Execution Model Inversion (#2666)

* Execution Model Inversion

This PR inverts the execution model -- from recursively calling nodes to
using a topological sort of the nodes. This change allows for
modification of the node graph during execution. This allows for two
major advantages:

    1. The implementation of lazy evaluation in nodes. For example, if a
    "Mix Images" node has a mix factor of exactly 0.0, the second image
    input doesn't even need to be evaluated (and visa-versa if the mix
    factor is 1.0).

    2. Dynamic expansion of nodes. This allows for the creation of dynamic
    "node groups". Specifically, custom nodes can return subgraphs that
    replace the original node in the graph. This is an incredibly
    powerful concept. Using this functionality, it was easy to
    implement:
        a. Components (a.k.a. node groups)
        b. Flow control (i.e. while loops) via tail recursion
        c. All-in-one nodes that replicate the WebUI functionality
        d. and more
    All of those were able to be implemented entirely via custom nodes,
    so those features are *not* a part of this PR. (There are some
    front-end changes that should occur before that functionality is
    made widely available, particularly around variant sockets.)

The custom nodes associated with this PR can be found at:
https://github.com/BadCafeCode/execution-inversion-demo-comfyui

Note that some of them require that variant socket types ("*") be
enabled.

* Allow `input_info` to be of type `None`

* Handle errors (like OOM) more gracefully

* Add a command-line argument to enable variants

This allows the use of nodes that have sockets of type '*' without
applying a patch to the code.

* Fix an overly aggressive assertion.

This could happen when attempting to evaluate `IS_CHANGED` for a node
during the creation of the cache (in order to create the cache key).

* Fix Pyright warnings

* Add execution model unit tests

* Fix issue with unused literals

Behavior should now match the master branch with regard to undeclared
inputs. Undeclared inputs that are socket connections will be used while
undeclared inputs that are literals will be ignored.

* Make custom VALIDATE_INPUTS skip normal validation

Additionally, if `VALIDATE_INPUTS` takes an argument named `input_types`,
that variable will be a dictionary of the socket type of all incoming
connections. If that argument exists, normal socket type validation will
not occur. This removes the last hurdle for enabling variant types
entirely from custom nodes, so I've removed that command-line option.

I've added appropriate unit tests for these changes.

* Fix example in unit test

This wouldn't have caused any issues in the unit test, but it would have
bugged the UI if someone copy+pasted it into their own node pack.

* Use fstrings instead of '%' formatting syntax

* Use custom exception types.

* Display an error for dependency cycles

Previously, dependency cycles that were created during node expansion
would cause the application to quit (due to an uncaught exception). Now,
we'll throw a proper error to the UI. We also make an attempt to 'blame'
the most relevant node in the UI.

* Add docs on when ExecutionBlocker should be used

* Remove unused functionality

* Rename ExecutionResult.SLEEPING to PENDING

* Remove superfluous function parameter

* Pass None for uneval inputs instead of default

This applies to `VALIDATE_INPUTS`, `check_lazy_status`, and lazy values
in evaluation functions.

* Add a test for mixed node expansion

This test ensures that a node that returns a combination of expanded
subgraphs and literal values functions correctly.

* Raise exception for bad get_node calls.

* Minor refactor of IsChangedCache.get

* Refactor `map_node_over_list` function

* Fix ui output for duplicated nodes

* Add documentation on `check_lazy_status`

* Add file for execution model unit tests

* Clean up Javascript code as per review

* Improve documentation

Converted some comments to docstrings as per review

* Add a new unit test for mixed lazy results

This test validates that when an output list is fed to a lazy node, the
node will properly evaluate previous nodes that are needed by any inputs
to the lazy node.

No code in the execution model has been changed. The test already
passes.

* Allow kwargs in VALIDATE_INPUTS functions

When kwargs are used, validation is skipped for all inputs as if they
had been mentioned explicitly.

* List cached nodes in `execution_cached` message

This was previously just bugged in this PR.

comfy/caching.py

@@ -0,0 +1,299 @@
+import itertools
+from typing import Sequence, Mapping
+from comfy.graph import DynamicPrompt
+
+import nodes
+
+from comfy.graph_utils import is_link
+
+class CacheKeySet:
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        self.keys = {}
+        self.subcache_keys = {}
+
+    def add_keys(self, node_ids):
+        raise NotImplementedError()
+
+    def all_node_ids(self):
+        return set(self.keys.keys())
+
+    def get_used_keys(self):
+        return self.keys.values()
+
+    def get_used_subcache_keys(self):
+        return self.subcache_keys.values()
+
+    def get_data_key(self, node_id):
+        return self.keys.get(node_id, None)
+
+    def get_subcache_key(self, node_id):
+        return self.subcache_keys.get(node_id, None)
+
+class Unhashable:
+    def __init__(self):
+        self.value = float("NaN")
+
+def to_hashable(obj):
+    # So that we don't infinitely recurse since frozenset and tuples
+    # are Sequences.
+    if isinstance(obj, (int, float, str, bool, type(None))):
+        return obj
+    elif isinstance(obj, Mapping):
+        return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
+    elif isinstance(obj, Sequence):
+        return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
+    else:
+        # TODO - Support other objects like tensors?
+        return Unhashable()
+
+class CacheKeySetID(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        super().__init__(dynprompt, node_ids, is_changed_cache)
+        self.dynprompt = dynprompt
+        self.add_keys(node_ids)
+
+    def add_keys(self, node_ids):
+        for node_id in node_ids:
+            if node_id in self.keys:
+                continue
+            node = self.dynprompt.get_node(node_id)
+            self.keys[node_id] = (node_id, node["class_type"])
+            self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+class CacheKeySetInputSignature(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        super().__init__(dynprompt, node_ids, is_changed_cache)
+        self.dynprompt = dynprompt
+        self.is_changed_cache = is_changed_cache
+        self.add_keys(node_ids)
+
+    def include_node_id_in_input(self) -> bool:
+        return False
+
+    def add_keys(self, node_ids):
+        for node_id in node_ids:
+            if node_id in self.keys:
+                continue
+            node = self.dynprompt.get_node(node_id)
+            self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id)
+            self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+    def get_node_signature(self, dynprompt, node_id):
+        signature = []
+        ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
+        signature.append(self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
+        for ancestor_id in ancestors:
+            signature.append(self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
+        return to_hashable(signature)
+
+    def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+        node = dynprompt.get_node(node_id)
+        class_type = node["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        signature = [class_type, self.is_changed_cache.get(node_id)]
+        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT):
+            signature.append(node_id)
+        inputs = node["inputs"]
+        for key in sorted(inputs.keys()):
+            if is_link(inputs[key]):
+                (ancestor_id, ancestor_socket) = inputs[key]
+                ancestor_index = ancestor_order_mapping[ancestor_id]
+                signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
+            else:
+                signature.append((key, inputs[key]))
+        return signature
+
+    # This function returns a list of all ancestors of the given node. The order of the list is
+    # deterministic based on which specific inputs the ancestor is connected by.
+    def get_ordered_ancestry(self, dynprompt, node_id):
+        ancestors = []
+        order_mapping = {}
+        self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
+        return ancestors, order_mapping
+
+    def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+        inputs = dynprompt.get_node(node_id)["inputs"]
+        input_keys = sorted(inputs.keys())
+        for key in input_keys:
+            if is_link(inputs[key]):
+                ancestor_id = inputs[key][0]
+                if ancestor_id not in order_mapping:
+                    ancestors.append(ancestor_id)
+                    order_mapping[ancestor_id] = len(ancestors) - 1
+                    self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
+
+class BasicCache:
+    def __init__(self, key_class):
+        self.key_class = key_class
+        self.initialized = False
+        self.dynprompt: DynamicPrompt
+        self.cache_key_set: CacheKeySet
+        self.cache = {}
+        self.subcaches = {}
+
+    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        self.dynprompt = dynprompt
+        self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache)
+        self.is_changed_cache = is_changed_cache
+        self.initialized = True
+
+    def all_node_ids(self):
+        assert self.initialized
+        node_ids = self.cache_key_set.all_node_ids()
+        for subcache in self.subcaches.values():
+            node_ids = node_ids.union(subcache.all_node_ids())
+        return node_ids
+
+    def _clean_cache(self):
+        preserve_keys = set(self.cache_key_set.get_used_keys())
+        to_remove = []
+        for key in self.cache:
+            if key not in preserve_keys:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.cache[key]
+
+    def _clean_subcaches(self):
+        preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys())
+
+        to_remove = []
+        for key in self.subcaches:
+            if key not in preserve_subcaches:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.subcaches[key]
+
+    def clean_unused(self):
+        assert self.initialized
+        self._clean_cache()
+        self._clean_subcaches()
+
+    def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    def _get_immediate(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+        else:
+            return None
+
+    def _ensure_subcache(self, node_id, children_ids):
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        subcache = self.subcaches.get(subcache_key, None)
+        if subcache is None:
+            subcache = BasicCache(self.key_class)
+            self.subcaches[subcache_key] = subcache
+        subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
+        return subcache
+
+    def _get_subcache(self, node_id):
+        assert self.initialized
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        if subcache_key in self.subcaches:
+            return self.subcaches[subcache_key]
+        else:
+            return None
+
+    def recursive_debug_dump(self):
+        result = []
+        for key in self.cache:
+            result.append({"key": key, "value": self.cache[key]})
+        for key in self.subcaches:
+            result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()})
+        return result
+
+class HierarchicalCache(BasicCache):
+    def __init__(self, key_class):
+        super().__init__(key_class)
+
+    def _get_cache_for(self, node_id):
+        assert self.dynprompt is not None
+        parent_id = self.dynprompt.get_parent_node_id(node_id)
+        if parent_id is None:
+            return self
+
+        hierarchy = []
+        while parent_id is not None:
+            hierarchy.append(parent_id)
+            parent_id = self.dynprompt.get_parent_node_id(parent_id)
+
+        cache = self
+        for parent_id in reversed(hierarchy):
+            cache = cache._get_subcache(parent_id)
+            if cache is None:
+                return None
+        return cache
+
+    def get(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return cache._get_immediate(node_id)
+
+    def set(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        cache._set_immediate(node_id, value)
+
+    def ensure_subcache_for(self, node_id, children_ids):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        return cache._ensure_subcache(node_id, children_ids)
+
+class LRUCache(BasicCache):
+    def __init__(self, key_class, max_size=100):
+        super().__init__(key_class)
+        self.max_size = max_size
+        self.min_generation = 0
+        self.generation = 0
+        self.used_generation = {}
+        self.children = {}
+
+    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        super().set_prompt(dynprompt, node_ids, is_changed_cache)
+        self.generation += 1
+        for node_id in node_ids:
+            self._mark_used(node_id)
+
+    def clean_unused(self):
+        while len(self.cache) > self.max_size and self.min_generation < self.generation:
+            self.min_generation += 1
+            to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation]
+            for key in to_remove:
+                del self.cache[key]
+                del self.used_generation[key]
+                if key in self.children:
+                    del self.children[key]
+        self._clean_subcaches()
+
+    def get(self, node_id):
+        self._mark_used(node_id)
+        return self._get_immediate(node_id)
+
+    def _mark_used(self, node_id):
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key is not None:
+            self.used_generation[cache_key] = self.generation
+
+    def set(self, node_id, value):
+        self._mark_used(node_id)
+        return self._set_immediate(node_id, value)
+
+    def ensure_subcache_for(self, node_id, children_ids):
+        # Just uses subcaches for tracking 'live' nodes
+        super()._ensure_subcache(node_id, children_ids)
+
+        self.cache_key_set.add_keys(children_ids)
+        self._mark_used(node_id)
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.children[cache_key] = []
+        for child_id in children_ids:
+            self._mark_used(child_id)
+            self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
+        return self
+

comfy/cli_args.py

@@ -92,6 +92,10 @@ class LatentPreviewMethod(enum.Enum):
 
