Upload folder using huggingface_hub

.gitattributes

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 data/raw/3dDS9/3.png filter=lfs diff=lfs merge=lfs -text
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+data/EveryDream/scripts/BLIP/BLIP.gif filter=lfs diff=lfs merge=lfs -text
+models/Motion_Module/2fbkcvmxtmp filter=lfs diff=lfs merge=lfs -text
+models/Motion_Module/gqdawx6utmp filter=lfs diff=lfs merge=lfs -text
+models/Motion_Module/impovhmrtmp filter=lfs diff=lfs merge=lfs -text
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data/.cache/model_base_caption_capfilt_large.pth

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+size 896081425

data/EveryDream/'1.3.5'

@@ -0,0 +1,6 @@
+Requirement already satisfied: pandas in c:\users\texas\anaconda3\envs\animatediff\lib\site-packages (2.1.3)
+Requirement already satisfied: numpy<2,>=1.22.4 in c:\users\texas\anaconda3\envs\animatediff\lib\site-packages (from pandas) (1.26.0)
+Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\texas\anaconda3\envs\animatediff\lib\site-packages (from pandas) (2.8.2)
+Requirement already satisfied: pytz>=2020.1 in c:\users\texas\anaconda3\envs\animatediff\lib\site-packages (from pandas) (2023.3.post1)
+Requirement already satisfied: tzdata>=2022.1 in c:\users\texas\anaconda3\envs\animatediff\lib\site-packages (from pandas) (2023.3)
+Requirement already satisfied: six>=1.5 in c:\users\texas\anaconda3\envs\animatediff\lib\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)

data/EveryDream/.cache/model_base_caption_capfilt_large.pth

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data/EveryDream/.github/FUNDING.yml

@@ -0,0 +1,5 @@
+# These are supported funding model platforms
+
+github: victorchall
+patreon: everydream
+ko_fi: everydream

data/EveryDream/.gitignore

@@ -0,0 +1,139 @@
+# EveryDream
+/everydream-venv/**
+/laion/*.parquet
+/output/**
+/.cache/**
+/.venv/**
+/input/**
+/scripts/BLIP
+/.vscode/**
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

data/EveryDream/AutoCaption.ipynb

@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Please read the documentation here before you start.\n","\n","I suggest reading this doc before you connect to your runtime to avoid using credits or being charged while you figure it out.\n","\n","[Auto Captioning Readme](doc/AUTO_CAPTION.md)\n","\n","This notebook requires an Nvidia GPU instance. Any will do, you don't need anything power.  As low as 4GB should be fine.\n","\n","Only colab has automatic file transfers at this time.  If you are using another platform, you will need to manually download your output files."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":929,"status":"ok","timestamp":1667184580032,"user":{"displayName":"Victor Hall","userId":"00029068894644207946"},"user_tz":240},"id":"lWGx2LuU8Q_I","outputId":"d0eb4d03-f16d-460b-981d-d5f88447e85e"},"outputs":[],"source":["#download repo\n","!git clone https://github.com/victorchall/EveryDream.git\n","# Set working directory\n","%cd EveryDream"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4944,"status":"ok","timestamp":1667184754992,"user":{"displayName":"Victor Hall","userId":"00029068894644207946"},"user_tz":240},"id":"RJxfSai-8pkD","outputId":"0ac1b805-62a0-48aa-e0da-ee19503bb3f1"},"outputs":[],"source":["# install requirements\n","!pip install torch=='1.12.1+cu113' 'torchvision==0.13.1+cu113' --extra-index-url https://download.pytorch.org/whl/cu113\n","!pip install pandas>='1.3.5'\n","!git clone https://github.com/salesforce/BLIP scripts/BLIP\n","!pip install timm\n","!pip install fairscale=='0.4.4'\n","!pip install transformers=='4.19.2'\n","!pip install timm\n","!pip install aiofiles"]},{"cell_type":"markdown","metadata":{"id":"sbeUIVXJ-EVf"},"source":["# Upload your input images into the EveryDream/input folder\n","\n","![upload to input](demo/upload_images_caption.png)"]},{"cell_type":"markdown","metadata":{},"source":["## Please read the documentation for information on the parameters\n","\n","[Auto Captioning](doc/AUTO_CAPTION.md)\n","\n","*You cannot have commented lines between uncommented lines.  If you uncomment a line below, move it above any other commented lines.*\n","\n","*!python must remain the first line.*\n","\n","Default params should work fairly well."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18221,"status":"ok","timestamp":1667185808005,"user":{"displayName":"Victor Hall","userId":"00029068894644207946"},"user_tz":240},"id":"4TAICahl-RPn","outputId":"da7fa1a8-0855-403a-c295-4da31658d1f6"},"outputs":[],"source":["!python scripts/auto_caption.py \\\n","--img_dir input \\\n","--out_dir output \\\n","#--format mrwho \\\n","#--min_length 34 \\\n","#--q_factor 1.3 \\\n","#--nucleus \\"]},{"cell_type":"markdown","metadata":{"id":"HBrWnu1C_lN9"},"source":["## Download your captioned images from EveryDream/output\n","\n","If you're on a colab you can use the cell below to push your output to your Gdrive."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","!mkdir /content/drive/MyDrive/AutoCaption\n","!cp output/*.* /content/drive/MyDrive/AutoCaption"]},{"cell_type":"markdown","metadata":{},"source":["## If not on colab/gdrive, the following will zip up your files for extraction\n","\n","You'll still need to use your runtime's own download feature to download the zip.\n","\n","![output zip](demo/output_zip.png)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["!pip install patool\n","\n","import patoolib\n","\n","!mkdir output/zip\n","\n","!zip -r output/zip/output.zip output"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyN9ZSr0RyOQKdfeVsl2uOiE","collapsed_sections":[],"provenance":[{"file_id":"16QrivRfoDFvE7fAa7eLeVlxj78Q573E0","timestamp":1667185879409}]},"kernelspec":{"display_name":"Python 3.10.5 ('.venv': venv)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.5"},"vscode":{"interpreter":{"hash":"faf4a6abb601e3a9195ce3e9620411ceec233a951446de834cdf28542d2d93b4"}}},"nbformat":4,"nbformat_minor":0}

