feat(cache-provider): add on_set_prompt lifecycle hook for providers

Adds a new on_set_prompt() lifecycle hook on CacheProvider that fires
after the cache key set is prepared for a new prompt. Dispatched via
asyncio.create_task with errors swallowed (same fail-safe pattern as
on_store / on_lookup).

Why: BasicCache's lifecycle notifications to external providers were
incomplete. set_prompt is a key per-prompt event that providers need
visibility into — for example, to reset per-prompt timing/state used
for cost-aware caching policies (a provider can set t=0 here, then
measure elapsed at each on_store to estimate compute saved by a hit).

Backward-compatible: default implementation is a no-op, existing
providers compile and run unchanged. Providers that need the per-prompt
boundary override on_set_prompt().

CODEOWNERS

@@ -1,2 +1,5 @@
-# Admins
 * @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 @kijai
+
+/CODEOWNERS @comfyanonymous
+/.ci/ @comfyanonymous
+/.github/ @comfyanonymous

comfy/cli_args.py

@@ -110,13 +110,11 @@ parser.add_argument("--preview-method", type=LatentPreviewMethod, default=Latent
 
 parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
 
-CACHE_RAM_AUTO_GB = -1.0
-
 cache_group = parser.add_mutually_exclusive_group()
+cache_group.add_argument("--cache-ram", nargs='*', type=float, default=[], metavar="GB", help="Use RAM pressure caching with the specified headroom thresholds. This is the default caching mode. The first value sets the active-cache threshold; the optional second value sets the inactive-cache/pin threshold. Defaults when no values are provided: active 25%% of system RAM (min 4GB, max 32GB), inactive 75%% of system RAM (min 12GB, max 96GB).")
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
 cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-cache_group.add_argument("--cache-ram", nargs='?', const=CACHE_RAM_AUTO_GB, type=float, default=0, help="Use RAM pressure caching with the specified headroom threshold. If available RAM drops below the threshold the cache removes large items to free RAM. Default (when no value is provided): 25%% of system RAM (min 4GB, max 32GB).")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
@@ -245,6 +243,9 @@ if comfy.options.args_parsing:
 else:
     args = parser.parse_args([])
 
+if args.cache_ram is not None and len(args.cache_ram) > 2:
+    parser.error("--cache-ram accepts at most two values: active GB and inactive GB")
+
 if args.windows_standalone_build:
     args.auto_launch = True
 

comfy/lora.py

@@ -484,16 +484,23 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori
 
     return weight
 
-def prefetch_prepared_value(value, allocate_buffer, stream):
+def prefetch_prepared_value(value, counter, destination, stream, copy):
     if isinstance(value, torch.Tensor):
-        dest = allocate_buffer(comfy.memory_management.vram_aligned_size(value))
-        comfy.model_management.cast_to_gathered([value], dest, non_blocking=True, stream=stream)
+        size = comfy.memory_management.vram_aligned_size(value)
+        offset = counter[0]
+        counter[0] += size
+        if destination is None:
+            return value
+
+        dest = destination[offset:offset + size]
+        if copy:
+            comfy.model_management.cast_to_gathered([value], dest, non_blocking=True, stream=stream)
         return comfy.memory_management.interpret_gathered_like([value], dest)[0]
     elif isinstance(value, weight_adapter.WeightAdapterBase):
-        return type(value)(value.loaded_keys, prefetch_prepared_value(value.weights, allocate_buffer, stream))
+        return type(value)(value.loaded_keys, prefetch_prepared_value(value.weights, counter, destination, stream, copy))
     elif isinstance(value, tuple):
-        return tuple(prefetch_prepared_value(item, allocate_buffer, stream) for item in value)
+        return tuple(prefetch_prepared_value(item, counter, destination, stream, copy) for item in value)
     elif isinstance(value, list):
-        return [prefetch_prepared_value(item, allocate_buffer, stream) for item in value]
+        return [prefetch_prepared_value(item, counter, destination, stream, copy) for item in value]
 
     return value

comfy/memory_management.py

@@ -15,7 +15,7 @@ class TensorFileSlice(NamedTuple):
     size: int
 
 
-def read_tensor_file_slice_into(tensor, destination):
+def read_tensor_file_slice_into(tensor, destination, stream=None, destination2=None):
 
     if isinstance(tensor, QuantizedTensor):
         if not isinstance(destination, QuantizedTensor):
@@ -23,12 +23,17 @@ def read_tensor_file_slice_into(tensor, destination):
         if tensor._layout_cls != destination._layout_cls:
             return False
 
-        if not read_tensor_file_slice_into(tensor._qdata, destination._qdata):
+        if not read_tensor_file_slice_into(tensor._qdata, destination._qdata, stream=stream,
+                                           destination2=(destination2._qdata if destination2 is not None else None)):
             return False
 
         dst_orig_dtype = destination._params.orig_dtype
         destination._params.copy_from(tensor._params, non_blocking=False)
         destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype)
+        if destination2 is not None:
+            dst_orig_dtype = destination2._params.orig_dtype
+            destination2._params.copy_from(destination._params, non_blocking=True)
+            destination2._params = dataclasses.replace(destination2._params, orig_dtype=dst_orig_dtype)
         return True
 
     info = getattr(tensor.untyped_storage(), "_comfy_tensor_file_slice", None)
@@ -48,6 +53,17 @@ def read_tensor_file_slice_into(tensor, destination):
     if info.size == 0:
         return True
 
+    hostbuf = getattr(destination.untyped_storage(), "_comfy_hostbuf", None)
+    if hostbuf is not None:
+        stream_ptr = getattr(stream, "cuda_stream", 0) if stream is not None else 0
+        device_ptr = destination2.data_ptr() if destination2 is not None else 0
+        hostbuf.read_file_slice(file_obj, info.offset, info.size,
+                                offset=destination.data_ptr() - hostbuf.get_raw_address(),
+                                stream=stream_ptr,
+                                device_ptr=device_ptr,
+                                device=None if destination2 is None else destination2.device.index)
+        return True
+
     buf_type = ctypes.c_ubyte * info.size
     view = memoryview(buf_type.from_address(destination.data_ptr()))
 
@@ -151,7 +167,7 @@ def set_ram_cache_release_state(callback, headroom):
     extra_ram_release_callback = callback
     RAM_CACHE_HEADROOM = max(0, int(headroom))
 
-def extra_ram_release(target):
+def extra_ram_release(target, free_active=False):
     if extra_ram_release_callback is None:
         return 0
-    return extra_ram_release_callback(target)
+    return extra_ram_release_callback(target, free_active=free_active)

comfy/model_management.py

@@ -31,6 +31,7 @@ from contextlib import nullcontext
 import comfy.memory_management
 import comfy.utils
 import comfy.quant_ops
+import comfy_aimdo.host_buffer
 import comfy_aimdo.vram_buffer
 
 class VRAMState(Enum):
@@ -495,6 +496,14 @@ except:
 
 current_loaded_models = []
 
+DIRTY_MMAPS = set()
+
+PIN_PRESSURE_HYSTERESIS = 256 * 1024 * 1024
+
+#Freeing registerables on pressure does imply a GPU sync, so go big on
+#the hysteresis so each expensive sync gives us back a good chunk.
+REGISTERABLE_PIN_HYSTERESIS = 2048 * 1024 * 1024
+
 def module_size(module):
     module_mem = 0
     sd = module.state_dict()
@@ -503,27 +512,46 @@ def module_size(module):
         module_mem += t.nbytes
     return module_mem
 
-def module_mmap_residency(module, free=False):
-    mmap_touched_mem = 0
-    module_mem = 0
-    bounced_mmaps = set()
-    sd = module.state_dict()
-    for k in sd:
-        t = sd[k]
-        module_mem += t.nbytes
-        storage = t._qdata.untyped_storage() if isinstance(t, comfy.quant_ops.QuantizedTensor) else t.untyped_storage()
-        if not getattr(storage, "_comfy_tensor_mmap_touched", False):
-            continue
-        mmap_touched_mem += t.nbytes
-        if not free:
-            continue
-        storage._comfy_tensor_mmap_touched = False
-        mmap_obj = storage._comfy_tensor_mmap_refs[0]
-        if mmap_obj in bounced_mmaps:
-            continue
-        mmap_obj.bounce()
-        bounced_mmaps.add(mmap_obj)
-    return mmap_touched_mem, module_mem
+def mark_mmap_dirty(storage):
+    mmap_refs = getattr(storage, "_comfy_tensor_mmap_refs", None)
+    if mmap_refs is not None:
+        DIRTY_MMAPS.add(mmap_refs[0])
+
+def free_pins(size, evict_active=False):
+    freed_total = 0
+    for loaded_model in reversed(current_loaded_models):
+        if size <= 0:
+            return freed_total
+        model = loaded_model.model
+        if model is not None and model.is_dynamic() and (evict_active or not model.model.dynamic_pins[model.load_device]["active"]):
+            freed = model.partially_unload_ram(size)
+            freed_total += freed
+            size -= freed
+    return freed_total
+
+def ensure_pin_budget(size, evict_active=False):
+    shortfall = size + comfy.memory_management.RAM_CACHE_HEADROOM / 2 - psutil.virtual_memory().available
+    if shortfall <= 0:
+        return True
+
+    to_free = shortfall + PIN_PRESSURE_HYSTERESIS
+    return free_pins(to_free, evict_active=evict_active) >= shortfall
+
+def ensure_pin_registerable(size, evict_active=False):
+    shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY
+    if MAX_PINNED_MEMORY <= 0:
+        return False
+    if shortfall <= 0:
+        return True
+
+    shortfall += REGISTERABLE_PIN_HYSTERESIS
+    for loaded_model in reversed(current_loaded_models):
+        model = loaded_model.model
+        if model is not None and model.is_dynamic() and (evict_active or not model.model.dynamic_pins[model.load_device]["active"]):
+            shortfall -= model.unregister_inactive_pins(shortfall)
+            if shortfall <= 0:
+                return True
+    return shortfall <= REGISTERABLE_PIN_HYSTERESIS
 
 class LoadedModel:
     def __init__(self, model):
@@ -553,9 +581,6 @@ class LoadedModel:
     def model_memory(self):
         return self.model.model_size()
 
-    def model_mmap_residency(self, free=False):
-        return self.model.model_mmap_residency(free=free)
-
     def model_loaded_memory(self):
         return self.model.loaded_size()
 
@@ -635,15 +660,9 @@ WINDOWS = any(platform.win32_ver())
 
 EXTRA_RESERVED_VRAM = 400 * 1024 * 1024
 if WINDOWS:
-    import comfy.windows
     EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue
     if total_vram > (15 * 1024):  # more extra reserved vram on 16GB+ cards
         EXTRA_RESERVED_VRAM += 100 * 1024 * 1024
-    def get_free_ram():
-        return comfy.windows.get_free_ram()
-else:
-    def get_free_ram():
-        return psutil.virtual_memory().available
 
 if args.reserve_vram is not None:
     EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024
@@ -657,7 +676,6 @@ def minimum_inference_memory():
 
 def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):
     cleanup_models_gc()
-    comfy.memory_management.extra_ram_release(max(pins_required, ram_required))
     unloaded_model = []
     can_unload = []
     unloaded_models = []
@@ -673,11 +691,9 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
     for x in can_unload_sorted:
         i = x[-1]
         memory_to_free = 1e32
-        pins_to_free = 1e32
-        if not DISABLE_SMART_MEMORY or device is None:
+        if current_loaded_models[i].model.is_dynamic() and (not DISABLE_SMART_MEMORY or device is None):
             memory_to_free = 0 if device is None else memory_required - get_free_memory(device)
-            pins_to_free = pins_required - get_free_ram()
-            if current_loaded_models[i].model.is_dynamic() and for_dynamic:
+            if 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()
@@ -685,18 +701,6 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
         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)
-        if pins_to_free > 0:
-            logging.debug(f"PIN Unloading {current_loaded_models[i].model.model.__class__.__name__}")
-            current_loaded_models[i].model.partially_unload_ram(pins_to_free)
-
-    for x in can_unload_sorted:
-        i = x[-1]
-        ram_to_free = ram_required - psutil.virtual_memory().available
-        if ram_to_free <= 0 and i not in unloaded_model:
-            continue
-        resident_memory, _ = current_loaded_models[i].model_mmap_residency(free=True)
-        if resident_memory > 0:
-            logging.debug(f"RAM Unloading {current_loaded_models[i].model.model.__class__.__name__}")
 
     for i in sorted(unloaded_model, reverse=True):
         unloaded_models.append(current_loaded_models.pop(i))
@@ -762,29 +766,16 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             model_to_unload.model.detach(unpatch_all=False)
             model_to_unload.model_finalizer.detach()
 
-
     total_memory_required = {}
-    total_pins_required = {}
-    total_ram_required = {}
     for loaded_model in models_to_load:
         device = loaded_model.device
         total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device)
-        resident_memory, model_memory = loaded_model.model_mmap_residency()
-        pinned_memory = loaded_model.model.pinned_memory_size()
-        #FIXME: This can over-free the pins as it budgets to pin the entire model. We should
-        #make this JIT to keep as much pinned as possible.
-        pins_required = model_memory - pinned_memory
-        ram_required = model_memory - resident_memory
-        total_pins_required[device] = total_pins_required.get(device, 0) + pins_required
-        total_ram_required[device] = total_ram_required.get(device, 0) + ram_required
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
             free_memory(total_memory_required[device] * 1.1 + extra_mem,
                         device,
-                        for_dynamic=free_for_dynamic,
-                        pins_required=total_pins_required[device],
-                        ram_required=total_ram_required[device])
+                        for_dynamic=free_for_dynamic)
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
@@ -1180,6 +1171,7 @@ STREAM_CAST_BUFFERS = {}
 LARGEST_CASTED_WEIGHT = (None, 0)
 STREAM_AIMDO_CAST_BUFFERS = {}
 LARGEST_AIMDO_CASTED_WEIGHT = (None, 0)
+STREAM_PIN_BUFFERS = {}
 
 DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE = 16 * 1024 ** 3
 
@@ -1220,21 +1212,66 @@ def get_aimdo_cast_buffer(offload_stream, device):
     if cast_buffer is None:
         cast_buffer = comfy_aimdo.vram_buffer.VRAMBuffer(DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE, device.index)
         STREAM_AIMDO_CAST_BUFFERS[offload_stream] = cast_buffer
-
     return cast_buffer
+
+def get_pin_buffer(offload_stream):
+    pin_buffer = STREAM_PIN_BUFFERS.get(offload_stream, None)
+    if pin_buffer is None:
+        pin_buffer = comfy_aimdo.host_buffer.HostBuffer(0, 0, pinned_hostbuf_size(8 * 1024**3))
+        STREAM_PIN_BUFFERS[offload_stream] = pin_buffer
+    elif offload_stream is not None:
+        event = getattr(pin_buffer, "_comfy_event", None)
+        if event is not None:
+            event.synchronize()
+            delattr(pin_buffer, "_comfy_event")
+    return pin_buffer
+
+def resize_pin_buffer(pin_buffer, size):
+    global TOTAL_PINNED_MEMORY
+    old_size = pin_buffer.size
+    if size <= old_size:
+        return True
+    growth = size - old_size
+    comfy.memory_management.extra_ram_release(comfy.memory_management.RAM_CACHE_HEADROOM)
+    ensure_pin_budget(growth, evict_active=True)
+    ensure_pin_registerable(growth, evict_active=True)
+    try:
+        pin_buffer.extend(size=size, reallocate=True)
+    except RuntimeError:
+        return False
+    TOTAL_PINNED_MEMORY += pin_buffer.size - old_size
+    return True
+
 def reset_cast_buffers():
+    global TOTAL_PINNED_MEMORY
     global LARGEST_CASTED_WEIGHT
     global LARGEST_AIMDO_CASTED_WEIGHT
 
     LARGEST_CASTED_WEIGHT = (None, 0)
     LARGEST_AIMDO_CASTED_WEIGHT = (None, 0)
-    for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS):
+    for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS) | set(STREAM_PIN_BUFFERS):
         if offload_stream is not None:
             offload_stream.synchronize()
     synchronize()
 
+    for mmap_obj in DIRTY_MMAPS:
+        mmap_obj.bounce()
+    DIRTY_MMAPS.clear()
+
+    for pin_buffer in STREAM_PIN_BUFFERS.values():
+        TOTAL_PINNED_MEMORY -= pin_buffer.size
+    TOTAL_PINNED_MEMORY = max(0, TOTAL_PINNED_MEMORY)
+
+    for loaded_model in current_loaded_models:
+        model = loaded_model.model
+        if model is not None and model.is_dynamic():
+            model.model.dynamic_pins[model.load_device]["active"] = False
+            model.partially_unload_ram(1e30, subsets=[ "patches" ])
+            model.model.dynamic_pins[model.load_device]["patches"] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, pinned_hostbuf_size(model.model_size())), [], [-1], [0])
+
     STREAM_CAST_BUFFERS.clear()
     STREAM_AIMDO_CAST_BUFFERS.clear()
+    STREAM_PIN_BUFFERS.clear()
     soft_empty_cache()
 
 def get_offload_stream(device):
@@ -1280,7 +1317,7 @@ def sync_stream(device, stream):
     current_stream(device).wait_stream(stream)
 
 
-def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
+def cast_to_gathered(tensors, r, non_blocking=False, stream=None, r2=None):
     wf_context = nullcontext()
     if stream is not None:
        wf_context = stream
@@ -1288,17 +1325,20 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
            wf_context = wf_context.as_context(stream)
 
     dest_views = comfy.memory_management.interpret_gathered_like(tensors, r)
+    dest2_views = comfy.memory_management.interpret_gathered_like(tensors, r2) if r2 is not None else None
     with wf_context:
         for tensor in tensors:
             dest_view = dest_views.pop(0)
+            dest2_view = dest2_views.pop(0) if dest2_views is not None else None
             if tensor is None:
                 continue
-            if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view):
+            if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view, stream=stream, destination2=dest2_view):
                 continue
             storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage()
-            if hasattr(storage, "_comfy_tensor_mmap_touched"):
-                storage._comfy_tensor_mmap_touched = True
+            mark_mmap_dirty(storage)
             dest_view.copy_(tensor, non_blocking=non_blocking)
+            if dest2_view is not None:
+                dest2_view.copy_(dest_view, non_blocking=non_blocking)
 
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=None):
@@ -1339,14 +1379,18 @@ TOTAL_PINNED_MEMORY = 0
 MAX_PINNED_MEMORY = -1
 if not args.disable_pinned_memory:
     if is_nvidia() or is_amd():
+        ram = get_total_memory(torch.device("cpu"))
         if WINDOWS:
-            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.40  # Windows limit is apparently 50%
+            MAX_PINNED_MEMORY = ram * 0.40  # Windows limit is apparently 50%
         else:
-            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.90
+            MAX_PINNED_MEMORY = ram * 0.90
         logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
 
 PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"])
 
+def pinned_hostbuf_size(size):
+    return max(0, int(min(size, MAX_PINNED_MEMORY) * 2))
+
 def discard_cuda_async_error():
     try:
         a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
@@ -1378,8 +1422,8 @@ def pin_memory(tensor):
         return False
 
     size = tensor.nbytes
-    if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:
-        return False
+    comfy.memory_management.extra_ram_release(comfy.memory_management.RAM_CACHE_HEADROOM)
+    ensure_pin_registerable(size)
 
     ptr = tensor.data_ptr()
     if ptr == 0:
@@ -1416,7 +1460,8 @@ def unpin_memory(tensor):
         return False
 
     if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
-        TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
+        size = PINNED_MEMORY.pop(ptr)
+        TOTAL_PINNED_MEMORY -= size
         return True
     else:
         logging.warning("Unpin error.")

comfy/model_patcher.py

@@ -35,6 +35,7 @@ import comfy.model_management
 import comfy.ops
 import comfy.patcher_extension
 import comfy.utils
+import comfy_aimdo.host_buffer
 from comfy.comfy_types import UnetWrapperFunction
 from comfy.quant_ops import QuantizedTensor
 from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
@@ -117,6 +118,8 @@ def string_to_seed(data):
     return comfy.utils.string_to_seed(data)
 
 class LowVramPatch:
+    is_lowvram_patch = True
+
     def __init__(self, key, patches, convert_func=None, set_func=None):
         self.key = key
         self.patches = patches
@@ -124,11 +127,21 @@ class LowVramPatch:
         self.set_func = set_func
         self.prepared_patches = None
 
-    def prepare(self, allocate_buffer, stream):
-        self.prepared_patches = [
-            (patch[0], comfy.lora.prefetch_prepared_value(patch[1], allocate_buffer, stream), patch[2], patch[3], patch[4])
+    def memory_required(self):
+        counter = [0]
+        for patch in self.patches[self.key]:
+            comfy.lora.prefetch_prepared_value(patch[1], counter, None, None, False)
+        return counter[0]
+
+    def prepare(self, destination, stream, copy=True, commit=True):
+        counter = [0]
+        prepared_patches = [
+            (patch[0], comfy.lora.prefetch_prepared_value(patch[1], counter, destination, stream, copy), patch[2], patch[3], patch[4])
             for patch in self.patches[self.key]
         ]
+        if commit:
+            self.prepared_patches = prepared_patches
+        return prepared_patches
 
     def clear_prepared(self):
         self.prepared_patches = None
@@ -341,9 +354,6 @@ class ModelPatcher:
         self.size = comfy.model_management.module_size(self.model)
         return self.size
 
-    def model_mmap_residency(self, free=False):
-        return comfy.model_management.module_mmap_residency(self.model, free=free)
-
     def loaded_size(self):
         return self.model.model_loaded_weight_memory
 
@@ -1118,8 +1128,12 @@ class ModelPatcher:
         # Pinned memory pressure tracking is only implemented for DynamicVram loading
         return 0
 
+    def loaded_ram_size(self):
+        # Loaded RAM pressure tracking is only implemented for DynamicVram loading
+        return 0
+
     def partially_unload_ram(self, ram_to_unload):
-        pass
+        return 0
 
     def detach(self, unpatch_all=True):
         self.eject_model()
@@ -1550,6 +1564,16 @@ class ModelPatcherDynamic(ModelPatcher):
         super().__init__(model, load_device, offload_device, size, weight_inplace_update)
         if not hasattr(self.model, "dynamic_vbars"):
             self.model.dynamic_vbars = {}
+        if not hasattr(self.model, "dynamic_pins"):
+            self.model.dynamic_pins = {}
+        if self.load_device not in self.model.dynamic_pins:
+            self.model.dynamic_pins[self.load_device] = {
+                "weights": (comfy_aimdo.host_buffer.HostBuffer(0, 0, 0), [], [-1], [0]),
+                "patches": (comfy_aimdo.host_buffer.HostBuffer(0, 0, 0), [], [-1], [0]),
+                "hostbufs_initialized": False,
+                "failed": False,
+                "active": False,
+            }
         self.non_dynamic_delegate_model = None
         assert load_device is not None
 
@@ -1611,6 +1635,14 @@ class ModelPatcherDynamic(ModelPatcher):
             self.unpatch_hooks()
 
             vbar = self._vbar_get(create=True)
+            pin_state = self.model.dynamic_pins[self.load_device]
+            if not pin_state["hostbufs_initialized"]:
+                hostbuf_size = comfy.model_management.pinned_hostbuf_size(self.model_size())
+                pin_state["weights"] = (comfy_aimdo.host_buffer.HostBuffer(0, 64 * 1024 * 1024, hostbuf_size), [], [-1], [0])
+                pin_state["patches"] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, hostbuf_size), [], [-1], [0])
+                pin_state["hostbufs_initialized"] = True
+            pin_state["failed"] = False
+            pin_state["active"] = True
             if vbar is not None:
                 vbar.prioritize()
 
@@ -1636,7 +1668,9 @@ class ModelPatcherDynamic(ModelPatcher):
                     if key in self.patches:
                         if comfy.lora.calculate_shape(self.patches[key], weight, key) != weight.shape:
                             return (True, 0)
-                        setattr(m, param_key + "_lowvram_function", LowVramPatch(key, self.patches))
+                        lowvram_patch = LowVramPatch(key, self.patches)
+                        lowvram_patch._pin_state = pin_state
+                        setattr(m, param_key + "_lowvram_function", lowvram_patch)
                         num_patches += 1
                     else:
                         setattr(m, param_key + "_lowvram_function", None)
@@ -1653,6 +1687,9 @@ class ModelPatcherDynamic(ModelPatcher):
 
                 def force_load_param(self, param_key, device_to):
                     key = key_param_name_to_key(n, param_key)
+                    weight, _, _ = get_key_weight(self.model, key)
+                    if weight is None:
+                        return
                     if key in self.backup:
                         comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
                     self.patch_weight_to_device(key, device_to=device_to, force_cast=True)
@@ -1662,17 +1699,23 @@ class ModelPatcherDynamic(ModelPatcher):
 
                 if hasattr(m, "comfy_cast_weights"):
                     m.comfy_cast_weights = True
-                    m.pin_failed = False
                     m.seed_key = n
+                    m._pin_state = pin_state
                     set_dirty(m, dirty)
 
-                    force_load, v_weight_size = setup_param(self, m, n, "weight")
-                    force_load_bias, v_weight_bias = setup_param(self, m, n, "bias")
-                    force_load = force_load or force_load_bias
-                    v_weight_size += v_weight_bias
+                    #Models that mix tiny and giant weights can causing lopsided stream buffer
+                    #rotations and stall. force the tinys over.
+                    if module_mem > 16 * 1024:
+                        force_load, v_weight_size = setup_param(self, m, n, "weight")
+                        force_load_bias, v_weight_bias = setup_param(self, m, n, "bias")
+                        force_load = force_load or force_load_bias
+                        v_weight_size += v_weight_bias
+                        if force_load:
+                            logging.info(f"Module {n} has resizing Lora - force loading")
+                    else:
+                        force_load=True
 
                     if force_load:
-                        logging.info(f"Module {n} has resizing Lora - force loading")
                         force_load_param(self, "weight", device_to)
                         force_load_param(self, "bias", device_to)
                     else:
@@ -1740,23 +1783,58 @@ class ModelPatcherDynamic(ModelPatcher):
 
         return freed
 
-    def pinned_memory_size(self):
-        total = 0
-        loading = self._load_list(for_dynamic=True)
-        for x in loading:
-            _, _, _, _, m, _ = x
-            pin = comfy.pinned_memory.get_pin(m)
-            if pin is not None:
-                total += pin.numel() * pin.element_size()
-        return total
+    def loaded_ram_size(self):
+        return (self.model.dynamic_pins[self.load_device]["weights"][0].size +
+                self.model.dynamic_pins[self.load_device]["patches"][0].size)
 
-    def partially_unload_ram(self, ram_to_unload):
-        loading = self._load_list(for_dynamic=True, default_device=self.offload_device)
-        for x in loading:
-            *_, m, _ = x
-            ram_to_unload -= comfy.pinned_memory.unpin_memory(m)
-            if ram_to_unload <= 0:
-                return
+    def pinned_memory_size(self):
+        return (self.model.dynamic_pins[self.load_device]["weights"][3][0] +
+                self.model.dynamic_pins[self.load_device]["patches"][3][0])
+
+    def unregister_inactive_pins(self, ram_to_unload, subsets=[ "weights", "patches" ]):
+        freed = 0
+        pin_state = self.model.dynamic_pins[self.load_device]
+        for subset in subsets:
+            hostbuf, stack, stack_split, pinned_size = pin_state[subset]
+            split = stack_split[0]
+            while split >= 0:
+                module, offset = stack[split]
+                split -= 1
+                stack_split[0] = split
+                if not module._pin_registered:
+                    continue
+                size = module._pin.numel() * module._pin.element_size()
+                if torch.cuda.cudart().cudaHostUnregister(module._pin.data_ptr()) != 0:
+                    comfy.model_management.discard_cuda_async_error()
+                    continue
+                module._pin_registered = False
+                comfy.model_management.TOTAL_PINNED_MEMORY = max(0, comfy.model_management.TOTAL_PINNED_MEMORY - size)
+                pinned_size[0] = max(0, pinned_size[0] - size)
+                freed += size
+                ram_to_unload -= size
+                if ram_to_unload <= 0:
+                    return freed
+        return freed
+
+    def partially_unload_ram(self, ram_to_unload, subsets=[ "weights", "patches" ]):
+        freed = 0
+        pin_state = self.model.dynamic_pins[self.load_device]
+        for subset in subsets:
+            hostbuf, stack, stack_split, pinned_size = pin_state[subset]
+            while len(stack) > 0:
+                module, offset = stack.pop()
+                size = module._pin.numel() * module._pin.element_size()
+                del module._pin
+                hostbuf.truncate(offset, do_unregister=module._pin_registered)
+                stack_split[0] = min(stack_split[0], len(stack) - 1)
+                if module._pin_registered:
+                    comfy.model_management.TOTAL_PINNED_MEMORY = max(0, comfy.model_management.TOTAL_PINNED_MEMORY - size)
+                    pinned_size[0] = max(0, pinned_size[0] - size)
+                freed += size
+                ram_to_unload -= size
+                if ram_to_unload <= 0:
+                    return freed
+        return freed
 
     def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):
         #This isn't used by the core at all and can only be to load a model out of

comfy/ops.py

@@ -75,6 +75,8 @@ except:
 
 cast_to = comfy.model_management.cast_to #TODO: remove once no more references
 
+STREAM_PIN_BUFFER_HEADROOM = 8 * 1024 * 1024
+
 def cast_to_input(weight, input, non_blocking=False, copy=True):
     return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
@@ -91,6 +93,9 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
     offload_stream = None
     cast_buffer = None
     cast_buffer_offset = 0
+    stream_pin_hostbuf = None
+    stream_pin_offset = 0
+    stream_pin_queue = []
 
     def ensure_offload_stream(module, required_size, check_largest):
         nonlocal offload_stream
@@ -124,6 +129,22 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
         cast_buffer_offset += buffer_size
         return buffer
 
+    def get_stream_pin_buffer_offset(buffer_size):
+        nonlocal stream_pin_hostbuf
+        nonlocal stream_pin_offset
+
+        if buffer_size == 0 or offload_stream is None:
+            return None
+
+        if stream_pin_hostbuf is None:
+            stream_pin_hostbuf = comfy.model_management.get_pin_buffer(offload_stream)
+            if stream_pin_hostbuf is None:
+                return None
+
+        offset = stream_pin_offset
+        stream_pin_offset += buffer_size
+        return offset
+
     for s in comfy_modules:
         signature = comfy_aimdo.model_vbar.vbar_fault(s._v)
         resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature)
@@ -162,23 +183,47 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
         if xfer_dest is None:
             xfer_dest = get_cast_buffer(dest_size)
 
-        if signature is None and pin is None:
-            comfy.pinned_memory.pin_memory(s)
-            pin = comfy.pinned_memory.get_pin(s)
-        else:
-            pin = None
+        def cast_maybe_lowvram_patch(xfer_source, xfer_dest, stream):
+            if xfer_source is not None:
+                if getattr(xfer_source, "is_lowvram_patch", False):
+                    xfer_source.prepare(xfer_dest, stream, copy=True, commit=False)
+                else:
+                    comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=stream)
 
-        if pin is not None:
-            comfy.model_management.cast_to_gathered(xfer_source, pin)
-            xfer_source = [ pin ]
-        #send it over
-        comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=offload_stream)
+        def handle_pin(m, pin, source, dest, subset="weights", size=None):
+            if pin is not None:
+                cast_maybe_lowvram_patch([pin], dest, offload_stream)
+                return
+            if signature is None:
+                comfy.pinned_memory.pin_memory(m, subset=subset, size=size)
+                pin = comfy.pinned_memory.get_pin(m, subset=subset)
+                if pin is not None:
+                    if isinstance(source, list):
+                        comfy.model_management.cast_to_gathered(source, pin, non_blocking=non_blocking, stream=offload_stream, r2=dest)
+                    else:
+                        cast_maybe_lowvram_patch(source, pin, None)
+                        cast_maybe_lowvram_patch([ pin ], dest, offload_stream)
+                    return
+            if pin is None:
+                pin_offset = get_stream_pin_buffer_offset(size)
+                if pin_offset is not None:
+                    stream_pin_queue.append((source, pin_offset, size, dest))
+                    return
+            cast_maybe_lowvram_patch(source, dest, offload_stream)
+
+        handle_pin(s, pin, xfer_source, xfer_dest, size=dest_size)
 
         for param_key in ("weight", "bias"):
-            lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
-            if lowvram_fn is not None:
+            lowvram_source = getattr(s, param_key + "_lowvram_function", None)
+            if lowvram_source is not None:
                 ensure_offload_stream(s, cast_buffer_offset, False)
-                lowvram_fn.prepare(lambda size: get_cast_buffer(size), offload_stream)
+                lowvram_size = lowvram_source.memory_required()
+                lowvram_dest = get_cast_buffer(lowvram_size)
+                lowvram_source.prepare(lowvram_dest, None, copy=False, commit=True)
+
+                pin = comfy.pinned_memory.get_pin(lowvram_source, subset="patches")
+                handle_pin(lowvram_source, pin, lowvram_source, lowvram_dest, subset="patches", size=lowvram_size)
+
 
         prefetch["xfer_dest"] = xfer_dest
         prefetch["cast_dest"] = cast_dest
@@ -186,6 +231,23 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
         prefetch["needs_cast"] = needs_cast
         s._prefetch = prefetch
 
+    if stream_pin_offset > 0:
+        if stream_pin_hostbuf.size < stream_pin_offset:
+            if not comfy.model_management.resize_pin_buffer(stream_pin_hostbuf, stream_pin_offset + STREAM_PIN_BUFFER_HEADROOM):
+                for xfer_source, _, _, xfer_dest in stream_pin_queue:
+                    cast_maybe_lowvram_patch(xfer_source, xfer_dest, offload_stream)
+                return offload_stream
+        stream_pin_tensor = comfy_aimdo.torch.hostbuf_to_tensor(stream_pin_hostbuf)
+        stream_pin_tensor.untyped_storage()._comfy_hostbuf = stream_pin_hostbuf
+        for xfer_source, pin_offset, pin_size, xfer_dest in stream_pin_queue:
+            pin = stream_pin_tensor[pin_offset:pin_offset + pin_size]
+            if isinstance(xfer_source, list):
+                comfy.model_management.cast_to_gathered(xfer_source, pin, non_blocking=non_blocking, stream=offload_stream, r2=xfer_dest)
+            else:
+                cast_maybe_lowvram_patch(xfer_source, pin, None)
+                comfy.model_management.cast_to_gathered([ pin ], xfer_dest, non_blocking=non_blocking, stream=offload_stream)
+        stream_pin_hostbuf._comfy_event = offload_stream.record_event()
+
     return offload_stream
 
 

comfy/pinned_memory.py

@@ -2,42 +2,62 @@ import comfy.model_management
 import comfy.memory_management
 import comfy_aimdo.host_buffer
 import comfy_aimdo.torch
+import torch
 
 from comfy.cli_args import args
 
-def get_pin(module):
-    return getattr(module, "_pin", None)
+def get_pin(module, subset="weights"):
+    pin = getattr(module, "_pin", None)
+    if pin is None or module._pin_registered or args.disable_pinned_memory:
+        return pin
 
-def pin_memory(module):
-    if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None:
+    _, _, stack_split, pinned_size = module._pin_state[subset]
+    size = pin.nbytes
+    comfy.model_management.ensure_pin_registerable(size)
+
+    if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
+        comfy.model_management.discard_cuda_async_error()
+        return pin
+
+    module._pin_registered = True
+    stack_split[0] = max(stack_split[0], module._pin_stack_index)
+    comfy.model_management.TOTAL_PINNED_MEMORY += size
+    pinned_size[0] += size
+    return pin
+
+def pin_memory(module, subset="weights", size=None):
+    pin_state = module._pin_state
+    if args.disable_pinned_memory:
         return
 
-    size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
+    pin = get_pin(module, subset)
+    if pin is not None or pin_state["failed"]:
+        return
 
-    if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:
-        module.pin_failed = True
+    hostbuf, stack, stack_split, pinned_size = pin_state[subset]
+    if size is None:
+        size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
+    offset = hostbuf.size
+    registerable_size = size + max(0, hostbuf.size - pinned_size[0])
+
+    comfy.memory_management.extra_ram_release(comfy.memory_management.RAM_CACHE_HEADROOM)
+    if (not comfy.model_management.ensure_pin_budget(size) or
+        not comfy.model_management.ensure_pin_registerable(registerable_size)):
+        pin_state["failed"] = True
         return False
 
     try:
-        hostbuf = comfy_aimdo.host_buffer.HostBuffer(size)
+        hostbuf.extend(size=size)
     except RuntimeError:
-        module.pin_failed = True
+        pin_state["failed"] = True
         return False
 
-    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)
-    module._pin_hostbuf = hostbuf
+    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
+    module._pin.untyped_storage()._comfy_hostbuf = hostbuf
+    stack.append((module, offset))
+    module._pin_registered = True
+    module._pin_stack_index = len(stack) - 1
+    stack_split[0] = max(stack_split[0], module._pin_stack_index)
     comfy.model_management.TOTAL_PINNED_MEMORY += size
+    pinned_size[0] += size
     return True
-
-def unpin_memory(module):
-    if get_pin(module) is None:
-        return 0
-    size = module._pin.numel() * module._pin.element_size()
-
-    comfy.model_management.TOTAL_PINNED_MEMORY -= size
-    if comfy.model_management.TOTAL_PINNED_MEMORY < 0:
-        comfy.model_management.TOTAL_PINNED_MEMORY = 0
-
-    del module._pin
-    del module._pin_hostbuf
-    return size

comfy/utils.py

@@ -113,7 +113,6 @@ def load_safetensors(ckpt):
                         "_comfy_tensor_file_slice",
                         comfy.memory_management.TensorFileSlice(f, threading.get_ident(), data_base_offset + start, end - start))
                 setattr(storage, "_comfy_tensor_mmap_refs", (model_mmap, mv))
-                setattr(storage, "_comfy_tensor_mmap_touched", False)
                 sd[name] = tensor
 
     return sd, header.get("__metadata__", {}),
@@ -1451,4 +1450,3 @@ def deepcopy_list_dict(obj, memo=None):
 
     memo[obj_id] = res
     return res
-

comfy/windows.py

@@ -1,52 +0,0 @@
-import ctypes
-import logging
-import psutil
-from ctypes import wintypes
-
-import comfy_aimdo.control
-
-psapi = ctypes.WinDLL("psapi")
-kernel32 = ctypes.WinDLL("kernel32")
-
-class PERFORMANCE_INFORMATION(ctypes.Structure):
-    _fields_ = [
-        ("cb", wintypes.DWORD),
-        ("CommitTotal", ctypes.c_size_t),
-        ("CommitLimit", ctypes.c_size_t),
-        ("CommitPeak", ctypes.c_size_t),
-        ("PhysicalTotal", ctypes.c_size_t),
-        ("PhysicalAvailable", ctypes.c_size_t),
-        ("SystemCache", ctypes.c_size_t),
-        ("KernelTotal", ctypes.c_size_t),
-        ("KernelPaged", ctypes.c_size_t),
-        ("KernelNonpaged", ctypes.c_size_t),
-        ("PageSize", ctypes.c_size_t),
-        ("HandleCount", wintypes.DWORD),
-        ("ProcessCount", wintypes.DWORD),
-        ("ThreadCount", wintypes.DWORD),
-    ]
-
-def get_free_ram():
-    #Windows is way too conservative and chalks recently used uncommitted model RAM
-    #as "in-use". So, calculate free RAM for the sake of general use as the greater of:
-    #
-    #1: What psutil says
-    #2: Total Memory - (Committed Memory - VRAM in use)
-    #
-    #We have to subtract VRAM in use from the comitted memory as WDDM creates a naked
-    #commit charge for all VRAM used just incase it wants to page it all out. This just
-    #isn't realistic so "overcommit" on our calculations by just subtracting it off.
-
-    pi = PERFORMANCE_INFORMATION()
-    pi.cb = ctypes.sizeof(pi)
-
-    if not psapi.GetPerformanceInfo(ctypes.byref(pi), pi.cb):
-        logging.warning("WARNING: Failed to query windows performance info. RAM usage may be sub optimal")
-        return psutil.virtual_memory().available
-
-    committed = pi.CommitTotal * pi.PageSize
-    total = pi.PhysicalTotal * pi.PageSize
-
-    return max(psutil.virtual_memory().available,
-               total - (committed - comfy_aimdo.control.get_total_vram_usage()))
-

comfy_api/latest/_caching.py

@@ -21,6 +21,10 @@ class CacheProvider(ABC):
     Exceptions from provider methods are caught by the caller and never break execution.
     """
 
+    async def on_set_prompt(self) -> None:
+        """Called after prompt cache keys are prepared. Dispatched via asyncio.create_task."""
+        pass
+
     @abstractmethod
     async def on_lookup(self, context: CacheContext) -> Optional[CacheValue]:
         """Called on local cache miss. Return CacheValue if found, None otherwise."""

comfy_execution/caching.py

@@ -164,6 +164,7 @@ class BasicCache:
         await self.cache_key_set.add_keys(node_ids)
         self.is_changed_cache = is_changed_cache
         self.initialized = True
+        await self._notify_providers_set_prompt()
 
     def all_node_ids(self):
         assert self.initialized
@@ -263,6 +264,24 @@ class BasicCache:
             except Exception as e:
                 _logger.warning(f"Cache provider {provider.__class__.__name__} error on store: {e}")
 
+    async def _notify_providers_set_prompt(self):
+        from comfy_execution.cache_provider import (
+            _has_cache_providers, _get_cache_providers, _logger
+        )
+
+        if not self.enable_providers:
+            return
+        if not _has_cache_providers():
+            return
+
+        for provider in _get_cache_providers():
+            try:
+                task = asyncio.create_task(self._safe_provider_set_prompt(provider))
+                self._pending_store_tasks.add(task)
+                task.add_done_callback(self._pending_store_tasks.discard)
+            except Exception as e:
+                _logger.warning(f"Cache provider {provider.__class__.__name__} error on set_prompt: {e}")
+
     @staticmethod
     async def _safe_provider_store(provider, context, cache_value):
         from comfy_execution.cache_provider import _logger
@@ -271,6 +290,14 @@ class BasicCache:
         except Exception as e:
             _logger.warning(f"Cache provider {provider.__class__.__name__} async store error: {e}")
 
