Memory Efficient Training With Gradient Checkpointing

via “memory-efficient inference with attention slicing and gradient checkpointing”

text-to-image model by undefined. 15,28,067 downloads.

Unique: Provides optional attention slicing and gradient checkpointing as first-class pipeline features, enabling fine-grained memory-compute tradeoffs without code changes; slicing is applied transparently during inference

vs others: More flexible than fixed memory budgets; attention slicing is simpler than custom kernels (xFormers) but less efficient; gradient checkpointing is standard PyTorch but requires explicit enablement