This blog describes an optimized approach for data-driven AI application architecture where AI calls are made directly from the Oracle vector database.

Generative AI systems are built in blocks, each performing a distinct function and interacting with other blocks to achieve a larger goal.

A user study comparing SDXL and Midjourney v5.1 using the PartiPrompts benchmark reveals that SDXL slightly outperforms Midjourney in terms of prompt adherence

Learn how Diffusion Models use Probability Flow ODEs, stochastic equations, and denoising score matching to generate images from noise.

The SDXL model faces limitations in rendering complex structures, achieving photorealism, addressing biases, handling concept bleeding, and improving long text

This paper outlines potential SDXL improvements, including single-stage models, better text synthesis, transformer optimizations, and enhanced efficiency.

Explore how the integration of an improved autoencoder and a multi-stage training process enhances the image quality in SDXL.

Discover how multi-aspect training in SDXL adapts to real-world image diversity by accommodating varying sizes and aspect ratios.

Explore the micro-conditioning techniques in SDXL that address image size limitations and cropping issues, enhancing image synthesis quality.

Discover the architectural advancements in SDXL, including a heterogeneous distribution of transformer blocks and more.

Explore modular improvements to the Stable Diffusion architecture, designed for flexible integration with any model.

Discover SDXL, a significant advancement in text-to-image synthesis, featuring a larger UNet backbone and novel conditioning techniques.

Authors:

Traditional FID scores may no longer effectively measure the quality of modern generative text-to-image models like SDXL.

Explore the latest advancements in Stable Diffusion technology as presented by the Stability AI Applied Research team.

Nucleoid uses an intermediate language as a bridge between Neural Networks and Symbolic Systems.