Extensions/RandomLoRALoader
ComfyUI Extension

RandomLoRALoader

A ComfyUI custom node for randomly selecting and applying LoRAs from multiple folders with support for trigger words, sample prompts, and strength randomization.

By shin131002·Created 7 months ago·Updated 7 months ago· 2
shin131002/RandomLoRALoader
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Nodes (3)

Filtered Random LoRA Loader
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Filtered Random LoRA Loader (LBW)
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Random LoRA Loader
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Readme

Random LoRA Loader for ComfyUI

English | 日本語版 README

A ComfyUI custom node package for randomly selecting and applying LoRAs. Includes three nodes:

  1. Random LoRA Loader - Select from 3 folders simultaneously
  2. Filtered Random LoRA Loader - Single folder with keyword filtering
  3. Filtered Random LoRA Loader (LBW) - 🆕 LBW support with automatic SD1.5/SDXL detection (NEW v1.2.0)

Random LoRA Loader Workflow Example Filtered Random LoRA Loader Workflow Example FilteredRandomLoRALoader(LBW) Workflow Example

⚠️ Important: Supported Models

These nodes are designed for SD1.5 and SDXL models ONLY.

  • ✅ Supported: Stable Diffusion 1.5, Stable Diffusion XL (SDXL)
  • ❌ NOT Supported: Flux, SD3, SDXL Turbo, Pony, or other architectures

The LBW node's block weight feature specifically targets SD1.5/SDXL U-Net architecture. Other model types will not work correctly with LBW.

Nodes Overview

Random LoRA Loader (Original)

Select LoRAs from up to 3 different folders. Ideal for folder-based organization.

Use case:

  • Different folders for styles, characters, concepts
  • Fixed LoRA categories
  • Simple folder-based workflow

Filtered Random LoRA Loader

Select LoRAs from a single folder using keyword filtering. Ideal for dynamic selection and large collections.

Use case:

  • All LoRAs in one folder, filter by keywords
  • Dynamic filtering with AND/OR conditions
  • Metadata search capability
  • Recommended for chaining multiple instances

Filtered Random LoRA Loader (LBW) 🆕 NEW v1.2.0

Advanced node with LoRA Block Weight (LBW) support for precise effect control.

Use case:

  • Separate control of style vs. structure
  • Fine-tuned LoRA application
  • Professional workflows requiring precision
  • 4 preset modes + custom input

See README_LBW.md for detailed LBW documentation.

Key Features

Common Features (All Nodes)

  • Multi-Source Metadata Reading: Automatically retrieve trigger words and sample prompts with priority order:
    1. .metadata.json format (ComfyUI Lora Manager) - Priority 1
    2. .info format (Civitai Helper) - Priority 2
    3. Embedded metadata in LoRA file - Priority 3
  • Strength Randomization:
    • Range specification with negative support: 0.4-0.8, -0.8--0.3 (v1.2.0)
    • 0.1 increments for ranges: Easier to observe effect differences
    • Fixed values rounded to 2 decimals: 0.847 → 0.85
  • Flexible Trigger Word Retrieval:
    • json_combined: Combine all trigger word patterns (with deduplication)
    • json_random: Randomly select one pattern
    • json_sample_prompt: Randomly retrieve sample prompts
    • metadata: Read directly from embedded metadata
  • Sample Prompt Optimization: Automatically remove LoRA syntax from samples and apply node-configured strength
  • Dual Text Outputs: Separate positive_text and negative_text outputs
  • ComfyUI Standard Seed Control: Supports fixed/randomize/increment/decrement
  • Wildcard Encode Integration: Works seamlessly with Wildcard Encode (Inspire)
  • LoRA Syntax Auto-Removal: Cleans up LoRA syntax in additional_prompt to prevent noise
  • Preview Image Support: Display preview images/videos for selected LoRAs (v1.1.0)
  • Duplicate Filename Handling: Exclude duplicate filenames across subfolders (v1.1.0)

