DiT-MoE

Large-scale Parameter Diffusion Transformer Model

CommonProductProgrammingDeep LearningDiffusion Models

DiT-MoE is a diffusion transformer model implemented in PyTorch that can scale up to 16 billion parameters while competing with dense networks and demonstrating highly optimized inference capabilities. It represents cutting-edge technology in deep learning for handling large-scale datasets, carrying significant research and application value.

Provide PyTorch model definitions
Include pre-trained weights
Support training and sampling code
Support large-scale parameter expansion
Optimized inference capabilities
Provide expert routing analysis tools
Include synthetic data generation scripts

\Target audience includes deep learning researchers and developers
particularly those seeking efficient model architectures in fields such as image processing and natural language processing. The DiT-MoE model is especially suited for scenarios involving the handling of large-scale datasets and complex model training due to its high inference capability and large-scale parameter handling capabilities.\

Research projects focused on image generation and style transfer
Used as a foundational model architecture for natural language processing tasks
Serves as an educational tool to help students understand the workings of large-scale neural networks

1. Visit the GitHub page and clone or download the DiT-MoE model code.
2. Set up the runtime environment according to the provided README.md file.
3. Use the provided scripts for model training or sampling.
4. Utilize the expert routing analysis tools to optimize model performance.
5. Adjust configuration files as needed to fit different training or inference tasks.

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DiT-MoE Visit Over Time

Monthly Visits

494758773

Bounce Rate

37.69%

Page per Visit

5.7

Visit Duration

00:06:29

DiT-MoE Visit Trend

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