Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion

• URL: http://arxiv.org/abs/2407.07249v1
• Date: Tue, 9 Jul 2024 21:58:26 GMT
• Title: Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion
• Authors: Yu Cao, Shaogang Gong,
• Abstract summary: Conditional Diffusion Relaxing Inversion (CRDI) is designed to enhance distribution diversity in synthetic image generation. CRDI does not rely on fine-tuning based on only a few samples. It focuses on reconstructing each target image instance and expanding diversity through few-shot learning.
• Score: 37.18537753482751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of Few-Shot Image Generation (FSIG) using Deep Generative Models (DGMs), accurately estimating the distribution of target domain with minimal samples poses a significant challenge. This requires a method that can both capture the broad diversity and the true characteristics of the target domain distribution. We present Conditional Relaxing Diffusion Inversion (CRDI), an innovative `training-free' approach designed to enhance distribution diversity in synthetic image generation. Distinct from conventional methods, CRDI does not rely on fine-tuning based on only a few samples. Instead, it focuses on reconstructing each target image instance and expanding diversity through few-shot learning. The approach initiates by identifying a Sample-wise Guidance Embedding (SGE) for the diffusion model, which serves a purpose analogous to the explicit latent codes in certain Generative Adversarial Network (GAN) models. Subsequently, the method involves a scheduler that progressively introduces perturbations to the SGE, thereby augmenting diversity. Comprehensive experiments demonstrates that our method surpasses GAN-based reconstruction techniques and equals state-of-the-art (SOTA) FSIG methods in performance. Additionally, it effectively mitigates overfitting and catastrophic forgetting, common drawbacks of fine-tuning approaches.

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