Merge pull request #21 from xmarre/codex/disable-load-trim-highvram

Disable eager loader trim in sticky/highvram workflows

kj_loader.py

@@ -395,6 +395,21 @@ def _estimate_model_load_bytes(
 
 
 def _estimate_checkpoint_aux_component_bytes(ckpt_path: str, *, kind: str) -> int:
+    """
+    Estimate the byte size of a specific checkpoint auxiliary component (e.g., CLIP or VAE).
+    
+    If `ckpt_path` is a safetensors file this attempts a component-specific estimate via
+    `estimate_checkpoint_component_bytes`. If that returns `None` or the file is not
+    safetensors, the function falls back to the file size on disk.
+    
+    Parameters:
+        ckpt_path (str): Path to the checkpoint file.
+        kind (str): Component kind to estimate (for example `"clip"`, `"vae"`, or other
+            checkpoint component identifiers accepted by `estimate_checkpoint_component_bytes`).
+    
+    Returns:
+        int: Estimated number of bytes required by the requested component.
+    """
     estimated = None
     if _is_safetensors_path(ckpt_path):
         estimated = estimate_checkpoint_component_bytes(ckpt_path, kind)
@@ -403,6 +418,46 @@ def _estimate_checkpoint_aux_component_bytes(ckpt_path: str, *, kind: str) -> in
     return int(estimated)
 
 
+def _env_bool(name: str) -> bool | None:
+    """
+    Parse a boolean-like environment variable value.
+    
+    Parameters:
+        name (str): Environment variable name to read.
+    
+    Returns:
+        bool | None: `True` if the variable value is one of "1", "true", "yes", or "on";
+        `False` if it is one of "0", "false", "no", or "off"; `None` if the variable is unset
+        or contains an unrecognized value.
+    """
+    value = os.environ.get(name, "").strip().lower()
+    if value in {"1", "true", "yes", "on"}:
+        return True
+    if value in {"0", "false", "no", "off"}:
+        return False
+    return None
+
+
+def _should_trim_before_load(*, effective_policy: str) -> bool:
+    """
+    Decides whether to perform adaptive VRAM trimming before loading based on environment overrides, the effective residency policy, and runtime flags.
+    
+    Parameters:
+        effective_policy (str): The resolved residency policy name to evaluate (e.g., "sticky_gpu").
+    
+    Returns:
+        True if trimming should run before load, False otherwise.
+    """
+    env_override = _env_bool("COMFYUI_GPU_RESIDENT_LOAD_TRIM")
+    if env_override is not None:
+        return env_override
+    if effective_policy == "sticky_gpu":
+        return False
+    if getattr(args, "highvram", False) or getattr(args, "gpu_only", False):
+        return False
+    return True
+
+
 def _maybe_trim_before_load(
     *,
     loader_name: str,
@@ -410,7 +465,26 @@ def _maybe_trim_before_load(
     explicit_device: torch.device | None,
     required_bytes: int,
     keep_models: tuple[Any, ...] = (),
+    enabled: bool = True,
 ) -> None:
+    """
+    Attempt to free GPU VRAM proactively to create headroom for a forthcoming load.
+    
+    If trimming is enabled and an explicit CUDA device is provided and `required_bytes` > 0,
+    calls the adaptive VRAM trimmer to free memory while preserving any `keep_models`.
+    Logs an informational message when memory was freed and a warning if the trimmer
+    could not reach the target headroom.
+    
+    Parameters:
+        loader_name (str): Short name used in log messages for the entity requesting the trim.
+        reason (str): Human-readable reason for the trim (included in logs).
+        explicit_device (torch.device | None): The explicit device to trim on; trimming is skipped if `None` or not CUDA.
+        required_bytes (int): Estimated number of bytes needed for the upcoming load; trimming is skipped if <= 0.
+        keep_models (tuple[Any, ...], optional): Objects to preserve from eviction while trimming (defaults to ()).
+        enabled (bool, optional): If `False`, the function is a no-op (defaults to True).
+    """
+    if not enabled:
+        return
     if explicit_device is None or explicit_device.type != "cuda":
         return
     if required_bytes <= 0:
@@ -601,6 +675,26 @@ def _load_resident_diffusion_model(
     policy_override: str | None = None,
     keep_models: tuple[Any, ...] = (),
 ) -> Any:
+    """
+    Load a diffusion model state dict from `source_path`, applying residency-aware VRAM trimming, optional extra UNet state merging, backend flags, and post-load model patches, and bind or reuse the resulting model for future loads.
+    
+    Parameters:
+        loader_name (str): Human-readable loader identifier used for logging.
+        cache_scope (str): Logical cache scope used when constructing the loader key.
+        source_path (str): Filesystem path to the diffusion model checkpoint.
+        note (str): Short note describing the load context (used when binding).
+        weight_dtype (str): Weight dtype selection used to build model options.
+        compute_dtype (str): Compute dtype selection applied to the loaded model.
+        patch_cublaslinear (bool): Whether to enable the cublas linear optimization during load.
+        sage_attention (str): SageAttention mode to apply to the model after loading.
+        enable_fp16_accumulation (bool): If true, enable fp16 accumulation backend flag during load.
+        extra_state_dict (str | None): Optional path to an additional state dict whose matching UNet keys will be merged into the main state dict.
+        policy_override (str | None): Optional residency policy override name (affects trimming decision).
+        keep_models (tuple[Any, ...]): Iterable of already-loaded objects whose residency should be preserved during trimming.
+    
+    Returns:
+        The loaded diffusion model object.
+    """
     model_options = _build_model_options(weight_dtype)
     effective_policy = _effective_policy_name(policy_override)
     loader_key = _make_loader_key(
@@ -632,6 +726,7 @@ def _load_resident_diffusion_model(
             extra_state_dict=extra_state_dict,
         ),
         keep_models=keep_models,
+        enabled=_should_trim_before_load(effective_policy=effective_policy),
     )
 
     with _temporary_backend_flags(
@@ -697,6 +792,20 @@ def _load_checkpoint_clip_only(
     loader_name: str,
     keep_models: tuple[Any, ...] = (),
 ):
+    """
+    Load or reuse the CLIP (text encoder) component from a checkpoint file.
+    
+    This resolves a loader cache key, attempts to reuse a previously loaded CLIP, and if absent loads the checkpoint (safetensors or torch format), optionally applies model-config-based extraction or older-quant conversion, constructs a CLIP object when weights are present, and binds the result for reuse.
+    
+    Parameters:
+        ckpt_path (str): Filesystem path to the checkpoint file.
+        policy_override (str | None): Optional residency policy override used to form the loader key.
+        loader_name (str): Human-readable name used in log messages and trimming decisions.
+        keep_models (tuple[Any, ...]): Sequence of model-like objects to preserve during any pre-load VRAM trimming.
+    
+    Returns:
+        clip (comfy.sd.CLIP | None): A constructed CLIP/text-encoder instance when weights are available, or `None` if no CLIP weights were found.
+    """
     loader_key = _checkpoint_component_loader_key("clip", policy_override)
     reused_clip = REGISTRY.lookup_live_object(kind=KIND_CLIP, source_path=ckpt_path, loader_key=loader_key)
     if reused_clip is not None:
@@ -707,6 +816,7 @@ def _load_checkpoint_clip_only(
     is_safetensors = _is_safetensors_path(ckpt_path)
     header_info = checkpoint_component_info_from_header(ckpt_path) if is_safetensors else None
     model_config = None if header_info is None else header_info.get("model_config")
+    effective_policy = _effective_policy_name(policy_override)
     explicit_device = REGISTRY.explicit_load_device(kind=KIND_CLIP, source_path=ckpt_path)
     _maybe_trim_before_load(
         loader_name=loader_name,
@@ -714,6 +824,7 @@ def _load_checkpoint_clip_only(
         explicit_device=explicit_device,
         required_bytes=_estimate_checkpoint_aux_component_bytes(ckpt_path, kind=KIND_CLIP),
         keep_models=keep_models,
+        enabled=_should_trim_before_load(effective_policy=effective_policy),
     )
     with REGISTRY.load_context(kind=KIND_CLIP, source_path=ckpt_path, explicit_device=explicit_device, cache_key=loader_key):
         if is_safetensors and model_config is None:
@@ -783,6 +894,22 @@ def _load_checkpoint_vae_only(
     loader_name: str,
     keep_models: tuple[Any, ...] = (),
 ):
+    """
+    Load only the VAE component from a checkpoint, reusing a live VAE when available and binding the loaded VAE for reuse.
+    
+    Parameters:
+        ckpt_path (str): Filesystem path to the checkpoint (safetensors or torch file).
+        policy_override (str | None): Optional residency policy override used to resolve trimming/loading behavior.
+        loader_name (str): Human-readable name used in logging messages.
+        keep_models (tuple[Any, ...]): Objects to preserve from eviction when freeing VRAM before loading.
+    
+    Returns:
+        vae (comfy.sd.VAE | None): The loaded VAE object, or `None` if no VAE weights were found in the checkpoint.
+    
+    Side effects:
+        - May trigger adaptive VRAM trimming before loading depending on the effective policy and runtime flags.
+        - Binds the resulting VAE (if any) into the live-object registry for reuse.
+    """
     loader_key = _checkpoint_component_loader_key("vae", policy_override)
     reused_vae = REGISTRY.lookup_live_object(kind=KIND_VAE, source_path=ckpt_path, loader_key=loader_key)
     if reused_vae is not None:
@@ -793,6 +920,7 @@ def _load_checkpoint_vae_only(
     is_safetensors = _is_safetensors_path(ckpt_path)
     header_info = checkpoint_component_info_from_header(ckpt_path) if is_safetensors else None
     model_config = None if header_info is None else header_info.get("model_config")
+    effective_policy = _effective_policy_name(policy_override)
     explicit_device = REGISTRY.explicit_load_device(kind=KIND_VAE, source_path=ckpt_path)
     _maybe_trim_before_load(
         loader_name=loader_name,
@@ -800,6 +928,7 @@ def _load_checkpoint_vae_only(
         explicit_device=explicit_device,
         required_bytes=_estimate_checkpoint_aux_component_bytes(ckpt_path, kind=KIND_VAE),
         keep_models=keep_models,
+        enabled=_should_trim_before_load(effective_policy=effective_policy),
     )
     with REGISTRY.load_context(kind=KIND_VAE, source_path=ckpt_path, explicit_device=explicit_device, cache_key=loader_key):
         if is_safetensors and model_config is None:

