We make an attempt to eliminate the inversion process and introduce Denoising Diffusion Consistent Model (DDCM), a sampling strategy that enables virtual inversion. DDCM leverages a diffusion process that significantly enhances consistency throughout the image generation phases, ensuring fidelity and speed in transforming and refining visual content.
We also present Unified Attention Control (UAC) for tuning-free image editing through natural language that integrates cross-attention and self-attention control within a unified framework.
