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/maxpool1d.rs

@@ -0,0 +1,134 @@
+use super::*;
+use burn_tensor::Tolerance;
+use burn_tensor::module::max_pool1d;
+
+#[test]
+fn test_max_pool1d_simple() {
+    let kernel_size = 4;
+    let padding = 0;
+    let stride = 1;
+    let dilation = 1;
+
+    let device = Default::default();
+    let x = TestAutodiffTensor::from_floats(
+        [[[0.9861, 0.5474, 0.4477, 0.0732, 0.3548, 0.8221]]],
+        &device,
+    )
+    .require_grad();
+    let x_grad_expected =
+        TestAutodiffTensor::<3>::from_floats([[[1., 1., 0., 0., 0., 1.]]], &device);
+
+    let output = max_pool1d(x.clone(), kernel_size, stride, padding, dilation, false);
+    let grads = output.backward();
+
+    // Asserts
+    let x_grad_actual = x.grad(&grads).unwrap();
+    x_grad_expected
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
+}
+
+#[test]
+fn test_max_pool1d_with_dilation() {
+    let kernel_size = 4;
+    let padding = 0;
+    let stride = 1;
+    let dilation = 2;
+
+    let device = Default::default();
+    let x = TestAutodiffTensor::from_floats(
+        [[[
+            0.5388, 0.0676, 0.7122, 0.8316, 0.0653, 0.9154, 0.1536, 0.9089, 0.8016, 0.7518, 0.2073,
+            0.0501, 0.8811, 0.5604, 0.5075, 0.4384, 0.9963, 0.9698, 0.4988, 0.2609, 0.3391, 0.2230,
+            0.4610, 0.5365, 0.6880,
+        ]]],
+        &device,
+    )
+    .require_grad();
+    let x_grad_expected = TestAutodiffTensor::<3>::from_floats(
+        [[[
+            0., 0., 1., 0., 0., 3., 0., 1., 2., 1., 0., 0., 2., 0., 0., 0., 4., 4., 0., 0., 0., 0.,
+            0., 0., 1.,
+        ]]],
+        &device,
+    );
+
+    let output = max_pool1d(x.clone(), kernel_size, stride, padding, dilation, false);
+    let grads = output.backward();
+
+    // Asserts
+    let x_grad_actual = x.grad(&grads).unwrap();
+    x_grad_expected
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
+}
+
+#[test]
+fn test_max_pool1d_complex() {
+    let kernel_size = 4;
+    let padding = 0;
+    let stride = 1;
+    let dilation = 1;
+
+    let device = Default::default();
+    let x = TestAutodiffTensor::from_floats(
+        [[[
+            0.5388, 0.0676, 0.7122, 0.8316, 0.0653, 0.9154, 0.1536, 0.9089, 0.8016, 0.7518, 0.2073,
+            0.0501, 0.8811, 0.5604, 0.5075, 0.4384, 0.9963, 0.9698, 0.4988, 0.2609, 0.3391, 0.2230,
+            0.4610, 0.5365, 0.6880,
+        ]]],
+        &device,
+    )
+    .require_grad();
+    let x_grad_expected = TestAutodiffTensor::<3>::from_floats(
+        [[[
+            0., 0., 0., 2., 0., 4., 0., 2., 1., 0., 0., 0., 4., 0., 0., 0., 4., 1., 1., 0., 0., 0.,
+            1., 1., 1.,
+        ]]],
+        &device,
+    );
+
+    let output = max_pool1d(x.clone(), kernel_size, stride, padding, dilation, false);
+    let grads = output.backward();
+
+    // Asserts
+    let x_grad_actual = x.grad(&grads).unwrap();
+    x_grad_expected
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
+}
+
+#[test]
+fn test_max_pool1d_complex_with_padding() {
+    let kernel_size = 4;
+    let padding = 2;
+    let stride = 1;
+    let dilation = 1;
+
+    let device = Default::default();
+    let x = TestAutodiffTensor::from_floats(
+        [[[
+            0.5388, 0.0676, 0.7122, 0.8316, 0.0653, 0.9154, 0.1536, 0.9089, 0.8016, 0.7518, 0.2073,
+            0.0501, 0.8811, 0.5604, 0.5075, 0.4384, 0.9963, 0.9698, 0.4988, 0.2609, 0.3391, 0.2230,
+            0.4610, 0.5365, 0.6880,
+        ]]],
+        &device,
+    )
+    .require_grad();
+    let x_grad_expected = TestAutodiffTensor::<3>::from_floats(
+        [[[
+            1., 0., 1., 2., 0., 4., 0., 2., 1., 0., 0., 0., 4., 0., 0., 0., 4., 1., 1., 0., 0., 0.,
+            1., 1., 3.,
+        ]]],
+        &device,
+    );
+
+    let output = max_pool1d(x.clone(), kernel_size, stride, padding, dilation, false);
+    let grads = output.backward();
+
+    // Asserts
+    let x_grad_actual = x.grad(&grads).unwrap();
+    x_grad_expected
+        .to_data()
+        .assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
+}