init all dit module with device and dtype for speed up#164
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Summary of Changes
Hello @qzzz95, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request optimizes the initialization of neural network modules within the Diffusion Transformer (DiT) architecture. By passing device and dtype arguments directly to nn.Linear layers during their creation, the model's parameters are instantiated on the correct hardware and with the desired precision from the outset, which can lead to faster model loading and execution.
Highlights
- Direct Device and Data Type Initialization: nn.Linear layers within FluxDiTBlock and DiTBlock are now explicitly initialized with device and dtype arguments.
- Performance Optimization: This change aims to improve performance by ensuring model parameters are created directly on the target device and with the correct data type, reducing the need for subsequent data transfers.
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Code Review
This pull request correctly adds device and dtype parameters to the initialization of several nn.Linear layers within the FluxDiT model. This change is crucial for performance optimization, particularly for speeding up model loading with meta device initialization. The modifications in FluxDoubleTransformerBlock and FluxSingleTransformerBlock are accurate and ensure that all relevant submodules are consistently initialized on the target device and with the specified data type. This is a valuable and well-executed improvement.
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