InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting

• URL: http://arxiv.org/abs/2603.23463v1
• Date: Tue, 24 Mar 2026 17:32:55 GMT
• Title: InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting
• Abstract summary: InverFill is a one-step inversion method tailored for inpainting.<n>It injects semantic information from the input masked image into the initial noise, enabling high-fidelity few-step inpainting.
• Score: 14.766289236102345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text-to-image models offer faster generation, but naively applying them to inpainting yields poor harmonization and artifacts between the background and inpainted region. We trace this cause to random Gaussian noise initialization, which under low function evaluations causes semantic misalignment and reduced fidelity. To overcome this, we propose InverFill, a one-step inversion method tailored for inpainting that injects semantic information from the input masked image into the initial noise, enabling high-fidelity few-step inpainting. Instead of training inpainting models, InverFill leverages few-step text-to-image models in a blended sampling pipeline with semantically aligned noise as input, significantly improving vanilla blended sampling and even matching specialized inpainting models at low NFEs. Moreover, InverFill does not require real-image supervision and only adds minimal inference overhead. Extensive experiments show that InverFill consistently boosts baseline few-step models, improving image quality and text coherence without costly retraining or heavy iterative optimization.

Related papers

• ImageRAGTurbo: Towards One-step Text-to-Image Generation with Retrieval-Augmented Diffusion Models [33.09645476860831]
  We propose ImageRAGTurbo, a novel approach to efficiently finetune few-step diffusion models via retrieval augmentation.<n>Given a text prompt, we retrieve relevant text-image pairs from a database and use them to condition the generation process.<n>Experiments show that our approach produces high-fidelity images without compromising latency compared to existing methods.
  arXiv  Detail & Related papers  (2026-02-13T05:59:57Z)
• FreeInpaint: Tuning-free Prompt Alignment and Visual Rationality Enhancement in Image Inpainting [98.04041133839088]
  Text-guided image inpainting endeavors to generate new content within specified regions of images using textual prompts from users.<n>We introduce FreeInpaint, a plug-and-play tuning-free approach that directly optimize the diffusion latents on the fly during inference to improve the faithfulness of the generated images.
  arXiv  Detail & Related papers  (2025-12-24T11:06:26Z)
• PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference [62.72779589895124]
  We make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework. We train a reward model with a dataset we construct, consisting of nearly 51,000 images annotated with human preferences. Experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-10-29T11:49:39Z)
• Fast constrained sampling in pre-trained diffusion models [80.99262780028015]
  We propose an algorithm that enables fast, high-quality generation under arbitrary constraints.<n>Our approach produces results that rival or surpass the state-of-the-art training-free inference methods.
  arXiv  Detail & Related papers  (2024-10-24T14:52:38Z)
• Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction [31.503662384666274]
  In science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain imaging modality. Motivated Score-based diffusion models, due to its empirical success, have emerged as an impressive candidate of an exemplary prior in image reconstruction.
  arXiv  Detail & Related papers  (2024-03-25T15:58:26Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation [53.04220377034574]
  We propose incorporating an analytical image attenuation process into the forward diffusion process for high-quality (un)conditioned image generation.<n>Our method represents the forward image-to-noise mapping as simultaneous textitimage-to-zero mapping and textitzero-to-noise mapping.<n>We have conducted experiments on unconditioned image generation, textite.g., CIFAR-10 and CelebA-HQ-256, and image-conditioned downstream tasks such as super-resolution, saliency detection, edge detection, and image inpainting.
  arXiv  Detail & Related papers  (2023-06-23T18:08:00Z)
• Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration [22.709205282657617]
  Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration. It produces more realistic and detailed images than existing regression-based methods.
  arXiv  Detail & Related papers  (2023-03-20T20:28:17Z)
• High-Fidelity Image Inpainting with GAN Inversion [23.49170140410603]
  In this paper, we propose a novel GAN inversion model for image inpainting, dubbed InvertFill. Within the encoder, the pre-modulation network leverages multi-scale structures to encode more discriminative semantics into style vectors. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions.
  arXiv  Detail & Related papers  (2022-08-25T03:39:24Z)

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.