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-autodiff/Cargo.toml

@@ -0,0 +1,45 @@
+[package]
+authors = ["nathanielsimard <nathaniel.simard.42@gmail.com>"]
+categories = ["science"]
+description = "Automatic differentiation backend for the Burn framework"
+edition.workspace = true
+keywords = ["deep-learning", "machine-learning", "data"]
+license.workspace = true
+name = "burn-autodiff"
+readme.workspace = true
+repository = "https://github.com/tracel-ai/burn/tree/main/crates/burn-autodiff"
+documentation = "https://docs.rs/burn-autodiff"
+version.workspace = true
+
+[lints]
+workspace = true
+
+[features]
+default = ["std", "tracing"]
+std = ["dep:parking_lot"]
+export_tests = []         # check checkpointer is_empty in tests
+
+tracing = [
+    "dep:tracing",
+    "burn-std/tracing",
+    "burn-backend/tracing",
+]
+
+[dependencies]
+burn-std = { path = "../burn-std", version = "=0.21.0-pre.2", default-features = false }
+burn-backend = { path = "../burn-backend", version = "=0.21.0-pre.2", default-features = false }
+
+
+derive-new = { workspace = true }
+spin = { workspace = true }
+parking_lot = { workspace = true, optional = true }
+log = { workspace = true }
+hashbrown = { workspace = true }
+num-traits = { workspace = true }
+portable-atomic = { workspace = true }
+tracing = { workspace = true, optional = true, features = ["default"] }
+
+
+[package.metadata.docs.rs]
+features = ["default"]
+rustdoc-args = ["--cfg", "docsrs"]

crates/stable-diffusion-burn/burn-crates/burn-autodiff/LICENSE-APACHE

@@ -0,0 +1 @@
+../../LICENSE-APACHE

crates/stable-diffusion-burn/burn-crates/burn-autodiff/LICENSE-MIT

@@ -0,0 +1 @@
+../../LICENSE-MIT

crates/stable-diffusion-burn/burn-crates/burn-autodiff/README.md

@@ -0,0 +1,8 @@
+# Burn Autodiff
+
+> [Burn](https://github.com/tracel-ai/burn) autodiff backend
+
+[![Current Crates.io Version](https://img.shields.io/crates/v/burn-autodiff.svg)](https://crates.io/crates/burn-autodiff)
+[![license](https://shields.io/badge/license-MIT%2FApache--2.0-blue)](https://github.com/tracel-ai/burn-autodiff/blob/master/README.md)
+
+For now only first order reverse mode autodiff is supported.

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/backend.rs

@@ -0,0 +1,138 @@
+use crate::{
+    checkpoint::strategy::{CheckpointStrategy, NoCheckpointing},
+    grads::Gradients,
+    tensor::AutodiffTensor,
+};
+use alloc::{format, string::String};
+use burn_backend::{
+    backend::{AutodiffBackend, Backend, ExecutionError},
+    tensor::{BoolTensor, IntTensor, QuantizedTensor},
+};
+use core::marker::PhantomData;
+
+/// Enable auto-differentiation on a backend.
+///
+/// This works as a backend decorator, extending the functionality of any backend with
+/// backpropagation.
+#[derive(Clone, Copy, Debug, Default)]
+pub struct Autodiff<B, C = NoCheckpointing> {
+    _b: PhantomData<B>,
+    _checkpoint_strategy: PhantomData<C>,
+}
+
+impl<B: Backend, C: CheckpointStrategy> Backend for Autodiff<B, C> {
+    type Device = B::Device;
+
+    type FloatTensorPrimitive = AutodiffTensor<B>;
+    type FloatElem = B::FloatElem;
+
+    type IntTensorPrimitive = B::IntTensorPrimitive;
+    type IntElem = B::IntElem;
+
+    type BoolTensorPrimitive = B::BoolTensorPrimitive;
+    type BoolElem = B::BoolElem;
+
+    type QuantizedTensorPrimitive = B::QuantizedTensorPrimitive;
+
+    fn ad_enabled(_device: &Self::Device) -> bool {
+        true
+    }
+
+    fn name(device: &Self::Device) -> String {
+        format!("autodiff<{}>", B::name(device))
+    }
+
+    fn seed(device: &B::Device, seed: u64) {
+        B::seed(device, seed)
+    }
+
+    fn sync(device: &B::Device) -> Result<(), ExecutionError> {
+        B::sync(device)
+    }
+
+    fn memory_persistent_allocations<Output, Input, Func: Fn(Input) -> Output>(
+        device: &Self::Device,
+        input: Input,
+        func: Func,
+    ) -> Output {
+        B::memory_persistent_allocations(device, input, func)
+    }
+
+    fn memory_cleanup(device: &Self::Device) {
+        B::memory_cleanup(device)
+    }
+
+    fn staging<'a, Iter>(data: Iter, device: &Self::Device)
+    where
+        Iter: Iterator<Item = &'a mut burn_backend::TensorData>,
+    {
+        B::staging(data, device);
+    }
+
+    fn supports_dtype(device: &Self::Device, dtype: burn_std::DType) -> bool {
+        B::supports_dtype(device, dtype)
+    }
+
+    fn dtype_usage(device: &Self::Device, dtype: burn_std::DType) -> burn_backend::DTypeUsageSet {
+        B::dtype_usage(device, dtype)
+    }
+}
+
+impl<B: Backend, C: CheckpointStrategy> AutodiffBackend for Autodiff<B, C> {
+    type InnerBackend = B;
+    type Gradients = Gradients;
+
+    fn backward(tensor: AutodiffTensor<B>) -> Gradients {
+        tensor.backward()
+    }
+
+    fn grad(tensor: &AutodiffTensor<B>, grads: &Gradients) -> Option<B::FloatTensorPrimitive> {
+        tensor.grad(grads)
+    }
+
+    fn grad_remove(
+        tensor: &AutodiffTensor<B>,
+        grads: &mut Gradients,
+    ) -> Option<B::FloatTensorPrimitive> {
+        tensor.grad_remove(grads)
+    }
+    fn inner(tensor: AutodiffTensor<B>) -> B::FloatTensorPrimitive {
+        tensor.primitive
+    }
+
+    fn from_inner(tensor: B::FloatTensorPrimitive) -> AutodiffTensor<B> {
+        AutodiffTensor::new(tensor)
+    }
+
+    fn grad_replace(
+        tensor: &AutodiffTensor<B>,
+        grads: &mut Self::Gradients,
+        grad: B::FloatTensorPrimitive,
+    ) {
+        tensor.grad_replace(grads, grad);
+    }
+
+    fn int_inner(tensor: IntTensor<Self>) -> IntTensor<Self::InnerBackend> {
+        tensor
+    }
+
+    fn bool_inner(tensor: BoolTensor<Self>) -> BoolTensor<Self::InnerBackend> {
+        tensor
+    }
+
+    fn int_from_inner(tensor: IntTensor<Self::InnerBackend>) -> IntTensor<Self> {
+        tensor
+    }
+
+    fn bool_from_inner(tensor: BoolTensor<Self::InnerBackend>) -> BoolTensor<Self> {
+        tensor
+    }
+
+    fn q_inner(tensor: QuantizedTensor<Self>) -> QuantizedTensor<Self::InnerBackend> {
+        tensor
+    }
+
+    fn q_from_inner(tensor: QuantizedTensor<Self::InnerBackend>) -> QuantizedTensor<Self> {
+        tensor
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/checkpoint/base.rs

@@ -0,0 +1,82 @@
+use super::{
+    retro_forward::RetroForwards,
+    state::{BackwardStates, State},
+};
+use crate::collections::HashMap;
+use crate::graph::NodeId;
+
+use alloc::{vec, vec::Vec};
+
+#[derive(new, Debug)]
+/// Links a [NodeId] to its autodiff graph [NodeRef]
+pub(crate) struct NodeTree {
+    map: HashMap<NodeId, Vec<NodeId>>,
+}
+
+impl NodeTree {
+    /// Gives the parents of the node in the autodiff graph
+    pub(crate) fn parents(&self, node_id: &NodeId) -> Option<Vec<NodeId>> {
+        self.map.get(node_id).cloned()
+    }
+}
+
+#[derive(new, Debug)]
+/// Struct responsible of fetching the output for a node in the autodiff graph during a backward pass
+pub struct Checkpointer {
+    backward_states: BackwardStates,
+    retro_forwards: RetroForwards,
+    node_tree: NodeTree,
+}
+
+impl Checkpointer {
+    /// Gives the output of the given node, by recursively asking parents to compute themselves
+    /// or give their pre-computed tensors.
+    pub fn retrieve_node_output<T>(&mut self, node_id: NodeId) -> T
+    where
+        T: Clone + Send + 'static,
+    {
+        self.topological_sort(node_id).into_iter().for_each(|node| {
+            self.retro_forwards
+                .execute_retro_forward(node, &mut self.backward_states)
+        });
+
+        self.backward_states.get_state::<T>(&node_id)
+    }
+
+    /// Sorts the ancestors of NodeId in a way such that all parents come before their children
+    /// Useful to avoid recursivity later when mutating the states
+    ///
+    /// The sort on a compute bound state or a memory bound that is already computed is trivial.
+    /// The match on State::Computed also serves as a stopping criterion for the sort,
+    /// we don't need to look higher than that during recursivity.
+    fn topological_sort(&self, node_id: NodeId) -> Vec<NodeId> {
+        match self.backward_states.get_state_ref(&node_id) {
+            Some(state) => match state {
+                State::Recompute { n_required: _ } => {
+                    let mut sorted = Vec::new();
+                    let parents = self.node_tree.parents(&node_id).unwrap();
+                    for parent_node in parents {
+                        let parent_sorted = self.topological_sort(parent_node);
+                        for ps in parent_sorted {
+                            if !sorted.contains(&ps) {
+                                sorted.push(ps)
+                            }
+                        }
+                    }
+                    sorted.push(node_id);
+                    sorted
+                }
+                State::Computed {
+                    state_content: _,
+                    n_required: _,
+                } => vec![node_id],
+            },
+            None => panic!("Node {node_id:?} is not in the backward_states. "),
+        }
+    }
+
+    /// Checks if checkpointer has been drained adequately. Useful for testing
+    pub fn is_empty(&self) -> bool {
+        self.backward_states.is_empty() && self.retro_forwards.is_empty()
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/checkpoint/builder.rs

@@ -0,0 +1,304 @@
+use crate::{
+    collections::HashMap,
+    graph::{ComputingProperty, NodeId},
+    tensor::AutodiffTensor,
+};
+use alloc::{boxed::Box, sync::Arc, vec::Vec};
+use burn_backend::Backend;
+use core::any::Any;
+
+use super::{
+    base::{Checkpointer, NodeTree},
+    retro_forward::{RetroForward, RetroForwards},
+    state::{BackwardStates, State},
+};
+
+#[derive(Debug)]
+/// Determines if a node should checkpoint its computed output or its retro_forward for recomputation
+/// The action is normally created by the child of the node, once the node is determined to be needed
+pub enum CheckpointingAction {
+    /// The node's already computed output should be saved
+    Computed {
+        /// The node
+        node_id: NodeId,
+        /// The node's output
+        state_content: Box<dyn Any + Send>,
+    },
+    /// The node should recompute itself when asked
+    Recompute {
+        /// The node
+        node_id: NodeId,
+        /// How the node should recompute itself
+        retro_forward: Arc<dyn RetroForward>,
+    },
+}
+
+// TODO: Remove that when proper client server.
+unsafe impl Send for CheckpointingAction {}
+
+impl CheckpointingAction {
+    /// Utility function to access the id of the node of the checkpointing action
+    pub fn id(&self) -> NodeId {
+        match self {
+            CheckpointingAction::Computed {
+                node_id: node_ref,
+                state_content: _,
+            } => *node_ref,
+            CheckpointingAction::Recompute {
+                node_id: node_ref,
+                retro_forward: _,
+            } => *node_ref,
+        }
+    }
+}
+
+#[derive(new, Debug, Default)]
+/// Accumulates checkpoints as checkpointing actions during the forward pass,
+/// and builds a checkpointer right before the backward pass
+pub struct CheckpointerBuilder {
+    explicit_actions: Vec<CheckpointingAction>,
+    backup_actions: Vec<CheckpointingAction>,
+}
+
+/// Determines if a checkpoint should impact the n_required values (Main)
+/// or if it should just keep the state in case it's required (Backup)
+///
+pub(crate) enum ActionType {
+    /// Explicit actions have been explicitly requested by some operation to retrieve their state
+    Explicit,
+    /// Backup actions are not always needed. They exist to save the output of an operation
+    /// whose child is memory bound, in case the state is indirectly needed when computing
+    /// the child's retro_forward. If no explicit action ever asks for the child's output, then
+    /// the backup output will go out of scope when the checkpointer is built.
+    Backup,
+}
+
+impl CheckpointerBuilder {
+    pub(crate) fn checkpoint<B: Backend>(
+        &mut self,
+        tensor: &AutodiffTensor<B>,
+        action_type: ActionType,
+    ) {
+        let action_list = match action_type {
+            ActionType::Explicit => &mut self.explicit_actions,
+            ActionType::Backup => &mut self.backup_actions,
+        };
+        match &tensor.node.properties {
+            ComputingProperty::ComputeBound | ComputingProperty::Ambiguous => {
+                action_list.push(CheckpointingAction::Computed {
+                    node_id: tensor.node.id,
+                    state_content: Box::new(tensor.primitive.clone()),
+                })
+            }
+            ComputingProperty::MemoryBound { retro_forward } => {
+                action_list.push(CheckpointingAction::Recompute {
+                    node_id: tensor.node.id,
+                    retro_forward: retro_forward.clone(),
+                })
+            }
+        }
+    }
+
+    pub(crate) fn extend(&mut self, other: CheckpointerBuilder) {
+        for other_action in other.explicit_actions {
+            self.explicit_actions.push(other_action)
+        }
+        for other_unsure in other.backup_actions {
+            self.backup_actions.push(other_unsure)
+        }
+    }
+
+    pub(crate) fn build(self, node_tree: NodeTree) -> Checkpointer {
+        let mut backward_states_map = HashMap::new();
+        let mut retro_forwards_map = HashMap::new();
+
+        // Find recursion stopping points
+        let stop_nodes: Vec<NodeId> = self.find_stop_nodes();
+
+        // We start by identifying how many times each node will be required.
+        let n_required_map = self.build_n_required_map(&node_tree, stop_nodes);
+
+        // Then we checkpoint the nodes with the corresponding n_required value
+        self.insert_checkpoints(
+            &mut backward_states_map,
+            &mut retro_forwards_map,
+            n_required_map,
+        );
+
+        Checkpointer::new(
+            BackwardStates::new(backward_states_map),
+            RetroForwards::new(retro_forwards_map),
+            node_tree,
+        )
+    }
+
+    fn find_stop_nodes(&self) -> Vec<NodeId> {
+        let mut stop_nodes = Vec::default();
+        for action in self
+            .explicit_actions
+            .iter()
+            .chain(self.backup_actions.iter())
+        {
+            match action {
+                CheckpointingAction::Computed {
+                    node_id: node_ref,
+                    state_content: _,
+                } => stop_nodes.push(*node_ref),
+                CheckpointingAction::Recompute {
+                    node_id: _,
+                    retro_forward: _,
+                } => {}
+            }
+        }
+        stop_nodes
+    }
+
+    fn build_n_required_map(
+        &self,
+        node_tree: &NodeTree,
+        stop_nodes: Vec<NodeId>,
+    ) -> HashMap<NodeId, usize> {
+        let mut n_required_map = HashMap::<NodeId, usize>::default();
+
+        for action in self.explicit_actions.iter() {
+            match action {
+                CheckpointingAction::Computed {
+                    node_id: node_ref,
+                    state_content: _,
+                } => {
+                    let id = *node_ref;
+                    match n_required_map.remove(&id) {
+                        Some(n) => {
+                            n_required_map.insert(id, n + 1);
+                        }
+                        None => {
+                            n_required_map.insert(id, 1);
+                        }
+                    };
+                }
+                CheckpointingAction::Recompute {
+                    node_id: node_ref,
+                    retro_forward: _,
+                } => {
+                    let id = *node_ref;
+                    Self::update_n_required_of_parents(
+                        id,
+                        &mut n_required_map,
+                        node_tree,
+                        &stop_nodes,
+                    );
+                }
+            }
+        }
+
+        n_required_map
+    }
+
+    fn insert_checkpoints(
+        mut self,
+        backward_states_map: &mut HashMap<NodeId, State>,
+        retro_forward_map: &mut HashMap<NodeId, Arc<dyn RetroForward>>,
+        n_required_map: HashMap<NodeId, usize>,
+    ) {
+        // We do not loop over checkpointing actions anymore because they can contain
+        // duplicates or miss some that are in backup. We loop over the n_required_map
+        // from which we use the ids to find them again in the checkpointing actions
+        for (node_id, n_required) in n_required_map {
+            // We find the checkpointing action for node_id. It's likely in checkpointing_actions
+            // so we check there first, otherwise it will be in backup.
+            // Technically it can be there several times but can never be of both types, so we can assume the first we find is fine
+
+            let action = match self
+                .explicit_actions
+                .iter()
+                .position(|action| action.id() == node_id)
+            {
+                Some(pos) => self.explicit_actions.remove(pos),
+                None => {
+                    let pos = self
+                        .backup_actions
+                        .iter()
+                        .position(|action| action.id() == node_id);
+                    self.backup_actions.remove(pos.unwrap_or_else(|| {
+                        panic!("Node {:?} is needed but never checkpointed", &node_id)
+                    }))
+                }
+            };
+
+            match action {
+                CheckpointingAction::Computed {
+                    node_id: _,
+                    state_content,
+                } => {
+                    self.checkpoint_compute(backward_states_map, node_id, state_content, n_required)
+                }
+                CheckpointingAction::Recompute {
+                    node_id: _,
+                    retro_forward,
+                } => self.checkpoint_lazy(
+                    backward_states_map,
+                    retro_forward_map,
+                    node_id,
+                    retro_forward,
+                    n_required,
+                ),
+            };
+        }
+    }
+
+    fn update_n_required_of_parents(
+        id: NodeId,
+        n_required_map: &mut HashMap<NodeId, usize>,
+        node_tree: &NodeTree,
+        stop_nodes: &Vec<NodeId>,
+    ) {
+        match n_required_map.remove(&id) {
+            Some(n) => {
+                n_required_map.insert(id, n + 1);
+            }
+            None => {
+                n_required_map.insert(id, 1);
+                if !stop_nodes.contains(&id)
+                    && let Some(parents) = node_tree.parents(&id)
+                {
+                    for p in parents {
+                        Self::update_n_required_of_parents(
+                            p,
+                            n_required_map,
+                            node_tree,
+                            stop_nodes,
+                        );
+                    }
+                }
+            }
+        }
+    }
+
+    fn checkpoint_compute(
+        &self,
+        backward_states_map: &mut HashMap<NodeId, State>,
+        node_id: NodeId,
+        state_content: Box<dyn Any + Send>,
+        n_required: usize,
+    ) {
+        backward_states_map.insert(
+            node_id,
+            State::Computed {
+                state_content,
+                n_required,
+            },
+        );
+    }
+
+    fn checkpoint_lazy(
+        &self,
+        backward_states_map: &mut HashMap<NodeId, State>,
+        retro_forward_map: &mut HashMap<NodeId, Arc<dyn RetroForward>>,
+        node_id: NodeId,
+        retro_forward: Arc<dyn RetroForward>,
+        n_required: usize,
+    ) {
+        retro_forward_map.insert(node_id, retro_forward);
+        backward_states_map.insert(node_id, State::Recompute { n_required });
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/checkpoint/mod.rs

