Text-Guided Image Editing With Time-Weighted Embedding Mixing
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Solution Overview
Problem
Existing text guided image editing methods require extensive training for each edit, are computationally expensive, and struggle with preserving the subject's identity and controlling edit strength effectively.
Innovation Solution
A method involving an embedding mixer that controls the mixing of base and edit embeddings using a time-dependent weight, ensuring that base embeddings dominate at early stages to maintain identity and edit embeddings influence finer details, combined with cross-attention maps to guide the editing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text guided image editing methods are used to make semantic changes to images, then editing capability is improved, but computational cost and training requirements increase
Solution Approach 1:
The method performs preliminary encoding of text prompts into embeddings before the diffusion process. By pre-computing the embedding representations of both base and edit prompts, the system avoids repeated heavy computations during each editing operation, reducing computational cost while maintaining editing capability
Solution Approach 2:
The method uses cross-attention maps generated from the base image diffusion process as a template for guiding the edit diffusion process. Instead of re-computing attention mechanisms from scratch, the system copies and adapts the cross-attention maps to guide the editing process, significantly reducing computational overhead
2Adaptability or versatility
If existing text guided image editing methods are used, then image modification is achieved, but subject identity preservation deteriorates
Solution Approach 1:
The method applies different embedding mixtures at different diffusion time steps. By controlling the mixing ratio dynamically throughout the diffusion process, the system preserves subject identity in early stages while allowing edit embeddings to influence finer details in later stages, achieving both identity preservation and effective modification
Solution Approach 2:
The mixing ratio between base and edit embeddings is made dynamic rather than static. The system adjusts the proportion of base and edit embeddings based on the diffusion time step, creating a time-dependent mixing strategy that adapts to different stages of the generation process to balance identity preservation and edit application
3Manufacturing precision
If per-edit training is performed to improve editing precision, then editing control is improved, but training time and computational resources increase
Solution Approach 1:
The method uses a universal text encoder and embedding space that works across different editing tasks. By training the text encoder once on diverse text-image pairs, the system achieves precise editing control for multiple different edits without requiring separate training for each edit type, eliminating per-edit training requirements
Solution Approach 2:
The system achieves different editing precisions by changing the parameters of the embedding mixing ratio rather than retraining the model. By adjusting the weight given to base versus edit embeddings, the system can control the strength and precision of edits without any additional training, making precision control a matter of parameter adjustment而非training
Data Source
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AI summary
A computer-implemented method comprising obtaining a base prompt and an edit prompt; converting the base and edit prompts to base and edit embeddings; repeating, for a plurality of iterations, the steps of: determining new edit embeddings based on: the base and edit embeddings, a time step relating to the iteration, and a weight dependent on the time step wherein the weight controls mixing of the base and edit embeddings; inputting the base embeddings into a diffusion model in a base reverse process arranged to update a base latent relating to the base image; and inputting the new edit embeddings into the diffusion model in an edit reverse process arranged to update an edit latent relating to an edited image, wherein cross-attention maps generated from the diffusion model in the base reverse process are input into the diffusion model in the edit reverse process. Finally, converting the edit latent to the edited image; and outputting the edited image.