Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models
This addresses the computational bottleneck for high-resolution image generation, making it accessible with limited hardware, though it is incremental as it builds on existing diffusion models.
The authors tackled the problem of generating high-resolution images with a single GPU by introducing Pixelsmith, a zero-shot text-to-image framework that scales pre-trained diffusion models by up to 1000x, enabling gigapixel image generation without additional cost.
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.