data/EveryDream/EveryDream_Tools.ipynb

@@ -0,0 +1,387 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/victorchall/EveryDream/blob/main/EveryDream_Tools.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title #Connect to Google Drive\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n"
+      ],
+      "metadata": {
+        "id": "Z_ZHfnQ52dg9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uJfwih4wAVgw"
+      },
+      "source": [
+        "# Please read the documentation here before you start.\n",
+        "\n",
+        "I suggest reading this doc before you connect to your runtime to avoid using credits or being charged while you figure it out.\n",
+        "\n",
+        "[Auto Captioning Readme](doc/AUTO_CAPTION.md)\n",
+        "\n",
+        "This notebook requires an Nvidia GPU instance. Any will do, you don't need anything power.  As low as 4GB should be fine.\n",
+        "\n",
+        "Only colab has automatic file transfers at this time.  If you are using another platform, you will need to manually download your output files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lWGx2LuU8Q_I"
+      },
+      "outputs": [],
+      "source": [
+        "#download repo\n",
+        "!git clone https://github.com/victorchall/EveryDream.git\n",
+        "# Set working directory\n",
+        "%cd EveryDream"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RJxfSai-8pkD"
+      },
+      "outputs": [],
+      "source": [
+        "# install requirements\n",
+        "!pip install torch=='1.12.1+cu113' 'torchvision==0.13.1+cu113' --extra-index-url https://download.pytorch.org/whl/cu113\n",
+        "!pip install pandas>='1.3.5'\n",
+        "!git clone https://github.com/salesforce/BLIP scripts/BLIP\n",
+        "!pip install timm\n",
+        "!pip install fairscale=='0.4.4'\n",
+        "!pip install transformers=='4.19.2'\n",
+        "!pip install timm\n",
+        "!pip install aiofiles\n",
+        "!pip install colorama"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Extract Frames from video\n",
+        "\n",
+        "Here we will use the folder input_vid and upload in the same way we did our images"
+      ],
+      "metadata": {
+        "id": "huQSI8Y-Bboz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python /scripts/extract_video_frames.py \\\n",
+        "--vid_dir input_vid \\\n",
+        "--out_dir output/vid \\\n",
+        "--format png \\\n",
+        "--interval 10 "
+      ],
+      "metadata": {
+        "id": "RDuBL4k8Avz-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Move the extracted frames to the input directory for captions"
+      ],
+      "metadata": {
+        "id": "iqcUzcRuCTLR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!cp -r output/vid input"
+      ],
+      "metadata": {
+        "id": "Uv8wAHSQAvrm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sbeUIVXJ-EVf"
+      },
+      "source": [
+        "# Upload your input images into the EveryDream/input folder\n",
+        "\n",
+        "![upload to input](https://github.com/victorchall/EveryDream/blob/main/demo/upload_images_caption.png?raw=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bscWH13SAVgz"
+      },
+      "source": [
+        "## Please read the documentation for information on the parameters\n",
+        "\n",
+        "[Auto Captioning](doc/AUTO_CAPTION.md)\n",
+        "\n",
+        "*You cannot have commented lines between uncommented lines.  If you uncomment a line below, move it above any other commented lines.*\n",
+        "\n",
+        "*!python must remain the first line.*\n",
+        "\n",
+        "Default params should work fairly well."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4TAICahl-RPn"
+      },
+      "outputs": [],
+      "source": [
+        "!python scripts/auto_caption.py \\\n",
+        "--img_dir input \\\n",
+        "--out_dir output \\\n",
+        "#--format mrwho \\\n",
+        "#--min_length 34 \\\n",
+        "#--q_factor 1.3 \\\n",
+        "#--nucleus \\\n",
+        "\n",
+        "## mutiple files can be targeted in succession\n",
+        "\n",
+        "#!python scripts/auto_caption.py \\\n",
+        "#--img_dir input/subfolder \\\n",
+        "#--out_dir output/subfolder \\\n",
+        "#--format mrwho \\\n",
+        "#--min_length 34 \\\n",
+        "#--q_factor 1.3 \\\n",
+        "#--nucleus \\"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Laion Downloader\n",
+        "\n",
+        "* --laion_dir: directory with laion parquet files, default is ./laion\n",
+        "\n",
+        "* --search_text: csv of words with AND logic, ex \\\"photo,man,dog\\\"\n",
+        "\n",
+        "* --out_dir: directory to download files to, ive defaulted this to inputs so they can be captioned \n",
+        "\n",
+        "* --log_dir: directory for logs, if ommitted will not log, logs may be large!\n",
+        "\n",
+        "* --column:column to search for matches, defaults is 'TEXT', but you could use 'URL' if you wanted\",\n",
+        "\n",
+        "* --limit: max number of matching images to download, warning: may be slightly imprecise due to concurrency and http errors, defaults is 100\n",
+        "\n",
+        "* --min_hw: min height AND width of image to download, default is 512\n",
+        "  \n",
+        "* --force: forces a full download of all images, even if no search is provided, USE CAUTION!\n",
+        "\n",
+        "* --parquet_skip: skips the first n parquet files on disk, useful to resume\n",
+        "        \n",
+        "* --verbose: additional logging of URL and TEXT \n",
+        "        \n",
+        "* --test: skips downloading, for checking filters, use with \"--verbose\"\n"
+      ],
+      "metadata": {
+        "id": "wY2f2LkPGSVa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python scripts/download_laion.py \\\n",
+        "--laion_dir ./laion \\\n",
+        "--search_text \"photo,man,dog\" \\\n",
+        "#--out_dir input \\\n",
+        "#--log_dir logs \\\n",
+        "#--column TEXT \\\n",
+        "#--limit 100 \\\n",
+        "#--min_hw 512 \\\n",
+        "#--force False \\\n",
+        "#--parquet_skip 0 \\\n",
+        "#--Verbose False \\\n",
+        "#--test not \\\n"
+      ],
+      "metadata": {
+        "id": "cxw60TTmEy2C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Here we can take our now captioned images and replace generic terms with our subjects\n",
+        "\n",
+        "* --find: will search for a word in this case man\n",
+        "\n",
+        "* --replace: will replace our found word with in this case bob smith\n",
+        "\n",
+        "* --append_only: this will allow us to add a tag at he end "
+      ],
+      "metadata": {
+        "id": "EBdLelNpDjYc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python scripts/filename_replace.py \\\n",
+        "--img_dir output \\\n",
+        "--find \"man\" \\\n",
+        "--replace \"bob smith\""
+      ],
+      "metadata": {
+        "id": "6Y1md3OHAvhw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we can chose to create text files based on our file names, this is usefull for images with very long discriptions or tag list, windows has a limit of 256 characters, and files will not transfer correctly to a windows program if they are longer, moving these files in a zip is fine however and causes no issues\n"
+      ],
+      "metadata": {
+        "id": "W0MspWmXJQuc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python scripts/createtxtfromfilename.py"
+      ],
+      "metadata": {
+        "id": "BpvenvyQJr9b"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Compress our images "
+      ],
+      "metadata": {
+        "id": "boVkDsiWJ_-P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python scripts/compress_img.py \\\n",
+        "--img_dir output \\\n",
+        "--out_dir output/compressed_images \\\n",
+        "--max_mp 1.5 \n",
+        "#--overwrite False \\\n",
+        "#--Quality 95  \\\n",
+        "#--noresize False \\\n",
+        "#--delete \\"
+      ],
+      "metadata": {
+        "id": "F6QYfylhKAII"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HBrWnu1C_lN9"
+      },
+      "source": [
+        "## Download your DataSet from EveryDream/output\n",
+        "\n",
+        "If you're on a colab you can use the cell below to push your output to your Gdrive."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ldW2sDLcAVgz"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "!mkdir /content/drive/MyDrive/Auto_Data_sets\n",
+        "!cp -r output/ /content/drive/MyDrive/Auto_Data_sets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B-HFqbP4AVgz"
+      },
+      "source": [
+        "## If not on colab/gdrive, the following will zip up your files for extraction\n",
+        "\n",
+        "You'll still need to use your runtime's own download feature to download the zip.\n",
+        "\n",
+        "![output zip](https://github.com/victorchall/EveryDream/blob/main/demo/output_zip.png?raw=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SVa80mrKAVg0"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install patool\n",
+        "\n",
+        "import patoolib\n",
+        "\n",
+        "!mkdir output/zip\n",
+        "\n",
+        "!zip -r output/zip/output.zip output"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "machine_shape": "hm",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3.10.5 ('.venv': venv)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.5"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "faf4a6abb601e3a9195ce3e9620411ceec233a951446de834cdf28542d2d93b4"
+      }
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

