We extend Textual Inversion to learn pseudo-words that represent a concept at different resolutions. This allows us to generate images that use the concept with different levels of detail and also to manipulate different resolutions using language. Once learned, the user can generate images at different levels of agreement to the original concept; "A photo of $S^*(0)$" produces the exact object while the prompt "A photo of $S^*(0.8)$" only matches the rough outlines and colors. Our framework allows us to generate images that use different resolutions of an image (e.g. details, textures, styles) as separate pseudo-words that can be composed in various ways. We open-soure our code in the following URL: https://github.com/giannisdaras/multires_textual_inversion
翻译:我们扩展了“ 文本转换” 以学习在不同分辨率上代表概念的假字。 这样可以让我们生成使用不同详细度的概念的图像, 并使用语言操控不同分辨率。 一旦学习, 用户可以生成与原始概念一致的不同级别图像; “ $S ⁇ ( 0)$ 的图片” 生成精确对象, 而提示“ $S ⁇ ( 0. 8) $ 的图片” 只匹配粗略大纲和颜色。 我们的框架允许我们生成图像, 将图像的不同分辨率( 如细节、 纹理、 样式) 用作可以不同方式组成的单独的伪词。 我们打开了以下 URL 的代码 : https:// github. com/ giannsdaras/ multures_ intversion 。