Model card: link the GitHub code repository

README.md

@@ -10,7 +10,8 @@ library_name: pytorch
 
 # cimp-denoise weights
 
-Weights for the `denoise` command from the `cimp-denoise` package (code: CIMP-flow). It denoises STEM images by
+Weights for the `denoise` command from the `cimp-denoise` package
+(code: [github.com/benke97/cimp-denoise](https://github.com/benke97/cimp-denoise)). 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.
 
@@ -24,12 +25,10 @@ steered by CMMP, a frozen encoder that describes an acquisition from its metadat
 ## Use
 
 ```
-pip install cimp-denoise          # or: pip install -e <CIMP-flow checkout>
+pip install git+https://github.com/benke97/cimp-denoise
 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.