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Text-to-image generation is a significant domain in modern computer visionand has achieved substantial improvements through the evolution of generativearchitectures. Among these, there are diffusion-based models that havedemonstrated essential quality enhancements. These models are generally splitinto two categories: pixel-level and latent-level approaches. We presentKandinsky1, a novel exploration of latent diffusion architecture, combining theprinciples of the image prior models with latent diffusion techniques. Theimage prior model is trained separately to map text embeddings to imageembeddings of CLIP. Another distinct feature of the proposed model is themodified MoVQ implementation, which serves as the image autoencoder component.Overall, the designed model contains 3.3B parameters. We also deployed auser-friendly demo system that supports diverse generative modes such astext-to-image generation, image fusion, text and image fusion, image variationsgeneration, and text-guided inpainting/outpainting. Additionally, we releasedthe source code and checkpoints for the Kandinsky models. Experimentalevaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, markingour model as the top open-source performer in terms of measurable imagegeneration quality.

Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion