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。

稳定服装：通过稳定扩散进行基于服装的生成