About DreamOmni2

DreamOmni2 is a research project focused on advancing the capabilities of AI-driven image editing and generation. Released in October 2025, this model represents a significant step forward in making visual content creation more intuitive and flexible for users across different skill levels and industries.

Project Background

The DreamOmni2 project emerged from research into multimodal instruction processing for visual content manipulation. The development team identified key limitations in existing approaches that relied exclusively on text instructions or worked only with concrete objects. These limitations made it difficult for users to achieve specific visual effects or communicate their exact creative intent to AI systems.

The research aimed to create a unified model that could understand instructions from both text and images, handle abstract concepts as well as concrete objects, and perform both editing and generation tasks within a single framework. This integration reduces complexity for users and enables more sophisticated workflows that previously required multiple separate tools.

Core Capabilities

DreamOmni2 handles two primary tasks through a unified architecture. The first task, multimodal instruction-based generation, creates new images from scratch based on user guidance provided through text descriptions and reference images. This capability extends beyond traditional subject-driven generation by supporting abstract attributes such as artistic styles, material textures, design patterns, and visual effects.

The second task, multimodal instruction-based editing, modifies existing images according to user instructions. The model accepts reference images to specify exact visual details that would be difficult to describe in text alone. This approach enables precise edits while maintaining strict consistency in areas that should remain unchanged.

Technical Innovation

The model incorporates several technical innovations to handle its complex tasks. An index encoding and position encoding shift scheme allows the system to process multiple reference images simultaneously without confusion. Joint training with a Vision Language Model improves instruction comprehension and enables the model to handle sophisticated requests that combine visual and textual information.

The training process involved developing a comprehensive data synthesis pipeline that generates examples covering both concrete objects and abstract concepts. This pipeline creates training data for extraction, editing, and generation tasks, ensuring the model learns to handle diverse scenarios effectively.

Performance and Benchmarks

Testing shows that DreamOmni2 achieves strong results in identity and pose consistency for concrete objects, performing competitively with commercial models in these areas. For abstract attribute handling, the model demonstrates capabilities that meet or exceed some commercial alternatives. The comprehensive benchmarks developed alongside the model provide standardized ways to evaluate performance on multimodal instruction-based tasks.

Research Applications

The project contributes to several active research areas including multimodal learning, instruction following, and unified model architectures. By releasing the model, code, and training pipeline as open source, the team enables other researchers to build on this work, conduct comparative studies, and explore new applications.

Practical Use Cases

DreamOmni2 finds applications across multiple domains. Creative professionals use it for design iteration, style exploration, and content creation. E-commerce businesses apply it for product visualization and virtual try-on scenarios. Entertainment industries utilize it for character design, concept art, and visual effects. Researchers employ it as a foundation for studies in computer vision and human-computer interaction.

Open Source Commitment

Released under the Apache 2.0 license, DreamOmni2 is freely available for research and most commercial applications. The complete codebase, model weights, inference scripts, and training data pipeline are publicly accessible. This open approach supports reproducibility, encourages collaboration, and lowers barriers to entry for developers and researchers interested in this technology.

Responsible Development

The development team acknowledges that AI image generation and editing technologies carry responsibilities regarding their use and impact. While the model enables creative expression and practical applications, users are expected to comply with local laws and use the technology responsibly. The project includes guidelines for appropriate use and disclaimers about potential misuse.

Future Directions

Ongoing development focuses on improving model efficiency, expanding capability to handle even more complex instructions, and refining output quality across diverse scenarios. The open-source nature of the project invites community contributions, which may lead to extensions, adaptations, and improvements beyond the core research team's roadmap.