feat: update workspace paths and enhance gitignore

- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution
- Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory
- Added Cargo.lock to gitignore with appropriate comment
- Reorganized IDE files section in gitignore for better clarity
- Added newline at end of file for proper formatting

crates/stable-diffusion-burn/burn-crates/burn-backend-tests/tests/autodiff/cross.rs

@@ -0,0 +1,103 @@
+use super::*;
+use burn_tensor::{TensorData, Tolerance};
+
+#[cfg(feature = "std")]
+use burn_backend_tests::might_panic;
+
+#[test]
+fn backward_basic() {
+    let device = Default::default();
+    let a = TestAutodiffTensor::<2>::from_data(
+        TensorData::from([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
+        &device,
+    )
+    .require_grad();
+    let b = TestAutodiffTensor::<2>::from_data(
+        TensorData::from([[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]),
+        &device,
+    )
+    .require_grad();
+
+    // Simple cross product; grad is a vector of ones.
+    let c = a.clone().cross(b.clone(), 1);
+    let grads = c.backward();
+
+    let a_grad = a.grad(&grads).unwrap().to_data();
+    let b_grad = b.grad(&grads).unwrap().to_data();
+
+    // For a: b×grad_out, where grad_out = [1,1,1]
+    let expected_a = TensorData::from([[-1.0, 2.0, -1.0], [-1.0, 2.0, -1.0]]);
+    // For b: grad_out×a
+    let expected_b = TensorData::from([[1.0, -2.0, 1.0], [1.0, -2.0, 1.0]]);
+
+    a_grad.assert_approx_eq::<FloatElem>(&expected_a, Tolerance::default());
+    b_grad.assert_approx_eq::<FloatElem>(&expected_b, Tolerance::default());
+}
+
+#[test]
+fn backward_after_sum() {
+    let device = Default::default();
+    let a = TestAutodiffTensor::<2>::from_data(
+        TensorData::from([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
+        &device,
+    )
+    .require_grad();
+    let b = TestAutodiffTensor::<2>::from_data(
+        TensorData::from([[4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]),
+        &device,
+    )
+    .require_grad();
+
+    // Sum reduces to scalar, but the gradient should be the same.
+    let c = a.clone().cross(b.clone(), 1).sum();
+    let grads = c.backward();
+
+    let a_grad = a.grad(&grads).unwrap().to_data();
+    let b_grad = b.grad(&grads).unwrap().to_data();
+
+    let expected_a = TensorData::from([[-1.0, 2.0, -1.0], [-1.0, 2.0, -1.0]]);
+    let expected_b = TensorData::from([[1.0, -2.0, 1.0], [1.0, -2.0, 1.0]]);
+
+    a_grad.assert_approx_eq::<FloatElem>(&expected_a, Tolerance::default());
+    b_grad.assert_approx_eq::<FloatElem>(&expected_b, Tolerance::default());
+}
+
+#[cfg(feature = "std")]
+#[might_panic(reason = "not implemented: Cross product on non-last dimension")]
+#[test]
+fn different_dim() {
+    // Also check when the cross is along a different dimension (e.g. dim 0).
+    let device = Default::default();
+    let a_raw = [[1.0, 4.0, 7.0], [2.0, 5.0, 8.0], [3.0, 6.0, 9.0]];
+    let b_raw = [[9.0, 6.0, 3.0], [8.0, 5.0, 2.0], [7.0, 4.0, 1.0]];
+
+    let a = TestTensor::<2>::from_data(TensorData::from(a_raw), &device);
+    let b = TestTensor::<2>::from_data(TensorData::from(b_raw), &device);
+    // Cross along dim 0. Some backends (for example CubeCL) may not support
+    // cross on non-last dimensions and will intentionally panic with a
+    // message like "Cross product on non-last dimension not yet implemented".
+    // In that case we treat the panic as a skipped test for that backend.
+    let out = a.cross(b.clone(), 0);
+
+    // Manually compute cross of each column vector using raw arrays
+    let expected = [
+        [
+            a_raw[1][0] * b_raw[2][0] - a_raw[2][0] * b_raw[1][0],
+            a_raw[1][1] * b_raw[2][1] - a_raw[2][1] * b_raw[1][1],
+            a_raw[1][2] * b_raw[2][2] - a_raw[2][2] * b_raw[1][2],
+        ],
+        [
+            a_raw[2][0] * b_raw[0][0] - a_raw[0][0] * b_raw[2][0],
+            a_raw[2][1] * b_raw[0][1] - a_raw[0][1] * b_raw[2][1],
+            a_raw[2][2] * b_raw[0][2] - a_raw[0][2] * b_raw[2][2],
+        ],
+        [
+            a_raw[0][0] * b_raw[1][0] - a_raw[1][0] * b_raw[0][0],
+            a_raw[0][1] * b_raw[1][1] - a_raw[1][1] * b_raw[0][1],
+            a_raw[0][2] * b_raw[1][2] - a_raw[1][2] * b_raw[0][2],
+        ],
+    ];
+
+    out.to_data()
+        .assert_approx_eq::<FloatElem>(&TensorData::from(expected), Tolerance::default());
+}