diff --git a/gates/bucket_node.py b/gates/bucket_node.py
new file mode 100644
index 0000000..9e62962
--- /dev/null
+++ b/gates/bucket_node.py
@@ -0,0 +1,72 @@
+"""BucketResize node: cover-crop an image (and optional mask) onto a Klein
+training bucket. Pure compute (torch + PIL); no comfy imports in run()."""
+import numpy as np
+import torch
+from PIL import Image
+
+from . import buckets
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+
+def _resize_crop_pil(pil, new_w, new_h, left, top, W, H):
+    pil = pil.resize((new_w, new_h), Image.LANCZOS)
+    return pil.crop((left, top, left + W, top + H))
+
+
+def fit_image(image, W, H):
+    """image [B,H,W,3] -> [B,H,W,3] at (W,H) using the first image's geometry."""
+    b, ih, iw = image.shape[0], image.shape[1], image.shape[2]
+    new_w, new_h, left, top, scale = buckets.cover_crop_params(iw, ih, W, H)
+    out = []
+    for i in range(b):
+        arr = (image[i].cpu().numpy() * 255.0).clip(0, 255).astype("uint8")
+        pil = _resize_crop_pil(Image.fromarray(arr), new_w, new_h, left, top, W, H)
+        out.append(torch.from_numpy(np.array(pil, dtype=np.float32) / 255.0))
+    return torch.stack(out, 0), scale
+
+
+def fit_mask(mask, W, H):
+    b, ih, iw = mask.shape[0], mask.shape[1], mask.shape[2]
+    new_w, new_h, left, top, _ = buckets.cover_crop_params(iw, ih, W, H)
+    out = []
+    for i in range(b):
+        arr = (mask[i].cpu().numpy() * 255.0).clip(0, 255).astype("uint8")
+        pil = _resize_crop_pil(Image.fromarray(arr, mode="L"), new_w, new_h, left, top, W, H)
+        out.append(torch.from_numpy(np.array(pil, dtype=np.float32) / 255.0))
+    return torch.stack(out, 0)
+
+
+class BucketResize:
+    CATEGORY = "Datasete Gates"
+    FUNCTION = "run"
+    RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING")
+    RETURN_NAMES = ("image", "mask", "width", "height", "label")
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "resolution": ("INT", {"default": 1280, "min": 64, "max": 8192}),
+                "divisible": ("INT", {"default": 64, "min": 8, "max": 256}),
+                "max_upscale": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 8.0, "step": 0.1}),
+            },
+            "optional": {"mask": ("MASK",)},
+        }
+
+    def run(self, image, resolution=1280, divisible=64, max_upscale=1.5, mask=None):
+        ih, iw = int(image.shape[1]), int(image.shape[2])
+        W, H = buckets.pick_bucket(iw, ih, resolution, divisible)
+        out_img, scale = fit_image(image, W, H)
+        if scale > max_upscale:
+            print(f"[BucketResize] cover scale {scale:.2f}x exceeds max_upscale "
+                  f"{max_upscale} for {iw}x{ih} -> {W}x{H}")
+        out_mask = fit_mask(mask, W, H) if mask is not None \
+            else torch.zeros((out_img.shape[0], H, W), dtype=torch.float32)
+        return (out_img, out_mask, W, H, f"{W}x{H}")
+
+
+NODE_CLASS_MAPPINGS = {"BucketResize": BucketResize}
+NODE_DISPLAY_NAME_MAPPINGS = {"BucketResize": "Bucket Resize (Klein 9B)"}
diff --git a/tests/test_bucket_node.py b/tests/test_bucket_node.py
new file mode 100644
index 0000000..1966283
--- /dev/null
+++ b/tests/test_bucket_node.py
@@ -0,0 +1,31 @@
+import torch
+from gates import bucket_node as bn
+
+
+def test_square_to_1280():
+    out, m, w, h, label = bn.BucketResize().run(image=torch.rand((1, 1000, 1000, 3)))
+    assert (w, h) == (1280, 1280)
+    assert out.shape == (1, 1280, 1280, 3)
+    assert m.shape == (1, 1280, 1280) and float(m.max()) == 0.0   # no mask -> zeros
+    assert label == "1280x1280"
+
+
+def test_landscape_bucket_shapes():
+    # tensor [B,H,W,3] with H=1000,W=2000 -> aspect 2.0 -> 1792x896
+    out, m, w, h, label = bn.BucketResize().run(image=torch.rand((1, 1000, 2000, 3)))
+    assert (w, h) == (1792, 896)
+    assert out.shape == (1, 896, 1792, 3)
+    assert label == "1792x896"
+
+
+def test_mask_resized_and_aligned():
+    out, m, w, h, _ = bn.BucketResize().run(
+        image=torch.rand((1, 1000, 1000, 3)), mask=torch.ones((1, 1000, 1000)))
+    assert m.shape == (1, 1280, 1280) and float(m.min()) > 0.9
+
+
+def test_outputs_are_on_grid():
+    out, m, w, h, _ = bn.BucketResize().run(
+        image=torch.rand((1, 777, 1333, 3)), resolution=1280, divisible=64)
+    assert w % 64 == 0 and h % 64 == 0
+    assert out.shape[1] == h and out.shape[2] == w