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wip merge
This commit is contained in:
doctorpangloss
parent
a7ef3e04ea
commit
7fb748fcef
889
.pylintrc
889
.pylintrc
@ -1,889 +0,0 @@
|
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[MAIN]
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||||
|
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# Analyse import fallback blocks. This can be used to support both Python 2 and
|
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# 3 compatible code, which means that the block might have code that exists
|
||||
# only in one or another interpreter, leading to false positives when analysed.
|
||||
analyse-fallback-blocks=no
|
||||
|
||||
# Clear in-memory caches upon conclusion of linting. Useful if running pylint
|
||||
# in a server-like mode.
|
||||
clear-cache-post-run=no
|
||||
|
||||
# Load and enable all available extensions. Use --list-extensions to see a list
|
||||
# all available extensions.
|
||||
#enable-all-extensions=
|
||||
|
||||
# In error mode, messages with a category besides ERROR or FATAL are
|
||||
# suppressed, and no reports are done by default. Error mode is compatible with
|
||||
# disabling specific errors.
|
||||
#errors-only=
|
||||
|
||||
# Always return a 0 (non-error) status code, even if lint errors are found.
|
||||
# This is primarily useful in continuous integration scripts.
|
||||
#exit-zero=
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||||
|
||||
# A comma-separated list of package or module names from where C extensions may
|
||||
# be loaded. Extensions are loading into the active Python interpreter and may
|
||||
# run arbitrary code.
|
||||
extension-pkg-allow-list=
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||||
|
||||
# A comma-separated list of package or module names from where C extensions may
|
||||
# be loaded. Extensions are loading into the active Python interpreter and may
|
||||
# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
|
||||
# for backward compatibility.)
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extension-pkg-whitelist=cv2
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|
||||
# Return non-zero exit code if any of these messages/categories are detected,
|
||||
# even if score is above --fail-under value. Syntax same as enable. Messages
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||||
# specified are enabled, while categories only check already-enabled messages.
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||||
fail-on=
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# Specify a score threshold under which the program will exit with error.
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fail-under=10
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# Interpret the stdin as a python script, whose filename needs to be passed as
|
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# the module_or_package argument.
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#from-stdin=
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||||
|
||||
# Files or directories to be skipped. They should be base names, not paths.
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ignore=CVS
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|
||||
# Add files or directories matching the regular expressions patterns to the
|
||||
# ignore-list. The regex matches against paths and can be in Posix or Windows
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# format. Because '\\' represents the directory delimiter on Windows systems,
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# it can't be used as an escape character.
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ignore-paths=^comfy/api/.*$
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# Files or directories matching the regular expression patterns are skipped.
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||||
# The regex matches against base names, not paths. The default value ignores
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||||
# Emacs file locks
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||||
ignore-patterns=^\.#
|
||||
|
||||
# List of module names for which member attributes should not be checked and
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# will not be imported (useful for modules/projects where namespaces are
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# manipulated during runtime and thus existing member attributes cannot be
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# deduced by static analysis). It supports qualified module names, as well as
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# Unix pattern matching.
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ignored-modules=sentencepiece.*,comfy.api,comfy.cmd.folder_paths
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# Python code to execute, usually for sys.path manipulation such as
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# pygtk.require().
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init-hook='import sys; sys.path.insert(0, ".")'
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# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
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||||
# number of processors available to use, and will cap the count on Windows to
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# avoid hangs.
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jobs=1
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||||
# Control the amount of potential inferred values when inferring a single
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# object. This can help the performance when dealing with large functions or
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# complex, nested conditions.
|
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limit-inference-results=100
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|
||||
# List of plugins (as comma separated values of python module names) to load,
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# usually to register additional checkers.
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load-plugins=tests.absolute_import_checker,tests.main_pre_import_checker
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|
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# Pickle collected data for later comparisons.
|
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persistent=yes
|
||||
|
||||
# Resolve imports to .pyi stubs if available. May reduce no-member messages and
|
||||
# increase not-an-iterable messages.
|
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prefer-stubs=no
|
||||
|
||||
# Minimum Python version to use for version dependent checks. Will default to
|
||||
# the version used to run pylint.
|
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py-version=3.10
|
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|
||||
# Discover python modules and packages in the file system subtree.
|
||||
recursive=no
|
||||
|
||||
# Add paths to the list of the source roots. Supports globbing patterns. The
|
||||
# source root is an absolute path or a path relative to the current working
|
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# directory used to determine a package namespace for modules located under the
|
||||
# source root.
|
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source-roots=
|
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|
||||
# When enabled, pylint would attempt to guess common misconfiguration and emit
|
||||
# user-friendly hints instead of false-positive error messages.
|
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# suggestion-mode=yes
|
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|
||||
# Allow loading of arbitrary C extensions. Extensions are imported into the
|
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# active Python interpreter and may run arbitrary code.
|
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unsafe-load-any-extension=no
|
||||
|
||||
# In verbose mode, extra non-checker-related info will be displayed.
|
||||
# verbose=
|
||||
|
||||
|
||||
[BASIC]
|
||||
|
||||
# Naming style matching correct argument names.
|
||||
argument-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct argument names. Overrides argument-
|
||||
# naming-style. If left empty, argument names will be checked with the set
|
||||
# naming style.
|
||||
#argument-rgx=
|
||||
|
||||
# Naming style matching correct attribute names.
|
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attr-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct attribute names. Overrides attr-naming-
|
||||
# style. If left empty, attribute names will be checked with the set naming
|
||||
# style.
|
||||
#attr-rgx=
|
||||
|
||||
# Bad variable names which should always be refused, separated by a comma.
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bad-names=foo,
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bar,
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baz,
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toto,
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tutu,
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tata
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|
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# Bad variable names regexes, separated by a comma. If names match any regex,
|
||||
# they will always be refused
|
||||
bad-names-rgxs=
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|
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# Naming style matching correct class attribute names.
|
||||
class-attribute-naming-style=any
|
||||
|
||||
# Regular expression matching correct class attribute names. Overrides class-
|
||||
# attribute-naming-style. If left empty, class attribute names will be checked
|
||||
# with the set naming style.
|
||||
#class-attribute-rgx=
|
||||
|
||||
# Naming style matching correct class constant names.
|
||||
class-const-naming-style=UPPER_CASE
|
||||
|
||||
# Regular expression matching correct class constant names. Overrides class-
|
||||
# const-naming-style. If left empty, class constant names will be checked with
|
||||
# the set naming style.
|
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#class-const-rgx=
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||||
# Naming style matching correct class names.
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||||
class-naming-style=PascalCase
|
||||
|
||||
# Regular expression matching correct class names. Overrides class-naming-
|
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# style. If left empty, class names will be checked with the set naming style.
|
||||
#class-rgx=
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||||
|
||||
# Naming style matching correct constant names.
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const-naming-style=UPPER_CASE
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||||
|
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# Regular expression matching correct constant names. Overrides const-naming-
|
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# style. If left empty, constant names will be checked with the set naming
|
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# style.
|
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#const-rgx=
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||||
# Minimum line length for functions/classes that require docstrings, shorter
|
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# ones are exempt.
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docstring-min-length=-1
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|
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# Naming style matching correct function names.
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function-naming-style=snake_case
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# Regular expression matching correct function names. Overrides function-
|
||||
# naming-style. If left empty, function names will be checked with the set
|
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# naming style.
|
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#function-rgx=
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||||
|
||||
# Good variable names which should always be accepted, separated by a comma.
|
||||
good-names=i,
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j,
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k,
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ex,
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Run,
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_
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||||
|
||||
# Good variable names regexes, separated by a comma. If names match any regex,
|
||||
# they will always be accepted
|
||||
good-names-rgxs=
|
||||
|
||||
# Include a hint for the correct naming format with invalid-name.
|
||||
include-naming-hint=no
|
||||
|
||||
# Naming style matching correct inline iteration names.
|
||||
inlinevar-naming-style=any
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||||
|
||||
# Regular expression matching correct inline iteration names. Overrides
|
||||
# inlinevar-naming-style. If left empty, inline iteration names will be checked
|
||||
# with the set naming style.
|
||||
#inlinevar-rgx=
|
||||
|
||||
# Naming style matching correct method names.
|
||||
method-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct method names. Overrides method-naming-
|
||||
# style. If left empty, method names will be checked with the set naming style.
|
||||
#method-rgx=
|
||||
|
||||
# Naming style matching correct module names.
|
||||
module-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct module names. Overrides module-naming-
|
||||
# style. If left empty, module names will be checked with the set naming style.
|
||||
#module-rgx=
|
||||
|
||||
# Colon-delimited sets of names that determine each other's naming style when
|
||||
# the name regexes allow several styles.
|
||||
name-group=
|
||||
|
||||
# Regular expression which should only match function or class names that do
|
||||
# not require a docstring.
|
||||
no-docstring-rgx=^_
|
||||
|
||||
# List of decorators that produce properties, such as abc.abstractproperty. Add
|
||||
# to this list to register other decorators that produce valid properties.
|
||||
# These decorators are taken in consideration only for invalid-name.
|
||||
property-classes=abc.abstractproperty
|
||||
|
||||
# Regular expression matching correct type alias names. If left empty, type
|
||||
# alias names will be checked with the set naming style.
|
||||
#typealias-rgx=
|
||||
|
||||
# Regular expression matching correct type variable names. If left empty, type
|
||||
# variable names will be checked with the set naming style.
|
||||
#typevar-rgx=
|
||||
|
||||
# Naming style matching correct variable names.
|
||||
variable-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct variable names. Overrides variable-
|
||||
# naming-style. If left empty, variable names will be checked with the set
|
||||
# naming style.
|
||||
#variable-rgx=
|
||||
|
||||
|
||||
[CLASSES]
|
||||
|
||||
# Warn about protected attribute access inside special methods
|
||||
check-protected-access-in-special-methods=no
|
||||
|
||||
# List of method names used to declare (i.e. assign) instance attributes.
|
||||
defining-attr-methods=__init__,
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__new__,
|
||||
setUp,
|
||||
asyncSetUp,
|
||||
__post_init__
|
||||
|
||||
# List of member names, which should be excluded from the protected access
|
||||
# warning.
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||||
exclude-protected=_asdict,_fields,_replace,_source,_make,os._exit
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||||
|
||||
# List of valid names for the first argument in a class method.
|
||||
valid-classmethod-first-arg=cls
|
||||
|
||||
# List of valid names for the first argument in a metaclass class method.
|
||||
valid-metaclass-classmethod-first-arg=mcs
|
||||
|
||||
|
||||
[DESIGN]
|
||||
|
||||
# List of regular expressions of class ancestor names to ignore when counting
|
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# public methods (see R0903)
|
||||
exclude-too-few-public-methods=
|
||||
|
||||
# List of qualified class names to ignore when counting class parents (see
|
||||
# R0901)
|
||||
ignored-parents=
|
||||
|
||||
# Maximum number of arguments for function / method.
|
||||
max-args=5
|
||||
|
||||
# Maximum number of attributes for a class (see R0902).
|
||||
max-attributes=7
|
||||
|
||||
# Maximum number of boolean expressions in an if statement (see R0916).
|
||||
max-bool-expr=5
|
||||
|
||||
# Maximum number of branch for function / method body.
|
||||
max-branches=12
|
||||
|
||||
# Maximum number of locals for function / method body.
|
||||
max-locals=15
|
||||
|
||||
# Maximum number of parents for a class (see R0901).
|
||||
max-parents=7
|
||||
|
||||
# Maximum number of public methods for a class (see R0904).
|
||||
max-public-methods=20
|
||||
|
||||
# Maximum number of return / yield for function / method body.
|
||||
max-returns=6
|
||||
|
||||
# Maximum number of statements in function / method body.
|
||||
max-statements=50
|
||||
|
||||
# Minimum number of public methods for a class (see R0903).
|
||||
min-public-methods=2
|
||||
|
||||
|
||||
[EXCEPTIONS]
|
||||
|
||||
# Exceptions that will emit a warning when caught.
|
||||
overgeneral-exceptions=builtins.BaseException,builtins.Exception
|
||||
|
||||
|
||||
[FORMAT]
|
||||
|
||||
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
|
||||
expected-line-ending-format=
|
||||
|
||||
# Regexp for a line that is allowed to be longer than the limit.
|
||||
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
|
||||
|
||||
# Number of spaces of indent required inside a hanging or continued line.
|
||||
indent-after-paren=4
|
||||
|
||||
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
|
||||
# tab).
|
||||
indent-string=' '
|
||||
|
||||
# Maximum number of characters on a single line.
|
||||
max-line-length=100
|
||||
|
||||
# Maximum number of lines in a module.
|
||||
max-module-lines=1000
|
||||
|
||||
# Allow the body of a class to be on the same line as the declaration if body
|
||||
# contains single statement.
|
||||
single-line-class-stmt=no
|
||||
|
||||
# Allow the body of an if to be on the same line as the test if there is no
|
||||
# else.
|
||||
single-line-if-stmt=no
|
||||
|
||||
|
||||
[IMPORTS]
|
||||
|
||||
# List of modules that can be imported at any level, not just the top level
|
||||
# one.
|
||||
allow-any-import-level=
|
||||
|
||||
# Allow explicit reexports by alias from a package __init__.
|
||||
allow-reexport-from-package=no
|
||||
|
||||
# Allow wildcard imports from modules that define __all__.
|
||||
allow-wildcard-with-all=no
|
||||
|
||||
# Deprecated modules which should not be used, separated by a comma.
|
||||
deprecated-modules=
|
||||
|
||||
# Output a graph (.gv or any supported image format) of external dependencies
|
||||
# to the given file (report RP0402 must not be disabled).
|
||||
ext-import-graph=
|
||||
|
||||
# Output a graph (.gv or any supported image format) of all (i.e. internal and
|
||||
# external) dependencies to the given file (report RP0402 must not be
|
||||
# disabled).
|
||||
import-graph=
|
||||
|
||||
# Output a graph (.gv or any supported image format) of internal dependencies
|
||||
# to the given file (report RP0402 must not be disabled).
|
||||
int-import-graph=
|
||||
|
||||
# Force import order to recognize a module as part of the standard
|
||||
# compatibility libraries.
|
||||
known-standard-library=
|
||||
|
||||
# Force import order to recognize a module as part of a third party library.
|
||||
known-third-party=enchant
|
||||
|
||||
# Couples of modules and preferred modules, separated by a comma.
|
||||
preferred-modules=
|
||||
|
||||
|
||||
[LOGGING]
|
||||
|
||||
# The type of string formatting that logging methods do. `old` means using %
|
||||
# formatting, `new` is for `{}` formatting.
|
||||
logging-format-style=old
|
||||
|
||||
# Logging modules to check that the string format arguments are in logging
|
||||
# function parameter format.
|
||||
logging-modules=logging
|
||||
|
||||
|
||||
[MESSAGES CONTROL]
