--- base_model: - Wan-AI/Wan2.2-TI2V-5B-Diffusers library_name: diffusers license: apache-2.0 language: - en - zh pipeline_tag: text-to-video tags: - diffusers - safetensors - text-to-video - image-to-video - video-generation - vbvr datasets: - Video-Reason/VBVR-Dataset --- # VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

## Overview Native visual reasoning, i.e., reasoning through visual generation, has recently emerged as a promising direction for studying visual intelligence beyond language. Yet progress remains bottlenecked by the lack of scalable training tasks, reliable feedback, and controlled comparisons across generative substrates. In this work, we introduce **VBVR-Pro**, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable. **1) Task scaling.** VBVR-Pro turns visual reasoning into a controlled task space of *300* procedurally generated tasks. Models trained on VBVR-Pro show strong transfer beyond the proposed suite across *six* held-out visual reasoning benchmarks such as RISE-Video, MME-CoF-Pro, and BabyVision. Further analysis validates that these gains reflect visual reasoning rather than instruction-pattern fitting. **2) Verifiable rewards.** VBVR-Pro provides verifiable reward scorers for task-grounded evaluation. Through a systematic study of leading MLLMs as judges, we identify recurring failure modes of the prevalent *VLM-as-a-judge* paradigm. In contrast, the proposed scorers are grounded on verifiable task-specific rules, achieve fine-grained alignment with human judgments. Importantly, they serve as reliable reward signals for large-scale multi-task reinforcement learning and demonstrate stronger post-RL performance across visual reasoning tasks. **3) Mechanism study.** VBVR-Pro enables controlled modality studies across more than *30* image, video, and interleaved generators. Our analysis shows that video generation remains strongest for tasks requiring persistent spatiotemporal state tracking, while interleaved generation provides a compute-efficient alternative by externalizing intermediate visual states. Critically, ablations and probing confirm the presence of vision-native trajectories, that are a more crucial substrate than explicit linguistic chains of thought for visual reasoning. We release all data, models, scorers, and code to facilitate future research. The models are presented in the paper [VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning](https://huggingface.co/papers/2602.20159). ## Models Zoo

Model	Base Architecture	Other Remarks
Image Generation Models
VBVR-Pro-BAGEL	BAGEL-7B-MoT	Complete model
VBVR-Pro-FLUX2-dev	FLUX.2-dev	Complete model, Diffusers format
VBVR-Pro-FLUX2-dev-diffsynth	FLUX.2-dev	LoRA model, DiffSynth format
VBVR-Pro-Qwen-Image-Edit	Qwen-Image-Edit-2511	Complete model, Diffusers format
VBVR-Pro-Qwen-Image-Edit-diffsynth	Qwen-Image-Edit-2511	LoRA model, DiffSynth format
Interleaved Image Generation Models
VBVR-Pro-ThinkMorph	ThinkMorph-7B	Complete model
VBVR-Pro-SenseNova-U1	SenseNova-U1-8B-MoT	Complete model
Video Generation Models
VBVR-Pro-LTX2.3	LTX-Video-2.3	Complete model, Diffusers format
VBVR-Pro-LTX2.3-diffsynth	LTX-Video-2.3	LoRA model, DiffSynth format
VBVR-Pro-Wan2.1-I2V-14B	Wan2.1-I2V-14B-720P	Complete model, Diffusers format
VBVR-Pro-Wan2.1-I2V-14B-diffsynth	Wan2.1-I2V-14B-720P	LoRA model, DiffSynth format
VBVR-Pro-Wan2.2-I2V-A14B	Wan2.2-I2V-A14B	Complete model, Diffusers format
VBVR-Pro-Wan2.2-I2V-A14B-diffsynth	Wan2.2-I2V-A14B	LoRA model, DiffSynth format
VBVR-Pro-Wan2.2-TI2V-5B	Wan2.2-TI2V-5B	Complete model, Diffusers format
VBVR-Pro-Wan2.2-TI2V-5B-diffsynth	Wan2.2-TI2V-5B	LoRA model, DiffSynth format

