Compare commits
@@ -0,0 +1,28 @@
|
||||
name: Publish to Comfy registry
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
- master
|
||||
paths:
|
||||
- "pyproject.toml"
|
||||
|
||||
permissions:
|
||||
issues: write
|
||||
|
||||
jobs:
|
||||
publish-node:
|
||||
name: Publish Custom Node to registry
|
||||
runs-on: ubuntu-latest
|
||||
if: ${{ github.repository_owner == 'shin131002' }}
|
||||
steps:
|
||||
- name: Check out code
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
submodules: true
|
||||
- name: Publish Custom Node
|
||||
uses: Comfy-Org/publish-node-action@v1
|
||||
with:
|
||||
## Add your own personal access token to your Github Repository secrets and reference it here.
|
||||
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
|
||||
+30
-2
@@ -4,13 +4,41 @@ __pycache__/
|
||||
*$py.class
|
||||
*.so
|
||||
.Python
|
||||
*.egg-info/
|
||||
dist/
|
||||
build/
|
||||
|
||||
# IDE
|
||||
# IDEs
|
||||
.vscode/
|
||||
.idea/
|
||||
*.swp
|
||||
*.swo
|
||||
*~
|
||||
|
||||
# OS
|
||||
.DS_Store
|
||||
Thumbs.db
|
||||
Thumbs.db
|
||||
desktop.ini
|
||||
|
||||
# Personal folders
|
||||
old/
|
||||
backup/
|
||||
test/
|
||||
tmp/
|
||||
temp/
|
||||
archive/
|
||||
_old/
|
||||
_backup/
|
||||
_test/
|
||||
|
||||
# Development files
|
||||
DEVELOPMENT_SUMMARY.md
|
||||
note_article.md
|
||||
release_notes_*.md
|
||||
*_backup.md
|
||||
*_fixed.toml
|
||||
__init___no_advanced.py
|
||||
|
||||
# User data
|
||||
*.log
|
||||
*.cache
|
||||
|
||||
+193
@@ -0,0 +1,193 @@
|
||||
# Changelog
|
||||
|
||||
All notable changes to this project will be documented in this file.
|
||||
|
||||
## [1.4.0] - 2026-09-24
|
||||
|
||||
### Changed
|
||||
|
||||
#### ⚠️ `positive_text` no longer contains LoRA syntax; new `lora_text` output
|
||||
- `positive_text` is now the positive prompt **without** `<lora:...>` tags — exactly what is encoded into the `positive` CONDITIONING output — so it can be fed straight into a text encoder
|
||||
- The previous content, tags included, now comes from a new **`lora_text`** output, added as the **last** slot
|
||||
- All other outputs keep their positions and types, so existing workflow connections stay valid (ComfyUI connects outputs by position)
|
||||
- Action for existing workflows:
|
||||
- `positive_text` fed a text encoder: nothing to do
|
||||
- `positive_text` was used to record the LoRA syntax (saved prompts, metadata): reconnect that link to `lora_text`
|
||||
- Applies to all three nodes (Random LoRA Loader, Filtered Random LoRA Loader, Filtered Random LoRA Loader (LBW))
|
||||
|
||||
#### ⚠️ Filtered nodes: cleaner encoded prompt
|
||||
- Filtered Random LoRA Loader / (LBW) removed `<lora:...>` tags before encoding but left the separators behind, so the positive conditioning was encoded from text like `masterpiece, , trig_a, , , trig_c`
|
||||
- Runs of commas and leading/trailing commas are now tidied before encoding, matching the 3-folder node. This applies to the negative prompt too (e.g. a trailing `, ` is no longer encoded)
|
||||
- Because the encoded text changes, **results with the same seed may differ from 1.3.0 and earlier**. Keep using an earlier version if identical results are required
|
||||
|
||||
### Fixed
|
||||
- Random LoRA Loader: a run of three or more commas left after removing tags from the additional prompt was only partly collapsed
|
||||
|
||||
---
|
||||
|
||||
## [1.3.0] - 2026-09-11
|
||||
|
||||
### Fixed
|
||||
|
||||
#### Symlinked folders were skipped during scanning
|
||||
- Folder scans now follow symbolic links, matching ComfyUI's own behaviour (`folder_paths.recursive_search` uses `followlinks=True`)
|
||||
- Previously, a subfolder that was a symlink was skipped entirely, so LoRAs behind a link never appeared as candidates — even though they showed up fine in ComfyUI's native LoRA dropdown
|
||||
- Affects all three nodes:
|
||||
- **Random LoRA Loader**: `glob.glob("**/*.safetensors")` replaced with `os.walk(followlinks=True)` (glob's `**` does not descend into symlinked directories)
|
||||
- **Filtered Random LoRA Loader** / **Filtered Random LoRA Loader (LBW)**: `os.walk()` now passes `followlinks=True`
|
||||
- Symlink loops are detected via a `realpath` set and skipped, so a cyclic link cannot hang the scan
|
||||
- A folder reachable through two different links is no longer scanned twice
|
||||
|
||||
#### Redundant error logging on non-safetensors LoRAs
|
||||
- `_load_embedded_metadata` now returns early for `.pt` / `.ckpt` files instead of calling `safe_open`, which can only read `.safetensors`
|
||||
- Previously every selected `.pt` / `.ckpt` produced a spurious "metadata read error" log line
|
||||
- Trigger words for these formats still come from `.metadata.json` / `.info` sidecar files as before
|
||||
|
||||
### Changed
|
||||
|
||||
#### Seed reproducibility — candidate list is now sorted
|
||||
- The candidate file list is sorted before random selection
|
||||
- Previously the order came straight from the filesystem, so **the same seed could produce different selections on different machines**. Sorting makes a seed reproducible across environments
|
||||
- ⚠️ Because the ordering changed, an existing workflow may select a different set of LoRAs after upgrading even with a fixed seed. If a result you were relying on changes, re-roll or re-pick the seed
|
||||
|
||||
#### Random LoRA Loader: extension support aligned with the Filtered nodes
|
||||
- Now scans `.safetensors`, `.pt`, and `.ckpt` (previously `.safetensors` only)
|
||||
- All three nodes now return an identical candidate list for the same folder
|
||||
- Extension matching is case-insensitive, so `.SAFETENSORS` is picked up on Linux as well (glob was case-sensitive there)
|
||||
|
||||
---
|
||||
|
||||
## [1.2.0] - 2026-01-13
|
||||
|
||||
### Added
|
||||
|
||||
#### New Node: Filtered Random LoRA Loader (LBW)
|
||||
- **LoRA Block Weight (LBW) support** for precise effect control
|
||||
- **Automatic SD1.5/SDXL detection** - no manual model type selection needed
|
||||
- **4 preset modes**: Style Focused, Character Focused, Structure/Composition Only, Balanced/Soft
|
||||
- **Preset: Random mode** - randomly selects one of the 4 presets
|
||||
- **Direct Input mode** - custom weight specification with auto-adjustment
|
||||
- **Automatic weight adjustment** - handles mismatched element counts
|
||||
- Too few elements: pads with 1.0 at the end
|
||||
- Too many elements: truncates from the end
|
||||
- U-Net block structure support:
|
||||
- SDXL: 20 elements (BASE + IN:9 + MID:1 + OUT:9)
|
||||
- SD1.5: 17 elements (BASE + IN:6 + MID:1 + OUT:9)
|
||||
- LBW syntax output in `positive_text` for Wildcard Encode compatibility
|
||||
- Includes all features from Filtered Random LoRA Loader
|
||||
|
||||
#### Video Preview Support (All Nodes)
|
||||
- Video file preview support (.mp4, .webm, .avi, .mov)
|
||||
- Extracts first frame as static image
|
||||
- Requires optional `opencv-python` dependency
|
||||
- Graceful fallback to black screen with warning if opencv-python not installed
|
||||
- Priority order: static images → animated images → video files
|
||||
|
||||
### Changed
|
||||
|
||||
#### Model Compatibility
|
||||
- ⚠️ **Explicitly SD1.5/SDXL only** - other architectures (Flux, SD3, etc.) not supported
|
||||
- LBW feature specifically designed for SD1.5/SDXL U-Net structure
|
||||
|
||||
#### Dependencies
|
||||
- Added optional dependency: `opencv-python` (for video preview only)
|
||||
- Core functionality works without opencv-python
|
||||
|
||||
### Technical Details
|
||||
|
||||
#### LBW Implementation
|
||||
- Direct tensor weight manipulation for precise block control
|
||||
- Automatic LoRA type detection via output_blocks analysis
|
||||
- Compatible with serial node chaining (different LBW per node)
|
||||
- Zero weight (all 0) = complete LoRA disable
|
||||
|
||||
#### Preview Image Processing
|
||||
- Pillow-only support: .png, .jpg, .jpeg, .gif, .webp (always works)
|
||||
- opencv-python support: .mp4, .webm, .avi, .mov (optional)
|
||||
- First-frame extraction for animated/video formats
|
||||
- Consistent 1240px preprocessing
|
||||
|
||||
### Performance
|
||||
- No performance impact when LBW not used (Normal mode)
|
||||
- Metadata caching unchanged from v1.1.0
|
||||
|
||||
---
|
||||
|
||||
## [1.1.0] - 2026-01-04
|
||||
|
||||
### Added
|
||||
|
||||
#### New Node: Filtered Random LoRA Loader
|
||||
- Single folder with keyword filtering capabilities
|
||||
- Space-separated keyword search with phrase support (`"..."`)
|
||||
- AND/OR filtering modes
|
||||
- Metadata search with caching (instant 2nd+ time)
|
||||
- Designed for chaining multiple instances
|
||||
- Supports all features from Random LoRA Loader
|
||||
|
||||
#### Preview Image Output (Both Nodes)
|
||||
- New `preview` output (IMAGE type)
|
||||
- Long edge resized to 1240px (aspect ratio preserved)
|
||||
- Center-padded to 1240x1240 for batch compatibility
|
||||
- Partial filename matching (case-insensitive)
|
||||
- Example: `anime_v1.safetensors` matches `anime_v1.png`, `anime_v1_preview.jpg`, etc.
