BAGEL is an open-source, unified multimodal model developed by ByteDance's Seed team, designed to seamlessly integrate text, image, and video processing capabilities. Leveraging a Mixture-of-Transformer-Experts (MoT) architecture, BAGEL excels in tasks such as text-to-image generation, image editing, style transfer, and complex visual reasoning. Pretrained on extensive interleaved multimodal data, it demonstrates emergent abilities in understanding and generating high-fidelity, contextually rich outputs across various modalities. Key Features: - Unified Multimodal Processing: Combines text, image, and video understanding and generation within a single model. - Advanced Image Generation and Editing: Produces photorealistic images from text prompts and performs intelligent image editing. - Style Transfer: Transforms images across different artistic styles with minimal alignment data. - World Navigation and Future Prediction: Exhibits capabilities in 3D manipulation, future frame prediction, and environment navigation. - Open-Source Accessibility: Available under the Apache 2.0 license, allowing for fine-tuning, distillation, and deployment across platforms. Primary Value and Problem Solved: BAGEL addresses the need for a versatile, open-source model capable of performing complex multimodal tasks that were previously restricted to proprietary systems. By unifying understanding and generation across text, images, and videos, it empowers developers and researchers to create innovative applications in content creation, virtual environment simulation, and beyond, without the constraints of vendor lock-in.