Description: CSGO
create images in any style with content preserved! (2)
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The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on training free-based methods (e.g., image inversion) due to the scarcity of specific data. In this study, we present a data construction pipeline for content-style-stylized image triplets that generates and automatically cleanses stylized data triplets. Based on this pipeline, we construct a dataset IMAGStyle, the first large-scale st
Given any content image C and style image S, CSGO aims to generate a plausible target image by combining the content of one image with the style of another, ensuring that the target image maintains the original content's semantics while adopting the desired style.The following figure outlines our approach. It consists of two key components: (1) content control for extracting content information, which is injected into the base model via Controlnet and decoupled cross-attention module; and (2) style control