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.

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

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