĠSpe cial\",\n \"lo gger\",\n \"log ger\",\n \"logg er\",\n \"Ġ tail\",\n \"Ġt ail\",\n \"Ġta il\",\n \"Ġtai l\",\n \"Ġ unknown\",\n \"Ġun known\",\n \"Ġunk nown\",\n \"Ġunknow n\",\n \"d ed\",\n \"de d\",\n \"Ġapp rec\",\n \"Ġap prec\",\n \"( db\",\n \"(d b\",\n \"c f\",\n \"1 55\",\n \"15 5\",\n \"Ġ assign\",\n \"Ġas sign\",\n \"Ġass ign\",\n \"- out\",\n \"-o ut\",\n \"Ġ Mont\",\n \"ĠM ont\",\n \"ĠMon t\",\n \"ĠMo nt\",\n \"d p\",\n \"w idget\",\n \"wid get\",\n \"Ġ stone\",\n \"Ġs tone\",\n \"Ġst one\",\n \"Ġsto ne\",\n \"- primary\",\n \"-pr imary\",\n \". grid\",\n \".g rid\",\n \".gr id\",\n \"Res ults\",\n \"Result s\",\n \"a zz\",\n \"az z\",\n \"Ġ daughter\",\n \"Ġda ughter\",\n \"Ġ curr\",\n \"Ġc urr\",\n \"Ġcur r\",\n \"Ġcu rr\",\n \"1 75\",\n \"17 5\",\n \"Ġ lin\",\n \"Ġl in\",\n \"Ġli n\",\n \"Ġ south\",\n \"Ġs outh\",\n \"Ġso uth\",\n \"Ġsou th\",\n \"Ġsout h\",\n \"form s\",\n \"fo rms\",\n \"for ms\",\n \"Ġ OUT\",\n \"ĠO UT\",\n \"ĠOU T\",\n \"l ette\",\n \"le tte\",\n \"let te\",\n \"lett e\",\n \"a ks\",\n \"ak s\",\n \"ig ure\",\n \"igu re\",\n \"Ġ EU\",\n \"ĠE U\",\n \"var iable\",\n \"vari able\",\n \"Ġ brief\",\n \"Ġb rief\",\n \"Ġbr ief\",\n \"Ġbri ef\",\n \"Ġ Scott\",\n \"ĠS cott\",\n \"ĠSc ott\",\n \"ĠScot t\",\n \"ĠSco tt\",\n \"Ġ conference\",\n \"Ġcon ference\",\n \"Ġconf erence\",\n \"Ġconfer ence\",\n \"a nda\",\n \"an da\",\n \"and a\",\n \"_ lock\",\n \"_l ock\",\n \"_lo ck\",\n \"_loc k\",\n \"o ral\",\n \"or al\",\n \"ora l\",\n \"Ġe ine\",\n \"Ġein e\",\n \"Ġei ne\",\n \"O RS\",\n \"OR S\",\n \"//// ////////////////////////////////////////////////////////////\",\n \"//////// ////////////////////////////////////////////////////////\",\n \"//////////////// ////////////////////////////////////////////////\",\n \"//////////////////////////////// ////////////////////////////////\",\n \"//////////// ////////////////////////////////////////////////////\",\n \"//////////////////////////////////////////////// ////////////////\",\n \"//////////////////////////////////////////////////////// ////////\",\n \"//////////////////////////////////////////////////////////// ////\",\n \"//////////////////////////////////////////////////// ////////////\",\n \"es so\",\n \"ess o\",\n \"Ġ ris\",\n \"Ġr is\",\n \"Ġri s\",\n \"Ġ gender\",\n \"Ġg ender\",\n \"Ġge nder\",\n \"Ġgen der\",\n \"es tic\",\n \"est ic\",\n \"esti c\",\n \"L icense\",\n \"Lic ense\",\n \"( out\",\n \"(o ut\",\n \"Ġ ms\",\n \"Ġm s\",\n \"S ee\",\n \"Se e\",\n \"Ġw illing\",\n \"Ġwill ing\",\n \"Ġwil ling\",\n \"a ze\",\n \"az e\",\n \"Ġ sports\",\n \"Ġs ports\",\n \"Ġsp orts\",\n \"Ġsport s\",\n \"Ġspo rts\",\n \"Ġspor ts\",\n \"Ġ yes\",\n \"Ġy es\",\n \"Ġye s\",\n \"l u\",\n \"Ġp urs\",\n \"Ġpur s\",\n \"Ġpu rs\",\n \"/ javascript\",\n \"/j avascript\",\n \"/java script\",\n \"/jav ascript\",\n \"- pro\",\n \"-p ro\",\n \"-pr o\",\n \"nav bar\",\n \"_ product\",\n \"_pro duct\",\n \"_prod uct\",\n \"/ bootstrap\",\n \"/boot strap\",\n \"Ġdr iving\",\n \"Ġdriv ing\",\n \"Ġdri ving\",\n \"Ġ Ä\",\n \"Ġpro pos\",\n \"Ġprop os\",\n \"ul tip\",\n \"ult ip\",\n \"ulti p\",\n \"up lic\",\n \". email\",\n \".e mail\",\n \".em ail\",\n \"Ġ approx\",\n \"Ġapp rox\",\n \"Ġap prox\",\n \"Ġappro x\",\n \"( cl\",\n \"(c l\",\n \"Ġ wear\",\n \"Ġw ear\",\n \"Ġwe ar\",\n \"Ġ reply\",\n \"Ġre ply\",\n \"Ġrep ly\",\n \"Ġrepl y\",\n \"as set\",\n \"ass et\",\n \"asse t\",\n \"Ġ ice\",\n \"Ġi ce\",\n \"Ġic e\",\n \"Ġ tx\",\n \"Ġt x\",\n \"k r\",\n \"Ġ Germany\",\n \"ĠGerman y\",\n \"ĠGer many\",\n \"ĠGerm any\",\n \"Ġ George\",\n \"ĠGe orge\",\n \"ĠGeorg e\",\n \"Ġ cb\",\n \"Ġc b\",\n \"ĉ err\",\n \"ĉe rr\",\n \"M ove\",\n \"Mo ve\",\n \"Mov e\",\n \"Ġ poly\",\n \"Ġp oly\",\n \"Ġpol y\",\n \"Ġpo ly\",\n \"v oice\",\n \"vo ice\",\n \"} \\\"\",\n \"Ġ animal\",\n \"Ġan imal\",\n \"Ġanim al\",\n \"Ġani mal\",\n \"A v\",\n \"Ġ Location\",\n \"ĠL ocation\",\n \"ĠLo cation\",\n \"ĠLoc ation\",\n \"Ġ native\",\n \"Ġn ative\",\n \"Ġnat ive\",\n \"] [\\\"\",\n \"][ \\\"\",\n \"< double\",\n \" \\\"\",\n \"s tat\",\n \"st at\",\n \"sta t\",\n \"Ġ },čĊ\",\n \"Ġ} ,čĊ\",\n \"Ġ}, čĊ\",\n \"< span\",\n \" =\",\n \"Ð ±\",\n \"1 39\",\n \"13 9\",\n \"i va\",\n \"iv a\",\n \". AutoSize\",\n \".Auto Size\",\n \"Ġ Lat\",\n \"ĠL at\",\n \"ĠLa t\",\n \"_ ext\",\n \"_e xt\",\n \"_ex t\",\n \"Initial ize\",\n \". register\",\n \".reg ister\",\n \"1 56\",\n \"15 6\",\n \"O PY\",\n \"OP Y\",\n \"Ġ reverse\",\n \"Ġre verse\",\n \"Ġrev erse\",\n \"Ġrevers e\",\n \"Ġrever se\",\n \"_ dis\",\n \"_d is\",\n \"_di s\",\n \"' ][\",\n \"'] [\",\n \"Ġ prompt\",\n \"Ġp rompt\",\n \"Ġprom pt\",\n \"on to\",\n \"ont o\",\n \"Ġ Journal\",\n \"ĠJ ournal\",\n \"ĠJo urnal\",\n \"r outer\",\n \"ro uter\",\n \"rou ter\",\n \"route r\",\n \"Ġ mysqli\",\n \"Ġm ysqli\",\n \"Ġmys qli\",\n \"Ġmysql i\",\n \"# else\",\n \") \\\"\",\n \"- xs\",\n \"-x s\",\n \"l ets\",\n \"le ts\",\n \"let s\",\n \"p han\",\n \"ph an\",\n \"pha n\",\n \". 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(\",\n \"D P\",\n \"Ġ MessageBox\",\n \"ĠMessage Box\",\n \"Ġphot ograph\",\n \"Ġphoto graph\",\n \"1 64\",\n \"16 4\",\n \"U RI\",\n \"UR I\",\n \"é t\",\n \"l ow\",\n \"lo w\",\n \"Ġ Grand\",\n \"ĠG rand\",\n \"ĠGr and\",\n \"ĠGran d\",\n \"ĠGra nd\",\n \".p ersistence\",\n \".persist ence\",\n \"Ġmain tain\",\n \"Ġmaint ain\",\n \"Ġ nums\",\n \"Ġn ums\",\n \"Ġnum s\",\n \"Ġnu ms\",\n \"Ġ zip\",\n \"Ġz ip\",\n \"Ġzi p\",\n \"i als\",\n \"ial s\",\n \"ia ls\",\n \"Ġ Gets\",\n \"ĠG ets\",\n \"ĠGet s\",\n \"ĠGe ts\",\n \"p eg\",\n \"pe g\",\n \"Ġ Buffer\",\n \"ĠB uffer\",\n \"ĠBuff er\",\n \"ĠBu ffer\",\n \"ĠBuf fer\",\n \"~~ ~~\",\n \"ra structure\",\n \"Ġ PL\",\n \"ĠP L\",\n \"u en\",\n \"ue n\",\n \"ob by\",\n \"obb y\",\n \"size of\",\n \"siz eof\",\n \"Ġ pic\",\n \"Ġp ic\",\n \"Ġpi c\",\n \"Ġ seed\",\n \"Ġs eed\",\n \"Ġse ed\",\n \"Ġsee d\",\n \"Ġex perienced\",\n \"Ġexperience d\",\n \"Ġexperi enced\",\n \"Ġ odd\",\n \"Ġo dd\",\n \"Ġod d\",\n \"Ġ kick\",\n \"Ġk ick\",\n \"Ġki ck\",\n \"Ġ procedure\",\n \"Ġpro cedure\",\n \"Ġproced ure\",\n \"av igator\",\n \"avig ator\",\n \"- on\",\n \"-o n\",\n \", j\",\n \"Ġ Although\",\n \"ĠAl though\",\n \"Ġ userId\",\n \"Ġuser Id\",\n \"ac cept\",\n \"acc ept\",\n \"B lue\",\n \"Bl ue\",\n \"I Color\",\n \"IC olor\",\n \"l ayer\",\n \"la yer\",\n \"lay er\",\n \"a vailable\",\n \"av ailable\",\n \"avail able\",\n \"Ġ ends\",\n \"Ġe nds\",\n \"Ġen ds\",\n \"Ġend s\",\n \". table\",\n \".t able\",\n \".tab le\",\n \".ta ble\",\n \"Ġ dataset\",\n \"Ġd ataset\",\n \"Ġdata set\",\n \"Ġdat aset\",\n \"Ġdatas et\",\n \"b us\",\n \"bu s\",\n \"Ġ explain\",\n \"Ġex plain\",\n \"Ġexp lain\",\n \"Ġexpl ain\",\n \"( pro\",\n \"(p ro\",\n \"(pr o\",\n \"ĠCommit tee\",\n \"Ġn oted\",\n \"Ġnot ed\",\n \"Ġno ted\",\n \"Ġnote d\",\n \"] :Ċ\",\n \"]: Ċ\",\n \"D im\",\n \"Di m\",\n \"st dio\",\n \"std io\",\n \"1 54\",\n \"15 4\",\n \". \\\",Ċ\",\n \".\\\" ,Ċ\",\n \".\\\", Ċ\",\n \"_ source\",\n \"_s ource\",\n \"1 81\",\n \"18 1\",\n \"Ġ Week\",\n \"ĠWe ek\",\n \"Ġ Edge\",\n \"ĠE dge\",\n \"ĠEd ge\",\n \"Ġoper ating\",\n \"Ġopera ting\",\n \"Ġ este\",\n \"Ġe ste\",\n \"Ġes te\",\n \"Ġest e\",\n \"i pl\",\n \"ip l\",\n \"3 30\",\n \"33 0\",\n \"ag ination\",\n \"agi nation\",\n \"agina tion\",\n \"Ġpro ceed\",\n \"Ġproc eed\",\n \"Ġ animation\",\n \"Ġan imation\",\n \"Ġanim ation\",\n \". Models\",\n \".Model s\",\n \".Mod els\",\n \".Mode ls\",\n \"Ġ Watch\",\n \"ĠW atch\",\n \"ĠWat ch\",\n \"i at\",\n \"ia t\",\n \"Ġop pon\",\n \"Ġopp on\",\n \"/ A\",\n \"Re port\",\n \"Rep ort\",\n \"Repo rt\",\n \"Ġ sounds\",\n \"Ġs ounds\",\n \"Ġso unds\",\n \"Ġsound s\",\n \"Ġsou nds\",\n \"_ buf\",\n \"_b uf\",\n \"IE LD\",\n \"IEL D\",\n \"Ġb und\",\n \"Ġbu nd\",\n \"Ġbun d\",\n \"ĉ get\",\n \"ĉg et\",\n \". pr\",\n \".p r\",\n \"( tmp\",\n \"(t mp\",\n \"(tm p\",\n \"Ġ kid\",\n \"Ġk id\",\n \"Ġki d\",\n \"> ĊĊĊ\",\n \">Ċ ĊĊ\",\n \">ĊĊ Ċ\",\n \"Ġ yang\",\n \"Ġy ang\",\n \"Ġya ng\",\n \"Ġyan g\",\n \"Not Found\",\n \"Ñ Ĩ\",\n \"m ath\",\n \"ma th\",\n \"mat h\",\n \"@ gmail\",\n \"@g mail\",\n \"Ġ LIMIT\",\n \"ĠL IMIT\",\n \"ĠLI MIT\",\n \"red ients\",\n \"redient s\",\n \"redi ents\",\n \"Ġ vent\",\n \"Ġv ent\",\n \"Ġve nt\",\n \"Ġven t\",\n \"av igate\",\n \"avig ate\",\n \"avi gate\",\n \"L ook\",\n \"Lo ok\",\n \"Ġ religious\",\n \"Ġrel igious\",\n \"Ġrelig ious\",\n \"Ġ rand\",\n \"Ġr and\",\n \"Ġran d\",\n \"Ġra nd\",\n \"r io\",\n \"ri o\",\n \"( GL\",\n \"(G L\",\n \"_ ip\",\n \"_i p\",\n \"u an\",\n \"ua n\",\n \"ici ency\",\n \"icie ncy\",\n \"Ġ Change\",\n \"ĠCh ange\",\n \"ĠCha nge\",\n \"ĠChan ge\",\n \"ĠChang e\",\n \"> čĊčĊ\",\n \">čĊ čĊ\",\n \"Ġ Entity\",\n \"ĠE ntity\",\n \"ĠEnt ity\",\n \"Ġren contre\",\n \"Ġrencont re\",\n \"Ġrencontr e\",\n \"Ġ Ret\",\n \"ĠR et\",\n \"ĠRe t\",\n \"p lan\",\n \"pl an\",\n \"pla n\",\n \"é n\",\n \"BO OL\",\n \"u ries\",\n \"ur ies\",\n \"uri es\",\n \"t rain\",\n \"tr ain\",\n \"tra in\",\n \"Def inition\",\n \"= ===========\",\n \"== ==========\",\n \"==== ========\",\n \"======== ====\",\n \"=== =========\",\n \"=========== =\",\n \"========= ===\",\n \"========== ==\",\n \"====== ======\",\n \"===== =======\",\n \"======= =====\",\n \"z z\",\n \"4 50\",\n \"45 0\",\n \"An imation\",\n \"Anim ation\",\n \"Ġ OK\",\n \"ĠO K\",\n \"_ menu\",\n \"_m enu\",\n \"_me nu\",\n \". bl\",\n \".b l\",\n \"_ score\",\n \"_s core\",\n \"_sc ore\",\n \"Ġ acad\",\n \"Ġa cad\",\n \"Ġac ad\",\n \"( System\",\n \"(S ystem\",\n \"Ġ refresh\",\n \"Ġre fresh\",\n \"Ġref resh\",\n \"Ġrefr esh\",\n \"' =>$\",\n \"'=> $\",\n \"'= >$\",\n \". Graphics\",\n \".G raphics\",\n \".Graph ics\",\n \"am ento\",\n \"ament o\",\n \"amen to\",\n \"p id\",\n \"pi d\",\n \"t c\",\n \"Ġ tips\",\n \"Ġt ips\",\n \"Ġti ps\",\n \"Ġtip s\",\n \"Ġ homes\",\n \"Ġh omes\",\n \"Ġhome s\",\n \"Ġhom es\",\n \"Ġho mes\",\n \"Ġ fuel\",\n \"Ġf uel\",\n \"Ġfu el\",\n \"Ġfue l\",\n \"â ĸ\",\n \"_ helper\",\n \"_h elper\",\n \"_help er\",\n \"Ġ ĠčĊ\",\n \"ĠĠ čĊ\",\n \"Ġ Room\",\n \"ĠR oom\",\n \"ĠRo om\",\n \"ĠRoo m\",\n \". Close\",\n \".C lose\",\n \".Cl ose\",\n \"_ attr\",\n \"_at tr\",\n \"_att r\",\n \"Ġ Mount\",\n \"ĠM ount\",\n \"ĠMo unt\",\n \"ĠMou nt\",\n \"Ġ Ev\",\n \"ĠE v\",\n \"ar ser\",\n \"ars er\",\n \"arse r\",\n \"_ top\",\n \"_t op\",\n \"_to p\",\n \"e ah\",\n \"ea h\",\n \"Ġ Delete\",\n \"ĠDe lete\",\n \"ĠDel ete\",\n \"ãĢ į\",\n \"u ke\",\n \"uk e\",\n \"Ġ usage\",\n \"Ġu sage\",\n \"Ġus age\",\n \"Ġusa ge\",\n \"a ria\",\n \"ar ia\",\n \"ari a\",\n \"_ dev\",\n \"_d ev\",\n \"_de v\",\n \"Ġ texture\",\n \"Ġtext ure\",\n \"Ġtex ture\",\n \"Ġtextu re\",\n \"Ġ conversation\",\n \"Ġcon versation\",\n \"Ġconvers ation\",\n \"e per\",\n \"ep er\",\n \"B ean\",\n \"Be an\",\n \"d one\",\n \"do ne\",\n \"don e\",\n \"non atomic\",\n \"Ġ Second\",\n \"ĠSe cond\",\n \"ĠSec ond\",\n \"Ġsh ooting\",\n \"Ġshoot ing\",\n \"Ġsho oting\",\n \"_ pre\",\n \"_p re\",\n \"_pr e\",\n \"Com ponents\",\n \"Component s\",\n \"Comp onents\",\n \"Ġ ]ĊĊ\",\n \"Ġ] ĊĊ\",\n \"Ġ]Ċ Ċ\",\n \"_ _,\",\n \"__ ,\",\n \"st itution\",\n \"stit ution\",\n \". Char\",\n \".C har\",\n \".Ch ar\",\n \"> ();ĊĊ\",\n \">( );ĊĊ\",\n \">();Ċ Ċ\",\n \">() ;ĊĊ\",\n \">(); ĊĊ\",\n \"Ġpres ented\",\n \"Ġpresent ed\",\n \"Ġpresente d\",\n \"Ġ wa\",\n \"Ġw a\",\n \"o ker\",\n \"ok er\",\n \"oke r\",\n \"- ĊĊ\",\n \"-Ċ Ċ\",\n \"i ner\",\n \"in er\",\n \"ine r\",\n \"Ġbe coming\",\n \"Ġbec oming\",\n \"Ġ incident\",\n \"Ġinc ident\",\n \"Ġincid ent\",\n \"A tt\",\n \"At t\",\n \"1 62\",\n \"16 2\",\n \"Ġreve aled\",\n \"Ġreveal ed\",\n \"f orc\",\n \"fo rc\",\n \"for c\",\n \"Ġ boot\",\n \"Ġb oot\",\n \"Ġbo ot\",\n \"Ġboo t\",\n \". page\",\n \".p age\",\n \".pa ge\",\n \".pag e\",\n \"En umerator\",\n \"Enumer ator\",\n \"Enum erator\",\n \"1 65\",\n \"16 5\",\n \"_ ->\",\n \"_- >\",\n \"Ph oto\",\n \"Phot o\",\n \"Ġ spring\",\n \"Ġs pring\",\n \"Ġsp ring\",\n \"Ġspr ing\",\n \". \\\",\",\n \".\\\" ,\",\n \"Ġ Dictionary\",\n \"ĠD ictionary\",\n \"B JECT\",\n \"BJ ECT\",\n \"Ġ locations\",\n \"Ġl ocations\",\n \"Ġloc ations\",\n \"Ġlocation s\",\n \"Ġ samples\",\n \"Ġs amples\",\n \"Ġsample s\",\n \"Ġsam ples\",\n \"Ġsamp les\",\n \"Input Stream\",\n \"Ġ Brown\",\n \"ĠB rown\",\n \"ĠBr own\",\n \"ĠBro wn\",\n \"ĠBrow n\",\n \"Ġ stats\",\n \"Ġst ats\",\n \"Ġstat s\",\n \"Ġsta ts\",\n \"q uality\",\n \"qu ality\",\n \"qual ity\",\n \"Ñ ħ\",\n \"- dis\",\n \"-d is\",\n \"-di s\",\n \"Ġhelp ing\",\n \"Ġhel ping\",\n \"Ġ ped\",\n \"Ġp ed\",\n \"Ġpe d\",\n \"2 24\",\n \"22 4\",\n \"( se\",\n \"(s e\",\n \"Ġ Who\",\n \"ĠW ho\",\n \"ĠWh o\",\n \"a lian\",\n \"al ian\",\n \"ali an\",\n \"alia n\",\n \"in ternal\",\n \"int ernal\",\n \"inter nal\",\n \"intern al\",\n \"Ġ ft\",\n \"Ġf t\",\n \"> ().\",\n \">( ).\",\n \">() .\",\n \"- >{\",\n \"-> {\",\n \"Ġ mine\",\n \"Ġm ine\",\n \"Ġmin e\",\n \"Ġmi ne\",\n \"Ġ sector\",\n \"Ġs ector\",\n \"Ġse ctor\",\n \"Ġsec tor\",\n \"Ġsect or\",\n \"Ġ gro\",\n \"Ġg ro\",\n \"Ġgr o\",\n \"Ġopport unities\",\n \"Ġopportun ities\",\n \"Ġ ü\",\n \"Ġà ¼\",\n \"Ġ mp\",\n \"Ġm p\",\n \"Ġalleg ed\",\n \"Ġalle ged\",\n \"Ġallege d\",\n \"Ġdoub t\",\n \"Ġdou bt\",\n \"M ouse\",\n \"Mo use\",\n \"A bout\",\n \"Ab out\",\n \"_ part\",\n \"_p art\",\n \"_par t\",\n \"_pa rt\",\n \"Ġ chair\",\n \"Ġc hair\",\n \"Ġch air\",\n \"Ġcha ir\",\n \"Ġchai r\",\n \"Ġ stopped\",\n \"Ġst opped\",\n \"Ġstop ped\",\n \"Ġsto pped\",\n \"1 61\",\n \"16 1\",\n \"l oop\",\n \"lo op\",\n \"loo p\",\n \"ent ities\",\n \"enti ties\",\n \"Ġ apps\",\n \"Ġa pps\",\n \"Ġapp s\",\n \"Ġap ps\",\n \"ans ion\",\n \"ansi on\",\n \"Ġ mental\",\n \"Ġm ental\",\n \"Ġmen tal\",\n \"Ġment al\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"F R\",\n \"Ġdef end\",\n \"Ġdefe nd\",\n \"c are\",\n \"ca re\",\n \"car e\",\n \"Ġ ideal\",\n \"Ġi deal\",\n \"Ġide al\",\n \"Ġidea l\",\n \"/ api\",\n \"/a pi\",\n \"/ap i\",\n \"ur face\",\n \"urf ace\",\n \"0 11\",\n \"01 1\",\n \"Ġ ele\",\n \"Ġe le\",\n \"Ġel e\",\n \"u lator\",\n \"ul ator\",\n \"ula tor\",\n \"Ġ Rights\",\n \"ĠR ights\",\n \"ĠRight s\",\n \"angu ages\",\n \"anguage s\",\n \"Ġf unds\",\n \"Ġfun ds\",\n \"Ġfund s\",\n \"Ġfu nds\",\n \"Ġ adapt\",\n \"Ġad apt\",\n \"Ġada pt\",\n \"Ġadap t\",\n \"At tributes\",\n \"Attribute s\",\n \"Attrib utes\",\n \"Ġ deploy\",\n \"Ġde ploy\",\n \"Ġdep loy\",\n \"o pts\",\n \"op ts\",\n \"opt s\",\n \"Ġ validation\",\n \"Ġvalid ation\",\n \"Ġvalida tion\",\n \"Ġconcern s\",\n \"u ce\",\n \"uc e\",\n \". num\",\n \".n um\",\n \"ul ture\",\n \"ult ure\",\n \"ultur e\",\n \"i la\",\n \"il a\",\n \"Ġ cup\",\n \"Ġc up\",\n \"Ġcu p\",\n \"Ġ pure\",\n \"Ġp ure\",\n \"Ġpur e\",\n \"Ġpu re\",\n \". Fore\",\n \".F ore\",\n \".For e\",\n \"1 83\",\n \"18 3\",\n \"Ġ HashMap\",\n \"ĠHash Map\",\n \". valueOf\",\n \".value Of\",\n \"a sm\",\n \"as m\",\n \"M O\",\n \"Ġ cs\",\n \"Ġc s\",\n \"Ġ stores\",\n \"Ġst ores\",\n \"Ġstore s\",\n \"Ġstor es\",\n \"Ġsto res\",\n \"Ġ ************************************************************************\",\n \"Ġ**************************************************************** ********\",\n \"Ġ******************************** ****************************************\",\n \"Ġ******** ****************************************************************\",\n \"Ġ**************** ********************************************************\",\n \"Ġ************************ ************************************************\",\n \"Ġ**************************************** ********************************\",\n \"Ġ******************************************************** ****************\",\n \"Ġ************************************************ ************************\",\n \"Ġ communication\",\n \"Ġcomm unication\",\n \"Ġcommunic ation\",\n \"Ġcommun ication\",\n \"m em\",\n \"me m\",\n \". EventHandler\",\n \".Event Handler\",\n \". Status\",\n \".S tatus\",\n \".St atus\",\n \".Stat us\",\n \"_ right\",\n \"_r ight\",\n \".set On\",\n \"S heet\",\n \"She et\",\n \"Ġ identify\",\n \"Ġident ify\",\n \"ener ated\",\n \"ene rated\",\n \"enerate d\",\n \"ord ered\",\n \"order ed\",\n \"orde red\",\n \"Ġ \\\"[\",\n \"Ġ\\\" [\",\n \"Ġs we\",\n \"Ġsw e\",\n \"Con dition\",\n \"Cond ition\",\n \"Ġ According\",\n \"ĠA ccording\",\n \"ĠAcc ording\",\n \"ĠAccord ing\",\n \"Ġ prepare\",\n \"Ġpre pare\",\n \"Ġprepar e\",\n \"Ġprep are\",\n \"Ġ rob\",\n \"Ġr ob\",\n \"Ġro b\",\n \"P ool\",\n \"Po ol\",\n \"Ġ sport\",\n \"Ġs port\",\n \"Ġsp ort\",\n \"Ġspo rt\",\n \"Ġspor t\",\n \"r v\",\n \"Ġ Router\",\n \"ĠR outer\",\n \"ĠRoute r\",\n \"ĠRo uter\",\n \"ĠRou ter\",\n \"ĠRout er\",\n \"Ġ alternative\",\n \"Ġaltern ative\",\n \"Ġalter native\",\n \"( []\",\n \"([ ]\",\n \"Ġ Chicago\",\n \"ĠCh icago\",\n \"ĠChic ago\",\n \"i pher\",\n \"ip her\",\n \"iph er\",\n \"is che\",\n \"isc he\",\n \"isch e\",\n \"Ġ Director\",\n \"ĠD irector\",\n \"ĠDirect or\",\n \"ĠDir ector\",\n \"ĠDire ctor\",\n \"k l\",\n \"Ġ Wil\",\n \"ĠW il\",\n \"ĠWi l\",\n \"ke ys\",\n \"key s\",\n \"Ġ mysql\",\n \"Ġm ysql\",\n \"Ġmy sql\",\n \"Ġmys ql\",\n \"Ġ welcome\",\n \"Ġw elcome\",\n \"Ġwel come\",\n \"k ing\",\n \"ki ng\",\n \"kin g\",\n \"Ġ Manager\",\n \"ĠM anager\",\n \"ĠMan ager\",\n \"ĠManage r\",\n \"ĠMana ger\",\n \"Ġ caught\",\n \"Ġca ught\",\n \") }Ċ\",\n \")} Ċ\",\n \"S core\",\n \"Sc ore\",\n \"_ PR\",\n \"_P R\",\n \"Ġ survey\",\n \"Ġs urvey\",\n \"Ġsur vey\",\n \"Ġsurv ey\",\n \"Ġsurve y\",\n \"h ab\",\n \"ha b\",\n \"He aders\",\n \"Header s\",\n \"Head ers\",\n \"A DER\",\n \"AD ER\",\n \"ADE R\",\n \"Ġ decor\",\n \"Ġde cor\",\n \"Ġdec or\",\n \"Ġdeco r\",\n \"Ġturn s\",\n \"Ġtur ns\",\n \"Ġ radius\",\n \"Ġr adius\",\n \"Ġrad ius\",\n \"Ġradi us\",\n \"er rupt\",\n \"err upt\",\n \"C or\",\n \"Co r\",\n \"Ġ mel\",\n \"Ġm el\",\n \"Ġme l\",\n \"Ġ intr\",\n \"Ġin tr\",\n \"Ġint r\",\n \"( q\",\n \"Ġ AC\",\n \"ĠA C\",\n \"a mos\",\n \"am os\",\n \"amo s\",\n \"M AX\",\n \"MA X\",\n \"Ġ Grid\",\n \"ĠG rid\",\n \"ĠGr id\",\n \"ĠGri d\",\n \"Ġ Jesus\",\n \"ĠJ esus\",\n \"ĠJes us\",\n \"ĠJe sus\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \". DE\",\n \".D E\",\n \"Ġ ts\",\n \"Ġt s\",\n \"Ġ linked\",\n \"Ġlink ed\",\n \"Ġlin ked\",\n \"f ree\",\n \"fr ee\",\n \"fre e\",\n \"Ġ Qt\",\n \"ĠQ t\",\n \"Ġ /**čĊ\",\n \"Ġ/ **čĊ\",\n \"Ġ/* *čĊ\",\n \"Ġ/** čĊ\",\n \"Ġf aster\",\n \"Ġfa ster\",\n \"Ġfast er\",\n \"Ġfas ter\",\n \"c tr\",\n \"ct r\",\n \"_ J\",\n \"D T\",\n \". Check\",\n \".C heck\",\n \".Ch eck\",\n \"Ġ combination\",\n \"Ġcomb ination\",\n \"Ġcombin ation\",\n \"Ġint ended\",\n \"Ġintend ed\",\n \"- the\",\n \"-t he\",\n \"-th e\",\n \"- type\",\n \"-t ype\",\n \"1 82\",\n \"18 2\",\n \"e ctors\",\n \"ect ors\",\n \"ec tors\",\n \"ector s\",\n \"a mi\",\n \"am i\",\n \"u ting\",\n \"ut ing\",\n \"uti ng\",\n \"utin g\",\n \"Ġ uma\",\n \"Ġu ma\",\n \"Ġum a\",\n \"X ML\",\n \"XM L\",\n \"U CT\",\n \"UC T\",\n \"A p\",\n \"Ġ Random\",\n \"ĠR andom\",\n \"ĠRand om\",\n \"ĠRan dom\",\n \"Ġ ran\",\n \"Ġr an\",\n \"Ġra n\",\n \". sort\",\n \".s ort\",\n \".so rt\",\n \"Ġ sorted\",\n \"Ġs orted\",\n \"Ġsort ed\",\n \"Ġsor ted\",\n \"Ġsorte d\",\n \". Un\",\n \".U n\",\n \"4 01\",\n \"40 1\",\n \"_ PER\",\n \"_P ER\",\n \"_PE R\",\n \"it ory\",\n \"itor y\",\n \"ito ry\",\n \"Ġ priority\",\n \"Ġp riority\",\n \"Ġprior ity\",\n \"Ġpriorit y\",\n \"Ġ Gal\",\n \"ĠG al\",\n \"ĠGa l\",\n \"Ġ Old\",\n \"ĠO ld\",\n \"ĠOl d\",\n \"h ot\",\n \"ho t\",\n \"Ġ Display\",\n \"ĠD isplay\",\n \"ĠDis play\",\n \"ĠDisp lay\",\n \"( sub\",\n \"(s ub\",\n \"_ TH\",\n \"_T H\",\n \"_ Y\",\n \"Ġ Care\",\n \"ĠC are\",\n \"ĠCar e\",\n \"ĠCa re\",\n \"lo ading\",\n \"load ing\",\n \"K ind\",\n \"Kin d\",\n \"Ki nd\",\n \"_ handle\",\n \"_h andle\",\n \"_hand le\",\n \", ,\",\n \"r ase\",\n \"ra se\",\n \"ras e\",\n \"_ replace\",\n \"_re place\",\n \"_rep lace\",\n \". addEventListener\",\n \".add EventListener\",\n \"Ġ RT\",\n \"ĠR T\",\n \"1 72\",\n \"17 2\",\n \"Ġ entered\",\n \"Ġen tered\",\n \"Ġent ered\",\n \"Ġenter ed\",\n \"g ers\",\n \"ge rs\",\n \"ger s\",\n \"Ġ ich\",\n \"Ġi ch\",\n \"Ġic h\",\n \"( start\",\n \"(st art\",\n \"2 05\",\n \"20 5\",\n \"/ app\",\n \"/a pp\",\n \"/ap p\",\n \"Ġbr other\",\n \"Ġbro ther\",\n \"Ġbroth er\",\n \"M emory\",\n \"Mem ory\",\n \"Memo ry\",\n \"Out let\",\n \"Ġ utf\",\n \"Ġu tf\",\n \"Ġut f\",\n \"p rec\",\n \"pr ec\",\n \"pre c\",\n \"Ġ navigation\",\n \"Ġn avigation\",\n \"Ġnav igation\",\n \"Ġnavig ation\",\n \"O RK\",\n \"OR K\",\n \"Ġ dst\",\n \"Ġd st\",\n \"Ġds t\",\n \"D etail\",\n \"De tail\",\n \"Det ail\",\n \"Ġaud ience\",\n \"Ġaudi ence\",\n \"Ġ dur\",\n \"Ġd ur\",\n \"Ġdu r\",\n \"Ġ cluster\",\n \"Ġcl uster\",\n \"un ched\",\n \"unc hed\",\n \"unch ed\",\n \"Ġ ],\",\n \"Ġ] ,\",\n \"Ġcomfort able\",\n \". values\",\n \".value s\",\n \".val ues\",\n \"Ġ Total\",\n \"ĠT otal\",\n \"ĠTo tal\",\n \"ĠTot al\",\n \"Ġ snap\",\n \"Ġs nap\",\n \"Ġsn ap\",\n \"Ġsna p\",\n \"Ġstand ards\",\n \"Ġstandard s\",\n \"Ġper formed\",\n \"Ġperform ed\",\n \"Ġperfor med\",\n \"h and\",\n \"ha nd\",\n \"han d\",\n \"( \\\"@\",\n \"(\\\" @\",\n \"å Ń\",\n \"Ġ phil\",\n \"Ġp hil\",\n \"Ġph il\",\n \"Ġphi l\",\n \"i br\",\n \"ib r\",\n \"t rim\",\n \"tr im\",\n \"tri m\",\n \"Ġ forget\",\n \"Ġf orget\",\n \"Ġfor get\",\n \"Ġforg et\",\n \"Ġforge t\",\n \"1 57\",\n \"15 7\",\n \"Ġ doctor\",\n \"Ġdo ctor\",\n \"Ġdoc tor\",\n \". 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\"ĠĠĠĠĠ Ċ\",\n \"Ġ River\",\n \"ĠR iver\",\n \"ĠRiv er\",\n \"ĠRi ver\",\n \"_ rows\",\n \"_r ows\",\n \"_row s\",\n \"_ro ws\",\n \"Ġhelp ful\",\n \"Ġmass ive\",\n \"- --Ċ\",\n \"-- -Ċ\",\n \"--- Ċ\",\n \"Ġme asures\",\n \"Ġmeasure s\",\n \"Ġmeas ures\",\n \"0 07\",\n \"00 7\",\n \"Ġ Runtime\",\n \"ĠR untime\",\n \"ĠRun time\",\n \"Ġw orry\",\n \"Ġwor ry\",\n \"Ġ Spec\",\n \"ĠS pec\",\n \"ĠSp ec\",\n \"ĠSpe c\",\n \"ĉ D\",\n \"ãĢ ij\",\n \"Ġ ){Ċ\",\n \"Ġ) {Ċ\",\n \"Ġ){ Ċ\",\n \"Ġw orse\",\n \"Ġwor se\",\n \"Ġwors e\",\n \"( filename\",\n \"(f ilename\",\n \"(file name\",\n \"(fi lename\",\n \"(fil ename\",\n \"Ġ lay\",\n \"Ġl ay\",\n \"Ġla y\",\n \"Ġ magic\",\n \"Ġm agic\",\n \"Ġmag ic\",\n \"Ġ Their\",\n \"ĠThe ir\",\n \"o ul\",\n \"ou l\",\n \"st roy\",\n \"str oy\",\n \"stro y\",\n \"Ġ Where\",\n \"ĠW here\",\n \"ĠWh ere\",\n \"ĠWhe re\",\n \"2 80\",\n \"28 0\",\n \"Ġs udden\",\n \"Ġsu dden\",\n \"Ġsud den\",\n \"Ġd efe\",\n \"Ġde fe\",\n \"Ġdef e\",\n \"Ġ binding\",\n \"Ġb inding\",\n \"Ġbin ding\",\n \"Ġbind ing\",\n \"Ġ flight\",\n \"Ġf light\",\n \"Ġfl ight\",\n \"Ġ OnInit\",\n \"ĠOn Init\",\n \"Ġ Women\",\n \"ĠW omen\",\n \"ĠWo men\",\n \"Ġ Policy\",\n \"ĠP olicy\",\n \"ĠPol icy\",\n \"ĠPolic y\",\n \"Ġdr ugs\",\n \"Ġdrug s\",\n \"Ġdru gs\",\n \"ish ing\",\n \"ishi ng\",\n \"(' ../\",\n \"('. ./\",\n \"Ġ Mel\",\n \"ĠM el\",\n \"ĠMe l\",\n \"p eat\",\n \"pe at\",\n \"t or\",\n \"to r\",\n \"Ġpro posed\",\n \"Ġprop osed\",\n \"Ġpropos ed\",\n \"Ġpropose d\",\n \"Ġst ated\",\n \"Ġstate d\",\n \"Ġstat ed\",\n \"Ġsta ted\",\n \"_ RES\",\n \"_RE S\",\n \"_R ES\",\n \"Ġ east\",\n \"Ġe ast\",\n \"Ġeas t\",\n \"Ġea st\",\n \"2 12\",\n \"21 2\",\n \"Ġ CONDITION\",\n \"ĠCON DITION\",\n \"_ desc\",\n \"_d esc\",\n \"_de sc\",\n \"_des c\",\n \"Ġw inning\",\n \"Ġwin ning\",\n \"f olio\",\n \"fo lio\",\n \"fol io\",\n \"M apper\",\n \"Map per\",\n \"Ma pper\",\n \"Ġ Pan\",\n \"ĠP an\",\n \"ĠPa n\",\n \"ĠA nge\",\n \"ĠAn ge\",\n \"ĠAng e\",\n \".s ervlet\",\n \".serv let\",\n \"Ġ copies\",\n \"Ġc opies\",\n \"Ġco pies\",\n \"Ġcop ies\",\n \"L M\",\n \"Ġ vm\",\n \"Ġv m\",\n \"å į\",\n \"Ġ dictionary\",\n \"Ġd ictionary\",\n \"S eg\",\n \"Se g\",\n \"1 77\",\n \"17 7\",\n \"e lines\",\n \"el ines\",\n \"eline s\",\n \"eli nes\",\n \"elin es\",\n \"Ġ Send\",\n \"ĠS end\",\n \"ĠSe nd\",\n \"ĠSen d\",\n \"Ġ iron\",\n \"Ġi ron\",\n \"Ġir on\",\n \"Ġ Fort\",\n \"ĠF ort\",\n \"ĠFor t\",\n \"ĠFo rt\",\n \"1 66\",\n \"16 6\",\n \". domain\",\n \".d omain\",\n \".do main\",\n \".dom ain\",\n \"Ġde bate\",\n \"Ġdeb ate\",\n \"Not Null\",\n \"e q\",\n \"a cher\",\n \"ac her\",\n \"ach er\",\n \"ache r\",\n \"l f\",\n \"ĉ fmt\",\n \"ĉf mt\",\n \"Ġl awy\",\n \"Ġla wy\",\n \"Ġlaw y\",\n \"1 78\",\n \"17 8\",\n \"Ä Ł\",\n \"Ġ Men\",\n \"ĠM en\",\n \"ĠMe n\",\n \"Ġ trim\",\n \"Ġt rim\",\n \"Ġtr im\",\n \"Ġtri m\",\n \"( NULL\",\n \"(N ULL\",\n \"Ġ !!\",\n \"Ġ! !\",\n \"Ġ pad\",\n \"Ġp ad\",\n \"Ġpa d\",\n \"Ġfollow s\",\n \"Ġfoll ows\",\n \"\\\" ][\\\"\",\n \"\\\"] [\\\"\",\n \"\\\"][ \\\"\",\n \"r equ\",\n \"re qu\",\n \"req u\",\n \"Ġ Ep\",\n \"ĠE p\",\n \". github\",\n \".g ithub\",\n \".git hub\",\n \"( img\",\n \"(i mg\",\n \"(im g\",\n \"e to\",\n \"et o\",\n \"( '\\\\\",\n \"(' \\\\\",\n \"S ervices\",\n \"Service s\",\n \"Serv ices\",\n \"umbn ail\",\n \"_ main\",\n \"_m ain\",\n \"_ma in\",\n \"p leted\",\n \"pl eted\",\n \"ple ted\",\n \"plete d\",\n \"plet ed\",\n \"fort unately\",\n \"fortunate ly\",\n \"Ġ windows\",\n \"Ġw indows\",\n \"Ġwindow s\",\n \"Ġwind ows\",\n \"Ġ plane\",\n \"Ġp lane\",\n \"Ġpl ane\",\n \"Ġplan e\",\n \"Ġpla ne\",\n \"Ġ Connection\",\n \"ĠCon nection\",\n \"ĠConnect ion\",\n \"ĠConn ection\",\n \". local\",\n \".l ocal\",\n \".loc al\",\n \".lo cal\",\n \"u ard\",\n \"ua rd\",\n \"uar d\",\n \"} \\\\\",\n \"= =\\\"\",\n \"== \\\"\",\n \"a ndon\",\n \"an don\",\n \"and on\",\n \"ando n\",\n \"Ġ Roy\",\n \"ĠR oy\",\n \"ĠRo y\",\n \"w est\",\n \"we st\",\n \"1 58\",\n \"15 8\",\n \"ig inal\",\n \"igin al\",\n \"igi nal\",\n \"em ies\",\n \"emi es\",\n \"emie s\",\n \"i tz\",\n \"it z\",\n \"' ):Ċ\",\n \"') :Ċ\",\n \"'): Ċ\",\n \"Ġ Peter\",\n \"ĠP eter\",\n \"ĠPe ter\",\n \"ĠPet er\",\n \"ĠPete r\",\n \"Ġt ough\",\n \"Ġto ugh\",\n \"Ġtou gh\",\n \"Ġre duced\",\n \"Ġred uced\",\n \"Ġredu ced\",\n \"Ġreduce d\",\n \"Ġ calculate\",\n \"Ġc alculate\",\n \"Ġcal culate\",\n \"Ġcalcul ate\",\n \"Ġcalc ulate\",\n \"Ġ rapid\",\n \"Ġr apid\",\n \"Ġrap id\",\n \"Ġra pid\",\n \"c ustomer\",\n \"custom er\",\n \"cust omer\",\n \"Ġ efficient\",\n \"Ġeff icient\",\n \"Ġeffic ient\",\n \"Ġ medium\",\n \"Ġm edium\",\n \"Ġmed ium\",\n \"Ġmedi um\",\n \"Ġ fell\",\n \"Ġf ell\",\n \"Ġfe ll\",\n \"Ġfel l\",\n \". ref\",\n \".re f\",\n \".r ef\",\n \"Ġ Cas\",\n \"ĠC as\",\n \"ĠCa s\",\n \"Ġ feedback\",\n \"Ġfe edback\",\n \"Ġfeed back\",\n \"S peed\",\n \"Sp eed\",\n \"Spe ed\",\n \"( output\",\n \"(out put\",\n \"a je\",\n \"aj e\",\n \"Ġ categories\",\n \"Ġc ategories\",\n \"Ġcategor ies\",\n \"Ġcategorie s\",\n \"Ġ fee\",\n \"Ġf ee\",\n \"Ġfe e\",\n \"} ;\",\n \"Ġ deleted\",\n \"Ġde leted\",\n \"Ġdel eted\",\n \"Ġdelete d\",\n \"Ġdelet ed\",\n \"Ġdele ted\",\n \"r eh\",\n \"re h\",\n \"Ġ proof\",\n \"Ġp roof\",\n \"Ġpro of\",\n \"D esc\",\n \"De sc\",\n \"Des c\",\n \"B uild\",\n \"Bu ild\",\n \"Ġs ides\",\n \"Ġside s\",\n \"Ġsi des\",\n \"Ġsid es\",\n \". ArrayList\",\n \".Array List\",\n \"- %\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"Ø ±\",\n \". match\",\n \".m atch\",\n \".mat ch\",\n \"л и\",\n \"Ġfe els\",\n \"Ġfeel s\",\n \"Ġfee ls\",\n \"Ġachie ve\",\n \"Ġach ieve\",\n \"Ġc lim\",\n \"Ġcl im\",\n \"Ġcli m\",\n \"_ ON\",\n \"_O N\",\n \"Ġ CD\",\n \"ĠC D\",\n \"Ġ teacher\",\n \"Ġt eacher\",\n \"Ġte acher\",\n \"Ġteach er\",\n \"Ġtea cher\",\n \"_ current\",\n \"_c urrent\",\n \"_cur rent\",\n \"_curr ent\",\n \"b n\",\n \"_ PL\",\n \"_P L\",\n \"is ting\",\n \"ist ing\",\n \"isti ng\",\n \"E nable\",\n \"En able\",\n \"G EN\",\n \"GE N\",\n \"Ġ tv\",\n \"Ġt v\",\n \"Ġ sock\",\n \"Ġs ock\",\n \"Ġso ck\",\n \"Ġsoc k\",\n \"Ġ plays\",\n \"Ġp lays\",\n \"Ġpl ays\",\n \"Ġplay s\",\n \"Ġpla ys\",\n \"Ġ discount\",\n \"Ġdis count\",\n \"Ġdisc ount\",\n \"Ġdisco unt\",\n \"Ġ KE\",\n \"ĠK E\",\n \"Ġ Debug\",\n \"ĠDe bug\",\n \"ĠDeb ug\",\n \"F ore\",\n \"For e\",\n \"Fo re\",\n \"Ġ Iraq\",\n \"ĠI raq\",\n \"ĠIr aq\",\n \"Ġ appearance\",\n \"Ġap pearance\",\n \"Ġappear ance\",\n \"M on\",\n \"Mo n\",\n \"Ġ styled\",\n \"Ġst yled\",\n \"Ġstyle d\",\n \"Ġsty led\",\n \"Ġstyl ed\",\n \"Ġ Human\",\n \"ĠH uman\",\n \"ĠHum an\",\n \"ĠHu man\",\n \"i ot\",\n \"io t\",\n \"Ġ History\",\n \"ĠH istory\",\n \"ĠHi story\",\n \"ĠHistor y\",\n \"ĠHist ory\",\n \"Ġs ac\",\n \"Ġsa c\",\n \"Ġ Collection\",\n \"ĠC ollection\",\n \"ĠCol lection\",\n \"ĠColl ection\",\n \"ĠCollect ion\",\n \"Ġ recommended\",\n \"Ġre commended\",\n \"Ġrecomm ended\",\n \"Ġrecommend ed\",\n \". Selected\",\n \".Se lected\",\n \".Select ed\",\n \"Ġ organizations\",\n \"Ġorgan izations\",\n \"Ġorganization s\",\n \"Ġorganiz ations\",\n \"Ġdis covered\",\n \"Ġdiscover ed\",\n \"co hol\",\n \"coh ol\",\n \"a das\",\n \"ad as\",\n \"ada s\",\n \"Ġ Thomas\",\n \"ĠTh omas\",\n \"ĠThom as\",\n \"M ay\",\n \"Ma y\",\n \"Ġcon serv\",\n \"Ġcons erv\",\n \"Ġconse rv\",\n \"Ġd omin\",\n \"Ġdo min\",\n \"Ġdom in\",\n \"Ġ Follow\",\n \"ĠF ollow\",\n \"ĠFol low\",\n \"Ġ Section\",\n \"ĠS ection\",\n \"ĠSe ction\",\n \"ĠSec tion\",\n \"ĠSect ion\",\n \"Ġ Thanks\",\n \"ĠTh anks\",\n \"ĠThank s\",\n \"ĠThan ks\",\n \"User name\",\n \"Ġ recipe\",\n \"Ġrec ipe\",\n \"Ġrecip e\",\n \"Ġwonder ful\",\n \". sleep\",\n \".s leep\",\n \"_ if\",\n \"_i f\",\n \"ĉ ĊĉĊ\",\n \"ĉĊ ĉĊ\",\n \"or no\",\n \"orn o\",\n \"Ġ ru\",\n \"Ġr u\",\n \"_ target\",\n \"_t arget\",\n \"_tar get\",\n \". \\\"\\\"\",\n \".\\\" \\\"\",\n \"à ¦\",\n \"Event Args\",\n \"Ġ inputs\",\n \"Ġin puts\",\n \"Ġinput s\",\n \"Ġinp uts\",\n \"Ġf if\",\n \"Ġfi f\",\n \"Ġ vision\",\n \"Ġv ision\",\n \"Ġvis ion\",\n \"c y\",\n \"Ġ Series\",\n \"ĠS eries\",\n \"ĠSe ries\",\n \"ĠSer ies\",\n \"ĠSerie s\",\n \") (((\",\n \")( ((\",\n \")(( (\",\n \"Ġtr ading\",\n \"Ġtrad ing\",\n \"Ġtra ding\",\n \"Ġtradi ng\",\n \"Ġ marker\",\n \"Ġm arker\",\n \"Ġmark er\",\n \"Ġmar ker\",\n \"B egin\",\n \"Be gin\",\n \"Ġ typically\",\n \"Ġtyp ically\",\n \"Ġtypical ly\",\n \"Ġc auses\",\n \"Ġca uses\",\n \"Ġcause s\",\n \"Ġcaus es\",\n \"d ropdown\",\n \"drop down\",\n \"_ DEBUG\",\n \"_DE BUG\",\n \"2 60\",\n \"26 0\",\n \"Ġ detect\",\n \"Ġd etect\",\n \"Ġdet ect\",\n \"c ountry\",\n \"count ry\",\n \"! \\\");Ċ\",\n \"!\\\" );Ċ\",\n \"!\\\"); Ċ\",\n \"!\\\") ;Ċ\",\n \"ĉ R\",\n \"a ppy\",\n \"ap py\",\n \"app y\",\n \"Ġc ref\",\n \"Ġcr ef\",\n \"Ġcre f\",\n \"( '<\",\n \"(' <\",\n \"\\\" =>\",\n \"Ġ LE\",\n \"ĠL E\",\n \"re ader\",\n \"read er\",\n \"rea der\",\n \"Ġ administr\",\n \"Ġadmin istr\",\n \"à µ\",\n \"u cket\",\n \"uc ket\",\n \"uck et\",\n \"Ġf ashion\",\n \". char\",\n \".c har\",\n \".ch ar\",\n \"i zar\",\n \"iz ar\",\n \"iza r\",\n \"Ġ disable\",\n \"Ġd isable\",\n \"Ġdis able\",\n \"Ġs uc\",\n \"Ġsu c\",\n \"Ġ Live\",\n \"ĠL ive\",\n \"ĠLi ve\",\n \"ĠLiv e\",\n \"i ssue\",\n \"iss ue\",\n \"Ġ metadata\",\n \"Ġm etadata\",\n \"Ġmet adata\",\n \"Ġmeta data\",\n \"f lags\",\n \"fl ags\",\n \"flag s\",\n \"Ġ ðŁ\",\n \"Ġð Ł\",\n \"Ġ committed\",\n \"Ġcom mitted\",\n \"Ġcomm itted\",\n \"Ġcommit ted\",\n \"Ġ va\",\n \"Ġv a\",\n \"Ġ rough\",\n \"Ġr ough\",\n \"Ġro ugh\",\n \"Ġrou gh\",\n \"Ġ '''Ċ\",\n \"Ġ' ''Ċ\",\n \"Ġ'' 'Ċ\",\n \"Ġ''' Ċ\",\n \"Ġ highlight\",\n \"Ġhigh light\",\n \"_ vars\",\n \"_v ars\",\n \"_var s\",\n \"_va rs\",\n \"V O\",\n \"Ġ encoding\",\n \"Ġen coding\",\n \"Ġenc oding\",\n \"- Z\",\n \"_ sign\",\n \"_s ign\",\n \"_sig n\",\n \"_si gn\",\n \"$ (\\\"#\",\n \"$( \\\"#\",\n \"$(\\\" #\",\n \"Ġ rain\",\n \"Ġr ain\",\n \"Ġra in\",\n \"re atest\",\n \"reate st\",\n \"reat est\",\n \"rea test\",\n \"Ġ END\",\n \"ĠE ND\",\n \"ĠEN D\",\n \"S election\",\n \"Se lection\",\n \"Select ion\",\n \"Sel ection\",\n \"Sele ction\",\n \"Ġ candidates\",\n \"Ġc andidates\",\n \"Ġcandid ates\",\n \"Ġcandidate s\",\n \"Ġ sav\",\n \"Ġs av\",\n \"Ġsa v\",\n \". Empty\",\n \"Ġdec isions\",\n \"Ġdecision s\",\n \"Ġdecis ions\",\n \"Ġcoll abor\",\n \"r idge\",\n \"ri dge\",\n \"rid ge\",\n \"f eed\",\n \"fe ed\",\n \"fee d\",\n \"r ession\",\n \"ress ion\",\n \"Ġ persons\",\n \"Ġper sons\",\n \"Ġperson s\",\n \"Ġpers ons\",\n \"Ġperso ns\",\n \"V M\",\n \"0 08\",\n \"00 8\",\n \"e ga\",\n \"eg a\",\n \"_ BIT\",\n \"_B IT\",\n \"A ccording\",\n \"Acc ording\",\n \"ack ed\",\n \"ac ked\",\n \"Ġdoll ars\",\n \"Ġdollar s\",\n \"_ loss\",\n \"_l oss\",\n \"_lo ss\",\n \"Ġ Cost\",\n \"ĠC ost\",\n \"ĠCo st\",\n \"ĠCos t\",\n \"} \\\"Ċ\",\n \"}\\\" Ċ\",\n \"Not ification\",\n \"Ġpro stit\",\n \"Ġpros tit\",\n \"Ġprost it\",\n \"Ġ authority\",\n \"Ġauthor ity\",\n \". rec\",\n \".re c\",\n \".r ec\",\n \"Ġsp okes\",\n \"Ġspoke s\",\n \"Ġspo kes\",\n \"Ġ Today\",\n \"ĠT oday\",\n \"ĠTo day\",\n \"ĠTod ay\",\n \"i stant\",\n \"is tant\",\n \"ist ant\",\n \"istan t\",\n \"ista nt\",\n \"Ġ Head\",\n \"ĠH ead\",\n \"ĠHe ad\",\n \"âĢĿ .\",\n \"er tainment\",\n \"ert ainment\",\n \"ertain ment\",\n \"c ean\",\n \"ce an\",\n \"cea n\",\n \"c ulate\",\n \"cul ate\",\n \"cu late\",\n \"Ġ ven\",\n \"Ġv en\",\n \"Ġve n\",\n \"How ever\",\n \"_ arr\",\n \"_a rr\",\n \"_ar r\",\n \"Ġ tokens\",\n \"Ġt okens\",\n \"Ġtoken s\",\n \"Ġtok ens\",\n \"G raph\",\n \"Gr aph\",\n \"Ġ Jud\",\n \"ĠJ ud\",\n \"ĠJu d\",\n \"Ġ Virgin\",\n \"ĠVir gin\",\n \"Ġ Serial\",\n \"ĠS erial\",\n \"ĠSe rial\",\n \"ĠSer ial\",\n \"un ning\",\n \"unn ing\",\n \"M utable\",\n \"Mu table\",\n \"Mut able\",\n \"a gers\",\n \"ag ers\",\n \"age rs\",\n \"ager s\",\n \". csv\",\n \".c sv\",\n \".cs v\",\n \"Ġdevelop ing\",\n \"Ġdevel oping\",\n \"Ġ instructions\",\n \"Ġin structions\",\n \"Ġinstruction s\",\n \"Ġinstr uctions\",\n \"Ġinstruct ions\",\n \"Ġ promise\",\n \"Ġp romise\",\n \"Ġpro mise\",\n \"Ġprom ise\",\n \"Ġ requested\",\n \"Ġrequest ed\",\n \"Ġrequ ested\",\n \"_ encode\",\n \"_en code\",\n \"_enc ode\",\n \"/ \\\"\",\n \"Ġ Icon\",\n \"ĠI con\",\n \"u ilt\",\n \"ui lt\",\n \"uil t\",\n \"- day\",\n \"-d ay\",\n \"-da y\",\n \"Ġ intelligence\",\n \"Ġint elligence\",\n \"Ġintellig ence\",\n \". IS\",\n \".I S\",\n \"Ġ Observable\",\n \"ĠO bservable\",\n \"ĠObserv able\",\n \"Ġ Hard\",\n \"ĠH ard\",\n \"ĠHar d\",\n \"ĠHa rd\",\n \"B ool\",\n \"Bo ol\",\n \"2 11\",\n \"21 1\",\n \"id ential\",\n \"ident ial\",\n \". Anchor\",\n \".An chor\",\n \"Ġ selling\",\n \"Ġs elling\",\n \"Ġsell ing\",\n \"Ġsel ling\",\n \"C I\",\n \"A GES\",\n \"AG ES\",\n \"AGE S\",\n \"t le\",\n \"tl e\",\n \"b ur\",\n \"bu r\",\n \"UFF ER\",\n \"UF FER\",\n \"R Y\",\n \"Ġb igger\",\n \"Ġbig ger\",\n \"Ġbi gger\",\n \"Ġ rat\",\n \"Ġr at\",\n \"Ġra t\",\n \"Ġf amous\",\n \"Ġfam ous\",\n \"Ġ typename\",\n \"Ġtype name\",\n \"Ġtyp ename\",\n \"Ġ explained\",\n \"Ġexpl ained\",\n \"Ġexplain ed\",\n \"} }Ċ\",\n \"}} Ċ\",\n \"Ġn uclear\",\n \"Ġnu clear\",\n \"Ġnucle ar\",\n \"- N\",\n \"Ġcr isis\",\n \"Ġcri sis\",\n \"Ġcris is\",\n \"Ġ Enter\",\n \"ĠEn ter\",\n \"ĠEnt er\",\n \"Ġ answers\",\n \"Ġan swers\",\n \"Ġanswer s\",\n \"Ġans wers\",\n \"/ ${\",\n \"/$ {\",\n \"/ pl\",\n \"/p l\",\n \"Ġ sequ\",\n \"Ġs equ\",\n \"Ġse qu\",\n \"Ġseq u\",\n \"_ next\",\n \"_n ext\",\n \"_ne xt\",\n \"m ask\",\n \"ma sk\",\n \"mas k\",\n \"Ġ standing\",\n \"Ġst anding\",\n \"Ġstand ing\",\n \"Ġstan ding\",\n \"Ġpl enty\",\n \"Ġple nty\",\n \"Ġ Cross\",\n \"ĠC ross\",\n \"ĠCr oss\",\n \"ĠCro ss\",\n \"ĠCros s\",\n \"ĉ ret\",\n \"ĉr et\",\n \"ĉre t\",\n \"d ro\",\n \"dr o\",\n \"Ġ Cast\",\n \"ĠC ast\",\n \"ĠCas t\",\n \"ĠCa st\",\n \"1 67\",\n \"16 7\",\n \"= true\",\n \"Ġ Chris\",\n \"ĠCh ris\",\n \"ĠChr is\",\n \"i cio\",\n \"ic io\",\n \"ici o\",\n \"Ġ Mike\",\n \"ĠM ike\",\n \"ĠMi ke\",\n \"ĠMik e\",\n \"D ecimal\",\n \"De cimal\",\n \"Dec imal\",\n \"add Component\",\n \"L en\",\n \"Le n\",\n \"Ġ cock\",\n \"Ġc ock\",\n \"Ġco ck\",\n \"Ġcoc k\",\n \"Ġ #{\",\n \"Ġ# {\",\n \"U RN\",\n \"UR N\",\n \"< tr\",\n \"\",\n \"\\\\\\\" >\",\n \"Ġ *=\",\n \"Ġ* =\",\n \"Ġ PS\",\n \"ĠP S\",\n \"Ġdanger ous\",\n \"[ p\",\n \"O ME\",\n \"OM E\",\n \"O ther\",\n \"Ot her\",\n \"Ġ StringBuilder\",\n \"ĠString Builder\",\n \"P oints\",\n \"Point s\",\n \"Po ints\",\n \"he ading\",\n \"head ing\",\n \"hea ding\",\n \"Ġ currency\",\n \"Ġc urrency\",\n \"Ġcurr ency\",\n \"Ġ percentage\",\n \"Ġper centage\",\n \"Ġpercent age\",\n \"_ API\",\n \"_A PI\",\n \"_AP I\",\n \"Ġ classic\",\n \"Ġclass ic\",\n \"Ġcl assic\",\n \"Ġclas sic\",\n \"t head\",\n \"th ead\",\n \"the ad\",\n \"Ġ MO\",\n \"ĠM O\",\n \"F E\",\n \"I dx\",\n \"Id x\",\n \"a wait\",\n \"aw ait\",\n \"awa it\",\n \"awai t\",\n \"Ġ è\",\n \"Ġà ¨\",\n \"Ġacc ident\",\n \"Ġ variant\",\n \"Ġv ariant\",\n \"Ġvar iant\",\n \"Ġvari ant\",\n \"Ġm yst\",\n \"Ġmy st\",\n \"Ġmys t\",\n \"Ġ Land\",\n \"ĠL and\",\n \"ĠLa nd\",\n \"ĠLan d\",\n \"Ġ Bre\",\n \"ĠB re\",\n \"ĠBr e\",\n \"Ġh arm\",\n \"Ġhar m\",\n \"Ġha rm\",\n \"Ġ Acc\",\n \"ĠA cc\",\n \"ĠAc c\",\n \"Ġ charged\",\n \"Ġch arged\",\n \"Ġchar ged\",\n \"Ġcharg ed\",\n \"Ġcharge d\",\n \"i ones\",\n \"ion es\",\n \"io nes\",\n \"ione s\",\n \"Vis ibility\",\n \"ar ry\",\n \"arr y\",\n \"Ġ Language\",\n \"ĠL anguage\",\n \"Ġ walking\",\n \"Ġw alking\",\n \"Ġwalk ing\",\n \"Ġwal king\",\n \"\\\" .ĊĊ\",\n \"\\\". ĊĊ\",\n \"\\\".Ċ Ċ\",\n \"i fer\",\n \"if er\",\n \"ife r\",\n \"Ġleaders hip\",\n \"Ġleader ship\",\n \". 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The\",\n \".T he\",\n \".Th e\",\n \"In ternal\",\n \"Int ernal\",\n \"Inter nal\",\n \"Intern al\",\n \"ĠCON TR\",\n \"ĠCONT R\",\n \"M ock\",\n \"Mo ck\",\n \"R IGHT\",\n \"Ġ '{\",\n \"Ġ' {\",\n \"Ġ controls\",\n \"Ġcontrol s\",\n \"Ġcontr ols\",\n \"Ġcontro ls\",\n \"M at\",\n \"Ma t\",\n \"Ġ mand\",\n \"Ġm and\",\n \"Ġman d\",\n \"Ġma nd\",\n \"Ġ extended\",\n \"Ġext ended\",\n \"Ġextend ed\",\n \"O k\",\n \"Ġ embed\",\n \"Ġem bed\",\n \"Ġemb ed\",\n \"Ġ planet\",\n \"Ġplan et\",\n \"Ġplane t\",\n \"Ġpla net\",\n \"Ġ Non\",\n \"ĠN on\",\n \"ĠNo n\",\n \"- ch\",\n \"-c h\",\n \") \\\",\",\n \")\\\" ,\",\n \"e par\",\n \"ep ar\",\n \"Ġbel ieved\",\n \"Ġbelie ved\",\n \"Ġbelieve d\",\n \"Ġ Environment\",\n \"ĠEn vironment\",\n \"Ġ Friend\",\n \"ĠF riend\",\n \"ĠFri end\",\n \"- res\",\n \"-r es\",\n \"-re s\",\n \"Ġ handling\",\n \"Ġhand ling\",\n \"Ġhan dling\",\n \"n ic\",\n \"ni c\",\n \"- level\",\n \"-le vel\",\n \"s cri\",\n \"sc ri\",\n \"scr i\",\n \"X ml\",\n \"B E\",\n \"u ngen\",\n \"un gen\",\n \"ung en\",\n \"unge n\",\n \"Ġ alter\",\n \"Ġal ter\",\n \"Ġalt er\",\n \"Ġalte r\",\n \"[ idx\",\n \"[i dx\",\n \"[id x\",\n \"P op\",\n \"Po p\",\n \"c am\",\n \"ca m\",\n \"Ġ (((\",\n \"Ġ( ((\",\n \"Ġ(( (\",\n \"Ġ shipping\",\n \"Ġsh ipping\",\n \"Ġship ping\",\n \"Ġ battery\",\n \"Ġb attery\",\n \"Ġbatter y\",\n \"Ġbatt ery\",\n \"Ġbat tery\",\n \"iddle ware\",\n \"M C\",\n \"Ġ impl\",\n \"Ġi mpl\",\n \"Ġim pl\",\n \"Ġimp l\",\n \"ot ation\",\n \"ota tion\",\n \"Ġ Lab\",\n \"ĠL ab\",\n \"ĠLa b\",\n \"< form\",\n \" {{\",\n \">{ {\",\n \"Ġ Resource\",\n \"ĠRe source\",\n \"ĠRes ource\",\n \"Ġ Standard\",\n \"ĠSt andard\",\n \"ĠStand ard\",\n \"Ġ Prem\",\n \"ĠP rem\",\n \"ĠPr em\",\n \"ĠPre m\",\n \"up dated\",\n \"update d\",\n \"upd ated\",\n \"iv alent\",\n \"ival ent\",\n \"Ġ assets\",\n \"Ġas sets\",\n \"Ġass ets\",\n \"Ġasset s\",\n \"_ temp\",\n \"_t emp\",\n \"_tem p\",\n \"_te mp\",\n \"Ġinter ests\",\n \"Ġinterest s\",\n \"Ġinteres ts\",\n \"Ġ hardware\",\n \"Ġh ardware\",\n \"Ġhard ware\",\n \"Ġ Rom\",\n \"ĠR om\",\n \"ĠRo m\",\n \"Ġ Share\",\n \"ĠS hare\",\n \"ĠSh are\",\n \"ĠSha re\",\n \"ĠShar e\",\n \"Ġ ''Ċ\",\n \"Ġ' 'Ċ\",\n \"Ġ'' Ċ\",\n \"Ġ *,\",\n \"Ġ* ,\",\n \"Ġ Take\",\n \"ĠT ake\",\n \"ĠTa ke\",\n \"ĠTak e\",\n \"Ġ Images\",\n \"ĠIm ages\",\n \"ĠImage s\",\n \"ĠImag es\",\n \"_ CHECK\",\n \"_C HECK\",\n \"( typeof\",\n \"(type of\",\n \"(typ eof\",\n \"Ġ Jun\",\n \"ĠJ un\",\n \"ĠJu n\",\n \"\\\\< ^\",\n \"Ġ liqu\",\n \"Ġl iqu\",\n \"Ġli qu\",\n \"Ġw orst\",\n \"Ġwor st\",\n \"Ġwo rst\",\n \"Ġwors t\",\n \"ymb ols\",\n \"ymbol s\",\n \"ĉ ĉĉĠĠĠ\",\n \"ĉĉ ĉĠĠĠ\",\n \"ĉĉĉ ĠĠĠ\",\n \"ĉĉĉĠ ĠĠ\",\n \"ĉĉĉĠĠ Ġ\",\n \"Ġ drivers\",\n \"Ġdr ivers\",\n \"Ġdriver s\",\n \"Ġdrive rs\",\n \"Ġdriv ers\",\n \"Ġdri vers\",\n \"Ġ Document\",\n \"ĠD ocument\",\n \"ĠDoc ument\",\n \"e no\",\n \"en o\",\n \"Ġ Technology\",\n \"ĠTechn ology\",\n \"Ġ approved\",\n \"Ġap proved\",\n \"Ġappro ved\",\n \"Ġapprove d\",\n \"u mps\",\n \"um ps\",\n \"ump s\",\n \"Ġ snow\",\n \"Ġs now\",\n \"Ġsn ow\",\n \"Ġsno w\",\n \"form ance\",\n \"forma nce\",\n \"forman ce\",\n \"_ ASSERT\",\n \"_A SSERT\",\n \"_ASS ERT\",\n \"u its\",\n \"ui ts\",\n \"uit s\",\n \"2 07\",\n \"20 7\",\n \"Ù Ĩ\",\n \"Ġd ifferences\",\n \"Ġdif ferences\",\n \"Ġdiffer ences\",\n \"Ġdifference s\",\n \". Visible\",\n \".V isible\",\n \"ĉ ĉĉčĊ\",\n \"ĉĉ ĉčĊ\",\n \"ĉĉĉ čĊ\",\n \"Ġ Ps\",\n \"ĠP s\",\n \"_ fetch\",\n \"_f etch\",\n \"Ġ todo\",\n \"Ġt odo\",\n \"Ġto do\",\n \"Ġtod o\",\n \". ',Ċ\",\n \".' ,Ċ\",\n \".', Ċ\",\n \"Ġ sel\",\n \"Ġs el\",\n \"Ġse l\",\n \"ur ers\",\n \"ure rs\",\n \"urer s\",\n \"in valid\",\n \"Ġ tweet\",\n \"Ġt weet\",\n \"Ġtwe et\",\n \"Ġtwee t\",\n \"V EL\",\n \"VE L\",\n \"Ġresearch ers\",\n \"Ġresearcher s\",\n \"Ġ sprintf\",\n \"Ġs printf\",\n \"Ġsprint f\",\n \"Ġ RO\",\n \"ĠR O\",\n \"Ġ pel\",\n \"Ġp el\",\n \"Ġpe l\",\n \". Trans\",\n \".T rans\",\n \".Tr ans\",\n \"Ġ illegal\",\n \"Ġil legal\",\n \"Ġill egal\",\n \"Ġilleg al\",\n \"d ialog\",\n \"di alog\",\n \"dia log\",\n \"sm arty\",\n \"smart y\",\n \"l g\",\n \"_ MIN\",\n \"_M IN\",\n \"_MI N\",\n \"Ġ hero\",\n \"Ġh ero\",\n \"Ġhe ro\",\n \"Ġher o\",\n \"f inal\",\n \"fin al\",\n \"fi nal\",\n \"Ġ pp\",\n \"Ġp p\",\n \". Le\",\n \".L e\",\n \"Ġ ci\",\n \"Ġc i\",\n \"ĉ RT\",\n \"ĉR T\",\n \"Ġs uggested\",\n \"Ġsuggest ed\",\n \"p df\",\n \"pd f\",\n \"a ching\",\n \"ach ing\",\n \"achi ng\",\n \"Ġ Ro\",\n \"ĠR o\",\n \"Ġ Properties\",\n \"ĠP roperties\",\n \"ĠProp erties\",\n \"ĠProper ties\",\n \"Ġ Si\",\n \"ĠS i\",\n \"Ġbu ying\",\n \"Ġbuy ing\",\n \"Ġ mu\",\n \"Ġm u\",\n \"Ġ lands\",\n \"Ġl ands\",\n \"Ġla nds\",\n \"Ġland s\",\n \"Ġlan ds\",\n \"if iers\",\n \"ifier s\",\n \"ifi ers\",\n \"ifie rs\",\n \"Ġ FILE\",\n \"ĠF ILE\",\n \"ĠFI LE\",\n \"ĠFIL E\",\n \"RO UP\",\n \"Ġ holder\",\n \"Ġh older\",\n \"Ġhold er\",\n \"Ġho lder\",\n \"Ġhol der\",\n \"Ġ Son\",\n \"ĠS on\",\n \"ĠSo n\",\n \"Ġsym pt\",\n \"Ġsymp t\",\n \". route\",\n \".r oute\",\n \".ro ute\",\n \") ?\",\n \"Ġ argc\",\n \"Ġar gc\",\n \"Ġarg c\",\n \"Ġ fort\",\n \"Ġf ort\",\n \"Ġfor t\",\n \"Ġfo rt\",\n \"Ġcas ino\",\n \"Ġcasi no\",\n \"_ category\",\n \"_c ategory\",\n \"Ġ forum\",\n \"Ġf orum\",\n \"Ġfor um\",\n \"Ġfo rum\",\n \"2 15\",\n \"21 5\",\n \"p refix\",\n \"pre fix\",\n \"pref ix\",\n \"ap ture\",\n \"apt ure\",\n \"T ube\",\n \"Tu be\",\n \"e ms\",\n \"em s\",\n \"im ize\",\n \"imi ze\",\n \"imiz e\",\n \"Ġn ue\",\n \"Ġnu e\",\n \"a us\",\n \"au s\",\n \"c ourse\",\n \"co urse\",\n \"cour se\",\n \"A TOR\",\n \"AT OR\",\n \"ATO R\",\n \"( )),\",\n \"() ),\",\n \"()) ,\",\n \"Ad vertis\",\n \"IN GS\",\n \"ING S\",\n \"Ġac know\",\n \"Ġack now\",\n \"ĠK orea\",\n \"ĠKore a\",\n \"ĠKo rea\",\n \"ĠKor ea\",\n \"p ling\",\n \"pl ing\",\n \"Ġ worker\",\n \"Ġwork er\",\n \"Ġwor ker\",\n \"PL IED\",\n \"h al\",\n \"ha l\",\n \"Ġ Richard\",\n \"ĠRich ard\",\n \"ĠRic hard\",\n \"E lements\",\n \"Element s\",\n \"El ements\",\n \"Elem ents\",\n \"Ele ments\",\n \"ĉ ĉĉĠ\",\n \"ĉĉ ĉĠ\",\n \"ĉĉĉ Ġ\",\n \"s tar\",\n \"st ar\",\n \"sta r\",\n \"Ġ relationships\",\n \"Ġrelations hips\",\n \"Ġrelationship s\",\n \"Ġrelation ships\",\n \"Ġ cheap\",\n \"Ġc heap\",\n \"Ġche ap\",\n \"A CH\",\n \"AC H\",\n \"Ġ XML\",\n \"ĠX ML\",\n \"ĠXM L\",\n \", &\",\n \"Ġ Louis\",\n \"ĠL ouis\",\n \"ĠLo uis\",\n \"ĠLou is\",\n \"Ġ ride\",\n \"Ġr ide\",\n \"Ġrid e\",\n \"Ġri de\",\n \"_ FAIL\",\n \"_F AIL\",\n \"_FA IL\",\n \"Ġ chunk\",\n \"Ġch unk\",\n \"Ġchu nk\",\n \"[ s\",\n \"_ OUT\",\n \"_O UT\",\n \"Ġ chosen\",\n \"Ġch osen\",\n \"Ġcho sen\",\n \"Ġchose n\",\n \"_ [\",\n \"/ (\",\n \"Ġ Jeff\",\n \"ĠJ eff\",\n \"ĠJe ff\",\n \"_ sl\",\n \"_s l\",\n \"p riv\",\n \"pr iv\",\n \"pri v\",\n \"Ġ Canadian\",\n \"ĠCan adian\",\n \"Ġ unable\",\n \"Ġu nable\",\n \"Ġun able\",\n \"Ġuna ble\",\n \"Ġunab le\",\n \"_ FLAG\",\n \"_F LAG\",\n \"_FL AG\",\n \"Ġ nos\",\n \"Ġn os\",\n \"Ġno s\",\n \"h igh\",\n \"hi gh\",\n \"Ġ lift\",\n \"Ġl ift\",\n \"Ġli ft\",\n \"Ġlif t\",\n \"f un\",\n \"fu n\",\n \"( ){\",\n \"() {\",\n \"el ly\",\n \"ell y\",\n \"ycler View\",\n \"_ as\",\n \"_a s\",\n \"_ LIST\",\n \"_L IST\",\n \"Ġ radi\",\n \"Ġr adi\",\n \"Ġrad i\",\n \"Ġra di\",\n \". getValue\",\n \".get Value\",\n \"3 04\",\n \"30 4\",\n \"ĠAnge les\",\n \"ĠAngel es\",\n \"Ġ Span\",\n \"ĠS pan\",\n \"ĠSp an\",\n \"ĠSpa n\",\n \"_ instance\",\n \"_in stance\",\n \"_inst ance\",\n \"i tors\",\n \"it ors\",\n \"itor s\",\n \"ito rs\",\n \"2 08\",\n \"20 8\",\n \"Ġ migration\",\n \"Ġm igration\",\n \"Ġmigr ation\",\n \"Ġmig ration\",\n \"A K\",\n \"O h\",\n \" ®\",\n \". selected\",\n \".se lected\",\n \".select ed\",\n \".sel ected\",\n \"Ġ GT\",\n \"ĠG T\",\n \"Ġ advance\",\n \"Ġadv ance\",\n \"Ġ Style\",\n \"ĠSt yle\",\n \"ĠSty le\",\n \". DataGridView\",\n \".Data GridView\",\n \"e ction\",\n \"ect ion\",\n \"ec tion\",\n \"Ñ İ\",\n \"p io\",\n \"pi o\",\n \"r og\",\n \"ro g\",\n \"Ġ shopping\",\n \"Ġsh opping\",\n \"Ġshop ping\",\n \"Ġsho pping\",\n \"Ġ Rect\",\n \"ĠR ect\",\n \"ĠRe ct\",\n \"ĠRec t\",\n \"I lluminate\",\n \"O U\",\n \"ĉ array\",\n \"ĉa rray\",\n \"ĉarr ay\",\n \"ĉar ray\",\n \"Ġsub stantial\",\n \"Ġsubstant ial\",\n \"Ġp regn\",\n \"Ġpre gn\",\n \"Ġpreg n\",\n \"Ġprom ote\",\n \"Ġpromot e\",\n \"Ġpromo te\",\n \"I EW\",\n \"IE W\",\n \". Layout\",\n \".L ayout\",\n \"Ġsign s\",\n \"Ġsig ns\",\n \"/ .\",\n \"Ġ letters\",\n \"Ġlet ters\",\n \"Ġletter s\",\n \"Ġlett ers\",\n \"B oard\",\n \"Bo ard\",\n \"c trl\",\n \"ct rl\",\n \"ctr l\",\n \"\\\" \\\\\",\n \"Ġ Jones\",\n \"ĠJ ones\",\n \"ĠJo nes\",\n \"ĠJon es\",\n \"Ġ vertex\",\n \"Ġver tex\",\n \"Ġvert ex\",\n \"Ġverte x\",\n \"Ġ ja\",\n \"Ġj a\",\n \"Ġaff ili\",\n \"Ġ wealth\",\n \"Ġwe alth\",\n \"ĉ default\",\n \"ĉd efault\",\n \"ĉdef ault\",\n \"ĉde fault\",\n \"Ġsign ificantly\",\n \"Ġsignific antly\",\n \"Ġsignificant ly\",\n \"Ġ ec\",\n \"Ġe c\",\n \"Ġ xs\",\n \"Ġx s\",\n \"act ual\",\n \"ac tual\",\n \". per\",\n \".p er\",\n \".pe r\",\n \"_ step\",\n \"_s tep\",\n \"_st ep\",\n \"_ste p\",\n \"an vas\",\n \"m ac\",\n \"ma c\",\n \"Ġtrans l\",\n \"Ġtran sl\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"It erator\",\n \"Iter ator\",\n \"Ġ och\",\n \"Ġo ch\",\n \"Ġoc h\",\n \"ag nostic\",\n \"agnost ic\",\n \"Ġ During\",\n \"ĠD uring\",\n \"ĠDu ring\",\n \"ĠDur ing\",\n \"Ġ DEFAULT\",\n \"ĠD EFAULT\",\n \"ĠDE FAULT\",\n \"Ġt ill\",\n \"Ġti ll\",\n \"Ġtil l\",\n \"Ġ signature\",\n \"Ġsign ature\",\n \"Ġsig nature\",\n \"Ġ bird\",\n \"Ġb ird\",\n \"Ġbi rd\",\n \"Ġbir d\",\n \"Ġ Ol\",\n \"ĠO l\",\n \"3 10\",\n \"31 0\",\n \"Ġ Ir\",\n \"ĠI r\",\n \"H S\",\n \"av atar\",\n \"ava tar\",\n \"ESS AGE\",\n \"Ġe lev\",\n \"Ġel ev\",\n \"Ġele v\",\n \"Ġ mt\",\n \"Ġm t\",\n \"Ġ Nav\",\n \"ĠN av\",\n \"ĠNa v\",\n \"Ġrel ax\",\n \"Ġ plate\",\n \"Ġp late\",\n \"Ġpl ate\",\n \"Ġpla te\",\n \"Ġplat e\",\n \"I TEM\",\n \"IT EM\",\n \"ITE M\",\n \"( date\",\n \"(d ate\",\n \"(dat e\",\n \"(da te\",\n \". not\",\n \".n ot\",\n \".no t\",\n \"Ġ grade\",\n \"Ġg rade\",\n \"Ġgr ade\",\n \"Ġgrad e\",\n \"Ġgra de\",\n \"Ġ }),Ċ\",\n \"Ġ} ),Ċ\",\n \"Ġ}) ,Ċ\",\n \"Ġ}), Ċ\",\n \"? \\\"ĊĊ\",\n \"?\\\" ĊĊ\",\n \"?\\\"Ċ Ċ\",\n \"i ences\",\n \"ie nces\",\n \"ience s\",\n \"ien ces\",\n \"H igh\",\n \"Hi gh\",\n \"Ġ DIS\",\n \"ĠD IS\",\n \"ĠDI S\",\n \"2 31\",\n \"23 1\",\n \"dis abled\",\n \"disable d\",\n \"Q UI\",\n \"QU I\",\n \"Ġ 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Common\",\n \".Com mon\",\n \".Comm on\",\n \"T ip\",\n \"Ti p\",\n \"A NK\",\n \"AN K\",\n \"Ġ FC\",\n \"ĠF C\",\n \"D uring\",\n \"Du ring\",\n \"Dur ing\",\n \"Ġ Symfony\",\n \"ĠSym fony\",\n \"Ġdef ensive\",\n \"k m\",\n \") >\",\n \"a rchive\",\n \"arch ive\",\n \"arc hive\",\n \"Ġ URI\",\n \"ĠU RI\",\n \"ĠUR I\",\n \"y cling\",\n \"yc ling\",\n \"ycl ing\",\n \"- o\",\n \"Ġ Website\",\n \"ĠWe bsite\",\n \"ĠWeb site\",\n \"A MP\",\n \"AM P\",\n \"4 05\",\n \"40 5\",\n \"ish ment\",\n \"Ġdo ctors\",\n \"Ġdoc tors\",\n \"Ġdoctor s\",\n \"D irect\",\n \"Dir ect\",\n \"Di rect\",\n \"Dire ct\",\n \"A RI\",\n \"AR I\",\n \"Ġ Redirect\",\n \"ĠRe direct\",\n \"ĠRed irect\",\n \"i eren\",\n \"ie ren\",\n \"ier en\",\n \"iere n\",\n \"9 60\",\n \"96 0\",\n \"_ dist\",\n \"_d ist\",\n \"_dis t\",\n \"_di st\",\n \"y o\",\n \"Ġ Progress\",\n \"ĠPro gress\",\n \"Ġz um\",\n \"Ġzu m\",\n \"Ġme mor\",\n \"Ġmem or\",\n \"Ġmemo r\",\n \"Ġ ED\",\n \"ĠE D\",\n \"Ġ jur\",\n \"Ġj ur\",\n \"Ġju r\",\n 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\"place ment\",\n \"Ġ Host\",\n \"ĠH ost\",\n \"ĠHo st\",\n \"ĠHos t\",\n \"S ound\",\n \"So und\",\n \"Sou nd\",\n \"и м\",\n \"Ġs ought\",\n \"Ġso ught\",\n \"Ġsou ght\",\n \"4 02\",\n \"40 2\",\n \"m id\",\n \"mi d\",\n \"Ġ salary\",\n \"Ġs alary\",\n \"Ġsal ary\",\n \"Ġsala ry\",\n \"o gg\",\n \"og g\",\n \"âĦ ¢\",\n \"b ul\",\n \"bu l\",\n \"Ġw ir\",\n \"Ġwi r\",\n \"valid ator\",\n \"_ STAT\",\n \"_ST AT\",\n \"_STA T\",\n \". store\",\n \".st ore\",\n \"Ġ Battle\",\n \"ĠB attle\",\n \"ĠBat tle\",\n \"ĠBatt le\",\n \"ı n\",\n \"Ġ -->ĊĊ\",\n \"Ġ-- >ĊĊ\",\n \"Ġ-->Ċ Ċ\",\n \"Ġ--> ĊĊ\",\n \"Tr ump\",\n \"d ot\",\n \"do t\",\n \"Ġ CONT\",\n \"ĠC ONT\",\n \"ĠCON T\",\n \"ĠCO NT\",\n \". fetch\",\n \".f etch\",\n \"Ġcont inu\",\n \"Ġcontin u\",\n \"w as\",\n \"wa s\",\n \"Ġf raud\",\n \"Ġfr aud\",\n \"Ġfra ud\",\n \"Ġfrau d\",\n \"_ tmp\",\n \"_t mp\",\n \"_tm p\",\n \"m itter\",\n \"mit ter\",\n \"mitt er\",\n \". pictureBox\",\n \".p ictureBox\",\n \".picture Box\",\n \"G A\",\n \"Ġ tournament\",\n \"Ġt ournament\",\n \". 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Top\",\n \".T op\",\n \".To p\",\n \"3 73\",\n \"37 3\",\n \". Page\",\n \".P age\",\n \"={ `\",\n \"Ġ ;čĊ\",\n \"Ġ; čĊ\",\n \"de pth\",\n \"dep th\",\n \"dept h\",\n \"m ann\",\n \"man n\",\n \"ma nn\",\n \"W D\",\n \"Ġ Som\",\n \"ĠS om\",\n \"ĠSo m\",\n \". Right\",\n \".R ight\",\n \"Ġ )}Ċ\",\n \"Ġ) }Ċ\",\n \"Ġ trait\",\n \"Ġt rait\",\n \"Ġtr ait\",\n \"Ġtra it\",\n \"Ġtrai t\",\n \"à Ĺ\",\n \"i ac\",\n \"ia c\",\n \"Ġ rv\",\n \"Ġr v\",\n \"S ample\",\n \"Sam ple\",\n \". Xml\",\n \".X ml\",\n \"o pped\",\n \"op ped\",\n \"opp ed\",\n \"Ġ ÑĦ\",\n \"ĠÑ Ħ\",\n \"l ists\",\n \"li sts\",\n \"list s\",\n \"lis ts\",\n \"Ġt ear\",\n \"Ġte ar\",\n \"Ġtea r\",\n \"ivers ary\",\n \". collection\",\n \".c ollection\",\n \".col lection\",\n \".collect ion\",\n \".coll ection\",\n \"ĠCon stitution\",\n \"ĠConst itution\",\n \"ĠConstit ution\",\n \"Ġ HttpResponse\",\n \"ĠHttp Response\",\n \"Ġbr ill\",\n \"Ġbri ll\",\n \"Ġ Prom\",\n \"ĠP rom\",\n \"ĠPro m\",\n \"ĠPr om\",\n \"h over\",\n \"ho ver\",\n \"3 66\",\n \"36 6\",\n \"Ġ Miami\",\n \"ĠM iami\",\n \"ĠMi ami\",\n \"ĠMia mi\",\n \"Ġar gue\",\n \"Ġarg ue\",\n \"_ float\",\n \"_f loat\",\n \"5 04\",\n \"50 4\",\n \"Ġ ãĤ\",\n \"Ġ nat\",\n \"Ġn at\",\n \"Ġna t\",\n \"ĠT al\",\n \"ĠTa l\",\n \"Ġ integration\",\n \"Ġint egration\",\n \"Ġinteg ration\",\n \"Ġintegr ation\",\n \"( cur\",\n \"(c ur\",\n \"Ġre moving\",\n \"Ġrem oving\",\n \"Ġ coeff\",\n \"Ġc oeff\",\n \"Ġco eff\",\n \"Ġcoef f\",\n \"Ġ Though\",\n \"ĠTh ough\",\n \"ĠThou gh\",\n \"Ġ forecast\",\n \"Ġfor ecast\",\n \"Ġfore cast\",\n \"4 08\",\n \"40 8\",\n \"ĠV egas\",\n \"ĠVe gas\",\n \"ĠVega s\",\n \"ĠVeg as\",\n \"S ite\",\n \"Si te\",\n \"Sit e\",\n \"3 46\",\n \"34 6\",\n \"Ġt rab\",\n \"Ġtr ab\",\n \"Ġtra b\",\n \"Ġ Henry\",\n \"ĠHen ry\",\n \"- i\",\n \"Ġinv olves\",\n \"Ġinvol ves\",\n \"Ġinvolve s\",\n \"B T\",\n \"Ġs lo\",\n \"Ġsl o\",\n \"In voke\",\n \"Inv oke\",\n \"Ġl ucky\",\n \"Ġluck y\",\n \"Ġlu cky\",\n \"Ġluc ky\",\n \"0 25\",\n \"02 5\",\n \"r at\",\n \"ra t\",\n \"Ġ ?Ċ\",\n \"Ġ? Ċ\",\n \"Ġ handled\",\n \"Ġhand led\",\n \"Ġhandle d\",\n \"( fd\",\n \"(f d\",\n \"cont ents\",\n \"content s\",\n \"conte nts\",\n \"Ġ OFF\",\n \"ĠO FF\",\n \"ĠOF F\",\n \"R F\",\n \"Ġ sty\",\n \"Ġs ty\",\n \"Ġst y\",\n \"Ġ Motor\",\n \"ĠM otor\",\n \"ĠMo tor\",\n \"ĠMot or\",\n \"ĠMoto r\",\n \"t ery\",\n \"ter y\",\n \"te ry\",\n \"t ax\",\n \"ta x\",\n \"M AP\",\n \"MA P\",\n \"Ġ Mrs\",\n \"ĠM rs\",\n \"ĠMr s\",\n \"Ġ phones\",\n \"Ġph ones\",\n \"Ġphone s\",\n \"Ġphon es\",\n \"Ġ UIView\",\n \"ĠUI View\",\n \"\\\" )));Ċ\",\n \"\\\") ));Ċ\",\n \"\\\")) );Ċ\",\n \"\\\"))) ;Ċ\",\n \"\\\"))); Ċ\",\n \"( dev\",\n \"(d ev\",\n \"(de v\",\n \"ĠI rish\",\n \"ĠIr ish\",\n \"ĠIris h\",\n \"0 19\",\n \"01 9\",\n \"Ġ ws\",\n \"Ġw s\",\n \"D I\",\n \"_ OFFSET\",\n \"_OFF SET\",\n \"Ġ Events\",\n \"ĠE vents\",\n \"ĠEvent s\",\n \"ĠEven ts\",\n \"ĠEv ents\",\n \"ĠEve nts\",\n \"Ġst ages\",\n \"Ġstage s\",\n \"Ġsta ges\",\n \"Ġstag es\",\n \"Ġ }//\",\n \"Ġ} //\",\n \"Ġh aben\",\n \"Ġhab en\",\n \"Ġha ben\",\n \"Ġhabe n\",\n \"ST ANCE\",\n \"Ġ Sin\",\n \"ĠS in\",\n \"ĠSi n\",\n \"Ġ Money\",\n \"ĠM oney\",\n \"ĠMon ey\",\n \"ĠMo ney\",\n \"( top\",\n \"(t op\",\n \"(to p\",\n \"Ġ appointment\",\n \"Ġapp ointment\",\n \"Ġappoint ment\",\n \"V ERSION\",\n \"VER SION\",\n \"VERS ION\",\n \"m etadata\",\n \"met adata\",\n \"meta data\",\n \"_ comment\",\n \"_com ment\",\n \"_comm ent\",\n \"Ġcolle agues\",\n \"Ġcolleague s\",\n \"m aps\",\n \"ma ps\",\n \"map s\",\n \"â ĺ\",\n \"Ċ ĉĊ\",\n \"( al\",\n \"(a l\",\n \"_ req\",\n \"_re q\",\n \"_r eq\",\n \"Ġf ut\",\n \"Ġfu t\",\n \"Ġ architecture\",\n \"Ġarch itecture\",\n \"Ġarchitect ure\",\n \"Ġarchit ecture\",\n \"3 51\",\n \"35 1\",\n \"ĠWH ETHER\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"_ screen\",\n \"_s creen\",\n \"_sc reen\",\n \"_scr een\",\n \"Ġstyle Urls\",\n \"Ġ monster\",\n \"Ġmon ster\",\n \". up\",\n \".u p\",\n \"ph ia\",\n \"phi a\",\n \"Ġ processor\",\n \"Ġprocess or\",\n \"Ġproc essor\",\n \"Ġprocesso r\",\n \"Ġ Terr\",\n \"ĠT err\",\n \"ĠTe rr\",\n \"ĠTer r\",\n \"= ',\",\n \"=' ,\",\n \"Ġ Manufact\",\n \"ĠMan ufact\",\n \"Ġ NT\",\n \"ĠN T\",\n \"k el\",\n \"ke l\",\n \"i bern\",\n \"ib ern\",\n \"iber n\",\n \"ibe rn\",\n \"ĉ file\",\n \"ĉf ile\",\n \"ĉfi le\",\n \"A li\",\n \"Al i\",\n \"rient ation\",\n \"Ġ //!\",\n \"Ġ// !\",\n \"ap ore\",\n \"apor e\",\n \"apo re\",\n \"an eous\",\n \"ane ous\",\n \"Ġ Creat\",\n \"ĠC reat\",\n \"ĠCr eat\",\n \"ĠCre at\",\n \"f older\",\n \"fo lder\",\n \"fol der\",\n \"fold er\",\n \"4 15\",\n \"41 5\",\n \"Ġ hay\",\n \"Ġh ay\",\n \"Ġha y\",\n \"Sup press\",\n \"( left\",\n \"(l eft\",\n \"(le ft\",\n \"Ġe uro\",\n \"Ġeu ro\",\n \"Ġdis claimer\",\n \"u stry\",\n \"us try\",\n \"ust ry\",\n \"ustr y\",\n \"s hips\",\n \"sh ips\",\n \"ship s\",\n \"shi ps\",\n \"_ fd\",\n \"_f d\",\n \"Ġ Fa\",\n \"ĠF a\",\n \"_ insert\",\n \"_in sert\",\n \"_ins ert\",\n \"Ġ rol\",\n \"Ġr ol\",\n \"Ġro l\",\n \"if ting\",\n \"ift ing\",\n \"Ġ Comments\",\n \"ĠCom ments\",\n \"ĠComm ents\",\n \"ĠComment s\",\n \"_ br\",\n \"_b r\",\n \"Ġlos ses\",\n \"Ġloss es\",\n \"Ġ Added\",\n \"ĠAd ded\",\n \"ĠAdd ed\",\n \"ch arg\",\n \"char g\",\n \"cha 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Mouse\",\n \".M ouse\",\n \"at eral\",\n \"ate ral\",\n \"ater al\",\n \"p aper\",\n \"pa per\",\n \"Con st\",\n \"Co nst\",\n \"Cons t\",\n \"Ġ VR\",\n \"ĠV R\",\n \"_ entity\",\n \"_e ntity\",\n \"_ent ity\",\n \"_ CTRL\",\n \"_C TRL\",\n \"_CT RL\",\n \"Ġ Protection\",\n \"ĠPro tection\",\n \"ĠProt ection\",\n \"ĠProte ction\",\n \"ĠProtect ion\",\n \"Ġ GM\",\n \"ĠG M\",\n \"Ġ Study\",\n \"ĠSt udy\",\n \"ĠStud y\",\n \"Ġ soup\",\n \"Ġs oup\",\n \"Ġso up\",\n \"Ġsou p\",\n \"o time\",\n \"ot ime\",\n \"oti me\",\n \"' use\",\n \"'u se\",\n \"] \\\"\",\n \"/ users\",\n \"/user s\",\n \"/use rs\",\n \"/us ers\",\n \"a ug\",\n \"au g\",\n \"Ġ Hong\",\n \"ĠH ong\",\n \"ĠHon g\",\n \"ĠHo ng\",\n \"_ norm\",\n \"_n orm\",\n \"_no rm\",\n \"ãģ ¨\",\n \"Ġse cre\",\n \"Ġsec re\",\n \"( Build\",\n \"(B uild\",\n \"Ġ Contract\",\n \"ĠCon tract\",\n \"ĠCont ract\",\n \"ĠContr act\",\n \"o las\",\n \"ol as\",\n \"ola s\",\n \"Ġs auce\",\n \"Ġsa uce\",\n \"Ġsau ce\",\n \"Ġag gressive\",\n \"Ġaggress ive\",\n \"Ġagg ressive\",\n \"Ġ racial\",\n \"Ġr acial\",\n \"Ġrac ial\",\n \"Ġra cial\",\n \"char acter\",\n \"@ @\",\n \"Ġ compile\",\n \"Ġcom pile\",\n \"Ġcomp ile\",\n \"Ġcompil e\",\n \"Ġ Void\",\n \"ĠV oid\",\n \"ĠVo id\",\n \"_ rem\",\n \"_re m\",\n \"_r em\",\n \"_ memory\",\n \"_m emory\",\n \"_mem ory\",\n \"3 48\",\n \"34 8\",\n \"k k\",\n \"Ġ mic\",\n \"Ġm ic\",\n \"Ġmi c\",\n \"S ame\",\n \"Sam e\",\n \"Sa me\",\n \"U tility\",\n \"Util ity\",\n \"Ut ility\",\n \"Ġ Html\",\n \"ĠH tml\",\n \"Ġ Xml\",\n \"ĠX ml\",\n \"ĠXm l\",\n \"Re ady\",\n \"Read y\",\n \"Ġg all\",\n \"Ġga ll\",\n \"Ġgal l\",\n \"Ġalleg edly\",\n \"Ġalleged ly\",\n \"ĉ ĉĉĉĠĠĠ\",\n \"ĉĉ ĉĉĠĠĠ\",\n \"ĉĉĉĉ ĠĠĠ\",\n \"ĉĉĉ ĉĠĠĠ\",\n \"ĉĉĉĉĠ ĠĠ\",\n \"ĉĉĉĉĠĠ Ġ\",\n \"Ġ Metal\",\n \"ĠM etal\",\n \"ĠMe tal\",\n \"ĠMet al\",\n \"ĠMeta l\",\n \"Ġ Personal\",\n \"ĠPerson al\",\n \"ĠPers onal\",\n \"ĠPersona l\",\n \"Ġborder Radius\",\n \"rx js\",\n \"object s\",\n \"obj ects\",\n \"Ġwant ing\",\n \"Ġwan ting\",\n \"Ġb owl\",\n \"Ġbo wl\",\n \"Ġbow l\",\n \"v endor\",\n \"offset of\",\n \"Ġ Rs\",\n \"ĠR s\",\n \"Ġ Rating\",\n \"ĠR ating\",\n \"ĠRa ting\",\n \"ĠRat ing\",\n \"Ġr ally\",\n \"Ġrall y\",\n \"_ NODE\",\n \"_N ODE\",\n \"_NO DE\",\n \"4 18\",\n \"41 8\",\n \"Ġ Mix\",\n \"ĠM ix\",\n \"ĠMi x\",\n \"Ġad vertis\",\n \"Ġadvert is\",\n \"4 85\",\n \"48 5\",\n \"6 67\",\n \"66 7\",\n \"Ġnarr ative\",\n \"s al\",\n \"sa l\",\n \"Ġ mc\",\n \"Ġm c\",\n \"S Error\",\n \"SE rror\",\n \"Ġf ingers\",\n \"Ġfin gers\",\n \"Ġfinger s\",\n \"Ġfing ers\",\n \"Ġac company\",\n \"Ġaccom pany\",\n \"Ġaccomp any\",\n \"Ġt ired\",\n \"Ġti red\",\n \"Ġtire d\",\n \"Ġtir ed\",\n \"Ġ stride\",\n \"Ġst ride\",\n \"Ġstr ide\",\n \"Ġstri de\",\n \"Ġ gui\",\n \"Ġg ui\",\n \"Ġgu i\",\n \"e list\",\n \"el ist\",\n \"eli st\",\n \"L ocale\",\n \"Lo cale\",\n \"Local e\",\n \"Loc ale\",\n \"Ġre leases\",\n \"Ġrelease s\",\n \"Ġrele ases\",\n \"i king\",\n \"ik ing\",\n \"iki ng\",\n \"Ġ anger\",\n \"Ġa nger\",\n \"Ġan ger\",\n \"Ġang er\",\n \"Ġange r\",\n \") ))ĊĊ\",\n \")) )ĊĊ\",\n \")))Ċ Ċ\",\n \"))) ĊĊ\",\n \"al lest\",\n \"all est\",\n \"alle st\",\n \"alles t\",\n \"Sum mary\",\n \"( O\",\n \"( for\",\n \"(f or\",\n \"Ġbasket ball\",\n \"Ġ roads\",\n \"Ġro ads\",\n \"Ġroad s\",\n \"Ġ Install\",\n \"ĠInst all\",\n \"Ġ Fab\",\n \"ĠF ab\",\n \"ĠFa b\",\n \"it map\",\n \"itm ap\",\n \"4 75\",\n \"47 5\",\n \"Ġ ))Ċ\",\n \"Ġ) )Ċ\",\n \"Ġ)) Ċ\",\n \"Ġ intersection\",\n \"Ġinter section\",\n \"Ġintersect ion\",\n \"Ġinters ection\",\n \"igh bor\",\n \"ighb or\",\n \"ĠB ry\",\n \"ĠBr y\",\n \"Ġ HERE\",\n \"ĠH ERE\",\n \"ĠHE RE\",\n \"ĠHER E\",\n \"S oftware\",\n \"So ftware\",\n \"Soft ware\",\n \"el fare\",\n \"elf are\",\n \"a cs\",\n \"ac s\",\n \"6 22\",\n \"62 2\",\n \"Ġtr ailer\",\n \"Ġtrail er\",\n \"Ġtra iler\",\n \"Ġtrai ler\",\n \". getClass\",\n \".get Class\",\n \".getC lass\",\n \"ch ars\",\n \"char s\",\n \"cha rs\",\n \"Ġreg ulation\",\n \"Ġregul ation\",\n \"Ġre fers\",\n \"Ġref ers\",\n \"Ġrefer s\",\n \"Ġd estruction\",\n \"Ġde struction\",\n \"Ġdestruct ion\",\n \"Ġ continuous\",\n \"Ġcontin uous\",\n \"Ġcontinu ous\",\n \"Ġ Austin\",\n \"ĠA ustin\",\n \"ĠAust in\",\n \"ĠAus tin\",\n \"ĠAu stin\",\n \"é ¢\",\n \"a kan\",\n \"ak an\",\n \"aka n\",\n \". window\",\n \".w indow\",\n \".wind ow\",\n \"Ġ Templates\",\n \"ĠT emplates\",\n \"ĠTem plates\",\n \"ĠTemplate s\",\n \"ĠTemp lates\",\n \"ĠTempl ates\",\n \"Ġabs ence\",\n \": n\",\n \"Ġdis order\",\n \"f lash\",\n \"fl ash\",\n \"Ġde let\",\n \"Ġdel et\",\n \"Ġdele t\",\n \"bo ards\",\n \"board s\",\n \"Ġ Ġĉ\",\n \"ĠĠ ĉ\",\n \"R OP\",\n \"RO P\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġac qu\",\n \"Ġlaw suit\",\n \"Ġlaws uit\",\n \"Ġ Reviews\",\n \"ĠRe views\",\n \"ĠReview s\",\n \"Ġgar age\",\n \"Ġga rage\",\n \"t imer\",\n \"time r\",\n \"ti mer\",\n \"tim er\",\n \"Ġ ej\",\n \"Ġe j\",\n \"Ġ Rectangle\",\n \"ĠRect angle\",\n \"Ġ flowers\",\n \"Ġfl owers\",\n \"Ġflow ers\",\n \"Ġflo wers\",\n \"Ġflower s\",\n \"3 98\",\n \"39 8\",\n \"i lst\",\n \"il st\",\n \"ils t\",\n \"Ġ Instance\",\n \"ĠIn stance\",\n \"ĠInst ance\",\n \"S uper\",\n \"Sup er\",\n \"Su per\",\n \"d et\",\n \"de t\",\n \"dis posing\",\n \"disp osing\",\n \"Ġ ES\",\n \"ĠE S\",\n \"Ġ IC\",\n \"ĠI C\",\n \"v ere\",\n \"ver e\",\n \"ve re\",\n \"S k\",\n \"_ channels\",\n \"_ch annels\",\n \"_channel s\",\n \"_chan nels\",\n \"p uted\",\n \"put ed\",\n \"pu ted\",\n \"pute d\",\n \"/ null\",\n \"/n ull\",\n \"n nen\",\n \"nn en\",\n \"4 31\",\n \"43 1\",\n \"Ġ Gallery\",\n \"ĠG allery\",\n \"ĠGall ery\",\n \"_ global\",\n \"_g lobal\",\n \"_glob al\",\n \"Auth entication\",\n \"Ġ Rank\",\n \"ĠR ank\",\n \"ĠRa nk\",\n \"ĠRan k\",\n \"Ġ blocked\",\n \"Ġb locked\",\n \"Ġbl ocked\",\n \"Ġblock ed\",\n \"Ġbloc ked\",\n \"Ġc alm\",\n \"Ġcal m\",\n \"Ġca lm\",\n \"m arket\",\n \"mark et\",\n \"mar ket\",\n \"ĉ val\",\n \"ĉv al\",\n \"ĉva l\",\n \"Ġ aug\",\n \"Ġa ug\",\n \"Ġau g\",\n \"p eriod\",\n \"per iod\",\n \"peri od\",\n \"Ġ Constant\",\n \"ĠCon stant\",\n \"ĠCons tant\",\n \"ĠConst ant\",\n \"Ġ ?>\\\">Ċ\",\n \"Ġ?> \\\">Ċ\",\n \"Ġ?>\\\" >Ċ\",\n \"Ġ?>\\\"> Ċ\",\n \"Ġ lobby\",\n \"Ġl obby\",\n \"Ġlob by\",\n \"p al\",\n \"pa l\",\n \"3 79\",\n \"37 9\",\n \"Ġ sink\",\n \"Ġs ink\",\n \"Ġsi nk\",\n \"Ġsin k\",\n \"5 08\",\n \"50 8\",\n \"i ah\",\n \"ia h\",\n \"Ð ¡\",\n \"ur name\",\n \"urn ame\",\n \"Ġcon ver\",\n \"Ġconv er\",\n \"Ġinvest igate\",\n \"Ġinvestig ate\",\n \"Ch rist\",\n \"Chris t\",\n \"Chr ist\",\n \"H ub\",\n \"Hu b\",\n \"Ġ IND\",\n \"ĠI ND\",\n \"ĠIN D\",\n \"Ġ Ped\",\n \"ĠP ed\",\n \"ĠPe d\",\n \"u ras\",\n \"ur as\",\n \"ura s\",\n \"ĉ url\",\n \"ĉu rl\",\n \"Ġ Tro\",\n \"ĠT ro\",\n \"ĠTr o\",\n \"Ġ preferences\",\n \"Ġp references\",\n \"Ġpre ferences\",\n \"Ġprefer ences\",\n \"Ġpreference s\",\n \"Ġguar anteed\",\n \"Ġguarante ed\",\n \"Ġguarantee d\",\n \"` ĊĊ\",\n \"`Ċ Ċ\",\n \"Ġport ions\",\n \"Ġportion s\",\n \"Ġe valu\",\n \"Ġev alu\",\n \"Ġeval u\",\n \"' > < /\",\n \"( ){ĊĊ\",\n \"() {ĊĊ\",\n \"(){Ċ Ċ\",\n \"(){ ĊĊ\",\n \"en coded\",\n \"enc oded\",\n \"encode d\",\n \"enco ded\",\n \"z illa\",\n \"zi lla\",\n \". Class\",\n \".C lass\",\n \".Cl ass\",\n \"Ġ *_\",\n \"Ġ* _\",\n \"_ '\",\n \"Ġview ed\",\n \"Ġvi ewed\",\n \"Ġvie wed\",\n \"Ġ Philadelphia\",\n \"ĠPhil adelphia\",\n \". rows\",\n \".r ows\",\n \".row s\",\n \".ro ws\",\n \"Add ed\",\n \"Ad ded\",\n \"Ġ Touch\",\n \"ĠT ouch\",\n \"ĠTo uch\",\n \"ĠTou ch\",\n \"8 40\",\n \"84 0\",\n \". delegate\",\n \".de legate\",\n \"quee ze\",\n \"s lide\",\n \"sl ide\",\n \"Ġ Senior\",\n \"ĠSen ior\",\n \"( tag\",\n \"(t ag\",\n \"Ġinter views\",\n \"Ġinterview s\",\n \"Ġs ua\",\n \"Ġsu a\",\n \"a tas\",\n \"at as\",\n \"ata s\",\n \"@ ĊĊ\",\n \"@Ċ Ċ\",\n \"d istance\",\n \"di stance\",\n \"dist ance\",\n \"Ġ sein\",\n \"Ġs ein\",\n \"Ġse in\",\n \"Ġsei n\",\n \"l atest\",\n \"la test\",\n \"late st\",\n \"lat est\",\n \"lates t\",\n \"Ġ Prince\",\n \"ĠPr ince\",\n \"ĠPri nce\",\n \"Ġlux ury\",\n \"Ġre fr\",\n \"Ġref r\",\n \"Ġ Kitchen\",\n \"ĠK itchen\",\n \"ĠKit chen\",\n \"Ñ Ħ\",\n \"( at\",\n \"(a t\",\n \"F inal\",\n \"Fin al\",\n \"Fi nal\",\n \"ü ck\",\n \"üc k\",\n \"_ zero\",\n \"_z ero\",\n \"Ġ ABC\",\n \"ĠA BC\",\n \"ĠAB C\",\n \"Ġ Manchester\",\n \"ĠMan chester\",\n \"Ġ cow\",\n \"Ġc ow\",\n \"Ġco w\",\n \"C OL\",\n \"CO L\",\n \"_ NUMBER\",\n \"_NUM BER\",\n \"ch anges\",\n \"change s\",\n \"chan ges\",\n \"chang es\",\n \"g enerate\",\n \"gen erate\",\n \"gener ate\",\n \"gene rate\",\n \". Printf\",\n \".Print f\",\n \"3 69\",\n \"36 9\",\n \"s hare\",\n \"sh are\",\n \"sha re\",\n \"St ock\",\n \"Ġ PT\",\n \"ĠP T\",\n \"A nim\",\n \"An im\",\n \"a nga\",\n \"an ga\",\n \"ang a\",\n \"Ġ ig\",\n \"Ġi g\",\n \"up loads\",\n \"upload s\",\n \"Ġ packed\",\n \"Ġp acked\",\n \"Ġpack ed\",\n \"Ġpac ked\",\n \"Ġ }];Ċ\",\n \"Ġ} ];Ċ\",\n \"Ġ}] ;Ċ\",\n \"( sender\",\n \"(s ender\",\n \"(se nder\",\n \"(send er\",\n \"Ġ Wire\",\n \"ĠW ire\",\n \"ĠWi re\",\n \"ĠWir e\",\n \"i sons\",\n \"is ons\",\n \"ison s\",\n \"iso ns\",\n \"Ġplay off\",\n \"\\\\ E\",\n \"6 08\",\n \"60 8\",\n \"/ R\",\n \"Ġ headed\",\n \"Ġhe aded\",\n \"Ġhead ed\",\n \"Al pha\",\n \"( order\",\n \"(ord er\",\n \"(or der\",\n \"Ġop ponents\",\n \"Ġopp onents\",\n \"Ġoppon ents\",\n \"Ġopponent s\",\n \"ack son\",\n \"acks on\",\n \"_ member\",\n \"_m ember\",\n \"_mem ber\",\n \"T urn\",\n \"Tur n\",\n \"Tu rn\",\n \"ĠSov iet\",\n \"ìĹ IJ\",\n \"a uge\",\n \"au ge\",\n \"aug e\",\n \"4 48\",\n \"44 8\",\n \"Ġ incoming\",\n \"Ġin coming\",\n \"Ġinc oming\",\n \"Ġincom ing\",\n \"Ġ jak\",\n \"Ġj ak\",\n \"Ġja k\",\n \"- game\",\n \"-g ame\",\n \"Ġ Male\",\n \"ĠM ale\",\n \"ĠMal e\",\n \"ĠMa le\",\n \"Ġ Month\",\n \"ĠM onth\",\n \"ĠMon th\",\n \"ĠMo nth\",\n \"ĠMont h\",\n \"St age\",\n \". exe\",\n \".e xe\",\n \".ex e\",\n \"Own Property\",\n \". setItem\",\n \".set Item\",\n \"Ġ dc\",\n \"Ġd c\",\n \"ä½ ľ\",\n \"Ġb rut\",\n \"Ġbr ut\",\n \"Ġbru t\",\n \"Ġattempt ing\",\n \". len\",\n \".l en\",\n \".le n\",\n \"Ġjud gment\",\n \"Ġs ab\",\n \"Ġsa b\",\n \"Ġ cad\",\n \"Ġc ad\",\n \"Ġca d\",\n \"Ġ Items\",\n \"ĠI tems\",\n \"ĠIt ems\",\n \"ĠItem s\",\n \"com fort\",\n \"el ize\",\n \"eli ze\",\n \"/ log\",\n \"/l og\",\n \"/lo g\",\n \"Ġentre prene\",\n \"Ġ compiler\",\n \"Ġc ompiler\",\n \"Ġcom piler\",\n \"Ġcomp iler\",\n \"Ġcompile r\",\n \"Ġcompil er\",\n \"_ validation\",\n \"_valid ation\",\n \"r eview\",\n \"re view\",\n \"rev iew\",\n \"Ġ textBox\",\n \"Ġtext Box\",\n \"Ġ fraction\",\n \"Ġf raction\",\n \"Ġfr action\",\n \"Ġfra ction\",\n \"Ġfract ion\",\n \"Ġfrac tion\",\n \"Ġ Bal\",\n \"ĠB al\",\n \"ĠBa l\",\n \"> ;ĊĊ\",\n \">;Ċ Ċ\",\n \">; 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Rec\",\n \".R ec\",\n \".Re c\",\n \"TR UE\",\n \"Ġint ensity\",\n \"Ġintens ity\",\n \"M arker\",\n \"Mark er\",\n \"Mar ker\",\n \". compare\",\n \".com pare\",\n \".comp are\",\n \"f fic\",\n \"ff ic\",\n \"ffi c\",\n \"C ookie\",\n \"Co okie\",\n \"Cook ie\",\n \"Ġ Baby\",\n \"ĠB aby\",\n \"ĠBa by\",\n \"ĠBab y\",\n \"Ġ BigDecimal\",\n \"ĠB igDecimal\",\n \"ĠBig Decimal\",\n \"i let\",\n \"il et\",\n \"ile t\",\n \"ĠHOLD ERS\",\n \"ĠHOLDER S\",\n \"Ġ Lady\",\n \"ĠL ady\",\n \"ĠLa dy\",\n \"ĠLad y\",\n \"Ġ lung\",\n \"Ġl ung\",\n \"Ġlu ng\",\n \"Ġlun g\",\n \"Ġ Alabama\",\n \"ĠAl abama\",\n \"Ġ dess\",\n \"Ġd ess\",\n \"Ġde ss\",\n \"Ġdes s\",\n \"` );Ċ\",\n \"`) ;Ċ\",\n \"Ġ Builder\",\n \"ĠB uilder\",\n \"ĠBuild er\",\n \"ĠBu ilder\",\n \"_ region\",\n \"_reg ion\",\n \"Ġ neutral\",\n \"Ġne utral\",\n \"Ġneut ral\",\n \"Ġneutr al\",\n \"9 09\",\n \"90 9\",\n \"B oth\",\n \"Bo th\",\n \"Bot h\",\n \"Ġ hp\",\n \"Ġh p\",\n \"Ġ horn\",\n \"Ġh orn\",\n \"Ġhor n\",\n \"Ġho rn\",\n \"Ġ segments\",\n \"Ġse gments\",\n \"Ġseg ments\",\n \"Ġsegment s\",\n \"Ġ EC\",\n \"ĠE C\",\n \"\\\" =>\\\"\",\n \"\\\"=> \\\"\",\n \"( rec\",\n \"(r ec\",\n \"(re c\",\n \"Ġ Pi\",\n \"ĠP i\",\n \"G M\",\n \"Ġl aptop\",\n \"Ġlap top\",\n \"S calar\",\n \"Sc alar\",\n \"Scala r\",\n \"4 63\",\n \"46 3\",\n \"i sd\",\n \"is d\",\n \"- dialog\",\n \"-d ialog\",\n \"-di alog\",\n \"Ġ Anderson\",\n \"ĠAnd erson\",\n \"ĠAnders on\",\n \"Ġmis takes\",\n \"Ġmist akes\",\n \"Ġmi stakes\",\n \"Ġmistake s\",\n \"7 08\",\n \"70 8\",\n \"Ġ Han\",\n \"ĠH an\",\n \"ĠHa n\",\n \"j es\",\n \"je s\",\n \"est ination\",\n \"esti nation\",\n \"4 36\",\n \"43 6\",\n \"Ġprom ises\",\n \"Ġpromise s\",\n \"b id\",\n \"bi d\",\n \"Ġ Scient\",\n \"ĠS cient\",\n \"ĠSc ient\",\n \"ĠSci ent\",\n \"G IN\",\n \"GI N\",\n \"Ġ Performance\",\n \"ĠPer formance\",\n \"ĠPerform ance\",\n \"b age\",\n \"ba ge\",\n \"bag e\",\n \". users\",\n \".user s\",\n \".use rs\",\n \".us ers\",\n \"le ading\",\n \"lead ing\",\n \"lea ding\",\n \"Ġ oral\",\n \"Ġo ral\",\n \"Ġor al\",\n \"Ġora l\",\n \"G raphics\",\n \"Graph ics\",\n \"Graphic s\",\n \"4 88\",\n \"48 8\",\n \"_ PTR\",\n \"_P TR\",\n \"_PT R\",\n \"5 18\",\n \"51 8\",\n \"h ang\",\n \"ha ng\",\n \"han g\",\n \"Ġin ev\",\n \"Ġi nev\",\n \"Ġine v\",\n \"p rocessing\",\n \"process ing\",\n \"F actor\",\n \"Fact or\",\n \"Fac tor\",\n \"Fa ctor\",\n \"Ġ NA\",\n \"ĠN A\",\n \"$ string\",\n \"$s tring\",\n \"$str ing\",\n \"Ġ grounds\",\n \"Ġgr ounds\",\n \"Ġground s\",\n \"Ġgro unds\",\n \"Ġgrou nds\",\n \".Save Changes\",\n \"c lock\",\n \"cl ock\",\n \"clo ck\",\n \"9 41\",\n \"94 1\",\n \"cri pcion\",\n \"Ġ Newton\",\n \"ĠNew ton\",\n \"g c\",\n \". includes\",\n \".in cludes\",\n \".include s\",\n \"Ġ blast\",\n \"Ġb last\",\n \"Ġbl ast\",\n \"Ġblas t\",\n \"Ġbla st\",\n \"Ġ' -'\",\n \"Ġ'- '\",\n \"Ġp uede\",\n \"Ġpued e\",\n \"Ġpu ede\",\n \"4 69\",\n \"46 9\",\n \". Session\",\n \".S ession\",\n \"Ġ grep\",\n \"Ġg rep\",\n \"Ġgr ep\",\n \"Ġgre p\",\n \"_ final\",\n \"_f inal\",\n \"_fin al\",\n \"Ġ Gay\",\n \"ĠG ay\",\n \"ĠGa y\",\n \"Ġ Give\",\n \"ĠG ive\",\n \"ĠGi ve\",\n \"i ri\",\n \"ir i\",\n \"- star\",\n \"-s tar\",\n \"-st ar\",\n \"Ġ UIImage\",\n \"ĠUI Image\",\n \"_ epoch\",\n \"_ep och\",\n \"u bb\",\n \"ub b\",\n \"e nth\",\n \"en th\",\n \"ent h\",\n \"Ġ elite\",\n \"Ġe lite\",\n \"Ġel ite\",\n \"Ġelit e\",\n \"Ġcampaign s\",\n \"Ġ Porno\",\n \"ĠP orno\",\n \"ĠPorn o\",\n \"ĠPor no\",\n \"_ assign\",\n \"_as sign\",\n \"_ass ign\",\n \"Prot ocol\",\n \"Proto col\",\n \"Ġ Being\",\n \"ĠB eing\",\n \"ĠBe ing\",\n \"ĠBei ng\",\n \"Ġ Airport\",\n \"ĠAir port\",\n \"Ġcon ventional\",\n \"Ġconvent ional\",\n \"Ġconvention al\",\n \"Ġ Wat\",\n \"ĠW at\",\n \"ĠWa t\",\n \"Ġ CI\",\n \"ĠC I\",\n \"E TA\",\n \"ET A\",\n \"Ġ Anthony\",\n \"ĠAnth ony\",\n \"Ġ tablet\",\n \"Ġtable t\",\n \"Ġtab let\",\n \"( format\",\n \"(form at\",\n \"(for mat\",\n \"Ġconsist ently\",\n \"Ġconsistent ly\",\n \"ĠI owa\",\n \"ĠIo wa\",\n \"4 74\",\n \"47 4\",\n \"Ġ avatar\",\n \"Ġav atar\",\n \"Ġava tar\",\n \"0 27\",\n \"02 7\",\n \". cursor\",\n \".c ursor\",\n \"! [\",\n \"Ġh anging\",\n \"Ġhang ing\",\n \"Ġhan ging\",\n \"Ġhangi ng\",\n \"H er\",\n \"He r\",\n \"S uch\",\n \"Su ch\",\n \"Suc h\",\n \"' ;ĊĊĊ\",\n \"';Ċ ĊĊ\",\n \"';ĊĊ Ċ\",\n \"'; ĊĊĊ\",\n \"org eous\",\n \"orge ous\",\n \"( )==\",\n \"() ==\",\n \"Ġ viewModel\",\n \"Ġview Model\",\n \"Ġ ãĥ\",\n \"Ġ els\",\n \"Ġe ls\",\n \"Ġel s\",\n \"Ġ Agent\",\n \"ĠA gent\",\n \"ĠAg ent\",\n \"ĠAge nt\",\n \"F etch\",\n \"a por\",\n \"ap or\",\n \"apo r\",\n \"Ġ cx\",\n \"Ġc x\",\n \"p read\",\n \"pr ead\",\n \"pre ad\",\n \"ĠP ier\",\n \"ĠPi er\",\n \"ĠPie r\",\n \"o eff\",\n \"oe ff\",\n \"6 16\",\n \"61 6\",\n \"S n\",\n \"8 90\",\n \"89 0\",\n \"Ġ Virtual\",\n \"ĠV irtual\",\n \"ĠVir tual\",\n \"ĠVirt ual\",\n \"A pr\",\n \"Ap r\",\n \". White\",\n \".Wh ite\",\n \"6 15\",\n \"61 5\",\n \"_ MOD\",\n \"_M OD\",\n \"_MO D\",\n \"Ġ Points\",\n \"ĠP oints\",\n \"ĠPoint s\",\n \"ĠPo ints\",\n \"å¤ ±\",\n \"Ġ genes\",\n \"Ġg enes\",\n \"Ġge nes\",\n \"Ġgen es\",\n \"Ġgene s\",\n \"Ġ vendor\",\n \"Ġv endor\",\n \"Ġvend or\",\n \"Ġmain stream\",\n \"< src\",\n \"Ċ\",\n \"Ġ< >Ċ\",\n \"Ġ<> Ċ\",\n \"F ilename\",\n \"File name\",\n \"Fi lename\",\n \"Fil ename\",\n \"Ġs ne\",\n \"Ġsn e\",\n \"Ġ Football\",\n \"ĠF ootball\",\n \"ĠFoot ball\",\n \"Ġr ival\",\n \"Ġri val\",\n \"Ġriv al\",\n \"Ġdis aster\",\n \"i onic\",\n \"ion ic\",\n \"io nic\",\n \"ioni c\",\n \"Ġ Damage\",\n \"ĠD amage\",\n \"ĠDa mage\",\n \"ĠDam age\",\n \". Resource\",\n \".Re source\",\n \".Res ource\",\n \"- en\",\n \"-e n\",\n \"Ġ Types\",\n \"ĠT ypes\",\n \"ĠType s\",\n \"ĠTy pes\",\n \"ĠTyp es\",\n \"get String\",\n \"( board\",\n \"(b oard\",\n \"Ġ bol\",\n \"Ġb ol\",\n \"Ġbo l\",\n \"p lain\",\n \"pl ain\",\n \"pla in\",\n \"z ym\",\n \"zy m\",\n \"ภ²\",\n \"Ġ scanner\",\n \"Ġsc anner\",\n \"Ġscan ner\",\n \"i lder\",\n \"il der\",\n \"ild er\",\n \"ilde r\",\n \"_ msgs\",\n \"_msg s\",\n \"_ms gs\",\n \"æ ı\",\n \"( intent\",\n \"(int ent\",\n \"(in tent\",\n \"Ġ destruct\",\n \"Ġd estruct\",\n \"Ġde struct\",\n \"Ġb ust\",\n \"Ġbu st\",\n \"Ġbus t\",\n \"Ġ Employ\",\n \"ĠE mploy\",\n \"ĠEm ploy\",\n \"ĠEmp loy\",\n \"o ni\",\n \"on i\",\n \"Ġ UIViewController\",\n \"ĠUI ViewController\",\n \"ĠUIView Controller\",\n \"Ġo dds\",\n \"Ġodd s\",\n \"Ġod ds\",\n \"e arer\",\n \"ear er\",\n \"ea rer\",\n \"Ge ometry\",\n \"Geo metry\",\n \"Geom etry\",\n \"Ġ yii\",\n \"Ġy ii\",\n \"Ġyi i\",\n \"_ EXPORT\",\n \"_EX PORT\",\n \"_EXP ORT\",\n \"Ġ Attack\",\n \"ĠAtt ack\",\n \"Ġn iet\",\n \"Ġnie t\",\n \"Ġni et\",\n \"Ġim pression\",\n \"Ġimp ression\",\n \"Ġimpress ion\",\n \"Ġimpr ession\",\n \"Ġ Gil\",\n \"ĠG il\",\n \"ĠGi l\",\n \"_ prob\",\n \"_p rob\",\n \"_pro b\",\n \"_pr ob\",\n \"5 28\",\n \"52 8\",\n \"Ġ CF\",\n \"ĠC F\",\n \"Ġ Experience\",\n \"ĠEx perience\",\n \"/ plugins\",\n \"/pl ugins\",\n \"/plugin s\",\n \". 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Mvc\",\n \".M vc\",\n \"u ir\",\n \"ui r\",\n \"( \\\"$\",\n \"(\\\" $\",\n \"Ġ connecting\",\n \"Ġconnect ing\",\n \"Read Only\",\n \"u ru\",\n \"ur u\",\n \"ĠE ag\",\n \"ĠEa g\",\n \"B IT\",\n \"BI T\",\n \"_ DEL\",\n \"_D EL\",\n \"_DE L\",\n \"å §\",\n \"arr ass\",\n \"arra ss\",\n \"ex ternal\",\n \"ext ernal\",\n \"extern al\",\n \"exter nal\",\n \"Ġ YOUR\",\n \"ĠY OUR\",\n \"ĠYOU R\",\n \"ĠB rew\",\n \"ĠBr ew\",\n \"ĠBre w\",\n \"Ġ Five\",\n \"ĠF ive\",\n \"ĠFi ve\",\n \"Ġ resize\",\n \"Ġre size\",\n \"Ġres ize\",\n \"i gid\",\n \"ig id\",\n \"igi d\",\n \"e ration\",\n \"er ation\",\n \"era tion\",\n \"6 53\",\n \"65 3\",\n \"Ġ Ñį\",\n \"ĠÑ į\",\n \"5 36\",\n \"53 6\",\n \"åĬ ł\",\n \"0 39\",\n \"03 9\",\n \"Ġ Catch\",\n \"ĠC atch\",\n \"ĠCat ch\",\n \"Ù ģ\",\n \"Ġ Leon\",\n \"ĠL eon\",\n \"ĠLe on\",\n \"ĠLeo n\",\n \"a mil\",\n \"am il\",\n \"ami l\",\n \". Body\",\n \".B ody\",\n \"C lip\",\n \"Cl ip\",\n \"Cli p\",\n \"/ list\",\n \"/l ist\",\n \"/li st\",\n \". br\",\n \".b r\",\n \"Edit Text\",\n \"ĉ db\",\n \"ĉd b\",\n \". Game\",\n \".G ame\",\n \"( BuildContext\",\n \"(Build Context\",\n \"back end\",\n \". Red\",\n \".R ed\",\n \".Re d\",\n \"f acebook\",\n \"face book\",\n \"5 29\",\n \"52 9\",\n \". urls\",\n \".url s\",\n \".ur ls\",\n \"m r\",\n \"rol led\",\n \"roll ed\",\n \"- ------\",\n \"-- -----\",\n \"---- ---\",\n \"--- ----\",\n \"----- --\",\n \"------ -\",\n \"Ġint ervention\",\n \"Ġinter vention\",\n \"Ġinterven tion\",\n \"Ġinterv ention\",\n \"Ġret irement\",\n \"Ġretire ment\",\n \"Ġretir ement\",\n \"Ġ Kit\",\n \"ĠK it\",\n \"ĠKi t\",\n \"Ġ PRE\",\n \"ĠP RE\",\n \"ĠPR E\",\n \"Upper Case\",\n \"Ġ Socket\",\n \"ĠS ocket\",\n \"ĠSo cket\",\n \"ĠSoc ket\",\n \"Ġ :-\",\n \"Ġ: -\",\n \"Ġstud ying\",\n \"Ġstudy ing\",\n \"Ġ Metro\",\n \"ĠM etro\",\n \"ĠMe tro\",\n \"ĠMet ro\",\n \"ar ded\",\n \"ard ed\",\n \"arde d\",\n \"Ġcon versations\",\n \"Ġconvers ations\",\n \"Ġconversation s\",\n \"C alled\",\n \"Call ed\",\n \"Cal led\",\n \"Ġex amine\",\n \"Ġexam ine\",\n \"ert ificate\",\n \". gz\",\n \".g z\",\n \"- responsive\",\n \"-res ponsive\",\n \"Ġ refund\",\n \"Ġre fund\",\n \"Ġref und\",\n \"_ network\",\n \"_n etwork\",\n \"_net work\",\n \"0 26\",\n \"02 6\",\n \"all owed\",\n \"allow ed\",\n \"allo wed\",\n \"em pt\",\n \"emp t\",\n \"Ġme als\",\n \"Ġmeal s\",\n \"C ategories\",\n \"Ġtravel ing\",\n \"Ġtrav eling\",\n \"Ġ kg\",\n \"Ġk g\",\n \"Ġsh ame\",\n \"Ġsha me\",\n \"Ġsham e\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġexplicit ly\",\n \"Ġmath ematic\",\n \"Ġ Suite\",\n \"ĠS uite\",\n \"ĠSu ite\",\n \"ĠSuit e\",\n \"Ġ RGB\",\n \"ĠR GB\",\n \"ĠRG B\",\n \"****** /\",\n \"***** */\",\n \"Ġm ixture\",\n \"Ġmix ture\",\n \"l earning\",\n \"le arning\",\n \"lear ning\",\n \"learn ing\",\n \". template\",\n \".t emplate\",\n \".temp late\",\n \".tem plate\",\n \"at ts\",\n \"att s\",\n \"w x\",\n \"ĉ ctx\",\n \"ĉc tx\",\n \"ĉct x\",\n \". properties\",\n \".p roperties\",\n \".prop erties\",\n \"Ġdr inks\",\n \"Ġdrink s\",\n \"Ġ Either\",\n \"ĠE ither\",\n \"set Text\",\n \". getData\",\n \".get Data\",\n \".getD ata\",\n \". zip\",\n \".z ip\",\n \"Ġreve als\",\n \"Ġreveal s\",\n \"< table\",\n \".Ċ\",\n \"/> .Ċ\",\n \"/>. Ċ\",\n \"Ġr anked\",\n \"Ġrank ed\",\n \"Ġran ked\",\n \"_ impl\",\n \"_i mpl\",\n \"_im pl\",\n \"_imp l\",\n \"Ġ Handles\",\n \"ĠH andles\",\n \"ĠHand les\",\n \"ĠHandle s\",\n \"Ġhost ed\",\n \"Ġho sted\",\n \"Ġhos ted\",\n \"Ġ updating\",\n \"Ġup dating\",\n \"Ġupd ating\",\n \"al bum\",\n \"é Ŀ\",\n \"Ġ shader\",\n \"Ġsh ader\",\n \"Ġsha der\",\n \"Ġshade r\",\n \"Ed itors\",\n \"Edit ors\",\n \"Editor s\",\n \"- round\",\n \"-r ound\",\n \"-ro und\",\n \"[ ]{\",\n \"[] {\",\n \"Ġ sep\",\n \"Ġs ep\",\n \"Ġse p\",\n \"Ġ Hi\",\n \"ĠH i\",\n \"T EM\",\n \"TE M\",\n \"look up\",\n \"loo kup\",\n \". man\",\n \".m an\",\n \".ma n\",\n \"_ INPUT\",\n \"_IN PUT\",\n \"Ġthreat ened\",\n \"Ġthreaten ed\",\n \"_ IMPORT\",\n \"_IM PORT\",\n \"_IMP ORT\",\n \"Ġ drops\",\n \"Ġd rops\",\n \"Ġdr ops\",\n \"Ġdrop s\",\n \"Ġdro ps\",\n \"r uit\",\n \"ru it\",\n \"s id\",\n \"si d\",\n \"b oth\",\n \"bo th\",\n \"bot h\",\n \"Ġ Excel\",\n \"ĠEx cel\",\n \"ĠExc el\",\n \"Ġ jer\",\n \"Ġj er\",\n \"Ġje r\",\n \"ord inary\",\n \"ordin ary\",\n \"е й\",\n \"еР¹\",\n \"V IEW\",\n \"VI EW\",\n \"re ply\",\n \"rep ly\",\n \"Ġ ):Ċ\",\n \"Ġ) :Ċ\",\n \"Ġ): Ċ\",\n \"col ors\",\n \"color s\",\n \"colo rs\",\n \"ver ified\",\n \"_ Tr\",\n \"_T r\",\n \"_ parse\",\n \"_p arse\",\n \"_par se\",\n \"_pars e\",\n \"Ġcon gress\",\n \"Ġcongr ess\",\n \"Ġcong ress\",\n \"6 17\",\n \"61 7\",\n \"P romise\",\n \"Pro mise\",\n \"Prom ise\",\n \"i nts\",\n \"in ts\",\n \"int s\",\n \"Ġ Mother\",\n \"ĠM other\",\n \"ĠMo ther\",\n \"ĠMot her\",\n \". Api\",\n \".A pi\",\n \".Ap i\",\n \"Ġ Duration\",\n \"ĠD uration\",\n \"ĠDu ration\",\n \"ĠDur ation\",\n \"Ġ firstName\",\n \"Ġfirst Name\",\n \"inherit doc\",\n \"ĠM ars\",\n \"ĠMar s\",\n \"ĠMa rs\",\n \"Ġ apr\",\n \"Ġa pr\",\n \"Ġap r\",\n \"O DY\",\n \"OD Y\",\n \"Ġvis its\",\n \"Ġvisit s\",\n \"6 31\",\n \"63 1\",\n \"Ġhe aling\",\n \"Ġheal ing\",\n \"let ters\",\n \"letter s\",\n \"lette rs\",\n \"lett ers\",\n \") ));čĊ\",\n \")) );čĊ\",\n \"))) ;čĊ\",\n \"))); čĊ\",\n \"f uture\",\n \"fu ture\",\n \". 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prep\",\n \"Ġp rep\",\n \"Ġpr ep\",\n \"Ġpre p\",\n \"Ġaw arded\",\n \"Ġaward ed\",\n \"ì§ Ģ\",\n \"Ġ Corn\",\n \"ĠC orn\",\n \"ĠCo rn\",\n \"ĠCor n\",\n \"Ġ Nature\",\n \"ĠN ature\",\n \"ĠNa ture\",\n \"ĠNat ure\",\n \"ĠNatur e\",\n \"UD IO\",\n \"7 37\",\n \"73 7\",\n \"Ġ proj\",\n \"Ġp roj\",\n \"Ġpro j\",\n \"Ġpr oj\",\n \"- pre\",\n \"-p re\",\n \"-pr e\",\n \"[ u\",\n \"F eatures\",\n \"Fe atures\",\n \"Feature s\",\n \"Feat ures\",\n \"Ġ isEqual\",\n \"Ġis Equal\",\n \"B inary\",\n \"Bin ary\",\n \"s ig\",\n \"si g\",\n \"Ġcon fusion\",\n \"Ġconf usion\",\n \"5 46\",\n \"54 6\",\n \"5 68\",\n \"56 8\",\n \"Ġ Hat\",\n \"ĠH at\",\n \"ĠHa t\",\n \"Ġkt ó\",\n \". configure\",\n \".con figure\",\n \".config ure\",\n \".conf igure\",\n \"M ON\",\n \"MO N\",\n \"4 94\",\n \"49 4\",\n \"/ edit\",\n \"/e dit\",\n \"_ Add\",\n \"_A dd\",\n \"_Ad d\",\n \", true\",\n \",tr ue\",\n \"5 41\",\n \"54 1\",\n \"Ġ cli\",\n \"Ġc li\",\n \"Ġcl i\",\n \"Error Message\",\n \"- loader\",\n \"-l oader\",\n \"-lo ader\",\n \"-load er\",\n \"Dim ensions\",\n \"Dimension s\",\n \"ulti ply\",\n \"ultip ly\",\n \"Ġ {!!\",\n \"Ġ{ !!\",\n \"Ġ{! !\",\n \"Ġ SqlCommand\",\n \"ĠSql Command\",\n \"Ġ spoken\",\n \"Ġsp oken\",\n \"Ġspoke n\",\n \"Ġspo ken\",\n \"Ġ pics\",\n \"Ġp ics\",\n \"Ġpi cs\",\n \"Ġpic s\",\n \"Ġ toy\",\n \"Ġt oy\",\n \"Ġto y\",\n \"( Key\",\n \"(K ey\",\n \"Ġ Loop\",\n \"ĠL oop\",\n \"ĠLo op\",\n \"Ø ¨\",\n \"E ATURE\",\n \"EA TURE\",\n \"in ction\",\n \"inc tion\",\n \"inct ion\",\n \"_ setup\",\n \"_set up\",\n \"w rapper\",\n \"wrap per\",\n \"wr apper\",\n \"Ġt ong\",\n \"Ġto ng\",\n \"Ġton g\",\n \"c ular\",\n \"cul ar\",\n \"cu lar\",\n \"O pt\",\n \"Op t\",\n \". Pl\",\n \".P l\",\n \"= \\\",\",\n \"=\\\" ,\",\n \"( length\",\n \"(l ength\",\n \"(len gth\",\n \"(le ngth\",\n \"u mn\",\n \"um n\",\n \"Ġ chrom\",\n \"Ġch rom\",\n \"Ġchr om\",\n \"Ġs event\",\n \"Ġse vent\",\n \"Ġseven t\",\n \"Ġsev ent\",\n \"Ġ IllegalArgumentException\",\n \"ĠIl legalArgumentException\",\n \"ĠIllegal ArgumentException\",\n \"4 78\",\n \"47 8\",\n \"ĉ start\",\n \"ĉst art\",\n \"Ġbe gun\",\n \"Ġbeg un\",\n \"CE PTION\",\n \"CEPT ION\",\n \"d ataset\",\n \"data set\",\n \"dat aset\",\n \"datas et\",\n \"8 25\",\n \"82 5\",\n \"Ġ Failed\",\n \"ĠF ailed\",\n \"ĠFa iled\",\n \"ĠFail ed\",\n \"c ols\",\n \"co ls\",\n \"col s\",\n \"4 59\",\n \"45 9\",\n \"Ġk nee\",\n \"Ġkn ee\",\n \"Ġkne e\",\n \"i more\",\n \"im ore\",\n \"imo re\",\n \". splice\",\n \".sp lice\",\n \"s hell\",\n \"sh ell\",\n \"she ll\",\n \"ig gers\",\n \"igger s\",\n \"igg ers\",\n \"Ġ themes\",\n \"Ġth emes\",\n \"Ġthe mes\",\n \"Ġthem es\",\n \"Ġtheme s\",\n \"9 95\",\n \"99 5\",\n \"Ġ DJ\",\n \"ĠD J\",\n \"Ġ Assistant\",\n \"ĠAss istant\",\n \"ĠAssist ant\",\n \"- $\",\n \"M aybe\",\n \"May be\",\n \"Ġ ordering\",\n \"Ġorder ing\",\n \"Ġord ering\",\n \"ĠInt elligence\",\n \"ĠIntelli gence\",\n \"ĠMass achusetts\",\n \"Ġf ailing\",\n \"Ġfa iling\",\n \"Ġfail ing\",\n \"el son\",\n \"els on\",\n \"G reat\",\n \"Gr eat\",\n \"Gre at\",\n \"= i\",\n \". rest\",\n \".re st\",\n \".r est\",\n \".res t\",\n \"Ġ invite\",\n \"Ġinv ite\",\n \"Ġinvit e\",\n \"- disable\",\n \"-d isable\",\n \"-dis able\",\n \". GroupBox\",\n \".Group Box\",\n \"âĢĻ est\",\n \"âĢĻe st\",\n \"âĢĻes t\",\n \"Ġt ackle\",\n \"Ġtack le\",\n \"Ġtac kle\",\n \"g v\",\n \"et ter\",\n \"ette r\",\n \"ett er\",\n \"Ġ ),čĊ\",\n \"Ġ) ,čĊ\",\n \"Ġ), čĊ\",\n \"_ rules\",\n \"_r ules\",\n \"_rule s\",\n \"_ru les\",\n \". warn\",\n \".w arn\",\n \"function s\",\n \"fun ctions\",\n \"ĠChrist ians\",\n \"ĠChristian s\",\n \"Ġb acked\",\n \"Ġback ed\",\n \"Ġbac ked\",\n \"Ġ slider\",\n \"Ġs lider\",\n \"Ġsl ider\",\n \"Ġslide r\",\n \"Ġslid er\",\n \"Ġenjoy ing\",\n \"Ġenjo ying\",\n \"n est\",\n \"ne st\",\n \"nes t\",\n \"Ġh ij\",\n \"Ġhi j\",\n \"_ ms\",\n \"_m s\",\n \"/ /*\",\n \"// *\",\n \"An notations\",\n \"Annotation s\",\n \"Ġ Variables\",\n \"ĠVariable s\",\n \"ĠVari ables\",\n \"< V\",\n \"( server\",\n \"(s erver\",\n \"(serv er\",\n \"Ġ Oracle\",\n \"ĠOr acle\",\n \"e lements\",\n \"el ements\",\n \"element s\",\n \"ele ments\",\n \"elem ents\",\n \"Ġ organisation\",\n \"Ġorgan isation\",\n \"Ġorganis ation\",\n \"_ pointer\",\n \"_point er\",\n \"_po inter\",\n \"Ġ Headers\",\n \"ĠHe aders\",\n \"ĠHead ers\",\n \"ĠHeader s\",\n \"[ d\",\n \"Ġ deadline\",\n \"Ġdead line\",\n \"i ssa\",\n \"is sa\",\n \"iss a\",\n \"Ġ knife\",\n \"Ġkn ife\",\n \"Ġ NASA\",\n \"ĠN ASA\",\n \"ĠNAS A\",\n \"ĠNA SA\",\n \"Ġ Height\",\n \"ĠH eight\",\n \"ĠHe ight\",\n \"7 84\",\n \"78 4\",\n \"Ġ Async\",\n \"ĠA sync\",\n \"ĠAs ync\",\n \"Ġ venue\",\n \"Ġven ue\",\n \". dom\",\n \".d om\",\n \".do m\",\n \"bour ne\",\n \"bou rne\",\n \"ĠH awai\",\n \"ĠHaw ai\",\n \"Ġ memo\",\n \"Ġm emo\",\n \"Ġme mo\",\n \"Ġmem o\",\n \"i ctions\",\n \"ict ions\",\n \"iction s\",\n \"Ġsur veillance\",\n \"Ġsurve illance\",\n \"o mi\",\n \"om i\",\n \"/ assets\",\n \"/as sets\",\n \"5 87\",\n \"58 7\",\n \"Ġ edu\",\n \"Ġe du\",\n \"Ġed u\",\n \"Ä Ľ\",\n \"Ġr oster\",\n \"Ġro ster\",\n \"Ġros ter\",\n \"Ġrost er\",\n \"Ġh ired\",\n \"Ġhi red\",\n \"Ġhire d\",\n \"Ġ Tok\",\n \"ĠT ok\",\n \"ĠTo k\",\n \"Ġ placement\",\n \"Ġpl acement\",\n \"Ġplace ment\",\n \"Ġplac ement\",\n \"ur ations\",\n \"uration s\",\n \"urat ions\",\n \"Ġ setState\",\n \"Ġset State\",\n \"ĠMag azine\",\n \"Ġhor ror\",\n \"Ġho rror\",\n \"Ġhorr or\",\n \"T ry\",\n \"Tr y\",\n \"Ġ lag\",\n \"Ġl ag\",\n \"Ġla g\",\n \"Ġ Everyone\",\n \"ĠEvery one\",\n \"t hur\",\n \"th ur\",\n \") );čĊčĊ\",\n \")) ;čĊčĊ\",\n \"));čĊ čĊ\",\n \")); čĊčĊ\",\n \". return\",\n \".re turn\",\n \".r eturn\",\n \".ret urn\",\n \"Ġsy mp\",\n \"Ġsym p\",\n \"âĸĪ âĸĪ\",\n \"Ġn ights\",\n \"Ġnight s\",\n \"work er\",\n \"wor ker\",\n \"Ġ ale\",\n \"Ġa le\",\n \"Ġal e\",\n \"ennes see\",\n \". step\",\n \".s tep\",\n \".st ep\",\n \"Ġs ynchronized\",\n \"Ġsynchron ized\",\n \"Ġsynchronize d\",\n \"4 87\",\n \"48 7\",\n \"o uri\",\n \"ou ri\",\n \"our i\",\n \"D oes\",\n \"Do es\",\n \". change\",\n \".ch ange\",\n \"f on\",\n \"fo n\",\n \". setBackground\",\n \".set Background\",\n \"ir cular\",\n \"irc ular\",\n \"4 76\",\n \"47 6\",\n \"+ -\",\n \"ĠC IA\",\n \"ĠCI A\",\n \"7 29\",\n \"72 9\",\n \"Ġ Jane\",\n \"ĠJ ane\",\n \"ĠJan e\",\n \"ĠJa ne\",\n \"Ġ Similar\",\n \"ĠS imilar\",\n \"ĠSim ilar\",\n \"- I\",\n \"level and\",\n \"lev eland\",\n \"Ġpro spect\",\n \"Ġpros pect\",\n \"_ found\",\n \"_f ound\",\n \"ĉ color\",\n \"ĉc olor\",\n \"ĉcol or\",\n \".D iagnostics\",\n \".Di agnostics\",\n \"Ġ announce\",\n \"Ġann ounce\",\n \"Ġannounc e\",\n \"Ġanno unce\",\n \"Ġass umes\",\n \"Ġassum es\",\n \"Ġassume s\",\n \"/ tr\",\n \"/t r\",\n \"Ġ bd\",\n \"Ġb d\",\n \"9 87\",\n \"98 7\",\n \"Ġ Carbon\",\n \"ĠC arbon\",\n \"ĠCar bon\",\n \"ĠCarb on\",\n \"Ġanal ys\",\n \"Ġanaly s\",\n \"Ġana lys\",\n \"5 64\",\n \"56 4\",\n \". dest\",\n \".d est\",\n \".de st\",\n \".des t\",\n \"n ik\",\n \"ni k\",\n \"Ġ Lie\",\n \"ĠL ie\",\n \"ĠLi e\",\n \"- index\",\n \"-in dex\",\n \"-ind ex\",\n \"Draw able\",\n \"Ġ TAG\",\n \"ĠT AG\",\n \"ĠTA G\",\n \"Ġ triangle\",\n \"Ġt riangle\",\n \"Ġtr iangle\",\n \"Ġtri angle\",\n \"Ġtriang le\",\n \"_ FLOAT\",\n \"_F LOAT\",\n \"ĉ ĉĠĠĠĠĠ\",\n \"ĉĉ ĠĠĠĠĠ\",\n \"ĉĉĠĠĠ ĠĠ\",\n \"ĉĉĠ ĠĠĠĠ\",\n \"ĉĉĠĠ ĠĠĠ\",\n \"ĉĉĠĠĠĠ Ġ\",\n \". black\",\n \".b lack\",\n \".bl ack\",\n \"v ue\",\n \"vu e\",\n \"c uracy\",\n \"cur acy\",\n \"cura cy\",\n \"Ġa ffects\",\n \"Ġaff ects\",\n \"Ġaffect s\",\n \"9 06\",\n \"90 6\",\n \"Ġsur ely\",\n \"Ġsure ly\",\n \"S lider\",\n \"Sl ider\",\n \"Slide r\",\n \"u ki\",\n \"uk i\",\n \"c ery\",\n \"ce ry\",\n \"cer y\",\n \"Ġ unter\",\n \"Ġun ter\",\n \"Ġunt er\",\n \". profile\",\n \".pro file\",\n \".pr ofile\",\n \".prof ile\",\n \"or don\",\n \"ord on\",\n \"ordo n\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"le ave\",\n \"lea ve\",\n \"Ġsmart phone\",\n \"g ie\",\n \"gi e\",\n \"Ġcon spir\",\n \"Ġcons pir\",\n \"Ġ tutorial\",\n \"Ġt utorial\",\n \"Ġtut orial\",\n \"Ġtutor ial\",\n \"Ġtuto rial\",\n \"ç± »\",\n \"Ġ cab\",\n \"Ġc ab\",\n \"Ġca b\",\n \"7 65\",\n \"76 5\",\n \"Ġ Summary\",\n \"ĠSum mary\",\n \"* ĊĊ\",\n \"*Ċ Ċ\",\n \"ä h\",\n \"\\\" This\",\n \"\\\"T his\",\n \"Ġ slides\",\n \"Ġsl ides\",\n \"Ġslide s\",\n \"Ġslid es\",\n \"\\\" \",\n \"() >\",\n \"c ycle\",\n \"cy cle\",\n \"cycl e\",\n \"ĠB ull\",\n \"ĠBul l\",\n \"ĠBu ll\",\n \"p aths\",\n \"path s\",\n \"pat hs\",\n \"pa ths\",\n \"Ġu np\",\n \"Ġun p\",\n \"Ġview DidLoad\",\n \"_ Model\",\n \"_M odel\",\n \"_Mode l\",\n \"_Mod el\",\n \"Ġ assertTrue\",\n \"Ġassert True\",\n \"Ġ rated\",\n \"Ġr ated\",\n \"Ġrate d\",\n \"Ġrat ed\",\n \"Ġra ted\",\n \"De cl\",\n \"Dec l\",\n \"ver ted\",\n \"vert ed\",\n \"verte d\",\n \"Ġ Dat\",\n \"ĠD at\",\n \"ĠDa t\",\n \"b rew\",\n \"br ew\",\n \"bre w\",\n \"Ġpoint ing\",\n \"M s\",\n \"Ġ Pointer\",\n \"ĠPoint er\",\n \"ĠPo inter\",\n \") '\",\n \"_ non\",\n \"_n on\",\n \"_no n\",\n \"5 27\",\n \"52 7\",\n \"Ġ SEC\",\n \"ĠS EC\",\n \"ĠSE C\",\n \"Ġ yeah\",\n \"Ġy eah\",\n \"Ġye ah\",\n \"g ency\",\n \"ge ncy\",\n \"gen cy\",\n \"initial ize\",\n \"f ly\",\n \"fl y\",\n \"7 11\",\n \"71 1\",\n \"[ pos\",\n \"[p os\",\n \", g\",\n \"T ele\",\n \"Te le\",\n \"Tel e\",\n \"0 34\",\n \"03 4\",\n \"Ġj oke\",\n \"Ġjo ke\",\n \"Ġ clause\",\n \"Ġcl ause\",\n \"Ġcla use\",\n \". findById\",\n \".find ById\",\n \".findBy Id\",\n \"e nes\",\n \"en es\",\n \"ene s\",\n \"( instance\",\n \"(in stance\",\n \"(inst ance\",\n \"6 26\",\n \"62 6\",\n \" £\",\n \"9 15\",\n \"91 5\",\n \"Ġs lic\",\n \"Ġsl ic\",\n \"_ home\",\n \"_h ome\",\n \"_hom e\",\n \"Ġ */}Ċ\",\n \"Ġ*/ }Ċ\",\n \"_ pages\",\n \"_p ages\",\n \"_page s\",\n \"_pag es\",\n \"_pa ges\",\n \"( service\",\n \"(s ervice\",\n \"(serv ice\",\n \"9 05\",\n \"90 5\",\n \"R P\",\n \"Ġ Among\",\n \"ĠA mong\",\n \"ĠAm ong\",\n \". getCurrent\",\n \".get Current\",\n \".getC urrent\",\n \"8 06\",\n \"80 6\",\n \"ã Ĥ¹\",\n \"ãĤ ¹\",\n \"Ġs lee\",\n \"Ġsl ee\",\n \"Ġsle e\",\n \"= [Ċ\",\n \">[ Ċ\",\n \"o ler\",\n \"ol er\",\n \"ole r\",\n \"Ġli bert\",\n \"Ġlib ert\",\n \"Ġliber t\",\n \"Ġ `Ċ\",\n \"Ġ` Ċ\",\n \"Ġw enn\",\n \"Ġwe nn\",\n \"Ġwen n\",\n \"l ated\",\n \"la ted\",\n \"late d\",\n \"lat ed\",\n \"Ġ immune\",\n \"Ġimm une\",\n \"Ġimmun e\",\n \"( Node\",\n \"(N ode\",\n \"Ġ Problem\",\n \"ĠPro blem\",\n \"ĠProb lem\",\n \"ĠProble m\",\n \"Ġ Abs\",\n \"ĠA bs\",\n \"ĠAb s\",\n \"l ogs\",\n \"lo gs\",\n \"log s\",\n \"Ġ ../\",\n \"Ġ. ./\",\n \"Ġ.. /\",\n \"Ġ ADC\",\n \"ĠA DC\",\n \"ĠAD C\",\n \"Ġ }}\\\">Ċ\",\n \"Ġ} }\\\">Ċ\",\n \"Ġ}} \\\">Ċ\",\n \"Ġ}}\\\" >Ċ\",\n \"Ġ}}\\\"> Ċ\",\n \"> ');Ċ\",\n \">' );Ċ\",\n \">') ;Ċ\",\n \">'); Ċ\",\n \"= b\",\n \"Ġ Wind\",\n \"ĠW ind\",\n \"ĠWin d\",\n \"ĠWi nd\",\n \"l ahoma\",\n \"lah oma\",\n \"Ġ allocate\",\n \"Ġal locate\",\n \"Ġall ocate\",\n \"Ġalloc ate\",\n \"Ġallo cate\",\n \"o rian\",\n \"or ian\",\n \"oria n\",\n \"ori an\",\n \"Ġpr escription\",\n \"Ġpre scription\",\n \"Ġpres cription\",\n \"- quality\",\n \"-q uality\",\n \"-qu ality\",\n \"Ġ Mayor\",\n \"ĠMay or\",\n \"ĠMa yor\",\n \"ĠMayo r\",\n \"8 55\",\n \"85 5\",\n \"in ely\",\n \"ine ly\",\n \"inel y\",\n \"end foreach\",\n \"Ġ Complex\",\n \"ĠCom plex\",\n \"ĠComp lex\",\n \"ĠComple x\",\n \"k om\",\n \"ko m\",\n \"7 09\",\n \"70 9\",\n \"T Y\",\n \"7 90\",\n \"79 0\",\n \"] ].\",\n \"]] .\",\n \". Style\",\n \".St yle\",\n \"_ many\",\n \"_m any\",\n \"_man y\",\n \"_ma ny\",\n \"', '$\",\n \"',' $\",\n \"Ġbar rier\",\n \"Ġbarr ier\",\n \"Ġ Fetch\",\n \"ĠF etch\",\n \"ĠFet ch\",\n \"Ġ Marvel\",\n \"ĠMar vel\",\n \"Ġres ist\",\n \"о го\",\n \"ог о\",\n \"b idden\",\n \"bi dden\",\n \"bid den\",\n \"Ġ Runnable\",\n \"ĠR unnable\",\n \"ĠRun nable\",\n \": false\",\n \":f alse\",\n \"8 99\",\n \"89 9\",\n \"Ġbuild s\",\n \"Ġ Stage\",\n \"ĠSt age\",\n \"ĠSta ge\",\n \"Ġ dub\",\n \"Ġd ub\",\n \"Ġdu b\",\n \"em po\",\n \"emp o\",\n \". site\",\n \".s ite\",\n \".si te\",\n \"5 58\",\n \"55 8\",\n \"; ĊĊĊĊ\",\n \";Ċ ĊĊĊ\",\n \";ĊĊ ĊĊ\",\n \";ĊĊĊ Ċ\",\n \"9 94\",\n \"99 4\",\n \"Ġ Denver\",\n \"ĠDen ver\",\n \"Ġre vel\",\n \"Ġrev el\",\n \"Ġreve l\",\n \"Ġtrigger ed\",\n \"Ġ dice\",\n \"Ġd ice\",\n \"Ġdi ce\",\n \"Ġdic e\",\n \"_ fail\",\n \"_f ail\",\n \"_fa il\",\n \"Ġ gc\",\n \"Ġg c\",\n \"8 33\",\n \"83 3\",\n \"5 89\",\n \"58 9\",\n \"ĉ X\",\n \"Ġ Throwable\",\n \"ĠTh rowable\",\n \"ĠThrow able\",\n \"7 75\",\n \"77 5\",\n \". router\",\n \".r outer\",\n \".route r\",\n \".ro uter\",\n \"ĠRe volution\",\n \"ĠRev olution\",\n \"ÑĢ а\",\n \"_ NON\",\n \"_N ON\",\n \"_NO N\",\n \"0 55\",\n \"05 5\",\n \"Ł ¥\",\n \"5 78\",\n \"57 8\",\n \"Ġ elder\",\n \"Ġe lder\",\n \"Ġel der\",\n \"Ġelde r\",\n \"Ġab road\",\n \"Ġ е\",\n \"ĠÐ µ\",\n \"Ġ Adult\",\n \"ĠAd ult\",\n \"b lr\",\n \"bl r\",\n \"g lyphicon\",\n \"glyph icon\",\n \"6 13\",\n \"61 3\",\n \"Ġprom oting\",\n \"Ġpromot ing\",\n \"Ġpromo ting\",\n \"Ġ iz\",\n \"Ġi z\",\n \"Ġ Solid\",\n \"ĠS olid\",\n \"ĠSo lid\",\n \"ĠSol id\",\n \"6 45\",\n \"64 5\",\n \"_ loader\",\n \"_l oader\",\n \"_lo ader\",\n \"_load er\",\n \"ear ly\",\n \". enabled\",\n \".en abled\",\n \".enable d\",\n \"- edit\",\n \"-e dit\",\n \"-ed it\",\n \"Ġ UL\",\n \"ĠU L\",\n \"_ play\",\n \"_p lay\",\n \"_pl ay\",\n \"Ġ Interrupt\",\n \"ĠInt errupt\",\n \"ĠInter rupt\",\n \"ĠInterr upt\",\n \"Ġadv antages\",\n \"Ġadvant ages\",\n \"Ġadvantage s\",\n \"u cle\",\n \"uc le\",\n \"Ġmechan ical\",\n \"Ġmechanic al\",\n \"Ġmech anical\",\n \".table LayoutPanel\",\n \"Ġ Working\",\n \"ĠWork ing\",\n \"ĠWor king\",\n \"Ġ anonymous\",\n \"Ġan onymous\",\n \"Ġanonym ous\",\n \"Ġanon ymous\",\n \"R ating\",\n \"Ra ting\",\n \"ig ious\",\n \"igi ous\",\n \"_ phone\",\n \"_p hone\",\n \"_ph one\",\n \".addAction Listener\",\n \"Ġf ran\",\n \"Ġfr an\",\n \"Ġfra n\",\n \"un den\",\n \"und en\",\n \"unde n\",\n \"Ġ *)&\",\n \"Ġ* )&\",\n \"Ġ*) &\",\n \"_ bool\",\n \"_b ool\",\n \"_bo ol\",\n \"ul ative\",\n \"Ġ cone\",\n \"Ġc one\",\n \"Ġcon e\",\n \"Ġco ne\",\n \"Ġ Mult\",\n \"ĠM ult\",\n \"ĠMu lt\",\n \"ĠMul t\",\n \"Ġm ö\",\n \"Ġ Forward\",\n \"ĠFor ward\",\n \"] ):Ċ\",\n \"]) :Ċ\",\n \"]): Ċ\",\n \"Ġconvin ced\",\n \"Ġconvince d\",\n \"Ġconvinc ed\",\n \"act ed\",\n \"ac ted\",\n \"6 43\",\n \"64 3\",\n \"ãģ ĵ\",\n \"Ġ Configure\",\n \"ĠCon figure\",\n \"ĠConfig ure\",\n \"ĠConf igure\",\n \"Ġce iling\",\n \"Ġceil ing\",\n \"D er\",\n \"De r\",\n \"Ġpass engers\",\n \"Ġpassenger s\",\n \"G roups\",\n \"Group s\",\n \"Gro ups\",\n \"Ġs occer\",\n \"Ġsoc cer\",\n \"/ W\",\n \"av iors\",\n \"avior s\",\n \"avi ors\",\n \"s with\",\n \"sw ith\",\n \"Ġ Zone\",\n \"ĠZ one\",\n \"ĠZo ne\",\n \". Options\",\n \".O ptions\",\n \".Option s\",\n \"Ġ Mom\",\n \"ĠM om\",\n \"ĠMo m\",\n \"i eder\",\n \"ie der\",\n \"ied er\",\n \"Array s\",\n \"Ar rays\",\n \"Arr ays\",\n \"Ġtreat ments\",\n \"Ġtreatment s\",\n \"Ġprotect ing\",\n \"f ac\",\n \"fa c\",\n \"Ġ pickle\",\n \"Ġp ickle\",\n \"Ġpick le\",\n \"Ġpic kle\",\n \"Button Item\",\n \"7 13\",\n \"71 3\",\n \"Ġ blocking\",\n \"Ġb locking\",\n \"Ġbl ocking\",\n \"Ġblock ing\",\n \"Ġbloc king\",\n \"st rar\",\n \"str ar\",\n \"stra r\",\n \"à ²\",\n \"Ġ Export\",\n \"ĠEx port\",\n \"ĠExp ort\",\n \"ĠExpo rt\",\n \"Ġth rew\",\n \"Ġthr ew\",\n \"ot ta\",\n \"ott a\",\n \"Ġ BASE\",\n \"ĠB ASE\",\n \"ĠBAS E\",\n \"ĠBA SE\",\n \". ws\",\n \".w s\",\n \".LE ADING\",\n \"order By\",\n \"_ delay\",\n \"_d elay\",\n \"_de lay\",\n \"_del ay\",\n \"Ġ Pu\",\n \"ĠP u\",\n \". dll\",\n \".d ll\",\n \"Ġ Choose\",\n \"ĠCh oose\",\n \"ĠCho ose\",\n \"9 92\",\n \"99 2\",\n \"Pol ice\",\n \"Po lice\",\n \"Ġ BEGIN\",\n \"ĠB EGIN\",\n \"ĠBE GIN\",\n \"bo xes\",\n \"box es\",\n \"Ġ diamond\",\n \"Ġd iamond\",\n \"Ġdiam ond\",\n \"Ġdia mond\",\n \", l\",\n \"Ġ ĉĉĉ\",\n \"Ġĉ ĉĉ\",\n \"Ġĉĉ ĉ\",\n \"Ġc urious\",\n \"Ġcur ious\",\n \"Ġcu rious\",\n \"6 24\",\n \"62 4\",\n \"t v\",\n \"Ġerot ische\",\n \"Ġerotisch e\",\n \"ack ages\",\n \"ackage s\",\n \"ĉ Set\",\n \"ĉS et\",\n \"T ick\",\n \"Ti ck\",\n \". border\",\n \".b order\",\n \"static method\",\n \"Ġ cher\",\n \"Ġc her\",\n \"Ġch er\",\n \"Ġche r\",\n \"in voice\",\n \"inv oice\",\n \"Ġc ru\",\n \"Ġcr u\",\n \"Ġde fect\",\n \"Ġdef ect\",\n \"Ġdefe ct\",\n \"_ metadata\",\n \"_m etadata\",\n \"_meta data\",\n \"_met adata\",\n \"re lation\",\n \"rel ation\",\n \"i kan\",\n \"ik an\",\n \"ika n\",\n \"[ N\",\n \"( Qt\",\n \"(Q t\",\n \"( Base\",\n \"(B ase\",\n \"æģ ¯\",\n \"b eat\",\n \"be at\",\n \"Ġ Empty\",\n \"ĠEm pty\",\n \"ĠEmp ty\",\n \"ĉ o\",\n \"_ shift\",\n \"_s hift\",\n \"_sh ift\",\n \"Ġreg ret\",\n \"7 22\",\n \"72 2\",\n \"Th ose\",\n \"Tho se\",\n \"C ent\",\n \"Ce nt\",\n \"ĠPort ug\",\n \"ĠIs lands\",\n \"ĠIsl ands\",\n \"ĠIsland s\",\n \"Ġ TIME\",\n \"ĠT IME\",\n \"ĠTIM E\",\n \"ĠTI ME\",\n \"Man agement\",\n \"Manage ment\",\n \"Mana gement\",\n \"9 96\",\n \"99 6\",\n \"- sp\",\n \"-s p\",\n \"5 39\",\n \"53 9\",\n \"ê me\",\n \"êm e\",\n \"Ġn otion\",\n \"Ġnot ion\",\n \"Ġno tion\",\n \"un ifu\",\n \"uni fu\",\n \"P K\",\n \"8 26\",\n \"82 6\",\n \"è¡ Į\",\n \"ĠC URLOPT\",\n \"ĠCUR LOPT\",\n \"ĠCURL OPT\",\n \"\\\\ \\\"\\\\\",\n \"\\\\\\\" \\\\\",\n \"U V\",\n \"ç º\",\n \"d ra\",\n \"dr a\",\n \"c ou\",\n \"co u\",\n \"= `\",\n \"Ġ Destroy\",\n \"ĠD estroy\",\n \"ĠDe stroy\",\n \"ĠDest roy\",\n \"r p\",\n \". cancel\",\n \".c ancel\",\n \".can cel\",\n \"G G\",\n \"r untime\",\n \"run time\",\n \"Ġ Vue\",\n \"ĠV ue\",\n \"ĠVu e\",\n \"Ġpro gressive\",\n \"Ġprogress ive\",\n \"Ġprog ressive\",\n \"/ services\",\n \"/s ervices\",\n \"/service s\",\n \"Ġ runner\",\n \"Ġr unner\",\n \"Ġrun ner\",\n \"_ FRAME\",\n \"_FR AME\",\n \". ToolStripMenuItem\",\n \".ToolStrip MenuItem\",\n \"Ġ ','\",\n \"Ġ' ,'\",\n \"Ġ', '\",\n \"d elay\",\n \"de lay\",\n \"del ay\",\n \"= utf\",\n \"=u tf\",\n \"Ġscreen ing\",\n \"Ġscre ening\",\n \"Ġp ulling\",\n \"Ġpull ing\",\n \"Ġpul ling\",\n \"o mas\",\n \"om as\",\n \"oma s\",\n \"Ġ anth\",\n \"Ġa nth\",\n \"Ġan th\",\n \"Ġant h\",\n \"- new\",\n \"-n ew\",\n \"-ne w\",\n \"/ local\",\n \"/l ocal\",\n \"/lo cal\",\n \"Ġ iPad\",\n \"Ġi Pad\",\n \"ĠiP ad\",\n \"Ġ twitter\",\n \"Ġt witter\",\n \"Ġtw itter\",\n \"Ġd ying\",\n \"Ġdy ing\",\n \"Ġhe aven\",\n \"Ġheav en\",\n \"Ġ UInt\",\n \"ĠU Int\",\n \"ĠUI nt\",\n \"Ġ Senator\",\n \"ĠSen ator\",\n \"Ġpre sum\",\n \"Ġpres um\",\n \"Ġ Walker\",\n \"ĠW alker\",\n \"ĠWalk er\",\n \"ĠWal ker\",\n \"Ġover come\",\n \"e tection\",\n \"et ection\",\n \"ete ction\",\n \"etect ion\",\n \"Ġemb arrass\",\n \"Ch ina\",\n \"Chi na\",\n \"6 39\",\n \"63 9\",\n \"In clude\",\n \"Inc lude\",\n \"R OLL\",\n \"RO LL\",\n \"ROL L\",\n \"Ġ dataType\",\n \"Ġdata Type\",\n \"D avid\",\n \"Da vid\",\n \"ภ£\",\n \"l op\",\n \"lo p\",\n \"- month\",\n \"-m onth\",\n \"-mon th\",\n \"Ġ scar\",\n \"Ġs car\",\n \"Ġsc ar\",\n \"Ġsca r\",\n \"Ġ Safe\",\n \"ĠS afe\",\n \"ĠSaf e\",\n \"ĠSa fe\",\n \"Ġ ****************************************************************\",\n \"Ġ******************************** ********************************\",\n \"Ġ******** ********************************************************\",\n \"Ġ**************** ************************************************\",\n \"Ġ************************ ****************************************\",\n \"Ġ**************************************** ************************\",\n \"Ġ******************************************************** ********\",\n \"Ġ************************************************ ****************\",\n \"Ġaccess ories\",\n \"Ġaccessor ies\",\n \"Ġr amp\",\n \"Ġra mp\",\n \"Ġram p\",\n \"_ USE\",\n \"_U SE\",\n \"_US E\",\n \"Ġcon trad\",\n \"Ġcont rad\",\n \"Ġcontr ad\",\n \"Ġcontra d\",\n \") )]Ċ\",\n \")) ]Ċ\",\n \"))] Ċ\",\n \"Ġp rest\",\n \"Ġpr est\",\n \"Ġpre st\",\n \"Ġpres t\",\n \"Ġ HR\",\n \"ĠH R\",\n \"ĠR ap\",\n \"ĠRa p\",\n \"Ġ usize\",\n \"Ġu size\",\n \"Ġus ize\",\n \"Ġ capability\",\n \"Ġcap ability\",\n \"Ġc ort\",\n \"Ġco rt\",\n \"Ġcor t\",\n \"- next\",\n \"-n ext\",\n \"-ne xt\",\n \"0 77\",\n \"07 7\",\n \"6 27\",\n \"62 7\",\n \"Ġbur den\",\n \"8 22\",\n \"82 2\",\n \"_ reader\",\n \"_re ader\",\n \"_read er\",\n \"Ġ @@\",\n \"Ġ@ @\",\n \"reg ular\",\n \"Ġ Ka\",\n \"ĠK a\",\n \"0 36\",\n \"03 6\",\n \"M AN\",\n \"MA N\",\n \"Ġ astr\",\n \"Ġa str\",\n \"Ġas tr\",\n \"Ġast r\",\n \"Ġ' ')Ċ\",\n \"Ġ'' )Ċ\",\n \"Ġ'') Ċ\",\n \"Ġ fed\",\n \"Ġf ed\",\n \"Ġfe d\",\n \"Ġp arsing\",\n \"Ġpar sing\",\n \"Ġpars ing\",\n \"Ġ Years\",\n \"ĠY ears\",\n \"ĠYear s\",\n \"ĠYe ars\",\n \"Ġ broker\",\n \"Ġb roker\",\n \"Ġbr oker\",\n \"Ġbro ker\",\n \"Ġbroke r\",\n \"\\\": {\\\"\",\n \"Ġ akt\",\n \"Ġa kt\",\n \"Ġak t\",\n \"In ventory\",\n \"ab eled\",\n \"abel ed\",\n \"abe led\",\n \"Ġarg parse\",\n \"* ******Ċ\",\n \"** *****Ċ\",\n \"**** ***Ċ\",\n \"****** *Ċ\",\n \"*** ****Ċ\",\n \"***** **Ċ\",\n \"******* Ċ\",\n \"vers ation\",\n \"Ġ cord\",\n \"Ġc ord\",\n \"Ġco rd\",\n \"Ġcor d\",\n \"Ġ Ti\",\n \"ĠT i\",\n \"Ġ hopefully\",\n \"Ġhope fully\",\n \"Ġhop efully\",\n \"Ġhopeful ly\",\n \"Ġ ah\",\n \"Ġa h\",\n \"v erb\",\n \"ver b\",\n \"ve rb\",\n \"Ġst olen\",\n \"Ġstole n\",\n \"Ġsto len\",\n \"Ġstol en\",\n \". Entry\",\n \".En try\",\n \".Ent ry\",\n \"Ġex pecting\",\n \"Ġexpect ing\",\n \"O rientation\",\n \"Ġ powered\",\n \"Ġp owered\",\n \"Ġpower ed\",\n \"Ġpow ered\",\n \"Ġ persist\",\n \"Ġp ersist\",\n \"Ġpers ist\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"' ]);\",\n \"'] );\",\n \"']) ;\",\n \"' )),Ċ\",\n \"') ),Ċ\",\n \"')) ,Ċ\",\n \"')), Ċ\",\n \"Ġ Cash\",\n \"ĠC ash\",\n \"ĠCas h\",\n \"ĠCa sh\",\n \"ĉ item\",\n \"ĉi tem\",\n \"ĉit em\",\n \"8 18\",\n \"81 8\",\n \"g rades\",\n \"gr ades\",\n \"grad es\",\n \"grade s\",\n \"gra des\",\n \"r opol\",\n \"ro pol\",\n \"rop ol\",\n \"b asic\",\n \"ba sic\",\n \"bas ic\",\n \"Ġ \\\");čĊ\",\n \"Ġ\\\" );čĊ\",\n \"Ġ\\\") ;čĊ\",\n \"Ġ\\\"); čĊ\",\n \"Ġa wards\",\n \"Ġaw ards\",\n \"Ġaward s\",\n \"( range\",\n \"(r ange\",\n \"(ra nge\",\n \"- all\",\n \"-a ll\",\n \"-al l\",\n \"Ġ IBOutlet\",\n \"ĠIB Outlet\",\n \"Ġ Indeed\",\n \"ĠInd eed\",\n \"---------------------------------------------------------------- ------------\",\n \"------------ ----------------------------------------------------------------\",\n \"------------------------------------------------ ----------------------------\",\n \"------ ----------------------------------------------------------------------\",\n \"---------------------------- ------------------------------------------------\",\n \"---------------------------------------------------------------------- ------\",\n \"Ġstom ach\",\n \"Ġsto mach\",\n \"Ġ flower\",\n \"Ġf lower\",\n \"Ġfl ower\",\n \"Ġflow er\",\n \"Ġflo wer\",\n \"Ġs ew\",\n \"Ġse w\",\n \"_ times\",\n \"_t imes\",\n \"_time s\",\n \"_tim es\",\n \"_ti mes\",\n \"a vis\",\n \"av is\",\n \"avi s\",\n \"Q String\",\n \"QS tring\",\n \"Ġ Routes\",\n \"ĠR outes\",\n \"ĠRoute s\",\n \"ĠRo utes\",\n \"ĠRou tes\",\n \"ĠRout es\",\n \"_ prot\",\n \"_p rot\",\n \"_pro t\",\n \"_pr ot\",\n \"Ġcom edy\",\n \"Ġcome dy\",\n \"Ġcomed y\",\n \"Ġ logout\",\n \"Ġlog out\",\n \"Ġlogo ut\",\n \"Ġwood en\",\n \"Ġwo oden\",\n \"Ġwoo den\",\n \"Ġ poster\",\n \"Ġp oster\",\n \"Ġpos ter\",\n \"Ġpost er\",\n \"Ġpo ster\",\n \"Ġposte r\",\n \"p iece\",\n \"pi ece\",\n \"pie ce\",\n \". 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ing\",\n \"Ġ ff\",\n \"Ġf f\",\n \"_ uuid\",\n \"_u uid\",\n \"u las\",\n \"ul as\",\n \"ula s\",\n \"Ġdem ocratic\",\n \"Ġdemocr atic\",\n \"Ġdemocrat ic\",\n \". entities\",\n \".ent ities\",\n \"S eries\",\n \"Se ries\",\n \"Ser ies\",\n \"aph ore\",\n \"Ġnew er\",\n \"Ġne wer\",\n \"} (\",\n \"S EC\",\n \"SE C\",\n \"a iro\",\n \"air o\",\n \"ai ro\",\n \"Ġcom mod\",\n \"Ġcomm od\",\n \"Ġpriv ilege\",\n \"Ġprivile ge\",\n \"Ġde ux\",\n \"Ġ Hop\",\n \"ĠH op\",\n \"ĠHo p\",\n \". '/\",\n \".' /\",\n \"c tic\",\n \"ct ic\",\n \". ';Ċ\",\n \".' ;Ċ\",\n \"< ?=\",\n \" C\",\n \"ĠWar ren\",\n \"Ġ optimizer\",\n \"Ġopt imizer\",\n \"Ġoptim izer\",\n \"Ġoptimize r\",\n \"ĠSER VICES\",\n \"ĠSERVICE S\",\n \"_ oper\",\n \"_op er\",\n \"_o per\",\n \"get Attribute\",\n \"ĠMc K\",\n \"_ self\",\n \"_s elf\",\n \"_se lf\",\n \"_sel f\",\n \"0 84\",\n \"08 4\",\n \". rs\",\n \".r s\",\n \"\\\" )ĊĊĊ\",\n \"\\\") ĊĊĊ\",\n \"\\\")Ċ ĊĊ\",\n \"\\\")ĊĊ Ċ\",\n \"Get Component\",\n \"er ce\",\n \"erc e\",\n \"Ġt ous\",\n \"Ġto us\",\n \"Ġtou s\",\n \"un its\",\n \"unit s\",\n \"uni ts\",\n \"' ]);čĊ\",\n \"'] );čĊ\",\n \"']) ;čĊ\",\n \"']); čĊ\",\n \"Z oom\",\n \"/ E\",\n \"Ġob sc\",\n \"Ġobs c\",\n \"Ġfast est\",\n \"Ġfas test\",\n \"on line\",\n \"Ġpeace ful\",\n \"f fen\",\n \"ff en\",\n \"ffe n\",\n \"Ġ cargo\",\n \"Ġc argo\",\n \"Ġcar go\",\n \"Ġcarg o\",\n \"ĉ pr\",\n \"ĉp r\",\n \"Ġse eks\",\n \"Ġsee ks\",\n \"Ġseek s\",\n \"z u\",\n \"0 74\",\n \"07 4\",\n \"T rim\",\n \"Tr im\",\n \"Tri m\",\n \"Ġ ward\",\n \"Ġw ard\",\n \"Ġwar d\",\n \"Ġwa rd\",\n \"Ġv erd\",\n \"Ġver d\",\n \"Ġve rd\",\n \"Ġ blogs\",\n \"Ġb logs\",\n \"Ġbl ogs\",\n \"Ġblog s\",\n \"Ġblo gs\",\n \". exceptions\",\n \".ex ceptions\",\n \".exception s\",\n \"Ġ Premium\",\n \"ĠP remium\",\n \"ĠPre mium\",\n \"ĠPrem ium\",\n \"ĠPremi um\",\n \"ĠN etherlands\",\n \"S afe\",\n \"Sa fe\",\n \"F inish\",\n \"Fin ish\",\n \"Fi nish\",\n \"Ġ Album\",\n \"ĠAl bum\",\n \"ĠAlb um\",\n \"_ ACC\",\n \"_A CC\",\n \"_AC C\",\n \"= this\",\n \"=t his\",\n \"v irtual\",\n \"vir tual\",\n \"virt ual\",\n \"] >\",\n \"_ LABEL\",\n \"_L ABEL\",\n \"_LA BEL\",\n \"Ġ Nich\",\n \"ĠN ich\",\n \"ĠNic h\",\n \"ĠNi ch\",\n \"_ win\",\n \"_w in\",\n \"Ġ Aaron\",\n \"ĠA aron\",\n \"W P\",\n \"; $\",\n \"a ims\",\n \"ai ms\",\n \"aim s\",\n \"Ġ ImageView\",\n \"ĠImage View\",\n \"Ġend less\",\n \"Ġendl ess\",\n \"E RA\",\n \"ER A\",\n \"_ DISABLE\",\n \"_DIS ABLE\",\n \"Ġ cancelled\",\n \"Ġcancel led\",\n \"Ġcancell ed\",\n \"Ġcanc elled\",\n \"- us\",\n \"-u s\",\n \"Ġ inspection\",\n \"Ġins pection\",\n \"Ġinspect ion\",\n \"Ġinsp ection\",\n \"e min\",\n \"em in\",\n \"emi n\",\n \"Ġ Grey\",\n \"ĠG rey\",\n \"ĠGr ey\",\n \"ĠGre y\",\n \"- open\",\n \"-o pen\",\n \"-op en\",\n \"Ġ iterations\",\n \"Ġiter ations\",\n \"Ġiteration s\",\n \". owner\",\n \".o wner\",\n \"Ġk eras\",\n \"Ġke ras\",\n \"Ġker as\",\n \". Password\",\n \".P assword\",\n \".Pass word\",\n \"ĠR y\",\n \"Ġ INS\",\n \"ĠI NS\",\n \"ĠIN S\",\n \"A ir\",\n \"Ai r\",\n \"Ġ Several\",\n \"ĠSe veral\",\n \"ĠSever al\",\n \"ĠSev eral\",\n \".Tab Stop\",\n \"IN GLE\",\n \"ING LE\",\n \"Ġ Hair\",\n \"ĠH air\",\n \"ĠHa ir\",\n \"ĠHai r\",\n \"Ġ Canvas\",\n \"ĠC anvas\",\n \"ĠCan vas\",\n \"A AAA\",\n \"AA AA\",\n \"AAA A\",\n \"Ġf law\",\n \"Ġfl aw\",\n \"Ġfla w\",\n \"c edes\",\n \"ce des\",\n \"ced es\",\n \"cede s\",\n \". Report\",\n \".Re port\",\n \"í Ĭ\",\n \"Ġ Tips\",\n \"ĠT ips\",\n \"ĠTi ps\",\n \"ĠTip s\",\n \"cript ors\",\n \"criptor s\",\n \". transaction\",\n \".trans action\",\n \". Spring\",\n \".S pring\",\n \".Sp ring\",\n \"Ġ viewer\",\n \"Ġview er\",\n \"Ġvie wer\",\n \"Ġins ights\",\n \"Ġinsight s\",\n \"è¾ ĵ\",\n \"ord ion\",\n \"U INT\",\n \"UI NT\",\n \"se ek\",\n \"see k\",\n \"Ġ Auf\",\n \"ĠA uf\",\n \"ĠAu f\",\n \"ìŀ IJ\",\n \"Ġ strain\",\n \"Ġs train\",\n \"Ġst rain\",\n \"Ġstr ain\",\n \"Ġstra in\",\n \"To oltip\",\n \"Tool tip\",\n \"Ġ dz\",\n \"Ġd z\",\n \"ig nal\",\n \"ign al\",\n \"a dt\",\n \"ad t\",\n \"Ġ uc\",\n \"Ġu c\",\n \"f inite\",\n \"fin ite\",\n \"fi nite\",\n \"Ġ nm\",\n \"Ġn m\",\n \". cmd\",\n \".c md\",\n \".cm d\",\n \"ĠMy Sql\",\n \"[ data\",\n \"[d ata\",\n \".j ackson\",\n \". tree\",\n \".t ree\",\n \".tr ee\",\n \"Request Param\",\n \"_ agent\",\n \"_a gent\",\n \"_ag ent\",\n \"_age nt\",\n \"\\\" )]čĊ\",\n \"\\\") ]čĊ\",\n \"\\\")] čĊ\",\n \"Ġas sass\",\n \"Ġass ass\",\n \"( Constants\",\n \"(Constant s\",\n \"(Const ants\",\n \": ss\",\n \":s s\",\n \"Ġ MAN\",\n \"ĠM AN\",\n \"ĠMA N\",\n \"+- +-\",\n \"Ġ Bottom\",\n \"ĠB ottom\",\n \"ĠBot tom\",\n \"ĠBott om\",\n \"pr ints\",\n \"print s\",\n \"pri nts\",\n \"Ġ Same\",\n \"ĠS ame\",\n \"ĠSam e\",\n \"ĠSa me\",\n \"@ Autowired\",\n \"s wap\",\n \"sw ap\",\n \"i ción\",\n \"ic ión\",\n \"ici ón\",\n \"Ġprot esters\",\n \"Ġprote sters\",\n \"Ġprotest ers\",\n \"Ġprotester s\",\n \"Ġh oney\",\n \"Ġhon ey\",\n \"Ġho ney\",\n \"Ġhone y\",\n \"Ġ Veter\",\n \"ĠV eter\",\n \"ĠVe ter\",\n \"ĠVet er\",\n \"( Calendar\",\n \"(C alendar\",\n \"- ad\",\n \"-a d\",\n \"ĠBro oklyn\",\n \"ĠBrook lyn\",\n \"L ife\",\n \"Li fe\",\n \"_ VAR\",\n \"_V AR\",\n \"z ech\",\n \"ze ch\",\n \"Ġ CALL\",\n \"ĠC ALL\",\n \"ĠCA LL\",\n \"ĠCAL L\",\n \"_ CAST\",\n \"_C AST\",\n \"_CA ST\",\n \"ĠE lection\",\n \"ĠEl ection\",\n \"ĠElect ion\",\n \"ĠEle ction\",\n \"Ġ thickness\",\n \"Ġth ickness\",\n \"Ġthick ness\",\n \"V ery\",\n \"Ver y\",\n \"Ve ry\",\n \"_ INTEGER\",\n \"_IN TEGER\",\n \"- dev\",\n \"-d ev\",\n \"-de v\",\n \") )))\",\n \")) ))\",\n \"))) )\",\n \"a pat\",\n \"ap at\",\n \"apa t\",\n \"o ooo\",\n \"oo oo\",\n \"ooo o\",\n \"d emo\",\n \"de mo\",\n \"dem o\",\n \"Ġ parseFloat\",\n \"Ġparse Float\",\n \"Ġ Rather\",\n \"ĠR ather\",\n \"ĠRa ther\",\n \"ĠRat her\",\n \"ĠRath er\",\n \"ST IT\",\n \"m aker\",\n \"ma ker\",\n \"make r\",\n \"mak er\",\n \"[ current\",\n \"[c urrent\",\n \"[cur rent\",\n \"[curr ent\",\n \"chron o\",\n \"chr ono\",\n \"Ġ christ\",\n \"Ġch rist\",\n \"Ġchr ist\",\n \"ãģ ª\",\n \"Ġ Detail\",\n \"ĠD etail\",\n \"ĠDe tail\",\n \"ĠDet ail\",\n \"Æ° á»\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"Ġs ul\",\n \"Ġsu l\",\n \"id ency\",\n \"ide ncy\",\n \"iden cy\",\n \"Q ue\",\n \"Qu e\",\n \"Ġe legant\",\n \"Ġeleg ant\",\n \"a pons\",\n \"ap ons\",\n \"apon s\",\n \"apo ns\",\n \"Ġd ishes\",\n \"Ġdis hes\",\n \"Ġdish es\",\n \"Ġint egers\",\n \"Ġinteg ers\",\n \"Ġinteger s\",\n \"Ġinte gers\",\n \"( read\",\n \"(r ead\",\n \"(re ad\",\n \"0 57\",\n \"05 7\",\n \"find ViewById\",\n \"Ġ Amount\",\n \"ĠA mount\",\n \"ĠAm ount\",\n \"Ġ Skip\",\n \"ĠS kip\",\n \"ĠSk ip\",\n \"ĠSki p\",\n \"Ġhab its\",\n \"Ġha bits\",\n \"Ġhabit s\",\n \"* )(\",\n \"*) (\",\n \"Ġmon sters\",\n \"Ġmonster s\",\n \"M AC\",\n \"MA C\",\n \": end\",\n \":e nd\",\n \"Ġf rank\",\n \"Ġfr ank\",\n \"Ġfra nk\",\n \"Ġfran k\",\n \"As sembly\",\n \"Ġ dfs\",\n \"Ġd fs\",\n \"Ġdf s\",\n \"Ġn eut\",\n \"Ġne ut\",\n \"Ġneu t\",\n \"_ TYPES\",\n \"_T YPES\",\n \"_TYPE S\",\n \"_TYP ES\",\n \"e qual\",\n \"eq ual\",\n \"equ al\",\n \"lo yd\",\n \"loy d\",\n \"( uri\",\n \"(u ri\",\n \"(ur i\",\n \"Ġ chi\",\n \"Ġc hi\",\n \"Ġch i\",\n \"Ġdef endant\",\n \"Ġdefend ant\",\n \"Ġconf licts\",\n \"Ġconflic ts\",\n \"Ġconflict s\",\n \"Ġconfl icts\",\n \"Ġ vil\",\n \"Ġv il\",\n \"Ġvi l\",\n \"- js\",\n \"-j s\",\n \"Ġ Peace\",\n \"ĠPe ace\",\n \"Ġ mutable\",\n \"Ġm utable\",\n \"Ġmut able\",\n \"Ġmu table\",\n \") sender\",\n \")s ender\",\n \"Ġ Focus\",\n \"ĠF ocus\",\n \"ĠFo cus\",\n \"å» º\",\n \"Ġapprec iated\",\n \"Ġappreciate d\",\n \"s leep\",\n \"Ġ RED\",\n \"ĠR ED\",\n \"ĠRE D\",\n \"C ulture\",\n \"Ġdesign ers\",\n \"Ġdesigner s\",\n \"_ generator\",\n \"_g enerator\",\n \"_gen erator\",\n \"_gener ator\",\n \"c odes\",\n \"co des\",\n \"code s\",\n \"cod es\",\n \"/ ex\",\n \"/e x\",\n \". GetValue\",\n \".Get Value\",\n \"um bled\",\n \"umb led\",\n \"umble d\",\n \".scal ajs\",\n \".scala js\",\n \"pe ror\",\n \"per or\",\n \"Ġveter ans\",\n \"Ġveteran s\",\n \"Ġ })čĊ\",\n \"Ġ} )čĊ\",\n \"Ġ}) čĊ\",\n \"Ġun fortunately\",\n \"Ġunfortunate ly\",\n \"_ CREATE\",\n \"_C REATE\",\n \"_CRE ATE\",\n \"_CREAT E\",\n \"M ass\",\n \"Ma ss\",\n \"Mas s\",\n \"Ġ CLAIM\",\n \"ĠCL AIM\",\n \"Ġ Meet\",\n \"ĠMe et\",\n \"_ support\",\n \"_s upport\",\n \"_sup port\",\n \"_supp ort\",\n \"B ank\",\n \"Ban k\",\n \"Ba nk\",\n \"( ).Ċ\",\n \"() .Ċ\",\n \"(). Ċ\",\n \"D ark\",\n \"Da rk\",\n \"Dar k\",\n \"_ LOW\",\n \"_L OW\",\n \"_LO W\",\n \"Ġ Mining\",\n \"ĠM ining\",\n \"ĠMin ing\",\n \"ĠMini ng\",\n \"ĠMi ning\",\n \"Ġ Owner\",\n \"ĠO wner\",\n \"ĠOwn er\",\n \"ĠOw ner\",\n \"i era\",\n \"ie ra\",\n \"ier a\",\n \"Client e\",\n \"Cl iente\",\n \"Cli ente\",\n \"Ġencour aging\",\n \"> S\",\n \"Ġboy friend\",\n \"Ġ Half\",\n \"ĠH alf\",\n \"ĠHa lf\",\n \"ĠHal f\",\n \"Ġ ACC\",\n \"ĠA CC\",\n \"ĠAC C\",\n \"A ff\",\n \"Af f\",\n \"_ ar\",\n \"_a r\",\n \"- life\",\n \"-l ife\",\n \"-li fe\",\n \"c x\",\n \".J Button\",\n \"iz ado\",\n \"iza do\",\n \". zero\",\n \".z ero\",\n \".open qa\",\n \"o ton\",\n \"ot on\",\n \"oto n\",\n \". textContent\",\n \".text Content\",\n \"Ġt oll\",\n \"Ġto ll\",\n \"Ġtol l\",\n \"a tie\",\n \"at ie\",\n \"ati e\",\n \"Ġball ot\",\n \"Ġbal lot\",\n \"Ġballo t\",\n \"- number\",\n \"-n umber\",\n \"-num ber\",\n \". Exception\",\n \".Ex ception\",\n \"ĉ params\",\n \"ĉparam s\",\n \"c ircle\",\n \"circ le\",\n \"cir cle\",\n \"- map\",\n \"-m ap\",\n \"Ġ nap\",\n \"Ġn ap\",\n \"Ġna p\",\n \"Ġ Robot\",\n \"ĠR obot\",\n \"ĠRob ot\",\n \"ĠRo bot\",\n \"Ġ Ich\",\n \"ĠI ch\",\n \"reg istration\",\n \"registr ation\",\n \"regist ration\",\n \"A mazon\",\n \"Am azon\",\n \"roll ment\",\n \"( exp\",\n \"(e xp\",\n \"(ex p\",\n \"Ġt anks\",\n \"Ġtank s\",\n \"Ġtan ks\",\n \"ĠG ordon\",\n \"ĠGor don\",\n \"ĠGord on\",\n \"Ġm achinery\",\n \"Ġmachine ry\",\n \"Ġmach inery\",\n \"Ġ baseline\",\n \"Ġb aseline\",\n \"Ġbase line\",\n \"Ġbas eline\",\n \"æ ĭ\",\n \"0 86\",\n \"08 6\",\n \"Ø ©\",\n \"Ġ Convention\",\n \"ĠCon vention\",\n \"ĠConv ention\",\n \"ĉ config\",\n \"ĉcon fig\",\n \"ĉconf ig\",\n \"o okies\",\n \"ook ies\",\n \"ookie s\",\n \"m ult\",\n \"mu lt\",\n \"mul t\",\n \"Rec ords\",\n \"Record s\",\n \"Ġ EST\",\n \"ĠE ST\",\n \"ĠES T\",\n \"Ġgar bage\",\n \"Ġcon form\",\n \"Ġconf orm\",\n \"i dal\",\n \"id al\",\n \"ida l\",\n \"Ġb arg\",\n \"Ġbar g\",\n \"Ġba rg\",\n \"Ġsurv ived\",\n \"Ġsurvive d\",\n \"Ġsurviv ed\",\n \"Ġinvestig ations\",\n \"Ġinvestigation s\",\n \"9 35\",\n \"93 5\",\n \".contains Key\",\n \"---- ----------------------------------------------------------------------Ċ\",\n \"---------------------------------------------------------------- ----------Ċ\",\n \"---------------------------------------------------------------------- ----Ċ\",\n \"or tion\",\n \"ort ion\",\n \"Ġh orr\",\n \"Ġhor r\",\n \"Ġho rr\",\n \"_ http\",\n \"_h ttp\",\n \"_ht tp\",\n \"Ġ mant\",\n \"Ġm ant\",\n \"Ġman t\",\n \"Ġma nt\",\n \"] ;čĊčĊ\",\n \"]; čĊčĊ\",\n \"];čĊ čĊ\",\n \"b inary\",\n \"bin ary\",\n \"9 48\",\n \"94 8\",\n \"e mpl\",\n \"em pl\",\n \"emp l\",\n \"Ġin quiry\",\n \"Ġ Meanwhile\",\n \"ĠMean while\",\n \"0 98\",\n \"09 8\",\n \"Ġcollect ing\",\n \".Entity Framework\",\n \"\\\" ,ĊĊ\",\n \"\\\", ĊĊ\",\n \"\\\",Ċ Ċ\",\n \"Ġ Pic\",\n \"ĠP ic\",\n \"ĠPi c\",\n \"@ Inject\",\n \"ick ness\",\n \"Ġ Binding\",\n \"ĠB inding\",\n \"ĠBind ing\",\n \"ĠBin ding\",\n \"Ġcont rolling\",\n \"Ġcontrol ling\",\n \"re verse\",\n \"rev erse\",\n \"Ġch airs\",\n \"Ġchair s\",\n \"Ġcha irs\",\n \"Ġchai rs\",\n \"semb led\",\n \"sem bled\",\n \"semble d\",\n \"sembl ed\",\n \"( add\",\n \"(a dd\",\n \"(ad d\",\n \"Dis abled\",\n \"Disable d\",\n \"a nas\",\n \"an as\",\n \"ana s\",\n \". translate\",\n \".trans late\",\n \"- ----------Ċ\",\n \"-- ---------Ċ\",\n \"---- -------Ċ\",\n \"-------- ---Ċ\",\n \"--- --------Ċ\",\n \"----- ------Ċ\",\n \"---------- -Ċ\",\n \"------ -----Ċ\",\n \"----------- Ċ\",\n \"------- ----Ċ\",\n \"--------- --Ċ\",\n \"Ġref lected\",\n \"Ġreflect ed\",\n \"Ġrefl ected\",\n \"\\\" ]ĊĊ\",\n \"\\\"] ĊĊ\",\n \"\\\"]Ċ Ċ\",\n \"Ex ternal\",\n \"Ext ernal\",\n \"Extern al\",\n \"Ar row\",\n \"Arr ow\",\n \"S ingleton\",\n \"Single ton\",\n \"Sing leton\",\n \"% x\",\n \"Ġ Å\",\n \"Ġan cest\",\n \"Ġance st\",\n \"Ġanc est\",\n \"ĠOr 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Wait\",\n \".W ait\",\n \"Ġ Pictures\",\n \"ĠP ictures\",\n \"ĠPicture s\",\n \"ĠPic tures\",\n \"Ġr het\",\n \"Ġrh et\",\n \"Ġrhe t\",\n \"Ġ thermal\",\n \"Ġth ermal\",\n \"Ġther mal\",\n \"Ġtherm al\",\n \"Ġp raise\",\n \"Ġpr aise\",\n \"Ġpra ise\",\n \"< >();ĊĊ\",\n \"<>();Ċ Ċ\",\n \"<>( );ĊĊ\",\n \"<> ();ĊĊ\",\n \"Ġ Spider\",\n \"ĠSp ider\",\n \"ĠSpi der\",\n \"P ause\",\n \"Pa use\",\n \"ĠB aker\",\n \"ĠBa ker\",\n \"ĠBak er\",\n \"ĠBake r\",\n \"Ġs lower\",\n \"Ġsl ower\",\n \"Ġslow er\",\n \"Ġslo wer\",\n \"Ġ }]Ċ\",\n \"Ġ} ]Ċ\",\n \"Ġ}] Ċ\",\n \"_ enqueue\",\n \"_en queue\",\n \"Ġdis appeared\",\n \"Ġdisappe ared\",\n \"Ġdisappear ed\",\n \"Ġ Ticket\",\n \"ĠT icket\",\n \"ĠTi cket\",\n \"ĠTick et\",\n \"ĠTic ket\",\n \"IN UX\",\n \"INU X\",\n \"_ LOCAL\",\n \"_LO CAL\",\n \"_LOC AL\",\n \"аÑģ Ñģ\",\n \"@Inject able\",\n \"comm unity\",\n \"G estureRecognizer\",\n \"Gesture Recognizer\",\n \"åĽ ½\",\n \"Ġs cales\",\n \"Ġsc ales\",\n \"Ġscale s\",\n \"Ġsca les\",\n \"Ġscal es\",\n \"Ġ -(\",\n \"Ġ- (\",\n \"/ '+\",\n \"/' +\",\n \"Ġ Sit\",\n \"ĠS it\",\n \"ĠSi t\",\n \"Ġexecutive s\",\n \"Ġexecut ives\",\n \"ar ding\",\n \"ard ing\",\n \"ardi ng\",\n \"ardin g\",\n \"Ġad vers\",\n \"Ġadv ers\",\n \"Ġback wards\",\n \"Ġbackward s\",\n \"ĉ context\",\n \"ĉcon text\",\n \"ĉcont ext\",\n \"ĠH amp\",\n \"ĠHam p\",\n \"ĠHa mp\",\n \"Ġ PF\",\n \"ĠP F\",\n \"Ġ Deck\",\n \"ĠD eck\",\n \"ĠDe ck\",\n \"ĠDec k\",\n \"Ġ Craig\",\n \"ĠC raig\",\n \"ĠCra ig\",\n \"A merican\",\n \"Americ an\",\n \"America n\",\n \"Ġ bell\",\n \"Ġb ell\",\n \"Ġbe ll\",\n \"Ġbel l\",\n \"Ġp rol\",\n \"Ġpro l\",\n \"Ġpr ol\",\n \"u fen\",\n \"uf en\",\n \"ufe n\",\n \"Ġ rng\",\n \"Ġr ng\",\n \"Ġrn g\",\n \"ar shal\",\n \"ars hal\",\n \"Ġ Simply\",\n \"ĠSim ply\",\n \"ĠSimpl y\",\n \"first name\",\n \"sh ore\",\n \"J uly\",\n \"Jul y\",\n \"Ju ly\",\n \"Ġm ortality\",\n \"Ġmort ality\",\n \"Ġmortal ity\",\n \"ĠâĨĴ ĊĊ\",\n \"H elpers\",\n \"Helper s\",\n \"Help ers\",\n \"Hel pers\",\n \"Ġ benchmark\",\n \"Ġb enchmark\",\n \"Ġbench mark\",\n \"e made\",\n \"em ade\",\n \"ema de\",\n \"Ġorgan isations\",\n \"Ġorganis ations\",\n \"Ġorganisation s\",\n \".g son\",\n \".gs on\",\n \"Ġ TextField\",\n \"ĠT extField\",\n \"ĠText Field\",\n \"Ġcivil ians\",\n \"Ġciv ilians\",\n \"Ġcivilian s\",\n \". Arrays\",\n \".Array s\",\n \".Ar rays\",\n \"ĠMiss issippi\",\n \"Ġinter mediate\",\n \"Ġintermedi ate\",\n \"get User\",\n \"_ cluster\",\n \"_cl uster\",\n \"Rel ative\",\n \"fore ign\",\n \".querySelector All\",\n \"Fore ignKey\",\n \"Foreign Key\",\n \"Ġreason ably\",\n \"- --------Ċ\",\n \"-- -------Ċ\",\n \"---- -----Ċ\",\n \"-------- -Ċ\",\n \"--- ------Ċ\",\n \"----- ----Ċ\",\n \"------ ---Ċ\",\n \"------- --Ċ\",\n \"--------- Ċ\",\n \"C ards\",\n \"Card s\",\n \"Car ds\",\n \"ĠK am\",\n \"ĠKa m\",\n \"Ġ Thor\",\n \"ĠT hor\",\n \"ĠTh or\",\n \"Ġ roller\",\n \"Ġr oller\",\n \"Ġro ller\",\n \"Ġroll er\",\n \"Ġrol ler\",\n \"- element\",\n \"-e lement\",\n \"-el ement\",\n \"Ġ Currency\",\n \"ĠC urrency\",\n \"d die\",\n \"dd ie\",\n \"AL LY\",\n \"ALL Y\",\n \"Ġ RA\",\n \"ĠR A\",\n \"Ġper met\",\n \"Ġperm et\",\n \"Ġperme t\",\n \"a aaa\",\n \"aa aa\",\n \"aaa a\",\n \"Ġhome work\",\n \"Ġhom ework\",\n \"ĠV it\",\n \"ĠVi t\",\n \"Ġm old\",\n \"Ġmo ld\",\n \"Ġmol d\",\n \"ĠF er\",\n \"ĠFe r\",\n \"[ start\",\n \"Ġstat istical\",\n \"Ġstatist ical\",\n \"Ġstatistic al\",\n \"Ġsc ary\",\n \"Ġsca ry\",\n \"Ġscar y\",\n \"_ HOME\",\n \"_H OME\",\n \". Begin\",\n \".B egin\",\n \".Be gin\",\n \"Con struct\",\n \"o genic\",\n \"og enic\",\n \"ogen ic\",\n \"oge nic\",\n \"ĠDEAL INGS\",\n \"Ġtamb ién\",\n \"i xon\",\n \"ix on\",\n \"ixo n\",\n \". ind\",\n \".in d\",\n \".i nd\",\n \"a cre\",\n \"ac re\",\n \"acr e\",\n \"Ġ transforms\",\n \"Ġtrans forms\",\n \"Ġtransform s\",\n \"ĠN ap\",\n \"ĠNa p\",\n \". Block\",\n \".B lock\",\n \".Bl ock\",\n \"uss ia\",\n \"p iration\",\n \"pi ration\",\n \"pir ation\",\n \"ul ent\",\n \"ule nt\",\n \"ulen t\",\n \"Ġ ceil\",\n \"Ġc eil\",\n \"Ġce il\",\n \"Cl ause\",\n \"Cla use\",\n \"n aire\",\n \"na ire\",\n \"T ES\",\n \"TE S\",\n \"Ġn eat\",\n \"Ġne at\",\n \"S TD\",\n \"ST D\",\n \"Ġ RegExp\",\n \"ĠReg Exp\",\n \"per form\",\n \"perf orm\",\n \": )\",\n \"Ġun ions\",\n \"Ġunion s\",\n \"Ġuni ons\",\n \"Ġs ublic\",\n \"Ġsub lic\",\n \"Ġw inds\",\n \"Ġwin ds\",\n \"Ġwind s\",\n \"Ġwi nds\",\n \"lo ating\",\n \"loat ing\",\n \"g lich\",\n \"gl ich\",\n \"gli ch\",\n \"Ġ pagination\",\n \"Ġp agination\",\n \"Ġpag ination\",\n \"Ġpagina tion\",\n \"S kill\",\n \"Sk ill\",\n \"App ly\",\n \"Ap ply\",\n \"Ġ Operator\",\n \"ĠO perator\",\n \"ĠOper ator\",\n \"ĠOp erator\",\n \"ĠOpera tor\",\n \"ist ogram\",\n \"isto gram\",\n \"Ġ qualities\",\n \"Ġqual ities\",\n \"Ġquali ties\",\n \"C ross\",\n \"Cr oss\",\n \"Cro ss\",\n \"Ġd ecom\",\n \"Ġde com\",\n \"Ġdec om\",\n \"Ġdeco m\",\n \"] ,\\\"\",\n \"], \\\"\",\n \"Ġ Juan\",\n \"ĠJ uan\",\n \"ĠJu an\",\n \". modal\",\n \".m odal\",\n \".mod al\",\n \".mo dal\",\n \". Child\",\n \".Ch ild\",\n \"Ġ Roger\",\n \"ĠR oger\",\n \"ĠRo ger\",\n \"ĠRog er\",\n \"STIT UTE\",\n \":CGRect Make\",\n \"a lette\",\n \"al ette\",\n \"ale tte\",\n \"alet te\",\n \"Ġ sta\",\n \"Ġs ta\",\n \"Ġst a\",\n \"a side\",\n \"as ide\",\n \"asi de\",\n \"Ġ blur\",\n \"Ġbl ur\",\n \"Ġ Wa\",\n \"ĠW a\",\n \"if etime\",\n \"ife time\",\n \"r eed\",\n \"re ed\",\n \"ree d\",\n \"control s\",\n \"contr ols\",\n \"contro ls\",\n \"Ġ bins\",\n \"Ġb ins\",\n \"Ġbi ns\",\n \"Ġbin s\",\n \"Ġ пол\",\n \"Ġп ол\",\n \"Ġпо л\",\n \"* /,Ċ\",\n \"*/ ,Ċ\",\n \"*/, Ċ\",\n \"U IS\",\n \"UI S\",\n \"ĠR ou\",\n \"ĠRo u\",\n \"Ġ Demo\",\n \"ĠD emo\",\n \"ĠDe mo\",\n \"ĠDem o\",\n \"- awesome\",\n \"Ġ Chain\",\n \"ĠCh ain\",\n \"ĠCha in\",\n \"Ġh asta\",\n \"Ġhas ta\",\n \"Ġha sta\",\n \"Ġhast a\",\n \"ĠB art\",\n \"ĠBar t\",\n \"ĠBa rt\",\n \". KEY\",\n \".K EY\",\n \"Ġ vendors\",\n \"Ġv endors\",\n \"Ġvend ors\",\n \"Ġvendor s\",\n \"no follow\",\n \"nof ollow\",\n \"Ġ Dest\",\n \"ĠD est\",\n \"ĠDe st\",\n \"ĠDes t\",\n \"_ builder\",\n \"_b uilder\",\n \"_build er\",\n \"Ġarg ues\",\n \"Ġargue s\",\n \"_ answer\",\n \"_an swer\",\n \"_ans wer\",\n \"g oto\",\n \"go to\",\n \"got o\",\n \"Ġ RESULT\",\n \"ĠRES ULT\",\n \"Ġ MON\",\n \"ĠM ON\",\n \"ĠMO N\",\n \"Ġp oder\",\n \"Ġpo der\",\n \"Ġpod er\",\n \"Ġpode r\",\n \"o ons\",\n \"oo ns\",\n \"oon s\",\n \"_ CASE\",\n \"_C ASE\",\n \"_CA SE\",\n \"Ġrep lic\",\n \"Ġrepl ic\",\n \"Ġfin ancing\",\n \"Ġfinanc ing\",\n \"Ġfinan cing\",\n \"Ġ DATE\",\n \"ĠD ATE\",\n \"ĠDA TE\",\n \"ĠDAT E\",\n \"c ern\",\n \"ce rn\",\n \"cer n\",\n \"_ track\",\n \"_t rack\",\n \"_tr ack\",\n \"_tra ck\",\n \"t ies\",\n \"ti es\",\n \"tie s\",\n \"/ logo\",\n \"/l ogo\",\n \"/log o\",\n \"/lo go\",\n \"ĠNE GLIGENCE\",\n \"get Type\",\n \"> T\",\n \"b et\",\n \"be t\",\n \"g irl\",\n \"gi rl\",\n \"ĠINCIDENT AL\",\n \"- site\",\n \"-s ite\",\n \". trigger\",\n \".tr igger\",\n \"Ġ Lisa\",\n \"ĠL isa\",\n \"ĠLi sa\",\n \"ĠLis a\",\n \"_ inputs\",\n \"_in puts\",\n \"_input s\",\n \"_inp uts\",\n \"Ġrel atives\",\n \"Ġrelative s\",\n \"Ġrelativ es\",\n \"Ġrelat ives\",\n \"Logged In\",\n \"Con figure\",\n \"Config ure\",\n \"Conf igure\",\n \"I K\",\n \". accept\",\n \".ac cept\",\n \".acc ept\",\n \"Re sume\",\n \"Res ume\",\n \"Ġ Draft\",\n \"ĠD raft\",\n \"ĠDr aft\",\n \"ĠDra ft\",\n \"Ġ *>(\",\n \"Ġ* >(\",\n \"Ġ*> (\",\n \"Ġ WA\",\n \"ĠW A\",\n \"ed ian\",\n \"edia n\",\n \"edi an\",\n \"er ness\",\n \"ern ess\",\n \"erne ss\",\n \"ernes s\",\n \"Ġ LayoutInflater\",\n \"ĠLayout Inflater\",\n \"* /čĊčĊ\",\n \"*/ čĊčĊ\",\n \"*/čĊ čĊ\",\n \"o thy\",\n \"ot hy\",\n \"oth y\",\n \"Ġoblig ation\",\n \"Ġobl igation\",\n \"Sub scribe\",\n \"Ġ thumbnail\",\n \"Ġth umbnail\",\n \"ex ist\",\n \"Ġins isted\",\n \"Ġinsist ed\",\n \"Ġ UICollectionView\",\n \"ĠU ICollectionView\",\n \"ĠUI CollectionView\",\n \"Ġ Angular\",\n \"ĠAng ular\",\n \"Ġtable ts\",\n \"Ġtab lets\",\n \"Ġtablet s\",\n \"Ġ Impact\",\n \"ĠImp act\",\n \"ãĢį ĊĊ\",\n \"ãĢįĊ Ċ\",\n \"a ho\",\n \"ah o\",\n \"Ġcharacter istic\",\n \"g d\",\n \"Ġ= ================================================\",\n \"Ġ================= ================================\",\n \"Ġ================================= ================\",\n \"o urt\",\n \"ou rt\",\n \"our t\",\n \"` .