---
title: SRUM_7B_BAGEL
canonical_url: "https://www.modelscope.cn/models/SOTAowner/SRUM_7B_BAGEL"
md_url: "https://www.modelscope.cn/models/SOTAowner/SRUM_7B_BAGEL.md"
repository: SOTAowner/SRUM_7B_BAGEL
last_updated: 2025-10-03
pipeline_tag: any-to-any
tasks:
  - any-to-any
parameters: 83.8M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
downloads: 51
stars: 0
---

# SRUM_7B_BAGEL

> SRUM_7B_BAGEL - SOTAowner 在 ModelScope 开源的模型。SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models Weiyang Jin, Yuwei Niu, Jiaqi Liao, Chengqi Duan, Aoxue Li, Shenghua Gao, Xihui Liu :email: contact: xihuiliu@hku.hk We present SRUM, a post-training…

- **Repository**: SOTAowner/SRUM_7B_BAGEL
- **Tasks**: any-to-any
- **Parameters**: 83.8M
- **Downloads**: 51
- **Stars**: 0
- **Last updated**: 2025-10-03

Source: https://www.modelscope.cn/models/SOTAowner/SRUM_7B_BAGEL

---

<p align="center">
  <a href="https://waynejin0918.github.io/srum_web/">
    <img
      src="https://img.shields.io/badge/SRUM-Website-blue"
      alt="SRUM Website"
    />
  </a>

</p>

# SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
> [Weiyang Jin*](https://github.com/WayneJin0918), [Yuwei Niu*](https://purshow.github.io/), Jiaqi Liao, [Chengqi Duan](https://scholar.google.com/citations?user=r9qb4ZwAAAAJ&hl=en), Aoxue Li, [Shenghua Gao](https://scholar.google.com/citations?user=fe-1v0MAAAAJ&hl=en), [Xihui Liu :email: ](https://xh-liu.github.io/)
>
> contact: xihuiliu@hku.hk
> 
> We present **SRUM**, a post-training reward fine-tuning method based on Unified Multimodal Models (UMMs) leverages UMMs' inherent understanding capabilities to boost their generative abilities, bridging the gaps in performance caused by conflicts during the previous training phase. SRUM demonstrates exceptional generalization across both common positions and world knowledge..
The figure below showcases SRUM's qualitative performance compared with SFT and Base Model.

<!-- ## 🧠 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="center"><img src="assets/arch.png" width="95%"></p>


## 🌱 Emerging Properties
<p align="center"><img src="assets/emerging_curves.png" width="95%"></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. -->

## 📮 Notice
<!-- **Call for Bad Cases:** If you have encountered any cases where the model performs poorly, we would greatly appreciate it if you could share them in the [issue#11](https://github.com/ByteDance-Seed/Bagel/issues/11) or [Discord](https://discord.gg/Z836xxzy). -->
Follow the Bagel's original settings, you should focus:

**About Inference Hyperparameters:**
- **`cfg_text_scale`:** Controls how strongly the model follows the text prompt. `1.0` disables text guidance. Typical range: `4.0–8.0`.
- **`cfg_image_scale`:** Controls how much the model preserves input image details. `1.0` disables image guidance. Typical range: `1.0–2.0`.
- **`cfg_interval`:** Fraction of denoising steps where CFG is applied. Later steps can skip CFG to reduce computation. Typical: `[0.4, 1.0]`.
- **`timestep_shift`:** Shifts the distribution of denoising steps. Higher values allocate more steps at the start (affects layout); lower values allocate more at the end (improves details).
- **`num_timesteps`:** Total denoising steps. Typical: `50`.
- **`cfg_renorm_min`:** Minimum value for CFG-Renorm. `1.0` disables renorm. Typical: `0`.
- **`cfg_renorm_type`:** CFG-Renorm method:  
  - `global`: Normalize over all tokens and channels (default for T2I).
  - `channel`: Normalize across channels for each token.
  - `text_channel`: Like `channel`, but only applies to text condition (good for editing, may cause blur).
- **If edited images appear blurry, try `global` CFG-Renorm, decrease `cfg_renorm_min` or decrease `cfg_scale`.**


## 📊 Benchmarks

### 1. Composition

| T2I Model | 3d spatial | Color | Complex | Nonspatial | Numeracy | Shape | Spatial | Texture | Overall |
|-------|-----------|-------|---------|------------|----------|-------|---------|---------|---------|
| FLUX.1-dev | 76.39 | 90.63 | 83.51 | 87.47 | 75.30 | 80.20 | 84.23 | 87.07 | 83.10 |
| FLUX.1-schnell | 79.38 | 84.53 | 81.96 | 85.55 | 72.82 | 82.20 | 85.49 | 86.38 | 82.29 |
| SD-3-medium | 77.83 | 91.63 | 84.73 | 86.12 | 72.80 | 83.72 | 88.20 | 89.03 | 84.26 |
| SD-xl-base-1 | 72.25 | 77.75 | 75.00 | 85.28 | 57.14 | 72.18 | 77.08 | 78.38 | 74.38 |

| Unified Model | 3d spatial | Color | Complex | Nonspatial | Numeracy | Shape | Spatial | Texture | Overall |
|-------|-----------|-------|---------|------------|----------|-------|---------|---------|---------|
| Janus-Pro | 76.17 | 84.25 | 80.28 | 80.47 | 56.43 | 65.14 | 79.67 | 69.67 | 74.01 |
| Show-o2 | 88.61 | 87.73 | 87.88 | 85.91 | 69.74 | 73.99 | 86.60 | 82.17 | 82.83 |
| BLIP3o | 81.73 | 89.92 | 85.55 | 84.78 | 71.67 | 83.75 | 92.47 | 87.45 | 84.66 |
| OmniGen2 | 82.21 | 92.22 | 86.87 | 88.51 | 72.00 | 83.95 | 90.07 | 90.88 | 85.84 |
| Bagel | 77.98 | 89.30 | 83.32 | 85.03 | 70.40 | 81.94 | 81.52 | 87.93 | 82.18 |
| Bagel (CoT) | 84.66 | 88.85 | 86.10 | 85.64 | 75.36 | 84.33 | 82.71 | 88.07 | 84.46 |
| BLIP3o+SRUM | 83.78↑ | 90.22↑ | 86.57↑ | 85.10↑ | 74.52↑ | 85.44↑ | 93.88↑ | 86.52↓ | 85.75↑ |
| Bagel+SRUM | 83.10↑ | 92.90↑ | 88.69↑ | 88.47↑ | 78.52↑ | 84.23↑ | 86.92↑ | 89.57↑ | 86.55↑ |
| Bagel+SRUM (CoT) 🏆| 88.60↑ | 92.90↑ | 91.31↑ | 90.48↑ | 80.12↑ | 84.47↑ | 89.93↑ | 89.15↑ | 88.37↑ |

### 2. Reasoning-informed


| **Model** | **Entity** | **Idiom** | **Scientific** | **Textual Image** | **Average** |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Bagel | 49.70 | 34.46 | 47.52 | 43.59 | 43.82 |
| Bagel+SFT | 50.53 | 39.43 | 47.45 | 44.08 | 45.37 |
| Bagel+SRUM | **52.85** | **40.51** | **47.83** | **45.83** | **46.75** |

*Performance comparison of Bagel models across four categories and their average scores. **Bold values** indicate the best performance in each column.*

## 📜 License
SRUM is licensed under the Apache 2.0.