In this paper, we introduce StableGarment, a unified framework to tackle
garment-centric(GC) generation tasks, including GC text-to-image, controllable
GC text-to-image, stylized GC text-to-image, and robust virtual try-on. The
main challenge lies in retaining the intricate textures of the garment while
maintaining the flexibility of pre-trained Stable Diffusion. Our solution
involves the development of a garment encoder, a trainable copy of the
denoising UNet equipped with additive self-attention (ASA) layers. These ASA
layers are specifically devised to transfer detailed garment textures, also
facilitating the integration of stylized base models for the creation of
stylized images. Furthermore, the incorporation of a dedicated try-on
ControlNet enables StableGarment to execute virtual try-on tasks with
precision. We also build a novel data engine that produces high-quality
synthesized data to preserve the model's ability to follow prompts. Extensive
experiments demonstrate that our approach delivers state-of-the-art (SOTA)
results among existing virtual try-on methods and exhibits high flexibility
with broad potential applications in various garment-centric image generation.

StableGarment 是一个统一框架，用于解决以服装为中心的生成任务，包括文本到图像、可控的文本到图像、风格化的文本到图像和鲁棒的虚拟试衣。通过开发一个具有加性自注意力 (ASA) 层的服装编码器和专用的试衣 ControlNet，以及生成高质量合成数据的新数据引擎，我们的方法在虚拟试衣方面达到了最先进的结果，并在各种以服装为中心的图像生成方面展示了高灵活性和广泛的潜在应用。