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cs.CV2026
Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision
Long Cui, Xiaoqian Liu, Qi Qin +4
Existing image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inh…
cs.CV2026
LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
Inclusion AI, Tiwei Bie, Haoxing Chen +15
We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a natively integrated framework. Its…
cs.CV2026
TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows
Zhenglin Cheng, Peng Sun, Jianguo Li +1
Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation. These models are t…