Table 1.

Summary table of patch-based and biomedical generative models. Top: The patch-based generative models built upon the GAN methodology; Middle: The patch-based generative models built upon DDM methodology; Bottom; Miscellaneous generative models for biomedical applications.

GAN-based model	Backbone	Patch resolution	Conditional input	Patch → large image generation	Summary
SinGAN [55]	Conv2d blocks	coarse-to-fine	N/A	Input scaling	Adversarial training with a single image.
Coco-GAN [40]	Residual blocks	64 × 64	Coordinate	Feature merging	Synthesize large images by small patches conditioned on local coordinates and latent vectors.
ALIS [57]	StyleGAN2	256 × 256	Coordinate	Latent code interpolation	Interpolation between the learned latent codes with regard to a 2d coordinate system.
InfiniteGAN [41]	StyleGAN2	101 × 101	Coordinate	Padding-free convolution	Introducing a structure synthesizer and padding-free generator.
Anyres-GAN [13]	StyleGAN3	64 × 64	Coordinate	Increasing coordinate sampling rate	Two-stage training paradigm that learns the global information and then learns the patch details.
Diffusion-based model
SinDDM [35]	Residual blocks	coarse-to-fine	learned text codes	Input scaling	Similar to SingGAN, the application of single-image training paradigm.
Patch diffusion [61]	UNet	random	learned text codes	N/A	Reduce the training memory footprint via patch-wise operations and position embedding concatenation.
Patchdiffusion [43]	UNet	64 × 64	learned text codes	N/A	Reshaping the image into non-overlapping grid patches.
Path-dm [18]	UNet	64 × 64	learned text codes and coordinate	Feature collage	Performing the latent representation collage for both the training and inference.
Biomedical generative model
Phenexplain [37]	StyleGAN2	128 × 128,256 × 256	Drug perturbation label	N/A	Condition on drug perturbation with different concentration levels.
GILEA [66]	StyleGAN2	64 × 64	N/A	N/A	Perturbation on real constructed cells by GAN inversion.
Grid-shift diffusion [24]	UNet	coarse-to-fine	N/A	Grid-shift sampling	Synthesis of WSIs by grid-shift technique.
RNA-cdm [11]	UNet	256 × 256	1-D mRNA vector	N/A	Synthesis of WSI tiles using bulk RNA-sequence.
SST-editing [67]	StyleGAN2	128 × 128	1-D mRNA vector	N/A	Reconstruction of cellular morphological images using sub-cellular gene expression patterns.
IST-editing [64]	StyleGAN2	133 × 133	3-D mRNA array	Padding-free convolution and overlapped spatial mRNA array	Reconstruction of the gigapixel mouse pup using sub-cellular gene expression patterns.