Duplicate from dreambooth-hackathon/leaderboard

Co-authored-by: Lewis Tunstall <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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

README.md

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+---
+title: Leaderboard
+emoji: 😻
+colorFrom: purple
+colorTo: gray
+sdk: gradio
+sdk_version: 3.14.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: dreambooth-hackathon/leaderboard
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ae4232b9-fb9f-419a-9992-8481d1de6b61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# |export\n",
+    "import gradio as gr\n",
+    "import pandas as pd\n",
+    "from huggingface_hub import list_models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "51d7a652-f6d2-4cee-b787-88fc0fae0acd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# |export\n",
+    "def make_clickable_model(model_name, link=None):\n",
+    "    if link is None:\n",
+    "        link = \"https://huggingface.co/\" + model_name\n",
+    "    # Remove user from model name\n",
+    "    return f'<a target=\"_blank\" href=\"{link}\">{model_name.split(\"/\")[-1]}</a>'\n",
+    "\n",
+    "\n",
+    "def make_clickable_user(user_id):\n",
+    "    link = \"https://huggingface.co/\" + user_id\n",
+    "    return f'<a  target=\"_blank\" href=\"{link}\">{user_id}</a>'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "82d94a98-0e69-4400-9cb1-2e90ef6da519",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# |export\n",
+    "def get_submissions(category):\n",
+    "    submissions = list_models(filter=[\"dreambooth-hackathon\", category], full=True)\n",
+    "    leaderboard_models = []\n",
+    "\n",
+    "    for submission in submissions:\n",
+    "        # user, model, likes\n",
+    "        user_id = submission.id.split(\"/\")[0]\n",
+    "        leaderboard_models.append(\n",
+    "            (\n",
+    "                make_clickable_user(user_id),\n",
+    "                make_clickable_model(submission.id),\n",
+    "                submission.likes,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    df = pd.DataFrame(data=leaderboard_models, columns=[\"User\", \"Model\", \"Likes\"])\n",
+    "    df.sort_values(by=[\"Likes\"], ascending=False, inplace=True)\n",
+    "    df.insert(0, \"Rank\", list(range(1, len(df) + 1)))\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "7579bfc6-ddf6-444d-ab7e-505734d86e4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7894\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7894/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# |export\n",
+    "block = gr.Blocks()\n",
+    "\n",
+    "with block:\n",
+    "    gr.Markdown(\n",
+    "        \"\"\"# The DreamBooth Hackathon Leaderboard\n",
+    "    \n",
+    "    Welcome to the leaderboard for the DreamBooth Hackathon! This is a community event where particpants **personalise a Stable Diffusion model** by fine-tuning it with a powerful technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242). This technique allows one to implant a subject (e.g. your pet or favourite dish) into the output domain of the model such that it can be synthesized with a _unique identifier_ in the prompt. \n",
+    "    \n",
+    "    This competition is composed of 5 _themes_, where each theme will collect models belong to one of the categories shown in the tabs below. We'll be **giving out prizes to the top 3 most liked models per theme**, and you're encouraged to submit as many models as you want!\n",
+    "    \n",
+    "    For details on how to participate, check out the hackathon's guide [here](https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md).\n",
+    "    \"\"\"\n",
+    "    )\n",
+    "    with gr.Tabs():\n",
+    "        with gr.TabItem(\"Animal 🐨\"):\n",
+    "            with gr.Row():\n",
+    "                animal_data = gr.components.Dataframe(\n",
+    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
+    "                )\n",
+    "            with gr.Row():\n",
+    "                data_run = gr.Button(\"Refresh\")\n",
+    "                data_run.click(\n",
+    "                    get_submissions, inputs=gr.Variable(\"animal\"), outputs=animal_data\n",
+    "                )\n",
+    "        with gr.TabItem(\"Science 🔬\"):\n",
+    "            with gr.Row():\n",
+    "                science_data = gr.components.Dataframe(\n",
+    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
+    "                )\n",
+    "            with gr.Row():\n",
+    "                data_run = gr.Button(\"Refresh\")\n",
+    "                data_run.click(\n",
+    "                    get_submissions, inputs=gr.Variable(\"science\"), outputs=science_data\n",
+    "                )\n",
+    "        with gr.TabItem(\"Food 🍔\"):\n",
+    "            with gr.Row():\n",
+    "                food_data = gr.components.Dataframe(\n",
+    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
+    "                )\n",
+    "            with gr.Row():\n",
+    "                data_run = gr.Button(\"Refresh\")\n",
+    "                data_run.click(\n",
+    "                    get_submissions, inputs=gr.Variable(\"food\"), outputs=food_data\n",
+    "                )\n",
+    "        with gr.TabItem(\"Landscape 🏔\"):\n",
+    "            with gr.Row():\n",
+    "                landscape_data = gr.components.Dataframe(\n",
+    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
+    "                )\n",
+    "            with gr.Row():\n",
+    "                data_run = gr.Button(\"Refresh\")\n",
+    "                data_run.click(\n",
+    "                    get_submissions,\n",
+    "                    inputs=gr.Variable(\"landscape\"),\n",
+    "                    outputs=landscape_data,\n",
+    "                )\n",
+    "        with gr.TabItem(\"Wilcard 🔥\"):\n",
+    "            with gr.Row():\n",
