fbTools (frost-byte Tools)

A comprehensive collection of custom nodes for ComfyUI focused on storytelling, scene management, and prompt templating workflows.

Features

🎬 Story & Scene Management

• Story Nodes: Create, edit, and manage multi-scene stories with ordered sequences
• Scene Nodes: Build scenes with poses, masks, depth maps, and multiple prompt types
• Scene Selection: Dynamic scene loading with automatic resource management

📝 Prompt System

• PromptCollection: Flexible v2 prompt system with metadata (categories, descriptions, tags)
• Backward Compatibility: Auto-migration from v1 format with preservation of legacy data
• Multiple Prompt Types: Support for character, scene, quality, and custom prompts

📚 Libber (Template System)

• Interactive Table Editor: Edit key-value templates with inline editing
• Click-to-Insert: Click any template key to insert it with delimiters
• Auto-Save: Changes automatically saved after modifications
• Smart Discovery: Automatically finds and loads libbers from disk
• Cursor Tracking: Maintains cursor position across focus changes with native undo/redo

🎭 Pose & Depth Processing

• Multiple Pose Formats: DWPose, OpenPose, DensePose, and face detection
• NLF 3D Pose: Neural Lifting Framework for advanced 3D pose estimation (optional, requires ComfyUI-SCAIL-Pose)
• Depth Estimation: Depth Anything v2, MiDaS, Zoe, and more
• Mask Generation: Character segmentation with background control

🖼️ Image Processing

• TailEnhancePro: Advanced frame enhancement with deflicker, color matching, and sharpening
• Aspect Ratio: Qwen-specific aspect ratio calculation and layout detection
• SAM Preprocessing: Prepare images for Segment Anything Model

🖼️ Image Compositing

• SubjectLayerDefine: Define a single subject layer — image, optional mask, fractional padding, canvas offset, and background removal model
• SubjectCompositor: Composite 1–20 subject layers onto a canvas; output a merged composite, individual per-subject images, or both

🎛️ LoRA Scene Management

• LoraEntryDefine: Define one LoRA for a specific model target with per-LoRA audio guard (LTX2.3), enable/disable toggle, and separate model/clip strengths
• LoraStackCollect: Collect 1–20 LoRA entries into a JSON string for scene persistence; optionally merges with existing stack JSON
• LoraStackApply: Apply the right LoRAs at inference time by model target — patching MODEL+CLIP directly (LTX2.3, Flux, Qwen, Z-Image), building a LORA_STACK (Wan2.2-Native), or building a WANVIDLORA (Wan2.2-Wrapper)

Dataset Captioning

• Dataset Captioner: Run a VLM over a directory, write one .txt per image
• Dataset Caption Editor: Batch edit captions: prepend trigger word, find/replace
• Dataset Caption Viewer: Interactive table — view, edit and re-caption images in-graph
• Dataset Export Summary: Dataset health check: counts, word stats, missing captions, CSV export
• Caption Model Unloader: Release captioner from VRAM before running generation

Quickstart

1. Install ComfyUI
2. Install ComfyUI-Manager
3. Install required dependencies (see below)
4. Look up "fb-tools" or "comfyui-fbTools" in ComfyUI-Manager, or manually clone:

   cd ComfyUI/custom_nodes
   git clone https://github.com/frost-byte/comfyui-fbTools.git

5. Restart ComfyUI

Dependencies

Required

• comfyui_controlnet_aux - Required for pose detection, depth estimation, and canny edge detection
  • Provides: DWPose, OpenPose, DensePose, face detection
  • Provides: Depth Anything v2, MiDaS, Zoe depth estimation
  • Provides: Canny edge detection
  • Used by: All Scene nodes (SceneCreate, SceneUpdate, etc.)

