Spaces:
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Name mask classes from the request, not the canonical label table
The legend showed "label 2" where hepatic tumor should be. The cause is worse than a missing name: the saved CT mask does not use canonical 132-class IDs at all. Upstream's filter_mask_with_organs (scripts/sample_mask.py:154) rewrites each requested organ to its 1-based position in the request: for i in range(len(anatomy_list)): combine_label[combine_label == anatomy_list[i]] = -(i + 1) So [liver, hepatic tumor, spleen, gallbladder] yields mask values 1..4 in that order. Naming those through label_dict.json happened to be right for liver and spleen and wrong for everything else -- "hepatic tumor" landed on dummy1 and rendered as a placeholder, while gallbladder was confidently displayed as "pancreas". Silent mislabelling is the real defect here; the placeholder is just the visible symptom. Every preset was affected: Full chest 5/5 wrong (all five lung lobes shown as liver/spleen/pancreas/kidney), Whole abdomen 9/10, Pelvis organs 3/3, Liver + lesion 2/4. GenerationResul…ca3f97e
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Initial commit: NV-Generate Gradio showcase
A Hugging Face Spaces app that surfaces NVIDIA Medtech's three open-weight 3D medical image generators (CT, MR, MR Brain) under one console-style UI, all running the latest MAISI-v2 rectified-flow variants. Architecture ============ - app.py — Gradio Blocks entrypoint. Hero card landing → per-model workspace switched via gr.Group visibility. Auto-clones the NV-Generate-CTMR upstream repo on first run so the same flow works locally and on HF Spaces. - pipelines/ base.py — GenerationRequest / GenerationResult dataclasses. _runner.py — Shared driver: chdir into upstream, write modified configs into configs/_temp/, invoke scripts.diff_model_infer or scripts.inference, harvest the resulting NIfTI(s). ct.py — Paired image + 132-class mask (rflow-ct). mr.py — Multi-contrast MR image-only (rflow-mr). mr_brain.py — Brain MR multi-sequence (rflow-mr-brain). - viewer/ niivue_embed.py — Render…ab1db83