|
||||
|
||||
#### Duplicate Filename Handling (Both Nodes)
|
||||
- New `unique_by_filename` parameter (default: `True`)
|
||||
- Prevents selecting same LoRA from different subfolders
|
||||
- Example: `/style/lora.safetensors` and `/backup/lora.safetensors` → only one selected
|
||||
- Logs duplicate detections with paths
|
||||
|
||||
### Changed
|
||||
|
||||
#### Both Nodes
|
||||
- **Breaking**: Split `additional_prompt` into `additional_prompt_positive` and `additional_prompt_negative`
|
||||
- Unified strength precision to 1 decimal place (was 2 for Filtered, 1 for Random)
|
||||
- Improved LoRA syntax removal from CONDITIONING generation
|
||||
|
||||
#### Filtered Random LoRA Loader
|
||||
- Better handling when `num_loras` exceeds available files (fills with duplicates)
|
||||
- Removed `control_after_generate` (use ComfyUI's default seed control)
|
||||
|
||||
### Fixed
|
||||
|
||||
#### Both Nodes
|
||||
- Fixed negative_text output in `json_sample_prompt` mode
|
||||
- Fixed LoRA syntax (`<lora:...>`) appearing in metadata trigger words
|
||||
- Fixed parameter order consistency
|
||||
|
||||
#### Random LoRA Loader
|
||||
- Fixed duplicate parameter in INPUT_TYPES
|
||||
|
||||
### Performance
|
||||
|
||||
#### Metadata Search Caching
|
||||
- Initial load time (SSD):
|
||||
- 1,000 files: ~2 seconds
|
||||
- 5,000 files: ~10 seconds
|
||||
- 10,000 files: ~20 seconds
|
||||
- 20,000 files: ~40 seconds
|
||||
- 50,000 files: ~100 seconds
|
||||
- 2nd+ time: Instant (<100ms)
|
||||
- Memory usage: ~150MB per 10,000 files
|
||||
- Progress display for large collections
|
||||
|
||||
---
|
||||
|
||||
## [1.0.0] - 2025-12-30
|
||||
|
||||
### Initial Release
|
||||
|
||||
#### Random LoRA Loader
|
||||
- 3-group folder selection
|
||||
- Multi-source metadata reading (`.metadata.json`, `.info`, embedded)
|
||||
- Strength randomization with range specification
|
||||
- Multiple trigger word sources
|
||||
- Sample prompt support with LoRA syntax removal
|
||||
- Wildcard Encode (Inspire) compatibility
|
||||
|
||||
#### Core Features
|
||||
- No additional dependencies (uses Python stdlib + ComfyUI)
|
||||
- Automatic trigger word extraction
|
||||
- CONDITIONING output with cleaned prompts
|
||||
- Seed control integration
|
||||
+609
@@ -0,0 +1,609 @@
|
||||
# Filtered Random LoRA Loader (LBW)
|
||||
|
||||
**LoRA Block Weight (LBW) support with automatic SD1.5/SDXL detection**
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Filtered Random LoRA Loader (LBW) is the Filtered Random LoRA Loader with LoRA Block Weight (LBW) support added. It lets you control precisely which parts of the U-Net a LoRA is applied to.
|
||||
|
||||
---
|
||||
|
||||
## Features
|
||||
|
||||
### **Core Features (shared with Filtered Random LoRA Loader)**
|
||||
- ✅ Random LoRA selection from a single folder
|
||||
- ✅ Keyword filtering (AND/OR modes)
|
||||
- ✅ Metadata search (filename or embedded metadata)
|
||||
- ✅ Preview images (static/animated/video support)
|
||||
- ✅ Trigger word extraction
|
||||
- ✅ Duplicate removal by filename
|
||||
- ✅ Direct serial connection support
|
||||
|
||||
### **Advanced Features (LBW)**
|
||||
- ✅ **LoRA Block Weight (LBW) support**: set a separate strength for each U-Net block to control style, character, composition and so on independently
|
||||
- ✅ **Automatic SD1.5/SDXL detection**
|
||||
- ✅ **4 preset modes + custom input**
|
||||
- ✅ **Automatic weight adjustment**
|
||||
|
||||
---
|
||||
|
||||
## LoRA Block Weight (LBW)
|
||||
|
||||
### **What is LBW?**
|
||||
|
||||
LoRA Block Weight lets you control which parts of the U-Net the LoRA affects. The Stable Diffusion U-Net is made up of three parts:
|
||||
|
||||
```
|
||||
INPUT blocks → Structure, composition, layout
|
||||
↓
|
||||
MIDDLE block → Overall features
|
||||
↓
|
||||
OUTPUT blocks → Style, details, fine-tuning
|
||||
```
|
||||
|
||||
By adjusting the weight of each block, you can precisely control the LoRA's effect.
|
||||
|
||||
---
|
||||
|
||||
## Weight Modes
|
||||
|
||||
### **1. Normal (All 1.0)** - Default
|
||||
All blocks weighted at 1.0 (standard LoRA application, no LBW).
|
||||
|
||||
---
|
||||
|
||||
### **2. Style Focused**
|
||||
Enables OUTPUT blocks only.
|
||||
|
||||
**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`
|
||||
|
||||
**Best for:**
|
||||
- Style LoRAs (anime style, watercolor, etc.)
|
||||
- Art style and effect LoRAs
|
||||
- Changing the visual aesthetic
|
||||
|
||||
**Effect:**
|
||||
- ✅ Strong style changes
|
||||
- ❌ Minimal structure/character changes (ideal when you want to keep the existing composition)
|
||||
|
||||
---
|
||||
|
||||
### **3. Character Focused**
|
||||
Enables INPUT (early) + MIDDLE + OUTPUT blocks.
|
||||
|
||||
**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`
|
||||
|
||||
**Best for:**
|
||||
- Character and person LoRAs
|
||||
- Concept LoRAs
|
||||
- Preserving faces/features
|
||||
|
||||
**Effect:**
|
||||
- ✅ Strong character features
|
||||
- ✅ Balanced overall effect
|
||||
|
||||
---
|
||||
|
||||
### **4. Structure/Composition Only**
|
||||
Enables INPUT + MIDDLE blocks 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`
|
||||
|
||||
**Best for:**
|
||||
- Pose LoRAs
|
||||
- Composition and layout LoRAs
|
||||
|
||||
**Effect:**
|
||||
- ✅ Strong structure/pose changes
|
||||
- ❌ Minimal style changes
|
||||
|
||||
---
|
||||
|
||||
### **5. Balanced / Soft**
|
||||
Soft application across selected blocks. The later OUTPUT blocks are turned down to avoid over-application.
