Bagel

Open-source unified multimodal AI is used for understanding, generating, and editing.

#ai-image generation and editing #ai-other

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Introduction

Open-source unified multimodal AI for understanding, generation, editing.

What is a bagel?

Developed by ByteDance’s Seed team, BAGEL is an open-source, Apache 2.0-compatible unified multimodal model designed to enable advanced capabilities in image/text understanding, generation, editing, and navigation. The performance it offers is on par with that of proprietary systems such as GPT-4o and Gemini 2.0. BAGEL can be fine-tuned and distilled, and it can be deployed anywhere, providing precise, accurate, and realistic results thanks to its native multimodal architecture.

How to use BAGEL?

Users can utilize BAGEL through its unified multimodal interface to input both images and text, and receive output in various mixed formats. It enables multiple rounds of dialogue, allows for the generation of high-fidelity images and video frames, supports image editing, applies style transfer techniques, facilitates navigation within virtual environments, and enables interaction with the model by providing relevant prompts—thus leveraging its combined capabilities and cognitive patterns.

The core functions of BAGEL

Unified Multimodal Model
Image/text understanding
Image/text generation (highly realistic images, video frames)
Image editing (while preserving visual identity and details)
Style transfer
Navigation (in various environments)
Combination ability (multi-round dialogue)
Thinking patterns (enhancing generation and editing through reasoning)
Pre-training based on the initialization of large language models
Expert Hybrid Transformer Architecture (MoT)

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Updated：2026-11-07