+    "                wildcard_data = gr.components.Dataframe(\n",
+    "                    type=\"pandas\", datatype=[\"number\", \"markdown\", \"markdown\", \"number\"]\n",
+    "                )\n",
+    "            with gr.Row():\n",
+    "                data_run = gr.Button(\"Refresh\")\n",
+    "                data_run.click(\n",
+    "                    get_submissions,\n",
+    "                    inputs=gr.Variable(\"wildcard\"),\n",
+    "                    outputs=wildcard_data,\n",
+    "                )\n",
+    "\n",
+    "    block.load(get_submissions, inputs=gr.Variable(\"animal\"), outputs=animal_data)\n",
+    "    block.load(get_submissions, inputs=gr.Variable(\"science\"), outputs=science_data)\n",
+    "    block.load(get_submissions, inputs=gr.Variable(\"food\"), outputs=food_data)\n",
+    "    block.load(get_submissions, inputs=gr.Variable(\"landscape\"), outputs=landscape_data)\n",
+    "    block.load(get_submissions, inputs=gr.Variable(\"wildcard\"), outputs=wildcard_data)\n",
+    "\n",
+    "\n",
+    "block.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "17ff7d33-0c9a-4ca0-bb7b-ba1661063035",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7894\n"
+     ]
+    }
+   ],
+   "source": [
+    "block.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "339fee32-8a83-435d-b882-55b5f0994774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nbdev.export import nb_export\n",
+    "\n",
+    "nb_export(\"app.ipynb\", lib_path=\".\", name=\"app\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "29f6746e-fbc3-4087-b2d8-46cd1a55e16e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing requirements.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile requirements.txt\n",
+    "pandas\n",
+    "huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63e8d8ea-31cc-4ddc-a08c-d9cbf02a909d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hf",
+   "language": "python",
+   "name": "hf"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
+
+# %% app.ipynb 0
+import gradio as gr
+import pandas as pd
+from huggingface_hub import list_models
+
+# %% app.ipynb 1
+def make_clickable_model(model_name, link=None):
+    if link is None:
+        link = "https://huggingface.co/" + model_name
+    # Remove user from model name
+    return f'<a target="_blank" href="{link}">{model_name.split("/")[-1]}</a>'
+
+
+def make_clickable_user(user_id):
+    link = "https://huggingface.co/" + user_id
+    return f'<a  target="_blank" href="{link}">{user_id}</a>'
+
+# %% app.ipynb 2
+def get_submissions(category):
+    submissions = list_models(filter=["dreambooth-hackathon", category], full=True)
+    leaderboard_models = []
+
+    for submission in submissions:
+        # user, model, likes
+        user_id = submission.id.split("/")[0]
+        leaderboard_models.append(
+            (
+                make_clickable_user(user_id),
+                make_clickable_model(submission.id),
+                submission.likes,
+            )
+        )
+
+    df = pd.DataFrame(data=leaderboard_models, columns=["User", "Model", "Likes"])
+    df.sort_values(by=["Likes"], ascending=False, inplace=True)
+    df.insert(0, "Rank", list(range(1, len(df) + 1)))
+    return df
+
+# %% app.ipynb 3
+block = gr.Blocks()
+
+with block:
+    gr.Markdown(
+        """# The DreamBooth Hackathon Leaderboard
+    
+    Welcome to the leaderboard for the DreamBooth Hackathon! This is a community event where particpants **personalise a Stable Diffusion model** by fine-tuning it with a powerful technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242). This technique allows one to implant a subject (e.g. your pet or favourite dish) into the output domain of the model such that it can be synthesized with a _unique identifier_ in the prompt. 
+    
+    This competition is composed of 5 _themes_, where each theme will collect models belong to one of the categories shown in the tabs below. We'll be **giving out prizes to the top 3 most liked models per theme**, and you're encouraged to submit as many models as you want!
+    
+    For details on how to participate, check out the hackathon's guide [here](https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md).
+    """
+    )
+    with gr.Tabs():
+        with gr.TabItem("Animal 🐨"):
+            with gr.Row():
+                animal_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions, inputs=gr.Variable("animal"), outputs=animal_data
+                )
+        with gr.TabItem("Science 🔬"):
+            with gr.Row():
+                science_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions, inputs=gr.Variable("science"), outputs=science_data
+                )
+        with gr.TabItem("Food 🍔"):
+            with gr.Row():
+                food_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions, inputs=gr.Variable("food"), outputs=food_data
+                )
+        with gr.TabItem("Landscape 🏔"):
+            with gr.Row():
+                landscape_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions,
+                    inputs=gr.Variable("landscape"),
+                    outputs=landscape_data,
+                )
+        with gr.TabItem("Wilcard 🔥"):
+            with gr.Row():
+                wildcard_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions,
+                    inputs=gr.Variable("wildcard"),
+                    outputs=wildcard_data,
+                )
+
+    block.load(get_submissions, inputs=gr.Variable("animal"), outputs=animal_data)
+    block.load(get_submissions, inputs=gr.Variable("science"), outputs=science_data)
+    block.load(get_submissions, inputs=gr.Variable("food"), outputs=food_data)
+    block.load(get_submissions, inputs=gr.Variable("landscape"), outputs=landscape_data)
+    block.load(get_submissions, inputs=gr.Variable("wildcard"), outputs=wildcard_data)
+
+
+block.launch()

requirements.txt

@@ -0,0 +1,2 @@
+pandas
+huggingface_hub