Merge afa96152b8 into 593be209a4

* feat: add essentials_category to nodes and blueprints for Essentials tab

Add ESSENTIALS_CATEGORY or essentials_category to 12 node classes and all
36 blueprint JSONs. Update SubgraphEntry TypedDict and subgraph_manager to
extract and pass through the field.

Fixes COM-15221

Amp-Thread-ID: https://ampcode.com/threads/T-019c83de-f7ab-7779-a451-0ba5940b56a9

* fix: import NotRequired from typing_extensions for Python 3.10 compat

* refactor: keep only node class ESSENTIALS_CATEGORY, remove blueprint/subgraph changes

Frontend will own blueprint categorization separately.

* fix: remove essentials_category from CreateVideo (not in spec)

---------

Co-authored-by: guill <jacob.e.segal@gmail.com>

.gitignore

@@ -2,6 +2,7 @@ __pycache__/
 *.py[cod]
 /output/
 /input/
+/cache/
 !/input/example.png
 /models/
 /temp/

README.md

@@ -38,6 +38,8 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 
 ## Get Started
 
+### Local
+
 #### [Desktop Application](https://www.comfy.org/download)
 - The easiest way to get started.
 - Available on Windows & macOS.
@@ -49,8 +51,13 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 #### [Manual Install](#manual-install-windows-linux)
 Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
 
-## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
-See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
+### Cloud
+
+#### [Comfy Cloud](https://www.comfy.org/cloud)
+- Our official paid cloud version for those who can't afford local hardware.
+
+## Examples
+See what ComfyUI can do with the [newer template workflows](https://comfy.org/workflows) or old [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.

comfy/cli_args.py

@@ -83,6 +83,8 @@ fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
 fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
 
+parser.add_argument("--fp16-intermediates", action="store_true", help="Experimental: Use fp16 for intermediate tensors between nodes instead of fp32.")
+
 parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")

comfy/float.py

@@ -209,3 +209,39 @@ def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16x, seed=
         output_block[i:i + slice_size].copy_(block)
 
     return output_fp4, to_blocked(output_block, flatten=False)
+
+
+def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0):
+    def roundup(x_val, multiple):
+        return ((x_val + multiple - 1) // multiple) * multiple
+
+    if pad_32x:
+        rows, cols = x.shape
+        padded_rows = roundup(rows, 32)
+        padded_cols = roundup(cols, 32)
+        if padded_rows != rows or padded_cols != cols:
+            x = torch.nn.functional.pad(x, (0, padded_cols - cols, 0, padded_rows - rows))
+
+    F8_E4M3_MAX = 448.0
+    E8M0_BIAS = 127
+    BLOCK_SIZE = 32
+
+    rows, cols = x.shape
+    x_blocked = x.reshape(rows, -1, BLOCK_SIZE)
+    max_abs = torch.amax(torch.abs(x_blocked), dim=-1)
+
+    # E8M0 block scales (power-of-2 exponents)
+    scale_needed = torch.clamp(max_abs.float() / F8_E4M3_MAX, min=2**(-127))
+    exp_biased = torch.clamp(torch.ceil(torch.log2(scale_needed)).to(torch.int32) + E8M0_BIAS, 0, 254)
+    block_scales_e8m0 = exp_biased.to(torch.uint8)
+
+    zero_mask = (max_abs == 0)
+    block_scales_f32 = (block_scales_e8m0.to(torch.int32) << 23).view(torch.float32)
+    block_scales_f32 = torch.where(zero_mask, torch.ones_like(block_scales_f32), block_scales_f32)
+
+    # Scale per-block then stochastic round
+    data_scaled = (x_blocked.float() / block_scales_f32.unsqueeze(-1)).reshape(rows, cols)
+    output_fp8 = stochastic_rounding(data_scaled, torch.float8_e4m3fn, seed=seed)
+
+    block_scales_e8m0 = torch.where(zero_mask, torch.zeros_like(block_scales_e8m0), block_scales_e8m0)
+    return output_fp8, to_blocked(block_scales_e8m0, flatten=False).view(torch.float8_e8m0fnu)

