Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing

• URL: http://arxiv.org/abs/2504.13490v2
• Date: Sat, 02 Aug 2025 05:53:06 GMT
• Title: Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing
• Abstract summary: ELECT (Early-timestep Latent Evaluation for Candidate Selection) is a framework that selects reliable seeds by estimating background mismatches at early diffusion timesteps.<n>It ranks seed candidates by a background inconsistency score, filtering unsuitable samples early based on background consistency while preserving editability.<n>Experiments show that ELECT reduces computational costs (by 41 percent on average) while improving background consistency and instruction adherence, achieving around 40 percent success rates in previously failed cases without any external supervision or training.
• Score: 32.56049667145546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite recent advances in diffusion models, achieving reliable image generation and editing remains challenging due to the inherent diversity induced by stochastic noise in the sampling process. Instruction-guided image editing with diffusion models offers user-friendly capabilities, yet editing failures, such as background distortion, frequently occur. 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 applicability, and evaluating multiple seeds increases computational complexity. To address this, we first establish a 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, identifying 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 preserving editability. Beyond standalone seed selection, ELECT integrates into instruction-guided editing pipelines and extends to Multimodal Large-Language Models (MLLMs) for joint seed and prompt selection, further improving results when seed selection alone is insufficient. Experiments show that ELECT reduces computational costs (by 41 percent on average and up to 61 percent) while improving background consistency and instruction adherence, achieving around 40 percent success rates in previously failed cases - without any external supervision or training.

