mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 06:40:48 +08:00
Improvements for Wan 2.2 support
- add xet support and add the xet cache to manageable directories - xet is enabled by default - fix logging to root in various places - improve logging about model unloading and loading - TorchCompileNode now supports the VAE - torchaudio missing will cause less noise in the logs - feature flags will assume to be supporting everything in the distributed progress context - fixes progress notifications
This commit is contained in:
doctorpangloss
parent
b5a50301f6
commit
03e5430121
@ -84,7 +84,7 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool
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stream_handler = logging.StreamHandler()
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stream_handler.setFormatter(logging.Formatter(
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"%(asctime)s [%(name)s] [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
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"%(asctime)s [%(levelname)s] [%(name)s] [%(filename)s:%(lineno)d] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S"
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))
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@ -86,6 +86,9 @@ def init_default_paths(folder_names_and_paths: FolderNames, configuration: Optio
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if "HF_HUB_CACHE" in os.environ:
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hf_cache_paths.additional_absolute_directory_paths.append(os.environ.get("HF_HUB_CACHE"))
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hf_xet = ModelPaths(["xet"], supported_extensions=set())
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if "HF_XET_CACHE" in os.environ:
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hf_xet.additional_absolute_directory_paths.append(os.environ.get("HF_XET_CACHE"))
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model_paths_to_add = [
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ModelPaths(["checkpoints"], supported_extensions=set(supported_pt_extensions)),
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ModelPaths(["configs"], additional_absolute_directory_paths=[get_package_as_path("comfy.configs")], supported_extensions={".yaml"}),
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@ -107,6 +110,7 @@ def init_default_paths(folder_names_and_paths: FolderNames, configuration: Optio
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ModelPaths(["classifiers"], supported_extensions=set()),
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ModelPaths(["huggingface"], supported_extensions=set()),
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hf_cache_paths,
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hf_xet,
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]
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for model_paths in model_paths_to_add:
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if replace_existing:
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@ -18,7 +18,7 @@ from .. import options
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from ..app import logger
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os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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os.environ["HF_XET_HIGH_PERFORMANCE"] = "True"
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os.environ["TORCHINDUCTOR_FX_GRAPH_CACHE"] = "1"
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os.environ["TORCHINDUCTOR_AUTOGRAD_CACHE"] = "1"
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os.environ["BITSANDBYTES_NOWELCOME"] = "1"
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@ -221,7 +221,7 @@ class PromptServer(ExecutorToClientProgress):
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handler_args={'max_field_size': 16380},
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middlewares=middlewares)
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self.sockets = dict()
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self.sockets_metadata = dict()
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self._sockets_metadata = dict()
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self.web_root = (
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FrontendManager.init_frontend(args.front_end_version)
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if args.front_end_root is None
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@ -278,16 +278,16 @@ class PromptServer(ExecutorToClientProgress):
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sid,
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)
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logging.info(
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logger.info(
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f"Feature flags negotiated for client {sid}: {client_flags}"
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)
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first_message = False
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except json.JSONDecodeError:
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logging.warning(
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logger.warning(
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f"Invalid JSON received from client {sid}: {msg.data}"
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)
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except Exception as e:
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logging.error(f"Error processing WebSocket message: {e}")
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logger.error(f"Error processing WebSocket message: {e}")
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finally:
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self.sockets.pop(sid, None)
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self.sockets_metadata.pop(sid, None)
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@ -1236,3 +1236,11 @@ class PromptServer(ExecutorToClientProgress):
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message = encode_text_for_progress(node_id, text)
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self.send_sync(BinaryEventTypes.TEXT, message, sid)
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@property
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def sockets_metadata(self):
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return self._sockets_metadata
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@sockets_metadata.setter
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def sockets_metadata(self, value):
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self._sockets_metadata = value
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@ -1,10 +1,11 @@
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from __future__ import annotations # for Python 3.7-3.9
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import PIL.Image
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import concurrent.futures
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import typing
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from enum import Enum
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from typing import Optional, Literal, Protocol, Union, NamedTuple, List
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import PIL.Image
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from typing_extensions import NotRequired, TypedDict
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from .encode_text_for_progress import encode_text_for_progress
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@ -79,11 +80,39 @@ class DependencyExecutionErrorMessage(TypedDict):
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current_inputs: list[typing.Never]
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class ActiveNodeProgressState(TypedDict, total=True):
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value: float
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max: float
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# a string value from the NodeState enum