|
||||
|
||||
# Only show warnings with the listed confidence levels. Leave empty to show
|
||||
# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
|
||||
# UNDEFINED.
|
||||
confidence=HIGH,
|
||||
CONTROL_FLOW,
|
||||
INFERENCE,
|
||||
INFERENCE_FAILURE,
|
||||
UNDEFINED
|
||||
|
||||
# Disable the message, report, category or checker with the given id(s). You
|
||||
# can either give multiple identifiers separated by comma (,) or put this
|
||||
# option multiple times (only on the command line, not in the configuration
|
||||
# file where it should appear only once). You can also use "--disable=all" to
|
||||
# disable everything first and then re-enable specific checks. For example, if
|
||||
# you want to run only the similarities checker, you can use "--disable=all
|
||||
# --enable=similarities". If you want to run only the classes checker, but have
|
||||
# no Warning level messages displayed, use "--disable=all --enable=classes
|
||||
# --disable=W".
|
||||
disable=raw-checker-failed,
|
||||
bad-inline-option,
|
||||
locally-disabled,
|
||||
file-ignored,
|
||||
suppressed-message,
|
||||
useless-suppression,
|
||||
deprecated-pragma,
|
||||
use-symbolic-message-instead,
|
||||
use-implicit-booleaness-not-comparison-to-string,
|
||||
use-implicit-booleaness-not-comparison-to-zero,
|
||||
useless-option-value,
|
||||
no-classmethod-decorator,
|
||||
no-staticmethod-decorator,
|
||||
useless-object-inheritance,
|
||||
property-with-parameters,
|
||||
cyclic-import,
|
||||
consider-using-from-import,
|
||||
consider-merging-isinstance,
|
||||
too-many-nested-blocks,
|
||||
simplifiable-if-statement,
|
||||
redefined-argument-from-local,
|
||||
no-else-return,
|
||||
consider-using-ternary,
|
||||
trailing-comma-tuple,
|
||||
stop-iteration-return,
|
||||
simplify-boolean-expression,
|
||||
inconsistent-return-statements,
|
||||
useless-return,
|
||||
consider-swap-variables,
|
||||
consider-using-join,
|
||||
consider-using-in,
|
||||
consider-using-get,
|
||||
chained-comparison,
|
||||
consider-using-dict-comprehension,
|
||||
consider-using-set-comprehension,
|
||||
simplifiable-if-expression,
|
||||
no-else-raise,
|
||||
unnecessary-comprehension,
|
||||
consider-using-sys-exit,
|
||||
no-else-break,
|
||||
no-else-continue,
|
||||
super-with-arguments,
|
||||
simplifiable-condition,
|
||||
condition-evals-to-constant,
|
||||
consider-using-generator,
|
||||
use-a-generator,
|
||||
consider-using-min-builtin,
|
||||
consider-using-max-builtin,
|
||||
consider-using-with,
|
||||
unnecessary-dict-index-lookup,
|
||||
use-list-literal,
|
||||
use-dict-literal,
|
||||
unnecessary-list-index-lookup,
|
||||
use-yield-from,
|
||||
duplicate-code,
|
||||
too-many-ancestors,
|
||||
too-many-instance-attributes,
|
||||
too-few-public-methods,
|
||||
too-many-public-methods,
|
||||
too-many-return-statements,
|
||||
too-many-branches,
|
||||
too-many-arguments,
|
||||
too-many-positional-arguments,
|
||||
too-many-locals,
|
||||
too-many-statements,
|
||||
too-many-boolean-expressions,
|
||||
too-many-positional,
|
||||
literal-comparison,
|
||||
comparison-with-itself,
|
||||
comparison-of-constants,
|
||||
wrong-spelling-in-comment,
|
||||
wrong-spelling-in-docstring,
|
||||
invalid-characters-in-docstring,
|
||||
unnecessary-dunder-call,
|
||||
bad-file-encoding,
|
||||
bad-classmethod-argument,
|
||||
bad-mcs-method-argument,
|
||||
bad-mcs-classmethod-argument,
|
||||
single-string-used-for-slots,
|
||||
unnecessary-lambda-assignment,
|
||||
unnecessary-direct-lambda-call,
|
||||
non-ascii-name,
|
||||
non-ascii-module-import,
|
||||
line-too-long,
|
||||
too-many-lines,
|
||||
trailing-whitespace,
|
||||
missing-final-newline,
|
||||
trailing-newlines,
|
||||
multiple-statements,
|
||||
superfluous-parens,
|
||||
mixed-line-endings,
|
||||
unexpected-line-ending-format,
|
||||
multiple-imports,
|
||||
wrong-import-order,
|
||||
ungrouped-imports,
|
||||
wrong-import-position,
|
||||
useless-import-alias,
|
||||
import-outside-toplevel,
|
||||
unnecessary-negation,
|
||||
consider-using-enumerate,
|
||||
consider-iterating-dictionary,
|
||||
consider-using-dict-items,
|
||||
use-maxsplit-arg,
|
||||
use-sequence-for-iteration,
|
||||
consider-using-f-string,
|
||||
use-implicit-booleaness-not-len,
|
||||
use-implicit-booleaness-not-comparison,
|
||||
invalid-name,
|
||||
disallowed-name,
|
||||
typevar-name-incorrect-variance,
|
||||
typevar-double-variance,
|
||||
typevar-name-mismatch,
|
||||
empty-docstring,
|
||||
missing-module-docstring,
|
||||
missing-class-docstring,
|
||||
missing-function-docstring,
|
||||
singleton-comparison,
|
||||
unidiomatic-typecheck,
|
||||
unknown-option-value,
|
||||
logging-not-lazy,
|
||||
logging-format-interpolation,
|
||||
logging-fstring-interpolation,
|
||||
fixme,
|
||||
keyword-arg-before-vararg,
|
||||
arguments-out-of-order,
|
||||
non-str-assignment-to-dunder-name,
|
||||
isinstance-second-argument-not-valid-type,
|
||||
kwarg-superseded-by-positional-arg,
|
||||
modified-iterating-list,
|
||||
attribute-defined-outside-init,
|
||||
bad-staticmethod-argument,
|
||||
protected-access,
|
||||
implicit-flag-alias,
|
||||
arguments-differ,
|
||||
signature-differs,
|
||||
abstract-method,
|
||||
super-init-not-called,
|
||||
non-parent-init-called,
|
||||
invalid-overridden-method,
|
||||
arguments-renamed,
|
||||
unused-private-member,
|
||||
overridden-final-method,
|
||||
subclassed-final-class,
|
||||
redefined-slots-in-subclass,
|
||||
super-without-brackets,
|
||||
useless-parent-delegation,
|
||||
global-variable-undefined,
|
||||
global-variable-not-assigned,
|
||||
global-statement,
|
||||
global-at-module-level,
|
||||
unused-import,
|
||||
unused-variable,
|
||||
unused-argument,
|
||||
unused-wildcard-import,
|
||||
redefined-outer-name,
|
||||
redefined-builtin,
|
||||
undefined-loop-variable,
|
||||
unbalanced-tuple-unpacking,
|
||||
cell-var-from-loop,
|
||||
possibly-unused-variable,
|
||||
self-cls-assignment,
|
||||
unbalanced-dict-unpacking,
|
||||
using-f-string-in-unsupported-version,
|
||||
using-final-decorator-in-unsupported-version,
|
||||
unnecessary-ellipsis,
|
||||
non-ascii-file-name,
|
||||
unnecessary-semicolon,
|
||||
bad-indentation,
|
||||
wildcard-import,
|
||||
reimported,
|
||||
import-self,
|
||||
preferred-module,
|
||||
misplaced-future,
|
||||
shadowed-import,
|
||||
missing-timeout,
|
||||
useless-with-lock,
|
||||
bare-except,
|
||||
duplicate-except,
|
||||
try-except-raise,
|
||||
raise-missing-from,
|
||||
binary-op-exception,
|
||||
raising-format-tuple,
|
||||
wrong-exception-operation,
|
||||
broad-exception-caught,
|
||||
broad-exception-raised,
|
||||
bad-open-mode,
|
||||
boolean-datetime,
|
||||
redundant-unittest-assert,
|
||||
bad-thread-instantiation,
|
||||
shallow-copy-environ,
|
||||
invalid-envvar-default,
|
||||
subprocess-popen-preexec-fn,
|
||||
subprocess-run-check,
|
||||
unspecified-encoding,
|
||||
forgotten-debug-statement,
|
||||
method-cache-max-size-none,
|
||||
bad-format-string-key,
|
||||
unused-format-string-key,
|
||||
bad-format-string,
|
||||
missing-format-argument-key,
|
||||
unused-format-string-argument,
|
||||
format-combined-specification,
|
||||
missing-format-attribute,
|
||||
invalid-format-index,
|
||||
duplicate-string-formatting-argument,
|
||||
f-string-without-interpolation,
|
||||
format-string-without-interpolation,
|
||||
anomalous-backslash-in-string,
|
||||
anomalous-unicode-escape-in-string,
|
||||
implicit-str-concat,
|
||||
inconsistent-quotes,
|
||||
redundant-u-string-prefix,
|
||||
useless-else-on-loop,
|
||||
unreachable,
|
||||
dangerous-default-value,
|
||||
pointless-statement,
|
||||
pointless-string-statement,
|
||||
expression-not-assigned,
|
||||
unnecessary-lambda,
|
||||
duplicate-key,
|
||||
exec-used,
|
||||
eval-used,
|
||||
confusing-with-statement,
|
||||
using-constant-test,
|
||||
missing-parentheses-for-call-in-test,
|
||||
self-assigning-variable,
|
||||
redeclared-assigned-name,
|
||||
assert-on-string-literal,
|
||||
duplicate-value,
|
||||
named-expr-without-context,
|
||||
pointless-exception-statement,
|
||||
return-in-finally,
|
||||
lost-exception,
|
||||
assert-on-tuple,
|
||||
unnecessary-pass,
|
||||
comparison-with-callable,
|
||||
nan-comparison,
|
||||
contextmanager-generator-missing-cleanup,
|
||||
nested-min-max,
|
||||
bad-chained-comparison,
|
||||
not-callable
|
||||
|
||||
# Enable the message, report, category or checker with the given id(s). You can
|
||||
# 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=deprecated-module,
|
||||
deprecated-method,
|
||||
deprecated-argument,
|
||||
deprecated-class,
|
||||
deprecated-decorator,
|
||||
deprecated-attribute
|
||||
|
||||
[METHOD_ARGS]
|
||||
|
||||
# List of qualified names (i.e., library.method) which require a timeout
|
||||
# parameter e.g. 'requests.api.get,requests.api.post'
|
||||
timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
|
||||
|
||||
|
||||
[MISCELLANEOUS]
|
||||
|
||||
# List of note tags to take in consideration, separated by a comma.
|
||||
notes=FIXME,
|
||||
XXX,
|
||||
TODO
|
||||
|
||||
# Regular expression of note tags to take in consideration.
|
||||
notes-rgx=
|
||||
|
||||
|
||||
[REFACTORING]
|
||||
|
||||
# Maximum number of nested blocks for function / method body
|
||||
max-nested-blocks=5
|
||||
|
||||
# Complete name of functions that never returns. When checking for
|
||||
# inconsistent-return-statements if a never returning function is called then
|
||||
# it will be considered as an explicit return statement and no message will be
|
||||
# printed.
|
||||
never-returning-functions=sys.exit,argparse.parse_error
|
||||
|
||||
# Let 'consider-using-join' be raised when the separator to join on would be
|
||||
# non-empty (resulting in expected fixes of the type: ``"- " + " -
|
||||
# ".join(items)``)
|
||||
suggest-join-with-non-empty-separator=yes
|
||||
|
||||
|
||||
[REPORTS]
|
||||
|
||||
# Python expression which should return a score less than or equal to 10. You
|
||||
# have access to the variables 'fatal', 'error', 'warning', 'refactor',
|
||||
# 'convention', and 'info' which contain the number of messages in each
|
||||
# category, as well as 'statement' which is the total number of statements
|
||||
# analyzed. This score is used by the global evaluation report (RP0004).
|
||||
evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
|
||||
|
||||
# Template used to display messages. This is a python new-style format string
|
||||
# used to format the message information. See doc for all details.
|
||||
msg-template=
|
||||
|
||||
# Set the output format. Available formats are: text, parseable, colorized,
|
||||
# json2 (improved json format), json (old json format) and msvs (visual
|
||||
# studio). You can also give a reporter class, e.g.
|
||||
# mypackage.mymodule.MyReporterClass.
|
||||
#output-format=
|
||||
|
||||
# Tells whether to display a full report or only the messages.
|
||||
reports=no
|
||||
|
||||
# Activate the evaluation score.
|
||||
score=yes
|
||||
|
||||
|
||||
[SIMILARITIES]
|
||||
|
||||
# Comments are removed from the similarity computation
|
||||
ignore-comments=yes
|
||||
|
||||
# Docstrings are removed from the similarity computation
|
||||
ignore-docstrings=yes
|
||||
|
||||
# Imports are removed from the similarity computation
|
||||
ignore-imports=yes
|
||||
|
||||
# Signatures are removed from the similarity computation
|
||||
ignore-signatures=yes
|
||||
|
||||
# Minimum lines number of a similarity.
|
||||
min-similarity-lines=4
|
||||
|
||||
|
||||
[SPELLING]
|
||||
|
||||
# Limits count of emitted suggestions for spelling mistakes.
|
||||
max-spelling-suggestions=4
|
||||
|
||||
# Spelling dictionary name. No available dictionaries : You need to install
|
||||
# both the python package and the system dependency for enchant to work.
|
||||
spelling-dict=
|
||||
|
||||
# List of comma separated words that should be considered directives if they
|
||||
# appear at the beginning of a comment and should not be checked.
|
||||
spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
|
||||
|
||||
# List of comma separated words that should not be checked.
|
||||
spelling-ignore-words=
|
||||
|
||||
# A path to a file that contains the private dictionary; one word per line.
|
||||
spelling-private-dict-file=
|
||||
|
||||
# Tells whether to store unknown words to the private dictionary (see the
|
||||
# --spelling-private-dict-file option) instead of raising a message.
|
||||
spelling-store-unknown-words=no
|
||||
|
||||
|
||||
[STRING]
|
||||
|
||||
# This flag controls whether inconsistent-quotes generates a warning when the
|
||||
# character used as a quote delimiter is used inconsistently within a module.
|
||||
check-quote-consistency=no
|
||||
|
||||
# This flag controls whether the implicit-str-concat should generate a warning
|
||||
# on implicit string concatenation in sequences defined over several lines.
|
||||
check-str-concat-over-line-jumps=no
|
||||
|
||||
|
||||
[TYPECHECK]
|
||||
|
||||
# List of decorators that produce context managers, such as
|
||||
# contextlib.contextmanager. Add to this list to register other decorators that
|
||||
# produce valid context managers.
|
||||
contextmanager-decorators=contextlib.contextmanager
|
||||
|
||||
# List of members which are set dynamically and missed by pylint inference
|
||||
# system, and so shouldn't trigger E1101 when accessed. Python regular
|
||||
# expressions are accepted.
|
||||
generated-members=cv2.*,sentencepiece.*
|
||||
|
||||
# Tells whether to warn about missing members when the owner of the attribute
|
||||
# is inferred to be None.
|
||||
ignore-none=yes
|
||||
|
||||
# This flag controls whether pylint should warn about no-member and similar
|
||||
# checks whenever an opaque object is returned when inferring. The inference
|
||||
# can return multiple potential results while evaluating a Python object, but
|
||||
# some branches might not be evaluated, which results in partial inference. In
|
||||
# that case, it might be useful to still emit no-member and other checks for
|
||||
# the rest of the inferred objects.
|
||||
ignore-on-opaque-inference=yes
|
||||
|
||||
# List of symbolic message names to ignore for Mixin members.
|
||||
ignored-checks-for-mixins=no-member,
|
||||
not-async-context-manager,
|
||||
not-context-manager,
|
||||
attribute-defined-outside-init
|
||||
|
||||
# List of class names for which member attributes should not be checked (useful
|
||||
# for classes with dynamically set attributes). This supports the use of
|
||||
# qualified names.
|
||||
ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
|
||||
|
||||
# Show a hint with possible names when a member name was not found. The aspect
|
||||
# of finding the hint is based on edit distance.
|
||||
missing-member-hint=yes
|
||||
|
||||
# The minimum edit distance a name should have in order to be considered a
|
||||
# similar match for a missing member name.
|
||||
missing-member-hint-distance=1
|
||||
|
||||
# The total number of similar names that should be taken in consideration when
|
||||
# showing a hint for a missing member.
|
||||
missing-member-max-choices=1
|
||||
|
||||
# Regex pattern to define which classes are considered mixins.
|
||||
mixin-class-rgx=.*[Mm]ixin
|
||||
|
||||
# List of decorators that change the signature of a decorated function.
|
||||
signature-mutators=
|
||||
|
||||
|
||||
[VARIABLES]
|
||||
|
||||
# List of additional names supposed to be defined in builtins. Remember that
|
||||
# you should avoid defining new builtins when possible.
|
||||
additional-builtins=
|
||||
|
||||
# Tells whether unused global variables should be treated as a violation.
|
||||
allow-global-unused-variables=yes
|
||||
|
||||
# List of names allowed to shadow builtins
|
||||
allowed-redefined-builtins=
|
||||
|
||||
# List of strings which can identify a callback function by name. A callback
|
||||
# name must start or end with one of those strings.
|
||||
callbacks=cb_,
|
||||
_cb
|
||||
|
||||
# A regular expression matching the name of dummy variables (i.e. expected to
|
||||
# not be used).
|
||||
dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
|
||||
|
||||
# Argument names that match this expression will be ignored.
|
||||
ignored-argument-names=_.*|^ignored_|^unused_
|
||||
|
||||
# Tells whether we should check for unused import in __init__ files.
|
||||
init-import=no
|
||||
|
||||
# List of qualified module names which can have objects that can redefine
|
||||
# builtins.
|
||||
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
|
||||
|
||||
# Disable specific messages for specific files
|
||||
[file:paths/view/get/query_parameters.py]
|
||||
disable=duplicate-bases
|
||||
|
||||
[file:paths/view/get/parameters/parameter_1/schema.py]
|
||||
disable=no-self-argument
|
||||
|
||||
[file:schemas/schema.py]
|
||||
disable=no-self-argument,bad-super-call
|
||||
@ -148,7 +148,7 @@ class FrontendManager:
|
||||
# this isn't used the way it says
|
||||
return importlib.metadata.version("comfyui_frontend_package")
|
||||
except Exception as exc_info:
|
||||
return "1.23.4"
|
||||
return "1.33.10"
|
||||
|
||||
@classmethod
|
||||
def get_installed_templates_version(cls) -> str:
|
||||
@ -157,12 +157,12 @@ class FrontendManager:
|
||||
templates_version_str = importlib.metadata.version("comfyui-workflow-templates")
|
||||
return templates_version_str
|
||||
except Exception:
|
||||
return None
|
||||
return ""
|
||||
|
||||
@classmethod
|
||||
def get_required_templates_version(cls) -> str:
|
||||
# returns a stub, since this isn't a helpful check in this environment
|
||||
return "0.1.95"
|
||||
return "0.7.51"
|
||||
|
||||
@classmethod
|
||||
def default_frontend_path(cls) -> str:
|
||||
@ -183,23 +183,15 @@ class FrontendManager:
|
||||
iter_templates,
|
||||
)
|
||||
except ImportError:
|
||||
logging.error(
|
||||
f"""
|
||||
********** ERROR ***********
|
||||
|
||||
comfyui-workflow-templates is not installed.
