Merge branch 'master' into feat/cache-provider-api

- Hold references to pending store tasks to prevent "Task was destroyed
  but it is still pending" warnings (bigcat88)
- Parallel cache lookups with asyncio.gather instead of sequential
  awaits for better performance (bigcat88)
- Delegate Caching.register/unregister_provider to existing functions
  in cache_provider.py instead of reimplementing (bigcat88)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

README.md

@@ -38,8 +38,6 @@ 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.
@@ -51,13 +49,8 @@ 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).
 
-### 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/).
+## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+See what ComfyUI can do with the [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

@@ -83,8 +83,6 @@ 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

@@ -209,39 +209,3 @@ 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

@@ -11,7 +11,6 @@ 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
@@ -537,7 +536,7 @@ 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.to(comfy.model_management.intermediate_device()))
+                    output.append(sample)
                 return
 
             up_block = self.up_blocks[idx]

comfy/memory_management.py

@@ -1,68 +1,9 @@
 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

@@ -400,7 +400,7 @@ 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", "gfx1150", "gfx1151"]):  # TODO: more arches, TODO: gfx950
+                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
                 if rocm_version >= (7, 0):
                    if any((a in arch) for a in ["gfx1200", "gfx1201"]):
@@ -505,28 +505,6 @@ 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)
@@ -554,9 +532,6 @@ 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()
 
@@ -658,7 +633,7 @@ 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, pins_required=0, ram_required=0):
+def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, ram_required=0):
     cleanup_models_gc()
     unloaded_model = []
     can_unload = []
@@ -671,14 +646,13 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins
                 can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
-    can_unload_sorted = sorted(can_unload)
-    for x in can_unload_sorted:
+    for x in sorted(can_unload):
         i = x[-1]
         memory_to_free = 1e32
-        pins_to_free = 1e32
+        ram_to_free = 1e32
         if not DISABLE_SMART_MEMORY:
             memory_to_free = memory_required - get_free_memory(device)
-            pins_to_free = pins_required - get_free_ram()
+            ram_to_free = ram_required - get_free_ram()
             if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                 #don't actually unload dynamic models for the sake of other dynamic models
                 #as that works on-demand.
@@ -687,18 +661,9 @@ 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:
+        if ram_to_free > 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))
@@ -764,27 +729,17 @@ 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:
-        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
+        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
 
     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])
+            free_memory(total_memory_required[device] * 1.1 + extra_mem, device, for_dynamic=free_for_dynamic, ram_required=total_ram_required[device])
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
@@ -1050,12 +1005,6 @@ 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")
@@ -1276,11 +1225,6 @@ 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)
 
 
@@ -1718,19 +1662,6 @@ 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

@@ -297,9 +297,6 @@ 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 get_ram_usage(self):
         return self.model_size()
 
@@ -1066,10 +1063,6 @@ 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
 
@@ -1660,16 +1653,6 @@ 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

@@ -306,40 +306,10 @@ 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):
-            # 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):
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 super().__init__(in_features, out_features, bias, device, dtype)
                 return
 
@@ -360,21 +330,32 @@ 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
-                or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
+            if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
                 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.in_features, self.out_features),
-                bias_shape=(self.out_features,),
-            )
+            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")
 
 
         def reset_parameters(self):
@@ -566,53 +547,6 @@ 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
@@ -867,22 +801,6 @@ 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)
@@ -1032,15 +950,12 @@ 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

@@ -1,7 +1,6 @@
+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
 
@@ -13,31 +12,18 @@ def pin_memory(module):
         return
     #FIXME: This is a RAM cache trigger event
     size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ])
-
-    if comfy.model_management.MAX_PINNED_MEMORY <= 0 or (comfy.model_management.TOTAL_PINNED_MEMORY + size) > comfy.model_management.MAX_PINNED_MEMORY:
+    pin = torch.empty((size,), dtype=torch.uint8)
+    if comfy.model_management.pin_memory(pin):
+        module._pin = pin
+    else:
         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.TOTAL_PINNED_MEMORY -= size
-    if comfy.model_management.TOTAL_PINNED_MEMORY < 0:
-        comfy.model_management.TOTAL_PINNED_MEMORY = 0
-
+    comfy.model_management.unpin_memory(module._pin)
     del module._pin
-    del module._pin_hostbuf
     return size

comfy/quant_ops.py

@@ -43,18 +43,6 @@ 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
 
 # ==============================================================================
@@ -96,31 +84,6 @@ 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):
@@ -174,8 +137,6 @@ 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": {
@@ -196,14 +157,6 @@ 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

@@ -871,16 +871,13 @@ 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)).to(dtype=self.vae_output_dtype())
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         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) +
@@ -890,16 +887,16 @@ 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)).to(dtype=self.vae_output_dtype())
+            decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         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)).to(dtype=self.vae_output_dtype())
+            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()
 