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state: Literal["pending", "running", "finished", "error"]
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node_id: str
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prompt_id: str
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display_node_id: str
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parent_node_id: str
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real_node_id: str
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class ProgressStateMessage(TypedDict, total=True):
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prompt_id: str
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nodes: dict[str, ActiveNodeProgressState]
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ExecutedMessage = ExecutingMessage
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SendSyncEvent = Union[Literal["status", "execution_error", "executing", "progress", "executed"], BinaryEventTypes, None]
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SendSyncEvent = Union[Literal["status", "execution_error", "executing", "progress", "executed", "progress_state"], BinaryEventTypes, None]
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SendSyncData = Union[StatusMessage, ExecutingMessage, DependencyExecutionErrorMessage, ExecutionErrorMessage, ExecutionInterruptedMessage, ProgressMessage, UnencodedPreviewImageMessage, tuple[UnencodedPreviewImageMessage, PreviewImageMetadata], bytes, bytearray, str, None]
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SendSyncData = Union[ProgressStateMessage, StatusMessage, ExecutingMessage, DependencyExecutionErrorMessage, ExecutionErrorMessage, ExecutionInterruptedMessage, ProgressMessage, UnencodedPreviewImageMessage, tuple[PIL.Image.Image, PreviewImageMetadata], bytes, bytearray, str, None]
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class SocketsMetadata(TypedDict, total=True):
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feature_flags: dict[str, typing.Any]
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class DefaultSocketsMetadata(TypedDict, total=True):
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__unimplemented: Literal[True]
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SocketsMetadataType = dict[str, SocketsMetadata] | DefaultSocketsMetadata
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class ExecutorToClientProgress(Protocol):
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@ -108,6 +137,10 @@ class ExecutorToClientProgress(Protocol):
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"""
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return False
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@property
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def sockets_metadata(self) -> SocketsMetadataType:
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return {"__unimplemented": True}
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def send_sync(self,
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event: SendSyncEvent,
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data: SendSyncData,
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@ -9,7 +9,7 @@ logger = logging.getLogger(__name__)
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try:
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import torchaudio # pylint: disable=import-error
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except:
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logger.warning("torchaudio missing, ACE model will be broken")
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logger.debug("torchaudio missing, ACE model will be broken")
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import torchvision.transforms as transforms
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from .music_vocoder import ADaMoSHiFiGANV1
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@ -10,7 +10,7 @@ logger = logging.getLogger(__name__)
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try:
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from torchaudio.transforms import MelScale # pylint: disable=import-error
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except:
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logger.warning("torchaudio missing, ACE model will be broken")
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logger.debug("torchaudio missing, ACE model will be broken")
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from .... import model_management
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@ -10,6 +10,8 @@ from .... import ops
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ops = ops.disable_weight_init
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logger = logging.getLogger(__name__)
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if model_management.xformers_enabled_vae():
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import xformers # pylint: disable=import-error
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import xformers.ops # pylint: disable=import-error
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@ -242,7 +244,7 @@ def slice_attention(q, k, v):
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steps *= 2
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if steps > 128:
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raise e
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logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
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logger.warning("out of memory error, increasing steps and trying again {}".format(steps))
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return r1
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@ -296,20 +298,20 @@ def pytorch_attention(q, k, v):
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out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
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out = out.transpose(2, 3).reshape(orig_shape)
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except model_management.OOM_EXCEPTION:
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logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
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logger.warning("scaled_dot_product_attention OOMed: switched to slice attention")
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out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
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return out
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def vae_attention():
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if model_management.xformers_enabled_vae():
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logging.debug("Using xformers attention in VAE")
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logger.debug("Using xformers attention in VAE")
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return xformers_attention
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elif model_management.pytorch_attention_enabled_vae():
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logging.debug("Using pytorch attention in VAE")
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logger.debug("Using pytorch attention in VAE")
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return pytorch_attention
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else:
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logging.debug("Using split attention in VAE")
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logger.debug("Using split attention in VAE")
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return normal_attention
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@ -650,7 +652,7 @@ class Decoder(nn.Module):
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2 ** (self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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logging.debug("Working with z of shape {} = {} dimensions.".format(
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logger.debug("Working with z of shape {} = {} dimensions.".format(
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self.z_shape, np.prod(self.z_shape)))
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# z to block_in