patches.py

@@ -2,10 +2,12 @@ from __future__ import annotations
 
 import contextlib
 import functools
+import inspect
 import json
 import logging
 import os
 import struct
+import threading
 from typing import Any, Callable
 
 import torch
@@ -39,6 +41,8 @@ _WARNED_PICKLE_GPU_PATHS: set[str] = set()
 _SAFE_TENSORS_COMPONENT_CACHE_MAX = 32
 _STICKY_PROTECTION_VRAM_FLOOR_RATIO = 0.125
 _STICKY_PROTECTION_VRAM_FLOOR_CEIL_BYTES = 16 * 1024 ** 3
+_TILED_VAE_MEMORY_LOCK_ATTR = "_gpu_resident_loader_tiled_memory_lock"
+_TILED_VAE_LOCK_INIT = threading.Lock()
 _SAFETENSORS_DTYPE_MAP = {
     "BOOL": torch.bool,
     "U8": torch.uint8,
@@ -682,6 +686,56 @@ def _sticky_vae_free_memory(*, device: Any, patcher: Any) -> int:
     return max(0, int(patcher.get_free_memory(device)))
 
 
+def _sticky_model_load_requirement(*, device: Any, models: tuple[Any, ...]) -> int | None:
+    import comfy.model_management as model_management
+
+    loaded_model_cls = getattr(model_management, "LoadedModel", None)
+    is_device_cpu = getattr(model_management, "is_device_cpu", None)
+    if device is None or loaded_model_cls is None:
+        return None
+    if callable(is_device_cpu) and is_device_cpu(device):
+        return 0
+
+    requested_models: set[Any] = set()
+    for model in models:
+        if model is None:
+            continue
+        requested_models.add(model)
+        additional_models = getattr(model, "model_patches_models", None)
+        if callable(additional_models):
+            try:
+                for additional in additional_models():
+                    if additional is not None:
+                        requested_models.add(additional)
+            except Exception as exc:
+                _LOG.debug(
+                    "GPU Resident Loader: failed to enumerate patched models for sticky VAE preflight: %s",
+                    exc,
+                )
+                return None
+
+    total_required = 0
+    for model in requested_models:
+        try:
+            loaded_model = loaded_model_cls(model)
+            loaded_device = getattr(loaded_model, "device", None)
+            if loaded_device != device:
+                continue
+            if callable(is_device_cpu) and is_device_cpu(loaded_device):
+                continue
+            total_required += max(0, int(loaded_model.model_memory_required(loaded_model.device)))
+        except Exception:
+            return None
+    return total_required
+
+
+def _sticky_model_load_target(model_load_required: int | None) -> int | None:
+    if model_load_required is None:
+        return None
+    required = max(0, int(model_load_required))
+    return (required * 11 + 9) // 10
+
+
 def _prepare_sticky_vae_batch(
     *,
     device: Any,
@@ -690,9 +744,12 @@ def _prepare_sticky_vae_batch(
     total_batch_count: int,
 ) -> tuple[int, int, bool]:
     free_memory = _sticky_vae_free_memory(device=device, patcher=patcher)
+    model_load_required = _sticky_model_load_requirement(device=device, models=(patcher,))
+    model_load_target = _sticky_model_load_target(model_load_required)
+    batch_budget = 0 if model_load_target is None else max(0, free_memory - model_load_target)
     batch_number = _sticky_safe_batch_number(
         batch_count=total_batch_count,
-        free_memory=free_memory,
+        free_memory=batch_budget,
         memory_used=total_memory_used,
         device=device,
     )
@@ -701,7 +758,11 @@ def _prepare_sticky_vae_batch(
