 Training  **Training Data** We used the following dataset for training the model:  - Approximately 100 million images with Japanese captions, including the Japanese subset of [LAION-5B](https://laion.ai/blog/laion-5b/).  **Training Procedure** Japanese Stable Diffusion has the same architecture as Stable Diffusion and was trained by using Stable Diffusion. Because Stable Diffusion was trained on English dataset and the CLIP tokenizer is basically for English, we had 2 stages to transfer to a language-specific model, inspired by [PITI](https://arxiv.org/abs/2205.12952).  1. Train a Japanese-specific text encoder with our Japanese tokenizer from scratch with the latent diffusion model fixed. This stage is expected to map Japanese captions to Stable Diffusion's latent space.  2. Fine-tune the text encoder and the latent diffusion model jointly. This stage is expected to generate Japanese-style images more.   [//]: 