  In this work, we introduce Pixelsmith, a zero-shot text-to-image generative
framework to sample images at higher resolutions with a single GPU. We are the
first to show that it is possible to scale the output of a pre-trained
diffusion model by a factor of 1000, opening the road for gigapixel image
generation at no additional cost. Our cascading method uses the image generated
at the lowest resolution as a baseline to sample at higher resolutions. For the
guidance, we introduce the Slider, a tunable mechanism that fuses the overall
structure contained in the first-generated image with enhanced fine details. At
each inference step, we denoise patches rather than the entire latent space,
minimizing memory demands such that a single GPU can handle the process,
regardless of the image's resolution. Our experimental results show that
Pixelsmith not only achieves higher quality and diversity compared to existing
techniques, but also reduces sampling time and artifacts. The code for our work
is available at https://github.com/Thanos-DB/Pixelsmith.