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
+cache_group = parser.add_mutually_exclusive_group()
+cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
+cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
+
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
 attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")

comfy/graph.py

@@ -0,0 +1,237 @@
+import nodes
+
+from comfy.graph_utils import is_link
+
+class DependencyCycleError(Exception):
+    pass
+
+class NodeInputError(Exception):
+    pass
+
+class NodeNotFoundError(Exception):
+    pass
+
+class DynamicPrompt:
+    def __init__(self, original_prompt):
+        # The original prompt provided by the user
+        self.original_prompt = original_prompt
+        # Any extra pieces of the graph created during execution
+        self.ephemeral_prompt = {}
+        self.ephemeral_parents = {}
+        self.ephemeral_display = {}
+
+    def get_node(self, node_id):
+        if node_id in self.ephemeral_prompt:
+            return self.ephemeral_prompt[node_id]
+        if node_id in self.original_prompt:
+            return self.original_prompt[node_id]
+        raise NodeNotFoundError(f"Node {node_id} not found")
+
+    def has_node(self, node_id):
+        return node_id in self.original_prompt or node_id in self.ephemeral_prompt
+
+    def add_ephemeral_node(self, node_id, node_info, parent_id, display_id):
+        self.ephemeral_prompt[node_id] = node_info
+        self.ephemeral_parents[node_id] = parent_id
+        self.ephemeral_display[node_id] = display_id
+
+    def get_real_node_id(self, node_id):
+        while node_id in self.ephemeral_parents:
+            node_id = self.ephemeral_parents[node_id]
+        return node_id
+
+    def get_parent_node_id(self, node_id):
+        return self.ephemeral_parents.get(node_id, None)
+
+    def get_display_node_id(self, node_id):
+        while node_id in self.ephemeral_display:
+            node_id = self.ephemeral_display[node_id]
+        return node_id
+
+    def all_node_ids(self):
+        return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
+
+    def get_original_prompt(self):
+        return self.original_prompt
+
+def get_input_info(class_def, input_name):
+    valid_inputs = class_def.INPUT_TYPES()
+    input_info = None
+    input_category = None
+    if "required" in valid_inputs and input_name in valid_inputs["required"]:
+        input_category = "required"
+        input_info = valid_inputs["required"][input_name]
+    elif "optional" in valid_inputs and input_name in valid_inputs["optional"]:
+        input_category = "optional"
+        input_info = valid_inputs["optional"][input_name]
+    elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]:
+        input_category = "hidden"
+        input_info = valid_inputs["hidden"][input_name]
+    if input_info is None:
+        return None, None, None
+    input_type = input_info[0]
+    if len(input_info) > 1:
+        extra_info = input_info[1]
+    else:
+        extra_info = {}
+    return input_type, input_category, extra_info
+
+class TopologicalSort:
+    def __init__(self, dynprompt):
+        self.dynprompt = dynprompt
+        self.pendingNodes = {}
+        self.blockCount = {} # Number of nodes this node is directly blocked by
+        self.blocking = {} # Which nodes are blocked by this node
+
+    def get_input_info(self, unique_id, input_name):
+        class_type = self.dynprompt.get_node(unique_id)["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        return get_input_info(class_def, input_name)
+
+    def make_input_strong_link(self, to_node_id, to_input):
+        inputs = self.dynprompt.get_node(to_node_id)["inputs"]
+        if to_input not in inputs:
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all")
+        value = inputs[to_input]
+        if not is_link(value):
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant")
+        from_node_id, from_socket = value
+        self.add_strong_link(from_node_id, from_socket, to_node_id)
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        self.add_node(from_node_id)
+        if to_node_id not in self.blocking[from_node_id]:
+            self.blocking[from_node_id][to_node_id] = {}
+            self.blockCount[to_node_id] += 1
+        self.blocking[from_node_id][to_node_id][from_socket] = True
+
+    def add_node(self, unique_id, include_lazy=False, subgraph_nodes=None):
+        if unique_id in self.pendingNodes:
+            return
+        self.pendingNodes[unique_id] = True
+        self.blockCount[unique_id] = 0
+        self.blocking[unique_id] = {}
+
+        inputs = self.dynprompt.get_node(unique_id)["inputs"]
+        for input_name in inputs:
+            value = inputs[input_name]
+            if is_link(value):
+                from_node_id, from_socket = value
+                if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+                    continue
+                input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
+                is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+                if include_lazy or not is_lazy:
+                    self.add_strong_link(from_node_id, from_socket, unique_id)
+
+    def get_ready_nodes(self):
+        return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
+
+    def pop_node(self, unique_id):
+        del self.pendingNodes[unique_id]
+        for blocked_node_id in self.blocking[unique_id]:
+            self.blockCount[blocked_node_id] -= 1
+        del self.blocking[unique_id]
+
+    def is_empty(self):
+        return len(self.pendingNodes) == 0
+
+class ExecutionList(TopologicalSort):
+    """
+    ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
+    it can still be returned to the graph after having further dependencies added.
+    """
+    def __init__(self, dynprompt, output_cache):
+        super().__init__(dynprompt)
+        self.output_cache = output_cache
+        self.staged_node_id = None
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        if self.output_cache.get(from_node_id) is not None:
+            # Nothing to do
+            return
+        super().add_strong_link(from_node_id, from_socket, to_node_id)
+
+    def stage_node_execution(self):
+        assert self.staged_node_id is None
+        if self.is_empty():
+            return None, None, None
+        available = self.get_ready_nodes()
+        if len(available) == 0:
+            cycled_nodes = self.get_nodes_in_cycle()
+            # Because cycles composed entirely of static nodes are caught during initial validation,
+            # we will 'blame' the first node in the cycle that is not a static node.
+            blamed_node = cycled_nodes[0]
+            for node_id in cycled_nodes:
+                display_node_id = self.dynprompt.get_display_node_id(node_id)
+                if display_node_id != node_id:
+                    blamed_node = display_node_id
+                    break
+            ex = DependencyCycleError("Dependency cycle detected")
+            error_details = {
+                "node_id": blamed_node,
+                "exception_message": str(ex),
+                "exception_type": "graph.DependencyCycleError",
+                "traceback": [],
+                "current_inputs": []
+            }
+            return None, error_details, ex
+        next_node = available[0]
+        # If an output node is available, do that first.
+        # Technically this has no effect on the overall length of execution, but it feels better as a user
+        # for a PreviewImage to display a result as soon as it can
+        # Some other heuristics could probably be used here to improve the UX further.
+        for node_id in available:
+            class_type = self.dynprompt.get_node(node_id)["class_type"]
+            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+            if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                next_node = node_id
+                break
+        self.staged_node_id = next_node
+        return self.staged_node_id, None, None
+
+    def unstage_node_execution(self):
+        assert self.staged_node_id is not None
+        self.staged_node_id = None
+
+    def complete_node_execution(self):
+        node_id = self.staged_node_id
+        self.pop_node(node_id)
+        self.staged_node_id = None
+
+    def get_nodes_in_cycle(self):
+        # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
+        # We're skipping some of the performance optimizations from the original TopologicalSort to keep
+        # the code simple (and because having a cycle in the first place is a catastrophic error)
+        blocked_by = { node_id: {} for node_id in self.pendingNodes }
+        for from_node_id in self.blocking:
+            for to_node_id in self.blocking[from_node_id]:
+                if True in self.blocking[from_node_id][to_node_id].values():
+                    blocked_by[to_node_id][from_node_id] = True
+        to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        while len(to_remove) > 0:
+            for node_id in to_remove:
+                for to_node_id in blocked_by:
+                    if node_id in blocked_by[to_node_id]:
+                        del blocked_by[to_node_id][node_id]
+                del blocked_by[node_id]
+            to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        return list(blocked_by.keys())
+
+class ExecutionBlocker:
+    """
+    Return this from a node and any users will be blocked with the given error message.
+    If the message is None, execution will be blocked silently instead.
+    Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
+    possible, a lazy input will be more efficient and have a better user experience.
+    This functionality is useful in two cases:
+    1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
+       like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
+       lazy evaluation to let it conditionally disable itself.)
+    2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
+       (I would recommend not making nodes like this in the future -- instead, make multiple nodes with
+       different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
+    """
+    def __init__(self, message):
+        self.message = message
+

comfy/graph_utils.py

@@ -0,0 +1,139 @@
+def is_link(obj):
+    if not isinstance(obj, list):
+        return False
+    if len(obj) != 2:
+        return False
+    if not isinstance(obj[0], str):
+        return False
+    if not isinstance(obj[1], int) and not isinstance(obj[1], float):
+        return False
+    return True
+
+# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end
+class GraphBuilder:
+    _default_prefix_root = ""
+    _default_prefix_call_index = 0
+    _default_prefix_graph_index = 0
+
+    def __init__(self, prefix = None):
+        if prefix is None:
+            self.prefix = GraphBuilder.alloc_prefix()
+        else:
+            self.prefix = prefix
+        self.nodes = {}
+        self.id_gen = 1
+
+    @classmethod
+    def set_default_prefix(cls, prefix_root, call_index, graph_index = 0):
+        cls._default_prefix_root = prefix_root
+        cls._default_prefix_call_index = call_index
+        cls._default_prefix_graph_index = graph_index
+
+    @classmethod
+    def alloc_prefix(cls, root=None, call_index=None, graph_index=None):
+        if root is None:
+            root = GraphBuilder._default_prefix_root
+        if call_index is None:
+            call_index = GraphBuilder._default_prefix_call_index
+        if graph_index is None:
+            graph_index = GraphBuilder._default_prefix_graph_index
+        result = f"{root}.{call_index}.{graph_index}."
+        GraphBuilder._default_prefix_graph_index += 1
+        return result
+
+    def node(self, class_type, id=None, **kwargs):
+        if id is None:
+            id = str(self.id_gen)
+            self.id_gen += 1
+        id = self.prefix + id
+        if id in self.nodes:
+            return self.nodes[id]
+
+        node = Node(id, class_type, kwargs)
+        self.nodes[id] = node
+        return node
+
+    def lookup_node(self, id):
+        id = self.prefix + id
+        return self.nodes.get(id)
+
+    def finalize(self):
+        output = {}
+        for node_id, node in self.nodes.items():
+            output[node_id] = node.serialize()
+        return output
+
+    def replace_node_output(self, node_id, index, new_value):
+        node_id = self.prefix + node_id
+        to_remove = []
+        for node in self.nodes.values():
+            for key, value in node.inputs.items():
+                if is_link(value) and value[0] == node_id and value[1] == index:
+                    if new_value is None:
+                        to_remove.append((node, key))
+                    else:
+                        node.inputs[key] = new_value
+        for node, key in to_remove:
+            del node.inputs[key]
+
+    def remove_node(self, id):
+        id = self.prefix + id
+        del self.nodes[id]
+
+class Node:
+    def __init__(self, id, class_type, inputs):
+        self.id = id
+        self.class_type = class_type
+        self.inputs = inputs
+        self.override_display_id = None
+
+    def out(self, index):
+        return [self.id, index]
+
+    def set_input(self, key, value):
+        if value is None:
+            if key in self.inputs:
+                del self.inputs[key]
+        else:
+            self.inputs[key] = value
+
+    def get_input(self, key):
+        return self.inputs.get(key)
+
+    def set_override_display_id(self, override_display_id):
+        self.override_display_id = override_display_id
+
+    def serialize(self):
+        serialized = {
+            "class_type": self.class_type,
+            "inputs": self.inputs
+        }
+        if self.override_display_id is not None:
+            serialized["override_display_id"] = self.override_display_id
+        return serialized
+
+def add_graph_prefix(graph, outputs, prefix):
+    # Change the node IDs and any internal links
+    new_graph = {}
+    for node_id, node_info in graph.items():
+        # Make sure the added nodes have unique IDs
+        new_node_id = prefix + node_id
+        new_node = { "class_type": node_info["class_type"], "inputs": {} }
+        for input_name, input_value in node_info.get("inputs", {}).items():
+            if is_link(input_value):
+                new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]]
+            else:
+                new_node["inputs"][input_name] = input_value
+        new_graph[new_node_id] = new_node
+
+    # Change the node IDs in the outputs
+    new_outputs = []
+    for n in range(len(outputs)):
+        output = outputs[n]
+        if is_link(output):
+            new_outputs.append([prefix + output[0], output[1]])
+        else:
+            new_outputs.append(output)
+
+    return new_graph, tuple(new_outputs)
+

custom_nodes/example_node.py.example

@@ -54,7 +54,8 @@ class Example:
                     "min": 0, #Minimum value
                     "max": 4096, #Maximum value
                     "step": 64, #Slider's step
-                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                    "display": "number", # Cosmetic only: display as "number" or "slider"
+                    "lazy": True # Will only be evaluated if check_lazy_status requires it
                 }),
                 "float_field": ("FLOAT", {
                     "default": 1.0,
@@ -62,11 +63,14 @@ class Example:
                     "max": 10.0,
                     "step": 0.01,
                     "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                    "display": "number"}),
+                    "display": "number",
+                    "lazy": True
+                }),
                 "print_to_screen": (["enable", "disable"],),
                 "string_field": ("STRING", {
                     "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node
-                    "default": "Hello World!"
+                    "default": "Hello World!",
+                    "lazy": True
                 }),
             },
         }
@@ -80,6 +84,23 @@ class Example:
 
     CATEGORY = "Example"
 
+    def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen):
+        """
+            Return a list of input names that need to be evaluated.
+
+            This function will be called if there are any lazy inputs which have not yet been
+            evaluated. As long as you return at least one field which has not yet been evaluated
+            (and more exist), this function will be called again once the value of the requested
+            field is available.
+
+            Any evaluated inputs will be passed as arguments to this function. Any unevaluated
+            inputs will have the value None.
+        """
+        if print_to_screen == "enable":
+            return ["int_field", "float_field", "string_field"]
+        else:
+            return []
+
     def test(self, image, string_field, int_field, float_field, print_to_screen):
         if print_to_screen == "enable":
             print(f"""Your input contains:

execution.py

@@ -5,6 +5,7 @@ import threading
 import heapq
 import time
 import traceback
+from enum import Enum
 import inspect
 from typing import List, Literal, NamedTuple, Optional
 
