Despite recent advances in diffusion models, achieving reliable image generation and editing results remains challenging due to the inherent diversity induced by stochastic noise in the sampling process. Particularly, instruction-guided image editing with diffusion models offers user-friendly editing capabilities, yet editing failures, such as background distortion, frequently occur across different attempts. Users often resort to trial and error, adjusting seeds or prompts to achieve satisfactory results, which is inefficient.
While seed selection methods exist for Text-to-Image (T2I) generation, they depend on external verifiers, limiting their applicability, and evaluating multiple seeds increases computational complexity, reducing practicality.
To address this, we first establish a new multiple-seed-based image editing baseline using background consistency scores, achieving Best-of-N performance without supervision. Building on this, we introduce ELECT (Early-timestep Latent Evaluation for Candidate selecTion), a zero-shot framework that selects reliable seeds by estimating background mismatches at early diffusion timesteps, identfying the seed that retains the background while modifying only the foreground. ELECT ranks seed candidates by a background inconsistency score, filtering unsuitable samples early based on background consistency while fully preserving editability. Beyond standalone seed selection, ELECT integrates into instruction-guided editing pipelines and extends to Multimodal Large-Language Models (MLLMs) for joint seed + prompt selection, further improving results when seed selection alone is insufficient. Experiments show that ELECT reduces computational costs (by 41% on average and up to 61%) while improving background consistency and instruction adherence, achieving around 40% success rates in previously failed cases—without any external supervision or training.
