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533f988

---
license: apache-2.0
language:
  - en
pipeline_tag: any-to-any
tags:
  - image-generation
  - interleaved-generation
  - multimodal-reasoning
  - visual-reasoning
  - vbvr-pro
base_model: ThinkMorph/ThinkMorph-7B
base_model_relation: finetune
---

# 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">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"><strong>VBVR-Pro-ThinkMorph</strong></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

This repository contains a VBVR-Pro fine-tune of [ThinkMorph-7B](https://huggingface.co/ThinkMorph/ThinkMorph-7B). It takes an initial image and a text instruction, reasons with interleaved text and images, and can generate one or more sequential images before returning its final answer.

The released weights are the EMA checkpoint from training step 25,000. The training checkpoint was stored in FP32; this Hugging Face export is converted to BF16, matching the precision of the original ThinkMorph-7B release.

### Repository contents

| File | Purpose |
| --- | --- |
| `ema.safetensors` | Complete BF16 fine-tuned ThinkMorph model state |
| `ae.safetensors` | Image autoencoder used for encoding and generation |
| `llm_config.json` | Qwen2-MoT language-model configuration |
| `vit_config.json` | SigLIP vision-encoder configuration |
| `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt` | Tokenizer assets |
| `generation_config.json` | Default text-generation settings |
| `config.json` | Checkpoint family identifier |

This is a full checkpoint, not a LoRA or other adapter. No separate base-model download is needed after this repository has been downloaded.

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

### Method 1: Standalone ThinkMorph inference

#### Requirements

- Linux and a CUDA-capable GPU
- Python 3.10
- The [ThinkMorph](https://github.com/ThinkMorph/ThinkMorph) inference code and its dependencies

For straightforward inference, an 80 GB-class GPU is recommended. The ThinkMorph loader can also distribute the model across multiple visible GPUs.

```bash
git clone https://github.com/ThinkMorph/ThinkMorph.git
cd ThinkMorph
conda create -n thinkmorph python=3.10 -y
conda activate thinkmorph
pip install -r requirements.txt
```

#### Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Video-Reason/VBVR-Pro-ThinkMorph",
    local_dir="models/VBVR-Pro-ThinkMorph",
    allow_patterns=["*.json", "*.safetensors", "*.txt", "*.md"],
)
```

#### Inference

Run the following from the ThinkMorph repository. The same downloaded directory is passed as both `model_path` and `config_path` because this release is self-contained.

```python
import random

import numpy as np
import torch
from PIL import Image

from inferencer import InterleaveInferencer
from scripts.cza.inference import load_model

model_path = "models/VBVR-Pro-ThinkMorph"

random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)

model, vae, tokenizer, token_ids, vae_transform, vit_transform = load_model(
    model_path,
    model_path,
)
inferencer = InterleaveInferencer(
    model, vae, tokenizer, vae_transform, vit_transform, token_ids
)

system_prompt = (
    "Let's think step by step to answer the question. For text-based thinking, "
    "enclose the process within <think> </think>. For visual thinking, enclose "
    "the content within <image_start> </image_end>. Finally conclude with the "
    "final answer wrapped in <answer> </answer>"
)

input_image = Image.open("first_frame.png").convert("RGB")
output = inferencer(
    image=input_image,
    text="Move the object to the requested destination while preserving the scene.",
    understanding_output=False,
    think=True,
    system_prompt=system_prompt,
    max_think_token_n=4096,
    do_sample=True,
    text_temperature=0.3,
    cfg_text_scale=4.0,
    cfg_img_scale=2.0,
    cfg_interval=[0.0, 1.0],
    timestep_shift=3.0,
    num_timesteps=50,
    cfg_renorm_min=0.0,
    cfg_renorm_type="text_channel",
    max_rounds=10,
)

for index, item in enumerate(output):
    if isinstance(item, Image.Image):
        item.save(f"generated_{index}.png")
    else:
        print(item, end="")
```

The settings above reproduce the first-attempt settings used by the existing VBVR-Pro evaluator:

| Setting | Value |
| --- | --- |
| Seed | 42 |
| Maximum thinking tokens | 4096 |
| Text temperature | 0.3 |
| Text CFG scale | 4.0 |
| Image CFG scale | 2.0 |
| Denoising steps | 50 |
| Timestep shift | 3.0 |
| Maximum interleaved rounds | 10 |

### 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-ThinkMorph \
  --image_paths first_frame.png \
  --prompt "Solve the task step by step." \
  --think --max_rounds 10 --num_images 2 \
  --output outputs
```

## Notes

- The model uses the custom ThinkMorph/BAGEL architecture and is not directly loadable with `transformers.AutoModel` or a Diffusers pipeline.
- Inputs should follow the same image-plus-instruction format used by ThinkMorph. Generated images appear as `PIL.Image.Image` objects interleaved with text strings.
- More generated images, larger resolutions, and longer reasoning traces increase runtime and memory use.
- This checkpoint may inherit limitations and biases from its base model and fine-tuning data. Validate outputs for your application and use generated content responsibly.

## Citation

```bibtex
@misc{gu2025thinkmorphemergentpropertiesmultimodal,
  title={ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning},
  author={Jiawei Gu and Yunzhuo Hao and Huichen Will Wang and Linjie Li and Michael Qizhe Shieh and Yejin Choi and Ranjay Krishna and Yu Cheng},
  year={2025},
  eprint={2510.27492},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2510.27492},
}
```

## License

This release follows the Apache 2.0 license declared by ThinkMorph-7B. See the base model repository for additional context.