+    @staticmethod
+    async def _safe_provider_set_prompt(provider):
+        from comfy_execution.cache_provider import _logger
+        try:
+            await provider.on_set_prompt()
+        except Exception as e:
+            _logger.warning(f"Cache provider {provider.__class__.__name__} async set_prompt error: {e}")
+
     async def _check_providers_lookup(self, node_id, cache_key):
         from comfy_execution.cache_provider import (
             _has_cache_providers, _get_cache_providers,

comfy_extras/mediapipe/face_geometry.py

@@ -0,0 +1,111 @@
+"""Pure-numpy port of MediaPipe's face_geometry (FACE_LANDMARK_PIPELINE mode)
++ weighted Procrustes solver. Computes the 4x4 facial transformation matrix.
+"""
+
+from __future__ import annotations
+
+import math
+import numpy as np
+
+
+def _solve_weighted_orthogonal_problem(src: np.ndarray, tgt: np.ndarray, weights: np.ndarray) -> np.ndarray:
+    """Weighted orthogonal Procrustes (similarity). Returns 4x4 M with
+    `target ≈ M @ homogeneous(source)` in the weighted LS sense. fp64 for
+    SVD stability. Port of procrustes_solver.cc."""
+    sqrt_w = np.sqrt(weights.astype(np.float64))
+    w_total = float((sqrt_w ** 2).sum())
+    ws = src.astype(np.float64) * sqrt_w
+    wt = tgt.astype(np.float64) * sqrt_w
+
+    c_w = (ws @ sqrt_w) / w_total
+    centered = ws - np.outer(c_w, sqrt_w)
+    U, _S, Vt = np.linalg.svd(wt @ centered.T, full_matrices=True)
+    # Disallow reflection: flip the least-significant axis when det(U)·det(V)<0.
+    post, pre = U.copy(), Vt.T.copy()
+    if np.linalg.det(post) * np.linalg.det(pre) < 0:
+        post[:, 2] *= -1.0
+    R = post @ pre.T
+
+    denom = float((centered * ws).sum())
+    if denom < 1e-12:
+        raise ValueError("Procrustes denominator collapsed (degenerate source).")
+    scale = float((R @ centered * wt).sum()) / denom
+    translation = ((wt - scale * (R @ ws)) @ sqrt_w) / w_total
+
+    M = np.eye(4, dtype=np.float64)
+    M[:3, :3] = scale * R
+    M[:3, 3] = translation
+    return M
+
+
+def _estimate_scale(canonical: np.ndarray, runtime: np.ndarray, weights: np.ndarray) -> float:
+    """scale = ‖first column of M[:3]‖ per geometry_pipeline.cc::EstimateScale."""
+    return float(np.linalg.norm(_solve_weighted_orthogonal_problem(canonical, runtime, weights)[:3, 0]))
+
+
+def solve_facial_transformation_matrix(
+    landmarks_normalized: np.ndarray,
+    canonical_vertices: np.ndarray,
+    procrustes_indices: np.ndarray,
+    procrustes_weights: np.ndarray,
+    image_width: int,
+    image_height: int,
+    # face_geometry_calculator_options.pbtxt defaults
+    vertical_fov_degrees: float = 63.0,
+    near: float = 1.0,
+) -> np.ndarray:
+    """4x4 facial transformation matrix via two-pass scale recovery
+    `landmarks_normalized` is (N, 3) in MediaPipe normalized convention: x, y
+    in [0,1] with TOP-LEFT origin, z in width-scaled units.
+    """
+
+    h_near = 2.0 * near * math.tan(0.5 * math.radians(vertical_fov_degrees))
+    w_near = image_width * h_near / image_height
+
+    sub = procrustes_indices.astype(np.int64)
+    screen = landmarks_normalized[sub].T.astype(np.float64).copy()
+    canon = canonical_vertices[sub].T.astype(np.float64).copy()
+    weights = procrustes_weights.astype(np.float64)
+
+    # ProjectXY (TOP_LEFT y-flip, then scale all 3 axes; z uses x-scale).
+    screen[1] = 1.0 - screen[1]
+    screen[0] = screen[0] * w_near - 0.5 * w_near
+    screen[1] = screen[1] * h_near - 0.5 * h_near
+    screen[2] = screen[2] * w_near
+    depth_offset = float(screen[2].mean())
+
+    def _unproject(s: np.ndarray, scale: float) -> np.ndarray:
+        s = s.copy()
+        s[2] = (s[2] - depth_offset + near) / scale
+        s[0] *= s[2] / near
+        s[1] *= s[2] / near
+        s[2] *= -1.0
+        return s
+
+    first = screen.copy()
+    first[2] *= -1.0
+    s1 = _estimate_scale(canon, first, weights) # 1st pass: Procrustes on projected XY
+    s2 = _estimate_scale(canon, _unproject(screen, s1), weights) # 2nd pass: rescale z by s1, un-project XY
+    return _solve_weighted_orthogonal_problem(canon, _unproject(screen, s1 * s2), weights).astype(np.float32)
+
+
+def transformation_matrix_from_detection(face_dict: dict, image_width: int, image_height: int, canonical_data: dict) -> np.ndarray:
+    """Adapt a FaceLandmarker face dict to MP's normalized convention and solve.
+    FaceMesh emits (x, y, z) in 192-canonical units; MP's geometry expects
+    z_norm = z_canonical * scale_x / image_width"""
+
+    lmks_xy, lmks_3d = face_dict["landmarks_xy"], face_dict["landmarks_3d"]
+    aug = np.concatenate([lmks_3d[:, :2].astype(np.float64), np.ones((lmks_xy.shape[0], 1))], axis=1)
+    M, *_ = np.linalg.lstsq(aug, lmks_xy.astype(np.float64), rcond=None)
+    scale_x = float(np.linalg.norm(M[0]))
+    z_scale = scale_x / image_width if scale_x > 1e-6 else 1.0 / image_width
+
+    normalized = np.empty((lmks_xy.shape[0], 3), dtype=np.float32)
+    normalized[:, 0] = lmks_xy[:, 0] / image_width
+    normalized[:, 1] = lmks_xy[:, 1] / image_height
+    normalized[:, 2] = lmks_3d[:, 2] * z_scale
+    return solve_facial_transformation_matrix(
+        normalized, canonical_data["canonical_vertices"],
+        canonical_data["procrustes_indices"], canonical_data["procrustes_weights"],
+        image_width=image_width, image_height=image_height,
+    )