data/EveryDream/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
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+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

data/EveryDream/README.MD

@@ -0,0 +1,63 @@
+# EveryDream Tools
+
+This repo will contain tools for data engineering efforts for people interested in taking their fine tuning beyond the initial DreamBooth paper implementations for Stable Diffusion, and may be useful for other image projects.
+
+If you are looking for trainers, check out [EveryDream 2.0](https://github.com/victorchall/EveryDream2trainer).  This is just a toolkit repo for data work but works in concert with that trainer.
+
+For instance with Stable Diffusion, by using ground truth Laion data mixed in with training data to replace "regularization" images, together with clip-interrogated captioning or original TEXT caption from laion, or human-geneated labels, training quality can be improved.  These are a significant steps towards towards full fine tuning capabilities. 
+
+Captioned training together with regularization has enabled multi-subject and multi-style training at the same time, and can scale to larger training efforts.
+
+As an example project, you can download a large scale model for Final Fantasy 7 Remake here: https://huggingface.co/panopstor/ff7r-stable-diffusion and be sure to also follow up on the gist link at the bottom for more information along with links to example output of a multi-model fine tuning. 
+
+Join the EveryDream discord here: https://discord.gg/uheqxU6sXN
+
+## Tools
+
+[Download scrapes using Laion](./doc/LAION_SCRAPE.md) - Web scrapes images off the web using Laion data files (runs on CPU).
+
+[Auto Captioning](./doc/AUTO_CAPTION.md) - Uses BLIP interrogation to caption images for training (includes colab notebook, needs minimal GPU).
+
+[File renaming](./doc/FILE_RENAME.md) - Simple script for replacing generic pronouns that come out of clip in filenames with proper names (ex "a man" -> "john doe", "a person" -> "jane doe").
+
+*See clip_rename.bat for an example to chain captioning and renaming together.*
+
+[Compress images](./doc/COMPRESS_IMG.md) - Compresses images to WEBP with a given size (ex 1.5 megapixels) to reduce disk usage if you've downloaded some massive PNG data sets (ex. FFHQ) and wish to save some disk space.
+
+[Training](https://github.com/victorchall/EveryDream2trainer) (separate repo) - Fine tuning
+
+[Image Caption GUI](./doc/CAPTION_GUI.md) and [Video frame extractor](./doc/VIDEO_EXTRACTOR.md) courtesy of [MStevenson](https://github.com/mstevenson/)
+
+[General Tools Notebook](EveryDream_Tools.ipynb) Collection of various tools in this codebase by [Nawnie](https://github.com/nawnie) if you prefer to use Notebook GUI instead of the command line.
+
+## Install
+
+You can use conda or venv.  This was developed on Python 3.10.5 but may work on older newer versions.
+
+One step venv setup:
+
+    create_venv.bat
+
+Don't forget to activate every time you open the command prompt later.
+
+    activate_venv.bat
+
+To use conda instead of venv:
+
+    conda env create -f environment.yaml
+
+    pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+
+    git clone https://github.com/salesforce/BLIP scripts/BLIP
+
+    conda activate everydream
+
+Or you if you wish to configure your own venv, container/WSL, or Linux:
+
+    pip install -r requirements.txt
+
+    pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+
+    git clone https://github.com/salesforce/BLIP scripts/BLIP
+
+Thanks to the SalesForce team for the [BLIP tool](https://github.com/salesforce/BLIP). It uses CLIP to produce sane sentences like you would expect to see in alt-text.

