  In the realm of fashion design, sketches serve as the canvas for expressing
an artist's distinctive drawing style and creative vision, capturing intricate
details like stroke variations and texture nuances. The advent of
sketch-to-image cross-modal translation technology has notably aided designers.
However, existing methods often compromise these sketch details during image
generation, resulting in images that deviate from the designer's intended
concept. This limitation hampers the ability to offer designers a precise
preview of the final output. To overcome this challenge, we introduce HAIFIT, a
novel approach that transforms sketches into high-fidelity, lifelike clothing
images by integrating multi-scale features and capturing extensive feature map
dependencies from diverse perspectives. Through extensive qualitative and
quantitative evaluations conducted on our self-collected dataset, our method
demonstrates superior performance compared to existing methods in generating
photorealistic clothing images. Our method excels in preserving the distinctive
style and intricate details essential for fashion design applications. In
addition, our method also has obvious advantages in model training and
inference speed, contributing to reducing designers' time costs and improving
design efficiency.
