simplify interpolate function and layer

src/refiners/fluxion/layers/sampling.py

@@ -16,15 +16,26 @@ class Interpolate(Module):
     This layer wraps [`torch.nn.functional.interpolate`][torch.nn.functional.interpolate].
     """
 
-    def __init__(self) -> None:
+    def __init__(
+        self,
+        mode: str = "nearest",
+        antialias: bool = False,
+    ) -> None:
         super().__init__()
+        self.mode = mode
+        self.antialias = antialias
 
     def forward(
         self,
         x: Tensor,
         shape: Size,
     ) -> Tensor:
-        return interpolate(x, shape)
+        return interpolate(
+            x=x,
+            size=shape,
+            mode=self.mode,
+            antialias=self.antialias,
+        )
 
 
 class Downsample(Chain):

src/refiners/fluxion/utils.py

@@ -40,12 +40,18 @@ def pad(x: Tensor, pad: Iterable[int], value: float = 0.0, mode: str = "constant
     return _pad(input=x, pad=pad, value=value, mode=mode)  # type: ignore
 
 
-def interpolate(x: Tensor, factor: float | torch.Size, mode: str = "nearest") -> Tensor:
-    return (
-        _interpolate(x, scale_factor=factor, mode=mode)
-        if isinstance(factor, float | int)
-        else _interpolate(x, size=factor, mode=mode)
-    )  # type: ignore
+def interpolate(
+    x: Tensor,
+    size: torch.Size,
+    mode: str = "nearest",
+    antialias: bool = False,
+) -> Tensor:
+    return _interpolate(  # type: ignore
+        input=x,
+        size=size,
+        mode=mode,
+        antialias=antialias,
+    )
 
 
 # Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py

@@ -228,7 +228,7 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
 
         mask_tensor = torch.tensor(data=np.array(object=mask).astype(dtype=np.float32) / 255.0).to(device=self.device)
         mask_tensor = (mask_tensor > 0.5).unsqueeze(dim=0).unsqueeze(dim=0).to(dtype=self.dtype)
-        self.mask_latents = interpolate(x=mask_tensor, factor=torch.Size(latents_size))
+        self.mask_latents = interpolate(x=mask_tensor, size=torch.Size(latents_size))
 
         init_image_tensor = image_to_tensor(image=target_image, device=self.device, dtype=self.dtype) * 2 - 1
         masked_init_image = init_image_tensor * (1 - mask_tensor)

src/refiners/foundationals/segment_anything/model.py

@@ -232,9 +232,9 @@ class SegmentAnything(fl.Chain):
         Returns:
             The postprocessed masks.
         """
-        masks = interpolate(masks, factor=torch.Size((self.image_size, self.image_size)), mode="bilinear")
+        masks = interpolate(masks, size=torch.Size((self.image_size, self.image_size)), mode="bilinear")
         masks = masks[..., : target_size[0], : target_size[1]]  # remove padding added at `preprocess_image` time
-        masks = interpolate(masks, factor=torch.Size(original_size), mode="bilinear")
+        masks = interpolate(masks, size=torch.Size(original_size), mode="bilinear")
         return masks