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kaizensuper/
leoma-VBVR

Name: leoma-VBVR
Availability: InStock

Wan-AI/Wan2.2-I2V-A14B-Diffusers

finetune

apache-2.0

WanImageToVideoPipeline

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VBVR: A Very Big Video Reasoning Suite

Code

arXiv

🔥Please check out our newly released VBVR-Wan2.1 (Diffusers format), VBVR-Wan2.1-diffsynth (DiffSynth LoRA format), and VBVR-LTX2.3-diffsynth (DiffSynth LoRA format; Diffusers does not yet support LTX-Video-2.3, so only the DiffSynth LoRA format is released for this model).

Overview

Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, enabling intuitive reasoning over motion, interaction, and causality. Rapid progress in video models has focused primarily on visual quality. Systematically studying video reasoning and its scaling behavior suffers from a lack of video reasoning (training) data.

To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks and over one million video clips—approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities.

Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization to unseen reasoning tasks. Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning.

The model was presented in the paper A Very Big Video Reasoning Suite.

Models Zoo

Model	Base Architecture	Other Remarks
VBVR-Wan2.1	Wan2.1-I2V-14B-720P	Diffusers format
VBVR-Wan2.2	Wan2.2-I2V-A14B	Diffusers format
VBVR-Wan2.1-diffsynth	Wan2.1-I2V-14B-720P	DiffSynth LoRA format
VBVR-Wan2.2-diffsynth	Wan2.2-I2V-A14B	DiffSynth LoRA format
VBVR-LTX2.3-diffsynth	LTX-Video-2.3	DiffSynth LoRA format

Release Information

VBVR-Wan2.2 is trained from Wan2.2-I2V-A14B without architectural modifications, as the goal of VBVR-Wan2.2 is to investigate data scaling behavior and provide a strong baseline model for the video reasoning research community. Leveraging the VBVR-Dataset, which constitutes one of the largest video reasoning datasets to date, VBVR-Wan2.2 achieved highest score on VBVR-Bench.

In this release, we present VBVR-Wan2.2, VBVR-Dataset, VBVR-Bench-Data and VBVR-Bench-Leaderboard.

Model	Overall	ID	ID-Abst.	ID-Know.	ID-Perc.	ID-Spat.	ID-Trans.	OOD	OOD-Abst.	OOD-Know.	OOD-Perc.	OOD-Spat.	OOD-Trans.
Human	0.974	0.960	0.919	0.956	1.00	0.95	1.00	0.988	1.00	1.00	0.990	1.00	0.970
Open-source Models
CogVideoX1.5-5B-I2V	0.273	0.283	0.241	0.328	0.257	0.328	0.305	0.262	0.281	0.235	0.250	0.254	0.282
HunyuanVideo-I2V	0.273	0.280	0.207	0.357	0.293	0.280	0.316	0.265	0.175	0.369	0.290	0.253	0.250
Wan2.2-I2V-A14B	0.371	0.412	0.430	0.382	0.415	0.404	0.419	0.329	0.405	0.308	0.343	0.236	0.307
LTX-2	0.313	0.329	0.316	0.362	0.326	0.340	0.306	0.297	0.244	0.337	0.317	0.231	0.311
Proprietary Models
Seedance 2.0	0.544	0.570	0.593	0.498	0.618	0.514	0.602	0.517	0.643	0.398	0.492	0.427	0.556
Runway Gen-4 Turbo	0.403	0.392	0.396	0.409	0.429	0.341	0.363	0.414	0.515	0.429	0.419	0.327	0.373
Sora 2	0.546	0.569	0.602	0.477	0.581	0.572	0.597	0.523	0.546	0.472	0.525	0.462	0.546
Kling 2.6	0.369	0.408	0.465	0.323	0.375	0.347	0.519	0.330	0.528	0.135	0.272	0.356	0.359
Veo 3.1	0.480	0.531	0.611	0.503	0.520	0.444	0.510	0.429	0.577	0.277	0.420	0.441	0.404
Data Scaling Strong Baseline
VBVR-LTX2.3	0.516	0.580	0.608	0.631	0.529	0.454	0.680	0.453	0.608	0.577	0.409	0.414	0.388
VBVR-Wan2.1	0.592	0.724	0.705	0.710	0.727	0.719	0.784	0.461	0.674	0.592	0.387	0.461	0.387
VBVR-Wan2.2	0.685	0.760	0.724	0.750	0.782	0.745	0.833	0.610	0.768	0.572	0.547	0.618	0.615

QuickStart

Installation

We recommend using uv to manage the environment.

uv installation guide: https://docs.astral.sh/uv/getting-started/installation/#installing-uv

bash

pip install torch>=2.4.0 torchvision>=0.19.0 transformers Pillow huggingface_hub[cli]
uv pip install git+https://github.com/huggingface/diffusers

Example Code