Related papers

• Adaptive Inference-Time Scaling via Early-Step Latent Verification for Image Editing [49.40982704974727]
  An unsuitable initial noise may lead to unsatisfactory editing results.<n>Recent inference-time scaling methods address this issue by sampling multiple initial noises and selecting better candidates.<n>We propose VeriLatent, a plug-and-play adaptive inference-time scaling framework with early-step latent verification for image editing.
  arXiv  Detail & Related papers  (2026-06-13T08:30:33Z)
• Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection [8.116206114186392]
  We show that attention dynamics over prompt core tokens, the content-bearing words, strongly predict final generation quality.<n>We introduce Attention-Based Seed Selection (ABSS), a training-free, plug-and-play method that ranks seeds for a given prompt by leveraging cross-attention to core tokens during the denoising process.
  arXiv  Detail & Related papers  (2026-05-19T08:33:10Z)
• Toward Early Quality Assessment of Text-to-Image Diffusion Models [23.306273801765297]
  Recent text-to-image (T2I) diffusion and flow-matching models can produce highly realistic images from natural language prompts.<n>Probe-Select is a plug-in module that enables efficient evaluation of image quality within the generation process.
  arXiv  Detail & Related papers  (2026-03-03T10:25:46Z)
• Image Diffusion Preview with Consistency Solver [86.95276270716477]
  We introduce Diffusion Preview, a novel paradigm employing rapid, low-step sampling to generate preliminary outputs for user evaluation.<n>Existing acceleration methods, including training-free solvers and post-training distillation, struggle to deliver high-quality previews.<n>We propose Consistencyr derived from general linear multistep methods, a lightweight, trainable high-order solver via Reinforcement Learning.
  arXiv  Detail & Related papers  (2025-12-15T17:47:49Z)
• TTSnap: Test-Time Scaling of Diffusion Models via Noise-Aware Pruning [53.52543819839442]
  A prominent approach to test-time scaling for text-to-image diffusion models formulates the problem as a search over multiple noise seeds.<n>We propose test-time scaling with noise-aware pruning (TTSnap), a framework that prunes low-quality candidates without fully denoising them.
  arXiv  Detail & Related papers  (2025-11-27T09:14:26Z)
• Noise Projection: Closing the Prompt-Agnostic Gap Behind Text-to-Image Misalignment in Diffusion Models [9.683618735282414]
  In text-to-image generation, different initial noises induce distinct denoising paths with a pretrained Stable Diffusion (SD) model.<n>While this pattern could output diverse images, some of them may fail to align well with the prompt.<n>We propose a noise projector that applies text-conditioned refinement to the initial noise before denoising.
  arXiv  Detail & Related papers  (2025-10-16T10:14:34Z)
• Seed Selection for Human-Oriented Image Reconstruction via Guided Diffusion [0.0]
  A recent diffusion-based approach generates human-oriented images from machine-oriented images without extra information.<n>It utilizes a single random seed, which may lead to suboptimal image quality.<n>We propose a seed selection method that identifies the optimal seed from multiple candidates to improve image quality.
  arXiv  Detail & Related papers  (2025-05-26T07:29:53Z)
• PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing [63.38854614997581]
  We introduce PostEdit, a method that incorporates a posterior scheme to govern the diffusion sampling process.<n>The proposed PostEdit achieves state-of-the-art editing performance while accurately preserving unedited regions.<n>The method is both inversion- and training-free, necessitating approximately 1.5 seconds and 18 GB of GPU memory to generate high-quality results.
  arXiv  Detail & Related papers  (2024-10-07T09:04:50Z)
• Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis [7.234618871984921]
  An emerging area of research aims to learn deep generative models with limited training data. We propose RS-IMLE, a novel approach that changes the prior distribution used for training. This leads to substantially higher quality image generation compared to existing GAN and IMLE-based methods.
  arXiv  Detail & Related papers  (2024-09-26T00:19:42Z)
• Improved Distribution Matching Distillation for Fast Image Synthesis [54.72356560597428]
  We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images.
  arXiv  Detail & Related papers  (2024-05-23T17:59:49Z)
• TiNO-Edit: Timestep and Noise Optimization for Robust Diffusion-Based Image Editing [12.504661526518234]
  We present TiNO-Edit, an SD-based method that focuses on optimizing the noise patterns and diffusion timesteps during editing. We propose a set of new loss functions that operate in the latent domain of SD, greatly speeding up the optimization. Our method can be easily applied to variations of SD including Textual Inversion and DreamBooth.
  arXiv  Detail & Related papers  (2024-04-17T07:08:38Z)
• Diffusion Posterior Proximal Sampling for Image Restoration [27.35952624032734]
  We present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep.
  arXiv  Detail & Related papers  (2024-02-25T04:24:28Z)
• The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling [78.6155095947769]
  Skip-Tuning is a simple yet surprisingly effective training-free tuning method on the skip connections. Our method can achieve 100% FID improvement for pretrained EDM on ImageNet 64 with only 19 NFEs (1.75) While Skip-Tuning increases the score-matching losses in the pixel space, the losses in the feature space are reduced.
  arXiv  Detail & Related papers  (2024-02-23T08:05:23Z)
• Test-Time Degradation Adaptation for Open-Set Image Restoration [35.94643881619977]
  We propose a test-time degradation adaptation framework for open-set image restoration. Our method achieves comparable even better performance than task-specific methods.
  arXiv  Detail & Related papers  (2023-12-02T13:35:48Z)
• Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning [73.75282761503581]
  We propose DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data. Our experiments on test datasets with distribution shifts and unseen categories demonstrate that DiffTPT improves the zero-shot accuracy by an average of 5.13%.
  arXiv  Detail & Related papers  (2023-08-11T09:36:31Z)
• Filter-Guided Diffusion for Controllable Image Generation [9.576951996461363]
  Filter-Guided Diffusion is a diffusion-based generative model for zero shot image-to-image translation and editing.<n>FGD supports finer control over the strength and frequencies of guidance and can work with non-deterministic samplers to produce greater variety.<n>We conduct extensive quantitative and qualitative experiments to evaluate the performance of FGD in translation tasks and also demonstrate its potential in localized editing when used with masks.
  arXiv  Detail & Related papers  (2023-06-29T17:44:18Z)
• Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping [62.78338049381917]
  Fine-tuning pretrained contextual word embedding models to supervised downstream tasks has become commonplace in natural language processing. We experiment with four datasets from the GLUE benchmark, fine-tuning BERT hundreds of times on each while varying only the random seeds. We find substantial performance increases compared to previously reported results, and we quantify how the performance of the best-found model varies as a function of the number of fine-tuning trials.
  arXiv  Detail & Related papers  (2020-02-15T02:40:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.