This study performs an ablation analysis of Vector Quantized Generative Adversarial Networks (VQGANs), concentrating on image-to-image synthesis utilizing a single NVIDIA A100 GPU. The current work explores the nuanced effects of varying critical parameters including the number of epochs, image count, and attributes of codebook vectors and latent dimensions, specifically within the constraint of limited resources. Notably, our focus is pinpointed on the vector quantization loss, keeping other hyperparameters and loss components (GAN loss) fixed. This was done to delve into a deeper understanding of the discrete latent space, and to explore how varying its size affects the reconstruction. Though, our results do not surpass the existing benchmarks, however, our findings shed significant light on VQGAN's behaviour for a smaller dataset, particularly concerning artifacts, codebook size optimization, and comparative analysis with Principal Component Analysis (PCA). The study also uncovers the promising direction by introducing 2D positional encodings, revealing a marked reduction in artifacts and insights into balancing clarity and overfitting.

本研究对使用单个NVIDIA A100 GPU进行图像合成的向量量化生成对抗网络（VQGANs）进行消融分析，重点关注了关键参数（包括训练轮数、图像数量、编码书向量和潜在尺寸等）在有限资源约束下的微妙效应，并特别关注了向量量化损失及离散潜在空间的大小对重建结果的影响，研究还揭示了引入二维位置编码的有望方向，以减少伪影，平衡清晰度和过拟合。

图像合成中的向量量化损失分析：一项用于 VQGAN 的单 GPU 消除研究