## Release Information VBVR-Pro fine-tuned **Wan2.2-TI2V-5B** for instruction-based text/image-to-video generation. It was trained on the [VBVR-Dataset](https://huggingface.co/datasets/Video-Reason/VBVR-Dataset). This is the **merged model**: the rank-32 LoRA has already been fused into the DiT weights. Load it directly with [diffusers](https://github.com/huggingface/diffusers); no adapter needs to be loaded separately. | Format | Repository | |---|---| | Merged / Diffusers (this repo) | `VBVR-Pro-Wan2.2-TI2V-5B` | | DiffSynth LoRA | `VBVR-Pro-Wan2.2-TI2V-5B-diffsynth` | In this release, we present all models presented in paper [**VBVR-Pro-Trained-Models**](https://huggingface.co/collections/Video-Reason/VBVR-Pro), [**VBVR-Pro-Dataset-Video**](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video), [**VBVR-Pro-Dataset-Image**](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Image), [**VBVR-Pro-Bench**](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-Bench), [**VBVR-Pro-Code**](https://github.com/Video-Reason/VBVR-Pro) and [**VBVR-Bench-Leaderboard**](https://video-reason.com/pro/bench/#leaderboard). ## VBVR-Pro Benchmark Results

Models	Overall	In-Domain by Category						Out-of-Domain by Category
Models	Overall	Avg.	Abst.	Know.	Perc.	Spat.	Trans.	Avg.	Abst.	Know.	Perc.	Spat.	Trans.
Image Generation Models
Proprietary Models
Qwen-Image-2.0	0.313	0.248	0.269	0.196	0.225	0.170	0.132	0.378	0.341	0.235	0.391	0.384	0.080
Seedream-5.0-Pro	0.557	0.485	0.518	0.312	0.509	0.401	0.217	0.629	0.507	0.455	0.661	0.559	0.202
Open-source Models
BAGEL-7B-MoT	0.089	0.066	0.039	0.085	0.067	0.046	0.027	0.111	0.201	0.031	0.073	0.028	0.121
FLUX.2-dev	0.157	0.108	0.088	0.109	0.072	0.100	0.066	0.206	0.197	0.165	0.184	0.241	0.077
Qwen-Image-Edit	0.134	0.108	0.092	0.082	0.100	0.109	0.056	0.159	0.176	0.063	0.141	0.182	0.082
Strong Baselines
VBVR-Pro-BAGEL	0.172	0.168	0.199	0.105	0.110	0.213	0.055	0.176	0.254	0.104	0.148	0.015	0.145
VBVR-Pro-FLUX.2	0.407	0.484	0.483	0.323	0.367	0.449	0.336	0.330	0.361	0.272	0.255	0.454	0.128
VBVR-Pro-Qwen-Image	0.322	0.332	0.298	0.217	0.193	0.431	0.222	0.311	0.341	0.239	0.233	0.413	0.181
Interleaved Image Generation Models
Proprietary Models
GPT-Image-2	0.507	0.428	0.456	0.318	0.428	0.206	0.300	0.587	0.398	0.413	0.633	0.480	0.303
Nano Banana Pro	0.564	0.480	0.518	0.422	0.512	0.285	0.174	0.648	0.553	0.499	0.657	0.585	0.220
Open-source Models
ThinkMorph-7B	0.154	0.113	0.100	0.082	0.101	0.148	0.031	0.195	0.176	0.166	0.163	0.253	0.103
VBVR-SenseNova-U1	0.408	0.469	0.356	0.313	0.373	0.386	0.477	0.347	0.291	0.317	0.275	0.480	0.238
SenseNova-U1-8B-MoT	0.565	0.533	0.501	0.395	0.544	0.355	0.349	0.597	0.448	0.495	0.533	0.717	0.401
Strong Baselines
VBVR-Pro-ThinkMorph	0.373	0.402	0.403	0.344	0.238	0.454	0.184	0.344	0.367	0.224	0.238	0.535	0.257