Optional

• ComfyUI-SCAIL-Pose - Required for NLF (Neural Lifting Framework) 3D pose generation

  • Provides: Advanced 3D pose estimation and rendering with torch/taichi backends
  • Used by: SceneUpdate node (update_nlf_pose parameter)
  • Without this: Basic DWPose functionality still works, NLF features gracefully disabled
  • Optional dependency: taichi for faster GPU-accelerated rendering

• ComfyUI-WanVideoWrapper - Required only for LoRA functionality in Scene nodes

  • Provides: WANVIDLORA type for high/low quality LoRA configurations
  • Used by: SceneWanVideoLoraMultiSave node

Optional LoRA Apply Note

The LoraStackApply node requires comfy.sd.load_lora_for_models for direct-apply targets (LTX2.3, Flux2/Klein, Qwen, Z-Image) — available in the standard ComfyUI install. For Wan2.2-Native targets, connect the lora_stack output to easy-use's loraStack node. For Wan2.2-Wrapper targets, connect the wanvid_lora output to ComfyUI-WanVideoWrapper.

Optional Compositing Backend

Install only if you use the Subject Compositor / SubjectLayerDefine nodes and want automatic background removal:

# BiRefNet via rembg (recommended — GPU-accelerated)
pip install "rembg[gpu]"

# CPU-only fallback
pip install rembg

Models (~100–400 MB each) are downloaded on first use and cached in ~/.u2net/ or the rembg cache directory. If you prefer to supply your own mask, leave remove_background off and connect any upstream MASK output to SubjectLayerDefine.

Optional Captioning Backends

Install one or more of these only if you use Dataset Captioning nodes:

# Qwen2.5-VL (recommended; image-focused, ~16GB VRAM in bf16)
pip install "transformers>=4.50.0" accelerate qwen-vl-utils

# Qwen2.5-Omni (heavier omni model, ~20GB VRAM)
pip install "transformers>=4.50.0" accelerate qwen-omni-utils

# Gemini Flash (cloud, no local VRAM needed)
pip install google-generativeai
export GEMINI_API_KEY=your_key_here

# Optional: 8-bit quantization (~50% VRAM reduction)
pip install bitsandbytes

Installation via ComfyUI-Manager:

1. Open ComfyUI-Manager
2. Search for "controlnet aux" and install
3. (Optional) Search for "SCAIL-Pose" or "ComfyUI-SCAIL-Pose" if using NLF pose features
4. (Optional) Search for "WanVideoWrapper" if using LoRA features
5. Restart ComfyUI

Node Categories

All nodes are organized under the 🧊 frost-byte category in ComfyUI.

Story Nodes (🧊 frost-byte/Story)

• StoryCreate: Create a new story with an initial scene
• StoryEdit: Add, remove, reorder, or modify scenes in a story
• StoryView: Preview and select scenes with automatic resource loading
• StorySave: Persist story configuration to JSON
• StoryLoad: Load story from JSON file
• StorySceneBatch: Create ordered list of scene descriptors for iteration
• StoryScenePick: Select and load a specific scene by index

Scene Nodes (🧊 frost-byte/Scene)

• SceneCreate: Create a new scene with all processing options
• SceneUpdate: Modify existing scene properties
• SceneView: View and preview scene data with images
• SceneSelect: Dynamic scene loading with widget updates
• SceneSave: Save scene data to disk
• SceneInput: Load scene from directory
• SceneOutput: Output scene images and data
• SceneWanVideoLoraMultiSave: Save video LoRA configurations

Libber Nodes (🧊 frost-byte/Libber)

• LibberManager: Interactive table editor for creating and managing templates
  • Inline editing with action buttons (Add, Update, Remove)
  • Sticky controls (Load, Save, Create)
  • Auto-save after modifications
  • Smart auto-loading from memory or disk
• LibberApply: Apply template substitutions to text
  • Click-to-insert functionality with delimiter wrapping
  • Always-visible Refresh button
  • Dynamic table display with cursor tracking
  • Native undo/redo support

Compositing Nodes (🧊 frost-byte/compositing)

• SubjectLayerDefine: Define one subject layer per image. Specify fractional padding, canvas offset, and optional background removal. Outputs a SUBJECT_LAYER token consumed by SubjectCompositor.
• SubjectCompositor: Composite 1–20 SUBJECT_LAYER inputs onto a canvas. Outputs a merged composite image, an individual_images batch (one per layer), a layer_count int, and the snapped canvas dimensions.

