DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation (2303.06285v1)

Abstract: Text-driven image manipulation remains challenging in training or inference flexibility. Conditional generative models depend heavily on expensive annotated training data. Meanwhile, recent frameworks, which leverage pre-trained vision-LLMs, are limited by either per text-prompt optimization or inference-time hyper-parameters tuning. In this work, we propose a novel framework named \textit{DeltaEdit} to address these problems. Our key idea is to investigate and identify a space, namely delta image and text space that has well-aligned distribution between CLIP visual feature differences of two images and CLIP textual embedding differences of source and target texts. Based on the CLIP delta space, the DeltaEdit network is designed to map the CLIP visual features differences to the editing directions of StyleGAN at training phase. Then, in inference phase, DeltaEdit predicts the StyleGAN's editing directions from the differences of the CLIP textual features. In this way, DeltaEdit is trained in a text-free manner. Once trained, it can well generalize to various text prompts for zero-shot inference without bells and whistles. Code is available at https://github.com/Yueming6568/DeltaEdit.

An Analysis of DeltaEdit: A Text-Free Approach to Text-Driven Image Manipulation

The paper "DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation" explores the challenges and potential solutions for text-driven image manipulation without relying on traditional text-image training pairs. Traditional approaches generally necessitate large datasets of annotated images and texts or leverage pre-trained vision-LLMs with complex inferencing or optimization requirements. DeltaEdit addresses these constraints by proposing a text-free training paradigm that offers flexibility and generalization capabilities.

The core innovation of DeltaEdit lies in leveraging a well-aligned space, denoted as the CLIP delta space, where differences in visual features between images correlate well with differences in textual features between source and target texts. Utilizing the latent space alterations of StyleGAN, the DeltaEdit framework effectively maps these differences to StyleGAN's editing directions, enabling manipulation without direct text-image supervision.

Methodology and Results

The DeltaEdit framework adopts a text-free training strategy. During training, differences in CLIP visual features are used to learn mappings to StyleGAN's latent space, effectively predicting how changes in text description should alter an image. This paradigm enables training without textual annotations but supports zero-shot inferencing based on natural language inputs post-training.

Significantly, DeltaEdit demonstrates the potential for generating accurate and disentangled edits conditioned purely on natural language prompts across various domains including facial edits, non-human objects like cats and churches, and settings shown in LSUN datasets. The approach is evaluated against established systems—TediGAN, and different variants of StyleCLIP—showing substantial improvements in visual realism, attribute disentanglement, and inference efficiency. Numerical results underline its competitive edge, with the framework achieving a Fréchet Inception Distance (FID) of 10.29, indicating high-quality image outputs.

The success of DeltaEdit hinges on its effective training without text-image pairs and generalization to unseen text prompts. The systematic architecture adopted, comprising the Delta Mapper and its associated coarse-to-fine modules, facilitates efficient learning and accurate mapping from image to text changes as demonstrated in comprehensive empirical evaluations.

Implications and Future Directions

DeltaEdit's contribution to the text-driven image manipulation field is twofold: simplifying training requirements by eliminating the dependency on large annotated datasets and improving inference efficiency. These advantages present practical implications for scalable image manipulation systems, particularly beneficial in domains where annotated datasets are challenging to acquire.

Theoretically, the paper underscores the significance of exploring joint visual-textual feature spaces, prompting further inquiry into how such spaces can unify multi-modal data for various generative tasks beyond image manipulation.

In terms of future research, expansion of DeltaEdit could explore refining the aligned feature space further or investigate integration with other generative models beyond StyleGAN for broader application scopes. Moreover, probing into the fine-grained semantic adjustments, as architects in the CLIP space, could yield even more nuanced manipulation systems. Additionally, leveraging advances in model interpretability could lead to an even deeper understanding of why certain attribute manipulations succeed or fail under different conditions within this framework.

Thus, the paper provides a meaningful advancement in text-driven image manipulation methodologies, with practical and theoretical implications that could significantly shape future generative AI technologies.