EditMGT applies masked generative transformers with attention consolidation and region-hold sampling to deliver state-of-the-art localized image editing at 6x the speed of diffusion methods.
UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
VACE unifies reference-to-video generation, video-to-video editing, and masked video-to-video editing in one Diffusion Transformer framework using a Video Condition Unit for inputs and a Context Adapter for task injection.
Develops a synthetic data pipeline for training sequential decomposition in generative image editing, showing robust gains with complexity and sim-to-real transfer via co-training.
A study deriving mathematical formulations and bounds for diffusion editing objectives while empirically comparing methods on fidelity and control metrics and discussing ethical issues.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
citing papers explorer
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Masked Generative Transformer Is What You Need for Image Editing
EditMGT applies masked generative transformers with attention consolidation and region-hold sampling to deliver state-of-the-art localized image editing at 6x the speed of diffusion methods.
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VACE: All-in-One Video Creation and Editing
VACE unifies reference-to-video generation, video-to-video editing, and masked video-to-video editing in one Diffusion Transformer framework using a Video Condition Unit for inputs and a Context Adapter for task injection.
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Towards Robust Sequential Decomposition for Complex Image Editing
Develops a synthetic data pipeline for training sequential decomposition in generative image editing, showing robust gains with complexity and sim-to-real transfer via co-training.
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On the Controllability-Fidelity Frontier in Diffusion Editing
A study deriving mathematical formulations and bounds for diffusion editing objectives while empirically comparing methods on fidelity and control metrics and discussing ethical issues.
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Toward Native Multimodal Modeling: A Roadmap
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.