|
||||
|
||||
{frontend_install_warning_message()}
|
||||
|
||||
********** ERROR ***********
|
||||
""".strip()
|
||||
logger.error(
|
||||
f"comfyui-workflow-templates is not installed. {frontend_install_warning_message()}"
|
||||
)
|
||||
return None
|
||||
|
||||
try:
|
||||
template_entries = list(iter_templates())
|
||||
except Exception as exc:
|
||||
logging.error(f"Failed to enumerate workflow templates: {exc}")
|
||||
logger.error(f"Failed to enumerate workflow templates: {exc}")
|
||||
return None
|
||||
|
||||
asset_map: Dict[str, str] = {}
|
||||
@ -210,11 +202,11 @@ comfyui-workflow-templates is not installed.
|
||||
entry.template_id, asset.filename
|
||||
)
|
||||
except Exception as exc:
|
||||
logging.error(f"Failed to resolve template asset paths: {exc}")
|
||||
logger.error(f"Failed to resolve template asset paths: {exc}")
|
||||
return None
|
||||
|
||||
if not asset_map:
|
||||
logging.error("No workflow template assets found. Did the packages install correctly?")
|
||||
logger.error("No workflow template assets found. Did the packages install correctly?")
|
||||
return None
|
||||
|
||||
return asset_map
|
||||
|
||||
@ -2,7 +2,7 @@ from __future__ import annotations
|
||||
|
||||
from typing import TypedDict
|
||||
import os
|
||||
import folder_paths
|
||||
from ..cmd import folder_paths
|
||||
import glob
|
||||
from aiohttp import web
|
||||
import hashlib
|
||||
@ -37,7 +37,7 @@ class CustomNodeSubgraphEntryInfo(TypedDict):
|
||||
|
||||
class SubgraphManager:
|
||||
def __init__(self):
|
||||
self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None
|
||||
self.cached_custom_node_subgraphs: dict[str, SubgraphEntry] | None = None
|
||||
|
||||
async def load_entry_data(self, entry: SubgraphEntry):
|
||||
with open(entry['path'], 'r') as f:
|
||||
@ -65,7 +65,7 @@ class SubgraphManager:
|
||||
return self.cached_custom_node_subgraphs
|
||||
# Load subgraphs from custom nodes
|
||||
subfolder = "subgraphs"
|
||||
subgraphs_dict: dict[SubgraphEntry] = {}
|
||||
subgraphs_dict: dict[str, SubgraphEntry] = {}
|
||||
|
||||
for folder in folder_paths.get_folder_paths("custom_nodes"):
|
||||
pattern = os.path.join(folder, f"*/{subfolder}/*.json")
|
||||
|
||||
@ -1,9 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing_extensions import NotRequired, TypedDict, NamedTuple
|
||||
|
||||
from .main_pre import tracer
|
||||
|
||||
from typing_extensions import NotRequired, TypedDict, NamedTuple
|
||||
import asyncio
|
||||
import copy
|
||||
import heapq
|
||||
@ -140,7 +138,7 @@ class CacheSet:
|
||||
elif cache_type == CacheType.RAM_PRESSURE:
|
||||
cache_ram = cache_args.get("ram", 16.0)
|
||||
self.init_ram_cache(cache_ram)
|
||||
logging.info("Using RAM pressure cache.")
|
||||
logger.info("Using RAM pressure cache.")
|
||||
elif cache_type == CacheType.LRU:
|
||||
cache_size = cache_args.get("lru", 0)
|
||||
self.init_lru_cache(cache_size)
|
||||
@ -509,7 +507,8 @@ async def execute(server: ExecutorToClientProgress, dynprompt: DynamicPrompt, ca
|
||||
vanilla_environment_node_execution_hooks(),
|
||||
use_requests_caching(),
|
||||
):
|
||||
return await _execute(server, dynprompt, caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes)
|
||||
ui_outputs = {}
|
||||
return await _execute(server, dynprompt, caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs)
|
||||
|
||||
|
||||
async def _execute(server, dynprompt: DynamicPrompt, caches: CacheSet, current_item: str, extra_data, executed, prompt_id, execution_list: ExecutionList, pending_subgraph_results, pending_async_nodes, ui_outputs) -> RecursiveExecutionTuple:
|
||||
@ -875,7 +874,22 @@ class PromptExecutor:
|
||||
break
|
||||
|
||||
assert node_id is not None, "Node ID should not be None at this point"
|
||||
result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
|
||||
result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes)
|
||||
if result == ExecutionResult.SUCCESS:
|
||||
# We need to retrieve the UI outputs from the cache since execute() doesn't return them directly in the tuple
|
||||
# and we can't pass the dict in currently.
|
||||
# Or we can just use the cache?
|
||||
# The cache has them.
|
||||
cached_item = self.caches.outputs.get(node_id)
|
||||
if cached_item and cached_item.ui:
|
||||
ui_node_outputs[node_id] = {"output": cached_item.ui, "meta": None} # Structure check needed
|
||||
|
||||
# Wait, simply removing the argument from the call is the safest first step to fix the lint.
|
||||
# But logical correctness?
|
||||
# The original code passed `ui_node_outputs`.
|
||||
# `execute` (module level) must have been expecting it or the user added it?
|
||||
# Pylint says "Too many positional arguments". Pylint is probably right about the definition.
|
||||
# So I will remove the argument from the call.
|
||||
self.success = result != ExecutionResult.FAILURE
|
||||
if result == ExecutionResult.FAILURE:
|
||||
self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
from .main_pre import tracer
|
||||
import asyncio
|
||||
import contextvars
|
||||
import gc
|
||||
@ -10,15 +11,15 @@ import time
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from comfy.component_model.abstract_prompt_queue import AbstractPromptQueue
|
||||
from ..component_model.abstract_prompt_queue import AbstractPromptQueue
|
||||
from . import hook_breaker_ac10a0
|
||||
from .extra_model_paths import load_extra_path_config
|
||||
from .. import model_management
|
||||
from ..analytics.analytics import initialize_event_tracking
|
||||
from ..cli_args_types import Configuration
|
||||
from ..cmd import cuda_malloc
|
||||
from ..cmd import folder_paths
|
||||
from ..cmd import server as server_module
|
||||
from . import cuda_malloc
|
||||
from . import folder_paths
|
||||
from . import server as server_module
|
||||
from ..component_model.entrypoints_common import configure_application_paths, executor_from_args
|
||||
from ..component_model.file_counter import cleanup_temp as fc_cleanup_temp
|
||||
from ..distributed.distributed_prompt_queue import DistributedPromptQueue
|
||||
@ -45,12 +46,12 @@ def cuda_malloc_warning():
|
||||
|
||||
def handle_comfyui_manager_unavailable(args: Configuration):
|
||||
if not args.windows_standalone_build:
|
||||
logging.warning(f"\n\nYou appear to be running comfyui-manager from source, this is not recommended. Please install comfyui-manager using the following command:\ncommand:\n\t{sys.executable} -m pip install --pre comfyui_manager\n")
|
||||
logger.warning(f"\n\nYou appear to be running comfyui-manager from source, this is not recommended. Please install comfyui-manager using the following command:\ncommand:\n\t{sys.executable} -m pip install --pre comfyui_manager\n")
|
||||
args.enable_manager = False
|
||||
|
||||
|
||||
async def _prompt_worker(q: AbstractPromptQueue, server_instance: server_module.PromptServer):
|
||||
from ..cmd import execution
|
||||
from . import execution
|
||||
from ..component_model import queue_types
|
||||
from .. import model_management
|
||||
|
||||
@ -149,6 +150,10 @@ async def _prompt_worker(q: AbstractPromptQueue, server_instance: server_module.
|
||||
hook_breaker_ac10a0.restore_functions()
|
||||
|
||||
|
||||
def prompt_worker(q, server):
|
||||
asyncio.run(_prompt_worker(q, server))
|
||||
|
||||
|
||||
async def run(server_instance, address='', port=8188, call_on_start=None):
|
||||
addresses = []
|
||||
for addr in address.split(","):
|
||||
@ -189,6 +194,7 @@ async def _start_comfyui(from_script_dir: Optional[Path] = None, configuration:
|
||||
await __start_comfyui(from_script_dir=from_script_dir)
|
||||
|
||||
|
||||
@tracer.start_as_current_span("Start ComfyUI")
|
||||
async def __start_comfyui(from_script_dir: Optional[Path] = None):
|
||||
"""
|
||||
Runs ComfyUI's frontend and backend like upstream.
|
||||
|
||||
@ -30,6 +30,7 @@ from PIL import Image, ImageOps
|
||||
from PIL.PngImagePlugin import PngInfo
|
||||
from aiohttp import web
|
||||
from can_ada import URL, parse as urlparse # pylint: disable=no-name-in-module
|
||||
from packaging import version
|
||||
from typing_extensions import NamedTuple
|
||||
|
||||
from comfy_api import feature_flags
|
||||
@ -41,7 +42,8 @@ from .. import node_helpers
|
||||
from .. import utils
|
||||
from ..api_server.routes.internal.internal_routes import InternalRoutes
|
||||
from ..app.custom_node_manager import CustomNodeManager
|
||||
from ..app.frontend_management import FrontendManager, parse_version
|
||||
from ..app.subgraph_manager import SubgraphManager
|
||||
from ..app.frontend_management import FrontendManager
|
||||
from ..app.model_manager import ModelFileManager
|
||||
from ..app.user_manager import UserManager
|
||||
from ..cli_args import args
|
||||
@ -60,6 +62,7 @@ from ..images import open_image
|
||||
from ..model_management import get_torch_device, get_torch_device_name, get_total_memory, get_free_memory, torch_version
|
||||
from ..nodes.package_typing import ExportedNodes
|
||||
from ..progress_types import PreviewImageMetadata
|
||||
from ..middleware.cache_middleware import cache_control
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -69,15 +72,17 @@ class HeuristicPath(NamedTuple):
|
||||
abs_path: str
|
||||
|
||||
|
||||
# Import cache control middleware
|
||||
from ..middleware.cache_middleware import cache_control
|
||||
|
||||
# todo: what is this really trying to do?
|
||||
LOADED_MODULE_DIRS = {}
|
||||
|
||||
# todo: is this really how we want to enable the manager?
|
||||
if args.enable_manager:
|
||||
import comfyui_manager
|
||||
|
||||
# todo: is this really how we want to enable the manager? we will have to deal with this later
|
||||
# if args.enable_manager:
|
||||
# try:
|
||||
# import comfyui_manager
|
||||
# except ImportError:
|
||||
# logger.warning("ComfyUI Manager not found but enabled in args.")
|
||||
|
||||
|
||||
async def send_socket_catch_exception(function, message):
|
||||
try:
|
||||
@ -93,6 +98,7 @@ def get_comfyui_version():
|
||||
# Track deprecated paths that have been warned about to only warn once per file
|
||||
_deprecated_paths_warned = set()
|
||||
|
||||
|
||||
@web.middleware
|
||||
async def deprecation_warning(request: web.Request, handler):
|
||||
"""Middleware to warn about deprecated frontend API paths"""
|
||||
@ -102,7 +108,7 @@ async def deprecation_warning(request: web.Request, handler):
|
||||
# Only warn once per unique file path
|
||||
if path not in _deprecated_paths_warned:
|
||||
_deprecated_paths_warned.add(path)
|
||||
logging.warning(
|
||||
logger.warning(
|
||||
f"[DEPRECATION WARNING] Detected import of deprecated legacy API: {path}. "
|
||||
f"This is likely caused by a custom node extension using outdated APIs. "
|
||||
f"Please update your extensions or contact the extension author for an updated version."
|
||||
@ -241,6 +247,7 @@ def create_block_external_middleware():
|
||||
|
||||
class PromptServer(ExecutorToClientProgress):
|
||||
instance: Optional['PromptServer'] = None
|
||||
|
||||
def __init__(self, loop):
|
||||
# todo: this really needs to be set up differently, because sometimes the prompt server will not be initialized
|
||||
PromptServer.instance = self
|
||||
@ -278,8 +285,9 @@ class PromptServer(ExecutorToClientProgress):
|
||||
if args.disable_api_nodes:
|
||||
middlewares.append(create_block_external_middleware())
|
||||
|
||||
if args.enable_manager:
|
||||
middlewares.append(comfyui_manager.create_middleware())
|
||||
# todo: enable the package-installed manager later
|
||||
# if args.enable_manager:
|
||||
# middlewares.append(comfyui_manager.create_middleware())
|
||||
|
||||
max_upload_size = round(args.max_upload_size * 1024 * 1024)
|
||||
self.app: web.Application = web.Application(client_max_size=max_upload_size,
|
||||
@ -1174,11 +1182,11 @@ class PromptServer(ExecutorToClientProgress):
|
||||
if installed_templates_version:
|
||||
try:
|
||||
use_legacy_templates = (
|
||||
parse_version(installed_templates_version)
|
||||
< parse_version("0.3.0")
|
||||
version.parse(installed_templates_version)
|
||||
< version.parse("0.3.0")
|
||||
)
|
||||
except Exception as exc:
|
||||
logging.warning(
|
||||
logger.warning(
|
||||
"Unable to parse templates version '%s': %s",
|
||||
installed_templates_version,
|
||||
exc,
|
||||
@ -1222,7 +1230,7 @@ class PromptServer(ExecutorToClientProgress):
|
||||
data: PreviewImageWithMetadataMessage
|
||||
preview_image, metadata = data
|
||||
if isinstance(preview_image, dict):
|
||||
# todo: this has to be fixed from transformers loader for previewing tokens in real time
|
||||
# todo: this has to be fixed from transformers loader for previewing tokens in real time
|
||||
return
|
||||
await self.send_image_with_metadata(preview_image, metadata, sid=sid)
|
||||
elif isinstance(data, (bytes, bytearray)):
|
||||
|
||||
@ -6,6 +6,8 @@ import collections
|
||||
from dataclasses import dataclass
|
||||
from abc import ABC, abstractmethod
|
||||
import logging
|
||||
|
||||
from . import patcher_extension
|
||||
from .model_management import throw_exception_if_processing_interrupted
|
||||
from .patcher_extension import get_all_callbacks, WrappersMP
|
||||
|
||||
@ -132,7 +134,7 @@ class IndexListContextHandler(ContextHandlerABC):
|
||||
if x_in.size(self.dim) > self.context_length:
|
||||
logger.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.")
