随着扩散模型（Diffusion Models）的迭代演进，图像生成已经日臻成熟。然而，在 多实例图像生成（Multi-Instance Image Generation, MIG） ...

数据集的痛点：很多专注身份的方法都附带了庞大规模的高质量数据集，但是这些数据集基本都没有提供精确的布局与身份标注对，而且奇缺实例较多的复杂场景，限制了模型的训练效果。少数包含布局标注的数据集，除了缺乏复杂场景外这一“通病”外，其参考图像与真值图像之间 ...

On Wednesday, Stability AI released Stable Diffusion XL 1.0 (SDXL), its next-generation open weights AI image synthesis model. It can generate novel images from text descriptions and produces more ...

Here’s what the neolabs are building and why their approaches could unlock new opportunities and cost structures for startups ...

All over the AI field, teams are unlocking new functionality by changing the ways that the models work. Some of this has to do with input compression and changing the memory requirements for LLMs, or ...

The development of large language models (LLMs) is entering a pivotal phase with the emergence of diffusion-based architectures. These models, spearheaded by Inception Labs through its new Mercury ...

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, ...

Diffusion models gradually refine and produce a requested output, sometimes starting from random noise—values generated by the model itself—and sometimes working from user-provided data. Think of ...