\",\n \"App ro\",\n \"Ap pro\",\n \"Co ordinate\",\n \"Coord inate\",\n \"Re member\",\n \"Rem ember\",\n \"Ġ marine\",\n \"Ġm arine\",\n \"Ġmar ine\",\n \"Ġma rine\",\n \"Ġmari ne\",\n \"Ġmarin e\",\n \"] =='\",\n \"]= ='\",\n \"]== '\",\n \"Ġ Administrator\",\n \"ĠAdmin istrator\",\n \"ĠAdministr ator\",\n \". getDefault\",\n \".get Default\",\n \".getD efault\",\n \"Ġ forgot\",\n \"Ġf orgot\",\n \"Ġfor got\",\n \"Ġforg ot\",\n \"Ġ Structure\",\n \"ĠStruct ure\",\n \"V ue\",\n \"Vu e\",\n \"ar sing\",\n \"ars ing\",\n \"arsi ng\",\n \"m oment\",\n \"mo ment\",\n \"mom ent\",\n \"k w\",\n \"_ cursor\",\n \"_c ursor\",\n \"Att ack\",\n \"Ġath letic\",\n \"Ġdiagn osed\",\n \"Ġdiagnose d\",\n \"Ġ ende\",\n \"Ġe nde\",\n \"Ġen de\",\n \"Ġend e\",\n \"åĪ łéĻ¤\",\n \"H ouse\",\n \"Ho use\",\n \"Ġ PARAM\",\n \"ĠP ARAM\",\n \"ĠPA RAM\",\n \"ĠPAR AM\",\n \"ĠPARA M\",\n \"Ġ wiki\",\n \"Ġw iki\",\n \"Ġwi ki\",\n \"Ġwik i\",\n \"Ġ Opp\",\n \"ĠO pp\",\n \"ĠOp p\",\n \"Ġcons ervation\",\n \"Ġconserv ation\",\n \"Ġ snd\",\n \"Ġs nd\",\n \"Ġsn d\",\n \"_ tem\",\n \"_t em\",\n \"_te m\",\n \"sub str\",\n \"subst r\",\n \"subs tr\",\n \"ĠC ape\",\n \"ĠCap e\",\n \"ĠCa pe\",\n \". sim\",\n \".s im\",\n \".si m\",\n \"U TION\",\n \"UT ION\",\n \"a nan\",\n \"an an\",\n \"ana n\",\n \"âĢĻ un\",\n \"Ġ gy\",\n \"Ġg y\",\n \"- work\",\n \"-w ork\",\n \"Ġcomp elling\",\n \"Ġcompel ling\",\n \"= '#\",\n \"=' #\",\n \"ĉ sub\",\n \"ĉs ub\",\n \"Ġ directories\",\n \"Ġdirect ories\",\n \"Ġdirector ies\",\n \"íĬ ¸\",\n \"Ġ touches\",\n \"Ġtouch es\",\n \"Ġtou ches\",\n \"out ines\",\n \"outine s\",\n \". Collection\",\n \".C ollection\",\n \".Col lection\",\n \"s chedule\",\n \"sched ule\",\n \". lat\",\n \".l at\",\n \"Ġ Doctrine\",\n \"ĠDo ctrine\",\n \"C AA\",\n \"CA A\",\n \"Ġ Refer\",\n \"ĠRe fer\",\n \"ĠRef er\",\n \"Ġshift s\",\n \"Ġ likelihood\",\n \"Ġlik elihood\",\n \"pr eter\",\n \"pre ter\",\n \"pret er\",\n \"Ġ Female\",\n \"ĠF emale\",\n \"ĠFe male\",\n \"ĠFem ale\",\n \"Ġinter cept\",\n \"Ġ lou\",\n \"Ġl ou\",\n \"Ġlo u\",\n \"çĻ »\",\n \"Ġ rug\",\n \"Ġr ug\",\n \"Ġru g\",\n \"ĠC rown\",\n \"ĠCr own\",\n \"ĠCro wn\",\n \"ĠCrow n\",\n \"Ġ ****************************************************************************\",\n \"Ġ************************************************************************ ****\",\n \"Ġ************************************************************************** **\",\n \"Ġ**************************************************************** ************\",\n \"Ġ**** ************************************************************************\",\n \"Ġ******************************************************** ********************\",\n \"Ġ************************************************ ****************************\",\n \"- product\",\n \"-pro duct\",\n \"-produ ct\",\n \"Ġprompt ed\",\n \"u ngle\",\n \"un gle\",\n \"ung le\",\n \"d ocker\",\n \"do cker\",\n \"doc ker\",\n \"dock er\",\n \"Ġ Tu\",\n \"ĠT u\",\n \"Ġ Unique\",\n \"ĠUn ique\",\n \"ĠUni que\",\n \"_ Error\",\n \"_E rror\",\n \"_Err or\",\n \"u los\",\n \"ul os\",\n \"ulo s\",\n \"Ġ âĦ\",\n \"Ġâ Ħ\",\n \"Ġ (`\",\n \"Ġ( `\",\n \"G etting\",\n \"Get ting\",\n \"_ scal\",\n \"_s cal\",\n \"_sc al\",\n \"Ġ Enh\",\n \"ĠE nh\",\n \"ĠEn h\",\n \"ü t\",\n \"Ġsust ained\",\n \"Ġsustain ed\",\n \"Ġ patches\",\n \"Ġp atches\",\n \"Ġpat ches\",\n \"Ġpatch es\",\n \"Ġpros per\",\n \"ĠG aza\",\n \"ĠGa za\",\n \"ĠGaz a\",\n \"_ light\",\n \"_l ight\",\n \"_li ght\",\n \"Ġin cons\",\n \"Ġinc ons\",\n \"Ġincon s\",\n \"- -------Ċ\",\n \"-- ------Ċ\",\n \"---- ----Ċ\",\n \"-------- Ċ\",\n \"--- -----Ċ\",\n \"----- ---Ċ\",\n \"------ --Ċ\",\n \"------- -Ċ\",\n \"ĉ ĉĠĠĠĠĠĠ\",\n \"ĉĉ ĠĠĠĠĠĠ\",\n \"ĉĉĠĠĠ ĠĠĠ\",\n \"ĉĉĠ ĠĠĠĠĠ\",\n \"ĉĉĠĠ ĠĠĠĠ\",\n \"ĉĉĠĠĠĠ ĠĠ\",\n \"ĉĉĠĠĠĠĠ Ġ\",\n \"S F\",\n \"C N\",\n \": \\\";Ċ\",\n \":\\\" ;Ċ\",\n \"ĠColl ins\",\n \"( *)\",\n \"(* )\",\n \"Ġcomp ilation\",\n \"Ġcompil ation\",\n \"' ]čĊ\",\n \"'] čĊ\",\n \"Ġcon sequence\",\n \"Ġconsequ ence\",\n \"Ġconse quence\",\n \", ...\",\n \",. ..\",\n \"Ġ dm\",\n \"Ġd m\",\n \"Ġ BLOCK\",\n \"ĠB LOCK\",\n \"ĠBL OCK\",\n \"Cl uster\",\n \"Ġ ski\",\n \"Ġs ki\",\n \"Ġsk i\",\n \"( argc\",\n \"(arg c\",\n \"(ar gc\",\n \"T uple\",\n \"Tu ple\",\n \"Ġj oins\",\n \"Ġjoin s\",\n \"Ġjo ins\",\n \"ĠSher iff\",\n \"W ar\",\n \"Wa r\",\n \"in di\",\n \"ind i\",\n \"Ġcom mented\",\n \"Ġcomm ented\",\n \"Ġcomment ed\",\n \"H OST\",\n \"HO ST\",\n \"Ġ invitation\",\n \"Ġinv itation\",\n \"Ġinvit ation\",\n \"apan ese\",\n \"Ġper mits\",\n \"Ġpermit s\",\n \"Ġperm its\",\n \"preced ented\",\n \"_ zone\",\n \"_z one\",\n \"Ġ Amy\",\n \"ĠA my\",\n \"ĠAm y\",\n \"_ RD\",\n \"_R D\",\n \"Min imum\",\n \"Ġinv ocation\",\n \"Ġinvo cation\",\n \". enable\",\n \".e nable\",\n \".en able\",\n \"i chten\",\n \"ich ten\",\n \"icht en\",\n \"ichte n\",\n \"- owned\",\n \"\\\" id\",\n \"_PO INTER\",\n \"_POINT ER\",\n \"F ac\",\n \"Fa c\",\n \"Ġspec ifications\",\n \"Ġspecific ations\",\n \"Ġspecification s\",\n \"Ġn omination\",\n \"Ġno mination\",\n \"Ġnom ination\",\n \"Ġnomin ation\",\n \"Ġ gp\",\n \"Ġg p\",\n \"< (\",\n \"Ġ robots\",\n \"Ġro bots\",\n \"Ġrob ots\",\n \"Ġrobot s\",\n \"Ġ Jerry\",\n \"ĠJ erry\",\n \"ĠJer ry\",\n \"Ġ holders\",\n \"Ġh olders\",\n \"Ġhold ers\",\n \"Ġholder s\",\n \"Ġhol ders\",\n \"Ġ wand\",\n \"Ġw and\",\n \"Ġwa nd\",\n \"Ġwan d\",\n \"c ms\",\n \"cm s\",\n \"Ġ }))Ċ\",\n \"Ġ} ))Ċ\",\n \"Ġ}) )Ċ\",\n \"Ġ})) Ċ\",\n \". Toast\",\n \".To ast\",\n \"ĠI List\",\n \"ĠIL ist\",\n \"B ased\",\n \"Base d\",\n \"Bas ed\",\n \"Ba sed\",\n \"z oom\",\n \"zo om\",\n \"/ style\",\n \"/st yle\",\n \"ĠB eck\",\n \"ĠBe ck\",\n \"ĠBec k\",\n \"M en\",\n \"Me n\",\n \"Ġcontrib uting\",\n \"Ġ undo\",\n \"Ġu ndo\",\n \"Ġun do\",\n \"Ġund o\",\n \"Ġ OH\",\n \"ĠO H\",\n \"Ġadd Object\",\n \"Ġe igen\",\n \"Ġei gen\",\n \"Ġeig en\",\n \"sign up\",\n \"éĶ Ļ\",\n \"Ġd istant\",\n \"Ġdis tant\",\n \"Ġdist ant\",\n \"Ġdi stant\",\n \"PAR ATOR\",\n \"Ġ Mari\",\n \"ĠM ari\",\n \"ĠMar i\",\n \"ĠMa ri\",\n \"Ġ má\",\n \"Ġm á\",\n \"E mp\",\n \"Em p\",\n \"ó s\",\n \"Ġ ìĪĺ\",\n \"Ġì Īĺ\",\n \"ĠìĪ ĺ\",\n \"e vt\",\n \"ev t\",\n \"+ j\",\n \"p ark\",\n \"par k\",\n \"pa rk\",\n \"Ġ Stay\",\n \"ĠSt ay\",\n \"ĠSta y\",\n \"ĠD un\",\n \"ĠDu n\",\n \"Ġs oy\",\n \"Ġso y\",\n \"> %\",\n \"az ines\",\n \"azine s\",\n \"azi nes\",\n \"Ġti empo\",\n \"( me\",\n \"(m e\",\n \"p resent\",\n \"pre sent\",\n \"pres ent\",\n \". This\",\n \".T his\",\n \".Th is\",\n \"Ġed itors\",\n \"Ġedit ors\",\n \"Ġeditor s\",\n \"Ġedi tors\",\n \"F IELD\",\n \". Work\",\n \".W ork\",\n \"ĠUn iverse\",\n \"ĠUnivers e\",\n \"ĠUni verse\",\n \"ĠUniv erse\",\n \"Ġdr unk\",\n \"Ġdru nk\",\n \". timer\",\n \".t imer\",\n \".time r\",\n \".tim er\",\n \"Ġal tered\",\n \"Ġalt ered\",\n \"Ġalter ed\",\n \"Ġalte red\",\n \"ĠN ar\",\n \"ĠNa r\",\n \"ëł ¥\",\n \". Active\",\n \".Act ive\",\n \"id or\",\n \"ido r\",\n \"ç Ń\",\n \".delta Time\",\n \"Ġawk ward\",\n \"& quot\",\n \"ĠS afari\",\n \"ĠSaf ari\",\n \"Ġt ricks\",\n \"Ġtr icks\",\n \"Ġtri cks\",\n \"Ġtrick s\",\n \"M ENTS\",\n \"MENT S\",\n \"div ision\",\n \"di vision\",\n \"Ġvar ying\",\n \"Ġva rying\",\n \"Ġvary ing\",\n \"ĠHigh way\",\n \"Ġphot ographer\",\n \"Ġphotograph er\",\n \"ĠSt ewart\",\n \"ĠSte wart\",\n \"Ġ lasting\",\n \"Ġl asting\",\n \"Ġlast ing\",\n \"Ġlas ting\",\n \". Pre\",\n \".P re\",\n \".Pr e\",\n \".amazon aws\",\n \"Ġ Luck\",\n \"ĠL uck\",\n \"ĠLuc k\",\n \"ĠLu ck\",\n \". Description\",\n \".D escription\",\n \".De scription\",\n \".Des cription\",\n \"Ġ Naz\",\n \"ĠN az\",\n \"ĠNa z\",\n \"n eg\",\n \"ne g\",\n \"Ġc ó\",\n \"<< \\\"\\\\\",\n \"<<\\\" \\\\\",\n \"Ġ Surv\",\n \"ĠS urv\",\n \"ĠSur v\",\n \"ĠSu rv\",\n \"Ġ Unc\",\n \"ĠU nc\",\n \"ĠUn c\",\n \"Rec ipe\",\n \". BorderStyle\",\n \".Border Style\",\n \"Ġmod ifications\",\n \"Ġmodification s\",\n \"Ġmodific ations\",\n \"- at\",\n \"-a t\",\n \"AT FORM\",\n \"h dr\",\n \"hd r\",\n \"a ko\",\n \"ak o\",\n \"Ġsub license\",\n \"Ġsublic ense\",\n \"Ġ Jump\",\n \"ĠJ ump\",\n \"ĠJu mp\",\n \"Ġbe im\",\n \"Ġbei m\",\n \"ĠMan hattan\",\n \". bool\",\n \".b ool\",\n \".bo ol\",\n \"_ hw\",\n \"_h w\",\n \"ÑĤ ÑĮ\",\n \"B in\",\n \"Bi n\",\n \"Ġ gateway\",\n \"Ġg ateway\",\n \"Ġgate way\",\n \"\\\" \\\":\",\n \"\\\"\\\" :\",\n \"Ġ UIS\",\n \"ĠU IS\",\n \"ĠUI S\",\n \": \\\"+\",\n \":\\\" +\",\n \"- def\",\n \"-d ef\",\n \"-de f\",\n \"Ġ Regular\",\n \"ĠReg ular\",\n \"/ testing\",\n \"/t esting\",\n \"/test ing\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"string stream\",\n \"Ġdis par\",\n \"Ġdi spar\",\n \"Ġdisp ar\",\n \"Ġm obil\",\n \"Ġmo bil\",\n \"Ġmob il\",\n \"- read\",\n \"-r ead\",\n \"-re ad\",\n \"Ġ Adapter\",\n \"ĠAd apter\",\n \"ĠAda pter\",\n \"ĠAdapt er\",\n \"ĠCh ampions\",\n \"ĠChampion s\",\n \"ĠChamp ions\",\n \"Ġ scheduler\",\n \"Ġs cheduler\",\n \"Ġsched uler\",\n \"Ġschedule r\",\n \"Ġ kills\",\n \"Ġk ills\",\n \"Ġkill s\",\n \"Ġkil ls\",\n \"Ġ Multiple\",\n \"ĠM ultiple\",\n \"ĠMulti ple\",\n \"ĠMult iple\",\n \"ĠMultip le\",\n \"i rror\",\n \"ir ror\",\n \"Ġg ods\",\n \"Ġgo ds\",\n \"Ġgod s\",\n \"A DO\",\n \"AD O\",\n \"a kte\",\n \"ak te\",\n \"akt e\",\n \"Ġ Usuario\",\n \"ĠUs uario\",\n \".c ircular\",\n \"Ġre cept\",\n \"Ġrec ept\",\n \"Ġrece pt\",\n \"Ġrecep t\",\n \"Ġ Expr\",\n \"ĠEx pr\",\n \"ĠExp r\",\n \"Ġelder ly\",\n \"Ġnice ly\",\n \"Ġnic ely\",\n \"Ġb este\",\n \"Ġbe ste\",\n \"Ġbest e\",\n \"Ġbes te\",\n \"W ant\",\n \"Wa nt\",\n \"Ġclass ical\",\n \"Ġclassic al\",\n \". sprite\",\n \".s prite\",\n \".sp rite\",\n \"ob jc\",\n \"obj c\",\n \"ĠM ason\",\n \"ĠMa son\",\n \"ĠMas on\",\n \"Ġs istema\",\n \"Ġsist ema\",\n \"Ġsistem a\",\n \". 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ACTION\",\n \".A CTION\",\n \".AC TION\",\n \".ACT ION\",\n \"F ig\",\n \"Fi g\",\n \"_ radius\",\n \"_r adius\",\n \"_rad ius\",\n \"_radi us\",\n \"Ġ Sync\",\n \"ĠS ync\",\n \"ĠSy nc\",\n \"ĠSyn c\",\n \"d ucer\",\n \"du cer\",\n \"duc er\",\n \"duce r\",\n \"f ailure\",\n \"fail ure\",\n \"en sed\",\n \"ens ed\",\n \"ense d\",\n \"p time\",\n \"pt ime\",\n \"B M\",\n \"_ datetime\",\n \"_d atetime\",\n \"_date time\",\n \"_dat etime\",\n \"qu ivo\",\n \"quiv o\",\n \"qui vo\",\n \"QUE UE\",\n \"èĢ ħ\",\n \"App ear\",\n \"Ap pear\",\n \"Appe ar\",\n \"Ġsum mit\",\n \"Ġsumm it\",\n \": void\",\n \":v oid\",\n \"Ġ vine\",\n \"Ġv ine\",\n \"Ġvi ne\",\n \"Ġvin e\",\n \"è ®¤\",\n \"è® ¤\",\n \"on ne\",\n \"onn e\",\n \"_ TRANS\",\n \"_TR ANS\",\n \"_TRAN S\",\n \"_TRA NS\",\n \". green\",\n \".g reen\",\n \".gr een\",\n \"_ cc\",\n \"_c c\",\n \"Ġhung ry\",\n \"Ġ \\\">\",\n \"Ġ\\\" >\",\n \"( ));čĊčĊ\",\n \"() );čĊčĊ\",\n \"()) ;čĊčĊ\",\n \"());čĊ čĊ\",\n \"()); čĊčĊ\",\n \"Ex tract\",\n \"Ext ract\",\n \"Extra ct\",\n \"i zens\",\n \"iz ens\",\n \"ize ns\",\n \"izen s\",\n \"Ġ solver\",\n \"Ġs olver\",\n \"Ġsol ver\",\n \"Ġsolve r\",\n \"N otify\",\n \"Not ify\",\n \"Ġ english\",\n \"Ġeng lish\",\n \"Ġ Shopping\",\n \"ĠSh opping\",\n \"ĠShop ping\",\n \"ĠSho pping\",\n \"inter faces\",\n \"interface s\",\n \"R EQ\",\n \"RE Q\",\n \"Ġil leg\",\n \"Ġill eg\",\n \"Ġ UIImageView\",\n \"ĠUI ImageView\",\n \"ĠUIImage View\",\n \"Ġ disconnect\",\n \"Ġdis connect\",\n \"Ġ Until\",\n \"ĠUn til\",\n \"ĠUnt il\",\n \"ĠCons ervative\",\n \"ĠConserv ative\",\n \"@ Column\",\n \"Ġshift ed\",\n \"Ġ :čĊ\",\n \"Ġ: čĊ\",\n \"Ġf ich\",\n \"Ġfi ch\",\n \"Ġfic h\",\n \"Ġd la\",\n \"Ġdl a\",\n \"Ġs hoe\",\n \"Ġsh oe\",\n \"Ġsho e\",\n \"\\\" ),čĊ\",\n \"\\\") ,čĊ\",\n \"\\\"), čĊ\",\n \"ul arity\",\n \"ular ity\",\n \"_ RESP\",\n \"_RE SP\",\n \"_R ESP\",\n \"_RES P\",\n \"We ather\",\n \"UI Application\",\n \". iterator\",\n \".it erator\",\n \".iter ator\",\n \"Ġ aging\",\n \"Ġa ging\",\n \"Ġag ing\",\n \". 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choice\",\n \"_ch oice\",\n \"ĠG oth\",\n \"ĠGo th\",\n \"ĠGot h\",\n \"Ġ Sites\",\n \"ĠS ites\",\n \"ĠSi tes\",\n \"ĠSite s\",\n \"ĠSit es\",\n \"M G\",\n \"_ border\",\n \"_b order\",\n \"Ind ices\",\n \"Com parer\",\n \"Comp arer\",\n \"Compar er\",\n \"Compare r\",\n \"ĠRe distribution\",\n \"ĠRed istribution\",\n \"ĠRedis tribution\",\n \"Ġclose t\",\n \"Ġclos et\",\n \"Ġclo set\",\n \"Ġvers atile\",\n \"Ġversa tile\",\n \"In puts\",\n \"Input s\",\n \"**** ****************\",\n \"******** ************\",\n \"**************** ****\",\n \"****** **************\",\n \"************ ********\",\n \"************** ******\",\n \"Ġob esity\",\n \"qu iz\",\n \"qui z\",\n \"g ra\",\n \"gr a\",\n \"( global\",\n \"(g lobal\",\n \"åĬ ¡\",\n \"Ġ collector\",\n \"Ġcol lector\",\n \"Ġcoll ector\",\n \"Ġcollect or\",\n \"Ġcolle ctor\",\n \"Ġ kor\",\n \"Ġk or\",\n \"Ġko r\",\n \"o vable\",\n \"ov able\",\n \"ova ble\",\n \"A DC\",\n \"AD C\",\n \"Ġ EventHandler\",\n \"ĠEvent Handler\",\n \". nc\",\n \".n c\",\n \"Ġplay back\",\n \"ient os\",\n \"ien tos\",\n \"iento s\",\n \"_ perm\",\n \"_p erm\",\n \"_per m\",\n \"_pe rm\",\n \"_ WARNING\",\n \"_W ARNING\",\n \"_WARN ING\",\n \"ĠOlymp ics\",\n \"ĠOlympic s\",\n \". norm\",\n \".n orm\",\n \".no rm\",\n \"Ġ Broadcast\",\n \"ĠB roadcast\",\n \"ĠBroad cast\",\n \"_ small\",\n \"_s mall\",\n \"_sm all\",\n \"d rive\",\n \"dr ive\",\n \". iloc\",\n \".i loc\",\n \".il oc\",\n \"Ġ typed\",\n \"Ġt yped\",\n \"Ġtype d\",\n \"Ġtyp ed\",\n \"Ġty ped\",\n \"M EM\",\n \"ME M\",\n \"_ cons\",\n \"_c ons\",\n \"_con s\",\n \"_co ns\",\n \"D METHOD\",\n \"DM ETHOD\",\n \"Ġ lun\",\n \"Ġl un\",\n \"Ġlu n\",\n \". distance\",\n \".d istance\",\n \".di stance\",\n \".dist ance\",\n \"( par\",\n \"(p ar\",\n \"(pa r\",\n \"p oon\",\n \"po on\",\n \"Ġ bast\",\n \"Ġb ast\",\n \"Ġbas t\",\n \"Ġba st\",\n \"act ivities\",\n \"activ ities\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \": čĊčĊ\",\n \":čĊ čĊ\",\n \"S ER\",\n \"SE R\",\n \") &&\",\n \")& &\",\n \"_ lst\",\n \"_l st\",\n \"_ls t\",\n \"ĠPol ish\",\n \"ĠPo lish\",\n \"Ġkn ocked\",\n \"Ġknock ed\",\n \"Ġfrustr ation\",\n \"au kee\",\n \"Ġph osph\",\n \"iqu id\",\n \"iq uid\",\n \"_ coeff\",\n \"_c oeff\",\n \"_co eff\",\n \"_coef f\",\n \"æŃ ¤\",\n \"L atest\",\n \"La test\",\n \"Lat est\",\n \"Late st\",\n \"ĠD ust\",\n \"ĠDu st\",\n \"T ipo\",\n \"Tip o\",\n \"Ti po\",\n \"Ġmain tains\",\n \"Ġmaint ains\",\n \"Ġmaintain s\",\n \"Ġ marsh\",\n \"Ġmar sh\",\n \"Ġmars h\",\n \"inc inn\",\n \"inci nn\",\n \"l bl\",\n \"lb l\",\n \"C are\",\n \"Car e\",\n \"Ca re\",\n \"Ġneighborhood s\",\n \"_ gpio\",\n \"_g pio\",\n \"_gp io\",\n \"ĠAr senal\",\n \"ĠArs enal\",\n \"D em\",\n \"De m\",\n \"ĠW he\",\n \"ĠWh e\",\n \"_ hook\",\n \"_h ook\",\n \"Ġl dc\",\n \"Ġld c\",\n \"ĠHar per\",\n \"ĠBer keley\",\n \"Ġgrad uated\",\n \"Ġgraduate d\",\n \"Ġgradu ated\",\n \"Per cent\",\n \"Ġarr iving\",\n \"Ġarriv ing\",\n \"Ġ Adventure\",\n \"ĠAd venture\",\n \"ĠAdvent ure\",\n \"( scope\",\n \"(s cope\",\n \"(sc ope\",\n \"( '*\",\n \"(' *\",\n \"qu arter\",\n \"ĠM arie\",\n \"ĠMar ie\",\n \"ĠMa rie\",\n \"ĠMari e\",\n \"Spe aking\",\n \"Speak ing\",\n \"_ codegen\",\n \"_code gen\",\n \"_cod egen\",\n \"Ġim mun\",\n \"Ġimm un\",\n \"c aster\",\n \"ca ster\",\n \"cast er\",\n \"cas ter\",\n \"ãĤ Į\",\n \"åķ Ĩ\",\n \"Ġ Dimensions\",\n \"ĠDim ensions\",\n \"ĠDimension s\",\n \". record\",\n \".re cord\",\n \".rec ord\",\n \"Ġ texto\",\n \"Ġtext o\",\n \"Ġtex to\",\n \"Ġ Michelle\",\n \"ĠMich elle\",\n \"ĠMichel le\",\n \"ĠMiche lle\",\n \"P ending\",\n \"Pen ding\",\n \"( by\",\n \"(b y\",\n \"_ PAR\",\n \"_P AR\",\n \"_PA R\",\n \"u cht\",\n \"uch t\",\n \"uc ht\",\n \"b ee\",\n \"be e\",\n \". Thread\",\n \".T hread\",\n \".Th read\",\n \"am pire\",\n \"amp ire\",\n \"k now\",\n \"kn ow\",\n \"Ġ Clinical\",\n \"ĠClin ical\",\n \"ĠClinic al\",\n \"Ġmargin Bottom\",\n \"Ġd istinguish\",\n \"Ġdistingu ish\",\n \". Full\",\n \".F ull\",\n \". undefined\",\n \".un defined\",\n \"ĠSequ elize\",\n \"#### ########################################################################\",\n \"################ ############################################################\",\n \"################################################################ ############\",\n \"############ ################################################################\",\n \"################################################ ############################\",\n \"######################################################################## ####\",\n \"############################ ################################################\",\n \"############################################################ ################\",\n \"Ġ educated\",\n \"Ġeduc ated\",\n \"Ġeducate d\",\n \"_ OVER\",\n \"_O VER\",\n \"åº ı\",\n \"Ġ ÂłĠÂł\",\n \"ĠÂł ĠÂł\",\n \"ĠÂłĠ Âł\",\n \"_ each\",\n \"_e ach\",\n \"Ġ urge\",\n \"Ġur ge\",\n \"Ġurg e\",\n \"de part\",\n \"dep art\",\n \"Ġdon ors\",\n \"Ġdonor s\",\n \"Ġ Au\",\n \"ĠA u\",\n \"Ġb illions\",\n \"Ġbill ions\",\n \"Ġbillion s\",\n \"Ġbelong ing\",\n \"_ age\",\n \"_a ge\",\n \"_ag e\",\n \"_ Int\",\n \"_I nt\",\n \"_In t\",\n \"Ġsub stances\",\n \"Ġsubstance s\",\n \"Ġsubst ances\",\n \"m achine\",\n \"ma chine\",\n \"mach ine\",\n \"! !!ĊĊ\",\n \"!! !ĊĊ\",\n \"!!! ĊĊ\",\n \"!!!Ċ Ċ\",\n \"Ġjson ify\",\n \"ib bean\",\n \"Ġ Cad\",\n \"ĠC ad\",\n \"ĠCa d\",\n \"Ġ endTime\",\n \"Ġend Time\",\n \"Ġc ycling\",\n \"Ġcy cling\",\n \"Ġcycl ing\",\n \"Ġcyc ling\",\n \"Ġ UITextField\",\n \"ĠUI TextField\",\n \"ĠUIT extField\",\n \"Ġle verage\",\n \"Ġlever age\",\n \"Ġleve rage\",\n \"Ġvan illa\",\n \"e at\",\n \"ea t\",\n \"L aunch\",\n \"La unch\",\n \"( pt\",\n \"(p t\",\n \"st ates\",\n \"state s\",\n \"stat es\",\n \"sta tes\",\n \"Ġ Controls\",\n \"ĠControl s\",\n \"ĠContr ols\",\n \"Ġ Respons\",\n \"ĠRes pons\",\n \"ĠResp ons\",\n \"Ġ Jake\",\n \"ĠJ ake\",\n \"ĠJa ke\",\n \"ĠJak e\",\n \"Ġa sleep\",\n \"Ġas leep\",\n \"fort unate\",\n \".next Line\",\n \"Size Mode\",\n \"ì Ŀ¼\",\n \"ìĿ ¼\",\n \"Testing Module\",\n \"G erman\",\n \"Ger man\",\n \"ĠInvest ig\",\n \". reverse\",\n \".re verse\",\n \".rev erse\",\n \"Ġ BACK\",\n \"ĠB ACK\",\n \"ĠBA CK\",\n \"( DateTime\",\n \"(Date Time\",\n \"Ġnon profit\",\n \"Ġ Expect\",\n \"ĠEx pect\",\n \"ĠExp ect\",\n \"Ġt anto\",\n \"Ġtan to\",\n \"Ġtant o\",\n \"' ]),\",\n \"'] ),\",\n \"']) ,\",\n \"ĉ the\",\n \"ĉt he\",\n \"ĉth e\",\n \"M ultiple\",\n \"Multi ple\",\n \"Mult iple\",\n \"Multip le\",\n \"(get Activity\",\n \"_ WAIT\",\n \"_W AIT\",\n \"Ġj á\",\n \"de cor\",\n \"dec or\",\n \"lev ance\",\n \"Ġ GitHub\",\n \"ĠGit Hub\",\n \"m ination\",\n \"min ation\",\n \"mi nation\",\n \"mina tion\",\n \"_ quantity\",\n \"_qu antity\",\n \"_quant ity\",\n \". Scanner\",\n \".Sc anner\",\n \".Scan ner\",\n \"ĠL ion\",\n \"ĠLi on\",\n \"éĶĻ 误\",\n \"Ġ dre\",\n \"Ġd re\",\n \"Ġdr e\",\n \"Ġtan tra\",\n \"Ġtant ra\",\n \"Ġtantr a\",\n \"Ġ contentType\",\n \"Ġcontent Type\",\n \"Ġ fid\",\n \"Ġf id\",\n \"Ġfi d\",\n \"_ alt\",\n \"_a lt\",\n \"_al t\",\n \"NS IndexPath\",\n \"- pl\",\n \"-p l\",\n \"åĮ ĸ\",\n \"Ġantib iot\",\n \"t ables\",\n \"table s\",\n \"ta bles\",\n \"tab les\",\n \"tabl es\",\n \"a cial\",\n \"ac ial\",\n \"aci al\",\n \"acia l\",\n \"Ġ Registry\",\n \"ĠReg istry\",\n \"ĠRegistr y\",\n \"Ġo live\",\n \"Ġol ive\",\n \"Ġoli ve\",\n \"i gers\",\n \"ig ers\",\n \"ige rs\",\n \"iger s\",\n \"Ġ subscriber\",\n \"Ġsub scriber\",\n \"Ġsubscri ber\",\n \"Ġsubscribe r\",\n \"_ pres\",\n \"_p res\",\n \"_pr es\",\n \"_pre s\",\n \"Ġ Syntax\",\n \"ĠS yntax\",\n \"ĠSy ntax\",\n \"ĠSyn tax\",\n \"Ġl overs\",\n \"Ġlo vers\",\n \"Ġlove rs\",\n \"Ġlover s\",\n \"Ġlov ers\",\n \". Byte\",\n \".B yte\",\n \".By te\",\n \"ol ders\",\n \"old ers\",\n \"older s\",\n \"_ forward\",\n \"_for ward\",\n \"al ways\",\n \"C aption\",\n \"Cap tion\",\n \"Ca ption\",\n \"Capt ion\",\n \"P riv\",\n \"Pr iv\",\n \"Pri v\",\n \"ĠT ampa\",\n \"ĠTam pa\",\n \"is ateur\",\n \"-labelled by\",\n \"Ġ ToString\",\n \"ĠTo String\",\n \"Ġ ìĤ¬\",\n \"Ġì Ĥ¬\",\n \"ĠìĤ ¬\",\n \"Ġinit iated\",\n \"Ġiniti ated\",\n \"Ġinitiate d\",\n \"W F\",\n \"Ġinstitution al\",\n \"in ject\",\n \"Ġ Scr\",\n \"ĠS cr\",\n \"ĠSc r\",\n \"Ġ doctrine\",\n \"Ġdo ctrine\",\n \"Ġdoctr ine\",\n \"Ġsp acious\",\n \"Ġspac ious\",\n \"i sure\",\n \"is ure\",\n \"isu re\",\n \"Ġ Ana\",\n \"ĠA na\",\n \"ĠAn a\",\n \"\\\" time\",\n \"ess aging\",\n \"essa ging\",\n \"Ġ cid\",\n \"Ġc id\",\n \"Ġci d\",\n \"ĠN an\",\n \"ĠNa n\",\n \"Ġin complete\",\n \"Ġincom plete\",\n \"T AG\",\n \"TA G\",\n \"- build\",\n \"-b uild\",\n \"Dec ember\",\n \"Ġres idual\",\n \"Ġresid ual\",\n \"( PDO\",\n \"(P DO\",\n \"Ġ Listen\",\n \"ĠL isten\",\n \"ĠList en\",\n \"ĠLi sten\",\n \"ĠLis ten\",\n \"ĠListe n\",\n \"Ġ glyph\",\n \"Ġg lyph\",\n \"Ġgly ph\",\n \"Ġg aps\",\n \"Ġgap s\",\n \"Ġga ps\",\n \"n ea\",\n \"ne a\",\n \". 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Bind\",\n \".B ind\",\n \"Ġp alm\",\n \"Ġpa lm\",\n \"Ġpal m\",\n \"clear fix\",\n \"ö s\",\n \"ĠG onz\",\n \"ĠGo nz\",\n \"ĠGon z\",\n \"Ġwe aken\",\n \"Ġweak en\",\n \"D rive\",\n \"Dr ive\",\n \"en ido\",\n \"eni do\",\n \"l ld\",\n \"ll d\",\n \"o box\",\n \"ob ox\",\n \"obo x\",\n \"an ean\",\n \"ane an\",\n \"G ot\",\n \"Go t\",\n \"ä¿ Ŀ\",\n \"Reg ex\",\n \"æ ĥ\",\n \"Ġsa lad\",\n \"Ġsal ad\",\n \"Ġsala d\",\n \"as sis\",\n \"ass is\",\n \"assi s\",\n \"\\\" net\",\n \"inherit Doc\",\n \"Ġ RV\",\n \"ĠR V\",\n \"qu ier\",\n \"qui er\",\n \"Ġ clazz\",\n \"Ġcl azz\",\n \"Ġcla zz\",\n \"ı ÅŁ\",\n \"oster one\",\n \"oste rone\",\n \"Ġair line\",\n \"Ġairl ine\",\n \".list dir\",\n \"Ġdown loading\",\n \"Ġdownload ing\",\n \"ĠP alm\",\n \"ĠPal m\",\n \"ĠPa lm\",\n \"w aukee\",\n \"& lt\",\n \". BL\",\n \".B L\",\n \"_ INLINE\",\n \"_IN LINE\",\n \"of fs\",\n \"off s\",\n \"< <(\",\n \"<< (\",\n \"_ news\",\n \"_n ews\",\n \"_new s\",\n \"_ne ws\",\n \"Ġch ase\",\n \"Ġcha se\",\n \"/ ><\",\n \"/> <\",\n \"Ġe uros\",\n \"Ġeu ros\",\n \"Ġeuro s\",\n \"ĠEgypt ian\",\n \"ĠSt ainless\",\n \"_ BOOL\",\n \"_BO OL\",\n \"Ġ Guild\",\n \"ĠG uild\",\n \"ĠGu ild\",\n \"ĠGui ld\",\n \"ĠGuil d\",\n \"ĠD ynam\",\n \"ĠDy nam\",\n \"ĠDyn am\",\n \"[ indexPath\",\n \"[index Path\",\n \"Ġ ï\",\n \"Ġmem orable\",\n \"Ġmemor able\",\n \"ĠCh ampion\",\n \"ĠChamp ion\",\n \"Resource Manager\",\n \". Login\",\n \".Log in\",\n \".Lo gin\",\n \"Ġ Former\",\n \"ĠFor mer\",\n \"ĠForm er\",\n \"y ped\",\n \"ype d\",\n \"yp ed\",\n \"Ġl leg\",\n \"Ġll eg\",\n \"Ġlle g\",\n \"; \\\",\",\n \";\\\" ,\",\n \"D WORD\",\n \"DW ORD\",\n \"Ġtax i\",\n \"Ġta xi\",\n \"Ġb ombs\",\n \"Ġbomb s\",\n \"Ġbom bs\",\n \"r ah\",\n \"ra h\",\n \". tags\",\n \".t ags\",\n \".tag s\",\n \".ta gs\",\n \"_ tests\",\n \"_t ests\",\n \"_test s\",\n \"_te sts\",\n \"s tones\",\n \"st ones\",\n \"ston es\",\n \"stone s\",\n \"sto nes\",\n \"âĢĿ )\",\n \"[ g\",\n \"r type\",\n \"rt ype\",\n \"Ġ vu\",\n \"Ġv u\",\n \"Ġhost ile\",\n \"Ġho stile\",\n \"Ġhos tile\",\n \"Ch ars\",\n \"Char s\",\n \"Cha rs\",\n \"ĠPatri ots\",\n \"ĠPatriot s\",\n \"/ status\",\n \"/s tatus\",\n \"/st atus\",\n \"/stat us\",\n \"< B\",\n \"Ġ Income\",\n \"ĠIn come\",\n \"ĠInc ome\",\n \"ĠD ad\",\n \"ĠDa d\",\n \"Ġpat rol\",\n \"_ CHANGE\",\n \"_CH ANGE\",\n \"_CHAN GE\",\n \"Ġup graded\",\n \"Ġupgrade d\",\n \"Ġ china\",\n \"Ġch ina\",\n \"Ġchi na\",\n \"Ġchin a\",\n \"set q\",\n \"Start ed\",\n \"Star ted\",\n \".U ndef\",\n \".Un def\",\n \"Ġ checksum\",\n \"Ġcheck sum\",\n \"Ġchecks um\",\n \"Ġfrustr ated\",\n \"{ o\",\n \"Ġe nf\",\n \"Ġen f\",\n \"Ġ woods\",\n \"Ġw oods\",\n \"Ġwood s\",\n \"Ġwo ods\",\n \"Ġwoo ds\",\n \"Ġ Anyone\",\n \"ĠAny one\",\n \"En code\",\n \"Enc ode\",\n \"ĠQt Widgets\",\n \"a reas\",\n \"are as\",\n \"area s\",\n \"Ġsh eer\",\n \"Ġshe er\",\n \"s ki\",\n \"sk i\",\n \"end point\",\n \"_ Test\",\n \"_T est\",\n \"S oup\",\n \"So up\",\n \"Sou p\",\n \"~~~~~~~~ ~~~~~~~~\",\n \"( files\",\n \"(f iles\",\n \"(file s\",\n \"(fi les\",\n \"(fil es\",\n \"ĉ ĉĉĉĉčĊ\",\n \"ĉĉ ĉĉĉčĊ\",\n \"ĉĉĉĉ ĉčĊ\",\n \"ĉĉĉ ĉĉčĊ\",\n \"ĉĉĉĉĉ čĊ\",\n \". spark\",\n \".s park\",\n \".sp ark\",\n \"Ġvalue d\",\n \"Ġval ued\",\n \"Ġvalu ed\",\n \"Ġ %Ċ\",\n \"Ġ% Ċ\",\n \". controls\",\n \".control s\",\n \"ĠXCTAssert Equal\",\n \"Ġf ame\",\n \"Ġfam e\",\n \"Ġfa me\",\n \"ĠR ic\",\n \"ĠRi c\",\n \"D OT\",\n \"DO T\",\n \"ĠAlbert a\",\n \"ĠAlb erta\",\n \"ä½ ¿\",\n \"o sal\",\n \"os al\",\n \"osa l\",\n \".Web Controls\",\n \"Ġ ------------\",\n \"Ġ- -----------\",\n \"Ġ-- ----------\",\n \"Ġ---- --------\",\n \"Ġ--- ---------\",\n \"Ġ----- -------\",\n \"Ġ---------- --\",\n \"Ġ------ ------\",\n \"Ġ-------- ----\",\n \"Ġ------- -----\",\n \"Ġ--------- ---\",\n \"Ġ----------- -\",\n \"Ġ Mis\",\n \"ĠM is\",\n \"ĠMi s\",\n \"Ġ SYS\",\n \"ĠS YS\",\n \"ĠSY S\",\n \"Non null\",\n \"= item\",\n \"=i tem\",\n \"Ġ expire\",\n \"Ġex pire\",\n \"Ġexp ire\",\n \"De code\",\n \"Dec ode\",\n \"_ operation\",\n \"_op eration\",\n \"_o peration\",\n \"_oper ation\",\n \"Ġ Validator\",\n \"ĠValid ator\",\n \". CENTER\",\n \".C ENTER\",\n \"uff s\",\n \"uf fs\",\n \"* m\",\n \"Ġa vant\",\n \"Ġav ant\",\n \"Ġava nt\",\n \"Ġavan t\",\n \"æ¬ ¡\",\n \"âĢľ You\",\n \". permission\",\n \".per mission\",\n \".perm ission\",\n \".. .)\",\n \"... )\",\n \"Ġ Lic\",\n \"ĠL ic\",\n \"ĠLi c\",\n \"_ coords\",\n \"_co ords\",\n \"_coord s\",\n \". nombre\",\n \".n ombre\",\n \".nom bre\",\n \"c lo\",\n \"cl o\",\n \". Internal\",\n \".In ternal\",\n \".Int ernal\",\n \".Inter nal\",\n \"Ġ Cho\",\n \"ĠC ho\",\n \"ĠCh o\",\n \"_ sw\",\n \"_s w\",\n \"ĉ Il\",\n \"ĉI l\",\n \"c lk\",\n \"cl k\",\n \"Ġ castle\",\n \"Ġc astle\",\n \"Ġcas tle\",\n \"Ġcast le\",\n \"( layer\",\n \"(l ayer\",\n \"p it\",\n \"pi t\",\n \"Ġ guided\",\n \"Ġgu ided\",\n \"Ġguide d\",\n \"Ġguid ed\",\n \"Ġgui ded\",\n \"Ġ âĸĪ\",\n \"Ġâĸ Ī\",\n \"Ġsup erb\",\n \"Ġsuper b\",\n \"Ġsup plements\",\n \"Ġsupplement s\",\n \"Ġsuppl ements\",\n \"Ġsupp lements\",\n \"_ cent\",\n \"_c ent\",\n \"_ce nt\",\n \"Ġ peek\",\n \"Ġpe ek\",\n \"Ġpee k\",\n \"IN ARY\",\n \"INA RY\",\n \". ContentAlignment\",\n \".Content Alignment\",\n \"f alls\",\n \"fall s\",\n \"fal ls\",\n \"\\\" ));\",\n \"\\\") );\",\n \"\\\")) ;\",\n \"W all\",\n \"Wal l\",\n \"Wa ll\",\n \") .čĊ\",\n \"). čĊ\",\n \"Ġ Danny\",\n \"ĠD anny\",\n \"ĠDan ny\",\n \"ĠDann y\",\n \"irm ingham\",\n \"IAL IZ\",\n \"( create\",\n \"(c reate\",\n \"\\\" In\",\n \"\\\"I n\",\n \"Service Provider\",\n \"Ġpr iced\",\n \"Ġprice d\",\n \"Ġpri ced\",\n \"m acro\",\n \"ma cro\",\n \"mac ro\",\n \"a mac\",\n \"am ac\",\n \"ama c\",\n \". box\",\n \".b ox\",\n \".bo x\",\n \"- ---Ċ\",\n \"-- --Ċ\",\n \"---- Ċ\",\n \"--- -Ċ\",\n \"ãĥ «\",\n \"Ġ Suit\",\n \"ĠS uit\",\n \"ĠSu it\",\n \"u rst\",\n \"ur st\",\n \"urs t\",\n \"b ru\",\n \"br u\",\n \"ourn als\",\n \"ournal s\",\n \"num ero\",\n \"numer o\",\n \"_ _()Ċ\",\n \"__ ()Ċ\",\n \"__( )Ċ\",\n \"__() Ċ\",\n \"D as\",\n \"Da s\",\n \"ĠM itt\",\n \"ĠMi tt\",\n \"ĠMit t\",\n \"u der\",\n \"ud er\",\n \"ude r\",\n \"? \\\\\",\n \"f u\",\n \"[ B\",\n \"Ġ: )ĊĊ\",\n \"Ġ:) ĊĊ\",\n \"Ġ:)Ċ Ċ\",\n \"( inter\",\n \"(int er\",\n \"(in ter\",\n \"br ains\",\n \"bra ins\",\n \"brain s\",\n \"Ġatt itudes\",\n \"Ġattitude s\",\n \"Ver ify\",\n \"Ġsign atures\",\n \"Ġsignature s\",\n \"ack Bar\",\n \"Ġ gd\",\n \"Ġg d\",\n \"J ack\",\n \"Ja ck\",\n \"Jac k\",\n \". cat\",\n \".c at\",\n \".ca t\",\n \"Ġ zz\",\n \"Ġz z\",\n \"w arf\",\n \"wa rf\",\n \"war f\",\n \"F TER\",\n \"FT ER\",\n \"\\\" );ĊĊĊ\",\n \"\\\");Ċ ĊĊ\",\n \"\\\") ;ĊĊĊ\",\n \"\\\");ĊĊ Ċ\",\n \"\\\"); ĊĊĊ\",\n \"A live\",\n \"Al ive\",\n \"Ali ve\",\n \"I CLE\",\n \"IC LE\",\n \"Ġ Whatever\",\n \"ĠWh atever\",\n \"ĠWhat ever\",\n \"Ġ outlined\",\n \"Ġout lined\",\n \"Ġoutline d\",\n \"s prite\",\n \"sp rite\",\n \"spr ite\",\n \"е в\",\n \"еР²\",\n \"_ AB\",\n \"_A B\",\n \"_ DEPTH\",\n \"_DE PTH\",\n \"Ġcr ushed\",\n \"Ġcrush ed\",\n \"Ġcru shed\",\n \"Ġcrus hed\",\n \"a aa\",\n \"aa a\",\n \"( ev\",\n \"(e v\",\n \"æľ º\",\n \"An ti\",\n \"Ant i\",\n \"I CO\",\n \"IC O\",\n \"is EqualTo\",\n \"isEqual To\",\n \". sun\",\n \".s un\",\n \"i culo\",\n \"ic ulo\",\n \"s ale\",\n \"sa le\",\n \"sal e\",\n \"_ hex\",\n \"_h ex\",\n \"_he x\",\n \"Ġ Vk\",\n \"ĠV k\",\n \"ap tor\",\n \"apt or\",\n \"Un ion\",\n \"Uni on\",\n \"Ġ Discount\",\n \"ĠDis count\",\n \"ĠDisc ount\",\n \"ĠDisco unt\",\n \"l ista\",\n \"li sta\",\n \"list a\",\n \"lis ta\",\n \".Undef Or\",\n \"Ġ automation\",\n \"Ġa utomation\",\n \"Ġautom ation\",\n \"Ġautomat ion\",\n \"N or\",\n \"No r\",\n \"å¯ ¹\",\n \"åı Ĥæķ°\",\n \"åıĤ æķ°\",\n \"Ġre flex\",\n \"Ġref lex\",\n \"Ġrefl ex\",\n \"ĠLa ure\",\n \"ĠLaur e\",\n \"ĠLau re\",\n \".showMessage Dialog\",\n \". temp\",\n \".t emp\",\n \".te mp\",\n \".tem p\",\n \"Ġ akan\",\n \"Ġa kan\",\n \"Ġak an\",\n \"Ġaka n\",\n \"Ġ_ _____\",\n \"Ġ__ ____\",\n \"Ġ___ ___\",\n \"Ġ____ __\",\n \"Ġ_____ _\",\n \".Is True\",\n \"A RED\",\n \"AR ED\",\n \"ARE D\",\n \"a gle\",\n \"ag le\",\n \"E nergy\",\n \"En ergy\",\n \"Ġquant ities\",\n \"âĢĻ é\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"Ġcitizens hip\",\n \"Ġcitizen ship\",\n \"m outh\",\n \"mo uth\",\n \"Ġin appropriate\",\n \"Ġ Outdoor\",\n \"ĠOut door\",\n \"White Space\",\n \"An onymous\",\n \"lo ads\",\n \"load s\",\n \"webElement Properties\",\n \"T en\",\n \"Te n\",\n \"Ġacc idents\",\n \"Ġaccident s\",\n \"Ġ advertisement\",\n \"Ġad vertisement\",\n \"Ġadvertis ement\",\n \"Ġadvertise ment\",\n \"ĠY emen\",\n \"ĠYe men\",\n \"( call\",\n \"(c all\",\n \"(cal l\",\n \"(ca ll\",\n \"Ġsl avery\",\n \"Ġslave ry\",\n \"Ġsla very\",\n \"Ġslav ery\",\n \"Ñģ п\",\n \"ĠL am\",\n \"ĠLa m\",\n \"_ BITS\",\n \"_B ITS\",\n \"_BIT S\",\n \"o mega\",\n \"om ega\",\n \"ome ga\",\n \"ĠO le\",\n \"ĠOl e\",\n \"Ġkid n\",\n \"Ġki dn\",\n \"_ An\",\n \"_A n\",\n \"ĠR aid\",\n \"ĠRa id\",\n \"ĠRai d\",\n \"C reation\",\n \"Cre ation\",\n \"Creat ion\",\n \"s aved\",\n \"save d\",\n \"sa ved\",\n \"sav ed\",\n \"Ġpro port\",\n \"Ġprop ort\",\n \"Ġpropor t\",\n \"W ARNING\",\n \"WAR NING\",\n \"WARN ING\",\n \"\\\\ P\",\n \"Ġ pwd\",\n \"Ġp wd\",\n \"Ġpw d\",\n \"Data Reader\",\n \"is cher\",\n \"isc her\",\n \"ische r\",\n \"isch er\",\n \"ad eon\",\n \"ade on\",\n \"Ġ Predict\",\n \"ĠP redict\",\n \"ĠPre dict\",\n \"ĠPred ict\",\n \"Ġreason ing\",\n \"Ġdestroy ing\",\n \"H el\",\n \"He l\",\n \"* d\",\n \"ĠLeg isl\",\n \"_ Pr\",\n \"_P r\",\n \"ĉ ĉĉĠĠĠĠĠĠĠ\",\n \"ĉĉ ĉĠĠĠĠĠĠĠ\",\n \"ĉĉĉ ĠĠĠĠĠĠĠ\",\n \"ĉĉĉĠĠĠ ĠĠĠĠ\",\n \"ĉĉĉĠ ĠĠĠĠĠĠ\",\n \"ĉĉĉĠĠ ĠĠĠĠĠ\",\n \"ĉĉĉĠĠĠĠ ĠĠĠ\",\n \"ĉĉĉĠĠĠĠĠ ĠĠ\",\n \"ĉĉĉĠĠĠĠĠĠ Ġ\",\n \"Ġsym path\",\n \"Ġsymp ath\",\n \"Ġch ess\",\n \"Ġche ss\",\n \"Ġ mam\",\n \"Ġm am\",\n \"Ġma m\",\n \": hover\",\n \":h over\",\n \"Ġcon verts\",\n \"Ġconvert s\",\n \"Ġconv erts\",\n \"Ġconver ts\",\n \"Ġp ela\",\n \"Ġpe la\",\n \"Ġpel a\",\n \"Ġpro gression\",\n \"Ġprogress ion\",\n \"Ġprog ression\",\n \"Ġ\\\" _\\\"\",\n \"Ġ\\\"_ \\\"\",\n \"ĠG ill\",\n \"ĠGi ll\",\n \"ĠGil l\",\n \"ĉ show\",\n \"ĉs how\",\n \"ĉsh ow\",\n \"Ġsupposed ly\",\n \"ac curacy\",\n \"acc uracy\",\n \"accur acy\",\n \"e lin\",\n \"el in\",\n \"eli n\",\n \"Ġunf olding\",\n \"Ġunfold ing\",\n \"Ġ Hyper\",\n \"ĠH yper\",\n \"ĠHy per\",\n \"ĠHyp er\",\n \"Ġw anna\",\n \"Ġwann a\",\n 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Look\",\n \".L ook\",\n \".Lo ok\",\n \"Ġ ents\",\n \"Ġe nts\",\n \"Ġen ts\",\n \"Ġent s\",\n \"Ġl ors\",\n \"Ġlo rs\",\n \"Ġlor s\",\n \"Ġ BIG\",\n \"ĠB IG\",\n \"ĠBI G\",\n \"f ab\",\n \"fa b\",\n \"Ġaverage d\",\n \"Ġaver aged\",\n \"Ġ Feedback\",\n \"ĠFe edback\",\n \"ĠFeed back\",\n \"ĠW ells\",\n \"ĠWell s\",\n \"ĠWel ls\",\n \"Ġm artial\",\n \"Ġmart ial\",\n \"Ġind ul\",\n \"Ġindu l\",\n \"ĠComm unist\",\n \"ĠCommun ist\",\n \"ĠF orex\",\n \"ĠFor ex\",\n \"ĠFore x\",\n \"ĠFo rex\",\n \"ĠAgricult ure\",\n \"ĠAgr iculture\",\n \"\\\" [\",\n \"Ġqu ar\",\n \"Ġq uar\",\n \"Ġqua r\",\n \"ĠK ont\",\n \"ĠKon t\",\n \"ĠKo nt\",\n \"ĉ view\",\n \"ĉv iew\",\n \". Bytes\",\n \".Byte s\",\n \".By tes\",\n \"d esktop\",\n \"des ktop\",\n \"desk top\",\n \"Ġ Makes\",\n \"ĠM akes\",\n \"ĠMake s\",\n \"ĠMa kes\",\n \"ĠMak es\",\n \"akes peare\",\n \". Nullable\",\n \".Null able\",\n \"Ġspot light\",\n \"V B\",\n \"o wy\",\n \"ow y\",\n \"( torch\",\n \"(t orch\",\n \"(to rch\",\n \"t ridge\",\n \"tr idge\",\n \"tri dge\",\n \"_ bounds\",\n \"_b ounds\",\n \"_bound s\",\n \"_bo unds\",\n \"Ġapolog ize\",\n \". addItem\",\n \".add Item\",\n \"an td\",\n \"ant d\",\n \"* );Ċ\",\n \"*) ;Ċ\",\n \", u\",\n \"( gen\",\n \"(g en\",\n \"ç» ĵ\",\n \"re ator\",\n \"reat or\",\n \"rea tor\",\n \"ĠC ord\",\n \"ĠCo rd\",\n \"ĠCor d\",\n \"o upper\",\n \"ou pper\",\n \"oup per\",\n \". metro\",\n \".m etro\",\n \".me tro\",\n \".met ro\",\n \"Ġ ew\",\n \"Ġe w\",\n \"Ġ WORD\",\n \"ĠW ORD\",\n \". After\",\n \".A fter\",\n \"Ġdet ained\",\n \"Ġdetain ed\",\n \"ĠH ammer\",\n \"ĠHam mer\",\n \"ĠHamm er\",\n \"ex isting\",\n \"exist ing\",\n \"Ġ ost\",\n \"Ġo st\",\n \"Ġos t\",\n \"Ġmon ument\",\n \"- custom\",\n \"-c ustom\",\n \"User ID\",\n \"Ġ Nom\",\n \"ĠN om\",\n \"ĠNo m\",\n \"Ġre jection\",\n \"Ġreject ion\",\n \"Ġrej ection\",\n \"( dim\",\n \"(d im\",\n \"(di m\",\n \"Ġ singleton\",\n \"Ġs ingleton\",\n \"Ġsingle ton\",\n \"Ġsing leton\",\n \"ĉ die\",\n \"ĉd ie\",\n \"ar iance\",\n \"ari ance\",\n \"arian ce\",\n \"aria nce\",\n \"re ports\",\n \"rep orts\",\n \"report s\",\n \"repo rts\",\n \"] !=\",\n \"e lda\",\n \"el da\",\n \"eld a\",\n \"Ġpreval ence\",\n \"_ regs\",\n \"_re gs\",\n \"_reg s\",\n \". \\\".\",\n \".\\\" .\",\n \"Ġfemin ist\",\n \"Code c\",\n \"Co dec\",\n \"Cod ec\",\n \"Ġ **Ċ\",\n \"Ġ* *Ċ\",\n \"Ġ** Ċ\",\n \"( labels\",\n \"(label s\",\n \"_ MARK\",\n \"_M ARK\",\n \"_MA RK\",\n \"FA ILED\",\n \"FAIL ED\",\n \"Ġadminister ed\",\n \"W N\",\n \"ĠĠ ĠĠĠĠĠĠĉĉ\",\n \"ĠĠĠĠ ĠĠĠĠĉĉ\",\n \"ĠĠĠĠĠĠĠĠ ĉĉ\",\n \"ĠĠĠĠĠĠĠ Ġĉĉ\",\n \"ĠĠĠĠĠ ĠĠĠĉĉ\",\n \"ĠĠĠĠĠĠ ĠĠĉĉ\",\n \"ĠĠĠĠĠĠĠĠĉ ĉ\",\n \"Ġ noun\",\n \"Ġn oun\",\n \"Ġno un\",\n \"Ġnou n\",\n \"w ig\",\n \"wi g\",\n \"Ġg otta\",\n \"Ġgot ta\",\n \"Ġ rif\",\n \"Ġr if\",\n \"Ġri f\",\n \"- im\",\n \"-i m\",\n \"ĠPaul o\",\n \"ĠPa ulo\",\n \"ĠCommand Type\",\n \"] ))ĊĊ\",\n \"]) )ĊĊ\",\n \"])) ĊĊ\",\n \"]))Ċ Ċ\",\n \"- zero\",\n \"-z ero\",\n \"Tr aining\",\n \"Train ing\",\n \"Tra ining\",\n \"Ġ lord\",\n \"Ġl ord\",\n \"Ġlo rd\",\n \"Ġlor d\",\n \"_ art\",\n \"_a rt\",\n \"_ar t\",\n \"re ddit\",\n \"red dit\",\n \"redd it\",\n \"C ert\",\n \"Ce rt\",\n \"Ġp eso\",\n \"Ġpe so\",\n \"Ġpes o\",\n \"R ot\",\n \"Ro t\",\n \"Ġen danger\",\n \"Ġend anger\",\n \". dr\",\n \".d r\",\n \"user Info\",\n \"u nts\",\n \"un ts\",\n \"unt s\",\n \"n v\",\n \"ĠTr ailer\",\n \"ĠTra iler\",\n \"ĠTrail er\",\n \"- first\",\n \"-f irst\",\n \"-fi rst\",\n \"( make\",\n \"(m ake\",\n \"Ġbenef ici\",\n \"- black\",\n \"-b lack\",\n \"-bl ack\",\n \"i ÃŁ\",\n \"Ġund oubtedly\",\n \"Ġm ex\",\n \"Ġme x\",\n \"ĠAn cient\",\n \"ĠAnc ient\",\n \"( as\",\n \"(a s\",\n \"Ġdes cent\",\n \"Ġdesc ent\",\n \"P ick\",\n \"Pic k\",\n \"Pi ck\",\n \"Ġrep lica\",\n \"Ġrepl ica\",\n \"Ġreplic a\",\n \"$ obj\",\n \"$o bj\",\n \"ä hr\",\n \"äh r\",\n \"Ġar rows\",\n \"Ġarr ows\",\n \"Ġarrow s\",\n \"f ty\",\n \"ft y\",\n \"ĠLib ya\",\n \"u ga\",\n \"ug a\",\n \"ch arged\",\n \"char ged\",\n \"charge d\",\n \"charg ed\",\n \"T ur\",\n \"Tu r\",\n \"Ġh omic\",\n \"Ġhom ic\",\n \"Ġho mic\",\n \"is sen\",\n \"iss en\",\n \"isse n\",\n \"Ġ Fake\",\n \"ĠF ake\",\n \"ĠFa ke\",\n \"ĠFak e\",\n \"Ġbe ers\",\n \"Ġbeer s\",\n \"Ġbee rs\",\n \"Ġsc attered\",\n \"Ġscatter ed\",\n \"( Time\",\n \"(T ime\",\n \"UT IL\",\n \"Ġbureauc r\",\n \"Ġbureau cr\",\n \"/ plain\",\n \"/p lain\",\n \"/pl ain\",\n \"Ġst icking\",\n \"Ġstick ing\",\n \"F AIL\",\n \"FA IL\",\n \"ĠC ovid\",\n \"ĠCo vid\",\n \"ĠCov id\",\n \"Th ird\",\n \"_ present\",\n \"_p resent\",\n \"_pre sent\",\n \"_pres ent\",\n \"ĠP ierre\",\n \"ĠPi erre\",\n \"ĠPier re\",\n \"Ġ ëª\",\n \"Ġë ª\",\n \"Ġ[ ...]ĊĊ\",\n \"Ġ[... ]ĊĊ\",\n \"Ġ[...] ĊĊ\",\n \"P rob\",\n \"Pro b\",\n \"Pr ob\",\n \"Ġ Traffic\",\n \"ĠTra ffic\",\n \"ĠTraff ic\",\n \"i cao\",\n \"ic ao\",\n \"ica o\",\n \"do ctor\",\n \"doc tor\",\n \"Ġ ),ĊĊ\",\n \"Ġ) ,ĊĊ\",\n \"Ġ),Ċ Ċ\",\n \"Ġ), ĊĊ\",\n \"T abs\",\n \"Tab s\",\n \"Ta bs\",\n \"a lu\",\n \"al u\",\n \"ï¼ļ âĢľ\",\n \"Ġin herent\",\n \"Ġinher ent\",\n \"_ No\",\n \"_N o\",\n \"r itis\",\n \"rit is\",\n \"Ġ Proof\",\n \"ĠP roof\",\n \"ĠPro of\",\n \". basename\",\n \".b asename\",\n \".base name\",\n \"ä¼ ļ\",\n \"Ġc him\",\n \"Ġch im\",\n \"Ġchi m\",\n \"Ġ Protected\",\n \"ĠProt ected\",\n \"ĠProtect ed\",\n \"c rit\",\n \"cri t\",\n \"cr it\",\n \"Ġp rone\",\n \"Ġpro ne\",\n \"Ġpr one\",\n \"Ġpron e\",\n \"Ġ кон\",\n \"Ġк он\",\n \"Ġко н\",\n \"Ġ Heroes\",\n \"ĠHer oes\",\n \"ĠHero es\",\n \"Ġan xious\",\n \"Ġanx ious\",\n \"Ġ anos\",\n \"Ġa nos\",\n \"Ġan os\",\n \"Ġano s\",\n \"Ġweek ends\",\n \"Ġweekend s\",\n \"Ġs ext\",\n \"Ġse xt\",\n \"Ġsex t\",\n \"Ġre ducer\",\n \"Ġred ucer\",\n \"Ġredu cer\",\n \"Ġreduce r\",\n \"= UTF\",\n \"h alf\",\n \"ha lf\",\n \"hal f\",\n \"ĠS aw\",\n \"ĠSa w\",\n \". mm\",\n \".m m\",\n \"Ġn ueva\",\n \"Ġnu eva\",\n \"Ġnue va\",\n \".current Target\",\n \". lua\",\n \".l ua\",\n \".lu a\",\n \"_EXT ENSION\",\n \"ĉ reg\",\n \"ĉr eg\",\n \"ĉre g\",\n \"Ġ Ctrl\",\n \"ĠC trl\",\n \"ĠCt rl\",\n \"_ align\",\n \"_al ign\",\n \"accept able\",\n \"Ġr ushing\",\n \"Ġrush ing\",\n \"f rac\",\n \"fr ac\",\n \"fra c\",\n \"Ġbo asts\",\n \"Ġboast s\",\n \"Ġboa sts\",\n \"F ive\",\n \"Fi ve\",\n \" ±\",\n \"Ġ Temperature\",\n \"ĠT emperature\",\n \"ĠTem perature\",\n \"ĠTemper ature\",\n \"> ):\",\n \">) :\",\n \"Ġch arter\",\n \"Ġchar ter\",\n \"Ġchart er\",\n \"RE ATED\",\n \"REATE D\",\n \"REAT ED\",\n \"REA TED\",\n \"Ġsub jected\",\n \"Ġsubject ed\",\n \"Ġsubj ected\",\n \"Ġ opc\",\n \"Ġo pc\",\n \"Ġop c\",\n \"health y\",\n \"使 çĶ¨\",\n \"ĠScient ific\",\n \"Ġ frau\",\n \"Ġfr au\",\n \"Ġfra u\",\n \"ri ages\",\n \"ria ges\",\n \"riage s\",\n \"ภĶ\",\n \". inventory\",\n \".in ventory\",\n \"at ionale\",\n \"ation ale\",\n \"ational e\",\n \"M ad\",\n \"Ma d\",\n \"min utes\",\n \"minute s\",\n \"> >();Ċ\",\n \">> ();Ċ\",\n \">>( );Ċ\",\n \">>() ;Ċ\",\n \"Ġ Env\",\n \"ĠE nv\",\n \"ĠEn v\",\n \"Ġrecord ings\",\n \"Ġrecording s\",\n \"Ġsusp icion\",\n \"sql ite\",\n \"sq lite\",\n \"ĉ read\",\n \"ĉr ead\",\n \"ĉre ad\",\n \"ãģ ¦\",\n \"Ġwor ries\",\n \".put String\",\n \"ĠSh anghai\",\n \"( uid\",\n \"(u id\",\n \"(ui d\",\n \"r er\",\n \"re r\",\n \"ĠvÃŃ de\",\n \"\\\" ):\",\n \"\\\") :\",\n \"Ġmethod ology\",\n \"Ġк оÑĤоÑĢ\",\n \"Ġко ÑĤоÑĢ\",\n \"ĠкоÑĤ оÑĢ\",\n \"c cc\",\n \"cc c\",\n \"av ad\",\n \"ava d\",\n \"Ġin duction\",\n \"Ġind uction\",\n \"Ġindu ction\",\n \"ĉ Thread\",\n \"ĉT hread\",\n \", string\",\n \",s tring\",\n \",str ing\",\n \",st ring\",\n \"ạ i\",\n \"neh men\",\n \"u ition\",\n \"ui tion\",\n \"uit ion\",\n \"Ġ* __\",\n \"Ġ*_ _\",\n \".e mf\",\n \".em f\",\n \"Ġ ìľ\",\n \"Ġì ľ\",\n \"/ themes\",\n \"/th emes\",\n \"/theme s\",\n \"/the mes\",\n \"Ġ Nine\",\n \"ĠN ine\",\n \"ĠNi ne\",\n \"ĠNin e\",\n \". One\",\n \".On e\",\n \".O ne\",\n \"Ġ Embed\",\n \"ĠEm bed\",\n \"ĠEmb ed\",\n \"Ġf az\",\n \"Ġfa z\",\n \"u ations\",\n \"uation s\",\n \"uat ions\",\n \"Ġprivate ly\",\n \"Ġpriv ately\",\n \"Ġprivat ely\",\n \"Ġ ling\",\n \"Ġl ing\",\n \"Ġli ng\",\n \"Ġlin g\",\n \"[ F\",\n \"u shi\",\n \"us hi\",\n \"ush i\",\n \"Ġlaunch es\",\n \"( KEY\",\n \"(K EY\",\n \"G MT\",\n \"GM T\",\n \"Ġa iming\",\n \"Ġaim ing\",\n \"Ġai ming\",\n \"pat ible\",\n \"ĠB iden\",\n \"ĠBi den\",\n \"ĠBid en\",\n \"i w\",\n \"Ġ Degree\",\n \"ĠD egree\",\n \"ĠDe gree\",\n \"ĠDeg ree\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"Ġ$ ('<\",\n \"Ġ$( '<\",\n \"Ġ$(' <\",\n \"á rios\",\n \"ário s\",\n \"ár ios\",\n \"to UpperCase\",\n \"ìł ľ\",\n \"Ġ EUR\",\n \"ĠE UR\",\n \"ĠEU R\",\n \"Ġovers ight\",\n \"Ġtable sp\",\n \"Ġtables p\",\n \"Up dates\",\n \"Update s\",\n \".m akedirs\",\n \".make dirs\",\n \"Ġ humidity\",\n \"Ġh umidity\",\n \"Ġhum idity\",\n \"Ġhumid ity\",\n \"/ template\",\n \"/t emplate\",\n \"/temp late\",\n \"Al ways\",\n \"( IS\",\n \"(I S\",\n \"_ cert\",\n \"_c ert\",\n \"_ce rt\",\n \"D ig\",\n \"Di g\",\n \"Ġunder way\",\n \"or ton\",\n \"ort on\",\n \"ĠHur ricane\",\n \"Ġsp ends\",\n \"Ġspe nds\",\n \"Ġspend s\",\n \"Ġ Segment\",\n \"ĠS egment\",\n \"ĠSe gment\",\n \"ĠSeg ment\",\n \"Ġ flies\",\n \"Ġf lies\",\n \"Ġfl ies\",\n \"Ġ Toggle\",\n \"ĠT oggle\",\n \"ĠL ynch\",\n \"ĠLyn ch\",\n \"Ġs enses\",\n \"Ġsense s\",\n \"Ġsens es\",\n \"Ġsen ses\",\n \"ĠK os\",\n \"ĠKo s\",\n \"set Enabled\",\n \"ist ically\",\n \"istic ally\",\n \"istical ly\",\n \"Ġ tester\",\n \"Ġt ester\",\n \"Ġte ster\",\n \"Ġtest er\",\n \"Ġtes ter\",\n \"Ġteste r\",\n \"Ġadministr ators\",\n \"Ġadministrator s\",\n \"Ġt agged\",\n \"Ġtag ged\",\n \"Ð ĵ\",\n \"Ġ shortcut\",\n \"Ġshort cut\",\n \"Ġ Resolution\",\n \"ĠRe solution\",\n \"ĠRes olution\",\n \"Ġsuper vision\",\n \"Ġsuperv ision\",\n \"ĠAsh ley\",\n \"Tr acking\",\n \"Track ing\",\n \"ul atory\",\n \"ulator y\",\n \"an del\",\n \"and el\",\n \"ande l\",\n \"i sten\",\n \"is ten\",\n \"ist en\",\n \"iste n\",\n \"Ġun re\",\n \"Ġunr e\",\n \"( diff\",\n \"(d iff\",\n \"(di ff\",\n \"AN TS\",\n \"ANT S\",\n \"Ġr ider\",\n \"Ġrid er\",\n \"Ġride r\",\n \"Ġri der\",\n \"Ġs Äħ\",\n \". Series\",\n \".S eries\",\n \".Se ries\",\n \"_ orders\",\n \"_order s\",\n \"_or ders\",\n \"_ord ers\",\n \"ORIZ ONTAL\",\n \"Ġret ention\",\n \"ãĢĤ čĊčĊ\",\n \"\\\"> čĊčĊ\",\n \"\\\">čĊ čĊ\",\n \"Ġdi agonal\",\n \"Ġdiag onal\",\n \"Ġdiagon al\",\n \"ĠC ancellationToken\",\n \"_ Internal\",\n \"_In ternal\",\n \"_Int ernal\",\n \"_Inter nal\",\n \"Ġr uin\",\n \"Ġru in\",\n \". Qt\",\n \".Q t\",\n \"ocr atic\",\n \"ocrat ic\",\n \"T el\",\n \"Te l\",\n \"Ġ Answers\",\n \"ĠAn swers\",\n \"ĠAnswer s\",\n \"ĠAns wers\",\n \"m atic\",\n \"ma tic\",\n \"mat ic\",\n \"Ġ xp\",\n \"Ġx p\",\n \"a tem\",\n \"at em\",\n \"ate m\",\n \"_ jobs\",\n \"_j obs\",\n \"_job s\",\n \"_ any\",\n \"_a ny\",\n \"_an y\",\n \"Ġsen iors\",\n \"Ġsenior s\",\n \"Ġseni ors\",\n \"Ġland mark\",\n \"ĠQ List\",\n \"Ġman eu\",\n \"Ġmane u\",\n \"ot ify\",\n \"oti fy\",\n \"/ \\\";Ċ\",\n \"/\\\" ;Ċ\",\n \"/ server\",\n \"/s erver\",\n \"ĠPhil osoph\",\n \"u tenant\",\n \"ut enant\",\n \"ute nant\",\n \"uten ant\",\n \"( io\",\n \"(i o\",\n \"h z\",\n \"Ġ authenticated\",\n \"Ġauth enticated\",\n \"Ġauthentic ated\",\n \"Ġauthenticate d\",\n \"d v\",\n \"- Compatible\",\n \"Origin ally\",\n \"Original ly\",\n \"Orig inally\",\n \", function\",\n \",f unction\",\n \"ãĢĤ čĊ\",\n \"ĠRepresent ative\",\n \"as ily\",\n \"asi ly\",\n \"asil y\",\n \"irc uit\",\n \". dt\",\n \".d t\",\n \"( math\",\n \"(m ath\",\n \"(mat h\",\n \". Marshal\",\n \".M arshal\",\n \".Mar shal\",\n \"[ ,\",\n \"Ġ Cities\",\n \"ĠC ities\",\n \"ĠCit ies\",\n \"ĠCi ties\",\n \"_ turn\",\n \"_t urn\",\n \"| )Ċ\",\n \"Ġ cantidad\",\n \"Ġc antidad\",\n \"Ġcant idad\",\n \"al ter\",\n \"alt er\",\n \"alte r\",\n \"ĉ ui\",\n \"ĉu i\",\n \"ĠNe braska\",\n \"Ġsk irt\",\n \"Ġski rt\",\n \". bg\",\n \".b g\",\n \"Shared Preferences\",\n \"( style\",\n \"(st yle\",\n \"Ġg rief\",\n \"Ġgr ief\",\n \"Ġgri ef\",\n \"g ew\",\n \"ge w\",\n \"Ġsaf eg\",\n \"Ġsafe g\",\n \"o lang\",\n \"ol ang\",\n \"ola ng\",\n \"olan g\",\n \"_ lists\",\n \"_l ists\",\n \"_list s\",\n \"_li sts\",\n \"ì Ľ\",\n \"Ġgran ite\",\n \"Ġgra nite\",\n \"Ġhot test\",\n \"Ġhott est\",\n \". jdbc\",\n \".j dbc\",\n \".jd bc\",\n \". Customer\",\n \".C ustomer\",\n \".Custom er\",\n \"Ġ âī¤\",\n \"Ġâī ¤\",\n \"Ġw aar\",\n \"Ġwa ar\",\n \"_ scene\",\n \"_s cene\",\n \"_sc ene\",\n \"+ '/\",\n \"+' /\",\n \"ĠJ TextField\",\n \"ĠJText Field\",\n \"ĠJT extField\",\n \"Ġs eating\",\n \"Ġse ating\",\n \"Ġsea ting\",\n \"Ġseat ing\",\n \"Ġw ears\",\n \"Ġwe ars\",\n \"Ġwear s\",\n \"Ġ` /\",\n \"C ases\",\n \"Case s\",\n \"Ca ses\",\n \"Cas es\",\n \"Ġ Youtube\",\n \"ĠY outube\",\n \"ĠYou tube\",\n \"ı m\",\n \"Ġb alcon\",\n \"Ġbal con\",\n \", G\",\n \"Meta Data\",\n \"Met aData\",\n \"- price\",\n \"-p rice\",\n \"-pr ice\",\n \"S CR\",\n \"SC R\",\n \"Un ity\",\n \"Unit y\",\n \"Uni ty\",\n \"Ġtr unk\",\n \"={ `${\",\n \"={` ${\",\n \"Ġearth quake\",\n \"Ġearthqu ake\",\n \"P artial\",\n \"Part ial\",\n \"Ġ subst\",\n \"Ġsu bst\",\n \"Ġsub st\",\n \"Ġsubs t\",\n \"Ġel imin\",\n \"Ġelim in\",\n \"=\\\" '.\",\n \"=\\\"' .\",\n \"//* [@\",\n \"//*[ @\",\n \"Ġsup ervisor\",\n \"Ġsuper visor\",\n \"Ġsuperv isor\",\n \"vr olet\",\n \"vro let\",\n \"_ article\",\n \"_art icle\",\n \"Ġ pane\",\n \"Ġp ane\",\n \"Ġpa ne\",\n \"Ġpan e\",\n \"b io\",\n \"bi o\",\n \"Ġmot ors\",\n \"Ġmo tors\",\n \"Ġmotor s\",\n \"Ġmoto rs\",\n \"N M\",\n \"F rank\",\n \"Fr ank\",\n \"Fran k\",\n \"Fra nk\",\n \"Ġon ion\",\n \"- word\",\n \"-w ord\",\n \"Item ClickListener\",\n \"ItemClick Listener\",\n \"Ġ brit\",\n \"Ġb rit\",\n \"Ġbr it\",\n \"Ġbri t\",\n \"end encies\",\n \"enden cies\",\n \"Com puter\",\n \"Comp uter\",\n \"Compute r\",\n \"Comput er\",\n \"_ running\",\n \"_r unning\",\n \"_run ning\",\n \"( day\",\n \"(d ay\",\n \"(da y\",\n \"- he\",\n \"-h e\",\n \"( named\",\n \"(n amed\",\n \"(name d\",\n \"ĠS ach\",\n \"ĠSa ch\",\n \"ĠSac h\",\n \"о Ñĩ\",\n \"c ampaign\",\n \"camp aign\",\n \". Abstract\",\n \".A bstract\",\n \".Ab stract\",\n \".Abs tract\",\n \"( wrapper\",\n \"(w rapper\",\n \". pay\",\n \".p ay\",\n \".pa y\",\n \"Ġ uw\",\n \"Ġu w\",\n \"G eo\",\n \"Ge o\",\n \"r ails\",\n \"ra ils\",\n \"rai ls\",\n \"rail s\",\n \"/ select\",\n \"/s elect\",\n \"/se lect\",\n \"i chte\",\n \"ic hte\",\n \"ich te\",\n \"icht e\",\n \"s ons\",\n \"so ns\",\n \"son s\",\n \"E VENT\",\n \"EV ENT\",\n \"Ġal iment\",\n \"Ġali ment\",\n \"Pro viders\",\n \"Provider s\",\n \"Provid ers\",\n \"Provide rs\",\n \"Prov iders\",\n \"A wait\",\n \"Aw ait\",\n \"_INTER VAL\",\n \". off\",\n \".of f\",\n \".o ff\",\n \"Ġgl uten\",\n \"Ġglut en\",\n \"Ġglu ten\",\n \"_ cloud\",\n \"_c loud\",\n \"_cl oud\",\n \"Ġ wen\",\n \"Ġw en\",\n \"Ġwe n\",\n \". extract\",\n \".ex tract\",\n \".ext ract\",\n \".extra ct\",\n \"ĉ button\",\n \"ĉb utton\",\n \"/ MM\",\n \"/M M\",\n \"P arty\",\n \"Par ty\",\n \"Part y\",\n \"Ġdem ographic\",\n \"Ġdemo graphic\",\n \"_ errno\",\n \"_err no\",\n \"Ġh iking\",\n \"Ġhi king\",\n \"Ġhik ing\",\n \"(' ')Ċ\",\n \"\\\", @\\\"\",\n \"Ġ wit\",\n \"Ġw it\",\n \"Ġwi t\",\n \"r á\",\n \"ol ogie\",\n \"olog ie\",\n \"olo gie\",\n \"ologi e\",\n \"Ġ Styles\",\n \"ĠSt yles\",\n \"ĠStyle s\",\n \"ĠSty les\",\n \"ĠBrowser Module\",\n \". 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\". labels\",\n \".label s\",\n \".lab els\",\n \"Ġ LV\",\n \"ĠL V\",\n \"wcs store\",\n \"ĠJ esse\",\n \"ĠJes se\",\n \"ĠJe sse\",\n \"ĠJess e\",\n \"ภ«\",\n \"T rade\",\n \"Tr ade\",\n \"Trad e\",\n \"Tra de\",\n \"Ġpredecess or\",\n \"ë Ĥ\",\n \"f inally\",\n \"fin ally\",\n \"final ly\",\n \"_ general\",\n \"_g eneral\",\n \"_gen eral\",\n \"_gene ral\",\n \"_gener al\",\n \"oggle r\",\n \"ogg ler\",\n \"_ REGION\",\n \"_REG ION\",\n \"n ement\",\n \"ne ment\",\n \"nem ent\",\n \"Ġb logger\",\n \"Ġblog ger\",\n \"Ġblo gger\",\n \"ĠHar bor\",\n \"Ġ Dataset\",\n \"ĠD ataset\",\n \"ĠData set\",\n \"ĠDat aset\",\n \"[ w\",\n \"Ġattend ees\",\n \"Ġattendee s\",\n \". ico\",\n \".i co\",\n \".ic o\",\n \"max imum\",\n \". Unlock\",\n \".Un lock\",\n \"_ SYNC\",\n \"_S YNC\",\n \"_SY NC\",\n \"_SYN C\",\n \"ág ina\",\n \"Ġd owns\",\n \"Ġdown s\",\n \"Ġdow ns\",\n \"ĠW ii\",\n \"ĠWi i\",\n \"] )/\",\n \"]) /\",\n \"Ġk icking\",\n \"Ġkick ing\",\n \"un ication\",\n \"unic ation\",\n \"uni cation\",\n \"Ġ DAC\",\n \"ĠD AC\",\n \"ĠDA C\",\n \"Ġ IDS\",\n \"ĠI DS\",\n \"ĠID S\",\n \"ĠR ental\",\n \"ĠRen tal\",\n \"ĠRent al\",\n \"Ġ currentTime\",\n \"Ġcurrent Time\",\n \"Ġvacc ines\",\n \"Ġvaccine s\",\n \"ĠD evil\",\n \"ĠDe vil\",\n \"ĠDev il\",\n \"Ġn ors\",\n \"Ġno rs\",\n \"Ġnor s\",\n \"_ mouse\",\n \"_m ouse\",\n \"_mo use\",\n \"ur rection\",\n \"urre ction\",\n \"urr ection\",\n \"urrect ion\",\n \"( no\",\n \"(n o\",\n \"Ġ >čĊ\",\n \"Ġ> čĊ\",\n \"Ġag gression\",\n \"Ġaggress ion\",\n \"Ġagg ression\",\n \"Ġbre eding\",\n \"Ġbreed ing\",\n \"Ġbree ding\",\n \". symbol\",\n \".s ymbol\",\n \".sym bol\",\n \"i man\",\n \"im an\",\n \"ima n\",\n \"Absolute Path\",\n \"Ġ WHO\",\n \"ĠW HO\",\n \"ĠWH O\",\n \"_ flush\",\n \"_f lush\",\n \"_fl ush\",\n \"- root\",\n \"-r oot\",\n \"-ro ot\",\n \"a rna\",\n \"ar na\",\n \"arn a\",\n \"& M\",\n \"Ġf athers\",\n \"Ġfa thers\",\n \"Ġfather s\",\n \"Ġ Rocket\",\n \"ĠR ocket\",\n \"ĠRock et\",\n \"ĠRo cket\",\n \"ĠRoc ket\",\n \"i veau\",\n \"ive au\",\n \"Ġw ander\",\n \"Ġwa nder\",\n \"Ġwand er\",\n \"Ġwan der\",\n \"Ġcom pos\",\n \"Ġcomp os\",\n \"ĠWar rior\",\n \"Ġ Seat\",\n \"ĠS eat\",\n \"ĠSe at\",\n \"ĠSea t\",\n \"ĠCl inic\",\n \"ĠClin ic\",\n \"ĠCli nic\",\n \"_ invoice\",\n \"_in voice\",\n \"_inv oice\",\n \"( dispatch\",\n \"(dis patch\",\n \"Product o\",\n \"at uring\",\n \"atur ing\",\n \"atu ring\",\n \"oss ier\",\n \"ĠM AY\",\n \"ĠMA Y\",\n \"Ġd agger\",\n \"Ġda gger\",\n \"Ġdag ger\",\n \"Ġsan itized\",\n \"Ġsanit ized\",\n \"Ġsanitize d\",\n \"Ġ RFC\",\n \"ĠR FC\",\n \"ĠRF C\",\n \"Ġp roph\",\n \"Ġpro ph\",\n \"Ġpr oph\",\n \"Ġprop h\",\n \"Ġu rine\",\n \"Ġur ine\",\n \"Ġuri ne\",\n \"Ġgr ind\",\n \"Ġgri nd\",\n \"Ġgrin d\",\n \"Ġ Expanded\",\n \"ĠExp anded\",\n \"ĠExpand ed\",\n \"des cripcion\",\n \"- fw\",\n \"-f w\",\n \"ĠK erry\",\n \"ĠKer ry\",\n \"ĠKerr y\",\n \"= name\",\n \"=n ame\",\n \"Ġ chk\",\n \"Ġc hk\",\n \"Ġch k\",\n \"Ġn ationally\",\n \"Ġnational ly\",\n \"Ġnation ally\",\n \"Ġt hee\",\n \"Ġth ee\",\n \"Ġthe e\",\n \"I nc\",\n \"In c\",\n \"Ġ ?>>\",\n \"Ġ? >>\",\n \"Ġ?> >\",\n \". RadioButton\",\n \".R adioButton\",\n \".Http ServletResponse\",\n \".HttpServlet Response\",\n \"/ Y\",\n \"ĉ field\",\n \"ĉf ield\",\n \"ĉfi eld\",\n \"Ġ homme\",\n \"Ġhom me\",\n \"y per\",\n \"ype r\",\n \"yp er\",\n \"Ph ysical\",\n \"Phys ical\",\n \"= v\",\n \"Ġd riv\",\n \"Ġdr iv\",\n \"Ġdri v\",\n \"Ġ Errors\",\n \"ĠError s\",\n \"ĠEr rors\",\n \"ĠErr ors\",\n \"Ġc Äĥ\",\n \"De ath\",\n \"Ġ WINDOW\",\n \"ĠW INDOW\",\n \"Ġpo et\",\n \"Ġ Sharp\",\n \"ĠSh arp\",\n \"ĠSha rp\",\n \"ĠShar p\",\n \"Ġ Immutable\",\n \"ĠIm mutable\",\n \"ĠImm utable\",\n \"ĉ create\",\n \"ĉc reate\",\n \"Ġge ht\",\n \"Ġgeh t\",\n \"ĠRe form\",\n \"ĠRef orm\",\n \"a iser\",\n \"ai ser\",\n \"ais er\",\n \"aise r\",\n \"Ġ Initialization\",\n \"ĠInitial ization\",\n \"Ġimm unity\",\n \"Ġimmun ity\",\n \". compose\",\n \".com pose\",\n \".comp ose\",\n \"Ġlate ncy\",\n \"Ġlat ency\",\n \"Ġlaten cy\",\n \"ĠLeban on\",\n \"ĠPar ad\",\n \"ĠPa rad\",\n \"ĠPara d\",\n \"Ġf uels\",\n \"Ġfuel s\",\n \"Ġfu els\",\n \"Ġfue ls\",\n \"ĠEx hib\",\n \"c oh\",\n \"co h\",\n \"% \\\">Ċ\",\n \"%\\\" >Ċ\",\n \"%\\\"> Ċ\",\n \"Ġ CLI\",\n \"ĠC LI\",\n \"ĠCL I\",\n \") initWith\",\n \")init With\",\n \"- Za\",\n \"-Z a\",\n \"_ CLEAR\",\n \"_C LEAR\",\n \"_CL EAR\",\n \"re gn\",\n \"reg n\",\n \"Ġfin ances\",\n \"Ġfinance s\",\n \"Ġfinanc es\",\n \"Ġfinan ces\",\n \". standard\",\n \".st andard\",\n \"_ CATEGORY\",\n \"_C ATEGORY\",\n \". library\",\n \".l ibrary\",\n \".lib rary\",\n \"Ġtravel ers\",\n \"Ġtraveler s\",\n \"_ wp\",\n \"_w p\",\n \"Ġ Evaluation\",\n \"ĠE valuation\",\n \"ĠEval uation\",\n \"ĠEvalu ation\",\n \"start ing\",\n \"star ting\",\n \"Ġ )),Ċ\",\n \"Ġ) ),Ċ\",\n \"Ġ)) ,Ċ\",\n \"ep isode\",\n \"Ġ Variant\",\n \"ĠV ariant\",\n \"ĠVar iant\",\n \"ĠVari ant\",\n \"Ġ daemon\",\n \"Ġda emon\",\n \"ĠJ ulia\",\n \"ĠJul ia\",\n \"ĠJu lia\",\n \"ĠJuli a\",\n \"Ġ NR\",\n \"ĠN R\",\n \"Ġd oubles\",\n \"Ġdouble s\",\n \"Ġdoub les\",\n \"Ġdou bles\",\n \"< v\",\n \"/ runtime\",\n \"/r untime\",\n \"/run time\",\n \"Ġ interpreter\",\n \"Ġinter preter\",\n \"Ġinterpret er\",\n \"Ġinterpre ter\",\n \"Ġ INDEX\",\n \"ĠIN DEX\",\n \"ĠIND EX\",\n \"ĠHol mes\",\n \"_ DIM\",\n \"_D IM\",\n \"_DI M\",\n \"Ġp addle\",\n \"Ġpad dle\",\n \"Ġpadd le\",\n \"_ example\",\n \"_ex ample\",\n \"_exam ple\",\n \"Ġ foreground\",\n \"Ġfore ground\",\n \". routes\",\n \".r outes\",\n \".route s\",\n \".ro utes\",\n \"Ġs owie\",\n \"Ġso wie\",\n \"Ġsow ie\",\n \"S UCCESS\",\n \"Ġ CDC\",\n \"ĠC DC\",\n \"ĠCD C\",\n \"Ġ BD\",\n \"ĠB D\",\n \"_ -\",\n \"as ured\",\n \"asure d\",\n \"asu red\",\n \"W riting\",\n \"Wr iting\",\n \"Ġ currentPage\",\n \"Ġcurrent Page\",\n \"( answer\",\n \"(ans wer\",\n \"(an swer\",\n \"Ġ ASCII\",\n \"ĠA SCII\",\n \"ĠASC II\",\n \"à ¨\",\n \"Ġsocial ly\",\n \"Ġsoc ially\",\n \"Ġsoci ally\",\n \"y yy\",\n \"yy y\",\n \"ĠSpecial ist\",\n \"( customer\",\n \"(c ustomer\",\n \"(custom er\",\n \"ist ani\",\n \"istan i\",\n \"ista ni\",\n \"k est\",\n \"ke st\",\n \"kes t\",\n \"ĠM ak\",\n \"ĠMa k\",\n \"Ġt ho\",\n \"Ġth o\",\n \". pt\",\n \".p t\",\n \"( comment\",\n \"(com ment\",\n \"(comm ent\",\n \"Ġ Converter\",\n \"ĠCon verter\",\n \"ĠConvert er\",\n \"g am\",\n \"ga m\",\n \"b ins\",\n \"bin s\",\n \"bi ns\",\n \". tele\",\n \".t ele\",\n \".te le\",\n \".tel e\",\n \"ĠVeter ans\",\n \"ĠVeteran s\",\n \"_ ALLOC\",\n \"_AL LOC\",\n \"_ALL OC\",\n \"олÑĮзов аÑĤ\",\n \"inn amon\",\n \"; width\",\n \"o hl\",\n \"oh l\",\n \"Ġf antas\",\n \"Ġfan tas\",\n \"Ġfant as\",\n \"Ġs ung\",\n \"Ġsu ng\",\n \"Ġsun g\",\n \"ĉ K\",\n \"( Json\",\n \"(J son\",\n \"Ġneighbour hood\",\n \"Ġv ow\",\n \"Ġvo w\",\n \"Ġs ins\",\n \"Ġsi ns\",\n \"Ġsin s\",\n \"on acci\",\n \"ona cci\",\n \"Ġ epochs\",\n \"Ġepoch s\",\n \"im agen\",\n \"image n\",\n \"ima gen\",\n \"imag en\",\n \". Change\",\n \".Ch ange\",\n \".my batis\",\n \"Se ek\",\n \"See k\",\n \"W ER\",\n \"WE R\",\n \"管 çIJĨ\",\n \"Ġinter ess\",\n \"Ġinte ress\",\n \"Ġinteres s\",\n \"_ Event\",\n \"_E vent\",\n \"ed erland\",\n \"eder land\",\n \"Ġterr itor\",\n \"Ġci udad\",\n \"uc ked\",\n \"uck ed\",\n \"Ġsn ack\",\n \"Ġsna ck\",\n \"Ġtrans ported\",\n \"Ġtransport ed\",\n \"Ġtransporte d\",\n \"Ġ Manifest\",\n \"ĠMan ifest\",\n \"Ġ DAT\",\n \"ĠD AT\",\n \"ĠDA T\",\n \"_ theta\",\n \"_th eta\",\n \"_the ta\",\n \"Ġw ont\",\n \"Ġwon t\",\n \"Ġwo nt\",\n \". ĊĊĊĊĊĊĊĊĊĊ\",\n \".ĊĊ ĊĊĊĊĊĊĊĊ\",\n \".Ċ ĊĊĊĊĊĊĊĊĊ\",\n \".ĊĊĊĊ ĊĊĊĊĊĊ\",\n \".ĊĊĊ ĊĊĊĊĊĊĊ\",\n \".ĊĊĊĊĊĊ ĊĊĊĊ\",\n \".ĊĊĊĊĊĊĊĊ ĊĊ\",\n \".ĊĊĊĊĊ ĊĊĊĊĊ\",\n \"Ĭ¶ æĢģ\",\n \"ĠE pic\",\n \"ĠEp ic\",\n \"D eck\",\n \"De ck\",\n \"Dec k\",\n \"l tra\",\n \"lt ra\",\n \"ltr a\",\n \"_ ZERO\",\n \"_Z ERO\",\n \"Ġ[ ];\",\n \"Ġ[] ;\",\n \"/ scripts\",\n \"/s cripts\",\n \"/script s\",\n \"Ġ --------------------------------------------------------------------------------\",\n \"Ġ---------------------------------------------------------------- ----------------\",\n \"Ġ---- ----------------------------------------------------------------------------\",\n \"Ġ---------------- ----------------------------------------------------------------\",\n \"Ġ------------------------------------------------ --------------------------------\",\n \"Ġ-------------------------------- ------------------------------------------------\",\n \"Ġ---------------------------------------------------------------------------- ----\",\n \"Ġ---------- ----------------------------------------------------------------------\",\n \"Ġ------------------------------------------------------------ --------------------\",\n \"Ġ------------------------------------------------------------------------- -------\",\n \"æĥ ħ\",\n \"Ġ weed\",\n \"Ġw eed\",\n \"Ġwe ed\",\n \"Ġwee d\",\n \"N BC\",\n \"NB C\",\n \"Ġr aped\",\n \"Ġrap ed\",\n \"Ġra ped\",\n \"Ġrape d\",\n \"Ġ Gateway\",\n \"ĠG ateway\",\n \"ĠGate way\",\n \"ĠGat eway\",\n \"[ M\",\n \"Ġ Timeout\",\n \"ĠTime out\",\n \"ench mark\",\n \". ViewModel\",\n \".View Model\",\n \"Ġporn os\",\n \"Ġpor nos\",\n \"Ġporno s\",\n \"Ġ Ya\",\n \"ĠY a\",\n \"th ritis\",\n \"thr itis\",\n \"ĠFl ynn\",\n \"ĠFly nn\",\n \"Ġ mega\",\n \"Ġm ega\",\n \"Ġme ga\",\n \"Ġmeg a\",\n \"a cin\",\n \"ac in\",\n \"aci n\",\n \"Ġtr ibal\",\n \"Ġtri bal\",\n \"Ġtrib al\",\n \". apple\",\n \".app le\",\n \".ap ple\",\n \"Ġ Blo\",\n \"ĠB lo\",\n \"ĠBl o\",\n \"â n\",\n \"i bi\",\n \"ib i\",\n \"r ov\",\n \"ro v\",\n \"ĠL ives\",\n \"ĠLive s\",\n \"ĠLi ves\",\n \"ĠLiv es\",\n \"^ .\",\n \"get Request\",\n \"Ġ Establish\",\n \"ĠEst ablish\",\n \"cont ainers\",\n \"container s\",\n \"contain ers\",\n \"Ġst arring\",\n \"Ġstar ring\",\n \"Ġcele brities\",\n \"Ġcelebr ities\",\n \"Ġ Relative\",\n \"ĠRel ative\",\n \"ĠHe ights\",\n \"ĠHeight s\",\n \"Ġtq dm\",\n \"ĠNorth west\",\n \"i vic\",\n \"iv ic\",\n \"ivi c\",\n \"ĉ cl\",\n \"ĉc l\",\n \"Ġautom otive\",\n \"ent ric\",\n \"entr ic\",\n \"Ġ fortunate\",\n \"Ġfort unate\",\n \"Ġfire place\",\n \"Ġfi replace\",\n \"se ud\",\n \"n ickname\",\n \"nick name\",\n \"; s\",\n \"_ CAL\",\n \"_C AL\",\n \"_CA L\",\n \"h alt\",\n \"ha lt\",\n \"hal t\",\n \"( ns\",\n \"(n s\",\n \"_ deleted\",\n \"_de leted\",\n \"_delete d\",\n \"_del eted\",\n \"De velopment\",\n \"Dev elopment\",\n \"Develop ment\",\n \"m ovies\",\n \"movie s\",\n \"mov ies\",\n \"Ġid entities\",\n \"Ġident ities\",\n \"Ġprompt ly\",\n \"ا ÙĨ\",\n \"ا٠Ĩ\",\n \"Ġ ante\",\n \"Ġa nte\",\n \"Ġan te\",\n \"Ġant e\",\n \"Ġ\\\" ','\",\n \"Ġ\\\"' ,'\",\n \"Ġ\\\"', '\",\n \"åı £\",\n \"imp se\",\n \"imps e\",\n \"Ġy ap\",\n \"Ġya p\",\n \"Type Name\",\n \"Ġb itch\",\n \"Ġbit ch\",\n \"Ġassoci ates\",\n \"Ġassociate s\",\n \"Ġassoc iates\",\n \"HE ME\",\n \"- empty\",\n \"-em pty\",\n \"Ġ ت\",\n \"ĠØ ª\",\n \"ol vers\",\n \"olve rs\",\n \"olver s\",\n \"olv ers\",\n \"Ġp istol\",\n \"Ġpist ol\",\n \"Ġpis tol\",\n \"Sc oped\",\n \"Scope d\",\n \"ag ner\",\n \"agn er\",\n \"agne r\",\n \"' ]=='\",\n \"'] =='\",\n \"']= ='\",\n \"']== '\",\n \"Ġ IMP\",\n \"ĠI MP\",\n \"ĠIM P\",\n \"e xc\",\n \"ex c\",\n \"Ġo mitted\",\n \"Ġom itted\",\n \"Ġomit ted\",\n \"Ġmind set\",\n \"Ġminds et\",\n \"Ġ [](\",\n \"Ġ[ ](\",\n \"Ġ[] (\",\n \"Ġ orn\",\n \"Ġo rn\",\n \"Ġor n\",\n \"_ CAM\",\n \"_C AM\",\n \"_CA M\",\n \"A vg\",\n \"Av g\",\n \"Localized String\",\n \"ĠN atur\",\n \"ĠNa tur\",\n \"ĠNat ur\",\n \"Ġ composer\",\n \"Ġcom poser\",\n \"Ġcomp oser\",\n \"Ġcompose r\",\n \"Ġcompos er\",\n \"Ġ Playing\",\n \"ĠPl aying\",\n \"ĠPlay ing\",\n \"ĠPla ying\",\n \"Ġover d\",\n \"Ġov erd\",\n \"_ utf\",\n \"_u tf\",\n \"_ut f\",\n \". sk\",\n \".s k\",\n \"ĠF ol\",\n \"ĠFo l\",\n \"$ page\",\n \"$p age\",\n \", Object\",\n \",O bject\",\n \"Ġb ees\",\n \"Ġbe es\",\n \"Ġbee s\",\n \"al ary\",\n \"ala ry\",\n \"alar y\",\n \"b ullet\",\n \"bul let\",\n \"bull et\",\n \"_ library\",\n \"_l ibrary\",\n \"_lib rary\",\n \"O ffer\",\n \"Of fer\",\n \"Off er\",\n \"loc ated\",\n \"locate d\",\n \"Ġ (_,\",\n \"Ġ( _,\",\n \"Ġ(_ ,\",\n \"âĢľ He\",\n \"Ġ Owners\",\n \"ĠOwn ers\",\n \"ĠOwner s\",\n \"ĠOw ners\",\n \") ).Ċ\",\n \")) .Ċ\",\n \")). Ċ\",\n \"Ġb ri\",\n \"Ġbr i\",\n \". Admin\",\n \".Ad min\",\n \"k tion\",\n \"kt ion\",\n \"лÑİ Ñĩ\",\n \"Ġerot ici\",\n \"Ġerotic i\",\n \"Cancel led\",\n \"Ġ agr\",\n \"Ġa gr\",\n \"Ġag r\",\n \"re views\",\n \"review s\",\n \"_ dma\",\n \"_d ma\",\n \"_dm a\",\n \"R ICT\",\n \"RI CT\",\n \"RIC T\",\n \"Ġ gfx\",\n \"Ġg fx\",\n \"Ġgf x\",\n \"m pi\",\n \"mp i\",\n \"p po\",\n \"pp o\",\n \"Ġ //@\",\n \"Ġ// @\",\n \"Ġ/ /@\",\n \"Ġ uppercase\",\n \"Ġupper case\",\n \"Ġcomm itting\",\n \"Ġcommit ting\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"User Data\",\n \"Ġv ai\",\n \"Ġva i\",\n \"ĉ sort\",\n \"ĉs ort\",\n \"Ġcongr at\",\n \"Ġcong rat\",\n \"Ġd ioxide\",\n \"Ġdi oxide\",\n \"д а\",\n \". area\",\n \".a rea\",\n \".ar ea\",\n \".are a\",\n \"ĠJosh ua\",\n \"ĠJos hua\",\n \"ĠK och\",\n \"ĠKo ch\",\n \"_ break\",\n \"_b reak\",\n \"a zure\",\n \"az ure\",\n \"azu re\",\n \"is tical\",\n \"ist ical\",\n \"istic al\",\n \"isti cal\",\n \"istica l\",\n \"_AL PHA\",\n \"_ views\",\n \"_view s\",\n \"_vi ews\",\n \"Ġelim inating\",\n \"Ġelimin ating\",\n \"O MB\",\n \"OM B\",\n \"e numer\",\n \"en umer\",\n \"enu mer\",\n \"enum er\",\n \"ĠH ydro\",\n \"ĠHy dro\",\n \"( *(\",\n \"(* (\",\n \"ERT ICAL\",\n \"Ġinev itably\",\n \"Ġst ole\",\n \"Ġsto le\",\n \"Ġstol e\",\n \"- east\",\n \"-e ast\",\n \"i eron\",\n \"ie ron\",\n \"ier on\",\n \"iero n\",\n \"Ġ linger\",\n \"Ġl inger\",\n \"Ġli nger\",\n \"Ġlin ger\",\n \"Ġling er\",\n \"/ doc\",\n \"/d oc\",\n \"/do c\",\n \"Å º\",\n \"Ġ Already\",\n \"ĠAl ready\",\n \"as io\",\n \"asi o\",\n \"Ġ --Ċ\",\n \"Ġ- -Ċ\",\n \"Ġ-- Ċ\",\n \"Ġ abbrev\",\n \"Ġabb rev\",\n \"Ġ Atom\",\n \"ĠA tom\",\n \"ĠAt om\",\n \"h im\",\n \"hi m\",\n \"Ġ INSERT\",\n \"ĠINS ERT\",\n \"s un\",\n \"su n\",\n \"âĻ ª\",\n \"CON NECT\",\n \"CONN ECT\",\n \"er ator\",\n \"era tor\",\n \"ĠM anning\",\n \"ĠMan ning\",\n \"ĠMann ing\",\n \"Ġ :(\",\n \"Ġ: (\",\n \"g as\",\n \"ga s\",\n \"= >'\",\n \"=> '\",\n \"Ġquery set\",\n \"; }čĊ\",\n \";} čĊ\",\n \"Ġ Population\",\n \"ĠPop ulation\",\n \"uted String\",\n \"res ident\",\n \"resi dent\",\n \"_ FONT\",\n \"_F ONT\",\n \"Ġ Respond\",\n \"ĠRes pond\",\n \"ĠResp ond\",\n \"Ġobsc ure\",\n \"Ġ observable\",\n \"Ġo bservable\",\n \"Ġobserv able\",\n \"ĠContrib utors\",\n \"ĠContributor s\",\n \"k on\",\n \"ko n\",\n \"ĠM usk\",\n \"ĠMus k\",\n \"ĠMu sk\",\n \"ex ao\",\n \"ĠT ub\",\n \"ĠTu b\",\n \"Boot Application\",\n \"S OR\",\n \"SO R\",\n \". 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Iterator\",\n \".It erator\",\n \".Iter ator\",\n \"- rich\",\n \"-r ich\",\n \"ro cess\",\n \"roc ess\",\n \"G D\",\n \"Ġcar bohydr\",\n \"Ġ fried\",\n \"Ġf ried\",\n \"Ġfr ied\",\n \"Ġfri ed\",\n \"ĠEm ployment\",\n \"ĠEmp loyment\",\n \"ĠEmploy ment\",\n \"ìŀ ¥\",\n \"ĠLeon ard\",\n \"ĠLeo nard\",\n \"_ ${\",\n \"_$ {\",\n \"qu ares\",\n \"quare s\",\n \"qua res\",\n \"Ġcompan ions\",\n \"Ġcompanion s\",\n \"Ġp aris\",\n \"Ġpar is\",\n \"Ġpa ris\",\n \"Ġpari s\",\n \"Ġst imulation\",\n \"Ġstim ulation\",\n \"ĠZ oo\",\n \"ĠZo o\",\n \"Ġre levance\",\n \"Ġrelev ance\",\n \"Ġ Colour\",\n \"ĠCol our\",\n \"ĠColo ur\",\n \"Ġs pear\",\n \"Ġsp ear\",\n \"Ġspe ar\",\n \"ot ional\",\n \"otion al\",\n \"oti onal\",\n \"Ġ Lite\",\n \"ĠL ite\",\n \"ĠLi te\",\n \"ĠLit e\",\n \"ĠK osten\",\n \"ĠKo sten\",\n \"ĠKos ten\",\n \"Ġ ó\",\n \"Ġà ³\",\n \"_ attachment\",\n \"_att achment\",\n \"_attach ment\",\n \"orph ic\",\n \"orp hic\",\n \"Ġda mit\",\n \"Ġdam it\",\n \"Ġ dlg\",\n \"Ġd lg\",\n \"Ġdl g\",\n \"Ġth rive\",\n \"Ġthr ive\",\n \"CH ANGE\",\n \"CHAN GE\",\n \"Ġ Apparently\",\n \"ĠApp arently\",\n \"Ġa tual\",\n \"Ġat ual\",\n \"Ġro oted\",\n \"Ġroot ed\",\n \"( images\",\n \"(image s\",\n \"(im ages\",\n \"a wi\",\n \"aw i\",\n \"ar iat\",\n \"ari at\",\n \"aria t\",\n \"Ġch erry\",\n \"Ġcher ry\",\n \"ST ATIC\",\n \"STAT IC\",\n \"m nt\",\n \"mn t\",\n \"Ġ UserId\",\n \"ĠUser Id\",\n \"il let\",\n \"ill et\",\n \"ille t\",\n \"ĠHis panic\",\n \"ĠHispan ic\",\n \"Ġ nak\",\n \"Ġn ak\",\n \"Ġna k\",\n \"Ġcent ro\",\n \"Ġcentr o\",\n \"Ġcen tro\",\n \"Ġ dims\",\n \"Ġd ims\",\n \"Ġdi ms\",\n \"Ġdim s\",\n \"_ initialize\",\n \"_initial ize\",\n \"ı k\",\n \"ĠCent ers\",\n \"ĠCenter s\",\n \"R EN\",\n \"RE N\",\n \"Ġevolution ary\",\n \"Ġ Topics\",\n \"ĠTo pics\",\n \"ĠTop ics\",\n \"ĠTopic s\",\n \"_ damage\",\n \"_d amage\",\n \"_da mage\",\n \"e mer\",\n \"em er\",\n \"eme r\",\n \"Ġr und\",\n \"Ġrun d\",\n \"Ġru nd\",\n \"Ġpun ished\",\n \"Ġpunish ed\",\n \"Ġc ubic\",\n \"Ġcu bic\",\n \"Ġcub ic\",\n \"f air\",\n \"fa ir\",\n \"[ ];ĊĊ\",\n \"[] ;ĊĊ\",\n \"[];Ċ Ċ\",\n \"Ġin stantiate\",\n \"Ġinstant iate\",\n \"Ġover see\",\n \"Ġovers ee\",\n \"Ġoverse e\",\n \"- delete\",\n \"-de lete\",\n \"-del ete\",\n \"unt eer\",\n \"unte er\",\n \"start Time\",\n \"Ġ Pipeline\",\n \"ĠP ipeline\",\n \"ĠPipe line\",\n \"ĠPip eline\",\n \"_ GAME\",\n \"_G AME\",\n \"ĠC ir\",\n \"ĠCi r\",\n \"ĉ Null\",\n \"ĉN ull\",\n \". Formatting\",\n \".Format ting\",\n \"uc umber\",\n \"ĠR ide\",\n \"ĠRid e\",\n \"ĠRi de\",\n \"Ġz oo\",\n \"Ġzo o\",\n \"Ġ checker\",\n \"Ġcheck er\",\n \"Ġche cker\",\n \"åIJ Į\",\n \"= C\",\n \"Ġg rit\",\n \"Ġgr it\",\n \"Ġgri t\",\n \"\\\" );//\",\n \"\\\") ;//\",\n \"\\\"); //\",\n \"_ xy\",\n \"_x y\",\n \"Ġ Declaration\",\n \"ĠDe claration\",\n \"Ġ callable\",\n \"Ġcall able\",\n \"F oo\",\n \"Fo o\",\n \"Ġ ListItem\",\n \"ĠList Item\",\n \"Ġin accur\",\n \"m lin\",\n \"ml in\",\n \"ĉ Data\",\n \"ĉD ata\",\n \"Ġev olving\",\n \"a wan\",\n \"aw an\",\n \"awa n\",\n \"Ġc afe\",\n \"Ġca fe\",\n \"Ġcaf e\",\n \"f olk\",\n \"fo lk\",\n \"fol k\",\n \"_ IDX\",\n \"_ID X\",\n \"_I DX\",\n \"Ġ Anything\",\n \"ĠAny thing\",\n \"ĠPalest ine\",\n \"ĠPalestin e\",\n \"Ġ GridView\",\n \"ĠGrid View\",\n \"Ġcol ony\",\n \"Ġcolon y\",\n \"ĠGerman s\",\n \"ĠGer mans\",\n \"ĠGerm ans\",\n \"( +\",\n \". pid\",\n \".p id\",\n \".pi d\",\n \". jsx\",\n \".j sx\",\n \".js x\",\n \"ĠSup erior\",\n \"ĠSuper ior\",\n \"Christ ian\",\n \"ĠL ect\",\n \"ĠLe ct\",\n \"ĉ Game\",\n \"ĉG ame\",\n \"Ġinstrument al\",\n \"An imations\",\n \"Animation s\",\n \"Anim ations\",\n \"д ал\",\n \"да л\",\n \"ĠM oses\",\n \"ĠMo ses\",\n \"ĠMos es\",\n \"ĉĉ čĊĉĉčĊ\",\n \"ĉĉčĊ ĉĉčĊ\",\n \"z s\",\n \"k te\",\n \"kt e\",\n \"ä¸ ļ\",\n \"_ DIST\",\n \"_D IST\",\n \"_DIS T\",\n \"_DI ST\",\n \"b itmap\",\n \"bit map\",\n \"d B\",\n \"Ġp ersistence\",\n \"Ġpers istence\",\n \"Ġpersist ence\",\n \"ÑĢ оÑģ\",\n \"ÑĢо Ñģ\",\n \"$ l\",\n \"B ron\",\n \"Br on\",\n \"Bro n\",\n \"Ġ {|\",\n \"Ġ{ |\",\n \"_ chart\",\n \"_c hart\",\n \"_ch art\",\n \"_char t\",\n \"Ġ Consum\",\n \"ĠCon sum\",\n \"ĠCons um\",\n \"Ġh emp\",\n \"Ġhe mp\",\n \"Ġhem p\",\n \"Ġ \\\"))Ċ\",\n \"Ġ\\\" ))Ċ\",\n \"Ġ\\\") )Ċ\",\n \"Ġ\\\")) Ċ\",\n \"Ġatt ackers\",\n \"Ġattack ers\",\n \"Ġattacker s\",\n \"Ġknowledge able\",\n \"Ġc et\",\n \"Ġce t\",\n \"Ġvir uses\",\n \"Ġvirus es\",\n \"' I\",\n \"Ġpitch er\",\n \"Ġpit cher\",\n \"Ġswe eping\",\n \"Ġsweep ing\",\n \"= list\",\n \"=l ist\",\n \"apt ops\",\n \"aptop s\",\n \". depth\",\n \".de pth\",\n \".dep th\",\n \"Ġinstruct ed\",\n \"Ġ Rus\",\n \"ĠR us\",\n \"ĠRu s\",\n \"benh avn\",\n \"Ġ ин\",\n \"Ġи н\",\n \"S ports\",\n \"Sp orts\",\n \"Sport s\",\n \"Spo rts\",\n \"Ġon set\",\n \"Ġons et\",\n \"æĿ ĥ\",\n \". RED\",\n \".R ED\",\n \".RE D\",\n \"_ si\",\n \"_s i\",\n \"ĠP ST\",\n \"ĠPS T\",\n \". onChange\",\n \".on Change\",\n \"> tag\",\n \">t ag\",\n \"ĠR oh\",\n \"ĠRo h\",\n \"_ character\",\n \"_char acter\",\n \"ĠL aws\",\n \"ĠLa ws\",\n \"ĠLaw s\",\n \"Ġ Bachelor\",\n \"ĠB achelor\",\n \"ĠBach elor\",\n \"_ swap\",\n \"_s wap\",\n \"_sw ap\",\n \".re activex\",\n \"Ġreward ing\",\n \"Ġrew arding\",\n \"M edium\",\n \"Med ium\",\n \"- [\",\n \"Ġ Recently\",\n \"ĠRec ently\",\n \"ĠRecent ly\",\n \"J oint\",\n \"Join t\",\n \"Jo int\",\n \"part ition\",\n \"Ġ Minutes\",\n \"ĠMin utes\",\n \"ĠMinute s\",\n \"Ġ indo\",\n \"Ġin do\",\n \"Ġi ndo\",\n \"Ġind o\",\n \"Ġabsor bed\",\n \"Ġabsorb ed\",\n \"Ġ GN\",\n \"ĠG N\",\n \"_ IND\",\n \"_IN D\",\n \"_I ND\",\n \"Ġs aber\",\n \"Ġsa ber\",\n \"Ġsab er\",\n \"Ġsabe r\",\n \"S pawn\",\n \"Sp awn\",\n \"out puts\",\n \"output s\",\n \"ĠJeff rey\",\n \"Ġmed ieval\",\n \"Ġmedi eval\",\n \"h ed\",\n \"he d\",\n \"G uide\",\n \"Gui de\",\n \"Guid e\",\n \"Gu ide\",\n \"Ġpsych o\",\n \"Ġpsy cho\",\n \"Ġg lam\",\n \"Ġgl am\",\n \"E lim\",\n \"El im\",\n \"äd chen\",\n \"_ plain\",\n \"_p lain\",\n \"_pl ain\",\n \"Ġ Sau\",\n \"ĠS au\",\n \"ĠSa u\",\n \"- four\",\n \"-f our\",\n \"Ġanaly zing\",\n \"QUE RY\",\n \"QU ERY\",\n \"Ġtom ato\",\n \"_ buttons\",\n \"_button s\",\n \"_but tons\",\n \"V EN\",\n \"VE N\",\n \". setStatus\",\n \".set Status\",\n \". Url\",\n \".U rl\",\n \"+ ĊĊ\",\n \"+Ċ Ċ\",\n \"Ġcompl aining\",\n \"Ġcomplain ing\",\n \"d egree\",\n \"de gree\",\n \"deg ree\",\n \"conf irmed\",\n \"confirm ed\",\n \"Ġsu bt\",\n \"Ġsub t\",\n \"p arsed\",\n \"par sed\",\n \"parse d\",\n \"pars ed\",\n \"Ġt orque\",\n \"Ġtor que\",\n \"Ġtrouble d\",\n \"Ġtroub led\",\n \"Ġtrou bled\",\n \"Ġ TARGET\",\n \"ĠT ARGET\",\n \"ĠTAR GET\",\n \"Ġtrad emarks\",\n \"Ġtrade marks\",\n \"Ġtrademark s\",\n \"Ġ Coordinate\",\n \"ĠCo ordinate\",\n \"ĠCoord inate\",\n \"ĠV iv\",\n \"ĠVi v\",\n \"Ġ //}ĊĊ\",\n \"Ġ// }ĊĊ\",\n \"Ġ//}Ċ Ċ\",\n \"Ġapr ès\",\n \". getPosition\",\n \".get Position\",\n \".getP osition\",\n \"( KeyCode\",\n \"(Key Code\",\n \"ĠSil va\",\n \"Ġ meteor\",\n \"Ġm eteor\",\n \"Ġmet eor\",\n \"Ġendorse ment\",\n \"Ġendors ement\",\n \"Over view\",\n \"Ġ Poss\",\n \"ĠP oss\",\n \"ĠPo ss\",\n \"ĠPos s\",\n \". Inject\",\n \".In ject\",\n \"Ġeven ly\",\n \"Ġ visualization\",\n \"Ġvisual ization\",\n \"Ġ wchar\",\n \"Ġw char\",\n \"Ġwc har\",\n \"ĠH DMI\",\n \"ĠHD MI\",\n \"Ġf unct\",\n \"Ġfun ct\",\n \"Ġfunc t\",\n \"ick name\",\n \"',' ','\",\n \"','', '\",\n \"Ġfor wards\",\n \"Ġforward s\",\n \"Managed Object\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĉ server\",\n \"ĉs erver\",\n \"ĠOut look\",\n \"ĠChron icle\",\n \"ĠChronic le\",\n \"Ġdub bed\",\n \"Ġd ok\",\n \"Ġdo k\",\n \"ĠW ear\",\n \"ĠWe ar\",\n \". AL\",\n \".A L\",\n \"p aren\",\n \"par en\",\n \"pare n\",\n \"pa ren\",\n \". Interface\",\n \".Inter face\",\n \"Inter faces\",\n \"Interface s\",\n \". cod\",\n \".c od\",\n \".co d\",\n \"Ġd ib\",\n \"Ġdi b\",\n \".Global ization\",\n \"ĠAc ademic\",\n \"ĠAcad emic\",\n \"ĠAcadem ic\",\n \"Ġas sms\",\n \"Ġass ms\",\n \"A utom\",\n \"Auto m\",\n \"Aut om\",\n \"Au tom\",\n \"Ġ lw\",\n \"Ġl w\",\n \"Ġ NW\",\n \"ĠN W\",\n \"Ġ&& čĊ\",\n \"Ġproble ma\",\n \"Ġproblem a\",\n \"Ġprobl ema\",\n \"ĠManufact uring\",\n \"l imits\",\n \"li mits\",\n \"lim its\",\n \"limit s\",\n \"- mobile\",\n \"-m obile\",\n \"Ġ filme\",\n \"Ġfil me\",\n \"Ġfilm e\",\n \"/ map\",\n \"/m ap\",\n \"Ġd oit\",\n \"Ġdo it\",\n \"Ġdoi t\",\n \"ĠI nk\",\n \"ĠIn k\",\n \"Ġs ued\",\n \"Ġsu ed\",\n \"Ġsue d\",\n \". arr\",\n \".a rr\",\n \".ar r\",\n \"Ġunder min\",\n \"Ġ Proc\",\n \"ĠP roc\",\n \"ĠPro c\",\n \"ĠPr oc\",\n \"croll View\",\n \"_ _$\",\n \"__ $\",\n \"Ġside walk\",\n \"Ġsidew alk\",\n \"( that\",\n \"(t hat\",\n \"(th at\",\n \"ภ·\",\n \"[ q\",\n \"gram mar\",\n \"Ġt ë\",\n \"q uito\",\n \"qu ito\",\n \"quit o\",\n \"qui to\",\n \"Ġsp iral\",\n \"Ġspir al\",\n \"Ġspi ral\",\n \"ext ended\",\n \"extend ed\",\n \"Ġf ocal\",\n \"Ġfoc al\",\n \"Ġfo cal\",\n \"Ġdig ging\",\n \"p as\",\n \"pa s\",\n \"ĠT all\",\n \"ĠTal l\",\n \"ĠTa ll\",\n \". proxy\",\n \".pro xy\",\n \".pr oxy\",\n \"i tures\",\n \"it ures\",\n \"iture s\",\n \"itu res\",\n \"itur es\",\n \"T RACT\",\n \"TR ACT\",\n \"TRA CT\",\n \"Ġ Realm\",\n \"ĠRe alm\",\n \"ĠReal m\",\n \"Ġf eder\",\n \"Ġfe der\",\n \"Ġfed er\",\n \"Ġ oriented\",\n \"Ġorient ed\",\n \"Ġori ented\",\n \"Ġ Alternative\",\n \"ĠAltern ative\",\n \"ĠAlter native\",\n \"Ġ owe\",\n \"Ġo we\",\n \"Ġow e\",\n \"Ġs ourced\",\n \"Ġsource d\",\n \"Ġsour ced\",\n \"in ker\",\n \"ink er\",\n \"inke r\",\n \". det\",\n \".d et\",\n \".de t\",\n \"S ep\",\n \"Se p\",\n \"Ġ Qui\",\n \"ĠQ ui\",\n \"ĠQu i\",\n \"ĠPal mer\",\n \"ĠPalm er\",\n \"( _,\",\n \"(_ ,\",\n \"s amples\",\n \"sample s\",\n \"sam ples\",\n \"samp les\",\n \"o yer\",\n \"oy er\",\n \"ul lan\",\n \"ull an\",\n \"ulla n\",\n \"q uez\",\n \"qu ez\",\n \"que z\",\n \"Ed ges\",\n \"Edge s\",\n \"Ġsh out\",\n \"Ġsho ut\",\n \"Ġ Achie\",\n \"ĠA chie\",\n \"ĠAch ie\",\n \"Ġ haar\",\n \"Ġh aar\",\n \"Ġha ar\",\n \"_ Construct\",\n \"_Con struct\",\n \"Ġprem ature\",\n \"Ġre vert\",\n \"Ġrev ert\",\n \"Ġreve rt\",\n \"Ġrever t\",\n \"' ).Ċ\",\n \"') .Ċ\",\n \"'). Ċ\",\n \"Ġs chn\",\n \"Ġsc hn\",\n \"Ġsch n\",\n \"filter ed\",\n \"fil tered\",\n \"filt ered\",\n \"null ptr\",\n \"S aved\",\n \"Save d\",\n \"Sa ved\",\n \"it ecture\",\n \"itect ure\",\n \"C LA\",\n \"CL A\",\n \"Ġ vl\",\n \"Ġv l\",\n \"s tell\",\n \"st ell\",\n \"ste ll\",\n \"ĉ Me\",\n \"ĉM e\",\n \"ĠL ip\",\n \"ĠLi p\",\n \"n ational\",\n \"nat ional\",\n \"nation al\",\n \"Ġwh olly\",\n \"Ġspr ings\",\n \"Ġspring s\",\n \". Timer\",\n \".T imer\",\n \".Time r\",\n \"ĉ src\",\n \"ĉs rc\",\n \"e lsen\",\n \"el sen\",\n \"else n\",\n \"els en\",\n \"åħ ¶\",\n \"Ġcommunic ating\",\n \"Ġ Quiz\",\n \"ĠQu iz\",\n \"ĠQui z\",\n \"Ġt eng\",\n \"Ġte ng\",\n \"Ġten g\",\n \"Ġg ez\",\n \"Ġge z\",\n \"Ġ Outside\",\n \"ĠOut side\",\n \"ĠOuts ide\",\n \". Sign\",\n \".S ign\",\n \"( cs\",\n \"(c s\",\n \"Ġdisp utes\",\n \"Ġdispute s\",\n \"Ġdisput es\",\n \"ĠWe iss\",\n \"ĠWei ss\",\n \"an nes\",\n \"ann es\",\n \"anne s\",\n \"> No\",\n \">N o\",\n \"ĠB ach\",\n \"ĠBa ch\",\n \"ĠBac h\",\n \".remove All\",\n \"re fer\",\n \"ref er\",\n \"/ dashboard\",\n \"/d ashboard\",\n \"Ġ Ajax\",\n \"ĠA jax\",\n \"ĠAj ax\",\n \"Index Changed\",\n \"Ġ Weak\",\n \"ĠWe ak\",\n \"' \\\"Ċ\",\n \"'\\\" Ċ\",\n \"Ġs ights\",\n \"Ġsight s\",\n \"Ġsigh ts\",\n \"access Token\",\n \"ĠJ oi\",\n \"ĠJo i\",\n \"( domain\",\n \"(d omain\",\n \"(dom ain\",\n \"(do main\",\n \"ĉ cv\",\n \"ĉc v\",\n \"Ġcontin uation\",\n \"Ġcontinu ation\",\n \"Ġcontinua tion\",\n \"Ġp lum\",\n \"Ġpl um\",\n \"Ġplu m\",\n \"a dir\",\n \"ad ir\",\n \"adi r\",\n \". setMessage\",\n \".set Message\",\n \"Ġ ï¼Į\",\n \"Ġï¼ Į\",\n \"Ġsw allow\",\n \"Ġswal low\",\n \"ĠL amp\",\n \"ĠLa mp\",\n \"ĠLam p\",\n \"Ġ qw\",\n \"Ġq w\",\n \"Ġ uu\",\n \"Ġu u\",\n \"C oin\",\n \"Co in\",\n \"u bic\",\n \"ub ic\",\n \"ubi c\",\n \"ĠDe als\",\n \"ĠDeal s\",\n \"r ace\",\n \"ra ce\",\n \"rac e\",\n \"Ġdict ator\",\n \"Ġm eme\",\n \"Ġme me\",\n \"Ġmem e\",\n \"turn ed\",\n \"tur ned\",\n \"ĠJul ie\",\n \"ĠJu lie\",\n \"ĠJuli e\",\n \".grid Column\",\n \"Ġp uppy\",\n \"Ġpup py\",\n \"Ġpu ppy\",\n \"Ġp am\",\n \"Ġpa m\",\n \"Ġ ){čĊ\",\n \"Ġ) {čĊ\",\n \"Ġ){ čĊ\",\n \"Ġinv iting\",\n \"Ġinvit ing\",\n \"Ġf rench\",\n \"Ġfr ench\",\n \"Ġfren ch\",\n \"v im\",\n \"vi m\",\n \"Ġwr apping\",\n \"Ġwrap ping\",\n \"Ġ#- }Ċ\",\n \"( [-\",\n \"([ -\",\n \"Ear ly\",\n \"Ġsh iny\",\n \"Ġshin y\",\n \". faces\",\n \".f aces\",\n \".face s\",\n \".fac es\",\n \".fa ces\",\n \"Ġre bell\",\n \"Ġreb ell\",\n \"Ġrebel l\",\n \"abc def\",\n \"abcd ef\",\n \"ä lt\",\n \"äl t\",\n \"Ġest imation\",\n \"Ġestim ation\",\n \"ph ys\",\n \"phy s\",\n \"los ures\",\n \"losure s\",\n \"_ REL\",\n \"_RE L\",\n \"_R EL\",\n \"Ġex clusion\",\n \"Ġexclus ion\",\n \"Ġexcl usion\",\n \"ĠSk ype\",\n \"ĠSky pe\",\n \"we ise\",\n \"wei se\",\n \"weis e\",\n \"- stop\",\n \"-s top\",\n \"-st op\",\n \"no thing\",\n \"ĠE gg\",\n \"ĠEg g\",\n \"is ors\",\n \"iso rs\",\n \"isor s\",\n \"Rich ard\",\n \"Ġcounsel ing\",\n \"Ġcom mem\",\n \"Ġcomm em\",\n \"Ġcomme m\",\n \"ĠQ MessageBox\",\n \"ĠSy nd\",\n \"ĠSyn d\",\n \"ĠF rost\",\n \"ĠFr ost\",\n \"ĠFro st\",\n \"ĠCom petition\",\n \"ĠCompet ition\",\n \"ĠA wake\",\n \"ĠAw ake\",\n \"Ġ ted\",\n \"Ġt ed\",\n \"Ġte d\",\n \"ic iones\",\n \"ici ones\",\n \"icio nes\",\n \"icion es\",\n \"ĠDev Components\",\n \"VERTISE MENT\",\n \"o tti\",\n \"ot ti\",\n \"ott i\",\n \". runner\",\n \".r unner\",\n \".run ner\",\n \"Ġunique ly\",\n \"Ġuniqu ely\",\n \"Ġuniq uely\",\n \". flag\",\n \".f lag\",\n \".fl ag\",\n \"ĉ rs\",\n \"ĉr s\",\n \"_ generic\",\n \"_g eneric\",\n \"_gen eric\",\n \"_gene ric\",\n \"_gener ic\",\n \"Ġ` ``Ċ\",\n \"Ġ`` `Ċ\",\n \"Ġ``` Ċ\",\n \"ACH INE\",\n \"ACHI NE\",\n \"Ġm ein\",\n \"Ġme in\",\n \"( Application\",\n \"(App lication\",\n \"( br\",\n \"(b r\",\n \"Ġrat ios\",\n \"Ġratio s\",\n \": ,\",\n \"ĠX CTest\",\n \"ĠXCT est\",\n \"ĠXC Test\",\n \"ustain able\",\n \"- www\",\n \"-w ww\",\n \"it les\",\n \"itle s\",\n \"_ TEMP\",\n \"_T EMP\",\n \"_TE MP\",\n \"_TEM P\",\n \"Ġs yst\",\n \"Ġsys t\",\n \"Ġsy st\",\n \"umeric UpDown\",\n \"ĉ assertTrue\",\n \"ĉassert True\",\n \"Ġ wf\",\n \"Ġw f\",\n \". peek\",\n \".pe ek\",\n \"ĠB ulg\",\n \"ĠBul g\",\n \"ĠBu lg\",\n \"Ġterr ifying\",\n \". MODE\",\n \".M ODE\",\n \".MOD E\",\n \"Ġ GW\",\n \"ĠG W\",\n \"á r\",\n \"Ġ fic\",\n \"Ġf ic\",\n \"Ġfi c\",\n \"Ġcommit ments\",\n \"Ġcommitment s\",\n \"- tech\",\n \"-t ech\",\n \"-te ch\",\n \"Ġ Liquid\",\n \"ĠL iquid\",\n \"ĠLiqu id\",\n \"o pez\",\n \"op ez\",\n \"ope z\",\n \"z heimer\",\n \"a ña\",\n \"añ a\",\n \"- media\",\n \"-m edia\",\n \"-me dia\",\n \"-med ia\",\n \"( animated\",\n \"(an imated\",\n \"_ goal\",\n \"_go al\",\n \"Ġg um\",\n \"Ġgu m\",\n \"y stone\",\n \"yst one\",\n \"ys tone\",\n \". SET\",\n \".S ET\",\n \".SE T\",\n \"ĠW end\",\n \"ĠWe nd\",\n \"ĠWen d\",\n \"set CellValue\",\n \"Ġ msgs\",\n \"Ġmsg s\",\n \"Ġms gs\",\n \"c ash\",\n \"ca sh\",\n \"cas h\",\n \"AL LOC\",\n \"ALL OC\",\n \"/ aws\",\n \"/a ws\",\n \"Ġmicro wave\",\n \"Ġmic rowave\",\n \". Pointer\",\n \".Point er\",\n \"ĉ Console\",\n \"ĉCon sole\",\n \"_ sorted\",\n \"_s orted\",\n \"_sort ed\",\n \"ĠF ilip\",\n \"ĠFil ip\",\n \"ĠFi lip\",\n \"P rod\",\n \"Pro d\",\n \"Pr od\",\n \"Ġ// !<\",\n \"Ġ//! <\",\n \"in group\",\n \"ing roup\",\n \"Ġ ks\",\n \"Ġk s\",\n \"_T RI\",\n \"_TR I\",\n \"Ġteas poon\",\n \"Ġ ATT\",\n \"ĠA TT\",\n \"ĠAT T\",\n \"Ġre covering\",\n \"Ġrecover ing\",\n \"Ġ GLOBAL\",\n \"ĠG LOBAL\",\n \". Par\",\n \".P ar\",\n \"Ġ/ >;Ċ\",\n \"Ġ/> ;Ċ\",\n \"Ġmar ble\",\n \"ul ators\",\n \"ula tors\",\n \"ulator s\",\n \"Ġ Cycle\",\n \"ĠC ycle\",\n \"ĠCy cle\",\n \"ĠCycl e\",\n \"ĠCyc le\",\n \"Ġher bs\",\n \"Ġherb s\",\n \"_ metric\",\n \"_m etric\",\n \"_met ric\",\n \") !\",\n \"_C LOCK\",\n \"_CL OCK\",\n \"_ Button\",\n \"_B utton\",\n \"H arry\",\n \"Har ry\",\n \"è¿ Ľ\",\n \"Ġstr ains\",\n \"Ġstrain s\",\n \"Ġstra ins\",\n \"Ġ AppBar\",\n \"ĠApp Bar\",\n \"Ġ Chan\",\n \"ĠC han\",\n \"ĠCh an\",\n \"ĠCha n\",\n \"/ video\",\n \"/v ideo\",\n \"Ġ bam\",\n \"Ġb am\",\n \"Ġba m\",\n \". Progress\",\n \".Pro gress\",\n \"$ f\",\n \"l emen\",\n \"le men\",\n \"lem en\",\n \"leme n\",\n \"Ġir regular\",\n \"ĠD uncan\",\n \"ĠDun can\",\n \"ĠM int\",\n \"ĠMin t\",\n \"ĠMi nt\",\n \"- video\",\n \"-v ideo\",\n \"ঠ¾\",\n \"ó wn\",\n \"ów n\",\n \"Ġ EMPTY\",\n \"ĠEM PTY\",\n \"ĠEMP TY\",\n \"Ġst acked\",\n \"Ġstack ed\",\n \"Ġ HA\",\n \"ĠH A\",\n \"_ cut\",\n \"_c ut\",\n \"_cu t\",\n \"Ġwhere in\",\n \"ĠW ays\",\n \"ĠWay s\",\n \"ĠWa ys\",\n \"( counter\",\n \"(c ounter\",\n \"(count er\",\n \"(co unter\",\n \"è¯ ķ\",\n \"Form Group\",\n \"Ġb lew\",\n \"Ġbl ew\",\n \"Ġble w\",\n \"c ourses\",\n \"co urses\",\n \"course s\",\n \"cour ses\",\n \"Ġ productos\",\n \"Ġproduct os\",\n \"Ġproducto s\",\n \"r ys\",\n \"ry s\",\n \"Ġ Restr\",\n \"ĠR estr\",\n \"ĠRe str\",\n \"ĠRes tr\",\n \"ĠRest r\",\n \"Ġst yling\",\n \"Ġsty ling\",\n \"Ġstyl ing\",\n \"> s\",\n \"Ġp iv\",\n \"Ġpi v\",\n \"Ġit ertools\",\n \"Ġiter tools\",\n \"get Repository\",\n \"Ġ Ik\",\n \"ĠI k\",\n \"_ devices\",\n \"_device s\",\n \"_dev ices\",\n \"lay ui\",\n \"Ġhalf way\",\n \"Ġfran ç\",\n \"Ġt uning\",\n \"Ġtu ning\",\n \"Ġtun ing\",\n \"O A\",\n \"_ Node\",\n \"_N ode\",\n \"_No de\",\n \"ar de\",\n \"ard e\",\n \"Ġf ierce\",\n \"Ġfi erce\",\n \"Ġfier ce\",\n \"Ġfierc e\",\n \"l icted\",\n \"lic ted\",\n \"lict ed\",\n \"# čĊ\",\n \"Ġbreak through\",\n \"ĠE rik\",\n \"ĠEr ik\",\n \"Ġb ride\",\n \"Ġbr ide\",\n \"Ġbri de\",\n \"Ġbrid e\",\n \"Ġ .\\\"\",\n \"Ġ. \\\"\",\n \"c ulus\",\n \"cul us\",\n \"cu lus\",\n \"in side\",\n \"ins ide\",\n \"insi de\",\n \"ĠIndian apolis\",\n \"Ġ EE\",\n \"ĠE E\",\n \"Ġy og\",\n \"Ġyo g\",\n \"ur ret\",\n \"urre t\",\n \"urr et\",\n \". fs\",\n \".f s\",\n \". grad\",\n \".g rad\",\n \".gr ad\",\n \"_ cards\",\n \"_c ards\",\n \"_card s\",\n \"_car ds\",\n \"_ accuracy\",\n \"_ac curacy\",\n \"_acc uracy\",\n \"_e pi\",\n \"_ep i\",\n \"qu eda\",\n \"que da\",\n \"/ org\",\n \"/or g\",\n \"/o rg\",\n \"é ªĮ\",\n \"éª Į\",\n \"Ġcom pte\",\n \"Ġcomp te\",\n \"Ġcompt e\",\n \") )[\",\n \")) [\",\n \"Out side\",\n \"G reater\",\n \"Great er\",\n \"Gre ater\",\n \"Ġ Renderer\",\n \"ĠRender er\",\n \". actor\",\n \".a ctor\",\n \".ac tor\",\n \".act or\",\n \"Account s\",\n \"Ac counts\",\n \"I dle\",\n \"Id le\",\n \"_ hours\",\n \"_h ours\",\n \"_hour s\",\n \"er ner\",\n \"ern er\",\n \"erne r\",\n \"J oined\",\n \"Join ed\",\n \"Jo ined\",\n \"Ġme nj\",\n \"Ġmen j\",\n \"re quires\",\n \"require s\",\n \"requ ires\",\n \"Ġ OPER\",\n \"ĠO PER\",\n \"ĠOP ER\",\n \".remove Child\",\n \"ĉ sp\",\n \"ĉs p\",\n \"Ġ esse\",\n \"Ġe sse\",\n \"Ġes se\",\n \"Ġess e\",\n \"r ift\",\n \"ri ft\",\n \"rif t\",\n \"x FE\",\n \"xF E\",\n \"ĠSh akespeare\",\n \"____ ________\",\n \"________ ____\",\n \"Ġbudget s\",\n \"Ġbud gets\",\n \"Model State\",\n \"fill able\",\n \"- component\",\n \"-com ponent\",\n \"-comp onent\",\n \"o cos\",\n \"oc os\",\n \"oco s\",\n \"Ġ BUTTON\",\n \"ĠB UTTON\",\n \"ĠBUT TON\",\n \"/ io\",\n \"/i o\",\n \", out\",\n \",o ut\",\n \"s ms\",\n \"sm s\",\n \"Th omas\",\n \"Tho mas\",\n \"ĠAr med\",\n \"ĠArm ed\",\n \"re sume\",\n \"res ume\",\n \"Ġrot ating\",\n \"Ġ Vault\",\n \"ĠV ault\",\n \"ĠVa ult\",\n \"Ġs eus\",\n \"Ġse us\",\n \"Ġseu s\",\n \". (*\",\n \".( *\",\n \"Ġa mino\",\n \"Ġam ino\",\n \"Ġami no\",\n \"Ġ[ ]);ĊĊ\",\n \"Ġ[] );ĊĊ\",\n \"Ġ[]) ;ĊĊ\",\n \"Ġ[]);Ċ Ċ\",\n \"Ġprov oc\",\n \"n ox\",\n \"no x\",\n \". GetEnumerator\",\n \".Get Enumerator\",\n \"= ======Ċ\",\n \"== =====Ċ\",\n \"==== ===Ċ\",\n \"=== ====Ċ\",\n \"====== =Ċ\",\n \"===== ==Ċ\",\n \"======= Ċ\",\n \"æĸ Ļ\",\n \"_ scroll\",\n \"_s croll\",\n \"_sc roll\",\n \"_scr oll\",\n \"Ġfil med\",\n \"Ġfilm ed\",\n \"Ġfilme d\",\n \"ĠS oci\",\n \"ĠSo ci\",\n \"ĠSoc i\",\n \"g ap\",\n \"ga p\",\n \"g ro\",\n \"gr o\",\n \"V ote\",\n \"Vo te\",\n \"\\\" But\",\n \"\\\"B ut\",\n \"_ RC\",\n \"_R C\",\n \"An imal\",\n \"Anim al\",\n \" Ģ\",\n \"ib ile\",\n \"ibil e\",\n \"ibi le\",\n \"Ġaw aken\",\n \"Ġawake n\",\n \"o rest\",\n \"or est\",\n \"ore st\",\n \"ores t\",\n \"in ja\",\n \"ĠI van\",\n \"ĠIv an\",\n \"( Command\",\n \"Ġ *****\",\n \"Ġ* ****\",\n \"Ġ** ***\",\n \"Ġ*** **\",\n \"Ġ**** *\",\n \"Î ·\",\n \"Ġkv inder\",\n \"Ġkvin der\",\n \"Ġkvinde r\",\n \"/ helpers\",\n \"/h elpers\",\n \"/help ers\",\n \"/helper s\",\n \"_ cases\",\n \"_c ases\",\n \"_case s\",\n \"_ca ses\",\n \"t g\",\n \"ìĦ ¸\",\n \"Register ed\",\n \"ĉ pass\",\n \"ĉp ass\",\n \"_ digits\",\n \"_d igits\",\n \"_digit s\",\n \"Ġcon tour\",\n \"Ġcont our\",\n \"Ġinf ants\",\n \"Ġinfant s\",\n \"Ġjust ification\",\n \"Ġ Fortunately\",\n \"ĠFort unately\",\n \"Cont r\",\n \"Con tr\",\n \"ĠonCreate View\",\n \"_ SAMPLE\",\n \"_S AMPLE\",\n \"_SAMPL E\",\n \"Ġallow Null\",\n \"Ġn ud\",\n \"Ġnu d\",\n \"Ġf etched\",\n \"Ġfetch ed\",\n \"Ġfet ched\",\n \"_ equ\",\n \"_e qu\",\n \"_eq u\",\n \"Ġ Unable\",\n \"ĠU nable\",\n \"ĠUn able\",\n \"ĠUna ble\",\n \"= \\\\\\\"\\\"\",\n \"=\\\\\\\" \\\"\",\n \"=\\\\ \\\"\\\"\",\n \"> {Ċ\",\n \">{ Ċ\",\n \"Ġcommit tees\",\n \"Ġcommittee s\",\n \"ist ema\",\n \"iste ma\",\n \"istem a\",\n \"+ \\\".\",\n \"+\\\" .\",\n \"ÃŃ an\",\n \"ÃŃa n\",\n \"m ant\",\n \"man t\",\n \"ma nt\",\n \"Ġsou theast\",\n \"Ġsouth east\",\n \"ï¼Į Ċ\",\n \"dialog s\",\n \"dia logs\",\n \"PRO JECT\",\n \"ch arger\",\n \"char ger\",\n \"charge r\",\n \"charg er\",\n \"- port\",\n \"-p ort\",\n \"-po rt\",\n \"( uuid\",\n \"(u uid\",\n \". export\",\n \".ex port\",\n \".exp ort\",\n \"S ix\",\n \"Si x\",\n \"Ġ RP\",\n \"ĠR P\",\n \"P rem\",\n \"Pr em\",\n \"Pre m\",\n \"Ġcon science\",\n \"Ġconsc ience\",\n \"Ġmargin Right\",\n \"_ distribution\",\n \"_d istribution\",\n \"_dis tribution\",\n \"y aml\",\n \"ya ml\",\n \"res izing\",\n \"resi zing\",\n \"D ock\",\n \"Do ck\",\n \"Doc k\",\n \"Ġ Locations\",\n \"ĠL ocations\",\n \"ĠLocation s\",\n \"ĠLoc ations\",\n \"G Y\",\n \"S eed\",\n \"Se ed\",\n \"See d\",\n \"B UFFER\",\n \"BUF FER\",\n \"BUFF ER\",\n \"os sip\",\n \"oss ip\",\n \"u llen\",\n \"ul len\",\n \"ull en\",\n \"ulle n\",\n \"Th ings\",\n \"Thing s\",\n \"Thin gs\",\n \"- self\",\n \"-s elf\",\n \"-se lf\",\n \". poll\",\n \".p oll\",\n \".po ll\",\n \".pol l\",\n \"PL AYER\",\n \"PLAY ER\",\n \"Ġ å®\",\n \"Ġå ®\",\n \"G ROUP\",\n \"Ġ Away\",\n \"ĠA way\",\n \"ĠAw ay\",\n \"Ġg ospel\",\n \"x fd\",\n \"xf d\",\n \"M ary\",\n \"Mar y\",\n \"Ma ry\",\n \"Ġ Portable\",\n \"ĠPort able\",\n \"ĠPor table\",\n \"T URE\",\n \"TU RE\",\n \"Ġutil is\",\n \"Ġut ilis\",\n \"Ġse it\",\n \"Ġsei t\",\n \"Ġ strand\",\n \"Ġs trand\",\n \"Ġst rand\",\n \"Ġstr and\",\n \"Ġstran d\",\n \"Ġstra nd\",\n \"Ġtrans c\",\n \"Ġtran sc\",\n \"Ġ (^\",\n \"Ġ( ^\",\n \"ĠAl fred\",\n \"ĠAlf red\",\n \". mem\",\n \".m em\",\n \".me m\",\n \". circle\",\n \".c ircle\",\n \"Ġ ~/\",\n \"Ġ~ /\",\n \"for cing\",\n \"forc ing\",\n \"Ġ riot\",\n \"Ġr iot\",\n \"Ġri ot\",\n \"Ġrio t\",\n \"p rox\",\n \"pr ox\",\n \"pro x\",\n \"TH ON\",\n \"iz ación\",\n \"iza ción\",\n \"Ġ NI\",\n \"ĠN I\",\n \"r ost\",\n \"ro st\",\n \"ros t\",\n \"Ġdis pro\",\n \"Ġdisp ro\",\n \"_ instances\",\n \"_in stances\",\n \"_instance s\",\n \"_inst ances\",\n \"ï¼Į âĢľ\",\n \"ograph er\",\n \"ogra pher\",\n \"en das\",\n \"end as\",\n \"enda s\",\n \"ĠIs aac\",\n \"ĠIsa ac\",\n \"ĠP ine\",\n \"ĠPin e\",\n \"ĠPi ne\",\n \"/ dis\",\n \"/d is\",\n \"Ġcolor With\",\n \"it erate\",\n \"ite rate\",\n \"iter ate\",\n \"_ stride\",\n \"_st ride\",\n \"_str ide\",\n \"Ġp unto\",\n \"Ġpun to\",\n \"Ġpunt o\",\n \". EventArgs\",\n \".Event Args\",\n \"( center\",\n \"(c enter\",\n \"Ġneighb oring\",\n \"Ġneighbor ing\",\n \"ĠPr ison\",\n \"ĠPri son\",\n \"Ġ Messenger\",\n \"ĠM essenger\",\n \"ĠMess enger\",\n \"Ġepid emic\",\n \"Ġepidemi c\",\n \"d ao\",\n \"da o\",\n \"_ complex\",\n \"_com plex\",\n \"_comp lex\",\n \"Ġg ravel\",\n \"Ġgr avel\",\n \"Ġgrave l\",\n \"Ġgra vel\",\n \"Ġgrav el\",\n \"_D IP\",\n \"_DI P\",\n \"é ment\",\n \"ém ent\",\n \"ĠA ri\",\n \"ĠAr i\",\n \"_ bitmap\",\n \"_b itmap\",\n \"_bit map\",\n \". quit\",\n \".q uit\",\n \".qu it\",\n \"( valid\",\n \"(val id\",\n \"(va lid\",\n \"Ġ pend\",\n \"Ġp end\",\n \"Ġpe nd\",\n \"Ġpen d\",\n \"Ġrespir atory\",\n \"Ġre bound\",\n \"Ġreb ound\",\n \"Default Value\",\n \"ãĥ Ń\",\n \"Ġcom mits\",\n \"Ġcomm its\",\n \"Ġcommit s\",\n \". tests\",\n \".t ests\",\n \".test s\",\n \".te sts\",\n \"_ fr\",\n \"_f r\",\n \"i tet\",\n \"it et\",\n \"ite t\",\n \". sf\",\n \".s f\",\n \"Ġspace craft\",\n \"c ritical\",\n \"cri tical\",\n \"cr itical\",\n \"crit ical\",\n \"Ġde pressed\",\n \"Ġdep ressed\",\n \"Ġdepr essed\",\n \"Ġdepress ed\",\n \"ĠAny Object\",\n \"Ġu nb\",\n \"Ġun b\",\n \"Ġdis cern\",\n \"Ġdisc ern\",\n \"( mysql\",\n \"(m ysql\",\n \"(my sql\",\n \"L atin\",\n \"La tin\",\n \"Lat in\",\n \"ĠB og\",\n \"ĠBo g\",\n \"ĠWild life\",\n \"To File\",\n \"ToF ile\",\n \"i oxid\",\n \"iox id\",\n \"@ RestController\",\n \"Ġ\\\" $(\",\n \"Ġ\\\"$ (\",\n \"Ġ <<\\\"\",\n \"Ġ< <\\\"\",\n \"Ġ<< \\\"\",\n \"Ġdef ects\",\n \"Ġdefe cts\",\n \"Ġdefect s\",\n \"Ġ datum\",\n \"Ġd atum\",\n \"Ġdat um\",\n \"h in\",\n \"hi n\",\n \"Ġreal izar\",\n \"Ġrealiz ar\",\n \"Ġrealiza r\",\n \"any ahu\",\n \"anya hu\",\n \"Ġ Sig\",\n \"ĠS ig\",\n \"ĠSi g\",\n \"@ Data\",\n \"ad aptive\",\n \"ada ptive\",\n \"adapt ive\",\n \"ĠC atherine\",\n \". cr\",\n \".c r\",\n \"Ġ COOKIE\",\n \"ĠCO OKIE\",\n \"Ġ pictured\",\n \"Ġp ictured\",\n \"Ġpicture d\",\n \"Ġpict ured\",\n \"ĠF ighter\",\n \"ĠFight er\",\n \"Query able\",\n \"Ġ Anyway\",\n \"ĠAny way\",\n \"ĠGL FW\",\n \"_ namespace\",\n \"_n amespace\",\n \"_name space\",\n \"_names pace\",\n \"_ ft\",\n \"_f t\",\n \"Ġ ])\",\n \"Ġ] )\",\n \"O rganization\",\n \"Organ ization\",\n \"Ġconstit utes\",\n \"Ġconstitu tes\",\n \"Ġconstitute s\",\n \"Ġqu and\",\n \"Ġqua nd\",\n \"Ġquan d\",\n \"( chunk\",\n \"(ch unk\",\n \"\\\" />čĊ\",\n \"\\\"/ >čĊ\",\n \"\\\"/> čĊ\",\n \"ĠL akes\",\n \"ĠLa kes\",\n \"ĠLake s\",\n \"ĠLak es\",\n \"main window\",\n \"Car thy\",\n \"Cart hy\",\n \"s pin\",\n \"sp in\",\n \"spi n\",\n \"( csv\",\n \"(c sv\",\n \"(cs v\",\n \": red\",\n \":r ed\",\n \"- commerce\",\n \"-com merce\",\n \"-comm erce\",\n \"ภ¹\",\n \"Ġdis covering\",\n \"Ġdiscover ing\",\n \"Ġ eco\",\n \"Ġe co\",\n \"Ġec o\",\n \"_ fac\",\n \"_f ac\",\n \"_fa c\",\n \"ince ton\",\n \"inc eton\",\n \"ĠGreen s\",\n \"ĠGre ens\",\n \"ĠGree ns\",\n \"j wt\",\n \"Ø µ\",\n \"ĠBron cos\",\n \"Ġ Goods\",\n \"ĠG oods\",\n \"ĠGo ods\",\n \"ĠGood s\",\n \"( GTK\",\n \"(G TK\",\n \"Ġ returnValue\",\n \"Ġreturn Value\",\n \"Ġsi empre\",\n \"Ġne utr\",\n \"Ġneu tr\",\n \"Ġneut r\",\n \"w ent\",\n \"we nt\",\n \"wen t\",\n \"ĠN atal\",\n \"ĠNa tal\",\n \"ĠNat al\",\n \"Ġenthusi astic\",\n \"Ġenthusiast ic\",\n \"á» į\",\n \"F N\",\n \"/ database\",\n \"/d atabase\",\n \"/data base\",\n \"/dat abase\",\n \"C atalog\",\n \"Cat alog\",\n \"Ġb run\",\n \"Ġbr un\",\n \"Ġbru n\",\n \"ĠK ash\",\n \"ĠKa sh\",\n \"ĠKas h\",\n \"_ Pl\",\n \"_P l\",\n \"isc rim\",\n \", width\",\n \",w idth\",\n \"Ġin mates\",\n \"Ġinmate s\",\n \"Ass ignment\",\n \"Assign ment\",\n \"ĠH aven\",\n \"ĠHave n\",\n \"ĠHa ven\",\n \"ĠHav en\",\n \"Ġplay ground\",\n \"ex am\",\n \"@ Controller\",\n \"ul iar\",\n \"uli ar\",\n \"ulia r\",\n \". getParent\",\n \".get Parent\",\n \".getP arent\",\n \"Ġ \\\";ĊĊ\",\n \"Ġ\\\" ;ĊĊ\",\n \"Ġ\\\";Ċ Ċ\",\n \"Ġ\\\"; ĊĊ\",\n \": size\",\n \":s ize\",\n \"iss ors\",\n \"issor s\",\n \"Ġf is\",\n \"Ġfi s\",\n \"Ġ alc\",\n \"Ġa lc\",\n \"Ġal c\",\n \"ens ation\",\n \"ensa tion\",\n \"ĠN ixon\",\n \"ĠNi xon\",\n \"Ġ mighty\",\n \"Ġmight y\",\n \"- str\",\n \"-s tr\",\n \"-st r\",\n \"_ special\",\n \"_s pecial\",\n \"_sp ecial\",\n \"_spec ial\",\n \"_ ADC\",\n \"_A DC\",\n \"_AD C\",\n \"Ġ Twig\",\n \"ĠT wig\",\n \"ĠTw ig\",\n \"um bling\",\n \"umb ling\",\n \"- address\",\n \"-add ress\",\n \"-ad dress\",\n \"Ġher oin\",\n \"Ġhero in\",\n \"Y TE\",\n \"YT E\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ\",\n \"F riend\",\n \"Fri end\",\n \"Ġ ave\",\n \"Ġa ve\",\n \"Ġav e\",\n \"Ġ PNG\",\n \"ĠP NG\",\n \"ĠPN G\",\n \"ĠKur dish\",\n \"ĠKurd ish\",\n \"DataSet Changed\",\n \"Ġbl ades\",\n \"Ġblade s\",\n \"Ġbla des\",\n \"b ral\",\n \"br al\",\n \"bra l\",\n \"S team\",\n \"St eam\",\n \"Ste am\",\n \"Ġs igu\",\n \"Ġsi gu\",\n \"Ġsig u\",\n \"IRT UAL\",\n \"a cos\",\n \"ac os\",\n \"aco s\",\n \"U DP\",\n \"UD P\",\n \"( database\",\n \"(d atabase\",\n \"(data base\",\n \"(dat abase\",\n \"h ec\",\n \"he c\",\n \"Ġ Strings\",\n \"ĠString s\",\n \"ĠStr ings\",\n \"_ scalar\",\n \"_s calar\",\n \"_sc alar\",\n \"_scal ar\",\n \"ĉ desc\",\n \"ĉd esc\",\n \"ĉdes c\",\n \"ĉde sc\",\n \"Ġ TLS\",\n \"ĠT LS\",\n \"ĠTL S\",\n \"; \\\"Ċ\",\n \";\\\" Ċ\",\n \"ĠCor byn\",\n \"Simple Name\",\n \"u ell\",\n \"ue ll\",\n \"uel l\",\n \"Ġ Entre\",\n \"ĠEn tre\",\n \"ĠEnt re\",\n \"ĠEntr e\",\n \"ell ites\",\n \"ellite s\",\n \"elli tes\",\n \"- place\",\n \"-p lace\",\n \"-pl ace\",\n \"Ġfrank ly\",\n \"ĠE rf\",\n \"ĠEr f\",\n \"C EL\",\n \"CE L\",\n \"Ġpa ÃŃs\",\n \"Ġh edge\",\n \"Ġhe dge\",\n \"Ġhed ge\",\n \"Ġ latent\",\n \"Ġla tent\",\n \"Ġlate nt\",\n \"Ġlat ent\",\n \"Ġlaten t\",\n \"Ġ IRQ\",\n \"ĠIR Q\",\n \"ĠH erald\",\n \"ĠHer ald\",\n \"ĠHera ld\",\n \"Ġ Prec\",\n \"ĠP rec\",\n \"ĠPr ec\",\n \"ĠPre c\",\n \"ë³ ´\",\n \". 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Prop\",\n \".P rop\",\n \".Pro p\",\n \".Pr op\",\n \". ''\",\n \".' '\",\n \". Generated\",\n \".G enerated\",\n \".Generate d\",\n \".New Guid\",\n \"at rigesimal\",\n \"il lon\",\n \"ill on\",\n \"illo n\",\n \"Ġstat istic\",\n \"Ġstatist ic\",\n \"s pecies\",\n \"sp ecies\",\n \"spec ies\",\n \"spe cies\",\n \"h olding\",\n \"hold ing\",\n \"hol ding\",\n \"Dr upal\",\n \"Ġfundament ally\",\n \"Ġfundamental ly\",\n \"Ġbond age\",\n \"Ġres olutions\",\n \"Ġresolution s\",\n \"Inline Data\",\n \"\\\\ Type\",\n \"es tion\",\n \"est ion\",\n \"esti on\",\n \". wrap\",\n \".w rap\",\n \".wr ap\",\n \"Ġwar riors\",\n \"Ġwarrior s\",\n \"Ġ LOCAL\",\n \"ĠLO CAL\",\n \"ĠLOC AL\",\n \"A rchive\",\n \"Arch ive\",\n \"Arc hive\",\n \"Ġembr aced\",\n \"Ġembrace d\",\n \"á» §\",\n \". Ver\",\n \".V er\",\n \"ĠAff ordable\",\n \"ole sale\",\n \"oles ale\",\n \"Ġ Applied\",\n \"ĠApp lied\",\n \"ĠAp plied\",\n \"ĠAppl ied\",\n \"Ġ Conversion\",\n \"ĠCon version\",\n \"ĠConv ersion\",\n \"ĠConvers ion\",\n \"m ega\",\n \"me ga\",\n \"meg a\",\n \"_ cam\",\n \"_c am\",\n \"_ca m\",\n \"Ġcer emon\",\n \"Ġcere mon\",\n \"a urus\",\n \"au rus\",\n \"aur us\",\n \"ĠV olk\",\n \"ĠVol k\",\n \"ĠVo lk\",\n \". opens\",\n \".open s\",\n \".op ens\",\n \"/ about\",\n \"/a bout\",\n \"Ġ Std\",\n \"ĠS td\",\n \"ĠSt d\",\n \"j ournal\",\n \"jo urnal\",\n \"jour nal\",\n \"( )){čĊ\",\n \"() ){čĊ\",\n \"()) {čĊ\",\n \"()){ čĊ\",\n \", \\\"\\\\\",\n \",\\\" \\\\\",\n \"( Arrays\",\n \"(Array s\",\n \"ĠD ense\",\n \"ĠDen se\",\n \"ase ña\",\n \"än ner\",\n \"änn er\",\n \"/ stat\",\n \"/s tat\",\n \"/st at\",\n \"user Data\",\n \"Ġg erman\",\n \"Ġger man\",\n \"Ġgerm an\",\n \"Ġ tz\",\n \"Ġt z\",\n \"w orthy\",\n \"worth y\",\n \"wort hy\",\n \"wor thy\",\n \"Format Exception\",\n \"ph erd\",\n \"pher d\",\n \"phe rd\",\n \"Ġsm iles\",\n \"Ġsmile s\",\n \"Ġ Whenever\",\n \"ĠWh enever\",\n \"ĠWhen ever\",\n \"ĠWhe never\",\n \"( adapter\",\n \"(ad apter\",\n \".bad logic\",\n \"Ġbrief ing\",\n \". GridColumn\",\n \".Grid Column\",\n \"- char\",\n \"-c har\",\n \"-ch ar\",\n \"d imension\",\n \"dim ension\",\n \"ĠC opper\",\n \"ĠCo pper\",\n \"ĠCop per\",\n \"ĠCopp er\",\n \"Ġn inth\",\n \"Ġni nth\",\n \"Ġnin th\",\n \"Ġ' {{\",\n \"Ġ'{ {\",\n \"Ġ rav\",\n \"Ġr av\",\n \"Ġra v\",\n \"_ Table\",\n \"_T able\",\n \"_Tab le\",\n \"Ġderiv atives\",\n \"Ġderivative s\",\n \"Ġ Raise\",\n \"ĠR aise\",\n \"ĠRa ise\",\n \"ĠRai se\",\n \"ĠF ut\",\n \"ĠFu t\",\n \"ar mor\",\n \"arm or\",\n \"- padding\",\n \"-p adding\",\n \"-pad ding\",\n \"Ġre min\",\n \"Ġr emin\",\n \"Ġrem in\",\n \"ĉ style\",\n \"ĉst yle\",\n \"Ġ Membership\",\n \"ĠMember ship\",\n \"ĠMembers hip\",\n \"Ġsp reads\",\n \"Ġspread s\",\n \"Ġspre ads\",\n \"Ġg alleries\",\n \"Ġgall eries\",\n \"ĠClark e\",\n \"ĠClar ke\",\n \"Ġcon ception\",\n \"Ġconcept ion\",\n \"Ġconce ption\",\n \"min ute\",\n \"Ġab usive\",\n \"_ adj\",\n \"_a dj\",\n \"_ad j\",\n \"Ġterr ific\",\n \"Ġo vert\",\n \"Ġover t\",\n \"Ġov ert\",\n \"our cing\",\n \"Ġ entrada\",\n \"Ġent rada\",\n \"Ġentr ada\",\n \"Ġentra da\",\n \"level s\",\n \"lev els\",\n \"Ġcrit ique\",\n \"Ġres pects\",\n \"Ġrespect s\",\n \"Ġresp ects\",\n \"ĠM MA\",\n \"ĠMM A\",\n \"i ene\",\n \"ie ne\",\n \"ien e\",\n \"Ġen caps\",\n \"Ġenc aps\",\n \"ĠRay mond\",\n \"Div ider\",\n \"Di vider\",\n \"i vable\",\n \"iv able\",\n \"iva ble\",\n \"b az\",\n \"ba z\",\n \"Ġ@ _;Ċ\",\n \"Ġ@_ ;Ċ\",\n \"ĠCl aire\",\n \"ĠCla ire\",\n \"ĠClair e\",\n \"Ġur ging\",\n \"Ġurg ing\",\n \"C EE\",\n \"CE E\",\n \"Ġtrans former\",\n \"Ġtransform er\",\n \"dis cord\",\n \"disc ord\",\n \"ĠJ ourney\",\n \"t os\",\n \"to s\",\n \"Ġcompet itions\",\n \"Ġcompetition s\",\n \"Ġcompetit ions\",\n \"Ġ OBJ\",\n \"ĠO BJ\",\n \"ĠOB J\",\n \"ĠB is\",\n \"ĠBi s\",\n \"Ġrelax ation\",\n \"i dy\",\n \"id y\",\n \"_ INSTANCE\",\n \"_IN STANCE\",\n \"_INST ANCE\",\n \"Ġ Pref\",\n \"ĠP ref\",\n \"ĠPr ef\",\n \"ĠPre f\",\n \"d ados\",\n \"da dos\",\n \"dad os\",\n \"ici encies\",\n \"ĠMedia Query\",\n \"Ġ Cube\",\n \"ĠC ube\",\n \"ĠCub e\",\n \"ĠCu be\",\n \"Ġ Strange\",\n \"ĠSt range\",\n \"ĠStr ange\",\n \"ĠStra nge\",\n \"g pu\",\n \"gp u\",\n \"( days\",\n \"(d ays\",\n \"(day s\",\n \"(da ys\",\n \"_ InitStruct\",\n \"_Init Struct\",\n \"Ġf ingerprint\",\n \"Ġfinger print\",\n \"e mat\",\n \"em at\",\n \"ema t\",\n \"ĠG ecko\",\n \"ĠGe cko\",\n \"Ġ rails\",\n \"Ġr ails\",\n \"Ġrail s\",\n \"Ġra ils\",\n \"ĠL um\",\n \"ĠLu m\",\n \"s traction\",\n \"st raction\",\n \"str action\",\n \"stract ion\",\n \"stra ction\",\n \"ig ung\",\n \"igu ng\",\n \"( movie\",\n \"(m ovie\",\n \"_ dictionary\",\n \"_d ictionary\",\n \"_ interrupt\",\n \"_int errupt\",\n \"_inter rupt\",\n \"Ġ QC\",\n \"ĠQ C\",\n \"i ked\",\n \"ik ed\",\n \"ike d\",\n \"append Child\",\n \"rec ipient\",\n \"r é\",\n \"V e\",\n \"Ġt owel\",\n \"Ġto wel\",\n \"Ġtow el\",\n \".last IndexOf\",\n \"Ġplace bo\",\n \"Ġplac ebo\",\n \"Ġ Wie\",\n \"ĠW ie\",\n \"ĠWi e\",\n \". esp\",\n \".e sp\",\n \".es p\",\n \"( Debug\",\n \"oper ative\",\n \"Ġde ceased\",\n \"Ġdece ased\",\n \"& id\",\n \"ĉ mutex\",\n \"ĉm utex\",\n \"e lic\",\n \"el ic\",\n \"eli c\",\n \"Ġb apt\",\n \"Ġba pt\",\n \"ĉ čĊčĊ\",\n \"ĉčĊ čĊ\",\n \"Ġfar ther\",\n \"Ġfart her\",\n \"H alf\",\n \"Ha lf\",\n \"Hal f\",\n \". disable\",\n \".d isable\",\n \".dis able\",\n \".menu Strip\",\n \"le ccion\",\n \"lec cion\",\n \"Ġ resultCode\",\n \"Ġresult Code\",\n \"Ġc ans\",\n \"Ġcan s\",\n \"Ġca ns\",\n \"- election\",\n \"-e lection\",\n \"-elect ion\",\n \"-el ection\",\n \"f emale\",\n \"fe male\",\n \"_ FIX\",\n \"_F IX\",\n \"aus ible\",\n \"Ġ POWER\",\n \"ĠP OWER\",\n \"ĠPO WER\",\n \"ĠPOW ER\",\n \"Ġre construction\",\n \"Ġrecon struction\",\n \"Ġreconstruct ion\",\n \"Ġsc ans\",\n \"Ġscan s\",\n \"Ġsca ns\",\n \".Xtra Bars\",\n \"âĢĺ s\",\n \"Re moved\",\n \"Rem oved\",\n \"Remove d\",\n \"Ġpara graphs\",\n \"Ġparagraph s\",\n \"_ margin\",\n \"_m argin\",\n \"_mar gin\",\n \"Ġl ymph\",\n \"Ġly mph\",\n \"Ġ bos\",\n \"Ġb os\",\n \"Ġbo s\",\n \"l ington\",\n \"ling ton\",\n \"ĠBapt ist\",\n \"Ġadvertis ements\",\n \"Ġadvertisement s\",\n \"Ġadvertise ments\",\n \"Ġ Manage\",\n \"ĠMan age\",\n \"ĠMa nage\",\n \"ĠMana ge\",\n \"/ yyyy\",\n \"/y yyy\",\n \"I OUS\",\n \"IO US\",\n \"EN CES\",\n \"ENCE S\",\n \"ENC ES\",\n \"ĠF iction\",\n \"ĠFi ction\",\n \"ĉ menu\",\n \"ĉm enu\",\n \"ĉme nu\",\n \"ĠFile OutputStream\",\n \"o van\",\n \"ov an\",\n \"ova n\",\n \"ĠF eng\",\n \"ĠFe ng\",\n \"ĠFen g\",\n \"Ġsk ipping\",\n \"Ġskip ping\",\n \"Ġski pping\",\n \"get Class\",\n \"getC lass\",\n \"an ni\",\n \"ann i\",\n \"Ġre bounds\",\n \"Ġreb ounds\",\n \"Ġrebound s\",\n \"Ġpublic ity\",\n \"Ġpub licity\",\n \"Ġpubli city\",\n \"Ġin gres\",\n \"Ġing res\",\n \"Ġingr es\",\n \"us ement\",\n \"use ment\",\n \"Ġthought ful\",\n \". Chart\",\n \".C hart\",\n \".Ch art\",\n \".Char t\",\n \"Ġh atte\",\n \"Ġha tte\",\n \"Ġhat te\",\n \"pass port\",\n \"pas sport\",\n \"Ġhook ed\",\n \"Ġho oked\",\n \"Ġ Lens\",\n \"ĠL ens\",\n \"ĠLe ns\",\n \"ĠLen s\",\n \"Ġflag ship\",\n \"Ġflags hip\",\n \"Ġs tip\",\n \"Ġst ip\",\n \"Ġ GEN\",\n \"ĠG EN\",\n \"ĠGE N\",\n \"Ġcl ues\",\n \"Ġclue s\",\n \"i pv\",\n \"ip v\",\n \"ĠR ise\",\n \"ĠRi se\",\n \"ĠRis e\",\n \"ĠG ew\",\n \"ĠGe w\",\n \"table name\",\n \"tab lename\",\n \"tabl ename\",\n \"Ġfore most\",\n \"_ validate\",\n \"_valid ate\",\n \"_ analysis\",\n \"_an alysis\",\n \"o lla\",\n \"ol la\",\n \"oll a\",\n \"Ġqual ifications\",\n \"Ġqualification s\",\n \"Ġd istributions\",\n \"Ġdistrib utions\",\n \"Ġdistribution s\",\n \"ĠF lower\",\n \"ĠFl ower\",\n \"ĠFlo wer\",\n \"ĠFlow er\",\n \"Ġt ense\",\n \"Ġten se\",\n \"Ġtens e\",\n \"Ġthank ful\",\n \"Ġcl utch\",\n \"Ġun ified\",\n \"ro ads\",\n \"road s\",\n \"Ġs iti\",\n \"Ġsit i\",\n \"Ġsi ti\",\n \"Ġst all\",\n \"Ġsta ll\",\n \"Ġstal l\",\n \"_P RIORITY\",\n \"_PRI ORITY\",\n \"c stdlib\",\n \"_ USERNAME\",\n \"_USER NAME\",\n \". bytes\",\n \".by tes\",\n \".byte s\",\n \"? page\",\n \"?p age\",\n \"er malink\",\n \"erm alink\",\n \"ermal ink\",\n \"ĠVe get\",\n \"ĠVeg et\",\n \"/v nd\",\n \"- author\",\n \"-a uthor\",\n \"-auth or\",\n \"-aut hor\",\n \". NONE\",\n \".N ONE\",\n \".NO NE\",\n \"ĠCon current\",\n \"ĠConc urrent\",\n \"ĠC ry\",\n \"ĠCr y\",\n \"Ġst arters\",\n \"Ġstart ers\",\n \"Ġstar ters\",\n \"Ġstarter s\",\n \"Ġ Interaction\",\n \"ĠInter action\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"Ġ LEVEL\",\n \"ĠLE VEL\",\n \"E ll\",\n \"El l\",\n \"Ġ comboBox\",\n \"Ġcom boBox\",\n \"Ġcombo Box\",\n \"ĠThe resa\",\n \"ĠTh eresa\",\n \"ĠThere sa\",\n \"ĠTher esa\",\n \"t ek\",\n \"te k\",\n \"_ Handle\",\n \"_H andle\",\n \"Ġ aby\",\n \"Ġa by\",\n \"Ġab y\",\n \".g dx\",\n \", end\",\n \",e nd\",\n \",en d\",\n \"( Local\",\n \"(L ocal\",\n \"O l\",\n \"kn ife\",\n \"a rial\",\n \"ar ial\",\n \"ari al\",\n \"aria l\",\n \"ĠH off\",\n \"ĠHo ff\",\n \"ĠHof f\",\n \"Ġprostituer ade\",\n \"Do ctor\",\n \"Doc tor\",\n \"In stances\",\n \"Instance s\",\n \"Inst ances\",\n \". SetValue\",\n \".Set Value\",\n \"ĉ from\",\n \"ĉf rom\",\n \"ĉfr om\",\n \"Ġlux urious\",\n \"In dent\",\n \"Ind ent\",\n \"Al locator\",\n \"All ocator\",\n \"Alloc ator\",\n \"_ DRAW\",\n \"_D RAW\",\n \"_DR AW\",\n \"(\\\" ,\\\",\",\n \"(\\\", \\\",\",\n \"(\\\",\\\" ,\",\n \"ĠF rances\",\n \"ĠFr ances\",\n \"ĠFrance s\",\n \"ĠFranc es\",\n \"ĠFra nces\",\n \"ĠFran ces\",\n \"Ġ groupBox\",\n \"Ġgroup Box\",\n \"( schema\",\n \"(s chema\",\n \"Print f\",\n \"O RIES\",\n \"OR IES\",\n \"- gradient\",\n \"-g radient\",\n \"Ġre put\",\n \"Ġrep ut\",\n \"a rin\",\n \"ar in\",\n \"ari n\",\n \"_ DONE\",\n \"_D ONE\",\n \"_DO NE\",\n \"in cre\",\n \"inc re\",\n \"incr e\",\n \"ig nty\",\n \"ign ty\",\n \"Ġex ert\",\n \"Ġexe rt\",\n \"Ġ -.\",\n \"Ġ- .\",\n \"/ App\",\n \"/A pp\",\n \"- through\",\n \"-th rough\",\n \"Ġdec lining\",\n \"Ġdecl ining\",\n \"Ġdes sert\",\n \"Ġdess ert\",\n \"Ġinc umb\",\n \"Ġ designation\",\n \"Ġdesign ation\",\n \". PORT\",\n \".P ORT\",\n \".PO RT\",\n \", strong\",\n \",str ong\",\n \",st rong\",\n \"Ġ sandbox\",\n \"Ġs andbox\",\n \"Ġsand box\",\n \"Ġw ines\",\n \"Ġwin es\",\n \"Ġwine s\",\n \"Ġwi nes\",\n \"ĠP av\",\n \"ĠPa v\",\n \"$ str\",\n \"$s tr\",\n \"ask ell\",\n \"Ġh ö\",\n \"Ġ PY\",\n \"ĠP Y\",\n \"Get Instance\",\n \"Text Input\",\n \"game Object\",\n \"/ events\",\n \"/e vents\",\n \"/event s\",\n \"created At\",\n \"Ġlocal Var\",\n \"Ġ WHITE\",\n \"ĠW HITE\",\n \"ĠWH ITE\",\n \"p ered\",\n \"pe red\",\n \"per ed\",\n \"i lege\",\n \"ile ge\",\n \"eff icient\",\n \", color\",\n \",c olor\",\n \",col or\",\n \"c ate\",\n \"ca te\",\n \"cat e\",\n \"ĠC afe\",\n \"ĠCa fe\",\n \"ĠCaf e\",\n \"Ġsimilar ities\",\n \"Ġp umps\",\n \"Ġpump s\",\n \"Ġpu mps\",\n \"ĠHun gary\",\n \"ĠHung ary\",\n \". Username\",\n \".User name\",\n \"Ġs kate\",\n \"Ġsk ate\",\n \"Ġska te\",\n \"Ġtouchdown s\",\n \"Ġacceler ate\",\n \"Ġaccel erate\",\n \"ĠH elen\",\n \"ĠHe len\",\n \"ĠHel en\",\n \"O MEM\",\n \"OM EM\",\n \"OME M\",\n \"ĠK un\",\n \"ĠKu n\",\n \"_ vol\",\n \"_v ol\",\n \"Ġ findAll\",\n \"Ġfind All\",\n \"ĠMens chen\",\n \"a head\",\n \"ah ead\",\n \") ;\\\"\",\n \"); \\\"\",\n \"k ommen\",\n \"kom men\",\n \"Ġposs essed\",\n \"Ġpossess ed\",\n \".arg max\",\n \". transition\",\n \".t ransition\",\n \".trans ition\",\n \"A RP\",\n \"AR P\",\n \"OL UME\",\n \"OLUM E\",\n \"( script\",\n \"(s cript\",\n \"Ġ Ðĺ\",\n \"ĠÐ ĺ\",\n \"Ġ Finding\",\n \"ĠF inding\",\n \"ĠFin ding\",\n \"ĠFind ing\",\n \"o nces\",\n \"on ces\",\n \"once s\",\n \"I o\",\n \"B old\",\n \"Bo ld\",\n \"Ġrenew al\",\n \"_D IALOG\",\n \"Ġdis reg\",\n \"IN TERN\",\n \"INT ERN\",\n \"INTER N\",\n \"Ġt oute\",\n \"Ġto ute\",\n \"Ġtou te\",\n \"Ġtout e\",\n \"Ġelect r\",\n \"Ġele ctr\",\n \"ĠG ross\",\n \"ĠGr oss\",\n \"ĠGro ss\",\n \"ĠGros s\",\n \"ĉ true\",\n \"ĉtr ue\",\n \". 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NO\",\n \".N O\",\n \"\\\" encoding\",\n \"Ġ Blockchain\",\n \"ĠBlock chain\",\n \"Ġlaws uits\",\n \"Ġlawsuit s\",\n \"Ġ Maint\",\n \"ĠM aint\",\n \"ĠMain t\",\n \"ĠMa int\",\n \"ĠMai nt\",\n \"ch ten\",\n \"cht en\",\n \"chte n\",\n \"Ġét ait\",\n \"Ġktó re\",\n \"_ ctl\",\n \"_c tl\",\n \"_ct l\",\n \"( timer\",\n \"(t imer\",\n \"(time r\",\n \"(ti mer\",\n \"B attle\",\n \"Bat tle\",\n \"i zo\",\n \"iz o\",\n \"ay ed\",\n \"aye d\",\n \"I OR\",\n \"IO R\",\n \"ĠGlas gow\",\n \"Ġs ynth\",\n \"Ġsy nth\",\n \"Ġsyn th\",\n \"Ġsynt h\",\n \"_ logs\",\n \"_l ogs\",\n \"_log s\",\n \"_lo gs\",\n \". pose\",\n \".p ose\",\n \".pos e\",\n \".po se\",\n \"_Adjust orThunk\",\n \"( (&\",\n \"(( &\",\n \"Ġun sure\",\n \"Ġuns ure\",\n \"Ġunsur e\",\n \"y state\",\n \"yst ate\",\n \"íķĺ ëĬĶ\",\n \"O ULD\",\n \"OU LD\",\n \". ng\",\n \".n g\",\n \"Ġdefault dict\",\n \"work space\",\n \"works pace\",\n \"Ġselect ive\",\n \"Ġsel ective\",\n \"Picker Controller\",\n \"YNAM IC\",\n \". methods\",\n \".method s\",\n \"Ġpath ways\",\n \"Ġpathway s\",\n \"Ġ Few\",\n \"ĠF ew\",\n \"ĠFe w\",\n \"K G\",\n \"C RYPT\",\n \"CRY PT\",\n \"follow ing\",\n \"ĠD LC\",\n \"ĠDL C\",\n \"ĠS ara\",\n \"ĠSar a\",\n \"ĠSa ra\",\n \"Ġ preset\",\n \"Ġp reset\",\n \"Ġpre set\",\n \"Ġpres et\",\n \"e structor\",\n \"estr uctor\",\n \"estruct or\",\n \"ĠK urt\",\n \"ĠKur t\",\n \"ĠKu rt\",\n \"Ġair plane\",\n \"Ġ omp\",\n \"Ġo mp\",\n \"Ġom p\",\n \"Ġ Parents\",\n \"ĠPar ents\",\n \"ĠParent s\",\n \"ĠParen ts\",\n \"ĠPare nts\",\n \"ĠMart inez\",\n \"ĠMartin ez\",\n \". complete\",\n \".com plete\",\n \".comp lete\",\n \"Ġbroad ly\",\n \"Ġs care\",\n \"Ġsc are\",\n \"Ġsca re\",\n \"Ġscar e\",\n \"ĠM é\",\n \"Ġelim ination\",\n \"Ġelimin ation\",\n \"Ġp oured\",\n \"Ġpo ured\",\n \"Ġpour ed\",\n \"Ġpou red\",\n \"/ sw\",\n \"/s w\",\n \"Ġcom un\",\n \"Ġco mun\",\n \"Ġm asc\",\n \"Ġma sc\",\n \"Ġmas c\",\n \"ĠOrgan ic\",\n \"ĠOrg anic\",\n \"Ġ StringUtils\",\n \"ĠString Utils\",\n \"ĠStringUtil s\",\n \"il ateral\",\n \"ilate ral\",\n \"ilater al\",\n \"Ġreluct ant\",\n \"- age\",\n \"-a ge\",\n \"-ag e\",\n \"Ġ nz\",\n \"Ġn z\",\n \". \\\"\\\\\",\n \".\\\" \\\\\",\n \"Ġpast or\",\n \"Ġpas tor\",\n \"Ġpa stor\",\n \"a lez\",\n \"al ez\",\n \"ale z\",\n \"Ġe fect\",\n \"Ġef ect\",\n \"p rov\",\n \"pr ov\",\n \"pro v\",\n \"/ init\",\n \"/i nit\",\n \"/in it\",\n \"Ġp enn\",\n \"Ġpe nn\",\n \"Ġpen n\",\n \"u nds\",\n \"un ds\",\n \"und s\",\n \"Ġ ssize\",\n \"Ġs size\",\n \"Ġss ize\",\n \"Ġ Proj\",\n \"ĠP roj\",\n \"ĠPro j\",\n \"ĠPr oj\",\n \"b asename\",\n \"base name\",\n \"bas ename\",\n \"Ġsh ells\",\n \"Ġshell s\",\n \"Ġshel ls\",\n \"ĠN eck\",\n \"ĠNe ck\",\n \"ĠNec k\",\n \"ĠEn forcement\",\n \"v ided\",\n \"vid ed\",\n \"vi ded\",\n \"vide d\",\n \"s town\",\n \"st own\",\n \"sto wn\",\n \"S phere\",\n \"Sp here\",\n \"$ r\",\n \"us sen\",\n \"uss en\",\n \"a fil\",\n \"af il\",\n \"afi l\",\n \"Ġ Telegram\",\n \"ĠTele gram\",\n \"Ġanaly tical\",\n \"Ġanalytic al\",\n \"н Ñĭе\",\n \"нÑĭ е\",\n \"us ually\",\n \"usu ally\",\n \"usual ly\",\n \"x n\",\n \"Ġhistor ian\",\n \"Ġhist orian\",\n \"Ġhistoria n\",\n \"Ġhisto rian\",\n \"ĠGreg ory\",\n \"ol ph\",\n \"Ġ Una\",\n \"ĠU na\",\n \"ĠUn a\",\n \"Ġcon tributes\",\n \"Ġcontrib utes\",\n \"Ġcontribute s\",\n \"% -\",\n \"anti ago\",\n \"ÑĢ ед\",\n \"ÑĢе д\",\n \". region\",\n \".reg ion\",\n \"Ġab rupt\",\n \"ĠUnsupported OperationException\",\n \"Ġ TASK\",\n \"ĠT ASK\",\n \"ĠTA SK\",\n \"ĠTAS K\",\n \"_ finish\",\n \"_f inish\",\n \"_fin ish\",\n \"Ġnot orious\",\n \"Ġ Vs\",\n \"ĠV s\",\n \"Ġ MQ\",\n \"ĠM Q\",\n \"Ġs unset\",\n \"Ġsun set\",\n \"Ġun acceptable\",\n \"ar cer\",\n \"arc er\",\n \"Ġill umin\",\n \"Ġillum in\",\n \"ĠO rb\",\n \"ĠOr b\",\n \"Ġ bh\",\n \"Ġb h\",\n \"E ste\",\n \"Est e\",\n \"Es te\",\n \"_ dispatch\",\n \"_dis patch\",\n \"_disp atch\",\n \"Ġr ipped\",\n \"Ġrip ped\",\n \"Ġri pped\",\n \"Ġtou jours\",\n \"Ġ Parcel\",\n \"ĠPar cel\",\n \"_ ll\",\n \"_l l\",\n \". userName\",\n \".user Name\",\n \". classes\",\n \".c lasses\",\n \".class es\",\n \".cl asses\",\n \"S OURCE\",\n \"( Number\",\n \"(N umber\",\n \"е лÑı\",\n \"ел Ñı\",\n \"Ġhead phones\",\n \"Ġheadphone s\",\n \"( side\",\n \"(s ide\",\n \"(si de\",\n \"(sid e\",\n \"con stitution\",\n \"const itution\",\n \"an nah\",\n \"ann ah\",\n \"anna h\",\n \"čĊ ĠĠĠĠĠĠĠĠčĊ\",\n \"Ġcl iff\",\n \"Ġcli ff\",\n \"- ref\",\n \"-r ef\",\n \"-re f\",\n \"Ġmost rar\",\n \"Ġmo strar\",\n \"Ġmostr ar\",\n \"Ġmostra r\",\n \"ĠP owell\",\n \"ĠPo well\",\n \"ĠPow ell\",\n \"+ y\",\n \"Ġ BG\",\n \"ĠB G\",\n \"_ fragment\",\n \"_f ragment\",\n \"_fr agment\",\n \"_frag ment\",\n \". 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FullName\",\n \".Full Name\",\n \"Ġf aut\",\n \"Ġfa ut\",\n \"ĠE asily\",\n \"_ dynamic\",\n \"_d ynamic\",\n \"_dyn amic\",\n \"Ġfr amed\",\n \"Ġframe d\",\n \"Ġfra med\",\n \"Ġfram ed\",\n \"Ġm otive\",\n \"Ġmot ive\",\n \"Ġmotiv e\",\n \"è· ¯\",\n \"s am\",\n \"sa m\",\n \"Ġ marca\",\n \"Ġmar ca\",\n \"Ġmarc a\",\n \"ĠText EditingController\",\n \"Ġd estructor\",\n \"Ġde structor\",\n \"Ġdestruct or\",\n \"c ream\",\n \"cre am\",\n \"cr eam\",\n \"Ġr ude\",\n \"Ġru de\",\n \"Ġrud e\",\n \"Ġ Bold\",\n \"ĠB old\",\n \"ĠBo ld\",\n \"ĠBol d\",\n \"ĠInd igenous\",\n \"Ġ gens\",\n \"Ġg ens\",\n \"Ġge ns\",\n \"Ġgen s\",\n \"Ġrel acion\",\n \"( system\",\n \"(s ystem\",\n \"(sys tem\",\n \"Ġ UIFont\",\n \"ĠUI Font\",\n \"ĠUIF ont\",\n \"_ charge\",\n \"_ch arge\",\n \"_char ge\",\n \"U STER\",\n \"US TER\",\n \"UST ER\",\n \"E V\",\n \". Namespace\",\n \".N amespace\",\n \".Name space\",\n \".Names pace\",\n \"Ġmer ger\",\n \"Ġmerge r\",\n \"Ġmerg er\",\n \"Ġ calloc\",\n \"Ġc alloc\",\n \"Ġcall oc\",\n \"Ġcal loc\",\n \"g ang\",\n \"ga ng\",\n \"gan g\",\n \"Bad Request\",\n \"Ġs per\",\n \"Ġsp er\",\n \"Ġspe r\",\n \"- design\",\n \"-d esign\",\n \"-de sign\",\n \"-des ign\",\n \"Ġ âĩ\",\n \"Ġâ ĩ\",\n \"C han\",\n \"Ch an\",\n \"Cha n\",\n \"Ġorg anism\",\n \"Ġorgan ism\",\n \"Ġorganis m\",\n \", )\",\n \"= id\",\n \"=i d\",\n \"_ plane\",\n \"_p lane\",\n \"_pl ane\",\n \"_plan e\",\n \"Ġ Cases\",\n \"ĠC ases\",\n \"ĠCas es\",\n \"ĠCase s\",\n \"ĠCa ses\",\n \"el fast\",\n \"elf ast\",\n \"ĠLegisl ature\",\n \"ĠF aker\",\n \"ĠFa ker\",\n \"ĠFake r\",\n \"ĠFak er\",\n \"Ġinv oking\",\n \"Ġinvo king\",\n \"- utils\",\n \"-util s\",\n \"( ).'\",\n \"() .'\",\n \"(). '\",\n \". face\",\n \".f ace\",\n \".fac e\",\n \".fa ce\",\n \"Ġguard ian\",\n \"my Modal\",\n \"Ġ clipboard\",\n \"Ġclip board\",\n \"ĠA TM\",\n \"ĠAT M\",\n \"Ġpe as\",\n \"Ġpea s\",\n \"ĠS ylv\",\n \"ĠSy lv\",\n \". calc\",\n \".c alc\",\n \".ca lc\",\n \".cal c\",\n \"Ġ Contacts\",\n \"ĠCont acts\",\n \"ĠContact s\",\n \"int Value\",\n \"Ġmod ifying\",\n \"Ġmodify ing\",\n \"ĠB arb\",\n \"ĠBar b\",\n \"ĠBa rb\",\n \". loss\",\n \".l oss\",\n \".lo ss\",\n \"_ percentage\",\n \"_per centage\",\n \"_percent age\",\n \"As ked\",\n \"Ask ed\",\n \"( lst\",\n \"(l st\",\n \"(ls t\",\n \"ateg orical\",\n \"ategor ical\",\n \"ategori cal\",\n \"atego rical\",\n \"- files\",\n \"-f iles\",\n \"-file s\",\n \"-fi les\",\n \"ĠRom ania\",\n \"ĠRo mania\",\n \"ĠRoman ia\",\n \"ĠRoma nia\",\n \". Ac\",\n \".A c\",\n \"Ġ hai\",\n \"Ġh ai\",\n \"Ġha i\",\n \"Ġ Flying\",\n \"ĠF lying\",\n \"ĠFl ying\",\n \"ĠFly ing\",\n \"Ġ ż\",\n \"ĠÅ ¼\",\n \"j p\",\n \"ĠTr ainer\",\n \"ĠTra iner\",\n \"ĠTrain er\",\n \". arc\",\n \".a rc\",\n \".ar c\",\n \"_ deg\",\n \"_d eg\",\n \"_de g\",\n \"Ġtrace back\",\n \"Or Fail\",\n \"F LOW\",\n \"FL OW\",\n \". old\",\n \".o ld\",\n \".ol d\",\n \"o ya\",\n \"oy a\",\n \"g mt\",\n \"gm t\",\n \"is empty\",\n \"Ġvacc ination\",\n \"Ġ obsolete\",\n \"Ġob solete\",\n \"recogn ized\",\n \"Ġru ined\",\n \"Ġruin ed\",\n \"ĠR ein\",\n \"ĠRe in\",\n \"ĠRei n\",\n \"Ġ Tracking\",\n \"ĠTr acking\",\n \"ĠTrack ing\",\n \"x fb\",\n \"xf b\",\n \"ا ÛĮ\",\n \"Ġv ære\",\n \"Ġvæ re\",\n \"Ġbr yster\",\n \"Ġ ITS\",\n \"ĠI TS\",\n \"ĠIT S\",\n \"Ġdes tiny\",\n \"Ġdest iny\",\n \"Ġdestin y\",\n \"Ġs wear\",\n \"Ġsw ear\",\n \"Ġswe ar\",\n \"Ġre des\",\n \"Ġr edes\",\n \"Ġred es\",\n \"Ġrede s\",\n \"Ġ clf\",\n \"Ġc lf\",\n \"Ġcl f\",\n \"Ġfl ipped\",\n \"Ġflip ped\",\n \"ĉ head\",\n \"ĉh ead\",\n \"B luetooth\",\n \"Bl uetooth\",\n \"Ġ Overrides\",\n \"ĠOver rides\",\n \"ĠOverride s\",\n \": Boolean\",\n \"_ =\",\n \"_ lr\",\n \"_l r\",\n \"s pawn\",\n \"sp awn\",\n \"spa wn\",\n \": index\",\n \"VAL UES\",\n \"VALUE S\",\n \"is key\",\n \"isk ey\",\n \"iske y\",\n \"? \\\");Ċ\",\n \"?\\\" );Ċ\",\n \".syn thetic\",\n \"Ġ Checking\",\n \"ĠCheck ing\",\n \"struct ures\",\n \"structure s\",\n \"i ping\",\n \"ip ing\",\n \"ipi ng\",\n \"Ġvoc als\",\n \"Ġvocal s\",\n \"- Up\",\n \"-U p\",\n \"ĠManufact urers\",\n \"ĠManufacturer s\",\n \"ĠMar riage\",\n \"ĠMarr iage\",\n \"代 çłģ\",\n \"Ġgar ner\",\n \"Ġgarn er\",\n \"_ Client\",\n \"_C lient\",\n \"_Cl ient\",\n \"par allel\",\n \"paralle l\",\n \"RI END\",\n \"Ġvine gar\",\n \"se gue\",\n \"seg ue\",\n \"J B\",\n \"Ġcont acting\",\n \"Ġcontact ing\",\n \"ĠCar roll\",\n \"ĠCarr oll\",\n \"Ġout reach\",\n \"Ġoutr each\",\n \"t ensor\",\n \"_ variant\",\n \"_v ariant\",\n \"_var iant\",\n \"Ġt heat\",\n \"Ġth eat\",\n \"Ġthe at\",\n \"l icable\",\n \"lic able\",\n \"lica ble\",\n \"{ |\",\n \"t iny\",\n \"ti ny\",\n \"tin y\",\n \"_ letter\",\n \"_l etter\",\n \"Ġp encil\",\n \"Ġpen cil\",\n \"Ġpenc il\",\n \"HeadersHeight SizeMode\",\n \"il tro\",\n \"ilt ro\",\n \"iltr o\",\n \".auto configure\",\n \". drag\",\n \".d rag\",\n \".dr ag\",\n \". useState\",\n \".use State\",\n \"Ġ BMI\",\n \"ĠB MI\",\n \"ĠBM I\",\n \"h int\",\n \"hi nt\",\n \"hin t\",\n \"Com pile\",\n \"Comp ile\",\n \"* \\\\\",\n \"en ary\",\n \"ena ry\",\n \"Ġ lvl\",\n \"Ġl vl\",\n \"Ġlv l\",\n \". Cache\",\n \".C ache\",\n \"+ =\\\"\",\n \"+= \\\"\",\n \"_ tv\",\n \"_t v\",\n \"ruit ment\",\n \"Ġf read\",\n \"Ġfr ead\",\n \"Ġfre ad\",\n \"Art icles\",\n \"Article s\",\n \"f ila\",\n \"fi la\",\n \"fil a\",\n \"Ġpack aged\",\n \"Ġpackage d\",\n \"âĺ Ĩ\",\n \"AT HER\",\n \"ATH ER\",\n \"ĠPl anned\",\n \"ĠPlan ned\",\n \"s cheme\",\n \"sch eme\",\n \"Ġdi ary\",\n \"Ġdia ry\",\n \"Ġoff enses\",\n \"Ġoffense s\",\n \"Ġoffen ses\",\n \"/ F\",\n \"Ġ Stick\",\n \"ĠS tick\",\n \"ĠSt ick\",\n \"Ġc erc\",\n \"Ġce rc\",\n \"Ġcer c\",\n \"ĠS lee\",\n \"ĠSl ee\",\n \"ĠSle e\",\n \"ĉ ĉĠĠĠĠĠĠĠĠ\",\n \"ĉĉ ĠĠĠĠĠĠĠĠ\",\n \"ĉĉĠĠĠ ĠĠĠĠĠ\",\n \"ĉĉĠ ĠĠĠĠĠĠĠ\",\n \"ĉĉĠĠ ĠĠĠĠĠĠ\",\n \"ĉĉĠĠĠĠĠĠĠ Ġ\",\n \"ĉĉĠĠĠĠ ĠĠĠĠ\",\n \"ĉĉĠĠĠĠĠ ĠĠĠ\",\n \"ĉĉĠĠĠĠĠĠ ĠĠ\",\n \"< Image\",\n \"\",\n \";' >\",\n \"ĉ col\",\n \"ĉc ol\",\n \"V G\",\n \"_ boolean\",\n \"_bool ean\",\n \"_bo olean\",\n \"re cent\",\n \"rec ent\",\n \"rece nt\",\n \"Ġ *)ĊĊ\",\n \"Ġ* )ĊĊ\",\n \"Ġ*) ĊĊ\",\n \"Ġ*)Ċ Ċ\",\n \"ĠRain bow\",\n \"om men\",\n \"omm en\",\n \"Ġl ur\",\n \"Ġlu r\",\n \"Ġop pression\",\n \"Ġopp ression\",\n \"Ġoppress ion\",\n \"(\\\" ,\\\");Ċ\",\n \"(\\\", \\\");Ċ\",\n \"(\\\",\\\" );Ċ\",\n \"ĠFac ility\",\n \"DEF INED\",\n \"DEFINE D\",\n \"Ġn eon\",\n \"Ġne on\",\n \"Ġneo n\",\n \"Ġoff ender\",\n \"Ġoffend er\",\n \"Ġoffen der\",\n \"A FP\",\n \"AF P\",\n \"Ġ Cleaning\",\n \"ĠC leaning\",\n \"ĠClean ing\",\n \"ĠCle aning\",\n \"[ ]):\",\n \"[] ):\",\n \"[]) :\",\n \"Ġund ocumented\",\n \". Repositories\",\n \".Re positories\",\n \"ĠG uitar\",\n \"ĠGu itar\",\n \"ĠGui tar\",\n \"аÑģÑģ ив\",\n \"S kills\",\n \"Sk ills\",\n \"Skill s\",\n \"Ġtest imon\",\n \"Ġtestim on\",\n \"rypt ography\",\n \"ĠAm ber\",\n \"ĠAmb er\",\n \"ĠSt alin\",\n \"ĠSta lin\",\n \"Ġl one\",\n \"Ġlo ne\",\n \"Ġlon e\",\n \"Ġap enas\",\n \"Ġape nas\",\n \"Ġdi eses\",\n \"Ġdie ses\",\n \"Ġdies es\",\n \"Ġdiese s\",\n \"Ġ Arduino\",\n \"ĠAr duino\",\n \"è½ ¬\",\n \"= =-\",\n \"== -\",\n \"_ Act\",\n \"_A ct\",\n \"Ġ coded\",\n \"Ġc oded\",\n \"Ġco ded\",\n \"Ġcode d\",\n \"Ġcod ed\",\n \"âĸ ł\",\n \"am burger\",\n \"amb urger\",\n \"amburg er\",\n \"- links\",\n \"-l inks\",\n \"-link s\",\n \"Ġar mour\",\n \"Ġarm our\",\n \". High\",\n \".H igh\",\n \"get Content\",\n \"getC ontent\",\n \"s tag\",\n \"st ag\",\n \"sta g\",\n \"Ġ heck\",\n \"Ġh eck\",\n \"Ġhe ck\",\n \"Ġ ìĹĨ\",\n \"ĠìĹ Ĩ\",\n \"ĠMc Connell\",\n \"ĠCon cert\",\n \"ĠConc ert\",\n \"ĠConce rt\",\n \"Ġ Alloc\",\n \"ĠAl loc\",\n \"ĠAll oc\",\n \"ä re\",\n \"är e\",\n \". replaceAll\",\n \".replace All\",\n \"Ġpart itions\",\n \"Ġpartition s\",\n \"r ott\",\n \"ro tt\",\n \"rot t\",\n \"ĠF le\",\n \"ĠFl e\",\n \"_ TREE\",\n \"_T REE\",\n \"_TR EE\",\n \"reason able\",\n \"Ġ Reporting\",\n \"ĠRep orting\",\n \"ĠReport ing\",\n \"Ġbillion aire\",\n \"s cores\",\n \"sc ores\",\n \"score s\",\n \"m ins\",\n \"min s\",\n \"mi ns\",\n \"- eye\",\n \"-e ye\",\n \"M ORE\",\n \"MO RE\",\n \"ab ort\",\n \"abor t\",\n \"abo rt\",\n \"ĠS WT\",\n \"ĠSW T\",\n \"Ġin verted\",\n \"Ġinvert ed\",\n \"Ġ Teachers\",\n \"ĠTe achers\",\n \"ĠTeacher s\",\n \"ĠTea chers\",\n \"ĠTeach ers\",\n \"; n\",\n \"Ġ astro\",\n \"Ġa stro\",\n \"Ġas tro\",\n \"Ġast ro\",\n \"Ġastr o\",\n \"н ов\",\n \"но в\",\n \"а ниÑĨ\",\n \"ан иÑĨ\",\n \"ани ÑĨ\",\n \"product o\",\n \"c ountries\",\n \"count ries\",\n \"ĠO wen\",\n \"ĠOw en\",\n \"Ġcont amination\",\n \"Ġcontamin ation\",\n \"Ġconta mination\",\n \"Ġv ibe\",\n \"Ġvi be\",\n \"Ġvib e\",\n \"ĠE lli\",\n \"ĠEl li\",\n \"ĠEll i\",\n \". script\",\n \".s cript\",\n \"ĠO live\",\n \"ĠOl ive\",\n \"ĠOliv e\",\n \"D MA\",\n \"DM A\",\n \"v ier\",\n \"vi er\",\n \"vie r\",\n \": semicolon\",\n \"- module\",\n \"-m odule\",\n \"-mod ule\",\n \"g ressive\",\n \"gress ive\",\n \"a gu\",\n \"ag u\",\n \"_ players\",\n \"_p layers\",\n \"_pl ayers\",\n \"_player s\",\n \"_play ers\",\n \"Ġresult ados\",\n \"Ġresultado s\",\n \"start ed\",\n \"star ted\",\n \"scroll Top\",\n \"= ====\",\n \"== ===\",\n \"==== =\",\n \"=== ==\",\n \"Ġweigh ing\",\n \"Ġ[ [[\",\n \"Ġ[[ [\",\n \"z ahl\",\n \"za hl\",\n \"( NS\",\n \"(N S\",\n \"Ġ Assertion\",\n \"ĠAssert ion\",\n \"le ague\",\n \"lea gue\",\n \".set TextColor\",\n \".setText Color\",\n \"ĉ Message\",\n \"ĉM essage\",\n \"Ġm oms\",\n \"Ġmom s\",\n \"Ġmo ms\",\n \"_ AF\",\n \"_A F\",\n \". wh\",\n \".w h\",\n \"A LS\",\n \"AL S\",\n \"Ġaut re\",\n \"Ġau tre\",\n \"] ĊĊĊĊ\",\n \"]Ċ ĊĊĊ\",\n \"]ĊĊ ĊĊ\",\n \"]ĊĊĊ Ċ\",\n \". opacity\",\n \".op acity\",\n \"ĠBudd hist\",\n \"ĠBuddh ist\",\n \"Ġde af\",\n \"ĠOrgan isation\",\n \"( Global\",\n \"(G lobal\",\n \"en sch\",\n \"ens ch\",\n \"Ġhead ache\",\n \"ĠA lien\",\n \"ĠAl ien\",\n \"ĠAli en\",\n \"_ inode\",\n \"_in ode\",\n \"_i node\",\n \"ĠSt ark\",\n \"ĠStar k\",\n \"ĠSta rk\",\n \"Ġ æī\",\n \"Ġæ ī\",\n \"-l nd\",\n \"-ln d\",\n \"o ref\",\n \"or ef\",\n \"ore f\",\n \"_ feat\",\n \"_f eat\",\n \"_fe at\",\n \"Ġpedest rian\",\n \"Ġnom inal\",\n \"Ġnomin al\",\n \"Ġball oon\",\n \"Ġbal loon\",\n \"Ġballo on\",\n \"Ġ sprites\",\n \"Ġs prites\",\n \"Ġsp rites\",\n \"Ġspr ites\",\n \"Ġsprite s\",\n \"Prototype Of\",\n \"ĠA post\",\n \"ĠAp ost\",\n \"Ġ FEATURE\",\n \"ĠF EATURE\",\n \"ĠFE ATURE\",\n \"O H\",\n \"Ġre cess\",\n \"Ġr ecess\",\n \"Ġrec ess\",\n \"Ġrece ss\",\n \"ĠD onna\",\n \"ĠDon na\",\n \"con sumer\",\n \"cons umer\",\n \"consum er\",\n \"consume r\",\n \"$ GLOBALS\",\n \"ĠG IF\",\n \"ĠGI F\",\n \"- frame\",\n \"-f rame\",\n \"-fr ame\",\n \"In icio\",\n \"Ini cio\",\n \"Ġpass ages\",\n \"Ġpassage s\",\n \"Date String\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \". byte\",\n \".b yte\",\n \".by te\",\n \"B ug\",\n \"Bu g\",\n \"initial izer\",\n \"initialize r\",\n \"p kt\",\n \"pk t\",\n \"od ium\",\n \"odi um\",\n \"Ġ DER\",\n \"ĠD ER\",\n \"ĠDE R\",\n \". ops\",\n \".op s\",\n \".o ps\",\n \"l eri\",\n \"le ri\",\n \"ler i\",\n \"Ġgift ed\",\n \"Ġgif ted\",\n \"Ġ detach\",\n \"Ġde tach\",\n \"Ġdet ach\",\n \"ter rain\",\n \"terra in\",\n \"terr ain\",\n \"el ters\",\n \"elt ers\",\n \"elter s\",\n \"ãģ ı\",\n \". loader\",\n \".l oader\",\n \".load er\",\n \".lo ader\",\n \"ĠN GO\",\n \"ĠNG O\",\n \"str ncmp\",\n \"K h\",\n \"( fontSize\",\n \"(font Size\",\n \"r ocket\",\n \"ro cket\",\n \"rock et\",\n \"roc ket\",\n \"Ġpreced ent\",\n \"ĠAur ora\",\n \"Ġ Experiment\",\n \"ĠEx periment\",\n \"i sphere\",\n \"is phere\",\n \"isp here\",\n \"En coded\",\n \"Enc oded\",\n \"Encode d\",\n \"Ġ âĢĵĊĊ\",\n \"ĠâĢĵ ĊĊ\",\n \"ĠâĢĵĊ Ċ\",\n \"Ġpy ramid\",\n \"ĠAnn iversary\",\n \"o fil\",\n \"of il\",\n \"ofi l\",\n \"ë Ł\",\n \"( plugin\",\n \"(pl ugin\",\n \"C oeff\",\n \"Co eff\",\n \"Ġco operate\",\n \"Ġcooper ate\",\n \"Ġcoop erate\",\n \"Ġpredomin antly\",\n \"Ġpredominant ly\",\n \"I SM\",\n \"IS M\",\n \"Ph rase\",\n \"_ DEFINE\",\n \"_DE FINE\",\n \"_DEF INE\",\n \"_DEFIN E\",\n \"F lip\",\n \"Fl ip\",\n \"AMIL Y\",\n \"AMI LY\",\n \"ĠMark ets\",\n \"ĠMarket s\",\n \"Ġ StreamReader\",\n \"ĠStream Reader\",\n \"Ġ Combine\",\n \"ĠC ombine\",\n \"ĠCom bine\",\n \"ĠComb ine\",\n \"Ġmanus cript\",\n \"z za\",\n \"zz a\",\n \", tp\",\n \",t p\",\n \"Wh atever\",\n \"What ever\",\n \"IT ICAL\",\n \"igh bour\",\n \"ighb our\",\n \"Data Provider\",\n \". Texture\",\n \".Text ure\",\n \"priv acy\",\n \". 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Valid\",\n \".Val id\",\n \"Ġ FileName\",\n \"ĠFile Name\",\n \"iv ative\",\n \"k ed\",\n \"ke d\",\n \"- war\",\n \"-w ar\",\n \"Ġm aturity\",\n \"Ġmat urity\",\n \"и д\",\n \"Ġ miner\",\n \"Ġm iner\",\n \"Ġmin er\",\n \"Ġmi ner\",\n \"Ġmine r\",\n \"Re ducers\",\n \"Reduc ers\",\n \"Reducer s\",\n \"Reduce rs\",\n \"Ġ LatLng\",\n \"ĠL atLng\",\n \"ĠLat Lng\",\n \"_ STD\",\n \"_S TD\",\n \"_ST D\",\n \"D igits\",\n \"Digit s\",\n \"Dig its\",\n \"C alc\",\n \"Cal c\",\n \"Ca lc\",\n \"- upload\",\n \"-up load\",\n \"Ġhand ic\",\n \"Ġhan dic\",\n \"ี à¹Ī\",\n \"eg rated\",\n \"egr ated\",\n \"egrate d\",\n \"egra ted\",\n \"Ġ STM\",\n \"ĠS TM\",\n \"ĠST M\",\n \"C lients\",\n \"Client s\",\n \"Cl ients\",\n \"Cli ents\",\n \"ĠTur bo\",\n \"S YNC\",\n \"SY NC\",\n \"Ġphot ographers\",\n \"Ġphotograph ers\",\n \"Ġphotographer s\",\n \". Out\",\n \".O ut\",\n \". character\",\n \".char acter\",\n \"B UILD\",\n \"BU ILD\",\n \". unlock\",\n \".un lock\",\n \"Ġar ises\",\n \"Ġarise s\",\n \"Ġ Commands\",\n \"ĠComm ands\",\n \"ĠCommand s\",\n \"(\\\" \\\");čĊ\",\n \"(\\\"\\\" );čĊ\",\n \"(\\\"\\\") ;čĊ\",\n \"(\\\"\\\"); čĊ\",\n \"_ FORE\",\n \"_F ORE\",\n \"_FOR E\",\n \"; ',\",\n \";' ,\",\n \"+ \\\"'\",\n \"+\\\" '\",\n \". Images\",\n \".Image s\",\n \".Im ages\",\n \"\\\" ){\",\n \"\\\") {\",\n \"ĠM eyer\",\n \"ĠMe yer\",\n \"ĠMey er\",\n \"Ġneg atively\",\n \"Ġnegative ly\",\n \"Ġ DLL\",\n \"ĠD LL\",\n \"ĠDL L\",\n \"Ġ exe\",\n \"Ġe xe\",\n \"Ġex e\",\n \"Ġdef iciency\",\n \"Ġwild ly\",\n \"- switch\",\n \"-s witch\",\n \"-sw itch\",\n \"con struction\",\n \"construct ion\",\n \"Ġexception ally\",\n \"Ġexceptional ly\",\n \"ĠL iz\",\n \"ĠLi z\",\n \"/ java\",\n \"/j ava\",\n \"/jav a\",\n \"Ġthe irs\",\n \"Ġtheir s\",\n \"ĠCon temporary\",\n \"ĠCont emporary\",\n \"l is\",\n \"li s\",\n \".fill Rect\",\n \"ĠN FC\",\n \"ĠNF C\",\n \"Ġre he\",\n \"Ġreh e\",\n \"( numbers\",\n \"(num bers\",\n \"(number s\",\n \"Ġr aster\",\n \"Ġra ster\",\n \"Ġras ter\",\n \"Ġrast er\",\n \"Ġfig uring\",\n \"Ġfigur ing\",\n \"Ġshow c\",\n \"Ġsho wc\",\n \"ĠJ ill\",\n \"ĠJi ll\",\n \"Ġar cade\",\n \"Ġarc ade\",\n \"ĠConstruct s\",\n \"m dl\",\n \"md l\",\n \"( '|\",\n \"(' |\",\n \"Ġident ifiers\",\n \"Ġidentifier s\",\n \"Ġ stellar\",\n \"Ġst ellar\",\n \"( Connection\",\n \"Ġ\\\" {{\",\n \"Ġ\\\"{ {\",\n \"y or\",\n \"yo r\",\n \"( mysqli\",\n \"(m ysqli\",\n \"(mysql i\",\n \"Ġd ove\",\n \"Ġdo ve\",\n \"Ġdov e\",\n \"Of Birth\",\n \". disconnect\",\n \".dis connect\",\n \"_ hi\",\n \"_h i\",\n \"Ġzw ischen\",\n \"ĠGr und\",\n \"i ros\",\n \"ir os\",\n \"iro s\",\n \"_ Array\",\n \"_A rray\",\n \". onclick\",\n \".on click\",\n \"an som\",\n \"ans om\",\n \"An swers\",\n \"Answer s\",\n \"Ans wers\",\n \"ĉ remove\",\n \"ĉre move\",\n \"F a\",\n \"Ġh urry\",\n \"Ġhur ry\",\n \"- inf\",\n \"-in f\",\n \"-i nf\",\n \"Ġ getClass\",\n \"Ġget Class\",\n \"ĠgetC lass\",\n \"ĠReg ulation\",\n \"Ġ FLAGS\",\n \"ĠFLAG S\",\n \"m isc\",\n \"mi sc\",\n \"mis c\",\n \"K en\",\n \"Ke n\",\n \"_ heading\",\n \"_head ing\",\n \"_he ading\",\n \"G Hz\",\n \"GH z\",\n \"- entry\",\n \"-en try\",\n \"Ġbi ography\",\n \"S ig\",\n \"Si g\",\n \"- mf\",\n \"-m f\",\n \"W atcher\",\n \"Watch er\",\n \"Wat cher\",\n \"âĢľ A\",\n \"} px\",\n \"Ġsp icy\",\n \"Ġspi cy\",\n \"_ sq\",\n \"_s q\",\n \"L ost\",\n \"Lo st\",\n \"Los t\",\n \"( track\",\n \"(t rack\",\n \"(tr ack\",\n \"а ли\",\n \"ал и\",\n \"Desc ending\",\n \"< bits\",\n \" ((\",\n \">( (\",\n \"s urvey\",\n \"sur vey\",\n \"Ġ íĺ\",\n \"Ġí ĺ\",\n \".. .')