@@ -0,0 +1,9 @@
+/// Checkpointer module
+pub mod base;
+pub(crate) mod builder;
+/// RetroForward module
+pub mod retro_forward;
+/// BackwardStates module
+pub mod state;
+/// CheckpointStrategy module
+pub mod strategy;

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/checkpoint/retro_forward.rs

@@ -0,0 +1,116 @@
+use crate::collections::HashMap;
+use crate::graph::NodeId;
+
+use alloc::sync::Arc;
+use core::fmt::Debug;
+
+use super::state::{BackwardStates, State};
+
+/// Definition of the forward function of a node, called during retropropagation only.
+/// This is different from the normal forward function because it reads and writes from
+/// the [BackwardStates] map instead of having a clear function signature.
+pub trait RetroForward: Debug + Send + 'static {
+    /// Applies the forward pass for retropropagation.
+    fn forward(&self, states: &mut BackwardStates, out_node: NodeId);
+}
+
+#[derive(new, Debug)]
+/// Links [NodeId]s to their corresponding [RetroForward]
+pub(crate) struct RetroForwards {
+    map: HashMap<NodeId, Arc<dyn RetroForward>>,
+}
+
+impl RetroForwards {
+    /// Executes the [RetroForward] for a given [NodeId] if the node's
+    /// [State] is [State::Recompute], otherwise does nothing.
+    pub(crate) fn execute_retro_forward(
+        &mut self,
+        node_id: NodeId,
+        backward_states: &mut BackwardStates,
+    ) {
+        if let State::Recompute { n_required: _ } = backward_states
+            .get_state_ref(&node_id)
+            .unwrap_or_else(|| panic!("Should find node {node_id:?}"))
+        {
+            // Retro forwards are always used only once because afterwards their state is computed
+            let retro_forward = self.map.remove(&node_id).unwrap();
+            retro_forward.forward(backward_states, node_id);
+        }
+    }
+
+    pub(crate) fn is_empty(&self) -> bool {
+        self.map.is_empty()
+    }
+}
+
+#[macro_export]
+/// Creates a RetroForward struct for unary scalar operations
+macro_rules! retro_unary_scalar {
+    (
+        $name:ident,
+        $ops:expr
+    ) => {
+        #[derive(new, Debug, Clone)]
+        struct $name<B: Backend> {
+            lhs_id: NodeId,
+            rhs: Scalar,
+            _backend: PhantomData<B>,
+        }
+
+        impl<B: Backend> RetroForward for $name<B> {
+            fn forward(&self, states: &mut BackwardStates, out_node: NodeId) {
+                let lhs = states.get_state::<B::FloatTensorPrimitive>(&self.lhs_id);
+                let out = $ops(lhs, self.rhs);
+                states.save(out_node, out)
+            }
+        }
+    };
+}
+
+#[macro_export]
+/// Creates a RetroForward struct for unary scalar operations
+macro_rules! retro_unary {
+    (
+        $name:ident,
+        $ops:expr
+    ) => {
+        #[derive(new, Debug, Clone)]
+        struct $name<B: Backend> {
+            input_id: NodeId,
+            _backend: PhantomData<B>,
+        }
+
+        impl<B: Backend> RetroForward for $name<B> {
+            fn forward(&self, states: &mut BackwardStates, out_node: NodeId) {
+                let input = states.get_state::<B::FloatTensorPrimitive>(&self.input_id);
+                let out = $ops(input);
+                states.save(out_node, out)
+            }
+        }
+    };
+}
+
+#[macro_export]
+/// Creates a RetroForward struct for binary operations
+macro_rules! retro_binary {
+    (
+        $name:ident,
+        $ops:expr
+    ) => {
+        #[derive(new, Debug, Clone)]
+        struct $name<B: Backend> {
+            lhs_id: NodeId,
+            rhs_id: NodeId,
+            _backend: PhantomData<B>,
+        }
+
+        impl<B: Backend> RetroForward for $name<B> {
+            fn forward(&self, states: &mut BackwardStates, out_node: NodeId) {
+                let lhs = states.get_state::<B::FloatTensorPrimitive>(&self.lhs_id);
+                let rhs = states.get_state::<B::FloatTensorPrimitive>(&self.rhs_id);
+                let out = $ops(lhs, rhs);
+                states.save(out_node, out)
+            }
+        }
+    };
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/checkpoint/state.rs

@@ -0,0 +1,144 @@
+use core::any::Any;
+
+use crate::collections::HashMap;
+use crate::graph::NodeId;
+use alloc::boxed::Box;
+
+/// In order to accept arbitrary node output in the same hashmap, we need to upcast them to any.
+pub(crate) type StateContent = Box<dyn Any + Send>;
+
+#[derive(Debug)]
+/// The state contained at one node. Encapsulates the node output if precomputed,
+/// or clearly asks that it needs to be recomputed from the parents.
+/// Also keeps track of the number of times the state is required so it can be removed
+/// from the map of states on its last use.
+pub(crate) enum State {
+    /// The state was not checkpointed, will need to recompute it from the node's parents
+    Recompute { n_required: usize },
+    /// The state was checkpointed or computed during retropropagation and can be directly accessed
+    Computed {
+        state_content: StateContent,
+        n_required: usize,
+    },
+}
+
+impl State {
+    /// Returns a reference to the (not yet) downcasted node output, if checkpointed
+    pub(crate) fn to_state_content(&self) -> &StateContent {
+        match self {
+            State::Recompute { n_required: _ } => {
+                unreachable!(
+                    "Can't get state content of recompute state. A child has likely been accessed before its parents."
+                )
+            }
+            State::Computed {
+                state_content,
+                n_required: _,
+            } => state_content,
+        }
+    }
+
+    /// Returns a (not yet) downcasted node output, if checkpointed
+    pub(crate) fn into_state_content(self) -> StateContent {
+        match self {
+            State::Recompute { n_required: _ } => {
+                unreachable!(
+                    "Can't get state content of recompute state. A child has likely been accessed before its parents."
+                )
+            }
+            State::Computed {
+                state_content,
+                n_required: _,
+            } => state_content,
+        }
+    }
+
+    /// Returns the number of time the state is required
+    pub(crate) fn n_required(&self) -> usize {
+        match self {
+            State::Recompute { n_required } => *n_required,
+            State::Computed {
+                state_content: _,
+                n_required,
+            } => *n_required,
+        }
+    }
+}
+
+#[derive(new, Default, Debug)]
+/// Links [NodeId]s to their current state
+pub struct BackwardStates {
+    map: HashMap<NodeId, State>,
+}
+
+impl BackwardStates {
+    /// Returns the output in the state of the given [NodeId],
+    /// and decrements the number of times this state is required.
+    /// This function always gives ownership of the output, but will clone it if needed for further uses.
+    pub fn get_state<T>(&mut self, node_id: &NodeId) -> T
+    where
+        T: Clone + Send + 'static,
+    {
+        // Fetch the state and decrement its number of required
+        let state = self.map.remove(node_id).unwrap();
+        let remaining_n_required = state.n_required() - 1;
+
+        // Downcast the state to whatever it is supposed to be
+        // If still needed after giving ownership, we copy it back to the hashmap
+        if remaining_n_required > 0 {
+            let new_stored_state = match state {
+                State::Recompute { n_required: _ } => unreachable!(),
+                State::Computed {
+                    state_content,
+                    n_required: _,
+                } => State::Computed {
+                    state_content,
+                    n_required: remaining_n_required,
+                },
+            };
+
+            let downcasted = new_stored_state
+                .to_state_content()
+                .downcast_ref::<T>()
+                .unwrap()
+                .clone();
+
+            self.insert_state(*node_id, new_stored_state);
+
+            downcasted
+        } else {
+            let downcasted = state.into_state_content().downcast::<T>().unwrap();
+            *downcasted
+        }
+    }
+
+    /// Returns a reference to the [State] of the given node
+    /// Useful when we need [State] information without needing the underlying tensor
+    pub(crate) fn get_state_ref(&self, node_id: &NodeId) -> Option<&State> {
+        self.map.get(node_id)
+    }
+
+    /// Associates a [State] to its [NodeId]
+    pub(crate) fn insert_state(&mut self, node_id: NodeId, state: State) {
+        self.map.insert(node_id, state);
+    }
+
+    /// Saves the output to the state of the given [NodeId].
+    pub fn save<T>(&mut self, node_id: NodeId, saved_output: T)
+    where
+        T: Clone + Send + 'static,
+    {
+        let n_required = self.get_state_ref(&node_id).unwrap().n_required();
+        self.insert_state(
+            node_id,
+            State::Computed {
+                state_content: Box::new(saved_output),
+                n_required,
+            },
+        );
+    }
+
+    pub(crate) fn is_empty(&self) -> bool {
+        self.map.is_empty()
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/checkpoint/strategy.rs

@@ -0,0 +1,102 @@
+use core::fmt::Debug;
+
+use burn_backend::Backend;
+
+use crate::{graph::ComputingProperty, tensor::AutodiffTensor};
+use alloc::sync::Arc;
+
+use super::{
+    builder::{ActionType, CheckpointerBuilder},
+    retro_forward::RetroForward,
+};
+
+/// Strategy for the amount of checkpointing to do during autodiff
+pub trait CheckpointStrategy: Clone + Copy + Debug + Default + Send + Sync + 'static {
+    /// May modify the compute property depending on the strategy
+    fn compute_property<R: RetroForward>(retro_forward: R) -> ComputingProperty;
+
+    /// Checkpoints parents if necessary in the strategy
+    fn checkpoint_parents<'a, B2, A>(
+        parents: A,
+        builder: &mut CheckpointerBuilder,
+    ) -> Result<(), CheckpointingError>
+    where
+        B2: Backend,
+        A: IntoIterator<Item = &'a AutodiffTensor<B2>>;
+}
+
+#[derive(Debug)]
+/// Error that can happen when trying to checkpoint a tensor.
+pub enum CheckpointingError {
+    /// When a parent is untracked, we can't easily checkpoint its state, since we don't know the
+    /// requirements in advanced.
+    UntrackedParent,
+}
+
+#[derive(Clone, Copy, Debug, Default)]
+/// All operations are considered compute bound, notwithstanding how they are marked
+pub struct NoCheckpointing {}
+
+impl CheckpointStrategy for NoCheckpointing {
+    /// An operation marked as memory bound is actually compute bound.
+    fn compute_property<R: RetroForward>(_retro_forward: R) -> ComputingProperty {
+        ComputingProperty::ComputeBound
+    }
+
+    /// An operation marked as memory bound is actually compute bound.
+    /// It's therefore useless to checkpoint the parents
+    fn checkpoint_parents<'a, B2, A>(
+        _parents: A,
+        _builder: &mut CheckpointerBuilder,
+    ) -> Result<(), CheckpointingError>
+    where
+        B2: Backend,
+        A: IntoIterator<Item = &'a AutodiffTensor<B2>>,
+    {
+        // Nothing to do here
+        Ok(())
+    }
+}
+
+#[derive(Clone, Copy, Debug, Default)]
+/// Operation properties are as they are marked (compute or memory bound)
+pub struct BalancedCheckpointing {}
+
+impl CheckpointStrategy for BalancedCheckpointing {
+    /// An operation marked as memory bound is memory bound.
+    /// When memory bound, an operation needs to save its RetroForward
+    fn compute_property<R: RetroForward>(retro_forward: R) -> ComputingProperty {
+        ComputingProperty::MemoryBound {
+            retro_forward: Arc::new(retro_forward),
+        }
+    }
+
+    /// An operation marked as memory bound is really memory bound.
+    /// Since the operation may not checkpoint its parents but may need them indirectly
+    /// if asked to recompute itself, the method needs to know the parent tensors to maybe checkpoint them
+    fn checkpoint_parents<'a, B2, A>(
+        parents: A,
+        builder: &mut CheckpointerBuilder,
+    ) -> Result<(), CheckpointingError>
+    where
+        B2: Backend,
+        A: IntoIterator<Item = &'a AutodiffTensor<B2>>,
+    {
+        let mut can_checkpoint = true;
+
+        for tensor in parents.into_iter() {
+            if let crate::graph::Requirement::None = tensor.node.requirement {
+                can_checkpoint = false;
+            } else {
+                builder.checkpoint(tensor, ActionType::Backup);
+            }
+        }
+
+        if !can_checkpoint {
+            *builder = CheckpointerBuilder::default();
+            return Err(CheckpointingError::UntrackedParent);
+        }
+
+        Ok(())
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/grads.rs

