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Running on Zero
| title: Agent-GenPath | |
| emoji: 🔬 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.31.0 | |
| app_file: app.py | |
| short_description: Pathology patch synthesis with the Agent-GenPath DiT | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| # Agent-GenPath — Pathology Patch Synthesis | |
| Demo for **"Agent-Driven Generative Priors for Pathology Image Synthesis and Segmentation"** | |
| ([arXiv 2611.06701](https://arxiv.org/abs/2611.06701)). | |
| Samples 256×256 synthetic H&E pathology patches from the paper's pretrained unconditional | |
| flow-matching Diffusion Transformer ([`yscript/agent-genpath`](https://huggingface.co/yscript/agent-genpath)), | |
| using the reference Euler sampler from the official | |
| [Agent-GenPath repo](https://github.com/script-Yang/Agent-GenPath) (Apache-2.0). | |
| The backbone is trained on **TCGA-200K**, a 217,030-patch dataset the paper's pathology agent | |
| system curated from the public TCGA repository; the same backbone transfers to semantic | |
| segmentation with a lightweight head (PathSeg-380K). | |
| The model definition (`dit.py`) is copied verbatim from the official repository. | |