  Large Text-to-Image(T2I) diffusion models have shown a remarkable capability
to produce photorealistic and diverse images based on text inputs. However,
existing works only support limited language input, e.g., English, Chinese, and
Japanese, leaving users beyond these languages underserved and blocking the
global expansion of T2I models. Therefore, this paper presents AltDiffusion, a
novel multilingual T2I diffusion model that supports eighteen different
languages. Specifically, we first train a multilingual text encoder based on
the knowledge distillation. Then we plug it into a pretrained English-only
diffusion model and train the model with a two-stage schema to enhance the
multilingual capability, including concept alignment and quality improvement
stage on a large-scale multilingual dataset. Furthermore, we introduce a new
benchmark, which includes Multilingual-General-18(MG-18) and
Multilingual-Cultural-18(MC-18) datasets, to evaluate the capabilities of T2I
diffusion models for generating high-quality images and capturing
culture-specific concepts in different languages. Experimental results on both
MG-18 and MC-18 demonstrate that AltDiffusion outperforms current
state-of-the-art T2I models, e.g., Stable Diffusion in multilingual
understanding, especially with respect to culture-specific concepts, while
still having comparable capability for generating high-quality images. All
source code and checkpoints could be found in
https://github.com/superhero-7/AltDiffuson.