     if REGISTRY.get_policy() != "sticky_gpu" or device is None:
         return batch_number, batch_memory_used, False
 
-    target_free = _sticky_protection_target(batch_memory_used, device)
+    target_free = (
+        free_memory + 1
+        if model_load_target is None
+        else model_load_target + _sticky_protection_target(batch_memory_used, device)
+    )
     if free_memory < target_free:
         try:
             trim_resident_vram(
@@ -711,27 +772,63 @@ def _prepare_sticky_vae_batch(
                 sticky_floor_priority=0,
                 allow_partial_unload=True,
                 keep_models=(patcher,),
+                include_external=False,
             )
         except Exception as exc:
-            _LOG.debug("GPU Resident Loader: proactive VAE trim failed for %s bytes: %s", batch_memory_used, exc)
+            _LOG.debug(
+                "GPU Resident Loader: proactive VAE trim failed for batch=%s load=%s bytes: %s",
+                batch_memory_used,
+                model_load_required,
+                exc,
+            )
 
         free_memory = _sticky_vae_free_memory(device=device, patcher=patcher)
+        if free_memory < target_free and external_trim_enabled():
+            try:
+                trim_resident_vram(
+                    device=device,
+                    target_free_vram_bytes=target_free,
+                    respect_sticky=True,
+                    sticky_floor_priority=0,
+                    allow_partial_unload=True,
+                    keep_models=(patcher,),
+                    include_external=True,
+                )
+            except Exception as exc:
+                _LOG.debug(
+                    "GPU Resident Loader: second-chance external VAE trim failed for batch=%s load=%s bytes: %s",
+                    batch_memory_used,
+                    model_load_required,
+                    exc,
+                )
+            else:
+                _LOG.info(
+                    "GPU Resident Loader: native sticky VAE trim was insufficient; retried with external candidates enabled"
+                )
+
+            free_memory = _sticky_vae_free_memory(device=device, patcher=patcher)
+        batch_budget = 0 if model_load_target is None else max(0, free_memory - model_load_target)
         batch_number = _sticky_safe_batch_number(
             batch_count=total_batch_count,
-            free_memory=free_memory,
+            free_memory=batch_budget,
             memory_used=total_memory_used,
             device=device,
         )
         batch_memory_used = _scaled_batch_memory(total_memory_used, total_batch_count, batch_number)
-        target_free = _sticky_protection_target(batch_memory_used, device)
+        target_free = (
+            free_memory + 1
+            if model_load_target is None
+            else model_load_target + _sticky_protection_target(batch_memory_used, device)
+        )
 
     should_tile = free_memory < target_free and batch_number <= 1
     if should_tile:
         _LOG.info(
-            "GPU Resident Loader: skipping regular VAE pass and switching directly to tiled mode; free=%s target=%s batch_memory=%s",
+            "GPU Resident Loader: skipping regular VAE pass and switching directly to tiled mode; free=%s target=%s batch_memory=%s model_load=%s",
             free_memory,
             target_free,
             batch_memory_used,
+            model_load_required,
         )
     return batch_number, batch_memory_used, should_tile
 