@@ -12,87 +13,162 @@ import torch
 import nodes
 
 import comfy.model_management
+import comfy.graph_utils
+from comfy.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
+from comfy.graph_utils import is_link, GraphBuilder
+from comfy.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
+from comfy.cli_args import args
+
+class ExecutionResult(Enum):
+    SUCCESS = 0
+    FAILURE = 1
+    PENDING = 2
+
+class DuplicateNodeError(Exception):
+    pass
+
+class IsChangedCache:
+    def __init__(self, dynprompt, outputs_cache):
+        self.dynprompt = dynprompt
+        self.outputs_cache = outputs_cache
+        self.is_changed = {}
+
+    def get(self, node_id):
+        if node_id in self.is_changed:
+            return self.is_changed[node_id]
+
+        node = self.dynprompt.get_node(node_id)
+        class_type = node["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        if not hasattr(class_def, "IS_CHANGED"):
+            self.is_changed[node_id] = False
+            return self.is_changed[node_id]
+
+        if "is_changed" in node:
+            self.is_changed[node_id] = node["is_changed"]
+            return self.is_changed[node_id]
+
+        input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, self.outputs_cache)
+        try:
+            is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
+            node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
+        except:
+            node["is_changed"] = float("NaN")
+        finally:
+            self.is_changed[node_id] = node["is_changed"]
+        return self.is_changed[node_id]
+
+class CacheSet:
+    def __init__(self, lru_size=None):
+        if lru_size is None or lru_size == 0:
+            self.init_classic_cache() 
+        else:
+            self.init_lru_cache(lru_size)
+        self.all = [self.outputs, self.ui, self.objects]
+
+    # Useful for those with ample RAM/VRAM -- allows experimenting without
+    # blowing away the cache every time
+    def init_lru_cache(self, cache_size):
+        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.objects = HierarchicalCache(CacheKeySetID)
+
+    # Performs like the old cache -- dump data ASAP
+    def init_classic_cache(self):
+        self.outputs = HierarchicalCache(CacheKeySetInputSignature)
+        self.ui = HierarchicalCache(CacheKeySetInputSignature)
+        self.objects = HierarchicalCache(CacheKeySetID)
+
+    def recursive_debug_dump(self):
+        result = {
+            "outputs": self.outputs.recursive_debug_dump(),
+            "ui": self.ui.recursive_debug_dump(),
+        }
+        return result
 
-def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
+def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}):
     valid_inputs = class_def.INPUT_TYPES()
     input_data_all = {}
+    missing_keys = {}
     for x in inputs:
         input_data = inputs[x]
-        if isinstance(input_data, list):
+        input_type, input_category, input_info = get_input_info(class_def, x)
+        def mark_missing():
+            missing_keys[x] = True
+            input_data_all[x] = (None,)
+        if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)):
             input_unique_id = input_data[0]
             output_index = input_data[1]
-            if input_unique_id not in outputs:
+            if outputs is None:
-                input_data_all[x] = (None,)
+                mark_missing()
+                continue # This might be a lazily-evaluated input
+            cached_output = outputs.get(input_unique_id)
+            if cached_output is None:
+                mark_missing()
+                continue
+            if output_index >= len(cached_output):
+                mark_missing()
                 continue
-            obj = outputs[input_unique_id][output_index]
+            obj = cached_output[output_index]
             input_data_all[x] = obj
-        else:
+        elif input_category is not None:
-            if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
             input_data_all[x] = [input_data]
 
     if "hidden" in valid_inputs:
         h = valid_inputs["hidden"]
         for x in h:
             if h[x] == "PROMPT":
-                input_data_all[x] = [prompt]
+                input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}]
+            if h[x] == "DYNPROMPT":
+                input_data_all[x] = [dynprompt]
             if h[x] == "EXTRA_PNGINFO":
                 input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
             if h[x] == "UNIQUE_ID":
                 input_data_all[x] = [unique_id]
-    return input_data_all
+    return input_data_all, missing_keys
 
-def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
+def map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
     # check if node wants the lists
-    input_is_list = False
+    input_is_list = getattr(obj, "INPUT_IS_LIST", False)
-    if hasattr(obj, "INPUT_IS_LIST"):
-        input_is_list = obj.INPUT_IS_LIST
 
     if len(input_data_all) == 0:
         max_len_input = 0
     else:
-        max_len_input = max([len(x) for x in input_data_all.values()])
+        max_len_input = max(len(x) for x in input_data_all.values())
      
     # get a slice of inputs, repeat last input when list isn't long enough
     def slice_dict(d, i):
-        d_new = dict()
+        return {k: v[i if len(v) > i else -1] for k, v in d.items()}
-        for k,v in d.items():
-            d_new[k] = v[i if len(v) > i else -1]
-        return d_new
     
     results = []
-    if input_is_list:
+    def process_inputs(inputs, index=None):
         if allow_interrupt:
             nodes.before_node_execution()
-        results.append(getattr(obj, func)(**input_data_all))
+        execution_block = None
+        for k, v in inputs.items():
+            if isinstance(v, ExecutionBlocker):
+                execution_block = execution_block_cb(v) if execution_block_cb else v
+                break
+        if execution_block is None:
+            if pre_execute_cb is not None and index is not None:
+                pre_execute_cb(index)
+            results.append(getattr(obj, func)(**inputs))
+        else:
+            results.append(execution_block)
+
+    if input_is_list:
+        process_inputs(input_data_all, 0)
     elif max_len_input == 0:
-        if allow_interrupt:
+        process_inputs({})
-            nodes.before_node_execution()
-        results.append(getattr(obj, func)())
     else: 
         for i in range(max_len_input):
-            if allow_interrupt:
+            input_dict = slice_dict(input_data_all, i)
-                nodes.before_node_execution()
+            process_inputs(input_dict, i)
-            results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
     return results
 
-def get_output_data(obj, input_data_all):
+def merge_result_data(results, obj):
-    
-    results = []
-    uis = []
-    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
-
-    for r in return_values:
-        if isinstance(r, dict):
-            if 'ui' in r:
-                uis.append(r['ui'])
-            if 'result' in r:
-                results.append(r['result'])
-        else:
-            results.append(r)
-    
-    output = []
-    if len(results) > 0:
     # check which outputs need concatenating
+    output = []
     output_is_list = [False] * len(results[0])
     if hasattr(obj, "OUTPUT_IS_LIST"):
         output_is_list = obj.OUTPUT_IS_LIST
@@ -103,11 +179,50 @@ def get_output_data(obj, input_data_all):
             output.append([x for o in results for x in o[i]])
         else:
             output.append([o[i] for o in results])
+    return output
+
+def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None):
     
+    results = []
+    uis = []
+    subgraph_results = []
+    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
+    has_subgraph = False
+    for i in range(len(return_values)):
+        r = return_values[i]
+        if isinstance(r, dict):
+            if 'ui' in r:
+                uis.append(r['ui'])
+            if 'expand' in r:
+                # Perform an expansion, but do not append results
+                has_subgraph = True
+                new_graph = r['expand']
+                result = r.get("result", None)
+                if isinstance(result, ExecutionBlocker):
+                    result = tuple([result] * len(obj.RETURN_TYPES))
+                subgraph_results.append((new_graph, result))
+            elif 'result' in r:
+                result = r.get("result", None)
+                if isinstance(result, ExecutionBlocker):
+                    result = tuple([result] * len(obj.RETURN_TYPES))
+                results.append(result)
+                subgraph_results.append((None, result))
+        else:
+            if isinstance(r, ExecutionBlocker):
+                r = tuple([r] * len(obj.RETURN_TYPES))
+            results.append(r)
+            subgraph_results.append((None, r))
+    
+    if has_subgraph:
+        output = subgraph_results
+    elif len(results) > 0:
+        output = merge_result_data(results, obj)
+    else:
+        output = []
     ui = dict()    
     if len(uis) > 0:
         ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
-    return output, ui
+    return output, ui, has_subgraph
 
 def format_value(x):
     if x is None:
@@ -117,53 +232,145 @@ def format_value(x):
     else:
         return str(x)
 
-def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage):
+def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results):
     unique_id = current_item
-    inputs = prompt[unique_id]['inputs']
+    real_node_id = dynprompt.get_real_node_id(unique_id)
-    class_type = prompt[unique_id]['class_type']
+    display_node_id = dynprompt.get_display_node_id(unique_id)
+    parent_node_id = dynprompt.get_parent_node_id(unique_id)
+    inputs = dynprompt.get_node(unique_id)['inputs']
+    class_type = dynprompt.get_node(unique_id)['class_type']
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-    if unique_id in outputs:
+    if caches.outputs.get(unique_id) is not None:
-        return (True, None, None)
+        if server.client_id is not None:
-
+            cached_output = caches.ui.get(unique_id) or {}
-    for x in inputs:
+            server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id)
-        input_data = inputs[x]
+        return (ExecutionResult.SUCCESS, None, None)
-
-        if isinstance(input_data, list):
-            input_unique_id = input_data[0]
-            output_index = input_data[1]
-            if input_unique_id not in outputs:
-                result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage)
-                if result[0] is not True:
-                    # Another node failed further upstream
-                    return result
 
     input_data_all = None
     try:
-        input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
+        if unique_id in pending_subgraph_results:
+            cached_results = pending_subgraph_results[unique_id]
+            resolved_outputs = []
+            for is_subgraph, result in cached_results:
+                if not is_subgraph:
+                    resolved_outputs.append(result)
+                else:
+                    resolved_output = []
+                    for r in result:
+                        if is_link(r):
+                            source_node, source_output = r[0], r[1]
+                            node_output = caches.outputs.get(source_node)[source_output]
+                            for o in node_output:
+                                resolved_output.append(o)
+
+                        else:
+                            resolved_output.append(r)
+                    resolved_outputs.append(tuple(resolved_output))
+            output_data = merge_result_data(resolved_outputs, class_def)
+            output_ui = []
+            has_subgraph = False
+        else:
+            input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data)
             if server.client_id is not None:
-            server.last_node_id = unique_id
+                server.last_node_id = display_node_id
-            server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
+                server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
 
-        obj = object_storage.get((unique_id, class_type), None)
+            obj = caches.objects.get(unique_id)
             if obj is None:
                 obj = class_def()
-            object_storage[(unique_id, class_type)] = obj
+                caches.objects.set(unique_id, obj)
+
+            if hasattr(obj, "check_lazy_status"):
+                required_inputs = map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True)
+                required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
+                required_inputs = [x for x in required_inputs if isinstance(x,str) and (
+                    x not in input_data_all or x in missing_keys
+                )]
+                if len(required_inputs) > 0:
+                    for i in required_inputs:
+                        execution_list.make_input_strong_link(unique_id, i)
+                    return (ExecutionResult.PENDING, None, None)
+
+            def execution_block_cb(block):
+                if block.message is not None:
+                    mes = {
+                        "prompt_id": prompt_id,
+                        "node_id": unique_id,
+                        "node_type": class_type,
+                        "executed": list(executed),
 
-        output_data, output_ui = get_output_data(obj, input_data_all)
+                        "exception_message": f"Execution Blocked: {block.message}",
-        outputs[unique_id] = output_data
+                        "exception_type": "ExecutionBlocked",
+                        "traceback": [],
+                        "current_inputs": [],
+                        "current_outputs": [],
+                    }
+                    server.send_sync("execution_error", mes, server.client_id)
+                    return ExecutionBlocker(None)
+                else:
+                    return block
+            def pre_execute_cb(call_index):
+                GraphBuilder.set_default_prefix(unique_id, call_index, 0)
+            output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
         if len(output_ui) > 0:
-            outputs_ui[unique_id] = output_ui
+            caches.ui.set(unique_id, {
+                "meta": {
+                    "node_id": unique_id,
+                    "display_node": display_node_id,
+                    "parent_node": parent_node_id,
+                    "real_node_id": real_node_id,
+                },
+                "output": output_ui
+            })
             if server.client_id is not None:
-                server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
+                server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
+        if has_subgraph:
+            cached_outputs = []
+            new_node_ids = []
+            new_output_ids = []
+            new_output_links = []
+            for i in range(len(output_data)):
+                new_graph, node_outputs = output_data[i]
+                if new_graph is None:
+                    cached_outputs.append((False, node_outputs))
+                else:
+                    # Check for conflicts
+                    for node_id in new_graph.keys():
+                        if dynprompt.has_node(node_id):
+                            raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.")
+                    for node_id, node_info in new_graph.items():
+                        new_node_ids.append(node_id)
+                        display_id = node_info.get("override_display_id", unique_id)
+                        dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id)
+                        # Figure out if the newly created node is an output node
+                        class_type = node_info["class_type"]
+                        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+                        if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                            new_output_ids.append(node_id)
+                    for i in range(len(node_outputs)):
+                        if is_link(node_outputs[i]):
+                            from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1]
+                            new_output_links.append((from_node_id, from_socket))
+                    cached_outputs.append((True, node_outputs))
+            new_node_ids = set(new_node_ids)
+            for cache in caches.all:
+                cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused()
+            for node_id in new_output_ids:
+                execution_list.add_node(node_id)
+            for link in new_output_links:
+                execution_list.add_strong_link(link[0], link[1], unique_id)
+            pending_subgraph_results[unique_id] = cached_outputs
+            return (ExecutionResult.PENDING, None, None)
+        caches.outputs.set(unique_id, output_data)
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
 
         # skip formatting inputs/outputs
         error_details = {
-            "node_id": unique_id,
+            "node_id": real_node_id,
         }
 
