Drag-Based Image Editing With Point-Guided Diffusion Control
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Solution Overview
Problem
Existing image editing techniques using generative models lack fine-grained spatial control, are inefficient, and have low success rates due to lengthy processes like latent-optimization and gradient-based guidance, making them impractical for real-world deployment.
Innovation Solution
A machine learning model is redefined for drag-based editing as a conditional generation process, using a reference-only architecture to preserve image identity and incorporating handle and target point embeddings into self-attention modules, eliminating gradient computations and leveraging off-the-shelf acceleration modules for diffusion models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing image editing techniques using generative models are used, then image editing functionality is achieved, but the process is inefficient with lengthy operations like latent-optimization and gradient-based guidance
Solution Approach 1:
The patent extracts and eliminates the time-consuming gradient computation and latent optimization steps from the traditional image editing pipeline. By reformulating the problem as direct point-based conditional generation without requiring gradient-based guidance, the method removes these bottlenecks while maintaining editing functionality.
Solution Approach 2:
The patent performs preliminary encoding of handle and target points into embeddings that are injected into the diffusion model's self-attention modules before the main generation process. This pre-processing of spatial information allows the model to directly understand drag operations without requiring iterative optimization during editing.
2Manufacturing precision
If existing image editing techniques are used, then basic editing is possible, but fine-grained spatial control is lacking
Solution Approach 1:
The patent implements local quality control by encoding specific handle and target points that correspond to local regions of interest in the image. The point embeddings are injected into self-attention modules at specific locations, enabling precise local manipulation of image content while leaving other regions unaffected.
Solution Approach 2:
The patent introduces point embeddings as an intermediary representation between user drag operations and the diffusion model. These embeddings serve as a mediator that translates simple point-based user input into detailed spatial control signals that the model can process for fine-grained editing.
3Reliability
If existing image editing techniques are used, then editing functionality is achieved, but the success rate is low due to complexity
Solution Approach 1:
The patent replaces the mechanical gradient-based optimization system with a direct embedding-based conditional generation approach. Instead of using gradient descent and iterative optimization, the method substitutes these with pre-computed point embeddings that directly guide the diffusion process, eliminating the complexity of gradient computations.
Solution Approach 2:
The patent changes the parameter representation from continuous gradient fields to discrete point embeddings. By transforming the control signals from gradient-based continuous parameters to fixed-dimensional point embeddings, the system simplifies the editing process while improving reliability through more stable and predictable model behavior.
Data Source
AI summary
The present disclosure describes techniques for implementing drag-based image editing. Feature maps are generated based on latent representations of an image by a first sub-model of a machine learning model. The first sub-model is configured to preserve an identity of the image. Embeddings corresponding to at least one pair of points are generated by a second sub-model of the machine learning model. Each pair of points comprises a handle point and a target point. The handle point identifies an area of the image. The target point indicates a target location to which the area is to be relocated. The feature maps and the embeddings are injected into a third sub-model of the machine learning model to guide a process of generating a target image by the third sub-model. The target image depicts the area of the image relocated at the target location.