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@ -16,13 +16,13 @@
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import math
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import logging
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import torch
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import math
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from enum import Enum
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from typing import TypeVar, Type, Protocol, Any, Optional
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import torch
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from . import conds
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from . import latent_formats
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from . import model_management
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@ -50,9 +50,9 @@ from .ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
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from .ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
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from .ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
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from .ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
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from .ldm.omnigen.omnigen2 import OmniGen2Transformer2DModel
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from .ldm.pixart.pixartms import PixArtMS
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from .ldm.wan.model import WanModel, VaceWanModel, CameraWanModel
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from .ldm.omnigen.omnigen2 import OmniGen2Transformer2DModel
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from .model_management_types import ModelManageable
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from .model_sampling import CONST, ModelSamplingDiscreteFlow, ModelSamplingFlux, IMG_TO_IMG
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from .model_sampling import StableCascadeSampling, COSMOS_RFLOW, ModelSamplingCosmosRFlow, V_PREDICTION, \
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@ -60,6 +60,8 @@ from .model_sampling import StableCascadeSampling, COSMOS_RFLOW, ModelSamplingCo
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from .ops import Operations
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from .patcher_extension import WrapperExecutor, WrappersMP, get_all_wrappers
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logger = logging.getLogger(__name__)
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class ModelType(Enum):
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EPS = 1
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@ -149,8 +151,8 @@ class BaseModel(torch.nn.Module):
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self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
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if model_management.force_channels_last():
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self.diffusion_model.to(memory_format=torch.channels_last)
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logging.debug("using channels last mode for diffusion model")
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logging.debug("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
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logger.debug("using channels last mode for diffusion model")
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logger.debug("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
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else:
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self.operations = None
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self.model_type = model_type
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@ -161,8 +163,8 @@ class BaseModel(torch.nn.Module):
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self.adm_channels = 0
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self.concat_keys = ()
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logging.debug("model_type {}".format(model_type.name))
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logging.debug("adm {}".format(self.adm_channels))
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logger.debug("model_type {}".format(model_type.name))
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logger.debug("adm {}".format(self.adm_channels))
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self.memory_usage_factor = model_config.memory_usage_factor
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self.memory_usage_factor_conds = ()
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self.training = False
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@ -310,10 +312,10 @@ class BaseModel(torch.nn.Module):
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to_load = self.model_config.process_unet_state_dict(to_load)
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m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
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if len(m) > 0:
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logging.warning("unet missing: {}".format(m))
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logger.warning("unet missing: {}".format(m))
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if len(u) > 0:
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logging.warning("unet unexpected: {}".format(u))
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logger.warning("unet unexpected: {}".format(u))
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del to_load
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return self
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@ -1227,6 +1229,7 @@ class WAN21_Camera(WAN21):
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out['camera_conditions'] = conds.CONDRegular(camera_conditions)
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return out
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class WAN22(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=WanModel)
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@ -1252,6 +1255,7 @@ class WAN22(BaseModel):
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def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
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return latent_image
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class Hunyuan3Dv2(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=Hunyuan3Dv2Model)
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@ -1321,6 +1325,7 @@ class ACEStep(BaseModel):
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out['lyrics_strength'] = conds.CONDConstant(kwargs.get("lyrics_strength", 1.0))
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return out
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class Omnigen2(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=OmniGen2Transformer2DModel)
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@ -14,7 +14,7 @@ from pathlib import Path
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from typing import List, Optional, Final, Set
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# enable better transfer
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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os.environ["HF_XET_HIGH_PERFORMANCE"] = "True"
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import tqdm
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from huggingface_hub import hf_hub_download, scan_cache_dir, snapshot_download, HfFileSystem
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@ -486,6 +486,7 @@ KNOWN_VAES: Final[KnownDownloadables] = KnownDownloadables([
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HuggingFile("comfyanonymous/cosmos_1.0_text_encoder_and_VAE_ComfyUI", "vae/cosmos_cv8x8x8_1.0.safetensors"),
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HuggingFile("Comfy-Org/Lumina_Image_2.0_Repackaged", "split_files/vae/ae.safetensors", save_with_filename="lumina_image_2.0-ae.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.1_ComfyUI_repackaged", "split_files/vae/wan_2.1_vae.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/vae/wan2.2_vae.safetensors"),
