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/abs.rs

@@ -0,0 +1,58 @@
+use super::*;
+use burn_tensor::{TensorData, Tolerance, cast::ToElement};
+
+#[test]
+fn should_diff_abs() {
+    let data_1 = TensorData::from([[0.0, -1.0], [3.0, 4.0]]);
+    let data_2 = TensorData::from([[6.0, 7.0], [9.0, -10.0]]);
+
+    let device = Default::default();
+    let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
+    let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
+
+    let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().abs());
+    let tensor_4 = tensor_3.matmul(tensor_2.clone());
+    let grads = tensor_4.backward();
+
+    let grad_1 = tensor_1.grad(&grads).unwrap();
+    let grad_2 = tensor_2.grad(&grads).unwrap();
+
+    let expected = TensorData::from([[71.0, 107.0], [71.0, 107.0]]);
+    grad_1
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+
+    let expected = TensorData::from([[84.0, 42.0], [90.0, 54.0]]);
+    grad_2
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_abs_no_nans() {
+    let data_1 = TensorData::from([[6.0, 7.0], [9.0, -10.0]]);
+    let data_2 = TensorData::from([[0.0, -1.0], [3.0, 4.0]]);
+
+    let device = Default::default();
+    let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
+    let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
+
+    let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().abs());
+    let grads = tensor_3.backward();
+
+    let grad_1 = tensor_1.grad(&grads).unwrap();
+    let grad_2 = tensor_2.grad(&grads).unwrap();
+
+    let expected = TensorData::from([[1.0, 7.0], [1.0, 7.0]]);
+    grad_1
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+
+    let expected = TensorData::from([[0.0, -15.0], [-3.0, -3.0]]);
+    grad_2
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+
+    let contains_nan = grad_2.contains_nan();
+    assert!(!contains_nan.into_scalar().to_bool());
+}