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DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing
Created by
Haebom
Author
Yueming Lyu, Kang Zhao, Bo Peng, Huafeng Chen, Yue Jiang, Yingya Zhang, Jing Dong, Caifeng Shan
Outline
In this paper, we propose a novel concept called CLIP DeltaSpace to solve the efficiency and flexibility problems of text-based image editing. Unlike existing methods that suffer from the difficulties of large amounts of annotation data, optimization per text prompt, and hyperparameter tuning during inference, the DeltaEdit framework leverages the semantic alignment between visual and textual feature differences in CLIP. This enables text-free training and zero-shot inference for various text prompts. We verify the effectiveness and versatility of DeltaEdit through experiments on various generative models including GANs and diffusion models.
Takeaways, Limitations
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Takeaways:
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We present a novel framework that can significantly improve the efficiency of text-based image editing.
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Text-based image editing without collecting large amounts of annotation data.
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Zero-shot inference possible for a variety of text prompts.
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Applicable to various generative models such as GAN and diffusion models.
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Limitations:
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Further theoretical analysis of the concept of CLIP DeltaSpace is needed.
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Need to evaluate generalization performance on various image and text datasets.
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Potential performance degradation for certain types of image editing.
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Because it relies on the CLIP model, limitations of the CLIP model may affect the performance of DeltaEdit.
