Text-to-image customization, which aims to synthesize text-driven images for
the given subjects, has recently revolutionized content creation. Existing
works follow the pseudo-word paradigm, i.e., represent the given subjects as
pseudo-words and then compose them with the given text. However, the inherent
entangled influence scope of pseudo-words with the given text results in a
dual-optimum paradox, i.e., the similarity of the given subjects and the
controllability of the given text could not be optimal simultaneously. We
present RealCustom that, for the first time, disentangles similarity from
controllability by precisely limiting subject influence to relevant parts only,
achieved by gradually narrowing real text word from its general connotation to
the specific subject and using its cross-attention to distinguish relevance.
Specifically, RealCustom introduces a novel "train-inference" decoupled
framework: (1) during training, RealCustom learns general alignment between
visual conditions to original textual conditions by a novel adaptive scoring
module to adaptively modulate influence quantity; (2) during inference, a novel
adaptive mask guidance strategy is proposed to iteratively update the influence
scope and influence quantity of the given subjects to gradually narrow the
generation of the real text word. Comprehensive experiments demonstrate the
superior real-time customization ability of RealCustom in the open domain,
achieving both unprecedented similarity of the given subjects and
controllability of the given text for the first time. The project page is
this https URL

Text-to-image customization using RealCustom disentangles similarity from controllability by limiting subject influence, achieved through a train-inference decoupled framework, resulting in unprecedented similarity of given subjects and controllability of given text.

RealCustom: 实时开放领域文本到图像定制的实际文本缩窄

RealCustom: Narrowing Real Text Word for Real-Time Open-Domain  Text-to-Image Customization

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: this https URL

本研究将文本反转技术扩展到不同分辨率，学习生成代表概念的伪词，并利用语言进行不同分辨率图像的生成和处理，允许用户根据需要生成不同分辨率的图像，可用于图像生成的多个方面，开源代码位于指定 URL。