data/EveryDream/activate_venv.bat

@@ -0,0 +1 @@
+call .venv/scripts/activate.bat

data/EveryDream/clip_rename.bat

@@ -0,0 +1,6 @@
+python scripts/auto_caption.py --q_factor 1.4
+::python scripts/filename_replace.py --img_dir output --find "a woman" --replace "rihanna"
+::python scripts/filename_replace.py --img_dir output --find "a person" --replace "rihanna"
+::python scripts/filename_replace.py --img_dir output --find "a man" --replace "asap rocky"
+::python scripts/filename_replace.py --img_dir output --replace "Keira Knightley"
+::python scripts/filename_replace.py --img_dir output --append "by Giotto"

data/EveryDream/create_venv.bat

@@ -0,0 +1,15 @@
+python -m venv .venv
+call .venv/scripts/activate.bat
+if %errorlevel% neq 0 goto :error
+pip install -r requirements.txt
+pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+git clone https://github.com/salesforce/BLIP scripts/BLIP
+if %errorlevel% neq 0 goto :error
+
+goto :done
+
+:error
+echo Error occurred trying to install or activate venv.
+exit /b %errorlevel%
+
+:done

data/EveryDream/deactivate_venv.bat

@@ -0,0 +1 @@
+call .venv/scripts/deactivate.bat

data/EveryDream/doc/AUTO_CAPTION.md

@@ -0,0 +1,115 @@
+# Automatic captioning
+
+Automatic captioning uses Salesforce's BLIP to automatically create a clean sentence structure for captioning input images before training.
+
+By default this requires an Nvidia GPU, but is not terribly intensive work.  It should run fine on something like a 1050 Ti 4GB. You can even run this on the CPU by specifying `--torch_device cpu` as an argument. This will be slower than running on a Nvidia GPU, but will work even on Apple Silicon Macs.
+
+[EveryDream trainer](https://github.com/victorchall/EveryDream-trainer) no longer requires cropped images. You only need to crop to exclude stuff you don't want trained, or to improve the portion of face close ups in your data.  The EveryDream trainer now accepts multiple aspect ratios and can train on them natively.
+
+But if you do wish to crop for other trainers, you can use [Birme](https://www.birme.net/?target_width=512&target_height=512&auto_focal=false&image_format=webp&quality_jpeg=95&quality_webp=99) to crop and resize first.  There are various tools out there for this.
+
+
+
+## Execute
+
+Place input files into the /input folder
+
+    python scripts/auto_caption.py
+
+Files will be **copied** and renamed to the caption as the file name and placed into /output.
+
+## Colab notebook
+
+This will run quite well on a T4 instance on Google Colab.  Don't waste credits on more powerful GPUs.
+
+https://colab.research.google.com/github/victorchall/EveryDream/blob/main/AutoCaption.ipynb
+
+It should work on other GPU providers on minimal power Nvidia GPU instances, but you are on your own to upload and download files.
+
+## Additional command line args:
+
+### --img_dir
+
+Changes the default input directory to read for files.  Default is /input
+
+    python scripts/auto_caption.py --img_dir x:/data/my_cropped_images
+
+### --out_dir
+
+Changes the default output directory.  Default is /output
+
+    python scripts/auto_caption.py --out_dir x:/data/ready_to_train
+
+### --format
+
+The default behavior will simply name the file the caption .EXT and, if needed, add _n at the end to avoid collisions, for use with EveryDream trainer or Kane Wallmann's dream booth fork.
+
+ex output: *"a man in a blue suit and a woman in a black dress standing next to each other in front of a table with a potted plant on it.jpg"*
+
+"mrwho" or "joepenna" will add \[number\]@ as a prefix for use with MrWho's captioning system (on JoePenna dream both fork) which uses that naming standard to avoid file name collisions.
+
+    python scripts/auto_caption.py --format "mrwho"
+
+"txt" or "caption" will create a ".txt" or ".caption" file instead of renaming the image.  ".txt" sidecar is another option for EveryDream trainer instead of getting the caption from the filename itself, and ".caption" is an option for other trainers.
+
+    python scripts/auto_caption.py --format "txt"
+
+or
+
+    python scripts/auto_caption.py --format "caption"
+## Tweaks
+
+You may find the following setting useful to deal with issues with bad auto-captioning.  Start with defaults, and if you have issues with captions that seem inaccurate or reptitious try some of the following settings.
+
+### --nucleus
+
+Uses an alternative "nucleus" algorithm instead of the default "beam 16" algorithm.  Nucleus produces relatively short captions but reliably absent of repeated words and phrases, comparable to using beam 16 which can be adjusted further but may need more tweaking.
+
+
+    python scripts/auto_caption.py --nucleus
+
+![Beam vs Nucleus](../demo/beam_vs_nucleus.webp)
+
+See q_factor below. 0.3 to 3 seem to produce sensible prompts, though 0.01 and 2000 will still work fairly well.
+
+Additional caption example for above with nucleus with different q_factor values:
+
+nucleus q_factor 9999: *"a number of kites painted in different colors in a ceiling"*
+
+nucleus q_factor 200: *"a group of people waiting under art hanging from a ceiling"*
+
+nucleus q_factor 1: *"several people standing around with large colorful umbrellas"*
+
+nucleus q_factor 0.01: *"people are standing in an open building with colorful paper decorations"*
+
+nucleus q_factor 0.00001: (same as above)
+
+### --q_factor
+
+An tuning adjustment depending the algorithm used.
+
+For the default beam 16 algorithm it limits the ability of words and phrases to be repeated.  Higher value reduces repeated words and phrases.  0.6-1.4 are sensible values for beam 16.  Default is 1.0 and works well with the defaulted value min_length of 24.  Consider using higher values if you use a min_length higher than 24 with beam 16.
+
+For nucleus (--nucleus), it simply changes the opinion on the prompt and does not impact repeats.  Values ranging from 0.01 to 200 seem sensible and default of 1.0 usually works well.
+
+![Beam vs Nucleus](../demo/beam_vs_nucleus_2.webp)
+
+### --min_length
+
+Adjusts the minimum length of prompt, measured in tokens.  **Only applies to beam 16.**  Useful to adjust along with --q_factor to keep it from repeating.
+
+Default is 22.  Sensible values are 15 to 30, max is 48.  Larger values are much more prone to repeating phrases and should be accompanied by increasing --q_factor to avoid repeats.
+
+    python scripts/auto_caption.py --min_length 20
+
+    python scripts/auto_caption.py --min_length 34 --q_factor 1.4
+
+![Q vs Min for beam](../demo/beam_min_vs_q.webp)
+
+### Note
+
+If you continue to both increase min_length and q_factor with default beam algorithm in an attempt to get a really long caption without repeats it will generate oddly specific prompts. For example using the above image:
+
+--q_factor 1.9  --min_length 48:
+
+*"a painting of a group of people sitting at a table in a room with red drapes on the walls and gold trimmings on the ceiling, while one person is holding a wine glass in front of the other hand"*

