Visual Prompt Tuning for Domain-Adaptive Generative Image Transformers
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Solution Overview
Problem
Existing generative image models, such as GANs, autoregressive transformers, and continuous diffusion models, face limitations in generating images efficiently across different domains, with GANs lacking variety and realism, and other models being slow or requiring extensive computation.
Innovation Solution
A prompt token generator is trained to bias a pretrained generative image transformer towards specific domains using class and instance identifiers, allowing efficient knowledge transfer and generating diverse images by interpolating between prompt token sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If autoregressive transformers and continuous diffusion models are used to improve image quality, then image quality is improved, but generation speed becomes orders of magnitude slower
Solution Approach 1:
The patent segments the image generation task into two independent parts: (1) a pretrained generative image transformer that handles the heavy computational lifting for image synthesis, and (2) a small prompt token generator that handles domain adaptation. This segmentation allows the slow but high-quality transformer to be reused across domains without retraining, while the fast prompt generator adapts to new domains efficiently.
Solution Approach 2:
The generative image transformer is pretrained on a large dataset of diverse images before being used for domain-specific generation. This preliminary action captures general image generation capabilities, allowing the model to achieve high image quality quickly when combined with domain-specific prompt tokens, avoiding the need to retrain the entire transformer for each new domain.
2Productivity
If GANs are used for fast generation, then generation speed is improved, but image variety and realism are limited
Solution Approach 1:
The patent merges the advantages of different approaches by combining the fast inference capability of transformer-based models with the high image quality of diffusion models. The pretrained generative image transformer provides the foundation for high-quality generation, while the prompt token generator enables fast adaptation to different domains, achieving both speed and quality simultaneously.
3Productivity
If a pretrained generative image transformer is adapted to new domains, then domain adaptation efficiency is improved, but the number of training images required increases
Solution Approach 1:
The patent changes the parameters of the prompt token generator to adapt the transformer to different domains. By training the prompt generator on domain-specific identifiers and images, the system can efficiently adapt to new domains without requiring retraining of the entire generative image transformer, thus maintaining high adaptation efficiency while reducing the total number of training images needed.
Data Source
AI summary
Systems and methods for training and using a prompt token generator to generate a set of prompt tokens which, when fed into a pretrained generative image transformer (e.g., an autoregressive transformer, continuous diffusion model, non-autoregressive transformer, or discrete diffusion model), may bias the generative image transformer's output towards a particular domain (e.g., towards a particular class of images, towards a particular training instance, etc.). In some examples, the prompt token generator may be used to generate a set of different prompt token sequences, which may then be fed sequentially to a pretrained non-autoregressive generative image transformer as it iteratively generates each image in each time-step in order to introduce more diversity into the transformer's final output.