VBVR-Pro-Wan2.1-I2V-14B

Video-Reason/VBVR-Pro-Wan2.1-I2V-14B

VBVR-Pro-Wan2.1-I2V-14B is a image-to-video model from Video-Reason. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as apache-2.0.

<a href="https://video-reason.com/?v=pro" target="blank" <img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" / </a <a href="https://github.com/Video-Reason/VBVR-Pro-Ben…

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At a glance

Task: Image-to-Video
Library: diffusers
License: apache-2.0
Access: Public
Created: Aug 23, 2026
Updated: Aug 27, 2026
SHA: 3bc88566

Base models

Wan-AI/Wan2.1-I2V-14B-720P-Diffusers

Datasets

Video-Reason/VBVR-Pro-SFT-Video

Model card

From the Hugging Face model README

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.

Models Zoo

<table border="1" cellspacing="0" cellpadding="4" style="border-collapse: collapse; width: 100%;"> <thead> <tr> <th width="260" style="min-width: 260px;">Model</th> <th>Base Architecture</th> <th>Other Remarks</th> </tr> </thead> <tbody> <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="3" align="left">Image Generation Models</th></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-BAGEL">VBVR-Pro-BAGEL</a></td><td>BAGEL-7B-MoT</td><td>Complete model</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-FLUX2-dev">VBVR-Pro-FLUX2-dev</a></td><td>FLUX.2-dev</td><td>Complete model, Diffusers format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-FLUX2-dev-diffsynth">VBVR-Pro-FLUX2-dev-diffsynth</a></td><td>FLUX.2-dev</td><td>LoRA model, DiffSynth format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Qwen-Image-Edit">VBVR-Pro-Qwen-Image-Edit</a></td><td>Qwen-Image-Edit-2511</td><td>Complete model, Diffusers format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Qwen-Image-Edit-diffsynth">VBVR-Pro-Qwen-Image-Edit-diffsynth</a></td><td>Qwen-Image-Edit-2511</td><td>LoRA model, DiffSynth format</td></tr> <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="3" align="left">Interleaved Image Generation Models</th></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-ThinkMorph">VBVR-Pro-ThinkMorph</a></td><td>ThinkMorph-7B</td><td>Complete model</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-SenseNova-U1">VBVR-Pro-SenseNova-U1</a></td><td>SenseNova-U1-8B-MoT</td><td>Complete model</td></tr> <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="3" align="left">Video Generation Models</th></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-LTX2.3">VBVR-Pro-LTX2.3</a></td><td>LTX-Video-2.3</td><td>Complete model, Diffusers format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-LTX2.3-diffsynth">VBVR-Pro-LTX2.3-diffsynth</a></td><td>LTX-Video-2.3</td><td>LoRA model, DiffSynth format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.1-I2V-14B">VBVR-Pro-Wan2.1-I2V-14B</a></td><td>Wan2.1-I2V-14B-720P</td><td>Complete model, Diffusers format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.1-I2V-14B-diffsynth">VBVR-Pro-Wan2.1-I2V-14B-diffsynth</a></td><td>Wan2.1-I2V-14B-720P</td><td>LoRA model, DiffSynth format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-I2V-A14B">VBVR-Pro-Wan2.2-I2V-A14B</a></td><td>Wan2.2-I2V-A14B</td><td>Complete model, Diffusers format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-I2V-A14B-diffsynth">VBVR-Pro-Wan2.2-I2V-A14B-diffsynth</a></td><td>Wan2.2-I2V-A14B</td><td>LoRA model, DiffSynth format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B">VBVR-Pro-Wan2.2-TI2V-5B</a></td><td>Wan2.2-TI2V-5B</td><td>Complete model, Diffusers format</td></tr> <tr><td><a href="https://huggingface.co/Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-diffsynth">VBVR-Pro-Wan2.2-TI2V-5B-diffsynth</a></td><td>Wan2.2-TI2V-5B</td><td>LoRA model, DiffSynth format</td></tr> </tbody> </table>

Release Information

VBVR-Pro-Wan2.1-I2V-14B is trained from Wan2.1-I2V-14B without architectural modifications, as the goal is to investigate how reasoning patterns differ across generative modalities after task-specific training on VBVR-Pro-Dataset.

