feat(isolation): DynamicVRAM compatibility for process isolation

DynamicVRAM's on-demand model loading/offloading conflicted with  process isolation in three ways: RPC tensor transport stalls from mid-call GPU offload, race conditions between model lifecycle and active RPC operations, and false positive memory leak detection from changed finalizer patterns.

- Marshal CUDA tensors to CPU before RPC transport for dynamic models
- Add operation state tracking + quiescence waits at workflow boundaries
- Distinguish proxy reference release from actual leaks in cleanup_models_gc
- Fix init order: DynamicVRAM must initialize before isolation proxies
- Add RPC timeouts to prevent indefinite hangs on model unavailability
- Prevent proxy-of-proxy chains from DynamicVRAM model reload cycles
- Add torch.device/torch.dtype serializers for new DynamicVRAM RPC paths
- Guard isolation overhead so non-isolated workflows are unaffected
- Migrate env var to PYISOLATE_CHILD

.pyisolate_venvs/ComfyUI-DepthAnythingV2/cache/cache_key

@@ -0,0 +1 @@
+f03e4c88e21504c3

.pyisolate_venvs/ComfyUI-DepthAnythingV2/cache/node_info.json

@@ -0,0 +1,81 @@
+{
+  "DepthAnything_V2": {
+    "input_types": {
+      "required": {
+        "da_model": {
+          "__pyisolate_tuple__": [
+            "DAMODEL"
+          ]
+        },
+        "images": {
+          "__pyisolate_tuple__": [
+            "IMAGE"
+          ]
+        }
+      }
+    },
+    "return_types": [
+      "IMAGE"
+    ],
+    "return_names": [
+      "image"
+    ],
+    "function": "process",
+    "category": "DepthAnythingV2",
+    "output_node": false,
+    "output_is_list": null,
+    "is_v3": false,
+    "display_name": "Depth Anything V2"
+  },
+  "DownloadAndLoadDepthAnythingV2Model": {
+    "input_types": {
+      "required": {
+        "model": {
+          "__pyisolate_tuple__": [
+            [
+              "depth_anything_v2_vits_fp16.safetensors",
+              "depth_anything_v2_vits_fp32.safetensors",
+              "depth_anything_v2_vitb_fp16.safetensors",
+              "depth_anything_v2_vitb_fp32.safetensors",
+              "depth_anything_v2_vitl_fp16.safetensors",
+              "depth_anything_v2_vitl_fp32.safetensors",
+              "depth_anything_v2_vitg_fp32.safetensors",
+              "depth_anything_v2_metric_hypersim_vitl_fp32.safetensors",
+              "depth_anything_v2_metric_vkitti_vitl_fp32.safetensors"
+            ],
+            {
+              "default": "depth_anything_v2_vitl_fp32.safetensors"
+            }
+          ]
+        }
+      },
+      "optional": {
+        "precision": {
+          "__pyisolate_tuple__": [
+            [
+              "auto",
+              "bf16",
+              "fp16",
+              "fp32"
+            ],
+            {
+              "default": "auto"
+            }
+          ]
+        }
+      }
+    },
+    "return_types": [
+      "DAMODEL"
+    ],
+    "return_names": [
+      "da_v2_model"
+    ],
+    "function": "loadmodel",
+    "category": "DepthAnythingV2",
+    "output_node": false,
+    "output_is_list": null,
+    "is_v3": false,
+    "display_name": "DownloadAndLoadDepthAnythingV2Model"
+  }
+}

.pyisolate_venvs/ComfyUI-IsolationToolkit/cache/cache_key

@@ -0,0 +1 @@
+4b90e6876f4c0b8c

app/frontend_management.py

@@ -17,7 +17,7 @@ from importlib.metadata import version
 import requests
 from typing_extensions import NotRequired
 
-from utils.install_util import get_missing_requirements_message, requirements_path
+from utils.install_util import get_missing_requirements_message, get_required_packages_versions
 
 from comfy.cli_args import DEFAULT_VERSION_STRING
 import app.logger
@@ -45,25 +45,7 @@ def get_installed_frontend_version():
 
 
 def get_required_frontend_version():
-    """Get the required frontend version from requirements.txt."""
-    try:
-        with open(requirements_path, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if line.startswith("comfyui-frontend-package=="):
-                    version_str = line.split("==")[-1]
-                    if not is_valid_version(version_str):
-                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
-                        return None
-                    return version_str
-            logging.error("comfyui-frontend-package not found in requirements.txt")
-            return None
-    except FileNotFoundError:
-        logging.error("requirements.txt not found. Cannot determine required frontend version.")
-        return None
-    except Exception as e:
-        logging.error(f"Error reading requirements.txt: {e}")
-        return None
+    return get_required_packages_versions().get("comfyui-frontend-package", None)
 
 
 def check_frontend_version():
@@ -217,25 +199,7 @@ class FrontendManager:
 
     @classmethod
     def get_required_templates_version(cls) -> str:
-        """Get the required workflow templates version from requirements.txt."""
-        try:
-            with open(requirements_path, "r", encoding="utf-8") as f:
-                for line in f:
-                    line = line.strip()
-                    if line.startswith("comfyui-workflow-templates=="):
-                        version_str = line.split("==")[-1]
-                        if not is_valid_version(version_str):
-                            logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
-                            return None
-                        return version_str
-                logging.error("comfyui-workflow-templates not found in requirements.txt")
-                return None
-        except FileNotFoundError:
-            logging.error("requirements.txt not found. Cannot determine required templates version.")
-            return None
-        except Exception as e:
-            logging.error(f"Error reading requirements.txt: {e}")
-            return None
+        return get_required_packages_versions().get("comfyui-workflow-templates", None)
 
     @classmethod
     def default_frontend_path(cls) -> str:

comfy/cli_args.py

@@ -146,6 +146,7 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am
 
 parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
 parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
+parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.")
 
 parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
 
@@ -159,7 +160,6 @@ class PerformanceFeature(enum.Enum):
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
     AutoTune = "autotune"
-    DynamicVRAM = "dynamic_vram"
 
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
 
@@ -262,4 +262,4 @@ else:
     args.fast = set(args.fast)
 
 def enables_dynamic_vram():
-    return PerformanceFeature.DynamicVRAM in args.fast and not args.highvram and not args.gpu_only
+    return not args.disable_dynamic_vram and not args.highvram and not args.gpu_only and not args.novram and not args.cpu

comfy/context_windows.py

@@ -214,7 +214,7 @@ class IndexListContextHandler(ContextHandlerABC):
         mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
         matches = torch.nonzero(mask)
         if torch.numel(matches) == 0:
-            raise Exception("No sample_sigmas matched current timestep; something went wrong.")
+            return  # substep from multi-step sampler: keep self._step from the last full step
         self._step = int(matches[0].item())
 
     def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:

comfy/hooks.py

@@ -64,7 +64,7 @@ class EnumHookScope(enum.Enum):
     HookedOnly = "hooked_only"
 
 
-_ISOLATION_HOOKREF_MODE = args.use_process_isolation or os.environ.get("PYISOLATE_ISOLATION_ACTIVE") == "1"
+_ISOLATION_HOOKREF_MODE = args.use_process_isolation or os.environ.get("PYISOLATE_CHILD") == "1"
 
 
 class _HookRef:

comfy/isolation/__init__.py

@@ -26,6 +26,7 @@ PYISOLATE_VENV_ROOT.mkdir(parents=True, exist_ok=True)
 
 logger = logging.getLogger(__name__)
 _WORKFLOW_BOUNDARY_MIN_FREE_VRAM_BYTES = 2 * 1024 * 1024 * 1024
+_MODEL_PATCHER_IDLE_TIMEOUT_MS = 120000
 
 
 def initialize_proxies() -> None:
@@ -168,6 +169,11 @@ def _get_class_types_for_extension(extension_name: str) -> Set[str]:
 
 async def notify_execution_graph(needed_class_types: Set[str]) -> None:
     """Evict running extensions not needed for current execution."""
+    await wait_for_model_patcher_quiescence(
+        timeout_ms=_MODEL_PATCHER_IDLE_TIMEOUT_MS,
+        fail_loud=True,
+        marker="ISO:notify_graph_wait_idle",
+    )
 
     async def _stop_extension(
         ext_name: str, extension: "ComfyNodeExtension", reason: str
@@ -182,22 +188,33 @@ async def notify_execution_graph(needed_class_types: Set[str]) -> None:
         scan_shm_forensics("ISO:stop_extension", refresh_model_context=True)
 
     scan_shm_forensics("ISO:notify_graph_start", refresh_model_context=True)
+    isolated_class_types_in_graph = needed_class_types.intersection(
+        {spec.node_name for spec in _ISOLATED_NODE_SPECS}
+    )
+    graph_uses_isolation = bool(isolated_class_types_in_graph)
     logger.debug(
         "%s ISO:notify_graph_start running=%d needed=%d",
         LOG_PREFIX,
         len(_RUNNING_EXTENSIONS),
         len(needed_class_types),
     )
-    for ext_name, extension in list(_RUNNING_EXTENSIONS.items()):
-        ext_class_types = _get_class_types_for_extension(ext_name)
+    if graph_uses_isolation:
+        for ext_name, extension in list(_RUNNING_EXTENSIONS.items()):
+            ext_class_types = _get_class_types_for_extension(ext_name)
 
-        # If NONE of this extension's nodes are in the execution graph → evict
-        if not ext_class_types.intersection(needed_class_types):
-            await _stop_extension(
-                ext_name,
-                extension,
-                "isolated custom_node not in execution graph, evicting",
-            )
+            # If NONE of this extension's nodes are in the execution graph -> evict.
+            if not ext_class_types.intersection(needed_class_types):
+                await _stop_extension(
+                    ext_name,
+                    extension,
+                    "isolated custom_node not in execution graph, evicting",
+                )
+    else:
+        logger.debug(
+            "%s ISO:notify_graph_skip_evict running=%d reason=no isolated nodes in graph",
+            LOG_PREFIX,
+            len(_RUNNING_EXTENSIONS),
+        )
 
     # Isolated child processes add steady VRAM pressure; reclaim host-side models
     # at workflow boundaries so subsequent host nodes (e.g. CLIP encode) keep headroom.
@@ -211,7 +228,7 @@ async def notify_execution_graph(needed_class_types: Set[str]) -> None:
                 _WORKFLOW_BOUNDARY_MIN_FREE_VRAM_BYTES,
             )
             free_before = model_management.get_free_memory(device)
-            if free_before < required and _RUNNING_EXTENSIONS:
+            if free_before < required and _RUNNING_EXTENSIONS and graph_uses_isolation:
                 for ext_name, extension in list(_RUNNING_EXTENSIONS.items()):
                     await _stop_extension(
                         ext_name,
@@ -237,6 +254,11 @@ async def notify_execution_graph(needed_class_types: Set[str]) -> None:
 
 
 async def flush_running_extensions_transport_state() -> int:
+    await wait_for_model_patcher_quiescence(
+        timeout_ms=_MODEL_PATCHER_IDLE_TIMEOUT_MS,
+        fail_loud=True,
+        marker="ISO:flush_transport_wait_idle",
+    )
     total_flushed = 0
     for ext_name, extension in list(_RUNNING_EXTENSIONS.items()):
         flush_fn = getattr(extension, "flush_transport_state", None)
@@ -263,6 +285,50 @@ async def flush_running_extensions_transport_state() -> int:
     return total_flushed
 