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -11,6 +11,7 @@ from .causal_conv3d import CausalConv3d
 from .pixel_norm import PixelNorm
 from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
 import comfy.ops
+import comfy.model_management
 from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
@@ -536,7 +537,7 @@ class Decoder(nn.Module):
                     mark_conv3d_ended(self.conv_out)
                 sample = self.conv_out(sample, causal=self.causal)
                 if sample is not None and sample.shape[2] > 0:
-                    output.append(sample)
+                    output.append(sample.to(comfy.model_management.intermediate_device()))
                 return
 
             up_block = self.up_blocks[idx]

comfy/memory_management.py

@@ -1,9 +1,68 @@
 import math
+import ctypes
+import threading
+import dataclasses
 import torch
 from typing import NamedTuple
 
 from comfy.quant_ops import QuantizedTensor
 
+
+class TensorFileSlice(NamedTuple):
+    file_ref: object
+    thread_id: int
+    offset: int
+    size: int
+
+
+def read_tensor_file_slice_into(tensor, destination):
+
+    if isinstance(tensor, QuantizedTensor):
+        if not isinstance(destination, QuantizedTensor):
+            return False
+        if tensor._layout_cls != destination._layout_cls:
+            return False
+
+        if not read_tensor_file_slice_into(tensor._qdata, destination._qdata):
+            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)
+        return True
+
+    info = getattr(tensor.untyped_storage(), "_comfy_tensor_file_slice", None)
+    if info is None:
+        return False
+
+    file_obj = info.file_ref
+    if (destination.device.type != "cpu"
+            or file_obj is None
+            or threading.get_ident() != info.thread_id
+            or destination.numel() * destination.element_size() < info.size):
+        return False
+
+    if info.size == 0:
+        return True
+
+    buf_type = ctypes.c_ubyte * info.size
+    view = memoryview(buf_type.from_address(destination.data_ptr()))
+
+    try:
+        file_obj.seek(info.offset)
+        done = 0
+        while done < info.size:
+            try:
+                n = file_obj.readinto(view[done:])
+            except OSError:
+                return False
+            if n <= 0:
+                return False
+            done += n
+        return True
+    finally:
+        view.release()
+
 class TensorGeometry(NamedTuple):
     shape: any
     dtype: torch.dtype

comfy/model_management.py

@@ -32,6 +32,26 @@ import comfy.memory_management
 import comfy.utils
 import comfy.quant_ops
 
+
+def get_container_ram_total():
+    cgroup_v2_limit = "/sys/fs/cgroup/memory.max"
+    cgroup_v1_limit = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
+    try:
+        if os.path.exists(cgroup_v2_limit):
+            with open(cgroup_v2_limit, "r") as f:
+                limit = int(f.read().strip())
+        elif os.path.exists(cgroup_v1_limit):
+            with open(cgroup_v1_limit, "r") as f:
+                limit = int(f.read().strip())
+        else:
+            limit = psutil.virtual_memory().total
+        if limit == 9223372036854771712:
+            limit = psutil.virtual_memory().total
+        return limit
+    except:
+        return psutil.virtual_memory().total
+
+
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
     NO_VRAM = 1     #Very low vram: enable all the options to save vram
@@ -211,11 +231,13 @@ def get_total_memory(dev=None, torch_total_too=False):
         dev = get_torch_device()
 
     if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
-        mem_total = psutil.virtual_memory().total
+        # 核心修改：读取容器内存
+        mem_total = get_container_ram_total()
         mem_total_torch = mem_total
     else:
+        # GPU逻辑保持不变
         if directml_enabled:
-            mem_total = 1024 * 1024 * 1024 #TODO
+            mem_total = 1024 * 1024 * 1024
             mem_total_torch = mem_total
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
@@ -254,7 +276,7 @@ def mac_version():
         return None
 
 total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
-total_ram = psutil.virtual_memory().total / (1024 * 1024)
+total_ram = get_total_memory(torch.device("cpu")) / (1024 * 1024)
 logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 
 try:
@@ -400,7 +422,7 @@ try:
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
-                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):
@@ -505,6 +527,28 @@ 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
+
 class LoadedModel:
     def __init__(self, model):
         self._set_model(model)
@@ -532,6 +576,9 @@ 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()
 
@@ -633,7 +680,7 @@ def extra_reserved_memory():
 def minimum_inference_memory():
     return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()
 
