Logging
Image logging supports the following formats:- Numpy arrays
- PIL/Pillow images
- PyTorch tensors
- Matplotlib plots or figures
pluto.Image class.
| Parameter | Type | Description |
|---|---|---|
data | Union[str, PILImage.Image, matplotlib.figure.Figure, torch.Tensor, np.ndarray] | The image data to log. Can be a path to an image file, a PIL image, a Matplotlib figure, a PyTorch tensor, or a NumPy array. |
caption | str | A caption for the image. |
Viewing
Logged images appear as gallery cards, grouped by their metric label prefix. When comparing multiple runs, images from each run are shown side-by-side.
Step Navigation
If you log images at multiple training steps, use the step slider below the images to browse through different steps. This is useful for tracking how generated outputs (e.g., sample images, attention maps) evolve over the course of training. When multiple image groups are displayed in the same section, their step sliders can be linked so that changing the step on one group changes all of them simultaneously. Click the lock icon on the step navigator to toggle sync on or off.Fullscreen View
Click the expand button on any image card’s toolbar to open it in fullscreen. The fullscreen view displays the full multi-run comparison at viewport size. Use arrow keys to navigate between steps — the step slider is auto-focused when fullscreen opens.Sync Zoom
Click the settings icon on an image card to open the Image Settings popover. Enable Sync Zoom to persist the zoom level across image fullscreen views within the same group. When enabled, zooming in on one image carries over when you navigate to the next step. Sync Zoom is off by default.