【483】NoobAI-XL (NAI-XL) Eps-pred 100%

NoobAI-XL (NAI-XL)

About this version

Introduction to NoobAI-XL EPS 1.0 Vwe by Laxhar Dream Lab

尊敬的各位AIGC爱好者，

Dear All,

很高兴向大家介绍Laxhar Dream Lab推出的:NoobAI-XL EPS 1.0
该模型使用了1270万张图像（最新的Danbooru和e621完整数据集），在32*H100 GPUs上进行了32个epoch的训练（共计38.4亿步），已支持D站solo count 80图的角色和风格。

We are honored to introduce to you the NoobAI-XL EPS 1.0 model launched by Laxhar Dream Lab. This model has been trained on 12.7 million images, including the latest complete datasets from Danbooru and e621, and was trained for 32 epochs on 32 H100 GPUs (a total of 3.84 billion steps), now supporting D station solo count 80 characters and artistic styles.

• 特别鸣谢
• Special Acknowledgments

本版本训练过程中，来自nieta的算法实习生@li_li对trainer发挥了重要作用，在此进行特别鸣谢，感谢li_li作为Laxhar Lab成员的辛勤付出。

Lanyun作为本项目的算力赞助商，其对于开源社区的巨大贡献我们无以言表，Liblib AI在训练过程中提供了测试设备，也一同在此致谢。

同时，解构原典社群的伙伴们也在训练过程中进行了详细的测试与辅助工作，限于人数众多，无法一一鸣谢，在此对各位一并致以最诚挚的感谢!

In the training process of this version, algorithm intern @li_li from nieta played a significant role in the training, for which we express our special thanks here. We appreciate the hard work of li_li as a member of Laxhar Lab.

Lanyun, as the computational sponsor of this project, has made an invaluable contribution to the open-source community, for which we are immensely grateful. Liblib AI also provided testing equipment during the training process, and we extend our thanks to them as well.

At the same time, our partners at DCTN have also carried out detailed testing and auxiliary work during the training process. Due to the large number of people involved, it is not possible to thank each one individually, so we extend our sincerest gratitude to all!

• To do List

Laxhar Dream Lab目前正全力致力于进一步完善SDXL开源生态，后续我们的工作是开发v预测版与noob配套的专用controlNet，以及更多配套插件，提高模型的泛用度，这也是这个模型明明的初衷，即“菜鸟也能用的很好的模型。”

Laxhar Dream Lab is currently fully committed to further improving the SDXL open-source ecosystem. Our next steps include developing a v-prediction version and a dedicated controlNet for NoobAI-XL, as well as additional complementary plugins, to enhance the versatility of the model. This is also the original intention of this model - "A model that even noobs can use well."

我们衷心感谢所有参与过测试和训练的人员，感谢大家的支持，希望开源社区变得越来越好！

We sincerely thank all those who have participated in testing and training, and we appreciate everyone's support. We hope that the open-source community will continue to grow and improve.