@@ -806,6 +903,364 @@ def _wrap_vae_encode(func: Callable[..., Any]) -> Callable[..., Any]:
     return wrapper
 
 
+def _default_tiled_vae_axes(
+    *,
+    latent_dim: int,
+    extra_1d_channel: Any,
+    tile_x: int | None,
+    tile_y: int | None,
+    tile_t: int | None,
+    decode: bool,
+) -> tuple[int | None, int | None, int | None]:
+    if latent_dim == 3:
+        default_tile_x = 32 if decode else 512
+        default_tile_y = 32 if decode else 512
+        default_tile_t = 999 if decode else 9999
+    elif latent_dim == 1 or extra_1d_channel is not None:
+        default_tile_x = 256 * 2048
+        default_tile_y = None
+        default_tile_t = None
+    else:
+        default_tile_x = 64 if decode else 512
+        default_tile_y = 64 if decode else 512
+        default_tile_t = None
+
+    resolved_tile_x = default_tile_x if tile_x is None else max(1, int(tile_x))
+    resolved_tile_y = default_tile_y if tile_y is None else max(1, int(tile_y))
+    resolved_tile_t = default_tile_t if tile_t is None else max(1, int(tile_t))
+    return resolved_tile_x, resolved_tile_y, resolved_tile_t
+
+
+def _shape_with_capped_tail(shape: tuple[int, ...], tail_caps: dict[int, int | None]) -> tuple[int, ...]:
+    capped = list(shape)
+    for index, cap in tail_caps.items():
+        if cap is None:
+            continue
+        capped[index] = min(int(capped[index]), max(1, int(cap)))
+    return tuple(capped)
+
+
+def _tiled_vae_memory_shapes(
+    *,
+    shape: tuple[int, ...],
+    latent_dim: int,
+    extra_1d_channel: Any,
+    tile_x: int | None,
+    tile_y: int | None,
+    tile_t: int | None,
+    decode: bool,
+) -> list[tuple[int, ...]]:
+    resolved_tile_x, resolved_tile_y, resolved_tile_t = _default_tiled_vae_axes(
+        latent_dim=latent_dim,
+        extra_1d_channel=extra_1d_channel,
+        tile_x=tile_x,
+        tile_y=tile_y,
+        tile_t=tile_t,
+        decode=decode,
+    )
+
+    if latent_dim == 3:
+        return [
+            _shape_with_capped_tail(
+                shape,
+                {
+                    len(shape) - 3: resolved_tile_t,
+                    len(shape) - 2: resolved_tile_y,
+                    len(shape) - 1: resolved_tile_x,
+                },
+            )
+        ]
+
+    if latent_dim == 1 or extra_1d_channel is not None:
+        return [_shape_with_capped_tail(shape, {len(shape) - 1: resolved_tile_x})]
+
+    if decode:
+        return [
+            _shape_with_capped_tail(
+                shape,
+                {
+                    len(shape) - 2: resolved_tile_y,
+                    len(shape) - 1: resolved_tile_x,
+                },
+            )
+        ]
+
+    return [
+        _shape_with_capped_tail(
+            shape,
+            {
+                len(shape) - 2: resolved_tile_y,
+                len(shape) - 1: resolved_tile_x,
+            },
+        ),
+        _shape_with_capped_tail(
+            shape,
+            {
+                len(shape) - 2: max(1, resolved_tile_y // 2),
+                len(shape) - 1: max(1, resolved_tile_x * 2),
+            },
+        ),
+        _shape_with_capped_tail(
+            shape,
+            {
+                len(shape) - 2: max(1, resolved_tile_y * 2),
+                len(shape) - 1: max(1, resolved_tile_x // 2),
+            },
+        ),
+    ]
+
+
+@contextlib.contextmanager
+def _temporary_tiled_vae_memory_estimate(
+    self,
+    *,
+    decode: bool,
+    tile_x: int | None,
+    tile_y: int | None,
+    tile_t: int | None,
+) -> Any:
+    memory_attr = "memory_used_decode" if decode else "memory_used_encode"
+    original = getattr(self, memory_attr, None)
+    if not callable(original):
+        yield
+        return
+    had_instance_attr = memory_attr in getattr(self, "__dict__", {})
+    lock = getattr(self, _TILED_VAE_MEMORY_LOCK_ATTR, None)
+    if lock is None:
+        with _TILED_VAE_LOCK_INIT:
+            lock = getattr(self, _TILED_VAE_MEMORY_LOCK_ATTR, None)
+            if lock is None:
+                lock = threading.RLock()
+                setattr(self, _TILED_VAE_MEMORY_LOCK_ATTR, lock)
