DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation

Understand

Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts.

• However, their applications to diverse real images are still difficult due to the limited GAN inversion capability.
• Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts.
• To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models.