| import pandas as pd | |
| import gradio as gr | |
| import csv | |
| import json | |
| import os | |
| import shutil | |
| from huggingface_hub import Repository | |
| HF_TOKEN = os.environ.get("HUGGINGFACE_TOKEN") | |
| MODEL_INFO = [ | |
| "Model", | |
| "Avg", | |
| "Visual Quality", | |
| "Temporal Consistency", | |
| "Dynamic Degree", | |
| "Text-to-Video Alignment", | |
| "Factual Consistency" | |
| ] | |
| DATA_TITILE_TYPE = ['markdown', 'number', 'number', 'number', 'number', 'number',] | |
| SUBMISSION_NAME = "VideoScore-Leaderboard" | |
| SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/hexuan21/", SUBMISSION_NAME) | |
| CSV_DIR = "./VideoScore-Leaderboard/leaderboard_res.csv" | |
| COLUMN_NAMES = MODEL_INFO | |
| LEADERBORAD_INTRODUCTION = """# VideoScore Leaderboard | |
| 🏆 Welcome to the **VideoScore Leaderboard**! The leaderboard covers many popular text-to-video generative models and evaluates them on 5 dimensions: <br> | |
| "Visual Quality", "Temporal Consistency", "Dynamic Degree", "Text-to-Video Alignment", "Factual Consistency" | |
| We sample 200 prompts from <a href="https://arxiv.org/abs/2403.06098">VidProM</a> to generate 200 videos using various T2V models (for those closed-source model, we generate 100). | |
| <a href='https://hits.seeyoufarm.com'><img src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2FTIGER-Lab%2FVideoScore-Leaderboard&count_bg=%23C7C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false'></a> | |
| """ | |
| TABLE_INTRODUCTION = """ | |
| """ | |
| LEADERBORAD_INFO = """ | |
| """ | |
| CITATION_BUTTON_LABEL = "Copy the following snippet to cite the t2v models and the used metrics" | |
| CITATION_BUTTON_TEXT = r""" | |
| """ | |
| def get_df(): | |
| repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN) | |
| repo.git_pull() | |
| df = pd.read_csv(CSV_DIR) | |
| df['Model'] = df['Model'].apply(lambda x: f"[{x.split(']')[0][1:]}]({x.split('(')[1][:-1]})") | |
| df['Avg'] = df[["Visual Quality", | |
| "Temporal Consistency", | |
| "Dynamic Degree", | |
| "Text-to-Video Alignment", | |
| "Factual Consistency"]].mean(axis=1).round(2) | |
| df = df.sort_values(by=['Avg'], ascending=False) | |
| return df[COLUMN_NAMES] | |
| def refresh_data(): | |
| return get_df() |