data/EveryDream/doc/CAPTION_GUI.md

@@ -0,0 +1,20 @@
+# MStevenson's tools
+
+## Caption GUI
+
+    python scripts/image_caption_gui.py
+
+Python GUI tool to manually caption images for machine learning.
+
+A sidecar file is created for each image with the same name and a .txt extension. These are compatible with EveryDream Trainer. 
+
+### Controls:
+[control/command + o] to open a folder of images.
+
+[page down] and [page up] to go to next and previous images. Hold shift to skip 10 images.
+
+[shift + home] and [shift + end] to go to first and last images.
+
+[shift + delete] to move the current image into a '_deleted' folder.
+
+[escape] to exit the app.

data/EveryDream/doc/COMPRESS_IMG.md

@@ -0,0 +1,69 @@
+# Mass compressing images in a folder
+
+## How it Works
+
+This script will sweep a folder and compress all the images to a given total number of megapixels.  Aspect ratio is not changed and nothing is cropped.
+
+*Images will only be resized if they exceed the specified megapixel limit.* Images within the limit will not be resized.
+
+This script will also correct issues with images having EXIF directives that rotate the image.  Or, in other words, it will make sure the proper orientation is saved native to the output image as trainers may not respect EXIF rotation directives.
+
+EXIF rotation correction will take place regardless of whether images are resized.
+
+Note this script will not attempt to copy or move any ICC color profiles at this time.  Trainers likely do not respect this anyway...
+
+Defaults are 1.5 megapixels, output is WEBPb at "quality 95" which affects the compression ratio.  90-99 are sane values for quality.  For the purposes of training stable diffusion, 1.5 megapixels is a good balance between quality and file size, and quality of 90-99 is as well. 
+
+If you are hoping to use massive training files in the future as tech advances, you may wish to change the --max_mp setting to a higher value, but for now 1.5MP is more than enough to last for a few more advances in the technology.  Ultimately this is your choice.  EveryDream trainer is built to handle multiple aspects, but if you want to use images for another trainer and will crop square, you may wish to use a higher value to make sure the images remain large after croppy, or consider cropping carefully first *before* running this script. 
+
+## Usage
+
+    usage: compress_img.py [-h] [--img_dir IMG_DIR] [--out_dir OUT_DIR]
+                            [--max_mp MAX_MP] [--quality QUALITY] [--overwrite]
+                            [--noresize] [--delete]
+
+    Compress images in a directory.
+
+    options:
+    -h, --help         show this help message and exit
+    --img_dir IMG_DIR  path to image directory (default: 'input')
+    --out_dir OUT_DIR  path to output directory (default: IMG_DIR)
+    --max_mp MAX_MP    maximum megapixels (default: 1.5)
+    --quality QUALITY  save quality (default: 95, range: 0-100, suggested: 90+)
+    --overwrite        overwrite files in output directory
+    --noresize         do not resize, just fix orientation
+    --delete           delete original files after processing
+
+The most basic use will load images from the local `input` directory, scale and rotate all the images, then write them back to the same folder. Default size is 1.5 megapixels.
+
+    python scripts/compress_img.py
+
+To specify the image source directory, specify the `--img_dir`:
+
+    python scripts/compress_img.py --img_dir Q:\big_images
+
+To save compressed images to a different path, specify the `--out_dir`:
+
+    python scripts/compress_img.py --img_dir Q:\big_images --out_dir Q:\small_images
+
+If a specific image already exists in the output path, **it will be skipped**. For example, if you run the script twice, existing `.webp` images in the output directory will be skipped entirely. To overwrite existing files, use the `--overwrite` directive:
+
+    python scripts/compress_img.py --img_dir Q:\big_images --overwrite
+
+If you want to ensure no files are skipped, *without overwriting existing images,* use `--out_dir` to specify an empty output folder.
+
+If you want to delete the *original source image* after it has been resized, use the `--delete` directive:
+
+    python scripts/compress_img.py --img_dir Q:\big_images --delete
+
+The `--delete` directive will not delete the original if it was overwritten or skipped.
+
+To change the max megapixels, use the `--max_mp` option. For example, to set max megapixels to 2.0 and overwrite existing images in the output directory, see this example:
+
+    python scripts/compress_img.py --img_dir Q:\big_images --out_dir Q:\small_images --max_mp 2.0 --overwrite
+
+Once you are comfortable with what is going on and OK with removing original images, you can use this to just replace everything in-place (these are my preferred settings):
+
+    python scripts/compress_img.py --img_dir Q:\big_images --max_mp 1.5 --quality 99 --overwrite --delete
+
+This will compress all images in the `Q:\big_images` directory down to a maximum `1.5` megapixels, at quality `99`, and will `overwrite` any existing output images and `delete` the original, un-altered image.