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], folder_name="vae")
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KNOWN_HUGGINGFACE_MODEL_REPOS: Final[Set[str]] = {
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@ -546,6 +547,15 @@ KNOWN_UNET_MODELS: Final[KnownDownloadables] = KnownDownloadables([
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HuggingFile("Comfy-Org/Cosmos_Predict2_repackaged", "cosmos_predict2_2B_video2world_480p_16fps.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.1_ComfyUI_repackaged", "split_files/diffusion_models/wan2.1_vace_14B_fp16.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.1_ComfyUI_repackaged", "split_files/diffusion_models/wan2.1_fun_camera_v1.1_1.3B_bf16.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp16.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp16.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp16.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp16.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors"),
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HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors"),
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HuggingFile("lodestones/Chroma", "chroma-unlocked-v37.safetensors"),
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], folder_names=["diffusion_models", "unet"])
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@ -742,7 +742,6 @@ def _load_models_gpu(models: Sequence[ModelManageable], memory_required: int = 0
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loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
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current_loaded_models.insert(0, loaded_model)
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logger.debug(f"Loaded {loaded_model}")
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span = get_current_span()
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span.set_attribute("models_to_load", list(map(str, models_to_load)))
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@ -29,6 +29,7 @@ from typing import Callable, Optional
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import torch
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import torch.nn
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from humanize import naturalsize
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from natsort import natsorted
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from . import model_management, lora
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from . import patcher_extension
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@ -119,6 +120,7 @@ def wipe_lowvram_weight(m):
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if hasattr(m, "bias_function"):
|
||||
m.bias_function = []
|
||||
|
||||
|
||||
def move_weight_functions(m, device):
|
||||
if device is None:
|
||||
return 0
|
||||
@ -289,7 +291,7 @@ class ModelPatcher(ModelManageable):
|
||||
return self._force_cast_weights
|
||||
|
||||
@force_cast_weights.setter
|
||||
def force_cast_weights(self, value:bool) -> None:
|
||||
def force_cast_weights(self, value: bool) -> None:
|
||||
self._force_cast_weights = value
|
||||
|
||||
def lowvram_patch_counter(self):
|
||||
@ -475,7 +477,7 @@ class ModelPatcher(ModelManageable):
|
||||
self.add_object_patch("manual_cast_dtype", dtype)
|
||||
if dtype is not None:
|
||||
self.force_cast_weights = True
|
||||
self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this
|
||||
self.patches_uuid = uuid.uuid4() # TODO: optimize by preventing a full model reload for this
|
||||
|
||||
def add_weight_wrapper(self, name, function):
|
||||
self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function]
|
||||
@ -630,7 +632,6 @@ class ModelPatcher(ModelManageable):
|
||||
else:
|
||||
set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
|
||||
|
||||
|
||||
def _load_list(self) -> list[LoadingListItem]:
|
||||
loading = []
|
||||
for n, m in self.model.named_modules():
|
||||
@ -715,6 +716,7 @@ class ModelPatcher(ModelManageable):
|
||||
mem_counter += move_weight_functions(m, device_to)
|
||||
|
||||
load_completely.sort(reverse=True)
|
||||
models_loaded_regularly: list[str] = []
|
||||
for x in load_completely:
|
||||
n = x.name
|
||||
m = x.module
|
||||
@ -726,17 +728,17 @@ class ModelPatcher(ModelManageable):
|
||||
for param in params:
|
||||
self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
|
||||
|
||||
logger.debug("lowvram: loaded module regularly {} {}".format(n, m))
|
||||
models_loaded_regularly.append("name={} module={}".format(n, m))
|
||||
m.comfy_patched_weights = True
|
||||
|
||||
logger.debug("lowvram: loaded module regularly: {}".format(", ".join(models_loaded_regularly)))
|
||||
for x in load_completely:
|
||||
x.module.to(device_to)
|
||||
|
||||
if lowvram_counter > 0:
|
||||
logger.debug("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
|
||||
logger.debug("loaded partially lowvram_model_memory={}MB mem_counter={}MB patch_counter={}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
|
||||
self._memory_measurements.model_lowvram = True
|
||||
else:
|
||||
logger.debug("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
|
||||
logger.debug("loaded completely lowvram_model_memory={}MB mem_counter={}MB full_load={}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
|
||||
self._memory_measurements.model_lowvram = False
|
||||
if full_load:
|
||||
self.model.to(device_to)
|
||||
@ -812,6 +814,7 @@ class ModelPatcher(ModelManageable):
|
||||
self.object_patches_backup.clear()
|
||||
|
||||
def partially_unload(self, device_to, memory_to_free=0):
|
||||
freed_layers: list[str] = []
|
||||
with self.use_ejected():
|
||||
hooks_unpatched = False
|
||||
memory_freed = 0
|
||||
@ -867,7 +870,9 @@ class ModelPatcher(ModelManageable):
|
||||
m.comfy_cast_weights = True
|
||||
m.comfy_patched_weights = False
|
||||
memory_freed += module_mem
|
||||
logging.debug("freed {}".format(n))
|
||||
freed_layers.append(n)
|
||||
|
||||
logger.debug("freed {}".format(natsorted(freed_layers)))
|
||||
|
||||
self._memory_measurements.model_lowvram = True
|
||||
self._memory_measurements.lowvram_patch_counter += patch_counter
|
||||
@ -1190,7 +1195,7 @@ class ModelPatcher(ModelManageable):
|
||||
model_sd_keys_set = set(model_sd_keys)
|
||||
for key in cached_weights:
|
||||
if key not in model_sd_keys:
|
||||
logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}")
|
||||
logger.warning(f"Cached hook could not patch. Key does not exist in model: {key}")
|
||||
continue
|
||||
self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter)
|
||||
model_sd_keys_set.remove(key)
|
||||
@ -1203,7 +1208,7 @@ class ModelPatcher(ModelManageable):
|
||||
original_weights = self.get_key_patches()
|
||||
for key in relevant_patches:
|
||||
if key not in model_sd_keys:
|
||||
logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}")
|
||||
logger.warning(f"Cached hook would not patch. Key does not exist in model: {key}")
|
||||
continue
|
||||
self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights,
|
||||
memory_counter=memory_counter)
|
||||
@ -1265,7 +1270,7 @@ class ModelPatcher(ModelManageable):
|
||||
del out_weight
|
||||
del weight
|
||||
|
||||
def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
|
||||
def unpatch_hooks(self, whitelist_keys_set: set[str] = None) -> None:
|
||||
with self.use_ejected():
|
||||
if len(self.hook_backup) == 0:
|
||||
self.current_hooks = None
|
||||
|
||||
@ -7,9 +7,10 @@ from PIL import Image
|
||||
from tqdm import tqdm
|
||||
from typing_extensions import override
|
||||
|
||||
from .component_model.executor_types import ExecutorToClientProgress
|
||||
from .component_model.module_property import create_module_properties
|
||||
from .execution_context import current_execution_context
|
||||
from .progress_types import AbstractProgressRegistry
|
||||
from .progress_types import AbstractProgressRegistry, PreviewImageMetadata
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from comfy_execution.graph import DynamicPrompt
|
||||
@ -157,7 +158,7 @@ class WebUIProgressHandler(ProgressHandler):
|
||||
Handler that sends progress updates to the WebUI via WebSockets.
