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/div.rs

@@ -0,0 +1,105 @@
+use super::*;
+use burn_tensor::{TensorData, Tolerance};
+
+#[test]
+fn should_diff_div() {
+    let data_1 = TensorData::from([1.0, 7.0]);
+    let data_2 = TensorData::from([4.0, 7.0]);
+
+    let device = Default::default();
+    let tensor_1 = TestAutodiffTensor::<1>::from_data(data_1, &device).require_grad();
+    let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
+
+    let tensor_3 = tensor_1.clone().div(tensor_2.clone());
+    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([0.25, 0.14285715]);
+    grad_1
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+
+    let expected = TensorData::from([-0.0625, -0.14285715]);
+    grad_2
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_div_scalar() {
+    let data = TensorData::from([1.0, 7.0]);
+
+    let tensor = TestAutodiffTensor::<1>::from_data(data, &Default::default()).require_grad();
+    let tensor_out = tensor.clone().div_scalar(4.0);
+
+    let grads = tensor_out.backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    grad.to_data()
+        .assert_eq(&TensorData::from([0.25, 0.25]), false);
+}
+
+#[test]
+fn test_div_complex_1() {
+    let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
+    let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
+    let data_3 = TensorData::from([[2.0, 2.0], [2.0, 2.0]]);
+
+    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 = TestAutodiffTensor::from_data(data_3, &device).require_grad();
+
+    let tensor_4 = tensor_1.clone().div(tensor_2.clone());
+    let tensor_5 = tensor_4.div(tensor_3.clone());
+
+    let grads = tensor_5.backward();
+
+    let grad_1 = tensor_1.grad(&grads).unwrap();
+    let grad_2 = tensor_2.grad(&grads).unwrap();
+    let grad_3 = tensor_3.grad(&grads).unwrap();
+
+    let expected = TensorData::from([[0.1250, 0.07142857], [0.25, 0.16666667]]);
+    grad_1
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+
+    let expected = TensorData::from([[-0.03125, -0.07142857], [-1.6250, 0.16666667]]);
+    grad_2
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+    let expected = TensorData::from([[-0.0625, -0.25], [-1.6250, 0.25]]);
+    grad_3
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn test_div_complex_2() {
+    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());
+    let tensor_4 = tensor_3.div(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 tolerance = Tolerance::default().set_half_precision_absolute(2e-3);
+    let expected = TensorData::from([[2.00, 2.92857146], [1.36666667, 2.0]]);
+    grad_1
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, tolerance);
+
+    let expected = TensorData::from([[0.08333334, 0.09591837], [-0.05555558, -0.06714284]]);
+    grad_2
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&expected, tolerance);
+}