-def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_required=0):
+def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):
     cleanup_models_gc()
     unloaded_model = []
     can_unload = []
@@ -646,13 +693,14 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
-    for x in sorted(can_unload):
+    can_unload_sorted = sorted(can_unload)
+    for x in can_unload_sorted:
         i = x[-1]
         memory_to_free = 1e32
-        ram_to_free = 1e32
+        pins_to_free = 1e32
         if not DISABLE_SMART_MEMORY:
             memory_to_free = memory_required - get_free_memory(device)
-            ram_to_free = ram_required - get_free_ram()
+            pins_to_free = pins_required - get_free_ram()
             if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                 #don't actually unload dynamic models for the sake of other dynamic models
                 #as that works on-demand.
@@ -661,9 +709,18 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_
         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 ram_to_free > 0:
+        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__}")
-            current_loaded_models[i].model.partially_unload_ram(ram_to_free)
 
     for i in sorted(unloaded_model, reverse=True):
         unloaded_models.append(current_loaded_models.pop(i))
@@ -729,17 +786,27 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
 
 
     total_memory_required = {}
+    total_pins_required = {}
     total_ram_required = {}
     for loaded_model in models_to_load:
-        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
-        #x2, one to make sure the OS can fit the model for loading in disk cache, and for us to do any pinning we
-        #want to do.
-        #FIXME: This should subtract off the to_load current pin consumption.
-        total_ram_required[loaded_model.device] = total_ram_required.get(loaded_model.device, 0) + loaded_model.model_memory() * 2
+        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, ram_required=total_ram_required[device])
+            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 device in total_memory_required:
         if device != torch.device("cpu"):
@@ -1005,6 +1072,12 @@ def intermediate_device():
     else:
         return torch.device("cpu")
 
+def intermediate_dtype():
+    if args.fp16_intermediates:
+        return torch.float16
+    else:
+        return torch.float32
+
 def vae_device():
     if args.cpu_vae:
         return torch.device("cpu")
@@ -1225,6 +1298,11 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
             dest_view = dest_views.pop(0)
             if tensor is None:
                 continue
+            if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_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
             dest_view.copy_(tensor, non_blocking=non_blocking)
 
 
@@ -1662,6 +1740,19 @@ def supports_nvfp4_compute(device=None):
 
     return True
 
+def supports_mxfp8_compute(device=None):
+    if not is_nvidia():
+        return False
+
+    if torch_version_numeric < (2, 10):
+        return False
+
+    props = torch.cuda.get_device_properties(device)
+    if props.major < 10:
+        return False
+
+    return True
+
 def extended_fp16_support():
     # TODO: check why some models work with fp16 on newer torch versions but not on older
     if torch_version_numeric < (2, 7):

comfy/model_patcher.py

@@ -297,6 +297,9 @@ 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 get_ram_usage(self):
         return self.model_size()
 
@@ -1063,6 +1066,10 @@ class ModelPatcher:
 
             return self.model.model_loaded_weight_memory - current_used
 
+    def pinned_memory_size(self):
+        # Pinned memory pressure tracking is only implemented for DynamicVram loading
+        return 0
+
     def partially_unload_ram(self, ram_to_unload):
         pass
 
@@ -1653,6 +1660,16 @@ 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 partially_unload_ram(self, ram_to_unload):
         loading = self._load_list(for_dynamic=True, default_device=self.offload_device)
         for x in loading:

comfy/ops.py

@@ -306,10 +306,40 @@ class CastWeightBiasOp:
     bias_function = []
 
 class disable_weight_init:
+    @staticmethod
+    def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata,
+                                   missing_keys, unexpected_keys, weight_shape,
+                                   bias_shape=None):
+        assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
+        prefix_len = len(prefix)
+        for k, v in state_dict.items():
+            key = k[prefix_len:]
+            if key == "weight":
+                if not assign_to_params_buffers:
+                    v = v.clone()
+                module.weight = torch.nn.Parameter(v, requires_grad=False)
+            elif bias_shape is not None and key == "bias" and v is not None:
+                if not assign_to_params_buffers:
+                    v = v.clone()
+                module.bias = torch.nn.Parameter(v, requires_grad=False)
+            else:
+                unexpected_keys.append(k)
+
+        if module.weight is None:
+            module.weight = torch.nn.Parameter(torch.zeros(weight_shape), requires_grad=False)
+            missing_keys.append(prefix + "weight")
+
+        if bias_shape is not None and module.bias is None and getattr(module, "comfy_need_lazy_init_bias", False):
+            module.bias = torch.nn.Parameter(torch.zeros(bias_shape), requires_grad=False)
+            missing_keys.append(prefix + "bias")
+
     class Linear(torch.nn.Linear, CastWeightBiasOp):
 
         def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            # don't trust subclasses that BYO state dict loader to call us.
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
                 super().__init__(in_features, out_features, bias, device, dtype)
                 return
 
