title: Text2Layer: Layered Image Generation using Latent Diffusion Model
summary: Layer compositing is one of the most popular image editing workflows among
  both amateurs and professionals. Motivated by the success of diffusion models,
  we explore layer compositing from a layered image generation perspective.
  Instead of generating an image, we propose to generate background, foreground,
  layer mask, and the composed image simultaneously. To achieve layered image
  generation, we train an autoencoder that is able to reconstruct layered images
  and train diffusion models on the latent representation. One benefit of the
  proposed problem is to enable better compositing workflows in addition to the
  high-quality image output. Another benefit is producing higher-quality layer
  masks compared to masks produced by a separate step of image segmentation.
  Experimental results show that the proposed method is able to generate
  high-quality layered images and initiates a benchmark for future work.
paper_authors._id: 64b8a631cf14c2fabe980782
paper_authors.name: Xinyang Zhang
paper_authors._id: 64b8a631cf14c2fabe980783
paper_authors.name: Wentian Zhao
paper_authors._id: 64b8a631cf14c2fabe980784
paper_authors.name: Xin Lu
paper_authors._id: 64b8a631cf14c2fabe980785
paper_authors.name: Jeff Chien
paper_title: Text2Layer: Layered Image Generation using Latent Diffusion Model
paper_summary: Layer compositing is one of the most popular image editing workflows among
  both amateurs and professionals. Motivated by the success of diffusion models,
  we explore layer compositing from a layered image generation perspective.
  Instead of generating an image, we propose to generate background, foreground,
  layer mask, and the composed image simultaneously. To achieve layered image
  generation, we train an autoencoder that is able to reconstruct layered images
  and train diffusion models on the latent representation. One benefit of the
  proposed problem is to enable better compositing workflows in addition to the
  high-quality image output. Another benefit is producing higher-quality layer
  masks compared to masks produced by a separate step of image segmentation.
  Experimental results show that the proposed method is able to generate
  high-quality layered images and initiates a benchmark for future work.
paper_ai_summary: A method using autoencoders and diffusion models generates high-quality layered images, improving compositing workflows and mask quality.
paper_ai_keywords: layer compositing diffusion models layered image generation autoencoder latent representation layer mask image segmentation benchmark