 
+async def wait_for_model_patcher_quiescence(
+    timeout_ms: int = _MODEL_PATCHER_IDLE_TIMEOUT_MS,
+    *,
+    fail_loud: bool = False,
+    marker: str = "ISO:wait_model_patcher_idle",
+) -> bool:
+    try:
+        from comfy.isolation.model_patcher_proxy_registry import ModelPatcherRegistry
+
+        registry = ModelPatcherRegistry()
+        start = time.perf_counter()
+        idle = await registry.wait_all_idle(timeout_ms)
+        elapsed_ms = (time.perf_counter() - start) * 1000.0
+        if idle:
+            logger.debug(
+                "%s %s idle=1 timeout_ms=%d elapsed_ms=%.3f",
+                LOG_PREFIX,
+                marker,
+                timeout_ms,
+                elapsed_ms,
+            )
+            return True
+
+        states = await registry.get_all_operation_states()
+        logger.error(
+            "%s %s idle_timeout timeout_ms=%d elapsed_ms=%.3f states=%s",
+            LOG_PREFIX,
+            marker,
+            timeout_ms,
+            elapsed_ms,
+            states,
+        )
+        if fail_loud:
+            raise TimeoutError(
+                f"ModelPatcherRegistry did not quiesce within {timeout_ms} ms"
+            )
+        return False
+    except Exception:
+        if fail_loud:
+            raise
+        logger.debug("%s %s failed", LOG_PREFIX, marker, exc_info=True)
+        return False
+
+
 def get_claimed_paths() -> Set[Path]:
     return _CLAIMED_PATHS
 
@@ -320,6 +386,7 @@ __all__ = [
     "await_isolation_loading",
     "notify_execution_graph",
     "flush_running_extensions_transport_state",
+    "wait_for_model_patcher_quiescence",
     "get_claimed_paths",
     "update_rpc_event_loops",
     "IsolatedNodeSpec",

comfy/isolation/adapter.py

@@ -83,6 +83,33 @@ class ComfyUIAdapter(IsolationAdapter):
                     logging.getLogger(pkg_name).setLevel(logging.ERROR)
 
     def register_serializers(self, registry: SerializerRegistryProtocol) -> None:
+        import torch
+
+        def serialize_device(obj: Any) -> Dict[str, Any]:
+            return {"__type__": "device", "device_str": str(obj)}
+
+        def deserialize_device(data: Dict[str, Any]) -> Any:
+            return torch.device(data["device_str"])
+
+        registry.register("device", serialize_device, deserialize_device)
+
+        _VALID_DTYPES = {
+            "float16", "float32", "float64", "bfloat16",
+            "int8", "int16", "int32", "int64",
+            "uint8", "bool",
+        }
+
+        def serialize_dtype(obj: Any) -> Dict[str, Any]:
+            return {"__type__": "dtype", "dtype_str": str(obj)}
+
+        def deserialize_dtype(data: Dict[str, Any]) -> Any:
+            dtype_name = data["dtype_str"].replace("torch.", "")
+            if dtype_name not in _VALID_DTYPES:
+                raise ValueError(f"Invalid dtype: {data['dtype_str']}")
+            return getattr(torch, dtype_name)
+
+        registry.register("dtype", serialize_dtype, deserialize_dtype)
+
         def serialize_model_patcher(obj: Any) -> Dict[str, Any]:
             # Child-side: must already have _instance_id (proxy)
             if os.environ.get("PYISOLATE_CHILD") == "1":
@@ -193,6 +220,10 @@ class ComfyUIAdapter(IsolationAdapter):
                     f"ModelSampling in child lacks _instance_id: "
                     f"{type(obj).__module__}.{type(obj).__name__}"
                 )
+            # Host-side pass-through for proxies: do not re-register a proxy as a
+            # new ModelSamplingRef, or we create proxy-of-proxy indirection.
+            if hasattr(obj, "_instance_id"):
+                return {"__type__": "ModelSamplingRef", "ms_id": obj._instance_id}
             # Host-side: register with ModelSamplingRegistry and return JSON-safe dict
             ms_id = ModelSamplingRegistry().register(obj)
             return {"__type__": "ModelSamplingRef", "ms_id": ms_id}
@@ -211,22 +242,21 @@ class ComfyUIAdapter(IsolationAdapter):
             else:
                 return ModelSamplingRegistry()._get_instance(data["ms_id"])
 
-        # Register ModelSampling type and proxy
-        registry.register(
-            "ModelSamplingDiscrete",
-            serialize_model_sampling,
-            deserialize_model_sampling,
-        )
-        registry.register(
-            "ModelSamplingContinuousEDM",
-            serialize_model_sampling,
-            deserialize_model_sampling,
-        )
-        registry.register(
-            "ModelSamplingContinuousV",
-            serialize_model_sampling,
-            deserialize_model_sampling,
-        )
+        # Register all ModelSampling* and StableCascadeSampling classes dynamically
+        import comfy.model_sampling
+
+        for ms_cls in vars(comfy.model_sampling).values():
+            if not isinstance(ms_cls, type):
+                continue
+            if not issubclass(ms_cls, torch.nn.Module):
+                continue
+            if not (ms_cls.__name__.startswith("ModelSampling") or ms_cls.__name__ == "StableCascadeSampling"):
+                continue
+            registry.register(
+                ms_cls.__name__,
+                serialize_model_sampling,
+                deserialize_model_sampling,
+            )
         registry.register(
             "ModelSamplingProxy", serialize_model_sampling, deserialize_model_sampling
         )

comfy/isolation/extension_wrapper.py

@@ -389,7 +389,8 @@ class ComfyNodeExtension(ExtensionBase):
                 getattr(self, "name", "?"),
                 node_name,
             )
-            return self._wrap_unpicklable_objects(result)
+            wrapped = self._wrap_unpicklable_objects(result)
+            return wrapped
 
         if not isinstance(result, tuple):
             result = (result,)
@@ -400,7 +401,8 @@ class ComfyNodeExtension(ExtensionBase):
             node_name,
             len(result),
         )
-        return self._wrap_unpicklable_objects(result)
+        wrapped = self._wrap_unpicklable_objects(result)
+        return wrapped
 
     async def flush_transport_state(self) -> int:
         if os.environ.get("PYISOLATE_ISOLATION_ACTIVE") != "1":
@@ -443,7 +445,10 @@ class ComfyNodeExtension(ExtensionBase):
         if isinstance(data, (str, int, float, bool, type(None))):
             return data
         if isinstance(data, torch.Tensor):
-            return data.detach() if data.requires_grad else data
+            tensor = data.detach() if data.requires_grad else data
+            if os.environ.get("PYISOLATE_CHILD") == "1" and tensor.device.type != "cpu":
+                return tensor.cpu()
+            return tensor
 
         # Special-case clip vision outputs: preserve attribute access by packing fields
         if hasattr(data, "penultimate_hidden_states") or hasattr(

comfy/isolation/model_patcher_proxy.py

@@ -338,6 +338,34 @@ class ModelPatcherProxy(BaseProxy[ModelPatcherRegistry]):
     def apply_model(self, *args, **kwargs) -> Any:
         import torch
 
+        def _preferred_device() -> Any:
+            for value in args:
+                if isinstance(value, torch.Tensor):
+                    return value.device
+            for value in kwargs.values():
+                if isinstance(value, torch.Tensor):
+                    return value.device
+            return None
+
+        def _move_result_to_device(obj: Any, device: Any) -> Any:
+            if device is None:
+                return obj
+            if isinstance(obj, torch.Tensor):
+                return obj.to(device) if obj.device != device else obj
+            if isinstance(obj, dict):
+                return {k: _move_result_to_device(v, device) for k, v in obj.items()}
+            if isinstance(obj, list):
+                return [_move_result_to_device(v, device) for v in obj]
+            if isinstance(obj, tuple):
+                return tuple(_move_result_to_device(v, device) for v in obj)
+            return obj
+
+        # DynamicVRAM models must keep load/offload decisions in host process.
+        # Child-side CUDA staging here can deadlock before first inference RPC.
+        if self.is_dynamic():
+            out = self._call_rpc("inner_model_apply_model", args, kwargs)
+            return _move_result_to_device(out, _preferred_device())
+
         required_bytes = self._cpu_tensor_bytes(args) + self._cpu_tensor_bytes(kwargs)
         self._ensure_apply_model_headroom(required_bytes)
 
@@ -360,7 +388,8 @@ class ModelPatcherProxy(BaseProxy[ModelPatcherRegistry]):
             args_cuda = _to_cuda(args)
             kwargs_cuda = _to_cuda(kwargs)
 
-        return self._call_rpc("inner_model_apply_model", args_cuda, kwargs_cuda)
+        out = self._call_rpc("inner_model_apply_model", args_cuda, kwargs_cuda)
+        return _move_result_to_device(out, _preferred_device())
 
     def model_state_dict(self, filter_prefix: Optional[str] = None) -> Any:
         keys = self._call_rpc("model_state_dict", filter_prefix)
@@ -526,6 +555,13 @@ class ModelPatcherProxy(BaseProxy[ModelPatcherRegistry]):
     def memory_required(self, input_shape: Any) -> Any:
         return self._call_rpc("memory_required", input_shape)
 
+    def get_operation_state(self) -> Dict[str, Any]:
+        state = self._call_rpc("get_operation_state")
+        return state if isinstance(state, dict) else {}
+
+    def wait_for_idle(self, timeout_ms: int = 0) -> bool:
+        return bool(self._call_rpc("wait_for_idle", timeout_ms))
+
     def is_dynamic(self) -> bool:
         return bool(self._call_rpc("is_dynamic"))
 
@@ -771,6 +807,7 @@ class ModelPatcherProxy(BaseProxy[ModelPatcherRegistry]):
 class _InnerModelProxy:
     def __init__(self, parent: ModelPatcherProxy):
         self._parent = parent
+        self._model_sampling = None
 
     def __getattr__(self, name: str) -> Any:
         if name.startswith("_"):
@@ -793,7 +830,11 @@ class _InnerModelProxy:
                 manage_lifecycle=False,
             )
         if name == "model_sampling":
-            return self._parent._call_rpc("get_model_object", "model_sampling")
+            if self._model_sampling is None:
+                self._model_sampling = self._parent._call_rpc(
+                    "get_model_object", "model_sampling"
+                )
+            return self._model_sampling
         if name == "extra_conds_shapes":
             return lambda *a, **k: self._parent._call_rpc(
                 "inner_model_extra_conds_shapes", a, k

comfy/isolation/model_patcher_proxy_registry.py

@@ -2,8 +2,12 @@
 # RPC server for ModelPatcher isolation (child process)
 from __future__ import annotations
 
