843 lines
31 KiB
Markdown
843 lines
31 KiB
Markdown
# ListHelper Nodes Collection
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[中文版本](#中文版本) | [English Version](#english-version)
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---
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## English Version
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### Overview
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The **ListHelper** collection is a comprehensive set of custom nodes for ComfyUI that provides powerful list manipulation capabilities. This collection includes audio processing, text splitting, and number generation tools for enhanced workflow automation.
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### Included Nodes
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1. [AudioListCombine](#audiolistcombine-node)
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2. [NumberListGenerator](#numberlistgenerator-node)
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3. [PromptSplitByDelimiter](#promptsplitbydelimiter-node)
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4. [Qwen_TE_LLM](#qwen-node) - AI Photo Prompt Optimizer
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---
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## AudioListCombine Node
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### Overview
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The **AudioListCombine** node is a powerful custom node for ComfyUI that allows you to combine multiple audio files from a list into a single audio output. It supports various combination modes and audio processing options.
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### Features
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- **Multiple Combination Modes**: Concatenate, mix, or overlay audio files
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- **Automatic Sample Rate Conversion**: Unifies different sample rates to target rate
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- **Channel Normalization**: Automatically handles mono/stereo conversion
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- **Crossfade Support**: Smooth transitions between audio segments
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- **Audio Normalization**: Optional output level normalization
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- **Flexible Input**: Accepts audio lists from Impact Pack or other list-making nodes
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### Requirements
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- ComfyUI
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- Audio list creation nodes (e.g., Impact Pack's MakeAnyList, or custom list nodes)
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- Python libraries: `torch`, `torchaudio`
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### Usage
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#### Input Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `audio_list` | AUDIO | - | List of audio files (from Impact Pack or other list nodes) |
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| `combine_mode` | COMBO | "concatenate" | How to combine audio: concatenate/mix/overlay |
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| `fade_duration` | FLOAT | 0.0 | Crossfade duration in seconds (0.0-5.0) |
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| `normalize_output` | BOOLEAN | True | Whether to normalize output audio |
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| `target_sample_rate` | INT | 44100 | Target sample rate for output |
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#### Combine Modes
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1. **Concatenate**: Join audio files end-to-end in sequence
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- Supports crossfade transitions
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- Maintains chronological order
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- Best for: Creating audio sequences, podcasts, music playlists
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2. **Mix**: Average all audio files together
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- Pads shorter files with silence
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- Equal weight blending
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- Best for: Creating audio mashups, averaging multiple takes
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3. **Overlay**: Add all audio files together
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- Direct addition (may cause clipping)
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- Preserves original volumes
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- Best for: Adding sound effects, layering instruments
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#### Output
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| Output | Type | Description |
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|--------|------|-------------|
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| `audio` | AUDIO | Combined audio result |
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### Examples
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#### Example 1: Creating a Music Playlist
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```
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Audio File 1 →
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Audio File 2 → MakeAnyList → AudioListCombine (concatenate, fade=0.5s) → Save Audio
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Audio File 3 →
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```
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#### Example 2: Mixing Multiple Recordings
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```
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Recording 1 →
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Recording 2 → MakeAnyList → AudioListCombine (mix, normalize=True) → Save Audio
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Recording 3 →
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```
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#### Example 3: Adding Sound Effects
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```
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Background Music →
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Sound Effect 1 → MakeAnyList → AudioListCombine (overlay) → Save Audio
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Sound Effect 2 →
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```
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---
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## NumberListGenerator Node
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### Overview
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The NumberListGenerator node creates lists of numbers with customizable parameters, supporting both sequential and randomized output. It's perfect for batch processing, parameter sweeping, or any workflow requiring controlled number sequences.
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### Features
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- **Dual Output Format**: Generates both integer and float lists simultaneously
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- **Flexible Range Control**: Set minimum, maximum values and step size
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- **Sequential or Random**: Toggle between ordered and shuffled output
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- **Reproducible Results**: Optional seed parameter for consistent random generation
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- **Count Tracking**: Returns total number of generated values
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### Parameters
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**Required Inputs:**
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- **min_value** (Float): Starting value for the sequence (Range: -10,000 to 10,000, Default: 0.0)
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- **max_value** (Float): Maximum value upper bound (Range: -10,000 to 10,000, Default: 10.0)
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- **step** (Float): Increment between consecutive values (Range: 0.01 to 1,000, Default: 1.0)
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- **count** (Int): Number of values to generate (Range: 1 to 10,000, Default: 10)
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- **random** (Boolean): Enable random shuffling of the generated list (Default: False)
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**Optional Inputs:**
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- **seed** (Int): Random seed for reproducible results when random=True (Range: -1 to 1,000,000, Default: -1)
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**Outputs:**
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- **int_list**: List of integer values
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- **float_list**: List of float values
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- **total_count**: Total number of generated values
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### Usage Examples
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**Sequential Generation:**
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```
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min_value: 0, max_value: 20, step: 2, count: 10, random: False
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Output: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
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```
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**Random Generation:**
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```
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min_value: 1, max_value: 100, step: 5, count: 8, random: True, seed: 42
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Output: [16, 1, 31, 6, 21, 11, 26, 36] (shuffled)
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```
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---
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## PromptSplitByDelimiter Node
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### Overview
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The **PromptSplitByDelimiter** node is a versatile text processing tool that splits text content using customizable delimiters. It supports both simple string delimiters and advanced regular expressions, with optional random ordering and delimiter preservation.
