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f9f3c7a

---
base_model:
  - Qwen/Qwen-Image-Edit-2511
library_name: diffsynth
license: apache-2.0
pipeline_tag: image-to-image
tags:
  - diffsynth
  - lora
  - image-editing
  - vbvr
datasets:
  - Video-Reason/VBVR-Dataset
---

# VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

<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-Bench" target="_blank">
    <img alt="Code" src="https://img.shields.io/badge/Evaluation_code-VBVR_Pro_Bench-100000?style=flat-square&logo=github&logoColor=white" height="20" />
</a>
<a href="https://github.com/Video-Reason/VBVR-Pro" target="_blank">
    <img alt="Code" src="https://img.shields.io/badge/Training_Inferenceing-VBVR_Pro-100000?style=flat-square&logo=github&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/papers/2602.20159" target="_blank">
    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR_Pro-red?logo=arxiv" height="20" />
</a>
<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video" target="_blank">
    <img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Dataset-Data-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-Bench/tree/main" target="_blank">
    <img alt="Bench Data" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Bench-Data-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
<a href="https://video-reason.com/pro/bench/#leaderboard" target="_blank">
    <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Bench-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
</a>

## 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
<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"><strong>VBVR-Pro-Qwen-Image-Edit-diffsynth</strong></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 LoRA weights for **Qwen-Image-Edit-2511**, trained on the
[VBVR-Dataset](https://huggingface.co/datasets/Video-Reason/VBVR-Dataset)
for instruction-based image editing.

This is the **DiffSynth LoRA format** — a single safetensors file containing
only the LoRA adapter (rank 32). Load it with
[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) on top of
the base model.

| Format | Repository |
|---|---|
| DiffSynth LoRA (this repo) | `VBVR-Pro-Qwen-Image-Edit-diffsynth` |
| Merged (ready to use) | `VBVR-Pro-Qwen-Image-Edit` |

