DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching

Understand

Personalized image generation requires text-to-image generative models that capture the core features of a reference subject to allow for controlled generation across different contexts.

• Existing methods face challenges due to complex training requirements, high inference costs, limited flexibility, or a combination of these issues.
• In this paper, we introduce DreamCache, a scalable approach for efficient and high-quality personalized image generation.
• By caching a small number of reference image features from a subset of layers and a single timestep of the pretrained diffusion denoiser, DreamCache enables dynamic modulation of the generated image features through lightweight, trained conditioning adapters.