|
||||
|
||||
**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`
|
||||
|
||||
**Best for:**
|
||||
- Subtle effects and fine adjustment
|
||||
- Multi-LoRA workflows
|
||||
- General-purpose LoRAs
|
||||
|
||||
**Effect:**
|
||||
- ✅ Balanced, gentle application with a natural finish
|
||||
- ✅ Good for stacking multiple LoRAs
|
||||
|
||||
---
|
||||
|
||||
### **6. Preset: Random**
|
||||
Randomly picks one of the 4 presets above on each run.
|
||||
|
||||
```
|
||||
weight_mode: "Preset: Random"
|
||||
→ One of the 4 presets is picked on each run
|
||||
```
|
||||
|
||||
**Best for:**
|
||||
- Experimentation
|
||||
- Variation generation
|
||||
- Random effects
|
||||
|
||||
---
|
||||
|
||||
### **7. Direct Input**
|
||||
Custom weight specification.
|
||||
|
||||
**Format:**
|
||||
- SDXL: 20 comma-separated values
|
||||
- SD1.5: 17 comma-separated values
|
||||
|
||||
**Example (SDXL):**
|
||||
```
|
||||
weight_mode: "Direct Input"
|
||||
lbw_input: "1,0.5,0.5,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.7,0.7,0.7,1,1,1,1,1,1,1"
|
||||
```
|
||||
|
||||
**Auto-adjustment:**
|
||||
- If too few elements: pads with 1.0 at the end
|
||||
- If too many elements: truncates from the end
|
||||
|
||||
---
|
||||
|
||||
## Block Structure
|
||||
|
||||
### **SDXL (20 elements)**
|
||||
```
|
||||
BASE, IN00-IN08 (9), MID (1), OUT00-OUT08 (9)
|
||||
```
|
||||
|
||||
| Index | Block | Role |
|
||||
|-------|-------|------|
|
||||
| 0 | BASE | Overall base |
|
||||
| 1-9 | INPUT (IN00-08) | Structure/composition |
|
||||
| 10 | MIDDLE (M00) | Intermediate features |
|
||||
| 11-19 | OUTPUT (OUT00-08) | Style/details |
|
||||
|
||||
**Weight example:**
|
||||
```
|
||||
1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
|
||||
BASE,IN00-08 (OFF),M00,OUT00-08 (ON)
|
||||
→ LoRA applied to OUTPUT blocks only (style focused)
|
||||
```
|
||||
|
||||
### **SD1.5 (17 elements)**
|
||||
```
|
||||
BASE, IN01,IN02,IN04,IN05,IN07,IN08 (6), MID (1), OUT03-OUT11 (9)
|
||||
```
|
||||
|
||||
| Index | Block | Role |
|
||||
|-------|-------|------|
|
||||
| 0 | BASE | Overall base |
|
||||
| 1-6 | INPUT (IN01,02,04,05,07,08) | Structure/composition |
|
||||
| 7 | MIDDLE (M00) | Intermediate features |
|
||||
| 8-16 | OUTPUT (OUT03-11) | Style/details |
|
||||
|
||||
**Weight example:**
|
||||
```
|
||||
1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
|
||||
BASE,IN (OFF),M00,OUT (ON)
|
||||
→ LoRA applied to OUTPUT blocks only (style focused)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Automatic Detection
|
||||
|
||||
### **LoRA Type Detection**
|
||||
|
||||
The node automatically detects whether a LoRA is SD1.5 or SDXL by analyzing the model structure:
|
||||
|
||||
- **SD1.5**: `output_blocks_0` to `output_blocks_11` (12 blocks)
|
||||
- **SDXL**: `output_blocks_0` to `output_blocks_8` (9 blocks)
|
||||
|
||||
```
|
||||
SDXL detected → 20-element weights are used
|
||||
SD1.5 detected → 17-element weights are used
|
||||
```
|
||||
|
||||
**You don't need to specify the model type manually!** ✅
|
||||
|
||||
---
|
||||
|
||||
### **Weight Adjustment**
|
||||
|
||||
If the number of weights doesn't match the detected LoRA type, it is adjusted automatically:
|
||||
|
||||
**Example 1: Too few weights**
|
||||
```
|
||||
Input: 1,0,0 (3 elements)
|
||||
Detected: SDXL (requires 20)
|
||||
→ Auto-adjusted: 1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
```
|
||||
|
||||
**Example 2: Too many weights**
|
||||
```
|
||||
Input: 1,0,0,...,0,0,0 (25 elements)
|
||||
Detected: SDXL (requires 20)
|
||||
→ Auto-adjusted: 1,0,0,...,0,0,0 (first 20 only)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
### **Basic Workflow**
|
||||
|
||||
```
|
||||
[Filtered Random LoRA Loader (LBW)]
|
||||
MODEL → [KSampler]
|
||||
CLIP → [CLIP Text Encode]
|
||||
lora_text → [Show Text] (optional, for debugging)
|
||||
```
|
||||
|
||||
**Steps:**
|
||||
|
||||
1. **Place the node**
|
||||
```
|
||||
[Filtered Random LoRA Loader (LBW)]
|
||||
```
|
||||
|
||||
2. **Set the folder and keywords**
|
||||
```
|
||||
lora_folder_path: "path/to/loras"
|
||||
keyword_filter: "anime style"
|
||||
num_loras: 1
|
||||
```
|
||||
|
||||
3. **Choose the LBW mode**
|
||||
```
|
||||
weight_mode: "Style Focused"
|
||||
```
|
||||
|
||||
4. **Connect MODEL/CLIP**
|
||||
```
|
||||
MODEL → [KSampler]
|
||||
CLIP → [CLIP Text Encode]
|
||||
```
|
||||
|
||||
### **Parameters**
|
||||
|
||||
#### **LoRA Settings**
|
||||
- `lora_folder_path`: Path to LoRA folder
|
||||
- `num_loras`: Number of LoRAs to apply (0-20)
|
||||
- `model_strength`: LoRA strength for MODEL (e.g., "1.0" or "0.6-0.9")
|
||||
- `clip_strength`: LoRA strength for CLIP (e.g., "1.0" or "0.6-0.9")
|
||||
|
||||
> Folder scanning behaviour (supported extensions, symbolic links, sorted candidate order) is shared with the other nodes — see [Folder Scanning](README.md#folder-scanning) in the main README.
|
||||
|
||||
#### **Keyword Filter**
|
||||
- `keyword_filter`: Space-separated keywords (e.g., `style anime` or `"anime style" red`)
|
||||
- `filter_mode`: AND / OR
|
||||
- `search_in_metadata`: Search in JSON/embedded metadata (slower)
|
||||
|
||||
#### **LBW Settings**
|
||||
- `weight_mode`: Normal / 4 Presets / Random / Direct Input
|
||||
- `lbw_input`: Custom weights (only for Direct Input mode)
|
||||
|
||||
#### **Other**
|
||||
- `trigger_word_source`: Trigger word extraction method
|
||||
- `seed`: Random seed
|
||||
|
||||
---
|
||||
|
||||
## Output Format
|
||||
|
||||
### **LBW Syntax**
|
||||
|
||||
When LBW is applied, the `lora_text` output (`positive_text` before v1.4.0) includes LBW syntax:
|
||||
|
||||
**Normal (All 1.0):**
|
||||
```
|
||||
<lora:style_anime:0.8:0.8>
|
||||
```
|
||||
|
||||
**With LBW (Style Focused, SDXL):**
|
||||
```
|
||||
<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>
|
||||
```
|
||||
|
||||
**Note:** The LBW is applied internally to the MODEL. The syntax in `lora_text` is for reference/re-use with other nodes.