-        return (False, error_details, iex)
+        return (ExecutionResult.FAILURE, error_details, iex)
     except Exception as ex:
         typ, _, tb = sys.exc_info()
         exception_type = full_type_name(typ)
@@ -173,121 +380,36 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
             for name, inputs in input_data_all.items():
                 input_data_formatted[name] = [format_value(x) for x in inputs]
 
-        output_data_formatted = {}
+        logging.error(f"!!! Exception during processing !!! {ex}")
-        for node_id, node_outputs in outputs.items():
-            output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
-
-        logging.error(f"!!! Exception during processing!!! {ex}")
         logging.error(traceback.format_exc())
 
         error_details = {
-            "node_id": unique_id,
+            "node_id": real_node_id,
             "exception_message": str(ex),
             "exception_type": exception_type,
             "traceback": traceback.format_tb(tb),
-            "current_inputs": input_data_formatted,
+            "current_inputs": input_data_formatted
-            "current_outputs": output_data_formatted
         }
-
         if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
             logging.error("Got an OOM, unloading all loaded models.")
             comfy.model_management.unload_all_models()
 
-        return (False, error_details, ex)
+        return (ExecutionResult.FAILURE, error_details, ex)
 
     executed.add(unique_id)
 
-    return (True, None, None)
+    return (ExecutionResult.SUCCESS, None, None)
-
-def recursive_will_execute(prompt, outputs, current_item, memo={}):
-    unique_id = current_item
-
-    if unique_id in memo:
-        return memo[unique_id]
-
-    inputs = prompt[unique_id]['inputs']
-    will_execute = []
-    if unique_id in outputs:
-        return []
-
-    for x in inputs:
-        input_data = inputs[x]
-        if isinstance(input_data, list):
-            input_unique_id = input_data[0]
-            output_index = input_data[1]
-            if input_unique_id not in outputs:
-                will_execute += recursive_will_execute(prompt, outputs, input_unique_id, memo)
-
-    memo[unique_id] = will_execute + [unique_id]
-    return memo[unique_id]
-
-def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
-    unique_id = current_item
-    inputs = prompt[unique_id]['inputs']
-    class_type = prompt[unique_id]['class_type']
-    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-
-    is_changed_old = ''
-    is_changed = ''
-    to_delete = False
-    if hasattr(class_def, 'IS_CHANGED'):
-        if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
-            is_changed_old = old_prompt[unique_id]['is_changed']
-        if 'is_changed' not in prompt[unique_id]:
-            input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
-            if input_data_all is not None:
-                try:
-                    #is_changed = class_def.IS_CHANGED(**input_data_all)
-                    is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
-                    prompt[unique_id]['is_changed'] = is_changed
-                except:
-                    to_delete = True
-        else:
-            is_changed = prompt[unique_id]['is_changed']
-
-    if unique_id not in outputs:
-        return True
-
-    if not to_delete:
-        if is_changed != is_changed_old:
-            to_delete = True
-        elif unique_id not in old_prompt:
-            to_delete = True
-        elif class_type != old_prompt[unique_id]['class_type']:
-            to_delete = True
-        elif inputs == old_prompt[unique_id]['inputs']:
-            for x in inputs:
-                input_data = inputs[x]
-
-                if isinstance(input_data, list):
-                    input_unique_id = input_data[0]
-                    output_index = input_data[1]
-                    if input_unique_id in outputs:
-                        to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
-                    else:
-                        to_delete = True
-                    if to_delete:
-                        break
-        else:
-            to_delete = True
-
-    if to_delete:
-        d = outputs.pop(unique_id)
-        del d
-    return to_delete
 
 class PromptExecutor:
-    def __init__(self, server):
+    def __init__(self, server, lru_size=None):
+        self.lru_size = lru_size
         self.server = server
         self.reset()
 
     def reset(self):
-        self.outputs = {}
+        self.caches = CacheSet(self.lru_size)
-        self.object_storage = {}
-        self.outputs_ui = {}
         self.status_messages = []
         self.success = True
-        self.old_prompt = {}
 
     def add_message(self, event, data: dict, broadcast: bool):
         data = {
@@ -318,27 +440,14 @@ class PromptExecutor:
                 "node_id": node_id,
                 "node_type": class_type,
                 "executed": list(executed),
-
                 "exception_message": error["exception_message"],
                 "exception_type": error["exception_type"],
                 "traceback": error["traceback"],
                 "current_inputs": error["current_inputs"],
-                "current_outputs": error["current_outputs"],
+                "current_outputs": list(current_outputs),
             }
             self.add_message("execution_error", mes, broadcast=False)
         
-        # Next, remove the subsequent outputs since they will not be executed
-        to_delete = []
-        for o in self.outputs:
-            if (o not in current_outputs) and (o not in executed):
-                to_delete += [o]
-                if o in self.old_prompt:
-                    d = self.old_prompt.pop(o)
-                    del d
-        for o in to_delete:
-            d = self.outputs.pop(o)
-            del d
-
     def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
         nodes.interrupt_processing(False)
 
@@ -351,65 +460,58 @@ class PromptExecutor:
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
         with torch.inference_mode():
-            #delete cached outputs if nodes don't exist for them
+            dynamic_prompt = DynamicPrompt(prompt)
-            to_delete = []
+            is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs)
-            for o in self.outputs:
+            for cache in self.caches.all:
-                if o not in prompt:
+                cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
-                    to_delete += [o]
+                cache.clean_unused()
-            for o in to_delete:
-                d = self.outputs.pop(o)
-                del d
-            to_delete = []
-            for o in self.object_storage:
-                if o[0] not in prompt:
-                    to_delete += [o]
-                else:
-                    p = prompt[o[0]]
-                    if o[1] != p['class_type']:
-                        to_delete += [o]
-            for o in to_delete:
-                d = self.object_storage.pop(o)
-                del d
 
-            for x in prompt:
+            cached_nodes = []
-                recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
+            for node_id in prompt:
-
+                if self.caches.outputs.get(node_id) is not None:
-            current_outputs = set(self.outputs.keys())
+                    cached_nodes.append(node_id)
-            for x in list(self.outputs_ui.keys()):
-                if x not in current_outputs:
-                    d = self.outputs_ui.pop(x)
-                    del d
 
             comfy.model_management.cleanup_models(keep_clone_weights_loaded=True)
             self.add_message("execution_cached",
-                          { "nodes": list(current_outputs) , "prompt_id": prompt_id},
+                          { "nodes": cached_nodes, "prompt_id": prompt_id},
                           broadcast=False)
+            pending_subgraph_results = {}
             executed = set()
-            output_node_id = None
+            execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
-            to_execute = []
+            current_outputs = self.caches.outputs.all_node_ids()
-
             for node_id in list(execute_outputs):
-                to_execute += [(0, node_id)]
+                execution_list.add_node(node_id)
-
+
-            while len(to_execute) > 0:
+            while not execution_list.is_empty():
-                #always execute the output that depends on the least amount of unexecuted nodes first
+                node_id, error, ex = execution_list.stage_node_execution()
-                memo = {}
+                if error is not None:
-                to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1], memo)), a[-1]), to_execute)))
+                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                output_node_id = to_execute.pop(0)[-1]
-
-                # This call shouldn't raise anything if there's an error deep in
-                # the actual SD code, instead it will report the node where the
-                # error was raised
-                self.success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage)
-                if self.success is not True:
-                    self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
                     break
+
+                result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
+                if result == ExecutionResult.FAILURE:
+                    self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                    break
+                elif result == ExecutionResult.PENDING:
+                    execution_list.unstage_node_execution()
+                else: # result == ExecutionResult.SUCCESS:
+                    execution_list.complete_node_execution()
             else:
                 # Only execute when the while-loop ends without break
                 self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
 
-            for x in executed:
+            ui_outputs = {}
-                self.old_prompt[x] = copy.deepcopy(prompt[x])
+            meta_outputs = {}
+            all_node_ids = self.caches.ui.all_node_ids()
+            for node_id in all_node_ids:
+                ui_info = self.caches.ui.get(node_id)
+                if ui_info is not None:
+                    ui_outputs[node_id] = ui_info["output"]
+                    meta_outputs[node_id] = ui_info["meta"]
+            self.history_result = {
+                "outputs": ui_outputs,
+                "meta": meta_outputs,
+            }
             self.server.last_node_id = None
             if comfy.model_management.DISABLE_SMART_MEMORY:
                 comfy.model_management.unload_all_models()
@@ -426,17 +528,24 @@ def validate_inputs(prompt, item, validated):
     obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
 
     class_inputs = obj_class.INPUT_TYPES()
-    required_inputs = class_inputs['required']
+    valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
 
     errors = []
     valid = True
 
     validate_function_inputs = []
+    validate_has_kwargs = False
     if hasattr(obj_class, "VALIDATE_INPUTS"):
-        validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args
+        argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS)
-
+        validate_function_inputs = argspec.args
-    for x in required_inputs:
+        validate_has_kwargs = argspec.varkw is not None
+    received_types = {}
+
+    for x in valid_inputs:
+        type_input, input_category, extra_info = get_input_info(obj_class, x)
+        assert extra_info is not None
         if x not in inputs:
+            if input_category == "required":
                 error = {
                     "type": "required_input_missing",
                     "message": "Required input is missing",
@@ -449,8 +558,7 @@ def validate_inputs(prompt, item, validated):
             continue
 
         val = inputs[x]
-        info = required_inputs[x]
+        info = (type_input, extra_info)
-        type_input = info[0]
         if isinstance(val, list):
             if len(val) != 2:
                 error = {
@@ -469,8 +577,9 @@ def validate_inputs(prompt, item, validated):
             o_id = val[0]
             o_class_type = prompt[o_id]['class_type']
             r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
-            if r[val[1]] != type_input:
             received_type = r[val[1]]
+            received_types[x] = received_type
+            if 'input_types' not in validate_function_inputs and received_type != type_input:
                 details = f"{x}, {received_type} != {type_input}"
                 error = {
                     "type": "return_type_mismatch",
@@ -521,6 +630,9 @@ def validate_inputs(prompt, item, validated):
                 if type_input == "STRING":
                     val = str(val)
                     inputs[x] = val
+                if type_input == "BOOLEAN":
+                    val = bool(val)
+                    inputs[x] = val
             except Exception as ex:
                 error = {
                     "type": "invalid_input_type",
@@ -536,11 +648,11 @@ def validate_inputs(prompt, item, validated):
                 errors.append(error)
                 continue
 
-            if len(info) > 1:
+            if x not in validate_function_inputs and not validate_has_kwargs:
-                if "min" in info[1] and val < info[1]["min"]:
+                if "min" in extra_info and val < extra_info["min"]:
                     error = {
                         "type": "value_smaller_than_min",
-                        "message": "Value {} smaller than min of {}".format(val, info[1]["min"]),
+                        "message": "Value {} smaller than min of {}".format(val, extra_info["min"]),
                         "details": f"{x}",
                         "extra_info": {
                             "input_name": x,
@@ -550,10 +662,10 @@ def validate_inputs(prompt, item, validated):
                     }
                     errors.append(error)
                     continue
-                if "max" in info[1] and val > info[1]["max"]:
+                if "max" in extra_info and val > extra_info["max"]:
                     error = {
                         "type": "value_bigger_than_max",
-                        "message": "Value {} bigger than max of {}".format(val, info[1]["max"]),
+                        "message": "Value {} bigger than max of {}".format(val, extra_info["max"]),
                         "details": f"{x}",
                         "extra_info": {
                             "input_name": x,
@@ -564,7 +676,6 @@ def validate_inputs(prompt, item, validated):
                     errors.append(error)
                     continue
 
-            if x not in validate_function_inputs:
                 if isinstance(type_input, list):
                     if val not in type_input:
                         input_config = info
@@ -591,18 +702,20 @@ def validate_inputs(prompt, item, validated):
                         errors.append(error)
                         continue
 
-    if len(validate_function_inputs) > 0:
+    if len(validate_function_inputs) > 0 or validate_has_kwargs:
-        input_data_all = get_input_data(inputs, obj_class, unique_id)
+        input_data_all, _ = get_input_data(inputs, obj_class, unique_id)
         input_filtered = {}
         for x in input_data_all:
-            if x in validate_function_inputs:
+            if x in validate_function_inputs or validate_has_kwargs:
                 input_filtered[x] = input_data_all[x]
+        if 'input_types' in validate_function_inputs:
+            input_filtered['input_types'] = [received_types]
 
         #ret = obj_class.VALIDATE_INPUTS(**input_filtered)
         ret = map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS")
         for x in input_filtered:
             for i, r in enumerate(ret):
-                if r is not True:
+                if r is not True and not isinstance(r, ExecutionBlocker):
                     details = f"{x}"
                     if r is not False:
                         details += f" - {str(r)}"
@@ -613,8 +726,6 @@ def validate_inputs(prompt, item, validated):
                         "details": details,
                         "extra_info": {
                             "input_name": x,
-                            "input_config": info,
-                            "received_value": val,
                         }
                     }
                     errors.append(error)
@@ -780,7 +891,7 @@ class PromptQueue:
         completed: bool
         messages: List[str]
 