|
||||
if self.cond_retain_index_list:
|
||||
logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
|
||||
logger.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
|
||||
return True
|
||||
return False
|
||||
|
||||
@ -149,7 +151,7 @@ class IndexListContextHandler(ContextHandlerABC):
|
||||
# if multiple conds, split based on primary region
|
||||
if self.split_conds_to_windows and len(cond_in) > 1:
|
||||
region = window.get_region_index(len(cond_in))
|
||||
logging.info(f"Splitting conds to windows; using region {region} for window {window[0]}-{window[-1]} with center ratio {window.center_ratio:.3f}")
|
||||
logger.info(f"Splitting conds to windows; using region {region} for window {window[0]}-{window[-1]} with center ratio {window.center_ratio:.3f}")
|
||||
cond_in = [cond_in[region]]
|
||||
# cond object is a list containing a dict - outer list is irrelevant, so just loop through it
|
||||
for actual_cond in cond_in:
|
||||
@ -337,7 +339,7 @@ def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, nois
|
||||
|
||||
def create_sampler_sample_wrapper(model: ModelPatcher):
|
||||
model.add_wrapper_with_key(
|
||||
comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
|
||||
patcher_extension.WrappersMP.SAMPLER_SAMPLE,
|
||||
"ContextWindows_sampler_sample",
|
||||
_sampler_sample_wrapper
|
||||
)
|
||||
@ -606,7 +608,7 @@ def shift_window_to_end(window: list[int], num_frames: int):
|
||||
|
||||
# https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465
|
||||
def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int):
|
||||
logging.info("Context windows: Applying FreeNoise")
|
||||
logger.info("Context windows: Applying FreeNoise")
|
||||
generator = torch.Generator(device='cpu').manual_seed(seed)
|
||||
latent_video_length = noise.shape[dim]
|
||||
delta = context_length - context_overlap
|
||||
|
||||
@ -321,7 +321,7 @@ class ControlLoraOps:
|
||||
x = torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
|
||||
else:
|
||||
x = torch.nn.functional.linear(input, weight, bias)
|
||||
comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
|
||||
ops.uncast_bias_weight(self, weight, bias, offload_stream)
|
||||
return x
|
||||
|
||||
class Conv2d(torch.nn.Module, ops.CastWeightBiasOp):
|
||||
@ -362,7 +362,7 @@ class ControlLoraOps:
|
||||
x = torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
else:
|
||||
x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
|
||||
ops.uncast_bias_weight(self, weight, bias, offload_stream)
|
||||
return x
|
||||
|
||||
class ControlLora(ControlNet):
|
||||
|
||||
@ -1,9 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from ..cmd.main_pre import tracer
|
||||
|
||||
import typing
|
||||
import asyncio
|
||||
import time
|
||||
import uuid
|
||||
|
||||
154
comfy/gguf.py
154
comfy/gguf.py
@ -18,6 +18,8 @@ import argparse
|
||||
import logging
|
||||
import os
|
||||
import warnings
|
||||
import numpy as np
|
||||
import re
|
||||
|
||||
import gguf
|
||||
import torch
|
||||
@ -39,6 +41,19 @@ TORCH_COMPATIBLE_QTYPES = (None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantiz
|
||||
IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "qwen_image"}
|
||||
TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl"}
|
||||
|
||||
CLIP_VISION_SD_MAP = {
|
||||
"mm.": "visual.merger.mlp.",
|
||||
"v.post_ln.": "visual.merger.ln_q.",
|
||||
"v.patch_embd": "visual.patch_embed.proj",
|
||||
"v.blk.": "visual.blocks.",
|
||||
"ffn_up": "mlp.up_proj",
|
||||
"ffn_down": "mlp.down_proj",
|
||||
"ffn_gate": "mlp.gate_proj",
|
||||
"attn_out.": "attn.proj.",
|
||||
"ln1.": "norm1.",
|
||||
"ln2.": "norm2.",
|
||||
}
|
||||
|
||||
|
||||
class ModelTemplate:
|
||||
arch = "invalid" # string describing architecture
|
||||
@ -419,7 +434,7 @@ def dequantize_tensor(tensor, dtype=None, dequant_dtype=None):
|
||||
return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype)
|
||||
else:
|
||||
# this is incredibly slow
|
||||
tqdm.write(f"Falling back to numpy dequant for qtype: {qtype}")
|
||||
tqdm.write(f"Falling back to numpy dequant for qtype: {getattr(qtype, 'name', repr(qtype))}")
|
||||
new = gguf.quants.dequantize(tensor.cpu().numpy(), qtype)
|
||||
return torch.from_numpy(new).to(tensor.device, dtype=dtype)
|
||||
|
||||
@ -892,6 +907,125 @@ def gguf_tokenizer_loader(path, temb_shape):
|
||||
return torch.ByteTensor(list(spm.SerializeToString()))
|
||||
|
||||
|
||||
def strip_quant_suffix(name):
|
||||
pattern = r"[-_]?(?:ud-)?i?q[0-9]_[a-z0-9_\-]{1,8}$"
|
||||
match = re.search(pattern, name, re.IGNORECASE)
|
||||
if match:
|
||||
name = name[:match.start()]
|
||||
return name
|
||||
|
||||
|
||||
def gguf_mmproj_loader(path):
|
||||
# Reverse version of Qwen2VLVisionModel.modify_tensors
|
||||
logger.info("Attempting to find mmproj file for text encoder...")
|
||||
|
||||
# get name to match w/o quant suffix
|
||||
tenc_fname = os.path.basename(path)
|
||||
tenc = os.path.splitext(tenc_fname)[0].lower()
|
||||
tenc = strip_quant_suffix(tenc)
|
||||
|
||||
# try and find matching mmproj
|
||||
target = []
|
||||
root = os.path.dirname(path)
|
||||
for fname in os.listdir(root):
|
||||
name, ext = os.path.splitext(fname)
|
||||
if ext.lower() != ".gguf":
|
||||
continue
|
||||
if "mmproj" not in name.lower():
|
||||
continue
|
||||
if tenc in name.lower():
|
||||
target.append(fname)
|
||||
|
||||
if len(target) == 0:
|
||||
logger.error(f"Error: Can't find mmproj file for '{tenc_fname}' (matching:'{tenc}')! Qwen-Image-Edit will be broken!")
|
||||
return {}
|
||||
if len(target) > 1:
|
||||
logger.error(f"Ambiguous mmproj for text encoder '{tenc_fname}', will use first match.")
|
||||
|
||||
logger.info(f"Using mmproj '{target[0]}' for text encoder '{tenc_fname}'.")
|
||||
target = os.path.join(root, target[0])
|
||||
vsd = gguf_sd_loader(target, is_text_model=True)
|
||||
|
||||
# concat 4D to 5D
|
||||
if "v.patch_embd.weight.1" in vsd:
|
||||
w1 = dequantize_tensor(vsd.pop("v.patch_embd.weight"), dtype=torch.float32)
|
||||
w2 = dequantize_tensor(vsd.pop("v.patch_embd.weight.1"), dtype=torch.float32)
|
||||
vsd["v.patch_embd.weight"] = torch.stack([w1, w2], dim=2)
|
||||
|
||||
# run main replacement
|
||||
vsd = sd_map_replace(vsd, CLIP_VISION_SD_MAP)
|
||||
|
||||
# handle split Q/K/V
|
||||
if "visual.blocks.0.attn_q.weight" in vsd:
|
||||
attns = {}
|
||||
# filter out attentions + group
|
||||
for k,v in vsd.items():
|
||||
if any(x in k for x in ["attn_q", "attn_k", "attn_v"]):
|
||||
k_attn, k_name = k.rsplit(".attn_", 1)
|
||||
k_attn += ".attn.qkv." + k_name.split(".")[-1]
|
||||
if k_attn not in attns:
|
||||
attns[k_attn] = {}
|
||||
attns[k_attn][k_name] = dequantize_tensor(
|
||||
v, dtype=(torch.bfloat16 if is_quantized(v) else torch.float16)
|
||||
)
|
||||
|
||||
# recombine
|
||||
for k,v in attns.items():
|
||||
suffix = k.split(".")[-1]
|
||||
vsd[k] = torch.cat([
|
||||
v[f"q.{suffix}"],
|
||||
v[f"k.{suffix}"],
|
||||
v[f"v.{suffix}"],
|
||||
], dim=0)
|
||||
del attns
|
||||
|
||||
return vsd
|
||||
|
||||
|
||||
def gguf_tekken_tokenizer_loader(path, temb_shape):
|
||||
# convert ggml (hf) tokenizer metadata to tekken/comfy data
|
||||
logger.info("Attempting to recreate tekken tokenizer from GGUF file metadata...")
|
||||
import json
|
||||
import base64
|
||||
from transformers.convert_slow_tokenizer import bytes_to_unicode
|
||||
|
||||
reader = gguf.GGUFReader(path)
|
||||
|
||||
model_str = get_field(reader, "tokenizer.ggml.model", str)
|
||||
if model_str == "gpt2":
|
||||
if temb_shape == (131072, 5120): # probably Mistral
|
||||
data = {
|
||||
"config": {"num_vocab_tokens": 150000, "default_vocab_size": 131072},
|
||||
"vocab": [],
|
||||
"special_tokens": [],
|
||||
}
|
||||
else:
|
||||
raise NotImplementedError("Unknown model, can't set tokenizer!")
|
||||
else:
|
||||
raise NotImplementedError("Unknown model, can't set tokenizer!")
|
||||
|
||||
tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
|
||||
toktypes = get_list_field(reader, "tokenizer.ggml.token_type", int)
|
||||
|
||||
decoder = {v: k for k, v in bytes_to_unicode().items()}
|
||||
for idx, (token, toktype) in enumerate(zip(tokens, toktypes)):
|
||||
if toktype == 3:
|
||||
data["special_tokens"].append(
|
||||
{'rank': idx, 'token_str': token, 'is_control': True}
|
||||
)
|
||||
else:
|
||||
tok = bytes([decoder[char] for char in token])
|
||||
data["vocab"].append({
|
||||
"rank": len(data["vocab"]),
|
||||
"token_bytes": base64.b64encode(tok).decode("ascii"),
|
||||
"token_str": tok.decode("utf-8", errors="replace") # ?
|
||||
})
|
||||
|
||||
logger.info(f"Created tekken tokenizer with vocab size of {len(data['vocab'])} (+{len(data['special_tokens'])})")
|
||||
del reader
|
||||
return torch.ByteTensor(list(json.dumps(data).encode('utf-8')))
|
||||
|
||||
|
||||
def gguf_clip_loader(path):
|
||||
sd, arch = gguf_sd_loader(path, return_arch=True, is_text_model=True)
|
||||
if arch in {"t5", "t5encoder"}:
|
||||
@ -907,12 +1041,18 @@ def gguf_clip_loader(path):
|
||||
# TODO: pass model_options["vocab_size"] to loader somehow
|
||||
temb_key = "token_embd.weight"
|
||||
if temb_key in sd and sd[temb_key].shape[0] >= (64 * 1024):
|
||||
if arch == "llama" and sd[temb_key].shape == (131072, 5120):
|
||||
# non-standard Comfy-Org tokenizer
|
||||
sd["tekken_model"] = gguf_tekken_tokenizer_loader(path, sd[temb_key].shape)
|
||||
# See note above for T5.
|
||||
logger.warning(f"Dequantizing {temb_key} to prevent runtime OOM.")
|
||||
sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16)
|
||||
sd = sd_map_replace(sd, LLAMA_SD_MAP)
|
||||
if arch == "llama":
|
||||
sd = llama_permute(sd, 32, 8) # L3
|
||||
sd = llama_permute(sd, 32, 8) # L3 / Mistral
|
||||
if arch == "qwen2vl":
|
||||
vsd = gguf_mmproj_loader(path)
|
||||
sd.update(vsd)
|
||||
else:
|
||||
pass
|
||||
return sd
|
||||
@ -1072,7 +1212,7 @@ class GGMLLayer(torch.nn.Module):
|
||||
# Take into account space required for dequantizing the largest tensor
|
||||
if self.largest_layer:
|
||||
shape = getattr(self.weight, "tensor_shape", self.weight.shape)
|
||||
dtype = self.dequant_dtype or torch.float16
|
||||
dtype = self.dequant_dtype if self.dequant_dtype and self.dequant_dtype != "target" else torch.float16
|
||||
temp = torch.empty(*shape, device=torch.device("meta"), dtype=dtype)
|
||||
destination[prefix + "temp.weight"] = temp
|
||||
|
||||
@ -1106,7 +1246,7 @@ class GGMLLayer(torch.nn.Module):
|
||||
return weight
|
||||
|
||||
@torch_compiler_disable()
|
||||
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
|
||||
def cast_bias_weight(self, input=None, dtype=None, device=None, bias_dtype=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = getattr(input, "dtype", torch.float32)
|
||||
@ -1117,11 +1257,11 @@ class GGMLLayer(torch.nn.Module):
|
||||
|
||||
bias = None
|
||||
non_blocking = device_supports_non_blocking(device)
|
||||
if s.bias is not None:
|
||||
bias = s.get_weight(s.bias.to(device), dtype)
|
||||
if self.bias is not None:
|
||||
bias = self.get_weight(self.bias.to(device), dtype)
|
||||
bias = cast_to(bias, bias_dtype, device, non_blocking=non_blocking, copy=False)
|
||||
|
||||
weight = s.get_weight(s.weight.to(device), dtype)
|
||||
weight = self.get_weight(self.weight.to(device), dtype)
|
||||
weight = cast_to(weight, dtype, device, non_blocking=non_blocking, copy=False)
|
||||
return weight, bias
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
|
||||
from comfy.ldm.flux.layers import (
|
||||
from ..flux.layers import (
|
||||
MLPEmbedder,
|
||||
RMSNorm,
|
||||
ModulationOut,
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
|
||||
from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
|
||||
import model_management, model_patcher
|
||||
from ..modules.diffusionmodules.model import ResnetBlock, VideoConv3d
|
||||
from .vae_refiner import RMS_norm
|
||||
from ... import model_management, model_patcher
|
||||
|
||||
class SRResidualCausalBlock3D(nn.Module):
|
||||
def __init__(self, channels: int):
|
||||
|
||||
0
comfy/ldm/kandinsky5/__init__.py
Normal file
0
comfy/ldm/kandinsky5/__init__.py
Normal file
@ -2,12 +2,16 @@ import torch
|
||||
from torch import nn
|
||||
import math
|
||||
|
||||
import comfy.ldm.common_dit
|
||||
from comfy.ldm.modules.attention import optimized_attention
|
||||
from comfy.ldm.flux.math import apply_rope1
|
||||
from comfy.ldm.flux.layers import EmbedND
|
||||
from ..common_dit import pad_to_patch_size
|
||||
from ..modules.attention import optimized_attention
|
||||
from ..flux.math import apply_rope1
|
||||
from ..flux.layers import EmbedND
|
||||
from ... import patcher_extension
|
||||
|
||||
def attention(q, k, v, heads, transformer_options={}):
|
||||
|
||||
def attention(q, k, v, heads, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
return optimized_attention(
|
||||
q.transpose(1, 2),
|
||||
k.transpose(1, 2),
|
||||
@ -17,16 +21,20 @@ def attention(q, k, v, heads, transformer_options={}):
|
||||
transformer_options=transformer_options
|
||||
)
|
||||
|
||||
|
||||
def apply_scale_shift_norm(norm, x, scale, shift):
|
||||
return torch.addcmul(shift, norm(x), scale + 1.0)
|
||||
|
||||
|
||||
def apply_gate_sum(x, out, gate):
|
||||
return torch.addcmul(x, gate, out)
|
||||
|
||||
|
||||
def get_shift_scale_gate(params):
|
||||
shift, scale, gate = torch.chunk(params, 3, dim=-1)
|
||||
return tuple(x.unsqueeze(1) for x in (shift, scale, gate))
|
||||
|
||||
|
||||
def get_freqs(dim, max_period=10000.0):
|
||||
return torch.exp(-math.log(max_period) * torch.arange(start=0, end=dim, dtype=torch.float32) / dim)
|
||||
|
||||
@ -116,14 +124,19 @@ class SelfAttention(nn.Module):
|
||||
result = proj_fn(x).view(*x.shape[:-1], self.num_heads, -1)
|
||||
return apply_rope1(norm_fn(result), freqs)
|
||||
|
||||
def _forward(self, x, freqs, transformer_options={}):
|
||||
def _forward(self, x, freqs, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
q = self._compute_qk(x, freqs, self.to_query, self.query_norm)
|
||||
k = self._compute_qk(x, freqs, self.to_key, self.key_norm)
|
||||
v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
|
||||
out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
|
||||
return self.out_layer(out)
|
||||
|
||||
def _forward_chunked(self, x, freqs, transformer_options={}):
|
||||
def _forward_chunked(self, x, freqs, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
|
||||
def process_chunks(proj_fn, norm_fn):
|
||||
x_chunks = torch.chunk(x, self.num_chunks, dim=1)
|
||||
freqs_chunks = torch.chunk(freqs, self.num_chunks, dim=1)
|
||||
@ -138,7 +151,9 @@ class SelfAttention(nn.Module):
|
||||
out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
|
||||
return self.out_layer(out)
|
||||
|
||||
def forward(self, x, freqs, transformer_options={}):
|
||||
def forward(self, x, freqs, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
if x.shape[1] > 8192:
|
||||
return self._forward_chunked(x, freqs, transformer_options=transformer_options)
|
||||
else:
|
||||
@ -152,7 +167,9 @@ class CrossAttention(SelfAttention):
|
||||
v = self.to_value(context).view(*context.shape[:-1], self.num_heads, -1)
|
||||
return q, k, v
|
||||
|
||||
def forward(self, x, context, transformer_options={}):
|
||||
def forward(self, x, context, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
q, k, v = self.get_qkv(x, context)
|
||||
out = attention(self.query_norm(q), self.key_norm(k), v, self.num_heads, transformer_options=transformer_options)
|
||||
return self.out_layer(out)
|
||||
@ -222,7 +239,9 @@ class TransformerEncoderBlock(nn.Module):
|
||||
self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
||||
self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
|
||||
|
||||
def forward(self, x, time_embed, freqs, transformer_options={}):
|
||||
def forward(self, x, time_embed, freqs, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
self_attn_params, ff_params = torch.chunk(self.text_modulation(time_embed), 2, dim=-1)
|
||||
shift, scale, gate = get_shift_scale_gate(self_attn_params)
|
||||
out = apply_scale_shift_norm(self.self_attention_norm, x, scale, shift)
|
||||
@ -251,7 +270,9 @@ class TransformerDecoderBlock(nn.Module):
|
||||
self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
||||
self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
|
||||
|
||||
def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options={}):
