--- license: apache-2.0 base_model: - ByteDance-Seed/BAGEL-7B-MoT base_model_relation: finetune library_name: transformers pipeline_tag: image-to-image tags: - bagel - image-editing - image-generation - interleaved-generation - safetensors - vbvr-pro --- # 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 BAGEL is an image-editing and interleaved keyframe-generation fine-tune of [ByteDance-Seed/BAGEL-7B-MoT](https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT). This export uses training step **38,000** from `260717_NEO_VBVR_PRO_5e-6`. Given an initial image and a text instruction, the model generates the next edited image. It was trained for the VBVR-Bench Pro image setting with visual generation enabled, a maximum latent side of 64, and the BAGEL Mixture-of-Transformer-Experts language backbone. ### Checkpoint format The training checkpoint stores only the 797 trainable tensors in FP32. For this release, those tensors were merged with the 426 frozen ViT and connector tensors from the base BAGEL EMA checkpoint, then the complete 1,223-tensor model was converted to BF16. The FLUX autoencoder is provided separately as `ae.safetensors`, following the layout of [SenseNova-SI-1.1-BAGEL-7B-MoT](https://huggingface.co/sensenova/SenseNova-SI-1.1-BAGEL-7B-MoT). Training-only optimizer shards and scheduler state are not included. | File | Purpose | | --- | --- | | `model.safetensors` | Complete merged BAGEL model in BF16 | | `ae.safetensors` | FLUX autoencoder used by BAGEL | | `model.safetensors.index.json` | Weight-to-file map for the model and autoencoder | | `config.json` | Integrated BAGEL architecture configuration | | `llm_config.json`, `vit_config.json` | Language and vision encoder configurations | | `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt` | Qwen2 tokenizer assets | | `generation_config.json`, `preprocessor_config.json` | Text-generation and image-preprocessing defaults | 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 BAGEL inference #### Requirements - Linux and a CUDA-capable GPU - Python 3.10 or newer - PyTorch 2.5 or newer - Transformers 4.49 - Accelerate, Safetensors, Pillow, and FlashAttention - The [BAGEL codebase](https://github.com/bytedance-seed/BAGEL) Install the official BAGEL environment: ```bash git clone https://github.com/bytedance-seed/BAGEL.git cd BAGEL conda create -n bagel python=3.10 -y conda activate bagel pip install -r requirements.txt pip install flash_attn==2.5.8 --no-build-isolation ``` Download this repository: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="Video-Reason/VBVR-Pro-BAGEL", local_dir="models/VBVR-Pro-BAGEL", allow_patterns=["*.json", "*.safetensors", "*.md", "*.txt"], ) ``` #### Inference This export uses the original BAGEL model implementation. Construct `Bagel` with `visual_gen=True`, `visual_und=True`, `max_latent_size=64`, and `layer_module="Qwen2MoTDecoderLayer"`; load `model.safetensors` with Accelerate or Safetensors; and load the autoencoder from `ae.safetensors`. The evaluation path used for this checkpoint was: ```bash bash inference/run_bagel_vbvr_pro_eval.sh \ In-Domain_50 \ /path/to/VBVR-Pro-BAGEL/model.safetensors \ vigen 8 25 ``` The launcher invokes `inference/bagel_vbvr_pro.py`. It builds the BAGEL architecture from the base assets, supplies this fine-tuned checkpoint, and calls `InterleaveInferencer.interleave_inference` with a text instruction followed by the initial image. #### Evaluation defaults | Setting | Value | | --- | --- | | Input | `prompt.txt` and `first_frame.png` | | Output | `frame_1.png` | | Denoising steps | 50 | | Text CFG scale | 4.0 | | Image CFG scale | 2.0 | | CFG interval | `[0.0, 1.0]` | | Timestep shift | 3.0 | | CFG renormalization | `text_channel` | | Seed | 1 | When using the legacy BAGEL `app.py`, which looks specifically for `ema.safetensors`, create a local alias after downloading: ```bash ln -s model.safetensors models/VBVR-Pro-BAGEL/ema.safetensors python app.py --model_path models/VBVR-Pro-BAGEL ``` The alias is not part of the Hugging Face release because `model.safetensors` is the canonical weight filename used by the reference release layout. ### 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-BAGEL \ --image_paths first_frame.png \ --prompt "Move the object to the requested destination." \ --output output.png ``` ## Notes - The checkpoint is intended for BF16 CUDA inference. A high-memory GPU is recommended for direct single-GPU loading. - The released weights are a complete inference checkpoint; the original BAGEL EMA file is not needed after the merge. - Higher output resolutions and additional generated images increase runtime and memory use substantially. - Generated content should be reviewed before use in downstream applications. ## License This model is released under the Apache License 2.0, following the base BAGEL model. BAGEL is fine-tuned from Qwen2.5-7B-Instruct and SigLIP and uses the FLUX.1-schnell autoencoder; consult the upstream repositories for their notices. ## Citation ```bibtex @article{deng2025bagel, title = {Emerging Properties in Unified Multimodal Pretraining}, author = {Deng, Chaorui and Zhu, Deyao and Li, Kunchang and Gou, Chenhui and Li, Feng and Wang, Zeyu and Zhong, Shu and Yu, Weihao and Nie, Xiaonan and Song, Ziang and Shi, Guang and Fan, Haoqi}, journal = {arXiv preprint arXiv:2505.14683}, year = {2025} } ```