Ċ\",\n \"... ')Ċ\",\n \"...' )Ċ\",\n \"Ġ Divider\",\n \"ĠDi vider\",\n \"ĠDiv ider\",\n \"ĠDivide r\",\n \"o sl\",\n \"os l\",\n \"_ CANCEL\",\n \"_C ANCEL\",\n \"_CAN CEL\",\n \"_ prepare\",\n \"_pre pare\",\n \"_prep are\",\n \"s tin\",\n \"st in\",\n \"sti n\",\n \"ĠHe ath\",\n \"ĠHeat h\",\n \". PrimaryKey\",\n \".Primary Key\",\n \"Ġ âĨIJ\",\n \"ĠâĨ IJ\",\n \"ĠLocal DateTime\",\n \"ĠLocalDate Time\",\n \"Ġco operative\",\n \"Ġcooper ative\",\n \"L earning\",\n \"Le arning\",\n \"Learn ing\",\n \". enqueue\",\n \".en queue\",\n \"Ġ goog\",\n \"Ġg oog\",\n \"Ġgo og\",\n \"Ġgoo g\",\n \"Ġ Regression\",\n \"ĠRe gression\",\n \"ĠReg ression\",\n \"i mates\",\n \"im ates\",\n \"imate s\",\n \"ima tes\",\n \"imat es\",\n \"Ġvoy eur\",\n \"Ġ Drink\",\n \"ĠD rink\",\n \"ĠDr ink\",\n \"p lug\",\n \"pl ug\",\n \"Ġl ender\",\n \"Ġle nder\",\n \"Ġlen der\",\n \"Ġlend er\",\n \"m ana\",\n \"man a\",\n \"ma na\",\n \"Ġperson nes\",\n \"Ġpersonne s\",\n \"Ġpersonn es\",\n \"yp se\",\n \"yps e\",\n \"Ġ unlink\",\n \"Ġun link\",\n \"Ġunl ink\",\n \"ĠRa vens\",\n \"ĠRav ens\",\n \"ĠRaven s\",\n \"Ġh urd\",\n \"Ġhur d\",\n \"Ġhu rd\",\n \"Ġperiod ically\",\n \"Ġperiodic ally\",\n \"AR GS\",\n \"ARG S\",\n \"Ġ GH\",\n \"ĠG H\",\n \"char acters\",\n \"character s\",\n \".. .\\\"ĊĊ\",\n \"... \\\"ĊĊ\",\n \"...\\\" ĊĊ\",\n \"...\\\"Ċ Ċ\",\n \"- establish\",\n \"Ġ dn\",\n \"Ġd n\",\n \"( condition\",\n \"(con dition\",\n \"(cond ition\",\n \"Ġ Gravity\",\n \"ĠGr avity\",\n \"Ġes tas\",\n \"Ġest as\",\n \"Ġesta s\",\n \"_ focus\",\n \"_f ocus\",\n \"C reature\",\n \"Cre ature\",\n \"Cr eature\",\n \"Creat ure\",\n \"( site\",\n \"(s ite\",\n \"(si te\",\n \"Ġc arr\",\n \"Ġcar r\",\n \"Ġca rr\",\n \"Ġ RL\",\n \"ĠR L\",\n \"Ġ RI\",\n \"ĠR I\",\n \"ĠM oto\",\n \"ĠMo to\",\n \"ĠMot o\",\n \"A SF\",\n \"AS F\",\n \"Ġ Luckily\",\n \"ĠLuck ily\",\n \"ĉ Route\",\n \"ĉR oute\",\n \"Ġ entropy\",\n \"Ġent ropy\",\n \"Ġentr opy\",\n \"( \\\",\\\"\",\n \"(\\\" ,\\\"\",\n \"(\\\", \\\"\",\n \"C ollect\",\n \"Col lect\",\n \"Coll ect\",\n \"( contact\",\n \"(cont act\",\n \"ĠFlor ence\",\n \"ĠFlo rence\",\n \"Ġpremium s\",\n \"Ġpremi ums\",\n \"Ġl ifecycle\",\n \"Ġlife cycle\",\n \"Ġlif ecycle\",\n \"Ġb ans\",\n \"Ġban s\",\n \"Ġba ns\",\n \"x ef\",\n \"xe f\",\n \"Web Kit\",\n \"Ġ Floating\",\n \"ĠF loating\",\n \"ĠFloat ing\",\n \"ĠFlo ating\",\n \"Ġ cosa\",\n \"Ġc osa\",\n \"Ġco sa\",\n \"Ġcos a\",\n \"S pecific\",\n \"Spec ific\",\n \"ĠLo ans\",\n \"ĠLoan s\",\n \"b read\",\n \"br ead\",\n \"bre ad\",\n \"Ġdes criptors\",\n \"Ġdescriptor s\",\n \"Ġ{ :.\",\n \"Ġ{: .\",\n \"TH READ\",\n \"ĠT rent\",\n \"ĠTr ent\",\n \"ĠTre nt\",\n \"Ġs cop\",\n \"Ġsc op\",\n \"Ġsco p\",\n \"Q A\",\n \"ĠAn tar\",\n \"ĠAnt ar\",\n \"p el\",\n \"pe l\",\n \"_ difference\",\n \"_d ifference\",\n \"_diff erence\",\n \"_ changes\",\n \"_ch anges\",\n \"_change s\",\n \"_chan ges\",\n \"( ...)\",\n \"(... )\",\n \"(.. .)\",\n \"Ġ Rotation\",\n \"ĠR otation\",\n \"ĠRot ation\",\n \"ĠL GPL\",\n \"ĠLG PL\",\n \"Ġ JUST\",\n \"ĠJ UST\",\n \"( Task\",\n \"(T ask\",\n \"_ subset\",\n \"_sub set\",\n \"_subs et\",\n \"Ġ TRANS\",\n \"ĠTR ANS\",\n \"ĠTRAN S\",\n \"åĬ Ľ\",\n \"ĠS cout\",\n \"ĠSc out\",\n \"ĠSco ut\",\n \"- popup\",\n \"-p opup\",\n \"-pop up\",\n \"Ġsm oked\",\n \"Ġsmoke d\",\n \"Ġsmo ked\",\n \"_ Class\",\n \"_C lass\",\n \"_Cl ass\",\n \"Ġturn over\",\n \"Ġturno ver\",\n \"br akk\",\n \"bra kk\",\n \"ĠRock y\",\n \"ĠRo cky\",\n \"ĠRoc ky\",\n \"t as\",\n \"ta s\",\n \".Regular Expressions\",\n \"ĠElli ott\",\n \"ĠElliot t\",\n \"Ġ Spinner\",\n \"ĠSp inner\",\n \"ĠSpin ner\",\n \"DUCT ION\",\n \"DU CTION\",\n \"Ġl ibre\",\n \"Ġli bre\",\n \"Ġlib re\",\n \"Ġlibr e\",\n \"Ġmol to\",\n \"Ġmolt o\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\",\n \"Ġ FTP\",\n \"ĠF TP\",\n \"ĠFT P\",\n \"m peg\",\n \"mp eg\",\n \"( features\",\n \"(f eatures\",\n \"(feature s\",\n \"(fe atures\",\n \"Ġb ald\",\n \"Ġbal d\",\n \"Ġba ld\",\n \"ĠV id\",\n \"ĠVi d\",\n \"Ġsh outing\",\n \"Ġshout ing\",\n \"Ġsho uting\",\n \"L int\",\n \"Li nt\",\n \"Lin t\",\n \"Ġ sockets\",\n \"Ġs ockets\",\n \"Ġsocket s\",\n \"Ġsock ets\",\n \"Ġp row\",\n \"Ġpro w\",\n \"Ġpr ow\",\n \"Ġnou velle\",\n \"Ġnouvel le\",\n \"Ġnouve lle\",\n \"is card\",\n \"isc ard\",\n \"ĠS ponsor\",\n \"Ġ consulta\",\n \"Ġcons ulta\",\n \"Ġconsult a\",\n \"Ġconsul ta\",\n \") ));\",\n \")) );\",\n \"))) ;\",\n \"Ind ian\",\n \"India n\",\n \"ĠR aspberry\",\n \"Ġteam mate\",\n \"Ġ JWT\",\n \"ĠJ WT\",\n \"ĠJW T\",\n \"ĠG hana\",\n \"ĠGh ana\",\n \"Ġ cakes\",\n \"Ġc akes\",\n \"Ġca kes\",\n \"Ġcake s\",\n \"pr imer\",\n \"prim er\",\n \"prime r\",\n \"pri mer\",\n \"form a\",\n \"for ma\",\n \"erg arten\",\n \"_ Manager\",\n \"_M anager\",\n \"_Man ager\",\n \"Ġpre season\",\n \"G AME\",\n \"GA ME\",\n \"| \\\"\",\n \"ĠB rock\",\n \"ĠBr ock\",\n \"ĠBro ck\",\n \"Ġocc upy\",\n \"Ġoccup y\",\n \"Ġdecor ations\",\n \"Ġdecoration s\",\n \"á nd\",\n \"án d\",\n \"Ġ cot\",\n \"Ġc ot\",\n \"Ġco t\",\n \"Ġp aran\",\n \"Ġpar an\",\n \"Ġpara n\",\n \"Ġpa ran\",\n \"D isk\",\n \"Dis k\",\n \"Di sk\",\n \"r emain\",\n \"re main\",\n \"rem ain\",\n \"rema in\",\n \"> ?\",\n \"St rong\",\n \"Str ong\",\n \"Ġf rance\",\n \"Ġfr ance\",\n \"Ġfra nce\",\n \"Ġfran ce\",\n \"Ġfranc e\",\n \"ĠE ra\",\n \"ĠEr a\",\n \"- cr\",\n \"-c r\",\n \".Buffer edReader\",\n \".Buffered Reader\",\n \"ĠParad ise\",\n \"ĠV AT\",\n \"ĠVA T\",\n \"ĠAn ders\",\n \"ĠAnd ers\",\n \"Ġl imb\",\n \"Ġli mb\",\n \"Ġlim b\",\n \"amp oo\",\n \"ampo o\",\n \"Ġimper ative\",\n \"UT ILITY\",\n \"UTIL ITY\",\n \"Ġ Recognition\",\n \"ĠRec ognition\",\n \"ĠRecogn ition\",\n \"Ġragaz ze\",\n \"Ġp ops\",\n \"Ġpop s\",\n \"Ġpo ps\",\n \"y press\",\n \"yp ress\",\n \"Ġemb argo\",\n \"Ġembar go\",\n \"// {Ċ\",\n \"Ġs yll\",\n \"Ġsy ll\",\n \"P TR\",\n \"PT R\",\n \"åŃĺ åľ¨\",\n \"Ġdid nt\",\n \"Ġdidn t\",\n \"M ailer\",\n \"Mail er\",\n \"Ma iler\",\n \"Ġacad emics\",\n \"Ġacademic s\",\n \"ĠFr auen\",\n \"ĠFra uen\",\n \"ĠFrau en\",\n \"ne ider\",\n \"- rel\",\n \"-r el\",\n \"-re l\",\n \"Ġrain bow\",\n \"( In\",\n \"(I n\",\n \"Ġs liced\",\n \"Ġsl iced\",\n \"Ġslice d\",\n \"Ġslic ed\",\n \"= ============Ċ\",\n \"== ===========Ċ\",\n \"==== =========Ċ\",\n \"======== =====Ċ\",\n \"=== ==========Ċ\",\n \"============ =Ċ\",\n \"============= Ċ\",\n \"=========== ==Ċ\",\n \"========= ====Ċ\",\n \"========== ===Ċ\",\n \"====== =======Ċ\",\n \"===== ========Ċ\",\n \"======= ======Ċ\",\n \"( send\",\n \"(s end\",\n \"(se nd\",\n \"NSMutable Dictionary\",\n \"v os\",\n \"vo s\",\n \"( package\",\n \"(p ackage\",\n \"(pack age\",\n \"Ġord inance\",\n \"Ġordin ance\",\n \"view er\",\n \"vie wer\",\n \"ĠSan tos\",\n \"ĠSant os\",\n \"ĠSanto s\",\n \"- selling\",\n \"-s elling\",\n \"Ġ gov\",\n \"Ġg ov\",\n \"Ġgo v\",\n \"et tle\",\n \"ett le\",\n \"Ġfound ers\",\n \"Ġfo unders\",\n \"Ġfounder s\",\n \"Ġw aking\",\n \"Ġwa king\",\n \"s lashes\",\n \"sl ashes\",\n \"slash es\",\n \"-p ound\",\n \"-po und\",\n \"re cht\",\n \"rec ht\",\n \"rech t\",\n \"ا ت\",\n \"Ø§Ø ª\",\n \". onClick\",\n \".on Click\",\n \"Ġn ord\",\n \"Ġno rd\",\n \"Ġnor d\",\n \"st änd\",\n \"_ when\",\n \"_w hen\",\n \"_wh en\",\n \"U TERS\",\n \"UT ERS\",\n \"UTE RS\",\n \"i cc\",\n \"ic c\",\n \"Ġcaps ule\",\n \"ĠW id\",\n \"ĠWi d\",\n \"M arc\",\n \"Mar c\",\n \"Ma rc\",\n \"ภ¸\",\n \"r ored\",\n \"ro red\",\n \"ror ed\",\n \"U GE\",\n \"UG E\",\n \"LO UD\",\n \"Ġ Audit\",\n \"ĠA udit\",\n \"ĠAud it\",\n \"ĠAu dit\",\n \"ĠAudi t\",\n \"ip ients\",\n \"ipient s\",\n \"ipi ents\",\n \"op ian\",\n \"opia n\",\n \"opi an\",\n \"ĠS ue\",\n \"ĠSu e\",\n \"Ġwur den\",\n \"Ġwurde n\",\n \". Helpers\",\n \".H elpers\",\n \".Helper s\",\n \".Help ers\",\n \"Ġf actions\",\n \"Ġfact ions\",\n \"Ġfa ctions\",\n \"Ġfaction s\",\n \"[ np\",\n \"[n p\",\n \"- than\",\n \"-t han\",\n \"-th an\",\n \"Ġre co\",\n \"Ġr eco\",\n \"Ġrec o\",\n \"Ġ kas\",\n \"Ġk as\",\n \"Ġka s\",\n \"Ġ cmds\",\n \"Ġcmd s\",\n \"Ġcm ds\",\n \"/ network\",\n \"/n etwork\",\n \"/net work\",\n \"x bf\",\n \"xb f\",\n \"get Color\",\n \"getC olor\",\n \"Ġ biased\",\n \"Ġbi ased\",\n \"Ġbias ed\",\n \"ĠL ak\",\n \"ĠLa k\",\n \"D atas\",\n \"Data s\",\n \"Da tas\",\n \"Dat as\",\n \"v ents\",\n \"ve nts\",\n \"vent s\",\n \"ven ts\",\n \"Ġ ë²\",\n \"Ġë ²\",\n \"_ PS\",\n \"_P S\",\n \". Validate\",\n \".Valid ate\",\n \"Inv oker\",\n \"Invoke r\",\n \"Ġne uen\",\n \"Ġneu en\",\n \"Ġneue n\",\n \"Ġju venile\",\n \"Ġjuven ile\",\n \"V ISION\",\n \"VI SION\",\n \"VIS ION\",\n \"Ġde vote\",\n \"Ġdev ote\",\n \"Ġ linha\",\n \"Ġl inha\",\n \"Ġlin ha\",\n \"Ġlinh a\",\n \"Ġdiscount ed\",\n \"Ġdisco unted\",\n \"\\\\ Config\",\n \"Ġworth while\",\n \"Ġskin ny\",\n \"Ġ Courses\",\n \"ĠC ourses\",\n \"ĠCo urses\",\n \"ĠCour ses\",\n \"ĠCourse s\",\n \"le ys\",\n \"ley s\",\n \"ĠMort gage\",\n \"K evin\",\n \"Ke vin\",\n \"Ġann ounces\",\n \"Ġannounc es\",\n \"Ġannounce s\",\n \"] )*\",\n \"]) *\",\n \"res ervation\",\n \"Ġ æķ°\",\n \"Ġæķ °\",\n \"Ġprejud ice\",\n \"ĠString Comparison\",\n \"Ġbe ard\",\n \"Ġbear d\",\n \"- win\",\n \"-w in\",\n \"ĠS ão\",\n \"ĉ ms\",\n \"ĉm s\",\n \"j al\",\n \"ja l\",\n \"Ġ Earn\",\n \"ĠE arn\",\n \"ĠEar n\",\n \"ĠEa rn\",\n \"_ ports\",\n \"_p orts\",\n \"_port s\",\n \"_po rts\",\n \"_por ts\",\n \"Ġ Nombre\",\n \"ĠN ombre\",\n \"ĠNom bre\",\n \"_ COR\",\n \"_C OR\",\n \"_CO R\",\n \"Ġ BUILD\",\n \"ĠB UILD\",\n \"ĠBU ILD\",\n \". sound\",\n \".s ound\",\n \".so und\",\n \"Y ellow\",\n \"Ġlineback er\",\n \"Ġchar itable\",\n \"Ġcha ritable\",\n \"j ug\",\n \"ju g\",\n \"_NON NULL\",\n \"ĠD ental\",\n \"ĠDen tal\",\n \"ĠDent al\",\n \"\\\" >${\",\n \"\\\"> ${\",\n \"\\\">$ {\",\n \"ĉ match\",\n \"ĉm atch\",\n \"ĉmat ch\",\n \"R ussian\",\n \"Russia n\",\n \"Russ ian\",\n \"Rus sian\",\n \"Ġver sch\",\n \"Ġvers ch\",\n \"Ġp inned\",\n \"Ġpin ned\",\n \"Ġadopt ing\",\n \"Options Menu\",\n \"P ag\",\n \"Pa g\",\n \"Ġpair ing\",\n \"Ġpa iring\",\n \"Ġpai ring\",\n \"Ġt read\",\n \"Ġtr ead\",\n \"Ġtre ad\",\n \"erc ises\",\n \"ercise s\",\n \"Ġ Spread\",\n \"ĠS pread\",\n \"ĠSp read\",\n \"ĠSpr ead\",\n \") i\",\n \"Ġ BAD\",\n \"ĠB AD\",\n \"ĠBA D\",\n \"_ tf\",\n \"_t f\",\n \"UI ImageView\",\n \"UIImage View\",\n \"pop ulate\",\n \"b ab\",\n \"ba b\",\n \"Ġ Ïĥ\",\n \"ĠÏ ĥ\",\n \"[ ++\",\n \"Ġopi oid\",\n \"Ġ ##Ċ\",\n \"Ġ# #Ċ\",\n \"Ġ## Ċ\",\n \"d type\",\n \"dt ype\",\n \"ĠSt arts\",\n \"ĠStart s\",\n \"ĠStar ts\",\n \"ĠSta rts\",\n \"(' /')\",\n \"('/ ')\",\n \"Ġperson als\",\n \"Ġpersonal s\",\n \"Ġpersona ls\",\n \"- market\",\n \"-m arket\",\n \"-mark et\",\n \"-mar ket\",\n \"Ġredund ant\",\n \"ĠEss ential\",\n \"Ġsc rapy\",\n \"Ġscr apy\",\n \"Ġscrap y\",\n \"Ġ им\",\n \"Ġи м\",\n \"a cl\",\n \"ac l\",\n \"Ġ crear\",\n \"Ġc rear\",\n \"Ġcr ear\",\n \"Ġcre ar\",\n \"Ġcrea r\",\n \"ĠB end\",\n \"ĠBe nd\",\n \"ĠBen d\",\n \"Ġrel ieve\",\n \"Ġreli eve\",\n \"Ġrelie ve\",\n \"- room\",\n \"-r oom\",\n \"-ro om\",\n \"w ife\",\n \"wi fe\",\n \"Ġv Ãł\",\n \"ĠQ Point\",\n \"Ġqu asi\",\n \"Ġqua si\",\n \"Ġ methodName\",\n \"Ġmethod Name\",\n \"\\\\ xc\",\n \"\\\\x c\",\n \"ĠP eru\",\n \"ĠPer u\",\n \"ĠPe ru\",\n \"/ The\",\n \"/T he\",\n \". orm\",\n \".o rm\",\n \".or m\",\n \"Ġ viz\",\n \"Ġv iz\",\n \"Ġvi z\",\n \"/ pdf\",\n \"/p df\",\n \"Loc ated\",\n \"Ġconfront ation\",\n \"ĠChampionship s\",\n \"ĠChampions hips\",\n \"ĠChampion ships\",\n \"Ġhy pert\",\n \"Ġhyp ert\",\n \"Ġhyper t\",\n \"Ġhype rt\",\n \"Ġ dj\",\n \"Ġd j\",\n \"Ġ UserInfo\",\n \"ĠUser Info\",\n \"Ġ åĪĽå»º\",\n \"ĠåĪ Ľå»º\",\n \"\\\\ xb\",\n \"\\\\x b\",\n \"( sim\",\n \"(s im\",\n \"(si m\",\n \"Ġ ==Ċ\",\n \"Ġ= =Ċ\",\n \"Ġ== Ċ\",\n \"Ġst aging\",\n \"Ġsta ging\",\n \"Ġstag ing\",\n \"Ġdr astically\",\n \"Ġdrastic ally\",\n \"åŃ ¦\",\n \"l ords\",\n \"lor ds\",\n \"lord s\",\n \". less\",\n \".l ess\",\n \".le ss\",\n \"вед иÑĤе\",\n \"Ġ Bucket\",\n \"ĠB ucket\",\n \"ĠBuck et\",\n \"ĠBu cket\",\n \"ĠM am\",\n \"ĠMa m\",\n \". term\",\n \".t erm\",\n \".te rm\",\n \"_ pi\",\n \"_p i\",\n \"c zy\",\n \"cz y\",\n \". pub\",\n \".p ub\",\n \"p recio\",\n \"pre cio\",\n \"prec io\",\n \"preci o\",\n \"ĠV irt\",\n \"ĠVir t\",\n \"ĠVi rt\",\n \"Ġ roman\",\n \"Ġr oman\",\n \"Ġro man\",\n \"Ġrom an\",\n \"Ġroma n\",\n \"i tat\",\n \"it at\",\n \"ita t\",\n \"L ex\",\n \"Le x\",\n \"_ infos\",\n \"_in fos\",\n \"_info s\",\n \"_inf os\",\n \"Ä °\",\n \". other\",\n \".o ther\",\n \".ot her\",\n \"VE LO\",\n \"VEL O\",\n \"Ġ ponder\",\n \"Ġp onder\",\n \"Ġpo nder\",\n \"Ġpon der\",\n \"Ġpond er\",\n \"Ġh anno\",\n \"Ġhan no\",\n \"Ġhann o\",\n \"( Page\",\n \"(P age\",\n \"d oi\",\n \"do i\",\n \"Ġpol ite\",\n \"Ġpo lite\",\n \"Ġpolit e\",\n \"Ġprogram mer\",\n \"Ġprogramme r\",\n \"Ġprogramm er\",\n \"D ies\",\n \"Die s\",\n \"Di es\",\n \"$ d\",\n \"Ġre plication\",\n \"Ġrep lication\",\n \"Ġrepl ication\",\n \"Ġreplic ation\",\n \"Ġreplica tion\",\n \"add Column\",\n \"fr ican\",\n \"frica n\",\n \"Ġl eng\",\n \"Ġle ng\",\n \"Ġlen g\",\n \"b eer\",\n \"be er\",\n \"bee r\",\n \"o it\",\n \"oi t\",\n \"Ġw asting\",\n \"Ġwas ting\",\n \"Ġwast ing\",\n \"y lim\",\n \"yl im\",\n \"me asure\",\n \"N eg\",\n \"Ne g\",\n \"Ġpart ie\",\n \"Ġpar tie\",\n \"Ġparti e\",\n \". console\",\n \".con sole\",\n \".cons ole\",\n \"ĠGu inea\",\n \"ĠGui nea\",\n \"T EL\",\n \"TE L\",\n \"_ fact\",\n \"_f act\",\n \"_fac t\",\n \"_fa ct\",\n \". chunk\",\n \".ch unk\",\n \"Ġl ent\",\n \"Ġle nt\",\n \"Ġlen t\",\n \"Ġ aller\",\n \"Ġa ller\",\n \"Ġal ler\",\n \"Ġall er\",\n \"Ġalle r\",\n \"Ġ à¤ķ\",\n \"Ġठķ\",\n \"_ idle\",\n \"_id le\",\n \"_i dle\",\n \"Ġad missions\",\n \"Ġadm issions\",\n \"Ġadmission s\",\n \"JSON Array\",\n \"Ġv ibration\",\n \"Ġvibr ation\",\n \"Ġvib ration\",\n \". helpers\",\n \".h elpers\",\n \".helper s\",\n \".help ers\",\n \"å¤ ĸ\",\n \"Ġ hen\",\n \"Ġh en\",\n \"Ġhe n\",\n \"j ohn\",\n \"jo hn\",\n \"Ġ ìĥĿ\",\n \"Ġì ĥĿ\",\n \"Ġìĥ Ŀ\",\n \"Ġjud gement\",\n \"Ġjudge ment\",\n \"Ġg een\",\n \"Ġge en\",\n \"Ġgee n\",\n \"t erra\",\n \"ter ra\",\n \"terr a\",\n \"^ {\",\n \"ĠI z\",\n \"Ġc â\",\n \"in stances\",\n \"instance s\",\n \"inst ances\",\n \"instanc es\",\n \"Ġthreat ens\",\n \"Ġthreaten s\",\n \"Ġm üssen\",\n \"Kind OfClass\",\n \"Ġstoryt elling\",\n \"_ demo\",\n \"_d emo\",\n \"_de mo\",\n \"_dem o\",\n \"r ias\",\n \"ri as\",\n \"ria s\",\n \"Priv acy\",\n \"h 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Nil\",\n \".N il\",\n \"Ġa isle\",\n \"Ġais le\",\n \"Ġ Growing\",\n \"ĠG rowing\",\n \"ĠGr owing\",\n \"ĠGro wing\",\n \"ĠGrow ing\",\n \"Ġblog info\",\n \"/ sql\",\n \"/s ql\",\n \"_ ioctl\",\n \"_io ctl\",\n \"S caling\",\n \"Sc aling\",\n \"Ġ Monad\",\n \"ĠMon ad\",\n \"ĠMo nad\",\n \"ĠMona d\",\n \"_ cpp\",\n \"_c pp\",\n \"_cp p\",\n \"ĠH utch\",\n \"ĠHut ch\",\n \"ĠApple WebKit\",\n \"Exp ense\",\n \"_ JOB\",\n \"_J OB\",\n \"_JO B\",\n \"Ġpoint less\",\n \"From Body\",\n \"an tal\",\n \"ant al\",\n \"anta l\",\n \"Ġdepict ing\",\n \"Ġ CELL\",\n \"ĠC ELL\",\n \"ĠCE LL\",\n \"ĠCEL L\",\n \"Ġre fin\",\n \"Ġref in\",\n \"ĠC NC\",\n \"ĠCN C\",\n \"ì¹ ĺ\",\n \"_ dimensions\",\n \"_dim ensions\",\n \"_dimension s\",\n \"Ġ SAN\",\n \"ĠS AN\",\n \"ĠSA N\",\n \"Ġ aft\",\n \"Ġa ft\",\n \"Ġaf t\",\n \"Ġfoot steps\",\n \"c coli\",\n \"cc oli\",\n \"cco li\",\n \"_ PHONE\",\n \"_P HONE\",\n \"_PH ONE\",\n \"/ math\",\n \"/m ath\",\n \"/mat h\",\n \"- kind\",\n \"-k ind\",\n \"Ġ Means\",\n \"ĠMe ans\",\n \"ĠMean s\",\n \"ich ael\",\n \"icha el\",\n \". guna\",\n \".g una\",\n \"Ġinaug uration\",\n \"Ġinaugur ation\",\n \"-dr iving\",\n \"( delete\",\n \"(de lete\",\n \"(del ete\",\n \"Ġ totalCount\",\n \"Ġtotal Count\",\n \"_ MC\",\n \"_M C\",\n \". 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Unknown\",\n \".Un known\",\n \"C BC\",\n \"CB C\",\n \"ĠCarl son\",\n \". URI\",\n \".U RI\",\n \".UR I\",\n \"Ġp light\",\n \"Ġpl ight\",\n \"/ start\",\n \"/st art\",\n \"/star t\",\n \"ĠPerson nel\",\n \"Ġ PREFIX\",\n \"ĠP REFIX\",\n \"ĠPRE FIX\",\n \"ĠPREF IX\",\n \", **\",\n \",* *\",\n \"Ġli mite\",\n \"Ġlimit e\",\n \"Ġlim ite\",\n \"_ heat\",\n \"_h eat\",\n \"_he at\",\n \"% ï¼Į\",\n \"ĠD onne\",\n \"ĠDon ne\",\n \"get Node\",\n \"ĠScient ology\",\n \"Ġc omet\",\n \"Ġcom et\",\n \"Ġco met\",\n \"Ġcome t\",\n \"Ġwen ig\",\n \"A side\",\n \"As ide\",\n \"ĠM PEG\",\n \"ĠMP EG\",\n \"' ?\",\n \"vari ably\",\n \". endDate\",\n \".end Date\",\n \"Ġun cont\",\n \"Ġunc ont\",\n \"Ġuncon t\",\n \"Ġ Scores\",\n \"ĠS cores\",\n \"ĠSc ores\",\n \"ĠScore s\",\n \"ĠSco res\",\n \"ĠScor es\",\n \"Ġ LoginForm\",\n \"ĠLogin Form\",\n \". generated\",\n \".g enerated\",\n \".generate d\",\n \".gener ated\",\n \", ch\",\n \",c h\",\n \"- mar\",\n \"-m ar\",\n \"ĠN ed\",\n \"ĠNe d\",\n \"Ġ eventId\",\n \"Ġevent Id\",\n \"+ p\",\n \"ĠS IN\",\n \"ĠSI N\",\n \"/ reset\",\n \"/re set\",\n \"/res et\",\n \".RE ACT\",\n \"ĠMe ssi\",\n \"ĠMess i\",\n \"ĠMes si\",\n \"_R ANK\",\n \"_RA NK\",\n \".write File\",\n \"Ġc ripp\",\n \"Ġcr ipp\",\n \"Ġcri pp\",\n \"es thetic\",\n \"est hetic\",\n \"ERS IST\",\n \"Ġreim bursement\",\n \"Ġreimburse ment\",\n \"Current Value\",\n \"Ġu nin\",\n \"Ġun in\",\n \"Ġuni n\",\n \"Down Latch\",\n \"Ġpadding Right\",\n \"Ġst ocked\",\n \"Ġstock ed\",\n \"/ '.\",\n \"/' .\",\n \"Ġre payment\",\n \"Ġrep ayment\",\n \"Ġrepay ment\",\n \"t rak\",\n \"tr ak\",\n \"tra k\",\n \"/ backend\",\n \"/back end\",\n \"Ġиз мен\",\n \"C SR\",\n \"CS R\",\n \"Ġprevent ive\",\n \"Ġpant alla\",\n \"_ trim\",\n \"_t rim\",\n \"_tr im\",\n \"_tri m\",\n \"P edido\",\n \"Ped ido\",\n \"h ospital\",\n \"Ġmanage able\",\n \"route Params\",\n \"text ures\",\n \"texture s\",\n \"tex tures\",\n \". .....ĊĊ\",\n \".. ....ĊĊ\",\n \"... ...ĊĊ\",\n \".... ..ĊĊ\",\n \"..... .ĊĊ\",\n \"...... 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\"u nik\",\n \"un ik\",\n \"uni k\",\n \"N EXT\",\n \"NE XT\",\n \"A lle\",\n \"Al le\",\n \"All e\",\n \"Ġn xt\",\n \"Ġnx t\",\n \"M art\",\n \"Mar t\",\n \"Ma rt\",\n \"Ġ atl\",\n \"Ġa tl\",\n \"Ġat l\",\n \"Ġperiod o\",\n \"Ġperi odo\",\n \"_ you\",\n \"_y ou\",\n \"Ġ} )).\",\n \"Ġ}) ).\",\n \"Ġ})) .\",\n \"int estinal\",\n \".Adapter View\",\n \"Ġhes itant\",\n \"Ġcompar atively\",\n \"Ġcomparative ly\",\n \". 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PLAIN\",\n \".PL AIN\",\n \"Se attle\",\n \"Seat tle\",\n \". ordinal\",\n \"Ġ PIPE\",\n \"ĠPI PE\",\n \"Ġret orna\",\n \"Ġretorn a\",\n \"Ġj ugador\",\n \"Ġjug ador\",\n \"ĠB ret\",\n \"ĠBr et\",\n \"ĠBre t\",\n \"ĠâĶ ľ\",\n \"Ġp lush\",\n \"Ġpl ush\",\n \"Ġplus h\",\n \"Ġplu sh\",\n \"UL ATOR\",\n \"ULA TOR\",\n \"S orting\",\n \"Sort ing\",\n \".grid y\",\n \".gr idy\",\n \"ect omy\",\n \"_ activ\",\n \"_ac tiv\",\n \"_act iv\",\n \"r ack\",\n \"ra ck\",\n \"rac k\",\n \"Inter active\",\n \"ĠAntar ctica\",\n \"ĠAntarctic a\",\n \"Ġv engeance\",\n \"en so\",\n \"ens o\",\n \"_ known\",\n \"_k nown\",\n \"up plier\",\n \"upp lier\",\n \". 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Delay\",\n \".D elay\",\n \".De lay\",\n \"Ġk end\",\n \"Ġke nd\",\n \"Ġken d\",\n \"Ġfacilit ated\",\n \"Ġfacilitate d\",\n \"Ġfacil itated\",\n \". flatMap\",\n \".flat Map\",\n \"Ġs anta\",\n \"Ġsan ta\",\n \"Ġsant a\",\n \"ĉ Send\",\n \"ĉS end\",\n \"/ messages\",\n \"/m essages\",\n \"/message s\",\n \"Ġof Type\",\n \"ĉ swap\",\n \"ĉs wap\",\n \"ĉsw ap\",\n \"# plt\",\n \"ĠTur ks\",\n \"ĠTurk s\",\n \"N ES\",\n \"NE S\",\n \"Ġprogress ively\",\n \"Ġprogressive ly\",\n \"ĠRes idence\",\n \"Ġ TREE\",\n \"ĠT REE\",\n \"ĠTR EE\",\n \"ĠTRE E\",\n \"Ġn oen\",\n \"Ġno en\",\n \"Ġnoe n\",\n \"d io\",\n \"di o\",\n \"Ġ nelle\",\n \"Ġn elle\",\n \"Ġne lle\",\n \"Ġnel le\",\n \"Ġnell e\",\n \"Ġso gar\",\n \"Ġsog ar\",\n \"i tti\",\n \"it ti\",\n \"itt i\",\n \"week ly\",\n \"Ġambigu ity\",\n \"_ Settings\",\n \"_S ettings\",\n \"_Set tings\",\n \"W are\",\n \"War e\",\n \"Wa re\",\n \". neo\",\n \".n eo\",\n \".ne o\",\n \"_ DST\",\n \"_D ST\",\n \"_DS T\",\n \"Ġ æĸ¹\",\n \"Ġæĸ ¹\",\n \"p rep\",\n \"pr ep\",\n \"pre p\",\n \"l obby\",\n \"lob by\",\n \"@ email\",\n \"/ movie\",\n \"/m ovie\",\n \"Ġfun kc\",\n \"Ġfunk c\",\n \"Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ\",\n \"ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ\",\n \"ÂŃ s\",\n \"Ġguard ians\",\n \"Ġguardian s\",\n \"- pos\",\n \"-p os\",\n \"-po s\",\n \"Ġconfig uring\",\n \"Ġconfigur ing\",\n \"ĠC PS\",\n \"ĠCP S\",\n \"ĠD eus\",\n \"ĠDe us\",\n \"Ġvidé os\",\n \"Ġvidéo s\",\n \"_ empresa\",\n \"_emp resa\",\n \"Ġsl apped\",\n \"Ġsla pped\",\n \"Ġslap ped\",\n \"< Model\",\n \"',Ċ\",\n \"\\\"> ',Ċ\",\n \"\\\">' ,Ċ\",\n \"\\\">', Ċ\",\n \"_X DECREF\",\n \"ĠBuzz Feed\",\n \"_M ARGIN\",\n \"P LOY\",\n \". small\",\n \".s mall\",\n \".sm all\",\n \"Ġm imeType\",\n \"Ġmime Type\",\n \"Ġh olog\",\n \"Ġho log\",\n \"Ġhol og\",\n \"ĉ camera\",\n \"ĉc amera\",\n \"l ias\",\n \"li as\",\n \"lia s\",\n \"Ġsusp ense\",\n \"od ynam\",\n \"ody nam\",\n \"odyn am\",\n \"b au\",\n \"ba u\",\n \"Ġgrave yard\",\n \"_ named\",\n \"_n amed\",\n \"_name d\",\n \"_na med\",\n \"\\\": \\\"'\",\n \"\\\":\\\" '\",\n \"Ġ ************************************************\",\n \"Ġ******************************** ****************\",\n \"Ġ******** ****************************************\",\n \"Ġ**************** ********************************\",\n \"Ġ************************ ************************\",\n \"Ġ**************************************** ********\",\n \"Ġgame Over\",\n \"Ġ LENGTH\",\n \"ĠLE NGTH\",\n \"ĠLENG TH\",\n \"ĉ screen\",\n \"ĉs creen\",\n \"ĉsc reen\",\n \"Ġdo InBackground\",\n \"_ dependencies\",\n \"_depend encies\",\n \"_dep endencies\",\n \"Ġ rtc\",\n \"Ġr tc\",\n \"Ġrt c\",\n \"/ up\",\n \"/u p\",\n \"_ ROM\",\n \"_R OM\",\n \"_RO M\",\n \"H all\",\n \"Ha ll\",\n \"Hal l\",\n \"Ġdef iciencies\",\n \"( te\",\n \"(t e\",\n \"' #\",\n \"_ equiv\",\n \"_e quiv\",\n \"_equ iv\",\n \"Ġpre order\",\n \"ĠA xe\",\n \"ĠAx e\",\n \"о мÑĥ\",\n \"ом Ñĥ\",\n \".send File\",\n \"Ġ filt\",\n \"Ġf ilt\",\n \"Ġfil t\",\n \"Ġfi lt\",\n \"Ġ Limits\",\n \"ĠL imits\",\n \"ĠLim its\",\n \"ĠLi mits\",\n \"ĠLimit s\",\n \"ĠCaval iers\",\n \". discount\",\n \".dis count\",\n \".disc ount\",\n \"âĨ IJ\",\n \"ĠW it\",\n \"ĠWi t\",\n \"QRST UV\",\n \"Ġ ij\",\n \"Ġi j\",\n \"Ġt egen\",\n \"Ġte gen\",\n \"Ġteg en\",\n \"Ġ :\\\",\",\n \"Ġ: \\\",\",\n \"Ġ:\\\" ,\",\n \"diff iculty\",\n \"p unkt\",\n \"pun kt\",\n \"punk t\",\n \"ĠEm ails\",\n \"ĠEmail s\",\n \"ch lor\",\n \"chl or\",\n \"( fun\",\n \"(f un\",\n \". 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JWT\",\n \".J WT\",\n \"Ġaston ished\",\n \"B ush\",\n \"Bus h\",\n \"Bu sh\",\n \"( itemId\",\n \"(item Id\",\n \"Ġdet achment\",\n \"Ġdetach ment\",\n \"ĠEn core\",\n \"ĠEnc ore\",\n \"å° Ķ\",\n \"Ġre kl\",\n \"Ġr ekl\",\n \"Ġrek l\",\n \"Ġc ram\",\n \"Ġcr am\",\n \"Ġcra m\",\n \") $/\",\n \")$ /\",\n \".get Host\",\n \"_ recommend\",\n \"_re commend\",\n \"- HT\",\n \"-H T\",\n \"_cal ibration\",\n \"Auth enticate\",\n \".firebase app\",\n \"UN IX\",\n \"ĉ Camera\",\n \"ĉC amera\",\n \"ĠHE AP\",\n \"I deal\",\n \"Ide al\",\n \". office\",\n \".off ice\",\n \"Ġgoof y\",\n \"Ġgoo fy\",\n \"( Symbol\",\n \"(S ymbol\",\n \"Ġjo uer\",\n \"Ġjou er\",\n \"_part itions\",\n \"_partition s\",\n \"Ġrapid ement\",\n \"Ġrapide ment\",\n \"ĠGNU NET\",\n \"ĠGN UNET\",\n \"id User\",\n \"Ġsuper vise\",\n \"Ġsuperv ise\",\n \"( Contact\",\n \"A WN\",\n \"AW N\",\n \"ãģ ĺ\",\n \"Ġna am\",\n \"Ġa ust\",\n \"Ġau st\",\n \"Ġaus t\",\n \"åľ¨ 线\",\n \"_ softmax\",\n \"_soft max\",\n \"Allow 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\"Ġठ²\",\n \"Ġ ãĢĢĠãĢĢ\",\n \"ĠãĢĢ ĠãĢĢ\",\n \"ĠãĢĢĠ ãĢĢ\",\n \"ॠĤ\",\n \"ठ¦\",\n \"à¸Ń à¸ĩ\",\n \"ÙĪ ÙĨ\",\n \"ठµ\",\n \"a ÅŁ\",\n \"๠Ĥ\",\n \"ι κ\",\n \"Ġ र\",\n \"Ġठ°\",\n \"Ġ ви\",\n \"Ġв и\",\n \"à¥į य\",\n \"à¥įठ¯\",\n \"ा न\",\n \"ाठ¨\",\n \"Ġ از\",\n \"Ġا ز\",\n \"ĠØ§Ø ²\",\n \"ا Ùĩ\",\n \"ا٠ĩ\",\n \"Ľ i\",\n \"Ġh á»\",\n \"à¥ĭ à¤Ĥ\",\n \"i ế\",\n \"ĠÄij á»\",\n \"ठ¯\",\n \"Ï į\",\n \"Ġc ủ\",\n \"Ġ بر\",\n \"Ġب ر\",\n \"Ġ ÙħÛĮ\",\n \"ĠÙħ ÛĮ\",\n \"Ġ اÛĮ\",\n \"Ġا ÛĮ\",\n \"Ġ à¤Ĩ\",\n \"ĠठĨ\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"ि य\",\n \"िठ¯\",\n \"ÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁ\",\n \"в и\",\n \"ر د\",\n \"н Ñĥ\",\n \"ÙĬ ÙĨ\",\n \"ι α\",\n \"Ġ त\",\n \"Ġठ¤\",\n \"Ñĩ и\",\n \"Ġ à¤ķर\",\n \"Ġà¤ķ र\",\n \"ا ز\",\n \"Ø§Ø ²\",\n \"a ÄŁ\",\n \"Ġ à¤ī\",\n \"Ġठī\",\n \"ठ¬\",\n \"ÏĦ α\",\n \"ت ر\",\n \"Ùĩ ا\",\n \"ร ะ\",\n \"j ÃŃ\",\n \"Î ij\",\n \"а ÑĤи\",\n \"аÑĤ и\",\n \"Ġ à¤Ĺ\",\n \"ĠठĹ\",\n \"Ġ ÑĤа\",\n \"ĠÑĤ а\",\n \"Ú Ĩ\",\n \"ठľ\",\n \"า à¸Ļ\",\n \"าภĻ\",\n \"Ġ à¤Ń\",\n \"ĠठŃ\",\n \"ि à¤ķ\",\n \"िठķ\",\n \"á v\",\n \"Ġ Ú¯\",\n \"Ï İ\",\n \"า ย\",\n \"าภ¢\",\n \"Ġ à¤Ķ\",\n \"ĠठĶ\",\n \"ÅĻ ÃŃ\",\n \"ا ÙĪ\",\n \"ا٠Ī\",\n \"Ġ Ñī\",\n \"ĠÑ ī\",\n \"Ġ à¤Ķर\",\n \"Ġà¤Ķ र\",\n \"ен нÑı\",\n \"Ġ Ú©Ùĩ\",\n \"ĠÚ© Ùĩ\",\n \"ठ¡\",\n \"ÏĦ ο\",\n \"ε ι\",\n \"Ġ à¤ĩ\",\n \"Ġठĩ\",\n \"à¥į त\",\n \"à¥įठ¤\",\n \"ठŁ\",\n \"Û ±\",\n \"Ġ ØĮ\",\n \"ĠØ Į\",\n \"Ïģ ο\",\n \"η ÏĤ\",\n \"ë ¬\",\n \"Ñĸ н\",\n \"i á»ģ\",\n \"iá» ģ\",\n \"i ên\",\n \"iê n\",\n \"Ġ вÑĸд\",\n \"Ġв Ñĸд\",\n \"ĠвÑĸ д\",\n \"d ı\",\n \"ÙĦ ÛĮ\",\n \"Ġ ز\",\n \"ĠØ ²\",\n \"Ïģ α\",\n \"Ġ ÛĮ\",\n \"า à¸ĩ\",\n \"าภĩ\",\n \"Ġth á»\",\n \"Ġ à¹Ģà¸\",\n \"Ġà¹Ģ à¸\",\n \"i á»ĩn\",\n \"iá»ĩ n\",\n \"ا ÙĬ\",\n \"ا٠Ĭ\",\n \"ан нÑı\",\n \"ÑĢ е\",\n \"Î Ł\",\n \"å Ĵ\",\n \"ا Ø´\",\n \"Ø§Ø ´\",\n \"ा ल\",\n \"ाठ²\",\n \"ëħ Ħ\",\n \"Ġ य\",\n 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ÏĤ\",\n \"à¹Ģ à¸Ĥ\",\n \"à¹ĢภĤ\",\n \"å Ļ\",\n \"ि श\",\n \"िठ¶\",\n \"Ġ باز\",\n \"Ġب از\",\n \"Ġبا ز\",\n \"ÑĢ об\",\n \"ÑĢо б\",\n \"Ġγ ια\",\n \"μ ε\",\n \"Ġ باش\",\n \"Ġب اش\",\n \"Ġبا Ø´\",\n \"ा à¤ĩ\",\n \"ाठĩ\",\n \"Ġqu y\",\n \"Ġq uy\",\n \"λ ε\",\n \"ا Ùĥ\",\n \"ا٠ĥ\",\n \"Ġ ÑĢок\",\n \"ĠÑĢ ок\",\n \"Ġ Türk\",\n \"ĠT ürk\",\n \"ĠTür k\",\n \"Ġ Ð¥\",\n \"ĠÐ ¥\",\n \"ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ\",\n \"æ ©\",\n \"Ġph ải\",\n \"à¸Ħ วาม\",\n \"à¸Ħว าม\",\n \": ::\",\n \":: :\",\n \"l ÃŃ\",\n \"Ġj sou\",\n \"Ġjs ou\",\n \"ÛĮ ÙĦ\",\n \"ál nÃŃ\",\n \"áln ÃŃ\",\n \"Ķ Ķ\",\n \"í ĸī\",\n \"íĸ ī\",\n \"æĥ ³\",\n \"l á\",\n \"Ġ ÏĥÏħ\",\n \"ĠÏĥ Ïħ\",\n \"Ñĭ ва\",\n \"Ñĭв а\",\n \"Ġnh ất\",\n \"à¸Ń ม\",\n \"Û ¸\",\n \"e cek\",\n \"ec ek\",\n \"ece k\",\n \"Ñĸ ÑĢ\",\n \"ÙĪ Ø´\",\n \"λ α\",\n \"Ġ ÎĴ\",\n \"ĠÎ Ĵ\",\n \"о ÑĢа\",\n \"оÑĢ а\",\n \"Ùģ ت\",\n \"e dir\",\n \"ed ir\",\n \"edi r\",\n \"Ñĥ Ñħ\",\n \"ä¸ ĸ\",\n \"ĠУ кÑĢаÑĹ\",\n \"ĠУкÑĢа ÑĹ\",\n \"Ġ íĶ\",\n \"Ġí Ķ\",\n \"ά ν\",\n \"Ġ شر\",\n \"ĠØ´ ر\",\n \"ĠاÙĦ ج\",\n \"е ÑĢед\",\n \"еÑĢ ед\",\n \"еÑĢе д\",\n \"ìĺ ģ\",\n \"Ġh Ãłnh\",\n \"ï¿£ ï¿£\",\n \"м е\",\n \"ÑİÑĤ ÑģÑı\",\n \"ĠØ¥ ÙĦÙī\",\n \"ĠØ¥ÙĦ Ùī\",\n \"ìĹ ħ\",\n \"Ġ تر\",\n \"Ġت ر\",\n \"к ом\",\n \"ко м\",\n \"Ġ شد\",\n \"ĠØ´ د\",\n \"Ġ اÙĦÙĥ\",\n \"Ġا ÙĦÙĥ\",\n \"ĠاÙĦ Ùĥ\",\n \"Ġ ÏĥÏĦο\",\n \"ĠÏĥ ÏĦο\",\n \"à¥į द\",\n \"à¥įठ¦\",\n \"ëł ¤\",\n \"Ñĥ ваннÑı\",\n \"Ñĥв аннÑı\",\n \"Ġth ì\",\n \"ê ´Ģ\",\n \"ê´ Ģ\",\n \"κ ε\",\n \"س ب\",\n \"íĥ Ģ\",\n \"Ġ ï¼ı\",\n \"Ġï¼ ı\",\n \"Ġ à¹ģละ\",\n \"Ġà¹ģ ละ\",\n \"Ġà¹ģล ะ\",\n \"Ġ ÏĮ\",\n \"ĠÏ Į\",\n \"н иÑĨ\",\n \"ни ÑĨ\",\n \"Ġ ÐĿа\",\n \"ĠÐĿ а\",\n \"Ñı в\",\n \"l ü\",\n \"ι ο\",\n \"ÙĨ دÙĩ\",\n \"ÙĨد Ùĩ\",\n \"ÙĦ Ùĥ\",\n \"Ġng Ãły\",\n \"Ġnh ân\",\n \"Ġ ^{\",\n \"Ġ^ {\",\n \"ॠĥ\",\n \"Ġg erek\",\n \"Ġge rek\",\n \"Ġger ek\",\n \"Ġgere k\",\n \"ا رÙĩ\",\n \"ار Ùĩ\",\n \"Ġc Æ¡\",\n \"Ġ à¸ķ\",\n \"Ġภķ\",\n \"æ Ĥ\",\n \"çĶ °\",\n \"à¥Īà¤Ĥ ।\",\n \"ั ว\",\n \"v ÄĽ\",\n \"ö z\",\n \"и ли\",\n \"ил и\",\n \"Ġph áp\",\n \"Ġphá p\",\n \"ê¸ Ī\",\n \"Ġ ÎŁ\",\n \"ĠÎ Ł\",\n \"Ġp ÅĻi\",\n \"ĠpÅĻ i\",\n \"Ġ ìĸ´\",\n \"Ġìĸ ´\",\n \"Ġд ол\",\n \"Ġдо л\",\n \"ÙĪ رد\",\n \"ÙĪر د\",\n \"à¹Ģ ม\",\n \"à¹Ģภ¡\",\n \"Ïĥ ε\",\n \"า à¸Ĺ\",\n \"าภĹ\",\n \"o Ãłi\",\n \"ร ม\",\n \"Û ¶\",\n \"Ġ à¸ļ\",\n \"Ġภļ\",\n \"i yet\",\n \"iy et\",\n \"iye t\",\n \"ÏĦ αι\",\n \"ÏĦα ι\",\n \"ìĦ ł\",\n \"Ġ εÏĢ\",\n \"Ġε ÏĢ\",\n \"ि व\",\n \"िठµ\",\n \"ê¹ Į\",\n \"г а\",\n \"ĠÑģ лÑĥ\",\n \"ĠÑģл Ñĥ\",\n \"Ġh ình\",\n \"Ġ داÙĨ\",\n \"Ġد اÙĨ\",\n \"Ġà¤Ĺ य\",\n \"ÙĬ ا\",\n \"è ij\",\n \"à¤Ĥ त\",\n \"Ġ ساÙĦ\",\n \"Ġس اÙĦ\",\n \"ëł Ī\",\n \"l erin\",\n \"le rin\",\n \"ler in\",\n \"leri n\",\n \"à¥ĩ त\",\n \"à¥ĩठ¤\",\n \".: .:.:.:\",\n \".:.: .:.:\",\n \".:. :.:.:\",\n \".:.:.: .:\",\n \".:.:. :.:\",\n \".:.:.:. :\",\n \"Ġ ëħ\",\n \"Ġë ħ\",\n \"Ġ اÙĦØ¥\",\n \"ĠاÙĦ Ø¥\",\n \"ả ng\",\n \"ản g\",\n \"è Ħ\",\n \"ο λ\",\n \"п ов\",\n \"по в\",\n \"Ġ θ\",\n \"ĠÎ ¸\",\n \"Û ·\",\n \"Ġn ó\",\n \"Ġd Ã¼ÅŁ\",\n \"Ġdü ÅŁ\",\n \"Ġt iế\",\n \"Ġti ế\",\n \"ÙĪ ج\",\n \"Ġj sem\",\n \"Ġjs em\",\n \"Ạ¡ng\",\n \"ạ ng\",\n \"ạn g\",\n \"ãģĤ ãĤĭ\",\n \"à¸Ń à¸ļ\",\n \"ÙĪ ÙĬ\",\n \"à¤ķ र\",\n \"Ġ де\",\n \"Ġд е\",\n \"¯ ¼\",\n \"Ġ но\",\n \"Ġн о\",\n \"ÑĨ Ñĸй\",\n \"ÑĨÑĸ й\",\n \"Ïĥ ÏĦ\",\n \"к ие\",\n \"ки е\",\n \"Ïĥ ει\",\n \"Ïĥε ι\",\n \"ìķ Ī\",\n \"Ġh Æ¡n\",\n \"Ġà¤ķ ह\",\n \"ا ض\",\n \"Ø§Ø ¶\",\n \"ì ¸\",\n \"ãĥ Ł\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"ãĤĪ ãģĨ\",\n \"ा ,\",\n \"е ÑĢи\",\n \"еÑĢ и\",\n \"ë© °\",\n \"í ĶĦ\",\n \"íĶ Ħ\",\n \"Ġп оÑģÑĤ\",\n \"Ġпо ÑģÑĤ\",\n \"ĠпоÑģ ÑĤ\",\n \"Ø® ر\",\n \"à¥ĭ त\",\n \"â u\",\n \"к ой\",\n \"ко й\",\n \"d aki\",\n \"da ki\",\n \"í ħ\",\n \": :::::::::::::::\",\n \":: ::::::::::::::\",\n \":::: ::::::::::::\",\n \":::::: ::::::::::\",\n \":::::::: ::::::::\",\n \"::: :::::::::::::\",\n \"::::: :::::::::::\",\n \"::::::: :::::::::\",\n \"::::::::: :::::::\",\n \":::::::::: ::::::\",\n \"::::::::::: :::::\",\n \":::::::::::: ::::\",\n \"::::::::::::: :::\",\n \":::::::::::::: ::\",\n \"::::::::::::::: :\",\n \"Ġ öz\",\n \"Ġö z\",\n \"ÑĢ аж\",\n \"ÑĢаР¶\",\n \"ÑĢа ж\",\n \"nÃŃ ho\",\n \"ห ล\",\n \"Ġ ÏĥÏĦη\",\n \"ĠÏĥ ÏĦη\",\n \"ĠÄij á»ģ\",\n \"ĠÄijá» ģ\",\n \"Ġk á»\",\n \"i á»ĥn\",\n \"iá» ĥn\",\n \"iá»ĥ n\",\n \"ÅĻ i\",\n \"Ġkter é\",\n \"¢ ħ\",\n \"ü ç\",\n \"ÙĬ Ùģ\",\n \"Ġ lý\",\n \"Ġl ý\",\n \"Ġth á»Ŀi\",\n \"Ġthá» Ŀi\",\n \"Ġthá»Ŀ i\",\n \"Ġ ìĨĮ\",\n \"ĠìĨ Į\",\n \"н ÑĮ\",\n \"Ð Ĩ\",\n \"ÑĤ ÑĢ\",\n \"à¸ĩ าà¸Ļ\",\n \"к оÑĹ\",\n \"ко ÑĹ\",\n \"μ ο\",\n \"Ġs ür\",\n \"Ġsü r\",\n \"uy á»ģn\",\n \"uyá» ģn\",\n \"Ġ Ùħا\",\n \"ĠÙħ ا\",\n \"à¤Ĥ à¤Ĺ\",\n \"ĠÄij á»ĵng\",\n \"ĠÄijá»ĵ ng\",\n \"ò n\",\n \"à¥ģ ल\",\n \"à¥ģठ²\",\n \"à¥į प\",\n \"à¥įठª\",\n \"λ η\",\n \"Ùħ ر\",\n \"п ÑĢи\",\n \"пÑĢ и\",\n \"i yle\",\n \"iy le\",\n \"ा प\",\n \"ाठª\",\n \"Ġà¤ħ न\",\n \"Ġ ÑĶ\",\n \"ĠÑ Ķ\",\n \"Ġy ön\",\n \"Ġyö n\",\n \"ÙĦ Ùģ\",\n \"a dır\",\n \"ad ır\",\n \"adı r\",\n \"á ½\",\n \"Ġ ê³ł\",\n \"Ġê³ ł\",\n \"Ø® ص\",\n \"im iz\",\n \"imi z\",\n \"åľ ĭ\",\n \"Ġ над\",\n \"Ġн ад\",\n \"Ġна д\",\n \"Ġ ÅĻ\",\n \"ĠÅ Ļ\",\n \"н оÑģÑĤÑĸ\",\n \"но ÑģÑĤÑĸ\",\n \"ноÑģÑĤ Ñĸ\",\n \"ноÑģ ÑĤÑĸ\",\n \"Ġ اÙģ\",\n \"Ġا Ùģ\",\n \"а нÑĸ\",\n \"ан Ñĸ\",\n \"à¥ĩ à¤Ł\",\n \"à¥ĩठŁ\",\n \"Ġ ë§IJ\",\n \"Ġë§ IJ\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"Ġ ìĬ¤\",\n \"ĠìĬ ¤\",\n \"ิ à¸ķ\",\n \"å Ħ\",\n \"ÛĮ Ùĩ\",\n \"о ÑĪ\",\n \"ž it\",\n \"ži t\",\n \"ìĭ ¤\",\n \"à¥Ģ न\",\n \"Ġ î\",\n \"æ¥ Ń\",\n \"à¥ĩ न\",\n \"à¥ĩठ¨\",\n \"Ġ Ø°\",\n \"ĠØ °\",\n \"Ġl oại\",\n \"Ġlo ại\",\n \"à¹Ħ à¸Ľ\",\n \"Ñĸ к\",\n \"Ġ кÑĢа\",\n \"Ġк ÑĢа\",\n \"à¥ĭ र\",\n \"ุ à¸Ķ\",\n \"ĠاÙĦ ر\",\n \"ĠÑģ об\",\n \"ĠÑģо б\",\n \"า à¸Ĭ\",\n \"าภĬ\",\n \"Ġसà¤ķ त\",\n \"Ġ ÎĿ\",\n \"ĠÎ Ŀ\",\n \"ا ÙħÙĩ\",\n \"اÙħ Ùĩ\",\n \"à¹ī าà¸Ļ\",\n \"à¹īา à¸Ļ\",\n \"Ġtr ình\",\n \"Ġtrì nh\",\n \"Ġ اÙĦÙģ\",\n \"Ġا ÙĦÙģ\",\n \"ĠاÙĦ Ùģ\",\n \"Ġ اÙĦد\",\n \"ĠاÙĦ د\",\n \"u nun\",\n \"un un\",\n \"unu n\",\n \"о ÑĤов\",\n \"оÑĤ ов\",\n \"оÑĤо в\",\n \"Æ° ợ\",\n \"Æ°á» Ł\",\n \"ĠÑģ во\",\n \"ĠÑģв о\",\n \"ί αÏĤ\",\n \"ία ÏĤ\",\n \"ấ n\",\n \"ог да\",\n \"à¸Ĺ ย\",\n \"Ġb yl\",\n \"Ġby l\",\n \"ØŃ Ø¯\",\n \"à¸ģ ล\",\n \"ب Ùĩ\",\n \"Ġ vÄĽ\",\n \"Ġv ÄĽ\",\n \"è¢ «\",\n \"Ġ Ø¢Ùħ\",\n \"ĠØ¢ Ùħ\",\n \"ĠÄij iá»ģu\",\n \"å ¨\",\n \"Ġk dy\",\n \"Ġkd y\",\n \"Ġ بÙĪ\",\n \"Ġب ÙĪ\",\n \"ẫ n\",\n \"ìľ ł\",\n \"ा à¤ķ\",\n \"ाठķ\",\n \"k ů\",\n \"Ġtr Æ°á»Ŀng\",\n \"ic ké\",\n \"ick é\",\n \"н иÑı\",\n \"ни Ñı\",\n \"Ġ ÏĢοÏħ\",\n \"ĠÏĢ οÏħ\",\n \"ĠÏĢο Ïħ\",\n \"Æ°á»Ł ng\",\n \"н омÑĥ\",\n \"но мÑĥ\",\n \"ном Ñĥ\",\n \"à¹Ī à¸Ļ\",\n \"ู à¹Ī\",\n \"Ġk ết\",\n \"Ġkế t\",\n \"Ġ ï¼¼\",\n \"Ġï¼ ¼\",\n \"Ġ ìĭł\",\n \"Ġìĭ ł\",\n \"i ç\",\n \"Ġn Äĥng\",\n \"ĠnÄĥ ng\",\n \"Äį ÃŃ\",\n \"ÑĤ Ñı\",\n \"ÑĢ еб\",\n \"ÑĢе б\",\n \"Ùĭ ا\",\n \"Ú¯ ÛĮ\",\n \"ãĥ İ\",\n \"Ġkar ÅŁ\",\n \"в Ñĸ\",\n \"Ġph ần\",\n \"à¸Ī ะ\",\n \"ắ t\",\n \"ر Ø©\",\n \"ิ à¸ĩ\",\n \"ิ à¹Ī\",\n \"ा à¤Ī\",\n \"ाठĪ\",\n \"า à¸ŀ\",\n \"าภŀ\",\n \"ÙĨ ÛĮ\",\n \"ìĹ °\",\n \"b ÄĽ\",\n \"Ġ اÙĦص\",\n \"ĠاÙĦ ص\",\n \"í Ĺ\",\n \"Ġ سر\",\n \"Ġس ر\",\n \"l ara\",\n \"la ra\",\n \"lar a\",\n \"ëĭ ¨\",\n \"Ġ ÙĤر\",\n \"ĠÙĤ ر\",\n \"è İ\",\n \"ب د\",\n \"Ġй ого\",\n \"à¥į ह\",\n \"à¥įठ¹\",\n \"Ġc ách\",\n \"Ġcá ch\",\n \"Ġcác h\",\n \"íķĺ ê³ł\",\n \"Ġ ÏĢÏģο\",\n \"ĠÏĢ Ïģο\",\n \"Ġ تع\",\n \"Ġت ع\",\n \"Ĵ Ī\",\n \"Ġ вод\",\n \"Ġв од\",\n \"Ġво д\",\n \"ç¥ ŀ\",\n \"к им\",\n \"ки м\",\n \"Ġd á»±\",\n \"à¹Ģ ห\",\n \"à¹Ģภ«\",\n \"а на\",\n \"ан а\",\n \"Ġ ï½\",\n \"Ġï ½\",\n \"Ġb aÄŁ\",\n \"Ġba ÄŁ\",\n \"Ġप ह\",\n \"Ġ cao\",\n \"Ġc ao\",\n \"Ġca o\",\n \"Ïģ ÏĮ\",\n \"ÙĨ ج\",\n \"ा à¤ı\",\n \"ाठı\",\n \"Ġ å¹´\",\n \"Ġå¹ ´\",\n \"Ġngh iá»ĩp\",\n \"Ġnghiá»ĩ p\",\n \"Û² Û°\",\n \"к аÑı\",\n \"ка Ñı\",\n \"Ïģ ί\",\n \"Ġ бол\",\n \"Ġб ол\",\n \"Ġбо л\",\n \"Ġgi á\",\n \"Ġ зд\",\n \"Ġз д\",\n \"à¥ĩ ल\",\n \"à¥ĩठ²\",\n \"Ġc ấp\",\n \"à¹Ģ ส\",\n \"à¹Ģภª\",\n \"Ïģ γ\",\n \"Ġ ìĤ\",\n \"Ġì Ĥ\",\n \"d ÄĽ\",\n \"à¥ģ न\",\n \"à¥ģठ¨\",\n \"ì Ī\",\n \"ı lan\",\n \"ıl an\",\n \"л аÑģ\",\n \"ла Ñģ\",\n \"Ġ ว\",\n \"Ġภ§\",\n \"Ġ Ïĥε\",\n \"ĠÏĥ ε\",\n \"Ġ Ø«\",\n \"ĠØ «\",\n \"Ġ Ц\",\n \"ĠÐ ¦\",\n \"çĤ º\",\n \"Ġb üy\",\n \"Ġbü y\",\n \"е ÑĨ\",\n \"å¤ ª\",\n \"Ġब न\",\n \"о гÑĢа\",\n \"ог ÑĢа\",\n \"Ġп ÑĢоÑĤ\",\n \"ĠпÑĢ оÑĤ\",\n \"ĠпÑĢо ÑĤ\",\n \"Ġl ượng\",\n \"Ġd ön\",\n \"Ġdö n\",\n \"ร à¸ĩ\",\n \"а ло\",\n \"ал о\",\n \"Ġ جÙħ\",\n \"Ġج Ùħ\",\n \"à¥Ī ,\",\n \"Ġ 미\",\n \"Ġë ¯¸\",\n \"Ġ ê¹\",\n \"Ġê ¹\",\n \"ÙĪ ت\",\n \"à¥Ģ य\",\n \"à¸Ī าà¸ģ\",\n \"Ġch ất\",\n \"Î ©\",\n \"Ġkh ác\",\n \"Ġkhá c\",\n \"Ġth áng\",\n \"j Å¡ÃŃ\",\n \"ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł\",\n \"ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł\",\n \"ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł\",\n \"á»ij t\",\n \"ห ร\",\n \"Ñĸ л\",\n \"åħ ī\",\n \"å Ĥ\",\n \"ÙĦ Ø©\",\n \"Ġ ê±°\",\n \"Ġê± °\",\n \"о воÑĢ\",\n \"ов оÑĢ\",\n \"ово ÑĢ\",\n \"iá»ĥ u\",\n \"Ġ меÑĤ\",\n \"Ġм еÑĤ\",\n \"а ÑĶ\",\n \"Ġ ÑĩаÑģ\",\n \"ĠÑĩ аÑģ\",\n \"ĠÑĩа Ñģ\",\n \"Ïģ ε\",\n \"ì¹ ´\",\n \"âĢĮ Ø´\",\n \"ë¬ ¼\",\n \"ú c\",\n \"âĢĮ Ùĩا\",\n \"i á»ģn\",\n \"iá» ģn\",\n \"iá»ģ n\",\n \"st av\",\n \"sta v\",\n \"í ŀ\",\n \"ĠÙĨ ظ\",\n \"Ĩ Ĵ\",\n \"Ġ ÏĦα\",\n \"ĠÏĦ α\",\n \"Ġ заб\",\n \"Ġз аб\",\n \"Ġза б\",\n \"Ùĥ Ø©\",\n \"Ġг ÑĢÑĥ\",\n \"ĠгÑĢ Ñĥ\",\n \"в о\",\n \"Ġ Ùħج\",\n \"ĠÙħ ج\",\n \"Ġ sah\",\n \"Ġs ah\",\n \"Ġsa h\",\n \"ب ÙĦ\",\n \"ع Ø©\",\n \"Ñĥ ÑĪ\",\n \"ĠÑĤ ем\",\n \"ĠÑĤе м\",\n \"í ĭ\",\n \"e ck\",\n \"ec k\",\n \"Ïī ÏĤ\",\n \"ÙĬ ت\",\n \"ìĹ Ī\",\n \"ç ĭ\",\n \"Ø° ا\",\n \"ì łĢ\",\n \"ìł Ģ\",\n \"Ġн аÑģ\",\n \"Ġна Ñģ\",\n \"Ġ поÑĩ\",\n \"Ġп оÑĩ\",\n \"Ġпо Ñĩ\",\n \"æł ¡\",\n \"Ï Ī\",\n \"Ñģ кой\",\n \"Ñģк ой\",\n \"Ñģко й\",\n \"ü c\",\n \"ÙĤ ÙĦ\",\n \"Ġп оз\",\n \"Ġпо з\",\n \"Ġ оÑģоб\",\n \"ĠоÑģ об\",\n \"า ล\",\n \"าภ¥\",\n \"н Ñĭми\",\n \"нÑĭ ми\",\n \"нÑĭм и\",\n \"о лод\",\n \"ол од\",\n \"оло д\",\n \"è ¼\",\n \"Ġ دÛĮ\",\n \"Ġد ÛĮ\",\n \"Ġ ÑĥÑģÑĤ\",\n \"ĠÑĥ ÑģÑĤ\",\n \"ĠÑĥÑģ ÑĤ\",\n \"Ġ 무\",\n \"Ġë ¬´\",\n \"Ġë¬ ´\",\n \"ÙĬ س\",\n \"ë° ©\",\n \"à¥į à¤ļ\",\n \"à¥įठļ\",\n \"и ла\",\n \"ил а\",\n \"Ġn ên\",\n \"н ие\",\n \"ни е\",\n \"ι ν\",\n \"lar ını\",\n \"ların ı\",\n \"à¹Ģ à¸Ļ\",\n \"à¹ĢภĻ\",\n \"ÙĨ ت\",\n \"a ģı\",\n \"aÄŁ ı\",\n \"ım ız\",\n \"ımı z\",\n \"ĠاÙĦ Ø®\",\n \"à¹Ģ ว\",\n \"à¹Ģภ§\",\n \"à¥į न\",\n \"à¥įठ¨\",\n \"Ġ Ïħ\",\n \"ĠÏ ħ\",\n \"Ġ íĨ\",\n \"Ġí Ĩ\",\n \"Ạ»\",\n \"ิ à¹Ĥ\",\n \"α ÏĤ\",\n \"м еÑĤ\",\n \"ме ÑĤ\",\n \"Ġ zp\",\n \"Ġz p\",\n \"Ġje ho\",\n \"ี ยà¸Ļ\",\n \"ีย à¸Ļ\",\n \"ÑĦ оÑĢ\",\n \"ın ız\",\n \"ını z\",\n \"k lad\",\n \"kl ad\",\n \"kla d\",\n \"íĮ Į\",\n \"uy á»ĩ\",\n \"uyá» ĩ\",\n \"ι ά\",\n \"Ġ ãĢģ\",\n \"ĠãĢ ģ\",\n \"Ø´ ر\",\n \"æ© Ł\",\n \"Ġ تا\",\n \"Ġت ا\",\n \"Ġ зна\",\n \"Ġз на\",\n \"Ġзн а\",\n \"س تاÙĨ\",\n \"ست اÙĨ\",\n \"à¥ĩ र\",\n \"à¥ĩठ°\",\n \"ë§ ¤\",\n \"ç ĥ\",\n \"Ġ же\",\n \"Ġж е\",\n \"า à¸Ķ\",\n \"าภĶ\",\n \"Ġ ض\",\n \"ĠØ ¶\",\n \"é Ń\",\n \"Ġн аз\",\n \"Ġна з\",\n \"Ġ ÛĮا\",\n \"ĠÛĮ ا\",\n \"e né\",\n \"en é\",\n \"ั ย\",\n \"íĸ Īëĭ¤\",\n \"íĸĪ ëĭ¤\",\n \"Ġ بد\",\n \"Ġب د\",\n \"à¥ģ à¤ķ\",\n \"à¥ģठķ\",\n \"ÑĤ ов\",\n \"ÑĤо в\",\n \"ì° ¨\",\n \"Ùĩ د\",\n \"à¸Ķ ย\",\n \"Ġho ặc\",\n \"Ġ ÐŁÑĢи\",\n \"ĠÐŁ ÑĢи\",\n \"ĠÐŁÑĢ и\",\n \"ÙĨ ا\",\n \"çİ ĭ\",\n \"Ñĥ ваÑĤи\",\n \"Ñĥв аÑĤи\",\n \"Ñĥва ÑĤи\",\n \"à¸ļ ร\",\n \"Ġà¤ķ रत\",\n \"Ġà¤ķर त\",\n \"Ïĥ ηÏĤ\",\n \"Ïĥη ÏĤ\",\n \"Ø ¤\",\n \"éķ ·\",\n \"åħ ĭ\",\n \"Ġ دار\",\n \"Ġد ار\",\n \"ั à¹Ī\",\n \"Æ¡ i\",\n \"า à¸Ī\",\n \"าภĪ\",\n \"ý mi\",\n \"ým i\",\n \"ấ u\",\n \"Ġد ست\",\n \"Ġدس ت\",\n \"k em\",\n \"ke m\",\n \"Ġ оÑģнов\",\n \"ĠоÑģ нов\",\n \"ëª ¨\",\n \"Ïģ ά\",\n \"æ ħ\",\n \"Ġ اب\",\n \"Ġا ب\",\n \"ĠØ§Ø ¨\",\n \"å£ «\",\n \"Ħ ĸ\",\n \"Î Ķ\",\n \"ÙĬ Ùĥ\",\n \"í İ\",\n \"Ġy üz\",\n \"a dı\",\n \"ad ı\",\n \"า à¸ķ\",\n \"าภķ\",\n \"ä» Ģ\",\n \"ìĿ´ ëĭ¤\",\n \"Ġ zv\",\n \"Ġz v\",\n \"Ġ tÄĽ\",\n \"Ġt ÄĽ\",\n \"Ġ íĸ\",\n \"Ġí ĸ\",\n \"ठ¥\",\n \"Ġ लà¤Ĺ\",\n \"Ġल à¤Ĺ\",\n \"ìĺ Ģ\",\n \"Ġ ан\",\n \"Ġа н\",\n \"ç Ĺ\",\n \"ìĹ Ń\",\n \"н ÑĸÑģÑĤÑĮ\",\n \"нÑĸ ÑģÑĤÑĮ\",\n \"нÑĸÑģÑĤ ÑĮ\",\n \"Å ŀ\",\n \"Ġph át\",\n \"Ġphá t\",\n \"ÙĤ Ø©\",\n \"Ġth ế\",\n \"Ġ ï¾\",\n \"Ġï ¾\",\n \"ì² ľ\",\n \"Ġ ìĦł\",\n \"ĠìĦ ł\",\n \"à¹ĥ à¸Ĭ\",\n \"i êu\",\n \"iê u\",\n \"ÄŁ ini\",\n \"ÄŁi ni\",\n \"ÄŁin i\",\n \"ÙĤ د\",\n \"Ġkter ý\",\n \"Ñģ кий\",\n \"Ñģк ий\",\n 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ÙĨ\",\n \"Ä° L\",\n \"Ġ ê´ij\",\n \"Ġê ´ij\",\n \"Ġê´ ij\",\n \"Ġ PÅĻ\",\n \"ĠP ÅĻ\",\n \"ç ŀ\",\n \"ĠÑĤемп еÑĢаÑĤÑĥ\",\n \"Ġka bul\",\n \"Ġkab ul\",\n \"Ġbu dou\",\n \"Ġbud ou\",\n \"ÑĨÑĸ оналÑĮ\",\n \"ÑĨÑĸон алÑĮ\",\n \"ï½ ľ\",\n \"Ġç ocuk\",\n \"Ġçocu k\",\n \"ĠÑĤ ÑĸлÑĮки\",\n \"b yt\",\n \"by t\",\n \"ãĥ ¤\",\n \"ĠÑģÑĤ аÑĤ\",\n \"ĠÑģÑĤа ÑĤ\",\n \"Ġ æĿ±\",\n \"ĠæĿ ±\",\n \"le žit\",\n \"اس طة\",\n \"ุ ร\",\n \"i êm\",\n \"iê m\",\n \"ĠкÑĥлÑĮ ÑĤÑĥ\",\n \"Ġ пон\",\n \"Ġп он\",\n \"Ġпо н\",\n \"Ä© nh\",\n \"åĸ ľ\",\n \"н ев\",\n \"не в\",\n \"ÑĶ н\",\n \"ĠÑģо оÑĤ\",\n \"ë Ŀ\",\n \"çĪ ¾\",\n \"Ġtu á»ķi\",\n \"k anı\",\n \"kan ı\",\n \"สำ หร\",\n \"ا عت\",\n \"اع ت\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"д еÑĢж\",\n \"деÑĢ ж\",\n \"Ġоб 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\"Ġkol ay\",\n \"Ġkola y\",\n \"ĠÑĢазв иÑĤи\",\n \"ĠÑĢазви ÑĤи\",\n \"а ди\",\n \"ад и\",\n \"ئ ÙĬس\",\n \"a dıģı\",\n \"ad ıģı\",\n \"adı ģı\",\n \"adıģ ı\",\n \"Îij ÎĽ\",\n \"Ġ hoa\",\n \"Ġh oa\",\n \"Ġho a\",\n \"Ġ ศ\",\n \"Ġภ¨\",\n \"ı ÅŁtır\",\n \"Ä±ÅŁ tır\",\n \"ÑĢ Ñİ\",\n \"Ġк аÑĩе\",\n \"Ġка Ñĩе\",\n \"¼ åIJĪ\",\n \"åħ ´\",\n \"Ġ ê·¸ëŁ¬\",\n \"Ġê·¸ 룬\",\n \"Ġм ÑĸÑģÑĤ\",\n \"ĠмÑĸ ÑģÑĤ\",\n \"ĠмÑĸÑģ ÑĤ\",\n \"Ġм не\",\n \"Ġмн е\",\n \"ãĥ¼ ãĤº\",\n \"ãĥ¼ãĤ º\",\n \"ç§ Ģ\",\n \"Ġع ÙĦÙĬÙĩ\",\n \"ĠعÙĦ ÙĬÙĩ\",\n \"ĠعÙĦÙĬ Ùĩ\",\n \"Ġ ìĭľê°Ħ\",\n \"Ġìĭľ ê°Ħ\",\n \"Ġà¤ĺ र\",\n \"Ġ Ñĥг\",\n \"ĠÑĥ г\",\n \"åıij å±ķ\",\n \"ı ÅŁÄ±\",\n \"Ä±ÅŁ ı\",\n \"Ġ ìĪľ\",\n \"ĠìĪ ľ\",\n \"Ġ íĻľ\",\n \"ĠíĻ ľ\",\n \"æ¡ £\",\n \"Ġn okt\",\n \"Ġno kt\",\n \"Ġnok t\",\n \"l ém\",\n \"lé m\",\n \"ен нÑĭй\",\n \"Ġب Ùħ\",\n \"à¥ĩ य\",\n \"à¥ĩठ¯\",\n \"о дав\",\n \"од ав\",\n \"ода в\",\n \"à¹Ĥ ร\",\n \"ï¼Į æľī\",\n \"ا ÙĬات\",\n \"اÙĬ ات\",\n \"اÙĬا ت\",\n \"ا ÛĮÙĩ\",\n \"اÛĮ Ùĩ\",\n \"Ġà¤īप य\",\n \"Ġs 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æĺ¯\",\n \"ĠÙħ Ø«ÙĦ\",\n \"ĠÙħØ« ÙĦ\",\n \"ìľ ¨\",\n \"ï¾ ĺ\",\n \"åĪ ¸\",\n \"ç ¶ļ\",\n \"ç¶ ļ\",\n \"ج اد\",\n \"جا د\",\n \"Ġ кÑĥ\",\n \"Ġк Ñĥ\",\n \"åĢ ij\",\n \"o vu\",\n \"ov u\",\n \"Ġs Ä©\",\n \"Ġ ìłIJ\",\n \"Ġìł IJ\",\n \"ĠÑĥ ÑĢов\",\n \"ि à¤ļ\",\n \"िठļ\",\n \"ov ali\",\n \"ova li\",\n \"oval i\",\n \"Ġ ÙĪÙĨ\",\n \"ĠÙĪ ÙĨ\",\n \"Ġ ìĿĮ\",\n \"ĠìĿ Į\",\n \"Ġк г\",\n \"า à¸ĺ\",\n \"าภĺ\",\n \"ÏĦ Ïģα\",\n \"ÏĦÏģ α\",\n \"ž dy\",\n \"à¹Į à¸ķ\",\n \"Ġ nÄĽm\",\n \"ĠnÄĽ m\",\n \"Ġ Це\",\n \"ĠЦ е\",\n \"n oho\",\n \"no ho\",\n \"Ġëĭ¤ ìĭľ\",\n \"Ġté to\",\n \"Ġb iá»ĥu\",\n \"ĠY ön\",\n \"Ġpr áce\",\n \"Ġprá ce\",\n \"à¥ī र\",\n \"Ġch ÃŃ\",\n \"ов ой\",\n \"ово й\",\n \"Ġm ợ\",\n \"Ġmá» Ł\",\n \"èª ª\",\n \"Ïİ ÏĤ\",\n \"в олÑı\",\n \"во лÑı\",\n \"вол Ñı\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"å¯ ¦\",\n \"é» ŀ\",\n \"Ġà¤ı व\",\n \"Ïħ να\",\n \"Ïħν α\",\n \"å² ¡\",\n \"kh ông\",\n \"ĠpÅĻÃŃpad ÄĽ\",\n \"å Ĺ\",\n \"Ġبد ÙĪÙĨ\",\n \"Ïĥ κε\",\n \"Ïĥκ ε\",\n \"Ġdik kat\",\n \"ĠAn cak\",\n \"ĠAnc ak\",\n \"Ġt iá»ĩn\",\n \"Ġti á»ĩn\",\n \"éĿ Ļ\",\n \"Ġ ìĿ¼ë°ĺ\",\n \"ĠìĿ¼ ë°ĺ\",\n \"ĠÄį len\",\n \"ìķ ħ\",\n \"ा à¤ĩन\",\n \"ाà¤ĩ न\",\n \"ãģ£ ãģ¦ãģĦãģŁ\",\n \"ãģ£ãģ¦ ãģĦãģŁ\",\n \"ãģ£ãģ¦ãģĦ ãģŁ\",\n \"ĠìĿ´ ìļ©\",\n \"ÙĪ ÙħÛĮ\",\n \"ÙĪÙħ ÛĮ\",\n \"i ná\",\n \"in á\",\n \"ặ ng\",\n \"ặn g\",\n \"Ïİ Ïģα\",\n \"ÙĨ ÙĬØ©\",\n \"ÙĨÙĬ Ø©\",\n \"в аж\",\n \"ва ж\",\n \"è İ·\",\n \"èİ ·\",\n \"å© ļ\",\n \"ĠÅŁ u\",\n \"Ġ ãģĬ\",\n \"Ġد رب\",\n \"Ġدر ب\",\n \"Ġd iá»ħn\",\n \"ÅĻ eba\",\n \"ÅĻe ba\",\n \"ÅĻeb a\",\n \"as ının\",\n \"asını n\",\n \"ç½ij ç«Ļ\",\n \"н ÑĮого\",\n \"нÑĮ ого\",\n \"нÑĮо го\",\n \"ĠاÙĦØ£ ÙĪÙĦ\",\n \"ικ ÎŃÏĤ\",\n \"Ġz ÃŃsk\",\n \"о ло\",\n \"ол о\",\n \"ĠÑį ÑĤоÑĤ\",\n \"ĠÑįÑĤ оÑĤ\",\n \"ĠÑįÑĤо ÑĤ\",\n \"Ġp okud\",\n \"Ġpo kud\",\n \"Ġpok ud\",\n \"è² »\",\n \"е ÑĢÑĸв\",\n \"еÑĢ Ñĸв\",\n \"еÑĢÑĸ в\",\n \"ãĥķ ãĤ£\",\n \"ãĥķãĤ £\",\n \"иÑĤÑĥ а\",\n \"Ġv yd\",\n \"Ġvy d\",\n \"о лож\",\n \"ол ож\",\n \"оло ж\",\n \"л ÑıÑĤÑĮ\",\n \"лÑı ÑĤÑĮ\",\n \"ÙĤ Ùħ\",\n \"æ´ ĭ\",\n \"æ§ ĭ\",\n \"Ġغ ÛĮر\",\n \"Ġ stÅĻed\",\n \"Ġst ÅĻed\",\n \"ĠstÅĻ ed\",\n \"ظ ر\",\n \"Ġhiç bir\",\n \"θ εί\",\n \"θε ί\",\n \"z nik\",\n \"zn ik\",\n \"д Ñĭ\",\n \"l uv\",\n \"lu v\",\n \"ĠÙħ ؤ\",\n \"ĠÚ¯ رÙĪÙĩ\",\n \"Ġگر ÙĪÙĩ\",\n \"Ġ ï¼īĊ\",\n \"Ġï¼ī Ċ\",\n \"t eri\",\n \"ter i\",\n \"te ri\",\n \"ĠÏħ ÏĢο\",\n \"ĠÏħÏĢ ο\",\n \"v oj\",\n \"vo j\",\n \"Ġبع ض\",\n \"Ġb ilin\",\n \"Ġbi lin\",\n \"Ġbil in\",\n \"Ġ رÙĪØ´\",\n \"Ġر ÙĪØ´\",\n \"ĠرÙĪ Ø´\",\n \"Ġоб Ñıз\",\n \"Ġ ï»\",\n \"Ġï »\",\n \"س ÙĨ\",\n \"Ġ ÏĢα\",\n \"ĠÏĢ α\",\n \"í į¼\",\n \"Ġt ÃŃn\",\n \"ĠtÃŃ n\",\n \"Ġ ´\",\n \"Ġ ´\",\n \"ìĤ¬ ìĿ´íĬ¸\",\n \"ìĤ¬ìĿ´ íĬ¸\",\n \"Ġ podob\",\n \"Ġpo dob\",\n \"Ġpod ob\",\n \"Ñī ее\",\n \"Ñīе е\",\n \"Ġ åįĹ\",\n \"Ġåį Ĺ\",\n \"Ġb ych\",\n \"Ġby ch\",\n \"о зи\",\n \"оз и\",\n \"ĠV Äĥn\",\n \"ØŃ Ùĩ\",\n \"åѦ éĻ¢\",\n \"ĠÅĻ ekl\",\n \"ĠÅĻe kl\",\n \"ĠÅĻek l\",\n \"립 ëĭĪëĭ¤\",\n \"Ġп ÑĢоÑģ\",\n \"ĠпÑĢ оÑģ\",\n \"ĠпÑĢо Ñģ\",\n \"κ ά\",\n \"ĠbaÅŁ ladı\",\n \"ủ y\",\n \"Ñİ Ð´Ð¶\",\n \"ाà¤ı à¤Ĺ\",\n \"à¤Ĥ à¤ļ\",\n \"Ġ ê´Ģ볨\",\n \"Ġê´Ģ 볨\",\n \"Ġв опÑĢоÑģ\",\n \"ĠÑģÑĤ аÑĤÑĮ\",\n \"ĠÑģÑĤаÑĤ ÑĮ\",\n \"ĠÑģÑĤа ÑĤÑĮ\",\n \"Ġy atırım\",\n \"Ġyatır ım\",\n \"н Ñĥла\",\n \"нÑĥ ла\",\n \"нÑĥл а\",\n \"ر اÙģ\",\n \"را Ùģ\",\n \"Ġç eÅŁit\",\n \"Ġà¤ī द\",\n \"å¤ ®\",\n \"Ġпо Ñıв\",\n \"åĽ½ 家\",\n \"ĠÑģооÑĤвеÑĤ ÑģÑĤв\",\n \"ìķ ¡\",\n \"ĠØ® ÙĪاÙĩد\",\n \"ĠØ®ÙĪاÙĩ د\",\n \"ĠØ®ÙĪ اÙĩد\",\n \"Å¡ Å¡ÃŃ\",\n \"Âł п\",\n \"ĠNh Ãł\",\n \"' '''\",\n \"'' ''\",\n \"''' '\",\n \"ï½ ¨\",\n \"à ħ\",\n \"Ġ ïº\",\n \"Ġï º\",\n \"ĠØ¢Ùħ رÛĮÚ©\",\n \"lar ımız\",\n \"larım ız\",\n \"ج ا\",\n \"Ùģ ÙĤ\",\n \"Ġ á»\",\n \"Ġá »\",\n \"Ġ ìķł\",\n \"Ġìķ ł\",\n \"Ġز باÙĨ\",\n \"ĠÑĤ воÑĢ\",\n \"ĠÑĤв оÑĢ\",\n \"н иÑĩеÑģ\",\n \"ни ÑĩеÑģ\",\n \"ниÑĩ еÑģ\",\n \"Ġк ни\",\n \"Ø® داÙħ\",\n \"à¸Ł ร\",\n \"Ġ ì¹ĺ\",\n \"Ġì¹ ĺ\",\n \"ว าม\",\n \"ĠÙħ ÙĩÙħ\",\n \"ĠÙħÙĩ Ùħ\",\n \"Ġs tol\",\n \"Ġst ol\",\n \"Ġsto l\",\n \"Ġed ilen\",\n \"Ġedi len\",\n \"Ġedil en\",\n \"Ġ pek\",\n \"Ġp ek\",\n \"Ġpe k\",\n \"اÙĨ ات\",\n \"اÙĨا ت\",\n \"алÑĮ нÑĸ\",\n \"Ġнеоб ÑħÑĸд\",\n \"à¹Ħ ว\",\n \"Ġश र\",\n \"Ġ íĮIJ\",\n \"ĠíĮ IJ\",\n \"Ò ij\",\n \"Ġ ним\",\n \"Ġн им\",\n \"Ġни м\",\n \"Ġ à¸ĺ\",\n \"Ġภĺ\",\n \"æĺ ł\",\n \"äº Ĵ\",\n \"ĠbaÅŁ ar\",\n \"ž i\",\n \"Ġм ног\",\n \"Ġмн ог\",\n \"l endi\",\n \"len di\",\n \"á vajÃŃ\",\n \"áv ajÃŃ\",\n \"n ict\",\n \"ni ct\",\n \"nic t\",\n \"Ġд Ñĥм\",\n \"ĠдÑĥ м\",\n \"éĻ ©\",\n \"Ïĥ Ïĥ\",\n \"i ky\",\n \"ik y\",\n \"алÑĮ нÑĭй\",\n \"Ġ ÙħÙĨت\",\n \"ĠÙħ ÙĨت\",\n \"ĠÙħÙĨ ت\",\n \"å® ®\",\n \"- за\",\n \"-з а\",\n \"еÑĢ к\",\n \"å¡ Ķ\",\n \"Ġμε ÏĦα\",\n \"oÄŁ un\",\n \"oÄŁu n\",\n \"ÎĹ Îľ\",\n \"à¥Īà¤Ĥ ।Ċ\",\n \"à¥Īà¤Ĥ। Ċ\",\n \"Äį ky\",\n \"å¹³ åı°\",\n \"à¥ĭ श\",\n \"Ġ ona\",\n \"Ġo na\",\n \"Ġon a\",\n \"Ġ bec\",\n \"Ġb ec\",\n \"Ġbe c\",\n \"ì ¢\",\n \"Ġc ây\",\n \"Ġcâ y\",\n \"k ün\",\n \"kü n\",\n \"Ġ à¤Ī\",\n \"ĠठĪ\",\n \"Ġr á»Ļng\",\n \"еÑĢ б\",\n \"å¹ ¸\",\n \"ï¾ IJ\",\n \"ĠпÑĸд пÑĢиÑĶм\",\n \"çĶ £\",\n \"Ġ ÏĦε\",\n \"ĠÏĦ ε\",\n \"ĠÙĨÙĤ Ø´\",\n \"о виÑħ\",\n \"ов иÑħ\",\n \"ови Ñħ\",\n \"ĠÙģ Ùī\",\n \"Ðļ ак\",\n \"Ùİ Ø±\",\n \"Ġ Щ\",\n \"ĠÐ ©\",\n \"алÑĮ нÑĭÑħ\",\n \"Ġk üçük\",\n \"èŃ ·\",\n \"æĭ ħ\",\n \"i caret\",\n \"ic aret\",\n \"ica ret\",\n \"icare t\",\n \"Ġ رÙģت\",\n \"Ġر Ùģت\",\n \"ĠرÙģ ت\",\n \"Ġод ного\",\n \"Ġодно го\",\n \"ÑĪ им\",\n \"ÑĪи м\",\n \"Ġ бÑĸ\",\n \"Ġб Ñĸ\",\n \"Ġuygu lam\",\n \"Ġ æĭ\",\n \"Ġæ ĭ\",\n \"ä½ Ľ\",\n \"u cu\",\n \"uc u\",\n \"d ÃŃ\",\n \"Å ĺ\",\n \"ئ Ø©\",\n \"ê± ¸\",\n \"Ù Į\",\n \"Ġ ÎłÏģο\",\n \"ĠÎł Ïģο\",\n \"Ġye rine\",\n \"Ġyer ine\",\n \"Ġyeri ne\",\n \"ĠÑĸн ÑĦоÑĢма\",\n \"Ġ å¤ĸ\",\n \"Ġå¤ ĸ\",\n \"ä» ķ\",\n \"н ав\",\n \"на в\",\n \"a rası\",\n \"ar ası\",\n \"ara sı\",\n \"aras ı\",\n \"à¸Ńà¸Ļ à¹Ħลà¸Ļ\",\n \"ا شت\",\n \"اش ت\",\n \"ز ÙĬ\",\n \"æ© ĭ\",\n \"Ġ ãĤ«\",\n \"ĠãĤ «\",\n \"èĥ½ åĬĽ\",\n \"å¥ Ĺ\",\n \"Ġpro h\",\n \"Ġpr oh\",\n \"Ġп ÑĢава\",\n \"ĠпÑĢ ава\",\n \"ĠпÑĢав а\",\n \"ĠпÑĢа ва\",\n \"Ỽ p\",\n \"Ġ à¸Ĥà¸Ńà¸ĩ\",\n \"Ġà¸Ĥ à¸Ńà¸ĩ\",\n \"Ġ ë´\",\n \"Ġë ´\",\n \"Ġl úc\",\n \"Ġ éķ\",\n \"Ġé ķ\",\n \"ب ÙĪد\",\n \"بÙĪ د\",\n \"r upa\",\n \"ru pa\",\n \"rup a\",\n \"ا زÙħ\",\n \"از Ùħ\",\n \"Ġ кан\",\n \"Ġк ан\",\n \"Ġка н\",\n \"ılı m\",\n \"ıl ım\",\n \"Ġ Ùĩد\",\n \"ĠÙĩ د\",\n \"ãĢĢ ĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢ ĠãĢĢ\",\n \"ãĢĢĠ ãĢĢĠãĢĢ\",\n \"Ñĭ ваеÑĤ\",\n \"Ñĭв аеÑĤ\",\n \"Ñĭва еÑĤ\",\n \"Ø® اÙĨÙĩ\",\n \"خاÙĨ Ùĩ\",\n \"Ñĥ кÑĤ\",\n \"Ñĥк ÑĤ\",\n \"ĠçĻ¾ 度\",\n \"ĠnÄĽ co\",\n \"е мон\",\n \"ем он\",\n \"емо н\",\n \"Ġà¤ħ प\",\n \"Ġ ÎĮ\",\n \"ĠÎ Į\",\n \"ün ün\",\n \"ünü n\",\n \"æĸĩ åĮĸ\",\n \"ä¹ İ\",\n \"ä¸Ĭ çļĦ\",\n \"ÙĦ ÙĬÙħ\",\n \"ÙĦÙĬ Ùħ\",\n \"ĠtÄĽ ch\",\n \"ا سب\",\n \"اس ب\",\n \"âĢĻ ÑĶ\",\n \"Ġ Ú¯ÛĮ\",\n \"ĠÚ¯ ÛĮ\",\n \"Ġ ê·¼\",\n \"Ġê· ¼\",\n \"Ġtr ẻ\",\n \"μÎŃ Î½Î¿\",\n \"μÎŃν ο\",\n \"ãģĵãģ¨ ãĤĴ\",\n \"ìĿ´ ëĤĺ\",\n \"åĸ Ħ\",\n \"Ġtr ả\",\n \"åĪĨ æŀIJ\",\n \"Ġ dÄĽl\",\n \"Ġd ÄĽl\",\n \"ĠdÄĽ l\",\n \"Ñĥ Ñģка\",\n \"ÑĥÑģ ка\",\n \"ÑĥÑģк а\",\n \"Ġм ного\",\n \"Ġмн ого\",\n \"Ġмног о\",\n \"à¥Ī र\",\n \"μα ÏĦοÏĤ\",\n \"μαÏĦο ÏĤ\",\n \"Ġm ÃŃsto\",\n \"ĠmÃŃ sto\",\n \"ĠmÃŃst o\",\n \"Ġ ê°ģ\",\n \"Ġê° ģ\",\n \"Ġп ÑĢог\",\n \"ĠпÑĢ ог\",\n \"ĠпÑĢо г\",\n \"b aÅŁ\",\n \"ba ÅŁ\",\n \"а йÑĤе\",\n \"ай ÑĤе\",\n \"айÑĤ е\",\n \"Ġc á»ķ\",\n \"å¿ ľ\",\n \"ï¼ģ Ċ\",\n \"ç ı\",\n \"Ġbir çok\",\n \"Ġ íĺķ\",\n \"Ġíĺ ķ\",\n \"çµ Į\",\n \"ĠEv rop\",\n \"ĠÑģ оÑĨÑĸ\",\n \"ĠÑģо ÑĨÑĸ\",\n \"ä»ĸ çļĦ\",\n \"Ġ μÏĢο\",\n \"Ġμ ÏĢο\",\n \"ĠμÏĢ ο\",\n \"å¥ Ī\",\n \"Ġ Ú¯ÙĦ\",\n \"ĠÚ¯ ÙĦ\",\n \"ÙĪ ÙĦØ©\",\n \"ÙĪÙĦ Ø©\",\n \"æµ İ\",\n \"Ġ Ú©ÙĪ\",\n \"ĠÚ© ÙĪ\",\n \"± ä¹IJ\",\n \"ãģĹ ãģı\",\n \"ãģĹãģ ı\",\n \"çº ³\",\n \"ÑģÑĤв енно\",\n \"ÑģÑĤвен но\",\n \"éĽ ¢\",\n \"ा .\",\n \"Ġgerçek leÅŁtir\",\n \"ĠgerçekleÅŁ tir\",\n \"Ġ kır\",\n \"Ġk ır\",\n \"Ġkı r\",\n \"ì ³\",\n \"Ġг оÑģп\",\n \"å¹ ķ\",\n \"ìĦ ¼\",\n \"» .Ċ\",\n \"». Ċ\",\n \"к ÑĥÑĢ\",\n \"кÑĥ ÑĢ\",\n \"Ġ رÛĮ\",\n \"Ġر ÛĮ\",\n \"æĽ ¾\",\n \"ÙĪ رÙĬ\",\n \"ÙĪر ÙĬ\",\n \"лекÑģ анд\",\n \"ص Ùģ\",\n \"Ġc ảnh\",\n \"Ġcả nh\",\n \"å± Ĥ\",\n \"ãĤ Ĩ\",\n \"Ġ تس\",\n \"Ġت س\",\n \"ì° ½\",\n \"기 를\",\n \"Ġ à¹Ģà¸Ħ\",\n \"Ġà¹Ģ à¸Ħ\",\n \"Ġà¹ĢภĦ\",\n \"çŁ Ń\",\n \"Ġ ÑģÑĤÑĢо\",\n \"ĠÑģ ÑĤÑĢо\",\n \"ĠÑģÑĤ ÑĢо\",\n \"ĠÑģÑĤÑĢ о\",\n \"ĠÏĥ ÏĦιÏĤ\",\n \"ĠÏĥÏĦι ÏĤ\",\n \"à¥įय व\",\n \"Ġع ÙĦÙħ\",\n \"ĠعÙĦ Ùħ\",\n \"ĠÑģ иÑĤÑĥа\",\n \"ĠÑī одо\",\n \"ĠÑīо до\",\n \"åIJ Ľ\",\n \"Ùħ س\",\n \"ĠоÑĤ кÑĢÑĭ\",\n \"ĠоÑĤк ÑĢÑĭ\",\n \"Ġsp oj\",\n \"Ġspo j\",\n \"ĠÄij Äĥng\",\n \"Ġs avaÅŁ\",\n \"Ġsav aÅŁ\",\n \"ี ร\",\n \"sk ém\",\n \"ské m\",\n \"Ġ è¡Į\",\n \"Ġè¡ Į\",\n \"é ¹\",\n \"Ġ ÙĬÙħÙĥÙĨ\",\n \"ĠÙĬÙħ ÙĥÙĨ\",\n \"о вано\",\n \"ов ано\",\n \"ова но\",\n \"ован о\",\n \"Ġп ÑĢавилÑĮ\",\n \"ĠпÑĢав илÑĮ\",\n \"ĠпÑĢави лÑĮ\",\n \"ĠпÑĢавил ÑĮ\",\n \"Ġchiế c\",\n \"èĪ ¹\",\n \"éĵ ¶\",\n \"ĠоÑĤ д\",\n \"Ġ ìĿĢ\",\n \"ĠìĿ Ģ\",\n \"íħ Ķ\",\n \"Ġ Nej\",\n \"ĠN ej\",\n \"ĠNe j\",\n \"о не\",\n \"он е\",\n \"Ġk ız\",\n \"Ġkı z\",\n \"олог иÑĩеÑģ\",\n \"Ġ кÑĢаÑĹ\",\n \"ĠкÑĢа ÑĹ\",\n \"à¸ļ à¸Ńล\",\n \"æ¥ ¼\",\n \"Ġت ÙħاÙħ\",\n \"ĠتÙħ اÙħ\",\n \"Ġب ÛĮÙħ\",\n \"ĠبÛĮ Ùħ\",\n \"ĠÑģ Ñĥб\",\n \"ĠÑģÑĥ б\",\n \"v ý\",\n \"Ñģ кие\",\n \"Ñģк ие\",\n \"Ñģки е\",\n \"ëĮĢ ë¡ľ\",\n \"ëĮ Ģë¡ľ\",\n \"???? ????\",\n \"abilir siniz\",\n \"ан Ñģов\",\n \"анÑģ ов\",\n \"代 表\",\n \"Ġ매 매\",\n \"олог ÑĸÑĩ\",\n \"μ αν\",\n \"μα ν\",\n \"ак Ñģим\",\n \"акÑģ им\",\n \"ãĤ¤ ãĥ«\",\n \"Ġt ải\",\n \"Ġtả i\",\n \"Ùħ ÙĪ\",\n \"å® Ĺ\",\n \"n em\",\n \"ne m\",\n \"Ġkho ản\",\n \"Ġ паÑĤ\",\n \"Ġп аÑĤ\",\n \"Ġпа ÑĤ\",\n \"ан ÑĤа\",\n \"анÑĤ а\",\n \"Ġпом оÑī\",\n \"Ġ vod\",\n \"Ġv od\",\n \"Ġvo d\",\n \"Ġkay nak\",\n \"Ġkayn ak\",\n \"Ïĥ ÏĨ\",\n \"à¥Ĥ त\",\n \"du ÄŁ\",\n \"а ÑĤиÑģÑı\",\n \"аÑĤи ÑģÑı\",\n \"Ġ ç¥ŀ\",\n \"Ġç¥ ŀ\",\n \"ĠÑģ лова\",\n \"ĠÑģл ова\",\n \"ĠÑģлов а\",\n \"ĠÑģло ва\",\n \"ÑĢÑĥ кÑĤÑĥ\",\n \"ÑĢÑĥк ÑĤÑĥ\",\n \"ÑĢÑĥкÑĤ Ñĥ\",\n \"ĠmÄĽ sÃŃ\",\n \"Ùı Ùħ\",\n \"зна Ñĩа\",\n \"знаÑĩ а\",\n \"Ġ èī\",\n \"Ġè ī\",\n \"åѦ çĶŁ\",\n \"æ´ ¥\",\n \"Ùİ ÙĬ\",\n \"è§ Ī\",\n \"Ġ å®ī\",\n \"Ġå® ī\",\n \"Ġgör Ã¼ÅŁ\",\n \"ál nÄĽ\",\n \"áln ÄĽ\",\n \"ĠëĶ° ëĿ¼\",\n \"ĠÙħ ÙĪجÙĪد\",\n \"ĠÙħÙĪج ÙĪد\",\n \"ĠÄij ứ\",\n \"ĠÄijá» ©\",\n \"ĠçalÄ±ÅŁ malar\",\n \"ĠçalÄ±ÅŁma lar\",\n \"ĠÑı киÑħ\",\n \"ĠÑıк иÑħ\",\n \"Ġاج تÙħاع\",\n \"μ εν\",\n \"με ν\",\n \"èİ ī\",\n \"ç§ ¯\",\n \"ì¶ ķ\",\n \"à¥į शन\",\n \"à¥įश न\",\n \"Ġx ét\",\n \"Ġв ÑĤоÑĢ\",\n \"ĠвÑĤ оÑĢ\",\n \"çİ ©\",\n \"Âł ÐĿ\",\n \"ÑĪ ие\",\n \"ÑĪи е\",\n \"о ÑĢи\",\n \"оÑĢ и\",\n \"Ø£ س\",\n \"Ġthu á»ijc\",\n \"ëĭĪ ê¹Į\",\n \"ë ķĮ\",\n \"ÑĢ Ñĥп\",\n \"ÑĢÑĥ п\",\n \"Ñģ ÑıÑĤ\",\n \"ÑģÑı ÑĤ\",\n \"з Ñĭ\",\n \"ĠÑģ меÑĢ\",\n \"ĠÑģм еÑĢ\",\n \"Ġv yb\",\n \"Ġvy b\",\n \"ĠìĿ´ ìĥģ\",\n \"à¤ļ न\",\n \"Ġgel di\",\n \"Ġgeld i\",\n \"Û± Û°\",\n \"Û±Û °\",\n \"ικ Ïİν\",\n \"ĠÄIJ ức\",\n \"Ġд оÑģÑĤаÑĤ\",\n \"ĠдоÑģÑĤ аÑĤ\",\n \"Ġö nc\",\n \"Ġön c\",\n \"è¦ ª\",\n \"Ġ adı\",\n \"Ġa dı\",\n \"Ġad ı\",\n \"un ca\",\n \"unc a\",\n \"ĠاÙĦ تر\",\n \"ĠاÙĦت ر\",\n \"çķ ¶\",\n \"ĠФ едеÑĢа\",\n \"ĠФед еÑĢа\",\n \"лÑı ÑİÑĤÑģÑı\",\n \"лÑıÑİÑĤ ÑģÑı\",\n \"ĠÙĥ اÙĨت\",\n \"ĠÙĥاÙĨ ت\",\n \"æİ ¢\",\n \"Ġ Ñĥб\",\n \"ĠÑĥ б\",\n \"Ġ κο\",\n \"Ġκ ο\",\n \"ाà¤ĩ à¤Ł\",\n \"з н\",\n \"Ġm ôi\",\n \"Ġmô i\",\n \"Ġ ãĤµ\",\n \"ĠãĤ µ\",\n \"Ġна вÑĸ\",\n \"Ġнав Ñĸ\",\n \"ç» ¼åIJĪ\",\n \"Ġмин ÑĥÑĤ\",\n \"Ġми нÑĥÑĤ\",\n \"ĠминÑĥ ÑĤ\",\n \"d ık\",\n \"dı k\",\n \"ÑĢ Ñĥд\",\n \"ÑĢÑĥ д\",\n \"åľ ĸ\",\n \"ê° ¤\",\n \"ĠÄijo Ãłn\",\n \"è ¤\",\n \"à¥į वर\",\n \"à¥įव र\",\n \"ĠÃľn iversit\",\n \"а но\",\n \"ан о\",\n \"éĽ ¨\",\n \"ĠvÅ¡ech ny\",\n \"Ġëĭ¤ ìĿĮ\",\n \"ĠC umhur\",\n \"ĠCum hur\",\n \"Ġм Ñĥз\",\n \"ĠмÑĥ з\",\n \"a ÅŁtır\",\n \"aÅŁ tır\",\n \"Ġ ê±°ëŀĺ\",\n \"Ġê±° ëŀĺ\",\n \"Ġ é¡\",\n \"Ġé ¡\",\n \"žit ÃŃ\",\n \"ži tÃŃ\",\n \"Ġ à¸Ł\",\n \"ĠภŁ\",\n \"Ġthu ế\",\n \"Ġм Ñĥж\",\n \"ĠмÑĥ ж\",\n \"ĠÎij ν\",\n \"Ġد ÙĪÙħ\",\n \"ĠدÙĪ Ùħ\",\n \"ĠÑģ ин\",\n \"ĠÑģи н\",\n \"Ġ ÏīÏĤ\",\n \"ĠÏī ÏĤ\",\n \"m eler\",\n \"me ler\",\n \"mel er\",\n \"Ġ poÄį\",\n \"Ġp oÄį\",\n \"Ġpo Äį\",\n \"Ġколи Ñĩе\",\n \"ĠколиÑĩ е\",\n \"ĠK Äį\",\n \"è³ ½\",\n \"ĠоÑģ Ñĸб\",\n \"åı ¥\",\n \"ĠB öl\",\n \"à¸ĺ รรม\",\n \"Ġc ạnh\",\n \"å° ĩ\",\n \"Ġ ноÑģ\",\n \"Ġн оÑģ\",\n \"Ġно Ñģ\",\n \"èĦ ¸\",\n \"Ġgel ir\",\n \"о ÑĢон\",\n \"оÑĢ он\",\n \"оÑĢо н\",\n \"à¥įर à¤Ń\",\n \"ç» ĩ\",\n \"ุ à¹ī\",\n \"ाम ल\",\n \"Ġc âu\",\n \"Ġcâ u\",\n \"Ñij ÑĤ\",\n \"Ġ :|\",\n \"Ġ: |\",\n \"ãĤĮ ãģ¦\",\n \"Ġpos led\",\n \"Ġpo sled\",\n \"ãĤ¹ ãĥĨ\",\n \"ÑĸлÑĮ ÑĪ\",\n \"ен ÑĤÑĭ\",\n \"енÑĤ Ñĭ\",\n \"Ø® دÙħ\",\n \"Ġباش گاÙĩ\",\n \"Ġth Æ°\",\n \"á vánÃŃ\",\n \"áv ánÃŃ\",\n \"ává nÃŃ\",\n \"ëĬ IJ\",\n \"ĠØ£ ØŃ\",\n \"ر اد\",\n \"را د\",\n \"ĠبسÛĮ ار\",\n \"åĪ° äºĨ\",\n \"\\\" ;\\\"\",\n \"\\\"; \\\"\",\n \"å° İ\",\n \"Ġ ör\",\n \"Ġö r\",\n \"à¸Ĭ าà¸ķ\",\n \"g enus\",\n \"gen us\",\n \"Ġya kın\",\n \"Ġyak ın\",\n \"Ġ ÃŃt\",\n \"ĠÃŃ t\",\n \"reg num\",\n \"regn um\",\n \"Ġf iyat\",\n \"Ġfi yat\",\n \"н ÑĸÑħ\",\n \"нÑĸ Ñħ\",\n \"åľ° æĸ¹\",\n \"Ġbil gi\",\n \"Ġbilg i\",\n \"к ам\",\n \"ка м\",\n \"Ġs pol\",\n \"Ġsp ol\",\n \"Ġspo l\",\n \"ائ ÙĬ\",\n \"Ġ ÙĬÙĨ\",\n \"ĠÙĬ ÙĨ\",\n \"า หาร\",\n \"าห าร\",\n \"Ġب Ú¯\",\n \"é ĺħ\",\n \"éĺ ħ\",\n \"ĠاÙĦ شر\",\n \"ĠاÙĦØ´ ر\",\n \" ģ\",\n \"ĠÑĸн ÑĪиÑħ\",\n \"ĠÑĸнÑĪ иÑħ\",\n \"Ġtr ạng\",\n \"çģ £\",\n \"Ġc á»±c\",\n \"к ан\",\n \"ка н\",\n \"èĭ ı\",\n \"à Ķ\",\n \"Ġl á»Ŀi\",\n \"Ġlá» Ŀi\",\n \"Ñı Ñĩ\",\n \"Ġ ÙĪØŃ\",\n \"ĠÙĪ ØŃ\",\n \"ìĪ ľ\",\n \"Å ¸\",\n \"Ġв оÑģп\",\n \"Ġво Ñģп\",\n \"ĠвоÑģ п\",\n \"ì¡ Į\",\n \"Äį nÃŃch\",\n \"ÄįnÃŃ ch\",\n \"Ø® رÙī\",\n \"خر Ùī\",\n \"ائ ÙĬØ©\",\n \"ائÙĬ Ø©\",\n \"Ġsu ất\",\n \"æĩ ī\",\n \"ا ØŃÛĮ\",\n \"اØŃ ÛĮ\",\n \"Ġn áz\",\n \"Ġná z\",\n \"è¿Ļ ç§į\",\n \"Ġзаб езпеÑĩ\",\n \"Ġ ЧеÑĢ\",\n \"ĠЧ еÑĢ\",\n \"Ġзд ÑĸйÑģ\",\n \"åı ¦\",\n \"æĭ ¬\",\n \"à¥ģ ष\",\n \"à¥ģठ·\",\n \"μ ÏĨ\",\n \"ëĥ IJ\",\n \"Ðķ Ñģли\",\n \"é ¬\",\n \"Ġ íĥľ\",\n \"Ġíĥ ľ\",\n \"Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"Ġм л\",\n \"å´ İ\",\n \"Ùģ ع\",\n \"Ġ ÙĤدر\",\n \"ĠÙĤ در\",\n \"ĠÙĤد ر\",\n \"Ġv á»ijn\",\n \"å¦ ¹\",\n \"Ġ ÐĿаÑģ\",\n \"ĠÐĿ аÑģ\",\n \"ĠÐĿа Ñģ\",\n \"à¥į फ\",\n \"à¥įठ«\",\n \"ãĤ¸ ãĥ£\",\n \"Ġm ı\",\n \"ен Ñģ\",\n \"б Ñĥд\",\n \"бÑĥ д\",\n \"ĠØŃ ØªÙī\",\n \"Ġ ì²´\",\n \"Ġì² ´\",\n \"ĠÑĸ ÑģÑĤоÑĢ\",\n \"ĠÑĸÑģ ÑĤоÑĢ\",\n \"Ġgi ấy\",\n \"γ οÏģ\",\n \"γο Ïģ\",\n \"ëIJĺ ìĸ´\",\n \"Ġ íĤ\",\n \"Ġí Ĥ\",\n \"ĠÐŀд на\",\n \"ĠÙĨ ÙħÙĪد\",\n \"ĠÙĨÙħ ÙĪد\",\n \"Ġвип ад\",\n \"ĠìŀIJ ìĭł\",\n \"Ġj ste\",\n \"Ġjs te\",\n \"Ġ ëĵ±ë¡Ŀ\",\n \"Ġëĵ± ë¡Ŀ\",\n \"ek ten\",\n \"ekt en\",\n \"ekte n\",\n \"ĠÑĢ еÑĩ\",\n \"ĠÑĢе Ñĩ\",\n \"r odnÃŃ\",\n \"rod nÃŃ\",\n \"س تر\",\n \"ست ر\",\n \"ı t\",\n \"ä¹ħ ä¹ħ\",\n \"ĠØ® ÙĦاÙĦ\",\n \"ĠØ®ÙĦ اÙĦ\",\n \"Ġ ç¦\",\n \"Ġç ¦\",\n \"u luk\",\n \"ul uk\",\n \"ulu k\",\n \"l enen\",\n \"le nen\",\n \"len en\",\n \"lene n\",\n \"i lip\",\n \"il ip\",\n \"ili p\",\n \"è´ ¢\",\n \"Ġà¤ħ à¤ķ\",\n \"ĠY ıl\",\n \"Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"Ġ à¤Ŀ\",\n \"ĠठĿ\",\n \"ĠB ình\",\n \"Ġol muÅŁ\",\n \"Ġolm uÅŁ\",\n \"اÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©\",\n \"اÙĦØ¥ ÙĨجÙĦÙĬزÙĬØ©\",\n \"м енно\",\n \"мен но\",\n \"aln ız\",\n \"Ġ شرÙĥØ©\",\n \"Ġشر ÙĥØ©\",\n \"Ġس ÙĨØ©\",\n \"ĠسÙĨ Ø©\",\n \"è´ Ł\",\n \"ä½ľ åĵģ\",\n \"Ġ ìķ½\",\n \"Ġìķ ½\",\n \"ĠдÑĢÑĥг иÑħ\",\n \"ĠbaÄŁ lantı\",\n \"о дÑĥ\",\n \"од Ñĥ\",\n \"çļĦ æĺ¯\",\n \"ั à¸Ļà¸Ķ\",\n \"ัà¸Ļ à¸Ķ\",\n \"ĠкоÑĤоÑĢ ÑĭÑħ\",\n \"ĠاÙĦ ÙĪÙĦ\",\n \"ĠاÙĦÙĪ ÙĦ\",\n \"ê¸Ģ ìĥģìľĦ\",\n \"ĠÏĢ εÏģ\",\n \"ĠÏĢε Ïģ\",\n \"리 ìķĦ\",\n \"i bar\",\n \"ib ar\",\n \"iba r\",\n \"Ġ èĥ\",\n \"Ġè ĥ\",\n \"ãģŁ ãģĦ\",\n \"á j\",\n \"ĠìľĦ íķ´\",\n \"? âĢľĊĊ\",\n \"?âĢľ ĊĊ\",\n \"Ġ íİĺ\",\n \"Ġíİ ĺ\",\n \"Ġ ней\",\n \"Ġн ей\",\n \"Ġне й\",\n \"ĠÐĹ Ð°Ðº\",\n \"ĠÐĹа к\",\n \"Ġ ÐĴÑĸд\",\n \"ĠÐĴ Ñĸд\",\n \"е лÑĸ\",\n \"ел Ñĸ\",\n \"è¯ ¾\",\n \"åī ¯\",\n \"m adan\",\n \"ma dan\",\n \"mad an\",\n \"mada n\",\n \"æľ «\",\n \"ĠÏĢ ÏģÏĮ\",\n \"Ġп ÑģиÑħ\",\n \"Ġ ÑĤÑĸ\",\n \"ĠÑĤ Ñĸ\",\n \"Ùĥ ات\",\n \"Ġvys ok\",\n \"ê´Ģ 리\",\n \"ül tür\",\n \"ült ür\",\n \"Ġ à¹Ģà¸Ń\",\n \"Ġà¹Ģ à¸Ń\",\n \"Ġà¹ĢภŃ\",\n \"Ġ íķ©\",\n \"Ġíķ ©\",\n \"çĿ £\",\n \"Ġ ÑĢиÑģ\",\n \"ĠÑĢ иÑģ\",\n \"е ÑĢÑĮ\",\n \"еÑĢ ÑĮ\",\n \"ĠÚ© ÙĦÛĮ\",\n \"ĠÚ©ÙĦ ÛĮ\",\n \"Ġ ãĥŀ\",\n \"Ġãĥ ŀ\",\n \"Ġph ÃŃa\",\n \"ĠphÃŃ a\",\n \"å «\",\n \"ا Ú¯\",\n \"Ġ é¢\",\n \"Ġé ¢\",\n \"ĠÙĨ Ùģر\",\n \"ĠÙĨÙģ ر\",\n \"Ġ جاÙĨ\",\n \"Ġج اÙĨ\",\n \"Ġجا ÙĨ\",\n 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ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"à¹ĩ à¸Ī\",\n \"å¾ Ĵ\",\n \"t ach\",\n \"ta ch\",\n \"u muz\",\n \"um uz\",\n \"umu z\",\n \"κ η\",\n \"à Ĭ\",\n \"Ġ ün\",\n \"Ġü n\",\n \"ĠB ITTE\",\n \"ĠBIT TE\",\n \"ĠÙħ ربع\",\n \"ĠÙħر بع\",\n \"ĠÙħرب ع\",\n \"ãĤ· ãĥ¥\",\n \"िस à¤ķ\",\n \"Ø· ÙĪر\",\n \"Ġв оÑģ\",\n \"Ġво Ñģ\",\n \"ï¾ Ł\",\n \"Ġy ayın\",\n \"Ġyay ın\",\n \"ãģĭ ãĤĬ\",\n \"л иÑı\",\n \"ли Ñı\",\n \"Ġп ÑĢин\",\n \"ĠпÑĢ ин\",\n \"ĠпÑĢи н\",\n \"ij ng\",\n \"ĠÙĨ Ø®\",\n \"Ġl ze\",\n \"Ġlz e\",\n \"à¥įष ण\",\n \"Ġ бо\",\n \"Ġб о\",\n \"Ġ ê¸Ģ\",\n \"Ġê¸ Ģ\",\n \"Ġgel iÅŁtir\",\n \"ĠgeliÅŁ tir\",\n \"à¸Ľà¸£à¸° à¸Ĭ\",\n \"å½ ¡\",\n \"Ġ ãĤª\",\n \"ĠãĤ ª\",\n \"ãģĪ ãģ¦\",\n \"н ÑĥÑĤÑĮ\",\n \"нÑĥ ÑĤÑĮ\",\n \"нÑĥÑĤ ÑĮ\",\n \"Ġ ç½\",\n \"Ġç ½\",\n \"Ġ маг\",\n \"Ġм аг\",\n \"Ġма г\",\n \"ãģ« ãģ¤\",\n \"н оÑģÑĤей\",\n \"ноÑģÑĤ ей\",\n \"Ġ ÙĦÙĬ\",\n \"ĠÙĦ ÙĬ\",\n \"æĢ ª\",\n \"ÑıÑĤ 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\"à¹Ĥ รà¸ĩ\",\n \"à¹Ĥร à¸ĩ\",\n \"ì§Ģ ëħ¸\",\n \"Ùİ Ùij\",\n \"Îij ÎĻ\",\n \"á na\",\n \"án a\",\n \"an dır\",\n \"and ır\",\n \"ระ à¸ļà¸ļ\",\n \"ระà¸ļ à¸ļ\",\n \"oÄŁ lu\",\n \"า à¸Īะ\",\n \"าà¸Ī ะ\",\n \"ẩ y\",\n \"ا ÙĪÙĦ\",\n \"اÙĪ ÙĦ\",\n \"ĠмаÑĤ еÑĢÑĸ\",\n \"ÎŁ ÎĿ\",\n \"ÎŁÎ Ŀ\",\n \"Ġin formace\",\n \"Ġinform ace\",\n \"ت ع\",\n \"à¸ļ à¸Ļ\",\n \"ĠÄĮesk é\",\n \"ĠÄĮes ké\",\n \"Ġte mel\",\n \"Ġtem el\",\n \":::::::::::::::: ::::::::::::::::\",\n \"Ġ chia\",\n \"Ġch ia\",\n \"Ġchi a\",\n \"- Ñģ\",\n \"н еÑĢг\",\n \"не ÑĢг\",\n \"неÑĢ г\",\n \"Ġì° ¾\",\n \"ÑĢ ид\",\n \"ÑĢи д\",\n \"л оÑģÑĮ\",\n \"ло ÑģÑĮ\",\n \"ز ÙĦ\",\n \"ê°Ģ ëĬĶ\",\n \"a né\",\n \"an é\",\n \"Ġнав ÑĸÑĤÑĮ\",\n \"ĠнавÑĸ ÑĤÑĮ\",\n \"ä¸ĵ ä¸ļ\",\n \"Ġ 경기\",\n \"Ġê²½ 기\",\n \"Ġp ÅĻev\",\n \"ĠpÅĻ ev\",\n \"ĠpÅĻe v\",\n \"е ÑĤи\",\n \"еÑĤ и\",\n \"Ġ íĶĮ\",\n \"ĠíĶ Į\",\n \"н ÑıÑĤ\",\n \"нÑı ÑĤ\",\n \"à¥ģ श\",\n \"à¥ģठ¶\",\n \"лÑİ Ð´\",\n \"в иÑī\",\n \"ви Ñī\",\n \"å° ¾\",\n \"çļĦ äºĭ\",\n \"Ġ ëIJľ\",\n \"Ġë IJľ\",\n \"ĠëIJ ľ\",\n \"ر ÙĪÙģ\",\n \"رÙĪ Ùģ\",\n \"Ġ 女\",\n \"Ġå¥ ³\",\n \"κ ή\",\n \"Ġ Tuy\",\n \"ĠT uy\",\n \"ĠTu y\",\n \"Ġê²ĥ ìĿĦ\",\n \"Ġb unu\",\n \"Ġbu nu\",\n \"Ġbun u\",\n \"ĠÑĢаз лиÑĩ\",\n \"ĠD ün\",\n \"ãĤŃ ãĥ£\",\n \"ÑĢ ÑĥÑģ\",\n \"ÑĢÑĥ Ñģ\",\n \"Ġ мм\",\n \"Ġм м\",\n \"l oven\",\n \"lo ven\",\n \"love n\",\n \"lov en\",\n \"Ġot ev\",\n \"n oloj\",\n \"ES Ä°\",\n \"ü p\",\n \"Ġ èĤ\",\n \"Ġè Ĥ\",\n \"ικ ÏĮÏĤ\",\n \"ικÏĮ ÏĤ\",\n \"ض اء\",\n \"ضا Ø¡\",\n \"Ġ пеÑĩ\",\n \"Ġп еÑĩ\",\n \"ÅĻÃŃ klad\",\n \"ãģĵ ãĤį\",\n \"Å¡ tÃŃ\",\n \"Å¡t ÃŃ\",\n \"Ġبر Ú¯\",\n \"ãģĮ ãģĤãĤĭ\",\n \"Ñĸ ÑģÑĤ\",\n \"ÑĸÑģ ÑĤ\",\n \"à¥ī à¤ķ\",\n \"ÏĢ η\",\n \"ĠاÙĦÙħ ست\",\n \"ĠاÙĦÙħس ت\",\n \"Ġз ай\",\n \"Ġза й\",\n \"Ġch Æ°Æ¡ng\",\n \"о ÑĤÑĥ\",\n \"оÑĤ Ñĥ\",\n \"Ġ Сам\",\n \"ĠС ам\",\n \"Å¡ et\",\n \"Å¡e t\",\n \"ĠìŀĪ ìĹĪ\",\n \"ĠÙģ ار\",\n \"Ñĸ он\",\n \"ãĥĹ ãĥŃ\",\n \"Ġnh iá»ĩt\",\n \"in izi\",\n \"ini zi\",\n \"iniz i\",\n \"Ġc ož\",\n \"Ġco ž\",\n \"Ġà¤Ĩ न\",\n \"Ġsyst ém\",\n \"ر ÙĪع\",\n \"رÙĪ ع\",\n \"a 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\"ÑħодиÑĤ ÑĮ\",\n \"Ġsın ır\",\n \"ü cret\",\n \"üc ret\",\n \"s uz\",\n \"su z\",\n \"æ¨ Ĥ\",\n \"Ġ ì°½\",\n \"Ġì° ½\",\n \"Ïģ ίοÏħ\",\n \"Ïģί οÏħ\",\n \"åĪ ļ\",\n \"Ø® ÙĦ\",\n \"ëłĩ ê²Į\",\n \"ج د\",\n \"Ġμ αÏĤ\",\n \"Ġμα ÏĤ\",\n \"áºŃ m\",\n \"k ara\",\n \"ka ra\",\n \"kar a\",\n \"ãĤ« ãĥ¼\",\n \"Ġkter ou\",\n \"ìĽ ¨\",\n \"ÑĦи ÑĨи\",\n \"oÄŁ raf\",\n \"Ġна пÑĢи\",\n \"Ġнап ÑĢи\",\n \"ãģij ãģ©\",\n \"Ġ éļ\",\n \"Ġé ļ\",\n \"ت باÙĦ\",\n \"تب اÙĦ\",\n \"ëŁ ½\",\n \"ìĶ ¨\",\n \"íĮĮ ìĿ¼\",\n \"Ïĩ α\",\n \"Ġuz ak\",\n \"Ġd òng\",\n \"Ġг олоÑģ\",\n \"Ġгол оÑģ\",\n \"Ïĥ ÏĦή\",\n \"ÏĥÏĦ ή\",\n \"ι λ\",\n \"Ø· Ùģ\",\n \"Ġê·¸ ëħĢ\",\n \"ãĤ¿ ãĤ¤\",\n \"ا ÙĨÚ¯\",\n \"اÙĨ Ú¯\",\n \"i nou\",\n \"in ou\",\n \"ino u\",\n \"л он\",\n \"ло н\",\n \"à¹ĩ ม\",\n \"Ġब द\",\n \"Ġkon usunda\",\n \"Ġkonusu nda\",\n \"Ġkonus unda\",\n \"Ġn âng\",\n \"ãģ¾ãģĽ ãĤĵ\",\n \"Ñĥ ÑİÑĤÑĮÑģÑı\",\n \"ÑĥÑİ ÑĤÑĮÑģÑı\",\n \"ÑĥÑİÑĤÑĮ ÑģÑı\",\n \"åŁ ¹\",\n \"ен ко\",\n \"ìł ij\",\n \"Ġ ÑĤов\",\n \"ĠÑĤ ов\",\n \"ĠÑĤо в\",\n \"Ġt ÅĻeba\",\n \"ĠtÅĻ eba\",\n \"ز اÙĨ\",\n \"زا ÙĨ\",\n \"is yon\",\n \"isy on\",\n \"Ġ ген\",\n \"Ġг ен\",\n \"Ġге н\",\n \"Ġ Pokud\",\n \"ĠP okud\",\n \"ĠPo kud\",\n \"ĠPok ud\",\n \"âĢĮ اÙĨد\",\n \"âĢĮاÙĨ د\",\n \"Ġг ÑĢÑĥд\",\n \"ĠгÑĢÑĥ д\",\n \"ĠгÑĢ Ñĥд\",\n \"Ġ خرÛĮد\",\n \"Ġخر ÛĮد\",\n \"λ λα\",\n \"λλ α\",\n \"Ġp ÅĻÃŃm\",\n \"ĠpÅĻ ÃŃm\",\n \"ĠpÅĻÃŃ m\",\n \"Ġ æ³ķ\",\n \"Ġæ³ ķ\",\n \"Ġز ÙĨدگÛĮ\",\n \"ĠزÙĨد Ú¯ÛĮ\",\n \"ạ p\",\n \"Ġ íĬ¸\",\n \"ĠíĬ ¸\",\n \"ĠÄij á»Ļc\",\n \"ĠÄijá»Ļ c\",\n \"Ġê·¸ ë¦¬ê³ł\",\n \"Ġ그리 ê³ł\",\n \"н из\",\n \"ни з\",\n \"Ġ ÙĬÙĤ\",\n \"ĠÙĬ ÙĤ\",\n \"l aÅŁtır\",\n \"la ÅŁtır\",\n \"laÅŁ tır\",\n \"ĠпÑĢав о\",\n \"ĠпÑĢа во\",\n \"Ñĥ Ñģк\",\n \"ÑĥÑģ к\",\n \"å° ½\",\n \"Ġप ड\",\n \"éĵ ģ\",\n \"Ġ ì·¨\",\n \"Ġì ·¨\",\n \"ĠاÙĦ بÙĬ\",\n \"ĠاÙĦب ÙĬ\",\n \" ¸\",\n \"ิม à¸ŀ\",\n \"Ġs vÄĽ\",\n \"Ġsv ÄĽ\",\n \"Ġб ал\",\n \"Ġба л\",\n \"Ġm ôn\",\n \"Ġmô n\",\n \"ĠD ữ\",\n \"ĠØ´ دÙĨ\",\n \"Ġشد ÙĨ\",\n \"Ġ ÙģÙĦ\",\n \"ĠÙģ ÙĦ\",\n \"Ġv znik\",\n \"Ġvz nik\",\n \"Ġch 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į\",\n \"Ġgir iÅŁ\",\n \"ĠÄij áp\",\n \"ĠÄijá p\",\n \"@ n\",\n \"ов аÑĢи\",\n \"оваÑĢ и\",\n \"ова ÑĢи\",\n \"ĠØ® دا\",\n \"Ġخد ا\",\n \"Ġv ÄĽtÅ¡\",\n \"ĠvÄĽt Å¡\",\n \"ĠΣ Ïħ\",\n \"Ùģ Ø©\",\n \"аннÑı м\",\n \"ĠÑĩ лен\",\n \"æĶ¯ æĮģ\",\n \"å¨ ľ\",\n \"lar arası\",\n \"lara rası\",\n \"Ρ Îij\",\n \"Ġz iy\",\n \"Ġzi y\",\n \"Ġ êµIJìľ¡\",\n \"ĠêµIJ ìľ¡\",\n \"Ġh á»ĵi\",\n \"Ġhá»ĵ i\",\n \"าà¸Ħ าร\",\n \"าà¸Ħา ร\",\n \"im leri\",\n \"imler i\",\n \"è³ ¼\",\n \"ĠجÙĩ اÙĨ\",\n \"ĠÑĢоз мÑĸ\",\n \"Ñħ Ñĸв\",\n \"γ ε\",\n \"æ¨ ª\",\n \"ÎĻ ÎijΣ\",\n \"ÎĻÎij Σ\",\n \"ç¶ Ń\",\n \"Ġbi raz\",\n \"Ġbir az\",\n \"ĠÑĤак ого\",\n \"ĠÑĤа кого\",\n \"íĥ Ħ\",\n \"ĠбÑĥд ÑĥÑĤ\",\n \"ĠбÑĥ дÑĥÑĤ\",\n \"ĠбÑĥдÑĥ ÑĤ\",\n \"ĠÑĪ вид\",\n \"Ġ неÑģ\",\n \"Ġн еÑģ\",\n \"Ġне Ñģ\",\n \"ĠÙħ عÙĦÙĪÙħات\",\n \"ĠÙħعÙĦ ÙĪÙħات\",\n \"à¥ĩ यर\",\n \"à¥ĩय र\",\n \"Ġдв ÑĥÑħ\",\n \"å¿ħ è¦ģ\",\n \"å§ Ĩ\",\n \"Ġpo hled\",\n \"Ġpoh led\",\n \"ìĬ¤ íĦ°\",\n \"Ġ åįģ\",\n \"Ġåį ģ\",\n \"ĠØ£ ب\",\n \"веÑĢ дж\",\n \"веÑĢд ж\",\n \"Ġà¤ľ म\",\n \"ल त\",\n \"åľ° åĮº\",\n \"Ġ |[\",\n \"Ġ| [\",\n \"Ġв меÑģÑĤ\",\n \"ĠÚ© اÙħ\",\n \"Ġ ãĥIJ\",\n \"Ġãĥ IJ\",\n \"ãĥ¼ ãĥĸ\",\n \"ãĥ¼ãĥ ĸ\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"Ġ ìĥģíĴĪ\",\n \"Ġìĥģ íĴĪ\",\n \"à¹Ģล ย\",\n \"Äį né\",\n \"ĠÑģÑĢед ÑģÑĤва\",\n \"ĠÑģÑĢедÑģÑĤв а\",\n \"Ġ ÑĤаб\",\n \"ĠÑĤ аб\",\n \"ĠÑĤа б\",\n \"Ġ Ùħار\",\n \"ĠÙħ ار\",\n \"ĠÙħا ر\",\n \"Ġ hled\",\n \"Ġh led\",\n \"Ġhl ed\",\n \"д аÑĤ\",\n \"да ÑĤ\",\n \"ÙĪ ÛĮد\",\n \"ÙĪÛĮ د\",\n \"Ġ ãĥ©\",\n \"Ġãĥ ©\",\n \"ĠØ® د\",\n \"è¤ ĩ\",\n \"ç§ ĺ\",\n \"Ġ برد\",\n \"Ġب رد\",\n \"Ġبر د\",\n \"ĠÏĥ αÏĤ\",\n \"Ïİ ÏĥειÏĤ\",\n \"æĿ ¯\",\n \"λ Ïį\",\n \"å® ¿\",\n \"Ġ ëĤľ\",\n \"ĠëĤ ľ\",\n \"ï» Ł\",\n \"Ġözel likle\",\n \"Ġözellik le\",\n \"Ġкон Ñģ\",\n \"ĠÙħ غ\",\n \"ع ÙĬ\",\n \"à¹Į à¸ģ\",\n \"Ġ ÙĬت\",\n \"ĠÙĬ ت\",\n \"ĠÙħ شاÙĩ\",\n \"ĠÙħØ´ اÙĩ\",\n \"ĠTh anh\",\n \"ĠThan h\",\n \"ा à¤ľà¤¨\",\n \"à¤¾à¤ľ न\",\n \"¥ ¤\",\n \"Ġv lá\",\n \"Ġvl á\",\n \"ĠÙģ ض\",\n \"Τ ÎĻÎļ\",\n \"Ġна Ñĥков\",\n \"ĠнаÑĥк ов\",\n \"е лем\",\n \"ел ем\",\n \"еле м\",\n \"Ġd Ãłng\",\n \"ĠгоÑģп одаÑĢ\",\n \"Âł S\",\n \"и ÑĩеÑģкиÑħ\",\n \"иÑĩеÑģ киÑħ\",\n \"иÑĩеÑģки Ñħ\",\n \"ĠتÙĨ Ùĩا\",\n \"à¤ľ न\",\n \"Ġп ан\",\n \"Ġпа н\",\n \"åĨ ł\",\n \"Ġ ëĤĺëĬĶ\",\n \"ĠëĤĺ ëĬĶ\",\n \"t ÃŃ\",\n \"ä¸Ģ èµ·\",\n \"Ġlã nh\",\n \"Âł v\",\n \"ov ým\",\n \"ový m\",\n \"ز ب\",\n \"ĠجÙħع ÛĮت\",\n \"Ġ æµ·\",\n \"Ġæµ ·\",\n \"ĠоÑģ ÑĥÑīеÑģÑĤв\",\n \"à £i\",\n \"ã i\",\n \"ائ ر\",\n \"Ġ ë³ij\",\n \"Ġë³ ij\",\n \"á»ĭ nh\",\n \"Ġs á»Ńa\",\n \"Ġsá»Ń a\",\n \"à¥ĩà¤Ĥ ।\",\n \"ÄĽ jÅ¡ÃŃ\",\n \"ÄĽj Å¡ÃŃ\",\n \"Ġд ÑĸÑĤ\",\n \"ĠдÑĸ ÑĤ\",\n \"Ġ æĥ\",\n \"Ġæ ĥ\",\n \"mÄ±ÅŁ tı\",\n \"ر ØŃ\",\n \"Ġì§Ģ ê¸Ī\",\n \"å¦ »\",\n \"âĹ ĭ\",\n \"Ġ ì§ĢìĹŃ\",\n \"Ġì§Ģ ìĹŃ\",\n \"ÙĴ ÙĨ\",\n \"Ġur Äįit\",\n \"ĠurÄį it\",\n \"ÙĴ Ùħ\",\n \"z ÃŃ\",\n \"è ķ\",\n \"Ġ Ø´ÙĪر\",\n \"ĠØ´ ÙĪر\",\n \"ĠØ´ÙĪ ر\",\n \"Ġ Không\",\n \"ĠKh ông\",\n \"ÛĮ زÛĮ\",\n \"ÛĮز ÛĮ\",\n \"Ġз г\",\n \"Ġв не\",\n \"Ġpr ávÄĽ\",\n \"Ġprá vÄĽ\",\n \"Ġpráv ÄĽ\",\n \"è« ĭ\",\n \"ا ÙĬت\",\n \"اÙĬ ت\",\n \"ั à¸ģร\",\n \"ัà¸ģ ร\",\n \"Ġolduk ça\",\n \"ãĤģ ãĤĭ\",\n \"ĠT ây\",\n \"ëĿ¼ ìĿ¸\",\n \"èĻ ķ\",\n \"Ġs Æ°\",\n \"Ġ ник\",\n \"Ġн ик\",\n \"Ġни к\",\n \"Ù ł\",\n \"اش ÛĮÙĨ\",\n \"اشÛĮ ÙĨ\",\n \"e lerde\",\n \"el erde\",\n \"eler de\",\n \"ìĭľ ìķĦ\",\n \"Ġ Ñĥмов\",\n \"ĠÑĥ мов\",\n \"ĠÑĥм ов\",\n \"ĠçalÄ±ÅŁ an\",\n \"Ġ ë¸Ķ\",\n \"Ġë¸ Ķ\",\n \"ĠÑĤак им\",\n \"ĠÑĤа ким\",\n \"ÑĢ ин\",\n \"ÑĢи н\",\n \"Ġ Ø®ÙĦ\",\n \"ĠØ® ÙĦ\",\n \"a yd\",\n \"ay d\",\n \"Ġ ãĥ¡\",\n \"Ġãĥ ¡\",\n \"ей ÑĩаÑģ\",\n \"Ġdo prav\",\n \"Ġdop rav\",\n \"ãģĵ ãģ¨ãģ¯\",\n \"ãģĵãģ¨ ãģ¯\",\n \"Ġ ì¶Ķì²ľ\",\n \"Ġì¶Ķ ì²ľ\",\n \"å» ¶\",\n \"Ġ kı\",\n \"Ġk ı\",\n \"åı ¶\",\n \"ÑĢ иг\",\n \"ÑĢи г\",\n \"íħ ľ\",\n \"çĶ³ åįļ\",\n \"Ġ веÑĤ\",\n \"Ġв еÑĤ\",\n \"Ġве ÑĤ\",\n \"ĠпомоÑī ÑĮÑİ\",\n \"ĠاÙģ راد\",\n \"ĠاÙģر اد\",\n \"ÏĢ ει\",\n \"ÏĢε ι\",\n \"à¹Ģ สร\",\n \"à¹Ģส ร\",\n \"Ġgi ám\",\n \"Ġgiá m\",\n \"é İ\",\n \"h las\",\n \"hl as\",\n \"man ız\",\n \"manı z\",\n \"ан гл\",\n \"анг л\",\n \"Ġmu ž\",\n \"Âł K\",\n \"ÑĢед иÑĤ\",\n \"ÑĢеди ÑĤ\",\n \"设 å¤ĩ\",\n \"ι Ïĥμ\",\n \"ιÏĥ μ\",\n \"Ġc ải\",\n \"Ġcả i\",\n \"Ġ éĢļ\",\n \"ĠéĢ ļ\",\n \"Ġ Ùĥار\",\n \"ĠÙĥ ار\",\n \"Ġпод об\",\n \"ĠмеÑĤ ал\",\n \"ĠÑģам е\",\n \"л ÑĥÑĩ\",\n \"лÑĥ Ñĩ\",\n \"åĤ ³\",\n \"ĠÙĪÙĩ ÙĪ\",\n \"Ġ éĩį\",\n \"Ġéĩ į\",\n \"в ий\",\n \"ви й\",\n \"æ³ ģ\",\n \"Ġ æĿİ\",\n \"ĠæĿ İ\",\n \"ĠiliÅŁ kin\",\n \"ĠiliÅŁk in\",\n \"Ġεί Ïĩε\",\n \"çĬ ¯\",\n \"ÅĻej mÄĽ\",\n \"èŃ ĺ\",\n \"ç¨ ±\",\n \"μ μα\",\n \"μμ α\",\n \"Ġ ÙĦÛĮ\",\n \"ĠÙĦ ÛĮ\",\n \"Ùĩ اÙĬ\",\n \"Ùĩا ÙĬ\",\n \"Ġ опиÑģ\",\n \"Ġо пиÑģ\",\n \"Ġоп иÑģ\",\n \"Ú¯ رد\",\n \"گر د\",\n \"Ġг ÑĢ\",\n \"ĠAnimal ia\",\n \"ĠAnim alia\",\n \"ÐŁ о\",\n \"Ġb óng\",\n \"ĠдеÑĤ ей\",\n \"Ġl âu\",\n \"Ġ æķĻ\",\n \"Ġæķ Ļ\",\n \"Ġпо ÑıÑģ\",\n \"ĠاÙĦ Ø¢\",\n \"ั à¸Ļà¸ķ\",\n \"ัà¸Ļ à¸ķ\",\n \"Ġд ев\",\n \"Ġде в\",\n \"Ġ ÑĨей\",\n \"ĠÑĨ ей\",\n \"ĠÑĨе й\",\n \"ÑĮ в\",\n \"æĥ ł\",\n \"m aları\",\n \"ma ları\",\n \"mal arı\",\n \"malar ı\",\n \"im ler\",\n \"à¥Ī ।ĊĊ\",\n \"à¥Ī। ĊĊ\",\n \"à¥Ī।Ċ Ċ\",\n \"Ġ ном\",\n \"Ġн ом\",\n \"Ġно м\",\n \"z v\",\n \"Ġ à¸ģร\",\n \"Ġà¸ģ ร\",\n \"Ġpay laÅŁ\",\n \"Âł s\",\n \"ि सम\",\n \"िस म\",\n \"ÑģÑĤв еннÑĭÑħ\",\n \"ÑģÑĤвен нÑĭÑħ\",\n \"st oup\",\n \"sto up\",\n \"о нÑĸ\",\n \"он Ñĸ\",\n \"s tÃŃ\",\n \"st ÃŃ\",\n \"ĠØŃ Ú©\",\n \"ĠÚ¯ رÙģت\",\n \"ĠگرÙģ ت\",\n \"Ġگر Ùģت\",\n \"าà¸Ħ า\",\n \"д Ñı\",\n \"ÙĦ اث\",\n \"ÙĦا Ø«\",\n \"Ġzdrav ot\",\n \"ä¸Ĭ ãģĴ\",\n \"ãģ ¼\",\n \"e lere\",\n \"el ere\",\n \"eler e\",\n \"ele re\",\n \"ظ Ùħ\",\n \"ĠÑģ веÑĤ\",\n \"ĠÑģв еÑĤ\",\n \"о ÑĢг\",\n \"оÑĢ г\",\n \"ç« ¥\",\n \"ĠпеÑĢ 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éĺ¿\",\n \"Ġéĺ ¿\",\n \"ĠÙħ ارس\",\n \"ĠÙħا رس\",\n \"ĠÙħار س\",\n \"ĠçeÅŁit li\",\n \"Ġ ÅŁehir\",\n \"ĠÅŁ ehir\",\n \"ĠÅŁeh ir\",\n \"á tor\",\n \"át or\",\n \"à¹ī à¸Ĺ\",\n \"ìĿ´ ëĬĶ\",\n \"Ġ è²\",\n \"Ġè ²\",\n \"é¡ į\",\n \"ç ĻĤ\",\n \"çĻ Ĥ\",\n \"Ġ ниÑĩ\",\n \"Ġн иÑĩ\",\n \"Ġни Ñĩ\",\n \"Ġ ê°Ģì§Ģ\",\n \"Ġê°Ģ ì§Ģ\",\n \"ä¼ ¦\",\n \"r án\",\n \"rá n\",\n \"o stat\",\n \"os tat\",\n \"ost at\",\n \"osta t\",\n \"Ġ ÙĦÙĥ\",\n \"ĠÙĦ Ùĥ\",\n \"è º\",\n \"ĠNg Ãłnh\",\n \"Ġस द\",\n \"æľ Ĺ\",\n \"çĦ¶ åIJİ\",\n \"ãĤ¸ ãĤ§\",\n \"л еÑĢ\",\n \"ле ÑĢ\",\n \"ĠÐŀ на\",\n \"ĠÐŀн а\",\n \"س ÙĪÙĨ\",\n \"سÙĪ ÙĨ\",\n \"Ïģ ον\",\n \"Ïģο ν\",\n \"ĠدرÛĮ اÙģت\",\n \"ĠدرÛĮا Ùģت\",\n \"à¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ\",\n \"Ġ dál\",\n \"Ġd ál\",\n \"Ġdá l\",\n \"ĠмÑĸÑģ ÑĨе\",\n \"Ġд ней\",\n \"Ġ اÙĦات\",\n \"Ġا ÙĦات\",\n \"ĠاÙĦ ات\",\n \"Ġरह त\",\n \"ï¼Į 对\",\n \"è³ĩ æĸĻ\",\n \"ä»» ä½ķ\",\n \"é Ħ\",\n \"t aj\",\n \"ta j\",\n \"β ά\",\n \"Ġна до\",\n \"Ġнад о\",\n \"ĠÑģÑĤ Ñĥд\",\n \"ĠÅŁ eh\",\n \"ัà¸į à¸į\",\n \"à¥ĭ ब\",\n 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\"³³³ ³³\",\n \"Ġ ì¤Ħ\",\n \"Ġì¤ Ħ\",\n \"ि फ\",\n \"िठ«\",\n \"ĠÑĤ ол\",\n \"ĠÑĤо л\",\n \"ĠëĤ´ ê°Ģ\",\n \"âĸ ı\",\n \"Ġp há\",\n \"Ġph á\",\n \"ĠÑģп Ñĸв\",\n \"Ġ جÙħÙĬع\",\n \"ĠجÙħ ÙĬع\",\n \"Ġb ezpeÄį\",\n \"Ġbez peÄį\",\n \"Ġ æĹł\",\n \"ĠæĹ ł\",\n \"Ġv Å¡e\",\n \"ĠvÅ¡ e\",\n \"ÑģÑĤ вÑĥ\",\n \"ÑģÑĤв Ñĥ\",\n \"d ust\",\n \"du st\",\n \"o Å¡\",\n \"Ġت ارÙĬØ®\",\n \"ا ØŃØ©\",\n \"اØŃ Ø©\",\n \"ĠÙħشار ÙĥØ©\",\n \"Ġ ακ\",\n \"Ġα κ\",\n \"ั à¸Ļà¸Ļ\",\n \"ัà¸Ļ à¸Ļ\",\n \"éģ Ĭ\",\n \"Ġ ÑģоÑĤ\",\n \"ĠÑģ оÑĤ\",\n \"ĠÑģо ÑĤ\",\n \"Ġ каз\",\n \"Ġк аз\",\n \"Ġка з\",\n \"ĠÑĤ еÑĩение\",\n \"ĠÑĤеÑĩ ение\",\n \"ê¸ ´\",\n \"acak tır\",\n \"ê±° ëĤĺ\",\n \"ี ยม\",\n \"ีย ม\",\n \"ĠÑģ ÑĥÑħ\",\n \"ĠÑģÑĥ Ñħ\",\n \"ĠëĦĪ 무\",\n \"ãģı ãĤĭ\",\n \"ĠкоÑĤоÑĢ ой\",\n \"ا ÙĤØ©\",\n \"اÙĤ Ø©\",\n \"y ıl\",\n \"yı l\",\n \"ãĤ» ãĥĥãĥĪ\",\n \"ĠÑį лем\",\n \"æģ IJ\",\n \"ÙĨ اء\",\n \"ÙĨا Ø¡\",\n \"åħ ©\",\n \"Ġte Äı\",\n \"ä¸ ¥\",\n \"Ġì§Ī 문\",\n \"Ġ 为\",\n \"Ġä¸ º\",\n \"ìĭľ íĹĺ\",\n \"Ġп ÑĢок\",\n \"ĠпÑĢ ок\",\n \"ĠпÑĢо к\",\n \"u jeme\",\n \"uj eme\",\n \"uje me\",\n \"ü cü\",\n \"üc ü\",\n \"ĠاÙĦÙħ غ\",\n \"ĠØŃ Ø³Ø§Ø¨\",\n \"ĠØŃس اب\",\n \"ãģĹ ãģ¦ãģĦ\",\n \"ãģĹãģ¦ ãģĦ\",\n \"к ова\",\n \"ко ва\",\n \"ков а\",\n \"ĠÄij Ãło\",\n \"Ġп ÑĢиз\",\n \"ĠпÑĢ из\",\n \"ĠпÑĢи з\",\n \"ĠÙĪ ÙħÙĨ\",\n \"ĠÙĪÙħ ÙĨ\",\n \"Ġ оÑĢ\",\n \"Ġо ÑĢ\",\n \"à¸ģ à¸ķ\",\n \"а ÑĦ\",\n \"Ġ à¸ŀร\",\n \"Ġà¸ŀ ร\",\n \"ÑĨи ей\",\n \"æ ª\",\n \"Ġ působ\",\n \"Ġp ůsob\",\n \"Ġpů sob\",\n \"åŃ© åŃIJ\",\n \"Ġb ánh\",\n \"Ġbán h\",\n \"ĠÑĦоÑĢм Ñĥ\",\n \"ĠÑĦоÑĢ мÑĥ\",\n \"Ġ á»ķ\",\n \"Ġá» ķ\",\n \"Ġмен ее\",\n \"Ġмене е\",\n \"à¹ī าห\",\n \"à¹īา ห\",\n \"ни ÑĨа\",\n \"ниÑĨ а\",\n \"ี Ċ\",\n \"Ġв олоÑģ\",\n \"Ġвол оÑģ\",\n \"Ġار ائÙĩ\",\n \"第 ä¸ī\",\n \"ëIJĺ ìĹĪ\",\n \"Ġkıs m\",\n \"Ġkı sm\",\n \"ãĥ¼ ãĥĬ\",\n \"ãĥ¼ãĥ Ĭ\",\n \"ler imiz\",\n \"ÙĨ ÙĬÙĨ\",\n \"ÙĨÙĬ ÙĨ\",\n \"Ġ NgÆ°á»Ŀi\",\n \"ĠNg Æ°á»Ŀi\",\n \"ĠоÑĤ дел\",\n \"ĠоÑĤд ел\",\n \"çļĦ æŶåĢĻ\",\n \"о нов\",\n \"он ов\",\n \"Äį an\",\n \"i zm\",\n \"iz m\",\n \"ĠÑģоб ой\",\n 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\"Ġì± ħ\",\n \"Ġ hlas\",\n \"Ġh las\",\n \"Ġhl as\",\n \"е кÑĤив\",\n \"ек ÑĤив\",\n \"екÑĤ ив\",\n \"екÑĤи в\",\n \"ĠÏĢ λη\",\n \"lu ÄŁu\",\n \"luÄŁ u\",\n \"好 çļĦ\",\n \"ĠÚĨ ÙĪÙĨ\",\n \"ĠB eled\",\n \"ĠBe led\",\n \"ĠBel ed\",\n \"Ġen gel\",\n \"Ġeng el\",\n \"нÑı Ñı\",\n \"ĠyaÅŁ an\",\n \"Ñĩ ниÑħ\",\n \"ار ÙĬØ©\",\n \"ارÙĬ Ø©\",\n \"म त\",\n \"ãĥĭãĥĭ ãĥĭãĥĭ\",\n \"åĭ ¢\",\n \"Ġ åĨħ\",\n \"ĠåĨ ħ\",\n \"Ġíı¬ íķ¨\",\n \"Ġоб Ñģ\",\n \"Ġth ấp\",\n \"Ġd ây\",\n \"ãĥĸ ãĥ©\",\n \"а ÑĤÑĭ\",\n \"аÑĤ Ñĭ\",\n \"ĠÑģво ей\",\n \"ĠÑģвое й\",\n \"ãĤī ãģªãģĦ\",\n \"åıij çĶŁ\",\n \"e rece\",\n \"er ece\",\n \"ere ce\",\n \"Ġod bor\",\n \"Ġв неÑģ\",\n \"Ġвне Ñģ\",\n \"ĠÄIJ ảng\",\n \"ĠëıĮ ìķĦ\",\n \"ÄĽ li\",\n \"ÄĽl i\",\n \"ı sında\",\n \"ıs ında\",\n \"ısı nda\",\n \"Ġबद ल\",\n \"v nÃŃ\",\n \"vn ÃŃ\",\n \"ãģ® ãģ«\",\n \"Ġпо ÑĤом\",\n \"ĠпоÑĤ ом\",\n \"im de\",\n \"imd e\",\n \"a lama\",\n \"al ama\",\n \"ala ma\",\n \"alam a\",\n \"âĢ ª\",\n \"Ġstej nÄĽ\",\n \"е ÑĢе\",\n \"еÑĢ е\",\n \"éĴ ¢\",\n 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£\",\n \"Ġ íĦ\",\n \"Ġí Ħ\",\n \"æİ ī\",\n \"Ġs oru\",\n \"Ġso ru\",\n \"Ġsor u\",\n \"ĠФедеÑĢа ÑĨии\",\n \"ĠÑģиÑģÑĤем и\",\n \"æĸĻ çĦ¡æĸĻ\",\n \"Ġà¤ķ à¤Ń\",\n \"ĠÙĩ ÙĨد\",\n \"ĠÙĩÙĨ د\",\n \"ุà¸ĩ à¹Ģà¸Ĺà¸ŀ\",\n \"ĠOsman lı\",\n \"ĠпÑĢод олж\",\n \"Ġ ÙĪÙĦا\",\n \"ĠÙĪ ÙĦا\",\n \"ĠÙĪÙĦ ا\",\n \"ĠÄįlán ku\",\n \"Ġa dım\",\n \"Ġad ım\",\n \"Ġadı m\",\n \"ĠÏĢ αÏģά\",\n \"ĠÏĢαÏģ ά\",\n \"ĠÏĢα Ïģά\",\n \"Ġzá ÅĻÃŃ\",\n \"Ġ à¸Īำà¸ģ\",\n \"Ġà¸Īำ à¸ģ\",\n \"Ġп ен\",\n \"m enin\",\n \"me nin\",\n \"men in\",\n \"meni n\",\n \"Ġìĺ¤ ëĬĺ\",\n \"em iz\",\n \"emi z\",\n \"οÏį ÏĤ\",\n \"- स\",\n \"íķĺ ìĭľ\",\n \"ĠÑħ ви\",\n \"ĠÑħв и\",\n \"ãĤ° ãĥ©\",\n \"Ġп оÑĪ\",\n \"Ġпо ÑĪ\",\n \"ĠÐŀдна ко\",\n \"ĠÐŀднак о\",\n \"Ñĸд но\",\n \"íĺ ľ\",\n \"Ñī ими\",\n \"Ñīи ми\",\n \"Ñīим и\",\n \"èĥ ¸\",\n \"ĠÄ° lk\",\n \"ĠÄ°l k\",\n \"m ey\",\n \"me y\",\n \"Ġз да\",\n \"Ġзд а\",\n \"κ λη\",\n \"а лом\",\n \"ал ом\",\n \"ало м\",\n \"à¹Ģศ ษ\",\n \"ا ÙĨا\",\n \"اÙĨ ا\",\n \"Ġ ÎŁÎ¹\",\n \"ĠÎŁ ι\",\n \"Ġ åıĮ\",\n \"Ġåı 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\"ĠÑĢÑĥ ки\",\n \"ĠÑĢÑĥк и\",\n \"ĠصÙģ ØŃÙĩ\",\n \"Å¡ ky\",\n \"Å¡k y\",\n \"é» Ĵ\",\n \"èģ ļ\",\n \"ãģĭ ãģ«\",\n \"Ġs âu\",\n \"ед аг\",\n \"ĠÑģÑĤоÑĢ онÑĭ\",\n \"ĠÑģÑĤоÑĢон Ñĭ\",\n \"Ġ ruk\",\n \"Ġr uk\",\n \"Ġru k\",\n \"âĢĮ âĢĮ\",\n \"ĠØ¢ ÙĪر\",\n \"Ġع دÙħ\",\n \"Ġعد Ùħ\",\n \"õ i\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"Ġبا زار\",\n \"Ġباز ار\",\n \"Ġe deb\",\n \"Ġed eb\",\n \"Ġv ÄįetnÄĽ\",\n \"оп аÑģ\",\n \"Ġн ег\",\n \"Ġне г\",\n \"m ayan\",\n \"ma 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\"ĠmÄĽl i\",\n \"Ġδη μο\",\n \"å ¯¸\",\n \"å¯ ¸\",\n \"ĠÙĪ ÙĥاÙĨ\",\n \"ĠÙĪÙĥ اÙĨ\",\n \"Ġप ढ\",\n \"ĠвеÑĢ Ñħ\",\n \"Ġе Ñij\",\n \"C ách\",\n \"Các h\",\n \"ä½ľ 为\",\n \"ĠÐļ ол\",\n \"ĠÐļо л\",\n \"Ġ ве\",\n \"Ġв е\",\n \"Ġ деÑĢж\",\n \"Ġд еÑĢж\",\n \"ĠдеÑĢ ж\",\n \"em oc\",\n \"emo c\",\n \"ãģ¸ ãģ®\",\n \"Ġ аÑĢÑħ\",\n \"ĠаÑĢ Ñħ\",\n \"Ġk iếm\",\n \"Ġ æĺİ\",\n \"Ġæĺ İ\",\n \"ĠлÑİд ини\",\n \"ĠлÑİди ни\",\n \"ë ·\",\n \"ĠÙĪ اÙĦت\",\n \"ĠÙĪاÙĦ ت\",\n \"ĠÙĪا ÙĦت\",\n \"Ġ è°\",\n \"Ġè °\",\n \"çģ ¯\",\n \"íĻ ķ\",\n \"Ġ구 매\",\n \"Ġ ç§ij\",\n \"Ġç§ ij\",\n \"it nÃŃ\",\n \"и ÑĩеÑģкие\",\n \"иÑĩеÑģ кие\",\n \"иÑĩеÑģки е\",\n \"ĠÙĨ Ùģس\",\n \"ĠÙĨÙģ س\",\n \"Ġت ÙĦÙģ\",\n \"ĠتÙĦ Ùģ\",\n \"ا ÙģÛĮ\",\n \"اÙģ ÛĮ\",\n \"ĠØŃ Ø³ÙĨ\",\n \"ĠØŃس ÙĨ\",\n \"âĸ¡ âĸ¡\",\n \"ý vá\",\n \"ýv á\",\n \"ÄŁ ın\",\n \"ģı n\",\n \"ıyor uz\",\n \"ĠCh ÃŃ\",\n \"ĠÙ¾ÚĺÙĪÙĩ Ø´\",\n \"Ġ ÏĦÎŃ\",\n \"ĠÏĦ ÎŃ\",\n \"ĠÏĥ Ïĩε\",\n \"ĠÏĥÏĩ ε\",\n \"о леÑĤ\",\n \"ол еÑĤ\",\n \"α ιδ\",\n \"αι δ\",\n \"Ġh ạt\",\n \"Ġhạ t\",\n \"à¸ł 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\"ĠÙħÙħ Ú©ÙĨ\",\n \"Ġv hod\",\n \"Ġvh od\",\n \"ев ой\",\n \"ево й\",\n \"о вал\",\n \"ов ал\",\n \"ова л\",\n \"Ġн алеж\",\n \"Ġна леж\",\n \"Ġнал еж\",\n \"ï¼¼ :\",\n \"ย ะ\",\n \"ĠÙħ اشÛĮÙĨ\",\n \"Ġg á»Ńi\",\n \"al ım\",\n \"alı m\",\n \"Ġìµľ ìłĢ\",\n \"Ùij Ùĩ\",\n \"á»Ļ p\",\n \"à¥Ģ ।Ċ\",\n \"à¥Ģ। Ċ\",\n \"Ġ пиÑģ\",\n \"Ġп иÑģ\",\n \"Ġпи Ñģ\",\n \"Ġ вÑģÑı\",\n \"Ġв ÑģÑı\",\n \"ĠвÑģ Ñı\",\n \"Ñĩ ем\",\n \"Ñĩе м\",\n \"o zenÃŃ\",\n \"oz enÃŃ\",\n \"oze nÃŃ\",\n \"Ġ äºļæ´²\",\n \"Ġäºļ æ´²\",\n \"е ÑĢалÑĮ\",\n \"еÑĢ алÑĮ\",\n \"еÑĢа лÑĮ\",\n \"기 ëĬĶ\",\n \"Ġп ÑĢез\",\n \"ĠпÑĢ ез\",\n \"ĠпÑĢе з\",\n \"ĠعÙħ ÙĪÙħÛĮ\",\n \"и ÑĩниÑħ\",\n \"иÑĩ ниÑħ\",\n \"Ġ æ²³\",\n \"Ġæ² ³\",\n \"od nÃŃ\",\n \"åıª æĺ¯\",\n \"Ġpo dp\",\n \"Ġpod p\",\n \"à¹īà¸Ńà¸ĩ à¸ŀ\",\n \"ाय द\",\n \"ाà¤ĩ ल\",\n \"ล à¸Ķ\",\n \"ĠÑĢÑĸÑĪ еннÑı\",\n \"Ġ ÑĤÑĥÑĢ\",\n \"ĠÑĤ ÑĥÑĢ\",\n \"ĠÑĤÑĥ ÑĢ\",\n \"ÑģÑĮ кÑĥ\",\n \"ÑģÑĮк Ñĥ\",\n \"Ġsal dır\",\n \"ĠÐĽ ÑĮв\",\n \"ãĢģ Ċ\",\n \"ĠÙ¾ÛĮ ÙĪÙĨد\",\n \"åѦ ä¹ł\",\n \"λ Ïī\",\n \"o 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ÏģÏĮ\",\n \"Ġprac ovnÃŃ\",\n \"Ġpracov nÃŃ\",\n \"ĠÙĬ ÙĥÙĪÙĨ\",\n \"ĠÙĬÙĥ ÙĪÙĨ\",\n \"Ġb eÅŁ\",\n \"Ġbe ÅŁ\",\n \"ج ز\",\n \"ั à¸ļร\",\n \"ัà¸ļ ร\",\n \"ĠYön et\",\n \"Ġشر اÛĮØ·\",\n \"ĠتÙĪس عÙĩ\",\n \"çĹ ĩ\",\n \"à¸ĩ à¹Ģà¸Ľ\",\n \"ä¸Ģ 次\",\n \"ĠÐłÐ¾ÑģÑģий Ñģкой\",\n \"æľĢ é«ĺ\",\n \"Ġsp olu\",\n \"Ġspo lu\",\n \"Ġspol u\",\n \"д аеÑĤÑģÑı\",\n \"да еÑĤÑģÑı\",\n \"даеÑĤ ÑģÑı\",\n \"Ñĸ ÑĤÑĥ\",\n \"ÑĸÑĤ Ñĥ\",\n \"Ġоб ÑĢаÑĤ\",\n \"ĠобÑĢа ÑĤ\",\n \"e nek\",\n \"en ek\",\n \"ene k\",\n \"Ġ mek\",\n \"Ġm ek\",\n \"Ġme k\",\n \"å¦ Ī\",\n \"Ġдоп олниÑĤелÑĮ\",\n \"Ġ ç²\",\n \"Ġç ²\",\n \"ĠÙĦ ÙĦت\",\n \"ĠÙĦÙĦ ت\",\n \"ĠHaz iran\",\n \"æ¸ Ī\",\n \"à¹Į à¸Ĥà¸Ńà¸ĩ\",\n \"ĠÑĦ он\",\n \"Ġê²ĥ ìľ¼ë¡ľ\",\n \"Ġnh é\",\n \"Ġbu gün\",\n \"Ġbug ün\",\n \"ov ém\",\n \"ové m\",\n \"Ġза веÑĢ\",\n \"Ġзав еÑĢ\",\n \"Ġд виг\",\n \"Ġдв иг\",\n \"Ġдви г\",\n \"ä¼ Ļ\",\n \"Ġnu ôi\",\n \"меÑĢ ик\",\n \"ме ÑĢик\",\n \"ĠÙĨÙħ ÙĪÙĨÙĩ\",\n \"èį ·\",\n \"Ñĥ вала\",\n \"Ñĥв ала\",\n \"Ñĥва ла\",\n \"ç¿ »\",\n \"Ġs ân\",\n \"ог оÑİ\",\n \"ого Ñİ\",\n \"ا سÙĬØ©\",\n \"اس ÙĬØ©\",\n \"اسÙĬ Ø©\",\n \"Ñĥн кÑĤ\",\n \"Ñĥнк ÑĤ\",\n \"á nÃŃm\",\n \"án ÃŃm\",\n \"ánÃŃ m\",\n \"ен ное\",\n \"енно е\",\n \"Ġph út\",\n \"Ġ मर\",\n \"Ġम र\",\n \"ĠاÙĦ ÙĪØ·\",\n \"ĠاÙĦÙĪ Ø·\",\n \"Ġлег ко\",\n \"Ġ ãĢĭ\",\n \"ĠãĢ ĭ\",\n \"ë¡ľ ëĵľ\",\n \"ĠKas ım\",\n \"ÙĬ ÙĦÙĬ\",\n \"ÙĬÙĦ ÙĬ\",\n \"ĠbaÄŁlantı lar\",\n \"ĠÑĤ ÑĢÑĥд\",\n \"ĠÑĤÑĢ Ñĥд\",\n \"ĠÑĤÑĢÑĥ д\",\n \"Ø· Ùĩ\",\n \"Ġk vůli\",\n \"ÑģÑĤ оÑı\",\n \"Ġsat Ä±ÅŁ\",\n \"Ġh áºŃu\",\n \"ĠبÙĩ ترÛĮÙĨ\",\n \"ĠبÙĩتر ÛĮÙĨ\",\n \"ĠÑģ елÑĮ\",\n \"ĠÑģел ÑĮ\",\n \"ั à¸Ļว\",\n \"ัà¸Ļ ว\",\n \"o su\",\n \"os u\",\n \"य न\",\n \"åĽ ³\",\n \"ι δ\",\n \"ÛĮ تÛĮ\",\n \"ÛĮت ÛĮ\",\n \"ĠQu áºŃn\",\n \"Ġ ей\",\n \"Ġе й\",\n \"à¹Ģว ลา\",\n \"à¹Ģวล า\",\n \"ìĬ¤ íĥĢ\",\n \"ìĤ¬ 를\",\n \"Ġا ÙĩÙĦ\",\n \"ĠاÙĩ ÙĦ\",\n \"η γ\",\n \"Ġk á»·\",\n \"Ġká» ·\",\n \"Ġ наÑĤ\",\n \"Ġн аÑĤ\",\n \"Ġна ÑĤ\",\n \"âĢ ¡\",\n \"Ñĸ ÑĩниÑħ\",\n \"ÑĸÑĩ ниÑħ\",\n \"ĠÑĢазвиÑĤи Ñı\",\n \"ĠÑĢазви ÑĤиÑı\",\n \"e cial\",\n \"ec 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ÙĬÙĤ\",\n \"ÙĤÙĬ ÙĤ\",\n \"Ġotev ÅĻ\",\n \"Ġ fak\",\n \"Ġf ak\",\n \"Ġfa k\",\n \"Ġng uy\",\n \"Ġngu y\",\n \"б ов\",\n \"бо в\",\n \"l acaÄŁ\",\n \"lac aÄŁ\",\n \"ا طر\",\n \"اط ر\",\n \"ãģ« ãĤĪãĤĬ\",\n \"ãģ«ãĤĪ ãĤĬ\",\n \"æĺ¯ åľ¨\",\n \"Ġt ầng\",\n \"ìĿ¸ ìĿ´\",\n \"a ÅĻ\",\n \"ç ¢°\",\n \"ç¢ °\",\n \"ÏĮ με\",\n \"Ġ ê°Ī\",\n \"Ġê° Ī\",\n \"ĠØ£ ØŃد\",\n \"ĠØ£ØŃ Ø¯\",\n \"غ راÙģ\",\n \"غر اÙģ\",\n \"ĠÙĬ ØŃ\",\n \"ï½ §\",\n \"ĠاÙĦØŃÙĬ اة\",\n \"Ġ lep\",\n \"Ġl ep\",\n \"Ġle p\",\n \"Ġ ฮ\",\n \"Ġภ®\",\n \"t ae\",\n \"ta e\",\n \"Ġl Æ°Æ¡ng\",\n \"è½ ®\",\n \"Ġз мÑĸн\",\n \"ĠзмÑĸ н\",\n \"Ġзм Ñĸн\",\n \"ĠÐļи ÑĹв\",\n \"ĠмÑĸ ÑģÑı\",\n \"ĠмÑĸÑģ Ñı\",\n \"к ав\",\n \"ка в\",\n \"à¸ķ ะ\",\n \"Ġm noho\",\n \"Ġmn oho\",\n \"Ġmnoh o\",\n \"ĠNgh á»ĭ\",\n \"èĻ İ\",\n \"Ġ ãĥŁ\",\n \"Ġãĥ Ł\",\n \"Ġp ráci\",\n \"Ġpr áci\",\n \"Ġprá ci\",\n \"Ġg á»ijc\",\n \"ĠY eni\",\n \"ĠYe ni\",\n \"ĠYen i\",\n \"ا ضÙĬ\",\n \"اض ÙĬ\",\n \"Ġ èij\",\n \"Ġè ij\",\n \"Ġк ла\",\n \"Ġкл а\",\n \"ı ng\",\n \"ÏĦ εί\",\n \"ÏĦε ί\",\n \"Ġb eni\",\n \"Ġbe ni\",\n \"Ġben i\",\n \"Ġ عد\",\n \"Ġع د\",\n \"Ġ aktu\",\n \"Ġak tu\",\n \"Ġakt u\",\n \"ĠÙĪ ÙĤد\",\n \"ĠÙĪÙĤ د\",\n \"Ġпод гоÑĤов\",\n \"Ġgi ai\",\n \"Ġgia i\",\n \"( æ°´\",\n \"Ġs aç\",\n \"Ġsa ç\",\n \"ĠÙħÙĨ اسب\",\n \"ĠÙħÙĨاس ب\",\n \"âĸ ĭ\",\n \"ÙIJ Ùĩ\",\n \"é į\",\n \"à¸Ń à¸Ĺ\",\n \"ĠسÛĮ اسÛĮ\",\n \"o lit\",\n \"ol it\",\n \"oli t\",\n \"ĠاÙĦ جز\",\n \"ĠاÙĦج ز\",\n \"Ø· ÙĦب\",\n \"Ø·ÙĦ ب\",\n \"Ġ sey\",\n \"Ġs ey\",\n \"Ġse y\",\n \"e rence\",\n \"er ence\",\n \"ere nce\",\n \"eren ce\",\n \"ì´ Į\",\n \"ĠвнÑĥÑĤÑĢ ен\",\n \"ĠвнÑĥÑĤ ÑĢен\",\n \"Ġ à¸Ļาย\",\n \"Ġà¸Ļ าย\",\n \"ĠìķĬ ìķĺëĭ¤\",\n \"ĠìķĬìķĺ ëĭ¤\",\n \"o lik\",\n \"ol ik\",\n \"oli k\",\n \"æľĢ åIJİ\",\n \"ä» ª\",\n \"Ġ ÑĢÑĸд\",\n \"ĠÑĢ Ñĸд\",\n \"è¼ ĥ\",\n \"Ġ باب\",\n \"Ġب اب\",\n \"Ġبا ب\",\n \"Ñĥ ди\",\n \"Ñĥд и\",\n \"Ġ ÑģÑĤÑĥп\",\n \"ĠÑģÑĤ Ñĥп\",\n \"ĠÄij ứng\",\n \"ĠÄijứ ng\",\n \"ĠÅŁ öyle\",\n \"Ġ íķĻìĥĿ\",\n \"ĠíķĻ ìĥĿ\",\n \"Ġв лаÑģÑĤи\",\n \"Ġвла ÑģÑĤи\",\n \"ĠвлаÑģ ÑĤи\",\n \"Ġh ãng\",\n \"Ġhã ng\",\n \"à¹ī าว\",\n \"à¹īา ว\",\n \"ĠÚ© اÙĩØ´\",\n \"Ġ ëĵ¯\",\n \"Ġëĵ ¯\",\n \"ĠجÙħ ÙĦÙĩ\",\n \"Ġد کتر\",\n \"ad olu\",\n \"ado lu\",\n \"adol u\",\n \"Ġت بد\",\n \"Ġتب د\",\n \"ظ اÙħ\",\n \"Ġz naÄį\",\n \"Ġzn aÄį\",\n \"Ġد ÙĨÛĮ\",\n \"ĠدÙĨ ÛĮ\",\n \"Ġs ạn\",\n \"å¼ ±\",\n \"ÏĢ ι\",\n \"Ġ çIJĨ\",\n \"ĠçIJ Ĩ\",\n \"Ġ ÙģصÙĦ\",\n \"ĠÙģ صÙĦ\",\n \"и нг\",\n \"ин г\",\n \"Ðļ Ðŀ\",\n \"ĠС ов\",\n \"ĠСо в\",\n \"Ġziy aret\",\n \"Ġ دÙħ\",\n \"Ġد Ùħ\",\n \"ç« ¹\",\n \"Ġsah ibi\",\n \"is ayar\",\n \"isa yar\",\n \"isay ar\",\n \"ÄŁ a\",\n \"ĠпеÑĢÑĸ од\",\n \"Ġs na\",\n \"Ġsn a\",\n \"( æľ¨\",\n \"Ġ нее\",\n \"Ġн ее\",\n \"Ġне е\",\n \"ĠÑĦак ÑĤоÑĢ\",\n \"ĠÑĦакÑĤ оÑĢ\",\n \"м еж\",\n \"ме ж\",\n \"åº Ħ\",\n \"r áž\",\n \"rá ž\",\n \"ок ÑĢем\",\n \"Ġž al\",\n \"ิ à¹Ģศษ\",\n \"è± ª\",\n \"ou cÃŃ\",\n \"ĠU lus\",\n \"ĠUl us\",\n \"Ġtak že\",\n \"ا ÙĪÙĨ\",\n \"اÙĪ ÙĨ\",\n \"н иÑĤи\",\n \"ни ÑĤи\",\n \"ниÑĤ и\",\n \"нÑĮ о\",\n \"ëį ¸\",\n \"Ġ Ùĥرة\",\n \"ĠÙĥ رة\",\n \"ĠÙĥر Ø©\",\n \"åľ ³\",\n \"ĠArth ropoda\",\n \"ĠÑĤ одÑĸ\",\n \"ĠÑĤо дÑĸ\",\n \"Ġدر صد\",\n \"ุ รà¸ģ\",\n \"ุร à¸ģ\",\n \"ĠÑģв ого\",\n \"ĠÑģво го\",\n \"说 éģĵ\",\n \"Ġc ánh\",\n \"Ġcá nh\",\n \"Ġcán h\",\n \"æĵ Ĭ\",\n \"Ġ ä¸ĭè½½\",\n \"Ġä¸ĭ è½½\",\n \"èī ¾\",\n \"Ġnik dy\",\n \"Ø® Ø·\",\n \"ĠÑģ ейÑĩаÑģ\",\n \"ÙĪ ÙĬÙĦ\",\n \"ÙĪÙĬ ÙĦ\",\n \"a met\",\n \"am et\",\n \"ame t\",\n \"문 ìĿĺ\",\n \"ĠE ÄŁitim\",\n \"大 ä¼ļ\",\n \"Ġb ÅĻez\",\n \"за ÑĨÑĸÑı\",\n \"Ġty to\",\n \"н ай\",\n \"на й\",\n \"غ Ùħ\",\n \"Ġ é©\",\n \"Ġé ©\",\n \"计 ç®Ĺ\",\n \"Türk iye\",\n \"Ġmn ož\",\n \"åIJĪ ä½ľ\",\n \"æľį åĭĻ\",\n \"Ġkažd ý\",\n \"ĠÑİ ÑĢид\",\n \"Ġ βα\",\n \"Ġβ α\",\n \"à¥Ĥ à¤ļ\",\n \"åIJĮ ãģĺ\",\n \"Ġ çĭ\",\n \"Ġç ĭ\",\n \"ί ÏĦ\",\n \"ÙĪÛĮ ÙĨت\",\n \"ÙĪÛĮÙĨ ت\",\n \"ا ÙĨس\",\n \"اÙĨ س\",\n \"æľĢ 大\",\n \"Ġ Từ\",\n \"ĠT ừ\",\n \"éŃĶ æ³ķ\",\n \"Ġб ли\",\n \"Ġбл и\",\n \"ĠÑĤак ое\",\n \"ĠÑĤа кое\",\n \"ãģ ľ\",\n \"ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ\",\n \"ãĢĢĠ 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žitÃŃ\",\n \"Ġpoužit ÃŃ\",\n \"íĨ ¡\",\n \"Ġб лок\",\n \"Ġбл ок\",\n \"èĢ ¶\",\n \"ู ร\",\n \"Ġv üc\",\n \"Ø´ ÙĪد\",\n \"Ø´ÙĪ د\",\n \"и ма\",\n \"им а\",\n \"ни ÑĨип\",\n \"ниÑĨ ип\",\n \"ìĿ´ ëĵľ\",\n \"Ġ âĢIJ\",\n \"ĠâĢ IJ\",\n \"Ġ назнаÑĩ\",\n \"Ġна знаÑĩ\",\n \"Ġназ наÑĩ\",\n \"Ġназна Ñĩ\",\n \"Ġstr any\",\n \"Ġstran y\",\n \"Ġstra ny\",\n \"æ® ¿\",\n \"ĠاÙĦ رÙĪ\",\n \"ĠاÙĦر ÙĪ\",\n \"çº ¸\",\n \"åĪ ij\",\n \"ï¼Į ä»İ\",\n \"Ġ ë©´\",\n \"Ġë© ´\",\n \"ĠпÑĢовед еннÑı\",\n \"Ġh ava\",\n \"Ġha va\",\n \"Ġhav a\",\n \"ĠìĹĨ ìĹĪëĭ¤\",\n \"ĠìĹĨìĹĪ ëĭ¤\",\n \"å¢ŀ åĬł\",\n \"Ú ¾\",\n \"ç¼ º\",\n \"Ġع بار\",\n \"Ġعب ار\",\n \"Ġt ắc\",\n \"Ġin ÅŁa\",\n \"er se\",\n \"ers e\",\n \"ر ÙĬب\",\n \"رÙĬ ب\",\n \"Ġá»ķ n\",\n \"Ø£ Ø©\",\n \"ĠÏĢολ ι\",\n \"ĠÏĢο λι\",\n \"Ġm ắc\",\n \"Ñģ ол\",\n \"Ñģо л\",\n \"æ´ ŀ\",\n \"- го\",\n \"-г о\",\n \"ç¨ĭ 度\",\n \"ĠвикоÑĢиÑģÑĤ аннÑı\",\n \"âĢŀ ظ\",\n \"e lerinde\",\n \"eler inde\",\n \"eleri nde\",\n \"elerin de\",\n \"ĠNh Æ°ng\",\n \"ĠNhÆ° ng\",\n \"st 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Ø£\",\n \"ĠE ÄŁer\",\n \"åħ±åĴĮ åĽ½\",\n \"Ø° ر\",\n \"Ġd aÄŁ\",\n \"Ġda ÄŁ\",\n \"è¡Į ä¸ļ\",\n \"ê±°ëŀĺ ê°Ģ\",\n \"è´Ł è´£\",\n \"C ông\",\n \"ĠÑĦ илÑĮ\",\n \"ĠÑĦил ÑĮ\",\n \"Ġ аÑģ\",\n \"Ġа Ñģ\",\n \"Ġch ẳng\",\n \"ним аÑĤÑĮ\",\n \"нима ÑĤÑĮ\",\n \"Ġif ad\",\n \"Ġi fad\",\n \"Ġ ìħ\",\n \"Ġì ħ\",\n \"çĪ µ\",\n \"ĠÅĻe Å¡enÃŃ\",\n \"åĽ½ 产\",\n \"Ġкак ой\",\n \"Ġка кой\",\n \"Ġम ध\",\n \"ĠY ar\",\n \"ĠYa r\",\n \"ob raz\",\n \"obra z\",\n \"Ġon emoc\",\n \"Ġ âĤ\",\n \"Ġâ Ĥ\",\n \"åİŁ åĽł\",\n \"ĠÙĥ رد\",\n \"ĠÙĥر د\",\n \"Ġآز اد\",\n \"Ġad lı\",\n \"ĠH izmet\",\n \"ãĥ¼ ãĥij\",\n \"ãĥ¼ãĥ ij\",\n \"ÙĨ سÙĬØ©\",\n \"ÙĨس ÙĬØ©\",\n \"Ġв нÑĥÑĤ\",\n \"ĠвнÑĥ ÑĤ\",\n \"Ġd ále\",\n \"Ġdál e\",\n \"Ġdá le\",\n \"Îķ Î¥\",\n \"Ġ ÑĥÑħ\",\n \"ĠÑĥ Ñħ\",\n \"Ġ ÑĢев\",\n \"ĠÑĢ ев\",\n \"ĠÑĢе в\",\n \"Ġ меÑĪ\",\n \"Ġм еÑĪ\",\n \"ĠkoÅŁ ul\",\n \"ĠاÛĮ راÙĨÛĮ\",\n \"ĠاÛĮراÙĨ ÛĮ\",\n \"éĺ µ\",\n \"Ġ ëıĻìķĪ\",\n \"ĠëıĻ ìķĪ\",\n \"à¹Ģ à¸Ł\",\n \"à¹ĢภŁ\",\n \"ëłĪ 벨\",\n \"è¨Ń è¨Ī\",\n \"p rak\",\n \"pr ak\",\n \"pra k\",\n \"p oÄį\",\n \"po Äį\",\n \"اع دة\",\n \"اعد Ø©\",\n \"Ġas ker\",\n \"Ġask er\",\n \"ĠÙĪÛĮ ÚĺÙĩ\",\n \"ĠÙĪÛĮÚĺ Ùĩ\",\n \"ĠТ еÑĢ\",\n \"ĠТе ÑĢ\",\n \"mak ta\",\n \"makt a\",\n \"ĠÄįty ÅĻ\",\n \"Âł С\",\n \"âĢĮÚ©ÙĨ ÙĨد\",\n \"ï¼Į 並\",\n \"ĠÑĢоÑģ Ñĸй\",\n \"Ġu nut\",\n \"Ġun ut\",\n \"è¿Ļ ä¸Ģ\",\n \"o pak\",\n \"op ak\",\n \"opa k\",\n \"èĢ IJ\",\n \"Ġз амеÑĤ\",\n \"Ġза меÑĤ\",\n \"Ġзам еÑĤ\",\n \"à¹Į ล\",\n \"ب ÙĨ\",\n \"Ġ 몰\",\n \"Ġëª °\",\n \"Ġins anlar\",\n \"Ġinsan lar\",\n \"åı¯ æĺ¯\",\n \"æ¢ ¦\",\n \"к од\",\n \"ко д\",\n \"èĽ Ľ\",\n \"kl adnÃŃ\",\n \"klad nÃŃ\",\n \"ÑĢов од\",\n \"ÑĢо вод\",\n \"ĠмÑĸ ÑģÑĤа\",\n \"ĠмÑĸÑģ ÑĤа\",\n \"ĠмÑĸÑģÑĤ а\",\n \"åĩº äºĨ\",\n \"Ġп аÑģ\",\n \"Ġпа Ñģ\",\n \"о бов\",\n \"об ов\",\n \"Ú¯ اÙĩÛĮ\",\n \"گاÙĩ ÛĮ\",\n \"в ин\",\n \"ви н\",\n \"à¥įर ध\",\n \"Ġком пон\",\n \"Ġкомп он\",\n \"Ġ аÑĤ\",\n \"Ġа ÑĤ\",\n \"Ġa det\",\n \"Ġad et\",\n \"Ġade t\",\n \"Ġ ãĥģ\",\n \"Ġãĥ ģ\",\n \"ĠØ° ات\",\n \"ĠØŃ ÙĪ\",\n \"Ġtro chu\",\n \"à¹ģ หà¸Ļ\",\n \"à¹ģห à¸Ļ\",\n \"Ġзав жди\",\n \"ĠPart isi\",\n \"ĠParti si\",\n \"ĠS avaÅŁ\",\n \"ĠSav aÅŁ\",\n \"Ġs ÃŃd\",\n \"ĠsÃŃ d\",\n \"Ġ Ñģон\",\n \"ĠÑģ он\",\n \"ĠÑģо н\",\n \"ر ÙĬÙģ\",\n \"رÙĬ Ùģ\",\n \"Ġz cela\",\n \"åĺ ´\",\n \"ĠÑĦ ÑĥÑĤ\",\n \"il erek\",\n \"ile rek\",\n \"iler ek\",\n \"ilere k\",\n \"m alıdır\",\n \"malı dır\",\n \"Ġd á»±a\",\n \"Ġdá»± a\",\n \"à¸Ĺำ à¸ĩาà¸Ļ\",\n \"ĠÙĪÙĦ ÙĥÙĨ\",\n \"ĠÙĪÙĦÙĥ ÙĨ\",\n \"ãģª ãĤĵãģł\",\n \"ãģªãĤĵ ãģł\",\n \"ĠÚ© ÙħÛĮ\",\n \"ĠÚ©Ùħ ÛĮ\",\n \"Ġléka ÅĻ\",\n \"Ïģ Ïį\",\n \"ج Ùħع\",\n \"جÙħ ع\",\n \"ın ızı\",\n \"ını zı\",\n \"ınız ı\",\n \"ĠAn adolu\",\n \"ãģ«ãĤĪ ãģ£ãģ¦\",\n \"Ġê·¸ëŁ¬ ëĤĺ\",\n \"Ġ íĮĶ\",\n \"ĠíĮ Ķ\",\n \"Ñĸ ÑĤÑĮ\",\n \"ÑĸÑĤ ÑĮ\",\n \"Ġ ¦\",\n \"Ġ ¦\",\n \"ä¸į è¦ģ\",\n \"à¸ĸ ม\",\n \"Ġ ÙĬد\",\n \"ĠÙĬ د\",\n \"ĠpÅĻ ep\",\n \"ĠpÅĻe p\",\n \"Ġ è¦ģ\",\n \"Ġè¦ ģ\",\n \"ĠпÑĢо екÑĤ\",\n \"ĠпÑĢоек ÑĤ\",\n \"ĠÑĢе ги\",\n \"ĠÑĢег и\",\n \"Ġd ạy\",\n \"к ового\",\n \"ков ого\",\n \"ково го\",\n \"Ġ ıs\",\n \"Ġı s\",\n \"ĠK ı\",\n \"ĠÙģÙĬ Ùĩا\",\n \"ĠÙģÙĬÙĩ ا\",\n \"ÛĮ ات\",\n \"ÛĮا ت\",\n \"ĠÑģÑĤ ала\",\n \"ĠÑģÑĤал а\",\n \"ĠÑģÑĤа ла\",\n \"æĬ ľ\",\n \"Ñĥ ÑĢа\",\n \"ÑĥÑĢ а\",\n \"ĠÙ¾ اÛĮاÙĨ\",\n \"ĠپاÛĮ اÙĨ\",\n \"Ġپا ÛĮاÙĨ\",\n \"Ġitibar en\",\n \"а нÑĸÑĹ\",\n \"ан ÑĸÑĹ\",\n \"анÑĸ ÑĹ\",\n \"Ġо ÑĦоÑĢм\",\n \"л еÑĩ\",\n \"ле Ñĩ\",\n \"ε ξ\",\n \"æĶ¿ çŃĸ\",\n \"Ġ ç½ij\",\n \"Ġç½ ij\",\n \"å Ĥ¬\",\n \"åĤ ¬\",\n \"ĠìĿ´ 룰\",\n \"Ġkar deÅŁ\",\n \"Ñİ Ñīего\",\n \"ÑİÑī его\",\n \"л ки\",\n \"ĠاÛĮ اÙĦات\",\n \"ت Ùĩا\",\n \"تÙĩ ا\",\n \"Ġпод Ñħод\",\n \"ĠØŃ ÙĪÙĦ\",\n \"ĠØŃÙĪ ÙĦ\",\n \"ĠÑģов ÑĢем\",\n \"íĿ ¥\",\n \"Ġ 詳細\",\n \"Ġè© ³ç´°\",\n \"ı yı\",\n \"ıy ı\",\n \"ĠتÙĤ ÙĪ\",\n \"æ¯Ķ è¾ĥ\",\n \"Ġαν ÏĦι\",\n \"Ġ ΣΤ\",\n \"ĠΣ Τ\",\n \"ji Å¡Å¥\",\n \"yn ı\",\n \"Ġpo cházet\",\n \"- Ðļ\",\n \"Ġзав д\",\n \"Ùİ Ø³\",\n \"ç»ĵ æŀĦ\",\n \"Ùħ ار\",\n \"Ùħا ر\",\n \"ν οι\",\n \"νο ι\",\n \"ĠÎł εÏģι\",\n \"ĠγεÏģ ι\",\n \"èĩ £\",\n \"Ġna cházÃŃ\",\n \"Ġnach ázÃŃ\",\n \"ÏĦ Ïİ\",\n \"à¥įय त\",\n \"u yu\",\n \"uy u\",\n \"æķ Ĺ\",\n \"e bi\",\n \"eb i\",\n \"Ġë°Ķ ë¡ľ\",\n \"ĠгÑĢ н\",\n \"ĠاÙĦ اس\",\n \"Ġorg án\",\n \"Ġ edin\",\n \"Ġe din\",\n \"Ġed in\",\n \"Ġedi n\",\n \"åŁ ĥ\",\n \"à¹ģ à¸Ħ\",\n \"ĠØŃ Ø¯ÙĪد\",\n \"ĠØŃد ÙĪد\",\n \"ĠдÑĢÑĥг ой\",\n \"ĠдÑĢÑĥго й\",\n \"оÑģ ков\",\n \"оÑģк ов\",\n \"ĠS ợ\",\n \"ĠpÅĻ ib\",\n \"ĠpÅĻi b\",\n \"ä¿Ŀ æĬ¤\",\n \"Ùħ بر\",\n \"Ġ ãĥĨ\",\n \"Ġãĥ Ĩ\",\n \"Ġd oz\",\n \"Ġdo z\",\n \"op tera\",\n \"opt era\",\n \"ิล à¸Ľ\",\n \"د ارÛĮ\",\n \"دار ÛĮ\",\n \"دا رÛĮ\",\n \"æĦŁ è§ī\",\n \"代 çIJĨ\",\n \"ÙĨ دا\",\n \"ÙĨد ا\",\n \"ا ÙĬا\",\n \"اÙĬ ا\",\n \"ص ÙĨ\",\n \"Ġce lé\",\n \"Ġcel é\",\n \"Ġ è©ķ\",\n \"Ġè© ķ\",\n \"à¸ĩ à¸Ļ\",\n \"Ġ leh\",\n \"Ġl eh\",\n \"Ġle h\",\n \"èİ· å¾Ĺ\",\n \"ãĢĢ ï¾ī\",\n \"ĠìĦł ìĪĺ\",\n \"르 ëĬĶ\",\n \"à¤Ĩ र\",\n \"å§Ķ åijĺ\",\n \"æĹł çłģ\",\n \"Ġ è·\",\n \"Ġè ·\",\n \"Ġza jÃŃm\",\n \"Ġzaj ÃŃm\",\n \"ec ké\",\n \"eck é\",\n \"æ µľ\",\n \"æµ ľ\",\n \"ĠÑĥ нÑĸвеÑĢÑģиÑĤ\",\n \"ĠбÑİдж еÑĤ\",\n \"à¥ĩ .\",\n \"Ġv stup\",\n \"Ġ оÑī\",\n \"Ġо Ñī\",\n \"Ġ åľĭ\",\n \"Ġåľ ĭ\",\n \"ä¸ģ 缮\",\n \"Ġв едÑĮ\",\n \"Ġвед ÑĮ\",\n \"Ġë§IJ ìĿĦ\",\n \"Ġtek nik\",\n \"Ġtekn ik\",\n \"ãĢĢ ï½Į\",\n \"ãĢĢï½ Į\",\n \"ĠпÑĸд виÑī\",\n \"ĠÑģвÑıз и\",\n \"ĠÑģвÑı зи\",\n \"Ġتر جÙħ\",\n \" ī\",\n \"ĠÄij âu\",\n \"Ñĸ Ñĩного\",\n \"ÑĸÑĩ ного\",\n \"å°ij å¹´\",\n \"e cta\",\n \"ect a\",\n \"ec ta\",\n \"ि लत\",\n \"िल त\",\n \"ι οÏĤ\",\n \"ιο ÏĤ\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"t eg\",\n \"te g\",\n \"á» īnh\",\n \"á»ī nh\",\n \"¯ ¿\",\n \"Ġne bu\",\n \"Ġneb u\",\n \"ÙĬ ÙĬÙĨ\",\n \"о ÑıÑĤ\",\n \"оÑı ÑĤ\",\n \"é¤ Ĭ\",\n \"ĠاÙĤتص ادÛĮ\",\n \"ĠاÙĤتصاد ÛĮ\",\n \"âĢĻ nun\",\n \"âĢĻn un\",\n \"ĠÐĴ Ñĸк\",\n \"Ġng Äĥn\",\n \"ëĮĢ íķĻêµIJ\",\n \"ëĮĢíķĻ êµIJ\",\n \"é ı\",\n \"़ र\",\n \"ا باÙĨ\",\n \"اب اÙĨ\",\n \"Ùİ Ùĥ\",\n \"Ġet kil\",\n \"Ġetk il\",\n \"Ġetki l\",\n \"Ġch ắn\",\n \"Ġë°ľ ìĥĿ\",\n \"Ġtam amen\",\n \"Ġtamam en\",\n \"ĠÙħØŃ ÛĮØ·\",\n \"ü lü\",\n \"ül ü\",\n \"åģ¥ 康\",\n \"ĠÑĢаÑģÑĤ ениÑı\",\n \"ÏĢο ί\",\n \"Ġ è¶ħ\",\n \"Ġè¶ ħ\",\n \"á Äį\",\n \"ĠìĻ¸ ë¶Ģ\",\n \"ĠØ® ÛĮÙĦÛĮ\",\n \"ĠØ®ÛĮ ÙĦÛĮ\",\n \"Ġد ÙĪست\",\n \"ĠدÙĪ ست\",\n \"à¹Ģà¸Ĥ à¸ķ\",\n \"Ġk alan\",\n \"Ġka lan\",\n \"Ġkal an\",\n \"ë¨ ¼\",\n \"a vÄĽ\",\n \"av ÄĽ\",\n \"문 íĻĶ\",\n \"Ġди аг\",\n \"ĠÙĨ ÙĪÙĬس\",\n \"ĠÙĨÙĪ ÙĬس\",\n \"íķ ij\",\n \"à¸ŀ าะ\",\n \"ëĭ¤ ê°Ģ\",\n \"Ġn iá»ĩm\",\n \"Ġس ÙĪÙħ\",\n \"ĠسÙĪ Ùħ\",\n \"- м\",\n \"à¸Ķำ à¹Ģà¸Ļ\",\n \"à¹ĩ ว\",\n \"ãĢĤ ãģĵãģ®\",\n \"ç¯ ī\",\n \"Width Space\",\n \"Zero WidthSpace\",\n \"ائ ÙħØ©\",\n \"ائÙħ Ø©\",\n \"à¹Ħà¸ĭ à¸ķ\",\n \"ä¸ĭè½½ 次æķ°\",\n \"ä¼¼ ä¹İ\",\n \"ĠÑĤ в\",\n \"Ġzá kaz\",\n \"Ġج دا\",\n \"Ġجد ا\",\n \"Ġg ider\",\n \"Ġgi der\",\n \"Ġgid er\",\n \"ãĥ¼ ãĥĵ\",\n \"ãĥ¼ãĥ ĵ\",\n \"n ů\",\n \"Ġë§ģ íģ¬\",\n \"ĠdÃ¼ÅŁ ük\",\n \"Ñĥ нок\",\n \"Ñĥн ок\",\n \"Ġt óc\",\n \"ĠÑĤ ÑĢÑĥб\",\n \"ĠÑĤÑĢ Ñĥб\",\n \"ĠÑĤÑĢÑĥ б\",\n \"о кÑģ\",\n \"ок Ñģ\",\n \"Ġtr ải\",\n \"Ġtrả i\",\n \"Ġm iá»ħn\",\n \"Ġth Æ°á»Ľc\",\n \"Ġnh áºŃt\",\n \"Âł D\",\n \"m asının\",\n \"mas ının\",\n \"masını n\",\n \"è¼ ª\",\n \"ĠÎĿ ο\",\n \"er ç\",\n \"Ġdok once\",\n \"Ġdokon ce\",\n \"ĠG üven\",\n \"ĠGü ven\",\n \"ov aná\",\n \"ova ná\",\n \"ovan á\",\n \"е зд\",\n \"ез д\",\n \"Ñĸ нÑĮ\",\n \"Ñĸн ÑĮ\",\n \"èģ ²\",\n \"اÙĦ Ø£\",\n \"ï¼Į ä½Ĩæĺ¯\",\n \"ï¼Įä½Ĩ æĺ¯\",\n \"Ġпол ноÑģÑĤÑĮÑİ\",\n \"Port ály\",\n \"ĠØŃ Ø§Ùģظ\",\n \"à¥Ĥ à¤ķ\",\n \"ÑĢ Ñĥн\",\n \"ÑĢÑĥ н\",\n \"人 çī©\",\n \"Ġa çı\",\n \"Ġaç ı\",\n \"Ġp oru\",\n \"Ġpo ru\",\n \"Ġpor u\",\n \"er iod\",\n \"eri od\",\n \"ĠAmer ika\",\n \"ĠAmerik a\",\n \"çĩ Ł\",\n \"Ġ رÙĪد\",\n \"Ġر ÙĪد\",\n \"ĠرÙĪ د\",\n \"ĠкÑĢов и\",\n \"ÙĪ ÙĤت\",\n \"ÙĪÙĤ ت\",\n \"éĺ ¶\",\n \"ãĥ»âĶģ ãĥ»âĶģ\",\n \"ر ÙĬÙħ\",\n \"رÙĬ Ùħ\",\n \"åİĨ åı²\",\n \"ä¸ ¸\",\n \"Ġзн овÑĥ\",\n \"Ġзнов Ñĥ\",\n \"ĠÑģво его\",\n \"ĠÑģвое го\",\n \"бÑĥд ÑĮ\",\n \"ĠØŃ Ø¬Ùħ\",\n \"ĠØŃج Ùħ\",\n \"ĠδÏį ο\",\n \"ìķĪ ëĤ´\",\n \"Ġ ãģ§\",\n \"à¹Ī ะ\",\n \"Ùij Ùı\",\n \"çµIJ æŀľ\",\n \"âĢĻ i\",\n \"à¹Į ,\",\n \"åŃĺ äºİ\",\n \"Ġरà¤ĸ न\",\n \"ĠسرÙħ اÛĮÙĩ\",\n \"Ġг лÑĥб\",\n \"ĠглÑĥ б\",\n \"ĠÑĢаз Ñĸ\",\n \"ĠÑĢа зÑĸ\",\n \"Ñĩ нÑĸ\",\n \"ï¼Į åıĪ\",\n \"c ısı\",\n \"cı sı\",\n \"æľī åħ³\",\n \"ãĤ» ãĥ³\",\n \"èIJ ¨\",\n \"ĠGi áo\",\n \"ĠGiá o\",\n \"ĠاÙĦØ« اÙĨÙĬ\",\n \"ĠÑĢаз ом\",\n \"Ġ ÑĤÑĢо\",\n \"ĠÑĤ ÑĢо\",\n \"ĠÑĤÑĢ о\",\n \"Ġaçık lam\",\n \"åĨ³ å®ļ\",\n \"à¸Ń à¸Ľ\",\n \"åĶ ¯\",\n \"ĠÅŁ ark\",\n \"ĠÅŁar k\",\n \"Ġsist emi\",\n \"Ġsistem i\",\n \"Ġto prak\",\n \"Ġtop rak\",\n \"èĢĥ ãģĪ\",\n \"Ġпоп ÑĥлÑıÑĢ\",\n \"Ġ ëĨį\",\n \"ĠëĨ į\",\n \"ا ÙĬÙĨ\",\n \"اÙĬ ÙĨ\",\n \"स म\",\n \"Ġ ÂĢ\",\n \"Ġ Ģ\",\n \"Ġed erek\",\n \"Ġeder ek\",\n \"Ġg ec\",\n \"Ġge c\",\n \"ìĤ¬ ìĹħ\",\n \"ĠÑĢ оки\",\n \"ĠÑĢок и\",\n \"ĠбеÑĢ ем\",\n \"ĠخاÙĨÙĪ ادÙĩ\",\n \"Ġ èµ·\",\n \"Ġèµ ·\",\n \"Ġ ЧÑĤо\",\n \"ĠЧ ÑĤо\",\n \"Ġo bÄĽ\",\n \"Ġob ÄĽ\",\n \"и нÑĸ\",\n \"ин Ñĸ\",\n \"ìĿ´ ìĹĪ\",\n \"ĠIn di\",\n \"ĠInd i\",\n \"Ġд иÑĤ\",\n \"Ġди ÑĤ\",\n \"ãĥ¶ æľĪ\",\n \"Ġнем ного\",\n \"Ġzáklad ÄĽ\",\n \"à¹Ĥ à¸Ħ\",\n \"ĠÑģам ого\",\n \"ĠÑģамо го\",\n \"Ġب ØŃØ«\",\n \"ĠبØŃ Ø«\",\n \"Ġ æ¶\",\n \"Ġæ ¶\",\n \"ов ж\",\n \"Ġоб ÑĢаÑī\",\n \"ĠобÑĢа Ñī\",\n \"à Ĵ\",\n \"ว รร\",\n \"วร ร\",\n \"à¤Ĥ श\",\n \"ĠоÑĩ еÑĢед\",\n \"ĠÙģ رز\",\n \"ĠÙģر ز\",\n \"ëĮĢ íķľ\",\n \"Ġs izin\",\n \"Ġsi zin\",\n \"Ġsiz in\",\n \"Ġsizi n\",\n \"ر Ùģت\",\n \"رÙģ ت\",\n \"Ñİ Ñīим\",\n \"ÑİÑī им\",\n \"æ» ij\",\n \"a vir\",\n \"av ir\",\n \"avi r\",\n \"ĠÙĪ صÙĦ\",\n \"ĠÙĪص ÙĦ\",\n \"Ġqu ay\",\n \"Ġqua y\",\n \"Ġг ип\",\n \"ÑĢ ениÑı\",\n \"ÑĢе ниÑı\",\n \"ÑĢен иÑı\",\n \"à¥į वत\",\n \"à¥įव त\",\n \"ιν Ïīν\",\n \"à¤ľ ह\",\n \"Ġh Æ¡i\",\n \"Ġpo važ\",\n \"Ġpov až\",\n \"Ġع رب\",\n \"м енÑĤа\",\n \"мен ÑĤа\",\n \"менÑĤ а\",\n \"Ġо ÑģÑĤан\",\n \"ĠоÑģÑĤ ан\",\n \"ä¹ĭ éĹ´\",\n \"a cÃŃch\",\n \"ac ÃŃch\",\n \"acÃŃ ch\",\n \"ĠÑģказ ала\",\n \"ĠÑģказал а\",\n \"ìĿ´ ëĿ¼ëĬĶ\",\n \"ìĿ´ëĿ¼ ëĬĶ\",\n \"ĠØ´ اخ\",\n \"Ġëĭ¹ ìĭł\",\n \"ar lar\",\n \"arl ar\",\n \"Ġмл н\",\n \"åĨ ¬\",\n \". :.:.:\",\n \".: .:.:\",\n \".:.: .:\",\n \".:. :.:\",\n \".:.:. :\",\n \"Ġ θε\",\n \"Ġθ ε\",\n \"Ġher kes\",\n \"Ġherk es\",\n \"л Ñıд\",\n \"лÑı д\",\n \"ا Ùħا\",\n \"اÙħ ا\",\n \"Ġë ŃIJ\",\n \"Ïĥιμο ÏĢοι\",\n \"Ġ obraz\",\n \"Ġob raz\",\n \"Ġobr az\",\n \"Ġobra z\",\n \"غ اÙĦ\",\n \"B Æ°á»Ľc\",\n \"å° Ĭ\",\n \"ìŀIJ 를\",\n \"æĢ Ĵ\",\n \"οÏħ Ïģγ\",\n \"å¼ķ ãģį\",\n \"Ġkon uda\",\n \"Ġkonu da\",\n \"ĠاÙĦت ج\",\n \"Ġ krit\",\n \"Ġk rit\",\n \"Ġkr it\",\n \"å¿ į\",\n \"ĠìłĦìĦ¸ ê°Ģ\",\n \"г овоÑĢ\",\n \"го воÑĢ\",\n \"гов оÑĢ\",\n \"Ġist iyor\",\n \"о ки\",\n \"ок и\",\n \"ĠобеÑģп еÑĩ\",\n \"ĠобеÑģпе Ñĩ\",\n \"Ġay rıca\",\n \"Ġayrı ca\",\n \"à¹Ģ à¸ľ\",\n \"à¹Ģภľ\",\n \"а ÑĢод\",\n \"аÑĢ од\",\n \"Ä° Åŀ\",\n \"ĠجÙħ ÙĩÙĪرÛĮ\",\n \"ĠجÙħÙĩÙĪر ÛĮ\",\n \"ĠÑģво иÑħ\",\n \"ĠÑģвои Ñħ\",\n \"Ġprov ád\",\n \"Ġ ÑĢам\",\n \"ĠÑĢ ам\",\n \"ĠÑĢаР¼\",\n \"ĠÑĢа м\",\n \"ĠÙĤ ض\",\n \"л иÑĤелÑĮ\",\n \"лиÑĤ елÑĮ\",\n \"ãĤ± ãĥĥãĥĪ\",\n \"оÑģ оÑĦ\",\n \"Ġर हन\",\n \"Ġरह न\",\n \"k ový\",\n \"ko vý\",\n \"kov ý\",\n \"ì° ¸\",\n \"γ κα\",\n \"γκ α\",\n \"λ οι\",\n \"λο ι\",\n \"μ ÏĢο\",\n \"μÏĢ ο\",\n \"ĠÄij au\",\n \"ĠÄija u\",\n \"н иÑİ\",\n \"ни Ñİ\",\n \"Ġman žel\",\n \"Ġ íĺ¼\",\n \"Ġíĺ ¼\",\n \"ĠÑĤ иÑģ\",\n \"ĠÑĤи Ñģ\",\n \"ãĥĨ ãĥ«\",\n \"ab ilecek\",\n \"abil ecek\",\n \"abile cek\",\n \"abilece k\",\n \"н ин\",\n \"ни н\",\n \"à¸ģรรม à¸ģาร\",\n \"éł IJ\",\n \"Ġph ê\",\n \"j edn\",\n \"je dn\",\n \"jed n\",\n \"交 æµģ\",\n \"Ġвним ание\",\n \"об ÑĢеÑĤ\",\n \"Ġжиз нÑĮ\",\n \"ÑĢи ÑģÑĤи\",\n \"ÑĢиÑģÑĤ и\",\n \"ÑĢиÑģ ÑĤи\",\n \"à¥Ī à¤ļ\",\n \"Ġyüz den\",\n \"Ġyüzde n\",\n \"Ġg iy\",\n \"Ġgi y\",\n \"éļ Ķ\",\n 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ãĥij\",\n \"ãĥ³ãĥ ij\",\n \"μÎŃν Ïīν\",\n \"Ġgenel likle\",\n \"Ġدر ÙħاÙĨ\",\n \"Ù ª\",\n \"Ġak ıl\",\n \"ĠÐľ Ñĭ\",\n \"Ġet miÅŁ\",\n \"Ġetm iÅŁ\",\n \"Å¡ la\",\n \"Ġвозмож ноÑģÑĤÑĮ\",\n \"Ġвозможно ÑģÑĤÑĮ\",\n \"Ġgün cel\",\n \"Ġná ro\",\n \"å½¢ å¼ı\",\n \"Ġα ÏĢοÏĦε\",\n \"ĠαÏĢο ÏĦε\",\n \"ĠмÑĸÑģ ÑĨÑı\",\n \"Ġ رض\",\n \"Ġر ض\",\n \"ä¸į çŁ¥éģĵ\",\n \"ä¸įçŁ¥ éģĵ\",\n \"r ava\",\n \"ra va\",\n \"rav a\",\n \"ĠÎļ ά\",\n \"ิà¸Ļ à¸Ĺร\",\n \"ิà¸Ļà¸Ĺ ร\",\n \"Ġли ÑģÑĤÑĮ\",\n \"ĠлиÑģÑĤ ÑĮ\",\n \"èĨ ľ\",\n \"ãģ«ãģª ãĤĬ\",\n \"Ġ æĿ¾\",\n \"ĠæĿ ¾\",\n \"å® ı\",\n \"Ġм иÑģ\",\n \"Ġми Ñģ\",\n \"át nÃŃ\",\n \"Ġyıl lık\",\n \"ĠMerk ezi\",\n \"ĠMerkez i\",\n \"Ġiç eri\",\n \"Ġiçer i\",\n \"ÅĻ ÃŃž\",\n \"ÅĻÃŃ Å¾\",\n \"Ġp ÅĻe\",\n \"ĠpÅĻ e\",\n \"Ïĩ Ïģι\",\n \"Ġ åįĥ\",\n \"Ġåį ĥ\",\n \"Ġs rp\",\n \"Ġsr p\",\n \"à¹Ĥ à¸Ĺร\",\n \"à¹Ĥà¸Ĺ ร\",\n \"ĠK rál\",\n \"ĠKr ál\",\n \". 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оÑĤ\",\n \"Ġпло ÑĤ\",\n \"Ġn itel\",\n \"Ġni tel\",\n \"Ġnit el\",\n \"ìĬ¤ íģ¬\",\n \"ĠSon ra\",\n \"ĠÑģ боÑĢ\",\n \"ĠÑģб оÑĢ\",\n \"Ġ ÏĥοÏħ\",\n \"ĠÏĥ οÏħ\",\n \"Ġol mam\",\n \"Ġolm am\",\n \"Ġolma m\",\n \"Ġan aliz\",\n \"Ġanal iz\",\n \"à¹Į ว\",\n \"Ġm ỹ\",\n \"Ġmá» ¹\",\n \"ce ae\",\n \"cea e\",\n \"Ġ ден\",\n \"Ġд ен\",\n \"Ġде н\",\n \"веÑĢ жд\",\n \"веÑĢж д\",\n \"Ạ¢\",\n \"ãģĵ ãģ¨ãĤĤ\",\n \"ãģĵãģ¨ ãĤĤ\",\n \"ìĤ¬ íķŃ\",\n \"è¨Ģ ãģ£ãģŁ\",\n \"Ġ ì¹´ì§Ģëħ¸\",\n \"Ġì¹´ ì§Ģëħ¸\",\n \"ÑĢ иÑĤи\",\n \"ÑĢи ÑĤи\",\n \"ÑĢиÑĤ и\",\n \"Ġch ce\",\n \"Ġçev ir\",\n \"ÛĮ ÛĮÙĨ\",\n \"ä¼ļ è®®\",\n \"ัม à¸ŀ\",\n \"Ġ åĦ\",\n \"Ġå Ħ\",\n \"ĠÙ¾ در\",\n \"å¼ı ä¼ļ社\",\n \"Ġ ÑĨен\",\n \"ĠÑĨ ен\",\n \"ĠÑĨе н\",\n \"ิ à¸ĸ\",\n \"Ġji nak\",\n \"Ġjin ak\",\n \"Ġб лÑİ\",\n \"Ġбл Ñİ\",\n \"и ÑĨин\",\n \"иÑĨ ин\",\n \"ÙĴ Ùĩ\",\n \"Ú© ÙĪر\",\n \"Ú©ÙĪ ر\",\n \"Ġ ìķħ\",\n \"Ġìķ ħ\",\n \"e ksiyon\",\n \"ek siyon\",\n \"eks iyon\",\n \"ĠÑģ веÑĢ\",\n \"ĠÑģв еÑĢ\",\n \"ĠобÑĢаз ованиÑı\",\n \"Ġ ãĥĻ\",\n \"Ġãĥ Ļ\",\n \"æľī 人\",\n \"Ġbilg ileri\",\n \"Ġbilgi leri\",\n \"Ġbilgiler i\",\n \"Ġh ầu\",\n \"еÑĢ Ñĸг\",\n \"еÑĢÑĸ г\",\n \"Ġva Å¡e\",\n \"Ġn edir\",\n \"Ġne dir\",\n \"Ġned ir\",\n \"ä¸į å¾Ĺ\",\n \"ĠbaÅŁar ılı\",\n \"ĠbaÅŁarı lı\",\n \"Ġkay bet\",\n \"Ġkayb et\",\n \"å© ·\",\n \"ĠÐĿ ав\",\n \"ĠÐĿа в\",\n \"Ġê´Ģ íķľ\",\n \"Ñģ ÑĤÑİ\",\n \"ÑģÑĤ Ñİ\",\n \"å®ŀ éĻħ\",\n \"k lady\",\n \"kl ady\",\n \"klad y\",\n \"kla dy\",\n \"д аÑĤÑĮ\",\n \"да ÑĤÑĮ\",\n \"даÑĤ ÑĮ\",\n \"r aç\",\n \"ra ç\",\n \"Ġkuv vet\",\n \"à¸ģาร à¸Ĺ\",\n \"å ļ\",\n \"Ġ ÑĢеп\",\n \"ĠÑĢ еп\",\n \"ĠÑĢе п\",\n \"Ġ à¸Ŀ\",\n \"ĠภĿ\",\n \"ĠDi ÄŁer\",\n \"íĶĦ íĬ¸\",\n \"Ġnej vÄĽtÅ¡ÃŃ\",\n \"Ġìłģ ìļ©\",\n \"Ġonemoc nÄĽnÃŃ\",\n \"а ка\",\n \"ак а\",\n \"Ðł аз\",\n \"ĠÙģ Ø¥ÙĨ\",\n \"ãĤµ ãĤ¤ãĤº\",\n \"ãĤµãĤ¤ ãĤº\",\n \"Ġv lád\",\n \"Ġvl ád\",\n \"Ġvlá d\",\n \"Ġr ady\",\n \"Ġrad y\",\n \"Ġra dy\",\n \"ãĢģ ãģĵãĤĮ\",\n \"ÑģÑĤв ие\",\n \"lı ÄŁa\",\n \"lıģ a\",\n \"å ŃĶ\",\n \"åŃ Ķ\",\n \"Ġ áo\",\n \"Ġá o\",\n \"à¸Ń าà¸ģาศ\",\n \"Ġ à¤ıम\",\n \"Ġà¤ı म\",\n \"δ αÏĤ\",\n \"δα ÏĤ\",\n \"Ġа пÑĢ\",\n \"Ġап ÑĢ\",\n \"æİ Ľ\",\n \"Ġ ç«ĭ\",\n \"Ġç« ĭ\",\n \"âĸı âĸı\",\n \"ĠС м\",\n \"Ġne má\",\n \"Ġnem á\",\n \"Ġ è¢\",\n \"Ġè ¢\",\n \"νο μα\",\n \"νομ α\",\n \"ĠÙģ رÙĪد\",\n \"ĠÙģر ÙĪد\",\n \"ĠÙģرÙĪ د\",\n \"Ġül ke\",\n \"Ġülk e\",\n \"Ġ æĺŁ\",\n \"Ġæĺ Ł\",\n \"ั à¸Ļà¸ģ\",\n \"ัà¸Ļ à¸ģ\",\n \"ãģķãĤĵ ãģ®\",\n \"eÅŁ il\",\n \"ÄŁ iz\",\n \"ÄŁi z\",\n \"ĠÐij оÑĢ\",\n \"Ġt ầm\",\n \"ει ÏĦοÏħÏģγ\",\n \"Ġ γÏģα\",\n \"Ġγ Ïģα\",\n \"à¥įष à¤ķ\",\n \"Ġv ẻ\",\n \"Ġkend isine\",\n \"Ġkendisi ne\",\n \"ĠìķĮ ê³ł\",\n \"Ġêµ Ńìłľ\",\n \"ĠêµŃ ìłľ\",\n \"ĠnÄĽk do\",\n \"Ġ ÛĮÙĩ\",\n \"ĠÛĮ Ùĩ\",\n \"Ġکار بر\",\n \"ãĥĻ ãĥ«\",\n \"ï» ´\",\n \"Ġt uyên\",\n \"Ġtuy ên\",\n \"Ġç at\",\n \"Ġça t\",\n \"âĢIJ âĢIJ\",\n \" ı\",\n \"Ġ ìĤ¬ìĹħ\",\n \"ĠìĤ¬ ìĹħ\",\n \"é ĨĴ\",\n \"éĨ Ĵ\",\n \"æıIJ é«ĺ\",\n \"æ· ¡\",\n \"Ġ ÄŁ\",\n \"ĠÄ Ł\",\n \"èĸ ¦\",\n \"ãĢĭ ï¼Ī\",\n \"æ¡ ĥ\",\n \"ìĹ Ħ\",\n \"Ġ æŀĹ\",\n \"Ġæŀ Ĺ\",\n \"Ä Ĥ\",\n \"ĠÄĮ ech\",\n \"α ιο\",\n \"αι ο\",\n \"ĠØ· رÙĬÙĤ\",\n \"Ġطر ÙĬÙĤ\",\n \"Ġзав еÑĢÑĪ\",\n \"ĠзавеÑĢ ÑĪ\",\n \"تÙĪ بر\",\n \"ĠØŃ Ø¬\",\n \"ĠÎŃÏĩ οÏħν\",\n \"¿ ÃĤ\",\n \"Ġd ÄĽtÃŃ\",\n \"ĠdÄĽ tÃŃ\",\n \"ĠdÄĽt ÃŃ\",\n \"Ġiç ine\",\n \"Ġiçin e\",\n \"Ġiçi ne\",\n \"ĠCh úa\",\n \"ан нÑĭй\",\n \"аннÑĭ й\",\n \"ĠÙĪÛĮ Úĺ\",\n \"Ġna stav\",\n \"Ġnast av\",\n \"ıs ına\",\n \"ısı na\",\n \"ĠÑĹ Ð¼\",\n \"п он\",\n \"по н\",\n \"е нÑı\",\n \"ен Ñı\",\n \"ĠÙĪ ظ\",\n \"Ú¯ ÙĦ\",\n \"หล วà¸ĩ\",\n \"Ġza stav\",\n \"Ġzast av\",\n \"а кон\",\n \"ак он\",\n \"³³³³³³³³³³³³³³³³ ³³³³³³³³³³³³³³³³\",\n \"ĠK ır\",\n \"ĠKı r\",\n \"çµ ¶\",\n \"ĠоÑĢганÑĸ заÑĨÑĸÑĹ\",\n \"ĠоÑĢганÑĸз аÑĨÑĸÑĹ\",\n \"ĠоÑĢганÑĸза ÑĨÑĸÑĹ\",\n \"ãģŁ ãĤĬ\",\n \"Ø° ÙĬ\",\n \"Ġर à¤ķ\",\n \"amp iyon\",\n \"Ġ æ¸ħ\",\n \"Ġæ¸ ħ\",\n \"çľ¼ çĿĽ\",\n \"Ġìķ ĬìĿĢ\",\n \"ĠìķĬ ìĿĢ\",\n \"é¹ ¿\",\n \"Ġ å¿ĥ\",\n \"Ġå¿ ĥ\",\n \"ĠпÑĢек ÑĢаÑģ\",\n \"ĠÑģ егоднÑı\",\n \"Ġ सल\",\n \"Ġस ल\",\n \"ĠÏħ ÏĢÏĮ\",\n \"ĠÏħÏĢ ÏĮ\",\n \"ĠÐķ го\",\n \"ĠÐĽ и\",\n \"ãĤ¨ ãĥ«\",\n \"Ġл ÑİÑĤ\",\n \"ĠлÑİ ÑĤ\",\n \"é¥ °\",\n \"Ġvz dál\",\n \"¯ ÃĤ\",\n \"Ġна Ñıв\",\n \"Ġتش Ú©ÛĮÙĦ\",\n \"Ġس ÙĪÛĮ\",\n \"ĠسÙĪ ÛĮ\",\n \"Ġt ái\",\n \"Ġtá i\",\n \"Ġk apı\",\n \"Ġkap ı\",\n \"ĠsvÄĽt ÄĽ\",\n \"ĠsvÄĽ tÄĽ\",\n \"δ ÏĮν\",\n \"δÏĮ ν\",\n \"æ¼ ¢\",\n \"ì į¨\",\n \"ĠbaÅŁv ur\",\n \"ÑĢ ина\",\n \"ÑĢи на\",\n \"ÑĢин а\",\n \"Ġk elim\",\n \"Ġke lim\",\n \"Ġkel im\",\n \"аÑĤ ок\",\n \"аÑĤо к\",\n \"Ġκά θε\",\n \"ĠYük sek\",\n \"à¹ĩà¸Ļ à¸ľ\",\n \"éł Ĥ\",\n \"åIJĮ æĻĤ\",\n \"ÅŁ tır\",\n \"ÅŁt ır\",\n \"ว à¸ĩศ\",\n \"วà¸ĩ ศ\",\n \"o ty\",\n \"ot y\",\n \"Ġ ارد\",\n \"Ġا رد\",\n \"Ġار د\",\n \"ĠìŀIJìĭł ìĿĺ\",\n \"ĠÑıн ва\",\n \"üyor du\",\n \"æĿ ¨\",\n \"ĠâĢĵ Ċ\",\n \"ï¼Į å®ĥ\",\n \"е йн\",\n \"ей н\",\n \"ĠпеÑĢ еÑĤ\",\n \"ĠпеÑĢе ÑĤ\",\n \"ĠdeÄŁiÅŁik lik\",\n \"ĠогÑĢа ниÑĩ\",\n \"ìĦľ ìļ¸\",\n \"Ġgel iyor\",\n \"ĠÙ¾ Ø°ÛĮر\",\n \"åĵ ²\",\n \"ey in\",\n \"eyi n\",\n \"Ġëı Ī\",\n \"Ġun iverz\",\n \"Ġh ned\",\n \"Ġhn ed\",\n \"Ġt áºŃn\",\n \"vo ÅĻÃŃ\",\n \"voÅĻ ÃŃ\",\n \"Ġn iên\",\n \"Ġni ên\",\n \"dÄĽ podob\",\n \"ìĤ¬ 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\"ĠнаÑĩ алÑĮ\",\n \"Ġне дел\",\n \"Ġнед ел\",\n \"ĠÑģиÑģÑĤем Ñĥ\",\n \"ج ÙĬ\",\n \"اد ات\",\n \"ادا ت\",\n \"Ġ æ¢\",\n \"Ġæ ¢\",\n \"ĠجاÙħ عة\",\n \"ĠجاÙħع Ø©\",\n \"Ġ ä»İ\",\n \"Ġà¤ħ फ\",\n \"èĸ Ħ\",\n \"Ġب اÙĤ\",\n \"Ġبا ÙĤ\",\n \"ب ÙĬع\",\n \"بÙĬ ع\",\n \"ãģķ ãĤĮãģ¦\",\n \"ãģķãĤĮ ãģ¦\",\n \"ĠÃĩ alÄ±ÅŁ\",\n \"Ø®ÙĪ است\",\n \"ãĥĥ ãĤ·ãĥ¥\",\n \"ĠØŃ Ø³ÛĮÙĨ\",\n \"ĠØŃس ÛĮÙĨ\",\n \"Ġоб наÑĢÑĥж\",\n \"в Ñĸдом\",\n \"вÑĸ дом\",\n \"вÑĸд ом\",\n \"Ġh ôm\",\n \"л анд\",\n \"ла нд\",\n \"лан д\",\n \"Ġव à¤ľà¤¹\",\n \"س ÙĬÙĨ\",\n \"سÙĬ ÙĨ\",\n \"æł ı\",\n \"Ġna vÃŃc\",\n \"Ġnav ÃŃc\",\n \"ãĤµ ãĤ¤ãĥĪ\",\n \"ãĤµãĤ¤ ãĥĪ\",\n \"ĠÑı комÑĥ\",\n \"ĠÑıк омÑĥ\",\n \"Ġí Ľ\",\n \"ĠY ani\",\n \"ĠYan i\",\n \"ĠYa ni\",\n \"ãĤĵ ãģ§ãģĻ\",\n \"ãĤĵãģ§ ãģĻ\",\n \"Ġг ÑĢÑĥп\",\n \"ĠгÑĢÑĥ п\",\n \"ĠгÑĢ Ñĥп\",\n \"Äį ný\",\n \"ÑĨ ик\",\n \"ÑĨи к\",\n \"ÙĪ ÙĬر\",\n \"ÙĪÙĬ ر\",\n \"Ġ Xã\",\n \"ĠX ã\",\n \"Ġf yz\",\n \"Ġfy z\",\n \"Ġ ï½ī\",\n \"Ġï½ ī\",\n \"âĢĮ ترÛĮÙĨ\",\n \"âĢĮتر ÛĮÙĨ\",\n \"à¤Ł à¤ķ\",\n \"ÑĦоÑĢм 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ìĦľ\",\n \"à¸Ħà¹Ĥà¸Ļ à¹Ĥลย\",\n \"Ġ 好\",\n \"Ġå¥ ½\",\n \"Ġy ayım\",\n \"Ġyay ım\",\n \"ë§Į ëĤ¨\",\n \"ĠкиÑģ лоÑĤ\",\n \"ĠкиÑģл оÑĤ\",\n \"ĠÑį неÑĢг\",\n \"çĸ ¾\",\n \"Ġد Ø´\",\n \"Ġsor uml\",\n \"Ġsoru ml\",\n \"Ġза клад\",\n \"Ġзак лад\",\n \"à¸Ĭ à¸Ńà¸ļ\",\n \"ĠÙģرÙĩÙĨÚ¯ ÛĮ\",\n \"Ġà¤ı ल\",\n \"Ġë¹Ħ êµIJ\",\n \"l erce\",\n \"ler ce\",\n \"Ġ Ø·ÙĦب\",\n \"ĠØ· ÙĦب\",\n \"ĠØ·ÙĦ ب\",\n \"ãģ« ãģĹãģ¦\",\n \"ĠÑı коÑĹ\",\n \"ĠÑıк оÑĹ\",\n \"ĠاÙĦب تÙĩ\",\n \"ĠÐľ аÑĤ\",\n \"ĠÐľÐ° ÑĤ\",\n \"åį ĵ\",\n \"Ġ åħ¬åı¸\",\n \"Ġåħ¬ åı¸\",\n \"Ġsöy ley\",\n \"Ġsöyl ey\",\n \"ĠìĥĪ ë¡ľìļ´\",\n \"ĠÑĦ аÑĢ\",\n \"Ġalt ına\",\n \"Ġaltın a\",\n \"Ġaltı na\",\n \"Ġsta vu\",\n \"Ġstav u\",\n \"âĢĻ ı\",\n \"al izace\",\n \"aliz ace\",\n \"Ġви ÑģÑĤÑĥп\",\n \"æķĻ å¸Ī\",\n \"à¥Ģ à¤ıस\",\n \"à¥Ģà¤ı स\",\n \"o dÄĽ\",\n \"od ÄĽ\",\n \"ĠÑĨ Ñĸл\",\n \"ĠÑĨÑĸ л\",\n \"ĠëĮĢ ìĥģ\",\n \"ĠкоÑĤоÑĢ ом\",\n \"ĠкоÑĤ оÑĢом\",\n \"Ġظ رÙģ\",\n \"éİ ®\",\n \"اÙģ ÙĬØ©\",\n \"اÙģÙĬ Ø©\",\n \"Ġ ìĹĨìĿ´\",\n \"ĠìĹĨ ìĿ´\",\n \"ĠμÏĮ νο\",\n \"ĠC Æ¡\",\n \"å¯ »\",\n \"ÏĦ ιÏĥ\",\n \"ÏĦι Ïĥ\",\n \"Ġ ãĤĦ\",\n \"ĠãĤ Ħ\",\n \"Ġjed noho\",\n \"Ġjedn oho\",\n \"Ġjedno ho\",\n \"ا ا\",\n \"Ø§Ø §\",\n \"et ler\",\n \"Ġव स\",\n \"ĠÑĢазлиÑĩ нÑĭÑħ\",\n \"Ġج غراÙģ\",\n \"Ġth ừa\",\n \"Ġthá» «a\",\n \"ĠгÑĢомад Ñıн\",\n \"ॠ°\",\n \"ĠاÙĦØ£ Ø®\",\n \"Ġнаг ÑĢÑĥз\",\n \"ç¸ ¾\",\n \"à¥Ĥ ह\",\n \"ĠпÑĢÑıм о\",\n \"â Ĭ\",\n \"ĠاÙĦØ£ÙĪÙĦ Ùī\",\n \"æĸ° èģŀ\",\n \"Ġìĥģ íĻ©\",\n \"it esi\",\n \"ite si\",\n \"ites i\",\n \"ëį° ìĿ´íĬ¸\",\n \"æŃ ·\",\n \"ï¼ĮèĢĮ ä¸Ķ\",\n \"ãģ¯ ãģļ\",\n \"产 çĶŁ\",\n \"æ°Ĺ ãģĮ\",\n \"y slu\",\n \"ys lu\",\n \"ysl u\",\n \"ìĸ´ ëĤĺ\",\n \"ا Ú©Ùħ\",\n \"اک Ùħ\",\n \"âĢ ĥ\",\n \") ìĿĢ\",\n \"Ġجست ارÙĩاÛĮ\",\n \"ÙĪ Ø«\",\n \"ãħ İ\",\n \"Ġkav ram\",\n \"v ál\",\n \"vá l\",\n \"æľ Ń\",\n \"æĤ ł\",\n \"ìħ Ģ\",\n \"h rad\",\n \"hr ad\",\n \"hra d\",\n \"Ġت ÙĥÙĪÙĨ\",\n \"ĠتÙĥ ÙĪÙĨ\",\n \"ĠH òa\",\n \"å¹´ çļĦ\",\n \"Ġç arp\",\n \"Ġça rp\",\n \"Ġy olu\",\n \"Ġyo lu\",\n \"Ġyol u\",\n \"Ġdub na\",\n \"ĠÐĴ елик\",\n \"ĠÐĴели к\",\n \"Ġt ôn\",\n \"Ġtô n\",\n \"æ ķĮ\",\n \"æķ Į\",\n \"Ġc oi\",\n \"Ġco i\",\n \"Ġnak onec\",\n \"ĠÑį ÑĤÑĥ\",\n \"ĠÑįÑĤ Ñĥ\",\n \"íĨµ ëł¹\",\n \"ÑĪ ел\",\n \"ÑĪе л\",\n \"Ġneb yl\",\n \"Ġneby l\",\n \"in ç\",\n \"ب اÙĦØ¥ÙĨجÙĦÙĬزÙĬØ©\",\n \"باÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©\",\n \"ï¼ ¡\",\n \"о нÑĮ\",\n \"он ÑĮ\",\n \"Ġне маÑĶ\",\n \"Ġнем аÑĶ\",\n \"Ġê³ł ê°Ŀ\",\n \"ĠÙĤ طع\",\n \"ĠÙĤØ· ع\",\n \"ĠÑĤеÑĢиÑĤоÑĢ ÑĸÑĹ\",\n \"人 ãģ¯\",\n \"ĠΣ α\",\n \"éĤ£ äºĽ\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"ios per\",\n \"í Ĥ¨\",\n \"íĤ ¨\",\n \"r aki\",\n \"ra ki\",\n \"rak i\",\n \"اÛĮ ج\",\n \"Âł C\",\n \"Ġан алÑĸз\",\n \"ãĤı ãĤĬ\",\n \"ĠìķĦëĭ Į\",\n \"ĠاÙĦعÙħÙĦ ÙĬØ©\",\n \"ĠاÙĦعÙħ ÙĦÙĬØ©\",\n \"l ament\",\n \"la ment\",\n \"lam ent\",\n \"é» ¨\",\n \"u jÃŃcÃŃm\",\n \"ujÃŃ cÃŃm\",\n \"ujÃŃcÃŃ m\",\n \"Ġr ẻ\",\n \"ä¸į åĪ°\",\n \"Ġrez erv\",\n \"ĠاÙĦØ° ÙĬÙĨ\",\n \"ĠاÙĦØ°ÙĬ ÙĨ\",\n \"æĭ ¥\",\n \"Ðĺ н\",\n \"Ġतह त\",\n \"r esi\",\n \"re si\",\n \"res i\",\n \"Ġ ãĥ¢\",\n \"Ġãĥ ¢\",\n \"л ев\",\n \"ле в\",\n \"ãĢĢ r\",\n \"Ġ ä»Ĭ\",\n \"Ġö dem\",\n \"Ġpot rav\",\n \"ĠêµIJ ìĪĺ\",\n \"ÑĢ еди\",\n \"ÑĢед и\",\n \"ÑĢе ди\",\n \"ĠÎļ ÎijÎĻ\",\n \"Ġ наÑĩала\",\n \"ĠнаÑĩ ала\",\n \"Ġиз б\",\n \"ĠbÅĻez na\",\n \"Ġle dna\",\n \"Ġled na\",\n \"ÑĢ ÑĥÑİÑĤ\",\n \"ÑĢÑĥ ÑİÑĤ\",\n \"Ġ моÑĤ\",\n \"Ġм оÑĤ\",\n \"Ġмо ÑĤ\",\n \"åıĹ åĪ°\",\n \"ĠÑĢÑĥ кÑĥ\",\n \"ĠÑĢÑĥк Ñĥ\",\n \"Ỽ m\",\n \"ad ele\",\n \"ade le\",\n \"adel e\",\n \"ĠÑĢоз глÑı\",\n \"åħ IJ\",\n \"Ġر ÙĪاÙĨ\",\n \"ĠرÙĪ اÙĨ\",\n \"а ков\",\n \"ак ов\",\n \"Ñĥ ÑĢÑĭ\",\n \"ÑĥÑĢ Ñĭ\",\n \"Ġaz al\",\n \"ĠÑĥ кÑĢа\",\n \"ĠÑĥк ÑĢа\",\n \"пи он\",\n \"ĠÄįlov ÄĽ\",\n \"äºĮäºĮ äºĮäºĮ\",\n \"ا بÙĬ\",\n \"اب ÙĬ\",\n \"Ġas lında\",\n \"ë¹ Ī\",\n \"Ġв ÑĢаÑĩ\",\n \"ĠвÑĢа Ñĩ\",\n \"ë£ ¹\",\n \"Ġген еÑĢа\",\n \"à¸ģาร ส\",\n \"ĠÑģов Ñģем\",\n \"ÙĪ ÙĦا\",\n \"ÙĪÙĦ ا\",\n \"Ġश ब\",\n \"ाà¤ĸ ण\",\n \"ست اÙĨÛĮ\",\n \"ستاÙĨ ÛĮ\",\n \"æĬ ½\",\n \"Ġrů z\",\n \"ĠíĮIJ 매\",\n \"à¸ģาร à¸ķ\",\n \"ائ ÛĮ\",\n \"a sal\",\n \"as al\",\n \"asa l\",\n \"ĠÑĢабоÑĤ Ñĥ\",\n \"ĠÑĢаб оÑĤÑĥ\",\n \"ĠÑĢабо ÑĤÑĥ\",\n \"à¥ĭल न\",\n \"Ġ 马\",\n \"Ġé© ¬\",\n \"Ġl ai\",\n \"Ġla i\",\n \"ó i\",\n \"v ap\",\n \"va p\",\n \"ëħĦ ìĹIJëĬĶ\",\n \"ëħĦìĹIJ ëĬĶ\",\n \"ĠпеÑĢед баÑĩ\",\n \"Ġп леÑĩ\",\n \"Ġпл еÑĩ\",\n \"id det\",\n \"idd et\",\n \"ĠÑĩ оÑĢ\",\n \"i yan\",\n \"iy an\",\n \"iya n\",\n \"ãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ\",\n \"ĠØŃر ÙģÙĩ\",\n \"ĠØŃرÙģ Ùĩ\",\n \"大 éĺª\",\n \"Ñĩ ого\",\n \"Ġ ки\",\n \"Ġк и\",\n \"ا ÙĪÙĬ\",\n \"اÙĪ ÙĬ\",\n \"ĠbaÅŁ lan\",\n \"Ġmerk ezi\",\n \"Ġmerkez i\",\n \"© ©\",\n \"Ġر است\",\n \"Ġرا ست\",\n \"Ġ ëĬĶ\",\n \"ĠëĬ Ķ\",\n \"ĠÑģ ÑĢав\",\n \"ĠвнÑĥ ÑĤÑĢи\",\n \"ĠвнÑĥÑĤÑĢ и\",\n \"ĠвнÑĥÑĤ ÑĢи\",\n \"ãĢĢ ãĥİ\",\n \"åĿ Ľ\",\n \"Ġв ÑĤ\",\n \": :/\",\n \":: /\",\n \"Ġsöz leÅŁ\",\n \"Ġver diÄŁi\",\n \"Ġverdi ÄŁi\",\n \"ิ ยม\",\n \"ิย ม\",\n \"ĠÐŁ ÑĢоÑĤ\",\n \"ĠÐŁÑĢ оÑĤ\",\n \"ĠÐŁÑĢо ÑĤ\",\n \"Ùĥ ار\",\n \"Ġب ÙĨدÛĮ\",\n \"ĠبÙĨ دÛĮ\",\n \"ĠبÙĨد ÛĮ\",\n \"Ùı ÙĪ\",\n \"缴 æĴŃ\",\n \"ĠÙħ ÙĦÙĬ\",\n \"ĠÙħÙĦ ÙĬ\",\n \"Ġnut né\",\n \"ะà¹ģ à¸Ļà¸Ļ\",\n \"ĠM ã\",\n \"Ġ ì´\",\n \"Ġì ´\",\n \"à¹Ī าม\",\n \"à¹Īา ม\",\n \"м оÑģ\",\n \"мо Ñģ\",\n \"Ġпо Ñıви\",\n \"ĠпоÑıв и\",\n \"Ġn ghi\",\n \"Ġng hi\",\n \"Ġngh i\",\n \"Ġ ëIJĺëĬĶ\",\n \"ĠëIJĺ 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\"ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ\",\n \"ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢ\",\n \"æĪ ª\",\n \"ĠاÙĦسعÙĪد ÙĬØ©\",\n \"ĠëĤ¨ ìŀIJ\",\n \"ĠAng iosper\",\n \"???????? ????????\",\n \"Ġpr ům\",\n \"Ġprů m\",\n \"ĠплоÑī ад\",\n \"Ġ ÏĦÏģα\",\n \"ĠÏĦ Ïģα\",\n \"д аÑİÑĤ\",\n \"да ÑİÑĤ\",\n \"Ġsı nav\",\n \"Ġsın av\",\n \"Ġm ặc\",\n \"æ°´ å¹³\",\n \"Ġви глÑı\",\n \"Ġвиг лÑı\",\n \"Ġn ást\",\n \"Ġná st\",\n \"Ġnás t\",\n \"ĠобÑĭ Ñĩ\",\n \"ĠìĿ´ìķ¼ 기\",\n \"ë¹ Ľ\",\n \"ĠB aÄŁ\",\n \"ĠBa ÄŁ\",\n \"ĠاÙĦØ« اÙĦØ«\",\n \"Ġser vis\",\n \"Ġserv is\",\n \"Ġservi s\",\n \"Ġ 룬\",\n \"ĠëŁ ¬\",\n \"ом ина\",\n \"ί θ\",\n \"Ġ Ấ\",\n \"ĠẠ¤\",\n \"ê²½ 기\",\n \"Ġì¡ ¸\",\n \"ี à¸ļ\",\n \"Ġà¤ĺ à¤Łà¤¨\",\n \"Ġ à¸Ļาà¸ĩ\",\n \"Ġà¸Ļ าà¸ĩ\",\n \". 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\"åĿ ª\",\n \"Ġê·¸ ê°Ģ\",\n \"ت ÙĩÙħ\",\n \"تÙĩ Ùħ\",\n \"Ġб аж\",\n \"Ġба ж\",\n \"ا Ùģع\",\n \"اÙģ ع\",\n \"éĢļ 常\",\n \"ĠТ и\",\n \"γ νÏī\",\n \"ì¹ Ļ\",\n \"Ġznam ená\",\n \"ï¼¼ ï¼¼\",\n \"α ÏĢÏĮ\",\n \"åĨĻ 羣\",\n \"Ġ ï¼¼Ċ\",\n \"Ġï¼¼ Ċ\",\n \"åĬł å·¥\",\n \"èĤ¡ä»½ æľīéĻIJåħ¬åı¸\",\n \"Ñı ÑĤий\",\n \"ÑıÑĤ ий\",\n \"ÑıÑĤи й\",\n \"Ġh âl\",\n \"Ġç ab\",\n \"Ġça b\",\n \"ĠØŃ Ø§Ø¶Ø±\",\n \"P ÅĻ\",\n \"ĠاÙĦ تÙĤ\",\n \"ĠاÙĦت ÙĤ\",\n \"ξ ηÏĤ\",\n \"ξη ÏĤ\",\n \"б е\",\n \"Ġkh ám\",\n \"Ġkhá m\",\n \"Ġ âĮĴ\",\n \"Ġâ ĮĴ\",\n \"Ġ éķ¿\",\n \"Ġéķ ¿\",\n \"Ġ âĢ¦Ċ\",\n \"ĠâĢ¦ Ċ\",\n \"द म\",\n \"ĠSt udi\",\n \"ĠStud i\",\n \"Ġk odu\",\n \"Ġko du\",\n \"Ġkod u\",\n \"Ġkom unik\",\n \"Ġkomun ik\",\n \"Ġkat kı\",\n \"n ete\",\n \"ne te\",\n \"net e\",\n \"Ġr apor\",\n \"Ġrap or\",\n \"Ġra por\",\n \"éĨ ´\",\n \"ãĤī ãģĽ\",\n \"ĠнеÑģк олÑĮ\",\n \"Ġhá»į p\",\n \"ï¿£ ￣￣\",\n \"￣￣ ï¿£\",\n \"º ¼\",\n \"è£ Ĥ\",\n \"ед ÑĮ\",\n \"Ġا ÙĦاØŃ\",\n \"ĠاÙĦ اØŃ\",\n \"l adık\",\n \"la dık\",\n \"lad ık\",\n \"ladı 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ν\",\n \"ร à¸Ńà¸ļ\",\n \"Ġtr ùng\",\n \"à¹ģà¸Ĺ à¸Ļ\",\n \"Ġα κÏĮ\",\n \"Ġακ ÏĮ\",\n \"ĠÐĴеÑĢ Ñħов\",\n \"à¹ĥ à¸Ļส\",\n \"à¹ĥà¸Ļ ส\",\n \"ãĢģ ä½ķ\",\n \"åĩ ¦\",\n \"Ġ ç»ı\",\n \"Ġç» ı\",\n \"æ¨ ĵ\",\n \"اÙĨÚ¯ ÙĦÛĮسÛĮ\",\n \"Ġ lepÅ¡ÃŃ\",\n \"Ġlep Å¡ÃŃ\",\n \"Ġ å¼Ģå§ĭ\",\n \"Ġå¼Ģ å§ĭ\",\n \"éĻ º\",\n \"ĠÑĩ еÑĤÑĭ\",\n \"ĠÑĩеÑĤ Ñĭ\",\n \"ĠС еÑĢ\",\n \"оÑİ Ð·\",\n \"Ġx ung\",\n \"Ġxu ng\",\n \"åĵģ çīĮ\",\n \"Ġìĥģ íĥľ\",\n \"ĠÙĨ صب\",\n \"ĠÙĨص ب\",\n \"ĠÑĩ омÑĥ\",\n \"Ġتر Ú©ÛĮ\",\n \"Ġترک ÛĮ\",\n \"- ли\",\n \"o vÃŃ\",\n \"ov ÃŃ\",\n \"Ġا ÙĨج\",\n \"ĠاÙĨ ج\",\n \"çµ ¡\",\n \"Ġت ÙĪص\",\n \"ĠتÙĪ ص\",\n \"Ġ ì¿ł\",\n \"Ġì ¿ł\",\n \"Ġvar sa\",\n \"Ġva rsa\",\n \"Ġvars a\",\n \"ĠÑĢаз ÑĢабоÑĤ\",\n \"à¸Ĥ à¸Ńà¸ĩà¸Ħ\",\n \"à¸Ĥà¸Ńà¸ĩ à¸Ħ\",\n \"éŃ Ĥ\",\n \"Ġà¤Ĭ पर\",\n \"æĿ¥ 说\",\n \"ĠÑĨенÑĤ ÑĢалÑĮ\",\n \"ĠÑĨенÑĤÑĢ алÑĮ\",\n \"ĠÑĨенÑĤÑĢа лÑĮ\",\n \"ĠTak ım\",\n \"Ġon lar\",\n \"Ġسر عت\",\n \"好 åĥı\",\n \"Ġbu á»ķi\",\n \"ĠÐij ел\",\n \"Âł c\",\n \"Ø£ ت\",\n \"à¸Ĥ à¸ĵะ\",\n \"ãģ« åĩº\",\n \"Ġ+ **************\",\n \"ÏĦη κε\",\n \"ا جر\",\n \"اج ر\",\n \"Ġ âĢ²\",\n \"ĠâĢ ²\",\n \"ãĥ¼ ãĥ¬\",\n \"ãĥ¼ãĥ ¬\",\n \"é¥ Ń\",\n \"Ġج ÙĦس\",\n \"ĠجÙĦ س\",\n \"Ġب ستÙĩ\",\n \"Ġبس تÙĩ\",\n \"ว าà¸ĩ\",\n \"Ġ βά\",\n \"Ġβ ά\",\n \"Ġа меÑĢикан\",\n \"ĠPr emi\",\n \"ĠPre mi\",\n \"ĠPrem i\",\n \"m ae\",\n \"ma e\",\n \"ĠÑģ ÑĢеди\",\n \"ĠÑģÑĢед и\",\n \"Ạł\",\n \"Ġв ÑĢед\",\n \"ãĢĤ èĢĮ\",\n \"åĴ ²\",\n \"Ġê³µ ê°ľ\",\n \"èĤ ¥\",\n \"з виÑĩай\",\n \"Ġpro cent\",\n \"Ġproc ent\",\n \"и лоÑģÑĮ\",\n \"ил оÑģÑĮ\",\n \"ило ÑģÑĮ\",\n \"श न\",\n \"é łģ\",\n \"éł ģ\",\n \"е кÑĤи\",\n \"ек ÑĤи\",\n \"екÑĤ и\",\n \"د اشت\",\n \"دا شت\",\n \"íķĻ íļĮ\",\n \"ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ\",\n \"ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ\",\n \"ĠÙħد ÙĬÙĨØ©\",\n \"िल न\",\n \"Ġ èĹ\",\n \"Ġè Ĺ\",\n \"м иÑĢ\",\n \"ми ÑĢ\",\n \"Ġн оÑĢ\",\n \"Ġно ÑĢ\",\n \"Ġ íķĺì§Ģ\",\n \"Ġíķĺ ì§Ģ\",\n \"в еÑī\",\n \"ве Ñī\",\n \"nÄĽ m\",\n \"е ÑĢами\",\n \"еÑĢ ами\",\n \"еÑĢа ми\",\n \"Ġpra cov\",\n \"Ġprac ov\",\n \"ĠبÙĬ اÙĨات\",\n \"ĠÏĥ Ïįν\",\n \"ĠÏĥÏį ν\",\n \"Ġج Ø°\",\n \"ãģĦ ãģ§\",\n \"ĠB ÃŃ\",\n \"è± Ĩ\",\n \"Ġh mot\",\n \"Ġhm ot\",\n \"il eceÄŁi\",\n \"ilece ÄŁi\",\n \"Ġت اث\",\n \"Ġتا Ø«\",\n \"è´ ´\",\n \"Ġ ê¸ī\",\n \"Ġê¸ ī\",\n \"Ġm ysl\",\n \"Ġmy sl\",\n \"Ġmys l\",\n \"ĠìĿ´ íķ´\",\n \"Ġ기 ëĬ¥\",\n \"ĠТ ам\",\n \"ĠТа м\",\n \"ĠнаÑģ елениÑı\",\n \"ĠM ez\",\n \"ĠMe z\",\n \"Ġ모 르\",\n \"íĻĶ 를\",\n \"ĠÙĨسخ Ùĩ\",\n \"ĠتÙĦ ÙĪÛĮزÛĮ\",\n \"ĠÄįerv na\",\n \"Æ°á» ¡ng\",\n \"ص ØŃ\",\n \"ĠÑĤ ÑĢен\",\n \"ĠÑĤÑĢ ен\",\n \"Õ ¡\",\n \"Ġce lou\",\n \"Ġcel ou\",\n \"Å© i\",\n \"ìĹĨ ìĿ´\",\n \"nÃŃ ku\",\n \"nÃŃk u\",\n \"Ġprogram u\",\n \"à¥į पन\",\n \"à¥įप न\",\n \"Ġп ÑĢеж\",\n \"ĠпÑĢ еж\",\n \"ĠпÑĢе ж\",\n \"ا رب\",\n \"ار ب\",\n \"æľŁ éĸĵ\",\n \"Ġ μά\",\n \"Ġμ ά\",\n \"ëįĶ ëĭĪ\",\n \"ụ n\",\n \"ĠпеÑĢ еÑģÑĤ\",\n \"ĠпеÑĢе ÑģÑĤ\",\n \"ĠпеÑĢеÑģ ÑĤ\",\n \"对 äºİ\",\n \"è¿IJ è¡Į\",\n \"ĠÑĤ ан\",\n \"ĠÑĤа н\",\n \"Ġ ìĤ¬ìĿ´íĬ¸\",\n \"ĠìĤ¬ ìĿ´íĬ¸\",\n \"ĠìĤ¬ìĿ´ íĬ¸\",\n \"ĠQu ảng\",\n \"ĠQuản g\",\n \"Ġst ojÃŃ\",\n \"Ġsto jÃŃ\",\n \"ãĥŃ ãĥ¼\",\n \"Ú¯ ار\",\n \"Ġе неÑĢг\",\n \"Ġkter ým\",\n \"Ġkterý m\",\n \"ĠпÑĢи мÑĸ\",\n \"ĠпÑĢим Ñĸ\",\n \"ĠкаÑĢÑĤ и\",\n \"ĠкаÑĢ ÑĤи\",\n \"Ġz engin\",\n \"Ġzen gin\",\n \"ï¼Į åĨį\",\n \"Ġت رب\",\n \"Ġتر ب\",\n \"ĠÑĨенÑĤ ÑĢ\",\n \"ĠÑĨен ÑĤÑĢ\",\n \"ĠsaÄŁ lamak\",\n \"ĠsaÄŁlam ak\",\n \"ëĭ Ŀ\",\n \"ãģ® åŃIJ\",\n \"Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"Ġs Æ¡n\",\n \"ĠsÆ¡ n\",\n \"z ı\",\n \"ÑĤ аки\",\n \"ÑĤа ки\",\n \"ÑĤак и\",\n \"ÄĽ stÃŃ\",\n \"ÄĽst ÃŃ\",\n \"Ġ à¥\",\n \"Ġà ¥\",\n \"é ®\",\n \"åŁ¹ è®Ń\",\n \"Ġ ìĶ¨\",\n \"ĠìĶ ¨\",\n \"Ġbel ki\",\n \"ĠìĿ´ 벤íĬ¸\",\n \"ëĶĶ ìĸ´\",\n \"Ġs Ãłn\",\n \"ни кам\",\n \"ник ам\",\n \"ника м\",\n \"a lim\",\n \"al im\",\n \"ali m\",\n \"対 å¿ľ\",\n \"ĠS á»±\",\n \"éģĵ è·¯\",\n \"é«ĺ æ¸ħ\",\n \"Ġd õi\",\n \"ĠÙĦ ÙĢ\",\n \"Ġ èĤ¡\",\n \"ĠèĤ ¡\",\n \"ν ι\",\n \"âĢŀ J\",\n \"' nde\",\n \"'n de\",\n \"Îij Îĵ\",\n \"ãģ¨ ãģªãĤĭ\",\n \"ãģ¨ãģª ãĤĭ\",\n \"çĪ ¸\",\n \"ع ÙĦÛĮ\",\n \"عÙĦ ÛĮ\",\n \"Ïģι ÏĥÏĦ\",\n \"ÏģιÏĥ ÏĦ\",\n \"Ġe ÄŁit\",\n \"ĠeÄŁ it\",\n \"Ġзов нÑĸÑĪ\",\n \"Ġп ÑĢим\",\n \"ĠпÑĢ им\",\n \"ĠпÑĢи м\",\n \"س Ùħبر\",\n \"سÙħ بر\",\n \"ĠmÄĽst ÄĽ\",\n \"ĠÏĢεÏģι ÏĥÏĥÏĮÏĦε\",\n \"ÐIJ Ðł\",\n \"æĦŁ åĪ°\",\n \"Ġ문 ìĦľ\",\n \"ãģĭ ãĤĭ\",\n \"ÙĤÙĬ ÙĤØ©\",\n \"ÙĤÙĬÙĤ Ø©\",\n \"Ġв ÑĤÑĢа\",\n \"ĠвÑĤ ÑĢа\",\n \"Ġ à¸Ńำ\",\n \"Ġà¸Ń ำ\",\n \"Ñģ кÑĥÑİ\",\n \"Ñģк ÑĥÑİ\",\n \"د ÙĩاÛĮ\",\n \"دÙĩ اÛĮ\",\n \"ĠÄ° st\",\n \"ĠÄ°s t\",\n \"ĠÐĹ Ð°Ð²\",\n \"ĠÐĹа в\",\n \"Ġ éĥ½\",\n \"Ġé ĥ½\",\n \"Ġéĥ ½\",\n \"ÑĪ ем\",\n \"ÑĪе м\",\n \"Ġе ÑīÑij\",\n \"ĠÐľÐ¸Ñħ ай\",\n \"ĠÑĥпÑĢав лениÑı\",\n \"л еннÑĭе\",\n \"лен нÑĭе\",\n \"ĠzaÄį al\",\n \"æ¡ Į\",\n \"Ġп Ñĸз\",\n \"л ÑıÑĤÑĮÑģÑı\",\n \"лÑı ÑĤÑĮÑģÑı\",\n \"лÑıÑĤÑĮ ÑģÑı\",\n \"Ġ ìŀIJë£Į\",\n \"ĠìŀIJ ë£Į\",\n \"ãĢĢ ãĢĢĠ\",\n \"ãĢĢãĢĢ Ġ\",\n \"ĠK ral\",\n \"ĠKr al\",\n \"ĠKra l\",\n \"èĪ ī\",\n \"Ġà¤Ń व\",\n \"Ġ Ø®Ùħ\",\n \"ĠØ® Ùħ\",\n \"Ġа кадем\",\n \"Ġ isten\",\n \"Ġis ten\",\n \"Ġi sten\",\n \"Ġist en\",\n \"ĠиÑģ кÑĥÑģ\",\n \"ĠиÑģк ÑĥÑģ\",\n \"ĠعÙĨد Ùħا\",\n \"Ġا ÙĦاÙħ\",\n \"ĠاÙĦ اÙħ\",\n \"is mus\",\n \"ism us\",\n \"ismu s\",\n \"Ġayr ıntı\",\n \"Ġ Що\",\n \"ĠЩ о\",\n \"ĠÙĩ ÙĪØ´\",\n \"ĠÙĩÙĪ Ø´\",\n \"د ÙĪاج\",\n \"دÙĪ اج\",\n \"л аж\",\n \"ла ж\",\n \"ĠÚ©ÙĨ ار\",\n \"Âł R\",\n \"æĢ§ çļĦ\",\n \"Ñģ Ñĸм\",\n \"ÑģÑĸ м\",\n \"ĠM üz\",\n \"ĠMü z\",\n \"ÑĢ овиÑĩ\",\n \"ÑĢов иÑĩ\",\n \"ÑĢо виÑĩ\",\n \"Ġ Ω\",\n \"ĠÎ ©\",\n \"Ġìĸ´ ëĶĶ\",\n \"س ÙħØ©\",\n \"سÙħ Ø©\",\n \"Ġ ÑĢÑı\",\n \"ĠÑĢ Ñı\",\n \"Ġt Æ°Æ¡i\",\n \"ĠtÆ° Æ¡i\",\n \"ĠÑĢаÑģ Ñħод\",\n \"åı° çģ£\",\n \"ĠاÙĦ ÙĪÙĤت\",\n \"ĠاÙĦÙĪ ÙĤت\",\n \"بر اÛĮ\",\n \"ĠзÑĢоб иÑĤи\",\n \"Ġб ÑĥÑĢ\",\n \"ĠбÑĥ ÑĢ\",\n \"ĠÄįin nosti\",\n \"ĠÄįinnost i\",\n \"Ġص اØŃ\",\n \"ĠصÙĨ عت\",\n \"ĠصÙĨع ت\",\n \"Ġ Ø·ÙĦ\",\n \"ĠØ· ÙĦ\",\n \"ξ Ïį\",\n \"ĠtisÃŃ c\",\n \"ĠFr ansa\",\n \"ĠFran sa\",\n \"ì¦ ĺ\",\n \"è» ½\",\n \"Ñ ĺ\",\n \"ÏĮÏĦη ÏĦαÏĤ\",\n \"ÏĮÏĦηÏĦα ÏĤ\",\n \"ĠM illet\",\n \"ĠMill et\",\n \"ĠMil let\",\n \"ãĢĢ ãĥ¾\",\n \"ĠпÑĢ ием\",\n \"ĠпÑĢи ем\",\n \"ĠترجÙħ Ùĩ\",\n \"Ġس ÙĪد\",\n \"ĠسÙĪ د\",\n \"ĠsouÄįást ÃŃ\",\n \"ÐĶ о\",\n \"Ġtr ụ\",\n \"足 çIJĥ\",\n \"à¸Ľ à¸ģ\",\n \"Ġu stanov\",\n \"ÎŁ ÎĻ\",\n \"ÎŁÎ Ļ\",\n \"Ðŀ н\",\n \"Ġн еж\",\n \"Ġне ж\",\n \"к ог\",\n \"ко г\",\n \"ä¸Ģ çĤ¹\",\n \"Ġد ÙĪراÙĨ\",\n \"ĠدÙĪ راÙĨ\",\n \"ĠدÙĪر اÙĨ\",\n \"å½± éŁ¿\",\n \"el idir\",\n \"eli dir\",\n \"âĢŀ N\",\n \"es iyle\",\n \"esi yle\",\n \"ÑĢем енно\",\n \"ÑĢе менно\",\n \"Ġilet iÅŁim\",\n \"ม à¹Ģà¸ķ\",\n \"以 åīį\",\n \"ãĥĭ ãĥ¼\",\n \"鼻 話\",\n \"à¹Ĥ à¸ŀ\",\n \"ov ky\",\n \"Ġза мÑĸ\",\n \"Ġзам Ñĸ\",\n \"Ġव à¤ķ\",\n \" Ļ\",\n \"ĠвÑĸй ни\",\n \"Ġol madıģı\",\n \"Ġolm adıģı\",\n \"Ġolmadı ģı\",\n \"Ġolma dıģı\",\n \"æ¢ ģ\",\n \"ĠТ еп\",\n \"ĠТе п\",\n \"nÄĽ te\",\n \"nÄĽt e\",\n \"èħ ķ\",\n \"ìĤ¬ ëĬĶ\",\n \"m amak\",\n \"ma mak\",\n \"mam ak\",\n \"Ġc iz\",\n \"Ġci z\",\n \"æ£ Ĵ\",\n \"Ġ ï¼ı:\",\n \"Ġï¼ı :\",\n \"éģĭ åĭķ\",\n \"ĠÙĩ ÙĨا\",\n \"ĠÙĩÙĨ ا\",\n \"Ġ ê°ij\",\n \"Ġê° ij\",\n \"ĠÙĩÙĨÚ¯ اÙħ\",\n \"ĠuÄŁ ra\",\n \"å½ ¦\",\n \"Ġob jekt\",\n \"Ġobj ekt\",\n \"ãģ¨ ãģĻãĤĭ\",\n \"åĽ½ åĨħ\",\n \"ĠдеÑĢжав и\",\n \"ĠдеÑĢж ави\",\n \"Ġ èĮ\",\n \"Ġè Į\",\n \"Ġulus lararası\",\n \"Ù £\",\n \"Ġmut lak\",\n \"Ġз обов\",\n \"Ġ γεν\",\n \"Ġγ εν\",\n \"Ġγε ν\",\n \"à¹Ħà¸Ł à¸Ł\",\n \"Ġözg ür\",\n \"íĦ ¸\",\n \"Ġвипад кÑĥ\",\n \"Ġà¤ķ ब\",\n \"ĠاÙĦ خط\",\n \"ĠاÙĦØ® Ø·\",\n \"θη καν\",\n \"ï¼Į æĬĬ\",\n \"ÑıÑĤ ÑĤÑı\",\n \"Ġolmadı ģını\",\n \"Ġolma dıģını\",\n \"Âłk W\",\n \"ĠnÄĽkter ých\",\n \"ãĥĩ ãĥ«\",\n \"æ¤į çī©\",\n \"μι λοÏĤ\",\n \"ÐIJÑĢ ÑħÑĸв\",\n \"ĠТ о\",\n \"èĸ ¬\",\n \"ÑģÑĤв иÑı\",\n \"Ġ Ø®ÙĪاست\",\n \"ĠØ®ÙĪ است\",\n \"олог ÑĸÑĹ\",\n \"ÙĪ اÙĩد\",\n \"ÙĪاÙĩ د\",\n \"ÙĪا Ùĩد\",\n \"Ġ нак\",\n \"Ġн ак\",\n \"Ġна к\",\n \"ĠкоÑĤоÑĢ ÑĥÑİ\",\n \"Ġद à¤ķ\",\n \"âĢŀ M\",\n \"λ ια\",\n \"λι α\",\n \"æŃ ²\",\n \"第 åĽĽ\",\n \"à¤¾à¤ľ स\",\n \"Ġ( «\",\n \"Ġth ẻ\",\n \"､ Ċ\",\n \"ç£ ģ\",\n \"Ġ ÙĦÙĤ\",\n \"ĠÙĦ ÙĤ\",\n \"Ġ ìķĶ\",\n \"Ġìķ Ķ\",\n \"Ġн ового\",\n \"Ġнов ого\",\n \"ĠìķĦ 주\",\n \"Ġ ëIJĺìĸ´\",\n \"ĠëIJĺ ìĸ´\",\n \"Ġo lun\",\n \"Ġol un\",\n \"à ¾\",\n \"Ġkar iy\",\n \"Ġkari y\",\n \"ĠØŃ Ø³Ø¨\",\n \"ĠØŃس ب\",\n \"ĠìĿĺ 미\",\n \". Ðľ\",\n \"Ġoz naÄį\",\n \"ÙĦ سÙĦ\",\n \"ÙĦس ÙĦ\",\n \"ĠÐĴ ид\",\n \"ĠÐĴи д\",\n \"ë¡ľ ëĤĺ\",\n \"à¥įà¤Ł म\",\n \"í ľ´\",\n \"Ġbilg isayar\",\n \"ìĿ¸ ì§Ģ\",\n \"Ġв ов\",\n \"Ġво в\",\n \"nict vÃŃm\",\n \"nictvÃŃ m\",\n \"า à¸Ńย\",\n \"าà¸Ń ย\",\n \"Ġشخص ÛĮ\",\n \"п Ñĸон\",\n \"æľ¬ å½ĵ\",\n \"Ġب ÙĢ\",\n \"ĠмаÑģ ло\",\n \"ĠPh át\",\n \"Ġ ба\",\n \"Ġб а\",\n \"алÑĮ номÑĥ\",\n \"алÑĮно мÑĥ\",\n \"алÑĮном Ñĥ\",\n \"社 åĮº\",\n \"Ġ Ò\",\n \": ::|\",\n \":: :|\",\n \"::: |\",\n \"ê ´\",\n \"Ġ ä¸ĥ\",\n \"Ġä¸ ĥ\",\n \"ĠÙĪ اÙĦد\",\n \"ĠÙĪاÙĦ د\",\n \"ни ке\",\n \"ник е\",\n \"à¸Ń ลล\",\n \"à¸Ńล ล\",\n \"Ġyer leÅŁ\",\n \"Ġkom bin\",\n \"Ġkomb in\",\n \"u Å¡\",\n \"Ġо ÑĤÑĢи\",\n \"ĠоÑĤ ÑĢи\",\n \"ä¹ Į\",\n \"iÅŁ ti\",\n \"Ġs óng\",\n \"Ġsó ng\",\n \"λ ηÏĤ\",\n \"λη ÏĤ\",\n \"Ġк ÑĥÑĢÑģ\",\n \"ĠкÑĥÑĢ Ñģ\",\n \"à¹Ī าà¸Ħ\",\n \"à¹Īา à¸Ħ\",\n \"Ġ ÙĬس\",\n \"ĠÙĬ س\",\n \"Ġ داÙħ\",\n \"Ġد اÙħ\",\n \"çĴ° å¢ĥ\",\n \"Ñĩ енко\",\n \"Ñĩен ко\",\n \"ãĢį ãģ®\",\n \"ĠmÃŃ sta\",\n \"ĠmÃŃst a\",\n \"ĠÑĦ оÑĤ\",\n \"ĠpÅĻÃŃ zn\",\n \"ĠÑĢ аза\",\n \"ĠÑĢаз а\",\n \"ĠÑĢа за\",\n \"ç´ «\",\n \"lá da\",\n \"lád a\",\n \"ĠÑģпеÑĨи алиÑģÑĤ\",\n \"ĠبÛĮ ÙħارÛĮ\",\n \"ĠبÛĮÙħ ارÛĮ\",\n \"ĠبÛĮÙħار ÛĮ\",\n \"Ġëĵ £\",\n \"çĭ Ĺ\",\n \"ÙĪ ÙĪ\",\n \"ан ÑĸÑĤ\",\n \"анÑĸ ÑĤ\",\n \"ĠدÙĨ باÙĦ\",\n \"ĠÙħجÙħÙĪع Ø©\",\n \"ÃŃ na\",\n \"ÃŃn a\",\n \"ĠH alk\",\n \"ĠHa lk\",\n \"ĠHal k\",\n \"á jem\",\n \"áj em\",\n \"enÃŃ ze\",\n \"Ġim z\",\n \"« ng\",\n \"Ġ ÎķÎł\",\n \"ĠÎķ Îł\",\n \"ĠÙħ Ùĩد\",\n \"ĠÙħÙĩ د\",\n \"ìľĦìĽIJ íļĮ\",\n \"Ġìľł íĺķ\",\n \"ाप स\",\n \"Ġje ž\",\n \"ан Ñĸз\",\n \"анÑĸ з\",\n \"иÑĤ ай\",\n \"иÑĤа й\",\n \"á¿ ĸ\",\n \"ir ler\",\n \"irl er\",\n \"기 ê°Ħ\",\n \"Ġ воÑĢ\",\n \"Ġв оÑĢ\",\n \"Ġво ÑĢ\",\n \"Ġ Ïİ\",\n \"ĠÏ İ\",\n \"Ġpo zn\",\n \"Ġpoz n\",\n \"Ġ ساÙĨ\",\n \"Ġس اÙĨ\",\n \"å ¯¿\",\n \"å¯ ¿\",\n \"æĸ¯ çī¹\",\n \"Ġtu rist\",\n \"Ġtur ist\",\n \"ĠìŀIJ ìľł\",\n \"à¥Ģ à¤ĸ\",\n \"μ με\",\n \"μμ ε\",\n \"an sı\",\n \"ans ı\",\n \"ìĨĮ ëħĦ\",\n \"Ġted avi\",\n \"Ġtedav i\",\n \"Ñĩ еÑģÑĤва\",\n \"Ñĩе 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\"Ġkend ine\",\n \"Ġkendi ne\",\n \"Ġ اÙĪÙĨ\",\n \"Ġا ÙĪÙĨ\",\n \"ĠاÙĪ ÙĨ\",\n \"ãĢ Ķ\",\n \"ĠΣ Ïį\",\n \"à¹Ģ à¸Ħล\",\n \"à¹Ģà¸Ħ ล\",\n \"å¥ ¶\",\n \"ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"Ġú Äįet\",\n \"ĠúÄį et\",\n \"Ñĥ ла\",\n \"Ñĥл а\",\n \"éĢļ ä¿¡\",\n \"Ġì¦ IJ\",\n \". čĊĊ\",\n \".čĊ Ċ\",\n \"ĠÐľ ед\",\n \"ا عÙĬ\",\n \"اع ÙĬ\",\n \"Ġjeho ž\",\n \"ĠGü ney\",\n \"ĠGün ey\",\n \"ĠÎij ÏĢο\",\n \"ĠÎijÏĢ ο\",\n \"Ġп олÑĸ\",\n \"Ġпо лÑĸ\",\n \"Ġпол Ñĸ\",\n \"ü me\",\n \"üm e\",\n \"ho dob\",\n \"hod ob\",\n \"ĠÎĿ α\",\n \"ĠØ¢ ÙĦات\",\n \"ĠØ¢ÙĦ ات\",\n \"ĠpÅĻ iz\",\n \"ĠpÅĻi z\",\n \"Ġt avs\",\n \"Ġta vs\",\n \"Ġtav s\",\n \"Ġتب ÙĦÛĮ\",\n \"ãĥ³ ãĥĶ\",\n \"ãĥ³ãĥ Ķ\",\n \"Ø® رج\",\n \"خر ج\",\n \"Ġак кÑĥ\",\n \"Ġú prav\",\n \"ĠاØŃ Ø³Ø§Ø³\",\n \"ì¹´ ëĿ¼\",\n \"ı mızı\",\n \"ım ızı\",\n \"ımız ı\",\n \"ımı zı\",\n \"Ġд окÑĥменÑĤ\",\n \"Ġдок ÑĥменÑĤ\",\n \"ĠдокÑĥм енÑĤ\",\n \"Ġ اصÙĦ\",\n \"Ġا صÙĦ\",\n \"Ġاص ÙĦ\",\n \"ظ Ùĩ\",\n \"ĠìĿ¸ ê°Ħ\",\n \"Ġج رÛĮاÙĨ\",\n \"Ġجر ÛĮاÙĨ\",\n \"Î¥ ÎĿ\",\n \"ÑĩеÑģ каÑı\",\n \"Ñĩе ÑģкаÑı\",\n \"ÙĬ ÙĨÙĬØ©\",\n \"ÙĬÙĨ ÙĬØ©\",\n \"ÙĬÙĨÙĬ Ø©\",\n \"åĴ ¨\",\n \"æĹħ 游\",\n \"Ġ à¸Īำà¸Ļวà¸Ļ\",\n \"Ġà¸Īำ à¸Ļวà¸Ļ\",\n \"Ġ анг\",\n \"Ġа нг\",\n \"Ġан г\",\n \"Ïħ Ïĩ\",\n \"èĻ «\",\n \"ĠÙħ ÙĤر\",\n \"ĠÙħÙĤ ر\",\n \"ĠÙħÙĪسÛĮ ÙĤÛĮ\",\n \"ut ut\",\n \"ĠÐĽ е\",\n \"ĠÐŁ ÑĸÑģлÑı\",\n \"ãĤŃ ãĥ¼\",\n \"ุล าà¸Ħม\",\n \"åĩ ¡\",\n \"ÏĢ οÏį\",\n \"ÏĢο Ïį\",\n \"ĠÃĸ dül\",\n \"Ïĥ κ\",\n \"Ġ ÑĨÑİ\",\n \"ĠÑĨ Ñİ\",\n \"Ñĭ ваÑı\",\n \"Ñĭв аÑı\",\n \"Ñĭва Ñı\",\n \"ï½ŀ ï½ŀ\",\n \"ĠØ´ ÙħاÙĦ\",\n \"ĠØ´Ùħ اÙĦ\",\n \"ĠØ´Ùħا ÙĦ\",\n \"è¿ ħ\",\n \"ĠبÙĦ Ú©Ùĩ\",\n \"çİ Ľ\",\n \"Ġì§Ģ ëĤĺ\",\n \"ĠÙģ کر\",\n \"ĠÑģÑĤеп ени\",\n \"Ġна Ñĥки\",\n \"ĠнаÑĥк и\",\n \"çī© çIJĨ\",\n \"ÄĽ le\",\n \"ÄĽl e\",\n \"Ġо ÑģкÑĸлÑĮки\",\n \"ĠкÑĥлÑĮÑĤÑĥ ÑĢи\",\n \"ĠкÑĥлÑĮÑĤÑĥÑĢ и\",\n \"èĢĥ è¯ķ\",\n \"Ġmateri ál\",\n \"ĠÑģÑĤ воÑĢеннÑı\",\n \"ĠÑģÑĤвоÑĢ еннÑı\",\n \"Ġà¤ħ द\",\n \"æıIJ åĩº\",\n \"Ġè©ķ 価\",\n \"ÙĴ د\",\n \"Ġë§Įëĵ¤ ìĸ´\",\n \"duÄŁu nu\",\n \"duÄŁ unu\",\n \"ÙĬ ÙĨÙĩ\",\n \"ÙĬÙĨ Ùĩ\",\n \"åĵ ¦\",\n \"оÑĩ нÑĭÑħ\",\n \"ĠÙħ ض\",\n \"is mu\",\n \"ism u\",\n \"Ġ Ñĩай\",\n \"ĠÑĩ ай\",\n \"ĠÑĩа й\",\n \"ÙĪ رÙĪب\",\n \"ÙĪر ÙĪب\",\n \"Ġ англ\",\n \"Ġан гл\",\n \"Ġанг л\",\n \"oÄŁun luk\",\n \"ĠпÑĢед пол\",\n \"ĠÎŃ ÏīÏĤ\",\n \"ส à¸ĸ\",\n \"ĠÎķλλά δα\",\n \"ĠBi lg\",\n \"ĠBil g\",\n \"Ġ بات\",\n \"Ġب ات\",\n \"Ġبا ت\",\n \"ĠÐĽÑĮв Ñĸв\",\n \"Ġyap ılması\",\n \"Ġyapıl ması\",\n \"æ£Ģ æŁ¥\",\n \"æķ° åѦ\",\n \"Ġ :.:\",\n \"Ġ: .:\",\n \"Ġ:. :\",\n \"Ġ çİ©\",\n \"Ġçİ ©\",\n \"Îļ α\",\n \"à¹Ģà¸Ĺ à¸Ħà¹Ĥà¸Ļà¹Ĥลย\",\n \"Ġساخت ÙħاÙĨ\",\n \"ĠìĨĮ 리\",\n \"é¼ »\",\n \"Ġs mr\",\n \"Ġsm r\",\n \"Ġëĭ¤ìĸij íķľ\",\n \"Ġjed nánÃŃ\",\n \"Ġjedn ánÃŃ\",\n \"Ġjedná nÃŃ\",\n \"Ġserv isi\",\n \"Ġservi si\",\n \"Ġservis i\",\n \"Ġey lem\",\n \"Ġм али\",\n \"Ġмал и\",\n \"Ġма ли\",\n \"Ġvý hod\",\n \"éĻ¤ äºĨ\",\n \"ĠпоÑĢÑıд кÑĥ\",\n \"Ġn ový\",\n \"Ġno vý\",\n \"Ġnov ý\",\n \"å¤ ķ\",\n \"ĠнекоÑĤоÑĢ Ñĭе\",\n \"Ġ^ {}\",\n \"Ġ^{ }\",\n \"γÏī γ\",\n \"Ñĥ ÑĪки\",\n \"ÑĥÑĪ ки\",\n \"Ġp sik\",\n \"Ġps ik\",\n \"Ġpsi k\",\n \"ĠíĶĦ 리\",\n \"Ø´ اء\",\n \"Ġ ван\",\n \"Ġв ан\",\n \"Ġва н\",\n \"Ġس ÙĥاÙĨ\",\n \"ç¢ ¼\",\n \"ĠÎľ η\",\n \"ĠÑĥÑĢов енÑĮ\",\n \"ãĤµ ãĥ¼\",\n \"ĠاÙĦب ØŃر\",\n \"Ġd nÃŃ\",\n \"Ġdn ÃŃ\",\n \"à¸ģาร ศ\",\n \"e diÄŁi\",\n \"edi ÄŁi\",\n \"Ġbelir li\",\n \"Ùĭ ØĮ\",\n \"ĠzamÄĽst nan\",\n \"ĠzamÄĽstn an\",\n \"æŁ ±\",\n \"ا ÙģÙĬ\",\n \"اÙģ ÙĬ\",\n \"Ġh ải\",\n \"æĢĿ æĥ³\",\n \"Ġn eler\",\n \"Ġne ler\",\n \"Ġnel er\",\n \"Ġرس ÙħÛĮ\",\n \"ĠرسÙħ ÛĮ\",\n \"Ñģ еÑĢ\",\n \"Ñģе ÑĢ\",\n \"ãģĵãģ¨ ãģ§\",\n \"ĠZá kladnÃŃ\",\n \"л ова\",\n \"ло ва\",\n \"лов а\",\n \"к ÑĤÑĥ\",\n \"кÑĤ Ñĥ\",\n \"ÙĪس Ùģ\",\n \"Ñĸб лÑĸ\",\n \"Ì Ĥ\",\n \"ÑĢ д\",\n \"éĻ ³\",\n \"æį ·\",\n \"ĠyaÅŁ ayan\",\n \"à¥ģ à¤ļ\",\n \"à¥ģठļ\",\n \"ÑĸÑĤ ÑĤÑı\",\n \"Ġb á»ģ\",\n \"ëĤĺ ëĿ¼\",\n \"Ġм ÑıÑģ\",\n \"Ġ{ [%\",\n \"Ġ{[ %\",\n \"θ α\",\n \"Ġдоз волÑı\",\n \"Ġдозвол Ñı\",\n \"Ġ åIJĦ\",\n \"ĠåIJ Ħ\",\n \"ĠÐŁ еÑĢв\",\n \"ĠÐŁÐµÑĢ в\",\n \"ĠSaÄŁ lık\",\n \"ÑģÑĤоÑĢ 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олÑĮ\",\n \"Ġпо лÑĮ\",\n \"Ġпол ÑĮ\",\n \"ĠÙĩÙĨ ر\",\n \"ëĮĢ ë¹Ħ\",\n \"Ġخارج ÛĮ\",\n \"r act\",\n \"ra ct\",\n \"rac t\",\n \"ãĢĤ ãģĵãĤĮ\",\n \"ĠØ´ÙĪر اÛĮ\",\n \"л енно\",\n \"лен но\",\n \"Ġh isset\",\n \"Ġhis set\",\n \"Ġhiss et\",\n \"Ġc Ãłi\",\n \"ĠcÃł i\",\n \"ĠÑĦ оÑĤо\",\n \"ĠÑĦоÑĤ о\",\n \"æģ Ĵ\",\n \"Ġмед иÑĨин\",\n \"Ġмеди ÑĨин\",\n \"ÑģÑĤ вÑĸ\",\n \"ÑģÑĤв Ñĸ\",\n \"ĠاÙĦ عÙĦ\",\n \"ĠاÙĦع ÙĦ\",\n \"ĠпиÑģÑĮ мен\",\n \"ãĢĤ ãģ¾ãģŁ\",\n \"Ġvlast nÄĽ\",\n \"Ġп ода\",\n \"Ġпо да\",\n \"Ġпод а\",\n \"Ïģ οι\",\n \"Ïģο ι\",\n \"Ġ ìĦĿ\",\n \"ĠìĦ Ŀ\",\n \"ĠìĿ¼ ìĿ´\",\n \"Ġ ìĽĮ\",\n \"ĠìĽ Į\",\n \"ок Ñģи\",\n \"окÑģ и\",\n \"Ġos oby\",\n \"Ġosob y\",\n \"ÐŁÐ¾Ñģ ле\",\n \"ĠÑĸÑģÑĤоÑĢ ÑĸÑĹ\",\n \"ع ÙĦÙī\",\n \"عÙĦ Ùī\",\n \"н ка\",\n \"ت Ùħبر\",\n \"تÙħ بر\",\n \"à¥ĩ हर\",\n \"à¥ĩह र\",\n \"ĠJ ana\",\n \"ĠJan a\",\n \"ĠJa na\",\n \"ÙĦ ÙĬات\",\n \"ÙĦÙĬ ات\",\n \"ĠмаÑĢ ÑĤа\",\n \"ĠÐļи ÑĶ\",\n \"ĠÑĢоб оÑĤÑĥ\",\n \"ĠÑĢобоÑĤ Ñĥ\",\n \"Ġnh ấn\",\n \"и Ñģлов\",\n \"иÑģ лов\",\n \"ëŁ Ń\",\n \"Ġo 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б\",\n \"ĠÐł аÑģ\",\n \"ĠÐłÐ° Ñģ\",\n \"P CP\",\n \"PC P\",\n \"ĠCumhur baÅŁ\",\n \"од аÑĤелÑĮ\",\n \"ÃŃ sto\",\n \"ÃŃs to\",\n \"ÃŃst o\",\n \"Ġo znám\",\n \"Ġoz nám\",\n \"ãĥ¼ ãĥĭ\",\n \"ãĥ¼ãĥ ĭ\",\n \"Ġok uy\",\n \"Ġoku y\",\n \"o phy\",\n \"op hy\",\n \"oph y\",\n \"า à¸Ļà¸Ħร\",\n \"าà¸Ļ à¸Ħร\",\n \"ĠÎķ θν\",\n \"ay ım\",\n \"ayı m\",\n \"Ùİ Ø£\",\n \"æİ ¡\",\n \"Ġfunk ce\",\n \"Ġfunkc e\",\n \"æļ ĸ\",\n \"Ø· ار\",\n \"ĠÐĿ аг\",\n \"ĠÐĿа г\",\n \"Ġ ä¸ĩåĨĨ\",\n \"Ġä¸ĩ åĨĨ\",\n \"Ġ íĴį\",\n \"ĠíĴ į\",\n \"Ġ ä½ı\",\n \"Ġ ï¼İ\",\n \"Ġï¼ İ\",\n \"Ñĭ ваÑİÑĤÑģÑı\",\n \"Ñĭв аÑİÑĤÑģÑı\",\n \"Ñĭва ÑİÑĤÑģÑı\",\n \"ÑĭваÑİÑĤ ÑģÑı\",\n \"ĠP la\",\n \"ĠPl a\",\n \"ا ÙĬÙĦ\",\n \"اÙĬ ÙĦ\",\n \"Ġ무 ìĹĩ\",\n \"Ġкон еÑĩно\",\n \"к м\",\n \"à¤Ĥ पर\",\n \"à¤Ĥप र\",\n \"Ġ ìłķë¶Ģ\",\n \"Ġìłķ ë¶Ģ\",\n \"ĠëĤ´ 볤\",\n \"ãĤ° ãĥ«\",\n \"çģ °\",\n \"Ġc yk\",\n \"Ġcy k\",\n \"Ġжел Ñĥд\",\n \"ĠëĨĴ ìĿĢ\",\n \"çĶŁ åij½\",\n \"æµ ´\",\n \"Ġart Ä±ÅŁ\",\n \"Ġ Ðĩ\",\n \"ĠÐ ĩ\",\n \"ï¼ ²\",\n \"e kim\",\n \"ek im\",\n \"eki m\",\n \"ĠÑĦ едеÑĢа\",\n \"ĠвеÑĢеÑģ нÑı\",\n \"н иÑĤе\",\n \"ни ÑĤе\",\n \"ниÑĤ е\",\n \"ĠÄ°ÅŁ te\",\n \"ĠÙĪضع ÛĮت\",\n \"ãģķ ãģ¾\",\n \"ĠtÅĻ etÃŃ\",\n \"ĠtÅĻet ÃŃ\",\n \"u luÄŁ\",\n \"ulu ÄŁ\",\n \"ĠCumhur iyet\",\n \"ä¼ Ł\",\n \"Ġ ë§Ŀ\",\n \"Ġë§ Ŀ\",\n \"Ġver mek\",\n \"Ġverm ek\",\n \"Ġn alez\",\n \"Ġna lez\",\n \"Ġnal ez\",\n \"Ġnale z\",\n \"çĵ ¶\",\n \"Ġd iÅŁ\",\n \"Ġdi ÅŁ\",\n \"ĠH á»ĵng\",\n \"ĠHá»ĵ ng\",\n \"غ ÙĬرة\",\n \"غÙĬر Ø©\",\n \"å© Ĩ\",\n \"н ив\",\n \"ни в\",\n \"Ġr út\",\n \"' nda\",\n \"'n da\",\n \"Ġh roz\",\n \"Ġhr oz\",\n \"à¥ī प\",\n \"Ġза коном\",\n \"Ġзак оном\",\n \"Ġзакон ом\",\n \"Ġзако ном\",\n \"Ġjed nu\",\n \"Ġjedn u\",\n \"ĠKa dın\",\n \"ĠKad ın\",\n \"in dir\",\n \"ind ir\",\n \"indi r\",\n \"س ازÛĮ\",\n \"åĮº åŁŁ\",\n \"ĠkonuÅŁ tu\",\n \"Ġز ÙĨد\",\n \"ĠزÙĨ د\",\n \"ा ĊĊ\",\n \"ाĊ Ċ\",\n \"ĠÐIJ з\",\n \"à¸ĩ à¸Ĥà¸Ńà¸ĩ\",\n \"à¸ĩà¸Ĥ à¸Ńà¸ĩ\",\n \"ĠÑģвой ÑģÑĤва\",\n \"Ġìŀij íĴĪ\",\n \"пе ки\",\n \"Ġ å°±\",\n \"Ġå° ±\",\n \"ев ого\",\n \"ево го\",\n \"ĠtaÅŁ ıy\",\n \"ĠÙħÙĨ Ø·ÙĤØ©\",\n \"ĠÙħÙĨØ· ÙĤØ©\",\n \"ĠÃĩ ocuk\",\n \"Û² Û·\",\n \"ĠÏĥÏħ μÏĢ\",\n \"é£Ł åĵģ\",\n \"h á\",\n \"ï¼ ¯\",\n \"ÙĦ ÙħÙĩ\",\n \"ÙĦÙħ Ùĩ\",\n \"ãģ¨ãģª ãģ£ãģŁ\",\n \"о ÑĢÑĸ\",\n \"оÑĢ Ñĸ\",\n \"° }\",\n \"ĠtaÅŁ ın\",\n \"çŁ ¿\",\n \"ĠÑĩаÑģÑĤ ини\",\n \"ĠÑĩаÑģÑĤи ни\",\n \"ĠدÙĬ سÙħبر\",\n \"Ġ èī¯\",\n \"Ġèī ¯\",\n \"st ÅĻÃŃ\",\n \"Ġ ÑĨик\",\n \"ĠÑĨ ик\",\n \"ĠÑĨи к\",\n \"âĢķâĢķ âĢķâĢķ\",\n \"ĠÄ°ng iltere\",\n \"ĠÑģÑĤ ÑĢаÑĤег\",\n \"ĠÑģÑĤÑĢ аÑĤег\",\n \"ÃĦ Ÿ\",\n \"и Ñĩного\",\n \"иÑĩ ного\",\n \"иÑĩно го\",\n \"ÃŃ rk\",\n \"ÃŃr k\",\n \"ĠÎij Ïģ\",\n \"! âĢľĊĊ\",\n \"!