@@ -0,0 +1,85 @@
+use burn_backend::{
+    Backend, TensorMetadata, TensorPrimitive,
+    tensor::{FloatTensor, TensorContainer},
+};
+
+use crate::{
+    NodeId,
+    graph::{NodeRef, Requirement},
+    tensor::AutodiffTensor,
+};
+
+/// Gradient identifier.
+pub type GradID = u64;
+
+/// Gradients container used during the backward pass.
+pub struct Gradients {
+    container: TensorContainer<GradID>,
+}
+
+impl Gradients {
+    /// Creates a new gradients container.
+    pub fn new<B: Backend>(root_node: NodeRef, root_tensor: FloatTensor<B>) -> Self {
+        let mut gradients = Self {
+            container: TensorContainer::new(),
+        };
+        gradients.register::<B>(
+            root_node.id,
+            B::float_ones(
+                root_tensor.shape(),
+                &B::float_device(&root_tensor),
+                root_tensor.dtype().into(),
+            ),
+        );
+        gradients
+    }
+
+    /// Consumes the gradients for a given tensor.
+    ///
+    /// Each tensor should be consumed exactly 1 time if its gradients are only required during the
+    /// backward pass, otherwise, it may be consume multiple times.
+    pub fn consume<B: Backend>(&mut self, node: &NodeRef) -> FloatTensor<B> {
+        match node.requirement {
+            Requirement::Grad => self
+                .container
+                .get::<B>(&node.id.value)
+                .map(|tensor| tensor.tensor())
+                .expect("Can't consume the gradients before they are registered at least once."),
+            Requirement::GradInBackward => self
+                .container
+                .remove::<B>(&node.id.value)
+                .map(|tensor| tensor.tensor())
+                .expect("Can't consume the gradients before they are registered at least once."),
+            Requirement::None => panic!("Trying to consume the gradients for an untracked tensor"),
+        }
+    }
+
+    /// Removes a grad tensor from the container.
+    pub fn remove<B: Backend>(&mut self, tensor: &AutodiffTensor<B>) -> Option<FloatTensor<B>> {
+        self.container
+            .remove::<B>(&tensor.node.id.value)
+            .map(|tensor| tensor.tensor())
+    }
+
+    /// Gets a grad tensor from the container.
+    pub fn get<B: Backend>(&self, tensor: &AutodiffTensor<B>) -> Option<FloatTensor<B>> {
+        self.container
+            .get::<B>(&tensor.node.id.value)
+            .map(|tensor| tensor.tensor())
+    }
+
+    /// Register a grad tensor in the container.
+    ///
+    /// If the tensor already exists, add both tensors together before saving the result.
+    pub fn register<B: Backend>(&mut self, node_id: NodeId, value: FloatTensor<B>) {
+        if let Some(tensor_old) = self.container.remove::<B>(&node_id.value) {
+            self.container.register::<B>(
+                node_id.value,
+                TensorPrimitive::Float(B::float_add(value, tensor_old.tensor())),
+            );
+        } else {
+            self.container
+                .register::<B>(node_id.value, TensorPrimitive::Float(value));
+        }
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/graph/base.rs

@@ -0,0 +1,17 @@
+use super::NodeId;
+use crate::{checkpoint::base::Checkpointer, grads::Gradients, graph::Parent};
+use alloc::boxed::Box;
+
+/// Backward step for reverse mode autodiff.
+pub trait Step: Send + core::fmt::Debug {
+    /// Executes the step and consumes it.
+    fn step(self: Box<Self>, grads: &mut Gradients, checkpointer: &mut Checkpointer);
+    /// Depth of the operation relative to the first node added to a graph.
+    fn depth(&self) -> usize;
+    /// The node associated to the step.
+    fn node(&self) -> NodeId;
+    /// The parents of the node associated to the step.
+    fn parents(&self) -> &[Parent];
+}
+
+pub type StepBoxed = Box<dyn Step>;

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/graph/mod.rs

@@ -0,0 +1,9 @@
+mod base;
+mod node;
+mod requirement;
+
+pub mod traversal;
+
+pub use base::*;
+pub use node::*;
+pub use requirement::*;

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/graph/node.rs

@@ -0,0 +1,87 @@
+use alloc::{sync::Arc, vec::Vec};
+
+#[cfg(target_has_atomic = "64")]
+use core::sync::atomic::{AtomicU64, Ordering};
+#[cfg(not(target_has_atomic = "64"))]
+use portable_atomic::{AtomicU64, Ordering};
+
+use crate::checkpoint::retro_forward::RetroForward;
+use crate::runtime::AutodiffClientImpl;
+
+use super::Requirement;
+
+#[derive(Debug, Clone)]
+pub enum ComputingProperty {
+    ComputeBound,
+    MemoryBound {
+        retro_forward: Arc<dyn RetroForward>,
+    },
+    Ambiguous, // Maybe autotune someday
+}
+
+/// This is safe only because we only call RetroForward on the autodiff server.
+/// Therefore, the trait will never be used by multiple threads at the same time.
+///
+/// TODO: Find a way to avoid cloning the compute property, which will remove the need to add the
+/// Arc, which will make (dyn RetroForward) safely implement Send.
+unsafe impl Send for ComputingProperty {}
+/// unsafe Sync is required because Send is only implemented for Arc<Sync>, not Arc<Send>.
+unsafe impl Sync for ComputingProperty {}
+
+/// A node contains graph metadata and should be used wrapped in an Arc for cheap cloning.
+#[derive(new, Debug)]
+pub struct Node {
+    pub parents: Vec<Parent>,
+    pub order: usize,
+    pub id: NodeId,
+    pub requirement: Requirement,
+    pub properties: ComputingProperty,
+    pub client: AutodiffClientImpl,
+}
+pub type NodeRef = Arc<Node>;
+
+#[derive(new, Debug, Clone, PartialEq, Eq)]
+pub struct Parent {
+    pub id: NodeId,
+}
+
+impl Node {
+    /// Returns the [node](Node) only if gradients are required.
+    pub fn clone_if_require_grad(self: &Arc<Self>) -> Option<NodeRef> {
+        match self.requirement.is_none() {
+            true => None,
+            false => Some(self.clone()),
+        }
+    }
+}
+
+/// Unique identifier generated for each node.
+#[derive(Clone, Hash, PartialEq, Eq, Debug, Copy)]
+pub struct NodeId {
+    /// The integer representation of the id
+    pub value: u64,
+}
+
+impl core::fmt::Display for NodeId {
+    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
+        f.write_fmt(format_args!("NodeId({})", self.value))
+    }
+}
+
+impl NodeId {
+    /// Create a unique [node id](NodeId).
+    pub fn new() -> Self {
+        static COUNTER: AtomicU64 = AtomicU64::new(0);
+        let value = COUNTER.fetch_add(1, Ordering::Relaxed);
+        if value == u64::MAX {
+            panic!("NodeId overflowed");
+        }
+        Self { value }
+    }
+}
+
+impl Default for NodeId {
+    fn default() -> Self {
+        Self::new()
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/graph/requirement.rs

@@ -0,0 +1,38 @@
+use super::NodeRef;
+
+/// Requirement for each tensor in the graph.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum Requirement {
+    /// Operations that require gradients.
+    Grad,
+    /// Operations that require gradients only for backprop.
+    GradInBackward,
+    /// Operations that don't need gradients, therefore not to be included in the graph.
+    None,
+}
+
+impl Requirement {
+    /// Returns true if gradients are not required.
+    pub fn is_none(&self) -> bool {
+        matches!(self, Self::None)
+    }
+    /// Returns the right requirement from a list of nodes.
+    pub fn from_nodes(nodes: &[NodeRef]) -> Self {
+        if nodes.len() == 1 {
+            return nodes[0].requirement.infer(&Requirement::None);
+        }
+
+        nodes
+            .iter()
+            .map(|node| node.requirement)
+            .reduce(|acc, requirement| requirement.infer(&acc))
+            .unwrap_or(Requirement::None)
+    }
+
+    fn infer(&self, other: &Self) -> Self {
+        match self.is_none() && other.is_none() {
+            true => Self::None,
+            false => Self::GradInBackward,
+        }
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/graph/traversal.rs

@@ -0,0 +1,74 @@
+use super::{Step, StepBoxed};
+use crate::{
+    NodeId,
+    collections::{HashMap, HashSet},
+    graph::Parent,
+};
+use alloc::vec::Vec;
+
+/// Breadth for search algorithm.
+pub struct BreadthFirstSearch;
+
+pub trait TraversalItem {
+    fn id(&self) -> NodeId;
+    fn parents(&self) -> &[Parent];
+    fn parent_nodes(&self) -> Vec<NodeId> {
+        self.parents().iter().map(|p| p.id).collect()
+    }
+}
+
+impl BreadthFirstSearch {
+    /// Traverse the graph of backward steps from a root node.
+    pub fn traverse<F, I>(
+        &self,
+        root_id: NodeId,
+        root_step: I,
+        steps: &mut HashMap<NodeId, I>,
+        mut callback: F,
+    ) where
+        F: FnMut(NodeId, I),
+        I: TraversalItem,
+    {
+        let mut visited = HashSet::new();
+        let mut parents = Vec::new();
+
+        visited.insert(root_id);
+        parents.append(&mut root_step.parent_nodes());
+
+        callback(root_id, root_step);
+
+        while let Some(id) = parents.pop() {
+            let step = match steps.remove(&id) {
+                Some(step) => step,
+                None => continue,
+            };
+
+            let step_node = step.id();
+            let step_parents = step.parent_nodes();
+
+            if visited.contains(&step_node) {
+                continue;
+            }
+
+            visited.insert(step_node);
+
+            for id in step_parents.iter() {
+                if !visited.contains(id) {
+                    parents.push(*id);
+                }
+            }
+
+            callback(step_node, step);
+        }
+    }
+}
+
+impl TraversalItem for StepBoxed {
+    fn id(&self) -> NodeId {
+        Step::node(self.as_ref())
+    }
+
+    fn parents(&self) -> &[Parent] {
+        Step::parents(self.as_ref())
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/lib.rs

@@ -0,0 +1,43 @@
+#![cfg_attr(not(feature = "std"), no_std)]
+#![warn(missing_docs)]
+#![cfg_attr(docsrs, feature(doc_cfg))]
+
+//! # Burn Autodiff
+//!
+//! This autodiff library is a part of the Burn project. It is a standalone crate
+//! that can be used to perform automatic differentiation on tensors. It is
+//! designed to be used with the Burn Tensor crate, but it can be used with any
+//! tensor library that implements the `Backend` trait.
+
+#[macro_use]
+extern crate derive_new;
+
+extern crate alloc;
+
+/// Checkpoint module.
+pub mod checkpoint;
+/// Gradients module.
+pub mod grads;
+/// Operation module.
+pub mod ops;
+
+pub(crate) mod graph;
+// Exported for backend extension
+pub use graph::NodeId;
+pub(crate) mod tensor;
+pub(crate) mod utils;
+
+mod backend;
+
+pub(crate) mod runtime;
+
+pub use backend::*;
+
+/// A facade around for HashMap and HashSet.
+/// This avoids elaborate import wrangling having to happen in every module.
+mod collections {
+    #[cfg(not(feature = "std"))]
+    pub use hashbrown::{HashMap, HashSet};
+    #[cfg(feature = "std")]
+    pub use std::collections::{HashMap, HashSet};
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/activation.rs

@@ -0,0 +1,167 @@
+use core::marker::PhantomData;
+
+use crate::{
+    Autodiff,
+    checkpoint::{
+        base::Checkpointer, retro_forward::RetroForward, state::BackwardStates,
+        strategy::CheckpointStrategy,
+    },
+    grads::Gradients,
+    graph::NodeId,
+    ops::{Backward, Ops, OpsKind, unary},
+    retro_unary,
+};
+use burn_backend::{Backend, ops::ActivationOps, tensor::FloatTensor};
+
+impl<B: Backend, C: CheckpointStrategy> ActivationOps<Autodiff<B, C>> for Autodiff<B, C> {
+    fn gelu(tensor: FloatTensor<Self>) -> FloatTensor<Self> {
+        #[derive(Debug)]
+        struct Gelu;
+
+        retro_unary!(RetroGelu, B::gelu);
+
+        impl<B: Backend> Backward<B, 1> for Gelu {
+            type State = NodeId;
+
+            fn backward(
+                self,
+                ops: Ops<Self::State, 1>,
+                grads: &mut Gradients,
+                checkpointer: &mut Checkpointer,
+            ) {
+                let input = checkpointer.retrieve_node_output(ops.state);
+
+                unary::<B, _>(ops.parents, ops.node, grads, |grad| {
+                    B::gelu_backward(input, grad)
+                });
+            }
+        }
+
+        match Gelu
+            .prepare::<C>([tensor.node.clone()])
+            .memory_bound()
+            .retro_forward(RetroGelu::<B>::new(tensor.node.id))
+            .parents([&tensor])
+            .stateful()
+        {
+            OpsKind::Tracked(mut prep) => {
+                let state = prep.checkpoint(&tensor);
+                prep.finish(state, B::gelu(tensor.primitive.clone()))
+            }
+            OpsKind::UnTracked(prep) => prep.finish(B::gelu(tensor.primitive)),
+        }
+    }
+
+    fn relu(tensor: FloatTensor<Self>) -> FloatTensor<Self> {
+        #[derive(Debug)]
+        struct Relu;
+
+        retro_unary!(RetroRelu, B::relu);
+
+        impl<B: Backend> Backward<B, 1> for Relu {
+            type State = NodeId;
+
+            fn backward(
+                self,
+                ops: Ops<Self::State, 1>,
+                grads: &mut Gradients,
+                checkpointer: &mut Checkpointer,
+            ) {
+                let state = checkpointer.retrieve_node_output(ops.state);
+                unary::<B, _>(ops.parents, ops.node, grads, |grad| {
+                    B::relu_backward(state, grad)
+                });
+            }
+        }
+
+        match Relu
+            .prepare::<C>([tensor.node.clone()])
+            .memory_bound()
+            .retro_forward(RetroRelu::<B>::new(tensor.node.id))
+            .parents([&tensor])
+            .stateful()
+        {
+            OpsKind::Tracked(mut prep) => {
+                let state = prep.checkpoint(&tensor);
+                prep.finish(state, B::relu(tensor.primitive))
+            }
+            OpsKind::UnTracked(prep) => prep.finish(B::relu(tensor.primitive)),
+        }
+    }
+
+    fn sigmoid(tensor: FloatTensor<Self>) -> FloatTensor<Self> {
+        #[derive(Debug)]
+        struct Sigmoid;
+
+        retro_unary!(RetroSigmoid, B::sigmoid);
+
+        impl<B: Backend> Backward<B, 1> for Sigmoid {
+            type State = NodeId;
+
+            fn backward(
+                self,
+                ops: Ops<Self::State, 1>,
+                grads: &mut Gradients,
+                checkpointer: &mut Checkpointer,
+            ) {
+                let input = checkpointer.retrieve_node_output(ops.state);
+                let output = B::sigmoid(input);
+                unary::<B, _>(ops.parents, ops.node, grads, |grad| {
+                    B::sigmoid_backward(output, grad)
+                });
+            }
+        }
+
+        match Sigmoid
+            .prepare::<C>([tensor.node.clone()])
+            .memory_bound()
+            .retro_forward(RetroSigmoid::<B>::new(tensor.node.id))
+            .parents([&tensor])
+            .stateful()
+        {
+            OpsKind::Tracked(mut prep) => {
+                let state = prep.checkpoint(&tensor);
+                prep.finish(state, B::sigmoid(tensor.primitive))
+            }
+            OpsKind::UnTracked(prep) => prep.finish(B::sigmoid(tensor.primitive)),
+        }
+    }
+
+    fn log_sigmoid(tensor: FloatTensor<Self>) -> FloatTensor<Self> {
+        #[derive(Debug)]
+        struct LogSigmoid;
+
+        retro_unary!(RetroLogSigmoid, B::log_sigmoid);
+
+        impl<B: Backend> Backward<B, 1> for LogSigmoid {
+            type State = NodeId;
+
+            fn backward(
+                self,
+                ops: Ops<Self::State, 1>,
+                grads: &mut Gradients,
+                checkpointer: &mut Checkpointer,
+            ) {
+                let input = checkpointer.retrieve_node_output(ops.state);
+
+                unary::<B, _>(ops.parents, ops.node, grads, |grad| {
+                    B::log_sigmoid_backward(input, grad)
+                });
+            }
+        }
+
+        match LogSigmoid
+            .prepare::<C>([tensor.node.clone()])
+            .memory_bound()
+            .retro_forward(RetroLogSigmoid::<B>::new(tensor.node.id))
+            .parents([&tensor])
+            .stateful()
+        {
+            OpsKind::Tracked(mut prep) => {
+                let state = prep.checkpoint(&tensor);
+                prep.finish(state, B::log_sigmoid(tensor.primitive.clone()))
+            }
+            OpsKind::UnTracked(prep) => prep.finish(B::log_sigmoid(tensor.primitive)),
+        }
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/backward.rs

