We present LayerDiffusion, an approach enabling large-scale pretrained latent
diffusion models to generate transparent images. The method allows generation
of single transparent images or of multiple transparent layers. The method
learns a "latent transparency" that encodes alpha channel transparency into the
latent manifold of a pretrained latent diffusion model. It preserves the
production-ready quality of the large diffusion model by regulating the added
transparency as a latent offset with minimal changes to the original latent
distribution of the pretrained model. In this way, any latent diffusion model
can be converted into a transparent image generator by finetuning it with the
adjusted latent space. We train the model with 1M transparent image layer pairs
collected using a human-in-the-loop collection scheme. We show that latent
transparency can be applied to different open source image generators, or be
adapted to various conditional control systems to achieve applications like
foreground/background-conditioned layer generation, joint layer generation,
structural control of layer contents, etc. A user study finds that in most
cases (97%) users prefer our natively generated transparent content over
previous ad-hoc solutions such as generating and then matting. Users also
report the quality of our generated transparent images is comparable to real
commercial transparent assets like Adobe Stock.

我们提出了 LayerDiffusion 方法，使得大规模预训练潜在扩散模型能够生成透明图像。该方法通过将 alpha 通道透明度编码到预训练潜在扩散模型的潜在流形中，学习了 “潜在透明度”。通过调整潜在空间并微调预训练模型，可以将任何潜在扩散模型转换为透明图像生成器。我们通过人机协作收集 1M 个透明图像层对来训练该模型，并展示了潜在透明度在不同开源图像生成器和条件控制系统中的应用。用户研究发现，在大多数情况下（97%），用户更喜欢我们本地生成的透明内容，而不是之前的临时解决方案，如生成和镶嵌。用户还报告称我们生成的透明图像的质量可与 Adobe Stock 等真实商业透明资产相媲美。