Versatile Diffusion: Text, Images and Variations All in One Diffusion Model (2211.08332v4)

Abstract: Recent advances in diffusion models have set an impressive milestone in many generation tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted great interest. Despite the rapid landscape changes, recent new approaches focus on extensions and performance rather than capacity, thus requiring separate models for separate tasks. In this work, we expand the existing single-flow diffusion pipeline into a multi-task multimodal network, dubbed Versatile Diffusion (VD), that handles multiple flows of text-to-image, image-to-text, and variations in one unified model. The pipeline design of VD instantiates a unified multi-flow diffusion framework, consisting of sharable and swappable layer modules that enable the crossmodal generality beyond images and text. Through extensive experiments, we demonstrate that VD successfully achieves the following: a) VD outperforms the baseline approaches and handles all its base tasks with competitive quality; b) VD enables novel extensions such as disentanglement of style and semantics, dual- and multi-context blending, etc.; c) The success of our multi-flow multimodal framework over images and text may inspire further diffusion-based universal AI research. Our code and models are open-sourced at https://github.com/SHI-Labs/Versatile-Diffusion.

Versatile Diffusion: A Unified Framework for Text-to-Image, Image Variation, and Crossmodal Generation

Introduction

The interdisciplinary domain of artificial intelligence that converges generative models and multi-modal data processing has witnessed a remarkable revolution with the advent of diffusion models. These models have demonstrated unparalleled prowess in generating high fidelity outputs, be it images or text. Yet, the dichotomy of excelling at a single task versus achieving a multi-tasking generative model remains largely unbridged. The introduction of the Versatile Diffusion (VD) model aims to fill this chasm by presenting a unified framework capable of handling not just multiple tasks but also embracing the complexity of different modalities within a single model architecture.

Methodology

At its core, the Versatile Diffusion model innovates by implementing a multi-flow multimodal framework. This framework diverges from the traditional single-flow diffusion models by integrating sharable and swappable layer modules, facilitating cross-modal generality. The architecture is designed to support and switch between various tasks seamlessly, such as text-to-image, image-to-text, and image variations, by dynamically activating relevant data and context-specific layers based on the input modalities.

The methodological strength of VD lies in its robust training regime, wherein it undergoes progressive training starting from single-flow tasks and gradually escalating to more complex multi-flow tasks. This approach not only enhances the model's generalizability but also its adaptability to novel tasks. Additionally, VD skillfully manipulates the latent space for both image and text modalities, leveraging the strengths of VAEs and CLIP models for embedding contexts, which in turn fine-tunes the generative outputs tailored to the specifics of the given modality.

Experimental Results

The quantitative and qualitative analysis underscores VD's superiority over existing baselines across a spectrum of tasks. Specifically, for text-to-image generation, VD demonstrates an ability to produce high-quality, contextually relevant images that closely adhere to the provided text descriptions. Further, the image variation task illustrates VD's capability to generate semantically consistent variations of a given image, showcasing a profound understanding of image semantics and style. These achievements are emblematic of VD's innovative approach to generalized multi-flow modeling that transcends traditional generative boundaries.

Moreover, the model introduces several novel capabilities, including but not limited to, semantic-style disentanglement and dual-context blending. These functionalities enable VD to not only create variations but also manipulate images and text in a controlled manner, demonstrating a significant leap towards achieving creative and versatile generative models.

Discussion

The implications of the Versatile Diffusion model are manifold, bridging gaps between isolated generative tasks and paving the way for a new era of multi-modal, multi-task AI models. VD's framework opens avenues for exploring more intricate relationships between different data modalities, significantly reducing the overhead associated with training separate models for each task. This consolidation into a single model architecture, without compromising on performance across tasks, marks a pivotal advancement in efficient resource utilization and model scalability.

Looking forward, the versatility and adaptability of the VD model hold promise for extending its application beyond the domains of text and images to include other modalities such as audio, video, and 3D models. By continually expanding the boundaries of what is possible within the field of generative AI, VD contributes to the overarching goal of achieving a truly universal artificial intelligence.

Conclusion

In summation, the Versatile Diffusion model exemplifies a significant breakthrough in the field of generative AI by presenting a scalable, efficient, and highly versatile framework capable of mastering complex multi-task, multi-modal generative tasks. Through its innovative design and exemplary performance, VD not only enhances the current landscape of generative models but also sets a benchmark for future research and development toward realizing the full potential of universal AI.