|
||||
"""
|
||||
|
||||
def __init__(self, server_instance):
|
||||
def __init__(self, server_instance: ExecutorToClientProgress):
|
||||
super().__init__("webui")
|
||||
self.server_instance = server_instance
|
||||
|
||||
@ -216,7 +217,7 @@ class WebUIProgressHandler(ProgressHandler):
|
||||
self.server_instance.client_id,
|
||||
"supports_preview_metadata",
|
||||
):
|
||||
metadata = {
|
||||
metadata: PreviewImageMetadata = {
|
||||
"node_id": node_id,
|
||||
"prompt_id": prompt_id,
|
||||
"display_node_id": self.registry.dynprompt.get_display_node_id(
|
||||
@ -327,7 +328,7 @@ class ProgressRegistry(AbstractProgressRegistry):
|
||||
|
||||
# Global registry instance
|
||||
@_module_properties.getter
|
||||
def _global_progress_registry() -> ProgressRegistry:
|
||||
def _global_progress_registry() -> AbstractProgressRegistry | None:
|
||||
return current_execution_context().progress_registry
|
||||
|
||||
|
||||
|
||||
41
comfy/sd.py
41
comfy/sd.py
@ -41,23 +41,23 @@ from .model_management import load_models_gpu
|
||||
from .model_patcher import ModelPatcher
|
||||
from .t2i_adapter import adapter
|
||||
from .taesd import taesd
|
||||
from .text_encoders import ace
|
||||
from .text_encoders import aura_t5
|
||||
from .text_encoders import hidream
|
||||
from .text_encoders import cosmos
|
||||
from .text_encoders import flux
|
||||
from .text_encoders import genmo
|
||||
from .text_encoders import hidream
|
||||
from .text_encoders import hunyuan_video
|
||||
from .text_encoders import hydit
|
||||
from .text_encoders import long_clipl
|
||||
from .text_encoders import lt
|
||||
from .text_encoders import lumina2
|
||||
from .text_encoders import omnigen2
|
||||
from .text_encoders import pixart_t5
|
||||
from .text_encoders import sa_t5
|
||||
from .text_encoders import sd2_clip
|
||||
from .text_encoders import sd3_clip
|
||||
from .text_encoders import wan
|
||||
from .text_encoders import ace
|
||||
from .text_encoders import omnigen2
|
||||
from .utils import ProgressBar
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -280,7 +280,9 @@ class CLIP:
|
||||
|
||||
|
||||
class VAE:
|
||||
def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
|
||||
def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None, no_init=False):
|
||||
if no_init:
|
||||
return
|
||||
if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): # diffusers format
|
||||
sd = diffusers_convert.convert_vae_state_dict(sd)
|
||||
|
||||
@ -469,7 +471,7 @@ class VAE:
|
||||
ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post}
|
||||
self.first_stage_model = ShapeVAE(**ddconfig)
|
||||
self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
|
||||
elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio
|
||||
elif "vocoder.backbone.channel_layers.0.0.bias" in sd: # Ace Step Audio
|
||||
self.first_stage_model = MusicDCAE(source_sample_rate=44100)
|
||||
self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype)
|
||||
@ -511,6 +513,29 @@ class VAE:
|
||||
self.patcher = model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
|
||||
logger.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
|
||||
|
||||
def clone(self):
|
||||
n = VAE(no_init=True)
|
||||
n.memory_used_encode = self.memory_used_encode
|
||||
n.memory_used_decode = self.memory_used_decode
|
||||
n.downscale_ratio = self.downscale_ratio
|
||||
n.upscale_ratio = self.upscale_ratio
|
||||
n.latent_channels = self.latent_channels
|
||||
n.latent_dim = self.latent_dim
|
||||
n.output_channels = self.output_channels
|
||||
n.process_input = self.process_input
|
||||
n.process_output = self.process_output
|
||||
n.working_dtypes = self.working_dtypes.copy()
|
||||
n.disable_offload = self.disable_offload
|
||||
n.downscale_index_formula = self.downscale_index_formula
|
||||
n.upscale_index_formula = self.upscale_index_formula
|
||||
n.extra_1d_channel = self.extra_1d_channel
|
||||
n.first_stage_model = self.first_stage_model
|
||||
n.device = self.device
|
||||
n.vae_dtype = self.vae_dtype
|
||||
n.output_device = self.output_device
|
||||
n.patcher = self.patcher.clone()