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### Features
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- **Flexible Delimiter Support**: Use simple strings or regular expressions as delimiters
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- **Multi-language Support**: Native support for CJK (Chinese, Japanese, Korean) characters
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- **Regular Expression Mode**: Advanced pattern matching for complex splitting rules
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- **Delimiter Preservation**: Option to keep delimiters in the output
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- **Random Ordering**: Shuffle results with reproducible seed control
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- **Advanced Text Processing**: Handle multiple newlines, skip empty segments
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- **Selective Processing**: Skip content before first delimiter occurrence
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### Parameters
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| Parameter | Type | Default | Range | Description |
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|-----------|------|---------|--------|-------------|
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| `text` | STRING | - | - | Multiline text input to be split |
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| `delimiter` | STRING | "," | - | Delimiter string or regex pattern |
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| `use_regex` | BOOLEAN | False | - | Enable regular expression mode |
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| `keep_delimiter` | BOOLEAN | False | - | Preserve delimiters in output |
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| `start_index` | INT | 0 | 0-1000 | Starting index for selection |
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| `skip_every` | INT | 0 | 0-10 | Skip every N items |
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| `max_count` | INT | 10 | 1-1000 | Maximum items to return |
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| `skip_first_index` | BOOLEAN | False | - | Skip content before first delimiter |
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| `random_order` | BOOLEAN | False | - | Randomize output order |
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| `seed` | INT | 0 | 0-2147483647 | Random seed for reproducible results |
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### Output
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| Output | Type | Description |
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|--------|------|-------------|
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| `text_list` | STRING | List of split text segments |
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| `total_index` | INT | Total number of segments found |
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### Usage Examples
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#### Example 1: Simple Comma Splitting
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```
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Input: "apple,banana,cherry,date"
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Delimiter: ","
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Output: ["apple", "banana", "cherry", "date"]
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```
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#### Example 2: Chinese Chapter Splitting
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```
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Input: "前言第一章内容第二章内容第三章结尾"
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Delimiter: "第.*?章" (regex mode)
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Output: ["前言", "内容", "内容", "结尾"]
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```
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#### Example 3: Delimiter Preservation
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```
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Input: "AAA//BBB//CCC"
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Delimiter: "//"
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Keep Delimiter: True
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Output: ["AAA", "//BBB", "//CCC"]
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```
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#### Example 4: Random Chapter Selection
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```
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Input: "Chapter1\nChapter2\nChapter3\nChapter4"
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Delimiter: "\n"
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Random Order: True
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Max Count: 2
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Output: ["Chapter3", "Chapter1"] (randomized)
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```
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### Advanced Features
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#### Regular Expression Support
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- **Pattern Matching**: Use regex patterns for complex delimiter rules
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- **CJK Character Support**: `第\d+章` matches "第1章", "第2章", etc.
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- **Flexible Patterns**: `(章|節|段)` matches any of "章", "節", or "段"
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#### Text Processing Rules
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- **Newline Normalization**: Multiple consecutive newlines are treated as single newline
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- **Empty Delimiter Handling**: Empty delimiter automatically uses newline as fallback
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- **Whitespace Trimming**: Automatic trimming of leading/trailing whitespace
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#### Selection and Filtering
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- **Index Range**: Select specific range of results using `start_index` and `max_count`
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- **Skip Pattern**: Use `skip_every` to select every Nth item
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- **First Segment Skip**: Use `skip_first_index` to ignore content before first delimiter
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### Use Cases
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- **Document Processing**: Split books into chapters, articles into sections
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- **Data Extraction**: Extract structured data from formatted text
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- **Content Management**: Process multilingual content with CJK support
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- **Batch Processing**: Generate lists for downstream processing nodes
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- **Random Sampling**: Create randomized content selections
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---
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## Qwen_TE_LLM Node
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### Overview
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The **Qwen_TE_LLM** node is an AI-powered text generation node that uses the Qwen3-4B language model with intelligent GPU memory management and CPU offload support for seamless integration with ComfyUI. It can transform simple descriptions into detailed, professional prompts using customizable templates.