-    def task_done(self, item_id, outputs,
+    def task_done(self, item_id, history_result,
                   status: Optional['PromptQueue.ExecutionStatus']):
         with self.mutex:
             prompt = self.currently_running.pop(item_id)
@@ -793,9 +904,10 @@ class PromptQueue:
 
             self.history[prompt[1]] = {
                 "prompt": prompt,
-                "outputs": copy.deepcopy(outputs),
+                "outputs": {},
                 'status': status_dict,
             }
+            self.history[prompt[1]].update(history_result)
             self.server.queue_updated()
 
     def get_current_queue(self):

main.py

@@ -101,7 +101,7 @@ def cuda_malloc_warning():
             logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
 
 def prompt_worker(q, server):
-    e = execution.PromptExecutor(server)
+    e = execution.PromptExecutor(server, lru_size=args.cache_lru)
     last_gc_collect = 0
     need_gc = False
     gc_collect_interval = 10.0
@@ -121,7 +121,7 @@ def prompt_worker(q, server):
             e.execute(item[2], prompt_id, item[3], item[4])
             need_gc = True
             q.task_done(item_id,
-                        e.outputs_ui,
+                        e.history_result,
                         status=execution.PromptQueue.ExecutionStatus(
                             status_str='success' if e.success else 'error',
                             completed=e.success,

pytest.ini

@@ -1,6 +1,7 @@
 [pytest]
 markers = 
   inference: mark as inference test (deselect with '-m "not inference"')
+  execution: mark as execution test (deselect with '-m "not execution"')
 testpaths =
   tests
   tests-unit

server.py

@@ -423,6 +423,7 @@ class PromptServer():
             obj_class = nodes.NODE_CLASS_MAPPINGS[node_class]
             info = {}
             info['input'] = obj_class.INPUT_TYPES()
+            info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()}
             info['output'] = obj_class.RETURN_TYPES
             info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
             info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']
@@ -714,6 +715,9 @@ class PromptServer():
         site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
         await site.start()
 
+        self.address = address
+        self.port = port
+
         if verbose:
             logging.info("Starting server\n")
             logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address, port))

tests/inference/extra_model_paths.yaml

@@ -0,0 +1,4 @@
+# Config for testing nodes
+testing:
+    custom_nodes: tests/inference/testing_nodes
+

tests/inference/test_execution.py

@@ -0,0 +1,461 @@
+from io import BytesIO
+import numpy
+from PIL import Image
+import pytest
+from pytest import fixture
+import time
+import torch
+from typing import Union, Dict
+import json
+import subprocess
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import urllib.request
+import urllib.parse
+import urllib.error
+from comfy.graph_utils import GraphBuilder, Node
+
+class RunResult:
+    def __init__(self, prompt_id: str):
+        self.outputs: Dict[str,Dict] = {}
+        self.runs: Dict[str,bool] = {}
+        self.prompt_id: str = prompt_id
+
+    def get_output(self, node: Node):
+        return self.outputs.get(node.id, None)
+
+    def did_run(self, node: Node):
+        return self.runs.get(node.id, False)
+
+    def get_images(self, node: Node):
+        output = self.get_output(node)
+        if output is None:
+            return []
+        return output.get('image_objects', [])
+
+    def get_prompt_id(self):
+        return self.prompt_id
+
+class ComfyClient:
+    def __init__(self):
+        self.test_name = ""
+
+    def connect(self, 
+                    listen:str = '127.0.0.1', 
+                    port:Union[str,int] = 8188,
+                    client_id: str = str(uuid.uuid4())
+                    ):
+        self.client_id = client_id
+        self.server_address = f"{listen}:{port}"
+        ws = websocket.WebSocket()
+        ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id))
+        self.ws = ws
+
+    def queue_prompt(self, prompt):
+        p = {"prompt": prompt, "client_id": self.client_id}
+        data = json.dumps(p).encode('utf-8')
+        req =  urllib.request.Request("http://{}/prompt".format(self.server_address), data=data)
+        return json.loads(urllib.request.urlopen(req).read())
+
+    def get_image(self, filename, subfolder, folder_type):
+        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+        url_values = urllib.parse.urlencode(data)
+        with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response:
+            return response.read()
+
+    def get_history(self, prompt_id):
+        with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response:
+            return json.loads(response.read())
+
+    def set_test_name(self, name):
+        self.test_name = name
+
+    def run(self, graph):
+        prompt = graph.finalize()
+        for node in graph.nodes.values():
+            if node.class_type == 'SaveImage':
+                node.inputs['filename_prefix'] = self.test_name
+
+        prompt_id = self.queue_prompt(prompt)['prompt_id']
+        result = RunResult(prompt_id)
+        while True:
+            out = self.ws.recv()
+            if isinstance(out, str):
+                message = json.loads(out)
+                if message['type'] == 'executing':
+                    data = message['data']
+                    if data['prompt_id'] != prompt_id:
+                        continue
+                    if data['node'] is None:
+                        break
+                    result.runs[data['node']] = True
+                elif message['type'] == 'execution_error':
+                    raise Exception(message['data'])
+                elif message['type'] == 'execution_cached':
+                    pass # Probably want to store this off for testing
+
+        history = self.get_history(prompt_id)[prompt_id]
+        for o in history['outputs']:
+            for node_id in history['outputs']:
+                node_output = history['outputs'][node_id]
+                result.outputs[node_id] = node_output
+                if 'images' in node_output:
+                    images_output = []
+                    for image in node_output['images']:
+                        image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
+                        image_obj = Image.open(BytesIO(image_data))
+                        images_output.append(image_obj)
+                    node_output['image_objects'] = images_output
+
+        return result
+
+#
+# Loop through these variables
+#
+@pytest.mark.execution
+class TestExecution:
+    #
+    # Initialize server and client
+    #
+    @fixture(scope="class", autouse=True, params=[
+        # (use_lru, lru_size)
+        (False, 0),
+        (True, 0),
+        (True, 100),
+    ])
+    def _server(self, args_pytest, request):
+        # Start server
+        pargs = [
+            'python','main.py', 
+            '--output-directory', args_pytest["output_dir"],
+            '--listen', args_pytest["listen"],
+            '--port', str(args_pytest["port"]),
+            '--extra-model-paths-config', 'tests/inference/extra_model_paths.yaml',
+        ]
+        use_lru, lru_size = request.param
+        if use_lru:
+            pargs += ['--cache-lru', str(lru_size)]
+        print("Running server with args:", pargs)
+        p = subprocess.Popen(pargs)
+        yield
+        p.kill()
+        torch.cuda.empty_cache()
+
+    def start_client(self, listen:str, port:int):
+        # Start client
+        comfy_client = ComfyClient()
+        # Connect to server (with retries)
+        n_tries = 5
+        for i in range(n_tries):
+            time.sleep(4)
+            try:
+                comfy_client.connect(listen=listen, port=port)
+            except ConnectionRefusedError as e:
+                print(e)
+                print(f"({i+1}/{n_tries}) Retrying...")
+            else:
+                break
+        return comfy_client
+
+    @fixture(scope="class", autouse=True)
+    def shared_client(self, args_pytest, _server):
+        client = self.start_client(args_pytest["listen"], args_pytest["port"])
+        yield client
+        del client
+        torch.cuda.empty_cache()
+
+    @fixture
+    def client(self, shared_client, request):
+        shared_client.set_test_name(f"execution[{request.node.name}]")
+        yield shared_client
+
+    @fixture
+    def builder(self, request):
+        yield GraphBuilder(prefix=request.node.name)
+
+    def test_lazy_input(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1)
+
+        lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        output = g.node("SaveImage", images=lazy_mix.out(0))
+        result = client.run(g)
+
+        result_image = result.get_images(output)[0]
+        assert numpy.array(result_image).any() == 0, "Image should be black"
+        assert result.did_run(input1)
+        assert not result.did_run(input2)
+        assert result.did_run(mask)
+        assert result.did_run(lazy_mix)
+
+    def test_full_cache(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+
+        lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        g.node("SaveImage", images=lazy_mix.out(0))
+
+        client.run(g)
+        result2 = client.run(g)
+        for node_id, node in g.nodes.items():
+            assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached"
+
+    def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+
+        lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        g.node("SaveImage", images=lazy_mix.out(0))
+
+        client.run(g)
+        mask.inputs['value'] = 0.4
+        result2 = client.run(g)
+        assert not result2.did_run(input1), "Input1 should have been cached"
+        assert not result2.did_run(input2), "Input2 should have been cached"
+
+    def test_error(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        # Different size of the two images
+        input2 = g.node("StubImage", content="NOISE", height=256, width=256, batch_size=1)
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+
+        lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        g.node("SaveImage", images=lazy_mix.out(0))
+
+        try:
+            client.run(g)
+            assert False, "Should have raised an error"
+        except Exception as e:
+            assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
+
+    @pytest.mark.parametrize("test_value, expect_error", [
+        (5, True),
+        ("foo", True),
+        (5.0, False),
+    ])
+    def test_validation_error_literal(self, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        validation1 = g.node("TestCustomValidation1", input1=test_value, input2=3.0)
+        g.node("SaveImage", images=validation1.out(0))
+
+        if expect_error:
+            with pytest.raises(urllib.error.HTTPError):
+                client.run(g)
+        else:
+            client.run(g)
+
+    @pytest.mark.parametrize("test_type, test_value", [
+        ("StubInt", 5),
+        ("StubFloat", 5.0)
+    ])
+    def test_validation_error_edge1(self, test_type, test_value, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        stub = g.node(test_type, value=test_value)
+        validation1 = g.node("TestCustomValidation1", input1=stub.out(0), input2=3.0)
+        g.node("SaveImage", images=validation1.out(0))
+
+        with pytest.raises(urllib.error.HTTPError):
+            client.run(g)
+
+    @pytest.mark.parametrize("test_type, test_value, expect_error", [
+        ("StubInt", 5, True),
+        ("StubFloat", 5.0, False)
+    ])
+    def test_validation_error_edge2(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        stub = g.node(test_type, value=test_value)
+        validation2 = g.node("TestCustomValidation2", input1=stub.out(0), input2=3.0)
+        g.node("SaveImage", images=validation2.out(0))
+
+        if expect_error:
+            with pytest.raises(urllib.error.HTTPError):
+                client.run(g)
+        else:
+            client.run(g)
+
+    @pytest.mark.parametrize("test_type, test_value, expect_error", [
+        ("StubInt", 5, True),
+        ("StubFloat", 5.0, False)
+    ])
+    def test_validation_error_edge3(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        stub = g.node(test_type, value=test_value)
+        validation3 = g.node("TestCustomValidation3", input1=stub.out(0), input2=3.0)
+        g.node("SaveImage", images=validation3.out(0))
+
+        if expect_error:
+            with pytest.raises(urllib.error.HTTPError):
+                client.run(g)
+        else:
+            client.run(g)
+
+    @pytest.mark.parametrize("test_type, test_value, expect_error", [
+        ("StubInt", 5, True),
+        ("StubFloat", 5.0, False)
+    ])
+    def test_validation_error_edge4(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        stub = g.node(test_type, value=test_value)
+        validation4 = g.node("TestCustomValidation4", input1=stub.out(0), input2=3.0)
+        g.node("SaveImage", images=validation4.out(0))
+
+        if expect_error:
+            with pytest.raises(urllib.error.HTTPError):
+                client.run(g)
+        else:
+            client.run(g)
+
+    @pytest.mark.parametrize("test_value1, test_value2, expect_error", [
+        (0.0, 0.5, False),
+        (0.0, 5.0, False),
+        (0.0, 7.0, True)
+    ])
+    def test_validation_error_kwargs(self, test_value1, test_value2, expect_error, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        validation5 = g.node("TestCustomValidation5", input1=test_value1, input2=test_value2)
+        g.node("SaveImage", images=validation5.out(0))
+
+        if expect_error:
+            with pytest.raises(urllib.error.HTTPError):
+                client.run(g)
+        else:
+            client.run(g)
+
+    def test_cycle_error(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+
+        lazy_mix1 = g.node("TestLazyMixImages", image1=input1.out(0), mask=mask.out(0))
+        lazy_mix2 = g.node("TestLazyMixImages", image1=lazy_mix1.out(0), image2=input2.out(0), mask=mask.out(0))
+        g.node("SaveImage", images=lazy_mix2.out(0))
+
+        # When the cycle exists on initial submission, it should raise a validation error
+        with pytest.raises(urllib.error.HTTPError):
+            client.run(g)
+
+    def test_dynamic_cycle_error(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        generator = g.node("TestDynamicDependencyCycle", input1=input1.out(0), input2=input2.out(0))
+        g.node("SaveImage", images=generator.out(0))
+
+        # When the cycle is in a graph that is generated dynamically, it should raise a runtime error
+        try:
+            client.run(g)
+            assert False, "Should have raised an error"
+        except Exception as e:
+            assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
+            assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node"
+
+    def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        # Creating the nodes in this specific order previously caused a bug
+        save = g.node("SaveImage")
+        is_changed = g.node("TestCustomIsChanged", should_change=False)
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+
+        save.set_input('images', is_changed.out(0))
+        is_changed.set_input('image', input1.out(0))
+
+        result1 = client.run(g)
+        result2 = client.run(g)
+        is_changed.set_input('should_change', True)
+        result3 = client.run(g)
+        result4 = client.run(g)
+        assert result1.did_run(is_changed), "is_changed should have been run"
+        assert not result2.did_run(is_changed), "is_changed should have been cached"
+        assert result3.did_run(is_changed), "is_changed should have been re-run"
+        assert result4.did_run(is_changed), "is_changed should not have been cached"
+
+    def test_undeclared_inputs(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        input3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input4 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        average = g.node("TestVariadicAverage", input1=input1.out(0), input2=input2.out(0), input3=input3.out(0), input4=input4.out(0))
+        output = g.node("SaveImage", images=average.out(0))
+
+        result = client.run(g)
+        result_image = result.get_images(output)[0]
+        expected = 255 // 4
+        assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey"
+
+    def test_for_loop(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        iterations = 4
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        is_changed = g.node("TestCustomIsChanged", should_change=True, image=input2.out(0))
+        for_open = g.node("TestForLoopOpen", remaining=iterations, initial_value1=is_changed.out(0))
+        average = g.node("TestVariadicAverage", input1=input1.out(0), input2=for_open.out(2))
+        for_close = g.node("TestForLoopClose", flow_control=for_open.out(0), initial_value1=average.out(0))
+        output = g.node("SaveImage", images=for_close.out(0))
+
+        for iterations in range(1, 5):
+            for_open.set_input('remaining', iterations)
+            result = client.run(g)
+            result_image = result.get_images(output)[0]
+            expected = 255 // (2 ** iterations)
+            assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey"
+            assert result.did_run(is_changed)
+
+    def test_mixed_expansion_returns(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        val_list = g.node("TestMakeListNode", value1=0.1, value2=0.2, value3=0.3)
+        mixed = g.node("TestMixedExpansionReturns", input1=val_list.out(0))
+        output_dynamic = g.node("SaveImage", images=mixed.out(0))
+        output_literal = g.node("SaveImage", images=mixed.out(1))
+
+        result = client.run(g)
+        images_dynamic = result.get_images(output_dynamic)
+        assert len(images_dynamic) == 3, "Should have 2 images"
+        assert numpy.array(images_dynamic[0]).min() == 25 and numpy.array(images_dynamic[0]).max() == 25, "First image should be 0.1"
+        assert numpy.array(images_dynamic[1]).min() == 51 and numpy.array(images_dynamic[1]).max() == 51, "Second image should be 0.2"
+        assert numpy.array(images_dynamic[2]).min() == 76 and numpy.array(images_dynamic[2]).max() == 76, "Third image should be 0.3"
+
+        images_literal = result.get_images(output_literal)
+        assert len(images_literal) == 3, "Should have 2 images"
+        for i in range(3):
+            assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white"
+
+    def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0)
+        mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1)
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        rebatch = g.node("RebatchImages", images=mix.out(0), batch_size=3)
+        output = g.node("SaveImage", images=rebatch.out(0))
+
+        result = client.run(g)
+        images = result.get_images(output)
+        assert len(images) == 3, "Should have 3 image"
+        assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be 0.0"
+        assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5"
+        assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0"
+
+    def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+
+        output1 = g.node("PreviewImage", images=input1.out(0))
+        output2 = g.node("PreviewImage", images=input1.out(0))
+
+        result = client.run(g)
+        images1 = result.get_images(output1)
+        images2 = result.get_images(output2)
+        assert len(images1) == 1, "Should have 1 image"
+        assert len(images2) == 1, "Should have 1 image"
+