         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)).to(dtype=self.vae_output_dtype())
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         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):
@@ -908,7 +905,7 @@ 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)).to(dtype=self.vae_output_dtype())
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
         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)
@@ -917,7 +914,7 @@ 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)).to(dtype=self.vae_output_dtype())
+            encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
             out_channels = self.latent_channels
             upscale_amount = 1 / self.downscale_ratio
         else:
@@ -926,7 +923,7 @@ 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).to(dtype=self.vae_output_dtype())
+            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()
 
         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:
@@ -935,7 +932,7 @@ 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)).to(dtype=self.vae_output_dtype())
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
         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={}):
@@ -953,9 +950,9 @@ 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).to(dtype=self.vae_output_dtype()))
+                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
                 if pixel_samples is None:
-                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 pixel_samples[x:x+batch_number] = out
         except Exception as e:
             model_management.raise_non_oom(e)
@@ -1028,9 +1025,9 @@ 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).to(dtype=self.vae_output_dtype())
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
                 if samples is None:
-                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 samples[x:x + batch_number] = out
 
         except Exception as e:

comfy/utils.py

@@ -20,8 +20,6 @@
 import torch
 import math
 import struct
-import ctypes
-import os
 import comfy.memory_management
 import safetensors.torch
 import numpy as np
@@ -34,7 +32,7 @@ from einops import rearrange
 from comfy.cli_args import args
 import json
 import time
-import threading
+import mmap
 import warnings
 
 MMAP_TORCH_FILES = args.mmap_torch_files
@@ -83,17 +81,14 @@ _TYPES = {
 }
 
 def load_safetensors(ckpt):
-    import comfy_aimdo.model_mmap
+    f = open(ckpt, "rb")
+    mapping = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
+    mv = memoryview(mapping)
 
-    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()))
+    header_size = struct.unpack("<Q", mapping[:8])[0]
+    header = json.loads(mapping[8:8+header_size].decode("utf-8"))
 
-    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):]
+    mv = mv[8 + header_size:]
 
     sd = {}
     for name, info in header.items():
@@ -107,14 +102,7 @@ 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")
-                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
+                sd[name] = torch.frombuffer(mv[start:end], dtype=_TYPES[info["dtype"]]).view(info["shape"])
 
     return sd, header.get("__metadata__", {}),
 

comfy_api/latest/_input_impl/video_types.py

@@ -272,7 +272,7 @@ class VideoFromFile(VideoInput):
             has_first_frame = False
             for frame in frames:
                 offset_seconds = start_time - frame.pts * audio_stream.time_base
-                to_skip = max(0, int(offset_seconds * audio_stream.sample_rate))
+                to_skip = int(offset_seconds * audio_stream.sample_rate)
                 if to_skip < frame.samples:
                     has_first_frame = True
                     break
@@ -280,7 +280,7 @@ class VideoFromFile(VideoInput):
                 audio_frames.append(frame.to_ndarray()[..., to_skip:])
 
             for frame in frames:
-                if self.__duration and frame.time > start_time + self.__duration:
+                if frame.time > start_time + self.__duration:
                     break
                 audio_frames.append(frame.to_ndarray())  # shape: (channels, samples)
             if len(audio_frames) > 0:

comfy_api_nodes/nodes_hunyuan3d.py

@@ -1,7 +1,3 @@
-import zipfile
-from io import BytesIO
-
-import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, Types
@@ -21,10 +17,7 @@ from comfy_api_nodes.apis.hunyuan3d import (
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
-    bytesio_to_image_tensor,
-    download_url_to_bytesio,
     download_url_to_file_3d,
-    download_url_to_image_tensor,
     downscale_image_tensor_by_max_side,
     poll_op,
     sync_op,
@@ -43,68 +36,6 @@ def _is_tencent_rate_limited(status: int, body: object) -> bool:
     )
 