|
||||
return n
|
||||
|
||||
def throw_exception_if_invalid(self):
|
||||
if self.first_stage_model is None:
|
||||
raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")
|
||||
@ -920,7 +945,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
|
||||
elif clip_type == CLIPType.HIDREAM:
|
||||
clip_target.clip = hidream.hidream_clip(**t5xxl_detect(clip_data),
|
||||
clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
|
||||
clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
|
||||
clip_target.tokenizer = hidream.HiDreamTokenizer
|
||||
else: # CLIPType.MOCHI
|
||||
clip_target.clip = genmo.mochi_te(**t5xxl_detect(clip_data))
|
||||
@ -945,7 +970,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
|
||||
elif te_model == TEModel.LLAMA3_8:
|
||||
clip_target.clip = hidream.hidream_clip(**llama_detect(clip_data),
|
||||
clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
|
||||
clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
|
||||
clip_target.tokenizer = hidream.HiDreamTokenizer
|
||||
elif te_model == TEModel.QWEN25_3B:
|
||||
clip_target.clip = omnigen2.te(**llama_detect(clip_data))
|
||||
@ -1033,6 +1058,7 @@ def model_detection_error_hint(path, state_dict):
|
||||
return "\nHINT: This seems to be a Lora file and Lora files should be put in the lora folder and loaded with a lora loader node.."
|
||||
return ""
|
||||
|
||||
|
||||
def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
|
||||
logger.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
|
||||
model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
|
||||
@ -1097,7 +1123,6 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
|
||||
return None
|
||||
return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used'
|
||||
|
||||
|
||||
unet_weight_dtype = list(model_config.supported_inference_dtypes)
|
||||
if model_config.scaled_fp8 is not None:
|
||||
weight_dtype = None
|
||||
|
||||
@ -41,6 +41,7 @@ from einops import rearrange
|
||||
from torch.nn.functional import interpolate
|
||||
from tqdm import tqdm
|
||||
|
||||
from comfy_api import feature_flags
|
||||
from . import interruption, checkpoint_pickle
|
||||
from .cli_args import args
|
||||
from .component_model import files
|
||||
@ -48,6 +49,7 @@ from .component_model.deprecation import _deprecate_method
|
||||
from .component_model.executor_types import ExecutorToClientProgress, ProgressMessage
|
||||
from .component_model.queue_types import BinaryEventTypes
|
||||
from .execution_context import current_execution_context
|
||||
from .progress import get_progress_state
|
||||
|
||||
MMAP_TORCH_FILES = args.mmap_torch_files
|
||||
DISABLE_MMAP = args.disable_mmap
|
||||
@ -1106,22 +1108,23 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap=8, upscale_amou
|
||||
return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
|
||||
|
||||
|
||||
def _progress_bar_update(value: float, total: float, preview_image_or_data: Optional[Any] = None, client_id: Optional[str] = None, server: Optional[ExecutorToClientProgress] = None, node_id: str = None):
|
||||
server = server or current_execution_context().server
|
||||
# todo: this should really be from the context. right now the server is behaving like a context
|
||||
client_id = client_id or server.client_id
|
||||
def _progress_bar_update(value: float, total: float, preview_image_or_data: Optional[Any] = None, client_id: Optional[str] = None, server: Optional[ExecutorToClientProgress] = None, node_id: str = None, prompt_id: str = None):
|
||||
context = current_execution_context()
|
||||
server = server or context.server
|
||||
executing_context = context
|
||||
prompt_id = prompt_id or executing_context.task_id or server.last_prompt_id
|
||||
node_id = node_id or executing_context.node_id or server.last_node_id
|
||||
interruption.throw_exception_if_processing_interrupted()
|
||||
progress: ProgressMessage = {"value": value, "max": total, "prompt_id": server.last_prompt_id, "node": node_id or server.last_node_id}
|
||||
|
||||
progress: ProgressMessage = {"value": value, "max": total, "prompt_id": prompt_id, "node": node_id}