+import asyncio
 import gc
 import logging
+import threading
+import time
+from dataclasses import dataclass, field
 from typing import Any, Optional, List
 
 try:
@@ -43,12 +47,191 @@ from comfy.isolation.proxies.base import (
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class _OperationState:
+    lease: threading.Lock = field(default_factory=threading.Lock)
+    active_count: int = 0
+    active_by_method: dict[str, int] = field(default_factory=dict)
+    total_operations: int = 0
+    last_method: Optional[str] = None
+    last_started_ts: Optional[float] = None
+    last_ended_ts: Optional[float] = None
+    last_elapsed_ms: Optional[float] = None
+    last_error: Optional[str] = None
+    last_thread_id: Optional[int] = None
+    last_loop_id: Optional[int] = None
+
+
 class ModelPatcherRegistry(BaseRegistry[Any]):
     _type_prefix = "model"
 
     def __init__(self) -> None:
         super().__init__()
         self._pending_cleanup_ids: set[str] = set()
+        self._operation_states: dict[str, _OperationState] = {}
+        self._operation_state_cv = threading.Condition(self._lock)
+
+    def _get_or_create_operation_state(self, instance_id: str) -> _OperationState:
+        state = self._operation_states.get(instance_id)
+        if state is None:
+            state = _OperationState()
+            self._operation_states[instance_id] = state
+        return state
+
+    def _begin_operation(self, instance_id: str, method_name: str) -> tuple[float, float]:
+        start_epoch = time.time()
+        start_perf = time.perf_counter()
+        with self._operation_state_cv:
+            state = self._get_or_create_operation_state(instance_id)
+            state.active_count += 1
+            state.active_by_method[method_name] = (
+                state.active_by_method.get(method_name, 0) + 1
+            )
+            state.total_operations += 1
+            state.last_method = method_name
+            state.last_started_ts = start_epoch
+            state.last_thread_id = threading.get_ident()
+            try:
+                state.last_loop_id = id(asyncio.get_running_loop())
+            except RuntimeError:
+                state.last_loop_id = None
+        logger.debug(
+            "ISO:registry_op_start instance_id=%s method=%s start_ts=%.6f thread=%s loop=%s",
+            instance_id,
+            method_name,
+            start_epoch,
+            threading.get_ident(),
+            state.last_loop_id,
+        )
+        return start_epoch, start_perf
+
+    def _end_operation(
+        self,
+        instance_id: str,
+        method_name: str,
+        start_perf: float,
+        error: Optional[BaseException] = None,
+    ) -> None:
+        end_epoch = time.time()
+        elapsed_ms = (time.perf_counter() - start_perf) * 1000.0
+        with self._operation_state_cv:
+            state = self._get_or_create_operation_state(instance_id)
+            state.active_count = max(0, state.active_count - 1)
+            if method_name in state.active_by_method:
+                remaining = state.active_by_method[method_name] - 1
+                if remaining <= 0:
+                    state.active_by_method.pop(method_name, None)
+                else:
+                    state.active_by_method[method_name] = remaining
+            state.last_ended_ts = end_epoch
+            state.last_elapsed_ms = elapsed_ms
+            state.last_error = None if error is None else repr(error)
+            if state.active_count == 0:
+                self._operation_state_cv.notify_all()
+        logger.debug(
+            "ISO:registry_op_end instance_id=%s method=%s end_ts=%.6f elapsed_ms=%.3f error=%s",
+            instance_id,
+            method_name,
+            end_epoch,
+            elapsed_ms,
+            None if error is None else type(error).__name__,
+        )
+
+    def _run_operation_with_lease(self, instance_id: str, method_name: str, fn):
+        with self._operation_state_cv:
+            state = self._get_or_create_operation_state(instance_id)
+            lease = state.lease
+        with lease:
+            _, start_perf = self._begin_operation(instance_id, method_name)
+            try:
+                result = fn()
+            except Exception as exc:
+                self._end_operation(instance_id, method_name, start_perf, error=exc)
+                raise
+            self._end_operation(instance_id, method_name, start_perf)
+            return result
+
+    def _snapshot_operation_state(self, instance_id: str) -> dict[str, Any]:
+        with self._operation_state_cv:
+            state = self._operation_states.get(instance_id)
+            if state is None:
+                return {
+                    "instance_id": instance_id,
+                    "active_count": 0,
+                    "active_methods": [],
+                    "total_operations": 0,
+                    "last_method": None,
+                    "last_started_ts": None,
+                    "last_ended_ts": None,
+                    "last_elapsed_ms": None,
+                    "last_error": None,
+                    "last_thread_id": None,
+                    "last_loop_id": None,
+                }
+            return {
+                "instance_id": instance_id,
+                "active_count": state.active_count,
+                "active_methods": sorted(state.active_by_method.keys()),
+                "total_operations": state.total_operations,
+                "last_method": state.last_method,
+                "last_started_ts": state.last_started_ts,
+                "last_ended_ts": state.last_ended_ts,
+                "last_elapsed_ms": state.last_elapsed_ms,
+                "last_error": state.last_error,
+                "last_thread_id": state.last_thread_id,
+                "last_loop_id": state.last_loop_id,
+            }
+
+    def unregister_sync(self, instance_id: str) -> None:
+        with self._operation_state_cv:
+            instance = self._registry.pop(instance_id, None)
+            if instance is not None:
+                self._id_map.pop(id(instance), None)
+            self._pending_cleanup_ids.discard(instance_id)
+            self._operation_states.pop(instance_id, None)
+            self._operation_state_cv.notify_all()
+
+    async def get_operation_state(self, instance_id: str) -> dict[str, Any]:
+        return self._snapshot_operation_state(instance_id)
+
+    async def get_all_operation_states(self) -> dict[str, dict[str, Any]]:
+        with self._operation_state_cv:
+            ids = sorted(self._operation_states.keys())
+        return {instance_id: self._snapshot_operation_state(instance_id) for instance_id in ids}
+
+    async def wait_for_idle(self, instance_id: str, timeout_ms: int = 0) -> bool:
+        timeout_s = None if timeout_ms <= 0 else (timeout_ms / 1000.0)
+        deadline = None if timeout_s is None else (time.monotonic() + timeout_s)
+        with self._operation_state_cv:
+            while True:
+                active = self._operation_states.get(instance_id)
+                if active is None or active.active_count == 0:
+                    return True
+                if deadline is None:
+                    self._operation_state_cv.wait()
+                    continue
+                remaining = deadline - time.monotonic()
+                if remaining <= 0:
+                    return False
+                self._operation_state_cv.wait(timeout=remaining)
+
+    async def wait_all_idle(self, timeout_ms: int = 0) -> bool:
+        timeout_s = None if timeout_ms <= 0 else (timeout_ms / 1000.0)
+        deadline = None if timeout_s is None else (time.monotonic() + timeout_s)
+        with self._operation_state_cv:
+            while True:
+                has_active = any(
+                    state.active_count > 0 for state in self._operation_states.values()
+                )
+                if not has_active:
+                    return True
+                if deadline is None:
+                    self._operation_state_cv.wait()
+                    continue
+                remaining = deadline - time.monotonic()
+                if remaining <= 0:
+                    return False
+                self._operation_state_cv.wait(timeout=remaining)
 
     async def clone(self, instance_id: str) -> str:
         instance = self._get_instance(instance_id)
@@ -73,8 +256,14 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
             )
 
             registry = ModelSamplingRegistry()
-            sampling_id = registry.register(result)
+            # Preserve identity when upstream already returned a proxy. Re-registering
+            # a proxy object creates proxy-of-proxy call chains.
+            if isinstance(result, ModelSamplingProxy):
+                sampling_id = result._instance_id
+            else:
+                sampling_id = registry.register(result)
             return ModelSamplingProxy(sampling_id, registry)
+
         return detach_if_grad(result)
 
     async def get_model_options(self, instance_id: str) -> dict:
@@ -163,7 +352,11 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         return self._get_instance(instance_id).lowvram_patch_counter()
 
     async def memory_required(self, instance_id: str, input_shape: Any) -> Any:
-        return self._get_instance(instance_id).memory_required(input_shape)
+        return self._run_operation_with_lease(
+            instance_id,
+            "memory_required",
+            lambda: self._get_instance(instance_id).memory_required(input_shape),
+        )
 
     async def is_dynamic(self, instance_id: str) -> bool:
         instance = self._get_instance(instance_id)
@@ -186,7 +379,11 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         return None
 
     async def model_dtype(self, instance_id: str) -> Any:
-        return self._get_instance(instance_id).model_dtype()
+        return self._run_operation_with_lease(
+            instance_id,
+            "model_dtype",
+            lambda: self._get_instance(instance_id).model_dtype(),
+        )
 
     async def model_patches_to(self, instance_id: str, device: Any) -> Any:
         return self._get_instance(instance_id).model_patches_to(device)
@@ -198,8 +395,12 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         extra_memory: Any,
         force_patch_weights: bool = False,
     ) -> Any:
-        return self._get_instance(instance_id).partially_load(
-            device, extra_memory, force_patch_weights=force_patch_weights
+        return self._run_operation_with_lease(
+            instance_id,
+            "partially_load",
+            lambda: self._get_instance(instance_id).partially_load(
+                device, extra_memory, force_patch_weights=force_patch_weights
+            ),
         )
 
     async def partially_unload(
@@ -209,8 +410,12 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         memory_to_free: int = 0,
         force_patch_weights: bool = False,
     ) -> int:
-        return self._get_instance(instance_id).partially_unload(
-            device_to, memory_to_free, force_patch_weights
+        return self._run_operation_with_lease(
+            instance_id,
+            "partially_unload",
+            lambda: self._get_instance(instance_id).partially_unload(
+                device_to, memory_to_free, force_patch_weights
+            ),
         )
 
     async def load(
@@ -221,8 +426,12 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         force_patch_weights: bool = False,
         full_load: bool = False,
     ) -> None:
-        self._get_instance(instance_id).load(
-            device_to, lowvram_model_memory, force_patch_weights, full_load
+        self._run_operation_with_lease(
+            instance_id,
+            "load",
+            lambda: self._get_instance(instance_id).load(
+                device_to, lowvram_model_memory, force_patch_weights, full_load
+            ),
         )
 
     async def patch_model(
@@ -233,20 +442,29 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         load_weights: bool = True,
         force_patch_weights: bool = False,
     ) -> None:
-        try:
-            self._get_instance(instance_id).patch_model(
-                device_to, lowvram_model_memory, load_weights, force_patch_weights
-            )
-        except AttributeError as e:
-            logger.error(
-                f"Isolation Error: Failed to patch model attribute: {e}. Skipping."
-            )
-            return
+        def _invoke() -> None:
+            try:
+                self._get_instance(instance_id).patch_model(
+                    device_to, lowvram_model_memory, load_weights, force_patch_weights
+                )
+            except AttributeError as e:
+                logger.error(
+                    f"Isolation Error: Failed to patch model attribute: {e}. Skipping."
+                )
+                return
+
+        self._run_operation_with_lease(instance_id, "patch_model", _invoke)
 
     async def unpatch_model(
         self, instance_id: str, device_to: Any = None, unpatch_weights: bool = True
     ) -> None:
-        self._get_instance(instance_id).unpatch_model(device_to, unpatch_weights)
+        self._run_operation_with_lease(
+            instance_id,
+            "unpatch_model",
+            lambda: self._get_instance(instance_id).unpatch_model(
+                device_to, unpatch_weights
+            ),
+        )
 
     async def detach(self, instance_id: str, unpatch_all: bool = True) -> None:
         self._get_instance(instance_id).detach(unpatch_all)
@@ -262,26 +480,29 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
         self._get_instance(instance_id).pre_run()
 
     async def cleanup(self, instance_id: str) -> None:
-        try:
-            instance = self._get_instance(instance_id)
-        except Exception:
-            logger.debug(
-                "ModelPatcher cleanup requested for missing instance %s",
-                instance_id,
-                exc_info=True,
-            )
-            return
+        def _invoke() -> None:
+            try:
+                instance = self._get_instance(instance_id)
+            except Exception:
+                logger.debug(
+                    "ModelPatcher cleanup requested for missing instance %s",
+                    instance_id,
+                    exc_info=True,
+                )
+                return
 
-        try:
-            instance.cleanup()
-        finally:
-            with self._lock:
-                self._pending_cleanup_ids.add(instance_id)
-            gc.collect()
+            try:
+                instance.cleanup()
+            finally:
+                with self._lock:
+                    self._pending_cleanup_ids.add(instance_id)
+                gc.collect()
+
+        self._run_operation_with_lease(instance_id, "cleanup", _invoke)
 
     def sweep_pending_cleanup(self) -> int:
         removed = 0
-        with self._lock:
+        with self._operation_state_cv:
             pending_ids = list(self._pending_cleanup_ids)
             self._pending_cleanup_ids.clear()
             for instance_id in pending_ids:
@@ -289,17 +510,21 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
                 if instance is None:
                     continue
                 self._id_map.pop(id(instance), None)
+                self._operation_states.pop(instance_id, None)
                 removed += 1
+            self._operation_state_cv.notify_all()
 
         gc.collect()
         return removed
 
     def purge_all(self) -> int:
-        with self._lock:
+        with self._operation_state_cv:
             removed = len(self._registry)
             self._registry.clear()
             self._id_map.clear()
             self._pending_cleanup_ids.clear()
+            self._operation_states.clear()
+            self._operation_state_cv.notify_all()
         gc.collect()
         return removed
 
