Merge branch 'comfyanonymous:master' into master

comfy/ops.py

@@ -52,6 +52,16 @@ try:
 except (ModuleNotFoundError, TypeError):
     logging.warning("Could not set sdpa backend priority.")
 
+NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
+try:
+    if comfy.model_management.is_nvidia():
+        if torch.backends.cudnn.version() >= 91200 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
+            #TODO: change upper bound version once it's fixed'
+            NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
+            logging.info("working around nvidia conv3d memory bug.")
+except:
+    pass
+
 cast_to = comfy.model_management.cast_to #TODO: remove once no more references
 
 if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
@@ -151,6 +161,15 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
+        def _conv_forward(self, input, weight, bias, *args, **kwargs):
+            if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):
+                out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)
+                if bias is not None:
+                    out += bias.reshape((1, -1) + (1,) * (out.ndim - 2))
+                return out
+            else:
+                return super()._conv_forward(input, weight, bias, *args, **kwargs)
+
         def forward_comfy_cast_weights(self, input):
             weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)

comfy_api_nodes/nodes_veo2.py

@@ -393,7 +393,9 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=list(MODELS_MAP.keys()),
+                    options=[
+                        "veo-3.1-generate", "veo-3.1-fast-generate", "veo-3.0-generate-001", "veo-3.0-fast-generate-001"
+                    ],
                     default="veo-3.0-generate-001",
                     tooltip="Veo 3 model to use for video generation",
                     optional=True,

comfy_execution/caching.py

@@ -265,6 +265,26 @@ class HierarchicalCache(BasicCache):
         assert cache is not None
         return await cache._ensure_subcache(node_id, children_ids)
 
+class NullCache:
+
+    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        pass
+
+    def all_node_ids(self):
+        return []
+
+    def clean_unused(self):
+        pass
+
+    def get(self, node_id):
+        return None
+
+    def set(self, node_id, value):
+        pass
+
+    async def ensure_subcache_for(self, node_id, children_ids):
+        return self
+
 class LRUCache(BasicCache):
     def __init__(self, key_class, max_size=100):
         super().__init__(key_class)
@@ -316,157 +336,3 @@ class LRUCache(BasicCache):
             self._mark_used(child_id)
             self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
         return self
-
-
-class DependencyAwareCache(BasicCache):
-    """
-    A cache implementation that tracks dependencies between nodes and manages
-    their execution and caching accordingly. It extends the BasicCache class.
-    Nodes are removed from this cache once all of their descendants have been
-    executed.
-    """
-
-    def __init__(self, key_class):
-        """
-        Initialize the DependencyAwareCache.
-
-        Args:
-            key_class: The class used for generating cache keys.
-        """
-        super().__init__(key_class)
-        self.descendants = {}  # Maps node_id -> set of descendant node_ids
-        self.ancestors = {}    # Maps node_id -> set of ancestor node_ids
-        self.executed_nodes = set()  # Tracks nodes that have been executed
-
-    async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
-        """
-        Clear the entire cache and rebuild the dependency graph.
-
-        Args:
-            dynprompt: The dynamic prompt object containing node information.
-            node_ids: List of node IDs to initialize the cache for.
-            is_changed_cache: Flag indicating if the cache has changed.
-        """
-        # Clear all existing cache data
-        self.cache.clear()
-        self.subcaches.clear()
-        self.descendants.clear()
-        self.ancestors.clear()
-        self.executed_nodes.clear()
-
-        # Call the parent method to initialize the cache with the new prompt
-        await super().set_prompt(dynprompt, node_ids, is_changed_cache)
-
-        # Rebuild the dependency graph
-        self._build_dependency_graph(dynprompt, node_ids)
-
-    def _build_dependency_graph(self, dynprompt, node_ids):
-        """
-        Build the dependency graph for all nodes.
-
-        Args:
-            dynprompt: The dynamic prompt object containing node information.
-            node_ids: List of node IDs to build the graph for.
-        """
-        self.descendants.clear()
-        self.ancestors.clear()
-        for node_id in node_ids:
-            self.descendants[node_id] = set()
-            self.ancestors[node_id] = set()
-
-        for node_id in node_ids:
-            inputs = dynprompt.get_node(node_id)["inputs"]
-            for input_data in inputs.values():
-                if is_link(input_data):  # Check if the input is a link to another node
-                    ancestor_id = input_data[0]
-                    self.descendants[ancestor_id].add(node_id)
-                    self.ancestors[node_id].add(ancestor_id)
-
-    def set(self, node_id, value):
-        """
-        Mark a node as executed and store its value in the cache.
-
-        Args:
-            node_id: The ID of the node to store.
-            value: The value to store for the node.
-        """
-        self._set_immediate(node_id, value)
-        self.executed_nodes.add(node_id)
-        self._cleanup_ancestors(node_id)
-
-    def get(self, node_id):
-        """
-        Retrieve the cached value for a node.
-
-        Args:
-            node_id: The ID of the node to retrieve.
-
-        Returns:
-            The cached value for the node.
-        """
-        return self._get_immediate(node_id)
-
-    async def ensure_subcache_for(self, node_id, children_ids):
-        """
-        Ensure a subcache exists for a node and update dependencies.
-
-        Args:
-            node_id: The ID of the parent node.
-            children_ids: List of child node IDs to associate with the parent node.
-
-        Returns:
-            The subcache object for the node.
-        """
-        subcache = await super()._ensure_subcache(node_id, children_ids)
-        for child_id in children_ids:
-            self.descendants[node_id].add(child_id)
-            self.ancestors[child_id].add(node_id)
-        return subcache
-
-    def _cleanup_ancestors(self, node_id):
-        """
-        Check if ancestors of a node can be removed from the cache.
-
-        Args:
-            node_id: The ID of the node whose ancestors are to be checked.
-        """
-        for ancestor_id in self.ancestors.get(node_id, []):
-            if ancestor_id in self.executed_nodes:
-                # Remove ancestor if all its descendants have been executed
-                if all(descendant in self.executed_nodes for descendant in self.descendants[ancestor_id]):
-                    self._remove_node(ancestor_id)
-
-    def _remove_node(self, node_id):
-        """
-        Remove a node from the cache.
-
-        Args:
-            node_id: The ID of the node to remove.
-        """
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        if cache_key in self.cache:
-            del self.cache[cache_key]
-        subcache_key = self.cache_key_set.get_subcache_key(node_id)
-        if subcache_key in self.subcaches:
-            del self.subcaches[subcache_key]
-
-    def clean_unused(self):
-        """
-        Clean up unused nodes. This is a no-op for this cache implementation.
-        """
-        pass
-
-    def recursive_debug_dump(self):
-        """
-        Dump the cache and dependency graph for debugging.
-
-        Returns:
-            A list containing the cache state and dependency graph.
-        """
-        result = super().recursive_debug_dump()
-        result.append({
-            "descendants": self.descendants,
-            "ancestors": self.ancestors,
-            "executed_nodes": list(self.executed_nodes),
-        })
-        return result

