CoTIR fine-tunes a pre-trained image editing model using a differentiable CoT-style objective inspired by Lagrangian optimization to enable single-pass universal image restoration, supported by a new 5.2M-sample benchmark showing improved perceptual quality.
Lumina-omnilv: A unified multimodal framework for general low-level vision
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Task-aware localization via attention cues and feature centroids from source/target streams in IIE models improves non-edit consistency while preserving instruction following.
Step1X-Edit integrates a multimodal LLM with a diffusion decoder, trained on a custom high-quality dataset, to deliver image editing performance that surpasses open-source baselines and approaches proprietary models on the new GEdit-Bench.
citing papers explorer
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Universal Image Restoration via Internalized Chain-of-Thought Reasoning
CoTIR fine-tunes a pre-trained image editing model using a differentiable CoT-style objective inspired by Lagrangian optimization to enable single-pass universal image restoration, supported by a new 5.2M-sample benchmark showing improved perceptual quality.
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Rethinking Where to Edit: Task-Aware Localization for Instruction-Based Image Editing
Task-aware localization via attention cues and feature centroids from source/target streams in IIE models improves non-edit consistency while preserving instruction following.
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Step1X-Edit: A Practical Framework for General Image Editing
Step1X-Edit integrates a multimodal LLM with a diffusion decoder, trained on a custom high-quality dataset, to deliver image editing performance that surpasses open-source baselines and approaches proprietary models on the new GEdit-Bench.