diff --git a/perceptor/losses/lit.py b/perceptor/losses/lit.py
new file mode 100644
index 0000000..c298395
--- /dev/null
+++ b/perceptor/losses/lit.py
@@ -0,0 +1,92 @@
+from pathlib import Path
+import torch
+from lantern import module_device
+import lit
+
+from perceptor.transforms import resize
+from perceptor.losses.interface import LossInterface
+
+
+class LiT(LossInterface):
+    def __init__(
+        self,
+        name="LiT-L16L",
+        cache_dir=Path("models"),
+    ):
+        """
+        LiT image-text similarity
+
+        Args:
+            model_name (str): Name of the model to load. "LiT-B16B_2" or "LiT-L16L"
+            cache_dir (str): Path to the directory where the model is cached.
+        """
+        super().__init__()
+        self.model = lit.LiT(name, cache_dir)
+        self.encodings = None
+        self.weights = None
+
+    @property
+    def device(self):
+        return module_device(self)
+
+    def add_texts_(self, texts, weights=None):
+        return self.add_encodings_(self.model.encode_texts(texts), weights)
+
+    def add_images_(self, images, weights=None):
+        if images.shape[-2:] != self.model.image_size:
+            images = resize(images, out_shape=self.model.image_size)
+        return self.add_encodings_(self.model.encode_images(images), weights)
+
+    def add_encodings_(
+        self,
+        encodings,
+        weights=None,
+    ):
+        if isinstance(weights, list) or isinstance(weights, tuple):
+            weights = torch.tensor(weights)
+        elif weights is None:
+            weights = torch.ones_like(encodings[:, 0])
+
+        if self.encodings is None:
+            self.encodings = torch.nn.Parameter(
+                encodings.to(self.device), requires_grad=False
+            )
+            self.weights = torch.nn.Parameter(
+                weights.to(self.device),
+                requires_grad=False,
+            )
+        else:
+            self.encodings = torch.nn.Parameter(
+                torch.cat([self.encodings, encodings.to(self.device)]),
+                requires_grad=False,
+            )
+            self.weights = torch.nn.Parameter(
+                torch.cat([self.weights, weights.to(self.device)]),
+                requires_grad=False,
+            )
+        return self
+
+    def forward(self, images):
+        if images.shape[-2:] != self.model.image_size:
+            images = resize(images, out_shape=self.model.image_size)
+        image_encodings = self.model.encode_images(images)
+        spherical_distance = (
+            (image_encodings[:, None] - self.encodings[None, :])
+            .norm(dim=2)
+            .div(2)
+            .arcsin()
+            .square()
+            .mul(2)
+        )
+        return (spherical_distance * self.weights).mean()
+
+
+def test_lit():
+    torch.set_grad_enabled(False)
+    loss = LiT().add_texts_(["hello", "world"]).add_images_(torch.randn(1, 3, 256, 256))
+
+    image = torch.randn(1, 3, 256, 256).requires_grad_()
+    with torch.enable_grad():
+        loss(image).backward()
+
+    assert image.grad is not None
diff --git a/pyproject.toml b/pyproject.toml
index 4fe8dbc..d33de28 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "perceptor"
-version = "0.5.13"
+version = "0.5.14"
 description = "Modular image generation library"
 authors = ["Richard Löwenström <samedii@gmail.com>", "dribnet"]
 readme = "README.md"