Multimodal Control in Image Generation: A New Approach for More Complex Image Synthesis

The generation of images from text descriptions has made enormous progress in recent years. Modern text-to-image models can generate impressively realistic and detailed images. Controlling these models via additional conditions, such as edges or depth maps, expands the creative possibilities. However, a truly comprehensive approach for flexible, interleaved control that combines text and image inputs has been lacking until now.

A promising approach to solving this problem lies in the use of large multimodal models (LMMs). These models offer a shared representation space for text and images, which allows for precise alignment of both modalities. This alignment forms the basis for effective control of external diffusion models, which are responsible for the actual image generation. The idea of representing text and image information in a common space opens up new avenues for controlling the generation process.

An example of this approach is the "Dream Engine," a framework for flexible, text-image interleaved control of image generation models. This framework builds on established models like SD3.5, but replaces their text-based encoders with more versatile, multimodal encoders, such as QwenVL. By integrating multimodal information, more complex control mechanisms can be implemented that go beyond mere text descriptions.

The training of the Dream Engine takes place in two phases. First, a joint alignment of text and image is performed in the representation space of the multimodal encoder. This is followed by fine-tuning with multimodal, interleaved instructions. This two-stage training strategy enables effective integration of text and image information into the generation process.

Initial results show the potential of this approach. The Dream Engine achieves an overall score of 0.69 on the GenEval benchmark, putting it on par with state-of-the-art models like SD3.5 and FLUX. These results suggest that multimodal control is a promising way to further improve flexibility and control in image generation.

The development of frameworks like the Dream Engine underscores the importance of multimodal models for the future of image generation. The ability to seamlessly integrate text and image information opens up new perspectives for creative applications and more complex control options. Future research will focus on further improving the efficiency and precision of this control and exploring new application areas.

The combination of powerful text-to-image models and advanced multimodal encoders paves the way for a new generation of image generation systems. These systems offer a high degree of control and flexibility and enable the creation of images that precisely meet the users' requirements. Research in this area is progressing rapidly and promises further exciting developments in the future.

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