From Stains to RGB: Revisiting Rendering for Cell Painting Generation

Generating Cell Painting images requires balancing computational cost with biological utility. To reduce training costs, some existing approaches render multichannel fluorescence images into RGB and compress them using variational autoencoders (VAEs) pretrained on natural images for latent-space generation. Yet the separate effects of rendering and VAE compression on cellular feature recoverability and their implications for generative training remain unclear. Here we introduce CPGENeval, a benchmark that evaluates seven RGB rendering protocols and five VAEs across two datasets and compares pixel- and latent-space generation under a common training setup. Alongside FID, we measure whether cellular features computed from the original fluorescence channels can be recovered from RGB images and their VAE reconstructions, and whether generated images can be matched to real images of the same siRNA perturbation (perturbation retrieval). We find that (i) renderings that rank best by reconstruction FID (rFID) can leave native cellular features poorly recoverable; (ii) changing the rendering substantially changes generation FID (gFID) for the same generated samples, and even within a fixed rendering, gFID and perturbation retrieval can rank generators differently; and (iii) with appropriate rendering, RGB latent-space generation achieves stronger perturbation retrieval than pixel-space generation under most protocols, in substantially less training time. These findings call for reporting the rendering protocol with every FID, holding it fixed across compared models, and validating it with feature recovery and perturbation retrieval.