(doc/fluxion/utils) add/convert docstrings to mkdocstrings format

src/refiners/fluxion/utils.py

@@ -121,13 +121,21 @@ def images_to_tensor(
 
 
 def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtype: DType | None = None) -> Tensor:
-    """
+    """Convert a PIL Image to a Tensor.
-    Convert a PIL Image to a Tensor.
 
-    If the image is in mode `RGB` the tensor will have shape `[3, H, W]`, otherwise
+    Args:
-    `[1, H, W]` for mode `L` (grayscale) or `[4, H, W]` for mode `RGBA`.
+        image: The image to convert.
+        device: The device to use for the tensor.
+        dtype: The dtype to use for the tensor.
 
-    Values are clamped to the range `[0, 1]`.
+    Returns:
+        The converted tensor.
+
+    Note:
+        If the image is in mode `RGB` the tensor will have shape `[3, H, W]`,
+        otherwise `[1, H, W]` for mode `L` (grayscale) or `[4, H, W]` for mode `RGBA`.
+
+        Values are clamped to the range `[0, 1]`.
     """
     image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype)
 
@@ -147,13 +155,19 @@ def tensor_to_images(tensor: Tensor) -> list[Image.Image]:
 
 
 def tensor_to_image(tensor: Tensor) -> Image.Image:
-    """
+    """Convert a Tensor to a PIL Image.
-    Convert a Tensor to a PIL Image.
 
-    The tensor must have shape `[1, channels, height, width]` where the number of
+    Args:
-    channels is either 1 (grayscale) or 3 (RGB) or 4 (RGBA).
+        tensor: The tensor to convert.
 
-    Expected values are in the range `[0, 1]` and are clamped to this range.
+    Returns:
+        The converted image.
+
+    Note:
+        The tensor must have shape `[1, channels, height, width]` where the number of
+        channels is either 1 (grayscale) or 3 (RGB) or 4 (RGBA).
+
+        Expected values are in the range `[0, 1]` and are clamped to this range.
     """
     assert tensor.ndim == 4 and tensor.shape[0] == 1, f"Unsupported tensor shape: {tensor.shape}"
     num_channels = tensor.shape[1]
@@ -176,20 +190,38 @@ def safe_open(
     framework: Literal["pytorch", "tensorflow", "flax", "numpy"],
     device: Device | str = "cpu",
 ) -> dict[str, Tensor]:
+    """Open a SafeTensor file from disk.
+
+    Args:
+        path: The path to the file.
+        framework: The framework used to save the file.
+        device: The device to use for the tensors.
+
+    Returns:
+        The loaded tensors.
+    """
     framework_mapping = {
         "pytorch": "pt",
         "tensorflow": "tf",
         "flax": "flax",
         "numpy": "numpy",
     }
-    return _safe_open(str(path), framework=framework_mapping[framework], device=str(device))  # type: ignore
+    return _safe_open(
+        str(path),
+        framework=framework_mapping[framework],
+        device=str(device),
+    )  # type: ignore
 
 
 def load_tensors(path: Path | str, /, device: Device | str = "cpu") -> dict[str, Tensor]:
-    """
+    """Load tensors from a file saved with `torch.save` from disk.
-    Load tensors from a file saved with `torch.save` from disk using the `weights_only` mode
+
-    for additional safety (see `torch.load` for more details). Still, *only load data you trust* and
+    Note:
-    favor using `load_from_safetensors`.
+        This function uses the `weights_only` mode of `torch.load` for additional safety.
+
+    Warning:
+        Still, **only load data you trust** and favor using
+        [`load_from_safetensors`](refiners.fluxion.utils.load_from_safetensors) instead.
     """
     # see https://github.com/pytorch/pytorch/issues/97207#issuecomment-1494781560
     with warnings.catch_warnings():
@@ -205,15 +237,41 @@ def load_tensors(path: Path | str, /, device: Device | str = "cpu") -> dict[str,
 
 
 def load_from_safetensors(path: Path | str, device: Device | str = "cpu") -> dict[str, Tensor]:
+    """Load tensors from a SafeTensor file from disk.
+
+    Args:
+        path: The path to the file.
+        device: The device to use for the tensors.
+
+    Returns:
+        The loaded tensors.
+    """
     with safe_open(path=path, framework="pytorch", device=device) as tensors:  # type: ignore
         return {key: tensors.get_tensor(key) for key in tensors.keys()}  # type: ignore
 
 
 def save_to_safetensors(path: Path | str, tensors: dict[str, Tensor], metadata: dict[str, str] | None = None) -> None:
+    """Save tensors to a SafeTensor file on disk.
+
+    Args:
+        path: The path to the file.
+        tensors: The tensors to save.
+        metadata: The metadata to save.
+    """
     _save_file(tensors, path, metadata)  # type: ignore
 
 
 def summarize_tensor(tensor: torch.Tensor, /) -> str:
+    """Summarize a tensor.
+
+    This helper function prints the shape, dtype, device, min, max, mean, std, norm and grad of a tensor.
+
+    Args:
+        tensor: The tensor to summarize.
+
+    Returns:
+        The summary string.
+    """
     info_list = [
         f"shape=({', '.join(map(str, tensor.shape))})",
         f"dtype={str(object=tensor.dtype).removeprefix('torch.')}",