data/EveryDream/doc/FILE_RENAME.md

@@ -0,0 +1,36 @@
+# Filename Replace
+
+This is a very simple script to rename generic pronouns in files to proper names after using auto captioning.  This script does not create copies.  It renames the files in place.
+
+By default, it will replace "a man", "a woman", and "a person" with your supplied proper name.  This works well for single subject without tweaking.
+
+
+## Usage
+
+    python scripts/filename_replace.py --img_dir output --replace "john doe"
+
+*"a man standing in a park with birds on his shoulders.jpg"
+-> 
+"john doe standing in a park with birds on his shoulders.jpg"*
+
+## Append tags only
+
+    python scripts/filename_replace.py --img_dir "x:\myfiles" --append_only " by claude monet"
+
+This will simply append " by claude monet" without replacing anything, useful to add tags or artstyle keywords.
+
+## Chaining with auto caption
+
+You can chain together the auto_caption.py and file_rename.py to help deal with multiple people in photos in a simple shell script (bash or windows .bat) with a bit of thinking about what you replace and using --find to specify the pronoun to replace first more specifically than all three default pronouns. 
+
+    python scripts/auto_caption.py --q_factor 1.4 --img_dir input --out_dir output 
+    python scripts/filename_replace.py --img_dir output --find "a woman" --replace "rihanna" 
+    python scripts/filename_replace.py --img_dir output --replace "asap rocky"
+
+"a man and a woman standing next to each other in front of a green wall with leaves on it.webp" 
+->
+"asap rocky and rihanna standing next to each other in front of a green wall with leaves on it.webp"
+
+See clip_rename.bat in the root folder, modify it to your needs.
+
+Renaming is nearly instant as it is just renaming the files and not using and AI models or calculations, just a dumb find and replace on the filename. 

data/EveryDream/doc/LAION_SCRAPE.md

@@ -0,0 +1,51 @@
+# download_laion.py
+
+![](../demo/demo03.png)
+
+This script enables you to webscrape using the Laion parquet files which are available on Huggingface.co. 
+
+It has been tested with 2B-en-aesthetic, but may need minor tweaks for some other datasets that contain different columns.  Keep in mind some other files are purely sidecar metadata.
+
+https://huggingface.co/datasets/laion/laion2B-en-aesthetic
+
+**This tool does not work unless you download a set of Laion parquet files, above link is suggested.** Download all 128 .parquet files and place them in the /laion folder.
+
+The script will rename downloaded files to the best of its ability to the TEXT (caption) of the image with the original file extension, which can be plugged into the new class of caption-capable DreamBooth apps or the EveryDream trainer that will use the filename as the prompt for training.  
+
+One suggested use is to take this data and replace regularization images with ground truth data from the Laion dataset.
+
+It should execute quite quickly as it uses async task gathers for the the HTTP and fileio work. 
+
+Default folders are /laion for the parquet files and /output for downloaded images relative to the root folder, but consider disk space and point to another location if needed.
+
+## Examples
+
+Query all the parquet files in ./laion for any image with a caption (TEXT) containing "a man" and attempt top stop after downloading (approximately) 50 files:
+
+    python scripts/download_laion.py --search_text "a man" --limit 50
+
+Query for person with a leading and trailing space:
+
+    python scripts/download_laion.py --search_text " person " --limit 200
+
+Query for both "man" and "photo" anywhere in the caption, and write them to z:/myDumpFolder instead of the default folder.  Useful if you need to put them on another drive, NAS, etc.  The default limit of 100 images will apply since --limit is omitted:
+
+    python scripts/download_laion.py --search_text "man,photo" --out_dir "z:/myDumpFolder" --laion_dir "x:/datahoard/laion5b"
+
+## Performance
+
+Script should be reasonably fast depending on your internet speed.  I'm able to pull 10,000 images in about 3 1/2 minutes on 1 Gbit fiber.  
+
+## Other resources
+
+Nvidia has compiled a close up photo set: [ffhq-dataset](https://github.com/NVlabs/ffhq-dataset)
+
+## Batch run
+
+You can throw commands in a shell/cmd script to run several searches, but I will leave this exercise to the user.
+
+    python scripts/download_laion.py --search_text "jan van eyck" --limit 200
+    python scripts/download_laion.py --search_text " hokusai" --limit 200
+    python scripts/download_laion.py --search_text " bernini" --limit 200
+    python scripts/download_laion.py --search_text "Gustav Klimt" --limit 200
+    python scripts/download_laion.py --search_text "engon Schiele" --limit 200

