Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion

• URL: http://arxiv.org/abs/2402.16305v1
• Date: Mon, 26 Feb 2024 05:08:40 GMT
• Title: Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
• Authors: Xuantong Liu, Tianyang Hu, Wenjia Wang, Kenji Kawaguchi, Yuan Yao
• Abstract summary: Diffusion Probabilistic Models (DPMs) are dominant force in text-to-image generation tasks. We propose an alternative view of state-of-the-art DPMs as a way of inverting advanced Vision-Language Models (VLMs) By directly optimizing images with the supervision of discriminative VLMs, the proposed method can potentially achieve a better text-image alignment.
• Score: 35.21106030549071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs) face a critical challenge in controllability, struggling to adhere strictly to complex, multi-faceted instructions. In this work, we aim to address this alignment challenge for conditional generation tasks. First, we provide an alternative view of state-of-the-art DPMs as a way of inverting advanced Vision-Language Models (VLMs). With this formulation, we naturally propose a training-free approach that bypasses the conventional sampling process associated with DPMs. By directly optimizing images with the supervision of discriminative VLMs, the proposed method can potentially achieve a better text-image alignment. As proof of concept, we demonstrate the pipeline with the pre-trained BLIP-2 model and identify several key designs for improved image generation. To further enhance the image fidelity, a Score Distillation Sampling module of Stable Diffusion is incorporated. By carefully balancing the two components during optimization, our method can produce high-quality images with near state-of-the-art performance on T2I-Compbench.

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