@@ -330,32 +360,21 @@ class disable_weight_init:
         def _load_from_state_dict(self, state_dict, prefix, local_metadata,
                                 strict, missing_keys, unexpected_keys, error_msgs):
 
-            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
                 return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                                      missing_keys, unexpected_keys, error_msgs)
-            assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False)
-            prefix_len = len(prefix)
-            for k,v in state_dict.items():
-                if k[prefix_len:] == "weight":
-                    if not assign_to_params_buffers:
-                        v = v.clone()
-                    self.weight = torch.nn.Parameter(v, requires_grad=False)
-                elif k[prefix_len:] == "bias" and v is not None:
-                    if not assign_to_params_buffers:
-                        v = v.clone()
-                    self.bias = torch.nn.Parameter(v, requires_grad=False)
-                else:
-                    unexpected_keys.append(k)
-
-            #Reconcile default construction of the weight if its missing.
-            if self.weight is None:
-                v = torch.zeros(self.in_features, self.out_features)
-                self.weight = torch.nn.Parameter(v, requires_grad=False)
-                missing_keys.append(prefix+"weight")
-            if self.bias is None and self.comfy_need_lazy_init_bias:
-                v = torch.zeros(self.out_features,)
-                self.bias = torch.nn.Parameter(v, requires_grad=False)
-                missing_keys.append(prefix+"bias")
+            disable_weight_init._lazy_load_from_state_dict(
+                self,
+                state_dict,
+                prefix,
+                local_metadata,
+                missing_keys,
+                unexpected_keys,
+                weight_shape=(self.in_features, self.out_features),
+                bias_shape=(self.out_features,),
+            )
 
 
         def reset_parameters(self):
@@ -547,6 +566,53 @@ class disable_weight_init:
                 return super().forward(*args, **kwargs)
 
     class Embedding(torch.nn.Embedding, CastWeightBiasOp):
+        def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,
+                     norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,
+                     _freeze=False, device=None, dtype=None):
+            # don't trust subclasses that BYO state dict loader to call us.
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
+                super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
+                                 norm_type, scale_grad_by_freq, sparse, _weight,
+                                 _freeze, device, dtype)
+                return
+
+            torch.nn.Module.__init__(self)
+            self.num_embeddings = num_embeddings
+            self.embedding_dim = embedding_dim
+            self.padding_idx = padding_idx
+            self.max_norm = max_norm
+            self.norm_type = norm_type
+            self.scale_grad_by_freq = scale_grad_by_freq
+            self.sparse = sparse
+            # Keep shape/dtype visible for module introspection without reserving storage.
+            embedding_dtype = dtype if dtype is not None else torch.get_default_dtype()
+            self.weight = torch.nn.Parameter(
+                torch.empty((num_embeddings, embedding_dim), device="meta", dtype=embedding_dtype),
+                requires_grad=False,
+            )
+            self.bias = None
+            self.weight_comfy_model_dtype = dtype
+
+        def _load_from_state_dict(self, state_dict, prefix, local_metadata,
+                                strict, missing_keys, unexpected_keys, error_msgs):
+
+            if (not comfy.model_management.WINDOWS
+                or not comfy.memory_management.aimdo_enabled
+                or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
+                return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
+                                                     missing_keys, unexpected_keys, error_msgs)
+            disable_weight_init._lazy_load_from_state_dict(
+                self,
+                state_dict,
+                prefix,
+                local_metadata,
+                missing_keys,
+                unexpected_keys,
+                weight_shape=(self.num_embeddings, self.embedding_dim),
+            )
+
         def reset_parameters(self):
             self.bias = None
             return None
@@ -801,6 +867,22 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                             orig_shape=(self.out_features, self.in_features),
                         )
 
+                    elif self.quant_format == "mxfp8":
+                        # MXFP8: E8M0 block scales stored as uint8 in safetensors
+                        block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys,
+                                                             dtype=torch.uint8)
+
+                        if block_scale is None:
+                            raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}")
+
+                        block_scale = block_scale.view(torch.float8_e8m0fnu)
+
+                        params = layout_cls.Params(
+                            scale=block_scale,
+                            orig_dtype=MixedPrecisionOps._compute_dtype,
+                            orig_shape=(self.out_features, self.in_features),
+                        )
+
                     elif self.quant_format == "nvfp4":
                         # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale)
                         tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys)
@@ -950,12 +1032,15 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
 def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
     nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device)
+    mxfp8_compute = comfy.model_management.supports_mxfp8_compute(load_device)
 
     if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
         logging.info("Using mixed precision operations")
         disabled = set()
         if not nvfp4_compute:
             disabled.add("nvfp4")
+        if not mxfp8_compute:
+            disabled.add("mxfp8")
         if not fp8_compute:
             disabled.add("float8_e4m3fn")
             disabled.add("float8_e5m2")

comfy/pinned_memory.py

@@ -1,6 +1,7 @@
-import torch
 import comfy.model_management
 import comfy.memory_management
+import comfy_aimdo.host_buffer
+import comfy_aimdo.torch
 
 from comfy.cli_args import args
 
@@ -12,18 +13,31 @@ def pin_memory(module):
         return
     #FIXME: This is a RAM cache trigger event
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
-    pin = torch.empty((size,), dtype=torch.uint8)
-    if comfy.model_management.pin_memory(pin):
-        module._pin = pin
-    else:
+
+    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
         return False
+
+    try:
+        hostbuf = comfy_aimdo.host_buffer.HostBuffer(size)
+    except RuntimeError:
+        module.pin_failed = True
+        return False
+
+    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)
+    module._pin_hostbuf = hostbuf
+    comfy.model_management.TOTAL_PINNED_MEMORY += 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.unpin_memory(module._pin)
+
+    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/quant_ops.py

@@ -43,6 +43,18 @@ except ImportError as e:
     def get_layout_class(name):
         return None
 
+_CK_MXFP8_AVAILABLE = False
+if _CK_AVAILABLE:
+    try:
+        from comfy_kitchen.tensor import TensorCoreMXFP8Layout as _CKMxfp8Layout
+        _CK_MXFP8_AVAILABLE = True
+    except ImportError:
+        logging.warning("comfy_kitchen does not support MXFP8, please update comfy_kitchen.")
+
+if not _CK_MXFP8_AVAILABLE:
+    class _CKMxfp8Layout:
+        pass
+
 import comfy.float
 
 # ==============================================================================
@@ -84,6 +96,31 @@ class _TensorCoreFP8LayoutBase(_CKFp8Layout):
         return qdata, params
 
 
+class TensorCoreMXFP8Layout(_CKMxfp8Layout):
+    @classmethod
+    def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
+        if tensor.dim() != 2:
+            raise ValueError(f"MXFP8 requires 2D tensor, got {tensor.dim()}D")
+
+        orig_dtype = tensor.dtype
+        orig_shape = tuple(tensor.shape)
+
+        padded_shape = cls.get_padded_shape(orig_shape)
+        needs_padding = padded_shape != orig_shape
+
+        if stochastic_rounding > 0:
+            qdata, block_scale = comfy.float.stochastic_round_quantize_mxfp8_by_block(tensor, pad_32x=needs_padding, seed=stochastic_rounding)
+        else:
+            qdata, block_scale = ck.quantize_mxfp8(tensor, pad_32x=needs_padding)
+
+        params = cls.Params(
+            scale=block_scale,
+            orig_dtype=orig_dtype,
+            orig_shape=orig_shape,
+        )
+        return qdata, params
+
+
 class TensorCoreNVFP4Layout(_CKNvfp4Layout):
     @classmethod
     def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_ops=False):
@@ -137,6 +174,8 @@ register_layout_class("TensorCoreFP8Layout", TensorCoreFP8Layout)
 register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
 register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
+if _CK_MXFP8_AVAILABLE:
+    register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 
 QUANT_ALGOS = {
     "float8_e4m3fn": {
@@ -157,6 +196,14 @@ QUANT_ALGOS = {
     },
 }
 
