fix(isolation-lifecycle): execution/model ejection parity + fenced sampler device handling

add pyisolate==0.9.1 to requirements.txt

comfy/hooks.py

@@ -14,6 +14,9 @@ if TYPE_CHECKING:
 import comfy.lora
 import comfy.model_management
 import comfy.patcher_extension
+from comfy.cli_args import args
+import uuid
+import os
 from node_helpers import conditioning_set_values
 
 # #######################################################################################################
@@ -61,8 +64,37 @@ class EnumHookScope(enum.Enum):
     HookedOnly = "hooked_only"
 
 
+_ISOLATION_HOOKREF_MODE = args.use_process_isolation or os.environ.get("PYISOLATE_ISOLATION_ACTIVE") == "1"
+
+
 class _HookRef:
-    pass
+    def __init__(self):
+        if _ISOLATION_HOOKREF_MODE:
+            self._pyisolate_id = str(uuid.uuid4())
+
+    def _ensure_pyisolate_id(self):
+        pyisolate_id = getattr(self, "_pyisolate_id", None)
+        if pyisolate_id is None:
+            pyisolate_id = str(uuid.uuid4())
+            self._pyisolate_id = pyisolate_id
+        return pyisolate_id
+
+    def __eq__(self, other):
+        if not _ISOLATION_HOOKREF_MODE:
+            return self is other
+        if not isinstance(other, _HookRef):
+            return False
+        return self._ensure_pyisolate_id() == other._ensure_pyisolate_id()
+
+    def __hash__(self):
+        if not _ISOLATION_HOOKREF_MODE:
+            return id(self)
+        return hash(self._ensure_pyisolate_id())
+
+    def __str__(self):
+        if not _ISOLATION_HOOKREF_MODE:
+            return super().__str__()
+        return f"PYISOLATE_HOOKREF:{self._ensure_pyisolate_id()}"
 
 
 def default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
@@ -168,6 +200,8 @@ class WeightHook(Hook):
                 key_map = comfy.lora.model_lora_keys_clip(model.model, key_map)
             else:
                 key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
+            if self.weights is None:
+                self.weights = {}
             weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False)
         else:
             if target == EnumWeightTarget.Clip:

comfy/k_diffusion/sampling.py

@@ -1,4 +1,5 @@
 import math
+import os
 from functools import partial
 
 from scipy import integrate
@@ -12,8 +13,8 @@ from . import deis
 from . import sa_solver
 import comfy.model_patcher
 import comfy.model_sampling
-
 import comfy.memory_management
+from comfy.cli_args import args
 from comfy.utils import model_trange as trange
 
 def append_zero(x):
@@ -191,6 +192,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
     """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
+    isolation_active = args.use_process_isolation or os.environ.get("PYISOLATE_ISOLATION_ACTIVE") == "1"
+    if isolation_active:
+        target_device = sigmas.device
+        if x.device != target_device:
+            x = x.to(target_device)
+        s_in = s_in.to(target_device)
+
     for i in trange(len(sigmas) - 1, disable=disable):
         if s_churn > 0:
             gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.

comfy/model_base.py

@@ -76,7 +76,6 @@ class ModelType(Enum):
     FLUX = 8
     IMG_TO_IMG = 9
     FLOW_COSMOS = 10
-    IMG_TO_IMG_FLOW = 11
 
 
 def model_sampling(model_config, model_type):
@@ -109,11 +108,17 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.FLOW_COSMOS:
         c = comfy.model_sampling.COSMOS_RFLOW
         s = comfy.model_sampling.ModelSamplingCosmosRFlow
-    elif model_type == ModelType.IMG_TO_IMG_FLOW:
-        c = comfy.model_sampling.IMG_TO_IMG_FLOW
 
     class ModelSampling(s, c):
-        pass
+        def __reduce__(self):
+            """Ensure pickling yields a proxy instead of failing on local class."""
+            try:
+                from comfy.isolation.model_sampling_proxy import ModelSamplingRegistry, ModelSamplingProxy
+                registry = ModelSamplingRegistry()
+                ms_id = registry.register(self)
+                return (ModelSamplingProxy, (ms_id,))
+            except Exception as exc:
+                raise RuntimeError("Failed to serialize ModelSampling for isolation.") from exc
 
     return ModelSampling(model_config)
 