Filtered Nodes (Filtered & LBW)

  • Keyword Filtering: Space-separated keywords with phrase support
  • AND/OR Modes: Flexible filtering logic
  • Metadata Search: Search in filenames or embedded metadata
  • Fast Caching: Instant metadata access after first load

LBW Node Only 🆕

  • LoRA Block Weight Support: Control which U-Net blocks are affected
  • Automatic SD1.5/SDXL Detection: No manual model type selection needed
  • 4 Preset Modes:
    • Style Focused - OUTPUT blocks only
    • Character Focused - Balanced IN+MID+OUT
    • Structure/Composition Only - INPUT+MID blocks only
    • Balanced / Soft - Gentle application
  • Preset: Random Mode: Randomly select one of the 4 presets
  • Direct Input Mode: Custom weight specification
  • Automatic Weight Adjustment: Handles mismatched element counts

Installation

⚠️ Optional: Video Preview Support

For video file preview (.mp4, .webm, .avi, .mov), opencv-python is REQUIRED:

pip install opencv-python

Without opencv-python:

  • ✅ Static images (.png, .jpg, .jpeg) work
  • ✅ Animated images (.gif, .webp) work
  • ❌ Video files will show BLACK SCREEN (with warning message in console)

LoRA functionality works normally regardless of opencv-python installation. Only preview display is affected.

Requirements

  • ComfyUI (works with standard installation)
  • Python 3.9+
  • Core dependencies included with ComfyUI ✅

Steps

cd ComfyUI/custom_nodes
git clone https://github.com/YOUR_USERNAME/RandomLoRALoader.git
# Or manually create RandomLoRALoader folder and copy files

Restart ComfyUI.

Note: Uses only Python standard library and ComfyUI bundled libraries for core functionality. opencv-python is optional for video preview only.

Uninstallation

# Navigate to custom_nodes folder
cd ComfyUI/custom_nodes

# Remove RandomLoRALoader folder
rm -rf RandomLoRALoader

# On Windows
rmdir /s RandomLoRALoader

Or manually delete the RandomLoRALoader folder and restart ComfyUI.

Usage

Basic Usage (Random LoRA Loader)

  1. Add Node: Search for "Random LoRA Loader" in the node browser
  2. Connect MODEL/CLIP: Connect base model and CLIP to inputs
  3. Set Folder Paths:
    • Group 1: Style LoRA folder path
    • Group 2: Character LoRA folder path (optional)
    • Group 3: Concept LoRA folder path (optional)
  4. Configure Each Group:
    • num_loras: Number of LoRAs to select from each group
    • model_strength / clip_strength: Application strength (fixed value or random range)
  5. Connect Outputs:
    • MODEL / CLIP: Connect to KSampler
    • positive / negative: Connect to Set Conditioning or KSampler
    • positive_text / negative_text: Connect to Show Text for verification (recommended)

Wildcard Encode Integration (Recommended)

This node works seamlessly with Wildcard Encode (Inspire) for dynamic prompt generation combined with random LoRA selection.

Wildcard Encode + Random LoRA Loader Workflow

Recommended Connection

[Wildcard Encode (Inspire)]
  text: "__style__, {red|blue|green}, <lora:base_effect:0.5>"
  ↓
  ├─ populated_text ──→ [Random LoRA Loader]
  │                     additional_prompt_positive
  └─ MODEL/CLIP ──────→ model/clip input
  
[Random LoRA Loader]
  Folder 1: character LoRAs
  Folder 2: concept LoRAs
  ↓
  MODEL/CLIP/CONDITIONING → [KSampler]

How It Works

  1. Wildcard Encode Processing:

    • Wildcard expansion: __style__ → "anime style"
    • Choice expansion: {red|blue|green} → "blue"
    • LoRA syntax processing: <lora:base_effect:0.5> → applied to MODEL
    • Result: populated_text = "anime style, blue, lora:base_effect:0.5"

