What is Emu3.5
Emu3.5 (Wujie·Emu3.5) is a multimodal world model released by BAAI. Through end-to-end pretraining on over 10 trillion multimodal tokens (mainly sourced from internet videos, with a total duration of about 790 years), it learns and internalizes the dynamic laws of the real physical world and possesses native world modeling capabilities. The model is based on a 34B dense Transformer architecture and adopts the "next-state prediction" objective, achieving a unified understanding and generation of three modalities: text, images, and video. Emu3.5 has multiple innovations and breakthroughs. It proposes Discrete Diffusion Adaptation (DiDA) technology, which increases image generation speed by nearly 20 times, solving the speed bottleneck of autoregressive models in image generation. It demonstrates strong capabilities in tasks such as visual storytelling, visual guidance, general image editing and generation, world modeling and exploration, and can generate illustrated stories, step-by-step tutorials, high-quality images, as well as continuous visual sequence generation in virtual environments and decomposition of complex robotic manipulation tasks.
Main features of Emu3.5
-
Multimodal content generation: It can generate high-quality illustrated content, including images, text, and combinations of both, suitable for creative content creation fields such as advertising, film and television, and gaming.
-
Visual storytelling: It can generate immersive illustrated stories around any theme, with coherent logic and consistent visual style, providing new narrative methods for fields such as education and entertainment.
-
Visual guidance: It generates step-by-step tutorials with visual examples, intuitively demonstrating operation processes such as drawing and handicrafts, helping users understand and execute tasks more clearly.
-
General image editing and generation: It performs excellently on image editing tasks, enabling open-world editing and spatiotemporal operations, and its accuracy and naturalness in text rendering also surpass existing leading models.
-
World modeling and exploration: It can generate visual sequences of continuous movement in virtual environments, maintaining consistency in scene geometry, semantics, and appearance, and can be used in fields such as virtual reality and game development.
-
Embodied manipulation: It decomposes complex robotic manipulation tasks into a series of subtasks with language instructions and keyframe images, providing a foundation for training more general embodied agents and advancing the development of robotics technology.
Technical principles of Emu3.5
-
Native multimodal architecture: Based on a 34B dense Transformer model, through the "Next-State Prediction" objective, it achieves unified understanding and generation of three modalities: text, images, and video, breaking the boundaries between modalities.
-
Large-scale pretraining: End-to-end pretraining is conducted on data of over 10 trillion multimodal tokens, which are mainly sourced from internet videos and their corresponding speech transcription text, with a total video duration of about 790 years. This large-scale pretraining enables the model to learn the physical dynamics and causal laws of the real world.
-
Discrete Diffusion Adaptation (DiDA) technology: To solve the bottleneck of autoregressive models in image generation speed, DiDA technology is proposed, increasing image generation speed by nearly 20 times while maintaining generation quality, bridging the gap between autoregressive models and diffusion models.
-
Supervised fine-tuning: Fine-tuned on a high-quality dataset containing 150 billion samples, covering a variety of complex tasks, establishing a unified multimodal interaction interface, and improving the model's ability to understand and execute specific instructions.
-
Large-Scale Multimodal Reinforcement Learning: Building a complex multidimensional reward system that simultaneously evaluates multiple metrics such as the aesthetic quality of generated content, image-text alignment, and narrative coherence, further improving the model's multimodal reasoning and generation quality through reinforcement learning.
Emu3.5 Project Address
- Project Website：https://zh.emu.world
- Technical Paper：https://zh.emu.world/Emu35_tech_report.pdf
Emu3.5 Application Scenarios
-
Content Creation: Generate high-quality image-text content, suitable for creative fields such as advertising, film and television, and gaming, providing rich visual and narrative materials.
-
Education and Training: Generate immersive image-text stories and step-by-step tutorials to help students better understand and master knowledge, enhancing the learning experience.
-
Virtual Reality and Game Development: Generate continuous visual sequences in virtual environments, maintain scene consistency, and provide powerful content support for virtual reality and game development.
-
Robot Control and Embodied Intelligence: Decompose complex robot operation tasks into subtasks, provide language instructions and keyframe images, and help robots understand and execute complex tasks.
-
Image Editing and Design: Performs excellently on image editing tasks, can achieve open-world editing and spatiotemporal operations, and provides designers with efficient tools.
-
Intelligent Customer Service and Interaction: By generating answers with both images and text, provide more intuitive and richer information, improving user experience and interaction effectiveness.