Show-o: A unified AI model that unifies multimodal understanding and generation using a single transformer

This paper presents Show-o, a unified transformer model that integrates multimodal understanding and generation capabilities within a single architecture. As artificial intelligence advances, there has been significant progress in multimodal understanding (e.g., visual question answering) and generation (e.g., text-to-image synthesis) separately. However, unifying these capabilities into one model remains a challenge. Show-o addresses this problem by innovatively combining discrete diffusion and autoregressive modeling techniques, allowing it to handle both text and image modalities effectively.

Current approaches to multimodal ai typically involve separate models for understanding and generation tasks. For example, models like LLaVA excel at multimodal understanding, while diffusion models like Stable Diffusion focus on image generation. Some recent attempts at unification, such as NExT-GPT, use separate components for different tasks. Instead, researchers propose Show-o, a single transformer that unifies both capabilities. Show-o is based on a pre-trained large language model (LLM) and incorporates autoregressive text modeling and discrete diffusion denoising for images. This allows it to handle diverse input types and generate various outputs, including text responses, photos, and mixed-modality content.

Show-o’s architecture is based on existing LLMs but incorporates a QK-Norm operation at each attention layer. It uses a unified request strategy to format multiple input types, allowing for seamless handling of multimodal data. The model employs an “omnipresent attention” mechanism that applies causal attention to text tokens and full attention to image tokens, allowing for efficient processing of both modalities. Show-o’s training process consists of three stages. Initially, the model learns image token embeddings and pixel dependencies. This is followed by image and text alignment for understanding and generation tasks. Finally, the model undergoes fine-tuning with high-quality data to improve its performance.

Show-o demonstrates impressive performance on several benchmarks. Multimodal understanding tasks achieve comparable or superior results to specialized models despite having fewer parameters. For example, on the VQAv2 benchmark, Show-o outperforms larger unified models such as NExT-GPT and Chameleon. On image generation, the model achieves a competitive FID score of 9.24 on the MSCOCO 30K dataset, outperforming some larger models trained on larger datasets. Despite its smaller size, the GenEval benchmark for text-to-image generation performs comparable or better than specialized models such as SDXL and SD3. Furthermore, it exhibits capabilities on downstream tasks such as extrapolation and text-guided image embedding without the need for fine-tuning. It also shows potential for mixed-modality generation such as creating video keyframes with corresponding text descriptions.

Show-o represents a significant advancement in multimodal ai by unifying understanding and generation capabilities within a single, efficient transformer architecture. Despite its relatively small size, its ability to achieve comparable or superior performance to models specialized in various tasks highlights its potential as a versatile base model for multimodal ai applications. The integration of discrete diffusion and autoregressive modeling techniques allows Show-o to handle different modalities in distinct yet cohesive ways. This approach simplifies the model architecture and enables new possibilities in mixed-modality tasks and efficient downstream applications.

While there are still areas for improvement, such as text recognition and object counting, Show-o’s performance and versatility make it a promising step toward more integrated and capable ai systems. As research in this direction continues, we may see even more powerful unified models that can seamlessly understand and output across multiple modalities, potentially revolutionizing several ai application fields.

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