@@ -0,0 +1,88 @@
+use super::{Ops, OpsPrep};
+use crate::{
+    checkpoint::{base::Checkpointer, builder::CheckpointerBuilder, strategy::CheckpointStrategy},
+    grads::Gradients,
+    graph::{ComputingProperty, NodeRef, Requirement},
+    utils::duplicate,
+};
+use burn_backend::Backend;
+
+/// Trait for all operations.
+///
+/// # Notes
+///
+/// Concrete types implementing this trait should not have any state.
+/// If a state is necessary during the backward pass,
+/// they should be declared with the associated type 'State'.
+pub trait Backward<B, const N: usize>: Send + core::fmt::Debug
+where
+    Self: Sized + 'static,
+    B: Backend,
+{
+    /// Associated type to compute the backward pass.
+    type State: Clone + Send + core::fmt::Debug + 'static;
+
+    /// The backward pass.
+    fn backward(
+        self,
+        ops: Ops<Self::State, N>,
+        grads: &mut Gradients,
+        checkpointer: &mut Checkpointer,
+    );
+
+    /// Prepare the backward ops.
+    fn prepare<C: CheckpointStrategy>(
+        self,
+        nodes: [NodeRef; N],
+    ) -> OpsPrep<Self, B, Self::State, C, N> {
+        let requirement = Requirement::from_nodes(&nodes);
+        OpsPrep::new(
+            nodes,
+            requirement,
+            self,
+            ComputingProperty::Ambiguous, // If not specified we start with ambiguous
+            CheckpointerBuilder::default(),
+        )
+    }
+}
+
+/// Execute a binary operation during the backward step.
+pub fn binary<B, FLhs, FRhs>(
+    parents: [Option<NodeRef>; 2],
+    node: NodeRef,
+    grads: &mut Gradients,
+    func_lhs: FLhs,
+    func_rhs: FRhs,
+) where
+    B: Backend,
+    FLhs: FnOnce(B::FloatTensorPrimitive) -> B::FloatTensorPrimitive,
+    FRhs: FnOnce(B::FloatTensorPrimitive) -> B::FloatTensorPrimitive,
+{
+    let [grad_4lhs, grad_4rhs] = duplicate(&parents, Some(grads.consume::<B>(&node)));
+    let [node_lhs, node_rhs] = parents;
+
+    if let Some(node) = node_lhs {
+        let grad = func_lhs(grad_4lhs.unwrap());
+        grads.register::<B>(node.id, grad)
+    }
+
+    if let Some(node) = node_rhs {
+        let grad = func_rhs(grad_4rhs.unwrap());
+        grads.register::<B>(node.id, grad)
+    }
+}
+
+/// Execute a unary operation during the backward step.
+pub fn unary<B, F>(parents: [Option<NodeRef>; 1], node: NodeRef, grads: &mut Gradients, func: F)
+where
+    B: Backend,
+    F: FnOnce(B::FloatTensorPrimitive) -> B::FloatTensorPrimitive,
+{
+    let [parent_node] = parents;
+    let grad = grads.consume::<B>(&node);
+
+    if let Some(node) = parent_node {
+        let grad = func(grad);
+        grads.register::<B>(node.id, grad)
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/base.rs

@@ -0,0 +1,317 @@
+use super::Backward;
+use crate::{
+    checkpoint::{
+        base::Checkpointer,
+        builder::{ActionType, CheckpointerBuilder},
+        retro_forward::RetroForward,
+        strategy::CheckpointStrategy,
+    },
+    grads::Gradients,
+    graph::{ComputingProperty, NodeId, NodeRef, Parent, Requirement, Step},
+    tensor::AutodiffTensor,
+};
+use alloc::boxed::Box;
+use burn_backend::{Backend, TensorMetadata, tensor::FloatTensor};
+use burn_std::Shape;
+use core::marker::PhantomData;
+
+/// Operation in preparation.
+///
+/// Each mode has its own set of functions to minimize cloning for unused backward states.
+#[derive(new)]
+pub struct OpsPrep<Backward, B, S, C, const N: usize, Mode = Init> {
+    nodes: [NodeRef; N],
+    requirement: Requirement,
+    backward: Backward,
+    compute_property: ComputingProperty,
+    checkpointer_builder: CheckpointerBuilder,
+    checkpoint_strategy: PhantomData<C>,
+    phantom_backend: PhantomData<B>,
+    phantom_state: PhantomData<S>,
+    marker: PhantomData<Mode>,
+}
+
+/// Operation is initialized
+pub struct Init;
+/// Operation has been tagged as memory bound
+pub struct MemoryBound;
+/// Memory bound operation has received its RetroForward
+pub struct MemoryBoundRetroForward;
+/// Operation's compute property is fixed
+pub struct ComputePropertyDone;
+/// Tracked operation tag.
+pub struct Tracked;
+/// Untracked operation tag.
+pub struct UnTracked;
+
+impl<BO, B, S, C, const N: usize> OpsPrep<BO, B, S, C, N, Init>
+where
+    B: Backend,
+    BO: Backward<B, N, State = S>,
+{
+    /// Indicates that the operation is compute bound, meaning its computation
+    /// is heavy and should not be recomputed
+    pub fn compute_bound(self) -> OpsPrep<BO, B, S, C, N, ComputePropertyDone> {
+        OpsPrep::new(
+            self.nodes,
+            self.requirement,
+            self.backward,
+            ComputingProperty::ComputeBound,
+            self.checkpointer_builder,
+        )
+    }
+
+    /// Indicates that the operation is memory bound, meaning its computation
+    /// is light and can be recomputed
+    pub fn memory_bound(self) -> OpsPrep<BO, B, S, C, N, MemoryBound> {
+        OpsPrep::new(
+            self.nodes,
+            self.requirement,
+            self.backward,
+            self.compute_property,
+            self.checkpointer_builder,
+        )
+    }
+}
+
+impl<BO, B, S, C, const N: usize> OpsPrep<BO, B, S, C, N, MemoryBound>
+where
+    B: Backend,
+    BO: Backward<B, N, State = S>,
+    C: CheckpointStrategy,
+{
+    /// Registers the retro forward, if needed
+    pub fn retro_forward<R: RetroForward>(
+        self,
+        retro_forward: R,
+    ) -> OpsPrep<BO, B, S, C, N, MemoryBoundRetroForward> {
+        OpsPrep::new(
+            self.nodes,
+            self.requirement,
+            self.backward,
+            C::compute_property(retro_forward),
+            self.checkpointer_builder,
+        )
+    }
+}
+
+impl<BO, B, S, C, const N: usize> OpsPrep<BO, B, S, C, N, MemoryBoundRetroForward>
+where
+    B: Backend,
+    BO: Backward<B, N, State = S>,
+    C: CheckpointStrategy,
+{
+    /// Checkpoints the parents, if needed
+    pub fn parents<'a, B2, A>(mut self, parents: A) -> OpsPrep<BO, B, S, C, N, ComputePropertyDone>
+    where
+        B2: Backend,
+        A: IntoIterator<Item = &'a AutodiffTensor<B2>>,
+    {
+        let compute_property = match C::checkpoint_parents(parents, &mut self.checkpointer_builder)
+        {
+            Ok(..) => self.compute_property,
+            Err(..) => ComputingProperty::ComputeBound,
+        };
+
+        OpsPrep::new(
+            self.nodes,
+            self.requirement,
+            self.backward,
+            compute_property,
+            self.checkpointer_builder,
+        )
+    }
+}
+
+impl<BO, B, C, const N: usize> OpsPrep<BO, B, (), C, N, ComputePropertyDone>
+where
+    B: Backend,
+    BO: Backward<B, N, State = ()>,
+{
+    /// Prepare a stateless operation.
+    pub fn stateless(self, output: FloatTensor<B>) -> AutodiffTensor<B> {
+        match self.stateful() {
+            OpsKind::Tracked(prep) => prep.finish((), output),
+            OpsKind::UnTracked(prep) => prep.finish(output),
+        }
+    }
+}
+
+impl<BO, B, S, C, const N: usize> OpsPrep<BO, B, S, C, N, ComputePropertyDone>
+where
+    B: Backend,
+    S: Clone + Send + core::fmt::Debug + 'static,
+    BO: Backward<B, N, State = S>,
+{
+    /// Prepare an operation that requires a state during the backward pass.
+    pub fn stateful(self) -> OpsKind<BO, B, S, C, N> {
+        match self.requirement.is_none() {
+            false => OpsKind::Tracked(OpsPrep::new(
+                self.nodes,
+                self.requirement,
+                self.backward,
+                self.compute_property,
+                self.checkpointer_builder,
+            )),
+            true => OpsKind::UnTracked(OpsPrep::new(
+                self.nodes,
+                self.requirement,
+                self.backward,
+                self.compute_property,
+                self.checkpointer_builder,
+            )),
+        }
+    }
+}
+
+impl<BO, B, S, C, const N: usize> OpsPrep<BO, B, S, C, N, UnTracked>
+where
+    B: Backend,
+    S: Clone + Send + core::fmt::Debug + 'static,
+    BO: Backward<B, N, State = S>,
+{
+    /// Finish the preparation of an untracked operation and returns the output tensor.
+    pub fn finish(self, output: FloatTensor<B>) -> AutodiffTensor<B> {
+        let output = AutodiffTensor::from_parents(
+            output,
+            &self.nodes,
+            self.requirement,
+            self.compute_property,
+        );
+        let parents = self.nodes.map(|node| node.clone_if_require_grad());
+        let ops = Ops::new(parents, output.node.clone(), ());
+
+        // We register the ops in the graph even if untracked, otherwise memory bound operations
+        // that have an untracked parent would not be able to retrieve it
+        output.register_step(UntrackedOpsStep::new(ops), self.checkpointer_builder)
+    }
+}
+
+impl<BO, B, S, C, const N: usize> OpsPrep<BO, B, S, C, N, Tracked>
+where
+    B: Backend,
+    S: Clone + Send + core::fmt::Debug + 'static,
+    BO: Backward<B, N, State = S>,
+{
+    /// Finish the preparation of a tracked operation and returns the output tensor.
+    pub fn finish(self, state: S, output: FloatTensor<B>) -> AutodiffTensor<B> {
+        let output = AutodiffTensor::from_parents(
+            output,
+            &self.nodes,
+            self.requirement,
+            self.compute_property,
+        );
+        let parents = self.nodes.map(|node| node.clone_if_require_grad());
+        let ops = Ops::new(parents, output.node.clone(), state);
+
+        output.register_step(OpsStep::new(ops, self.backward), self.checkpointer_builder)
+    }
+
+    /// Checkpoints the tensor
+    pub fn checkpoint(&mut self, tensor: &AutodiffTensor<B>) -> NodeId {
+        self.checkpointer_builder
+            .checkpoint(tensor, ActionType::Explicit);
+
+        tensor.node.id
+    }
+}
+
+/// Enum used before finishing tracked and untracked operations.
+pub enum OpsKind<BO, B, S, C, const N: usize> {
+    /// Tracked operation preparation.
+    Tracked(OpsPrep<BO, B, S, C, N, Tracked>),
+    /// Untracked operation preparation.
+    UnTracked(OpsPrep<BO, B, S, C, N, UnTracked>),
+}
+
+/// Operation containing its parent nodes, its own node and the backward step state.
+#[derive(new, Debug)]
+pub struct Ops<S, const N: usize> {
+    /// Parents nodes.
+    pub parents: [Option<NodeRef>; N],
+    /// The node.
+    pub node: NodeRef,
+    /// The state.
+    pub state: S,
+}
+
+/// Operation implementing backward [step](Step) with type erasing.
+#[derive(new, Debug)]
+struct OpsStep<B, T, SB, const N: usize>
+where
+    B: Backend,
+    T: Backward<B, N, State = SB>,
+    SB: Clone + Send + core::fmt::Debug + 'static,
+{
+    ops: Ops<SB, N>,
+    backward: T,
+    phantom: PhantomData<B>,
+}
+
+impl<B, T, SB, const N: usize> Step for OpsStep<B, T, SB, N>
+where
+    B: Backend,
+    T: Backward<B, N, State = SB>,
+    SB: Clone + Send + core::fmt::Debug + 'static,
+{
+    fn step(self: Box<Self>, grads: &mut Gradients, checkpointer: &mut Checkpointer) {
+        self.backward.backward(self.ops, grads, checkpointer);
+    }
+
+    fn node(&self) -> NodeId {
+        self.ops.node.id
+    }
+
+    fn parents(&self) -> &[Parent] {
+        &self.ops.node.parents
+    }
+
+    fn depth(&self) -> usize {
+        self.ops.node.order
+    }
+}
+
+#[derive(new, Debug)]
+struct UntrackedOpsStep<const N: usize> {
+    ops: Ops<(), N>,
+}
+
+impl<const N: usize> Step for UntrackedOpsStep<N> {
+    fn step(self: Box<Self>, _grads: &mut Gradients, _checkpointer: &mut Checkpointer) {
+        // Nothing to do
+    }
+
+    fn node(&self) -> NodeId {
+        self.ops.node.id
+    }
+
+    fn parents(&self) -> &[Parent] {
+        &self.ops.node.parents
+    }
+    fn depth(&self) -> usize {
+        self.ops.node.order
+    }
+}
+
+/// Make sure the grad tensor has the given shape.
+///
+/// If broadcasting happened during the forward pass, the gradients will be sum along the
+/// broadcasted dimension.
+pub fn broadcast_shape<B: Backend>(mut grad: FloatTensor<B>, shape: &Shape) -> FloatTensor<B> {
+    let shape_grad = grad.shape();
+    let ndims = shape_grad.num_dims();
+
+    for i in 0..ndims {
+        if shape_grad[i] != shape[i] {
+            if shape[i] != 1 {
+                panic!(
+                    "Invalid broadcast shapes: Next grad shape {:?}, Previous grad shape {:?}. {}",
+                    shape, shape_grad, "Expected the shape of the next grad to be 1."
+                );
+            }
+            grad = B::float_sum_dim(grad, i);
+        }
+    }
+
+    grad
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/bool_tensor.rs

