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Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models

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arxiv 2406.07251 v3 pith:IF3A7SRB submitted 2024-06-11 cs.CV cs.AI

Athanasios Tragakis , Marco Aversa , Chaitanya Kaul , Roderick Murray-Smith , Daniele Faccio This is my paper
classification cs.CVcs.AI
keywords imagehigherpixelsmithgenerationintroduceresolutionresolutionssample
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders

    cs.CV 2026-01 conditional novelty 6.0 of 10

    UPLiFT shows that iterative 2× feature upsampling with a locally-defined attention operator beats cross-attention-based upsamplers on dense prediction while scaling linearly with token count.

  2. HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A training-free diffusion sampling pipeline combines patch-wise DDIM inversion with wavelet-domain frequency guidance to generate coherent 4096x4096 images from SDXL.

  3. APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A training-free add-on for latent diffusion models that fixes patch statistics and re-schedules noise, improving detail in high-resolution images while reducing sampling steps.

  4. HaineiFRDM: Structure-Preserving Diffusion for Film Restoration under Fast Motion and Diverse Defects

    cs.CV 2025-12 reject novelty 4.0 of 10

    HaineiFRDM combines ControlNet-style guidance, patch-based training, global context fusion, and frequency-domain texture matching to restore high-resolution films on one 24GB GPU, but the manuscript provides no experi...

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