ComfyUI version v0.3.66

This reverts commit b1467da480.

comfy/ldm/chroma_radiance/model.py

@@ -189,15 +189,15 @@ class ChromaRadiance(Chroma):
         nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size)
         nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P]
 
+        # Reshape for per-patch processing
+        nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
+        nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
+
         if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size:
             # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than
             # the tile size.
-            img_dct = self.forward_tiled_nerf(img_out, nerf_pixels, B, C, num_patches, patch_size, params)
+            img_dct = self.forward_tiled_nerf(nerf_hidden, nerf_pixels, B, C, num_patches, patch_size, params)
         else:
-            # Reshape for per-patch processing
-            nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
-            nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
-
             # Get DCT-encoded pixel embeddings [pixel-dct]
             img_dct = self.nerf_image_embedder(nerf_pixels)
 
@@ -240,17 +240,8 @@ class ChromaRadiance(Chroma):
             end = min(i + tile_size, num_patches)
 
             # Slice the current tile from the input tensors
-            nerf_hidden_tile = nerf_hidden[:, i:end, :]
-            nerf_pixels_tile = nerf_pixels[:, i:end, :]
-
-            # Get the actual number of patches in this tile (can be smaller for the last tile)
-            num_patches_tile = nerf_hidden_tile.shape[1]
-
-            # Reshape the tile for per-patch processing
-            # [B, NumPatches_tile, D] -> [B * NumPatches_tile, D]
-            nerf_hidden_tile = nerf_hidden_tile.reshape(batch * num_patches_tile, params.hidden_size)
-            # [B, NumPatches_tile, C*P*P] -> [B*NumPatches_tile, C, P*P] -> [B*NumPatches_tile, P*P, C]
-            nerf_pixels_tile = nerf_pixels_tile.reshape(batch * num_patches_tile, channels, patch_size**2).transpose(1, 2)
+            nerf_hidden_tile = nerf_hidden[i * batch:end * batch]
+            nerf_pixels_tile = nerf_pixels[i * batch:end * batch]
 
             # get DCT-encoded pixel embeddings [pixel-dct]
             img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)

comfy/model_detection.py

@@ -213,7 +213,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["nerf_mlp_ratio"] = 4
                 dit_config["nerf_depth"] = 4
                 dit_config["nerf_max_freqs"] = 8
-                dit_config["nerf_tile_size"] = 32
+                dit_config["nerf_tile_size"] = 512
                 dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
                 dit_config["nerf_embedder_dtype"] = torch.float32
         else:

comfy/model_management.py

@@ -333,6 +333,7 @@ SUPPORT_FP8_OPS = args.supports_fp8_compute
 try:
     if is_amd():
         torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
+        logging.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.")
         try:
             rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
         except:
@@ -371,6 +372,9 @@ try:
 except:
     pass
 
+if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
+    torch.backends.cudnn.benchmark = True
+
 try:
     if torch_version_numeric >= (2, 5):
         torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)

comfy/ops.py

@@ -58,7 +58,7 @@ except (ModuleNotFoundError, TypeError):
 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):
+        if torch.backends.cudnn.version() >= 91002 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.")
@@ -67,9 +67,6 @@ except:
 
 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:
-    torch.backends.cudnn.benchmark = True
-
 def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 

comfy_execution/caching.py

@@ -265,26 +265,6 @@ 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)
@@ -336,3 +316,157 @@ 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

@@ -153,9 +153,8 @@ 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):
-                        if not self.is_cached(from_node_id):
-                            node_ids.append(from_node_id)
+                    if (include_lazy or not is_lazy) and 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:
@@ -195,34 +194,10 @@ 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():
@@ -302,8 +277,6 @@ 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):

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.65"
+__version__ = "0.3.66"

cuda_malloc.py

@@ -1,6 +1,6 @@
 import os
 import importlib.util
-from comfy.cli_args import args
+from comfy.cli_args import args, PerformanceFeature
 import subprocess
 
 #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
@@ -75,8 +75,9 @@ if not args.cuda_malloc:
                 spec.loader.exec_module(module)
                 version = module.__version__
 
-        if int(version[0]) >= 2 and "+cu" in version: #enable by default for torch version 2.0 and up only on cuda torch
-            args.cuda_malloc = cuda_malloc_supported()
+        if int(version[0]) >= 2 and "+cu" in version:  # enable by default for torch version 2.0 and up only on cuda torch
+            if PerformanceFeature.AutoTune not in args.fast:  # Autotune has issues with cuda malloc
+                args.cuda_malloc = cuda_malloc_supported()
     except:
         pass
 

execution.py

@@ -18,7 +18,7 @@ from comfy_execution.caching import (
     BasicCache,
     CacheKeySetID,
     CacheKeySetInputSignature,
-    NullCache,
+    DependencyAwareCache,
     HierarchicalCache,
     LRUCache,
 )
@@ -91,13 +91,13 @@ class IsChangedCache:
 class CacheType(Enum):
     CLASSIC = 0
     LRU = 1
-    NONE = 2
+    DEPENDENCY_AWARE = 2
 
 
 class CacheSet:
     def __init__(self, cache_type=None, cache_size=None):
-        if cache_type == CacheType.NONE:
-            self.init_null_cache()
+        if cache_type == CacheType.DEPENDENCY_AWARE:
+            self.init_dependency_aware_cache()
             logging.info("Disabling intermediate node cache.")
         elif cache_type == CacheType.LRU:
             if cache_size is None:
@@ -120,12 +120,11 @@ class CacheSet:
         self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
         self.objects = HierarchicalCache(CacheKeySetID)
 
