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LensErase: New foundation model for image element removal released

Researchers have developed LensErase, a new foundation model capable of removing diverse elements from images, including objects, text, and visual effects. To support this capability, they created LensErase-100M, a dataset comprising over 101 million image pairs. The model adapts a text-to-image model called Lens and incorporates a novel positional encoding method, Mask-Aware RoPE, to integrate source image and erase mask information efficiently. LensErase demonstrates significant improvements across multiple benchmarks, including a newly proposed LensErase-Bench, and utilizes TraceFusion for high-fidelity content preservation. AI

IMPACT This model could enable new applications in image editing and content manipulation, potentially impacting creative industries and digital media.

RANK_REASON The item describes a new research paper detailing a novel foundation model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LensErase: New foundation model for image element removal released

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The item describes a new research paper detailing a novel foundation model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    LensErase: A Foundation Model for Erase Anything

    We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse objects, visual text, watermarks, associated visual effects, multiple-object removal, and a wide range…