✨Step1X-Edit: A Practical Framework for General Image Editing

📝 Summary:
Step1X-Edit is a new image editing model combining multimodal LLM with a diffusion decoder. It significantly outperforms open-source models and approaches the quality of proprietary models like GPT-4o. This bridges the gap in general image editing capabilities.

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.17761
• PDF: https://arxiv.org/pdf/2504.17761
• Github: https://github.com/stepfun-ai/Step1X-Edit

🔹 Models citing this paper:
• https://huggingface.co/stepfun-ai/Step1X-Edit
• https://huggingface.co/stepfun-ai/Step1X-Edit-v1p2
• https://huggingface.co/stepfun-ai/Step1X-Edit-v1p2-preview

✨ Datasets citing this paper:
• https://huggingface.co/datasets/stepfun-ai/GEdit-Bench

✨ Spaces citing this paper:
• https://huggingface.co/spaces/johnnyclem/stepfun-ai-Step1X-Edit
• https://huggingface.co/spaces/Osuii/stepfun-ai-Step1X-Edit
• https://huggingface.co/spaces/Paus/stepfun-ai-Step1X-Edit

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#ImageEditing #AI #LLM #DiffusionModels #ComputerVision

✨EditThinker: Unlocking Iterative Reasoning for Any Image Editor

📝 Summary:
EditThinker proposes a deliberative framework for image editing, simulating human iterative critique and refinement of instructions. It uses an MLLM as a reasoning engine to enhance instruction-following capability. This significantly improves the performance of any image editor.

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05965
• PDF: https://arxiv.org/pdf/2512.05965
• Project Page: https://appletea233.github.io/think-while-edit/

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#ImageEditing #MLLM #AI #Reasoning #ComputerVision

✨SpotEdit: Selective Region Editing in Diffusion Transformers

📝 Summary:
SpotEdit is a training-free framework for selective image editing in diffusion transformers. It avoids reprocessing stable regions by reusing their features, combining them with edited areas. This reduces computation and preserves unchanged regions, enhancing efficiency and precision.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22323
• PDF: https://arxiv.org/pdf/2512.22323
• Project Page: https://biangbiang0321.github.io/SpotEdit.github.io
• Github: https://biangbiang0321.github.io/SpotEdit.github.io

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#ImageEditing #DiffusionModels #ComputerVision #AIResearch #DeepLearning

✨VIBE: Visual Instruction Based Editor

📝 Summary:
VIBE is a compact image editor using a 2B-parameter guidance model and a 1.6B-parameter diffusion model. It achieves high-quality, source-consistent edits with low computational cost, outperforming larger models. VIBE fits in 24GB GPU memory and generates 2K images in 4 seconds.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02242
• PDF: https://arxiv.org/pdf/2601.02242
• Project Page: https://riko0.github.io/VIBE/
• Github: https://github.com/ai-forever/vibe

🔹 Models citing this paper:
• https://huggingface.co/iitolstykh/VIBE-Image-Edit

✨ Spaces citing this paper:
• https://huggingface.co/spaces/iitolstykh/VIBE-Image-Edit-DEMO

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#ImageEditing #DiffusionModels #GenerativeAI #EfficientAI #AI

✨Alterbute: Editing Intrinsic Attributes of Objects in Images

📝 Summary:
Alterbute is a diffusion method for editing intrinsic object attributes like color or shape, while preserving identity and scene context. It uses a relaxed training objective and Visual Named Entities for scalable, identity-preserving supervision, outperforming existing methods.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2601.10714
• PDF: https://arxiv.org/pdf/2601.10714
• Project Page: https://talreiss.github.io/alterbute/
• Github: https://talreiss.github.io/alterbute/

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#Alterbute #DiffusionModels #ImageEditing #ComputerVision #AIResearch

✨PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks

📝 Summary:
PlanViz is a new benchmark evaluating unified multimodal models for image generation and editing in computer-use planning tasks. It features route planning, work diagramming, and web&UI displaying sub-tasks, using a task-adaptive PlanScore to assess correctness, visual quality, and efficiency.

🔹 Publication Date: Published on Feb 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06663
• PDF: https://arxiv.org/pdf/2602.06663
• Project Page: https://github.com/lijunxian111/PlanViz
• Github: https://github.com/lijunxian111/PlanViz/releases/tag/v1

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#MultimodalAI #ImageGeneration #ImageEditing #ComputerVision #Benchmarking

✨From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors

📝 Summary:
PhysicEdit addresses physically implausible image editing by modeling edits as predictive physical state transitions. It uses a dual-thinking diffusion framework guided by a vision-language model, greatly enhancing physical realism.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21778
• PDF: https://arxiv.org/pdf/2602.21778
• Project Page: https://liangbingzhao.github.io/statics2dynamics/
• Github: https://github.com/liangbingzhao/PhysicEdit

✨ Datasets citing this paper:
• https://huggingface.co/datasets/metazlb/PhysicTran38K

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#ImageEditing #DiffusionModels #ComputerVision #PhysicsAI #AIResearch

✨SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing

📝 Summary:
This paper presents SpatialEdit-Bench, a new benchmark and dataset for fine-grained image spatial editing. It introduces SpatialEdit-16B, a model that substantially outperforms prior methods on spatial manipulation, offering precise control over object layout and camera viewpoints.

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04911
• PDF: https://arxiv.org/pdf/2604.04911
• Project Page: https://github.com/EasonXiao-888/SpatialEdit
• Github: https://github.com/EasonXiao-888/SpatialEdit

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#ImageEditing #ComputerVision #DeepLearning #AI #Benchmark

✨RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details

📝 Summary:
RefineAnything is a multimodal diffusion model for region-specific image refinement. It fixes local detail collapse while strictly preserving backgrounds using a Focus-and-Refine strategy and boundary-aware loss. This provides a practical solution for high-precision local editing.

🔹 Publication Date: Published on Apr 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06870
• PDF: https://arxiv.org/pdf/2604.06870
• Project Page: https://limuloo.github.io/RefineAnything/
• Github: https://github.com/limuloo/RefineAnything

==================================

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#DiffusionModels #ImageEditing #ComputerVision #DeepLearning #GenerativeAI

✨Probing Visual Planning in Image Editing Models

📝 Summary:
This paper redefines visual planning as a single-step image transformation using abstract puzzles for evaluation. Their EAR paradigm and AMAZE dataset reveal that current AI models, despite finetuning, cannot match human zero-shot efficiency, highlighting a gap in visual reasoning.

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22868
• PDF: https://arxiv.org/pdf/2604.22868
• Project Page: https://spatigen.github.io/amaze.io/
• Github: https://github.com/spatigen/amaze

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#VisualPlanning #ImageEditing #ComputerVision #AIResearch #MachineLearning