@@ -974,10 +979,6 @@ class LTXV(BaseModel):
         if keyframe_idxs is not None:
             out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)
 
-        guide_attention_entries = kwargs.get("guide_attention_entries", None)
-        if guide_attention_entries is not None:
-            out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
-
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
@@ -1030,10 +1031,6 @@ class LTXAV(BaseModel):
         if latent_shapes is not None:
             out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes)
 
-        guide_attention_entries = kwargs.get("guide_attention_entries", None)
-        if guide_attention_entries is not None:
-            out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
-
         return out
 
     def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
@@ -1477,12 +1474,6 @@ class WAN22(WAN21):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return latent_image
 
-class WAN21_FlowRVS(WAN21):
-    def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG_FLOW, image_to_video=False, device=None):
-        model_config.unet_config["model_type"] = "t2v"
-        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
-        self.image_to_video = image_to_video
-
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)

comfy/model_management.py

@@ -350,7 +350,7 @@ AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'
 
 try:
     if is_amd():
-        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName.split(':')[0]
+        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
         if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
             if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1':
                 torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
@@ -378,7 +378,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):
@@ -570,7 +570,13 @@ class LoadedModel:
             self._patcher_finalizer.detach()
 
     def is_dead(self):
-        return self.real_model() is not None and self.model is None
+        # Model is dead if the weakref to model has been garbage collected
+        # This can happen with ModelPatcherProxy objects between isolated workflows
+        if self.model is None:
+            return True
+        if self.real_model is None:
+            return False
+        return self.real_model() is None
 
 
 def use_more_memory(extra_memory, loaded_models, device):
@@ -616,6 +622,7 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
     unloaded_model = []
     can_unload = []
     unloaded_models = []
+    isolation_active = os.environ.get("PYISOLATE_ISOLATION_ACTIVE") == "1"
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
@@ -624,6 +631,17 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
+    if can_unload and isolation_active:
+        try:
+            from pyisolate import flush_tensor_keeper  # type: ignore[attr-defined]
+        except Exception:
+            flush_tensor_keeper = None
+        if callable(flush_tensor_keeper):
+            flushed = flush_tensor_keeper()
+            if flushed > 0:
+                logging.debug("][ MM:tensor_keeper_flush | released=%d", flushed)
+                gc.collect()
+
     for x in sorted(can_unload):
         i = x[-1]
         memory_to_free = 1e32
@@ -645,7 +663,13 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
             current_loaded_models[i].model.partially_unload_ram(ram_to_free)
 
     for i in sorted(unloaded_model, reverse=True):
-        unloaded_models.append(current_loaded_models.pop(i))
+        unloaded = current_loaded_models.pop(i)
+        model_obj = unloaded.model
+        if model_obj is not None:
+            cleanup = getattr(model_obj, "cleanup", None)
+            if callable(cleanup):
+                cleanup()
+        unloaded_models.append(unloaded)
 
     if len(unloaded_model) > 0:
         soft_empty_cache()
@@ -767,25 +791,28 @@ def loaded_models(only_currently_used=False):
 
 
 def cleanup_models_gc():
-    do_gc = False
-
     reset_cast_buffers()
-
+    dead_found = False
     for i in range(len(current_loaded_models)):
-        cur = current_loaded_models[i]
+        if current_loaded_models[i].is_dead():
-        if cur.is_dead():
+            dead_found = True
-            logging.info("Potential memory leak detected with model {}, doing a full garbage collect, for maximum performance avoid circular references in the model code.".format(cur.real_model().__class__.__name__))
-            do_gc = True
             break
 
-    if do_gc:
+    if dead_found:
+        logging.info("Potential memory leak detected with model NoneType, doing a full garbage collect, for maximum performance avoid circular references in the model code.")
         gc.collect()
         soft_empty_cache()
 