  2. Random LoRA Loader Processing:

    • Auto-removes LoRA syntax from populated_text: "anime style, blue"
    • Random LoRA selection: character_alice, concept_magic
    • Final prompt: "anime style, blue, alice, blonde hair, magic circle"
    • MODEL/CLIP: base_effect + character_alice + concept_magic (all applied)

Benefits

  • ✅ Delegate wildcard functionality to Wildcard Encode (specialized tool)
  • ✅ Delegate random LoRA selection to this node (simple configuration)
  • ✅ Prompt information fully inherited
  • ✅ LoRA syntax automatically cleaned (no noise)

3-Group Example

Group 1: style LoRAs
  - num_loras_1: 2
  - model_strength_1: "0.6-0.9"  ← Random (0.1 increments)
  - clip_strength_1: "0.6-0.9"   ← Random (0.1 increments)

Group 2: character LoRAs
  - num_loras_2: 1
  - model_strength_2: "1.0"      ← Fixed
  - clip_strength_2: "1.0"       ← Fixed

Group 3: Unused
  - lora_folder_path_3: (empty)
  - num_loras_3: 0

→ Result: 2 style LoRAs (variable strength) + 1 character LoRA (fixed strength) = 3 LoRAs total

Settings

Common Settings

| Setting | Description | Default | |---------|-------------|---------| | token_normalization | Token normalization method | none | | weight_interpretation | Prompt emphasis notation interpretation | A1111 | | additional_prompt_positive | Additional positive prompt (combined with trigger words) | (empty) | | additional_prompt_negative | Additional negative prompt | (empty) | | trigger_word_source | Trigger word source | json_combined | | seed | Random selection seed | 0 |

Important Notes on additional_prompt

Supported:

  • Regular prompt text
  • Text input from other nodes (e.g., Wildcard Encode's populated_text)

Not Supported (automatically removed):

  • <lora:xxx:0.8> format LoRA syntax
  • {a|b|c} format wildcard syntax
  • __filename__ format wildcard syntax

Why:

  • This node applies LoRAs via folder specification
  • Wildcard functionality should be handled by dedicated nodes (Wildcard Encode, etc.)
  • Writing LoRA syntax causes filename to become meaningful tokens as noise

Example (problematic case):

Input: "1girl, <lora:anime_style:0.8>, beautiful"
↓
CLIP tokenization: [1girl, lora, anime, style, 0, 8, beautiful]
                            ^^^^^ ^^^^^
                            Unintended words added to prompt ❌

Correct usage:

additional_prompt_positive: "1girl, beautiful"  ← No LoRA syntax ✅
LoRA application: Use folder specification feature ✅

Or, when connecting from Wildcard Encode, LoRA syntax is automatically removed:

[Wildcard Encode] populated_text: "1girl, <lora:style:0.8>, beautiful"
       ↓
[This Node] additional_prompt_positive received → LoRA syntax auto-removed
       ↓
Final prompt: "1girl, beautiful" ✅

Group Settings (1-3)

Each group can be configured individually:

| Setting | Description | Default | |---------|-------------|---------| | lora_folder_path_X | LoRA folder absolute path | (empty) | | include_subfolders_X | Include subfolders | true | | unique_by_filename_X | Exclude duplicate filenames | true | | model_strength_X | MODEL application strength | "1.0" | | clip_strength_X | CLIP application strength | "1.0" | | num_loras_X | Number of LoRAs to select | Group 1: 1, Groups 2/3: 0 |

Strength Specification (Important)

Strength fields accept fixed values or random ranges, including negative values.