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### Features
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- **Automatic Model Detection**: Automatically finds Qwen safetensors files in `text_encoders` or `clip` folders
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- **Smart Memory Management**: Automatically detects GPU memory and chooses optimal loading strategy
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- Full GPU mode (≥7.5GB free): ~26-30 tokens/second
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- CPU Offload mode (<7.5GB free): ~1-2 tokens/second, coexists with other models
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- **Template System**: Select from pre-made prompt templates in the `Prompt` folder or use custom prompts
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- **Bilingual Support**: Handles both Chinese and English inputs
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- **Automatic Config Download**: Downloads required model configuration files from HuggingFace
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- **Think Tag Removal**: Automatically removes `<think>...</think>` reasoning tags from output
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### Requirements
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- **GPU**: NVIDIA GPU with CUDA support (12GB+ recommended, works with less using CPU offload)
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- **Model Files**:
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- **Required**: `qwen_3_4b.safetensors` (or similar Qwen3-4B safetensors file)
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- **Location**: Place in either `ComfyUI/models/text_encoders/` or `ComfyUI/models/clip/`
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- **Python Packages**: transformers, safetensors, torch
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### Model Setup
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1. Download Qwen3-4B safetensors from [Hugging Face](https://huggingface.co/Qwen/Qwen3-4B)
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- Recommended filename: `qwen_3_4b.safetensors`
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2. Place the file in `ComfyUI/models/text_encoders/` or `ComfyUI/models/clip/`
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3. First run will automatically download configuration files to a `_config` subfolder
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### Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `user_prompt` | STRING | "A girl in a coffee shop" | Your input text/description |
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| `prompt_template` | COMBO | "Custom" | Select a template from the Prompt folder or use "Custom" |
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| `system_prompt` | STRING | "" | Custom system prompt (used when template is "Custom") |
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| `max_new_tokens` | INT | 2048 | Maximum length of generated text |
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| `temperature` | FLOAT | 0.7 | Creativity level (0.0-2.0) |
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| `do_sample` | BOOLEAN | True | Enable sampling for varied outputs |
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| `top_p` | FLOAT | 0.9 | Nucleus sampling threshold |
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| `top_k` | INT | 50 | Top-k sampling parameter |
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### Template System
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The node supports customizable prompt templates stored in the `Prompt` folder as `.md` files:
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- **Custom**: Use the `system_prompt` parameter directly
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- **Template Files**: Select from available `.md` files in the `Prompt` folder
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- Templates automatically replace the `system_prompt` parameter when selected
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### Loading Strategies
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The node automatically selects the best loading strategy based on available GPU memory:
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#### Strategy 1: Full GPU Loading
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- **Condition**: ≥7.5GB available GPU memory
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- **Performance**: ~26-30 tokens/second
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- **Use Case**: Standalone usage or minimal ComfyUI memory usage
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#### Strategy 2: CPU Offload
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- **Condition**: <7.5GB available GPU memory
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- **Performance**: ~1-2 tokens/second (slower but reliable)
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- **Use Case**: When ComfyUI has loaded large models
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- **Advantage**: No memory conflicts, coexists with other models
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#### Strategy 3: CPU Mode
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- **Condition**: No CUDA available
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- **Use Case**: CPU-only environments
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### Output
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| Output | Type | Description |
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|--------|------|-------------|
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| `text` | STRING | Detailed professional photography prompt |
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### Usage Examples
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#### Example 1: Using a Template
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```
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User Prompt: "一個女孩在咖啡廳"
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Template: (photography template from Prompt folder)
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Output: "A young woman sitting by the window in a cozy coffee shop, warm afternoon sunlight streaming through large glass windows creating soft shadows, wearing casual outfit, holding a cup of coffee, wooden table with laptop and notebook, blurred background with other customers, shallow depth of field, bokeh effect, warm color temperature, golden hour lighting..."
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```
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#### Example 2: Custom System Prompt
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```
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User Prompt: "a cat sitting on a windowsill"
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Template: "Custom"
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System Prompt: "Describe the scene in poetic detail"
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Output: "A calico cat sitting on a sunlit windowsill, its long fur catching golden-hour light from the right, creating soft shadows across its face and body, wearing a quiet expression of peaceful solitude, surrounded by indoor plants and a wooden bookshelf in the background..."