|
||||
# todo: is this still necessary?
|
||||
if isinstance(preview_image_or_data, dict):
|
||||
progress["output"] = preview_image_or_data
|
||||
|
||||
# this is responsible for encoding the image
|
||||
get_progress_state().update_progress(node_id, value, total, preview_image_or_data)
|
||||
server.send_sync("progress", progress, client_id)
|
||||
|
||||
# todo: investigate a better way to send the image data, since it needs the node ID
|
||||
if preview_image_or_data is not None and not isinstance(preview_image_or_data, dict):
|
||||
server.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image_or_data, client_id)
|
||||
|
||||
|
||||
def set_progress_bar_enabled(enabled: bool):
|
||||
warnings.warn(
|
||||
"The global method 'set_progress_bar_enabled' is deprecated and will be removed in a future version. Use current_execution_context().server.receive_all_progress_notifications instead.",
|
||||
|
||||
@ -43,7 +43,8 @@ def get_connection_feature(
|
||||
def supports_feature(
|
||||
sockets_metadata: Dict[str, Dict[str, Any]],
|
||||
sid: str,
|
||||
feature_name: str
|
||||
feature_name: str,
|
||||
force=True,
|
||||
) -> bool:
|
||||
"""
|
||||
Check if a connection supports a specific feature.
|
||||
@ -52,10 +53,13 @@ def supports_feature(
|
||||
sockets_metadata: Dictionary of socket metadata
|
||||
sid: Session ID of the connection
|
||||
feature_name: Name of the feature to check
|
||||
force (bool): If it cannot be determined, assume True
|
||||
|
||||
Returns:
|
||||
Boolean indicating if feature is supported
|
||||
"""
|
||||
if sockets_metadata is None or "__unimplemented" in sockets_metadata:
|
||||
return force
|
||||
return get_connection_feature(sockets_metadata, sid, feature_name, False) is True
|
||||
|
||||
|
||||
|
||||
@ -11,6 +11,7 @@ from comfy import model_management
|
||||
from comfy.language.transformers_model_management import TransformersManagedModel
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from comfy.nodes.package_typing import CustomNode, InputTypes
|
||||
from comfy.sd import VAE
|
||||
from comfy_api.torch_helpers import set_torch_compile_wrapper
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -45,6 +46,7 @@ def write_atomic(
|
||||
|
||||
|
||||
torch._inductor.codecache.write_atomic = write_atomic
|
||||
# torch._inductor.utils.is_big_gpu = lambda *args: True
|
||||
|
||||
|
||||
class TorchCompileModel(CustomNode):
|
||||
@ -52,10 +54,10 @@ class TorchCompileModel(CustomNode):
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"model": ("MODEL,VAE",),
|
||||
},
|
||||
"optional": {
|
||||
"object_patch": ("STRING", {"default": DIFFUSION_MODEL}),
|
||||
"object_patch": ("STRING", {"default": ""}),
|
||||
"fullgraph": ("BOOLEAN", {"default": False}),
|
||||
"dynamic": ("BOOLEAN", {"default": False}),
|
||||
"backend": (TORCH_COMPILE_BACKENDS, {"default": "inductor"}),
|
||||
@ -64,15 +66,14 @@ class TorchCompileModel(CustomNode):
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
RETURN_TYPES = ("MODEL,VAE",)
|
||||
FUNCTION = "patch"
|
||||
RETURN_NAMES = ("model or vae",)
|
||||
|
||||
CATEGORY = "_for_testing"
|
||||
EXPERIMENTAL = True
|
||||
|
||||
def patch(self, model: ModelPatcher, object_patch: str | None = DIFFUSION_MODEL, fullgraph: bool = False, dynamic: bool = False, backend: str = "inductor", mode: str = "max-autotune", torch_tensorrt_optimization_level: int = 3) -> tuple[Callable]:
|
||||
if object_patch is None:
|
||||
object_patch = DIFFUSION_MODEL
|
||||
def patch(self, model: ModelPatcher | VAE | torch.nn.Module, object_patch: str | None = "", fullgraph: bool = False, dynamic: bool = False, backend: str = "inductor", mode: str = "max-autotune", torch_tensorrt_optimization_level: int = 3) -> tuple[Callable]:
|
||||
compile_kwargs = {
|
||||
"fullgraph": fullgraph,
|
||||
"dynamic": dynamic,
|
||||
@ -99,17 +100,26 @@ class TorchCompileModel(CustomNode):
|
||||
}
|
||||
move_to_gpu = True
|
||||
del compile_kwargs["mode"]
|
||||
if isinstance(model, ModelPatcher) or isinstance(model, TransformersManagedModel):
|
||||
m = model.clone()
|
||||
if isinstance(model, (ModelPatcher, TransformersManagedModel, VAE)):
|
||||
to_return = model.clone()
|
||||
object_patches = [p.strip() for p in object_patch.split(",")]
|
||||
patcher: ModelPatcher
|
||||
if isinstance(to_return, VAE):
|
||||
patcher = to_return.patcher
|
||||
object_patches = ["encoder", "decoder"]
|
||||
else:
|
||||
patcher = to_return
|
||||
if object_patch is None or len(object_patches) == 0:
|
||||
object_patches = [DIFFUSION_MODEL]
|
||||
if move_to_gpu:
|
||||
model_management.unload_all_models()
|
||||
model_management.load_models_gpu([m])
|
||||
set_torch_compile_wrapper(m, object_patch=object_patch, **compile_kwargs)
|
||||
m.add_object_patch(object_patch, torch.compile(model=m.get_model_object(object_patch), **compile_kwargs))
|
||||
model_management.load_models_gpu([patcher])
|
||||
set_torch_compile_wrapper(patcher, keys=object_patches, **compile_kwargs)