Fixed Value

Input: "1.0"
→ Always applied at 1.0

Input: "0.55"
→ Rounded to 0.55 (2 decimals)

Input: "-0.5"
→ Negative LoRA at -0.5 ✅ (v1.2.0)

Random Range

Format: min-max

Input: "0.6-0.9"
→ Random from [0.6, 0.7, 0.8, 0.9] (0.1 increments)

Input: "-0.8--0.3"
→ Random from [-0.8, -0.7, -0.6, -0.5, -0.4, -0.3] ✅ (v1.2.0)

Input: "-0.5-0.5"
→ Random from [-0.5, -0.4, ..., 0.4, 0.5] ✅

v1.2.0 Improvements:

  • ✅ Negative range support: -0.8--0.3 now works correctly
  • ✅ 0.1 increments for all ranges: Easier to observe effect differences
  • ✅ Fixed values rounded to 2 decimals: 0.847 → 0.85

Behavior

All Nodes:

  • Range: 0.1 increments list → random selection
  • Fixed: Rounded to 2 decimals

Example:

model_strength_1: "0.6-0.9"
→ Each execution randomly selects from [0.6, 0.7, 0.8, 0.9]
→ Console: "[RandomLoRALoader] Selected strength: 0.7"

MODEL Strength vs CLIP Strength

MODEL Strength

  • Affects: Image itself (art style, composition, colors, shapes)
  • Effect: Visual characteristics of generated image
  • Example:
    • High value: Strong art style transfer
    • Low value: Subtle style hints

CLIP Strength

  • Affects: Prompt interpretation (concept understanding, language-image association)
  • Effect: How strongly the model follows prompt instructions
  • Example:
    • High value: Strong semantic guidance from trigger words
    • Low value: Weak semantic influence

Recommended Settings

For most use cases:

model_strength: 0.8
clip_strength: 0.8
→ Balanced application

For subtle style application:

model_strength: 0.4-0.6
clip_strength: 0.8-1.0
→ Light visual effect, strong semantic guidance

For strong style override:

model_strength: 1.0-1.2
clip_strength: 0.6-0.8
→ Strong visual effect, moderate semantic guidance

Trigger Word Retrieval Methods

json_combined (Default)

Combines all trigger word patterns from JSON metadata.

Source:

  1. activation text field
  2. ss_tag_frequency keys (first 3 highest frequency)
  3. modelspec.description keywords
  4. ss_output_name keywords

Example:

{
  "activation text": "anime style, detailed eyes",
  "ss_tag_frequency": {
    "1girl": 500,
    "blue eyes": 300,
    "long hair": 250
  }
}

Result: "anime style, detailed eyes, 1girl, blue eyes, long hair"

json_random

Randomly selects one trigger word pattern.

Example:

Candidates:
- "anime style, detailed eyes"
- "1girl, blue eyes, long hair"
- "anime, girl, portrait"

Random selection: "1girl, blue eyes, long hair"

json_sample_prompt

Retrieves sample prompts from JSON metadata.

Source fields (priority order):

  1. sample_prompts
  2. civitai.images[0].meta.prompt

Processing:

  • Automatically removes LoRA syntax (<lora:xxx:0.8>)
  • Replaces with node-configured strength
  • Extracts negative prompt if available

Example:

{
  "sample_prompts": "1girl, <lora:style:0.9>, beautiful, detailed eyes"
}

Result: "1girl, beautiful, detailed eyes" (LoRA syntax removed)

metadata

Reads trigger words directly from embedded LoRA file metadata.

Source:

  • Safetensors metadata keys
  • Similar to json_combined but reads from LoRA file directly

Use when:

  • No external JSON files available
  • Want to use original training metadata

Outputs

All nodes provide the following outputs:

| Output | Type | Description | |--------|------|-------------| | MODEL | MODEL | Model with LoRAs applied | | CLIP | CLIP | CLIP with LoRAs applied | | positive | CONDITIONING | Positive conditioning (LoRA syntax removed) | | negative | CONDITIONING | Negative conditioning | | positive_text | STRING | Full positive prompt text with LoRA syntax | | negative_text | STRING | Full negative prompt text | | preview | IMAGE | Batch of preview images for selected LoRAs (v1.1.0) |

