Hi, I am ZhiHan Gao, living in Shantou, China.

I specialize in developing audio, video, and image processing algorithms, and I share my open-source projects on GitHub. If you find my projects useful, please consider buying me a coffee. Your support is greatly appreciated!

Professional Experience

• 👨🏽‍💻 I have worked at leading tech companies including Baidu, KingSoft, and more.
• 📱 Developed algorithms for multiple applications:
  • ToolWiz Photos
  • Mypic-AI Make up Master
  • DOUPAI
• 💡Delivered AI-based technical customization services and successfully implemented and delivered several AI projects.

Research Progress and Achievements

• 🌱 Here are some of my past research endeavors and achievements in deep learning and statistical algorithms:

  • Deep Learning

    • A Trimap-Free Solution for Real-Time Automatic Portrait Matting on Mobile Devices
    • A Robust Optimizer With Accelerated Convergence Capability in Deep Learning
    • A General and Adaptive Robust Loss Structure Scheme
    • A Robust Loss Weighting Solution For Learning Long-Tail Data
    • Image Synthesis and Semantic Manipulation Using Stable Diffusion Networks
    • Stable Diffusion Architecture Optimization And Deployment On Mobile Devices
    • A Robust Solution For Accelerated Training Convergence And Learning Long-Tail Data
    • A Arbitrary Resolution Super Resolution Solution for Real World
    • Accelerate Stable Diffusion FP16 Inference Deployment Optimization with TensorRT
    • Port Stable Diffusion X4 Upscaler To TensorFlow And Support FP16 Inference Deployment
    • Port Stable Diffusion PromptGen (GPT2) To TensorFlow And Support ONNX Inference Deployment
    • Stable Diffusion Architectural Distillation
    • Content-aware 3-view synthesis based on Stable Diffusion in Game Art
    • Super Resolution Solution based on Stable Diffusion
    • Video Editing techniques based on Stable Diffusion
    • Port Stable Diffusion XL 1.0 To TensorFlow And Support FP16 Inference Deployment
    • A Plug-And-Play Algorithm For Asynchronous Inference With Frequency-Domain Decomposable Reconstruction For Arbitrary Visual Scenes
    • Stable Diffusion Inference With PyTorch Weights And More Features Like Stable Diffusion Web UI In Keras 3.x
    • FLUX.1 Support FP16 Inference Deployment and Low Memory Lora Training In PyTorch
    • LLM from Scratch with PyTorch
    • Enhanced FaceFusion: Decoupled Modules and Optimized Inference for Visual Performance
    • Ultra High-Resolution Portrait Retouching
    • Training-Free Universal High-Resolution Synthesis for Any Video Model
    • Chunked Flash Attention in Keras
    • Robustness and Speed, Effortlessly: An Adaptive, Efficient Optimizer for Stable Training
      • Learning-Rate-Free
      • Warmup-Free
      • Normalization-Free
      • Corrected Gradient Accumulation → Large-Batch-Equivalent Performance
      • Long-Tailed Gradient Mitigation
      • Accelerated Convergence
      • Memory-Efficient
    • Loss Regularization: A Novel Approach to Enhance Model Generalization and Convergence
    • A Simple Yet Effective Approach to Multi-Task Learning via Dynamic Loss Weighting
    • A Parameter-Free Weight Regularization Approach
    • Towards Stable Batch Normalization via Adaptive Moving Averages
    • AdamSage: An Adaptive Optimizer for Mixed-Precision and Large-Batch Training
      • Full PyTorch AdamW Inheritance — A drop-in replacement requiring zero code changes.
      • Corrected Gradient Accumulation — Delivers true large-batch-equivalent performance.
      • Learning-Rate-Free — Eliminates a critical hyperparameter.
      • Unified Closure & AMP GradScaler Support — Ensures seamless mixed-precision training.
    • Memory-Efficient Training

  • Statistical Algorithms

    • Real time and embedded implementation of speech enhancement algorithms based on Minimum Mean-Square Error Short-Time Spectral Amplitude estimation (MMSE-STSA)

Collaboration and Contact

• 👯 I’m looking to collaborate on audio and image algorithms
  • 🤔 Reach me on
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• 💬 Any paid technical service or solution consulting
  • 📫 Reach me on mail:
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