LoRA Scene Nodes (🧊 frost-byte/lora)

• LoraEntryDefine: Define one LoRA for a specific model target. Supports enabled toggle, audio weight guard (LTX2.3), and separate model/clip strengths.
• LoraStackCollect: Collect up to 20 LORA_ENTRY inputs into a JSON string for scene persistence. Autogrow inputs. Optionally merges with existing JSON.
• LoraStackApply: Apply a persisted LoRA stack at inference time. Filters by model_target; routes to direct model patching, LORA_STACK (easy-use), or WANVIDLORA (WanVideoWrapper) output as appropriate.

Image Processing Nodes

• TailEnhancePro: Frame enhancement with deflicker, color matching, and sharpening
• TailSplit: Split image batches into main and tail sections
• OpaqueAlpha: Create opaque alpha masks for images
• SAMPreprocessNHWC: Prepare images for SAM predictor
• QwenAspectRatio: Calculate aspect ratios for Qwen models

Utility Nodes

• SubdirLister: List subdirectories with full paths
• NodeInputSelect: Select and output node input metadata

Dataset Captioning Nodes

• Dataset Captioner: Run caption generation over a folder of images and write one .txt per image
• Dataset Caption Editor: Batch edit caption files with prepend/append/find/replace operations
• Dataset Caption Viewer: Review images and captions in a table UI with per-image re-caption/clear actions
• Dataset Export Summary: Report dataset health and optionally export dataset_summary.csv
• Caption Model Unloader: Explicitly unload cached caption models from VRAM

Documentation

📖 Core Documentation

Node Systems

• Libber Nodes: Template system for reusable text snippets
• Story Nodes: Multi-scene story building system
• Scene Nodes: Scene management with poses, depth, and masks
• Dataset Caption Nodes: Dataset captioning workflow, node parameters, API routes, and troubleshooting
• Scene Prompt System: Scene prompt architecture and usage
• Story Video: Video generation from stories
• Subject Compositor (inline below): Multi-subject image compositing with SubjectLayerDefine + SubjectCompositor
• LoRA Scene Nodes (inline below): Per-target LoRA persistence and apply with LoraEntryDefine + LoraStackCollect + LoraStackApply

Mask System (NEW!)

• Mask System Guide: Generic mask system with arbitrary mask names
  • Custom mask definitions (not limited to "girl", "male", "combined")
  • Mask types: transparent and color-keyed
  • Background variant support
  • Migration guide from legacy system

Prompt Management

• Scene Prompt Usage: How to use scene prompts
• Scene Prompt Manager Tabs: UI tabs reference

UI Documentation

• Video Prompt UI Layout: Video prompt interface design
• Video Prompt UX Implementation: Video prompt user experience

🔧 Development & Debugging

• Debugging Guide: Runtime debug flag system and troubleshooting
• Development Notes: Developer notes and implementation details
• Implementation Steps: Feature implementation history

🧪 Testing Documentation

All testing documentation is in docs/testing/:

• Testing Strategy: Overall testing approach
• Testing Guide: How to run and write tests
• Test Results: Test coverage and results
• Test Summary: Testing overview
• Test Coverage Summary: Coverage metrics
• Story Edit Testing: Story editor testing procedures
• Scene Tabs Testing: Scene UI testing procedures

💻 Frontend Architecture

• JavaScript Overview: Frontend modular architecture
• Integration Guide: How to use API clients
• Modular Architecture: Architecture decisions
• Quick Reference: API quick reference

Key Features Explained

Libber Template System

The Libber system provides a powerful template/substitution mechanism for reusable text snippets:

Example:

# Define templates
libs = {
    "chunky": "incredibly thick, and %yummy%",
    "yummy": "delicious",
    "character": "A %chunky% warrior"
}

# Apply substitutions
"Look at this %character%!"
# Result: "Look at this A incredibly thick, and delicious warrior!"

Features:

• Recursive substitution with depth limiting
• Custom delimiters (default: %)
• Interactive table editor in LibberManager
• Click-to-insert in LibberApply
• File-based persistence

Story Building Workflow

1. Create Story: Use StoryCreate to initialize a story with first scene
2. Add Scenes: Use StoryEdit to add more scenes with configurations
3. Preview: Use StoryView to preview and select scenes
4. Batch Process: Use StorySceneBatch + StoryScenePick for iterative generation
5. Save: Use StorySave to persist story configuration

Each scene can have:

• Custom mask type and background settings
• Specific prompt type (character, quality, custom)
• Depth map selection
• Pose image selection

PromptCollection V2

The new prompt system supports unlimited named prompts with metadata:

collection = PromptCollection()
collection.add_prompt(
    "lighting",
    "soft diffused lighting, golden hour",
    category="scene",
    description="Lighting setup",
    tags=["lighting", "atmosphere"]
)

Features:

• Automatic v1 → v2 migration with backup
• Metadata: categories, descriptions, tags
• Backward compatible with legacy fields
• REST API for JavaScript integration

Subject Compositor

Compose two or more subjects onto a single canvas for use with image-conditioning workflows (LTX-Video, Qwen Edit, ReferenceLatent, etc.).

[SubjectLayerDefine]  [SubjectLayerDefine]  ...up to 20
        |                    |
        └─────── layer_0 ────┘
                    ↓
           [SubjectCompositor]
                    ↓
     composite / individual_images / layer_count

SubjectLayerDefine inputs

Input	Type	Default	Notes
image	IMAGE	—	Input image
mask	MASK	optional	Pre-computed alpha mask. Overrides bg removal if connected.
pad_top/bottom/left/right	FLOAT	0.0	Padding as fraction of longer dimension. 0.2 = 20% padding.
offset_x	FLOAT	0.0	Horizontal offset. 0=center, 1.0=right edge, −1.0=left edge.
offset_y	FLOAT	0.0	Vertical offset. 0=center, 1.0=bottom, −1.0=top.
remove_background	BOOLEAN	True	Auto background removal via rembg/BiRefNet.
bg_model	COMBO	BiRefNet-general	Background removal model.

Output: SUBJECT_LAYER (custom type passed to SubjectCompositor)

SubjectCompositor inputs

Input	Type	Default	Notes
canvas_width	INT	1344	Target width. Snapped to divisible_by.
canvas_height	INT	768	Target height. Snapped to divisible_by.
canvas_color	STRING	#222222	Background color. Accepts hex, named colors, "transparent".
output_mode	COMBO	both	composite / individual / both
divisible_by	INT	32	Snap dimensions to this multiple. 32 for LTX/video models.
layer_0..N	SUBJECT_LAYER	—	Autogrow inputs. Connect SubjectLayerDefine outputs.

Outputs: composite (IMAGE), individual_images (IMAGE batch [N, H, W, 3]), layer_count (INT)

Padding semantics

Padding is specified as a fraction of the image's longer dimension:

pad = 0.2,  image = 800×600
longer = 800
pad_pixels = 0.2 × 800 = 160 px added on that side

This makes the subject appear smaller relative to the canvas and to other layers — the primary use case for selectively scaling subjects down.

Offset coordinate system

(−1, −1) ── (0, −1) ── (1, −1)
    |            |            |
(−1,  0) ── (0,  0) ── (1,  0)   ← center of canvas
    |            |            |
(−1,  1) ── (0,  1) ── (1,  1)

The subject's center is placed at the computed canvas position. Values beyond ±1.0 are allowed and will partially clip the subject at the edge.