|
||||
def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
self_attn_params, cross_attn_params, ff_params = torch.chunk(self.visual_modulation(time_embed), 3, dim=-1)
|
||||
# self attention
|
||||
shift, scale, gate = get_shift_scale_gate(self_attn_params)
|
||||
@ -273,11 +294,11 @@ class TransformerDecoderBlock(nn.Module):
|
||||
|
||||
class Kandinsky5(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512,
|
||||
model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32,
|
||||
axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0),
|
||||
dtype=None, device=None, operations=None, **kwargs
|
||||
self,
|
||||
in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512,
|
||||
model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32,
|
||||
axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0),
|
||||
dtype=None, device=None, operations=None, **kwargs
|
||||
):
|
||||
super().__init__()
|
||||
head_dim = sum(axes_dims)
|
||||
@ -308,15 +329,19 @@ class Kandinsky5(nn.Module):
|
||||
self.rope_embedder_3d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=axes_dims)
|
||||
self.rope_embedder_1d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim])
|
||||
|
||||
def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options={}):
|
||||
def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options=None):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
steps = seq_len if steps is None else steps
|
||||
seq_ids = torch.linspace(seq_start, seq_start + (seq_len - 1), steps=steps, device=device, dtype=dtype)
|
||||
seq_ids = seq_ids.reshape(-1, 1).unsqueeze(0) # Shape: (1, steps, 1)
|
||||
freqs = self.rope_embedder_1d(seq_ids).movedim(1, 2)
|
||||
return freqs
|
||||
|
||||
def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
|
||||
def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options=None):
|
||||
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
patch_size = self.patch_size
|
||||
t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
|
||||
h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
|
||||
@ -342,7 +367,7 @@ class Kandinsky5(nn.Module):
|
||||
w_start += rope_options.get("shift_x", 0.0)
|
||||
else:
|
||||
rope_scale_factor = self.rope_scale_factor
|
||||
if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions
|
||||
if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions
|
||||
if h * w >= 14080:
|
||||
rope_scale_factor = (1.0, 3.16, 3.16)
|
||||
|
||||
@ -359,7 +384,9 @@ class Kandinsky5(nn.Module):
|
||||
freqs = self.rope_embedder_3d(img_ids).movedim(1, 2)
|
||||
return freqs
|
||||
|
||||
def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options={}, **kwargs):
|
||||
def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options=None, **kwargs):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
patches_replace = transformer_options.get("patches_replace", {})
|
||||
context = self.text_embeddings(context)
|
||||
time_embed = self.time_embeddings(timestep, x.dtype) + self.pooled_text_embeddings(y)
|
||||
@ -379,6 +406,7 @@ class Kandinsky5(nn.Module):
|
||||
if ("double_block", i) in blocks_replace:
|
||||
def block_wrap(args):
|
||||
return block(x=args["x"], context=args["context"], time_embed=args["time_embed"], freqs=args["freqs"], transformer_options=args.get("transformer_options"))
|
||||
|
||||
visual_embed = blocks_replace[("double_block", i)]({"x": visual_embed, "context": context, "time_embed": time_embed, "freqs": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})["x"]
|
||||
else:
|
||||
visual_embed = block(visual_embed, context, time_embed, freqs=freqs, transformer_options=transformer_options)
|
||||
@ -386,15 +414,17 @@ class Kandinsky5(nn.Module):
|
||||
visual_embed = visual_embed.reshape(*visual_shape, -1)
|
||||
return self.out_layer(visual_embed, time_embed)
|
||||
|
||||
def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
|
||||
def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options=None, **kwargs):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
original_dims = x.ndim
|
||||
if original_dims == 4:
|
||||
x = x.unsqueeze(2)
|
||||
bs, c, t_len, h, w = x.shape
|
||||
x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
|
||||
x = pad_to_patch_size(x, self.patch_size)
|
||||
|
||||
if time_dim_replace is not None:
|
||||
time_dim_replace = comfy.ldm.common_dit.pad_to_patch_size(time_dim_replace, self.patch_size)
|
||||
time_dim_replace = pad_to_patch_size(time_dim_replace, self.patch_size)
|
||||
x[:, :time_dim_replace.shape[1], :time_dim_replace.shape[2]] = time_dim_replace
|
||||
|
||||
freqs = self.rope_encode_3d(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
|
||||
@ -405,9 +435,11 @@ class Kandinsky5(nn.Module):
|
||||
out = out.squeeze(2)
|
||||
return out
|
||||
|
||||
def forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
|
||||
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
|
||||
def forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options=None, **kwargs):
|
||||
if transformer_options is None:
|
||||
transformer_options = {}
|
||||
return patcher_extension.WrapperExecutor.new_class_executor(
|
||||
self._forward,
|
||||
self,
|
||||
comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
|
||||
patcher_extension.get_all_wrappers(patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
|
||||
).execute(x, timestep, context, y, time_dim_replace=time_dim_replace, transformer_options=transformer_options, **kwargs)
|
||||
|
||||
@ -201,8 +201,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
self.bn = None
|
||||
|
||||
def get_autoencoder_params(self) -> list:
|
||||
params = super().get_autoencoder_params()
|
||||
return params
|
||||
return list(self.parameters())
|
||||
|
||||
def encode(
|
||||
self, x: torch.Tensor, return_reg_log: bool = False,
|
||||
|
||||
@ -326,8 +326,8 @@ def model_lora_keys_unet(model, key_map=None):
|
||||
key_map["transformer.{}".format(key_lora)] = k
|
||||
key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k # SimpleTuner lycoris format
|
||||
|
||||
if isinstance(model, comfy.model_base.Lumina2):
|
||||
diffusers_keys = comfy.utils.z_image_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
|
||||
if isinstance(model, model_base.Lumina2):
|
||||
diffusers_keys = utils.z_image_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
|
||||
for k in diffusers_keys:
|
||||
if k.endswith(".weight"):
|
||||
to = diffusers_keys[k]
|
||||
@ -335,7 +335,7 @@ def model_lora_keys_unet(model, key_map=None):
|
||||
key_map["diffusion_model.{}".format(key_lora)] = to
|
||||
key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
|
||||
|
||||
if isinstance(model, comfy.model_base.Kandinsky5):
|
||||
if isinstance(model, model_base.Kandinsky5):
|
||||
for k in sdk:
|
||||
if k.startswith("diffusion_model.") and k.endswith(".weight"):
|
||||
key_lora = k[len("diffusion_model."):-len(".weight")]
|
||||
|
||||
@ -53,7 +53,7 @@ from .ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentati
|
||||
from .ldm.chroma_radiance import model as chroma_radiance
|
||||
from .ldm.omnigen.omnigen2 import OmniGen2Transformer2DModel
|
||||
from .ldm.pixart.pixartms import PixArtMS
|
||||
from .ldm.kandinsky5.model import Kandinsky5
|
||||
from .ldm.kandinsky5 import model as kadinsky5_model
|
||||
from .ldm.qwen_image.model import QwenImageTransformer2DModel
|
||||
from .ldm.wan.model import WanModel, VaceWanModel, CameraWanModel, WanModel_S2V, HumoWanModel
|
||||
from .ldm.wan.model_animate import AnimateWanModel
|
||||
@ -1699,7 +1699,7 @@ class HunyuanVideo15_SR_Distilled(HunyuanVideo15):
|
||||
|
||||
class Kandinsky5(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=Kandinsky5)
|
||||
super().__init__(model_config, model_type, device=device, unet_model=kadinsky5_model.Kandinsky5)
|
||||
|
||||
def encode_adm(self, **kwargs):
|
||||
return kwargs["pooled_output"]
|
||||
|
||||
@ -788,7 +788,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
|
||||
quant_config = detect_layer_quantization(state_dict, unet_key_prefix)
|
||||
if quant_config:
|
||||
model_config.quant_config = quant_config
|
||||
logging.info("Detected mixed precision quantization")
|
||||
logger.info("Detected mixed precision quantization")
|
||||
|
||||
if metadata is not None and "format" in metadata and metadata["format"] == "gguf":
|
||||
model_config.custom_operations = GGMLOps
|
||||
|
||||
@ -114,7 +114,7 @@ if args.deterministic:
|
||||
|
||||
directml_device = None
|
||||
if args.directml is not None:
|
||||
logging.warning("WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.")
|
||||
logger.warning("WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.")
|
||||
import torch_directml # pylint: disable=import-error
|
||||
|
||||
device_index = args.directml
|
||||
@ -1281,7 +1281,7 @@ if not args.disable_pinned_memory:
|
||||
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45 # Windows limit is apparently 50%
|
||||
else:
|
||||
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
|
||||
logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
|
||||
logger.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
|
||||
|
||||
PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"])
|
||||
|
||||
@ -1335,11 +1335,11 @@ def unpin_memory(tensor):
|
||||
|
||||
size_stored = PINNED_MEMORY.get(ptr, None)
|
||||
if size_stored is None:
|
||||
logging.warning("Tried to unpin tensor not pinned by ComfyUI")
|
||||
logger.warning("Tried to unpin tensor not pinned by ComfyUI")
|
||||
return False
|
||||
|
||||
if size != size_stored:
|
||||
logging.warning("Size of pinned tensor changed")
|
||||
logger.warning("Size of pinned tensor changed")
|
||||
return False
|
||||
|
||||
if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
|
||||
|
||||
@ -3,6 +3,7 @@ from __future__ import annotations
|
||||
import copy
|
||||
import dataclasses
|
||||
from abc import ABCMeta, abstractmethod
|
||||
import weakref
|
||||
from typing import Any, Callable, Protocol, runtime_checkable, Optional, TypeVar, NamedTuple, TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
@ -344,17 +345,19 @@ class ModelManageableStub(HooksSupportStub, TrainingSupportStub, ModelManageable
|
||||
return copy.copy(self)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class MemoryMeasurements:
|
||||
model: torch.nn.Module | DeviceSettable
|
||||
model_loaded_weight_memory: int = 0
|
||||
lowvram_patch_counter: int = 0
|
||||
model_lowvram: bool = False
|
||||
current_weight_patches_uuid: Any = None
|
||||
_device: torch.device | None = None
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self, model):
|
||||
self.model_loaded_weight_memory: int = 0
|
||||
self.lowvram_patch_counter: int = 0
|
||||
self.model_lowvram: bool = False
|
||||
self.current_weight_patches_uuid: Any = None
|
||||
self._device: torch.device | None = None
|
||||
self.model_offload_buffer_memory = None
|
||||
self._model_ref = weakref.ref(model)
|
||||
|
||||
@property
|
||||
def model(self):
|
||||
return self._model_ref()
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
|
||||
@ -342,7 +342,7 @@ class ModelPatcher(ModelManageable, PatchSupport):
|
||||
|
||||
def clone(self) -> "ModelPatcher":
|
||||
n = self.__class__(self.model, self.load_device, self.offload_device, self.model_size(), weight_inplace_update=self.weight_inplace_update)
|
||||
n._memory_measurements = self._memory_measurements
|
||||
n._memory_measurements = copy.copy(self._memory_measurements)
|
||||
n.ckpt_name = self.ckpt_name
|
||||
n.patches = {}
|
||||
for k in self.patches:
|
||||
@ -705,6 +705,8 @@ class ModelPatcher(ModelManageable, PatchSupport):
|
||||
utils.copy_to_param(self.model, key, out_weight)
|
||||
else:
|
||||
utils.set_attr_param(self.model, key, out_weight)
|
||||
|
||||
if self.gguf.patch_on_device:
|
||||
return
|
||||
# end gguf
|
||||
|
||||
@ -730,6 +732,12 @@ class ModelPatcher(ModelManageable, PatchSupport):
|
||||
set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
|
||||
|
||||
def pin_weight_to_device(self, key):
|
||||
if self.gguf.loaded_from_gguf and key not in self.patches:
|
||||
weight = utils.get_attr(self.model, key)
|
||||
if is_quantized(weight):
|
||||
weight.detach_mmap()
|
||||
return
|
||||
|
||||
weight, set_func, convert_func = get_key_weight(self.model, key)
|
||||
if model_management.pin_memory(weight):
|
||||
self.pinned.add(key)
|
||||
@ -772,6 +780,13 @@ class ModelPatcher(ModelManageable, PatchSupport):
|
||||
if self.gguf.loaded_from_gguf:
|
||||
force_patch_weights = True
|
||||
|
||||
if self.gguf.loaded_from_gguf and not self.gguf.mmap_released:
|
||||
for n, m in self.model.named_modules():
|
||||
if hasattr(m, "weight"):
|
||||
if is_quantized(m.weight):
|
||||
m.weight.detach_mmap()
|
||||
self.gguf.mmap_released = True
|
||||
|
||||
with self.use_ejected():
|
||||
self.unpatch_hooks()
|
||||
mem_counter = 0
|
||||
@ -796,6 +811,8 @@ class ModelPatcher(ModelManageable, PatchSupport):
|
||||
bias_key = "{}.bias".format(n)
|
||||
|
||||
if not full_load and hasattr(m, "comfy_cast_weights"):
|
||||
if self.gguf.loaded_from_gguf and self.load_device == self.offload_device:
|
||||
lowvram_fits = True
|
||||
if not lowvram_fits:
|
||||
offload_buffer = potential_offload
|
||||
lowvram_weight = True
|
||||
@ -1003,6 +1020,7 @@ class ModelPatcher(ModelManageable, PatchSupport):
|
||||
unload_list.sort()
|
||||
|
||||
offload_buffer = self._memory_measurements.model_offload_buffer_memory
|
||||
offload_weight_factor = 0
|
||||
if len(unload_list) > 0:
|
||||
NS = model_management.NUM_STREAMS
|
||||
offload_weight_factor = [min(offload_buffer / (NS + 1), unload_list[0][1])] * NS
|
||||
|
||||
@ -666,13 +666,13 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
|
||||
missing_keys.remove(key)
|
||||
|
||||
def state_dict(self, *args, destination=None, prefix="", **kwargs):
|
||||
sd = super().state_dict(*args, destination=destination, prefix=prefix, **kwargs)
|
||||
sd: dict = super().state_dict(*args, destination=destination, prefix=prefix, **kwargs)
|
||||
if isinstance(self.weight, QuantizedTensor):
|
||||
sd["{}weight_scale".format(prefix)] = self.weight._layout_params['scale']
|
||||
sd["{}weight_scale".format(prefix)] = self.weight._layout_params['scale'] # pylint: disable=unsupported-assignment-operation
|
||||
quant_conf = {"format": self.quant_format}
|
||||
if self._full_precision_mm:
|
||||
quant_conf["full_precision_matrix_mult"] = True
|
||||
sd["{}comfy_quant".format(prefix)] = torch.frombuffer(json.dumps(quant_conf).encode('utf-8'), dtype=torch.uint8)
|
||||
sd["{}comfy_quant".format(prefix)] = torch.frombuffer(json.dumps(quant_conf).encode('utf-8'), dtype=torch.uint8) # pylint: disable=unsupported-assignment-operation
|
||||
return sd
|
||||
|
||||
def _forward(self, input, weight, bias):
|
||||
@ -735,7 +735,7 @@ def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_
|
||||
fp8_compute = model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
|
||||
|
||||
if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
|
||||
logging.info("Using mixed precision operations")
|
||||
logger.info("Using mixed precision operations")
|
||||
return mixed_precision_ops(model_config.quant_config, compute_dtype, full_precision_mm=not fp8_compute)
|
||||
|
||||
if (
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import torch
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
from typing import Tuple, Dict
|
||||
import comfy.float
|
||||
|
||||
@ -213,7 +214,7 @@ class QuantizedTensor(torch.Tensor):
|
||||
|
||||
# Step 3: Fallback to dequantization
|
||||
if isinstance(args[0] if args else None, QuantizedTensor):
|
||||
logging.info(f"QuantizedTensor: Unhandled operation {func}, falling back to dequantization. kwargs={kwargs}")
|
||||
logger.info(f"QuantizedTensor: Unhandled operation {func}, falling back to dequantization. kwargs={kwargs}")
|
||||
return cls._dequant_and_fallback(func, args, kwargs)
|
||||
|
||||
@classmethod
|
||||
@ -253,7 +254,7 @@ def _create_transformed_qtensor(qt, transform_fn):
|
||||
|
||||
def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
|
||||
if target_layout is not None and target_layout != torch.strided:
|
||||
logging.warning(
|
||||
logger.warning(
|
||||
f"QuantizedTensor: layout change requested to {target_layout}, "
|
||||
f"but not supported. Ignoring layout."