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```
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### Memory Management
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The node includes intelligent memory management:
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1. **Pre-Load Check**: Checks GPU memory before loading
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2. **Automatic Cleanup**: Clears CUDA cache if needed
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3. **Dynamic Strategy**: Chooses loading strategy based on available memory
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4. **Device Distribution**: Shows how model layers are distributed across GPU/CPU
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5. **Clear Error Messages**: Provides detailed error information and solutions
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### Performance Considerations
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- **First Load**: 7-130 seconds depending on hardware and memory
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- **Subsequent Loads**: Model stays in memory, near-instant
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- **Full GPU**: Fast inference (~26-30 tokens/second)
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- **CPU Offload**: Slower inference (~1-2 tokens/second) but prevents crashes
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- **Memory Usage**: ~7.5GB GPU memory for full GPU mode
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### Troubleshooting
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**Model not found**
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- Ensure a Qwen safetensors file is in `models/text_encoders/` or `models/clip/`
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- Recommended filename: `qwen_3_4b.safetensors` (containing "qwen", "3", and "4b")
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- The node automatically searches for compatible Qwen model files
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**Out of memory**
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- Node will automatically use CPU offload
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- If still failing, close other GPU applications
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- Consider restarting ComfyUI to free memory
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**Slow inference**
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- Using CPU offload (expected behavior with low GPU memory)
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- For faster inference, free up GPU memory
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**Config download fails**
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- Check internet connection
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- Manually download from Hugging Face if needed
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### Testing
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A test script is provided: `test_qwen_node.py`
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```bash
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python test_qwen_node.py
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```
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This tests model loading, memory management, and inference capabilities.
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---
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## 中文版本
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### 概述
