diff --git a/gemma/gm/nn/vision/_image.py b/gemma/gm/nn/vision/_image.py
index ce4182e0..a357c14e 100644
--- a/gemma/gm/nn/vision/_image.py
+++ b/gemma/gm/nn/vision/_image.py
@@ -23,7 +23,10 @@
 from kauldron import typing
 import numpy as np
 from PIL import Image
-import tensorflow as tf
+import warnings
+# Removed TensorFlow dependency: use PIL + NumPy + JAX for decoding/resizing.
+# Note: inputs are expected to be image arrays (H,W,C) in uint8 or floats.
+# Keep a small fallback warning if the input array isn't a standard type.
 
 _IMAGE_MEAN = (127.5,) * 3
 _IMAGE_STD = (127.5,) * 3
@@ -69,17 +72,28 @@ def pre_process_image(
   Returns:
     The pre-processed image.
   """
-  # all inputs are expected to have been jpeg compressed.
-  # TODO(eyvinec): we should remove tf dependency.
-  image = jnp.asarray(
-      tf.image.decode_jpeg(tf.io.encode_jpeg(image), channels=3)
-  )
-  image = jax.image.resize(
-      image,
-      shape=(image_height, image_width, 3),
-      method="bilinear",
-      antialias=True,
-  )
+  # Accept numpy / jax arrays or PIL images. Convert to uint8 ndarray for PIL.
+  arr = np.asarray(image)
+
+  # If floats in [0, 1], convert to 0-255 uint8
+  if np.issubdtype(arr.dtype, np.floating):
+    if arr.max() <= 1.0:
+      arr = (arr * 255.0).round().astype(np.uint8)
+    else:
+      arr = np.clip(arr, 0, 255).round().astype(np.uint8)
+  else:
+    arr = arr.astype(np.uint8)
+
+  # PIL expects shape (W, H) ordering for resize tuple; Image.fromarray handles H,W,C.
+  pil = Image.fromarray(arr)
+  # Use bilinear resizing; PIL's LANCZOS is a high-quality downsample filter but
+  # bilinear better matches previous `jax.image.resize(..., method='bilinear')`.
+  pil = pil.resize((image_width, image_height), resample=Image.BILINEAR)
+
+  # Back to numpy -> jax
+  resized = np.asarray(pil).astype(np.float32)
+  image = jnp.asarray(resized)
+
   image = normalize_images(image)
   image = jnp.clip(image, -1, 1)
   return image
diff --git a/gemma/peft/_einsum_utils.py b/gemma/peft/_einsum_utils.py
index 847acb9e..359c4531 100644
--- a/gemma/peft/_einsum_utils.py
+++ b/gemma/peft/_einsum_utils.py
@@ -28,7 +28,7 @@ def get_lora_einsum_str_and_shapes(
 ) -> tuple[str, _Shape, _Shape]:
   """Extract the LoRA decomposition from the original einsum parameters.
 
-  This function reqrites a einsum string `inputs,weights->outputs` into
+  This function rewrites an einsum string `inputs,weights->outputs` into
   `inputs,a,b->outputs`.
 
   Args:
@@ -66,7 +66,7 @@ def get_lora_einsum_str_and_shapes(
 
   lora_einsum_str = f'{inputs},{a_str},{b_str}->{outputs}'
 
-  # This assume there's no elipsis in the weights.
+  # This assumes there's no ellipsis in the weights.
   weights_str_to_dim = dict(zip(weights, weights_shape))
   weights_str_to_dim[rank_dim] = rank
   a_shape = tuple(weights_str_to_dim[c] for c in a_str)