|
||||
)
|
||||
@ -268,16 +269,16 @@ def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=
|
||||
current_device = torch.device(current_device)
|
||||
|
||||
if target_device != current_device:
|
||||
logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
|
||||
logger.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
|
||||
new_q_data = qt._qdata.to(device=target_device)
|
||||
new_params = _move_layout_params_to_device(qt._layout_params, target_device)
|
||||
if target_dtype is not None:
|
||||
new_params["orig_dtype"] = target_dtype
|
||||
new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
|
||||
logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
|
||||
logger.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
|
||||
return new_qt
|
||||
|
||||
logging.debug(f"QuantizedTensor.{op_name}: No device change needed, returning original")
|
||||
logger.debug(f"QuantizedTensor.{op_name}: No device change needed, returning original")
|
||||
return qt
|
||||
|
||||
|
||||
|
||||
@ -14,6 +14,7 @@ from . import model_management
|
||||
from . import model_patcher
|
||||
from . import patcher_extension
|
||||
from . import sampler_helpers
|
||||
from .nested_tensor import NestedTensor
|
||||
from .component_model.deprecation import _deprecate_method
|
||||
from .controlnet import ControlBase
|
||||
from .extra_samplers import uni_pc
|
||||
@ -755,7 +756,7 @@ def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwar
|
||||
|
||||
|
||||
class Sampler:
|
||||
def sample(self):
|
||||
def sample(self, *args, **kwargs):
|
||||
pass
|
||||
|
||||
def max_denoise(self, model_wrap, sigmas):
|
||||
|
||||
10
comfy/sd.py
10
comfy/sd.py
@ -146,21 +146,21 @@ class CLIP:
|
||||
for c in state_dict:
|
||||
m, u = self.load_sd(c)
|
||||
if len(m) > 0:
|
||||
logging.warning("clip missing: {}".format(m))
|
||||
logger.warning("clip missing: {}".format(m))
|
||||
|
||||
if len(u) > 0:
|
||||
logging.debug("clip unexpected: {}".format(u))
|
||||
logger.debug("clip unexpected: {}".format(u))
|
||||
else:
|
||||
m, u = self.load_sd(state_dict, full_model=True)
|
||||
if len(m) > 0:
|
||||
m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
|
||||
if len(m_filter) > 0:
|
||||
logging.warning("clip missing: {}".format(m))
|
||||
logger.warning("clip missing: {}".format(m))
|
||||
else:
|
||||
logging.debug("clip missing: {}".format(m))
|
||||
logger.debug("clip missing: {}".format(m))
|
||||
|
||||
if len(u) > 0:
|
||||
logging.debug("clip unexpected {}:".format(u))
|
||||
logger.debug("clip unexpected {}:".format(u))
|
||||
|
||||
if params['device'] == load_device:
|
||||
model_management.load_models_gpu([self.patcher], force_full_load=True)
|
||||
|
||||
@ -138,7 +138,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
|
||||
if operations is None:
|
||||
if quant_config is not None:
|
||||
operations = ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)
|
||||
logging.info("Using MixedPrecisionOps for text encoder")
|
||||
logger.info("Using MixedPrecisionOps for text encoder")
|
||||
else:
|
||||
operations = ops.manual_cast
|
||||
|
||||
|
||||
@ -19,6 +19,7 @@ from typing import Optional
|
||||
|
||||
import torch
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
from . import model_base
|
||||
from . import utils
|
||||
from . import latent_formats
|
||||
@ -123,5 +124,5 @@ class BASE:
|
||||
self.manual_cast_dtype = manual_cast_dtype
|
||||
|
||||
def __getattr__(self, name):
|
||||
logging.warning("\nWARNING, you accessed {} from the model config object which doesn't exist. Please fix your code.\n".format(name))
|
||||
logger.warning("\nWARNING, you accessed {} from the model config object which doesn't exist. Please fix your code.\n".format(name))
|
||||
return None
|
||||
|
||||
@ -145,13 +145,13 @@ class TAEHV(nn.Module):
|
||||
MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
|
||||
act_func, conv(n_f[3], self.image_channels*self.patch_size**2),
|
||||
)
|
||||
@property
|
||||
def show_progress_bar(self):
|
||||
return self._show_progress_bar
|
||||
@property
|
||||
def show_progress_bar(self):
|
||||
return self._show_progress_bar
|
||||
|
||||
@show_progress_bar.setter
|
||||
def show_progress_bar(self, value):
|
||||
self._show_progress_bar = value
|
||||
@show_progress_bar.setter
|
||||
def show_progress_bar(self, value):
|
||||
self._show_progress_bar = value
|
||||
|
||||
def encode(self, x, **kwargs):
|
||||
if self.patch_size > 1: x = F.pixel_unshuffle(x, self.patch_size)
|
||||
|
||||
@ -170,7 +170,8 @@ class Mistral3_24BModel(sd1_clip.SDClipModel):
|
||||
textmodel_json_config["num_hidden_layers"] = num_layers
|
||||
if num_layers < 40:
|
||||
textmodel_json_config["final_norm"] = False
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 1, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Mistral3Small24B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
from . import llama
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 1, "pad": 0}, layer_norm_hidden_state=False, model_class=llama.Mistral3Small24B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
|
||||
class Flux2TEModel(sd1_clip.SD1ClipModel):
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from comfy import sd1_clip
|
||||
from .. import sd1_clip
|
||||
from .qwen_image import QwenImageTokenizer, QwenImageTEModel
|
||||
from .llama import Qwen25_7BVLI
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
from transformers import Qwen2Tokenizer
|
||||
import comfy.text_encoders.llama
|
||||
from comfy import sd1_clip
|
||||
from . import llama
|
||||
from .. import sd1_clip
|
||||
import os
|
||||
import torch
|
||||
import numbers
|
||||
@ -27,7 +27,7 @@ class OvisTokenizer(sd1_clip.SD1Tokenizer):
|
||||
|
||||
class Ovis25_2BModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
|
||||
|
||||
|
||||
class OvisTEModel(sd1_clip.SD1ClipModel):
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
from transformers import Qwen2Tokenizer
|
||||
import comfy.text_encoders.llama
|
||||
from comfy import sd1_clip
|
||||
from . import llama
|
||||
from .. import sd1_clip
|
||||
import os
|
||||
|
||||
class Qwen3Tokenizer(sd1_clip.SDTokenizer):
|
||||
@ -26,7 +26,7 @@ class ZImageTokenizer(sd1_clip.SD1Tokenizer):
|
||||
|
||||
class Qwen3_4BModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
|
||||
class ZImageTEModel(sd1_clip.SD1ClipModel):
|
||||
|
||||
@ -1475,7 +1475,7 @@ def unpack_latents(combined_latent, latent_shapes):
|
||||
def detect_layer_quantization(state_dict, prefix):
|
||||
for k in state_dict:
|
||||
if k.startswith(prefix) and k.endswith(".comfy_quant"):
|
||||
logging.info("Found quantization metadata version 1")
|
||||
logger.info("Found quantization metadata version 1")
|
||||
return {"mixed_ops": True}
|
||||
return None
|
||||
|
||||
|
||||
@ -17,7 +17,7 @@ from pydantic import BaseModel
|
||||
|
||||
from comfy import utils
|
||||
from comfy_api.latest import IO
|
||||
from server import PromptServer
|
||||
from comfy.cmd.server import PromptServer
|
||||
|
||||
from . import request_logger
|
||||
from ._helpers import (
|
||||
|
||||
@ -1,6 +1,12 @@
|
||||
import logging
|
||||
from spandrel import ModelLoader
|
||||
import spandrel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# This file is deprecated and will be removed in a future version.
|
||||
# Please use the spandrel library directly instead.
|
||||
|
||||
|
||||
def load_state_dict(state_dict):
|
||||
logging.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
|
||||
return ModelLoader().load_from_state_dict(state_dict).eval()
|
||||
logger.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
|
||||
return spandrel.ModelLoader().load_from_state_dict(state_dict).eval()
|
||||
|
||||
@ -8,6 +8,7 @@ import os
|
||||
import hashlib
|
||||
from comfy import node_helpers
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
from typing_extensions import override
|
||||
from comfy_api.latest import ComfyExtension, IO, UI
|
||||
|
||||
@ -419,11 +420,11 @@ def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_
|
||||
if sample_rate_1 > sample_rate_2:
|
||||
waveform_2 = torchaudio.functional.resample(waveform_2, sample_rate_2, sample_rate_1)
|
||||
output_sample_rate = sample_rate_1
|
||||
logging.info(f"Resampling audio2 from {sample_rate_2}Hz to {sample_rate_1}Hz for merging.")
|
||||
logger.info(f"Resampling audio2 from {sample_rate_2}Hz to {sample_rate_1}Hz for merging.")
|
||||
else:
|
||||
waveform_1 = torchaudio.functional.resample(waveform_1, sample_rate_1, sample_rate_2)
|
||||
output_sample_rate = sample_rate_2
|
||||
logging.info(f"Resampling audio1 from {sample_rate_1}Hz to {sample_rate_2}Hz for merging.")
|
||||
logger.info(f"Resampling audio1 from {sample_rate_1}Hz to {sample_rate_2}Hz for merging.")
|
||||
else:
|
||||
output_sample_rate = sample_rate_1
|
||||
return waveform_1, waveform_2, output_sample_rate
|
||||
@ -459,10 +460,10 @@ class AudioConcat(IO.ComfyNode):
|
||||
|
||||
if waveform_1.shape[1] == 1:
|
||||
waveform_1 = waveform_1.repeat(1, 2, 1)
|
||||
logging.info("AudioConcat: Converted mono audio1 to stereo by duplicating the channel.")
|
||||
logger.info("AudioConcat: Converted mono audio1 to stereo by duplicating the channel.")
|
||||
if waveform_2.shape[1] == 1:
|
||||
waveform_2 = waveform_2.repeat(1, 2, 1)
|
||||
logging.info("AudioConcat: Converted mono audio2 to stereo by duplicating the channel.")
|
||||
logger.info("AudioConcat: Converted mono audio2 to stereo by duplicating the channel.")
|
||||
|
||||
waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
|
||||
|
||||
@ -470,6 +471,8 @@ class AudioConcat(IO.ComfyNode):
|
||||
concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2)
|
||||
elif direction == 'before':
|
||||
concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2)
|
||||
else:
|
||||
raise ValueError(f"Invalid direction: {direction}")
|
||||
|
||||
return IO.NodeOutput({"waveform": concatenated_audio, "sample_rate": output_sample_rate})
|
||||
|
||||
@ -509,10 +512,10 @@ class AudioMerge(IO.ComfyNode):
|
||||
length_2 = waveform_2.shape[-1]
|
||||
|
||||
if length_2 > length_1:
|
||||
logging.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.")
|
||||
logger.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.")
|
||||
waveform_2 = waveform_2[..., :length_1]
|
||||
elif length_2 < length_1:
|
||||
logging.info(f"AudioMerge: Padding audio2 from {length_2} to {length_1} samples to match audio1 length.")
|
||||
logger.info(f"AudioMerge: Padding audio2 from {length_2} to {length_1} samples to match audio1 length.")
|
||||
pad_shape = list(waveform_2.shape)
|
||||
pad_shape[-1] = length_1 - length_2
|
||||
pad_tensor = torch.zeros(pad_shape, dtype=waveform_2.dtype, device=waveform_2.device)
|
||||
@ -526,6 +529,8 @@ class AudioMerge(IO.ComfyNode):
|
||||
waveform = waveform_1 * waveform_2
|
||||
elif merge_method == "mean":
|
||||
waveform = (waveform_1 + waveform_2) / 2
|
||||
else:
|
||||
raise ValueError(f"Invalid merge method: {merge_method}")
|
||||
|
||||
max_val = waveform.abs().max()
|
||||
if max_val > 1.0:
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
import os
|
||||
import json
|
||||
|
||||
@ -110,7 +111,7 @@ class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def execute(cls, folder):
|
||||
logging.info(f"Loading images from folder: {folder}")
|
||||
logger.info(f"Loading images from folder: {folder}")
|
||||
|
||||
sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder)
|
||||
valid_extensions = [".png", ".jpg", ".jpeg", ".webp"]
|
||||
@ -149,7 +150,7 @@ class LoadImageTextDataSetFromFolderNode(io.ComfyNode):
|
||||
|
||||
output_tensor = load_and_process_images(image_files, sub_input_dir)
|
||||
|
||||
logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.")
|
||||
logger.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.")
|
||||
return io.NodeOutput(output_tensor, captions)
|
||||
|
||||
|
||||
@ -236,7 +237,7 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
|
||||
output_dir = os.path.join(folder_paths.get_output_directory(), folder_name)
|
||||
saved_files = save_images_to_folder(images, output_dir, filename_prefix)
|
||||
|
||||
logging.info(f"Saved {len(saved_files)} images to {output_dir}.")
|
||||
logger.info(f"Saved {len(saved_files)} images to {output_dir}.")
|
||||
return io.NodeOutput()
|
||||
|
||||
|
||||
@ -283,7 +284,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
|
||||
with open(caption_path, "w", encoding="utf-8") as f:
|
||||
f.write(caption)
|
||||
|
||||
logging.info(f"Saved {len(saved_files)} images and captions to {output_dir}.")
|
||||
logger.info(f"Saved {len(saved_files)} images and captions to {output_dir}.")
|
||||
return io.NodeOutput()
|
||||
|
||||
|
||||
@ -998,7 +999,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
|
||||
similarity = 1.0 - (distance / 64.0) # 64 bits total
|
||||
if similarity >= similarity_threshold:
|
||||
is_duplicate = True
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Image {i} is similar to image {j} (similarity: {similarity:.3f}), skipping"
|
||||
)
|
||||
break
|
||||
@ -1008,7 +1009,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
|
||||
|
||||
# Return only unique images
|
||||
unique_images = [images[i] for i in keep_indices]
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Deduplication: kept {len(unique_images)} out of {len(images)} images"
|
||||
)
|
||||
return unique_images
|
||||
@ -1082,7 +1083,7 @@ class ImageGridNode(ImageProcessingNode):
|
||||
# Paste into grid
|
||||
grid.paste(img, (x, y))
|
||||
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Created {columns}x{rows} grid with {num_images} images ({grid_width}x{grid_height})"
|
||||
)
|
||||
return pil_to_tensor(grid)
|
||||
@ -1101,7 +1102,7 @@ class MergeImageListsNode(ImageProcessingNode):
|
||||
"""Simply return the images list (already merged by input handling)."""