comfy_extras/mediapipe/face_landmarker.py

@@ -0,0 +1,682 @@
+"""Pure-PyTorch port of MediaPipe's face_landmarker_v2_with_blendshapes.task:
+BlazeFace detector → FaceMesh v2 → ARKit-52 blendshapes."""
+
+from __future__ import annotations
+
+import math
+from functools import lru_cache
+from typing import List, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from scipy.special import expit
+from torch import Tensor, nn
+
+
+# Values below must stay verbatim with the published face_landmarker_v2 graph
+
+# face_blendshapes_graph.cc::kLandmarksSubsetIdxs
+_BS_INPUT_INDICES: Tuple[int, ...] = (
+    0, 1, 4, 5, 6, 7, 8, 10, 13, 14, 17, 21, 33, 37, 39, 40, 46, 52, 53, 54,
+    55, 58, 61, 63, 65, 66, 67, 70, 78, 80, 81, 82, 84, 87, 88, 91, 93, 95,
+    103, 105, 107, 109, 127, 132, 133, 136, 144, 145, 146, 148, 149, 150, 152,
+    153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 168, 172, 173, 176, 178,
+    181, 185, 191, 195, 197, 234, 246, 249, 251, 263, 267, 269, 270, 276, 282,
+    283, 284, 285, 288, 291, 293, 295, 296, 297, 300, 308, 310, 311, 312, 314,
+    317, 318, 321, 323, 324, 332, 334, 336, 338, 356, 361, 362, 365, 373, 374,
+    375, 377, 378, 379, 380, 381, 382, 384, 385, 386, 387, 388, 389, 390, 397,
+    398, 400, 402, 405, 409, 415, 454, 466, 468, 469, 470, 471, 472, 473, 474,
+    475, 476, 477,
+)
+
+# face_blendshapes_graph.cc::kCategoryNames
+BLENDSHAPE_NAMES: Tuple[str, ...] = (
+    "_neutral", "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft",
+    "browOuterUpRight", "cheekPuff", "cheekSquintLeft", "cheekSquintRight",
+    "eyeBlinkLeft", "eyeBlinkRight", "eyeLookDownLeft", "eyeLookDownRight",
+    "eyeLookInLeft", "eyeLookInRight", "eyeLookOutLeft", "eyeLookOutRight",
+    "eyeLookUpLeft", "eyeLookUpRight", "eyeSquintLeft", "eyeSquintRight",
+    "eyeWideLeft", "eyeWideRight", "jawForward", "jawLeft", "jawOpen",
+    "jawRight", "mouthClose", "mouthDimpleLeft", "mouthDimpleRight",
+    "mouthFrownLeft", "mouthFrownRight", "mouthFunnel", "mouthLeft",
+    "mouthLowerDownLeft", "mouthLowerDownRight", "mouthPressLeft",
+    "mouthPressRight", "mouthPucker", "mouthRight", "mouthRollLower",
+    "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", "mouthSmileLeft",
+    "mouthSmileRight", "mouthStretchLeft", "mouthStretchRight",
+    "mouthUpperUpLeft", "mouthUpperUpRight", "noseSneerLeft", "noseSneerRight",
+)
+
+# face_detection.pbtxt — short-range BlazeFace.
+_BF_NUM_LAYERS = 4
+_BF_INPUT_SIZE = 128
+_BF_STRIDES = (8, 16, 16, 16)
+_BF_ANCHOR_OFFSET_X = 0.5
+_BF_ANCHOR_OFFSET_Y = 0.5
+_BF_ASPECT_RATIOS = (1.0,)
+_BF_INTERP_SCALE_AR = 1.0
+_BF_BOX_SCALE = 128.0
+_BF_KP_OFFSET = 4
+_BF_SCORE_CLIP = 100.0
+_BF_MIN_SCORE = 0.5
+
+# face_detection_full_range.pbtxt — 48x48 grid at stride 4, 1 anchor/cell.
+_BF_FR_INPUT_SIZE = 192
+_BF_FR_GRID = 48
+_BF_FR_NUM_ANCHORS = _BF_FR_GRID * _BF_FR_GRID
+_BF_FR_BOX_SCALE = 192.0
+_BF_FR_SCORE_CLIP = 100.0
+
+_FM_INPUT_SIZE = 192
+
+# Face ROI: 1.5xbbox rect warped anisotropically into 192x192.
+_FACE_LEFT_EYE_KP = 0
+_FACE_RIGHT_EYE_KP = 1
+_FACE_ROI_SCALE_X = 1.5
+_FACE_ROI_SCALE_Y = 1.5
+_FACE_ROI_TARGET_ANGLE = 0.0
+
+
+def _tf_same_pad(x: Tensor, kernel: int, stride: int) -> Tensor:
+    """TF SAME pad (asymmetric on stride-2; PyTorch's symmetric pad undershoots by 1 px)."""
+    H, W = x.shape[-2], x.shape[-1]
+    pad_h = max(((H + stride - 1) // stride - 1) * stride + kernel - H, 0)
+    pad_w = max(((W + stride - 1) // stride - 1) * stride + kernel - W, 0)
+    if pad_h == 0 and pad_w == 0:
+        return x
+    return F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))
+
+
+# BlazeFace short-range: stem 5x5/s2 → 16 BlazeBlocks → parallel heads at
+# 16²x88 (2 anchors/cell) and 8²x96 (6/cell) = 896 anchors. (in, out, stride):
+_BLAZEFACE_BLOCKS = [
+    (24, 24, 1), (24, 28, 1), (28, 32, 2), (32, 36, 1),
+    (36, 42, 1), (42, 48, 2), (48, 56, 1), (56, 64, 1),
+    (64, 72, 1), (72, 80, 1), (80, 88, 1), (88, 96, 2),
+    (96, 96, 1), (96, 96, 1), (96, 96, 1), (96, 96, 1),
+]
+
+
+class BlazeFaceBlock(nn.Module):
+    """DW 3x3 + PW + residual. Residual max-pools on stride>1, channel-pads on out_ch>in_ch."""
+
+    def __init__(self, in_ch: int, out_ch: int, stride: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        self.in_ch, self.out_ch, self.stride = in_ch, out_ch, stride
+        self.depthwise = ops.Conv2d(in_ch, in_ch, 3, stride=stride, padding=0, groups=in_ch, bias=True, device=device, dtype=dtype)
+        self.pointwise = ops.Conv2d(in_ch, out_ch, 1, padding=0, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = F.max_pool2d(x, 2, 2) if self.stride > 1 else x
+        if self.out_ch > self.in_ch:
+            residual = F.pad(residual, (0, 0, 0, 0, 0, self.out_ch - self.in_ch))
+        x = _tf_same_pad(x, 3, self.stride) if self.stride > 1 else F.pad(x, (1, 1, 1, 1))
+        return F.relu(self.pointwise(self.depthwise(x)) + residual)
+
+
+class BlazeFace(nn.Module):
+    """Short-range BlazeFace: (B, 3, 128, 128) in [-1, 1] → 896 anchors x 17."""
+
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.stem = ops.Conv2d(3, 24, 5, stride=2, padding=0, bias=True, **kw)
+        self.blocks = nn.ModuleList(BlazeFaceBlock(i, o, s, device=device, dtype=dtype, operations=operations)
+                                    for (i, o, s) in _BLAZEFACE_BLOCKS)
+        # 16²x2 + 8²x6 = 512 + 384 = 896 anchors.
+        self.cls_16 = ops.Conv2d(88, 2, 1, padding=0, bias=True, **kw)
+        self.cls_8 = ops.Conv2d(96, 6, 1, padding=0, bias=True, **kw)
+        self.reg_16 = ops.Conv2d(88, 32, 1, padding=0, bias=True, **kw)
+        self.reg_8 = ops.Conv2d(96, 96, 1, padding=0, bias=True, **kw)
+
+    def forward(self, image_chw_normalized: Tensor) -> tuple[Tensor, Tensor]:
+        x = F.relu(self.stem(_tf_same_pad(image_chw_normalized, 5, 2)))
+        # 16x16 tap is block-10 output (before the 88→96 stride-2 in block 11).
+        for i in range(11):
+            x = self.blocks[i](x)
+        feat_16 = x
+        for i in range(11, 16):
+            x = self.blocks[i](x)
+        feat_8 = x
+
+        def flat(t, a, k):  # NHWC flatten → (B, H*W*A, K)
+            B, _, H, W = t.shape
+            return t.permute(0, 2, 3, 1).reshape(B, H * W * a, k)
+
+        cls = torch.cat([flat(self.cls_16(feat_16), 2, 1), flat(self.cls_8(feat_8), 6, 1)], dim=1)
+        reg = torch.cat([flat(self.reg_16(feat_16), 2, 16), flat(self.reg_8(feat_8), 6, 16)], dim=1)
+        return reg, cls
+
+
+# BlazeFace full-range (face_detection_full_range_sparse.tflite): MobileNetV2-ish
+# backbone + top-down FPN, 192² input → 2304 anchors at the 48x48 grid.
+class FRBlock(nn.Module):
+    """Double inverted residual: DW → PW(mid) → DW → PW(out) [+ residual].
+
+    Per source tflite: dw* have no fused activation, pw1 is always ReLU, pw2
+    is ReLU only when no residual (else ReLU fuses into the ADD).
+    """
+
+    def __init__(self, in_ch: int, mid_ch: int, out_ch: int, stride: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.has_residual = (in_ch == out_ch and stride == 1)
+        self.dw1 = ops.Conv2d(in_ch, in_ch, 3, stride=stride, padding=0, groups=in_ch, bias=True, **kw)
+        self.pw1 = ops.Conv2d(in_ch, mid_ch, 1, padding=0, bias=True, **kw)
+        self.dw2 = ops.Conv2d(mid_ch, mid_ch, 3, stride=1, padding=0, groups=mid_ch, bias=True, **kw)
+        self.pw2 = ops.Conv2d(mid_ch, out_ch, 1, padding=0, bias=True, **kw)
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = x if self.has_residual else None
+        x = F.relu(self.pw1(self.dw1(F.pad(x, (1, 1, 1, 1)))))
+        x = self.pw2(self.dw2(F.pad(x, (1, 1, 1, 1))))
+        return F.relu(x + residual) if residual is not None else F.relu(x)
+
+
+# (in_ch, mid_ch, out_ch, stride). Stages downsample 96²x32 → 48²x64 → 24²x128
+# → 12²x192 → 6²x384. Lateral taps at indices 4, 7, 10 (see _FR_LATERAL_*).
+_FR_BACKBONE_BLOCKS = [
+    (32, 8, 32, 1),    (32, 8, 32, 1),                                            # 96²x32
+    (32, 16, 64, 2),   (64, 16, 64, 1),   (64, 16, 64, 1),                        # 48²x64 — tap[0]
+    (64, 32, 128, 2),  (128, 32, 128, 1), (128, 32, 128, 1),                      # 24²x128 — tap[1]
+    (128, 48, 192, 2), (192, 48, 192, 1), (192, 48, 192, 1),                      # 12²x192 — tap[2]
+    (192, 96, 384, 2), (384, 96, 384, 1), (384, 96, 384, 1), (384, 96, 384, 1),   # 6²x384
+]
+_FR_LATERAL_TAP_INDICES = (4, 7, 10)
+_FR_LATERAL_CHANNELS = ((64, 48), (128, 64), (192, 96))  # (in, out) per side-conv
+
+# Decoder blocks per FPN level (after upsample-and-merge with the lateral).
+_FR_DECODER_BLOCKS = [
+    [(96, 48, 96, 1), (96, 48, 96, 1)],  # 12²x96
+    [(64, 32, 64, 1), (64, 32, 64, 1)],  # 24²x64
+    [(48, 24, 48, 1)],                   # 48²x48 — feeds the heads
+]
+
+
+def _dcr_depth_to_space(t: Tensor, r: int, c_out: int) -> Tensor:
+    """TF DEPTH_TO_SPACE in DCR layout (input channels = (i, j, c_out)).
+    pixel_shuffle uses CRD which permutes output channels for c_out > 1."""
+    B_, _, H_, W_ = t.shape
+    t = t.reshape(B_, r, r, c_out, H_, W_)
+    t = t.permute(0, 3, 4, 1, 5, 2).contiguous()
+    return t.reshape(B_, c_out, H_ * r, W_ * r)
+
+
+class BlazeFaceFullRange(nn.Module):
+    """Full-range face detector: (B, 3, 192, 192) in [-1, 1] → 2304 anchors x 17 values."""
+
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        mk_block = lambda i, m, o, s: FRBlock(i, m, o, s, device=device, dtype=dtype, operations=operations)
+        self.stem = ops.Conv2d(3, 32, 3, stride=2, padding=0, bias=True, **kw)
+        self.backbone = nn.ModuleList(mk_block(i, m, o, s) for (i, m, o, s) in _FR_BACKBONE_BLOCKS)
+        self.lateral_convs = nn.ModuleList(ops.Conv2d(i, o, 1, padding=0, bias=True, **kw) for (i, o) in _FR_LATERAL_CHANNELS)
+        self.top_conv = ops.Conv2d(384, 96, 1, padding=0, bias=True, **kw)
+        self.decoder_levels = nn.ModuleList(
+            nn.ModuleList(mk_block(i, m, o, s) for (i, m, o, s) in lvl) for lvl in _FR_DECODER_BLOCKS
+        )
+        # 96→64 before 12→24, 64→48 before 24→48.
+        self.decoder_reduce_convs = nn.ModuleList([
+            ops.Conv2d(96, 64, 1, padding=0, bias=True, **kw),
+            ops.Conv2d(64, 48, 1, padding=0, bias=True, **kw),
+        ])
+        # Heads mix 2x2-cell info via DW-stride-2 + depth_to_space block_size=2.
+        self.cls_conv = ops.Conv2d(48, 4, 1, padding=0, bias=True, **kw)
+        self.cls_dw = ops.Conv2d(4, 4, 3, stride=2, padding=0, groups=4, bias=True, **kw)
+        self.reg_conv = ops.Conv2d(48, 64, 1, padding=0, bias=True, **kw)
+        self.reg_dw = ops.Conv2d(64, 64, 3, stride=2, padding=0, groups=64, bias=True, **kw)
+
+    def forward(self, image_chw_normalized: Tensor) -> tuple[Tensor, Tensor]:
+        # Symmetric pad-1 throughout (full-range tflite uses explicit TF PAD, not SAME).
+        x = F.relu(self.stem(F.pad(image_chw_normalized, (1, 1, 1, 1))))
+        tap_set = set(_FR_LATERAL_TAP_INDICES)
+        laterals: list[Tensor] = []
+        for i, blk in enumerate(self.backbone):
+            x = blk(x)
+            if i in tap_set:
+                laterals.append(x)
+
+        # top_conv / lateral_convs / decoder_reduce_convs all have fused ReLU in the tflite.
+        p = F.relu(self.top_conv(x))
+        laterals_rev = list(reversed(laterals))
+        lateral_convs_rev = list(reversed(self.lateral_convs))
+        for level in range(len(self.decoder_levels)):
+            lateral = laterals_rev[level]
+            p = F.interpolate(p, size=lateral.shape[-2:], mode="bilinear", align_corners=False)
+            p = p + F.relu(lateral_convs_rev[level](lateral))
+            for blk in self.decoder_levels[level]:
+                p = blk(p)
+            if level < len(self.decoder_reduce_convs):
+                p = F.relu(self.decoder_reduce_convs[level](p))
+
+        c = self.cls_dw(F.pad(self.cls_conv(p), (1, 1, 1, 1)))
+        c = _dcr_depth_to_space(c, r=2, c_out=1)
+        r = self.reg_dw(F.pad(self.reg_conv(p), (1, 1, 1, 1)))
+        r = _dcr_depth_to_space(r, r=2, c_out=16)
+        B = c.shape[0]
+        cls_out = c.permute(0, 2, 3, 1).reshape(B, _BF_FR_NUM_ANCHORS, 1)
+        reg_out = r.permute(0, 2, 3, 1).reshape(B, _BF_FR_NUM_ANCHORS, 16)
+        return reg_out, cls_out
+
+
+@lru_cache(maxsize=1)
+def _blazeface_full_range_anchors() -> np.ndarray:
+    """2304 anchors over 48x48; anchor_w=anchor_h=1 (fixed_anchor_size)."""
+    feat = _BF_FR_GRID
+    yy, xx = np.meshgrid(np.arange(feat, dtype=np.float32), np.arange(feat, dtype=np.float32), indexing="ij")
+    cx, cy, ones = (xx + 0.5) / feat, (yy + 0.5) / feat, np.ones_like(xx)
+    return np.stack([cx, cy, ones, ones], axis=-1).reshape(_BF_FR_NUM_ANCHORS, 4)
+
+
+def _decode_blazeface_full_range(regressors: np.ndarray, classificators: np.ndarray,
+                                 score_thresh: float = _BF_MIN_SCORE) -> np.ndarray:
+    """Same decode as short-range with 2304-anchor grid and box_scale=192."""
+    scores = expit(np.clip(classificators[:, 0], -_BF_FR_SCORE_CLIP, _BF_FR_SCORE_CLIP))
+    keep = scores >= score_thresh
+    if not keep.any():
+        return np.empty((0, 17), dtype=np.float32)
+    r = regressors[keep] / _BF_FR_BOX_SCALE
+    a = _blazeface_full_range_anchors()[keep]
+    cxs, cys, aws, ahs = a[:, 0:1], a[:, 1:2], a[:, 2:3], a[:, 3:4]
+    xc, yc = r[:, 0:1] * aws + cxs, r[:, 1:2] * ahs + cys
+    w, h = r[:, 2:3] * aws, r[:, 3:4] * ahs
+    out = np.empty((r.shape[0], 17), dtype=np.float32)
+    out[:, 0:1], out[:, 1:2], out[:, 2:3], out[:, 3:4] = xc - w / 2, yc - h / 2, xc + w / 2, yc + h / 2
+    out[:, 4:16:2] = r[:, _BF_KP_OFFSET::2] * aws + cxs
+    out[:, 5:16:2] = r[:, _BF_KP_OFFSET + 1::2] * ahs + cys
+    out[:, 16] = scores[keep]
+    return out
+
+
+# FaceMesh (face_landmarks_detector.tflite): PReLU variant of BlazeBlock,
+# 17 blocks, heads for 478x3 landmarks + presence.
+_FACEMESH_BLOCKS = [  # (in_ch, out_ch, stride)
+    (16, 16, 1),  (16, 16, 1),  (16, 32, 2),  (32, 32, 1), (32, 32, 1), (32, 64, 2),
+    (64, 64, 1),  (64, 64, 1),  (64, 128, 2), (128, 128, 1), (128, 128, 1), (128, 128, 2),
+    (128, 128, 1), (128, 128, 1), (128, 128, 2), (128, 128, 1), (128, 128, 1),
+]
+
+
+class FaceMeshBlock(nn.Module):
+    """PReLU BlazeBlock: PReLU between DW and PW, and after the residual add."""
+
+    def __init__(self, in_ch: int, out_ch: int, stride: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.in_ch, self.out_ch, self.stride = in_ch, out_ch, stride
+        self.depthwise = ops.Conv2d(in_ch, in_ch, 3, stride=stride, padding=0, groups=in_ch, bias=True, **kw)
+        self.prelu_dwise = nn.PReLU(num_parameters=in_ch, **kw)
+        self.pointwise = ops.Conv2d(in_ch, out_ch, 1, padding=0, bias=True, **kw)
+        self.prelu_out = nn.PReLU(num_parameters=out_ch, **kw)
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = F.max_pool2d(x, 2, 2) if self.stride > 1 else x
+        if self.out_ch > self.in_ch:
+            residual = F.pad(residual, (0, 0, 0, 0, 0, self.out_ch - self.in_ch))
+        x = _tf_same_pad(x, 3, self.stride) if self.stride > 1 else F.pad(x, (1, 1, 1, 1))
+        return self.prelu_out(self.pointwise(self.prelu_dwise(self.depthwise(x))) + residual)
+
+
+class FaceMesh(nn.Module):
+    NUM_LANDMARKS = 478
+
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.stem = ops.Conv2d(3, 16, 3, stride=2, padding=0, bias=True, **kw)
+        self.prelu_stem = nn.PReLU(num_parameters=16, **kw)
+        self.blocks = nn.ModuleList(FaceMeshBlock(i, o, s, device=device, dtype=dtype, operations=operations)
+                                    for (i, o, s) in _FACEMESH_BLOCKS)
+        self.head_reduce = ops.Conv2d(128, 8, 1, padding=0, bias=True, **kw)
+        self.prelu_head_reduce = nn.PReLU(num_parameters=8, **kw)
+        self.head_block = FaceMeshBlock(8, 8, 1, device=device, dtype=dtype, operations=operations)
+        self.head_presence = ops.Conv2d(8, 1, 3, padding=0, bias=True, **kw)
+        self.head_landmarks = ops.Conv2d(8, self.NUM_LANDMARKS * 3, 3, padding=0, bias=True, **kw)
+
+    def forward(self, face_chw_normalized: Tensor) -> tuple[Tensor, Tensor]:
+        """(B, 3, 192, 192) in [0, 1] → ((B, 478, 3) landmarks in 192-canonical, (B,) presence)."""
+        x = self.prelu_stem(self.stem(_tf_same_pad(face_chw_normalized, 3, 2)))
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.prelu_head_reduce(self.head_reduce(x))
+        x = self.head_block(x)
+        B = x.shape[0]
+        presence = self.head_presence(x).reshape(B)
+        lmks = self.head_landmarks(x).reshape(B, self.NUM_LANDMARKS, 3)
+        return lmks, presence
+
+