Output mode guide

Mode	Use when…
composite	Feeding a single merged image to ReferenceLatent or TextEncoderQwenImageEditPlus
individual	Feeding separate per-subject images to multiple ReferenceLatent nodes or a multi-reference conditioning node
both	You want flexibility without re-running the compositor

Background removal models

Model	Best for
BiRefNet-general	General subjects, objects, scenery
BiRefNet-portrait	Human faces and portraits
BiRefNet-general-lite	Faster, slightly lower quality
u2net	General — good fallback
u2net_human_seg	Human silhouettes
isnet-general-use	High-detail foreground extraction

Using an external mask instead of auto removal

If your workflow already has a bg-removal node (e.g. RMBG, BiRefNet from ComfyUI-BRIA), connect its MASK output to the mask input of SubjectLayerDefine. The remove_background flag is ignored when a mask is connected.

---

LoRA Scene Nodes

Persist and apply LoRA settings per model target. Replaces the abandoned LTX2MasterLoaderLD and consolidates LoRA management across LTX2.3, Wan2.2 (Native and Wrapper), Flux2/Klein, Qwen, and Z-Image.

# Scene definition (save once)
LoraEntryDefine (LTX2.3, my_character.safetensors, audio_enabled=True)
      ↓ lora_entry
LoraEntryDefine (Wan2.2-Native, my_character_wan.safetensors)
      ↓ lora_entry
LoraStackCollect
      ↓ stack_json  →  [Scene Node / String storage]
      ↓ lora_stack_data

# Inference (LTX2.3 pipeline)
[Scene Node] → stack_json
                    ↓
LoraStackApply (model_target=LTX2.3)
  model ← [your model]
  clip  ← [your clip]
      ↓ model  →  [LTX2.3 sampler]
      ↓ clip   →  [text encoder]

LoraEntryDefine inputs

Input	Default	Notes
lora	None	File picker from loras folder
model_target	LTX2.3	Which pipeline this LoRA applies to
strength_model	1.0	UNet/transformer weight strength
strength_clip	1.0	Text encoder strength (ignored where N/A)
enabled	True	Toggle off without removing from stack
audio_enabled	True	LTX2.3 only: include audio weights

Output: LORA_ENTRY (custom type)

LoraStackCollect inputs

Input	Notes
entry_0..N	Autogrow LORA_ENTRY inputs (up to 20)
existing_json	Optional — merge with existing scene JSON

Outputs: lora_stack_data (LORA_STACK_DATA), stack_json (STRING), entry_count (INT)

LoraStackApply inputs

Input	Notes
model_target	Must match the target you set in LoraEntryDefine
lora_stack_data	Connect from LoraStackCollect (preferred)
stack_json	OR a JSON STRING from a scene node
model	MODEL to patch (optional)
clip	CLIP to patch (optional)
prev_lora_stack	Wan2.2-Native: chain from existing LORA_STACK
prev_wanvid_lora	Wan2.2-Wrapper: chain from existing WANVIDLORA
low_mem_load	Wan2.2-Wrapper infrastructure setting
merge_loras	Wan2.2-Wrapper infrastructure setting

Outputs: model (MODEL), clip (CLIP), lora_stack (LORA_STACK), wanvid_lora (WANVIDLORA), applied_count (INT)

Output behaviour by target

Target	model	clip	lora_stack	wanvid_lora
LTX2.3	✓ patched	✓ patched	—	—
Wan2.2-Native	passthrough	passthrough	✓ built	—
Wan2.2-Wrapper	passthrough	passthrough	—	✓ built
Flux2/Klein	✓ patched	✓ patched	—	—
Qwen	✓ patched	✓ patched	—	—
Z-Image	✓ patched	✓ patched	—	—

LTX2.3 audio guard

The audio_enabled flag controls whether audio-related weight keys are included. When audio_enabled=False, keys containing these strings are stripped: audio, vocoder, speech, audio_stream, cross_modal, video_to_audio, av_ca.

This replicates the behaviour of the abandoned LTX2MasterLoaderLD node, now per-LoRA with V3 API.

---

Dataset Captioning Workflow

Use this flow when preparing LoRA training captions:

[Dataset Captioner]
  |
  v
[Dataset Caption Editor]   <- optional post-processing (trigger word, find/replace)
  |
  v
[Dataset Caption Viewer]   <- review/edit/re-caption individual images
  |
  v
[Dataset Export Summary]   <- verify coverage and caption statistics

Then feed your dataset directory into your training configuration.

