Supports most practical Explicit Runge-Kutta (ERK) methods. Tested on SD1.5, SDXL, and SD3. Decrease it to set more strict tolerances (better results) in exchange for slower inference times. Increase ...
Abstract: Noisy gradient algorithms have emerged as one of the most popular algorithms for distributed optimization with massive data. Choosing proper step-size schedules is an important task to tune ...
Abstract: In non-circular signals, the drawback of the fixed step-size of the widely linear complex-valued least mean square (WL-CNLMS) algorithm results in the algorithm being suboptimal. To address ...
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