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
<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><u>0.313</u></td><td><u>0.248</u></td><td><u>0.269</u></td><td><u>0.196</u></td><td><u>0.225</u></td><td><u>0.170</u></td><td><u>0.132</u></td><td><u>0.378</u></td><td><u>0.341</u></td><td><u>0.235</u></td><td><u>0.391</u></td><td><u>0.384</u></td><td><u>0.080</u></td></tr>
    <tr><td>Seedream-5.0-Pro</td><td><strong>0.557</strong></td><td><strong>0.485</strong></td><td><strong>0.518</strong></td><td><strong>0.312</strong></td><td><strong>0.509</strong></td><td><strong>0.401</strong></td><td><strong>0.217</strong></td><td><strong>0.629</strong></td><td><strong>0.507</strong></td><td><strong>0.455</strong></td><td><strong>0.661</strong></td><td><strong>0.559</strong></td><td><strong>0.202</strong></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><u>0.066</u></td><td>0.039</td><td><u>0.085</u></td><td>0.067</td><td>0.046</td><td>0.027</td><td>0.111</td><td><strong>0.201</strong></td><td>0.031</td><td>0.073</td><td>0.028</td><td><strong>0.121</strong></td></tr>
    <tr><td>FLUX.2-dev</td><td><strong>0.157</strong></td><td><strong>0.108</strong></td><td><u>0.088</u></td><td><strong>0.109</strong></td><td><u>0.072</u></td><td><u>0.100</u></td><td><strong>0.066</strong></td><td><strong>0.206</strong></td><td><u>0.197</u></td><td><strong>0.165</strong></td><td><strong>0.184</strong></td><td><strong>0.241</strong></td><td>0.077</td></tr>
    <tr><td>Qwen-Image-Edit</td><td><u>0.134</u></td><td><strong>0.108</strong></td><td><strong>0.092</strong></td><td>0.082</td><td><strong>0.100</strong></td><td><strong>0.109</strong></td><td><u>0.056</u></td><td><u>0.159</u></td><td>0.176</td><td><u>0.063</u></td><td><u>0.141</u></td><td><u>0.182</u></td><td><u>0.082</u></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><u>0.145</u></td></tr>
    <tr><td>VBVR-Pro-FLUX.2</td><td><strong>0.407</strong></td><td><strong>0.484</strong></td><td><strong>0.483</strong></td><td><strong>0.323</strong></td><td><strong>0.367</strong></td><td><strong>0.449</strong></td><td><strong>0.336</strong></td><td><strong>0.330</strong></td><td><strong>0.361</strong></td><td><strong>0.272</strong></td><td><strong>0.255</strong></td><td><strong>0.454</strong></td><td>0.128</td></tr>
    <tr><td>VBVR-Pro-Qwen-Image</td><td><u>0.322</u></td><td><u>0.332</u></td><td><u>0.298</u></td><td><u>0.217</u></td><td><u>0.193</u></td><td><u>0.431</u></td><td><u>0.222</u></td><td><u>0.311</u></td><td><u>0.341</u></td><td><u>0.239</u></td><td><u>0.233</u></td><td><u>0.413</u></td><td><strong>0.181</strong></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><u>0.507</u></td><td><u>0.428</u></td><td><u>0.456</u></td><td><u>0.318</u></td><td><u>0.428</u></td><td><u>0.206</u></td><td><strong>0.300</strong></td><td><u>0.587</u></td><td><u>0.398</u></td><td><u>0.413</u></td><td><u>0.633</u></td><td><u>0.480</u></td><td><strong>0.303</strong></td></tr>
    <tr><td>Nano Banana Pro</td><td><strong>0.564</strong></td><td><strong>0.480</strong></td><td><strong>0.518</strong></td><td><strong>0.422</strong></td><td><strong>0.512</strong></td><td><strong>0.285</strong></td><td><u>0.174</u></td><td><strong>0.648</strong></td><td><strong>0.553</strong></td><td><strong>0.499</strong></td><td><strong>0.657</strong></td><td><strong>0.585</strong></td><td><u>0.220</u></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><u>0.408</u></td><td><u>0.469</u></td><td><u>0.356</u></td><td><u>0.313</u></td><td><u>0.373</u></td><td><strong>0.386</strong></td><td><strong>0.477</strong></td><td><u>0.347</u></td><td><u>0.291</u></td><td><u>0.317</u></td><td><u>0.275</u></td><td><u>0.480</u></td><td><u>0.238</u></td></tr>
    <tr><td>SenseNova-U1-8B-MoT</td><td><strong>0.565</strong></td><td><strong>0.533</strong></td><td><strong>0.501</strong></td><td><strong>0.395</strong></td><td><strong>0.544</strong></td><td><u>0.355</u></td><td><u>0.349</u></td><td><strong>0.597</strong></td><td><strong>0.448</strong></td><td><strong>0.495</strong></td><td><strong>0.533</strong></td><td><strong>0.717</strong></td><td><strong>0.401</strong></td></tr>
