Understand
State-of-the-arts text-to-image generation models such as Imagen and Stable Diffusion Model have succeed remarkable progresses in synthesizing high-quality, feature-rich images with high resolution guided by human text prompts.
- Since certain characteristics of image content \emph{e.g.}, very specific object entities or styles, are very hard to be accurately described by text, some example-based image generation approaches have been proposed, \emph{i.e.} generating new concepts based on absorbing the salient features of a few input references.
- Despite of acknowledged successes, these methods have struggled on accurately capturing the reference examples' characteristics while keeping diverse and high-quality image generation, particularly in the one-shot scenario (\emph{i.e.} given only one reference).
- To tackle this problem, we propose a simple yet effective framework, namely DreamArtist, which adopts a novel positive-negative prompt-tuning learning strategy on the pre-trained diffusion model, and it has shown to well handle the trade-off between the accurate controllability and fidelity of image generation with only one reference example.
Reading the bibliography…