@@ -0,0 +1,161 @@
+use crate::{Autodiff, checkpoint::strategy::CheckpointStrategy, tensor::AutodiffTensor};
+use alloc::vec::Vec;
+
+use burn_backend::{
+    Backend, ExecutionError, Scalar, TensorData,
+    ops::BoolTensorOps,
+    tensor::{BoolTensor, Device, IntTensor},
+};
+use burn_std::Shape;
+
+impl<B: Backend, C: CheckpointStrategy> BoolTensorOps<Self> for Autodiff<B, C> {
+    fn bool_from_data(data: TensorData, device: &Device<B>) -> BoolTensor<B> {
+        B::bool_from_data(data, device)
+    }
+
+    async fn bool_into_data(tensor: BoolTensor<B>) -> Result<TensorData, ExecutionError> {
+        B::bool_into_data(tensor).await
+    }
+
+    fn bool_into_int(tensor: BoolTensor<B>) -> IntTensor<B> {
+        B::bool_into_int(tensor)
+    }
+
+    fn bool_to_device(tensor: BoolTensor<B>, device: &Device<B>) -> BoolTensor<B> {
+        B::bool_to_device(tensor, device)
+    }
+
+    fn bool_device(tensor: &BoolTensor<B>) -> Device<B> {
+        B::bool_device(tensor)
+    }
+
+    fn bool_reshape(tensor: BoolTensor<B>, shape: Shape) -> BoolTensor<B> {
+        B::bool_reshape(tensor, shape)
+    }
+
+    fn bool_slice(tensor: BoolTensor<B>, slices: &[burn_std::Slice]) -> BoolTensor<B> {
+        B::bool_slice(tensor, slices)
+    }
+
+    fn bool_empty(shape: Shape, device: &Device<B>) -> BoolTensor<B> {
+        B::bool_empty(shape, device)
+    }
+
+    fn bool_zeros(shape: Shape, device: &Device<B>) -> BoolTensor<B> {
+        B::bool_zeros(shape, device)
+    }
+
+    fn bool_ones(shape: Shape, device: &Device<B>) -> BoolTensor<B> {
+        B::bool_ones(shape, device)
+    }
+
+    fn bool_slice_assign(
+        tensor: BoolTensor<Self>,
+        slices: &[burn_std::Slice],
+        value: BoolTensor<Self>,
+    ) -> BoolTensor<Self> {
+        B::bool_slice_assign(tensor, slices, value)
+    }
+
+    fn bool_cat(tensors: Vec<BoolTensor<B>>, dim: usize) -> BoolTensor<B> {
+        B::bool_cat(tensors, dim)
+    }
+
+    fn bool_equal(lhs: BoolTensor<B>, rhs: BoolTensor<B>) -> BoolTensor<B> {
+        B::bool_equal(lhs, rhs)
+    }
+
+    fn bool_not(tensor: BoolTensor<B>) -> BoolTensor<B> {
+        B::bool_not(tensor)
+    }
+
+    fn bool_and(lhs: BoolTensor<B>, rhs: BoolTensor<B>) -> BoolTensor<B> {
+        B::bool_and(lhs, rhs)
+    }
+
+    fn bool_or(lhs: BoolTensor<B>, rhs: BoolTensor<B>) -> BoolTensor<B> {
+        B::bool_or(lhs, rhs)
+    }
+
+    fn bool_xor(lhs: BoolTensor<B>, rhs: BoolTensor<B>) -> BoolTensor<B> {
+        B::bool_xor(lhs, rhs)
+    }
+
+    fn bool_into_float(tensor: BoolTensor<B>) -> <Autodiff<B> as Backend>::FloatTensorPrimitive {
+        AutodiffTensor::new(B::bool_into_float(tensor))
+    }
+
+    fn bool_swap_dims(
+        tensor: <Autodiff<B> as Backend>::BoolTensorPrimitive,
+        dim1: usize,
+        dim2: usize,
+    ) -> <Autodiff<B> as Backend>::BoolTensorPrimitive {
+        B::bool_swap_dims(tensor, dim1, dim2)
+    }
+
+    fn bool_permute(tensor: BoolTensor<Self>, axes: &[usize]) -> BoolTensor<Self> {
+        B::bool_permute(tensor, axes)
+    }
+
+    fn bool_flip(tensor: BoolTensor<B>, axes: &[usize]) -> BoolTensor<B> {
+        B::bool_flip(tensor, axes)
+    }
+
+    async fn bool_argwhere(tensor: BoolTensor<B>) -> IntTensor<B> {
+        B::bool_argwhere(tensor).await
+    }
+
+    fn bool_expand(tensor: BoolTensor<B>, shape: Shape) -> BoolTensor<B> {
+        B::bool_expand(tensor, shape)
+    }
+
+    fn bool_repeat_dim(tensor: BoolTensor<B>, dim: usize, times: usize) -> BoolTensor<B> {
+        B::bool_repeat_dim(tensor, dim, times)
+    }
+
+    fn bool_unfold(
+        tensor: BoolTensor<Self>,
+        dim: usize,
+        size: usize,
+        step: usize,
+    ) -> BoolTensor<Self> {
+        B::bool_unfold(tensor, dim, size, step)
+    }
+
+    fn bool_mask_where(
+        tensor: BoolTensor<Self>,
+        mask: BoolTensor<Self>,
+        source: BoolTensor<Self>,
+    ) -> BoolTensor<Self> {
+        B::bool_mask_where(tensor, mask, source)
+    }
+
+    fn bool_mask_fill(
+        tensor: BoolTensor<Self>,
+        mask: BoolTensor<Self>,
+        value: Scalar,
+    ) -> BoolTensor<Self> {
+        B::bool_mask_fill(tensor, mask, value)
+    }
+
+    fn bool_gather(
+        dim: usize,
+        tensor: BoolTensor<Self>,
+        indices: IntTensor<Self>,
+    ) -> BoolTensor<Self> {
+        B::bool_gather(dim, tensor, indices)
+    }
+
+    fn bool_scatter_or(
+        dim: usize,
+        tensor: BoolTensor<Self>,
+        indices: IntTensor<Self>,
+        value: BoolTensor<Self>,
+    ) -> BoolTensor<Self> {
+        B::bool_scatter_or(dim, tensor, indices, value)
+    }
+
+    fn bool_equal_elem(lhs: BoolTensor<Self>, rhs: Scalar) -> BoolTensor<Self> {
+        B::bool_equal_elem(lhs, rhs)
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/int_tensor.rs

@@ -0,0 +1,406 @@
+use crate::{Autodiff, checkpoint::strategy::CheckpointStrategy, tensor::AutodiffTensor};
+use alloc::vec::Vec;
+
+use burn_backend::{
+    Backend, Distribution, ExecutionError, Scalar, TensorData,
+    ops::IntTensorOps,
+    tensor::{BoolTensor, Device, IntTensor},
+};
+use burn_std::{IntDType, Shape};
+
+impl<B: Backend, C: CheckpointStrategy> IntTensorOps<Self> for Autodiff<B, C> {
+    fn int_from_data(data: TensorData, device: &Device<Self>) -> IntTensor<B> {
+        B::int_from_data(data, device)
+    }
+
+    async fn int_into_data(tensor: IntTensor<B>) -> Result<TensorData, ExecutionError> {
+        B::int_into_data(tensor).await
+    }
+
+    fn int_to_device(tensor: IntTensor<B>, device: &Device<Self>) -> IntTensor<B> {
+        B::int_to_device(tensor, device)
+    }
+
+    fn int_device(tensor: &IntTensor<B>) -> Device<Self> {
+        B::int_device(tensor)
+    }
+
+    fn int_reshape(tensor: IntTensor<B>, shape: Shape) -> IntTensor<B> {
+        B::int_reshape(tensor, shape)
+    }
+
+    fn int_slice(tensor: IntTensor<B>, slices: &[burn_std::Slice]) -> IntTensor<B> {
+        B::int_slice(tensor, slices)
+    }
+
+    fn int_empty(
+        shape: Shape,
+        device: &<Autodiff<B> as Backend>::Device,
+        dtype: IntDType,
+    ) -> IntTensor<B> {
+        B::int_empty(shape, device, dtype)
+    }
+
+    fn int_slice_assign(
+        tensor: IntTensor<B>,
+        slices: &[burn_std::Slice],
+        value: IntTensor<B>,
+    ) -> IntTensor<B> {
+        B::int_slice_assign(tensor, slices, value)
+    }
+
+    fn int_cat(tensors: Vec<IntTensor<B>>, dim: usize) -> IntTensor<B> {
+        B::int_cat(tensors, dim)
+    }
+
+    fn int_equal(lhs: IntTensor<B>, rhs: IntTensor<B>) -> BoolTensor<B> {
+        B::int_equal(lhs, rhs)
+    }
+
+    fn int_equal_elem(lhs: IntTensor<B>, rhs: Scalar) -> BoolTensor<B> {
+        B::int_equal_elem(lhs, rhs)
+    }
+
+    fn int_add(lhs: IntTensor<B>, rhs: IntTensor<B>) -> IntTensor<B> {
+        B::int_add(lhs, rhs)
+    }
+
+    fn int_add_scalar(lhs: IntTensor<B>, rhs: Scalar) -> IntTensor<B> {
+        B::int_add_scalar(lhs, rhs)
+    }
+
+    fn int_clamp_min(tensor: IntTensor<B>, min: Scalar) -> IntTensor<B> {
+        B::int_clamp_min(tensor, min)
+    }
+
+    fn int_clamp_max(tensor: IntTensor<B>, max: Scalar) -> IntTensor<B> {
+        B::int_clamp_max(tensor, max)
+    }
+
+    fn int_clamp(tensor: IntTensor<B>, min: Scalar, max: Scalar) -> IntTensor<B> {
+        B::int_clamp(tensor, min, max)
+    }
+
+    fn int_sub(lhs: IntTensor<B>, rhs: IntTensor<B>) -> IntTensor<B> {
+        B::int_sub(lhs, rhs)
+    }
+
+    fn int_sub_scalar(lhs: IntTensor<B>, rhs: Scalar) -> IntTensor<B> {
+        B::int_sub_scalar(lhs, rhs)
+    }
+
+    fn int_mul(lhs: IntTensor<B>, rhs: IntTensor<B>) -> IntTensor<B> {
+        B::int_mul(lhs, rhs)
+    }
+
+    fn int_mul_scalar(lhs: IntTensor<B>, rhs: Scalar) -> IntTensor<B> {
+        B::int_mul_scalar(lhs, rhs)
+    }
+
+    fn int_div(lhs: IntTensor<B>, rhs: IntTensor<B>) -> IntTensor<B> {
+        B::int_div(lhs, rhs)
+    }
+
+    fn int_div_scalar(lhs: IntTensor<B>, rhs: Scalar) -> IntTensor<B> {
+        B::int_div_scalar(lhs, rhs)
+    }
+
+    fn int_remainder(lhs: IntTensor<B>, rhs: IntTensor<B>) -> IntTensor<B> {
+        B::int_remainder(lhs, rhs)
+    }
+
+    fn int_remainder_scalar(lhs: IntTensor<B>, rhs: Scalar) -> IntTensor<B> {
+        B::int_remainder_scalar(lhs, rhs)
+    }
+
+    fn int_matmul(lhs: IntTensor<B>, rhs: IntTensor<B>) -> IntTensor<B> {
+        B::int_matmul(lhs, rhs)
+    }
+
+    fn int_neg(tensor: IntTensor<B>) -> IntTensor<B> {
+        B::int_neg(tensor)
+    }
+
+    fn int_zeros(shape: Shape, device: &Device<Self>, dtype: IntDType) -> IntTensor<B> {
+        B::int_zeros(shape, device, dtype)
+    }
+
+    fn int_ones(shape: Shape, device: &Device<Self>, dtype: IntDType) -> IntTensor<B> {
+        B::int_ones(shape, device, dtype)
+    }
+
+    fn int_full(
+        shape: Shape,
+        fill_value: Scalar,
+        device: &Device<Self>,
+        dtype: IntDType,
+    ) -> IntTensor<B> {
+        B::int_full(shape, fill_value, device, dtype)
+    }
+
+    fn int_sum(tensor: IntTensor<B>) -> IntTensor<B> {
+        B::int_sum(tensor)
+    }
+
+    fn int_sum_dim(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_sum_dim(tensor, dim)
+    }
+
+    fn int_mean(tensor: IntTensor<B>) -> IntTensor<B> {
+        B::int_mean(tensor)
+    }
+
+    fn int_mean_dim(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_mean_dim(tensor, dim)
+    }
+
+    fn int_cumsum(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_cumsum(tensor, dim)
+    }
+
+    fn int_cumprod(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_cumprod(tensor, dim)
+    }
+
+    fn int_cummin(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_cummin(tensor, dim)
+    }
+
+    fn int_cummax(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_cummax(tensor, dim)
+    }
+
+    fn int_repeat_dim(tensor: IntTensor<B>, dim: usize, times: usize) -> IntTensor<B> {
+        B::int_repeat_dim(tensor, dim, times)
+    }
+
+    fn int_greater(lhs: IntTensor<B>, rhs: IntTensor<B>) -> BoolTensor<B> {
+        B::int_greater(lhs, rhs)
+    }
+
+    fn int_greater_elem(lhs: IntTensor<B>, rhs: Scalar) -> BoolTensor<B> {
+        B::int_greater_elem(lhs, rhs)
+    }
+
+    fn int_greater_equal(lhs: IntTensor<B>, rhs: IntTensor<B>) -> BoolTensor<B> {
+        B::int_greater_equal(lhs, rhs)
+    }
+
+    fn int_greater_equal_elem(lhs: IntTensor<B>, rhs: Scalar) -> BoolTensor<B> {
+        B::int_greater_equal_elem(lhs, rhs)
+    }
+
+    fn int_lower(lhs: IntTensor<B>, rhs: IntTensor<B>) -> BoolTensor<B> {
+        B::int_lower(lhs, rhs)
+    }
+
+    fn int_lower_elem(lhs: IntTensor<B>, rhs: Scalar) -> BoolTensor<B> {
+        B::int_lower_elem(lhs, rhs)
+    }
+
+    fn int_lower_equal(lhs: IntTensor<B>, rhs: IntTensor<B>) -> BoolTensor<B> {
+        B::int_lower_equal(lhs, rhs)
+    }
+
+    fn int_lower_equal_elem(lhs: IntTensor<B>, rhs: Scalar) -> BoolTensor<B> {
+        B::int_lower_equal_elem(lhs, rhs)
+    }
+
+    fn int_gather(dim: usize, tensor: IntTensor<B>, indices: IntTensor<B>) -> IntTensor<B> {
+        B::int_gather(dim, tensor, indices)
+    }
+
+    fn int_scatter_add(
+        dim: usize,
+        tensor: IntTensor<B>,
+        indices: IntTensor<B>,
+        value: IntTensor<B>,
+    ) -> IntTensor<B> {
+        B::int_scatter_add(dim, tensor, indices, value)
+    }
+
+    fn int_select(tensor: IntTensor<B>, dim: usize, indices: IntTensor<B>) -> IntTensor<B> {
+        B::int_select(tensor, dim, indices)
+    }
+
+    fn int_select_add(
+        tensor: IntTensor<B>,
+        dim: usize,
+        indices: IntTensor<B>,
+        value: IntTensor<B>,
+    ) -> IntTensor<B> {
+        B::int_select_add(tensor, dim, indices, value)
+    }
+
+    fn int_mask_where(
+        tensor: IntTensor<B>,
+        mask: BoolTensor<B>,
+        value: IntTensor<B>,
+    ) -> <Autodiff<B> as Backend>::IntTensorPrimitive {
+        B::int_mask_where(tensor, mask, value)
+    }
+
+    fn int_mask_fill(
+        tensor: IntTensor<B>,
+        mask: BoolTensor<B>,
+        value: Scalar,
+    ) -> <Autodiff<B> as Backend>::IntTensorPrimitive {
+        B::int_mask_fill(tensor, mask, value)
+    }
+
+    fn int_argmax(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_argmax(tensor, dim)
+    }
+    fn int_argmin(tensor: IntTensor<B>, dim: usize) -> IntTensor<B> {
+        B::int_argmin(tensor, dim)
+    }
+    fn int_max(tensor: B::IntTensorPrimitive) -> B::IntTensorPrimitive {
+        B::int_max(tensor)
+    }
+    fn int_max_dim(tensor: B::IntTensorPrimitive, dim: usize) -> B::IntTensorPrimitive {
+        B::int_max_dim(tensor, dim)
+    }
+    fn int_max_dim_with_indices(
+        tensor: B::IntTensorPrimitive,
+        dim: usize,
+    ) -> (B::IntTensorPrimitive, B::IntTensorPrimitive) {
+        B::int_max_dim_with_indices(tensor, dim)
+    }
+    fn int_min(tensor: B::IntTensorPrimitive) -> B::IntTensorPrimitive {
+        B::int_min(tensor)
+    }
+    fn int_min_dim(tensor: B::IntTensorPrimitive, dim: usize) -> B::IntTensorPrimitive {
+        B::int_min_dim(tensor, dim)
+    }
+    fn int_min_dim_with_indices(
+        tensor: B::IntTensorPrimitive,
+        dim: usize,
+    ) -> (B::IntTensorPrimitive, B::IntTensorPrimitive) {
+        B::int_min_dim_with_indices(tensor, dim)
+    }
+    fn int_abs(tensor: B::IntTensorPrimitive) -> B::IntTensorPrimitive {
+        B::int_abs(tensor)
+    }
+    fn int_into_float(
+        tensor: <Autodiff<B> as Backend>::IntTensorPrimitive,
+    ) -> <Autodiff<B> as Backend>::FloatTensorPrimitive {
+        AutodiffTensor::new(B::int_into_float(tensor))
+    }
+
+    fn int_swap_dims(
+        tensor: <Autodiff<B> as Backend>::IntTensorPrimitive,
+        dim1: usize,
+        dim2: usize,
+    ) -> <Autodiff<B> as Backend>::IntTensorPrimitive {
+        B::int_swap_dims(tensor, dim1, dim2)
+    }
+
+    fn int_random(
+        shape: Shape,
+        distribution: Distribution,
+        device: &Device<Self>,
+    ) -> IntTensor<Self> {
+        B::int_random(shape, distribution, device)
+    }
+
+    fn int_arange(range: core::ops::Range<i64>, device: &Device<Self>) -> IntTensor<Self> {
+        B::int_arange(range, device)
+    }
+
+    fn int_permute(tensor: IntTensor<Self>, axes: &[usize]) -> IntTensor<Self> {
+        B::int_permute(tensor, axes)
+    }
+
+    fn int_flip(tensor: IntTensor<Self>, axes: &[usize]) -> IntTensor<Self> {
+        B::int_flip(tensor, axes)
+    }
+
+    fn int_sign(tensor: IntTensor<Self>) -> IntTensor<Self> {
+        B::int_sign(tensor)
+    }
+
+    fn int_prod(tensor: IntTensor<Self>) -> IntTensor<Self> {
+        B::int_prod(tensor)
+    }
+
+    fn int_prod_dim(tensor: IntTensor<Self>, dim: usize) -> IntTensor<Self> {
+        B::int_prod_dim(tensor, dim)
+    }
+
+    fn int_expand(tensor: IntTensor<B>, shape: Shape) -> IntTensor<B> {
+        B::int_expand(tensor, shape)
+    }
+
+    fn int_sort(tensor: IntTensor<Self>, dim: usize, descending: bool) -> IntTensor<Self> {
+        B::int_sort(tensor, dim, descending)
+    }
+
+    fn int_sort_with_indices(
+        tensor: IntTensor<Self>,
+        dim: usize,
+        descending: bool,
+    ) -> (IntTensor<Self>, IntTensor<Self>) {
+        B::int_sort_with_indices(tensor, dim, descending)
+    }
+
+    fn int_argsort(tensor: IntTensor<Self>, dim: usize, descending: bool) -> IntTensor<Self> {
+        B::int_argsort(tensor, dim, descending)
+    }
+
+    fn bitwise_and(lhs: IntTensor<Self>, rhs: IntTensor<Self>) -> IntTensor<Self> {
+        B::bitwise_and(lhs, rhs)
+    }
+
+    fn bitwise_and_scalar(lhs: IntTensor<Self>, rhs: Scalar) -> IntTensor<Self> {
+        B::bitwise_and_scalar(lhs, rhs)
+    }
+
+    fn bitwise_or(lhs: IntTensor<Self>, rhs: IntTensor<Self>) -> IntTensor<Self> {
+        B::bitwise_or(lhs, rhs)
+    }
+
+    fn bitwise_or_scalar(lhs: IntTensor<Self>, rhs: Scalar) -> IntTensor<Self> {
+        B::bitwise_or_scalar(lhs, rhs)
+    }
+
+    fn bitwise_xor(lhs: IntTensor<Self>, rhs: IntTensor<Self>) -> IntTensor<Self> {
+        B::bitwise_xor(lhs, rhs)
+    }
+
+    fn bitwise_xor_scalar(lhs: IntTensor<Self>, rhs: Scalar) -> IntTensor<Self> {
+        B::bitwise_xor_scalar(lhs, rhs)
+    }
+
+    fn bitwise_not(tensor: IntTensor<Self>) -> IntTensor<Self> {
+        B::bitwise_not(tensor)
+    }
+
+    fn bitwise_left_shift(lhs: IntTensor<Self>, rhs: IntTensor<Self>) -> IntTensor<Self> {
+        B::bitwise_left_shift(lhs, rhs)
+    }
+
+    fn bitwise_left_shift_scalar(lhs: IntTensor<Self>, rhs: Scalar) -> IntTensor<Self> {
+        B::bitwise_left_shift_scalar(lhs, rhs)
+    }
+
+    fn bitwise_right_shift(lhs: IntTensor<Self>, rhs: IntTensor<Self>) -> IntTensor<Self> {
+        B::bitwise_right_shift(lhs, rhs)
+    }
+
+    fn bitwise_right_shift_scalar(lhs: IntTensor<Self>, rhs: Scalar) -> IntTensor<Self> {
+        B::bitwise_right_shift_scalar(lhs, rhs)
+    }
+
+    fn int_cast(tensor: IntTensor<Self>, dtype: IntDType) -> IntTensor<Self> {
+        B::int_cast(tensor, dtype)
+    }
+
+    fn int_unfold(
+        tensor: IntTensor<Self>,
+        dim: usize,
+        size: usize,
+        step: usize,
+    ) -> IntTensor<Self> {
+        B::int_unfold(tensor, dim, size, step)
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/maxmin.rs

