用标签控制虚拟试衣生成，让服装图像更真实可控。
Controllable Clothing: Precise Labels and Generation for Virtual Try-On with Latent Diffusion Models
- 通过新增长度、风格等标签增强图像数据
- 训练适配器实现对生成图像的精确控制
- 适合电商零售场景提升试穿真实性
本文提出一种面向虚拟试衣（VITON）的图像生成引导方法。利用开源AI模型为服装图像添加长度、风格等新标签，构建标签与图像配对的数据集，并训练适配器以实现可控生成。该方法可生成更丰富多样的服装图像，使零售商能确保生成结果贴近实际穿着效果，避免误导消费者。
原文摘要 · Abstract (English)
In this technical report, I present a new method for guiding image generation in the context of Virtual- Try-On (VITON). The proposed method leverages new open source Ai models to augment the image data with labels, such as lengths and styles. By training adapters with these labels paired with images of the garments, the model can produce a more diverse set of images that the user can control. For the end user, such as a retailer, this means that they can assure that the produced image is as true to the true fit as possible, not misleading consumers
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