|
||||
|
||||
---
|
||||
|
||||
## Examples
|
||||
|
||||
### **Example 1: Change the style only**
|
||||
|
||||
```
|
||||
[Load Checkpoint] → [Filtered Random LoRA Loader (LBW)]
|
||||
weight_mode: "Style Focused"
|
||||
keyword_filter: "anime"
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
**Result:** The composition stays the same; only the style becomes anime-like
|
||||
|
||||
---
|
||||
|
||||
### **Example 2: Staged application with multiple LoRAs**
|
||||
|
||||
```
|
||||
[Load Checkpoint]
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Structure/Composition Only)
|
||||
keyword_filter: "pose"
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Style Focused)
|
||||
keyword_filter: "watercolor"
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
**Result:** Pose adjustment → watercolor style applied
|
||||
|
||||
---
|
||||
|
||||
### **Example 3: Character + style**
|
||||
|
||||
```
|
||||
[Load Checkpoint]
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Character Focused)
|
||||
keyword_filter: "character_name"
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Style Focused)
|
||||
keyword_filter: "art_style"
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
**Result:** Character features preserved + style changed
|
||||
|
||||
---
|
||||
|
||||
## Tips
|
||||
|
||||
### **1. Preset Selection Guide**
|
||||
|
||||
| LoRA Type | Recommended Preset | Why |
|
||||
|-----------|-------------------|-----|
|
||||
| Style/Art | Style Focused | OUTPUT blocks control style |
|
||||
| Character | Character Focused | Balanced IN+MID+OUT preserves features |
|
||||
| Pose/Composition | Structure/Composition Only | INPUT blocks control layout |
|
||||
| General/fine adjustment | Balanced / Soft | Gentle, stackable effect |
|
||||
|
||||
---
|
||||
|
||||
### **2. Effect Visibility**
|
||||
|
||||
The difference between presets is **more visible** with:
|
||||
- ✅ Strong LoRAs (high model_strength)
|
||||
- ✅ Single LoRA application (num_loras=1)
|
||||
- ✅ Style or concept LoRAs
|
||||
|
||||
The difference is **less visible** with:
|
||||
- ⚠️ Weak LoRAs (low model_strength)
|
||||
- ⚠️ Multiple LoRAs applied simultaneously
|
||||
- ⚠️ LoRAs whose training effect is spread evenly across blocks
|
||||
|
||||
---
|
||||
|
||||
### **3. Zero Weight = No Effect**
|
||||
|
||||
Setting all weights to 0 completely disables the LoRA effect:
|
||||
```
|
||||
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
|
||||
```
|
||||
This is equivalent to not applying the LoRA at all.
|
||||
|
||||
**Weight guide:**
|
||||
- **0**: Completely off
|
||||
- **0.5**: Weaker
|
||||
- **1.0**: Standard
|
||||
- **1.5 or more**: Stronger (watch for over-application)
|
||||
|
||||
---
|
||||
|
||||
### **4. Serial Connection**
|
||||
|
||||
For applying different LBW settings to different LoRAs, connect nodes in series:
|
||||
```
|
||||
[Filtered Random LoRA Loader (LBW)] (Style Focused)
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Character Focused)
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
All LoRAs within a single node instance use the same LBW setting.
|
||||
|
||||
**Tips:**
|
||||
- **Structure → style** is the basic order
|
||||
- Strength can also be adjusted per node
|
||||
|
||||
---
|
||||
|
||||
### **5. Troubleshooting**
|
||||
|
||||
**If there is no effect:**
|
||||
- Check that the weights aren't all 0
|
||||
- Check that the LoRA is loaded correctly
|
||||
- Check the console for the LBW application message
|
||||
|
||||
**If the effect is too strong:**
|
||||
- Lower model_strength
|
||||
- Try the Balanced / Soft preset
|
||||
- Adjust the weights to 0.5–0.8
|
||||
|
||||
---
|
||||
|
||||
## Requirements
|
||||
|
||||
### **⚠️ Supported Models**
|
||||
**This node is 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 feature specifically targets SD1.5/SDXL U-Net architecture. Other model types will not work correctly.
|
||||
|
||||
---
|
||||
|
||||
### **Dependencies**
|
||||
- ComfyUI
|
||||
- Pillow (included with ComfyUI)
|
||||
- torch (included with ComfyUI)
|
||||
|
||||
### **⚠️ Optional: Video Preview**
|
||||
**For video preview support (.mp4, .webm, .avi, .mov), opencv-python is REQUIRED:**
|
||||
```bash
|
||||
pip install opencv-python
|
||||
```
|
||||
|
||||
**Without opencv-python:**
|
||||
- ✅ Static/animated images work
|
||||
- ❌ Video files show black screen
|
||||
|
||||
---
|
||||
|
||||
## Preview Images
|
||||
|
||||
Supports the following formats (in priority order):
|
||||
|
||||
1. **Static images** (.png, .jpg, .jpeg)
|
||||
2. **Animated images** (.gif, .webp) - first frame
|
||||
3. **Video files** (.mp4, .webm, .avi, .mov) - first frame (requires opencv-python)
|
||||
|
||||
The node automatically searches for preview files matching the LoRA filename.
|
||||
|
||||
**File matching:**
|
||||
- Files that start with the LoRA filename (case-insensitive)
|
||||
- Example: matches for `style_anime.safetensors`:
|
||||
- `style_anime.png` ✅
|
||||
- `style_anime_preview.jpg` ✅
|
||||
- `STYLE_ANIME.PNG` ✅
|
||||
|
||||
---
|
||||
|
||||
## Advanced: Custom Presets
|
||||
|
||||
To create your own presets or change the existing ones, you can edit the source file directly.
|
||||
|
||||
### **How to Edit**
|
||||
|
||||
**File location:**
|
||||
```
|
||||
ComfyUI/custom_nodes/RandomLoRALoader/filtered_random_lora_loader_lbw.py
|
||||
```
|
||||
|
||||
**Lines to edit:** 29–41 (`SDXL_PRESETS` for SDXL, `SD15_PRESETS` for SD1.5)
|
||||
|
||||
### **Example 1: Add your own preset**
|
||||
|
||||
```python
|
||||
SDXL_PRESETS = {
|
||||
"Style Focused": "1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1",
|
||||
"Character Focused": "1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1",
|
||||
"Structure/Composition Only": "1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0",
|
||||
"Balanced / Soft": "1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0",
|
||||
# ↓ Your own preset
|
||||
"My Custom Mix": "0.5,0.5,0.5,0.5,0,0,0,0,0,0,1,0.8,0.8,0.8,0.8,0.8,0.5,0.5,0.5,0.5"
|
||||
}
|
||||
```
|
||||
|
||||
### **Example 2: Adjust an existing preset**
|
||||
|
||||
```python
|
||||
SDXL_PRESETS = {
|
||||
# Make Style Focused stronger
|
||||
"Style Focused": "1,0,0,0,0,0,0,0,0,0,0,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2",
|
||||
|
||||
# Make Character Focused milder
|
||||
"Character Focused": "0.8,0.8,0.8,0.8,0,0,0,0,0,0,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8",
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
### **Example 3: Rename presets**
|
||||
|
||||
```python
|
||||
SDXL_PRESETS = {
|
||||
"Style Only": "1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1",
|
||||
"Character First": "1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1",
|
||||
"Composition Only": "1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0",
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
### **Important Notes**
|
||||
|
||||
⚠️ **Before editing:**
|
||||
- Make a backup of the original file
|
||||
- Watch the Python syntax (commas, quotes)
|
||||
|
||||
⚠️ **Number of weights:**
|
||||
- SDXL presets: exactly 20 elements
|
||||
- SD1.5 presets: exactly 17 elements
|
||||
- Mismatched counts are auto-adjusted, but specifying them exactly is more reliable
|
||||
|
||||
⚠️ **After editing:**
|
||||
- Fully restart ComfyUI to apply the changes
|
||||
- Check the console for syntax errors
|
||||
|
||||
💡 **Tip:** If editing the file is a hassle, use the "Direct Input" mode!
|
||||
|
||||
---
|
||||
|
||||
## Disclaimer
|
||||
|
||||
- No technical support provided
|
||||
- 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
|
||||
|
||||
---
|
||||
|
||||
## Changelog
|
||||
|
||||
### v1.2.0 (2026-01-13)
|
||||
- ✅ Added LoRA Block Weight (LBW) support
|
||||
- ✅ Automatic SD1.5/SDXL detection
|
||||
- ✅ 4 preset modes + custom input
|
||||
- ✅ Automatic weight adjustment
|
||||
- ✅ Video preview support (.mp4, .webm, etc.)