âĢľ ĊĊ\",\n \"Ġ 깨\",\n \"Ġê¹ ¨\",\n \"à¥ģà¤Ĩ त\",\n \"ĠدÙĨÛĮ ا\",\n \"ĠدÙĨ ÛĮا\",\n \"l ÃŃn\",\n \"lÃŃ n\",\n \"Ġà¤ķ ड\",\n \"ĠÙħ بت\",\n \"ĠÙħب ت\",\n \"ем ÑĭÑħ\",\n \"о би\",\n \"об и\",\n \"ย à¸Ļà¸ķ\",\n \"ยà¸Ļ à¸ķ\",\n \"à¤Ĥध न\",\n \"ÚĨ ÛĮ\",\n \"Ġ çŁ¥\",\n \"Ġç Ł¥\",\n \"ĠçŁ ¥\",\n \"ĠXu ân\",\n \"a daki\",\n \"ad aki\",\n \"ada ki\",\n \"Ġ orta\",\n \"Ġor ta\",\n \"Ġort a\",\n \"æł¹ æľ¬\",\n \"åħ± åIJĮ\",\n \"н ений\",\n \"не ний\",\n \"нен ий\",\n \"ب ÙĬرة\",\n \"بÙĬ رة\",\n \"بÙĬر Ø©\",\n \"çŃ ĭ\",\n \"ïº Ķ\",\n \"âĢĮ ÙĩاÙĬ\",\n \"âĢĮÙĩا ÙĬ\",\n \"Ġö deme\",\n \"Ġödem e\",\n \"ĠØ¢ÙĨ ÚĨÙĩ\",\n \"Ġза Ñıви\",\n \"ĠзаÑıв и\",\n \"ĠÙĨÙĤ Ø´Ùĩ\",\n \"ĠÙĨÙĤØ´ Ùĩ\",\n \"Ġ ç³»\",\n \"Ġç ³»\",\n \"Ġç³ »\",\n \"à¥ĭ ।\",\n \"Ġì§Ģ ìłķ\",\n \"Ġin sp\",\n \"Ġins p\",\n \"Ġ ÑĤен\",\n \"ĠÑĤ ен\",\n \"ĠÑĤе н\",\n \"Ġت Ø·\",\n \"Ġqu ảng\",\n \"Ġquả ng\",\n \"Ġquản g\",\n \"åī £\",\n \"ãģı ãģ®\",\n \"ĠÑĨ им\",\n \"ĠÑĨи м\",\n \"k ovi\",\n \"ko vi\",\n \"kov i\",\n \"i yah\",\n \"iy ah\",\n \"iya h\",\n \"Ġ ëIJľëĭ¤\",\n \"ĠëIJľ ëĭ¤\",\n \"ص Ùĩ\",\n \"ĠÄij u\",\n \"Ġsu á»ijt\",\n \"ı ma\",\n \"ım a\",\n \"ì§Ģ ê³ł\",\n \"Ì ĥ\",\n \"à¸ļ าย\",\n \"ĠCert if\",\n \"ĠCer tif\",\n \"ĠÑĥÑģ ÑĸÑħ\",\n \"ĠÑĥÑģÑĸ Ñħ\",\n \"à¸ķะ ว\",\n \"εί ÏĦε\",\n \"Ġ č\",\n \"Ġмож ливÑĸÑģÑĤÑĮ\",\n \"Ġможлив ÑĸÑģÑĤÑĮ\",\n \"Ġ -âĢIJ\",\n \"Ġ- âĢIJ\",\n \"Ġ íĺ¹\",\n \"Ġíĺ ¹\",\n \"ìĤ¬ ì§Ħ\",\n \"Ġд аниÑħ\",\n \"Ġда ниÑħ\",\n \"Ġдан иÑħ\",\n \"Ġzah áj\",\n \"주 ëĬĶ\",\n \"Ġг ид\",\n \"n iž\",\n \"ni ž\",\n \"Ġ^{ °}\",\n \"Ġk ro\",\n \"Ġkr o\",\n \"Äį en\",\n \"Äįe n\",\n \"ÏĨ ι\",\n \"ımız da\",\n \"Ġ æ¹ĸ\",\n \"Ġæ¹ ĸ\",\n \"Ġпов ÑĢежд\",\n \"Ġì¡´ ìŀ¬\",\n \"à¸Ļ าà¸Ļ\",\n \"à¸Ļา à¸Ļ\",\n \"μÎŃ Î½Î¿ÏĤ\",\n \"μÎŃν οÏĤ\",\n \"μÎŃνο ÏĤ\",\n \"æ½ ľ\",\n \"ï¼Į 使\",\n \"Ġd osp\",\n \"Ġdo sp\",\n \"Ġdos p\",\n \"Ġl iá»ģn\",\n \"Ġli á»ģn\",\n \"ัà¸ļ à¸Ħวาม\",\n \"ัà¸ļà¸Ħ วาม\",\n \"ĠÑĢабоÑĤ е\",\n \"ĠÑĢаб оÑĤе\",\n \"ĠÑĢабо ÑĤе\",\n \"Ġмай бÑĥÑĤ\",\n \"à¹Ģà¸ģ ษ\",\n \"B aÅŁ\",\n \"Ba ÅŁ\",\n \"Ġ æĿ±äº¬\",\n \"ĠæĿ± 京\",\n \"наÑĩ ала\",\n \"δ ει\",\n \"δε ι\",\n \"à¥Ī प\",\n \"Ñĸ мÑĸ\",\n \"Ñĸм Ñĸ\",\n \"Ġf izik\",\n \"Ġfi zik\",\n \"Ġfiz ik\",\n \"ว ล\",\n \"ä¼ į\",\n \"Ġ à¸Ĭà¸Ļะ\",\n \"Ġà¸Ĭ à¸Ļะ\",\n \"' ÑıÑĤ\",\n \"'Ñı ÑĤ\",\n \"н ил\",\n \"ни л\",\n \"и нов\",\n \"ин ов\",\n \"ĠÄijo án\",\n \"รว à¸Ī\",\n \"f et\",\n \"fe t\",\n \"à¹Į à¹Ĥ\",\n \"Ġ маÑĤи\",\n \"Ġм аÑĤи\",\n \"ĠмаÑĤ и\",\n \"Ġма ÑĤи\",\n \"é¨ İ\",\n \"Ðļ Т\",\n \"à¹Ģส à¸Ļà¸Ń\",\n \"à¹Ģสà¸Ļ à¸Ń\",\n \"Ġм ав\",\n \"Ġма в\",\n \"lı ģına\",\n \"lıģı na\",\n \"lıģ ına\",\n \"lıģın a\",\n \"Ġпо Ñĩина\",\n \"ĠпоÑĩ ина\",\n \"ู à¸ķร\",\n \"ูà¸ķ ร\",\n \"ÑĨ еÑĢ\",\n \"ÑĨе ÑĢ\",\n \"uj ete\",\n \"uje te\",\n \"ujet e\",\n \"Ġtah min\",\n \"Ġвим ог\",\n \"า à¸Ł\",\n \"าภŁ\",\n \"е дж\",\n \"ед ж\",\n \"ÏĦ εÏį\",\n \"ÏĦε Ïį\",\n \"ad la\",\n \"ĠÄij Æ°Æ¡ng\",\n \"Ġد استاÙĨ\",\n \"Ġbas ın\",\n \"Ġba sın\",\n \"ĠÑħ в\",\n \"Ġ reak\",\n \"Ġre ak\",\n \"ĠоÑĤ меÑĤ\",\n \"æ³ ¥\",\n \"Ġm áte\",\n \"Ġmá te\",\n \"Ġmát e\",\n \"Ġzo run\",\n \"Ġzor un\",\n \"ãģ¨æĢĿ ãģĨ\",\n \"Ġدر جة\",\n \"ĠвÑĸд ÑģÑĥÑĤ\",\n \"Ġع اÙħÙĦ\",\n \"ĠعاÙħ ÙĦ\",\n \"èĶ µ\",\n \"Ġson raki\",\n \"Ġsonra ki\",\n \"Ġmoh li\",\n \"Ġmohl i\",\n \"и ваеÑĤ\",\n \"ив аеÑĤ\",\n \"ива еÑĤ\",\n \"ĠпÑĸд ÑģÑĤав\",\n \"Ġost rov\",\n \"Ġostr ov\",\n \"ान व\",\n \"âĢŀ P\",\n \"Ġви знаÑĩа\",\n \"ĠвизнаÑĩ а\",\n \"Ġprav dÄĽpodob\",\n \"Ġz az\",\n \"Ġza z\",\n \"ìĿ´ 를\",\n \"Ġдж еÑĢ\",\n \"ĠÐł ад\",\n \"ĠÐłÐ° д\",\n \"ĠÑģеÑĢÑĮ ез\",\n \"Ġ дем\",\n \"Ġд ем\",\n \"Ġде м\",\n \"ÏĢ ή\",\n \"ĠÐĦ вÑĢоп\",\n \"ĠÐĦв ÑĢоп\",\n \"ĠÄįesk é\",\n \"ĠÄįe ské\",\n \"ï¾ ı\",\n \"Ġ ØŃÙĬ\",\n \"ĠØŃ ÙĬ\",\n \"ì¼ ĢìĿ´\",\n \"ì¼Ģ ìĿ´\",\n \"ĠØ® ÙĪÙĨ\",\n \"ĠØ®ÙĪ ÙĨ\",\n \"Âł L\",\n \"ãģĦ ãģ«\",\n \"из неÑģ\",\n \"ĠÙħ ÙĤاÙħ\",\n \"ĠÙħÙĤ اÙħ\",\n \"ĠاÙĦ ØŃÙĦ\",\n \"ĠاÙĦØŃ ÙĦ\",\n \"ëĨ į\",\n \"ĠØ¢ ÛĮا\",\n \"ĠØ¢ÛĮ ا\",\n \"ç¿ ¼\",\n \"ï¼ ½\",\n \"æ¸ IJ\",\n \"ли вÑĸ\",\n \"лив Ñĸ\",\n \"ãģĦ ãģ¦ãģĦãĤĭ\",\n \"ãģĦãģ¦ ãģĦãĤĭ\",\n \"Ġ ÎijÎł\",\n \"ĠÎij Îł\",\n \"ĠиÑģполÑĮз ÑĥеÑĤÑģÑı\",\n \"ĠиÑģполÑĮзÑĥ еÑĤÑģÑı\",\n \"Ġm át\",\n \"Ġmá t\",\n \"Ġμε γά\",\n \"Ġμεγ ά\",\n \"ëħ ¼\",\n \"æµ· éģĵ\",\n \"ĠÙħØ´Ú© ÙĦات\",\n \"ĠÙħØ´Ú©ÙĦ ات\",\n \"Ñĩ на\",\n \"'; ';\",\n \"Ġ μία\",\n \"Ġμ ία\",\n \"Ïģ Ïİν\",\n \"ÏģÏİ Î½\",\n \"Ġby ste\",\n \"ĠÑįлек ÑĤÑĢи\",\n \"ĠÑįлекÑĤÑĢ и\",\n \"ĠY ardım\",\n \"ĠYard ım\",\n \"ĠYar dım\",\n \"Ġh át\",\n \"Ġhá t\",\n \"ĠÐĶ еÑĢжав\",\n \". С\",\n \"Ġo rada\",\n \"Ġor ada\",\n \"Ġora da\",\n \"Ġal anı\",\n \"Ġalan ı\",\n \"åľ° åŁŁ\",\n \"ĠدÙĩ ÙĨد\",\n \"мен ÑĪ\",\n \"ĠоÑĢг анов\",\n \"ĠоÑĢган ов\",\n \"Ġع ص\",\n \"ู à¸ĩส\",\n \"ูà¸ĩ ส\",\n \"ĠØ´ عر\",\n \"Ġشع ر\",\n \"Ġìĸ »\",\n \"Ġά λλ\",\n \"Ġάλ λ\",\n \"Ġg ói\",\n \"Ġgó i\",\n \"ĠÙĨ اØŃ\",\n \"å¼ ĺ\",\n \"à¥įथ ल\",\n \"i lim\",\n \"il im\",\n \"ili m\",\n \"ëIJĺ ì§Ģ\",\n \"Ġкон ÑĨе\",\n \"ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł\",\n \"Ġì¤Ģ ë¹Ħ\",\n \"Ġostat nÃŃ\",\n \"Ġvlá dy\",\n \"Ġvlád y\",\n \"ĠÑģо биÑĢа\",\n \"ĠÑģоб иÑĢа\",\n \"ĠìĹŃ ìĤ¬\",\n \"à¹ģ à¸ģรม\",\n \"à¹ģà¸ģ รม\",\n \". ï¼ı\",\n \"Ùı ÙĪÙĨ\",\n \"ÙıÙĪ ÙĨ\",\n \"Ù¾ س\",\n \"ĠW ikip\",\n \"ĠWi kip\",\n \"ĠWiki p\",\n \"ĠWik ip\",\n \"Ġ æ¾\",\n \"Ġæ ¾\",\n \"Ġж аÑĢ\",\n \"容 æĺĵ\",\n \"ĠprostÅĻed nictvÃŃm\",\n \"Ġž eny\",\n \"Ġže ny\",\n \"Ġžen y\",\n \"Ġèı²å¾ĭ宾 çĶ³åįļ\",\n \"а ÑİÑĤÑģÑı\",\n \"аÑİÑĤ ÑģÑı\",\n \"Ġm iêu\",\n \"Ġmi êu\",\n \"Ġp enÃŃze\",\n \"δ ιά\",\n \"δι ά\",\n \"ol dur\",\n \"old ur\",\n \"ĠпÑĢимеÑĢ но\",\n \"ĠìŀĪ ê³ł\",\n \"à¸ĩ à¸Ńย\",\n \"к овий\",\n \"ко вий\",\n \"ков ий\",\n \". ÎŁ\",\n \"à¹ĥ à¸Ħร\",\n \"çĭ ł\",\n \"ĠÐŁ Ñĸв\",\n \"æĶ¹ éĿ©\",\n \"ĠÐĿаÑģ еление\",\n \"Å¡et ÅĻ\",\n \"ÙĴ ب\",\n \"Ġ âĶĢ\",\n \"ĠâĶ Ģ\",\n \"غ ÙĬÙĦ\",\n \"ĠдÑĸÑıлÑĮ нÑĸÑģÑĤÑĮ\",\n \"ĠÙĦ ÙĬس\",\n \"ĠÙĦÙĬ س\",\n \"Ġìĭľ ìŀ¥\",\n \"ãĥŁ ãĥ¥\",\n \"ĠÚ© ÙĪت\",\n \"ĠÚ©ÙĪ ت\",\n \"ĠÎĵ ι\",\n \"ิ à¹Ģว\",\n \"e ktor\",\n \"ek tor\",\n \"ekt or\",\n \"ĠбÑĥд Ñĥ\",\n \"ĠбÑĥ дÑĥ\",\n \"но важ\",\n \"нов аж\",\n \"нова ж\",\n \"Ñī аеÑĤÑģÑı\",\n \"Ñīа еÑĤÑģÑı\",\n \"Ġng ôn\",\n \"ĠvÄĽ c\",\n \"å¾ IJ\",\n \"à¸Ńà¹Ģม ร\",\n \"ัà¸į à¸Ĭ\",\n \"ĠиÑģполÑĮз ÑĥÑİÑĤ\",\n \"ĠиÑģполÑĮзÑĥ ÑİÑĤ\",\n \"r ubu\",\n \"ru bu\",\n \"rub u\",\n \"Ġnh á»±a\",\n \"ãģĮ ãģĬ\",\n \"ĠÐĵ аÑĢ\",\n \"о ÑĢе\",\n \"оÑĢ е\",\n \"Ġз олоÑĤ\",\n \"æ Ł³\",\n \"æŁ ³\",\n \"Ġ ÙĬØ´\",\n \"ĠÙĬ Ø´\",\n \"Ġповин нÑĸ\",\n \"اÙĤ تص\",\n \"ÙĦ ØŃ\",\n \"ĠокÑĤ ÑıбÑĢÑı\",\n \"ĠnÄĽk dy\",\n \"Ġо бÑĢа\",\n \"Ġоб ÑĢа\",\n \"ست Ú¯ÛĮ\",\n \"符 åIJĪ\",\n \"Ġth iá»ĥu\",\n \"æĺ¯ ä»Ģä¹Ī\",\n \"Ġro zs\",\n \"Ġroz s\",\n \"ì½ľ 걸\",\n \"Ġк аÑĦ\",\n \"Ġка ÑĦ\",\n \"åIJĮ æŃ¥\",\n \"ì¼ ĵ\",\n \"ÏĢ ÏĦÏħ\",\n \"à¸ł ายà¹ĥà¸Ļ\",\n \"ι ÏĥÏĦή\",\n \"ιÏĥ ÏĦή\",\n \"ĠدÙĪÙĦ ار\",\n \"ĠÙħا ÙĬÙĪ\",\n \"ĠÙħاÙĬ ÙĪ\",\n \"Ġ peÄį\",\n \"Ġp eÄį\",\n \"Ġpe Äį\",\n \"ัà¸ļ ม\",\n \"ÎĻ ÎĶ\",\n \"ı ydı\",\n \"ıy dı\",\n \"ัà¸ģ à¸Ĺ\",\n \"à¸Ľà¸£à¸° à¸ĸม\",\n \"κ αι\",\n \"κα ι\",\n \"Ġpro dej\",\n \"Ġprod ej\",\n \"ĠиÑİ Ð»Ñı\",\n \"Ġv Å©\",\n \"é© ±\",\n \"Ġh vÄĽ\",\n \"Ġhv ÄĽ\",\n \"æĥ³ è¦ģ\",\n \"ç¯ Ħ\",\n \"ç ak\",\n \"ça k\",\n \"Ġм Ñıг\",\n \"ım ın\",\n \"ımı n\",\n \"Ġdisp ozici\",\n \"Ġu kaz\",\n \"Ġuk az\",\n \"r acak\",\n \"ra cak\",\n \"rac ak\",\n \"Ġболез ни\",\n \"ว à¹Ĥม\",\n \"Ġз ел\",\n \"ĠÐĴ ики\",\n \"ĠÐĴи ки\",\n \"ĠÐĴик и\",\n \"ĠÐł од\",\n \"ูà¸ģ à¸Ħ\",\n \"í ij¸\",\n \"Ġth ải\",\n \"ĠbaÄŁ ımsız\",\n \"ĠÑĢоÑģ Ñģий\",\n \"ĠÐļ ам\",\n \"ĠÐļа м\",\n \"ĠиÑģполÑĮзов аниÑı\",\n \"ĠиÑģполÑĮз ованиÑı\",\n \"ĠиÑģполÑĮзовани Ñı\",\n \"ĠØŃ Ø°\",\n \"Âł ³³³³³³³³\",\n \"³³ ³³³³³³³\",\n \"³³³³ ³³³³³\",\n \"³³³ ³³³³³³\",\n \"³³³³³³³³ Âł\",\n \"³³³³³³³ ³³\",\n \"³³³³³ ³³³³\",\n \"³³³³³³ ³³³\",\n \"ĠاÙĨت ÙĤاÙĦ\",\n \"Ġаб ÑģолÑİÑĤ\",\n \"Ġ Ä±ÅŁÄ±k\",\n \"ĠÄ±ÅŁÄ± k\",\n \"ÏĦο γÏģαÏĨ\",\n \"ĠболÑĮÑĪ ой\",\n \"Ġعب ارت\",\n \"Ġعبار ت\",\n \"ÃŃ Å¾\",\n \"Ġدر ست\",\n \"Ġدرس ت\",\n \"ĠÑģл ово\",\n \"ĠÑģлов о\",\n \"ĠÑģло во\",\n \"à¥Ī Ċ\",\n \"ب ÙĪب\",\n \"بÙĪ ب\",\n \"ĠÐĴ оÑĤ\",\n \"ĠÐĴо ÑĤ\",\n \"ว à¹Ħà¸Ľ\",\n \"Ġbil inen\",\n \"Ġbilin en\",\n \"Ġ ÙĤÙĬ\",\n \"ĠÙĤ ÙĬ\",\n \"Ġbun ların\",\n \"Ġbunlar ın\",\n \"Ġbunları n\",\n \"Ùij ت\",\n \"Ġbas it\",\n \"Ġba sit\",\n \"ë¦ ¿\",\n \"ائ رة\",\n \"ائر Ø©\",\n \"Ġp ů\",\n \"Ġed ilmiÅŁ\",\n \"Ġedilm iÅŁ\",\n \"Ġedil miÅŁ\",\n \"Ġ ä½IJ\",\n \"ĠYön etim\",\n \"ĠYönet im\",\n \"Ùħ ÛĮر\",\n \"ÙħÛĮ ر\",\n \"Ġsp ou\",\n \"Ġspo u\",\n \"æ·± åľ³\",\n \"Ġвза ÑĶм\",\n \"ÎĻ ÎĽ\",\n \"Ð ĥ\",\n \"ĠдеÑĢжав ноÑĹ\",\n \"Ġ mrt\",\n \"Ġm rt\",\n \"Ġmr t\",\n \"ĠDe mir\",\n \"ĠDem ir\",\n \"é» İ\",\n \"ĠÑĢег ÑĥлÑıÑĢ\",\n \"Ġник огда\",\n \"å¼ ¾\",\n \"à¥ī ड\",\n \"Ġг лаз\",\n \"Ġгла з\",\n \"ĠÙħÛĮ Ú©ÙĨ\",\n \"ĠÙħÛĮÚ© ÙĨ\",\n \"éĻIJ å®ļ\",\n \"Ġнав к\",\n \"Ġпод ÑĤ\",\n \"ĠتصÙĪ ÛĮر\",\n \"ĠاÙĦØŃ Ø¯ÙĬØ«\",\n \"Ġdo Å¡lo\",\n \"нÑİ Ñİ\",\n \"ĠÑģ Ñħод\",\n \"ĠÑģÑħ од\",\n \"Ø· ÙĤØ©\",\n \"ĠÑģенÑĤ ÑıбÑĢÑı\",\n \"çī¹ æ®Ĭ\",\n \"à¸ģาร à¹ģà¸Ĥ\",\n \"á zd\",\n \"áz d\",\n \"ÑĶ ÑĤе\",\n \"ĠΣ ε\",\n \"ĠÙĦ ÙĥÙĦ\",\n \"ĠÙĦÙĥ ÙĦ\",\n \"åIJį åŃĹ\",\n \"اÙĨ ÛĮا\",\n \"اÙĨÛĮ ا\",\n \"Ġc ins\",\n \"Ġcin s\",\n \"Ġci ns\",\n \"기 ìĹħ\",\n \"Ġ éŁ³\",\n \"Ġé Ł³\",\n \"ĠéŁ ³\",\n \"éł ĥ\",\n \"ย าย\",\n \"ยา ย\",\n \"ìļ ķ\",\n \"ĠvÃŃ tÄĽz\",\n \"à¥įर ब\",\n \"Ġشر ÙĤÛĮ\",\n \"ĠشرÙĤ ÛĮ\",\n \"ĠbezpeÄį nost\",\n \"Ġçerç ev\",\n \"Ġ 맼\",\n \"Ġë§ Ľ\",\n \"c ky\",\n \"ck y\",\n \"ĵ ¨\",\n \"ĠÑĥм оваÑħ\",\n \"ĠÑĥмов аÑħ\",\n \"л иÑħ\",\n \"ли Ñħ\",\n \"m eniz\",\n \"men iz\",\n \"meni z\",\n \"Ġب Ú¯ÛĮر\",\n \"Ġبگ ÛĮر\",\n \"ÙĨ Ùī\",\n \"Ġ à¸ģารà¹ģà¸Ĥ\",\n \"Ġà¸ģาร à¹ģà¸Ĥ\",\n \"ι Ïĥε\",\n \"ιÏĥ ε\",\n \"âĢ³ E\",\n \"Ġdönem inde\",\n \"리 ì¹´\",\n \"Ġ åĪ°\",\n \"ĠåĪ °\",\n \"Ġhu kuk\",\n \"Ġhuku k\",\n \"а ÑĤоÑĢа\",\n \"аÑĤ оÑĢа\",\n \"аÑĤоÑĢ а\",\n \"аÑĤо ÑĢа\",\n \"ĠاÙĦ عÙĨ\",\n \"ĠاÙĦع ÙĨ\",\n \"ïº ĺ\",\n \"ün üz\",\n \"ünü z\",\n \"Ñģ оÑĤ\",\n \"Ñģо ÑĤ\",\n \"ุ ษ\",\n \"Ġd Æ°Æ¡ng\",\n \"ov ny\",\n \"Ġ ÑĦоÑĢма\",\n \"ĠÑĦоÑĢм а\",\n \"ĠÑĦоÑĢ ма\",\n \"ãģĹ ãģ®\",\n \"ãģĹãģ ®\",\n \"ز ÙĬز\",\n \"زÙĬ ز\",\n \"ĠاÙĦÙĨ اس\",\n \"Ġ Ñĩим\",\n \"ĠÑĩ им\",\n \"ĠÑĩи м\",\n \"大 人\",\n \"Ú¯ ÙĬ\",\n \"ĠÐĵ оÑģп\",\n \"é¢Ĩ 导\",\n \"Ġn inh\",\n \"Ġni nh\",\n \"Ġnin h\",\n \"Ġร าà¸Ħา\",\n \"Ġราà¸Ħ า\",\n \"ÙĤ اء\",\n \"ìī ¬\",\n \"ĠìĿ´ ìłĦ\",\n \"ĠÃ¶ÄŁret men\",\n \"ĠÑĨвеÑĤ а\",\n \"ен ноÑģÑĤÑĮ\",\n \"енно ÑģÑĤÑĮ\",\n \"大 ãģį\",\n \"ĠмиÑģÑĤ еÑĨÑĤ\",\n \"ر ÙĪت\",\n \"رÙĪ ت\",\n \"p oÅĪ\",\n \"po ÅĪ\",\n \"ĠÅŀ irket\",\n \"ĠкÑĢаÑģ ив\",\n \"ĠÑĢеÑģ ÑĥÑĢÑģ\",\n \"ĠÑĢеÑģÑĥÑĢ Ñģ\",\n \"ä¹ ¾\",\n \"Ġ ÙģÙĩ\",\n \"ĠÙģ Ùĩ\",\n \"ĠY Ãĸ\",\n \"èĬ ³\",\n \"μ ÏīÏĤ\",\n \"ÄĽ ji\",\n \"ÄĽj i\",\n \"Ġв лаж\",\n \"Ġвла ж\",\n \"ĠÑĥв ели\",\n \"ا ذا\",\n \"اذ ا\",\n \"ãĢĤ å¦Ĥæŀľ\",\n \"ĠпÑĢи ÑģÑĥÑĤÑģÑĤв\",\n \"ĠẤ n\",\n \"æĢ ĸ\",\n \"ĠÐľ еÑĤ\",\n \"Ġje dna\",\n \"Ġjed na\",\n \"Ġjedn a\",\n \"Ġc ục\",\n \"Ġcụ c\",\n \"ĠاÙĨت شار\",\n \"Ġз окÑĢема\",\n \"и ÑĩеÑģки\",\n \"иÑĩеÑģ ки\",\n \"ĠкÑĢаÑĹ Ð½Ð¸\",\n \"ĠкÑĢаÑĹн и\",\n \"и ÑĢÑĥ\",\n \"иÑĢ Ñĥ\",\n \"ĠÑĸн ÑĤеÑĢ\",\n \"ĠÑĸнÑĤ еÑĢ\",\n \"Ġан алог\",\n \"Ñ Ľ\",\n \"ี à¸ĭ\",\n \"н Ñĥли\",\n \"нÑĥ ли\",\n \"нÑĥл и\",\n \"ĠN inh\",\n \"ĠNi nh\",\n \"ĠNin h\",\n \"еÑĢ аÑĤоÑĢ\",\n \"еÑĢа ÑĤоÑĢ\",\n \"Ġr uce\",\n \"Ġru ce\",\n \"ĠÑĪ кÑĸ\",\n \"ĠÑĪк Ñĸ\",\n \"تر ÙĨت\",\n \"Ġson rası\",\n \"Ġsonra sı\",\n \"Ġ æį\",\n \"Ġæ į\",\n \"ÑĨен ÑĤÑĢа\",\n \"ÑĨенÑĤ ÑĢа\",\n \"Ġà¸Ńำ à¹Ģà¸ł\",\n \"Ø· ÙĬ\",\n \"ï¼Į å½ĵ\",\n \"ĠÑĤ ÑĢеÑħ\",\n \"ĠÑĤÑĢ еÑħ\",\n \"Âł H\",\n \"æ´ ª\",\n \"ãĥ³ ãĥĦ\",\n \"ãĥ³ãĥ Ħ\",\n \"ĠвÑĸдповÑĸд алÑĮ\",\n \"âĢĻ daki\",\n \"âĢĻd aki\",\n \"âĢĻda ki\",\n \"á ÅĻi\",\n \"áÅĻ i\",\n \"ĠpÅĻ em\",\n \"ĠpÅĻe m\",\n \"t uk\",\n \"tu k\",\n \"ĠÙģر ÙħÙĪد\",\n \"ĠÙģرÙħ ÙĪد\",\n \"Ġ ìĿ¸ì¦Ŀ\",\n \"ĠìĿ¸ ì¦Ŀ\",\n \"สำ à¸Ļ\",\n \"ìĥģ ìĿĺ\",\n \"ÅĻ ÃŃm\",\n \"ÅĻÃŃ m\",\n \"æ¾ ¤\",\n \"Ġ ÑĢей\",\n \"ĠÑĢ ей\",\n \"ĠÑĢе й\",\n \"ĠлÑİб ой\",\n \"u jte\",\n \"uj te\",\n \"ë³µ ì§Ģ\",\n \"Ġ درس\",\n \"Ġد رس\",\n \"Ġدر س\",\n \"ĠÐĴ лади\",\n \"ĠÑģво им\",\n \"ĠÑģвои м\",\n \"ĠìĿ¸íĦ° ëĦ·\",\n \"è± Ĭ\",\n \"Ġн алог\",\n \"Ġнал ог\",\n \"ãĤĪ ãģ³\",\n \"ĠØ® اطر\",\n \"Ġ ìŀħëĭĪëĭ¤\",\n \"Ġìŀħ ëĭĪëĭ¤\",\n \"ãĢĤ ãģĹãģĭãģĹ\",\n \"л аг\",\n \"ла г\",\n \"å° ĸ\",\n \"ëĭ ¥\",\n \"ìĬ¤ ëĬĶ\",\n \"ìĭł ì²Ń\",\n \"ãĥĩ ãĥ¼ãĤ¿\",\n \"ĠÑĥÑĢов нÑı\",\n \"Ġ무 ìĬ¨\",\n \"ĠاÙĦØ£ رض\",\n \"à¹ī à¸ķ\",\n \"Ỽ t\",\n \"ĠÙĨÛĮ رÙĪ\",\n \"ĠÙĨÛĮر ÙĪ\",\n \"å¢ ¨\",\n \"ãĤ¶ ãĥ¼\",\n \"r uba\",\n \"ru ba\",\n \"rub a\",\n \"ĠÙĨØ´ دÙĩ\",\n \"и лÑı\",\n \"ил Ñı\",\n \"a cÃŃm\",\n \"ac ÃŃm\",\n \"acÃŃ m\",\n \"ãĥ© ãĤ¯\",\n \"X H\",\n \"Ġس رد\",\n \"Ġسر د\",\n \"Ġद स\",\n \"t ember\",\n \"tem ber\",\n \"ĠDoÄŁ um\",\n \"ĠDoÄŁu m\",\n \"ĠпÑĢ оÑĢ\",\n \"ĠпÑĢо ÑĢ\",\n \"θ οÏĤ\",\n \"θο ÏĤ\",\n \"ĠiÅŁ e\",\n \"à¸Ń à¸Ł\",\n \"л аÑĪ\",\n \"ла ÑĪ\",\n \"اص ÙĦÙĩ\",\n \"اصÙĦ Ùĩ\",\n \"l ivÄĽ\",\n \"li vÄĽ\",\n \"liv ÄĽ\",\n \"ë¶Ģ ë¶Ħ\",\n \"н ак\",\n \"на к\",\n \"åįģ ä¸ī\",\n \"ส าห\",\n \"à¸Ľà¸£à¸°à¹Ģà¸Ĺศ à¹Ħà¸Ĺย\",\n \"ãĤŃ ãĥ³ãĤ°\",\n \"ĠмеÑĤ оÑİ\",\n \"Ġkullan arak\",\n \"âij ¡\",\n \"ÛĮز ات\",\n \"ĠÙħÙĪب اÛĮÙĦ\",\n \"ĠзнаÑĩ иÑĤ\",\n \"Ġзна ÑĩиÑĤ\",\n \"Ġorgan izace\",\n \"Ġorganiz ace\",\n \"ÑĢ ии\",\n \"ÑĢи и\",\n \"ov na\",\n \"Ġ ê²½ìłľ\",\n \"Ġê²½ ìłľ\",\n \"ãĢģ å½¼\",\n \"Ġम स\",\n \"Ġ à¹Ĥà¸Ľà¸£\",\n \"Ġà¹Ĥ à¸Ľà¸£\",\n \"L ARI\",\n \"LA RI\",\n \"LAR I\",\n \"æĩ Ĥ\",\n \"Ġ ва\",\n \"Ġв а\",\n \"ĠÙĥ ÙĨت\",\n \"ĠÙĥÙĨ ت\",\n \"ĠÑĢабоÑĤ а\",\n \"ĠÑĢаб оÑĤа\",\n \"ĠÑĢабо ÑĤа\",\n \"Âł ĠÂłĠÂł\",\n \"ÂłĠ ÂłĠÂł\",\n \"ÂłĠÂł ĠÂł\",\n \"好 äºĨ\",\n \"ĠzamÄĽst n\",\n \"ж енÑĮ\",\n \"же нÑĮ\",\n \"жен ÑĮ\",\n \"Ġu kon\",\n \"Ġuk on\",\n \"nÄĽ né\",\n \"nÄĽn é\",\n \"Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"ĠاÙĦخاص Ø©\",\n \"ĠÄį asu\",\n \"ĠÄįas u\",\n \"å°ı 说\",\n \"ĠØŃر کت\",\n \"æij Ħ\",\n \"Ïĩ Ïī\",\n \"ĠÑģв еж\",\n \"æĸ° éĹ»\",\n \"Ġ ìĭ±\",\n \"Ġìĭ ±\",\n \"Ġe ÄŁer\",\n \"ĠeÄŁ er\",\n \"Ġsitu ace\",\n \"Ġ ç·¨\",\n \"Ġç ·¨\",\n \"Ġç· ¨\",\n \"f ik\",\n \"fi k\",\n \"ë§ Īëĭ¤\",\n \"ë§Ī ëĭ¤\",\n \"Îķ Îļ\",\n \"Ġê°ľ ìµľ\",\n \"Ġc Ãł\",\n \"ا دث\",\n \"اد Ø«\",\n \"Ġsay ıda\",\n \"Ġsayı da\",\n \"ĠØ£ ÙģضÙĦ\",\n \"ĠØ£Ùģ ضÙĦ\",\n \"æ³ķ éĻ¢\",\n \"Ġ .,\",\n \"Ġ. ,\",\n \"ĠTh Æ°Æ¡ng\",\n \"Ïģ ÏĮÏĤ\",\n \"ÏģÏĮ ÏĤ\",\n \"ãģĹ ãĤĪãģĨ\",\n \"Ç İ\",\n \"æ ij¸\",\n \"æij ¸\",\n \"Ġ éĻ³\",\n \"ĠéĻ ³\",\n \"¥ IJ\",\n \"ฤ à¸Ķ\",\n \"Ġgi ảng\",\n \"Ġgiả ng\",\n \"Ġgiản g\",\n \"ĠлÑİ Ð±Ð¾Ð²\",\n \"ĠлÑİб ов\",\n \"Ġek ran\",\n \"о пиÑģ\",\n \"оп иÑģ\",\n \"еж дÑĥ\",\n \"Ġназ ва\",\n \"æĭ ĵ\",\n \"ı f\",\n \"à¹Ī à¸ģ\",\n \"и ÑĩнÑĸ\",\n \"иÑĩ нÑĸ\",\n \"Ġ ê³Ħíļį\",\n \"Ġê³Ħ íļį\",\n \"à¸ł าà¸Ħม\",\n \"à¸łà¸²à¸Ħ ม\",\n \"Ġ اپ\",\n \"Ġا Ù¾\",\n \"리 ìĿĺ\",\n \"ãģ§ãģĻ ãģĮ\",\n \"Ġkon ci\",\n \"Ġکار خاÙĨÙĩ\",\n \"Ġ ä½ķ\",\n \"ĠÑĤ ва\",\n \"ĠÑĤв а\",\n \"ĠÐŁ оÑģÑĤ\",\n \"ĠÐŁÐ¾ ÑģÑĤ\",\n \"ĠÐŁÐ¾Ñģ ÑĤ\",\n \"ĠапÑĢ елÑı\",\n \"ĠاÙĦع راÙĤ\",\n \"ä¸Ń åįİ\",\n \"à¹ĩ à¸Ńà¸ģ\",\n \"à¥įत à¤ķ\",\n \"Ġz ájem\",\n \"Ġzá jem\",\n \"Ġدر جÙĩ\",\n \"Ġब à¥ľ\",\n \"ĠÑģÑĤ ÑĢан\",\n \"ĠÑģÑĤÑĢ ан\",\n \"ĠÑģÑĤÑĢа н\",\n \"èѦ å¯Ł\",\n \"Ġyer leÅŁtir\",\n \"ĠyerleÅŁ tir\",\n \"ĠV Å©\",\n \"ç¾İ åħĥ\",\n \"Ġì¡° ê¸Ī\",\n \"Ġ รà¸Ńà¸ĩ\",\n \"Ġร à¸Ńà¸ĩ\",\n \"Ġak adem\",\n \"Ġaka dem\",\n \"à¸Ħ à¸ĵะ\",\n \"Ġpoz it\",\n \"Ġkon eÄį\",\n \"Ġkone Äį\",\n \"è°ĥ æŁ¥\",\n \"Ġ ãģĭ\",\n \"ĠÄįerv ence\",\n \"ĠOd kazy\",\n \"ĠëıĦ ìĭľ\",\n \"ั สà¸Ķ\",\n \"ัส à¸Ķ\",\n \"Ġg ái\",\n \"ĠÐł об\",\n \"Ġб оÑı\",\n \"Ġбо Ñı\",\n \"æī ©\",\n \"å¼Ģ å±ķ\",\n \"a nik\",\n \"an ik\",\n \"ani k\",\n \"Ġvy ž\",\n \"ĠbaÅŁ lay\",\n \"Ġbak Ä±ÅŁ\",\n \"ek ce\",\n \"ÑģÑĤ ика\",\n \"ÑģÑĤи ка\",\n \"еÑĢа ÑĤÑĥÑĢа\",\n \"еÑĢаÑĤÑĥ ÑĢа\",\n \"Ġë¶Ħ ë¥ĺ\",\n \"ĠPo Äįet\",\n \"od áÅĻ\",\n \"ëĭĺ ìĿĺ\",\n \"Ġk lid\",\n \"Ġkl id\",\n \"Ġkli d\",\n \"Û² Û¹\",\n \"ĠÚĨ ÛĮز\",\n \"m ür\",\n \"Ġs ứ\",\n \"ÙĬا ÙĨØ©\",\n \"ÙĬاÙĨ Ø©\",\n \"åĬ ±\",\n \"Ġ oku\",\n \"Ġo ku\",\n \"Ġok u\",\n \"Ġв оди\",\n \"Ġво ди\",\n \"Ġвод и\",\n \"ĠزÛĮر ا\",\n \"ĠزÛĮ را\",\n \"大 åĪ©\",\n \"ĠÙĦ ÛĮÙĨÚ©\",\n \"ĠÙĬ جب\",\n \"ĠÙĬج ب\",\n \"Ùħ ÛĮÙĦ\",\n \"ÙħÛĮ ÙĦ\",\n \"ĠÏĥ ÏĦÏģα\",\n \"æĻ ĵ\",\n \"ิ สà¸ķ\",\n \"ิส à¸ķ\",\n \"ĠÅŁ iddet\",\n \"ĠÑĢекомен да\",\n \"Ġpožad av\",\n \"Ġп ÑĸÑģ\",\n \"åħ¬ å¼ı\",\n \"Ġ Ú¯ÛĮرÛĮ\",\n \"ĠÚ¯ ÛĮرÛĮ\",\n \"ĠÚ¯ÛĮ رÛĮ\",\n \"ĠÚ¯ÛĮر ÛĮ\",\n \"к ÑĤа\",\n \"кÑĤ а\",\n \"ĠÙħÙĨ اطÙĤ\",\n \"Ġfirm y\",\n \"Ġfir my\",\n \"Ġ à¹Ħà¸Ľ\",\n \"Ġà¹Ħ à¸Ľ\",\n \"Ġ ÎŃÏģγ\",\n \"ĠÎŃ Ïģγ\",\n \"å¿« éĢŁ\",\n \"ãģĮ ãģªãģĦ\",\n \"н еÑģÑĤи\",\n \"не ÑģÑĤи\",\n \"неÑģ ÑĤи\",\n \"Ġ ç²¾\",\n \"Ġç² ¾\",\n \"ÑĢ ади\",\n \"ÑĢа ди\",\n \"ÑĢад и\",\n \"ãĤĴ ãģĭ\",\n \"ïº ª\",\n \"ky nÄĽ\",\n \"Ġह त\",\n \"t ak\",\n \"ta k\",\n \"ĠÙĬÙĪÙĨ ÙĬÙĪ\",\n \"ö ÄŁ\",\n \"Ġ ÑĢÑĥк\",\n \"ĠÑĢ Ñĥк\",\n \"ĠÑĢÑĥ к\",\n \"åľĭ éļĽ\",\n \"Ñİ ÑģÑĮ\",\n \"Ġдав но\",\n \"Ġp opis\",\n \"Ġpop is\",\n \"Ġpo pis\",\n \"ĠB Ä°L\",\n \"ĠÙĨ ÙĤد\",\n \"ĠÙĨÙĤ د\",\n \"ĠÑģп ож\",\n \"ÑĨион нÑĭÑħ\",\n \"ĠÑĪ п\",\n \"Ñĥ ÑİÑīиÑħ\",\n \"ÑĥÑİ ÑīиÑħ\",\n \"ÑĥÑİÑī иÑħ\",\n \"ĠвоздÑĥ Ñħ\",\n \"ÑĤ ие\",\n \"ÑĤи е\",\n \"ĠU ž\",\n \"ÏĮ δ\",\n \"à¸ģร าà¸Ħม\",\n \"Ġalan ında\",\n \"Ġalanı nda\",\n \"Ġs ắt\",\n \"ãĥIJ ãĤ¤\",\n \"Ng Ãły\",\n \"Ġ ë¹Į\",\n \"Ġë¹ Į\",\n \"ï¼ī ãģ¯\",\n \"Ġ ä¿¡\",\n \"Ðķ С\",\n \"ĠT ato\",\n \"ĠTa to\",\n \"ĠTat o\",\n \"Ġún ora\",\n \"e rap\",\n \"er ap\",\n \"era p\",\n \"Ä ł\",\n \"ĠT áºŃp\",\n \"Ġкомп ании\",\n \"ãĥ© ãĤ¤ãĥĪ\",\n \"ãĥ©ãĤ¤ ãĥĪ\",\n \"éľĢ æ±Ĥ\",\n \"Ġت ÙĪÙĤ\",\n \"ĠتÙĪ ÙĤ\",\n \"âĢĻ âĢĻ\",\n \"ëŀį ëĭĪëĭ¤\",\n \"ĠквÑĸÑĤ нÑı\",\n \"Ġoyun cu\",\n \"ÂĢÂĢÂĢÂĢ ÂĢÂĢÂĢÂĢ\",\n \"åĨ Ĭ\",\n \"Ġyap mÄ±ÅŁ\",\n \"ัà¸ĩ à¹Ħม\",\n \"Ġзап аÑħ\",\n \"á la\",\n \"ál a\",\n \"ĠÑĤеÑħ 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\"ĠÐij ез\",\n \"ĠبÙĦ ÙĨد\",\n \"м м\",\n \"ÑĢ ел\",\n \"ÑĢе л\",\n \"N Ä°\",\n \"Ġ ãĥ¯\",\n \"Ġãĥ ¯\",\n \"éĭ ¼\",\n \"ĠÑģв Ñı\",\n \"Ġ åIJİ\",\n \"ĠåIJ İ\",\n \"Ġmu ht\",\n \"Ġmuh t\",\n \"ĠпÑĢоблем и\",\n \"ĠÑĤÑıж ел\",\n \"ĠС ем\",\n \"ฤษ à¸łà¸²à¸Ħม\",\n \"à¹Ī าà¸ķ\",\n \"à¹Īา à¸ķ\",\n \"ör ü\",\n \"üy orum\",\n \"üyor um\",\n \"ĠاÙĦØ£ ØŃ\",\n \"ĠÑģÑĤÑĢ аÑĪ\",\n \"ĠÑģÑĤÑĢа ÑĪ\",\n \"h oo\",\n \"ho o\",\n \"ध र\",\n \"Ġt lak\",\n \"Ġtl ak\",\n \"Ġsrp na\",\n \"ifik ace\",\n \"Ġ reh\",\n \"Ġre h\",\n \"Ġr eh\",\n \"Ġм инÑĥ\",\n \"Ġмин Ñĥ\",\n \"Ġми нÑĥ\",\n \"ãĢĢ j\",\n \"ĠгÑĢÑĥ пи\",\n \"ĠгÑĢÑĥп и\",\n \"Ġ άλ\",\n \"Ġά λ\",\n \"Ġolur sa\",\n \"λογ ία\",\n \"ĠÐĴ ик\",\n \"ĠÐĴи к\",\n \"Ġmüc adel\",\n \"Ġz ávÄĽ\",\n \"Ġzá vÄĽ\",\n \"Ġzáv ÄĽ\",\n \"ĠÑĦев ÑĢа\",\n \"Äį ná\",\n \"à¹Į à¹Ģà¸ĭ\",\n \"ĠÙĦ ÙĦØŃ\",\n \"ĠÙĦÙĦ ØŃ\",\n \"ÑĢ ип\",\n \"ÑĢи п\",\n \"Ġб Ñĥк\",\n \"ĠбÑĥ к\",\n \"ãģĪ ãģªãģĦ\",\n \"Ġpo rad\",\n \"Ġpor ad\",\n \"Ġsa mostat\",\n \"Ġsam ostat\",\n \"Ġsamo stat\",\n \"Ġt esis\",\n \"Ġte sis\",\n \"Ġtes is\",\n \"اب ÙĤÙĩ\",\n \"ابÙĤ Ùĩ\",\n \"Ġجد ÙĬدة\",\n \"ĠجدÙĬد Ø©\",\n \"éĢ Ĵ\",\n \"âĶģ âĶ\",\n \"س ÛĮÙĨ\",\n \"سÛĮ ÙĨ\",\n \"Ġgerek tiÄŁini\",\n \"ียà¸Ļ à¸ļ\",\n \"è¨Ģ ãģ£ãģ¦\",\n \"ĠÑĸн ÑĤеÑĢеÑģ\",\n \"ĠÑĸнÑĤеÑĢ еÑģ\",\n \"ĠÑı ким\",\n \"ĠÑıк им\",\n \"Ġ æĢ»\",\n \"ĠæĢ »\",\n \"k ovou\",\n \"ko vou\",\n \"kov ou\",\n \"Ġd emek\",\n \"Ġde mek\",\n \"Ġdem ek\",\n \"اÙĨ ÙĬا\",\n \"اÙĨÙĬ ا\",\n \"Ġdom ů\",\n \"Å¡ nÃŃ\",\n \"ate ÅĻ\",\n \"åĢ «\",\n \"δο Ïĥη\",\n \"Ġ 기ìĹħ\",\n \"Ġ기 ìĹħ\",\n \"åĶ ĩ\",\n \"ì¹ ł\",\n \"Ñĸ дÑĥ\",\n \"Ñĸд Ñĥ\",\n \"린 ìĿ´\",\n \"æľĢ åĪĿ\",\n \"è ¸ı\",\n \"è¸ ı\",\n \"æĥ³ åĪ°\",\n \"à¥į बर\",\n \"à¥įब र\",\n \"Ġ ìŀĶ\",\n \"Ġìŀ Ķ\",\n \"ĠÑĢаз нÑĭÑħ\",\n \"k rom\",\n \"kr om\",\n \"ι αν\",\n \"ια ν\",\n \"Ġд ÑĢÑĥз\",\n \"ĠдÑĢÑĥ з\",\n \"ĠдÑĢ Ñĥз\",\n \"ä »¿\",\n \"ä» ¿\",\n \"Ġê·¸ ëłĩ\",\n \"Ġд алÑĸ\",\n \"Ġда лÑĸ\",\n \"Ġдал Ñĸ\",\n \"æķĪ æŀľ\",\n \"Ġह व\",\n \"è¼ Ŀ\",\n \"Ġì°¸ ê³ł\",\n \"Ġ ìĨĶ\",\n \"ĠìĨ Ķ\",\n \"Ġz nal\",\n \"Ġzn al\",\n \"ĠпеÑĢ Ñģ\",\n \"ÙIJ Ùij\",\n \"ĠÑĤ еж\",\n \"ĠÑĤе ж\",\n \"åĭ Ł\",\n \"ι θ\",\n \"Äį ů\",\n \"Ġe kip\",\n \"Ġek ip\",\n \"Ġk hung\",\n \"Ġkh ung\",\n \"Ġkhu ng\",\n \"éĹ ĺ\",\n \"ĠتصÙħ ÛĮÙħ\",\n \"о иÑĤ\",\n \"ĠÑħ ол\",\n \"æĬ ŀ\",\n \"a mam\",\n \"am am\",\n \"ama m\",\n \"Ġâĸ ³\",\n \"ãģ ĩ\",\n \"Ġع ÙĨÙĩ\",\n \"ĠعÙĨ Ùĩ\",\n \"Ġì°¸ ê°Ģ\",\n \"ĠÎļ ÏĮ\",\n \"åı¤ å±ĭ\",\n \"к овоÑĹ\",\n \"ков оÑĹ\",\n \"ково ÑĹ\",\n \"ศ à¸Ī\",\n \"олог иÑı\",\n \"ĠÙħØ« بت\",\n \"ĠÐļÑĢа ÑĹна\",\n \"ĠмеÑģÑı ÑĨев\",\n \"Ġalın an\",\n \"ĠÏĢÏģα γμα\",\n \"Ġ ìŀ¡ëĭ´\",\n \"Ġìŀ¡ ëĭ´\",\n \"Ġп лод\",\n \"Ġпл од\",\n \"Ġпло д\",\n \"ĠÑĤка ни\",\n \"ÑģÑĭ лки\",\n \"ÑģÑĭл ки\",\n \"سط س\",\n \"ra nÄĽ\",\n \"ran ÄĽ\",\n \"к аж\",\n \"ка ж\",\n \"е маÑĤи\",\n \"ем аÑĤи\",\n \"ема ÑĤи\",\n \"Ġز ÛĮست\",\n \"ĠزÛĮ ست\",\n \"æ¿ Ł\",\n \"Ġpop lat\",\n \"Ġpo plat\",\n \"γ ÎŃν\",\n \"íĨł íĨł\",\n \"Ġt ây\",\n \"Ġìµľ ê·¼\",\n \"ãĥ© ãĥ³ãĤ¹\",\n \"ãĥ©ãĥ³ ãĤ¹\",\n \"Ġgün eÅŁ\",\n \"Ġ ÙģÙĤ\",\n \"ĠÙģ ÙĤ\",\n \"ĠsaÄŁ layan\",\n \"ĠsaÄŁlay an\",\n \"ĠØŃ Ø²Ø¨\",\n \"à¥ģल न\",\n \"ĠB ilim\",\n \"ĠBi lim\",\n \"ĠBil im\",\n \"ĠB atı\",\n \"ĠBa tı\",\n \"ĠBat ı\",\n \"æł· çļĦ\",\n \"δ ικ\",\n \"δι κ\",\n \"α ÏģίοÏħ\",\n \"αÏģ ίοÏħ\",\n \"Ġ ìĽĢ\",\n \"ĠìĽ Ģ\",\n \"Ġl á»Ńa\",\n \"ÙĨ ÙĪع\",\n \"çİ ²\",\n \"а ном\",\n \"ан ом\",\n \"ано м\",\n \"Ġst átnÃŃ\",\n \"Ġstát nÃŃ\",\n \"Ġ äºİ\",\n \"Ġm ùi\",\n \"ĠÄij á»Ļt\",\n \"ĠÄijá»Ļ t\",\n \"æ² ĥ\",\n \"åħ¬ åľĴ\",\n \"ĠÑģ ÑĮогоднÑĸ\",\n \"но Ñģи\",\n \"ноÑģ и\",\n \"Z a\",\n \"Ġд ли\",\n \"ĠÏĥÏħν ÎŃ\",\n \"ĠV á»ĭ\",\n \"m av\",\n \"ma v\",\n \"ĠM üslüman\",\n \"/ ï¼ı\",\n \"ĠзаÑī иÑĤ\",\n \"é ĸī\",\n \"éĸ ī\",\n \"Ġ çģ«\",\n \"Ġçģ «\",\n \"Ġ å·Ŀ\",\n \"Ġå· Ŀ\",\n \"Ġ аж\",\n \"Ġа ж\",\n \"è¿ĩ æĿ¥\",\n \"à¸Ĺ าà¸Ļ\",\n \"ĠAr aÅŁtır\",\n \"ĠAra ÅŁtır\",\n \"Õ¡ Õ\",\n \"Ġpo mÄĽr\",\n \"Ġpom ÄĽr\",\n \"Ġd ům\",\n \"Ġdů m\",\n \"å¦ ®\",\n \"Ġhlav nÄĽ\",\n \"Ġfin ans\",\n \"Ġfinan s\",\n \"Ġ γνÏī\",\n \"Ġγ νÏī\",\n \"ÏĥÏĦη μα\",\n \"ï¼Į çĶ¨\",\n \"ìĭŃ ìĭľìĺ¤\",\n \"ĠÙħ ثاÙĦ\",\n \"ĠÙħØ« اÙĦ\",\n \"- Ðij\",\n \"ÑĨÑĸй нÑĸ\",\n \"Ġد ستÙĩ\",\n \"Ġدست Ùĩ\",\n \"Ġدس تÙĩ\",\n \"à¥ī स\",\n \"ÑĢ Ñĸп\",\n \"ÑĢÑĸ п\",\n \"ĠpÅĻi pom\",\n \"ĠpÅĻip om\",\n \"Ġ ÙĪÙĦÙĬ\",\n \"ĠÙĪ ÙĦÙĬ\",\n \"ĠÙĪÙĦ ÙĬ\",\n \"ĠÙĪ زÙĨ\",\n \"ĠÙĪز ÙĨ\",\n \"Ġelekt rik\",\n \"Ġelektr ik\",\n \"ĠQu ân\",\n \"i vé\",\n \"iv é\",\n \"Ġl ẽ\",\n \"ç®Ģ åįķ\",\n \"Ġon lara\",\n \"Ġonlar a\",\n \"оÑģ лав\",\n \"ìĭľ íĤ¤\",\n \"ëª ¬\",\n \"ĠÙħÙĤ دار\",\n \"ĠÙħÙĤد ار\",\n \"ĠOr ta\",\n \"ĠOrt a\",\n \"ĠS eç\",\n \"ĠSe ç\",\n \"ĠÙĨÙĪÙģ Ùħبر\",\n \"ุà¸Ļ ายà¸Ļ\",\n \"ĠÑĥм ови\",\n \"ĠÑĥмов и\",\n \"Ġपर म\",\n \"Ġ strom\",\n \"Ġst rom\",\n \"Ġstr om\",\n \"Ġstro m\",\n \"ĠкÑĢа Ñīе\",\n \"ç§ ¦\",\n \"缸 æīĭ\",\n \"鼻 è¦ĸ\",\n \"Ġuygu lama\",\n \"Ġuygulam a\",\n \"Ġ ÑĢиз\",\n \"ĠÑĢ из\",\n \"æĪ ²\",\n \"य र\",\n \"ĠH lav\",\n \"Ġ ìĭ¸\",\n \"Ġìĭ ¸\",\n \"Ġли пнÑı\",\n \"ÅĪ ujÃŃ\",\n \"ÑĢ из\",\n \"ÑĢи з\",\n \"é«ĺ éĢŁ\",\n \"缸 å½ĵ\",\n \"k enin\",\n \"ke nin\",\n \"ken in\",\n \"Ġо ÑģÑĤанов\",\n \"ĠоÑģÑĤ анов\",\n \"ĠоÑģÑĤан ов\",\n \"Ġbit k\",\n \"Ġbi tk\",\n \"ova ného\",\n \"ovan ého\",\n \"ované ho\",\n \"ĠÐľ аÑĢи\",\n \"ĠÐľÐ°ÑĢ и\",\n \"ĠÐľÐ° ÑĢи\",\n \"èµ ¶\",\n \"ì½ ©\",\n \"Ġölç ü\",\n \"ĠС еÑĢед\",\n \"ĠСеÑĢ ед\",\n \"ĠTh á»Ŀi\",\n \"Ïī να\",\n \"Ïīν α\",\n \"ÙĪ بة\",\n \"ÙĪب Ø©\",\n \"Ġch ụp\",\n \"âĢĮ د\",\n \"Ġch áy\",\n \"ĠÐĴ ели\",\n \"Ġоб ÑģÑĤ\",\n \"ĠобÑģ ÑĤ\",\n \"Ġìĭľ ì¦Į\",\n \"د ÙħØ©\",\n \"دÙħ Ø©\",\n \"п од\",\n \"по д\",\n \"l ue\",\n \"lu e\",\n \"ĠдÑĸ лÑıн\",\n \"ĠÙ¾ ÙĪست\",\n \"ĠاÙĦ ÙĨس\",\n \"ĠاÙĦÙĨ س\",\n \"èĤ Į\",\n \"ìĪĺ 를\",\n \"Ġú rov\",\n \"ĠÙħØ´ Ú©ÙĦ\",\n \"ĠÙħØ´Ú© ÙĦ\",\n \"éĩįè¤ĩ éĩįè¤ĩ\",\n \"н ез\",\n \"не з\",\n \"Ġdop oruÄį\",\n \"Ġtas arım\",\n \"Ġtasar ım\",\n \"íģ¬ 기\",\n \"ìĿ´ ìħĺ\",\n \"Ġde set\",\n \"Ġdes et\",\n \"Ġdese t\",\n \"ĠÙħرتب Ø·\",\n \"ัà¸Ĵ à¸Ļา\",\n \"ัà¸Ĵà¸Ļ า\",\n \"' ı\",\n \"Ñĩ ки\",\n \"ĠìŀĪ ëįĺ\",\n \"ÑĪ ка\",\n \"n ám\",\n \"ná m\",\n \"ÑģÑĤ ÑĢов\",\n \"ÑģÑĤÑĢ ов\",\n \"ÑģÑĤÑĢо в\",\n \"à¥į सर\",\n \"à¥įस र\",\n 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Åŀ\",\n \"Ġп ÑĢоз\",\n \"ĠпÑĢ оз\",\n \"ĠпÑĢо з\",\n \"ÙĨت اج\",\n \"鼻 åŃIJ\",\n \".: .:.\",\n \".:.: .\",\n \".:. :.\",\n \"è¨ ĵ\",\n \"и ÑĩеÑģкое\",\n \"иÑĩеÑģ кое\",\n \"Ġн оги\",\n \"Ġно ги\",\n \"Ġног и\",\n \"Ġ λÎŃ\",\n \"Ġλ ÎŃ\",\n \"Ġsık ıntı\",\n \"Ġê°Ģ 족\",\n \"ĠتÙĨ ظÙĬÙģ\",\n \"ĠتÙĨظ ÙĬÙģ\",\n \"Ġö dül\",\n \"ĠaÅŁaģı daki\",\n \"Ġž elez\",\n \"Ġže lez\",\n \"ĠاÙĦع دÙĬد\",\n \"غ ÙĨ\",\n \"Ġокон Ñĩ\",\n \"ÑĢем Ñı\",\n \"ÑĢе мÑı\",\n \"L Ä°\",\n \"Ġne jd\",\n \"Ġnej d\",\n \"Ġ ÏĢλα\",\n \"ĠÏĢ λα\",\n \"Ñģ ко\",\n \"Ñģк о\",\n \"Ġ ìĪĻ\",\n \"ĠìĪ Ļ\",\n \"ĠÙ¾ ÙĪÙĦ\",\n \"θεν ήÏĤ\",\n \"Ġ주 ìļĶ\",\n \"Ġ æĬ¥\",\n \"ĠæĬ ¥\",\n \"ĠÙħ Ùħا\",\n \"ĠÙħÙħ ا\",\n \"Ðł Ð¡Ðł\",\n \"ĠÑĢа дÑĸ\",\n \"ĠÑĢад Ñĸ\",\n \"ä¸Ģ ç§į\",\n \"é¾ Ħ\",\n \"Ġsö yl\",\n \"Ġsöy l\",\n \"Ïģκε ια\",\n \"Ïģκ εια\",\n \"Ġзем лÑĸ\",\n \"Ġve Äįer\",\n \"g eç\",\n \"ge ç\",\n \"س تÙħ\",\n \"ست Ùħ\",\n \"Ġse fer\",\n \"ĠÑģ вÑĸд\",\n \"ĠÑģв Ñĸд\",\n \"ï»Ł ï»\",\n \"а лов\",\n \"ал ов\",\n \"ало в\",\n \"ìĬ¤ 를\",\n \"âī ¥\",\n \"ĠتÙĦ ÙģÙĨ\",\n \"ĠتÙĦÙģ ÙĨ\",\n \"åİ» äºĨ\",\n \"़ à¥ĭà¤Ĥ\",\n \"़à¥ĭ à¤Ĥ\",\n \"ĠÑĦоÑĢм е\",\n \"ĠÑĦоÑĢ ме\",\n \"d üm\",\n \"dü m\",\n \"åħ ģ\",\n \"ÑĢ ап\",\n \"ÑĢаР¿\",\n \"ÑĢа п\",\n \"ĠV Æ°Æ¡ng\",\n \"à¸Ńะ à¹Ħร\",\n \"ัà¸ģษ à¸ĵ\",\n \"Ġ åį³\",\n \"Ġåį ³\",\n \"ĠاÙĦ رÙħ\",\n \"ĠاÙĦر Ùħ\",\n \"ĠзаÑħиÑģÑĤ Ñĥ\",\n \"° E\",\n \"o dÃŃ\",\n \"od ÃŃ\",\n \"Ġव न\",\n \"ĠÄij èn\",\n \"Ġ åıĹ\",\n \"Ġåı Ĺ\",\n \"èIJ½ ãģ¡\",\n \"Ġ zim\",\n \"Ġz im\",\n \"Ġzi m\",\n \"리 ì¦Ī\",\n \"èĪ Ĵ\",\n \"Ġзб ÑĸÑĢ\",\n \"Ġ ä»·æł¼\",\n \"ĠлÑİ Ð´Ð¸Ð½Ð°\",\n \"ĠлÑİд ина\",\n \"ĠлÑİди на\",\n \"ĠÐŁÐ¾Ñģ иланнÑı\",\n \"и Ñī\",\n \"ĠÎ ¨\",\n \"ิà¸ģ ายà¸Ļ\",\n \"ิà¸ģา ยà¸Ļ\",\n \"Ġbu dete\",\n \"Ġbud ete\",\n \"Ġbude te\",\n \"Ġз ÑĢоÑģÑĤ\",\n \"Ġ vyk\",\n \"Ġv yk\",\n \"Ġvy k\",\n \"ĠÐĹ ÐµÐ¼\",\n \"ĠиÑİ Ð½Ñı\",\n \"ĠmÄĽ lo\",\n \"ĠmÄĽl o\",\n \"ÙĦ اÙģ\",\n \"ÙĦا Ùģ\",\n \"Ġ ÙĪØ´\",\n \"ĠÙĪ Ø´\",\n \"ĠÑģп ÑĢави\",\n \"ĠÑģпÑĢав и\",\n \"ãģĻ ãģİ\",\n \"ĠгÑĢа дÑĥ\",\n \"ĠгÑĢ адÑĥ\",\n \"R oz\",\n \"Ro z\",\n \"ι νή\",\n \"ιν ή\",\n \"Ġch á»ĵng\",\n \"ä¸Ģ åį·\",\n \"Ġ Xem\",\n \"ĠX em\",\n \"ĠÑģимв ол\",\n \"ĠÑģим вол\",\n \"Ġod mÃŃt\",\n \"ĠÑĢÑıд ом\",\n \"ĠÑĢÑı дом\",\n \"ĠÑĩеÑĢв нÑı\",\n \"à¸ģระ à¸Ĺ\",\n \"人 人\",\n \"æ°Ĺ æĮģãģ¡\",\n \"un daki\",\n \"und aki\",\n \"unda ki\",\n \"åľĭ 家\",\n \"εÏģ μαν\",\n \"Ġ лÑĮ\",\n \"Ġл ÑĮ\",\n \"ĠN üfus\",\n \"Ġм еÑĢе\",\n \"ĠмеÑĢ е\",\n \"بر اÙĬر\",\n \"н аннÑı\",\n \"Ġ наÑĢ\",\n \"Ġн аÑĢ\",\n \"Ġна ÑĢ\",\n \"Ġt ấm\",\n \"æĸ½ å·¥\",\n \"é¡ ¯\",\n \"Ġh è\",\n \"æĺİ çĻ½\",\n \"Ġдо гов\",\n \"Ġдог ов\",\n \"ĠÙģ رÙħ\",\n \"ĠÙģر Ùħ\",\n \"èĢ Ĺ\",\n \"ìĬ¤ ìĿĺ\",\n \"ìĦ¸ ëĮĢ\",\n \"è¯ ļ\",\n \"Ġнеб олÑĮ\",\n \"Ġ à¸Ľà¸£à¸°à¸ģ\",\n \"Ġà¸Ľà¸£à¸° à¸ģ\",\n \"Ġì¹ ¼\",\n \"Ġov liv\",\n \"Ġ NGC\",\n \"ĠN GC\",\n \"ĠNG C\",\n \"ãĢĤ ä¸į\",\n \"ا ÙĦÙī\",\n \"اÙĦ Ùī\",\n \"æī £\",\n \". 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ÙĪاÙĨ\",\n \"ĠÙĪ اÙĨ\",\n \"ĠÙĪا ÙĨ\",\n \"Îľ ε\",\n \"Ġ μον\",\n \"Ġμ ον\",\n \"Ġμο ν\",\n \"Ñĥ ÑĪка\",\n \"ÑĥÑĪ ка\",\n \"or dum\",\n \"ord um\",\n \"æ¸ħ æ¥ļ\",\n \"ĠDe ÄŁ\",\n \"ÏĢ Ïģο\",\n \"ĠÙĪاÙĦ تÙĬ\",\n \"ĠÙĪاÙĦت ÙĬ\",\n \"Ġp okus\",\n \"Ġpo kus\",\n \"Ġpok us\",\n \"íĽĦ 기\",\n \"é¥ ®\",\n \"æĹħ è¡Į\",\n \"Ġжен Ñīин\",\n \"ĠdoÄŁru dan\",\n \"Ġ Ñıб\",\n \"ĠÑı б\",\n \"Ġza ÄįÃŃ\",\n \"ĠzaÄį ÃŃ\",\n \"Ġë³´ ìŬ\",\n \"- CP\",\n \"-C P\",\n \"åIJ ¨\",\n \"à¥ĭ à¤ĸ\",\n \"ÑĢ огÑĢа\",\n \"ÑĢо гÑĢа\",\n \"ÑĢог ÑĢа\",\n \"ler di\",\n \"ìĬ ´\",\n \"Ùı ÙĪا\",\n \"ÙıÙĪ ا\",\n \"Ġustanov enÃŃ\",\n \"Ġд оÑģÑĤав\",\n \"Ġдо ÑģÑĤав\",\n \"ĠдоÑģÑĤ ав\",\n \"Ġfır sat\",\n \"ĠاÙĦÙħÙĩ ÙĨØ©\",\n \"ĠвеÑī еÑģÑĤва\",\n \"ĠвеÑīеÑģÑĤв а\",\n \"Ġн еÑģп\",\n \"Ġне Ñģп\",\n \"ĠнеÑģ п\",\n \"ĠاÙĦکتر ÙĪÙĨ\",\n \"t aÅŁ\",\n \"ta ÅŁ\",\n \"æĪ Ĵ\",\n \"Ġy urt\",\n \"Ġyu rt\",\n \"Ġgir di\",\n \"ĠÐļ Ñĥб\",\n \"Ġ 를\",\n \"Ġë¥ ¼\",\n \"ุ à¹Į\",\n \"ãģĿãģĨ ãģª\",\n \"à¹ī Ċ\",\n \"ĠвÑĭ бÑĢа\",\n \"ĠвÑĭб ÑĢа\",\n \"k 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\"Ġγ λÏİ\",\n \"E NÃį\",\n \"EN Ãį\",\n \"ë ®¤\",\n \"ŃIJ ï¸ı\",\n \"Ġ æ¯ı\",\n \"Ġæ¯ ı\",\n \"Ġ æĸ¼\",\n \"Ġæĸ ¼\",\n \"Ġκα λÏį\",\n \"ĠТ ом\",\n \"ĠТо м\",\n \"ul ur\",\n \"ulu r\",\n \"Ġak ce\",\n \"ĠÙħÙĪ جب\",\n \"ĠÙħÙĪج ب\",\n \"e siz\",\n \"es iz\",\n \"esi z\",\n \"н Ñıв\",\n \"нÑı в\",\n \"алÑĮ нÑĥÑİ\",\n \"алÑĮнÑĥ Ñİ\",\n \"ал ÑĸÑģÑĤ\",\n \"алÑĸ ÑģÑĤ\",\n \"Ġв аÑĢÑĸ\",\n \"ĠваÑĢ Ñĸ\",\n \"Ġва ÑĢÑĸ\",\n \"ĠÙħؤ س\",\n \"ĠÙħ اÛĮÙĦ\",\n \"ĠÙħا ÛĮÙĦ\",\n \"ĠμεÏĦα ξÏį\",\n \"åĩº ãģĻ\",\n \"Ġv á»Ŀi\",\n \"Ġvá» Ŀi\",\n \"ëŁ ´\",\n \"ï¼ ĭ\",\n \"æ¯ İ\",\n \"Ġt abi\",\n \"Ġtab i\",\n \"Ġta bi\",\n \"âĤ ĥ\",\n \"æ£ĭ çīĮ\",\n \"Ġ ÃIJ\",\n \"Ġà IJ\",\n \"ĠпÑĢоÑĦеÑģ Ñĸй\",\n \"Ñĥв аннÑĸ\",\n \"Îľ Îł\",\n \"Ġж ил\",\n \"Úĺ ÙĨ\",\n \"л ÑĥÑĪ\",\n \"лÑĥ ÑĪ\",\n \"á½ ´\",\n \"о веÑĢ\",\n \"ов еÑĢ\",\n \"ове ÑĢ\",\n \"è¾¼ ãģ¿\",\n \"ĠÐľ акÑģим\",\n \"ĠÐľÐ°Ðº Ñģим\",\n \"Ġвз глÑıд\",\n \"Ġн аÑĤÑĥ\",\n \"Ġна ÑĤÑĥ\",\n \"ĠнаÑĤ Ñĥ\",\n \"म à¤ķ\",\n \"ĠÑħ ими\",\n \"ĠÑĢозÑĤа ÑĪ\",\n \"ÙĪ راÙĨ\",\n \"ÙĪر اÙĨ\",\n \"ÙĪرا ÙĨ\",\n \"ĠØ´Ùĩر ÙĩاÛĮ\",\n \"æ© Łèĥ½\",\n \"æ©Ł èĥ½\",\n \"Ø® Ø°\",\n \"ĠÑģво ÑĶÑĹ\",\n \"ĠÑģвоÑĶ ÑĹ\",\n \"н ÑıеÑĤ\",\n \"нÑı еÑĤ\",\n \"Ġgh ế\",\n \"ĠpÅĻed ch\",\n \"ÑĶ ÑĪ\",\n \"огÑĢаÑĦ ÑĸÑı\",\n \"Ġ à¸Ĺำà¹ĥห\",\n \"Ġà¸Ĺำ à¹ĥห\",\n \"åĿ Ĭ\",\n \"Ïģ Ïīν\",\n \"ÏģÏī ν\",\n \"า ระ\",\n \"าร ะ\",\n \"ĠK ết\",\n \"ĠKế t\",\n \"Ġch ặt\",\n \"Ġ éĻĪ\",\n \"ĠéĻ Ī\",\n \"ĠdÄĽ lat\",\n \"ĠdÄĽl at\",\n \"ĠбÑĥд ÑĥÑī\",\n \"ĠбÑĥдÑĥ Ñī\",\n \"ĠAç ık\",\n \"æłª å¼ıä¼ļ社\",\n \"ĠÐŁ аÑĢ\",\n \"ĠK hu\",\n \"ĠKh u\",\n \"ãĢģ æĸ°\",\n \"Ġб ой\",\n \"Ġбо й\",\n \"ë§Ī íĬ¸\",\n \"ĠÑģоп ÑĢов\",\n \"س اب\",\n \"н иÑģÑĤ\",\n \"ни ÑģÑĤ\",\n \"å¼ ĥ\",\n \"Ġ Ø´ÙĨاس\",\n \"ĠØ´ÙĨ اس\",\n \"ен ном\",\n \"енно м\",\n \"Ġ 项\",\n \"Ġé¡ ¹\",\n \"èīº æľ¯\",\n \"о зем\",\n \"оз ем\",\n \"ĠÑĢеÑĪ ениÑı\",\n \"l ady\",\n \"la dy\",\n \"lad y\",\n \"ĠвÑģ ей\",\n \"ĠвÑģе й\",\n \"æĶ» åĩ»\",\n \"Ġê²° ìłķ\",\n \"ãĢĢ ï¾ŀ\",\n \"Ġê°IJ ëıħ\",\n \"- ÐIJ\",\n \"Ġm ÃŃr\",\n \"ĠmÃŃ r\",\n \"à¥ģप à¤ı\",\n \"нÑĸ 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ой\",\n \"Ġпо й\",\n \"Ġ جÙĬ\",\n \"Ġج ÙĬ\",\n \"к ÑĥваÑĤи\",\n \"кÑĥ ваÑĤи\",\n \"Ġá» ŀ\",\n \"Ġب شر\",\n \"Ġبش ر\",\n \"Ġ ÙĥÙĬÙĦ\",\n \"ĠÙĥ ÙĬÙĦ\",\n \"Ñī еÑģÑĤво\",\n \"Ñīе ÑģÑĤво\",\n \"ÑīеÑģÑĤв о\",\n \"ĠìŬ íĸī\",\n \"ا ÙħÙĬ\",\n \"اÙħ ÙĬ\",\n \"в ÑĸлÑĮ\",\n \"вÑĸ лÑĮ\",\n \"ĠPr vnÃŃ\",\n \"Ġ ÙĪسÛĮ\",\n \"ĠÙĪ سÛĮ\",\n \"ĠÙĪس ÛĮ\",\n \"ĠÄIJ á»\",\n \"æĪ¿ éĹ´\",\n \"åľ¨çº¿ éĺħ读\",\n \"æķ ·\",\n \"Ġt rai\",\n \"Ġtr ai\",\n \"Ġtra i\",\n \"ä¿ Ĺ\",\n \"ĠÑģамоÑģÑĤоÑıÑĤелÑĮ но\",\n \"ĠÑĤÑĢеб ÑĥеÑĤÑģÑı\",\n \"ĠÑĤÑĢебÑĥеÑĤ ÑģÑı\",\n \"δ Ïģα\",\n \"ĠÑĢеÑĩ ов\",\n \"Ġв Ñĸк\",\n \"ĠвÑĸ к\",\n \"Ġ ÑĢÑĥÑĩ\",\n \"ĠÑĢ ÑĥÑĩ\",\n \"ĠÑĢÑĥ Ñĩ\",\n \"å¥ §\",\n \"ĠolduÄŁ una\",\n \"ĠolduÄŁu na\",\n \"ев Ñĭе\",\n \"Ġ à¸Ħล\",\n \"Ġà¸Ħ ล\",\n \"ا ÙĦÙĤ\",\n \"اÙĦ ÙĤ\",\n \"ĠÑĸм енÑĸ\",\n \"ĠÑĸмен Ñĸ\",\n \"æĶ» æĴĥ\",\n \"ĠÑĥнивеÑĢ ÑģиÑĤ\",\n \"Ġth Äĥm\",\n \"ĠлиÑģÑĤоп ада\",\n \"२ ०\",\n \"Ø® ÙĬ\",\n \"Îķ Îł\",\n \"Ġart tır\",\n \"Ġس خت\",\n \"Ġسخ ت\",\n \"ï¼Ī æĺŃåĴĮ\",\n \"ĠÎŁ Ïħ\",\n \"и ваниÑı\",\n \"ив 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\"ά β\",\n \"ĠÎł ο\",\n \"Ġê·¸ 림\",\n \"Ñĩ аÑİÑĤ\",\n \"Ñĩа ÑİÑĤ\",\n \"à¸ĩ à¸ķ\",\n \"íĥ ĢìĿ´\",\n \"íĥĢ ìĿ´\",\n \"æī ¬\",\n \"Ġpo jist\",\n \"Ġpoj ist\",\n \"Ġ çłĶ\",\n \"Ġçł Ķ\",\n \"Ġ åıĸ\",\n \"Ġåı ĸ\",\n \"Ġüzer indeki\",\n \"Ġüzerinde ki\",\n \"j Å¡ÃŃch\",\n \"jÅ¡ÃŃ ch\",\n \"à¥Ģद व\",\n \"æª ¢\",\n \"ĠмаÑĤеÑĢи алов\",\n \"ĠмаÑĤеÑĢиал ов\",\n \"и ваннÑı\",\n \"ив аннÑı\",\n \"Ġ å°Ĩ\",\n \"Ġå° Ĩ\",\n \"л л\",\n \"Ġнаб лÑİд\",\n \"ĠнаблÑİ Ð´\",\n \"ĠG öz\",\n \"ĠGö z\",\n \"Ġв зÑı\",\n \"Ġвз Ñı\",\n \"çĶµ è§Ĩ\",\n \"Ġв ак\",\n \"Ġва к\",\n \"ç¿ Ķ\",\n \"Ġвза им\",\n \"Ġg itti\",\n \"Ġgi tti\",\n \"Ġgit ti\",\n \"it eleri\",\n \"ite leri\",\n \"itel eri\",\n \"itele ri\",\n \"ä»· åĢ¼\",\n \"ĠاÙĦ تص\",\n \"ĠاÙĦت ص\",\n \"िन à¤ķ\",\n \"éĢļ ãĤĬ\",\n \"ĠÑģ ÑĦеÑĢ\",\n \"ĠÑģÑĦ еÑĢ\",\n \"çĻº 売\",\n \"âĿ ¤\",\n \"ĠÚ¯ÙĪØ´ ÛĮ\",\n \"ĠÚ¯ÙĪ Ø´ÛĮ\",\n \"аг аÑĤо\",\n \"ĠÏĥÏħ γκ\",\n \"ав иÑģ\",\n \"ави Ñģ\",\n \"æĤ£ èĢħ\",\n \"ĠØ® اÙħ\",\n \"ÎĻÎļ ÎĹΣ\",\n \"ÎĻÎļÎĹ Î£\",\n \"ınız da\",\n \"pan ÄĽl\",\n \"pa nÄĽl\",\n \"ĠÄIJ á»ĭa\",\n \"à¹ģละ ส\",\n \"Ġ ãĤĤ\",\n \"ĠãĤ Ĥ\",\n \"Ġsonuc unda\",\n \"Ġsonucu nda\",\n \"ìĿ į\",\n \"e less\",\n \"el ess\",\n \"ele ss\",\n \"ĠN ha\",\n \"ĠNh a\",\n \"Ġzak áz\",\n \"Ġв оÑģÑĤ\",\n \"Ġво ÑģÑĤ\",\n \"ĠвоÑģ ÑĤ\",\n \"ĠvzdÄĽl ávánÃŃ\",\n \"- ม\",\n \"Ġmet rů\",\n \"ĠپاÛĮ ÛĮÙĨ\",\n \"Ġپا ÛĮÛĮÙĨ\",\n \"ĠÑĢаÑģÑĤ ение\",\n \"Ġmu á»iji\",\n \"èµĦ éĩij\",\n \"ĠÅŁ üph\",\n \"ÙĬ ÙĦÙħ\",\n \"ÙĬÙĦ Ùħ\",\n \"ĠdÃ¼ÅŁÃ¼n c\",\n \"Ġк Ñĸм\",\n \"ĠÏĩÏī ÏģίÏĤ\",\n \"áz ev\",\n \"áze v\",\n \"ĠDe ÄŁer\",\n \"ĠDeÄŁ er\",\n \"å·¥ æ¥Ń\",\n \"Ġر Ùħز\",\n \"ĠرÙħ ز\",\n \"Ġal espoÅĪ\",\n \"ĠпÑĢе ÑģÑĤÑĥп\",\n \"ĠпÑĢеÑģÑĤ Ñĥп\",\n \"ĠعÙĦ اÙĪÙĩ\",\n \"Ġme rak\",\n \"Ġmer ak\",\n \"à¹Į :\",\n \"çİ° åľº\",\n \"ÑĨ веÑĤ\",\n \"Ġप à¥ľ\",\n \"Ġëĭ¤ìĿĮ ê³¼\",\n \"u dic\",\n \"ud ic\",\n \"udi c\",\n \"ĠL ep\",\n \"ĠLe p\",\n \"Ġод нÑĸ\",\n \"Ġa larak\",\n \"Ġal arak\",\n \"å®ī æİĴ\",\n \"Ġ à¸Ĥà¸Ļาà¸Ķ\",\n \"Ġà¸Ĥ à¸Ļาà¸Ķ\",\n \"re zent\",\n \"rez ent\",\n \"is inden\",\n \"isin den\",\n \"isinde n\",\n \"ر ÙĪÛĮ\",\n \"رÙĪ ÛĮ\",\n \"Ġp lu\",\n \"Ġpl u\",\n \"ç«ĭ ãģ¦\",\n \"Ñĭ ваниÑı\",\n \"Ñĭв аниÑı\",\n \"Ñĭва ниÑı\",\n \"Ġr ast\",\n \"Ġra st\",\n \"Ġras t\",\n \"Ġdüzen lem\",\n \"je zd\",\n \"jez d\",\n \"Ġве ÑīеÑģÑĤв\",\n \"ĠвеÑī еÑģÑĤв\",\n \"ĠдиÑĢ екÑĤоÑĢ\",\n \"ÑĦ ÑĦ\",\n \"t ainment\",\n \"tain ment\",\n \"ĠاÙĦ ÙĪز\",\n \"ĠاÙĦÙĪ ز\",\n \"l anda\",\n \"land a\",\n \"la nda\",\n \"lan da\",\n \"ĠÙĨÚ¯ Ùĩد\",\n \"ĠпÑĢоÑĤив оп\",\n \"ãģ£ ãģı\",\n \"ãģ£ãģ ı\",\n \"ãģ¨ãģª ãĤĬ\",\n \"Ġë°ľ 견\",\n \"i ctor\",\n \"ic tor\",\n \"ict or\",\n \"ãĤ¸ ãĤª\",\n \"ÎŁ Φ\",\n \"ÎŁÎ ¦\",\n \"ĠÑģклад Ñĸ\",\n \"Ġob sahuje\",\n \"Ġobsah uje\",\n \"ĠUkr a\",\n \"ĠUk ra\",\n \"æķ ¦\",\n \"ĠÏĩ αÏģα\",\n \"ĠÏĩα Ïģα\",\n \"ĠÑĢег Ñĥли\",\n \"俺 ãģ¯\",\n \"ัà¸ķ ว\",\n \"éĦ ī\",\n \"Ġب اÛĮ\",\n \"Ġبا ÛĮ\",\n \"éĬ ·\",\n \"ĠN ẵng\",\n \"л од\",\n \"ло д\",\n \"ا رÙģ\",\n \"ار Ùģ\",\n \"æ´ ģ\",\n \"ĠëıĻ ìĿ¼\",\n \"ÑĤив ного\",\n \"âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ\",\n \"Ġ- :-\",\n \"Ġ-: -\",\n \"ì» ¬\",\n \"ĠÑĪ аг\",\n \"ìłĦ ìŀIJ\",\n \"çļĦ äºĭæĥħ\",\n \"çļĦäºĭ æĥħ\",\n \"ĠÑĢег Ñĸ\",\n \"िय ल\",\n \"ĠÐĿ аз\",\n \"ĠÐĿа з\",\n \"ĠÐĻ ого\",\n \"ĠÐł ом\",\n \"ĠÃĸr neÄŁin\",\n \"Ġп ÑĢеÑģ\",\n \"ĠпÑĢ еÑģ\",\n \"ĠпÑĢе Ñģ\",\n \"u luÄŁu\",\n \"ulu ÄŁu\",\n \"uluÄŁ u\",\n \"Ġза дов\",\n \"Ġзад ов\",\n \"ÅĻ eh\",\n \"ÅĻe h\",\n \"æ¯ķ ä¸ļ\",\n \"Ġth áºŃp\",\n \"ëĤ ¸\",\n \"Ġdlou hodob\",\n \"Ġdlouho dob\",\n \"дÑĸ лÑĥ\",\n \"дÑĸл Ñĥ\",\n \"a lat\",\n \"al at\",\n \"ala t\",\n \"ä» °\",\n \"о ком\",\n \"ок ом\",\n \"око м\",\n \"ĠÑĦ ÑĸлÑĮ\",\n \"ĠÑĦÑĸл ÑĮ\",\n \"ĠNg ân\",\n \"Ġ ترÙĥ\",\n \"Ġت رÙĥ\",\n \"Ġتر Ùĥ\",\n \"ĠÑĤ Ñī\",\n \"ر ÙĪد\",\n \"رÙĪ د\",\n \"ç uk\",\n \"çu k\",\n \"ra nÃŃ\",\n \"ran ÃŃ\",\n \"Ġdo laÅŁ\",\n \"Ġdol aÅŁ\",\n \"ĠQ uang\",\n \"ĠQu ang\",\n \"ĠpÅĻed pok\",\n \"Ġnám ÄĽstÃŃ\",\n \"ой Ñĩив\",\n \"çĭ Ģ\",\n \"Ġб изнеÑģ\",\n \"ãģŁ ãģı\",\n \"ĠìĿ¸ ì²ľ\",\n \"о ÑĢо\",\n \"оÑĢ о\",\n \"ĠKü rt\",\n \"ĠKür t\",\n \"ê·¸ 룬\",\n \"ÑĨ аÑĤÑĮ\",\n \"ÑĨа ÑĤÑĮ\",\n \"ĠB ên\",\n \"Ġ acı\",\n \"Ġa cı\",\n \"Ġac ı\",\n \"Ú© Ø´\",\n \"ï¼Ī å¹³æĪIJ\",\n \"Ġ èģĶ\",\n \"Ġèģ Ķ\",\n \") ãĢģ\",\n \"d iler\",\n \"di ler\",\n \"Ñĩ иÑĤÑĮ\",\n \"ÑĩиÑĤ ÑĮ\",\n \"Ñĩи ÑĤÑĮ\",\n \"Ư á»\",\n \"éĻ ¶\",\n \"il eceÄŁini\",\n \"ilece ÄŁini\",\n \"ileceÄŁi ni\",\n \"Ġv Å¡em\",\n \"ĠvÅ¡ em\",\n \"ĠvÅ¡e m\",\n \"å¼Ģ å¥ĸ\",\n \"è§Ħ 模\",\n \"ul muÅŁ\",\n \"Ġ åĪĺ\",\n \"Ġå Īĺ\",\n \"ĠåĪ ĺ\",\n \"е о\",\n \"еР¾\",\n \"ĠпеÑĢев ÑĸÑĢ\",\n \"åĪĨ åĪ«\",\n \"Ġjed ná\",\n \"Ġjedn á\",\n \"li ÄŁe\",\n \"liÄŁ e\",\n \"ĠرÙħ ضاÙĨ\",\n \"ık lı\",\n \"ıkl ı\",\n \"Ùĩ ÙĢ\",\n \"éĩį çĤ¹\",\n \"Ñĩ иваеÑĤÑģÑı\",\n \"Ñĩи ваеÑĤÑģÑı\",\n \"Ñĩив аеÑĤÑģÑı\",\n \"Ñĩива еÑĤÑģÑı\",\n \"ë¡ľ ìĦľ\",\n \"ÏĦ εÏģο\",\n \"ÏĦε Ïģο\",\n \"ÏĦεÏģ ο\",\n \"åľ° ä¸ĭ\",\n \"д наннÑı\",\n \"дн аннÑı\",\n \"Ġng ược\",\n \"ॠª\",\n \"ĠÎij λ\",\n \"Ġa lacak\",\n \"Ġal acak\",\n \"Ġ à¹Ģà¸ĩ\",\n \"Ġà¹Ģ à¸ĩ\",\n \"Ġà¹Ģภĩ\",\n \"اÛĮ ÙĨد\",\n \"اÛĮÙĨ د\",\n \"Ġh Ãłi\",\n \"ÑĢо 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\"ĠA ÅŁ\",\n \"Ġзем лÑİ\",\n \"ĠдомаÑĪ ниÑħ\",\n \"ĠÑĥ веÑĢ\",\n \"ĠÑĥв еÑĢ\",\n \"A LI\",\n \"AL I\",\n \"г ан\",\n \"га н\",\n \"Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"Ġdo stan\",\n \"Ġdos tan\",\n \"Ġdost an\",\n \"ez pe\",\n \"ãģĭ ãģĦ\",\n \"ر ÙģتÙĩ\",\n \"رÙģ تÙĩ\",\n \"رÙģت Ùĩ\",\n \"Ġм ÑĥÑģ\",\n \"ĠмÑĥ Ñģ\",\n \"à¹Į à¸Ł\",\n \"è¦ º\",\n \"али за\",\n \"ализ а\",\n \"ĠÑĥÑĩ ÑĢежд\",\n \"ĠÚ© اÙĦ\",\n \"Ġetk isi\",\n \"Ġetki si\",\n \"ä½Ĩ æĺ¯\",\n \"Ġsou vis\",\n \"ĠSav aÅŁÄ±\",\n \"ĠSavaÅŁ ı\",\n \"Ġب سبب\",\n \"ÎŁ ι\",\n \"ÎŁÎ ¹\",\n \"è ļ\",\n \"Ġ 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\"Ġser best\",\n \"ÅĻÃŃ z\",\n \"ĠíĺĦ ëĮĢ\",\n \"ãĢĮ ãģĿãģĨ\",\n \"çĤ ¸\",\n \"om ik\",\n \"omi k\",\n \"ĠÄ° ran\",\n \"Ġer iÅŁ\",\n \"ĠÑģ ела\",\n \"ĠÑģел а\",\n \"Ġار زÛĮ\",\n \"Ġارز ÛĮ\",\n \"ãĥĪ ãĥª\",\n \"ĠB ÄĽ\",\n \"е кÑĥ\",\n \"ек Ñĥ\",\n \"Ч ÑĤобÑĭ\",\n \"ЧÑĤо бÑĭ\",\n \"Ġanlam da\",\n \"Îij Îĺ\",\n \"ĠLINE AR\",\n \"ĠLIN EAR\",\n \"æľī çĤ¹\",\n \"ÑĤ аÑĢ\",\n \"ÑĤа ÑĢ\",\n \"it ler\",\n \"itle r\",\n \"Ġn ÃŃž\",\n \"ĠnÃŃ Å¾\",\n \"ĠС ÑģÑĭлки\",\n \"å ¶\",\n \"Ġв пол\",\n \"Ġвп ол\",\n \"ĠدÙĤÛĮ ÙĤÙĩ\",\n \"ĠدÙĤÛĮÙĤ Ùĩ\",\n \"Ġ ä½ĵ\",\n \"ر Ùī\",\n \"ëĶ °\",\n \"Ġà¤ķ व\",\n \"Ġж иÑĢ\",\n \"æij Ĩ\",\n \"Ġì¤ij ìĭ¬\",\n \"Ġк Ñĥб\",\n \"ĠкÑĥ б\",\n \"Ġz lep\",\n \"ĠÑĢÑĭ б\",\n \"é³ ´\",\n \"à¹ģà¸ľ à¸Ļ\",\n \"Ġ íĢ\",\n \"Ġí Ģ\",\n \"ĠÐĿ еÑĤ\",\n \"ĠÐĿе ÑĤ\",\n \"ž itÄĽ\",\n \"žit ÄĽ\",\n \"ži tÄĽ\",\n \"Ġb Äĥng\",\n \"ĠH ava\",\n \"ĠHa va\",\n \"ĠHav a\",\n \"Ġ모 ëį¸\",\n \"ĠH ãy\",\n \"ĠìĿ´ ê²ĥ\",\n \"Ġìĥģ ìĦ¸\",\n \"me miÅŁ\",\n \"mem iÅŁ\",\n \"ĠθÎŃ Ïĥη\",\n \"ण न\",\n \"ĠskuteÄį 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\"ĠpÅĻ en\",\n \"ĠpÅĻe n\",\n \"ĠÑĦоÑĢм ÑĥваннÑı\",\n \"ĠÑĦоÑĢмÑĥ ваннÑı\",\n \"ü mÃ¼ÅŁ\",\n \"üm Ã¼ÅŁ\",\n \"ümü ÅŁ\",\n \"Ġ δο\",\n \"Ġδ ο\",\n \"ımız ın\",\n \"ımızı n\",\n \"Ġ é¢Ħ\",\n \"Ġé¢ Ħ\",\n \"оÑģÑĤ ÑĮÑİ\",\n \"оÑģÑĤÑĮ Ñİ\",\n \"ĠоÑĤкÑĢÑĭ ÑĤ\",\n \"Ġأغ سطس\",\n \"ĠA sp\",\n \"ĠAs p\",\n \"ĠÑĥ зн\",\n \"ĠÑĥз н\",\n \"ĠÙĪ است\",\n \"ĠÙĪا ست\",\n \"ĠÙĪاس ت\",\n \"e lerle\",\n \"eler le\",\n \"èķ ī\",\n \"Ġت Ú©ÙĨ\",\n \"Ġتک ÙĨ\",\n \"Ñĥ мÑĥ\",\n \"Ñĥм Ñĥ\",\n \"à¹Į à¸ĭ\",\n \"ाद न\",\n \"ĠâĢĭ âĢĭâĢĭ\",\n \"ĠâĢĭâĢĭ âĢĭ\",\n \"Ġa lıyor\",\n \"Ġal ıyor\",\n \"Ġî ¡\",\n \"Ùħ دة\",\n \"Ùħد Ø©\",\n \"Ġ Ïĥει\",\n \"ĠÏĥ ει\",\n \"ĠÏĥε ι\",\n \"Ġ è¿Ļ\",\n \"Ġè¿ Ļ\",\n \"ĠÅŀ ehir\",\n \"ен ÑĤами\",\n \"енÑĤ ами\",\n \"енÑĤа ми\",\n \"ãĤ¿ ãĥ«\",\n \"ห าย\",\n \"หา ย\",\n \"ай ÑĤ\",\n \"Ġh arc\",\n \"Ġhar c\",\n \"Ġha rc\",\n \"ãĢĤ ãģĬ\",\n \"Ġتأ Ø«ÛĮر\",\n \"า à¸Ĭà¸Ļ\",\n \"าà¸Ĭ à¸Ļ\",\n \"Ġth áºŃm\",\n \"Ġ æ¿\",\n \"Ġæ ¿\",\n \"Ġm Å©i\",\n \"Ġprv nÃŃm\",\n \"ĠprvnÃŃ m\",\n \"Ġбаг 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Ħ\",\n \"Ġ iddi\",\n \"Ġid di\",\n \"i Äįka\",\n \"iÄį ka\",\n \"struk ce\",\n \"mÃ¼ÅŁ tür\",\n \"Ïħ ÏĦÏĮ\",\n \"ë¡ ±\",\n \"Ġal maktadır\",\n \"Ġalmak tadır\",\n \"ени Ñıми\",\n \"ениÑı ми\",\n \"ียà¸Ļ ร\",\n \"à¹ĩ à¸Ļว\",\n \"à¹ĩà¸Ļ ว\",\n \"и кÑĥ\",\n \"ик Ñĥ\",\n \"е нка\",\n \"ен ка\",\n \"âĢĻ yi\",\n \"âĢĻy i\",\n \"Ġpo hod\",\n \"Ġpoh od\",\n \"Ġ زر\",\n \"Ġز ر\",\n \"Ġx ấu\",\n \"Ġ à¸łà¸²à¸©\",\n \"Ġà¸ł าษ\",\n \"Âł Ðŀ\",\n \"Ġ δικ\",\n \"Ġδ ικ\",\n \"Ġδι κ\",\n \"Ġназ ива\",\n \"åıª èĥ½\",\n \"大 éĩı\",\n \"ĠÄij ế\",\n \"Ġ 第äºĮ\",\n \"Ġ第 äºĮ\",\n \"ĠkiÅŁ ilerin\",\n \"ĠkiÅŁi lerin\",\n \"ĠkiÅŁiler in\",\n \"Ġdob ré\",\n \"Ġdobr é\",\n \"é© ¾\",\n \"Ġdůležit é\",\n \"ë¡ ¤\",\n \"μÎŃ Î½Î¿Ïħ\",\n \"μÎŃν οÏħ\",\n \"μÎŃνο Ïħ\",\n \"Ġtr ú\",\n \"Ġbiç im\",\n \"Ġ ÐĿÐIJ\",\n \"ĠÐĿ ÐIJ\",\n \"Ġ å¾Į\",\n \"Ġå¾ Į\",\n \"Ġdu yg\",\n \"Ġduy g\",\n \"åŀ Ĥ\",\n \"ÐĨ ÐĨ\",\n \"Ġet meye\",\n \"Ġetm eye\",\n \"Ġetme ye\",\n \"ĠÙĦب اس\",\n \"Ġд вÑĸ\",\n \"Ġдв Ñĸ\",\n \"Ġ 긴\",\n \"Ġê¸ ´\",\n \"ÑĨ 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\"éĸĭ çĻº\",\n \"ĠдеÑĢ ева\",\n \"ĠдеÑĢев а\",\n \"- ÐĶ\",\n \"à¸ĩ à¸Ĭ\",\n \"ิà¸ķ ย\",\n \"ĠاÙĦÙĤ اÙĨÙĪÙĨ\",\n \"ãĤ¹ ãĤ«\",\n \"l ÃŃž\",\n \"lÃŃ Å¾\",\n \"Ġан ализ\",\n \"Ġproblém y\",\n \"æĸĩ åѦ\",\n \"çĹħ éĻ¢\",\n \"Ñģ ед\",\n \"Ñģе д\",\n \"ï¼Į å°ı\",\n \"Ġعش ÙĤ\",\n \"ãģ° ãģĭãĤĬ\",\n \"Ġع ÙĤد\",\n \"ĠعÙĤ د\",\n \"ØŃ ÙĬØ©\",\n \"ØŃÙĬ Ø©\",\n \"Ġë°Ķ ëŀįëĭĪëĭ¤\",\n \"inc lu\",\n \"incl u\",\n \"Ġ ëĵľë¦½ëĭĪëĭ¤\",\n \"Ġëĵľ 립ëĭĪëĭ¤\",\n \"åį« çĶŁ\",\n \"Ġвид Ñĥ\",\n \"Ġви дÑĥ\",\n \"ุ à¸ļาล\",\n \"ุà¸ļ าล\",\n \"ÑĢ ÑĥкÑĤ\",\n \"ÑĢÑĥ кÑĤ\",\n \"ÑĢÑĥк ÑĤ\",\n \"ĠоÑģ вÑĸÑĤ\",\n \"ĠоÑģвÑĸ ÑĤ\",\n \"Ġvel ký\",\n \"Ġvelk ý\",\n \"Ġch tÄĽl\",\n \"ĠchtÄĽ l\",\n \"æīĵ å¼Ģ\",\n \"Ġзакон одаÑĤелÑĮ\",\n \"ан Ñģи\",\n \"анÑģ и\",\n \"ì¶ ĺ\",\n \"ĠÙħر اج\",\n \"åģľ æŃ¢\",\n \"Ġво но\",\n \"ìłķ ìĿ´\",\n \"Ġroz sah\",\n \"Ġrozs ah\",\n \"Ġ æĻ´\",\n \"ĠæĻ ´\",\n \"Ġza jist\",\n \"Ġzaj ist\",\n \"Âł м\",\n \"tı ģını\",\n \"tıģ ını\",\n \"Ġhizmet i\",\n \". 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\"nÄĽjÅ¡ÃŃ ch\",\n \"αλ ÏįÏĦε\",\n \"ĠH akk\",\n \"ĠHa kk\",\n \"ĠHak k\",\n \"访 éĹ®\",\n \"ĠÑĤ еÑĩ\",\n \"ĠÑĤе Ñĩ\",\n \"ĠL á»ĭch\",\n \"Ġدش ÙħÙĨ\",\n \"Î Į\",\n \"Ġ ÏĢε\",\n \"ĠÏĢ ε\",\n \"Ġза мов\",\n \"Ġзам ов\",\n \"Ġb irim\",\n \"Ġbi rim\",\n \"Ġbir im\",\n \"Ġbiri m\",\n \"ãĤ· ãĤ¹ãĥĨãĥł\",\n \"ĠÏĢÏģο ÏĬ\",\n \"Ĭ ìĿĢ\",\n \"в иг\",\n \"ви г\",\n \"Ġëıħ ìĿ¼\",\n \"ĠÑĢев олÑİ\",\n \"Ġ é¦Ļ港\",\n \"Ġé¦Ļ 港\",\n \"Ġ lez\",\n \"Ġl ez\",\n \"Ġle z\",\n \"ĠبÛĮ Ùħار\",\n \"ĠبÛĮÙħ ار\",\n \"Ġduy gu\",\n \"Ġduyg u\",\n \"Ġë Ľ°\",\n \"Ġa macı\",\n \"Ġam acı\",\n \"Ġama cı\",\n \"Ġamac ı\",\n \"à¥įय प\",\n \"ĠìŀIJ ìĦ¸\",\n \"اÙĪ ÛĮر\",\n \"اÙĪÛĮ ر\",\n \"Ġs pole\",\n \"Ġsp ole\",\n \"Ġspo le\",\n \"Ġspol e\",\n \"Ãĸ L\",\n \"Ġ جع\",\n \"Ġج ع\",\n \"ÙĦ ÛĮÙħ\",\n \"ÙĦÛĮ Ùħ\",\n \"ãģªãģ© ãģ®\",\n \"à¸Ľà¸£à¸°à¸ª à¸ļ\",\n \"ĠnaÅ¡ ich\",\n \"ĠпÑĢедÑģÑĤав лÑıеÑĤ\",\n \"Ġзд об\",\n \"Ġo bou\",\n \"Ġob ou\",\n \"Ø® ÙĪاÙĨ\",\n \"Ø®ÙĪ اÙĨ\",\n \"ãĥ¬ ãĥĥãĥĪ\",\n \"о дейÑģÑĤв\",\n \"од ейÑģÑĤв\",\n \"Ú© 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à¸Ĥาย\",\n \"Ġà¸Ĥ าย\",\n \"Ġsv az\",\n \"ĠØ´ رÙĤ\",\n \"Ġشر ÙĤ\",\n \"Ġд ÑĭÑħ\",\n \"Ġиз бав\",\n \"Ġизб ав\",\n \"ĠÑı кÑĸй\",\n \"ĠÑıк Ñĸй\",\n \"ĠÑıкÑĸ й\",\n \"ĠÎľ ον\",\n \"Ġg ön\",\n \"Ġgö n\",\n \"ĠUkr aj\",\n \"ĠUk raj\",\n \"ĠUkra j\",\n \"ัà¸Ļ à¸Ńà¸Ńà¸ģ\",\n \"ัà¸Ļà¸Ń à¸Ńà¸ģ\",\n \"Ġม à¸ģราà¸Ħม\",\n \"и ÑĤов\",\n \"иÑĤ ов\",\n \"Ġanal ý\",\n \"Ġana lý\",\n \"ĠоÑĤ меÑĩ\",\n \"Ġبر اÙī\",\n \"âĪ ı\",\n \"ัà¸ģ à¸ģ\",\n \"æĭ¥ æľī\",\n \"ĠÑĸнÑĪ ого\",\n \"Ġкомп анÑĸÑĹ\",\n \"Ġk ÅĻes\",\n \"ĠÑĢаб оÑĩ\",\n \"ĠÑĢабо Ñĩ\",\n \"a dÃŃ\",\n \"ad ÃŃ\",\n \"ìł ł\",\n \"à¹Ħ หà¸Ļ\",\n \"à¥ģब ह\",\n \"âĢĻ deki\",\n \"âĢĻd eki\",\n \"âĢĻde ki\",\n \"çħ ¤\",\n \"ĠпаÑĢ Ñĥ\",\n \"Ġпа ÑĢÑĥ\",\n \"ìĦ Ń\",\n \"Ġнеп оÑģÑĢед\",\n \"ĠÄ° b\",\n \"Ġà¸ŀ ฤศà¸Ī\",\n \"íĭ ´\",\n \"Ġ ëłĪìĿ´\",\n \"ĠëłĪ ìĿ´\",\n \"ĠTh á»ķ\",\n \"Ñı еÑĤ\",\n \"ائ ج\",\n \"» çĴĥ\",\n \"ÐĴ Ðŀ\",\n \"åĸ Ĭ\",\n \"Ġ 第ä¸ī\",\n \"Ġ第 ä¸ī\",\n \"Ġвок ÑĢÑĥг\",\n \"Ñĩ енÑĮ\",\n \"Ñĩе нÑĮ\",\n \"Ñĩен ÑĮ\",\n \"Ġolan ak\",\n \"Ġola nak\",\n \"t ura\",\n \"tu ra\",\n \"tur a\",\n \"Ġ ÙħÙĬÙĦ\",\n \"ĠÙħ ÙĬÙĦ\",\n \"ĠÙħÙĬ ÙĦ\",\n \"ey di\",\n \"ĠÙħد ÙĬر\",\n \"Ġnel ze\",\n \"ัว à¸Ńย\",\n \"ìħ ľ\",\n \"Ġhlav u\",\n \"Ġkor uy\",\n \"ÑĨ ин\",\n \"ÑĨи н\",\n \"ĠдиÑģ ÑĨип\",\n \"ĠÙħ اÙĨد\",\n \"ĠÙħا ÙĨد\",\n \"ĠÙħاÙĨ د\",\n \"Ġпод ÑĢоб\",\n \"Т Ðŀ\",\n \"ÙĤر ار\",\n \"à¹ģà¸Ļะ à¸Ļำ\",\n \"문 ìĿĦ\",\n \"æĮ¯ ãĤĬ\",\n \"P ÅĻi\",\n \"PÅĻ i\",\n \"Ġy ên\",\n \"श à¤ķ\",\n \"Âł je\",\n \"ĠÐļон ÑģÑĤиÑĤÑĥ\",\n \"à¥ģ ह\",\n \"à¥ģठ¹\",\n \"ĠÙ¾ ا\",\n \"ìĨĮ 를\",\n \"Ġд ела\",\n \"Ġдел а\",\n \"Ġде ла\",\n \"к ид\",\n \"ки д\",\n \"à¹Ĥ à¸Ĭ\",\n \"커 ìĬ¤\",\n \"dÄĽ len\",\n \"dÄĽl en\",\n \"à¤Ķ र\",\n \"äºİ æĺ¯\",\n \"ĠÙĩÙħ ÛĮØ´Ùĩ\",\n \"ĠbaÅŁ lam\",\n \"Ġ ìĽ¨\",\n \"ĠìĽ ¨\",\n \"Ġden eyim\",\n \"Ġdeney im\",\n \"Ġü ye\",\n \"Ġüy e\",\n \"Ġ νÏĮ\",\n \"Ġν ÏĮ\",\n \"Ġà¤ĸ ड\",\n \"n ÄĽl\",\n \"nÄĽ l\",\n \"ĠÑģÑĦ еÑĢÑĸ\",\n \"ĠÑģÑĦеÑĢ Ñĸ\",\n \"à¸Ńà¸Ķ à¸ł\",\n \"ä¸Ģ å¹´\",\n \"Ġvur gu\",\n \"Äŀ Ä°\",\n \"âĢĻ Ċ\",\n \"ĠÑĸн ÑĪими\",\n \"ĠÑĸнÑĪ ими\",\n \"Ġз менÑĪ\",\n \"Ġठĭ\",\n \"Ġв ека\",\n \"Ġве ка\",\n \"ĠØŃÚ©ÙĪÙħ ت\",\n \"ĠتÙħ اÙħÛĮ\",\n \"ĠتÙħاÙħ ÛĮ\",\n \"Ġs mrt\",\n \"Ġsm rt\",\n \"Ġsmr t\",\n \"Ġh ủy\",\n \"Ġyap ılmÄ±ÅŁ\",\n \"Ġyapıl mÄ±ÅŁ\",\n \"à¹ī à¸ľ\",\n \"ĠY en\",\n \"ĠYe n\",\n \"Ġ Ñĥл\",\n \"ĠÑĥ л\",\n \"ĠS vÄĽt\",\n \"ĠSv ÄĽt\",\n \"ั à¸Ħ\",\n \"ĠmÄĽsÃŃ ců\",\n \"д енÑĤи\",\n \"ден ÑĤи\",\n \"Ġ ï¾ĺ\",\n \"Ġï¾ ĺ\",\n \"Ġпол иÑĤи\",\n \"Ġполи ÑĤи\",\n \"ĠполиÑĤ и\",\n \"s kyt\",\n \"sk yt\",\n \"sky t\",\n \"ä¹Ł æľī\",\n \"Ġê°Ļ ìĬµëĭĪëĭ¤\",\n \"Ġê·¸ëŀĺ ìĦľ\",\n \"ÏĦε Ïģη\",\n \"ÏĦεÏģ η\",\n \"Ñĩ еÑĢ\",\n \"Ñĩе ÑĢ\",\n \"ĠÃľNÄ°VERS Ä°TESÄ°\",\n \"ส à¸ł\",\n \"Ġ สร\",\n \"Ġส ร\",\n \"ान द\",\n \"ĠaÅŁ ırı\",\n \"λ ίοÏħ\",\n \"λί οÏħ\",\n \"Ġ ÙĦÙģ\",\n \"ĠÙĦ Ùģ\",\n \"ÃŃ nu\",\n \"ÃŃn u\",\n \"à¸Ń าร\",\n \"ÑĤ ÑĥÑĢа\",\n \"ÑĤÑĥ ÑĢа\",\n \"ÑĤÑĥÑĢ а\",\n \"ĠÄįesk ých\",\n \"ĠÄįe ských\",\n \"ĠÄįeský ch\",\n \"Ġph ức\",\n \"以 为\",\n \"ÏģÏī ÏĢα\",\n \"ĠاÙĨر ÚĺÛĮ\",\n \"» )\",\n \"a lardan\",\n \"alar dan\",\n \"alarda n\",\n 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\"Ġ Ïĥκ\",\n \"ĠÏĥ κ\",\n \"ãĤ¹ ãĥŀ\",\n \"ĠÙħس ائÙĦ\",\n \"ĠاÙĦاج تÙħاع\",\n \"åľ° çĤ¹\",\n \"ا ÛĮاÙĨ\",\n \"اÛĮ اÙĨ\",\n \"ĠÐŀ к\",\n \"ê¸ Ķ\",\n \"e lease\",\n \"el ease\",\n \"ele ase\",\n \"Ġطب ÙĤÙĩ\",\n \"ĠطبÙĤ Ùĩ\",\n \"éij ij\",\n \"Ġì½Ķ ë¡ľëĤĺ\",\n \"é¼ ł\",\n \"大 åħ¨\",\n \"ĠпÑĢи веÑģÑĤи\",\n \"ĠпÑĢив еÑģÑĤи\",\n \"Ġاب تد\",\n \"Ġابت د\",\n \"리 ë¡ľ\",\n \"ĠÑģÑĤÑĢ анÑĭ\",\n \"ĠÑģÑĤÑĢа нÑĭ\",\n \"ĠÑģÑĤÑĢан Ñĭ\",\n \"ĠzatÃŃm co\",\n \"Ġh uyết\",\n \"Ġhuy ết\",\n \"سÛĮ ÙĪÙĨ\",\n \"Ġsor du\",\n \"âĢĮ رس\",\n \"ĠÑĦ ÑĢон\",\n \"Ġed ip\",\n \"Ġedi p\",\n \"ÙĨ Ú¯ÛĮ\",\n \"ÙĨÚ¯ ÛĮ\",\n \"Ġк иÑĢ\",\n \"Ġки ÑĢ\",\n \"Ġ íķ´ìķ¼\",\n \"Ġíķ´ ìķ¼\",\n \"ì» ´\",\n \"ÑĨик лоп\",\n \"ĠпÑĢимен ениÑı\",\n \"Ġоб л\",\n \"éļ ª\",\n \"Ġk romÄĽ\",\n \"Ġkro mÄĽ\",\n \"æł¸ å¿ĥ\",\n \"ra him\",\n \"rah im\",\n \"о ÑĢд\",\n \"оÑĢ д\",\n \"Ġl Ãłnh\",\n \"ĠlÃł nh\",\n \"ĠlÃłn h\",\n \"Ġо ÑģÑĤÑĢов\",\n \"ĠоÑģÑĤ ÑĢов\",\n \"; |\",\n \"b uz\",\n \"bu z\",\n \"Ġ ÏĦÏģο\",\n \"ĠÏĦ Ïģο\",\n \"ĠÐĴ аÑĢ\",\n \"æī 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h\",\n \"ĠLi nh\",\n \"ebilir iz\",\n \"Ġзав ÑĤÑĢа\",\n \"μ εÏģο\",\n \"με Ïģο\",\n \"μεÏģ ο\",\n \"Ġ ÎŃν\",\n \"ĠÎŃ Î½\",\n \"ÑıÑĤ но\",\n \"Ġд оÑĢож\",\n \"Ġдо ÑĢож\",\n \"ĠдоÑĢ ож\",\n \"åıĤ çħ§\",\n \"Ïĥ ιο\",\n \"Ïĥι ο\",\n \"à¹ī à¹Ģà¸ģ\",\n \"a ných\",\n \"an ých\",\n \"aný ch\",\n \"ç· ł\",\n \"Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ\",\n \"ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ\",\n \"åĬĽ çļĦ\",\n \"ĠS ır\",\n \"ĠSı r\",\n \"Ġ ì§ĢëıĦ\",\n \"Ġì§Ģ ëıĦ\",\n \"ç· Ĭ\",\n \"ĠpoÄį tu\",\n \"ï¼Į ä¸İ\",\n \"ä¸ĸ ç´Ģ\",\n \"ем ого\",\n \"емо го\",\n \"Ġhus us\",\n \"Ġhu sus\",\n \"Ġölçü de\",\n \"Ġtr ục\",\n \"Ġtrụ c\",\n \"à¸Ľà¸¥ à¸Ńà¸Ķà¸ł\",\n \"Âłp ÅĻÃŃ\",\n \"ĠBöl gesi\",\n \"м ом\",\n \"мо м\",\n \"ãģ« ãģ¦\",\n \"Ġ쪽 ì§Ģ\",\n \"ÄĽt Å¡\",\n \"ĠìĦ± ê³µ\",\n \"र त\",\n \"ur du\",\n \"urd u\",\n \"ĠìĽĢ ì§ģ\",\n \"ÑŁ ÐŃ\",\n \"nÃŃ kem\",\n \"nÃŃk em\",\n \"ĠskuteÄį nosti\",\n \"Ġ даÑĤ\",\n \"Ġд аÑĤ\",\n \"Ġда ÑĤ\",\n \"n eum\",\n \"ne um\",\n \"ĠÑĤаб леÑĤ\",\n \"j vu\",\n \"Ġs edm\",\n \"Ġse dm\",\n \"Ġsed m\",\n \"س ÙĬØ©\",\n \"سÙĬ Ø©\",\n \"ĠкоÑĢ об\",\n \"Ġко ÑĢоб\",\n \"em mel\",\n \"emm el\",\n \"emme l\",\n \"ãģ¤ ãģij\",\n \"é¦Ļ èķī\",\n \"Ġشخص ÛĮت\",\n \"ĠشخصÛĮ ت\",\n \"ä¸Ĭ äºĨ\",\n \"ÙĪ را\",\n \"ÙĪر ا\",\n \"ĠаÑĤ моÑģ\",\n \"Ġ лей\",\n \"Ġл ей\",\n \"Ġz prav\",\n \"Ġzp rav\",\n \"Ġëķ ħ\",\n \"ู à¸Ĺ\",\n \"Ġ اسر\",\n \"Ġا سر\",\n \"Ġاس ر\",\n \"ĠAy dın\",\n \"ĠعÙħ ÙĦÙĬØ©\",\n \"ĠعÙħÙĦ ÙĬØ©\",\n \"Ġд ÑĸÑĶ\",\n \"ĠдÑĸ ÑĶ\",\n \"Ġd ök\",\n \"Ġdö k\",\n \"Ġफ ल\",\n \"ĠìĤ¬ëŀĮ ëĵ¤ìĿ´\",\n \"ĠнаÑĤÑĥ ÑĢалÑĮ\",\n \"æŁ ľ\",\n \"温 度\",\n \"Ġk les\",\n \"Ġkl es\",\n \"Ġkle s\",\n \"Ġин веÑģÑĤи\",\n \"s üz\",\n \"æĴ °\",\n \"Ġ ãĤ¢ãĥ«\",\n \"ĠãĤ¢ ãĥ«\",\n \"Ġ èĴ\",\n \"Ġè Ĵ\",\n \"ад ки\",\n \"Ġk lÃŃÄį\",\n \"Ïĩ εί\",\n \"Ïĩε ί\",\n \"ĠTh iết\",\n \"ĠThi ết\",\n \"ĠسرÛĮ ع\",\n \"ĠÏĢεÏģιο Ïĩή\",\n \"ÙĪ ÙĤÙģ\",\n \"ÙĪÙĤ Ùģ\",\n \"Ïģ ÏīÏĥη\",\n \"ÏģÏī Ïĥη\",\n \"ĠسÙĦ اÙħت\",\n \"ĠسÙĦاÙħ ت\",\n \"ëĵ¤ ëıĦ\",\n \"ĠveÅĻej né\",\n \"Ġvi tam\",\n \"Ġvit am\",\n \"Ġvita m\",\n \"ĠبازÛĮ گر\",\n \"ĠÑĢеÑĨеп ÑĤ\",\n \"ĠìľĦ íķ´ìĦľ\",\n \"ĠìľĦíķ´ ìĦľ\",\n \"ĠØ£Ùĥ بر\",\n \"Ġk üt\",\n \"Ġkü t\",\n \"민 주\",\n \"Ġté ž\",\n \"Ġ å¼ķ\",\n \"Ġå¼ ķ\",\n \"ÑĩаÑģ но\",\n \"çļĦ åľ°\",\n \"Ġarchit ekt\",\n \"Ġбак ÑĤеÑĢ\",\n \"Ġ ãģį\",\n \"Ġ одеÑĢж\",\n \"Ġо деÑĢж\",\n \"Ġод еÑĢж\",\n \"Ġتج ارÛĮ\",\n \"éĿ Ī\",\n \"Ġre cep\",\n \"Ġrec ep\",\n \"Ġrece p\",\n \"é© ¶\",\n \"Ġد ÙĩÙĩ\",\n \"ĠدÙĩ Ùĩ\",\n \"è² Į\",\n \"çµIJ å©ļ\",\n \"ılı ç\",\n \"ãģĭãĤī ãģ¯\",\n \"å¿ĥ éĩĮ\",\n \"æĬķ è³ĩ\",\n \"è² Ŀ\",\n \"ĠкÑĥлÑĮÑĤÑĥ ÑĢÑĭ\",\n \"ĠкÑĥлÑĮÑĤÑĥÑĢ Ñĭ\",\n \"Ġ å°ij\",\n \"Ġå° ij\",\n \"à¹ģ à¸ŀร\",\n \"à¹ģà¸ŀ ร\",\n \"γ κÏĮ\",\n \"γκ ÏĮ\",\n \"ar ım\",\n \"arı m\",\n \"Ġاس اسÛĮ\",\n \"Ġاساس ÛĮ\",\n \"Ġposled nÃŃch\",\n \"ĠposlednÃŃ ch\",\n \"ĠÙħ ÙħÙĨ\",\n \"ĠÙħÙħ ÙĨ\",\n \"Ġпоз иÑĤив\",\n \"Ġпози ÑĤив\",\n \"ìł ¤\",\n \"Ñĥ вавÑģÑı\",\n \"Ñĥв авÑģÑı\",\n \"Ñĥва вÑģÑı\",\n \"Ñĥвав ÑģÑı\",\n \"Ġجز ئ\",\n \"ìĿ´ ìŀIJ\",\n \"Ġин ÑģÑĤÑĢÑĥк\",\n \"Ġη λεκ\",\n \"Ġde mir\",\n \"Ġdem ir\",\n \"Ġdemi r\",\n \"ä¸Ńæĸĩ åŃĹå¹ķ\",\n \"Ġعاش ÙĤ\",\n \"Ġب اÙĦÙĤ\",\n \"Ġبا ÙĦÙĤ\",\n \"ĠباÙĦ ÙĤ\",\n \"Ġ maz\",\n \"Ġm az\",\n \"Ġma z\",\n \"ά νι\",\n \"άν ι\",\n \"Ġ dÃ¼ÄŁ\",\n \"Ġd Ã¼ÄŁ\",\n \"Ġdü ÄŁ\",\n \"Ġ κÏģα\",\n \"Ġκ Ïģα\",\n \"ĠбÑĥд ÑĤо\",\n \"ç¦ı åĪ©\",\n \"ĠпÑĢед назнаÑĩ\",\n \"Ùħ ÙĦØ©\",\n \"ÙħÙĦ Ø©\",\n \"ĠбÑĥд инкÑĥ\",\n \"Å¥ an\",\n \"íķ Ģ\",\n \"ç´¹ ä»ĭ\",\n \"Ú© ز\",\n \"ĠкаÑĦ ед\",\n \"ãģ« è¦ĭ\",\n \"าà¸ķร à¸IJาà¸Ļ\",\n \"ë¡ľ ëĬĶ\",\n \"i vÄĽ\",\n \"iv ÄĽ\",\n \"èĥ½ æºIJ\",\n \"ï¼Į åħ¨\",\n \"ĠÑĥ див\",\n \"ĠÑĥд ив\",\n \"Ġë§Į ëĤĺ\",\n \"ÐĴ ÐIJ\",\n \"ĠG ür\",\n \"ĠGü r\",\n \"ĠдÑĢÑĥг им\",\n \"Ïĥ Ïį\",\n \"Ġ oÄŁlu\",\n \"ĠoÄŁ lu\",\n \"Ġê°Ģ ê¹Į\",\n \"ĠзнаÑĩ иÑĤелÑĮно\",\n \"ĠзнаÑĩиÑĤ елÑĮно\",\n \"о зÑĸ\",\n \"оз Ñĸ\",\n \"Ġm á»±c\",\n \"ĠB eÅŁ\",\n \"ĠBe ÅŁ\",\n \"Ġ jezd\",\n \"Ġje zd\",\n \"á vÄĽ\",\n \"áv ÄĽ\",\n \"ÏĦη Ïĥε\",\n \"ãģ¦ãģĦ ãģªãģĦ\",\n \"ĠСв ÑıÑĤ\",\n \"Ġम श\",\n \"ĠΤ οÏħ\",\n \"ĠΤο Ïħ\",\n \"声 ãĤĴ\",\n \"ĠÑģам ое\",\n \"ĠÑģамо е\",\n \"Ġ åĮº\",\n \"ĠåĮ º\",\n \"ĠìĤ¬ëŀĮ ìĿĢ\",\n \"ĠÙħ ÙĦت\",\n \"ĠÙħÙĦ ت\",\n \"Ġj oker\",\n \"Ġjo ker\",\n \"Ġjoke r\",\n \"Ġne ob\",\n \"Ġneo b\",\n \"ĠÑĤ ака\",\n \"ĠÑĤак а\",\n \"ĠÑĤа ка\",\n \"ĠÙĩ Ùģت\",\n \"Ġδε δο\",\n \"ĠзаÑħ оп\",\n \"ĠاÙĦÙħ خت\",\n \"ез да\",\n \"езд а\",\n \"Ġíķľ ë²Ī\",\n \"Ġع اÙħØ©\",\n \"ĠعاÙħ Ø©\",\n \"Ġdo state\",\n \"Ġdost ate\",\n \"Ġdostat e\",\n \"Ġp lav\",\n \"Ġpl av\",\n \"Ġpla v\",\n \"楽 ãģĹ\",\n \".;.; .;.;\",\n \"в аÑĶ\",\n \"ва ÑĶ\",\n \"Ġbụ i\",\n \"ĠÄij ỡ\",\n \"ĠÄijá» ¡\",\n \"Ġmys lÃŃ\",\n \"Ġmysl ÃŃ\",\n \"ĠÙĨ ار\",\n \"Ġn út\",\n \"Ġм ала\",\n \"Ġмал а\",\n \"Ġма ла\",\n \"Τ Ρ\",\n \"ĠاÙĦرÙħ زÙĬØ©\",\n \"la dım\",\n \"lad ım\",\n \"ladı m\",\n \"ä¸Ģ ç·Ĵ\",\n \"ĠiÅŁ ç\",\n \"l ivé\",\n \"li vé\",\n \"liv é\",\n \"르 ê²Į\",\n \"ан наÑı\",\n \"ظËĨ Ø·\",\n \"Ġd ừng\",\n \"ÙĦÙĥ تر\",\n \"çŃĶ æ¡Ī\",\n \"ĠÙħÙĪÙĤع ÛĮت\",\n \"ĠÑĸн озем\",\n \"ĠиÑģ Ñĩ\",\n \"Ġнеп ÑĢавилÑĮ\",\n \"b akan\",\n \"ba kan\",\n \"bak an\",\n \"Ġ çīĪ\",\n \"Ġçī Ī\",\n \"ен нÑİ\",\n \"à¸ĩ à¹Ģศ\",\n \"à¸Ħวาม à¸Ħ\",\n \"% .Ċ\",\n \"%. 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ÛĮد\",\n \"Ġگر دÛĮد\",\n \"ØŃ ÙĦ\",\n \"lu vÃŃ\",\n \"luv ÃŃ\",\n \"v éd\",\n \"vé d\",\n \"Ġìĺ ·\",\n \"Ġε ÏĢα\",\n \"ĠεÏĢ α\",\n \"ĠÑĤи ÑģÑıÑĩ\",\n \"ĠÑĤиÑģ ÑıÑĩ\",\n \"Ġê½ ĥ\",\n \"ĠP US\",\n \"ĠPU S\",\n \"ĠдÑĥм кÑĥ\",\n \"Ġ âĢĿĊ\",\n \"ĠâĢĿ Ċ\",\n \"ĠìĬ¤ íı¬ì¸ł\",\n \"Ùĩ Ùĩ\",\n \"Ġg ắng\",\n \"Ġgắn g\",\n \"ิ à¸łà¸²à¸ŀ\",\n \"éĩĮ éĿ¢\",\n \"br ıs\",\n \"Ġz áb\",\n \"Ġzá b\",\n \"κ αÏĤ\",\n \"κα ÏĤ\",\n \"ĠåıĮ 线\",\n \"ล ล\",\n \"ĠÄIJ Ãłi\",\n \"ĠÄIJÃł i\",\n \"åѸ æł¡\",\n \"ĠÑĢаÑģп ÑĢед\",\n \"ĠÑģÑĤан еÑĤ\",\n \"ĠÑģÑĤа неÑĤ\",\n \"Ġл ак\",\n \"Ġла к\",\n \"Ġпод к\",\n \"Ġg ören\",\n \"Ġgö ren\",\n \"Ġgör en\",\n \"Ġgöre n\",\n \"르 ê³ł\",\n \"ĠÑĦ ÑĢÑĥкÑĤ\",\n \"íĵ¨ íĦ°\",\n \"ãģĻ ãĤĮãģ°\",\n \"ãĤĴ ä½ľ\",\n \"à¸Ńà¸Ńà¸ģ à¹ģà¸ļà¸ļ\",\n \"Ġku lak\",\n \"Ġkul ak\",\n \"ĠíĶĮ ëłĪìĿ´\",\n \"ĠØŃ Ø¯ÙĬØ«\",\n \"ĠØŃد ÙĬØ«\",\n \"ãģĨ ãĤĵ\",\n \"Ġм Ñĸк\",\n \"ĠмÑĸ к\",\n \"à¤ĩस à¤ķ\",\n \"ĠÑĥ ÑĤоÑĩ\",\n \"ĠÑĥÑĤ оÑĩ\",\n \"ĠÙĥ Ø«ÙĬر\",\n \"ĠY ine\",\n \"ĠYi ne\",\n \"ĠYin e\",\n \"ัว หà¸Ļ\",\n \"н ÑĸÑĹ\",\n \"нÑĸ ÑĹ\",\n \"åį ¢\",\n \"Ñĥ Ñģлов\",\n \"ÑĥÑģ лов\",\n \"ìĽĮ íģ¬\",\n \"Ġà¤ħ à¤ĸ\",\n \"ĠÑĨ Ñĸка\",\n \"ĠÑĨÑĸ ка\",\n \"ìĦł ìĿĦ\",\n \"ĠØ£ ر\",\n \"гал ÑĤеÑĢ\",\n \"angl icky\",\n \"ĠÑģ оÑģÑĥд\",\n \"ĠÑģоÑģ Ñĥд\",\n \"ĠÑĥ Ñıв\",\n \"ĠпÑĢодÑĥк ÑĨÑĸÑĹ\",\n \"Ġc hua\",\n \"Ġch ua\",\n \"Ġchu a\",\n \"Ġd án\",\n \"Ġdá n\",\n \"ाम à¤Ĺ\",\n \"ئ ت\",\n \"ĠФ ед\",\n \"Ġh rom\",\n \"Ġhr om\",\n \"íķ´ ë³´\",\n \"ĠØ¢ÙĨ ÙĦاÛĮÙĨ\",\n \"- пÑĢав\",\n \"-п ÑĢав\",\n \"Ġì¤ijìļĶ íķľ\",\n \"Ġв кÑĥ\",\n \"Ġвк Ñĥ\",\n \"Ġ 大éĺª\",\n \"Ġ大 éĺª\",\n \"Ġt erk\",\n \"Ġte rk\",\n \"Ġter k\",\n \"Ġпод Ñĸб\",\n \"ĠвÑĸд вÑĸд\",\n \"à¥Į à¤Ł\",\n \"è³ £\",\n \"Ġب تÙĨ\",\n \"Ġبت ÙĨ\",\n \"Ġبع ضÛĮ\",\n \"Ġبعض ÛĮ\",\n \"ãģª ãģĬ\",\n \"ä»ĸ åĢij\",\n \"Ġtavs iye\",\n \"ĠM ısır\",\n \"ĠØ¥ Ø°\",\n \"Ġ æIJ\",\n \"Ġæ IJ\",\n \"íķĺ ëĤĺ\",\n \"ĠÙĪ Ø®\",\n \"ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ\",\n \"ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ\",\n \"Ġta kový\",\n \"Ġtak ový\",\n \"Ġtako vý\",\n \"Ġबन न\",\n \"Ġз ÑĢениÑı\",\n \"ĠÙĪ ÙģÙĤ\",\n \"ĠÙĪÙģ ÙĤ\",\n \"ë¹Ħ ìķĦ\",\n \"Ġпом ожеÑĤ\",\n \"åĮĹ å¸Ĥ\",\n \"dık ları\",\n \"Ġ éĵģ\",\n \"Ġéĵ ģ\",\n \"Ġakt uálnÃŃ\",\n \"Ġaktu álnÃŃ\",\n \"Ġв в\",\n \"ãĤĤ ãģªãģĦ\",\n \"íĨµ ìĭł\",\n \"ÏĦα Ïĥη\",\n \"Ġìĥģ ëĮĢ\",\n \"Ġ æł¡\",\n \"Ġæł ¡\",\n \"ãĢĤ éĤ£\",\n \"ĠرÙĪسÛĮ Ùĩ\",\n \"Ġtelev izyon\",\n \"å¹´ é¾Ħ\",\n \"ĠÐijоÑĢ иÑģ\",\n \"리 ìĸ´\",\n \"Ġz veÅĻej\",\n \"ж но\",\n \"ĠÐŀ ÑģÑĤ\",\n \"ĠÐŀÑģ ÑĤ\",\n \"ĠмÑĥж Ñĩин\",\n \"Ġy eÅŁil\",\n \"ĠСов еÑĤ\",\n \"ĠСо веÑĤ\",\n \"ĠB ÃĸL\",\n \"ĠТак ож\",\n \"Ġob nov\",\n \"ĠпÑĢи надлеж\",\n \"ĠвиÑģ нов\",\n \"Ø· Ùħ\",\n \"ĠìĹĨ ìĸ´\",\n \"ĠM ùa\",\n \"ä½ı å®ħ\",\n \"åĮ» åѦ\",\n \"Ġна ÑĢез\",\n \"ĠнаÑĢ ез\",\n \"ãĥĭ ï¾Ĩ\",\n \"ĠM ặt\",\n \"Ġvu ông\",\n \"ä¸Ģ åĮº\",\n \"ĠẢ nh\",\n \"ÑĢ иÑĦ\",\n \"ÑĢи ÑĦ\",\n \"ä¿Ŀ éĻ©\",\n \"ĠÏĩÏģή Ïĥη\",\n \"åIJĮ æĦı\",\n \"Ġ æīĵ\",\n \"Ġæī ĵ\",\n \"e tÄĽ\",\n \"et ÄĽ\",\n \"ĠÙĪ 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ει\",\n \"ÏĪε ι\",\n \"Ġ ẩn\",\n \"ت س\",\n \"ĠÑĤ ай\",\n \"ĠÑĤа й\",\n \"Ġнев озможно\",\n \"åıĬ ãģ³\",\n \"r oti\",\n \"ro ti\",\n \"rot i\",\n \"ï½ Ń\",\n \"д ом\",\n \"до м\",\n \"ой но\",\n \"å£ Ĭ\",\n \"说 çļĦ\",\n \"Ġsk oro\",\n \"ni ÄįnÃŃ\",\n \"niÄį nÃŃ\",\n \"ĠProf es\",\n \"ĠÑħ ÑĢониÑĩеÑģ\",\n \"Ġ주 문\",\n \"ĠZ n\",\n \"ĠÑģ лой\",\n \"ĠÑģл ой\",\n \"ĠÑģло й\",\n \"Îł Ïģο\",\n \"æĮĩ æķ°\",\n \"ĠпеÑĢ еÑĪ\",\n \"ĠпеÑĢе ÑĪ\",\n \"à¥ģà¤ķ स\",\n \"Ġê°Ģ ìłķ\",\n \"Ġ íķĺë©´\",\n \"Ġíķĺ ë©´\",\n \"Û±Û¹ Û´\",\n \"к Ñĥл\",\n \"кÑĥ л\",\n \"ÙĬ ÙĦا\",\n \"ÙĬÙĦ ا\",\n \"ĠدÙĪب ارÙĩ\",\n \"| l\",\n \"ĠÐľ Ñĥ\",\n \"н ила\",\n \"ни ла\",\n \"нил а\",\n \"ãģ¦ ãģĦãģ¾ãģĻ\",\n \"ãģ¦ãģĦ ãģ¾ãģĻ\",\n \"m acı\",\n \"ma cı\",\n \"mac ı\",\n \"ãģŁ ãģ¡ãģ¯\",\n \"ãģŁãģ¡ ãģ¯\",\n \"ĠاÙĦÙĥ تاب\",\n \"移 åĭķ\",\n \"λ μ\",\n \"_ ï¼ı\",\n \"Ġê°Ģ ìŀħ\",\n \"èħ ¾\",\n \"ĠпÑĢез иденÑĤ\",\n \"Ġë¶Ħ ìķ¼\",\n \"a hy\",\n \"ah y\",\n \"Å¡et ÅĻenÃŃ\",\n \"Å¡etÅĻ enÃŃ\",\n \"éĵ º\",\n \"ĠpÅĻÃŃ ro\",\n \"Ðķ Т\",\n \"ĠìļĶ ì²Ń\",\n \"Ġmoh lo\",\n \"Ġmohl o\",\n \"å¿ĥ çIJĨ\",\n \"Ġvysok é\",\n \"& uuml\",\n \"ÏĦ ικα\",\n \"ÏĦικ α\",\n \"ÏĦι κα\",\n \"ìĹħ ì²´\",\n \"ãģ§ ãģĤ\",\n \"ราย à¸ĩาà¸Ļ\",\n \"ĠpÅĻÃŃspÄĽ v\",\n \"Ġet miÅŁtir\",\n \"Ġetm iÅŁtir\",\n \"ĠetmiÅŁ tir\",\n \"她 们\",\n \"ÏĢ λα\",\n \"ÏĢλ α\",\n \"ứ a\",\n \"Ġ 说\",\n \"Ġè¯ ´\",\n \"ĠÑģо Ñģед\",\n \"ĠÑģоÑģ ед\",\n \"åĩ ī\",\n \"ĠÐł е\",\n \"åİŁ æĿ¥\",\n \"ĠÐIJÑĢ Ñħ\",\n \"ب ÙĬÙĨ\",\n \"بÙĬ ÙĨ\",\n \"åľ° 说\",\n \"Ġ ört\",\n \"Ġö rt\",\n \"Ġör t\",\n \"ĠΣ εÏĢ\",\n \"ĠΣε ÏĢ\",\n \"ÂŃ ÙĩاÛĮ\",\n \"ĠاÙĦ اÙĤتص\",\n \"å°½ 管\",\n \"ÑĤ Ñĭй\",\n \"ÑĤÑĭ й\",\n \"t ains\",\n \"ta ins\",\n \"tain s\",\n \"ÙĢ ÙĦ\",\n \"ç§ijæĬĢ æľīéĻIJåħ¬åı¸\",\n \"æı ®\",\n \"ัà¸ķ à¸ĸ\",\n \"á»Ĺ ng\",\n \"ล าà¸Ķ\",\n \"ลา à¸Ķ\",\n \"æļ ®\",\n \"ĠÙĨÙģ سÙĩ\",\n \"ĠÙĨÙģس Ùĩ\",\n \"Ġ çľĭ\",\n \"Ġçľ ĭ\",\n \"Ġ ãģ¿\",\n \"Ġt arım\",\n \"Ġtar ım\",\n \"Û±Û¹ Ûµ\",\n \"ĠÎ Ĭ\",\n \"Ġkom plex\",\n \"Ġkomple x\",\n \"Ġkomp lex\",\n \"ĠNh Ä©\",\n \"è´¹ çĶ¨\",\n \"Ġکاربر اÙĨ\",\n \"ÅĪ ovánÃŃ\",\n \"Ġ ků\",\n \"Ġk ů\",\n \"д ап\",\n \"да п\",\n \"Îķ Χ\",\n \"ê·¸ ëŀĺ\",\n \"Ġdön dü\",\n \"人 åĵ¡\",\n \"ĠT iá»ĥu\",\n \"ĠÙĪÛĮر اÛĮØ´\",\n \"Ġö ngör\",\n \"ĠÙĪ غÙĬر\",\n \"ĠÑģ кÑĢÑĭ\",\n \"ĠÑģк ÑĢÑĭ\",\n \"âĢIJ '\",\n \"Ġ немÑĥ\",\n \"Ġн емÑĥ\",\n \"Ġне мÑĥ\",\n \"Ġнем Ñĥ\",\n \"ĠH á»ĩ\",\n \"ĠHá» ĩ\",\n \"Ġdüzen li\",\n \"ĠsoutÄĽ že\",\n \"ãĢģ ãĥŀ\",\n \"ÏĦο μα\",\n \"ÄĽ lÃŃ\",\n \"ÄĽl ÃŃ\",\n \"ĠØ£ ÙĦÙħاÙĨ\",\n \"ĠØ£ÙĦ ÙħاÙĨ\",\n \"çł ²\",\n \"Ġtr Ãł\",\n \"Ġ ä¸ĸçķĮ\",\n \"Ġä¸ĸ çķĮ\",\n \"ay ız\",\n \"ayı z\",\n \"ım lı\",\n \"ĠاÙĦØ£ Ùģ\",\n \"íķĺ ëĬĶëį°\",\n \"íķĺëĬĶ ëį°\",\n \"в ано\",\n \"ва но\",\n \"ван о\",\n \"ĠpÅĻi Äįemž\",\n \"Ùĥ ÙĬب\",\n \"ÙĥÙĬ ب\",\n \"ĠмаÑĤ емаÑĤи\",\n \"м ени\",\n \"мен и\",\n \"ме ни\",\n \"ĠпÑĢо екÑĤÑĥ\",\n \"ĠпÑĢоек ÑĤÑĥ\",\n \"ĠпÑĢоекÑĤ Ñĥ\",\n \"ี à¹Ĥà¸Ń\",\n \"ีà¹Ĥ à¸Ń\",\n \"о Ñĥ\",\n \"ĠاÙĦ شرÙĥØ©\",\n \"ĠاÙĦشر ÙĥØ©\",\n \"æ³ £\",\n \"ÙĪÙĤ ÙĬت\",\n \"ÑĪ ив\",\n \"ÑĪи в\",\n \"Ġperson el\",\n \"Ġpersone l\",\n \"Ġperso nel\",\n \"Ø´ 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ÙĦات\",\n \"ÙĪÙĦ ات\",\n \"ÙĪÙĦا ت\",\n \"в аÑĤиÑģÑı\",\n \"ва ÑĤиÑģÑı\",\n \"ваÑĤи ÑģÑı\",\n \"Ġk ök\",\n \"Ġkö k\",\n \"ÙĨ ب\",\n \"ĠвÑĭÑģок ой\",\n \"ãĥ¼ ãĥ¼\",\n \"ãĥ¼ãĥ ¼\",\n \"éĶ ¦\"\n ]\n }\n}" }, { "path": "comfy/text_encoders/llama_tokenizer/tokenizer_config.json", "content": "{\n \"add_bos_token\": true,\n \"add_eos_token\": false,\n \"add_prefix_space\": null,\n \"added_tokens_decoder\": {\n \"128000\": {\n \"content\": \"<|begin_of_text|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128001\": {\n \"content\": \"<|end_of_text|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128002\": {\n \"content\": \"<|reserved_special_token_0|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128003\": {\n \"content\": \"<|reserved_special_token_1|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128004\": {\n \"content\": \"<|reserved_special_token_2|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128005\": {\n \"content\": \"<|reserved_special_token_3|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128006\": {\n \"content\": \"<|start_header_id|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128007\": {\n \"content\": \"<|end_header_id|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128008\": {\n \"content\": \"<|reserved_special_token_4|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128009\": {\n \"content\": \"<|eot_id|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128010\": {\n \"content\": \"<|reserved_special_token_5|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128011\": {\n \"content\": \"<|reserved_special_token_6|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128012\": {\n \"content\": \"<|reserved_special_token_7|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128013\": {\n \"content\": \"<|reserved_special_token_8|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128014\": {\n \"content\": \"<|reserved_special_token_9|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128015\": {\n \"content\": \"<|reserved_special_token_10|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128016\": {\n \"content\": \"<|reserved_special_token_11|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128017\": {\n \"content\": \"<|reserved_special_token_12|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128018\": {\n \"content\": \"<|reserved_special_token_13|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128019\": {\n \"content\": \"<|reserved_special_token_14|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128020\": {\n \"content\": \"<|reserved_special_token_15|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128021\": {\n \"content\": \"<|reserved_special_token_16|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128022\": {\n \"content\": \"<|reserved_special_token_17|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128023\": {\n \"content\": \"<|reserved_special_token_18|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128024\": {\n \"content\": \"<|reserved_special_token_19|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128025\": {\n \"content\": \"<|reserved_special_token_20|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128026\": {\n \"content\": \"<|reserved_special_token_21|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128027\": {\n \"content\": \"<|reserved_special_token_22|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128028\": {\n \"content\": \"<|reserved_special_token_23|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128029\": {\n \"content\": \"<|reserved_special_token_24|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128030\": {\n \"content\": \"<|reserved_special_token_25|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128031\": {\n \"content\": \"<|reserved_special_token_26|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128032\": {\n \"content\": \"<|reserved_special_token_27|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128033\": {\n \"content\": \"<|reserved_special_token_28|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128034\": {\n \"content\": \"<|reserved_special_token_29|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128035\": {\n \"content\": \"<|reserved_special_token_30|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128036\": {\n \"content\": \"<|reserved_special_token_31|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128037\": {\n \"content\": \"<|reserved_special_token_32|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128038\": {\n \"content\": \"<|reserved_special_token_33|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128039\": {\n \"content\": \"<|reserved_special_token_34|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128040\": {\n \"content\": \"<|reserved_special_token_35|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128041\": {\n \"content\": \"<|reserved_special_token_36|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128042\": {\n \"content\": \"<|reserved_special_token_37|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128043\": {\n \"content\": \"<|reserved_special_token_38|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128044\": {\n \"content\": \"<|reserved_special_token_39|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128045\": {\n \"content\": \"<|reserved_special_token_40|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128046\": {\n \"content\": \"<|reserved_special_token_41|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128047\": {\n \"content\": \"<|reserved_special_token_42|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128048\": {\n \"content\": \"<|reserved_special_token_43|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128049\": {\n \"content\": \"<|reserved_special_token_44|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128050\": {\n \"content\": \"<|reserved_special_token_45|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128051\": {\n \"content\": \"<|reserved_special_token_46|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128052\": {\n \"content\": \"<|reserved_special_token_47|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128053\": {\n \"content\": \"<|reserved_special_token_48|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128054\": {\n \"content\": \"<|reserved_special_token_49|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128055\": {\n \"content\": \"<|reserved_special_token_50|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128056\": {\n \"content\": \"<|reserved_special_token_51|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128057\": {\n \"content\": \"<|reserved_special_token_52|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128058\": {\n \"content\": \"<|reserved_special_token_53|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128059\": {\n \"content\": \"<|reserved_special_token_54|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128060\": {\n \"content\": \"<|reserved_special_token_55|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128061\": {\n \"content\": \"<|reserved_special_token_56|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128062\": {\n \"content\": \"<|reserved_special_token_57|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128063\": {\n \"content\": \"<|reserved_special_token_58|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128064\": {\n \"content\": \"<|reserved_special_token_59|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128065\": {\n \"content\": \"<|reserved_special_token_60|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128066\": {\n \"content\": \"<|reserved_special_token_61|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128067\": {\n \"content\": \"<|reserved_special_token_62|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128068\": {\n \"content\": \"<|reserved_special_token_63|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128069\": {\n \"content\": \"<|reserved_special_token_64|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128070\": {\n \"content\": \"<|reserved_special_token_65|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128071\": {\n \"content\": \"<|reserved_special_token_66|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128072\": {\n \"content\": \"<|reserved_special_token_67|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128073\": {\n \"content\": \"<|reserved_special_token_68|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128074\": {\n \"content\": \"<|reserved_special_token_69|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128075\": {\n \"content\": \"<|reserved_special_token_70|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128076\": {\n \"content\": \"<|reserved_special_token_71|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128077\": {\n \"content\": \"<|reserved_special_token_72|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128078\": {\n \"content\": \"<|reserved_special_token_73|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128079\": {\n \"content\": \"<|reserved_special_token_74|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128080\": {\n \"content\": \"<|reserved_special_token_75|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128081\": {\n \"content\": \"<|reserved_special_token_76|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128082\": {\n \"content\": \"<|reserved_special_token_77|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128083\": {\n \"content\": \"<|reserved_special_token_78|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128084\": {\n \"content\": \"<|reserved_special_token_79|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128085\": {\n \"content\": \"<|reserved_special_token_80|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128086\": {\n \"content\": \"<|reserved_special_token_81|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128087\": {\n \"content\": \"<|reserved_special_token_82|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128088\": {\n \"content\": \"<|reserved_special_token_83|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128089\": {\n \"content\": \"<|reserved_special_token_84|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n 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\"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128209\": {\n \"content\": \"<|reserved_special_token_204|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128210\": {\n \"content\": \"<|reserved_special_token_205|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128211\": {\n \"content\": \"<|reserved_special_token_206|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128212\": {\n \"content\": \"<|reserved_special_token_207|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128213\": {\n \"content\": \"<|reserved_special_token_208|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128214\": {\n \"content\": \"<|reserved_special_token_209|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128215\": {\n \"content\": \"<|reserved_special_token_210|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128216\": {\n \"content\": \"<|reserved_special_token_211|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128217\": {\n \"content\": \"<|reserved_special_token_212|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128218\": {\n \"content\": \"<|reserved_special_token_213|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128219\": {\n \"content\": \"<|reserved_special_token_214|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128220\": {\n \"content\": \"<|reserved_special_token_215|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128221\": {\n \"content\": \"<|reserved_special_token_216|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128222\": {\n \"content\": \"<|reserved_special_token_217|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128223\": {\n \"content\": \"<|reserved_special_token_218|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128224\": {\n \"content\": \"<|reserved_special_token_219|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128225\": {\n \"content\": \"<|reserved_special_token_220|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128226\": {\n \"content\": \"<|reserved_special_token_221|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128227\": {\n \"content\": \"<|reserved_special_token_222|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128228\": {\n \"content\": \"<|reserved_special_token_223|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128229\": {\n \"content\": \"<|reserved_special_token_224|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128230\": {\n \"content\": \"<|reserved_special_token_225|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128231\": {\n \"content\": \"<|reserved_special_token_226|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128232\": {\n \"content\": \"<|reserved_special_token_227|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128233\": {\n \"content\": \"<|reserved_special_token_228|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128234\": {\n \"content\": \"<|reserved_special_token_229|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128235\": {\n \"content\": \"<|reserved_special_token_230|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128236\": {\n \"content\": \"<|reserved_special_token_231|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128237\": {\n \"content\": \"<|reserved_special_token_232|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128238\": {\n \"content\": \"<|reserved_special_token_233|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128239\": {\n \"content\": \"<|reserved_special_token_234|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128240\": {\n \"content\": \"<|reserved_special_token_235|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128241\": {\n \"content\": \"<|reserved_special_token_236|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128242\": {\n \"content\": \"<|reserved_special_token_237|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128243\": {\n \"content\": \"<|reserved_special_token_238|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128244\": {\n \"content\": \"<|reserved_special_token_239|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128245\": {\n \"content\": \"<|reserved_special_token_240|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128246\": {\n \"content\": \"<|reserved_special_token_241|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128247\": {\n \"content\": \"<|reserved_special_token_242|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128248\": {\n \"content\": \"<|reserved_special_token_243|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128249\": {\n \"content\": \"<|reserved_special_token_244|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128250\": {\n \"content\": \"<|reserved_special_token_245|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128251\": {\n \"content\": \"<|reserved_special_token_246|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128252\": {\n \"content\": \"<|reserved_special_token_247|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128253\": {\n \"content\": \"<|reserved_special_token_248|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128254\": {\n \"content\": \"<|reserved_special_token_249|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128255\": {\n \"content\": \"<|reserved_special_token_250|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128256\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128257\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"128258\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n }\n },\n \"bos_token\": \"<|begin_of_text|>\",\n \"chat_template\": \"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\",\n \"clean_up_tokenization_spaces\": true,\n \"eos_token\": \"<|end_of_text|>\",\n \"legacy\": true,\n \"model_input_names\": [\n \"input_ids\",\n \"attention_mask\"\n ],\n \"model_max_length\": 1000000000000000019884624838656,\n \"pad_token\": \"\",\n \"padding_side\": \"right\",\n \"processor_class\": \"LlavaProcessor\",\n \"tokenizer_class\": \"LlamaTokenizer\",\n \"unk_token\": \"\",\n \"use_default_system_prompt\": false\n}\n" }, { "path": "comfy/text_encoders/long_clipl.py", "content": "\n\ndef model_options_long_clip(sd, tokenizer_data, model_options):\n w = sd.get(\"clip_l.text_model.embeddings.position_embedding.weight\", None)\n if w is None:\n w = sd.get(\"clip_g.text_model.embeddings.position_embedding.weight\", None)\n else:\n model_name = \"clip_g\"\n\n if w is None:\n w = sd.get(\"text_model.embeddings.position_embedding.weight\", None)\n if w is not None:\n if \"text_model.encoder.layers.30.mlp.fc1.weight\" in sd:\n model_name = \"clip_g\"\n elif \"text_model.encoder.layers.1.mlp.fc1.weight\" in sd:\n model_name = \"clip_l\"\n else:\n model_name = \"clip_l\"\n\n if w is not None:\n tokenizer_data = tokenizer_data.copy()\n model_options = model_options.copy()\n model_config = model_options.get(\"model_config\", {})\n model_config[\"max_position_embeddings\"] = w.shape[0]\n model_options[\"{}_model_config\".format(model_name)] = model_config\n tokenizer_data[\"{}_max_length\".format(model_name)] = w.shape[0]\n return tokenizer_data, model_options\n" }, { "path": "comfy/text_encoders/longcat_image.py", "content": "import re\nimport numbers\nimport torch\nfrom comfy import sd1_clip\nfrom comfy.text_encoders.qwen_image import Qwen25_7BVLITokenizer, Qwen25_7BVLIModel\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nQUOTE_PAIRS = [(\"'\", \"'\"), ('\"', '\"'), (\"\\u2018\", \"\\u2019\"), (\"\\u201c\", \"\\u201d\")]\nQUOTE_PATTERN = \"|\".join(\n [\n re.escape(q1) + r\"[^\" + re.escape(q1 + q2) + r\"]*?\" + re.escape(q2)\n for q1, q2 in QUOTE_PAIRS\n ]\n)\nWORD_INTERNAL_QUOTE_RE = re.compile(r\"[a-zA-Z]+'[a-zA-Z]+\")\n\n\ndef split_quotation(prompt):\n matches = WORD_INTERNAL_QUOTE_RE.findall(prompt)\n mapping = []\n for i, word_src in enumerate(set(matches)):\n word_tgt = \"longcat_$##$_longcat\" * (i + 1)\n prompt = prompt.replace(word_src, word_tgt)\n mapping.append((word_src, word_tgt))\n\n parts = re.split(f\"({QUOTE_PATTERN})\", prompt)\n result = []\n for part in parts:\n for word_src, word_tgt in mapping:\n part = part.replace(word_tgt, word_src)\n if not part:\n continue\n is_quoted = bool(re.match(QUOTE_PATTERN, part))\n result.append((part, is_quoted))\n return result\n\n\nclass LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.max_length = 512\n\n def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):\n parts = split_quotation(text)\n all_tokens = []\n for part_text, is_quoted in parts:\n if is_quoted:\n for char in part_text:\n ids = self.tokenizer(char, add_special_tokens=False)[\"input_ids\"]\n all_tokens.extend(ids)\n else:\n ids = self.tokenizer(part_text, add_special_tokens=False)[\"input_ids\"]\n all_tokens.extend(ids)\n\n if len(all_tokens) > self.max_length:\n all_tokens = all_tokens[: self.max_length]\n logger.warning(f\"Truncated prompt to {self.max_length} tokens\")\n\n output = [(t, 1.0) for t in all_tokens]\n # Pad to max length\n self.pad_tokens(output, self.max_length - len(output))\n return [output]\n\n\nclass LongCatImageTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(\n embedding_directory=embedding_directory,\n tokenizer_data=tokenizer_data,\n name=\"qwen25_7b\",\n tokenizer=LongCatImageBaseTokenizer,\n )\n self.longcat_template_prefix = \"<|im_start|>system\\nAs an image captioning expert, generate a descriptive text prompt based on an image content, suitable for input to a text-to-image model.<|im_end|>\\n<|im_start|>user\\n\"\n self.longcat_template_suffix = \"<|im_end|>\\n<|im_start|>assistant\\n\"\n\n def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):\n skip_template = False\n if text.startswith(\"<|im_start|>\"):\n skip_template = True\n if text.startswith(\"<|start_header_id|>\"):\n skip_template = True\n if text == \"\":\n text = \" \"\n\n base_tok = getattr(self, \"qwen25_7b\")\n if skip_template:\n tokens = super().tokenize_with_weights(\n text, return_word_ids=return_word_ids, disable_weights=True, **kwargs\n )\n else:\n prefix_ids = base_tok.tokenizer(\n self.longcat_template_prefix, add_special_tokens=False\n )[\"input_ids\"]\n suffix_ids = base_tok.tokenizer(\n self.longcat_template_suffix, add_special_tokens=False\n )[\"input_ids\"]\n\n prompt_tokens = base_tok.tokenize_with_weights(\n text, return_word_ids=return_word_ids, **kwargs\n )\n prompt_pairs = prompt_tokens[0]\n\n prefix_pairs = [(t, 1.0) for t in prefix_ids]\n suffix_pairs = [(t, 1.0) for t in suffix_ids]\n\n combined = prefix_pairs + prompt_pairs + suffix_pairs\n tokens = {\"qwen25_7b\": [combined]}\n\n return tokens\n\n\nclass LongCatImageTEModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(\n device=device,\n dtype=dtype,\n name=\"qwen25_7b\",\n clip_model=Qwen25_7BVLIModel,\n model_options=model_options,\n )\n\n def encode_token_weights(self, token_weight_pairs, template_end=-1):\n out, pooled, extra = super().encode_token_weights(token_weight_pairs)\n tok_pairs = token_weight_pairs[\"qwen25_7b\"][0]\n count_im_start = 0\n if template_end == -1:\n for i, v in enumerate(tok_pairs):\n elem = v[0]\n if not torch.is_tensor(elem):\n if isinstance(elem, numbers.Integral):\n if elem == 151644 and count_im_start < 2:\n template_end = i\n count_im_start += 1\n\n if out.shape[1] > (template_end + 3):\n if tok_pairs[template_end + 1][0] == 872:\n if tok_pairs[template_end + 2][0] == 198:\n template_end += 3\n\n if template_end == -1:\n template_end = 0\n\n suffix_start = None\n for i in range(len(tok_pairs) - 1, -1, -1):\n elem = tok_pairs[i][0]\n if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral):\n if elem == 151645:\n suffix_start = i\n break\n\n out = out[:, template_end:]\n\n if \"attention_mask\" in extra:\n extra[\"attention_mask\"] = extra[\"attention_mask\"][:, template_end:]\n if extra[\"attention_mask\"].sum() == torch.numel(extra[\"attention_mask\"]):\n extra.pop(\"attention_mask\")\n\n if suffix_start is not None:\n suffix_len = len(tok_pairs) - suffix_start\n if suffix_len > 0 and out.shape[1] > suffix_len:\n out = out[:, :-suffix_len]\n if \"attention_mask\" in extra:\n extra[\"attention_mask\"] = extra[\"attention_mask\"][:, :-suffix_len]\n if extra[\"attention_mask\"].sum() == torch.numel(\n extra[\"attention_mask\"]\n ):\n extra.pop(\"attention_mask\")\n\n return out, pooled, extra\n\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class LongCatImageTEModel_(LongCatImageTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n\n return LongCatImageTEModel_\n" }, { "path": "comfy/text_encoders/lt.py", "content": "from comfy import sd1_clip\nimport os\nfrom transformers import T5TokenizerFast\nfrom .spiece_tokenizer import SPieceTokenizer\nimport comfy.text_encoders.genmo\nimport torch\nimport comfy.utils\nimport math\nimport itertools\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) #pad to 128?\n\n\nclass LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"t5xxl\", tokenizer=T5XXLTokenizer)\n\n\ndef ltxv_te(*args, **kwargs):\n return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)\n\n\nclass Gemma3_Tokenizer():\n def state_dict(self):\n return {\"spiece_model\": self.tokenizer.serialize_model()}\n\n def tokenize_with_weights(self, text, return_word_ids=False, image=None, llama_template=None, skip_template=True, **kwargs):\n self.llama_template = \"system\\nYou are a helpful assistant.\\nuser\\n{}\\nmodel\\n\"\n self.llama_template_images = \"system\\nYou are a helpful assistant.\\nuser\\n\\n{}\\n\\nmodel\\n\"\n\n if image is None:\n images = []\n else:\n samples = image.movedim(-1, 1)\n total = int(896 * 896)\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 = comfy.utils.common_upscale(samples, width, height, \"area\", \"disabled\").movedim(1, -1)\n images = [s[:, :, :, :3]]\n\n if text.startswith(''):\n skip_template = True\n\n if skip_template:\n llama_text = text\n else:\n if llama_template is None:\n if len(images) > 0:\n llama_text = self.llama_template_images.format(text)\n else:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n\n text_tokens = super().tokenize_with_weights(llama_text, return_word_ids)\n\n if len(images) > 0:\n embed_count = 0\n for r in text_tokens:\n for i, token in enumerate(r):\n if token[0] == 262144 and embed_count < len(images):\n r[i] = ({\"type\": \"image\", \"data\": images[embed_count]},) + token[1:]\n embed_count += 1\n return text_tokens\n\nclass Gemma3_12BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n special_tokens = {\"\": 262144, \"\": 106}\n super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1024, pad_left=True, disable_weights=True, tokenizer_args={\"add_bos\": True, \"add_eos\": False, \"special_tokens\": special_tokens}, tokenizer_data=tokenizer_data)\n\n\nclass LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"gemma3_12b\", tokenizer=Gemma3_12BTokenizer)\n\n\nclass Gemma3_12BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"all\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n self.dtypes = set()\n self.dtypes.add(dtype)\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"start\": 2, \"pad\": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):\n tokens_only = [[t[0] for t in b] for b in tokens]\n embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)\n comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)\n return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106]) # 106 is \n\nclass DualLinearProjection(torch.nn.Module):\n def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None):\n super().__init__()\n self.audio_aggregate_embed = operations.Linear(in_dim, out_dim_audio, bias=True, dtype=dtype, device=device)\n self.video_aggregate_embed = operations.Linear(in_dim, out_dim_video, bias=True, dtype=dtype, device=device)\n\n def forward(self, x):\n source_dim = x.shape[-1]\n x = x.movedim(1, -1)\n x = (x * torch.rsqrt(torch.mean(x**2, dim=2, keepdim=True) + 1e-6)).flatten(start_dim=2)\n\n video = self.video_aggregate_embed(x * math.sqrt(self.video_aggregate_embed.out_features / source_dim))\n audio = self.audio_aggregate_embed(x * math.sqrt(self.audio_aggregate_embed.out_features / source_dim))\n return torch.cat((video, audio), dim=-1)\n\nclass LTXAVTEModel(torch.nn.Module):\n def __init__(self, dtype_llama=None, device=\"cpu\", dtype=None, text_projection_type=\"single_linear\", model_options={}):\n super().__init__()\n self.dtypes = set()\n self.dtypes.add(dtype)\n self.compat_mode = False\n self.text_projection_type = text_projection_type\n\n self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer=\"all\", layer_idx=None)\n self.dtypes.add(dtype_llama)\n\n operations = self.gemma3_12b.operations # TODO\n\n if self.text_projection_type == \"single_linear\":\n self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)\n elif self.text_projection_type == \"dual_linear\":\n self.text_embedding_projection = DualLinearProjection(3840 * 49, 4096, 2048, dtype=dtype, device=device, operations=operations)\n\n\n def enable_compat_mode(self): # TODO: remove\n from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector\n operations = self.gemma3_12b.operations\n dtype = self.text_embedding_projection.weight.dtype\n device = self.text_embedding_projection.weight.device\n self.audio_embeddings_connector = Embeddings1DConnector(\n split_rope=True,\n double_precision_rope=True,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n\n self.video_embeddings_connector = Embeddings1DConnector(\n split_rope=True,\n double_precision_rope=True,\n dtype=dtype,\n device=device,\n operations=operations,\n )\n self.compat_mode = True\n\n def set_clip_options(self, options):\n self.execution_device = options.get(\"execution_device\", self.execution_device)\n self.gemma3_12b.set_clip_options(options)\n\n def reset_clip_options(self):\n self.gemma3_12b.reset_clip_options()\n self.execution_device = None\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs = token_weight_pairs[\"gemma3_12b\"]\n\n out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)\n out = out[:, :, -torch.sum(extra[\"attention_mask\"]).item():]\n out_device = out.device\n if comfy.model_management.should_use_bf16(self.execution_device):\n out = out.to(device=self.execution_device, dtype=torch.bfloat16)\n\n if self.text_projection_type == \"single_linear\":\n out = out.movedim(1, -1).to(self.execution_device)\n out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)\n out = out.reshape((out.shape[0], out.shape[1], -1))\n out = self.text_embedding_projection(out)\n\n if self.compat_mode:\n out_vid = self.video_embeddings_connector(out)[0]\n out_audio = self.audio_embeddings_connector(out)[0]\n out = torch.concat((out_vid, out_audio), dim=-1)\n extra = {}\n else:\n extra = {\"unprocessed_ltxav_embeds\": True}\n elif self.text_projection_type == \"dual_linear\":\n out = self.text_embedding_projection(out)\n extra = {\"unprocessed_ltxav_embeds\": True}\n\n return out.to(device=out_device, dtype=torch.float), pooled, extra\n\n def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):\n return self.gemma3_12b.generate(tokens[\"gemma3_12b\"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)\n\n def load_sd(self, sd):\n if \"model.layers.47.self_attn.q_norm.weight\" in sd:\n return self.gemma3_12b.load_sd(sd)\n else:\n sdo = comfy.utils.state_dict_prefix_replace(sd, {\"text_embedding_projection.aggregate_embed.weight\": \"text_embedding_projection.weight\", \"text_embedding_projection.\": \"text_embedding_projection.\"}, filter_keys=True)\n if len(sdo) == 0:\n sdo = sd\n\n missing_all = []\n unexpected_all = []\n\n for prefix, component in [(\"text_embedding_projection.\", self.text_embedding_projection)]:\n component_sd = {k.replace(prefix, \"\"): v for k, v in sdo.items() if k.startswith(prefix)}\n if component_sd:\n missing, unexpected = component.load_state_dict(component_sd, strict=False, assign=getattr(self, \"can_assign_sd\", False))\n missing_all.extend([f\"{prefix}{k}\" for k in missing])\n unexpected_all.extend([f\"{prefix}{k}\" for k in unexpected])\n\n if \"model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.2.attn1.to_q.bias\" not in sd: # TODO: remove\n ww = sd.get(\"model.diffusion_model.audio_embeddings_connector.transformer_1d_blocks.0.attn1.to_q.bias\", None)\n if ww is not None:\n if ww.shape[0] == 3840:\n self.enable_compat_mode()\n sdv = comfy.utils.state_dict_prefix_replace(sd, {\"model.diffusion_model.video_embeddings_connector.\": \"\"}, filter_keys=True)\n self.video_embeddings_connector.load_state_dict(sdv, strict=False, assign=getattr(self, \"can_assign_sd\", False))\n sda = comfy.utils.state_dict_prefix_replace(sd, {\"model.diffusion_model.audio_embeddings_connector.\": \"\"}, filter_keys=True)\n self.audio_embeddings_connector.load_state_dict(sda, strict=False, assign=getattr(self, \"can_assign_sd\", False))\n\n return (missing_all, unexpected_all)\n\n def memory_estimation_function(self, token_weight_pairs, device=None):\n constant = 6.0\n if comfy.model_management.should_use_bf16(device):\n constant /= 2.0\n\n token_weight_pairs = token_weight_pairs.get(\"gemma3_12b\", [])\n m = min([sum(1 for _ in itertools.takewhile(lambda x: x[0] == 0, sub)) for sub in token_weight_pairs])\n\n num_tokens = sum(map(lambda a: len(a), token_weight_pairs)) - m\n num_tokens = max(num_tokens, 642)\n return num_tokens * constant * 1024 * 1024\n\ndef ltxav_te(dtype_llama=None, llama_quantization_metadata=None, text_projection_type=\"single_linear\"):\n class LTXAVTEModel_(LTXAVTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, text_projection_type=text_projection_type, model_options=model_options)\n return LTXAVTEModel_\n\n\ndef sd_detect(state_dict_list, prefix=\"\"):\n for sd in state_dict_list:\n if \"{}text_embedding_projection.audio_aggregate_embed.bias\".format(prefix) in sd:\n return {\"text_projection_type\": \"dual_linear\"}\n if \"{}text_embedding_projection.weight\".format(prefix) in sd or \"{}text_embedding_projection.aggregate_embed.weight\".format(prefix) in sd:\n return {\"text_projection_type\": \"single_linear\"}\n return {}\n\n\ndef gemma3_te(dtype_llama=None, llama_quantization_metadata=None):\n class Gemma3_12BModel_(Gemma3_12BModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return Gemma3_12BModel_\n" }, { "path": "comfy/text_encoders/lumina2.py", "content": "from comfy import sd1_clip\nfrom .spiece_tokenizer import SPieceTokenizer\nimport comfy.text_encoders.llama\nfrom comfy.text_encoders.lt import Gemma3_Tokenizer\nimport comfy.utils\n\nclass Gemma2BTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n special_tokens = {\"\": 107}\n super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={\"add_bos\": True, \"add_eos\": False, \"special_tokens\": special_tokens}, tokenizer_data=tokenizer_data)\n\n def state_dict(self):\n return {\"spiece_model\": self.tokenizer.serialize_model()}\n\nclass Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n special_tokens = {\"\": 262144, \"\": 106}\n super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={\"add_bos\": True, \"add_eos\": False, \"special_tokens\": special_tokens}, disable_weights=True, tokenizer_data=tokenizer_data)\n\nclass LuminaTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"gemma2_2b\", tokenizer=Gemma2BTokenizer)\n\nclass NTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"gemma3_4b\", tokenizer=Gemma3_4BTokenizer)\n\nclass Gemma2_2BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"hidden\", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"start\": 2, \"pad\": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass Gemma3_4BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"hidden\", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"start\": 2, \"pad\": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\nclass Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"hidden\", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):\n llama_quantization_metadata = model_options.get(\"llama_quantization_metadata\", None)\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"start\": 2, \"pad\": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B_Vision, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n def process_tokens(self, tokens, device):\n embeds, _, _, embeds_info = super().process_tokens(tokens, device)\n comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)\n return embeds\n\nclass LuminaModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, name=\"gemma2_2b\", clip_model=Gemma2_2BModel):\n super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)\n\n\ndef te(dtype_llama=None, llama_quantization_metadata=None, model_type=\"gemma2_2b\"):\n if model_type == \"gemma2_2b\":\n model = Gemma2_2BModel\n elif model_type == \"gemma3_4b\":\n model = Gemma3_4BModel\n elif model_type == \"gemma3_4b_vision\":\n model = Gemma3_4B_Vision_Model\n\n class LuminaTEModel_(LuminaModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)\n return LuminaTEModel_\n" }, { "path": "comfy/text_encoders/mt5_config_xl.json", "content": "{\n \"d_ff\": 5120,\n \"d_kv\": 64,\n \"d_model\": 2048,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 1,\n \"dense_act_fn\": \"gelu_pytorch_tanh\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": true,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"mt5\",\n \"num_decoder_layers\": 24,\n \"num_heads\": 32,\n \"num_layers\": 24,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 250112\n}\n" }, { "path": "comfy/text_encoders/newbie.py", "content": "import torch\n\nimport comfy.model_management\nimport comfy.text_encoders.jina_clip_2\nimport comfy.text_encoders.lumina2\n\nclass NewBieTokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.gemma = comfy.text_encoders.lumina2.Gemma3_4BTokenizer(embedding_directory=embedding_directory, tokenizer_data={\"spiece_model\": tokenizer_data[\"gemma_spiece_model\"]})\n self.jina = comfy.text_encoders.jina_clip_2.JinaClip2Tokenizer(embedding_directory=embedding_directory, tokenizer_data={\"spiece_model\": tokenizer_data[\"jina_spiece_model\"]})\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"gemma\"] = self.gemma.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"jina\"] = self.jina.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n raise NotImplementedError\n\n def state_dict(self):\n return {}\n\nclass NewBieTEModel(torch.nn.Module):\n def __init__(self, dtype_gemma=None, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n dtype_gemma = comfy.model_management.pick_weight_dtype(dtype_gemma, dtype, device)\n self.gemma = comfy.text_encoders.lumina2.Gemma3_4BModel(device=device, dtype=dtype_gemma, model_options=model_options)\n self.jina = comfy.text_encoders.jina_clip_2.JinaClip2TextModel(device=device, dtype=dtype, model_options=model_options)\n self.dtypes = {dtype, dtype_gemma}\n\n def set_clip_options(self, options):\n self.gemma.set_clip_options(options)\n self.jina.set_clip_options(options)\n\n def reset_clip_options(self):\n self.gemma.reset_clip_options()\n self.jina.reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_gemma = token_weight_pairs[\"gemma\"]\n token_weight_pairs_jina = token_weight_pairs[\"jina\"]\n\n gemma_out, gemma_pooled, gemma_extra = self.gemma.encode_token_weights(token_weight_pairs_gemma)\n jina_out, jina_pooled, jina_extra = self.jina.encode_token_weights(token_weight_pairs_jina)\n\n return gemma_out, jina_pooled, gemma_extra\n\n def load_sd(self, sd):\n if \"model.layers.0.self_attn.q_norm.weight\" in sd:\n return self.gemma.load_sd(sd)\n else:\n return self.jina.load_sd(sd)\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class NewBieTEModel_(NewBieTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"llama_quantization_metadata\"] = llama_quantization_metadata\n super().__init__(dtype_gemma=dtype_llama, device=device, dtype=dtype, model_options=model_options)\n return NewBieTEModel_\n" }, { "path": "comfy/text_encoders/omnigen2.py", "content": "from transformers import Qwen2Tokenizer\nfrom comfy import sd1_clip\nimport comfy.text_encoders.llama\nimport os\n\n\nclass Qwen25_3BTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"qwen25_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)\n\n\nclass Omnigen2Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"qwen25_3b\", tokenizer=Qwen25_3BTokenizer)\n self.llama_template = '<|im_start|>system\\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\\n<|im_start|>user\\n{}<|im_end|>\\n'\n\n def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs):\n if llama_template is None:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs)\n\nclass Qwen25_3BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\nclass Omnigen2Model(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"qwen25_3b\", clip_model=Qwen25_3BModel, model_options=model_options)\n\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class Omnigen2TEModel_(Omnigen2Model):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return Omnigen2TEModel_\n" }, { "path": "comfy/text_encoders/ovis.py", "content": "from transformers import Qwen2Tokenizer\nimport comfy.text_encoders.llama\nfrom comfy import sd1_clip\nimport os\nimport torch\nimport numbers\n\nclass Qwen3Tokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"qwen25_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen3_2b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=284, pad_token=151643, tokenizer_data=tokenizer_data)\n\n\nclass OvisTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"qwen3_2b\", tokenizer=Qwen3Tokenizer)\n self.llama_template = \"<|im_start|>user\\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background: {}<|im_end|>\\n<|im_start|>assistant\\n\\n\\n\\n\\n\"\n\n def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):\n if llama_template is None:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n\n tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)\n return tokens\n\nclass Ovis25_2BModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)\n\n\nclass OvisTEModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"qwen3_2b\", clip_model=Ovis25_2BModel, model_options=model_options)\n\n def encode_token_weights(self, token_weight_pairs, template_end=-1):\n out, pooled = super().encode_token_weights(token_weight_pairs)\n tok_pairs = token_weight_pairs[\"qwen3_2b\"][0]\n count_im_start = 0\n if template_end == -1:\n for i, v in enumerate(tok_pairs):\n elem = v[0]\n if not torch.is_tensor(elem):\n if isinstance(elem, numbers.Integral):\n if elem == 4004 and count_im_start < 1:\n template_end = i\n count_im_start += 1\n\n if out.shape[1] > (template_end + 1):\n if tok_pairs[template_end + 1][0] == 25:\n template_end += 1\n\n out = out[:, template_end:]\n return out, pooled, {}\n\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class OvisTEModel_(OvisTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if dtype_llama is not None:\n dtype = dtype_llama\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return OvisTEModel_\n" }, { "path": "comfy/text_encoders/pixart_t5.py", "content": "import os\n\nfrom comfy import sd1_clip\nimport comfy.text_encoders.t5\nimport comfy.text_encoders.sd3_clip\nfrom comfy.sd1_clip import gen_empty_tokens\n\nfrom transformers import T5TokenizerFast\n\nclass T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def gen_empty_tokens(self, special_tokens, *args, **kwargs):\n # PixArt expects the negative to be all pad tokens\n special_tokens = special_tokens.copy()\n special_tokens.pop(\"end\")\n return gen_empty_tokens(special_tokens, *args, **kwargs)\n\nclass PixArtT5XXL(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"t5xxl\", clip_model=T5XXLModel, model_options=model_options)\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding\n\nclass PixArtTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"t5xxl\", tokenizer=T5XXLTokenizer)\n\ndef pixart_te(dtype_t5=None, t5_quantization_metadata=None):\n class PixArtTEModel_(PixArtT5XXL):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"t5xxl_quantization_metadata\"] = t5_quantization_metadata\n if dtype_t5 is not None:\n dtype = dtype_t5\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return PixArtTEModel_\n" }, { "path": "comfy/text_encoders/qwen25_tokenizer/merges.txt", "content": "#version: 0.2\nĠ Ġ\nĠĠ ĠĠ\ni n\nĠ t\nĠĠĠĠ ĠĠĠĠ\ne r\nĠĠ Ġ\no n\nĠ a\nr e\na t\ns t\ne n\no r\nĠt h\nĊ Ċ\nĠ c\nl e\nĠ s\ni t\na n\na r\na l\nĠth e\n; Ċ\nĠ p\nĠ f\no u\nĠ =\ni s\nĠĠĠĠ ĠĠĠ\nin g\ne s\nĠ w\ni on\ne d\ni c\nĠ b\nĠ d\ne t\nĠ m\nĠ o\nĉ ĉ\nr o\na s\ne l\nc t\nn d\nĠ in\nĠ h\nen t\ni d\nĠ n\na m\nĠĠĠĠĠĠĠĠ ĠĠĠ\nĠt o\nĠ re\n- -\nĠ {\nĠo f\no m\n) ;Ċ\ni m\nč Ċ\nĠ (\ni l\n/ /\nĠa nd\nu r\ns e\nĠ l\ne x\nĠ S\na d\nĠ \"\nc h\nu t\ni f\n* *\nĠ }\ne m\no l\nĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\nt h\n) Ċ\nĠ{ Ċ\nĠ g\ni g\ni v\n, Ċ\nc e\no d\nĠ v\nat e\nĠ T\na g\na y\nĠ *\no t\nu s\nĠ C\nĠ st\nĠ I\nu n\nu l\nu e\nĠ A\no w\nĠ '\ne w\nĠ <\nat ion\n( )\nĠf or\na b\nor t\nu m\nam e\nĠ is\np e\nt r\nc k\nâ Ģ\nĠ y\ni st\n-- --\n. ĊĊ\nh e\nĠ e\nl o\nĠ M\nĠb e\ner s\nĠ on\nĠc on\na p\nu b\nĠ P\nĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\nas s\nin t\n> Ċ\nl y\nur n\nĠ $\n; ĊĊ\na v\np ort\ni r\n- >\nn t\nct ion\nen d\nĠd e\nit h\nou t\nt urn\nou r\nĠĠĠĠ Ġ\nl ic\nre s\np t\n= =\nĠth is\nĠw h\nĠ if\nĠ D\nv er\nag e\nĠ B\nh t\nex t\n= \"\nĠth at\n** **\nĠ R\nĠ it\nes s\nĠ F\nĠ r\no s\nan d\nĠa s\ne ct\nk e\nro m\nĠ //\nc on\nĠ L\n( \"\nq u\nl ass\nĠw ith\ni z\nd e\nĠ N\nĠa l\no p\nu p\ng et\nĠ} Ċ\ni le\nĠa n\nat a\no re\nr i\nĠp ro\n; čĊ\nĉĉ ĉĉ\nt er\na in\nĠ W\nĠ E\nĠc om\nĠre turn\nar t\nĠ H\na ck\nim port\nub lic\nĠ or\ne st\nm ent\nĠ G\nab le\nĠ -\nin e\nil l\nin d\ner e\n: :\nit y\nĠ +\nĠt r\nel f\nig ht\n( '\nor m\nul t\nst r\n. .\n\" ,\nĠy ou\ny pe\np l\nĠn ew\nĠ j\nĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\nĠf rom\nĠ ex\nĠ O\nl d\nĠ [\no c\n: Ċ\nĠs e\nĠ le\n---- ----\n. s\n{ Ċ\n' ,\nan t\nĠa t\nas e\n. c\nĠc h\n< /\nav e\nan g\nĠa re\nĠin t\nâĢ Ļ\n_ t\ner t\ni al\na ct\n} Ċ\niv e\nod e\no st\nĠc lass\nĠn ot\no g\nor d\nal ue\nal l\nf f\n( );Ċ\non t\nim e\na re\nĠ U\nĠp r\nĠ :\ni es\niz e\nu re\nĠb y\ni re\nĠ} ĊĊ\n. p\nĠs h\nic e\na st\npt ion\ntr ing\no k\n_ _\nc l\n# #\nĠh e\nar d\n) .\nĠ @\ni ew\nĉĉ ĉ\nĠw as\ni p\nth is\nĠ u\nĠT he\nid e\na ce\ni b\na c\nr ou\nĠw e\nj ect\nĠp ublic\na k\nv e\nat h\no id\nĠ= >\nu st\nq ue\nĠre s\n) )\n' s\nĠ k\nan s\ny st\nun ction\n**** ****\nĠ i\nĠ us\np p\non e\na il\n== ==\nn ame\nĠst r\nĠ /\nĠ &\na ch\nd iv\nyst em\nel l\nĠh ave\ner r\nou ld\nul l\np on\nĠ J\n_ p\nĠ= =\nig n\nS t\n. Ċ\nĠp l\n) ;ĊĊ\nf orm\np ut\nou nt\n} ĊĊ\nd d\nit e\nĠg et\nr r\nom e\nĠ âĢ\nar am\nc c\nĠ* /\nE R\nI n\nle s\n_ s\non g\ni e\nĠc an\nĠ V\ner v\np r\nĠ un\nro w\nb er\nĠd o\nl l\nĠ el\nĠs elf\nat ed\nar y\nĠ .\n' ]\nu d\nĠ en\nĠT h\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ\nt e\n_ c\nu ct\nĠa b\nor k\n. get\nĠ #\na w\nres s\no b\nN ame\nap p\n[ '\nĠal l\nor y\nit ion\nan ce\ne ar\nĠcon t\nv ent\ni a\nĠw ill\nI N\nĠĠĠĠĠĠĠĠ Ġ\nre turn\nĠ< /\nd ata\n) ĊĊ\nR e\np le\nil d\nth er\nĠy our\n\" Ċ\n( $\nĠ out\n) ,\nĠh as\nS tring\ns o\nĠ up\na x\nĠde f\nĠb o\ng e\nal se\nO N\np er\nic h\nĠb ut\nĠ Ċ\nĠ _\n_ m\nad d\nque st\nod el\ns elf\ner y\nf t\nen s\n// //\na ke\n. C\nĠg o\nĠf unction\nĠ K\niv ate\nĠ im\nĠcon st\n. t\nĠ*/ Ċ\n) ;čĊ\nĠv oid\nĠs et\nĠS ystem\nc ri\n( )Ċ\nl i\nĉ if\n. m\nal ly\ns et\ne p\nâĢĻ s\nb o\nde f\n' ,Ċ\nĠm e\nĠ !\nat ch\n\" >\n\" ,Ċ\ne c\nĠI n\np h\nĠ |\n_ f\nĠv ar\nen ce\nI d\nre e\nin k\nle ct\nu g\net h\nĠel se\n-------- --------\ncon t\nĠs o\nat ic\nĠl o\np ro\nt on\ns s\now n\nab el\no int\nou s\nel d\nS T\nT he\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nR E\n\" :\nol or\nt p\ne g\nke y\nu de\nĠS t\nou nd\nĠa r\n\" );Ċ\nen er\ns er\nb ject\ness age\nf er\nĠm ore\nation s\nent s\nĠh is\nĠthe y\n. S\nĠ Y\nu se\nn e\nis h\nol d\n_ d\ni o\ni eld\nĠp er\nC ont\ning s\n## ##\nĠd ata\nĠs a\ne f\nf o\nĠon e\nen g\nĠd is\nA T\nĠn ame\nĠtr ue\nv al\nle d\n. f\nĠn e\nĠ end\n. T\nc re\nar k\nlo g\nE x\nerr or\n_ id\nur re\nang e\nĠn ull\nrr ay\nĠm y\np an\nic t\nat or\nV iew\nL ist\nĉ return\nâĢ Ŀ\nĠp re\nĠ x\ncl ude\nar g\no v\n. h\nĠ >\nĠthe ir\n' )\nir st\nic k\ng h\nL E\nO R\nĠpr ivate\nt em\nčĊ čĊ\nus er\nĠ )\nc om\n. A\n\" ;Ċ\nĠ id\nre ad\nĠwh o\n_ b\n\" >Ċ\nĠt ime\nĠm an\nr y\n==== ====\nrou p\nro p\np ublic\nv el\num ber\nb le\nĠwh ich\n******** ********\nĠan y\nĠf alse\nw e\nĠv alue\nĠl i\n\" )\nnd er\ng r\nĠn o\np aram\nf ig\n.c om\nĠa pp\n_ l\nion s\n. D\nĠC h\nĠab out\nĠa dd\nĠs u\nĠstr ing\nI D\nĠo ver\nstr ing\n. l\nour ce\n_ C\n] Ċ\nĠ qu\nĠS tring\nc a\nS E\nĠ ro\ns h\nu al\nT ype\ns on\nn ew\ner n\nĠa g\nA R\n] ;Ċ\n] .\nĠ ?\nic al\nĠd es\nut h\ni x\nay s\nĠt ype\n' t\na ult\nĠin ter\nv ar\n. b\nĠp art\n. d\nurre nt\nI T\nE N\nen c\n( f\nr a\nv alue\nch o\nut ton\no se\nĠ! =\nat er\nà ©\nre ate\nol l\np os\ny le\nn g\nA L\nus ing\nam es\nĠ{ čĊ\nat es\nel y\nĠw ork\nĠ em\nin al\nĠs p\nĠwh en\n.s et\nĠĠĠĠ ĠĠ\n) :Ċ\nt o\nqu ire\nind ow\nle ment\npe ct\nas h\n[ i\nĠu se\n. F\npe c\nĠa d\no ve\nce ption\neng th\nin clude\nad er\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\nat us\nT h\nit le\nr it\nv oid\n() .\n( Ċ\nĠof f\nĠo ther\nĠ& &\n' ;Ċ\nm s\nĠbe en\nĠt e\nm l\nc o\nn c\nerv ice\nĠ %\n** Ċ\nan n\nad e\nĊĊ ĊĊ\nlo ck\ncon st\npon se\nĠs up\n+ +\nd ate\nĠa cc\nĠh ad\nĠb u\nĠR e\nĠw ere\nĠf ile\nĠw ould\nĠâĢ ľ\nv en\nis s\nĠ our\nc lass\nr aw\nĠy ear\nD ata\nĠv al\nĠs ome\nf ter\ny s\nĠ// /\nrou nd\nv iew\nĠp e\nĠth ere\nĠsa id\nd u\no f\nl ine\n/ *\nd uct\nĠh er\nĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\nR es\nĠc o\nĠcom m\nis e\nm in\nĠĠĠĠ Ċ\n# include\neth od\n. P\nut e\nĠas s\nI nt\nas k\nlo c\nĠli ke\nod y\nĠle t\nlo ad\nĠa m\nro l\nĠg r\ny p\nĠal so\nĠI t\nur l\nif ic\nor s\n_ P\n_ n\nig h\nĠth an\nC om\nA N\nU L\nat ing\nĠTh is\nre f\n_ S\nĠst atic\nro ll\nĠj ust\nĠres ult\ni an\nid th\nĠthe m\n) );Ċ\nd er\nre ak\nC on\n: //\nu le\n.. .\nar ch\nem ent\nĠ< <\nus h\nen se\nar r\nĠint o\nc ess\nam p\ni ed\num ent\nĠ \\\n] ,\nw o\nal s\nĠwh at\nan c\nV alue\n= '\nol um\nĠp os\nag es\nay er\nĠs c\nu es\n\" )Ċ\n_ T\nĠl ist\n( s\nĠc ase\nC h\nĉĉĉĉ ĉ\n//// ////\npon ent\nĠ z\nĠk n\nle t\nD E\nre d\nĠf e\nĠ} ,Ċ\nĠ ,\n( t\nĠf irst\n' );Ċ\nw ord\nĠ import\nĠa ct\nĠch ar\nC T\nĠT r\nop le\n= {\nĉ f\ni ent\nc ent\n. j\nle ction\n) )Ċ\nĠon ly\nĠpr int\nm er\n. W\no ck\nĠ --\nT ext\nĠo p\nan k\nĠit s\nĠb ack\n[ \"\nĠne ed\nĠc l\nĠs ub\nĠl a\n( (\n. \"\nO bject\nĠst art\nf ile\n( self\nn er\ne y\nĠus er\nĠ ent\nĠC om\nit s\nĠC on\nou ble\now er\nit em\nver y\nĠW e\nlic k\nĠ Q\nph p\nt tp\n' :\nic s\nĠu nder\nĠ* Ċ\n. L\n) ;\nic es\nĠre g\n) čĊ\nĉ public\nS S\nĠth en\nre at\ni ous\n. G\ne k\nire ct\nhe ck\ncri pt\nn ing\nĠU n\nĠm ay\nĠW h\nB o\nI tem\nstr uct\n. st\nre am\nib le\nlo at\nĠor g\nu nd\ns um\n_ in\n.. /\n_ M\nĠh ow\nr ite\n' Ċ\nT o\nw w\nĠpe ople\nind ex\n. n\nht tp\n( m\nect or\nĠin d\nĠj av\n] ,Ċ\nĠH e\n_ st\nf ul\no le\n) {Ċ\nĠsh ould\nop y\nel p\ni er\n_ name\ners on\nI ON\not e\nĠt est\nĠb et\nrr or\nul ar\nã Ģ\nĠ Ð\nb s\nt ing\nĠm ake\nT r\nĠa fter\nar get\nR O\nolum n\nr c\n_ re\ndef ine\nĠr ight\nr ight\nd ay\nĠl ong\n[ ]\n( p\nt d\ncon d\nĠP ro\nĠre m\nption s\nv id\n. g\nĠ ext\nĠ __\n' )Ċ\np ace\nm p\nĠm in\nst ance\na ir\na ction\nw h\nt ype\nut il\na it\n< ?\nI C\nt ext\nĠp h\nĠf l\n. M\ncc ess\nb r\nf ore\ners ion\n) ,Ċ\n. re\nate g\nĠl oc\nin s\n- s\ntr ib\nĠI nt\nĠa rray\n, \"\nP ro\n( c\ness ion\n> ĊĊ\nĠs he\n\" ]\nap h\nĠex p\nert y\nĠS e\nĠp ar\nun c\nE T\nĠre ad\npr int\nĠre l\nĠfor m\nĠd r\nEx ception\nin put\nĠtr ans\n#### ####\nord er\nB y\nĠa w\nit ies\nu ff\npl ay\n. add\nĠâĢ ĵ\nĠw ant\nĠcom p\nment s\nĠ| |\na z\nb e\nĠn umber\nĠre quire\nĠE x\nĠc ol\nĠ key\nem ber\nĠt wo\nĠs ize\nĠwh ere\nU T\nres ult\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nou gh\nor ld\no od\nu ch\nat ive\ng er\nare nt\nĠ/ *\nĠar g\nĠwh ile\n( this\nĠre c\nĠd if\nSt ate\nĠs pec\nr ide\n_ F\nĠlo ok\nA M\nil ity\net er\nâĢĻ t\nĊĊ Ċ\nay out\n---------------- ----------------\nag er\nĠc ould\nĠb r\nend s\nu res\nĠkn ow\net s\nĠI f\nĠS h\n. w\nb ack\nĠs er\nĠ+ =\nĠf r\n() );Ċ\nĠh and\nI nd\nUL L\nI m\n() ;ĊĊ\nĠm ost\nĠtr y\nĠn ow\nrou gh\n> čĊ\nack age\nĠh im\n. _\nif y\nĠb reak\nĠ );Ċ\nre n\n# define\nit t\nĠa p\nĉ c\n( n\nĠY ou\n: ĊĊ\n- m\nĠe very\nust om\nli ent\noc ument\ncri ption\nE rror\n- b\nÐ ¾\n] [\ntr ans\nĠp oint\nĠst d\nĠf il\nT ime\nĠm od\nĠ ->\nĠ error\na h\nĠt ext\nroll er\nlo se\nq l\nĠp ol\n> <\n. B\n- c\nĠop en\nĠe st\nĠĠĠĠĠĠĠĠ Ċ\nĠn ext\nI M\nÑ Ĥ\nO T\nà ³\nĠf ollow\ncont ent\nĠĠĠĠĠĠĠĠ ĠĠĠĠ\nĠin clud\nH E\nĠR es\nĠh ref\nÐ ¸\nĠc ar\nyp es\nim age\nU n\nĠbo ol\nA D\nĠg ame\n.F orm\nrow s\n* /\nvel op\n.D rawing\nĠp ath\nis ion\nĠe ach\nĠP l\n_t ype\nP ath\nne ction\nĠa v\n' ).\nĠsup port\nEN T\nre m\n\" ).\nĠo wn\nĠc or\nc ount\nm iss\nu ally\nĠm em\nst d\ni ence\nse arch\n\" ĊĊ\nF orm\nĠs ex\nen ame\nĠs ign\nĠ et\nĠĠĠĠĠĠĠĠ ĠĠ\n', '\nĠA pp\nĠth ose\no ff\nĠ err\nĠs ystem\nĠbe st\nc ode\nĠs ame\nĠd i\nus s\nĠc reate\nath er\nA rray\n. in\nf e\nS ervice\nU N\nat s\nĠ Z\nal th\nĠm ade\ntr ue\nA B\nĠm ark\nr id\nif ied\n, čĊ\ny n\np ress\nĠg roup\nĠf in\nĠL icense\nF ield\neg er\nĠw orld\nin ess\nt y\nĠpro cess\n( b\nĠc re\nar n\niv es\nĠm ain\nide o\n_ g\nA G\nval id\nim g\nP I\nĠc olor\nĠre port\nĠt ake\nri b\nO M\nĠd ay\nRe quest\nĠs k\nb ers\nĉ s\n.A dd\no ot\nIm age\nĠcom ple\nol lection\nĠto p\nĠf ree\nA S\nD e\nĠO n\nI G\net a\nD ate\nĠa ction\nO ver\nit or\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nn ot\nĠind ex\nh er\nic on\nO n\n;čĊ čĊ\niv ity\nm and\n.W indows\nO L\nĠre al\nĠm ax\nl and\n.. ..\nr aph\nĠbu ild\nle g\nass word\n? ĊĊ\nâĢ ¦\no ok\nu ck\nĠm essage\nt est\niv ers\nĠin put\nĠar t\nĠbet ween\nG et\nent er\ng round\nen e\nà ¡\n.l ength\nN ode\n( i\nC lass\nf or\nĠâĢ Ķ\nt en\no in\nĠ ke\nu i\nĠI N\nĠt able\ns ub\nĠL e\nĠhe ad\nĠm ust\n//////// ////////\n. util\nCont ext\nĠor der\nĠm ov\no ver\nĠcont in\nĠs ay\nst atic\n.T ext\nĠclass Name\npan y\nĠt er\nhe ad\nr g\nĠpro duct\nTh is\n. âĢĿ\nĠB ut\nlo y\nĠd ouble\ns g\nĠpl ace\n. x\nm essage\nĠin formation\npr ivate\nĠo per\nc ed\nd b\n\"> \nater ial\nile d\nĠp ut\nQ u\nÑ Ģ\nun g\nm ap\nĉĉĉĉ ĉĉĉĉ\nĠle vel\nCom ponent\nbo ok\ncre en\n_ RE\nĠcon fig\nã ģ\nO r\n. data\nĠd ocument\n\", \"\ntrib ute\nu x\nL og\nfer ence\np ost\n_ e\nĠloc al\nand om\nass ert\nV al\nlect ed\nin a\natab ase\nA dd\nĠcont ent\n.p rint\ns igned\nr ic\n.\" ĊĊ\nĠf a\n! ĊĊ\n- f\niv ed\nĠ quest\n. ex\nĠf loat\nĠde velop\nо Ð\nM ap\nad ing\nĠpos s\nU E\nn amespace\n_ O\nĉ b\n.G et\n> (\nj son\netail s\nĠto o\nĠext ends\nĠN one\nĠf ore\n( String\nform at\nĠg reat\nint er\nca le\nÑ ģ\nr on\niv ing\nE nt\nenc y\nx t\no y\nĠmon th\nĠh app\nĠsup er\nb ar\ndef ault\n_ de\nord s\nl n\n( {Ċ\nĠI nd\nas es\nĠt itle\nĠcont ext\no h\n- p\nE m\nĠm et\nT est\nĠl ife\n_ v\nĠU S\nU I\noc ation\nm d\nĠ[ Ċ\nĠ ]\ns w\nĠin cre\ns cript\nent ial\nw ays\n. de\nĠs rc\nĠc atch\nĠA meric\n// Ċ\nĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\nĠp ay\npl it\nâĢ Ķ\nĠc oun\nob j\n.ph p\nĠch ange\neth ing\n' re\nast er\nlo s\nl ation\nĠĠ Ċ\nL e\nà ¤\n( {\nread y\nĠN o\nĠpos ition\nĠo ld\nĠbo ok\nable d\nb ug\nH and\n} ;ĊĊ\nis play\nav ing\nĠgo ver\nĠv ersion\nS ystem\nn ect\nres ponse\nSt yle\nU p\nang u\nĠth ree\nin it\ner o\nĠl aw\nend if\nĠb ase\nem ail\n( l\n_ V\nĠcon f\nAT E\nĠd uring\nt es\nĠcon sole\nĠP r\nĠs pe\nv es\np ath\nial og\nd ition\n_t o\nard s\nĠagain st\net work\nĠP h\n_ L\nc ur\nim it\nW ith\nĠp ower\ni um\n' ;ĊĊ\nĠw om\nle ft\nour ces\nat ri\nĠI m\nĠM an\nor th\n$ {\nqu als\nes e\n_s ize\nĠis s\not al\n- g\ni que\nr ame\nĠw idth\ner g\n) (\nitt le\nT R\nĠThe y\nenc es\nr l\non s\nĠl abel\n. y\n- t\nup date\nan el\ns c\n.t o\nĠpro ject\nà ¼\nĠe lement\nĠsu ccess\nĉĉ Ċ\n.s h\nr am\nch ed\n() )Ċ\nĠ( Ċ\nĠd ate\nĠto t\n_ ST\nA ll\nific ation\nĉ var\nĠt ri\nch em\nm y\nĠb ig\nĠA d\nĠA t\not s\nn um\nA ct\nĠm ap\ner a\nco pe\n. $\n, âĢĿ\nĠp op\nĠf ew\nĠl en\nu id\net ers\nu les\nà Ń\ns ource\nhttp s\nĠd em\nĠe ar\n######## ########\nĠm atch\nor ies\nac es\nĠC l\nĠn ode\nir c\nloc al\nun ity\n} ;Ċ\nĠan other\n< <\nog le\nĠs it\new ork\nT E\n. I\nN S\nolog y\nou ght\n.C ont\n> >\nĠc are\nst ate\nĉ private\nĠe ffect\n++ )\n_f ile\nend ing\nL ine\nF or\ni or\nĠS c\nĠf un\n.S ize\nĉ else\n] )\nst art\nv ious\nĠ} ,\nour s\nĠle g\nĠs ervice\nĠs ince\nir on\nL abel\nĠn on\nĠl os\nict ion\nĠf ull\nact er\nbo ard\ng ress\nĠt urn\nith er\n.s ize\nĠb ody\nres h\net urn\n( _\ny les\norm al\np i\nĠsom ething\n! --\nu int\nĠpro du\nĠst and\nĠpro ble\nĠav ailable\nm t\nĠB l\nĠ ...\nĠb lock\nIn put\nĠke ep\nC ount\nop en\nĠ[ '\nĠth row\nuild er\nA ction\nĠth ings\nTr ue\nĠ url\nĠB o\nprint f\nĠre d\nj s\n.c reate\nĠO r\nSt atus\nIn stance\nĠcont rol\nĠcom e\nĠc ustom\nloc ation\nm odel\nĠ čĊ\nĠs ource\nĠe as\n. out\n] ĊĊ\none y\nĠaw ait\nĠpart ic\nA P\nub lish\nod es\n_p ro\np ly\nrit er\nĠpro v\nĠm ill\nH T\n] )Ċ\nĠch ang\nĠas k\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\nĠout put\nĠem ail\n.p ush\nĠ} čĊčĊ\nin ation\natri x\nT able\nu ccess\n] );Ċ\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠdis c\n( [\nĠb usiness\nhe ight\n. html\nt a\nf ield\nĠrequire d\n_ R\nĠgover n\n} čĊčĊ\nle x\n. ,\nĠS et\nur ch\n// /\nt s\na f\nĠm ight\nist ory\nS tr\nĠne ver\nRes ponse\nar se\nad a\nĠH ow\nĠ* )\nĠ ;\nĠh ard\nA d\nĠinter n\nus ed\n( data\nm od\nann el\nĠn p\nug g\nĠ/ >Ċ\nĠcal led\nb ody\nĠch o\n( r\n_s et\nir d\nĠ> =\nĠ} ;Ċ\nĠo ptions\nĠG ener\nĠhe ight\nP oint\nY ou\net y\nC lick\nĠsm all\nĠ ide\nĠacc ess\nangu age\nĠprot ected\nĠj ob\nĠTh ere\nD ef\nĠadd ress\nĠu int\nN ot\no o\nap s\n< div\nain ed\nat ur\nĠs um\n- w\nĠD ate\nĠl ittle\nĠf ri\nY PE\nĠp ort\ne h\npr ing\n_p ath\nĠst atus\na im\nbo ol\nĠap pe\nĠo s\n. name\nens ion\n_ G\nĠup date\nCon fig\na ff\nER R\nĠ< =\nat ely\n# if\nu ction\nĠT e\nĠl ink\nĠU ser\n.f ind\n. org\nm e\nĠg iven\nO ut\n# endif\nĠbet ter\nP age\nĠfe el\nen n\nM L\nĠal ready\nĠinclud ing\no ogle\nr u\nic ally\npro p\nle an\nout er\nĠal ways\nord ing\nI f\nor age\nĠp arent\nv is\nĉĉĉĉ ĉĉĉ\nĠg ot\nst and\nĠle ss\n/ s\nĠA ss\nap t\nire d\nĠA dd\nĠacc ount\np loy\nĠd er\nres ent\nĠl ot\nĠval id\nĉ d\nĠb it\npon ents\nĠfollow ing\n_ ex\nS ON\nĠs ure\noc ial\nĠp rom\nert ies\nhe ader\n.p ro\nĠbo olean\nĠse arch\nk en\nĠor ig\nĠ er\nE d\nE M\na ut\nl ing\nal ity\nBy Id\nb ed\nĉc ase\neth er\npos it\nĠinv est\nĠO R\nĠs ays\nmiss ion\nAM E\nĠtem p\no ad\nĠre st\nin fo\nĠinter est\nA rg\nĠper form\npon s\nĠV iew\nĠv er\nl ib\n( const\nU til\nList ener\nar ge\nĠm ult\nĠd ie\nĠs ite\n../ ../\nE L\nĠval ues\nĠ} )Ċ\np en\nN o\nic ro\nĠbe h\nĠ' ./\nac y\nre c\n() ->\nĉ ĠĠĠ\n\" ))\nCont ent\n_ W\nple ment\nĠw on\nĠv ideo\nad i\np oint\n% %\nĠg l\nerv ed\nv iron\nI F\nut ed\nã ĥ\n' m\nĠc ert\nĠpro f\nĠc ell\nar i\nĠpl ayer\na is\nĠc ost\nĠh um\n( R\nĠoff ic\nk s\n.t ext\nat ures\nĠtot al\nĠ*/ ĊĊ\no pe\nĠst at\nU M\nĠlo ad\night s\nĠc lear\nu ro\nĠte chn\nup port\nI R\nĠ row\nĠse em\nĠ q\nĠsh ort\nĠN ot\nip p\nG roup\nse ction\nm ax\nir l\nĠover ride\nĠcom pany\nĠd one\n\" );čĊ\nĠg re\n. Re\nĠbel ie\nr ist\nĠhe alth\nAN T\n() ĊĊ\nĠB e\n. value\nĠG r\nott om\nĠarg s\nP T\nst atus\nf unc\num ents\n- h\nN umber\n: čĊ\nĠL og\ner ver\nĠ) ,Ċ\nam ent\nĠob j\nin c\nĠchild ren\nic y\nI Z\nand s\nab ly\nĠdist rib\nĠc ur\ner ial\nĠd ays\nre ated\nre ct\n- l\nir m\nidd en\nom b\nĠin itial\n.j s\nĠ â\nQu ery\nĠon line\nim al\n. con\na u\nU rl\ncont rol\nire ction\nĠin stance\nOR T\nĠF r\nwh ere\nĠjav ax\nĠorg an\nap ter\nĠre ason\no ptions\nĠM ar\n( a\nĠwith in\n.âĢĿ ĊĊ\nO DE\n_ DE\nad min\nend ed\nĠdes ign\nĠD ata\nun e\nĠF ile\nro ot\nĠc ent\nĠa rr\n_ add\nl en\np age\n, '\n_ str\nĠb ro\nab ility\nou th\n/ c\np ose\nirt ual\near ch\n_ url\narg in\nH ttp\nĠs chool\nav a\nĠcons ider\n.l abel\nĠA rray\nwe b\no pt\n.print ln\nul ation\nĠf unc\nP L\nĠ\" \\\nĠT ext\nact ory\n(f unction\nn ull\nĠen g\nd own\nĠin clude\nĠE n\nĠD r\nĠd b\n! !\ns ide\nĠin it\nquire d\nĠS he\nC olumn\nre act\nĠan n\nĠst op\nĠl ater\nĠTh at\nent ion\nd f\nU G\nI LE\nĠc lient\nra ft\nff er\nPO ST\nel per\nĠlo ve\nqu ote\nou d\nĠj son\nĠab le\nĠm en\nA X\nĠC opyright\nà ¶\nav ig\nre q\nC lient\n} );Ċ\n.C om\ner c\nil t\npec ial\n_c om\nro om\n. Name\nĠg ive\nam b\ni ke\nĠcon dition\ncl ient\nator s\n: \"\nĠc opy\nut ure\nivers ity\nern al\n{ {\nĠC an\nou nc\nd o\nĠo cc\nĠapp ro\nth ers\nz e\nĠe ither\nĠF l\nĠimport ant\nĠle ad\nat tr\nAR T\nE qual\nĠd a\net ch\nent ity\nĠfam ily\nadd ing\nĠo ption\nĠex ist\nic a\nĠO bject\n' ve\nv ers\nition al\nout put\nĠTr ue\nĠO F\n_t ime\nĠof fer\nĠ} );ĊĊ\nH ER\neg in\n\" \"\nĠw ater\nĠc he\nĠM y\nore d\nĠst ep\nanc es\nC K\nA Y\nà ¸\nstr uction\n( C\nou ch\nSt ream\nact ive\nam a\nEnt ity\npro duct\n() {Ċ\nĠgovern ment\nĠI D\naj or\nA nd\nĠdis play\nÐ »\nĠt imes\nĠf our\nĠf ar\nĠpres ent\nĠN S\nĠ\\ Ċ\nue st\nĠb as\ne cho\nch ild\nif ier\nHand ler\nĠl ib\nProp erty\ntrans lation\nĠro om\nĠon ce\nĠ[ ]\ncent er\n================ ================\nĠresult s\nĠcontin ue\nĠt alk\n_ get\nĠg row\n.s w\ne b\nĠP ublic\nO P\nec ute\nol s\nĠ **\n\" );ĊĊ\nĠm ass\nure d\n.c lass\nom ic\nĠme an\nip s\nĠa ut\n);čĊ čĊ\nĠun til\nĠmark et\nĠare a\nu it\nĠl ength\nĠW ith\nstruct or\ne vent\n\"> <\nĠS p\nI V\nĠm us\nif f\nĠk ind\na uthor\nound s\nm b\n_ key\nw idth\nposit ory\nĠl ight\nu k\nR ow\noh n\nal f\nviron ment\napp er\nollection s\nĠs ide\n_in fo\nĠex ample\nim ary\nĠw r\nĠc amp\ncri be\n\" /\nĠm iss\nw ay\nĠb ased\nĠpl an\nV is\nom ain\nun k\nĠaw ay\nU P\n< T\nO S\ni od\nĠM on\nâĢĻ re\nĠli k\nà §\niv ely\n. v\nim er\niz er\nS ub\nĠbut ton\nĠU p\nĠexper ience\nC L\nĠre nder\n_ value\nĠn ear\nUR L\nal t\nĠcoun try\nib ility\n() ,Ċ\ne ad\nĠa uthor\nĠspec ific\nb ase\n( name\non es\nĠD o\nĠal ong\ny ear\nĠexp ress\n. '\nen v\nĠbeg in\nĠso ftware\nĠim p\nĠw in\nó n\nĠth ing\nTr ans\nĠT HE\nĠ< ?\nĠwh y\nĠdoes n\ni j\ng ing\nĉ g\nĠs ingle\noff set\nar ning\nog raph\nle y\n_c ount\nĠan al\ncre ate\n/ m\nĠR eg\nun ch\n= $\nis k\nĠright s\n( M\nĠ\"\" \"Ċ\nap er\n.m odel\nĠp o\nem pty\nart ment\nĠa nt\nĠWh en\nĠwom en\nĠE d\nĠse ason\nĠde st\nà £\n( h\nĠposs ible\nĠse ver\nĠb tn\nĠdid n\nĠs ent\nĠen c\nĠcomm and\nĠ ],Ċ\n_ x\nĠre cent\nol ution\nv ector\nĠB y\nĠM ay\nĠA ct\n» ¿\nĠm oney\nIN T\nbs ite\nĉ p\n. čĊ\nï »¿\ns l\natter n\nĠC lass\nĠto ld\nud io\nc urrent\nĠe qu\nĠa uto\nĠSt ate\nd a\nms g\n)) ;ĊĊ\nĠwork ing\nĠqu ery\nĠB r\nĠw indow\na uth\non ly\nĉ t\nĠle ast\nag n\nĠex pl\nit ter\nar ing\nĠc olumn\nĠGener al\n\": \"\ner al\nri or\nĠrec ord\nI B\nE X\nĠd at\nĠm aking\nu ed\nĠC ar\nem p\n\" .\nĠM ed\nĠc lose\nĠper cent\nĠp ast\n( g\n: (\nĠw rite\nĠm ove\nĠp at\nCont rol\n.T o\nĠv i\n*/ Ċ\nin ate\n' ll\nag ed\nN ull\nĠspec ial\nIZ E\nĠc ity\n/* Ċ\nĠE ng\nix ed\nin ary\np y\nĠe ff\nar io\nĠt ell\nav or\nĠse lect\nle vel\nim um\nop er\nB uilder\nI P\n') ,Ċ\nes c\nĠf ont\n\" ;ĊĊ\nĠA m\nish ed\nill s\nInt er\nO W\nĠcour se\nĠl ate\nidd le\nĠam ount\nĠas ync\nin o\nc ul\nĠ ì\nand le\n_ user\nĠb en\nĠC al\nĠ$ _\nĠR ep\nĠen ough\nT oken\n. user\n( j\nS c\nW idth\nn ow\nat form\nĠlook ing\nĠh old\nM odule\nIT Y\nv o\nis on\n.D ata\ny c\nĠp ot\nĠTr ump\nid ual\nid es\nr t\nĠprop erty\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ\nam ework\ng o\nĠl ow\nĠpar a\nĠpr ice\nur y\nĠto day\nro y\nĠ' /\nĠpol it\nĠ' '\nym b\nP h\nĠad v\nĠatt ack\nĠS te\nRO M\nan a\nĠme ans\nĠst ory\nid s\nak en\nĠme et\nĠm om\nĠâĢ ĺ\nĠ? >\nĠd en\nob ile\nch ange\nĠĠĠĠĠĠĠĠ ĠĠĠĠĊ\nic i\nn a\nĠF orm\nĠs ort\nSe lect\np are\nĠth ought\n_ con\nĠt ask\noc us\nĠD E\nĠM in\nĠo pt\nĉb reak\num er\nK E\nth en\nĠd et\nĠT est\nport s\nĠre view\n(' /\nm ove\nĠsw itch\nER T\np atch\nann ot\nã Ĥ\nĠab ove\nit ive\nĠquest ion\nĠQ u\nãĢĤ ĊĊ\ng le\nĠw ord\nĠprov ide\nĠR eturn\nĠre search\nã o\nu str\nĠp ublish\nchem a\n} }\nĠC ON\n- in\nall back\nĠco ver\n\\ \\\nc olor\nĠI S\nĠwh ether\nim ate\nis c\nB ar\nĠd iv\nB e\nour n\nĠh aving\nle m\npl ayer\nab s\nam era\nne y\nĠex c\nget her\npl ied\na o\n[ $\nĠ+ +\ni pe\nsh ow\n/ d\n[ :\nag ement\nle v\n_ ID\nr ary\nad es\n_ se\na use\nĠem ploy\nĠ*/ čĊ\nĠf re\nĠ' @\nĠcomple t\nĠl arge\nr al\n\\ x\nĠf ac\n< String\nĠcre ated\nup er\n.st ate\nĠh ost\nener ic\n/ b\n( !\nwh ile\ni as\nB UG\nĠ );ĊĊ\nĠro le\nRe g\nĠC olor\nSt art\nĠp orn\nt op\nĠwe b\nĠde v\nĠde al\n++ )Ċ\nInt eger\npos ition\n. on\nĠ( \"\nä ¸\nĠproble m\ns v\nĠp ress\nAB LE\nAT ION\nĠSe e\nan ch\nĠth ough\nle ep\nĠ< !--\nĠpoint s\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\n. J\nĠ ::\np tr\nD B\n++ ;Ċ\n.p ng\nn ode\nso ft\npon d\nĠe ver\n-------------------------------- --------------------------------\nM enu\n(' #\nĠs ervices\np g\n} )Ċ\nparam s\nĠact ually\nĠ\" /\nEm pty\nM ethod\nĠid ent\nun ic\nĠmill ion\nĠa ff\nst yle\nĠcon c\ni os\nign ment\nUL T\nP r\n\" ;čĊ\nĠunder stand\nu ary\nĠhapp en\nĠser ver\nĠC o\nS C\nĠle s\nĠfile s\nG rid\ns ql\nĠof ten\nĠin fo\n_ tr\ns rc\non y\nĠsp ace\num b\nĠpass word\nĠst ore\n, ĊĊ\nĠWh at\ng ed\nĠF alse\nU s\nsw er\n_ index\nĠform at\nm ost\ns m\nN ew\nĠd etails\nĠpro b\nĠAN D\n() čĊ\nil ar\nĠ$ {\nry pt\n.C ollections\n$ this\nĠF ree\n_ of\n(f alse\nd ated\nĠ> >\nĠf ace\nCT ION\nĠs ave\nĠt yp\nde v\n(\" #\nAG E\ncont ainer\ned it\nQ L\nĠitem s\nĠs ocial\ni en\nĠRe act\n) .