Join LUXED AI, the best AI community: <a target="_blank" rel="ugc" href="https://discord.gg/HxfP9TnctJ">https://discord.gg/HxfP9TnctJ</a> <img src="https://resouces.modelscope.cn/proxy-image/eab28bf5995ed66879ec21a7af64f18f5a5c91fdc7e5aed28b5d76402fd120d0.png" width="400" />💚 <a target="_blank" rel="ugc" href="https://research.nvidia.com/labs/toronto-ai/chronoedit/">ChronoEdit</a>    ｜    🖥️ <a target="_blank" rel="ugc" href="https://github.com/nv-tlabs/ChronoEdit">GitHub</a>    |   🤗 <a target="_blank" rel="ugc" href="https://huggingface.co/collections/nvidia/chronoedit">Hugging Face</a>   |   🤖 <a target="_blank" rel="ugc" href="https://huggingface.co/spaces/nvidia/ChronoEdit">Gradio Demo</a>   |    📑 <a target="_blank" rel="ugc" href="https://arxiv.org/abs/2510.04290">Paper</a> ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation ChronoEdit-14B enables physics-aware image editing and action-conditioned world simulation through temporal reasoning. It distills priors from a 14B-parameter pretrained video generative model and separates inference into (i) a video reasoning stage for latent trajectory denoising, and (ii) an in-context editing stage for pruning trajectory tokens. ChronoEdit-14B was developed by NVIDIA as part of the ChronoEdit family of multimodal foundation models. This model is ready for commercial use.<img src="https://research.nvidia.com/labs/toronto-ai/chronoedit/assets/method.png" alt="ChronoEdit Method Overview" />Overview of the ChronoEdit pipeline. From right to left, the denoising process begins in the temporal reasoning stage, where the model imagines and denoises a short trajectory of intermediate frames. These intermediate frames act as reasoning tokens, guiding how the edit should unfold in a physically consistent manner. For efficiency, the reasoning tokens are discarded in the subsequent editing frame generation stage, where the target frame is further refined into the final edited image.

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