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**ListHelper** 集合是 ComfyUI 的全面自定義節點集，提供強大的列表操作功能。此集合包含音頻處理、文本分割和數字生成工具，用於增強工作流程自動化。
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### 包含的節點
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1. [AudioListCombine 音頻列表合併](#audiolistcombine-音頻列表合併節點)
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2. [NumberListGenerator 數字列表生成器](#numberlistgenerator-數字列表生成節點)
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3. [PromptSplitByDelimiter 提示分割器](#promptsplitbydelimiter-提示分割節點)
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4. [Qwen_TE_LLM AI照片提示詞優化器](#Qwen_TE_LLM-ai照片提示詞優化器)
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5. [AudioToFrameCount](#AudioToFrameCount)
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6. [AudioSplitToList](#AudioSplitToList)
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7. [CeilDivide](#CeilDivide)
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---
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## AudioListCombine 音頻列表合併節點
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### 概述
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**AudioListCombine** 節點是 ComfyUI 的強大自定義節點，允許您將音頻清單中的多個音頻文件合併為單一音頻輸出。支持多種合併模式和音頻處理選項。
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### 功能特色
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- **多種合併模式**：串接、混音或覆疊音頻文件
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- **自動採樣率轉換**：統一不同採樣率至目標採樣率
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- **聲道標準化**：自動處理單聲道/立體聲轉換
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- **交叉淡化支持**：音頻片段間的平滑過渡
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- **音頻標準化**：可選的輸出音量標準化
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- **靈活輸入**：接受來自 Impact Pack 或其他清單製作節點的音頻清單
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### 使用方法
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#### 輸入參數
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| 參數 | 類型 | 預設值 | 說明 |
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|------|------|--------|------|
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| `audio_list` | AUDIO | - | 音頻文件清單（來自 Impact Pack 或其他清單節點）|
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| `combine_mode` | COMBO | "concatenate" | 合併方式：concatenate/mix/overlay |
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| `fade_duration` | FLOAT | 0.0 | 交叉淡化持續時間（秒，0.0-5.0）|
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| `normalize_output` | BOOLEAN | True | 是否標準化輸出音頻 |
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| `target_sample_rate` | INT | 44100 | 目標輸出採樣率 |
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#### 合併模式
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1. **Concatenate（串接）**：按順序將音頻文件首尾相連
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- 支持交叉淡化過渡
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- 保持時間順序
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- 適用於：創建音頻序列、播客、音樂播放清單
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2. **Mix（混音）**：將所有音頻文件平均混合
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- 較短文件用靜音填充
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- 等權重混合
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- 適用於：創建音頻混搭、平均多個錄音
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3. **Overlay（覆疊）**：將所有音頻文件直接相加
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- 直接加法（可能造成削波）
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- 保持原始音量
|
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- 適用於：添加音效、樂器分層
|
||
|
||
#### 輸出
|
||
|
||
| 輸出 | 類型 | 說明 |
|
||
|------|------|------|
|
||
| `audio` | AUDIO | 合併後的音頻結果 |
|
||
|
||
### 使用範例
|
||
|
||
#### 範例 1：創建音樂播放清單
|
||
```
|
