Get Started
Research questionHow can few-step diffusion defer high-resolution denoising without causing transition artifacts?High-resolution denoising dominates the cost of few-step diffusion inference. Moving from low to high resolution late in the trajectory can create distribution mismatch and visible artifacts, while few remaining steps provide little opportunity to correct them.
Diffusion Models
Image Generation
Inference Optimization
Latest papersRecent research connected to this question, newest first.SelfLift: Accelerating Few-Step Diffusion via Self-Recovering Resolution TransitionThe source studies progressive-resolution few-step image diffusion, including a training-free transition-repair variant and an on-policy self-recovery variant. Evidence is reported for FLUX.2-Klein and Z-Image-Turbo, with latency and generation-quality results.research paper · Sep 2, 2026
Related questions
How should diffusion samplers allocate limited neural evaluations without sacrificing generated image quality?How can masked diffusion language models retain generation quality with only a few denoising steps?How can continuous diffusion language models reduce denoising steps without sacrificing text-generation quality?How can diffusion models preserve fine perceptual detail when reconstructing high-resolution images from low-resolution inputs?
Home
Topics
Search
Library