CELL-FM weights

Checkpoints of CELL-FM.

File	Model
condenseq/cellfm_seq2img.bin	CELL-FM CS sequence-to-image generator, trained with IDPs held out (includes the ESM-C 600M encoder)
condenseq/vae.bin	Image VAE, 160x160, 3 down blocks, 4 latent channels
condenseq/vit_cls.bin	ViT condensed/diffuse classifier, 2-channel 160x160 input
condenseq/reference_nucleus.npy	The DAPI channel every CondenSeq generation is conditioned on: (1, 160, 160) float32 in [-1, 1]. CondenSeq protein index 12626, image 0 — the same one the offline seq2img runs used
hpa/cellfm_seq2img.bin	CELL-FM sequence-to-image generator for HPA, trained with test proteins held out; 256x256, 3-channel conditioning (includes the ESM-C 600M encoder)
hpa/cellfm_vs.bin	CELL-FM virtual-staining generator for HPA, trained on all HPA proteins; 256x256, 3-channel conditioning (includes the ESM-C 600M encoder)
hpa/vae.bin	Image VAE at 256x256, the one both hpa/cellfm_seq2img.bin and hpa/cellfm_vs.bin were trained against
hpa/vit.bin	Image-embedding ViT for HPA: 12 layers, 512 hidden, 2048 MLP, 8 heads, patch 4, 256x256 input. Trained to identify the protein in an image over 13,908 classes; the embedding is the representation, not the prediction. Four input channels — protein, nucleus, ER, microtubules
hpa/cellfm_img2seq.bin	CELL-FM image-to-sequence model for HPA, 512x512, 3-channel conditioning (includes the ESM-C 600M encoder)
hpa/vae_512.bin	Image VAE at 512x512, the one hpa/cellfm_img2seq.bin was trained against — a different model from hpa/vae.bin, not a rename
opencell/cellfm_vs.bin	CELL-FM virtual-staining generator for OpenCell, fine-tuned on all OpenCell proteins and meant for those proteins only; 256x256, single nucleus conditioning channel (includes the ESM-C 600M encoder)
opencell/vae.bin	Image VAE at 256x256, the one opencell/cellfm_vs.bin was trained against — OpenCell-finetuned, so not interchangeable with hpa/vae.bin despite the matching shape
opencell/vit.bin	The same ViT fine-tuned on OpenCell, 1,311 classes. Identical backbone to hpa/vit.bin, but its input stem takes two channels — protein and nucleus — where the HPA one takes four, so the two cannot be swapped: the count is fixed in conv_proj and a mismatch fails there
opencell/vs_anchor_nucleus.npy	The nucleus every OpenCell generation is conditioned on: (1, 256, 256) float32 in [-1, 1]. Gene ATG7, crop CID001813_FID00035838_proj_11 — bit-for-bit the conditioning channel of the published offline run
opencell/vs_genes.csv	OpenCell's 1,311 genes: name, protein name, UniProt accession, Ensembl id, localization annotation and sequence. The metadata table minus its image paths
opencell/vs_reference_cells.npz	Four proteins in two matched pairs (POLR1A/SNRPF nuclear, LSM14A/DDX6 both P-body), each with one real OpenCell crop as a (nucleus, protein) float16 pair — the image shown beside the generated one
hpa/anchor_cell.npy	The fixed cell every NLS-screening image is conditioned on: (3, 256, 256) float32 in [-1, 1], channels nucleus, ER, microtubules. HPA gene H3C13, cell crop 1194_B2_2_4
hpa/anchor_masks.npz	Two 256x256 boolean masks over that cell, nucleus and cell; cytoplasm is cell & ~nucleus
hpa/pls_anchor_nls.npz	The cell PLS generation conditions on for nuclear signals: cell (3, 512, 512) nucleus/ER/microtubules and protein (1, 512, 512), float32 in [-1, 1]. HPA gene PPM1G (Nucleoplasm), crop 392_B9_1_11
hpa/pls_anchor_nes.npz	The same for export signals. HPA gene DIAPH1 (Cytosol, Plasma membrane), crop 1608_B3_1_1
hpa/proteome_aa_counts.json	Residue counts over the 12,894 HPA proteins (7,940,784 residues), the proteome baseline the frequency analysis compares against
hpa/pls_reference_nls.csv	The 320 published NLS signals: 20 independent draws at each of 16 tail lengths, 10-25 aa
hpa/pls_reference_nes.csv	The same 320 for export signals

Hyperparameters for the CondenSeq models are set in pipeline.py in the Space and mirror scripts/cell_fm_cs/evaluate_seq2img.sh and scripts/vit_cls_condenseq_img/pretrain.sh in the CELL-FM repository. The HPA hyperparameters are spelled out in notebooks/nls_screening.ipynb and mirror scripts/cell_fm/evaluate_virtual_staining_hpa_dict.sh. The img2seq pair mirrors scripts_local/cell_fm/evaluate_img2seq_hpa_v2.sh: 512 px, sample_size 128, encoder_patch_size 8, img_generator_patch_size 4, 8 attention heads. The OpenCell pair mirrors scripts/cell_fm/evaluate_virtual_staining_opencell.sh: 256 px, sample_size 64, encoder_patch_size 4, img_generator_patch_size 2, 18 attention heads, cell_image nucl — spelled out in notebooks/opencell_vs.ipynb. That single conditioning channel is the one architectural difference a caller can see: the HPA generator takes three.

Each generator must be loaded with the VAE it was trained against — pairing cellfm_img2seq.bin with the 256 px vae.bin gives a latent-size mismatch.