DeepSeek Janus Pro model
Here are the contents translated into English:
DeepSeek is a laboratory that has driven the development of multimodal unified models, and recently released a series of research results, including Janus Pro and Janus Flow. These models aim to address some challenges in processing multimodal data.
Janus Pro is a model designed to understand and generate multimodal data, which can output images with a resolution of 384x384, but still exhibit fine details and accurate semantic understanding. This shows that Janus Pro has significant importance in the development of multimodal unified models.
However, Janus Pro also has some limitations, such as limited input resolution and poor performance on certain tasks. But through innovative architectural design and decoupled training strategies, the model proves that “understanding” and “generation” can be achieved separately and optimally within a unified framework.
Janus Flow is a more complex unified model that directly combines the understanding framework based on visual encoder and large language models with the generation framework based on rectified flow. It also adopts some key strategies, such as decoupled encoders and aligned representations, to improve the performance of unified models.
DeepSeek’s research results show that Janus Flow can surpass previous unified models, achieving a total score of 0.63 and outperforming other specific generation models. These achievements give new directions for the development of multimodal unified models and expectations for future technological breakthroughs.
At this time, bigflyingbird expresses gratitude to audience friends for their tolerance and support, hopes to bring more good programs to everyone, and wishes everyone in the new year to be happy and healthy, with all things going well.
Translation
DeepSeek是推动了多模态大一统模型发展的一个实验室，它最近发布了一系列研究成果，包括Janus Pro和Janus Flow。这些模型试图解决多模态数据处理中的一些挑战。
Janus Pro是一个旨在理解和生成多模态数据的模型，能够输出分辨率仅为384×384的图片，但却展现出了细致的细节和准确的语义理解。这表明Janus Pro对多模态大一统模型的发展具有重要意义。
然而，Janus Pro也有一些局限性，如输入分辨率受到限制，对于某些任务表现可能不佳。但是，这个模型通过创新的架构设计和解耦的训练策略，证明了“理解”和“生成”的两个独立任务，可以在一个统一框架下达到各自的最优状态。
Janus Flow是一个更为复杂的大一统模型，它将基于视觉编码器和大语言模型的理解框架与基于校正流Rectified Flow的生成框架直接融合，实现了端到端训练。它还采用了一些关键策略，如解耦编码器、对齐表征等来提高统一模型的性能。
DeepSeek的研究结果表明，JanusFlow能够超越之前的大一统模型，在总体得分上达到了0.63，并超过了其他生成特定模型。这些成果给出了多模态大一统模型的发展新方向，以及对未来技术突破的期待。
在此新年之际，大飞表达了对观众朋友们包容和支持的感谢，希望能为大家带来更多好的节目，也祝愿大家在新的一年里，红红火火，万事如意。
Reference:
https://github.com/deepseek-ai/Janus; https://github.com/deepseek-ai/Janus/blob/main/janus_pro_tech_report.pdf