-        for i in range(len(current_loaded_models)):
+        for i in range(len(current_loaded_models) - 1, -1, -1):
             cur = current_loaded_models[i]
             if cur.is_dead():
-                logging.warning("WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.".format(cur.real_model().__class__.__name__))
+                logging.warning("WARNING, memory leak with model NoneType. Please make sure it is not being referenced from somewhere.")
+                leaked = current_loaded_models.pop(i)
+                model_obj = getattr(leaked, "model", None)
+                if model_obj is not None:
+                    cleanup = getattr(model_obj, "cleanup", None)
+                    if callable(cleanup):
+                        cleanup()
 
 
 def archive_model_dtypes(model):
@@ -802,6 +829,11 @@ def cleanup_models():
 
     for i in to_delete:
         x = current_loaded_models.pop(i)
+        model_obj = getattr(x, "model", None)
+        if model_obj is not None:
+            cleanup = getattr(model_obj, "cleanup", None)
+            if callable(cleanup):
+                cleanup()
         del x
 
 def dtype_size(dtype):

comfy/samplers.py

@@ -11,12 +11,14 @@ from functools import partial
 import collections
 import math
 import logging
+import os
 import comfy.sampler_helpers
 import comfy.model_patcher
 import comfy.patcher_extension
 import comfy.hooks
 import comfy.context_windows
 import comfy.utils
+from comfy.cli_args import args
 import scipy.stats
 import numpy
 
@@ -213,6 +215,7 @@ def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torc
     return executor.execute(model, conds, x_in, timestep, model_options)
 
 def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
+    isolation_active = args.use_process_isolation or os.environ.get("PYISOLATE_ISOLATION_ACTIVE") == "1"
     out_conds = []
     out_counts = []
     # separate conds by matching hooks
@@ -294,9 +297,17 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
                 patches = p.patches
 
             batch_chunks = len(cond_or_uncond)
-            input_x = torch.cat(input_x)
+            if isolation_active:
+                target_device = model.load_device if hasattr(model, "load_device") else input_x[0].device
+                input_x = torch.cat(input_x).to(target_device)
+            else:
+                input_x = torch.cat(input_x)
             c = cond_cat(c)
-            timestep_ = torch.cat([timestep] * batch_chunks)
+            if isolation_active:
+                timestep_ = torch.cat([timestep] * batch_chunks).to(target_device)
+                mult = [m.to(target_device) if hasattr(m, "to") else m for m in mult]
+            else:
+                timestep_ = torch.cat([timestep] * batch_chunks)
 
             transformer_options = model.current_patcher.apply_hooks(hooks=hooks)
             if 'transformer_options' in model_options:
@@ -327,9 +338,17 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
             for o in range(batch_chunks):
                 cond_index = cond_or_uncond[o]
                 a = area[o]
+                out_t = output[o]
+                mult_t = mult[o]
+                if isolation_active:
+                    target_dev = out_conds[cond_index].device
+                    if hasattr(out_t, "device") and out_t.device != target_dev:
+                        out_t = out_t.to(target_dev)
+                    if hasattr(mult_t, "device") and mult_t.device != target_dev:
+                        mult_t = mult_t.to(target_dev)
                 if a is None:
-                    out_conds[cond_index] += output[o] * mult[o]
+                    out_conds[cond_index] += out_t * mult_t
-                    out_counts[cond_index] += mult[o]
+                    out_counts[cond_index] += mult_t
                 else:
                     out_c = out_conds[cond_index]
                     out_cts = out_counts[cond_index]
@@ -337,8 +356,8 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
                     for i in range(dims):
                         out_c = out_c.narrow(i + 2, a[i + dims], a[i])
                         out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
-                    out_c += output[o] * mult[o]
+                    out_c += out_t * mult_t
-                    out_cts += mult[o]
+                    out_cts += mult_t
 