Text Output Format

positive_text example:

<lora:style_anime:0.8:0.8>, <lora:character_alice:1.0:1.0>, anime style, alice, blonde hair, 1girl, beautiful

LBW node positive_text example:

<lora:style_anime:0.8:0.8:lbw=1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1>, anime style, 1girl, beautiful

positive (CONDITIONING) content:

anime style, alice, blonde hair, 1girl, beautiful
(LoRA syntax removed for clean prompt)

Preview Images (v1.1.0)

Supported formats (priority order):

  1. Static images (.png, .jpg, .jpeg) - Always works
  2. Animated images (.gif, .webp) - First frame, always works
  3. Video files (.mp4, .webm, .avi, .mov) - First frame, requires opencv-python

File matching:

  • Files starting with the LoRA filename (case-insensitive)
  • Example: style_anime.safetensors matches:
    • style_anime.png ✅
    • style_anime_preview.jpg ✅
    • STYLE_ANIME.PNG ✅

Filtered Random LoRA Loader

Single folder with keyword filtering.

Filtered Random LoRA Loader Workflow

Key Features

  • Keyword Filtering: Filter LoRAs by space-separated keywords
  • AND/OR Modes: Flexible filtering logic
  • Phrase Matching: Use quotes for exact phrases
  • Metadata Search: Search in filenames or embedded metadata
  • Fast Caching: Instant metadata access after first load
  • Serial Connection Ready: Chain multiple instances for complex workflows

Parameters

| Parameter | Description | Default | |-----------|-------------|---------| | lora_folder_path | LoRA folder path | (empty) | | keyword_filter | Space-separated keywords or quoted phrases | (empty) | | filter_mode | AND / OR | AND | | search_in_metadata | Search in JSON/embedded metadata | false | | num_loras | Number of LoRAs to select | 1 | | model_strength | MODEL strength (fixed or range) | "1.0" | | clip_strength | CLIP strength (fixed or range) | "1.0" | | include_subfolders | Include subfolders | true | | unique_by_filename | Exclude duplicate filenames | true |

Keyword Filter Syntax

Basic keywords (AND mode):

keyword_filter: "anime girl"
→ Matches files containing BOTH "anime" AND "girl"

OR mode:

filter_mode: OR
keyword_filter: "anime realistic"
→ Matches files containing "anime" OR "realistic"

Phrase matching:

keyword_filter: '"anime style" detailed'
→ Must contain exact phrase "anime style" AND word "detailed"

Multiple phrases:

keyword_filter: '"anime style" "detailed eyes" red'
→ Must contain "anime style" AND "detailed eyes" AND "red"

Metadata Search

Filename search (default):

search_in_metadata: false
→ Fast, searches only filenames

Metadata search:

search_in_metadata: true
→ Slower, searches JSON/embedded metadata
→ Cached after first search (instant on 2nd+)

Performance:

  • Initial load (SSD):
    • 1,000 files: ~2 seconds
    • 5,000 files: ~10 seconds
    • 10,000 files: ~20 seconds
  • 2nd+ time: Instant (<100ms)
  • Memory: ~150MB per 10,000 files

Use Cases

Example 1: Character Selection

lora_folder_path: "/path/to/all_loras"
keyword_filter: "character girl"
filter_mode: AND
num_loras: 1

Result: Select 1 character LoRA containing both "character" and "girl"

Example 2: Multiple Instances (Recommended)

Filtered Random LoRA Loader x2 Workflow

[Load Checkpoint]
  ↓
[Filtered Random LoRA Loader #1]
  keyword_filter: "style anime"
  num_loras: 1
  ↓
[Filtered Random LoRA Loader #2]
  keyword_filter: "character"
  num_loras: 1
  ↓
[KSampler]

Benefits:

  • Different filtering per category
  • Independent strength settings
  • More flexibility

Example 3: Metadata Search

search_in_metadata: true
keyword_filter: "detailed eyes"

Searches in:

  • Filenames
  • JSON activation text
  • JSON ss_tag_frequency
  • JSON modelspec.description
  • Embedded LoRA metadata

Filtered Random LoRA Loader (LBW) 🆕 NEW v1.2.0

Advanced node with LoRA Block Weight support.