||
音頻文件 1 →
|
||
音頻文件 2 → MakeAnyList → AudioListCombine (concatenate, fade=0.5s) → 保存音頻
|
||
音頻文件 3 →
|
||
```
|
||
|
||
#### 範例 2：混合多個錄音
|
||
```
|
||
錄音 1 →
|
||
錄音 2 → MakeAnyList → AudioListCombine (mix, normalize=True) → 保存音頻
|
||
錄音 3 →
|
||
```
|
||
|
||
#### 範例 3：添加音效
|
||
```
|
||
背景音樂 →
|
||
音效 1 → MakeAnyList → AudioListCombine (overlay) → 保存音頻
|
||
音效 2 →
|
||
```
|
||
|
||
---
|
||
|
||
## NumberListGenerator 數字列表生成節點
|
||
|
||
### 概述
|
||
NumberListGenerator 節點可根據自訂參數創建數字列表，支援有序和隨機輸出。非常適合批次處理、參數掃描或任何需要受控數字序列的工作流程。
|
||
|
||
### 功能特色
|
||
- **雙重輸出格式**: 同時生成整數和浮點數列表
|
||
- **靈活範圍控制**: 設定最小值、最大值和步長
|
||
- **有序或隨機**: 可切換有序和打亂輸出
|
||
- **可重現結果**: 可選種子參數確保隨機生成的一致性
|
||
- **計數追蹤**: 返回生成數值的總數
|
||
|
||
### 參數說明
|
||
|
||
**必需輸入:**
|
||
- **min_value / 最小值** (Float): 序列的起始值 (範圍: -10,000 到 10,000，預設: 0.0)
|
||
- **max_value / 最大值** (Float): 最大值上限 (範圍: -10,000 到 10,000，預設: 10.0)
|
||
- **step / 步長** (Float): 連續數值間的增量 (範圍: 0.01 到 1,000，預設: 1.0)
|
||
- **count / 數量** (Int): 要生成的數值數量 (範圍: 1 到 10,000，預設: 10)
|
||
- **random / 隨機** (Boolean): 啟用生成列表的隨機打亂 (預設: False)
|
||
|
||
**可選輸入:**
|
||
- **seed / 種子** (Int): 隨機種子，用於可重現結果 (範圍: -1 到 1,000,000，預設: -1)
|
||
|
||
**輸出:**
|
||
- **int_list / 整數列表**: 整數值列表
|
||
- **float_list / 浮點數列表**: 浮點數值列表
|
||
- **total_count / 總計數**: 生成數值的總數
|
||
|
||
### 使用範例
|
||
|
||
**有序數字生成:**
|
||
```
|
||
最小值: 0，最大值: 20，步長: 2，數量: 10，隨機: False
|
||
輸出: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
|
||
```
|
||
|
||
**隨機數字生成:**
|
||
```
|
||
最小值: 1，最大值: 100，步長: 5，數量: 8，隨機: True，種子: 42
|
||
輸出: [16, 1, 31, 6, 21, 11, 26, 36] (已打亂)
|
||
```
|
||
|
||
---
|
||
|
||
## PromptSplitByDelimiter 提示分割節點
|
||
|
||
### 概述
|
||
|
||
**PromptSplitByDelimiter** 節點是一個多功能的文本處理工具，使用可自訂的分隔符分割文本內容。支援簡單字符串分隔符和高級正規表示式，並具有可選的隨機排序和分隔符保留功能。
|
||
|
||
### 功能特色
|
||
|
||
- **靈活的分隔符支援**：使用簡單字符串或正規表示式作為分隔符
|
||
- **多語言支援**：原生支援中日韓（CJK）文字
|
||
- **正規表示式模式**：高級模式匹配，用於複雜的分割規則
|
||
- **分隔符保留**：可選擇在輸出中保留分隔符
|
||
- **隨機排序**：使用可重現的種子控制打亂結果
|
||
- **高級文本處理**：處理多個換行符、跳過空白片段
|
||
- **選擇性處理**：跳過第一個分隔符出現前的內容
|
||
|
||
### 參數說明
|
||
|
||
| 參數 | 類型 | 預設值 | 範圍 | 說明 |
|
||
|------|------|--------|------|------|
|
||
| `text` | STRING | - | - | 要分割的多行文本輸入 |
|
||
| `delimiter` | STRING | "," | - | 分隔符字符串或正規表示式模式 |
|
||
| `use_regex` | BOOLEAN | False | - | 啟用正規表示式模式 |
|
||
| `keep_delimiter` | BOOLEAN | False | - | 在輸出中保留分隔符 |
|
||
| `start_index` | INT | 0 | 0-1000 | 選擇的起始索引 |
|
||
| `skip_every` | INT | 0 | 0-10 | 跳過每 N 個項目 |
|
||
| `max_count` | INT | 10 | 1-1000 | 返回的最大項目數 |
|
||
| `skip_first_index` | BOOLEAN | False | - | 跳過第一個分隔符前的內容 |
|
||
| `random_order` | BOOLEAN | False | - | 隨機輸出順序 |
|
||
| `seed` | INT | 0 | 0-2147483647 | 可重現結果的隨機種子 |
|
||
|
||
### 輸出
|
||
|
||
| 輸出 | 類型 | 說明 |
|
||
|------|------|------|
|
||
| `text_list` | STRING | 分割的文本片段列表 |
|
||
| `total_index` | INT | 找到的片段總數 |
|
||
|
||
### 使用範例
|
||
|
||
#### 範例 1：簡單逗號分割
|
||
```
|
||
輸入: "蘋果,香蕉,櫻桃,棗子"
|
||
分隔符: ","
|
||
輸出: ["蘋果", "香蕉", "櫻桃", "棗子"]
|
||
```
|
||
|
||
#### 範例 2：中文章節分割
|
||
```
|
||
輸入: "前言第一章內容第二章內容第三章結尾"
|
||
分隔符: "第.*?章" (正規表示式模式)
|
||
輸出: ["前言", "內容", "內容", "結尾"]
|
||
```
|
||
|
||
#### 範例 3：分隔符保留
|
||
```
|
||
輸入: "AAA//BBB//CCC"
|
||
分隔符: "//"
|
||
保留分隔符: True
|
||
輸出: ["AAA", "//BBB", "//CCC"]
|
||
```
|
||
|
||
#### 範例 4：隨機章節選擇
|
||
```
|
||
輸入: "第一章\n第二章\n第三章\n第四章"
|
||
分隔符: "\n"
|
||
隨機順序: True
|
||
最大數量: 2
|
||
輸出: ["第三章", "第一章"] (已隨機化)
|
||
```
|
||
|
||
### 進階功能
|
||
|
||
#### 正規表示式支援
|
||
- **模式匹配**：使用正規表示式模式進行複雜的分隔符規則
|
||
- **中日韓文字支援**：`第\d+章` 匹配 "第1章"、"第2章" 等
|
||
- **靈活模式**：`(章|節|段)` 匹配 "章"、"節" 或 "段" 中的任何一個
|
||
|
||
#### 文本處理規則
|
||
- **換行標準化**：多個連續換行符被視為單個換行符
|
||
- **空分隔符處理**：空分隔符自動使用換行符作為後備
|
||
- **空白修剪**：自動修剪前導/尾隨空白
