BAGEL: ByteDance's Open-Source Unified Multimodal Foundation Model

ByteDance's Seed team recently open-sourced BAGEL (Bagel AI Generated Everything Lab), a unified multimodal foundation model with 7 billion active parameters (14 billion total parameters). This model outperforms Qwen2.5-VL and InternVL-2.5 on standard multimodal understanding benchmarks, has text-to-image quality competitive with SD3, and also possesses "world modeling" capabilities such as free-form visual manipulation, multi-view synthesis, and world navigation, opening up new possibilities for multimodal AI applications.

BAGEL's core capabilities cover three main areas:

1. Multimodal Understanding: Outperforms existing open-source models on standard benchmarks like MME, MMBench, and MMMU; reasoning ability is comparable to Gemini 2.0;
2. Text-to-Image Generation: Quality is competitive with the professional model SD3, achieving unification of understanding and generation tasks;
3. Image Editing & World Modeling: Not only excels in traditional editing scenarios but also extends to tasks beyond traditional models, such as free-form manipulation, multi-view synthesis, and world navigation.

BAGEL uses the Mixture-of-Transformers (MoT) architecture, with 7 billion active parameters out of a total 14 billion, balancing capability and inference efficiency. Its unified multimodal design (instead of separate encoders/decoders) brings three key advantages:

• A more consistent multimodal representation space;
• Efficient knowledge transfer (understanding → generation);
• Simplified deployment (single model replaces multiple specialized models).

BAGEL performs outstandingly in multiple benchmark tests:

• Multimodal Understanding: Outperforms models like Qwen2.5-VL-7B on benchmarks including MME, MMBench, MMMU, MM-Vet, and MathVista;
• Reasoning Ability: Performance is comparable to Gemini 2.0 on KRIS-Bench and RISEBench;
• Text-to-Image: Quality is competitive with SD3; Evaluation code has been open-sourced for easy reproduction and comparison.

Installation & Deployment:

1. Clone the repository: git clone https://github.com/bytedance-seed/BAGEL.git;
2. Environment setup: Create a conda environment and install dependencies;
3. Model download: Obtain via Hugging Face Hub;
4. WebUI launch: Supports different VRAM configurations (e.g., direct launch for 32GB+, NF4 quantization for 12-32GB). Inference Tuning: Core parameters include cfg_text_scale (text guidance strength), cfg_image_scale (image detail preservation), etc. Adjust according to needs to optimize generation results.

BAGEL has the following areas for improvement:

• High computational resource requirements (full-precision inference requires large VRAM);
• Occasional perspective inconsistencies in multi-view synthesis for complex scenes;
• Need to improve understanding accuracy for complex long-text prompts;
• Need to optimize fine-grained control for some editing tasks. The team encourages the community to share "bad cases" to guide future iterations.

Community Ecosystem: Since BAGEL's open-source release, it has spawned derivative projects such as quantized versions (DF11, INT8), ComfyUI nodes, Docker support, and Windows installation guides. The official team provides an online demo on Hugging Face Space and documentation. Conclusion: BAGEL marks a new stage for open-source multimodal models. Its unified architecture is capable of both understanding and generation tasks, providing a powerful tool for researchers, developers, and creators. The ecosystem will continue to grow in the future.