@@ -743,17 +968,52 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
     async def inner_model_memory_required(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        return self._get_instance(instance_id).model.memory_required(*args, **kwargs)
+        return self._run_operation_with_lease(
+            instance_id,
+            "inner_model_memory_required",
+            lambda: self._get_instance(instance_id).model.memory_required(
+                *args, **kwargs
+            ),
+        )
 
     async def inner_model_extra_conds_shapes(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        return self._get_instance(instance_id).model.extra_conds_shapes(*args, **kwargs)
+        return self._run_operation_with_lease(
+            instance_id,
+            "inner_model_extra_conds_shapes",
+            lambda: self._get_instance(instance_id).model.extra_conds_shapes(
+                *args, **kwargs
+            ),
+        )
 
     async def inner_model_extra_conds(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        return self._get_instance(instance_id).model.extra_conds(*args, **kwargs)
+        def _invoke() -> Any:
+            result = self._get_instance(instance_id).model.extra_conds(*args, **kwargs)
+            try:
+                import torch
+                import comfy.conds
+            except Exception:
+                return result
+
+            def _to_cpu(obj: Any) -> Any:
+                if torch.is_tensor(obj):
+                    return obj.detach().cpu() if obj.device.type != "cpu" else obj
+                if isinstance(obj, dict):
+                    return {k: _to_cpu(v) for k, v in obj.items()}
+                if isinstance(obj, list):
+                    return [_to_cpu(v) for v in obj]
+                if isinstance(obj, tuple):
+                    return tuple(_to_cpu(v) for v in obj)
+                if isinstance(obj, comfy.conds.CONDRegular):
+                    return type(obj)(_to_cpu(obj.cond))
+                return obj
+
+            return _to_cpu(result)
+
+        return self._run_operation_with_lease(instance_id, "inner_model_extra_conds", _invoke)
 
     async def inner_model_state_dict(
         self, instance_id: str, args: tuple, kwargs: dict
@@ -767,82 +1027,177 @@ class ModelPatcherRegistry(BaseRegistry[Any]):
     async def inner_model_apply_model(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        instance = self._get_instance(instance_id)
-        target = getattr(instance, "load_device", None)
-        if target is None and args and hasattr(args[0], "device"):
-            target = args[0].device
-        elif target is None:
-            for v in kwargs.values():
-                if hasattr(v, "device"):
-                    target = v.device
-                    break
+        def _invoke() -> Any:
+            import torch
 
-        def _move(obj):
-            if target is None:
+            instance = self._get_instance(instance_id)
+            target = getattr(instance, "load_device", None)
+            if target is None and args and hasattr(args[0], "device"):
+                target = args[0].device
+            elif target is None:
+                for v in kwargs.values():
+                    if hasattr(v, "device"):
+                        target = v.device
+                        break
+
+            def _move(obj):
+                if target is None:
+                    return obj
+                if isinstance(obj, (tuple, list)):
+                    return type(obj)(_move(o) for o in obj)
+                if hasattr(obj, "to"):
+                    return obj.to(target)
                 return obj
-            if isinstance(obj, (tuple, list)):
-                return type(obj)(_move(o) for o in obj)
-            if hasattr(obj, "to"):
-                return obj.to(target)
-            return obj
 
-        moved_args = tuple(_move(a) for a in args)
-        moved_kwargs = {k: _move(v) for k, v in kwargs.items()}
-        result = instance.model.apply_model(*moved_args, **moved_kwargs)
-        return detach_if_grad(_move(result))
+            moved_args = tuple(_move(a) for a in args)
+            moved_kwargs = {k: _move(v) for k, v in kwargs.items()}
+            result = instance.model.apply_model(*moved_args, **moved_kwargs)
+            moved_result = detach_if_grad(_move(result))
+
+            # DynamicVRAM + isolation: returning CUDA tensors across RPC can stall
+            # at the transport boundary. Marshal dynamic-path results as CPU and let
+            # the proxy restore device placement in the child process.
+            is_dynamic_fn = getattr(instance, "is_dynamic", None)
+            if callable(is_dynamic_fn) and is_dynamic_fn():
+                def _to_cpu(obj: Any) -> Any:
+                    if torch.is_tensor(obj):
+                        return obj.detach().cpu() if obj.device.type != "cpu" else obj
+                    if isinstance(obj, dict):
+                        return {k: _to_cpu(v) for k, v in obj.items()}
+                    if isinstance(obj, list):
+                        return [_to_cpu(v) for v in obj]
+                    if isinstance(obj, tuple):
+                        return tuple(_to_cpu(v) for v in obj)
+                    return obj
+
+                return _to_cpu(moved_result)
+            return moved_result
+
+        return self._run_operation_with_lease(instance_id, "inner_model_apply_model", _invoke)
 
     async def process_latent_in(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        return detach_if_grad(
-            self._get_instance(instance_id).model.process_latent_in(*args, **kwargs)
-        )
+        import torch
+
+        def _invoke() -> Any:
+            instance = self._get_instance(instance_id)
+            result = detach_if_grad(instance.model.process_latent_in(*args, **kwargs))
+
+            # DynamicVRAM + isolation: returning CUDA tensors across RPC can stall
+            # at the transport boundary. Marshal dynamic-path results as CPU and let
+            # the proxy restore placement when needed.
+            is_dynamic_fn = getattr(instance, "is_dynamic", None)
+            if callable(is_dynamic_fn) and is_dynamic_fn():
+                def _to_cpu(obj: Any) -> Any:
+                    if torch.is_tensor(obj):
+                        return obj.detach().cpu() if obj.device.type != "cpu" else obj
+                    if isinstance(obj, dict):
+                        return {k: _to_cpu(v) for k, v in obj.items()}
+                    if isinstance(obj, list):
+                        return [_to_cpu(v) for v in obj]
+                    if isinstance(obj, tuple):
+                        return tuple(_to_cpu(v) for v in obj)
+                    return obj
+
+                return _to_cpu(result)
+            return result
+
+        return self._run_operation_with_lease(instance_id, "process_latent_in", _invoke)
 
     async def process_latent_out(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        instance = self._get_instance(instance_id)
-        result = instance.model.process_latent_out(*args, **kwargs)
-        try:
-            target = None
-            if args and hasattr(args[0], "device"):
-                target = args[0].device
-            elif kwargs:
-                for v in kwargs.values():
-                    if hasattr(v, "device"):
-                        target = v.device
-                        break
-            if target is not None and hasattr(result, "to"):
-                return detach_if_grad(result.to(target))
-        except Exception:
-            logger.debug(
-                "process_latent_out: failed to move result to target device",
-                exc_info=True,
-            )
-        return detach_if_grad(result)
+        import torch
+
+        def _invoke() -> Any:
+            instance = self._get_instance(instance_id)
+            result = instance.model.process_latent_out(*args, **kwargs)
+            moved_result = None
+            try:
+                target = None
+                if args and hasattr(args[0], "device"):
+                    target = args[0].device
+                elif kwargs:
+                    for v in kwargs.values():
+                        if hasattr(v, "device"):
+                            target = v.device
+                            break
+                if target is not None and hasattr(result, "to"):
+                    moved_result = detach_if_grad(result.to(target))
+            except Exception:
+                logger.debug(
+                    "process_latent_out: failed to move result to target device",
+                    exc_info=True,
+                )
+            if moved_result is None:
+                moved_result = detach_if_grad(result)
+
+            is_dynamic_fn = getattr(instance, "is_dynamic", None)
+            if callable(is_dynamic_fn) and is_dynamic_fn():
+                def _to_cpu(obj: Any) -> Any:
+                    if torch.is_tensor(obj):
+                        return obj.detach().cpu() if obj.device.type != "cpu" else obj
+                    if isinstance(obj, dict):
+                        return {k: _to_cpu(v) for k, v in obj.items()}
+                    if isinstance(obj, list):
+                        return [_to_cpu(v) for v in obj]
+                    if isinstance(obj, tuple):
+                        return tuple(_to_cpu(v) for v in obj)
+                    return obj
+
+                return _to_cpu(moved_result)
+            return moved_result
+
+        return self._run_operation_with_lease(instance_id, "process_latent_out", _invoke)
 
     async def scale_latent_inpaint(
         self, instance_id: str, args: tuple, kwargs: dict
     ) -> Any:
-        instance = self._get_instance(instance_id)
-        result = instance.model.scale_latent_inpaint(*args, **kwargs)
-        try:
-            target = None
-            if args and hasattr(args[0], "device"):
-                target = args[0].device
-            elif kwargs:
-                for v in kwargs.values():
-                    if hasattr(v, "device"):
-                        target = v.device
-                        break
-            if target is not None and hasattr(result, "to"):
-                return detach_if_grad(result.to(target))
-        except Exception:
-            logger.debug(
-                "scale_latent_inpaint: failed to move result to target device",
-                exc_info=True,
-            )
-        return detach_if_grad(result)
+        import torch
+
+        def _invoke() -> Any:
+            instance = self._get_instance(instance_id)
+            result = instance.model.scale_latent_inpaint(*args, **kwargs)
+            moved_result = None
+            try:
+                target = None
+                if args and hasattr(args[0], "device"):
+                    target = args[0].device
+                elif kwargs:
+                    for v in kwargs.values():
+                        if hasattr(v, "device"):
+                            target = v.device
+                            break
+                if target is not None and hasattr(result, "to"):
+                    moved_result = detach_if_grad(result.to(target))
+            except Exception:
+                logger.debug(
+                    "scale_latent_inpaint: failed to move result to target device",
+                    exc_info=True,
+                )
+            if moved_result is None:
+                moved_result = detach_if_grad(result)
+
+            is_dynamic_fn = getattr(instance, "is_dynamic", None)
+            if callable(is_dynamic_fn) and is_dynamic_fn():
+                def _to_cpu(obj: Any) -> Any:
+                    if torch.is_tensor(obj):
+                        return obj.detach().cpu() if obj.device.type != "cpu" else obj
+                    if isinstance(obj, dict):
+                        return {k: _to_cpu(v) for k, v in obj.items()}
+                    if isinstance(obj, list):
+                        return [_to_cpu(v) for v in obj]
+                    if isinstance(obj, tuple):
+                        return tuple(_to_cpu(v) for v in obj)
+                    return obj
+
+                return _to_cpu(moved_result)
+            return moved_result
+
+        return self._run_operation_with_lease(
+            instance_id, "scale_latent_inpaint", _invoke
+        )
 
     async def load_lora(
         self,

comfy/isolation/model_sampling_proxy.py

@@ -3,6 +3,9 @@ from __future__ import annotations
 
 import asyncio
 import logging
+import os
+import threading
+import time
 from typing import Any
 
 from comfy.isolation.proxies.base import (
@@ -16,6 +19,22 @@ from comfy.isolation.proxies.base import (
 logger = logging.getLogger(__name__)
 
 
+def _describe_value(obj: Any) -> str:
+    try:
+        import torch
+    except Exception:
+        torch = None
+    try:
+        if torch is not None and isinstance(obj, torch.Tensor):
+            return (
+                "Tensor(shape=%s,dtype=%s,device=%s,id=%s)"
+                % (tuple(obj.shape), obj.dtype, obj.device, id(obj))
+            )
+    except Exception:
+        pass
+    return "%s(id=%s)" % (type(obj).__name__, id(obj))
+
+
 def _prefer_device(*tensors: Any) -> Any:
     try:
         import torch
@@ -49,6 +68,24 @@ def _to_device(obj: Any, device: Any) -> Any:
     return obj
 