     for i in range(len(out_conds)):
         out_conds[i] /= out_counts[i]
@@ -392,14 +411,31 @@ class KSamplerX0Inpaint:
         self.inner_model = model
         self.sigmas = sigmas
     def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
+        isolation_active = args.use_process_isolation or os.environ.get("PYISOLATE_ISOLATION_ACTIVE") == "1"
         if denoise_mask is not None:
+            if isolation_active and denoise_mask.device != x.device:
+                denoise_mask = denoise_mask.to(x.device)
             if "denoise_mask_function" in model_options:
                 denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
             latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask
+            if isolation_active:
+                latent_image = self.latent_image
+                if hasattr(latent_image, "device") and latent_image.device != x.device:
+                    latent_image = latent_image.to(x.device)
+                scaled = self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=latent_image)
+                if hasattr(scaled, "device") and scaled.device != x.device:
+                    scaled = scaled.to(x.device)
+            else:
+                scaled = self.inner_model.inner_model.scale_latent_inpaint(
+                    x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image
+                )
+            x = x * denoise_mask + scaled * latent_mask
         out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
         if denoise_mask is not None:
-            out = out * denoise_mask + self.latent_image * latent_mask
+            latent_image = self.latent_image
+            if isolation_active and hasattr(latent_image, "device") and latent_image.device != out.device:
+                latent_image = latent_image.to(out.device)
+            out = out * denoise_mask + latent_image * latent_mask
         return out
 
 def simple_scheduler(model_sampling, steps):

cuda_malloc.py

@@ -92,7 +92,7 @@ if args.cuda_malloc:
     env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None)
     if env_var is None:
         env_var = "backend:cudaMallocAsync"
-    else:
+    elif not args.use_process_isolation:
         env_var += ",backend:cudaMallocAsync"
 
     os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var

execution.py

@@ -1,7 +1,9 @@
 import copy
+import gc
 import heapq
 import inspect
 import logging
+import os
 import sys
 import threading
 import time
@@ -261,20 +263,31 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
                 pre_execute_cb(index)
             # V3
             if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)):
-                # if is just a class, then assign no state, just create clone
+                # Check for isolated node - skip validation and class cloning
-                if is_class(obj):
+                if hasattr(obj, "_pyisolate_extension"):
-                    type_obj = obj
+                    # Isolated Node: The stub is just a proxy; real validation happens in child process
-                    obj.VALIDATE_CLASS()
+                    if v3_data is not None:
-                    class_clone = obj.PREPARE_CLASS_CLONE(v3_data)
+                        inputs = _io.build_nested_inputs(inputs, v3_data)
-                # otherwise, use class instance to populate/reuse some fields
+                        # Inject hidden inputs so they're available in the isolated child process
+                        inputs.update(v3_data.get("hidden_inputs", {}))
+                    f = getattr(obj, func)
+                # Standard V3 Node (Existing Logic)
+
                 else:
-                    type_obj = type(obj)
+                    # if is just a class, then assign no resources or state, just create clone
-                    type_obj.VALIDATE_CLASS()
+                    if is_class(obj):
-                    class_clone = type_obj.PREPARE_CLASS_CLONE(v3_data)
+                        type_obj = obj
-                f = make_locked_method_func(type_obj, func, class_clone)
+                        obj.VALIDATE_CLASS()
-                # in case of dynamic inputs, restructure inputs to expected nested dict
+                        class_clone = obj.PREPARE_CLASS_CLONE(v3_data)
-                if v3_data is not None:
+                    # otherwise, use class instance to populate/reuse some fields
-                    inputs = _io.build_nested_inputs(inputs, v3_data)
+                    else:
+                        type_obj = type(obj)
+                        type_obj.VALIDATE_CLASS()
+                        class_clone = type_obj.PREPARE_CLASS_CLONE(v3_data)
+                    f = make_locked_method_func(type_obj, func, class_clone)
+                    # in case of dynamic inputs, restructure inputs to expected nested dict
+                    if v3_data is not None:
+                        inputs = _io.build_nested_inputs(inputs, v3_data)
             # V1
             else:
                 f = getattr(obj, func)
@@ -536,6 +549,14 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     tasks = [x for x in output_data if isinstance(x, asyncio.Task)]
                     await asyncio.gather(*tasks, return_exceptions=True)
                     unblock()
+
+                # Keep isolation node execution deterministic by default, but allow
+                # opt-out for diagnostics.
+                isolation_sequential = os.environ.get("COMFY_ISOLATE_SEQUENTIAL", "1").lower() in ("1", "true", "yes")
+                if args.use_process_isolation and isolation_sequential:
+                    await await_completion()
+                    return await execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs)
+
                 asyncio.create_task(await_completion())
                 return (ExecutionResult.PENDING, None, None)
         if len(output_ui) > 0:
@@ -647,6 +668,22 @@ class PromptExecutor:
         self.status_messages = []
         self.success = True
 