comfy_execution/graph.py

@@ -153,8 +153,9 @@ class TopologicalSort:
                         continue
                     _, _, 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) and not self.is_cached(from_node_id):
+                    if (include_lazy or not is_lazy):
-                        node_ids.append(from_node_id)
+                        if not self.is_cached(from_node_id):
+                            node_ids.append(from_node_id)
                         links.append((from_node_id, from_socket, unique_id))
 
         for link in links:
@@ -194,10 +195,34 @@ class ExecutionList(TopologicalSort):
         super().__init__(dynprompt)
         self.output_cache = output_cache
         self.staged_node_id = None
+        self.execution_cache = {}
+        self.execution_cache_listeners = {}
 
     def is_cached(self, node_id):
         return self.output_cache.get(node_id) is not None
 
+    def cache_link(self, from_node_id, to_node_id):
+        if not to_node_id in self.execution_cache:
+            self.execution_cache[to_node_id] = {}
+        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
+        if not from_node_id in self.execution_cache_listeners:
+            self.execution_cache_listeners[from_node_id] = set()
+        self.execution_cache_listeners[from_node_id].add(to_node_id)
+
+    def get_output_cache(self, from_node_id, to_node_id):
+        if not to_node_id in self.execution_cache:
+            return None
+        return self.execution_cache[to_node_id].get(from_node_id)
+
+    def cache_update(self, node_id, value):
+        if node_id in self.execution_cache_listeners:
+            for to_node_id in self.execution_cache_listeners[node_id]:
+                self.execution_cache[to_node_id][node_id] = value
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        super().add_strong_link(from_node_id, from_socket, to_node_id)
+        self.cache_link(from_node_id, to_node_id)
+
     async def stage_node_execution(self):
         assert self.staged_node_id is None
         if self.is_empty():
@@ -277,6 +302,8 @@ class ExecutionList(TopologicalSort):
     def complete_node_execution(self):
         node_id = self.staged_node_id
         self.pop_node(node_id)
+        self.execution_cache.pop(node_id, None)
+        self.execution_cache_listeners.pop(node_id, None)
         self.staged_node_id = None
 
