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qgfvadfuvads/t2v_data_v2

DenseDPO T2V Broad-Pair Dataset (v2) Cross-model text-to-video (T2V) generation pairs for training video reward models (RM) and DPO-style preference learning. The HF Dataset Viewer renders each row as prompt + two videos side-by-side. Generation task All videos are generated T2V from a shared text prompt. For every pair, both videos share the same prompt, so the primary comparison axis is the model identity itself. Plan-A tier structure Models are… See the full description on the dataset page: https://huggingface.co/datasets/qgfvadfuvads/t2v_data_v2.

sourceHugging Facecc-by-nc-4.0updated 4mo agoView on Hugging Face
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DenseDPO T2V Broad-Pair Dataset (v2)

Cross-model text-to-video (T2V) generation pairs for training video reward models (RM) and DPO-style preference learning.

The HF Dataset Viewer renders each row as prompt + two videos side-by-side.

Generation task

All videos are generated T2V from a shared text prompt. For every pair, both videos share the same prompt, so the primary comparison axis is the model identity itself.

Plan-A tier structure

Models are grouped into two resolution tiers; pairs only form within a tier to keep resolution as a constant signal for the RM.

TierModelsResolutionFrames @ FPS
480p (low-res)Helios-Distilled, LongCat-Video384×640 / 480×83299@16 / 93@15
720p (high-res)LTX-Video-2.3-distilled, SkyReels-V2, Wan-2.2704×1280 / 720×1280121@24

Pair-types (one prompt → exactly one pair-type):

  • —480p: (helios, longcat)
  • —720p: (ltx, skyreels), (ltx, wan), (skyreels, wan)

File layout

t2v_data_v2_hub/
├── README.md
├── data/
│   └── train-*.parquet
└── pairs_summary.json

Schema

ColumnTypeNotes
pair_idstringunique pair id
tierstring"480p" or "720p"
model_a, model_bstringalphabetically sorted within a pair
video_a, video_bVideo()mp4 bytes embedded in parquet; renders inline in HF Viewer
stemstringfilename-derived prompt stem
promptstringshared text caption from the manifest
taskstringalways "T2V"

Loading

python
from datasets import load_dataset

ds = load_dataset("<hf-user>/t2v_data_v2", split="train")
print(ds[0]["prompt"])
print(ds[0]["video_a"])   # decord/pyav-decoded Video object
print(ds[0]["video_b"])

When loading from disk:

python
from datasets import load_dataset
ds = load_dataset("parquet", data_files="t2v_data_v2_hub/data/train-*.parquet",
                  split="train")

Video bytes are embedded directly in the parquet shards, so no videos/ folder is required.

Counts

See pairs_summary.json for the exact per-pair-type breakdown.

This build

  • —(helios, longcat) [480p]: 880 pairs
  • —(ltx, skyreels) [720p]: 800 pairs
  • —(ltx, wan) [720p]: 800 pairs
  • —(skyreels, wan) [720p]: 800 pairs
  • —Dropped-source pairs: 3280
  • —Selected T2V pairs: 3239
  • —Total pairs: 6519
  • —Orphan stems (1 model only, skipped): 0
  • —Cross-tier pairs (skipped): 0
  • —Stems with 3+ models (skipped): 0

Pushing to HF Hub

bash
hf upload-large-folder <hf-user>/t2v_data_v2 t2v_data_v2_hub/ --repo-type=dataset

Selected T2V Source

Selected T2V pairs from selectedt2vfiltered.json are included in the same train split with task set to T2V. Video bytes are embedded in 12 parquet shards; no videos/ folder is required.