+# FaceBlendshapes (MLP-Mixer "GhumMarkerPoserMlpMixerGeneral"):
+# 146x2 → token-reduce 146→96 → embed 2→64 → +cls token → 4x mixer → cls→52.
+_BS_NUM_INPUT_LANDMARKS = 146
+_BS_NUM_TOKENS_REDUCED = 96
+_BS_NUM_TOKENS = 97  # +1 cls
+_BS_TOKEN_DIM = 64
+_BS_TOKEN_MIX_HIDDEN = 384
+_BS_CHANNEL_MIX_HIDDEN = 256
+_BS_NUM_BLENDSHAPES = 52
+_BS_LN_EPS = 1e-6
+
+
+class MlpMixerBlock(nn.Module):
+    """MLP-Mixer block: token-mixing MLP (over tokens) → channel-mixing MLP (over dim).
+    Both pre-LN, both residual. LN has no beta (bias=False) to match MP."""
+
+    def __init__(self, num_tokens: int, token_dim: int, token_hidden: int, channel_hidden: int,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        # bias=False → no LN beta (matches MP).
+        self.ln1 = ops.LayerNorm(token_dim, eps=_BS_LN_EPS, bias=False, **kw)
+        self.ln2 = ops.LayerNorm(token_dim, eps=_BS_LN_EPS, bias=False, **kw)
+        self.token_mlp1 = ops.Linear(num_tokens, token_hidden, bias=True, **kw)
+        self.token_mlp2 = ops.Linear(token_hidden, num_tokens, bias=True, **kw)
+        self.channel_mlp1 = ops.Linear(token_dim, channel_hidden, bias=True, **kw)
+        self.channel_mlp2 = ops.Linear(channel_hidden, token_dim, bias=True, **kw)
+
+    def forward(self, x: Tensor) -> Tensor:
+        y = self.ln1(x).transpose(1, 2)
+        x = x + self.token_mlp2(F.relu(self.token_mlp1(y))).transpose(1, 2)
+        return x + self.channel_mlp2(F.relu(self.channel_mlp1(self.ln2(x))))
+
+
+class FaceBlendshapes(nn.Module):
+    def __init__(self, device=None, dtype=None, operations=None):
+        super().__init__()
+        ops = operations if operations is not None else nn
+        kw = dict(device=device, dtype=dtype)
+        self.token_reduce = ops.Linear(_BS_NUM_INPUT_LANDMARKS, _BS_NUM_TOKENS_REDUCED, bias=True, **kw)
+        self.token_embed = ops.Linear(2, _BS_TOKEN_DIM, bias=True, **kw)
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, _BS_TOKEN_DIM, **kw))
+        self.blocks = nn.ModuleList(
+            MlpMixerBlock(_BS_NUM_TOKENS, _BS_TOKEN_DIM, _BS_TOKEN_MIX_HIDDEN, _BS_CHANNEL_MIX_HIDDEN,
+                          device=device, dtype=dtype, operations=operations) for _ in range(4)
+        )
+        self.head = ops.Linear(_BS_TOKEN_DIM, _BS_NUM_BLENDSHAPES, bias=True, **kw)
+
+    @staticmethod
+    def _input_normalize(landmarks_2d: Tensor) -> Tensor:
+        # Centroid-subtract → L2 scale → x0.5. The 0.5 is baked into training.
+        centroid = landmarks_2d.mean(dim=1, keepdim=True)
+        x = landmarks_2d - centroid
+        mag = torch.sqrt((x * x).sum(dim=-1, keepdim=True))
+        scale = mag.mean(dim=1, keepdim=True)
+        return (x / scale.clamp(min=1e-12)) * 0.5
+
+    def forward(self, landmarks_2d: Tensor) -> Tensor:
+        """(B, 146, 2) → (B, 52) in [0, 1]. Input units don't matter (centroid + L2 normalize)."""
+        x = self._input_normalize(landmarks_2d)
+        x = self.token_reduce(x.transpose(1, 2)).transpose(1, 2)
+        x = self.token_embed(x)
+        cls = self.cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat([cls, x], dim=1)
+        for blk in self.blocks:
+            x = blk(x)
+        return torch.sigmoid(self.head(x[:, 0]))
+
+
+@lru_cache(maxsize=1)
+def _blazeface_anchors() -> np.ndarray:
+    """896 anchors per SsdAnchorsCalculator (fixed_anchor_size → anchor_w=anchor_h=1)."""
+    per_ar = len(_BF_ASPECT_RATIOS) + (1 if _BF_INTERP_SCALE_AR > 0 else 0)
+    layer_anchors: List[np.ndarray] = []
+    layer = 0
+    while layer < _BF_NUM_LAYERS:
+        stride = _BF_STRIDES[layer]
+        last = layer
+        while last < _BF_NUM_LAYERS and _BF_STRIDES[last] == stride:
+            last += 1
+        per_cell = per_ar * (last - layer)
+        feat = (_BF_INPUT_SIZE + stride - 1) // stride
+        yy, xx = np.meshgrid(np.arange(feat, dtype=np.float32), np.arange(feat, dtype=np.float32), indexing="ij")
+        cx, cy, ones = (xx + _BF_ANCHOR_OFFSET_X) / feat, (yy + _BF_ANCHOR_OFFSET_Y) / feat, np.ones_like(xx)
+        cell = np.stack([cx, cy, ones, ones], axis=-1).reshape(-1, 4)
+        layer_anchors.append(np.repeat(cell, per_cell, axis=0))
+        layer = last
+    out = np.concatenate(layer_anchors, axis=0)
+    assert out.shape == (896, 4), out.shape
+    return out
+
+
+def _decode_blazeface(regressors: np.ndarray, classificators: np.ndarray,
+                      score_thresh: float = _BF_MIN_SCORE) -> np.ndarray:
+    """Decode (regs (896,16), cls (896,1)) → (N, 17) = [xyxy, kp0x..kp5y, score] in [0, 1]."""
+    scores = expit(np.clip(classificators[:, 0], -_BF_SCORE_CLIP, _BF_SCORE_CLIP))
+    keep = scores >= score_thresh
+    if not keep.any():
+        return np.empty((0, 17), dtype=np.float32)
+    r = regressors[keep] / _BF_BOX_SCALE
+    a = _blazeface_anchors()[keep]  # (N, 4) cx, cy, 1, 1
+    cxs, cys, aws, ahs = a[:, 0:1], a[:, 1:2], a[:, 2:3], a[:, 3:4]
+    xc, yc = r[:, 0:1] * aws + cxs, r[:, 1:2] * ahs + cys
+    w, h = r[:, 2:3] * aws, r[:, 3:4] * ahs
+    out = np.empty((r.shape[0], 17), dtype=np.float32)
+    out[:, 0:1], out[:, 1:2], out[:, 2:3], out[:, 3:4] = xc - w / 2, yc - h / 2, xc + w / 2, yc + h / 2
+    out[:, 4:16:2] = r[:, _BF_KP_OFFSET::2] * aws + cxs
+    out[:, 5:16:2] = r[:, _BF_KP_OFFSET + 1::2] * ahs + cys
+    out[:, 16] = scores[keep]
+    return out
+
+
+def _weighted_nms(detections: np.ndarray, iou_thresh: float = 0.5) -> np.ndarray:
+    """MP weighted NMS — kept boxes are score-weighted averages of overlapping detections."""
+    if detections.shape[0] == 0:
+        return detections
+    dets = detections[np.argsort(-detections[:, 16])]
+    N = dets.shape[0]
+    areas = np.clip(dets[:, 2] - dets[:, 0], 0, None) * np.clip(dets[:, 3] - dets[:, 1], 0, None)
+    kept: List[np.ndarray] = []
+    used = np.zeros(N, dtype=bool)
+    for i in range(N):
+        if used[i]:
+            continue
+        ax1, ay1, ax2, ay2 = dets[i, 0:4]
+        merge_idx = [i]
+        for j in range(i + 1, N):
+            if used[j]:
+                continue
+            bx1, by1, bx2, by2 = dets[j, 0:4]
+            iw = max(0.0, min(ax2, bx2) - max(ax1, bx1))
+            ih = max(0.0, min(ay2, by2) - max(ay1, by1))
+            inter = iw * ih
+            union = areas[i] + areas[j] - inter
+            if union > 0 and inter / union > iou_thresh:  # strict > matches MP
+                merge_idx.append(j)
+                used[j] = True
+        used[i] = True
+        cluster = dets[merge_idx]
+        ws = cluster[:, 16:17]
+        ws_sum = ws.sum()
+        merged = np.copy(cluster[0])
+        if ws_sum > 0:
+            merged[:16] = (cluster[:, :16] * ws).sum(axis=0) / ws_sum
+        kept.append(merged)
+    return np.stack(kept, axis=0) if kept else np.empty((0, 17), dtype=np.float32)
+
+
+def _detection_to_face_rect(detection: np.ndarray, image_w: int, image_h: int) -> Tuple[float, float, float, float, float]:
+    """Detection (normalized) → rotated 1.5xbbox ROI in image pixels (anisotropic)."""
+    xmin, ymin, xmax, ymax = detection[0:4]
+    lx = detection[4 + _FACE_LEFT_EYE_KP * 2 + 0] * image_w
+    ly = detection[4 + _FACE_LEFT_EYE_KP * 2 + 1] * image_h
+    rx = detection[4 + _FACE_RIGHT_EYE_KP * 2 + 0] * image_w
+    ry = detection[4 + _FACE_RIGHT_EYE_KP * 2 + 1] * image_h
+    # Image-y-down convention: angle = target - atan2(-dy, dx).
+    angle = _FACE_ROI_TARGET_ANGLE - math.atan2(ly - ry, rx - lx)
+    return (float((xmin + xmax) * 0.5 * image_w),
+            float((ymin + ymax) * 0.5 * image_h),
+            float((xmax - xmin) * image_w * _FACE_ROI_SCALE_X),
+            float((ymax - ymin) * image_h * _FACE_ROI_SCALE_Y),
+            float(angle))
+
+
+def _sample_warp(image_chw: Tensor, src_x: Tensor, src_y: Tensor, padding_mode: str) -> Tensor:
+    """Bilinear-sample image_chw at corner-aligned (src_x, src_y)."""
+    H, W = int(image_chw.shape[-2]), int(image_chw.shape[-1])
+    grid = torch.stack([(2.0 * src_x + 1.0) / W - 1.0,
+                        (2.0 * src_y + 1.0) / H - 1.0], dim=-1).unsqueeze(0)
+    return F.grid_sample(image_chw.unsqueeze(0), grid, mode="bilinear",
+                         align_corners=False, padding_mode=padding_mode).squeeze(0)
+
+
+def _warp_face_crop(image_chw: Tensor, cx: float, cy: float, width: float, height: float,
+                    angle: float, output_size: int = _FM_INPUT_SIZE) -> Tensor:
+    """Rotated rect → output_size² with BORDER_REPLICATE. image_chw must be in [0, 1]."""
+    s_x, s_y = width / output_size, height / output_size
+    cos_a, sin_a = math.cos(angle), math.sin(angle)
+    arange = torch.arange(output_size, dtype=image_chw.dtype, device=image_chw.device) - output_size * 0.5
+    v_grid, u_grid = torch.meshgrid(arange, arange, indexing="ij")
+    src_x = cx + u_grid * s_x * cos_a - v_grid * s_y * sin_a
+    src_y = cy + u_grid * s_x * sin_a + v_grid * s_y * cos_a
+    return _sample_warp(image_chw, src_x, src_y, "border")
+
+
+def _blazeface_input_warp(image_chw_raw: Tensor, target: int = _BF_INPUT_SIZE) -> Tuple[Tensor, float, float, float]:
+    """Centered max(W,H) square → target² with BORDER_ZERO + [-1, 1] norm.
+
+    Sub-pixel grid_sample matters; integer-pad-then-resize drifts the bbox ~5%.
+    Returns (warped, sub_rect_cx, sub_rect_cy, sub_rect_size) — the triplet maps
+    tensor-normalized [0,1] detections back to image pixels.
+    """
+    H, W = int(image_chw_raw.shape[1]), int(image_chw_raw.shape[2])
+    sub_rect_size = float(max(W, H))
+    sub_rect_cx, sub_rect_cy = W * 0.5, H * 0.5
+    s = sub_rect_size / target
+    arange = torch.arange(target, dtype=image_chw_raw.dtype, device=image_chw_raw.device) - target * 0.5
+    v_grid, u_grid = torch.meshgrid(arange, arange, indexing="ij")
+    out = _sample_warp(image_chw_raw, sub_rect_cx + u_grid * s, sub_rect_cy + v_grid * s, "zeros")
+    return (out / 127.5) - 1.0, sub_rect_cx, sub_rect_cy, sub_rect_size
+
+
+class FaceLandmarker(nn.Module):
+    """BlazeFace → FaceMesh v2 → blendshapes. `detector_variant` selects 'short'
+    (128², ≤2m) or 'full' (192² FPN, ≤5m). State dict uses inner-module prefixes
+    `detector.*` / `mesh.*` / `blendshapes.*`; the outer FaceLandmarkerModel
+    wrapper rewrites `detector_{variant}.*` keys to `detector.*` before loading.
+    """
+
+    def __init__(self, device=None, dtype=None, operations=None, detector_variant: str = "short"):
+        super().__init__()
+        det_cls = {"short": BlazeFace, "full": BlazeFaceFullRange}.get(detector_variant)
+
+        self.detector_variant = detector_variant
+        self.detector = det_cls(device=device, dtype=dtype, operations=operations)
+        self.mesh = FaceMesh(device=device, dtype=dtype, operations=operations)
+        self.blendshapes = FaceBlendshapes(device=device, dtype=dtype, operations=operations)
+        self.register_buffer("_bs_idx", torch.tensor(_BS_INPUT_INDICES, dtype=torch.long), persistent=False)
+
+    def run_detector_batch(self, images_rgb_uint8: List[np.ndarray],
+                           score_thresh: float = _BF_MIN_SCORE,
+                           iou_thresh: float = 0.5):
+        """Batched detector pass. Returns (img_raws, sub_rects, sizes, per_frame_decoded)
+        where per_frame_decoded[b] is (N, 17) in tensor-normalized [0,1] coords."""
+        if not images_rgb_uint8:
+            return [], [], [], []
+        device, dtype = self.detector.stem.weight.device, self.detector.stem.weight.dtype
+        det_input_size, decode_fn = ((_BF_FR_INPUT_SIZE, _decode_blazeface_full_range)
+                                     if self.detector_variant == "full"
+                                     else (_BF_INPUT_SIZE, _decode_blazeface))
+
+        # Same-size frames: stack once and transfer once. Variable size falls back
+        # to per-image (only triggers for SAM3DBody's head crops).
+        sizes = [tuple(img.shape[:2]) for img in images_rgb_uint8]
+        if len(set(sizes)) == 1:
+            batch_chw = torch.from_numpy(np.stack(images_rgb_uint8, axis=0)).to(device, dtype).movedim(-1, -3).contiguous()
+            img_raws = [batch_chw[bi] for bi in range(batch_chw.shape[0])]
+        else:
+            img_raws = [torch.from_numpy(img).to(device, dtype).movedim(-1, -3).contiguous() for img in images_rgb_uint8]
+
+        warps = [_blazeface_input_warp(img_raw, det_input_size) for img_raw in img_raws]
+        det_crops = [w[0] for w in warps]
+        sub_rects = [(w[1], w[2], w[3]) for w in warps]
+
+        regs_b, cls_b = self.detector(torch.stack(det_crops, dim=0))
+        regs_np, cls_np = regs_b.float().cpu().numpy(), cls_b.float().cpu().numpy()
+        per_frame = []
+        for b in range(len(images_rgb_uint8)):
+            decoded = decode_fn(regs_np[b], cls_np[b], score_thresh=score_thresh)
+            per_frame.append(_weighted_nms(decoded, iou_thresh=iou_thresh) if decoded.shape[0] > 0 else decoded)
+        return img_raws, sub_rects, sizes, per_frame
+
+    def detect_batch(self, images_rgb_uint8: List[np.ndarray], num_faces: int = 1,
+                     score_thresh: float = _BF_MIN_SCORE) -> List[List[dict]]:
+        """Full pipeline batched across `images_rgb_uint8`. Returns one face-dict
+        list per image (empty if nothing detected). Face dict:
+            bbox_xyxy (4,) image pixels, blendshapes {52} ∈ [0,1],
+            landmarks_xy (478, 2) image pixels, landmarks_3d (478, 3) in
+            192-canonical (pre-transformation) units, presence float (raw logit).
+        """
+        img_raws, sub_rects, sizes, per_frame_dets = self.run_detector_batch(
+            images_rgb_uint8, score_thresh=score_thresh,
+        )
+        # tensor-normalized → image-normalized [0,1] for _detection_to_face_rect.
+        for b, decoded in enumerate(per_frame_dets):
+            if decoded.shape[0] == 0:
+                continue
+            cx, cy, size = sub_rects[b]
+            H, W = sizes[b]
+            sx0, sy0 = cx - size * 0.5, cy - size * 0.5
+            decoded[:, 0:16:2] = (sx0 + size * decoded[:, 0:16:2]) / W
+            decoded[:, 1:16:2] = (sy0 + size * decoded[:, 1:16:2]) / H
+            if num_faces > 0:
+                per_frame_dets[b] = decoded[: int(num_faces)]
+
+        # Collect every detected face across all frames into one mesh input.
+        face_params: List[Tuple[int, float, float, float, float, float, float]] = []
+        mesh_crops: List[Tensor] = []
+        for b, dets in enumerate(per_frame_dets):
+            if dets.shape[0] == 0:
+                continue
+            H, W = sizes[b]
+            img_for_mesh = img_raws[b] / 255.0
+            for det in dets:
+                cx, cy, w, h, angle = _detection_to_face_rect(det, W, H)
+                mesh_crops.append(_warp_face_crop(img_for_mesh, cx, cy, w, h, angle, _FM_INPUT_SIZE))
+                face_params.append((b, float(det[16]), cx, cy, w, h, angle))
+
+        results: List[List[dict]] = [[] for _ in range(len(images_rgb_uint8))]
+        if not mesh_crops:
+            return results
+
+        lmks_canon_b, presence_b = self.mesh(torch.stack(mesh_crops, dim=0))
+        bs_out_b = self.blendshapes(lmks_canon_b[:, self._bs_idx, :2])
+
+        # Batched canonical→image affine
+        params_t = torch.tensor(
+            [(cx, cy, w, h, math.cos(a), math.sin(a)) for (_b, _s, cx, cy, w, h, a) in face_params],
+            device=lmks_canon_b.device, dtype=lmks_canon_b.dtype,
+        )
+        cxs, cys, ws, hs, cos_a, sin_a = params_t.unbind(dim=1)
+        inv = 1.0 / _FM_INPUT_SIZE
+        u = lmks_canon_b[..., 0] - _FM_INPUT_SIZE * 0.5
+        v = lmks_canon_b[..., 1] - _FM_INPUT_SIZE * 0.5
+        lmks_xy_t = torch.stack([
+            cxs[:, None] + u * (ws * inv * cos_a)[:, None] - v * (hs * inv * sin_a)[:, None],
+            cys[:, None] + u * (ws * inv * sin_a)[:, None] + v * (hs * inv * cos_a)[:, None],
+        ], dim=-1)
+
+        lmks_xy_np = lmks_xy_t.float().cpu().numpy()
+        lmks_canon_np = lmks_canon_b.float().cpu().numpy()
+        presence_np = presence_b.float().cpu().numpy()
+        bs_np = bs_out_b.float().cpu().numpy()
+
+        for i, (b, score, *_) in enumerate(face_params):
+            lmks_xy = lmks_xy_np[i]
+            mn, mx = lmks_xy.min(0), lmks_xy.max(0)
+            results[b].append({
+                "bbox_xyxy": np.array([mn[0], mn[1], mx[0], mx[1]], dtype=np.float32),
+                "blendshapes": dict(zip(BLENDSHAPE_NAMES, bs_np[i].tolist())),
+                "landmarks_xy": lmks_xy,
+                "landmarks_3d": lmks_canon_np[i],
+                "presence": float(presence_np[i]),
+                "score": score,
+            })
+        return results