tests/inference/testing_nodes/testing-pack/__init__.py

@@ -0,0 +1,23 @@
+from .specific_tests import TEST_NODE_CLASS_MAPPINGS, TEST_NODE_DISPLAY_NAME_MAPPINGS
+from .flow_control import FLOW_CONTROL_NODE_CLASS_MAPPINGS, FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS
+from .util import UTILITY_NODE_CLASS_MAPPINGS, UTILITY_NODE_DISPLAY_NAME_MAPPINGS
+from .conditions import CONDITION_NODE_CLASS_MAPPINGS, CONDITION_NODE_DISPLAY_NAME_MAPPINGS
+from .stubs import TEST_STUB_NODE_CLASS_MAPPINGS, TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS
+
+# NODE_CLASS_MAPPINGS = GENERAL_NODE_CLASS_MAPPINGS.update(COMPONENT_NODE_CLASS_MAPPINGS)
+# NODE_DISPLAY_NAME_MAPPINGS = GENERAL_NODE_DISPLAY_NAME_MAPPINGS.update(COMPONENT_NODE_DISPLAY_NAME_MAPPINGS)
+
+NODE_CLASS_MAPPINGS = {}
+NODE_CLASS_MAPPINGS.update(TEST_NODE_CLASS_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(FLOW_CONTROL_NODE_CLASS_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(UTILITY_NODE_CLASS_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(CONDITION_NODE_CLASS_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(TEST_STUB_NODE_CLASS_MAPPINGS)
+
+NODE_DISPLAY_NAME_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS.update(TEST_NODE_DISPLAY_NAME_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(UTILITY_NODE_DISPLAY_NAME_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(CONDITION_NODE_DISPLAY_NAME_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS)
+

tests/inference/testing_nodes/testing-pack/conditions.py

@@ -0,0 +1,194 @@
+import re
+import torch
+
+class TestIntConditions:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}),
+                "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}),
+                "operation": (["==", "!=", "<", ">", "<=", ">="],),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    FUNCTION = "int_condition"
+
+    CATEGORY = "Testing/Logic"
+
+    def int_condition(self, a, b, operation):
+        if operation == "==":
+            return (a == b,)
+        elif operation == "!=":
+            return (a != b,)
+        elif operation == "<":
+            return (a < b,)
+        elif operation == ">":
+            return (a > b,)
+        elif operation == "<=":
+            return (a <= b,)
+        elif operation == ">=":
+            return (a >= b,)
+
+
+class TestFloatConditions:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "a": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}),
+                "b": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}),
+                "operation": (["==", "!=", "<", ">", "<=", ">="],),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    FUNCTION = "float_condition"
+
+    CATEGORY = "Testing/Logic"
+
+    def float_condition(self, a, b, operation):
+        if operation == "==":
+            return (a == b,)
+        elif operation == "!=":
+            return (a != b,)
+        elif operation == "<":
+            return (a < b,)
+        elif operation == ">":
+            return (a > b,)
+        elif operation == "<=":
+            return (a <= b,)
+        elif operation == ">=":
+            return (a >= b,)
+
+class TestStringConditions:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "a": ("STRING", {"multiline": False}),
+                "b": ("STRING", {"multiline": False}),
+                "operation": (["a == b", "a != b", "a IN b", "a MATCH REGEX(b)", "a BEGINSWITH b", "a ENDSWITH b"],),
+                "case_sensitive": ("BOOLEAN", {"default": True}),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    FUNCTION = "string_condition"
+
+    CATEGORY = "Testing/Logic"
+
+    def string_condition(self, a, b, operation, case_sensitive):
+        if not case_sensitive:
+            a = a.lower()
+            b = b.lower()
+
+        if operation == "a == b":
+            return (a == b,)
+        elif operation == "a != b":
+            return (a != b,)
+        elif operation == "a IN b":
+            return (a in b,)
+        elif operation == "a MATCH REGEX(b)":
+            try:
+                return (re.match(b, a) is not None,)
+            except:
+                return (False,)
+        elif operation == "a BEGINSWITH b":
+            return (a.startswith(b),)
+        elif operation == "a ENDSWITH b":
+            return (a.endswith(b),)
+
+class TestToBoolNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("*",),
+            },
+            "optional": {
+                "invert": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    FUNCTION = "to_bool"
+
+    CATEGORY = "Testing/Logic"
+
+    def to_bool(self, value, invert = False):
+        if isinstance(value, torch.Tensor):
+            if value.max().item() == 0 and value.min().item() == 0:
+                result = False
+            else:
+                result = True
+        else:
+            try:
+                result = bool(value)
+            except:
+                # Can't convert it? Well then it's something or other. I dunno, I'm not a Python programmer.
+                result = True
+
+        if invert:
+            result = not result
+
+        return (result,)
+
+class TestBoolOperationNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "a": ("BOOLEAN",),
+                "b": ("BOOLEAN",),
+                "op": (["a AND b", "a OR b", "a XOR b", "NOT a"],),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    FUNCTION = "bool_operation"
+
+    CATEGORY = "Testing/Logic"
+
+    def bool_operation(self, a, b, op):
+        if op == "a AND b":
+            return (a and b,)
+        elif op == "a OR b":
+            return (a or b,)
+        elif op == "a XOR b":
+            return (a ^ b,)
+        elif op == "NOT a":
+            return (not a,)
+
+
+CONDITION_NODE_CLASS_MAPPINGS = {
+    "TestIntConditions": TestIntConditions,
+    "TestFloatConditions": TestFloatConditions,
+    "TestStringConditions": TestStringConditions,
+    "TestToBoolNode": TestToBoolNode,
+    "TestBoolOperationNode": TestBoolOperationNode,
+}
+
+CONDITION_NODE_DISPLAY_NAME_MAPPINGS = {
+    "TestIntConditions": "Int Condition",
+    "TestFloatConditions": "Float Condition",
+    "TestStringConditions": "String Condition",
+    "TestToBoolNode": "To Bool",
+    "TestBoolOperationNode": "Bool Operation",
+}

tests/inference/testing_nodes/testing-pack/flow_control.py

@@ -0,0 +1,173 @@
+from comfy.graph_utils import GraphBuilder, is_link
+from comfy.graph import ExecutionBlocker
+from .tools import VariantSupport
+
+NUM_FLOW_SOCKETS = 5
+@VariantSupport()
+class TestWhileLoopOpen:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {
+            "required": {
+                "condition": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+            },
+        }
+        for i in range(NUM_FLOW_SOCKETS):
+            inputs["optional"][f"initial_value{i}"] = ("*",)
+        return inputs
+
+    RETURN_TYPES = tuple(["FLOW_CONTROL"] + ["*"] * NUM_FLOW_SOCKETS)
+    RETURN_NAMES = tuple(["FLOW_CONTROL"] + [f"value{i}" for i in range(NUM_FLOW_SOCKETS)])
+    FUNCTION = "while_loop_open"
+
+    CATEGORY = "Testing/Flow"
+
+    def while_loop_open(self, condition, **kwargs):
+        values = []
+        for i in range(NUM_FLOW_SOCKETS):
+            values.append(kwargs.get(f"initial_value{i}", None))
+        return tuple(["stub"] + values)
+
+@VariantSupport()
+class TestWhileLoopClose:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {
+            "required": {
+                "flow_control": ("FLOW_CONTROL", {"rawLink": True}),
+                "condition": ("BOOLEAN", {"forceInput": True}),
+            },
+            "optional": {
+            },
+            "hidden": {
+                "dynprompt": "DYNPROMPT",
+                "unique_id": "UNIQUE_ID",
+            }
+        }
+        for i in range(NUM_FLOW_SOCKETS):
+            inputs["optional"][f"initial_value{i}"] = ("*",)
+        return inputs
+
+    RETURN_TYPES = tuple(["*"] * NUM_FLOW_SOCKETS)
+    RETURN_NAMES = tuple([f"value{i}" for i in range(NUM_FLOW_SOCKETS)])
+    FUNCTION = "while_loop_close"
+
+    CATEGORY = "Testing/Flow"
+
+    def explore_dependencies(self, node_id, dynprompt, upstream):
+        node_info = dynprompt.get_node(node_id)
+        if "inputs" not in node_info:
+            return
+        for k, v in node_info["inputs"].items():
+            if is_link(v):
+                parent_id = v[0]
+                if parent_id not in upstream:
+                    upstream[parent_id] = []
+                    self.explore_dependencies(parent_id, dynprompt, upstream)
+                upstream[parent_id].append(node_id)
+
+    def collect_contained(self, node_id, upstream, contained):
+        if node_id not in upstream:
+            return
+        for child_id in upstream[node_id]:
+            if child_id not in contained:
+                contained[child_id] = True
+                self.collect_contained(child_id, upstream, contained)
+
+
+    def while_loop_close(self, flow_control, condition, dynprompt=None, unique_id=None, **kwargs):
+        assert dynprompt is not None
+        if not condition:
+            # We're done with the loop
+            values = []
+            for i in range(NUM_FLOW_SOCKETS):
+                values.append(kwargs.get(f"initial_value{i}", None))
+            return tuple(values)
+
+        # We want to loop
+        upstream = {}
+        # Get the list of all nodes between the open and close nodes
+        self.explore_dependencies(unique_id, dynprompt, upstream)
+
+        contained = {}
+        open_node = flow_control[0]
+        self.collect_contained(open_node, upstream, contained)
+        contained[unique_id] = True
+        contained[open_node] = True
+
+        # We'll use the default prefix, but to avoid having node names grow exponentially in size,
+        # we'll use "Recurse" for the name of the recursively-generated copy of this node.
+        graph = GraphBuilder()
+        for node_id in contained:
+            original_node = dynprompt.get_node(node_id)
+            node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id)
+            node.set_override_display_id(node_id)
+        for node_id in contained:
+            original_node = dynprompt.get_node(node_id)
+            node = graph.lookup_node("Recurse" if node_id == unique_id else node_id)
+            assert node is not None
+            for k, v in original_node["inputs"].items():
+                if is_link(v) and v[0] in contained:
+                    parent = graph.lookup_node(v[0])
+                    assert parent is not None
+                    node.set_input(k, parent.out(v[1]))
+                else:
+                    node.set_input(k, v)
+        new_open = graph.lookup_node(open_node)
+        assert new_open is not None
+        for i in range(NUM_FLOW_SOCKETS):
+            key = f"initial_value{i}"
+            new_open.set_input(key, kwargs.get(key, None))
+        my_clone = graph.lookup_node("Recurse")
+        assert my_clone is not None
+        result = map(lambda x: my_clone.out(x), range(NUM_FLOW_SOCKETS))
+        return {
+            "result": tuple(result),
+            "expand": graph.finalize(),
+        }
+
+@VariantSupport()
+class TestExecutionBlockerNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {
+            "required": {
+                "input": ("*",),
+                "block": ("BOOLEAN",),
+                "verbose": ("BOOLEAN", {"default": False}),
+            },
+        }
+        return inputs
+
+    RETURN_TYPES = ("*",)
+    RETURN_NAMES = ("output",)
+    FUNCTION = "execution_blocker"
+
+    CATEGORY = "Testing/Flow"
+
+    def execution_blocker(self, input, block, verbose):
+        if block:
+            return (ExecutionBlocker("Blocked Execution" if verbose else None),)
+        return (input,)
+
+FLOW_CONTROL_NODE_CLASS_MAPPINGS = {
+    "TestWhileLoopOpen": TestWhileLoopOpen,
+    "TestWhileLoopClose": TestWhileLoopClose,
+    "TestExecutionBlocker": TestExecutionBlockerNode,
+}
+FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS = {
+    "TestWhileLoopOpen": "While Loop Open",
+    "TestWhileLoopClose": "While Loop Close",
+    "TestExecutionBlocker": "Execution Blocker",
+}

