ITstyler: Image-optimized Text-based Style Transfer

Bai, Yunpeng; Liu, Jiayue; Chen, Dong; Yuan, Chun

doi:10.48550/arxiv.2301.10916

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Related Work 21 Neural Style Transfer1

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2023

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“…Though the aforementioned works can produce high-quality stylized images, a content image and a style image are required from the users. Several recent studies [1,7,23,41] contend that in certain scenarios, acquiring a desired style image may be challenging. Therefore, the task of text-guided image style transfer has been introduced, which substitutes the style image with a natural language sentence that conveys the desired stylistic attributes.…”

Section: Related Work 21 Neural Style Transfermentioning

confidence: 99%

ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors

Chen,

Pan,

Yao

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Figure 1: In this work, we explore a new task of text-driven stylized image generation, i.e., directly generating stylized images based on style images and text prompts that describe the content. A simple solution for this task is to combine a text-to-image model (text ⇒ image) and a style transfer network (content image ⇒ stylized image) in a two-stage manner. In contrast, our ControlStyle unifies both stages into one diffusion process, leading to high-fidelity stylized images with better visual quality.

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Section: Related Work 21 Neural Style Transfermentioning

confidence: 99%