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-train/src/lib.rs

@@ -0,0 +1,118 @@
+#![warn(missing_docs)]
+#![cfg_attr(docsrs, feature(doc_cfg))]
+
+//! A library for training neural networks using the burn crate.
+
+#[macro_use]
+extern crate derive_new;
+
+/// The checkpoint module.
+pub mod checkpoint;
+
+pub(crate) mod components;
+
+/// Renderer modules to display metrics and training information.
+pub mod renderer;
+
+/// The logger module.
+pub mod logger;
+
+/// The metric module.
+pub mod metric;
+
+pub use metric::processor::*;
+
+mod learner;
+
+pub use learner::*;
+
+mod evaluator;
+
+pub use evaluator::*;
+
+pub use components::*;
+
+#[cfg(test)]
+pub(crate) type TestBackend = burn_ndarray::NdArray<f32>;
+
+#[cfg(test)]
+pub(crate) mod tests {
+    use crate::TestBackend;
+    use burn_core::{prelude::Tensor, tensor::Bool};
+    use std::default::Default;
+
+    pub type TestAutodiffBackend = burn_autodiff::Autodiff<TestBackend>;
+
+    /// Probability of tp before adding errors
+    pub const THRESHOLD: f64 = 0.5;
+
+    #[derive(Debug, Default)]
+    pub enum ClassificationType {
+        #[default]
+        Binary,
+        Multiclass,
+        Multilabel,
+    }
+
+    /// Sample x Class shaped matrix for use in
+    /// classification metrics testing
+    pub fn dummy_classification_input(
+        classification_type: &ClassificationType,
+    ) -> (Tensor<TestBackend, 2>, Tensor<TestBackend, 2, Bool>) {
+        match classification_type {
+            ClassificationType::Binary => {
+                (
+                    Tensor::from_data([[0.3], [0.2], [0.7], [0.1], [0.55]], &Default::default()),
+                    // targets
+                    Tensor::from_data([[0], [1], [0], [0], [1]], &Default::default()),
+                    // predictions @ threshold=0.5
+                    //                     [[0], [0], [1], [0], [1]]
+                )
+            }
+            ClassificationType::Multiclass => {
+                (
+                    Tensor::from_data(
+                        [
+                            [0.2, 0.8, 0.0],
+                            [0.3, 0.6, 0.1],
+                            [0.7, 0.25, 0.05],
+                            [0.1, 0.15, 0.8],
+                            [0.9, 0.03, 0.07],
+                        ],
+                        &Default::default(),
+                    ),
+                    Tensor::from_data(
+                        // targets
+                        [[0, 1, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0]],
+                        // predictions @ top_k=1
+                        //   [[0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1], [1, 0,  0]]
+                        // predictions @ top_k=2
+                        //   [[1, 1, 0], [1, 1, 0], [1, 1, 0], [0, 1, 1], [1, 0,  1]]
+                        &Default::default(),
+                    ),
+                )
+            }
+            ClassificationType::Multilabel => {
+                (
+                    Tensor::from_data(
+                        [
+                            [0.1, 0.7, 0.6],
+                            [0.3, 0.9, 0.05],
+                            [0.8, 0.9, 0.4],
+                            [0.7, 0.5, 0.9],
+                            [1.0, 0.3, 0.2],
+                        ],
+                        &Default::default(),
+                    ),
+                    // targets
+                    Tensor::from_data(
+                        [[1, 1, 0], [1, 0, 1], [1, 1, 1], [0, 0, 1], [1, 0, 0]],
+                        // predictions @ threshold=0.5
+                        //   [[0, 1, 1], [0, 1, 0], [1, 1, 0], [1, 0, 1], [1, 0, 0]]
+                        &Default::default(),
+                    ),
+                )
+            }
+        }
+    }
+}