🥯 ByteDance Seed’s BAGEL: The All-in-One Multimodal AI Model 🥯
This is ByteDance’s BAGEL (Unified Model for Multimodal Understanding and Generation), a 7B parameter model that does something pretty remarkable: it can generate images from text, edit existing images, AND understand what’s in images—all in one unified model. No need to switch between different specialized models.
Original Project: ByteDance-Seed/Bagel
Research Paper: Emerging Properties in Unified Multimodal Pretraining
Model Weights: ByteDance-Seed/BAGEL-7B-MoT
Project Website: bagel-ai.org
About This Model
Most AI models are specialists—DALL-E generates images, InstructPix2Pix edits them, GPT-4V understands them. BAGEL breaks this pattern by doing all three tasks in a single 7B parameter model (14B total parameters using Mixture-of-Transformer-Experts architecture).
What makes this especially cool is that BAGEL developed these capabilities naturally during training. As ByteDance scaled up the training, they watched different abilities emerge at different stages: first basic understanding and generation, then editing, and finally sophisticated “intelligent editing” that requires deep visual reasoning.
Key capabilities: - Text-to-Image Generation: Creates images from text prompts with quality competitive with Stable Diffusion 3 - Image Editing: Modifies existing images based on natural language instructions - Image Understanding: Analyzes and answers questions about images with detailed explanations - Chain-of-Thought Reasoning: Can “think” through complex tasks step-by-step before generating output
Model Performance
BAGEL holds its own against much larger specialized models:
Understanding: Outperforms Qwen2.5-VL-7B on most vision-language benchmarks
Text-to-Image: Competitive with SD3-Medium, and with chain-of-thought enabled, outperforms FLUX-1-dev
Image Editing: Beats leading open-source editing models on standard benchmarks
How It Works
BAGEL uses a Mixture-of-Transformer-Experts (MoT) architecture with dual visual encoders—a VAE for pixel-level details and a ViT for semantic understanding. This combination allows it to understand images at both fine-grained and conceptual levels.
The model processes everything as sequences of tokens (text tokens and visual tokens), using a unified approach that lets it seamlessly switch between understanding and generation tasks.
Using the Model
The model automatically detects what you want to do based on your inputs:
- Text-to-Image: Just provide a text prompt
- Image Editing: Provide an image + editing instruction + set task to “image-editing”
- Image Understanding: Provide an image + question + set task to “image-understanding”
Enable “chain-of-thought reasoning” for more sophisticated results—the model will think through the problem before generating its response.
Technical Details
- Architecture: Mixture-of-Transformer-Experts with 7B active parameters (14B total)
- Training: Scaled on trillions of interleaved multimodal tokens
- Visual Processing: Dual encoders (VAE + ViT) for comprehensive image understanding
- Hardware Requirements: Needs a powerful GPU with 40GB+ VRAM (A100/H100 recommended)
Limitations
Like all AI models, BAGEL isn’t perfect. Complex editing instructions might not always be interpreted as intended, and the model occasionally produces artifacts. The unified approach, while impressive, sometimes means it’s not quite as specialized as dedicated single-purpose models for any given task.
Citation
@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}
}License
BAGEL is licensed under Apache 2.0. The original research and model are from ByteDance-Seed.
