  Image-to-image translation is a technique that focuses on transferring images
from one domain to another while maintaining the essential content
representations. In recent years, image-to-image translation has gained
significant attention and achieved remarkable advancements due to its diverse
applications in computer vision and image processing tasks. In this work, we
propose an innovative method for image translation between different domains.
For high-resolution image translation tasks, we use a grayscale adjustment
method to achieve pixel-level translation. For other tasks, we utilize the
Pix2PixHD model with a coarse-to-fine generator, multi-scale discriminator, and
improved loss to enhance the image translation performance. On the other hand,
to tackle the issue of sparse training data, we adopt model weight
initialization from other task to optimize the performance of the current task.