+if _CK_MXFP8_AVAILABLE:
+    QUANT_ALGOS["mxfp8"] = {
+        "storage_t": torch.float8_e4m3fn,
+        "parameters": {"weight_scale", "input_scale"},
+        "comfy_tensor_layout": "TensorCoreMXFP8Layout",
+        "group_size": 32,
+    }
+
 
 # ==============================================================================
 # Re-exports for backward compatibility

comfy/sd.py

@@ -871,13 +871,16 @@ class VAE:
                 pixels = torch.nn.functional.pad(pixels, (0, self.output_channels - pixels.shape[-1]), mode=mode, value=value)
         return pixels
 
+    def vae_output_dtype(self):
+        return model_management.intermediate_dtype()
+
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
         steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         output = self.process_output(
             (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
             comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
@@ -887,16 +890,16 @@ class VAE:
 
     def decode_tiled_1d(self, samples, tile_x=256, overlap=32):
         if samples.ndim == 3:
-            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         else:
             og_shape = samples.shape
             samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1))
-            decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float()
+            decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
 
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
 
     def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
@@ -905,7 +908,7 @@ class VAE:
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
@@ -914,7 +917,7 @@ class VAE:
 
     def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048):
         if self.latent_dim == 1:
-            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
             out_channels = self.latent_channels
             upscale_amount = 1 / self.downscale_ratio
         else:
@@ -923,7 +926,7 @@ class VAE:
             tile_x = tile_x // extra_channel_size
             overlap = overlap // extra_channel_size
             upscale_amount = 1 / self.downscale_ratio
-            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float()
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).to(dtype=self.vae_output_dtype())
 
         out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device)
         if self.latent_dim == 1:
@@ -932,7 +935,7 @@ class VAE:
             return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1)
 
     def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
     def decode(self, samples_in, vae_options={}):
@@ -950,9 +953,9 @@ class VAE:
 
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
+                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).to(dtype=self.vae_output_dtype()))
                 if pixel_samples is None:
-                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
                 pixel_samples[x:x+batch_number] = out
         except Exception as e:
             model_management.raise_non_oom(e)
@@ -1025,9 +1028,9 @@ class VAE:
             samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
-                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).to(dtype=self.vae_output_dtype())
                 if samples is None:
-                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
                 samples[x:x + batch_number] = out
 
         except Exception as e:

comfy/utils.py

@@ -20,6 +20,8 @@
 import torch
 import math
 import struct
+import ctypes
+import os
 import comfy.memory_management
 import safetensors.torch
 import numpy as np
@@ -32,7 +34,7 @@ from einops import rearrange
 from comfy.cli_args import args
 import json
 import time
-import mmap
+import threading
 import warnings
 
 MMAP_TORCH_FILES = args.mmap_torch_files
@@ -81,14 +83,17 @@ _TYPES = {
 }
 
 def load_safetensors(ckpt):
-    f = open(ckpt, "rb")
-    mapping = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
-    mv = memoryview(mapping)
+    import comfy_aimdo.model_mmap
 
-    header_size = struct.unpack("<Q", mapping[:8])[0]
-    header = json.loads(mapping[8:8+header_size].decode("utf-8"))
+    f = open(ckpt, "rb", buffering=0)
+    model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt)
+    file_size = os.path.getsize(ckpt)
+    mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get()))
 
-    mv = mv[8 + header_size:]
+    header_size = struct.unpack("<Q", mv[:8])[0]
+    header = json.loads(mv[8:8 + header_size].tobytes().decode("utf-8"))
+
+    mv = mv[(data_base_offset := 8 + header_size):]
 
     sd = {}
     for name, info in header.items():
@@ -102,7 +107,14 @@ def load_safetensors(ckpt):
             with warnings.catch_warnings():
                 #We are working with read-only RAM by design
                 warnings.filterwarnings("ignore", message="The given buffer is not writable")
-                sd[name] = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
+                tensor = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
+                storage = tensor.untyped_storage()
+                setattr(storage,
+                        "_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__", {}),
 