    <tr style="background-color: #dbeafe;"><th colspan="14" align="left">Strong Baselines</th></tr>
    <tr><td>VBVR-Pro-ThinkMorph</td><td><u>0.373</u></td><td><u>0.402</u></td><td><u>0.403</u></td><td><u>0.344</u></td><td><u>0.238</u></td><td><u>0.454</u></td><td><u>0.184</u></td><td><u>0.344</u></td><td><u>0.367</u></td><td><u>0.224</u></td><td><u>0.238</u></td><td><u>0.535</u></td><td><u>0.257</u></td></tr>
    <tr><td>VBVR-Pro-SenseNova-U1</td><td><strong>0.638</strong></td><td><strong>0.811</strong></td><td><strong>0.648</strong></td><td><strong>0.695</strong></td><td><strong>0.621</strong></td><td><strong>0.770</strong></td><td><strong>0.541</strong></td><td><strong>0.464</strong></td><td><strong>0.480</strong></td><td><strong>0.328</strong></td><td><strong>0.344</strong></td><td><strong>0.558</strong></td><td><strong>0.408</strong></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><u>0.275</u></td><td>0.299</td><td>0.252</td><td>0.267</td><td>0.157</td><td>0.305</td><td><u>0.305</u></td><td>0.233</td><td>0.252</td><td><u>0.312</u></td><td>0.219</td></tr>
    <tr><td>Kling V3</td><td><u>0.392</u></td><td><u>0.356</u></td><td>0.213</td><td><u>0.326</u></td><td><u>0.320</u></td><td><u>0.355</u></td><td><u>0.229</u></td><td><u>0.427</u></td><td>0.294</td><td><strong>0.564</strong></td><td><u>0.375</u></td><td>0.242</td><td><u>0.412</u></td></tr>
    <tr><td>SeedDance 2.0</td><td><strong>0.499</strong></td><td><strong>0.451</strong></td><td><strong>0.338</strong></td><td><strong>0.361</strong></td><td><strong>0.353</strong></td><td><strong>0.468</strong></td><td><strong>0.308</strong></td><td><strong>0.547</strong></td><td><strong>0.369</strong></td><td><u>0.511</u></td><td><strong>0.478</strong></td><td><strong>0.538</strong></td><td><strong>0.532</strong></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><u>0.182</u></td><td><u>0.157</u></td><td><u>0.082</u></td><td><u>0.131</u></td><td><u>0.110</u></td><td><u>0.161</u></td><td><u>0.156</u></td><td><u>0.207</u></td><td><u>0.224</u></td><td><u>0.139</u></td><td><u>0.140</u></td><td><u>0.195</u></td><td><u>0.273</u></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><strong>0.517</strong></td><td><strong>0.548</strong></td><td><strong>0.237</strong></td><td><strong>0.499</strong></td><td><strong>0.334</strong></td><td><strong>0.566</strong></td><td><strong>0.591</strong></td><td><strong>0.486</strong></td><td><strong>0.310</strong></td><td><strong>0.343</strong></td><td><strong>0.345</strong></td><td><strong>0.732</strong></td><td><strong>0.684</strong></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><u>0.562</u></td><td><u>0.730</u></td><td><u>0.617</u></td><td><u>0.580</u></td><td><u>0.452</u></td><td><u>0.676</u></td><td><u>0.623</u></td><td><u>0.395</u></td><td><u>0.410</u></td><td><u>0.305</u></td><td><u>0.230</u></td><td><u>0.617</u></td><td><u>0.439</u></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><strong>0.670</strong></td><td><strong>0.808</strong></td><td><strong>0.632</strong></td><td><strong>0.685</strong></td><td><strong>0.556</strong></td><td><strong>0.751</strong></td><td><strong>0.636</strong></td><td><strong>0.532</strong></td><td><strong>0.479</strong></td><td><strong>0.418</strong></td><td><strong>0.350</strong></td><td><strong>0.679</strong></td><td><strong>0.690</strong></td></tr>
  </tbody>
</table>

## Quick Start

### 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
```

#### Download the Qwen-Image-Edit-2511 base model

```bash
export MODELS_DIR="${PWD}/models"
mkdir -p "${MODELS_DIR}"
hf download Qwen/Qwen-Image-Edit-2511 \
  --local-dir "${MODELS_DIR}/Qwen/Qwen-Image-Edit-2511"
```

#### Run inference

```bash
python example.py \
  --model_path Video-Reason/VBVR-Pro-Qwen-Image-Edit-diffsynth \
  --base_model "${MODELS_DIR}/Qwen/Qwen-Image-Edit-2511" \
  --image_paths input.png \
  --prompt "Place the cup on the empty shelf." \
  --steps 40 --width 512 --height 512 \
  --output output.png
```

## Citation

```bibtex
@article{vbvr2025,
  title={VBVR: A Very Big Video Reasoning Suite},
  year={2025},
}
```