Inversion-Free Image Editing with Natural Language (2312.04965v1)

Abstract: Despite recent advances in inversion-based editing, text-guided image manipulation remains challenging for diffusion models. The primary bottlenecks include 1) the time-consuming nature of the inversion process; 2) the struggle to balance consistency with accuracy; 3) the lack of compatibility with efficient consistency sampling methods used in consistency models. To address the above issues, we start by asking ourselves if the inversion process can be eliminated for editing. We show that when the initial sample is known, a special variance schedule reduces the denoising step to the same form as the multi-step consistency sampling. We name this Denoising Diffusion Consistent Model (DDCM), and note that it implies a virtual inversion strategy without explicit inversion in sampling. We further unify the attention control mechanisms in a tuning-free framework for text-guided editing. Combining them, we present inversion-free editing (InfEdit), which allows for consistent and faithful editing for both rigid and non-rigid semantic changes, catering to intricate modifications without compromising on the image's integrity and explicit inversion. Through extensive experiments, InfEdit shows strong performance in various editing tasks and also maintains a seamless workflow (less than 3 seconds on one single A40), demonstrating the potential for real-time applications. Project Page: https://sled-group.github.io/InfEdit/

• The paper introduces InfEdit, an inversion-free framework that employs a novel Denoising Diffusion Consistent Model to eliminate the inversion bottleneck in text-guided image editing.
• It integrates Unified Attention Control to handle both rigid and non-rigid semantic modifications while preserving image consistency and quality.
• Empirical evaluations demonstrate that InfEdit performs real-time edits in under 3 seconds on an NVIDIA A40 GPU, significantly outperforming traditional inversion-based methods.

Inversion-Free Image Editing with Natural Language: A Detailed Exposition

This paper introduces an innovative approach to text-guided image manipulation utilizing diffusion models. It addresses the limitations encountered in inversion-based editing frameworks, specifically focusing on inefficiencies and the inability to balance consistency and accuracy effectively. The proposed solution, termed Inversion-Free Editing (InfEdit), employs a Denoising Diffusion Consistent Model (DDCM) that cleverly circumvents the explicit inversion process typically required in sampling.

Key Contributions

1. Denoising Diffusion Consistent Model (DDCM): The primary technical advancement presented in this paper is the DDCM, which leverages a special variance schedule that transforms the denoising step to mimic the form of multi-step consistency sampling. This approach effectively removes inversion, paving the way for more efficient image editing.
2. Unified Attention Control (UAC): The paper further innovates by integrating multiple attention control mechanisms into a unified framework. This aids in handling both rigid and non-rigid semantic changes without compromising image integrity.
3. InfEdit Framework: By combining DDCM and UAC, the InfEdit framework demonstrates significant improvements in efficiency, executing with ease in under 3 seconds on a single NVIDIA A40 GPU. This promotes real-time editing applications, marking substantial progress in the field.
4. Empirical Validation: The authors employ extensive empirical evaluations across diverse editing tasks, convincingly showcasing the superior performance of InfEdit over traditional inversion-based methodologies. The introduced model exhibits strong alignment with target prompts while preserving the original consistency in the images, achieving remarkable results in both efficiency and quality.

Technical Details

The paper identifies three main bottlenecks of inversion-based methods: a time-consuming inversion process, challenges in maintaining consistency and accuracy, and incompatibility with efficient consistency sampling. To tackle these, the authors introduce DDCM by eliminating the parameterization traditionally required in denoising diffusion models and employing a non-Markovian forward process where intermediate steps depend solely on a known initial sample. This approach underscores a virtual inversion strategy, facilitating seamless sampling without explicit inversion.

Attention mechanisms are optimized under the UAC, which unites cross-attention and mutual self-attention controls in the InfEdit framework. Notably, the cross-attention adapts global refinement and local blending techniques, while the self-attention ensures semantic integrity even amidst non-rigid transformations.

Implications and Future Directions

InfEdit is a methodological leap towards more sophisticated, efficient, and real-time-capable image editing solutions. By removing the reliance on inversion and integrating a robust attention control framework, InfEdit holds promise in expanding the horizons of AI-driven visual content creation and manipulation.

Future developments might explore the application of InfEdit within larger multimedia systems and extend its utility in other domains like video editing, further leveraging the intersection with LLMs for enhanced multi-modal interactions. Additionally, addressing biases inhering in training datasets remains a crucial area of work, ensuring responsible and equitable AI-driven creativity tools.

In summary, InfEdit provides a compelling paradigm for direct and efficient text-guided image editing, a significant step forward in harnessing diffusion models for real-world, language-driven applications.