 
+def _to_cpu_for_rpc(obj: Any) -> Any:
+    try:
+        import torch
+    except Exception:
+        return obj
+    if isinstance(obj, torch.Tensor):
+        t = obj.detach() if obj.requires_grad else obj
+        if t.is_cuda:
+            return t.to("cpu")
+        return t
+    if isinstance(obj, (list, tuple)):
+        converted = [_to_cpu_for_rpc(x) for x in obj]
+        return type(obj)(converted) if isinstance(obj, tuple) else converted
+    if isinstance(obj, dict):
+        return {k: _to_cpu_for_rpc(v) for k, v in obj.items()}
+    return obj
+
+
 class ModelSamplingRegistry(BaseRegistry[Any]):
     _type_prefix = "modelsampling"
 
@@ -199,14 +236,70 @@ class ModelSamplingProxy(BaseProxy[ModelSamplingRegistry]):
         rpc = self._get_rpc()
         method = getattr(rpc, method_name)
         result = method(self._instance_id, *args)
+        timeout_ms = self._rpc_timeout_ms()
+        start_epoch = time.time()
+        start_perf = time.perf_counter()
+        thread_id = threading.get_ident()
+        call_id = "%s:%s:%s:%.6f" % (
+            self._instance_id,
+            method_name,
+            thread_id,
+            start_perf,
+        )
+        logger.debug(
+            "ISO:modelsampling_rpc_start method=%s instance_id=%s call_id=%s start_ts=%.6f thread=%s timeout_ms=%s",
+            method_name,
+            self._instance_id,
+            call_id,
+            start_epoch,
+            thread_id,
+            timeout_ms,
+        )
         if asyncio.iscoroutine(result):
+            result = asyncio.wait_for(result, timeout=timeout_ms / 1000.0)
             try:
                 asyncio.get_running_loop()
-                return run_coro_in_new_loop(result)
+                out = run_coro_in_new_loop(result)
             except RuntimeError:
                 loop = get_thread_loop()
-                return loop.run_until_complete(result)
-        return result
+                out = loop.run_until_complete(result)
+        else:
+            out = result
+        logger.debug(
+            "ISO:modelsampling_rpc_after_await method=%s instance_id=%s call_id=%s out=%s",
+            method_name,
+            self._instance_id,
+            call_id,
+            _describe_value(out),
+        )
+        elapsed_ms = (time.perf_counter() - start_perf) * 1000.0
+        logger.debug(
+            "ISO:modelsampling_rpc_end method=%s instance_id=%s call_id=%s elapsed_ms=%.3f thread=%s",
+            method_name,
+            self._instance_id,
+            call_id,
+            elapsed_ms,
+            thread_id,
+        )
+        logger.debug(
+            "ISO:modelsampling_rpc_return method=%s instance_id=%s call_id=%s",
+            method_name,
+            self._instance_id,
+            call_id,
+        )
+        return out
+
+    @staticmethod
+    def _rpc_timeout_ms() -> int:
+        raw = os.environ.get(
+            "COMFY_ISOLATION_MODEL_SAMPLING_RPC_TIMEOUT_MS",
+            os.environ.get("COMFY_ISOLATION_LOAD_RPC_TIMEOUT_MS", "30000"),
+        )
+        try:
+            timeout_ms = int(raw)
+        except ValueError:
+            timeout_ms = 30000
+        return max(1, timeout_ms)
 
     @property
     def sigma_min(self) -> Any:
@@ -235,10 +328,24 @@ class ModelSamplingProxy(BaseProxy[ModelSamplingRegistry]):
     def noise_scaling(
         self, sigma: Any, noise: Any, latent_image: Any, max_denoise: bool = False
     ) -> Any:
-        return self._call("noise_scaling", sigma, noise, latent_image, max_denoise)
+        preferred_device = _prefer_device(noise, latent_image)
+        out = self._call(
+            "noise_scaling",
+            _to_cpu_for_rpc(sigma),
+            _to_cpu_for_rpc(noise),
+            _to_cpu_for_rpc(latent_image),
+            max_denoise,
+        )
+        return _to_device(out, preferred_device)
 
     def inverse_noise_scaling(self, sigma: Any, latent: Any) -> Any:
-        return self._call("inverse_noise_scaling", sigma, latent)
+        preferred_device = _prefer_device(latent)
+        out = self._call(
+            "inverse_noise_scaling",
+            _to_cpu_for_rpc(sigma),
+            _to_cpu_for_rpc(latent),
+        )
+        return _to_device(out, preferred_device)
 
     def timestep(self, sigma: Any) -> Any:
         return self._call("timestep", sigma)

comfy/isolation/proxies/base.py

@@ -2,9 +2,11 @@
 from __future__ import annotations
 
 import asyncio
+import concurrent.futures
 import logging
 import os
 import threading
+import time
 import weakref
 from typing import Any, Callable, Dict, Generic, Optional, TypeVar
 
@@ -119,6 +121,24 @@ def set_global_loop(loop: asyncio.AbstractEventLoop) -> None:
 class BaseProxy(Generic[T]):
     _registry_class: type = BaseRegistry  # type: ignore[type-arg]
     __module__: str = "comfy.isolation.proxies.base"
+    _TIMEOUT_RPC_METHODS = frozenset(
+        {
+            "partially_load",
+            "partially_unload",
+            "load",
+            "patch_model",
+            "unpatch_model",
+            "inner_model_apply_model",
+            "memory_required",
+            "model_dtype",
+            "inner_model_memory_required",
+            "inner_model_extra_conds_shapes",
+            "inner_model_extra_conds",
+            "process_latent_in",
+            "process_latent_out",
+            "scale_latent_inpaint",
+        }
+    )
 
     def __init__(
         self,
@@ -148,39 +168,89 @@ class BaseProxy(Generic[T]):
             )
         return self._rpc_caller
 
+    def _rpc_timeout_ms_for_method(self, method_name: str) -> Optional[int]:
+        if method_name not in self._TIMEOUT_RPC_METHODS:
+            return None
+        try:
+            timeout_ms = int(
+                os.environ.get("COMFY_ISOLATION_LOAD_RPC_TIMEOUT_MS", "120000")
+            )
+        except ValueError:
+            timeout_ms = 120000
+        return max(1, timeout_ms)
+
     def _call_rpc(self, method_name: str, *args: Any, **kwargs: Any) -> Any:
         rpc = self._get_rpc()
         method = getattr(rpc, method_name)
+        timeout_ms = self._rpc_timeout_ms_for_method(method_name)
         coro = method(self._instance_id, *args, **kwargs)
+        if timeout_ms is not None:
+            coro = asyncio.wait_for(coro, timeout=timeout_ms / 1000.0)
 
-        # If we have a global loop (Main Thread Loop), use it for dispatch from worker threads
-        if _GLOBAL_LOOP is not None and _GLOBAL_LOOP.is_running():
-            try:
-                # If we are already in the global loop, we can't block on it?
-                # Actually, this method is synchronous (__getattr__ -> lambda).
-                # If called from async context in main loop, we need to handle that.
-                curr_loop = asyncio.get_running_loop()
-                if curr_loop is _GLOBAL_LOOP:
-                    # We are in the main loop. We cannot await/block here if we are just a sync function.
-                    # But proxies are often called from sync code.
-                    # If called from sync code in main loop, creating a new loop is bad.
-                    # But we can't await `coro`.
-                    # This implies proxies MUST be awaited if called from async context?
-                    # Existing code used `run_coro_in_new_loop` which is weird.
-                    # Let's trust that if we are in a thread (RuntimeError on get_running_loop),
-                    # we use run_coroutine_threadsafe.
-                    pass
-            except RuntimeError:
-                # No running loop - we are in a worker thread.
-                future = asyncio.run_coroutine_threadsafe(coro, _GLOBAL_LOOP)
-                return future.result()
+        start_epoch = time.time()
+        start_perf = time.perf_counter()
+        thread_id = threading.get_ident()
+        try:
+            running_loop = asyncio.get_running_loop()
+            loop_id: Optional[int] = id(running_loop)
+        except RuntimeError:
+            loop_id = None
+        logger.debug(
+            "ISO:rpc_start proxy=%s method=%s instance_id=%s start_ts=%.6f "
+            "thread=%s loop=%s timeout_ms=%s",
+            self.__class__.__name__,
+            method_name,
+            self._instance_id,
+            start_epoch,
+            thread_id,
+            loop_id,
+            timeout_ms,
+        )
 
         try:
-            asyncio.get_running_loop()
-            return run_coro_in_new_loop(coro)
-        except RuntimeError:
-            loop = get_thread_loop()
-            return loop.run_until_complete(coro)
+            # If we have a global loop (Main Thread Loop), use it for dispatch from worker threads
+            if _GLOBAL_LOOP is not None and _GLOBAL_LOOP.is_running():
+                try:
+                    curr_loop = asyncio.get_running_loop()
+                    if curr_loop is _GLOBAL_LOOP:
+                        pass
+                except RuntimeError:
+                    # No running loop - we are in a worker thread.
+                    future = asyncio.run_coroutine_threadsafe(coro, _GLOBAL_LOOP)
+                    return future.result(
+                        timeout=(timeout_ms / 1000.0) if timeout_ms is not None else None
+                    )
+
+            try:
+                asyncio.get_running_loop()
+                return run_coro_in_new_loop(coro)
+            except RuntimeError:
+                loop = get_thread_loop()
+                return loop.run_until_complete(coro)
+        except asyncio.TimeoutError as exc:
+            raise TimeoutError(
+                f"Isolation RPC timeout in {self.__class__.__name__}.{method_name} "
+                f"(instance_id={self._instance_id}, timeout_ms={timeout_ms})"
+            ) from exc
+        except concurrent.futures.TimeoutError as exc:
+            raise TimeoutError(
+                f"Isolation RPC timeout in {self.__class__.__name__}.{method_name} "
+                f"(instance_id={self._instance_id}, timeout_ms={timeout_ms})"
+            ) from exc
+        finally:
+            end_epoch = time.time()
+            elapsed_ms = (time.perf_counter() - start_perf) * 1000.0
+            logger.debug(
+                "ISO:rpc_end proxy=%s method=%s instance_id=%s end_ts=%.6f "
+                "elapsed_ms=%.3f thread=%s loop=%s",
+                self.__class__.__name__,
+                method_name,
+                self._instance_id,
+                end_epoch,
+                elapsed_ms,
+                thread_id,
+                loop_id,
+            )
 
     def __getstate__(self) -> Dict[str, Any]:
         return {"_instance_id": self._instance_id}

comfy/k_diffusion/sampling.py

@@ -192,7 +192,7 @@ 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"
+    isolation_active = args.use_process_isolation or os.environ.get("PYISOLATE_CHILD") == "1"
     if isolation_active:
         target_device = sigmas.device
         if x.device != target_device:

comfy/ldm/wan/model.py

@@ -1621,3 +1621,118 @@ class HumoWanModel(WanModel):
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
         return x
+
+class SCAILWanModel(WanModel):
+    def __init__(self, model_type="scail", patch_size=(1, 2, 2), in_dim=20, dim=5120, operations=None, device=None, dtype=None, **kwargs):
+        super().__init__(model_type='i2v', patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs)
+
+        self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)
+
+    def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs):
+
+        if reference_latent is not None:
+            x = torch.cat((reference_latent, x), dim=2)
+
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        grid_sizes = x.shape[2:]
+        transformer_options["grid_sizes"] = grid_sizes
+        x = x.flatten(2).transpose(1, 2)
+
+        scail_pose_seq_len = 0
+        if pose_latents is not None:
+            scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype)
+            scail_x = scail_x.flatten(2).transpose(1, 2)
+            scail_pose_seq_len = scail_x.shape[1]
+            x = torch.cat([x, scail_x], dim=1)
+            del scail_x
+
+        # time embeddings
+        e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        # context
+        context = self.text_embedding(context)
+
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.cat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
+        for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+
+        # head
+        x = self.head(x, e)
+
+        if scail_pose_seq_len > 0:
+            x = x[:, :-scail_pose_seq_len]
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+
+        if reference_latent is not None:
+            x = x[:, :, reference_latent.shape[2]:]
+
+        return x
+
+    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}):
+        main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options)
+
+        if pose_latents is None:
+            return main_freqs
+
+        ref_t_patches = 0
+        if reference_latent is not None:
+            ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
+
+        F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
+
+        # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames
+        h_scale = h / H_pose
+        w_scale = w / W_pose
+
+        # 120 w-offset and shift 0.5 to place positions at midpoints (0.5, 2.5, ...) to match the original code
+        h_shift = (h_scale - 1) / 2
+        w_shift = (w_scale - 1) / 2
+        pose_transformer_options = {"rope_options": {"shift_y": h_shift, "shift_x": 120.0 + w_shift, "scale_y": h_scale, "scale_x": w_scale}}
+        pose_freqs = super().rope_encode(F_pose, H_pose, W_pose, t_start=t_start+ref_t_patches, device=device, dtype=dtype, transformer_options=pose_transformer_options)
+
+        return torch.cat([main_freqs, pose_freqs], dim=1)
+
+    def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs):
+        bs, c, t, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
+        if pose_latents is not None:
+            pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size)
+
+        t_len = t
+        if time_dim_concat is not None:
+            time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
+            x = torch.cat([x, time_dim_concat], dim=2)
+            t_len = x.shape[2]
+
+        reference_latent = None
+        if "reference_latent" in kwargs:
+            reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop("reference_latent"), self.patch_size)
+            t_len += reference_latent.shape[2]
+
+        freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent)
+        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w]

comfy/model_base.py

@@ -20,6 +20,7 @@ import comfy.ldm.hunyuan3dv2_1
 import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
+import os
 import comfy.ldm.lightricks.av_model
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from comfy.ldm.cascade.stage_c import StageC
@@ -76,6 +77,7 @@ class ModelType(Enum):
     FLUX = 8
     IMG_TO_IMG = 9
     FLOW_COSMOS = 10
+    IMG_TO_IMG_FLOW = 11
 
 
 def model_sampling(model_config, model_type):
@@ -108,17 +110,23 @@ 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
+
+    from comfy.cli_args import args
+    isolation_runtime_enabled = args.use_process_isolation or os.environ.get("PYISOLATE_CHILD") == "1"
 
     class ModelSampling(s, c):
-        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
+        if isolation_runtime_enabled:
+            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)
 
@@ -998,6 +1006,10 @@ 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):
@@ -1050,6 +1062,10 @@ 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):
@@ -1493,6 +1509,50 @@ 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 WAN21_SCAIL(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAILWanModel)
+        self.memory_usage_factor_conds = ("reference_latent", "pose_latents")
+        self.memory_usage_shape_process = {"pose_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]}
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        reference_latents = kwargs.get("reference_latents", None)
+        if reference_latents is not None:
+            ref_latent = self.process_latent_in(reference_latents[-1])
+            ref_mask = torch.ones_like(ref_latent[:, :4])
+            ref_latent = torch.cat([ref_latent, ref_mask], dim=1)
+            out['reference_latent'] = comfy.conds.CONDRegular(ref_latent)
+
+        pose_latents = kwargs.get("pose_video_latent", None)
+        if pose_latents is not None:
+            pose_latents = self.process_latent_in(pose_latents)
+            pose_mask = torch.ones_like(pose_latents[:, :4])
+            pose_latents = torch.cat([pose_latents, pose_mask], dim=1)
+            out['pose_latents'] = comfy.conds.CONDRegular(pose_latents)
+
+        return out
+
+    def extra_conds_shapes(self, **kwargs):
+        out = {}
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            out['reference_latent'] = list([1, 20, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
+
+        pose_latents = kwargs.get("pose_video_latent", None)
+        if pose_latents is not None:
+            out['pose_latents'] = [pose_latents.shape[0], 20, *pose_latents.shape[2:]]
+
+        return out
+
 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_detection.py

@@ -423,7 +423,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
         return dit_config
 
-    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
+    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
         dit_config = {}
         dit_config["image_model"] = "lumina2"
         dit_config["patch_size"] = 2
@@ -498,6 +498,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["model_type"] = "humo"
         elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
             dit_config["model_type"] = "animate"
+        elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "scail"
         else:
             if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
                 dit_config["model_type"] = "i2v"
@@ -531,8 +533,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
         return dit_config
 
-    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys:  # Hunyuan 3D 2.1
-
+    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys and f"{key_prefix}blocks.0.attn1.k_norm.weight" in state_dict_keys:  # Hunyuan 3D 2.1
         dit_config = {}
         dit_config["image_model"] = "hunyuan3d2_1"
         dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1]
@@ -1053,6 +1054,13 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
     elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
+    elif 'noise_refiner.0.attention.norm_k.weight' in state_dict:
+        n_layers = count_blocks(state_dict, 'layers.{}.')
+        dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0]
+        sd_map = comfy.utils.z_image_to_diffusers({"n_layers": n_layers, "dim": dim}, output_prefix=output_prefix)
+        for k in state_dict: # For zeta chroma
+            if k not in sd_map:
+                sd_map[k] = k
     elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')

comfy/model_management.py

@@ -180,6 +180,14 @@ def is_ixuca():
         return True
     return False
 
+def is_wsl():
+    version = platform.uname().release
+    if version.endswith("-Microsoft"):
+        return True
+    elif version.endswith("microsoft-standard-WSL2"):
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
@@ -475,6 +483,9 @@ except:
 
 current_loaded_models = []
 
+def _isolation_mode_enabled():
+    return args.use_process_isolation or os.environ.get("PYISOLATE_CHILD") == "1"
+
 def module_size(module):
     module_mem = 0
     sd = module.state_dict()
@@ -554,8 +565,9 @@ class LoadedModel:
                 if freed >= memory_to_free:
                     return False
         self.model.detach(unpatch_weights)
-        self.model_finalizer.detach()
-        self.model_finalizer = None
+        if self.model_finalizer is not None:
+            self.model_finalizer.detach()
+            self.model_finalizer = None
         self.real_model = None
         return True
 
@@ -569,14 +581,15 @@ class LoadedModel:
         if self._patcher_finalizer is not None:
             self._patcher_finalizer.detach()
 
+    def dead_state(self):
+        model_ref_gone = self.model is None
+        real_model_ref = self.real_model
+        real_model_ref_gone = callable(real_model_ref) and real_model_ref() is None
+        return model_ref_gone, real_model_ref_gone
+
     def is_dead(self):
-        # 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
+        model_ref_gone, real_model_ref_gone = self.dead_state()
+        return model_ref_gone or real_model_ref_gone
 
 
 def use_more_memory(extra_memory, loaded_models, device):
@@ -622,7 +635,7 @@ 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"
+    isolation_active = _isolation_mode_enabled()
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
@@ -649,12 +662,11 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
         if not DISABLE_SMART_MEMORY:
             memory_to_free = memory_required - get_free_memory(device)
             ram_to_free = ram_required - get_free_ram()
-
-        if current_loaded_models[i].model.is_dynamic() and for_dynamic:
-            #don't actually unload dynamic models for the sake of other dynamic models
-            #as that works on-demand.
-            memory_required -= current_loaded_models[i].model.loaded_size()
-            memory_to_free = 0
+            if current_loaded_models[i].model.is_dynamic() and for_dynamic:
+                #don't actually unload dynamic models for the sake of other dynamic models
+                #as that works on-demand.
+                memory_required -= current_loaded_models[i].model.loaded_size()
+                memory_to_free = 0
         if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free):
             logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
             unloaded_model.append(i)
@@ -728,7 +740,9 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         for i in to_unload:
             model_to_unload = current_loaded_models.pop(i)
             model_to_unload.model.detach(unpatch_all=False)
-            model_to_unload.model_finalizer.detach()
+            if model_to_unload.model_finalizer is not None:
+                model_to_unload.model_finalizer.detach()
+                model_to_unload.model_finalizer = None
 
 
     total_memory_required = {}
@@ -792,21 +806,55 @@ def loaded_models(only_currently_used=False):
 
 def cleanup_models_gc():
     reset_cast_buffers()
+    if not _isolation_mode_enabled():
+        dead_found = False
+        for i in range(len(current_loaded_models)):
+            if current_loaded_models[i].is_dead():
+                dead_found = True
+                break
+
+        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) - 1, -1, -1):
+                cur = current_loaded_models[i]
+                if cur.is_dead():
+                    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()
+        return
+
     dead_found = False
+    has_real_model_leak = False
     for i in range(len(current_loaded_models)):
-        if current_loaded_models[i].is_dead():
+        model_ref_gone, real_model_ref_gone = current_loaded_models[i].dead_state()
+        if model_ref_gone or real_model_ref_gone:
             dead_found = True
-            break
+            if real_model_ref_gone and not model_ref_gone:
+                has_real_model_leak = True
 
     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.")
+        if has_real_model_leak:
+            logging.info("Potential memory leak detected with model NoneType, doing a full garbage collect, for maximum performance avoid circular references in the model code.")
+        else:
+            logging.debug("Cleaning stale loaded-model entries with released patcher references.")
         gc.collect()
         soft_empty_cache()
 
         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 NoneType. Please make sure it is not being referenced from somewhere.")
+            model_ref_gone, real_model_ref_gone = cur.dead_state()
+            if model_ref_gone or real_model_ref_gone:
+                if real_model_ref_gone and not model_ref_gone:
+                    logging.warning("WARNING, memory leak with model NoneType. Please make sure it is not being referenced from somewhere.")
+                else:
+                    logging.debug("Cleaning stale loaded-model entry with released patcher reference.")
                 leaked = current_loaded_models.pop(i)
                 model_obj = getattr(leaked, "model", None)
                 if model_obj is not None:
@@ -824,7 +872,11 @@ def archive_model_dtypes(model):
 def cleanup_models():
     to_delete = []
     for i in range(len(current_loaded_models)):
-        if current_loaded_models[i].real_model() is None:
+        real_model_ref = current_loaded_models[i].real_model
+        if real_model_ref is None:
+            to_delete = [i] + to_delete
+            continue
+        if callable(real_model_ref) and real_model_ref() is None:
             to_delete = [i] + to_delete
 
     for i in to_delete:

comfy/model_patcher.py

@@ -308,15 +308,22 @@ class ModelPatcher:
     def get_free_memory(self, device):
         return comfy.model_management.get_free_memory(device)
 
-    def clone(self, disable_dynamic=False):
+    def get_clone_model_override(self):
+        return self.model, (self.backup, self.object_patches_backup, self.pinned)
+
+    def clone(self, disable_dynamic=False, model_override=None):
         class_ = self.__class__
-        model = self.model
         if self.is_dynamic() and disable_dynamic:
             class_ = ModelPatcher
-            temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
-            model = temp_model_patcher.model
+            if model_override is None:
+                if self.cached_patcher_init is None:
+                    raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.")
+                temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
+                model_override = temp_model_patcher.get_clone_model_override()
+        if model_override is None:
+            model_override = self.get_clone_model_override()
 
-        n = class_(model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
+        n = class_(model_override[0], self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -325,13 +332,12 @@ class ModelPatcher:
         n.object_patches = self.object_patches.copy()
         n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
         n.model_options = comfy.utils.deepcopy_list_dict(self.model_options)
-        n.backup = self.backup
-        n.object_patches_backup = self.object_patches_backup
         n.parent = self
-        n.pinned = self.pinned
 
         n.force_cast_weights = self.force_cast_weights
 
+        n.backup, n.object_patches_backup, n.pinned = model_override[1]
+
         # attachments
         n.attachments = {}
         for k in self.attachments:
@@ -1435,6 +1441,7 @@ class ModelPatcherDynamic(ModelPatcher):
             del self.model.model_loaded_weight_memory
         if not hasattr(self.model, "dynamic_vbars"):
             self.model.dynamic_vbars = {}
+        self.non_dynamic_delegate_model = None
         assert load_device is not None
 
     def is_dynamic(self):
@@ -1669,4 +1676,10 @@ class ModelPatcherDynamic(ModelPatcher):
     def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
         pass
 