tests/inference/testing_nodes/testing-pack/specific_tests.py

@@ -0,0 +1,335 @@
+import torch
+from .tools import VariantSupport
+from comfy.graph_utils import GraphBuilder
+
+class TestLazyMixImages:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image1": ("IMAGE",{"lazy": True}),
+                "image2": ("IMAGE",{"lazy": True}),
+                "mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "mix"
+
+    CATEGORY = "Testing/Nodes"
+
+    def check_lazy_status(self, mask, image1, image2):
+        mask_min = mask.min()
+        mask_max = mask.max()
+        needed = []
+        if image1 is None and (mask_min != 1.0 or mask_max != 1.0):
+            needed.append("image1")
+        if image2 is None and (mask_min != 0.0 or mask_max != 0.0):
+            needed.append("image2")
+        return needed
+
+    # Not trying to handle different batch sizes here just to keep the demo simple
+    def mix(self, mask, image1, image2):
+        mask_min = mask.min()
+        mask_max = mask.max()
+        if mask_min == 0.0 and mask_max == 0.0:
+            return (image1,)
+        elif mask_min == 1.0 and mask_max == 1.0:
+            return (image2,)
+
+        if len(mask.shape) == 2:
+            mask = mask.unsqueeze(0)
+        if len(mask.shape) == 3:
+            mask = mask.unsqueeze(3)
+        if mask.shape[3] < image1.shape[3]:
+            mask = mask.repeat(1, 1, 1, image1.shape[3])
+
+        result = image1 * (1. - mask) + image2 * mask,
+        return (result[0],)
+
+class TestVariadicAverage:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "variadic_average"
+
+    CATEGORY = "Testing/Nodes"
+
+    def variadic_average(self, input1, **kwargs):
+        inputs = [input1]
+        while 'input' + str(len(inputs) + 1) in kwargs:
+            inputs.append(kwargs['input' + str(len(inputs) + 1)])
+        return (torch.stack(inputs).mean(dim=0),)
+
+
+class TestCustomIsChanged:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+                "should_change": ("BOOL", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_is_changed"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_is_changed(self, image, should_change=False):
+        return (image,)
+    
+    @classmethod
+    def IS_CHANGED(cls, should_change=False, *args, **kwargs):
+        if should_change:
+            return float("NaN")
+        else:
+            return False
+
+class TestCustomValidation1:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("IMAGE,FLOAT",),
+                "input2": ("IMAGE,FLOAT",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_validation1"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_validation1(self, input1, input2):
+        if isinstance(input1, float) and isinstance(input2, float):
+            result = torch.ones([1, 512, 512, 3]) * input1 * input2
+        else:
+            result = input1 * input2
+        return (result,)
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, input1=None, input2=None):
+        if input1 is not None:
+            if not isinstance(input1, (torch.Tensor, float)):
+                return f"Invalid type of input1: {type(input1)}"
+        if input2 is not None:
+            if not isinstance(input2, (torch.Tensor, float)):
+                return f"Invalid type of input2: {type(input2)}"
+
+        return True
+
+class TestCustomValidation2:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("IMAGE,FLOAT",),
+                "input2": ("IMAGE,FLOAT",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_validation2"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_validation2(self, input1, input2):
+        if isinstance(input1, float) and isinstance(input2, float):
+            result = torch.ones([1, 512, 512, 3]) * input1 * input2
+        else:
+            result = input1 * input2
+        return (result,)
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, input_types, input1=None, input2=None):
+        if input1 is not None:
+            if not isinstance(input1, (torch.Tensor, float)):
+                return f"Invalid type of input1: {type(input1)}"
+        if input2 is not None:
+            if not isinstance(input2, (torch.Tensor, float)):
+                return f"Invalid type of input2: {type(input2)}"
+
+        if 'input1' in input_types:
+            if input_types['input1'] not in ["IMAGE", "FLOAT"]:
+                return f"Invalid type of input1: {input_types['input1']}"
+        if 'input2' in input_types:
+            if input_types['input2'] not in ["IMAGE", "FLOAT"]:
+                return f"Invalid type of input2: {input_types['input2']}"
+
+        return True
+
+@VariantSupport()
+class TestCustomValidation3:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("IMAGE,FLOAT",),
+                "input2": ("IMAGE,FLOAT",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_validation3"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_validation3(self, input1, input2):
+        if isinstance(input1, float) and isinstance(input2, float):
+            result = torch.ones([1, 512, 512, 3]) * input1 * input2
+        else:
+            result = input1 * input2
+        return (result,)
+
+class TestCustomValidation4:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("FLOAT",),
+                "input2": ("FLOAT",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_validation4"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_validation4(self, input1, input2):
+        result = torch.ones([1, 512, 512, 3]) * input1 * input2
+        return (result,)
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, input1, input2):
+        if input1 is not None:
+            if not isinstance(input1, float):
+                return f"Invalid type of input1: {type(input1)}"
+        if input2 is not None:
+            if not isinstance(input2, float):
+                return f"Invalid type of input2: {type(input2)}"
+
+        return True
+
+class TestCustomValidation5:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("FLOAT", {"min": 0.0, "max": 1.0}),
+                "input2": ("FLOAT", {"min": 0.0, "max": 1.0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_validation5"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_validation5(self, input1, input2):
+        value = input1 * input2
+        return (torch.ones([1, 512, 512, 3]) * value,)
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, **kwargs):
+        if kwargs['input2'] == 7.0:
+            return "7s are not allowed. I've never liked 7s."
+        return True
+
+class TestDynamicDependencyCycle:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("IMAGE",),
+                "input2": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "dynamic_dependency_cycle"
+
+    CATEGORY = "Testing/Nodes"
+
+    def dynamic_dependency_cycle(self, input1, input2):
+        g = GraphBuilder()
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+        mix1 = g.node("TestLazyMixImages", image1=input1, mask=mask.out(0))
+        mix2 = g.node("TestLazyMixImages", image1=mix1.out(0), image2=input2, mask=mask.out(0))
+
+        # Create the cyle
+        mix1.set_input("image2", mix2.out(0))
+
+        return {
+            "result": (mix2.out(0),),
+            "expand": g.finalize(),
+        }
+
+class TestMixedExpansionReturns:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input1": ("FLOAT",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE","IMAGE")
+    FUNCTION = "mixed_expansion_returns"
+
+    CATEGORY = "Testing/Nodes"
+
+    def mixed_expansion_returns(self, input1):
+        white_image = torch.ones([1, 512, 512, 3])
+        if input1 <= 0.1:
+            return (torch.ones([1, 512, 512, 3]) * 0.1, white_image)
+        elif input1 <= 0.2:
+            return {
+                "result": (torch.ones([1, 512, 512, 3]) * 0.2, white_image),
+            }
+        else:
+            g = GraphBuilder()
+            mask = g.node("StubMask", value=0.3, height=512, width=512, batch_size=1)
+            black = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+            white = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+            mix = g.node("TestLazyMixImages", image1=black.out(0), image2=white.out(0), mask=mask.out(0))
+            return {
+                "result": (mix.out(0), white_image),
+                "expand": g.finalize(),
+            }
+
+TEST_NODE_CLASS_MAPPINGS = {
+    "TestLazyMixImages": TestLazyMixImages,
+    "TestVariadicAverage": TestVariadicAverage,
+    "TestCustomIsChanged": TestCustomIsChanged,
+    "TestCustomValidation1": TestCustomValidation1,
+    "TestCustomValidation2": TestCustomValidation2,
+    "TestCustomValidation3": TestCustomValidation3,
+    "TestCustomValidation4": TestCustomValidation4,
+    "TestCustomValidation5": TestCustomValidation5,
+    "TestDynamicDependencyCycle": TestDynamicDependencyCycle,
+    "TestMixedExpansionReturns": TestMixedExpansionReturns,
+}
+
+TEST_NODE_DISPLAY_NAME_MAPPINGS = {
+    "TestLazyMixImages": "Lazy Mix Images",
+    "TestVariadicAverage": "Variadic Average",
+    "TestCustomIsChanged": "Custom IsChanged",
+    "TestCustomValidation1": "Custom Validation 1",
+    "TestCustomValidation2": "Custom Validation 2",
+    "TestCustomValidation3": "Custom Validation 3",
+    "TestCustomValidation4": "Custom Validation 4",
+    "TestCustomValidation5": "Custom Validation 5",
+    "TestDynamicDependencyCycle": "Dynamic Dependency Cycle",
+    "TestMixedExpansionReturns": "Mixed Expansion Returns",
+}

tests/inference/testing_nodes/testing-pack/stubs.py

@@ -0,0 +1,105 @@
+import torch
+
+class StubImage:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "content": (['WHITE', 'BLACK', 'NOISE'],),
+                "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stub_image"
+
+    CATEGORY = "Testing/Stub Nodes"
+
+    def stub_image(self, content, height, width, batch_size):
+        if content == "WHITE":
+            return (torch.ones(batch_size, height, width, 3),)
+        elif content == "BLACK":
+            return (torch.zeros(batch_size, height, width, 3),)
+        elif content == "NOISE":
+            return (torch.rand(batch_size, height, width, 3),)
+
+class StubMask:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "stub_mask"
+
+    CATEGORY = "Testing/Stub Nodes"
+
+    def stub_mask(self, value, height, width, batch_size):
+        return (torch.ones(batch_size, height, width) * value,)
+
+class StubInt:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("INT", {"default": 0, "min": -0xffffffff, "max": 0xffffffff, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "stub_int"
+
+    CATEGORY = "Testing/Stub Nodes"
+
+    def stub_int(self, value):
+        return (value,)
+
+class StubFloat:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0.0, "min": -1.0e38, "max": 1.0e38, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    FUNCTION = "stub_float"
+
+    CATEGORY = "Testing/Stub Nodes"
+
+    def stub_float(self, value):
+        return (value,)
+
+TEST_STUB_NODE_CLASS_MAPPINGS = {
+    "StubImage": StubImage,
+    "StubMask": StubMask,
+    "StubInt": StubInt,
+    "StubFloat": StubFloat,
+}
+TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS = {
+    "StubImage": "Stub Image",
+    "StubMask": "Stub Mask",
+    "StubInt": "Stub Int",
+    "StubFloat": "Stub Float",
+}

tests/inference/testing_nodes/testing-pack/tools.py

@@ -0,0 +1,53 @@
+
+def MakeSmartType(t):
+    if isinstance(t, str):
+        return SmartType(t)
+    return t
+
+class SmartType(str):
+    def __ne__(self, other):
+        if self == "*" or other == "*":
+            return False
+        selfset = set(self.split(','))
+        otherset = set(other.split(','))
+        return not selfset.issubset(otherset)
+
+def VariantSupport():
+    def decorator(cls):
+        if hasattr(cls, "INPUT_TYPES"):
+            old_input_types = getattr(cls, "INPUT_TYPES")
+            def new_input_types(*args, **kwargs):
+                types = old_input_types(*args, **kwargs)
+                for category in ["required", "optional"]:
+                    if category not in types:
+                        continue
+                    for key, value in types[category].items():
+                        if isinstance(value, tuple):
+                            types[category][key] = (MakeSmartType(value[0]),) + value[1:]
+                return types
+            setattr(cls, "INPUT_TYPES", new_input_types)
+        if hasattr(cls, "RETURN_TYPES"):
+            old_return_types = cls.RETURN_TYPES
+            setattr(cls, "RETURN_TYPES", tuple(MakeSmartType(x) for x in old_return_types))
+        if hasattr(cls, "VALIDATE_INPUTS"):
+            # Reflection is used to determine what the function signature is, so we can't just change the function signature
+            raise NotImplementedError("VariantSupport does not support VALIDATE_INPUTS yet")
+        else:
+            def validate_inputs(input_types):
+                inputs = cls.INPUT_TYPES()
+                for key, value in input_types.items():
+                    if isinstance(value, SmartType):
+                        continue
+                    if "required" in inputs and key in inputs["required"]:
+                        expected_type = inputs["required"][key][0]
+                    elif "optional" in inputs and key in inputs["optional"]:
+                        expected_type = inputs["optional"][key][0]
+                    else:
+                        expected_type = None
+                    if expected_type is not None and MakeSmartType(value) != expected_type:
+                        return f"Invalid type of {key}: {value} (expected {expected_type})"
+                return True
+            setattr(cls, "VALIDATE_INPUTS", validate_inputs)
+        return cls
+    return decorator
+