|
||
|
||
#### 選擇和過濾
|
||
- **索引範圍**：使用 `start_index` 和 `max_count` 選擇特定範圍的結果
|
||
- **跳過模式**：使用 `skip_every` 選擇每第 N 個項目
|
||
- **第一片段跳過**：使用 `skip_first_index` 忽略第一個分隔符前的內容
|
||
|
||
### 使用案例
|
||
|
||
- **文檔處理**：將書籍分割為章節，將文章分割為段落
|
||
- **數據提取**：從格式化文本中提取結構化數據
|
||
- **內容管理**：處理支援中日韓的多語言內容
|
||
- **批次處理**：為下游處理節點生成列表
|
||
- **隨機抽樣**：創建隨機化的內容選擇
|
||
|
||
---
|
||
|
||
## Qwen_TE_LLM AI照片提示詞優化器
|
||
|
||
### 概述
|
||
|
||
**Qwen_TE_LLM** 節點是一個 AI 驅動的文本生成節點，使用 Qwen3-4B 語言模型，具備智能 GPU 記憶體管理和 CPU Offload 支援，可與 ComfyUI 無縫整合。可使用自訂模板將簡單描述轉換為詳細、專業的提示詞。
|
||
|
||
### 功能特色
|
||
|
||
- **自動模型偵測**：自動在 `text_encoders` 或 `clip` 資料夾中尋找 Qwen safetensors 檔案
|
||
- **智能記憶體管理**：自動檢測 GPU 記憶體並選擇最佳載入策略
|
||
- 完全 GPU 模式（可用 ≥7.5GB）：~26-30 tokens/秒
|
||
- CPU Offload 模式（可用 <7.5GB）：~1-2 tokens/秒，可與其他模型共存
|
||
- **模板系統**：從 `Prompt` 資料夾選擇預製模板或使用自訂提示詞
|
||
- **雙語支援**：處理中文和英文輸入
|
||
- **自動配置下載**：從 HuggingFace 自動下載所需的模型配置檔案
|
||
- **思考標籤移除**：自動移除 `<think>...</think>` 推理標籤
|
||
|
||
### 需求
|
||
|
||
- **GPU**：支援 CUDA 的 NVIDIA GPU（建議 12GB+，記憶體較少時使用 CPU offload）
|
||
- **模型檔案**：
|
||
- **必需**：`qwen_3_4b.safetensors`（或類似的 Qwen3-4B safetensors 檔案）
|
||
- **位置**：放在 `ComfyUI/models/text_encoders/` 或 `ComfyUI/models/clip/`
|
||
- **Python 套件**：transformers、safetensors、torch
|
||
|
||
### 模型設置
|
||
|
||
1. 從 [Hugging Face](https://huggingface.co/Qwen/Qwen3-4B) 下載 Qwen3-4B safetensors
|
||
- 建議檔名：`qwen_3_4b.safetensors`
|
||
2. 將檔案放在 `ComfyUI/models/text_encoders/` 或 `ComfyUI/models/clip/`
|
||
3. 首次執行會自動下載配置檔案至 `_config` 子資料夾
|
||
|
||
### 參數說明
|
||
|
||
| 參數 | 類型 | 預設值 | 說明 |
|
||
|------|------|--------|------|
|
||
| `user_prompt` | STRING | "A girl in a coffee shop" | 您的輸入文字/描述 |
|
||
| `prompt_template` | COMBO | "Custom" | 從 Prompt 資料夾選擇模板或使用 "Custom" |
|
||
| `system_prompt` | STRING | "" | 自訂系統提示詞（模板為 "Custom" 時使用）|
|
||
| `max_new_tokens` | INT | 2048 | 生成文字的最大長度 |
|
||
| `temperature` | FLOAT | 0.7 | 創意程度 (0.0-2.0) |
|
||
| `do_sample` | BOOLEAN | True | 啟用採樣以產生變化輸出 |
|
||
| `top_p` | FLOAT | 0.9 | Nucleus 採樣閾值 |
|
||
| `top_k` | INT | 50 | Top-k 採樣參數 |
|
||
|
||
### 模板系統
|
||
|
||
節點支援儲存在 `Prompt` 資料夾中的自訂提示詞模板（`.md` 檔案）：
|
||
- **Custom**：直接使用 `system_prompt` 參數
|
||
- **模板檔案**：從 `Prompt` 資料夾中的 `.md` 檔案選擇
|
||
- 選擇模板時會自動取代 `system_prompt` 參數
|
||
|
||
### 載入策略
|
||
|
||
節點會根據可用 GPU 記憶體自動選擇最佳載入策略：
|
||
|
||
#### 策略 1：完全 GPU 載入
|
||
- **條件**：可用 GPU 記憶體 ≥7.5GB
|
||
- **效能**：~26-30 tokens/秒
|
||
- **適用**：獨立使用或 ComfyUI 記憶體佔用較少時
|
||
|
||
#### 策略 2：CPU Offload
|
||
- **條件**：可用 GPU 記憶體 <7.5GB
|
||
- **效能**：~1-2 tokens/秒（較慢但可靠）
|
||
- **適用**：ComfyUI 已載入大型模型時
|
||
- **優點**：無記憶體衝突，可與其他模型共存
|
||
|
||
#### 策略 3：CPU 模式
|
||
- **條件**：無 CUDA 可用
|
||
- **適用**：純 CPU 環境
|
||
|
||
### 輸出
|
||
|
||
| 輸出 | 類型 | 說明 |
|
||
|------|------|------|
|
||
| `text` | STRING | 詳細的專業攝影提示詞 |
|
||
|
||
### 使用範例
|
||
|
||
#### 範例 1：使用模板
|
||
```
|
||
使用者提示詞: "一個女孩在咖啡廳"
|
||
模板: (Prompt 資料夾中的攝影模板)
|
||
輸出: "A young woman sitting by the window in a cozy coffee shop, warm afternoon sunlight streaming through large glass windows creating soft shadows, wearing casual outfit, holding a cup of coffee, wooden table with laptop and notebook, blurred background with other customers, shallow depth of field, bokeh effect, warm color temperature, golden hour lighting..."
|
||
```
|
||
|
||
#### 範例 2：自訂系統提示詞
|
||
```
|
||
使用者提示詞: "a cat sitting on a windowsill"
|
||
模板: "Custom"
|
||
系統提示詞: "用詩意的細節描述場景"
|
||
輸出: "A calico cat sitting on a sunlit windowsill, its long fur catching golden-hour light from the right, creating soft shadows across its face and body, wearing a quiet expression of peaceful solitude, surrounded by indoor plants and a wooden bookshelf in the background..."