In this release, we present all models presented in paper VBVR-Pro-Trained-Models, VBVR-Pro-Dataset-Video, VBVR-Pro-Dataset-Image, VBVR-Pro-Bench, VBVR-Pro-Code and VBVR-Bench-Leaderboard.

VBVR-Pro Benchmark Results

<table border="1" cellspacing="0" cellpadding="4" style="border-collapse: collapse; width: 100%; font-size: 12px;"> <thead> <tr> <th rowspan="2" width="260" style="min-width: 260px;">Models</th> <th rowspan="2">Overall</th> <th colspan="6">In-Domain by Category</th> <th colspan="6">Out-of-Domain by Category</th> </tr> <tr> <th>Avg.</th><th>Abst.</th><th>Know.</th><th>Perc.</th><th>Spat.</th><th>Trans.</th> <th>Avg.</th><th>Abst.</th><th>Know.</th><th>Perc.</th><th>Spat.</th><th>Trans.</th> </tr> </thead> <tbody> <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="14" align="left">Image Generation Models</th></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Proprietary Models</th></tr> <tr><td>Qwen-Image-2.0</td><td>0.313</td><td>0.248</td><td>0.269</td><td>0.196</td><td>0.225</td><td>0.170</td><td>0.132</td><td>0.378</td><td>0.341</td><td>0.235</td><td>0.391</td><td>0.384</td><td>0.080</td></tr> <tr><td>Seedream-5.0-Pro</td><td>0.557</td><td>0.485</td><td>0.518</td><td>0.312</td><td>0.509</td><td>0.401</td><td>0.217</td><td>0.629</td><td>0.507</td><td>0.455</td><td>0.661</td><td>0.559</td><td>0.202</td></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Open-source Models</th></tr> <tr><td>BAGEL-7B-MoT</td><td>0.089</td><td>0.066</td><td>0.039</td><td>0.085</td><td>0.067</td><td>0.046</td><td>0.027</td><td>0.111</td><td>0.201</td><td>0.031</td><td>0.073</td><td>0.028</td><td>0.121</td></tr> <tr><td>FLUX.2-dev</td><td>0.157</td><td>0.108</td><td>0.088</td><td>0.109</td><td>0.072</td><td>0.100</td><td>0.066</td><td>0.206</td><td>0.197</td><td>0.165</td><td>0.184</td><td>0.241</td><td>0.077</td></tr> <tr><td>Qwen-Image-Edit</td><td>0.134</td><td>0.108</td><td>0.092</td><td>0.082</td><td>0.100</td><td>0.109</td><td>0.056</td><td>0.159</td><td>0.176</td><td>0.063</td><td>0.141</td><td>0.182</td><td>0.082</td></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Strong Baselines</th></tr> <tr><td>VBVR-Pro-BAGEL</td><td>0.172</td><td>0.168</td><td>0.199</td><td>0.105</td><td>0.110</td><td>0.213</td><td>0.055</td><td>0.176</td><td>0.254</td><td>0.104</td><td>0.148</td><td>0.015</td><td>0.145</td></tr> <tr><td>VBVR-Pro-FLUX.2</td><td>0.407</td><td>0.484</td><td>0.483</td><td>0.323</td><td>0.367</td><td>0.449</td><td>0.336</td><td>0.330</td><td>0.361</td><td>0.272</td><td>0.255</td><td>0.454</td><td>0.128</td></tr> <tr><td>VBVR-Pro-Qwen-Image</td><td>0.322</td><td>0.332</td><td>0.298</td><td>0.217</td><td>0.193</td><td>0.431</td><td>0.222</td><td>0.311</td><td>0.341</td><td>0.239</td><td>0.233</td><td>0.413</td><td>0.181</td></tr> <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="14" align="left">Interleaved Image Generation Models</th></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Proprietary Models</th></tr> <tr><td>GPT-Image-2</td><td>0.507</td><td>0.428</td><td>0.456</td><td>0.318</td><td>0.428</td><td>0.206</td><td>0.300</td><td>0.587</td><td>0.398</td><td>0.413</td><td>0.633</td><td>0.480</td><td>0.303</td></tr> <tr><td>Nano Banana Pro</td><td>0.564</td><td>0.480</td><td>0.518</td><td>0.422</td><td>0.512</td><td>0.285</td><td>0.174</td><td>0.648</td><td>0.553</td><td>0.499</td><td>0.657</td><td>0.585</td><td>0.220</td></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Open-source Models</th></tr> <tr><td>ThinkMorph-7B</td><td>0.154</td><td>0.113</td><td>0.100</td><td>0.082</td><td>0.101</td><td>0.148</td><td>0.031</td><td>0.195</td><td>0.176</td><td>0.166</td><td>0.163</td><td>0.253</td><td>0.103</td></tr> <tr><td>VBVR-SenseNova-U1</td><td>0.408</td><td>0.469</td><td>0.356</td><td>0.313</td><td>0.373</td><td>0.386</td><td>0.477</td><td>0.347</td><td>0.291</td><td>0.317</td><td>0.275</td><td>0.480</td><td>0.238</td></tr> <tr><td>SenseNova-U1-8B-MoT</td><td>0.565</td><td>0.533</td><td>0.501</td><td>0.395</td><td>0.544</td><td>0.355</td><td>0.349</td><td>0.597</td><td>0.448</td><td>0.495</td><td>0.533</td><td>0.717</td><td>0.401</td></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Strong