Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models

Abstract: Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a particular region of the output space or sampling evenly over a range of characteristics. For efficient sampling in these scenarios, we propose Generative Visual Prompt (PromptGen), a framework for distributional control over pre-trained generative models by incorporating knowledge of other off-the-shelf models. PromptGen defines control as energy-based models (EBMs) and samples images in a feed-forward manner by approximating the EBM with invertible neural networks, avoiding optimization at inference. Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e.g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses. (4) Finally, PromptGen reveals that the CLIP model shows a "reporting bias" when used as control, and PromptGen can further de-bias this controlled distribution in an iterative manner. The code is available at https://github.com/ChenWu98/Generative-Visual-Prompt.

• The paper demonstrates a unified framework (PromptGen) that employs energy-based models in the latent space to control generative outputs without requiring fine-tuning.
• It utilizes invertible neural networks to replace iterative sampling methods, enabling efficient feed-forward generation and reducing computation.
• Empirical results show improved FID and KL divergence metrics, promoting effective bias mitigation and enhanced diversity in image generation.

A Critical Analysis on Generative Visual Prompt: Distributional Control of Generative Models

The paper "Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models" tackles the challenge of controlling outputs from unsupervised generative models—a task that has historically been cumbersome due to its reliance on model-specific modifications or extensive labeled data. In the context of generative models such as GANs and diffusion models, which excel in capturing complex data distributions but often lack precise controllability, this work presents a framework named PromptGen. This framework stands out by offering a unified approach that leverages off-the-shelf models to steer the distributional outcomes of pre-trained generative models, effectively broadening their application scope without additional fine-tuning of the original models.

Methodology Overview

PromptGen employs an innovative approach by defining control objectives through energy-based models (EBMs) in the latent space of pre-trained generative models. Central to its design is the use of invertible neural networks (INNs) to approximate EBMs efficiently, thereby enabling feed-forward sampling and eliminating the need for iterative optimization during inference—a significant advantage over traditional methods such as Langevin dynamics sampling used in PPGMs. This framework harnesses diverse off-the-shelf models, including CLIP for text-based control, classifiers for de-biasing, and inverse graphics models for identity and pose manipulation. Through this modularity, PromptGen facilitates various controls, such as de-biasing and identity-preserving transformations, manifesting a generalist approach previously unexplored with such efficiency.

Empirical Validation and Results

The empirical evaluation of PromptGen on multiple generative models like StyleGAN2, StyleNeRF, and diffusion autoencoders corroborates its efficacy in controlled sample generation. Notably, experiments demonstrate its capability to de-bias race and age distributions in generated images by leveraging classifiers trained on external datasets. Furthermore, PromptGen's extension in handling iterative controls reveals interesting biases within models like CLIP, allowing for bias mitigation in multi-step training and inference—showcasing its practical utility.

The paper backs its claims with quantitative metrics such as the Frechet Inception Distance (FID) and KL divergence to ensure unbiased sampling, lending robustness to its approach. The results demonstrate marked improvements over baseline methods, particularly highlighting PromptGen's ability to maintain image diversity and quality even under constrained or iteratively controlled settings.

Implications and Future Directions

From a theoretical perspective, PromptGen proposes a significant shift in how distributional control can be achieved in unsupervised generative models without overhauling the base model's training. By mapping out the potential of EBMs combined with INNs for sampling, this work contributes a vital piece to the puzzle of how best to utilize pre-trained models across varied downstream tasks with minimal computation.

Practically, PromptGen suggests a pathway for developing adaptable AI systems capable of nuanced outputs tailored by user-specified criteria, thus bringing more responsibility and fairness to generative AI applications. However, it also raises questions about the dependency on the accuracy and biases inherent in off-the-shelf models, signaling an important area for future research concerning bias detection and de-biasing mechanisms.

With the foundation this paper sets, future advancements could explore integrating PromptGen with domain adaptation strategies, potentially expanding its applicability across more diverse domains or datasets it wasn't originally designed for. The exploration of learned energy functions beyond existing models could further refine control granularity and achieve even greater alignment with user objectives.

In conclusion, this paper presents a comprehensive framework that not only enhances the controllability of generative models but also provides a toolkit for ethical AI deployment concerning bias and representation. Its core innovations lay groundwork for both the practical deployment and theoretical advancements of generative AI technologies.