FilteredRandomLoRALoader(LBW) Workflow Example

What is LBW?

LoRA Block Weight allows you to control which parts of the U-Net are affected by the LoRA:

INPUT blocks  → Structure, composition, layout
   ↓
MIDDLE block  → Overall features
   ↓
OUTPUT blocks → Style, details, fine-tuning

Key Features

  • 4 Preset Modes:
    • Style Focused: OUTPUT blocks only (style without changing structure)
    • Character Focused: Balanced IN+MID+OUT (character features)
    • Structure/Composition Only: INPUT+MID blocks (layout without style)
    • Balanced / Soft: Gentle application (natural results)
  • Preset: Random: Randomly select one of the 4 presets
  • Direct Input: Custom weight specification
  • Automatic SD1.5/SDXL Detection: No manual model type selection
  • Automatic Weight Adjustment: Handles mismatched element counts

Parameters

Same as Filtered Random LoRA Loader, plus:

| Parameter | Description | Default | |-----------|-------------|---------| | weight_mode | LBW preset or Direct Input | Normal (All 1.0) | | lbw_input | Custom weights (for Direct Input) | (empty) |

Weight Modes

Normal (All 1.0)

Standard LoRA application without block weighting.

Style Focused

SDXL: 1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
SD1.5: 1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
  • OUTPUT blocks only
  • Style without changing structure
  • Use for: Art style LoRAs, effect LoRAs

Character Focused

SDXL: 1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
SD1.5: 1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
  • Balanced IN+MID+OUT
  • Character features preserved
  • Use for: Character LoRAs, person LoRAs

Structure/Composition Only

SDXL: 1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0
SD1.5: 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0
  • INPUT+MID blocks only
  • Structure without changing style
  • Use for: Pose LoRAs, composition LoRAs

Balanced / Soft

SDXL: 1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0
SD1.5: 1,1,1,1,0,0,0,1,1,1,1,1,0,0,0,0,0
  • Gentle application
  • Natural results
  • Use for: General LoRAs, multi-LoRA workflows

Preset: Random

Randomly selects one of the 4 presets above each execution.

Direct Input

weight_mode: Direct Input
lbw_input: "1,0.5,0.5,0,0,0,0,0,0,0,1,0.8,0.8,0.8,0.8,0.8,0.5,0.5,0.5,0.5"
  • Custom weight specification
  • SDXL: 20 elements, SD1.5: 17 elements
  • Auto-adjusted if count doesn't match

Use Cases

Example 1: Style Only

[Filtered Random LoRA Loader (LBW)]
  keyword_filter: "anime watercolor"
  weight_mode: Style Focused
  ↓
[KSampler]

Result: Applies anime watercolor style without changing composition

Example 2: Structure then Style

[Load Checkpoint]
  ↓
[Filtered Random LoRA Loader (LBW)]
  keyword_filter: "pose"
  weight_mode: Structure/Composition Only
  ↓
[Filtered Random LoRA Loader (LBW)]
  keyword_filter: "oil painting"
  weight_mode: Style Focused
  ↓
[KSampler]

Result: Pose adjustment → Oil painting style applied

Example 3: Custom Weights

weight_mode: Direct Input
lbw_input: "1,0.5,0.5,0.5,0,0,0,0,0,0,1,1,1,1,1,1,0.5,0.5,0.5,0.5"

Result: Custom block weight distribution

Advanced: Custom Presets

You can edit presets directly in the source file:

File: filtered_random_lora_loader_lbw.py (lines 29-41)

See README_LBW.md for detailed customization guide.