+    def get_non_dynamic_delegate(self):
+        model_patcher = self.clone(disable_dynamic=True, model_override=self.non_dynamic_delegate_model)
+        self.non_dynamic_delegate_model = model_patcher.get_clone_model_override()
+        return model_patcher
+
+
 CoreModelPatcher = ModelPatcher

comfy/sampler_helpers.py

@@ -66,6 +66,18 @@ def convert_cond(cond):
         out.append(temp)
     return out
 
+def cond_has_hooks(cond):
+    for c in cond:
+        temp = c[1]
+        if "hooks" in temp:
+            return True
+        if "control" in temp:
+            control = temp["control"]
+            extra_hooks = control.get_extra_hooks()
+            if len(extra_hooks) > 0:
+                return True
+    return False
+
 def get_additional_models(conds, dtype):
     """loads additional models in conditioning"""
     cnets: list[ControlBase] = []

comfy/samplers.py

@@ -212,10 +212,11 @@ def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torc
         _calc_cond_batch,
         comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True)
     )
-    return executor.execute(model, conds, x_in, timestep, model_options)
+    result = executor.execute(model, conds, x_in, timestep, model_options)
+    return result
 
 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"
+    isolation_active = args.use_process_isolation or os.environ.get("PYISOLATE_CHILD") == "1"
     out_conds = []
     out_counts = []
     # separate conds by matching hooks
@@ -272,7 +273,8 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
                     for k, v in to_run[tt][0].conditioning.items():
                         cond_shapes[k].append(v.size())
 
-                if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory:
+                memory_required = model.memory_required(input_shape, cond_shapes=cond_shapes)
+                if memory_required * 1.5 < free_memory:
                     to_batch = batch_amount
                     break
 
@@ -411,7 +413,7 @@ 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"
+        isolation_active = args.use_process_isolation or os.environ.get("PYISOLATE_CHILD") == "1"
         if denoise_mask is not None:
             if isolation_active and denoise_mask.device != x.device:
                 denoise_mask = denoise_mask.to(x.device)
@@ -777,7 +779,11 @@ class KSAMPLER(Sampler):
         else:
             model_k.noise = noise
 
-        noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
+        max_denoise = self.max_denoise(model_wrap, sigmas)
+        model_sampling = model_wrap.inner_model.model_sampling
+        noise = model_sampling.noise_scaling(
+            sigmas[0], noise, latent_image, max_denoise
+        )
 
         k_callback = None
         total_steps = len(sigmas) - 1
@@ -982,6 +988,8 @@ class CFGGuider:
 
     def inner_set_conds(self, conds):
         for k in conds:
+            if self.model_patcher.is_dynamic() and comfy.sampler_helpers.cond_has_hooks(conds[k]):
+                self.model_patcher = self.model_patcher.get_non_dynamic_delegate()
             self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
 
     def __call__(self, *args, **kwargs):

comfy/sd.py

@@ -204,7 +204,7 @@ def load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip
 
 
 class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}):
+    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}, disable_dynamic=False):
         if no_init:
             return
         params = target.params.copy()
@@ -233,7 +233,8 @@ class CLIP:
         model_management.archive_model_dtypes(self.cond_stage_model)
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
-        self.patcher = comfy.model_patcher.CoreModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
+        ModelPatcher = comfy.model_patcher.ModelPatcher if disable_dynamic else comfy.model_patcher.CoreModelPatcher
+        self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         #Match torch.float32 hardcode upcast in TE implemention
         self.patcher.set_model_compute_dtype(torch.float32)
         self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
@@ -267,9 +268,9 @@ class CLIP:
         logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
         self.tokenizer_options = {}
 
-    def clone(self):
+    def clone(self, disable_dynamic=False):
         n = CLIP(no_init=True)
-        n.patcher = self.patcher.clone()
+        n.patcher = self.patcher.clone(disable_dynamic=disable_dynamic)
         n.cond_stage_model = self.cond_stage_model
         n.tokenizer = self.tokenizer
         n.layer_idx = self.layer_idx
@@ -1164,14 +1165,21 @@ class CLIPType(Enum):
     LONGCAT_IMAGE = 26
 
 
-def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
+
+def load_clip_model_patcher(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
+    clip = load_clip(ckpt_paths, embedding_directory, clip_type, model_options, disable_dynamic)
+    return clip.patcher
+
+def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
     clip_data = []
     for p in ckpt_paths:
         sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True)
         if model_options.get("custom_operations", None) is None:
             sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix="", metadata=metadata)
         clip_data.append(sd)
-    return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
+    clip = load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options, disable_dynamic=disable_dynamic)
+    clip.patcher.cached_patcher_init = (load_clip_model_patcher, (ckpt_paths, embedding_directory, clip_type, model_options))
+    return clip
 
 
 class TEModel(Enum):
@@ -1276,7 +1284,7 @@ def llama_detect(clip_data):
 
     return {}
 
-def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
+def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
     clip_data = state_dicts
 
     class EmptyClass:
@@ -1496,7 +1504,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         parameters += comfy.utils.calculate_parameters(c)
         tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
 
-    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options)
+    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options, disable_dynamic=disable_dynamic)
     return clip
 
 def load_gligen(ckpt_path):
@@ -1541,8 +1549,10 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)
     if out is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))
-    if output_model:
+    if output_model and out[0] is not None:
         out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options))
+    if output_clip and out[1] is not None:
+        out[1].patcher.cached_patcher_init = (load_checkpoint_guess_config_clip_only, (ckpt_path, embedding_directory, model_options, te_model_options))
     return out
 
 def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
@@ -1553,6 +1563,14 @@ def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None,
             disable_dynamic=disable_dynamic)
     return model
 
+def load_checkpoint_guess_config_clip_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
+    _, clip, *_ = load_checkpoint_guess_config(ckpt_path, False, True, False,
+            embedding_directory=embedding_directory, output_model=False,
+            model_options=model_options,
+            te_model_options=te_model_options,
+            disable_dynamic=disable_dynamic)
+    return clip.patcher
+
 def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None, disable_dynamic=False):
     clip = None
     clipvision = None
@@ -1638,7 +1656,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
                 parameters = comfy.utils.calculate_parameters(clip_sd)
-                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options)
+                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options, disable_dynamic=disable_dynamic)
             else:
                 logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
 

comfy/supported_models.py

@@ -1268,6 +1268,16 @@ class WAN21_FlowRVS(WAN21_T2V):
         out = model_base.WAN21_FlowRVS(self, image_to_video=True, device=device)
         return out
 
+class WAN21_SCAIL(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "scail",
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device)
+        return out
+
 class Hunyuan3Dv2(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan3d2",
@@ -1710,6 +1720,6 @@ class LongCatImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]

comfy_api_nodes/nodes_gemini.py

@@ -789,8 +789,6 @@ class GeminiImage2(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
             model = "gemini-3.1-flash-image-preview"
-            if response_modalities == "IMAGE+TEXT":
-                raise ValueError("IMAGE+TEXT is not currently available for the Nano Banana 2 model.")
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -895,7 +893,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "response_modalities",
-                    options=["IMAGE"],
+                    options=["IMAGE", "IMAGE+TEXT"],
                     advanced=True,
                 ),
                 IO.Combo.Input(
@@ -925,6 +923,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
             ],
             outputs=[
                 IO.Image.Output(),
+                IO.String.Output(),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,

comfy_execution/jobs.py

@@ -20,7 +20,7 @@ class JobStatus:
 
 
 # Media types that can be previewed in the frontend
-PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d'})
+PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
 
 # 3D file extensions for preview fallback (no dedicated media_type exists)
 THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
@@ -75,6 +75,23 @@ def normalize_outputs(outputs: dict) -> dict:
         normalized[node_id] = normalized_node
     return normalized
 
+# Text preview truncation limit (1024 characters) to prevent preview_output bloat
+TEXT_PREVIEW_MAX_LENGTH = 1024
+
+
+def _create_text_preview(value: str) -> dict:
+    """Create a text preview dict with optional truncation.
+
+    Returns:
+        dict with 'content' and optionally 'truncated' flag
+    """
+    if len(value) <= TEXT_PREVIEW_MAX_LENGTH:
+        return {'content': value}
+    return {
+        'content': value[:TEXT_PREVIEW_MAX_LENGTH],
+        'truncated': True
+    }
+
 
 def _extract_job_metadata(extra_data: dict) -> tuple[Optional[int], Optional[str]]:
     """Extract create_time and workflow_id from extra_data.
@@ -221,23 +238,43 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                 continue
 
             for item in items:
-                normalized = normalize_output_item(item)
-                if normalized is None:
-                    continue
+                if not isinstance(item, dict):
+                    # Handle text outputs (non-dict items like strings or tuples)
+                    normalized = normalize_output_item(item)
+                    if normalized is None:
+                        # Not a 3D file string — check for text preview
+                        if media_type == 'text':
+                            count += 1
+                            if preview_output is None:
+                                if isinstance(item, tuple):
+                                    text_value = item[0] if item else ''
+                                else:
+                                    text_value = str(item)
+                                text_preview = _create_text_preview(text_value)
+                                enriched = {
+                                    **text_preview,
+                                    'nodeId': node_id,
+                                    'mediaType': media_type
+                                }
+                                if fallback_preview is None:
+                                    fallback_preview = enriched
+                        continue
+                    # normalize_output_item returned a dict (e.g. 3D file)
+                    item = normalized
 
                 count += 1
 
                 if preview_output is not None:
                     continue
 
-                if isinstance(normalized, dict) and is_previewable(media_type, normalized):
+                if is_previewable(media_type, item):
                     enriched = {
-                        **normalized,
+                        **item,
                         'nodeId': node_id,
                     }
-                    if 'mediaType' not in normalized:
+                    if 'mediaType' not in item:
                         enriched['mediaType'] = media_type
-                    if normalized.get('type') == 'output':
+                    if item.get('type') == 'output':
                         preview_output = enriched
                     elif fallback_preview is None:
                         fallback_preview = enriched

comfy_extras/nodes_hooks.py

@@ -248,7 +248,7 @@ class SetClipHooks:
 
     def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None):
         if hooks is not None:
-            clip = clip.clone()
+            clip = clip.clone(disable_dynamic=True)
             if apply_to_conds:
                 clip.apply_hooks_to_conds = hooks
             clip.patcher.forced_hooks = hooks.clone()

comfy_extras/nodes_mahiro.py

@@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Mahiro",
-            display_name="Mahiro CFG",
+            display_name="Positive-Biased Guidance",
             category="_for_testing",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[
@@ -20,27 +20,35 @@ class Mahiro(io.ComfyNode):
                 io.Model.Output(display_name="patched_model"),
             ],
             is_experimental=True,
+            search_aliases=[
+                "mahiro",
+                "mahiro cfg",
+                "similarity-adaptive guidance",
+                "positive-biased cfg",
+            ],
         )
 
