In this paper, we present DiffSketcher, an innovative algorithm that creates vectorized free-hand sketches using natural language input.

Gradient clipping is commonly used in training deep neural networks partly due to its practicability in relieving the exploding gradient problem. Recently, Zhang et al. [2020a] show that clipped (stochastic) Gradient Descent (GD) converges faster than vanilla GD/SGD via introducing a new assumption called (L0,L1)smoothness, which characterizes the violent fluctuation of gradients typically encountered in deep neural networks.

To validate the robustness, we design extensive experiments across diverse prompts in terms of length, format, and content.