We present Kandinsky 3.0, a large-scale text-to-image generation model based
on latent diffusion, continuing the series of text-to-image Kandinsky models
and reflecting our progress to achieve higher quality and realism of image
generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0
leverages a two times larger U-Net backbone, a ten times larger text encoder
and removes diffusion mapping. We describe the architecture of the model, the
data collection procedure, the training technique, and the production system of
user interaction. We focus on the key components that, as we have identified as
a result of a large number of experiments, had the most significant impact on
improving the quality of our model compared to the others. By our side-by-side
comparisons, Kandinsky becomes better in text understanding and works better on
specific domains. Project page: this https URL

Kandinsky 3.0 是一种基于潜在扩散的大规模文本到图像生成模型，通过两倍更大的 U-Net 主干网、十倍更大的文本编码器和去除扩散映射等关键组件的改进，取得了与其他模型相比更高质量和真实感的图像生成效果。