|
||||
|
||||
### v1.1.0 (2026-01-04)
|
||||
- ✅ Added Filtered Random LoRA Loader
|
||||
- ✅ Keyword filtering with AND/OR modes
|
||||
- ✅ Metadata search support
|
||||
- ✅ Preview image display
|
||||
|
||||
### v1.0.0 (2025-12-30)
|
||||
- ✅ Initial release
|
||||
- ✅ Random LoRA Loader (3 groups)
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
MIT License
|
||||
|
||||
---
|
||||
|
||||
## Author
|
||||
|
||||
konohana
|
||||
|
||||
---
|
||||
|
||||
**Enjoy precise LoRA control with Block Weights!** 🎨✨
|
||||
@@ -0,0 +1,609 @@
|
||||
# Filtered Random LoRA Loader (LBW)
|
||||
|
||||
**LoRAブロックウェイト（LBW）対応、SD1.5/SDXL自動検出付き**
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 概要
|
||||
|
||||
Filtered Random LoRA Loader (LBW) は、Filtered Random LoRA LoaderにLoRAブロックウェイト（LBW）機能を追加したノードです。U-Netのどの部分にLoRAを適用するかを細かく制御できます。
|
||||
|
||||
---
|
||||
|
||||
## 主な機能
|
||||
|
||||
### **基本機能（Filtered Random LoRA Loaderと共通）**
|
||||
- ✅ 1つのフォルダからLoRAをランダム選択
|
||||
- ✅ キーワードフィルタ（AND/ORモード）
|
||||
- ✅ メタデータ検索（ファイル名または埋め込みメタデータ）
|
||||
- ✅ プレビュー画像（静止画・アニメーション画像・動画に対応）
|
||||
- ✅ トリガーワードの取得
|
||||
- ✅ ファイル名による重複除外
|
||||
- ✅ そのまま直列接続が可能
|
||||
|
||||
### **拡張機能（LBW）**
|
||||
- ✅ **LoRAブロックウェイト（LBW）対応**：U-Netのブロックごとに個別の強度を設定し、画風・キャラクター・構図などを分離して制御
|
||||
- ✅ **SD1.5/SDXLの自動検出**
|
||||
- ✅ **4つのプリセットモード＋カスタム入力**
|
||||
- ✅ **ウェイト数の自動調整**
|
||||
|
||||
---
|
||||
|
||||
## LoRAブロックウェイト（LBW）
|
||||
|
||||
### **LBWとは？**
|
||||
|
||||
LoRAブロックウェイトを使うと、U-Netのどの部分にLoRAを効かせるかを制御できます。Stable DiffusionのU-Netは次の3つの部分で構成されています。
|
||||
|
||||
```
|
||||
INPUT blocks → 構造、構図、レイアウト
|
||||
↓
|
||||
MIDDLE block → 全体的な特徴
|
||||
↓
|
||||
OUTPUT blocks → 画風、ディテール、細部の調整
|
||||
```
|
||||
|
||||
ブロックごとにウェイトを調整することで、LoRAの効き方を細かく制御できます。
|
||||
|
||||
---
|
||||
|
||||
## ウェイトモード
|
||||
|
||||
### **1. Normal (All 1.0)** - デフォルト
|
||||
すべてのブロックのウェイトが1.0（LBWを使わない通常のLoRA適用）です。
|
||||
|
||||
---
|
||||
|
||||
### **2. Style Focused（画風特化）**
|
||||
OUTPUTブロックのみを有効にします。
|
||||
|
||||
**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`
|
||||
|
||||
**向いているもの:**
|
||||
- 画風LoRA（アニメ風、水彩風など）
|
||||
- スタイルLoRA、エフェクトLoRA
|
||||
- 見た目の雰囲気を変えたい場合
|
||||
|
||||
**効果:**
|
||||
- ✅ 画風が強く変わる
|
||||
- ❌ 構図やキャラクターはほとんど変わらない（既存の構図を保ちたい場合に最適）
|
||||
|
||||
---
|
||||
|
||||
### **3. Character Focused（キャラ重視）**
|
||||
INPUT前半＋MIDDLE＋OUTPUTを有効にします。
|
||||
|
||||
**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`
|
||||
|
||||
**向いているもの:**
|
||||
- キャラクターLoRA、人物LoRA
|
||||
- コンセプトLoRA
|
||||
- 顔や特徴を保ちたい場合
|
||||
|
||||
**効果:**
|
||||
- ✅ キャラクターの特徴が強く出る
|
||||
- ✅ 全体的にバランスの取れた効き方
|
||||
|
||||
---
|
||||
|
||||
### **4. Structure/Composition Only（構造・構図のみ）**
|
||||
INPUT＋MIDDLEブロックのみを有効にします。
|
||||
|
||||
**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`
|
||||
|
||||
**向いているもの:**
|
||||
- ポーズLoRA
|
||||
- 構図LoRA、レイアウトLoRA
|
||||
|
||||
**効果:**
|
||||
- ✅ 構造やポーズが強く変わる
|
||||
- ❌ 画風はほとんど変わらない
|
||||
|
||||
---
|
||||
|
||||
### **5. Balanced / Soft（バランス・穏やか）**
|
||||
選んだブロックに穏やかに適用します。OUTPUT後半を弱めて過剰な効きを防ぎます。
|
||||
|
||||
**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`
|
||||
|
||||
**向いているもの:**
|
||||
- 控えめに効かせたい場合、微調整
|
||||
- 複数のLoRAを併用するワークフロー
|
||||
- 汎用LoRA
|
||||
|
||||
**効果:**
|
||||
- ✅ バランスの取れた穏やかな効き方で、自然な仕上がり
|
||||
- ✅ 複数のLoRAを重ねやすい
|
||||
|
||||
---
|
||||
|
||||
### **6. Preset: Random**
|
||||
上記4つのプリセットから、実行ごとに1つをランダムに選びます。
|
||||
|
||||
```
|
||||
weight_mode: "Preset: Random"
|
||||
→ 実行ごとに4つのプリセットから1つ選択
|
||||
```
|
||||
|
||||
**向いているもの:**
|
||||
- 試行錯誤
|
||||
- バリエーションの生成
|
||||
- ランダムな効果
|
||||
|
||||
---
|
||||
|
||||
### **7. Direct Input**
|
||||
ウェイトを直接指定します。
|
||||
|
||||
**形式:**
|
||||
- SDXL: カンマ区切りで20個の値
|
||||
- SD1.5: カンマ区切りで17個の値
|
||||
|
||||
**例（SDXL）:**
|
||||
```
|
||||
weight_mode: "Direct Input"
|
||||
lbw_input: "1,0.5,0.5,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.7,0.7,0.7,1,1,1,1,1,1,1"
|
||||
```
|
||||
|
||||
**自動調整:**
|
||||
- 要素数が足りない場合：末尾を1.0で埋める
|
||||
- 要素数が多すぎる場合：末尾から切り詰める
|
||||
|
||||
---
|
||||
|
||||
## ブロック構造
|
||||
|
||||
### **SDXL（20要素）**
|
||||
```
|
||||
BASE, IN00-IN08 (9), MID (1), OUT00-OUT08 (9)
|
||||
```
|
||||
|
||||
| インデックス | ブロック | 役割 |
|
||||
|------------|---------|------|
|
||||
| 0 | BASE | 全体ベース |
|
||||
| 1-9 | INPUT (IN00-08) | 構造・構図 |
|
||||
| 10 | MIDDLE (M00) | 中間特徴 |
|
||||
| 11-19 | OUTPUT (OUT00-08) | 画風・ディテール |
|
||||
|
||||
**ウェイト例:**
|
||||
```
|
||||
1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
|
||||
BASE,IN00-08 (OFF),M00,OUT00-08 (ON)
|
||||
→ OUTPUTブロックのみLoRA適用（画風特化）
|
||||
```
|
||||
|
||||
### **SD1.5（17要素）**
|
||||
```
|
||||
BASE, IN01,IN02,IN04,IN05,IN07,IN08 (6), MID (1), OUT03-OUT11 (9)
|
||||
```
|
||||
|
||||
| インデックス | ブロック | 役割 |
|
||||
|------------|---------|------|
|
||||
| 0 | BASE | 全体ベース |
|
||||
| 1-6 | INPUT (IN01,02,04,05,07,08) | 構造・構図 |
|
||||
| 7 | MIDDLE (M00) | 中間特徴 |
|
||||
| 8-16 | OUTPUT (OUT03-11) | 画風・ディテール |
|
||||
|
||||
**ウェイト例:**
|
||||
```
|
||||
1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
|
||||
BASE,IN (OFF),M00,OUT (ON)
|
||||
→ OUTPUTブロックのみLoRA適用（画風特化）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 自動検出
|
||||
|
||||
### **LoRAタイプの検出**
|
||||
|
||||
ノードはモデル構造を解析して、LoRAがSD1.5用かSDXL用かを自動で判定します。
|
||||
|
||||
- **SD1.5**: `output_blocks_0`〜`output_blocks_11`（12ブロック）
|
||||
- **SDXL**: `output_blocks_0`〜`output_blocks_8`（9ブロック）
|
||||
|
||||
```
|
||||
SDXL検出 → 20要素のウェイトを使用
|
||||
SD1.5検出 → 17要素のウェイトを使用
|
||||
```
|
||||
|
||||
**モデルの種類を手動で指定する必要はありません** ✅
|
||||
|
||||
---
|
||||
|
||||
### **ウェイト数の自動調整**
|
||||
|
||||
入力したウェイトの数が、検出したLoRAタイプと合わない場合は自動で調整します。
|
||||
|
||||
**例1: 足りない場合**
|
