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README.md

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+---
+license: apache-2.0

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bagel_7b_mot/README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: any-to-any
+library_name: bagel-mot
+---
+
+
+<p align="left">
+  <img src="https://lf3-static.bytednsdoc.com/obj/eden-cn/nuhojubrps/banner.png" alt="BAGEL" width="480"/>
+</p>
+
+
+# 🥯 BAGEL • Unified Model for Multimodal Understanding and Generation
+
+
+
+<p align="left">
+  <a href="https://bagel-ai.org/">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Website-0A66C2?logo=safari&logoColor=white" style="display: inline-block; vertical-align: middle;"
+      alt="BAGEL Website"
+    />
+  </a>
+  <a href="https://arxiv.org/abs/2505.14683">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Paper-red?logo=arxiv&logoColor=red" style="display: inline-block; vertical-align: middle;"
+      alt="BAGEL Paper on arXiv"
+    />
+  </a>
+  <a href="https://github.com/bytedance-seed/BAGEL" target="_blank" style="margin: 2px;">
+      <img 
+        alt="Github" src="https://img.shields.io/badge/BAGEL-Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;"
+        alt="BAGEL Codebase"
+      />
+  </a>
+  <a href="https://demo.bagel-ai.org/">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Demo-blue?logo=googleplay&logoColor=white" style="display: inline-block; vertical-align: middle;"
+      alt="BAGEL Demo"
+    />
+  </a>
+  <a href="https://discord.com/invite/Z836xxzy">
+    <img
+      src="https://img.shields.io/badge/BAGEL-Discord-green?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"
+      alt="BAGEL Discord"
+    />
+  </a>
+
+  
+</p>
+
+
+> We present **BAGEL**, an open‑source multimodal foundation model with 7B active parameters (14B total) trained on large‑scale interleaved multimodal data. BAGEL outperforms the current top‑tier open‑source VLMs like Qwen2.5-VL and InternVL-2.5 on standard multimodal understanding leaderboards, and delivers text‑to‑image quality that is competitive with strong specialist generators such as SD3.
+Moreover, BAGEL demonstrates superior qualitative results in classical image‑editing scenarios than the leading open-source models. More importantly, it extends to free-form visual manipulation, multiview synthesis, and world navigation, capabilities that constitute "world-modeling" tasks beyond the scope of previous image-editing models.
+
+
+This repository hosts the model weights for **BAGEL**. For installation, usage instructions, and further documentation, please visit our [GitHub repository](https://github.com/bytedance-seed/BAGEL).
+
+
+
+<p align="left"><img src="https://github.com/ByteDance-Seed/Bagel/raw/main/assets/teaser.webp" width="80%"></p>
+
+
+
+
+
+
+## 🧠 Method
+BAGEL adopts a Mixture-of-Transformer-Experts (MoT) architecture to maximize the model’s capacity to learn from richly diverse multimodal information. Following the same principle of capacity maximization, it utilizes two separate encoders to capture pixel-level and semantic-level features of an image. The overall framework follows a Next Group of Token Prediction paradigm, where the model is trained to predict the next group of language or visual tokens as a compression target.
+
+BAGEL scales MoT’s capacity through Pre-training, Continued Training, and Supervised Finetuning on trillions of interleaved multimodal tokens spanning language, image, video, and web data. It surpasses open models on standard understanding and generation benchmarks and demonstrates advanced in-context multimodal abilities like free-form image editing, future frame prediction, 3D manipulation, world navigation, and sequential reasoning.
+
+<p align="left"><img src="https://github.com/ByteDance-Seed/Bagel/raw/main/assets/arch.png" width="50%"></p>
+
+
+## 🌱 Emerging Properties
+<p align="left"><img src="https://github.com/ByteDance-Seed/Bagel/raw/main/assets/emerging_curves.png" width="50%"></p>
+
+As we scale up BAGEL’s pretraining with more multimodal tokens, we observe consistent performance gains across understanding, generation, and editing tasks. Different capabilities emerge at distinct training stages—multimodal understanding and generation appear early, followed by basic editing, while complex, intelligent editing emerges later. This staged progression suggests an emergent pattern, where advanced multimodal reasoning builds on well-formed foundational skills. Ablation studies further show that combining VAE and ViT features significantly improves intelligent editing, underscoring the importance of visual-semantic context in enabling complex multimodal reasoning and further supporting its role in the emergence of advanced capabilities.
+
+
+
+## 📊 Benchmarks
+### 1. Visual Understanding
+| Model | MME ↑ | MMBench ↑ |   MMMU ↑ | MM-Vet ↑ | MathVista ↑ |
+| ------------------- | ----------: | ----------: | -------: | -------: | ----------: |
+| Janus-Pro-7B        | -  |     79.2 |     41.0 |     50.0 |           – |
+| Qwen2.5-VL-7B      | 2347    |   83.5 | **58.6** |     67.1 |           68.2 |
+| **BAGEL**    | **2388**  |  **85.0** |     55.3 | **67.2** |    **73.1** |
+### 2. Text-to-Image Generation · GenEval
+| Model        | Overall ↑ |
+| ------------ | --------- |
+| FLUX-1-dev   | 0.82      |
+| SD3-Medium   | 0.74      |
+| Janus-Pro-7B | 0.80      |
+| **BAGEL**    | **0.88**  |
+### 3. Image Editing
+| Model         | GEdit-Bench-EN (SC) ↑ | GEdit-Bench-EN (PQ) ↑ | GEdit-Bench-EN (O) ↑ | IntelligentBench ↑ |
+| ------------- | --------------------- | --------------------- | ------------------- | ------------------ |
+| Step1X-Edit   | 7.09                  | 6.76                  | **6.70**            | 14.9               |
+| Gemini-2-exp. | 6.73                  | 6.61                  | 6.32                | **57.6**           |
+| **BAGEL**     | **7.36**              | **6.83**              | 6.52                | 44.0               |
+| **BAGEL+CoT** | –                   | –                     | –                   | 55.3               |
+
+## License
+BAGEL is licensed under the Apache 2.0 license. It is finetuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) and [siglip-so400m-14-384-flash-attn2](https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2) model, and uses the [FLUX.1-schnell VAE model](https://huggingface.co/black-forest-labs/FLUX.1-schnell), all under Apache 2.0.
+
+## ✍️ 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}
+}
+```