 
-class ObjZipResult:
-    __slots__ = ("obj", "texture", "metallic", "normal", "roughness")
-
-    def __init__(
-        self,
-        obj: Types.File3D,
-        texture: Input.Image | None = None,
-        metallic: Input.Image | None = None,
-        normal: Input.Image | None = None,
-        roughness: Input.Image | None = None,
-    ):
-        self.obj = obj
-        self.texture = texture
-        self.metallic = metallic
-        self.normal = normal
-        self.roughness = roughness
-
-
-async def download_and_extract_obj_zip(url: str) -> ObjZipResult:
-    """The Tencent API returns OBJ results as ZIP archives containing the .obj mesh, and texture images.
-
-    When PBR is enabled, the ZIP may contain additional metallic, normal, and roughness maps
-    identified by their filename suffixes.
-    """
-    data = BytesIO()
-    await download_url_to_bytesio(url, data)
-    data.seek(0)
-    if not zipfile.is_zipfile(data):
-        data.seek(0)
-        return ObjZipResult(obj=Types.File3D(source=data, file_format="obj"))
-    data.seek(0)
-    obj_bytes = None
-    textures: dict[str, Input.Image] = {}
-    with zipfile.ZipFile(data) as zf:
-        for name in zf.namelist():
-            lower = name.lower()
-            if lower.endswith(".obj"):
-                obj_bytes = zf.read(name)
-            elif any(lower.endswith(ext) for ext in (".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".webp")):
-                stem = lower.rsplit(".", 1)[0]
-                tensor = bytesio_to_image_tensor(BytesIO(zf.read(name)), mode="RGB")
-                matched_key = "texture"
-                for suffix, key in {
-                    "_metallic": "metallic",
-                    "_normal": "normal",
-                    "_roughness": "roughness",
-                }.items():
-                    if stem.endswith(suffix):
-                        matched_key = key
-                        break
-                textures[matched_key] = tensor
-    if obj_bytes is None:
-        raise ValueError("ZIP archive does not contain an OBJ file.")
-    return ObjZipResult(
-        obj=Types.File3D(source=BytesIO(obj_bytes), file_format="obj"),
-        texture=textures.get("texture"),
-        metallic=textures.get("metallic"),
-        normal=textures.get("normal"),
-        roughness=textures.get("roughness"),
-    )
-
-
 def get_file_from_response(
     response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
 ) -> ResultFile3D | None:
@@ -162,7 +93,6 @@ class TencentTextToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
-                IO.Image.Output(display_name="texture_image"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -221,14 +151,14 @@ class TencentTextToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            obj_result.obj,
-            obj_result.texture,
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
+            ),
         )
 
 
@@ -281,10 +211,6 @@ class TencentImageToModelNode(IO.ComfyNode):
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
                 IO.File3DGLB.Output(display_name="GLB"),
                 IO.File3DOBJ.Output(display_name="OBJ"),
-                IO.Image.Output(display_name="texture_image"),
-                IO.Image.Output(display_name="optional_metallic"),
-                IO.Image.Output(display_name="optional_normal"),
-                IO.Image.Output(display_name="optional_roughness"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -378,17 +304,14 @@ class TencentImageToModelNode(IO.ComfyNode):
             response_model=To3DProTaskResultResponse,
             status_extractor=lambda r: r.Status,
         )
-        obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url)
         return IO.NodeOutput(
             f"{task_id}.glb",
             await download_url_to_file_3d(
                 get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
             ),
-            obj_result.obj,
-            obj_result.texture,
-            obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3),
-            obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3),
-            obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3),
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj", task_id=task_id
+            ),
         )
 
 
@@ -508,8 +431,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
             ],
             outputs=[
                 IO.File3DGLB.Output(display_name="GLB"),
-                IO.File3DOBJ.Output(display_name="OBJ"),
-                IO.Image.Output(display_name="texture_image"),
+                IO.File3DFBX.Output(display_name="FBX"),
             ],
             hidden=[
                 IO.Hidden.auth_token_comfy_org,
@@ -558,8 +480,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
         )
         return IO.NodeOutput(
             await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
-            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
-            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "texture_image").Url),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
         )
 
 
@@ -733,7 +654,7 @@ class TencentHunyuan3DExtension(ComfyExtension):
             TencentTextToModelNode,
             TencentImageToModelNode,
             TencentModelTo3DUVNode,
-            Tencent3DTextureEditNode,
+            # Tencent3DTextureEditNode,
             Tencent3DPartNode,
             TencentSmartTopologyNode,
         ]

comfy_api_nodes/nodes_kling.py

@@ -1459,7 +1459,6 @@ 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

@@ -833,7 +833,6 @@ 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

@@ -148,10 +148,9 @@ class CacheKeySetInputSignature(CacheKeySet):
                     self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
 
 class BasicCache:
-    def __init__(self, key_class, enable_providers=False):
+    def __init__(self, key_class):
         self.key_class = key_class
         self.initialized = False
-        self.enable_providers = enable_providers
         self.dynprompt: DynamicPrompt
         self.cache_key_set: CacheKeySet
         self.cache = {}
@@ -240,8 +239,6 @@ class BasicCache:
             CacheValue, _contains_self_unequal, _logger
         )
 
-        if not self.enable_providers:
-            return
         if not _has_cache_providers():
             return
         if not self._is_external_cacheable_value(value):
@@ -277,8 +274,6 @@ class BasicCache:
             CacheValue, _contains_self_unequal, _logger
         )
 