@@ -0,0 +1,27 @@
+use super::{Backward, Ops, unary};
+use crate::{checkpoint::base::Checkpointer, grads::Gradients};
+use burn_backend::{Backend, TensorMetadata};
+use burn_std::Shape;
+
+#[derive(Debug)]
+pub(crate) struct MaxMinDim;
+
+impl<B: Backend> Backward<B, 1> for MaxMinDim {
+    type State = (B::IntTensorPrimitive, Shape, usize);
+
+    fn backward(
+        self,
+        ops: Ops<Self::State, 1>,
+        grads: &mut Gradients,
+        _checkpointer: &mut Checkpointer,
+    ) {
+        unary::<B, _>(ops.parents, ops.node, grads, |grad| {
+            let (indices, shape, dim) = ops.state;
+            let device = B::float_device(&grad);
+            let dtype = grad.dtype();
+            let zeros = B::float_zeros(shape, &device, dtype.into());
+
+            B::float_scatter_add(dim, zeros, indices, grad)
+        });
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/mod.rs

@@ -0,0 +1,15 @@
+mod activation;
+mod backward;
+mod base;
+mod bool_tensor;
+mod int_tensor;
+mod module;
+mod qtensor;
+mod tensor;
+mod transaction;
+
+pub(crate) mod maxmin;
+pub(crate) mod sort;
+
+pub use backward::*;
+pub use base::*;

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/qtensor.rs

@@ -0,0 +1,106 @@
+use burn_backend::{
+    Backend, ExecutionError, TensorData,
+    ops::QTensorOps,
+    tensor::{
+        Device, FloatTensor, IntTensor, QuantizedTensor,
+        quantization::QuantizationParametersPrimitive,
+    },
+};
+use burn_std::{QuantScheme, Shape};
+
+use crate::{Autodiff, checkpoint::strategy::CheckpointStrategy};
+
+impl<B: Backend, C: CheckpointStrategy> QTensorOps<Self> for Autodiff<B, C> {
+    fn q_from_data(_data: TensorData, _device: &Device<Self>) -> QuantizedTensor<Self> {
+        todo!()
+    }
+
+    fn quantize(
+        _tensor: FloatTensor<Self>,
+        _scheme: &QuantScheme,
+        _qparams: QuantizationParametersPrimitive<Self>,
+    ) -> QuantizedTensor<Self> {
+        todo!() // required for QAT
+    }
+
+    fn quantize_dynamic(
+        _tensor: FloatTensor<Self>,
+        _scheme: &QuantScheme,
+    ) -> QuantizedTensor<Self> {
+        todo!()
+    }
+
+    fn dequantize(_tensor: QuantizedTensor<Self>) -> FloatTensor<Self> {
+        todo!()
+    }
+
+    fn q_device(tensor: &QuantizedTensor<Self>) -> Device<Self> {
+        B::q_device(tensor)
+    }
+
+    fn q_to_device(
+        _tensor: QuantizedTensor<Self>,
+        _device: &Device<Self>,
+    ) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_reshape(tensor: QuantizedTensor<Self>, shape: Shape) -> QuantizedTensor<Self> {
+        B::q_reshape(tensor, shape)
+    }
+
+    async fn q_into_data(tensor: QuantizedTensor<Self>) -> Result<TensorData, ExecutionError> {
+        B::q_into_data(tensor).await
+    }
+
+    fn q_swap_dims(
+        _tensor: QuantizedTensor<Self>,
+        _dim1: usize,
+        _dim2: usize,
+    ) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_permute(_tensor: QuantizedTensor<Self>, _axes: &[usize]) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_flip(_tensor: QuantizedTensor<Self>, _axes: &[usize]) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_gather(
+        _dim: usize,
+        _tensor: QuantizedTensor<Self>,
+        _indices: IntTensor<Self>,
+    ) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_select(
+        _tensor: QuantizedTensor<Self>,
+        _dim: usize,
+        _indices: IntTensor<Self>,
+    ) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_slice(
+        _tensor: QuantizedTensor<Self>,
+        _slices: &[burn_std::Slice],
+    ) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+
+    fn q_argmax(tensor: QuantizedTensor<Self>, dim: usize) -> IntTensor<Self> {
+        B::q_argmax(tensor, dim)
+    }
+
+    fn q_argmin(tensor: QuantizedTensor<Self>, dim: usize) -> IntTensor<Self> {
+        B::q_argmin(tensor, dim)
+    }
+
+    fn q_expand(_tensor: QuantizedTensor<Self>, _shape: Shape) -> QuantizedTensor<Self> {
+        unimplemented!()
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/sort.rs

@@ -0,0 +1,27 @@
+use super::{Backward, Ops, unary};
+use crate::{checkpoint::base::Checkpointer, grads::Gradients};
+use burn_backend::{Backend, TensorMetadata};
+use burn_std::Shape;
+
+#[derive(Debug)]
+pub(crate) struct SortDim;
+
+impl<B: Backend> Backward<B, 1> for SortDim {
+    type State = (B::IntTensorPrimitive, Shape, usize);
+
+    fn backward(
+        self,
+        ops: Ops<Self::State, 1>,
+        grads: &mut Gradients,
+        _checkpointer: &mut Checkpointer,
+    ) {
+        unary::<B, _>(ops.parents, ops.node, grads, |grad| {
+            let (indices, shape, dim) = ops.state;
+            let device = B::float_device(&grad);
+            let dtype = grad.dtype();
+            let zeros = B::float_zeros(shape, &device, dtype.into());
+
+            B::float_scatter_add(dim, zeros, indices, grad)
+        });
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/ops/transaction.rs

@@ -0,0 +1,24 @@
+use burn_backend::{
+    Backend, ExecutionError,
+    ops::{TransactionOps, TransactionPrimitive},
+};
+
+use crate::{Autodiff, checkpoint::strategy::CheckpointStrategy};
+
+impl<B: Backend, C: CheckpointStrategy> TransactionOps<Self> for Autodiff<B, C> {
+    async fn tr_execute(
+        transaction: TransactionPrimitive<Self>,
+    ) -> Result<burn_backend::ops::TransactionPrimitiveData, ExecutionError> {
+        B::tr_execute(TransactionPrimitive::new(
+            transaction
+                .read_floats
+                .into_iter()
+                .map(|t| t.primitive)
+                .collect(),
+            transaction.read_qfloats,
+            transaction.read_ints,
+            transaction.read_bools,
+        ))
+        .await
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/runtime/client.rs

@@ -0,0 +1,18 @@
+use crate::{
+    checkpoint::builder::CheckpointerBuilder,
+    grads::Gradients,
+    graph::StepBoxed,
+    tensor::{AutodiffTensor, NodeRefCount},
+};
+use burn_backend::Backend;
+
+/// Client used to communicate with the autodiff server.
+pub trait AutodiffClient: Send + Clone {
+    /// Register a new step.
+    fn register(&self, node_id: NodeRefCount, step: StepBoxed, actions: CheckpointerBuilder);
+    /// Call backpropagation from the given tensor.
+    fn backward<B: Backend>(&self, tensor: AutodiffTensor<B>) -> Gradients;
+}
+
+/// Client implementation in used.
+pub type AutodiffClientImpl = super::graph::GraphMutexClient;