     @classmethod
     def execute(cls, model) -> io.NodeOutput:
         m = model.clone()
+
         def mahiro_normd(args):
-            scale: float = args['cond_scale']
-            cond_p: torch.Tensor = args['cond_denoised']
-            uncond_p: torch.Tensor = args['uncond_denoised']
-            #naive leap
+            scale: float = args["cond_scale"]
+            cond_p: torch.Tensor = args["cond_denoised"]
+            uncond_p: torch.Tensor = args["uncond_denoised"]
+            # naive leap
             leap = cond_p * scale
-            #sim with uncond leap
+            # sim with uncond leap
             u_leap = uncond_p * scale
             cfg = args["denoised"]
             merge = (leap + cfg) / 2
             normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
             normm = torch.sqrt(merge.abs()) * merge.sign()
             sim = F.cosine_similarity(normu, normm).mean()
-            simsc = 2 * (sim+1)
-            wm = (simsc*cfg + (4-simsc)*leap) / 4
+            simsc = 2 * (sim + 1)
+            wm = (simsc * cfg + (4 - simsc) * leap) / 4
             return wm
+
         m.set_model_sampler_post_cfg_function(mahiro_normd)
         return io.NodeOutput(m)
 

comfy_extras/nodes_wan.py

@@ -1456,6 +1456,63 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
         return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image)
 
 
+class WanSCAILToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanSCAILToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+                io.Image.Input("reference_image", optional=True),
+                io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
+                io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
+                io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step to use pose conditioning."),
+                io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step to use pose conditioning."),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, reference_image=None, clip_vision_output=None, pose_video=None) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+
+        ref_latent = None
+        if reference_image is not None:
+            reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            ref_latent = vae.encode(reference_image[:, :, :, :3])
+
+        if ref_latent is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
+            negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True)
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        if pose_video is not None:
+            pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
+            pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
+            positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
+            negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(positive, negative, out_latent)
+
+
 class WanExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -1476,6 +1533,7 @@ class WanExtension(ComfyExtension):
             WanAnimateToVideo,
             Wan22ImageToVideoLatent,
             WanInfiniteTalkToVideo,
+            WanSCAILToVideo,
         ]
 
 async def comfy_entrypoint() -> WanExtension:

execution.py

@@ -43,6 +43,8 @@ from comfy_execution.utils import CurrentNodeContext
 from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
 from comfy_api.latest import io, _io
 
+_AIMDO_VBAR_RESET_UNAVAILABLE_LOGGED = False
+
 
 class ExecutionResult(Enum):
     SUCCESS = 0
@@ -540,7 +542,17 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     if args.verbose == "DEBUG":
                         comfy_aimdo.control.analyze()
                     comfy.model_management.reset_cast_buffers()
-                    comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
+                    vbar_lib = getattr(comfy_aimdo.model_vbar, "lib", None)
+                    if vbar_lib is not None:
+                        comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
+                    else:
+                        global _AIMDO_VBAR_RESET_UNAVAILABLE_LOGGED
+                        if not _AIMDO_VBAR_RESET_UNAVAILABLE_LOGGED:
+                            logging.warning(
+                                "DynamicVRAM backend unavailable for watermark reset; "
+                                "skipping vbar reset for this process."
+                            )
+                            _AIMDO_VBAR_RESET_UNAVAILABLE_LOGGED = True
 
             if has_pending_tasks:
                 pending_async_nodes[unique_id] = output_data
@@ -669,6 +681,8 @@ class PromptExecutor:
         self.success = True
 
     async def _notify_execution_graph_safe(self, class_types: set[str], *, fail_loud: bool = False) -> None:
+        if not args.use_process_isolation:
+            return
         try:
             from comfy.isolation import notify_execution_graph
             await notify_execution_graph(class_types)
@@ -678,12 +692,34 @@ class PromptExecutor:
             logging.debug("][ EX:notify_execution_graph failed", exc_info=True)
 
     async def _flush_running_extensions_transport_state_safe(self) -> None:
+        if not args.use_process_isolation:
+            return
         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)
 
+    async def _wait_model_patcher_quiescence_safe(
+        self,
+        *,
+        fail_loud: bool = False,
+        timeout_ms: int = 120000,
+        marker: str = "EX:wait_model_patcher_idle",
+    ) -> None:
+        if not args.use_process_isolation:
+            return
+        try:
+            from comfy.isolation import wait_for_model_patcher_quiescence
+
+            await wait_for_model_patcher_quiescence(
+                timeout_ms=timeout_ms, fail_loud=fail_loud, marker=marker
+            )
+        except Exception:
+            if fail_loud:
+                raise
+            logging.debug("][ EX:wait_model_patcher_quiescence failed", exc_info=True)
+
     def add_message(self, event, data: dict, broadcast: bool):
         data = {
             **data,
@@ -725,16 +761,17 @@ 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}")
+        if args.use_process_isolation:
+            # 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"))
 
@@ -754,6 +791,11 @@ class PromptExecutor:
                     # 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._wait_model_patcher_quiescence_safe(
+                        fail_loud=False,
+                        timeout_ms=120000,
+                        marker="EX:boundary_cleanup_wait_idle",
+                    )
                     await self._flush_running_extensions_transport_state_safe()
                     comfy.model_management.unload_all_models()
                     comfy.model_management.cleanup_models_gc()
@@ -794,6 +836,11 @@ class PromptExecutor:
                 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._wait_model_patcher_quiescence_safe(
+                    fail_loud=True,
+                    timeout_ms=120000,
+                    marker="EX:notify_graph_wait_idle",
+                )
                 await self._notify_execution_graph_safe(pending_class_types, fail_loud=True)
 
             while not execution_list.is_empty():

main.py

@@ -25,6 +25,15 @@ from app.assets.scanner import seed_assets
 import itertools
 import logging
 
+import comfy_aimdo.control
+
+if enables_dynamic_vram():
+    if not comfy_aimdo.control.init():
+        logging.warning(
+            "DynamicVRAM requested, but comfy-aimdo failed to initialize early. "
+            "Will fall back to legacy model loading if device init fails."
+        )
+
 if '--use-process-isolation' in sys.argv:
     from comfy.isolation import initialize_proxies
     initialize_proxies()
@@ -208,11 +217,6 @@ import gc
 if 'torch' in sys.modules:
     logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.")
 
-import comfy_aimdo.control
-
-if enables_dynamic_vram():
-    comfy_aimdo.control.init()
-
 import comfy.utils
 
 if not IS_PYISOLATE_CHILD:
@@ -228,7 +232,7 @@ if not IS_PYISOLATE_CHILD:
 import comfy.memory_management
 import comfy.model_patcher
 
-if enables_dynamic_vram():
+if enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl():
     if comfy.model_management.torch_version_numeric < (2, 8):
         logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows")
     elif comfy_aimdo.control.init_device(comfy.model_management.get_torch_device().index):

node_helpers.py

@@ -1,5 +1,6 @@
 import hashlib
 import torch
+import logging
 
 from comfy.cli_args import args
 
@@ -21,6 +22,36 @@ def conditioning_set_values(conditioning, values={}, append=False):
 
     return c
 
+def conditioning_set_values_with_timestep_range(conditioning, values={}, start_percent=0.0, end_percent=1.0):
+    """
+    Apply values to conditioning only during [start_percent, end_percent], keeping the
+    original conditioning active outside that range. Respects existing per-entry ranges.
+    """
+    if start_percent > end_percent:
+        logging.warning(f"start_percent ({start_percent}) must be <= end_percent ({end_percent})")
+        return conditioning
+
+    EPS = 1e-5 # the sampler gates entries with strict > / <, shift boundaries slightly to ensure only one conditioning is active per timestep
+    c = []
+    for t in conditioning:
+        cond_start = t[1].get("start_percent", 0.0)
+        cond_end   = t[1].get("end_percent",   1.0)
+        intersect_start = max(start_percent, cond_start)
+        intersect_end   = min(end_percent,   cond_end)
+
+        if intersect_start >= intersect_end: # no overlap: emit unchanged
+            c.append(t)
+            continue
+
+        if intersect_start > cond_start: # part before the requested range
+            c.extend(conditioning_set_values([t], {"start_percent": cond_start, "end_percent": intersect_start - EPS}))
+
+        c.extend(conditioning_set_values([t], {**values, "start_percent": intersect_start, "end_percent": intersect_end}))
+
+        if intersect_end < cond_end: # part after the requested range
+            c.extend(conditioning_set_values([t], {"start_percent": intersect_end + EPS, "end_percent": cond_end}))
+    return c
+
 def pillow(fn, arg):
     prev_value = None
     try:

pyproject.toml

@@ -10,6 +10,25 @@ homepage = "https://www.comfy.org/"
 repository = "https://github.com/comfyanonymous/ComfyUI"
 documentation = "https://docs.comfy.org/"
 
+[tool.comfy.host]
+allow_network = false
+writable_paths = ["/dev/shm", "/tmp"]
+
+[tool.comfy.host.whitelist]
+"ComfyUI-Crystools" = "*"
+"ComfyUI-Florence2" = "*"
+"ComfyUI-GGUF" = "*"
+"ComfyUI-KJNodes" = "*"
+"ComfyUI-LTXVideo" = "*"
+"ComfyUI-Manager" = "*"
+"comfyui-depthanythingv2" = "*"
+"comfyui-kjnodes" = "*"
+"comfyui-videohelpersuite" = "*"
+"comfyui_controlnet_aux" = "*"
+"rgthree-comfy" = "*"
+"was-ns" = "*"
+"websocket_image_save.py" = "*"
+
 [tool.ruff]
 lint.select = [
   "N805", # invalid-first-argument-name-for-method

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.4
+comfyui-workflow-templates==0.9.5
 comfyui-embedded-docs==0.4.3
 torch
 torchsde
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.2
+comfy-aimdo>=0.2.4
 requests
 
 #non essential dependencies:

tests-unit/app_test/frontend_manager_test.py

@@ -49,6 +49,12 @@ def mock_provider(mock_releases):
     return provider
 
 
+@pytest.fixture(autouse=True)
+def clear_cache():
+    import utils.install_util
+    utils.install_util.PACKAGE_VERSIONS = {}
+
+
 def test_get_release(mock_provider, mock_releases):
     version = "1.0.0"
     release = mock_provider.get_release(version)

tests/execution/test_jobs.py

@@ -38,13 +38,13 @@ class TestIsPreviewable:
     """Unit tests for is_previewable()"""
 
     def test_previewable_media_types(self):
-        """Images, video, audio, 3d media types should be previewable."""
-        for media_type in ['images', 'video', 'audio', '3d']:
+        """Images, video, audio, 3d, text media types should be previewable."""
+        for media_type in ['images', 'video', 'audio', '3d', 'text']:
             assert is_previewable(media_type, {}) is True
 
     def test_non_previewable_media_types(self):
         """Other media types should not be previewable."""
-        for media_type in ['latents', 'text', 'metadata', 'files']:
+        for media_type in ['latents', 'metadata', 'files']:
             assert is_previewable(media_type, {}) is False
 
     def test_3d_extensions_previewable(self):

utils/install_util.py

@@ -1,5 +1,7 @@
 from pathlib import Path
 import sys
+import logging
+import re
 
 # The path to the requirements.txt file
 requirements_path = Path(__file__).parents[1] / "requirements.txt"
@@ -16,3 +18,34 @@ Please install the updated requirements.txt file by running:
 {sys.executable} {extra}-m pip install -r {requirements_path}
 If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem.
 """.strip()
+
+
+def is_valid_version(version: str) -> bool:
+    """Validate if a string is a valid semantic version (X.Y.Z format)."""
+    pattern = r"^(\d+)\.(\d+)\.(\d+)$"
+    return bool(re.match(pattern, version))
+
+
+PACKAGE_VERSIONS = {}
+def get_required_packages_versions():
+    if len(PACKAGE_VERSIONS) > 0:
+        return PACKAGE_VERSIONS.copy()
+    out = PACKAGE_VERSIONS
+    try:
+        with open(requirements_path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip().replace(">=", "==")
+                s = line.split("==")
+                if len(s) == 2:
+                    version_str = s[-1]
+                    if not is_valid_version(version_str):
+                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
+                        continue
+                    out[s[0]] = version_str
+        return out.copy()
+    except FileNotFoundError:
+        logging.error("requirements.txt not found.")
+        return None
+    except Exception as e:
+        logging.error(f"Error reading requirements.txt: {e}")
+        return None