DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing (2310.08785v1)

Abstract: Text-guided image editing faces significant challenges to training and inference flexibility. Much literature collects large amounts of annotated image-text pairs to train text-conditioned generative models from scratch, which is expensive and not efficient. After that, some approaches that leverage pre-trained vision-LLMs are put forward to avoid data collection, but they are also limited by either per text-prompt optimization or inference-time hyper-parameters tuning. To address these issues, we investigate and identify a specific space, referred to as CLIP DeltaSpace, where the CLIP visual feature difference of two images is semantically aligned with the CLIP textual feature difference of their corresponding text descriptions. Based on DeltaSpace, we propose a novel framework called DeltaEdit, which maps the CLIP visual feature differences to the latent space directions of a generative model during the training phase, and predicts the latent space directions from the CLIP textual feature differences during the inference phase. And this design endows DeltaEdit with two advantages: (1) text-free training; (2) generalization to various text prompts for zero-shot inference. Extensive experiments validate the effectiveness and versatility of DeltaEdit with different generative models, including both the GAN model and the diffusion model, in achieving flexible text-guided image editing. Code is available at https://github.com/Yueming6568/DeltaEdit.

DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing

The paper "DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing" expands upon earlier work on text-driven image manipulation, specifically the DeltaEdit framework initially introduced at CVPR 2023. Central to this paper is the exploration and identification of a CLIP DeltaSpace, a semantic-aligned feature space designed to enable text-free training and zero-shot inference for various unseen text prompts in image editing contexts.

The revised manuscript offers comprehensive enhancements and novel insights that address limitations and extend the versatility of the initial framework. Key developments in this paper are as follows:

1. Extended Literature Review and Methodology: The document includes a thorough review of recent advancements in text-guided image editing facilitated by diffusion models. This sets the groundwork for a refined analysis of the DeltaSpace concept. By delineating DeltaEdit's application across multiple generative models — namely GANs and diffusion models — the paper illuminates the method's broader applicability and adaptability beyond previous constraints to only GAN models.
2. Introduction of a Style-conditioned Diffusion Model: The authors present a novel style-conditioned diffusion model in Section 4.2, which integrates the Style space from StyleGAN to exert control over the forward and reverse processes within the conditional diffusion model. This innovation enhances detailed reconstruction capabilities and significantly advances image editing quality.
3. Expanded Evaluations and Comparisons: The inclusion of new comparative studies and performance evaluations fortifies the findings, particularly highlighting the robust functionality of the DeltaEdit-G model. The addition of StyleMC as a comparison metric in figures and tables enhances the credibility of the proposed approach. Furthermore, new evaluations provide deeper insights into DeltaEdit-D's functionality, showcasing semantically meaningful latent interpolation, real image reconstruction, style mixing, and adaptable text-guided editing.
4. Comparative Analysis of DeltaEdit-G and DeltaEdit-D: The paper introduces a systematic comparison between DeltaEdit-G and DeltaEdit-D, offering a nuanced understanding of their respective strengths and weaknesses.

The theoretical and practical implications of this research are noteworthy. The development of a semantically-aligned feature space in an image editing framework suggests significant potential for future applications in AI-driven creative processes. Additionally, the zero-shot inference capability underscores promising advancements in deploying AI systems for tasks requiring minimal data-specific training.

In conclusion, this paper contributes meaningfully to the discipline by refining and extending the established DeltaEdit framework, offering a more adaptable and effective tool for text-guided image editing. Future exploration could focus on further optimizing the semantic alignment and practical deployment in diverse real-world contexts, potentially transforming the interface between textual inputs and visual outputs in AI systems.