|
||
```
|
||
|
||
### 記憶體管理
|
||
|
||
節點包含智能記憶體管理：
|
||
|
||
1. **載入前檢查**：載入前檢查 GPU 記憶體
|
||
2. **自動清理**：需要時清理 CUDA 快取
|
||
3. **動態策略**：根據可用記憶體選擇載入策略
|
||
4. **設備分佈**：顯示模型層在 GPU/CPU 的分佈情況
|
||
5. **清楚錯誤訊息**：提供詳細的錯誤資訊和解決方案
|
||
|
||
### 效能考量
|
||
|
||
- **首次載入**：7-130 秒（取決於硬體和記憶體）
|
||
- **後續載入**：模型保留在記憶體中，幾乎即時
|
||
- **完全 GPU**：快速推理（~26-30 tokens/秒）
|
||
- **CPU Offload**：較慢推理（~1-2 tokens/秒）但防止崩潰
|
||
- **記憶體使用**：完全 GPU 模式約 7.5GB GPU 記憶體
|
||
|
||
### 疑難排解
|
||
|
||
**找不到模型**
|
||
- 確保 Qwen safetensors 檔案在 `models/text_encoders/` 或 `models/clip/`
|
||
- 建議檔名：`qwen_3_4b.safetensors`（包含 "qwen"、"3" 和 "4b"）
|
||
- 節點會自動搜尋相容的 Qwen 模型檔案
|
||
|
||
**記憶體不足**
|
||
- 節點會自動使用 CPU offload
|
||
- 若仍失敗，關閉其他 GPU 應用程式
|
||
- 考慮重啟 ComfyUI 以釋放記憶體
|
||
|
||
**推理速度慢**
|
||
- 正在使用 CPU offload（低 GPU 記憶體時的預期行為）
|
||
- 若要更快推理，釋放 GPU 記憶體
|
||
|
||
**配置下載失敗**
|
||
- 檢查網路連線
|
||
- 必要時從 Hugging Face 手動下載
|
||
|
||
### 測試
|
||
|
||
提供測試腳本：`test_qwen_node.py`
|
||
|
||
```bash
|
||
python test_qwen_node.py
|
||
```
|
||
|
||
此腳本測試模型載入、記憶體管理和推理能力。
|
||
|
||
---
|
||
|
||
### Performance Considerations / 性能考慮
|
||
- **Memory Usage**: Large audio files and long text strings may require significant RAM
|
||
- **Processing Speed**: Regular expressions may be slower than simple string operations
|
||
- **File Formats**: AudioListCombine supports all formats compatible with torchaudio
|
||
|
||
### 中文技術說明
|
||
- **記憶體使用**：大型音頻文件和長文本字符串可能需要大量 RAM
|
||
- **處理速度**：正規表示式可能比簡單字符串操作慢
|
||
- **文件格式**：AudioListCombine 支援所有與 torchaudio 兼容的格式
|
||
|
||
### Common Issues / 常見問題
|
||
|
||
**Audio list is empty / 音頻列表為空**
|
||
- Ensure list creation nodes have connected inputs / 確保列表創建節點已連接輸入
|
||
|
||
**Regular expression errors / 正規表示式錯誤**
|
||
- Check pattern syntax, node will fallback to string mode / 檢查模式語法，節點將回退到字符串模式
|
||
|
||
**Memory issues with large files / 大文件記憶體問題**
|
||
- Process files in smaller batches / 以較小批次處理文件
|
||
|
||
## AudioToFrameCount
|
||
|
||
|
||
|
||
### 功能特色
|
||
|
||
**輸入音檔以串接圖片數目**：依照所需影片格數計算音檔長度，輸入為音檔，輸出為一固定值，可用於重複單一圖片配合音檔長度
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|
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## CeilDivide
|
||
|
||
|
||
|
||
### 功能特色
|
||
|
||
**無條件進位**：將AB相除結果無條件進位，以避免因尾數被捨去迴圈數目不足，導致多段影片或音檔分離時，末段音檔未被採樣
|
||
|
||
## AudioSplitToList
|
||
|
||
|
||
|
||
### 功能特色
|
||
|
||
**分割音檔為清單**：將聲音檔分割為清單，長篇數字人時可分段採樣，目前已經更新音檔處理的邏輯，增加FADE功能
|
||
|
||
**必需輸入:**
|
||
- **videofps / 畫格** (Float): 每秒多少格
|
||
- **samplefps / 分段採樣格** (Int): 每段採樣多少格，範例，如果是25格，分段採樣格是75，則音檔將會分隔為每三秒一個單位
|
||
- **pad_last_segment / 補足音檔** (Boolean): 將音檔長度插入空白，對齊最後一格的分段採樣 (預設: False)
|
||
|
||
**輸出:**
|
||
- **cycle / 整數**: 分割為多少段
|
||
- **audio_list / 音檔清單**: 分割完成的音檔，可直接放入audio輸入，會依序處理
|
||
|
||
|
||
## License / 授權
|
||
|
||
MIT License
|
||
|
||
## Contributing / 貢獻
|
||
|
||
歡迎提交 Issue 和 Pull Request！
|
||
Welcome to submit Issues and Pull Requests!
|
||
|
||
## Support / 支援
|
||
|
||
如有問題請在 GitHub Issues 中回報。
|
||
For questions, please report in GitHub Issues. |