Baselines</th></tr> <tr><td>VBVR-Pro-ThinkMorph</td><td>0.373</td><td>0.402</td><td>0.403</td><td>0.344</td><td>0.238</td><td>0.454</td><td>0.184</td><td>0.344</td><td>0.367</td><td>0.224</td><td>0.238</td><td>0.535</td><td>0.257</td></tr> <tr><td>VBVR-Pro-SenseNova-U1</td><td>0.638</td><td>0.811</td><td>0.648</td><td>0.695</td><td>0.621</td><td>0.770</td><td>0.541</td><td>0.464</td><td>0.480</td><td>0.328</td><td>0.344</td><td>0.558</td><td>0.408</td></tr> <tr style="border-top: 4px solid #6b7280; background-color: #e5e7eb;"><th colspan="14" align="left">Video Generation Models</th></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Proprietary Models</th></tr> <tr><td>Veo 3.1</td><td>0.309</td><td>0.312</td><td>0.275</td><td>0.299</td><td>0.252</td><td>0.267</td><td>0.157</td><td>0.305</td><td>0.305</td><td>0.233</td><td>0.252</td><td>0.312</td><td>0.219</td></tr> <tr><td>Kling V3</td><td>0.392</td><td>0.356</td><td>0.213</td><td>0.326</td><td>0.320</td><td>0.355</td><td>0.229</td><td>0.427</td><td>0.294</td><td>0.564</td><td>0.375</td><td>0.242</td><td>0.412</td></tr> <tr><td>SeedDance 2.0</td><td>0.499</td><td>0.451</td><td>0.338</td><td>0.361</td><td>0.353</td><td>0.468</td><td>0.308</td><td>0.547</td><td>0.369</td><td>0.511</td><td>0.478</td><td>0.538</td><td>0.532</td></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Open-source Models</th></tr> <tr><td>HunyuanVideo-I2V</td><td>0.054</td><td>0.054</td><td>0.023</td><td>0.064</td><td>0.015</td><td>0.084</td><td>0.032</td><td>0.053</td><td>0.088</td><td>0.014</td><td>0.028</td><td>0.062</td><td>0.055</td></tr> <tr><td>CogVideoX1.5-5B-I2V</td><td>0.085</td><td>0.100</td><td>0.061</td><td>0.118</td><td>0.069</td><td>0.092</td><td>0.060</td><td>0.070</td><td>0.125</td><td>0.038</td><td>0.051</td><td>0.040</td><td>0.024</td></tr> <tr><td>Wan2.1-I2V-14B</td><td>0.100</td><td>0.105</td><td>0.052</td><td>0.125</td><td>0.091</td><td>0.102</td><td>0.052</td><td>0.095</td><td>0.112</td><td>0.073</td><td>0.071</td><td>0.123</td><td>0.044</td></tr> <tr><td>Wan2.2-TI2V-5B</td><td>0.094</td><td>0.066</td><td>0.029</td><td>0.073</td><td>0.050</td><td>0.083</td><td>0.031</td><td>0.122</td><td>0.156</td><td>0.052</td><td>0.106</td><td>0.063</td><td>0.099</td></tr> <tr><td>Wan2.2-I2V-14B-720P</td><td>0.182</td><td>0.157</td><td>0.082</td><td>0.131</td><td>0.110</td><td>0.161</td><td>0.156</td><td>0.207</td><td>0.224</td><td>0.139</td><td>0.140</td><td>0.195</td><td>0.273</td></tr> <tr><td>LTX2.3-I2AV</td><td>0.112</td><td>0.106</td><td>0.062</td><td>0.109</td><td>0.070</td><td>0.133</td><td>0.055</td><td>0.119</td><td>0.161</td><td>0.135</td><td>0.086</td><td>0.091</td><td>0.050</td></tr> <tr><td>VBVR-Wan2.2</td><td>0.517</td><td>0.548</td><td>0.237</td><td>0.499</td><td>0.334</td><td>0.566</td><td>0.591</td><td>0.486</td><td>0.310</td><td>0.343</td><td>0.345</td><td>0.732</td><td>0.684</td></tr> <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Strong Baselines</th></tr> <tr><td>VBVR-Pro-LTX2.3</td><td>0.425</td><td>0.527</td><td>0.409</td><td>0.510</td><td>0.346</td><td>0.460</td><td>0.390</td><td>0.324</td><td>0.381</td><td>0.108</td><td>0.201</td><td>0.477</td><td>0.386</td></tr> <tr><td>VBVR-Pro-Wan2.1-I2V-14B</td><td>0.562</td><td>0.730</td><td>0.617</td><td>0.580</td><td>0.452</td><td>0.676</td><td>0.623</td><td>0.395</td><td>0.410</td><td>0.305</td><td>0.230</td><td>0.617</td><td>0.439</td></tr> <tr><td>VBVR-Pro-Wan2.2-TI2V-5B</td><td>0.470</td><td>0.641</td><td>0.528</td><td>0.556</td><td>0.373</td><td>0.565</td><td>0.557</td><td>0.300</td><td>0.333</td><td>0.127</td><td>0.161</td><td>0.505</td><td>0.409</td></tr> <tr><td>VBVR-Pro-Wan2.2-I2V-14B</td><td>0.670</td><td>0.808</td><td>0.632</td><td>0.685</td><td>0.556</td><td>0.751</td><td>0.636</td><td>0.532</td><td>0.479</td><td>0.418</td><td>0.350</td><td>0.679</td><td>0.690</td></tr> </tbody> </table>

