Update README.md

README.md

@@ -25,7 +25,7 @@ base_model_relation: finetune
 
 ## What the model does
 
-CPathOGen synthesizes a 512 x 512 H&E tile from a cellular spatial map and a morphology/appearance vector. A learned spatial encoder creates latent-space features that are concatenated with the noisy image latent. Blockwise FiLM modules apply the morphology controls during denoising. The released checkpoint is the custom `checkpoint-30000_FID58/checkpoint-30000` model used in the project's inference workflows.
+CPathOGen synthesizes a 512 x 512 H&E tile from a cellular spatial map and a morphology/appearance vector. A learned spatial encoder creates latent-space features that are concatenated with the noisy image latent. Blockwise FiLM modules apply the morphology controls during denoising.
 
 Researchers can keep diffusion noise fixed, change a requested control, and compare outputs of a frozen pathology model on the resulting matched images. This release contains the generator; CellViT++ candidate ranking and independent nucleus analysis require their own software and checkpoints.