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/runtime/graph.rs

@@ -0,0 +1,335 @@
+use super::{AutodiffClient, server::AutodiffServer};
+use crate::{
+    NodeId,
+    checkpoint::builder::CheckpointerBuilder,
+    grads::Gradients,
+    graph::{Parent, StepBoxed},
+    runtime::server::NodeCleaner,
+    tensor::{AutodiffTensor, NodeRefCount},
+};
+use alloc::sync::Arc;
+use alloc::vec::Vec;
+use burn_backend::Backend;
+use hashbrown::{HashMap, HashSet};
+
+#[cfg(feature = "std")]
+use parking_lot::{Mutex, MutexGuard};
+
+#[cfg(not(feature = "std"))]
+use spin::{Mutex, MutexGuard};
+
+/// A client for managing multiple graphs using mutex-based synchronization.
+///
+/// The biggest benefit of using this client implementation is that each graph can modify its own
+/// data without blocking other graphs, which is essential for multi-device training.
+///
+/// # Notes
+///
+/// The [AutodiffServer] fully supports multiple graphs with sharing nodes, however those type of
+/// graphs will be stored under a single mutex-protected graph by the client, limiting
+/// parallelisation.
+#[derive(Clone, new, Debug)]
+pub struct GraphMutexClient;
+
+/// Manages a collection of graphs, mapping [node ids](NodeId) to their respective graph.
+///
+/// The `GraphLocator` is responsible for selecting and merging graphs based on their IDs and parent
+/// dependencies, ensuring proper synchronization and server allocation.
+///
+/// # Notes
+///
+/// Multiple node ids can point to the same graph, where the autodiff graph is stored.
+#[derive(Default)]
+pub struct GraphLocator {
+    graphs: HashMap<NodeId, Arc<Graph>>,
+    /// We keep a mapping of each original node id (graph id) => all nodes that point to that graph.
+    /// This is to ensure that when merging graphs, we correctly move all previous graphs to
+    /// the new merged one.
+    keys: HashMap<NodeId, HashSet<NodeId>>,
+}
+
+/// Represents a single computation graph with a mutex-protected server.
+///
+/// Each `Graph` contains an [AutodiffServer] and the original [NodeId] where the server was
+/// first created.
+pub(crate) struct Graph {
+    origin: NodeId,
+    state: Mutex<GraphState>,
+}
+
+#[derive(Default)]
+struct GraphState {
+    server: AutodiffServer,
+}
+
+impl core::fmt::Debug for Graph {
+    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
+        f.debug_struct("Graph")
+            .field("origin", &self.origin)
+            .finish()
+    }
+}
+
+static STATE: Mutex<Option<GraphLocator>> = Mutex::new(None);
+
+impl GraphMutexClient {
+    /// Retrieves or creates a graph for the given [NodeId] and parent dependencies.
+    ///
+    /// # Parameters
+    /// - `node`: The unique identifier for the stream.
+    /// - `parents`: A slice of parent nodes that the stream depends on.
+    ///
+    /// # Returns
+    /// An `Arc<Graph>` representing the selected or newly created stream.
+    fn graph(node: NodeId, parents: &[Parent]) -> Arc<Graph> {
+        let mut state = STATE.lock();
+
+        match state.as_mut() {
+            Some(locator) => locator.select(node, parents),
+            None => {
+                let mut locator = GraphLocator::default();
+                let stream = locator.select(node, parents);
+                *state = Some(locator);
+                stream
+            }
+        }
+    }
+}
+
+impl AutodiffClient for GraphMutexClient {
+    fn register(&self, node_id_ref: NodeRefCount, step: StepBoxed, actions: CheckpointerBuilder) {
+        let node_id = *node_id_ref;
+        let graph = GraphMutexClient::graph(node_id, step.parents());
+        let mut state = graph.state.lock();
+
+        state.server.register(node_id_ref, step, actions);
+    }
+
+    fn backward<B: Backend>(&self, root: AutodiffTensor<B>) -> Gradients {
+        let node_id = root.node.id;
+        let graph = GraphMutexClient::graph(root.node.id, &[]);
+
+        let grads = Gradients::new::<B>(root.node, root.primitive);
+        let grads = {
+            let mut state = graph.state.lock();
+            state.server.backward::<GraphCleaner>(grads, node_id)
+        }; // lock released
+
+        GraphCleaner::cleanup_orphaned_entries();
+
+        grads
+    }
+}
+
+struct GraphCleaner<'a> {
+    guard: MutexGuard<'a, Option<GraphLocator>>,
+}
+
+impl<'a> GraphCleaner<'a> {
+    fn cleanup_orphaned_entries() {
+        let graphs = {
+            // Get the available graphs and release the lock
+            match STATE.lock().as_ref() {
+                Some(state) => state.graphs.clone(),
+                None => return,
+            }
+        };
+
+        let mut should_remove = Vec::new();
+        for graph in graphs.values() {
+            {
+                let mut guard = graph.state.lock();
+                // Double safety: in case it was marked as no longer useful, but other
+                // nodes are still relevant, we only check which nodes can safely be removed.
+                if !guard.server.maybe_useful() {
+                    guard
+                        .server
+                        .free_unused_roots(|node| should_remove.push(*node));
+                }
+            }
+        }
+
+        if !should_remove.is_empty() {
+            let mut state = STATE.lock();
+            if let Some(state) = state.as_mut() {
+                for node in should_remove {
+                    state.remove_entry(&node);
+                }
+            }
+        }
+    }
+}
+
+impl<'a> NodeCleaner for GraphCleaner<'a> {
+    fn init() -> Self {
+        let guard = STATE.lock();
+        Self { guard }
+    }
+
+    fn clean(&mut self, node: &NodeId) {
+        if let Some(state) = self.guard.as_mut() {
+            state.remove_entry(node);
+        }
+    }
+}
+
+impl GraphLocator {
+    /// Selects a single graph for the given [NodeId], considering parent dependencies.
+    ///
+    /// If multiple graphs are found, they are merged into a single one.
+    ///
+    /// # Parameters
+    /// - `node`: The node ID of the graph to select.
+    /// - `parents`: A slice of parent nodes that the graph depends on.
+    ///
+    /// # Returns
+    ///
+    /// An `Arc<Graph>` representing the selected or merged graph.
+    pub(crate) fn select(&mut self, node: NodeId, parents: &[Parent]) -> Arc<Graph> {
+        match self.analyse(node, parents) {
+            GraphAnalysis::NoCollision(graph) => {
+                if graph.origin != node {
+                    self.graphs.insert(node, graph.clone());
+                    self.register_key(graph.origin, node);
+                }
+
+                graph
+            }
+            GraphAnalysis::Collisions(graphs) => self.merge(node, graphs),
+        }
+    }
+
+    /// Analyses the graph for a given node and its parents, returning the associated `GraphAnalysis`.
+    fn analyse(&mut self, node: NodeId, parents: &[Parent]) -> GraphAnalysis {
+        // If no parents, there is no collision, therefore a single graph is ok.
+        if parents.is_empty() {
+            let graph = match self.graphs.get(&node) {
+                Some(val) => val.clone(),
+                None => self.new_graph(node),
+            };
+            return GraphAnalysis::NoCollision(graph);
+        };
+
+        // We collect all graphs of parents and of the current node based on their origin node id.
+        let mut graphs = HashMap::<NodeId, Arc<Graph>>::new();
+
+        if let Some(val) = self.graphs.get(&node) {
+            graphs.insert(val.origin, val.clone());
+        }
+
+        for parent in parents {
+            match self.graphs.get(&parent.id) {
+                Some(graph) => graphs.insert(graph.origin, graph.clone()),
+                None => continue,
+            };
+        }
+
+        if graphs.is_empty() {
+            return match self.graphs.get(&node) {
+                Some(old) => GraphAnalysis::NoCollision(old.clone()),
+                None => GraphAnalysis::NoCollision(self.new_graph(node)),
+            };
+        }
+
+        if graphs.len() == 1 {
+            return GraphAnalysis::NoCollision(graphs.drain().next().unwrap().1);
+        }
+
+        GraphAnalysis::Collisions(graphs)
+    }
+
+    /// Merges multiple graphs associated with a node into a single graph.
+    fn merge(&mut self, node: NodeId, mut graphs: HashMap<NodeId, Arc<Graph>>) -> Arc<Graph> {
+        let mut graphs = graphs.drain().map(|g| g.1);
+
+        let main = graphs.next().expect("At least one graph");
+        self.register_key(main.origin, node);
+
+        let mut state = main.state.lock();
+
+        for graph in graphs {
+            self.merge_two(&mut state, &main, graph);
+        }
+
+        self.graphs.insert(main.origin, main.clone());
+        self.graphs.insert(node, main.clone());
+
+        core::mem::drop(state);
+
+        main
+    }
+
+    /// Registers a key for a given origin node.
+    fn register_key(&mut self, origin: NodeId, key: NodeId) {
+        if !self.keys.contains_key(&origin) {
+            // Ensure an entry exists for this origin
+            self.keys.insert(origin, HashSet::new());
+        }
+
+        if origin != key {
+            // Register this node to point to the origin graph
+            self.keys.get_mut(&origin).unwrap().insert(key);
+        }
+    }
+
+    /// Merges two graphs by combining their states and updating graph mappings.
+    fn merge_two(&mut self, main_state: &mut GraphState, main: &Arc<Graph>, merged: Arc<Graph>) {
+        let mut locked = merged.state.lock();
+        let mut state_old = GraphState::default();
+        core::mem::swap(&mut state_old, &mut locked);
+        main_state.server.extend(state_old.server);
+
+        // Re-map merged origin to the main graph
+        self.graphs.insert(merged.origin, main.clone());
+
+        // Move all keys (node IDs) from the merged graph to the main graph
+        if let Some(locator_keys) = self.keys.remove(&merged.origin) {
+            for k in locator_keys.iter() {
+                self.graphs.insert(*k, main.clone());
+            }
+
+            let locator_keys_main = self
+                .keys
+                .get_mut(&main.origin)
+                .expect("Should be init before the merge.");
+            locator_keys_main.extend(locator_keys);
+        }
+    }
+
+    /// Creates a new graph for a given node.
+    fn new_graph(&mut self, origin: NodeId) -> Arc<Graph> {
+        let graph = Arc::new(Graph {
+            origin,
+            state: Mutex::new(GraphState::default()),
+        });
+        self.graphs.insert(origin, graph.clone());
+        self.keys.insert(origin, HashSet::new());
+        graph
+    }
+
+    fn remove_entry(&mut self, node: &NodeId) {
+        if let Some(graph) = self.graphs.remove(node) {
+            let mut remove = false;
+
+            if let Some(entry) = self.keys.get_mut(&graph.origin) {
+                entry.remove(node);
+                if entry.is_empty() {
+                    remove = true;
+                }
+            }
+
+            if remove {
+                self.keys.remove(&graph.origin);
+            }
+        }
+    }
+}
+
+/// Represents the analysis result of graph operations for a given node and its parents.
+#[derive(Debug)]
+enum GraphAnalysis {
+    /// No collision detected, contains the graph associated with the node.
+    NoCollision(Arc<Graph>),
+    /// Collision detected, contains a map of node IDs to their associated graphs.
+    Collisions(HashMap<NodeId, Arc<Graph>>),
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/runtime/memory_management.rs

@@ -0,0 +1,294 @@
+use crate::{
+    NodeId,
+    collections::{HashMap, HashSet},
+    graph::Parent,
+    tensor::NodeRefCount,
+};
+use alloc::{borrow::ToOwned, sync::Arc, vec, vec::Vec};
+use core::mem;
+
+#[derive(Default, Debug)]
+pub struct GraphMemoryManagement {
+    nodes: HashMap<NodeRefCount, Vec<NodeId>>,
+    leaves: HashSet<NodeId>,
+    statuses: HashMap<NodeId, NodeMemoryStatus>,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+enum NodeMemoryStatus {
+    Useful,
+    Unavailable,
+    Unknown,
+}
+
+impl GraphMemoryManagement {
+    pub fn extend(&mut self, other: Self) {
+        self.nodes.extend(other.nodes);
+        self.leaves.extend(other.leaves);
+        self.statuses.extend(other.statuses);
+    }
+
+    /// Register a new node with its parent.
+    pub fn register(&mut self, node: NodeRefCount, parents: &[Parent]) {
+        let node_id = *node.as_ref();
+
+        for parent in parents.iter() {
+            self.leaves.remove(&parent.id);
+        }
+
+        self.leaves.insert(node_id);
+        self.nodes
+            .insert(node, parents.iter().map(|p| p.id).collect());
+    }
+
+    /// Free the node from the state.
+    pub fn consume_node(&mut self, node_id: NodeId) {
+        if !self.is_referenced(node_id) {
+            self.leaves.remove(&node_id);
+            self.nodes.remove(&node_id);
+        }
+    }
+
+    /// Free all nodes whose backward call has become impossible
+    ///
+    /// This function goes into three steps, which must happen for all leaves
+    /// before going into the next step. Then it deletes what can be safely deleted
+    pub(crate) fn free_unavailable_nodes(&mut self, mut on_free_graph: impl FnMut(&NodeId)) {
+        let leaves = self.leaves.clone();
+        let mut new_leaves = HashSet::new();
+        let mut deletables = Vec::new();
+
+        // When consuming nodes with a backward pass, some other backward passes become
+        // unavailable because some of their parents have been consumed. They are
+        // identified here.
+        for leaf in leaves.clone() {
+            self.unavailable_propagation(leaf);
+        }
+
+        // Among the available nodes that remain, some may be useless if no
+        // available node with a tensor reference exist in their descendance.
+        // But some may seem useless from some leaf but be useful from another one,
+        // hence the need to iterate on all leaves.
+        self.useful_propagation(leaves.clone());
+
+        // New leaves are the roots of a useful backward sub-tree.
+        // Deletables are everything not marked as useful.
+        for leaf in leaves {
+            self.identify_leaves_and_deletables(leaf, &mut new_leaves, &mut deletables);
+        }
+
+        // Replace leaves by the new ones and delete everything not useful anymore
+        mem::swap(&mut self.leaves, &mut new_leaves);
+
+        self.clear_unused_roots(&mut deletables);
+
+        self.statuses.clear();
+        for node_to_delete in deletables {
+            self.nodes.remove(&node_to_delete);
+            on_free_graph(&node_to_delete)
+        }
+    }
+
+    pub(crate) fn free_unused_roots(&mut self, mut on_free_graph: impl FnMut(&NodeId)) {
+        let mut deletables = Vec::new();
+        self.clear_unused_roots(&mut deletables);
+
+        for node_id in deletables {
+            self.nodes.remove(&node_id);
+            on_free_graph(&node_id);
+        }
+    }
+
+    fn clear_unused_roots(&self, to_delete: &mut Vec<NodeId>) {
+        for (id, parents) in self.nodes.iter() {
+            let is_useful = matches!(
+                self.statuses.get(id.as_ref()),
+                Some(NodeMemoryStatus::Useful)
+            );
+
+            // Check if parents are either empty or absent from self.nodes
+            let parents_absent = parents.iter().all(|p| !self.nodes.contains_key(p));
+
+            if !is_useful && Arc::strong_count(id) == 1 && parents_absent {
+                to_delete.push(*id.as_ref())
+            }
+        }
+    }
+
+    fn unavailable_propagation(&mut self, node_id: NodeId) -> NodeMemoryStatus {
+        // If already visited
+        if let Some(status) = self.statuses.get(&node_id) {
+            return status.clone();
+        }
+
+        match self.nodes.get(&node_id).cloned() {
+            // If node exists and any of its parents is unavailable, it is unavailable as well
+            // If node exists but the parents vec is empty, it is a tensor that never had parents;
+            //  the status remains unknown
+            Some(parents) => {
+                let mut node_status = NodeMemoryStatus::Unknown;
+                for parent in parents {
+                    let parent_status = self.unavailable_propagation(parent);
+                    if let NodeMemoryStatus::Unavailable = parent_status {
+                        node_status = NodeMemoryStatus::Unavailable;
+                    }
+                }
+                self.statuses.insert(node_id, node_status.clone());
+                node_status
+            }
+            // If node does not exist, it was
+            // deleted, so this and all its descendants are unavailable
+            None => {
+                self.statuses.insert(node_id, NodeMemoryStatus::Unavailable);
+                NodeMemoryStatus::Unavailable
+            }
+        }
+    }
+
+    fn useful_propagation(&mut self, leaves: HashSet<NodeId>) {
+        // Accumulate visited nodes
+        let mut explored = HashSet::new();
+        let mut tagged_useful = HashSet::new();
+
+        // Queue of nodes to visit
+        let mut to_tag_useful = PopNodeSet::default();
+        let mut to_explore = PopNodeSet::new(leaves);
+
+        // Utility function to iterate over a node's parents
+        let parents = |node_id| {
+            self.nodes
+                .get(&node_id)
+                .cloned()
+                .unwrap_or_default()
+                .into_iter()
+        };
+
+        loop {
+            // Pop a node id, greedily looking at tag_useful ones first
+            let (node_id, status) = match to_tag_useful.pop() {
+                Some(node_id) => (node_id, NodeMemoryStatus::Useful),
+                None => match to_explore.pop() {
+                    Some(node_id) => {
+                        let node_status = self
+                            .statuses
+                            .get(&node_id)
+                            .expect("All nodes should have received a status during unavailable_propagation")
+                            .to_owned();
+
+                        if let NodeMemoryStatus::Unknown = node_status {
+                            match self.is_referenced(node_id) {
+                                true => (node_id, NodeMemoryStatus::Useful),
+                                false => (node_id, NodeMemoryStatus::Unknown),
+                            }
+                        } else {
+                            (node_id, node_status)
+                        }
+                    }
+                    None => {
+                        // There are no nodes in the queues anymore
+                        break;
+                    }
+                },
+            };
+
+            match status {
+                NodeMemoryStatus::Useful => {
+                    tagged_useful.insert(node_id);
+                    for parent in parents(node_id) {
+                        // The node can be explored, as long as it's not already tagged useful
+                        if !(tagged_useful.contains(&parent) || to_tag_useful.contains(&parent)) {
+                            to_tag_useful.insert(parent);
+                        }
+                    }
+                }
+                _ => {
+                    explored.insert(node_id);
+                    for parent in parents(node_id) {
+                        if !(explored.contains(&parent) || to_explore.contains(&parent)) {
+                            to_explore.insert(parent);
+                        }
+                    }
+                }
+            }
+
+            self.statuses.insert(node_id, status);
+        }
+    }
+
+    fn identify_leaves_and_deletables(
+        &self,
+        leaf_id: NodeId,
+        new_leaves: &mut HashSet<NodeId>,
+        to_delete: &mut Vec<NodeId>,
+    ) {
+        let mut visited = HashSet::new();
+        let mut to_visit = vec![leaf_id];
+
+        while let Some(node_id) = to_visit.pop() {
+            visited.insert(node_id);
+
+            match self
+                .statuses
+                .get(&node_id)
+                .expect("Node should have status")
+            {
+                NodeMemoryStatus::Useful => {
+                    new_leaves.insert(node_id);
+                }
+                _ => {
+                    to_delete.push(node_id);
+
+                    for parent in self
+                        .nodes
+                        .get(&node_id)
+                        .cloned()
+                        .unwrap_or_default()
+                        .into_iter()
+                    {
+                        if !visited.contains(&parent) {
+                            to_visit.push(parent);
+                        }
+                    }
+                }
+            };
+        }
+    }
+
+    fn is_referenced(&self, node_id: NodeId) -> bool {
+        match self.nodes.get_key_value(&node_id) {
+            Some((key, _value)) => Arc::strong_count(key) > 1,
+            None => panic!("Node should be in the nodes map"),
+        }
+    }
+
+    pub(crate) fn maybe_useful(&self) -> bool {
+        self.nodes.keys().any(|node| Arc::strong_count(node) > 1)
+    }
+}
+
+/// Wrapper over hash set for fast popping of any node
+#[derive(new, Default)]
+struct PopNodeSet {
+    hash_set: HashSet<NodeId>,
+}
+
+impl PopNodeSet {
+    #[inline(always)]
+    fn pop(&mut self) -> Option<NodeId> {
+        self.hash_set
+            .iter()
+            .next()
+            .copied()
+            .and_then(|node_id| self.hash_set.take(&node_id))
+    }
+
+    #[inline(always)]
+    fn contains(&self, node_id: &NodeId) -> bool {
+        self.hash_set.contains(node_id)
+    }
+
+    #[inline(always)]
+    fn insert(&mut self, node_id: NodeId) {
+        self.hash_set.insert(node_id);
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/runtime/mod.rs