-        if not self.enable_providers:
-            return None
         if not _has_cache_providers():
             return None
         if _contains_self_unequal(cache_key):
@@ -301,7 +296,7 @@ class BasicCache:
                         _logger.warning(f"Provider {provider.__class__.__name__} returned invalid outputs")
                         continue
                     from execution import CacheEntry
-                    return CacheEntry(ui=result.ui, outputs=list(result.outputs))
+                    return CacheEntry(ui=result.ui or {}, outputs=list(result.outputs))
             except Exception as e:
                 _logger.warning(f"Cache provider {provider.__class__.__name__} error on lookup: {e}")
 
@@ -359,8 +354,8 @@ class BasicCache:
         return result
 
 class HierarchicalCache(BasicCache):
-    def __init__(self, key_class, enable_providers=False):
-        super().__init__(key_class, enable_providers=enable_providers)
+    def __init__(self, key_class):
+        super().__init__(key_class)
 
     def _get_cache_for(self, node_id):
         assert self.dynprompt is not None
@@ -437,8 +432,8 @@ class NullCache:
         return self
 
 class LRUCache(BasicCache):
-    def __init__(self, key_class, max_size=100, enable_providers=False):
-        super().__init__(key_class, enable_providers=enable_providers)
+    def __init__(self, key_class, max_size=100):
+        super().__init__(key_class)
         self.max_size = max_size
         self.min_generation = 0
         self.generation = 0
@@ -506,8 +501,8 @@ RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
 class RAMPressureCache(LRUCache):
 
-    def __init__(self, key_class, enable_providers=False):
-        super().__init__(key_class, 0, enable_providers=enable_providers)
+    def __init__(self, key_class):
+        super().__init__(key_class, 0)
         self.timestamps = {}
 
     def clean_unused(self):

comfy_extras/nodes_audio.py

@@ -19,7 +19,6 @@ 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(
@@ -186,7 +185,6 @@ 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

@@ -14,7 +14,6 @@ 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

@@ -58,7 +58,6 @@ 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

@@ -21,7 +21,6 @@ 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"),

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.17.0"
+__version__ = "0.16.4"

execution.py

@@ -127,15 +127,15 @@ class CacheSet:
 
     # Performs like the old cache -- dump data ASAP
     def init_classic_cache(self):
-        self.outputs = HierarchicalCache(CacheKeySetInputSignature, enable_providers=True)
+        self.outputs = HierarchicalCache(CacheKeySetInputSignature)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_lru_cache(self, cache_size):
-        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size, enable_providers=True)
+        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_ram_cache(self, min_headroom):
-        self.outputs = RAMPressureCache(CacheKeySetInputSignature, enable_providers=True)
+        self.outputs = RAMPressureCache(CacheKeySetInputSignature)
         self.objects = HierarchicalCache(CacheKeySetID)
 
     def init_null_cache(self):

manager_requirements.txt

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

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-store")
+        response.headers.setdefault("Cache-Control", "no-cache")
         return response
 
     # Early return for non-image files - no cache headers needed

nodes.py

@@ -81,7 +81,6 @@ class CLIPTextEncode(ComfyNodeABC):
 
 
 class ConditioningCombine:
-    ESSENTIALS_CATEGORY = "Image Generation"
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
@@ -1212,6 +1211,9 @@ class GLIGENTextBoxApply:
         return (c, )
 
 class EmptyLatentImage:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -1230,7 +1232,7 @@ 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=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
         return ({"samples": latent, "downscale_ratio_spacial": 8}, )
 
 
@@ -1722,8 +1724,6 @@ 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.to(dtype=dtype))
-            output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
 
             if img.format == "MPO":
                 break  # ignore all frames except the first one for MPO format
@@ -1779,7 +1779,6 @@ class LoadImage:
         return True
 
 class LoadImageMask:
-    ESSENTIALS_CATEGORY = "Image Tools"
     SEARCH_ALIASES = ["import mask", "alpha mask", "channel mask"]
 
     _color_channels = ["alpha", "red", "green", "blue"]
@@ -1888,7 +1887,6 @@ class ImageScale:
         return (s,)
 
 class ImageScaleBy:
-    ESSENTIALS_CATEGORY = "Image Tools"
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
 
     @classmethod

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.17.0"
+version = "0.16.4"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.41.20
+comfyui-frontend-package==1.41.18
 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>=0.2.12
+comfy-aimdo>=0.2.10
 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-store, must-revalidate'
+            response.headers['Cache-Control'] = 'no-cache'
             response.headers["Pragma"] = "no-cache"
             response.headers["Expires"] = "0"
             return response

tests-unit/server_test/test_cache_control.py

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