Graph Theory and Additive Combinatorics
Exploring Structure and Randomness

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Bias-Variance Trade-Off in Statistics at MIT OpenCourseWare

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Greetings.
As part of our research, we want to write a review article in the field of pathology. Friends who are interested in the 2nd and 3rd places on this topic can participate.

✅ Approximate start time: April 10th.

Journal: scientific reports https://www.nature.com/srep/

Price:
2: $400
3: $300

I will help with complete explanations and how to write each section.

@Raminmousa
@Machine_learn
@Paper4money

Solar Power Generation Forecasting in Europe: A Time Series Analysis Python Code

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📚 Introduction to Linux for Bioinformatics
💥Booklet



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Mathematics for Computer Science



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InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity

20 Mar 2025 · Liming Jiang, Qing Yan, Yumin Jia, Zichuan Liu, Hao Kang, Xin Lu ·

Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust frameworks leveraging DiTs for this task. InfU addresses significant issues of existing methods, such as insufficient identity similarity, poor text-image alignment, and low generation quality and aesthetics. Central to InfU is InfuseNet, a component that injects identity features into the DiT base model via residual connections, enhancing identity similarity while maintaining generation capabilities. A multi-stage training strategy, including pretraining and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample (SPMS) data, further improves text-image alignment, ameliorates image quality, and alleviates face copy-pasting. Extensive experiments demonstrate that InfU achieves state-of-the-art performance, surpassing existing baselines. In addition, the plug-and-play design of InfU ensures compatibility with various existing methods, offering a valuable contribution to the broader community.

Paper: https://arxiv.org/pdf/2503.16418v1.pdf

Code: https://github.com/bytedance/infiniteyou

Dataset: 10,000 People - Human Pose Recognition Data

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