comfy_api_nodes/nodes_kling.py

@@ -1459,6 +1459,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
             node_id="KlingOmniProEditVideoNode",
             display_name="Kling 3.0 Omni Edit Video",
             category="api node/video/Kling",
+            essentials_category="Video Generation",
             description="Edit an existing video with the latest model from Kling.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),

comfy_api_nodes/nodes_recraft.py

@@ -833,6 +833,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode):
             node_id="RecraftVectorizeImageNode",
             display_name="Recraft Vectorize Image",
             category="api node/image/Recraft",
+            essentials_category="Image Tools",
             description="Generates SVG synchronously from an input image.",
             inputs=[
                 IO.Image.Input("image"),

comfy_execution/caching.py

@@ -1,5 +1,6 @@
 import asyncio
 import bisect
+import os
 import gc
 import itertools
 import psutil
@@ -523,8 +524,41 @@ class RAMPressureCache(LRUCache):
 
     def poll(self, ram_headroom):
         def _ram_gb():
-            return psutil.virtual_memory().available / (1024**3)
-
+            fallback_to_host_available = False
+            cgroup_mem_limit_path = "/sys/fs/cgroup/memory.max"
+            cgroup_mem_usage_path = "/sys/fs/cgroup/memory.current"
+        
+            if not os.path.exists(cgroup_mem_limit_path):
+                cgroup_mem_limit_path = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
+                cgroup_mem_usage_path = "/sys/fs/cgroup/memory/memory.usage_in_bytes"
+    
+            try:
+                with open(cgroup_mem_limit_path, "r") as f:
+                    raw_limit = f.read().strip()
+                    if raw_limit == "max":
+                        fallback_to_host_available = True
+                        mem_limit = psutil.virtual_memory().total
+                    else:
+                        mem_limit = int(raw_limit)
+                        if mem_limit == 9223372036854771712:
+                            fallback_to_host_available = True
+                            mem_limit = psutil.virtual_memory().total
+            except (FileNotFoundError, ValueError):
+                fallback_to_host_available = True
+                mem_limit = psutil.virtual_memory().total
+    
+            if fallback_to_host_available:
+                return psutil.virtual_memory().available / (1024**3)
+    
+            try:
+                with open(cgroup_mem_usage_path, "r") as f:
+                    mem_used = int(f.read().strip())
+            except (FileNotFoundError, ValueError):
+                mem_used = psutil.virtual_memory().total - psutil.virtual_memory().available
+    
+            mem_available = max(0, mem_limit - mem_used)
+            return mem_available / (1024**3)
+    
         if _ram_gb() > ram_headroom:
             return
         gc.collect()

comfy_extras/nodes_audio.py

@@ -19,6 +19,7 @@ class EmptyLatentAudio(IO.ComfyNode):
             node_id="EmptyLatentAudio",
             display_name="Empty Latent Audio",
             category="latent/audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 IO.Int.Input(
@@ -185,6 +186,7 @@ class SaveAudioMP3(IO.ComfyNode):
             search_aliases=["export mp3"],
             display_name="Save Audio (MP3)",
             category="audio",
+            essentials_category="Audio",
             inputs=[
                 IO.Audio.Input("audio"),
                 IO.String.Input("filename_prefix", default="audio/ComfyUI"),

comfy_extras/nodes_image_compare.py

@@ -14,6 +14,7 @@ class ImageCompare(IO.ComfyNode):
             display_name="Image Compare",
             description="Compares two images side by side with a slider.",
             category="image",
+            essentials_category="Image Tools",
             is_experimental=True,
             is_output_node=True,
             inputs=[

comfy_extras/nodes_images.py

@@ -58,6 +58,7 @@ class ImageCropV2(IO.ComfyNode):
             search_aliases=["trim"],
             display_name="Image Crop",
             category="image/transform",
+            essentials_category="Image Tools",
             inputs=[
                 IO.Image.Input("image"),
                 IO.BoundingBox.Input("crop_region", component="ImageCrop"),

comfy_extras/nodes_post_processing.py

@@ -21,6 +21,7 @@ class Blend(io.ComfyNode):
             node_id="ImageBlend",
             display_name="Image Blend",
             category="image/postprocessing",
+            essentials_category="Image Tools",
             inputs=[
                 io.Image.Input("image1"),
                 io.Image.Input("image2"),