||||
```
|
||||
入力: 1,0,0（3要素）
|
||||
検出: SDXL（20要素必要）
|
||||
→ 自動調整: 1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
```
|
||||
|
||||
**例2: 多すぎる場合**
|
||||
```
|
||||
入力: 1,0,0,...,0,0,0（25要素）
|
||||
検出: SDXL（20要素必要）
|
||||
→ 自動調整: 1,0,0,...,0,0,0（先頭20要素のみ）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 使い方
|
||||
|
||||
### **基本的なワークフロー**
|
||||
|
||||
```
|
||||
[Filtered Random LoRA Loader (LBW)]
|
||||
MODEL → [KSampler]
|
||||
CLIP → [CLIP Text Encode]
|
||||
lora_text → [Show Text]（任意、確認用）
|
||||
```
|
||||
|
||||
**手順:**
|
||||
|
||||
1. **ノードを配置**
|
||||
```
|
||||
[Filtered Random LoRA Loader (LBW)]
|
||||
```
|
||||
|
||||
2. **フォルダとキーワードを設定**
|
||||
```
|
||||
lora_folder_path: "path/to/loras"
|
||||
keyword_filter: "anime style"
|
||||
num_loras: 1
|
||||
```
|
||||
|
||||
3. **LBWモードを選択**
|
||||
```
|
||||
weight_mode: "Style Focused"
|
||||
```
|
||||
|
||||
4. **MODEL/CLIPを接続**
|
||||
```
|
||||
MODEL → [KSampler]
|
||||
CLIP → [CLIP Text Encode]
|
||||
```
|
||||
|
||||
### **パラメータ**
|
||||
|
||||
#### **LoRA設定**
|
||||
- `lora_folder_path`: LoRAフォルダのパス
|
||||
- `num_loras`: 適用するLoRAの数（0〜20）
|
||||
- `model_strength`: MODELに対するLoRAの強度（例: "1.0" または "0.6-0.9"）
|
||||
- `clip_strength`: CLIPに対するLoRAの強度（例: "1.0" または "0.6-0.9"）
|
||||
|
||||
> フォルダスキャンの挙動（対象拡張子、シンボリックリンクの追跡、候補リストのソート）は他のノードと共通です。詳細は[README_ja.md の「フォルダスキャンについて」](README_ja.md#フォルダスキャンについて)を参照してください。
|
||||
|
||||
#### **キーワードフィルタ**
|
||||
- `keyword_filter`: スペース区切りのキーワード（例: `style anime` や `"anime style" red`）
|
||||
- `filter_mode`: AND / OR
|
||||
- `search_in_metadata`: JSONや埋め込みメタデータも検索対象にする（遅くなります）
|
||||
|
||||
#### **LBW設定**
|
||||
- `weight_mode`: Normal / 4つのプリセット / Random / Direct Input
|
||||
- `lbw_input`: カスタムウェイト（Direct Inputモードのみ）
|
||||
|
||||
#### **その他**
|
||||
- `trigger_word_source`: トリガーワードの取得方法
|
||||
- `seed`: ランダムシード
|
||||
|
||||
---
|
||||
|
||||
## 出力形式
|
||||
|
||||
### **LBW構文**
|
||||
|
||||
LBWを適用した場合、`lora_text`出力（v1.4.0より前は`positive_text`）にLBW構文が含まれます。
|
||||
|
||||
**Normal (All 1.0):**
|
||||
```
|
||||
<lora:style_anime:0.8:0.8>
|
||||
```
|
||||
|
||||
**LBW適用時（Style Focused、SDXL）:**
|
||||
```
|
||||
<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>
|
||||
```
|
||||
|
||||
**注:** LBWはノード内部でMODELに適用済みです。`lora_text`の構文は、記録や他のノードでの再利用のためのものです。
|
||||
|
||||
---
|
||||
|
||||
## 使用例
|
||||
|
||||
### **例1: 画風のみ変更**
|
||||
|
||||
```
|
||||
[Load Checkpoint] → [Filtered Random LoRA Loader (LBW)]
|
||||
weight_mode: "Style Focused"
|
||||
keyword_filter: "anime"
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
**結果:** 構図はそのまま、画風のみアニメ風に
|
||||
|
||||
---
|
||||
|
||||
### **例2: 複数LoRAで段階適用**
|
||||
|
||||
```
|
||||
[Load Checkpoint]
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Structure/Composition Only)
|
||||
keyword_filter: "pose"
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Style Focused)
|
||||
keyword_filter: "watercolor"
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
**結果:** ポーズ調整 → 水彩画風適用
|
||||
|
||||
---
|
||||
|
||||
### **例3: キャラ + 画風**
|
||||
|
||||
```
|
||||
[Load Checkpoint]
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Character Focused)
|
||||
keyword_filter: "character_name"
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Style Focused)
|
||||
keyword_filter: "art_style"
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
**結果:** キャラ特徴保持 + 画風変更
|
||||
|
||||
---
|
||||
|
||||
## ヒント・Tips
|
||||
|
||||
### **1. プリセット選択ガイド**
|
||||
|
||||
| LoRAタイプ | 推奨プリセット | 理由 |
|
||||
|-----------|--------------|------|
|
||||
| 画風・スタイル | Style Focused | OUTPUTブロックが画風を担うため |
|
||||
| キャラクター | Character Focused | IN＋MID＋OUTのバランスで特徴を保つため |
|
||||
| ポーズ・構図 | Structure/Composition Only | INPUTブロックがレイアウトを担うため |
|
||||
| 汎用・微調整 | Balanced / Soft | 穏やかで重ねやすいため |
|
||||
|
||||
---
|
||||
|
||||
### **2. 効果の見え方**
|
||||
|
||||
プリセットごとの違いが**見えやすい**条件:
|
||||
- ✅ 強めのLoRA（model_strengthが高い）
|
||||
- ✅ 1つだけ適用（num_loras=1）
|
||||
- ✅ 画風・コンセプト系のLoRA
|
||||
|
||||
違いが**見えにくい**条件:
|
||||
- ⚠️ 弱めのLoRA（model_strengthが低い）
|
||||
- ⚠️ 複数のLoRAを同時に適用
|
||||
- ⚠️ 学習の効き方がブロック全体に均等に分散しているLoRA
|
||||
|
||||
---
|
||||
|
||||
### **3. ウェイト0 = 効果なし**
|
||||
|
||||
すべてのウェイトを0にすると、LoRAの効果は完全に無効になります。
|
||||
```
|
||||
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
|
||||
```
|
||||
これはLoRAを適用しないのと同じです。
|
||||
|
||||
**ウェイト調整の目安:**
|
||||
- **0**: 完全無効
|
||||
- **0.5**: 弱め
|
||||
- **1.0**: 標準
|
||||
- **1.5以上**: 強め（効きすぎに注意）
|
||||
|
||||
---
|
||||
|
||||
### **4. 直列接続**
|
||||
|
||||
LoRAごとに異なるLBW設定を使いたい場合は、ノードを直列に接続します。
|
||||
```
|
||||
[Filtered Random LoRA Loader (LBW)] (Style Focused)
|
||||
↓
|
||||
[Filtered Random LoRA Loader (LBW)] (Character Focused)
|
||||
↓
|
||||
[KSampler]
|
||||
```
|
||||
|
||||
1つのノード内で選ばれたLoRAは、すべて同じLBW設定で適用されます。
|
||||
|
||||
**コツ:**
|
||||
- **構造 → 画風の順**が基本
|
||||
- 強度もノードごとに個別に調整可能
|
||||
|
||||
---
|
||||
|
||||
### **5. トラブルシューティング**
|
||||
|
||||
**効果が出ない場合:**
|
||||
- ウェイトがすべて0になっていないか確認
|
||||
- LoRAが正しく読み込まれているか確認
|
||||
- コンソールでLBW適用のメッセージを確認
|
||||
|
||||
**効果が強すぎる場合:**
|
||||
- model_strengthを下げる
|
||||
- Balanced / Softプリセットを試す
|
||||
- ウェイトを0.5〜0.8に調整
|
||||
|
||||
---
|
||||
|
||||
## 要件
|
||||
|
||||
### **⚠️ 対応モデル**
|
||||
**本ノードはSD1.5とSDXLモデル専用です。**
|
||||
|
||||
- ✅ **対応:** Stable Diffusion 1.5、Stable Diffusion XL（SDXL）
|
||||
- ❌ **非対応:** Flux、SD3、SDXL Turbo、Pony、その他のアーキテクチャ
|
||||
|
||||
LBW機能は、SD1.5/SDXLのU-Netアーキテクチャ専用に設計されています。他のモデルタイプでは正常に動作しません。
|
||||
|
||||
---
|
||||
|
||||
### **依存関係**
|
||||
- ComfyUI
|
||||
- Pillow（ComfyUIに含まれる）
|
||||
- torch（ComfyUIに含まれる）
|
||||
|
||||
### **⚠️ オプション: 動画プレビュー**
|