data/EveryDream/doc/VIDEO_EXTRACTOR.md

@@ -0,0 +1,27 @@
+# Video frame extractor
+
+## Usage
+
+Place video files into the top level of a directory.
+
+Execute `python scripts/extract_video_frames.py --vid_dir path/to/videos` to iterate over all files, extract frames at regular intervals, and save full resolution frame images to disk.
+
+This tool supports a wide variety of input video containers and codecs (via OpenCV), and exports jpg or png files.
+
+## Arguments
+
+### --vid_dir
+
+Required directory path for input video files.
+
+### --out_dir
+
+Optional directory path in which to store extracted frame images. Defaults to a directory named 'output' that will be created inside the specified videos directory.
+
+### --format
+
+The format for image files saved to disk. Defaults to `png`, or optionally `jpg`.
+
+### --interval
+
+The number of seconds between frame captures. Defaults to 10 seconds.

data/EveryDream/environment.yaml

@@ -0,0 +1,9 @@
+name: edtools
+dependencies:
+  - pandas>=1.4.3
+  - aiofiles>=22.1.0
+  - colorama>=0.4.5
+  - aiohttp>=3.8.3
+  - timm
+  - fairscale==0.4.4
+  - transformers==4.19.2

data/EveryDream/laion/Put LAION parquets here.txt

@@ -0,0 +1,2 @@
+Suggested set is here: https://huggingface.co/datasets/laion/laion2B-en-aesthetic
+this is the default folder unless specified otherwise

data/EveryDream/requirements.txt

@@ -0,0 +1,10 @@
+pandas>=1.4.3
+pyarrow>=9.0.0
+aiofiles>=22.1.0
+colorama>=0.4.5
+aiohttp>=3.8.3
+#open_clip_torch>=1.26.12
+timm
+fairscale==0.4.4
+transformers==4.19.2
+opencv-python>=4.6.0

data/EveryDream/scripts/BLIP/CODEOWNERS

@@ -0,0 +1,2 @@
+# Comment line immediately above ownership line is reserved for related gus information. Please be careful while editing.
+#ECCN:Open Source

data/EveryDream/scripts/BLIP/CODE_OF_CONDUCT.md

@@ -0,0 +1,105 @@
+# Salesforce Open Source Community Code of Conduct
+
+## About the Code of Conduct
+
+Equality is a core value at Salesforce. We believe a diverse and inclusive
+community fosters innovation and creativity, and are committed to building a
+culture where everyone feels included.
+
+Salesforce open-source projects are committed to providing a friendly, safe, and
+welcoming environment for all, regardless of gender identity and expression,
+sexual orientation, disability, physical appearance, body size, ethnicity, nationality, 
+race, age, religion, level of experience, education, socioeconomic status, or 
+other similar personal characteristics.
+
+The goal of this code of conduct is to specify a baseline standard of behavior so
+that people with different social values and communication styles can work
+together effectively, productively, and respectfully in our open source community.
+It also establishes a mechanism for reporting issues and resolving conflicts.
+
+All questions and reports of abusive, harassing, or otherwise unacceptable behavior
+in a Salesforce open-source project may be reported by contacting the Salesforce
+Open Source Conduct Committee at ossconduct@salesforce.com.
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of gender 
+identity and expression, sexual orientation, disability, physical appearance, 
+body size, ethnicity, nationality, race, age, religion, level of experience, education, 
+socioeconomic status, or other similar personal characteristics.
+
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+## Attribution
+
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data/EveryDream/scripts/BLIP/LICENSE.txt

@@ -0,0 +1,12 @@
+Copyright (c) 2022, Salesforce.com, Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
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data/EveryDream/scripts/BLIP/README.md