ĊĊ\nĠm ar\nĠre du\nĠR E\n.p ut\nĠm ajor\nC ell\nn ext\nĠexpect ed\nĠy et\nĠin div\ntrib utes\nat is\nam ed\nĠf ood\nS ource\n( string\nĠ+ Ċ\nit es\nd r\nĠmem bers\nĠcom b\nitem s\nĠP er\nT H\n= True\nĠb ar\n_ SE\ncom m\n( w\n)ĊĊ Ċ\nĠs end\nĠin c\nun signed\nF A\nĠparam s\napp ing\nro s\nug in\nf a\nĠcon nection\nĠ} ;ĊĊ\nĠbe come\nM ode\nĠe v\nĠdif f\nĠUn ited\nHe ight\nful ly\nim ages\nĠm akes\nĠg lobal\nĠcont act\n' :Ċ\nĠab s\nа Ð\nf loat\nĠex cept\nĠP ol\nCh ild\nt yp\nĠcert ain\ni ón\nO UT\nĠim pro\nile s\nĠ-- >Ċ\nĠP art\nval ues\nos s\n/ **\nil it\nĠE vent\ncur ity\nst er\nĠchar acter\nĠnew s\nĠ\" ,\nĠde vice\nc el\nlog in\nhe et\nDef ault\n@ \"\nĉ Ġ\nc lick\n( value\nĠA b\nĠpre vious\nERR OR\noc al\nĠm aterial\nĠbel ow\nĠCh rist\nĠmed ia\nco ver\nĠU I\nĠf ail\nĠbl ack\nĠcom ponent\nĠAmeric an\nĠadd ed\nĠbu y\nst it\nĠc ame\nĠde lete\nprop erty\nod ing\nĠc ard\nrop s\nĠhttp s\nĠro ot\nĠhand le\nC C\nB ack\nem plate\nĠget ting\n_b y\nm ail\n_s h\n. assert\nĠD ec\n( true\nĠcom put\nĠcl aim\n' =>\nĠS ub\nĠa ir\nop s\nn av\nem ents\n( id\nĠent er\nang ed\nE nd\nĠloc ation\nĠn ight\nĠdo ing\nĠR ed\nl in\n}ĊĊ Ċ\nvid er\nĠp ick\nĠw atch\ness ages\nĠhum an\nĠd am\np end\nd ir\nĠt ax\nĠg irl\nre et\nĠbo x\nĠstr ong\n( v\nre l\nĠinter face\nĠm sg\nf ect\n_ at\nĠh ouse\nĠtr ack\n' );ĊĊ\nj e\nĠJ ohn\nist r\n( S\nub e\nĠc e\nitt ed\nV ER\n* )\np arent\nĠapp lication\nan y\n.sw ing\nĠp ack\n\\ u\nĠpr act\nĠse ction\nct x\nĠun signed\n.P oint\nĠO ne\nÄ ±\nip le\na id\nÑ ĥ\nV ector\nby te\nĠw ait\nĠà ł\nà ¥\nĠto gether\nĠth rows\nF O\n' ))\nh ost\nis ing\n. view\nĠter ms\nfr amework\n- r\nĠapp ly\nĠs ession\nO ptions\nugg est\nĠo thers\nw itter\nĠf und\nIn it\n__ (\nens or\nG ET\nĠsever al\ni i\n[ j\nI O\nĠtem plate\nP osition\nĠe con\nach ine\nĠ il\n.s pring\nm ain\nel t\nim ent\nRe c\nm m\nĠUn iversity\nurs or\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\nG L\nict ure\nith ub\nc er\nc ast\nF rom\na les\nĠsub ject\np assword\nn y\nĠes c\n.w rite\nï¼ Į\nWh at\n. H\nĠh istory\nĠF e\nĠindiv idual\nun it\nĠ-- >\nĠd u\nI ST\nĠus ers\nf s\nf alse\nun t\nT itle\nĠm ot\nĠf uture\nach ed\nĠstart ed\nĠm ode\nĠ' <\n_ array\nĠa x\n'] ;Ċ\ni res\nTh ere\nug ht\nt ml\npos ed\nic ult\nĠto ok\nĠg ames\nĠ} }\nĠ? >Ċ\nĠproduct s\nI s\nĠb ad\nĠD es\n.p ath\n' ĊĊ\nĠP ost\nav el\n( :\nĠneed s\nĠkn own\nF l\nĠex ec\nĠse en\num e\nĠb order\nĠl ive\ntem p\nP er\nĠvar iable\ni et\nĠD ef\nĠg e\nem e\n_b ack\nf irst\nĠprovid ed\n//////////////// ////////////////\nĠfil ename\nĠh ope\nul y\na uto\nf ind\n_ string\nb tn\nit ude\nAt tribute\nĠyou ng\n.t xt\nĠwe bsite\nĠP rop\nĠe y\n> ();Ċ\nion al\nAR R\niction ary\nur ther\n. \nt x\nĠp ur\nu el\nymb ol\nu ation\nang er\nĠback ground\nec ess\nef ined\n.... ....\nĠdes cription\nĠrep resent\n\") );Ċ\npress ion\nrow ser\nĠser ies\nward s\n($ _\na ise\nĠh ot\nac ity\nri es\naction s\nC reate\nad io\namp les\nĠorig inal\nens ive\nf ont\nst ream\n using\n.spring framework\nser ver\nĠb ill\nAC K\nil ename\nĠfr ame\nĠ= Ċ\nEd it\nadi us\nĠd raw\nank s\nĠd eter\nĠcom es\n_ int\nĠfore ach\nang le\nĠe lect\npect ed\nHe ader\nist ration\nF alse\nĠG ame\nĠfil ter\nAct ivity\nĠl arg\nin ition\nĠ\" <\nis ed\nĠrem ove\nĠTr ans\nm et\nse e\nForm at\nCom mand\nĠE X\nN one\nĠfr ont\nA SE\nĠR ec\nound ation\nĠv o\n= \\\"\n( *\nCh ange\n.W rite\ng roup\ni ents\nu y\n******************************** ********************************\nĠd ig\nh r\n( -\nĠg en\nn umber\nve c\nuro pe\nent ry\nL L\nĠst e\nVal id\n'] ,\n_p aram\nĠse lected\nĠacc ording\nĠD is\nĠ util\nB uffer\n_ error\nĠass oci\n_S IZE\nĠw or\nĠprint f\nr ag\n ł\nD D\nĠV al\nĠact iv\nE ng\net ime\nĠv irtual\na ign\na ur\nĠP res\nĠEx ception\nĠany thing\nĠO ff\nĠh ours\nĠw ar\nArg s\nag ing\nĠmodel s\nĠT ime\nO b\nam s\nj oy\nĠear ly\n. read\nĠc enter\nĠIn itial\nĠl anguage\nl ength\nx y\nĠs n\nĠin f\nP ost\nĠag o\nĠeas y\n_c ode\nĠAN Y\n_ ch\nĠdown load\n( T\nav ed\nâĢ ĵ\nĠstud ents\nĠf ig\nl ight\nx x\nĠbu ffer\nĠD ep\nĠM ath\nIT H\nĠvar i\nĠd ue\nF actory\nĠp or\nĠe p\not ype\nĠcan not\nĠwh ite\n< int\nter n\nĠreg ister\nĠpre d\ncl us\n_d ate\nĠ/ **\nĠa uth\nĠ[ ]Ċ\nĠper iod\nn own\nĠv ot\nĠs creen\n' d\nT ypes\nĠt mp\nе Ð\nur al\nĠben ef\n_ y\nĠn et\nĠSt ates\n'] ['\nĠN e\nĠN OT\nĠn eg\nĠcomm on\ns cope\nĠc red\ng es\n_T YPE\nĠs uggest\no om\n.ĊĊ Ċ\nĠac cept\nĠr andom\ner m\nĠV ector\nw ith\nT ER\n( str\nĠres pons\nĠh it\n.S et\ngr id\nri a\nĠc lick\nund le\nC ase\nins ert\nUtil s\nĠ\"\" \"\nĠim plement\nat al\ntem pt\ntem plate\noc r\nreturn s\nĠplay ers\nus ers\ned ef\nĠTh ese\nĠam ong\nĠde b\nh a\n.get Element\nĠc irc\nĠan swer\nĠw alk\nĠt reat\nĠG e\nĠC reate\nĠa ge\nĠre q\nO ST\nang ular\nÑ ı\nĠf ive\nĠdistrib uted\nĠfri end\nT P\nĠc lean\now s\n.Control s\nd is\nĠw ords\n. io\nz y\nĠhe ader\nĠC heck\nâĢĻ m\nj ust\nh older\n=\" čĊ\n. annot\nĠcol lection\n' .\nĠsim ilar\nĠt aken\n(\" %\nOr der\n'] Ċ\n-m d\nĠT H\nac ed\nĠis n\n/ j\nĠs on\ngr aph\nĠInt eger\nĠn ecess\nre en\nĠ um\nĠ\\ <\nĠmom ent\nĠbr ing\nĠind ic\nys is\nLe vel\nver se\nurre nc\n_t est\nĠent ire\nD own\nĠ}ĊĊ Ċ\n( result\nĠRe ad\nà ¨\nM od\nĠtry ing\n\") ,Ċ\nĠm ember\nĠC or\nOD O\n- control\nun time\nĠS im\nD ialog\npl ot\n_ on\nĠph ys\n} /\nĠn amespace\nĉ čĊ\nac c\nPl ayer\nA RE\nĠf oot\nĠbo ard\np art\nĠs us\nw ise\nĠM c\nĠp ush\nAT A\nĠp lease\nri ed\nwe et\nb it\nid ed\nV E\nĠS w\nU B\nĠt ypes\ned ia\nĠc los\nace book\nWh en\nĠed it\nig ger\nĠen erg\nCont ainer\nĠph ot\nĠC ount\nĠE urope\n.I s\nĠR uss\npe ed\nĠS tr\nĠp y\nĠc ult\nĠdef ined\ncc ount\nĠob t\n.L ocation\nĠth read\nil le\nĠinst ead\nstr ong\nĠS ec\nU RE\nĠide a\n. se\nem y\nselect ed\nCon nection\nac ing\nth read\n.n ext\nĠc oll\nĠfil m\nist ic\nĠcomp et\nĠcon n\nth ough\nĠcom pan\nock et\nĠte ach\n= (\nĠph one\nĠact ive\nde lete\ntr ies\nĠm o\nĠde ath\n} );ĊĊ\noc ol\nW idget\nĠart icle\nro du\nand id\nÑ ĭ\nĠC r\nk a\n() :\nlo od\nĉĉĉ Ċ\nĠal most\nĠs ell\nerv let\nri p\nUn it\nĠapp lic\nĠcon nect\nĠfe ature\nĠv ia\n' ),\nĠl im\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠG u\nEng ine\nĠen s\nĠen vironment\nb lock\nHER E\nN ULL\ng y\nt ag\n) ).\nex p\nĠcom pl\nĠinst all\nĠcomple te\nque ue\natur al\nĠgener al\nth on\nĠask ed\no res\n( res\nĠres erved\nS P\nĠâĢ ¦\nÅ Ĥ\nĠsign ific\nO ff\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠA g\nĠJ ust\nĠE rror\nĠin fl\nad ata\nĠ icon\nask s\n' '\n_ LO\n? .\nac count\nĠ( *\n' )ĊĊ\nr ap\n_ var\nĠF OR\nĠpart y\nĠY our\nc at\nstr y\n. new\nbo ot\nĠN ov\nĠv ector\nĠn ormal\nĠf urther\nRe pository\nĠd atabase\natt le\nĠmus ic\nĠspe ed\nĠd oc\npro cess\nIG HT\n.p arse\nĠt aking\nĠvi ol\nce ed\nĠA fter\nĠfor ward\nĠc rit\n\"/ >Ċ\nro t\nĠfa iled\nef ore\nĠconc ern\no e\nb a\nĠs ender\nĠter m\nh as\n=\" #\nĠpot ential\nN um\nĠpublish ed\n.c lose\nĠIm age\nstr aint\nU D\nĠO b\nĠprob ably\nl im\n\" :Ċ\nolum e\nĠcon sum\nag ue\nens ions\nĠinvest ig\n- year\n') ;\n-s m\nĠen joy\nor ig\ner ing\nc p\nle ased\nple ments\nĠreturn s\np at\nB O\nĠH ouse\n.L abel\nĠwe ight\nigh b\nĠcondition s\nĠex ception\nd escription\nĠtr ad\n- to\nĠ{ }\nĠmod ule\nEN D\n. ap\n.p rops\nĠcon structor\nav es\nĠf avor\nĠN ow\n; i\nĠM ain\n_ k\ner ies\nâĢĻ ll\ntrans form\nimest amp\nP re\nĠm er\n. res\nst ant\nL ocation\n_N AME\nĠlos s\nĠ ĊĊ\nn et\nĠeng ine\nB lock\nĠiss ues\nĠpar se\nĠB ar\nĠst ay\nĠJ SON\nĠd om\nair s\nw ner\nĠl ower\n\", čĊ\nĠD em\nuf act\nĠp s\nĠper fect\nR L\nĠed uc\nl s\nem ory\nARR ANT\nu ge\nĠex act\n. key\nal led\ne ch\nie f\n\\ /\no ke\nĠfor mer\nal loc\nĠs ix\nid a\nĠm argin\nĠhe art\nal d\np ack\n.getElement ById\nĠW ARRANT\nĠr ather\nĠbuild ing\ner man\nlic e\nĠquest ions\niz es\nle ge\nirect ory\nĠj e\nĠc as\npro ps\nut f\nĠse curity\nĠhow ever\nwe ight\nĠins ide\nĠpres ident\nCh ar\nĠW ITH\n.m ap\nĠgr aph\nĠt ag\n_st atus\nĠat tempt\nop p\nus es\nĉ const\nĠr ound\n, $\nĠfri ends\nEm ail\n? >\nRes ource\nKE Y\nos p\n. query\nĠN orth\nable s\nist rib\n_c lass\nel lo\nTh at\nÐ º\npecial ly\nĠPres ident\nĠcamp aign\nĠal t\nare a\nĠch all\nĠop port\n.C on\nĠenerg y\nli ke\n. string\ning ton\n) *\ny y\nĠprof ession\nir th\nĠse g\næ ľ\nĠh or\ni ers\nc an\nĠbeh ind\nPro duct\nf g\nĠS k\n.j pg\n? :\n] ;ĊĊ\nĠcall back\nĠH ttp\nÑ Į\nl ong\nM S\nAT H\nĠr aise\nĠwant ed\nrow n\nut or\nl t\n] =\nel ine\nM A\nĠse par\nc s\nse mb\nD is\nbs erv\nĠW ill\nĠpol icy\nĠth ird\nph one\nĠb ed\n/ g\n. __\nĠIn c\niz ing\n.re move\nin stance\n.t ype\nĠs erv\nE ach\nĠh ar\nĠM essage\n( key\nSE LECT\nP os\n)) ;čĊ\nĠre comm\nĠtr aining\nĠE nt\nĠCh ar\nic ht\n(f ile\nĠp rior\nG ame\nĠex it\nParam s\n.c ore\nP C\nn es\nanc ed\n( request\nP assword\n} >Ċ\nĠm ag\nĠre lease\nĠsh all\nud ent\nĠS outh\nand o\n: '\n.Tab Index\ns k\nann er\nis set\nĠout side\nled ge\nĠ å\nĠR ob\nĠim m\n! Ċ\nĠWe b\nD es\nB C\nanc ial\nR oute\nD ec\nfer ences\nĠp urch\nĠM odel\nct or\ng n\n_st art\n_ un\n. *\nis es\nĠg round\nĠun ique\nĠbe aut\n{ \"\nĠp our\nĠO ct\nĠt ree\nset s\n_ res\n') ->\n_re g\n(\" \\\nĠby te\nB l\nĠd ating\nĠm atter\nĠR em\nĠ' ../\nĠA ug\nĠL a\nĠ$ (\nourn al\ni am\nĠshow s\nw rite\nĠb all\nĠsim ply\nĠf ast\nĠmem ory\nA SS\nĠO f\nov ed\nant e\na ul\nist ry\n)) );Ċ\nĠf it\n< string\nĠpolit ical\nanc el\n_ .\nc ard\n.c urrent\no ch\n_ image\n\\ t\n# Ċ\n( L\nĠindu stry\ncom ing\nĠex tra\nĠreport ed\n.st art\nĠres ources\nĠim g\nfl ow\n_E X\n(n ull\nĠP re\nĠwr ong\ninter face\nParam eter\nn ers\ná »\nt ure\ners ist\noun try\nĠseem s\nal ance\nde st\nĉ String\nĠm aint\nĠun it\nact ers\nĠT R\nif ul\nexport s\npro ject\nApp lication\nleg ate\nĠt akes\nter m\nĠet c\nust er\nĠappe ar\nadd ress\nĠf em\nh s\nĠh om\n, -\nĠdiff icult\nĠcom ing\nO pen\nĠset tings\nĠW ar\nĠTh en\nĠaut om\nĠF oundation\nĠqu ite\nD escription\nĠb log\ni qu\nP S\n_f ield\nJ son\nSS ION\nĠS ch\nĠL O\nĠdes cri\nĠevery one\nĠpret ty\nĠlong er\nĠm enu\nĠcurrent ly\nse c\nĠrelations hip\n################ ################\nĠM ap\nas et\nĠparam eters\nĠcr ush\n\" čĊ\nIL ITY\nig ration\nĠc out\nt otal\nĠn ames\nnd ef\n\") ;\nri end\nyn amic\nĠeff ort\nĠact ual\nĠfield s\nO UN\nt ers\nĠf ix\n_m odel\nĠc ases\nC A\nM y\nInter face\nĠS E\n] ]\nal le\nĠN ational\nĠArray List\nin line\n. V\nar a\nref ix\nas c\nRe ader\nĠÐ ¿\nast ic\n( ()\nC l\n.annot ation\nĠperform ance\nail y\n.to String\n.n et\nview s\n. end\nay ers\nl ate\nĠA pr\ned eral\n'] )\n.b ody\nĠhigh er\n_f l\nc r\nal ert\n_n ode\nĠG oogle\nĠit self\nA uth\nurrenc y\nĠsignific ant\napp end\nĠres pect\nstr ap\nĠun a\nriter ia\nP ORT\n.ap ache\nOut put\nĠpro gress\nĠm id\nĠM icrosoft\nĠres ource\nab lish\nĠd im\n. load\n.A pp\nĠd irection\nĠadd itional\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ\nĠnum bers\nĠcompan ies\n.T h\nĠs ound\nuser name\nĠstat ement\nĠal ert\nĠcon tract\nh ome\n_l ength\n.Com ponent\ne v\n. Ex\nï¼ ļ\n\" ;\nĠH igh\nĠ )ĊĊ\nĠP oint\nop h\nĠl ines\n-> _\n\" )ĊĊ\no x\napp lication\nĠ ]Ċ\nĊĊĊĊ ĊĊ\nĠso on\nction s\ning er\nĠj oin\nĠP e\nĠ ë\nĠl as\n. E\nc ss\n/ or\nĠSt art\nĠT O\nĠsub s\ncon n\ncom ponents\nDE BUG\nqu are\nF unction\nend ar\n. index\nĠf ill\nÄ Ļ\nĠcho ose\nh ow\nĠAmeric a\nass ets\n-------- ----\nĠV alue\nĠoff ice\nĠv eh\nĠtrans form\nĠAr t\nĠin de\nĠf n\nĠim plements\nang o\nple te\n+ \"\nt mp\nam ily\nĠhas h\nmiss ions\nE ST\ng t\nPro vider\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\nĠfl ag\nĠpartic ip\nd en\nĠReturn s\nĠnot e\nü r\np m\nide os\nĠspec ified\nĠE N\nest er\nol id\nĠup on\n( std\nĉ v\nĠ' \\\nu z\nĠv ert\nĠv ict\nĉ self\nĠ\" $\n. k\nĠgroup s\ng ithub\nl ang\nĠm ut\nT O\nĠv e\nĠP lease\n;ĊĊ Ċ\nac cess\nĠ{ \"\nre a\nĠr isk\nick er\nog gle\nĉ while\nAN G\n.s end\nĠwom an\nĠget s\nĠ ign\nĠI d\n_ log\nON E\nĠe vid\nĠH ar\n_s ub\nĠend l\nĠinclud ed\n() );ĊĊ\nĠA p\nig r\nĠs em\nĠBl ack\nd oc\n_t able\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\n- up\nĠca use\nĠ ..\nĠv an\n_d ict\nĠf ocus\nIN D\nCE SS\n.L og\nĠmult iple\nid o\nĠreg ard\n- M\nand ler\nour se\nĠde g\n. U\nĠadd ition\nĠvar ious\nĠrece ive\nе н\nĠH T\nOb j\nD F\nĠincre ase\nĠO pen\n] ;\nĠcomm it\n? Ċ\nateg ories\nat ory\nsh ip\nĠM ich\nĠh tml\nrom ise\nĠle ave\nĠstr ateg\nav en\nĠCon sole\nk nown\n- n\n_ LE\n.com ponent\nĠb re\nS ession\ni ance\nĠal ign\ntyp edef\n_ result\nĠW HERE\n.s plit\nĠread ing\nFA ULT\nĠc lo\nĠnot ice\n_p r\nar ter\nĠlo ck\nĠstand ard\net ic\nell ow\nĠp adding\nĠH is\nĠst ates\n_c ast\n( P\na a\nĠintern al\ne an\nĠP RO\nĠK ey\nĠes pecially\nm ing\nĠc ross\nĠn ational\n_ object\nf ilter\nĠs cript\n. update\n_ i\nĠAss ert\n/ core\n%% %%\nĠproble ms\nist or\nĠ. =\nĠar ch\nĠwrit ten\nĠm ilit\nM ENT\n. ch\nca pe\nĠM us\n_ config\nĠA PI\nfo ot\nĠim ages\nend l\n. In\nF irst\nĠpl atform\n.pro t\nO ption\nst e\nĠT ODO\nĠfor ce\n. cont\nĉ echo\nĠD av\nP tr\n( B\nR T\nĠB ase\n] ['\nĠann ounc\ncon sole\nĠP y\nd s\n. as\nĠpre vent\nap an\nĠ{ '\n} '\nĠde ad\nV AL\nQ UE\n**************************************************************** ********\nĠch arg\nR eturn\nĠf ul\nd om\nĠr ules\nĠmod ify\nĠe val\nh am\nat ement\n\\ <\nul a\n= False\nR A\nĠcont ains\nĠst ack\nm ar\nĠ{ }Ċ\nĠund efined\nA ss\nĠCh ina\nve y\n* Ċ\nĠplay ing\n) /\nact or\nĠb ottom\nli er\nĠN umber\nĠcou ple\nD C\nĠS O\ng or\n.set Text\ns uccess\ncom mand\nF ilter\nĠO ur\n_ item\nĠc tx\nĠro ad\nV ersion\nc ase\nur t\nav ior\ny ch\nsemb ly\nĠPro duct\nĠh eld\na fe\nĠinclud es\n< quote\nĠa void\nĠF in\nĠM od\nĠt ab\nan o\nà ±\nipp ing\n- e\nĠins ert\nt arget\nch an\n.M odel\nIM E\n\\ Ċ\nĠm achine\nav y\nĠN O\nĠInt er\nĠoper ation\nmod al\nT ag\n] :\nĠprodu ction\nĠare as\nĠre n\n_f rom\nn bsp\nĠoper ator\nm en\napp ed\n_p er\nz en\n(\" .\n.s ave\n=\" {{\nĠt or\n( response\nĠc andid\nĠcon v\na iled\nĠL ib\ncom p\nur a\nï¿ ½\nĠH ere\nĠarg ument\nh ood\nĠest ablish\nograph y\nĠon Click\namb da\nĠs ch\nĠmov ie\nĠse c\nĠact ivity\nØ §\nĠs ql\n_ all\ninc ip\nĠprovid es\nĠs ys\nack et\nĠwas n\nĠus es\nĠF unction\n.g oogle\nĠRes ult\nVis ible\nag ma\nel come\nĠS y\nĠC ent\nAL SE\nac ión\nEX T\nĠl icense\nĠL ong\nĠacc om\nĠab ility\n. height\nAct ive\nolog ical\nol y\n)) ,\n.S e\nĠparam eter\npr ite\nAB ILITY\n.s ervice\nĠG roup\n_ query\nĠI tem\nin ing\nĠj ud\nim s\nf ix\nind er\nag ram\nĠfunction s\nĠexper i\nĠE m\nĠro t\nĠp en\n.b tn\nĠA S\n#if def\nĠcho ice\nĠP age\n_P RO\nQ U\nå ı\nant ity\n Ń\nword s\nĠread only\nĠf lex\nprot ected\nĠAn y\nĠchar acters\nenc ed\nĠJ uly\nil er\nC ard\nur ance\nĠre v\n.e vent\nal y\nĠwon der\nĠP ort\nĠleg al\nro le\nĠt en\nĠgo es\nM P\nwh ite\n): čĊ\n)) čĊ\nĠre ference\nĠm is\nĠPro ject\nick s\n> &\nC ON\nĠre pl\nĠreg ular\nSt orage\nram ework\nĠgo al\nĠt ouch\n.w idget\nĠbu ilt\nd es\nP art\n( re\nĠw orth\nh ib\ng ame\nĠÐ ²\nac ion\nĠWh ite\n(t ype\n( `\nĠn atural\nĠin j\nĠcal cul\nĠApr il\n. List\nĠassoci ated\nĉ System\n~ ~\n= [\nĠst orage\nĠby tes\nĠtr avel\nĠs ou\nĠpass ed\n! =\nas cript\n. open\nĠgr id\nĠb us\nĠrec ogn\nA b\nĠh on\nĠC enter\nĠpre c\nb uild\nHT ML\nĠS an\nĠcoun tries\na led\nt oken\nk t\nĠqu al\nL ast\nad ow\nĠman ufact\nid ad\nj ango\nN ext\nx f\n. a\nĠporn o\nĠP M\ner ve\nit ing\n_ th\nc i\n= None\ng s\nĠlog in\nat ives\n'] );Ċ\nÄ ħ\nĠ ill\nI A\nchild ren\nD O\nĠlevel s\nĠ{ {\nĠlook s\nĠ\" #\nTo String\nĠnecess ary\nĠĠĠ Ċ\nc ell\nEn try\nĠ' #\nĠext rem\nSelect or\nĠplace holder\nL oad\nĠre leased\nO RE\nEn umer\nĠT V\nSE T\nin q\nP ress\nĠDep artment\nĠprop erties\nĠres pond\nS earch\na el\nĠre qu\nĠB ook\n/ Ċ\n( st\nĠfin ancial\nick et\n_in put\nĠth reat\n( in\nStr ip\nì Ŀ\nç ão\nĠevid ence\n)) ;\nĠB ro\nĠ[ ];Ċ\nĠ ou\nb uf\nS cript\nd at\nĠr ule\n# import\n=\" /\nS erial\nĠstart ing\n[ index\na e\nĠcon trib\ns ession\n_ new\nut able\no ber\nĠ\" ./\nĠlog ger\nĠrecent ly\nĠreturn ed\nč čĊ\n)) )Ċ\nition s\nĠse ek\nĠcomm unic\nĠ\" .\nĠuser name\nE CT\nD S\nĠother wise\nĠG erman\n. aw\nAd apter\nix el\nĠsystem s\nĠd rop\nĠstruct ure\nĠ$ (\"#\nenc ies\nann ing\nĠL ink\nĠRes ponse\nĠst ri\nÅ ¼\nĠD B\næ Ĺ\nand roid\nsub mit\not ion\n( @\n.t est\nĊĊĊĊ ĊĊĊĊ\n] ;čĊ\nĠdirect ly\nĠ\" %\nr is\nel ta\nA IL\n) {čĊ\nm ine\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\n( k\nb on\nas ic\np ite\n__ _\nM ax\nĠerror s\nĠWh ile\nĠarg uments\nĠens ure\nR ight\n-b ased\nWe b\nĠ- =\nĠint rodu\nĠIn st\nĠW ash\nord in\nj oin\nD atabase\nĠgr ad\nĠus ually\nIT E\nProp s\n? >Ċ\nĠG o\n@ Override\nRE F\nĠ ip\nĠA ustral\nĠ ist\nView ById\nĠser ious\nĠcustom er\n.prot otype\nod o\nc or\nĠdo or\nĠWITH OUT\nĠpl ant\nĠbeg an\nĠdist ance\n() ).\nĠch ance\nĠor d\nc ame\npr agma\nĠprot ect\nrag ment\nĠN ode\nen ing\nÑ ĩ\nĠr oute\nĠS chool\nh i\nĠne ighb\nA fter\nlic it\nĠcon tr\nĠpr imary\nA A\n.Write Line\nutil s\nĠb i\nR ed\n.L inq\n. object\nĠlead ers\nun ities\nĠg un\non th\nĠDe v\nF ILE\nĠcom ments\n_l en\nar row\nam ount\nR ange\ns ert\nGrid View\nĠup dated\nĠM o\nĠin form\noci ety\nal a\nA ccess\nĠh ab\nĠc reat\n_ arg\nĠJan uary\nĠD ay\n\") čĊ\nup le\nd ocument\ngor ith\nm enu\nĠO ver\nb b\n.t itle\n_ out\nĠle d\nur i\nĠ? >Ċ\nr un\nĠsc ene\n( array\nde vice\n_t itle\nag on\n] čĊ\nab y\nĠbe came\nbo olean\nĠp ark\nĠC ode\nup load\nrid ay\nĠSept ember\nF e\nĠs en\nc ing\nF L\nC ol\nut s\n_p age\nin n\nĠim plied\nal ing\nĠyour self\n.C ount\ncon f\nĠa ud\n_in it\n. )\nĠw rote\nN G\n. Error\nä »\n.f or\nĠe qual\nĠRe quest\nĠser ial\nĠallow s\nX X\nĠm iddle\nch or\nà ¸\nerv al\n.C olumn\nread ing\nĠesc ort\nĠAug ust\nĠquick ly\nĠwe ap\nĠC G\nrop ri\nh o\nĠc op\n( struct\nĠB ig\nĠv s\nĠfre qu\n. Value\nĠaction s\nĠpro per\nĠin n\nĠobject s\nĠm atrix\nav ascript\nĠon es\n.g roup\nĠgre en\nĠp aint\nool s\ny cl\nenc ode\nol t\ncom ment\n. api\nD ir\nĠun e\niz ont\n.p osition\nĠdes igned\n_ val\nav i\nir ing\nt ab\nĠl ayer\nĠview s\nĠre ve\nra el\nĠO N\nr ics\nn p\nĠc ore\n() );čĊ\nM ain\nĠexp ert\nĉĉ čĊ\n_ en\nĠ/ >\nut ter\nI AL\nail s\nĠK ing\n*/ ĊĊ\nĠM et\n_ end\nadd r\nor a\nĠ ir\nM in\nĠsur pr\nĠre pe\nĠdirect ory\nP UT\n- S\nĠe lection\nh aps\n.p re\nc m\nVal ues\nĠ\" Ċ\nc olumn\niv il\nLog in\nin ue\nĠbeaut iful\nĠse cret\n(e vent\nĠch at\num s\nĠorig in\nĠeffect s\nĠman agement\nill a\nt k\nĠset ting\nĠC our\nĠmass age\nĉ end\nĠhapp y\nĠfin ish\nĠc amera\nĠV er\nĠDem ocr\nĠH er\n( Q\ncon s\nit a\nĠ' .\n{ }\nĉ C\nĠst uff\nĠ :Ċ\nĠA R\nT ask\nh idden\ner os\nIG N\nat io\nĠHe alth\nol ute\nEnt er\n' >\nĠT witter\nĠCount y\ns cribe\nĠ= >Ċ\nĠh y\nf it\nĠmilit ary\nĠsa le\nre quired\nn on\nboot strap\nh old\nr im\n- old\nĠD own\nĠm ention\ncont act\n_g roup\nod ay\nĠto wn\nĠsol ution\nu ate\nell ing\n] ->\not es\nent al\nom en\nosp ital\nĠS up\n_ EN\nĠsl ow\nSE SSION\nĠbl ue\nag o\nĠl ives\nĠ ^\n. un\nin st\nen ge\nĠcustom ers\nĠc ast\nud get\nï¼ ģ\nic ens\nĠdeter min\nSe lected\n_ pl\nue ue\nĠd ark\n// ĊĊ\ns i\nther n\nĠJ apan\n/ w\nP U\nĠE ast\nov ie\nĠp ackage\nĠn or\nĠap i\nb ot\n\" ];Ċ\n_p ost\nul ate\nĠcl ub\n') );Ċ\nĠlo op\nPI O\nion e\nsh ot\nIn itial\nĠplay ed\nreg ister\nrou ght\n_m ax\nac ement\nm atch\nraph ics\nA ST\nĠexist ing\nĠcomple x\nD A\n.C h\n.com mon\nm o\nĠ' ../../\nit o\nĠanal ysis\nĠdel iver\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ\nid x\nà ł\nong o\nĠEng lish\n< !--\nĠcomput er\nEN SE\nĠp as\nĠr ais\nH ash\nĠm obile\nĠo wner\nF IG\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nth es\nĠat tr\nw d\n.t ime\naw n\nĠtreat ment\nĠA c\n. View\nim pl\nm ore\np ass\nĠh a\n.f rom\nĠle ading\nFF FF\n( error\n. ui\nat ar\nad ers\nd ates\nĠz u\nĠfl ow\nT arget\nĠinvol ved\nĠi o\npar se\n$ _\nhe st\n. int\n- item\nas y\nS p\nĠsh ift\nN T\nĠt f\n_T R\n. web\nC S\nĠ} )\nĠey es\n_ z\n' );čĊ\nif orn\nĠ{ @\nĠn ice\n.l ist\nĠĠĠĠ čĊ\nĠf loor\nĠred irect\nĠU K\n( ['\nĠw ish\nĠcap t\nleg al\nĠI O\nĠst age\n. String\nĠA fr\nig en\nĠS H\nDe lete\nell s\nĠsol id\nĠmeet ing\nĠwork ed\nĠed itor\nin y\nÐ ¼\n_ read\n. Id\ne ff\nOff set\nch a\nUS ER\nĉĉ ĠĠĠ\nipp ed\nĠd ict\nĠR un\n.h pp\nĠan g\nx ml\nim ple\nĠmed ical\n_t oken\ncon nect\nĠh our\nĠcont roller\n_m essage\nU ID\nG r\nand ed\n_C H\nĠbook s\nĠspe ak\nam ing\nĠm ount\nRec ord\nĉ struct\n.W eb\nond on\nĠ// Ċ\nĠf elt\n.A uto\nid ge\n_p os\nP R\nĠmod ern\nC ollection\n_m sg\nC D\nĠL o\nĠsecond s\nib ly\n.e quals\nĠintern ational\n# pragma\noo th\nW riter\ni ate\nĠce le\nĠB it\niv o\niv ery\nr d\nHE CK\nĠc ache\n.c ount\nĠro ll\n.Re ad\nRE D\nĠset up\nizont al\nmodel s\narg v\nĠconsider ed\n=\" ../\nset tings\nĠR el\nĠgrow th\nĠm ix\nĠWash ington\nĠpl t\nĠI M\ná º\nĠturn ed\nĠDate Time\nĠW ed\n( url\nĠ\" -\nĠlet ter\nAs ync\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ\nĠOct ober\n_l ine\nĠatt ention\nĠcol lect\nĠH ash\nĠim ag\nT ree\nĠsit uation\net te\n_n o\nIV E\nĠv on\n.t arget\nĠknow ledge\nĠdr ive\n.p ost\nĠb lood\nĠc it\npr imary\nĠconfig uration\nte e\nĠph oto\nis ode\nTr ace\nĠg ave\nĠsh ot\nĠA ir\nĠm other\npr ice\nĠmor ning\n)) {Ċ\n- x\nĠtr ade\nĠdes c\nĠ&& Ċ\nĠparent s\nA pi\nå Ī\nt ed\nw er\nĠ æ\nĠs y\nĠK e\nPar ser\nå ħ\nanc y\nĠpie ce\niforn ia\nto String\nr an\nid ing\nPT ION\ncom es\n/ lic\n.c lient\nE l\nL ong\nĠprofession al\nru pt\nv a\nĠcomplet ely\nĠpract ice\nĠse lection\nR em\nin i\nĠc am\nRE E\nĠsit es\np a\nAT US\nÑģ ÑĤ\narr ant\n* (\n_ KEY\nĠB utton\nĠF riday\nse qu\nĠre ader\nĠm essages\nè ¯\nĠbu f\nK e\nĠn ov\nH P\nM sg\nal ign\nar ily\nĠ' ,\n_w ith\nĠd as\nĠhe ard\nat omic\nri al\n) [\nĠdis e\n@ end\nĠg old\nĠf air\nĠsa les\n. Button\nstr ict\ns ave\nĠme asure\nĠ\" +\nec ause\nView Controller\nĠT able\n.p aram\nĠdec ided\n(( (\nIN FO\nĠopport unity\nT e\nIC ENSE\ncc ording\nk i\nĠU N\nĠcont ain\nĠman ager\nĠp ain\nĠF ire\nrom e\nĠpl ans\nF ound\nl ay\nĠDec ember\nĠinfl u\nà º\nren ch\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ\naz ing\nb rief\nc all\nwo od\nĠload ed\nĠgr and\n/ f\nim p\n_ U\nST R\nâĢ ¢\nĠcred it\n.C olor\nor ge\nQUE ST\nĠdiffer ence\nĠP C\nw args\nĠp ub\nund ay\nĠf ra\n.m ax\nĠtri ed\nann els\ns end\nĠreport s\nĠad ult\nä º\nĠcons ist\nĠSt reet\nĠPro gram\nS QL\nM atrix\nounc il\n- A\nĉ w\nĠwho se\nĠrel ig\nĠS ex\nĠg ives\nn one\n.m essage\n( G\n.aw t\n- right\nĠNov ember\nell ig\nut ive\nÄ ĥ\nover n\nĠeas ily\nĠide as\nĠÐ ½\n/c ss\nly ing\nel le\nC an\n_c olor\nоР²\nĠp air\nng th\nĠs plit\nd rop\nart y\non a\nĠcap ital\nĠhe ar\nĠex ists\nĉ log\nem o\nR un\no i\nĠpar ser\nĠM ethod\nĠeduc ation\n[ k\nĠlib rary\n> \";Ċ\n_ UN\nĉ std\nod ed\nĠcall s\nh ere\nR el\nĠbr and\nback ground\ng a\n_add ress\n_param s\nC ategory\nĠInd ia\n_e vent\nĠ ing\nR ender\n.c l\nump y\nĠp et\nF C\nĠA nt\nEx t\nĠchar ge\nen ed\ngr ad\nE O\nĠdep end\nĠ .ĊĊ\nfr ame\nĠd f\nĠh uge\nĠP ART\ned s\n; ;\nĠA M\nĠbas ic\nĠL et\nlic h\nĠar m\nĠst ar\nĠf ederal\nW ork\nĠcar ry\nĠIs rael\n( obj\n={ {\nĠs aved\nĠs yn\nĠconst ant\nV ENT\nĠpos itive\nĠcon duct\nĠsk in\nĠear lier\nĠl ayout\nĠI P\nO UR\nĠt im\nstyles heet\n_ cl\nĠC ard\n++ ){Ċ\nĠtem per\nĠDav id\nĉ try\n.d art\nĠwant s\nĠp icture\nĠv ideos\nĠCom m\nis ions\n_M AX\nM apping\n- content\nĠE ar\n- de\nĠpre m\nbr uary\nĠcom ponents\nĠthrough out\nĠp ull\nĠp ages\nent e\nres pond\nĠg as\ncript or\nĠed ge\nĠb ound\nA CT\n**** **\nĠcre ating\nĠC H\nĠnull ptr\nB r\n+ '\n.c o\n> ::\nĠle arning\n.L ength\n_S H\nĠpat ients\nA IN\nĠk ids\nĠcom fort\nĠsh own\nug ins\nĠB ack\nell a\n_C L\nĠl at\nĠdis patch\nĠclass es\n. at\n.b egin\nĠsuccess ful\nb an\nĠobt ain\nĠS l\nĠl ack\niter ator\nTh read\n(s ize\nĠn one\n.h as\n_ X\ns ort\nn ap\np et\nb in\nĠCan ada\nThe y\nĠd ans\nĠM at\n< td\nĠh air\nĠ' ',Ċ\nĠc u\nĠlaw s\nlet ed\np ed\nĠp ow\nĠk new\n_C OM\n_ ,\nĠM ag\nid ents\n( req\nĠ ),\n- center\nĠw ide\nĠA uthor\nst ants\nĠjob s\nĠm ath\net imes\nBo olean\nĠs cope\n_ is\nĠme as\nĠkey s\nel ay\nĠexact ly\n'=> '\nĠP aul\nm as\nĉ print\n(l en\nf d\nĠ) ;\n. Event\nq li\nir it\nield s\nom an\nĠT op\nĠv ote\nĠm ask\nĠthem e\n- Ċ\nĠpro ps\nĠf ine\nĠwrit er\n_ offset\nc ar\nĠal tern\nĠc opyright\nĠdest roy\npp er\nĠgener ate\npp ed\nâĢĻ d\nĠĠĠĠĠĠ Ċ\nm ake\nĠSh ow\nĠb rowser\nĠfavor ite\nĠcare er\nĠhappen ed\n( char\nĠrecomm end\nĠl iter\n.f ilter\ngr ade\nĠ £\nPh one\nom s\nĠn amed\n- label\nip o\nĠO ther\nĠp anel\nĠro ck\nS cale\nĉ assert\nÐ ´\nĠtr ust\nfr ont\nĠdem on\nA r\nN et\nĠecon omic\nfoot er\nĠr ace\n(n ode\nĠO ption\ns plit\nĠphys ical\nif est\nĠrem oved\n. http\n)) ,Ċ\nĠlook ed\n' ;\nd ing\ng est\natur day\n/lic enses\nPr ice\nĠd ro\nĠto wards\nĠun s\nĠC L\nĉ static\nĠ rows\nĠdef ine\n.re place\nĠf ather\nĠDes ign\nass ign\nm ut\nDe vice\nD id\n') )Ċ\nomet ry\nay load\nĠh istor\nĠP aram\nĠBo olean\nĠn ature\nĠj s\nĠn ation\ni h\nĠdis cover\nse m\nHand le\nĉ r\nĠTe chn\nĠw all\n{ $\n@ property\nĠ\" ../\nĠex am\n.d raw\nopp ing\nĠnear ly\nĠco ol\nĠinde pend\nRE S\nĠhand ler\nĠMon day\nĠs un\nSt yles\nous ly\nĠ ĉ\nv est\nD isplay\n( y\natic ally\nĠpred ict\ny ing\nĠsom etimes\n\" ]Ċ\nĠdr ink\nĠb ul\nific ations\n. insert\n.re g\nĠtest s\nAl ignment\nĠal leg\nĠat tribute\nĠN ote\nĠmy self\nart s\nN ow\nĠinterest ing\nli ents\nĠpop ulation\nĠCal ifornia\n\" I\nå ¹\nĠgre ater\nues day\nĠth ous\nĠcost s\nĠla unch\n\\ Http\nk er\nb and\nĠPl ay\nĠb and\n.sh ape\nes ome\nart icle\n.r f\nĠw er\ná s\nem bers\nus r\nB A\nic an\net t\nvalid ate\nult i\nĠimmedi ately\nz er\nĠfig ure\no es\nell er\nirc le\nĠS ign\n.d b\nĠr ank\nBy tes\nĠproject s\n_re c\nUL AR\nA PI\nĠL ine\nP ort\nĠp oll\nĠg iving\nid ence\n-- Ċ\nĠpl ot\nic ial\nĠw arrant\nIT ION\nĠD ouble\nĠbill ion\ngorith m\nĠequ ipment\nD ATE\nĠ@ \"\nE E\nĠp le\ni ation\nĠhead ers\nĠpro ced\n.Component Model\nĠOb ama\nĠp a\nĠB est\nim ately\n.get String\n. \\\nmp loy\nĠr aw\n_b lock\nund red\n\" },Ċ\n.Group Layout\nĠb rought\nNS String\nth row\ncre ated\n.N ew\n_ view\nC P\nep s\nO p\nĠgr atis\nĠ' \"\nĠinter view\n\"\" \"Ċ\nĠpart ial\nĠa ria\nb ing\nA uthor\nBo ok\nĠP at\num an\nUs ers\npl us\nĠD irect\nven ue\nal pha\nUC CESS\nĠC all\nĠ );čĊ\nim ated\nĠrem ain\nĠant i\nĠL ondon\nĠsaf ety\nPO SE\no les\ncont roller\nBy te\nĠCour t\nĠPh il\nĠAss oci\nen a\nå IJ\n_ST R\nco in\nresh old\nĠb atch\n_C lick\nentic ation\n> ';Ċ\nent y\nĠbegin ning\nĠz ero\nĠCon vert\nĠt err\nĠp aid\nĠincre ased\nc atch\n-s ize\nact ivity\ne quals\nĠque ue\nĠ\" '\nĠIntern ational\nĠf ür\nurs day\nĠsc ient\nall ow\nax is\nĠapp ropri\ned ge\nĠid x\nS uccess\nent ifier\n: \\\nx is\nĠmax imum\nark s\nĠb irth\n( index\nĠmay be\n.p y\nfile s\nĠlim ited\n_ check\nlo ok\npl ies\nĠmov ement\n'] .\nĠbro ad\nĠB E\nĠUn ityEngine\n.c pp\nĠE very\nAd min\nĠf ans\np ared\nĊ ĠĠĠĠĊ\nĠfore ign\nĠp an\nĠt our\nĠOr der\nĠmov ing\nĠa uf\nC all\nc b\nÅ Ł\nvent ory\nĠS ql\nĠful ly\nClick Listener\nW ORD\nĠannounc ed\n) čĊčĊ\nĠagre ed\nri e\nĠe arn\n_l ink\n. array\n(t ext\nĠmaterial s\n, p\nff ff\nv g\nĠ ©\nĠun less\naj ax\nLO G\nĠsex ual\nĠ\\ \"\n- time\nĠco ach\nĠsupport ed\nĠphot os\nif orm\n.C reate\n) ]\nri er\nĠd ialog\nav er\nig e\n) +\n_id x\n: [\n_m in\nĠC ong\nĠpress ure\nĠteam s\nS ign\nb egin\nri an\nNE SS\nL S\nĠimpro ve\nĠS unday\nĠdef inition\nig er\nroll ers\nĠthink ing\nT emplate\n- F\nĠem erg\npl ates\nĠUS A\n.set State\nĠAl so\nre v\nĠen able\nĠC O\nPE CT\nĠcon cept\n) -\nĠâĢ ¢\nĠset s\nĠmean ing\nem on\nĠCon s\nc mp\ned er\nann ed\nicens ed\nĠS uper\nĠd aily\nĠmult i\n_ u\nĠchall eng\n_m ode\nĠP romise\nĠstr ict\nj o\nint on\n( list\nOn ly\n> {\nĠveh icle\ní ķ\nĠPl ayer\nĠD el\nĠp ool\n. url\nnes day\n();čĊ čĊ\nĠ\" );Ċ\nL ocal\n. \");Ċ\nĠorgan ization\nre nder\nĠApp lication\nĠsum mer\nex pected\nN A\nĠr ap\n_ obj\nĠsur face\nĠP UR\nĠ}, ĊĊ\nĠvariable s\n(m essage\nĠop in\n.b ack\nа н\nĠwork ers\nv m\nC o\nught er\nĠm aster\nĠ\" \",\nĠst ories\n. User\nĠcele br\nines e\nB S\nĠCom mand\nash board\nĠo g\nk g\n. image\n.st yle\nĠstep s\nĠB en\n( args\nĠP erson\n, y\nĠofficial s\n| Ċ\nĠsk ills\nv c\nĠbuild er\nĠg ar\nA ccount\nĠA uth\nç Ķ\n'] )Ċ\nĠA T\nn n\n. Int\nSS ERT\nĠeffect ive\nLE TE\nĠto ols\nAR D\nĠdig ital\nD ouble\nĠF ind\nR C\nĠin line\n/ r\nAR AM\nAS K\nĠint ent\na ight\n_add r\nĠrequest s\n.f irst\nĠde bug\nĠsp ent\n() ));Ċ\nÅ Ľ\nĠpr incip\nLog ger\nclud es\n. use\nĠsur v\nmed ia\nĠFe bruary\nĠM ac\nĠmiss ing\nĠw ife\nĠtalk ing\nĠM ake\nĠc art\nĠloc ated\nE nc\n- a\nch ron\nĠc ards\nĠgu y\nĠp ers\nĠY es\nate ver\nĠA ng\nol ar\nĠE ven\nĠacc ur\nĠP ower\nĠG old\nc lear\nPro cess\nĠrec ords\nĠk illed\n.c lear\nĠWARRANT IES\nĠpur pose\npan el\nJ ECT\nÃŃ a\nĠex erc\nW S\n/ L\n. exports\nĠ__ _\nĠs in\nS ervlet\nĠd é\n.de lete\nro ke\nS l\nug h\near s\nĠpoint er\nĠh op\nall ery\nĠo bs\nco very\nĉ char\nĉĉĉĉ ĉĉĉĉĉĉ\nĉ def\noc ity\nitch en\nul ations\nĠF IT\nĠ ).\nstraint s\nvent ion\nĠrequ ires\nĠO per\nM E\nOUN T\nal let\nĠn orm\nI RE\nex as\nĠprogram s\nĠwe ak\n' .$\nu ing\nĉ ĠĠĠĠĠĠĠ\nĠm il\nĠf irm\ninit ely\n_VAL UE\nap se\natis f\nĠdem and\n_m od\nĠdescri bed\nĠpl aces\nV ID\nĠal one\nĠex port\nĠv ec\nĠM ax\nĠactiv ities\nict ures\ng ener\nĠm a\nĤ ¬\nĠexpress ion\nC allback\n_ content\nĠM ost\nĠtest ing\nE C\nCH ANT\nĠad just\n.Th reading\n( ctx\nĠag ree\nig hest\nĠu i\nĠL aw\n. Y\n> ĊĊ\n.ex ample\nber g\nĠmov ed\nĉ e\nĠS aturday\nĠpay load\nÄ ĩ\n) :ĊĊ\nĠbe y\nur er\n< script\nĠs ymbol\nĠass um\nĠp ul\nE ffect\nĠh undred\nTo ol\nak ed\ncon nection\nĠvo ice\nĠp d\nĠtrans action\nĠlink s\nE rr\nĠInd ian\nT C\natal og\nn i\ns ign\n<< \"\nj i\ny a\nĠdemon str\nul ated\n. St\nĠinst it\nĠbo ost\nĠcell s\nol ic\n.P ro\n: ,\n\"> \\\nĠth us\nĠReg ister\nh ol\nĠCh inese\nĠpost ed\nĠm agn\nab ilities\nĠdise ase\nĠrem ains\nĠPro f\n- form\nĠc in\norg an\nic ate\nĠst ress\n] *\nĠ ----------------------------------------------------------------\n_ context\nor ry\nĠd ied\nm at\nĠstart s\n.M essage\nĠrun s\nĠgu ide\nĠwarrant y\nential s\nd ict\nĠS ize\nul er\nĠrespons ible\n_SE T\nĠcont aining\nĠPr ice\n| |\nF S\nĠem p\n_b utton\n( uint\nĠsu ff\np th\nĠdef initely\nput e\nĠmarket ing\nĠW H\nĠS ie\n+ =\nOL OR\nĠcons ult\nĠs igned\nĠse quence\nle e\nĠrequire ments\nh y\nEx press\nM T\nse y\nĠ ult\nå ®\nellig ence\nĠanal y\nĠd ress\neng ine\nĠG reat\nĠAnd roid\nĠA lex\nm ode\nD ictionary\n.D ate\nä ½\nV ICE\nĠfam ilies\nĠRuss ian\nĠT imes\n.c all\n$ (\nPro file\nĠf older\nch es\nĠleg is\n_ row\nun es\nÙ Ħ\nĠ} ).\nAss ert\nag en\nĠH and\nI ter\nĠbig gest\nore ach\nĠpol ic\nĠper missions\nĠshow ed\nĠE lement\nĠtop ic\nâĢĶ âĢĶ\nro ad\nĠB ank\nrec ord\nĠpart ners\nĠR ef\ness ions\nĠass ess\nU ST\nĠPart y\npro du\nL C\nĠ ul\n. form\nh ide\nc opy\nUT F\nĠSO FTWARE\nčĊčĊ čĊ\nĠL in\nun a\nug ar\nĠadmin istration\nĠopen ing\nĠsc an\nĠcontin ued\ncom ponent\n.s p\nĠhapp ens\num my\nĠP R\n.F ile\nĠDown load\nLo ading\nd i\nĠwait ing\n_A DD\nT ab\n.query Selector\nĠecon omy\nĠF rench\nt xt\nĠf ant\n_ ;Ċ\nH older\nS H\nĠn umpy\nĠst reet\nĠm ale\n\\ Model\nang ing\nĠB ill\nĠprevious ly\nB I\nĠSec ret\nĠm ist\nĠF ield\nup s\nĠPro cess\nĠke pt\nĠO T\nĠtrad itional\n. i\nam in\nĠhelp s\nAn y\norig in\nilt ers\nj u\nd esc\nĠA ccount\nĠ) čĊ\nk top\nol ly\nĠf s\nĠ ê\nĠ ut\nĠcent ral\n(t est\n.A n\nĠs atisf\nG R\nĠF ull\nĠhe at\nib er\nĠon to\nm os\nS chema\nĠfact ory\n\" .$\naw s\nSt atement\n(t arget\nĉ new\n.b e\nĠg uest\nĠm al\nAR Y\nĠre ached\nĠm ouse\nĠchall enge\nĉd ouble\nĠT em\nĠt error\nĠex tract\n_T O\nĠsepar ate\nĠm ir\nh elp\nĠcap acity\nĠProp erty\nk an\n_c reate\nĠL ight\n.p arent\nĠunderstand ing\nĠeas ier\nĠ| =\nĠen h\nĠf at\nĠprot est\nam m\n_ AT\n- of\nil s\nĠO h\nĠps ych\nĠ$ .\nind s\nĠrel ative\nsh op\nsh ort\nĠS and\nuest ion\nĠf ear\n/ ĊĊ\n. context\nĠschool s\nĠser ve\nz one\n_d b\nĠmajor ity\nex ample\nĠl ang\nĉ ĠĠ\nReg ister\nend o\nĠprocess ing\n_t emplate\n- user\nĠe g\nC OM\nĠBl ue\ni ro\nĠrem ote\nĠI T\n#! /\nĠred istrib\nra z\nĠS ince\nĠT ur\nBack ground\n== =\nĠref lect\nĠpro s\nc md\nĠwh om\nCom pat\nĠA re\nId entifier\nĠTh om\n_ port\ng u\nĠmon itor\nr m\nĠpat ient\nver ter\nĠg ain\n- ui\nIn st\nĠd ies\nA rea\n_f ilter\nĠgr at\nĠreal ity\nord inate\nol ved\nCont act\nĠcompl iance\n_ or\nĠV ar\nd l\nĠapp end\nG ER\n(m ax\n.re nder\nĠd ynamic\nordin ates\n_ options\n_c olumn\nĠb atter\ns pace\nL a\nĠS ource\n/b in\nĠd os\nĠBo ard\nĠTh read\nĠA L\n( config\nĠM er\nĠm iles\n_ header\nETH OD\niz z\nĠbenef it\nĠinteg r\n(c urrent\nul o\n. default\nĠD iv\nĠt on\no th\nerv ation\ned om\nĠb aby\nce ived\n.t op\nrior ity\nĠL ocal\nri age\nĠattack s\nĠh ospital\nĠfem ale\nĠLog in\nĠFl or\nĠch ain\nash ion\nText ure\nS ave\nĠf arm\n.cont ains\n.T est\nĠknow s\nĠgener ally\nip eline\nĠme ant\nenc ia\nĠn icht\nĠcont ents\nP M\nched ule\n( line\nC G\nj ob\nĠRe al\nu er\nf irm\nĠ Ø\net ro\n\" `Ċ\nĠspe ech\nĠth r\nfore ach\nĠw arn\nĉ l\nĠhe avy\n< li\nN e\nĠinvestig ation\nM ath\n- title\nĠch urch\nĠdes pite\nch ain\nĠwh atever\nar ian\nf n\nĠm eta\n} )ĊĊ\nU FF\nĠregard ing\n_S UCCESS\nm es\nĠInt ent\nĠres olve\npos s\nir a\nfor ce\no ice\nà ¢\nĠp m\nĠup dates\nA rr\nĠ Ñ\ntest ing\nĠto ward\nnt ax\në ĭ\nĠlist en\nĠgo als\nInstance State\nD r\nĠr are\nĠtr ail\nKe ys\nC al\nC ar\nĠPe ople\nĉ local\nclass es\nRe ference\n.for Each\nem b\nact iv\nĠpr im\nred ict\nĠr ad\næķ °\n.B ack\nĠsp read\nĠc lock\nĠv ir\ned itor\nĠeffort s\nĠbr anch\nĠind ust\nĠmot or\nĠam b\nĠdat etime\nĠren cont\nĠChrist ian\nĠAmeric ans\nf ull\nĠf mt\n.m ain\nĠca used\n_ update\nĠCont ent\nAT CH\nĠb ath\nĠE ach\nĠr adio\nach ment\nuz z\nSub mit\nĠre strict\nab in\nĠL oad\nĠext ension\nĠess ay\nĠh at\navi our\nto Be\n\": [\nĠoffer ed\nĠv ill\n(d ouble\næĹ ¥\nb c\n_f ree\nĠM iss\nĠB er\nĠ è\nĠL ike\nĠhelp ed\n.get Name\n_ AL\nĠsp irit\nĠAp ache\nw s\nĠthere fore\n( params\n_ img\nĠpe ace\nĠinc or\nĠEX PECT\nĠmin or\nip es\nĉ data\nselect or\nc ity\ntr ie\n.b ase\n_f rame\nĠopen ed\n/ json\nL Y\nn u\n.D e\nt f\nm argin\n.P arse\nĠp i\nĠe q\nb d\nField s\nĠT ree\nĠb an\nist an\nĊ ĠĠĠĠĠĠĠĠĊ\nĉg l\nĠprodu ced\ns ystem\nM ark\n_h ash\nĠb g\nĠconst it\nĠLe ague\nĠmiss ion\n_ format\n([ Ċ\nclus ion\n! \"\nÐ ·\nb reak\nĉs witch\nĠth er\nTrans form\nĠfoot ball\n- link\nr oute\n. auth\nĠb ag\nov ers\nĠen abled\nĠr ac\n( I\nC R\nanc ing\nĠman aged\n_ q\nNG TH\nĠm ac\nĠA uto\nament e\nĠ' ',\n.App end\nĠp in\n. item\nack ing\nĠocc as\np erson\nĠt i\n.Re g\nĠh aven\nĠg lass\nĠ\" )\n_ char\nres ource\nĠep isode\nĠ' _\nĠE s\nĠEar th\nÂł Âł\nUP DATE\nĠS ou\nu is\nt ypes\nĠm as\nĠf av\nĠcon struct\n_r ate\ner as\nĠ| Ċ\nrop erties\nĠext ernal\nĠap plied\nĠpre fix\not ed\nl ers\nĠc old\nĠS P\nĠCh urch\nĠOut put\nlos ed\nç ļ\nific ate\noper ation\nher it\nx FF\n. env\n_ err\nos h\nD irection\nC ancel\nĠFr ank\nĠfind ing\n. )ĊĊ\nĠr outer\nãĥ »\ns es\nĠc row\n== '\nĠs and\nĠr id\nit ure\nĠent re\nĠo bserv\nĠv ac\nð Ł\n- T\nA rt\nn ight\n. search\nĠex change\nĠdistr ict\n. os\nĠdep artment\nĠdoc uments\nĠcent ury\nĠN ext\nH ost\nĠK IND\nĠsus p\n- P\nre nd\n. em\nu ite\nist ers\n( json\nĠAn n\nw t\nat i\nĠHT ML\nwh en\nD irectory\nĠsh ut\n< a\ned y\nĠhealth y\nĠtemper ature\nĠG en\nĠmet al\nĠsub mit\nĠD O\nĠat tract\nĠ{ };Ċ\nĠW ord\nĠl l\nĠseem ed\nk o\nI ED\nĠl abor\n.Cont ext\nĠas set\ny ou\nĠc ars\nĠC olumn\nĠr é\nĠs quare\nĠNS String\nâĢĿ ,\nap es\n.. .Ċ\nĠthan ks\n( props\nĠt ick\nĠexper iment\nĠpr ison\nt ree\n- text\nĠIO Exception\n-w idth\n_ST ATUS\nf ast\n-b ody\n- header\nĠgu ar\ncre te\nĠT im\nĠclear ly\nĠRepublic an\nĠjust ify\nи ÑĤ\nĉ ĠĠĠĠ\nc ache\n; //\nĠpres ence\nĠfact ors\nĠemploy ee\n] ))\nM ember\nĠselect or\nb or\nĠM ex\nçļ Ħ\nut ex\n_t ag\nail ure\nĠN et\nĠre li\nE G\nĠf printf\nĠte en\nlo ss\nĠle aving\nDe legate\nĠbe at\nĠmin ute\nsub scribe\nĠredistrib ute\nCon stants\nĠcan cer\n/ {\nB L\nĠs pan\nĠCh ild\nC enter\nĠear th\nY S\nĠLe vel\nĠse a\n.s upport\n.in ner\n. 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Request\nĠT oken\n_p osition\nĠn om\n- term\nĠrem aining\ni ostream\nĠpie ces\nap y\nĠL ess\nr ange\numb n\npr ise\n_ option\nIm pl\nk wargs\nĠbusiness es\nAl ert\nĠpart ies\nĠCont ainer\nĠPr ivate\nĠPl an\nĠregister ed\nĠj our\nack er\nен и\n/ >\nch at\nse ct\nĠcre ation\nolut ely\nĠinst ant\nĠdel ivery\nick en\ny es\nĠFr anc\nbl ing\nend a\n[ (\n_r ange\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\nĠsched ule\nCon n\nĠthan k\nx d\nĠh ook\nĠdocument ation\nParam eters\nH ello\nv t\nĠart icles\nĠw est\ndef ined\n. select\nok ens\nĠV AL\n.f ile\nres et\nĠmy s\nĠM A\n] ),\nĠc ities\nrel ated\nå Ľ\nĠappe ared\nĠw id\n.p anel\nĠIn s\n. entity\nĠde cre\nĠL ou\n(t ime\nĠTh ank\n.create Element\nĠmention ed\noun ce\nĠT ry\nĠW all\n/ images\nĠM enu\n' čĊ\nĠE r\nĠcrit ic\nĠY ear\n( param\nĠf lo\nN N\noot er\nĠ ];Ċ\nĠA ff\n\" github\nroom s\nĠh yp\ng lobal\nĠa vec\næľ Ī\nĠcomplet ion\nĠcon d\nonym ous\n( temp\nĠst ars\nĠre levant\nĠcover ed\nĠel im\n_t ypes\n( bool\nĠt u\n_ex ists\nĠsec ure\nĠst ored\n] /\nx F\nĠCont roller\nĠm igr\nM I\nĠD en\nĠann ual\nU IL\n- and\nĠcr ime\nb el\nĠk itchen\n@ g\n_p h\nourn ament\nĠS ocial\nĠS pecial\nlog ger\nĠt ail\nĠun known\nd ed\nĠapp rec\n(d b\nc f\nĠass ign\n- out\nĠM ont\nd p\nw idget\nĠst one\n- primary\n. grid\nResult s\naz z\nĠda ughter\nĠcur r\nĠl in\nĠs outh\nform s\nĠO UT\nlet te\nak s\nig ure\nĠE U\nvar iable\nĠb rief\nĠSc ott\nĠcon ference\nand a\n_ lock\nor al\nĠe ine\nOR S\n//////////////////////////////// ////////////////////////////////\ness o\nĠr is\nĠg ender\nest ic\nL icense\n( out\nĠm s\nSe e\nĠwill ing\naz e\nĠs ports\nĠy es\nl u\nĠp urs\n/j avascript\n- pro\nnav bar\n_pro duct\n/ bootstrap\nĠdr iving\nĠ Ä\nĠpro pos\nult ip\nup lic\n. email\nĠappro x\n( cl\nĠwe ar\nĠrep ly\nass et\nĠ ice\nĠt x\nk r\nĠGerman y\nĠGe orge\nĠc b\nĉ err\nM ove\nĠpol y\nvo ice\n} \"\nĠan imal\nA v\nĠL ocation\nĠn ative\n] [\"\n< double\nĠm ais\n, int\nĠpre par\nĠinter val\nplement ation\n_ ERR\nĠb ug\n> \"\nst at\nĠ} ,čĊ\n< span\nĠfa ith\nĠ rom\npre v\nĠE lect\nF ind\nĠg od\not or\n// ----------------------------------------------------------------\norig inal\nC pp\nĠSen ate\nĠposition s\nĠweap ons\nĠco ff\nĠpur poses\np ol\nĠim press\nĠanim als\n. Entity\n(n p\nĠmur der\nĠ` `\nfl ag\nĠsol utions\nĠAct ive\nĠb right\n.d ate\nĠsit u\nï¼ Ī\n. ID\nĠs ie\n), čĊ\nak t\nS pace\n.d at\n.index Of\nh an\naz ine\nĠZ e\nĠcr ash\n( /\n> =\nÐ ±\niv a\n.Auto Size\nĠL at\n_ ext\nInitial ize\n.reg ister\nOP Y\nĠre verse\n_d is\n'] [\nĠprom pt\nont o\nĠJ ournal\nr outer\nĠmys qli\n# else\n) \"\n-x s\nlet s\nph an\n. LE\nW ill\nĠaff ord\nĠsk ill\n-t oggle\nN C\nB ind\nT S\nJ ust\niter al\nY P\nĉ unsigned\nĠw ind\n)) :Ċ\nĠw arning\nĠW ater\nĠd raft\nĠc m\nĠs am\nĠhold ing\nz ip\nĠSc ience\nĠsup posed\nG en\nĠdi et\n< h\nĠP ass\nv i\nĠhus band\n� �\nn ote\nĠAb out\nĠIn stitute\nĠcl imate\n.Form at\nĠn ut\nest ed\nĠapp arent\nĠhold s\nf i\nnew s\nC M\nv ideo\n': '\nD ITION\np ing\nĠsen ior\nw a\n-- >Ċ\n_ default\nĠD atabase\nre p\nE SS\nner gy\n.F ind\n_m ask\nĠr ise\nĠk ernel\n:: $\n. Q\nĠoffer ing\nde cl\nĠC S\nĠlist ed\nĠmost ly\neng er\nĠblock s\nol o\nĠgover ning\n\\ F\nĠcon cent\n.get Text\nĠm b\nĠocc urred\nĠchang ing\nSc ene\n_C ODE\nB eh\n\" The\nĠt ile\nĠAssoci ation\nĉ P\nal ty\n_ ad\nod ies\ni ated\nĠpre pared\nposs ible\nĠm ort\nTE ST\nĠign ore\nĠcal c\nĠr s\nĠassert Equals\nĠs z\nĠTH IS\n. \"Ċ\nĠcan vas\nj ava\nĠd ut\nVAL ID\n.s ql\n. input\nĠa ux\nS up\nĠart ist\nV ec\n_T IME\n.string ify\net ween\nĠC ategory\nĠ[ -\nĠDev Express\nĠJ ul\nĠr ing\n. ed\nY Y\nL et\nText Field\nĠfl at\n_p rint\nĠOT HER\nad ian\nĠcheck ed\ne le\nAl ign\nstand ing\nĠ[ ],\nĠl ab\nuck y\nĠChrist mas\n( image\n.m odule\nĠl ots\nĠslight ly\n(f inal\ner ge\nè ¿\nĠPol ice\nĠR ight\nĠaw ard\nĠO S\nĠ{ }ĊĊ\nĠp tr\nov es\nic ated\nеР¼\nĠman age\nolid ay\nAm ount\nool Strip\nt body\nN av\nw rap\nB B\nĠwatch ing\nari os\nĠoption al\n_ K\nĠL icensed\n.M ap\nT imer\nĠA P\nĠRe v\n( o\n, c\num in\neta iled\nĠH y\nĠbl ank\nag ger\nĠS elf\n() [\n.m ake\near n\nch annel\n< pre\nble m\n_p assword\n_s p\nic ing\ne z\nĠthe ory\nĠT er\n, n\nlog o\nĠHT TP\n() ))\n.h andle\n> ;Ċ\nW orld\nĠpy thon\nĠl if\nĠtr av\nĠcon ven\ncom pany\nĠCl ub\nV er\nB tn\nĠz one\nproduct s\nĠE duc\nĠver ify\nĠM il\non o\n] );ĊĊ\nEN CE\nĠpack et\nĠc er\nĠen umer\nĠpar s\nform ed\nĠocc up\nt re\nĠexerc ise\nD ay\n_s um\nĠask ing\napt ion\nĠord ers\nĠsp ending\nĠE RR\n.D is\nĠU til\nâĢľ I\n\\ '\n? )\n/ >Ċ\nĠem ot\nĠinflu ence\nĠAfr ica\natt ers\nÙ ħ\n.s ession\nĠch ief\nĉĉĉĉĉĉĉĉ ĉĉĉ\nĠto m\nclud ed\nser ial\n_h andler\n.T ype\nap ed\nĠpolic ies\n- ex\n- tr\nbl ank\nmer ce\nĠcover age\nĠr c\n_m atrix\n_ box\nĠcharg es\nĠB oston\nP e\nĠcirc um\nĠfil led\nĠn orth\nicture Box\nĉ res\nè ®\nĠter min\nĠ[ âĢ¦\nIRE CT\nĠb er\nĠ\" ../../\nret ch\n.c ode\n_c ol\nĠGovern ment\nĠarg v\nĠL ord\nas i\nEx ec\nĉ let\nvert is\nĠdiscuss ion\nen ance\nout ube\ntype of\nĠs erved\nĠP ut\nĉ x\nĠs weet\nB efore\nateg y\n. of\nĠM aterial\nS ort\nON T\nig ital\nWh y\nĠs ust\nĠ ç\nab et\nĠseg ment\nĠ[ ],Ċ\nĠMus lim\nĠfind ViewById\nc ut\n_T EXT\nĠM ary\nĠlo ved\nĠl ie\nĠJ O\nĠis set\nmon th\nĠpr ime\nt i\nĠCar ol\nU se\nĠP op\nĠS ave\nInt erval\nex ecute\nd y\nĠI ran\n_ cont\nĉ T\nĠph ase\ncheck box\nwe ek\nĠh ide\nĠt il\nĠj u\nC ustom\nb urg\n/ M\nT ON\nĠqu ant\nĠr ub\nix els\nĠinst alled\nĠd ump\nĠproper ly\n( List\nĠdec ide\napp ly\nH as\nĠkeep ing\nĠcitiz ens\nĠj oint\np ool\nS ocket\n_ op\nĠweap on\ngn ore\nĠEx ec\nott en\nĠM S\nĠ( -\nĠRe view\nĠex amples\nĠt ight\n! (\nD P\nĠMessage Box\nĠphot ograph\nUR I\né t\nl ow\nĠGr and\n.p ersistence\nĠmaint ain\nĠnum s\nĠz ip\nial s\nĠG ets\npe g\nĠB uffer\n~~ ~~\nra structure\nĠP L\nu en\nob by\nsize of\nĠp ic\nĠse ed\nĠexperi enced\nĠo dd\nĠk ick\nĠproced ure\navig ator\n- on\n, j\nĠAl though\nĠuser Id\nac cept\nBl ue\nIC olor\nl ayer\nav ailable\nĠend s\n.t able\nĠdat aset\nb us\nĠexpl ain\n( pro\nĠCommit tee\nĠnot ed\n] :Ċ\nD im\nstd io\n. \",Ċ\n_s ource\nĠWe ek\nĠEd ge\nĠoper ating\nĠest e\ni pl\nag ination\nĠpro ceed\nĠanim ation\n.Model s\nĠW atch\ni at\nĠopp on\n/ A\nRe port\nĠs ounds\n_b uf\nIEL D\nĠbu nd\nĉ get\n.p r\n(t mp\nĠk id\n>ĊĊ Ċ\nĠy ang\nNot Found\nÑ Ĩ\nm ath\n@g mail\nĠL IMIT\nred ients\nĠv ent\navig ate\nL ook\nĠrelig ious\nĠr and\nri o\n( GL\n_ ip\nu an\nici ency\nĠCh ange\n> čĊčĊ\nĠEnt ity\nĠrencont re\nĠR et\npl an\né n\nBO OL\nur ies\ntr ain\nDef inition\n======== ====\nz z\nAn imation\nĠO K\n_m enu\n.b l\n_s core\nĠac ad\n( System\nĠref resh\n'=> $\n.G raphics\nament o\np id\nt c\nĠt ips\nĠhom es\nĠf uel\nâ ĸ\n_h elper\nĠĠ čĊ\nĠR oom\n.C lose\n_ attr\nĠM ount\nĠE v\nar ser\n_t op\ne ah\nĠDe lete\nãĢ į\nu ke\nĠus age\nar ia\n_de v\nĠtext ure\nĠconvers ation\ne per\nBe an\nd one\nnon atomic\nĠSe cond\nĠshoot ing\n_p re\nCom ponents\nĠ] ĊĊ\n__ ,\nstit ution\n.Ch ar\n> ();ĊĊ\nĠpresent ed\nĠw a\nok er\n- ĊĊ\nin er\nĠbe coming\nĠinc ident\nAt t\nĠreve aled\nfor c\nĠbo ot\n.p age\nEnumer ator\n_ ->\nPh oto\nĠs pring\n. \",\nĠD ictionary\nB JECT\nĠloc ations\nĠs amples\nInput Stream\nĠB rown\nĠst ats\nqual ity\nÑ ħ\n-d is\nĠhelp ing\nĠp ed\n( se\nĠWh o\nal ian\nint ernal\nĠf t\n> ().\n-> {\nĠm ine\nĠs ector\nĠg ro\nĠopport unities\nĠà ¼\nĠm p\nĠalleg ed\nĠdoub t\nM ouse\nAb out\n_p art\nĠch air\nĠstop ped\nlo op\nent ities\nĠapp s\nans ion\nĠm ental\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ\nF R\nĠdef end\nc are\nĠide al\n/ api\nur face\nĠe le\nul ator\nĠR ights\nangu ages\nĠfund s\nĠad apt\nAt tributes\nĠdep loy\nopt s\nĠvalid ation\nĠconcern s\nu ce\n.n um\nult ure\nil a\nĠc up\nĠp ure\n.F ore\nĠHash Map\n.value Of\nas m\nM O\nĠc s\nĠst ores\nĠ ************************************************************************\nĠcommunic ation\nm em\n.Event Handler\n. Status\n_ right\n.set On\nS heet\nĠident ify\nener ated\norder ed\nĠ\" [\nĠs we\nCon dition\nĠA ccording\nĠpre pare\nĠro b\nP ool\nĠs port\nr v\nĠR outer\nĠaltern ative\n( []\nĠCh icago\nip her\nis che\nĠDirect or\nk l\nĠW il\nkey s\nĠmy sql\nĠw elcome\nk ing\nĠMan ager\nĠca ught\n) }Ċ\nS core\n_P R\nĠsur vey\nh ab\nHe aders\nAD ER\nĠdec or\nĠturn s\nĠr adius\nerr upt\nC or\nĠm el\nĠin tr\n( q\nĠA C\nam os\nM AX\nĠG rid\nĠJes us\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ\n.D E\nĠt s\nĠlink ed\nf ree\nĠQ t\nĠ/** čĊ\nĠf aster\nct r\n_ J\nD T\n.C heck\nĠcomb ination\nĠint ended\n- the\n- type\nect ors\nam i\nut ing\nĠum a\nX ML\nU CT\nA p\nĠR andom\nĠr an\n.s ort\nĠsort ed\n. Un\n_P ER\nit ory\nĠprior ity\nĠG al\nĠO ld\nh ot\nĠD isplay\n(s ub\n_T H\n_ Y\nĠC are\nload ing\nK ind\n_h andle\n, ,\nr ase\n_re place\n.add EventListener\nĠR T\nĠenter ed\ng ers\nĠ ich\n( start\n/ app\nĠbro ther\nM emory\nOut let\nĠ utf\npre c\nĠn avigation\nOR K\nĠd st\nD etail\nĠaud ience\nĠd ur\nĠcl uster\nun ched\nĠ ],\nĠcomfort able\n. values\nĠT otal\nĠsn ap\nĠstand ards\nĠperform ed\nh and\n(\" @\nå Ń\nĠph il\nib r\ntr im\nĠfor get\nĠdo ctor\n.Text Box\nicon s\n, s\nĠO p\nS m\nSt op\nĉ List\nĉ u\nCom ment\n_V ERSION\n.X tra\nP erson\nr b\nLO B\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ\nĠCent ral\nIC K\nra q\nĠput ting\nĠm d\nĠL ove\nPro gram\nB order\no or\nĠallow ing\na fter\nĠent ries\nĠMay be\n] ).\nĠSh ort\n) \\\n.n ow\nf riend\nĠpre fer\nĠG PIO\nos is\nĠGame Object\nĠsk ip\nĠcompet ition\n_m atch\nlic ations\n_CON T\n.group Box\nĠal s\n\" We\n_e q\nl an\n_ search\nĠMus ic\nas is\nĠb ind\nĠIs land\nr um\n( E\nĠse at\nV ideo\nĠa ck\nree k\n={ ()\nĠr ating\nĠrestaur ant\nDE X\n(b uf\npp ing\nual ity\nĠle ague\nĠfoc used\nap on\n$ data\nCL UD\nCLUD ING\nĠabs olute\n( query\nĠtell s\nA ng\nĠcomm unities\nĠhon est\nok ing\nĠap art\nar ity\n/ $\n_m odule\nĠE nc\n. an\n.Con fig\nC re\nĠsh ock\nĠAr ab\nI ENT\n/ re\nĠre trie\nycl er\nis a\nĠO rgan\n. graph\nĠ í\nĠB AS\nEn um\nĠposs ibly\nÑĢ аÐ\nĠJapan ese\nĠc raft\nĠPl ace\nĠtal ent\nĠfund ing\nĠconf irmed\nĠc ycle\n/ x\nG E\nĠhe aring\nĠpl ants\nĠm outh\np ages\nor ia\nĠRem ove\n_t otal\nĠo d\noll apse\ndo or\nĠb ought\nĠadd r\nAR CH\n_d im\ndd en\nĠdec ades\nRE QUEST\nĠvers ions\nf ire\nĠmov es\nf b\nĠcoff ee\n.con nect\nĠR ow\nĠs chema\nS cope\n- Type\nĠfight ing\nĠret ail\nĠmod ified\nT F\nFile s\nn ie\n_com mand\nst one\nĠ ÑĤ\n_ thread\nĠb ond\nĠDevelop ment\nĠp t\nF ORM\nple t\nĠident ified\nc pp\nĠc oding\nok ed\nĠM aster\nID TH\nĠres idents\nred it\nĠPh oto\n= -\nun te\nate ur\n_ST ATE\nĠS ing\nĠshe et\n. val\nor se\nĠh ers\nĠdetermin ed\nCom mon\nĠw ed\n_ queue\nP H\nĠAt l\ncre d\n/L ICENSE\nĠm es\nĠadv anced\n.j ava\n.S h\nG o\nk ill\nf p\n_set tings\nĠp al\nĠtr uck\nĠcomb ined\nĠ\" ${\nĠCor por\nĠjo ined\nĠJ ose\nĠC up\nun s\nest ival\nlev ision\nĠbro ken\nĠmar riage\nĠWest ern\nĠrep resents\nĠT itle\nĠs s\n.A ss\nongo ose\nient o\n< >();Ċ\nĠabs olutely\nĠsm ooth\nTER N\nĠUn less\nW ord\nĠmer ge\nig an\nĠV ol\nĠn n\n.get Id\nĠÐ ·\nĠsex y\nĠseek ing\nS ingle\n. this\nĠk om\nb ound\n; \"\nĠfont Size\n_d f\nĠinj ury\n( H\nĠiss ued\n_ END\n: self\nĠp atch\nĠle aves\nĠad opt\nFile Name\nãĢ IJ\nĠexec utive\nĠBy te\n] ))Ċ\nĠn u\nout ing\nclud ing\n- R\n. options\nĠsub stant\nav ax\nĠB UT\nĠtechn ical\nĠtw ice\nĠm ás\nĠun ivers\ny r\nĠdr ag\nĠD C\nĠs ed\nĠb ot\nĠP al\nĠH all\nforc ement\nĠa uch\n.m od\nnot ation\n_file s\n.l ine\n_fl ag\n[ name\nĠres olution\nĠb ott\n(\" [\nend e\n( arr\nF ree\n( @\"\nĠD istrict\nPE C\n: -\nP icker\nĠJ o\nĠĠĠĠĠ Ċ\nĠR iver\n_ rows\nĠhelp ful\nĠmass ive\n--- Ċ\nĠmeas ures\nĠR untime\nĠwor ry\nĠS pec\nĉ D\nãĢ ij\nĠ) {Ċ\nĠwor se\n(f ilename\nĠl ay\nĠmag ic\nĠThe ir\nou l\nst roy\nĠWh ere\nĠsu dden\nĠdef e\nĠb inding\nĠfl ight\nĠOn Init\nĠW omen\nĠPol icy\nĠdrug s\nish ing\n(' ../\nĠM el\npe at\nt or\nĠpro posed\nĠst ated\n_RE S\nĠe ast\nĠCON DITION\n_d esc\nĠwin ning\nfol io\nM apper\nĠP an\nĠAn ge\n.s ervlet\nĠcop ies\nL M\nĠv m\nå į\nĠd ictionary\nS eg\nel ines\nĠS end\nĠ iron\nĠF ort\n.d omain\nĠdeb ate\nNot Null\ne q\nach er\nl f\nĉf mt\nĠlaw y\nÄ Ł\nĠM en\nĠtr im\n( NULL\nĠ! !\nĠp ad\nĠfollow s\n\"] [\"\nre qu\nĠE p\n.g ithub\n( img\net o\n(' \\\nS ervices\numbn ail\n_m ain\nple ted\nfort unately\nĠw indows\nĠpl ane\nĠCon nection\n. local\nu ard\n} \\\n== \"\nand on\nĠR oy\nw est\nig inal\nem ies\nit z\n') :Ċ\nĠP eter\nĠt ough\nĠredu ced\nĠcalcul ate\nĠrap id\nc ustomer\nĠeff icient\nĠmed ium\nĠf ell\n. ref\nĠC as\nĠfeed back\nS peed\n( output\naj e\nĠc ategories\nĠfe e\n} ;\nĠde leted\nre h\nĠpro of\nD esc\nB uild\nĠs ides\n.Array List\n- %\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ\nØ ±\n.m atch\nл и\nĠfe els\nĠachie ve\nĠcl im\n_ ON\nĠC D\nĠteach er\n_c urrent\nb n\n_P L\nist ing\nEn able\nG EN\nĠt v\nĠso ck\nĠpl ays\nĠdis count\nĠK E\nĠDe bug\nF ore\nĠI raq\nĠappear ance\nM on\nĠst yled\nĠH uman\ni ot\nĠH istory\nĠs ac\nĠC ollection\nĠrecomm ended\n.Se lected\nĠorgan izations\nĠdiscover ed\nco hol\nad as\nĠThom as\nM ay\nĠcons erv\nĠdom in\nĠF ollow\nĠSe ction\nĠTh anks\nUser name\nĠrec ipe\nĠwonder ful\n.s leep\n_ if\nĉĊ ĉĊ\norn o\nĠr u\n_t arget\n.\" \"\nà ¦\nEvent Args\nĠinput s\nĠf if\nĠv ision\nc y\nĠS eries\n) (((\nĠtr ading\nĠmark er\nB egin\nĠtyp ically\nĠca uses\ndrop down\n_DE BUG\nĠdet ect\nc ountry\n! \");Ċ\nĉ R\napp y\nĠc ref\n(' <\n\" =>\nĠL E\nread er\nĠadmin istr\nà µ\nuck et\nĠf ashion\n. char\niz ar\nĠdis able\nĠsu c\nĠL ive\niss ue\nĠmet adata\nfl ags\nĠ ðŁ\nĠcomm itted\nĠv a\nĠr ough\nĠ'' 'Ċ\nĠhigh light\n_var s\nV O\nĠenc oding\n- Z\n_s ign\n$ (\"#\nĠr ain\nreate st\nĠEN D\nSe lection\nĠcandid ates\nĠs av\n. 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Z\ncre ment\nad ium\nst ock\nĠserv ing\nĠB on\nĠline ar\nĠT arget\nĠR adio\nH L\nSh ader\nom atic\nag ues\nin ity\nd iff\n_ iterator\nqu ot\nĠ ,Ċ\nc allback\nĠsympt oms\n[ _\nĠB ul\nĠF eb\nund o\n_ account\nĠtyp edef\nи Ñģ\ntr as\nUser Id\nĠP enn\nĠSup reme\n} >\nuser Id\nĠK im\nĠg a\nĠart ists\nå ¸\nĠAb stract\nok emon\nĠh am\no val\nĠch a\nat en\nå Ĩ\nF ixed\nĠvul ner\nĠParam eters\nqu antity\n.C lear\nServlet Request\nĠy a\nĠsou l\ntrans action\nĠsol o\nĠp airs\næ Ķ\nĠG re\n_ word\nĠC C\nĠg i\nz ie\nĠsched uled\nrot ation\ngy pt\nul ous\n:: _\nĠE ll\n< !\nĉĉ ĠĠ\nl p\nah a\nC opyright\nĠdr am\nĠdi agram\nĠM em\nĠg arden\nCom p\nĠattempt s\nuff ix\n> ()\nĠphil osoph\n_re l\nå ¼\nĠs v\n.se cond\nant o\n.J son\nĠTe le\n_ local\n_s end\nĠas pects\nì Ĺ\nIB LE\nĠr ail\nĠwid ely\nash ed\ni ar\nin f\nup per\nd jango\n_result s\niss ing\nĠequ ivalent\nOUN D\nĠt y\nĠpotential ly\nAdvertis ement\nĠRec ord\nresent ation\n_w idget\nound ing\nĠrelig ion\nĠcons c\nĠL im\n. am\nH tml\nĠ' :\nP ATH\n_s pec\nort ed\nid ades\n_sh ape\nĠkeep s\n.S ave\nĠL oc\nor i\nĠT EST\nunic ip\nĠreg ions\nĠbelie ves\n/ en\npos ite\n{ '\npre pare\n_ const\ns ample\nĠWill iams\nĠstr t\n_ Get\nĠAnd rew\n. active\nĠl ayers\nVisual Style\naz y\nĠK n\nĠac id\nĠAs ia\nĠex cess\nĉm y\nĠkey board\nens us\nĠcre w\nĠmiss ed\nm aster\nĠW ild\nĠnew ly\nĠwin ner\nĠst ub\nic ode\n.m ove\nD omain\nĠS ar\nĠfore st\nLE D\nclaim er\n.ex it\nĠW indow\nĠres istance\nĠC HECK\n(\" -\nĠR yan\nĠp ipe\nĠco ast\nDE F\n// !\n_ off\nex it\nĠult imately\nimit ive\nĠKe ep\nĠhistor ical\nĠany way\nĠJack son\nock er\nER N\nĠU INT\ny ntax\nER Y\nis ms\nĠc n\nĠocc urs\nĠ; ;\nText View\nA E\n/ img\nĠy esterday\n- default\nĠt iny\nĠpro c\nĠal ive\nĠRE G\n. th\near ing\n.get Logger\n< link\n_ login\nF older\nab c\nlyph icon\nн о\nĠnot iced\nod igo\nĠed ition\nim ator\n. 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ĊĊĊĊ\naur ant\nP refix\nĠinstit utions\n: int\nĠex posure\np attern\nĠLin ux\n.n umber\nred ient\nArgument Exception\nĠCh ief\n\" },\nĠelect ronic\nr ong\ner d\nsp Net\nra it\n/ ',\nĠOh io\nCont rollers\nĠcontin uing\nĠT emplate\nĠE th\ns z\n/ env\nEn v\n% .\nart ers\n) ((\nĠT ABLE\nĠà ®\nper ature\npro gress\nP res\nê °\nim plementation\nĠb ien\nĠstre ets\n_M SG\nNew s\n## #\n: /\nĠcut ting\nx B\nress ed\n_EN ABLE\nl ab\nĠca using\n] ));Ċ\nb ra\nx FFFF\nil ly\nplet ion\nw ill\n_b ar\nĠstruct ures\nĠI mp\nÛ Į\nĠ< >\nĠ ----------------\n_B UFFER\n.d ir\nĠpl ain\nĠpe er\ng g\noint s\nĠsomew hat\nĠw et\nĠemploy ment\nĠtick ets\nir ms\nĠt uple\ns is\n$ sql\nr ig\nĠcon version\nĠg es\nĠconfig ure\neg r\nĠC a\nĠ__ ('\nou ston\n.t oken\nBl ack\nĠmag azine\nA W\n. IN\nos ing\nĠbro ke\nĠC ru\nDE LETE\nĠdestroy ed\n(M ath\nĠappro val\n-d om\nĠI II\ntable View\nĠdesign s\nĠcrush ing\nĠcons ent\ndir name\nom p\nĠc rypt\n? (\nor ough\n. o\nĉ list\nams ung\n.\"\" \"Ċ\nerr ing\nG oogle\n_p air\n_IN IT\nrem arks\nĠg ear\nF ill\nl ife\n} \")Ċ\nĠsuit able\nĠsurpr ised\n_RE QUEST\nĠman ifest\natt en\nĠfr ustr\nov ement\n.c lick\nĠi i\nĠexp ansion\nig s\nP arse\n.Reg ular\nR ob\n_l ayout\nì ł\nĠtrans lation\nĠBe aut\nB est\n_C OLOR\n< label\nĠliqu id\nIT S\nĠpro d\nĠoper ate\nUI Kit\nĠn atur\narg ument\n_d etail\nĠCent re\nĠ\" --\nĠ}} \"\nlo cale\n.t v\n_se q\nĠup coming\nCh art\nĠDiv ision\nĠclin ical\nCom pany\nS epar\nl as\nĠH un\n: s\nĠhead ing\nоР³\nĠ\" \");Ċ\n[ id\nb ia\nĠst retch\nic ide\nĠre produ\n.pro ject\nleg end\nend ers\nĠrespons es\nĠon t\nrit ical\nĠref uge\nĠL i\nĠ: ĊĊ\nĠTh ree\n.cont roller\n_IN DEX\n_F OR\n\\Model s\nj ax\nĉex it\nĠâ ĸ\nĠc overs\nĉ y\n- .\nIND OW\nĠfail s\nin cludes\nĠf ault\nĠl y\nñ o\n.s lice\nILE D\nĠP ur\nĠAs ian\n_b atch\n.M ax\nv l\nĠCOPY RIGHT\nĠg iant\nĠMan ual\nĠC opy\nClass Name\nHe alth\nC ursor\nIB Outlet\nĠt we\næ ³\n_label s\nĠcol lected\nĠfurn iture\nĠdeal ing\nControl s\nĠHot el\nck s\nĠch ose\nâĶ Ģ\nod d\nS R\nÙ Ĭ\nì Ħ\nĠacc ord\nĠM ove\nĠM ode\nĠM ock\nĠthread s\n++ ++\nĠO ptions\nRef resh\nĠD id\n'] ->\nu cc\n_ch annel\n. abs\nĠ{ },Ċ\nĠW al\ner ior\nĠmain ly\nĠDr iver\nNotFound Exception\nĠcount s\ne am\nĠ& =\nQ uestion\nĠA li\nĠany more\nd etail\nt ail\nĠm ile\nĠF air\nĠs orry\nĠsurround ing\nĠad m\nDe v\nĠmari juana\nĠS ound\nĠA sh\nF D\nTe am\n. port\nĠ[ ]ĊĊ\nub ble\nĠas c\nĠint ention\nA cc\nch i\nust ers\nĠins pired\nse g\nCL U\nĠman ip\nM etadata\nCon nect\nĠB eh\nĠfind ings\nĠas sembly\nw orld\nĠrem ained\nĠu id\n( .\nĠm x\nLo op\nĊĊĊĊ Ċ\nĠfant astic\nwh o\nak i\nĠB asic\nĠY et\nĠUs ers\nik ip\nĠhead s\nĠMich igan\n_ it\nĠTor onto\nĠrec ording\nĠsub mitted\n_var iable\nmedi ate\n.graph ics\nĠst ood\nĠre ar\nvel ocity\n_M ESSAGE\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nro les\nĠT our\n_ year\nend ment\namp s\nĠIre land\nm al\nĠyoung er\nĠstrugg le\nĠc able\nĠSD L\n(' -\nan es\nĠNe ed\n.R ow\nP ol\nĠP H\n_s cript\nag em\nĠB as\n_s pace\n. loc\n: i\nad r\nĠengine ering\nit en\n) &\nĠu k\nĠL ittle\n_C OUNT\nx A\nArray List\næ į\nĠ\" \")Ċ\nAn chor\nĠh ang\nt witter\nĠcompet itive\n.s rc\nãģ Ĺ\nĠtrans late\nĠCre ates\nook s\nĠR oll\n'' 'Ċ\n/ sh\ns ome\nEnc oding\n.res olve\nĠdesign er\nĠSt orage\nĠz a\nĠN ever\nĠsomew here\nĠbox es\n.s ource\nĠpy game\nĠgrow n\n.t w\n() ),Ċ\n', ['\nĠoppon ent\n(s rc\n.l ayer\nAP P\nĠAct iv\nĠguest s\nĠVAL UES\n};ĊĊ Ċ\n.n ative\nĠamount s\n. 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old\n_d isplay\nou ver\nor er\nRes et\nĠsurg ery\nĠstrateg ies\n/m aterial\n_ unit\nĠc ouncil\n.P er\nĠâĢ ŀ\nĠre form\nF ramework\nĠlist ing\n_b tn\nĠb is\n% d\neg as\nĠsudden ly\n_S ER\nĠa o\n_d irectory\nf as\nĠprem ium\nĠtrack ing\nĠB L\nĠm ature\nĠbath room\nĠ'/ '\nĠÄ ij\nPer formed\nĠsold iers\narn ings\nĠwalk ed\n- con\nb ottom\nĠsurpr ising\nĠg ene\nUs uario\n.DE FAULT\nĠM IT\nC ODE\nĠE gypt\np icker\nys ql\nAT URE\nd etails\nĠCon ference\nIn formation\nĠM ail\n-d own\nr aries\nb ro\nĠsubject s\nĠ' *\nè¯ ·\nor ient\n: @\nver bose\nE F\nĠto ler\neng ers\nĠend point\nĠstr ange\nĠcol on\nĠpre ferred\nde p\nĠE V\nARR AY\nĠw he\nĠp up\n_n odes\nĠtalk ed\nĠinstit ution\ndb c\nĠex posed\nte en\nĠFr ont\nT T\n_N ONE\n\\/ \\/\npro gram\nĠencour age\n. `\nsh ire\nĠIsl am\ne en\nN I\n' \"\n.W idth\nĠlik ed\nĠ{ ...\nĠSystem s\nĠvot re\nĠmanufact uring\nCon verter\nĠIn f\nì ļ\nD TO\nĠin ches\nĠ à¤\nà ¹\nĠChar les\nB U\n\")) ;ĊĊ\nĠL abor\nun n\nĠest im\nm obile\nĠL earn\n_C ALL\nâ Ħ\nĠind ices\nĠt ub\nikip edia\nC ost\nrow able\në ¡\ng age\nĠfunction ality\nuzz le\nem os\n.l ib\nĠd ass\nеРº\nenn a\nĠsh ots\nĠrest ore\n/ D\nFor Key\n], [\nal ias\nl int\n.st ream\næ ł\n_FORM AT\nĠsil ver\n.re pository\nĠlegis l\n.B order\n_fe atures\nPer mission\nĠhous es\nĠW ars\n_COM P\nĠinj uries\nĠconstant ly\nfl utter\nEN U\nĠCon f\nĠrecogn ized\nĠpract ical\nĠde cent\nB J\n] );\nast y\nĠAct ivity\n-m ode\nĠsl ide\n.IsNullOr Empty\nĠY OU\nP ower\nind ices\nĠqual ified\nĠthrow n\nh ello\nĠN ick\nl ah\nas sembly\nĠSm all\nold ing\nSh ould\nĠSil ver\n(saved InstanceState\nĠtog gle\n.N ot\nC trl\n: nil\nĠCont inue\nĠB oot\næ ī\nĠM ur\nd on\nĠF A\nS napshot\nĠassoci ation\nfo x\n, a\naz ione\n] )čĊ\nCT YPE\nĠf ade\nĠD ar\n.n avigation\nĠl uck\nSC RI\nĠDe ad\nĠterm inal\n_LE NGTH\nĠeff iciency\nĠun w\nĠn arrow\niment o\n( Color\nĠSe a\n_ area\n, A\n_ opt\nĠHill ary\n.t ask\nĠJ ac\nast ed\nĠAd am\nĠIl legal\nĠsearch ing\nInstance Of\nJ ava\nĠForm at\nĠreal ized\nĠChild ren\nĠk il\n(f rame\nâĢĿ .ĊĊ\nĠscen ario\n\"] );Ċ\nĠincred ible\nli x\nIO Exception\nĠQ uest\nil ty\nĠun lock\nâ Ĥ¬\nĠre ferences\nĠV ert\nB inding\neg ative\nĠwr ap\n.d atabase\n( content\nB uf\nĠTr ad\nĠA ud\ntr ace\n.m ock\nĠther apy\nĉ L\n.To Int\nĠKing dom\nB us\nha ust\n\"\" \"ĊĊ\n( end\n.draw able\n[ ];Ċ\nĠH ospital\nĠph arm\n---- -\nĠA G\né d\n> \");Ċ\nĠw allet\nat able\n) $\nĠmonth ly\nĠdi agnostic\nS ymbol\nĠiter ator\nun finished\nĠimm igration\ns r\nRO W\n(g ame\nĠclo thes\nĠU nt\nĠactiv ation\n_C on\n.h ash\nĠinitial ly\n.H ash\nĠcut s\nf ound\nĠSt ory\nÑĨ и\nac ao\n_T YP\npro to\nest r\n-p age\nah r\nĠincor rect\nĠJose ph\nTextBox Column\n_st yle\nĠD aniel\ns heet\nĠl iv\nl ined\nĠr a\nR untime\n_ empty\nsl ug\n_ struct\në Ĭ\nm u\nĠper mitted\nĠreg ional\nĠsob re\nĠS uch\nĠ[ _\nĠro of\n.Al ignment\nt imes\n.m sg\nĠche st\nĠT ab\nĠest a\nä n\nĠsubs cription\n( command\ns pecial\nĠme al\n\") :Ċ\n_ ctx\nĠclos ely\net ry\n- be\nad el\nĠR am\nig est\nĠSpan ish\nĠcommit ment\nĠw ake\n* >(\nP 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Equals\nF B\nĠRes ources\nroll ing\nĠpass es\nĠN um\nrot ate\net ype\n\\ \",\nĠsens itive\nĠt all\n? âĢĿĊĊ\nPro xy\ni y\n_ section\nâĢĶâĢĶ âĢĶâĢĶ\nbr id\nĠcirc uit\nat an\nEN C\nĠdr iven\nĠvot ed\nĠeduc ational\nĠinter action\nabet es\nĠt one\nĠInitialize Component\nĠmer ely\nĠì ŀ\nco okie\n_ div\nĠUIL abel\nvel y\n} );čĊ\n_ ENT\n#+ #+\nart icles\nĠSou thern\nĠstrong er\nĠG iven\nĠE ric\nĠI R\nab stract\nU nder\nn able\nĠincre ment\nov en\nĠco in\n_t imer\nĠsuffer ed\nĠF REE\n'] .\"\nĠQue en\nst ats\nĠmeet ings\nĠenter ing\nĠalong side\n(s ession\nit als\nĠfound ation\nĠC redit\n. div\n_ ALL\npc ion\n_st at\nick ing\nDefault s\n_s rc\nĠoutput s\n/ B\nĠent hus\n-b l\n.Fore Color\nĉ temp\nF ace\nĠinter act\nĠwe ird\nM ount\nre ll\nud ents\nĠrequire ment\nĠS us\nI ER\nĠe lected\nre ference\nĠM E\nĠserv ers\n.w ait\nĠsnap shot\nil ton\nĠtri es\nĠt ipo\n.T ime\n> w\nĠmount ain\nĠp ounds\nĠ[ ...\nex ists\nĠng On\n_M AP\nĠf lying\nxi ety\nĉ value\n_D B\nun o\nĠse ats\nT URN\n. author\n! )\nor ce\nĠindic ated\n.s in\nĠass ignment\nim iento\nĠF rame\n_g en\nin ery\n_ )\nm essages\n.set tings\nĠMe an\nĠM useum\nir q\natt ach\nĠPalest in\n_ QU\n_t ags\nĠcas ual\nem en\nASS WORD\n$ s\nĠC irc\nоР¹\net ric\n/ P\nĠep och\n< head\n_C MD\nĠg it\nĠpen alty\nor ph\n_ users\nours es\n.Date Time\natern ion\n_pro ject\nĠsuper ior\nĠD am\nĠSe attle\nX Y\n> The\nĠA k\nĠgr ass\n/* čĊ\n(d is\nĠgun s\nĠt b\nĠK evin\n. args\nĠA h\nop ed\n( J\ncolumn s\narg uments\nĠWith Events\n_f ull\nĠDef ense\nS imple\nĠdeath s\nĠext ensive\nĠSt ill\nĠEx pression\nĠAg ency\nĠperform ing\nF X\nĠus uario\nU AL\nS ide\nod os\napt op\nĠcred entials\n_c ap\nat ient\nĠDis ney\nĠa i\nĠch ip\nĠvol t\n.make Text\n%%%%%%%% %%%%%%%%\nĠbelie f\n_LO C\nĠC ivil\nN avigation\nĠreve al\nĠviol ent\nĠF il\nĠc atalog\nem ed\nsc an\n. control\nĠconstit ution\nC ountry\nSepar ator\n_A PP\ntop ic\nuet ooth\nM IN\nĠdes criptor\ny t\nET HER\nĠdistrib ute\n' }Ċ\n.tr im\n.L ine\nĠl bl\nassert Equals\nĠD et\nomb ok\n( width\nĠt ort\nĠEXP RESS\nac o\nUs ing\nĠBr and\nw all\nEM ENT\nĠComm unic\n< uint\nĠG UI\nEG IN\nĠR ange\n/ i\nĠT aylor\nc ost\nĠrespond ed\nĠTh eme\nn ce\nIS H\nĠfeat uring\nReturn s\nĠK r\nĠ .Ċ\nĠn am\n_c b\nTest ing\nĠ{ },\ny al\n.f ield\nĠ/ =\n_SH ORT\nm ates\nTest Case\nain less\nĠeval uation\n_ ITEM\nĠPac ific\nĉ k\nĠc ant\nĠR os\n) s\nĠf et\nSTR ING\nĠDis pose\ng al\nĠJ oin\nĠP orn\nĠCath olic\nAR GET\ncp u\nç łģ\n.sc roll\nIS ING\nifest yle\nanc ement\nĠm erc\nĠB rowser\neter min\nĠover flow\nAv ailable\nĠbott le\n: UI\nific ial\nĠco ord\nclar ation\nĠcon j\nG LOBAL\nok u\nĠk wargs\ncond itions\nul um\nĠg enu\nĠH ero\nå İ\nĠun expected\nĠDAM AGES\nĠk a\nĠC ould\nUP PORT\nĠPh otos\nĠconf ident\nĠdet ected\nde g\nrg b\nĠstrong ly\nĠ} ;čĊ\nĠ) :\nĠle ct\nurs ive\nRO L\nĠWe ight\nĠent ertainment\nĠ) );Ċ\nĠg onna\nĠb b\n.d o\nG S\nĠmist ake\nD L\nĠPROVID ED\near ning\nL imit\niss ions\n[ v\nä¸ į\nir ty\nD el\nĠunder lying\npre ne\nĠj aw\nĠD I\npe er\nĠobject ive\nĠde posit\nĠk on\nĠes p\n.set Visibility\n/ login\n< typename\nĠfr anch\n/ e\nPar allel\nĠsc ored\nĠH on\nĠV ill\nig a\nĠant icip\n_ assert\nĠO pt\nĠdescri bes\nw an\nm ount\nĠmonitor ing\nĠt out\nëĬ Ķ\n}, {\n................ ................\n= int\nĠc ust\n---- --\nĠatmos phere\nP AR\nort e\nIS IBLE\nĠI ron\nĠNot ification\n.log ging\nĠBO OL\n-p oint\nĠaf raid\nent a\nĠtom orrow\n@ implementation\nĠeng age\nĠAn th\nĠF loor\nĠU l\nTo ols\nĠb ab\nĠcare ful\nãģ Ħ\nĠcruc ial\nĠcalcul ated\nĠS A\nĠw y\nD X\n_T AG\nind ed\nĠj et\nĠEngine ering\n.M AX\nen z\nv d\nĠpublic ation\nĠ## #\nĠfac ed\nra ham\nĠC apt\nAs set\nĠCon stants\nĠlo ans\n_ IP\nĠF ish\nRed uc\n_m at\nDate Format\n_m e\n[] []\nĠintegr ity\nĠC ourse\nlob als\nĠfac ilit\nĠem br\nĠN g\n.S ystem\nĠmanufact urers\nĠpro ven\n.on Create\nĠal arm\nĠ §\nĠcomm only\nic os\næĸ °\nĠSt ation\n} ).\nĠF ilm\nw i\nç ī\nĠeng aged\nSt ats\nĠgovern ments\nĠafford able\n_p roperty\nĠag es\n(' --\nĠf ör\nĠProf essor\nĠhy dro\nP ush\nĠorgan ized\nAc cept\né m\n_c ell\nĠn b\np b\nArt icle\nĠrem oval\nĠauth entication\nĠF R\nl ide\nĠple asure\nap ol\nĠpart ition\nĠS ide\nĠcr imes\nĠdem o\nhold ers\nĠPak istan\nIn struction\nĠexpect ations\n.sc ene\nĠ' )\nh es\nino is\n_P ro\nĠm olec\nand al\n_sh ort\nĠdefault s\nĠn ations\nin en\nĠr t\nO CK\nP acket\nS B\nĠSH ALL\n_cont ents\nise conds\nvert y\ná t\nG uid\nn om\nĠcon clusion\n. 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');Ċ\nex pect\ned Reader\nĠM ajor\nĠin ch\nCom par\nĠpre view\nĠill ness\nĠCONTR ACT\nĠInd epend\nu uid\nĠn ome\nĠt c\nĠA venue\nis an\nĠph rase\n_m ove\n\") [\nĠprov ision\nĠconcent r\n_ IR\nĠU t\n() +\nĠn as\n! ,\nĠRob in\ni ations\nat itude\nĠp x\nĠWith out\n/b ash\nek t\nre ement\nOb server\nĠReg ion\nUBL IC\nĠ{ //\nK N\nå ·\nGame Object\nå ¾\nenc oding\nĠ** *\nproject s\nĠt k\nĠche ese\nEM PL\nar o\nĠا ÙĦ\nĠcons ists\nref resh\nure au\nĠSc anner\nĠso il\nĠfl avor\nData Source\nEx ecute\nени е\nĠsh it\nåĪ Ĩ\n< any\nĠretrie ve\nĠbelong s\n.st rip\nabs olute\nĠexp anded\nbo y\n): -\nĠresc ue\n.J Label\nĠre ly\nĠal ignment\n-f amily\nĠre nd\nOLUM N\nĠb orrow\nĠqu otes\nĠL ew\nĠsh ower\nĠDE LETE\n_lo op\n! \"ĊĊ\nĉ re\nĠattempt ed\naver age\nĠP aint\nquis ition\nol en\nĠliter ature\nĠRe ference\n_TEXT URE\nĠS eg\nĠInd ust\nct ype\nD UCT\n_H OST\nĠTr ade\nĠpl ugins\nĠbre ast\nul se\nĠcreat ure\nãģ Ļ\nĠW i\nĠsup plied\nc oll\n! 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Ċ\nĠhand led\n(f d\ncont ents\nĠO FF\nR F\nĠst y\nĠM otor\nter y\nt ax\nM AP\nĠMr s\nĠph ones\nĠUI View\n\")) );Ċ\n( dev\nĠIr ish\nĠw s\nD I\n_OFF SET\nĠEvent s\nĠst ages\nĠ} //\nĠhab en\nST ANCE\nĠS in\nĠM oney\n(t op\nĠappoint ment\nVER SION\nmet adata\n_com ment\nĠcolle agues\nmap s\nâ ĺ\nĊ ĉĊ\n( al\n_re q\nĠf ut\nĠarchitect ure\nĠWH ETHER\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\n_s creen\nĠstyle Urls\nĠmon ster\n. up\nph ia\nĠprocess or\nĠT err\n= ',\nĠMan ufact\nĠN T\nk el\nib ern\nĉf ile\nA li\nrient ation\nĠ// !\nap ore\nane ous\nĠC reat\nf older\nĠh ay\nSup press\n( left\nĠe uro\nĠdis claimer\nustr y\nsh ips\n_f d\nĠF a\n_in sert\nĠro l\nif ting\nĠCom ments\n_b r\nĠloss es\nĠAdd ed\nch arg\nĠп о\n_s ystem\nĠS ometimes\nĠSp ain\n(g roup\nial is\nĠdoll ar\nĠAr gs\nqu ires\nĠT en\n.s css\nĠsurv ive\nus age\nĠj un\nim iter\nï¼ģ ĊĊ\nĠfif th\nt oggle\nĠdecl ine\n($ \"\n(L ong\ning e\nĠpil ot\n-l ight\n-r adius\nĠpod cast\nĠnatur ally\nP ages\nä¸ º\nĠDes pite\nĠlight ing\nĠcr ate\nĠB inary\nĠredu cing\nĠe leg\nĠM ouse\nĠTest Bed\nĠbefore Each\n_ ARRAY\nRed irect\nĠf lood\nĠsh ips\nĠelectric ity\n)* (\nê ¸\nĠV iet\nher o\nĠd ia\nĠK ent\nhe art\nĠthreat s\n_ acc\nĠs ymbols\nis chen\n_in st\nC riterion\nĠT IM\n. Height\nĠ âĢĻ\n();ĊĊ Ċ\nProduct s\n_S P\nĠC y\nĠdepend ent\nest e\nĠdat os\nd it\nаР²\nIGN AL\nĠless on\n\"> '\nĠC over\nĠH ope\nĠT imer\nĠd ad\nvid ers\nĠPh ot\n/ ?\nrop y\nom ing\nas ion\nĠ\\ (\nĠE T\nĠRe ading\nĠep isodes\nl m\nech a\nĠne uro\nĠhar mon\nĠlib eral\n- ind\nD ATA\nĠevery day\nĠdiv ided\nĠActive Record\nfig ure\nU A\nä ¹\nriend ly\nte ch\n.game Object\nиÑĤ ÑĮ\nĠmo on\nft ime\nĠno ch\nĠT ORT\nĠV M\n.in itial\n( child\nĠmus ical\nĠo c\nb as\nĠH ay\n_l ong\nĠmem set\nile y\nadel phia\nS V\nro at\n_t x\nĠl on\nĠngOn Init\nb p\nĠGold en\nAC HE\nĠwor ried\naz i\nE ar\nT ake\n(f p\nbur gh\n_ Data\ng res\nĠO nt\np us\nĠtrans parent\nĠp ocket\nĠr am\nigr ations\n. čĊčĊ\nĠ[ (\nĠadopt ed\nĠreported ly\nĠD ream\nĠ} ));Ċ\nlos ing\nĠte eth\nĠBook s\n\", &\nenn y\nLE MENT\nĠg el\nĠPl ant\n! âĢĿ\n.h ost\nĠRep ly\nre ngth\nĠrecogn ition\nĠ}} >Ċ\nL A\nĠmir ror\nĠassist ant\n( device\nĠspirit ual\nb uilder\n §\nĠou tr\nĠt t\nĠP ER\nĠrad ical\nMethod s\nĠp ace\nud y\nĠg ut\nĠG reek\nĠnon atomic\nĠP aper\n_G PIO\nĠob st\n.A d\nviron ments\nĠS ov\n( con\nĠTrans action\n. assign\nĉc atch\nel ter\nĠbit coin\n_G R\nĠčĊ\nmet ic\nĠtrans formation\nåı ·\nĠr gb\nistrib utions\nĠimp licit\n/ in\ndest ination\nаÑĤ ÑĮ\nZ ero\nĠun set\n. where\n.g o\nĠform ation\nĠdeclar ation\n() čĊčĊ\nĠEx pl\nĉĉĉ ĠĠ\n/ pro\n.J SON\nĠdes k\n.sub str\n//---------------------------------------------------------------- ------------\nly n\np son\ndis able\nĠF unc\nĉ Assert\nĠM ARK\nĠdefe at\nĠbl ind\nĠconst ants\n. headers\nUIL D\nĠexp enses\nP ixel\nĠh r\nĠf el\nĠEast ern\n_d el\nĠC ub\nĠs q\nĉc ount\nĠD irectory\nĠex clus\nĠhistor ic\nĠ ------------------------------------------------\nĠcom position\nĠdata GridView\nĠB urn\nĠB C\nM aster\nĠsp awn\nĠbe aring\n.Set Active\nil o\nĠg allery\nĠfound ed\nĠav ailability\n.s qrt\nĠp es\nĠD OM\nm ate\nO ct\nĠmatch ed\nit ivity\nĠan xiety\n.pr ice\nĠIn stant\nì Ĭ\nĠt ut\nIC ollection\n.sh ared\n_s ql\nt bl\nlib rary\n_de stroy\nerm al\nĠNot es\nĠE in\nĠsou thern\nĠOTHER WISE\nĠmac ro\n.l ower\ncl s\nContent View\n.l ink\nconst ant\nĠB es\nĠsome body\nn b\n\"> {\n( local\n.. ...\nĠN ull\nm x\nĠà §\nĠp ause\n-------- ---\n_M O\nĠC M\nĠfor Key\nĠD VD\nĠclose st\n_DE VICE\nĠSte phen\nĠB BC\nĠTr avel\nP aint\nĠResult s\nĠR ule\nĠt p\nĠrat ings\nc in\nc sv\n> /\nĠG OP\nl ad\nĠ ÑĢ\nĠindex Path\nm atrix\n= f\nars ed\nĠ} );\nĠC os\nĠS core\nĠt ak\nĠE SP\nĠIN C\n_N ULL\n-f lex\n\"] [\nint o\nel and\nAuthor ization\n_F ALSE\nĠg ate\nĠv id\nist ent\nT IME\nĠre write\nĠt ie\nĠarch ive\n.event s\n.get Parameter\nĠPer mission\nĠprogram me\nĠ é\nj ud\nĠcam eras\n(s ys\nĠSy rian\nĠimpro vements\nĠh ip\nĠsu icide\nĠsch olar\nĠcompat ible\nrem ote\n.d own\nF UNCTION\nĠman aging\nĠUI Kit\n. raw\n>> >>\nĠdem ands\nell ite\nĠd ent\nĠM icro\nåı ĸ\n'] [$\nĠI E\nim ension\nĠt rem\nĠg ained\n.w ith\n. ok\nh ou\nĠb om\namp aign\nĠjoin ing\nf ish\nĠadd Subview\nĠnor thern\n.c or\nore t\nD ie\nin ish\n_com p\nĠatt ended\nĠcoll apse\nĠS S\nac ent\n_E QUAL\nĠDe ep\nR GB\nĉ test\nol ves\nus et\nUn ityEngine\nw riter\nRes olver\n, %\nif ference\n_re move\nond a\nĠfem me\nde code\nBr anch\nĠfl ush\nĠinnov ative\nTest s\nĠ[' ./\nĠcover ing\n. admin\nultip art\n(l ambda\n namespace\nĠS port\nĠ! (\nac les\nĠde pression\nĠK ong\nĠp ert\nĠCon n\nĠOther wise\n/ home\ns upported\nĠp ink\nĠinv ited\nñ os\n_en abled\nĠ- Ċ\nF W\nen ers\nĠM Y\nĠsuggest ions\nCan vas\nĠf er\nĠMarket ing\n@ Test\nunt u\nĠV en\nĠC ou\niv als\nDon ald\nlim ited\nĉĉĉĉĉĉ Ċ\nĠanal yst\n( entry\nĠrepresent ative\n_at tributes\nĠf ur\n.h ide\nres p\nado res\nrid es\nĠJ osh\nro bot\nĠN AT\nĠs esso\nĠintegr ated\n: true\npart s\nĠst upid\n: event\n@end section\nĠp u\n.T able\nĠY ii\n` ;ĊĊ\nĠcl ang\n=\" \">\neng an\n_param eters\n.int ernal\nĠMod ern\nĠmet ric\nĠsem i\n={ {Ċ\n.am azon\nĠB B\naint y\nview port\nĠstart Activity\ndis patch\n**** *\nĠfl av\niffer ent\n[ this\nĠst ake\nĠarg ued\nvious ly\n.w ork\nĠO ak\nO ld\n( async\nnot es\nĠfl ip\nĠdis ag\nĠT E\nĉ error\n< '\nĠ» ĊĊ\nĠfilter ed\nĠM ach\nĠh ung\n_d ump\n_s amples\n-dis miss\nĠr ay\nIm plemented\nD K\nĠj ed\nĠbreak s\nĠf its\n. gr\nĠZ ero\nor o\nĠequ ally\nĠ' [\nĠconcern ing\n< meta\nplay ers\n_P OS\n_s im\nJ an\nĠyour s\nĉ N\nĠsp ir\nĠch ampion\nĠAn alysis\nap a\nĠNS Log\n_l ines\nñ a\nĉĉ ĠĠĠĠĠĠĠ\n.S c\nRe p\netro it\nur able\nM IT\ncom pat\nown ed\n_ind ices\n], čĊ\nĠdis covery\nĠDie go\nob i\n. Index\nĠtrend s\nPL AY\n.n o\nĠl ens\n_c fg\nĠan no\nag an\nĠperiod s\nter ms\ny z\nĠattack ed\nib ration\nPEC IAL\n_ grad\nĠaccord ance\n.Read Line\n.de vice\nri x\n. container\nm ay\nerc ise\nĠL u\nĠr g\nĠÑģ ÑĤ\nĉĉĊ ĉĉĊ\n( un\nTERN AL\nĠless ons\nĠalleg ations\nĠtrans mission\n.Re f\nM obile\nĠT ournament\nĠN ut\nĠG a\nĠCap ital\ndef inition\n- exp\nc lean\nĠfant asy\nĠenh ance\nent ence\n'] :Ċ\nack ets\nĠcelebr ate\n@ \",\nSerialize Field\nĠarray s\nt b\nĉ st\n[ assembly\n( reg\n.c ategory\nĠimpro ving\nĠsal ope\nByte Array\nOr iginal\nĠ[ {Ċ\nåĽ ŀ\nĠCl in\noen ix\nĠS amsung\nĠmaint ained\nĠag enda\nf ail\nĠpres ents\nĠtim ing\n.m ark\n' ><\nĠprom ot\nĠin cl\n_ only\në¥ ¼\nĠAtt orney\n- date\nĠlands cape\nĠf u\nS Y\n.p rop\nĠA rr\np ag\nParallel Group\n': čĊ\nĠlog s\na unch\nunc i\nn ama\nTable Cell\niss ues\n. {\nec urity\n_ex ec\nold s\nĠhost s\nĠpro to\n_ import\n_s ort\nĠB ow\nĠN ormal\nĠF arm\n.create ParallelGroup\nR otation\n. err\nĠp leased\nit age\n.W h\nĉĉ ĠĠĠĠ\nM R\nĠM ORE\nĠN atural\n_ transform\nB ASE\nener al\nut down\n.common s\nW T\nĠa an\n. Result\nd og\nĠclick ing\n), ĊĊ\n# line\nOper ator\nĠc iv\nĠm erg\nob uf\nng then\nĠ[ {\nĠcan cell\ntr igger\n. :\nW ORK\ndecl are\nĠdecre ase\nÅĽ ci\nlo om\n.N one\nĠM I\nĠJ ason\nĠhealth care\niam ond\ns ylvania\n* x\nĠR a\n[ b\nĠprint ing\nph abet\nĠLab our\nop per\nĠz ijn\n-t arget\n_F UNCTION\nĠo ct\nени Ñı\nåľ ¨\nĠwest ern\nĠcomput ers\nĠR ET\nHash Map\n[ String\nget Value\n_D ATE\n.N ext\nĠF if\né l\nick ed\næ İ\n-M M\nĠ{ ĊĊĊ\nĠcontact s\nĠdig its\nPro du\nĠunus ual\nĠrapid ly\nt ures\nĠang ry\nc ancel\nxx xx\n_p arser\nid ity\n_P REFIX\nĠme hr\nĠrare ly\net he\nop es\nĠ% .\nwork s\nĠthe ta\nĠcontrib ution\nĠT ony\nĠsqu ad\nаР¹\nĠî n\nth ere\nout ed\nĉ q\nĻ Ĥ\ng ood\nL I\né¡ µ\nĠL iving\niz abeth\nĠk t\nĠD allas\n] ],Ċ\nĠ/ >ĊĊ\nĠrais ing\n/r outer\n_g ame\nĠC UR\nz ens\n. es\nĠfont Weight\n(f unc\nnot ification\nĠ'../../ ../\nĠbl ame\nãĢĤ ĊĊĊĊ\nan co\nId entity\nf ollow\nĠart s\nx s\nĠofficial ly\nĠSt udio\nĠrecommend ations\nĠloc ale\nĠam ateur\nĠEn able\nĠcap s\n. End\n- add\n_g shared\nĠC T\nFor ce\nĊ ĠĠĠĠĠĠĠĠĠĠĠĠĊ\nĠor ange\nĠl p\nĠanswer ed\n.G rid\nĠd ual\nĠstrateg ic\nĠnob ody\nĠf atal\n_ est\n( el\nĠì ł\nĠB udd\nA IT\n_f actor\n- one\nĠH AVE\n\" čĊčĊ\nPro f\nĠä r\nstr ings\nĠdir ty\nĠF ace\nĠB egin\nĠB us\nĠw is\nåŃ Ĺ\nĠspe aker\nĠcar rier\nĠO m\nĠhad n\nAll ow\n:: __\nĠver b\nĠCom plete\nĠE asy\nĠb ills\nĠĠ ĊĊ\nVert ical\nĠpr on\nĠDef ine\nĠlook up\nvariable s\nĠpand as\num es\nĠinn oc\nĠset Up\nĠCh ampionship\nart ist\nĠC Type\nF oundation\n๠Ī\nĠSet up\nĠrec ipes\nĠU IColor\nĠF ight\nĠauthor ized\n_c lick\n_s uccess\nang an\nĠMount ain\nĠDo ctor\nĠeg g\nĠMedic ine\nc les\n` .Ċ\n[ int\nd ashboard\nĠApp ro\n-d r\nĠprodu ces\nĠrent al\nĠre load\nĠarr ival\nsp ot\nĠund ert\nĠequ ipped\nĠpro ved\nĠcent ers\nĠdef ines\nal so\nĠop acity\nĠUn fortunately\nĠIll inois\nĠн е\nĠTem ple\nĠTr ail\nĠK elly\nĠmeasure ment\nĠsepar ated\n-c ircle\nH ey\nĠRE AD\nig its\nĠ ib\nĠM OD\natter y\nаР·\nĠv end\nен ÑĤ\nĠHttp Client\ns afe\n_A SS\nic it\nĠCon struct\nĠC lo\nĠS ix\n_T OKEN\n(b lock\nĠwarn ed\n/* !\n! Ċ\nĠinnov ation\n_ \"\nĠ );čĊčĊ\nĠsp ots\nĠcho osing\n.c s\nĠflex ible\nU Int\nĠscr atch\n- al\nĠf estival\nĠout standing\n================================ ================\nM ean\nĠO regon\ns ymbol\n. account\nd ney\n'' '\n! \",\nĠpart icle\nà ĥ\n[ MAX\nIV ER\nER ENCE\nNS Mutable\nĠColum bia\n_ ĊĊ\n.f r\nĠc ogn\nV R\nĠMethod s\nĠM ade\nĠB R\nĠEl se\nĠeg gs\nĠsw ing\nĠIn v\nĠdise ases\nĠf irms\nĠle mma\n}` );Ċ\nl ings\nĠg ym\numin um\n.T rim\nM em\nĠcritic ism\nibern ate\n_T X\nion i\nĠguid ance\nĠrepeated ly\nĠsup plier\nĠpaint ing\n.F ragment\ned Exception\nĠw iring\nĠcour ts\nW EB\næľ ī\n\\ .\nill ance\nĠb rows\nĠP attern\nPL ICATION\nĠSum mer\nCh ain\nĠc ute\nmer cial\nĠd il\nĠFrank lin\nĉg lobal\nIN CLUDING\nh istory\nĠl st\nQ t\nSD L\nal ia\ni ere\n( ...\nĉc in\niff s\nvel ope\nĠR oot\ncl uster\nUser Name\nign e\n< S\nĠf est\nĠindic ating\nke eper\nĠc ada\né g\ncons in\nĠG B\nĠl b\nem ony\n-icon s\n_d oc\nAct or\ne lem\n.De lete\nĠin fection\nĠPriv acy\nĠgreat ly\nĠP os\nĠT reat\nFl ow\nĠattract ive\nĠMar c\ns udo\ntes y\n- an\nab ama\nĠW ould\nĠsu ck\nindex Path\nĠE t\nT imes\nĠclub s\n_ass oc\nĠac quired\n(\" :\nĠint ense\n.m aps\nEx pected\nT oggle\nĠa y\nĠl ifestyle\n-c alled\nĠS now\nV olume\nĠcann abis\nĠD irection\nĠLim ited\n-s pecific\nĠd owntown\n/ icons\nĠre ven\nL eg\n= null\nKey board\n') ).\nĠ\"\" ;čĊ\nĠatt itude\n.n avigate\n- error\nAM PLE\nĠJ ay\nv r\nc ow\n.com pile\nĠmem ories\n_m ark\nĠMin nesota\nĠk osten\nĠprob ability\nw arning\nĠgen etic\nF ixture\nĠHash Set\nN ombre\n_m onth\nÆ °\n- start\nxy gen\nĉ ft\ni agnostics\nĠMat thew\nĠconcept s\nĠcon str\n. State\nи н\nN ov\nÎ ±\nĠP anel\nä¸ ª\ncom pare\n> ()Ċ\nĠapply ing\nĠprom ised\nĠo x\nnc ia\nĠValid ation\nort s\n_c ur\ne lect\ney e\n( Data\nĠreport er\nĠB uff\nĠs r\nĠ\" ;\nick y\nĠtemp or\nS N\nĠres ident\npi res\nys ical\nĠend orse\nĠS ong\nis Empty\nle et\n_ util\nĠdist ingu\nĠT alk\nĠM ot\n( default\n.A rg\ngorith ms\n_ words\nim mer\n_res et\nf amily\nW W\nĠsav ings\nĠâĢ Ŀ\n_en able\nside bar\nRun ning\nĠal i\nĠtest im\nĠwarn ings\nĠCh em\nĠEx it\nĠfound er\npect or\nĠr m\n_d ataset\nĠD as\nĠh an\nGet ty\ná l\nĠn y\nĠpo verty\nĠresult ed\n.b y\nĠVis it\nĠobt aining\n/ '.$\nĠĠĠĠĠĠĠĠĠĠĠ Ċ\nsh all\n_LE FT\nUI Image\n_ Name\nh ave\nĠN ob\nl r\n- footer\nĠn aked\nĠG arden\n\\F acades\nĠgrad uate\nĠfranch ise\npl ane\nĠcontrib utions\nĠstring With\nĠc rypto\nĠmov ements\nath ers\nĠlif etime\nĠcommunic ate\nj ar\nĠFr agment\n_ IF\nĠN avy\nĠF igure\nĠsim ulation\n_st op\nĠreport ers\nĠvers us\naj a\nĠÎ ±\nĠgovern or\nList Item\nĠse aled\n.Back ground\ned i\nash ing\nĠl ip\nĠI h\nmer ge\nĠn ec\nel ocity\nATE G\nĠse eds\nĠflo ating\n_F A\nw alk\nĉ user\n_de pth\nĠw age\n@ app\nN il\n( [\"\n( vector\nĠsecret ary\nĠj Panel\nve z\n³³ ³³\nd irection\nĠE P\nĠh unt\nJson Property\nĠP ORT\n] \",\nаР¿\nĠFore ign\npan ic\nĠtri als\nĠA le\nĠr ural\n- value\nauthor ized\nĠScot land\n.d rop\nĠM T\nç ±\nrow th\nFile Path\nĠrec all\nif le\nĠc el\nĠSE LECT\nk n\n_c ase\nĠc rop\ns ure\np ot\nIC S\nĠst em\nĠindust ries\nP ut\nĠa ber\nroad cast\nIcon s\n) \")Ċ\næĪIJ åĬŁ\ng ui\nĠassum ed\nĠr x\nE A\nè §\nEL L\nĠdo se\nĠin e\nĠde eper\nl ider\nĠord inary\nĠg olf\n_IM AGE\nĠN AME\n(m odule\nĠat om\nĠbel t\nĠoff ices\nb eta\nĠphilosoph y\n( JSON\n-f ield\nĠintrodu ce\nĠconven ience\nopt im\n> \"Ċ\nath y\nĠemploy er\nqu ate\nĠed ited\nArg uments\nĠN ations\n__ )\nĠno se\nĠS ample\n' )ĊĊĊ\nĠc ake\n.get Attribute\nH D\nMod ified\nĠpredict ed\nÅ Ħ\nan ie\nS orry\n(d oc\nw ind\nie ve\nĠprov isions\nAT ER\nOT E\nM Y\n.A utowired\nĠB ath\n. Boolean\nĠback end\n.M ouse\nater al\np aper\nCon st\nĠV R\n_ entity\n_C TRL\nĠProte ction\nĠG M\nĠStud y\nĠsou p\not ime\n' use\n] \"\n/ users\na ug\nĠH ong\n_n orm\nãģ ¨\nĠse cre\n(B uild\nĠCon tract\nol as\nĠsa uce\nĠaggress ive\nĠrac ial\nchar acter\n@ @\nĠcomp ile\nĠV oid\n_re m\n_m emory\nk k\nĠm ic\nS ame\nU tility\nĠH tml\nĠX ml\nRead y\nĠg all\nĠalleged ly\nĉĉĉĉ ĠĠĠ\nĠMet al\nĠPerson al\nĠborder Radius\nrx js\nobject s\nĠwant ing\nĠb owl\nv endor\noffset of\nĠR s\nĠR ating\nĠr ally\n_N ODE\nĠM ix\nĠadvert is\nĠnarr ative\ns al\nĠm c\nSE rror\nĠf ingers\nĠaccom pany\nĠt ired\nĠstr ide\nĠgu i\nel ist\nLoc ale\nĠrele ases\nik ing\nĠan ger\n)) )ĊĊ\nalle st\nSum mary\n( O\n(f or\nĠbasket ball\nĠroad s\nĠInst all\nĠF ab\nit map\nĠ) )Ċ\nĠinter section\nighb or\nĠB ry\nĠHER E\nSo ftware\nelf are\nac s\nĠtrail er\n.get Class\nch ars\nĠreg ulation\nĠref ers\nĠde struction\nĠcontin uous\nĠAust in\né ¢\nak an\n.w indow\nĠTem plates\nĠabs ence\n: n\nĠdis order\nfl ash\nĠde let\nbo ards\nĠĠ ĉ\nRO P\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠac qu\nĠlaws uit\nĠRe views\nĠgar age\nt imer\nĠe j\nĠRect angle\nĠflow ers\nil st\nĠIn stance\nS uper\nd et\ndis posing\nĠE S\nĠI C\nver e\nS k\n_ch annels\nput ed\n/ null\nnn en\nĠG allery\n_g lobal\nAuth entication\nĠR ank\nĠblock ed\nĠcal m\nmark et\nĉ val\nĠa ug\nper iod\nĠCon stant\nĠ?> \">Ċ\nĠl obby\np al\nĠs ink\nia h\nÐ ¡\nurn ame\nĠcon ver\nĠinvestig ate\nCh rist\nH ub\nĠIN D\nĠP ed\nur as\nĉ url\nĠT ro\nĠpre ferences\nĠguarante ed\n` ĊĊ\nĠport ions\nĠeval u\n' > ;ĊĊ\n.AutoScale Mode\nĠc ats\nĠreg istry\nul us\nF I\np ayload\n- search\nĠstay ing\nac ious\nDec oration\nRe view\nIn f\nKe ep\nit is\n, String\nCo ord\nĠper o\nS ex\nĠAtl anta\nuest a\nArg b\n> *\n} _\nF ooter\nĠemploy ed\n_b ound\nv ide\n.f unc\n$ scope\nĠsp o\nĠAn al\nounc ed\nar ound\nĠrestr iction\nĠsh ops\nå Ģ\nĠLat in\n-c ol\nĠbare ly\nĠE uro\nE r\nĠfa ire\n_d istance\n_un lock\nQu ote\nIV ATE\nĠå Ī\nĠaim ed\nĠRet rie\n. iter\nĠwr apped\nĠagre ements\nstr ument\n( product\nĠstud ied\n.set Value\nĠy e\nĠC ache\nMB OL\nĠquarter back\nĠsy ntax\n.getElements By\n.v ersion\nwe bsite\nRun ner\n_s ingle\nat iv\nĠAl tern\nĠBeaut iful\nright arrow\nĠd iversity\npl ash\n( co\n.F ill\nĠtyp ing\nĠcl ar\nH it\nO O\nac co\nw orth\nĠscript s\nĠMuslim s\nĠL L\nerv ing\n( boolean\nĠbase ball\nĠC AN\nMA IL\nde pend\nĠrespect ive\nĠconst expr\n.* ;ĊĊ\n'] ))Ċ\nĠy ard\nĠident ical\nif ecycle\nUS H\nup iter\n. validate\ncl i\nIST ER\nInd icator\nF ail\nĠdemocr acy\n. var\nĠsatisf ied\n------------ -\nenc er\nh or\nĠr ounds\nDA O\no a\nĠfl ask\n= c\n[ ]Ċ\n/d ist\nĠpart e\nĠconfirm ation\ner on\naw are\n\nĠdepend encies\nĠV ideos\n- row\nĠ** /Ċ\nĠn ou\nĠh over\næ ŀ\nĠn in\nĠUS D\nM ac\n_L oad\nĠout comes\n_s ocket\nĠqu eries\nw m\nĠhit ting\nin ux\nM ich\nud ge\nAT AB\nĠvulner able\nä ¾\nĠport folio\n: YES\nĉm ap\nB ound\nĠiter ation\nin cess\nĠact ors\nĠQ ual\n_c lean\nãĢij ãĢIJ\nMS G\nG reen\nĠOff icer\nĠsm oking\n> ',\nĠF lo\n++ ;\noly gon\nĠbul k\nĠdr ama\nĠexception s\nos ed\nĠ+ čĊ\nĠleg acy\nC V\nĠcontrib uted\nĠTer ms\nĠb t\nĠunt uk\nĠal ien\n=== Ċ\nĉ Vector\nĠl s\nOn line\n.f acebook\nnum eric\nock ets\nA ut\nb ury\n-re dux\nĠRed istributions\nGLOBAL S\nurrenc ies\nĠt ons\nâĢĻ ,\nĠà ª\n(c ol\nĠS ymbol\nĠstay ed\nĠM L\nĠm unicip\nĠsex o\nS en\nn r\nĠg ains\nĠshort ly\n.M enu\nà ½\nKN OWN\nĠoper ators\n- V\nĠPat rick\n/ add\n_C O\nir ation\n(p ost\nPost s\n/ _\nĠpl ug\nĠintellect ual\nĠmet ab\nĠpregn ancy\nĠPrem ier\nn m\nĠpred iction\nĠMin istry\nTh ree\nval uate\nĠMin i\nb u\nоР·\n< ul\nĠd d\nol ving\nĠC ut\nĠs chem\n.tr ain\nit ate\nĠr ice\nĠbird s\nãģ «\nm iddle\nstruction s\nĠn erv\na que\nĠfl u\nĠsurv ival\nĠGal axy\nĠF ant\n. Order\nAt trib\nirt s\né c\nM ovie\nĠcon ce\nqu arters\nĠm ood\n.Add Range\nĠres olved\nãĥ Ī\nĠburn ing\nĉĉĉĉ čĊ\nĠW E\nĠhost ing\nL AB\nĠman agers\nĠstre ngthen\n< const\nĠFire base\non ed\nĠJ ean\n' \";čĊ\nĠS av\n.B old\nĠen ables\nĉt mp\nĠman ually\nĠS qu\nuser id\n.f unction\n.c ache\nLO PT\n.S ervices\ndd it\nt im\n< img\nĠTh ings\nĠEvery thing\nĠa pt\nem and\nĠroll ing\në ¦\n. level\nĠst om\nĠW inter\nĠview ing\n( values\nocom plete\nv ia\nup o\nĠabort ion\ni ère\nï¼ ij\n_B UTTON\n_d omain\nĠb ra\nĠA st\nin as\nĠstat ist\nc od\nL R\nĠdr ives\nĠfollow ers\nĠall ies\nĉc urrent\necess ary\nĠdam aged\n_ pt\nand les\noun tries\nĠsim ult\ne u\nĠcontrovers ial\n_G ROUP\nĠr ib\n. Info\n: mm\n.n ormal\n_ADD RESS\nĠ íķ\nadd le\nĠD ur\n. Element\nW arnings\nĠcred its\nĠin hib\nĠem issions\nĠh az\n.y outube\nugg ed\nĠbo ther\nĠK ansas\nĠF ixed\nĠTest s\nĠF IX\nUn iform\nĠk ont\n>> >\nst ation\nlo re\nat ype\nish op\n/ ****************************************************************\nCom boBox\nĠvac ation\nĠiniti ative\nĠdefault Value\ncon cat\nĠK h\nĠW elcome\nized Name\nM igration\nĠgrad ient\nH ot\nĠhard ly\nel o\nĠStud ents\nĠlo ose\nat z\n.S end\n' /\nĠunivers al\nĠenter prise\nĠreg ex\nĠvis itor\nĠF ly\nSe q\nภĻ\nĠVis ual\nĠlib raries\nato es\nP ayment\nĠp ent\nĠgather ed\nVRT X\nĠD M\nS plit\nĠlet ting\nÐ Ŀ\n_error s\nep och\nP ARAM\nc u\nÑģÑĤ в\nol utions\nEdit ing\nfont s\nĠalloc ated\nĠB ased\n( Y\nĠJud ge\nĠbro thers\nFILE S\nç o\nw b\n_P I\n' ^\nĠs word\n.s ervices\nĠn l\nT im\nig g\nĠMo ore\nĠcrypt oc\nåĩ º\n_post s\not ate\n? '\n... .ĊĊ\nĠk l\n=\" $\nĠdec oration\nẠ¡\nĠD IRECT\nG UI\n) =>{Ċ\nĠnews letter\nĠprec is\n(p oint\nĠEqu ipment\nut y\nĠD ave\nĠparticip ation\nu arios\nx it\n.A s\nET ER\nor ous\nĠsh ield\n[] >\nilit ary\n. origin\nĠprom otion\nU nt\nĠc t\nTR A\nView Holder\nĠsig ma\nd elta\nare house\ncon tract\n( Vector\nĠcompet e\n/ form\n/ components\nĠn r\nĠInd ones\nĠо ÑĤ\nĠV olume\n.f iles\n(res p\n/ models\nĠsur f\nstand ard\n/ o\nĠXCT Assert\nV ICES\n.C ode\nSE D\nĠact ivate\nD elta\nĠlimit ation\nri j\nĠpregn ant\n: ^(\nĠs our\np ie\nĠexp ense\nic ation\nĠL arge\nĠ ±\nĠB owl\n(model s\n/ N\nP a\n.re load\nĠwonder ing\nExec ution\nĉ ĠĠĠĠĠĠ\nĠG raphics\nĠCont in\n_j ob\nĠget Name\nĠM agn\nĠD WORD\nm ad\nĠn h\nfe atures\n} \");Ċ\nhe ets\n(tr ain\nz n\nĠrecru it\n.con nection\nĠbar rel\nĠste am\n_set ting\nĠang ular\nane ously\nĠb il\nĠN orm\n(! $\nib t\n% (\nĠpos it\nĠF ather\nint endo\nL ive\nĠport s\nĠme j\nĠland ing\npon der\nĠc od\n_HE ADER\n.M argin\nĠball s\nĠdiscuss ions\nĠbl end\nH ex\nĠfarm ers\nĠmaint aining\nĠĠĠ čĊ\ns yn\n[ T\nr us\nuff ers\nĠcontrib utors\n_s ys\n.De bug\nĠconstruct ed\nom es\n? id\nsl ider\nĠsup pliers\nscri ber\np es\nÐ ŀ\n\": čĊ\n\\ Controller\n)) ĊĊĊ\nĠl ua\nM ulti\nEN S\nS rc\nĠpet ition\nĠsl ave\nlook ing\nV ERT\nĉ vector\nS pecial\nh h\nan ne\nĠN iger\n/ views\nz ing\nend ant\n< C\ns peed\nĠ{ };ĊĊ\nBegin Init\nĠf open\n@ RequestMapping\nEnd Init\nĠp unch\nS ender\né Ķ\nget Message\n/t ypes\n.P I\n(' ');Ċ\noc used\n( all\nĠdrop down\n). __\nĠV in\n.Fore ignKey\ncan f\nou red\nĠOrgan ization\nĠÐ °\nĠC ulture\n(cl s\n, _\nrg ba\nìĿ ĺ\n.data GridView\nĠdo zen\nĠG es\n_sh ared\nn ick\nĠh osp\nom eter\nĠclaim ing\nib les\nri k\næĺ ¯\nen ario\nĠd engan\nob b\nm ont\n_r ank\n('/ ',\nĠap olog\nP s\n_p ower\nĠG ree\nĠful fill\nĠfire base\nĠf are\nĠH im\nĠbe an\nâĢ¦ .\nĠS PI\n_R X\nĠper ception\nrel ative\ncomp ile\nu um\nut os\na uc\nĠAs k\nĠindic ator\n/ th\n.set String\nĠWis consin\n.D omain\nĠart ificial\nDe velop\nĠSar ah\nĠl ying\n( search\nĠEmp ire\nurr ing\næŶ éĹ´\n=\" ${\nĠget Id\nĠP ayment\ntrans ition\nĠ ].\nix in\nV T\n- select\nĠdemonstr ated\nĠlast Name\nemploy ment\n.get Property\nĠf ought\nfile Name\nĠP ers\n-c ard\na str\nattr s\nĠprom inent\nDes ign\nanc ouver\nãģĹ ãģ\nard o\nse cret\nĠr ag\nĠpo ison\n-m an\n, omitempty\nĉ un\nit zer\nĠCas ino\nĠR oss\n- foot\n(result s\nPl an\nĠlas er\nê¸ °\n_D R\nF acebook\nĠbo ards\nst a\n] ],\nĠt iles\nS IZE\nĠ= ~\nĠprem ier\noc ab\nĠenc oded\nĠres erve\nĠAfghan istan\nĠList Node\nurl s\nĠsub mission\nĠne u\nĠ# +#\n_P OST\nĠmo ist\nell i\nellig ent\n. alert\nó d\nb re\nĠCol lect\nĠgraph ic\nĠlong itude\nĠPro vid\nĠCal culate\nx ffff\nc riteria\nĠw aters\nro ck\nlo quent\nĠT rib\nĠbur st\nĠsuff ix\n.Ext ensions\nish es\niv el\nĠLI KE\nĠGet ty\n.Action Event\n.s lf\nĠH AL\nup al\nE AR\nud i\n_time out\nU F\nĠSing apore\nĠAd vent\n_int erval\ncha ft\nĠE mer\nĠtele phone\nĠTur k\n_ interface\nĠO wn\nĠencour aged\n< Object\n_T ext\nĠOnt ario\nĠApp ly\n.f irebase\nĠant ib\nP riority\nene z\nD ays\nc id\nurre nce\n; /\ninn ed\nÑģ Ñı\nĠve z\nf w\n// $\natt ack\nĠstart up\nain ers\n.f ragment\nop acity\n( conn\nhe im\n.n etwork\n( stream\nĠN ON\nt ol\nĠX box\nĠD S\nĠc ached\nĠprostit utas\nĠB alt\n(' [\nĠno except\n\" '\nĠs d\n. valid\n_ ag\nĠr aces\nĠro d\nitud es\n< >(\n.Pro duct\nForm s\nNE W\nP ay\nĉ boolean\n_ contact\nĠElect ric\nsk ip\nĠw ur\nĠch ronic\n_d river\nĠS ab\nĠU lt\nĠR ad\nST ATUS\nĠLew is\nO B\nĠgift s\n.Re c\nTR UE\nĠint ensity\nMark er\n.com pare\nff ic\nC ookie\nĠB aby\nĠBig Decimal\nile t\nĠHOLD ERS\nĠL ady\nĠl ung\nĠAl abama\nĠd ess\n` );Ċ\nĠB uilder\n_reg ion\nĠne utral\nBo th\nĠh p\nĠh orn\nĠseg ments\nĠE C\n\"=> \"\n( rec\nĠP i\nG M\nĠl aptop\nSc alar\nis d\n-d ialog\nĠAnd erson\nĠmist akes\nĠH an\nj es\nest ination\nĠprom ises\nb id\nĠSc ient\nG IN\nĠPer formance\nb age\n. users\nle ading\nĠor al\nG raphics\n_P TR\nh ang\nĠin ev\nprocess ing\nF actor\nĠN A\n$ string\nĠground s\n.Save Changes\nc lock\ncri pcion\nĠNew ton\ng c\n.in cludes\nĠbl ast\nĠ'- '\nĠpued e\n.S ession\nĠgre p\n_f inal\nĠG ay\nĠG ive\nir i\n-st ar\nĠUI Image\n_ep och\nub b\nent h\nĠel ite\nĠcampaign s\nĠP orno\n_ assign\nProt ocol\nĠBe ing\nĠAir port\nĠconvent ional\nĠW at\nĠC I\nET A\nĠAnth ony\nĠtable t\n( format\nĠconsist ently\nĠI owa\nĠav atar\n.c ursor\n! [\nĠh anging\nH er\nS uch\n';ĊĊ Ċ\norge ous\n() ==\nĠview Model\nĠ ãĥ\nĠel s\nĠAg ent\nF etch\nap or\nĠc x\np read\nĠP ier\noe ff\nS n\nĠV irtual\nA pr\n.Wh ite\n_M OD\nĠPoint s\nå¤ ±\nĠgen es\nĠv endor\nĠmain stream\n< src\nĠEl izabeth\nDec oder\n- state\nĠG lass\nnc y\nadi ans\n_m on\nĠRem ote\nĠwire less\nĠM i\nå ī\nè¡ ¨\nst age\nĠT ile\nll ib\nV ariant\n== Ċ\nĠgold en\n(Q String\n.put Extra\nĠD om\nĠAn imation\nĠinter active\nif act\néĻ ¤\nLE T\nĠfrequ ent\nĠ< >Ċ\nF ilename\nĠs ne\nĠFoot ball\nĠr ival\nĠdis aster\nion ic\nĠD amage\n. Resource\n- en\nĠT ypes\nget String\n( board\nĠb ol\npl ain\nz ym\nภ²\nĠsc anner\nild er\n_msg s\næ ı\n(int ent\nĠde struct\nĠb ust\nĠE mploy\non i\nĠUI ViewController\nĠodd s\near er\nGe ometry\nĠy ii\n_EX PORT\nĠAtt ack\nĠn iet\nĠim pression\nĠG il\n_pro b\nĠC F\nĠEx perience\n/pl ugins\n.M ethod\nĠbelie fs\nN ative\n_b uild\nĠv ig\nĠr anks\ncover ed\ns uch\nG uard\n.p ack\nadd er\niv ia\nl ng\nĠв Ñĭ\nT imestamp\n_n ow\nĠp oker\nĠun c\nĠsh apes\n-t ypes\n_per iod\np k\nĠveter an\nĠson o\nĠappoint ed\nover flow\n.d river\n_c at\nut t\npl ant\nim b\nĠAc cept\nĠconc ert\nĉ node\nĉ z\n? >čĊ\nĠb anned\nĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠto xic\nĠdisap pe\nÈ Ľ\nĠgr ace\nate ful\nRe ply\nĠCru z\nĠsc rap\nĠkey words\ns imp\nĠmort gage\nĠcy ber\nĠEx ecute\nĠlat itude\nif u\n.C OM\nd bo\nĠsort s\nĠG as\nom ial\n.L ocal\nCell s\n.Re place\nString s\n.f it\nĠTh ird\n% \",Ċ\nĠ{} \".\nĠS ony\nĠ[ :\nĠfall en\n. ')Ċ\nin h\nĠM C\nĠred is\nC odes\nĠprofile s\nh ook\nReduc er\n_F UNC\nĠn avigate\nstr len\nĠh orm\ná ŀ\nĠS R\n. boot\nĠdig est\nĉ header\n.find One\næ ģ\nDb Type\nn ia\n_m erge\nĠdon ne\n/ Getty\n_CH AR\nĠb ands\n. URL\nart ial\nĠf req\nĠs ist\nN g\nĠrender ing\n\\ Core\nWidget s\nĠV A\nĠactiv ists\nSt e\n= _\nall a\nSt amp\nĠload s\nĠx x\nĠL earning\n.M vc\nu ir\n(\" $\nĠconnect ing\nRead Only\nur u\nĠE ag\nB IT\n_DE L\nå §\narr ass\next ernal\nĠY OUR\nĠB rew\nĠF ive\nĠres ize\nig id\ner ation\nĠÑ į\nåĬ ł\nĠC atch\nÙ ģ\nĠLe on\nam il\n.B ody\nCl ip\n/ list\n.b r\nEdit Text\nĉ db\n.G ame\n(Build Context\nback end\n.R ed\nface book\n.url s\nm r\nrol led\n---- ---\nĠinter vention\nĠretire ment\nĠK it\nĠP RE\nUpper Case\nĠS ocket\nĠ: -\nĠstudy ing\nĠMet ro\nard ed\nĠconvers ations\nC alled\nĠexam ine\nert ificate\n.g z\n-res ponsive\nĠref und\n_n etwork\nallow ed\nem pt\nĠme als\nC ategories\nĠtravel ing\nĠk g\nĠsh ame\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠexplicit ly\nĠmath ematic\nĠS uite\nĠR GB\n****** /\nĠmix ture\nlear ning\n.t emplate\natt s\nw x\nĉ ctx\n.p roperties\nĠdrink s\nĠE ither\nset Text\n.get Data\n.z ip\nĠreve als\n< table\n.Hash Map\nĠH ur\n) \");Ċ\n.f ramework\nĠST ART\nfeed back\nĠsaf ely\n. icon\nconfig ure\n. lock\n.l ayers\n/> .Ċ\nĠrank ed\n_ impl\nĠHand les\nĠhost ed\nĠup dating\nal bum\né Ŀ\nĠsh ader\nEdit ors\n- round\n[] {\nĠse p\nĠH i\nTE M\nlook up\n.m an\n_IN PUT\nĠthreat ened\n_IM PORT\nĠd rops\nru it\ns id\nbo th\nĠEx cel\nĠj er\nord inary\nеР¹\nV IEW\nre ply\nĠ) :Ċ\ncolor s\nver ified\n_T r\n_p arse\nĠcon gress\nP romise\nint s\nĠM other\n.A pi\nĠD uration\nĠfirst Name\ninherit doc\nĠM ars\nĠa pr\nOD Y\nĠvis its\nĠhe aling\nlet ters\n)) );čĊ\nf uture\n.F ramework\nĠk iss\nĠinv olve\nĠsil ent\nad ows\nĠany body\ns ch\nĠsole ly\n- img\nĠprop ri\nĠin struct\nĠlic enses\nĠm eth\nĠcond em\nĠD omain\nĠHarr is\nĠs Ã¥\nCE PT\nB atch\n@ extends\nĠCONTR IBUT\n.Data Frame\n_p acket\nrec ision\nĠfoc using\n. ht\n__ \":Ċ\n: Get\nĠK C\nĠpass age\nSeg ment\n_c enter\n-z A\n_B L\nĠconv in\nĠclass ified\nĠNS Mutable\n_ ap\nt ile\nRect angle\n(n ums\nv ens\nĠUI Button\nĠF eder\nam o\nĠout line\nĠPar ser\nĠâ ī\nĠWork s\n.S chema\nĠeng ines\n_com mon\n_ old\nĠset ContentView\nĠ/// <\nĠB T\nf m\nĠd ivers\n_ weights\nem ark\nĠA CT\nĠpro portion\nover lay\n.dir name\nĠG it\n_REF ERENCE\n< >\nl b\n_r ule\nè´ ¥\nĠPut in\nĠsleep ing\n() :čĊ\nĠpres erve\nĠpar liament\nĠLook ing\nĠpick ing\nĠDis patch\nĠsl ip\në ĵ\nĠL yn\n_sign al\nconfig uration\nĠP itt\nad en\npro cedure\nĠenthus i\nf ight\nĠCons ider\nĠt orn\nConn ected\n.c os\n_group s\nĠTh ink\nĠdel iber\nĠres id\nwork ing\n.column s\nĠCal led\nĠes lint\n> \",\n_D OWN\nh ist\nĠAdv anced\nĠre wards\nact ors\nĠsil ence\nĠmy th\nĠne ur\nĠa uction\n.Get String\nek s\n( project\nĉ msg\nĉ output\nĠcomplaint s\n, S\nĠt bl\nĠ, ĊĊ\nri ors\nah ren\nĠlawy ers\nre dux\n_s ymbol\noff ee\n_RES ULT\n( Name\nUT C\n.current Time\nĠorgan is\n. arg\nĠmin im\nw ick\nĠrece ives\nB alance\nĠspeak s\nĠD ays\nĠBel ow\nt ipo\nP resent\nĠres erv\nh p\nĠr it\n_R IGHT\n-- )\nĠchair man\nD IS\nĠBO OST\nĠexper iments\n__ );Ċ\nĠst amp\nĠf ert\nĠf ond\nT er\nel ve\nure n\n+ i\nend ency\nĠvirt ually\n... \"\nï½ ŀ\n- cent\n_un ique\nĠpr icing\nm ic\nRES H\nĠ:: :\nĠan notation\nĠC ircle\nong odb\nit as\nĠ% (\n( component\nĠо б\n( port\n-h our\n. obj\nL BL\nĠj ury\nGB T\nĠsp y\nĠProf essional\nĠ\"\" ;ĊĊ\nĠstri king\nĠdiscrim ination\nĠp ays\nlic t\nent es\nĠthrow ing\nĠPl ugin\n( def\nĠRuntime Exception\nĠM igration\nĠd ic\nb ag\non ia\nĠcor ruption\n( Map\nĠpr z\n.d to\nĠac quire\nState ToProps\nĠlo ving\nоР¶\n_p attern\nĠemot ions\nĠpublish er\n_b e\nĠcoup les\no j\nĠCh art\nĠt rop\n.t ool\nĠestablish ment\nĠd ol\nĠto wer\nĠl ane\nĠSy dney\nĠfill ing\nclaim ed\nĠdialog ue\nĠcon vention\nbook ing\npare ncy\næ ±\nĠGener ic\n\\ Schema\nĠr anges\n/ ch\nĠpan els\nĠr uled\nçĶ Ł\n.t s\n_s ets\nĠclean up\nPre vious\nĠAn imal\n($ (\nĠA ve\noll ar\n_e val\nĉ Name\n(t ree\nĠ\" ]\nĠdut ies\n=' /\nClick ed\nĠdifferent ly\nĠCl ark\nĠd it\nolog ists\nĠsy nd\nĠs ends\n- known\nk b\nĠMod al\nit ative\nĠr acing\nĠhigh lights\nĠSim on\nĠCapt ain\nä¿ ¡\nĠC B\ncont in\nar an\nĠphys ics\nret ty\net al\n.m d\nax ios\nĠspeak ers\nĠpre p\nĠaward ed\nì§ Ģ\nĠC orn\nĠN ature\nUD IO\nĠpro j\n- pre\n[ u\nFe atures\nĠis Equal\nB inary\ns ig\nĠconf usion\nĠH at\nĠkt ó\n.config ure\nM ON\n/ edit\n_A dd\n, true\nĠc li\nError Message\n- loader\nDim ensions\nultip ly\nĠ{ !!\nĠSql Command\nĠsp oken\nĠp ics\nĠto y\n( Key\nĠLo op\nØ ¨\nE ATURE\nin ction\n_set up\nw rapper\nĠt ong\nc ular\nO pt\n.P l\n=\" ,\n(l ength\num n\nĠch rom\nĠse vent\nĠIllegal ArgumentException\nĉ start\nĠbeg un\nCE PTION\ndat aset\nĠF ailed\ncol s\nĠkne e\nim ore\n.sp lice\nsh ell\nig gers\nĠthem es\nĠD J\nĠAss istant\n- $\nMay be\nĠorder ing\nĠInt elligence\nĠMass achusetts\nĠfail ing\nel son\nG reat\n= i\n.re st\nĠinv ite\n-dis able\n.Group Box\nâĢĻ est\nĠtack le\ng v\net ter\nĠ), čĊ\n_r ules\n.w arn\nfunction s\nĠChrist ians\nĠback ed\nĠsl ider\nĠenjoy ing\nn est\nĠh ij\n_m s\n// *\nAn notations\nĠVariable s\n< V\n( server\nĠOr acle\nelement s\nĠorgan isation\n_point er\nĠHe aders\n[ d\nĠdead line\niss a\nĠkn ife\nĠNAS A\nĠHe ight\nĠAs ync\nĠven ue\n.d om\nbour ne\nĠHaw ai\nĠmem o\nict ions\nĠsurve illance\nom i\n/ assets\nĠed u\nÄ Ľ\nĠro ster\nĠh ired\nĠT ok\nĠpl acement\nur ations\nĠset State\nĠMag azine\nĠhor ror\nT ry\nĠl ag\nĠEvery one\nth ur\n)) ;čĊčĊ\n. return\nĠsy mp\nâĸĪ âĸĪ\nĠn ights\nwork er\nĠa le\nennes see\n.st ep\nĠsynchron ized\nour i\nDo es\n. change\nf on\n.set Background\nirc ular\n+ -\nĠC IA\nĠJ ane\nĠSim ilar\n- I\nlevel and\nĠpros pect\n_f ound\nĉc olor\n.D iagnostics\nĠann ounce\nĠassum es\n/ tr\nĠb d\nĠCar bon\nĠanal ys\n.de st\nn ik\nĠL ie\n- index\nDraw able\nĠT AG\nĠtri angle\n_F LOAT\nĉĉ ĠĠĠĠĠ\n.bl ack\nv ue\ncur acy\nĠaffect s\nĠsure ly\nSl ider\nuk i\nc ery\nĠun ter\n.pro file\nord on\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nle ave\nĠsmart phone\ng ie\nĠcons pir\nĠt utorial\nç± »\nĠc ab\nĠSum mary\n* ĊĊ\nä h\n\" This\nĠsl ides\n\" \nc ycle\nĠB ull\npath s\nĠun p\nĠview DidLoad\n_M odel\nĠassert True\nĠr ated\nDe cl\nvert ed\nĠD at\nb rew\nĠpoint ing\nM s\nĠPoint er\n) '\n_n on\nĠSE C\nĠy eah\ng ency\ninitial ize\nf ly\n[ pos\n, g\nTe le\nĠj oke\nĠcl ause\n.find ById\nen es\n( instance\n £\nĠs lic\n_h ome\nĠ*/ }Ċ\n_p ages\n(s ervice\nR P\nĠAm ong\n.get Current\nãĤ ¹\nĠs lee\n= [Ċ\nol er\nĠlib ert\nĠ` Ċ\nĠw enn\nl ated\nĠimm une\n( Node\nĠPro blem\nĠA bs\nlog s\nĠ ../\nĠA DC\nĠ}} \">Ċ\n> ');Ċ\n= b\nĠW ind\nlah oma\nĠalloc ate\nor ian\nĠpres cription\n- quality\nĠMay or\nin ely\nend foreach\nĠCom plex\nk om\nT Y\n] ].\n. 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EX\n.st orage\n.W e\nĠto ast\nĠAdd itionally\nĠN OW\n_ UPDATE\nĠtrans ferred\nth a\n.D isplay\n_ ui\nID EO\nĠmeaning ful\nĠMos cow\n, this\nĠVict oria\næĶ ¹\nĠÐ Ł\n.st ack\nĠB arn\npared Statement\n: string\nĠb ij\nĠST ATE\nĠemploy ers\nĉ input\n( |\nĠle x\nin voke\nĉ num\n++ ,\nat ial\nors es\nĠfor k\n_t xt\nĠAnton io\nĠ( <\naver se\nĠdev ast\nãĢ Ģ\n.D ec\nĠG ard\n/ ui\n. %\ntr i\nĠrol led\nValue Pair\nitt en\nĠTh er\nĠv rou\nĠFl ow\nĠFin ance\nĠCom b\nH C\n.set Visible\nis l\nĠp k\nĠup set\n( raw\nĠV ice\ne atures\nĠL ang\nLook ing\nĠA ST\nĠtri ps\nĠJust in\nb rowser\n=\" '.$\n. vertices\n- co\n}/ {\nĠ? ,\nĠD omin\nĠBel g\n\" <\nĠsup pose\nadd y\nĠwalk s\nERR U\n_f ilters\nPre ferred\nsc ene\nе Ñģ\nĠAff airs\nĠ\"# {\nĠon Submit\nĠstock s\n/ view\ng ree\n- get\nh it\nJ o\n.get C\nInitial ized\nÑĤ и\nc uts\n( Type\nĠAg reement\nĠViet nam\nĠ/* !\nĠp izza\n- view\n_ em\nĠl hs\nĠm uy\nĠId ent\nĠF riends\nĠab und\n_A D\n.t imestamp\n- '\nĠd uplicate\nĠhun ting\nĠregul atory\nia o\nam ous\nĠEnt ertainment\n[ A\niat ric\n_CL IENT\nĠK ids\n/p kg\nB reak\n)) );ĊĊ\nĠSh ape\nĠrel ating\nInt errupt\nable Opacity\nemb re\nĠmyst ery\nĠjournal ists\nrit able\n.L ink\nĠstop ping\nCRE T\n.D B\nĠpopular ity\nĠg ew\nĠim pr\nset Value\nFL AG\nĉm ax\nĠb ake\nw y\nĠEcon omic\nĠen contr\nĠf name\n/ de\nR ank\nĠbug s\n.s m\nĠmed ian\nD OWN\nĠS ure\nAt Index\nĠD ick\nĠ( __\n.d elta\nF r\nĠsuggest ing\nĠRec yclerView\n, e\nST ART\n/************************************************************************ ****\nxf ord\nĠrece ipt\nCL AIM\nread only\nĠeng aging\nC a\nas ma\nĠens uring\nEng lish\nĠV ancouver\nhy th\nĠpurch asing\nĠP I\n. word\n(s p\n.h ome\n: def\nĠg ig\nĠV e\nfor um\nĠM itch\nB ay\n_F L\nĠs oll\n_column s\nĠminor ity\nb ird\nĠhand ed\nSS L\nST AT\nĠnerv ous\nĥ ½\nĠfile Path\nCRE ATE\nA w\nĠp ens\nse ed\nĠCom pute\nol k\nĠAs set\nre ach\n'), čĊ\nn avigation\nL F\n/ util\nĠP ub\nĠâ Ķ\nc ion\n## Ċ\nII I\nTag Name\nĠam id\nper mission\nif iable\nxFFFF FFFF\nн и\n.B uffer\n_ irq\nd ark\nĠret val\n.f ire\nprodu ction\n.list en\nĠWe ather\nĠbuy ers\n. ne\ner p\nĠP ent\nĠw elfare\nĠpage Size\nĠSt adium\nert a\nĠle v\namp a\nP ager\nĠcharg ing\nĠNet flix\n| null\n_r andom\n.x path\nĠst ere\nĠIS IS\npons es\n( loc\ney ond\nĠOff icial\nĠMary land\nData Type\n_p ar\n{ },\nĠEn joy\n_SH IFT\nĠA wards\n_ENT RY\nĠseem ingly\nentic ate\nĠheart s\n_ ;ĊĊ\nĠH IV\nĠindiv id\nĠFl ag\n_ ctrl\nĠC allback\n, z\nĠG PU\nĉ obj\nĠPh oenix\nĠB US\nĠrub ber\n_A UTH\nĠSol utions\n( location\nVariable s\n.set Enabled\n_h igh\nW O\nG esture\nĠre try\nĠobject ForKey\nallow een\nĠm os\nĠC ele\nĠik ke\n(c ell\nĠM ODE\nren a\nĠdescri bing\nĠph i\nĠr d\nĠdes erve\nĠwhe els\nå¸ Ĥ\nĠcrit ics\nN amespace\nĠF ra\nĠ ĊĊĊĊ\nĠall a\nĠrequ iring\næľ Ł\nut ation\nĠdelay ed\nĠadministr ative\nĠb ay\n.h idden\nT ex\nĠbound aries\nĠ] );ĊĊ\nĠFollow ing\n~ /\nF i\n_con v\n_T ITLE\nĠdes de\nICollection View\nAli as\nĠb ite\npat ient\n_COMM AND\nCom pleted\nĉ elif\n( <\nB usiness\nĠP ool\nĠpurs ue\nĠB an\n_st eps\n_DE CL\num ble\nĠcom bo\nĠL ayer\n.x r\nĠd up\n-------- -\nĠmod ifier\nro b\nre z\nĠath letes\nUs ed\nw ear\nĠlegit imate\nĠ\" ĊĊ\nĠh v\nSt d\nĠH old\nĠsurv iv\nĠAll iance\nĠEar ly\nBeh avior\n(f ont\n/lib s\nĠrect angle\nĠs inger\nĠam p\nEqual To\nĠ\" .\"\nĠgirl friend\nå ±\nline ar\nobs erv\nĠpi ù\nĠcomple ment\nWith Value\n(p assword\nt ake\nBl ank\nĠCom par\n' \",\n_p olicy\nm ongoose\n_FA ILED\n.re port\nR atio\n.Perform Layout\nus able\nm ers\n_re nder\nPE ED\nĠles b\nĉ E\n_t ool\nĠl adies\nо Ñģ\n)) ))Ċ\n;; ;;\n.d ot\nĠn est\npe ak\nuk kit\nec a\n_S W\nĠ& (\nĠOk lahoma\nĠbank ing\nĠN intendo\nĠreprodu ce\n_element s\n_m ac\npro xy\nĠremark able\n}/ ${\nĠout s\n.has Next\nM ODE\nĠan ime\n.con n\nUn ique\nD om\nĠimportant ly\nitt y\nĠju ice\nT w\nĠPart ners\nĠattack ing\nĠport able\nam iento\n.P ictureBox\n.g en\nĠopt imal\nĠre cre\nĠjournal ist\nĠEx tract\nĠMore over\nĠmargin Top\n.A p\nĠf iring\nNa N\nĉ template\nаР´\n. En\nĠdef ence\nĠT el\nil en\nj an\n= data\nĠU rl\nĠRe uters\n(t otal\nĠFif th\nĠess ays\nĠinterpret ation\nĠchar ity\nĠR ules\nĠsub section\nst yled\naz er\nl ags\nL IST\nĠupload ed\nĠtr ash\nĠreg istr\nĠsell er\n>' ;čĊ\nĠstart Time\nç Ļ\ns y\n(Http ServletRequest\nĠtr ap\nG C\nĠembed ded\nĠsurround ed\nim its\nT X\nyl inder\nĠF al\nĠsent ences\nĠJ a\nIF ICATION\nwe apon\nov ation\nĠco at\nĠinter pol\nĠl ips\nĠK y\nĠv ectors\n_ am\nĠint ake\n.w orld\nĠin box\nĠM AC\n_ ab\n(name of\nĠent ert\nĠgather ing\nĠS IM\n++ .\nny a\n' }}\nĠUP DATE\nĠp ac\n( html\nĠS ant\ni ating\nĠIde as\nĠspr ay\nĠH art\nĠver ification\nades h\n/ modules\nĠM ind\nĠSized Box\nĠsh elter\nĠher oes\natt y\nĠcert ified\ns j\nĠê tre\nÅĤ o\nĠpublish ing\nĠMal ays\n.get User\nĠPro vider\nĠLinked List\nĠB or\nRO UND\nd id\nt ain\np ire\nĠJ enn\nt el\nand e\n_f ront\nĠMc G\nTest Method\nภŃ\nĠoccasion ally\nĠW ales\nĠexerc ises\nĠÐ Ĵ\n- plus\nĠvalid ator\nĠpr ayer\nL ATED\n_ author\nĠlab our\n++ Ċ\n-e quiv\nĠG PL\nĠface book\ns imple\ng ly\nProcess or\nip y\nĠ* >\nĠcle ared\nĠP ush\nĠpen is\nStruct ure\nli j\nĠM organ\nĠhand ful\n\" .Ċ\n| \\\nĠ ********************************\nĠA qu\n_ IC\n.load s\nĠm eter\nĠMar ine\n:: {\nĠT S\nĠArray s\n.T itle\nGR AM\nter min\nĠco inc\nEl se\n_st ates\n-r un\nm embers\nast ro\nĠon Press\nĠbe ings\nĠabandon ed\nĠtax p\nown ers\n.m ode\nĠdiagn osis\nĠ_ Ċ\nĠK night\nĉ A\nĠob serve\n), '\n! \")Ċ\nĠPar a\nĠvari ation\n( False\nĠAnt i\nĠg ri\nĠhome less\n? v\nĠbe z\n.S erver\nre lease\nĠP atri\nĠchar s\nĠrank ing\nactiv ation\nĠw ides\nq r\n.S ql\nac ular\nĠB ot\n_s ync\nĠhapp iness\nĠvolunte ers\nĠs its\n/ <\n[ e\n(file Name\nĠcap ac\nĠMar ia\nf ather\nĠgr am\n* i\nĠcas o\n_d raw\nĠR aw\nĠIter ator\nĠP adding\nP D\nBO X\nĠS PECIAL\nĠfe cha\nĠv ide\nĠLe ader\nä» ¥\n$ (\".\nĠdiam eter\nĠm ild\nĠrock s\napp ings\nd irectory\n.fl ush\nĠJ ess\nUN IT\nĠP ear\nĠmand atory\nS ur\nq t\nĠstream s\nĠco operation\nĠS ac\nĠche aper\nĉ ch\nan imation\nf are\n( height\n( True\nN Y\nĠw rest\nĠpoll s\nĠencounter ed\nĠMarket able\n_P ASSWORD\n_SE LECT\nĠArab ia\n_c lock\nĠv oy\nĠи з\nĠst ir\nis ible\n-e ffect\n.c reated\nĠto ys\nĠTrad able\nĠr ust\nĠstr cpy\n_t imestamp\nĠtalent ed\n, null\nĠJ obs\nĠPort land\nĠweak ness\nTh row\nĠAng el\nä¿ ®\nĠun cert\nï¼ī Ċ\nĠìĿ ´\nWh ich\nĠ[- ]:\nS omething\nĠconv icted\nk le\ned ium\nĠbranch es\nĠb ases\nç ®\nĠcomplex ity\nĠF ig\n. reshape\n$ db\n_CON ST\nĠT es\n.r untime\nĠden y\nĠB SD\nĠk r\nh att\nĠSt atic\nĠunivers ities\nRe place\nĠdro ve\nĠad oles\n_pl ugin\nĠL GBT\nĠt ex\ndu ction\nED I\nĠT ed\n_ URI\nĠre ception\nart en\n.S ingle\nr ice\nsc ious\n_b g\nĠw ages\nĠS ervlet\nUIL ayout\nĠform atted\n.M od\n< class\nis en\nĠrepresent atives\n\"] =\nĠport al\nĠHun ter\nĠh iring\n__ )Ċ\nric ulum\nu o\nli est\nĠt ears\nL at\nĠliter al\n.In sert\nĠc urs\nĠCom put\nĠterror ism\nĠswe ep\nĠ[] čĊ\nĠpass enger\nĠeast ern\nĠtwe ets\nĠoper ated\nw nd\nĠS yn\n.t ools\nĠW M\nul ates\nĠbacter ia\n( bytes\n.set Data\nĠvis ibility\n// ================================================================\nel m\nĠgener ating\nĠm v\nĠk h\nj en\n/ search\nĠaccount ing\nse gment\nact ic\n. ip\nĠdeploy ment\nĠfoot er\n> ',Ċ\nĠexpand ing\nĠHam ilton\nĠCon trib\n.T ables\nAct iv\nH H\nocom merce\n_ ;\nĠamong st\now ing\nĠC old\nAP H\nĠpsych ological\n_t ensor\nĠpack aging\nĠSw eden\nĠp are\nĠag gregate\nĠmoder ate\n_h and\nĠdesign ated\nĠdr um\nĠget User\nĠC reek\n_s cope\nĠTrans fer\nĠM arg\nĠfight ers\nW nd\nĠS el\nĠLa unch\nĠemerg ing\nif rame\nĠAdd itional\nĠf ears\nĠsat ellite\n_ :\nĠdis posing\nGet Value\nHttp Post\nAT IVE\nul ary\nView s\nĠatt ending\nĠT ennessee\nĠM ission\nĠmedic ation\nĠW y\nĠAn na\nØ ¹\nĠVert ex\n.t ypes\nO rgan\n.DataGridView TextBoxColumn\nĠR S\nĠtemp o\n( App\nVersion UID\n.p oint\nĠD utch\nH ours\nL U\nĠqu oted\n.b uilder\nĠPer fect\nĠAl ways\n_t wo\nĠexclus ively\nĠC ra\nific ar\nĠA WS\ning ham\ncom plex\nk ernel\nĠgr avity\nĠw i\nĠover view\nĠW ant\nĠW P\n( sh\n. rotation\nSt ates\nĠTe en\n_com ponents\nì Īĺ\nRe ceived\nĠly rics\nrit es\nĉĉĉĉĉ Ġ\n-A merican\n[ num\n/ python\nĠU ART\nĠapp le\nĠJon athan\nĠmoment um\nภ±\nĤ ¹\nĠm ich\nand ra\nĠbi ological\nĠM ens\nĠ% %\nelse a\nĠMex ican\n.rand int\nĠt ale\nĠValid ate\nĠdefe ated\n.ht m\nĠcop per\n= /\ncos ystem\nĠr ip\ndec imal\n.V ISIBLE\nĠT a\nĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ\nĠdownload ed\nen vironment\nĠnom ine\nbuild ing\nĠSp ot\nipher al\nĠal to\nqu et\nĠF T\n/ get\n/m aster\nW IN\nåħ ĥ\nW est\narg c\nĠprodu cers\nĠM uch\n_st orage\ncred it\nCON T\nĠv et\nĠvo ices\n(' ',\nĠinstr uments\nĠM SG\nes se\nre pository\nom ics\nĠdeal er\nSt ill\nĠb anner\nasc ii\nĠrem arks\n[ js\nĠshort er\ng ulp\nĠmyst er\nĠk un\nĠB ird\nĠti ene\nn ut\nĠU m\nĠw ise\nY eah\nINE SS\n_b egin\n- heading\nC ourse\nĠ čĊčĊ\nomb ie\ngrad ed\nĠG PS\nĠ że\nF it\nc aption\nö n\n/ image\nl ia\n(m od\nĠle ak\nen za\n/ H\nĠH appy\nD ist\nn x\nĠGovern or\n(l ast\nte acher\nĠS ent\ns upport\nject ory\nĠ Ùħ\nReg istration\nĠGr ay\n, false\nĠadjust ed\n( settings\n< R\nĠM age\nĠpl aint\n_ )Ċ\nĉ it\nomet ric\n. bootstrap\nĠcar ries\nI p\nĠ! $\nĠswim ming\nĠMar io\nĠQuest ions\nP ACE\næĸ ¹\ne or\n}} \"\nĠo ven\nĠK on\nĠwis dom\nĠac quisition\ness ment\nag ine\nĠexpress ions\nSequential Group\nF ront\nul pt\naw k\n'] )ĊĊ\n_ AR\nĠanal og\nul in\n_PR INT\nĠL G\nĠb lob\nĠFurther more\n_com ponent\nĠC ole\nL AN\nSCRI PTION\nĠl ap\nicens ing\n_TIME OUT\nĠF ro\nĠli ability\nĠcom posed\n.create SequentialGroup\n_p erson\nĠbe am\nĉ ĠĠĠĠĠĠĠĠ\nĠNot Found\n. 'Ċ\nÃŃ s\n.Text View\nP DF\nĠk ar\n__ ('\nĠ\" :\"\n_m essages\nĠhar vest\n.h istory\n> 'Ċ\n-f old\næ Ĭ\nĠBet ter\nĠ\"\\ <\nsp acing\nĠfurn ished\nos er\n] }Ċ\nĠ$ \"\np ull\n.P ost\n( ip\nĹ ı\n.f ront\nnt e\nĠF M\ng uid\nĠnegot iations\nagon al\nĠtrem end\nunge on\nAd v\ncar ousel\nÃŁ e\n_DE SC\nĠham mer\nẠŃ\nĠĠĠĠĠĠĠĠ ĊĊ\n-c ore\n-s ervice\nĠcorn ers\nĠS F\np red\n> A\nĠJ Label\nĠrom antic\nĠtestim ony\nos c\nĠGener ation\nas ures\n_int ernal\nĠprint s\nĠ] )Ċ\nĠC leveland\nre po\nD isc\nĠ\" >Ċ\n�� ��\nĠne arest\n_t b\n( require\nEO F\n- child\nĠbu dd\n.Xtra Editors\nalt ies\n\\\": \\\"\nW ords\nĠloc ally\nĠpurch ases\nDraw er\nex tract\nĠexec ut\n} '.\nuser data\nĠfocus es\n-min ute\nĠP ublish\nog o\nĠmount ains\nB ot\n} >{\nĠt ension\nro d\nm esh\nĠtransform ed\n, R\n() }Ċ\n.l ong\nĠg orgeous\nĠS chedule\nĠol dest\nĠsub process\n( IN\ny ect\nĠCo oper\narn ess\nĠMon itor\n.p art\nĠN BC\nĠc otton\nĠh ol\nĠrg ba\nĠB io\nCont inue\nP od\nĠparticip ating\nclus ions\n(By Val\nà ¬\nĠH OW\n_set opt\nĠaccompany ing\nat on\nĠ/ \\\nĠAuth entication\ni én\nĠBar ack\n/* .\nĠe ager\nĠC ancel\n< lemma\nep h\nĉ window\nĠinc idents\n), (\n.D es\nib e\nĠFunction s\nĠhosp itals\nĠo xygen\nroot Scope\nĠd rew\nĉ request\nnot ice\nak u\nam ents\nf ar\nĠprec ise\n_w rapper\nĠlisten ers\nA Z\n.b ounds\nĠA verage\nfield set\n_ axis\nĠexam ination\n' .Ċ\nmon s\n++) {čĊ\nĠForm s\níķ ľ\nCpp Method\n_tr ace\nĠengine er\nĠFl at\nĠrev ision\nĠhe ating\n/ profile\n.r u\np riority\nĠin fer\n_ST REAM\nĠ* )(\n> $\nOLE AN\nOK IE\nIB ILITY\nU AGE\nĠSur vey\nĠres ign\nw ing\nĠsecre ts\nĠch ips\nJSON Object\nDes ktop\n_SY MBOL\n(res ource\nĠĊ\nĠnew est\nul i\nĠdes ert\nĠd ip\nĠP ow\nĠequ ation\nĠposs ibilities\nĠF ed\nos ph\nĠ[ %\nĠb ubble\nether lands\nĠc ement\n. auto\n_ AN\nâĢĻ .\nse lection\nĠB ond\nD en\n- O\n.get Type\n.W indow\np res\nĠsw inger\n\" })Ċ\nĠp ip\nĠm ice\nĠcomp ound\n- plugin\nik o\nĠcent uries\nic ular\n-in line\nĉ key\n> \\<\nEN SION\nĠ[ čĊ\nĠprecis ely\nĠét é\nĠP ast\nĠCam bridge\n-f ull\nĠanaly ze\nĠSte ven\nĠn em\nd ue\nore n\nĠmus cles\nij ing\n/ -\nĠKenn edy\nR M\noss ible\nĠact ress\nĠd olor\nå½ ķ\nNe ed\n.t oggle\nĠR ace\nw ers\n.m aterial\nĠD ue\nĠP el\n# print\nĠindepend ence\nex us\nSh adow\nĠenc oder\n( level\nĠSw ift\n.d oc\n_se lection\nĠserial VersionUID\nLabel s\nĠperform ances\n.T ag\nĠN HL\niz en\n/ UIKit\n_CONT ROL\nĠearn ings\nĠAl t\n_H ANDLE\nC tx\nĠpers u\nĠtr an\nç ¨\n_CH ANNEL\nĠsatisf action\nĠG P\nio x\nm itt\nland o\nĠp ig\ninal s\nê ncia\nS urface\nĠU UID\nĠbenef icial\nĠsequ ences\nĉmem set\nĠmag ical\n «\nĠw orn\nAS C\npop up\nCOM P\n_b efore\nen ess\nU i\nL es\n.re quire\n.Serial izable\nadd Gap\nĠauthor ization\n.py plot\nurr ay\nlat itude\nfr ames\naj s\nĠcomp ass\nĠobserv ations\n_s up\n.en viron\nĠtri ple\nĠRub y\nĠdr ain\n_F ILTER\nS an\nUM P\nNull Exception\nĠG ab\now e\nĠTurk ish\n_se quence\nĠGr ant\nuel a\nĠw o\nĠc ube\ni q\nĠdis orders\nĠextra ordinary\nĠc trl\nĠSe q\nent r\nĠsan ctions\nuts ch\nRe ports\nĠin herit\nPer iod\nĠphot ography\nĠF ramework\nĠspecial ist\nĠ? 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ĊĊ\nĉ title\nY LE\nĉ message\nU UID\nOLD ER\nĠH H\nĠStyle Sheet\nĠaccess ed\n. validation\nt asks\nĠpoll ution\n.c anvas\nĠing redient\nĠC abin\nA h\nold own\nĠNO I\nĠà Ĺ\n[ f\ned uc\ny alty\n(n ot\n_ State\nam en\nĠda o\nud ad\nell ers\n} &\nlic ity\n_W INDOW\nĠt atto\nval or\n.R ange\nĠrefer enced\nĠRes erve\nM oney\nSCRI PT\n/ product\ncho ices\nĠt in\nãĤ ĵ\nĠsepar ator\nĠp kg\nam med\nĠM AT\n! !ĊĊ\nĠr aid\nĠmotiv ation\nĠX P\nĠBack ground\nĠQu aternion\n.define Property\nik er\nĉp arent\nĠOrigin ally\nant age\nĠH ans\nĠtim eline\n.c ur\nop ic\nĠSe qu\nm ust\nĠCo al\nĠform atter\n_R GB\nĠ_ (\"\n'} ),Ċ\nĠ= ================\nĠF UNCTION\nĠl ng\nic ates\nl ive\n_ engine\nĠtown s\n')) ĊĊ\nĠP K\n( api\nĉs canf\npack et\n.ph one\ná Ģ\nĠAnd y\n_N AMES\nPL Y\nĠmin s\nim i\nĠbr ick\nĠbl ade\n.std out\n}` ;Ċ\nSh ift\nĉs b\nĠCheck s\nĠphenomen on\nAv atar\nĠmin istry\nro se\nĉ File\nĠtit led\n( LOG\nĠg an\ndes ign\n(), čĊ\nĠb ones\nst m\nÅĽ Äĩ\nĠInput Stream\nĠvol unt\nĠSerial izable\nĠfight er\nĠDr ag\nT witter\nĠsubs id\nç ¼\nĠfor ums\n.load ing\nlog ged\n_ this\nĠterr ain\nĠir re\nĠIn g\nĠC N\n_object s\n. uid\nĠconscious ness\nT INGS\nĠG all\nĠport ray\nĠDevelop er\nĠparticip ant\nĠ\" ;čĊ\n/ model\nĠOper ations\n^ \\\nĠL ater\nĠrais es\n-n one\n.m eta\n=' .$\nFin ished\nĠrepl acing\nĠsam pling\nĠJ en\n\" There\nRE AL\nA LE\nìĬ ¤\nOr ders\n_param eter\nĠOlymp ic\nĠtr ès\nĠare na\ni ol\n; ?>\nĠimpact s\nĠW S\n: get\nĠfl ights\nĠRuss ell\nc amera\nF n\ns igma\nĠfor cing\nĠloc als\nĠdepart ure\nĠcelebr ation\nĠS ay\nï¼ Ĵ\nĠH ills\n.has OwnProperty\nĠtyp ings\n.A PI\nĠdon ation\nOperation Exception\n.Act ivity\nc plusplus\nĠChar lie\nĠimport ed\nĠd ann\nĠoccas ions\nĠimplement ing\nĠpur ple\n.d ialog\nSQL Exception\nern o\nĠw ars\nĠpast e\nĠdecre ased\nĠhar sh\nĠel abor\ninput s\nĠView s\nĠerror Message\n_m ul\nĉ write\nĠC op\nĠAnn ual\n(b utton\nĠv ida\nb ars\nĠHar vard\nĉex pect\nĠindex es\nĠdocument ary\nĠf lesh\nOR LD\nĠD elta\nM AND\nBr ush\n-c olumn\nĠdevelop ments\nmethod Visitor\ns lice\nĠP DO\nĠinvest ing\nir able\nĠxml ns\nï¼ Ľ\nart a\nĠthe ories\n_c ity\nĠ$ __\nCre ating\n( pr\nD ropdown\nism atch\nĠN ET\n'] )){Ċ\nĠVal ues\nĠSE O\nĠST AT\nĠe cosystem\nĠtem pt\nĠ\\ \\\nĠ// {Ċ\nĠChrist opher\nĠKent ucky\nĠHttp ServletResponse\nĠhy brid\ny on\nĠfeed ing\nĠEx tra\nN orm\nIT CH\nĠSe an\nĠUp load\nm un\np ur\nĠp ersistent\nĠID C\nĠPer form\n.m erge\n_ room\nMean while\n! ='\nĠW el\nArgs Constructor\n.D atabase\nĠcount ing\n() *\nĶ åĽŀ\nĠT OP\nm ill\nĠD T\nIGN ED\nĠK B\nĠcomp ly\nS outh\n_c ollection\nCh apter\nĠexpl aining\n_ AM\n_t s\nc ards\nĠqu el\nĠp ole\nĠtouch down\nĠO thers\nĠpe ers\nĠType Error\nĠsix th\nĠche er\nĠdis pute\nus c\n) ],\nth umb\nĠh iding\nĠS IG\nlik es\nĠP AGE\n.Ref lection\nĠhead quarters\nT ING\nĠG host\nM LE\n$ Ċ\nĠcontr ary\next end\n'] ).\nFF ECT\nĠP interest\númer o\nric ane\nĉs ession\nĠcr ystal\n- Control\novern ment\nog raf\n- action\nv olume\nft en\nĠun con\nĠan imate\nĠle ase\nsc r\nĠref use\nãĢ ĭ\nft p\nin formation\nĠeval uated\nĠin jection\nĠj ack\nĠwork shop\næ³ ¨\nPT H\nĠT s\noff er\nĉ os\nĠking dom\nM issing\nĠlaw makers\next Field\nĠsing ing\nab i\n/ client\n.m edia\nATEG ORY\nSign ature\n% ',Ċ\nĠF uck\n][ :\nĠsens ors\n/ com\nĠPr imary\n.S QL\n_pro gram\nĠp ills\nĠinteg ral\nĠfle et\nĠdro pping\n.s l\nBe en\nĠp ets\nĠadvis ed\nĠdr agon\n_ EDIT\n( im\nF ER\nĠDr ug\n(r andom\nĠcomp ression\nou st\n[ %\nĠbuy er\nh op\nR oles\nman age\nĠpain ful\nĠBr anch\n-mod al\nen ant\nĠM esh\n/ font\nĠG raham\nĠâ ĺ\nĠn c\nĠFranc is\nĠspec ification\nĠdam ages\n- config\nĠthe oret\nsec ure\n_m ulti\naceut ical\nĠdemand ing\nen ne\nIST S\n() ));ĊĊ\nRe ason\nRe cent\nph ase\nĠps y\n_M AN\nĠvolunte er\nå ¿\nistrib uted\nli o\nĠproduct ivity\n_com m\nS pring\nn is\n. weight\nĠC ancer\nAl loc\nĠT weet\nĠsepar ately\nĉ check\n_p roperties\n. Unit\n_CL K\nĠg t\nĠ( );ĊĊ\nĠhand y\nĠThom pson\nĠunn ecessary\nĠRe ader\nG N\n= request\nĠU tility\n.Re pository\nĠA x\nhy dr\nie u\nĠth y\nĠl t\n_m ail\nä¿® æĶ¹\nail and\nĠPhil ip\nĠbit ter\nĠbet ting\nĠtim ed\nock s\n' a\nĠal gorithms\nĠre interpret\nĠto ss\nro gen\nĠhop ed\n( selected\nĠvent ure\nTE X\nĠLe ave\n.Sub string\nĠgr ateful\nuk a\nĠCon sumer\nĠag greg\nC ircle\nภģ\n_block s\nĠleg ally\nĠ\" |\nãĥ ĥ\n. board\n.A b\nFunction s\nrec ipe\nè ĩ\nĠO xford\nĠwho les\n.B uild\n_ch anged\nh ai\nĠdepart ments\nI mp\nĠcoal ition\nIN FRINGEMENT\nĠemp ower\nitch es\nN orth\nĠinfl amm\nON SE\nĠmiss ile\nĠR aj\nĠIss ue\nĠat oi\nca led\n.Cont rollers\nĠW olf\nĠcrush ers\ná» ĩ\n.A uth\n.add Attribute\nh is\nĠbo ots\n.c lean\nc amp\nĠten ant\nĠt une\nĠ{} '.\nĠwork out\nRe po\nĠpartial ly\nMI SSION\nj amin\nĠS B\nĠdetermin ation\nĠ' ');Ċ\nĠB eng\nĠv os\nĠin hab\n/ lang\ns burgh\nExec utor\nh one\nĠCh allenge\n_link s\n.Le vel\nĠunder ground\n-c ode\nĠoptim ization\nlog ging\n_de st\nĠsn ake\nĠchemical s\n_IMPORT ED\nado op\nĠTH AT\nman aged\nĠredu ces\nĠRE AL\nĠG uy\n_GENER IC\n/ ********************************\n. amount\nĠd ere\nget Time\nĠp ant\nan onymous\nĠharmon y\nĠAl an\nĠscen arios\nĠd irt\nht ags\nM c\nSh ell\nr in\n{ čĊčĊ\n.p ow\nĉ client\nĠconspir acy\nĠad mission\nĠReg ional\nĠView Controller\nĠPhilipp ines\nĠde pos\nĠp ap\nĠP ad\nP aul\n.Com boBox\nĠt utor\nĠRec ipe\nw riting\nĠcontrib utor\nOT H\nSm all\nV I\nĠh acer\ne qu\nĠEx amples\nh uman\n.m essages\nĉt yp\nĠ( čĊ\nĠS SL\nLE N\nĠRom ney\n( grid\nĉ min\nĠ> ĊĊ\nĠfr uits\nĠvot er\nIn line\npan e\nĠC ollections\nchar set\nĠsp am\nz b\nitem ap\nĠsucceed ed\n_C OL\nĠel apsed\nim eter\nĠrecover ed\nT ensor\nhatt an\n.set up\nist o\n( head\nĠS IZE\nĠtact ics\nĠdist ur\nĠpre val\nici os\n( Value\n_c ols\nĠF at\nĠse al\nĠs ons\nĠens ures\nĠpress ing\n= &\nigen ous\nĠharass ment\n_ JSON\nĠign or\nyn omial\nom er\n_st atic\nĠsignific ance\nĠcirc les\n_S ystem\nĠdiscipl ine\nĠdress ed\nĠs phere\nĠclim b\n_ actions\nĠB ab\nĠ' =',\n_s chema\n\" use\nĠund ers\nĠc ups\n.s creen\n/ new\nĠappe aring\nT OP\nvis ed\ncl ang\nĠinvestig ators\nĠmyster ious\nĠprom ising\nĠqual ify\nĠc ave\nĠequ ip\n= x\nG T\n( link\n. velocity\n. erase\not er\n++++ ++++\npro fit\nĠz ones\n_ uid\n- ser\nĠobject ives\nĠmil f\nweb kit\n(m atch\nne h\nĠAssoci ated\nĠT odo\n= d\nC am\nĠv ocal\nĠs udo\n( EX\nĠtr ou\nAB C\n.b ean\nĠG round\nĠRE ST\nwe ets\nIn g\nim on\n_b us\nĠC OLOR\nun to\nĠf oss\nĠLink s\nä ng\n/ forms\npr ises\nĠachie vement\nC ALL\nел ÑĮ\nĠVer ify\n_S OURCE\napt cha\nID D\n_re ference\nG old\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ\nRe ceiver\nĠa j\n_d irection\n} ]\nĠCom pet\nĠb ang\nĠC ass\n- url\nte chn\nĠJer usalem\nlong itude\n' );čĊčĊ\nĠwin ners\nT asks\nĠD MA\nĠtool tip\nİ ·\nĠB ra\n_d uration\ncur y\nparent s\n---- >(\nĠK ir\nĠint ros\nĠsk etch\nĠsk illed\nĠim mer\nĠade quate\n_re p\n( header\n_ like\nĠper ceived\nss h\nĠassum ing\nĠf f\n_u uid\nul as\nĠdemocr atic\n. entities\nS eries\naph ore\nĠnew er\n} (\nSE C\nai ro\nĠcomm od\nĠprivile ge\nĠde ux\nĠH op\n.' /\nct ic\n. ';Ċ\n C\nĠWar ren\nĠoptim izer\nĠSER VICES\n_ oper\nget Attribute\nĠMc K\n_s elf\n.r s\n\" )ĊĊĊ\nGet Component\ner ce\nĠt ous\nun its\n'] );čĊ\nZ oom\n/ E\nĠobs c\nĠfast est\non line\nĠpeace ful\nff en\nĠc argo\nĉ pr\nĠseek s\nz u\nTr im\nĠw ard\nĠver d\nĠblog s\n.exception s\nĠPrem ium\nĠN etherlands\nS afe\nFin ish\nĠAl bum\n_A CC\n= this\nv irtual\n] >\n_L ABEL\nĠN ich\n_w in\nĠA aron\nW P\n; $\naim s\nĠImage View\nĠend less\nER A\n_DIS ABLE\nĠcancel led\n- us\nĠins pection\nem in\nĠG rey\n- open\nĠiter ations\n. owner\nĠk eras\n.P assword\nĠR y\nĠIN S\nA ir\nĠSe veral\n.Tab Stop\nING LE\nĠH air\nĠCan vas\nAA AA\nĠfl aw\nced es\n.Re port\ní Ĭ\nĠT ips\ncript ors\n.trans action\n.S pring\nĠview er\nĠins ights\nè¾ ĵ\nord ion\nU INT\nse ek\nĠA uf\nìŀ IJ\nĠstr ain\nTo oltip\nĠd z\nign al\nad t\nĠu c\nfin ite\nĠn m\n.c md\nĠMy Sql\n[ data\n.j ackson\n.t ree\nRequest Param\n_ agent\n\") ]čĊ\nĠass ass\n( Constants\n: ss\nĠM AN\n+- +-\nĠB ottom\nprint s\nĠS ame\n@ Autowired\nsw ap\nici ón\nĠprotest ers\nĠh oney\nĠV eter\n(C alendar\n- ad\nĠBrook lyn\nL ife\n_V AR\nze ch\nĠC ALL\n_C AST\nĠE lection\nĠthick ness\nV ery\n_IN TEGER\n- dev\n)) ))\nap at\noo oo\nd emo\nĠparse Float\nĠR ather\nST IT\nm aker\n[ current\nchron o\nĠch rist\nãģ ª\nĠD etail\nÆ° á»\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠs ul\nid ency\nQ ue\nĠeleg ant\nap ons\nĠdish es\nĠinteg ers\n( read\nfind ViewById\nĠAm ount\nĠSk ip\nĠhab its\n* )(\nĠmon sters\nM AC\n: end\nĠfr ank\nAs sembly\nĠd fs\nĠne ut\n_TYP ES\ne qual\nloy d\n( uri\nĠch i\nĠdefend ant\nĠconflic ts\nĠv il\n- js\nĠPe ace\nĠmut able\n) sender\nĠF ocus\nå» º\nĠapprec iated\ns leep\nĠR ED\nC ulture\nĠdesign ers\n_g enerator\nc odes\n/ ex\n.Get Value\numb led\n.scal ajs\nper or\nĠveter ans\nĠ} )čĊ\nĠun fortunately\n_C REATE\nM ass\nĠCL AIM\nĠMe et\n_s upport\nB ank\n() .Ċ\nD ark\n_LO W\nĠMin ing\nĠO wner\nier a\nClient e\nĠencour aging\n> S\nĠboy friend\nĠH alf\nĠA CC\nA ff\n_ ar\n-l ife\nc x\n.J Button\niz ado\n.z ero\n.open qa\not on\n.text Content\nĠto ll\nat ie\nĠball ot\n- number\n. Exception\nĉ params\nc ircle\n-m ap\nĠn ap\nĠRob ot\nĠI ch\nreg istration\nAm azon\nroll ment\n( exp\nĠt anks\nĠG ordon\nĠmach inery\nĠbas eline\næ ĭ\nØ ©\nĠCon vention\nĉ config\nook ies\nm ult\nRec ords\nĠE ST\nĠgar bage\nĠcon form\nid al\nĠb arg\nĠsurv ived\nĠinvestig ations\n.contains Key\n---------------------------------------------------------------- ----------Ċ\nort ion\nĠhor r\n_ http\nĠm ant\n] ;čĊčĊ\nb inary\nem pl\nĠin quiry\nĠMean while\nĠcollect ing\n.Entity Framework\n\", ĊĊ\nĠP ic\n@ Inject\nick ness\nĠB inding\nĠcont rolling\nre verse\nĠch airs\nsemb led\n( add\nDis abled\nan as\n.trans late\n-------- ---Ċ\nĠref lected\n\"] ĊĊ\nEx ternal\nAr row\nSingle ton\n% x\nĠ Å\nĠan cest\nĠOr leans\nĉc md\nĠprohib ited\nith metic\n(ch annel\n_c ss\nFor ward\n.s ocket\nĠl uc\nâ Ĩ\nĠFire fox\nĠM ovies\n) _\n. ends\n( shape\nĠde alt\nĠs aves\nĠgl ory\nĠmej or\nĠbreath ing\nĠ eller\nget Data\nĠang les\nĠtool bar\nĠsp acing\nIP S\nĠflo ors\n_ACT IVE\nĠsh uffle\n/ shared\nĠE le\ned 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It\nC lean\n> \\<^\nĠi os\ns ound\n\"] ;\nĠfre ed\nrot tle\nĠL ower\n[ count\nå Ŀ\nĠp ale\nĠWay ne\near th\n_c ategories\nU CK\n.m etadata\nĠsum mon\nH OME\nолÑĮ з\nĠmanufact ured\nĠdo ck\nĠcompet itors\n_MODE L\nok ia\nĠH ey\nÎ ¿\nĠback ward\nĠPO SS\nrop a\nĠc ri\n_O BJ\nTrans port\n-h igh\nĠerot ik\n_s lot\nĠart ic\n_f ramework\n-ser if\nĠSql DbType\n') (\n+ \"/\nĠw ore\nS il\nĠst oring\nĠPh ase\nu ant\nĠb ump\nin ho\nĠd ign\nĠback s\nq q\n(h ash\nĠge o\nĠt ender\nLog o\n! )Ċ\nĠM X\nĠAr thur\nesso a\n_C h\nĠbed rooms\n=\"# \"><\nĠth roat\nins ic\n.int eger\nĠpr imitive\nTruth y\nĠfacilit ate\nĠcreat ivity\nĠD NS\nĠg ra\nue z\nĠcount less\nĠPol and\n' M\nĠD ist\nĠv est\nĠcert ification\ná» ij\nh eld\next ensions\n( static\nĠgr ades\nĠU ber\nãģ Ł\nĠ[ ])Ċ\ndat os\nĠget Data\nĠCh arg\nĠB S\n.m icrosoft\n.v ideo\n.d irection\n->{ '\nl ua\nape st\nĠbo iler\nere k\nĠdec ides\n.j ar\nIS C\nĠW ords\n(C ON\nEMPL ATE\nree ze\nsh ots\napp s\nunt ed\n.set Name\n:: <\n-b old\nê ²\nå¯ Ĩ\nLong rightarrow\nĠunf air\nĠear ning\nĠsh elf\nURE MENT\nĠid le\n_M ENU\n.C ustom\nAG ER\n- \"\n_s witch\nb ecause\n) view\nm are\n_ condition\nĠStart ing\nM vc\n(p re\nd ump\n_LO CK\nat etime\n.c allback\nĠC er\nop ol\nib rary\nĠres ervation\nĉĉĉĉĉĉĉ Ċ\nlect or\ngrad uate\nĠgener ous\nĠ ion\nric ao\nm q\n_com plete\n(c ursor\nĠForm Control\n: center\nĠsub stitute\nĠPl anning\nĠp ension\nĠrecommend ation\nĠT ags\nĠg ef\nĠalbum s\nĠwash ing\nro c\nĠtr ains\nat ings\nĠex ponent\nack bar\n- ln\ná g\n.Data Annotations\nĠE IF\nĠMalays ia\nĉ PORT\non us\nĠcle ver\nĠpe u\n> ĊĊĊĊ\nĠArg uments\nĠdebug ging\n( right\n' D\ncom pute\nĠfin est\nOR AGE\nĠspect acular\nph rase\nĠind ia\nĠlegend ary\nb irth\nĠcom posite\nĠg rows\nĠT D\nĠep id\nĠlaunch ing\n] ][\nMin utes\nĠCh a\nĠclean ed\nĠwitness es\nuk an\nĉ Type\nĠhab e\npar agraph\nĠJ Panel\nĠH ann\nĠvar ied\nĠP okemon\nĠM UST\nåĬ ¨\n.vis ibility\nop up\n^ [\n.exp and\nĠ\" ',\n.f asterxml\n_ auto\nĠShe et\nmark er\nPar cel\new s\nĠStr ategy\n-m aking\nĠun ve\nĠtrail ing\nĠclick s\nĠGet Component\nĉ content\nIG ENCE\nERN EL\nNSMutable Array\nĠb reat\nĠharm ful\n¶ Ī\nĠbes ides\nĠb oring\nĠbrut al\nv ang\n(p arse\nqu ick\nĠpy test\nĠswitch ing\n() ]Ċ\nĠì Ħ\nL ER\nĉf ont\nĠnet t\n) ]ĊĊ\n(/ \\\næŀ ľ\nto Array\nĠbre ed\nĠC AR\nĠWe apon\nA bs\nt ot\nĠset Name\napt ive\nĠ: ,\nĠesc aped\nord en\nĠP ri\nth umbnail\nĠdescri ptions\n/ styles\nĠPC I\nĠal phabet\nastic search\nNOT E\nĠc ialis\nĠGr iff\nĠpor que\nĠprote ins\npl ays\nĠst ating\nĠimag ination\nĠfac ial\nĠMe chan\nĠarr anged\n_ used\nĠarrang ements\nĠP ipe\nhost name\nĠprov inc\nT it\n.Flat Style\nĠS plit\nĠLo ader\n.c c\nĠclin ic\n---------------- ------------\nĠb aking\nĠEN T\nne ath\nãĢģ ĊĊ\nAN E\n.EntityFramework Core\napp ers\n. ic\nĠNg Module\nĠF ORM\nĠ' ;\n-pro fit\nh w\nen emy\nĠE ye\nĠca ution\nt own\nĠur ged\nĠJim my\nynchron ous\n-s ized\nm aking\n, {\n] ',\n_ Object\nah oma\nĠactiv ist\nIN VAL\nĠCom mercial\nĠOr lando\n(t ab\nĠØ ¨\nAl gorithm\nĠher itage\nGet Mapping\nĠfail ures\nri os\nat iva\nĠt et\nĠcar pet\n( Z\nth ree\nĠdisc losure\n. 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ĊĊĊ\nĠexp ires\nĠspokes person\nint erval\nĠà ¢\nĠque en\n(n il\ning o\nHe ap\nÙ İ\nĠcompl ain\nS ym\nĠCl one\nĠR u\nĠW ILL\nĠCr ystal\n/ content\ning en\noint ment\nLast Name\nav icon\nĠIB M\nĠDim ension\nan h\nicip ants\nĠAn ne\n.pro gress\nĠal go\nob il\nĠV oice\nĠF E\nĠg li\nĠv ed\nĠprevent s\n\\ Column\nĠfol k\nett i\nĠm n\nĠCL ASS\nĠdisplay ing\nĠK l\nĠF err\nd uto\n. ib\nĠd ados\n' name\n-s pace\nĠit alian\nĠin verse\nĠd ense\nut er\nĠI Enumerator\n-s ign\nĠnation wide\nĠperson a\nĠsol ved\nĠdram atically\nLog out\nĠgr av\nĠanalys es\nol lo\nĠl amp\n. team\nĠE rot\n= [\"\nĠd ancing\nĠ?> /\nĠc ater\nff e\nĠSh a\nĠB os\nĠRE QUIRE\nĠMon ster\nĠR B\nĠI DE\nĠsu its\nĠform Data\n( theta\nĠsp atial\n= NULL\nĠSql Connection\nĠ à\nĠV enez\nĠMor ning\nĠpublic ations\nĠNON INFRINGEMENT\nfirst Name\nud s\nW ould\n_HE AD\nĠinvest ed\nst able\nf red\nĠcommand er\nSE S\nâĢĶ a\nan che\nĠM ovement\në ³\nS uite\nĠjur isdiction\në¦ ¬\nĠB eth\nj Query\nĠIs a\nĠd ental\n, *\nĠL imit\nili ation\n=\" {\nb ast\nĠt urb\nis y\nO OK\nĠadvoc ate\nim ag\nLE CTION\nл ÑĮ\n(c ategory\n.de c\nĠun iqu\n_s n\nĠattract ed\nĠà ī\nĠRun ning\n_ edges\nĠDis able\n_A S\nåĽ ¾\nĠnetwork ing\n_br anch\nH aving\ntoBe Truthy\nG I\nĠcamp s\nse p\n-p art\nĠ)ĊĊ ĊĊĊĊĊĊ\nustral ia\nĠRe ports\nrit o\nĠwa ist\n_pl us\nĠW W\n-p erson\nApr il\nĠs ar\n.t ar\nĠagricult ural\nt ic\nĠt cp\nĠset Value\nagent o\nĠAp pe\np iler\nCA DE\nĠan che\natch er\nĠcom ics\nĠl bs\n_se gment\n'] =$\nitt ers\nich er\nG INE\nĠutil ize\nĠC ursor\n_ex pression\nĠd ag\n< long\nĠr hyth\næı IJ\nĠconsult ation\nY et\n\")) ĊĊ\n_M AC\nc ould\nĠ' \\\\\nĠV o\nĉ http\nĠg s\nph er\n- grid\nJ ames\nJ ul\nĠsch on\nĠtensor flow\nĠLOG GER\nam as\nĠsc ipy\nĠconv iction\n. ag\nĠadministr ator\n)) {čĊ\nĠn un\n\" group\nP or\nĠnur se\nex pression\nak y\nĠHe avy\n. opt\n.get All\nĠover l\n/ \",\n_c ountry\nç İ\nĠG ENER\n_r oute\nĠD al\n ´\nol oad\nĠuncomfort able\n(m enu\nĠhost name\n' \");Ċ\nĠcalcul ations\n-c lick\nĠprotect ive\nãĤ ¯\n_F orm\nung s\nAct ual\nm f\nĠProcess ing\nĠIn ventory\n(m atrix\napp ropriate\nw eg\nij a\nĠch r\nĠr ifle\n-w sj\nk ar\nĠindepend ently\nI OS\nĠconsist ency\nv n\n/s ystem\nĠCh anges\nĠexp ose\nici ents\nĠrel ate\nĉ next\nè ¨\nud es\nĠglass es\nF XML\n.... ..\nĠP df\nĠappro ve\nĠ{ \\\nĠexist e\n)) (\nARE NT\nоР¿\nĠL atest\nĠNiger ia\n.Inter faces\nĠrem oves\nEn emy\nĠen force\nvert s\nĉ pos\n_text ure\nW ARD\nĠINC IDENT\n( container\nĠdef ending\nĠR X\nĠH ook\nbr is\nĠFl ask\nGr ay\n. )Ċ\nvis ibility\nĠRedirectTo Action\nerr al\n_e lem\nĠres on\nfront end\n_variable s\nater ia\nĠ+ \"\nave led\nRI X\nĠdef icit\n_C heck\nYY YY\nTo One\nsp y\nĠun ited\nend ent\nĠp ode\nãģ Į\nC AT\n(f mt\nĠBon us\nĠre ck\n º\nMod ules\nĠvac uum\nR adio\nĠDAM AGE\nP en\nĠPark er\n; ;Ċ\nĠRe ally\n_n eg\np ending\nĠnomine e\nĠC ategories\nĠUl tra\nWe apon\nĠdef ender\nI ss\nĠG ender\nĠD ress\nĠimpr ison\nĠbank rupt\nimension al\nPH A\nĠStr ateg\nĠPROF ITS\nĠp atri\n//////////////////////////////////////////////////////////////// ////////////////\nde legate\nĠfor State\nĠdev oted\n_m ake\nĠterror ists\nĠS nap\n_n av\nĠA A\nĠI an\nĉ app\nPl acement\n_h dr\n< K\nĠs ang\nst roke\n- Q\n> x\n.T ask\nm oney\nib aba\n' });Ċ\nĠSpec ific\nĠLine ar\n_O PT\nHash Code\n( Player\n.Contains Key\nĠcoll apsed\ntrans parent\n_R ANGE\nView er\n(c fg\nĠsort ing\nĠinf ected\nĠN ach\nĠaccommod ate\n.element s\n_P ART\nĠSex y\n= get\n( year\nĠx hr\n: ]\nows ki\nĠsum mar\nĠ ¿\nĠint e\nĠwork flow\nĠTai wan\nvers ions\nåı ij\nĠsurprising ly\nĠopt ical\nĠpro ces\nĠdisag ree\nĠnue vo\nĠC AM\nsort ed\nle ases\nist le\nId ent\nĉ event\nject ed\nCh unk\nV ars\n.pro vider\nĠproceed ings\nĠin clusive\nĠart work\nend ants\nï¼ļ Ċ\nse en\nĠl ig\nĠm akers\n_f un\nĠlength s\nPath Variable\n[ item\nภµ\nDe ad\nFFFF FF\nĠUr ban\nup les\nich en\n(null ptr\n.s pec\n, System\nUR ATION\n(j ob\nå¼ ı\nĠtrack er\nÅ Ļ\nĠM R\nĠSQL ite\nĠd to\nĠ; ;Ċ\nĠm int\nĠInt roduction\nca o\nĠquestion ed\nĠf itted\nrev ision\ns q\nĠm ig\n_un its\n_ async\nĠf lick\n});ĊĊ Ċ\nĠnot re\n}` ,\nF ilters\nĠm undo\n_d ays\nĠfr m\nut c\nĠval s\new idth\nĠGener ator\nĠArt ist\nĠID s\nĠArt icles\nre ater\nĠComponent Fixture\n. =\nĠr ou\n- no\n.b ukkit\neg g\nĠD iff\natic s\nÑĥ Ñĩ\nâĢĶ ĊĊ\nĠChar lotte\nby e\nĠ} );čĊčĊ\nĠV ik\nĠB row\nĠl v\nĠG ib\n-w ing\nGL IGENCE\n(I l\nĠEngine er\n.W ait\nĠP ictures\nĠr het\nĠth ermal\nĠpr aise\n< >();ĊĊ\nĠSp ider\nP ause\nĠB aker\nĠsl ower\nĠ} ]Ċ\n_en queue\nĠdisappe ared\nĠT icket\nIN UX\n_LOC AL\nаÑģ Ñģ\n@Inject able\ncomm unity\nGesture Recognizer\nåĽ ½\nĠsca les\nĠ- (\n/ '+\nĠS it\nĠexecut ives\nard ing\nĠad vers\nĠback wards\nĉ context\nĠH amp\nĠP F\nĠDe ck\nĠCra ig\nA merican\nĠb ell\nĠpro l\nuf en\nĠr ng\nar shal\nĠSim ply\nfirst name\nsh ore\nJ uly\nĠmort ality\nĠâĨĴ ĊĊ\nHelp ers\nĠbench mark\nem ade\nĠorganis ations\n.g son\nĠText Field\nĠciv ilians\n.Array s\nĠMiss issippi\nĠinter mediate\nget User\n_cl uster\nRel ative\nfore ign\n.querySelector All\nFore ignKey\nĠreason ably\n-------- -Ċ\nC ards\nĠK am\nĠTh or\nĠroll er\n-e lement\nĠC urrency\ndd ie\nALL Y\nĠR A\nĠper met\naa aa\nĠhom ework\nĠV it\nĠm old\nĠF er\n[ start\nĠstatist ical\nĠsc ary\n_H OME\n.B egin\nCon struct\nogen ic\nĠDEAL INGS\nĠtamb ién\nix on\n. ind\nac re\nĠtransform s\nĠN ap\n.B lock\nuss ia\npir ation\nul ent\nĠce il\nCl ause\nna ire\nT ES\nĠne at\nST D\nĠReg Exp\nper form\n: )\nĠun ions\nĠs ublic\nĠw inds\nlo ating\ng lich\nĠp agination\nS kill\nApp ly\nĠOper ator\nist ogram\nĠqual ities\nC ross\nĠde com\n], \"\nĠJ uan\n.mod al\n.Ch ild\nĠRog er\nSTIT UTE\n:CGRect Make\na lette\nĠst a\nas ide\nĠbl ur\nĠW a\nif etime\nre ed\ncontrol s\nĠb ins\nĠп ол\n*/ ,Ċ\nU IS\nĠR ou\nĠDem o\n- awesome\nĠCh ain\nĠh asta\nĠB art\n. KEY\nĠvend ors\nnof ollow\nĠD est\n_b uilder\nĠarg ues\n_ answer\ng oto\nĠRES ULT\nĠM ON\nĠp oder\no ons\n_C ASE\nĠrep lic\nĠfin ancing\nĠD ATE\nc ern\n_tr ack\nt ies\n/ logo\nĠNE GLIGENCE\nget Type\n> T\nb et\ng irl\nĠINCIDENT AL\n-s ite\n.tr igger\nĠL isa\n_input s\nĠrel atives\nLogged In\nConfig ure\nI K\n. accept\nRes ume\nĠD raft\nĠ* >(\nĠW A\ned ian\nern ess\nĠLayout Inflater\n*/ čĊčĊ\noth y\nĠoblig ation\nSub scribe\nĠth umbnail\nex ist\nĠins isted\nĠU ICollectionView\nĠAng ular\nĠtable ts\nĠImp act\nãĢį ĊĊ\nah o\nĠcharacter istic\ng d\nĠ= ================================================\nour t\n` .