+
+    def estimated(shape, dtype, *args, **kwargs):
+        shapes = _tiled_vae_memory_shapes(
+            shape=tuple(int(dim) for dim in shape),
+            latent_dim=int(getattr(self, "latent_dim", 2)),
+            extra_1d_channel=getattr(self, "extra_1d_channel", None),
+            tile_x=tile_x,
+            tile_y=tile_y,
+            tile_t=tile_t,
+            decode=decode,
+        )
+        return max(int(original(candidate, dtype, *args, **kwargs)) for candidate in shapes)
+
+    with lock:
+        setattr(self, memory_attr, estimated)
+        try:
+            yield
+        finally:
+            if had_instance_attr:
+                setattr(self, memory_attr, original)
+            else:
+                delattr(self, memory_attr)
+
+
+@functools.lru_cache(maxsize=None)
+def _tiled_vae_supported_kwargs(func: Callable[..., Any]) -> frozenset[str]:
+    return frozenset(inspect.signature(func).parameters)
+
+
+def _call_tiled_vae(
+    func: Callable[..., Any],
+    self,
+    data,
+    *,
+    tile_x=None,
+    tile_y=None,
+    overlap=None,
+    tile_t=None,
+    overlap_t=None,
+):
+    kwargs = {}
+    supported_kwargs = _tiled_vae_supported_kwargs(func)
+    if "tile_x" in supported_kwargs:
+        kwargs["tile_x"] = tile_x
+    if "tile_y" in supported_kwargs:
+        kwargs["tile_y"] = tile_y
+    if "overlap" in supported_kwargs:
+        kwargs["overlap"] = overlap
+    if "tile_t" in supported_kwargs:
+        kwargs["tile_t"] = tile_t
+    if "overlap_t" in supported_kwargs:
+        kwargs["overlap_t"] = overlap_t
+    return func(self, data, **kwargs)
+
+
+def _should_prefer_tiled_vae_encode(vae: Any, pixel_samples: Any) -> bool:
+    if REGISTRY.get_policy() != "sticky_gpu":
+        return False
+    if vae is None or pixel_samples is None:
+        return False
+
+    try:
+        vae.throw_exception_if_invalid()
+        prepared = vae.vae_encode_crop_pixels(pixel_samples)
+        prepared = prepared.movedim(-1, 1)
+        if int(getattr(vae, "latent_dim", 2)) == 3 and prepared.ndim < 5:
+            if not getattr(vae, "not_video", False):
+                prepared = prepared.movedim(1, 0).unsqueeze(0)
+            else:
+                prepared = prepared.unsqueeze(2)
+
+        memory_used = vae.memory_used_encode(prepared.shape, vae.vae_dtype)
+        _, _, should_tile = _prepare_sticky_vae_batch(
+            device=getattr(vae, "device", None),
+            patcher=getattr(vae, "patcher", None),
+            total_memory_used=memory_used,
+            total_batch_count=prepared.shape[0],
+        )
+        return bool(should_tile)
+    except Exception as exc:
+        _LOG.debug("GPU Resident Loader: failed to preflight sticky VAE encode preference: %s", exc)
+        return False
+
+
+def _call_bound_tiled_vae(func: Callable[..., Any], pixel_samples: Any, *args: Any, **kwargs: Any) -> Any:
+    supported_kwargs = _tiled_vae_supported_kwargs(getattr(func, "__func__", func))
+    filtered_kwargs = {key: value for key, value in kwargs.items() if key in supported_kwargs}
+    return func(pixel_samples, *args, **filtered_kwargs)
+
+
+@contextlib.contextmanager
+def _temporary_prefer_tiled_vae_encode(vae: Any):
+    original_encode = getattr(vae, "encode", None)
+    encode_tiled = getattr(vae, "encode_tiled", None)
+    if not callable(original_encode) or not callable(encode_tiled):
+        yield
+        return
+
+    had_instance_attr = "encode" in getattr(vae, "__dict__", {})
+    lock = getattr(vae, _TILED_VAE_MEMORY_LOCK_ATTR, None)
+    if lock is None:
+        with _TILED_VAE_LOCK_INIT:
+            lock = getattr(vae, _TILED_VAE_MEMORY_LOCK_ATTR, None)
+            if lock is None:
+                lock = threading.RLock()
+                setattr(vae, _TILED_VAE_MEMORY_LOCK_ATTR, lock)
+
+    def prefer_encode(pixel_samples, *args, **kwargs):
+        if _should_prefer_tiled_vae_encode(vae, pixel_samples):
+            return _call_bound_tiled_vae(encode_tiled, pixel_samples, *args, **kwargs)
+        return original_encode(pixel_samples, *args, **kwargs)
+
+    with lock:
+        setattr(vae, "encode", prefer_encode)
+        try:
+            yield
+        finally:
+            if had_instance_attr:
+                setattr(vae, "encode", original_encode)
+            else:
+                delattr(vae, "encode")
+
+
+def _wrap_vae_encode_for_inpaint_node(func: Callable[..., Any]) -> Callable[..., Any]:
+    supported_kwargs = frozenset(inspect.signature(func).parameters)
+
+    @functools.wraps(func)
+    def wrapper(self, *args, **kwargs):
+        # Filter kwargs to only include those supported by the wrapped function
+        filtered_kwargs = {key: value for key, value in kwargs.items() if key in supported_kwargs}
+
+        # Supply default grow_mask_by if not present and supported
+        if "grow_mask_by" in supported_kwargs and "grow_mask_by" not in filtered_kwargs:
+            filtered_kwargs["grow_mask_by"] = 6
+
+        if REGISTRY.get_policy() != "sticky_gpu":
+            return func(self, *args, **filtered_kwargs)
+
+        # Extract vae from args for the context manager
+        vae = args[0] if args else kwargs.get("vae")
+        with _temporary_prefer_tiled_vae_encode(vae):
+            return func(self, *args, **filtered_kwargs)
+
+    return wrapper
+
+
+def _wrap_inpaint_model_conditioning_node(func: Callable[..., Any]) -> Callable[..., Any]:
+    @functools.wraps(func)
+    def wrapper(self, positive, negative, pixels, vae, mask, noise_mask=True):
+        if REGISTRY.get_policy() != "sticky_gpu":
+            return func(self, positive, negative, pixels, vae, mask, noise_mask=noise_mask)
+        with _temporary_prefer_tiled_vae_encode(vae):
+            return func(self, positive, negative, pixels, vae, mask, noise_mask=noise_mask)
+
+    return wrapper
+
+
+def _wrap_vae_encode_tiled(func: Callable[..., Any]) -> Callable[..., Any]:
+    @functools.wraps(func)
+    def wrapper(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        if REGISTRY.get_policy() != "sticky_gpu":
+            return _call_tiled_vae(
+                func,
+                self,
+                pixel_samples,
+                tile_x=tile_x,
+                tile_y=tile_y,
+                overlap=overlap,
+                tile_t=tile_t,
+                overlap_t=overlap_t,
+            )
+
+        with _temporary_tiled_vae_memory_estimate(
+            self,
+            decode=False,
+            tile_x=tile_x,
+            tile_y=tile_y,
+            tile_t=tile_t,
+        ):
+            return _call_tiled_vae(
+                func,
+                self,
+                pixel_samples,
+                tile_x=tile_x,
+                tile_y=tile_y,
+                overlap=overlap,
+                tile_t=tile_t,
+                overlap_t=overlap_t,
+            )
+
+    return wrapper
+
+
+def _wrap_vae_decode_tiled(func: Callable[..., Any]) -> Callable[..., Any]:
+    @functools.wraps(func)
+    def wrapper(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        if REGISTRY.get_policy() != "sticky_gpu":
+            return _call_tiled_vae(
+                func,
+                self,
+                samples,
+                tile_x=tile_x,
+                tile_y=tile_y,
+                overlap=overlap,
+                tile_t=tile_t,
+                overlap_t=overlap_t,
+            )
+
+        with _temporary_tiled_vae_memory_estimate(
+            self,
+            decode=True,
+            tile_x=tile_x,
+            tile_y=tile_y,
+            tile_t=tile_t,
+        ):
+            return _call_tiled_vae(
+                func,
+                self,
+                samples,
+                tile_x=tile_x,
+                tile_y=tile_y,
+                overlap=overlap,
+                tile_t=tile_t,
+                overlap_t=overlap_t,
+            )
+
+    return wrapper
+
+
 def _wrap_vae_decode(func: Callable[..., Any]) -> Callable[..., Any]:
     @functools.wraps(func)
     def wrapper(self, samples_in, vae_options={}):
@@ -1072,7 +1527,7 @@ def _wrap_free_memory(func: Callable[..., Any]) -> Callable[..., Any]:
         EXTERNAL_REGISTRY.refresh_runtime_state()
 
