Aurora introduces a VLM-based agent that converts raw user video edit requests into structured conditioning inputs for a unified diffusion transformer, improving performance on underspecified tasks via a new benchmark.
EditMGT: Unleashing potentials of masked generative transformers in image editing
6 Pith papers cite this work. Polarity classification is still indexing.
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A co-trained adapter framework enables mask-free local editing in DiTs by factorizing edit semantics from spatial location and jointly learning a mask predictor.
UniEditBench unifies image and video editing evaluation with a nine-plus-eight operation taxonomy and cost-effective 4B/8B distilled MLLM evaluators that align with human judgments.
SIGMA generates accurate IML masks via semantic feature differencing and instruction-guided cross-modal refinement, yielding a 1.1M training set that boosts six detectors by 18.34% F1 on five datasets.
LLaDA2.0-Uni unifies multimodal understanding and generation inside one discrete diffusion large language model with a semantic tokenizer, MoE backbone, and diffusion decoder.
Using understanding tasks as direct supervision during post-training improves image generation and editing in unified multimodal models.
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LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
LLaDA2.0-Uni unifies multimodal understanding and generation inside one discrete diffusion large language model with a semantic tokenizer, MoE backbone, and diffusion decoder.