Quick Start

Method 1: Standalone Diffusers inference

1. Install Diffusers

pip install -U diffusers transformers accelerate pillow imageio imageio-ffmpeg

2. Run `example.py`

The included example.py loads the merged checkpoint directly with Diffusers and enables model CPU offloading.

python example.py \
  --model_path Video-Reason/VBVR-Pro-Wan2.1-I2V-14B \
  --image input.png \
  --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 and create its unified inference environment:

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:

python example.py \
  --model_path Video-Reason/VBVR-Pro-Wan2.1-I2V-14B \
  --image_paths input.png \
  --prompt "The subject walks toward the doorway." \
  --num_frames 81 --width 832 --height 480 \
  --output output.mp4

Citation

@misc{xu2026vbvrproscalableverifiablesuite,
      title={VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning}, 
      author={Junxiang Xu and Ruisi Wang and Fanyi Pu and Maijunxian Wang and Ran Ji and Tongxi Zhou and Chenyang Gu and Jing Zuo and Hongcan Xiao and Yimeng Geng and Wanqi Yin and Wei Chen and Oscar Qian and Zhengan Yan and Ziqi Huang and Haiwen Diao and Liang Pan and Bo Li and Xiangyu Fan and Dezhi Luo and Fengyuan Yu and Zehong Zhao and Qingying Gao and Tinghui Zhu and Yilan Zhang and Jingqi Tong and Pinyuan Feng and Zhengze Jiang and Letian Wang and Ziyu Guo and Renrui Zhang and Jieneng Chen and Sonia Joseph and Constantin Venhoff and Saman Motamed and Mengyue Yang and Chandra Sripada and Alan Yuille and Philip Torr and Lvmin Zhang and Vikash Kumar and Daniel Khashabi and Nikolaus Kriegeskorte and Raphaël Millière and Vincent C. Müller and Anyi Rao and Quan Wang and Ziwei Liu and Dahua Lin and Lei Yang and Hokin Deng and Zhongang Cai},
      year={2026},
      eprint={2608.26105},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.26105}, 
}

Task: Image-to-Video
Library: diffusers
License: apache-2.0
Created: Aug 23, 2026
Updated: Aug 27, 2026