Tips & Tricks

Serial Connection for Multiple Effects

Connect nodes in series to apply different LoRA categories:

[Load Checkpoint]
  ↓
[Filtered Random LoRA Loader (LBW)] (Structure/Composition Only)
  keyword_filter: "pose"
  ↓
[Filtered Random LoRA Loader (LBW)] (Style Focused)
  keyword_filter: "watercolor"
  ↓
[KSampler]

Benefits:

  • Different settings per category
  • Cumulative LoRA effects
  • More control over final result

Strength Randomization for Variety

Use ranges instead of fixed values:

model_strength_1: "0.6-0.9"
clip_strength_1: "0.6-0.9"

Effect:

  • Each generation uses different strength
  • More variety in results
  • Easier to find optimal strength

Keyword Filtering Best Practices

Use specific keywords:

✅ Good: "anime girl"
❌ Too broad: "anime"

Combine with metadata search:

search_in_metadata: true
keyword_filter: "detailed eyes"

Use phrases for precision:

keyword_filter: '"anime style" detailed'
→ Must contain exact phrase "anime style" AND word "detailed"

Wildcard Encode + Random LoRA Loader

Ultimate dynamic workflow:

[Wildcard Encode (Inspire)]
  text: "__style__, __pose__, {red|blue|green}"
  ↓
  populated_text ──→ [Random LoRA Loader]
                     additional_prompt_positive
  ↓
[KSampler]

Result:

  • Wildcard expansion for prompts
  • Random LoRA selection for variety
  • Clean prompt without LoRA syntax
  • Maximum flexibility

Preview Images for Organization

Enable preview display to:

  • See which LoRAs were selected
  • Verify LoRA appearance before generation
  • Organize LoRA collections visually

Recommended workflow:

[Random LoRA Loader]
  preview ──→ [Preview Image] (for verification)
  MODEL ──→ [KSampler]

Troubleshooting

No LoRAs Found

Check:

  1. LoRA folder path is correct (absolute path)
  2. .safetensors files exist in the folder
  3. include_subfolders setting if LoRAs are in subfolders
  4. Console for error messages

Example valid paths:

Windows: C:/ComfyUI/models/loras/style
Linux/Mac: /home/user/ComfyUI/models/loras/style

No Trigger Words Found

Check:

  1. Metadata files (.metadata.json or .info) exist
  2. JSON files contain required fields:
    • activation text
    • ss_tag_frequency
    • sample_prompts
    • civitai.trainedWords or civitai.images
  3. Try different trigger_word_source settings

Verify JSON format:

{
  "activation text": "anime style, detailed",
  "ss_tag_frequency": {...},
  "modelspec.description": "...",
  "sample_prompts": "..."
}

LoRA Not Applied

Check:

  1. num_loras is not 0
  2. Strength values are not all 0
  3. MODEL/CLIP outputs are connected properly
  4. Console for LoRA application messages
  5. For LBW node: weight_mode is not "Normal (All 1.0)" with all weights set to 0

Preview Images Not Showing

Check:

  1. Preview files match LoRA filename
  2. For video files: opencv-python is installed (pip install opencv-python)
  3. Supported formats: .png, .jpg, .jpeg, .gif, .webp, .mp4, .webm, .avi, .mov
  4. File permissions are correct

Without opencv-python:

  • Static images work ✅
  • Animated images work ✅
  • Video files show black screen ⚠️

Strength Not Working with Negative Values (Fixed in v1.2.0)

Previous issue (v1.1.0):

Input: "-0.5"
Result: 1.0 ❌ (converted to default)

Fixed in v1.2.0:

Input: "-0.5"
Result: -0.5 ✅ (negative LoRA applied correctly)

Input: "-0.8--0.3"
Result: Random from [-0.8, -0.7, -0.6, -0.5, -0.4, -0.3] ✅

Wildcard Encode Issues

If wildcard text appears in output:

  1. Check connection: Connect Wildcard Encode's populated_text to additional_prompt_positive
  2. Wrong output: Don't connect text (input) to this node
  3. LoRA syntax appearing: This node automatically removes it

Example workflow:

[Wildcard Encode (Inspire)]
  populated_text ──→ [Random LoRA Loader]
  (not "text")       additional_prompt_positive

Duplicate LoRAs Selected

Solution: Enable unique_by_filename

unique_by_filename_1: true

This prevents selecting the same LoRA from different subfolders:

/style/lora.safetensors
/backup/lora.safetensors
→ Only one selected ✅

Detailed Documentation

Disclaimer and Support Policy

Disclaimer

  • This node is provided as-is with no technical support
  • No warranty or guarantee of functionality
  • No guaranteed compatibility with future ComfyUI updates
  • Bug reports and feature requests may not be addressed
  • Use at your own risk

Support Status

  • ❌ No individual support via issues or email
  • ❌ No guaranteed bug fixes or feature additions
  • ✅ Code is open source - feel free to fork and modify
  • ✅ Community discussions welcome (no promises of response)

Reporting Issues

While support is not guaranteed, you can:

  1. Check existing issues in the repository
  2. Review this README and troubleshooting section
  3. Open an issue (may or may not be addressed)
  4. Fork and fix it yourself

License

MIT License

Changelog

v1.2.0 (2026-01-13)

Added

  • ✅ NEW NODE: Filtered Random LoRA Loader (LBW)
  • ✅ LoRA Block Weight (LBW) support with 4 presets
  • ✅ Automatic SD1.5/SDXL detection for LBW
  • ✅ Preset: Random mode for LBW
  • ✅ Direct Input mode for custom LBW weights
  • ✅ Automatic weight adjustment for LBW
  • ✅ Negative value support for strength: -0.5, -0.8--0.3 now work correctly
  • ✅ Video preview support: .mp4, .webm, .avi, .mov (requires opencv-python)
  • ✅ Strength precision improvements:
    • Range specification: 0.1 increments for all nodes
    • Fixed values: Rounded to 2 decimals

Fixed

  • ✅ Critical bug: Negative single values ("-0.5") now work correctly (previously converted to 1.0)
  • ✅ Critical bug: Negative ranges ("-0.8--0.3") now work correctly (previously caused errors)
  • ✅ Improved strength parsing with regex pattern matching

Changed

  • ✅ Unified strength behavior across all 3 nodes
  • ✅ Renamed node: "Filtered Random LoRA Loader (Advanced)" → "Filtered Random LoRA Loader (LBW)"
  • ✅ Enhanced documentation with model compatibility warnings

v1.1.0 (2026-01-04)

Added

  • ✅ NEW NODE: Filtered Random LoRA Loader
  • ✅ Keyword filtering with AND/OR modes
  • ✅ Metadata search with caching
  • ✅ Preview image output (IMAGE type)
  • ✅ Duplicate filename handling (unique_by_filename)
  • ✅ Animated image support (.gif, .webp)

Changed

  • ✅ Breaking: Split additional_prompt into additional_prompt_positive and additional_prompt_negative
  • ✅ Unified strength precision to 1 decimal place
  • ✅ Improved LoRA syntax removal

Fixed

  • ✅ Fixed negative_text output in json_sample_prompt mode
  • ✅ Fixed LoRA syntax appearing in metadata trigger words
  • ✅ Fixed duplicate parameter in INPUT_TYPES

v1.0.0 (2025-12-30)

Added

  • ✅ Initial release
  • ✅ Random LoRA Loader with 3-group support
  • ✅ Multi-source metadata reading
  • ✅ Strength randomization with range specification
  • ✅ Trigger word extraction
  • ✅ Wildcard Encode compatibility
  • ✅ CONDITIONING output with cleaned prompts

References

Enjoy flexible LoRA randomization with precise control! 🎲✨