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+## BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
+
+## Announcement: BLIP is now officially integrated into [LAVIS](https://github.com/salesforce/LAVIS) - a one-stop library for language-and-vision research and applications!
+
+<img src="BLIP.gif" width="700">
+
+This is the PyTorch code of the <a href="https://arxiv.org/abs/2201.12086">BLIP paper</a> [[blog](https://blog.salesforceairesearch.com/blip-bootstrapping-language-image-pretraining/)]. The code has been tested on PyTorch 1.10.
+To install the dependencies, run <pre/>pip install -r requirements.txt</pre> 
+
+Catalog:
+- [x] Inference demo
+- [x] Pre-trained and finetuned checkpoints
+- [x] Finetuning code for Image-Text Retrieval, Image Captioning, VQA, and NLVR2
+- [x] Pre-training code
+- [x] Zero-shot video-text retrieval
+- [x] Download of bootstrapped pre-training datasets 
+
+
+### Inference demo:
+Run our interactive demo using [Colab notebook](https://colab.research.google.com/github/salesforce/BLIP/blob/main/demo.ipynb) (no GPU needed).
+The demo includes code for: 
+1. Image captioning
+2. Open-ended visual question answering
+3. Multimodal / unimodal feature extraction
+4. Image-text matching
+
+Try out the [Web demo](https://huggingface.co/spaces/Salesforce/BLIP), integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). 
+
+Replicate web demo and Docker image is also available at [![Replicate](https://replicate.com/salesforce/blip/badge)](https://replicate.com/salesforce/blip)
+
+### Pre-trained checkpoints:
+Num. pre-train images | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L 
+--- | :---: | :---: | :---: 
+14M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_14M.pth">Download</a>| - | -
+129M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth">Download</a> | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large.pth">Download</a>
+
+### Finetuned checkpoints:
+Task | BLIP w/ ViT-B | BLIP w/ ViT-B and CapFilt-L | BLIP w/ ViT-L 
+--- | :---: | :---: | :---:
+Image-Text Retrieval (COCO) | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth">Download</a>| - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco.pth">Download</a>
+Image-Text Retrieval (Flickr30k) | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth">Download</a>|  - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_flickr.pth">Download</a>
+Image Captioning (COCO) | - | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth">Download</a> | 
+VQA | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth">Download</a>| <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth">Download</a> | - 
+NLVR2 | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth">Download</a>| - | - 
+
+
+### Image-Text Retrieval:
+1. Download COCO and Flickr30k datasets from the original websites, and set 'image_root' in configs/retrieval_{dataset}.yaml accordingly.
+2. To evaluate the finetuned BLIP model on COCO, run:
+<pre>python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
+--config ./configs/retrieval_coco.yaml \
+--output_dir output/retrieval_coco \
+--evaluate</pre> 
+3. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/retrieval_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
+--config ./configs/retrieval_coco.yaml \
+--output_dir output/retrieval_coco </pre> 
+
+### Image-Text Captioning:
+1. Download COCO and NoCaps datasets from the original websites, and set 'image_root' in configs/caption_coco.yaml and configs/nocaps.yaml accordingly.
+2. To evaluate the finetuned BLIP model on COCO, run:
+<pre>python -m torch.distributed.run --nproc_per_node=8 train_caption.py --evaluate</pre> 
+3. To evaluate the finetuned BLIP model on NoCaps, generate results with: (evaluation needs to be performed on official server)
+<pre>python -m torch.distributed.run --nproc_per_node=8 eval_nocaps.py </pre> 
+4. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set 'pretrained' in configs/caption_coco.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=8 train_caption.py </pre> 
+
+### VQA:
+1. Download VQA v2 dataset and Visual Genome dataset from the original websites, and set 'vqa_root' and 'vg_root' in configs/vqa.yaml.
+2. To evaluate the finetuned BLIP model, generate results with: (evaluation needs to be performed on official server)
+<pre>python -m torch.distributed.run --nproc_per_node=8 train_vqa.py --evaluate</pre> 
+3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/vqa.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=16 train_vqa.py </pre> 
+
+### NLVR2:
+1. Download NLVR2 dataset from the original websites, and set 'image_root' in configs/nlvr.yaml.
+2. To evaluate the finetuned BLIP model, run
+<pre>python -m torch.distributed.run --nproc_per_node=8 train_nlvr.py --evaluate</pre> 
+3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set 'pretrained' in configs/nlvr.yaml as "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth". Then run:
+<pre>python -m torch.distributed.run --nproc_per_node=16 train_nlvr.py </pre> 
+
+### Finetune with ViT-L:
+In order to finetune a model with ViT-L, simply change the config file to set 'vit' as large. Batch size and learning rate may also need to be adjusted accordingly (please see the paper's appendix for hyper-parameter details). <a href="https://github.com/facebookresearch/fairscale">Gradient checkpoint</a> can also be activated in the config file to reduce GPU memory usage. 
+
+### Pre-train:
+1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'image': path_of_image, 'caption': text_of_image}. 
+2. In configs/pretrain.yaml, set 'train_file' as the paths for the json files .
+3. Pre-train the model using 8 A100 GPUs:
+<pre>python -m torch.distributed.run --nproc_per_node=8 pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain </pre> 
+
+### Zero-shot video-text retrieval:
+1. Download MSRVTT dataset following the instructions from https://github.com/salesforce/ALPRO, and set 'video_root' accordingly in configs/retrieval_msrvtt.yaml.
+2. Install [decord](https://github.com/dmlc/decord) with <pre>pip install decord</pre> 
+3. To perform zero-shot evaluation, run
+<pre>python -m torch.distributed.run --nproc_per_node=8 eval_retrieval_video.py</pre> 
+
+### Pre-training datasets download:
+We provide bootstrapped pre-training datasets as json files. Each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {'url': url_of_image, 'caption': text_of_image}. 
+
+Image source | Filtered web caption | Filtered synthetic caption by ViT-B | Filtered synthetic caption by ViT-L
+--- | :---: | :---: | :---:
+CC3M+CC12M+SBU |  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered_large.json">Download</a>
+LAION115M | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_synthetic_filtered.json">Download</a>|  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_synthetic_filtered_large.json">Download</a>
+
+### Citation
+If you find this code to be useful for your research, please consider citing.
+<pre>
+@inproceedings{li2022blip,
+      title={BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, 
+      author={Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi},
+      year={2022},
+      booktitle={ICML},
+}</pre>
+
+### Acknowledgement
+The implementation of BLIP relies on resources from <a href="https://github.com/salesforce/ALBEF">ALBEF</a>, <a href="https://github.com/huggingface/transformers">Huggingface Transformers</a>, and <a href="https://github.com/rwightman/pytorch-image-models/tree/master/timm">timm</a>. We thank the original authors for their open-sourcing.

data/EveryDream/scripts/BLIP/SECURITY.md

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+## Security
+
+Please report any security issue to [security@salesforce.com](mailto:security@salesforce.com)
+as soon as it is discovered. This library limits its runtime dependencies in
+order to reduce the total cost of ownership as much as can be, but all consumers
+should remain vigilant and have their security stakeholders review all third-party
+products (3PP) like this one and their dependencies.