tests/inference/testing_nodes/testing-pack/util.py

@@ -0,0 +1,364 @@
+from comfy.graph_utils import GraphBuilder
+from .tools import VariantSupport
+
+@VariantSupport()
+class TestAccumulateNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "to_add": ("*",),
+            },
+            "optional": {
+                "accumulation": ("ACCUMULATION",),
+            },
+        }
+
+    RETURN_TYPES = ("ACCUMULATION",)
+    FUNCTION = "accumulate"
+
+    CATEGORY = "Testing/Lists"
+
+    def accumulate(self, to_add, accumulation = None):
+        if accumulation is None:
+            value = [to_add]
+        else:
+            value = accumulation["accum"] + [to_add]
+        return ({"accum": value},)
+
+@VariantSupport()
+class TestAccumulationHeadNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "accumulation": ("ACCUMULATION",),
+            },
+        }
+
+    RETURN_TYPES = ("ACCUMULATION", "*",)
+    FUNCTION = "accumulation_head"
+
+    CATEGORY = "Testing/Lists"
+
+    def accumulation_head(self, accumulation):
+        accum = accumulation["accum"]
+        if len(accum) == 0:
+            return (accumulation, None)
+        else:
+            return ({"accum": accum[1:]}, accum[0])
+
+class TestAccumulationTailNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "accumulation": ("ACCUMULATION",),
+            },
+        }
+
+    RETURN_TYPES = ("ACCUMULATION", "*",)
+    FUNCTION = "accumulation_tail"
+
+    CATEGORY = "Testing/Lists"
+
+    def accumulation_tail(self, accumulation):
+        accum = accumulation["accum"]
+        if len(accum) == 0:
+            return (None, accumulation)
+        else:
+            return ({"accum": accum[:-1]}, accum[-1])
+
+@VariantSupport()
+class TestAccumulationToListNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "accumulation": ("ACCUMULATION",),
+            },
+        }
+
+    RETURN_TYPES = ("*",)
+    OUTPUT_IS_LIST = (True,)
+
+    FUNCTION = "accumulation_to_list"
+
+    CATEGORY = "Testing/Lists"
+
+    def accumulation_to_list(self, accumulation):
+        return (accumulation["accum"],)
+
+@VariantSupport()
+class TestListToAccumulationNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "list": ("*",),
+            },
+        }
+
+    RETURN_TYPES = ("ACCUMULATION",)
+    INPUT_IS_LIST = (True,)
+
+    FUNCTION = "list_to_accumulation"
+
+    CATEGORY = "Testing/Lists"
+
+    def list_to_accumulation(self, list):
+        return ({"accum": list},)
+
+@VariantSupport()
+class TestAccumulationGetLengthNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "accumulation": ("ACCUMULATION",),
+            },
+        }
+
+    RETURN_TYPES = ("INT",)
+
+    FUNCTION = "accumlength"
+
+    CATEGORY = "Testing/Lists"
+
+    def accumlength(self, accumulation):
+        return (len(accumulation['accum']),)
+        
+@VariantSupport()
+class TestAccumulationGetItemNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "accumulation": ("ACCUMULATION",),
+                "index": ("INT", {"default":0, "step":1})
+            },
+        }
+
+    RETURN_TYPES = ("*",)
+
+    FUNCTION = "get_item"
+
+    CATEGORY = "Testing/Lists"
+
+    def get_item(self, accumulation, index):
+        return (accumulation['accum'][index],)
+        
+@VariantSupport()
+class TestAccumulationSetItemNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "accumulation": ("ACCUMULATION",),
+                "index": ("INT", {"default":0, "step":1}),
+                "value": ("*",),
+            },
+        }
+
+    RETURN_TYPES = ("ACCUMULATION",)
+
+    FUNCTION = "set_item"
+
+    CATEGORY = "Testing/Lists"
+
+    def set_item(self, accumulation, index, value):
+        new_accum = accumulation['accum'][:]
+        new_accum[index] = value
+        return ({"accum": new_accum},)
+
+class TestIntMathOperation:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}),
+                "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}),
+                "operation": (["add", "subtract", "multiply", "divide", "modulo", "power"],),
+            },
+        }
+
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "int_math_operation"
+
+    CATEGORY = "Testing/Logic"
+
+    def int_math_operation(self, a, b, operation):
+        if operation == "add":
+            return (a + b,)
+        elif operation == "subtract":
+            return (a - b,)
+        elif operation == "multiply":
+            return (a * b,)
+        elif operation == "divide":
+            return (a // b,)
+        elif operation == "modulo":
+            return (a % b,)
+        elif operation == "power":
+            return (a ** b,)
+
+
+from .flow_control import NUM_FLOW_SOCKETS
+@VariantSupport()
+class TestForLoopOpen:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "remaining": ("INT", {"default": 1, "min": 0, "max": 100000, "step": 1}),
+            },
+            "optional": {
+                f"initial_value{i}": ("*",) for i in range(1, NUM_FLOW_SOCKETS)
+            },
+            "hidden": {
+                "initial_value0": ("*",)
+            }
+        }
+
+    RETURN_TYPES = tuple(["FLOW_CONTROL", "INT",] + ["*"] * (NUM_FLOW_SOCKETS-1))
+    RETURN_NAMES = tuple(["flow_control", "remaining"] + [f"value{i}" for i in range(1, NUM_FLOW_SOCKETS)])
+    FUNCTION = "for_loop_open"
+
+    CATEGORY = "Testing/Flow"
+
+    def for_loop_open(self, remaining, **kwargs):
+        graph = GraphBuilder()
+        if "initial_value0" in kwargs:
+            remaining = kwargs["initial_value0"]
+        while_open = graph.node("TestWhileLoopOpen", condition=remaining, initial_value0=remaining, **{(f"initial_value{i}"): kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)})
+        outputs = [kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)]
+        return {
+            "result": tuple(["stub", remaining] + outputs),
+            "expand": graph.finalize(),
+        }
+
+@VariantSupport()
+class TestForLoopClose:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "flow_control": ("FLOW_CONTROL", {"rawLink": True}),
+            },
+            "optional": {
+                f"initial_value{i}": ("*",{"rawLink": True}) for i in range(1, NUM_FLOW_SOCKETS)
+            },
+        }
+
+    RETURN_TYPES = tuple(["*"] * (NUM_FLOW_SOCKETS-1))
+    RETURN_NAMES = tuple([f"value{i}" for i in range(1, NUM_FLOW_SOCKETS)])
+    FUNCTION = "for_loop_close"
+
+    CATEGORY = "Testing/Flow"
+
+    def for_loop_close(self, flow_control, **kwargs):
+        graph = GraphBuilder()
+        while_open = flow_control[0]
+        sub = graph.node("TestIntMathOperation", operation="subtract", a=[while_open,1], b=1)
+        cond = graph.node("TestToBoolNode", value=sub.out(0))
+        input_values = {f"initial_value{i}": kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)}
+        while_close = graph.node("TestWhileLoopClose",
+                flow_control=flow_control,
+                condition=cond.out(0),
+                initial_value0=sub.out(0),
+                **input_values)
+        return {
+            "result": tuple([while_close.out(i) for i in range(1, NUM_FLOW_SOCKETS)]),
+            "expand": graph.finalize(),
+        }
+
+NUM_LIST_SOCKETS = 10
+@VariantSupport()
+class TestMakeListNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value1": ("*",),
+            },
+            "optional": {
+                f"value{i}": ("*",) for i in range(1, NUM_LIST_SOCKETS)
+            },
+        }
+
+    RETURN_TYPES = ("*",)
+    FUNCTION = "make_list"
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "Testing/Lists"
+
+    def make_list(self, **kwargs):
+        result = []
+        for i in range(NUM_LIST_SOCKETS):
+            if f"value{i}" in kwargs:
+                result.append(kwargs[f"value{i}"])
+        return (result,)
+
+UTILITY_NODE_CLASS_MAPPINGS = {
+    "TestAccumulateNode": TestAccumulateNode,
+    "TestAccumulationHeadNode": TestAccumulationHeadNode,
+    "TestAccumulationTailNode": TestAccumulationTailNode,
+    "TestAccumulationToListNode": TestAccumulationToListNode,
+    "TestListToAccumulationNode": TestListToAccumulationNode,
+    "TestAccumulationGetLengthNode": TestAccumulationGetLengthNode,
+    "TestAccumulationGetItemNode": TestAccumulationGetItemNode,
+    "TestAccumulationSetItemNode": TestAccumulationSetItemNode,
+    "TestForLoopOpen": TestForLoopOpen,
+    "TestForLoopClose": TestForLoopClose,
+    "TestIntMathOperation": TestIntMathOperation,
+    "TestMakeListNode": TestMakeListNode,
+}
+UTILITY_NODE_DISPLAY_NAME_MAPPINGS = {
+    "TestAccumulateNode": "Accumulate",
+    "TestAccumulationHeadNode": "Accumulation Head",
+    "TestAccumulationTailNode": "Accumulation Tail",
+    "TestAccumulationToListNode": "Accumulation to List",
+    "TestListToAccumulationNode": "List to Accumulation",
+    "TestAccumulationGetLengthNode": "Accumulation Get Length",
+    "TestAccumulationGetItemNode": "Accumulation Get Item",
+    "TestAccumulationSetItemNode": "Accumulation Set Item",
+    "TestForLoopOpen": "For Loop Open",
+    "TestForLoopClose": "For Loop Close",
+    "TestIntMathOperation": "Int Math Operation",
+    "TestMakeListNode": "Make List",
+}

web/extensions/core/groupNode.js

@@ -959,8 +959,8 @@ export class GroupNodeHandler {
 		const executed = handleEvent.call(
 			this,
 			"executed",
-			(d) => d?.node,
+			(d) => d?.display_node,
-			(d, id, node) => ({ ...d, node: id, merge: !node.resetExecution })
+			(d, id, node) => ({ ...d, node: id, display_node: id, merge: !node.resetExecution })
 		);
 
 		const onRemoved = node.onRemoved;

web/extensions/core/widgetInputs.js

@@ -3,7 +3,7 @@ import { app } from "../../scripts/app.js";
 import { applyTextReplacements } from "../../scripts/utils.js";
 
 const CONVERTED_TYPE = "converted-widget";
-const VALID_TYPES = ["STRING", "combo", "number", "BOOLEAN"];
+const VALID_TYPES = ["STRING", "combo", "number", "toggle", "BOOLEAN"];
 const CONFIG = Symbol();
 const GET_CONFIG = Symbol();
 const TARGET = Symbol(); // Used for reroutes to specify the real target widget

web/scripts/api.js

@@ -128,7 +128,7 @@ class ComfyApi extends EventTarget {
 						    this.dispatchEvent(new CustomEvent("progress", { detail: msg.data }));
 						    break;
 					    case "executing":
-						    this.dispatchEvent(new CustomEvent("executing", { detail: msg.data.node }));
+						    this.dispatchEvent(new CustomEvent("executing", { detail: msg.data.display_node }));
 						    break;
 					    case "executed":
 						    this.dispatchEvent(new CustomEvent("executed", { detail: msg.data }));

web/scripts/app.js

@@ -1348,7 +1348,7 @@ export class ComfyApp {
 		api.addEventListener("executed", ({ detail }) => {
 			if (this.workflowManager.activePrompt ?.workflow
 				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
-			const output = this.nodeOutputs[detail.node];
+			const output = this.nodeOutputs[detail.display_node];
 			if (detail.merge && output) {
 				for (const k in detail.output ?? {}) {
 					const v = output[k];
@@ -1359,9 +1359,9 @@ export class ComfyApp {
 					}
 				}
 			} else {
-				this.nodeOutputs[detail.node] = detail.output;
+				this.nodeOutputs[detail.display_node] = detail.output;
 			}
-			const node = this.graph.getNodeById(detail.node);
+			const node = this.graph.getNodeById(detail.display_node);
 			if (node) {
 				if (node.onExecuted)
 					node.onExecuted(detail.output);

web/scripts/ui.js

@@ -239,7 +239,11 @@ class ComfyList {
 								onclick: async () => {
 									await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow, true, false);
 									if (item.outputs) {
-										app.nodeOutputs = item.outputs;
+										app.nodeOutputs = {};
+										for (const [key, value] of Object.entries(item.outputs)) {
+											const realKey = item?.meta?.[key]?.display_node ?? key;
+											app.nodeOutputs[realKey] = value;
+										}
 									}
 								},
 							}),