Model card

README.md

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+---
+license: cc-by-4.0
+tags:
+  - electron-microscopy
+  - stem
+  - denoising
+  - flow-matching
+library_name: pytorch
+---
+
+# cimp-denoise weights
+
+Weights for the `denoise` command from the `cimp-denoise` package (code: CIMP-flow). It denoises STEM images by
+rendering a frame as it would look after a longer exposure. The model is a conditional flow-matching U-Net (x2x),
+steered by CMMP, a frozen encoder that describes an acquisition from its metadata or from the image itself.
+
+| file | what it is | trained from |
+|---|---|---|
+| `cmmp.pt` | frozen CMMP encoder pair (image + 7-number acquisition metadata) | CMMP |
+| `x2x.pt` | flow denoiser, metadata mode | `x2x_v2_local` ep500 |
+| `x2x_cmmp.pt` | flow denoiser, CMMP mode (also trained with CMMP image embeddings as sources) | `x2x_v3_imgsrc` ep100 |
+| `supervised.pt` | supervised denoiser; supplies the "clean look" target in CMMP mode | `sup_v1` ep100 |
+
+## Use
+
+```
+pip install cimp-denoise          # or: pip install -e <CIMP-flow checkout>
+denoise frame.emd                 # downloads these files to ~/.cache/cimp-denoise on first use
+```
+
+While this repository is private, set `HF_TOKEN` to a token with read access before the first run.
+
+Defaults: 8 draws × 4 sampler steps, about 5 s per 512 × 512 px and 90 s per 2048 × 2048 px on a desktop GPU.
+Metadata mode (Velox .emd) is told min(recorded dwell, 1 µs) → 16× that. CMMP mode needs only the image.
+
+## Limitations
+
+- Trained on HAADF data from one microscope. BF and DF channels are processed, but the models were not trained on them.
+- Very faint lattice fringes near the noise level can be smoothed away.
+- Images must be at least 128 px per side.