lvyufeng/denoising-diffusion-mindspore
Implementation of Denoising Diffusion Probabilistic Model in MindSpore
Provides a modular U-Net architecture with configurable timesteps and loss functions (L1/L2), integrated with MindSpore's mixed precision training (AMP) and exponential moving average (EMA) for stable convergence. Includes a high-level `Trainer` class supporting gradient accumulation, DDIM acceleration for faster inference, and direct dataset folder integration—eliminating boilerplate training code.
No commits in the last 6 months. Available on PyPI.
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46
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10
Language
Python
License
MIT
Category
Last pushed
Dec 16, 2022
Monthly downloads
21
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0
Dependencies
1
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