comfy_extras/nodes_mediapipe.py

@@ -0,0 +1,502 @@
+"""ComfyUI nodes for the pure-PyTorch MediaPipe Face Landmarker port.
+
+Custom IO types:
+  FACE_LANDMARKER  — FaceLandmarkerModel wrapper (ModelPatcher inside)
+  FACE_LANDMARKS   — {"frames": List[List[face_dict]], "image_size": (H, W),
+                      "connection_sets": dict[str, frozenset[(int, int)]]}
+                     face_dict: bbox_xyxy, blendshapes, landmarks_xy,
+                                landmarks_3d, presence, score, transformation_matrix
+
+MediaPipeFaceLandmarker also emits the core BOUNDING_BOX type — pair with DrawBBoxes.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import torch
+from PIL import Image, ImageColor, ImageDraw
+from tqdm.auto import tqdm
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.model_patcher
+import comfy.utils
+import folder_paths
+from comfy_api.latest import ComfyExtension, io
+
+from comfy_extras.mediapipe.face_landmarker import FaceLandmarker
+from comfy_extras.mediapipe.face_geometry import transformation_matrix_from_detection
+
+
+FaceLandmarkerType = io.Custom("FACE_LANDMARKER")
+FaceLandmarksType = io.Custom("FACE_LANDMARKS")
+
+_CANONICAL_KEYS = ("canonical_vertices", "procrustes_indices", "procrustes_weights")
+_CONTOUR_PARTS = ("face_oval", "left_eye", "right_eye", "left_eyebrow", "right_eyebrow", "lips")
+
+
+class FaceLandmarkerModel:
+    """Loaded FaceLandmarker variants + ModelPatcher per variant.
+
+    Safetensors layout: `detector_short.*` / `detector_full.*` plus shared
+    `mesh.*`, `blendshapes.*`, `canonical_*`, and `topology.*`.
+    PReLU forces plain-nn / fp32 (manual_cast strands buffers across devices).
+    """
+
+    def __init__(self, state_dict: dict):
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = torch.float32
+
+        # FACEMESH_* connection sets, embedded as int32 (N, 2) under topology.*.
+        base: dict[str, frozenset] = {}
+        for k in [k for k in state_dict if k.startswith("topology.")]:
+            base[k[len("topology."):]] = frozenset(map(tuple, state_dict.pop(k).tolist()))
+        base["contours"] = frozenset().union(*(base[p] for p in _CONTOUR_PARTS))
+        base["all"] = base["contours"] | base["irises"] | base["nose"]
+
+        self.connection_sets: dict[str, frozenset] = base
+        self.canonical_data: dict[str, np.ndarray] = {k: state_dict.pop(k).numpy() for k in _CANONICAL_KEYS}
+
+        shared = {k: v for k, v in state_dict.items() if k.startswith(("mesh.", "blendshapes."))}
+
+        self.models: dict[str, FaceLandmarker] = {}
+        self.patchers: dict[str, comfy.model_patcher.ModelPatcher] = {}
+        for variant in ("short", "full"):
+            prefix = f"detector_{variant}."
+            sub = dict(shared)
+            sub.update({f"detector.{k[len(prefix):]}": v for k, v in state_dict.items() if k.startswith(prefix)})
+            fl = FaceLandmarker(device=offload_device, dtype=self.dtype, operations=None, detector_variant=variant).eval()
+            fl.load_state_dict(sub, strict=False)
+
+            self.models[variant] = fl
+            self.patchers[variant] = comfy.model_patcher.CoreModelPatcher(
+                fl, load_device=self.load_device, offload_device=offload_device,
+                size=comfy.model_management.module_size(fl),
+            )
+
+    def detect_batch(self, images, num_faces: int, score_thresh: float, variant: str):
+        comfy.model_management.load_model_gpu(self.patchers[variant])
+        return self.models[variant].detect_batch(images, num_faces=num_faces, score_thresh=score_thresh)
+
+
+def _image_to_uint8(image: torch.Tensor) -> np.ndarray:
+    return image[..., :3].mul(255.0).add_(0.5).clamp_(0, 255).to(torch.uint8).cpu().numpy()
+
+
+def _parse_color(color: str) -> tuple[int, int, int]:
+    try:
+        return ImageColor.getrgb(color)[:3]
+    except ValueError:
+        return (0, 255, 0)
+
+
+def _copy_face(face: dict) -> dict:
+    """Shallow copy of a face_dict with array-fields cloned so callers can mutate."""
+    return {
+        "bbox_xyxy":    face["bbox_xyxy"].copy(),
+        "blendshapes":  dict(face["blendshapes"]),
+        "landmarks_xy": face["landmarks_xy"].copy(),
+        "landmarks_3d": face["landmarks_3d"].copy(),
+        "presence":     face["presence"],
+        "score":        face["score"],
+    }
+
+
+def _lerp_face(a: dict, b: dict, t: float) -> dict:
+    return {
+        "bbox_xyxy":    (1 - t) * a["bbox_xyxy"]    + t * b["bbox_xyxy"],
+        "blendshapes":  {k: (1 - t) * a["blendshapes"][k] + t * b["blendshapes"][k] for k in a["blendshapes"]},
+        "landmarks_xy": (1 - t) * a["landmarks_xy"] + t * b["landmarks_xy"],
+        "landmarks_3d": (1 - t) * a["landmarks_3d"] + t * b["landmarks_3d"],
+        "presence":     (1 - t) * a["presence"] + t * b["presence"],
+        "score":        (1 - t) * a["score"]    + t * b["score"],
+    }
+
+
+def _match_faces(a: list[dict], b: list[dict]) -> list[tuple[int, int]]:
+    """Greedy nearest-neighbour pairing of faces between two frames by bbox
+    centre distance. Unmatched (when counts differ) are dropped."""
+    if not a or not b:
+        return []
+    centers_a = np.array([(0.5 * (f["bbox_xyxy"][0] + f["bbox_xyxy"][2]),
+                           0.5 * (f["bbox_xyxy"][1] + f["bbox_xyxy"][3])) for f in a])
+    centers_b = np.array([(0.5 * (f["bbox_xyxy"][0] + f["bbox_xyxy"][2]),
+                           0.5 * (f["bbox_xyxy"][1] + f["bbox_xyxy"][3])) for f in b])
+    dists = np.linalg.norm(centers_a[:, None] - centers_b[None], axis=-1)
+    pairs: list[tuple[int, int]] = []
+    used_a: set[int] = set()
+    used_b: set[int] = set()
+    candidates = sorted((dists[ia, ib], ia, ib) for ia in range(len(a)) for ib in range(len(b)))
+    for _, ia, ib in candidates:
+        if ia in used_a or ib in used_b:
+            continue
+        pairs.append((ia, ib))
+        used_a.add(ia)
+        used_b.add(ib)
+    return pairs
+
+
+def _fill_missing_frames(frames: list[list[dict]], mode: str) -> None:
+    """In-place fill empty frame slots from neighbouring detections. Multi-face
+    aware: pairs faces across bracketing frames by greedy bbox-centre NN.
+    When counts differ, unmatched faces are dropped from the synthesised frame."""
+    if mode == "empty":
+        return
+    valid = [i for i, fr in enumerate(frames) if fr]
+    if not valid:
+        return  # nothing to fill from
+    if mode == "previous":
+        last: list[dict] = []
+        for i, fr in enumerate(frames):
+            if fr:
+                last = fr
+            elif last:
+                frames[i] = [_copy_face(f) for f in last]
+        return
+    # interpolate: lerp between bracketing valid frames; clamp at ends.
+    for i in range(len(frames)):
+        if frames[i]:
+            continue
+        prev_i = max((v for v in valid if v < i), default=None)
+        next_i = min((v for v in valid if v > i), default=None)
+        if prev_i is None:
+            frames[i] = [_copy_face(f) for f in frames[next_i]]
+        elif next_i is None:
+            frames[i] = [_copy_face(f) for f in frames[prev_i]]
+        else:
+            t = (i - prev_i) / (next_i - prev_i)
+            pairs = _match_faces(frames[prev_i], frames[next_i])
+            frames[i] = [_lerp_face(frames[prev_i][a], frames[next_i][b], t) for a, b in pairs]
+
+
+def _ordered_rings(edges: frozenset[tuple[int, int]]) -> list[list[int]]:
+    """Walk an unordered edge set into one or more closed-loop vertex rings
+    (handles multi-loop sets like FACEMESH_LIPS: outer + inner)."""
+    adj: dict[int, set[int]] = {}
+    for a, b in edges:
+        adj.setdefault(a, set()).add(b)
+        adj.setdefault(b, set()).add(a)
+    visited: set[int] = set()
+    rings: list[list[int]] = []
+    for start in adj:
+        if start in visited:
+            continue
+        ring = [start]
+        visited.add(start)
+        prev, cur = -1, start
+        while True:
+            nxt = next((v for v in adj[cur] if v != prev), None)
+            if nxt is None or nxt == start:
+                break
+            ring.append(nxt)
+            visited.add(nxt)
+            prev, cur = cur, nxt
+        rings.append(ring)
+    return rings
+
+
+class LoadMediaPipeFaceLandmarker(io.ComfyNode):
+    """Load MediaPipe Face Landmarker v2 weights. Contains both detector variants
+    (short / full), shared mesh, blendshapes, and canonical geometry."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadMediaPipeFaceLandmarker",
+            display_name="Load MediaPipe Face Landmarker",
+            category="loaders",
+            inputs=[
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("mediapipe"),
+                               tooltip="Face Landmarker safetensors from models/mediapipe/."),
+            ],
+            outputs=[FaceLandmarkerType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model_name) -> io.NodeOutput:
+        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("mediapipe", model_name), safe_load=True)
+        wrapper = FaceLandmarkerModel(sd)
+        return io.NodeOutput(wrapper)
+
+
+# Per-frame fallback modes for detection failures in a batch.
+_FALLBACK_MODES = ("empty", "previous", "interpolate")
+
+
+class MediaPipeFaceLandmarker(io.ComfyNode):
+    """BlazeFace → FaceMesh v2 → ARKit-52 blendshapes, batched across the
+    input. Also emits a BOUNDING_BOX list (landmark-extent bbox per face) —
+    pair with DrawBBoxes for detector-only viz or MediaPipeFaceMeshVisualize
+    for the mesh overlay."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MediaPipeFaceLandmarker",
+            display_name="MediaPipe Face Landmarker",
+            category="image/detection",
+            inputs=[
+                FaceLandmarkerType.Input("face_landmarker"),
+                io.Image.Input("image"),
+                io.Combo.Input("detector_variant", options=["short", "full", "both"], default="short",
+                               tooltip="Face detector range. 'short' is tuned for close-up faces "
+                                       "(within ~2 m of the camera); 'full' covers farther / smaller "
+                                       "faces (up to ~5 m) but is slower. 'both' runs both detectors and "
+                                       "keeps whichever found more faces per frame (~2× detection cost)."),
+                io.Int.Input("num_faces", default=1, min=0, max=16, step=1,
+                             tooltip="Maximum faces to return per frame. 0 = no cap (return all detected)."),
+                io.Float.Input("min_confidence", default=0.5, min=0.0, max=1.0, step=0.01, advanced=True,
+                               tooltip="BlazeFace score threshold. Lower to catch small/occluded faces."),
+                io.Combo.Input("missing_frame_fallback", options=list(_FALLBACK_MODES), default="empty", advanced=True,
+                               tooltip="Per-frame behaviour when detection fails in a batch. "
+                                       "'empty' leaves the frame faceless. 'previous' copies the most recent successful "
+                                       "detection. 'interpolate' lerps landmarks/bbox/blendshapes between bracketing "
+                                       "successful frames. Multi-face: pairs faces across frames by greedy bbox-centre NN."),
+            ],
+            outputs=[
+                FaceLandmarksType.Output(display_name="face_landmarks"),
+                io.BoundingBox.Output("bboxes"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, face_landmarker, image, detector_variant, num_faces, min_confidence,
+                missing_frame_fallback) -> io.NodeOutput:
+        canonical = face_landmarker.canonical_data
+        img_np = _image_to_uint8(image)
+        B, H, W = img_np.shape[:3]
+        chunk = 16
+        is_both = detector_variant == "both"
+        total_work = 2 * B if is_both else B
+        pbar = comfy.utils.ProgressBar(total_work)
+
+        def _run(variant: str) -> list[list[dict]]:
+            res: list[list[dict]] = []
+            with tqdm(total=B, desc=f"MediaPipe Face Landmarker ({variant})") as tq:
+                for i in range(0, B, chunk):
+                    end = min(i + chunk, B)
+                    res.extend(face_landmarker.detect_batch(
+                        [img_np[bi] for bi in range(i, end)],
+                        num_faces=int(num_faces),
+                        score_thresh=float(min_confidence),
+                        variant=variant,
+                    ))
+                    pbar.update_absolute(min(pbar.current + (end - i), total_work))
+                    tq.update(end - i)
+            return res
+
+        if is_both:
+            short_res = _run("short")
+            full_res = _run("full")
+            # Per-frame keep whichever found more faces (tie → short).
+            frames: list[list[dict]] = [
+                short_res[bi] if len(short_res[bi]) >= len(full_res[bi]) else full_res[bi]
+                for bi in range(B)
+            ]
+        else:
+            frames = _run(detector_variant)
+        _fill_missing_frames(frames, missing_frame_fallback)
+        bboxes = []
+        for per_frame in frames:
+            per_bb = []
+            for f in per_frame:
+                f["transformation_matrix"] = transformation_matrix_from_detection(f, W, H, canonical)
+                x1, y1, x2, y2 = (float(v) for v in f["bbox_xyxy"])
+                per_bb.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1, "label": "face", "score": float(f["score"])})
+            bboxes.append(per_bb)
+        return io.NodeOutput({"frames": frames, "image_size": (H, W),
+                              "connection_sets": face_landmarker.connection_sets}, bboxes)
+
+
+# Topology keys unioned by the 'all' connections preset (contour parts + irises + nose).
+_ALL_CONNECTION_PARTS: tuple[str, ...] = (*_CONTOUR_PARTS, "irises", "nose")
+_CUSTOM_FEATURES: tuple[tuple[str, bool], ...] = (
+    ("face_oval",     True),
+    ("lips",          True),
+    ("left_eye",      True),
+    ("right_eye",     True),
+    ("left_eyebrow",  True),
+    ("right_eyebrow", True),
+    ("irises",        True),
+    ("nose",          True),
+    ("tesselation",   False),
+)
+
+
+class MediaPipeFaceMeshVisualize(io.ComfyNode):
+    """Draw a FACEMESH_* subset over an image. Topology travels with the
+    FACE_LANDMARKS payload (set at detection time)."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MediaPipeFaceMeshVisualize",
+            display_name="MediaPipe Face Mesh Visualize",
+            category="image/detection",
+            inputs=[
+                FaceLandmarksType.Input("face_landmarks"),
+                io.Image.Input("image", optional=True, tooltip="If not connected, a black canvas will be used."),
+                io.DynamicCombo.Input(
+                    "connections",
+                    tooltip="'all' = oval+eyes+brows+lips+irises+nose. 'fill' = solid face_oval polygon (silhouette mask). 'custom' = toggle each feature individually (including 'tesselation', the full 2547-edge wireframe).",
+                    options=[
+                        io.DynamicCombo.Option("all", []),
+                        io.DynamicCombo.Option("fill", []),
+                        io.DynamicCombo.Option("custom", [
+                            io.Boolean.Input(feat, default=default,
+                                             tooltip=f"Draw the '{feat}' connection set.")
+                            for feat, default in _CUSTOM_FEATURES
+                        ]),
+                    ],
+                ),
+                io.Color.Input("color", default="#00ff00"),
+                io.Int.Input("thickness", default=1, min=0, max=8, step=1,
+                             tooltip="Edge line thickness in pixels. 0 disables edge drawing."),
+                io.Int.Input("point_size", default=2, min=0, max=16, step=1,
+                             tooltip="Landmark dot radius in pixels. 0 disables point drawing."),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, face_landmarks, connections, color, thickness, point_size, image=None) -> io.NodeOutput:
+        sets = face_landmarks["connection_sets"]
+        sel = connections["connections"]
+        fill_rings: list[list[int]] | None = None
+        if sel == "fill":
+            fill_rings = _ordered_rings(sets["face_oval"])
+            edges = frozenset()
+        elif sel == "custom":
+            parts = [feat for feat, _ in _CUSTOM_FEATURES if connections.get(feat, False)]
+            edges = frozenset().union(*(sets[p] for p in parts))
+        else:  # "all"
+            edges = frozenset().union(*(sets[p] for p in _ALL_CONNECTION_PARTS))
+        rgb, thick, psize = _parse_color(color), int(thickness), int(point_size)
+        frames = face_landmarks["frames"]
+        if image is None:
+            H, W = face_landmarks["image_size"]
+            img_np = np.zeros((len(frames), H, W, 3), dtype=np.uint8)
+        else:
+            img_np = _image_to_uint8(image)
+        B = img_np.shape[0]
+        n_frames = len(frames)
+        pbar = comfy.utils.ProgressBar(B)
+        out = np.empty_like(img_np)
+        for bi in range(B):
+            faces = frames[bi] if bi < n_frames else []
+            out[bi] = _draw_mesh(img_np[bi], faces, edges, rgb, thick, psize, fill_rings)
+            pbar.update_absolute(bi + 1)
+        return io.NodeOutput(torch.from_numpy(out).to(
+            device=comfy.model_management.intermediate_device(),
+            dtype=comfy.model_management.intermediate_dtype(),
+        ).div_(255.0))
+
+
+def _draw_mesh(image_rgb: np.ndarray, faces: list, edges,
+               rgb: tuple[int, int, int], thickness: int,
+               point_size: int, fill_rings: list[list[int]] | None = None) -> np.ndarray:
+    draw_edges = thickness > 0 and edges
+    if not faces or (fill_rings is None and not draw_edges and point_size <= 0):
+        return image_rgb.copy()
+    pil = Image.fromarray(image_rgb)
+    draw = ImageDraw.Draw(pil)
+    r = point_size * 0.5
+    if fill_rings is not None:
+        for f in faces:
+            lmks = f["landmarks_xy"]
+            for ring in fill_rings:
+                draw.polygon([(float(lmks[i, 0]), float(lmks[i, 1])) for i in ring], fill=rgb)
+        return np.asarray(pil)
+    for f in faces:
+        lmks = f["landmarks_xy"]
+        n = lmks.shape[0]
+        if draw_edges:
+            for a, b in edges:
+                if a < n and b < n:
+                    draw.line([(float(lmks[a, 0]), float(lmks[a, 1])),
+                               (float(lmks[b, 0]), float(lmks[b, 1]))], fill=rgb, width=thickness)
+        if point_size == 1:
+            draw.point(lmks.flatten().tolist(), fill=rgb)
+        elif point_size > 1:
+            for x, y in lmks:
+                draw.ellipse((float(x) - r, float(y) - r, float(x) + r, float(y) + r), fill=rgb)
+    return np.asarray(pil)
+
+
+# Mask region presets — closed-loop topologies only.
+_MASK_REGIONS: tuple[str, ...] = ("face_oval", "lips", "left_eye", "right_eye", "irises")
+_MASK_CUSTOM_FEATURES: tuple[tuple[str, bool], ...] = (
+    ("face_oval",  True),
+    ("lips",       False),
+    ("left_eye",   False),
+    ("right_eye",  False),
+    ("irises",     False),
+)
+
+
+class MediaPipeFaceMask(io.ComfyNode):
+    """Binary mask from face landmarks, filled polygon per face. One mask per
+    frame in the batch; faces in the same frame composite (union)."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="MediaPipeFaceMask",
+            display_name="MediaPipe Face Mask",
+            category="image/detection",
+            inputs=[
+                FaceLandmarksType.Input("face_landmarks"),
+                io.DynamicCombo.Input(
+                    "regions",
+                    tooltip="'all' = union of face_oval+lips+eyes+irises (which collapses to face_oval since it encloses the rest). 'custom' = toggle each region individually for combos like lips+eyes.",
+                    options=[
+                        io.DynamicCombo.Option("all", []),
+                        io.DynamicCombo.Option("custom", [
+                            io.Boolean.Input(reg, default=default,
+                                             tooltip=f"Include the '{reg}' region in the mask.")
+                            for reg, default in _MASK_CUSTOM_FEATURES
+                        ]),
+                    ],
+                ),
+            ],
+            outputs=[io.Mask.Output()],
+        )
+
+    @classmethod
+    def execute(cls, face_landmarks, regions) -> io.NodeOutput:
+        sets = face_landmarks["connection_sets"]
+        sel = regions["regions"]
+        if sel == "custom":
+            picked = [reg for reg, _ in _MASK_CUSTOM_FEATURES if regions.get(reg, False)]
+        else:
+            picked = list(_MASK_REGIONS)
+        rings = [r for reg in picked for r in _ordered_rings(sets[reg])]
+        frames = face_landmarks["frames"]
+        H, W = face_landmarks["image_size"]
+        masks = np.zeros((len(frames), H, W), dtype=np.uint8)
+        pbar = comfy.utils.ProgressBar(len(frames))
+        for bi, per_frame in enumerate(frames):
+            if per_frame:
+                pil = Image.new("L", (W, H), 0)
+                draw = ImageDraw.Draw(pil)
+                for f in per_frame:
+                    lmks = f["landmarks_xy"]
+                    for ring in rings:
+                        draw.polygon([(float(lmks[i, 0]), float(lmks[i, 1])) for i in ring], fill=255)
+                masks[bi] = np.asarray(pil)
+            pbar.update_absolute(bi + 1)
+        return io.NodeOutput(torch.from_numpy(masks).to(
+            device=comfy.model_management.intermediate_device(),
+            dtype=comfy.model_management.intermediate_dtype(),
+        ).div_(255.0))
+
+
+class MediaPipeFaceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [LoadMediaPipeFaceLandmarker, MediaPipeFaceLandmarker, MediaPipeFaceMeshVisualize, MediaPipeFaceMask]
+
+
+async def comfy_entrypoint() -> MediaPipeFaceExtension:
+    return MediaPipeFaceExtension()