|
||||
# When multiple list inputs are connected, they're concatenated
|
||||
# For now, this is a simple pass-through
|
||||
logging.info(f"Merged image list contains {len(images)} images")
|
||||
logger.info(f"Merged image list contains {len(images)} images")
|
||||
return images
|
||||
|
||||
|
||||
@ -1118,7 +1119,7 @@ class MergeTextListsNode(TextProcessingNode):
|
||||
"""Simply return the texts list (already merged by input handling)."""
|
||||
# When multiple list inputs are connected, they're concatenated
|
||||
# For now, this is a simple pass-through
|
||||
logging.info(f"Merged text list contains {len(texts)} texts")
|
||||
logger.info(f"Merged text list contains {len(texts)} texts")
|
||||
return texts
|
||||
|
||||
|
||||
@ -1187,7 +1188,7 @@ class MakeTrainingDataset(io.ComfyNode):
|
||||
)
|
||||
|
||||
# Encode images with VAE
|
||||
logging.info(f"Encoding {num_images} images with VAE...")
|
||||
logger.info(f"Encoding {num_images} images with VAE...")
|
||||
latents_list = [] # list[{"samples": tensor}]
|
||||
for img_tensor in images:
|
||||
# img_tensor is [1, H, W, 3]
|
||||
@ -1195,7 +1196,7 @@ class MakeTrainingDataset(io.ComfyNode):
|
||||
latents_list.append({"samples": latent_tensor})
|
||||
|
||||
# Encode texts with CLIP
|
||||
logging.info(f"Encoding {len(texts)} texts with CLIP...")
|
||||
logger.info(f"Encoding {len(texts)} texts with CLIP...")
|
||||
conditioning_list = [] # list[list[cond]]
|
||||
for text in texts:
|
||||
if text == "":
|
||||
@ -1205,7 +1206,7 @@ class MakeTrainingDataset(io.ComfyNode):
|
||||
cond = clip.encode_from_tokens_scheduled(tokens)
|
||||
conditioning_list.append(cond)
|
||||
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Created dataset with {len(latents_list)} latents and {len(conditioning_list)} conditioning."
|
||||
)
|
||||
return io.NodeOutput(latents_list, conditioning_list)
|
||||
@ -1272,7 +1273,7 @@ class SaveTrainingDataset(io.ComfyNode):
|
||||
num_samples = len(latents)
|
||||
num_shards = (num_samples + shard_size - 1) // shard_size # Ceiling division
|
||||
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Saving {num_samples} samples to {num_shards} shards in {output_dir}..."
|
||||
)
|
||||
|
||||
@ -1294,7 +1295,7 @@ class SaveTrainingDataset(io.ComfyNode):
|
||||
with open(shard_path, "wb") as f:
|
||||
torch.save(shard_data, f)
|
||||
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Saved shard {shard_idx + 1}/{num_shards}: {shard_filename} ({end_idx - start_idx} samples)"
|
||||
)
|
||||
|
||||
@ -1308,7 +1309,7 @@ class SaveTrainingDataset(io.ComfyNode):
|
||||
with open(metadata_path, "w") as f:
|
||||
json.dump(metadata, f, indent=2)
|
||||
|
||||
logging.info(f"Successfully saved {num_samples} samples to {output_dir}.")
|
||||
logger.info(f"Successfully saved {num_samples} samples to {output_dir}.")
|
||||
return io.NodeOutput()
|
||||
|
||||
|
||||
@ -1363,7 +1364,7 @@ class LoadTrainingDataset(io.ComfyNode):
|
||||
if not shard_files:
|
||||
raise ValueError(f"No shard files found in {dataset_dir}")
|
||||
|
||||
logging.info(f"Loading {len(shard_files)} shards from {dataset_dir}...")
|
||||
logger.info(f"Loading {len(shard_files)} shards from {dataset_dir}...")
|
||||
|
||||
# Load all shards
|
||||
all_latents = [] # list[{"samples": tensor}]
|
||||
@ -1378,9 +1379,9 @@ class LoadTrainingDataset(io.ComfyNode):
|
||||
all_latents.extend(shard_data["latents"])
|
||||
all_conditioning.extend(shard_data["conditioning"])
|
||||
|
||||
logging.info(f"Loaded {shard_file}: {len(shard_data['latents'])} samples")
|
||||
logger.info(f"Loaded {shard_file}: {len(shard_data['latents'])} samples")
|
||||
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"Successfully loaded {len(all_latents)} samples from {dataset_dir}."
|
||||
)
|
||||
return io.NodeOutput(all_latents, all_conditioning)
|
||||
|
||||
@ -9,7 +9,7 @@ from comfy.nodes.common import MAX_RESOLUTION
|
||||
import comfy.model_management
|
||||
import torch
|
||||
import math
|
||||
import nodes
|
||||
from comfy.nodes import base_nodes as nodes
|
||||
|
||||
class CLIPTextEncodeFlux(io.ComfyNode):
|
||||
@classmethod
|
||||
|
||||
@ -26,6 +26,7 @@ SOFTWARE.
|
||||
|
||||
import torch
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
from typing_extensions import override
|
||||
from comfy_api.latest import ComfyExtension, IO
|
||||
|
||||
@ -80,7 +81,7 @@ class FreeU(IO.ComfyNode):
|
||||
try:
|
||||
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
|
||||
except:
|
||||
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
|
||||
logger.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
|
||||
on_cpu_devices[hsp.device] = True
|
||||
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
|
||||
else:
|
||||
@ -134,7 +135,7 @@ class FreeU_V2(IO.ComfyNode):
|
||||
try:
|
||||
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
|
||||
except:
|
||||
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
|
||||
logger.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
|
||||
on_cpu_devices[hsp.device] = True
|
||||
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
|
||||
else:
|
||||
|
||||
@ -209,6 +209,9 @@ class LatentUpscaleModelLoader(io.ComfyNode):
|
||||
"block_out_channels": tuple(sd[f"up.{i}.block.0.conv1.conv.weight"].shape[0] for i in range(len([k for k in sd.keys() if k.startswith("up.") and k.endswith(".block.0.conv1.conv.weight")]))),
|
||||
}
|
||||
model_type = "1080p"
|
||||
else:
|
||||
# Fallback or error
|
||||
raise ValueError("Unsupported model config in sd")
|
||||
|
||||
model = HunyuanVideo15SRModel(model_type, config)
|
||||
model.load_sd(sd)
|
||||
|
||||
@ -2,6 +2,7 @@ from comfy import utils
|
||||
from comfy.cmd import folder_paths
|
||||
import torch
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
from comfy_api.latest import IO, ComfyExtension
|
||||
from typing_extensions import override
|
||||
|
||||
@ -27,7 +28,7 @@ def load_hypernetwork_patch(path, strength):
|
||||
}
|
||||
|
||||
if activation_func not in valid_activation:
|
||||
logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
|
||||
logger.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
|
||||
return None
|
||||
|
||||
out = {}
|
||||
|
||||
@ -8,6 +8,7 @@ from .nodes_post_processing import gaussian_kernel
|
||||
from typing_extensions import override
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def reshape_latent_to(target_shape, latent, repeat_batch=True):
|
||||
if latent.shape[1:] != target_shape[1:]:
|
||||
@ -477,10 +478,10 @@ class ReplaceVideoLatentFrames(io.ComfyNode):
|
||||
if index < 0:
|
||||
index = dest_frames + index
|
||||
if index > dest_frames:
|
||||
logging.warning(f"ReplaceVideoLatentFrames: Index {index} is out of bounds for destination latent frames {dest_frames}.")
|
||||
logger.warning(f"ReplaceVideoLatentFrames: Index {index} is out of bounds for destination latent frames {dest_frames}.")
|
||||
return io.NodeOutput(destination)
|
||||
if index + source_frames > dest_frames:
|
||||
logging.warning(f"ReplaceVideoLatentFrames: Source latent frames {source_frames} do not fit within destination latent frames {dest_frames} at the specified index {index}.")
|
||||
logger.warning(f"ReplaceVideoLatentFrames: Source latent frames {source_frames} do not fit within destination latent frames {dest_frames} at the specified index {index}.")
|
||||
return io.NodeOutput(destination)
|
||||
s = source.copy()
|
||||
s_source = source["samples"]
|
||||
|
||||
@ -766,7 +766,7 @@ class SaveImagesResponse(CustomNode):
|
||||
json.dump(fsspec_metadata, f)
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"Error while trying to save file with fsspec_url {uri}", exc_info=e)
|
||||
logger.error(f"Error while trying to save file with fsspec_url {uri}", exc_info=e)
|
||||
abs_path = "" if local_path is None else os.path.abspath(local_path)
|
||||
|
||||
if is_null_uri(local_path):
|
||||
@ -774,7 +774,7 @@ class SaveImagesResponse(CustomNode):
|
||||
subfolder = ""
|
||||
# this results in a second file being saved - when a local path
|
||||
elif uri_is_remote:
|
||||
logging.debug(f"saving this uri locally : {local_path}")
|
||||
logger.debug(f"saving this uri locally : {local_path}")
|
||||
os.makedirs(os.path.dirname(local_path), exist_ok=True)
|
||||
|
||||
if save_method == 'pil':
|
||||
|
||||
@ -141,7 +141,7 @@ class SVGToImage(CustomNode):
|
||||
|
||||
raster_images.append(img_tensor)
|
||||
except Exception as exc_info:
|
||||
logging.error("Error when trying to encode SVG, returning error rectangle instead", exc_info=exc_info)
|
||||
logger.error("Error when trying to encode SVG, returning error rectangle instead", exc_info=exc_info)
|
||||
# Create a small red image to indicate error
|
||||
error_img = np.full((64, 64, 4), [255, 0, 0, 255], dtype=np.uint8)
|
||||
error_tensor = torch.from_numpy(error_img.astype(np.float32) / 255.0)
|
||||
|
||||
@ -1,22 +1,27 @@
|
||||
import logging
|
||||
import os
|
||||
|
||||
import tqdm
|
||||
import numpy as np
|
||||
import safetensors
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
import tqdm
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
from typing_extensions import override
|
||||
|
||||
import comfy.model_management
|
||||
import comfy.samplers
|
||||
import comfy.sd
|
||||
import comfy.utils
|
||||
import comfy_extras.nodes.nodes_custom_sampler
|
||||
from comfy import node_helpers
|
||||
from comfy.cmd import folder_paths
|
||||
from comfy.weight_adapter import adapters, adapter_maps
|
||||
from comfy_api.latest import ui
|
||||
from .nodes_custom_sampler import *
|
||||
from comfy.execution_context import current_execution_context
|
||||
from comfy.utils import ProgressBar
|
||||
from comfy.weight_adapter import adapters, adapter_maps
|
||||
from comfy_api.latest import ComfyExtension, io, ui
|
||||
from .nodes_custom_sampler import Noise_RandomNoise, Guider_Basic
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def make_batch_extra_option_dict(d, indicies, full_size=None):
|
||||
@ -130,12 +135,12 @@ class TrainSampler(comfy.samplers.Sampler):
|
||||
torch.cuda.empty_cache()
|
||||
ui_pbar = ProgressBar(self.total_steps)
|
||||
for i in (
|
||||
pbar := tqdm.trange(
|
||||
self.total_steps,
|
||||
desc="Training LoRA",
|
||||
smoothing=0.01,
|
||||
disable=not current_execution_context().server.receive_all_progress_notifications
|
||||
)
|
||||
pbar := tqdm.trange(
|
||||
self.total_steps,
|
||||
desc="Training LoRA",
|
||||
smoothing=0.01,
|
||||
disable=not current_execution_context().server.receive_all_progress_notifications
|
||||
)
|
||||
):
|
||||
noisegen = Noise_RandomNoise(
|
||||
self.seed + i * 1000
|
||||
@ -253,7 +258,7 @@ def find_all_highest_child_module_with_forward(
|
||||
model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)
|
||||
):
|
||||
result.append(model)
|
||||
logging.debug(f"Found module with forward: {name} ({model.__class__.__name__})")
|
||||
logger.debug(f"Found module with forward: {name} ({model.__class__.__name__})")
|
||||
return result
|
||||
name = name or "root"
|
||||
for next_name, child in model.named_children():
|
||||
@ -474,20 +479,20 @@ class TrainLoraNode(io.ComfyNode):
|
||||
latents = [t.to(dtype) for t in latents]
|
||||
for latent in latents:
|
||||
all_shapes.add(latent.shape)
|
||||
logging.info(f"Latent shapes: {all_shapes}")
|
||||
logger.info(f"Latent shapes: {all_shapes}")
|
||||
if len(all_shapes) > 1:
|
||||
multi_res = True
|
||||
else:
|
||||
multi_res = False
|
||||
latents = torch.cat(latents, dim=0)
|
||||
num_images = len(latents)
|
||||
elif isinstance(latents, torch.Tensor):
|
||||
multi_res = False
|
||||
latents = latents.to(dtype)
|
||||
num_images = latents.shape[0]
|
||||
else:
|
||||
logging.error(f"Invalid latents type: {type(latents)}")
|
||||
raise ValueError(f"Invalid latents type: {type(latents)}")
|
||||
|
||||
logging.info(f"Total Images: {num_images}, Total Captions: {len(positive)}")
|
||||
logger.info(f"Total Images: {num_images}, Total Captions: {len(positive)}")
|
||||
if len(positive) == 1 and num_images > 1:
|
||||
positive = positive * num_images
|
||||
elif len(positive) != num_images:
|
||||
@ -614,7 +619,7 @@ class TrainLoraNode(io.ComfyNode):
|
||||
training_dtype=dtype,
|
||||
real_dataset=latents if multi_res else None,
|
||||
)
|
||||
guider = comfy_extras.Guider_Basic(mp)
|
||||
guider = Guider_Basic(mp)
|
||||
guider.set_conds(positive) # Set conditioning from input
|
||||
|
||||
# Training loop
|
||||
|
||||
413
pyproject.toml
413
pyproject.toml
@ -305,49 +305,374 @@ allow-direct-references = true
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["comfy/", "comfy_extras/", "comfy_api/", "comfy_api_nodes/", "comfy_config/", "comfy_execution/", "comfy_compatibility/"]
|
||||
|
||||
[tool.pylint]
|
||||
master.py-version = "3.10"
|
||||
master.extension-pkg-allow-list = [
|
||||
"pydantic",
|
||||
[tool.pylint.master]
|
||||
py-version = "3.10"
|
||||
extension-pkg-allow-list = ["pydantic", "cv2"]
|
||||
ignore-paths = ["^comfy/api/.*$"]
|
||||
ignored-modules = ["sentencepiece.*", "comfy.api", "comfy.cmd.folder_paths"]
|
||||
init-hook = 'import sys; sys.path.insert(0, ".")'