+    async def _notify_execution_graph_safe(self, class_types: set[str], *, fail_loud: bool = False) -> None:
+        try:
+            from comfy.isolation import notify_execution_graph
+            await notify_execution_graph(class_types)
+        except Exception:
+            if fail_loud:
+                raise
+            logging.debug("][ EX:notify_execution_graph failed", exc_info=True)
+
+    async def _flush_running_extensions_transport_state_safe(self) -> None:
+        try:
+            from comfy.isolation import flush_running_extensions_transport_state
+            await flush_running_extensions_transport_state()
+        except Exception:
+            logging.debug("][ EX:flush_running_extensions_transport_state failed", exc_info=True)
+
     def add_message(self, event, data: dict, broadcast: bool):
         data = {
             **data,
@@ -688,6 +725,17 @@ class PromptExecutor:
         asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs))
 
     async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
+        # Update RPC event loops for all isolated extensions
+        # This is critical for serial workflow execution - each asyncio.run() creates
+        # a new event loop, and RPC instances must be updated to use it
+        try:
+            from comfy.isolation import update_rpc_event_loops
+            update_rpc_event_loops()
+        except ImportError:
+            pass  # Isolation not available
+        except Exception as e:
+            logging.getLogger(__name__).warning(f"Failed to update RPC event loops: {e}")
+
         set_preview_method(extra_data.get("preview_method"))
 
         nodes.interrupt_processing(False)
@@ -701,6 +749,20 @@ class PromptExecutor:
         self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
 
         with torch.inference_mode():
+            if args.use_process_isolation:
+                try:
+                    # Boundary cleanup runs at the start of the next workflow in
+                    # isolation mode, matching non-isolated "next prompt" timing.
+                    self.caches = CacheSet(cache_type=self.cache_type, cache_args=self.cache_args)
+                    await self._flush_running_extensions_transport_state_safe()
+                    comfy.model_management.unload_all_models()
+                    comfy.model_management.cleanup_models_gc()
+                    comfy.model_management.cleanup_models()
+                    gc.collect()
+                    comfy.model_management.soft_empty_cache()
+                except Exception:
+                    logging.debug("][ EX:isolation_boundary_cleanup_start failed", exc_info=True)
+
             dynamic_prompt = DynamicPrompt(prompt)
             reset_progress_state(prompt_id, dynamic_prompt)
             add_progress_handler(WebUIProgressHandler(self.server))
@@ -727,6 +789,13 @@ class PromptExecutor:
             for node_id in list(execute_outputs):
                 execution_list.add_node(node_id)
 
+            if args.use_process_isolation:
+                pending_class_types = set()
+                for node_id in execution_list.pendingNodes.keys():
+                    class_type = dynamic_prompt.get_node(node_id)["class_type"]
+                    pending_class_types.add(class_type)
+                await self._notify_execution_graph_safe(pending_class_types, fail_loud=True)
+
             while not execution_list.is_empty():
                 node_id, error, ex = await execution_list.stage_node_execution()
                 if error is not None:
@@ -757,6 +826,7 @@ class PromptExecutor:
                 "outputs": ui_outputs,
                 "meta": meta_outputs,
             }
+            comfy.model_management.cleanup_models_gc()
             self.server.last_node_id = None
             if comfy.model_management.DISABLE_SMART_MEMORY:
                 comfy.model_management.unload_all_models()

requirements.txt

@@ -33,3 +33,5 @@ pydantic-settings~=2.0
 PyOpenGL
 PyOpenGL-accelerate
 glfw
+
+pyisolate==0.9.1