Captioner Inputs

Dataset Captioner supports:

• input_directory, output_directory, recursive
• captioner_type: qwen_vl (recommended), qwen_omni, or gemini_flash
• instruction, trigger_word, clean_caption
• device: auto, cuda, or cpu
• use_8bit (requires bitsandbytes)
• override_existing, unload_after, gemini_api_key

Outputs: dataset_path, caption_count, failed_count

Dataset Caption Editor runs in dry-run mode by default (dry_run=true) and only writes changes when disabled.

Dataset Caption Viewer provides thumbnail rows, caption editing, per-image re-caption, and clear-caption actions.

The current viewer table viewport is intentionally fixed-height for layout stability; the table scrolls internally.

Dataset Export Summary reports total/captioned/missing counts and caption length stats; set export_csv=true to write dataset_summary.csv.

Batch Caption Edits via Fish Script

Use scripts/dataset_caption_edit.fish for repeatable multi-pass find/replace edits against the /fbtools/dataset_caption/edit API.

# Dry-run (default)
fish scripts/dataset_caption_edit.fish --dataset rara \
  --pass 'old phrase=>new phrase' \
  --pass 'another old=>another new'

# Apply changes
fish scripts/dataset_caption_edit.fish --dataset rara --apply \
  --pass 'old phrase=>new phrase' \
  --pass 'another old=>another new'

Notes:

• Pass pairs are formatted as find=>replace.
• Script payload uses find_text and replace_text fields expected by the API.
• Use --output <dir> when captions are stored in a separate output directory.

VRAM Guidance

Model	Precision	Approx VRAM
Qwen2.5-VL-7B	bf16	~16 GB
Qwen2.5-VL-7B	8-bit	~8 GB
Qwen2.5-Omni-7B	bf16	~20 GB
Qwen2.5-Omni-7B	8-bit	~11 GB
Gemini Flash	cloud	0 GB

LoRA Captioning Tips

• Set trigger_word in Dataset Captioner instead of relying on prompt wording for consistency.
• Review captions in Dataset Caption Viewer to correct hallucinations before training.
• Aim for moderate caption length (roughly 60-150 words) and use Dataset Export Summary to validate.

Development

Setup

To install development dependencies and pre-commit hooks:

cd comfyui-fbTools
pip install -e .[dev]
pre-commit install

The -e flag installs in "editable" mode, so changes are immediately reflected when ComfyUI restarts.

Project Structure

comfyui-fbTools/
├── extension.py              # Main Python extension with all nodes
├── prompt_models.py          # Data models (PromptMetadata, PromptCollection)
├── utils/                    # Python utilities
│   ├── io.py                # File I/O operations
│   ├── util.py              # General utilities
│   ├── pose.py              # Pose detection utilities
│   └── images.py            # Image processing utilities
├── js/                       # JavaScript frontend code
│   ├── fb_tools.js          # Main extension registration
│   ├── api/                 # REST API clients
│   │   ├── libber.js        # Libber API
│   │   ├── prompt_collection.js  # PromptCollection API
│   │   ├── scene.js         # Scene API
│   │   └── story.js         # Story API
│   ├── nodes/               # Node-specific handlers
│   │   ├── libber.js        # LibberManager & LibberApply
│   │   ├── scene.js         # SceneSelect handler
│   │   └── story.js         # StoryEdit & StoryView handlers
│   └── utils/               # Shared JavaScript utilities
├── tests/                    # Python unit tests
│   ├── test_prompt_collection.py
│   └── test_libber.py
└── js-tests/                # JavaScript unit tests
    ├── prompt_collection_api.test.js
    └── libber_api.test.js

Testing

Python Tests

# Run all tests
python -m pytest tests/ -v

# Run specific test file
python -m pytest tests/test_libber.py -v

# With coverage report
python -m pytest tests/ --cov=. --cov-report=html

Test Coverage:

• ✅ 70+ tests across PromptCollection and Libber systems
• ✅ Unit tests for data models
• ✅ Integration tests for workflows
• ✅ Edge case and boundary testing
• ✅ File I/O operations

See Testing Guide for detailed instructions.

