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yinanhe
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[Init]
Browse files- README.md +5 -5
- app.py +130 -0
- requirements.txt +3 -0
README.md
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---
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title:
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emoji:
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colorFrom: indigo
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colorTo:
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sdk: streamlit
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sdk_version: 1.
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app_file: app.py
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pinned: false
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license:
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Leaderboard
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emoji: π₯
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colorFrom: indigo
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colorTo: pink
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sdk: streamlit
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sdk_version: 1.26.0
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app_file: app.py
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pinned: false
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license: cc-by-sa-4.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import streamlit as st
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st.set_page_config(layout="wide")
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import pandas as pd
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import os
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import json
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import shutil
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from huggingface_hub import Repository
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REFERENCE_NAME = "references"
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SUBMISSION_NAME = "vbench_leaderboard_submission"
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SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/VBench/", SUBMISSION_NAME)
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TEST_SETS = [
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"subject consistency",
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"background consistency",
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"temporal flickering",
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"motion smoothness",
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"dynamic degree",
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"aesthetic quality",
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"imaging quality",
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"object class",
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"multiple objects",
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"human action",
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"color",
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"spatial relationship",
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"scene",
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"appearance style",
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"temporal style",
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"overall consistency"
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]
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style = """
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<style>
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th {
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font-size: 10px;
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}
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</style>
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"""
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CSV_RESULTS_FILE = os.path.join(SUBMISSION_NAME, "results.csv")
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HF_TOKEN = os.environ.get("HF_TOKEN")
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try:
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submission_repo = Repository(
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local_dir="vbench_leaderboard_submission", clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset"
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)
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except Exception as e:
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print(e)
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submission_repo.git_pull()
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all_submissions = [
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file_name
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for file_name in os.listdir(SUBMISSION_NAME)
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if file_name.endswith('.json')
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]
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all_results = pd.read_csv(CSV_RESULTS_FILE)
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with open(os.path.join(SUBMISSION_NAME, "verified_model.txt")) as f:
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verified_model = [i.strip() for i in f.readlines()]
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all_results['verified'] = all_results['name'].apply(lambda x: 'β' if x in verified_model else ' ')
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# Write table form CSV
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table = all_results.copy()
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table = table.round(2)
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# Streamlit
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st.markdown("# VBench ")
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st.markdown(
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f"""
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This is the leaderboard of VBench: Comprehensive Benchmark Suite for Video Generative Models (VBench).
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"""
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)
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sort_option = st.selectbox(
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'Choose a column to sort by',
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table.columns[1:-1],
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)
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table = table.sort_values(by=sort_option, ascending=False)
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st.write(style + table.to_markdown(index=False), unsafe_allow_html=True)
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st.markdown(
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"""
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For more information, refer to the paper submission on [Arxiv](https://).
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"""
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)
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st.markdown(
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"""
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## Submitting to VBench
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\n
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To submit to VBench, download the prompt suite from [VBench/Prompt](https://huggingface.co/datasets//). Upload your zipped submissions for scoring and placement on the leaderboard.
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\n
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Should you experience any issues, open an issue using the link [new discussion](http:) and tag `@vbench`.
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"""
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)
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# Using the "with" syntax
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with st.form(key="my_form"):
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uploaded_file = st.file_uploader("Choose a json file")
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submit_button = st.form_submit_button(label="Submit")
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if submit_button:
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if uploaded_file is None:
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raise ValueError("Please make sure to have uploaded a json file.")
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submission = uploaded_file.name.split(".json")[0]
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with st.spinner(f"Uploading {submission}..."):
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with open(os.path.join(submission_repo.local_dir, os.path.basename(uploaded_file.name)),'wb') as f:
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f.write(uploaded_file.getvalue())
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submission_repo.push_to_hub()
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with st.spinner(f"Update Score for {submission}..."):
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results = {"name": submission}
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upload_score = json.loads(uploaded_file.getvalue())
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for info in upload_score:
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results[info['dimension']] = info['final_score']
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all_results.loc[len(all_results)] = results
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all_results.to_csv(CSV_RESULTS_FILE, index=False)
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commit_url = submission_repo.push_to_hub()
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st.success('Please refresh this space (CTRL+R) to see your result')
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requirements.txt
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@@ -0,0 +1,3 @@
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huggingface_hub==0.8.1
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pandas
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streamlit
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