|
||||
# m.add_object_patch(object_patch, torch.compile(model=m.get_model_object(object_patch), **compile_kwargs))
|
||||
# todo: do we want to move something back off the GPU?
|
||||
# if move_to_gpu:
|
||||
# model_management.unload_all_models()
|
||||
return m,
|
||||
return to_return,
|
||||
elif isinstance(model, torch.nn.Module):
|
||||
if move_to_gpu:
|
||||
model_management.unload_all_models()
|
||||
@ -119,7 +129,7 @@ class TorchCompileModel(CustomNode):
|
||||
model.to(device=model_management.unet_offload_device())
|
||||
return res,
|
||||
else:
|
||||
logging.warning("Encountered a model that cannot be compiled")
|
||||
logger.warning("Encountered a model that cannot be compiled")
|
||||
return model,
|
||||
except OSError as os_error:
|
||||
try:
|
||||
@ -132,7 +142,7 @@ class TorchCompileModel(CustomNode):
|
||||
torch._inductor.utils.clear_inductor_caches() # pylint: disable=no-member
|
||||
except Exception:
|
||||
pass
|
||||
logging.error(f"An exception occurred while trying to compile {str(model)}, gracefully skipping compilation", exc_info=exc_info)
|
||||
logger.error(f"An exception occurred while trying to compile {str(model)}, gracefully skipping compilation", exc_info=exc_info)
|
||||
return model,
|
||||
|
||||
|
||||
@ -160,7 +170,7 @@ class QuantizeModel(CustomNode):
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
|
||||
def warn_in_place(self, model: ModelPatcher):
|
||||
logging.warning(f"Quantizing {model} this way quantizes it in place, making it insuitable for cloning. All uses of this model will be quantized.")
|
||||
logger.warning(f"Quantizing {model} this way quantizes it in place, making it insuitable for cloning. All uses of this model will be quantized.")
|
||||
|
||||
def execute(self, model: ModelPatcher, strategy: str = _QUANTIZATION_STRATEGIES[0]) -> tuple[ModelPatcher]:
|
||||
model = model.clone()
|
||||
@ -179,7 +189,7 @@ class QuantizeModel(CustomNode):
|
||||
"final_layer",
|
||||
}
|
||||
if strategy == "quanto":
|
||||
logging.warning(f"Quantizing {model} will produce poor results due to Optimum's limitations")
|
||||
logger.warning(f"Quantizing {model} will produce poor results due to Optimum's limitations")
|
||||
self.warn_in_place(model)
|
||||
from optimum.quanto import quantize, qint8 # pylint: disable=import-error
|
||||
exclusion_list = [
|
||||
|
||||
@ -57,7 +57,7 @@ dependencies = [
|
||||
"pyjwt[crypto]",
|
||||
"kornia>=0.7.0",
|
||||
"mpmath>=1.0,!=1.4.0a0",
|
||||
"huggingface_hub[hf_transfer]>0.20",
|
||||
"huggingface_hub[hf_xet]>=0.32.0",
|
||||
"lazy-object-proxy",
|
||||
"lazy_loader>=0.3",
|
||||
"can_ada",
|
||||
@ -76,7 +76,8 @@ dependencies = [
|
||||
"wrapt>=1.16.0",
|
||||
"certifi",
|
||||
"spandrel>=0.3.4",
|
||||
"numpy>=1.24.4",
|
||||
# https://github.com/conda-forge/numba-feedstock/issues/158 until numba is released with support for a later version of numpy
|
||||
"numpy>=1.24.4,<2.3",
|
||||
"soundfile",
|
||||
"watchdog",
|
||||
"PySoundFile",
|
||||
|
||||
@ -15,7 +15,7 @@ import requests
|
||||
|
||||
os.environ['OTEL_METRICS_EXPORTER'] = 'none'
|
||||
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
|
||||
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
|
||||
os.environ["HF_XET_HIGH_PERFORMANCE"] = "True"
|
||||
# fixes issues with running the testcontainers rabbitmqcontainer on Windows
|
||||
os.environ["TC_HOST"] = "localhost"
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user