Spaces:
Running
Download README.md from PrunaAI/P-Bench: direct link, hf CLI and curl.
- Browser
- Download file 1.55 kB
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https://huggingface.co/spaces/PrunaAI/P-Bench/resolve/refs%2Fpr%2F10/README.md
- Command line
-
hf download hf://spaces/PrunaAI/P-Bench@refs/pr/10/README.md
-
curl -L -o README.md https://huggingface.co/spaces/PrunaAI/P-Bench/resolve/refs%2Fpr%2F10/README.md
title: P-Bench
emoji: 🥇
colorFrom: green
colorTo: indigo
sdk: gradio
pinned: true
license: apache-2.0
short_description: A cost/quality/speed Leaderboard for Inference Providers!
sdk_version: 5.19.0
tags:
- leaderboard
P-Bench
Compare text-to-image, text-to-video, image-to-video, and video-to-video models on quality, speed, and price. This repo is the Gradio dashboard: leaderboards, Pareto plots, and side-by-side samples.
The live Space is PrunaAI/P-Bench.
The current P-Video-2 Pro evaluation snapshots are
data/t2v-pro.csv (text-to-video) and
data/i2v-pro.csv (image-to-video). Both feed the leaderboards and
price/time Pareto plots with Datapoint and Rapidata Elo. All
models appear in the video-generation leaderboards, including variants with
missing scores. Missing scores and ranks display as dashes; Pareto plots
include only models with the required score and price or timing. Image-to-video
sample media is not included in these exports.
Rapidata Elo comes directly from these exports.
Run locally
From the repo root:
python -m venv .venv
source .venv/bin/activate
pip install "gradio==5.19.0" pandas -r requirements.txt
python app.py
requirements.txt lists Plotly. Gradio and pandas are required locally;
Hugging Face Spaces installs Gradio from the YAML sdk_version above.
Deploy
origin is the Space (https://huggingface.co/spaces/PrunaAI/P-Bench).
Publish the current branch to the live app with:
git push origin HEAD:main