Optimizer Abstraction With Multiple Algorithms And Learning Rate Scheduling

via “scheduler-agnostic sampling with multiple algorithm support”

text-to-image model by undefined. 20,22,003 downloads.

Unique: Provides scheduler abstraction enabling algorithm swapping without pipeline changes; supports 8+ sampling strategies (DDPM, DDIM, Euler, DPM++, etc.) with independent step count and noise schedule configuration

vs others: More flexible than fixed sampling algorithms; enables faster inference than DDPM-only models; comparable to other scheduler-agnostic implementations but with more algorithm options and better documentation