\nApp ro\nCo ordinate\nRem ember\nĠmar ine\n] =='\nĠAdmin istrator\n.get Default\nĠforg ot\nĠStruct ure\nV ue\nars ing\nm oment\nk w\n_c ursor\nAtt ack\nĠath letic\nĠdiagn osed\nĠend e\nåĪ łéĻ¤\nH ouse\nĠP ARAM\nĠw iki\nĠO pp\nĠcons ervation\nĠs nd\n_t em\nsub str\nĠC ape\n.s im\nUT ION\nan an\nâĢĻ un\nĠg y\n- work\nĠcomp elling\n=' #\nĉs ub\nĠdirect ories\níĬ ¸\nĠtouch es\nout ines\n.C ollection\ns chedule\n.l at\nĠDo ctrine\nCA A\nĠRe fer\nĠshift s\nĠlik elihood\npre ter\nĠF emale\nĠinter cept\nĠl ou\nçĻ »\nĠr ug\nĠC rown\nĠ************************************************************************ ****\n- product\nĠprompt ed\nung le\nd ocker\nĠT u\nĠUn ique\n_ Error\nul os\nĠâ Ħ\nĠ( `\nGet ting\n_s cal\nĠEn h\nü t\nĠsust ained\nĠp atches\nĠpros per\nĠG aza\n_l ight\nĠin cons\n-------- Ċ\nĉĉ ĠĠĠĠĠĠ\nS F\nC N\n: \";Ċ\nĠColl ins\n( *)\nĠcomp ilation\n'] čĊ\nĠcon sequence\n, ...\nĠd m\nĠB LOCK\nCl uster\nĠsk i\n(arg c\nT uple\nĠjo ins\nĠSher iff\nW ar\nind i\nĠcomment ed\nH OST\nĠinv itation\napan ese\nĠperm its\npreced ented\n_z one\nĠA my\n_R D\nMin imum\nĠinv ocation\n.en able\nicht en\n- owned\n\" id\n_PO INTER\nF ac\nĠspecific ations\nĠnom ination\nĠg p\n< (\nĠrob ots\nĠJ erry\nĠhold ers\nĠw and\nc ms\nĠ} ))Ċ\n.To ast\nĠI List\nB ased\nz oom\n/ style\nĠBe ck\nM en\nĠcontrib uting\nĠund o\nĠO H\nĠadd Object\nĠe igen\nsign up\néĶ Ļ\nĠdist ant\nPAR ATOR\nĠM ari\nĠm á\nE mp\nó s\nĠì Īĺ\nev t\n+ j\np ark\nĠSt ay\nĠD un\nĠso y\n> %\naz ines\nĠti empo\n(m e\np resent\n.Th is\nĠedit ors\nF IELD\n.W ork\nĠUn iverse\nĠdr unk\n.t imer\nĠalter ed\nĠN ar\nëł ¥\n.Act ive\nid or\nç Ń\n.delta Time\nĠawk ward\n& quot\nĠSaf ari\nĠtr icks\nMENT S\ndiv ision\nĠvary ing\nĠHigh way\nĠphotograph er\nĠSt ewart\nĠlast ing\n.P re\n.amazon aws\nĠL uck\n.D escription\nĠN az\nn eg\nĠc ó\n<<\" \\\nĠSur v\nĠU nc\nRec ipe\n.Border Style\nĠmod ifications\n- at\nAT FORM\nh dr\nak o\nĠsublic ense\nĠJ ump\nĠbe im\nĠMan hattan\n. bool\n_h w\nÑĤ ÑĮ\nB in\nĠg ateway\n\" \":\nĠU IS\n:\" +\n- def\nĠReg ular\n/ testing\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nstring stream\nĠdis par\nĠmob il\n- read\nĠAd apter\nĠCh ampions\nĠsched uler\nĠk ills\nĠM ultiple\nir ror\nĠgod s\nAD O\nak te\nĠUs uario\n.c ircular\nĠre cept\nĠEx pr\nĠelder ly\nĠnic ely\nĠbest e\nW ant\nĠclass ical\n.s prite\nobj c\nĠM ason\nĠsist ema\n.Bl ack\nes o\nĠZe it\nĠdiv id\nĠent ers\n_sub 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M\n_dat etime\nqu ivo\nQUE UE\nèĢ ħ\nAp pear\nĠsum mit\n: void\nĠv ine\nè® ¤\non ne\n_TR ANS\n.g reen\n_ cc\nĠhung ry\nĠ\" >\n() );čĊčĊ\nEx tract\niz ens\nĠsol ver\nNot ify\nĠeng lish\nĠSh opping\ninter faces\nRE Q\nĠil leg\nĠUI ImageView\nĠdis connect\nĠUnt il\nĠConserv ative\n@ Column\nĠshift ed\nĠ: čĊ\nĠf ich\nĠd la\nĠsh oe\n\"), čĊ\nular ity\n_RE SP\nWe ather\nUI Application\n. iterator\nĠag ing\n.P arent\now ie\n(e qual\nĠCon v\n/ default\nĠmeas uring\n.pre v\n.Is Valid\n.F at\nĠs Äĥ\nkey words\nwith out\nĠso vere\nĠex changes\nĠm elt\nĠis lands\nĠInt egr\nĠjump ing\nĠg le\nĠjournal ism\nĠd ated\nLocal ized\nĠRef resh\nPart icle\nĠa a\nĠSTR ICT\nĠb od\n.Pro cess\n_A UTO\nĠP ublished\ne very\nĠtechn ological\nls x\nĠir rit\nAdd itional\nĠdel imiter\n_l anguage\n- area\nbo ys\nĠT ube\nĠw at\nĠmechan ics\n_ owner\nSp ell\nĠSt ories\n.Append Line\nTable View\nh em\nst ick\noll ower\nI FF\nĠU V\noll ision\nS UB\nĠcompar able\nĠdon de\ns ales\nll vm\nĠ} ],Ċ\nOTT OM\nĠPur pose\nL 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our\nĠrom ance\nuc ceed\n. choice\nĠad ip\n_d ims\nSerial izable\nãĤ ĭ\n.j ob\nĠpro g\nuch ar\nĠg ently\nĠR SS\nict ured\n_ENABLE D\nĉ label\naw ks\nĠEn sure\nrem ember\nìł ķ\nĠtrans mit\n{{ $\n.Trans action\nur se\n_rel ative\nĠs ized\nĠX X\nĠPr incess\nĠL arry\nĠpr ó\nĠÑģÑĤ ÑĢ\nĠs isters\nestr uct\nĠcheck point\n: length\nĠCar los\n/ icon\n_T ARGET\nT okens\nĠpat ience\nĠSe lected\nq ty\n.show Message\nĠwild life\nĠP rops\nb m\n- arrow\nĠpar cel\nfire base\nĠBen jamin\ncess o\n.t im\nĠG arc\n. any\nĠHOW EVER\nĠK o\nĠgrab bed\n_f rames\nĠobject AtIndex\nĠADV ISED\nĠsub ur\nĉ GL\nĠ}) }Ċ\n-l ength\nìĭ ľ\nĠPot ter\n_b uff\n.g ui\nĠEnc oding\nE lect\n-m essage\nĠ �\nĠ ÈĻi\nĠArgument NullException\nа ÑĨи\nĠmin imize\nĠrespond ing\n$_ ['\nĠInd ividual\ná c\nĠIN TER\nĠmast urb\nĠB in\n(' $\nëĵ ľ\nĠopen ly\nĠ> <\nĠun to\nolog ically\nĠM ul\nVID IA\nĠsl im\nĠCommission er\n( on\nĠunder neath\n/ db\nv ote\n( Message\nĠP ope\nDef ined\nĠsw ift\nur f\nĠadapt ed\nSE L\nĠreven ues\nĠdiv ine\n= 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UTF\nĠpick up\nĠgro cery\nĠP ure\nĠeas iest\nPh il\n.f eature\n(\" *\nĠinvest or\nt ok\nĠj ar\nL os\nâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ\n. queue\n-s peed\nM al\num blr\nĠCON ST\nĠH RESULT\nĠD ance\n(file Path\nĠattrib uted\nॠį\nĠB und\nco ins\nĠs ão\nĠp ir\nperson al\nĠpre lim\nĠprop ose\nĠT L\n] ])\nĠSub scription\nĠK re\n, len\n.First OrDefault\n) --\n_product s\n.Get Bytes\nSh ip\nĠenc rypt\nĠS G\nĠM yst\nh ir\nĠiter ate\nĠint end\n.mock ito\nĠch apters\n( angle\nĠV lad\nè® ¾\n' .ĊĊ\nResponse Body\nĠAb d\nde al\nĠbar riers\n-out line\nb ill\nĠF alls\n_se cond\n. include\n. ceil\nĠoccup ation\nph ony\n.move To\nĠJenn ifer\nAST ER\n; \"><\nĠEn abled\nĠtermin ate\nĠI o\nl ations\nĠTHE ORY\nĠear liest\nĠr ack\nĠSc ar\nsh ake\nch ip\nĠu v\nĠall iance\nп иÑģ\nĠGOOD S\nz ione\nĠV I\nĠ{ -\nĠfilter ing\nĠmis con\n.Dock Style\nĠb ush\nĠj unk\næ Į\nĠQ UE\nĠhook s\nĠfirm ware\nĠmiddle ware\nd ic\nĠOak land\nĠarr ives\nP ayload\np ixel\n] |\nĠstart Date\n.P RO\n_a udio\nĠmid field\nigid body\nĠSw iss\nĠCl ip\nĠD ump\nĠText Box\nĠg eh\ny ield\nod s\nĠrefer endum\nBack end\nĠC ream\nĠdomin ated\nĠArch ive\nĠrid ers\n.prepare Statement\nĠqu ando\nĠche f\nw iki\nin el\nam pling\n(\" \\\\\nĠs ag\n_pro xy\nãģ ķ\np do\n.getElementsBy TagName\nĠdemonstr ation\nĠN PC\nĠarch ivo\nend ance\nĠefficient ly\n( actual\n.t ableView\nĠm ush\nĠbe ars\n_thread s\nj as\nah un\nĠne ural\nĠdesign ing\nĠG DP\nĠlift ed\nçĽ ®\nĠJ oint\nĠIn clude\nĠGi ants\nĠwithdraw al\nĠR ent\nn ative\nĠSe ek\ngress ion\n_C PU\n\\ S\nĠSh ield\nĠsol ic\nĠbo om\nyect o\nĠmanufact ure\nĠâĢ ĭ\nĠb box\nĠearth qu\nollect ors\n:@\" %\nĠlo ops\nJ e\nalk ing\nĠWh ats\nĠBo ys\n. book\nARG E\n_p ixel\nĠsus pects\nÎ ¹\nus p\nĠBM W\nie ces\n(p erson\nå¼ Ģ\né »\nĠPod cast\nĠb ou\n( Item\nà »\n( Input\nHttp Get\nĠb urg\n) ^\nBO ARD\n*/ ,\nĠg ulp\nĠB enn\nĠdeck s\n.status Code\nĠac ute\nĠh ug\nug u\nĠp led\n,\" %\nh ape\nĠз ап\nĠMain e\n.re al\nĠd alam\nĠMin or\n.F loat\ndis p\nĠt l\nĠen count\n=> $\nĠf g\nte es\nĠRec omm\nä l\nĠchem istry\nBlock s\nO ID\nĠfore x\nĠApp end\nĠ{ *\nĠSup ply\nCG Float\n(b l\nĠat e\nador a\nĠg ust\nAss oci\n> .Ċ\nF ETCH\n.s erial\nwidget s\nard less\nie fs\n_F ULL\nernet es\nĠP red\nØ Ń\näº ĭ\nub ernetes\nĠL aura\nĠl abeled\nHigh light\nĠanno ying\n/ update\n(d escription\nĠintim id\n$ c\n\")) )Ċ\n.A P\nĠ[] *\nĠEX IT\n.H ost\nĠOP EN\n.send Message\n_c amera\n_t ile\nĠth erm\nonom ous\nĠdis adv\nĠna ar\nindex Of\nĠP P\n.prot ocol\nAF E\nĠtext ures\n################################ ################\numb ai\n.st ats\nĠG E\nĠi e\nĠST D\nĠM ann\n.ref lect\nK B\nĠd ive\n.w av\n/* ----------------------------------------------------------------\n/ settings\n.l ifecycle\nĠda ughters\nor us\nub er\nN ING\nst ri\nĠT ip\nĠz n\nĠswitch ed\nin et\nuff y\nĠTransport ation\n( conf\nfr ica\nĠX L\nĠLe ad\n_per cent\n< Map\nĠthr ust\nor b\nik k\nĠtra uma\nAccess or\nĠF it\nĠString Buffer\nex pl\n(s creen\nĠaud iences\nĠO PTION\n_ round\n[ node\nbe h\n-> __\nper missions\nĠD etermine\n.M an\nĠadv ances\n. 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client\nes Module\n-b ase\nĠret reat\n_s imple\nĉĉĉĉĉĉ Ġ\nfe e\n') čĊčĊ\nControl Item\nĠsubscri bers\nple ase\nĠE ff\nĠp ound\nĠBy tes\nĠTe a\n_ activity\nĠmax im\nĠop code\nB SD\n. constant\n; }\nomb res\nĠcare ers\n) .ĊĊĊĊ\nĠsp reading\n-exp anded\nĠOr d\namar in\nĠmob ility\nUn fortunately\nak k\nN L\n_ redirect\nĠP G\nĠS ensor\nb ol\nt ap\n_MEM ORY\nĠUI Alert\nplit ude\nWe bsite\nĠLog o\nlo ve\n[ ind\nĠalto gether\nĠwonder ed\nĠes per\nĠLib eral\nĠo ss\nĠel it\nĠst iff\nod ox\n_ment ions\nĠDou glas\n_p id\nĠC K\nĠinitWith Frame\n.b log\np kg\nang hai\nQUI RED\nu u\nĠm kdir\nAT AL\nĠun h\nin ces\nst h\nĠhypo thesis\nĠc ata\nĠT B\nĠCl ar\nĠpre decess\nĠsitu ated\n-w orld\n)) /\nĠhead lines\n.st at\nĠout break\nsp ath\n_FLAG S\nĠServlet Exception\nS un\nF ROM\nĠD ir\nãĥ»ãĥ» ãĥ»\n_co ord\nĠOpt im\nMon itor\n.b it\nXX X\nĠtod as\nf eld\nÑĢ и\nim ir\nĠpolit ically\nĠmolec ular\nĠtrad ed\nĠ{{ $\nĠSw edish\nĠ'@ /\n_RE AL\nĠw arehouse\nt oday\n, L\nor p\n< section\n- br\nym e\nĠUser Service\nĠlib erty\nĠmoment o\n( Image\n< size\nS ch\nĠj og\ni ology\narent ly\nĠquant um\nĠAb u\nĠr im\nĠman a\nFont Size\nBuild ing\nst airs\nAIL ABLE\nĠ& '\nĠs ect\nĠs igh\n(b atch\n.I Container\np oll\nĠCor ps\nÎ µ\nar u\nĠK ay\n.r ange\n_click ed\nĠRobert s\n.N etwork\nfin ish\n- Man\nĠcolleg es\nĠF ine\n\")) ,Ċ\nf ilm\nĠrem inded\nĠgest ure\nout il\nĠthread ing\nĠobj et\nĠt ours\nactiv ated\n.m kdir\n= user\nĠre de\nf ü\n_SY STEM\np v\nĠcon gr\nĠmass asje\nĠpract ition\nUn iversity\nĠtab index\nÐ ĺ\nS ets\nĠcount ies\ng uest\nf an\nĠword en\n.d i\nна Ñĩ\n ¿\nig Decimal\nĠsh ore\nĠg ö\nĠrep airs\nĠhelp ers\nĠcenter ed\nOL LOW\nĠmap StateToProps\nĠc ents\n< A\nĠexpect ation\nOct ober\nĠbg color\nca les\n.C ON\nĠV el\nĠcry ing\n-se ason\nĠfunction ing\n_LOC ATION\nü ss\nber y\nPar a\nomin ator\n- le\nĠeth ical\nhas htags\nemp lo\nĠn úmero\n( activity\n.St op\n.str ftime\nIL D\nĠto e\nĉ Node\n\") čĊčĊ\nĠPu erto\nĠexec uting\nĠG UID\nĠoppos ing\nal ph\nĠexhib it\n_fl ash\nĠme 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ĊĊĊ\n.w riter\nĠH our\nDb Context\nio v\nam on\nre pr\né ĥ\nĉf i\n'] ]\nĠD ry\n. ro\nĠO bserv\næł ĩ\nForm er\nĠB alance\nĉ json\nĠpr zy\nI SS\n( sock\nĠL INE\nĠde ce\nĠal ly\nĠtend ency\nF un\nĠschem es\nĠinter ven\næĺ İ\nĠad verse\nquote lev\nĠsacr ific\n_s ide\nĠmut ex\nAG IC\nĠocc urring\nĠCommunic ation\num ar\nç¼ ĸ\nĠTreat ment\n.p erson\nĠL C\nĠe ch\n( (\"\nĠDise ase\nä d\nĠA Z\n.A ccount\nĠcontinu ously\nEND ING\nĠRET URN\n- string\n.f ilename\nsyn thesize\nRes ponder\n( opts\nreg s\nĠn uest\nPe er\n// ------------------------------------------------\nĠg auge\nĠK in\n.s chema\nĠarr ange\nĠBl ake\n_Type Info\nC over\nĠHamp shire\nP aper\n-in ner\nutil ity\nĠcross origin\nF OR\nĠign oring\nĠD D\nav an\nĠtrad itions\nĠget String\nĠeth ics\nĠMaterial s\nDE SC\nĠen zym\nio let\nĠCh ip\nĠMc Donald\nĠn erve\nç Ħ\n\") ]\næ± Ĥ\nĠS ugar\n_S IM\nj peg\nĠdiscret ion\nĠT N\nbo ve\nĠMin imum\nĠForm Group\nĠwork force\nĠExec ution\nerr er\nĉ ĠĠĠĠĉ\nĠpres cribed\n.Text Align\nOP EN\nĠP B\nim ity\nĠEx ternal\n° C\nĠApplication Controller\nĠb arr\nimp licit\n_d ot\nĠCol on\nC OLOR\n.Pro ject\n* }Ċ\npl aint\nget Text\nĠindivid ually\nĠcheck box\nU Y\nĠL amb\nĠdys function\nĠL ar\nà °\nĠCre ating\n');ĊĊ Ċ\n\" They\nloc ations\n_C ORE\nInter action\numbn ails\nĠPart ner\nb rit\nĠless er\nĠSl ot\nset Attribute\nĠW ave\n.p o\n/ store\nĠbrows ing\n_p d\nsum e\ns ed\nCur ve\nĠpl asma\nĠsusp icious\nìĿ ¸\nĠB ah\nĠExp licit\n_C C\n.Client Size\n\\ View\nĠsub stit\nlo on\nĠG AME\nĠB rid\nĽ 建\n_ User\nĠsqu ares\nf one\nĠsac red\nug hs\n] interface\nĠTh row\nĠK irk\nĠemp ire\nĠassess ed\nT ax\nĠHe aven\n-b uffer\n_STAT IC\nén é\n-b ordered\nĠpun ct\n(m ode\nĠke ine\nS ent\nĠCal cul\nĠE ve\nĠsty lish\nĠoil s\n.Test Case\nĠtrad emark\nĠliter ary\nĠconcentr ations\nĠRel ations\n( Class\nĠstd in\nĠv æ\nback up\n. 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margin\nless ness\nop ard\num atic\n() ));čĊ\nĠf als\n(c ache\nType Id\néĢ ļ\n_ choice\nĠGo th\nĠS ites\nM G\n_b order\nInd ices\nCompar er\nĠRed istribution\nĠclo set\nĠvers atile\nInput s\n**************** ****\nĠob esity\nqu iz\ngr a\n(g lobal\nåĬ ¡\nĠcollect or\nĠk or\nov able\nAD C\nĠEvent Handler\n. nc\nĠplay back\nient os\n_p erm\n_W ARNING\nĠOlymp ics\n.n orm\nĠBroad cast\n_sm all\ndr ive\n. iloc\nĠtyp ed\nM EM\n_con s\nDM ETHOD\nĠl un\n.d istance\n(p ar\npo on\nĠb ast\nactiv ities\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\n: čĊčĊ\nS ER\n) &&\n_l st\nĠPol ish\nĠknock ed\nĠfrustr ation\nau kee\nĠph osph\niqu id\n_c oeff\næŃ ¤\nL atest\nĠD ust\nT ipo\nĠmaint ains\nĠmar sh\ninc inn\nl bl\nC are\nĠneighborhood s\n_g pio\nĠAr senal\nD em\nĠW he\n_h ook\nĠl dc\nĠHar per\nĠBer keley\nĠgrad uated\nPer cent\nĠarr iving\nĠAdvent ure\n(s cope\n(' *\nqu arter\nĠMar ie\nSpe aking\n_code gen\nĠimm un\nc aster\nãĤ Į\nåķ Ĩ\nĠDim ensions\n.rec ord\nĠtext o\nĠMich elle\nP ending\n( by\n_P AR\nuch t\nbe e\n.Th read\namp ire\nk now\nĠClin ical\nĠmargin Bottom\nĠdistingu ish\n.F ull\n. undefined\nĠSequ elize\n################################################################ ############\nĠeduc ated\n_O VER\nåº ı\nĠÂł ĠÂł\n_e ach\nĠur ge\nde part\nĠdon ors\nĠA u\nĠbill ions\nĠbelong ing\n_ age\n_ Int\nĠsub stances\nm achine\n!! !ĊĊ\nĠjson ify\nib bean\nĠC ad\nĠend Time\nĠc ycling\nĠUIT extField\nĠle verage\nĠvan illa\ne at\nLa unch\n( pt\nst ates\nĠControl s\nĠRes pons\nĠJ ake\nĠas leep\nfort unate\n.next Line\nSize Mode\nìĿ ¼\nTesting Module\nG erman\nĠInvest ig\n.re verse\nĠB ACK\n( DateTime\nĠnon profit\nĠEx pect\nĠt anto\n'] ),\nĉ the\nM ultiple\n(get Activity\n_W AIT\nĠj á\nde cor\nlev ance\nĠGit Hub\nmin ation\n_qu antity\n.Sc anner\nĠL ion\néĶĻ 误\nĠd re\nĠtan tra\nĠcontent Type\nĠf id\n_ alt\nNS IndexPath\n- pl\nåĮ ĸ\nĠantib iot\ntable s\nac ial\nĠReg istry\nĠol ive\nig ers\nĠsubscri ber\n_p res\nĠSy ntax\nĠlo vers\n. 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Enable\nĠInstant iate\noll en\në© ´\nĠCall ing\n_h our\nAD A\nĠsh y\n) **\nĠ== >\nĠes pecial\nĠinterpre ted\n! =\"\nĠpharm acy\n.s ingle\nĠC ialis\nĠpar as\n.to UpperCase\nĠDem on\nPr ime\nĠrank ings\nAdd ing\n_H ASH\nĠEx am\nÚ ©\nĠVict or\nOk ay\n\"] ;čĊ\nĠfort une\nĠF ETCH\nexp and\n.Inter op\nĠb arn\næ ¶Ī\nue vo\nĠspec ulation\nâĶĢâĶĢ âĶĢâĶĢ\nĠN u\nĠBl ues\n(f name\nĠinhab it\nĠ\\\" %\nC ES\nular io\n_c r\nĠvalid ated\nĠmid night\nank ing\nĠincorpor ate\nĠpurs uit\nEX P\npr ime\nP id\n- US\nĠN urs\nĠW heel\né ĺ\nĠin p\nĠsupport ive\n.m ember\nĠSh ot\n.Check Box\nĠaff irm\nT or\nFull Year\nĠconsider ably\ncred entials\n_ opts\nR oll\n( round\nĠcom ent\n_U ART\nĠext ending\nR G\nresult ado\nit u\n.get Session\nĠattr action\n& D\n$ html\nĠJess ica\nĠAssoci ate\na ñ\n_ ed\nĠL ag\nĠorig ins\n()) ->\nadd EventListener\nIAL OG\nåIJ ¦\n.Com pare\nAl bum\nĠK u\n< Q\narg est\nĠpro long\nĠconfig urations\nĠaccident ally\n_ph oto\nĠ'' ;čĊ\nĠver se\nB ob\nĠfarm ing\ndel ivery\nĠM ack\nĠuse Selector\n.bootstrap cdn\nkeep ing\nen y\n. upload\nĠM ETHOD\ncre ator\n< _\nĠE aster\n. --\nUI Button\nãĤ ī\nom eters\nĠsh ine\nĠh ogy\n\\ s\nĠh arness\n.C ell\nĠlif ting\nĠcomb ines\nĠOcc up\nex clude\npat ial\nĠres pir\n_f it\nĠfif ty\nĠM ol\nĠtun ed\n-d imensional\nĠq s\nĠto ps\n> \";ĊĊ\nquis ite\nch annels\n/ res\nĠAn alytics\n.app compat\n/ to\nĠon Error\n( attr\nIR M\nĠrag az\n- as\n.Se cond\norient ed\nĠdon n\nĠlight ning\nf id\nĠP le\nãģ¾ ãģĻ\nt ro\n.Tr ue\nO bservable\n× Ļ\numb ing\nĠpros pective\n-f ilter\nĠpurs uant\n(p oints\n.B ind\nĠp alm\nclear fix\nö s\nĠG onz\nĠwe aken\nDr ive\nen ido\nl ld\nob ox\nane an\nG ot\nä¿ Ŀ\nReg ex\næ ĥ\nĠsal ad\nass is\n\" net\ninherit Doc\nĠR V\nqu ier\nĠcl azz\nı ÅŁ\noster one\nĠair line\n.list dir\nĠdownload ing\nĠP alm\nw aukee\n& lt\n.B L\n_IN LINE\noff s\n<< (\n_new s\nĠch ase\n/ ><\nĠeuro s\nĠEgypt ian\nĠSt ainless\n_BO OL\nĠG uild\nĠD ynam\n[index Path\nĠ ï\nĠmemor able\nĠCh ampion\nResource Manager\n.Log in\nĠForm er\nyp ed\nĠl leg\n; \",\nD WORD\nĠtax i\nĠbom bs\nra h\n.t ags\n_test s\nst ones\nâĢĿ )\n[ g\nr type\nĠv u\nĠhost ile\nCh ars\nĠPatri ots\n/ status\n< B\nĠIn come\nĠD ad\nĠpat rol\n_CH ANGE\nĠup graded\nĠch ina\nset q\nStart ed\n.U ndef\nĠcheck sum\nĠfrustr ated\n{ o\nĠen f\nĠwood s\nĠAny one\nEnc ode\nĠQt Widgets\nare as\nĠshe er\nsk i\nend point\n_T est\nS oup\n~~~~~~~~ ~~~~~~~~\n(f iles\nĉĉĉĉĉ čĊ\n.sp ark\nĠval ued\nĠ% Ċ\n.control s\nĠXCTAssert Equal\nĠf ame\nĠR ic\nD OT\nĠAlbert a\nä½ ¿\nos al\n.Web Controls\nĠ ------------\nĠM is\nĠS YS\nNon null\n= item\nĠexp ire\nDec ode\n_ operation\nĠValid ator\n.C ENTER\nuff s\n* m\nĠav ant\næ¬ ¡\nâĢľ You\n.per mission\n... )\nĠL ic\n_co ords\n.n ombre\nc lo\n.Int ernal\nĠCh o\n_s w\nĉ Il\ncl k\nĠcast le\n(l ayer\np it\nĠgu ided\nĠâĸ Ī\nĠsuper b\nĠsup plements\n_c ent\nĠpe ek\nIN ARY\n.Content Alignment\nf alls\n\")) ;\nW all\n). čĊ\nĠD anny\nirm ingham\nIAL IZ\n( create\n\" In\nService Provider\nĠpr iced\nmac ro\nam ac\n. box\n---- Ċ\nãĥ «\nĠS uit\nur st\nbr u\nourn als\nnum ero\n__ ()Ċ\nD as\nĠM itt\nud er\n? \\\nf u\n[ B\nĠ: )ĊĊ\n(int er\nbr ains\nĠatt itudes\nVer ify\nĠsign atures\nack Bar\nĠg d\nJ ack\n.c at\nĠz z\nwar f\nFT ER\n\");ĊĊ Ċ\nAl ive\nIC LE\nĠWh atever\nĠout lined\ns prite\nеР²\n_A B\n_DE PTH\nĠcrush ed\naa a\n(e v\næľ º\nAnt i\nIC O\nis EqualTo\n.s un\nic ulo\ns ale\n_h ex\nĠV k\napt or\nUn ion\nĠDis count\nlist a\n.Undef Or\nĠautom ation\nN or\nå¯ ¹\nåı Ĥæķ°\nĠref lex\nĠLa ure\n.showMessage Dialog\n.t emp\nĠa kan\nĠ__ ____\n.Is True\nARE D\nag le\nE nergy\nĠquant ities\nâĢĻ é\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠcitizens hip\nm outh\nĠin appropriate\nĠOut door\nWhite Space\nAn onymous\nload s\nwebElement Properties\nT en\nĠacc idents\nĠadvertis ement\nĠY emen\n(c all\nĠsl avery\nÑģ п\nĠL am\n_BIT S\nome ga\nĠO le\nĠkid n\n_A n\nĠR aid\nCre ation\ns aved\nĠpro port\nW ARNING\n\\ P\nĠp wd\nData Reader\nis cher\nade on\nĠP redict\nĠreason ing\nĠdestroy ing\nH el\n* d\nĠLeg isl\n_P r\nĉĉĉ ĠĠĠĠĠĠĠ\nĠsymp ath\nĠch ess\nĠm am\n: hover\nĠconvert s\nĠp ela\nĠprogress ion\nĠ\"_ \"\nĠG ill\nĉ show\nĠsupposed ly\nac curacy\nel in\nĠunf olding\nĠHy per\nĠw anna\nĠup s\n( #\nĠCr iminal\n( Point\nat Lng\nact ly\nĠcontract ors\n'] }\ndraul ic\nód igo\nĠT T\nĠW ide\nĠAR G\n_ ic\nFLAG S\nS chool\nĠclear ing\n-be ing\n={ [\n, const\nman ent\nOver lay\n(' \"\néĩ ı\nĠT imestamp\nĠmail ing\nĠC ake\n.Th at\nĠmed itation\nq p\nĠemp resa\nĠL ions\nĠw eld\nĠLinked In\nĠc ush\nĠgen ome\n.Index Of\nag ain\nĠf allback\nĠcamp ing\nre dd\n-strip ed\nĠd v\nFe bruary\nĠPro xy\nus k\nĠdies el\nW RITE\nRE AK\nL orem\n.In voke\n- div\nInter ceptor\nĠD H\nia les\nĠvill ages\nØ ´\nĠEN V\nS ys\n.X R\nĠpo em\nà Ĥ\nc ade\npl ots\nĠ{ (\n.g it\n/s vg\nnc mp\nĠÄ į\nain es\nåĩ ½æķ°\nĠ( )ĊĊ\nops is\nĠRel ationship\n_ aut\nĠB omb\nĉ com\n* sizeof\noff icial\n_p ayload\nĉĉĉĉĉ ĠĠ\n.m anager\nĠA round\nĉs end\nĠEx ercise\nĠB illy\niv i\nĠneed ing\n_url s\n_t asks\nĠH em\nĠtear Down\nenc rypt\n.t ie\nĠas m\nIC H\nĠCGRect Make\nìĦ ±\nul ong\nĠit r\nĠG ST\nĠoffer ings\nro be\nEE E\noper ators\n_PRO P\nind ent\nA DE\nor f\në IJ\nĠbless ed\nvas cular\nĠcon oc\nH appy\nB ridge\nilit ation\nj oint\nĠAdmin istr\n- transform\nĠmeant ime\n/ K\nĠBed room\nĠrig id\nĠbrows ers\nEM PTY\n.S erialize\n_ ED\nĠst itch\nĠj an\nell t\nĠbr ace\nĠtr ails\np ublished\nå¯Ĩ çłģ\n} ')Ċ\nĠac ids\nĠ! !!\n_d irect\n> ());Ċ\naj Äħ\n_O CC\nĠplan ets\næ Ł¥\nĠDub lin\nĠser ie\n.print f\nde ep\n` )\nĠ\\ $\nĠÎ ¼\n_V IDEO\nend ors\nĠC rypto\nF ar\n.Trans parent\n.T R\nias m\n_tr aining\nĠteach es\nĠB elt\nĠlimit ing\nĠK ath\nĠIndex Path\nĠachie vements\nĠser á\ninterop Require\nĠdis se\n.I f\narm ing\nuls ion\nP o\n_DE TAIL\nProt otype\nĠC AL\nĠagre es\n.v o\n.Execute NonQuery\nĠTop ic\nĠ' {}\nAr m\nĠe cc\nM ag\nĠserial ized\nĉ conn\nc ached\n= tf\nĠByte Array\nprot obuf\nvar char\nĉ ASSERT\nĠlist e\n_tr igger\n· ¸\nFe el\nT ahoma\nĠL ik\nĠstruct ured\nerg us\n.In itial\n_ ge\ncl js\n.cont act\nĠand ere\n$ stmt\n_C URRENT\nĠDis cover\n$ res\nform atter\nH a\nvang st\nĠem erge\nãĢĤ âĢĿ\nĠCabin et\n-s quare\néĥ ¨\nĠr age\nĠA J\nĠV T\nsh adow\nĠFa ith\nen ames\npret ty\nhas il\npart y\nĠvar char\nĠf otos\nĠal um\nĠBelg ium\n.y label\nĠde j\n_num bers\nĠh u\n.set Adapter\nĠUs ually\n(s ample\n.Sh ared\nĠbook ed\nĠ>> =\nĠmin erals\n\">\npro g\nbo o\n_m d\n_p ack\n(ex press\nut z\n\\ Auth\n, id\nĠCh ile\nact ice\nĠrecruit ment\nĠpos es\nĠvulner ability\ninst anc\nor um\nd ess\nĠx l\n%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%\n( fig\nĠdelet ing\n.d el\n) ')Ċ\nĠWeek ly\n?? 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Stream\n.g uild\nĠn og\nĠPortug al\nĠUnt er\nPr imitive\nb oss\nĠDe utsch\nĠerot ic\nĠstr conv\n.Try Parse\nĠgr ams\n.S uccess\n_p k\nĠHar vey\n-m inded\n.c ountry\n[] \"\nĠang el\nĠbe ats\nĠV or\nil io\n.m aster\ns omething\nĠP ACK\n( if\nRequest Body\nĠant es\n/w idget\nĠmod o\nĠA W\nfind er\nĠoptim ized\nĠmiss iles\nN B\nĉint ernal\nt ex\nĠS ri\nĠdam aging\nĠM ais\n- Allow\nĠZ h\n- alt\nĠ ));ĊĊ\nè ī\nĠinflu ences\nĠc atal\n_REG ISTER\nĠAPI s\n-cent ury\nĠbi ology\nĠAct ual\nĠhe els\nTR ACE\n_D IG\nD ataset\nĠM atter\nĠclass ifier\n.w ikipedia\nĠRog ers\nĠdon ated\nraw ler\nen en\nĠcas inos\nort al\nĠpr ive\ns pe\nduc ers\n. ep\nĠgr asp\nac ji\nĠd airy\nĠb uses\n.com m\n. ins\nĠI RS\nĠBe er\nad c\no ard\n_M ET\nĠ' +'\nr ans\nĠkind a\nĠâĶ Ĥ\nĠM aur\nаР³\nĠband width\nib us\nĠD ifferent\n(m at\nĠRes ume\n_UN S\nest ablish\nĠfon ction\nSub scription\n_com pany\nĠlight ly\n.con firm\n.y aml\nĠBo ost\nCom merce\n- template\n_DEL AY\nĠH I\nĠn avig\n(S ender\nĠH S\n_ \"+\nĠRE QUEST\nĠw ifi\n=\" \"Ċ\n]) ->\nĠro pe\nĠviol ated\nĠgl ance\nĠK urd\nĠè ®\nde ck\nĠIS BN\nĠin fect\nĠF oo\nĠget ter\nĠt ener\nap pe\n.h h\n_h ot\n< AM\np oly\n! \",Ċ\nĠconver ting\nĠW WE\nRO S\n(' {\nCom mit\n) L\nĠO re\nĠsp arse\nĠdis posal\nĠcan celed\nåIJ İ\nĠa er\nĠvin yl\ná» ĥ\nrec ogn\nark ing\nĠtrick y\n* s\nĠproceed s\nĠis o\nĠco conut\nĠcraft ed\nIEL DS\nĠquest o\nĠcomm un\n_CON NECT\nĠtraff icking\nDe ep\na ções\nc odigo\nve au\nĠbet ray\nint a\nT ED\næ r\nm art\n_B US\n/ sc\nial ly\nĠcigaret tes\nè¯ ģ\n(n n\nĠmodel ing\n/ products\nw arn\nĠmet ro\nĠI v\n& )\nĠC able\nÎ »\nCompar ison\ng ary\nĠB A\nP ART\nĠp v\n_up dated\nC redit\north y\nobserv able\nĠthe atre\nB LE\n; }ĊĊ\nla unch\n_str ings\nug o\nĠR PG\n- auth\nÐ ł\nhol m\nĠP and\nU id\nĠim ply\nìľ ¼\n'] ='\n/ User\nĠstr cat\nнÑĭ й\nData Adapter\nĠland sc\nĠdipl omatic\nï¼ ĵ\n************************************************************************ ****\nĠCh icken\nĠbc rypt\n.In f\n[ col\nĠQu antity\n- position\nĠdiet ary\nĠfil mm\nIs rael\nPre v\nĠMill ion\nĠrem ed\nĠbill ing\nĠout doors\n.t m\nĠn ad\nF org\nZ Z\nĠs sl\n], '\nK T\nf req\n= document\nbl ur\n¬ ¸\nĠJeff erson\nC s\n(s ave\nĠstr ap\nInd ia\nĠide ology\nBO SE\nĠF P\n( ans\nĠfe ver\nĠY am\nK ing\nà ²\nAT ING\nbo hydr\nroll back\nĠnew Node\nĠN VIDIA\nĠhon our\nĠCon firm\nxb d\nĠsuccess or\n/ u\nl iv\nourn aments\nAtt achment\nĠgr up\nĠtri be\nĠca res\ne ft\n_s ame\n' label\nĠ ãĢIJ\nM otor\nĠin exp\nĠ\" (\"\n_POS ITION\nĠval ley\nĠResult Set\nĠpres erved\nĠmut ations\nĠquestion ing\nmun ition\nparse Int\nĠS r\nĠMet adata\nâĢĿ ï¼Į\ntimestamp s\nĠtrans itions\ní Ļ\nÑ Ĭ\ni om\n.D o\nĠp ine\nĠf ung\nĠtrans mitted\nct ime\nĠF am\nRe vision\nB as\nUP ER\nD estination\ntoHave BeenCalled\nĠun fortunate\nIN ES\n_pro f\nAm ong\nĠCy ber\nĠB attery\ngen re\nĠView Model\n- =\nĠutil ized\np aint\n.Integer Field\nern ity\ncomp iler\nâĢĭ ĊĊ\nĠM asters\n.To Array\nĠstrt ol\nĠUkrain ian\n} ));Ċ\nĠsh emale\n\" That\nfor all\n/ download\nĠrhet oric\n.l atitude\nĠWH EN\nĠshock ing\nIF IC\n.N ormal\n_F OLDER\nĠdr ift\nĠmount ing\n- book\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ\nĠWire less\n> \".$\nĠrel ies\n( Console\nInt ernational\n-> {$\nM id\nĠdis sert\ndd s\nĠdepos its\nĉd river\n# ga\npr ising\nprint ln\nĠpres enter\nĠmin es\nC SS\nĠD ual\n(! 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(Ċ\nS aturday\nĠsoci eties\nĠTh an\nĠC od\n= @\nĠattach ments\n.m obile\nĠsp ite\nĠb ounce\nraw l\ninstanc etype\nĠTr uck\nĠmanip ulation\n( Config\n-in st\nĠst or\nit ution\nPreferred Gap\nĠmain AxisAlignment\nĠlist ened\n'' 'ĊĊ\nott age\n- project\n.AP PLICATION\nĉ root\nĠwh it\nĠb ilder\nĠk er\nĠappl iances\nrow ave\nìĿ Ģ\nematic s\nĠO rg\nop ing\n_SE ARCH\nĠch am\nadd ContainerGap\nĠ( ).\nĠAr row\nIl legal\nCurrent ly\nĠus a\nĠpassword s\nĠre nown\nav ern\nĠEv il\nĠconc at\nĠdu o\nĠv ale\nĠBe an\nĠindic ators\ncm ath\nĠP ump\nNov ember\nific ant\n_DOM AIN\nreg ar\nĠPort al\n\" $\nĠformer ly\n\"] :Ċ\nĠVis ibility\n.getElementsBy ClassName\n_RE D\nĠch ampions\nà ´\nVal or\n_ es\n* a\n-re peat\nB and\n.st age\nĠbure auc\nC nt\net en\n- function\nĠm uito\nP ID\n_ editor\nĠcrash ed\nde ad\nk at\nag h\nĠEX T\nass er\n-sm all\nĠreal iz\n( Entity\nú s\nĠAct ually\nĠEl ite\nĠhel m\n(non atomic\nash er\nComm unity\nall eng\nir y\nĠG rowth\nĠs ue\nĠfrequ encies\n_des criptor\n.At tribute\nĠrecip ients\n_N S\n/ \"+\nib an\nĠath lete\nĠI gn\n_D MA\n(d s\nĠRequire ments\nAD I\nere z\n\\ Admin\nbr aska\nĠR ust\nRel ation\nC OD\nĠV ERSION\nem ma\n)) {\n.D uration\nĠC amb\n- logo\nĠread able\nĠcre ators\n() ];Ċ\nUp Down\n-h alf\n.get Month\n(s f\nP ic\nĠhun ger\n.t x\nĠexceed ed\n_se ed\n( ^\n_s k\n.per form\nĠ> ::\nĠm ongo\n= float\nbind Param\nSm art\nif a\nĠse curities\nĠpre jud\nĠ, \"\nĠcor ps\nĠv ra\namac are\nit err\n(M edia\nuch e\nĠc ob\nĠlib er\n. geometry\nLoc ator\nĠsl iding\nĠsurg ical\n_C UR\nĠcon sect\n[ *\nĠRes ort\nSt ub\n_DO UBLE\nĠS oph\nĠelect oral\n_dis able\nĠÑģ о\nĠLight ning\nĠment ions\noc y\nĠle aked\nĠrelax ing\nPres enter\nv sp\nĠgu ilt\n=- =-\n.re ply\nĠMir ror\nC amp\nĠ+#+ #+#+\nĠ+#+#+#+ #+#+\n.A uthor\nĠdirect ive\n-h ook\níĦ °\n}ĊĊ ĊĊĊ\n@ pytest\n_r and\nm is\nĠcolor ful\nu je\nlass es\nĠClass es\n.h ave\n% ),\né¢ ĺ\nĠdistur bing\nsub string\nĠK oh\nIn vest\np urchase\nĠrec ycling\nĠA RT\nier archy\nĠf ps\n.check Box\níķ ´\n_m aterial\nduc ation\nĠf w\nud it\nĠreview ing\nĠS id\nS yntax\nĠW ritten\narg ar\nUM E\n/ q\nClass ifier\nOff icial\nĠj azz\nĠom ega\nPh ysics\nĠl ugar\n_access or\n.command s\nAb ility\nĠB atch\nR AM\nĠencount ers\n. Qu\nBY TE\nĠD istribution\nĠus o\nĠReco very\nappro ved\nĠden ial\n/sh are\nLinked List\n)čĊčĊ čĊ\nudd y\nĠf ines\nĠr y\nUn icode\nĉ render\nĠprem ises\nĠp on\nali ases\n/F oundation\nc uda\nĠC ock\n,: )\n(f older\nĠm éd\ndr ag\nĠtal ents\nĠĠĠ ĊĊ\nе ÑģÑĤв\nm ob\n.y ml\nĠa ster\nĠdis cre\ngo al\nĠGT X\nĠS UCCESS\nĠL ONG\n(f ind\nĠsing ular\n_s z\nĠEth ereum\n.. Ċ\nĠir res\n')) {Ċ\nĠmin isters\nSt eps\nivers al\nĠNever theless\n- led\nĠ( %)\nç¡ ®\nĠtime zone\nĠstr anger\n(re nder\nĠsh util\nĠm ph\nĠtri o\npp y\nĠpred omin\nĠend ors\nĠRuss ians\nĉ row\nĠw izard\n.s erialize\nĠcompl ained\nĠs ido\nĠdelight ed\n-m e\nĠR av\nH uman\nad ays\nrec v\nWork ing\nJ ump\nĠÃ¥ r\nĠAut omatic\n_B ase\næł ¼\naur ants\n ¯\næ ¸\n(C Type\nIF I\n( amount\nĠbelie ving\n= mysql\nĠf ir\nĠrest oration\nere co\nÐ ¢\n_ '+\nĠe book\nĠde bris\n(input s\nAY OUT\nĠscre aming\nav ia\nland er\nĠdist ress\nĠas sembled\nĠA void\n( thread\nĠR PC\n_EX IT\n( queue\nи ÑģÑĤ\nD ll\nĠsk ull\n_p ub\nche z\nmin ate\nens en\nĠins ane\nb ounds\nĠR osen\nĠcondition ing\nprocess ed\nv ideos\nf our\n.Con v\n| ;Ċ\nPerson al\ncer pt\n:UIControlState Normal\nĠdos es\nĠKar l\nĠFre qu\n.B ASE\nĠV ote\nĠcon current\nĠMessageBox Icon\nĠà ĸ\nĠDub ai\nĠR etail\n: number\nĠOb server\nĠBig Integer\n_ origin\n_W ORK\nF rames\nĠnot ably\n. âĢľ\nĠtrop ical\nĠn iche\nam ina\n.s ys\n(t okens\nmod ify\nos it\nst rom\nĠCom ics\nO PTION\nT icket\nĠfact ories\nĠdis put\n_F ile\nĠFin n\nee e\nĠDisc ord\n_m oney\n.t pl\n_s afe\nL B\nĠgl ut\nJ K\n.fl ow\n- cont\ng os\nĠhor izon\nĠR ush\n:: *\nP ipe\null a\nbor ough\nhe imer\n(m ove\n( Text\n} );čĊčĊ\nw elcome\nĠCom ponents\nĠgovern ance\nc losed\nĉm argin\nĠla undry\nĠTerm inal\niz ards\n. âĢĶ\n.rem ote\n.r adius\nĠQue bec\nĠd h\nT ech\nĠM ist\ns eller\n_l iteral\nĠgen ius\nĠbr ains\ng em\nĠMe asure\nĠcata st\nr ance\n.Text Field\nĠconsum ing\nĠ'\\ ''\noubted ly\nĠC ertain\nE v\nert i\nbe ing\nEx perience\nĠ// [\nĠArab ic\nĠC rist\nĠAz ure\nĠhor a\nl adesh\n\\ Blueprint\nd ar\n.re l\nĠsup rem\nĠRe agan\nĠAt tributes\n-s idebar\nĠuse Styles\nĠA irlines\nĠh ills\n/x html\nv inc\n_m ock\nĊ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ\nĠP ill\n.Layout Style\nĠCommand er\n] <\nsign ature\nĠ{ }čĊ\nĠhat red\nĠë ĭ\nole sterol\nĠ ********\nancell or\nc rop\nT IM\nĉĉ ĊĊ\nys qli\nuit ive\nĉun set\n_s el\nĠmen us\nt ick\nĠconstit ute\nĠElement s\nĠRed is\nagg io\n_f p\n_de pend\nem as\nCA ST\nor ange\nj on\nĠEm ily\nĠpot atoes\nĠre ceptor\nĠElect ronic\nĠL ights\nĠcomb ining\nĠSome one\nĠ######## .\nĠT OD\n/ show\nX d\n.\" '\naf x\nĠtr agic\nSt yled\nĠMar co\nG allery\nd ale\n.âĢĿ ĊĊĊĊ\né rie\n/s ervice\näº Ĩ\nĠamb ient\n_SET TINGS\n.Ad apter\nl ene\nĠtrav els\nNot ice\nĠcle ans\nĠF em\nch air\nÑĥ н\n/ my\n_b ad\nĠEcon omics\nIS A\n_C NT\n(M enu\näº İ\nĠR idge\nĠlength y\nD ot\nĠjump s\nĠhe y\n$ pdf\nĠw orm\nĠs ut\nĠsh er\niam o\nĠCal c\ntrie ve\nĠc ops\nĠCh rom\nĠreg ulated\nreat ment\nĠHigh er\nok s\nĠde ze\nLOC ATION\nongs To\nĠfin ite\nĠvar ies\nĠposition ed\n' il\néĩ ij\nĠh ike\n(d one\nplay list\nĠad a\nĠcoast al\nĠN ancy\n.DateTime Field\nCpp CodeGen\nĠSimilar ly\nre ur\nĠCon tr\nĠH idden\nĠB eta\natch ed\n_inst all\n. Output\nLook up\nĠRich mond\nqu ared\nĠm anga\n-control s\nĠBern ard\nL arge\nĠslic es\nĠoff ence\nĠM ega\nĠest ar\nĠjoint s\nĠsum m\n_pl atform\nB uff\n.add Subview\nĠret ained\nLet ter\n.d im\nĠess ere\nĠS caffold\nEX PECT\nĉ RE\n.long itude\nü nd\nĠstat ue\n.add Widget\nĠCar ibbean\nadd PreferredGap\nil de\nUIL abel\nĠOp port\nĠimper ial\nurs ion\nĠmand ate\nĠpromot ional\nĠv k\nia ÅĤ\nĠp yl\nĠCre ation\nоз д\nĠsim pler\n. what\nĠRec ent\nSt orm\n. quantity\nĠL ov\n\" -\nubb les\n_not ification\n(w orld\nur ger\n* (-\n: \"Ċ\nh m\nans hip\nĠAl most\nĠmotor cycle\n_f ee\nĠabsor b\nĠVin cent\nĠsound ed\nÃŃ st\nĠpharm aceutical\nht ag\nĠKind le\nital ize\nĠEm peror\noust ic\nĠspecial ists\nåħ ¬\nBorder Style\n/ \\\nRE LATED\n(', ',\n(ex pr\nĠh t\nåį Ī\n_C reate\nĠspecial ly\nĠ[] ;čĊ\nĠhe el\nĠse pt\n_ arch\n(in itial\n% .ĊĊ\n\\\", \\\"\nĠdiscuss es\nĠu pt\nĠ[ &\nĠman us\n.h and\nĠM AIN\nĠDen mark\nĠ], čĊ\nĠcr yst\nĠn ack\nCo ords\n_in ner\nĠmid st\nĠaw ake\nĠÐ ŀ\n-b reak\nÃŃ vel\n_P ASS\nĠParam s\nĠdet r\nĠsp ider\nĠCon cept\nĠpre nd\nCH ED\n.Ex it\nĠpop ulated\nĠvirt ue\n_SE SSION\nĠnou vel\no auth\nĠд аннÑĭ\nr ink\n.Header Text\natur ated\nĠer st\nĠå ħ\nॠĩ\n_vis ible\ney er\nĠli able\nĠde be\nĠb w\n{- #\n_W IN\ndf s\nH over\nĠP UT\n- angle\nĠnob le\nĠtr aces\nenc v\nĠuser Data\n_in s\nĠS uz\nĠnews letters\nĠMod i\nĠentreprene urs\nĠtrib ute\nĠrum ors\nĠr r\nĠQu arter\nê³ ł\nĠfeed s\nó g\nĠen velope\nĠle ar\nĠk ø\ndevelop er\nSim ilar\n: \")Ċ\nsub scription\nMod ifier\nital ic\nĠn asty\nĠtermin ation\nĠchar ming\nĠâ Ł\nton s\n.tr ace\nh ots\nĠU R\nM ont\nĠjust ified\nĠG ang\nine a\nĠb og\n( ap\n_ $\nĠcont amin\n.D ot\nĉ Debug\n( exports\nĠpa ired\nĠAss ignment\nĠautom obile\nĵ į\nĠph ases\nv w\n@ SuppressWarnings\n= \\\nr ant\n- ed\nĉ await\nĠcert ificates\n'> \"\nĠint act\nCT RL\nM ike\ngreg ation\nAT TERN\nĠre public\n_up per\nili ary\nĠcomput ation\nh ire\nĠSh in\n_ ANY\nĠManufact urer\nĠC arm\nĠbear ings\n_c omb\nc ad\nur istic\nĠwholes ale\nĠdon or\n.inter faces\npress o\nĠBr un\n-c lose\npro ve\n_S K\nĉf rame\net ros\nĠP ain\n_EX P\nĠL T\n_f s\n.dat as\nĉ ss\nvo ir\nĠA xis\nM ajor\n=\" <\n[ h\nĠprof ess\nigr ate\n(s core\nKey word\n\" os\nĠĠĠĠ ĉĊ\nan alysis\nĠre play\n.p ass\n\\ d\nt ls\nĠsan ct\n.l ight\n_m obile\nÑģÑĤ ÑĮ\nĉt otal\nu ity\nĠpa used\nN AS\nĠen core\nlo e\nĠ-* -ĊĊ\n.h igh\nam pler\nĠSec ure\nĠfrag ments\n_ vel\nill ary\nĠSte in\nĠD awn\nĠmax imize\nภ¢\nĠ/ ^\nĠcontin ually\nĠsh adows\nĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠI ActionResult\nĠinform ación\nC HECK\n.Selected Item\nb undle\nol ley\n< Int\nAIN ER\nĠW ing\ntit les\nount ain\nC Y\nĠLoc ale\nform er\n< context\nR adioButton\n_s chedule\nĠfab ulous\nRob ert\n_PRO FILE\nĠg ates\nIM P\nĠPent agon\ng old\nb ach\nemploy ees\nR otate\nĠch amp\nĠsel bst\nAl tern\nĠconvert View\n/ ,\nĠ~ (\nSt reet\n_ place\nĠpersonal ized\nP ublisher\nĠSO CK\n_NAMES PACE\nĠStand ards\nso ever\n_C ENTER\nInter est\nô t\ntem perature\nView port\nget Resource\nĠeat en\nĠsem pre\nĠab normal\nĠc ylinder\nĠtroub les\nn od\nÑĭ в\ng ames\n_g l\nPl ane\ng rey\n_t bl\n.Component Placement\nĠCh ase\nLog ging\nman y\nì Ĩ\nĠfl ame\n=\"<\nĠtra jectory\n_r ing\nĠhydro gen\ntr on\nĠstat ute\nĠcondition al\nĠtr ay\n-s chool\n(w idget\n$ config\nĠrequest ing\n. uint\net on\nbrit ies\nOf Type\nAD MIN\np redict\nĠg egen\nĠH app\nOC UMENT\nĠA part\nĠ---- -\nro e\nu ide\njust ify\nĠSqu ad\nĠprof es\n.b ot\n_c urrency\ninn en\nĠM umbai\nĠNum bers\navana ugh\nagn itude\nâĢľ There\n= http\nçī ĩ\nĠv b\n+' {{ $\nĠin ode\ns il\nĠh ace\nĠsever ely\nĠOver view\nĠspr aw\nĠbeach es\n: left\n· »\n($ {\nĠF IRST\nĠSp a\n- ass\nĠb aise\nĠN ODE\nĠP izza\nP et\n(se q\n\\ \">Ċ\nCppMethod Pointer\nĠv p\nĠi a\n_se conds\nem et\n/b lob\n_TH RESH\n... čĊ\nD est\nĠN H\n.data Source\nit és\nĠJ ak\ns ell\nĠwork shops\n< u\nĠr ivals\nĠEX ISTS\nh om\n-t oken\ncompat ible\n.J Panel\nĠphys icians\nart in\nĠdes irable\nĠdistinct ive\n.D ep\ng id\nili ate\n, max\nĠprem iere\nĠq Debug\nĠadvoc acy\nĠwh isper\nP t\nĠun changed\n_q ty\n请 æ±Ĥ\nSe ason\navel ength\nĠP ul\nĠd ÃŃa\n'] ]],Ċ\nal is\n(\" &\nbor o\nĠb m\nĠR adi\nw rong\nĠGo ing\nime Type\nij i\n- feedback\nĠN ames\nĠB apt\nĠprob able\nĠE ther\nĠPolit ics\n_prot ocol\nlin ing\nS at\nĠcor rel\n.Pr imary\n(null able\nRI ORITY\nĠcolor ing\nĠutil izing\nd as\nĠexport ed\nĠcar riers\nCon v\n. editor\ni ó\n(h andles\nĠapprec iation\n. import\nĠAust ria\nĠStr ip\nil ight\nĠappropri ately\nĠP rest\nĠW ir\nĠUI Application\nal chemy\nĠM ob\nĠD etermin\nergus on\nregister ed\n_con vert\nĠVlad imir\n.Show Dialog\nref lect\nĠsh ook\nĠass ure\nĠO ften\nĠcivil ization\nĠvocab ulary\nfore ground\nĠS cope\nĠunw anted\nact ing\nĠ( []\nĠmark ing\n. original\nĠMO VE\nĠsport ing\nception s\nNS Number\nS izes\nĠprovinc ial\n_Tr ans\nĠproblem atic\nd igit\nĠEm ma\nlock s\nĠC rew\nib a\n') :\nish a\nĠm amm\nĠocc ured\nw cs\n(r ule\nĠmerch andise\nes pecially\nĠT win\nĠn aming\nĠs log\nĠimpro ves\nĠad her\n: text\n.h adoop\n_HT TP\n.to List\n.dis abled\nĠl enses\n.in i\nĠR are\nĠUb untu\nĠsc ram\nol ation\ntit ulo\nEvery thing\nĠnod ded\nicht ig\n_const ant\nz c\nl ift\nĠNot ify\nond o\nĠIN F\n(\" +\nĠK az\nĠd read\n.m apper\nle ur\nĠCome y\nĠN B\nic ers\n.P ush\nĠH ack\nĠBrazil ian\n_pro d\nĠ// ĊĊ\nĠb icycle\nĠun available\nĠadoles cent\nbl k\nĠmit ig\n_bl ue\nì ĺ\nfade In\nĠUtil ities\nĠM N\n; k\n< style\n- status\nind o\nĠinn ings\nĠg j\nĠ|| =\n.e u\n: Number\nĠcuis ine\nĠURL s\nie k\nĠw ires\nĉ ps\nie g\n.m k\nso ap\nĠsom etime\nĠst ap\n_s eries\n.T arget\næ º\n.dest ination\nOUN TER\nR aises\n& A\nĠsmart phones\nNI Env\n.s dk\nĠhelicopt er\nĠim pe\nĠB irth\nA U\nb readcrumbs\nco ords\nĠexplo red\nĠl od\nĠI p\ng able\nian e\nĠart ifacts\nBox Layout\nا ر\nlist ener\n.c art\nĠH uff\nĠHind u\nĠData Types\nĠDr upal\nIGN ORE\nĠoffset s\nĠR TC\n- login\næ ®\nĠQ Object\nĠprosec utor\nR ock\n_ch at\nW ay\nì ²\nĠneg lig\nĠd ude\n; <\nĠdeleg ates\n_f ailed\n/ dev\n/ work\n( New\net able\n() \"\n( Icons\nĠp ork\nĠModel AndView\nĠV IP\nĠK or\nm ix\nĠox id\nĠSC REEN\nĠFour th\n/ \",Ċ\nĠte e\nĠSte vens\nt icks\nĠp ledge\nib bon\nĠLo an\nĠne o\nn umpy\nĠShared Preferences\n- oriented\nĠLogger Factory\nĠGraph QL\nzen ia\n\" _\nW omen\n.c ast\nĠdeliber ately\n+ b\nĠAr n\nfont Size\nĠm aze\nĠbl amed\n.m as\n} )čĊ\neler ik\nĠsc anning\nĠWork shop\nĠfind en\nĠca ut\nUI Font\n( return\nal in\ncast le\n//////////////////////////////////////////////////////////////// ////////\nĠincent ive\nop ath\nb lob\nĠcigaret te\nĠfert il\n*/ ĊĊĊ\nĠSh ar\nĊ ĠĠĠĠĠĠĊ\nĠunc ertain\nĠS ton\nOper ations\nĠSp encer\nĠdef in\nĠS olo\non est\n·» åĬł\nĠu omo\nG ive\nĠdent ro\n; padding\nent ai\nĠC ars\nĠenthus iasm\nĠOper ating\nS kip\npar ation\nĠprotect s\nĠre ver\nd g\nĠC incinnati\nĠconsect etur\nĠm uss\nemploy ed\na uses\nink le\n. Values\n£ ¼\nlo v\n_W ARN\nĠbook mark\nĠAp ollo\n. axis\nĠm ét\nĠop ener\nĠtum or\nd an\nĠelement ary\nĠsk ipped\nĠK er\nas ia\n_res p\nĠdem ol\nĠCan adians\nĠt astes\nU Integer\nĠ' ${\n.aw s\nRO ID\nri ans\nM Q\nord able\nĠcous in\nProp agation\n(S ession\nph alt\nUL D\nĠSc alar\nĠblo ody\nĠ à¦\n.m ask\n, q\nĠUn its\nĠcent res\nĠPr im\n. ]ĊĊ\nĠSh aw\nP rom\nĠTh ought\nCheck er\n_output s\n( chan\nE INVAL\nĠb ob\n_c mp\nP ed\nĠmat rices\nĠvrou wen\nĠgenu inely\nhigh light\n(d isplay\n) !=\nĠdel icate\nĠL uther\nĠM iles\nĠuser ID\n% =\nate urs\n_B UF\n---- ---Ċ\nimit ives\nĠsh elves\nsl ow\n_in formation\nLE G\nW r\n.form s\ncel and\n/ un\n: &\n.âĢĻ ĊĊ\n=\" %\nĠpro st\nĠfont size\nuc ión\nget ic\nam t\n=\" .\nDec or\nB rit\nĠ\"\" ).\nĠfound ing\n.File Name\nĠT ier\nĠdisc lose\ná m\n.s yn\n.View Holder\nlic ant\n_st age\nMon day\nĠdes erialize\nt alk\nĠtradition ally\næĢ ģ\nØ ®\nLE X\nĠe h\nĉ ROM\nĠ{ })Ċ\nQuest ions\nnc py\nĠfix ing\nк Ñĥ\n_ Key\n: x\nĠSTR ING\nĠÑĦ ай\nĉ left\nĠBen ch\nell ij\nUR RED\nĠDi agram\n} catch\n/ time\nĠMiss ing\ndb name\nĠs ore\nĠW alt\nugg ing\nrep resent\nĠG S\nne ys\nĉ page\nĠvol can\n(b tn\nĠexceed s\nĠ erg\nĠpil ots\nĠS ed\ners ions\nĠpat ron\nR V\n/ top\n. asset\n_c ross\n. Editor\n.t b\nĠwel coming\nSC REEN\n) findViewById\nC oder\n \",Ċ\n_P in\nues e\nĠover rides\n_ ready\nAdv anced\nĠop i\n-c art\n(\"/ \",\nĠDe b\nCR Y\nĠVert ical\nĠO VER\nĠCorpor ate\nĠ\"\" ;\nĠste pping\ne j\nĠaccus ations\nĠor az\n_t ail\nĠindu ced\nĠel astic\nĠbl own\n, //\nĠbackground s\nâĢĻ une\n-s dk\nĠset Interval\nĠincent ives\nĠveget able\n_ On\nexp anded\np ix\n_sh ader\nĠSP DX\n@ example\nĠW rapper\n.Z ero\nPos itive\nĠsp inner\nĠinvent ed\nĠG ates\nоÑĤ оÑĢ\nĠcompar isons\nè ·\n.pr imary\ndata Provider\nadd itional\nĉ options\ns napshot\n.set Horizontal\nĠ\" {}\nĠFish er\nhal ten\n< Type\nĠmax Length\nĠM t\nĠê° Ģ\n.jet brains\nĠident ifies\nĠflow ing\nĠDisc ussion\nats by\nĠsch w\nught y\nĠr ivers\n.un ique\n_PH Y\ned ral\n( ll\nĠcs rf\npp ers\nü l\nĠEs pecially\nport ed\nĠHarr ison\n****** */Ċ\nText Color\nìĬ µ\nw ire\nĠstatus Code\nĠFin ish\nc ence\nĠMcC ain\nĠW or\n( await\nĠ) ->\nĠRegister ed\nIN ED\nk al\npar ison\nĠobj eto\nV i\nmand a\nĠrenew ed\nĠS of\ness el\n.nd array\nĠcr ap\nç® ¡\n.ab spath\n( up\nĠclear ance\nĠT W\n_C OPY\nĠĠĠĠĠĠĠĠĠĠĠĠ ĉ\nĠforest s\nĠarg uably\nĠA SS\nhe y\nam el\n_f ore\nĠSou theast\nĠab used\nĠpract icing\naked irs\nä¸ »\n_res ources\nĠp ond\n.F ixed\nLast Error\nĠPsych ology\nĠ\" //\n! :\nRe usable\nĠmens aje\nĠro spy\nĠb our\nĠvar ieties\nĠem path\n(( {\n_ org\nĠM es\nĠMag ento\nIST ORY\nUn less\nĠh j\nĠD uty\nJ un\n, size\nĠpaint ings\nĠdisp ens\nd art\nĠbehavior al\nĠr pc\ncal culate\nfr uit\n_m m\nĉp thread\nMax Length\nĠc urrencies\n_cap acity\nĠO z\nĠfire arm\nĠcoeff icient\nĠbankrupt cy\nw art\nĠfat igue\nAV A\nĠes pa\n_p c\nĠQu otes\n_L IGHT\nĠT ickets\nĠrel ates\nĠpublish ers\nĠunlock ed\nĠ// ----------------------------------------------------------------\nĠInterrupt edException\nĠout look\nr n\nĠreb els\nW ritten\nĠas ian\not to\nĠ ĉĉĉĉ\n_g pu\nT xt\n.Image View\nĠsu is\n_t ables\n.Rec yclerView\nĠwhat soever\nè ģ\n] ++;Ċ\nassert True\n_ verify\nĠR ivers\nĠ ][\nJ et\nid ian\nS ibling\nĠgen res\n.A ccess\nOP S\nĠtr ivial\nภª\nal en\nв ед\nĠS word\nĠscrut iny\n(c b\nĠcomm erce\nĠguarante es\n_ad v\nĠL ET\nrec io\nĠh ilar\nĠback yard\nãĢ ı\nĠillustr ated\n/v endor\n. Util\nĠw ow\nLO Y\nĠMar shal\n\"> '.$\nĠB ak\nĠmod ifiers\nd ictionary\nĠSt re\nm ultiple\n\")) ,\nĠC ort\n'] \").\n( admin\nĠCre ator\nInt ernet\n( ms\nlog y\nDECL ARE\nĠMarc us\n<< <<\nãģ ł\n_m y\n(in st\nĠsc iences\nND ER\n. enter\nĠit u\nĠbeh ave\nP an\nomb ies\n=' <\n')) ;čĊ\nĠM ENU\nĠWork ers\n.No Error\nĠbind ings\nĠdis abilities\n{ \\\nĠM unicip\nĠco res\nur ple\nĠN okia\nus ions\nĠF itness\n.handle Change\nĠjav ascript\nìļ Ķ\n( dec\nĠpack ing\n-de pend\nĠtrans cript\nz eros\n_ alert\n? \",Ċ\nlib s\n± оÑĤ\nĠ| ĊĊ\ntr ained\nĠG ent\nĠR ab\nx p\n_config uration\nå¤ ©\n_ accept\n.rec yclerview\n: url\nĠMu hammad\nĠprivile ges\n_b ank\nuk u\nw allet\nĠRO OT\nĠenc uent\n? family\nĉ position\nĠc g\nĠprec ip\nmethod s\n_f ast\nin crement\nĠT iger\n_OCC URRED\nqu ip\nĠH AS\n_d om\nĠw reck\nb j\nĠd ern\nĠorg ans\n. entries\nĠ_ ('\nram ento\nĠJam ie\nĠp unk\nIP P\nĠprogram a\nĠatt ain\nĠpro ves\n/s ign\nĠanswer ing\nĠl adder\n************************ ****\nĠW almart\nĠCONT ENT\nduct or\nĠver bal\nĠP ID\nc rypto\n_CALL BACK\nĠ= ================================\nĠpot ent\nĠshort s\n.U ri\n.un iform\n; border\nĠW er\nĠhere in\nll a\nĠI hr\nP ixmap\nl iteral\n! )ĊĊ\ng eneric\nr ust\n_script s\nost o\nit us\nĠCoal ition\nĠrem ot\nde ploy\nĠEag le\nãĢģ ãĢĮ\nĠimportant e\nĉ object\nĠseason al\nne j\naid u\nBind View\nĠSi erra\n-b g\nĠmake Styles\n[ offset\nG ames\nĠhorm one\nAR IO\nhead s\n( select\nĠStart ed\n@ param\n_de cl\n_b log\nĠa ño\n\\ Api\nĠMil waukee\nPro vid\nAn imated\nĠcool er\nĠSe ed\n. Edit\nÏ Ħ\nĠT aking\nĠborder Color\n-found er\n.Logger Factory\nĠ\"\" ĊĊ\nAL T\nĠL ate\nEDI ATE\nĠ);ĊĊ Ċ\naf a\nĠcancell ation\nAt om\nĠB irmingham\nemp resa\nHE MA\nasc al\nĠup side\n.V ersion\nĠF older\nĠE ight\nĠV intage\nĠApp Delegate\nĠPre vention\n.se parator\nST M\n( room\ngener ator\nĠc attle\nĉ Z\nĠPart icle\n' };Ċ\nĠneighb ours\nĠState less\nĠalt itude\nĠsa int\nоб ав\nĠconv inc\nĠCont ents\nĠje une\n(t s\nSerial ization\n(c ollection\nĠJ azz\nĠD od\nĠR och\nac io\ncomm ended\nDEF INE\n.on load\nĠspecial ty\nPL ACE\n_MO VE\nĠaccount able\nRe uters\nĠf icken\nĠde pr\nW ow\nV oid\n.s pace\nภĹ\nĠt q\nĠP ets\n< $\n(C urrent\nber ries\nplan ation\nĠlist Of\nĠTh u\nĠPR INT\nĠm ismo\nĠdo i\nch k\nĠUn icode\n( role\nĠvir gin\n< Point\n_RESP ONSE\n-h ouse\nĠVenez uela\nEM AIL\nĠp úb\n_ex ist\nB all\n.C L\nre ferences\nĠBeautiful Soup\nĉ Expect\nTH IS\nÑĥ д\nb ane\nĠtemp oral\nER IC\net as\nĠrefresh ing\nĠsec ular\n@ synthesize\nac cur\nĠn ella\nĠS OL\n.p ipe\nCh annels\nèĩ ª\nĠinsert ion\ná» ĭ\nel ia\nĠadjust able\nCan ada\nĠI TEM\nĠcur ves\nĠChe ap\nlet ing\nĠoptim istic\nal lo\nĠpolit ician\n_down load\n= edge\nORT H\nĠmodel o\nart o\n. rotate\nĠs elenium\næĪ ij\n_al ias\nĠrenown ed\n.' .\nĠc zy\nĠal les\n.Com piler\nĠB ass\nConn ector\n.R ole\nL INK\nĠc riterion\nlem etry\nSuccess fully\n/p ng\nĠey eb\nasp berry\n( gr\nĠd angers\nĠcorrect ed\nĠgl ow\nĠelabor ate\nĠB ears\naw ai\n=\" '+\nĠpromot ions\nĠmathematic al\nĠ\" `\n_Generic Class\nĠChe f\n.S ort\ntable Name\nR IC\nĠvolunt ary\nĠBl ade\n-e lect\nĠCom bat\nĠAb ility\nĠab dom\nĠd uck\nT mp\nåħ ¨\nĠer ase\n.P h\nĠDefault s\np artment\n_US B\nê te\n; '\nĠp ads\nĠOb amacare\n.T otal\nĠdiv ert\nĠcr icket\nĠrecre ational\n( red\nĠC le\nR U\nĠmist aken\nĠMont ana\nĠstr ive\n_sl ider\nĠPl astic\nĠdecor ated\nĠV P\nlic o\nĉf alse\nĠpre fs\n( \\\"\n_f alse\ni endo\nĠ@ $\nB ucket\nact ical\nĠZ hang\n.c ols\n.B inding\nĠw ax\n_ST ORAGE\nĠlaw n\nĠr f\n.Sc ene\nĠCal culator\n.d esign\nĠres il\nл ем\nE mploy\nĠPr ices\nĠP WM\nag i\n.e valuate\nĉ param\nĠbr ass\nbb en\nĠinflamm ation\null ivan\nĠan not\nĠp H\niam eter\nĠB TC\n( box\nStory board\nĠcl ay\n.assert Raises\n| string\n.App ly\nĠmatch er\nund ed\nĠsatisf ying\nĠìł ķ\nRender ing\n_app ro\nind rome\nAN EL\n_f ix\nbr ush\n.M atch\nĠsm iling\non aut\nS unday\nĠdelet ion\nĠencour ages\nP ull\nĠreven ge\nĠqu arry\ntr ade\nĠc ables\n(d elta\nites pace\nĠf h\n.b unifu\nĠvi el\n_IN CLUDED\nĠT ail\nad ar\nof s\nĠmet als\ng om\n_method s\nĠn j\n.St d\n(w in\n$ ('\nĠt urtle\nur on\nĠen rolled\nĠH z\nĠBox Decoration\nĠp ont\nrel ationship\nB i\n³ »\nĠmas cul\nĠsh ades\nĠv r\nĠLog ic\nĠa in\nĠD IST\nĠcoll ar\n\" profile\nGenerated Value\nĠP ossible\nĠe ines\nĥ ģ\n.time out\nĠE c\nĠjer sey\n.D ouble\nĠqual ifying\nv or\nCRE EN\n_A pp\n_rec v\nĠali ens\nIt s\nE sc\ni ator\nĠE clipse\nĠg h\nV ict\nĉ html\nto o\n. const\nĠant erior\nĠW u\n(key s\nĠul tr\n_p oly\nĠT ap\nĠB ud\nA WS\nĠcrash es\n_t ot\nCont in\n-h anded\nalth ough\nภļ\nific ent\nĠde ve\nut ory\nĠW orth\n_M S\nĠfloor ing\nĠsell ers\nĠThank sgiving\nĠp ng\nĠval ores\nĠslee ve\nĠfil le\nÐ IJ\nĠappoint ments\nĠv im\nUser Info\nBO OST\nĠpos ed\ninitial ized\n.product s\nĠLeaders hip\nman uel\n' %\nem arks\nPer centage\n(d ist\n. avatar\n(h Object\nä» Ĭ\n_ iff\nic one\n; )\n_n il\nĠab ol\nе ÑģÑĤ\nĠven ues\n.Con vert\n! ')Ċ\n.B itmap\nsk in\n_C OLUMN\nRe v\nG RESS\ng ow\nĠw ished\ntract s\n.assert False\nĠscreens hot\nĠfo is\nCom b\nLine Width\nĠGr ab\nĠint ensive\nĉ sh\n+ )\n.first Name\n_PRO CESS\nĠt ilt\nit ored\n.L OG\nĠb ak\nĠintention ally\n.play ers\n(c anvas\n)) )čĊ\n.Pro vider\n_P UBLIC\nT alk\nĠL iv\nched ulers\nĠl c\nad ic\nfeature d\n.res ources\nFull Name\nĠmean while\nB uffers\nĠres olver\nĠS AP\n_T E\nG NU\nĠForms Module\n_ wh\nĠS we\n.widget s\nĠcabin ets\nĠsus cept\nĠB ott\nactiv ex\nav ar\nant ics\nĠ\" =\"\n_k wargs\nĠgame Object\nĠAng le\n.I ter\nmar sh\nĠB irthday\nĠC MS\nrequest s\nĠPear l\n_E OL\nĠlin ux\n( org\n_M ouse\n.con structor\nĠz d\nĠk icks\nart isan\nĠe ax\nK n\npon ge\nĠFin land\nĠmet res\nĠAss essment\npart ner\n/ pre\n! ',Ċ\n[ Int\nĠos lo\ndate picker\n/ String\nop lay\nĠHe brew\n, double\nĠtrab al\n+\" \\\nĉ EIF\n/ text\n_F IRST\nĠP ete\nĠe go\nĠextr as\nP DO\nĠreg ulate\nĠQ Widget\nst s\nĠSh ows\nĠN HS\n.c ourse\np thread\nĠF uel\n.t imes\nĠ °\nĠstr ides\n($ ('#\n( words\nĠrhyth m\nĠsp ont\nĠsens ation\nĠsp ike\nC losing\n页 éĿ¢\nN umeric\nĠbreat he\nĠfin ale\n_F ACT\nin ion\nĠch ill\nĠform ally\nANG ED\nĠ' :'\nĠпÑĢ и\na q\nĠFab ric\n(l at\nĠPr incipal\nĠer ro\noc ale\nN om\nĠf ost\n_C USTOM\n.int ellij\nert ools\nĠcl asse\nadi ents\nĠfundra ising\nEN E\n_OPTION S\n_ ob\n// }Ċ\nĠprote ctions\n.se ed\nN V\nterm inal\n;; ;\nP redicate\nĠì ¶\nĠbomb ing\nG F\nĠch ew\n)) ).\nqual ified\n] ={\nlist en\nC ENT\nd igest\nE ast\nĠd iver\nĠend points\nĠe e\nĠcolle ague\nĠdissert ation\n_com mit\n_D AT\n. rc\nĠbre asts\nĠR ug\nĠP il\nContract s\nĠBry an\nWeb View\nĠconcent rate\nĠIn ner\nĠ' |\nstd out\n_S ub\n> -->Ċ\nV ol\nĠS SD\n)) ),\n. Optional\nĠnurs es\nĠor b\n_ pe\n);čĊ čĊčĊ\npl aced\ness er\nĠther apeutic\nĠwhites pace\nĠa ston\nSuccess ful\nĠpr aised\nĠW es\nĠe ighth\nir al\nĠvrou w\nĠf action\n_b ias\nĠw itch\nĠnp c\n(s b\nĠRod rig\n_b ig\nDep endency\nĠAb raham\nard i\nC AR\nn os\nĠabund ance\nĠnut rients\nin stein\n.V ert\nĠI SS\n< U\nĠsum s\n_h ist\nĠfar mer\nĠA br\nSh ot\nĠBad Request\nĠh ass\nĠR ails\nĠaffili ated\næĿ ¥\nĠer f\nIN F\nĠView Holder\nmin i\nĠR oth\nĠfaith ful\nĠPhill ips\nAND OM\n]. [\n_P AY\nĠAr ctic\nf aker\nD igit\nM ale\nstd err\nse ys\nĠ Å¡\n_rem ote\nli que\nĠin def\nĠIndust ries\nit ra\n_p airs\n< iostream\nĠsal aries\nik en\n.F rame\nPL IC\n_S PEC\nĠMed iterr\nĠsystem atic\nĠinter rog\nIcon Button\nse a\nint ro\nĠIss ues\nenc rypted\nĠintern ationally\nĠsn printf\nĠpast a\nĠBrad ley\n_ Status\nAL K\n_P AD\n.l aunch\n< select\nĠhar dest\nĠph y\nĠ(( *\n-s lide\nĠNob ody\nS u\nĠas ÃŃ\nclose st\n_initial izer\nĠsupport er\n-g en\nĠt ales\nĠcor p\n_f u\ns at\nne ighbor\n.M igrations\nĠal gun\nĠsin on\n.S pec\n? ,Ċ\n.G L\nm ale\nĠmon itors\nyl an\n-L icense\n.m atches\nĠA BS\nĠM ast\nĠW allet\n($ (\"#\nDir ty\nĠco pe\nĠinterpol ation\nous ed\nĠJ ets\n.F LAG\n.C ancel\n.Event s\nne ver\nĠM Hz\n> D\nĠs ervlet\nbast ian\nĠ> &\nS ID\n_cl k\nĠdiv isions\n} ',Ċ\nĠd ildo\nĠpar ade\nm ajor\nĠab oard\n; ++\nĠf usion\n\"}, {\"\nĠDialog Result\nĉ arr\n- em\n_n r\n(h andler\n.N ET\n.Xtra Reports\nĠSh ah\nĠB rief\n- ,\nĠprec io\nĉĉĉ ĠĠĠĠĠĠ\nĠt ant\nĠGrand e\n/ xml\n_IC ON\nĠR etro\nun que\nĠn ag\nto Fixed\nX L\nĠdecl aring\nĠCon crete\nĠAm azing\nĉprint k\nĠdeb ates\nD ATED\nĠaest hetic\nemet ery\nRouting Module\nĠNash ville\nW AYS\nĠw olf\nĠobserv ers\nOT A\nans on\nĠe a\nĠgreen house\nĵį ä½ľ\nĠst air\nĠimmigr ant\n_app ly\npe are\nĠBloom berg\n_PL AYER\nRes p\næŃ £\nCho oser\nĠI Collection\nP eter\nEr ro\n.detect Changes\nMap s\nĠs queeze\nĠHom es\nweg ian\nĠformat ting\nĠnegot iate\nul d\nĠN ep\nĠQ B\nĠeconom ies\nĠ*/ ,\nĠredu nd\nĠA ber\n.IsNullOr WhiteSpace\nyc led\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ\n_S h\nĠske pt\nĠre created\nĠget Type\nĠmarg ins\nĠcolon ial\nch arts\n// @\nĠprocess ors\nè¯ ´\nb atis\næĦ ı\nator io\nmention ed\nP atient\nĠpre y\nCheck box\n_x path\n.s kip\nĠMorm on\nĠMemory Stream\nCRE MENT\nĠk u\nm eld\n\\ Data\nĠK ernel\nil tr\néĢ ģ\n( profile\nCar bon\nRO LE\n( pl\n] *(\n.m emory\nĠmed al\nĠadvis or\nit ät\nĠh dr\nier ung\nĠProvid es\n( alpha\nĠteen agers\n- parser\n.L atLng\n] ()Ċ\nĠfel ony\nĉĉĉĊ ĉĉĉĊ\nBO OK\nĠsl ash\nĠclear fix\nĠPro phet\nå® ¹\nright ness\n-f i\n.k ind\nert on\nJ im\nĠmanip ulate\nĠworks heet\nol in\nst ars\nĠart ifact\n_EM PTY\nĉm ain\n------------- ' ;\nĠexpress ing\nĠI Q\nĠF act\n/************************************************************************ *******Ċ\n_m ass\n)) :\nĠcon dom\nĠcreate State\nomet own\nĠir r\nĠ> (\n> B\niter ation\nãĥ ª\nĠshirt s\nount y\n-> $\n_S IGN\nĠD ale\nĠj j\nE asy\nF re\nĠN y\nĠch lor\nmatch ed\nĠG erm\n- UA\nĠN athan\neduc ation\n-y ard\n- che\nh ouses\nr itional\nĠprox imity\nĠdies em\náºŃ p\nĠd rought\n.a udio\nĠLe o\nĠfavor able\nin ch\nĠD aw\nrib ly\n_st udent\nid able\nO VE\nĠlack s\nounc ing\n.b usiness\nĠre open\nmay be\n_G LOBAL\nĠdress es\nĠEd wards\nens ible\nĠHard ware\nĠEx cellent\nĠTime Unit\nCTION S\nĠsched ules\nĠseg ue\nOp ens\nam men\n- Identifier\nĠst aring\nĠhapp ily\nĠH ob\n' _\nĠ\" );\nament os\net ched\nĠ/> }Ċ\n. Users\nĠinterrupt ed\nContact s\nĠreg istro\nin burgh\nCH A\n_ imp\nph is\ns ay\nĠretail er\n.N ODE\n/ maps\n_L AST\nĠCh arge\n_g uard\nColl ider\nĠStateless Widget\n\": [\"\n(\" ../../\niox ide\nĠS und\nĠ'' ;\nun set\nadd Widget\nл Ñİ\nel les\nalk er\nA rc\nĠded uct\nG UILayout\nĠV illa\nĠfor bidden\n_ where\nĠ\\ /\nĠT ib\n_A X\n] čĊčĊ\nĠB ir\nĠb end\nĠMA KE\nĠM ET\nĠfut ures\nĠweight ed\n\"\" \"čĊ\nĠauthor ize\n(pro gram\n}, {\"\nĠcoeff icients\nê s\nPer Page\nĠBath room\nĠPublish ing\nG PL\nĠsub missions\nĠNUM BER\nj Äħ\nĠaddition ally\nem pre\nĠSh el\not yp\nS olution\nĠth under\n_ ec\nĠĊ ĠĠĠĠĊ\nĠF ellow\nĠk ay\nĠnew State\nONT AL\nIm plementation\n.L ook\nĠ ents\nĠl ors\nĠB IG\nf ab\nĠaver aged\nĠFe edback\nĠW ells\nĠm artial\nĠind ul\nĠComm unist\nĠFore x\nĠAgricult ure\n\" [\nĠqu ar\nĠK ont\nĉ view\n. Bytes\ndes ktop\nĠM akes\nakes peare\n.Null able\nĠspot light\nV B\now y\n(t orch\ntr idge\n_b ounds\nĠapolog ize\n.add Item\nant d\n* );Ċ\n, u\n(g en\nç» ĵ\nre ator\nĠC ord\nou pper\n.m etro\nĠ ew\nĠW ORD\n.A fter\nĠdet ained\nĠHam mer\nex isting\nĠo st\nĠmon ument\n-c ustom\nUser ID\nĠN om\nĠre jection\n(d im\nĠsingle ton\nĉd ie\nari ance\nre ports\n] !=\neld a\nĠpreval ence\n_reg s\n.\" .\nĠfemin ist\nCode c\nĠ **Ċ\n(label s\n_M ARK\nFA ILED\nĠadminister ed\nW N\nĠĠĠĠĠĠĠĠ ĉĉ\nĠn oun\nw ig\nĠg otta\nĠr if\n- im\nĠPaul o\nĠCommand Type\n] ))ĊĊ\n-z ero\nTr aining\nĠl ord\n_ art\nre ddit\nC ert\nĠpes o\nR ot\nĠend anger\n.d r\nuser Info\nun ts\nn v\nĠTrail er\n-f irst\n(m ake\nĠbenef ici\n-bl ack\ni ÃŁ\nĠund oubtedly\nĠm ex\nĠAnc ient\n( as\nĠdes cent\nP ick\nĠrep lica\n$ obj\nä hr\nĠar rows\nft y\nĠLib ya\nug a\ncharg ed\nT ur\nĠh omic\niss en\nĠF ake\nĠbe ers\nĠsc attered\n( Time\nUT IL\nĠbureauc r\n/pl ain\nĠstick ing\nFA IL\nĠC ovid\nTh ird\n_p resent\nĠPier re\nĠë ª\nĠ[... ]ĊĊ\nPro b\nĠTra ffic\nica o\ndo ctor\nĠ), ĊĊ\nT abs\nal u\nï¼ļ âĢľ\nĠinher ent\n_N o\nrit is\nĠPro of\n.b asename\nä¼ ļ\nĠch im\nĠProt ected\nc rit\nĠpr one\nĠк он\nĠHero es\nĠan xious\nĠan os\nĠweek ends\nĠs ext\nĠredu cer\n= UTF\nh alf\nĠS aw\n.m m\nĠnue va\n.current Target\n.l ua\n_EXT ENSION\nĉ reg\nĠC trl\n_ align\naccept able\nĠrush ing\nfr ac\nĠbo asts\nF ive\n ±\nĠTem perature\n> ):\nĠchar ter\nRE ATED\nĠsubject ed\nĠop c\nhealth y\n使 çĶ¨\nĠScient ific\nĠfra u\nri ages\nภĶ\n.in ventory\nation ale\nM ad\nmin utes\n>> ();Ċ\nĠEn v\nĠrecord ings\nĠsusp icion\nsql ite\nĉ read\nãģ ¦\nĠwor ries\n.put String\nĠSh anghai\n( uid\nr er\nĠvÃŃ de\n\") :\nĠmethod ology\nĠк оÑĤоÑĢ\ncc c\nav ad\nĠindu ction\nĉ Thread\n, string\nạ i\nneh men\nu ition\nĠ* __\n.em f\nĠì ľ\n/th emes\nĠN ine\n. One\nĠEm bed\nĠf az\nu ations\nĠpriv ately\nĠl ing\n[ F\nush i\nĠlaunch es\n( KEY\nG MT\nĠaim ing\npat ible\nĠB iden\ni w\nĠD egree\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ\nĠ$ ('<\ná rios\nto UpperCase\nìł ľ\nĠE UR\nĠovers ight\nĠtable sp\nUp dates\n.m akedirs\nĠhum idity\n/ template\nAl ways\n( IS\n_c ert\nD ig\nĠunder way\nort on\nĠHur ricane\nĠsp ends\nĠSeg ment\nĠfl ies\nĠT oggle\nĠLyn ch\nĠs enses\nĠK os\nset Enabled\nist ically\nĠtest er\nĠadministr ators\nĠtag ged\nÐ ĵ\nĠshort cut\nĠRes olution\nĠsuperv ision\nĠAsh ley\nTr acking\nul atory\nand el\nist en\nĠun re\n(d iff\nANT S\nĠr ider\nĠs Äħ\n.S eries\n_ orders\nORIZ ONTAL\nĠret ention\nãĢĤ čĊčĊ\nĠdi agonal\nĠC ancellationToken\n_ Internal\nĠru in\n.Q t\nocr atic\nT el\nĠAn swers\nm atic\nĠx p\nat em\n_j obs\n_ any\nĠsen iors\nĠland mark\nĠQ List\nĠman eu\not ify\n/ \";Ċ\n/ server\nĠPhil osoph\nuten ant\n( io\nh z\nĠauthentic ated\nd v\n- Compatible\nOrigin ally\n, function\nãĢĤ čĊ\nĠRepresent ative\nas ily\nirc uit\n.d t\n(m ath\n.M arshal\n[ ,\nĠC ities\n_ turn\n| )Ċ\nĠcant idad\nal ter\nĉ ui\nĠNe braska\nĠsk irt\n.b g\nShared Preferences\n( style\nĠg rief\ng ew\nĠsaf eg\nol ang\n_l ists\nì Ľ\nĠgran ite\nĠhott est\n.j dbc\n.C ustomer\nĠâī ¤\nĠwa ar\n_sc ene\n+' /\nĠJ TextField\nĠse ating\nĠwe ars\nĠ` /\nC ases\nĠY outube\nı m\nĠbal con\n, G\nMeta Data\n- price\nSC R\nUn ity\nĠtr unk\n={` ${\nĠearthqu ake\nPart ial\nĠsub st\nĠelim in\n=\" '.\n//* [@\nĠsuperv isor\nvro let\n_ article\nĠp ane\nb io\nĠmot ors\nN M\nF rank\nĠon ion\n- word\nItem ClickListener\nĠb rit\nend encies\nCom puter\n_r unning\n( day\n- he\n(n amed\nĠS ach\nо Ñĩ\nc ampaign\n.Ab stract\n(w rapper\n.p ay\nĠu w\nGe o\nr ails\n/ select\nicht e\nson s\nE VENT\nĠal iment\nPro viders\nA wait\n_INTER VAL\n. off\nĠgl uten\n_cl oud\nĠw en\n.ex tract\nĉ button\n/ MM\nPart y\nĠdem ographic\n_err no\nĠh iking\n(' ')Ċ\n\", @\"\nĠw it\nr á\nolog ie\nĠSt yles\nĠBrowser Module\n.Request Mapping\nic ans\nP AGE\ncre ation\nĠF erguson\nud ed\nnum bers\nĠGT K\nĠpresent ations\nĠB obby\n_s pan\nest yle\nĠilleg ally\nabel a\nĠbattle field\ncap acity\nter ror\n] \");Ċ\nĠwar rior\nle ader\nĠDB G\nĠRe venue\nĠvig il\nĠcounter parts\n( Error\nACT ER\nĠhe eft\nĠselection s\nze ug\nt om\n-t wo\n. ;Ċ\n_st atement\nĠA id\nĠV ul\n_r gb\nĠpr izes\nĠedit able\nĉ form\nın ı\n.de cor\nD emo\nlic es\nĠen ctype\nrat ulations\nĠR OS\n_ch ars\nĠJ ahr\npart ial\nÑĥ ÑĤ\nĠRe ceive\nĠL ands\nAP TER\nĠch opped\n.. \"\nĠAn aly\nĠU ID\nĠR adeon\nĠB ee\nĠun m\n> M\n.find all\nToken izer\nĠWH AT\nĠs j\nD rawing\nE ss\nON D\nĬ ¶\n(p acket\nâĢĶ but\nInv ocation\nĠN uclear\n? ;Ċ\nĠgrand es\nĠC rypt\nrem ark\nĠ'../../ ../../\nĠin ability\nm agic\nc ats\nĠsim ulate\n: ${\nin flate\nĠen er\n: NO\nip les\nĠmer it\nĠR ated\nĠgl ue\n/b log\nĠg ren\nĠthr illed\n.C H\nunc an\nĠPR IMARY\nĠper sec\nĠfe ared\n.M IN\nĠThe ater\né Ĵ\nategor ie\næ® µ\nĠappet ite\ns quare\nĠAlex and\n.User Id\n_g t\n_ enter\nĠgradu ates\nFragment Manager\nAuthor ize\n-N LS\n(M y\nĠtri umph\nust ing\n_PARAM S\nChar acters\n(: ,:,\n_B UILD\nM Hz\nĠwash ed\nĠun cle\nSte ve\nard own\n ${\n_confirm ation\nĠtro phy\nWork s\nĠElect ronics\nĠMediterr anean\n_m etrics\nĠannounc ing\nĠD AY\n_pro to\nĠp ear\nbase Url\nĉĉĉĉĉĉĉĉ Ċ\nĠcoord ination\n: N\n.an imate\nĠC otton\n_h it\nâ ľ\nĠjet zt\nif ter\n(f ields\nown load\nific acion\n.c uda\nĠLi u\n> equals\nĠA ce\nÑĢаР¼\nĠSuper man\nĠGarc ia\nĠarrest s\nag ar\nĠ{} )\nĠmac ros\nrou pe\nê tre\nĠtw isted\nstr uments\n_ (\"\n_ vertices\nĠTrans ition\nи к\n[ max\nm ind\nĠaccess Token\nĠun le\nm us\nc op\nĠF actor\nĠcon ced\nĠre tr\n.l inalg\n-s lider\nob l\n_Static Fields\nĠz ombie\ns elling\nĠch ap\nĠsh aking\nĠTrans late\nĠAm sterdam\nĠE TH\n_EX TERN\nk d\n_d isc\nĠpreced ing\nĠpri x\nObject Name\n_mod ified\nard ware\nĠ?> \">\nĠD W\n` ${\nĠ?> \">ĊĊ\nĠspin ning\n_p ending\nMatch ers\n. Keys\nĠP V\nen us\nant is\nĠdisc ard\nĠh aul\nĠem pir\nĠpath way\nĠo ak\nм ен\n-ind uced\nĠimp air\nĠCal gary\n.is Hidden\nd z\n_ include\nĠg m\nĠ' ('\nP Y\nuggest ions\nĠcommod ity\nc ro\n/ sub\nĠget Instance\nĠLeg acy\nĠK il\nB al\n( short\nIn form\n+ x\n* r\nĠHope fully\nor ate\nĠmach en\nĠtreat y\nĠO ri\n.p ublic\n-h orizontal\nĠtact ic\nĠb ord\nw ares\nĠam mo\nĠL ists\nĠequ ations\n/ her\nĠNS W\nB ounding\n_C ollections\nĠav ail\n.Drop Down\nè °\nĠh h\nĠl Ãł\n.p b\nĠmemor ial\nĠAT TR\nĠexhaust ed\nĠt sp\nĉ redirect\nĠlik ewise\nST ER\nL java\nĠcondem ned\noca ust\n(str ict\nĠexem pt\nĠs ms\nĠex agger\nS YS\nĠl ounge\n: ^\nĠto dd\nde b\nator ial\nĠPort er\nĠtu ition\nĠexem pl\nĠp aren\n.line To\nĠkid ney\nĠç a\nĠc ui\nï¼Į 请\nX C\nĠmo ż\nĠnomin ated\nl ung\nIm Gui\nĠB uzz\nĠstere o\nport al\nres as\nĠk lass\nĠdraft ed\nĠproject ile\n/g pl\n(param eters\n* )Ċ\nĠassist ed\nĠNS Integer\ns itemap\n:n th\n.View s\n.Argument Parser\nĠme er\nz ier\nĠD ig\nĊ\nĠpl ag\np ine\nĠblank et\nĠ: -\nĠl cd\n------------ ---\n(\" \"\nĠtact ical\nĠRon ald\nex tr\nĠF est\nĠf uer\n-n avigation\nĠk b\ngh ost\nĠhandle Change\n_cl s\n() !=\nCom parator\n.v m\nĠCo x\n_re view\n/ @\n_c ookie\nĠrecogn ised\nld ap\nThread s\nĠSex ual\nĠB earing\n(S QL\nĠx r\nĠth igh\nURL Connection\nĠSU V\nĠm Context\nĠinc idence\nĠE ste\n.s up\n_t e\n(EX IT\nC MD\n/ \">\nAl most\nĠU ne\nĠand eren\nĠSingle ton\nĠb ore\nTh ink\nĠn arc\n] initWith\n_sh op\n(str ategy\n! ',\nher its\nĠDes k\n_m achine\n.net ty\nı nda\n= <\nĠQ R\nĠS idebar\n.split Container\nĠon Success\nĠmon key\nEn joy\n(n odes\npect rum\nĠ(* (\nĉU INT\n, height\nĠNetwork s\n.t ail\n.l inspace\nĠ\" ...\nList en\nÆ ¡\n.Ch annel\n- defined\nRe peat\nad just\nER M\n_ application\n.assert NotNull\n- stream\nĠr abbit\nĠposition ing\nĠw oke\nĠf ing\nĠmulti player\nĠregister ing\nun til\nÃ¥ n\n( ::\nuss ions\nĠpot ato\nĠE quals\n.S up\n/ap ache\nĠ( =\n. \")\n.p tr\nĠSpe ech\n.cl ip\nĠGab riel\nĠmusic ian\n/ issues\n.sh op\nĠH ier\n_RE T\n_b ucket\nãĥ ¡\nav s\nĠro z\nfl ower\nWrite Barrier\nĠMil an\nĠlegisl ature\nĠD oll\nĠprov ing\n.concat enate\nâķ IJ\nĠg char\ncdn js\nb les\nĠList ing\nл о\n.xr Label\nĠS ak\njust ice\nĠVal entine\nun less\nĠp iger\n(r un\nĠtest ified\nAN A\nĠRem oves\n)) ));Ċ\nrec ated\nĠRuntime Method\nĠcon qu\nãĤ ¢\nĠt issues\nail er\nét é\n- Star\nĠfl ames\n.set Icon\nĠsup ern\nĠvag ina\n- variable\nĠwell ness\nC UR\nĠbel le\n.get Request\nĠp oco\nben h\nag ens\nĠsp 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Visibility\nĠenter prises\n.Set up\n(sc ene\nĠRe active\nur ge\nb w\n.P ut\np ersist\n.c ookie\nĠAud i\n` s\nsup plier\n( Form\n ¡\n_s o\nĮ Ģ\nĠLeg ion\nt te\nN d\nL oss\n( attrs\n.sc atter\nĠg room\nĠgl impse\nĠn ails\nĠcum ulative\nĠf azer\n_s ervices\n.N um\nib ilit\n_res olution\nĠT x\numin ium\nop a\n.s chedule\nsm tp\nภķ\nur ry\nü k\ngo og\n_sign ature\n.int o\nĠSte ps\nĠhome owners\nĠNS URL\nĠP AC\nĠĠĠĠĠĠĠĠĠĠĠĠ ĊĊ\n> ')Ċ\nen h\nĠinc ap\n$ MESS\nĠmo ins\nĠF i\nĠoff season\npress ions\n> .Ċ\nĠGr ass\nĠGo al\n_p df\nHand lers\nĠstack s\n.get FullYear\n=[ ];Ċ\nè½ ¦\n, V\n(s plit\nÑĥн к\nĠbake ca\nĠ~ /.\npe z\nt ails\nĠG len\nĠset Image\nĠCom ic\nB LOCK\nĉ This\no ader\nĠcapital ist\n_ST EP\n( Boolean\nĠCor rect\nr ina\nĠconc aten\nå® ŀ\n() :ĊĊ\nĠun anim\nll i\nal ars\n- ne\nĠdiv or\nĠKick starter\n]. _\n< number\n/m enu\nGR APH\nvis itor\nĠimpro per\n_N EXT\nĠb isa\nbackground Color\n/ input\nĠmo i\nGo al\nli qu\nĠmiscon duct\nĠcompr ises\naw ns\nĠP ie\nra is\nrole um\nĠcur se\ny u\n_p oll\n.current User\nES H\n]) [\nĠstory t\n)? ;Ċ\n* =\nĠB urg\n/ layout\n_back end\n; ?> * '+\nåĿ Ģ\nac ency\n( URL\n_h alf\n= l\nĠlist View\n( section\n.to Array\n+ /\nĠRodrig uez\nist ream\nĠelig ibility\n:: -\n.new Instance\nP B\nĠAs sets\nĠCom posite\nĠL abs\nĠHam as\n++ );Ċ\nĠbl k\nĠNe o\nL uc\n@ login\nĠun aware\n.m et\n_RE LEASE\n( ST\nAM IL\nri ke\nĠ( ){Ċ\n(s printf\nĠAccount s\nĠV IEW\nĠA j\nãĤ °\nĠwh isk\nĠid i\nĠro de\nĠih n\nĠElement ary\nQ ty\nĠintrig uing\nĠå ¤\nJ obs\nĉ offset\nĠAh med\nĠTal iban\nĠè İ·åıĸ\nĠinject ed\n.Auth entication\n_line ar\n.Dec imal\nĠapp les\nĠshare holders\nĠb aked\n.d iff\nĠE ddie\nok ers\nĠconfront ed\nvo ices\nĠt us\nĠSp in\nN ODE\n_ Un\nCT X\n/g oogle\nTem perature\nĠ' ').\nĠmagn ificent\nĠstart Index\nsemb les\nAny one\nz k\neh en\nĠD ame\n. strict\nĠrepl aces\nĠline back\nĠpush es\nĠche ek\nĠSh i\n_BY TES\nRE A\nả n\n_CON NECTION\nG ateway\nĠTr avis\nĠA X\nĠBas ically\nĠUp grade\nà ª\nth emes\nerm o\nk or\nF emale\n_att ach\nĠìĤ¬ ìļ©\nĠpo z\n============ ==Ċ\n(s ymbol\nĠS ector\n__ )ĊĊ\n_p adding\nï¼ļ \"\nĠf abs\nĠr anged\nset Name\nĠp error\nâ Ĺ\nĠFile Reader\nĠful filled\n_C urrent\nĠdom inate\nĠsm ugg\nPost Mapping\n_for ce\nĠb loc\nĠG iant\n(v ideo\nĠC U\nSystem Service\nĠ elf\nĠkont akt\në ª\nke es\ngt k\nĠparam Int\nĠmark up\nu ales\nĠaccount ed\nĠgang bang\nRY PT\nĠW rong\nĠcred ited\nĠM ESSAGE\nĠfl aws\nĠbb w\nĠmetab olic\nĠO EM\n/ event\n(C ollectors\nmont on\nap pear\nĠopt ed\nĠche at\nĠd av\nĠPro ceed\nĠê ¸\nank ed\nи з\nans k\nĠH ang\nĠC ler\nĠdis gu\nĠc map\n.cl js\nĠa ument\nle z\nĠJo ined\n_re ceived\nĠa erial\not el\nĠgre et\n\" s\nĠGen esis\nĠCal if\npan ion\nĠtail ored\nm apping\nand Expect\n.tr ack\nat omy\nĠO w\null ah\n.Y es\nĠSimple Name\ndb h\n' en\nĠnons ense\nĠphilosoph ical\n(get Context\nĠis so\nĠA CE\nstart Date\nĠb ÄĻd\nĠAUTH OR\nĠGlo be\nĠinsect s\n_A l\nush ing\nè® °\n/ Home\nĠLocal Date\nneed ed\nhes ive\nĠill usion\näº Į\nĠtr at\nx o\n/d etail\n_M ATCH\nĠbroad band\nĠw al\nĠIllegal StateException\nIRE CTION\nĠnor theast\nes ium\nĠClient e\nul ance\nnt y\nĠt ecn\nDev ices\nĠgr ains\nĠO g\nĠS EL\nud iant\nĠ++ ;Ċ\nĠexplan ations\noc co\nĠdi ets\nĠco hort\n( controller\n.Iter ator\n-r ich\nro cess\nG D\nĠcar bohydr\nĠfri ed\nĠEmploy ment\nìŀ ¥\nĠLeon ard\n_ ${\nqu ares\nĠcompan ions\nĠpar is\nĠstim ulation\nĠZ oo\nĠre levance\nĠCol our\nĠspe ar\not ional\nĠL ite\nĠK osten\nĠà ³\n_att achment\norph ic\nĠdam it\nĠd lg\nĠthr ive\nCH ANGE\nĠApp arently\nĠat ual\nĠroot ed\n( images\naw i\nari at\nĠch erry\nSTAT IC\nm nt\nĠUser Id\nil let\nĠHis panic\nĠn ak\nĠcent ro\nĠdim s\n_initial ize\nı k\nĠCent ers\nRE N\nĠevolution ary\nĠTop ics\n_d amage\nem er\nĠr und\nĠpun ished\nĠcub ic\nf air\n[] ;ĊĊ\nĠinstant iate\nĠover see\n- delete\nunte er\nstart Time\nĠP ipeline\n_G AME\nĠC ir\nĉ Null\n.Format ting\nuc umber\nĠR ide\nĠz oo\nĠcheck er\nåIJ Į\n= C\nĠg rit\n\"); //\n_x y\nĠDe claration\nĠcall able\nF oo\nĠList Item\nĠin accur\nml in\nĉ Data\nĠev olving\naw an\nĠca fe\nfol k\n_ID X\nĠAny thing\nĠPalest ine\nĠGrid View\nĠcol ony\nĠGerm ans\n( +\n.p id\n.js x\nĠSuper ior\nChrist ian\nĠL ect\nĉ Game\nĠinstrument al\nAnim ations\nд ал\nĠMos es\nĉĉčĊ ĉĉčĊ\nz s\nk te\nä¸ ļ\n_D IST\nbit map\nd B\nĠp ersistence\nÑĢ оÑģ\n$ l\nB ron\nĠ{ |\n_ch art\nĠCon sum\nĠh emp\nĠ\" ))Ċ\nĠattack ers\nĠknowledge able\nĠc et\nĠvir uses\n' I\nĠpitch er\nĠsweep ing\n= list\napt ops\n.de pth\nĠinstruct ed\nĠR us\nbenh avn\nĠи н\nS ports\nĠon set\næĿ ĥ\n. RED\n_s i\nĠP ST\n.on Change\n> tag\nĠR oh\n_char acter\nĠLaw s\nĠB achelor\n_s wap\n.re activex\nĠreward ing\nMed ium\n- [\nĠRec ently\nJ oint\npart ition\nĠMin utes\nĠind o\nĠabsor bed\nĠG N\n_IN D\nĠsab er\nSp awn\noutput s\nĠJeff rey\nĠmed ieval\nh ed\nGu ide\nĠpsy cho\nĠgl am\nE lim\näd chen\n_pl ain\nĠS au\n-f our\nĠanaly zing\nQU ERY\nĠtom ato\n_button s\nV EN\n.set Status\n. Url\n+ ĊĊ\nĠcompl aining\ndeg ree\nconf irmed\nĠsub t\np arsed\nĠtor que\nĠtroub led\nĠT ARGET\nĠtrad emarks\nĠCo ordinate\nĠV iv\nĠ// }ĊĊ\nĠapr ès\n.get Position\n(Key Code\nĠSil va\nĠmet eor\nĠendorse ment\nOver view\nĠP oss\n.In ject\nĠeven ly\nĠvisual ization\nĠw char\nĠH DMI\nĠfun ct\nick name\n',' ','\nĠfor wards\nManaged Object\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ\nĉ server\nĠOut look\nĠChron icle\nĠdub bed\nĠd ok\nĠW ear\n.A L\npare n\n. Interface\nInter faces\n.c od\nĠd ib\n.Global ization\nĠAcad emic\nĠass ms\nAut om\nĠl w\nĠN W\nĠ&& čĊ\nĠproble ma\nĠManufact uring\nlim its\n-m obile\nĠfil me\n/ map\nĠdo it\nĠIn k\nĠsu ed\n. arr\nĠunder min\nĠPro c\ncroll View\n__ $\nĠsidew alk\n( that\nภ·\n[ q\ngram mar\nĠt ë\nqu ito\nĠspir al\next ended\nĠf ocal\nĠdig ging\np as\nĠT all\n.pro xy\nit ures\nTR ACT\nĠRe alm\nĠf eder\nĠorient ed\nĠAltern ative\nĠo we\nĠsour ced\nink er\n.d et\nS ep\nĠQ ui\nĠPal mer\n(_ ,\ns amples\noy er\null an\nque z\nEd ges\nĠsh out\nĠA chie\nĠha ar\n_Con struct\nĠprem ature\nĠre vert\n'). Ċ\nĠs chn\nfilter ed\nnull ptr\nS aved\nitect ure\nCL A\nĠv l\nst ell\nĉ Me\nĠL ip\nn ational\nĠwh olly\nĠspr ings\n.T imer\nĉs rc\nels en\nåħ ¶\nĠcommunic ating\nĠQu iz\nĠt eng\nĠge z\nĠOut side\n.S ign\n(c s\nĠdisput es\nĠWe iss\nann es\n> No\nĠB ach\n.remove All\nre fer\n/d ashboard\nĠA jax\nIndex Changed\nĠWe ak\n' \"Ċ\nĠs ights\naccess Token\nĠJ oi\n(d omain\nĉc v\nĠcontin uation\nĠpl um\nad ir\n.set Message\nĠ ï¼Į\nĠsw allow\nĠL amp\nĠq w\nĠu u\nC oin\nub ic\nĠDe als\nr ace\nĠdict ator\nĠmem e\nturn ed\nĠJul ie\n.grid Column\nĠpup py\nĠp am\nĠ) {čĊ\nĠinv iting\nĠf rench\nv im\nĠwr apping\nĠ#- }Ċ\n([ -\nEar ly\nĠsh iny\n.f aces\nĠreb ell\nabc def\nä lt\nĠest imation\nph ys\nlos ures\n_RE L\nĠex clusion\nĠSk ype\nwe ise\n-st op\nno thing\nĠE gg\nis ors\nRich ard\nĠcounsel ing\nĠcomm em\nĠQ MessageBox\nĠSy nd\nĠFro st\nĠCompet ition\nĠAw ake\nĠt ed\nic iones\nĠDev Components\nVERTISE MENT\nott i\n.run ner\nĠuniqu ely\n.fl ag\nĉ rs\n_g eneric\nĠ`` `Ċ\nACH INE\nĠme in\n( Application\n( br\nĠrat ios\n: ,\nĠXCT est\nustain able\n- www\nit les\n_T EMP\nĠs yst\numeric UpDown\nĉassert True\nĠw f\n. peek\nĠBul g\nĠterr ifying\n.M ODE\nĠG W\ná r\nĠf ic\nĠcommit ments\n- tech\nĠL iquid\nope z\nz heimer\na ña\n-m edia\n( animated\n_go al\nĠg um\nyst one\n.S ET\nĠW end\nset CellValue\nĠmsg s\nc ash\nAL LOC\n/ aws\nĠmic rowave\n.Point er\nĉ Console\n_s orted\nĠFil ip\nPro d\nĠ//! <\ning roup\nĠk s\n_T RI\nĠteas poon\nĠAT T\nĠrecover ing\nĠG LOBAL\n.P ar\nĠ/> ;Ċ\nĠmar ble\nul ators\nĠC ycle\nĠher bs\n_m etric\n) !\n_C LOCK\n_ Button\nH arry\nè¿ Ľ\nĠstr ains\nĠApp Bar\nĠCh an\n/v ideo\nĠb am\n.Pro gress\n$ f\nlem en\nĠir regular\nĠD uncan\nĠM int\n-v ideo\nঠ¾\nó wn\nĠEM PTY\nĠstack ed\nĠH A\n_c ut\nĠwhere in\nĠW ays\n(count er\nè¯ ķ\nForm Group\nĠble w\nc ourses\nĠproduct os\nry s\nĠRest r\nĠsty ling\n> s\nĠp iv\nĠit ertools\nget Repository\nĠI k\n_dev ices\nlay ui\nĠhalf way\nĠfran ç\nĠtun ing\nO A\n_N ode\nar de\nĠfier ce\nlic ted\n# čĊ\nĠbreak through\nĠE rik\nĠb ride\nĠ. \"\ncul us\nins ide\nĠIndian apolis\nĠE E\nĠy og\nurre t\n.f s\n. grad\n_c ards\n_ac curacy\n_ep i\nqu eda\n/ org\né ªĮ\nĠcom pte\n)) [\nOut side\nG reater\nĠRender er\n. actor\nAccount s\nId le\n_h ours\nern er\nJo ined\nĠmen j\nrequ ires\nĠO PER\n.remove Child\nĉs p\nĠes se\nr ift\nxF E\nĠSh akespeare\n________ ____\nĠbudget s\nModel State\nfill able\n- component\noc os\nĠBUT TON\n/ io\n, out\ns ms\nTh omas\nĠAr med\nres ume\nĠrot ating\nĠV ault\nĠse us\n. 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',Ċ\nĠc yl\n( ax\nĠW I\nur ally\nĠBr asil\nĠsen za\nale y\non en\nĠb ah\nĠmolec ule\nR ad\nè¿ °\nAN CH\n- background\n- agent\nĠprol ifer\n: boolean\nĠt ide\nerial izer\n_ ;čĊ\nF ee\n** )\nerg y\nĠHon or\n.Log ging\nir is\nĠunder mine\nĠD y\nĠt yr\nĠde que\nĠdam er\n([] )Ċ\n.layout ControlItem\npe ated\nC AN\nrag ments\nL and\n) ]);Ċ\nĠS ah\nĠDE CL\nWith in\nĠN amespace\nan other\nsem bling\n.des cribe\nCon sum\nĠF ear\ng iven\nOr ange\n< boolean\nĠstead ily\npa Repository\nĠresult Set\n_ ENTER\n_re peat\nĠt ones\nĠPRO P\nn al\npart icle\nĠsign aling\nĠaccess ory\nĉĉĉĉĉĉ ĠĠ\nĠvie le\nĠNo ah\n- ag\nĠmur ders\nĠa ired\nĠPL AY\nĠS ullivan\n_C ore\nĠul ong\nĠblog ging\n> This\nĠdata Index\nĠprint able\nĠE yes\n_target s\n(P y\n. over\nĠbr u\nam pton\nĠplaint iff\n< Key\nb ull\nĠ⣠¨\nIss ue\n.cor nerRadius\nC ritical\n_p hi\n. angle\nĠdynam ically\n! \");čĊ\n> );Ċ\nin vest\n.* ĊĊ\nĠt élé\nĠsuper f\nĠcas cade\nDT D\nĠviv id\nĠsubsid ies\nĠH ass\nĠcoll aps\nĠcer amic\n{} \".\nĠLeak age\n-tr ash\ncoll apsed\n-s ocial\nĠCh ad\nĠincl ined\nĠst o\nĠstory board\n.p ayment\nstack overflow\nĠRaid ers\nĠ# '\nolic ies\nìľ¼ ë¡ľ\nem ap\nĠk j\nĠqu ota\nĠGard ens\në² Ī\nĠAng els\nĠof t\nĠlower case\nĠi Param\nĠche apest\nun ta\n_p kt\nic ators\nĠle urs\nĠdecre ases\nĉ define\nPRE C\namm ers\nĠPre paredStatement\n(d irection\nĠcre ws\nark ed\nĠMem phis\nĠS ell\nG TK\nĠm aid\n: disable\néĽ Ĩ\nĠP f\nĠal beit\nopen h\n?> \">Ċ\n.get Source\n(s cale\nD u\nĠP IL\n_ref resh\nĠbet s\n(c ar\nĠV on\n| --------------------------------------------------------------------------Ċ\nĠGr at\nM uch\n( Dialog\n.stop Propagation\nĠte k\nĠex its\n'], $\nĠphone Number\nuc s\nec imal\n------------ --\nin p\n.po jo\nĠcor pus\nĠpractition ers\n.p ic\n\" testing\nĠstring By\n.Not Null\nĠr ang\n.D ynamic\n_R ender\nаÑĤ а\nWait ing\nĠW ik\nĠoverwhel med\n% \">\nĠA E\n}} >Ċ\nu w\n_t yp\nĠbuck ets\nĠgre eting\nĠla ughter\nĠant agon\nuggest ion\n- email\nĉt op\nĠer os\n_tr i\nĠiss uing\nĠh á\nĠisol ate\nOver flow\n, E\nĠnut ritional\nĠAbb ott\nĠn f\n.t ouch\n.fetch all\n_z ip\n\") }Ċ\nĠam at\nĠC isco\nĠn Ã¥\nPLE X\nĠse i\nf oto\n.to Json\nå¤ ļ\nĠKle in\nĠlib c\nĠmin ers\nå ¢\n- print\nĠP ride\nT odos\nĠmask ed\nĠset Data\nĠtele fon\nĠunh appy\nĠT ables\nge b\n( debug\n_all owed\n- access\nĠlog istics\nĠg ems\nĠM ature\nĠr sp\nĠAl le\n.get Bytes\n\\ web\nynchron ized\nPar agraph\nĠth rottle\n.sql ite\ncons ulta\nĠSe ah\nC e\nĠsub mar\nER E\nV ous\nĠre ddit\nĠsql alchemy\n-m ile\noc ide\nP our\n}} \">Ċ\nst ead\nĠ@ (\nĠ[ ])\nĠAd s\nĠover load\nr idden\nĠDes ert\nĠW rap\nĠPortug uese\net z\nĉf irst\nĠmile stone\næĹ ł\nÑĥ Ñī\n(s uccess\n< Vector\nco ol\nĠ[ ]);Ċ\nerv als\nĠin vert\n\" io\ncur so\nfr agment\nĠfeas ible\n.set Position\nĠel m\nĠimag in\n@ Spring\nĠb ats\npu és\nga lement\nns ic\ngi ene\nell ation\nĠBa iley\nSh ar\nĠT ul\nĠH K\nĠfree zing\ngl m\nce ans\n-c ut\n_c ircle\nåij ĺ\nn egative\nĠind ian\ns alt\nĠt ing\nĉm od\nĠs int\nak in\num l\nĠText Input\nĠpop ped\nT MP\nĠpark ed\n×Ļ ×\nĠF usion\nĠhe ater\nET F\nro zen\nh all\nĠM ik\nlev ard\n- heart\nĉ order\nM aking\nĠpled ged\nĠdir s\n$ post\nĠH err\nstant iate\n, \"Ċ\n.get Color\nĠS AT\nĠtimed elta\nĠM ai\nĉm ethod\nĠid iot\nĠTr av\nident ified\nĠDiv ine\n.get Path\nD ash\nĠinf iltr\nĠhandle Submit\nbro ok\n.g eneric\n.short cuts\n................................ ................................\nĠdat ings\nĠM V\n #\n} \"ĊĊ\nĠimprison ment\nason ic\nrou d\nuc ion\næĬ ¥\nĠdia lect\nĠon Mouse\nconst expr\n.label Control\nĠwe aker\nĠman kind\nĠRE CE\nĠd iz\nĠapp Bar\nĠqu é\nf ra\n_default s\nĠal iqu\n_at om\n: indexPath\nĠmiss es\nĠvis ually\nĠH ands\nSTR U\ni ates\n_ asset\nF inder\nmid t\nĠsn acks\n(__ ('\n. uri\nĠIn strument\nven ir\n($ __\n.Dot NetBar\nĠconfig s\nĠguess ed\nि à¤\nĠinitial izer\nĠ? \",\nĠVer izon\nman ifest\nge ben\n.d etails\nG ate\npons ible\nĠEl im\n, str\nĠwrit ings\nĠD erek\nĠCo ordinator\nĠpill ow\nĠnotice able\nR s\nĠduplic ates\nern els\nk J\n.z z\noll and\nĠSE CTION\n_f name\nuff led\n'].' \")Ċ\nĠD ollar\nĠem oji\nCar ousel\n- player\nĠadjust ing\nĠjug a\nalleng es\ng ene\n(body Parser\nlop edia\nĠBeh ind\nĠslee ves\nĠdrag ging\nĠChe vrolet\nĠb iz\niv ities\nĠFrequ ency\n, char\n.W HITE\n_pre view\n) ';Ċ\n_ ax\nION S\n.c pu\n.input s\nUB E\n_fe ed\nĠSup plement\n! ).\nes us\nĠU DP\nĠmicro phone\nĠconf irms\n.is NotEmpty\n\":\" \",Ċ\n_S CREEN\nĉ expected\n+-+- +-+-\nĠH ait\nfast call\nĠdep ict\nv b\n_p icture\nĉd escription\nĠW ife\nuc i\nĠv icious\nä» ĸ\nue ba\nĠset User\nãģ ¡\nĠd iving\nĠoper a\nuser content\nar ah\n) },\ny un\nvel t\nĠun covered\nĠh ips\nĠosc ill\nĠassert ing\nĠX i\n.re store\nke a\nĠsp elling\nĠder ive\nab we\nĠD ow\n.set Type\n_v s\nĠco zy\n.c ategories\nO rg\n_m gr\nĠd ungeon\ncollection View\nĠBl ank\nac ias\nä ä\n_clean up\n_ACT IVITY\nĠtri angles\n.Menu Item\nĠip hone\nĠW on\n] ]ĊĊ\nĠCompar ison\n.D oc\nĠcan onical\nĠSud an\n') {\nUp Inside\nb uiltin\nENC Y\nx be\nĠch uck\nĠcontrad ict\nĠnuest ro\nĠarchitect ural\nĠF ib\nĠcomp ares\n* k\nC fg\nçĦ ¡\nnt en\nMatch es\nĠDOWN LOAD\n_HAND LER\nman agement\n[ S\nEN G\nÂĢ Â\nf ang\nĠsl ipped\nĠL anka\nesc aping\nĠtack les\nĠPed ro\n.P rop\n.' '\n.G enerated\n.New Guid\nat rigesimal\nill on\nĠstat istic\nspec ies\nhold ing\nDr upal\nĠfundament ally\nĠbond age\nĠres olutions\nInline Data\n\\ Type\nest ion\n.w rap\nĠwar riors\nĠLOC AL\nArch ive\nĠembr aced\ná» §\n.V er\nĠAff ordable\noles ale\nĠAp plied\nĠCon version\nm ega\n_c am\nĠcer emon\naur us\nĠVol k\n.op ens\n/ about\nĠSt d\nj ournal\n()) {čĊ\n,\" \\\n( Arrays\nĠD ense\nase ña\nän ner\n/ stat\nuser Data\nĠg erman\nĠt z\nworth y\nFormat Exception\nph erd\nĠsm iles\nĠWh enever\n( adapter\n.bad logic\nĠbrief ing\n.Grid Column\n- char\ndim ension\nĠC opper\nĠnin th\nĠ' {{\nĠr av\n_T able\nĠderiv atives\nĠR aise\nĠF ut\narm or\n-p adding\nĠre min\nĉ style\nĠMembers hip\nĠspread s\nĠgall eries\nĠClar ke\nĠcon ception\nmin ute\nĠab usive\n_ad j\nĠterr ific\nĠover t\nour cing\nĠentr ada\nlevel s\nĠcrit ique\nĠrespect s\nĠM MA\ni ene\nĠenc aps\nĠRay mond\nDiv ider\niv able\nb az\nĠ@ _;Ċ\nĠCl aire\nĠur ging\nCE E\nĠtransform er\ndisc ord\nĠJ ourney\nt os\nĠcompet itions\nĠO BJ\nĠB is\nĠrelax ation\nid y\n_IN STANCE\nĠP ref\nd ados\nici encies\nĠMedia Query\nĠC ube\nĠStr ange\ng pu\n(d ays\n_Init Struct\nĠfinger print\nem at\nĠGe cko\nĠr ails\nĠL um\nstr action\nig ung\n(m ovie\n_d ictionary\n_int errupt\nĠQ C\nik ed\nappend Child\nrec ipient\nr é\nV e\nĠtow el\n.last IndexOf\nĠplace bo\nĠW ie\n.es p\n( Debug\noper ative\nĠdece ased\n& id\nĉm utex\nel ic\nĠb apt\nĉ čĊčĊ\nĠfar ther\nH alf\n.dis able\n.menu Strip\nle ccion\nĠresult Code\nĠc ans\n-e lection\nf emale\n_F IX\naus ible\nĠP OWER\nĠrecon struction\nĠsc ans\n.Xtra Bars\nâĢĺ s\nRem oved\nĠparagraph s\n_m argin\nĠl ymph\nĠb os\nling ton\nĠBapt ist\nĠadvertis ements\nĠMan age\n/ yyyy\nIO US\nENC ES\nĠF iction\nĉm enu\nĠFile OutputStream\nov an\nĠF eng\nĠsk ipping\nget Class\nann i\nĠreb ounds\nĠpublic ity\nĠing res\nuse ment\nĠthought ful\n.Ch art\nĠhat te\npass port\nĠhook ed\nĠL ens\nĠflag ship\nĠst ip\nĠG EN\nĠcl ues\nip v\nĠR ise\nĠG ew\ntab lename\nĠfore most\n_ validate\n_an alysis\noll a\nĠqual ifications\nĠdistrib utions\nĠFl ower\nĠt ense\nĠthank ful\nĠcl utch\nĠun ified\nro ads\nĠsit i\nĠst all\n_P RIORITY\nc stdlib\n_USER NAME\n.by tes\n? page\nermal ink\nĠVe get\n/v nd\n- author\n.N ONE\nĠCon current\nĠC ry\nĠstart ers\nĠInter action\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ\nĠLE VEL\nE ll\nĠcom boBox\nĠTh eresa\nte k\n_H andle\nĠab y\n.g dx\n, end\n(L ocal\nO l\nkn ife\nar ial\nĠH off\nĠprostituer ade\nDo ctor\nInst ances\n.Set Value\nĉf rom\nĠlux urious\nInd ent\nAlloc ator\n_D RAW\n(\", \",\nĠFr ances\nĠgroup Box\n(s chema\nPrint f\nOR IES\n- gradient\nĠre put\nar in\n_D ONE\nin cre\nig nty\nĠex ert\nĠ- .\n/ App\n-th rough\nĠdecl ining\nĠdess ert\nĠinc umb\nĠdesign ation\n.P ORT\n, strong\nĠsand box\nĠw ines\nĠP av\n$ str\nask ell\nĠh ö\nĠP Y\nGet Instance\nText Input\ngame Object\n/ events\ncreated At\nĠlocal Var\nĠWH ITE\nper ed\nile ge\neff icient\n, color\nc ate\nĠC afe\nĠsimilar ities\nĠp umps\nĠHung ary\n.User name\nĠsk ate\nĠtouchdown s\nĠacceler ate\nĠH elen\nOM EM\nĠK un\n_v ol\nĠfind All\nĠMens chen\na head\n); \"\nkom men\nĠpossess ed\n.arg max\n.trans ition\nAR P\nOLUM E\n(s cript\nĠÐ ĺ\nĠF inding\non ces\nI o\nB old\nĠrenew al\n_D IALOG\nĠdis reg\nINT ERN\nĠt oute\nĠelect r\nĠG ross\nĉ true\n.F ields\nĠW IDTH\nĠD ent\nĠà ģ\nNS Notification\nĠa os\nĠme lee\n. Validation\nĠDE C\n-depend ent\nĠsu ic\nT raits\n$ message\nĠD ear\nĉ FILE\nl anguages\n.P rot\n.add r\n-g eneration\nIC ON\nĠtrans plant\n-d escription\nĠch asing\nĠche es\nĠ} */Ċ\nTr ad\nqu eries\n/widget s\nsub package\nĠes pec\nĠcr acked\nĠcompet itor\nP urchase\n- team\nolec ular\nor Thunk\n& P\nĠrel ent\n/ #{\nĠproduct Id\nĠè ¾\nĠL av\nĠAl ter\n.M ode\nAD IO\ngr p\næ ·»åĬł\nQu it\nĠdepth s\n-c ategory\nĠD ATABASE\nS PELL\nĠFal con\nĠQString List\nĠ'' .\nĠIn stitution\nd amage\naz or\nbel ongsTo\nver ages\nĠN ONE\nipp ets\n, \\Ċ\nĠfoot print\n_ archive\nn ak\n.get Field\nĠRef lection\nĠ' ]\nĠH BO\n_dis count\nĠin cest\nĠD odge\nĠW ade\n.N O\n\" encoding\nĠBlock chain\nĠlaws uits\nĠM aint\nch ten\nĠét ait\nĠktó re\n_ ctl\n(t imer\nB attle\niz o\nay ed\nI OR\nĠGlas gow\nĠsyn th\n_log s\n.p ose\n_Adjust orThunk\n(( &\nĠuns ure\nyst ate\níķĺ ëĬĶ\nO ULD\n. ng\nĠdefault dict\nwork space\nĠselect ive\nPicker Controller\nYNAM IC\n.method s\nĠpath ways\nĠF ew\nK G\nCRY PT\nfollow ing\nĠD LC\nĠS ara\nĠpres et\nestruct or\nĠK urt\nĠair plane\nĠo mp\nĠParent s\nĠMart inez\n.com plete\nĠbroad ly\nĠsc are\nĠM é\nĠelim ination\nĠpou red\n/ sw\nĠcom un\nĠm asc\nĠOrgan ic\nĠString Utils\nil ateral\nĠreluct ant\n- age\nĠn z\n.\" \\\nĠpast or\nale z\nĠe fect\npro v\n/ init\nĠp enn\nund s\nĠs size\nĠPro j\nbas ename\nĠsh ells\nĠNe ck\nĠEn forcement\nvid ed\nst own\nS phere\n$ r\nuss en\naf il\nĠTele gram\nĠanaly tical\nнÑĭ е\nus ually\nx n\nĠhistor ian\nĠGreg ory\nol ph\nĠUn a\nĠcon tributes\n% -\nanti ago\nÑĢ ед\n.reg ion\nĠab rupt\nĠUnsupported OperationException\nĠT ASK\n_f inish\nĠnot orious\nĠV s\nĠM Q\nĠsun set\nĠun acceptable\nar cer\nĠill umin\nĠOr b\nĠb h\nE ste\n_dis patch\nĠr ipped\nĠtou jours\nĠPar cel\n_ ll\n.user Name\n.class es\nS OURCE\n( Number\nел Ñı\nĠhead phones\n(s ide\nconst itution\nann ah\nčĊ ĠĠĠĠĠĠĠĠčĊ\nĠcl iff\n- ref\nĠmo strar\nĠPow ell\n+ y\nĠB G\n_f ragment\n.P ort\nĠreal izing\nparam ref\nĠh ometown\n@ Table\n+\" --}}Ċ\nF rench\nEntity Manager\nĠPl ain\n//////////////////////////////////////////////////////////////// ////\n ³\n( RE\nc apt\nĠorgan isms\nĠj ets\nol ocation\nĠApp RoutingModule\nĠgl orious\næľ į\nĠdisc arded\nĉĉĉĉ ĠĠĠĠĠ\nĠArn old\nl ug\nĠpar l\nĠhorm ones\nĠm ah\nĠSon ic\nĠorgan izers\n_PL ATFORM\n.in v\nĠch ord\nvent ional\nĉ of\nEp isode\n. Enum\nunk t\nĠD h\nĠJ ared\nĠN ak\nĠint ends\nEnd ian\nĠa ustralia\n_c v\n(res olve\nĠclin ics\nlik ed\nASH INGTON\nin ha\n' *\nĠN P\n_b eh\nĠh f\nĠw ür\nc ategoria\n$ form\nĠsub way\nĠis Active\npop ular\nC our\nĠco oldown\nĠa insi\nĠGL uint\nere al\nĠarray Of\nĠh atch\n======== ==\nress es\n_P P\n. ^\n_dec ay\nĠB less\nmet rics\nĠCOPY ING\nĠDump ster\nĠJos é\nĠDesign s\n<\nĠ\" }Ċ\ntime zone\nĠe er\nmax cdn\nĠE SC\nig aret\n_conn ected\n_re verse\nĠquestion able\nĠUS C\nĠtut ti\nĠdrop out\nĠActiv ities\nĠW inds\n')) );Ċ\nĠcon gest\nÄŁ ı\nĠprolong ed\nè¿ Ļ\nĠCross AxisAlignment\nLE EP\nĠVAL ID\nĠG az\nĠdepend ence\nĠP rix\n.Compiler Services\nj ump\nĠstr at\nc irc\nĠC USTOM\nx aa\nĠb mp\nĠb ureau\nĠw aren\nN X\n( Window\nĠChrist ie\n_F E\nĠt n\nĠOm ega\ncommunic ations\nHome Page\ncom pletion\nĠsupply ing\nYP ES\ná vel\nåĪ ¶\n(c lick\n\\ Contracts\n/ questions\nĠe z\nAM S\n.m esh\nĠ' \\Ċ\nRob ot\nJson Object\nĠD F\nĠProcess or\n_sh ould\n.prot obuf\n- users\nĠemb ry\nF ONT\nĠstart ups\nĠData Source\n) #\nuro s\n_C olor\nĠstand alone\n} [\nj d\nĠforg ive\nĠng x\nĠGener ally\nĠconfig urable\n/ order\nĠv as\n') \";Ċ\nĠR R\nĠT roy\nĠcomprom ised\nĠSw an\nint endent\nCent ral\n_ keeper\nĠar quivo\nĠRead Only\n_cur ve\nk v\nent in\nè ±\nĠE y\n.im read\nĠP am\nif fe\nat ivity\nxb c\nĠgr im\n-f illed\nnames e\n'] :\nĠa ur\nĠGib son\n.Mouse Event\nĠl ado\navad oc\nĠfam il\nĠM oder\nf ps\nãĢĢ ãĢĢ\n- example\nĠAl zheimer\nĠU tf\n_arg uments\nCon clusion\ntext Content\nrem aining\nĠinterrupt s\nĠBack up\nĠM ong\nĠrecept ors\nh istor\n.cor outines\nĠsh outed\nAl arm\nĠcomb ust\nĠg rote\nult ural\n( ids\n---------------------------------------------------------------- ----------------\nipl inary\nO pts\nĠY ale\nlocal Storage\nĠequ ival\nĠF leet\n\\ b\n* pi\nĠQ Label\næ ¡\nĠv x\nĠA CL\nĠsu cesso\nĠper c\nĠNot re\nĠan arch\nR ing\nsp b\nĠstr pos\nst ores\nĠMap le\n(Main Activity\n(\" \"))\nĠview Holder\nQu ad\nĠig ual\nors che\n.m argin\nĠind ie\nĠfr anc\nĠForm Builder\nĠPart icip\n.fl ash\nĠstorm s\nU lt\nĠf en\n[ new\nE ver\n=\" Ċ\nĠlocal ized\n_f ollow\nĠn ave\nĠdomin ance\n(t ile\nJ ournal\nĠV C\nĠpenet ration\nï¼ ķ\nĠcomp artment\nĠb ids\nForm atted\n****** /ĊĊ\n(c ity\nâĢĶ it\n[ C\nĠuse Callback\na ub\n) ?.\nĠV AR\nĠSe bastian\nĠM oss\nĠabund ant\nG reg\nÑĤ а\n_c i\nĠbib li\nCR M\nĠAt tempt\nism e\nd ash\nãĢ İ\n_m u\n.Formatting Enabled\nInd eed\n-d irect\nĠsuck ing\nĠp ne\nocab ulary\nĠPack ers\n.N avigation\nĠp ied\ncri bing\nĠSt uart\n.To Double\nĠSecond ary\nS aving\nĠD ut\nĠM add\nM agic\n, H\n.document Element\nĠB ST\nĠdiff ers\nĠmore over\n_ nd\nSE ARCH\nп ÑĢав\næ ´\nto Match\nĠdecre asing\n-m ember\namp us\n( boost\nD aily\nData GridView\nĠHttp Context\nĠh ipp\n_work ers\n-l anguage\né ĵ\nĠconsist ed\nath ing\nĠMer cury\n$ content\nĠpract iced\nĠMod ules\n_D AY\nĠweakness es\nĠL odge\nĠn ar\nĠM ate\nĠj p\nĠHttp Headers\nĠsm o\nĠT OKEN\n] )(\nĠaqu i\nsw agen\nĠs rv\nĉ ans\nA round\nĠMan uel\nĠfiction al\nĠIM G\nĠ. '\nĠB erry\nĠwall paper\nsex ual\nier o\nĠ çļĦ\nìĨ Į\nBacking Field\nĠAd rian\nBASE PATH\nĠrepe ats\nĠbl ues\nĠunp redict\n_c oll\nst acle\nĠT umblr\nĠEl f\nĠass urance\nĠc ensus\nĠIM PORT\nEND ER\nan os\nĠ= (\nĠEll is\n\" ĊĊĊĊ\n.w in\nĠA bove\nal on\n_t ick\nĠrepresent ations\nĠæ ķ\nw id\nĠAr ms\nList a\n_f ailure\n_c m\n.Flat Appearance\nĠthr one\nP atch\nĠV oy\neng l\nĠnegot iating\n> `\nĠshoot s\nĠF PS\n.Y ear\nĠK iss\nenc ión\nreet ing\nFrom File\nĠresign ation\nØ ·\nĠtw ins\nÆ°á» £\nĠge bru\n.get Content\n.T ree\nĠEmploy ees\nĠF IFA\nĠcert ainty\n(C l\nĠtot als\nedit able\nॠĢ\n.Report ing\nM as\nqu iet\n.r ules\nĠV O\ncon exion\n, K\nĠalloc ator\nĠPow der\n\\ Repository\nBe at\n_t ipo\nĠ[' ',\n_IN TR\nĠ<< <\n< hr\n\") ==\nugg age\nĠC raw\nĠé galement\nĠg inger\nĠprim era\nĠprod uto\nlt k\n.User Name\nĠstr error\nm ith\n_n b\nĠdis comfort\n']; ?> \");čĊ\ndrop IfExists\nĠB eg\n_H AL\nĠcross AxisAlignment\nĠE vidence\nĠpec uliar\nĠinstit ute\nve is\nĠf ft\nà ģ\nĠzo ekt\nan aly\nĠHom eland\nĠpen etr\nudden ly\nĉ element\nĠB ren\nĠTr udeau\nĠCub an\nj am\nus lim\n_e v\nĠst ems\n} %\nĿ å§ĭ\nĠbrand ing\nĠcorrespond ence\n.j query\n¢ åįķ\nĠRead s\n(Http StatusCode\nass in\n(s lot\nĠGrad uate\n/// <\nĠinform ations\nEN ABLE\nĠp uis\nĠfind er\nĠBr is\nĠnett steder\n_m id\nĠo gs\nĠSter ling\nĠar rog\nstr ftime\n| ĊĊ\nĠvo x\nĠReg ardless\nĠes o\nĠCom fort\n.Boolean Field\nĠu h\nAC Y\nĠsque ez\nĠV ic\ncont ro\n. lo\nĠ ire\nĠCom edy\në ¶\nĠorigin ated\nĠsh ipment\n| max\n_g uid\nlev ation\nна Ñı\n( undefined\nĠD DR\nĠshoot ings\nĠLat ino\nEND OR\nĠaver aging\nĠgre eted\nĠthe aters\nо е\nĠd B\nĠg st\nĠdef inite\n. Storage\n.h er\nĠa fore\nĠRe ality\nĠGod s\nvers ed\nĠhands ome\nĠex cluding\n( ad\nQu otes\nĠS cheme\n? q\nĠT amil\nT icks\nĠp est\n' n\nĠporn ography\n_mod al\nĠ ----------\nĠdis posable\nF REE\nĠsh ark\nC HE\nĠdep icted\nĠdemonstr ations\nĠK illed\nĠR ULE\nĠobs essed\nĠsimpl ified\nPost al\nĠconcept ual\nĠp st\nL as\n_PRO JECT\nucceed ed\nol u\nÄŁ i\nĠpersonal ities\nĠres hape\nĠenc losed\nĉp tr\nĠtutor ials\nĠexpl oded\n_DIRECT ORY\nåĨħ 容\nĠcan on\nĠrecogn ise\nP AD\nĠAppro x\nĠRest ore\nĠImport ant\nĠheav ier\n.Se quential\nEar th\nĠMil k\n.set Request\n.t em\nĠre construct\nĠskept ical\n_Pr ivate\nBU F\nqu a\n: a\nĠse k\nĠd well\noss a\nĠreward ed\nи й\n(top ic\n_part ition\nĠ__ ________________\nKey words\nĠFr anco\nL ite\nĠn aken\nĠз а\nO BJECT\nĠcraft s\nĠSw ap\n.X na\n.Con nect\nĠbalcon y\n(re al\nĠBarn es\nb ir\nĠTw enty\nay an\nat ars\nĠProp el\nĠIh nen\nUp grade\nĠcur b\n- second\nĠn eph\n.p res\nìŀ ħ\n.se q\nĠp added\n\" ?\nj l\nãĥ ¬\n') a\nCo ordinates\nĠen acted\nENT S\nĠl ac\n.f inal\nĠPhp Storm\nc alled\nĠin quiries\n.m iddleware\nĠD owntown\n/ ';Ċ\nĠkil omet\nac cel\nĠqu ien\nw string\nset Data\nĠman era\nĠmod ular\nrim p\nĠtar iffs\nâĢĻ il\n_TH ROW\n/c olor\nĠHT MLElement\nĠcar ro\nĠpr ere\nĠplot ting\nĠPos itive\nĠMach ines\nOT ES\ná» Ľ\nple asant\nĠal te\nĠa inda\nth ese\nĠc ors\nip ay\nĠAdvis ory\nĠRub io\nj q\nĠl imestone\nĠdet ached\n设 ç½®\nten ant\nĠDep th\nal ore\nĠÑģÑĤÑĢ ок\nĠF ORE\nĠL ay\np resentation\n) ');Ċ\n.sub plots\nÏ ĥ\nN OW\nG ar\nhand les\nab ra\nput ies\nĠElect rical\nM iddle\nrop ic\nĠJ D\nĠD yn\nĠB ristol\nĠMc Carthy\nĠstri ker\nĠenumer able\nĠEv an\n.default s\nqu ences\n) ||\nĉt oken\nâ Ĺı\n-d ropdown\nST ORE\nĠGraph ic\n( pp\nEx pl\nĠup wards\nĠD istributed\nĠW EB\nJ er\nis NaN\nçĶŁ æĪIJ\n> R\nüss en\nef s\nĠun cover\nĠl ud\n.cal culate\nĠint ptr\nĠmidfield er\n. Headers\nĠm f\nere f\n.M etro\nĠSpe aking\n: b\nĠcryptoc urrencies\nĠdem ons\nĉ EXPECT\nĠw icked\ny outube\n: Int\nĠHind i\nĠC AT\nĠØ ¹\nr ar\nom ore\n/ per\n/lic ense\nĠre im\nĠawait ing\nĠle thal\nĠE F\nround ed\nĠPl atinum\nĠвÑģ е\n.co ords\n.De vice\n/ item\nĠW enn\ncompile Components\nĠK inder\n.remove Item\nĠand a\nbn b\nĠpr a\n( transaction\nĠembarrass ing\nĉ BOOL\n.content View\nĠevent data\nat ore\nĠprovided In\nir ma\nĠz ona\n_H W\næ Ļ\nĠst ove\nĠcounter part\n_Pro duct\n_MAN AGER\nĠinfr ing\nĠE RA\n_p arty\nÑ ij\nĠin ici\n_ Request\nĠmir acle\nĠcancel Button\nS py\nat ó\nĠpol ish\nĠNic ole\n.display Name\n\\Request s\nĠuse History\nRouter Module\nĠst ared\nID ER\nÑĥнк ÑĨи\nĠnot a\n$ arr\npec ified\nĠto pp\n_DR IVER\n/ ng\nå ł\n_t m\n% timeout\n< s\nĠ( *)\nĠHttp Request\n_TR ACK\n(n ote\nĠExp lore\n_s erv\nĠç »\nB inder\n+ \",\n. att\nĠEth i\nĠc ódigo\n=' \\\n.l ines\n( Of\nå° Ĩ\nmiss ible\nĠv é\nĠac oustic\nĠcraft ing\nn it\n.b a\nĠLuc y\nĠi Pod\nĠpup ils\n-m ax\n_w r\n(c p\nĠRE PORT\nĠd ns\nĠRe ferences\nĠundert aken\nĠkø benhavn\nĠch ai\nĠC roat\n_ Log\nrown ed\n_m ed\nĉ date\n# __\nĠcost umes\nĠRe quires\naff le\nç Ĭ¶æĢģ\n-S emit\nela ide\nеÑĤ од\nĠp estic\nĠd ra\nDOC UMENT\nĠ... čĊ\n}` }Ċ\nĠA uction\nĠD ock\nxxxx xxxx\n(get String\nħ į\nĠborder Width\nĠMach inery\nĠpredict able\n.S H\nĠam plitude\n.for Root\nIN avigation\nTable Model\nat trib\nĠmaneu ver\nĠexc av\nB ERS\nĠd apat\nĠinstall ations\n.A sync\nĠr ays\n= âĢĿ\n; ččĊ\n.c rypto\n_db g\nĠEnum erable\nOf Size\n_epoch s\nm w\nM ENU\nout line\nĠP apers\n============ Ċ\nĠuniform s\nĠG ig\n- package\nĠJen kins\nĠHome Page\n.is Selected\nĠmechan ic\nM K\nĠS ounds\n//---------------------------------------------------------------------------- -Ċ\nĠresearch ing\nĠinf os\nograph ics\ners et\n([' /\nĠTim ber\n. agent\n.to JSON\n_command s\npar ing\n_ad just\n.n ome\n(g lm\nStatus Bar\nfile path\n? âĢĻ\nĠdetect ive\nĠunser er\nĠTib et\nEN DED\n(se ed\nĠsne ak\nĠam or\n=\" //\nĠPan thers\nall ax\nĠL IVE\nĉD WORD\n]= -\nĠtorn ado\n/ min\nĠlung s\n-c urrent\nĠBook ing\nåĪĹ è¡¨\nĠenjoy ment\nठ°\nJ A\ntyp ed\n.B tn\nf at\nug al\nĠSh ares\nĠdis gr\nĠB AR\nĠFO X\nOp code\nĠS z\nkey down\niction aries\nĠdetail ing\n} ))Ċ\nĠp ok\nĠdemonstr ating\nĠnot ation\nl ayers\n@ if\nĠN PR\n.strict Equal\nĠRec ipes\n.T ensor\nĠliqu or\nĠdeb ts\n.ends With\nW heel\n.P os\nCS V\n$ arity\nĠun stable\n( loss\nENS OR\nĠele ven\nĠL opez\nĠHop kins\ncon om\nĠS eth\nĠpo ems\nQu ant\nĠg sl\nĠsy rup\nĠs ibling\nĠc ass\n-v ous\nö t\n_P ATTERN\n_SE CTION\nest imated\nup grade\n.m ongodb\nĠBo at\n_C TX\nĠfetch ing\nust in\npi el\nM arg\nRef lection\nĠd uct\nĠMunicip al\nĠb x\n.Get Current\nml ink\nĠAccount ing\nĠGene va\n_P os\nĠpass er\nĠhear ings\ncom pan\nĠfrag ile\nInitial izer\nwalk er\n.M aterial\nĠHun ting\ntrys ide\nĠk at\nĠcl erk\ná Ł\ndo ing\nĉg roup\nĠsan ction\n.l b\nĠL azy\nĠCon straint\nP agination\nĠpou vez\nĠInd icates\nM ER\nĠcour s\nĠyear ly\nĠgros se\nabb rev\nĠD ON\nĠproceed ed\nent lich\nĠproperty Name\nĠTe aching\nst adt\nĠc utoff\norn ers\nĠa frica\nĠrend ers\nĠYan kees\nĠTool bar\nsp aces\n.fill Style\nĠseg undo\n_str len\n.F irebase\nå¤ Ħ\nĠmention ing\n\\ (\nĠVal ve\nSet ter\nĠsp ans\nĠAl cohol\nĠLet ters\n\\x e\nĠT K\n_B LE\n.get Result\n< Player\nĠP att\nĠeas ing\nĠtur key\nĠF en\n') \"\nĠconf ined\nĠin clus\nSup erview\n(with Identifier\nenc ial\nĠstuff ed\nTh eta\nĠeconom ists\n} ));ĊĊ\nco okies\nĠRo ose\nĠChe ese\nĠfich ier\nĠen forced\nAB B\nno ÅĽci\n_AL LOW\nĠrecru ited\nĠexpend iture\n-n ight\nĠassert NotNull\n_ex ecute\nĠØ ¯\nIN DEX\n_F MT\nĠresc ued\nĠMonth ly\nĠCons ervation\nĠG eb\nOb ama\nEp och\nic ies\nĠOr t\nĠso it\n( icon\nF riends\nm ol\nĠground ed\nĠC ause\nad ena\nWE EN\nĠL un\nIT IVE\n. loop\n_un til\nĠcor r\n.ed ges\nĠhyp oth\nched uling\ntrans lator\nĠÐ ľ\nR om\nãĢij ĊĊ\nĠX amarin\nĠviol ating\n. anchor\n--- ĊĊ\nĠtr ader\nAD VERTISEMENT\nĠuns ere\nĠD AO\nĠbl ond\nĠP AT\n.g lob\nĠè¾ ĵ\nĠsplit ting\nĠun subscribe\nĠatmos pheric\nĠTr im\nĠcit ation\nĠin ference\nĠF t\nĠDar win\nfind One\nĠG el\n( Convert\nĠaccess or\n; text\n(s orted\nĠjud ged\n); \\\n: p\nĠme ine\nĠS lim\n.Command s\nĠper ceive\ncoh olic\n< Data\n.entry Set\nĠassert False\nĠPat rol\nense m\nÅĤ Äħ\n¨ ¡\nW IDTH\nĠRes cue\nĠU IF\n_THRESH OLD\nĠMich el\nATER IAL\nopens ource\nĠD iana\nĠinv ites\n_B ODY\nĠreserv oir\nĠro i\nc ust\n(t c\nï¼ģ \");Ċ\nĠfest ivals\nĠperform ers\nĠclim bed\nĠj ungle\nString Length\nĠunlaw ful\nier re\nvertis ement\nĠst akes\nĠh ats\nMod ify\nĠLET TER\n.H ide\nĠstat utory\n_ white\nĠPer l\nuten berg\nem ple\n.W orld\nĠoverlook ed\nĠcon cludes\n/* ================================================================\n-w ise\nĉ stream\npop ulation\nĠevent o\nĠillustr ations\nft s\nĠaut of\nĠPro cedure\nĠdes erved\n-t imes\nĠg ol\nN SError\ncre st\nĠPak istani\nany ch\nget Current\nĠl ar\nnt l\nĠRe becca\nĠm ateria\nĠfind By\n/ ad\nCallback s\nĠAl s\nĠKat ie\nĠObservable Collection\nĠDocument ation\nTyp ed\nĠCulture Info\nĠTim othy\nĠlater al\n\" type\nĠun authorized\nĠteach ings\nĠdebug ger\n[ value\nĠal ors\nĠu z\nĠsc atter\nĠdown ward\nĠmig li\nstatus Code\nĠ( ))\nĠM W\nĠм ож\nRO SS\n.b uf\nĠfair y\nĠInf rastructure\n=> \"\nt lement\n$ (\"\nFrom String\nĠB ild\nĠconvent ions\n_n ative\nĠIns pector\nĠP ist\nub ar\nĠreg s\nĠP ilot\nTh us\n>' +\nĠc ela\n.new s\n( Product\nL iving\nR ussia\nĠfac et\net ical\nĠ[' $\n/ [\nĠD ire\nĠg ases\nĠIN FORMATION\nĠE at\nĠFor ums\nĠChar acters\n_m et\nĠìĭ ľ\nĠk ings\nach ie\nĠL ambda\nĠtim ers\nĠLight ing\nĠCase y\nadd ir\nand ex\n. answer\nĠH ip\nĠPr incip\nStart Date\nĠ ãĢĮ\nt res\nĠ& #\n.Max Value\nĠPro blems\nĠlat ex\nOf Class\nĠLyn n\n// '\nĠvoy age\nĠshut tle\nĠRoll er\nĠRuntime Error\nuy a\nD ic\nĉb uilder\nĠbul lying\nĠsimple st\n.c alled\nĠL R\nĠmor ality\nĠst urdy\ntr acking\n.sw agger\n_B IND\nIT OR\n-url encoded\nĠÑ ħ\nĠTr inity\nĠtr aps\nĠ| -\nĠset Text\nĠbarg ain\nĠbr akes\n.get Code\nĠmigr ate\nĠrib bon\n) return\nĠcharg er\nac om\nADI US\nĠAmb assador\n-a fter\nĠann i\nĉs pin\nCon cept\nĠHend erson\nĠH OST\n.r ank\nĠNor theast\nĠber lin\nĠrequ is\n.f eed\nĠsource Mapping\nĠRen contre\n. ajax\nnest js\nĠtre k\nĠN acional\nĠ& [\nĠpay able\nort ex\nĠde pt\nfield Name\nĠcomple tes\nĠR VA\nĠon ions\nal ignment\nForm ats\nĠ' {$\nHash Set\nĠB od\n.Invariant Culture\nĠsettlement s\nĠhy dr\n. updated\nvent h\n( seconds\n=\"/ \"\nĠweb page\n( ĊĊ\nĠt ir\nĠto es\nĠBr ick\nĠamb ition\nP ot\n= max\nET IME\nĠdep ot\nc alls\nĠNor wegian\n` :\nĠbur ger\nĠprofess ors\nĠAl locate\n-third s\n-ch art\nĠfor d\n* N\n.k otlin\nĠpaper work\nĠDE VICE\n% @\",\nres pect\n(m p\né «ĺ\n- if\nĠcush ion\nob ot\nĠpar c\nSP ACE\nĠNet anyahu\nĠself ish\nfe at\nĠclient es\n-to ols\nĠpor ch\nĠj q\n. verbose\nĠlib erals\n] )ĊĊĊ\np ies\nNot Blank\n( term\nÈĽ i\n_Param s\n.normal ize\nB ullet\nAS IC\n(h ex\n_client e\n+ ,\n_D I\nĠforth coming\n} \")]Ċ\nse o\nU m\n> Name\nĠcomfort ably\nirection al\nW ITH\n/ pr\nĠP oor\nĠVit amin\nv ic\nG H\nĠprior it\nĠN N\nĠC losed\n¤ í\nĠis Open\n\\ Console\nAnd Feel\n.S UCCESS\n_OPER ATION\npol ation\nĠT as\nps z\n> '.\nC URRENT\nV endor\nhost s\nĠE rd\n>tag ger\nĠsourceMapping URL\nĠmar athon\n_c losed\nĠexem ption\nĠrecogn izes\nides how\n' $\n('/ ');Ċ\nm its\nwar z\nĠCh erry\nµ ¬\nn or\nport e\nĠw l\n_back up\n.get Boolean\n.get Resource\nĠdefinit ive\n. EditText\nĠs ÃŃ\n.C ONT\nĠPL AYER\n.c ards\nĠSh ore\n('/ ')Ċ\ncl uir\nWeb Driver\n(m onth\n-re lease\nĠins pector\nå £\nĠN F\n_cl ip\nåŃ IJ\nĠinteract ing\n.t mp\nĠ'' 'ĊĊ\nĠde e\nĠfro st\n\"] ))Ċ\nĠPl aces\nTh rows\nf ork\n/ day\ni Phone\nĠM IC\nĠfold ing\nĠcro re\nĠCh iefs\npher ical\n( price\n.Write String\nĠexit ing\n] ',Ċ\night ing\nIng redient\n( vertex\nĠscroll View\nh f\n: new\nSE N\nse ctor\nĠsp ins\nĠS cheduler\note chn\nsem icolon\nFont OfSize\nĠSpecific ally\nfl amm\n.Object Id\nĠcont a\n_per missions\nĉF ROM\nIC ODE\n/ kg\nĠHot els\n-m ed\nĠD in\nĠn avy\nget Param\nĠm end\nĠportray ed\nĠMet ropolitan\nPaint er\nĠref erral\n_g ood\nĠmar vel\nosa ic\n> (&\n. ur\nĠest os\nWill iam\nĠtim ber\nĠquel ques\nĠDoc uments\n.X aml\nĠbatch es\néģ ĵ\nĠRe leased\nT ail\nCO OKIE\nhe id\n_st ation\nĠV ia\nS ale\nĠRe peat\nĠprom in\nĠZ o\n- forward\nĠI on\nit ary\nĠj us\n- request\nĠproud ly\nĠStream ing\n(Mouse Event\nĠS print\n_ rotation\nRe positories\nĠt art\nĠÑģ в\nĠm appings\nè ª\nC u\nC ycle\nĠb un\nĉl ua\nãĥ ī\nĠ(( !\nĠcollect ively\nĠCon d\nĠwsz yst\n(l ib\nopenh agen\n_s kip\n.Column Header\né Ĥ\nperi enced\nı è¿°\n_p rops\nĠcontr ace\nĠmatch up\nab etic\n.m embers\nRE CT\n(d at\nĠs og\nren om\n_M ethod\nCustom ers\nfull name\nZ N\nre try\nĠk ap\nĠNe u\nè Ĭ\nadd Child\nwill Return\n_p ermalink\nĠener getic\nĠW et\nĠMor r\nĠg cd\ncount s\n, type\nd ig\n( Login\nĠcr acks\nĠbacter ial\nĠMe at\nĠArm strong\nĠBron ze\nĠapprox imate\n_dir s\nlig a\nÅĤ ad\nĠkind ness\nĠcont re\nĠE VERY\nM ET\nĠannounc ements\ng pio\nĠWaitFor Seconds\nĠPhotos hop\nĠdis contin\n/ dd\nĠtop ology\nan ical\n. interface\nauc oup\n.Hash Set\nARI ANT\n(r outes\nĠT eh\nĠh ype\n] \").\nĠsl am\nĠbro th\n- inter\nĠR id\n-m anager\nCancel ar\nĠP agination\nĠsound track\nĠpost erior\nĠscr ub\ncre ating\n- *\nir teen\n.d y\n.s ymmetric\nĠ\"\" .\n============ ===\nĠch assis\nĠnumberOf Rows\nDevelop er\n_b ins\nĠO UR\nri eb\nPro s\nĠwi ÄĻ\n\" d\nĠasync io\nze igen\n_s pi\n.A LL\nĠscre ws\nCh inese\nĠapi Key\nĠun successful\nĠSeah awks\nOR G\nç« ł\nĠprofession ally\nĠCou pon\nåŃĹ æ®µ\nCon vention\nĠpol ym\næī ĭ\nĠsalv ation\nĠengine ered\nĠW rest\nĠG CC\nĠwar mer\nLayout Constraint\nĠag grav\nScript s\nvent ure\nĠrefriger ator\nĠinnov ations\nĠRun ner\nN IC\nĠRoll ing\nControl Events\nĠlo os\np ac\nĉ panel\nef e\nĠBudd ha\n------------ --Ċ\nåº ĵ\n(for Key\nĠl umin\nĠ( ?\nĠA IDS\n, user\nim ientos\ncontent Type\nant lr\né ¦\nĠW elt\nProdu ction\nm ight\nĠV II\n\", (\nĠobserv ing\nĠdeliber ate\n( control\nĠwith d\nĠsem ana\nST ACK\nuch en\nN ice\nĠDeutsch land\nĠSpec ifies\nd ma\niz io\nĠF acts\n_pop up\nĠDirect ors\n{ :\n[ R\nĠÑį леменÑĤ\nĠpl at\nĠdirect ing\nä¸ ī\nĠGil bert\nâĢ¦ .ĊĊ\n.q ml\nĠthere after\nĠdis position\nd raft\nĠsurge on\nĠIns ider\nBl end\nĠT rev\ntr insic\nTop ics\nrie ve\n_FILE NAME\nĠaut res\nJ ose\nProdu cer\ner us\nĠpet it\nĠN EXT\nĠF ilters\nĠreplic ate\n\"] ).\nĠl enders\n] \",Ċ\n; charset\nCpp Object\nĠfl oral\nĠT ipo\nĠcirc uits\ne asy\n(& $\nitt a\nery l\n_COMM ON\n'}} >Ċ\n-back ed\n(var iable\n( Index\nĠvo ir\n_loc ations\n++) {\nĠLouis ville\nĠgrat itude\n.Mock ito\nĠP owers\nie urs\nĠge ographic\nra le\nĠc ra\nĠSp urs\niph ertext\nAC ION\n- common\nĠvict ories\nĠFinal s\n.sh uffle\n-m illion\n_PRO C\nass ume\nĠil s\nDB C\nBoot Test\nĠl avor\n.test ing\n. ast\n\"] /\nm oid\nĠqual ification\nges ch\nĉ put\nĠair ports\nJ I\nTe acher\n_un iform\nĠn ama\nĠB ast\nert ype\nc apture\nget All\nĠReyn olds\noo led\n.com ments\nĠch in\n). *\nĠи ли\nt gl\nud os\nĠd ÃŃas\nch ai\n.pro gram\nĠps z\nĉ icon\nph il\nent ral\n_WR AP\nov i\nĠnost alg\nIn finity\nĉy ield\nĠvit amins\nQu aternion\nS ink\n_g oods\nĠ ........\nĠW ings\nur idad\n-st ory\n\"] )ĊĊ\nidel ity\nType Def\nG tk\nĠí Į\n_M ain\nĠche z\nĠR aven\nĠpay roll\nĠfreel ance\nLL U\nĠM end\ned ay\nApi ModelProperty\n.Form BorderStyle\nĠeconom ist\nstan bul\nĠfre ight\n-A gent\n(m eta\nĠsym metry\nĠ' ..\n.C alendar\n- aut\ng f\np ent\nyc lopedia\nĠwish ing\nĊĊĊĊĊĊĊĊ ĊĊĊĊ\nĠgentle man\nĠê ³\n= #\nĠlect ures\nâĢľ In\nĠ! _\nĠh b\nĠV endor\nRecent ly\n_n otes\næıIJ 示\n\" My\nHeaders Height\n_S O\nĠunw illing\nĠsuper hero\ng io\nps y\nĠPe er\nj avax\n& apos\nĠCr isis\nord inal\nMem cpy\n++++++++ ++++++++\n- val\nĠwork book\n- ap\n= k\nĠmetal lic\n_ peer\nBy PrimaryKey\n_S D\nu ator\n_SH ADER\n) Math\n.Trans form\nĠc ows\nPh i\nĠC lem\n(_ (\"\nĠL ud\n-d elay\nĠSec urities\nĠOrth odox\nSym fony\n(re port\nĠent ertain\nE PS\niz oph\nex ual\nIR D\nä» İ\nĠl ith\nĠsanit ize\nĠfemin ine\nIS BN\n.auth entication\n_p ipeline\n/ constants\nĠCON F\nĠluc r\nric ia\n.t tf\n.set Content\nĠst an\nore an\nĠL loyd\n.raw Value\nĠg or\nĠBrow ns\nRe gression\nĠlower ing\nna issance\nĠbl ows\nĠam azed\nĠun related\nRe views\nĠrub y\nĠMod ifier\nĠgi ants\n. thread\nĠcontain ment\nĠStart Coroutine\num at\nore lease\nĠR andy\n@ endif\nD igest\nĠsubur ban\n=\" );Ċ\nĠann once\n. variable\n\\F oundation\nĠa cre\nV an\nĠt uples\nd ns\nĠStand ing\n_l arge\nĠbox ing\nSupport ActionBar\nĠFort une\nĠR um\n_m ultiple\narch ical\nĠf write\n_ quote\nĠfool ish\nĠcompr ising\nĠо п\n- selected\nv f\nma id\nN ama\n(d atetime\nĠindirect ly\ng art\nfix tures\nch os\nĠH alo\nĠrec urring\n- news\nv il\nĠNurs ing\n- produ\nĠH Q\n\\Http Foundation\nenc i\nau en\nĠv y\nocr acy\nĠdeleg ation\nĠas phalt\nĠset Selected\nk ok\n/ rest\nmet ics\nĠNS Date\nĠtravel led\nĠrec ib\nĠm ime\nCL IENT\nĠG U\nĠH ANDLE\n/ Q\n[ z\nĠbother ed\nĠBB Q\nç as\n_ex amples\n_F IN\nĠwhite Color\nĠastr onom\n-d ir\nĠsovere ign\nĠb reeze\nĠin ning\nĠEd monton\ng li\n.blog spot\njs x\nĠvers a\nĠMoh ammed\n.J ob\n-t oggler\nĠп олÑĮзоваÑĤ\nard on\nĠnew born\nĠnav al\nnote q\nĠtum blr\nĠh entai\nĠTyp ically\nĠlo ot\n.S prite\nFl ight\nĠw avelength\n-s k\nĠEl le\n_ exports\nĠ Ñı\nĠI H\nizoph ren\nĠí ģ\n_pr imary\nĠmo is\nĠB N\nĠsystem ic\nĠdifer entes\nIN CT\nĠ'' ĊĊ\n$ q\nWidget Item\ncl ide\n$ file\nL emma\n/ table\nag rid\nĠMongo DB\nint e\nĠapp rent\nÂŃ ing\n.D b\nĠà Ĥ\nham mer\n=' ';Ċ\nĠbro kers\nit lement\nsembl ies\nE le\n{ x\nĠlast name\n< -\nĠfl atten\n_b and\n.R oot\n.read FileSync\n==== ==\n.r x\n? čĊ\nĠmetaph or\nT i\ncon te\nĠdeb it\nĠcont empt\nCpp Type\næĶ ¯\nForm Field\nr atio\nos opher\nĠimpl ant\nP URE\nĠal ta\n_man agement\nĠref ine\nĠCheck Box\nĠChar l\n- version\ncond itional\nven ues\nĠrif les\nĠoff spring\nĠmill ing\nĠshar ply\nĠunder water\n( origin\n_ Control\nĠ. $\nPl ugins\nĠdry ing\nĠillustr ates\n- u\nĠveget arian\nn pc\nHe art\n; ',Ċ\ncom ma\nte enth\nas an\n/s pec\n_m oves\n-m argin\nĠing en\n³³ Âł\nĠpro jet\nĠo tra\nĠbr as\n. utc\nĠsle pt\n= sub\nab ilit\npost er\nĠs dk\nounc ill\nĠw d\nPre paredStatement\nĠDr um\n( attribute\nĠEther net\nĉ DB\nCal ifornia\nc ube\n[ I\n.C reated\nĠH M\nĠtr acing\nForms Module\n- you\n.c urrency\nfeed ing\nĠt body\nL i\nacc ion\nn as\nĠtr ouver\nN ONE\n\"} ,čĊ\nĠf tp\nWith Identifier\npol ate\nFile Info\nĠpurs ued\nĠĠĠĠčĊ ĠĠĠĠčĊ\nDE SCRIPTION\n} */Ċ\nFrom Nib\nĠdecor ative\n_S SL\n(ch at\nT LS\nĠsurpr ises\nal culate\nĠS plash\n( Configuration\nĠS EM\nim son\n/lib rary\n< Double\n. robot\n³³³³ ³³³³\nĠCP F\nĠUnder standing\nĠcos metic\nĠX t\nt ips\n+ k\n(\" '\nĠP DT\nW AR\n.get Object\nĠTrad itional\n.sl ug\nĠDi pl\n=\" \",\nĠFil ms\nĠAn im\n.h elp\nĠemb assy\nĠBoot s\nĠb unk\n-r isk\nĠp ci\nĠ/ \\.\nĠI PT\nĠcrash ing\nĠip v\n_ ke\nĠRES P\n.Log Error\nĠinade quate\nI on\nĠF ür\nric ula\nĠshould Be\nal ready\n'].\" \nG ED\nfa q\nĠoption ally\n_D is\nĠSuccess ful\nĠC ensus\nĠinc arcer\n_C ARD\nĠav iation\nĠG ym\nAuthor ity\n.B ean\nsh ader\nNot Exist\n_Text Changed\nĠST OP\n( team\n\" H\nw g\nĠgr inder\nĠstri pe\nĠpres ervation\nCl aim\navers al\nware house\ntarget s\nTr ust\nĠal lev\n, www\nous se\n_ch an\n_S ize\nsystem s\nĠobj ection\nĠK ane\nĠcor ros\nĠD SL\nĠu a\nĠM H\nĠStrateg ic\n_t cp\nĠê° Ĵ\nĠborrow ed\nĠA ch\nĉ command\nĠg ps\nle ston\niche ver\nĠU A\nĠassault ed\nĠspecial izes\nĉ search\nHot el\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ čĊ\nĠP itch\nĠ Ùģ\nREAD Y\nĠparent al\nĠg éné\nĠdonn ées\nĠdet ain\nT ARGET\nĠprotagon ist\nĠclear Interval\nĠIcon Button\nĠGet All\nType Info\nE H\nâĢľ They\nĠ{ [\nĠg ag\nĠ Ú©\nĠD ropdown\n.f ree\ng one\nim ens\nĠinst al\nĉc url\n_C AN\nĠB one\nï¼ Ķ\nony ms\n-g overnment\n.binding Navigator\nĠD ans\nĠMc L\n( en\n>( _\nÐĴ Ñĭ\n.* ;čĊ\n= j\n-c or\nS on\n.ToolStrip Item\n- around\n_X ML\nend Date\nĠsl ack\nĠrot ated\nĠno qa\nĠc ottage\nĠencontr ar\n_s kill\nhou ette\n! 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')Ċ\nĠDiv ider\nos l\n_C ANCEL\n_pre pare\nst in\nĠHe ath\n.Primary Key\nĠâĨ IJ\nĠLocal DateTime\nĠcooper ative\nL earning\n.en queue\nĠgo og\nĠReg ression\nim ates\nĠvoy eur\nĠDr ink\npl ug\nĠl ender\nman a\nĠperson nes\nyp se\nĠun link\nĠRav ens\nĠhur d\nĠperiod ically\nARG S\nĠG H\nchar acters\n... \"ĊĊ\n- establish\nĠd n\n( condition\nĠGr avity\nĠest as\n_f ocus\nCreat ure\n(s ite\nĠc arr\nĠR L\nĠR I\nĠM oto\nAS F\nĠLuck ily\nĉ Route\nĠent ropy\n(\" ,\"\nCol lect\n( contact\nĠFlo rence\nĠpremium s\nĠlif ecycle\nĠb ans\nx ef\nWeb Kit\nĠFlo ating\nĠcos a\nSpec ific\nĠLo ans\nb read\nĠdes criptors\nĠ{ :.\nTH READ\nĠT rent\nĠsc op\nQ A\nĠAnt ar\np el\n_d ifference\n_ch anges\n(... )\nĠR otation\nĠLG PL\nĠJ UST\n(T ask\n_sub set\nĠTR ANS\nåĬ Ľ\nĠSc out\n-p opup\nĠsm oked\n_C lass\nĠturn over\nbr akk\nĠRock y\nt as\n.Regular Expressions\nĠElli ott\nĠSp inner\nDU CTION\nĠlib re\nĠmol to\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ\nĠF TP\nm peg\n(f eatures\nĠb ald\nĠV id\nĠsh outing\nL int\nĠsock ets\nĠpro w\nĠnouvel le\nisc ard\nĠS ponsor\nĠconsult a\n)) );\nInd ian\nĠR aspberry\nĠteam mate\nĠJ WT\nĠGh ana\nĠc akes\npr imer\nform a\nerg arten\n_M anager\nĠpre season\nG AME\n| \"\nĠBro ck\nĠoccup y\nĠdecor ations\ná nd\nĠc ot\nĠpar an\nD isk\nrem ain\n> ?\nStr ong\nĠfr ance\nĠE ra\n-c r\n.Buffer edReader\nĠParad ise\nĠV AT\nĠAnd ers\nĠlim b\namp oo\nĠimper ative\nUT ILITY\nĠRec ognition\nĠragaz ze\nĠpop s\nyp ress\nĠemb argo\n// {Ċ\nĠsy ll\nP TR\nåŃĺ åľ¨\nĠdid nt\nMail er\nĠacad emics\nĠFra uen\nne ider\n- rel\nĠrain bow\n( In\nĠslic ed\n============ =Ċ\n(s end\nNSMutable Dictionary\nv os\n(p ackage\nĠord inance\nview er\nĠSant os\n-s elling\nĠgo v\nett le\nĠfound ers\nĠw aking\nsl ashes\n-p ound\nre cht\nا ت\n.on Click\nĠn ord\nst änd\n_ when\nUT ERS\nic c\nĠcaps ule\nĠW id\nM arc\nภ¸\nro red\nUG E\nLO UD\nĠAud it\nip ients\nop ian\nĠS ue\nĠwur den\n.H elpers\nĠf actions\n[ np\n-th an\nĠre co\nĠk as\nĠcmd s\n/n etwork\nxb f\nget Color\nĠbi ased\nĠL ak\nD atas\nvent s\nĠë ²\n_P S\n. Validate\nInv oker\nĠne uen\nĠju venile\nV ISION\nĠdev ote\nĠlin ha\nĠdiscount ed\n\\ Config\nĠworth while\nĠskin ny\nĠC ourses\nle ys\nĠMort gage\nK evin\nĠannounc es\n]) *\nres ervation\nĠæķ °\nĠprejud ice\nĠString Comparison\nĠbe ard\n-w in\nĠS ão\nĉ ms\nj al\nĠE arn\n_ ports\nĠN ombre\n_C OR\nĠB UILD\n.s ound\nY ellow\nĠlineback er\nĠchar itable\nj ug\n_NON NULL\nĠD ental\n\"> ${\nĉm atch\nR ussian\nĠvers ch\nĠp inned\nĠadopt ing\nOptions Menu\nP ag\nĠpair ing\nĠt read\nerc ises\nĠSp read\n) i\nĠB AD\n_t f\nUI ImageView\npop ulate\nb ab\nĠÏ ĥ\n[ ++\nĠopi oid\nĠ## Ċ\nd type\nĠStart s\n('/ ')\nĠperson als\n-mark et\nĠredund ant\nĠEss ential\nĠscrap y\nĠи м\na cl\nĠcre ar\nĠB end\nĠrel ieve\n- room\nw ife\nĠv Ãł\nĠQ Point\nĠqu asi\nĠmethod Name\n\\x c\nĠPer u\n/ The\n. orm\nĠv iz\n/p df\nLoc ated\nĠconfront ation\nĠChampionship s\nĠhyp ert\nĠd j\nĠUser Info\nĠåĪ Ľå»º\n\\x b\n(s im\nĠ== Ċ\nĠst aging\nĠdr astically\nåŃ ¦\nl ords\n. less\nвед иÑĤе\nĠB ucket\nĠM am\n. term\n_p i\nc zy\n.p ub\nprec io\nĠV irt\nĠrom an\nit at\nL ex\n_inf os\nÄ °\n. other\nVE LO\nĠp onder\nĠh anno\n( Page\ndo i\nĠpol ite\nĠprogram mer\nD ies\n$ d\nĠrep lication\nadd Column\nfr ican\nĠl eng\nbe er\no it\nĠw asting\nyl im\nme asure\nN eg\nĠpart ie\n.con sole\nĠGu inea\nTE L\n_f act\n.ch unk\nĠl ent\nĠall er\nĠठķ\n_id le\nĠad missions\nJSON Array\nĠv ibration\n.h elpers\nå¤ ĸ\nĠh en\nj ohn\nĠì ĥĿ\nĠjud gement\nĠge en\nter ra\n^ {\nĠI z\nĠc â\ninst ances\nĠthreat ens\nĠm üssen\nKind OfClass\nĠstoryt elling\n_d emo\nri as\nPriv acy\nh ift\nĠY i\nes or\níķ ł\nens itivity\n.W riter\nภĤ\nD istrict\n.get JSONObject\nIm pro\n(get Resources\nĠS PELL\nrodu ce\nĠslow ed\nĠlin ewidth\nĠhonest y\nĠCo ord\nĠF ork\nĠDispatch Queue\nĠCl iff\nĠW iring\n_TIM ESTAMP\noll ah\nav oid\n++ ];Ċ\nsem antic\n-c ss\nĠv eto\nĠM err\nĠlegisl ators\nCEE DED\nĠquestion naire\nĠP ills\nCal culate\n(c ore\n' e\nĠdis like\nĠPre ferences\n_EX TERNAL\nè° ĥ\nĠd odge\næľį åĬ¡\n.n ames\n.draw Image\n_p rom\nuck land\nĠ<$ >\nı z\n/s ite\né¡ ¹\nrop he\nĠcomp elled\nĠl aptops\nĠun i\nC LOSE\nĠcasual ties\nĠUn iform\nTerm inal\n. \",\"\nD AT\n(T reeNode\nĠGand hi\n(st mt\nAX B\n* M\nĠumb rella\nan imal\nĠgr pc\nĠwhere by\nĠfloat s\nĉ arg\nĠdb g\nĠexceed ing\nEvent Type\n.SaveChanges Async\nĠ{ {{\nĠow ed\nahren heit\nĠì §\nĠequ ipo\nur ai\nĠid ol\n] \")Ċ\n_m ajor\nĠentire ty\ninger print\nç os\n/ account\nĉ right\nurs os\nĠE DT\n_INS ERT\nĠsh ining\nĠ< :\nEdge Insets\nĠcolon ies\n. IM\nĉĠ ĉ\nRO AD\nCC CC\npl acing\nĠget Activity\nem acs\n' %(\n.click ed\nĠTh em\nis ia\nBus car\n.re name\nĠo ath\nĠafter ward\nĠU FO\nAP S\nĠJackson ville\n.s ome\nConf irmed\n.s can\nig Integer\nDecor ator\nsh ield\nress ive\n.d id\n请 è¾ĵåħ¥\nĠsh utter\nD am\nĠparent ing\ney ed\n$ item\n-de velop\nĠextract s\nĠdecentral ized\nĠEl sa\n_sp in\n]) +\n-in itial\nĠmult itude\nĠsens ory\nĠMODE L\nĠsafeg uard\nì ¹\nĠhunt ers\nĠT iny\nIN O\ndecor ate\nĠNo Such\nH o\n( Response\nĠr uler\nĉ short\nĠc aster\nĠclient Id\nĠp db\nëı Ħ\nit ic\nĠGame State\nĠnew Item\n)ĊĊ ĊĊĊĊ\nou is\nn oc\n.BL ACK\n_V ECTOR\n---------- ();\n.get P\nany e\nĠneur on\nif old\nĠK nown\nBit coin\nAny way\nay ette\nĠ' ['\nÃł nh\nm gr\nĠcor related\nĠn ause\nĠment ality\nhas Many\nĠF G\namp ie\nIT U\nF s\n.S p\n_b etween\nDep endencies\nou g\nPlace holder\n= text\nĠMan aging\nocal ypse\nåĮ Ĺ\n_m ag\nf ld\nâ ij\nC AM\nĠHelp ers\nĠd ost\n/ out\nĠassass ination\n.get Image\nĠKenn y\n.' )ĊĊ\n){ //\nĠR anger\nĠg ek\nĠsinc ere\n< Value\nĠD OT\nĠVict ory\nĠleg ends\nĠpr isons\n(ex pression\nĠR abbit\n_s entence\nĠbit es\nĠon Failure\nĠâĪ Ī\nK im\n.g ender\nĠÎ »\nĠ[ .\n\"] );\nland ing\n-d igit\nTE MP\nĉ entry\nĠstrt ok\nĠdesc endants\num no\nĠlean ing\nĠspecific s\nq n\nĠSp art\nĠpor r\nEDIATE K\nĠse per\n' aut\nĠSTE P\nĠBorder Layout\nĠret ros\nĠSalv ador\nĠEN GINE\nx dc\nT weet\nv k\nĠì ²\n] <<\nhet ics\nc oding\nRe ach\n.re q\ngu ide\n.s cope\nsh irt\nrog ate\nSET TING\nĠProte in\nĠe ing\n. 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ist\nĠchat ting\nĠM ant\nĠ% @\nĠ\"\" );ĊĊ\nĠd gv\nĠíķ ¨\n.re peat\n_M essage\nĠadvis ers\n/ path\nĠk es\n) } .ĊĊ\nogen esis\nĠOPTION S\nupt ools\nĠmilit ant\nĠex ited\nig ar\nĠCOM M\nĠDis posable\nay cast\nĠrow span\nĠsyn thes\nĠsond ern\nĠĊ\nĠJ acket\nR ATION\n.getSelected Item\n- init\nĠReg isters\n_se p\nĠTool kit\n.d ict\nĠx label\n\\ Table\nt oc\n_com bo\nĠComp act\nĠr ugged\nà¥ĩ à¤\n-man agement\n')}} \">Ċ\nĠSt amp\nı l\nro x\nĠlandsc apes\n_NOT E\nmon ary\nc ab\nĠmo et\nx af\nrc ode\n- cli\n_g ate\n[ event\nSP ORT\ng ia\nĠS UPER\n/ Login\n_sh utdown\nint errupt\nĠpret ending\nĠfr inge\nĠRed s\nĠC UDA\nĠUN IX\nv it\nĠbr ig\ndr v\nĠConn ector\nThere fore\nĠl ia\nD etection\n_ actor\nĠtemp file\nĠecc entric\n- role\nĠpad x\nd ent\nWest ern\nĠê ·¸\nĠApplication Record\nĠcampaign ing\n_run ner\nĠC ivic\nale igh\nĠdire kt\n.s ul\nĠĠ ĉĉĉ\nant en\nĠiss uer\nĠassert ions\n( orig\nAT IO\nĠlean ed\nä s\n.D TO\nexpl ode\n.O bservable\nĠstagger ing\nĠkidn apped\nĠprogram mers\nĠInn 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y\nĠAlb any\nĠè §\nll er\nagn a\nwork ers\n.bl ank\nĠPr ayer\nM IC\nĠresil ience\nTe X\nĠL anguages\nst udy\nĉc urr\nĠenzym es\nSl ug\nĠíĮ Į\nstr al\nĠtum ors\nĠseg unda\n=' {\nin struction\nĠL isp\n/ info\nĠ\" {$\n,: ),\nĠg v\n( ErrorMessage\nĠ' =\n}- ${\n.Doc uments\n\" Well\nĠreminis cent\nĠg az\niro pr\neh r\nĠsup pressed\ners h\n.scroll To\nĠcad ena\nĠgame State\nÃŃ m\n( conv\nĠTom orrow\nĠC CT\nM ongo\nul g\n.C amera\n.hand lers\nm ph\nĠst k\nĠgen etics\nAC ING\nTr ivia\nĠB am\n(m arker\n.St retch\nĠSun ni\nĠBet ty\n.t olist\nun likely\n.Rect angle\nob solete\nIL ON\ninner Text\nemb ourg\na N\nĠV ehicles\nun lock\n: utf\nn ob\nĠSee ing\nĠNE VER\nĠt ls\nĠfil les\nĠbenef ited\nĠCl int\n*/ ),\n.f old\nĠpos ible\nA DED\nth ouse\n.D AL\nĠO dd\nro kes\nĠSun ny\nĠPartial Eq\n_B uffer\nĠLe vi\nlong rightarrow\neld on\ng ages\n_w arn\n.Create Table\nĠD ip\n_ questions\n.log ic\nĠ# \"\n={() =>\nĠt ep\nĠju icy\nì Ĥ¬\nen ko\nia lect\nÙ ī\nĠon board\nĠæ ı\nĉ rt\n_ UTF\nĠQ Action\nâĢ ŀ\n( 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Book\nx ad\nĠG one\nĉd p\nĠdev otion\nĠst m\nĠobs ess\nĠBack end\nQu eries\nI k\n// ****************************************************************\nĠdivid ends\n.parent Element\n} \")ĊĊ\nĠMaterial PageRoute\n: num\nĠexp lic\nĠO L\nle ast\nO ops\niment os\nĠins urers\nĠhero ic\nĉf ields\n.img ur\n.btn Cancel\nĠDetect ive\n(s m\nĠMutable LiveData\n.l ab\n(( [\nĠha irst\nĠTrans actions\nå¼Ģ å§ĭ\nĠstd Class\nuent o\nG IS\n_c od\nInstruction s\nC alls\nPointer Type\nĠR w\nĠassort ment\nĠD IG\n+ r\n_C ERT\nĠinst ability\nĠv ib\non as\nĠro ku\nap ellido\nĠan gl\nprene ur\nĠfluid s\nise ase\nĠde ed\nqu ist\n_CONST ANT\nĠequ ilibrium\n_de legate\nĠQuant um\nre i\nCap abilities\nrect angle\n? ><\nal ien\nĠJ ug\nD NA\nT ickets\nOcc urs\nĠHaw k\n.setHorizontal Group\n\\ Collection\nff iti\nĠre arr\n.setVertical Group\nĠc avity\nĠadult e\nFac ade\n- wh\nĠL OL\nØ °\nĠgrand parents\nSw ift\nĉw x\næīĢ æľī\nif en\nff set\nB eyond\n// }ĊĊ\nĠw ager\nĠb ury\nĠcomm ence\nreg istro\nsc ient\nĠPer cent\nĠд олж\n( identifier\n.set Model\nĠs eldom\nnt on\nĠappl iance\nam us\nrys ler\nĠpant ies\nengu ins\nĠmim ic\nĠon Changed\nĠal coholic\n.reload Data\nCh arge\nĠF ax\nĠj ScrollPane\nEmp resa\nĠsh attered\nx ba\nFont s\n? s\nĠpost season\nret ain\n_r ates\nĠrequest Code\n.t odo\n´ s\nCH K\nĠKeep ing\nenge ance\nĠvs code\nIPP ING\nDefault CloseOperation\n_ raise\nĠO culus\nogram s\nra j\npc i\nĠcorros ion\n.handle Submit\nAccess ible\nĠP iano\nl ittle\nAC L\nÄĩ e\n.un wrap\nĠCon vers\nĠLe ben\nione er\nĠMer chant\nĠJ orge\nĠembr acing\nĠvent a\ná st\nĠvi ene\n< QString\nĠexplos ions\nĠdistur bed\n.\" <\nm emo\nĠAb original\nĠcomple to\nTex Parameter\nĠuom ini\n( agent\nÑĥ ÑĢ\nĠWh olesale\n/ am\nĠBook mark\ndr agon\nĠglo ve\nĠ\" \"));Ċ\niv ariate\nnow rap\nIn Children\n.B r\nĠcon exion\nĠback bone\nĠe clipse\nĠpersec ution\n': ĊĊ\n/ link\nĠP ero\nand as\nĠT ek\n. \");\n-an alysis\nĠer ad\nMar shal\nĠanch ors\nog er\nĠconver gence\nst icky\nĠnave g\nint ern\n_DE SCRIPTOR\nĠConsult ant\nĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ\nĠA uch\nĠer re\nÅĽ li\nĠHor izon\ncol a\nInstall ation\nhot mail\nC NN\n.C ollectors\nch s\n(tr ace\nĠEnc rypt\nĠ---- --\nĠBase Controller\nĠag ua\nĠre active\nid l\nĠclass Names\nĉ Session\nĠDod gers\nH ad\n_l v\nIs Valid\nĠHEL P\nut to\nĠVer ification\nĠget env\n_p a\n.b mp\n: f\nĠLou ise\n(' ;\n/ socket\nGr anted\n.c alendar\n( IP\nĠP X\n.R oom\nĠprogram m\nens i\nĠtablesp oons\nĠle ve\nĠmo str\n.t ipo\n/ an\n(d i\nĠb iod\nĠdb Context\nĠJS X\nĉ results\n. 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Up\nĠno op\nc io\nĉ ST\nĠdecre ment\nĠmagn esium\n_ rotate\nS it\nĠnieu we\nĠter med\níķ ©ëĭĪëĭ¤\nĠur g\n_t ouch\nĠsw arm\nĠcl ave\nth est\nĠL af\nH X\nĠH ulk\nĠplaint ext\nĠSof a\nget Session\nL ed\nĠecosystem s\nhe i\nĠK ills\nĠhus bands\nÑħ ÑĢан\n(d om\n_t iles\nNib Name\nĠdon ating\n. acc\nĠlifes pan\n.b n\n_RG CTX\næ ¥\nans en\nĠmod elling\nLayout Params\nĠonChange Text\nrs a\n- location\n.P e\n(b us\n(s ong\nĠprodu k\nĠSH OULD\nĠC J\nĠs os\nĠHome Controller\n.load ed\n(D ocument\n.s ocial\nt iles\nĠl ame\n= df\n.parse Long\nĠpr ac\nĠdet ox\nĠV E\nĠpunt os\nĠdo ctr\nĠan cor\nCA PE\nĠc mb\nçĦ ¶\n*) \"\n:// /\nValue Type\nĠmort gages\n; q\nĠRock ets\ns port\nUG C\nct s\nãĤ ģ\nie ur\nĠAppe al\n(n b\n//////////////////////////////////////////////// ////////\nIM ATION\nĠC res\nĠMan ip\nC ause\nat ypes\nman ufacturer\n# ----------------------------------------------------------------------------\nĠsp or\nes on\nĠpun ched\nĠbook marks\nĠBul k\nComplete Listener\nĠTalk ing\nĠEr nest\nĠrub bish\nk ills\nĠDE FIN\nĠneighbour ing\nar lo\nĠP CA\nĉm atrix\nlo k\nĠat las\nĠG ur\nĠw yn\n-n egative\nĠt ul\nĠre lic\nĠV oltage\nĠPre is\nĠJ NICALL\nĠPM ID\nak et\nĉ attr\nĠet iqu\nĠM J\nĠG mail\ncl r\n_exec ution\néĶ ®\npos itor\n. af\nN r\nGe orgia\nTop ology\nĠperch é\nĠmus lim\nĠepid emi\nĠsab ot\nact us\nĠë ĮĢ\nĠIO Error\n. est\np refs\nĠKr ish\n.Read Key\nNAS A\nu ção\n_D b\numer ator\nW ide\n(st atement\n.end point\n.... .....\nĠ[ *\nstream s\nm time\nP x\nat r\nĠt pl\nR oman\nĠscen ic\n.n z\nĠSe conds\nsub menu\nĠìĭ ¤í\n_b undle\nĠde ÄŁ\nĠS isters\npre ferences\nĠport a\nAd visor\nmax Length\nĠG REAT\n__ (Ċ\nole st\nĠLabel s\nĠen fer\nĠĠĠĠĠĠ ĊĊ\nĠThe ft\n_F ILL\nĠW ise\n) application\nun ami\n> ())Ċ\nADD RESS\nB ST\net zt\nĠQ gs\nS ense\nException Handler\nĠCh u\n.get OwnProperty\nĠexerc ised\niot ic\nĠRe leases\nĠp interest\nol ie\nis oft\nĠsequ encing\nĠpad re\n] ));čĊ\n(r adius\n.m ed\naint ies\n.Object Model\nĠem ple\nĠseg uro\nSt ars\nĠqual itative\nlem n\ná» ±\n> \").\nĠg x\n-c ert\nĠAST M\nĠfull name\nĠte lemetry\nĠCamb odia\n_ ul\nĠCl are\nC USTOM\nQ C\nĠUn s\nĠHTTP S\nĠPark inson\nancy box\n',' .\nT ue\n.get Last\nĠab i\nÄħ d\nA st\nĠEd iting\n.Un ity\nj mp\nĠm ats\nĠshared Preferences\nCapt ain\n.page Size\nĠr tl\nĠan meld\nRuntime Object\nĠdemand e\n(\" ;\nse ite\n-head ed\nĠK ra\nĠF ONT\n` \\\nClass NotFoundException\n. avg\natic al\nA j\nĠpermit ting\nPro j\nERR Q\nĠcre ampie\nĠBuy er\n-mod ules\nĠSund ays\n| `Ċ\nĠday time\nĠ+ (\nĠgl itch\nĠOper and\nĠtox ins\niny a\nD NS\nĠS as\nC ake\nĠNation als\n.add To\nĠs inking\nĠcompreh ension\nĠsc or\nag ements\nĠt ard\nĠmarch ing\nĠM TV\nĠs ane\nCreate Info\nẠ¯\nĠend Index\nĉ layout\nĠåIJ į\nS ITE\nĠT HERE\nĠ[ {'\nopath ic\nĠtrans mitter\n/ body\nĠp und\nĠC losing\nĠset attr\nĠbound ed\nAt las\nsum ing\n(t imes\npar er\nyn om\nfe it\nĠf rem\n- leg\nĠBr as\n> #\nĠì¶ ľëł¥\nĠIN STANCE\nĠC ouch\n_host s\nlik elihood\n.M arker\nĠM asks\nĠcere al\nutil ities\nĠelement al\nĠdist orted\nin active\nc ry\nW L\nUPPORT ED\n.Th rows\n/s chema\nser ie\n.\" ',\nĠBened ict\n-p icker\nig gs\nĠPir ate\nåij¨ æľŁ\nĠTh ema\nĠSouth ampton\nĠarray With\nĠPaul a\nĠpredict or\n- Ass\n.user id\nĠper i\nĠexagger ated\nur ate\narse ille\nĠCon cent\nĠP ik\nĠ@ _;ĊĊ\nĠform ations\nĠden omin\n\"/> .Ċ\nended or\nĠpan cre\nĠam t\nĠon Resume\non Delete\nĠB CH\n) (\"\nm ovement\nĠpot assium\n čĊčĊ\nĠMah m\n} \";ĊĊ\nĠd q\nĠPublish ers\nĠAm pl\nĠDani elle\nĠt ern\nèµ ·\nno ÅĽÄĩ\ne in\nĠAsync Storage\nun ger\nrou w\nĠsc issors\n/ assert\n.b ucket\n/ archive\n_M an\nĠint oler\nĠ() =>\nĠÐĴ Ñĭ\nĠsa i\n.x y\n.