     def get_nodes_in_cycle(self):

comfy_extras/nodes_easycache.py

@@ -244,6 +244,8 @@ class EasyCacheHolder:
             self.total_steps_skipped += 1
         batch_offset = x.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
+            # slice out only what is relevant to this cond
+            batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)]
             # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video)
             if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]:
                 if not self.allow_mismatch:
@@ -261,9 +263,8 @@ class EasyCacheHolder:
                             slicing.append(slice(None, dim_u))
                     else:
                         slicing.append(slice(None))
-                slicing = [slice(i*batch_offset,(i+1)*batch_offset)] + slicing
+                batch_slice = batch_slice + slicing
-                x = x[slicing]
+            x[batch_slice] += self.uuid_cache_diffs[uuid].to(x.device)
-            x += self.uuid_cache_diffs[uuid].to(x.device)
         return x
 
     def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):

execution.py

@@ -18,7 +18,7 @@ from comfy_execution.caching import (
     BasicCache,
     CacheKeySetID,
     CacheKeySetInputSignature,
-    DependencyAwareCache,
+    NullCache,
     HierarchicalCache,
     LRUCache,
 )
@@ -91,13 +91,13 @@ class IsChangedCache:
 class CacheType(Enum):
     CLASSIC = 0
     LRU = 1
-    DEPENDENCY_AWARE = 2
+    NONE = 2
 
 
 class CacheSet:
     def __init__(self, cache_type=None, cache_size=None):
-        if cache_type == CacheType.DEPENDENCY_AWARE:
+        if cache_type == CacheType.NONE:
-            self.init_dependency_aware_cache()
+            self.init_null_cache()
             logging.info("Disabling intermediate node cache.")
         elif cache_type == CacheType.LRU:
             if cache_size is None:
@@ -120,11 +120,12 @@ class CacheSet:
         self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
         self.objects = HierarchicalCache(CacheKeySetID)
 
-    # only hold cached items while the decendents have not executed
+    def init_null_cache(self):
-    def init_dependency_aware_cache(self):
+        self.outputs = NullCache()
-        self.outputs = DependencyAwareCache(CacheKeySetInputSignature)
+        #The UI cache is expected to be iterable at the end of each workflow
-        self.ui = DependencyAwareCache(CacheKeySetInputSignature)
+        #so it must cache at least a full workflow. Use Heirachical
-        self.objects = DependencyAwareCache(CacheKeySetID)
+        self.ui = HierarchicalCache(CacheKeySetInputSignature)
+        self.objects = NullCache()
 
     def recursive_debug_dump(self):
         result = {
@@ -135,7 +136,7 @@ class CacheSet:
 
 SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
 
-def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}):
+def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
     is_v3 = issubclass(class_def, _ComfyNodeInternal)
     if is_v3:
         valid_inputs, schema = class_def.INPUT_TYPES(include_hidden=False, return_schema=True)
@@ -153,10 +154,10 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, e
         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 outputs is None:
+            if execution_list is None:
                 mark_missing()
                 continue # This might be a lazily-evaluated input
-            cached_output = outputs.get(input_unique_id)
+            cached_output = execution_list.get_output_cache(input_unique_id, unique_id)
             if cached_output is None:
                 mark_missing()
                 continue
@@ -405,6 +406,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             cached_output = caches.ui.get(unique_id) or {}
             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)
         get_progress_state().finish_progress(unique_id)
+        execution_list.cache_update(unique_id, caches.outputs.get(unique_id))
         return (ExecutionResult.SUCCESS, None, None)
 
     input_data_all = None
@@ -434,7 +436,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     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]
+                            node_output = execution_list.get_output_cache(source_node, unique_id)[source_output]
                             for o in node_output:
                                 resolved_output.append(o)
 
