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/cummin.rs

@@ -0,0 +1,117 @@
+use super::*;
+use burn_tensor::{TensorData, Tolerance};
+
+#[test]
+fn should_diff_cummin() {
+    // Simple test to verify cummin gradients work
+    let device = Default::default();
+    let tensor = TestAutodiffTensor::<1>::from_data(TensorData::from([3.0, 2.0, 4.0]), &device)
+        .require_grad();
+
+    let output = tensor.clone().cummin(0);
+    let grads = output.sum().backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    // PyTorch reference: [1.0, 2.0, 0.0]
+    let expected = TensorData::from([1.0, 2.0, 0.0]);
+    grad.to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_cummin_2d() {
+    // Test 2D cummin gradients
+    let device = Default::default();
+    let tensor = TestAutodiffTensor::<2>::from_data(
+        TensorData::from([[3.0, 2.0, 4.0], [5.0, 1.0, 3.0]]),
+        &device,
+    )
+    .require_grad();
+
+    let output = tensor.clone().cummin(1);
+    let grads = output.sum().backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    // PyTorch reference: [[1.0, 2.0, 0.0], [1.0, 2.0, 0.0]]
+    let expected = TensorData::from([[1.0, 2.0, 0.0], [1.0, 2.0, 0.0]]);
+    grad.to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_cummin_duplicate_values() {
+    // Test with duplicate minimum values - critical edge case
+    let device = Default::default();
+    let tensor =
+        TestAutodiffTensor::<1>::from_data(TensorData::from([3.0, 2.0, 2.0, 4.0]), &device)
+            .require_grad();
+
+    let output = tensor.clone().cummin(0);
+    let grads = output.sum().backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    // input:  [3.0, 2.0, 2.0, 4.0]
+    // cummin: [3.0, 2.0, 2.0, 2.0]
+    // PyTorch reference: [1.0, 1.0, 2.0, 0.0]
+    // Position 2 gets grad from itself + position 3
+    let expected = TensorData::from([1.0, 1.0, 2.0, 0.0]);
+    grad.to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_cummin_all_same() {
+    // Test with all same values
+    let device = Default::default();
+    let tensor = TestAutodiffTensor::<1>::from_data(TensorData::from([2.0, 2.0, 2.0]), &device)
+        .require_grad();
+
+    let output = tensor.clone().cummin(0);
+    let grads = output.sum().backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    // PyTorch reference: [1.0, 1.0, 1.0]
+    // Each position matches cummin, so each gets its own gradient
+    let expected = TensorData::from([1.0, 1.0, 1.0]);
+    grad.to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_cummin_decreasing() {
+    // Test with decreasing sequence
+    let device = Default::default();
+    let tensor =
+        TestAutodiffTensor::<1>::from_data(TensorData::from([5.0, 4.0, 3.0, 2.0]), &device)
+            .require_grad();
+
+    let output = tensor.clone().cummin(0);
+    let grads = output.sum().backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    // PyTorch reference: [1.0, 1.0, 1.0, 1.0]
+    // Each position is a new minimum
+    let expected = TensorData::from([1.0, 1.0, 1.0, 1.0]);
+    grad.to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}
+
+#[test]
+fn should_diff_cummin_2d_duplicates() {
+    // Test 2D with duplicate values
+    let device = Default::default();
+    let tensor = TestAutodiffTensor::<2>::from_data(
+        TensorData::from([[3.0, 2.0, 2.0, 4.0], [5.0, 1.0, 1.0, 3.0]]),
+        &device,
+    )
+    .require_grad();
+
+    let output = tensor.clone().cummin(1);
+    let grads = output.sum().backward();
+    let grad = tensor.grad(&grads).unwrap();
+
+    // PyTorch reference: [[1.0, 1.0, 2.0, 0.0], [1.0, 1.0, 2.0, 0.0]]
+    let expected = TensorData::from([[1.0, 1.0, 2.0, 0.0], [1.0, 1.0, 2.0, 0.0]]);
+    grad.to_data()
+        .assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
+}