\" čĊ\nĠur inary\nes ub\nIST ICS\nĠÎ º\nĠcompl iments\nĠtypings Japgolly\nih ar\nExp ansion\nĠS erving\n_st udents\nĠX BOOLE\n( il\nĠì² ĺ\nĠj ó\n(t ol\n( JS\nĉC G\nĠD RAW\ntw ig\nĠo at\n_sm ooth\nĠC SL\nĠos ob\nĠens uing\nĠbank er\nĠBack pack\n_p ing\nĠwish list\n= ax\nĉĠĠĠ Ċ\nDis ney\nstead y\n\"> %\nĠproph ets\nĠZ X\nĠminimal ist\n.PL AIN\nSe attle\n. ordinal\nĠPI PE\nĠret orna\nĠjug ador\nĠB ret\nĠâĶ ľ\nĠpl 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çĦ¶æĺ¯\næıIJ æ¡Ī\nçĶµåķĨ å¹³åı°\nåģļ åĪ°äºĨ\næĿľ ç»Ŀ\nå®ī åįĵ\nä¸ĸçķĮ åIJĦåľ°\nåīį éĢĶ\næ´Ĺ åĩĢ\nå¥ĭ åĬĽ\nåŁİå¸Ĥ 建设\nå¤ļ åĬŁèĥ½\nä¼ļ éĢłæĪIJ\nåıijå¸ĥ ä¼ļä¸Ĭ\n究 ç«Łæĺ¯\nåĪĨ 红\nçŁ¥ èŃĺ\néĿ¢ æĿ¿\næĹł 声\næĢ¥ éľĢ\n失 çľł\nçĪ¸ å¦Ī\näº Ĥ\nåħ¨ æĻ¯\nç»ıåħ¸ çļĦ\nåī§ ä¸Ń\né¢Ĩ导 ä¸ĭ\nåħļ åĨħ\nåħ¥ ä¾µ\næĭī æĸ¯\nä¸Ģ å¹ķ\nåĬł ä¹ĭ\nèĤ Ĩ\nèĭ± æł¼\nèĭ±æł¼ åħ°\nå·§ åħĭ\nå·§åħĭ åĬĽ\nä¸Ģ å¿ĥ\nèģ Ĥ\nå¾Ģå¾Ģ æĺ¯\n管çIJĨ å±Ĥ\nçĻ» åħ¥\n建ç«ĭ èµ·\n建 åĽ½\nåŃIJ 宫\nåºĶ ä»ĺ\næİ¢ 究\n第ä¸Ģ ä½į\nä½Ļ 家\nçŃī æ´»åĬ¨\næīĢ èĩ´\nè¾ĥ å¿«\næĺ¯ éĿŀ\næıIJ åIJį\näºĮ èĢħ\nåıªåī© ä¸ĭ\nåħ¶ä¸Ń åĮħæĭ¬\nç¼ĸ ç¨ĭ\nçł´ ç¢İ\nä¸Ń 举\nå·¥ä½ľ æĬ¥åijĬ\nçŃ¾ åIJį\néħĴ ä¸ļ\nçŁ¥ æĻĵ\nçĥŃ å¿ĥ\néĿŀ åĩ¡\nèIJ¥ä¸ļ æī§\nèIJ¥ä¸ļæī§ çħ§\n人大 代表\nä¸Ģ个 æĸ°çļĦ\nå¨ģ æµ·\néĤ£ 人\n涨 ä»·\næ¶Ī çģŃ\néļ¾ å¿ĺ\nç¶ĵ é©Ĺ\nåı£ è¢ĭ\nç³» æķ°\næĸĩ ä¸Ń\n好 转\næĸ° 鼶åĶ®\n讲述 äºĨ\nå¼Ģ çĽĺ\nçķĻ ç»Ļ\næħ¢æħ¢ çļĦ\næĤ² 伤\næľ¬ æľŁ\näºĨ å¤ļå°ij\nè¿Ļ 让\nåIJĮ çŃī\næ¸ħ æĺİ\n个 åŁİå¸Ĥ\næºĸ åĤĻ\nåĩłä¹İ æĺ¯\n强 åĬĽ\nä¿ ¯\næ°´ 稻\nåĽºå®ļ çļĦ\næł¸ åĩĨ\n说 æľį\n顯 示\nè¿Ļ å¥Ĺ\næĻºæħ§ åŁİå¸Ĥ\nå±ĭ 顶\nä¸į æĿ¥\nçĶŁ é²ľ\nçŁ¥ æĥħ\næĬķ 身\nåijĬè¯ī æĪij们\nä¸ī åĽĽ\nä¸ĩ ä¸Ģ\nè¾Ĩ 车\n为 ä¹ĭ\nåĪ° æŶåĢĻ\nè¿Ļ æīįæĺ¯\nåIJį çīĮ\nåºŁ æ°´\nåݻ年 åIJĮæľŁ\nå¹´ éĻIJ\néģĭ åĭķ\nåıĮ çľ¼\nè¦ģ ç´§\n对 çŃĸ\nåľº é¦Ĩ\nçĻ¾ ç§ij\nè¶Ĭ éĩİ\nå¯Į åIJ«\n大å¤ļæķ° 人\næľĢ å°ij\nåı¬ åĶ¤\nåħ¸ èĮĥ\nåĨľ æľº\næŃ£ æĸĩ\nåºĶçĶ¨ äºİ\næ·± èĢķ\nä¿ Ń\nä»Ģä¹Ī ä¸ľè¥¿\nå¥Ĺ é¤IJ\nå½ĵ éĢī\nå·¦ æīĭ\nè°ĥ çIJĨ\næĻļ é¤IJ\néļ¾ åħ³\nåĩŃ è¯ģ\nçĪ± 人\næĮĩ è´£\nè´£ ç¼ĸ\nçļĦä¸Ģ 款\néĵ ²\nåįģ 个\nèĢ »\næľįåĬ¡ åķĨ\nåľ° çĭ±\nè¿ŀ å¿Ļ\nåĽ° æĥij\nçļ ĵ\nä¸į åIJĥ\nçİ°åľ¨ å·²ç»ı\nçĽĺ çĤ¹\nä¸įåģľ åľ°\n管çIJĨ 模å¼ı\nè¿Ļ 段æŶéĹ´\næ¤ °\n礼 åĮħ\næµģ 转\næī« çłģ\néĽĨä¸Ń åľ¨\næ±Ĥ åĬ©\nåįĬ 个\nå¿«éĢŁ å¢ŀéķ¿\nå¾Ģ ä¸ĭ\nè¯Ħ åĪĨ\nå°± æĥ³\nåķĨåĬ¡ éĥ¨\næľī éĹ®é¢ĺ\nèİ· åĪ©\næ¯Ľ çĹħ\næĦŁ åºĶ\nèī¯ æĢ§\nåĪĨ æѧ\nåĨ ī\næĪij们 çİ°åľ¨\nè¦ģ åĬłå¼º\nå·§ å¦Ļ\nèŀº æĹĭ\nåĪĩ æį¢\nçĭ Ħ\n顺 çķħ\nå°¤åħ¶ æĺ¯åľ¨\nèĬĿ 麻\néļ¾ è¿ĩ\næĹĹ å¸ľ\nå¤į åį°\nå¤įåį° 件\nå¿ħ éľĢ\n对å¤ĸ å¼ĢæĶ¾\néļ¾ åıĹ\nåİŁæĿ¥ æĺ¯\nç®Ĺ äºĨ\né«ĺ å±±\n离 èģĮ\nçµĦ ç¹\nçµĦç¹ Ķ\nå±ģ èĤ¡\nçĻ¾ 家\néģĩ ä¸Ĭ\næĺĶ æĹ¥\nä¸į 容\nçĽij管 éĥ¨éŨ\n主 æĦı\næµģ 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¯\nï ®\nา ม\nâ ĩ\nðŁ ¥\nï Ń\nðĿ Ļ\n×ķ× ł\ná ½\nĠ× Ľ\nðŁ ļ\nâ ļ\nï §\n×ij ר\n×Ļ× ł\ná ´\nĠ× Ĺ\ná ¼\nðĿ Ĺ\nĠ× ¢\n×Ļ× Ķ\nãģ£ ãģŁ\nãģĵ ãģ¨\ná ¸\nÙĬ ÙĨ\nãģª ãģĦ\nا ع\nภ¨\nà¹Ī à¸ĩ\n×Ļ× ĵ\n×ŀ ש\ná Ī\n׳ ×Ļ\n×Ļ× ij\nï ¥\nðĿ ĵ\nĠ× Ļ\n× ļ\nั à¸ĩ\nâ ĵ\nï ¤\nĠاÙĦ Ø£\nา à¸ģ\nà¹ī à¸Ļ\nà¹Ģ ร\n×ķ× Ŀ\ná ¹\nภ¶\n×Ļ× §\nภĭ\nà¸Ħ ร\nภĺ\nั à¸ģ\nðŁ ķ\nÙĪ ÙĨ\nà¸Ń ย\nâ Ĭ\nðĿ Ĵ\nĠاÙĦ ع\nา à¸Ļ\n×Ļ× Ł\nÙĦ ÙĬ\n×Ļ× ©\nà¸Ľ ระ\nà¹Ģ à¸Ľ\nĠ× ł\n×ķ× ¡\nภł\nÙħ ÙĨ\n×ķ× ¢\n×ķ× ŀ\nâ Į\nðŁ §\nà¹ĩ à¸Ļ\nภį\nã İ\ná µ\nĠاÙĦ س\n×ķ× §\nห ล\nðŁ ĩ\nâ ı\nðŁ ¦\nĠ×Ķ ×ŀ\nÙĪ ا\nĠ× ª\nר ×IJ\nà¸Ń à¸Ļ\nภ©\nà¹Ī ว\n×ķ× ¦\ní Ĺ\nã Ħ\nï ¨\nï ¹\nâ İ\nï ²\nðĿ ļ\nð IJ\nà¸Ħ ว\nห à¸Ļ\nĠ× ¨\nب ÙĬ\nร à¹Į\nر ا\nØ´ ر\n×ķ× Ĺ\n×ķ× ¤\n×ķ× ©\n×ķ× Ĵ\ní Ŀ\nâ Ľ\nà¸ķ ิ\nà¹Ģ à¸ģ\nï ³\nï ±\nà¸Ķ à¹ī\në ¹\nï ¬\ná ¿\nðŁ Ľ\nðĿ ĸ\nà¹Īา à¸ĩ\nู à¹ī\nĠ×Ķ ×IJ\nĠاÙĦ ØŃ\nפ ר\nÙĪ Ùħ\nà¹Ģ ล\ní ĸ\n×Ļ× ¢\nì Ī\ní ĵ\nðŁ ħ\ná ł\nà¸Ħว าม\nà¸Ī ะ\n׳ ×Ķ\nĠ× §\nภŁ\nà¹ī à¸ĩ\nห ม\nت Ùħ\n׾ ×Ļ\nÙĬ د\nà¹Ī à¸Ļ\n׊ר\nש ר\nà¹Ģ à¸Ĺ\n×ŀ ר\në ĸ\nع ÙĦ\n×ŀ ×¢\nâ ²\n׾ ×Ķ\nĠ× ¤\nà¸Ń à¸ģ\nس ÙĦ\n×Ļ× ŀ\nÙĤ ÙĬ\ní İ\nت ØŃ\n×Ļ× ¡\n×Ļ× Ĺ\ní Ľ\nï °\nâ ½\ná ī\ná Ĭ\ná ¨\nÙĩ ا\nĠ׾ ×Ķ\n×ķ× IJ\nÙħ ا\nà¹īà¸Ń à¸ĩ\nر ب\nĠاÙĦ ج\n×ŀ ×ĵ\nÙħ ÙĦ\nت ر\nà¹Ģ à¸Ķ\nק ר\ní ħ\nì ¼\nê ¿\nã Ī\ná IJ\nðŁ Ĺ\nê ¦\ná ĭ\nðĿ Ķ\nà¹Ģà¸Ľ à¹ĩà¸Ļ\nà¹ĥ ห\nม า\nว à¹Īา\nม ี\nี à¹ī\nà¹Ħม à¹Ī\nÙĨ ÙĬ\nØ ¤\nร า\n×ķ ×Ļ\nãĤĪ ãģĨ\nิ à¸Ķ\n×Ļ× ¤\n׊׾\nÙĤ د\nà¹Ģ ส\n×Ļ× ĺ\nà¸ģ ล\nר ׼\n×ķ× Ľ\n×Ļ× Ľ\në Ī\në ĥ\nðŁ ĸ\ná ħ\nâ ¼\nã ī\nà¹Ħ à¸Ķà¹ī\nת ×Ļ\n×Ļ× IJ\nĠاÙĦ Ø¥\nà¸ł า\nร ิ\nÙĤ Ø©\nØŃ Ø¯\nê »\nì ±\nת ×Ĺ\nì º\nâ ĭ\ná Ħ\ná ¾\nâ µ\nâ ¾\nĠÙĪ اÙĦ\n׳ ×ķ\nÙ Ģ\nÙĬ ا\nà¸ģ à¹ĩ\n×ŀ ×Ķ\nãģĦ ãĤĭ\nع د\nĠاÙĦ ÙĨ\nĠ×Ķ ש\nØ ¦\nั à¹īà¸ĩ\nร ัà¸ļ\nÙĪ ÙĤ\nãģ§ ãģį\nà¹Ģ à¸ŀ\n׼ ׾\n×ĺ ר\nั à¸Ķ\nà¸Ń า\nì ¢\nà¸Ń à¸ļ\nà¸ķ ร\nà¹Ģ à¸Ĭ\nì Ķ\nãģĹ ãģ¾\në ģ\në ķ\nðŁ Ļ\nâ Ĵ\ná ¶\nà¹ģ ล\nÙĨ ا\nà¹ĥห à¹ī\nà¹Ħ à¸Ľ\n× £\nั ว\nา à¸ĩ\n×ĵ ר\n×ij ׾\nפ ×Ļ\nĠ× ĵ\nĠاÙĦ Ùģ\nà¹Ģ à¸Ĥ\nש ×Ķ\n×IJ ר\në ¬\nãģ« ãģª\nÑĢ о\nว ิ\nÙħ ر\n×IJ ת\nÙĥ ر\nس ب\nÙĨ ت\nãģĹ ãģĦ\nا ج\nà¸Ń รà¹Į\nÙĥ ÙĦ\nس Ùħ\nส ิ\n×Ļ× ¦\në Ŀ\ní ľ\nì ī\ná Ĩ\nÙĩ Ùħ\nà¸Ļ ีà¹ī\nãģĤ ãĤĭ\nãģĦ ãģ¦\nس ÙĬ\n׾ ×IJ\nد ر\nãģ ļ\nÙĪ ج\nĠاÙĦ 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Ľ\nâ ¸\nì IJ\nà »\ná ļ\nâ »\nê Ļ\nâ §\nð Ĵ\nðĿ ĩ\nĠ×IJ ת\nĠÙĦ ÙĦ\nĠØ£ ÙĨ\nĠ×ķ ×Ķ\nãģ« ãģ¯\nĠ×Ļ ש\nت Ùĩ\nÃŃ nh\nÙĬ ات\nĠ×ij ×ŀ\nà¸Ļั à¹īà¸Ļ\nà¸Ļ à¹īำ\nÃł o\nà¸ķ าม\nãģ® ãģ¯\nd ır\nĠn ghi\nặ t\n×ŀ ×Ļ×Ŀ\nãģ¦ ãģĦãĤĭ\nĠ×ij ת\nหร ืà¸Ń\nĠس ÙĬ\nãģª ãĤī\nà¹Ĥà¸Ķ ย\nı yor\nà¸Ńี à¸ģ\ná»ĩ nh\nÑĭ м\nà¸Ĺุ à¸ģ\nĠ׾ ×Ĺ\nĠ×Ķ ר\nĠ×Ķ ×Ļ\nà¸ŀ ระ\nà¹Ģว ลา\nĠØ º\nẫ n\nm Ä±ÅŁ\n׼ ×Ķ\ná»ij n\nãģ§ ãģĹãĤĩãģĨ\nãĥ ¢\nà¸Ľ ี\nס ×Ļ\nãģĵ ãĤį\nĠ׾ פ\nร à¸ĸ\nê¸ Ī\nà¸ģ วà¹Īา\në ¬´\ná»į ng\nãĤĵ ãģ§\nãĤĪãģĨ ãģª\ná»ĵ i\nãĤ ¬\nส à¹Īà¸ĩ\n×Ļ׳ ×Ķ\nà¸ĸ ูà¸ģ\nà¸Ī ัà¸Ķ\nĠ×Ķ ×Ĵ\nãĥ ľ\n×ŀ ×ķת\nÙĪ Ùĥ\nëĭ ¨\nĠØ «\nãģ® ãģĮ\nà¹Ģห à¹ĩà¸Ļ\nع ا\nà¸Ļ ิ\nÅ ŀ\nà¸Ń ะ\nãģĪ ãĤĭ\nØ« ÙĦ\nØŃÙħ د\nà¹Ģà¸ģ ิà¸Ķ\nפ שר\nפ ×Ķ\nม ิ\nئ ÙĬس\nà¸Ĺำ à¹ĥหà¹ī\n×¢ ×ĵ\nìĭ ¤\nà¸Ĭà¹Īว ย\nĠاÙĦÙħ ÙĨ\nز ÙĬ\nع ÙĬ\nĠ׼ ×IJ\nạ nh\ná» ¹\nãĤĵ ãģª\nส ู\nצ ר\nÆ°á»Ľ ng\n×ķ ×ķ×Ķ\nà¹Ĥ ล\nĠاÙĦ Ùĩ\nว า\nหล าย\nÑī е\nà¸Ĥ à¹īà¸Ń\nà¹īà¸Ń ย\nب Ø·\nка Ñı\nĠØ ¢\nĠи Ñģ\nĠاÙĦ غ\nà¸ģ า\nà¸Ļ à¹Īา\nÙĬ ÙĪ\n×ij ×ķר\ná»ħ n\nว à¸ĩ\n×Ļ× ĸ\nì² Ń\nн им\nëŁ °\n×Ĵ ×ķר\nص ØŃ\nÙĦ ÙĪ\n×Ĺ ×ķת\nส ุ\nرÙĬ ÙĤ\nס ×ĺ\nĠ×ŀ ×¢\nãĥĨ ãĤ£\nà¸Ħ ิà¸Ķ\nãĤį ãģĨ\nà¹Ħ ล\nà¸Ļ à¹Į\ná»ı i\nÑģÑĤÑĢ о\nส à¸Ķ\nส าร\nÙĪÙĦ Ø©\nầ m\nร à¹Īว\nรà¹Īว ม\nร ุ\nĠاÙĦس ÙĬ\nìĺ ģ\nĠ×ŀ ×ij\nפ ×ĺ\nà¸ķิ à¸Ķ\n×ĺ ×Ļ×Ŀ\nĠë ¬´\nÙĤد Ùħ\nĠdü ÅŁ\nائ ÙĦ\nм Ñĭ\nØŃ Ø³\nÙĪ ص\n×Ļק ×Ķ\nãģ§ãģ¯ ãģªãģĦ\nà¹Ģ หม\nоÑĢ ÑĤ\ní Ĩµ\nãģ IJ\nк ÑĢа\nีย ว\nع ار\nئ Ø©\níĥ Ģ\nãģ«ãģª ãĤĬ\nج Ø©\nÙĪÙĤ ع\nÑĮ Ñı\n×ķצ ×Ķ\nש ×Ŀ\nب ÙĤ\nĠ×Ļ ×Ķ\nÙĬ Ø·\nım ız\nд еÑĢж\n×Ļש ר×IJ׾\nغ ÙĬر\nร à¸Ńà¸ĩ\nà¹Ģรีย à¸Ļ\nĠ×Ķ ×ĺ\nหม าย\nÙħ Ùĩ\nاÙģ Ø©\nĠо ÑĢг\nÙĪ Ùī\nãĥ© ãĤ¤\n×ŀ ׳×Ķ\nĠÄij o\nĠг оÑĢ\nاÙħ Ø©\næ¥ ½\nØ« ÙĬر\nà¸ģิ à¸Ī\ná»ĵ n\nÙĨ ب\nÑĢÑĥ д\nìĹ Ī\nĠ×Ĺ ×ijר\nÑĢаР¶\nạ ch\nت ÙĪ\nà¹Ĥ ม\n×ij ×Ļ×ij\nĠí Ĩµ\naca ģı\nجÙĦ س\nà¹Ģà¸Ľ ล\nว à¸Ķ\nà¸Ń ล\nãģŁ ãĤĬ\nà¸Ľ ัà¸į\nĠìķ Į\nعر Ùģ\nà¹Ħ à¸Ł\nØ£ Ø®\nå¤ļ ãģĦ\nà¸Ķ ัà¸ĩ\nØ´ Ùģ\nãģ£ãģ¦ ãģĦãģ¾ãģĻ\n׼ ×ł×¡\nÑĨ е\nеÑģ п\nÙħ اÙħ\nà¸ŀื à¹īà¸Ļ\nиÑĩеÑģ ки\nØ® د\nÙĥ ÙĪÙħ\nĠ×Ķ ר×IJש\nت اب\né£Ł ãģ¹\nื à¸Ļ\nоÑĢ о\nĠb öl\n×ķ×Ĺ ×ĵ\nدÙĬ ر\nắ m\nد ع\nãģķ ãģĽ\nà¸ĺ ร\nà¸ĺร รม\nãģĭ ãĤĤ\nå¤ļ ãģı\nr ä\nس ع\n×Ļ׾ ×Ķ\nض ر\nĠاÙĦ شر\n×ĸ ×ķר\n×¢ ×ijר\nạ m\nалÑĮ но\nر ÙĨ\nاÙħ ج\n׼ ×ļ\nd ıģ\nд ен\nض ا\nÙĦÙĬ Ùħ\nĠê·¸ 룬\nتÙħ اع\nار ÙĬØ®\nà¹Ĥ à¸ķ\nĠÑģ ÑĢед\nĠ׳ ×ķס\nÙĤ بÙĦ\nоÑĤ ов\nle ÅŁtir\nĠм еÑģÑĤ\nسÙĦ Ùħ\nĠ×¢ צ\nĠاÙĦس ÙĦ\nеÑĤ ÑĮ\nاب Ø©\nн ак\nสà¸ĸ าà¸Ļ\nĠ×ij ׳\nà¸ļ ัà¸Ļ\n׼ ׳\nĠö ÄŁ\nãģ¨ è¨Ģ\nuy ến\ndi ÄŁ\náºŃ u\nÑĢ аÑģ\nãĤ· ãĥ§ãĥ³\nn ız\n×ķ×ĵ ×Ķ\nت س\nÙħ اÙĦ\nà¹Ģห à¸ķุ\nย ว\nà¸ŀ ัà¸ģ\nãģĦ ãģªãģĦ\nĠк аÑĩ\nล à¹Į\nר׼ ת\nÅŁt ur\n×ŀ ×ķס\nãģ ¥\nб ол\nعÙħ اÙĦ\n×ķר ת\nÑĨи он\nศ ึà¸ģ\nภı\nÑĢ ен\nاس ÙĬ\nائ ر\nà¹Ĥ à¸Ľà¸£\nĠse ç\nغ ÙĬ\nÑį ÑĤ\nен н\nãģª ãģ®\n×Ļש ×Ķ\n×Ļפ ×ķר\nãģŁãĤģ ãģ«\nز Ø©\nĠç oc\nãĤ¯ ãĥª\nÑĪ ен\nãĤı ãģij\nرÙĬ د\nĠÑĢ аÑģÑģ\nÙĥ ات\nส à¸Ńà¸ļ\nce ÄŁi\nãĤ¿ ãĤ¤\nà¸ļ ร\nĠاÙĦ بر\n׳ ×ķ×¢\nr ün\nرا ض\nศา ส\nà¸ķ รà¹Į\nãģį ãģŁ\n×ķ׾ ×ĵ\nеÑĢ и\níĹ ĺ\nắ p\nت عÙĦ\nÙĥ د\nиÑĤелÑĮ но\nØ· Ùģ\nĠав ÑĤом\nĠ×ŀ צ\nÑĪи Ñħ\nات Ùģ\nĠÑħ оÑĤ\nÙİ Ø§\nãģı ãĤĭ\n×Ķ פ\nà¹Ĥ à¸Ĺ\nà¹ģ à¸ŀ\nà¹Ī à¸Ńย\nĠاÙĦÙħ Ø´\nà¸ģาร à¸ĵà¹Į\nани з\n×Ķ ׾\nظ Ùħ\nย ุ\nli ÄŁ\nà¹Ħ à¸Ĥ\nà¸ĸ ืà¸Ń\nö z\nãģij ãģ¦\nà¹Ģ à¸ľ\nุ ม\nãĥĹ ãĥ¬\nĠ×Ķ×IJ ×Ĺר\nخت ÙĦÙģ\nภİ\nÙĦا ØŃ\nĠdü zen\nצ ×Ķ\nس اء\n×ķר ×ļ\n×ķ×ĵ ×Ļ\nÑĢа ÑĦ\nÅŁt ır\nãģ« åħ¥\nãģĪ ãģ°\nص ÙĪÙĦ\nĠÐľ оÑģ\nا Ùĩر\nãģ£ ãģ\nĠлÑİ Ð±\n×Ļ×¢ ×Ķ\nĠ×Ķ×ŀ ק\nสิ à¸Ĺ\nสิà¸Ĺ à¸ĺิ\n×Ļ׳ ×Ŀ\nÙĦا Ùģ\nà¸ŀัà¸Ļ à¸ĺ\n×ķ×IJ ×Ķ\nม ั\nà¸Ĥ à¸ĵะ\nд оÑĢ\nãģ¨ ãģª\nà¸ģระ à¸Ĺ\nac ı\n×ķ׾ ×ķ×Ĵ\nÑĥ ÑĪ\nãĥ¥ ãĥ¼\nãĥ ¦\nÙħ ست\nĠa ÅŁ\nש ק\nפ ת×Ĺ\nาย à¸Ļ\ní ĩ\në ¢\nï ·\ní ī\nì µ\nì ¬\nðĿ Ľ\nì Ĵ\në Ļ\nê §\ná ĸ\nâ ¨\nâ ±\ná ĺ\nð ĸ\nà ł\ná Ķ\nðIJ Ń\nữ ng\nÅ© ng\nĠ×Ķ ת\nĠاÙĦ ا\nĠ×ŀ ת\nà¸ĸ ึà¸ĩ\nò n\ná»ĭ nh\nнÑĭ м\nĠc ả\nà¸Ķ ู\nĠ à¹ģà¸ķà¹Ī\nĠ×ij ×Ķ\nó i\nãģ¨ ãģĹãģ¦\nú ng\nĠØ °\nĠ×Ķ ׳\nĠب ÙĨ\nÙĦ اÙĦ\nà¹Ħ à¸Ĺย\ná»ĩ p\nt ı\nม ัà¸Ļ\nằ ng\ná»ij t\nк ом\nà¸ĭ ึà¹Īà¸ĩ\nà¸Ħร ัà¸ļ\nà¸ļ à¹īาà¸Ļ\nĠاÙĦ ÙĬ\nl ü\nÙĪ س\nãģł ãģ£ãģŁ\nà¹Ģ à¸ĩ\nĠê³ µ\nн Ñĥ\nãĤĪ ãĤĬ\nм Ñĥ\nà¹Ģà¸Ĥ า\nãĤ Ģ\nни е\nãģ«ãģª ãĤĭ\náºŃ y\nĠÙĪ ا\nëł ¤\nש ×ķ\ná p\n×ĵ ×ķ\nãģ§ ãģĹãģŁ\nع ض\nÑģк ой\næĦŁ ãģĺ\nÑİÑĤ ÑģÑı\nĠ×Ļ ׼×ķ׾\nãĤĵ ãģł\nв и\nà¹Ģล à¹Īà¸Ļ\nìĿ´ ëĭ¤\nĠÙĦ Ùĩ\nà¸Ħ ืà¸Ń\nت Ùĥ\nÙħ ÙĥÙĨ\na ģı\n׳ ×ĵ\në¯ ¼\nà¹Ħ ว\nสำ ห\nสำห รัà¸ļ\nÑģл ед\nt ır\nĠÙĦ ÙĬ\nĠاÙĦع ÙħÙĦ\n×ij ×ķת\n×ij ×Ļ×Ŀ\nà¸Ħ ำ\nà¹Ģà¸Ħร ืà¹Īà¸Ńà¸ĩ\nlı ģı\nืà¸Ń à¸ĩ\nج د\níŀ Ī\nìĭ ¬\n×¢ ×ķת\nส ิà¸Ļ\nÑĩ и\nر ض\nà¹Ģà¸Ľ ิà¸Ķ\nà¸Ħ à¹Īา\nìĦ ł\nÙĪر Ø©\nק ×ĺ\nìľ ł\nع ÙħÙĦ\n×IJ ×Ļ×Ŀ\n׾ ×Ļ×Ŀ\nà¹ĥห à¸į\nà¹ĥหà¸į à¹Ī\nừ a\ná»į i\nãģ ¶\nÃŃ ch\nãĥĩ ãĤ£\n×ķר ×Ļ×Ŀ\nÑģ о\nìķ ½\nов а\nÑĩ аÑģÑĤ\nà¹Ģà¸Ī à¹īา\nп ÑĢо\nĠ×ŀ ×Ĺ\nãĥ İ\n×ķ×Ļ ×ķת\nĠд е\në§ Ī\nì§ ģ\n×Ļפ ×Ķ\nĠاÙĦع اÙĦÙħ\në¥ ´\nר×IJ ×Ķ\nuy á»ĥn\n×¢ ×Ļ\nม ืà¸Ń\nØ¥ ÙĨ\nร ู\nĠØ ²\n×Ļ ×ķ×Ŀ\nà¸ķ à¹īà¸Ļ\nãģ¦ ãģĦãģ¾ãģĻ\nÙħ اÙĨ\nĠÐ ¥\nà¸Ľà¸£à¸° à¹Ģà¸Ĺศ\ná» ³\n׾ ×ij\nà¹Ģà¸Ķ à¹ĩ\nãģŁ ãģ¡\nà¸Ĺี ม\nà¸Ļ ะ\nìĹ °\nĠìł Ģ\nÙĦ Ùĩ\nợ i\nĠاÙĦ ز\nد ار\nãĤ³ ãĥ³\nм ин\nà¹ģห à¹Īà¸ĩ\nà¸Ķ ัà¸ļ\n׼ ר\nж а\níĸ Ī\n×ŀ ×ĸ\nợ i\nà¸Ķ า\nĠع بد\nà¹ģ ร\n×IJת ר\n×¢ ׳×Ļ\nà¹Ģ à¸Ħ\n×ķצ ר\nì§Ģ ë§Į\nائ Ùħ\nØ£ س\nuy á»ģn\nĠ×IJ ׳\n׊׳×ķ\n×ĸ ×Ļ\nร à¹īาà¸Ļ\nĠÐł оÑģ\nĠÐłÐ¾Ñģ Ñģ\nرب ÙĬØ©\nt ür\nãĤĭ ãģĵãģ¨\nظ ر\nб Ñĭ\nà¸Ĺีà¹Ī สุà¸Ķ\nĠצ ר\nèĩª åĪĨ\nл аÑģ\nĠÑı в\nĠÑıв лÑı\nà¸ŀร à¹īà¸Ńม\nà¸Ńา à¸Ī\nà¸ļริ à¸ģาร\nĠç ı\nëį ĺ\nĠاÙĦÙħ ست\nت Ø´\nש ×ķ×ij\nãĤ ´\nĠyap ıl\nĠاÙĦ Ø°\nุ à¹Īม\nà¸ĸ à¹īา\nìĦ ¤\nì° ¨\nв аÑĢ\nà¹Ģà¸ŀ ิà¹Īม\nÆ°á»Ľ i\nÙĥ س\nà¸Ńย าà¸ģ\nãģ¦ ãĤĤ\nĠг од\nÙĬ ار\nà¸ķ à¸Ńà¸Ļ\nĠиг ÑĢ\nà¹Ħà¸Ķà¹ī รัà¸ļ\nĠاÙĦÙħ ر\nÙĤ ت\nĠë ĺ\nĠëĺ IJ\nẩ n\nãģĻãĤĭ ãģĵãģ¨\n×Ĵ ×Ŀ\nĠ×ij ×ij\nت د\nÙĪ ار\nãĤ ®\nп ол\nĠм ог\nتر Ùĥ\nÙĪ Ø«\nĠç ık\nا Ø©\nà¹Ģà¸Ķ ียว\nมี à¸Ħวาม\nĠ×ŀ ×Ĵ\nص Ùģ\nĠТ ак\nĠ׼ ת\n×Ļ×ĵ ×Ļ\nов оÑĢ\nầ y\nสิ à¹Īà¸ĩ\nب ت\nür ü\nÙĨ ج\nหล ัà¸ģ\n×Ļ×Ķ ×Ŀ\nÙĤ ص\nз Ñĭ\n×Ľ×ª ×ij\nÆ° u\nm ız\nĠìĦ ¸\nл ог\nÙħ ÙĬÙĦ\nÙĬ ج\níĴ Ī\nà¸ŀ à¸ļ\nห ัว\nз на\nר ק\nà¹Ĥ ร\nĠ×ij ס\nĠBaÅŁ kan\nĠëĶ °\nà¸Ń ัà¸Ļ\nีà¹Īย ว\nн еÑģ\nà¹Ģà¸Ķ ิà¸Ļ\nÙĬ اÙĨ\n×ķ׾ ×Ļ\nا خت\nצ ×ķת\nãģĵ ãģĵ\nĠاÙĦ اÙĨ\nĠпÑĢо ÑĨ\nãģ¾ ãģł\n׼ ס\nĠاÙĦ Ø¢\nÙĬ ز\nĠاÙĦد ÙĪÙĦ\nĠíķĺ ëĤĺ\nض ع\nê» ĺ\nÅĽ wi\nย ิ\nãģ¡ãĤĥ ãĤĵ\nĠÙħ Ø´\nà¸ĺ ี\nãģ¨ ãģį\n׳×Ļ ×ķת\nĠë ¯\nĠë¯ ¸\nĠs ı\nëĭĪ ê¹Į\nĠп л\nغ ÙĦ\nà¹ģ รà¸ĩ\nب ÙĬر\nãģĤãĤĬ ãģ¾ãģĽãĤĵ\nê· ¼\nĠy üz\nĠdeÄŁ er\nåł´ åIJĪ\ná» ¡\nм аÑĤ\nรา à¸Ĭ\nÙĪر ÙĬ\nж ен\nãģ¾ ãĤĬ\nãģ® ä¸Ń\n×Ļ×ĵ ×¢\nà¸Ń ุ\nà¸ļ à¸Ńล\nà¸Ľà¸±à¸į หา\nز Ùħ\nÄŁ a\nà¸Ń ืà¹Ī\nà¸Ńืà¹Ī à¸Ļ\nп л\nĠне обÑħодим\n׼ ×ij\nà¹Ģ ศ\nקר ×Ķ\nì² ĺ\nëł ¨\n×ŀק ×ķ×Ŀ\njÄħ c\nÙĩ ÙĦ\nĠ×¢ ×ij×ķ×ĵ\nà¹Ħม à¹ī\nà¸ģล ัà¸ļ\n×ķ׼ ׾\nק ×ĵ\nاÙĦ ÙĬØ©\nر Ùĩ\nãģij ãĤĮãģ°\nĠÙĨ Ùģس\nãĤ¢ ãĥ«\nìĹ Īëĭ¤\nק ×ķר\nн еÑĢ\nب اب\nãĤ ¶\nسب ب\nÙĦ ÙĬÙĦ\nص ÙĨ\nص در\nế m\nà¸Ĭà¹Īว à¸ĩ\nØŃ ÙĨ\nĠ×ij ×Ĵ\n×ŀ ×ķ×¢\n׾ ×Ĺ\n大 ãģį\nت ب\nн еÑĤ\n×Ļ×ij ×Ķ\nб л\nãĥĹ ãĥª\nاص Ø©\nãģ¤ ãģij\n×Ļ×ŀ ×ķש\nãģĮ ãģĤ\nëĭ ´\nãģĭãĤĤ ãģĹ\nãģĭãĤĤãģĹ ãĤĮ\nãģ¡ ãĤī\n×ij ×ĺ\nĠba ÄŁ\n×Ļ×Ĺ ×¡\n×ij ×ķ×¢\nล ี\nפע ×Ļ׾\nим и\ng ÅĤ\nĠим е\nخد اÙħ\n×IJ ×Ļר\nĠy apt\nãģ¨ ãģĦ\nà¸ĩ à¹Īาย\n׾×Ļ ×ķ\nØŃد Ø«\nرا ÙĤ\nĠÄIJ i\nاد ر\nãģĵãģ¨ ãĤĤ\n×ij ×Ļר\nĠв з\nض اÙģ\nת ×ķ׼\nÑĢ ом\nر ات\nà¹Ģà¸Ĺ à¹Īา\nãģĺ ãĤĥ\nãģĿ ãģĵ\nاج تÙħاع\nà¹īà¸Ń à¸Ļ\nÙĤ Ùħ\në³ ¸\nÄ ŀ\nש ×Ļ×ķ\n×ij ׳×Ļ\nìľĦ ìĽIJ\nà¹ģ à¸Ī\n×Ĺ ×ķר\nدÙĬ ÙĨØ©\nت Ø·\nằ m\nò a\nย à¸Ńà¸Ķ\nĠëĭ ¹\nสุ à¸Ĥ\n×ĵר ×ļ\nد ÙĨ\nس ÙĬÙĨ\nÙĪÙĤ Ùģ\nÑĨ Ñĭ\nг оÑĤов\nеж дÑĥ\nà¸ŀ วà¸ģ\nاÙĤ تص\nاÙĤتص اد\ncz ÄĻ\nni ÄĻ\nÑĢ еб\nØŃ ÙĪ\nà¸Ĺ à¹Į\nãĤĪ ãģŃ\nд ж\nà¸ģล à¹Īาว\nدÙĬ Ø«\nãĤ³ ãĥŁ\nÙĤ ÙĪÙħ\nĠت ØŃ\nà¹Ģ à¸ķิ\nاÙģ ظ\nà¸Ī ุ\nرÙĬ اض\n×ŀש ×ļ\nà¹Ĥ ย\nеÑĢ е\nãģ¿ ãģŁãģĦ\nìĿ´ ëĿ¼\nĠاÙĦÙħ ÙĪ\nĠÑģÑĤ о\nà¹Ģรà¹ĩ ว\nĠд еÑĤ\nĠÑģ дел\nà¹Ģà¸Ĭ ืà¹Īà¸Ń\nפ ׳×Ļ\nÙĪض ÙĪع\n×ij ס\nà¹ģ à¸Ķ\nó c\nริ ม\nÑĢаР´\nìĪ ł\nãĥ¼ãĤ º\nãģ« ãģĬ\nи но\nפ ×Ļ׾\nà¸Ĭั à¹Īà¸Ļ\n×Ĺ×ĵ ש\nà¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ\n׳ ×Ļס\nغ رب\nãĤ¸ ãĥ£\nส ัà¸ĩ\nà¹Ģ à¸Ĺีà¹Ī\nà¹Ģà¸Ĺีà¹Ī ยว\nëŁ ¼\nà¹ģ à¸Ł\nãĥ¼ãĤ ·\nãĥ¼ãĤ· ãĥ§ãĥ³\nĠвоз мож\nجÙħ ÙĪع\n×ijר ×Ļ×Ŀ\nãĥĪ ãĥ©\nĠкаÑĩ еÑģÑĤв\nØ· ÙĬ\nÑĤ Ñı\nצ ×ķ×¢\nÄŁ ını\nع ÙĦÙī\nا Ø°\nÙĪاÙĤ ع\nÙħ ÙĪا\nائ ÙĬÙĦ\nк ол\ná»ģ m\nà¸ľà¸¥ ิà¸ķ\n×Ļ׳ ×ĺר\nس Ùĥ\nש ×Ļר\nศึà¸ģ ษา\nà¸ļ ั\nÑĩ аÑģ\n×ķפ ×Ķ\n×Ļפ ×ķ׾\nĠاÙĦس اب\nرÙĬ ب\nĠاÙĦ بÙĬ\nãĤ¹ ãĥĨ\nÑĩ ен\nà¹ģ à¸ľ\nĠ׳ ש\nز ÙĬد\nØŃ Ø§Ø¯\nëį Ķ\nرÙĪ ع\nà¸Ĺุ à¸Ļ\nส มา\nc zeÅĦ\n×Ļ×ĵ ×Ķ\nãģ§ ãģĤ\nĠçoc uk\nØ® ب\nà¸ļ าย\nà¸Ľà¸£à¸° à¸Ĭา\n×ŀש ׾\nãģª ãģĭ\nà¸ģ าย\nãĥģ ãĥ£\nаÑĢ и\nĠÑĩ а\nà¸Ķ ำ\nà¸Ĺั à¹Īว\nÑĥ Ñħ\nĠö z\nĠì¢ ĭ\nج رÙĬ\nائ ÙĤ\nà¸ł ัย\nØ· ار\nد ارة\nÄ© nh\nØ« ÙĨ\nzell ik\nاÙĦ ت\nĠg eli\nãĥķãĤ ©\nол од\nرب ع\nשת ×ŀש\nà¸ļร ร\níĿ ¬\nĠü rün\nĠê·¸ ëłĩ\nศาส à¸ķรà¹Į\nãģ ľ\n×Ļ×ij ׾\nĠпÑĢед ÑģÑĤав\nسط ÙĬÙĨ\nãĤĴ 使\nĠпом оÑī\n×ķק ר\nãĥ¯ ãĥ¼\nĠyö net\n×Ļק ר\nà¸Ĥ า\nеÑĢи ал\nØŃ Ùģ\nĠ×Ļ צ\nà¸Ĺ ิ\nå£ ²\nà¸Ļ à¸Ńà¸ģ\n×ķ׼ ר\níĻ ľ\nủ y\nĠاÙĦÙĤ ر\n×Ļ×ij ×ķת\nÅĽ ni\nÙħ شار\nượ t\nĠÙĦ دÙĬ\nÑĤ ел\nĠØ¥ ÙĦÙĬ\nعÙĦ ÙĪÙħ\nìķ ĺ\nв иÑĤ\nà¸Ħ ะ\nyr ı\nãģ¨ ãģ£ãģ¦\nà¹Ģ à¸ī\nà¸ĸ าม\nÙĤ ار\nعÙĦ اÙħ\nặ ng\nÙħ ÙĴ\n×Ļ×ŀ ת\nسب Ø©\nãĤ¯ ãĥ©\n×ķס ×£\nĠпÑĢ ин\nãģĦ ãĤį\nس اس\nعت بر\nวิ à¸Ĺย\nวิà¸Ĺย า\nس Ùĥر\nãĤ· ãĥ§\nãģ ģ\nัà¸ģ ษ\n×ij ×ķ×Ķ\nห ย\nãģ¾ ãĤĮ\nĠоÑĢг аниз\nказ ал\nĠÑģв Ñıз\nuy ết\nĠпÑĢо из\nĠק ×ĺ\nà¹ģà¸ģ à¹ī\nп ÑĥÑģ\nĠê·¸ ê²ĥ\nëĬ IJ\nл екÑģ\nãĥ¼ãĥ Ĺ\nà¸ķ ำ\nת×Ĺ ×Ļ׾\nà¸Ńà¸ĩ à¸Ħà¹Į\nẠµ\n׳ צ\nØ£ Ø´\nØ´ Ùĩ\nย ะ\nà¸ģ à¸İ\nĠاÙĦØ¥ سÙĦاÙħ\nед ÑĮ\nãģ² ãģ¨\nëıĦ ë¡Ŀ\nãģ© ãģ®\nÑĥ в\nеÑĩ ение\nĠاÙĦت ج\nãģ« è¡Į\nĠп озв\nãĤı ãĤĬ\nÙĦ اث\níķĺ ìĺĢ\nĠм аÑĢ\nĠkon uÅŁ\nãĥ¬ ãĤ¹\nãĤĴ æĮģ\nĠоÑģ нов\n×Ĺ ×ij\nÙĪج ÙĪد\nפ ×ķף\nв оÑĢ\nĠн ик\nãģĭ ãĤĭ\nÅŁtır ma\n×Ļס ×ĺ\nØ£ ÙĦ\nห à¹Į\nи она\nлÑĮ н\nĠг оÑģ\nĠÐľÐ¾Ñģ к\nÑĢ об\n×ķ×IJ ×Ļ\nãģĬãĤĬ ãģ¾ãģĻ\nãģ£ãģ ±\nк л\nà¸Ļ à¸Ķà¹Į\nرÙĬ Ùģ\nاس ب\nĠÑĢ еÑĪ\nĠд ол\nãģ¹ ãģį\n×Ļ×ij ×ķר\nм еÑī\nĠна ÑĪ\nà¹ģ à¸Ľà¸¥\nÑĢ иÑĤ\nкÑĥ Ñģ\nи ÑĢа\nаÑĤ ÑĥÑĢ\nÙĪا صÙĦ\nà¹Ģà¸ľ ย\nà¸Ń ำ\nà¹Ģà¸ģ ิà¸Ļ\nغ Ùħ\nãģĻ ãģİ\nlı kl\nÅĦ sk\nê² ¬\n×Ļ׼ ×Ķ\n׊ש×ij\nÙĪر ÙĬØ©\nĠд ейÑģÑĤв\n×Ĺ׾ ×ĺ\nĠ׾ ×ŀ×¢\nצ׾ ×Ļ×Ĺ\nеÑĩ а\nÙģ اع\n×Ĵ ×Ļ×ĵ\náºŃ m\nÄĻ b\nØ´ ع\nãģı ãĤĬ\nà¸ŀ ุ\nед еÑĢ\nà¸Ĥ à¸Ļ\nà¸Ħ าร\nĠболÑĮ ÑĪ\nãģı ãģªãĤĬ\nà¸ĵ า\n×ĵ ×ķ×Ĵ\nĠм н\nä¸Ĭ ãģĮ\nç¶ļ ãģį\nฤ ษ\nภĨ\nØ® ÙĬ\nà¹Ģà¸Ĺ à¸ŀ\nสั ม\nà¹Ģส à¸Ļ\nà¹Ģสà¸Ļ à¸Ń\nãĥ ´\nĠи ÑģÑĤ\nبا شر\nĠÑĥ ÑĢов\n×ŀ ×ķ×ĸ\nab ı\nwa ż\n×ķצ ×IJ×Ķ\nÑĤ веÑĢ\nà¸ŀัà¸Ļà¸ĺ à¹Į\n׳ ×Ĵ×ĵ\nãĤĭ ãģĵãģ¨ãģĮãģ§ãģį\nĠÑĤÑĢ еб\nà¸ģร ุà¸ĩ\nØŃت اج\nà¹Ģ à¸Ħล\nã Ĩ\nÄĻ tr\nĠszcz eg\nĠר ש\nà¸Ĺ à¸ĺ\nĠн ек\nĠнек оÑĤоÑĢ\nв ÑĪ\nÐ ¬\nà¹Īว ย\nล ุ\nб ÑĢÑı\nหม ูà¹Ī\nà¹ģ à¸ķà¸ģ\nר׼ ×Ļ×Ŀ\nĠí ĸī\nã i\nÙĥر Ø©\nâ Ń\ní IJ\nã į\ná ģ\nâ ®\nâ ¥\nì ®\nà ¿\nâ ¿\ná Ĥ\ná ¤\nâ ł\ní Ł\nðIJ į\nðIJ °\nðĿ Ĩ\nðŁ Ī\nĠ×¢ ׾\nĠع ÙĨ\nĠÙħ ع\nĠ×ĸ ×Ķ\nĠÙħ ا\nĠm Ãł\nĠd ụ\ná»ĩ c\nа Ñħ\ns ı\níķĺ ê³ł\nĠ×ķ ×ij\nĠÐŁ о\n×ķת ר\nĠÙĦ Ùħ\nĠ×ķ ׾\nãģĹãģ¦ ãģĦãĤĭ\nĠ×ŀ ×Ļ\nĠب ÙĬÙĨ\nз а\nĠÙĥ اÙĨ\nĠ×Ķ ×Ļ×Ķ\nëħ Ħ\n×IJ ×ķ\nд и\nĠпеÑĢ е\nd ı\nĠ׾ ש\nĠש ×ŀ\nãģĮ ãģĤãĤĭ\nãģĦ ãģĦ\nÑĢ е\nק ×ķ\nи ли\nм е\nÙĬ ت\nãģ§ ãģĤãĤĭ\nĠв о\nà¹ĥ หม\nà¹ĥหม à¹Ī\nĠש ×ij\nĠ à¹Ĥà¸Ķย\nÙĬ Ùĩ\nãģ§ãģĻ ãģĮ\nãģ¨ ãģ¯\nר ×ķ\nĠ à¸ĭึà¹Īà¸ĩ\nãģ§ãģį ãĤĭ\nм о\nà¹Ģà¸ŀ ืà¹Īà¸Ń\nצ ×ķ\n×ĺ ×ķ\nìķ Ī\nĠh á»į\nà¹Ģà¸ĩ ิà¸Ļ\nĠاÙĦ ب\nĠ มี\në¬ ¼\nÑģ е\nëĵ¤ ìĿ´\nĠë§ IJ\nĠl Ỽ\na ÅĤ\n×Ĺ ×ijר\nĠd á»±\nÙĬ Ø«\nĠth á»ĭ\nà¸ģà¹Ī à¸Ńà¸Ļ\nĠ×ij ׼׾\nãģ ¸\nãģ¨æĢĿ ãģĦãģ¾ãģĻ\nả nh\nย า\nÙģ ا\nส ี\nà¸ķ า\në² ķ\nãĥª ãĥ¼\nรา à¸Ħา\nĠ×ķ ׾×IJ\nãģ¨ ãģĵãĤį\nà¹Ģล ืà¸Ń\ndi ÄŁi\nÙĪ اÙĨ\nĠ׾×Ķ ת\nรว ม\nפ ×Ļ×Ŀ\nà¸ľ ม\nж и\nc ı\nÑĢ од\nĠkar ÅŁÄ±\n×Ĵ ×ķ\nãģ« ãģ¤\nãģ«ãģ¤ ãģĦãģ¦\nr Ãł\n×Ļ×ķת ר\nĠìĨ Į\nק ×Ķ\nÑģÑĤв о\nãģij ãģ©\ng é\nà¸Ķ à¹īาà¸Ļ\nçļĦ ãģ«\nĠÙĬ ÙħÙĥÙĨ\nìĨ į\nÙĬ Ùĥ\nà¹Ħว à¹ī\nÑģки й\nì m\nĠ׾×IJ ×Ĺר\nà¸Ńา หาร\nĠà¹Ģ à¸ŀ\nรา ะ\nล ูà¸ģ\nÑģÑĤ а\nĠìľ ł\nÙĤ ÙĪÙĦ\nб оÑĢ\nÑģк ого\nหล ัà¸ĩ\nà¸Ĥ à¹Īาว\nà¹Ģม ืà¸Ńà¸ĩ\nê° ģ\nt Ãł\nÙĬ ÙĬÙĨ\nعر ض\në° ©\nĠëı Ļ\nĠà¹Ģ à¸Ľ\nĠà¹Ģà¸Ľ à¹ĩà¸Ļ\nç i\nli ÄŁi\nìĹIJ ê²Į\nãĤ¿ ãĥ¼\nĠ׾ ת\nפ ×ķת\nà¸Ĥ à¸Ń\nر س\nìł IJ\nà¸ľ à¹Īาà¸Ļ\nÑĦ и\nج ÙĨ\nì¢ ħ\nĠ×Ķ פ\nĠn go\ná»ĭ a\nĠtá» ķ\nĠê·¸ 리\nà¹Ģม ืà¹Īà¸Ń\nØ° Ùĥر\nìĸ ij\nìĹ Ń\n×ĺ ׾\nk ı\nĠع ÙħÙĦ\nĠع ÙĨد\nà¸ĭ ืà¹īà¸Ń\nĠê± °\nв е\nr ü\nà¹Ģ à¸Ńา\nส à¹Į\nà¸Ī à¸Ļ\nס ת\nĠgi ả\nãĤĭ ãģ¨\nà¸ģำ ลัà¸ĩ\nн ей\nà¸Ī ริ\nà¸Īริ à¸ĩ\nĠë į\nĠëį Ķ\nà¸Ħà¹Ī ะ\nì n\nĠsü re\nĠqu y\nà¸ļ าà¸ĩ\nåıĸ ãĤĬ\nר ×Ĺ\n×ij ת\nãģĮ ãģĤãĤĬãģ¾ãģĻ\nר ש\nìĹIJ ëĬĶ\nĠ×IJ פשר\nay ı\nãģĮ ãĤī\nØŃ Ø¨\nан Ñģ\nس ÙĪ\nĠпÑĢ е\nد ÙĪ\nãģ« ãĤĪ\nà¹Ģà¸ģ ม\nสู à¸ĩ\nm akt\nmakt ad\nmaktad ır\nĠön em\n×Ļ×ŀ ×Ļ×Ŀ\nб о\nÙĪ ÙĬØ©\nรู à¸Ľ\nà¹Ĥล à¸ģ\nÙħ ÙĬع\nÑģÑĤ Ñĥп\nà¹Ĥ à¸Ń\nدÙĬ ÙĨ\nì¤ ij\nãģĹãģ ı\nà¹Ģส ีย\nв Ñĭ\nÙħ ت\níĺ Ħ\nãĥIJ ãĥ¼\nا Ø´\nק ס\nĠtá» ¥\nล à¸Ķ\nÙģ Ø©\ní ijľ\nر ج\nk ÅĤad\nĠÅŁ ey\nĠØ£ Ùħ\nĠà¹Ģ ม\nĠب ÙĦ\nÑģ каÑı\nãģ¨ ãģ®\nĠìĭ ¤\nấ m\nห à¹īà¸Ńà¸ĩ\nà¸Ĭ ม\nd ü\nĠç ek\nĠê³ ł\n×Ĵ ×ij\nà¸Ĭี วิ\nà¸Ĭีวิ à¸ķ\nÙģض ÙĦ\nภ¯\nç ı\nĠب Ø´\nĠÙĩ ÙĨا\nãģį ãģ¾ãģĹãģŁ\nt ü\nĠìĺ ģ\nĠTür k\nк ÑĤ\nפר ס\nãģ¨ãģĦãģĨ ãģĵãģ¨\ní ĶĦ\nà¹ģร à¸ģ\nר ×ķף\nĠar as\n×ŀצ ×IJ\nĠtá» ī\nس ا\nà¸ŀ à¸Ń\nĠاÙĦÙħ ØŃ\nãĥ ¤\nĠاÙĦ است\nÙģ ÙĨ\n×Ļ×ŀ ×Ķ\nر ت\nãģ¨ ãĤĤ\nĠна Ñģ\nп ÑĢи\nĠ×Ĺ ×ķ\nи ла\nÙĬ Ø´\nĠgö z\nĠ×ij ׳×Ļ\nım ı\nĠÑĤ еÑħ\nĠh á»Ļ\nغ ر\nк он\nاØŃ Øª\nĠ à¸ŀ\nà¸Ń à¸Ńà¸Ļ\nà¸Ńà¸Ńà¸Ļ à¹Ħล\nà¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į\nÑħ о\nÑı в\nà¹ģ สà¸Ķ\nà¹ģสà¸Ķ à¸ĩ\nà¹Ģà¸ŀ ียà¸ĩ\nÑĤ ов\nا ÙĬ\nĠ×Ķ ×ĵ\nĠ×ķ ׼\nãĤī ãģĦ\n×ķפ ף\nĠë ¶Ī\nล à¸Ńà¸ĩ\nØ· اÙĦ\nĠн и\nĠÙħ ست\nế c\nĠש ׼\nĠëķĮ 문\nวัà¸Ļ à¸Ĺีà¹Ī\n×Ļ׾ ×ĵ\nØŃ Ø§\nе ÑĨ\nĠc ứ\n×ĵ ×ķר\nĠÙħ ØŃ\nר׼ ×ij\nبÙĬ ع\nни и\nĠاÙĦØ£ ÙĪÙĦ\nà¸Ħว ร\nãģ¨æĢĿ ãģĨ\nĠС о\nائ ÙĬØ©\nر اء\nоÑģ об\nĠب Ø£ÙĨ\n×¢ ×ķ×ĵ\nĠÑĤ е\nãģĵ ãģĨ\nÑģÑĤ ÑĢа\nай н\nĠsö z\nت ÙĨا\nà¸Ń ิ\nặ p\nĠìķĦ ëĭĪ\níķ Ń\nĠר×IJ ש\nĠ à¹Ħà¸Ķà¹ī\nĠ×Ĵ ×ĵ\nĠס פר\nобÑī е\nĠÙĪ Ø¥\nada ÅŁ\nãģ¡ ãĤĩ\nק ×ķ׾\nÑĢ ез\nĠdÃ¼ÅŁ ün\nĠ×ij ×IJ×ŀ\nĠìĸ´ ëĸ\nער ×ij\nн ее\nĠÑģÑĤÑĢ ан\nس اÙĨ\nyn ı\nĠاÙĦر ئÙĬس\nãģĹãģ ª\nĠ׳ ת\nãģ«ãģª ãģ£ãģŁ\ng ü\nåıĹ ãģij\n׾ ת\nìł Ī\nëĬĶ ëį°\nØ® ÙĬر\nà¸ķà¹īà¸Ńà¸ĩ à¸ģาร\nĠÙĦ Ø£ÙĨ\nĠch á»ĭ\nÙĪ Ø©\nà¹ĥ ส\në¶Ģ íĦ°\níķĺ ë©´\nữ u\nà¹Ģหม ืà¸Ńà¸Ļ\nб еÑĢ\nĠìĿ´ ìļ©\nĠÑģ еб\nwiÄĻ ks\nĠ׳ ×¢\nÑĤ ÑĥÑĢ\nĠngh Ä©\nש ×ķ×ĺ\nti ÄŁi\nĠde ÄŁi\n×IJ ×ij\nĠ×ŀ ×ŀ\nãĥĹ ãĥŃ\nwa ÅĤ\nà¸Ī ึà¸ĩ\nØ® دÙħ\n×IJ ×Ŀ\nÄ±ÅŁ ı\ncz Äħ\nר ×ĵ\nĠÑĢ Ñĥб\nخر Ùī\nãģ® æĸ¹\nĠд енÑĮ\n×Ĺ ×Ļ×Ŀ\nеÑĤ е\nëĤ ľ\n×IJ ×Ĵ\n×¢ ×ķר\në³ Ħ\nåIJĮ ãģĺ\nãĤ ²\nר ×ļ\n×ķש ×IJ\nìľ ¡\nا Ø®\nצ ×Ļ×Ķ\ná»± a\nãģĪ ãģ¦\nש×Ķ ×ķ\nан ÑĤ\nลา à¸Ķ\nин г\në¡ ł\nاع د\nÙĪ سط\nĠв оп\nĠвоп ÑĢоÑģ\nÙħ ÙĬÙĨ\nà¸Ħ à¸ĩ\n×Ļר ×Ļ×Ŀ\nc ów\nê² ©\nĠê·¸ 룰\nĠì§ Ħ\nĠש ׾×Ķ\nà¹Ģร ิà¹Īม\nà¸Ĭ à¸Ńà¸ļ\nд еÑĤ\nÑİÑī иÑħ\nà¸ļ à¸Ńà¸ģ\næĢĿ ãģĦ\nع ÙĬد\nס ×ŀ\n×Ĵ ×Ļ×¢\nצ ×ĵ\nب ات\nĠëĶ° ëĿ¼\nà¸Ī ัà¸ĩ\nãģłãģij ãģ§\n×¢ ×Ļר\nĠÑĩ ел\nĠÑĩел ов\nĠÑĩелов ек\nãĥĥ ãĥģ\nà¹Ģà¸ģ ีà¹Īยว\nà¸Ķ ิ\nĠפ ×¢\n×Ļ×ŀ ×Ļ\në° ĺ\nØ® ار\n×ij ×Ļת\n×¢ ×Ļ×Ŀ\nü yor\nãĤģ ãģ¦\nк лад\nĠ à¸Īาà¸ģ\nà¹Ģà¸Ħ ย\nส à¸Ńà¸ĩ\nà¹ģ à¸Ħà¹Ī\nẫ u\nหà¸Ļ ัà¸ĩ\nש׾ ×ķ×Ŀ\nاÙĨ ÙĬØ©\nåĩº ä¼ļ\nåĩºä¼ļ ãģĦ\nà¸ł าย\nà¸ļา à¸Ĺ\nà¸Ĭา ว\nmu ÅŁ\nĠ׾ק ×ij׾\nãĤ· ãĥ£\nĠÄ° ÅŁ\n×Ĵ×ĵ ×ķ׾\nج عÙĦ\në³ Ģ\nยิ à¹Īà¸ĩ\nà¸Ļ าย\nà¸Ļ ีà¹Ī\nวิ à¸ĺี\nãĤī ãģªãģĦ\nëł Ī\nĠ문 ìłľ\nĠ à¸ģ\nà¸Ĺำ à¸ĩาà¸Ļ\nà¹Ģว à¹ĩà¸ļ\nÑĦ е\n楽 ãģĹ\nสำ à¸Ħ\nสำà¸Ħ ัà¸į\nر Ùħ\nãģķãĤĮ ãģ¦\nĠоб ла\nר×IJ ×Ļ\nหม à¸Ķ\nÙĨ ÙĬØ©\nли н\nĠe ÄŁ\nit im\nëł ¹\nص اÙĦ\nÅĽ l\nà¸ľ ิà¸Ķ\nãĥŀ ãĥ³\nåħ¥ ãĤĮ\nà¹Ģà¸ķ à¸Ńรà¹Į\nار ÙĬ\nĠÐ ¦\nd ür\nส วย\në¦ ½\nرÙĥ Ø©\nĠh ã\n×Ļת ×Ķ\nà¸Ĥ à¸Ļา\nà¸Ĥà¸Ļา à¸Ķ\nà¸Īำ à¸Ļ\nà¸Īำà¸Ļ วà¸Ļ\nש ×ķק\nĠд ом\nì± ħ\nãģĭ ãģij\nפ ×ķ׾\nà¸Ĭ าย\nÑģ моÑĤÑĢ\nÑģл Ñĥж\nש ×IJ׾\nкÑĢÑĭ ÑĤ\nĠìŀ ĺ\né«ĺ ãģĦ\nĠÑĢ Ñĥк\nÙĨ ص\nд ав\nÆ°á» ¡\nưỡ ng\nر اÙħ\n×Ļ׳ ×Ļ×Ŀ\nãĥ© ãĥ¼\nëĦ ¤\nĠت ع\nl ke\n好 ãģį\næĮģ ãģ¡\nĠë§ İ\nĠy ük\nĠÑģоÑģÑĤ ав\nенÑĤ ÑĢ\npe ÅĤ\nà¹Ģà¸Ľà¸¥ ีà¹Īย\nà¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ\níı ī\nãĤĦ ãģĻ\n×Ĺ ×ĸ\n×ijר ×Ķ\në£ ¨\nìĶ Ģ\nبØŃ Ø«\nà¹Ģà¸ķ à¹ĩ\nów i\nب Ùĩ\nãģį ãģ¾ãģĻ\nĠ×¢ ×ŀ\n×Ĵ ×ķ׾\nез д\nÙĬÙģ Ø©\nสà¸Ļ à¹ĥà¸Ī\nĠת ׾\nÑı Ñī\nĠس ÙĨ\nĠÙĪا ØŃد\nĠÑģ м\nlad ı\nı ld\n×Ļר ת\nีย à¸Ļ\nת×Ĺ ×ª\nĠж из\nà¸ŀ ั\nà¸ŀั à¸Ĵ\nà¸ŀัà¸Ĵ à¸Ļา\nà¸Ĭ ิ\nا Ø®ÙĦ\nãģ£ãģ¦ ãģĦãģŁ\nรั à¸IJ\nãĤģ ãĤĭ\nà¹Ĥ à¸ģ\nĠT á»ķ\nĠh akk\nر Ùģ\nìł Ģ\nÑģ об\nãģª ãģijãĤĮãģ°\nÙĩ ÙĪ\nĠë² ķ\nãĤ Ĩ\nĠاÙĦس عÙĪد\nĠ×IJ תר\nØ§Ø º\nĠ׾ ×ĵ\nà¹ģ à¸ķ\nà¹ģà¸ķ à¹Īà¸ĩ\níĮ Į\nÑĥп иÑĤÑĮ\nà¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī\n×ij ת×Ļ\nà¹ĩ à¸ģ\nÅĤ at\nĠê°ľ ìĿ¸\nìłķ ë³´\nÑĤ ал\nĠgü ven\nĠÄ° l\nĠê° ģ\nĠب ت\n×ŀ ×ķ׳×Ķ\nĠاÙĦØŃ ÙĥÙĪÙħ\nÙĤ ات\nà¹ģ à¸ģà¹Ī\nห าà¸ģ\nн ÑĮ\nà¸Ľ รัà¸ļ\nมา à¸ĵ\nĠне Ñģк\nĠØ ¶\nสม ั\nสมั à¸Ħร\nãģĮ ãģĤãĤĬ\nм еÑģÑĤ\nĠ×IJ צ׾\nĠкомп ани\nס ר\nÙĬÙħ Ø©\nĠÑħ оÑĢо\nĠÑħоÑĢо ÑĪ\nĠ×Ļ ×ķ×ĵ\nü s\n×Ĵ ×Ļש\nà¸ļ à¸Ĺ\nتÙĨ ظ\nว าà¸ĩ\nม หา\nĠ׼ ×ķ׾\nà¸Ĥ à¹īาà¸ĩ\në° ľ\nг од\nд ан\nãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ\nãģĵ ãģ¡ãĤī\nãĥIJ ãĤ¤\nece ÄŁi\nدÙĬ دة\nÙĨ Ùī\nĠëĭ¤ ìĿĮ\nว ี\nغ ا\nли з\nà¹Ģà¸Ķ ิ\nà¹Ģà¸Ķิ ม\nĠÙĬ ست\nĠy ılı\nko ÅĦ\nãģ§ãģĹãĤĩãģĨ ãģĭ\nãģĤ ãģª\nãģĤãģª ãģŁ\nÑĨ ен\nĠÙĪ ز\n×IJ ×Ļש\nà¹Ī à¸Ń\nر ØŃ\nê´ ij\nÑĢа ÑģÑĤ\nĠ×Ķ ׾\nãģĹãģ¦ ãĤĤ\n×ŀר ׼\n×ŀר׼ ×ĸ\néģķ ãģĦ\nãģŁ ãģı\nĠÑģ Ñĥд\nв еÑģÑĤи\nĠíķĦ ìļĶ\nãĥķ ãĤ§\nÑĤелÑĮ но\nà¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ\nÅĤu ż\nà¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ\nש ×ķר\nĠ×ŀ ×ĵ\n×ķ×¢ ׾\nÙĦ اÙħ\nà¹Ħ à¸ĭ\nл ей\nкÑĥ ÑĢ\nẠ¢\nà¸Ĺ าà¸Ļ\nì§ ij\nĠгоÑĢ од\nר ס\n׾ ×ķ×Ĵ\nmas ını\nĠл ÑĥÑĩ\nล à¹Īา\nìļ ¸\nש ×ĺ\nĠÐĺ н\ní Ĥ¤\nÙĪÙĦ ا\nìķ ł\nĠØ£ÙĬ ضا\nÙĥ ار\nĠاÙĦت ع\nส ูà¹Ī\nãĤ ¼\n×ij ×Ļ×IJ\nย à¸ģ\nĠØŃ ÙĤ\nر بÙĬ\nãģĺãĤĥ ãģªãģĦ\nรัà¸ģ ษา\nÑħод иÑĤ\nà¸ķ à¸Ńà¸ļ\n׳ ×ĺ×Ļ\nĠاÙĦÙħ ج\nتÙħ ع\nов аÑĤÑĮ\nÙĦ ÙĬÙĨ\n×Ļ×ŀ ×ķת\nĠm ù\nn ÄĻ\nĠد ÙĬ\n׼ ש×Ļ×ķ\nĠhi ç\në ijIJ\nÙĪ اء\nÙĪ Ø·\nĠاÙĦ بÙĦ\nà¹ģม à¹ī\nק ×ķת\nÙĪج د\nå§ĭ ãĤģ\nÙĬ ئة\nĠë§ ¤\nص بØŃ\nפ ×IJ\nг оÑĢ\nס ×Ķ\nبÙĬ ÙĤ\nย าà¸ģ\nĠн ад\nÙĬ Ùij\nĠب ÙĪ\nס ×ķר\nÙħ ÙĥاÙĨ\nר ×ij\n×Ĵ ×ĸ\nצ ת\nb ilit\nл аг\nĠN go\n×IJ ×ķר\nà¸ķ à¸Ļ\níĬ ¹\nà¸Ĺีà¹Ī à¸Ķี\nà¸Ľà¸£à¸° à¸Īำ\nов ание\nãģĦ ãģ¤\nãĥĥãĤ¯ ãĤ¹\nåIJĪ ãĤı\nåIJĪãĤı ãģĽ\n×Ļ׳ ×ķ×Ļ\nạ y\nØ« ÙĤ\nĠпÑĢ об\nĠпÑĢоб лем\nÅŁ eh\nÅŁeh ir\nع ادة\nاÙĨ ÙĪÙĨ\nà¸ķัว à¹Ģà¸Ńà¸ĩ\nì¶ ķ\nı lan\nб ан\nãĥ³ ãĥī\nà¸Ī ี\nĠ×Ķש ׳×Ļ\nп оÑĤ\n×ķ׾ ×Ļ×Ŀ\nล ัà¸ļ\nĠÑį ÑĤи\n×ijק ש\në¹Ħ ìĬ¤\nà¸Ńยà¹Īาà¸ĩ à¹Ħร\n×Ļ׾ ×Ļ\nà¹ĥà¸Ĭ à¹Ī\nĠاÙĦ ÙĥÙĦ\nãĥļ ãĥ¼ãĤ¸\nص Ø©\nÑĤи ÑĢ\nãĤĵ ãģ©\nзÑĭ к\nwy ż\nÙĩ ÙĬ\nĠÙħ ÙĦÙĬ\nĠвид е\nظ اÙħ\nدا ÙĪÙĦ\n×ŀ ת×Ļ\nĠs ık\nà¹Ģà¸ķิ ม\nãĤ¢ ãĤ¤\nка Ñħ\nצ ×Ļ׾\nà¹Ģà¸Ĭ à¹Īà¸Ļ\nм аг\nмаг аз\nмагаз ин\nà¸Ľ ั\nà¸Ľà¸± à¸Ī\nĠש ×Ļר×ķת\nีย ม\nãĥĸ ãĥ«\nĠد ÙĪÙĦ\nקר ×Ļ×Ŀ\nÙĩ Ùı\nов о\nĠü ret\nد ÙĪÙĨ\nà¹ģà¸Ļ ว\nà¹Ģà¸Ļ ืà¹īà¸Ń\nĠÑĦ оÑĤ\nãĥ ĺ\nãģ¤ ãģĭ\nÑı Ñģ\nĠíķĺëĤĺ ëĭĺ\nائ ع\nĠп лаÑĤ\nìĺ Ī\nĠdost ÄĻp\nÙĪج Ùĩ\nĠ×Ķ ×Ĺ×Ļ\n׳ ×Ļק\nд ей\ní ĽĦ\nı y\nبØŃ Ø±\nà¹Ģส ริม\nĠ׾ ×Ĵ\nØ°Ùĩ ب\nج ÙĬÙĦ\nرÙĥ ز\nĠë ħ\nĠëħ ¸\nפ×Ļ׾ ×ķ\nãģ¾ ãģļ\niri ÅŁ\nĠÙĥ ÙĬÙģ\nĠ×ij צ\nĠêµ IJ\nÑĢоÑģ Ñģ\nĠØ´ ÙĬ\nĠiç er\n×Ĵ ×ķ×ij×Ķ\nмен но\n×¢ ×ij×Ļר\n×ķ×ŀ ×Ķ\nãĤī ãģĹãģĦ\nãģ ¼\nÑī ин\nè²· ãģĦ\nجÙħÙĪع Ø©\nĠdön em\nĠ×ij ×IJר\nв еÑģÑĤ\n×ķר ×ķת\nس Ùģ\nà¹ģà¸Ĺ à¸Ļ\nĠд окÑĥменÑĤ\nĠا ÙĬ\nج اÙĨ\nצ×ķ×¢ ×Ļ\nĠоÑģ об\nĠاÙĦÙħ س\nÑĢаР±\nà¸ł ู\nà¸Ķ าว\nл екÑĤ\nع ÙĤ\n×ķ×ĵ ×ķת\nĠol u\nĠolu ÅŁtur\nãģ¾ ãģ¾\nед ин\nà¹Ģ à¸Ńà¸ģ\nãĤµ ãĤ¤\nëĦ Ī\nØ· ÙĨÙĬ\nØ· ÙĤØ©\nĠÐł аз\nÙĦ Ùij\nÑĩ ем\nĠ׾ ×ĺ\nสั à¹Īà¸ĩ\nسر ائÙĬÙĦ\nĠפר ×ĺ×Ļ\nд еÑģÑĮ\nĠ׳ ׼\nاÙĨ ب\nÙĬا Ø©\nÙħ بر\nĠk ı\nà¸Ľ à¸ı\nà¸Ľà¸ı ิ\nà¸ļั à¸ķิ\n׳ ת×Ļ\nìĨ ¡\nر اب\nà¹ĥ à¸ķ\nà¹ĥà¸ķ à¹ī\n×Ļ׳ ת\nÙĪ ÙĬر\nĠ×Ķ×ŀ ×Ļ\nей ÑĩаÑģ\nק ×ķ×ij\nدر اس\nĠÙħ ÙĤ\nرÙĬ ÙĨ\nØ® اص\nãģĬ éĩij\nĠج دا\nãģĨ ãģ¡\nëħ ¸\nır ım\næ§ ĺ\nãģ« å¯\nãģ«å¯ ¾\nÑĨ ев\nĠv ard\nĠÐIJ н\ne ÄŁ\nÑģÑĤв енно\nÐ ¨\nس د\nà¸ģ ุ\nà¹ģà¸ľ à¸Ļ\nรูà¹ī ส\nรูà¹īส ึà¸ģ\nات ØŃاد\nÑij ÑĤ\n×Ĺ ×ķק\nãģĻ ãģIJ\nØ· ÙĦاÙĤ\nĠק ×ķ×ĵ\nà¹ĥà¸Ĭ à¹īà¸ĩ\nà¹ĥà¸Ĭà¹īà¸ĩ าà¸Ļ\nãĥ¼ãĤ ¿\nĠs ür\nÑĢ ок\në³ ij\nสมา à¸Ĭ\nสมาà¸Ĭ ิà¸ģ\nãĥķ ãĥ¬\nè¾¼ ãģ¿\nãĤ» ãĥ³\nĠê°Ģ ì§Ģ\nà¸ľ à¹īา\nÑįÑĤ омÑĥ\nиÑĤ ел\nà¸ł ั\nภij\nãĥĸ ãĥ©\n×Ľ×ª ×ķ×ij\n׳ ×Ŀ\nен нÑĭе\n×¢ ×¨×Ľ×ª\nĠì Ĥ\nĠìĤ ´\nà¸Ĥ à¹īา\n׳ ×ķס\nãĥ¬ ãĥĵ\nÑĢ еÑģ\nà¹Ģล à¸Ĥ\nØ« اÙĦ\nìĹ Ĩ\nĠÑĩ аÑģÑĤ\nา ศ\nãĥª ãĤ¢\nu ç\n×Ļ׼ ×ķת\nล à¹īาà¸Ļ\ni ë\nãĤ¸ ãĤ§\nà¸Ī à¸Ń\nÙĪ ØŃد\n×Ļצ ×ķ×ij\nĠ×ij ש׾\nок о\nض Ø©\nØ° ر\nĠÑĥ д\nÄ° L\n×ķצ ×Ļ×Ŀ\n×ĸ ×ŀף\nà¸Ľ à¸ģ\níķĻ êµIJ\nس اÙħ\nà¹Ħ à¸Ķ\nละ à¹Ģà¸Ń\nละà¹Ģà¸Ń ีย\nละà¹Ģà¸Ńีย à¸Ķ\nả y\nаÑĨи он\nãĤ¹ ãĤ¯\nפ ×ķס\nร à¹Īาà¸ĩ\nен нÑĭй\nع ÙĨ\nعÙĦ ÙĨ\nائ Ùģ\nd ÄĻ\nؤ ÙĪÙĦ\n׾×ķ ×ķ\nĠ×ij ש×ij\nä»Ĭ åĽŀ\nĠاÙĦج ÙĨ\nد اد\nwa Äĩ\nãĥª ãĥ³\nĠìŀIJ ìĭł\nاÙĨ ÙĬا\nãĥ¡ ãĥª\nÙĦ ÙĪÙĨ\nà¸Ĺ à¹Īà¸Ńà¸ĩ\nà¸Ĺà¹Īà¸Ńà¸ĩ à¹Ģà¸Ĺีà¹Īยว\nاÙģ ÙĬ\nĠли ÑĪ\nÙħ ÙĬØ©\nоÑĤ веÑĤ\nÑĩ ин\nà Ĭ\nãĥ¡ ãĥ³\nå® Ł\néļĽ ãģ«\nĠÑĢаР¹\nãĤ¦ ãĥ³\n×Ļר ×ķש\n×Ļר×ķש ׾×Ļ×Ŀ\nม ะ\nĠar a\nказ аÑĤÑĮ\nà¸ķ ัà¸Ķ\nÑĥÑİ ÑĤ\nĠü st\n×Ĵ ×ķ×ij\n×Ĵ×ķ×ij ×ķת\nmal ı\nег од\nегод нÑı\nاÙģ ÙĤ\nà¸Ĭ à¹Īà¸Ńà¸ĩ\nĠö zellik\n×Ļצ ×ķר\nĠmi ÄĻd\nĠili ÅŁ\nĠна Ñħод\n×¢ ×ĸר\n׾ ×Ľ×ª\nÙĨت اج\nĠÑģ ем\nà¸Ī à¹Īาย\nà¸ķร ว\nà¸ķรว à¸Ī\nפר ×ķ\nà¸Ĥ ัà¸ļ\nãģ ŀ\nĠп ло\nк олÑĮ\n×ŀ×¢ ×ĺ\níķĺ ìĭľ\njÄħ ce\nÙĨ اÙĨ\nลี à¸ģ\nн ÑĥÑĤ\nĠоб ÑĢаз\nÙĥ بر\nĠاÙĦÙĪ Ø·ÙĨ\nãģķãģĽ ãģ¦\nÙĤ اء\n×ŀ×ĵ ×Ļ׳\ny ü\nפ ×Ļת\n׳ ×ķף\nÙħÙĨ ظ\nหà¸Ļ ัà¸ģ\nìŀ Ī\nãĤ« ãĥ¼ãĥī\nع ÙĨÙĬ\nп од\nض اء\nà¸Ļ à¸ķà¹Į\n×ŀש פ\nว à¹Į\nר ×ķק\nส ืà¹Īà¸Ń\nפק ×Ļ×ĵ\nãģªãĤī ãģªãģĦ\nĠìŬ 룬\nÙĦ ج\nÑī иÑĤ\nãĥĥ ãĤ·\nÙĦÙĬ س\nĠÙĦ Ùħا\nìł ij\n×ij ×Ļף\nãĥģ ãĤ§\nĠgü ç\nĠch ứ\n×ķצ ×IJ\nקר ×ij\nà¹Ĥ à¸ŀ\nоÑĩ но\nסק ×Ļ\nש׾ ×Ŀ\nصر Ùģ\nĠL Ãł\n×¢ ×Ļת\ná» ·\nà¹Ĥ à¸Ńà¸ģ\nà¹Ĥà¸Ńà¸ģ า\nà¹Ĥà¸Ńà¸ģา ส\nĠ×Ķ ×ĵ×ijר\nà¸Ļั à¹Īà¸Ļ\nز ر\nнак о\níļ į\nãĤĤ ãģ¡\nãĤĤãģ¡ ãĤį\nãĤĤãģ¡ãĤį ãĤĵ\nاÙħ ت\nعد اد\nи нÑĭ\nÅĤy w\nà¸Ħ à¸ĵะ\nà¸Ĺ ะ\nkt ör\n×Ļ×Ĺ ×Ķ\nĠм е\nĠме ÑģÑı\n׳×Ķ ×Ĵ\nĠÑģ ÑĥÑīеÑģÑĤв\nà¸Ļ ัà¸Ļ\nÑĦ ÑĦ\nек ÑĤив\nعÙĦÙĪÙħ ات\nб Ñĥд\nà¸Ļัà¸ģ à¸ĩาà¸Ļ\nหà¸Ļà¹īา à¸Ĺีà¹Ī\nÙĤÙĬ ÙĤ\nãĤ· ãĥ³\nãģ« éĸ¢\n×IJר ×Ĵ\nĠпÑĢ оÑĤ\nĠпÑĢоÑĤ ив\nĠìŀĪ ìĸ´\nÙĤÙĬ ÙĤØ©\nìĹ ĩ\nk ür\nãģ«ãģªãĤĬ ãģ¾ãģĹãģŁ\nĠде ÑıÑĤ\nĠдеÑıÑĤ елÑĮ\nפ×ķר ×ĺ\nà¸Ł à¹īา\nà¹Ģ à¸ł\nĠавÑĤом аÑĤ\n×ĸ ×Ļק\nĠold uk\nع اÙħ\nĠÑĤ оÑĢ\nyrı ca\nê Ì\nãĤŃ ãĥ³ãĤ°\nãģ« ãģ¨ãģ£ãģ¦\nà¹Ģà¸ī à¸ŀ\nà¹Ģà¸īà¸ŀ าะ\nãģ¯ ãģļ\n×ŀ ×IJ×Ļ\nสะ à¸Ķ\nสะà¸Ķ วà¸ģ\nìľ¼ ë©°\nà¸ģ ี\nภ¬\nĠ×¢ ×ķש\nà¸łà¸² ษา\nà¸Ĺ ัà¸Ļ\nac akt\nacakt ır\nاع دة\nĠÑĥÑģл Ñĥг\nס ר×ĺ\n×ķ×ŀ ×ķת\n×Ķ ×ķר\n×ŀ ×ķ×ij\n×ŀ×ķ×ij ף\nسÙĬ اس\nاتÙģ اÙĤ\n×Ķ צ׾\nÙħؤ س\nĠp ó\nĠк ни\n×Ļ׼ ×ķ׾\nà¹Ģหล ืà¸Ń\n׼׾ ׼\n׳ ×ĸ\nÑĪи е\nr ès\nĠاÙĦØŃ ÙĤ\nлÑı ÑĢ\nห à¸į\nหà¸į ิà¸ĩ\nר×Ĵ ×Ļש\nà¹Ģส à¹īà¸Ļ\nש×ij ×ķף\nô tel\nап ÑĢ\nапÑĢ имеÑĢ\nاب ÙĦ\nĠÑĢаз виÑĤ\nĠп олÑĮз\nĠС еÑĢ\n×ķ×ij ×Ļ\nr óż\nìĭ Ń\nãĤ¯ ãĥĪ\nãģĹ ãĤĪãģĨ\nà¸ģร ม\nØŃ ÙĥÙĪÙħ\nà¹Ĥ à¸ļ\nà¸Ĺ à¹īาย\nĠM á\nĠÑĤ Ñĭ\nà¸Ħร ัว\nÑĢÑĥ б\nạ p\nĠm ÅĤ\nĠmÅĤ od\nĠgör Ã¼ÅŁ\nĠgeli ÅŁ\nÆ°Æ¡ i\n×ŀש ק\nÙĢÙĢ ÙĢÙĢ\nรา ว\nãģĹãģ £\nãģĹãģ£ ãģĭãĤĬ\nĠÐļ он\nĠk ê\nà¹Ĥà¸Ĺ ร\nèIJ½ ãģ¡\nåĩº ãģ¦\nล ัà¸ģษ\nĠ×Ĵ ×ij×ķ×Ķ\nãĥĻ ãĥ«\nê±° ëĤĺ\në§ IJ\n×Ļ׾ ×ĵ×Ļ×Ŀ\nĠëĦ Ī\n×ŀר ×Ļ\nร ส\nãĥŃ ãĥ³\nи ло\nноÑģÑĤÑĮ Ñİ\n×ĸר ×Ĺ\nп он\nĠ×Ķש ׾\nê²ł ìĬµëĭĪëĭ¤\nĠki ÅŁ\nĠÐļ и\nว ร\nد اع\nÅŁ im\nÙĨ Ùij\nв аÑĤ\nرا Ùĥ\nب اÙĦ\nид е\nĠ×Ķ×ŀ ×Ĺ\nìĸ µ\nتÙģ اع\nØ£ ت\nëĬ ĺ\nש ×Ļת\nست Ùħر\nĠÑĦ ак\nĠاÙĦØ£Ùħ رÙĬ\nëŀ ¨\nاس Ùħ\nĠa ÄŁ\nĠç ev\nÙĥ ÙĪر\nãģķ ãģ¾\nĠç öz\nĠر س\nÄħ da\nสà¸Ļ ุ\nãģĹãģ¦ ãģıãĤĮ\nн Ñİ\nleÅŁ me\nãĤª ãĥ³\nãģ¨ ãģªãĤĬ\nava ÅŁ\n×ĺ ×Ļ×ij\nØŃ Ø¶\n×ķצ ×IJ×ķת\nÙĨ ÙħÙĪ\nı t\nĠÑħ а\nĠÑħа ÑĢак\nĠÑħаÑĢак ÑĤеÑĢ\nĠd ÅĤ\nãĥĹ ãĥ©\nà¸Ĭ ุม\nà¹Ī à¸Ńà¸Ļ\n×ķ×ij ׾\nÑģ ол\n×ĵ ×Ĵ\nаÑĢ аÑĤ\nn ivers\nĠgerçek leÅŁtir\nĠاÙĦ ÙĦÙĬ\nระ ยะ\nĠÙħ ختÙĦÙģ\nĠgö nder\nÙģ ار\ndo ÄŁ\ndoÄŁ an\nص ÙĦاØŃ\nĠyay ın\nãĥĨ ãĥ³\nรว à¸Ī\n×Ļ×Ĺ ×Ļ×ĵ\nünk ü\nÑĨи алÑĮн\nà¸ļ ู\nม ุ\nh ä\nØ® Ùģ\nå¢ Ĺ\nå¢Ĺ ãģĪ\nеÑĩ но\nĠاÙĦس ÙĨ\nà¸Ĥ าว\nim di\nÐ «\nà¸Ļà¸Ńà¸ģ à¸Īาà¸ģ\nà¸ļา ล\nת ש\nĠdüzen le\nмÑĭ Ñģл\nãģı ãģª\nż u\nĠwsp óÅĤ\nĠн аз\nınd aki\nتر Ø©\nÅŁ ek\nĠö d\nĠÙĪ Ùĥ\nĠпозв олÑı\nĠת ×ķ׼\nÙħÙĨ تج\në§ ī\nĠاÙĦØ« ÙĦاث\nаÑĨи Ñİ\nÙĪر ÙĪ\nÑĭв аеÑĤ\nخص ص\nĠاÙĦÙģ ÙĦ\nĠاÙĦÙģÙĦ سطÙĬÙĨ\nØ¥ جر\nإجر اء\nاÙĨت Ø®\nاÙĨتخ اب\nار ÙĬØ©\n×ķ Ö\nØ¢ ÙĨ\n×ŀ×¢ ×ķת\nĠм ал\nĠ×IJ ×Ĺ\nà¸Ĺ à¹īà¸Ńà¸ĩ\nze ÅĽ\nĠë§Į ëĵ¤\nرÙĬ ع\näºĭ ãĤĴ\nà¸ļริ หาร\n׾ ×ŀ×Ļ×ĵ\nĠм Ñĥж\nت رÙĪ\nĠباÙĦ Ø¥\nפ ×Ļק\nز ÙħØ©\nĠÃ¶ÄŁ renc\nãĥ ¶\nاÙħ عة\nק×ij ×ķצ\n×ŀ ׳×ķת\nرÙĬ Ùħ\nĠо каз\nãģłãģij ãģ©\nĠh ız\nĠש ×IJת\nãĤ¢ ãĥ¼\nĠmożli wo\nìĦ ¼\nÙĪ اب\nог ÑĢаÑĦ\nĠعبد اÙĦ\nãĤĴ è¡Į\nب ÙĬÙĦ\nĠÄ° ç\nย าย\nĠÑĥ ÑĩаÑģÑĤ\nÑĦ еÑģÑģ\nÑĦеÑģÑģ иона\nẠ¤\nÙĨ ÙĬÙĨ\nعد ÙĦ\nสร ร\nدÙĬ ÙĦ\n×ij ×Ļק\nczy ÅĤ\nÑĢом е\nĠм ед\nìĻ Ķ\nãĥ© ãĤ¤ãĥ³\nĠÑĤ еп\nеÑĢ ÑĮ\ni ÄŁi\nв ели\nÑĢи ÑģÑĤ\nס ×ķפ\n×ŀ׾ ×Ĺ\nĠاÙĦØ¥ ÙĨ\nĠ׾×Ķ ש\nè¶Ĭ ãģĹ\nĠÑĢ Ñĭ\n×ķ×IJ ר\nرÙĩ اب\nפ ×ķ×IJ×Ļ\nĠгоÑģ Ñĥд\nĠгоÑģÑĥд аÑĢ\nĠгоÑģÑĥдаÑĢ ÑģÑĤв\nĠاÙĦØ£Ùħ ÙĬر\nÙħ ج\nà¹Ģหม าะ\nÑĢ ев\nà¸Ĭี à¸ŀ\nãĥķ ãĥĪ\nиÑĩ но\nĠاÙĦÙħ ؤ\nĠi ht\níħ ľ\nد ÙĨÙĬ\nر ص\nла ÑģÑĤ\nà¹Ģหล à¹Īา\nılı r\nร à¸ĵà¹Į\n×ŀש ×Ļ×ļ\nĠd á»ĭ\nØ·Ùģ اÙĦ\n×ĺ ×ķף\nĠ×ij ×Ļ׳\nãģ¾ ãģ£ãģŁ\nлож ениÑı\nتØŃ Ø±\nب اØŃ\nà¹Ģส ืà¹īà¸Ń\nãģĻ ãģĶ\nlt ür\nà¸ĩ าม\nĠt ü\nĠпÑĢ им\nĠпÑĢим ен\nĠhay at\nëĥ IJ\nëĭ Į\n׳×Ļ ×ķ\nвед ен\nìħ ¨\nà¸Ī ัย\nà¸ģà¹Ī à¸Ń\nĠв од\nоÑģÑĤ оÑı\nн аÑĤ\nà¹ģ หล\nسÙħ ÙĬ\nà¸Ķำ à¹Ģà¸Ļ\nà¸Ķำà¹Ģà¸Ļ ิà¸Ļ\nw ód\nö yle\nãĥĢ ãĤ¤\nÑĪи й\nмеÑī ен\nãģĹãģ¾ ãģĨ\nãĥī ãĥ©\nÙĪض ØŃ\nà¸Ńà¸Ļ ุ\nĠاÙĦ اجتÙħاع\nlaÅŁ ma\nà¸Ħ à¸Ńà¸Ļ\n×ŀר ×Ļ×Ŀ\nÙĨ اÙħج\nשר ×ķת\nاÙĦ Ø£\nĠksi Äħż\nĠа н\nÑĢаР¹\nاÙĩر Ø©\n×ŀ×ĵ ×Ķ\nä¸Ģ ç·\nä¸Ģç· Ĵ\nä¸Ģç·Ĵ ãģ«\nÑĢиÑĤ оÑĢ\nd ıkl\nà¹ģ à¸ĸ\nà¹ģà¸Ĥ à¹Īà¸ĩ\nекÑĤ оÑĢ\n×ŀס ×¢\nÑĢак ÑĤи\nu ÄŁu\n×ķ×ij ת\nสู à¸ķร\nĠçalÄ±ÅŁ m\nĠçalÄ±ÅŁm alar\nĠа на\nãĥĽ ãĥ¼ãĥł\nĠböl üm\nĠب ص\nол оÑģ\nĠìķĬ ëĬĶ\nà¹Ī ะ\nÙĪ تر\nä¹ Ĺ\nست خداÙħ\nפ×Ļ ×Ļס\nפ×Ļ×Ļס ×ij\nפ×Ļ×Ļס×ij ×ķק\nĠк ÑĢаÑģ\nли к\nرÙĬ ØŃ\n×ŀש ׾×Ķ\nà¹Ģย ีà¹Īย\nà¹Ģยีà¹Īย ม\nв иÑģ\nом н\nÄŁ un\nãĥŃ ãĥ¼ãĥ³\nØ£ تÙĬ\nà¸ķร ี\nçĶ³ ãģĹ\nتÙħ ر\nìĹ ĪìĬµëĭĪëĭ¤\nĠÙĪ غÙĬر\nred ni\nĠاÙĦص Ùģ\nĠна ÑģÑĤоÑı\nĠнаÑģÑĤоÑı Ñī\nà¸ķ รา\nĠÑĥÑģл ов\nĠÑĥÑģлов иÑı\nÑĨ еп\n×Ķ ×Ĺ׾×ĺ\nØ· ÙĬع\nĠB akan\nĠاÙĦ رÙĪ\nилÑĮ но\nĠм еÑĤ\nà¸Ķ à¸Ńà¸ģ\nãģĭãĤī ãģªãģĦ\nĠпо ÑģÑĤоÑı\nĠпоÑģÑĤоÑı н\nĠÑĩ аÑģ\nü c\nwr ó\nб ÑĥÑĢ\nãĥIJ ãĥĥãĤ¯\nãĥ©ãĥ³ ãĥī\nĠо гÑĢ\nสั à¸į\nสัà¸į à¸įา\nมั à¹Īà¸Ļ\nà¸Ħ à¸Ńม\nal ık\nĠн ед\nüm üz\nĠÅĽ wie\né rio\n×Ļ×IJ ×Ķ\nدÙħ ات\nı rl\nĠоÑĤ з\nĠоÑĤз Ñĭв\nä»ĺ ãģį\nĠkaż de\nмин иÑģÑĤ\nãĤ° ãĥ«\në° ĸ\nез н\nاÙĦ Ùģ\nĠש ק׾\nÙħ ض\nãĥĿ ãĥ¼ãĥĪ\nÙħÙĨ ت\nÙĤÙĬ اÙħ\nØ´ ÙĨ\n×Ļר ×ķ×¢\nãĤŃãĥ£ ãĥ³\nдоÑĢ ов\n×ŀ ×Ļת×Ļ\nÙĪÙĦ ÙĪج\nÙĥ اÙģ\nĠÑĢаз лиÑĩ\nиÑĤ еÑĤ\nн олог\nลà¸ĩ à¸Ĺุà¸Ļ\nĠyak laÅŁ\nãĥ¬ ãĤ¤\nê²ł ëĭ¤\næ±Ĥ ãĤģ\nرÙĪ Ùģ\nĠí Ĭ\nĠíĬ ¹\nãģ£ ãģıãĤĬ\nà¸Ħวาม à¸Ħิà¸Ķ\n×Ķ ×Ļס×ĺ\nØ¥ ÙĤ\nãģ¦ ãģĦ\nà¹Ĥ à¸Ĭ\nĠBü yük\nĠФ едеÑĢ\nÑĨи н\nÑĢов а\nĠاÙĦ اÙĤتصاد\nĠch á\nà¸ĺ าà¸Ļ\në¥ ł\nà¹Ħ à¸ķ\nÃŃ pio\nÙĭ ا\nĠоб Ñıз\nÙĩ ج\nĠì¤ij ìļĶ\nãģ® ãģ§ãģ¯ãģªãģĦ\nبار اة\nãĤ¤ ãĥ«\nĠн оÑĢм\ná»ī nh\nm ö\nmö glich\nÑĨи п\nãĤ¢ ãĤ¯\n×Ķ ×Ļ\nÑĨи алÑĮно\nĠÅĽ wi\nت ÙĤ\nĠÑģÑĤо им\nبÙĬ عÙĬ\nĠ׾ ש×ŀ\nг лÑı\nглÑı д\nãģ¦ ãģıãĤĮ\nÄĻd zi\nà¸Ĥ ั\nà¸Ĥั à¹īà¸Ļ\nØ· ÙĤ\nĠìĹ Ń\nãģ£ãģ¦ãģĹãģ¾ ãģĨ\nĠdeÄŁer l\nĠdeÄŁerl endir\nĠü lk\nĠмн ог\n๠ĭ\në¿ IJ\nĠУ кÑĢа\nÄŁ ini\nĠбез оп\nĠбезоп аÑģ\nà¸Ńà¸Ńà¸ģ à¹ģà¸ļà¸ļ\nØ§Ø ¸\nØŃد اث\nл еÑĢ\n×Ļ× ¥\n×Ļ׳×ĺר ׳×ĺ\nlar ınız\nØŃÙĬ ØŃ\nż eli\nà¸Ń ัà¸ĩ\nà¸Ńัà¸ĩ à¸ģ\nà¸Ńัà¸ĩà¸ģ ฤษ\nĠоÑĤ лиÑĩ\nั ส\nëŀ į\nож но\nãĤ¹ ãĥĿ\nĠÑħ оÑĩ\nĠк ап\nеÑĩ ен\nØŃÙĦ Ø©\nÙĬا Ùĩ\nна л\n×ķצ ר×Ļ×Ŀ\nĠk ald\nåĥ į\nĠاÙĦØ´ خص\nĠз на\nĠwz gl\nż ycz\nê° Ŀ\nà¸ŀ ลัà¸ĩ\níģ ¼\nĠö l\nĠb ụ\nØ´ Ùĩر\nĠз ам\nĠд ев\n×Ļ×ĺ ת\nتعÙĦ ÙĤ\nÙĪÙħ Ø©\nãĤĴ ä½ľ\nãģį ãģ¦\ní ĥĿ\nras ında\nãĤĴ æİ¢\nĠÙħ باشر\nراج ع\nĠв озд\nÙħØŃ Ø§\n×ķש ר\nĠиÑģÑĤ оÑĢ\nม ัà¸ģ\nt ıģ\nØ« ار\nتر ÙĨت\nà¹ģà¸Ĥ à¹ĩ\nà¹ģà¸Ĥà¹ĩ à¸ĩ\nп оÑĩ\nĠ×ij ×IJ×ķת\në¯ Ģ\nëĿ¼ ëıĦ\nà¸Ĭ ัà¸Ķ\nส à¸ķà¹Į\nãĥĭ ãĥĥãĤ¯\nид енÑĤ\nĠг ÑĢÑĥпп\nت Ø®\nẠł\nย ืà¸Ļ\nย ัà¸Ļ\nó ry\nT Ãľ\nãģĹ ãĤĥ\nĠпÑĢов ед\nлÑı еÑĤ\nÙħ Ø®\nย à¸Ńม\n×Ľ×ł×¡ ת\nĠاÙĦÙħ ÙĨت\nĠol mad\nר׼ ×ĸ×Ļ\nĠв ÑģÑĤÑĢ\nĠиÑģ Ñģлед\nÑĤвеÑĢ ж\nبد ÙĪ\nеÑĢ ÑĤ\nï» ·\n± ħ\nสัม à¸ŀัà¸Ļà¸ĺà¹Į\nิ à¹Īà¸Ļ\nצ ×Ļ×ij\nwiÄĻ t\nĠì° ¸\nĠz wiÄħz\nسب ÙĪع\nãĥĥ ãĤ°\nà¸Ľà¸¥ à¸Ńà¸Ķ\nà¸Ľà¸¥à¸Ńà¸Ķ à¸łà¸±à¸¢\nãĤĤ ãĤĬ\nÙĤد س\nĠspr z\nĠsprz eda\nĠist edi\nĠk hu\nĠд ен\nĠko ÅĦ\nĠ×ij ×Ĺ×Ļ\nà¹Ģà¸Ĺ à¹īา\n×ķס ×Ļ×£\nãĥĭ ãĥ¥ãĥ¼\nĠпÑĢед оÑģÑĤ\nĠпÑĢедоÑģÑĤ ав\nà¹Ĥ à¸Ł\né v\nĠاÙĦص ØŃ\nصØŃ Ø§Ø¨\nà¹Ģà¸Ī à¹ĩà¸ļ\nвл ек\nวั à¸ķ\nà¸ĸ ุ\nãģĵãģ¨ãģĮãģ§ãģį ãģ¾ãģĻ\nÙĤÙĬ ÙĤÙĬ\n×ķ׊ר\nÑĭ ÑĪ\nĠоÑĤ но\nĠоÑĤно ÑĪ\nоб илÑĮ\nÙģ ØŃ\nı nt\nınt ı\nĠ׾ ×ij×ĵ\ní İĺìĿ´ì§Ģ\nãĥĬ ãĥ«\nĠÙħ ساء\n×Ļ×ĺ ×ij\nÑĮ еÑĢ\nëĦ ·\nÑĭ ÑĤа\nĠоÑĩ еÑĢ\nà¸Ķ ืà¹Ī\nà¸Ķืà¹Ī ม\nĠN gh\nت عب\nÙĦاÙĤ ات\n×ķ׾×ķ×Ĵ ×Ļ×Ķ\nĠìĿ´ ê²ĥ\nĠ×Ķ ×ijר\nìľ µ\nà¹Ģà¸Ħล ืà¹Īà¸Ńà¸Ļ\nÙĩ Ø©\nà¸Īำ à¹Ģà¸Ľà¹ĩà¸Ļ\nå¤ī ãģĪ\nwi ÅĽcie\nch od\nchod zÄħ\nв ÑĢо\n×ŀ×Ĺ ×Ļר\nĠy ı\nĠyı ll\nì¡ Į\nà¹Ħ หว\nãģªãģı ãģª\nĠзав иÑģ\nĠìĺĪ ìĪĺ\nÙģ Ø°\nủ ng\nà¸ŀุ à¸Ĺà¸ĺ\nз н\nlay an\nãĤ ¡\nà¸ģà¹ĩ à¸ķาม\nĠsaÄŁ lam\nร à¸ĵ\nĠÑģ иÑĤ\nĠÑģиÑĤ Ñĥ\nĠاÙĦت ÙĨ\n×Ķ ×ĸ\nĠØ· ÙĪÙĬÙĦ\nta ÅĤ\nĠgö rd\nå¤ī ãĤı\nëĥ ¥\nà¸Ħà¹Ī à¸Ńย\n×IJ ×ķ×ĺ\nëħ IJ\nãĥ©ãĥ³ ãĤ¹\nวั à¸Ĵ\nวัà¸Ĵ à¸Ļ\nĠol uÅŁ\nפע ×ķ׾\nĠszczeg óÅĤ\nà¸Ħา สิ\nà¸Ħาสิ à¹Ĥà¸Ļ\npow ied\nĠÑĤ еб\nหà¸Ļ à¹Īวย\nĠм ил\nØŃ Ùĥ\nà¸Ĺ à¸Ķ\nĠмаÑĤ еÑĢиал\nÅĤ ow\nà¹Ģà¸ģ ีย\nĠÑģов еÑĢ\nãĤ ©\nà¸Ľ ริ\nĠи Ñİ\nнаÑĩ ен\nÑĢен д\nmu ÅŁtur\nĠпÑĢод Ñĥк\nз д\nÑı ÑĤи\nÑıÑĤи Ñı\nà¹Ģม ีย\nرات ÙĬج\nĠam acı\nש ×ķ׾\nש×ķ׾ ×Ĺ\nสะ à¸Ńา\nสะà¸Ńา à¸Ķ\nפ×Ĵ ×¢\nعب Ø©\nd ın\níħ Ķ\nĠ×ŀש ×Ĺק\nĠfi yat\nĠз аÑı\nĠзаÑı в\nà¹Ĥ หล\nà¹Ĥหล à¸Ķ\nà¸ģรุà¸ĩ à¹Ģà¸Ĺà¸ŀ\nצ×Ļ ×Ļף\nìļ ±\nÙħ ب\nÙħب اد\nland ır\nĠв еÑģÑĮ\nĠh ük\nĠÐĴ оз\nÑĩиÑĤ Ñĭва\nว ล\n×ķצ ×¢\nà¸Ĥà¸ĵะ à¸Ĺีà¹Ī\nĠaÅŁ aģı\n׾×IJ ×ķ×ŀ×Ļ\ntr zym\nÃ¤ÃŁ ig\nowo ÅĽci\nãģĿ ãĤĤ\nĠroz wiÄħz\nĠgÅĤ ówn\nм онÑĤ\n×ŀ ×ķ×ŀ\nĠÑģÑĤ ан\nÙĦا ÙĤØ©\np rowad\nprowad zi\nĠÑģоÑģÑĤ оÑı\n×Ļ×IJ ×ķת\nr ı\ng ı\nãĥij ãĥij\nĠна лиÑĩ\n×Ķ צע\nĠ׳ ×Ķ\nà¸Ħ ัà¸ļ\nع راض\nи ж\nÙĩ ائÙĬ\nãĤī ãģı\nож еÑĤ\nĠоб оÑĢ\nĠобоÑĢ Ñĥд\nØ£ سÙĦ\nà¹ĩ à¸Ķ\nÑĢÑĥ ÑĤ\nدÙĬ ÙħÙĤ\nدÙĬÙħÙĤ را\nĠjest e\n×ķ×ķ ×Ļר\n×ij×ĵ ×Ļק\nдеÑĢж ива\nãģĬ ãģı\newn ÄĻtr\newnÄĻtr zn\nà¸ŀ ฤ\nĠ×IJ ×ķ×Ķ\nת×Ĺ ×ķש\nĠz ob\nд Ñĥм\nĠÑģ Ñĭ\nÙĬر ا\nĠwiÄĻ ks\nà¹ģà¸ķà¸ģ 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á»ĩc\nĠn Äĥm\nĠth ì\nĠh á»įc\nĠÙĪ ت\nt é\nĠا ÙĨ\nĠt ôi\nĠ×IJ ׳×Ļ\nĠ׾ ×Ļ\nĠ×ŀ ×ķ\nĠng Ãły\nĠn Æ°á»Ľc\nĠ×Ķ ×Ļ×IJ\nĠ×IJ ×Ļ\nĠh Æ¡n\nĠÙĩ Ø°Ùĩ\nĠÙĪ ÙĬ\nĠاÙĦ Ø°ÙĬ\nĠ×ķ ×ŀ\nĠgi á\nĠnh ân\nĠch ÃŃnh\nĠm ình\nĠÐĿ а\nĠth ế\nĠ×Ļ ×ķתר\nĠ×IJ ×Ŀ\nĠn ên\nĠh ợ\nĠhợ p\nĠc òn\nĠÙĩ ÙĪ\nĠc Æ¡\nĠr ất\nĠVi á»ĩt\nĠب عد\nĠש ×Ļ\nĠth á»Ŀi\nĠc ách\nĠÄij á»ĵng\nĠн о\nĠtr Æ°á»Ŀng\nØ Ł\nĠÄij á»ĭnh\nĠÄiji á»ģu\n×Ļ ×Ļ×Ŀ\nĠth á»±c\nn ın\nĠh ình\nĠn ói\nĠc ùng\nĠ×Ķ ×Ķ\nĠØ¥ ÙĨ\nĠ×IJ ×ij׾\nĠnh Æ°ng\nĠbi ết\nĠж е\nĠch úng\nĠÄij ang\nĠØ° ÙĦÙĥ\nĠl ên\nĠkh ách\nĠn Ãło\nĠs á»Ń\nĠkh ác\nĠë° ı\nĠl ý\n×Ļ ×Ļ\nĠÄij ây\nĠ׾ ×ŀ\nĠc ần\nĠtr ình\nĠph át\nãģ« ãĤĤ\nп о\nĠn Äĥng\nĠb á»Ļ\nĠv ụ\nĠÄij á»Ļ\nÑĩ е\nĠnh áºŃn\nĠtr Æ°á»Ľc\nĠ×¢ ×ĵ\nĠh Ãłnh\nĠØ® ÙĦاÙĦ\nĠl ượng\nĠc ấp\nĠtá» ±\nĠv ì\nĠt Æ°\nĠch ất\nĠ׼ ×ŀ×ķ\nĠg ì\nĠש ׳\nĠt ế\nת ×ķ\nĠnghi á»ĩp\nĠm ặt\nĠÙĥ Ùħا\nĠ×ij ×Ļף\nĠר ק\nĠth ấy\nĠmá y\nĠÙģ Ùī\nĠd ân\nĠ×IJ ×Ĺ×ĵ\nĠt âm\nĠ׼ ×ļ\nĠ׾ ×ķ\nв о\nĠt ác\nĠto Ãłn\nĠÙĪ Ùħ\nĠk ết\nĠ หรืà¸Ń\nĠÙĪاÙĦ Ùħ\nĠÄiji á»ĥm\nĠ×ĸ ×ķ\nĠ×ij ×ķ\n׼ ×ķת\nĠh 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×ķ×ĵ\nĠn ó\nĠth áng\nà¹Ģลืà¸Ń à¸ģ\nר ×Ķ\nĠt Äĥng\nĠcá i\nĠtri á»ĥn\nĠ×IJ×ķת ×ķ\nìłģ ìĿ¸\nĠC ông\nĠ׾×Ķ ×Ļ×ķת\nĠг ода\nи Ñİ\nĠب عض\nĠ à¸ģาร\nèī¯ ãģĦ\nÙĪ ت\nĠli ên\nĠÐĿ о\nĠÐĿ е\nçļĦ ãģª\nĠÙħ ت\nĠÑĤак же\nĠкоÑĤоÑĢ Ñĭе\nĠ×Ļ ×ĵ×Ļ\nĠtr á»įng\nãĤµ ãĤ¤ãĥĪ\nìłģ ìľ¼ë¡ľ\nĠt áºŃp\nĠש ׾×Ļ\níķĺ ê²Į\nĠt Ãłi\nĠÐ ¯\nĠr á»ĵi\nا Ùĥ\nĠth Æ°Æ¡ng\nĠ×Ķ ×ĸ×Ķ\nĠÙĪ ÙħÙĨ\nà¸Ĺีà¹Ī มี\nĠcu á»Ļc\nĠbü yük\nãģ¨ ãģĭ\nĠ×ij ×Ļ×ķתר\nĠl ần\nĠgö re\nĠtr ợ\nĠ×ĺ ×ķ×ij\nÑĤÑĮ ÑģÑı\nĠth á»ijng\nĠ׼ ש\nĠti êu\nĠ×ŀ×IJ ×ķ×ĵ\nØ Ľ\nk Äħ\nĠ à¹ĥà¸Ļ\nĠv ấn\nĠש ׾×ķ\nĠÄij á»ģu\nÙģ ت\nĠê²ĥ ìĿ´\nĠh óa\nĠاÙĦع اÙħ\nĠÙĬ ÙĪÙħ\nк ой\nĠbi á»ĩt\nÑģÑĤ о\nĠ×Ķ ×Ļ×ķ\nà¸Ĺีà¹Ī à¸Īะ\nĠ×ĵ ×Ļ\nĠ×IJ ×ļ\nĠá n\nص ÙĪر\nĠtr ÃŃ\nĠÐŁÑĢ о\nĠl á»±c\nãģĹãģ¦ ãģĦãģ¾ãģĻ\nĠb Ãłi\nĠ×ĸ ×IJת\nĠb áo\nà¸ļ à¸Ļ\nĠëĮĢ íķľ\nĠti ế\nĠtiế ng\nĠb ên\nãģķãĤĮ ãĤĭ\ns ión\nĠt ìm\n×¢ ×ķ\nm é\nни Ñı\nãģ» ãģ©\nĠà¹Ģà¸ŀ ราะ\nب Ø©\nĠë¶ Ħ\nĠ×IJ ×ĸ\nà¸Ĺ à¹Īาà¸Ļ\nת ×Ŀ\nĠth êm\nĠho ạt\ny ı\n×ĸ ×ķ\nĠgi á»Ŀ\nĠb án\nà¸Ĥ าย\nÑĩ а\nĠ à¹Ĩ\nĠاÙĦÙħ ت\nĠоÑĩ енÑĮ\nĠb ất\nĠtr ẻ\nÑĤ ÑĢ\nĠØ£ ÙĨÙĩ\nĠØ« Ùħ\nĠ׼ ×ŀ×Ķ\nĠkh ó\nĠr ằng\nĠÙĪ ÙģÙĬ\nни й\nĠho Ãłn\nt ó\nĠ×IJ שר\nĠìĥĿ ê°ģ\nÑģ а\nĠ׼ ×ijר\nĠÑįÑĤ ом\nlar ının\nĠch Æ°a\nз и\nĠd ẫn\nĠÐļ ак\nج ÙĪ\nĠбÑĭ ло\nĠÙĬ ت\nn ı\nÅĤ am\nĠÙĪÙĩ ÙĪ\n×ij ×ķ\nп и\nר ת\nĠqu á»ijc\nж д\nĠÄij Æ¡n\nÙĥت ب\nĠm ắt\nระ à¸ļ\nระà¸ļ à¸ļ\nĠÙĥ اÙĨت\nĠth ân\nสิà¸Ļ à¸Ħà¹īา\n×Ĵ ×Ļ\nĠph Æ°Æ¡ng\nà¹Ħมà¹Ī à¹Ħà¸Ķà¹ī\nĠìĦ ±\nĠC ác\nĠ×Ķ×ŀ ×ķ\nĠÑĤ ем\nĠ×ĵ ×ķ\nà¸Ńะ à¹Ħร\nĠv Äĥn\nãģª ãģ®ãģ§\nĠN á»Ļi\nĠ×¢ ×ķ\nãĤīãĤĮ ãĤĭ\nĠs áng\nĠgö ster\nãģĵãģ¨ ãĤĴ\nĠtaraf ından\nĠм а\nĠпоÑģл е\nĠ׳ ×Ļת\nĠ׳×Ļת ף\nĠл еÑĤ\nĠ׾ ׳×ķ\nÑģ Ñģ\nĠ×Ļ ×ķ\nп е\nĠÙĪ ÙĦÙĥ\nĠÙĪÙĦÙĥ ÙĨ\nĠngo Ãłi\nĠÄij á»ĭa\nr zÄħd\ndz iaÅĤ\nĠÙħ ر\nиÑĤÑĮ ÑģÑı\nĠ×IJ×Ĺר ×Ļ\nĠ׾ ׼׾\nà¸Ĥ à¹īà¸Ńม\nà¸Ĥà¹īà¸Ńม ูล\nĠб ол\nĠбол ее\nجÙħ ع\nл еÑĤ\nĠl á»ĭch\nĠÙħ Ø«ÙĦ\nĠ그리 ê³ł\nĠth ứ\nĠdeÄŁ il\nÙĪ ØŃ\nĠש׾ ×ļ\nĠÙħ ØŃÙħد\nĠn ếu\nĠÄij á»ķi\nĠv ừa\nĠm á»įi\nĠо ни\nĠl úc\nĠÙĬ ÙĥÙĪÙĨ\nì§ Ī\nĠש׾ ׳×ķ\nĠÐĶ о\nĠש ׳×Ļ\nล ิ\n×IJ פשר\nĠs ức\nê¶ Į\nĠ ứng\nà¹Ħมà¹Ī มี\nØ·ÙĦ ب\nĠÑĩ ем\nĠch uyên\nĠth ÃŃch\nĠ×ķ ×Ļ\níķ ©\nĠÙħ صر\nд о\nĠÄij ất\nĠch ế\nà¸Ĭ ืà¹Īà¸Ń\nĠìĭ ł\nĠØ¥ ذا\nĠر ئÙĬس\nĠש ×Ļש\nĠgiả m\nÑģ ка\nlar ında\nĠs ợ\nĠtÃŃ ch\nĠÙĦ ÙĥÙĨ\nĠب Ùħ\n×¢ ×ķ×ij\n×¢×ķ×ij ×ĵ\nÅĤÄħ cz\nları na\nĠש ×Ŀ\nĠÙĦ ت\nĠש×Ķ ×ķ×IJ\nt ów\nĠëĭ¤ 른\nĠØ£ Ùĥثر\nãģ® ãģ§ãģĻ\n׼ ×Ļ×Ŀ\nĠolduÄŁ unu\nãģĭ ãģª\nãĤĤ ãģĨ\nÙĬ ØŃ\nĠnh ìn\nĠngh á»ĩ\nãģ«ãģª ãģ£ãģ¦\nп а\nĠquy ết\nÙĦ ÙĤ\nt á\nĠlu ôn\nĠÄij ặc\nĠ×IJ ר\nĠtu á»ķi\ns ão\nìĻ ¸\nر د\nĠبÙĩ ا\nĠ×Ķ×Ļ ×ķ×Ŀ\n×ķ ×ķ×Ļ\nãģ§ãģĻ ãģŃ\nĠÑĤ ого\nĠth ủ\nãģĹãģŁ ãģĦ\nر ÙĤ\nĠb ắt\nг Ñĥ\nĠtá» Ń\nÑĪ а\nĠ à¸Ľà¸µ\nĠ×Ķ×IJ ×Ŀ\níı ¬\nż a\nĠ×IJת ×Ķ\nĠn á»Ļi\nĠph ÃŃ\nĠÅŁek ilde\nĠl á»Ŀi\nd ıģı\nĠ׼×IJ ף\nĠt üm\nĠm ạnh\nĠM ỹ\nãģĿ ãĤĵãģª\nĠnh á»ı\nãģª ãģĮãĤī\nĠb ình\nı p\nà¸ŀ า\nĠÄij ánh\nĠÙĪ ÙĦ\nר ×ķת\nĠ×IJ ×Ļ×ļ\nĠch uyá»ĥn\nÙĥ ا\nãĤĮ ãĤĭ\nà¹ģม à¹Ī\nãĤĪ ãģı\nĠÙĪ ÙĤد\níĸ Īëĭ¤\nĠn Æ¡i\nãģ«ãĤĪ ãģ£ãģ¦\nĠvi ết\nĠà¹Ģà¸ŀ ืà¹Īà¸Ń\nëIJĺ ëĬĶ\nاد ÙĬ\nĠÙģ Ø¥ÙĨ\nì¦ Ŀ\nĠÄij ặt\nĠh Æ°á»Ľng\nĠx ã\nĠönem li\nãģł ãģ¨\nĠm ẹ\nĠ×ij ×Ļ\nĠ×ĵ ×ijר\nĠv áºŃt\nĠÄij ạo\nĠdá»± ng\nĠÑĤ ом\nĠÙģÙĬ Ùĩا\nĠج ÙħÙĬع\nĠthu áºŃt\nst ÄĻp\nĠti ết\nØ´ ÙĬ\nĠе Ñīе\nãģĻãĤĭ ãģ¨\nĠmÃł u\nĠÑįÑĤ ого\nĠv ô\nĠÐŃ ÑĤо\nĠth áºŃt\nĠn ữa\nĠbi ến\nĠn ữ\nĠ׾ ׼×Ŀ\n×Ļ ×Ļף\nĠس ت\nĠÐŀ ÑĤ\nĠph ụ\nê¹Į ì§Ģ\nĠ׾ ×ļ\nĠk ỳ\nà¹ĥ à¸Ħร\nĠg ây\nĠÙĦ ÙĦÙħ\nĠtụ c\nت ÙĬÙĨ\nĠtr ợ\nĠ׾ פ×Ļ\nĠb á»ij\nĠÐļ а\nĠÄij ình\now Äħ\ns ında\nĠkhi ến\ns ız\nĠк огда\nס ׾\nĠбÑĭ л\nà¸Ļ à¹īà¸Ńย\nобÑĢаР·\nĠê²ĥ ìĿ´ëĭ¤\nëĵ¤ ìĿĢ\nãģ¸ ãģ®\nĠà¹Ģม ืà¹Īà¸Ń\nĠph ục\nĠ׊׾ק\nĠh ết\nĠÄij a\nà¹Ģà¸Ķà¹ĩ à¸ģ\níĺ ķ\nl ÃŃ\nê¸ ī\nĠع دد\nĠÄij á»ĵ\nĠg ần\nĠ×Ļ ×ķ×Ŀ\nĠs Ä©\nÑĢ Ñıд\nĠquy á»ģn\nĠ×IJ ׾×IJ\nÙĩ Ùħا\n׳ ×Ļ×Ķ\n׾ ×ķת\nĠ×Ķר ×ij×Ķ\nĠti ên\nĠal ın\nĠd á»ħ\n人 ãģĮ\nно Ñģ\nл ÑģÑı\nĠÄij Æ°a\nส าว\nиÑĢов ан\nĠ×ŀס פר\n×Ĵ ף\nĠki ến\nĠÐ ¨\np é\nб Ñĥ\nов ой\nб а\nĠØ¥ ÙĦا\n×IJ ׾×Ļ\nĠx ây\nĠb ợi\nĠש ×ķ\n人 ãģ®\nק ×Ļ×Ŀ\nà¹Ģà¸Ķ ืà¸Ńà¸Ļ\nĠkh á\nĠ×ķ ׾×Ķ\n×ĵ ×ķת\nĠ×¢ ×ij×ķר\nĠبش ÙĥÙĦ\nĠÙĩÙĨا Ùĥ\nÑĤ ÑĢа\nĠ íķĺëĬĶ\nร à¸Ńà¸ļ\nowa ÅĤ\nh é\nĠdi á»ħn\nĠ×Ķ ׼׾\nĠØ£ س\nĠch uyá»ĩn\nระ à¸Ķัà¸ļ\nĠNh ững\nĠ×IJ ×Ĺת\nĠØŃ ÙĪÙĦ\nл ов\n׳ ר\nĠ×ķ ׳\nĠch Æ¡i\nĠiç inde\nÑģÑĤв Ñĥ\nĠph á»ij\nĠÑģ Ñĥ\nç§ģ ãģ¯\nĠch ứng\nĠv á»±c\nà¹ģ à¸Ń\nĠl áºŃp\nĠtừ ng\nå°ij ãģĹ\nĠNg uy\nĠNguy á»ħn\nĠÙģÙĬ Ùĩ\nĠб а\n×Ļ ×Ļת\nĠ×ľ×¢ ש×ķת\nĠ×ŀ ׼\nĠnghi á»ĩm\nĠм ного\nĠе е\nëIJĺ ìĸ´\nĠl ợi\nĠ׾ ׾×IJ\nĠ׼ ף\nĠch ÃŃ\nãģ§ ãģ®\n×Ĺ ×ķ\nש ×ķ×Ŀ\nĠ×ŀ ר\nĠÐĶ лÑı\nÅ ģ\nĠ׼×IJ שר\nĠM á»Ļt\nĠÙĪاÙĦ ت\nĠìĿ´ 룰\nÅŁ a\nĠchi ến\nĠaras ında\nĠ×ij ×IJתר\nãģķãĤĮ ãģ¦ãģĦãĤĭ\nØ´ ÙĥÙĦ\nĠt ượng\nĠت ت\nĠC ó\nĠb á»ı\nĠtá»ī nh\nĠkh ÃŃ\nĠпÑĢ оÑģÑĤ\nĠпÑĢоÑģÑĤ о\nĠÙĪ ÙĤاÙĦ\nĠgi áo\nĠN ếu\n×IJ ×ŀר\n×¢×ł×Ļ ×Ļף\níİ ¸\nÙĩد Ùģ\nĠB á»Ļ\nĠb Ãłn\nĠng uyên\nĠgü zel\nส าย\nì² ľ\n×ŀ ×ķר\nĠph ân\nס פק\nק ×ij׾\nĠاÙĦÙħ تØŃ\nĠاÙĦÙħتØŃ Ø¯Ø©\nائ د\nĠ×IJ ×ŀר\nĠki ÅŁi\nì¤ Ģ\nĠtr uyá»ģn\nĠÙĦ Ùĩا\nĠÐľ а\nà¸ļริ ษ\nà¸ļริษ ั\nà¸ļริษั à¸Ĺ\nĠש ׳×Ļ×Ŀ\nĠмен Ñı\nÅŁ e\nĠdi á»ĩn\nĠ×IJ׳ ×Ĺ׳×ķ\nk ü\nĠc á»ķ\nĠm á»Ĺi\nw ä\nÙħ ÙĬ\nĠhi á»ĥu\nëĭ ¬\nĠ×Ķ ×Ĺ׾\nĠt ên\nĠki á»ĩn\nÙĨ ÙĤÙĦ\nĠv á»ĩ\n×ĵ ת\nĠÐłÐ¾ÑģÑģ ии\nл Ñĥ\nĠاÙĦع ربÙĬØ©\nĠØ· رÙĬÙĤ\nĠ×Ķ×ij ×Ļת\nÑģ еÑĢ\nĠм не\nä u\nĠtri á»ĩu\nĠÄij ủ\nĠר ×ij\nت ÙĩÙħ\nà¸ĭ ี\nĠì§Ģ ê¸Ī\nli ÅĽmy\nد عÙħ\nãģł ãĤįãģĨ\nÑģки е\nĠh á»ıi\nĠק ×ķ\nÑĢÑĥ Ñģ\nÙĨ ظر\nãģ® ãĤĤ\nĠ×Ķ ׼×Ļ\nĠìĽ IJ\nÙĪ Ùĩ\nĠÙĪ Ùİ\nĠB ạn\nп лаÑĤ\nĠ×ŀ ×ŀש\nлÑİ Ð±\nĠнÑĥж но\nĠth Æ°\nãģ µ\nãģı ãĤīãģĦ\nر Ø´\nר ×ķ×Ĺ\nĠÙĬ تÙħ\nĠצר ×Ļ×ļ\nĠph á\nม à¸Ńà¸ĩ\nĠ×ij×IJ ×ķפף\nĠcả nh\nĠíķľ ëĭ¤\nĠ×Ķ×ŀ ת\nà¸ķà¹Īาà¸ĩ à¹Ĩ\nมี à¸ģาร\nÑģки Ñħ\nĠÐĴ Ñģе\nĠا ÙĪ\nج ÙĬ\nãģĵãģ¨ ãģ¯\nĠd Ãłi\nĠh á»ĵ\nèĩªåĪĨ ãģ®\nà¹Ħ หà¸Ļ\nëĵ¤ ìĿĦ\nĠV Äĥn\nĠд аж\nĠдаж е\nÑĭ ми\nлаÑģ ÑĮ\nÙĬ ÙĪÙĨ\nÙĨ ÙĪ\nc ó\nãģĹãģ¦ ãģĦãģŁ\nãģł ãģĭãĤī\nطاÙĦ ب\nĠc á»Ńa\nп ÑĢоÑģ\nãģªãģ© ãģ®\nรุ à¹Īà¸Ļ\nĠchi ếc\nл Ñĭ\nĠÑıвлÑı еÑĤÑģÑı\nĠn á»ķi\nãģ® ãģĬ\nĠ×IJת ×Ŀ\nĠëķĮ문 ìĹIJ\nà¸ģล าà¸ĩ\nĠbaÅŁ ka\nìĦ Ŀ\nĠÑĨ ел\nÙģ ÙĤ\nãģ«ãĤĪ ãĤĭ\nÙĤ ا\nĠçı kar\nĠcứ u\nØ· ا\nĠש ת\nà¹Ĥ à¸Ħ\nĠ×ŀ ׾\nĠ×Ķ פר\nĠг де\nĠØ® Ø·\nåīį ãģ«\nc jÄĻ\nĠ׊ש×ķ×ij\nר×Ĵ ×¢\nĠkho ảng\nĠÄij á»Ŀi\nĠÐł е\nĠо на\nĠ×IJ ׳×ķ\nãģ® ãģ«\nĠاÙĦØ° ÙĬÙĨ\nкÑĥ п\nãĤµ ãĥ¼ãĥ\nãĤµãĥ¼ãĥ ĵ\nãĤµãĥ¼ãĥĵ ãĤ¹\nв ал\nг е\nĠgi ữa\nĠKh ông\nĠâĹ ĭ\nà¸ģล ุà¹Īม\nĠÙħÙĨ Ø°\nà¸Ń à¹Īาà¸Ļ\nĠÑģп оÑģоб\nĠÄij á»Ļi\nĠdi ÄŁer\nĠ à¸ĸà¹īา\nÙħ Ø«ÙĦ\nĠ×Ķ×IJ ×Ļ\nĠد ÙĪÙĨ\nÙĬر اÙĨ\nÑī и\nبÙĨ اء\nĠØ¢ خر\nظ Ùĩر\nĠ×ij ׼\nĠاÙĦÙħ ع\nãĥ Ĵ\nĠt ất\nĠm ục\nĠdoÄŁ ru\nãģŁ ãĤī\nĠס ×ķ\nĠx ác\nร à¸Ń\nĠcÄĥ n\nĠон л\nĠонл айн\nĠk ý\nĠch ân\nĠ à¹Ħมà¹Ī\nاØŃ Ø©\nr án\n׳×Ļ ×Ļ×Ŀ\nĠ×ij ף\nĠÐ ĸ\nà¸ķร à¸ĩ\nд Ñĭ\nĠs ắc\nÙĦ ت\nãĥŃ ãĥ¼\nĠÙĦ ÙĨ\nĠר ×ķ\nĠd Æ°á»Ľi\nà¹Ģ à¸ĺ\nà¹Ģà¸ĺ à¸Ń\ne ÄŁi\nĠ×ķ ש\nĠÙĦ Ø£\nĠg ặp\nĠc á»ij\nãģ¨ ãģ¦ãĤĤ\nرÙĪ س\nĠ׾×Ķ ×Ļ\nĠë³ ¸\nä¸Ĭ ãģĴ\nĠm ức\nÑħ а\nĠìŀ ¬\nà¸ī ัà¸Ļ\nÑĢÑĥ ж\nĠaç ık\nÙĪ اÙĦ\nĠ×ĸ ×ŀף\n人 ãģ¯\nع ÙĬÙĨ\nÑı Ñħ\nĠ×Ĵ×ĵ ×ķ׾\nר ×ķ×ij\ng ó\nëĿ¼ ê³ł\nĠark adaÅŁ\nÙĨ شر\nĠгод Ñĥ\nĠболÑĮ ÑĪе\nãģ¡ãĤĩ ãģ£ãģ¨\nĠcâ u\nĠs át\níĶ ¼\nĠti ến\níķ´ ìķ¼\nĠÙĪ Ø£ÙĨ\nà¸Ļ าà¸Ļ\nĠ×ij×IJ×ŀ צע\nĠ×ij×IJ×ŀצע ×ķת\nĠ׾ ר\nĠqu ản\nĠÙĪاÙĦ Ø£\nĠ×IJ×ķת ×Ķ\nĠìĸ´ëĸ ¤\nĠê²ĥ ìĿĢ\nØŃس ÙĨ\nĠm ất\nà¸Ħ ูà¹Ī\nãĥ¬ ãĥ¼\nĠÐĶ а\nĠol ması\nĠthu á»Ļc\n׳ ×Ĺ\níĨ ł\nĠsö yle\nãģĿãģĨ ãģ§ãģĻ\nĠت ÙĥÙĪÙĨ\nл ÑĥÑĩ\n׾ ×Ļ×ļ\nĠØ£ ØŃد\nли ÑģÑĮ\nĠвÑģ его\nĠ×Ķר ×ij\nĠëª »\no ÄŁ\noÄŁ lu\nĠìĦ ł\nĠк аÑĢ\nà¸łà¸² à¸Ħ\ne ÅĦ\nĠ à¸ģà¹ĩ\nĠa ynı\nĠb Ãł\nãģªãĤĵ ãģ¦\nĠ모 ëĵł\nÙĤر ار\nãģĹãģª ãģĦ\nĠÐĴ о\nĠÙĪÙĩ ÙĬ\nни ки\nãĤĮ ãģŁ\nĠchu ẩn\nר ×¢\nÙģ رÙĬÙĤ\nãĤĴ åıĹãģij\nĠÄij úng\nб е\n׼ ×ķ×Ĺ\nп Ñĥ\nĠ×ķ ×Ĵ×Ŀ\n×ŀ ׳×Ļ\níĸ ¥\nצ ×Ļ×Ŀ\nà¸ĭ ิ\nÙĩ ÙĨ\nн ем\nĠ×ij×ij ×Ļת\nر ع\nĠ ส\nĠÄIJ Ãł\níķĺ ëĭ¤\nĠ ấy\n×Ĺ ×ķ×ĵ\n×Ĺ×ķ×ĵ ש\nĠÑĩеÑĢ ез\nÑĥ л\nĠB ình\nĠê²ĥ ìĿĦ\nĠ×Ĵ ר\nä»ĺ ãģij\n×Ĺ׾ ק\nĠت ÙĦÙĥ\nà¹ĥส à¹Ī\nsz Äħ\nÙĤ اÙħ\nد ÙĪر\nĠÙģ ÙĤØ·\nĠh ữu\nĠмог ÑĥÑĤ\nĠg á»įi\nĠק ר\nà¸Īะ มี\nت ÙĤدÙħ\nĠع بر\nĠ׾×Ķ ×Ŀ\nĠÑģам о\nס ×ĵר\nĠc Ãłng\nr ÃŃ\nĠìŀ ¥\nëĵ¤ ìĿĺ\nĠÙĦ Ùĥ\nп оÑĢÑĤ\nĠkh ả\nĠÑģеб Ñı\n׳ ף\nĠد ÙĪر\nĠm ợ\nĠcâ y\nĠf ark\nĠfark lı\nа ÑİÑĤ\nĠtr á»±c\nwiÄĻks z\nĠthu á»ijc\nĠت ØŃت\nت ÙĦ\nов Ñĭе\nëĤ ł\nĠв ам\nبÙĦ غ\nĠê°Ļ ìĿĢ\níĮ IJ\nÙĦ ب\nĠnas ıl\nĠод ин\nм ан\nĠعÙĦÙĬ Ùĩا\nб и\nĠפ ש×ķ×ĺ\n×ijר ×Ļ\nĠש ׳×Ķ\nĠëı Ħ\nĠÄIJ ại\nĠ×IJ×ķת ×Ŀ\nĠاÙĦØŃ Ø±\nĠб о\nà¸Ī ุà¸Ķ\nĠr õ\nĠdeÄŁi ÅŁ\nĠëĭ ¨\nĠÑģлÑĥÑĩ а\nĠÑģлÑĥÑĩа е\nĠ×IJ׳ ש×Ļ×Ŀ\n×ĵ ×£\nש×ij ת\nĠש׾ ׼×Ŀ\nĠch ú\nnik ów\nĠtan ı\nĠcá o\nĠÄij á\nĠ×IJ ×ĵ×Ŀ\nĠê° ķ\nĠnhi á»ĩm\nĠ׾ ס\nĠ×Ľ×ª ×ij\nĠ×Ķס פר\nĠÄij Äĥng\nĠë ijIJ\nà¸ľ ิ\nà¸ľà¸´ ว\nج ا\nĠê° IJ\nر Ø£\nست خدÙħ\nãģ«ãģªãĤĬ ãģ¾ãģĻ\nĠtá» ·\n×ĺ ×ķר\nг овоÑĢ\nĠв оÑģ\nĠÙħÙĨ Ùĩا\nиÑĢов аÑĤÑĮ\nĠÄij ầy\n׳ ×Ĵ\nĠÙħ ÙĪ\nĠÙħ ÙĪÙĤع\nר׼ ×Ļ\nت Ùı\nëª ¨\nĠת ×ķ\nÙĬا Ùĭ\nà¹ĥ à¸Ķ\nãĤĬ ãģ¾ãģĻ\nà¸Ńยูà¹Ī à¹ĥà¸Ļ\nĠØ£ ÙĪÙĦ\nĠØ£ خرÙī\nĠc Æ°\nص ار\n×ŀ׊ש×ij\nб ÑĢа\nÅĦ ski\nб ÑĢ\nĠÙĬ Ùı\nà¸ģ ิà¸Ļ\nĠch á»ijng\nÙħ Ùı\nĠ à¸Ħืà¸Ń\nĠت ÙĨ\nt ÃŃ\ny Äĩ\nĠm ạng\nÙģ ÙĪ\nĠdü nya\nק ר×IJ\nĠק ׾\nĠØŃ Ø§ÙĦ\nc ÃŃa\nĠà¹Ģ รา\nĠר ×ķצ×Ķ\nĠá p\në° ķ\nا ÙĤØ©\nни Ñİ\nĠ×IJ ׾×ķ\nĠ×ŀס ×ķ\nãģ§ãģ¯ ãģªãģı\nĠtr ả\nĠק שר\nmi ÅŁtir\nĠl Æ°u\nĠh á»Ĺ\nĠбÑĭ ли\nĠl ấy\nعÙĦ Ùħ\nĠö zel\næ°Ĺ ãģĮ\nĠ×ĵ ר×ļ\nÙħ د\ns ını\n׳ ×ķש×IJ\nr ów\nÑĩ еÑĢ\nêµIJ ìľ¡\nĠÐľ о\nл ег\nĠV Ỽi\nวัà¸Ļ à¸Ļีà¹ī\nÑİÑī ие\nãģĬ ãģĻ\nãģĬãģĻ ãģĻ\nãģĬãģĻãģĻ ãĤģ\nëı ħ\nĠ×Ļ×Ķ ×Ļ×Ķ\n×ŀ ×ĺר\nÑı ми\nĠl á»±a\nĠÄij ấu\nà¹Ģส ียà¸ĩ\nĠt Æ°Æ¡ng\nëĵ ±\nĠÑģÑĤ аÑĢ\nà¹ĥ à¸ļ\nว ัà¸Ķ\nĠÄ° stanbul\nĠ à¸Īะ\nà¸ķ ลาà¸Ķ\nĠب ÙĬ\nà¹ģà¸Ļ ะ\nà¹ģà¸Ļะ à¸Ļำ\nس اعد\nĠب Ø£\nĠki á»ĥm\nØŃ Ø³Ø¨\nà¸Ĭั à¹īà¸Ļ\nĠ×ķ ×¢×ķ×ĵ\nов ÑĭÑħ\nоÑģ нов\nĠtr Æ°á»Łng\nצ ×ij×¢\nĠÃŃ t\nĠk ỹ\ncr é\nÑı м\nêµ °\nãģĮ ãģªãģĦ\nÙĬÙĦ Ø©\nãĥķ ãĤ£\nر Ùī\nĠÙĬ جب\nĠ×IJ ×£\nĠc á»±c\nãĤīãĤĮ ãģŁ\nĠ à¸ľà¸¹à¹ī\nĠ à¸Ń\nlar ımız\nĠkad ın\nĠê·¸ ëŀĺ\nĠê·¸ëŀĺ ìĦľ\nĠëĺIJ ëĬĶ\nĠÄij ả\nĠÄijả m\nĠ×IJ ×ķ×ŀר\nĠy ếu\nci 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ש\np óÅĤ\nĠ×ŀ ×ĵ×Ļ\nujÄħ c\n×ŀ׳×Ķ ׾\nĠש×ij ×ķ\nĠ×Ķ×ŀש פ×ĺ\nĠ×IJ ׾×Ķ\nĠÙĪ Ø°ÙĦÙĥ\nà¹Ģà¸ŀ ราะ\nĠÄijo Ãłn\nĠíķ¨ ê»ĺ\nĠd ục\nØ´ ت\nĠ ula\nĠula ÅŁ\nĠqu ý\nĠ×Ķ ×Ĵ×ĵ×ķ׾\nà¸ķัà¹īà¸ĩ à¹ģà¸ķà¹Ī\nĠש ר\nØ´ Ùĩد\n׳ ש×Ļ×Ŀ\nà¸ŀ ล\nرÙĪ ا\nãĤĮ ãģ¦\nĠн иÑħ\nĠдел а\nãģ§ãģį ãģªãģĦ\nÅĤo ż\n×IJ ×Ĺר\nì ½Ķ\nãĤ¢ ãĥĥãĥĹ\nد Ùģع\nĠti á»ĩn\nĠkh á»ı\nĠkhá»ı e\nĠاÙĦع اÙħØ©\nãģ« ãģĤãĤĭ\nĠÄij á»Ļc\nì¡ ±\nĠc ụ\nй ÑĤе\nĠзак он\nĠпÑĢо екÑĤ\nìĸ ¸\nÙĦ ØŃ\nĠçalÄ±ÅŁ ma\nãĤĴ ãģĻãĤĭ\nÑħ и\nع اد\nĠ׳ ×ŀצ×IJ\nĠר ×Ļ\nà¸Ńà¸Ńà¸ģ มา\nĠT ôi\nĠth ần\nĠÙĬ ا\nล าย\nĠав ÑĤо\nĠsı ra\nĠÙĥ Ø«ÙĬر\nÙħ ÙĬز\nĠاÙĦع ÙĦÙħ\næĸ¹ ãģ¯\n×ķ×¢ ×ĵ\nĠобла ÑģÑĤи\n×Ļ׾ ×Ļ×Ŀ\nãģĮ åĩº\nà¸ĺ ุ\nà¸ĺุ ร\nà¸ĺุร à¸ģิà¸Ī\nÙĤت ÙĦ\nר×IJ ×ķ\nĠng u\nĠngu á»ĵn\nĠ มา\nĠпл ан\nt ório\nĠcu á»iji\nÑģк ом\nĠاÙĦÙħ اض\nĠاÙĦÙħاض ÙĬ\nĠ×ij×¢ ׾\nĠר ×ij×Ļ×Ŀ\nĠlu áºŃn\nÙĥ ÙĪ\nà¸Ĺัà¹īà¸ĩ หมà¸Ķ\nв ан\nĠtho ại\nà¹Ħ à¸Ń\nб иÑĢ\nĠاÙĦ ض\nت ا\nĠÑĢ од\nĠV Ãł\n×ŀ ×Ļף\nĠбÑĭ ла\nк ами\nĠÐĶ е\nt ık\nקר ×Ļ\nĠeÄŁ itim\nĠÙĥ بÙĬر\nب Ùĥ\nĠÙĦ ÙĪ\nв ой\nĠ ãģĵãģ®\nĠÑĤ ÑĢÑĥд\nmy ÅĽl\nĠs Æ°\nà¸ŀ ีà¹Ī\nĠ à¹ģลà¹īว\n×¢ ק\nĠ×Ĺ×ijר ת\nระ หว\nระหว à¹Īาà¸ĩ\n×Ļ ×Ļ×Ķ\nĠاÙĦÙĨ اس\nün ü\nĠ׾ ×ŀ×Ķ\nĠch Æ°Æ¡ng\nĠH á»ĵ\nار ت\nãĤĪãģĨ ãģ§ãģĻ\nl á\nק×Ļ ×Ļ×Ŀ\næľ¬ å½ĵ\næľ¬å½ĵ ãģ«\nãģĵãĤĵ ãģª\nÑģ ов\nĠ×ķ ×Ĺ\nà¹Ģà¸ģ à¹ĩà¸ļ\nĠк ÑĤо\nà¹Ĥร à¸Ħ\nĠØ´ رÙĥØ©\nع زÙĬ\nعزÙĬ ز\nØ·ÙĦ ÙĤ\nп ÑĥÑģÑĤ\nÙģ تØŃ\nëŀ Ģ\nĠhã y\nض Ùħ\në¦ °\nåł´åIJĪ ãģ¯\nãĤª ãĥ¼\nĠh ắn\nĠ×IJ ×ij×Ļ×ij\nĠש׾×Ķ ×Ŀ\nĠ×Ķ×Ļ ×Ļת×Ķ\nĠاÙĦد ÙĪÙĦØ©\nĠاÙĦ ÙĪÙĤ\nĠاÙĦÙĪÙĤ ت\nãģĤ ãģ¾ãĤĬ\nĠta ÅŁÄ±\nÄ° N\n×¢ סק\nãģ¦ ãģĦãģŁ\nĠtá»ķ ng\nĠاÙĦØ¥ ÙĨس\nĠاÙĦØ¥ÙĨس اÙĨ\nÑĢ еÑĪ\nĠg ái\nĠÑĨ ен\nĠÙģ ÙĤد\nÙħ ات\nãģķãĤĵ ãģ®\nĠph ù\n×ĺ ×Ķ\nĠÙĪاÙĦ تÙĬ\nĠب Ùĥ\nìĿ´ ëĤĺ\nк Ñģ\nÙħ ÙĬر\nĠv ùng\nĠاÙĦØ´ عب\nĠNh Æ°ng\nãĥĢ ãĥ¼\nĠ×Ĺ×Ļ ×Ļ×Ŀ\nĠØ´ خص\nק ×ķ×ĵ\nê² Ģ\n×¢ ש\n×¢ ×ķ׾×Ŀ\nצ ×ķר\nع ÙĤد\nĠiÅŁ lem\nĠ×Ķ×ij ×IJ\nĠd ưỡng\nà¸Ł รี\nĠph ÃŃa\nãģ®ä¸Ń ãģ§\nĠп и\nĠng Ãłnh\nним а\nĠÙĩ ÙĦ\nĠ×ķ ×IJת\nĠÄij áng\né quipe\nĠÑįÑĤ оÑĤ\nĠgö rev\në§ ¤\nĠqu ân\nå¼ķ ãģį\næĻĤ ãģ«\nĠب Ùħا\n×ŀ ×Ļת\nĠü lke\nĠ×ŀק ×ķ×Ŀ\n×ij ף\næ°Ĺ æĮģãģ¡\nĠë§İ ìĿĢ\nĠyük sek\nÑĨ енÑĤÑĢ\nĠÙħ جÙĦس\nç§ģ ãģ®\nÙĤد ر\nĠë¶Ģ ë¶Ħ\nĠì° ¨\nخر ج\nãģĭ ãģªãĤĬ\në³´ ëĭ¤\nĠ×ŀ ×Ļ×ĵ×¢\npeÅĤ ni\nĠx á»Ń\nìĹIJìĦľ ëĬĶ\nĠباÙĦ Ùħ\nĠÙĪ Ùħا\nĠÑįÑĤ ой\nب ÙĬÙĨ\nn ü\nØŃ Ø²\nØŃز ب\nĠÑĢабоÑĤ а\nĠNh áºŃt\nÙĦ اء\nĠëĵ ¤\nĠëĵ¤ ìĸ´\nãĤĦãģĻ ãģĦ\n×Ĺ×ĸ ק\nĠ×Ķ×Ĺ ×ijר×Ķ\nп иÑĤ\nãģĭãĤī ãģ®\nĠë§IJ ìĶĢ\nĠפ ×ķ\nÙĦ Ùİ\nà¹Ģà¸ķà¹ĩ ม\nĠÐļ о\nĠm ówi\nĠt ÃŃn\nר×Ĵ ש\nפר ק\nĠtr ạng\nĠÐŀ н\n×Ĺ ×ķ×¥\nĠعÙĨد Ùħا\nĠب ر\n使 ãģĦ\nĠr á»Ļng\nëĮĢ ë¡ľ\níĪ ¬\nĠktóry ch\nв ид\nลูà¸ģ à¸Ħà¹īา\nĠmog Äħ\nĠש ×Ĺ\n×ij ×Ĺר\nãĥĸ ãĥŃãĤ°\nĠTh Ãłnh\nĠ×Ķ ר×Ļ\nĠÑģÑĤ аÑĤÑĮ\nĠH á»Ļi\nà¸ļ à¹īาà¸ĩ\nçī¹ ãģ«\nĠÄIJ ức\nèĢħ ãģ®\n×¢ ×ŀ×ķ×ĵ\n×ĺר ×Ķ\nÐ ¥\nĠÙħ Ùħا\nĠe ÅŁ\nĠнеобÑħодим о\nник ов\nĠüzer inde\na ÅĤa\nĠchá»ĭ u\nĠاÙĦ دÙĬÙĨ\nأخ بار\nĠÄij au\nãģĮ å¤ļãģĦ\njÄħ cych\nد Ø®ÙĦ\nları nd\nlarınd an\nĠs ẻ\nà¸ŀิ à¹Ģศ\nà¸ŀิà¹Ģศ ษ\nת ף\nt ıģı\nĠlu áºŃt\nĠÅŀ e\nãĤ« ãĥ¼\nãģ® ãģĤãĤĭ\nĠ×Ķ×IJ תר\nĠاÙĦØ¢ ÙĨ\nıld ı\nĠá o\nĠнаÑĩ ал\nĠvi á»ĩn\nĠ×ij×¢ ×ķ׾×Ŀ\nз наÑĩ\n×Ļ×ĺ ×Ķ\nк ам\nĠÐĺ з\nà¹Ģà¸Ĥ ียà¸Ļ\nà¸Ļ à¹īà¸Ńà¸ĩ\nÑĤ ÑĢо\nà¹Ģ à¸Ł\nĠжиз ни\nĠ สà¹Īวà¸Ļ\nĠv áºŃn\nĠê´Ģ 볨\nĠl âu\nס ×ĺר\nק ש\nس ÙĬر\nĠ×IJ×ķת ×Ļ\nĠm ôi\nائ ب\nĠо ÑģÑĤа\nĠm ón\nĠ×ij ×ŀק×ķ×Ŀ\nĠد اخÙĦ\nĠ×IJ ×ķר\nĠв аÑģ\nÙĥ Ø´Ùģ\nìĺ ¨\nà¸ĸ à¹Īาย\nĠkullan ıl\nĠt ô\nãģ« ãĤĪãĤĬ\nĠëĺIJ íķľ\nĠ×¢×ij×ķ×ĵ ×Ķ\nĠri ê\nĠriê ng\nĠyak ın\nز ا\nÅ »\n×IJ ×ķ׼׾\nشار Ùĥ\nĠб еÑģ\n× ´\nĠا بÙĨ\nĠTá»ķ ng\nÙĨ ظ\nÅĽwi ad\nãĤµ ãĥ¼\nห าย\nĠG ün\nĠhakk ında\nà¹Ģà¸Ĥà¹īา มา\nز ÙĨ\nĠÐł о\nĠbi á»ĥn\nãģ© ãģĵ\nÙģ عÙĦ\nز ع\nפר ×ĺ\nĠ×Ķ ף\nØ£ ÙĩÙĦ\nĠth ất\nØŃ ÙħÙĦ\nÑĩ Ñĥ\nĠìĤ¬ ìĭ¤\nì° ¸\nĠìľĦ íķ´\nÙĪ ظ\nĠÐŁ од\nĠkho ản\nÑĤ ен\nĠÙģ اÙĦ\nÑģ ад\nà¸Ļ à¸Ńà¸Ļ\nĠاÙĦسعÙĪد ÙĬØ©\n\" ØĮ\nĠاÙĦ ÙĴ\nãĤī ãģļ\nĠto án\nĠch ắc\n׼ ×Ļר\nm éd\nméd ia\nز ÙĪ\nĠyan ı\nפ ׳×Ļ×Ŀ\nØŃ Ø¸\nĠб еÑģп\nĠбеÑģп лаÑĤ\nĠбеÑģплаÑĤ но\nĠØ£ ÙħاÙħ\nà¸Ń าย\nà¸Ńาย ุ\nר שת\nĠg á»ĵ\nĠgá»ĵ m\nĠu á»ijng\nص ب\nk ır\nãĥij ãĥ¼\nĠ׾×ĵ עת\nĠк ÑĥпиÑĤÑĮ\n׾ ×ķ×Ĺ\nÙĪض ع\nÙĤÙĬ Ùħ\nà¸Ľ า\nж ив\nà¸Ķ ิà¸Ļ\n×IJ ×ķפ\nà¹Ģล à¹ĩà¸ģ\nãĥĥ ãĥī\nиÑĩеÑģки Ñħ\nĠCh ủ\nкÑĢ аÑģ\nÙĪ صÙĦ\np ÅĤat\nм оÑĢ\nĠ×Ķ×IJ ×ķ\nà¸Ń ิà¸Ļ\nĠíķľ êµŃ\nгÑĢ е\nĠìłľ ê³µ\nì° ½\nĠê°ľìĿ¸ ìłķë³´\nĠngh á»ĭ\nà¸ĭ า\nØŃس اب\nĠby ÅĤa\nÙħÙĦ Ùĥ\nиÑĩеÑģки е\nĠb ác\nض ØŃ\nê¸ ¸\nש ×ŀ×¢\nĠìĸ´ëĸ »\nĠìĸ´ëĸ» ê²Į\nìĽ Į\nات Ùĩ\nà¹Ĥรà¸ĩ à¹ģ\nà¹Ĥรà¸ĩà¹ģ รม\nخد ÙħØ©\nĠÐł а\n׼×ķ׾ ×Ŀ\n×ŀש ×Ĺק\nĠÙĪ ÙĥاÙĨ\nס ×ķ×£\nĠاÙĦØŃÙĥÙĪÙħ Ø©\nĠ×ij ×ĺ\nĠtr áºŃn\nĠ×Ķ×¢ ×ķ׾×Ŀ\nĠÃŃ ch\nt Äħ\nש×ŀ ×ķ\nĠ×Ķר×IJש ×ķף\nĠíķĺ ê³ł\nãģķ ãĤī\nãģķãĤī ãģ«\nãģ« ãģĹãģ¦\nĠ à¸ľà¸¡\nãģ® ãĤĪãģĨãģª\nĠÙĪ ÙĤت\nãĥį ãĥĥãĥĪ\nÙĦ عب\nÙĪ Ø´\nìĺ ¬\nĠ หาà¸ģ\nĠm iaÅĤ\nà¸Ĺ à¸Ńà¸ĩ\nиÑĤ а\nا صر\nил ÑģÑı\nз е\nà¸Ľà¸£à¸° มาà¸ĵ\nãģĿãĤĮ ãģ¯\nĠb ır\nĠbır ak\nصÙĨ اع\nÐ ®\nØ´ عر\nĠ׳ ×Ĵ×ĵ\nĠب سبب\nãĥĿ ãĤ¤\nãĥĿãĤ¤ ãĥ³ãĥĪ\nĠاÙĦج ÙĪ\nĠнеÑģк олÑĮко\nĠki ếm\nÙģ Ùİ\nĠض د\n×ij×Ļ×ĺ ×ķ×Ĺ\nتاب ع\nÙĨ ز\nĠB ản\nĠaç ıkl\nĠaçıkl ama\nĠ à¸Ħุà¸ĵ\nà¸Ĺ า\nÅĤ ów\nØ· ب\nÙĨ ØŃÙĨ\nĠ×ŀק ×ķר\nĠÄ° s\nĠдом а\nĠ วัà¸Ļ\nĠd Ãłnh\nÑı н\nми ÑĢ\nĠm ô\nĠvÃł ng\nص اب\ns ının\nà¸Ħ ืà¸Ļ\nØ® بر\n×ĸ׼ ×ķ\nĠ×ŀ ש×Ķ×ķ\nm ü\nĠкомпани и\nĠ×Ķ×¢ ×Ļר\nĠÙĥ ÙĪ\nÙĤÙĦ ب\nĠlỼ p\nи ки\n׳ ×ij\nà¹Ĥ à¸Ħร\nà¹Ĥà¸Ħร à¸ĩ\nà¹Ĥà¸Ħรà¸ĩ à¸ģาร\n×ŀ×ķ×¢ ×ĵ\nÑıÑĤ ÑģÑı\nหลัà¸ĩ à¸Īาà¸ģ\nени Ñİ\nĠש ×¢\nĠb Æ°á»Ľc\nãĥ¡ ãĥ¼ãĥ«\nãĤĦ ãĤĬ\nĠ×Ļ×ķ×ĵ ×¢\nĠê´Ģ íķľ\nĠاÙĦØ£ Ùħر\nĠböl ge\nĠÑģв ой\nÙĦ س\nĠ×ŀ×Ļ ×ķ×Ĺ×ĵ\nĠëĤ´ ìļ©\nĠØ£ جÙĦ\nĠÄIJ ông\nĠ×ŀ ×ł×ª\nĠìĭľ ê°Ħ\nÙĥ Ùİ\nãģ¨ãģĦãģĨ ãģ®ãģ¯\nĠnale ży\nتÙĨظ ÙĬÙħ\nĠÑģозд а\nĠph é\nĠphé p\nãģ§ãģį ãģ¾ãģĻ\nĠع ÙĦÙħ\n大ãģį ãģª\nãĤ² ãĥ¼ãĥł\ní ħĮ\nĠ׼×ķ׾ ׾\nĠинÑĤеÑĢ неÑĤ\nĠT ừ\nãģ¨ ãģªãĤĭ\nز اÙĦ\nĠktóry m\nĠnh é\nìĪ ľ\nн ев\nд еÑĢ\nãĤ¢ ãĥĹãĥª\ni á»ĩu\n×ij ×Ļ׾\nĠت س\nĠÄIJ ây\nĠاÙĦØ® اصة\nĠà¹Ģ à¸Ĭ\nĠà¹Ģà¸Ĭ à¹Īà¸Ļ\nص اد\nĠd ạng\nس عر\nĠש ×Ļ×ŀ×ķש\n×Ĵ ×Ļ×Ŀ\nãģĮãģĤ ãģ£ãģŁ\nп ÑĢов\nпÑĢов од\nĠ×IJ ×Ļ׳×ķ\nĠ׾ ר×IJ\nĠ׾ר×IJ ×ķת\nĠØ£ ÙģضÙĦ\nĠØŃ ÙĦ\nĠØ£ بÙĪ\nê° ķ\nĠì§ ij\nãģ® ãĤĪãģĨãģ«\nĠפ ׳×Ļ\nס ×Ļ×Ŀ\nĠÙĪÙĩ ذا\nĠka ç\nĠé én\nĠê± ´\në° Ķ\nÑĥ з\nà¸Ĥà¸Ńà¸ĩ à¹Ģรา\ni ÅĤ\nĠÐľ Ñĭ\nĠch ết\nĠاÙĦØ« اÙĨÙĬ\n×IJ ק\nĠ×ķ ×¢×ľ\nĠاÙĦØ· ب\n×ij×ĺ ×Ĺ\nĠج دÙĬدة\nĠع دÙħ\nع ز\nสิà¹Īà¸ĩ à¸Ĺีà¹Ī\nãģĻ ãĤĮãģ°\nĠÄij ô\nì£ ł\nد ÙĤ\nн омÑĥ\nĠk á»ĥ\nãĤ¢ ãĥ³\nå¤ļãģı ãģ®\nà¸Ľà¸£à¸° à¸ģ\nà¸Ľà¸£à¸°à¸ģ à¸Ńà¸ļ\nפע×Ļ׾ ×ķת\nĠÑģÑĤ ол\nmay ı\nãģ¤ ãģĦ\nĠyılı nda\nĠ à¸Īึà¸ĩ\nkoÅĦ cz\nĠTh ông\nĠак ÑĤив\nн ÑģÑĤ\nнÑģÑĤ ÑĢÑĥ\nĠÃĸ z\nĠת ×ŀ×Ļ×ĵ\nĠÙĥ ÙĨت\nÑģ иÑģÑĤем\npr és\nprés ent\nĠn â\nĠnâ ng\ngÅĤ os\nĠÙĪز ÙĬر\nØŃ ØµÙĦ\nĠиме еÑĤ\nØŃ Ø±ÙĥØ©\nà¸ŀ à¹Īà¸Ń\nãĤĴ ãģĬ\nĠاست خداÙħ\n×IJ×Ļר ×ķ×¢\nä»ĸ ãģ®\nĠש×Ķ ×Ŀ\nãģĹãģŁ ãĤī\nש×ŀ ×Ļ\nÑģ ла\nm ı\nĠbaz ı\nĠíķĺ ì§Ģë§Į\n×ĵ ׾\nĠyapt ıģı\nãĥĬ ãĥ¼\n׾ ×Ļ׾×Ķ\nãģ¨ãģĦ ãģ£ãģŁ\nänd ig\nĠÅŁ a\nĠÙģÙĬ Ùħا\nиÑĤ елÑı\n×ŀ ×ķש\nà¸Ĥ à¸Ńà¸ļ\nl ük\nĠh á»ĵi\nĠëª ħ\nĠاÙĦÙĥ Ø«ÙĬر\nצ ×IJ\nĠhaz ır\nطر Ùģ\nا ÙĬا\nĠÄij ôi\nен д\nÙĦ غ\n×Ĺ ×ĸ×ķר\nĠвÑģ ег\nĠвÑģег да\nëIJĺ ê³ł\n×ĵ ×ķ×ĵ\nан а\nد ÙĪÙĦØ©\nĠho ạch\nع ÙĦا\nعÙĦا ج\nĠ×ķ ×¢×ĵ\n×Ķ ×Ŀ\nки й\nÙĦ ÙIJ\nĠ×¢ ׾×Ļ×ķ\nÑİÑī ий\nĠng ủ\nصÙĨ ع\nĠاÙĦع راÙĤ\nà¸ķà¹Īà¸Ń à¹Ħà¸Ľ\nãģŁãģı ãģķãĤĵ\nĠph ạm\nÙĦ اÙĨ\nات Ùĩا\nĠbö yle\nتÙĨ ÙģÙĬ\nتÙĨÙģÙĬ Ø°\nĠש×Ķ ×Ļ×IJ\nÑģ Ñĥ\nย าว\nĠש ×ķ׳×Ļ×Ŀ\nĠ×ŀ ×ķ׾\nĠÑģ ил\nĠ×IJ×Ĺר ×Ļ×Ŀ\nĠph ủ\nÙĤØ· ع\nĠTh ủ\nà¸Ľà¸£à¸°à¹Ģà¸Ĺศ à¹Ħà¸Ĺย\nÙĨ ÙĤ\nĠÄijo ạn\nĠب Ø¥\nп ÑĢедел\n×ķת ×ķ\nĠy arı\nпÑĢ е\nĠczÄĻ ÅĽci\nØŃ ÙĥÙħ\n×ķ׳ ×Ļת\nפע ׾\nãĤĴ ãģĹãģ¦\nĠktó rzy\n׾ ×Ŀ\nĠÄIJi á»ģu\nĠкоÑĤоÑĢ аÑı\nĠìĿ´ ìĥģ\nãģĤ ãģ£ãģŁ\nĠ×ŀ×ĵ ×ķ×ijר\nפ ×ķ×¢×ľ\nd ım\néĢļ ãĤĬ\nĠбÑĥд ÑĥÑĤ\nà¹Ģวà¹ĩà¸ļ à¹Ħà¸ĭ\nà¹Ģวà¹ĩà¸ļà¹Ħà¸ĭ à¸ķà¹Į\nا خر\n×Ĺ ×Ļ׾\nĠ×Ļ ׾\nĠ×Ļ׾ ×ĵ×Ļ×Ŀ\n×Ĺ ×Ļפ\n×Ĺ×Ļפ ×ķש\nĠd òng\nĠש ×ĸ×Ķ\nÑĮ е\nãģĤ ãģ¨\nìŀIJ ê°Ģ\n×IJ ×ĵ\nĠü z\nĠüz ere\nظ ÙĦ\nĠ×IJ ×ķ׾×Ļ\nĠ×ij ×Ļ×ķ×Ŀ\nÙĦ ات\nĠm ê\nì¹ ¨\nتØŃ Ø¯\nتØŃد Ø«\nĠØ® اصة\nĠب رÙĨ\nĠبرÙĨ اÙħج\nĠH Ãłn\n×Ĺ ×¡\nĠÙĪ ÙĦÙħ\n×¢ ×Ŀ\nĠm ı\nà¸Ł ัà¸ĩ\nש ×¢×Ķ\nÙĪÙģ ÙĤ\nס ×ij×Ļר\nалÑĮ нÑĭй\n×Ĺש ×ķ×ij\nĠn Ãłng\në³ ¼\nĠкоÑĤоÑĢ ÑĭÑħ\nĠ×Ĺ ×ķק\nt ör\nĠлÑĥÑĩ ÑĪе\nãĥij ãĥ³\nลà¹Īา สุà¸Ķ\nĠج دÙĬد\nÙĬد Ø©\nà¸Ĺ รà¸ĩ\nãĤĪãĤĬ ãĤĤ\nÙĦ ÙĦ\nãĤĤ ãģ£ãģ¨\nש×ĺ ×Ĺ\nĠ×ķ ×IJ×Ļ\nĠgi á»ijng\nØ¥ ضاÙģ\nק ת\në§ Ŀ\nĠzosta ÅĤ\nÑĢ оз\n×Ļפ ×Ļ×Ŀ\nĠ׼׾ ׾\nת×ķ׼ ף\ndıģ ını\nÙĤ سÙħ\nĠÑģ ÑĩиÑĤ\nĠÑģÑĩиÑĤ а\n×ĺ ×ķת\nĠ Æ°u\nĠØ¢ ÙĦ\nĠм ом\nĠмом енÑĤ\nĠاÙĦتع ÙĦÙĬÙħ\n×¢×ľ ×ķת\nĠch ữa\nĠy ön\nĠtr Ãł\nĠØŃ ÙĬÙĨ\nà¸ĭ ั\nĠC á\n×¢ ×ĸ\nĠاÙĦØ£ ÙħÙĨ\nc ÃŃ\nĠv á»ijn\nĠ à¸Ļาย\nоб ÑĢа\nק ×IJ\nĠthi ếu\nãĥŀ ãĥ¼\nส วà¸Ļ\nĠg á»Ń\nĠgá»Ń i\nĠê ¹\nĠê¹ Ģ\nĠthi á»ĩn\nÙĤ ع\nw ÄĻ\nĠн ам\nÑĤ ол\nĠs ân\nס ×ķ×Ĵ\nĠgeç ir\nÑĤ он\nев а\nĠÙĪ ضع\nĠع شر\nÑģ ло\nà¸Ī ัà¸ļ\nãĤ· ãĥ¼\nãĤĤ ãģĤãĤĬãģ¾ãģĻ\nĠv ẻ\nĠÄIJ á»ĥ\nر Ùģع\nĠاÙĦØ£ÙĪÙĦ Ùī\nÑĤ аÑĢ\nãģªãģı ãģ¦\nÙħ Ùİ\nqu ÃŃ\n×¢×ł×Ļ ×Ļ׳\nг ен\nĠh ôm\nà¸Ī า\nĠnh Ỽ\nĠاÙĦع ربÙĬ\n×IJ ף\nĠl á»Ļ\nĠje ÅĽli\nà¹Ģà¸Ĺà¹Īา à¸Ļัà¹īà¸Ļ\nĠØ£ÙĨ Ùĩا\nĠt uy\nĠtuy á»ĩt\nĠت ص\nĠتص ÙĨÙĬ\nĠتصÙĨÙĬ Ùģ\nĠê·¸ëŁ¬ ëĤĺ\nо ÑĨен\nà¸ģิà¸Ī à¸ģรรม\nãĤĦ ãģ£ãģ¦\nĠkh á»ıi\nĠl á»ĩ\nĠاÙĦÙħج تÙħع\nà¸Ńาà¸Ī à¸Īะ\nà¸Īะ à¹Ģà¸Ľà¹ĩà¸Ļ\nов Ñĭй\nר ×Ŀ\nร à¹īà¸Ńà¸Ļ\nש ×ŀש\n人 ãģ«\nĠüzer ine\nפר ×Ļ\ndu ÄŁu\nÑĩ ик\nĠmù a\nĠ×ŀת ×ķ×ļ\nĠc áºŃp\nĠت ارÙĬØ®\n×ij׾ ת×Ļ\nĠì¢ Ģ\nÙĦ ع\nب اÙĨ\nĠch út\nĠ×Ķ×ĸ ×ŀף\nn ée\nĠLi ên\nĠÙĦÙĦ Ø£\nØŃد ÙĪد\nĠ×¢ ׼ש×Ļ×ķ\nв оз\nĠyapt ı\nĠоб о\nà¹ĥหà¹ī à¸ģัà¸ļ\nĠ×ij×Ķ ×Ŀ\nãģı ãģ¦\nر أس\nĠÑģÑĢед ÑģÑĤв\nĠB Ãłi\nãģĵãģ¨ ãģ«\nĠìĤ¬ íļĮ\nĠ모 ëijIJ\n×ij ×IJ\nĠtr ắng\nĠاÙĦبÙĦ د\nĠHo Ãłng\nли бо\nĠдÑĢÑĥг иÑħ\nÄ° R\nÑĥм а\nĠJe ÅĽli\nãĤĤ ãģĹ\nĠv òng\nĠ×IJתר ×Ļ×Ŀ\nĠÄij á»įc\nĠв оÑĤ\nãģł ãģĮ\në° °\nà¸Ķู à¹ģล\nĠ×ŀ ׼׾\nìĹIJ ëıĦ\nг аз\nĠ׳×ķס פ×Ļ×Ŀ\nãģĵãģ¨ ãģ§\nĠت ÙĪ\nãģ§ ãģĤãĤĬ\nà¸Ļั à¹Īà¸ĩ\nĠможеÑĤ е\nsz ÄĻ\nãģ® ãģł\nĠÙħÙĨ Ùĩ\nĠb á»ķ\nĠb üt\nĠbüt ün\në³´ ê³ł\nĠch á»ĵng\nà¹ģà¸Ī à¹īà¸ĩ\nĠV ì\nĠØŃ Ø±\nĠgi ản\nĠÙħ دÙĬÙĨØ©\nتط بÙĬÙĤ\nà¸Ī ิ\næĹ¥ ãģ®\nб ил\nà¸ģ à¸Ńà¸ĩ\nê³ ³\nĠØ£ Ùħا\nìĨ IJ\nĠtr ái\nĠвÑģ ем\nĠس ÙĨØ©\nĠÑģай ÑĤ\nĠг оÑĤов\nп Ñĭ\nĠëIJ ł\nĠاÙĦØ® Ø·\nĠاÙĦرئÙĬس ÙĬØ©\nĠíķ ©ëĭĪëĭ¤\nĠìķĦëĭĪ ëĿ¼\nĠìĿ´ ëłĩ\nĠìĿ´ëłĩ ê²Į\n) ØĮ\nh ält\nĠØ£ Ùħر\nĠع Ùħر\nà¸ģà¹ĩ à¸Īะ\nĠ à¸Ĺำà¹ĥหà¹ī\nĠc ân\nĠ×ij ׾\nĠ×ij׾ ×ij×ĵ\nפ סק\nĠÙĬ ÙĤÙĪÙĦ\nн ÑĥÑĤÑĮ\nà¹ģ à¸Ħ\nĠק צת\nĠn ằm\nĠh òa\nbilit Ãł\nĠìĹĨ ëĭ¤\nĠ׼ פ×Ļ\nÑĢ ож\nлаг а\nĠ×Ķש ×Ļ\nĠNgo Ãłi\nĠÙĪ ج\nĠÙĪج ÙĪد\nĠìľĦ íķľ\nĠus ÅĤug\nĠtu ần\nd ź\n×ŀ ×ķף\nĠاÙĦع دÙĬد\nĠch ẳng\nสุà¸Ĥ à¸łà¸²à¸ŀ\nĠ×ij ×ĵר×ļ\nĠÑģеб е\nĠìŀĪ ìĿĦ\nĠاÙĦØŃ Ø§ÙĦ\nĠd á\nĠc Æ°á»Ŀi\nĠnghi ên\nie ÅĦ\nĠD Æ°Æ¡ng\nï¼ ħ\nØ´ د\nãģĦãģ¤ ãĤĤ\nĠвÑĭб оÑĢ\nĠc á»Ļng\nש ×Ļ׳×ķ×Ļ\nĠch ạy\nĠ×ij×¢ ׾×Ļ\nاخ بار\níķĺ ë©°\nż Äħ\nج از\nĠ׳ ר×IJ×Ķ\nศ ู\nศู à¸Ļ\nศูà¸Ļ ยà¹Į\n×Ĵ ×¢\nĠ×¢ ×ĵ×Ļ\nĠ×¢×ĵ×Ļ ×Ļף\nبر ا\nÑĨи й\nĠÄIJ á»ĵng\nÙĤ اÙĨÙĪÙĨ\nĠÄij ứng\nãģĹãģŁ ãĤĬ\nĠ×Ĺ×Ļ ×Ļ\nĠë IJľ\nĠëIJľ ëĭ¤\nĠм еждÑĥ\nà¸ŀวà¸ģ à¹Ģà¸Ĥา\nĠB ắc\nล ำ\në° ±\nĠíĻ ķ\nมาà¸ģ ม\nมาà¸ģม าย\nбан к\nà¸Ńา à¸ģาร\nĠh Ãł\nĠ׾ ׳\nà¸Ń à¸Ń\nĠë°Ķ ë¡ľ\nл ом\nm ática\nĠØŃ Ø¯\nاب ت\nà¸Ĺีà¹Ī à¸Ļีà¹Ī\nĠco ÅĽ\nÙģÙĬ دÙĬ\nÙģÙĬدÙĬ ÙĪ\nĠмеÑģÑĤ о\nĠph út\nมาà¸ģ à¸ģวà¹Īา\n×IJ פ\nب ÙIJ\nĠPh ú\nì± Ħ\nĠÙĪ سÙĦÙħ\nà¸Īี à¸Ļ\nпоÑĤ ÑĢеб\nĠ×Ĺ×ĵ ש×ķת\nØ´ ÙĪ\nĠעצ ×ŀ×ķ\nĠعÙħÙĦ ÙĬØ©\nà¸Ħุà¸ĵ à¸łà¸²à¸ŀ\nãģ¾ãģĻ ãģĮ\nدع ÙĪ\nطر ÙĤ\nà¹Ħมà¹Ī à¸ķà¹īà¸Ńà¸ĩ\në² Ķ\nìĬ ¹\nĠk ÃŃch\nĠìĹĨ ëĬĶ\nĠÑĤ ам\nĠÙĨ ØŃÙĪ\nĠاÙĦÙĤ اÙĨÙĪÙĨ\n×Ĺ ×ķ×Ŀ\nĠk ız\nĠ×ĵ ×Ļף\nĠвÑĢем ени\nãģ£ãģŁ ãĤĬ\nĠØ´ Ùĩر\nĠìĦľ ë¹ĦìĬ¤\n×¢ ש×Ķ\nĠgi ác\nĠاÙĦسÙĦ اÙħ\nĠ×IJ ש\nĠполÑĥÑĩ а\nà¸Īัà¸Ķ à¸ģาร\nк оÑĢ\nĠ×Ķ×ĺ ×ķ×ij\nราย à¸ģาร\n주 ìĿĺ\nà¹ģà¸ķà¹Ī ละ\nĠê·¸ëŁ° ëį°\nà¸Ĺีà¹Ī à¹Ģà¸Ľà¹ĩà¸Ļ\nĠת ×ķ×ļ\nبÙĬ اÙĨ\nÐ Ļ\noÅĽci Äħ\nÑĤ ок\nĠà Ķ\nĠÃĶ ng\nà¹Ħมà¹Ī à¹ĥà¸Ĭà¹Ī\nãģ¿ ãģ¦\nÐŁ о\nĠЧ ÑĤо\níĻ ©\n×ĺ ×ij×¢\nмеÑĤ ÑĢ\nĠ×ij ×ŀ×Ķ\nĠ×ij×ŀ×Ķ ׾\nĠ×ij×ŀ×Ķ׾ ×ļ\nÑĩ ÑĮ\nק ש×Ķ\nз нак\nзнак ом\nuj ÄĻ\n×Ļצ ר\nĠاÙĦÙħ ÙĦÙĥ\nı yla\n×IJ×ŀ ת\nà¸Ľ ิà¸Ķ\n×IJ ×Ĺ×ĵ\nر اد\nĠm áºŃt\nëĭ¤ ëĬĶ\nĠl ạnh\nש׾ ×ķש\nØŃ Ø¯ÙĬØ«\nت ز\nå¹´ ãģ®\nĠк ваÑĢ\nĠкваÑĢ ÑĤиÑĢ\nä½ľ ãĤĬ\nرÙĪ ب\nов ан\nĠТ е\nà¸Īำ à¸ģ\nà¸Īำà¸ģ ัà¸Ķ\nب اط\n×Ĵ ת\nĠм аÑĪ\nĠмаÑĪ ин\n×Ļצ ×Ķ\nãģ» ãģ¨\nãģ»ãģ¨ ãĤĵãģ©\nÃŃ do\nĠÑı зÑĭк\nà¸ļ ิà¸Ļ\nสà¸ĸาà¸Ļ à¸Ĺีà¹Ī\nĠìĹ ´\nãĤ¦ ãĤ§\nĠc Ãł\nп ан\nåı£ ãĤ³ãĥŁ\nĠر د\nاÙĤ ت\nĠÙĥ ب\nĠÙĥب ÙĬرة\nÑģÑĤ ал\nש×ŀ ×Ĺ\npos ición\nĠÙħÙĦÙĬ ÙĪÙĨ\nĠìĿ´ ìķ¼\nĠìĿ´ìķ¼ 기\nĠh út\nĠÅĽw iat\nĠë°© ë²ķ\nĠÑģв еÑĤ\nĠвиде о\nĠاÙĦÙĨ ظاÙħ\nĠtr á»Ŀi\nĠëĮĢ íķ´ìĦľ\nר ×ŀת\nت داÙĪÙĦ\n×ķר ×ĵ\nת ×ŀ\nת×ŀ ×ķ׳×ķת\nĠ×ŀ ף\nĠдв а\nĠ×Ķק ×ķ\næĹ¥ ãģ«\nĠ×Ķ×Ĵ ×Ļ×¢\nà¹Ģà¸ŀิà¹Īม à¹Ģà¸ķิม\nÙħار س\nĠê²ĥ ìŀħëĭĪëĭ¤\nãģªãģĦ ãģ¨\nĠnhi á»ĩt\nëIJ ©ëĭĪëĭ¤\nĠ×ij׳ ×ķש×IJ\nĠê°Ģ ìŀ¥\nĠv ợ\nĠÄij óng\nצ×Ļ׾ ×ķ×Ŀ\nê´Ģ ê³Ħ\nв аÑı\n×IJ ×Ļ×ĸ\n×IJ×Ļ×ĸ ×Ķ\nĠÙĨ ظاÙħ\nÙħØŃ Ø§Ùģظ\nĠt ải\n기 ëıĦ\nà¸Ľà¸±à¸Ī à¸Īุ\nà¸Ľà¸±à¸Īà¸Īุ à¸ļัà¸Ļ\n׼ ×ĵ×ķר\nĠìķĦ ìĿ´\n׼׳ ×Ļס\nà¹Ģ à¸ķร\nà¹Ģà¸ķร ียม\nĠngo ại\nĠدÙĪÙĦ ار\nĠr ẻ\nĠkh Äĥn\nعد د\nØ´ عب\nczy Äĩ\nĠاÙĦ Ùĥر\nĠÑĩеловек а\nĠÙĪ Ø¥ÙĨ\n×IJ ×ĺ\nĠth Æ¡\nĠاÙĦ رÙĬاض\nоп ÑĢедел\nопÑĢедел ен\n×Ķ ×ŀש×ļ\nĠÐĿ ово\nз Ñĭва\nĠاÙĦدÙĪÙĦ ÙĬ\nĠÄij áp\nĠк ÑĢед\nĠкÑĢед иÑĤ\nов ого\nĠm ôn\nà¸Ľà¸£à¸° à¹Ĥย\nà¸Ľà¸£à¸°à¹Ĥย à¸Ĭà¸Ļ\nà¸Ľà¸£à¸°à¹Ĥยà¸Ĭà¸Ļ à¹Į\nÑģÑĤ е\nĠTh á»ĭ\nد ÙĬØ©\n×ŀצ ×ķ\nÙģ ات\nק ×ĵ×Ŀ\nìĿ´ëĿ¼ ê³ł\nÙĪ Ø®\nĠ×Ĺ ×ĸ\nĠÑĦоÑĤ о\n׾ ×Ļת\nت Ùİ\nÙĪ بر\nй ÑĤи\nĠÃ¶ÄŁ ren\nĠ×Ķ×ĸ ×ķ\nĠv á»įng\nÙĤÙĪ Ø©\nĠT ây\nĠÐĿ и\nĠש ×ķ×ij\nãģ¨è¨Ģ ãĤıãĤĮ\nãģ© ãĤĵãģª\n׊צ×Ļ\nï½ ľ\nĠ×ķ×Ķ ×ķ×IJ\nä¸Ģ ãģ¤\nĠÑģÑĤо иÑĤ\nni Äħ\n×ĺר ×Ļ\nĠдеÑĤ ей\nнÑı ÑĤÑĮ\nĠÑģдел аÑĤÑĮ\nĠë§İ ìĿ´\nä½ķ ãģĭ\nãģĽ ãĤĭ\nà¹Ħ หม\nà¸ķิà¸Ķ à¸ķà¹Īà¸Ń\nĠ×ij ת×Ĺ\nĠ×ijת×Ĺ ×ķ×Ŀ\nìĻ Ħ\nì§Ģ ëĬĶ\nÑģÑĤ аÑĤ\nÑıÑģ н\nü b\nĠth ả\nĠ×ij×IJ×ŀ ת\nĠt uyến\n×ĵ ×Ļר×Ķ\nĠ×IJ ×Ļש×Ļ\n×ĸ׼ ר\nãģ° ãģĭãĤĬ\nĠx ét\n׼ ×Ļ×ķ\n׼×Ļ×ķ ×ķף\ndiÄŁ ini\nĠاÙĦÙħ ÙĪضÙĪع\nĠh áºŃu\nà¸Īาà¸ģ à¸ģาร\n×ijס ×Ļס\nĠ×ŀ×Ĵ ×Ļ×¢\n×ij ×Ļ×¢\nĠÙĪ جÙĩ\nà¹ģà¸Ķ à¸ĩ\nà¸Ļ าà¸ĩ\nĠÅŀ a\nì ¡´\në¡ Ģ\nà¸ķ ะ\nĠ×Ķ×Ĺ×Ļ ×Ļ×Ŀ\nÙģ ÙĬد\nãģ§ãģĻ ãģĭãĤī\nê· ľ\nź ni\nĠлÑİ Ð´ÐµÐ¹\nĠyüz de\nıy orum\nĠاÙĦ بØŃر\ne ño\nп аÑĢ\nÙĬ ÙĤØ©\nоб ÑĢ\nר ×ķ×ļ\nت ÙĪÙĤع\nĠاÙĦØ´ ÙĬØ®\nåĪĿ ãĤģãģ¦\nĠÑĤ елеÑĦ\nĠÑĤелеÑĦ он\nĠth ôi\nĠ×Ļ׼×ķ׾ ×Ļ×Ŀ\nĠÅŁ irk\nĠÅŁirk et\nĠìļ°ë¦¬ ê°Ģ\nĠÄij ông\nĠת ×ķ×ĵ×Ķ\nÑģмоÑĤÑĢ еÑĤÑĮ\nĠÙĦ ÙĩÙħ\nĠ׾ ׼\nĠN ó\nĠØŃ Ø§ÙĦØ©\nãģĦ ãģij\nקר ×ķ\naz ı\nãĤ³ ãĥ¼\nĠÙĦÙĦ ت\ns ınız\nĠH ải\n기 ìĪł\nยัà¸ĩ à¹Ħมà¹Ī\nëĭ¤ ê³ł\nפ ×Ĺ\nĠ׾×Ĵ ×ij×Ļ\nĠع ÙĨÙĩ\nĠк аз\nĠказ ино\nب ÙĪر\nÑĦ еÑĢ\nĠê°Ļ ìĿ´\nتس جÙĬÙĦ\nĠاÙĦÙħ رÙĥز\nĠTh ái\nд аÑĤÑĮ\n×ŀ×Ļ ×Ļ׾\nĠpay laÅŁ\nãģ¤ ãģ®\nà¹Ģร ืà¸Ń\nn ça\n׳ ×ķ×Ĺ\nĠ×IJ פ×Ļ׾×ķ\nãģ¨ èĢĥãģĪ\nãģ¨ãģĹãģ¦ ãģ¯\nà¹Ģà¸Ī à¸Ń\n×ŀ פ\nĠg iriÅŁ\nл иÑĤ\nÑĤ елÑı\nÑij н\næ°Ĺ ãģ«\nĠg ó\nĠgó p\nåĪĩ ãĤĬ\nĠ×Ķ ×Ĺ×ĵש\nж ал\nĠ×ĵ עת\néģķ ãģĨ\nà¹Ģà¸Ĥà¹īา à¹Ħà¸Ľ\nĠס ר×ĺ\ne ña\næĸ° ãģĹãģĦ\nر Ùİ\nĠÐIJ ÑĢ\nĠph ản\nà¸Īะ à¹Ħà¸Ķà¹ī\nĠ×ijצ ×ķר×Ķ\nØ´ اÙĩ\nشاÙĩ د\nÙĪر د\nà¹Ģà¸Ļืà¹Īà¸Ńà¸ĩ à¸Īาà¸ģ\nили ÑģÑĮ\nà¹ģละ à¸ģาร\nĠ×Ķ ×ĸ׼\nĠ×Ķ×ĸ׼ ×ķ×Ļ×ķת\nei ÃŁ\nãĥ ¨\nìĥ Ī\nĠÃĩ a\nÆ ¯\nש ×Ĵ\nÙĬÙĨ Ø©\nร à¹īà¸Ńà¸ĩ\nãĤµ ãĥ³\nÑĢоÑģÑģ ий\nÑĢоÑģÑģий Ñģк\na ÄŁa\nĠнаÑĩ ина\nĠص ÙĦÙī\nà¸Ĺุà¸ģ à¸Ħà¸Ļ\níļĮ ìĤ¬\nĠли ÑĨ\nØ´ ÙĬر\nĠØ´ÙĬ Ø¡\nÙĬÙĨ ا\nĠפ ×Ĺ×ķת\nĠiçer is\nĠiçeris inde\nĠØ£ ØŃÙħد\nĠże by\nì´ Ŀ\nĠп оказ\nĠи менно\nหà¸Ļัà¸ĩ ส\nหà¸Ļัà¸ĩส ืà¸Ń\nĠÑĤÑĢ е\nสัà¸ĩ à¸Ħม\nØ¥ ÙIJ\nãģĮ å¿ħè¦ģ\nÙĬÙij Ø©\nפ צ\níĭ °\nĠÙħ جاÙĦ\n׳ פש\nк ан\n×Ĺ ×ķפ\n×Ĺ×ķפ ש\nì²ĺ ëŁ¼\nов аÑı\nз ов\nĠh ạ\nĠdzi ÄĻki\n×Ļר ×ķ\nĠ׾ ×ŀצ\nĠ׾×ŀצ ×ķ×IJ\n×Ļ×ĵ ×ķ\nĠs ợ\nĠ׾×Ķ ×Ĵ×Ļ×¢\nק ×ij×¢\nĠchi á»ģu\nãĥŀ ãĤ¤\nĠd Ãłng\nà¹ģà¸Ł à¸Ļ\nĠü ye\n×Ļ׳ ×Ĵ\nà¹Ģรีย à¸ģ\nç§ģ ãģĮ\nth é\nĠÑĦ илÑĮ\nĠÑĦилÑĮ м\nĠNg Ãły\nĠж ен\nĠжен Ñīин\nج ÙĬد\nn ç\nà¸Ľ รา\n×Ļ×ŀ ×ķ\nĠn á»ģn\n×IJ ×ķ׾×Ŀ\nĠвозмож ноÑģÑĤÑĮ\nĠëĭ¤ ìĭľ\nè¦ĭ ãģŁ\nà¸ĸ à¸Ļ\nà¸ĸà¸Ļ à¸Ļ\nmız ı\nĠÙħ جÙħÙĪعة\nc jÄħ\nĠÐł Ф\nà¸ģำ หà¸Ļ\nà¸ģำหà¸Ļ à¸Ķ\nĠìŬ 기\nland ı\nни ÑĨ\nÑģÑĤв е\nĠ×ĵ ×ijר×Ļ×Ŀ\nĠsk ÅĤad\nãĤĬ ãģ¾ãģĹãģŁ\nĠоÑĤ кÑĢÑĭÑĤ\nнÑı ÑĤ\nĠÑģво ей\nà¸Ī ิà¸ķ\nĠкаÑĩеÑģÑĤв е\nĠet tiÄŁi\nìĤ¬ íķŃ\nĠاÙĦÙĬ ÙħÙĨ\nиÑĩеÑģки й\në¸ Į\nĠ×ij×IJר ×¥\nĠا سÙħ\nĠиз веÑģÑĤ\nr ão\nĠatt ivitÃł\nà¹Ģà¸Ľà¹ĩà¸Ļ à¸ģาร\nĠاÙĦد Ùĥت\nĠاÙĦدÙĥت ÙĪر\nĠÙĪاØŃد Ø©\nĠÑģ ÑĩеÑĤ\nĠпÑĢ иÑĩ\nĠпÑĢиÑĩ ин\nĠÙĪز ارة\nĠh uyá»ĩn\nĠÙĥ تاب\nà¹ģà¸Ļ à¹Īà¸Ļ\nà¹ģà¸Ļà¹Īà¸Ļ à¸Ńà¸Ļ\nĠgün ü\nг ÑĢÑĥз\nĠاÙĦØ® اص\nĠgör ül\n׾ ×ŀ×ĵ\nĠìłķ ëıĦ\n×ķ×ij ×Ļ׾\nĠ×ŀק צ×ķ×¢×Ļ\nĠоÑģоб енно\nà¸Ľà¸£à¸° à¸ģา\nà¸Ľà¸£à¸°à¸ģา ศ\naca ģını\në¶ ģ\nà¸łà¸¹ มิ\nĠÑį лекÑĤ\nĠÑįлекÑĤ ÑĢо\nĠק ש×Ķ\nسÙĦ Ø·\nà¸Ĭà¸Ļ ะ\n×¢ ×Ļ׾\nĠЧ е\nà¹ģà¸Ļ à¹Ī\nlı ÄŁ\nlıģ ın\nĠ×ŀ×¢ ×¨×Ľ×ª\n好ãģį ãģª\nมาà¸ģ à¸Ĥึà¹īà¸Ļ\n×ŀ×¢ ×ijר\nĠاÙĦÙħ غرب\nĠпеÑĢ и\nĠпеÑĢи од\nĠnh ạc\nا ÙĪÙĬ\nĠÙĪ عÙĦÙī\nأخ Ø°\nĠC ô\nתר ×ij×ķת\n×Ĵ ×Ķ\nĠktóre j\n×IJ ×Ļת\n×ij ×ķ×IJ\nд елÑĮ\nรี วิ\nรีวิ ว\nж Ñĥ\nĠ×ij×Ĺ ×ķ\nеÑĪ ÑĮ\nĠØ£ ÙĦÙģ\nĠاÙĦÙĪ Ø·ÙĨÙĬ\nĠاÙĦÙħÙĨ Ø·ÙĤØ©\nnÄħ Äĩ\nĠthi ên\nиÑĩеÑģк ой\nĠاÙĦÙħ ÙĦ\nĠع Ùħ\nס פר\nĠnh óm\nÙĪص Ùģ\nĠCh úng\nĠر ÙĤÙħ\nãģ¾ãģĹãģŁ ãģĮ\nal ité\nล ม\nĠëĤ´ ê°Ģ\n׾ק ×ķ×Ĺ\nĠS Æ¡n\npos ição\nmi ÄĻ\nĠtr ánh\nĠÄIJ á»Ļ\n׼ ×Ĺ\nãģĤ ãģ£ãģ¦\nà¸Ńย à¹Īา\nĠ×ŀ×Ĺ ×Ļר\nĠ×Ķ ×Ļת×Ķ\nà¸Ľ à¹Īา\nà¸Ńืà¹Īà¸Ļ à¹Ĩ\nØ´ ÙĤ\n×ł×¡ ×Ļ\në¦ ¼\nãģ¦ãģĹãģ¾ ãģĨ\nĠ×ŀ צ×ij\nãģ« åĩº\nÙħÙĪا Ø·ÙĨ\nยัà¸ĩ มี\nалÑĮ нÑĭе\nsan ız\nØ¥ سرائÙĬÙĦ\nĠvÃł i\nì¤ Ħ\nãģ¨æĢĿ ãģ£ãģ¦\n×Ļ ×ķ׳×Ļ\nçĶŁ ãģį\nĠs âu\nÑĩ иÑģÑĤ\nĠl á»ħ\nĠGi á\nà¸Ńุ à¸Ľ\nà¸Ńà¸¸à¸Ľ à¸ģร\nà¸Ńà¸¸à¸Ľà¸ģร à¸ĵà¹Į\nĠnh ẹ\nr ö\nס ×ĺ×Ļ\nãģķãĤĵ ãģĮ\nĠd ầu\nع Ùİ\nت را\n×Ĵ×ĵ ׾\nĠtécn ica\n׼ ׳×Ļ×Ŀ\nתק ש\nתקש ×ķרת\nĠн его\nét ait\nĠm á»ģm\nÑģ еÑĤ\nĠnh áºŃt\nĠ×ŀ ×¢×ľ\nĠ×Ķ×¢ ×ij×ķ×ĵ\nĠ×Ķ×¢×ij×ķ×ĵ ×Ķ\nĠ×Ĵ ×Ļ׾\nãģ¯ ãģªãģĦ\nائ ØŃ\nĠз деÑģÑĮ\n×IJ ×Ļ׳×ĺר\nÙħ ÙIJ\nĠ×Ļ ×Ĺ×ĵ\nر اÙģ\nì²ĺ 리\n×ĵ ×¢×ķת\nì¹ ľ\nĠТ о\nĠTh ế\nì¶ ©\nĠ׳׼ ×ķף\nعÙĬ Ø´\nни з\nĠج اÙĨب\n×ŀק צ×ķ×¢\nà¹Ĥ à¸ĭ\nÑģ ÑĥÑĤ\nìĸ´ ìļĶ\nãĤĴè¦ĭ ãģ¦\nار د\nĠaç ıl\nĠاÙĦØŃ ÙĬاة\nà¸ģà¹ĩ à¹Ħà¸Ķà¹ī\nãģĿãĤĮ ãĤĴ\nعض ÙĪ\nĠг ÑĢаж\nĠгÑĢаж дан\nà¸Īะ à¸ķà¹īà¸Ńà¸ĩ\nĠìĿ´ 룬\nĠìĿ´ëŁ¬ íķľ\nĠtr ách\nÙĨ Ùİ\nĠkı sa\nà Ķ\nÑĪ ка\nãģ® 人\nĠÐŁ оÑģ\nĠÐŁÐ¾Ñģ ле\nÑĥ лÑĮ\nÙĪا جÙĩ\nÙĤ رب\nà¸Ľà¸ıิ à¸ļัà¸ķิ\nê° Ļ\nĠ×ŀ ׳\nĠÑģво и\nبر اÙħج\nĠر ÙĪ\nпÑĢ од\nпÑĢод аж\nĠby ÅĤy\nวั ย\nĠgör ün\nĠà Ī\nÑİÑī им\nĠÑĤак ой\nÙģ ÙĪر\nĠÙģ عÙĦ\nĠб ел\nëIJ ł\ner ÃŃa\nĠÑģво Ñİ\nĠl ã\nĠlã nh\nà¹Ģà¸ŀืà¹Īà¸Ń à¹ĥหà¹ī\nÙĤ ÙĨ\nتط ÙĪÙĬر\nĠsay ı\nĠÑģ ейÑĩаÑģ\nĠ×IJ×Ĺר ת\nק ×ķפ×Ķ\nק×ķר ס\nĠس Ùħ\nĠ×ĺ ×Ļפ×ķ׾\nìĿ´ëĿ¼ ëĬĶ\nدراس Ø©\nèµ· ãģĵ\n×Ĺ ×Ļ׳\n×Ĺ×Ļ׳ ×ķ×ļ\n×ĵ ק\nĠë§ ŀ\nĠком анд\nĠÐij о\nĠиг ÑĢÑĭ\nà¸ļ ี\nĠØ£ Ùİ\nв ен\nĠاÙĦج دÙĬد\nĠÙĦ Ø¥\nĠ×ķ×IJ ׳×Ļ\nĠ×Ķס ×Ļ\nиÑĩеÑģк ого\nرÙĪ ØŃ\nà¸ģาร ศึà¸ģษา\nĠTr Æ°á»Ŀng\nиг ÑĢа\nıl ması\nĠм аÑģÑģ\nãģ¨ãģį ãģ«\nà¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ\nà¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา\nĠاÙĦساب ÙĤ\nĠ×ŀ×¢ ×ĺ\nв аÑĤÑĮ\nm Ã¼ÅŁ\nĠ׾ ׼×ļ\nĠt á»ĭch\nÙģ ÙĩÙħ\nتد رÙĬب\nØ´ Ùĥ\nĠ×ij ×ŀ×Ļ\nĠ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ\nÙĤØ· اع\nãģª ãģĹ\n×ķצ ×Ļ×IJ\nĠÙĪ سÙĬ\nз Ñĥ\nĠy at\nĠyat ırım\në§ İ\nĠth ắng\nãģĬ 客\nãģĬ客 æ§ĺ\nĠThi ên\nãģ«å¯¾ ãģĹãģ¦\nÑĢ иÑģ\nÙĨت ائ\nÙĨتائ ج\nĠ×ŀ שר\nĠ×ŀשר ×ĵ\nĠتع اÙĦ\nĠتعاÙĦ Ùī\nש ׳×Ļ\nÙĩ اÙħ\n×IJ׳ ש×Ļ×Ŀ\nĠżyc ia\nĠÑĢÑĥб лей\nÙĬ ض\nĠkat ıl\nĠÙħ ÙĪضÙĪع\nĠvard ır\nĠÙħÙĨ Ø·ÙĤØ©\nĠTr ần\nĠв еÑģ\nü p\nÙħ ÙĪÙĨ\nÑĪ ли\nĠn óng\nØ® ÙĦÙģ\nĠС ÑĤа\nĠд оÑĢ\nĠдоÑĢ ог\nĠwÅĤa ÅĽnie\neÄŁ in\nĠhi á»ĥm\nĠС ам\nê»ĺ ìĦľ\nĠÑĦ а\nãģ» ãģĨ\nãģ»ãģĨ ãģĮ\n×ķפ ×Ļ×¢\nê° Ī\nد ÙĪÙĦ\nĠthu ê\nĠch á»Ĺ\nĠëĭ¹ ìĭł\nãģij ãĤĮ\nãģijãĤĮ ãģ©\në³´ íĺ¸\nãģķãĤĮ ãģ¦ãģĦãģ¾ãģĻ\nĠнад о\nĠìĤ¬ëŀĮ ëĵ¤\nà¹Ģà¸Ĥ à¸ķ\nสม ัย\nz ÅĤ\nت ÙĪر\nĠש ת×Ļ\nv ê\nĠ×ijת ×ķ×ļ\nà¸Ĭ ัย\nãģĦ ãģ£ãģŁ\nìĿ ij\nĠt ầ\nĠtầ ng\nש ׼ר\nĠê¸ Ģ\nĠ×Ķש ׳×Ķ\nĠا ÙĨÙĩ\nç«ĭ ãģ¡\nr és\nfüh ren\nر ØŃÙħ\nê· ¹\nĠâĢ «\nĠsu ất\nà¸Ł ิ\nÙĬ Ùĩا\nĠاÙĦ اتØŃاد\nĠt uyá»ĥn\nãģ¾ ãĤĭ\nĠm ại\nĠng ân\nãĤ° ãĥ©\n欲 ãģĹãģĦ\nس ار\nãĤĤãģ® ãģ§ãģĻ\nки е\nĠseç im\nåħ¥ ãĤĬ\nãģªãģ© ãĤĴ\nÑĤ ÑĢи\nĠÑģп еÑĨ\nĠØ£ د\nĠод но\nÑĪ ел\nãĥĩ ãĥ¼ãĤ¿\nãĤ· ãĤ¹ãĥĨ\nãĤ·ãĤ¹ãĥĨ ãĥł\nè¡Į ãģį\nãģ¨æĢĿ ãģ£ãģŁ\nà¹Ģà¸ģิà¸Ķ à¸Ĥึà¹īà¸Ļ\nĠÑĤ ож\nĠÑĤож е\nĠs ạch\nĠÑģ ÑĢок\nĠкли енÑĤ\nĠÙħØ´ رÙĪع\nĠalt ında\nĠì ·¨\nä¸Ń ãģ®\nãģķãģĽ ãĤĭ\nãģĻ ãģ¹\nãģĻãģ¹ ãģ¦\nê°ľ ë°ľ\nĠÄij êm\nãģªãģĦ ãģ®ãģ§\nì² ł\n×¢ ×ij×ĵ\nĠd ấu\nà¸Ħà¸Ļ à¸Ĺีà¹Ī\nĠC ách\nتع ÙĦÙĬÙħ\nĠh ại\nãĤ» ãĥķãĥ¬\nĠÙĨÙģس Ùĩ\nĠíĨµ íķ´\nÑĪ ло\nĠнап ÑĢав\nĠнапÑĢав лен\nÑĢÑĥ Ñĩ\níĶ Į\nĠ×ijר ×Ļ×IJ\nãģ® ãģ¿\nãģ«ãģĬ ãģĦãģ¦\n×ij ׳ק\nãĤ¨ ãĥ³\nØ«ÙĦ اث\nĠm ỹ\nĠÑģай ÑĤе\nĠе мÑĥ\nت غÙĬ\nتغÙĬ ÙĬر\nخص ÙĪص\nÑĤе ли\nĠ×ķ׾ ׼ף\nפע ×Ŀ\nĠпо ÑįÑĤомÑĥ\nر اÙĨ\nиÑĤел ей\nпиÑģ ан\n×¢ ×¥\nĠìĤ¬ ìĹħ\nÙħ ز\nجÙħ ÙĬع\në©´ ìĦľ\nà¸ľà¸¥à¸´à¸ķ à¸łà¸±\nà¸ľà¸¥à¸´à¸ķà¸łà¸± à¸ĵ\nà¸ľà¸¥à¸´à¸ķà¸łà¸±à¸ĵ à¸ij\nà¸ľà¸¥à¸´à¸ķà¸łà¸±à¸ĵà¸ij à¹Į\nĠпÑĢ имеÑĢ\nãĤŃ ãĥ¼\nl â\nĠch Äĥm\n缮 ãģ®\nãģĦ ãģĭ\nãģ¨è¨Ģ ãģĨ\n×ĸ ×ķ×Ĵ\nĠ×ij ×ĵ×Ļ\nĠ×ij×ĵ×Ļ ×ķק\nãģĬ åºĹ\nà¸ķà¸Ńà¸Ļ à¸Ļีà¹ī\nĠph á»iji\nп ÑĤ\nสà¸Ļ าม\nØ· ÙĪ\nص اØŃ\nصاØŃ Ø¨\nĠD ü\nĠDü nya\nĠп ока\nп ал\nĠÄij ảo\nĠاÙĦÙģ ÙĪر\nĠاÙĦÙģÙĪر Ùĥس\nĠmá u\nкÑĢ еп\nĠاÙĦس اعة\nĠгоÑĢ ода\nÙģ صÙĦ\nай ÑĤе\nĠд ог\nĠдог овоÑĢ\nĠØ¥ Ø°\nĠ×ij׼׾ ׾\nÙĬ تÙĩ\n×Ĵ ×ijר\nĠbir ç\nĠbirç ok\n문 íĻĶ\nãģĿãģĨ ãģª\nرا ØŃ\nĠÙħ رة\nĠденÑĮ ги\nf ä\nà¸Ĥà¹īา ว\nĠÑģов ÑĢем\nĠÑģовÑĢем енн\n׾×Ĺ ×¥\nèī¯ ãģı\nĠÙģ Ø£\nĠ×ķ ×ĸ×Ķ\nĠз ани\nĠзани ма\nĠê°Ģì§Ģ ê³ł\nĠh Æ¡i\nãģªãģ® ãģĭ\nãĥĨ ãĥ¬ãĥĵ\nĠר ×ij×ķת\nà¸ķ ี\nĠ×ijש ×ł×ª\nĠT ại\nĠthu áºŃn\nÑģ ел\nÑij м\ndzi Äĩ\nĠÑģ ка\nĠÑģка Ñĩ\nĠÑģкаÑĩ аÑĤÑĮ\n×ķ×ŀ ×ķ\nг ла\nĠмин ÑĥÑĤ\nåĩº ãģĻ\nĠ×Ĺ×Ļ ×Ļ×ij\nĠת ×Ĵ×ķ×ij×Ķ\nà¸£à¸¹à¸Ľ à¹ģà¸ļà¸ļ\nни ÑĨа\nĠÄ° n\nĠØ£ ع\nĠض ÙħÙĨ\nÙħ ثاÙĦ\nĠyaÅŁ an\nĠìĹ° 구\nĠL ê\nש׾ ×Ĺ\nãģı ãģªãĤĭ\nìĹĨ ìĿ´\nĠÑĤ ÑĢи\nĠÑĩаÑģÑĤ о\nĠоб ÑĢаÑĤ\nп ло\nد Ø®\nدخ ÙĪÙĦ\nس Ùĩ\nà¸Ń าà¸ģ\nà¸Ńาà¸ģ าศ\nĠ׼ ×ĸ×Ķ\nĠ×Ķ×¢ סק\nĠاÙĦØ£ ÙĨ\nå¹´ ãģ«\n×¢ ש×ķ\nĠש ×¢×ķת\nĠm Ãłn\n×IJר ×Ļ\nsı yla\nÙģر ÙĤ\nни Ñħ\nĠت ست\nè¦ĭ ãģ¦\nØŃا ÙĪÙĦ\n×IJ ×Ļ׼×ķת\nĠbaÅŁ ladı\nst Äħ\nstÄħ pi\nà¸Ĺีà¹Ī à¹Ģรา\nÙĤر ر\nج اب\nĠ×ijר ×ķר\nà¹Ģà¸Ĥà¹īา à¹ĥà¸Ī\n×ŀ׊קר\nal ım\nĠס ×Ļפ×ķר\nãģ§ãģĤ ãĤĮãģ°\nĠש×ŀ ×ķר×ķת\nĠ×ķ ×ŀ×Ķ\nãģĵ ãģĿ\nid ée\nä¸ĭ ãģķãģĦ\nتÙĨا ÙĪÙĦ\nĠ ลà¹īาà¸Ļ\nĠìļ°ë¦¬ ëĬĶ\nاÙĨ ا\nÑģÑĤ ой\nб оÑĤ\nĠyaÅŁ am\nkö y\nØ¥ ÙĦ\nÑĢ Ñĭв\n기 ìĹħ\nĠ×Ķ×ŀ ×ĵ\nĠ×Ķ×ŀ×ĵ ×Ļ׳×Ķ\nد ب\n×¢ ×Ļ׳×Ļ\n×ŀ ת×Ĺ\nĠפ ר×Ļ\nãĥĭ ãĥ¼\nاÙħ ÙĬ\nĠnh ằm\nãĤĮ ãģªãģĦ\nت 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ÙĥاÙĨ\në ķĮ\nĠc ải\nĠCh ÃŃ\nÑĥÑĩ а\nìĿ µ\nĠx ảy\nà¸Ĭà¸Ļ ิà¸Ķ\nĠc áºŃu\nк ÑĢов\nss é\nĠÙĨ ÙĪع\nĠТ а\nØ® Ùħس\nפ×ķס ×ĺ\nĠm ắc\nĠÄij em\nà¸ģาร à¹ĥà¸Ĭà¹ī\nר ×ķס\nĠÐĽ е\nĠth á»Ń\nรà¹Īาà¸ĩ à¸ģาย\nüz ü\næĹ¥æľ¬ ãģ®\nê³¼ ìłķ\nש ×Ļ×IJ\nĠìŀĪ ê³ł\n×ij ×ķ׾\nìķ ħ\nĠÙĪاÙĦ ا\nĠÐĽ и\nĠвÑģ Ñij\nĠużytk ow\n×Ĺ ×ķ׾\nر Ùģض\nĠson uç\nãģĦ ãģ¾ãģĽãĤĵ\nìĤ¬ ìĹħ\nëĪ Ħ\nÑĤ ек\nĠud ziaÅĤ\nл ез\nĠ×Ķ×Ļ ×Ļת×Ļ\nãĤīãĤĮ ãģ¦\nÙħس ؤÙĪÙĦ\nر ار\nÑĤ ан\nĠÄij Ãło\nĠר ×ķ×ij\nĠ×ijש×ij ×Ļ׾\nä»ĬåĽŀ ãģ¯\nãĤ¸ ãĥ¥\nĠ×¢ ×ijר\nãģĽ ãģ¦\nп олÑĮ\nak lı\nĠk ÃŃnh\nد ت\nлож ение\nĠاÙĦÙħ ص\nĠاÙĦÙħص رÙĬ\nà¸Īริà¸ĩ à¹Ĩ\nĠاÙĦشر ÙĥØ©\nĠÄij á»ı\nãĥĽ ãĥĨ\nãĥĽãĥĨ ãĥ«\nÑį кон\nÑįкон ом\nĠÙĪ عÙĨ\nĠת ׳\nĠ×ª×ł ×IJ×Ļ\nĠاÙĦدÙĪÙĦ ÙĬØ©\nĠì§Ģ ìĹŃ\nãģ§ãģĻ ãģĭ\nĠв аÑĢи\nĠваÑĢи анÑĤ\nĠاÙĦع رب\nел а\nĠt Æ°á»Ľng\nsk Äħ\nĠm ặc\nส ัà¸ģ\nãĥĵ ãĥ¼\nĠ×ij ×Ĵ׾\nĠ×ij×Ĵ׾ ׾\nãĥķãĤ¡ ãĥ³\n×ij ×Ļצ\n×ij×Ļצ ×ķ×¢\nли ÑģÑĤ\nà¸Ł ุ\nà¸Łà¸¸ à¸ķ\nà¸Łà¸¸à¸ķ à¸ļà¸Ńล\nà¸Ŀ à¹Īาย\nìŀIJ ìĿĺ\nĠس ÙĪÙģ\nĠש ×Ķת\nĠê± ¸\n×¢ ×ij×ķ×ĵ\nãģĻãĤĭ ãģĵãģ¨ãģĮ\nĠÑĩа ÑģÑĤÑĮ\nãĤ¢ ãĥ¡ãĥª\nãĤ¢ãĥ¡ãĥª ãĤ«\nĠtak 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×ķ×Ĺ\nÙĨ Ùĥ\nĠاÙĦÙĨ ÙĤ\nãģ®ãģ§ ãģĹãĤĩãģĨ\nãģ®ãģ§ãģĹãĤĩãģĨ ãģĭ\nÙħ عرÙģ\nÙħعرÙģ Ø©\nÑĥÑī е\nĠ×ij×¢ ×Ļקר\nت صÙĦ\nĠ×Ķ×IJ ר\nĠ×Ķ×IJר ×¥\nĠÅŀ i\nà¸Ĥา à¸Ķ\níŀ ĺ\nãģªãĤĵ ãģ¨\nĠìĤ¬ëŀ ij\nl Ã¼ÄŁÃ¼\nب اء\nĠاÙĦØ¢ خر\nĠfam ÃŃlia\nĠTh áng\nÑī ениÑı\nãĤ¯ ãĥŃ\nĠTh ứ\næĽ¸ ãģį\nен ной\nìŀ ¡\nбл аг\nблаг о\nп ов\nà¹ģ ว\nà¸ĩ à¸Ħà¹Į\nà¸Ńัà¸Ļ à¸Ķัà¸ļ\nãģĤ ãģĴ\nร à¹īาย\nün ün\nĠ×Ļ׼×ķ׾ ×Ķ\nз он\nĠÐľ и\nмаÑĤ еÑĢиал\nĠë³´ ë©´\nØŃÙģ ظ\nê Ìģ\nãģ« ãģĻãĤĭ\nĠת ×IJ\nĠ×Ķס ×ķ\nĠÑģÑĤ оÑĢ\nĠÑģÑĤоÑĢ он\nãĥĪ ãĥĥãĥĹ\nÅĤo ÅĽÄĩ\nëħ ¼\nëĵ Ŀ\nĠÙĪاÙĦ ع\nì¶ Ķ\nĠ×Ļצ ×IJ\nĠÑĢаз дел\nалÑĮ наÑı\n×IJ׳ ש×Ļ\nspo ÅĤ\nspoÅĤ ec\nspoÅĤec zn\nØ¥ عÙĦ\nإعÙĦ اÙĨ\nÙĤÙĪ Ùī\níķĺë©´ ìĦľ\nتط ÙĪر\nĠsi êu\nỼ t\nд ви\nдви ж\nĠqu ần\nk ıl\nĠпÑĢи зна\nĠH ã\nĠHã y\nĠباÙĦ ت\nman ın\nãĤ« ãĥ«\nĠk á»·\nק ׾×Ļ\nëIJĺ ì§Ģ\nتعÙĦ Ùħ\nìĭľ ìĦ¤\nìĭ ¶\níĺ ¼\nÙĥ ÙĬÙģ\n売 ãĤĬ\nวิ à¸Ĭา\nб ал\nĠØ£ ØŃ\nĠдолж ен\nรา à¸ĩ\nราà¸ĩ วั\nราà¸ĩวั ล\nÙħ اء\nج ار\nÅ ļ\nĠ×ŀ×IJ ×ĸ\nר ×ŀ×Ķ\nãģĭãĤĤãģĹãĤĮ ãģªãģĦ\nét ude\nczÄħ c\nĠg ór\n×ł×¡ ×Ķ\nÙħ ÙĬد\nĠÐŁ еÑĢе\nØ£ خر\nãģĿãģ® å¾Į\nà¹Ģà¸Ķียว à¸ģัà¸Ļ\n×ŀ ×Ĵ×ķ\n×ŀ×Ĵ×ķ ×ķף\nд ов\nmas ına\n×¢ ׳×Ķ\nãĤ± ãĥĥãĥĪ\nס ×¢\nסע ×Ļ×£\nĠT Æ°\nĠt óc\níĻľ ëıĻ\nĠÐŀ д\nĠÐŀд нако\nĠdol ayı\nؤ Ùĥد\nê³Ħ íļį\n׾ ר\nв еÑĩ\nĠkh ợi\nĠth ủy\n×ĵ ף\nร à¸ģ\nà¸ļั à¸ķร\nà¹Ģà¸ģ à¹Īา\nĠاÙĦØ« اÙĦ\nĠاÙĦثاÙĦ Ø«\nĠpod rá\nער ×Ļ\nÙĨج اØŃ\nĠkh ắc\nì¸ ¡\nÄ° M\nãĤ» ãĥĥãĥĪ\nż enia\nĠ׾×Ĺ ×ijר\ner Ãł\nì ´Ī\nĠkü ç\nĠküç ük\nات ÙĩÙħ\nà¸ĭ à¹Į\nÙħشار ÙĥØ©\nĠاÙĦ بط\nĠd ây\nен нÑĭм\nà¸Ĺีà¹Ī à¹Ħมà¹Ī\nÙĤ Ùİ\nĠv ượt\nĠtr ì\nĠwp ÅĤyw\nA Åŀ\nз о\nĠاÙĦس ÙĬد\nà¸Ĺะ à¹Ģล\nĠÑģодеÑĢж а\nع Ø·ÙĬ\nĠاÙĦع ÙĨ\nèĢħ ãģĮ\nà¹Ģ หà¸Ļ\nà¹Ģหà¸Ļ ืà¸Ń\nĠb ÃŃ\nĠüzer inden\nĠV Å©\nĠnu ôi\nÙĨ Ùħ\nалÑĮ ного\n×¢ ×Ļף\nØŃ Ø¶Ø±\nĠоÑĤ дел\nëª ĩ\nìķ ¡\nĠÙĦدÙĬ Ùĩ\nìĻ ľ\nĠse ktör\nĠвозмож но\nĠÐĶ ж\nĠh ô\näºĭ ãģĮ\nиÑĢов ание\nалÑĮ ной\nĠ미 êµŃ\nر ØŃÙĦ\nĠÑįк Ñģ\nпÑĢав лÑı\nĠnh á»Ŀ\nĠÄij ẩ\nĠÄijẩ y\nÙģ Ùĥر\nĠÙĪØ£ ضاÙģ\nãĥIJ ãĤ¹\nת×ķ׼ ׳×Ļת\nÑĤел ей\nĠØ¥ÙĦÙĬ Ùĩ\nãģ¨è¨Ģ ãģ£ãģ¦\nĠдв е\nĠch ấp\nĠL ö\nà¸Ħล ิ\nà¸Ħลิ à¸Ľ\nĠس ÙĪر\nĠسÙĪر ÙĬا\n×ŀ×Ĺ ×ķ\nst ä\nд об\nĠni á»ĩm\nãģ® 大\nפר×ķ ×Ļק\nפר×ķ×Ļק ×ĺ\nĠCh âu\nĠ×ŀ×Ķ ×Ŀ\nÑģк им\nĠполÑĥÑĩ иÑĤÑĮ\nÙĬ ÙĪÙħ\nØ« ÙĪر\nפ×ķ׾ ×Ļ×ĺ\nפ×ķ׾×Ļ×ĺ ×Ļ\nĠмеÑģÑı ÑĨ\nåħ¨ ãģ¦\nĠاÙĦÙħ جÙĦس\nĠاÙĦت اÙĦÙĬ\nĠ׊ר\nåIJij ãģij\n׼ ×ŀ×Ķ\nб ед\nØ£ عض\nأعض اء\nÙĪÙĦ د\nวà¹Īา à¸Īะ\nĠb ánh\nà¸Ļิ ย\nà¸Ļิย ม\nà¸Ľà¸£à¸° à¸ģัà¸Ļ\nÑģÑĤав иÑĤÑĮ\nà¸ŀ à¸Ļัà¸Ļ\nĠÑį ÑĦÑĦ\nĠÑįÑĦÑĦ екÑĤив\nĠав ÑĤоÑĢ\nĠÄIJ Äĥng\nĠth Æ°á»Łng\nãĤĴ æĦŁãģĺ\nà¸ģัà¸ļ à¸ģาร\nå¾Į ãģ«\nĠya ÄŁ\nست اÙĨ\nĠli á»ģn\nãģĦ ãģ¾\ni êu\nà¹Ĥà¸Ķ à¸Ļ\nĠÙĦ Ø°ÙĦÙĥ\nà¹Ĥรà¸ĩ à¹Ģรียà¸Ļ\nצ ×Ļ×Ĵ\nĠاÙĦÙħ عÙĦÙĪÙħات\nç§ģ ãģŁãģ¡\nà¸Ĺีà¹Ī à¸Ħุà¸ĵ\nãģ«ãģª ãģ£ãģ¦ãģĦãĤĭ\n×ŀ×ĵ ×Ļ׳×Ķ\nס ׼×Ŀ\nĠв не\nà¸ŀ à¸Ļัà¸ģà¸ĩาà¸Ļ\nÑĢ ей\nà¹Ģà¸Īà¹īา หà¸Ļà¹īาà¸Ĺีà¹Ī\nĠHi á»ĩn\nĠméd ico\nĠتØŃ ÙĤÙĬÙĤ\nÑĮ ÑĤе\nmiÅŁ ti\nÙĤÙĬ ادة\nãĤı ãģĭãĤĬ\nมา à¸Īาà¸ģ\nëħ Ģ\nãģ«éĸ¢ ãģĻãĤĭ\n×IJר×Ĵ ×ķף\nm ètre\nĠעצ ×ŀ×Ļ\nĠCh úa\nรูà¹ī à¸Ī\nรูà¹īà¸Ī ัà¸ģ\nì£ Ħ\nëĭ µ\nà¹ģà¸Ĺ à¹ī\nĠgeç en\nĠlan ça\nĠاÙĦ بØŃØ«\n×ĵ ×ŀ×ķ\nãģ¯ ãģĺ\nãģ¯ãģĺ ãĤģ\nĠdön Ã¼ÅŁ\nè¿ij ãģı\nà¹Ģส ม\nà¹Ģสม à¸Ń\nëĿ ½\nĠü ç\ná» ŀ\nÑĪ аÑı\nà¸Ĺ ร\nØŃ ÙĤÙĬÙĤØ©\nà¸Ĥà¸Ńà¸ĩ à¸ģาร\nĠ무 ìĹĩ\nĠ×Ķ ׼ר\nĠاÙĦص ÙĬÙĨ\nĠлÑİ Ð´Ð¸\nà¸ķ 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×Ļ×ij\nĠÐļ аÑĢ\n׳×Ļת ×ķ×Ĺ\nĠק×ĺ ף\nר ×ĸ\nÙĪ غ\nèªŃ ãģ¿\nĠت ÙĤÙĪÙħ\nĠÙĥ اÙĦ\nà¸Ŀ ึà¸ģ\nĠë°ľ ìĥĿ\nológ ico\nر اع\nà¹ģà¸ģà¹ī à¹Ħà¸Ĥ\nĠÑĢабоÑĤ Ñĥ\nÙĨÙij Ùİ\nà¸Ńยูà¹Ī à¸Ĺีà¹Ī\nĠاÙĦØ« اÙĨÙĬØ©\nĠNh ân\nÑħ ваÑĤ\nö ne\nĠع دة\nà¹ģ สà¸ĩ\nÑĤ оп\nпÑĥÑģ ка\nشر اء\nĠÐļ ом\nĠפע ×ķ׾×Ķ\nìĤ¬ ìĿ´\nìĤ¬ìĿ´ íĬ¸\nè¡Į ãģ£ãģ¦\nĠ×Ķ ×Ķת\nĠÑģÑĤ оÑĢо\nĠÑģÑĤоÑĢо нÑĭ\nدر س\nà¸ĭ ู\nà¸ķà¹Ī ำ\nĠØ£ بÙĬ\nпод об\nãģ« ãģ¦\nار تÙģاع\nĠÙħ ؤ\nик ов\nge führt\nมืà¸Ń à¸ĸืà¸Ń\nĠÙĦ ÙĤد\nĠØ£ÙĨ Ùij\nسÙĬ طر\nãģ¾ãģļ ãģ¯\nס ×ĵ\nÑģк олÑĮко\nãģ¿ãģŁãģĦ ãģª\n×ĵר ×Ĵ\n×¢ ×Ļ×ĵ\nà¹ĥหà¹ī à¸ļริà¸ģาร\nĠÐĶ и\n×ij×¢ ×Ļ×ķת\nĠ×Ķ×Ĺ ×ķ\nпиÑģ ÑĮ\nĠاÙĦØ® ÙĦ\nб ав\nĠÄ° lk\nĠاÙĦØ® Ùħ\nĠاÙĦØ®Ùħ ÙĬس\nĠÙĬ ÙĤÙĪÙħ\næĻĤ ãģ®\nĠsÅĤ ow\nĠØ£ ÙĩÙħ\nØ®ÙĦ ÙĤ\nĠØ£ صبØŃ\nĠchứ a\nĠth ác\nÙģ اÙĦ\nĠch á»Ŀ\nĠاÙĦØ® ار\nĠاÙĦخار ج\nĠاÙĦخارج ÙĬØ©\nØ· ائر\nĠt Ãł\nĠtÃł u\nà¸ģล à¹īà¸Ńà¸ĩ\nĠاÙĦÙħر Ø£\nĠاÙĦÙħرأ Ø©\nåħ¨ ãģı\nĠÃĸ n\nçļĦ ãģ«ãģ¯\nĠpiè ce\n×Ĵ ×Ļ×ij\nĠاÙĦ ÙĪاÙĤع\nä»Ĭ ãģ®\nĠاÙĦÙħ ÙĤ\ncz nÄħ\nÙģع اÙĦ\nен ного\nĠÑĦак ÑĤ\nìĭł ì²Ń\nĠÐŀ ни\nĠاÙĦبÙĦ اد\nов иÑĩ\nëı Į\nÑĦ 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×Ļ×Ĺ\nà¸Ń à¹Īà¸Ńà¸Ļ\næĽ¸ ãģĦãģ¦\nĠng ữ\n×ij×Ļ×ĺ ×Ĺ\n×ij×Ļ×ĺ×Ĺ ×ķף\nĠs ẵ\nĠsẵ n\nì§Ģ ëıĦ\nĠпÑĢ еп\nĠпÑĢеп аÑĢаÑĤ\nĠна ÑĥÑĩ\nĠÃľ nivers\nĠÃľnivers ites\nĠÃľniversites i\nĠ×Ĵ×ĵ ×ķ׾×Ķ\nĠ×Ķ ×ł×ª\nĠ×Ķ×ł×ª ×ij×¢\nãģ§ãģĤ ãģ£ãģŁ\nĠmies iÄħ\nĠmiesiÄħ c\nг ÑĢам\nгÑĢам м\nĠبش Ø£ÙĨ\nĠÑħ ÑĢ\nק ×Ļ×ĵ\nק×Ļ×ĵ ×ķ×Ŀ\nØ´ Ùĥر\nĠ á»ķ\nĠá»ķ n\nãģĮãģĤ ãģ£ãģ¦\nãģķãĤĮ ãģ¾ãģĻ\nĠ×Ĺ ×ķ×ĵ\nĠ×Ĺ×ķ×ĵ ש×Ļ×Ŀ\nÙħÙĪا جÙĩ\nÙħÙĪاجÙĩ Ø©\nأش خاص\nب غ\nà¹Ģรียà¸Ļ รูà¹ī\nãģĹãģ¦ ãģĦãģı\nĠs ạn\nå¿ħ ãģļ\n׳ ×Ļ×Ĵ\n׳×Ļ×Ĵ ×ķ×ĵ\nباÙĦ غ\n׊ש×ŀ\n×Ĺש×ŀ ׾\nĠnap raw\nĠnapraw dÄĻ\nØ´Ùĩ اد\n×IJ ×ķ×Ķ\n×IJ×ķ×Ķ ×ij\nи ÑĨÑĭ\nĠ×Ķ ר׼×ij\nëŀ ij\nĠת ×¢\nĠ×Ķ ×Ļש\nĠ×Ķ×Ļש ר×IJ\nĠ×Ķ×Ļשר×IJ ׾×Ļ\nØ£ ÙħÙĨ\nÑİÑī аÑı\nsk ór\nLER Ä°\nĠ×Ķ×IJ×Ĺר ×ķף\n×¢ ׳ק\nĠÙĪ ÙĥÙĦ\nãģĵãģĵ ãģ§\nĠqu án\nliÄŁ in\nà¸ģà¸İ หมาย\nØ· Ùħ\nØ£ جÙĩ\nأجÙĩ زة\nĠEr doÄŁan\nãģ§ ãģĬ\nĠв ÑĢа\nĠвÑĢа Ñĩ\nĠPh ó\nà¸Ĭั à¹Īว\nà¸Ĭัà¹Īว à¹Ĥม\nà¸Ĭัà¹Īวà¹Ĥม à¸ĩ\nĠph úc\n×Ļפ ×ķת\n×¢×Ļ ×ķף\nĠduż o\nãĥģ ãĥ¼ãĥł\nĠÙĬ Ùİ\nĠзад аÑĩ\nĠ×Ĵ×ij×ķ×Ķ ×Ķ\nĠ׼ ׼׾\nлож ен\nét at\nĠng Äĥn\nèµ· ãģį\nĠTi ến\nص عب\nĠexperi 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imdi\n×Ļ×IJ ׾\nتأ Ø«ÙĬر\nØ£ سب\nأسب اب\nĠвÑĭполн ен\nл ок\nש ×Ļ×ij×Ķ\nĠl ắm\nĠTr Æ°á»Ľc\nĠ×Ķ×¢ ׾\n리 를\nĠÑĢ еж\nĠÑĢеж им\nint é\ninté gr\n×Ĵ ׳×Ļ\nĠاÙĦØ´ عر\nĠmil hões\nĠpeque ño\nãĤ³ ãĥ¼ãĤ¹\n×ķ׼ ×Ĺ\nà¹Ģà¸Ĭ à¹īา\nشر ÙĤ\nĠh Æ°Æ¡ng\nรัà¸IJ à¸ļาล\nà¸ģล าย\nà¸ģลาย à¹Ģà¸Ľà¹ĩà¸Ļ\nĠпод Ñħод\nתש ×ķ×ij×Ķ\nãģıãģª ãģ£ãģ¦\nĠاÙĦØ£Ùħ Ùħ\nĠH á»įc\nĠwspóÅĤ pr\nĠwspóÅĤpr ac\nÑĩ Ñĥв\nÑĩÑĥв ÑģÑĤв\nÃŃst ico\nà¹Ģà¸ģ าะ\nìĽ Ģ\nĠназ ад\nãĤĭ ãĤĪãģĨãģ«\nĠС Ш\nĠСШ ÐIJ\nм он\nĠAs ÃŃ\n×ķר ×Ĵ\nполн ен\n×ŀס ׾\n×ŀ×¡×ľ ×ķ׾\nà¹Ģลืà¸Ń à¸Ķ\nà¹Ģริà¹Īม à¸ķà¹īà¸Ļ\nĠاÙĦØ¥ Ùħ\nĠاÙĦØ¥Ùħ ارات\nצ×Ķ ר\nãĥ¡ãĥª ãĥĥãĥĪ\nĠпоÑĤ ом\nв из\nĠÙģ ترة\nå¾Į ãģ®\nÐĿ ÐIJ\n×ŀס ר\nÙĬر ÙĬ\npr é\nĠte ÅŁek\nĠteÅŁek kür\nĠöd eme\nد اÙĨ\nãģ¾ ãģĹãģ¦\n缮 ãģ«\nĠÑĤ еÑĩение\nl ard\nlard ır\nà¹Ģรา à¸Īะ\nס פ×Ļ\nĠÙĪÙĥ Ø°ÙĦÙĥ\nĠh át\nĠt á»Ļc\nà¸Ħุ ย\nĠb ức\nØŃ ÙĬÙĨ\nèģŀ ãģĦãģ¦\nÙħؤ شر\nĠNh Æ°\nĠмен ее\nละ à¸Ħร\nÑģ ин\nĠÑĢ ек\nĠÑĢек л\nĠÑĢекл ам\nĠÙģ ÙĩÙĪ\nĠ׾ ×ĸ\n×Ļ׳ ×ķת\nĠÅŁ art\nÑģÑĤав ка\nĠíı¬ íķ¨\nãģ«è¡Į ãģı\nï¼ Ŀ\nĠпозволÑı еÑĤ\nĠת×ķ׼ ׾×ķ\nов ал\nصÙĦ Ø©\nĠ׾ש ׳×ķת\nĠÐĺ гÑĢ\nÙħÙĨتج ات\nĠsat Ä±ÅŁ\nÑģ ко\nĠاÙĦØ«ÙĦاث اء\nĠ×Ķ×ĵ×ijר ×Ļ×Ŀ\nãģĹãģ¾ ãģĹãĤĩãģĨ\nبÙĤ Ùī\nåĬĽ ãĤĴ\nĠÃĩ ok\nãĥģ ãĥ¥\nà¹Ģà¸Ĭ ืà¹īà¸Ń\nยุ à¸Ħ\nศา ล\nĠק×ķ×ĵ ×Ŀ\n×ĸר ×Ļ×Ŀ\nãģ® åł´åIJĪ\nĠìķĬ ìķĺ\nãģĤãĤĬãģ¾ãģĻ ãģĮ\n×IJ שר\nè¡Į ãģı\nãģ» ãģĭ\næ°Ĺ ãģ«ãģªãĤĭ\nй деÑĤ\níķĺìĺĢ ëĭ¤\nستÙħر ار\nĠÐŁÑĢ е\nĠÑģ боÑĢ\nĠìķĦ 무\nç§ģ ãĤĤ\nع ص\nĠн иÑĩ\nĠниÑĩ его\nĠпÑĢи ем\nק ×ķ×ŀ\nĠìĪĺ ëıĦ\nĠì ¡´\nĠì¡´ ìŀ¬\nĠØ£ Ø«ÙĨ\nĠأثÙĨ اء\nĠÙĪاÙĦ ØŃ\nãģĮ ãģ§ãģįãĤĭ\nĠת ×Ķ\nĠת×Ķ ×Ļ×Ķ\nר ף\nĠÑģвÑıз и\n×Ĵ שת\nÑģп екÑĤ\nס ×ij×Ļ×ij\nס×ij×Ļ×ij ×Ķ\nĠíķĦìļĶ íķľ\nت خصص\nĠж ив\nĠжив оÑĤ\nĠMay ıs\nتع ا\nتعا ÙĪÙĨ\nĠعÙĨ Ùĩا\nów ki\nĠاÙĦÙģÙĦسطÙĬÙĨ ÙĬ\nãģłãģijãģ§ ãģªãģı\nìĿ¸ ì§Ģ\nĠاÙĦس ÙĪد\nĠاÙĦسÙĪد اÙĨ\nإجراء ات\nĠkö tü\nĠ×Ļ תר\n×Ĵ ×Ļש×Ķ\nĠצ ×ķר×ļ\nรà¸ĸ ย\nรà¸ĸย à¸Ļà¸ķà¹Į\nÑħ оÑĤ\nÐł ÐIJ\nÙĪ Ø·ÙĨ\nĠsay ısı\nס ×Ĺר\nÙħ ÙĪÙĦ\nãĤĴæĮģ ãģ£ãģ¦\nع اÙĨ\nĠt á»Ļi\nĠвÑĭ ÑĪе\nĠt ầm\nãĥĪ ãĥ¬\n×Ļצ ×ķ\nม ุม\nس ÙĪد\nìłĦ ìŀIJ\nãĤµ ãĥŃãĥ³\nìĤ° ìĹħ\nĠоÑģнов ан\nØ® Ùģض\nרצ ×Ķ\nبÙĬ ض\n×ķÖ ¹\nס×Ļ ×Ļ×¢\nĠש ×IJ×Ļ\nĠاÙĦÙĤر Ø¢ÙĨ\nĠТак же\n×ŀש ×ŀ×¢×ķת\nس ÙĩÙĦ\nĠ×Ķ ׳×Ķ\nãĤĴ ãģĹãģ¦ãģĦãĤĭ\n×Ļ ×Ļס\n×Ķ ×ķ×IJ\nĠB ÃŃ\nĠмал о\nĠëĶ°ëĿ¼ ìĦľ\nĠר ×Ĺ×ij\nãģĮ é«ĺãģĦ\nÙĪ اس\nìĤ ¼\n׳ ×¢\nãģ£ ãģ¡ãĤĥ\nĠT üm\nà¸Ńีà¸ģ à¸Ķà¹īวย\nãģĹãģ¦ ãģıãģłãģķãģĦ\nÙĨØ´ اط\nãĥĹ ãĥ©ãĥ³\nали ÑģÑĮ\n×ĵ ×ľ×ª\nĠwc zeÅĽ\nĠwczeÅĽ niej\nĠÑįÑĤ им\nĠthá»ĭ t\nà¸ļ ัà¸į\nà¸ļัà¸į à¸Ĭี\nãģļ ãģ£ãģ¨\nÑĢ ин\nĠswo jÄħ\níķĺëĬĶ ëį°\nĠë§Įëĵ¤ ìĸ´\nتش Ùĥ\nتشÙĥ ÙĬÙĦ\nائ Ùĩ\nĠ׾פ ×Ĺ×ķת\nãĥĭ ãĥ¥\nãĥĭãĥ¥ ãĥ¼ãĤ¹\n׼×IJ ף\nãģ§ãģį ãģŁ\nзв он\nĠsta ÅĤ\n×Ĺ×ijר ת×Ļ\nĠØ£ عÙĦÙĨ\nà¹ģà¸ļà¸ļ à¸Ļีà¹ī\nبد Ø¡\nãĤģ ãģŁ\nĠ×ŀש ×ŀ×¢×ķת\nĠ×ŀש×ŀ×¢×ķת ×Ļ\nör ü\nĠh ạnh\nz ähl\nĠL ý\nĠ×ij ×Ķת\nĠ×ij×Ķת ×IJ×Ŀ\nб аÑĢ\nì¦ Ī\nä»ĬåĽŀ ãģ®\nĠy ü\nĠyü ks\nĠyüks el\nãĤ½ ãĥ¼\nãģĤ ãĤĮ\nת ׾×ŀ×Ļ×ĵ\nãģ¤ ãģª\n×ij ׳×Ļ×Ŀ\nĠx ếp\nĠмÑĥж Ñĩин\nĠاÙĦÙĥ تاب\n׼ ×ŀ×ķת\nĠç e\nĠçe ÅŁ\nĠçeÅŁ it\nĠçeÅŁit li\n×ĵ ×Ļר×ķת\nà¸ļุ à¸į\nĠاÙĦØ¥ ÙĦÙĥ\nĠاÙĦØ¥ÙĦÙĥ ترÙĪ\nĠاÙĦØ¥ÙĦÙĥترÙĪ ÙĨÙĬ\nĠباÙĦØ¥ ض\nĠباÙĦإض اÙģØ©\nĠyö nel\nĠyönel ik\nmys ÅĤ\nà¸Ķà¹īวย à¸ģาร\nà¸ģาร à¸Ĺำ\nов Ñĭм\nØ£ زÙħØ©\næİ¢ ãģĹ\níļ ¨\nĠ×ķ×IJ ×Ŀ\nĠnghi êm\nÑĪ ин\nка л\nĠcrian ças\nèĩªåĪĨ 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ế\nت بع\nãģ«åħ¥ ãĤĮ\nÑİ ÑģÑĮ\níļĮ ìĿĺ\nç°¡ åį\nç°¡åį ĺ\nç°¡åįĺ ãģ«\nĠtr úc\nĠاÙĦÙĥ ÙĪÙĬ\nĠاÙĦÙĥÙĪÙĬ ت\nãĤıãģij ãģ§ãģĻ\nĠÑģв об\nĠÑģвоб од\nĠÑĥÑĩаÑģÑĤ ник\nสิ à¹īà¸Ļ\nĠпÑĢо ÑĦеÑģÑģиона\nĠпÑĢоÑĦеÑģÑģиона лÑĮн\nÑģп оÑĢ\n×Ĺ ×ķ×ij×Ķ\nÙħع ÙĨÙī\nĠاÙĦÙģ ترة\nสูà¸ĩ สุà¸Ķ\nãĤı ãģļ\nĠÄij è\nĠÄijè n\næ¯Ķ ãģ¹\nา à¸ĺิ\nĠmoż emy\nà¹ģ à¸ĭ\nà¸Īะ à¹Ħมà¹Ī\nĠs ắp\nÐļ Ðŀ\nĠprá ctica\nÙĪÙĥ اÙĦØ©\nè¾¼ ãĤĵãģ§\nológ ica\nĠе Ñī\nĠеÑī Ñij\nتع دÙĬÙĦ\nĠØ£ Ùĥد\nĠצר ×Ļ׼\nĠצר×Ļ׼ ×Ļ×Ŀ\nØ« Ùħ\nĠк ÑĢÑĥ\nĠкÑĢÑĥ п\n×ij×Ļק ×ķרת\nĠì¡° ê¸Ī\nãģ¨ãģį ãģ¯\nĠb ạc\nĠÑĢаÑģ пол\nĠÑĢаÑģпол ож\nĠÑĢаÑģполож ен\nز ÙĬÙĨ\nĠÐļ ÑĢоме\nĠاÙĦÙĨ ظر\n×Ķ ×ķ×ĵ\nĠاÙĦس بت\nãģ¨æĢĿ ãģĦ\nĠpa ÅĦst\nĠpaÅĦst w\nĠÙĦÙĬ ست\nĠбÑĥд Ñĥ\nà¸Ĺัà¸Ļ à¸Ĺี\nร าม\nØŃ ØµÙĪÙĦ\nãģĹãģ¦ãģıãĤĮ ãĤĭ\nĠاÙĦØ¥ سرائÙĬÙĦ\nĠاÙĦإسرائÙĬÙĦ ÙĬ\nãģĵãĤĮ ãģ¾ãģ§\nìĤ¬ 를\nĠs ürü\nà¹Ģว à¸Ńรà¹Į\nà¹Ģà¸ĭ à¸Ńรà¹Į\nĠutilis é\nĠÑģиÑģÑĤем а\nĠdw ó\nĠdwó ch\nĠpróp rio\nĠëĵ± ìĿĦ\narr êt\nĠЧ а\n×IJ×ŀ ׳×ķת\nعار ض\nà¹Ģà¸ģม สà¹Į\nĠ׾×Ķ ×ij×Ļף\nĠ׾ ×ij×Ĺ\nĠ׾×ij×Ĺ ×ķר\nสา à¸Ĥา\nĠÐľÐ¾Ñģк ве\nب عد\nĠاÙĦÙĤر ار\nĠÄIJ á»ĭa\nĠ×Ĺ ×Ĵ\nÙģ تر\nÙĪÙĨ Ø©\nĠ×Ķ×ĸ ×IJת\nå¸Ĥ ãģ®\nãģ» ãģĹãģĦ\nĠ×ij×¢ ×Ļר\nĠÑĤеп еÑĢÑĮ\nìĬµ ëĭĪê¹Į\nà¹Ħม à¹Īว\nà¹Ħมà¹Īว à¹Īา\nà¹Ħมà¹Īวà¹Īา à¸Īะ\n×ŀ ×IJ×Ķ\næĥħ åł±\næĥħåł± ãĤĴ\nغ ÙĨ\nĠпо Ñı\nĠпоÑı ви\néģİ ãģĶ\nتش غ\nتشغ ÙĬÙĦ\nв ел\nĠ×Ĺ ×ŀ\nãģ¨ãģªãĤĬ ãģ¾ãģĻ\nĠra ÄŁ\nĠraÄŁ men\nãģĭ ãģ©ãģĨ\nãģĭãģ©ãģĨ ãģĭ\nен ко\nì§Ģ ê³ł\nĠ×IJ׾ ×Ļ×Ķ\nĠØ£ ÙĦ\nà¸Īำ หà¸Ļ\nà¸Īำหà¸Ļ à¹Īาย\nnız ı\nĠ׾ק ×Ĺת\nØ£ ÙĩÙħ\nØ£ÙĩÙħ ÙĬØ©\nت غÙĬر\nש ×Ĺר\nס×ķפ ר\n×ĵ ×Ļר\nèī¯ ãģĭãģ£ãģŁ\n×ŀ׾×Ĺ ×ŀ×Ķ\nÑģÑĤв ие\nÑĤ ÑĢаÑĤ\nĠاÙĦØ£ Ø®\nĠاÙĦأخ ÙĬرة\nĠاÙĦØŃ ØµÙĪÙĦ\nĠcréd ito\nצ ×Ļ×¢\nãĥ¬ ãĥĻãĥ«\nبر ÙĬ\nëIJ IJ\nãģł ãģ£ãģ¦\nĠreal tÃł\nس Ùģر\n×ķ׳ ×ķ\n×Ĵ ×ķ×ĵ\n×Ĵ×ķ×ĵ ׾\nฮ า\nãģĹãģ¦ ãģĬãĤĬãģ¾ãģĻ\nĠg Ãł\nĠ׾×ij צע\nå¼ķ è¶ĬãģĹ\nĠ×ŀ ×Ļ׾×Ļ\nĠ×ŀ×Ļ׾×Ļ ×ķף\nÙħ در\nÙħدر سة\nפ ×ķ×ĺ\nà¸Ļà¹īำ มัà¸Ļ\nëģ Ŀ\nع Ùĥس\nĠÙĤ ض\nĠÑĢÑĭ б\nخط Ø·\n×ŀ×ķס ×ĵ\nĠ׼׾ ׾×Ļ\nĠкоÑĤоÑĢ ое\nצ×Ļ ×ķף\nĠмеÑģÑĤ а\nãģĭ ãģ¤\nг ÑĢÑĥпп\n׾ ×Ļ׾\nת ×ķ×IJר\në³µ ì§Ģ\nà¹ģà¸ľ à¹Īà¸Ļ\nĠ×ij×¢ ת\næĻĤéĸĵ ãĤĴ\nï¼ £\nãģ¨ãģĦãģĨãģĵãģ¨ ãģ§\nĠ׾×Ķ ק\nĠ׾ ×ĸ×Ķ\nĠìłĢ ëĬĶ\nĠاÙĦØ¥ رÙĩاب\nĠìŀĪëĬĶ ëį°\nĠÑĤ огда\nĠ×Ķ צ×Ļ\n×ķ׾ ×ĺ\nĠר פ×ķ×IJ×Ļ\nãģĵãģ¨ ãģ§ãģĻ\nĠÄij ÃŃch\nØŃ ÙĬا\nĠ×Ķ×ŀש ×Ĺק\nãģľ ãģ²\nĠ×ŀ×IJ פשר\nãģ¿ ãģ¾ãģĹãģŁ\nĠاÙĦØ£ÙħÙĬر ÙĥÙĬ\nÙħج تÙħع\nĠس اب\nĠساب ÙĤ\n׼ ×Ļ׾\nẠ¾\nãĥª ãĤ¹ãĥĪ\nĠì ĥ\nĠìĥ Ī\nĠìĥĪ ë¡ľ\nĠìĥĪë¡ľ ìļ´\nĠD á»ĭch\nà¹Ģหมาะ สม\nĠاÙĦÙĨ بÙĬ\n׾ ׾\nÙĨ ع\nÐĵ лав\nÐĵлав наÑı\nÙħر ض\nĠ×ķ ×ĵ\nت ÙĤÙĬ\nتÙĤÙĬ ÙĬÙħ\nĠb ảng\nĠÙģ ÙĤاÙĦ\n×¢ ×ŀ×Ļ\nд ÑĢа\nĠsu á»ijt\nسر عة\nĠc á»Ń\nĠ×Ķ ×Ļ×Ĺ×Ļ×ĵ\nسع ÙĬد\nà¸Ńา à¸Ĭีà¸ŀ\nĠس ÙĪاء\nãĤ½ ãĥķãĥĪ\nĠл иÑĩно\nĠÐļ оÑĢ\nاÙĩ تÙħ\nاÙĩتÙħ اÙħ\nà¸Ń à¸Ķี\nà¸Ńà¸Ķี à¸ķ\nãģIJ ãĤīãģĦ\nĠiht iya\nĠihtiya ç\nãģ¾ãģ§ ãģ®\nìĭľ ìĬ¤\nìĭľìĬ¤ íħľ\nÑĢÑĥ ÑĪ\nãĤĦ ãģ£ãģ±\nãĤĦãģ£ãģ± ãĤĬ\nк еÑĢ\nĠ ży\nĠży w\nкл он\nĠl ượt\nà ¾\nда Ñĩи\ntür k\nغ ÙĪ\nĠигÑĢ ок\nĠph ê\nĠש ×¢×ľ\nĠاÙĦÙħ دÙĨÙĬ\nĠìŬ룬 ë¶Ħ\nער ×Ļ×Ŀ\nÑħод ÑıÑĤ\nĠx ứ\nÐĹ Ð°\nĠÙģ رص\nà¸Īะ à¸Ĺำà¹ĥหà¹ī\níģ ´\n×¢ ×ij×ķר\nà¹Ģหลà¹Īา à¸Ļีà¹ī\nèĢĥãģĪ ãĤĭ\nÑĢ еÑģÑĤ\nн нÑĭй\nĠc ầm\nدا Ø®ÙĦ\nĠÙħÙĦÙĬ ار\nĠÐIJ л\nĠвÑĢем ен\nà¸Ĭà¹Īวย à¹ĥหà¹ī\nר×Ļ ×ķת\nëĵ ¯\n飲 ãģ¿\n׳ ׾\nשת ×£\nĠاÙĦسعÙĪد ÙĬ\nu ÃŁ\nìĿ¸ ëį°\nĠìĿ¼ ë°ĺ\nÅĤ ÄĻ\nĠm á»iji\n×ŀ ×Ļ׳\nĠاÙĦØ£ Ø·ÙģاÙĦ\nĠçı kan\né cole\nק ×Ļש\nק×Ļש ×ķר\nĠоÑģ ÑĥÑīеÑģÑĤв\nĠоÑģÑĥÑīеÑģÑĤв лÑı\n×ij ×IJר\nà¹Ħà¸Ľ à¸Ķà¹īวย\nĠ×¢ ×ķ׾×Ķ\nà¸ģà¹ĩ à¹Ħมà¹Ī\nãĥ¢ ãĥĩ\nãĥ¢ãĥĩ ãĥ«\nتØŃ ÙĪÙĦ\nĠод ного\nת×Ĺ×Ļ׾ ת\nĠت Ø®\nĠch cia\nĠchcia ÅĤ\nãĥIJ ãĥ³\nèĢħ ãģ¯\nĠÙħ ØŃÙĦ\nÑģл ож\nÑģлож н\nĠt ÄĻ\nĠçı kt\nĠçıkt ı\nĠC Æ¡\nà¹Ħà¸Ķà¹ī à¹Ģลย\nır ken\nà¹Ģà¸Ĥà¹īา สูà¹Ī\nÙħØŃ Ùĥ\nÙħØŃÙĥ ÙħØ©\nà¸Ħุ à¹īม\nà¸Ļà¹Īา à¸Īะ\nлÑİ Ð´\nде ÑģÑı\nдеÑģÑı ÑĤ\nĠлÑİб ой\nتØŃر ÙĬر\nצע ×ĵ\nĠе Ñij\nĠاÙĦØŃ ÙĥÙħ\nĠص باØŃ\nà¹Ģà¸ļ à¸Ńรà¹Į\nĠróż nych\nги б\nĠÑģ оÑĤ\nĠÑģоÑĤ ÑĢÑĥд\nĠÑģоÑĤÑĢÑĥд ник\nĠобÑĬ ем\nפ ×ĺר\nãģĻãģĶ ãģı\nãģ«éĸ¢ ãģĹãģ¦\nв ол\nØ« ÙħاÙĨ\nĠd ần\næĬ ľ\næĬľ ãģij\nĠ×¢ ש\nĠעש ×ķ×Ļ\nס ×ķף\nãģªãģ® ãģ§ãģĻ\nãģ¯ ãģ©ãģĨ\n×ŀ×¢ ר×ij\nï¼ °\nÙħ صر\nÙħÙĨ اسب\nÙħÙĨاسب Ø©\nä¸Ĭ ãģ®\n×IJ×Ļש ×ķר\nĠìĦ¤ ì¹ĺ\n×ŀ×ĵ×Ļ׳ ×ķת\n×ŀר ת\nãĤĭ ãģ®ãģĮ\nد Ùİ\nĠاÙĦشر Ùĥات\nìĭľ ê°Ħ\nĠÑĢеÑĪ ение\nãģĻãĤĭ ãģ®ãģ¯\nĠìŀIJìĭł ìĿĺ\n׾ ×ŀ×ķ\nãģ¨ãģĵãĤį ãģ§\nĠק צר\nĠmã i\nĠkü ltür\nãĥ©ãĤ¤ ãĥĸ\nà¸ľà¸¹à¹ī หà¸įิà¸ĩ\næĻĤéĸĵ ãģĮ\nклÑİÑĩ и\ndiÄŁ iniz\nมาà¸ģ à¹Ĩ\nتØŃ 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ı\nв ÑĪиÑħ\nãģĭãģ£ãģŁ ãģ§ãģĻ\nĠyapı yor\nĠاÙĦÙģ ت\nצר פת\nз доÑĢов\n×ij×¢ ׾\nĠ×IJ ×ŀ×Ļת×Ļ\nĠоб Ñĭ\nĠобÑĭ Ñĩ\nĠобÑĭÑĩ но\nĠ׾ ×ķ×ŀר\nت ÙĥÙĨ\nتÙĥÙĨ ÙĪÙĦÙĪج\nتÙĥÙĨÙĪÙĦÙĪج ÙĬا\nĠhakk ı\nĠÑĢаР²\nĠÑĢав но\nرÙĬ Ùĥ\nĠ×ij ×ŀ×Ļ×ĵ\nĠ×ij×ŀ×Ļ×ĵ ×Ķ\nà¹ģà¸ģ à¹īว\nĠìĸ ĺ\nĠìĸĺ 기\nãģĹãģ¦ ãģĦãģ¾ãģĹãģŁ\nĠkı sm\nĠkısm ı\nê± ¸\nåĨħ ãģ®\nì§ ķ\nà¹Ģหมืà¸Ńà¸Ļ à¸ģัà¸Ļ\nĠÙģ ÙIJ\nĠÙģÙIJ ÙĬ\nÙĤ اعدة\nĠmoż esz\nÙħ صاÙĦ\nÙħصاÙĦ ØŃ\nãģ¾ãģŁ ãģ¯\nб ег\nĠs ıc\nĠsıc ak\nÑĩ иÑģ\nÑĩиÑģ лен\nĠн ог\nãĥģãĥ£ ãĥ³\nãĥ« ãĥī\nĠgi ó\nĠs ını\nĠsını f\nив аÑĤÑĮ\nĠqu ên\nĠì łģ\nĠìłģ ìļ©\nĠJo ão\nÙģ اد\nĠGl ück\nà¸Ĺ à¸Ńà¸Ķ\nĠg ói\nï¼ Ĭ\nĠdé tail\nĠدÙĬ سÙħ\nĠدÙĬسÙħ بر\në¡ľ ìĦľ\n×ŀ ×ķ×Ĺ\nà¹Ħ ฮ\nĠоÑĤ д\nĠоÑĤд ÑĭÑħ\nĠkh uyến\nà¸Ħ à¸Ńย\nĠج ÙĨÙĬ\nĠجÙĨÙĬ Ùĩ\nĠاÙĦد Ùģاع\nà¸Ļà¹īำ หà¸Ļัà¸ģ\nĠìĤ¬ëŀĮ ëĵ¤ìĿ´\nĠth ừa\nĠÃ¶ÄŁrenc i\nĠпомоÑī и\nĠczÄĻ ÅĽÄĩ\nש ×ĺר\nĠN hi\nĠNhi á»ģu\n׳ צ×Ļ\nĠнаÑĪ ем\nĠkarÅŁÄ± laÅŁ\nĠ×Ķש ׳×Ļ×Ŀ\nĠÄIJ Æ°á»Ŀng\nĠtr ú\nĠÑĢазлиÑĩ нÑĭÑħ\nĠاÙĦØ´ Ùĩر\nĠ×ľ×¢ ×ķ׾×Ŀ\nØŃ Ø¬Ø±\nĠÄij á»ķ\nĠìĿĺ íķ´\nà¸ļ à¹Īà¸Ńย\nĠ×Ķ ×Ļ׾×ĵ\nãģ¨ãģª ãģ£ãģŁ\nĠ×Ĺ×ķ ×ķת\nĠש×Ļר×ķת ×Ļ\nÄħ cy\nس رÙĬ\nK Ä°\nפ ׳×ķ\nÑģÑĤÑĢÑĥк ÑĤÑĥÑĢ\nÑĤ ÑĢÑĥд\nĠ×Ķ קר\nĠ×Ķקר ×ķ×ij\nĠth áºŃm\nèģŀ ãģį\nÙĤÙĪ ÙĬ\nклÑİÑĩ ен\nÑĤе Ñħ\nÑĤеÑħ нолог\nè¡Į ãģ£ãģŁ\nĠ×ķ×IJ ×Ļף\nĠÅŁek lin\nĠÅŁeklin de\nr ô\nÑĢ ог\nĠнов Ñĭе\nĠס ×ij×Ļ×ij\nĠtecn ologÃŃa\nס ׼\n×¡×Ľ ×ķ×Ŀ\nĠÅŀ ub\nĠÅŀub at\nĠ×Ķ×ŀ ׾×IJ\nĠwy pos\nĠwypos aż\nãģ¯ ä½ķ\nãĤ¬ ãĥ³\nê° ĸ\nĠкак ие\nĠçocuk lar\nĠ׾צ ×ĵ\nĠkay ıt\nĠмеÑģÑĤ е\nÙħ دÙĬÙĨØ©\nĠ׼ ×Ĵ\nĠ׼×Ĵ ×ķף\nãģĹãģ¦ ãĤĭ\nĠÙħا ÙĬÙĪ\nãģ£ãģ¦ãģĹãģ¾ ãģ£ãģŁ\nĠпÑĢогÑĢамм Ñĭ\nà¹ģล à¸Ļà¸Ķà¹Į\nãĥ¯ ãĤ¤\nער ×ķ×¥\nÑģ ид\nĠB öyle\nĠì²ĺ ìĿĮ\nĠת פק×Ļ×ĵ\nĠTr ên\níĥ Ī\nĠÐłÐ¾ÑģÑģ ий\nĠÐłÐ¾ÑģÑģий Ñģкой\nĠs Ãłn\nĠrè gle\nĠyaklaÅŁ ık\nà¹Ģล ิà¸ģ\nĠد ائÙħ\nĠ×ķ ×Ĵ\nاب ر\nĠb è\nĠاÙĦ ÙĤدÙħ\nĠÑĢеÑĪ ениÑı\nhi ên\nÑĤи к\nÄ Ħ\nà¸ļรร ยาà¸ģ\nà¸ļรรยาà¸ģ าศ\nרצ ×ķף\nåĭķ ãģį\nĠGä ste\nĠ기 본\nĠÙĬ عرÙģ\nĠS á»Ń\ngÅĤ ÄĻb\nà¹Ģà¸Ń ส\n×IJ×ŀ ×Ļף\nĠп Ñĥнк\nĠпÑĥнк ÑĤ\nĠ×Ļ×ķ×ĵ ×¢×Ļ×Ŀ\nãĤ« ãĥ©ãĥ¼\nĠ×ijס ×ĵר\nĠbu á»ĵn\nй ÑĤ\nйÑĤ еÑģÑĮ\nãĤĴ æ±ĤãĤģ\nĠ×IJת ׼×Ŀ\nĠ모 르\nظ رÙĪÙģ\nÑĩ еÑģÑĤво\nìĸ´ ìĦľ\nĠод на\nĠkap ı\nĠëħ¸ 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ت\nĠ×ŀש פ\nĠ×ŀשפ ×ĺ×Ļ\nĠÙħ ÙĪاÙĤع\nè¦ ļ\nè¦ļ ãģĪ\n×ĵ ×Ļף\nà¹Ģรืà¹Īà¸Ńà¸ĩ ราว\nãģ¾ ãģĤ\nĠgh ế\nиÑĢÑĥ ÑİÑĤ\nà¸ģ ว\nà¸ģว à¹īาà¸ĩ\nĠпов еÑĢ\nĠповеÑĢ Ñħ\nĠповеÑĢÑħ ноÑģÑĤ\n׳ ×ĵר\nĠкон ÑĨе\nĠдолж на\nĠ×Ļש ×Ļר\nacaģı z\nìĹ Ķ\nĠn ÃŃvel\nĠö r\nĠör nek\nÙĥ Ùģ\nĠФедеÑĢ аÑĨии\nĠ구 ìĦ±\nหัว à¹ĥà¸Ī\nĠV áºŃy\nм ед\nмед и\nмеди ÑĨин\nмедиÑĨин Ñģк\nاز ÙĬ\n×Ĵ×ij ×ķ׾\nÑĦ ÑĢ\nĠzus ätzlich\nà¸ģ à¸ģ\nĠاÙĦاÙĤتصاد ÙĬØ©\nĠh è\nlu ÄŁun\nج Ùİ\nà¹Ħà¸Ł ลà¹Į\nÄIJ T\nãģĿãģ® ä»ĸ\nà¸Ĺิ à¹īà¸ĩ\nĠاÙĦØ£ ÙĪ\nر سÙħ\næ°Ĺ ãģ¥\nìĿ´ ë©°\nÑĮ ев\nص Ø·\nĠاÙĦاست Ø«\nĠاÙĦاستث Ùħار\nà¸Ńา à¸Ħาร\nĠÑĤоÑĩ но\nĠV ân\nà¸Ń ร\nà¸Ńร à¹Īà¸Ńย\nĠاÙĦس ÙĨØ©\nĠc Æ°á»Ľi\n×Ļ×Ķ ף\níį ¼\n話 ãģĹ\nâĹ ĭ\nĠìķĬ ìĿĢ\nãĥ¡ ãĥ¼ãĤ\nãĥ¡ãĥ¼ãĤ «\nãĥ¡ãĥ¼ãĤ« ãĥ¼\nĠÑĤеп ло\nå½¼ ãĤī\nĠÄ° z\nĠÄ°z mir\níĻ į\nĠr ượ\nĠrượ u\næĢĿãģĦ åĩº\nĠPh ạm\nĠchá u\nצ×Ļ ×ķת\nĠìĿ¼ 본\nìĤ¬ ëĬĶ\nĠÑģозд ан\nĠar acı\nĠ×¢ ר\nĠער ×Ļ׼×Ķ\nĠíķĺëĤĺëĭĺ ìĿĺ\ndzi ÅĤ\nà¸Ľà¸£à¸° à¸ĺาà¸Ļ\nĠser ÃŃa\nĠìŀĪ ëıĦë¡Ŀ\nدر ج\níķľëĭ¤ ëĬĶ\nà¸Ńา à¸Ĺ\nà¸Ńาà¸Ĺ ิà¸ķ\nà¸Ńาà¸Ĺิà¸ķ ยà¹Į\nÑĤелÑĮ нÑĭй\nĠØ® دÙħات\n×ŀ׳ ×ĺ\nĠl ược\nĠS Ãłi\nĠÙĪ اض\nĠÙĪاض ØŃ\nغ از\nĠdoÄŁ al\nĠ×ijש ×Ŀ\nĠд лин\nĠØ¥ طار\nĠ×ijס פר\nãĤĴ ä¸İ\nãĤĴä¸İ ãģĪ\nĠë²ķ ë¥ł\nĠÑĥ вели\nĠÑĥвели Ñĩи\nส à¹Ħà¸ķ\nสà¹Ħà¸ķ ลà¹Į\nà¹Ħ à¸ģล\n×ij׊ף\nĠìĿ´ íĽĦ\nĠm unic\nĠmunic ÃŃpio\nتÙħ Ø«ÙĦ\nĠÄij áo\nH ôtel\nĠl á»Ńa\nĠÄij ẳng\nÑĩ ки\nØ´ رÙĪ\nشرÙĪ Ø·\nĠìĿ´ 를\nÙĬ Ùĭا\n×ŀ׾ ×ļ\n×ŀ×Ķ ×Ļר×ķת\nĠобÑıз аÑĤелÑĮ\nĠобÑıзаÑĤелÑĮ но\né nergie\nĠmud ança\nĠm ụ\nĠmụ n\nĠn º\nĠاÙĦت عا\nĠاÙĦتعا ÙĪÙĨ\nĠاÙĦاجتÙħاع ÙĬØ©\nĠп лаÑģÑĤ\nĠëĵ± ìĿĺ\nãĥIJãĤ¤ ãĤ¯\nÙĩج ÙĪÙħ\nĠSa úde\nĠì¤ijìļĶ íķľ\nĠ×Ķצ ×Ļ×ij×ķר\nתק ף\nĠاÙĦعاÙĦÙħ ÙĬ\nĠболÑĮÑĪ ой\nĠÙĥ ÙĦÙħ\nĠÙĥÙĦÙħ Ø©\nãģ®ãģ§ãģ¯ãģªãģĦ ãģ§ãģĹãĤĩãģĨãģĭ\nĠÙħ باراة\nĠש×IJ ׳\nĠש×IJ׳ ×Ĺ׳×ķ\nãĤ¹ãĤ¿ ãĤ¤ãĥ«\nĠSa ÄŁ\nĠSaÄŁ lık\nĠh Æ°\n׳ ×Ĺ×Ķ\nĠ×ij קר×ij\nØ· عÙħ\nห ิà¸Ļ\nà¸Ĺุà¸ģ วัà¸Ļ\nà¸Ħรัà¹īà¸ĩ à¸Ĺีà¹Ī\nĠlÃł nh\nĠdonn é\nãģĽ ãģĦ\nجز ÙĬرة\nдоÑĢ ож\nì¼ ľ\nتÙĨظ ÙĬÙģ\nãĥģ ãĥ§\nĠald ıģı\nج اج\nĠÑĤ омÑĥ\nà¸Ľ ิ\nĠ×ijר שת\nãģıãģªãĤĬ ãģ¾ãģĻ\nĠпÑĢин ÑĨип\nĠ׊׾×ķ\nëı ¼\n×ķ×Ĵ ש\nس س\nà¸Ľ ู\nĠh ầu\næĦŁãģĺ ãĤĭ\nï¼ ´\nد ÙĪا\nĠÑģм ог\nscri ção\nĠth áºŃn\nĠר ×ķ×IJ×Ķ\nобÑĢаж ен\nĠاÙĦتج ارÙĬØ©\nØ· بÙĬع\njÄħc Äħ\níĸī ìľĦ\nĠнов Ñĭй\nĠ×ŀ ×Ĺ×ĵש\næĮ¯ ãĤĬ\ngu é\nĠ×IJ ×Ļר×ķ×¢\nĠ×IJ×Ļר×ķ×¢ ×Ļ×Ŀ\nĠاÙĦ Ø°Ùĩب\n×ĵ ×IJ\nت اÙĨ\nãģł ãģĹ\nà¸Ńั à¸ķรา\nà¹Ĥ à¸Ī\nبÙĦ اد\n×Ķ×Ļ ×Ļ׳×ķ\nĠÑģп е\nĠÑģпе ÑĨиалÑĮно\nĠÅĽwi ata\nãĤĵãģ§ãģĻ ãĤĪ\nشر ÙĥØ©\nĠpÅĤ yt\nĠsitu é\nĠ׼×IJ ׾×Ķ\nס ×ijר\nĠkaż d\nĠkażd ym\nãĤĴæĮģ ãģ¤\n׾×Ķ ׾\n׾×Ķ׾ ף\nĠwÅĤ as\nĠwÅĤas ne\nĠsaÄŁ lan\n×ŀ×¢ ׾×Ķ\nĠاÙĦا ÙĪÙĦ\nìĹIJìĦľ ëıĦ\n×IJ×Ļר ×ķפ×Ķ\nتÙĤ ÙĨÙĬØ©\nÙħ ائ\nÙħائ Ø©\nĠcompañ ÃŃa\nĠsü rek\nĠsürek li\nĠиÑģ кÑĥÑģ\nĠиÑģкÑĥÑģ ÑģÑĤв\nĠB ürger\nת ×Ĺר\nת×Ĺר ×ķת\nà¸ŀรà¹īà¸Ńม à¸ģัà¸ļ\nØ´ Ùħ\nà¸ĸืà¸Ń วà¹Īา\nè¾¼ ãĤĢ\nä¼ij ãģ¿\nĠاÙĦØ£ ب\nĠÑģÑĤоим оÑģÑĤÑĮ\nĠпÑĢав а\nmay ın\nห วย\nĠاÙĦØ· بÙĬعÙĬ\nà¸Ĺีà¹Ī à¸ŀัà¸ģ\nĠEst á\nÑĭва ÑİÑĤ\nب سÙĬ\nبسÙĬ Ø·\nĠ×ij×¢ ×ijר\nåı¯èĥ½ ãģ§ãģĻ\nĠ×ĵ ×ķ׾\nĠ×ĵ×ķ׾ ר\nÙĩ Ùİا\nвоÑĢ оÑĤ\nãģ¦ ãģĦãģ¾ãģĹãģŁ\nà¹Ĥà¸Ĺร ศ\nà¹Ĥà¸Ĺรศ ั\nà¹Ĥà¸Ĺรศั à¸ŀ\nà¹Ĥà¸Ĺรศัà¸ŀ à¸Ĺà¹Į\nĠק ׳\nĠاÙĦØ« ÙĨ\nĠاÙĦØ«ÙĨ ائÙĬØ©\nĠco ût\nà¸ķิà¸Ķ à¸ķัà¹īà¸ĩ\nĠö rg\nĠörg üt\nĠاÙĦØ® ÙĦÙĬ\nĠاÙĦØ®ÙĦÙĬ ج\nĠb á»įn\n×ķ׾×ķ×Ĵ ×Ļ\nëŀ ľ\nĠÐij олÑĮ\nĠÐijолÑĮ ÑĪ\n×Ĵ ×ijר×Ļ×Ŀ\nÙĤ ÙĬد\n×ij×Ļ×ĺ ×ķ×Ļ\næīĵ ãģ¡\nĠol muÅŁ\nf äh\nfäh ig\nล าà¸Ļ\nĠÙĤ طر\nש פ×Ķ\nèªŃ ãĤĵãģ§\nà¸Ĥ วา\nĠchi ếm\nãĤ¤ãĥ³ ãĤ¿\nãĤ¤ãĥ³ãĤ¿ ãĥ¼ãĥ\nãĤ¤ãĥ³ãĤ¿ãĥ¼ãĥ į\nãĤ¤ãĥ³ãĤ¿ãĥ¼ãĥį ãĥĥãĥĪ\nĠ׾ש×ŀ ×ķר\nĠت رÙĥ\nĠترÙĥ ÙĬا\nר ×ķ×ĺ\nãģ¨æĢĿ ãģĦãģ¾ãģĹãģŁ\nĠاÙĦت ÙĤ\nĠd Æ°\nãģ¦ãģıãĤĮ ãĤĭ\nãģĹãģŁ ãģĵãģ¨\nĠróż ne\nĠاÙĦØ· ÙģÙĦ\nĠPost é\nĠ×ŀש ×ķ×Ŀ\nÑį ÑĢ\nĠÑĢабоÑĤ аеÑĤ\nãĤ· ãĥª\nãĤ·ãĥª ãĥ¼ãĤº\nĠ×ij×Ķ ×Ĺ׾×ĺ\nק×Ķ ×Ļ׾×Ķ\nãĤ« ãĥ¡\nãĤ«ãĥ¡ ãĥ©\nï¼ ¯\nĠìĤ¬ ìĿ´\nĠk ì\nĠth Æ°á»Ľc\nض بط\nÙĤب ÙĪÙĦ\nåĪ¥ ãģ®\nĠparticul ière\nĠÑģво ем\nĠ×¢ סק\nĠעסק ×Ļ×Ŀ\n×ij×Ĺ ×Ļר×ķת\n×ij ×Ļ׳×ķ\nà¸ĭ à¸Ń\nĠ×¢ ×ķ×ijר\nãģłãģ£ãģŁ ãģ®ãģ§\nıld ıģı\nÙħ دار\nÙħدار س\n주 ìĭľ\nà¸Ńา ศ\nà¸Ńาศ ัย\nĠt ấm\nà¸ŀิ à¸Ī\nà¸ŀิà¸Ī าร\nà¸ŀิà¸Īาร à¸ĵา\nÑĤелÑĮ нÑĭе\nÑģк ÑĥÑİ\nÐľ Ðĺ\nà¹Ģà¸ģ า\nà¹Ģà¸ģา หล\nà¹Ģà¸ģาหล ี\n×ĵ ×Ĺ\nà¹Ģà¸Ĭ ิà¸ĩ\nĠد ÙĤÙĬÙĤØ©\níķĻ ìĥĿ\nĠש×IJ ׾×Ķ\nĠcontr ôle\nĠsit uação\nà¸Ĥà¸Ńà¸ĩ à¸ľà¸¹à¹ī\nÙĨ Ø·ÙĤ\nê³¼ íķĻ\nหลาย à¸Ħà¸Ļ\nĠn ắng\nÙĤ Ùı\nì¡° ê±´\nÑ ķ\nãĥĥ ãģ¨\n×ŀ ×Ļ׾×Ķ\nGr ün\n×Ļ ×Ļ×¢\n×Ļ×Ļ×¢ ×ķ×¥\n×ŀ׳ ׼\në ŃIJ\n×ŀ×¢ ×ŀ×ĵ\nสำ à¸Ļัà¸ģ\nج دد\nà¸Ħ ัà¸Ķ\nĠ×Ķ×ŀש פ\nĠ×Ķ×ŀשפ ×Ĺ×Ķ\n×ŀש ק׾\nÙĦ Ùı\nĠty tu\nĠtytu ÅĤ\nÑĪ ей\nĠìĿ¼ ë¶Ģ\nÑĪ ение\nĠph óng\nĠìĹŃ ìĤ¬\nãĤ« ãĥ³\nĠtú i\nĠÙĨ ÙĪÙģ\nĠÙĨÙĪÙģ Ùħبر\ngr ün\nĠاÙĦØ´ ÙħاÙĦ\nÅĽwi adc\nÅĽwiadc zenie\nער ×Ķ\nĠ×¢ ×ķ×ij\nĠ×¢×ķ×ij ×ĵ×Ļ×Ŀ\n×ĵ×ķ×Ĵ ×ŀ×IJ\nä»Ĭ ãģ¯\nĠv ão\nĠТ ем\nÑģ илÑĮ\nĠch ợ\nÙħ را\nÙħرا ÙĤب\nà¹Ħมà¹Ī รูà¹ī\nĠر ائع\n×IJ׳ ×Ĺ׳×ķ\nสà¹Īà¸ĩ à¹Ģสริม\nצ ×Ĺ\nĠìŀĪìĸ´ ìĦľ\nĠkur ulu\nĠkurulu ÅŁ\nĠÃĸ zellik\nĠÃĸzellik le\nĠת ×Ļק\nĠgh é\nĠspr zÄĻ\nĠsprzÄĻ t\nער ×ķת\nرا ØŃØ©\nãģ£ ãģį\nãģ£ãģį ãĤĬ\nĠìķĦ ëŀĺ\nstit uição\nĠдолж но\n×Ķ רש\n×Ķרש ×ŀ×Ķ\n×Ķ׾ ×ļ\nãģ¡ ãģª\nãģ¡ãģª ãģ¿\nãģ¡ãģªãģ¿ ãģ«\nפ ×Ĺ×ĵ\nĠاÙĦج ÙħÙĬع\n×ij×¢ ׾×Ļ\nĠtr ùng\nĠפ ת×Ĺ\n×ŀ׾×Ĺ ×ŀת\nãĥĨ ãĥ¼ãĥ\nãĥĨãĥ¼ãĥ ŀ\nÙħ تاب\nÙħتاب عة\nĠ모 ìĬµ\nÙĬ ص\nåIJĪ ãģĨ\nĠY ap\nĠYap ı\nĠÑģ казаÑĤÑĮ\nëª °\nà¸Ĺีà¹Ī สำà¸Ħัà¸į\nĠìĹĨ ìĬµëĭĪëĭ¤\nĠnh ắc\nĠülk eler\nĠмног ие\níķĺ ìħ¨\nมาà¸ģ à¸Ĺีà¹Īสุà¸Ķ\nà¸ģ à¹īา\nà¸ģà¹īา ว\nĠÄ° yi\nл еж\nлеж а\nãĤ¸ ãĥ§\nà¸Ĺั à¸ŀ\nا ÙĪر\nĠ×Ĺ×ijר ×Ļ\nĠ׾ ש×Ŀ\nì² «\nĠT á»Ń\n×ŀ ×ķ׳×Ļ\nÙĤ ÙĪد\nà¸ģระ à¹Ģà¸Ľ\nà¸ģระà¹Ģà¸Ľ à¹ĭ\nà¸ģระà¹Ģà¸Ľà¹ĭ า\nĠпÑĢоблем Ñĭ\nĠaç ıs\nĠaçıs ından\nĠ×Ķ×ŀ ׼\nĠÙħع ظÙħ\nÙĤÙĬ اس\nĠпÑĢод олж\nĠпÑĢодолж а\nĠver diÄŁi\nĠпÑĢед меÑĤ\nãģĦãģ¾ãģĻ ãģĮ\nĠëĶ° 른\nĠاÙĦ ÙĤÙĬاÙħ\nĠØ¥ÙĦÙĬ Ùĩا\nТ ÐIJ\nп оз\nãĤ· ãĥ¥\nä¸ĬãģĮ ãĤĬ\nà¹Ģà¸Ķิม à¸ŀัà¸Ļ\nà¸ģุ ล\nØŃر ÙĬØ©\nק×ij×ķצ ×ķת\në¯ ¿\nĠاÙĦÙħ ÙĨا\nĠاÙĦÙħÙĨا Ø·ÙĤ\nĠвÑĭп ол\nĠвÑĭпол нÑı\nãĥĭ ãĤ¢\nĠê²° êµŃ\n×Ĺ ×ķ×ŀ\n×Ĺ×ķ×ŀ ר×Ļ×Ŀ\nĠУкÑĢа инÑĭ\nห à¸Ńม\nר ×Ļס\nĠÑħоÑĤ ел\nĠобÑĢаз ованиÑı\nĠkh ẳng\nĠm Æ°a\nĠgör me\nĠgüç lü\nسع Ùī\nมัà¹Īà¸Ļ à¹ĥà¸Ī\níķĺ ê²łìĬµëĭĪëĭ¤\nĠпол Ñĥ\nĠfün f\nãģ¨æĢĿ ãģ£ãģ¦ãģĦãģ¾ãģĻ\nĠê·¸ê²ĥ ìĿĢ\nĠdÃ¼ÅŁÃ¼n ce\nìŀ ł\nĠH Æ°á»Ľng\nĠTi á»ĥu\nĠç ift\nãģij ãģ°\nà¸Īà¸Ļ à¸ĸึà¸ĩ\nà¸Ĺำ à¹Ħà¸Ķà¹ī\nĠìŀIJ ì²´\nĠd õ\nĠdõ i\nà¸Ī ัà¸Ļ\nà¸Īัà¸Ļ à¸Ĺ\nà¸Īัà¸Ļà¸Ĺ รà¹Į\nece ÄŁini\n׳×ķ×¢ ר\nغ ار\nĠاÙĦØ£ÙħرÙĬ ÙĥÙĬ\nداع Ø´\nĠбезопаÑģ ноÑģÑĤи\nĠб Ñİ\nĠбÑİ Ð´Ð¶\nĠбÑİдж еÑĤ\nãĥĬ ãĤ¤\nà¸ŀà¸ļ วà¹Īา\nda ÄŁ\n×IJ ×ķפף\níĹ Į\nãĥĢãĤ¤ ãĤ¨\nãĥĢãĤ¤ãĤ¨ ãĥĥãĥĪ\nĠëĮĢ íĨµ\nĠëĮĢíĨµ ëł¹\nD Ä°\nØ£ ØŃداث\nĠA ÄŁ\nĠAÄŁ ust\nĠAÄŁust os\nØŃÙĦ ÙĪÙĦ\nĠw ÅĽ\nĠwÅĽ ród\nĠÑģо оÑĤвеÑĤ\nĠÑģооÑĤвеÑĤ ÑģÑĤв\nĠÑģооÑĤвеÑĤÑģÑĤв ии\nĠLu áºŃt\nĠ׼׾ פ×Ļ\nĠв еÑī\nĠвеÑī еÑģÑĤв\nק ×Ļ×¥\nĠبÙĩ ذا\nعا Ø´\nà¹Ģà¸Ľà¹ĩà¸Ļ à¹Ģรืà¹Īà¸Ńà¸ĩ\nТ Ðķ\nĠ×ij×IJ ×Ļ׳×ĺר׳×ĺ\nس عد\nĠ×Ķ×ĺ ×Ļפ×ķ׾\nפ ×Ļס\nà¸ĩà¹Īาย à¹Ĩ\nĠGer ät\n׾ ×Ļ×ĵ×Ķ\nĠÑĢ иÑģк\n׾ק ×Ĺ\nн наÑı\nר ×Ļ×ĵ\nп ÑĢакÑĤи\nпÑĢакÑĤи к\nà¸Ĥัà¹īà¸Ļ à¸ķà¸Ńà¸Ļ\nà¸Ļà¹Īา รัà¸ģ\nlarınız ı\nà¸Ńà¸Ļุ à¸įา\nà¸Ńà¸Ļุà¸įา à¸ķ\nĠzdjÄĻ cia\nĠb ây\nÑģ ÑĢ\nÑģÑĢ оÑĩ\nãĥĭ ãĥ³ãĤ°\nĠö ner\nĠöner i\nĠнов ÑĭÑħ\nدع ÙĪØ©\nĠg ắn\nĠاÙĦÙĦ بÙĨ\nĠاÙĦÙĦبÙĨ اÙĨÙĬ\nãĥĨãĤ£ ãĥ¼\nĠص ØŃÙĬØŃ\nем ÑĭÑħ\nçĸ² ãĤĮ\nĠпÑĢо иÑģ\nĠпÑĢоиÑģ ÑħодиÑĤ\nส à¸ķิ\nĠT ết\nĠ×Ķ׾ ׾×ķ\nà¹Ģรืà¹Īà¸Ńà¸ĩ à¸Ļีà¹ī\n×ŀ×ij ׳×Ķ\nĠconte údo\nĠا خت\nĠاخت ÙĬار\nÙħ سÙĦ\nÙħسÙĦ سÙĦ\nëı Ī\nĠ׾ ×Ļ×ĵ\nà¸ŀิ à¸ĺี\nĠÑģов Ñģ\nĠÑģовÑģ ем\nãģĮãģĤãĤĬ ãģ¾ãģĹãģŁ\nĠsó ng\nØ¥ صÙĦاØŃ\në§ ģ\nÙģ ÙĬر\nĠJe żeli\nìłľ ëıĦ\nd ÅĤug\nìĥģ ìĿĦ\nĠc áºŃn\nĠhá»į p\nØ£ ست\nأست اذ\nĠ×ŀ ×Ļש×Ķ\nĠ×ŀ×Ļש×Ķ ×ķ\nĠd Ãły\nĠch Ãłng\nãģ¡ãĤĥãĤĵ ãģ¨\nĠÄij ám\nĠsw ój\nĠpoder á\nĠоÑĤлиÑĩ а\nĠpéri ode\nünd ig\n×ĺ×¢ ף\nÑģÑĤÑĢо иÑĤелÑĮ\nר 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ات\nĠÃľ ye\nliÄŁ inde\nà¸Ħะ à¹ģà¸Ļ\nà¸Ħะà¹ģà¸Ļ à¸Ļ\nãĤĭãģĵãģ¨ ãģ¯\nวิ à¹Ģà¸Ħร\nวิà¹Ģà¸Ħร าะ\nวิà¹Ģà¸Ħราะ หà¹Į\nĠвозмож ноÑģÑĤи\nĠاÙĦÙĨ ساء\nãĥīãĥ© ãĥŀ\nĠgü c\nĠgüc ü\nĠt Æ°á»Ŀng\nĠacomp aña\nãĤ¤ ãĥ©\nק צ×ij\nĠY ö\nĠYö net\nĠYönet im\nสัม à¸ľ\nà¸ªà¸±à¸¡à¸ľ ัส\nà¸Ļ าม\nĠÄij ợi\nà¹ģหà¹Īà¸ĩ à¸Ĭาà¸ķิ\nãģĿãĤĮ ãģ§ãĤĤ\nät ig\nת ×ķ×Ŀ\nĠbaÅŁ lat\nĠвÑģ ей\nת ×Ļק\nת×Ļק ×ķף\nĠNg ô\nĠGesch ä\nĠGeschä fts\nØ£ Ùħ\nØ£Ùħ راض\nà¹Ģà¸Ĺ à¸Ħà¸Ļ\nà¹Ģà¸Ĺà¸Ħà¸Ļ ิ\nà¹Ģà¸Ĺà¸Ħà¸Ļิ à¸Ħ\nĠм енÑĮ\nĠменÑĮ ÑĪе\nĠöl ç\nĠölç ü\nĠÙĬ جعÙĦ\nĠÄij ỡ\nש ×Ļ׾\nש×Ļ׾ ×ķ×ij\nĠGr Ã¶ÃŁe\nĠÙĩ اتÙģ\nรà¹īาà¸Ļ à¸Ńาหาร\n×Ķ׾ ×Ļ׼\n×Ķ׾×Ļ׼ ×Ļ\nиÑĢÑĥ ÑİÑī\nèĭ¥ ãģĦ\nĠÃĸ zel\nãģĦãģŁ ãĤī\nà¸Ħำ à¸ĸาม\nĠzosta ÅĤy\nĠ×Ķס ×Ļפ×ķר\n×Ķ ×ķ׾\n×Ķ×ķ׾ ×ļ\nà¹Ģà¸Ĭà¹Īà¸Ļ à¸ģัà¸Ļ\nà¹Ĥ à¸Ĩ\nà¹Ĥà¸Ĩ ษ\nà¹Ĥà¸Ĩษ à¸ĵา\n×IJר צ×ķת\n×Ĵר פ×Ļ\nĠao ût\nĠÙĬ رÙĬد\nت ÙĪج\nتÙĪج ÙĬÙĩ\nĠÑįÑĤ ап\nãĤ¹ãĤ¿ ãĥ³\nĠkr ó\nĠkró tk\nãĤĴ使 ãģĨ\nì ·¨\néĸ¢ ãĤı\nà¸Ķà¹īวย à¸Ħวาม\nà¸Ļำ à¹Ģสà¸Ļà¸Ń\nĠa yrıca\nà¸Ī à¹īาà¸ĩ\nĠÑĦоÑĤ огÑĢаÑĦ\nĠв еÑĩ\nĠвеÑĩ еÑĢ\nåĩº ãģĹãģŁ\nĠÐ¥ о\nĠ×ŀ ר×Ĵ×Ļש\nà¹ĥหà¹ī à¹Ģà¸Ľà¹ĩà¸Ļ\nãĤĴ 缮\nãĤĴ缮 æĮĩ\n׾ ×ŀ×Ļ×Ŀ\nnÄħ ÅĤ\nĠÑģÑĤ анд\nĠÑģÑĤанд аÑĢÑĤ\nĠSü d\nĠT âm\nاخت بار\nà¹Ģà¸ģ à¸Ńรà¹Į\nÙħس رØŃ\nĠbi á»ĩn\nب Ùı\nĠص اÙĦ\nĠصاÙĦ ØŃ\nĠPh ụ\níľ ´\nãĥ¬ãĥĵ ãĥ¥ãĥ¼\nĠbụ ng\nĠrég ime\nĠØ£ Ø´Ùĩر\nĠÑĢабоÑĤ ник\nà¸Ŀ ัà¸Ļ\nاع تÙħ\nاعتÙħ اد\nĠзам еÑĤ\nãģ¾ ãģ£ãģ¦\nĠch ặt\næĿ¥ ãĤĭ\nĠاÙĦÙĤ ÙĪات\nãģ«åħ¥ ãģ£ãģ¦\nتØŃ Ø§ÙĦÙģ\nÙħ زÙĬد\nĠÙĬ صÙĦ\nìĹ ¼\nà¹Ģà¸Ĭ à¹ĩ\nà¹Ģà¸Ĭà¹ĩ à¸Ħ\nĠk á»ĭ\nĠká»ĭ p\nĠìķĦ ì§ģ\n×IJ׳ ×Ĵ\nĠобла ÑģÑĤÑĮ\nĠpomoc Äħ\nĠ×ķ ש׾\nëĵł ì§Ģ\nĠGi ám\nĠSt ück\nĠchá y\nĠëĤĺ ìĺ¤\nש ×Ļ×ĺת\n×ŀ×ĵ ר\n×ŀ×ĵר ×Ļ×ļ\nĠsüre ç\nк ва\n×ij׾ ×Ļ×Ŀ\n×Ķ ת×Ļ\n×Ķת×Ļ ×Ļ×Ĺס\nÙĤب اÙĦ\nĠס ×ķ×Ĵ\nĠס×ķ×Ĵ ×Ļ\nÑģÑĤ олÑĮ\nä½ķ ãĤĤ\n×ĸ׼ ×ķר\nè²· ãģĨ\nå®ī ãģı\nà¸Ħรัà¹īà¸ĩ à¸Ļีà¹ī\nkö p\nĠÑģеÑĢ виÑģ\nоÑĩ нÑĭÑħ\nê±° ëŀĺ\nتأ Ùĥ\nتأÙĥ ÙĬد\n×ĵ ׾ק\nĠпо Ñĩем\nĠпоÑĩем Ñĥ\nпиÑģ аÑĤÑĮ\n×ij שר\nĠH Ãłng\nĠT ìm\nĠtr ừ\nãĤ» ãĥĥãĤ¯ãĤ¹\n×ķ׳ ×Ĵ\nmız da\nп Ñģи\nĠìŀĪ 기\nĠr út\nز اÙĨ\nتÙĨ ÙĪع\nÙħÙĤ ا\nÙħÙĤا ÙĪÙħØ©\nĠ׾צ ×ķר×ļ\nĠ×ij ×Ļר×ķש׾×Ļ×Ŀ\nãĥ´ ãĤ£\neb ile\nebile ceÄŁi\nãĥ¦ ãĥ¼ãĤ\nãĥ¦ãĥ¼ãĤ ¶\nãĥ¦ãĥ¼ãĤ¶ ãĥ¼\nãĤĴä½ľ ãĤĭ\nÑģ меÑĢ\nÑģмеÑĢ ÑĤ\nĠì§ ģ\nĠì§ģ ìłij\nĠÐŁ аÑĢ\nØŃ Ø§Ø¶\nØŃاض ر\nÙħ ÙĥاÙģ\nÙħÙĥاÙģ ØŃØ©\nล ิà¸Ļ\nãģ¦ ãģįãģ¦\nÑĢоÑģ л\nĠÄ°ÅŁ te\nÙĤص ÙĬر\nĠ×ij×Ĵ ×Ļ׾\nĠ×ŀת ×IJ×Ļ×Ŀ\nĠ×Ķ ×Ĺ×ĵ\nĠ×Ķ×Ĺ×ĵ ש×Ķ\nר ×ķ×¢\nĠprodukt ów\nĠÙħ صدر\nне ÑĨ\nĠاÙĦعÙħÙĦ ات\nĠçık ma\nĠد بÙĬ\nק ×Ļף\nת ×IJר\nת×IJר ×Ļ×ļ\n׳×Ļ ×Ļ×ĵ\nصر اع\nl ève\nצ ×Ļר\nà¸Ķ ัà¸Ļ\nà¹ĥหà¹ī à¹Ħà¸Ķà¹ī\nãĤ¿ãĤ¤ ãĥł\nĠgi ảng\nС ÐŁ\nĠاÙĦÙħ ØŃÙĦ\nĠاÙĦÙħØŃÙĦ ÙĬØ©\nĠT ất\n׾ ×ķ×ĺ\nh á»ķ\nĠam éric\nĠaméric ain\nĠ×ijש׾ ×ij\nĠ׾×IJ ×ķ×ŀ×Ļ\nĠpe ça\nĠÑĢаз нÑĭÑħ\nãģĦãĤĭ ãģ¨\nãĥĩ ãĥ³\nס קר\nĠ×Ķ×ŀ×Ĺ ×Ļר\nãģ¨ãģĦãģĨ ãĤĤãģ®\nرت بط\nĠиÑģÑĤ оÑĩ\nĠиÑģÑĤоÑĩ ник\nสมัà¸Ħร สมาà¸Ĭิà¸ģ\nĠ à¸Ĺัà¹īà¸ĩ\nĠà¸Ĺัà¹īà¸ĩ à¸Ļีà¹ī\nĠT áºŃp\nãģ£ãģ¦ ãģĦãģĨ\nĠاÙĦÙĪ صÙĪÙĦ\nĠdéc ada\nĠо ÑĦоÑĢм\nĠоÑĦоÑĢм лен\nสำหรัà¸ļ à¸ģาร\nĠog óln\nãģĨãģ¡ ãģ«\nĠvá rias\nãģĻãģİ ãĤĭ\nÙĪ Ùĩا\nà¹Ĥà¸Ľà¸£ à¸Ķ\nĠÐłÐ¾ÑģÑģ иÑı\n人 ãĢħ\nãģĹãģ¦ ãģįãģŁ\nĠsı rasında\nĠng ôn\nس ÙĨØ©\nتÙħ تع\n×ŀ׼ ×ij×Ļ\nĠnh ấn\n×¢ ×ŀ×Ļ×ĵ\ná» ¨\nж иÑĤÑĮ\nãĤī ãģĽ\ngr áf\ngráf ica\nĠÙĤ ÙĪÙĦ\nĠÙĤÙĪÙĦ Ùĩ\nëĭ¨ ì²´\nห à¹īา\nหà¹īา ม\n使 ãģ£ãģ¦\nת ×Ļ×ij\nת×Ļ×ij ת\ni á»ĥu\nà¹ģ à¸Ĭม\nà¹ģà¸Ĭม à¸Ľ\nà¹ģà¸Ĭà¸¡à¸Ľ à¹Į\nẠ¬\nĠëĤĺ ëĿ¼\nĠÙħباشر Ø©\nĠtr Äĥm\nسÙĥ ÙĪ\nĠاÙĦØ° Ùī\nĠbi ç\nĠbiç im\nت راجع\nĠоб еÑģп\nĠобеÑģп еÑĩ\nĠобеÑģпеÑĩ ива\nĠвозд ÑĥÑħ\nÑĭв аÑĤÑĮ\nÙĦ ØŃÙĤ\nĠMü dü\nĠMüdü rl\nĠMüdürl Ã¼ÄŁÃ¼\nĠyapt ır\nĠפר ס\nĠפרס ×ķ×Ŀ\nØ· ÙĪر\nÑģÑĤв оваÑĤÑĮ\nìŀ¥ ìĿĦ\nà¸Ĺีà¹Īà¸Ķี à¸Ĺีà¹Īสุà¸Ķ\nà¸Ńั ล\nÑĢ Ñİ\nÙħست ÙĤبÙĦ\nÑģл ÑĥÑĪ\nÑģлÑĥÑĪ а\nèªį ãĤģ\nĠ׾ ×Ļ×ŀ\nĠ׾×Ļ×ŀ ×ķ×ĵ×Ļ\nת ש×ķ×ij\nתש×ķ×ij ×ķת\nĠgerçekleÅŁtir il\nĠاÙĦ اتÙģاÙĤ\nĠÑĥÑĢов не\nĠÑĤ ÑĢав\nĠ×Ķ×ŀ ×ķף\nØŃÙģ اظ\nĠÙħ ÙIJ\nĠÙħÙIJ ÙĨ\nĠÙħÙIJÙĨ ÙĴ\nĠdem ás\n×ŀ×ķ×ĸ ×Ļק×Ķ\nש ×Ļ×Ĺ×Ķ\nĠb ú\nалÑĮ нÑĭм\nãĤı ãģŁ\nãĤıãģŁ ãģĹ\nĠاÙĦÙħÙĪ اد\nת ׼׳\n×ª×Ľ×ł ×ķף\nãĥŃ ãĥĥãĤ¯\nhi ếu\nĠÑĥ ме\nÙħØŃا ÙĪÙĦØ©\n×IJ ×ķשר\nĠкон кÑĥÑĢ\nĠконкÑĥÑĢ Ñģ\nĠ×ŀ ×ij×Ĺ\nĠ×ŀ×ij×Ĺ ×Ļ×ł×ª\nĠan lam\nĠanlam ı\nĠli á»ĩt\nĠв Ñħод\nĠH ình\nĠÙĨ ÙĬ\nĠÙĨÙĬ ÙĪز\nãĤ¸ãĥ£ ãĥ¼\n×ij ×Ļ×¥\nÑĤелÑĮ нÑĭÑħ\nà¸Ĺุà¸ģ à¸Ńยà¹Īาà¸ĩ\nĠkiÅŁ inin\nØ£ Ùĥثر\nĠиÑģÑĤоÑĢ ии\nĠë³Ģ íĻĶ\nפ׾ ס×ĺ\n×¤×ľ×¡×ĺ ×Ļ׳×Ļ\nĠÑģ еÑĤ\nĠÑģеÑĤ и\ndıģ ımız\níķĺ ëıĦë¡Ŀ\n×Ķ ר\n×Ķר ×ij×Ķ\nãģĻãĤĭãģĵãģ¨ ãģ¯\nĠphi ếu\nتØŃ Ø³ÙĬÙĨ\nĠÅĽ rod\nĠÅĽrod ow\nĠÅĽrodow isk\nĠÑĢаÑģ Ñħод\nبر ÙĬد\nĠر ÙĬ\nĠرÙĬ اÙĦ\nĠ×ķ ׼×ļ\nì§Ģ ìļĶ\n׼ ×ŀ×ķ\nĠ×¢×ľ ×Ļ×Ķ×Ŀ\nf ÃŃcio\nĠkar arı\ntıģ ını\nĠС ов\nĠСов еÑĤ\nãģĬéĩij ãĤĴ\nм еждÑĥ\nмеждÑĥ на\nмеждÑĥна ÑĢод\nмеждÑĥнаÑĢод н\nĠm á»Ŀi\nĠاÙĦØ¥ ÙĬر\nĠاÙĦØ¥ÙĬر اÙĨÙĬ\nĠاÙĦرÙĪ سÙĬ\nص ÙĨد\nصÙĨد ÙĪÙĤ\nĠاÙĦØ¥ÙĨ ترÙĨت\nĠt ắm\nĠÑĤак ого\nĠ×ij ׾×ķ×Ĵ\nĠü crets\nĠücrets iz\n×Ĺ×ĸ ×Ļר\nìĸ´ ìķ¼\nĠPh ần\nï¼ ľ\nĠ×ĺ ×ij×¢\nĠ×ĺ×ij×¢ ×Ļ\n×IJ×ŀ ×IJ\nاÙĤ ÙĦ\nĠcondi ções\nÙĤات ÙĦ\nĠÑĢезÑĥлÑĮÑĤаÑĤ е\nĠÑģво ими\nצ×ij ×Ļ×¢\ngé ni\nĠz es\nĠzes po\nĠzespo ÅĤ\nÑĪ ив\nĠפר×ĺ×Ļ ×ķת\nÙħست Ø´Ùģ\nÙħستشÙģ Ùī\nشر ع\nĠko ÅĽci\nĠ×Ķ×IJ ×Ļ׳×ĺר׳×ĺ\nĠЧ еÑĢ\nпоÑĩ ÑĤ\nĠactiv ités\nçŁ¥ ãģ£ãģ¦\nĠ×ij ×ĸ×Ķ\nĠyüz den\nãģªãĤĬ ãģ¾ãģĽãĤĵ\nĠíĺ ¹\nĠíĺ¹ ìĿĢ\nĠ×ŀש ׳×Ķ\nĠÐĴ еÑĢ\nĠ×ij×IJ×ķת ×ķ\néĿ¢ çĻ½\néĿ¢çĻ½ ãģĦ\nشر ØŃ\ngr ünde\nÙģ Ø´\nÙģØ´ ÙĦ\nĠsé jour\në´ IJ\nĠr ôle\nØ´ عار\nем Ñĭе\nĠاÙĦج سÙħ\nалÑĮ ное\nĠìĥģ íĥľ\nï¼ ¤\në¯Ģ ë¡ľ\nĠÙĨ ÙĤØ·\nĠÙĨÙĤØ· Ø©\nãģĿãģĨ ãģł\nãģĻãĤĭ ãģ®ãģĮ\nห ู\nĠnh á»ĭ\nĠeconóm 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zeÅĽ\nĠwrzeÅĽ nia\nĠÄIJ ầu\nà¹Ģà¸Ĥà¹īา รà¹Īวม\nnız da\nØ®ÙĬ ص\nĠgü nc\nĠgünc el\nĠÙĦÙĩ Ø°Ùĩ\nĠÙĬ عتبر\nlé gi\nãĤı ãģĭãĤĭ\nĠr ừng\nظ Ùĩ\nظÙĩ ÙĪر\nĠ×ŀ×ij ×Ļף\nĠ기 íĥĢ\nåĪĩ ãĤĮ\nlan mÄ±ÅŁ\nà¸Ĺีà¹Ī มีà¸Ħวาม\nĠh á»ģ\nت ÙĪجÙĩ\nĠاÙĦØ¥ دارة\nĠú til\nס פ×ķ\nà¸Ħวาม รัà¸ģ\nà¹Ĥ ฮ\nĠпол иÑĤ\nĠполиÑĤ ик\nĠsat ın\nĠÅŀ imdi\n×ŀ ×ķר×Ļ×Ŀ\nìķĺ ëĭ¤\n×Ĺ ×ķ×ķ\n×Ĺ×ķ×ķ ×Ļ×Ķ\nà¸Ħà¸Ńม à¸ŀิ\nà¸Ħà¸Ńมà¸ŀิ ว\nà¸Ħà¸Ńมà¸ŀิว à¹Ģà¸ķà¸Ńรà¹Į\nĠا ذا\nتخ اذ\nãĤ¨ ãĥ«\nĠpossibilit é\nยืà¸Ļ ยัà¸Ļ\nĠü nivers\nĠünivers ite\nĠاÙĦد ÙĪرÙĬ\nĠìķĬëĬĶ ëĭ¤\nĠìĦľ ë¡ľ\nØŃ Ø§ÙĦ\nĠë ¨\nĠë¨ ¼\nĠ먼 ìłĢ\nà¸Ĺีà¹Ī à¸ĸูà¸ģ\nì§ ľ\nĠsk óry\nлÑĮ ÑĨ\nà¹ĥà¸Ĭà¹ī à¹Ģวลา\n×ijק שת\nĠØ° ÙĪ\næĹ¥ ãĢħ\nĠкоÑĤоÑĢ ÑĥÑİ\nĠÑĥÑĢов енÑĮ\nê¹ ¨\nà¹Ħ à¸Ĺ\nãĤµ ãĥĹãĥª\nãĤ¸ ãĥ§ãĥ³\nãģĻ ãģ¹ãģį\nĠG ór\nãĥĪ ãĤ¤\nãĥĪãĤ¤ ãĥ¬\nĠyaÅŁ ama\nĠdá»ĭ p\nĠb ữa\nà¸ĭ ุ\nĠöl üm\nãģ£ãģ¦ ãģıãĤĭ\nà¸ģาร à¸Ħà¹īา\nש ער\nĠÑĤип а\nĠг еÑĢ\nĠгеÑĢ о\nרק ×¢\nĠu waż\nĠuważ a\nש×ŀ ף\nĠhast alık\nãĤıãĤĮ ãĤĭ\nba ÅŁÄ±\nÑĩ ÑĤо\nĠ×ij ×ŀר׼×ĸ\nĠìļ°ë¦¬ ìĿĺ\nĠÙĥاÙĨ ÙĪا\nĠØ£ بر\nĠأبر ÙĬÙĦ\nì¸ µ\nà¹Ħà¸Ĥ à¹Ī\nĠÙĪ 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ارة\nĠмног иÑħ\nобÑī а\nĠ×¢ סק×Ļ\nĠEdu cação\nק ש×Ļ×Ŀ\né tabl\nétabl issement\nĠд еле\nиÑĢÑĥ еÑĤÑģÑı\nØ¢ ثار\nĠ×Ķ×ŀ ר׼×ĸ×Ļ\nãĥIJ ãĥ«\nĠвÑģÑĤÑĢ еÑĩ\nãģĴ ãĤĭ\nĠci Äħ\nĠciÄħ gu\nÙĬ ست\nà¸łà¸² ว\nà¸łà¸²à¸§ ะ\nØ£ Ùħر\nĠо жи\nĠожи да\nĠ ủy\nãĥŀ ãĥ«\nر اس\nоÑĩ ной\nת ×Ĵ×ķ×ij×ķת\nتع رÙĬÙģ\nĠÑģо ÑĨиалÑĮно\nãĤĴ éĸĭ\nĠиÑģÑģлед ова\nĠd ú\nĠdú vida\nĠsk ÅĤ\nĠskÅĤ ada\nĠhä ufig\nĠвÑĭб ÑĢ\nĠвÑĭбÑĢ аÑĤÑĮ\nãģ®ãģ§ãģ¯ãģªãģĦ ãģĭ\nĠÑģ илÑĮно\nÑĤвеÑĢж ден\nר פ\nרפ ×ķ×IJ×Ķ\næĢĿ ãģĦãģ¾ãģĻ\nØŃر ص\nש×ķת ×£\nÙħس جد\nà¹Ĥà¸Ĭ วà¹Į\nем ÑģÑı\nв ÑĪие\nĠм л\nĠмл н\nĠ׾×Ķ ×ij×Ļ×IJ\nĠÙĬ تعÙĦÙĤ\nà¸ķ ูà¹ī\nĠп ÑĢаз\nĠпÑĢаз д\nĠпÑĢазд ник\nĠн ем\nĠнем ного\nĠs Ãłng\nتÙĨ سÙĬ\nتÙĨسÙĬ ÙĤ\nĠtá» Ŀ\nĠмед и\nãģ« æĪ\nãģ«æĪ »\nà¸Ħว à¹īา\nãģĭ ãģijãĤĭ\n×ij׾ ×ķת\nĠÑįк Ñģп\nĠÑįкÑģп еÑĢÑĤ\nĠдев ÑĥÑĪ\nĠдевÑĥÑĪ к\nĠØŃ Øµ\nÙĨØ´ Ø£\nãģĮãģĤãĤĭ ãģ®ãģ§\nĠت راÙħ\nĠتراÙħ ب\nأس ÙĪاÙĤ\nĠ׾פ ׳×ķת\nĠا ï»·\nãģ« ãģı\nãģ«ãģı ãģĦ\nĠØ£ عÙĦÙī\nĠ׾×Ķ ×ŀש×Ļ×ļ\nrä u\nש×ŀ ×Ļ×Ŀ\nåĪĨ ãģij\nãģĻ ãģ§\nãģĻãģ§ ãģ«\n×Ķ׾ ׼×Ķ\n×Ĺ׾ ×Ļ×£\nĠì ±ħ\nĠì±ħ ìŀĦ\nà¹Ģà¸Ī ริ\nà¹Ģà¸Īริ 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ãĥĭ\nĠdüzenle me\nĠmodal itÃł\nسر Ø·\nسرط اÙĨ\n×ŀ׼ ×ķף\nĠданнÑĭ й\nتر ت\nترت ÙĬب\nà¸ļาà¸ĩ à¸Ħà¸Ļ\nĠÄIJ á»ĭnh\nม ูล\nมูล à¸Ħà¹Īา\nÙĨ ÙĤص\nà¸ģาร รัà¸ģษา\nĠÑĦ он\nĠÑĦон д\nãĤĪãģĨ ãģ«ãģªãģ£ãģŁ\nÙħع اÙĦ\nÙħعاÙĦ جة\nĠOs man\nĠOsman lı\nиÑĩеÑģк ом\nà¸Ńยาà¸ģ à¸Īะ\nãģķãģ¾ ãģĸ\nãģķãģ¾ãģĸ ãģ¾\nãģķãģ¾ãģĸãģ¾ ãģª\nĠת ×ķ׼׾\n×¢ צ×ij\nĠاÙĦع سÙĥ\nĠاÙĦعسÙĥ رÙĬ\nĠvé hic\nĠvéhic ule\nĠ×Ļצ ×Ĺק\nĠاÙĦÙĪ ØŃ\nĠاÙĦÙĪØŃ ÙĬد\nĠاÙĦع دÙĪ\nĠQu ản\nĠê³µ ëıĻ\nبد ÙĦ\nĠÄij ảng\nĠm á»ĩnh\nĠnie zb\nĠniezb ÄĻ\nĠniezbÄĻ dn\nĠyayın lan\nобÑī и\nĠgö tür\nצ פ\nצפ ×ķ×Ļ\nĠÙĦÙĬ بÙĬ\nĠÙĦÙĬبÙĬ ا\nØŃ ÙĪا\nĠд об\nĠдоб ÑĢо\nиÑĢÑĥ ем\nĠاÙĦØŃÙĥÙĪÙħ ÙĬØ©\nm Ã¤ÃŁig\nĠed ición\nвлек аÑĤелÑĮ\nвлекаÑĤелÑĮ н\nĠת ש׾×ķ×Ŀ\nĠ×Ķש ×ķ׳×Ļ×Ŀ\nมิ à¸ĸุ\nมิà¸ĸุ à¸Ļ\nมิà¸ĸุà¸Ļ ายà¸Ļ\né£Łãģ¹ ãģ¦\nĠìĪĺ ì§ij\nס ×ij×Ļ\nĠиÑİ Ð»Ñı\nĠà¹Ħà¸Ķà¹ī à¹ģà¸ģà¹Ī\n׾×Ĺ ×Ŀ\ntr ä\nträ gt\nãģĿãĤĤ ãģĿãĤĤ\nÐĿ Ðķ\nĠв нÑĥÑĤ\nĠвнÑĥÑĤ ÑĢи\nãģ¨ ä¸Ģç·Ĵãģ«\nãĤ« ãĥķãĤ§\nĠ×ij×Ĺ ×ĵר\n×Ĺ ×ŀש\nãĤ¨ ãĥį\nãĤ¨ãĥį ãĥ«\nãĤ¨ãĥįãĥ« ãĤ®\nãĤ¨ãĥįãĥ«ãĤ® ãĥ¼\nà¸Ĥà¸Ńà¸ĩ à¸ķัวà¹Ģà¸Ńà¸ĩ\nبÙĤ اء\nפס ×Ļ׼\nפס×Ļ׼ ×ķ׾×ķ×Ĵ\nãĥ¡ ãĥĥ\nãĥ¡ãĥĥ ãĤ»\nãĥ¡ãĥĥãĤ» ãĥ¼ãĤ¸\nÙĦ ÙĤب\nA Äŀ\nשק ×Ļ×¢\nÙĤ ساÙħ\n×ĵ×ķ×Ĵ ×ŀ×Ķ\næ·± ãģĦ\níĸĪ ëĬĶëį°\nĠrozwiÄħz anie\nà¸Ļัà¹Īà¸Ļ à¹Ģà¸Ńà¸ĩ\n×Ļצ ×ij\nĠtr ông\nà¹ĥà¸Ĭà¹ī à¸ļริà¸ģาร\nĠاÙĦÙħÙĪ سÙħ\nĠдеÑĤ и\nãģĹãģĭ ãģªãģĦ\nס ×Ļף\nĠréfé rence\nà¹ģห à¹īà¸ĩ\nãĤĤãĤī ãģ£ãģŁ\nĠ׾ ר׼\nĠ׾ר׼ ×ķש\nشع ÙĪر\nĠÐij ог\nĠlaz ım\nĠ×Ļש ׳×Ŀ\nĠп аÑĢÑĤ\nĠпаÑĢÑĤ неÑĢ\nĠÑĥ ника\nĠÑĥника лÑĮн\nĠmaté riel\n×ŀר ק\nĠph Æ°á»Ŀng\nĠз ай\nĠзай м\nÙģ ÙĤد\nUnivers itÃł\n×¢ ר׼×Ļ×Ŀ\nĠba ño\nĠн оÑı\nĠноÑı бÑĢÑı\nà¸Ľ à¹īาย\nĠt ats\nĠtats äch\nĠtatsäch lich\nĠÑĤÑĢ еÑĤÑĮ\nÑį м\nãĥĻ ãĥ¼ãĤ¹\nĠnh á»±a\nìĬ¤ íģ¬\nĠعبداÙĦ ÙĦÙĩ\nĠת ×ķר×Ķ\nأش ÙĬ\nأشÙĬ اء\nĠÙĦÙĦ غا\nĠÙĦÙĦغا ÙĬØ©\nÙħ ÙĪاÙĤ\nÙħÙĪاÙĤ Ùģ\nĠgÅĤówn a\nĠart Ä±ÅŁ\nĠ×ŀק ×ķ×ŀ×Ļ\nãĤ¯ãĥ© ãĥĸ\nĠس ÙĪÙī\nĠìŬ ìĦ±\nاس ر\nاسر ائÙĬÙĦ\nĠ׳ ×Ľ×ª×ij\nย à¹īà¸Ńà¸Ļ\nĠdeber á\nĠph ẫu\nÑİÑī ем\nĠÙĦدÙĬ ÙĨا\n×ŀ×ĺ ×Ķ\nĠ׳ ×ķ׾×ĵ\nĠвÑģÑĤÑĢ еÑĩа\nãĤīãĤĮ ãģ¦ãģĦãģ¾ãģĻ\nĠcaÅĤ ej\nย ึ\nยึ à¸Ķ\nпоÑĤ ен\nпоÑĤен ÑĨи\nĠл иÑĤ\nĠлиÑĤ еÑĢ\nĠлиÑĤеÑĢ аÑĤÑĥÑĢ\nĠкажд ом\nĠíĮ IJ\nĠíĮIJ ëĭ¨\nà¸Ī ู\nĠpres ença\nãģªãĤĵ ãģ§\nÙħ ÙĬاÙĩ\nин ÑĦоÑĢм\nинÑĦоÑĢм аÑĨион\nинÑĦоÑĢмаÑĨион н\nĠìŀIJ ìĹ°\nר׼ ש\nĠöd ül\nç¶ļ ãģı\nĠп Ñģ\nĠпÑģ иÑħ\nĠпÑģиÑħ олог\nت Ø°Ùĥر\nĠìŀħ ìŀ¥\nล à¸Ķà¹Į\nìĦł ê±°\nãģ£ãģ¦ ãģĬãĤĬãģ¾ãģĻ\nĠ×Ļ ×¢\nĠ×Ļ×¢ ק×ij\nĠاÙĦØ· عاÙħ\nãĥĨ ãĤ¹ãĥĪ\nĠTu ấn\nĠparticip ación\n×ŀ×ķ×ŀ ×Ĺ×Ķ\n×Ĵר ס×Ķ\nĠاÙĦتÙĨ ÙģÙĬ\nĠاÙĦتÙĨÙģÙĬ Ø°ÙĬ\nĠбезопаÑģ н\nge f\ngef ähr\nØ´ ÙĪر\nĠmy ÅĽli\nÙĪا Ø´ÙĨ\nÙĪاشÙĨ Ø·ÙĨ\n׳×ķס ×¢\nÙĥ Ùĩ\nÙĥÙĩ رب\nÙĥÙĩرب اء\nĠmus iaÅĤ\nìĭ ¸\nãĥĸãĥ© ãĥĥãĤ¯\nĠcré é\nÙĨÙĩ ار\nowo ÅĽÄĩ\nÙħØŃا ÙĥÙħ\nĠwÅĤa ÅĽ\nĠwÅĤaÅĽ c\nĠwÅĤaÅĽc iciel\nĠÙĬ ؤ\nĠÙĬؤ دÙĬ\n×ŀ×¢ ×ķ׳\n×IJ ×ij׾\nخط Ø£\nĠÑħ олод\n×ĸ ×ķ׾\nãģĵãĤĮ ãĤī\nãģĵãĤĮãĤī ãģ®\nĠbás ica\nฤ à¸Ķ\nฤà¸Ķ ูà¸ģ\nฤà¸Ķูà¸ģ า\nฤà¸Ķูà¸ģา ล\nèIJ½ãģ¡ çĿĢ\nãģªãģĦ ãģĵãģ¨\nص ÙĪÙħ\nÙĨج ØŃ\n׳ק ×ķ×ĵ\n׳ק×ķ×ĵ ת\nкл аÑģÑģ\níķĺìĭľ ëĬĶ\nëĦ ĺ\nĠש×IJ ×Ļ׳×ķ\nĠС ейÑĩаÑģ\nmay acaģı\nĠyap ılır\nĠcategor ÃŃa\nعب اد\nĠТ еп\nĠТеп еÑĢÑĮ\n×Ķ×Ļס×ĺ ×ķר×Ļ\nh ế\nãĤ³ ãĥ¼ãĥī\nĠcabe ça\nج Ùħا\nجÙħا Ùĩ\nجÙħاÙĩ ÙĬر\nä½İ ãģĦ\nĠÑĤоваÑĢ ов\nà¸Ĭาว à¸ļà¹īาà¸Ļ\nĠÑģÑĤан ов\nĠÑģÑĤанов иÑĤÑģÑı\nĠавÑĤом обилÑĮ\nĠÑģлÑĥÑĩ ай\nà¸Ńั à¸ŀ\nĠG iriÅŁ\nĠìĿ¼ ëĭ¨\nĠпÑĢ оÑģ\nĠпÑĢоÑģ моÑĤÑĢ\nãģªãģıãģª ãģ£ãģŁ\nมี à¸Ľà¸±à¸įหา\nïº İ\néc oute\nĠÙħ ÙĪجÙĪد\nĠس رÙĬع\nĠÙĪÙĩ ÙĨا\nĠÙĪÙĩÙĨا Ùĥ\nà¸Ħุà¸ĵ สม\nà¸Ħุà¸ĵสม à¸ļัà¸ķิ\nĠìļ° ìĦł\nà¸ŀระ à¸ŀุà¸Ĺà¸ĺ\n好 ãģ¿\nظ ÙĦÙħ\nĠм акÑģ\nĠмакÑģ ималÑĮ\nĠмакÑģималÑĮ но\nãĥª ãĤ¢ãĥ«\nà¹ģมà¹ī วà¹Īา\nĠاÙĦØŃ ÙĪار\nãĥĹãĥ© ãĤ¹\nĠع ÙĦاÙĤØ©\nĠíĸī ëıĻ\nĠgönder il\nĠl ãi\nĠsaÄŁ lıkl\nĠsaÄŁlıkl ı\nĠÑĪ аг\nĠ×ij×IJר ×Ķ\nprowadzi Äĩ\nãģĦãģı ãģ¤ãģĭ\nĠبت ارÙĬØ®\nĠ×ij×IJ×ķת ×Ķ\nĠmó c\nĠÐľ не\nãĥĹãĥ¬ ãĥ¼\n×IJ ×ĸר×Ĺ\nåł´åIJĪ ãģ«ãģ¯\n使 ãģĪ\nà¹Ģร ืà¸Ńà¸Ļ\nĠÐŁ еÑĤ\nĠÐŁÐµÑĤ ÑĢ\nãģ«åħ¥ ãĤĭ\nÙħ ادة\nà¹Ģà¸ĩ ืà¹Īà¸Ńà¸Ļ\nà¹Ģà¸ĩืà¹Īà¸Ńà¸Ļ à¹Ħà¸Ĥ\nĠÑģоÑģÑĤоÑı ние\nôn ica\nĠÑĦ ев\nĠÑĦев ÑĢа\nĠÑĦевÑĢа лÑı\nĠ×ķ ×ĸ\nĠ×ķ×ĸ ×IJת\nà¸Ħร ิ\nà¸Ħริ ส\nĠÐķ Ñīе\nãģ£ãģ¦ãģĹãģ¾ ãģĦãģ¾ãģĹãģŁ\nĠпÑĢав иÑĤелÑĮ\nĠпÑĢавиÑĤелÑĮ ÑģÑĤв\nĠtä glich\nĠëĭ¹ ìĭľ\n×ŀ×ķ×¢ ×ŀ×ĵ\nĠдв оÑĢ\næī ķ\næīķ ãģĦ\nĠÑģÑĤан еÑĤ\nĠвозд ейÑģÑĤв\nĠвоздейÑģÑĤв и\nĠf ête\nà¹Ģส า\nתק ×ķ×ķ×Ķ\nĠu yar\nĠuyar ı\nà¸ģลัà¸ļ à¹Ħà¸Ľ\nĠgi Æ°á»Ŀng\nĠв а\nĠва ÑĪи\nĠÄij áºŃu\nĠSpa ÃŁ\nĠìķĦ ë§Ī\nà¹Ħà¸Ķà¹ī à¸ĩà¹Īาย\nĠ×Ķ×ŀ ×ijקש\næĸ° ãģŁ\næĸ°ãģŁ ãģª\nılı yor\nпл ан\nĠ×Ķ×ijר ×Ļ×IJ×ķת\nĠaÄŁ rı\nĠsay gı\n建 ãģ¦\nĠnaj wyż\nĠnajwyż sz\nسÙĬاس ات\nãģĬ å¾Ĺ\nĠاÙĦع ÙĦÙĬ\nĠاÙĦعÙĦÙĬ ا\nĠcoraz ón\nì¹ĺ ë£Į\nหัว à¸Ĥà¹īà¸Ń\nĠب ØŃÙĬ\nĠبØŃÙĬ Ø«\nзв езд\nبÙĪ ابة\nÐĽ Ðĺ\nÙĦا زÙħ\nĠroz p\nĠrozp oc\nĠrozpoc zÄĻ\n触 ãĤĮ\nĠاÙĦج ÙħÙĩ\nĠاÙĦجÙħÙĩ ÙĪر\nĠsp ÄĻd\nĠspÄĻd z\nวิà¸Ĺยา ศาสà¸ķรà¹Į\nив аеÑĤÑģÑı\nĠдан ной\nĠreprés ente\nĠÄij á»ĭch\nĠ×¢×ŀ ×ķק\nà¸Ńัà¸Ļ à¸ķร\nà¸Ńัà¸Ļà¸ķร าย\nĠestr atég\nĠestratég ia\npad ÅĤ\nĠв полн\nĠвполн е\nĠпÑĢедоÑģÑĤав лен\n×Ĺ׾ ×ķק\n×Ĺ׾×ķק ת\nãĤ¢ ãĥĬ\nĠاÙĦغ Ø°\nĠاÙĦغذ ائÙĬ\nĠÑĥ зн\nĠÑĥзн аÑĤÑĮ\nà¸ĭ à¹īาย\nå½ĵ ãģ¦\nØŃÙĬ اء\nĠbás ico\nק×ķ×ij ×¢\nĠاÙĦÙħ باراة\nĠاÙĦÙĩ اتÙģ\nĠ׼ ׳×Ĵ×ĵ\nà¸Ľà¸£à¸° หย\nà¸Ľà¸£à¸°à¸«à¸¢ ัà¸Ķ\nÐļ ак\nà¸Ĺีà¹Ī à¸Ļà¹Īา\nà¸Ĺีà¹Īà¸Ļà¹Īา สà¸Ļà¹ĥà¸Ī\nãģ¾ ãģģ\nï½ ¢\nÑģк оп\nĠson rasında\nĠur zÄħd\nĠurzÄħd zenia\n׼×ķ ×ķ׳\n׼×ķ×ķ׳ ת\nĠ׾×Ķת ×ŀ×ķ×ĵ\nĠ׾×Ķת×ŀ×ķ×ĵ ×ĵ\nĠÑģ ли\nĠÑģли ÑĪ\nĠÑģлиÑĪ ком\nĠÑģÑĤ Ñĥд\nĠÑģÑĤÑĥд енÑĤ\nĠ×Ķ ×ķ×ĵ\nĠ×Ķ×ķ×ĵ ×¢×Ķ\në¹Ħ ìļ©\nà¸Ńยาà¸ģ à¹ĥหà¹ī\nĠb á»ģ\nยุ à¸Ĺà¸ĺ\nÐĺ ÐĿ\nس ائر\nØ£ صÙĪÙĦ\nĠاÙĦغ رÙģ\nãģĵãģ¨ãĤĤ ãģĤãĤĬãģ¾ãģĻ\nè¾¼ ãģ¾ãĤĮ\nĠاÙĦساب ع\nĠc ủ\nãģĦãģŁãģł ãģĦãģŁ\nì§ ĵ\nìĤ¬ 무\npowied ź\nتÙģ Ùĥ\nتÙģÙĥ ÙĬر\nиÑĢов ки\nĠíĨµ íķ´ìĦľ\nãĤ¨ ãĤ¹ãĥĨ\nĠдеÑıÑĤелÑĮ ноÑģÑĤÑĮ\nĠданнÑĭ м\nĠ×¢ ×ķר\nĠ×¢×ķר ׼×Ļ\n×ķ×ĵ עת\nĠhayat ını\nĠb Äħd\nĠbÄħd ź\nobs ÅĤug\nà¹Ģà¸ŀียà¸ĩ à¹ģà¸Ħà¹Ī\nà¸ĭ à¹Īา\nè²ł ãģij\nĠÑģÑĤÑĢ ем\nĠÄij á»īnh\nĠÐł ÑĥÑģ\nĠN ữ\nĠ׾×Ķש ×Ļ×Ĵ\nĠjed noc\nĠjednoc ze\nĠjednocze ÅĽnie\nĠ×Ķ×Ĵ ×ij×ķ×Ķ\nأخ ÙĦاÙĤ\nĠнаÑģ ел\nĠнаÑģел ениÑı\nĠÙĬ ÙĨب\nĠÙĬÙĨب غÙĬ\nãģĮ ãģĭ\nãģĮãģĭ ãģĭ\n×Ĵ עת\nÐŀ Ðł\nĠналиÑĩ ии\nĠë§Ī ì§Ģ\nĠë§Īì§Ģ ë§ī\nĠíĸī ìĤ¬\nĠtre ÅĽci\nĠê°Ģ ì¹ĺ\nì¦ ĺ\nĠана лог\n×Ķצע ת\nв лад\nвлад е\nĠÑģдел ал\nĠ׳ ×Ĵ×Ļש\nĠ׳×Ĵ×Ļש ×ķת\nполн ение\nà¸Ĩ à¹Īา\nĠD ön\n׼׾׼ ׾×Ķ\n×ŀ×ĸ ×Ĵ\nÙħ Ùģ\nÙħÙģ Ùĩ\nÙħÙģÙĩ ÙĪÙħ\n×Ķ ×ĵ\n×Ķ×ĵ פס\n×Ķ×ĵפס ×Ķ\nãģĻãģİ ãģ¦\nĠг ÑĢ\nĠгÑĢ н\n×ŀ×ĺ ×ķס\nĠ기 ìĸµ\nï¾ Ł\nĠpÅĤ yn\nĠGr ünde\nĠBü cher\nĠwed ÅĤug\nãģ¾ãģł ãģ¾ãģł\nĠ׳×Ķ ×ĵר\nĠÙĬست Ø·ÙĬع\nĠHi á»ĩp\nãĤŃãĥ£ãĥ³ ãĥļ\nãĤŃãĥ£ãĥ³ãĥļ ãĥ¼ãĥ³\nĠth á»ķ\nĠeuropé enne\nà¸ļ ัà¸ĩ\nà¸ļัà¸ĩ à¸Ħัà¸ļ\nĠszczegóÅĤ owo\n׳ שק\nãĥķ ãĥ©ãĥ³ãĤ¹\n×ŀ×ķ×ŀ ×Ĺ×Ļ\nĠcom ún\nĠç arp\nØŃت ÙĬا\nØŃتÙĬا ج\nØŃتÙĬاج ات\nëĭ´ ëĭ¹\nä½ķ 度\nä½ķ度 ãĤĤ\n×ĵ ×ijק\nãģį ãĤĮ\nãģįãĤĮ ãģĦ\nĠк ам\nĠкам еÑĢ\nĠespecÃŃf ico\nĠtel éfono\nà¸ķัà¹īà¸ĩ à¸Ńยูà¹Ī\nI Åŀ\nãģ© ãĤĵãģ©\nãģ©ãĤĵãģ© ãĤĵ\nעצ ×ŀ×IJ×Ļ\nà¸Ķัà¸ĩ à¸Ļีà¹ī\nĠÑĦоÑĢм иÑĢов\nĠÑĦоÑĢмиÑĢов а\n×ķ×ŀ ×ij\nĠkullan ımı\nÐľ Ðŀ\n×¢ ש×Ļ\nעש×Ļ ×Ļ×Ķ\nĠön lem\nà¹Ģà¸Ń à¹ĩ\nà¹Ģà¸Ńà¹ĩ ม\n×ŀשק ×Ļ×¢\nר ×Ļ×Ĺ\nà¸Ĥ ัà¸Ķ\nĠíĻ ľ\nĠíĻľ ìļ©\nà¸ĭ ะ\nãĤĪãģĨ ãģ«ãģªãĤĬãģ¾ãģĹãģŁ\nĠÑĢаÑģ пÑĢ\nĠÑĢаÑģпÑĢ оÑģÑĤ\nĠÑĢаÑģпÑĢоÑģÑĤ ÑĢан\nĠÑĢаÑģпÑĢоÑģÑĤÑĢан ен\n׼×Ļ ×ķף\nÙĤب ض\nتص رÙĬØŃ\nتصرÙĬØŃ Ø§Øª\nĠо ÑĢи\nĠоÑĢи г\nĠоÑĢиг ина\nĠоÑĢигина л\nĠاÙĦع اÙĦÙĬ\nà¹ģหà¹Īà¸ĩ à¸Ļีà¹ī\nãĥķãĤ¡ ãĥ¼\nãģ¦ãģĦ ãģį\nãģ¦ãģĦãģį ãģŁãģĦ\nפ תר\nפתר ×ķ׳×ķת\nĠ×ij ×Ļ×Ĺ\nĠ×ij×Ļ×Ĺ ×ĵ\nĠod by\nĠodby ÅĤ\nĠоÑĩеÑĢ ед\nĠtr Æ°Æ¡ng\nãĤŃ ãĥ³\n×ŀ ×ķפ\n×ŀ×ķפ ×¢\nëĵľ 립\nëĵľë¦½ ëĭĪëĭ¤\nà¸ŀืà¹īà¸Ļ à¸IJาà¸Ļ\nìŀIJ 격\nĠVi á»ĩn\nĠDes pués\nĠ×IJ׾ ×Ļ׳×ķ\nĠdur ée\níĩ ´\nĠmü 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noÅĽci\nĠpewnoÅĽci Äħ\nĠ무 ìĬ¨\nØ¥ رس\nإرس اÙĦ\nĠд алÑĮ\nĠдалÑĮ ÑĪе\nĠ׾ ×ł×¡\nĠ×ľ×ł×¡ ×ķת\nหมูà¹Ī à¸ļà¹īาà¸Ļ\n×ŀס×ŀ ׼×Ļ\nأسÙĦ ÙĪب\nĠzw ÅĤ\nĠzwÅĤ as\nĠzwÅĤas zc\nĠzwÅĤaszc za\nĠпÑĢ еж\nĠпÑĢеж де\nĠоÑĢганиз аÑĨиÑı\nĠdön emin\nĠdönemin de\nĠ Ủ\nĠỦ y\nä¸ĭ ãģĴ\nĠпоÑģлед ние\nĠgü ne\nĠgüne ÅŁ\nĠ×IJ ×ĸר\nĠ×IJ×ĸר ×Ĺ×Ļ\nãģ§ãģĤ ãĤįãģĨ\nĠÙĨ ÙĤ\nĠÙĨÙĤ اط\næŃ£ ãģĹãģĦ\nĠÑĢ ег\nĠÑĢег иона\nĠFör der\nê²½ ìĺģ\ndıkl ar\ndıklar ını\ntrzym aÄĩ\nأش Ùĥ\nأشÙĥ اÙĦ\n×Ķת ×IJ\n×Ķת×IJ ×ŀ×Ķ\nà¸Ĺำà¹ĥหà¹ī à¹Ģà¸ģิà¸Ķ\nĠGeb ä\nĠGebä ude\nĠСеÑĢ г\nĠСеÑĢг ей\nĠз доÑĢов\nĠздоÑĢов ÑĮÑı\nĠr ãi\nĠпÑĢед ÑĥÑģ\nĠпÑĢедÑĥÑģ моÑĤÑĢ\nĠпÑĢедÑĥÑģмоÑĤÑĢ ен\nĠ×Ķצ ×Ļ×ij\nĠ×Ķצ×Ļ×ij ×ķר×Ļ\nĠdés ir\nĠн оÑĩ\nĠноÑĩ ÑĮ\nmöglich keiten\nĠ×IJ×Ĺר ×ķ׳×Ļ×Ŀ\nĠsoir ée\nĠNh áºŃn\nÙ ª\nà¸Ľà¸£à¸°à¸§à¸±à¸ķิ ศาสà¸ķรà¹Į\nêµIJ íĨµ\nĠØ£ Ø®ÙĬ\nĠdé cid\nĠdécid é\nĠwy ja\nĠwyja ÅĽni\nĠ สิ\nĠสิ à¸ĩ\nĠสิà¸ĩ หา\nĠสิà¸ĩหา à¸Ħม\nà¹ģ à¸Ńรà¹Į\nหà¸Ļà¹īา à¸Īà¸Ń\nס תר\nĠê ¶\nĠê¶ Į\nĠê¶Į 리\npl ätze\nب Ø·ÙĦ\nê±´ ìĦ¤\nĠ×IJ ×Ļ×ŀ×Ļ\nĠ×IJ×Ļ×ŀ×Ļ ×Ļ׾\nãģ ½\nتر اث\n×IJ׾ 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ни\nĠcompañ ero\nק צ×Ķ\n×ŀ×¢ ׳×Ļק\nÙħ ØŃÙħد\nĠc ámara\nĠп ед\nĠпед аг\nĠпедаг ог\nм аÑĢ\nмаÑĢ к\n×Ķת ׳×Ĵ×ĵ\nĠìĨĮ ê°ľ\nĠcom unitÃł\nê³ ¤\nĠNg Ãłi\nสà¸ĩ à¸ļ\nĠmieszkaÅĦ ców\nĠÙĨ ÙĩائÙĬ\niv ité\nĠи де\nĠиде алÑĮн\nĠØ£ سبÙĪع\nĠ×Ļ ×¢×ľ\nĠ׾ ר×IJש\nĠ׾ר×IJש ×ķ׳×Ķ\nĠзапиÑģ и\nĠкоÑĢ пÑĥÑģ\nวà¸ĩ ศ\nวà¸ĩศ à¹Į\nĠÐĶ м\nĠÐĶм иÑĤ\nĠÐĶмиÑĤ ÑĢ\nĠkön nt\nĠböl ges\nĠbölges inde\n׼ ×Ļ׼\n׼×Ļ׼ ר\nĠاÙĦØ¥ Ø«ÙĨ\nĠاÙĦإثÙĨ ÙĬÙĨ\nĠng á»Ļ\nì¹ ł\nد راج\nĠu da\nĠuda ÅĤo\nìº IJ\nبر ÙĨاÙħج\nĠÑģÑĥд еб\nĠÑģÑĥдеб н\nĠzun ächst\nĠEduc ación\nãģ¨ãģª ãģ£ãģ¦ãģĦãģ¾ãģĻ\nĠ×Ķ×IJ ×ŀ×Ļת×Ļ\nĠÄ° nt\nĠÄ°nt ernet\nĠcaÅĤ ego\nãĥĹãĥª ãĥ³\nØ¥ بد\nإبد اع\nĠпоÑĢ ÑĤал\nà¹Ĥà¸ķ à¹ī\nĠ×Ķק ש×ķר\nпл од\nĠÙħ د\nĠÙħد رÙĬد\n×ŀסע ×ĵ×Ķ\nĠØ´ÙĬ ئ\nĠØ´ÙĬئ ا\nà¸ģà¹Īà¸Ń สรà¹īาà¸ĩ\nĠì°¸ ê³ł\nà¹Ģà¸Ĺ ร\nà¹Ģà¸Ĺร à¸Ķ\nĠ×ij×ŀ קר×Ļ×Ŀ\nĠb ât\nĠbât iment\nåij¼ ãģ³\nç´ł æķµ\nç´łæķµ ãģª\nprzedsiÄĻbior st\nprzedsiÄĻbiorst w\nĠ×ł×ª ×ķ׳×Ļ×Ŀ\n×Ĺ׾ ×ķ×Ŀ\nร วย\nÙħ ÙĪضÙĪع\nĠÑģоб ÑĢан\nвед ÑĥÑī\nĠÑĤе аÑĤ\nĠÑĤеаÑĤ ÑĢ\nm eye\nmeye ceÄŁi\nĠpien iÄħ\nĠpieniÄħ d\nĠpieniÄħd ze\nÑĢез иденÑĤ\nØŃ ØµØ±\nìĺ ¥\nà¹Ģย ืà¸Ńà¸Ļ\nĠÑĥ ни\nĠÑĥни веÑĢ\nĠÑĥнивеÑĢ Ñģ\nĠÑĥнивеÑĢÑģ иÑĤеÑĤ\nĠاÙĦر ØŃ\nĠاÙĦرØŃ ÙħÙĨ\nĠÑĤеÑħ нолог\nĠÑĤеÑħнолог ии\nìĹIJ ëĦĪ\nìĹIJëĦĪ ì§Ģ\nĠíķ Ń\nĠíķŃ ìĥģ\nà¸ĺ า\nà¸ĺา à¸ķุ\nĠEspañ ol\n×ĵ×Ĵ ש\nĠêµ ī\nĠêµī ìŀ¥\nĠêµīìŀ¥ íŀĪ\nĠÅĤ at\nĠÅĤat wo\nĠk á»ĭch\nØ¥ ز\nإز اÙĦØ©\nĠдейÑģÑĤв ие\nĠsaÄŁ layan\nสุà¸Ķ ยà¸Ńà¸Ķ\nĠzosta Äĩ\nĠdispon ÃŃvel\nïº į\nver ständ\nverständ lich\ntw or\ntwor zyÄĩ\nع جز\nà¹Ģà¸Ĥ à¹īม\nยà¹Ī à¸Ńม\nĠstrat ég\nĠstratég ie\nà¸ľà¸¥ à¹Ħมà¹ī\nĠê°ģ ì¢ħ\nĠÙħ ÙĪا\nĠÙħÙĪا ض\nĠÙħÙĪاض ÙĬع\nاØŃ ØªØ¬\nاØŃتج اج\nĠ Ấ\nĠẤ n\n×ŀ ×ŀש׾×Ķ\nĠÅŁek il\n×ŀ ×Ĺ׾\n×ŀ×Ĺ׾ ×ķת\nĠ à¸ĺ\nĠà¸ĺ ัà¸Ļ\nĠà¸ĺัà¸Ļ วา\nĠà¸ĺัà¸Ļวา à¸Ħม\nĠìĭ¤ ìłľ\nĠìĭ¤ìłľ ë¡ľ\nì¤ij ìķĻ\nëįĶ ëĿ¼\nĠÑĪ иÑĢ\nĠÑĪиÑĢ око\nĠsol ución\nวาà¸ĩ à¹ģà¸ľà¸Ļ\n×IJ×ķ×ĺ ×ķ×ŀ\n×IJ×ķ×ĺ×ķ×ŀ ×ĺ×Ļ\nĠÑĢ еÑģÑĤ\nĠÑĢеÑģÑĤ оÑĢ\nĠÑĢеÑģÑĤоÑĢ ан\nëį ¸\nÑĤ ÑĢад\nÑĤÑĢад и\nÑĤÑĢади ÑĨион\nÑĤÑĢадиÑĨион н\nมะ à¹Ģรà¹ĩ\nมะà¹Ģรà¹ĩ à¸ĩ\nà¹Ĥ ส\nĠol masını\n×ŀ×ķס ר\nĠоÑĤноÑĪ ении\nĠê°ĢëĬ¥ ìĦ±\nĠy uk\nĠyuk arı\nìĨ Ķ\nĠÑģ ÑĦ\nĠÑģÑĦ еÑĢе\nĠק ×ķפ\nãĤ± ãĥ¼ãĤ\nãĤ±ãĥ¼ãĤ Ń\nâĢķ âĢķ\nĠاÙĦØ£ ÙĦÙħ\nĠاÙĦØ£ÙĦÙħ اÙĨÙĬ\nẢ N\nת×ķ׼ ׳×Ļ×ķת\nĠÑģÑĥÑīеÑģÑĤв ÑĥеÑĤ\næĪij ãĢħ\nĠاÙĦص ادر\nĠTr á»įng\nĠа д\nĠад миниÑģÑĤ\nĠадминиÑģÑĤ ÑĢа\nĠадминиÑģÑĤÑĢа ÑĨи\nĠдÑĢÑĥг ими\nÑģп еÑĪ\nعÙĦاÙħ ات\nĠа б\nĠаб Ñģол\nĠабÑģол ÑİÑĤ\nĠабÑģолÑİÑĤ но\nฤ à¸Ķู\né tr\nétr anger\nнÑı ÑĤи\nнÑıÑĤи е\n×¢ ×ķ׳\n×¢×ķ׳ ש\nĠÙĤ ائ\nĠÙĤائ ÙĦا\nĠм аÑģ\nĠмаÑģ ло\nãĥī ãĤ¤\nãĥīãĤ¤ ãĥĦ\nå¿ħè¦ģ ãģĮãģĤãĤĬãģ¾ãģĻ\n×ŀ×ķ×ĸ ×Ļ×IJ\n×ŀ×ķ×ĸ×Ļ×IJ ×ķף\nĠNgo ại\nĠkê nh\nà¸ģาร à¸Ńà¸Ńà¸ģà¹ģà¸ļà¸ļ\n×ŀ פק\n×ŀפק ×ĵ\nÙħÙĨ از\nÙħÙĨاز ÙĦ\në· °\níĹ ¤\nÙħÙĩ ارات\nĠpropri été\nפ×Ĵ ×Ļש×Ķ\nÑĩ ÑĢ\nÑĩÑĢ еж\nÑĩÑĢеж ден\n×Ķ ×ķצ×IJ×Ķ\nØŃÙĥ ÙĬÙħ\nĠíĻ Ī\nĠíĻĪ íİĺìĿ´ì§Ģ\nåİ ³\nåݳ ãģĹãģĦ\n×¢ ×ŀ×ĵ×Ķ\nĠAu ÃŁen\nسÙĪ Ø¡\në¹ Ī\nĠÙĪ Ø®\nĠÙĪØ® اصة\nин ÑĤеÑĢ\nинÑĤеÑĢ еÑģ\nèĩ´ ãģĹãģ¾ãģĻ\nĠhük üm\nà¹Ħà¸Ĥ มัà¸Ļ\nĠdav ran\nĠdavran Ä±ÅŁ\nà¹Ģà¸ķ ียà¸ĩ\nв ÑĢем\nвÑĢем енно\nà¹Ģà¸Ĺศ à¸ģา\nà¹Ģà¸Ĺศà¸ģา ล\nå¼ķ ãģ£\nå¼ķãģ£ è¶ĬãģĹ\n×IJר ×ķ×Ĺ\n×IJר×ķ×Ĺ ×ª\nà¹Ģ วิ\nà¹Ģวิ รà¹Į\nà¸Ńยà¹Īาà¸ĩ รวà¸Ķà¹Ģรà¹ĩว\nĠìŬ íĸī\nĠÑĢан ÑĮ\nĠÑĢанÑĮ ÑĪе\nĠzob ow\nĠzobow iÄħ\nĠzobowiÄħ z\nĠ×ķ׼ ×ŀ×ķ×ijף\nĠاÙĦÙħ Ùĩ\nĠاÙĦÙħÙĩ ÙĨÙĬ\nãĤ¢ ãĤ¸\nãĤ¢ãĤ¸ ãĤ¢\në°© ìĨ¡\nà¸Ńà¸Ńà¸ģ à¸ģำลัà¸ĩ\nà¸Ńà¸Ńà¸ģà¸ģำลัà¸ĩ à¸ģาย\nam éli\naméli orer\nå½ĵãģŁãĤĬ åīį\nĠreg elm\nĠregelm Ã¤ÃŁig\nãģĬ åĭ\nãģĬåĭ §\nãģĬåĭ§ ãĤģ\nĠm Æ°á»Ŀi\nبر Ùħج\nĠNat ürlich\nĠD Å©ng\nĠاÙĦر جاÙĦ\nĠthé p\nĠol muÅŁtur\n×ŀ×ķס ×Ļק×Ķ\nf älle\n주 íĥĿ\nĠاÙĦÙģ رص\nĠnaj wiÄĻks\nĠnajwiÄĻks zy\nĠça ÄŁ\nĠçaÄŁ rı\nì¸ ł\nĠvÃŃ ct\nĠvÃŃct ima\nĠÑģовеÑĢ ÑĪен\n×Ķ×Ļ ×Ļת×Ļ\nà¹Ģà¸Ķ ี\nà¹Ģà¸Ķี à¹ĭ\nà¹Ģà¸Ķีà¹ĭ ยว\nü yü\nĠд оп\nĠдоп олн\nĠдополн иÑĤелÑĮно\nà¹ģà¸ķà¸ģà¸ķà¹Īาà¸ĩ à¸ģัà¸Ļ\nĠá l\nĠál bum\nà¸Ľà¸£à¸°à¸Īำ à¸Ľà¸µ\nĠÑĦ едеÑĢ\nĠÑĦедеÑĢ алÑĮн\nĠobs ÅĤ\nĠobsÅĤ ugi\nà¹Ģร ืà¹Ī\nà¹Ģรืà¹Ī à¸Ńย\nà¹Ģรืà¹Īà¸Ńย à¹Ĩ\nëģ Į\nĠngh ìn\nĠBaÅŁkan lıģı\nتأ سÙĬ\nتأسÙĬ س\nĠ×ij×ij ×ķקר\nĠ×¢×ij×ķ×ĵ ×ķת\nĠبص ÙĪرة\nãĤıãģij ãģ§ãģ¯ãģªãģĦ\nführ er\nãĤ¹ ãĤŃ\nãĤ¹ãĤŃ ãĥ«\nĠاÙĦÙĤ ض\nĠاÙĦÙĤض ÙĬØ©\nĠдолж ноÑģÑĤ\nÙģار ÙĤ\nĠcomeç ou\nĠorganis é\nĠxu ân\nĠÑģообÑī аеÑĤ\nĠпÑĢи д\nĠпÑĢид еÑĤÑģÑı\nTÃľ RK\nãĥ¬ ãĥ¼ãĤ·ãĥ§ãĥ³\nKh ông\nاست Ùģ\nاستÙģ ادة\nä¸ĬãģĮ ãģ£ãģ¦\nĠum ie\nĠumie jÄĻ\nĠumiejÄĻ tn\nĠumiejÄĻtn oÅĽci\nëĤ ¸\nà¹Ģà¸Ļ à¸Ńรà¹Į\n×ĵ×ķ ×ķ×Ĺ\nÃŃs imo\nI ÃĬ\nIÃĬ N\nĠalcan ç\nĠ à¸ķุ\nĠà¸ķุ ลา\nĠà¸ķุลา à¸Ħม\nש׾ ×ĺ×ķף\nĠél è\nĠélè ves\nĠÄij u\nĠÄiju á»ķi\nĠØ£ Ùģ\nĠØ£Ùģ رÙĬ\nĠØ£ÙģرÙĬ ÙĤÙĬ\nĠØ£ÙģرÙĬÙĤÙĬ ا\nãĤĴæİ¢ ãģĻ\nĠпÑĢед ложениÑı\nج اد\nĠÑħоÑĤ ÑĮ\nÑģ ал\nÑģал он\nà¸Ľà¸£à¸° à¹Ģม\nà¸Ľà¸£à¸°à¹Ģม ิà¸Ļ\nãĤŃ ãĥĥãĥģ\nãĤŃãĥĥãĥģ ãĥ³\n×ij×ĵ×Ļק ×ķת\nĠch ù\nĠchù a\nÐĴ иде\nÐĴиде о\nиÑĢов ка\nĠÑħоÑĤ иÑĤе\nĠspéc ifique\nรส à¸Ĭาà¸ķิ\nè¾¼ ãĤĵãģł\n伸 ãģ³\n×Ķצ׾ ×Ĺת\nãģ©ãģ® ãĤĪãģĨãģ«\nسع ادة\nĠл ид\nĠлид еÑĢ\nม à¸ĩ\nมà¸ĩ à¸Ħล\nØŃ Ø§ÙħÙĦ\nหล ุà¸Ķ\nà¸Ńยà¹Īาà¸ĩ à¸ķà¹Īà¸Ń\nà¸Ńยà¹Īาà¸ĩà¸ķà¹Īà¸Ń à¹Ģà¸Ļืà¹Īà¸Ńà¸ĩ\nãģķãģĽãģ¦ éłĤ\nتس ÙĪÙĬ\nتسÙĪÙĬ ÙĤ\nĠaÅŁaģı d\nĠaÅŁaģıd aki\nĠÑĨ елÑĮ\nĠÑĨелÑĮ Ñİ\nĠAra ÅŁtırma\nà¸Ĥัà¸ļ รà¸ĸ\nÙĩ Ø°Ùĩ\nลà¸ĩ à¸Ĺะ\nลà¸ĩà¸Ĺะ à¹Ģà¸ļ\nลà¸ĩà¸Ĺะà¹Ģà¸ļ ียà¸Ļ\nتÙĥ اÙħÙĦ\nĠc io\nĠcio è\nãģ¦ ãģĬãģı\nĠاÙĦصØŃ ÙģÙĬ\nĠíĬ¹ ìłķ\nполн иÑĤÑĮ\nãĤĵ ãģĺãĤĥãģªãģĦ\nãĤĵãģĺãĤĥãģªãģĦ ãģĭ\nĠاÙĦج Ùĩ\nĠاÙĦجÙĩ ات\nĠÑĥÑģпеÑĪ но\nĠв ок\nĠвок 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à¸Ķ\nà¸ļาà¸Ķ à¹Ģà¸Īà¹ĩà¸ļ\nà¸Ŀ รั\nà¸Ŀรั à¹Īà¸ĩ\nà¸Ŀรัà¹Īà¸ĩ à¹Ģศ\nà¸Ŀรัà¹Īà¸ĩà¹Ģศ ส\nר ×¢×Ļ\nרע×Ļ ×ķ׳×ķת\nĠë Į\nĠëĮ ĵ\nĠëĮĵ ê¸Ģ\nĠnaj b\nĠnajb li\nĠnajbli ż\nĠnajbliż sz\nĠиÑģполÑĮз ÑĥеÑĤÑģÑı\nĠcient ÃŃf\nĠcientÃŃf ico\n×¢ ×ŀק\nĠg ợi\nØ´ ØŃÙĨ\nĠÅĽ m\nĠÅĽm ier\nĠÅĽmier ci\nà¸Ħาสิà¹Ĥà¸Ļ à¸Ńà¸Ńà¸Ļà¹Ħลà¸Ļà¹Į\n×Ĺש×ij ת×Ļ\nĠn ingu\nĠningu ém\nè¾¼ ãĤģ\nãģ ·\nĠÑĥ г\nĠÑĥг ол\nï½ °\nפת ×Ļ×Ĺ\nפת×Ļ×Ĺ ×ª\nĠ×Ķר×IJש ×ķ׳×Ļ×Ŀ\np ósito\nãĤŃ ãĥ¬ãĤ¤\nãģ© ãģĵãĤį\nà¹Ģà¸Ĺà¹Īา à¹Ħ\nà¹Ģà¸Ĺà¹Īาà¹Ħ หร\nà¹Ģà¸Ĺà¹Īาà¹Ħหร à¹Ī\nĠинÑĤеÑĢ ÑĮеÑĢ\nĠØŃ Ø§Ø¬\nĠØŃاج Ø©\nสี à¸Ĥาว\nìĸ ¼\nĠn á»Ļ\nĠná»Ļ p\nĠÃŃ nd\nĠÃŃnd ice\nสำ รวà¸Ī\nĠкажд ой\nĠhot éis\nĠnast ÄĻ\nĠnastÄĻ pn\nĠ×Ķק ×ķ×ĵ\nĠ×Ķק×ķ×ĵ ×Ŀ\nפ ×ķפ\nפ×ķפ ×ķ׾\nפ×ķפ×ķ׾ ר×Ļ\nвÑĪ ей\nãĤ·ãĥ³ ãĥĹ\nãĤ·ãĥ³ãĥĹ ãĥ«\nĠzdjÄĻ Äĩ\nĠгÑĢÑĥпп а\nĠпом еÑī\nĠпомеÑī ениÑı\nãģ©ãģĨ ãģĦãģĨ\nĠиÑģп ÑĭÑĤа\nĠog ÅĤ\nĠogÅĤ os\nĠogÅĤos zen\nĠogÅĤoszen i\nสรà¹īาà¸ĩ สรร\nสรà¹īาà¸ĩสรร à¸Ħà¹Į\nà¸ŀร รà¸ĵ\nĠçık Ä±ÅŁ\nĠÑĩаÑģÑĤ ноÑģÑĤи\nĠ×ķ ×Ļ×ķתר\nç¶ļãģį ãĤĴ\nç¶ļãģįãĤĴ èªŃ\nç¶ļãģįãĤĴèªŃ ãĤĢ\nà¸ģร ั\nà¸ģรั ม\nг ÑĢаÑĦ\nĠв лад\nĠвлад елÑĮ\nĠвладелÑĮ ÑĨ\nĠistedi ÄŁ\nĠistediÄŁ iniz\n×ij׾ ×¢\n×ij×ľ×¢ ×ĵ×Ļ\nÙħÙĪ اÙģ\nÙħÙĪاÙģ ÙĤØ©\nĠ×Ļ ×ķר\nĠ×Ļ×ķר ק\nãĤ«ãĥ¼ãĥī ãĥŃãĥ¼ãĥ³\nĠاÙĦÙħØ´ ÙĥÙĦ\nĠاÙĦÙħØ´ÙĥÙĦ Ø©\nĠêµŃ íļĮ\nס פ×ĺ\nספ×ĺ ×ŀ\nספ×ĺ×ŀ ×ijר\nĠìĸ´ ëłµ\nÙĥ اÙħ\nÙĥاÙħ ÙĬرا\nsch lü\nschlü sse\nĠØ« ÙĨ\nĠØ«ÙĨ ائÙĬ\nìī ½\nĠÐŀ Ñģоб\nĠÐŀÑģоб енно\nĠин веÑģÑĤи\nĠинвеÑģÑĤи ÑĨи\nاØŃ ØªÙħ\nاØŃتÙħ اÙĦ\nE Äŀ\nEÄŀ Ä°\níķĺ ê²łëĭ¤\nĠ×IJ ×ijר×Ķ\nĠ×IJ×ijר×Ķ ×Ŀ\nĠ×ij×Ĺ ×Ļ׳×Ŀ\nØ£ ÙĪض\nØ£ÙĪض اع\nĠdé l\nĠdél ai\nĠ×IJ×ķ×Ķ ×ij×Ļ×Ŀ\nĠÑģо Ñħ\nĠÑģоÑħ ÑĢ\nĠÑģоÑħÑĢ ани\nĠдоÑģÑĤ иж\nĠдоÑģÑĤиж ени\nสิà¹Īà¸ĩ à¹ģ\nสิà¹Īà¸ĩà¹ģ วà¸Ķ\nสิà¹Īà¸ĩà¹ģวà¸Ķ ล\nสิà¹Īà¸ĩà¹ģวà¸Ķล à¹īà¸Ńม\nĠاÙĦÙħ باشر\nĠÑĦ иг\nĠÑĦиг ÑĥÑĢ\nмож ем\n׾×ŀ×Ļ×ĵ ×Ķ\nĠcin é\nĠciné ma\nĠb ada\nĠbada ÅĦ\nجب ÙĩØ©\nĠд еп\nĠдеп ÑĥÑĤ\nĠдепÑĥÑĤ аÑĤ\nĠdist ância\nĠاÙĦÙħ عار\nĠاÙĦÙħعار ضة\nthè se\nü nc\nünc ü\nĠдан ного\nĠBel gi\nĠBelgi ë\nĠ×ij ×ijק\nĠ×ij×ijק ש×Ķ\nย à¹Īาà¸Ļ\nĠsol ução\nĠ×Ķצ ×ĺר\nĠ×Ķצ×ĺר פ×ķ\nĠØ£ÙĨ ØŃ\nĠØ£ÙĨØŃ Ø§Ø¡\nĠد ÙħØ´\nĠدÙħØ´ ÙĤ\nมั à¹ī\nมัà¹ī ย\nÙħ 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ен\nĠÑģÑĤ еп\nĠÑģÑĤеп ени\nĠاÙĦÙħ ÙĪجÙĪد\nĠاÙĦÙħÙĪجÙĪد Ø©\nล à¹īม\nĠnaj czÄĻ\nĠnajczÄĻ ÅĽcie\nĠnajczÄĻÅĽcie j\nĠz wy\nĠzwy k\nĠzwyk ÅĤ\nĠê·¸ëłĩ ì§Ģ\nà¸ģระ à¸Ī\nà¸ģระà¸Ī าย\nĠëĭ µ\nĠëĭµ ë³Ģ\nĠÑĢе ак\nĠÑĢеак ÑĨи\nĠÅĽwie ż\nĠÑģÑĤоим оÑģÑĤи\nÙħÙĨ اÙĤ\nÙħÙĨاÙĤ Ø´\nÙħÙĨاÙĤØ´ Ø©\nĠÑħоÑĩ Ñĥ\nãĥľ ãĥ¼ãĥī\nĠróż nic\nĠк ÑĢÑĭ\nĠкÑĢÑĭ ÑĪ\nâľ ĵ\nãĤ³ãĥ³ ãĥĨãĥ³\nãĤ³ãĥ³ãĥĨãĥ³ ãĥĦ\nĠпÑĢед поÑĩ\n×ŀר ×ij×Ļת\nĠØ´ Ùĥ\nĠØ´Ùĥ را\nĠд ал\nĠдал ек\nĠдалек о\nبر ÙĬØ·\nبرÙĬØ· اÙĨÙĬا\nع ÙĨا\nعÙĨا ÙĬØ©\nĠÑĢаÑģÑģ каз\nĠÑĢаÑģÑģказ Ñĭва\nØ£ ÙĦÙĪ\nØ£ÙĦÙĪ اÙĨ\næĮģ ãģ£ãģ¦\næĮģãģ£ãģ¦ ãģĦ\nÙħباد ئ\n×Ķ ×¢×ijר\n×Ķ×¢×ijר ת\nĠyay ı\nĠyayı ml\nĠyayıml a\nm át\nmát icos\nà¸ģ ัà¸ĩ\nà¸ģัà¸ĩ วล\nĠ׾ פת\nĠ×ľ×¤×ª ×ķ×Ĺ\nà¸ŀฤ à¸ķิ\nà¸ŀฤà¸ķิ à¸ģรรม\ní Ĥ¬\nĠок ÑĢÑĥг\nĠ×ŀצ ×ķ×ķ×Ķ\nÐĽ ени\nÐĽÐµÐ½Ð¸ н\nĠTri á»ģu\nãĤ³ãĥŁ ãĥ¥\nãĤ³ãĥŁãĥ¥ ãĥĭ\nãĤ³ãĥŁãĥ¥ãĥĭ ãĤ±\nãĤ³ãĥŁãĥ¥ãĥĭãĤ± ãĥ¼ãĤ·ãĥ§ãĥ³\nÙĥ ÙĨÙĬ\nÙĥÙĨÙĬ سة\nãĤĴ ä¸Ńå¿ĥ\nãĤĴä¸Ńå¿ĥ ãģ«\nĠmiÄĻd z\nĠmiÄĻdz yn\nĠmiÄĻdzyn ar\nĠmiÄĻdzynar od\nĠmiÄĻdzynarod ow\nÙĦ ÙĨ\nÙĦÙĨ دا\nبر Ø´\nبرش ÙĦÙĪÙĨ\nبرشÙĦÙĪÙĨ Ø©\nà¸ģระ à¸ķุ\nà¸ģระà¸ķุ à¹īà¸Ļ\nĠg ı\nĠgı da\nà¸Ľà¸£à¸° à¸Ĺัà¸ļ\nà¸Ľà¸£à¸°à¸Ĺัà¸ļ à¹ĥà¸Ī\nĠë¶Ī 구\nĠë¶Ī구 íķĺê³ł\nĠÙĨ Ø·\nĠÙĨØ· اÙĤ\nĠÐľ ожеÑĤ\nPr äs\nPräs ident\nĠÑģк оÑĢ\nĠÑģкоÑĢ оÑģÑĤÑĮ\nĠ×Ķ×ij ×ķקר\nеÑħ аÑĤÑĮ\nĠg ạo\nĠש×IJ ×Ļ׳×Ŀ\nĠ×ij׳ ×ķ×Ĵ\nĠ×ij׳×ķ×Ĵ ×¢\nĠо пиÑģание\nĠucz ni\nĠuczni ów\nà¹Ģà¸Ń à¹ĩà¸Ļ\nĠت Ø´\nĠتش رÙĬÙĨ\nĠnh ãn\në¹ ¨\nĠcaract ère\n×¢ ׾×Ļ\n×¢×ľ×Ļ ×Ļ×Ķ\n楽ãģĹ ãĤģãĤĭ\nĠÑģ аÑħ\nĠÑģаÑħ аÑĢ\nдÑĥм аÑĤÑĮ\nĠÐĴоз можно\nص ÙĬاÙĨ\nصÙĬاÙĨ Ø©\nöm ür\nส ล\nสล à¹ĩ\nสลà¹ĩ à¸Ń\nสลà¹ĩà¸Ń à¸ķ\në¡ ¯\nĠth ói\ngr Ã¶ÃŁe\nĠksi ÄĻ\nĠksiÄĻ g\nĠÑĢ ом\nĠÑĢом ан\nÙĤ اسÙħ\n×ŀ×ij ×ķ×Ĵ\n×ŀ×ij×ķ×Ĵ ר×Ļ×Ŀ\nbes ch\nbesch äft\nbeschäft ig\n×Ķצע ×Ķ\nĠÃģ rea\nĠзаÑıв к\nÄ ¹\nĠлÑİб ого\nĠ ม\nĠม à¸ģร\nĠมà¸ģร าà¸Ħม\nÑĦ из\nÑĦиз иÑĩеÑģк\nин ÑĦ\nинÑĦ ек\nинÑĦек ÑĨи\nاÙĦ Ø·\nاÙĦØ· ائÙģ\nĠкол л\nĠколл екÑĤив\nез жа\nĠس بØŃ\nĠسبØŃ Ø§ÙĨ\nĠسبØŃاÙĨ Ùĩ\nsch lä\nschlä ge\nĠд и\nĠди аг\nĠдиаг ноÑģÑĤ\nĠоÑĤмеÑĤ иÑĤÑĮ\nТ Ь\nĠاÙĦ در\nĠاÙĦدر اسÙĬ\nעצ ×ŀ\nעצ×ŀ ×IJ×ķת\nĠdém arch\nĠdémarch e\nĠ×ĺ ×ķ×¢\nĠ×ĺ×ķ×¢ ף\nĠfuncion ários\ná» µ\n׾ ׼×IJ\n׾׼×IJ ×ķר×Ķ\nà¸ĭ à¹Ī\nà¸ĭà¹Ī à¸Ńม\nĠÑĩ Ñĥв\nĠÑĩÑĥв ÑģÑĤво\nâĸ ¼\nп ÑĥÑī\nпÑĥÑī ен\nĠм еÑĢ\nĠмеÑĢ оп\nĠмеÑĢоп ÑĢи\nĠмеÑĢопÑĢи ÑıÑĤиÑı\nĠu çu\nĠuçu ÅŁ\nãĤĴåĪ©çĶ¨ ãģĻãĤĭ\na ÄŁ\naÄŁ lı\nìĺĪ ìĪł\nà¹ģ ยà¹Ī\nĠاÙĦÙĥ Ùħ\nĠاÙĦÙĥÙħ بÙĬ\nĠاÙĦÙĥÙħبÙĬ ÙĪتر\nت ÙĪÙĬ\nتÙĪÙĬ تر\nà¹Ģà¸Ĭ ีà¹Īยว\nà¹Ģà¸Ĭีà¹Īยว à¸Ĭา\nà¹Ģà¸Ĭีà¹Īยวà¸Ĭา à¸į\ná» Ķ\nĠhi ếm\nذا Ùĥرة\nĠ×Ķ×ŀ×Ļ ×ķ×Ĺ×ĵ\nĠìĪ ľ\nĠìĪľ ê°Ħ\nĠK ı\nĠKı sa\nĠgele ceÄŁi\nпÑĢо ÑĦеÑģÑģиона\nпÑĢоÑĦеÑģÑģиона л\nĠog ó\nĠogó le\nĠgÅĤ ów\nĠgÅĤów ne\nĠÑģÑĤ илÑĮ\n×IJ פ׾\n×IJפ׾ ×Ļק\n×IJפ׾×Ļק צ×Ļ×Ķ\nสม ารà¹Į\nสมารà¹Į à¸Ĺ\nสมารà¹Įà¸Ĺ à¹Ĥà¸Ł\nสมารà¹Įà¸Ĺà¹Ĥà¸Ł à¸Ļ\nĠth ánh\nÐŁ од\nÐŁÐ¾Ð´ ÑĢоб\nÐŁÐ¾Ð´ÑĢоб нее\nĠاÙĦت ÙĪÙĨ\nĠاÙĦتÙĪÙĨ سÙĬ\nĠbah çe\nà¹ģà¸ģà¹ī à¸Ľà¸±à¸įหา\né ducation\neu rop\neurop ä\neuropä ische\nĠK si\nĠKsi ÄĻ\nĠëĦ ĺ\nĠëĦĺ ìĸ´\nĠv üc\nĠvüc ud\nĠyay g\nĠyayg ın\nĠnie kt\nĠniekt óry\nĠniektóry ch\nãģŃ ãģĩ\nĠк аж\nĠкаж еÑĤÑģÑı\nк аж\nкаж еÑĤ\nĠاÙĦ دÙĬÙħÙĤرا\nĠاÙĦدÙĬÙħÙĤرا Ø·\nĠاÙĦدÙĬÙħÙĤراط ÙĬØ©\næŃ ©\næŃ© ãģĦãģ¦\nĠv az\nĠvaz 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¢\në¹ ¡\nìĶ ¹\nÅ ³\nË Ŀ\níİ ij\nï¾ ĵ\nðŁĴ ļ\nëĬ ij\nêº ¾\níĨ °\nà ¿\nÐ Ħ\nëĮ IJ\në½ Ģ\nì· Ħ\nðŁ ĵį\nðŁĻ Ī\nâĹ Ī\nê¿ ĩ\nì¼ Ħ\níİ «\nðŁĩ ·\nâĶ ĭ\nâļ ł\në± ī\nì į°\nìĻ Ī\nÉ ª\nïº ĭ\nðŁĺ ľ\nÎ Ł\nðŁ ĻĤ\nâļ ½\nÅ Ī\në¹ Ķ\níĮ ľ\n๠ı\nìĸ ¹\níĪ Ń\nðŁ¥ ĩ\nãĦ ´\nëĶ ¥\nìŃ Ī\nâĪ Ĩ\nëĸ ³\në± ĥ\nìŀ ¦\nï» IJ\nÎ ľ\nâľ §\nÏ į\nìł ĵ\nâĹ ķ\nëĴ Ģ\nï» Ģ\nðŁĶ ´\nê½ ģ\nëĮ Ī\nëİ Į\nãĤ İ\n⦠ģ\nì½ §\nï¯ ¾\nâĿ ¯\nภħ\nðŁĻ Ħ\nâĿ Ģ\nðŁĶ ¹\nâĩ IJ\nêµ µ\nâĩ Ķ\në¶ IJ\nðŁĴ Ľ\nÎ ¾\níĥ ¬\nâĿ Ħ\nÒ £\nãĢ °\nâĪ ij\nâĺ ¼\nâī ł\nÒ ¯\nïº ¯\nê¿ ¨\nâľ ĸ\nÊ ĸ\níĢ Ģ\nê¾ Ģ\níĹ Ŀ\nâĶ £\nãİ ľ\nëĶ Ľ\nëľ ¸\nï º«\nê¿ °\nðŁĩ ¹\nÇ IJ\nÛ Ĵ\në£ »\nïº ĸ\nÑ ļ\nëĬ ł\nÛ ķ\nê¹ ¡\në¿ ľ\nì² ¼\nï¨ ij\në¥ µ\nìį ¸\níħ ħ\níij ¹\nÖ Ģ\nï³ Į\nãħ £\nìij ¤\nì½ ķ\nëķ ł\nðŁĮ ¿\níĥ Ķ\nìĽ ģ\nÎ ¶\nâŀ ľ\nìĬ ĺ\níĽ Ĺ\në© §\nìī ĺ\nÕ ¶\ná¹ ĩ\nðŁİ ģ\nï½ ¿\nï¼ Ĥ\ná¼ IJ\nâľ ķ\nâŀ ¢\nëĦ ¨\nì» «\nì¯ Ķ\nì° ľ\nðŁĴ °\níħ Ŀ\nãİ ı\në³ ¶\nÒ ĵ\nâĨ ³\nìĥ ´\níģ ĺ\nâĸ Ģ\në² Ļ\nภĥ\ná½ ¶\nÄ ķ\n⬠ĩ\në¤ ĺ\nðŁİ µ\nâľ ļ\nïº ı\nÎ ¡\nâĹ ī\nðŁĴ «\nÐ Ī\nìĸ Ħ\nì§ Ļ\nï» ĥ\nðĿij Ĵ\nëŃ Ħ\nâĿ ¥\nâĿ ĸ\nâĺ Ŀ\nÊ ¹\nḠ¥\nâĢ ¿\nãħ ħ\nê¸ ģ\nëķ ¡\nëį ¥\nâĪ ©\nê» Ħ\në® Į\nÒ ±\nâĪ Ĺ\nëł Ļ\nïº Į\nË IJ\nðŁĺ ³\nðŁij ©\nðŁİ ¶\nì¿ µ\nðŁ¤ ©\nê· ¤\nëĮ Ķ\nïº IJ\nÏ İ\nì¶ ¥\nï½ Ĭ\ná¹ Ń\në¤ ¼\nâĸ «\nì§ ł\ná¼ Ģ\nê» ij\nëĮ ģ\níĢ ¸\nâĻ Ľ\nðŁĴ ŀ\nâĸ °\nðĿij ĸ\nëĿ ¤\nठ¦\nì´ ĺ\nðŁĺ ĩ\nëĶ ¤\nÎ Ĺ\nðŁĻ ĩ\nË Ľ\nì© ¡\nâĪ §\nÕ ¥\nÑ Ļ\nëIJ ¬\nëĸ Ħ\nðŁĮ ·\nìĹ Į\nðŁĺ ¥\nëĪ ´\nï» ļ\nÉ Ľ\nïº Ħ\nï» ı\nÅ Į\në² ļ\nìĭ £\nïº Ģ\nÎ ĵ\nðŁĺ Į\nË Ļ\nëŀ ı\nðŁĶ ¸\nðŁĵ ·\nëģ ½\níģ ½\nðŁĴ ¡\nðŁĮ ±\nëº ı\nìģ ł\nìĥ IJ\nëı Ĺ\nì¸ °\nëĪ ķ\nÎ Ŀ\nâģ ī\nðŁĮ ¼\níĮ ł\nâĭ ¯\náĥ ĺ\nâľ ¤\nê± Ķ\níĮ İ\nðŁĴ ¯\nìı Ļ\níĹ ī\nÙ Ń\nì½ °\nïº ¿\nï» ±\nì± Į\nâĺ ķ\nðŁİ Ģ\nÄ Ŀ\në° §\nìĤ ¿\náij ķ\nðŁį ĥ\nâĩ ¨\nÎ Ľ\në§ ´\në³ ķ\ná ijIJ\nâĸ ĵ\nðĿ ijľ\nâĻ »\níĤ ¥\nÕ ¸\nãĪ ±\nëº Ģ\nì² ¸\nïº Ľ\nðŁı Ĩ\nðŁĩ ª\nâĿ ĵ\nÄ Ģ\nì½ ¥\nðŁĩ §\ná½ ·\nâľ Ĥ\nìŀ ¼\nï§ ¡\nðŁĵ ¸\nâĻ ¯\nÉ Ķ\ná½ ¸\nâĮ ª\nï» ĸ\nï¥ §\nâļ «\nâĶ Ĺ\nðŁĮ Ī\nï» ©\nðŁĵ ²\nÏ Ī\nðŁĺ ¡\nðĿij İ\nìľ ½\nì§ ¬\nì§ Ĭ\ná½ ³\nìĮ ¤\nëĤ į\nâī Ĵ\nðŁij ¨\nâĺ ĺ\nÓ ©\nâĤ ĵ\nâĪ Ĥ\nï¹ ģ\nðŁĴ IJ\níħ ĥ\nðŁı ½\nê· Ħ\nðŁĺ ı\nðŁĮ 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ĩ\nà® IJ\nâĸ ¹\ná´ Ľ\nâĹ ĺ\nëº ¨\níĥ ī\nìĸ Į\nðŁIJ ¶\nãĤ ij\nË ĩ\nÅ ı\ná½ ¹\nìħ §\nï¹ °\nðĿij ¡\nðŁĶ Ŀ\nðŁĺ »\nðŁĴ ĥ\nðŁ¤ ¦\nðŁį Ĵ\níĢ µ\nâľ Ĩ\në¹ ´\nï§ ¤\nï» Ļ\ná´ Ĺ\nðŁĮ ´\nÍ ¾\nëĮ ij\nì¨ ĭ\nìµ ¸\nðŁİ Ī\nðŁı ł\ná½ ±\nÛ Ĩ\ná¿ ĸ\nâĢ Ľ\nì° ¼\níķ ¥\níĹ ´\nðŁĩ ¬\nì° Ŀ\nâĪ ł\nï¼ ĩ\nâĬ Ļ\nâĿ ij\nëĦ ĭ\nëŀ Ĺ\në° ī\nìĹ Ĭ\nì¢ Ĩ\níĮ ¥\nï° ²\nðŁĵ ĸ\nðŁĺ ®\nâļ ª\nðŁĺ ļ\nâĿ ŀ\nðĿij Ł\nðŁİ Ĥ\nÅ ķ\náIJ Ī\nêº ½\nì± ł\nïº Ŀ\nê¿ ī\náĥ ł\nðŁı ĥ\nðŁĴ ¸\nâĿ ģ\nâĹ ¾\nÚ ª\ná¹ ĥ\níĬ ¬\nðŁĩ ±\níİ Ń\nðŁĺ ŀ\në¾ °\ná¹ Ľ\nëĽ ¸\nâĿ Ĥ\nêĴ ³\nâĶ IJ\níĵ °\nâŀ ł\nê´ ĺ\nëħ ĺ\në» ¥\nì¾ ħ\nðŁĺ IJ\nâĪ ª\nðŁij ģ\nâĪ ´\nâĹ ģ\nëº IJ\nìŀ ¤\nì± Ĺ\nðŁı ¾\nÎ §\ná½ »\nâŀ ¥\nìŁ Ī\nï» ī\nâĸ Į\nãĥ ®\nðŁ¤ ¤\nâĩ ĵ\nì¼ ł\ná´ ı\në§ ¬\në» £\nðŁĴ ¬\nðŁį ĵ\nÄ ¸\nÙ ¹\nÊ ¿\ná½ °\nëķ ľ\nì° ¡\nì° »\níİ į\nðŁİ ¯\nðŁį Ĥ\nðŁij §\nâĻ ¢\náĨ ŀ\nâĻ §\nâļ ľ\nâľ ī\nëĵ ¦\nëŃ £\nìĪ ı\nìĵ ±\nÅ Ń\nÊ Ĭ\nâĴ ¸\nâĩ ©\nðŁĴ Ķ\nÕ µ\nÐ ī\nÒ »\në§ £\nìĽ ľ\nì¿ ¡\níĽ ħ\níĽ ¤\nïº ¢\nâľ ĭ\nâĪ Ī\nðŁĮ į\nÊ ľ\nëĬ ª\nëĴ ¹\nïº ²\nâĸ Ħ\nãħ Ī\nëļ ¤\níİ ©\nâĪ ¨\nðŁ¤ ª\náĥ ļ\nê³ ¶\níĬ ķ\nðŁĺ ¬\nâĪ «\nðŁij ĭ\nÒ IJ\níĬ ¿\nðŁĶ µ\nðŁĴ ¨\nðŁĮ Ļ\nëĩ ©\nâľ ³\në¨ ģ\nëº Ħ\nìĻ ij\nìº ħ\níı Ī\nðĿij Ļ\nðŁĴ ĺ\nãİ ¥\nâĿ ı\nâľ °\nï¯ ¿\nëµ IJ\nì¼ IJ\nïº ±\nÕ ´\nï¬ Ģ\nâľ ´\nðŁ¤ Ń\nðŁij Ĩ\n⼠Ķ\nê· ĵ\nìĮ Į\nðŁ¤ ·\nÛ Ķ\nðŁ§ ¡\nðŁĺ ĵ\nÎ ĸ\nâı °\nê² ľ\nëĭ ³\nëİ ħ\në° Ī\nï® IJ\nðŁı ¡\nâĨ ª\nâĵ Ķ\nâľ Ĭ\nÏ ²\nÜ IJ\nðŁĩ ³\nÖ Ĥ\nâľ ı\nìĸ Ĺ\nì« Ļ\nðŁĺ ²\nÄ Ń\nâĻ Ń\nâĶ ı\nâĹ Į\nðŁĺ ¯\náµ Ĵ\níĬ ł\nÄ ·\nÊ ģ\nठŁ\ná¹ ģ\ná¼ °\ná¿ Ĩ\nâ «\nâ« ¸\nëį «\nì³ ĩ\nì¼ ¤\níĽ ¨\nðŁĴ Ł\nÊ Ģ\nÊ ³\nëĵ IJ\nâķ °\nâĿ ĩ\nÇ Ģ\nÇ Ķ\nÉ ´\nâĺ ļ\nâĺ ľ\nê¶ Ĥ\nì« Ĵ\nì± Ī\nðŁĩ ¨\nðŁİ ¥\nðŁĵ Ŀ\nÄ §\nðĿ ijIJ\nÛ Ī\nठ¬\nì¬ IJ\níĹ ¥\nâĻ ¨\nðŁį ´\nï¹ ı\nË ĭ\nðŁ¥ º\nâĸ ¨\níĻ ĭ\nâĪ ħ\nëģ Ļ\nëŀ ł\nìĨ ¥\nâĢ ĸ\nðŁ¤ ĺ\nðŁIJ »\náµ ķ\nÇ Ŀ\nâĺ ı\nïº ļ\nï» Ĥ\nðŁļ ©\nìĪ Ł\nË Ĭ\n⤠µ\nðŁĴ §\nã ħį\në© ©\nÆ ¬\nÎ ĩ\nâĩ §\nâĵ ļ\nìĤ ¯\nìĪ ¯\nëĨ ĭ\nâľ ¯\nðŁļ Ģ\nÚ ĺ\nÚ ¨\nâľ Ń\nê² ħ\níĮ °\níľ Ļ\nðŁĮ Ĭ\nðŁİ ĵ\nðŁĺ Ļ\nË ĥ\nðŁĴ ģ\nðŁij İ\nâĺ ¹\nðŁĺ «\nðŁĴ »\nëĤ µ\nìĿ Ĭ\níĮ »\nÒ ³\ná½ ²\nâŀ ŀ\nëĤ ij\nëĿ Ī\nì£ ¤\nï» ¯\nðŁĩ ©\nðŁ¥ ³\nâĴ ¼\nðŁ¦ ĭ\nâĺ Ĥ\nðŁĺ °\nðŁĻ ĥ\nðŁĺ Ĵ\nÛ İ\nÏ ķ\nḠ¤\në£ ½\nìĬ ¥\nðĿij ī\nÉ IJ\nðŁį İ\nâķ ¯\nâķ ¹\nຠ²\nï¾ ł\në¹ ķ\nïº Ĩ\nÊ º\nÓ §\nâĨ ł\nëĥ ĩ\nìİ Ī\nìŁ ¤\nï± ¢\nâķ ¬\nâĺ ł\nðŁİ Ĭ\nãį į\nãİ İ\nâĺ °\nâľ ĥ\nãħ ī\në¯ Ī\në¹ ¤\nìı Ń\nðĿij ¢\nðŁIJ ¾\nÅ ĭ\nðŁij ¶\nâĶ Ľ\nï¿ ¢\náĥ ¡\nÄ ¼\nÅ Ĩ\nÑ IJ\nìĥ Ľ\nìĺ Į\nì± ¤\níħ ģ\níļ ĥ\nï³ Ĭ\nðĿij Ķ\nðŁĩ «\nâĭ °\nðŁĺ ¨\nâĤ ©\nÕ ¬\nḠį\ná» ´\nâĨ ĺ\nâĺ ¯\nãħ ı\nìł ¬\nâĻ Ķ\nðŁĶ Ķ\nðŁĺ ł\nðŁĻ Ĭ\nà® ľ\ná¹ ħ\nâĹ IJ\nâĿ Ī\nâŀ ½\nìĥ ħ\nðĿij ł\nÆ ¢\nâĭ Ļ\nê° Ľ\nëĿ µ\në£ Ł\nìı ľ\nïº ģ\nðŁĴ Ń\nâĬ ĥ\nðŁIJ °\nãħ Į\nÜ ĵ\nâŀ ķ\ná½ ģ\nìķ ³\nðĿij Ŀ\nðŁİ ¬\nÉ ¡\nठĹ\náIJ ī\nì© ľ\nì¶ §\nï³ ī\nï» ħ\nðĿIJ ŀ\nठ¶\nðŁĵ ¢\nðŁį ĭ\nðŁĴ ħ\nï¾ ķ\n⬠Ĩ\nâĪ µ\nðŁ¤ ij\náĥ £\nÆ Ħ\nÑ ¹\ná¼ Ķ\nê° ł\nê´ Į\nê· IJ\nëĽ ´\nì± ĺ\nï® Ń\nïº ¹\nïº ¾\nâľ Ĺ\nâĿ ¦\nðŁij ¦\náĥ Ĺ\nÙ ²\ná½ ´\nâĪ ı\nâľ ®\nê¹ °\në² µ\nìĦ Ģ\nì© Ŀ\nïº ŀ\nïº ½\nðŁĩ Ń\nË Ĥ\nðŁį ij\nðŁį Į\nðŁĶ »\nê¹ ¬\nìĬ Ń\nìľ ·\nðŁĽ ij\nÇ §\në¼ Ľ\nïº ¡\nïº º\nðĿij ļ\nðŁĵ ¦\nðŁĶ İ\nðŁĹ ĵ\náĥ Ķ\nâľ Ĵ\nâľ ¡\nðŁĮ µ\nâĶ ķ\nëĢ Ŀ\nðŁį Ĭ\nâĺ ĥ\nìĺ ħ\nঠ¬\nðŁ¦ ģ\nâİ ¯\nðŁIJ ķ\nÑ ¿\nॠ¤\n༠ĭ\nê· Ī\nì« Į\nðŁĩ °\nâĿ ī\nì« Ģ\níĿ Ħ\nðĿIJ ¢\nðŁļ ¨\nâĻ ¤\nðŁĺ ©\nðŁį į\nðŁĺ ij\nðŁļ ļ\nÖ Ħ\në «\në« ¼\nठı\ná¿ ·\nâĮ ©\nâĺ IJ\nâŀ £\nê¸ ±\nê¼ ¿\nëĦ Ŀ\nìı ´\nìļ ¤\nì¿ ±\níİ IJ\nðŁĴ ¢\nì´ IJ\nâĩ ij\nâĶ ĵ\nâģ ¾\nÜ Ŀ\nðŁ į°\nâ´ °\nÆ ı\nÏ Ł\nÚ º\nÛ ĥ\náĦ Ĵ\nâĪ Ł\nâĿ į\nãĦ ²\nìľ ħ\nì¤ ı\nðŁĩ ²\nêº Ħ\nðŁİ ¤\nâľ £\n⸠Ŀ\nï¸ µ\nຠ§\náĢ Ļ\nâķ ł\nÕ ¯\nâı ©\nðĿij £\nðŁĴ £\nÅ ĺ\nॠIJ\nâģ ĥ\nâĮ ĺ\nê» Į\nìĮ Ķ\nðĿij ĺ\nðŁ¤ ĵ\nÕ ¿\nठŃ\nâĮ ļ\nâľ Ŀ\nðŁIJ ¼\nË Į\nâķ ļ\nï¦ Ĺ\nâĿ ķ\nâķ £\nðŁIJ ±\nà® ¤\nÑ ¾\nठļ\nठľ\nìĪ Ħ\nìļ ľ\nðŁİ ®\nÉ Ĵ\nÚ ·\nຠį\nâĨ µ\nâ Īĺ\nâĿ Ĭ\në¿ į\nìIJ Ī\nìļ ĺ\nì¯ §\níĥ ¯\nìĸ ı\nï¸ °\nðŁĩ ¯\nðŁ§ ļ\nðŁĺ µ\nðŁĺ ·\nðŁĮ ³\nຠ¥\nÄ ī\nÄ ¥\nâľ ¶\ná¿ ¾\nâĬ ±\nâĺ ¾\nê° ī\nê¼ °\nëº ij\nðŁĶ Ĭ\nðŁĸ IJ\nÅ ¤\nÒ «\nà® ®\nâĮ Ī\nâĹ Ĺ\nëĦ µ\nëħ ľ\nëľ ¹\nðĿij ¥\nðŁĴ ¿\nðŁĽ Ĵ\nÊ Ĵ\náŀ ĵ\nðŁIJ Ŀ\nðŁ¦ Ħ\nðŁį ·\nâĺ Ł\nï¸ ¶\nðŁ¤ Ł\nÔ ±\nâĨ ²\nâĪ İ\nâľ «\nëĩ ½\nëı IJ\nëķ Ħ\nï¦ ³\nï§ Ŀ\nïº Ļ\nðŁij »\nðŁĵ º\nêµ ¼\nìĮ ©\nðŁĮ ²\nÈ ±\níĶ ķ\nðŁĺ ¤\nãĮ ¢\nÊ Ķ\nठ¡\ná¼ Ī\nëİ ĥ\në© ±\në® Ī\nðĿIJ «\nâĬ ķ\nëĥ ł\në» ¬\níĭ Ķ\nÕ ¤\ná¼ ±\nâľ ¥\nâĺ Ħ\nâĪ ¥\nâļ ķ\nðŁij Ħ\nðŁİ ħ\nຠĻ\nâĶ ¬\ná½ µ\nÕ ¾\nÖ ģ\nâĹ Ķ\nê¿ į\nëĸ µ\në© İ\në® ´\nìķ ´\náĥ ľ\ná¼ ¡\nâĶ Ĭ\nâķ ®\nâĹ ¼\nðŁį ¾\nðŁĽ į\nðŁij Ĺ\nðŁ¤ ŀ\nâľ Ħ\nÕ Ģ\nঠ²\nË ī\n⣠¨\nÄ ¯\nÏ Ĭ\ná´ ľ\në¹ ³\nï³ ĭ\nï¿ ł\nÄ ª\nâĤ ¸\nâľ ±\nê» IJ\nëĭ »\në§ ¸\nìŀ ¿\nì© ¨\nì ŃIJ\nì° ¿\níħ Ł\nðĿIJ §\nðĿij ij\nðŁĮ İ\nðŁĵ ®\nðŁķ Ķ\nâĹ Ļ\nâĹ »\nâŀ §\nìŁ Ŀ\nâľ ¬\nãĥ °\nâģ Ī\nâ ĵĺ\nðŁ ĴĮ\nï¬ ĥ\nຠĶ\nìĶ °\nðŁĺ ª\n× Ģ\nìĥ ¨\nïŃ ĭ\nðŁį ķ\nðŁĺ ´\nÏ ³\ná¼ Ħ\ná½ ħ\nâĩ ¢\nâķ Ń\nìĺ »\níĬ ¤\nÜ ĺ\n⤠´\nâĹ į\náŀ Ł\nðŁį º\náŀ ļ\nðŁı Ĭ\nðŁIJ ·\nÊ Į\ná½ º\nâģ »\nê½ Į\nëĪ Ĺ\në Ĺı\nì¿ °\níĢ ¼\níį ħ\nï· ²\nðŁĮ ı\nðŁį «\nðŁį ³\nðŁİ °\nðŁij °\nðŁĴ ²\nᥠĻ\nðŁIJ Ł\nï¿ ¡\nðŁĹ £\nðŁį ľ\nâľ ²\nãİ ¢\nðŁĶ °\ná¼ ¸\ná½ ij\nÄ İ\náĦ Ģ\nâĻ ķ\nëł Ŀ\nìĪ ´\nïŃ Ń\nÓ ľ\nÔ Ģ\nëĢ ľ\nëĥ Ķ\nìĬ Ľ\nì« ij\nìº ¥\nìº ¬\nðĿij ¦\nðŁĶ ¶\nì¾ ¨\nðĿIJ ļ\nðŁį »\nðŁĴ į\nðŁ¤ ¡\nðŁķ Ĭ\nâ½ ĩ\nâĵ IJ\nðŁį Ń\nðŁį ª\nðŁĶ Ĩ\nÒ ¡\ná´ ĩ\nÉ Ĺ\nÜ Ķ\nâĦ İ\nâĿ ĥ\nëĹ Ģ\nï² Ķ\nïº Ī\nðĿIJ »\nðŁĴ Ĭ\nðŁļ «\nÑ °\nÑ ³\nठ·\nâĹ ł\nðŁij ¤\nï¾ ĩ\nâĺ ĵ\nðŁį µ\nðŁ¤ ¨\nâĸ Ń\nà® ´\nÜ ¢\nÜ ¬\nà´ ®\nðŁķ º\nÔ ¹\nÕ £\nà´ ¯\ná ´Ģ\nâĮ ī\nâľ IJ\nâŀ ¦\nê¹ ½\nëĮ ľ\nðŁı ¥\nðŁĵ ©\nÒ ¹\nÓ ĺ\nठħ\nâĿ §\nÆ Ĺ\nâĹ ½\nðŁij «\nðŁİ §\nðŁij £\nâľ »\nðŁĻ ħ\nðŁĺ ĸ\nðŁĴ ®\nຠ°\nðŁĶ ľ\nðŁį Ħ\nðŁ¤ Ŀ\ná ĥĿ\náŀ Ģ\nâĩ ¦\nÊ ¾\nÒ ®\nÕ ¼\nठĨ\nâĹ ħ\nâļ ĵ\nâļ ĸ\nê¿ ©\në¯ Ħ\nìIJ IJ\nìŀ °\nì§ Ń\níĭ ĭ\níİ ¨\níĻ §\nï² ij\nðŁİ Ĺ\nÙ ³\nðŁij ¸\nঠ®\nðŁij ķ\nÚ µ\nâĢ ¾\nâŀ °\nðŁij ¯\nðŁİ ¼\nðŁı ģ\nÄ º\nÊ ı\nÚ ³\nâı ±\nê½ Ī\nëĿ Į\nìĮ ī\nìĹ ·\nìŀ ´\níĹ ¹\níľ ¨\nðĿĹ ²\nðŁĮ IJ\nðŁİ Ļ\nðŁı µ\níĽ Ļ\nðĿij ħ\nðŁĺ ¶\nâĵ ħ\nâķ ¥\nðŁį ı\nï¦ İ\nÕ ©\nðĿIJ Ħ\nÓ £\nÚ ¿\nâĻ ļ\nðŁĶ Ĺ\nḠ«\nâĭ ®\nâĸ ¦\n⼠½\nâľ µ\nãħ Ĩ\nãħ Ĭ\nëĦ Ļ\nëĿ ¨\në¥ Ħ\nìĦ ¦\nì§ °\nì§ ¹\níī Ī\nï§ ij\nï» ĩ\nðŁĮ ¾\nðŁı ĸ\nðŁIJ ij\nðŁĴ ³\nðŁĵ Ĩ\nÛ ĩ\nÜ ķ\ná½ ½\nëĦ ľ\nà´ ²\nà´ ³\nຠŃ\náĥ Ľ\nâĿ Ķ\nâij ħ\náĥ ¥\nðŁĵ ħ\nâŀ ³\ná´ µ\nï¹ ¡\nï¹ ¶\nÎ Ĩ\nठ¥\náī µ\nâĿ Ļ\nâĿ ±\nëī ł\nëİ ł\nëı Ľ\në¿ ħ\nìĶ ¸\níij ¯\níŀ ī\níŀ Ľ\nï§ Ħ\nïŃ ĺ\nïº ¦\nï» ¸\nðĿij Ĥ\nðĿij ı\nÏ ij\nÚ ł\náĢ Ķ\náŀ Ķ\ná¹ ¢\nëĦ ¸\nðĿIJ ¨\nðŁĩ ´\nÕ °\nðŁij ł\nðŁį Ĩ\nðŁı Ģ\nðŁ ijIJ\nðŁį ĩ\nðŁIJ £\náĪ Ń\nÜ ª\nðŁ ĮĢ\náŀ ĺ\nâĩ Ħ\nðĿIJ Ģ\nÊ Ļ\nâĶ ¼\nðŁı ¿\nÆ ·\nÈ ł\nÑ ½\nâĤ ¨\nê´ Ń\nê¹ »\nëĶ ¨\nìĪ Ģ\nì¾ °\níĨ Ī\nï® §\nï¯ ½\nðŁĶ ħ\nðŁĶ ®\nÅ ¢\nÊ °\nÑ ¸\nठ£\nâĬ Ĺ\nëª Ħ\nï¹ ·\nïº ħ\nðĿIJ µ\nðŁĮ ¶\nðŁĵ °\nðŁĶ ·\nðŁĸ Ĵ\nðŁ¤ ²\nëī ©\nðŁİ Ĩ\nðŁ§ IJ\nðŁį ®\nâĨ º\nâĿ ¢\nðŁij ª\nðŁij ±\nâĨ ¡\náŀ ı\nÚ ķ\nðŁį ¹\nðŁĴ Ģ\nË ®\nÓ ¨\nÖ ħ\nठĩ\nâĤ ¡\nâĪ ķ\nâĺ ī\nê¹ ¼\nê¼ IJ\nì½ ¸\nðĿIJ ¬\nðŁı ħ\nðŁij Ļ\nðŁĴ ī\nðŁ¤ Ļ\nÈ ĺ\nÉ ³\nÉ ¹\nÙ º\náĢ Ħ\ná¿ ³\nâļ ĺ\nâĿ Ĩ\nëĨ ī\nìĸ į\nìĺ ĩ\nì¥ ĺ\níĸ ħ\níĻ ij\nï® Ĭ\nï¿ Ń\nðĿĴ IJ\nðĿĹ ¢\nðŁĶ ĸ\nðŁĶ ¨\nðŁļ ij\nðŁļ ²\nÆ ¸\nâĹ ¥\nðĿIJ Ń\nðŁį ½\nâĹ ij\nâĵ ĩ\nðŁĶ ±\nâľ ¼\nï¹ ĥ\nâķ ±\nãĢ Ĺ\nðŁı ĭ\nðŁļ ´\nðĿIJ ®\nÄ ļ\nÕ ı\nÄ ¶\náĥ ij\ná¹ ¬\nÄ Ī\nÄ Ĵ\nÒ °\nÓ ķ\nâ IJ\nâIJ £\nâĹ ¢\nâļ Ļ\nãħ Ĺ\nê° ¬\nê³ ª\nê» Ģ\nëĦ ´\nëİ ģ\nëĿ Ķ\në¬ ½\nëŃ į\nìĩ ³\nì° ¹\níĮ ¹\níŀ Ŀ\nï® ĭ\nï ¶Ī\nðĿĴ Ĥ\nðŁ¥ Ģ\nðŁ¦ ħ\nÊ ĺ\ná¼ ij\nâģ İ\nðŁį ŀ\nâĨ ĸ\nâĨ Ļ\nðŁİ ĥ\nâĦ ¡\nâĭ ±\nðŁĶ į\nಠ¨\náµ ĥ\nâĶ «\n⦠¿\nðŁĩ »\nÆ ¤\nÒ ı\nÒ ·\nÛ ī\nà® ķ\nḠ³\nï¬ ±\nðŁĨ Ķ\nÚ Ń\nÛ ¦\náħ ¡\nâĦ ¹\nê¿ İ\nëķ Ķ\në¼ ī\nìļ §\nì² µ\nì´ ¨\níĬ Ī\níĸ IJ\nðĿĹ ĺ\nðŁĩ ¿\nðŁİ ĸ\nðŁij ħ\nðŁ ĵĺ\nðŁļ Ļ\nðŁĽ µ\nච½\n⼠µ\nðĿIJ ³\nðĿIJ ¸\nâļ Ķ\nðŁij Ń\nÓ ij\nâĶ ¯\nðŁħ ¿\nðŁĺ ¹\nï¿ «\nâ¼ ¤\nðŁĴ ĩ\nðŁĵ İ\nðŁĸ ĭ\nঠ¸\nðĿIJ į\nÄ ²\nÏ ĭ\nÑ ¬\nÚ ¬\nÜ Ĵ\ná´ ¬\nï¨ Ħ\nÉ £\nË ij\nÏ µ\nÒ Ŀ\nÛ ¥\nÜ ł\n๠Ľ\náĥ ķ\náĬ ķ\ná¾ ¶\nâĤ ·\nâĩ ¾\nâķ ©\nâĸ IJ\nâĺ ª\nâĺ ®\nâĿ ļ\nâĿ Ń\nâŀ ±\nâµ İ\nãı Ĭ\në© ĵ\nìĹ ¾\nìª Ħ\níĵ Į\níķ ¼\nïŃ ¬\nðĿij Ĩ\nðĿij ŀ\nðĿĸ Ĭ\nðŁİ ¸\nðŁı Ħ\nðŁij µ\nðŁĴ ł\nðŁĶ ĺ\nðŁ¥ Ĥ\nÅ ª\nà· ĥ\ná´ ¼\nâĬ °\në³ ı\në´ £\nï¥ ľ\nðŁĵ Ī\nðŁķ ¯\nðŁ§ Ģ\nâĻ IJ\nðŁĨ Ĺ\nðŁĵ ķ\nðŁ§ ģ\nÜ «\nâĿ IJ\nÕ ķ\nའķ\nâŀ Ŀ\nঠķ\nðĿIJ ¶\nÉ ¢\nÎ Ħ\náĨ ¢\nâĤ ±\nÕ į\nà¡ ķ\ná´ °\nḠ©\n⼠·\nâĿ ®\nê¡ ĵ\nëı ¤\nëĹ IJ\nëµ Į\nìij Ī\níı ¿\níĹ µ\nðĿIJ İ\nðŁĨ ĺ\nðŁı Ł\nÉ ¥\nÕ »\nà¡ Ķ\nठĸ\ná´ ¸\nâİ Ļ\nâİ ¥\nâı ³\nëģ ķ\nëĬ ī\nì¡ į\nì¹ ¡\nï¦ ¶\nï¬ Ł\nï® «\nï® ¯\nï± ĥ\nï ·»\nïº µ\nðĿĹ Ķ\nðĿĹ ¡\nðŁİ ¨\nðŁĶ Ĵ\nÚ Ľ\nठ§\nâŀ ¹\náĢ Ģ\nðŁį ħ\nâĹ ¤\nठł\nðŁIJ ¥\náĥ Ĵ\nðŁı Ŀ\nðŁį ¼\nãĮ §\nâĿ Ľ\nðŁIJ Ī\nঠ¯\náĢ ŀ\nãĢ ĸ\náŀ Ļ\nঠª\nÕ Ĩ\nâĬ Ĩ\nâľ ¾\nðŁIJ Ĺ\nï¹ ¿\nÄ ¦\nÜ Ł\nಠł\nಠ¥\náŀ ī\ná´ ¥\ná´ ©\ná½ Ģ\ná½ ¡\nâĨ ķ\nâŀ ¯\nê¡ ij\nëij £\në± Į\nìĪ ij\nìľ Ķ\nìŀ ½\nì¨ į\nðĿij Ģ\nðŁĮ Į\nðŁį ¦\nðŁį ©\nðŁIJ ļ\nðŁĵ Ĵ\nðŁĵ ¹\nðŁ¥ ij\nÄ ĭ\nË Ĺ\nÑ «\nÕ ¢\nÚ °\nâ ĮĢ\nâĹ Ĥ\nâĹ £\nâľ Ľ\nâĿ Ĵ\nâĿ ĺ\nâŀ Ļ\nâŀ ²\nãİ į\nê¡ IJ\nëŀ ĸ\nìĬ Ŀ\nìĽ ¤\nì¡ ĭ\nì¨ °\níĹ Ļ\nï¥ ¸\nï³ į\nï» İ\nðĿij ĵ\nðŁĵ Ĭ\nðŁļ ¼\nï¦ ģ\nðĿķ Ĵ\nðŁ ijľ\nðŁij ¿\nðŁĩ ½\nà· Ħ\nâĸ ´\nãį ī\nâĬ ĩ\nðŁ§ ¸\nÚ ¡\nâ¾ ĥ\nðŁĹ »\nâĵ ij\nðŁ¤ ¸\nðŁ¤ ¯\nêĴ °\nðĿIJ ĵ\nâĶ ´\nêĴ ±\náĢ ĺ\nâ ĽĦ\nï¹ ¹\nÓ Ķ\náĥ ±\nÜ ¡\nß ŀ\nâĻ ı\nâľ ¸\nìij ¨\nðĿIJ Ŀ\nðĿIJ ¥\nðŁį ī\nðŁij ¼\nðŁ¥ Ŀ\nÆ Ķ\nÝ ¬\nठ«\nຠļ\ná´ ´\ná½ ĸ\nâĤ ¶\nâİ ¢\nâĿ ħ\n⣠«\nãİ Ľ\në® ¨\nëº Į\në¼ ĺ\nìĨ Ŀ\nìľ ³\nìŀ Į\nì£ Ĺ\nìª ĺ\nì» ¹\nï· ¼\nïº Ĥ\nðĿIJ ´\nðĿIJ ¼\nðŁĮ ļ\nðŁı «\nðŁĴ ¤\nðŁĴ ¶\nðŁĴ ¼\nÊ ķ\nÊ ½\nâ² Ł\nãī ł\nê¡ Ĵ\nëľ Ģ\nìĥ ¾\nì¸ ¤\nï¥ ģ\nðĿļ Ĭ\nðŁļ ĥ\nâŀ Ľ\nìħ ´\náĦ ĭ\nâĩ Ĺ\nï§ ·\nâĺ ĸ\nðŁIJ ¦\n⸠ľ\nðŁĴ ´\nðŁ¤ ļ\nãĬ Ĺ\nâĮ Ľ\náĪ Ľ\n༠º\nâ½ ī\nðŁı ¢\nâĵ ŀ\nâĺ ½\nãĢ Ļ\nðŁ¤ ®\nÅ IJ\náĥ ¬\nðĿĹ »\nðŁį ĸ\nÆ Ĭ\nÊ Ł\nß ĭ\nठĭ\náµ Ķ\ná¿ ĥ\nâĦ ī\nâĮ ĭ\nâı ²\nâĵ Ī\nâĵ ¢\nâķ Ķ\nâļ ij\nâĿ ĭ\nâĿ İ\nâ µľ\nâµ £\nëĴ Ī\nëľ ģ\në¶ ĩ\nìį »\nìĺ Ń\nì§ ¢\níĹ Ģ\nï§ Ĭ\nï ¬¸\nï± ¡\nðĿIJ º\nðĿij §\nðĿĺ ¦\nðŁĵ ¥\nðŁĺ Ł\nðŁ¥ IJ\nÄ ĸ\nÉ ¨\náĢ IJ\náĥ ĵ\nẠĵ\ná¼ ¶\ná½ Ħ\nâĤ ¤\nâĮ ľ\nâĮ Ł\nâİ ł\n⼠¸\nâµ į\nâµ ı\nâµ ĵ\nãĢ ĺ\në ·¸\níħ ¼\nï¦ Į\nïŃ Ħ\nïŃ İ\nðĿĻ ļ\nðĿļ ĺ\n༠ĵ\nëŃ ħ\náIJ Ľ\nãİ ¾\nï¨ Ģ\nðŁĹ ½\nâĻ ŀ\nË ĸ\nâĹ ŀ\nðŁ¤ «\nðŁĺ Ĺ\nï½ ¦\nðŁ¤ ¢\nâģ ĩ\nãĢ µ\nðŁį Ķ\náĬ ł\nðŁĺ ¼\nðĿĹ ®\nðŁIJ ³\nðĿIJ ĭ\nðŁĨ ļ\nðŁĶ Ľ\nÑ »\nÜ ¨\nà® ²\nâľ ŀ\nâµ Ļ\nêµ £\nì¸ ¨\nðĿ IJľ\nðĿĺ °\nðŁĶ ½\nÇ »\nÇ ¿\nÊ ĩ\nÎ IJ\nÐ Ģ\nÑ ¡\nÑ ²\nÒ Ĵ\nÙ ¶\nß ķ\nච±\náIJ ģ\nâģ ŀ\nâĸ §\n⼠Ī\nâľ ľ\nâľ ¹\n⣠¹\n⤠ĩ\nê² Ĭ\nê¾ ľ\në¯ IJ\në³ IJ\nìħ ©\nìIJ ¬\nìij ¹\nï¤ Ķ\nï¦ ļ\nï¬ ł\nïŃ Ķ\nïº ¶\nðĿĴ ı\nðĿĸ Ĩ\nðĿĹ ¶\nðŁı Ĥ\nðŁIJ ½\nðŁĴ ©\nðŁĵ ½\nðŁĹ ¨\nðŁĹ º\nðŁĺ ¸\nðŁ¥ §\nÅ Ĺ\nÊ İ\nÒ Ļ\n× ²\nठĪ\ná¼ ´\ná¿ ij\nâµ ī\nãħ ĵ\nì½ ´\nðĿĸ ĵ\nðŁĵ Ĺ\nðŁĶ ª\nðŁĸ į\nÏ Ĵ\nðŁij ¬\náĥ Ļ\nâĨ ¬\nâĶ ¤\n⼠¹\nâĻ Ł\nðŁļ ¶\nðŁij ¾\nâĪ ĭ\nðŁIJ ¯\n༠İ\nâľ ·\nï¨ Ļ\nâĶ »\nðŁij ¹\náĦ ī\nຠª\nâ¾ ı\nâ½ ħ\nãİ ĸ\nÑ ´\nÕ ®\nÚ ¼\náĢ ķ\náĨ ¼\nëŃ ı\nðŁIJ ¸\nðŁļ £\nÆ Ŀ\nÔ »\náĥ ¢\nðŁį ¯\nÉ ¦\nÕ ¦\nâĻ ĭ\nï¬ «\nðĿĹ ¦\nÇ ļ\nÉ ±\nठī\ná´ Ħ\nâĻ ĵ\n⼠°\n⣠ª\nëĥ ĺ\në¢ ¸\nìĤ ij\nï® Ķ\nðĿķ ĸ\nðĿĹ §\nðŁĩ ¼\nðŁĵ ĭ\nðŁļ ľ\nðŁ¥ ¤\nÄ ®\nÅ ·\nß Ĭ\nॠ¥\nà® ª\náŀ Ħ\náµ Ģ\nḠħ\ná¼ ¢\nâĪ Ŀ\nâĬ ¹\nâĴ ¶\nâķ ´\n⼠±\n⼠³\n⼠º\nâŀ Ł\nãı Ħ\nê¸ Ķ\nê¹ Ł\nëĩ °\në¹ »\nìĤ ¥\nìĽ »\nì° Ł\níĥ °\níĨ º\níļ ½\nï¤ ´\nï¥ ¾\nï³ Ŀ\nðĿIJ ¦\nðĿĴ ľ\nðĿĴ Ł\nðĿļ Ĺ\nðŁİ Ń\nðŁı ĵ\nðŁı ³\nðŁı º\nðŁIJ į\nðŁij ĥ\nðŁĴ ı\nðŁ¤ ĸ\nðŁ¤ µ\nÕ ²\nâµ Ķ\nëĺ ¬\nï¦ £\nÊ Ĥ\náĨ «\náŀ ij\nðĿĸ İ\nðĿĹ ĸ\náĦ ĥ\nâĩ ł\náĢ ¡\nའĦ\nâŀ ¸\nï¦ Ļ\nâĩ ļ\nðŁIJ ¬\nðŁIJ ¢\nâ¾ Ĵ\nðŁIJ ¤\nðŁĶ «\nãĢ ŀ\nï¸ º\nðŁĺ º\nâ½ ´\nðŁĨ ķ\nâģ ¿\nðŁį ¨\nಠķ\nðŁļ ĺ\náŀ ħ\nঠħ\náŀ ¢\nਠľ\nâ ļĮ\nãĢ ½\nà· ´\nâĵ Ľ\náĢ ľ\nìĨ ¨\nË ©\nÜ Ĺ\nâĭ ¼\nðŁĻ ī\nÅ Ĭ\nÉ ĵ\nÊ ²\nÎ °\nÑ ¼\nÔ ¿\nà¡ IJ\n༠ľ\nའ¦\nᶠľ\nâĤ ²\nâĨ ¨\nâĬ ¥\nâķ §\nâĻ ľ\nãĭ ¡\në´ ¬\në¶ ij\nìī ¿\nìİ ħ\nìł ±\nì° §\nï² ¡\nðĿĴ Ľ\nðĿķ £\nðĿĹ ľ\nðŁį ²\nðŁİ ©\nðŁIJ IJ\nðŁIJ ł\nðŁij ½\nðŁĴ ij\nðŁĵ ľ\nðŁķ µ\nðŁ ļĮ\nðŁĽ £\nÊ ĭ\nÓ ¯\nÙ ¸\nß Ķ\nß Ļ\nà¡ ĵ\ná´ į\nḠ¿\nâı º\nâĸ ¥\në¤ ½\níľ ij\nðĿIJ ¹\nðĿĸ Ķ\nðĿļ İ\nðŁĵ Ħ\nðŁ¦ ·\nÆ ĥ\nঠŁ\nâĮ Ĥ\nâĺ Ń\nâ² ļ\nëĿ ķ\nðŁİ £\nà® ĩ\nའĨ\náħ µ\náĹ ľ\nâĢ ½\nâĮ £\nâģ ½\nðŁĵ ¬\nðŁ¤ §\nâĩ ª\nâ½ £\nâĹ Ł\nï¨ Ĺ\nêĴ ª\nðŁĽ Ģ\nÇ Ĥ\nðŁ¥ ¶\nðŁİ į\nï¿ ©\nðŁij Ĵ\náµ Ī\nï¸ ¿\náħ ©\nâ¾ ¦\nà° ¤\ná´ ĸ\nਠ¬\nຠĹ\n༠»\nÑ º\nਠª\ná´ ³\nðĿIJ Ī\nà» Ģ\ná´ ¿\nâĤ į\nâĩ ¡\n⼠ª\nðĿIJ Ĥ\nðĿĴ ķ\nðŁ IJľ\nÊ į\nÑ ±\nའĥ\në® IJ\nìĽ ¡\nìľ ģ\nðĿIJ ¿\nðĿķ ł\nðŁij Ľ\nÆ ª\nÏ º\nÓ ¬\nÙ ¿\nÝ £\nઠī\nà® ¹\nའij\náĨ ¯\náµ ĩ\nâĩ ¥\nâı ª\nâĻ °\nâļ Ń\nâļ ¾\nãħ Ħ\nêĢ °\nê° Ĺ\nê² ĭ\nê² »\nê¶ ľ\nê¼ ĩ\nê½ ¹\nëĤ Ł\nëħ Ī\nëĭ ¢\në§ Ł\nëª Ĩ\nëµ Ģ\nì½ ±\níĩ ĺ\níľ ľ\nï§ ¾\nï± µ\nï² ¢\nï² ¤\nðĿĴ Ĭ\nðĿĺ ¯\nðŁį Ĺ\nðŁı į\nðŁIJ ĺ\nðŁĵ ¡\nðŁĶ ŀ\nðŁ¤ ³\nðŁ¥ ģ\nðŁ¥ Ĺ\nðŁ¦ Ĭ\nÄ µ\nÆ ¦\nÇ µ\nÉ ¯\nÎ ı\nÕ Ħ\nÜ ¥\nའģ\nᨠł\nâķ «\nãİ ī\në· ´\nìĨ İ\nìİ Į\nì£ µ\níĽ ł\nï§ ª\nï³ ı\nï» º\nðĿij ģ\nðĿij ĩ\nðĿĴ Ĩ\nðŁİ ł\nðŁIJ Ķ\nðŁij Ł\nÅ ĸ\nठĮ\ná¾ ½\nê¦ Ĵ\nà® Ł\ná´ ±\nðŁı °\nðŁIJ ŀ\nའĢ\náĢ ħ\nâĬ ¿\nðŁIJ §\nἠģ\nâ¼ Ī\nâĶ ¿\nðŁ¥ ´\nâ¼ ¿\nðŁ§ ľ\nãħ ¿\nâĦ «\nãĢ ³\nãĬ Ļ\nâ¼ Ģ\nï ¦¬\nðŁı ¬\nðŁĵ »\náĬ Ľ\náĦ ħ\nຠĬ\nຠĽ\náħ ³\nðŁij ®\nà® ±\nâĺ ĩ\nðĿIJ ı\nà´ µ\nà» ģ\nའı\nའ¢\nᥠ±\nâĤ £\nï¥ ¦\nïŃ Ļ\nï´ ©\nï¹ Ĥ\nðŁį £\nðŁķ ¹\nÏ ĸ\nච¸\nຠ¢\náĭ Ń\nâİ Ŀ\nâĹ Ŀ\nâĻ Ī\nâĻ İ\nê½ ¥\nì³ Ķ\nì¼ ij\nï± °\nðĿij ĥ\nðŁĮ ª\nðŁį ¡\nÅ İ\nÊ ¦\nÑ §\nÓ İ\nÔ ´\nÚ Ī\nß ĵ\nß §\nठĶ\náĪ «\náĪ µ\náĹ ©\ná´ ł\ná¼ ł\nâĢ Ĺ\nâģ ij\nâĦ ı\nâĸ ĩ\nâ² £\nãĦ ³\nãī ®\nê³ Ĺ\nëĦ Ĵ\nëĸ «\në¡ Ħ\në¹ °\në½ ģ\nìĦ ģ\nìĮ ĺ\nìŁ Į\nì³ ī\nì¼ ķ\nï¬ »\nï³ İ\nï¹ ¸\nï¹ ¾\nðĿIJ Ĩ\nðĿij ·\nðĿĽ ¼\nðŁİ ı\nðŁİ ŀ\nðŁIJ Ļ\nðŁij Ĥ\nðŁĵ ģ\nðŁĸ ±\nðŁļ į\nðŁļ §\nðŁĽ ¡\nðŁ¤ Ĵ\nðŁ¥ ŀ\nðŁ¥ ©\nðŁ¦ Ģ\nðŁ¦ ĸ\nË ¢\nÜ ļ\nà® µ\náĢ ģ\náī °\nâı Ń\nâĻ ¿\nê³ ĺ\nëı Ŀ\nëķ ĥ\nìħ Į\nìĴ ¸\nìĽ Ł\níħ Ħ\níľ «\nï§ ĺ\nï¿ ¬\nðŁı ·\nðŁĶ §\nðŁ¥ Ī\nÆ ĸ\náŀ ĩ\náŀ ĸ\nâģ º\nâĹ ľ\nâŀ ©\nê¦ Ń\nëĻ ¤\nïŃ ¼\nðĿĻ ĸ\nðĿĻ £\nðĿĻ ¤\nðŁĮ Ŀ\nðŁĶ ij\nðŁĽ ł\nຠĩ\nâĺ £\nãĦ ¨\nðĿĸ Ĺ\nÓ ĵ\nâĨ £\nðŁ¥ ī\nðŁĮ ł\nðŁĺ ½\nãİ ł\nÅ §\nðŁIJ Ĵ\nï§ IJ\nðŁĺ ¿\nâĪ ¬\nðŁIJ ®\n⣠±\nಠ¡\nâ¾ ¼\nà° ²\nË ¶\nâĸ ¿\nÕ Ī\náŀ İ\náħ ¥\náŀ Ĺ\nÕ §\nðŁ¤ IJ\nðŁį ł\nঠ¤\nචº\nâĻ į\nìĺ Ļ\níĺ ĵ\nï¹ º\nðŁĽ ³\nÅ ī\ná´ İ\nâı ľ\nâĶ ³\nê¸ ·\nì¡ Ķ\nðĿĴ Ī\nðĿĴ į\nðĿĴ ¹\nðĿĵ ĩ\nðĿķ Ł\nðĿĹ ¹\nðŁĮ ħ\nðŁı ´\nÄ Ķ\nÄ ¤\nÅ µ\nÇ ¾\nÏ ŀ\nÏ ¶\nÔ ³\nÜ Ĩ\nß ©\nà¡ Ĵ\nठĺ\nචļ\nའĸ\náģ Ĭ\náĥ ŀ\náĦ Ĥ\náĭ «\ná´ º\nḠ£\nḠª\ná¹ Ĥ\ná¼ ·\ná¿ ĩ\nâĩ Į\nâı ¬\nâĻ Į\nâ® Ł\nâ´ »\nâµ Ł\nê¦ ķ\nê¦ ª\nê¦ ®\nê² Ħ\nê¾ IJ\nëĥ ij\nëķ ĭ\në¡ ¸\në¬ Ģ\nìĩ ¤\nìĪ ©\nìľ ķ\nìŃ ĺ\nì· °\nì ·¸\níľ Ģ\nï¤ £\nï§ į\nï± Ħ\nï³ ij\nðĿIJ ¤\nðĿĴ ĵ\nðĿĴ ¶\nðĿĹ ¼\nðĿĻ Ĭ\nðŁĩ ¾\nðŁĮ Ľ\nðŁĮ ®\nðŁİ ĩ\nðŁİ ²\nðŁı Ľ\nðŁij ¥\nðŁij ´\nðŁĴ Ĩ\nðŁĵ Ĥ\nðŁĵ §\nðŁķ IJ\nðŁĸ ķ\nðŁĺ §\nðŁĻ Ģ\nðŁļ Ĵ\nðŁĽ «\nðŁ¤ ł\nðŁ¥ ļ\nðŁ¥ Ľ\nðŁ¥ £\nÇ ¯\nÈ §\nÎ Ĭ\nÒ ²\n× °\nÛ ij\náĥ ©\náĦ Į\náĪ į\náī ¥\náı Ĥ\nâģ ±\nâĬ ¢\nâĹ ĵ\nâĿ °\në¿ ¡\nìĽ ©\níģ Ń\níĨ ³\níĬ Ħ\níĵ ¸\nï¥ £\nï¥ ´\nï± IJ\nï± ¯\nï³ ļ\nðĿĸ ĺ\nðĿĺ Ģ\nðŁIJ Ĭ\nðŁIJ Į\nðŁij ļ\nðŁĵ ĥ\nðŁļ Ľ\nðŁļ ª\nðŁ¤ °\nÄ ´\náĥ ®\náĹ ¨\nâĻ ®\nâ² ŀ\nãĪ Ķ\nì ħį\nãħ ĥ\nï¥ ¡\nຠ¡\nÕ İ\nÕ º\n⬠Ľ\nâ½ ¤\nðĿIJ ²\nâŀ µ\náĢ Ľ\nâĶ ħ\nâĨ Ł\nâ¼ Ĭ\nðŁĮ ½\nðŁļ ¿\nï¦ Ĭ\nãĦ £\n⼠©\nï© Ľ\nðŁį ±\nâ¾ ¨\nà´ ¤\náŀ ģ\nຠŀ\nÊ ļ\nðĿIJ Ĵ\nà´ ±\náŀ ľ\nà® ©\nà° Ĺ\nà´ ļ\nâĩ £\nï¦ ķ\nÕ ħ\nÆ ĺ\nâĤ ¦\nâĶ Ħ\nï¦ Ł\nï¦ «\nðĿIJ ģ\nðĿIJ ĥ\nðŁį ¸\nðŁIJ ²\nÅ ¶\nÉ ĸ\nß ĺ\nภ¦\nའĶ\náĨ ·\nâģ ķ\nâĵ Ĥ\nâĿ ľ\nï¥ ¥\nï¬ ®\nðĿĹ Ŀ\nðĿĹ ¿\nðŁİ ¾\nðŁĹ Ŀ\nðŁ¦ Į\nÆ ħ\nÇ ª\nÒ Ĺ\nÜ Ľ\nß ł\nà¡ ij\náī £\náĬ Ń\ná¹ ¡\nâŀ ¼\nâŀ ¾\nâ´ ±\nãī ¡\nê³ ¯\në½ Ī\nìĤ ĺ\nìī ij\nì «ĺ\níĮ ĥ\níĻ °\nï¤ Ĺ\nðŁĮ ¬\nðŁĮ °\nðŁį ¤\nÄ »\nÅ ĩ\nÆ ¨\nÉ ķ\nÒ ¢\nÒ º\nÖ į\n× ±\nÚ ±\nÚ ½\nÛ IJ\nठĽ\nà· Ģ\n๠ļ\nຠ«\ná´ ¹\ná ½Ķ\ná¾ ³\nâĤ Ĵ\nâĨ ´\nâĩ Ŀ\nâī ħ\nâ Į¨\nâĵ ĵ\nâĸ ¢\nâļ ¬\nâŀ Ń\nâ² Ĵ\nãİ ¿\nê¿ ´\nëĪ ±\nëį ¬\nëİ IJ\nëIJ «\nëĶ «\në± ģ\nìĥ ¥\níĮ ¼\nïŃ ĵ\nï® ¥\nï² °\nðĿIJ ĩ\nðĿIJ ij\nðĿij Į\nðĿĵ ª\nðĿķ ļ\nðĿĺ ª\nðĿĺ ¼\nðĿļ Ľ\nðŁĩ ¶\nðŁĮ Ħ\nðŁĮ ķ\nðŁĮ ¤\nðŁĮ §\nðŁį ¬\nðŁİ ĭ\nðŁİ »\nðŁı ¨\nðŁIJ ĩ\nðŁij ĵ\nðŁĵ IJ\nðŁĵ Ļ\nðŁĶ ¼\nðŁķ Ĵ\nðŁĸ ı\nðŁĸ ¥\nðŁ¤ ¬\nðŁ¥ Ĭ\nðŁ¥ Ĵ\nß Į\nຠĦ\ná¼ µ\nâķ ¡\nâ² ¤\nâ´ ¼\nâµ ¢\nãĪ ¯\nëĵ ¸\nëŁ ĩ\nëº į\nðĿĻ §\nðŁį Ī\nðŁĶ ¬\nðŁĸ Ĭ\nðŁ¤ ¾\nË ¡\nÜ ©\nâĮ ¡\nâŃ ij\nâ² ¦\në© ī\nì¼ Ń\nï¿ ¤\nðĿĴ İ\nðĿĹ ¥\nðŁIJ µ\nðŁķ ¶\nðŁķ ¸\nðŁ¤ ľ\nÕ ª\náĪ ĭ\nðŁ¥ µ\nï° ģ\náµ IJ\nâķ ĵ\náĢ ĸ\nâĭ Ī\nÉ ŀ\nâŀ ®\nॠ°\nãĨ ģ\nðŁĴ ±\nðŁı Ń\náĨ ¨\nðŁį ļ\nðŁ¦ IJ\ná´ »\nâĺ Į\nà´ ķ\nÕ ±\náħ ®\nðĿIJ Į\nÅ ¦\nຠķ\nâľ Ļ\nË ³\nÔ µ\nâķ Ĵ\nðĿĹ Ĺ\nðĿĹ ł\nÚ ļ\nঠ§\nâĨ Ŀ\nâĻ ī\nãĮ »\nì¹ Ĭ\nðĿĹ º\nðŁ§ ĺ\nì³ £\nï¬ Ŀ\nðŁij º\nÇ Ł\nÎ Ī\nÎ «\nÑ ¥\nÔ ²\nÕ ¨\nÜ ¦\nঠĨ\nঠ¥\náIJ ¢\ná¼ ģ\ná¼ ĺ\ná¼ ¦\nâĵ Ŀ\nãĪ °\nãİ Ĺ\nê² ¡\në¨ Ģ\nì£ Ķ\nì´ ¤\nìµ Ŀ\nï§ ´\nïŃ Ĭ\nï² Ł\nðĿIJ ·\nðĿij ĭ\nðĿĵ ī\nðĿĺ µ\nðŁĴ ·\nðŁĽ ©\nðŁ§ ¹\nÅ Ķ\nÊ ŀ\nË ¥\nÎ Į\nÑ ©\nÓ IJ\nÓ ł\nÚ ij\nÚ Ĵ\nß ¨\nઠĪ\náIJ ĥ\ná¹ ¯\nâĤ ĭ\nâĤ µ\nâĦ ħ\nâĦ ł\nâĪ £\nâī º\nâī »\nâĬ Ľ\nâĮ IJ\nâİ ĵ\nâĺ ¸\nâĻ Ĵ\nâļ Ĵ\nâľ ĩ\nâľ ł\nâ´ ·\nâµ ĸ\nãĦ ¸\nãī ¢\nãī °\nêĩ ´\nê´ ¸\nêº ł\nëĤ ı\nëĤ ¢\nëIJ Ģ\nëº ´\nìĥ ľ\nìį ħ\nì¤ «\nì± ¦\nìº ij\nì¼ ģ\nì¿ ³\níĤ ģ\níħ ¡\níĴ Ĥ\níĴ ī\níľ Ħ\nïŃ ª\nï® ¬\nï¯ ¦\nï± ª\nï² ı\nï ´Ģ\nï» Ĩ\nï¿ ¦\nðĿij Ĺ\nðĿĸ Ļ\nðŁĮ ¡\nðŁį Ŀ\nðŁį §\nðŁİ «\nðŁı ĺ\nðŁı ª\nðŁIJ ĭ\nðŁIJ Ľ\nðŁIJ º\nðŁij ĸ\nðŁij ŀ\nðŁij ·\nðŁĵ Ģ\nðŁ ĶĦ\nðŁĶ Į\nðŁķ Ļ\nðŁĻ į\nðŁĻ İ\nðŁ¦ į\nÇ °\nÉ Ł\nÊ Ĩ\nÔ ¼\nÚ ľ\nঠ¡\nঠ¶\náĴ ĥ\ná¼ ©\nâĵ ķ\nâ² Ī\nê° °\nê¹ ł\nêº ħ\nëĦ ¹\në¯ ĵ\níIJ Ī\nï§ ¶\nï® ij\nï² ¨\nðĿĴ ī\nðĿĴ Ķ\nðĿĹ ¨\nðĿĻ ŀ\nðĿļ Ĵ\nðĿļ ķ\nðŁIJ İ\nðŁ¤ ķ\nðŁ§ Ķ\nÏ °\nÔ Ŀ\nâĮ Ĭ\nâĴ ¾\nãī £\nïŃ ©\nðĿļ ŀ\nÊ ij\nঠ¦\náĦ ĩ\nâī ĥ\nâ² Ģ\nìŁ İ\nðĿij ¶\nðĿĵ ²\nðŁ İ·\nðŁļ ¹\nຠģ\náł ł\nãĦ ļ\nðŁIJ ¿\nἠļ\nâķ ³\nðŁIJ Ń\nâĴ ¹\nðĿĸ ļ\nâĻ ĸ\nãĪ ²\nâĨ ¾\náĦ Ĩ\nâķ Ľ\nðŁ¤ į\nâ½ ¥\nðŁ Į¨\nâĪ ®\nãĮ ĺ\nãį ij\nï¹ Ģ\nâĵ Ĺ\nâĬ Ħ\nðŁı ¹\nË Ĵ\nðŁ¤ ±\nãı ľ\nðŁİ Į\nï¥ Ń\nঠ£\nðŁİ ¹\nãĬ Ł\nà´ °\nðĿIJ Ķ\nà´ ¨\nའļ\nâľ º\nÕ ·\nðŁij ³\nঠľ\nâĺ ĭ\nâĻ Ĭ\nãĢ Ľ\nÈ ĭ\nà® °\náĥ ¨\nâĦ ķ\níij Ģ\nðĿĵ ĥ\nðŁ¦ Ķ\nÄ ¿\nÅ Ģ\nÆ ³\nÉ ļ\nÖ ĥ\nÜ £\nß Ł\nঠŃ\nৠ¡\nච»\nຠ£\nའĩ\nḠ¨\ná½ Ī\nâ½ ¬\nê¡ Ķ\nì³ Ħ\nï¨ ī\nðĿIJ ¡\nðĿĺ ¢\nðŁį ¿\nðŁİ Ł\nðŁı ī\nðŁĶ IJ\nðŁļ ħ\nðŁ¤ ½\nÆ į\nÇ «\nÇ ½\nÈ ļ\nÎ ī\nÓ ¤\nÓ ª\nÕ Ĭ\nÙ ¼\nÚ ´\nß Ŀ\nචľ\ná¼ ķ\ná¿ ¥\nâİ ŀ\nãĢ ļ\nãī ¤\nê³ ¸\nê· ģ\nëĵ Ħ\nëĵ ķ\nì¨ Ķ\nì± ¨\nðĿIJ ¾\nðĿij »\nðĿĶ ¼\nðĿķ Ŀ\nðĿĺ Ń\nðŁĨ Ļ\nðŁĵ ¤\nðŁĶ Ł\nðŁĹ ¼\nÄ ľ\nÆ ģ\nÆ ¿\nÇ ³\nÇ ·\nÉ ĥ\nÉ ł\nÊ ī\nÊ §\nË ²\nÏ ´\nÕ ģ\nÕ ŀ\nÖ ĩ\nÛ Ĥ\nÛ ĵ\nß Ĺ\nß ¦\nঠ¹\nà® ³\nà´ ¸\nà» Ĥ\náĪ Ŀ\náĪ ª\náĭ µ\náIJ Ĭ\náĴ ª\náļ ĸ\náŀ Ľ\ná´ ¢\náµ ı\náµ Ń\nᶠ«\nḠı\nẠĴ\ná¼ ¥\ná½ ķ\ná½ ¼\nâĤ Ĭ\nâĦ Ĥ\nâĦ ©\nâĩ ī\nâī £\nâĮ ł\nâİ Ł\nâı ®\nâķ ĺ\nâĹ ĸ\nâĺ ©\nâĻ ij\nâĻ ²\nâļ Ľ\nãĦ Ł\nãī ±\nãİ ļ\nê¡ ķ\nêª ĸ\nê° ¹\nê² Ĩ\nêµ Ħ\nëĩ ¬\nëĭ ¯\nëı ł\nëĴ ¬\nëĸ Ī\nëĸ ½\nëĺ Ķ\nëŀ ¸\në¸ ħ\në» ł\në¿ Ł\nìĤ µ\nìĬ ī\nìľ °\nìł ĭ\nìł Ķ\nì¥ ¡\nìŃ Ŀ\nì¼ ¬\níĪ ĩ\níī ľ\níį Ħ\níĽ ¾\níĿ £\nï¤ ©\nï¤ ¯\nï¦ ľ\nï¦ §\nï§ ľ\nï¨ Ī\nï¬ ª\nï ¬´\nïŃ ½\nï® ī\nï¯ ŀ\nï° Ĵ\nï± ĩ\nï¿ Ħ\nðĿIJ ħ\nðĿij Ħ\nðĿij º\nðĿĴ Ĺ\nðĿĵ ®\nðĿķ Ľ\nðĿķ ŀ\nðĿĸ ij\nðĿĺ ģ\nðĿĺ Ĩ\nðĿĺ ¶\nðĿĻ ¢\nðĿļ ľ\nðŁĮ ĥ\nðŁĮ ¦\nðŁį Ł\nðŁİ İ\nðŁı Ļ\nðŁIJ ©\nðŁIJ «\nðŁIJ ´\nðŁij Ķ\nðŁĵ ī\nðŁĵ Ľ\nðŁĶ ī\nðŁĸ ¼\nðŁĹ ĥ\nðŁĹ ¯\nðŁļ ĩ\nðŁļ IJ\nðŁļ µ\nðŁ¤ ¶\nðŁ¥ ĭ\nðŁ¥ ĵ\nðŁ¥ ®\nðŁ¦ İ\nðŁ¦ ł\nðŁ§ Ĵ\nðŁ§ ¨\nÆ IJ\nÇ į\nÓ Ģ\nÔ Ľ\nಠ°\nà´ Ļ\náĢ Ĵ\nê² Ŀ\nê¹ ¹\në© ¥\nìĸ Ķ\nï¤ ģ\nï¤ ı\nï¦ ī\nï¦ ĵ\nï§ ī\nï² Ŀ\nðĿĹ ŀ\nðĿĹ ±\nðŁĮ ĭ\nðŁį ¶\nঠļ\nìķ ľ\nðĿIJ ¯\nðĿļ Ŀ\nà° ¨\nའĺ\nའł\ná¡ ¥\ná¾ °\nâģ į\nâĶ °\n⬠ľ\nðĿIJ ł\nðĿij ¯\nðĿĹ Ľ\nðĿĵ »\nðĿĸ Ī\nâŀ »\náŀ ł\nâ¡ ±\nâ» ij\nðŁ§ µ\nï¦ ¢\nðŁij ĺ\nãĤ Ķ\nâ¼ Ł\nãĬ ¤\nï¦ Ŀ\nãĮ ¦\nâĢ ¸\nðŁĶ Ļ\nã ¹\nã¹ ¦\nï¹ ħ\nï© Į\nãī ¨\nï¸ ½\nâį ¥\nðŁļ ī\nðŁ¥ ľ\nâĵ ľ\nâ» Ŀ\nï¨ ľ\nðŁĴ 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Ľ\nðŁ¤ Ľ\nᨠ¾\nâŀ º\náķ ¯\nἠı\nâĩ Ĥ\nâĶ ¹\nâĻ Ĺ\nðŁĸ ¨\nê¦ ı\nઠ°\náļ ¨\nðŁ¤ ¥\nðŁ§ ¢\nãIJ Ĥ\nãĦ ¥\nðŁĸ Į\nâ¼ Ĵ\nãĬ §\nâį ©\nðŁ¦ ij\nâĶ ·\nï© IJ\nï© ¡\nðĵ Ī\nðĵĪ Ĵ\nâ» Ħ\nï¨ Ĵ\nâĦ ª\nÒ §\nÚ Į\nâĢ ¶\n⺠ł\nâ» ģ\nâĨ ¸\náĦ IJ\nãħ IJ\nà» Ħ\náĹ ª\nâĨ ¼\nâĩ ĭ\nâĩ ĺ\nâĮ ij\nâĸ ©\nðĿIJ Ĺ\nÄ Ĭ\nঠī\nìī ł\nÉ ¤\nß į\nß ı\náµ Ĺ\nâĤ ¥\nâĵ ī\nâĶ ł\nâĶ ¨\nâķ Ħ\nä ¤\nä¤ Ģ\nê» ¸\nï® ģ\nðĵ Ĥ\nðĵĤ ĥ\nðŁ¦ ķ\nÆ Ľ\nঠĩ\nãı ĺ\nï® ¼\nÚ ĵ\nÚ Ŀ\nঠĵ\nච¯\ná´ ħ\ná½ Ļ\nâģ ¼\nâĸ İ\nâ¼ ©\nä Ķ\näĶ Ģ\në» ¡\nìĽ ½\níģ Ħ\nï¥ ¼\nï± ī\nï¹ »\nðĿĸ ĭ\nðĿĻ Ī\nðĿĻ ª\nðĿ Ļ¶\nðŁIJ Ħ\nðŁIJ Ĩ\náİ ¢\nḠĮ\nâĿ ´\nðŁı ¸\nÈ Ŀ\nÉ ¸\nÎ ħ\nÏ ľ\nÓ ¢\nÕ ¹\nà´ ħ\nຠĪ\náĭ °\náij İ\náł µ\ná¡ ł\ná´ ī\nḠµ\ná¿ ´\nâĵ £\nâĶ ¶\nâ½ ¯\nê² ¥\nê¿ ĺ\nëģ İ\nëİ Ī\nëĶ ¯\në² °\nìĺ ¯\nìĽ ¸\nìŀ Ĺ\nì§ ĺ\nì¬ ¬\nì· ¬\níģ ħ\níĵ Ķ\níĽ Ŀ\nï¤ ®\nï¤ ¹\nï¥ ²\nï¯ ĸ\nðĿĵ ħ\nðĿĻ Ħ\nðŁĵ ¶\nðŁĹ Ĵ\nðŁ¥ Ķ\nðŁ¥ Ń\nÅ ®\nÅ ´\nÆ ī\nÆ «\nÇ ģ\nÇ £\nÇ º\nÇ ¼\nÈ į\nÈ ¯\nÉ ľ\nÊ ¬\nË ģ\nË ¤\nË µ\nÏ Ľ\nÒ ¤\nÒ ¬\nÓ ı\nÓ Ľ\nÓ ¡\nÓ ³\nÔ Į\nÔ ¬\nÕ ³\nÙ »\nÚ ī\nÚ §\nÜ ľ\nß ª\nठĿ\nঠĽ\nਠĨ\nઠķ\nઠ¡\nà® İ\nà° ¬\nൠ»\nൠ¼\nචł\nචŃ\nච¶\nà· Ĩ\n༠½\náĢ ļ\náħ ¢\náĨ ¸\náĪ Ģ\náĪ ķ\náĪ °\náī ¡\náī ¤\náĬ ¦\náĬ «\náĭ ĭ\náĭ į\náİ ¯\náij Ń\náķ Ĺ\nᣠĽ\nᥠĴ\ná© ī\náŃ º\ná´ ¡\náµ ĺ\náµ Ľ\nᶠł\nḠģ\nḠĭ\ná¹ Ļ\ná¹ Ŀ\ná¹ ¦\nẠħ\ná¼ Ĥ\ná½ ĥ\ná½ į\ná½ §\ná¾ ·\nâĢ µ\nâĤ İ\nâĦ Ŀ\nâħ Ģ\nâĨ ŀ\nâĨ §\nâĩ ħ\nâĪ ĥ\nâī ı\nâī ½\nâĬ ŀ\nâĬ ¡\nâĬ §\nâ Ĭ¶\nâĭ Ħ\nâİ Ĵ\nâİ ¡\nâİ £\nâİ ª\nâı İ\nâĵ ĥ\nâĵ ĸ\nâĵ ¨\nâķ ĭ\nâķ ĸ\nâķ ¢\nâķ ²\nâĸ Ĩ\nâĸ Ĭ\nâĸ į\nâĸ ®\nâĺ ¡\nâĺ ¦\nâĺ ±\nâĺ ¿\nâĻ ĺ\nâĻ Ŀ\nâļ °\n⼠ij\nâŀ ª\n⤠Ŀ\n⤠¢\n⤠·\n⧠«\n⨠Ń\n⨠¯\nâ± £\nâ² İ\nâµ Ľ\nãħ Ķ\nãĪ ı\nãī ²\nãī ³\nãĬ ij\nãĭ Ľ\nãİ IJ\nê² ¤\nê· ¿\nê¹ ŀ\nê» ¨\nê¼ į\nê¿ ¸\nëĥ ¬\nëĩ IJ\nëĭ ł\nëį ¯\nëĹ Į\nëĹ ij\në¥ Ģ\nëª ĥ\nëª ¯\në± ¡\në³ ĵ\në³ ½\në µľ\nìĤ ³\nìħ ¥\nìĩ ½\nìı ¨\nìı ¸\nìķ į\nìĸ ĸ\nìŁ ¨\nì¢ ĥ\nì¢ į\nì¥ ij\nì§ ¼\nì© ĥ\nì® ľ\nì® ¸\nì³ ij\nì´ ¥\nì¾ ĥ\níħ ¦\níĪ ¿\níĵ ½\níķ ³\níĸ ı\níĹ ł\níĿ «\nï¤ ĵ\nï¤ ĺ\nï¥ İ\nï¥ ¶\nï¦ ħ\nï¦ ½\nï§ ĩ\nï¬ Ĩ\nï¬ ³\nï® ĩ\nï® Ī\nï® Ŀ\nï® ©\nï® ±\nï¯ ĺ\nï¯ Ļ\nï¯ ¢\nï¯ £\nï¯ ¤\nï¯ ¥\nï± Ĥ\nï² Ĩ\nï² ª\nï´ ¼\nïº ī\nïº Ĭ\nïº ¥\nðĿij ¨\nðĿij ©\nðĿij ²\nðĿ ĴĮ\nðĿĴ ª\nðĿĴ ®\nðĿĵ Ĥ\nðĿĵ Ī\nðĿĵ ¯\nðĿĶ ¨\nðĿķ Ģ\nðĿķ Ĩ\nðĿķ ¦\nðĿķ §\nðĿķ «\nðĿķ ·\nðĿĹ µ\nðĿĹ ¸\nðĿĺ Ħ\nðĿĺ Ļ\nðĿĺ ł\nðĿĺ ¬\nðĿĻ į\nðĿĻ ij\nðĿĻ ¡\nðĿ Ļ¨\nðĿĻ ·\nðĿļ į\nðĿĽ ¿\nðŁ ĥ\nðŁĥ ı\nðŁħ ĺ\nðŁ ī\nðŁī ij\nðŁİ ¡\nðŁİ ª\nðŁİ ±\nðŁİ ³\nðŁİ º\nðŁı İ\nðŁı Ĺ\nðŁı ļ\nðŁı ŀ\nðŁı ¦\nðŁı §\nðŁIJ ģ\nðŁIJ ħ\nðŁIJ ĵ\nðŁĴ Ĥ\nðŁĵ ij\nðŁĵ ĵ\nðŁĵ ¨\nðŁĵ «\nðŁĶ ĭ\nðŁĶ Ń\nðŁĶ ¯\nðŁķ Ĺ\nðŁļ Ĥ\nðŁļ ¢\nðŁļ ¦\nðŁļ ¬\nðŁĽ ĭ\nðŁĽ Į\nðŁĽ ¬\nðŁĽ ¶\nðŁŁ ¡\nðŁ¥ ĺ\nðŁ¥ Ł\nðŁ¥ ¦\nðŁ¦ ĩ\nðŁ¦ Ī\nðŁ§ Ĭ\nðŁ§ Ĺ\nðŁ§ ¤\nÊ ·\nË ¹\ná¹ ļ\ná½ ¥\nâĦ Ł\nê² ¯\nê» «\në° ·\nìĥ Ĩ\nìĽ Ŀ\nì¨ ī\nì« ı\nï¯ ķ\nðĿľ ĭ\nÉ ²\nÒ Ń\nÓ Ī\nའĽ\náĭ ĵ\náĻ Ń\náł ©\ná¹ ®\nâĦ Ĵ\nâĨ »\nâµ ĥ\nëĢ ¨\nëł §\nìī ¥\nìĮ ľ\nìĹ ¶\nì¨ Ī\nìª ¾\níı ½\níļ Ķ\níĽ µ\nï¤ ¸\nï¦ IJ\nï§ Ĺ\nï§ ļ\nï¬ ¯\nðĿIJ Ĭ\nðĿķ Ĺ\nðĿĹ ļ\nðĿļ ĸ\nðŁħ ´\nÈ ĥ\nÉ Ŀ\nÏ ±\nÓ Ĺ\nठ¢\náħ ł\náī ¦\náij Į\náĴ ¼\náŀ ¡\náł ¨\náł Ń\nᨠħ\nᨠĶ\ná´ ĺ\nᶠ¦\nḠİ\ná¼ ħ\ná¼ ¹\nâĨ ¯\nâĵ İ\nãı Į\nê ī\nêī Ĥ\nëĨ §\nëĿ ±\nì¢ ¡\níĪ ½\nï¤ ĩ\nï¤ Ľ\nðĿIJ ķ\nðĿĵ ¸\nðĿĵ ¼\nðĿĹ ķ\nðĿĺ Ī\nðŁı £\nðŁı ¤\nðŁĹ Ħ\nÑ ·\nÒ ł\náµ ĸ\ná¼ ¨\në¬ Ħ\nï° ´\nâĪ ½\nÕ Ń\nÚ ¹\nॠŁ\náĢ Ĩ\náŀ Ĵ\nãĢ ¶\nê¦ «\nï¸ ĵ\nðĿIJ Ľ\nðĿĺ Ĺ\nðŁı ľ\nì« Ń\nðŁ§ ŀ\nའĤ\nâĨ ¿\nâĩ ı\nâĵ ģ\nâĶ §\nâķ ģ\nâķ ¤\nê¦ Ĺ\nê¦ ¤\nðŁı Ī\náŀ ķ\nÔ ½\nઠĹ\nଠĨ\nâķ ķ\nï½ ł\nâ¼ ¦\nâ¼ ¯\nâ¾ ·\nâĶ ĸ\nଠĵ\nâĺ Ĺ\nâį ĭ\nï¨ Ŀ\nâ¼ ¥\nï¦ ª\nâĦ Ĭ\nãĢ ´\nâį ¢\nð¡ Ī\nð¡Ī ½\nï© ¨\nãĢ »\nãı ĥ\nï¦ ¡\nï¨ ĺ\nðŁIJ ĥ\nðŁĨ ĸ\nðŁĹ ¾\nãĦ ĩ\nÞ ĭ\nâ¼ ¼\nï¨ Ń\nÞ Ģ\nÞ Ħ\nÞ Ī\nÞ IJ\nâĮ Ħ\nâ» ĺ\nãŁ ¢\ná ħ§\nðIJĮ ¿\nË »\nಠĹ\náĢ ĩ\náŀ Ĭ\nâķ ĩ\nãĩ ¼\nãİ °\nÕ Ĵ\nÜ Ī\nß ¥\nà¿ IJ\náĢ Ł\nâĨ ¥\nâķ Į\nâ½ Ģ\nâ½ °\nâ¾ Ĭ\nä Ħ\näĦ Ģ\nðĵ IJ\nðĵIJ į\nðŁİ ¦\nâĤ ¯\nâĬ ĺ\nâĦ į\nÊ µ\nÑ ¶\nÚ ĥ\nঠĶ\nà´ ¦\náİ ¶\náĵ ķ\ná¹ ¨\nâĤ ł\nâĩ °\nâĹ Ĵ\nâ¿ Ĭ\nê· ±\nì¹ ķ\níĪ ©\nïŃ Ģ\nðĿĴ ¸\nðĿĵ Ĭ\nðĿĺ ©\nÇ ¦\nÉ «\náĬ ¨\nÈ ¹\nÊ ¯\nÎ ª\nÚ Ģ\náĮ ¸\náİ »\náı ķ\náı ´\ná² Ĥ\ná½ ¨\nâı Ŀ\nâĺ Ļ\nëĥ ¨\nëĦ ¼\nëĪ Ļ\në£ ħ\nìĶ ¼\nìķ Ŀ\nìļ ¬\nìľ ±\nï¥ Ĥ\nï¦ ¹\nï¬ ¹\nïŃ ģ\nï³ Ī\nðĿĶ ħ\nðĿĺ ¤\nðĿĻ ı\nðĿĻ Ļ\nðŁķ ī\nðŁ§ Ļ\nḠij\nê´ ¼\nëģ į\nëĹ ´\nëĿ ³\në° ŀ\në° ¢\nëµ ĺ\nìĤ Ķ\nìĦ Ħ\nì¼ ļ\níĢ ł\níĬ ±\níĮ ĸ\nï¤ ij\nï¦ ´\nï¦ ¸\nï´ į\nðĿĺ ·\nÄ ¬\nÅ ¬\nÆ Ģ\nÆ ĭ\nÆ ľ\nÇ ij\nÇ ĺ\nÇ ŀ\nÇ ¥\nÇ ®\nÉ °\nÉ ¶\nÉ ·\nÉ ½\nÊ Ī\nÊ IJ\nË İ\nË Ł\nË ¦\nË ¯\nÏ IJ\nÏ ĵ\nÏ ¢\nÏ ¤\nÏ ª\nÏ Ń\nÏ ®\nÏ »\nÑ ł\nÑ Ń\nÒ ¨\nÓ Ŀ\nÔ ¡\nÔ ·\nÕ ī\nÕ ĵ\nÕ ĸ\nÕ ļ\nÕ Ŀ\nÖ İ\nØ ¿\nÚ ħ\nÚ į\nÚ Ķ\nÛ Ĭ\nÛ ¾\nÜ Ļ\nÝ Ĵ\nÝ ĺ\nß Ĵ\nß ĸ\nठĬ\nठIJ\nঠı\nঠĸ\nৠŁ\nઠ®\nઠ¹\nà® ħ\nà® Ĩ\nà° ¡\nà° °\nಠļ\nಠ®\nಠ¯\nà´ Ł\nà´ ·\nൠ¾\nචij\nචŀ\n༠¼\nའĵ\náĢ ĵ\náĤ ¦\náĥ ĸ\náĥ Ń\náĥ ¯\náħ ¨\náħ ª\náĨ °\náĪ ģ\náĪ İ\náĪ ĵ\náĪ ¥\náĪ ²\náĪ ´\náĪ »\náī ł\náī ²\náī ¶\náĬ £\náĬ ¥\náĬ ª\náĭ ĺ\náĭ ²\náĭ ¶\náĮ £\náį ¡\náį £\náİ ¬\náİ ¾\náIJ ¡\náķ ķ\náĸ ±\náĹ IJ\náĹ Ń\náĺ ī\náļ ±\nἠŁ\náŀ ¥\nᣠĶ\náł £\náł ª\náł °\náł ´\nᤠĸ\nᥠ£\ná ®\ná® ł\ná ¯\nᯠĻ\ná °\ná° į\ná´ Ĭ\ná´ ¾\náµ ģ\náµ İ\náµ ŀ\náµ ¤\nᶠħ\nᶠĺ\nᶠŁ\nᶠ¢\nᶠ¤\nᶠ±\nᶠ»\nḠī\nḠŀ\nḠº\ná¹ ĵ\ná¹ Ĺ\ná¹ ª\nẠĬ\nẠı\nẠĽ\ná¼ ĥ\ná¼ Į\ná¼ ¿\ná½ Ĥ\ná½ ĵ\ná½ Ĺ\ná½ ¦\ná¾ ±\ná¾ ´\ná¿ ĺ\ná¿ Ł\ná¿ ¸\nâģ ĺ\nâĤ ij\nâĤ Ľ\nâĤ ¿\nâĦ ĩ\nâĦ ŀ\nâĦ ±\nâĩ Ł\nâĩ ²\nâĪ ¤\nâĪ ¶\nâī Ĥ\nâī ¾\nâĬ ¨\nâĬ ³\nâĬ ·\nâĭ Į\nâĭ ĺ\nâĮ ķ\nâĮ ¥\nâĮ µ\nâĮ º\nâį £\nâį ²\nâį µ\nâİ ĩ\nâı ĥ\nâı IJ\nâı ł\nâı ¤\nâı ¶\nâı ¸\nâı ¹\nâij Ĥ\nâĴ ·\nâĴ º\nâĵ ¡\nâĵ ¤\nâĶ ¾\nâĸ ĺ\nâĸ µ\nâĹ ª\nâĹ ·\nâĺ ¨\nâĺ «\nâĺ ²\nâĺ ³\nâĻ Ĩ\nâļ ¤\nâļ ¥\n⼠ĵ\n⼠´\n⼠¾\nâŀ «\nâŀ ¿\n⣠·\n⤠ij\n⤠«\n⤠¶\n⤠½\n⧠ª\n⨠Ģ\nâ ©½\n⬠¡\n⬠¢\n⬠¤\nâ² ĸ\nâ² ª\nâµ Ģ\n⸠®\n⸠½\nãĢ ł\nãĢ ·\nãĦ Į\nãĦ ĺ\nãħ ij\nãĪ İ\nãĪ IJ\nãĬ ľ\nãĮ ĵ\nãĮ ł\nãİ Ł\nãİ ¤\nãİ §\n㬠®\nä Ī\näĪ Ģ\nä °\nä° Ģ\nê ħ\nêħ ī\nêĩ Ĺ\nê Ī\nêĪ į\nê§ Ĥ\nê§ Ĭ\nêª Ģ\nê² Ī\nê² į\nê³ Ģ\nêµ ł\nê½ IJ\nê¾ Ī\nê¿ ±\nëĥ ı\nëĦ ij\nëħ ¤\nëĩ ¸\nëĪ ¼\nëī ħ\nëĬ £\nëĭ º\nëį ŀ\nëIJ Į\nëķ ¸\nëĺ ł\nëĻ ĩ\nëĻ Ī\nëľ ½\nëŀ Ķ\nëł ľ\në£ IJ\në§ Ģ\në§ Ĭ\nëª Ģ\në¬ Ń\në¯ ¾\në³ ľ\në´ Ĭ\nëµ ī\në· ľ\në¸ Ģ\në¹ ĭ\nìģ Ħ\nìĤ £\nìĤ »\nìĦ µ\nìħ Ĵ\nìī Ī\nìī Ķ\nìĬ Į\nìĬ Ļ\nìIJ ´\nìĵ º\nìķ ļ\nìķ º\nìĸ ľ\nìĹ ª\nìĺ ľ\nìĻ ¤\nìļ Ľ\nìļ º\nìĿ ħ\nìĿ ı\nìĿ Ń\nìĿ ¶\nìł Ľ\nì¡ Ī\nì¢ ī\nì¢ Ķ\nì© ł\nìŃ Į\nì¯ ©\nì´ £\nì¸ ķ\nì¹ Ł\nì¾ ¡\nì¿ Ļ\níģ ĩ\níģ ī\níĩ Ģ\níĪ ¶\níĸ ij\níĸ ¤\níĹ ħ\níľ ı\níĿ Ŀ\nï¤ Ĵ\nï¤ ķ\nï¤ ¬\nï¥ ħ\nï¥ ĩ\nï¥ ı\nï¥ ļ\nï¥ Ł\nï¦ Ħ\nï¦ Ī\nï¦ ¨\nï¦ ©\nï¦ ²\nï§ ģ\nï§ ĥ\nï§ Ķ\nï§ ł\nï§ £\nï§ ®\nï ŃIJ\nïŃ ĸ\nïŃ ¦\nïŃ ´\nïŃ µ\nïŃ ¶\nïŃ ¸\nï® Į\nï® İ\nï® ŀ\nï® Ł\nï® ¡\nï® ª\nï¯ Ķ\nï¯ Ĺ\nï¯ ļ\nï¯ Ľ\nï¯ Ŀ\nï¯ Ł\nï¯ §\nï¯ ¨\nï¯ «\nï¯ ¯\nï¯ °\nï¯ ±\nï¯ ²\nï¯ ³\nï¯ ´\nï¯ µ\nï¯ ¶\nï° Ģ\nï± ħ\nï± Ķ\nï± ´\nï² ģ\nï³ ķ\nï· ½\nï¸ ķ\nï¸ ±\nï¹ £\nï¹ ½\nï» į\nï¾ ±\nðĿIJ Ļ\nðĿIJ ½\nðĿij ¤\nðĿij ®\nðĿij µ\nðĿĴ ĥ\nðĿĴ Ħ\nðĿĵ Ń\nðĿĵ ·\nðĿĶ ĸ\nðĿĶ ŀ\nðĿĶ ¢\nðĿĶ ¦\nðĿĶ ¬\nðĿķ Ħ\nðĿķ Ĭ\nðĿķ İ\nðĿķ Ļ\nðĿķ ľ\nðĿķ Ń\nðĿķ ³\nðĿķ ¸\nðĿķ ¾\nðĿ ĸī\nðĿĸ ı\nðĿĺ ĩ\nðĿĺ ī\nðĿĺ ĸ\nðĿĺ Ľ\nðĿĺ ŀ\nðĿĺ «\nðĿĺ ¾\nðĿĻ ĩ\nðĿĻ ī\nðĿĻ ĭ\nðĿĻ İ\nðĿĻ ĺ\nðĿĻ ¥\nðĿļ ĥ\nðĿļ IJ\nðĿļ Ķ\nðĿľ ĥ\nðŁĦ ·\nðŁħ Ŀ\nðŁħ ¾\nðŁĨ Ĥ\nðŁĨ ĵ\nðŁĮ Ĥ\nðŁĮ Ĩ\nðŁĮ ī\nðŁĮ ij\nðŁĮ ĺ\nðŁĮ ©\nðŁĮ «\nðŁį ¢\nðŁį ¥\nðŁİ Ľ\nðŁİ ¢\nðŁİ ´\nðŁij ¡\nðŁĴ ¾\nðŁĵ Ń\nðŁĶ Ī\nðŁĶ ¦\nðŁĶ ²\nðŁĶ ³\nðŁķ ĵ\nðŁķ ķ\nðŁķ ĺ\nðŁķ Ł\nðŁķ ·\nðŁĹ ³\nðŁļ Ħ\nðŁļ Ķ\nðŁļ ĸ\nðŁĽ IJ\nðŁĽ ¤\nðŁĽ ¸\nðŁ ł\nðŁł ³\nðŁ¤ ¹\nðŁ¥ ĥ\nðŁ¥ ¨\nðŁ¥ ª\nðŁ¥ ¾\nðŁ¦ ĥ\nðŁ¦ Ĵ\nðŁ¦ Ļ\nðŁ¦ ¶\nðŁ§ ł\nðŁ§ ª\nðŁ§ Ń\nðŁ§ ²\n𣠷\n𣷠Ń\nð¦ ĺ\nð¦ĺ Ĵ\nÆ ij\nÇ Ļ\nÈ ®\nØ ł\nÚ Ħ\nÜ Ģ\nß ¢\náī Ģ\náĬ IJ\náİ ł\nẠŀ\nëĪ ŀ\nëķ Ł\në£ ģ\në¤ Ĺ\nìĦ ¥\nìħ ij\nìĸ IJ\nìĽ Ľ\nì£ ķ\níİ ı\níĽ ĵ\nï¥ º\nï³ Ľ\nï´ «\nðĸ §\nðĸ§ ·\nðĿķ ģ\nðŁIJ ª\nðŁĴ Ī\nðŁĵ ł\nðŁķ Ľ\nðŁķ ´\nÑ Ŀ\nÓ Ĭ\nॠ²\nઠª\náĥ ¤\náį IJ\nᶠ°\ná¼ Ŀ\ná½ ©\nâĭ ĭ\nâĴ ½\nâĻ ¾\nâ ½Ķ\nâ¾ ¯\nãĦ Ĵ\nãħ ļ\nëIJ į\në· ģ\nìĭ Ģ\nìļ Ŀ\nì¥ °\nìº ´\níĭ ī\níĿ ½\nï¦ Ģ\nï¦ ¿\nï§ ħ\nï§ ĵ\nïŃ ¯\nï® Ĩ\nðIJ¤ ķ\nðĿIJ Ł\nðĿĴ ħ\nðĿĵ ľ\nðĿĶ °\nðĿĶ »\nðĿĺ į\nðĿĻ ¯\nðŁĦ ½\nðŁħ Ĥ\nðŁħ Ķ\nðŁħ ½\nðŁĵ ´\nðŁ§ ĸ\nÓ Ĵ\nḠ²\nëī ¼\nÇ ı\nÈ ĵ\nÊ ¸\nÕ Ĥ\nÛ ħ\nß ¡\nß £\nà® ¯\nà° Ī\nಠ¸\nຠ®\n༠ķ\náĢ İ\náĨ ¡\náIJ ĭ\náIJ ķ\náij ¯\náŀ Ĩ\nᨠķ\ná© Ī\nâģ ħ\nâĨ ļ\nâĶ İ\nâł ©\nâ² Ĥ\nâ² Ķ\nâ² ¨\nãĬ ļ\níĵ ²\nðĿij Ī\nðĿij ¬\nðĿij ¹\nðĿĴ ¾\nðĿĵ ±\nðĿĵ ½\nðĿķ ¯\nðĿķ »\nðĿĺ ½\nðĿļ Ĩ\nðŁĦ °\nðŁIJ ¨\nÒ ķ\nಠħ\nï¨ Ĩ\nðĿij °\nðŁĦ ¸\nÔ İ\nØ į\nÙ µ\nಠ¶\náĢ Ī\náĺ Ĺ\náł ¸\ná¡ ¡\nᨠ²\ná© ģ\ná´ ·\náµ §\nâķ ¨\nâļ ģ\nâ¾ Ŀ\nãĢ ¼\nãĦ ı\nêĴ «\nê¦ ¥\nê¦ ©\nê¦ ²\nìĺ ¼\níĵ IJ\nðĵ ĩ\nðĵĩ ¼\nðĿķ ¿\nðŁĽ ´\në¨ ľ\nಠµ\nà´ İ\n༠Ģ\nâĩ ĸ\nãĪ «\nâĵ Ģ\náħ ´\náļ ¾\nἠŀ\nἠ«\nᥠ´\nâĨ Ľ\nâĨ ¶\nâĩ ¤\nâķ Ł\nâĺ ·\nâļ IJ\nðŁ§ ´\ná¹ ³\nâĶ į\nâĶ Ĵ\nâĶ ©\nâĶ ¦\nâ¾ µ\nઠľ\nઠ¤\nâĩ Ļ\nâĶ ±\nâķ Ģ\nâ½ Ĭ\nï½ Ł\nଠ¡\nðł ®\nðł® ·\nâķ ĥ\nâ° Ķ\nãĬ ¦\nðŁİ IJ\nãĩ °\nâ¼ Ŀ\nâ¾ Ķ\nâ½ Ĵ\nâł Ĵ\nï¨ ¦\nï© Ĵ\nï¨ ²\nï© ĸ\nðĵı ¸\nãĮ ĥ\nðĸ ¤\nðĸ¤ IJ\nï¦ Ń\nâĬ ħ\nâ¾ ³\nä´ ¥\nï© ķ\nðŁĮ Ķ\náŀ ĭ\nâļ į\nâ¼ ĭ\nãİ ĺ\nðIJĮ ²\nÉ ©\náİ ij\nâĨ ®\nâĩ ĥ\nâļ İ\nãĩ ±\nãĭ ©\nãĮ ¶\nêĻ ª\nëİ ¬\nï¨ IJ\nï¨ Ľ\nï© Ĭ\nï© į\nðĵ ħ\nðĵħ º\nÏ ¡\nÈ ij\nÉ Ĥ\nÔ ĵ\nß İ\nà´ §\náĢ ī\náĢ ĭ\náĢ ij\náĢ ł\náļ Ļ\nᨠĦ\nᨠ©\nᨠ¹\ná© ĵ\nᬠľ\ná´ Ļ\náµ ij\nâĤ Ń\nâĨ °\nâľ ģ\nâ½ IJ\nãĭ ¯\nãĮ ½\níĨ ¢\nï¤ ¿\nðŁ Ĥ\nðŁĤ »\nÈ Ĵ\nÍ º\nÔ ¥\nÕ ij\nÚ ¶\nৠİ\nච®\nຠĸ\nຠľ\nຠ½\náĥ »\náħ ¯\náĭ ŀ\náĸ ķ\ná ´Ī\nᶠĨ\nḠľ\ná¹ ¼\ná¿ ¨\nâĦ ĭ\nâĦ Ń\nâĪ ±\nâĮ ĵ\nâĶ ĩ\nâĶ ¢\nâ± ®\nâ² Ħ\nãĩ ¾\nãĪ ¬\në¸ ¡\nìIJ ī\níĻ Ľ\nðĿķ ª\nÆ ¹\nÍ ²\nÓ ģ\nÛ ¼\nঠ«\náħ Ł\náī Ĩ\náį Ī\nẠĸ\ná½ ī\nâĶ ¸\nâ½ ©\nê ľ\nêľ ¥\nêµ ħ\nëĤ Ķ\nëĦ ł\nëĩ Ĺ\nëĻ Ŀ\nìļ ¯\nìļ ·\nìŁ Ľ\nì· IJ\níŁ ¬\níŁ ®\níŁ °\nï¦ Ĩ\nï¦ ±\nï² ŀ\nï³ ¤\nï³ ¥\nðIJĮ ¸\nðĿĶ ı\nðĿķ ®\nðĿĺ £\nঠĪ\nâı ı\nãĦ ĸ\nê² ĩ\nëĸ ĺ\nëľ ·\nëŀ Ĵ\në¡ ĵ\në¢ ī\në£ ĥ\në§ ĭ\në² ĭ\nìĤ ·\nìĪ ķ\nì Į¨\nìĵ »\nìĸ Ĭ\nìĻ ¬\nìĿ »\nì¦ ģ\nìµ ¤\nì· ĥ\níĢ ľ\níħ ī\níį ł\níı ħ\níij ±\níķ ķ\níĸ ł\níĿ ķ\nÆ Ļ\nÆ ļ\nÆ ŀ\nÇ ĥ\nÇ Ĭ\nÇ ľ\nÇ ¤\nÇ Ń\nÇ ¹\nÈ Ģ\nÈ ģ\nÈ ħ\nÈ ī\nÈ Ĺ\nÈ Ł\nÈ ¤\nÈ ¥\nÈ ¨\nÈ µ\nÈ º\nÈ »\nÉ Į\nÉ ®\nÊ ħ\nÊ ¥\nÊ ¨\nË ĵ\nË Ķ\nË ł\nË £\nË ¸\nÍ ´\nÏ Ĺ\nÏ ĺ\nÏ Ļ\nÏ ļ\nÏ Ŀ\nÏ ¨\nÏ ¬\nÏ ¾\nÏ ¿\nÑ ª\nÒ Ģ\nÒ ľ\nÒ ¼\nÒ ½\nÓ Ĥ\nÓ ħ\nÓ ĩ\nÓ į\nÓ ĸ\nÓ Ł\nÓ «\nÓ ±\nÔ Ĩ\nÔ ĩ\nÔ º\nÕ ĭ\nÖ ī\nØ Ī\nØ Ĭ\nØ ½\nØ ¾\nÙ ·\nÚ Ĥ\nÚ Ĭ\nÚ ĸ\nÚ Ĺ\nÚ £\nÚ «\nÚ ¸\nÛ Ģ\nÛ į\nÛ ½\nÜ ī\nÜ ¤\nÝ §\nÝ ´\nÞ ĥ\nÞ ¤\nÞ ¥\nß ļ\nß Ľ\nß ¤\nàł į\nàł ĵ\nàł ³\nà¡ ¢\nॠł\nৠł\nৠº\nਠĬ\nਠIJ\nਠ®\nਠ¯\nਠ°\nਠ¸\nઠĨ\nઠ³\nઠµ\nઠ½\nଠĮ\nଠĺ\nଠ½\nà® ĥ\nà® ¸\nà° Ĩ\nà° ķ\nà° ¦\nಠĨ\nಠĬ\nಠĮ\nಠIJ\nಠĽ\nಠ¤\nಠ¦\nಠª\nಠ²\nಠ¹\nà´ Ĩ\nà´ ı\nà´ Ĺ\nà´ «\nà´ ¹\nൠº\nൠ½\nචħ\nචĬ\nචĶ\nච§\nච«\nච°\n༠Ħ\n༠ħ\n༠Ĭ\nའĻ\nའ¡\nའ§\nà¿ Ģ\nà¿ Ļ\náĢ Ŀ\náĢ §\náĢ ©\náĢ ¿\náģ µ\náĤ ģ\náĤ ½\náĥ Ĥ\náĥ ª\náĦ Ĭ\náĦ ¢\náħ ¦\náħ Ń\náĨ ®\náĨ ±\náĨ »\ná ĩ\náĩ Ĥ\náĪ ħ\náĪ ī\náĪ Į\náĪ IJ\náĪ Ĵ\náĪ Ļ\náĪ ļ\náĪ ľ\náĪ ŀ\náĪ ©\náĪ ³\náĪ º\náĪ ½\náī ħ\náī ¢\náī ±\náī ´\náĬ ĥ\náĬ į\náĬ ĸ\náĬ ®\náĬ ¸\náĭ Ľ\náĭ Ŀ\náĭ ³\náĮ ģ\náĮ ħ\náĮ ¥\náĮ ¦\ná Į¨\náį Ĭ\náį į\náį ķ\náį ĸ\náį ¢\náį ¤\náİ Ĵ\náİ ª\náı ģ\náı IJ\náı Ł\náIJ Ĥ\náIJ ĸ\náIJ Ŀ\náIJ ŀ\náIJ Ł\náIJ ł\náij ĸ\náĴ ĭ\náĴ į\náĴ ¡\náĵ «\náĶ ķ\náķ ĭ\náķ ij\náķ Ļ\náķ ļ\náķ Ľ\náķ ¤\náķ ¦\náķ ®\náķ ¼\náĸ ĵ\náĹ Ĺ\náĹ ¢\náĹ ¯\náĹ ·\náĺ Ħ\náĺ ij\nἠĤ\nἠĻ\náŀ į\náł Ĩ\náł ¡\náł ¦\náł ®\náł ¯\náł ²\náł ·\ná¡ į\ná¡ ŀ\ná¡ ¤\ná ¡´\ná¡ µ\nᤠĵ\nᥠĸ\nᥠ°\nᨠ¦\nᨠ§\nᨠ¨\nᨠª\nᨠ¬\nᨠ¯\nᨠ³\nᨠµ\ná© ĥ\nᬠķ\náŃ £\ná ±\ná± ļ\ná² ł\ná´ ĵ\ná´ ¶\náµ Ĥ\náµ Į\náµ ¥\náµ ´\nᶠĩ\nḠĪ\nḠł\nḠ§\nḠ´\nḠ¾\ná¹ Ģ\ná¹ ĸ\ná¹ Ł\ná¹ ł\ná¹ «\ná¹ ±\ná¹ ·\ná¹ ¿\nẠĦ\nẠį\nẠij\nẠĹ\ná¼ ī\ná¼ ĵ\ná¼ Ń\ná½ ĭ\ná½ Ĵ\ná½ ł\ná½ £\ná¾ Ħ\ná¾ ı\ná¾ ij\ná¾ Ĺ\ná¾ ¦\ná¾ §\ná¾ ¾\ná¿ Ħ\ná¿ ĵ\ná¿ ¡\ná¿ ¬\nâģ ļ\nâĤ Į\nâĦ ģ\nâĦ Ķ\nâĦ £\nâĦ §\nâĦ ¯\nâĦ °\nâĦ ´\nâħ ħ\nâĨ ľ\nâĨ «\nâĨ Ń\nâĨ ±\nâĨ ¹\nâĨ ½\nâĩ ĩ\nâĩ ľ\nâĩ µ\nâĪ ī\nâĪ Ĭ\nâĪ ĸ\nâĪ ľ\nâĪ ¾\nâī Ģ\nâī ĭ\nâī Į\nâī ĵ\nâī ľ\nâī ´\nâī ¿\nâĬ Ĭ\nâĬ ĭ\nâĬ Ķ\nâĬ ĸ\nâĬ £\nâĬ ¦\nâĭ İ\nâĭ ª\nâĭ ²\nâĮ ¦\nâĮ §\nâį º\nâİ Ī\nâİ ¨\nâİ ¬\nâİ ³\nâİ ¼\nâİ ¾\nâı Į\nâı ļ\nâı «\nâı ¯\nâı µ\nâĴ ľ\nâĴ Ŀ\nâĴ «\nâĵ Ħ\nâĵ Ĭ\nâĵ Ļ\nâĵ ©\nâĶ ij\nâĶ Ļ\nâĶ ļ\nâĶ ¥\nâķ ħ\nâķ ī\nâķ į\nâķ ı\nâķ ŀ\nâĸ ļ\nâĸ ¯\nâĹ ĥ\nâĹ ļ\nâĹ ¬\nâĹ ´\nâĺ Ī\nâĺ ¤\nâĺ ¥\nâĺ §\nâĺ ¬\nâĻ ģ\nâĻ ±\nâļ ĥ\nâļ Ħ\nâļ ħ\nâļ ı\nâļ ļ\nâļ ŀ\nâļ Ł\nâļ ±\nâļ ²\nâľ Ģ\nâľ Ł\nâľ ¢\nâĿ µ\n⣠¡\n⣠¦\n⣠§\n⣠³\n⣠¾\n⣠¿\nâł ĩ\n⤠Ħ\n⤠º\n⥠Ĥ\n⥠¹\n⧠ī\n⧠¼\n⧠½\n⨠į\n⬠Ĭ\n⬠Ł\nâŃ ŀ\nâ® ŀ\nâ® ³\n⯠Ī\n⯠ij\nâ± ł\nâ± ±\nâ² Ń\nâ´ ¹\nâµ ķ\n⸠¾\nâ º«\nâ¼ Ĩ\nâ¼ ł\nâ½ Ł\nâ½ ¼\nâ¾ Ľ\nâ¾ §\nâ¿ ĥ\nâ¿ »\nãĤ ķ\nãĤ Ł\nãĦ Ľ\nãĦ ¡\nãĦ ¶\nãĦ º\nãħ Ĵ\nãħ Ł\nãĨ Ģ\nãĩ »\nãĪ ij\nãĪ Ń\nãĪ ®\nãĪ ³\nãĪ ¹\nãī ¥\nãī ¦\nãī ¹\nãī ¿\nãĬ ŀ\nãĬ ¨\nãĭ ij\nãĭ ¥\nãĭ ´\nãĭ º\nãİ Ħ\nãİ ķ\nãİ ¯\nãı Ĥ\nãı Ī\nãı ĵ\nãı ĸ\nãı ±\nãIJ ±\nãŁ ģ\nã ¢\n㢠¨\nã ¨\n㨠³\nã« ª\nã« ´\n㶠³\n㺠¾\nä Ģ\näĢ Ģ\nä ĭ\näĭ Į\nä ĮĢ\näIJ Ģ\nä łĢ\nä ł\näł ¼\nä §\nä§ ŀ\nä¨ °\nä¨ º\nä ´Ģ\nä ·\nä· ħ\nä ·¸\nê Ĥ\nêĤ «\nê Į\nêĮ ¼\nê į\nêį ²\nêĴ µ\nê ĵ\nêĵ ½\nêĻ Ń\nêĿ Ľ\nêĿ ¥\nê ŀ\nêŀ Ĭ\nê¦ Ĩ\nê¦ ĩ\nê¦ Ł\nê¦ ¨\nê§ Ī\nê ©\nê© Ł\nêª ĭ\nêª ij\nêª ķ\nêª Ĺ\nêª ľ\nêª ®\nêª ±\nêª »\nêª ¼\nê« Ģ\nê« Ŀ\nê° ĥ\nê° ĺ\nê± ľ\nê² ĵ\nê² ļ\nê³ Ļ\nê³ ¾\nê´ Ĺ\nê´ Ļ\nêµ Ľ\nê¶ ĥ\nê¶ ķ\nê¶ ¨\nê¸ ©\nê¸ ¿\nê ¹Ħ\nê¹ Ĩ\nê¹ ī\nê¹ ĵ\nê¹ ¢\nê¹ £\nê¹ ¸\nêº ³\nê¿ ı\nê¿ ķ\nê¿ §\nëĢ ©\nëģ ħ\nëĥ µ\nëĦ ĸ\nëĦ Ĺ\nëĦ ¢\nëħ Ĥ\nëĨ IJ\nëĩ ľ\nëĪ ĭ\nëĪ ļ\nëī į\nëī ¨\nëĬ ļ\nëĬ ¡\nëĭ ľ\nëĭ ª\nëĮ ĺ\nëĮ ¤\nëĮ ¸\nëİ Ł\nëı ¨\nëIJ Ħ\nëIJ ı\nëIJ ´\nëIJ ¸\nëij ģ\nëij ¿\nëĴ ¨\nëĵ ·\nëĶ ®\nëĶ ²\nëķ §\nëĸ Ķ\nëĸ ª\nëĺ Ń\nëļ Ģ\nëļ ł\nëĽ Ķ\nëĽ ©\nëľ ħ\nëŀ ķ\nëŀ °\nëŁ IJ\nëł ¡\në¡ ŀ\në¡ £\në¡ µ\në£ Ħ\në£ į\në¤ ³\në¦ į\në¦ ı\në¦ ³\në§ Ħ\në§ Ĩ\në§ į\në§ ľ\në§ «\në§ »\në¨ ®\në© Ĥ\në© Ń\nëª ´\në¬ ľ\në¬ ł\në¬ «\në¬ ¾\nëŃ ¬\në® ĺ\në® ¹\në¯ ķ\në¯ ľ\në° ¨\në° ª\në± Ķ\në² ĺ\në² Ľ\në² ±\në² ´\në´ ½\nëµ ¤\nëµ ¨\në· Ĺ\në· ĺ\në¸ ĵ\në¸ ľ\në¹ ª\nëº ĥ\nëº ĺ\nëº µ\në» ´\në¼ IJ\në¾ Ķ\nìģ Ń\nìĤ ł\nìĤ ®\nìĥ ı\nìĥ Ļ\nìĦ º\nìħ ¢\nìĨ Ģ\nìĨ ħ\nìĨ ¤\nìĨ ¦\nìĨ ¬\nìĩ ±\nìĪ µ\nìĭ ¨\nìĭ ´\nìĮ °\nìį ľ\nìİ Ĺ\nìİ ĺ\nìİ ¼\nìij ī\nìij Ŀ\nìij »\nìĴ Ķ\nìĴ ¯\nìĵ ©\nìķ IJ\nìķ ĸ\nìĸ ł\nìĸ ¾\nìĹ ĥ\nìĹ Ĺ\nìĹ ľ\nìĹ ¨\nìĺ Ĥ\nìĺ Ħ\nìĺ ı\nìĺ ¾\nìĺ ¿\nìľ §\nìĿ IJ\nìĿ ĸ\nìĿ ·\nìŀ į\nìŀ ı\nìŀ ¨\nìŀ ª\nìŀ ³\nìł ¡\nìł ´\nìł ¹\nì¡ Ģ\nì¡ ª\nì¡ µ\nì¢ IJ\nì¢ ¨\nì£ Į\nì£ Ļ\nì£ ³\nì¦ ij\nì§ ¥\nì§ ´\nì§ ¾\nì¨ ĵ\nì¨ ķ\nì© °\nì© »\nì© ¼\nìª Ĺ\nì¬ Ķ\nì¬ ĺ\nì® ®\nì¯ ķ\nì¯ ĺ\nì° İ\nì° ¯\nì± ĥ\nì± µ\nì² §\nì² ®\nì² ¯\nì³ ¬\nì´ ĭ\nì´ ¢\nìµ ¥\nì¶ £\nì¸ Ī\nì¸ Ļ\nìº ¤\nìº Ń\nì» ½\nì¼ Ļ\nì½ ¬\nì¾ Ģ\nì¿ ħ\nì¿ ½\níĢ ħ\níģ ¦\níĤ ħ\níĥ ¶\níĥ ¹\níĦ Ķ\níħ £\níĨ Ħ\níĨ §\níĨ ¹\níĩ ¼\níī ¤\níĬ ½\níĭ Ĥ\níĭ ij\níį Ī\níį Ļ\níį ¿\níİ ¶\níIJ Ŀ\níĴ ľ\níĵ Ŀ\níĵ ª\níĵ ±\níĵ ·\níĵ ¼\níĶ Ļ\níĶ ł\níķ ļ\níķ Ľ\níķ ŀ\níķ Ł\níķ §\níķ ¶\níĸ Ĭ\níĸ ĭ\níĸ į\níĸ Ķ\níĸ ĺ\níĸ ¡\níĸ ¬\níĹ £\níĹ ¿\níĺ ĸ\níĺ Ń\níļ °\níĽ į\níĽ ½\níĿ Ł\níĿ Ń\níĿ ´\níŀ ľ\nï¤ ī\nï¤ Ń\nï¤ ²\nï¤ µ\nï¤ ¼\nï¥ Ģ\nï¥ ij\nï¥ Ĵ\nï¥ ķ\nï¥ ĺ\nï¥ Ļ\nï¥ «\nï¥ ¬\nï¥ °\nï ¥¿\nï¦ ĭ\nï¦ ı\nï¦ Ķ\nï¦ ĸ\nï¦ ĺ\nï¦ Ľ\nï¦ ł\nï¦ ®\nï¦ ¯\nï¦ º\nï¦ »\nï¦ ¾\nï§ Ĩ\nï§ ĸ\nï§ Ľ\nï§ ŀ\nï§ Ł\nï§ §\nï§ ³\nï§ º\nï§ ½\nï¨ ĥ\nï¨ ļ\nï¨ ¢\nï© Ł\nï¬ ¤\nï¬ ¬\nï¬ ¼\nïŃ Ĵ\nïŃ ķ\nïŃ Ľ\nïŃ Ŀ\nïŃ ŀ\nïŃ Ł\nïŃ ¤\nïŃ §\nïŃ ¨\nïŃ ®\nïŃ °\nïŃ ±\nïŃ ·\nïŃ ¹\nïŃ »\nï® Ģ\nï® ĥ\nï® Ħ\nï® ħ\nï® į\nï® Ĵ\nï® ĵ\nï® ķ\nï® ¦\nï® ®\nï® °\nï¯ ĵ\nï¯ ľ\nï¯ ©\nï¯ ª\nï¯ ¬\nï¯ Ń\nï¯ ®\nï¯ ·\nï¯ ¹\nï¯ »\nï¯ ¼\nï° ĥ\nï° Į\nï° IJ\nï° ĺ\nï° Ļ\nï° ľ\nï° ŀ\nï° ¢\nï° ®\nï° °\nï° ¼\nï° ¿\nï± Ģ\nï± ģ\nï± Ī\nï± ĭ\nï± ı\nï± Ń\nï² Ģ\nï² ĩ\nï² Ī\nï² ĭ\nï² İ\nï² Ĵ\nï² ľ\nï² ł\nï² ¬\nï² »\nï³ ĩ\nï³ Ķ\nï³ £\nï³ «\nï´ ĺ\nï´ °\nï´ ½\nï ¶\nï¶ °\nï¸ ĸ\nï¸ ´\nï¸ ¹\nï¹ į\nï¹ Ĺ\nï¹ ¢\nï¹ ¤\nï¹ ©\nï¹ ±\nï¾ °\nï¿ Ĥ\nï¿ ®\nðIJĮ °\nðIJĮ ¹\nðIJĮ º\nðIJĮ ½\nðIJį Ĥ\nðIJį ĥ\nðIJį Ħ\nðIJ İ\nðIJİ ¹\nðIJ¤ Ĥ\nðIJ¤ į\nðIJ¤ ı\nðIJ¤ ĵ\nðIJŃ ī\nðIJŃ į\nðIJ° ĩ\nðIJ° °\nðij Ĥ\nðijĤ Ħ\nðij ĺ\nðijĺ ģ\nðĴ Ģ\nðĴĢ ¸\nðĴ ģ\nðĴģ º\nðĴ Ħ\nðĴĦ ·\nðĴ Ĭ\nðĴĬ ij\nðĴ ĭ\nðĴĭ Ĺ\nð ĴĮ\nðĴĮ ¨\nðĵĥ ¢\nðĵĥ °\nðĸ ł\nðĸł ļ\nðĿĦ ĥ\nðĿĦ ħ\nðĿĦ ķ\nðĿĦ Ļ\nðĿĦ ±\nðĿĦ ´\nðĿĦ ¹\nðĿħ İ\nðĿħ ª\nðĿĨ £\nðĿĨ ³\nðĿĨ ¹\nðĿĩ Ĭ\nðĿĩ Ĺ\nðĿĩ ļ\nðĿĩ ľ\nðĿĩ ł\nðĿIJ ī\nðĿIJ ĸ\nðĿIJ ĺ\nðĿIJ £\nðĿIJ ±\nðĿij Ĭ\nðĿij Ń\nðĿij ¼\nðĿij ½\nðĿĴ °\nðĿĴ ·\nðĿĴ ¿\nðĿĵ ģ\nðĿĵ ĭ\nðĿĵ İ\nðĿĵ Ĵ\nðĿ ĵĺ\nðĿĵ ¢\nðĿĵ ¦\nðĿĵ «\nðĿĵ ¿\nðĿĶ İ\nðĿĶ ±\nðĿĶ ´\nðĿĶ ·\nðĿĶ ¸\nðĿĶ ½\nðĿķ Ĥ\nðĿķ ĥ\nðĿķ ĭ\nðĿķ ı\nðĿķ IJ\nðĿķ ¥\nðĿķ ´\nðĿķ º\nðĿĸ IJ\nðĿĸ Ľ\nðĿĸ Ŀ\nðĿĸ ŀ\nðĿĹ ©\nðĿĹ ³\nðĿĹ ½\nðĿĺ Ĭ\nðĿĺ ĭ\nðĿĺ Ķ\nðĿĺ ±\nðĿĺ ´\nðĿĺ ¿\nðĿĻ Ĵ\nðĿĻ Ŀ\nðĿĻ Ł\nðĿĻ ¬\nðĿĻ Ń\nðĿĻ »\nðĿĻ ¾\nðĿļ Ī\nðĿļ ĭ\nðĿļ ij\nðĿļ Ł\nðĿļ ł\nðĿļ £\nðĿĽ ½\nðĿľ Ĥ\nðĿľ Ķ\nðĿľ Ļ\nðŁ Ģ\nðŁĢ Ħ\nðŁĦ ²\nðŁĦ ¶\nðŁħ IJ\nðŁħ ĸ\nðŁħ ļ\nðŁħ Ľ\nðŁħ ¦\nðŁħ ¶\nðŁħ »\nðŁħ ¼\nðŁĨ ĥ\nðŁĨ Ĩ\nðŁĨ İ\nðŁĪ ¯\nðŁĪ ²\nðŁĪ ¹\nðŁĮ ĩ\nðŁĮ ĵ\nðŁį ĺ\nðŁİ ij\nðŁİ ¿\nðŁı ı\nðŁı Ĵ\nðŁı ©\nðŁı ¯\nðŁIJ Ģ\nðŁij Ŀ\nðŁĴ ¹\nðŁĴ º\nðŁĵ Ł\nðŁĵ ª\nðŁĵ ¼\nðŁĶ Ģ\nðŁĶ Ĥ\nðŁĶ ĥ\nðŁĶ ĩ\nðŁĶ ĵ\nðŁĶ ¢\nðŁĶ ¤\nðŁĶ ©\nðŁķ ĸ\nðŁķ ļ\nðŁķ ľ\nðŁķ Ŀ\nðŁķ ŀ\nðŁķ ł\nðŁķ ¢\nðŁķ ³\nðŁĸ ĩ\nðŁĸ ij\nðŁĸ ¶\nðŁĹ ģ\nÑ ¨\nÚ İ\ná¡ Į\nḠ°\nẠĢ\ná¼ ®\ná½ Ŀ\nâĦ ¬\nâļ §\n⼠¤\nã³ ¬\nêĻ ĭ\nê¸ ij\nëĶ ī\nëĹ į\në¡ ij\në¯ ij\në» ħ\në¼ Ŀ\nìĦ IJ\nìī ¡\nìĭ ²\nìı ±\nìĹ ¤\nìĿ ©\nìĿ ¿\nìŁ Ļ\nìł °\nì¥ ī\níĬ Ń\níķ ®\nï® ı\nðŁħ ±\nðŁĨ Ĵ\nðŁķ ĭ\nÉ ĺ\nÊ ĵ\nÕ ĥ\nà´ ´\nའħ\náĨ º\náĪ Ĭ\náĪ ¨\náĪ ¾\náī IJ\náĮ ĥ\náĮ ½\náĶ Ń\náł Ĥ\náł ¬\nᨠ¸\ná© ĭ\nᶠı\ná¾ Ķ\ná¿ IJ\ná¿ ļ\nâĻ Ļ\nâļ Ĥ\nâļ Ĺ\nâ¡ ¢\n⤠¦\nëĸ °\në¤ Ĥ\në§ ł\në± ĭ\në± IJ\nìĽ ¢\nìľ ¾\nì³ ħ\nì» ģ\níģ »\níĥ Ļ\níĵ ĸ\níĵ Ń\níķ ±\níĽ ľ\nï¤ ħ\nï¤ Ĩ\nï¦ ĥ\nï§ ©\nï¨ Ĥ\nðIJ¤ Ķ\nðIJŃ ĵ\nðIJ° ¼\nðĿĵ ŀ\nðĿĵ °\nðĿĻ ľ\nðĿļ ģ\nðŁħ ¢\nðŁı ĩ\nÈ ²\nÊ ¶\nÔ Ī\nÔ ij\nÝ ĵ\nÝ ¥\nठij\nॠ±\nଠī\nà° ³\nà° µ\nಠŁ\náĢ ı\náģ ¼\náī ¨\náĬ Ĵ\náĭ ©\náĮ Ħ\náĮ Ķ\náIJ §\ná ĴĮ\náĶ ħ\náĶ Ĭ\náł Ħ\nᨠģ\nḠĥ\nḠ»\nâĶ ŀ\nâĺ µ\nâļ £\nâ² ¢\nãĪ ª\nä¶ µ\nê² Ļ\nê² ´\nê³ Ĥ\në¡ ¼\nìĨ Ĭ\nì¼ ĩ\níĭ į\níĵ ¬\níĵ ®\níĵ ¶\níĵ »\nï¤ ¦\nï¥ ł\nï¥ ±\nïŃ ²\nðIJŃ Ĭ\nðIJ ±ħ\nðĸ ¥\nðĸ¥ ¨\nðĿij ³\nðĿĵ ķ\nðĿĵ ¬\nðĿĵ ¹\nðĿĵ ¾\nðĿĶ ĵ\nðĿķ į\nðĿķ ¡\nðĿķ ±\nðĿĸ ĸ\nðĿĺ ı\nðĿĺ IJ\nðĿĺ ļ\nðĿĻ ®\nðĿĻ °\nðĿĻ ¸\nðĿĻ º\nðĿĻ ¼\nðĿĻ ½\nðĿĻ ¿\nðĿļ Ħ\nðĿļ ı\nðŁħ ħ\nðŁħ ĵ\nÆ Ī\nàł Į\náĻ ³\ná ļĮ\nἠħ\nἠIJ\nᤠĬ\nḠĬ\nâĶ ½\nâķ Ĭ\n⼠ĩ\n⼠ı\nâĿ ª\nâĿ «\n⣠°\nãĦ į\nãĦ ĵ\nãĦ §\nãħ ĸ\nãī «\nê¦ Ķ\nï± Ĭ\nຠĤ\náħ £\nᥠĶ\nᥠ¤\nâĨ ¤\nâĨ ·\nâĩ ŀ\nâĸ ¤\nâŀ ¶\nãĪ ¼\nï¨ ·\nðĵı §\nâĶ ²\nâĢ ´\nâĴ Ł\nâĴ ¡\nâ° Ĥ\nâ° į\nâ° İ\nâ° IJ\nâ° ij\nâ° Ł\nâ° ł\nâ° ¡\nâ¼ Ń\nãĬ ¥\nâĴ ł\nâ½ º\nãĩ º\nãĩ ½\nï¨ Ĭ\náķ ·\nâį ¨\n⺠Ł\nâ½ Ĺ\n" }, { "path": 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\"special\": true\n },\n \"151648\": {\n \"content\": \"<|box_start|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151649\": {\n \"content\": \"<|box_end|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151650\": {\n \"content\": \"<|quad_start|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151651\": {\n \"content\": \"<|quad_end|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151652\": {\n \"content\": \"<|vision_start|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151653\": {\n \"content\": \"<|vision_end|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151654\": {\n \"content\": \"<|vision_pad|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151655\": {\n \"content\": \"<|image_pad|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151656\": {\n \"content\": \"<|video_pad|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"151657\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": false\n },\n \"151658\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": false\n },\n \"151659\": {\n \"content\": \"<|fim_prefix|>\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n 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\"special\": false\n },\n \"151666\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": false\n },\n \"151667\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": false\n },\n \"151668\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": false\n }\n },\n \"additional_special_tokens\": [\n \"<|im_start|>\",\n \"<|im_end|>\",\n \"<|object_ref_start|>\",\n \"<|object_ref_end|>\",\n \"<|box_start|>\",\n \"<|box_end|>\",\n \"<|quad_start|>\",\n \"<|quad_end|>\",\n \"<|vision_start|>\",\n \"<|vision_end|>\",\n \"<|vision_pad|>\",\n \"<|image_pad|>\",\n \"<|video_pad|>\"\n ],\n \"bos_token\": null,\n \"chat_template\": \"{%- if tools %}\\n {{- '<|im_start|>system\\\\n' }}\\n {%- if messages[0]['role'] == 'system' %}\\n {{- messages[0]['content'] }}\\n {%- else %}\\n {{- 'You are a helpful assistant.' }}\\n {%- endif %}\\n {{- \\\"\\\\n\\\\n# Tools\\\\n\\\\nYou may call one or more functions to assist with the user query.