在数字内容创作、增强现实（AR）以及电影后期制作中，改变一张图片的照明条件——也就是“重光照（Relighting）”——一直是一项极具挑战性的任务。虽然近年来扩散模型（Diffusion Models, DM）在图像生成领域大放异彩，但在处理全场景重光照时，往往会陷入“光影玄学”：要么是物体的固有颜色随着光照乱变，要么是阴影的方向完全不符合物理逻辑。

何恺明（Kaiming He）教授带领的麻省理工学院团队，联合哈佛大学研究者，发布了一项名为 Drifting Models（漂移模型）的新研究。这项工作最核心的突破在于：它打破了扩散模型（Diffusion）或流匹配模型（Flow Matching）必须在推理时进行多次迭代的“魔咒”，仅需一次前向计算（1-NFE），就能在 ImageNet 256×256 任务上跑出令人惊叹的性能。

Following a string of controversies stemming from technical hiccups and licensing changes, AI startup Stability AI has announced its latest family of image-generation models. The new Stable Diffusion ...

Diffusion models generate incredible images by learning to reverse the process that, among other things, causes ink to spread through water. Ask DALL·E 2, an image generation system created by OpenAI, ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Black Forest Labs, a startup founded by the original creators of Stable ...

AI startup Stability AI continues to refine its generative AI models in the face of increasing competition — and ethical challenges. Today, Stability AI announced the launch of Stable Diffusion XL 1.0 ...

Membership Inference Authors, Creators & Presenters: Yan Pang (University of Virginia), Tianhao Wang (University of Virginia) PAPER Black-box Membership Inference Attacks against Fine-tuned Diffusion ...