||||
**動画ファイルのプレビュー（.mp4, .webm, .avi, .mov）にはopencv-pythonが必要です:**
|
||||
```bash
|
||||
pip install opencv-python
|
||||
```
|
||||
|
||||
**opencv-pythonなしの場合:**
|
||||
- ✅ 静止画・アニメーション画像は動作
|
||||
- ❌ 動画ファイルは黒画面
|
||||
|
||||
---
|
||||
|
||||
## プレビュー画像
|
||||
|
||||
以下の形式に対応しています（優先順位順）。
|
||||
|
||||
1. **静止画像**（.png, .jpg, .jpeg）
|
||||
2. **アニメーション画像**（.gif, .webp） - 1フレーム目
|
||||
3. **動画ファイル**（.mp4, .webm, .avi, .mov） - 1フレーム目（opencv-pythonが必要）
|
||||
|
||||
LoRAのファイル名に一致するプレビューファイルを自動で探します。
|
||||
|
||||
**ファイルマッチング:**
|
||||
- LoRAファイル名で始まるファイル（大文字小文字は区別しない）
|
||||
- 例: `style_anime.safetensors` にマッチ:
|
||||
- `style_anime.png` ✅
|
||||
- `style_anime_preview.jpg` ✅
|
||||
- `STYLE_ANIME.PNG` ✅
|
||||
|
||||
---
|
||||
|
||||
## 上級者向け: カスタムプリセット
|
||||
|
||||
独自のプリセットを作成したり、既存のプリセットを変更したい場合は、ソースファイルを直接編集できます。
|
||||
|
||||
### **編集方法**
|
||||
|
||||
**ファイル位置:**
|
||||
```
|
||||
ComfyUI/custom_nodes/RandomLoRALoader/filtered_random_lora_loader_lbw.py
|
||||
```
|
||||
|
||||
**編集箇所:** 29〜41行目（SDXL用が`SDXL_PRESETS`、SD1.5用が`SD15_PRESETS`）
|
||||
|
||||
### **例1: 独自プリセット追加**
|
||||
|
||||
```python
|
||||
SDXL_PRESETS = {
|
||||
"Style Focused": "1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1",
|
||||
"Character Focused": "1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1",
|
||||
"Structure/Composition Only": "1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0",
|
||||
"Balanced / Soft": "1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0",
|
||||
# ↓ 独自プリセット
|
||||
"My Custom Mix": "0.5,0.5,0.5,0.5,0,0,0,0,0,0,1,0.8,0.8,0.8,0.8,0.8,0.5,0.5,0.5,0.5"
|
||||
}
|
||||
```
|
||||
|
||||
### **例2: 既存プリセット調整**
|
||||
|
||||
```python
|
||||
SDXL_PRESETS = {
|
||||
# Style Focusedをより強く
|
||||
"Style Focused": "1,0,0,0,0,0,0,0,0,0,0,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2",
|
||||
|
||||
# Character Focusedをマイルドに
|
||||
"Character Focused": "0.8,0.8,0.8,0.8,0,0,0,0,0,0,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8",
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
### **例3: プリセット名変更**
|
||||
|
||||
```python
|
||||
SDXL_PRESETS = {
|
||||
"画風特化": "1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1",
|
||||
"キャラ重視": "1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1",
|
||||
"構図のみ": "1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0",
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
### **重要な注意事項**
|
||||
|
||||
⚠️ **編集前に:**
|
||||
- 元ファイルのバックアップを作成
|
||||
- Python構文（カンマ、引用符）に注意
|
||||
|
||||
⚠️ **ウェイト数:**
|
||||
- SDXLプリセット: 必ず20要素
|
||||
- SD1.5プリセット: 必ず17要素
|
||||
- 要素数が合わない場合は自動調整されますが、正確に指定する方が確実です
|
||||
|
||||
⚠️ **編集後:**
|
||||
- ComfyUIを完全に再起動して変更を適用
|
||||
- コンソールで構文エラーが出ていないか確認
|
||||
|
||||
💡 **ヒント:** ファイル編集が面倒な場合は「Direct Input」モードを使ってください！
|
||||
|
||||
---
|
||||
|
||||
## 免責事項
|
||||
|
||||
- 技術サポートは提供しません
|
||||
- 動作の保証はありません
|
||||
- 今後のComfyUIのアップデートとの互換性は保証しません
|
||||
- バグ報告や機能要望に対応できない場合があります
|
||||
- 自己責任でご利用ください
|
||||
|
||||
---
|
||||
|
||||
## 更新履歴
|
||||
|
||||
### v1.2.0（2026-01-13）
|
||||
- ✅ LoRAブロックウェイト（LBW）対応を追加
|
||||
- ✅ SD1.5/SDXLの自動検出
|
||||
- ✅ 4つのプリセットモード＋カスタム入力
|
||||
- ✅ ウェイト数の自動調整
|
||||
- ✅ 動画プレビュー対応（.mp4, .webm等）
|
||||
|
||||
### v1.1.0（2026-01-04）
|
||||
- ✅ Filtered Random LoRA Loaderを追加
|
||||
- ✅ キーワードフィルタ（AND/ORモード）
|
||||
- ✅ メタデータ検索に対応
|
||||
- ✅ プレビュー画像の表示
|
||||
|
||||
### v1.0.0（2025-12-30）
|
||||
- ✅ 初回リリース
|
||||
- ✅ Random LoRA Loader（3グループ）
|
||||
|
||||
---
|
||||
|
||||
## ライセンス
|
||||
|
||||
MIT License
|
||||
|
||||
---
|
||||
|
||||
## 作者
|
||||
|
||||
konohana
|
||||
|
||||
---
|
||||
|
||||
**ブロックウェイトで精密なLoRA制御を楽しんでください！** 🎨✨
|
||||
+1174
File diff suppressed because it is too large
Load Diff
+25
-1
@@ -3,6 +3,30 @@ RandomLoRALoader Custom Node for ComfyUI
|
||||
ランダムLoRA選択・適用ノード
|
||||
"""
|
||||
|
||||
from .random_lora_loader import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
from .random_lora_loader import RandomLoRALoader
|
||||
from .filtered_random_lora_loader import FilteredRandomLoRALoader
|
||||
|
||||
# LBW版は任意（読み込めない場合はスキップ）
|
||||
try:
|
||||
from .filtered_random_lora_loader_lbw import FilteredRandomLoRALoaderLBW
|
||||
HAS_LBW = True
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] Failed to load LBW version: {e}")
|
||||
HAS_LBW = False
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"RandomLoRALoader": RandomLoRALoader,
|
||||
"FilteredRandomLoRALoader": FilteredRandomLoRALoader,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"RandomLoRALoader": "Random LoRA Loader",
|
||||
"FilteredRandomLoRALoader": "Filtered Random LoRA Loader",
|
||||
}
|
||||
|
||||
# LBW版が利用可能な場合のみ追加
|
||||
if HAS_LBW:
|
||||
NODE_CLASS_MAPPINGS["FilteredRandomLoRALoaderLBW"] = FilteredRandomLoRALoaderLBW
|
||||
NODE_DISPLAY_NAME_MAPPINGS["FilteredRandomLoRALoaderLBW"] = "Filtered Random LoRA Loader (LBW)"
|
||||
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
|
||||
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Load Diff
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|
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|
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|
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|
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|
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|
After Width: | Height: | Size: 242 KiB |
@@ -0,0 +1,19 @@
|
||||
[project]
|
||||
name = "randomloraloader"
|
||||
description = "Randomly select and apply LoRAs with keyword filtering, LoRA Block Weight support, preview images, and multiple folder support for ComfyUI"
|
||||
version = "1.4.0"
|
||||
