FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866909309499080704 |
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| author | Suresh, Ananda Padhmanabhan Jain, Sanjana Noinongyao, Pavit Ganguly, Ankush Watchareeruetai, Ukrit Samacoits, Aubin |
| author_facet | Suresh, Ananda Padhmanabhan Jain, Sanjana Noinongyao, Pavit Ganguly, Ankush Watchareeruetai, Ukrit Samacoits, Aubin |
| contents | In recent years, language-driven artistic style transfer has emerged as a new type of style transfer technique, eliminating the need for a reference style image by using natural language descriptions of the style. The first model to achieve this, called CLIPstyler, has demonstrated impressive stylisation results. However, its lengthy optimisation procedure at runtime for each query limits its suitability for many practical applications. In this work, we present FastCLIPstyler, a generalised text-based image style transfer model capable of stylising images in a single forward pass for arbitrary text inputs. Furthermore, we introduce EdgeCLIPstyler, a lightweight model designed for compatibility with resource-constrained devices. Through quantitative and qualitative comparisons with state-of-the-art approaches, we demonstrate that our models achieve superior stylisation quality based on measurable metrics while offering significantly improved runtime efficiency, particularly on edge devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_03461 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations Suresh, Ananda Padhmanabhan Jain, Sanjana Noinongyao, Pavit Ganguly, Ankush Watchareeruetai, Ukrit Samacoits, Aubin Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning In recent years, language-driven artistic style transfer has emerged as a new type of style transfer technique, eliminating the need for a reference style image by using natural language descriptions of the style. The first model to achieve this, called CLIPstyler, has demonstrated impressive stylisation results. However, its lengthy optimisation procedure at runtime for each query limits its suitability for many practical applications. In this work, we present FastCLIPstyler, a generalised text-based image style transfer model capable of stylising images in a single forward pass for arbitrary text inputs. Furthermore, we introduce EdgeCLIPstyler, a lightweight model designed for compatibility with resource-constrained devices. Through quantitative and qualitative comparisons with state-of-the-art approaches, we demonstrate that our models achieve superior stylisation quality based on measurable metrics while offering significantly improved runtime efficiency, particularly on edge devices. |
| title | FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2210.03461 |