@@ -446,7 +448,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             has_subgraph = False
         else:
             get_progress_state().start_progress(unique_id)
-            input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data)
+            input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data)
             if server.client_id is not None:
                 server.last_node_id = display_node_id
                 server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
@@ -549,11 +551,15 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 subcache.clean_unused()
             for node_id in new_output_ids:
                 execution_list.add_node(node_id)
+                execution_list.cache_link(node_id, unique_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)
+        execution_list.cache_update(unique_id, output_data)
+
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
 

main.py

@@ -173,7 +173,7 @@ def prompt_worker(q, server_instance):
     if args.cache_lru > 0:
         cache_type = execution.CacheType.LRU
     elif args.cache_none:
-        cache_type = execution.CacheType.DEPENDENCY_AWARE
+        cache_type = execution.CacheType.NONE
 
     e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
     last_gc_collect = 0

tests/execution/test_execution.py

@@ -152,12 +152,12 @@ class TestExecution:
     # Initialize server and client
     #
     @fixture(scope="class", autouse=True, params=[
-        # (use_lru, lru_size)
+        { "extra_args" : [], "should_cache_results" : True },
-        (False, 0),
+        { "extra_args" : ["--cache-lru", 0], "should_cache_results" : True },
-        (True, 0),
+        { "extra_args" : ["--cache-lru", 100], "should_cache_results" : True },
-        (True, 100),
+        { "extra_args" : ["--cache-none"], "should_cache_results" : False },
     ])
-    def _server(self, args_pytest, request):
+    def server(self, args_pytest, request):
         # Start server
         pargs = [
             'python','main.py',
@@ -167,12 +167,10 @@ class TestExecution:
             '--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml',
             '--cpu',
         ]
-        use_lru, lru_size = request.param
+        pargs += [ str(param) for param in request.param["extra_args"] ]
-        if use_lru:
-            pargs += ['--cache-lru', str(lru_size)]
         print("Running server with args:", pargs)  # noqa: T201
         p = subprocess.Popen(pargs)
-        yield
+        yield request.param
         p.kill()
         torch.cuda.empty_cache()
 
@@ -193,7 +191,7 @@ class TestExecution:
         return comfy_client
 
     @fixture(scope="class", autouse=True)
-    def shared_client(self, args_pytest, _server):
+    def shared_client(self, args_pytest, server):
         client = self.start_client(args_pytest["listen"], args_pytest["port"])
         yield client
         del client
@@ -225,7 +223,7 @@ class TestExecution:
         assert result.did_run(mask)
         assert result.did_run(lazy_mix)
 
-    def test_full_cache(self, client: ComfyClient, builder: GraphBuilder):
+    def test_full_cache(self, client: ComfyClient, builder: GraphBuilder, server):
         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)
@@ -237,9 +235,12 @@ class TestExecution:
         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"
+            if server["should_cache_results"]:
+                assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached"
+            else:
+                assert result2.did_run(node), f"Node {node_id} was cached, but should have been run"
 
-    def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder):
+    def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder, server):
         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)
@@ -251,8 +252,12 @@ class TestExecution:
         client.run(g)
         mask.inputs['value'] = 0.4
         result2 = client.run(g)
-        assert not result2.did_run(input1), "Input1 should have been cached"
+        if server["should_cache_results"]:
-        assert not result2.did_run(input2), "Input2 should have been cached"
+            assert not result2.did_run(input1), "Input1 should have been cached"
+            assert not result2.did_run(input2), "Input2 should have been cached"
+        else:
+            assert result2.did_run(input1), "Input1 should have been rerun"
+            assert result2.did_run(input2), "Input2 should have been rerun"
 
     def test_error(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
@@ -411,7 +416,7 @@ class TestExecution:
         input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
         client.run(g)
 
-    def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
+    def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder, server):
         g = builder
         # Creating the nodes in this specific order previously caused a bug
         save = g.node("SaveImage")
@@ -427,7 +432,10 @@ class TestExecution:
         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"
+        if server["should_cache_results"]:
+            assert not result2.did_run(is_changed), "is_changed should have been cached"
+        else:
+            assert result2.did_run(is_changed), "is_changed should have been re-run"
         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"
 
@@ -514,7 +522,7 @@ class TestExecution:
         assert len(images2) == 1, "Should have 1 image"
 
     # This tests that only constant outputs are used in the call to `IS_CHANGED`
-    def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
+    def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder, server):
         g = builder
         input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1)
         test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5)
@@ -530,7 +538,11 @@ class TestExecution:
         images = result.get_images(output)
         assert len(images) == 1, "Should have 1 image"
         assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
-        assert not result.did_run(test_node), "The execution should have been cached"
+        if server["should_cache_results"]:
+            assert not result.did_run(test_node), "The execution should have been cached"
+        else:
+            assert result.did_run(test_node), "The execution should have been re-run"
+
 
     def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
         # Warmup execution to ensure server is fully initialized