Reversible Diffusion Image Editing for Stable Reconstruction
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Solution Overview
Problem
Existing denoising diffusion models (DDMs) for image editing suffer from significant distortions and instability, leading to inexact reconstructions of images, particularly when portions are intended to remain unchanged.
Innovation Solution
The use of a reversible denoising diffusion model (DDM) process, known as EDICT, which maintains two coupled noise vectors to iteratively update images in a reversible manner, allowing for exact recovery of the original image while making targeted edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing denoising diffusion models are used for image editing, then image generation capability is achieved, but significant distortions and instability occur in unchanged image portions
Solution Approach 1:
The patent segments the image editing process into two distinct phases: a forward diffusion process that introduces controlled noise to enable edits, and a backward diffusion process that recovers the original image structure. This segmentation allows the model to handle editing and reconstruction as separate, optimized operations, resolving the contradiction between edit capability and reconstruction accuracy
Solution Approach 2:
The patent applies the inversion principle by running the diffusion process in reverse: first forward-diffusing the original image to create a noisy intermediate representation, then backward-diffusing to recover the original image with edited portions. This inverted approach ensures that unchanged portions are accurately reconstructed while allowing targeted modifications, directly addressing the distortion and instability problems
2Adaptability or versatility
If diffusion models are used for image editing, then content changes can be made, but computational overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-computing the forward diffusion process and storing the noisy intermediate representations. When editing is needed, the system only needs to perform the backward diffusion process, significantly reducing the computational overhead compared to running the entire diffusion process from scratch. This preliminary preparation enables fast, versatile image editing
Solution Approach 2:
The patent creates a noisy copy of the original image through forward diffusion, then performs editing operations on this copy during backward diffusion. The original image remains intact and can be referenced for unchanged portions, allowing versatile edits without repeatedly processing the entire original image, thus reducing computational overhead
Data Source
AI summary
Embodiments described herein provide systems and methods for image editing, a first copy and a second copy of an input image are generated; noise is iteratively added to the first copy and the second copy by: updating the first copy based on a first inverted output of a denoising diffusion model (DDM) based on the second copy and a first caption and updating the second copy based on a second inverted output of the DDM based on the first copy and the first caption. A resultant noised image is iteratively denoised by a reverse process using the DDM conditioned on a second caption, thereby producing a final image.