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Fix #45 now test 3.10 and 3.12 images. NVIDIA doesn't support 3.11 at all!

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This commit is contained in:
doctorpangloss 2025-09-23 13:22:54 -07:00
parent f6d3962c77
commit 2a881a768e
6 changed files with 46 additions and 39 deletions
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12
.github/workflows/test.yml vendored
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@ -11,7 +11,7 @@ on: [ push ]
jobs:
build_and_execute_linux:
environment: "Testing"
name: Installation, Unit and Workflow Tests for Linux
name: Installation, Unit and Workflow Tests for Linux (${{ matrix.runner.friendly_name }})
runs-on: ${{ matrix.runner.labels }}
container: ${{ matrix.runner.container }}
strategy:
@ -19,7 +19,17 @@ jobs:
matrix:
runner:
- labels: [self-hosted, Linux, X64, cuda-3090-24gb]
friendly_name: "Python 3.12 CUDA 12.9.1 Torch-TensorRT 2.8.0a0"
container: "nvcr.io/nvidia/pytorch:25.06-py3"
- labels: [self-hosted, Linux, X64, cuda-3090-24gb]
friendly_name: "(LTS) Python 3.12 CUDA 12.8.1.012 Torch-TensorRT 2.7.0a0"
container: "nvcr.io/nvidia/pytorch:25.03-py3"
- labels: [self-hosted, Linux, X64, cuda-3090-24gb]
friendly_name: "Python 3.10 CUDA 12.6.2 Torch-TensorRT 2.5.0a0"
container: "nvcr.io/nvidia/pytorch:24.10-py3"
- labels: [self-hosted, Linux, X64, cuda-3090-24gb]
friendly_name: "Python 3.10 CUDA 12.3.2 Torch-TensorRT 2.2.0a0"
container: "nvcr.io/nvidia/pytorch:23.12-py3"
steps:
- run: |
apt-get update

13
.pylintrc
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@ -600,7 +600,6 @@ disable=raw-checker-failed,
preferred-module,
misplaced-future,
shadowed-import,
deprecated-module,
missing-timeout,
useless-with-lock,
bare-except,
@ -623,11 +622,6 @@ disable=raw-checker-failed,
unspecified-encoding,
forgotten-debug-statement,
method-cache-max-size-none,
deprecated-method,
deprecated-argument,
deprecated-class,
deprecated-decorator,
deprecated-attribute,
bad-format-string-key,
unused-format-string-key,
bad-format-string,
@ -678,7 +672,12 @@ disable=raw-checker-failed,
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where
# it should appear only once). See also the "--disable" option for examples.
enable=
enable=deprecated-module,
deprecated-method,
deprecated-argument,
deprecated-class,
deprecated-decorator,
deprecated-attribute
[METHOD_ARGS]

3
comfy/client/async_progress_iterable.py
View File

@ -1,7 +1,8 @@
import asyncio
import uuid
from asyncio import Task, Future
from typing import override, NamedTuple, Optional, AsyncIterable
from typing import NamedTuple, Optional, AsyncIterable
from typing_extensions import override
from .client_types import V1QueuePromptResponse, ProgressNotification
from ..component_model.executor_types import ExecutorToClientProgress, SendSyncEvent, SendSyncData

7
comfy/model_management_types.py
View File

@ -3,11 +3,11 @@ from __future__ import annotations
import copy
import dataclasses
from abc import ABCMeta, abstractmethod
from typing import Protocol, Optional, TypeVar, runtime_checkable, Callable, Any, NamedTuple, override, TYPE_CHECKING
from typing import Protocol, Optional, TypeVar, runtime_checkable, Callable, Any, NamedTuple, TYPE_CHECKING
import torch
import torch.nn
from typing_extensions import TypedDict, NotRequired
from typing_extensions import TypedDict, NotRequired, override
from .comfy_types import UnetWrapperFunction
from .latent_formats import LatentFormat
@ -109,8 +109,6 @@ class HooksSupportStub(HooksSupport, metaclass=ABCMeta):
if isinstance(model, BaseModel) or hasattr(model, "current_patcher") and isinstance(self, ModelManageable):
model.current_patcher = self
def prepare_state(self, *args, **kwargs):
pass
@ -219,7 +217,6 @@ class ModelManageableStub(HooksSupportStub, TrainingSupportStub, ModelManageable
:see: PatchSupport
"""
@abstractmethod
def patch_model(self, device_to: torch.device | None = None, lowvram_model_memory: int = 0, load_weights: bool = True,
force_patch_weights: bool = False) -> torch.nn.Module:

15
comfy/sd1_clip.py
View File

@ -8,14 +8,8 @@ import os
import re
import traceback
import zipfile
import logging
from pathlib import Path
try:
from importlib.resources.abc import Traversable # pylint: disable=no-name-in-module
except ImportError:
from importlib.abc import Traversable # pylint: disable=no-name-in-module
from typing import Tuple, Sequence, TypeVar, Callable
from typing import Tuple, Sequence, TypeVar, Callable, Optional, Union
import torch
from transformers import CLIPTokenizer, PreTrainedTokenizerBase
@ -27,6 +21,11 @@ from .component_model import files
from .component_model.files import get_path_as_dict, get_package_as_path
from .text_encoders.spiece_tokenizer import SPieceTokenizer
try:
from importlib.resources.abc import Traversable # pylint: disable=no-name-in-module
except ImportError:
from importlib.abc import Traversable # pylint: disable=no-name-in-module, deprecated-class
logger = logging.getLogger(__name__)
def gen_empty_tokens(special_tokens, length):
@ -547,7 +546,7 @@ SDTokenizerT = TypeVar('SDTokenizerT', bound='SDTokenizer')
class SDTokenizer:
def __init__(self, tokenizer_path: torch.Tensor | bytes | bytearray | memoryview | str | Path | Traversable = None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data=None, tokenizer_args=None):
def __init__(self, tokenizer_path: Optional[Union[torch.Tensor, bytes, bytearray, memoryview, str, Path, Traversable]] = None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data=None, tokenizer_args=None):
if tokenizer_data is None:
tokenizer_data = dict()
if tokenizer_args is None:

35
comfy_extras/nodes/nodes_easycache.py
View File

@ -8,6 +8,7 @@ import comfy.model_patcher
if TYPE_CHECKING:
from uuid import UUID
logger = logging.getLogger(__name__)
def easycache_forward_wrapper(executor, *args, **kwargs):
# get values from args
@ -32,7 +33,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
# if first cond marked this step for skipping, skip it and use appropriate cached values
if easycache.skip_current_step:
if easycache.verbose:
logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
logger.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
return easycache.apply_cache_diff(x, uuids)
if easycache.initial_step:
easycache.first_cond_uuid = uuids[0]
@ -46,13 +47,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
easycache.cumulative_change_rate += approx_output_change_rate
if easycache.cumulative_change_rate < easycache.reuse_threshold:
if easycache.verbose:
logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
logger.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
# other conds should also skip this step, and instead use their cached values
easycache.skip_current_step = True
return easycache.apply_cache_diff(x, uuids)
else:
if easycache.verbose:
logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
logger.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
easycache.cumulative_change_rate = 0.0
output: torch.Tensor = executor(*args, **kwargs)
@ -65,11 +66,11 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
easycache.approx_output_change_rates.append(approx_output_change_rate.item())
if easycache.verbose:
logging.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
logger.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
if input_change is not None:
easycache.relative_transformation_rate = output_change / input_change
if easycache.verbose:
logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
logger.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}")
# TODO: allow cache_diff to be offloaded
easycache.update_cache_diff(output, next_x_prev, uuids)
if has_first_cond_uuid:
@ -77,7 +78,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
easycache.output_prev_subsampled = easycache.subsample(output, uuids)
easycache.output_prev_norm = output.flatten().abs().mean()
if easycache.verbose:
logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
logger.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
return output
def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
@ -102,13 +103,13 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
easycache.cumulative_change_rate += approx_output_change_rate
if easycache.cumulative_change_rate < easycache.reuse_threshold:
if easycache.verbose:
logging.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
logger.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
# other conds should also skip this step, and instead use their cached values
easycache.skip_current_step = True
return easycache.apply_cache_diff(x)
else:
if easycache.verbose:
logging.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
logger.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
easycache.cumulative_change_rate = 0.0
output: torch.Tensor = executor(*args, **kwargs)
if easycache.has_output_prev_norm():
@ -120,18 +121,18 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
easycache.approx_output_change_rates.append(approx_output_change_rate.item())
if easycache.verbose:
logging.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
logger.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}")
if input_change is not None:
easycache.relative_transformation_rate = output_change / input_change
if easycache.verbose:
logging.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}")
logger.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}")
# TODO: allow cache_diff to be offloaded
easycache.update_cache_diff(output, next_x_prev)
easycache.x_prev_subsampled = easycache.subsample(next_x_prev)
easycache.output_prev_subsampled = easycache.subsample(output)
easycache.output_prev_norm = output.flatten().abs().mean()
if easycache.verbose:
logging.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
logger.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
return output
def easycache_calc_cond_batch_wrapper(executor, *args, **kwargs):
@ -152,22 +153,22 @@ def easycache_sample_wrapper(executor, *args, **kwargs):
# clone and prepare timesteps
guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
easycache: Union[EasyCacheHolder, LazyCacheHolder] = guider.model_options['transformer_options']['easycache']
logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}")
logger.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}")
return executor(*args, **kwargs)
finally:
easycache = guider.model_options['transformer_options']['easycache']
output_change_rates = easycache.output_change_rates
approx_output_change_rates = easycache.approx_output_change_rates
if easycache.verbose:
logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}")
logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
logger.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}")
logger.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
total_steps = len(args[3])-1
# catch division by zero for log statement; sucks to crash after all sampling is done
try:
speedup = total_steps/(total_steps-easycache.total_steps_skipped)
except ZeroDivisionError:
speedup = 1.0
logging.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({speedup:.2f}x speedup).")
logger.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({speedup:.2f}x speedup).")
easycache.reset()
guider.model_options = orig_model_options
@ -298,7 +299,7 @@ class EasyCacheHolder:
return True
if metadata == self.state_metadata:
return True
logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
logger.warning(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
self.reset()
return False
@ -432,7 +433,7 @@ class LazyCacheHolder:
return True
if metadata == self.state_metadata:
return True
logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
logger.warning(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
self.reset()
return False