JavaScript Tests

cd js/
npm install  # First time only
npm test

# Watch mode
npm run test:watch

# Coverage report
npm run test:coverage

Test Coverage:

• ✅ 30+ tests for API clients
• ✅ Mock utilities for testing without ComfyUI
• ✅ Integration tests for complete workflows
• ✅ Error handling scenarios

See TEST_RESULTS.md for detailed test coverage.

Code Quality

The project uses:

• ruff: Python linting and formatting
• pre-commit: Automatic hooks for code quality
• pytest: Python testing framework
• Jest: JavaScript testing framework

Architecture

Backend (Python)

• Pydantic Models: Type-safe data structures
• REST API: aiohttp endpoints for frontend integration
• State Management: Server-side session management with TTL
• File I/O: JSON-based persistence

Frontend (JavaScript)

• Modular Structure: Separate files for each API/node type
• API Clients: Centralized REST client classes
• Error Handling: Automatic toast notifications and logging
• Testability: Mock-friendly design with dependency injection

See Frontend Documentation for architecture details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Run tests and linting
5. Submit a pull request

Publishing to Registry

If you wish to share this extension:

1. Create account on https://registry.comfy.org
2. Add publisher ID to pyproject.toml
3. Create API key for publishing
4. Add REGISTRY_ACCESS_TOKEN to GitHub Secrets
5. Push to GitHub - action will auto-publish

See ComfyUI Registry docs for details.

License

See LICENSE file.

Support

• Issues: GitHub Issues
• Discord: ComfyUI Discord
• Documentation: See README files in repository

Changelog

See CHANGELOG.md for version history and release notes.

Recent Updates

2026-04-09: LoRA Scene Nodes

• ✅ LoraEntryDefine — define one LoRA for a specific model target with per-LoRA audio guard, enable toggle, and separate model/clip strengths
• ✅ LoraStackCollect — collect up to 20 LORA_ENTRY inputs into a persisted JSON stack with optional merge from existing JSON
• ✅ LoraStackApply — apply persisted stack at inference time; routes to direct patching (LTX2.3, Flux, Qwen, Z-Image), LORA_STACK (Wan2.2-Native), or WANVIDLORA (Wan2.2-Wrapper)
• ✅ Custom LORA_ENTRY and LORA_STACK_DATA types for type-safe wiring
• ✅ Replaces abandoned LTX2MasterLoaderLD with V3 API per-LoRA granularity

2026-04-08: Subject Compositor Nodes

• ✅ SubjectLayerDefine — define a subject layer with fractional padding, canvas offset, and optional background removal
• ✅ SubjectCompositor — composite 1–20 layers onto a canvas (composite, individual, or both output modes)
• ✅ Custom SUBJECT_LAYER type wiring between the two nodes
• ✅ Autogrow inputs (up to 20 layers) on SubjectCompositor
• ✅ Canvas dimension snapping (divisible_by, default 32 for LTX/video models)
• ✅ rembg/BiRefNet integration with model selector; external mask support

2025-01-18: Generic Mask System

• ✅ Arbitrary mask names (not limited to girl/male/combined)
• ✅ Mask types: TRANSPARENT and COLOR with RGB support
• ✅ Dynamic mask loading via masks.json
• ✅ Migration script for legacy scenes
• ✅ Full backward compatibility
• ✅ Updated all Scene and Story nodes
• ✅ Comprehensive documentation and tests

2024-12-19: Libber System Overhaul

• ✅ Interactive table editor in LibberManager
• ✅ Click-to-insert in LibberApply
• ✅ Always-visible Refresh button
• ✅ Auto-save after modifications
• ✅ Smart libber discovery and loading
• ✅ Modular JavaScript architecture
• ✅ Comprehensive test coverage

Previous Updates:

• PromptCollection V2 with metadata support
• Story building system with scene management
• Automatic v1→v2 migration with backward compatibility
• REST API for frontend integration
• Modular code organization