@@ -0,0 +1,6 @@
+mod client;
+mod memory_management;
+mod server;
+
+pub mod graph;
+pub use client::*;

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/runtime/server.rs

@@ -0,0 +1,143 @@
+use super::memory_management::GraphMemoryManagement;
+use crate::{
+    NodeId,
+    checkpoint::{
+        base::{Checkpointer, NodeTree},
+        builder::CheckpointerBuilder,
+    },
+    collections::HashMap,
+    grads::Gradients,
+    graph::{StepBoxed, traversal::BreadthFirstSearch},
+    tensor::NodeRefCount,
+};
+use alloc::vec::Vec;
+
+#[derive(Default)]
+pub struct AutodiffServer {
+    steps: HashMap<NodeId, StepBoxed>,
+    actions_builder: HashMap<NodeId, CheckpointerBuilder>,
+    memory_management: GraphMemoryManagement,
+}
+
+/// Defines how nodes are clean.
+pub trait NodeCleaner {
+    /// Initialize a new cleaner.
+    fn init() -> Self;
+    /// Cleans a single [node](NodeId).
+    fn clean(&mut self, node: &NodeId);
+}
+
+impl AutodiffServer {
+    pub fn extend(&mut self, other: AutodiffServer) {
+        self.steps.extend(other.steps);
+        self.actions_builder.extend(other.actions_builder);
+        self.memory_management.extend(other.memory_management);
+    }
+
+    pub fn register(&mut self, rc: NodeRefCount, step: StepBoxed, actions: CheckpointerBuilder) {
+        let parents = step.parents();
+        let node_id = *rc.as_ref();
+
+        self.memory_management.register(rc, parents);
+
+        self.steps.insert(node_id, step);
+        self.actions_builder.insert(node_id, actions);
+    }
+
+    pub fn backward<NC: NodeCleaner>(&mut self, grads: Gradients, node_id: NodeId) -> Gradients {
+        let step = self.steps.remove(&node_id).expect(
+            "Node should have a step registered, did you forget to call \
+             `Tensor::register_grad` on the tensor where you need gradients?",
+        );
+        let builder = self.actions_builder.remove(&node_id).unwrap();
+
+        let mut consumed = Vec::new();
+        let (tape, checkpointer) = self.build_tape(node_id, step, builder, &mut consumed);
+
+        let gradients = Self::execute_steps(tape, grads, checkpointer);
+
+        // Cleanup
+        let mut cleaner = NC::init();
+        self.memory_management
+            .free_unavailable_nodes(|node_id: &NodeId| {
+                self.steps.remove(node_id);
+                self.actions_builder.remove(node_id);
+                NC::clean(&mut cleaner, node_id);
+            });
+        for node_id in consumed {
+            cleaner.clean(&node_id)
+        }
+
+        gradients
+    }
+
+    pub(crate) fn free_unused_roots(&mut self, mut on_free_graph: impl FnMut(&NodeId)) {
+        self.memory_management.free_unused_roots(|node_id| {
+            self.steps.remove(node_id);
+            self.actions_builder.remove(node_id);
+            on_free_graph(node_id);
+        });
+    }
+
+    fn build_tape(
+        &mut self,
+        node: NodeId,
+        node_step: StepBoxed,
+        mut builder: CheckpointerBuilder,
+        consumed: &mut Vec<NodeId>,
+    ) -> (Vec<Vec<StepBoxed>>, Checkpointer) {
+        let mut tape = (0..node_step.depth())
+            .map(|_| Vec::with_capacity(1))
+            .collect::<Vec<_>>();
+
+        let mut tree = HashMap::default();
+
+        BreadthFirstSearch.traverse(node, node_step, &mut self.steps, |id, step| {
+            self.memory_management.consume_node(id);
+            // Clean up consumed node
+            consumed.push(id);
+
+            let depth = step.depth();
+
+            if depth == 0 {
+                return;
+            }
+
+            if let Some(steps) = tape.get_mut(depth - 1) {
+                let parents = step.parents().iter().map(|p| p.id).filter(|s| *s != id);
+                tree.insert(id, parents.collect());
+                steps.push(step);
+            }
+
+            if let Some(node_builder) = self.actions_builder.remove(&id) {
+                builder.extend(node_builder);
+            }
+        });
+
+        let checkpointer = builder.build(NodeTree::new(tree));
+
+        (tape, checkpointer)
+    }
+
+    fn execute_steps(
+        tape: Vec<Vec<StepBoxed>>,
+        mut grads: Gradients,
+        mut checkpointer: Checkpointer,
+    ) -> Gradients {
+        tape.into_iter().rev().for_each(|steps| {
+            steps
+                .into_iter()
+                .for_each(|step| step.step(&mut grads, &mut checkpointer))
+        });
+
+        // For checkpointing tests
+        #[cfg(feature = "export_tests")]
+        assert!(checkpointer.is_empty());
+
+        grads
+    }
+
+    pub(crate) fn maybe_useful(&self) -> bool {
+        self.memory_management.maybe_useful()
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/tensor.rs

@@ -0,0 +1,189 @@
+use crate::{
+    checkpoint::{base::Checkpointer, builder::CheckpointerBuilder},
+    grads::Gradients,
+    graph::{ComputingProperty, Node, NodeId, NodeRef, Parent, Requirement, Step},
+    runtime::{AutodiffClient, AutodiffClientImpl},
+};
+use alloc::{boxed::Box, sync::Arc, vec};
+use burn_backend::{Backend, TensorMetadata};
+
+#[derive(Debug, Clone)]
+pub struct AutodiffTensor<B: Backend> {
+    pub primitive: B::FloatTensorPrimitive,
+    pub node: NodeRef,
+    pub rc: NodeRefCount,
+}
+
+impl<B: Backend> TensorMetadata for AutodiffTensor<B> {
+    fn dtype(&self) -> burn_std::DType {
+        self.primitive.dtype()
+    }
+
+    fn shape(&self) -> burn_std::Shape {
+        self.primitive.shape()
+    }
+
+    fn rank(&self) -> usize {
+        self.primitive.rank()
+    }
+}
+
+pub type NodeRefCount = Arc<NodeId>;
+
+#[derive(new, Debug)]
+pub(crate) struct RootStep {
+    node: NodeRef,
+}
+
+impl Step for RootStep {
+    fn step(self: Box<Self>, _grads: &mut Gradients, _checkpointer: &mut Checkpointer) {
+        // Nothing to do
+    }
+
+    fn node(&self) -> NodeId {
+        self.node.id
+    }
+
+    fn parents(&self) -> &[Parent] {
+        &self.node.parents
+    }
+
+    fn depth(&self) -> usize {
+        self.node.order
+    }
+}
+
+impl<B: Backend> AutodiffTensor<B> {
+    /// Create a new leaf tensor.
+    pub fn new(primitive: B::FloatTensorPrimitive) -> Self {
+        let id = NodeId::new();
+        let node: NodeRef = Node::new(
+            vec![],
+            0,
+            id,
+            Requirement::None,
+            ComputingProperty::Ambiguous,
+            AutodiffClientImpl::new(),
+        )
+        .into();
+
+        Self {
+            rc: Arc::new(node.id),
+            primitive,
+            node: node.clone(),
+        }
+    }
+
+    pub fn is_tracked(&self) -> bool {
+        !self.node.requirement.is_none()
+    }
+
+    /// Mark the tensor as requiring gradients.
+    ///
+    /// # Panics
+    ///
+    /// It panics if the tensor is not a leaf.
+    pub fn require_grad(mut self) -> Self {
+        match self.node.requirement {
+            Requirement::Grad => self,
+            Requirement::GradInBackward => {
+                panic!("Can't convert a non leaf tensor into a tracked tensor")
+            }
+            Requirement::None => {
+                self.node = Node::new(
+                    vec![],
+                    0,
+                    self.node.id,
+                    Requirement::Grad,
+                    self.node.properties.clone(),
+                    self.node.client.clone(),
+                )
+                .into();
+                let step = RootStep::new(self.node.clone());
+
+                self.register_step(step, CheckpointerBuilder::default())
+            }
+        }
+    }
+
+    /// Create a tensor from parent infos.
+    pub fn from_parents(
+        primitive: B::FloatTensorPrimitive,
+        parent_nodes: &[NodeRef],
+        requirement: Requirement,
+        computing_properties: ComputingProperty,
+    ) -> Self {
+        let order = parent_nodes
+            .iter()
+            .map(|node| node.order)
+            .reduce(usize::max)
+            .unwrap_or(0)
+            + 1;
+
+        let client = parent_nodes
+            .first()
+            .map(|node| node.client.clone())
+            .unwrap_or_else(AutodiffClientImpl::new);
+
+        let node: NodeRef = Node::new(
+            parent_nodes
+                .iter()
+                .filter_map(|node| node.clone_if_require_grad())
+                .map(|node| Parent::new(node.id))
+                .collect(),
+            order,
+            NodeId::new(),
+            requirement,
+            computing_properties,
+            client,
+        )
+        .into();
+
+        Self {
+            rc: Arc::new(node.id),
+            primitive,
+            node,
+        }
+    }
+
+    /// Register a step into a graph for that tensor.
+    ///
+    /// # Warning
+    ///
+    /// This should be called only once per tensor.
+    pub fn register_step<S: Step + 'static>(
+        self,
+        step_that_created_the_tensor: S,
+        actions: CheckpointerBuilder,
+    ) -> Self {
+        self.node.client.register(
+            self.rc.clone(),
+            Box::new(step_that_created_the_tensor),
+            actions,
+        );
+        self
+    }
+
+    pub fn into_primitive(self) -> B::FloatTensorPrimitive {
+        self.primitive
+    }
+
+    pub fn backward(self) -> Gradients {
+        let client = self.node.client.clone();
+
+        AutodiffClient::backward::<B>(&client, self)
+    }
+
+    pub fn grad(&self, grads: &Gradients) -> Option<B::FloatTensorPrimitive> {
+        grads.get::<B>(self)
+    }
+
+    pub fn grad_remove(&self, grads: &mut Gradients) -> Option<B::FloatTensorPrimitive> {
+        grads.remove::<B>(self)
+    }
+
+    pub fn grad_replace(&self, grads: &mut Gradients, grad: B::FloatTensorPrimitive) {
+        grads.remove::<B>(self);
+        grads.register::<B>(self.node.id, grad);
+    }
+}

crates/stable-diffusion-burn/burn-crates/burn-autodiff/src/utils.rs

@@ -0,0 +1,25 @@
+use alloc::vec::Vec;
+
+use crate::graph::NodeRef;
+/// Duplicate the given object for each node that requires gradients.
+///
+/// # Notes
+///
+/// This is useful since you don't have to keep N cloned references alive event if just 1 node
+/// will be updated.
+///
+/// If the object is a tensor and if one reference exists, it can be updated inplace.
+pub fn duplicate<T: Clone + core::fmt::Debug, const N: usize>(
+    nodes: &[Option<NodeRef>; N],
+    obj: Option<T>,
+) -> [Option<T>; N] {
+    nodes
+        .iter()
+        .map(|node| match node {
+            Some(_) => obj.clone(),
+            None => None,
+        })
+        .collect::<Vec<_>>()
+        .try_into()
+        .unwrap()
+}