         for_dynamic = bool(kwargs.get("for_dynamic", args[0] if args else False))
-        if device is not None and not external_trim_enabled() and not for_dynamic:
+        if device is not None and external_trim_enabled() and not for_dynamic:
             fallback_target = memory_required
             if REGISTRY.get_policy() == "sticky_gpu":
                 fallback_target = max(fallback_target, _sticky_protection_target(memory_required, device))
@@ -1181,6 +1636,7 @@ def install_patches() -> None:
     import comfy.model_patcher as model_patcher
     import comfy.sd as comfy_sd
     import comfy.utils as comfy_utils
+    import nodes as comfy_nodes
 
     original_load_torch_file = _remember_original("utils.load_torch_file", comfy_utils.load_torch_file)
     if comfy_utils.load_torch_file is original_load_torch_file:
@@ -1228,6 +1684,30 @@ def install_patches() -> None:
     if comfy_sd.VAE.decode is original_vae_decode:
         comfy_sd.VAE.decode = _wrap_vae_decode(original_vae_decode)
 
+    original_vae_encode_tiled = _remember_original("sd.VAE.encode_tiled", comfy_sd.VAE.encode_tiled)
+    if comfy_sd.VAE.encode_tiled is original_vae_encode_tiled:
+        comfy_sd.VAE.encode_tiled = _wrap_vae_encode_tiled(original_vae_encode_tiled)
+
+    original_vae_decode_tiled = _remember_original("sd.VAE.decode_tiled", comfy_sd.VAE.decode_tiled)
+    if comfy_sd.VAE.decode_tiled is original_vae_decode_tiled:
+        comfy_sd.VAE.decode_tiled = _wrap_vae_decode_tiled(original_vae_decode_tiled)
+
+    if hasattr(comfy_nodes, "VAEEncodeForInpaint") and hasattr(comfy_nodes.VAEEncodeForInpaint, "encode"):
+        original_vae_encode_for_inpaint = _remember_original(
+            "nodes.VAEEncodeForInpaint.encode",
+            comfy_nodes.VAEEncodeForInpaint.encode,
+        )
+        if comfy_nodes.VAEEncodeForInpaint.encode is original_vae_encode_for_inpaint:
+            comfy_nodes.VAEEncodeForInpaint.encode = _wrap_vae_encode_for_inpaint_node(original_vae_encode_for_inpaint)
+
+    if hasattr(comfy_nodes, "InpaintModelConditioning") and hasattr(comfy_nodes.InpaintModelConditioning, "encode"):
+        original_inpaint_model_conditioning = _remember_original(
+            "nodes.InpaintModelConditioning.encode",
+            comfy_nodes.InpaintModelConditioning.encode,
+        )
+        if comfy_nodes.InpaintModelConditioning.encode is original_inpaint_model_conditioning:
+            comfy_nodes.InpaintModelConditioning.encode = _wrap_inpaint_model_conditioning_node(original_inpaint_model_conditioning)
+
     original_clip_vision_load = _remember_original("clip_vision.load", clip_vision.load)
     if clip_vision.load is original_clip_vision_load:
         clip_vision.load = _wrap_with_load_context(