LoReS: Low-Resolution Selection for Faster-and-Better Diffusion Generation

Diffusion inference pursues two opposed goals: making one trajectory cheaper, and spending more to select a better one. What divides them is the price of evidence — selection pays only if a candidate can be judged for far less than it costs to produce — so we move selection into the cheap stage of dynamic-resolution generation. Two measurements license this: at a fixed prompt the initial noise alone moves quality across almost the whole attainable range, for generation and editing alike, and a completed low-resolution trajectory agrees with the full-resolution image its handoff produces in content and ranking. LoReS (Low-Resolution noise-trajectory Selection) batches K low-resolution trajectories, ranks previews with one or several standardized verifiers, and refines a top-T pool rather than the argmax, re-choosing the winner on full-resolution evidence refinement exposes for free. Pooling pays because the cheap ranking is informative and cheap to widen: at K=8 its top-1 holds the true best for 32.5% of prompts against 12.5% for random, and T=2,4 give 53.5% and 76.5%. On FLUX.1-dev/DrawBench200, LoReS is the only training-free accelerator we test that is faster and better than the 50-step reference, by a wide margin: at reference latency ImageReward rises 32% with all eight metrics up, where every baseline falls below the reference on ImageReward. LoReS+FreqCa holds seven of eight up at 6.24×, pure LoReS six at 9.16×, and a 0.260× point lifts all eight; at the quality end K=80, T=8 search overtakes Best-of-16 at 2.4× lower latency. Editing repeats on two judges: 7.50→7.70 at 6.26× (Qwen-Image-Edit), 6.21→6.92 at 3.52× (FLUX.1-Kontext). LoReS composes with caching, quantization, and distillation; code will be released.