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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling

Research Multimodal & Generative

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TL;DR - ROMS-IMLE is a minimalist, single-step image generator that pairs Implicit Maximum Likelihood Estimation with a moderately sized convolutional network. It challenges the assumption that high-quality generation requires iterative denoising or transformer architectures.

  • Avoids variational inference, adversarial training, numerical integration, and iterative denoising.
  • Generates samples in one step for fast, parameter-efficient inference.
  • Achieves an FID of 2.56 on ImageNet 256 while maintaining good precision and recall.
  • Suggests gradual noise-to-data transformations are not essential for competitive image generation.

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ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling

arXiv cs.LG Chirag Vashist, Ke Li 2026-07-21 arXiv:2607.19332
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-21 14:38:59.127924 UTC

TL;DR - ROMS-IMLE is a minimalist, single-step image generator that pairs Implicit Maximum Likelihood Estimation with a moderately sized convolutional network. It challenges the assumption that high-quality generation requires iterative denoising or transformer architectures.

  • Avoids variational inference, adversarial training, numerical integration, and iterative denoising.
  • Generates samples in one step for fast, parameter-efficient inference.
  • Achieves an FID of 2.56 on ImageNet 256 while maintaining good precision and recall.
  • Suggests gradual noise-to-data transformations are not essential for competitive image generation.
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