-    def init_null_cache(self):
-        self.outputs = NullCache()
-        #The UI cache is expected to be iterable at the end of each workflow
-        #so it must cache at least a full workflow. Use Heirachical
-        self.ui = HierarchicalCache(CacheKeySetInputSignature)
-        self.objects = NullCache()
+    # only hold cached items while the decendents have not executed
+    def init_dependency_aware_cache(self):
+        self.outputs = DependencyAwareCache(CacheKeySetInputSignature)
+        self.ui = DependencyAwareCache(CacheKeySetInputSignature)
+        self.objects = DependencyAwareCache(CacheKeySetID)
 
     def recursive_debug_dump(self):
         result = {
@@ -136,7 +135,7 @@ class CacheSet:
 
 SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
 
-def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
+def get_input_data(inputs, class_def, unique_id, outputs=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)
@@ -154,10 +153,10 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
         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 execution_list is None:
+            if outputs is None:
                 mark_missing()
                 continue # This might be a lazily-evaluated input
-            cached_output = execution_list.get_output_cache(input_unique_id, unique_id)
+            cached_output = outputs.get(input_unique_id)
             if cached_output is None:
                 mark_missing()
                 continue
@@ -406,7 +405,6 @@ 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
@@ -436,7 +434,7 @@ 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 = execution_list.get_output_cache(source_node, unique_id)[source_output]
+                            node_output = caches.outputs.get(source_node)[source_output]
                             for o in node_output:
                                 resolved_output.append(o)
 
@@ -448,7 +446,7 @@ 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, execution_list, dynprompt, extra_data)
+            input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, caches.outputs, 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)
@@ -551,15 +549,11 @@ 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.NONE
+        cache_type = execution.CacheType.DEPENDENCY_AWARE
 
     e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
     last_gc_collect = 0

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.65"
+version = "0.3.66"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.28.7
-comfyui-workflow-templates==0.1.95
+comfyui-workflow-templates==0.2.1
 comfyui-embedded-docs==0.3.0
 torch
 torchsde

tests/execution/test_execution.py

@@ -152,12 +152,12 @@ class TestExecution:
     # Initialize server and client
     #
     @fixture(scope="class", autouse=True, params=[
-        { "extra_args" : [], "should_cache_results" : True },
-        { "extra_args" : ["--cache-lru", 0], "should_cache_results" : True },
-        { "extra_args" : ["--cache-lru", 100], "should_cache_results" : True },
-        { "extra_args" : ["--cache-none"], "should_cache_results" : False },
+        # (use_lru, lru_size)
+        (False, 0),
+        (True, 0),
+        (True, 100),
     ])
-    def server(self, args_pytest, request):
+    def _server(self, args_pytest, request):
         # Start server
         pargs = [
             'python','main.py',
@@ -167,10 +167,12 @@ class TestExecution:
             '--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml',
             '--cpu',
         ]
-        pargs += [ str(param) for param in request.param["extra_args"] ]
+        use_lru, lru_size = request.param
+        if use_lru:
+            pargs += ['--cache-lru', str(lru_size)]
         print("Running server with args:", pargs)  # noqa: T201
         p = subprocess.Popen(pargs)
-        yield request.param
+        yield
         p.kill()
         torch.cuda.empty_cache()
 
@@ -191,7 +193,7 @@ 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
@@ -223,7 +225,7 @@ class TestExecution:
         assert result.did_run(mask)
         assert result.did_run(lazy_mix)
 
-    def test_full_cache(self, client: ComfyClient, builder: GraphBuilder, server):
+    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)
@@ -235,12 +237,9 @@ class TestExecution:
         client.run(g)
         result2 = client.run(g)
         for node_id, node in g.nodes.items():
-            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"
+            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, server):
+    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)
@@ -252,12 +251,8 @@ class TestExecution:
         client.run(g)
         mask.inputs['value'] = 0.4
         result2 = client.run(g)
-        if server["should_cache_results"]:
-            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"
+        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
@@ -416,7 +411,7 @@ 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, server):
+    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")
@@ -432,10 +427,7 @@ class TestExecution:
         result3 = client.run(g)
         result4 = client.run(g)
         assert result1.did_run(is_changed), "is_changed should have been run"
-        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 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"
 
@@ -522,7 +514,7 @@ 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, server):
+    def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
         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)
@@ -538,11 +530,7 @@ 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"
-        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"
-
+        assert not result.did_run(test_node), "The execution should have been cached"
 
     def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
         # Warmup execution to ensure server is fully initialized