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Text-to-Image generation (TTI) technologies are advancing rapidly, especiallyin the English language communities. However, English-native TTI modelsinherently carry biases from English world centric training data, which createsa dilemma for development of other language-native TTI models. One commonchoice is fine-tuning the English-native TTI model with translated samples fromnon-English communities. It falls short of fully addressing the model biasproblem. Alternatively, training non-English language native models fromscratch can effectively resolve the English world bias, but diverges from theEnglish TTI communities, thus not able to utilize the strides continuouslygaining in the English TTI communities any more. To build non-English languagenative TTI model meanwhile keep compatability with the English TTI communities,we propose a novel model structure referred as "Bridge Diffusion Model" (BDM).The proposed BDM employs a backbone-branch network structure to learn thenon-English language semantics while keep the latent space compatible with theEnglish-native TTI backbone, in an end-to-end manner. The unique advantages ofthe proposed BDM are that it's not only adept at generating images thatprecisely depict non-English language semantics, but also compatible withvarious English-native TTI plugins, such as different checkpoints, LoRA,ControlNet, Dreambooth, and Textual Inversion, etc. Moreover, BDM canconcurrently generate content seamlessly combining both non-English native andEnglish-native semantics within a single image, fostering cultural interaction.We verify our method by applying BDM to build a Chinese-native TTI model,whereas the method is generic and applicable to any other language.

Bridge Diffusion Model: bridge non-English language-native text-to-image diffusion model with English communities