\\\\n\\\\nYou are provided with function signatures within XML tags:\\\\n\\\" }}\\n {%- for tool in tools %}\\n {{- \\\"\\\\n\\\" }}\\n {{- tool | tojson }}\\n {%- endfor %}\\n {{- \\\"\\\\n\\\\n\\\\nFor each function call, return a json object with function name and arguments within XML tags:\\\\n\\\\n{\\\\\\\"name\\\\\\\": , \\\\\\\"arguments\\\\\\\": }\\\\n<|im_end|>\\\\n\\\" }}\\n{%- else %}\\n {%- if messages[0]['role'] == 'system' %}\\n {{- '<|im_start|>system\\\\n' + messages[0]['content'] + '<|im_end|>\\\\n' }}\\n {%- else %}\\n {{- '<|im_start|>system\\\\nYou are a helpful assistant.<|im_end|>\\\\n' }}\\n {%- endif %}\\n{%- endif %}\\n{%- for message in messages %}\\n {%- if (message.role == \\\"user\\\") or (message.role == \\\"system\\\" and not loop.first) or (message.role == \\\"assistant\\\" and not message.tool_calls) %}\\n {{- '<|im_start|>' + message.role + '\\\\n' + message.content + '<|im_end|>' + '\\\\n' }}\\n {%- elif message.role == \\\"assistant\\\" %}\\n {{- '<|im_start|>' + message.role }}\\n {%- if message.content %}\\n {{- '\\\\n' + message.content }}\\n {%- endif %}\\n {%- for tool_call in message.tool_calls %}\\n {%- if tool_call.function is defined %}\\n {%- set tool_call = tool_call.function %}\\n {%- endif %}\\n {{- '\\\\n\\\\n{\\\"name\\\": \\\"' }}\\n {{- tool_call.name }}\\n {{- '\\\", \\\"arguments\\\": ' }}\\n {{- tool_call.arguments | tojson }}\\n {{- '}\\\\n' }}\\n {%- endfor %}\\n {{- '<|im_end|>\\\\n' }}\\n {%- elif message.role == \\\"tool\\\" %}\\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \\\"tool\\\") %}\\n {{- '<|im_start|>user' }}\\n {%- endif %}\\n {{- '\\\\n\\\\n' }}\\n {{- message.content }}\\n {{- '\\\\n' }}\\n {%- if loop.last or (messages[loop.index0 + 1].role != \\\"tool\\\") %}\\n {{- '<|im_end|>\\\\n' }}\\n {%- endif 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:151430,\"êĻĭ\":151431,\"ê¸ij\":151432,\"ëĶī\":151433,\"ëĹį\":151434,\"ë¡ij\":151435,\"ë¯ij\":151436,\"ë»ħ\":151437,\"ë¼Ŀ\":151438,\"ìĦIJ\":151439,\"ìī¡\":151440,\"ìĭ²\":151441,\"ìı±\":151442,\"ìŤ\":151443,\"ìĿ©\":151444,\"ìĿ¿\":151445,\"ìŁĻ\":151446,\"ìł°\":151447,\"ì¥ī\":151448,\"íĬŃ\":151449,\"íķ®\":151450,\"ï®ı\":151451,\"ðŁħ±\":151452,\"ðŁĨĴ\":151453,\"ðŁķĭ\":151454,\"Éĺ\":151455,\"Êĵ\":151456,\"Õĥ\":151457,\"à´´\":151458,\"à½ħ\":151459,\"áĨº\":151460,\"áĪĬ\":151461,\"áĪ¨\":151462,\"áĪ¾\":151463,\"áīIJ\":151464,\"áĮĥ\":151465,\"áĮ½\":151466,\"áĶŃ\":151467,\"áłĤ\":151468,\"ᳬ\":151469,\"ᨸ\":151470,\"á©ĭ\":151471,\"á¶ı\":151472,\"á¾Ķ\":151473,\"á¿IJ\":151474,\"á¿ļ\":151475,\"âĻĻ\":151476,\"âļĤ\":151477,\"âļĹ\":151478,\"â¡¢\":151479,\"⤦\":151480,\"ëĸ°\":151481,\"ë¤Ĥ\":151482,\"맳\":151483,\"ë±ĭ\":151484,\"ë±IJ\":151485,\"ìĽ¢\":151486,\"ìľ¾\":151487,\"ì³ħ\":151488,\"ì»ģ\":151489,\"íģ»\":151490,\"íĥĻ\":151491,\"íĵĸ\":151492,\"íĵŃ\":151493,\"íķ±\":151494,\"íĽľ\":151495,\"ï¤ħ\":151496,\"ï¤Ĩ\":151497,\"ï¦ĥ\":151498,\"里\":151499,\"ï¨Ĥ\":151500,\"ðIJ¤Ķ\":151501,\"ðIJŃĵ\":151502,\"ðIJ°¼\":151503,\"ðĿĵŀ\":151504,\"ðĿĵ°\":151505,\"ðĿĻľ\":151506,\"ðĿļģ\":151507,\"ðŁħ¢\":151508,\"ðŁıĩ\":151509,\"Ȳ\":151510,\"ʶ\":151511,\"ÔĪ\":151512,\"Ôij\":151513,\"Ýĵ\":151514,\"Ý¥\":151515,\"à¤ij\":151516,\"ॱ\":151517,\"à¬ī\":151518,\"à°³\":151519,\"à°µ\":151520,\"à²Ł\":151521,\"áĢı\":151522,\"áģ¼\":151523,\"áī¨\":151524,\"áĬĴ\":151525,\"áĭ©\":151526,\"áĮĦ\":151527,\"áĮĶ\":151528,\"áIJ§\":151529,\"áĴĮ\":151530,\"áĶħ\":151531,\"áĶĬ\":151532,\"áłĦ\":151533,\"á¨ģ\":151534,\"á¸ĥ\":151535,\"ḻ\":151536,\"âĶŀ\":151537,\"âĺµ\":151538,\"âļ£\":151539,\"â²¢\":151540,\"ãĪª\":151541,\"䶵\":151542,\"ê²Ļ\":151543,\"ê²´\":151544,\"ê³Ĥ\":151545,\"롼\":151546,\"ìĨĬ\":151547,\"ì¼ĩ\":151548,\"íĭį\":151549,\"íĵ¬\":151550,\"íĵ®\":151551,\"íĵ¶\":151552,\"íĵ»\":151553,\"臘\":151554,\"ï¥ł\":151555,\"辰\":151556,\"ïŃ²\":151557,\"ðIJŃĬ\":151558,\"ðIJ±ħ\":151559,\"ðĸ¥\":151560,\"ðĸ¥¨\":151561,\"ðĿij³\":151562,\"ðĿĵķ\":151563,\"ðĿĵ¬\":151564,\"ðĿĵ¹\":151565,\"ðĿĵ¾\":151566,\"ðĿĶĵ\":151567,\"ðĿķį\":151568,\"ðĿķ¡\":151569,\"ðĿķ±\":151570,\"ðĿĸĸ\":151571,\"ðĿĺı\":151572,\"ðĿĺIJ\":151573,\"ðĿĺļ\":151574,\"ðĿĻ®\":151575,\"ðĿĻ°\":151576,\"ðĿĻ¸\":151577,\"ðĿĻº\":151578,\"ðĿĻ¼\":151579,\"ðĿĻ½\":151580,\"ðĿĻ¿\":151581,\"ðĿļĦ\":151582,\"ðĿļı\":151583,\"ðŁħħ\":151584,\"ðŁħĵ\":151585,\"ÆĪ\":151586,\"àłĮ\":151587,\"áĻ³\":151588,\"áļĮ\":151589,\"áĽħ\":151590,\"áĽIJ\":151591,\"á¤Ĭ\":151592,\"á¸Ĭ\":151593,\"âĶ½\":151594,\"âķĬ\":151595,\"âĽĩ\":151596,\"âĽı\":151597,\"âĿª\":151598,\"âĿ«\":151599,\"⣰\":151600,\"ãĦį\":151601,\"ãĦĵ\":151602,\"ãĦ§\":151603,\"ãħĸ\":151604,\"ãī«\":151605,\"ê¦Ķ\":151606,\"ï±Ĭ\":151607,\"àºĤ\":151608,\"áħ£\":151609,\"á¥Ķ\":151610,\"ᥤ\":151611,\"âĨ¤\":151612,\"âĨ·\":151613,\"âĩŀ\":151614,\"âĸ¤\":151615,\"âŀ¶\":151616,\"ãĪ¼\":151617,\"嘆\":151618,\"ðĵı§\":151619,\"âĶ²\":151620,\"âĢ´\":151621,\"âĴŁ\":151622,\"âĴ¡\":151623,\"â°Ĥ\":151624,\"â°į\":151625,\"â°İ\":151626,\"â°IJ\":151627,\"â°ij\":151628,\"â°Ł\":151629,\"â°ł\":151630,\"â°¡\":151631,\"â¼Ń\":151632,\"ãĬ¥\":151633,\"âĴł\":151634,\"⽺\":151635,\"ãĩº\":151636,\"ãĩ½\":151637,\"ï¨Ĭ\":151638,\"áķ·\":151639,\"âį¨\":151640,\"âºŁ\":151641,\"â½Ĺ\":151642}" }, { "path": "comfy/text_encoders/qwen_image.py", "content": "from transformers import Qwen2Tokenizer\nfrom comfy import sd1_clip\nimport comfy.text_encoders.llama\nimport os\nimport torch\nimport numbers\n\nclass Qwen25_7BVLITokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"qwen25_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=3584, embedding_key='qwen25_7b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)\n\n\nclass QwenImageTokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=\"qwen25_7b\", tokenizer=Qwen25_7BVLITokenizer)\n self.llama_template = \"<|im_start|>system\\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\\n<|im_start|>user\\n{}<|im_end|>\\n<|im_start|>assistant\\n\"\n self.llama_template_images = \"<|im_start|>system\\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\\n<|im_start|>user\\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\\n<|im_start|>assistant\\n\"\n\n def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, **kwargs):\n skip_template = False\n if text.startswith('<|im_start|>'):\n skip_template = True\n if text.startswith('<|start_header_id|>'):\n skip_template = True\n if prevent_empty_text and text == '':\n text = ' '\n\n if skip_template:\n llama_text = text\n else:\n if llama_template is None:\n if len(images) > 0:\n llama_text = self.llama_template_images.format(text)\n else:\n llama_text = self.llama_template.format(text)\n else:\n llama_text = llama_template.format(text)\n tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)\n key_name = next(iter(tokens))\n embed_count = 0\n qwen_tokens = tokens[key_name]\n for r in qwen_tokens:\n for i in range(len(r)):\n if r[i][0] == 151655:\n if len(images) > embed_count:\n r[i] = ({\"type\": \"image\", \"data\": images[embed_count], \"original_type\": \"image\"},) + r[i][1:]\n embed_count += 1\n return tokens\n\n\nclass Qwen25_7BVLIModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=True, model_options={}):\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={\"pad\": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\nclass QwenImageTEModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n super().__init__(device=device, dtype=dtype, name=\"qwen25_7b\", clip_model=Qwen25_7BVLIModel, model_options=model_options)\n\n def encode_token_weights(self, token_weight_pairs, template_end=-1):\n out, pooled, extra = super().encode_token_weights(token_weight_pairs)\n tok_pairs = token_weight_pairs[\"qwen25_7b\"][0]\n count_im_start = 0\n if template_end == -1:\n for i, v in enumerate(tok_pairs):\n elem = v[0]\n if not torch.is_tensor(elem):\n if isinstance(elem, numbers.Integral):\n if elem == 151644 and count_im_start < 2:\n template_end = i\n count_im_start += 1\n\n if out.shape[1] > (template_end + 3):\n if tok_pairs[template_end + 1][0] == 872:\n if tok_pairs[template_end + 2][0] == 198:\n template_end += 3\n\n out = out[:, template_end:]\n\n extra[\"attention_mask\"] = extra[\"attention_mask\"][:, template_end:]\n if extra[\"attention_mask\"].sum() == torch.numel(extra[\"attention_mask\"]):\n extra.pop(\"attention_mask\") # attention mask is useless if no masked elements\n\n return out, pooled, extra\n\n\ndef te(dtype_llama=None, llama_quantization_metadata=None):\n class QwenImageTEModel_(QwenImageTEModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if llama_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = llama_quantization_metadata\n if dtype_llama is not None:\n dtype = dtype_llama\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return QwenImageTEModel_\n" }, { "path": "comfy/text_encoders/qwen_vl.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Optional, Tuple\nimport math\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\n\n\ndef process_qwen2vl_images(\n images: torch.Tensor,\n min_pixels: int = 3136,\n max_pixels: int = 12845056,\n patch_size: int = 14,\n temporal_patch_size: int = 2,\n merge_size: int = 2,\n image_mean: list = None,\n image_std: list = None,\n):\n if image_mean is None:\n image_mean = [0.48145466, 0.4578275, 0.40821073]\n if image_std is None:\n image_std = [0.26862954, 0.26130258, 0.27577711]\n\n batch_size, height, width, channels = images.shape\n device = images.device\n # dtype = images.dtype\n\n images = images.permute(0, 3, 1, 2)\n\n grid_thw_list = []\n img = images[0]\n\n factor = patch_size * merge_size\n\n h_bar = round(height / factor) * factor\n w_bar = round(width / factor) * factor\n\n if h_bar * w_bar > max_pixels:\n beta = math.sqrt((height * width) / max_pixels)\n h_bar = max(factor, math.floor(height / beta / factor) * factor)\n w_bar = max(factor, math.floor(width / beta / factor) * factor)\n elif h_bar * w_bar < min_pixels:\n beta = math.sqrt(min_pixels / (height * width))\n h_bar = math.ceil(height * beta / factor) * factor\n w_bar = math.ceil(width * beta / factor) * factor\n\n img_resized = F.interpolate(\n img.unsqueeze(0),\n size=(h_bar, w_bar),\n mode='bilinear',\n align_corners=False\n ).squeeze(0)\n\n normalized = img_resized.clone()\n for c in range(3):\n normalized[c] = (img_resized[c] - image_mean[c]) / image_std[c]\n\n grid_h = h_bar // patch_size\n grid_w = w_bar // patch_size\n grid_thw = torch.tensor([1, grid_h, grid_w], device=device, dtype=torch.long)\n\n pixel_values = normalized\n grid_thw_list.append(grid_thw)\n image_grid_thw = torch.stack(grid_thw_list)\n\n grid_t = 1\n channel = pixel_values.shape[0]\n pixel_values = pixel_values.unsqueeze(0).repeat(2, 1, 1, 1)\n\n patches = pixel_values.reshape(\n grid_t,\n temporal_patch_size,\n channel,\n grid_h // merge_size,\n merge_size,\n patch_size,\n grid_w // merge_size,\n merge_size,\n patch_size,\n )\n\n patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)\n flatten_patches = patches.reshape(\n grid_t * grid_h * grid_w,\n channel * temporal_patch_size * patch_size * patch_size\n )\n\n return flatten_patches, image_grid_thw\n\n\nclass VisionPatchEmbed(nn.Module):\n def __init__(\n self,\n patch_size: int = 14,\n temporal_patch_size: int = 2,\n in_channels: int = 3,\n embed_dim: int = 3584,\n device=None,\n dtype=None,\n ops=None,\n ):\n super().__init__()\n self.patch_size = patch_size\n self.temporal_patch_size = temporal_patch_size\n self.in_channels = in_channels\n self.embed_dim = embed_dim\n\n kernel_size = [temporal_patch_size, patch_size, patch_size]\n self.proj = ops.Conv3d(\n in_channels,\n embed_dim,\n kernel_size=kernel_size,\n stride=kernel_size,\n bias=False,\n device=device,\n dtype=dtype\n )\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = hidden_states.view(\n -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size\n )\n hidden_states = self.proj(hidden_states)\n return hidden_states.view(-1, self.embed_dim)\n\n\ndef rotate_half(x):\n x1 = x[..., : x.shape[-1] // 2]\n x2 = x[..., x.shape[-1] // 2 :]\n return torch.cat((-x2, x1), dim=-1)\n\n\ndef apply_rotary_pos_emb_vision(q, k, cos, sin):\n cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()\n q_embed = (q * cos) + (rotate_half(q) * sin)\n k_embed = (k * cos) + (rotate_half(k) * sin)\n return q_embed, k_embed\n\n\nclass VisionRotaryEmbedding(nn.Module):\n def __init__(self, dim: int, theta: float = 10000.0):\n super().__init__()\n self.dim = dim\n self.theta = theta\n\n def forward(self, seqlen: int, device) -> torch.Tensor:\n inv_freq = 1.0 / (self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float, device=device) / self.dim))\n seq = torch.arange(seqlen, device=inv_freq.device, dtype=inv_freq.dtype)\n freqs = torch.outer(seq, inv_freq)\n return freqs\n\n\nclass PatchMerger(nn.Module):\n def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2, device=None, dtype=None, ops=None):\n super().__init__()\n self.hidden_size = context_dim * (spatial_merge_size ** 2)\n self.ln_q = ops.RMSNorm(context_dim, eps=1e-6, device=device, dtype=dtype)\n self.mlp = nn.Sequential(\n ops.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype),\n nn.GELU(),\n ops.Linear(self.hidden_size, dim, device=device, dtype=dtype),\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.ln_q(x).reshape(-1, self.hidden_size)\n x = self.mlp(x)\n return x\n\n\nclass VisionAttention(nn.Module):\n def __init__(self, hidden_size: int, num_heads: int, device=None, dtype=None, ops=None):\n super().__init__()\n self.hidden_size = hidden_size\n self.num_heads = num_heads\n self.head_dim = hidden_size // num_heads\n self.scaling = self.head_dim ** -0.5\n\n self.qkv = ops.Linear(hidden_size, hidden_size * 3, bias=True, device=device, dtype=dtype)\n self.proj = ops.Linear(hidden_size, hidden_size, bias=True, device=device, dtype=dtype)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,\n cu_seqlens=None,\n optimized_attention=None,\n ) -> torch.Tensor:\n if hidden_states.dim() == 2:\n seq_length, _ = hidden_states.shape\n batch_size = 1\n hidden_states = hidden_states.unsqueeze(0)\n else:\n batch_size, seq_length, _ = hidden_states.shape\n\n qkv = self.qkv(hidden_states)\n qkv = qkv.reshape(batch_size, seq_length, 3, self.num_heads, self.head_dim)\n query_states, key_states, value_states = qkv.reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)\n\n if position_embeddings is not None:\n cos, sin = position_embeddings\n query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)\n\n query_states = query_states.transpose(0, 1).unsqueeze(0)\n key_states = key_states.transpose(0, 1).unsqueeze(0)\n value_states = value_states.transpose(0, 1).unsqueeze(0)\n\n lengths = cu_seqlens[1:] - cu_seqlens[:-1]\n splits = [\n torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)\n ]\n\n attn_outputs = [\n optimized_attention(q, k, v, self.num_heads, skip_reshape=True)\n for q, k, v in zip(*splits)\n ]\n attn_output = torch.cat(attn_outputs, dim=1)\n attn_output = attn_output.reshape(seq_length, -1)\n attn_output = self.proj(attn_output)\n\n return attn_output\n\n\nclass VisionMLP(nn.Module):\n def __init__(self, hidden_size: int, intermediate_size: int, device=None, dtype=None, ops=None):\n super().__init__()\n self.gate_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype)\n self.up_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype)\n self.down_proj = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype)\n self.act_fn = nn.SiLU()\n\n def forward(self, hidden_state):\n return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))\n\n\nclass VisionBlock(nn.Module):\n def __init__(self, hidden_size: int, intermediate_size: int, num_heads: int, device=None, dtype=None, ops=None):\n super().__init__()\n self.norm1 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)\n self.norm2 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)\n self.attn = VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops)\n self.mlp = VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,\n cu_seqlens=None,\n optimized_attention=None,\n ) -> torch.Tensor:\n residual = hidden_states\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.attn(hidden_states, position_embeddings, cu_seqlens, optimized_attention)\n hidden_states = residual + hidden_states\n\n residual = hidden_states\n hidden_states = self.norm2(hidden_states)\n hidden_states = self.mlp(hidden_states)\n hidden_states = residual + hidden_states\n\n return hidden_states\n\n\nclass Qwen2VLVisionTransformer(nn.Module):\n def __init__(\n self,\n hidden_size: int = 3584,\n output_hidden_size: int = 3584,\n intermediate_size: int = 3420,\n num_heads: int = 16,\n num_layers: int = 32,\n patch_size: int = 14,\n temporal_patch_size: int = 2,\n spatial_merge_size: int = 2,\n window_size: int = 112,\n device=None,\n dtype=None,\n ops=None\n ):\n super().__init__()\n self.hidden_size = hidden_size\n self.patch_size = patch_size\n self.spatial_merge_size = spatial_merge_size\n self.window_size = window_size\n self.fullatt_block_indexes = [7, 15, 23, 31]\n\n self.patch_embed = VisionPatchEmbed(\n patch_size=patch_size,\n temporal_patch_size=temporal_patch_size,\n in_channels=3,\n embed_dim=hidden_size,\n device=device,\n dtype=dtype,\n ops=ops,\n )\n\n head_dim = hidden_size // num_heads\n self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2)\n\n self.blocks = nn.ModuleList([\n VisionBlock(hidden_size, intermediate_size, num_heads, device, dtype, ops)\n for _ in range(num_layers)\n ])\n\n self.merger = PatchMerger(\n dim=output_hidden_size,\n context_dim=hidden_size,\n spatial_merge_size=spatial_merge_size,\n device=device,\n dtype=dtype,\n ops=ops,\n )\n\n def get_window_index(self, grid_thw):\n window_index = []\n cu_window_seqlens = [0]\n window_index_id = 0\n vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size\n\n for grid_t, grid_h, grid_w in grid_thw:\n llm_grid_h = grid_h // self.spatial_merge_size\n llm_grid_w = grid_w // self.spatial_merge_size\n\n index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)\n\n pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size\n pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size\n num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size\n num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size\n\n index_padded = F.pad(index, (0, pad_w, 0, pad_h), \"constant\", -100)\n index_padded = index_padded.reshape(\n grid_t,\n num_windows_h,\n vit_merger_window_size,\n num_windows_w,\n vit_merger_window_size,\n )\n index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(\n grid_t,\n num_windows_h * num_windows_w,\n vit_merger_window_size,\n vit_merger_window_size,\n )\n\n seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)\n index_padded = index_padded.reshape(-1)\n index_new = index_padded[index_padded != -100]\n window_index.append(index_new + window_index_id)\n\n cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_size * self.spatial_merge_size + cu_window_seqlens[-1]\n cu_window_seqlens.extend(cu_seqlens_tmp.tolist())\n window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()\n\n window_index = torch.cat(window_index, dim=0)\n return window_index, cu_window_seqlens\n\n def get_position_embeddings(self, grid_thw, device):\n pos_ids = []\n\n for t, h, w in grid_thw:\n hpos_ids = torch.arange(h, device=device).unsqueeze(1).expand(-1, w)\n hpos_ids = hpos_ids.reshape(\n h // self.spatial_merge_size,\n self.spatial_merge_size,\n w // self.spatial_merge_size,\n self.spatial_merge_size,\n )\n hpos_ids = hpos_ids.permute(0, 2, 1, 3).flatten()\n\n wpos_ids = torch.arange(w, device=device).unsqueeze(0).expand(h, -1)\n wpos_ids = wpos_ids.reshape(\n h // self.spatial_merge_size,\n self.spatial_merge_size,\n w // self.spatial_merge_size,\n self.spatial_merge_size,\n )\n wpos_ids = wpos_ids.permute(0, 2, 1, 3).flatten()\n\n pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))\n\n pos_ids = torch.cat(pos_ids, dim=0)\n max_grid_size = grid_thw[:, 1:].max()\n rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size, device)\n return rotary_pos_emb_full[pos_ids].flatten(1)\n\n def forward(\n self,\n pixel_values: torch.Tensor,\n image_grid_thw: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n optimized_attention = optimized_attention_for_device(pixel_values.device, mask=False, small_input=True)\n\n hidden_states = self.patch_embed(pixel_values)\n\n window_index, cu_window_seqlens = self.get_window_index(image_grid_thw)\n cu_window_seqlens = torch.tensor(cu_window_seqlens, device=hidden_states.device)\n cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)\n\n position_embeddings = self.get_position_embeddings(image_grid_thw, hidden_states.device)\n\n seq_len, _ = hidden_states.size()\n spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size\n\n hidden_states = hidden_states.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1)\n hidden_states = hidden_states[window_index, :, :]\n hidden_states = hidden_states.reshape(seq_len, -1)\n\n position_embeddings = position_embeddings.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1)\n position_embeddings = position_embeddings[window_index, :, :]\n position_embeddings = position_embeddings.reshape(seq_len, -1)\n position_embeddings = torch.cat((position_embeddings, position_embeddings), dim=-1)\n position_embeddings = (position_embeddings.cos(), position_embeddings.sin())\n\n cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum(\n dim=0,\n dtype=torch.int32,\n )\n cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)\n\n for i, block in enumerate(self.blocks):\n if i in self.fullatt_block_indexes:\n cu_seqlens_now = cu_seqlens\n else:\n cu_seqlens_now = cu_window_seqlens\n hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention)\n\n hidden_states = self.merger(hidden_states)\n return hidden_states\n" }, { "path": "comfy/text_encoders/sa_t5.py", "content": "from comfy import sd1_clip\nfrom transformers import T5TokenizerFast\nimport comfy.text_encoders.t5\nimport os\n\nclass T5BaseModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_config_base.json\")\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)\n\nclass T5BaseTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data)\n\nclass SAT5Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"t5base\", tokenizer=T5BaseTokenizer)\n\nclass SAT5Model(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, **kwargs):\n super().__init__(device=device, dtype=dtype, model_options=model_options, name=\"t5base\", clip_model=T5BaseModel, **kwargs)\n" }, { "path": "comfy/text_encoders/sd2_clip.py", "content": "from comfy import sd1_clip\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, model_options={}):\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}, return_projected_pooled=True, model_options=model_options)\n\nclass SD2ClipHTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):\n super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data)\n\nclass SD2Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"h\", tokenizer=SD2ClipHTokenizer)\n\nclass SD2ClipModel(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, **kwargs):\n super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name=\"h\", clip_model=SD2ClipHModel, **kwargs)\n" }, { "path": "comfy/text_encoders/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\": 49407,\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": "comfy/text_encoders/sd3_clip.py", "content": "from comfy import sd1_clip\nfrom comfy import sdxl_clip\nfrom transformers import T5TokenizerFast\nimport comfy.text_encoders.t5\nimport torch\nimport os\nimport comfy.model_management\nimport logging\nimport comfy.utils\n\nclass T5XXLModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, attention_mask=False, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_config_xxl.json\")\n t5xxl_quantization_metadata = model_options.get(\"t5xxl_quantization_metadata\", None)\n if t5xxl_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = t5xxl_quantization_metadata\n\n model_options = {**model_options, \"model_name\": \"t5xxl\"}\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)\n\n\ndef t5_xxl_detect(state_dict, prefix=\"\"):\n out = {}\n t5_key = \"{}encoder.final_layer_norm.weight\".format(prefix)\n if t5_key in state_dict:\n out[\"dtype_t5\"] = state_dict[t5_key].dtype\n\n quant = comfy.utils.detect_layer_quantization(state_dict, prefix)\n if quant is not None:\n out[\"t5_quantization_metadata\"] = quant\n\n return out\n\nclass T5XXLTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999):\n tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"t5_tokenizer\")\n super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data)\n\n\nclass SD3Tokenizer:\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)\n\n def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):\n out = {}\n out[\"g\"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)\n out[\"t5xxl\"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)\n return out\n\n def untokenize(self, token_weight_pair):\n return self.clip_g.untokenize(token_weight_pair)\n\n def state_dict(self):\n return {}\n\nclass SD3ClipModel(torch.nn.Module):\n def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device=\"cpu\", dtype=None, model_options={}):\n super().__init__()\n self.dtypes = set()\n if clip_l:\n self.clip_l = sd1_clip.SDClipModel(layer=\"hidden\", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)\n self.dtypes.add(dtype)\n else:\n self.clip_l = None\n\n if clip_g:\n self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)\n self.dtypes.add(dtype)\n else:\n self.clip_g = None\n\n if t5:\n dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)\n self.t5_attention_mask = t5_attention_mask\n self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)\n self.dtypes.add(dtype_t5)\n else:\n self.t5xxl = None\n\n logging.debug(\"Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}\".format(clip_l, clip_g, t5, dtype_t5))\n\n def set_clip_options(self, options):\n if self.clip_l is not None:\n self.clip_l.set_clip_options(options)\n if self.clip_g is not None:\n self.clip_g.set_clip_options(options)\n if self.t5xxl is not None:\n self.t5xxl.set_clip_options(options)\n\n def reset_clip_options(self):\n if self.clip_l is not None:\n self.clip_l.reset_clip_options()\n if self.clip_g is not None:\n self.clip_g.reset_clip_options()\n if self.t5xxl is not None:\n self.t5xxl.reset_clip_options()\n\n def encode_token_weights(self, token_weight_pairs):\n token_weight_pairs_l = token_weight_pairs[\"l\"]\n token_weight_pairs_g = token_weight_pairs[\"g\"]\n token_weight_pairs_t5 = token_weight_pairs[\"t5xxl\"]\n lg_out = None\n pooled = None\n out = None\n extra = {}\n\n if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:\n if self.clip_l is not None:\n lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)\n else:\n l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())\n\n if self.clip_g is not None:\n g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)\n if lg_out is not None:\n cut_to = min(lg_out.shape[1], g_out.shape[1])\n lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)\n else:\n lg_out = torch.nn.functional.pad(g_out, (768, 0))\n else:\n g_out = None\n g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())\n\n if lg_out is not None:\n lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))\n out = lg_out\n pooled = torch.cat((l_pooled, g_pooled), dim=-1)\n\n if self.t5xxl is not None:\n t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)\n t5_out, t5_pooled = t5_output[:2]\n if self.t5_attention_mask:\n extra[\"attention_mask\"] = t5_output[2][\"attention_mask\"]\n\n if lg_out is not None:\n out = torch.cat([lg_out, t5_out], dim=-2)\n else:\n out = t5_out\n\n if out is None:\n out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())\n\n if pooled is None:\n pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())\n\n return out, pooled, extra\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 elif \"text_model.encoder.layers.1.mlp.fc1.weight\" in sd:\n return self.clip_l.load_sd(sd)\n else:\n return self.t5xxl.load_sd(sd)\n\ndef sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_quantization_metadata=None, t5_attention_mask=False):\n class SD3ClipModel_(SD3ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"t5xxl_quantization_metadata\"] = t5_quantization_metadata\n super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)\n return SD3ClipModel_\n" }, { "path": "comfy/text_encoders/spiece_tokenizer.py", "content": "import torch\nimport os\n\nclass SPieceTokenizer:\n @staticmethod\n def from_pretrained(path, **kwargs):\n return SPieceTokenizer(path, **kwargs)\n\n def __init__(self, tokenizer_path, add_bos=False, add_eos=True, special_tokens=None):\n self.add_bos = add_bos\n self.add_eos = add_eos\n self.special_tokens = special_tokens\n import sentencepiece\n if torch.is_tensor(tokenizer_path):\n tokenizer_path = tokenizer_path.numpy().tobytes()\n\n if isinstance(tokenizer_path, bytes):\n self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)\n else:\n if not os.path.isfile(tokenizer_path):\n raise ValueError(\"invalid tokenizer\")\n self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)\n\n def get_vocab(self):\n out = {}\n for i in range(self.tokenizer.get_piece_size()):\n out[self.tokenizer.id_to_piece(i)] = i\n return out\n\n def __call__(self, string):\n if self.special_tokens is not None:\n import re\n special_tokens_pattern = '|'.join(re.escape(token) for token in self.special_tokens.keys())\n if special_tokens_pattern and re.search(special_tokens_pattern, string):\n parts = re.split(f'({special_tokens_pattern})', string)\n result = []\n for part in parts:\n if not part:\n continue\n if part in self.special_tokens:\n result.append(self.special_tokens[part])\n else:\n encoded = self.tokenizer.encode(part, add_bos=False, add_eos=False)\n result.extend(encoded)\n return {\"input_ids\": result}\n\n out = self.tokenizer.encode(string)\n return {\"input_ids\": out}\n\n def decode(self, token_ids, skip_special_tokens=False):\n\n if skip_special_tokens and self.special_tokens:\n special_token_ids = set(self.special_tokens.values())\n token_ids = [tid for tid in token_ids if tid not in special_token_ids]\n\n return self.tokenizer.decode(token_ids)\n\n def serialize_model(self):\n return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))\n" }, { "path": "comfy/text_encoders/t5.py", "content": "import torch\nimport math\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\nimport comfy.ops\n\nclass T5LayerNorm(torch.nn.Module):\n def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):\n super().__init__()\n self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))\n self.variance_epsilon = eps\n\n def forward(self, x):\n variance = x.pow(2).mean(-1, keepdim=True)\n x = x * torch.rsqrt(variance + self.variance_epsilon)\n return comfy.ops.cast_to_input(self.weight, x) * x\n\nactivations = {\n \"gelu_pytorch_tanh\": lambda a: torch.nn.functional.gelu(a, approximate=\"tanh\"),\n \"relu\": torch.nn.functional.relu,\n}\n\nclass T5DenseActDense(torch.nn.Module):\n def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):\n super().__init__()\n self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)\n self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)\n # self.dropout = nn.Dropout(config.dropout_rate)\n self.act = activations[ff_activation]\n\n def forward(self, x):\n x = self.act(self.wi(x))\n # x = self.dropout(x)\n x = self.wo(x)\n return x\n\nclass T5DenseGatedActDense(torch.nn.Module):\n def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):\n super().__init__()\n self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)\n self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)\n self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)\n # self.dropout = nn.Dropout(config.dropout_rate)\n self.act = activations[ff_activation]\n\n def forward(self, x):\n hidden_gelu = self.act(self.wi_0(x))\n hidden_linear = self.wi_1(x)\n x = hidden_gelu * hidden_linear\n # x = self.dropout(x)\n x = self.wo(x)\n return x\n\nclass T5LayerFF(torch.nn.Module):\n def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):\n super().__init__()\n if gated_act:\n self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)\n else:\n self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)\n\n self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)\n # self.dropout = nn.Dropout(config.dropout_rate)\n\n def forward(self, x):\n forwarded_states = self.layer_norm(x)\n forwarded_states = self.DenseReluDense(forwarded_states)\n # x = x + self.dropout(forwarded_states)\n x += forwarded_states\n return x\n\nclass T5Attention(torch.nn.Module):\n def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):\n super().__init__()\n\n # Mesh TensorFlow initialization to avoid scaling before softmax\n self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)\n self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)\n self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)\n self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)\n self.num_heads = num_heads\n\n self.relative_attention_bias = None\n if relative_attention_bias:\n self.relative_attention_num_buckets = 32\n self.relative_attention_max_distance = 128\n self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)\n\n @staticmethod\n def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):\n \"\"\"\n Adapted from Mesh Tensorflow:\n https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593\n\n Translate relative position to a bucket number for relative attention. The relative position is defined as\n memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to\n position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for\n small absolute relative_position and larger buckets for larger absolute relative_positions. All relative\n positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.\n This should allow for more graceful generalization to longer sequences than the model has been trained on\n\n Args:\n relative_position: an int32 Tensor\n bidirectional: a boolean - whether the attention is bidirectional\n num_buckets: an integer\n max_distance: an integer\n\n Returns:\n a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)\n \"\"\"\n relative_buckets = 0\n if bidirectional:\n num_buckets //= 2\n relative_buckets += (relative_position > 0).to(torch.long) * num_buckets\n relative_position = torch.abs(relative_position)\n else:\n relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))\n # now relative_position is in the range [0, inf)\n\n # half of the buckets are for exact increments in positions\n max_exact = num_buckets // 2\n is_small = relative_position < max_exact\n\n # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance\n relative_position_if_large = max_exact + (\n torch.log(relative_position.float() / max_exact)\n / math.log(max_distance / max_exact)\n * (num_buckets - max_exact)\n ).to(torch.long)\n relative_position_if_large = torch.min(\n relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)\n )\n\n relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)\n return relative_buckets\n\n def compute_bias(self, query_length, key_length, device, dtype):\n \"\"\"Compute binned relative position bias\"\"\"\n context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]\n memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]\n relative_position = memory_position - context_position # shape (query_length, key_length)\n relative_position_bucket = self._relative_position_bucket(\n relative_position, # shape (query_length, key_length)\n bidirectional=True,\n num_buckets=self.relative_attention_num_buckets,\n max_distance=self.relative_attention_max_distance,\n )\n values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads)\n values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)\n return values.contiguous()\n\n def forward(self, x, mask=None, past_bias=None, optimized_attention=None):\n q = self.q(x)\n k = self.k(x)\n v = self.v(x)\n if self.relative_attention_bias is not None:\n past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)\n\n if past_bias is not None:\n if mask is not None:\n mask = mask + past_bias\n else:\n mask = past_bias\n\n out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)\n return self.o(out), past_bias\n\nclass T5LayerSelfAttention(torch.nn.Module):\n def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):\n super().__init__()\n self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)\n self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)\n # self.dropout = nn.Dropout(config.dropout_rate)\n\n def forward(self, x, mask=None, past_bias=None, optimized_attention=None):\n output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)\n # x = x + self.dropout(attention_output)\n x += output\n return x, past_bias\n\nclass T5Block(torch.nn.Module):\n def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):\n super().__init__()\n self.layer = torch.nn.ModuleList()\n self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))\n self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))\n\n def forward(self, x, mask=None, past_bias=None, optimized_attention=None):\n x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)\n x = self.layer[-1](x)\n return x, past_bias\n\nclass T5Stack(torch.nn.Module):\n def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):\n super().__init__()\n\n self.block = torch.nn.ModuleList(\n [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]\n )\n self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)\n # self.dropout = nn.Dropout(config.dropout_rate)\n\n def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):\n mask = None\n if attention_mask is not None:\n mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])\n mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)\n\n intermediate = None\n optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)\n past_bias = None\n\n if intermediate_output is not None:\n if intermediate_output < 0:\n intermediate_output = len(self.block) + intermediate_output\n\n for i, l in enumerate(self.block):\n x, past_bias = l(x, mask, past_bias, optimized_attention)\n if i == intermediate_output:\n intermediate = x.clone()\n x = self.final_layer_norm(x)\n if intermediate is not None and final_layer_norm_intermediate:\n intermediate = self.final_layer_norm(intermediate)\n return x, intermediate\n\nclass T5(torch.nn.Module):\n def __init__(self, config_dict, dtype, device, operations):\n super().__init__()\n self.num_layers = config_dict[\"num_layers\"]\n model_dim = config_dict[\"d_model\"]\n inner_dim = config_dict[\"d_kv\"] * config_dict[\"num_heads\"]\n\n self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict[\"d_ff\"], config_dict[\"dense_act_fn\"], config_dict[\"is_gated_act\"], config_dict[\"num_heads\"], config_dict[\"model_type\"] != \"umt5\", dtype, device, operations)\n self.dtype = dtype\n self.shared = operations.Embedding(config_dict[\"vocab_size\"], model_dim, device=device, dtype=dtype)\n\n def get_input_embeddings(self):\n return self.shared\n\n def set_input_embeddings(self, embeddings):\n self.shared = embeddings\n\n def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs):\n if input_ids is None:\n x = embeds\n else:\n x = self.shared(input_ids, out_dtype=kwargs.get(\"dtype\", torch.float32))\n if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]:\n x = torch.nan_to_num(x) #Fix for fp8 T5 base\n return self.encoder(x, attention_mask=attention_mask, **kwargs)\n" }, { "path": "comfy/text_encoders/t5_config_base.json", "content": "{\n \"d_ff\": 3072,\n \"d_kv\": 64,\n \"d_model\": 768,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 1,\n \"dense_act_fn\": \"relu\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": false,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"t5\",\n \"num_decoder_layers\": 12,\n \"num_heads\": 12,\n \"num_layers\": 12,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 32128\n}\n" }, { "path": "comfy/text_encoders/t5_old_config_xxl.json", "content": "{\n \"d_ff\": 65536,\n \"d_kv\": 128,\n \"d_model\": 1024,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 1,\n \"dense_act_fn\": \"relu\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": false,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"t5\",\n \"num_decoder_layers\": 24,\n \"num_heads\": 128,\n \"num_layers\": 24,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 32128\n}\n" }, { "path": "comfy/text_encoders/t5_pile_config_xl.json", "content": "{\n \"d_ff\": 5120,\n \"d_kv\": 64,\n \"d_model\": 2048,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 2,\n \"dense_act_fn\": \"gelu_pytorch_tanh\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": true,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"umt5\",\n \"num_decoder_layers\": 24,\n \"num_heads\": 32,\n \"num_layers\": 24,\n \"output_past\": true,\n \"pad_token_id\": 1,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 32128\n}\n" }, { "path": "comfy/text_encoders/t5_tokenizer/special_tokens_map.json", "content": "{\n \"additional_special_tokens\": [\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n 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\"32095\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"32096\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"32097\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"32098\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n },\n \"32099\": {\n \"content\": \"\",\n \"lstrip\": false,\n \"normalized\": false,\n \"rstrip\": false,\n \"single_word\": false,\n \"special\": true\n }\n },\n \"additional_special_tokens\": [\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\",\n \"\"\n ],\n \"clean_up_tokenization_spaces\": true,\n \"eos_token\": \"\",\n \"extra_ids\": 100,\n \"legacy\": false,\n \"model_max_length\": 512,\n \"pad_token\": \"\",\n \"sp_model_kwargs\": {},\n \"tokenizer_class\": \"T5Tokenizer\",\n \"unk_token\": \"\"\n}\n" }, { "path": "comfy/text_encoders/umt5_config_base.json", "content": "{\n \"d_ff\": 2048,\n \"d_kv\": 64,\n \"d_model\": 768,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 1,\n \"dense_act_fn\": \"gelu_pytorch_tanh\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": true,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"umt5\",\n \"num_decoder_layers\": 12,\n \"num_heads\": 12,\n \"num_layers\": 12,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 256384\n}\n" }, { "path": "comfy/text_encoders/umt5_config_xxl.json", "content": "{\n \"d_ff\": 10240,\n \"d_kv\": 64,\n \"d_model\": 4096,\n \"decoder_start_token_id\": 0,\n \"dropout_rate\": 0.1,\n \"eos_token_id\": 1,\n \"dense_act_fn\": \"gelu_pytorch_tanh\",\n \"initializer_factor\": 1.0,\n \"is_encoder_decoder\": true,\n \"is_gated_act\": true,\n \"layer_norm_epsilon\": 1e-06,\n \"model_type\": \"umt5\",\n \"num_decoder_layers\": 24,\n \"num_heads\": 64,\n \"num_layers\": 24,\n \"output_past\": true,\n \"pad_token_id\": 0,\n \"relative_attention_num_buckets\": 32,\n \"tie_word_embeddings\": false,\n \"vocab_size\": 256384\n}\n" }, { "path": "comfy/text_encoders/wan.py", "content": "from comfy import sd1_clip\nfrom .spiece_tokenizer import SPieceTokenizer\nimport comfy.text_encoders.t5\nimport os\n\nclass UMT5XXlModel(sd1_clip.SDClipModel):\n def __init__(self, device=\"cpu\", layer=\"last\", layer_idx=None, dtype=None, model_options={}):\n textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"umt5_config_xxl.json\")\n super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"end\": 1, \"pad\": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options)\n\nclass UMT5XXlTokenizer(sd1_clip.SDTokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n tokenizer = tokenizer_data.get(\"spiece_model\", None)\n super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data)\n\n def state_dict(self):\n return {\"spiece_model\": self.tokenizer.serialize_model()}\n\n\nclass WanT5Tokenizer(sd1_clip.SD1Tokenizer):\n def __init__(self, embedding_directory=None, tokenizer_data={}):\n super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name=\"umt5xxl\", tokenizer=UMT5XXlTokenizer)\n\nclass WanT5Model(sd1_clip.SD1ClipModel):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}, **kwargs):\n super().__init__(device=device, dtype=dtype, model_options=model_options, name=\"umt5xxl\", clip_model=UMT5XXlModel, **kwargs)\n\ndef te(dtype_t5=None, t5_quantization_metadata=None):\n class WanTEModel(WanT5Model):\n def __init__(self, device=\"cpu\", dtype=None, model_options={}):\n if t5_quantization_metadata is not None:\n model_options = model_options.copy()\n model_options[\"quantization_metadata\"] = t5_quantization_metadata\n if dtype_t5 is not None:\n dtype = dtype_t5\n super().__init__(device=device, dtype=dtype, model_options=model_options)\n return WanTEModel\n" }, { "path": "comfy/utils.py", "content": "\"\"\"\n This file is part of ComfyUI.\n Copyright (C) 2024 Comfy\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 .\n\"\"\"\n\n\nimport torch\nimport math\nimport struct\nimport ctypes\nimport os\nimport comfy.memory_management\nimport safetensors.torch\nimport numpy as np\nfrom PIL import Image\nimport logging\nimport itertools\nfrom torch.nn.functional import interpolate\nfrom tqdm.auto import trange\nfrom einops import rearrange\nfrom comfy.cli_args import args\nimport json\nimport time\nimport threading\nimport warnings\n\nMMAP_TORCH_FILES = args.mmap_torch_files\nDISABLE_MMAP = args.disable_mmap\n\n\nif True: # ckpt/pt file whitelist for safe loading of old sd files\n class ModelCheckpoint:\n pass\n ModelCheckpoint.__module__ = \"pytorch_lightning.callbacks.model_checkpoint\"\n\n def scalar(*args, **kwargs):\n return None\n scalar.__module__ = \"numpy.core.multiarray\"\n\n from numpy import dtype\n from numpy.dtypes import Float64DType\n\n def encode(*args, **kwargs): # no longer necessary on newer torch\n return None\n encode.__module__ = \"_codecs\"\n\n torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])\n logging.info(\"Checkpoint files will always be loaded safely.\")\n\n\n# Current as of safetensors 0.7.0\n_TYPES = {\n \"F64\": torch.float64,\n \"F32\": torch.float32,\n \"F16\": torch.float16,\n \"BF16\": torch.bfloat16,\n \"I64\": torch.int64,\n \"I32\": torch.int32,\n \"I16\": torch.int16,\n \"I8\": torch.int8,\n \"U8\": torch.uint8,\n \"BOOL\": torch.bool,\n \"F8_E4M3\": torch.float8_e4m3fn,\n \"F8_E5M2\": torch.float8_e5m2,\n \"C64\": torch.complex64,\n\n \"U64\": torch.uint64,\n \"U32\": torch.uint32,\n \"U16\": torch.uint16,\n}\n\ndef load_safetensors(ckpt):\n import comfy_aimdo.model_mmap\n\n f = open(ckpt, \"rb\", buffering=0)\n model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt)\n file_size = os.path.getsize(ckpt)\n mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get()))\n\n header_size = struct.unpack(\" 0:\n message = e.args[0]\n if \"HeaderTooLarge\" in message:\n raise ValueError(\"{}\\n\\nFile path: {}\\n\\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.\".format(message, ckpt))\n if \"MetadataIncompleteBuffer\" in message:\n raise ValueError(\"{}\\n\\nFile path: {}\\n\\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.\".format(message, ckpt))\n raise e\n else:\n torch_args = {}\n if MMAP_TORCH_FILES:\n torch_args[\"mmap\"] = True\n\n pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)\n\n if \"state_dict\" in pl_sd:\n sd = pl_sd[\"state_dict\"]\n else:\n if len(pl_sd) == 1:\n key = list(pl_sd.keys())[0]\n sd = pl_sd[key]\n if not isinstance(sd, dict):\n sd = pl_sd\n else:\n sd = pl_sd\n return (sd, metadata) if return_metadata else 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 w = sd[k]\n params += w.nelement()\n return params\n\ndef weight_dtype(sd, prefix=\"\"):\n dtypes = {}\n for k in sd.keys():\n if k.startswith(prefix):\n w = sd[k]\n dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel()\n\n if len(dtypes) == 0:\n return None\n\n return max(dtypes, key=dtypes.get)\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\n return sd\n\ndef clip_text_transformers_convert(sd, prefix_from, prefix_to):\n sd = transformers_convert(sd, prefix_from, \"{}text_model.\".format(prefix_to), 32)\n\n tp = \"{}text_projection.weight\".format(prefix_from)\n if tp in sd:\n sd[\"{}text_projection.weight\".format(prefix_to)] = sd.pop(tp)\n\n tp = \"{}text_projection\".format(prefix_from)\n if tp in sd:\n sd[\"{}text_projection.weight\".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()\n return sd\n\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 if \"num_res_blocks\" not in unet_config:\n return {}\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 swap_scale_shift(weight):\n shift, scale = weight.chunk(2, dim=0)\n new_weight = torch.cat([scale, shift], dim=0)\n return new_weight\n\nMMDIT_MAP_BASIC = {\n (\"context_embedder.bias\", \"context_embedder.bias\"),\n (\"context_embedder.weight\", \"context_embedder.weight\"),\n (\"t_embedder.mlp.0.bias\", \"time_text_embed.timestep_embedder.linear_1.bias\"),\n (\"t_embedder.mlp.0.weight\", \"time_text_embed.timestep_embedder.linear_1.weight\"),\n (\"t_embedder.mlp.2.bias\", \"time_text_embed.timestep_embedder.linear_2.bias\"),\n (\"t_embedder.mlp.2.weight\", \"time_text_embed.timestep_embedder.linear_2.weight\"),\n (\"x_embedder.proj.bias\", \"pos_embed.proj.bias\"),\n (\"x_embedder.proj.weight\", \"pos_embed.proj.weight\"),\n (\"y_embedder.mlp.0.bias\", \"time_text_embed.text_embedder.linear_1.bias\"),\n (\"y_embedder.mlp.0.weight\", \"time_text_embed.text_embedder.linear_1.weight\"),\n (\"y_embedder.mlp.2.bias\", \"time_text_embed.text_embedder.linear_2.bias\"),\n (\"y_embedder.mlp.2.weight\", \"time_text_embed.text_embedder.linear_2.weight\"),\n (\"pos_embed\", \"pos_embed.pos_embed\"),\n (\"final_layer.adaLN_modulation.1.bias\", \"norm_out.linear.bias\", swap_scale_shift),\n (\"final_layer.adaLN_modulation.1.weight\", \"norm_out.linear.weight\", swap_scale_shift),\n (\"final_layer.linear.bias\", \"proj_out.bias\"),\n (\"final_layer.linear.weight\", \"proj_out.weight\"),\n}\n\nMMDIT_MAP_BLOCK = {\n (\"context_block.adaLN_modulation.1.bias\", \"norm1_context.linear.bias\"),\n (\"context_block.adaLN_modulation.1.weight\", \"norm1_context.linear.weight\"),\n (\"context_block.attn.proj.bias\", \"attn.to_add_out.bias\"),\n (\"context_block.attn.proj.weight\", \"attn.to_add_out.weight\"),\n (\"context_block.mlp.fc1.bias\", \"ff_context.net.0.proj.bias\"),\n (\"context_block.mlp.fc1.weight\", \"ff_context.net.0.proj.weight\"),\n (\"context_block.mlp.fc2.bias\", \"ff_context.net.2.bias\"),\n (\"context_block.mlp.fc2.weight\", \"ff_context.net.2.weight\"),\n (\"context_block.attn.ln_q.weight\", \"attn.norm_added_q.weight\"),\n (\"context_block.attn.ln_k.weight\", \"attn.norm_added_k.weight\"),\n (\"x_block.adaLN_modulation.1.bias\", \"norm1.linear.bias\"),\n (\"x_block.adaLN_modulation.1.weight\", \"norm1.linear.weight\"),\n (\"x_block.attn.proj.bias\", \"attn.to_out.0.bias\"),\n (\"x_block.attn.proj.weight\", \"attn.to_out.0.weight\"),\n (\"x_block.attn.ln_q.weight\", \"attn.norm_q.weight\"),\n (\"x_block.attn.ln_k.weight\", \"attn.norm_k.weight\"),\n (\"x_block.attn2.proj.bias\", \"attn2.to_out.0.bias\"),\n (\"x_block.attn2.proj.weight\", \"attn2.to_out.0.weight\"),\n (\"x_block.attn2.ln_q.weight\", \"attn2.norm_q.weight\"),\n (\"x_block.attn2.ln_k.weight\", \"attn2.norm_k.weight\"),\n (\"x_block.mlp.fc1.bias\", \"ff.net.0.proj.bias\"),\n (\"x_block.mlp.fc1.weight\", \"ff.net.0.proj.weight\"),\n (\"x_block.mlp.fc2.bias\", \"ff.net.2.bias\"),\n (\"x_block.mlp.fc2.weight\", \"ff.net.2.weight\"),\n}\n\ndef mmdit_to_diffusers(mmdit_config, output_prefix=\"\"):\n key_map = {}\n\n depth = mmdit_config.get(\"depth\", 0)\n num_blocks = mmdit_config.get(\"num_blocks\", depth)\n for i in range(num_blocks):\n block_from = \"transformer_blocks.{}\".format(i)\n block_to = \"{}joint_blocks.{}\".format(output_prefix, i)\n\n offset = depth * 64\n\n for end in (\"weight\", \"bias\"):\n k = \"{}.attn.\".format(block_from)\n qkv = \"{}.x_block.attn.qkv.{}\".format(block_to, end)\n key_map[\"{}to_q.{}\".format(k, end)] = (qkv, (0, 0, offset))\n key_map[\"{}to_k.{}\".format(k, end)] = (qkv, (0, offset, offset))\n key_map[\"{}to_v.{}\".format(k, end)] = (qkv, (0, offset * 2, offset))\n\n qkv = \"{}.context_block.attn.qkv.{}\".format(block_to, end)\n key_map[\"{}add_q_proj.{}\".format(k, end)] = (qkv, (0, 0, offset))\n key_map[\"{}add_k_proj.{}\".format(k, end)] = (qkv, (0, offset, offset))\n key_map[\"{}add_v_proj.{}\".format(k, end)] = (qkv, (0, offset * 2, offset))\n\n k = \"{}.attn2.\".format(block_from)\n qkv = \"{}.x_block.attn2.qkv.{}\".format(block_to, end)\n key_map[\"{}to_q.{}\".format(k, end)] = (qkv, (0, 0, offset))\n key_map[\"{}to_k.{}\".format(k, end)] = (qkv, (0, offset, offset))\n key_map[\"{}to_v.{}\".format(k, end)] = (qkv, (0, offset * 2, offset))\n\n for k in MMDIT_MAP_BLOCK:\n key_map[\"{}.{}\".format(block_from, k[1])] = \"{}.{}\".format(block_to, k[0])\n\n map_basic = MMDIT_MAP_BASIC.copy()\n map_basic.add((\"joint_blocks.{}.context_block.adaLN_modulation.1.bias\".format(depth - 1), \"transformer_blocks.{}.norm1_context.linear.bias\".format(depth - 1), swap_scale_shift))\n map_basic.add((\"joint_blocks.{}.context_block.adaLN_modulation.1.weight\".format(depth - 1), \"transformer_blocks.{}.norm1_context.linear.weight\".format(depth - 1), swap_scale_shift))\n\n for k in map_basic:\n if len(k) > 2:\n key_map[k[1]] = (\"{}{}\".format(output_prefix, k[0]), None, k[2])\n else:\n key_map[k[1]] = \"{}{}\".format(output_prefix, k[0])\n\n return key_map\n\nPIXART_MAP_BASIC = {\n (\"csize_embedder.mlp.0.weight\", \"adaln_single.emb.resolution_embedder.linear_1.weight\"),\n (\"csize_embedder.mlp.0.bias\", \"adaln_single.emb.resolution_embedder.linear_1.bias\"),\n (\"csize_embedder.mlp.2.weight\", \"adaln_single.emb.resolution_embedder.linear_2.weight\"),\n (\"csize_embedder.mlp.2.bias\", \"adaln_single.emb.resolution_embedder.linear_2.bias\"),\n (\"ar_embedder.mlp.0.weight\", \"adaln_single.emb.aspect_ratio_embedder.linear_1.weight\"),\n (\"ar_embedder.mlp.0.bias\", \"adaln_single.emb.aspect_ratio_embedder.linear_1.bias\"),\n (\"ar_embedder.mlp.2.weight\", \"adaln_single.emb.aspect_ratio_embedder.linear_2.weight\"),\n (\"ar_embedder.mlp.2.bias\", \"adaln_single.emb.aspect_ratio_embedder.linear_2.bias\"),\n (\"x_embedder.proj.weight\", \"pos_embed.proj.weight\"),\n (\"x_embedder.proj.bias\", \"pos_embed.proj.bias\"),\n (\"y_embedder.y_embedding\", \"caption_projection.y_embedding\"),\n (\"y_embedder.y_proj.fc1.weight\", \"caption_projection.linear_1.weight\"),\n (\"y_embedder.y_proj.fc1.bias\", \"caption_projection.linear_1.bias\"),\n (\"y_embedder.y_proj.fc2.weight\", \"caption_projection.linear_2.weight\"),\n (\"y_embedder.y_proj.fc2.bias\", \"caption_projection.linear_2.bias\"),\n (\"t_embedder.mlp.0.weight\", \"adaln_single.emb.timestep_embedder.linear_1.weight\"),\n (\"t_embedder.mlp.0.bias\", \"adaln_single.emb.timestep_embedder.linear_1.bias\"),\n (\"t_embedder.mlp.2.weight\", \"adaln_single.emb.timestep_embedder.linear_2.weight\"),\n (\"t_embedder.mlp.2.bias\", \"adaln_single.emb.timestep_embedder.linear_2.bias\"),\n (\"t_block.1.weight\", \"adaln_single.linear.weight\"),\n (\"t_block.1.bias\", \"adaln_single.linear.bias\"),\n (\"final_layer.linear.weight\", \"proj_out.weight\"),\n (\"final_layer.linear.bias\", \"proj_out.bias\"),\n (\"final_layer.scale_shift_table\", \"scale_shift_table\"),\n}\n\nPIXART_MAP_BLOCK = {\n (\"scale_shift_table\", \"scale_shift_table\"),\n (\"attn.proj.weight\", \"attn1.to_out.0.weight\"),\n (\"attn.proj.bias\", \"attn1.to_out.0.bias\"),\n (\"mlp.fc1.weight\", \"ff.net.0.proj.weight\"),\n (\"mlp.fc1.bias\", \"ff.net.0.proj.bias\"),\n (\"mlp.fc2.weight\", \"ff.net.2.weight\"),\n (\"mlp.fc2.bias\", \"ff.net.2.bias\"),\n (\"cross_attn.proj.weight\" ,\"attn2.to_out.0.weight\"),\n (\"cross_attn.proj.bias\" ,\"attn2.to_out.0.bias\"),\n}\n\ndef pixart_to_diffusers(mmdit_config, output_prefix=\"\"):\n key_map = {}\n\n depth = mmdit_config.get(\"depth\", 0)\n offset = mmdit_config.get(\"hidden_size\", 1152)\n\n for i in range(depth):\n block_from = \"transformer_blocks.{}\".format(i)\n block_to = \"{}blocks.{}\".format(output_prefix, i)\n\n for end in (\"weight\", \"bias\"):\n s = \"{}.attn1.\".format(block_from)\n qkv = \"{}.attn.qkv.{}\".format(block_to, end)\n key_map[\"{}to_q.{}\".format(s, end)] = (qkv, (0, 0, offset))\n key_map[\"{}to_k.{}\".format(s, end)] = (qkv, (0, offset, offset))\n key_map[\"{}to_v.{}\".format(s, end)] = (qkv, (0, offset * 2, offset))\n\n s = \"{}.attn2.\".format(block_from)\n q = \"{}.cross_attn.q_linear.{}\".format(block_to, end)\n kv = \"{}.cross_attn.kv_linear.{}\".format(block_to, end)\n\n key_map[\"{}to_q.{}\".format(s, end)] = q\n key_map[\"{}to_k.{}\".format(s, end)] = (kv, (0, 0, offset))\n key_map[\"{}to_v.{}\".format(s, end)] = (kv, (0, offset, offset))\n\n for k in PIXART_MAP_BLOCK:\n key_map[\"{}.{}\".format(block_from, k[1])] = \"{}.{}\".format(block_to, k[0])\n\n for k in PIXART_MAP_BASIC:\n key_map[k[1]] = \"{}{}\".format(output_prefix, k[0])\n\n return key_map\n\ndef auraflow_to_diffusers(mmdit_config, output_prefix=\"\"):\n n_double_layers = mmdit_config.get(\"n_double_layers\", 0)\n n_layers = mmdit_config.get(\"n_layers\", 0)\n\n key_map = {}\n for i in range(n_layers):\n if i < n_double_layers:\n index = i\n prefix_from = \"joint_transformer_blocks\"\n prefix_to = \"{}double_layers\".format(output_prefix)\n block_map = {\n \"attn.to_q.weight\": \"attn.w2q.weight\",\n \"attn.to_k.weight\": \"attn.w2k.weight\",\n \"attn.to_v.weight\": \"attn.w2v.weight\",\n \"attn.to_out.0.weight\": \"attn.w2o.weight\",\n \"attn.add_q_proj.weight\": \"attn.w1q.weight\",\n \"attn.add_k_proj.weight\": \"attn.w1k.weight\",\n \"attn.add_v_proj.weight\": \"attn.w1v.weight\",\n \"attn.to_add_out.weight\": \"attn.w1o.weight\",\n \"ff.linear_1.weight\": \"mlpX.c_fc1.weight\",\n \"ff.linear_2.weight\": \"mlpX.c_fc2.weight\",\n \"ff.out_projection.weight\": \"mlpX.c_proj.weight\",\n \"ff_context.linear_1.weight\": \"mlpC.c_fc1.weight\",\n \"ff_context.linear_2.weight\": \"mlpC.c_fc2.weight\",\n \"ff_context.out_projection.weight\": \"mlpC.c_proj.weight\",\n \"norm1.linear.weight\": \"modX.1.weight\",\n \"norm1_context.linear.weight\": \"modC.1.weight\",\n }\n else:\n index = i - n_double_layers\n prefix_from = \"single_transformer_blocks\"\n prefix_to = \"{}single_layers\".format(output_prefix)\n\n block_map = {\n \"attn.to_q.weight\": \"attn.w1q.weight\",\n \"attn.to_k.weight\": \"attn.w1k.weight\",\n \"attn.to_v.weight\": \"attn.w1v.weight\",\n \"attn.to_out.0.weight\": \"attn.w1o.weight\",\n \"norm1.linear.weight\": \"modCX.1.weight\",\n \"ff.linear_1.weight\": \"mlp.c_fc1.weight\",\n \"ff.linear_2.weight\": \"mlp.c_fc2.weight\",\n \"ff.out_projection.weight\": \"mlp.c_proj.weight\"\n }\n\n for k in block_map:\n key_map[\"{}.{}.{}\".format(prefix_from, index, k)] = \"{}.{}.{}\".format(prefix_to, index, block_map[k])\n\n MAP_BASIC = {\n (\"positional_encoding\", \"pos_embed.pos_embed\"),\n (\"register_tokens\", \"register_tokens\"),\n (\"t_embedder.mlp.0.weight\", \"time_step_proj.linear_1.weight\"),\n (\"t_embedder.mlp.0.bias\", \"time_step_proj.linear_1.bias\"),\n (\"t_embedder.mlp.2.weight\", \"time_step_proj.linear_2.weight\"),\n (\"t_embedder.mlp.2.bias\", \"time_step_proj.linear_2.bias\"),\n (\"cond_seq_linear.weight\", \"context_embedder.weight\"),\n (\"init_x_linear.weight\", \"pos_embed.proj.weight\"),\n (\"init_x_linear.bias\", \"pos_embed.proj.bias\"),\n (\"final_linear.weight\", \"proj_out.weight\"),\n (\"modF.1.weight\", \"norm_out.linear.weight\", swap_scale_shift),\n }\n\n for k in MAP_BASIC:\n if len(k) > 2:\n key_map[k[1]] = (\"{}{}\".format(output_prefix, k[0]), None, k[2])\n else:\n key_map[k[1]] = \"{}{}\".format(output_prefix, k[0])\n\n return key_map\n\ndef flux_to_diffusers(mmdit_config, output_prefix=\"\"):\n n_double_layers = mmdit_config.get(\"depth\", 0)\n n_single_layers = mmdit_config.get(\"depth_single_blocks\", 0)\n hidden_size = mmdit_config.get(\"hidden_size\", 0)\n\n key_map = {}\n for index in range(n_double_layers):\n prefix_from = \"transformer_blocks.{}\".format(index)\n prefix_to = \"{}double_blocks.{}\".format(output_prefix, index)\n\n for end in (\"weight\", \"bias\"):\n k = \"{}.attn.\".format(prefix_from)\n qkv = \"{}.img_attn.qkv.{}\".format(prefix_to, end)\n key_map[\"{}to_q.{}\".format(k, end)] = (qkv, (0, 0, hidden_size))\n key_map[\"{}to_k.{}\".format(k, end)] = (qkv, (0, hidden_size, hidden_size))\n key_map[\"{}to_v.{}\".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))\n\n k = \"{}.attn.\".format(prefix_from)\n qkv = \"{}.txt_attn.qkv.{}\".format(prefix_to, end)\n key_map[\"{}add_q_proj.{}\".format(k, end)] = (qkv, (0, 0, hidden_size))\n key_map[\"{}add_k_proj.{}\".format(k, end)] = (qkv, (0, hidden_size, hidden_size))\n key_map[\"{}add_v_proj.{}\".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))\n\n block_map = {\n \"attn.to_out.0.weight\": \"img_attn.proj.weight\",\n \"attn.to_out.0.bias\": \"img_attn.proj.bias\",\n \"norm1.linear.weight\": \"img_mod.lin.weight\",\n \"norm1.linear.bias\": \"img_mod.lin.bias\",\n \"norm1_context.linear.weight\": \"txt_mod.lin.weight\",\n \"norm1_context.linear.bias\": \"txt_mod.lin.bias\",\n \"attn.to_add_out.weight\": \"txt_attn.proj.weight\",\n \"attn.to_add_out.bias\": \"txt_attn.proj.bias\",\n \"ff.net.0.proj.weight\": \"img_mlp.0.weight\",\n \"ff.net.0.proj.bias\": \"img_mlp.0.bias\",\n \"ff.net.2.weight\": \"img_mlp.2.weight\",\n \"ff.net.2.bias\": \"img_mlp.2.bias\",\n \"ff_context.net.0.proj.weight\": \"txt_mlp.0.weight\",\n \"ff_context.net.0.proj.bias\": \"txt_mlp.0.bias\",\n \"ff_context.net.2.weight\": \"txt_mlp.2.weight\",\n \"ff_context.net.2.bias\": \"txt_mlp.2.bias\",\n \"ff.linear_in.weight\": \"img_mlp.0.weight\", # LyCoris LoKr\n \"ff.linear_in.bias\": \"img_mlp.0.bias\",\n \"ff.linear_out.weight\": \"img_mlp.2.weight\",\n \"ff.linear_out.bias\": \"img_mlp.2.bias\",\n \"ff_context.linear_in.weight\": \"txt_mlp.0.weight\",\n \"ff_context.linear_in.bias\": \"txt_mlp.0.bias\",\n \"ff_context.linear_out.weight\": \"txt_mlp.2.weight\",\n \"ff_context.linear_out.bias\": \"txt_mlp.2.bias\",\n \"attn.norm_q.weight\": \"img_attn.norm.query_norm.weight\",\n \"attn.norm_k.weight\": \"img_attn.norm.key_norm.weight\",\n \"attn.norm_added_q.weight\": \"txt_attn.norm.query_norm.weight\",\n \"attn.norm_added_k.weight\": \"txt_attn.norm.key_norm.weight\",\n }\n\n for k in block_map:\n key_map[\"{}.{}\".format(prefix_from, k)] = \"{}.{}\".format(prefix_to, block_map[k])\n\n for index in range(n_single_layers):\n prefix_from = \"single_transformer_blocks.{}\".format(index)\n prefix_to = \"{}single_blocks.{}\".format(output_prefix, index)\n\n for end in (\"weight\", \"bias\"):\n k = \"{}.attn.\".format(prefix_from)\n qkv = \"{}.linear1.{}\".format(prefix_to, end)\n key_map[\"{}to_q.{}\".format(k, end)] = (qkv, (0, 0, hidden_size))\n key_map[\"{}to_k.{}\".format(k, end)] = (qkv, (0, hidden_size, hidden_size))\n key_map[\"{}to_v.{}\".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))\n key_map[\"{}.proj_mlp.{}\".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4))\n\n block_map = {\n \"norm.linear.weight\": \"modulation.lin.weight\",\n \"norm.linear.bias\": \"modulation.lin.bias\",\n \"proj_out.weight\": \"linear2.weight\",\n \"proj_out.bias\": \"linear2.bias\",\n \"attn.norm_q.weight\": \"norm.query_norm.weight\",\n \"attn.norm_k.weight\": \"norm.key_norm.weight\",\n \"attn.to_qkv_mlp_proj.weight\": \"linear1.weight\", # Flux 2\n \"attn.to_out.weight\": \"linear2.weight\", # Flux 2\n }\n\n for k in block_map:\n key_map[\"{}.{}\".format(prefix_from, k)] = \"{}.{}\".format(prefix_to, block_map[k])\n\n MAP_BASIC = {\n (\"final_layer.linear.bias\", \"proj_out.bias\"),\n (\"final_layer.linear.weight\", \"proj_out.weight\"),\n (\"img_in.bias\", \"x_embedder.bias\"),\n (\"img_in.weight\", \"x_embedder.weight\"),\n (\"time_in.in_layer.bias\", \"time_text_embed.timestep_embedder.linear_1.bias\"),\n (\"time_in.in_layer.weight\", \"time_text_embed.timestep_embedder.linear_1.weight\"),\n (\"time_in.out_layer.bias\", \"time_text_embed.timestep_embedder.linear_2.bias\"),\n (\"time_in.out_layer.weight\", \"time_text_embed.timestep_embedder.linear_2.weight\"),\n (\"txt_in.bias\", \"context_embedder.bias\"),\n (\"txt_in.weight\", \"context_embedder.weight\"),\n (\"vector_in.in_layer.bias\", \"time_text_embed.text_embedder.linear_1.bias\"),\n (\"vector_in.in_layer.weight\", \"time_text_embed.text_embedder.linear_1.weight\"),\n (\"vector_in.out_layer.bias\", \"time_text_embed.text_embedder.linear_2.bias\"),\n (\"vector_in.out_layer.weight\", \"time_text_embed.text_embedder.linear_2.weight\"),\n (\"guidance_in.in_layer.bias\", \"time_text_embed.guidance_embedder.linear_1.bias\"),\n (\"guidance_in.in_layer.weight\", \"time_text_embed.guidance_embedder.linear_1.weight\"),\n (\"guidance_in.out_layer.bias\", \"time_text_embed.guidance_embedder.linear_2.bias\"),\n (\"guidance_in.out_layer.weight\", \"time_text_embed.guidance_embedder.linear_2.weight\"),\n (\"final_layer.adaLN_modulation.1.bias\", \"norm_out.linear.bias\", swap_scale_shift),\n (\"final_layer.adaLN_modulation.1.weight\", \"norm_out.linear.weight\", swap_scale_shift),\n (\"pos_embed_input.bias\", \"controlnet_x_embedder.bias\"),\n (\"pos_embed_input.weight\", \"controlnet_x_embedder.weight\"),\n }\n\n for k in MAP_BASIC:\n if len(k) > 2:\n key_map[k[1]] = (\"{}{}\".format(output_prefix, k[0]), None, k[2])\n else:\n key_map[k[1]] = \"{}{}\".format(output_prefix, k[0])\n\n return key_map\n\ndef z_image_to_diffusers(mmdit_config, output_prefix=\"\"):\n n_layers = mmdit_config.get(\"n_layers\", 0)\n hidden_size = mmdit_config.get(\"dim\", 0)\n n_context_refiner = mmdit_config.get(\"n_refiner_layers\", 2)\n n_noise_refiner = mmdit_config.get(\"n_refiner_layers\", 2)\n key_map = {}\n\n def add_block_keys(prefix_from, prefix_to, has_adaln=True):\n for end in (\"weight\", \"bias\"):\n k = \"{}.attention.\".format(prefix_from)\n qkv = \"{}.attention.qkv.{}\".format(prefix_to, end)\n key_map[\"{}to_q.{}\".format(k, end)] = (qkv, (0, 0, hidden_size))\n key_map[\"{}to_k.{}\".format(k, end)] = (qkv, (0, hidden_size, hidden_size))\n key_map[\"{}to_v.{}\".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))\n\n block_map = {\n \"attention.norm_q.weight\": \"attention.q_norm.weight\",\n \"attention.norm_k.weight\": \"attention.k_norm.weight\",\n \"attention.to_out.0.weight\": \"attention.out.weight\",\n \"attention.to_out.0.bias\": \"attention.out.bias\",\n \"attention_norm1.weight\": \"attention_norm1.weight\",\n \"attention_norm2.weight\": \"attention_norm2.weight\",\n \"feed_forward.w1.weight\": \"feed_forward.w1.weight\",\n \"feed_forward.w2.weight\": \"feed_forward.w2.weight\",\n \"feed_forward.w3.weight\": \"feed_forward.w3.weight\",\n \"ffn_norm1.weight\": \"ffn_norm1.weight\",\n \"ffn_norm2.weight\": \"ffn_norm2.weight\",\n }\n if has_adaln:\n block_map[\"adaLN_modulation.0.weight\"] = \"adaLN_modulation.0.weight\"\n block_map[\"adaLN_modulation.0.bias\"] = \"adaLN_modulation.0.bias\"\n for k, v in block_map.items():\n key_map[\"{}.{}\".format(prefix_from, k)] = \"{}.{}\".format(prefix_to, v)\n\n for i in range(n_layers):\n add_block_keys(\"layers.{}\".format(i), \"{}layers.{}\".format(output_prefix, i))\n\n for i in range(n_context_refiner):\n add_block_keys(\"context_refiner.{}\".format(i), \"{}context_refiner.{}\".format(output_prefix, i))\n\n for i in range(n_noise_refiner):\n add_block_keys(\"noise_refiner.{}\".format(i), \"{}noise_refiner.{}\".format(output_prefix, i))\n\n MAP_BASIC = [\n (\"final_layer.linear.weight\", \"all_final_layer.2-1.linear.weight\"),\n (\"final_layer.linear.bias\", \"all_final_layer.2-1.linear.bias\"),\n (\"final_layer.adaLN_modulation.1.weight\", \"all_final_layer.2-1.adaLN_modulation.1.weight\"),\n (\"final_layer.adaLN_modulation.1.bias\", \"all_final_layer.2-1.adaLN_modulation.1.bias\"),\n (\"x_embedder.weight\", \"all_x_embedder.2-1.weight\"),\n (\"x_embedder.bias\", \"all_x_embedder.2-1.bias\"),\n (\"x_pad_token\", \"x_pad_token\"),\n (\"cap_embedder.0.weight\", \"cap_embedder.0.weight\"),\n (\"cap_embedder.1.weight\", \"cap_embedder.1.weight\"),\n (\"cap_embedder.1.bias\", \"cap_embedder.1.bias\"),\n (\"cap_pad_token\", \"cap_pad_token\"),\n (\"t_embedder.mlp.0.weight\", \"t_embedder.mlp.0.weight\"),\n (\"t_embedder.mlp.0.bias\", \"t_embedder.mlp.0.bias\"),\n (\"t_embedder.mlp.2.weight\", \"t_embedder.mlp.2.weight\"),\n (\"t_embedder.mlp.2.bias\", \"t_embedder.mlp.2.bias\"),\n ]\n\n for c, diffusers in MAP_BASIC:\n key_map[diffusers] = \"{}{}\".format(output_prefix, c)\n\n return key_map\n\ndef repeat_to_batch_size(tensor, batch_size, dim=0):\n if tensor.shape[dim] > batch_size:\n return tensor.narrow(dim, 0, batch_size)\n elif tensor.shape[dim] < batch_size:\n return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, 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 resize_list_to_batch_size(l, batch_size):\n in_batch_size = len(l)\n if in_batch_size == batch_size or in_batch_size == 0:\n return l\n\n if batch_size <= 1:\n return l[:batch_size]\n\n output = []\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.append(l[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.append(l[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(' max_size:\n return None\n return f.read(length_of_header)\n\nATTR_UNSET={}\n\ndef resolve_attr(obj, attr):\n attrs = attr.split(\".\")\n for name in attrs[:-1]:\n obj = getattr(obj, name)\n return obj, attrs[-1]\n\ndef set_attr(obj, attr, value):\n obj, name = resolve_attr(obj, attr)\n prev = getattr(obj, name, ATTR_UNSET)\n if value is ATTR_UNSET:\n delattr(obj, name)\n else:\n setattr(obj, name, value)\n return prev\n\ndef set_attr_param(obj, attr, value):\n # Clone inference tensors (created under torch.inference_mode) since\n # their version counter is frozen and nn.Parameter() cannot wrap them.\n if (not torch.is_inference_mode_enabled()) and value.is_inference():\n value = value.clone()\n return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))\n\ndef set_attr_buffer(obj, attr, value):\n obj, name = resolve_attr(obj, attr)\n prev = getattr(obj, name, ATTR_UNSET)\n persistent = name not in getattr(obj, \"_non_persistent_buffers_set\", set())\n obj.register_buffer(name, value, persistent=persistent)\n return 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: str):\n \"\"\"Retrieves a nested attribute from an object using dot notation.\n\n Args:\n obj: The object to get the attribute from\n attr (str): The attribute path using dot notation (e.g. \"model.layer.weight\")\n\n Returns:\n The value of the requested attribute\n\n Example:\n model = MyModel()\n weight = get_attr(model, \"layer1.conv.weight\")\n # Equivalent to: model.layer1.conv.weight\n\n Important:\n Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when\n accessing nested model objects under `ModelPatcher.model`.\n \"\"\"\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 #the below API is strict and expects grayscale to be squeezed\n samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)\n images = [Image.fromarray(np.clip(255. * image.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 orig_shape = tuple(samples.shape)\n if len(orig_shape) > 4:\n samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1])\n samples = samples.movedim(2, 1)\n samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1])\n if crop == \"center\":\n old_width = samples.shape[-1]\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.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2)\n else:\n s = samples\n\n if upscale_method == \"bislerp\":\n out = bislerp(s, width, height)\n elif upscale_method == \"lanczos\":\n out = lanczos(s, width, height)\n else:\n out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)\n\n if len(orig_shape) == 4:\n return out\n\n out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width))\n return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width))\n\ndef get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):\n rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap))\n cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap))\n return rows * cols\n\n@torch.inference_mode()\ndef tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device=\"cpu\", downscale=False, index_formulas=None, pbar=None):\n dims = len(tile)\n\n if not (isinstance(upscale_amount, (tuple, list))):\n upscale_amount = [upscale_amount] * dims\n\n if not (isinstance(overlap, (tuple, list))):\n overlap = [overlap] * dims\n\n if index_formulas is None:\n index_formulas = upscale_amount\n\n if not (isinstance(index_formulas, (tuple, list))):\n index_formulas = [index_formulas] * dims\n\n def get_upscale(dim, val):\n up = upscale_amount[dim]\n if callable(up):\n return up(val)\n else:\n return up * val\n\n def get_downscale(dim, val):\n up = upscale_amount[dim]\n if callable(up):\n return up(val)\n else:\n return val / up\n\n def get_upscale_pos(dim, val):\n up = index_formulas[dim]\n if callable(up):\n return up(val)\n else:\n return up * val\n\n def get_downscale_pos(dim, val):\n up = index_formulas[dim]\n if callable(up):\n return up(val)\n else:\n return val / up\n\n if downscale:\n get_scale = get_downscale\n get_pos = get_downscale_pos\n else:\n get_scale = get_upscale\n get_pos = get_upscale_pos\n\n def mult_list_upscale(a):\n out = []\n for i in range(len(a)):\n out.append(round(get_scale(i, a[i])))\n return out\n\n output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device)\n\n for b in range(samples.shape[0]):\n s = samples[b:b+1]\n\n # handle entire input fitting in a single tile\n if all(s.shape[d+2] <= tile[d] for d in range(dims)):\n output[b:b+1] = function(s).to(output_device)\n if pbar is not None:\n pbar.update(1)\n continue\n\n out = output[b:b+1].zero_()\n out_div = torch.zeros([s.shape[0], 1] + mult_list_upscale(s.shape[2:]), device=output_device)\n\n positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]\n\n for it in itertools.product(*positions):\n s_in = s\n upscaled = []\n\n for d in range(dims):\n pos = max(0, min(s.shape[d + 2] - overlap[d], it[d]))\n l = min(tile[d], s.shape[d + 2] - pos)\n s_in = s_in.narrow(d + 2, pos, l)\n upscaled.append(round(get_pos(d, pos)))\n\n ps = function(s_in).to(output_device)\n mask = torch.ones([1, 1] + list(ps.shape[2:]), device=output_device)\n\n for d in range(2, dims + 2):\n feather = round(get_scale(d - 2, overlap[d - 2]))\n if feather >= mask.shape[d]:\n continue\n for t in range(feather):\n a = (t + 1) / feather\n mask.narrow(d, t, 1).mul_(a)\n mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a)\n\n o = out\n o_d = out_div\n for d in range(dims):\n o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])\n o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])\n\n o.add_(ps * mask)\n o_d.add_(mask)\n\n if pbar is not None:\n pbar.update(1)\n\n out.div_(out_div)\n return output\n\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 return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)\n\ndef model_trange(*args, **kwargs):\n if not comfy.memory_management.aimdo_enabled:\n return trange(*args, **kwargs)\n\n pbar = trange(*args, **kwargs, smoothing=1.0)\n pbar._i = 0\n pbar.set_postfix_str(\" Model Initializing ... \")\n\n _update = pbar.update\n\n def warmup_update(n=1):\n pbar._i += 1\n if pbar._i == 1:\n pbar.i1_time = time.time()\n pbar.set_postfix_str(\" Model Initialization complete! \")\n elif pbar._i == 2:\n #bring forward the effective start time based the the diff between first and second iteration\n #to attempt to remove load overhead from the final step rate estimate.\n pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)\n pbar.set_postfix_str(\"\")\n\n _update(n)\n\n pbar.update = warmup_update\n return pbar\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\n# Throttle settings for progress bar updates to reduce WebSocket flooding\nPROGRESS_THROTTLE_MIN_INTERVAL = 0.1 # 100ms minimum between updates\nPROGRESS_THROTTLE_MIN_PERCENT = 0.5 # 0.5% minimum progress change\n\nclass ProgressBar:\n def __init__(self, total, node_id=None):\n global PROGRESS_BAR_HOOK\n self.total = total\n self.current = 0\n self.hook = PROGRESS_BAR_HOOK\n self.node_id = node_id\n self._last_update_time = 0.0\n self._last_sent_value = -1\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 current_time = time.perf_counter()\n is_first = (self._last_sent_value < 0)\n is_final = (value >= self.total)\n has_preview = (preview is not None)\n\n # Always send immediately for previews, first update, or final update\n if has_preview or is_first or is_final:\n self.hook(self.current, self.total, preview, node_id=self.node_id)\n self._last_update_time = current_time\n self._last_sent_value = value\n return\n\n # Apply throttling for regular progress updates\n if self.total > 0:\n percent_changed = ((value - max(0, self._last_sent_value)) / self.total) * 100\n else:\n percent_changed = 100\n time_elapsed = current_time - self._last_update_time\n\n if time_elapsed >= PROGRESS_THROTTLE_MIN_INTERVAL and percent_changed >= PROGRESS_THROTTLE_MIN_PERCENT:\n self.hook(self.current, self.total, preview, node_id=self.node_id)\n self._last_update_time = current_time\n self._last_sent_value = value\n\n def update(self, value):\n self.update_absolute(self.current + value)\n\ndef reshape_mask(input_mask, output_shape):\n dims = len(output_shape) - 2\n\n if dims == 1:\n scale_mode = \"linear\"\n\n if dims == 2:\n input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1]))\n scale_mode = \"bilinear\"\n\n if dims == 3:\n if len(input_mask.shape) < 5:\n input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1]))\n scale_mode = \"trilinear\"\n\n mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)\n if mask.shape[1] < output_shape[1]:\n mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]\n mask = repeat_to_batch_size(mask, output_shape[0])\n return mask\n\ndef upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out):\n hi, wi = img_size_in\n ho, wo = img_size_out\n # if it's already the correct size, no need to do anything\n if (hi, wi) == (ho, wo):\n return mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0)\n if mask.ndim != 3:\n raise ValueError(f\"Got a mask of shape {list(mask.shape)}, expected [b, q, k] or [q, k]\")\n txt_tokens = mask.shape[1] - (hi * wi)\n # quadrants of the mask\n txt_to_txt = mask[:, :txt_tokens, :txt_tokens]\n txt_to_img = mask[:, :txt_tokens, txt_tokens:]\n img_to_img = mask[:, txt_tokens:, txt_tokens:]\n img_to_txt = mask[:, txt_tokens:, :txt_tokens]\n\n # convert to 1d x 2d, interpolate, then back to 1d x 1d\n txt_to_img = rearrange (txt_to_img, \"b t (h w) -> b t h w\", h=hi, w=wi)\n txt_to_img = interpolate(txt_to_img, size=img_size_out, mode=\"bilinear\")\n txt_to_img = rearrange (txt_to_img, \"b t h w -> b t (h w)\")\n # this one is hard because we have to do it twice\n # convert to 1d x 2d, interpolate, then to 2d x 1d, interpolate, then 1d x 1d\n img_to_img = rearrange (img_to_img, \"b hw (h w) -> b hw h w\", h=hi, w=wi)\n img_to_img = interpolate(img_to_img, size=img_size_out, mode=\"bilinear\")\n img_to_img = rearrange (img_to_img, \"b (hk wk) hq wq -> b (hq wq) hk wk\", hk=hi, wk=wi)\n img_to_img = interpolate(img_to_img, size=img_size_out, mode=\"bilinear\")\n img_to_img = rearrange (img_to_img, \"b (hq wq) hk wk -> b (hk wk) (hq wq)\", hq=ho, wq=wo)\n # convert to 2d x 1d, interpolate, then back to 1d x 1d\n img_to_txt = rearrange (img_to_txt, \"b (h w) t -> b t h w\", h=hi, w=wi)\n img_to_txt = interpolate(img_to_txt, size=img_size_out, mode=\"bilinear\")\n img_to_txt = rearrange (img_to_txt, \"b t h w -> b (h w) t\")\n\n # reassemble the mask from blocks\n out = torch.cat([\n torch.cat([txt_to_txt, txt_to_img], dim=2),\n torch.cat([img_to_txt, img_to_img], dim=2)],\n dim=1\n )\n return out\n\ndef pack_latents(latents):\n latent_shapes = []\n tensors = []\n for tensor in latents:\n latent_shapes.append(tensor.shape)\n tensors.append(tensor.reshape(tensor.shape[0], 1, -1))\n\n latent = torch.cat(tensors, dim=-1)\n return latent, latent_shapes\n\ndef unpack_latents(combined_latent, latent_shapes):\n if len(latent_shapes) > 1:\n output_tensors = []\n for shape in latent_shapes:\n cut = math.prod(shape[1:])\n tens = combined_latent[:, :, :cut]\n combined_latent = combined_latent[:, :, cut:]\n output_tensors.append(tens.reshape([tens.shape[0]] + list(shape)[1:]))\n else:\n output_tensors = [combined_latent]\n return output_tensors\n\ndef detect_layer_quantization(state_dict, prefix):\n for k in state_dict:\n if k.startswith(prefix) and k.endswith(\".comfy_quant\"):\n logging.info(\"Found quantization metadata version 1\")\n return {\"mixed_ops\": True}\n return None\n\ndef convert_old_quants(state_dict, model_prefix=\"\", metadata={}):\n if metadata is None:\n metadata = {}\n\n quant_metadata = None\n if \"_quantization_metadata\" not in metadata:\n scaled_fp8_key = \"{}scaled_fp8\".format(model_prefix)\n\n if scaled_fp8_key in state_dict:\n scaled_fp8_weight = state_dict[scaled_fp8_key]\n scaled_fp8_dtype = scaled_fp8_weight.dtype\n if scaled_fp8_dtype == torch.float32:\n scaled_fp8_dtype = torch.float8_e4m3fn\n\n if scaled_fp8_weight.nelement() == 2:\n full_precision_matrix_mult = True\n else:\n full_precision_matrix_mult = False\n\n out_sd = {}\n layers = {}\n for k in list(state_dict.keys()):\n if k == scaled_fp8_key:\n continue\n if not k.startswith(model_prefix):\n out_sd[k] = state_dict[k]\n continue\n k_out = k\n w = state_dict.pop(k)\n layer = None\n if k_out.endswith(\".scale_weight\"):\n layer = k_out[:-len(\".scale_weight\")]\n k_out = \"{}.weight_scale\".format(layer)\n\n if layer is not None:\n layer_conf = {\"format\": \"float8_e4m3fn\"} # TODO: check if anyone did some non e4m3fn scaled checkpoints\n if full_precision_matrix_mult:\n layer_conf[\"full_precision_matrix_mult\"] = full_precision_matrix_mult\n layers[layer] = layer_conf\n\n if k_out.endswith(\".scale_input\"):\n layer = k_out[:-len(\".scale_input\")]\n k_out = \"{}.input_scale\".format(layer)\n if w.item() == 1.0:\n continue\n\n out_sd[k_out] = w\n\n state_dict = out_sd\n quant_metadata = {\"layers\": layers}\n else:\n quant_metadata = json.loads(metadata[\"_quantization_metadata\"])\n\n if quant_metadata is not None:\n layers = quant_metadata[\"layers\"]\n for k, v in layers.items():\n state_dict[\"{}.comfy_quant\".format(k)] = torch.tensor(list(json.dumps(v).encode('utf-8')), dtype=torch.uint8)\n\n return state_dict, metadata\n\ndef string_to_seed(data):\n crc = 0xFFFFFFFF\n for byte in data:\n if isinstance(byte, str):\n byte = ord(byte)\n crc ^= byte\n for _ in range(8):\n if crc & 1:\n crc = (crc >> 1) ^ 0xEDB88320\n else:\n crc >>= 1\n return crc ^ 0xFFFFFFFF\n\ndef deepcopy_list_dict(obj, memo=None):\n if memo is None:\n memo = {}\n\n obj_id = id(obj)\n if obj_id in memo:\n return memo[obj_id]\n\n if isinstance(obj, dict):\n res = {deepcopy_list_dict(k, memo): deepcopy_list_dict(v, memo) for k, v in obj.items()}\n elif isinstance(obj, list):\n res = [deepcopy_list_dict(i, memo) for i in obj]\n else:\n res = obj\n\n memo[obj_id] = res\n return res\n\ndef normalize_image_embeddings(embeds, embeds_info, scale_factor):\n \"\"\"Normalize image embeddings to match text embedding scale\"\"\"\n for info in embeds_info:\n if info.get(\"type\") == \"image\":\n start_idx = info[\"index\"]\n end_idx = start_idx + info[\"size\"]\n embeds[:, start_idx:end_idx, :] /= scale_factor\n" }, { "path": "comfy/weight_adapter/glora.py", "content": "import logging\nfrom typing import Callable, Optional\n\nimport torch\nimport torch.nn.functional as F\nimport comfy.model_management\nfrom .base import WeightAdapterBase, weight_decompose\n\n\nclass GLoRAAdapter(WeightAdapterBase):\n name = \"glora\"\n\n def __init__(self, loaded_keys, weights):\n self.loaded_keys = loaded_keys\n self.weights = weights\n\n @classmethod\n def load(\n cls,\n x: str,\n lora: dict[str, torch.Tensor],\n alpha: float,\n dora_scale: torch.Tensor,\n loaded_keys: set[str] = None,\n ) -> Optional[\"GLoRAAdapter\"]:\n if loaded_keys is None:\n loaded_keys = set()\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 weights = (\n lora[a1_name],\n lora[a2_name],\n lora[b1_name],\n lora[b2_name],\n alpha,\n dora_scale,\n )\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 return cls(loaded_keys, weights)\n else:\n return None\n\n def calculate_weight(\n self,\n weight,\n key,\n strength,\n strength_model,\n offset,\n function,\n intermediate_dtype=torch.float32,\n original_weight=None,\n ):\n v = self.weights\n dora_scale = v[5]\n\n old_glora = False\n if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:\n rank = v[0].shape[0]\n old_glora = True\n\n if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:\n if (\n old_glora\n and v[1].shape[0] == weight.shape[0]\n and weight.shape[0] == weight.shape[1]\n ):\n pass\n else:\n old_glora = False\n rank = v[1].shape[0]\n\n a1 = comfy.model_management.cast_to_device(\n v[0].flatten(start_dim=1), weight.device, intermediate_dtype\n )\n a2 = comfy.model_management.cast_to_device(\n v[1].flatten(start_dim=1), weight.device, intermediate_dtype\n )\n b1 = comfy.model_management.cast_to_device(\n v[2].flatten(start_dim=1), weight.device, intermediate_dtype\n )\n b2 = comfy.model_management.cast_to_device(\n v[3].flatten(start_dim=1), weight.device, intermediate_dtype\n )\n\n if v[4] is not None:\n alpha = v[4] / rank\n else:\n alpha = 1.0\n\n try:\n if old_glora:\n lora_diff = (\n torch.mm(b2, b1)\n + torch.mm(\n torch.mm(\n weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2\n ),\n a1,\n )\n ).reshape(\n weight.shape\n ) # old lycoris glora\n else:\n if weight.dim() > 2:\n lora_diff = torch.einsum(\n \"o i ..., i j -> o j ...\",\n torch.einsum(\n \"o i ..., i j -> o j ...\",\n weight.to(dtype=intermediate_dtype),\n a1,\n ),\n a2,\n ).reshape(weight.shape)\n else:\n lora_diff = torch.mm(\n torch.mm(weight.to(dtype=intermediate_dtype), a1), a2\n ).reshape(weight.shape)\n lora_diff += torch.mm(b1, b2).reshape(weight.shape)\n\n if dora_scale is not None:\n weight = weight_decompose(\n dora_scale,\n weight,\n lora_diff,\n alpha,\n strength,\n intermediate_dtype,\n function,\n )\n else:\n weight += function(((strength * alpha) * lora_diff).type(weight.dtype))\n except Exception as e:\n logging.error(\"ERROR {} {} {}\".format(self.name, key, e))\n return weight\n\n def _compute_paths(self, x: torch.Tensor):\n \"\"\"\n Compute A path and B path outputs for GLoRA bypass.\n\n GLoRA: f(x) = Wx + WAx + Bx\n - A path: a1(a2(x)) - modifies input to base forward\n - B path: b1(b2(x)) - additive component\n\n Note:\n Does not access original model weights - bypass mode is designed\n for quantized models where weights may not be accessible.\n\n Returns: (a_out, b_out)\n \"\"\"\n v = self.weights\n # v = (a1, a2, b1, b2, alpha, dora_scale)\n a1 = v[0]\n a2 = v[1]\n b1 = v[2]\n b2 = v[3]\n alpha = v[4]\n\n dtype = x.dtype\n\n # Cast dtype (weights should already be on correct device from inject())\n a1 = a1.to(dtype=dtype)\n a2 = a2.to(dtype=dtype)\n b1 = b1.to(dtype=dtype)\n b2 = b2.to(dtype=dtype)\n\n # Determine rank and scale\n # Check for old vs new glora format\n old_glora = False\n if b2.shape[1] == b1.shape[0] == a1.shape[0] == a2.shape[1]:\n rank = a1.shape[0]\n old_glora = True\n\n if b2.shape[0] == b1.shape[1] == a1.shape[1] == a2.shape[0]:\n if old_glora and a2.shape[0] == x.shape[-1] and x.shape[-1] == x.shape[-1]:\n pass\n else:\n old_glora = False\n rank = a2.shape[0]\n\n if alpha is not None:\n scale = alpha / rank\n else:\n scale = 1.0\n\n # Apply multiplier\n multiplier = getattr(self, \"multiplier\", 1.0)\n scale = scale * multiplier\n\n # Use module info from bypass injection, not input tensor shape\n is_conv = getattr(self, \"is_conv\", False)\n conv_dim = getattr(self, \"conv_dim\", 0)\n kw_dict = getattr(self, \"kw_dict\", {})\n\n if is_conv:\n # Conv case - conv_dim is 1/2/3 for conv1d/2d/3d\n conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1]\n\n # Get module's stride/padding for spatial dimension handling\n module_stride = kw_dict.get(\"stride\", (1,) * conv_dim)\n module_padding = kw_dict.get(\"padding\", (0,) * conv_dim)\n kernel_size = getattr(self, \"kernel_size\", (1,) * conv_dim)\n in_channels = getattr(self, \"in_channels\", None)\n\n # Ensure weights are in conv shape\n # a1, a2, b1 are always 1x1 kernels\n if a1.ndim == 2:\n a1 = a1.view(*a1.shape, *([1] * conv_dim))\n if a2.ndim == 2:\n a2 = a2.view(*a2.shape, *([1] * conv_dim))\n if b1.ndim == 2:\n b1 = b1.view(*b1.shape, *([1] * conv_dim))\n # b2 has actual kernel_size (like LoRA down)\n if b2.ndim == 2:\n if in_channels is not None:\n b2 = b2.view(b2.shape[0], in_channels, *kernel_size)\n else:\n b2 = b2.view(*b2.shape, *([1] * conv_dim))\n\n # A path: a2(x) -> a1(...) - 1x1 convs, no stride/padding needed, a_out is added to x\n a2_out = conv_fn(x, a2)\n a_out = conv_fn(a2_out, a1) * scale\n\n # B path: b2(x) with kernel/stride/padding -> b1(...) 1x1\n b2_out = conv_fn(x, b2, stride=module_stride, padding=module_padding)\n b_out = conv_fn(b2_out, b1) * scale\n else:\n # Linear case\n if old_glora:\n # Old format: a1 @ a2 @ x, b2 @ b1\n a_out = F.linear(F.linear(x, a2), a1) * scale\n b_out = F.linear(F.linear(x, b1), b2) * scale\n else:\n # New format: x @ a1 @ a2, b1 @ b2\n a_out = F.linear(F.linear(x, a1), a2) * scale\n b_out = F.linear(F.linear(x, b2), b1) * scale\n\n return a_out, b_out\n\n def bypass_forward(\n self,\n org_forward: Callable,\n x: torch.Tensor,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n GLoRA bypass forward: f(x + a(x)) + b(x)\n\n Unlike standard adapters, GLoRA modifies the input to the base forward\n AND adds the B path output.\n\n Note:\n Does not access original model weights - bypass mode is designed\n for quantized models where weights may not be accessible.\n\n Reference: LyCORIS GLoRAModule._bypass_forward\n \"\"\"\n a_out, b_out = self._compute_paths(x)\n\n # Call base forward with modified input\n base_out = org_forward(x + a_out, *args, **kwargs)\n\n # Add B path\n return base_out + b_out\n\n def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:\n \"\"\"\n For GLoRA, h() returns the B path output.\n\n Note:\n GLoRA's full bypass requires overriding bypass_forward() since\n it also modifies the input to org_forward. This h() is provided for\n compatibility but bypass_forward() should be used for correct behavior.\n\n Does not access original model weights - bypass mode is designed\n for quantized models where weights may not be accessible.\n\n Args:\n x: Input tensor\n base_out: Output from base forward (unused, for API consistency)\n \"\"\"\n _, b_out = self._compute_paths(x)\n return b_out\n" }, { "path": "comfy/weight_adapter/lokr.py", "content": "import logging\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nimport comfy.model_management\nfrom .base import (\n WeightAdapterBase,\n WeightAdapterTrainBase,\n weight_decompose,\n factorization,\n)\n\n\nclass LokrDiff(WeightAdapterTrainBase):\n def __init__(self, weights):\n super().__init__()\n (\n lokr_w1,\n lokr_w2,\n alpha,\n lokr_w1_a,\n lokr_w1_b,\n lokr_w2_a,\n lokr_w2_b,\n lokr_t2,\n dora_scale,\n ) = weights\n self.use_tucker = False\n if lokr_w1_a is not None:\n _, rank_a = lokr_w1_a.shape[0], lokr_w1_a.shape[1]\n rank_a, _ = lokr_w1_b.shape[0], lokr_w1_b.shape[1]\n self.lokr_w1_a = torch.nn.Parameter(lokr_w1_a)\n self.lokr_w1_b = torch.nn.Parameter(lokr_w1_b)\n self.w1_rebuild = True\n self.ranka = rank_a\n\n if lokr_w2_a is not None:\n _, rank_b = lokr_w2_a.shape[0], lokr_w2_a.shape[1]\n rank_b, _ = lokr_w2_b.shape[0], lokr_w2_b.shape[1]\n self.lokr_w2_a = torch.nn.Parameter(lokr_w2_a)\n self.lokr_w2_b = torch.nn.Parameter(lokr_w2_b)\n if lokr_t2 is not None:\n self.use_tucker = True\n self.lokr_t2 = torch.nn.Parameter(lokr_t2)\n self.w2_rebuild = True\n self.rankb = rank_b\n\n if lokr_w1 is not None:\n self.lokr_w1 = torch.nn.Parameter(lokr_w1)\n self.w1_rebuild = False\n\n if lokr_w2 is not None:\n self.lokr_w2 = torch.nn.Parameter(lokr_w2)\n self.w2_rebuild = False\n\n self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False)\n\n @property\n def w1(self):\n if self.w1_rebuild:\n return (self.lokr_w1_a @ self.lokr_w1_b) * (self.alpha / self.ranka)\n else:\n return self.lokr_w1\n\n @property\n def w2(self):\n if self.w2_rebuild:\n if self.use_tucker:\n w2 = torch.einsum(\n \"i j k l, j r, i p -> p r k l\",\n self.lokr_t2,\n self.lokr_w2_b,\n self.lokr_w2_a,\n )\n else:\n w2 = self.lokr_w2_a @ self.lokr_w2_b\n return w2 * (self.alpha / self.rankb)\n else:\n return self.lokr_w2\n\n def __call__(self, w):\n w1 = self.w1\n w2 = self.w2\n # Unsqueeze w1 to match w2 dims for proper kron product (like LyCORIS make_kron)\n for _ in range(w2.dim() - w1.dim()):\n w1 = w1.unsqueeze(-1)\n diff = torch.kron(w1, w2)\n return w + diff.reshape(w.shape).to(w)\n\n def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Additive bypass component for LoKr training: efficient Kronecker product.\n\n Uses w1/w2 properties which handle both direct and decomposed cases.\n For create_train (direct w1/w2), no alpha scaling in properties.\n For to_train (decomposed), alpha/rank scaling is in properties.\n\n Args:\n x: Input tensor\n base_out: Output from base forward (unused, for API consistency)\n \"\"\"\n # Get w1, w2 from properties (handles rebuild vs direct)\n w1 = self.w1\n w2 = self.w2\n\n # Multiplier from bypass injection\n multiplier = getattr(self, \"multiplier\", 1.0)\n\n # Get module info from bypass injection\n is_conv = getattr(self, \"is_conv\", False)\n conv_dim = getattr(self, \"conv_dim\", 0)\n kw_dict = getattr(self, \"kw_dict\", {})\n\n # Efficient Kronecker application without materializing full weight\n # kron(w1, w2) @ x can be computed as nested operations\n # w1: [out_l, in_m], w2: [out_k, in_n, *k_size]\n # Full weight would be [out_l*out_k, in_m*in_n, *k_size]\n\n uq = w1.size(1) # in_m - inner grouping dimension\n\n if is_conv:\n conv_fn = (F.conv1d, F.conv2d, F.conv3d)[conv_dim - 1]\n\n B, C_in, *spatial = x.shape\n # Reshape input for grouped application: [B * uq, C_in // uq, *spatial]\n h_in_group = x.reshape(B * uq, -1, *spatial)\n\n # Ensure w2 has conv dims\n if w2.dim() == 2:\n w2 = w2.view(*w2.shape, *([1] * conv_dim))\n\n # Apply w2 path with stride/padding\n hb = conv_fn(h_in_group, w2, **kw_dict)\n\n # Reshape for cross-group operation\n hb = hb.view(B, -1, *hb.shape[1:])\n h_cross = hb.transpose(1, -1)\n\n # Apply w1 (always 2D, applied as linear on channel dim)\n hc = F.linear(h_cross, w1)\n hc = hc.transpose(1, -1)\n\n # Reshape to output\n out = hc.reshape(B, -1, *hc.shape[3:])\n else:\n # Linear case\n # Reshape input: [..., in_m * in_n] -> [..., uq (in_m), in_n]\n h_in_group = x.reshape(*x.shape[:-1], uq, -1)\n\n # Apply w2: [..., uq, in_n] @ [out_k, in_n].T -> [..., uq, out_k]\n hb = F.linear(h_in_group, w2)\n\n # Transpose for w1: [..., uq, out_k] -> [..., out_k, uq]\n h_cross = hb.transpose(-1, -2)\n\n # Apply w1: [..., out_k, uq] @ [out_l, uq].T -> [..., out_k, out_l]\n hc = F.linear(h_cross, w1)\n\n # Transpose back and flatten: [..., out_k, out_l] -> [..., out_l * out_k]\n hc = hc.transpose(-1, -2)\n out = hc.reshape(*hc.shape[:-2], -1)\n\n return out * multiplier\n\n def passive_memory_usage(self):\n return sum(param.numel() * param.element_size() for param in self.parameters())\n\n\nclass LoKrAdapter(WeightAdapterBase):\n name = \"lokr\"\n\n def __init__(self, loaded_keys, weights):\n self.loaded_keys = loaded_keys\n self.weights = weights\n\n @classmethod\n def create_train(cls, weight, rank=1, alpha=1.0):\n out_dim = weight.shape[0]\n in_dim = weight.shape[1] # Just in_channels, not flattened with kernel\n k_size = weight.shape[2:] if weight.dim() > 2 else ()\n\n out_l, out_k = factorization(out_dim, rank)\n in_m, in_n = factorization(in_dim, rank)\n\n # w1: [out_l, in_m]\n mat1 = torch.empty(out_l, in_m, device=weight.device, dtype=torch.float32)\n # w2: [out_k, in_n, *k_size] for conv, [out_k, in_n] for linear\n mat2 = torch.empty(\n out_k, in_n, *k_size, device=weight.device, dtype=torch.float32\n )\n\n torch.nn.init.kaiming_uniform_(mat2, a=5**0.5)\n torch.nn.init.constant_(mat1, 0.0)\n return LokrDiff((mat1, mat2, alpha, None, None, None, None, None, None))\n\n def to_train(self):\n return LokrDiff(self.weights)\n\n @classmethod\n def load(\n cls,\n x: str,\n lora: dict[str, torch.Tensor],\n alpha: float,\n dora_scale: torch.Tensor,\n loaded_keys: set[str] = None,\n ) -> Optional[\"LoKrAdapter\"]:\n if loaded_keys is None:\n loaded_keys = set()\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 (\n (lokr_w1 is not None)\n or (lokr_w2 is not None)\n or (lokr_w1_a is not None)\n or (lokr_w2_a is not None)\n ):\n weights = (\n lokr_w1,\n lokr_w2,\n alpha,\n lokr_w1_a,\n lokr_w1_b,\n lokr_w2_a,\n lokr_w2_b,\n lokr_t2,\n dora_scale,\n )\n return cls(loaded_keys, weights)\n else:\n return None\n\n def calculate_weight(\n self,\n weight,\n key,\n strength,\n strength_model,\n offset,\n function,\n intermediate_dtype=torch.float32,\n original_weight=None,\n ):\n v = self.weights\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 dora_scale = v[8]\n dim = None\n\n if w1 is None:\n dim = w1_b.shape[0]\n w1 = torch.mm(\n comfy.model_management.cast_to_device(\n w1_a, weight.device, intermediate_dtype\n ),\n comfy.model_management.cast_to_device(\n w1_b, weight.device, intermediate_dtype\n ),\n )\n else:\n w1 = comfy.model_management.cast_to_device(\n w1, weight.device, intermediate_dtype\n )\n\n if w2 is None:\n dim = w2_b.shape[0]\n if t2 is None:\n w2 = torch.mm(\n comfy.model_management.cast_to_device(\n w2_a, weight.device, intermediate_dtype\n ),\n comfy.model_management.cast_to_device(\n w2_b, weight.device, intermediate_dtype\n ),\n )\n else:\n w2 = torch.einsum(\n \"i j k l, j r, i p -> p r k l\",\n comfy.model_management.cast_to_device(\n t2, weight.device, intermediate_dtype\n ),\n comfy.model_management.cast_to_device(\n w2_b, weight.device, intermediate_dtype\n ),\n comfy.model_management.cast_to_device(\n w2_a, weight.device, intermediate_dtype\n ),\n )\n else:\n w2 = comfy.model_management.cast_to_device(\n w2, weight.device, intermediate_dtype\n )\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 else:\n alpha = 1.0\n\n try:\n lora_diff = torch.kron(w1, w2).reshape(weight.shape)\n if dora_scale is not None:\n weight = weight_decompose(\n dora_scale,\n weight,\n lora_diff,\n alpha,\n strength,\n intermediate_dtype,\n function,\n )\n else:\n weight += function(((strength * alpha) * lora_diff).type(weight.dtype))\n except Exception as e:\n logging.error(\"ERROR {} {} {}\".format(self.name, key, e))\n return weight\n\n def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Additive bypass component for LoKr: efficient Kronecker product application.\n\n Note:\n Does not access original model weights - bypass mode is designed\n for quantized models where weights may not be accessible.\n\n Args:\n x: Input tensor\n base_out: Output from base forward (unused, for API consistency)\n\n Reference: LyCORIS functional/lokr.py bypass_forward_diff\n \"\"\"\n # FUNC_LIST: [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]\n FUNC_LIST = [None, None, F.linear, F.conv1d, F.conv2d, F.conv3d]\n\n v = self.weights\n # v[0]=w1, v[1]=w2, v[2]=alpha, v[3]=w1_a, v[4]=w1_b, v[5]=w2_a, v[6]=w2_b, v[7]=t2, v[8]=dora\n w1 = v[0]\n w2 = v[1]\n alpha = v[2]\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\n use_w1 = w1 is not None\n use_w2 = w2 is not None\n tucker = t2 is not None\n\n # Use module info from bypass injection, not weight dimension\n is_conv = getattr(self, \"is_conv\", False)\n conv_dim = getattr(self, \"conv_dim\", 0)\n kw_dict = getattr(self, \"kw_dict\", {}) if is_conv else {}\n\n if is_conv:\n op = FUNC_LIST[conv_dim + 2]\n else:\n op = F.linear\n\n # Determine rank and scale\n rank = w1_b.size(0) if not use_w1 else w2_b.size(0) if not use_w2 else alpha\n scale = (alpha / rank if alpha is not None else 1.0) * getattr(\n self, \"multiplier\", 1.0\n )\n\n # Build c (w1)\n if use_w1:\n c = w1.to(dtype=x.dtype)\n else:\n c = w1_a.to(dtype=x.dtype) @ w1_b.to(dtype=x.dtype)\n uq = c.size(1)\n\n # Build w2 components\n if use_w2:\n ba = w2.to(dtype=x.dtype)\n else:\n a = w2_b.to(dtype=x.dtype)\n b = w2_a.to(dtype=x.dtype)\n if is_conv:\n if tucker:\n # Tucker: a, b get 1s appended (kernel is in t2)\n if a.dim() == 2:\n a = a.view(*a.shape, *([1] * conv_dim))\n if b.dim() == 2:\n b = b.view(*b.shape, *([1] * conv_dim))\n else:\n # Non-tucker conv: b may need 1s appended\n if b.dim() == 2:\n b = b.view(*b.shape, *([1] * conv_dim))\n\n # Reshape input by uq groups\n if is_conv:\n B, _, *rest = x.shape\n h_in_group = x.reshape(B * uq, -1, *rest)\n else:\n h_in_group = x.reshape(*x.shape[:-1], uq, -1)\n\n # Apply w2 path\n if use_w2:\n hb = op(h_in_group, ba, **kw_dict)\n else:\n if is_conv:\n if tucker:\n t = t2.to(dtype=x.dtype)\n if t.dim() == 2:\n t = t.view(*t.shape, *([1] * conv_dim))\n ha = op(h_in_group, a)\n ht = op(ha, t, **kw_dict)\n hb = op(ht, b)\n else:\n ha = op(h_in_group, a, **kw_dict)\n hb = op(ha, b)\n else:\n ha = op(h_in_group, a)\n hb = op(ha, b)\n\n # Reshape and apply c (w1)\n if is_conv:\n hb = hb.view(B, -1, *hb.shape[1:])\n h_cross_group = hb.transpose(1, -1)\n else:\n h_cross_group = hb.transpose(-1, -2)\n\n hc = F.linear(h_cross_group, c)\n\n if is_conv:\n hc = hc.transpose(1, -1)\n out = hc.reshape(B, -1, *hc.shape[3:])\n else:\n hc = hc.transpose(-1, -2)\n out = hc.reshape(*hc.shape[:-2], -1)\n\n return out * scale\n" }, { "path": "comfy/windows.py", "content": "import ctypes\nimport logging\nimport psutil\nfrom ctypes import wintypes\n\nimport comfy_aimdo.control\n\npsapi = ctypes.WinDLL(\"psapi\")\nkernel32 = ctypes.WinDLL(\"kernel32\")\n\nclass PERFORMANCE_INFORMATION(ctypes.Structure):\n _fields_ = [\n (\"cb\", wintypes.DWORD),\n (\"CommitTotal\", ctypes.c_size_t),\n (\"CommitLimit\", ctypes.c_size_t),\n (\"CommitPeak\", ctypes.c_size_t),\n (\"PhysicalTotal\", ctypes.c_size_t),\n (\"PhysicalAvailable\", ctypes.c_size_t),\n (\"SystemCache\", ctypes.c_size_t),\n (\"KernelTotal\", ctypes.c_size_t),\n (\"KernelPaged\", ctypes.c_size_t),\n (\"KernelNonpaged\", ctypes.c_size_t),\n (\"PageSize\", ctypes.c_size_t),\n (\"HandleCount\", wintypes.DWORD),\n (\"ProcessCount\", wintypes.DWORD),\n (\"ThreadCount\", wintypes.DWORD),\n ]\n\ndef get_free_ram():\n #Windows is way too conservative and chalks recently used uncommitted model RAM\n #as \"in-use\". So, calculate free RAM for the sake of general use as the greater of:\n #\n #1: What psutil says\n #2: Total Memory - (Committed Memory - VRAM in use)\n #\n #We have to subtract VRAM in use from the comitted memory as WDDM creates a naked\n #commit charge for all VRAM used just incase it wants to page it all out. This just\n #isn't realistic so \"overcommit\" on our calculations by just subtracting it off.\n\n pi = PERFORMANCE_INFORMATION()\n pi.cb = ctypes.sizeof(pi)\n\n if not psapi.GetPerformanceInfo(ctypes.byref(pi), pi.cb):\n logging.warning(\"WARNING: Failed to query windows performance info. RAM usage may be sub optimal\")\n return psutil.virtual_memory().available\n\n committed = pi.CommitTotal * pi.PageSize\n total = pi.PhysicalTotal * pi.PageSize\n\n return max(psutil.virtual_memory().available,\n total - (committed - comfy_aimdo.control.get_total_vram_usage()))\n\n" }, { "path": "comfy_api_nodes/__init__.py", "content": "" }, { "path": "tests/__init__.py", "content": "" }, { "path": "tests/inference/__init__.py", "content": "" }, { "path": "tests-unit/app_test/__init__.py", "content": "" }, { "path": "tests-unit/comfy_extras_test/__init__.py", "content": "" }, { "path": "tests-unit/folder_paths_test/__init__.py", "content": "" }, { "path": "tests-unit/prompt_server_test/__init__.py", "content": "" }, { "path": "utils/__init__.py", "content": "" } ]