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.1b2
+comfyui_manager==4.1b5

middleware/cache_middleware.py

@@ -32,7 +32,7 @@ async def cache_control(
     )
 
     if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point:
-        response.headers.setdefault("Cache-Control", "no-cache")
+        response.headers.setdefault("Cache-Control", "no-store")
         return response
 
     # Early return for non-image files - no cache headers needed

nodes.py

@@ -81,6 +81,7 @@ class CLIPTextEncode(ComfyNodeABC):
 
 
 class ConditioningCombine:
+    ESSENTIALS_CATEGORY = "Image Generation"
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
@@ -1211,9 +1212,6 @@ class GLIGENTextBoxApply:
         return (c, )
 
 class EmptyLatentImage:
-    def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
-
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -1232,7 +1230,7 @@ class EmptyLatentImage:
     SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"]
 
     def generate(self, width, height, batch_size=1):
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         return ({"samples": latent, "downscale_ratio_spacial": 8}, )
 
 
@@ -1724,6 +1722,8 @@ class LoadImage:
         output_masks = []
         w, h = None, None
 
+        dtype = comfy.model_management.intermediate_dtype()
+
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
@@ -1748,8 +1748,8 @@ class LoadImage:
                 mask = 1. - torch.from_numpy(mask)
             else:
                 mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
+            output_images.append(image.to(dtype=dtype))
+            output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
 
             if img.format == "MPO":
                 break  # ignore all frames except the first one for MPO format
@@ -1779,6 +1779,7 @@ class LoadImage:
         return True
 
 class LoadImageMask:
+    ESSENTIALS_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"]
 
     _color_channels = ["alpha", "red", "green", "blue"]
@@ -1887,6 +1888,7 @@ class ImageScale:
         return (s,)
 
 class ImageScaleBy:
+    ESSENTIALS_CATEGORY = "Image Tools"
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
 
     @classmethod

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.18
+comfyui-frontend-package==1.41.20
 comfyui-workflow-templates==0.9.21
 comfyui-embedded-docs==0.4.3
 torch
@@ -23,7 +23,7 @@ SQLAlchemy
 filelock
 av>=14.2.0
 comfy-kitchen>=0.2.8
-comfy-aimdo>=0.2.10
+comfy-aimdo>=0.2.12
 requests
 simpleeval>=1.0.0
 blake3

server.py

@@ -310,7 +310,7 @@ class PromptServer():
         @routes.get("/")
         async def get_root(request):
             response = web.FileResponse(os.path.join(self.web_root, "index.html"))
-            response.headers['Cache-Control'] = 'no-cache'
+            response.headers['Cache-Control'] = 'no-store, must-revalidate'
             response.headers["Pragma"] = "no-cache"
             response.headers["Expires"] = "0"
             return response

styles/default.csv

@@ -0,0 +1,3 @@
+name,prompt,negative_prompt
+❌Low Token,,"embedding:EasyNegative, NSFW, Cleavage, Pubic Hair, Nudity, Naked, censored"
+✅Line Art / Manga,"(Anime Scene, Toonshading, Satoshi Kon, Ken Sugimori, Hiromu Arakawa:1.2), (Anime Style, Manga Style:1.3), Low detail, sketch, concept art, line art, webtoon, manhua, hand drawn, defined lines, simple shades, minimalistic, High contrast, Linear compositions, Scalable artwork, Digital art, High Contrast Shadows, glow effects, humorous illustration, big depth of field, Masterpiece, colors, concept art, trending on artstation, Vivid colors, dramatic",

tests-unit/server_test/test_cache_control.py

@@ -28,31 +28,31 @@ CACHE_SCENARIOS = [
     },
     # JavaScript/CSS scenarios
     {
-        "name": "js_no_cache",
+        "name": "js_no_store",
         "path": "/script.js",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     {
-        "name": "css_no_cache",
+        "name": "css_no_store",
         "path": "/styles.css",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     {
-        "name": "index_json_no_cache",
+        "name": "index_json_no_store",
         "path": "/api/index.json",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     {
-        "name": "localized_index_json_no_cache",
+        "name": "localized_index_json_no_store",
         "path": "/templates/index.zh.json",
         "status": 200,
-        "expected_cache": "no-cache",
+        "expected_cache": "no-store",
         "should_have_header": True,
     },
     # Non-matching files