VBVR-Pro-SenseNova-U1	0.638	0.811	0.648	0.695	0.621	0.770	0.541	0.464	0.480	0.328	0.344	0.558	0.408
Video Generation Models
Proprietary Models
Veo 3.1	0.309	0.312	0.275	0.299	0.252	0.267	0.157	0.305	0.305	0.233	0.252	0.312	0.219
Kling V3	0.392	0.356	0.213	0.326	0.320	0.355	0.229	0.427	0.294	0.564	0.375	0.242	0.412
SeedDance 2.0	0.499	0.451	0.338	0.361	0.353	0.468	0.308	0.547	0.369	0.511	0.478	0.538	0.532
Open-source Models
HunyuanVideo-I2V	0.054	0.054	0.023	0.064	0.015	0.084	0.032	0.053	0.088	0.014	0.028	0.062	0.055
CogVideoX1.5-5B-I2V	0.085	0.100	0.061	0.118	0.069	0.092	0.060	0.070	0.125	0.038	0.051	0.040	0.024
Wan2.1-I2V-14B	0.100	0.105	0.052	0.125	0.091	0.102	0.052	0.095	0.112	0.073	0.071	0.123	0.044
Wan2.2-TI2V-5B	0.094	0.066	0.029	0.073	0.050	0.083	0.031	0.122	0.156	0.052	0.106	0.063	0.099
Wan2.2-I2V-14B-720P	0.182	0.157	0.082	0.131	0.110	0.161	0.156	0.207	0.224	0.139	0.140	0.195	0.273
LTX2.3-I2AV	0.112	0.106	0.062	0.109	0.070	0.133	0.055	0.119	0.161	0.135	0.086	0.091	0.050
VBVR-Wan2.2	0.517	0.548	0.237	0.499	0.334	0.566	0.591	0.486	0.310	0.343	0.345	0.732	0.684
Strong Baselines
VBVR-Pro-LTX2.3	0.425	0.527	0.409	0.510	0.346	0.460	0.390	0.324	0.381	0.108	0.201	0.477	0.386
VBVR-Pro-Wan2.1-I2V-14B	0.562	0.730	0.617	0.580	0.452	0.676	0.623	0.395	0.410	0.305	0.230	0.617	0.439
VBVR-Pro-Wan2.2-TI2V-5B	0.470	0.641	0.528	0.556	0.373	0.565	0.557	0.300	0.333	0.127	0.161	0.505	0.409
VBVR-Pro-Wan2.2-I2V-14B	0.670	0.808	0.632	0.685	0.556	0.751	0.636	0.532	0.479	0.418	0.350	0.679	0.690

## Quick Start ### Method 1: Standalone Diffusers inference #### 1. Install Diffusers ```bash pip install -U diffusers transformers accelerate pillow imageio imageio-ffmpeg ``` #### 2. Run `example.py` The included [`example.py`](example.py) loads the merged checkpoint directly with Diffusers and enables model CPU offloading. ```bash python example.py \ --model_path Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B \ --prompt "The subject walks toward the doorway." \ --num_frames 81 --width 832 --height 480 \ --output output.mp4 ``` ### Method 2: Unified VBVR-Pro inference Clone [Video-Reason/VBVR-Pro](https://github.com/Video-Reason/VBVR-Pro) and create its unified inference environment: ```bash git clone https://github.com/Video-Reason/VBVR-Pro.git cd VBVR-Pro/ uv sync --extra cu124 # or one of [cu118|cu121|cu124|cu126|cu128|cu129] source .venv/bin/activate ``` Then run the unified inference script: ```bash python example.py \ --model_path Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B \ --prompt "The subject walks toward the doorway." \ --num_frames 81 --width 832 --height 480 \ --output output.mp4 ``` ## Citation ```bibtex @article{vbvr2025, title={VBVR: A Very Big Video Reasoning Suite}, year={2025}, } ```