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bagel_7b_mot/config.json

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bagel_7b_mot/generation_config.json

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bagel_7b_mot/llm_config.json

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bagel_7b_mot/preprocessor_config.json

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bagel_7b_mot/vit_config.json

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unigendet/README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+tags:
+- text-to-image
+- fake-image-detection
+- unigendet
+- bagel
+base_model:
+- ByteDance-Seed/BAGEL-7B-MoT
+---
+
+<h1 align="center">[CVPR 2026] UniGenDet: A Unified Generative-Discriminative Framework</h1>
+
+<p align="center">
+  <b>
+    <a href="https://github.com/Zhangyr2022/">Yanran Zhang</a>,
+    <a href="https://wzzheng.net/#">Wenzhao Zheng</a><sup>†</sup>,
+    <a href="https://joeleelyf.github.io/">Yifei Li</a>,
+    <a href="https://yuby14.github.io/">Bingyao Yu</a>,
+    <a href="https://yzheng97.github.io/">Yu Zheng</a>,
+    <a href="https://leichenthu.github.io/">Lei Chen</a>,
+    <a href="https://scholar.google.com/citations?user=6a79aPwAAAAJ&hl=en">Jie Zhou</a><sup>*</sup>,
+    <a href="https://ivg.au.tsinghua.edu.cn/Jiwen_Lu/">Jiwen Lu</a>
+  </b>
+  <br/>
+  Department of Automation, Tsinghua University, China
+  <br/>
+  <sup>*</sup>Corresponding author &nbsp;&nbsp; <sup>†</sup>Project leader
+</p>
+
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/661cfae9a853782abad2a495/lBHJD1nNztgmdwc_WqVli.png" width="100%" alt="UniGenDet Teaser"/>
+</p>
+
+**UniGenDet** is a unified co-evolutionary framework that jointly optimizes image generation and generated-image detection in a single loop. By bridging generation and authenticity understanding through symbiotic multimodal self-attention, UniGenDet turns the traditional "generator vs. detector" arms race into a closed-loop collaboration. 
+
+This repository hosts the fine-tuned model weights for UniGenDet.
+
+### 🔗 Links
+- **GitHub Repository (Code & Detailed Instructions):** [Zhangyr2022/UniGenDet](https://github.com/Zhangyr2022/UniGenDet)
+- **Paper (arXiv):** [2604.21904](https://arxiv.org/abs/2604.21904v1)
+- **Project Website:** [UniGenDet Project Page](https://ivg-yanranzhang.github.io/UniGenDet/)
+
+### 🚀 Getting Started
+
+The UniGenDet model supports two main tasks:
+1. **Text-to-Image Generation (`t2i`)**
+2. **AI-Generated Image Detection and Explanation (`detection`)**
+
+To use these weights for generation, detection, or further fine-tuning, please refer to the official [GitHub repository](https://github.com/Zhangyr2022/UniGenDet). The repository provides a comprehensive `demo.py` script for interactive inference.
+
+**Quick Inference Example Setup:**
+1. Clone the GitHub repository: `git clone https://github.com/Zhangyr2022/UniGenDet.git`
+2. Install dependencies as outlined in the repo's `README.md`.
+3. Download the base BAGEL pretrained assets.
+4. Run `demo.py` pointing to this Hugging Face model directory.
+
+For complete installation, data preparation, training (GDUF/DIGA), and evaluation instructions, please consult the [main GitHub repository](https://github.com/Zhangyr2022/UniGenDet).
+
+### Citation
+
+```bibtex
+@article{zhang2026unigendet,
+  title   = {UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection},
+  author  = {Zhang, Yanran and Zheng, Wenzhao and Li, Yifei and Yu, Bingyao and Zheng, Yu and Chen, Lei and Zhou, Jie and Lu, Jiwen},
+  journal = {CoRR},
+  volume  = {abs/2604.21904},
+  year    = {2026},
+  url     = {[https://arxiv.org/abs/2604.21904](https://arxiv.org/abs/2604.21904)},
+}

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