Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
This addresses a key limitation in diffusion-based image editing for users needing high-fidelity manipulations, though it is incremental as it builds on prior inversion techniques.
The paper tackles the problem of text-guided image editing with diffusion models by proposing Editable Noise Map Inversion (ENM Inversion), which searches for optimal noise maps to improve adherence to target prompts while preserving source image content, resulting in outperformance over existing methods in preservation and edit fidelity across various tasks.
Text-to-image diffusion models have achieved remarkable success in generating high-quality and diverse images. Building on these advancements, diffusion models have also demonstrated exceptional performance in text-guided image editing. A key strategy for effective image editing involves inverting the source image into editable noise maps associated with the target image. However, previous inversion methods face challenges in adhering closely to the target text prompt. The limitation arises because inverted noise maps, while enabling faithful reconstruction of the source image, restrict the flexibility needed for desired edits. To overcome this issue, we propose Editable Noise Map Inversion (ENM Inversion), a novel inversion technique that searches for optimal noise maps to ensure both content preservation and editability. We analyze the properties of noise maps for enhanced editability. Based on this analysis, our method introduces an editable noise refinement that aligns with the desired edits by minimizing the difference between the reconstructed and edited noise maps. Extensive experiments demonstrate that ENM Inversion outperforms existing approaches across a wide range of image editing tasks in both preservation and edit fidelity with target prompts. Our approach can also be easily applied to video editing, enabling temporal consistency and content manipulation across frames.