BAGEL by ByteDance-Seed is an Apache 2.0 open-source unified multimodal model designed for advanced image/text understanding, generation, editing, and navigation. It offers capabilities comparable to proprietary systems like GPT-4o and Gemini 2.0. BAGEL can be fine-tuned, distilled, and deployed anywhere, providing precise, accurate, and photorealistic outputs through its natively multimodal architecture.
What is BAGEL?
How to use
BAGEL can be used through its unified multimodal interface, accepting both image and text inputs and outputs in a mixed format. Users can engage in multi-turn conversations, generate high-fidelity images and video frames, perform image editing, apply style transfers, navigate virtual environments, and leverage its compositional and thinking modes by providing prompts and interacting with the model.
Core Features
- Unified Multimodal Model
- Image/Text Understanding
- Image/Text Generation (photorealistic images, video frames)
- Image Editing (preserves visual identities and details)
- Style Transfer
- Navigation (in diverse environments)
- Compositional Abilities (multi-turn conversations)
- Thinking Mode (enhances generation and editing through reasoning)
- Pre-training initialized from large language models
- Mixture-of-Transformer-Experts (MoT) architecture
Use Cases
- Describing and understanding images (e.g., 'Tell me about this picture')
- Generating photorealistic images from text prompts (e.g., 'a photo of three antique glass magic potions')
- Editing images while preserving details (e.g., 'He squatted down and touched a dog's head')
- Transforming image styles (e.g., 'Change to 3D animated style')
- Navigating and interacting with virtual environments (e.g., 'After 0.40s, move forward')
- Engaging in multi-turn conversations with compositional reasoning (e.g., creating a slogan for a doll)
- Refining prompts for detailed and coherent visual outputs using a 'thinking' mode
FAQ
What is BAGEL?
BAGEL is an Apache 2.0 open-source unified multimodal model developed by ByteDance-Seed, designed for advanced image/text understanding, generation, editing, and navigation, with capabilities comparable to proprietary systems.
What are BAGEL's core capabilities?
BAGEL offers capabilities such as chat, image and text generation, image editing, style transfer, navigation, compositional reasoning, and a thinking mode to enhance outputs.
How does BAGEL compare to other models?
BAGEL offers comparable functionality to proprietary systems like GPT-4o and Gemini 2.0 and surpasses other open models on standard understanding and generation benchmarks.
When was BAGEL released?
BAGEL was released on May 20, 2025.
Pricing
Pros & Cons
Pros
- Open-source (Apache 2.0 license)
- Unified multimodal capabilities (image/text understanding, generation, editing, navigation)
- Functionality comparable to proprietary systems like GPT-4o and Gemini 2.0
- Can be fine-tuned, distilled, and deployed anywhere
- Capable of precise, accurate, and photorealistic outputs
- Handles mixed image and text inputs/outputs
- Strong reasoning and conversational abilities inherited from LLMs
- Effective for image editing, preserving visual identities and fine details
- Effortless style transfer with minimal alignment data
- Distills navigation knowledge from real-world data
- Engages in seamless multi-turn conversations
- Incorporates a thinking mode for nuanced and consistent outputs
- Scalable Mixture-of-Transformer-Experts (MoT) architecture
- Surpasses other open models on standard understanding and generation benchmarks
- Demonstrates advanced in-context multimodal abilities like future frame prediction and 3D manipulation
Cons
- No disadvantages explicitly mentioned in the provided content.