Basic data handling

Basic Data Handling

Basic Python functions for manipulating data that every programmer is used to. These nodes are very lightweight and require no additional dependencies.

Quickstart

Recommended Installation

1. Install ComfyUI
2. Install ComfyUI-Manager
3. Look up the "Basic data handling" extension in ComfyUI-Manager
4. Restart ComfyUI

Alternative (Manual Installation)

1. Install ComfyUI
2. Clone this repository under ComfyUI/custom_nodes
3. Restart ComfyUI

Node Categories

BOOLEAN

Boolean logic operations:

• Logic operations: and, or, not, xor, nand, nor

Cast

Type conversion nodes for ComfyUI data types: to BOOLEAN, to FLOAT, to INT, to STRING, to DICT, to LIST, to SET

Comparison

Value comparison nodes:

• Basic comparisons: equal (==), not equal (!=), greater than (>), greater than or equal (>=), less than (<), less than or equal (<=)
• String comparison: StringComparison with case-sensitive/insensitive options
• Special comparisons: NumberInRange, IsNull
• Container operations: CompareLength

Control Flow

Mechanisms to direct workflow execution:

• Conditional branching:
  • if/else - Routes based on a boolean condition
  • if/elif/.../else - Supports multiple conditional branches
  • switch/case - Selects from options based on an index
• Execution management:
  • disable flow - Conditionally enables/disables a flow
  • flow select - Directs output to either "true" or "false" path
  • force calculation - Prevents caching and forces recalculation
  • force execution order - Controls node execution sequence

Data List

ComfyUI list manipulation nodes (for processing individual items):

• Creation: create Data List (generic and type-specific versions)
• Modification: append, extend, insert, set item, shuffle, remove, pop, pop random
• Filtering: filter, filter select
• Access: get item, first, last, slice, index, contains
• Information: length, count
• Operations: sort, reverse, zip, min, max
• Conversion: convert to LIST, convert to SET

DICT

Dictionary manipulation nodes:

• Creation: create (generic and type-specific), create from items, create from lists, fromkeys
• Access: get, get_multiple, keys, values, items
• Modification: set, update, setdefault, merge
• Removal: pop, popitem, pop random, remove
• Information: length, contains_key
• Operations: filter_by_keys, exclude_keys, invert, compare
• Conversion: get_keys_values

FLOAT

Floating-point operation nodes:

• Creation: create FLOAT from string
• Basic arithmetic: add, subtract, multiply, divide, divide (zero safe), power
• Formatting: round (to specified decimal places)
• Conversion: to_hex, from_hex
• Analysis: is_integer, as_integer_ratio

INT

Integer operation nodes:

• Creation: create INT, create INT with base
• Basic arithmetic: add, subtract, multiply, divide, divide (zero safe), modulus, power
• Bit operations: bit_length, bit_count
• Byte conversion: to_bytes, from_bytes

LIST

Python list manipulation nodes (as a single variable):

• Creation: create LIST (generic and type-specific versions)
• Modification: append, extend, insert, remove, pop, pop random, set_item, shuffle
• Access: get_item, first, last, slice, index, contains
• Information: length, count
• Operations: sort, reverse, min, max
• Conversion: convert to data list, convert to SET

Math

Mathematical operations:

• Generic: formula
• Trigonometric functions: sin, cos, tan, asin, acos, atan, atan2
• Logarithmic/Exponential: log, log10, exp, sqrt
• Constants: pi, e
• Angle conversion: degrees, radians
• Rounding operations: floor, ceil
• Min/Max functions: min, max
• Other: abs

Path

File system path manipulation nodes:

• Basic operations: join, split, splitext, basename, dirname, normalize
• Path information: abspath, exists, is_file, is_dir, is_absolute, get_size, get_extension, set_extension, input_dir, output_dir
• Directory operations: list_dir, get_cwd
• Path searching: glob, common_prefix
• Path conversions: relative, expand_vars
• File loading: load STRING from file, load IMAGE from file, load IMAGE+MASK from file, load MASK from alpha channel, load MASK from greyscale/red
• File saving: save STRING to file, save IMAGE to file, save IMAGE+MASK to file

SET

Python set manipulation nodes (as a single variable):

• Creation: create SET (generic and type-specific versions)
• Modification: add, remove, discard, pop, pop random
• Information: length, contains
• Set operations: union, intersection, difference, symmetric_difference
• Set comparison: is_subset, is_superset, is_disjoint
• Conversion: convert to data list, convert to LIST

STRING

String manipulation nodes:

• Text case conversion: capitalize, casefold, lower, swapcase, title, upper
• Text inspection: contains, endswith, find, length, rfind, startswith
• Character type checking: isalnum, isalpha, isascii, isdecimal, isdigit, isidentifier, islower, isnumeric, isprintable, isspace, istitle, isupper
• Text formatting: center, expandtabs, ljust, rjust, zfill
• Text splitting/joining: join, split, rsplit, splitlines (with data list and LIST variants)
• Text modification: concat, count, replace, strip, lstrip, rstrip, removeprefix, removesuffix
• Encoding/escaping: decode, encode, escape, unescape, format_map

TENSOR

PyTorch tensor manipulation nodes:

• Creation: Tensor Create (from numbers, lists, or other tensors)
• Arithmetic: Tensor Binary Op (add, subtract, multiply, divide, power, remainder, floor_divide)
• Functions: Tensor Unary Op (abs, neg, exp, log, sin, cos, sqrt, sigmoid, relu)
• Reshaping: Tensor Reshape, Tensor Permute (dims)
• Access: Tensor Slice (supports Python-style slice strings like 0:10, :, 5)
• Combined: Tensor Join (concatenate or stack)
• Analysis: Tensor Info (returns shape, dtype, device)

Time

Date and time manipulation nodes:

• DateTime creation/conversion: TimeNow, TimeNowUTC, TimeToUnix, UnixToTime
• String formatting/parsing: TimeFormat, TimeParse
• Time calculations: TimeDelta, TimeAddDelta, TimeSubtractDelta, TimeDifference
• Component extraction: TimeExtract (year, month, day, hour, etc.)

Understanding Data Types

ComfyUI provides three different collection types that serve distinct purposes:

Collection Types and When to Choose Them

| Type | Description | When to Choose | |---------------|----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | data list | Native ComfyUI list where items are processed individually | • When you need ComfyUI to process each item individually<br>• For batch operations with parallel processing<br>• When connecting to nodes that expect individual inputs | | LIST | Python list passed as a single variable | • When you need ordered collections with preserved duplicates<br>• When index-based access is important<br>• When you need to work with the collection as a complete unit | | SET | Python set passed as a single variable | • When you need to ensure unique values only<br>• When you need fast membership testing<br>• For set operations (union, intersection, etc.)<br>• When element order doesn't matter |