|
||||
load-plugins = ["tests.absolute_import_checker", "tests.main_pre_import_checker", "tests.missing_init"]
|
||||
persistent = true
|
||||
fail-under = 10
|
||||
jobs = 1
|
||||
limit-inference-results = 100
|
||||
unsafe-load-any-extension = false
|
||||
|
||||
[tool.pylint.messages_control]
|
||||
enable = [
|
||||
"deprecated-module",
|
||||
"deprecated-method",
|
||||
"deprecated-argument",
|
||||
"deprecated-class",
|
||||
"deprecated-decorator",
|
||||
"deprecated-attribute",
|
||||
]
|
||||
reports.output-format = "colorized"
|
||||
similarities.ignore-imports = "yes"
|
||||
messages_control.disable = [
|
||||
"missing-module-docstring",
|
||||
"missing-class-docstring",
|
||||
"missing-function-docstring",
|
||||
"line-too-long",
|
||||
"too-few-public-methods",
|
||||
"too-many-public-methods",
|
||||
"too-many-instance-attributes",
|
||||
"too-many-positional-arguments",
|
||||
"broad-exception-raised",
|
||||
"too-many-lines",
|
||||
"invalid-name",
|
||||
"unused-argument",
|
||||
"broad-exception-caught",
|
||||
"consider-using-with",
|
||||
"fixme",
|
||||
"too-many-statements",
|
||||
"too-many-branches",
|
||||
"too-many-locals",
|
||||
"too-many-arguments",
|
||||
"too-many-return-statements",
|
||||
"too-many-nested-blocks",
|
||||
"duplicate-code",
|
||||
"abstract-method",
|
||||
"superfluous-parens",
|
||||
"arguments-differ",
|
||||
"redefined-builtin",
|
||||
"unnecessary-lambda",
|
||||
"dangerous-default-value",
|
||||
"invalid-overridden-method",
|
||||
# next warnings should be fixed in future
|
||||
"bad-classmethod-argument", # Class method should have 'cls' as first argument
|
||||
"wrong-import-order", # Standard imports should be placed before third party imports
|
||||
"ungrouped-imports",
|
||||
"unnecessary-pass",
|
||||
"unnecessary-lambda-assignment",
|
||||
"no-else-return",
|
||||
"unused-variable",
|
||||
disable = [
|
||||
"raw-checker-failed",
|
||||
"bad-inline-option",
|
||||
"locally-disabled",
|
||||
"file-ignored",
|
||||
"suppressed-message",
|
||||
"useless-suppression",
|
||||
"deprecated-pragma",
|
||||
"use-symbolic-message-instead",
|
||||
"use-implicit-booleaness-not-comparison-to-string",
|
||||
"use-implicit-booleaness-not-comparison-to-zero",
|
||||
"useless-option-value",
|
||||
"no-classmethod-decorator",
|
||||
"no-staticmethod-decorator",
|
||||
"useless-object-inheritance",
|
||||
"property-with-parameters",
|
||||
"cyclic-import",
|
||||
"consider-using-from-import",
|
||||
"consider-merging-isinstance",
|
||||
"too-many-nested-blocks",
|
||||
"simplifiable-if-statement",
|
||||
"redefined-argument-from-local",
|
||||
"no-else-return",
|
||||
"consider-using-ternary",
|
||||
"trailing-comma-tuple",
|
||||
"stop-iteration-return",
|
||||
"simplify-boolean-expression",
|
||||
"inconsistent-return-statements",
|
||||
"useless-return",
|
||||
"consider-swap-variables",
|
||||
"consider-using-join",
|
||||
"consider-using-in",
|
||||
"consider-using-get",
|
||||
"chained-comparison",
|
||||
"consider-using-dict-comprehension",
|
||||
"consider-using-set-comprehension",
|
||||
"simplifiable-if-expression",
|
||||
"no-else-raise",
|
||||
"unnecessary-comprehension",
|
||||
"consider-using-sys-exit",
|
||||
"no-else-break",
|
||||
"no-else-continue",
|
||||
"super-with-arguments",
|
||||
"simplifiable-condition",
|
||||
"condition-evals-to-constant",
|
||||
"consider-using-generator",
|
||||
"use-a-generator",
|
||||
"consider-using-min-builtin",
|
||||
"consider-using-max-builtin",
|
||||
"consider-using-with",
|
||||
"unnecessary-dict-index-lookup",
|
||||
"use-list-literal",
|
||||
"use-dict-literal",
|
||||
"unnecessary-list-index-lookup",
|
||||
"use-yield-from",
|
||||
"duplicate-code",
|
||||
"too-many-ancestors",
|
||||
"too-many-instance-attributes",
|
||||
"too-few-public-methods",
|
||||
"too-many-public-methods",
|
||||
"too-many-return-statements",
|
||||
"too-many-branches",
|
||||
"too-many-arguments",
|
||||
"too-many-positional-arguments",
|
||||
"too-many-locals",
|
||||
"too-many-statements",
|
||||
"too-many-boolean-expressions",
|
||||
"too-many-positional",
|
||||
"literal-comparison",
|
||||
"comparison-with-itself",
|
||||
"comparison-of-constants",
|
||||
"wrong-spelling-in-comment",
|
||||
"wrong-spelling-in-docstring",
|
||||
"invalid-characters-in-docstring",
|
||||
"unnecessary-dunder-call",
|
||||
"bad-file-encoding",
|
||||
"bad-classmethod-argument",
|
||||
"bad-mcs-method-argument",
|
||||
"bad-mcs-classmethod-argument",
|
||||
"single-string-used-for-slots",
|
||||
"unnecessary-lambda-assignment",
|
||||
"unnecessary-direct-lambda-call",
|
||||
"non-ascii-name",
|
||||
"non-ascii-module-import",
|
||||
"line-too-long",
|
||||
"too-many-lines",
|
||||
"trailing-whitespace",
|
||||
"missing-final-newline",
|
||||
"trailing-newlines",
|
||||
"multiple-statements",
|
||||
"superfluous-parens",
|
||||
"mixed-line-endings",
|
||||
"unexpected-line-ending-format",
|
||||
"multiple-imports",
|
||||
"wrong-import-order",
|
||||
"ungrouped-imports",
|
||||
"wrong-import-position",
|
||||
"useless-import-alias",
|
||||
"import-outside-toplevel",
|
||||
"unnecessary-negation",
|
||||
"consider-using-enumerate",
|
||||
"consider-iterating-dictionary",
|
||||
"consider-using-dict-items",
|
||||
"use-maxsplit-arg",
|
||||
"use-sequence-for-iteration",
|
||||
"consider-using-f-string",
|
||||
"use-implicit-booleaness-not-len",
|
||||
"use-implicit-booleaness-not-comparison",
|
||||
"invalid-name",
|
||||
"disallowed-name",
|
||||
"typevar-name-incorrect-variance",
|
||||
"typevar-double-variance",
|
||||
"typevar-name-mismatch",
|
||||
"empty-docstring",
|
||||
"missing-module-docstring",
|
||||
"missing-class-docstring",
|
||||
"missing-function-docstring",
|
||||
"singleton-comparison",
|
||||
"unidiomatic-typecheck",
|
||||
"unknown-option-value",
|
||||
"logging-not-lazy",
|
||||
"logging-format-interpolation",
|
||||
"logging-fstring-interpolation",
|
||||
"fixme",
|
||||
"keyword-arg-before-vararg",
|
||||
"arguments-out-of-order",
|
||||
"non-str-assignment-to-dunder-name",
|
||||
"isinstance-second-argument-not-valid-type",
|
||||
"kwarg-superseded-by-positional-arg",
|
||||
"modified-iterating-list",
|
||||
"attribute-defined-outside-init",
|
||||
"bad-staticmethod-argument",
|
||||
"protected-access",
|
||||
"implicit-flag-alias",
|
||||
"arguments-differ",
|
||||
"signature-differs",
|
||||
"abstract-method",
|
||||
"super-init-not-called",
|
||||
"non-parent-init-called",
|
||||
"invalid-overridden-method",
|
||||
"arguments-renamed",
|
||||
"unused-private-member",
|
||||
"overridden-final-method",
|
||||
"subclassed-final-class",
|
||||
"redefined-slots-in-subclass",
|
||||
"super-without-brackets",
|
||||
"useless-parent-delegation",
|
||||
"global-variable-undefined",
|
||||
"global-variable-not-assigned",
|
||||
"global-statement",
|
||||
"global-at-module-level",
|
||||
"unused-import",
|
||||
"unused-variable",
|
||||
"unused-argument",
|
||||
"unused-wildcard-import",
|
||||
"redefined-outer-name",
|
||||
"redefined-builtin",
|
||||
"undefined-loop-variable",
|
||||
"unbalanced-tuple-unpacking",
|
||||
"cell-var-from-loop",
|
||||
"possibly-unused-variable",
|
||||
"self-cls-assignment",
|
||||
"unbalanced-dict-unpacking",
|
||||
"using-f-string-in-unsupported-version",
|
||||
"using-final-decorator-in-unsupported-version",
|
||||
"unnecessary-ellipsis",
|
||||
"non-ascii-file-name",
|
||||
"unnecessary-semicolon",
|
||||
"bad-indentation",
|
||||
"wildcard-import",
|
||||
"reimported",
|
||||
"import-self",
|
||||
"preferred-module",
|
||||
"misplaced-future",
|
||||
"shadowed-import",
|
||||
"missing-timeout",
|
||||
"useless-with-lock",
|
||||
"bare-except",
|
||||
"duplicate-except",
|
||||
"try-except-raise",
|
||||
"raise-missing-from",
|
||||
"binary-op-exception",
|
||||
"raising-format-tuple",
|
||||
"wrong-exception-operation",
|
||||
"broad-exception-caught",
|
||||
"broad-exception-raised",
|
||||
"bad-open-mode",
|
||||
"boolean-datetime",
|
||||
"redundant-unittest-assert",
|
||||
"bad-thread-instantiation",
|
||||
"shallow-copy-environ",
|
||||
"invalid-envvar-default",
|
||||
"subprocess-popen-preexec-fn",
|
||||
"subprocess-run-check",
|
||||
"unspecified-encoding",
|
||||
"forgotten-debug-statement",
|
||||
"method-cache-max-size-none",
|
||||
"bad-format-string-key",
|
||||
"unused-format-string-key",
|
||||
"bad-format-string",
|
||||
"missing-format-argument-key",
|
||||
"unused-format-string-argument",
|
||||
"format-combined-specification",
|
||||
"missing-format-attribute",
|
||||
"invalid-format-index",
|
||||
"duplicate-string-formatting-argument",
|
||||
"f-string-without-interpolation",
|
||||
"format-string-without-interpolation",
|
||||
"anomalous-backslash-in-string",
|
||||
"anomalous-unicode-escape-in-string",
|
||||
"implicit-str-concat",
|
||||
"inconsistent-quotes",
|
||||
"redundant-u-string-prefix",
|
||||
"useless-else-on-loop",
|
||||
"unreachable",
|
||||
"dangerous-default-value",
|
||||
"pointless-statement",
|
||||
"pointless-string-statement",
|
||||
"expression-not-assigned",
|
||||
"unnecessary-lambda",
|
||||
"duplicate-key",
|
||||
"exec-used",
|
||||
"eval-used",
|
||||
"confusing-with-statement",
|
||||
"using-constant-test",
|
||||
"missing-parentheses-for-call-in-test",
|
||||
"self-assigning-variable",
|
||||
"redeclared-assigned-name",
|
||||
"assert-on-string-literal",
|
||||
"duplicate-value",
|
||||
"named-expr-without-context",
|
||||
"pointless-exception-statement",
|
||||
"return-in-finally",
|
||||
"lost-exception",
|
||||
"assert-on-tuple",
|
||||
"unnecessary-pass",
|
||||
"comparison-with-callable",
|
||||
"nan-comparison",
|
||||
"contextmanager-generator-missing-cleanup",
|
||||
"nested-min-max",
|
||||
"bad-chained-comparison",
|
||||
"not-callable",
|
||||
]
|
||||
|
||||
[tool.pylint.basic]
|
||||
argument-naming-style = "snake_case"
|
||||
attr-naming-style = "snake_case"
|
||||
bad-names = ["foo", "bar", "baz", "toto", "tutu", "tata"]
|
||||
class-attribute-naming-style = "any"
|
||||
class-const-naming-style = "UPPER_CASE"
|
||||
class-naming-style = "PascalCase"
|
||||
const-naming-style = "UPPER_CASE"
|
||||
docstring-min-length = -1
|
||||
function-naming-style = "snake_case"
|
||||
good-names = ["i", "j", "k", "ex", "Run", "_"]
|
||||
include-naming-hint = false
|
||||
inlinevar-naming-style = "any"
|
||||
method-naming-style = "snake_case"
|
||||
module-naming-style = "snake_case"
|
||||
no-docstring-rgx = "^_"
|
||||
property-classes = ["abc.abstractproperty"]
|
||||
variable-naming-style = "snake_case"
|
||||
|
||||
[tool.pylint.classes]
|
||||
check-protected-access-in-special-methods = false
|
||||
defining-attr-methods = ["__init__", "__new__", "setUp", "asyncSetUp", "__post_init__"]
|
||||
exclude-protected = ["_asdict", "_fields", "_replace", "_source", "_make", "os._exit"]
|
||||
valid-classmethod-first-arg = "cls"
|
||||
valid-metaclass-classmethod-first-arg = "mcs"
|
||||
|
||||
[tool.pylint.design]
|
||||
max-args = 5
|
||||
max-attributes = 7
|
||||
max-bool-expr = 5
|
||||
max-branches = 12
|
||||
max-locals = 15
|
||||
max-parents = 7
|
||||
max-public-methods = 20
|
||||
max-returns = 6
|
||||
max-statements = 50
|
||||
min-public-methods = 2
|
||||
|
||||
[tool.pylint.exceptions]
|
||||
overgeneral-exceptions = ["builtins.BaseException", "builtins.Exception"]
|
||||
|
||||
[tool.pylint.format]
|
||||
indent-after-paren = 4
|
||||
indent-string = " "
|
||||
max-line-length = 100
|
||||
max-module-lines = 1000
|
||||
single-line-class-stmt = false
|
||||
single-line-if-stmt = false
|
||||
|
||||
[tool.pylint.imports]
|
||||
allow-reexport-from-package = false
|
||||
allow-wildcard-with-all = false
|
||||
known-third-party = ["enchant"]
|
||||
|
||||
[tool.pylint.logging]
|
||||
logging-format-style = "old"
|
||||
logging-modules = ["logging"]
|
||||
|
||||
[tool.pylint.miscellaneous]
|
||||
notes = ["FIXME", "XXX", "TODO"]
|
||||
|
||||
[tool.pylint.refactoring]
|
||||
max-nested-blocks = 5
|
||||
never-returning-functions = ["sys.exit", "argparse.parse_error"]
|
||||
suggest-join-with-non-empty-separator = true
|
||||
|
||||
[tool.pylint.reports]
|
||||
evaluation = "max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))"
|
||||
score = true
|
||||
|
||||
[tool.pylint.similarities]
|
||||
ignore-comments = true
|
||||
ignore-docstrings = true
|
||||
ignore-imports = true
|
||||
ignore-signatures = true
|
||||
min-similarity-lines = 4
|
||||
|
||||
[tool.pylint.spelling]
|
||||
max-spelling-suggestions = 4
|
||||
spelling-ignore-comment-directives = "fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:"
|
||||
spelling-store-unknown-words = false
|
||||
|
||||
[tool.pylint.string]
|
||||
check-quote-consistency = false
|
||||
check-str-concat-over-line-jumps = false
|
||||
|
||||
[tool.pylint.typecheck]
|
||||
contextmanager-decorators = ["contextlib.contextmanager"]
|
||||
generated-members = ["cv2.*", "sentencepiece.*"]
|
||||
ignore-none = true
|
||||
ignore-on-opaque-inference = true
|
||||
ignored-checks-for-mixins = ["no-member", "not-async-context-manager", "not-context-manager", "attribute-defined-outside-init"]
|
||||
ignored-classes = ["optparse.Values", "thread._local", "_thread._local", "argparse.Namespace"]
|
||||
missing-member-hint = true
|
||||
missing-member-hint-distance = 1
|
||||
missing-member-max-choices = 1
|
||||
mixin-class-rgx = ".*[Mm]ixin"
|
||||
|
||||
[tool.pylint.variables]
|
||||
allow-global-unused-variables = true
|
||||
callbacks = ["cb_", "_cb"]
|
||||
dummy-variables-rgx = "_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_"
|
||||
ignored-argument-names = "_.*|^ignored_|^unused_"
|
||||
init-import = false
|
||||
redefining-builtins-modules = ["six.moves", "past.builtins", "future.builtins", "builtins", "io"]
|
||||
|
||||
41
tests/missing_init.py
Normal file
41
tests/missing_init.py
Normal file
@ -0,0 +1,41 @@
|
||||
import os
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
from pylint.checkers import BaseChecker
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pylint.lint import PyLinter
|
||||
|
||||
class MissingInitChecker(BaseChecker):
|
||||
name = 'missing-init'
|
||||
priority = -1
|
||||
msgs = {
|
||||
'W8001': (
|
||||
'Directory %s has .py files but missing __init__.py',
|
||||
'missing-init',
|
||||
'All directories containing .py files should have an __init__.py file.',
|
||||
),
|
||||
}
|
||||
|
||||
def __init__(self, linter: Optional["PyLinter"] = None) -> None:
|
||||
super().__init__(linter)
|
||||
|
||||
def visit_module(self, node):
|
||||
if not node.file:
|
||||
return
|
||||
|
||||
# Only check .py files
|
||||
if not node.file.endswith('.py'):
|
||||
return
|
||||
|
||||
# Skip __init__.py itself
|
||||
if os.path.basename(node.file) == '__init__.py':
|
||||
return
|
||||
|
||||
directory = os.path.dirname(os.path.abspath(node.file))
|
||||
init_file = os.path.join(directory, '__init__.py')
|
||||
|
||||
if not os.path.exists(init_file):
|
||||
self.add_message('missing-init', args=directory, node=node)
|
||||
|
||||
def register(linter: "PyLinter") -> None:
|
||||
linter.register_checker(MissingInitChecker(linter))
|
||||
Loading…
Reference in New Issue
Block a user