execution.py

@@ -2,6 +2,7 @@ import copy
 import heapq
 import inspect
 import logging
+import psutil
 import sys
 import threading
 import time
@@ -727,6 +728,7 @@ class PromptExecutor:
 
         self._notify_prompt_lifecycle("start", prompt_id)
         ram_headroom = int(self.cache_args["ram"] * (1024 ** 3))
+        ram_inactive_headroom = int(self.cache_args["ram_inactive"] * (1024 ** 3))
         ram_release_callback = self.caches.outputs.ram_release if self.cache_type == CacheType.RAM_PRESSURE else None
         comfy.memory_management.set_ram_cache_release_state(ram_release_callback, ram_headroom)
 
@@ -780,8 +782,14 @@ class PromptExecutor:
                         execution_list.complete_node_execution()
 
                     if self.cache_type == CacheType.RAM_PRESSURE:
-                        comfy.model_management.free_memory(0, None, pins_required=ram_headroom, ram_required=ram_headroom)
-                        ram_release_callback(ram_headroom, free_active=True)
+                        ram_release_callback(ram_inactive_headroom)
+                        ram_shortfall = ram_headroom - psutil.virtual_memory().available
+                        freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
+                        if freed < ram_shortfall:
+                            if freed > 64 * (1024 ** 2):
+                                # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
+                                time.sleep(0.05)
+                            ram_release_callback(ram_headroom, free_active=True)
                 else:
                     # Only execute when the while-loop ends without break
                     # Send cached UI for intermediate output nodes that weren't executed

folder_paths.py

@@ -60,6 +60,8 @@ folder_names_and_paths["geometry_estimation"] = ([os.path.join(models_dir, "geom
 
 folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)
 
+folder_names_and_paths["mediapipe"] = ([os.path.join(models_dir, "mediapipe")], supported_pt_extensions)
+
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
 input_directory = os.path.join(base_path, "input")

main.py

@@ -283,19 +283,25 @@ def _collect_output_absolute_paths(history_result: dict) -> list[str]:
 
 def prompt_worker(q, server_instance):
     current_time: float = 0.0
-    cache_ram = args.cache_ram
-    if cache_ram < 0:
+    cache_ram = 0
+    cache_ram_inactive = 0
+    if not args.cache_classic and not args.cache_none and args.cache_lru <= 0:
         cache_ram = min(32.0, max(4.0, comfy.model_management.total_ram * 0.25 / 1024.0))
+        cache_ram_inactive = min(96.0, max(12.0, comfy.model_management.total_ram * 0.75 / 1024.0))
+        if len(args.cache_ram) > 0:
+            cache_ram = args.cache_ram[0]
+        if len(args.cache_ram) > 1:
+            cache_ram_inactive = args.cache_ram[1]
 
-    cache_type = execution.CacheType.CLASSIC
-    if args.cache_lru > 0:
+    cache_type = execution.CacheType.RAM_PRESSURE
+    if args.cache_classic:
+        cache_type = execution.CacheType.CLASSIC
+    elif args.cache_lru > 0:
         cache_type = execution.CacheType.LRU
-    elif cache_ram > 0:
-        cache_type = execution.CacheType.RAM_PRESSURE
     elif args.cache_none:
         cache_type = execution.CacheType.NONE
 
-    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_args={ "lru" : args.cache_lru, "ram" : cache_ram } )
+    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_args={ "lru" : args.cache_lru, "ram" : cache_ram, "ram_inactive" : cache_ram_inactive } )
     last_gc_collect = 0
     need_gc = False
     gc_collect_interval = 10.0

nodes.py

@@ -2444,6 +2444,7 @@ async def init_builtin_extra_nodes():
         "nodes_hidream_o1.py",
         "nodes_save_3d.py",
         "nodes_moge.py",
+        "nodes_mediapipe.py",
     ]
 
     import_failed = []

requirements.txt

@@ -23,7 +23,7 @@ SQLAlchemy>=2.0.0
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.8
-comfy-aimdo==0.3.0
+comfy-aimdo==0.4.3
 requests
 simpleeval>=1.0.0
 blake3

tests/execution/test_async_nodes.py

@@ -14,7 +14,6 @@ from tests.execution.test_execution import ComfyClient, run_warmup
 class TestAsyncNodes:
     @fixture(scope="class", autouse=True, params=[
         (False, 0),
-        (True, 0),
         (True, 100),
     ])
     def _server(self, args_pytest, request):
@@ -29,6 +28,8 @@ class TestAsyncNodes:
         use_lru, lru_size = request.param
         if use_lru:
             pargs += ['--cache-lru', str(lru_size)]
+        else:
+            pargs += ['--cache-classic']
         # Running server with args: pargs
         p = subprocess.Popen(pargs)
         yield

tests/execution/test_execution.py

@@ -183,8 +183,7 @@ class TestExecution:
     # Initialize server and client
     #
     @fixture(scope="class", autouse=True, params=[
-        { "extra_args" : [], "should_cache_results" : True },
-        { "extra_args" : ["--cache-lru", 0], "should_cache_results" : True },
+        { "extra_args" : ["--cache-classic"], "should_cache_results" : True },
         { "extra_args" : ["--cache-lru", 100], "should_cache_results" : True },
         { "extra_args" : ["--cache-none"], "should_cache_results" : False },
     ])