  Diffusion models are able to generate photorealistic images in arbitrary
scenes. However, when applying diffusion models to image translation, there
exists a trade-off between maintaining spatial structure and high-quality
content. Besides, existing methods are mainly based on test-time optimization
or fine-tuning model for each input image, which are extremely time-consuming
for practical applications. To address these issues, we propose a new approach
for flexible image translation by learning a layout-aware image condition
together with a text condition. Specifically, our method co-encodes images and
text into a new domain during the training phase. In the inference stage, we
can choose images/text or both as the conditions for each time step, which
gives users more flexible control over layout and content. Experimental
comparisons of our method with state-of-the-art methods demonstrate our model
performs best in both style image translation and semantic image translation
and took the shortest time.