license = {file = "LICENSE"}
|
||||
|
||||
classifiers = [
|
||||
"Operating System :: OS Independent",
|
||||
"Environment :: GPU :: NVIDIA CUDA",
|
||||
"Environment :: GPU :: AMD ROCm",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/shin131002/RandomLoRALoader"
|
||||
|
||||
[tool.comfy]
|
||||
PublisherId = "konohana"
|
||||
DisplayName = "RandomLoRALoader"
|
||||
Icon = ""
|
||||
+585
-62
@@ -39,12 +39,15 @@ RandomLoRALoader - ComfyUI Custom Node V1
|
||||
- model_strength_3: MODEL適用強度
|
||||
- clip_strength_3: CLIP適用強度
|
||||
- num_loras_3: 選択するLoRA個数
|
||||
19. trigger_word_source: json_combined/json_random/json_sample_prompt（共通）
|
||||
19. trigger_word_source: json_combined/json_random/json_sample_prompt/metadata（共通）
|
||||
20. seed: ランダム選択のシード値（共通、ComfyUI標準のcontrol_before_generateで制御）
|
||||
|
||||
【その他仕様】
|
||||
- 外部JSONファイル（{LoRAファイル名}.metadata.json）からトリガーワード/作例を取得
|
||||
- JSON読み取り優先順位: trainedWords / images[].meta.prompt
|
||||
- メタデータ読み取り優先順位:
|
||||
1. {LoRAファイル名}.metadata.json (ComfyUI Lora Manager)
|
||||
2. {LoRAファイル名}.info (Civitai Helper)
|
||||
3. LoRA本体ファイルの埋め込みメタデータ
|
||||
- trigger_word_source="metadata"時は埋め込みメタデータのみを参照
|
||||
- 同じLoRAの重複選択なし（不足時は再選択で埋める）
|
||||
- json_sample_prompt選択時はpositive/negativeを分離
|
||||
- 作例プロンプト内のLoRA記述（<lora:xxx:x.x>）は削除
|
||||
@@ -63,9 +66,20 @@ import folder_paths
|
||||
import comfy.sd
|
||||
import comfy.utils
|
||||
|
||||
# LoRA埋め込みメタデータ読み込み用
|
||||
try:
|
||||
from safetensors.torch import safe_open
|
||||
SAFETENSORS_AVAILABLE = True
|
||||
except ImportError:
|
||||
SAFETENSORS_AVAILABLE = False
|
||||
print("[RandomLoRALoader] safetensors not available, embedded metadata reading disabled")
|
||||
|
||||
class RandomLoRALoader:
|
||||
"""ランダムLoRA選択・適用ノード（3グループ対応）"""
|
||||
|
||||
# クラス変数（opencv警告表示フラグ）
|
||||
_opencv_warning_shown = False
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
@@ -84,10 +98,15 @@ class RandomLoRALoader:
|
||||
"default": "A1111"
|
||||
}
|
||||
),
|
||||
"additional_prompt": ("STRING", {
|
||||
"additional_prompt_positive": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": True,
|
||||
"placeholder": "Additional prompt (e.g., 1girl, beautiful)"
|
||||
"placeholder": "Additional positive prompt"
|
||||
}),
|
||||
"additional_prompt_negative": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": True,
|
||||
"placeholder": "Additional negative prompt"
|
||||
}),
|
||||
# グループ1
|
||||
"lora_folder_path_1": ("STRING", {
|
||||
@@ -98,6 +117,10 @@ class RandomLoRALoader:
|
||||
"include_subfolders_1": ("BOOLEAN", {
|
||||
"default": True
|
||||
}),
|
||||
"unique_by_filename_1": ("BOOLEAN", {
|
||||
"default": True,
|
||||
"label": "Unique by filename (exclude duplicates)"
|
||||
}),
|
||||
"model_strength_1": ("STRING", {
|
||||
"default": "1.0",
|
||||
"multiline": False,
|
||||
@@ -124,6 +147,10 @@ class RandomLoRALoader:
|
||||
"include_subfolders_2": ("BOOLEAN", {
|
||||
"default": True
|
||||
}),
|
||||
"unique_by_filename_2": ("BOOLEAN", {
|
||||
"default": True,
|
||||
"label": "Unique by filename (exclude duplicates)"
|
||||
}),
|
||||
"model_strength_2": ("STRING", {
|
||||
"default": "1.0",
|
||||
"multiline": False,
|
||||
@@ -150,6 +177,10 @@ class RandomLoRALoader:
|
||||
"include_subfolders_3": ("BOOLEAN", {
|
||||
"default": True
|
||||
}),
|
||||
"unique_by_filename_3": ("BOOLEAN", {
|
||||
"default": True,
|
||||
"label": "Unique by filename (exclude duplicates)"
|
||||
}),
|
||||
"model_strength_3": ("STRING", {
|
||||
"default": "1.0",
|
||||
"multiline": False,
|
||||
@@ -169,7 +200,7 @@ class RandomLoRALoader:
|
||||
}),
|
||||
# 共通設定
|
||||
"trigger_word_source": (
|
||||
["json_combined", "json_random", "json_sample_prompt"],
|
||||
["json_combined", "json_random", "json_sample_prompt", "metadata"],
|
||||
{
|
||||
"default": "json_combined"
|
||||
}
|
||||
@@ -184,14 +215,19 @@ class RandomLoRALoader:
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING", "CONDITIONING", "CONDITIONING")
|
||||
RETURN_NAMES = ("MODEL", "CLIP", "positive_text", "negative_text", "positive", "negative")
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING", "CONDITIONING", "CONDITIONING", "IMAGE", "STRING")
|
||||
# 0〜6番はv1.4.0以前と位置・型とも同じ（保存済みワークフローは出力を番号で接続するため）。
|
||||
# v1.4.0での変更:
|
||||
# positive_text (2番) … <lora:...> を含まない。positive (CONDITIONING) にエンコード
|
||||
# される文字列そのもの
|
||||
# lora_text (7番) … 以前の positive_text の内容（<lora:...> 付き）。記録用
|
||||
RETURN_NAMES = ("MODEL", "CLIP", "positive_text", "negative_text", "positive", "negative", "preview", "lora_text")
|
||||
FUNCTION = "load_random_loras"
|
||||
CATEGORY = "loaders"
|
||||
|
||||
def _find_lora_files(self, folder_path, include_subfolders):
|
||||
"""
|
||||
指定フォルダ内のLoRAファイル（.safetensors）を検索
|
||||
指定フォルダ内のLoRAファイル（.safetensors / .pt / .ckpt）を検索
|
||||
|
||||
Args:
|
||||
folder_path: 検索対象フォルダの絶対パス
|
||||
@@ -204,9 +240,41 @@ class RandomLoRALoader:
|
||||
print(f"[RandomLoRALoader] フォルダが存在しません: {folder_path}")
|
||||
return []
|
||||
|
||||
pattern = "**/*.safetensors" if include_subfolders else "*.safetensors"
|
||||
lora_files = glob.glob(os.path.join(folder_path, pattern), recursive=include_subfolders)
|
||||
|
||||
lora_files = []
|
||||
# Filtered版と対象拡張子を揃えている
|
||||
extensions = ['.safetensors', '.pt', '.ckpt']
|
||||
|
||||
if include_subfolders:
|
||||
# glob の "**" はシンボリックリンクのディレクトリを辿らないため
|
||||
# os.walk(followlinks=True) に置き換えている。ComfyUI本体の
|
||||
# フォルダスキャン (folder_paths.recursive_search) と挙動を揃える
|
||||
# ためで、これがないとリンク先にしか存在しないLoRAが、
|
||||
# ComfyUIネイティブのドロップダウンには出るのにここでは
|
||||
# 候補に入らない、という食い違いが起きる。
|
||||
#
|
||||
# realpathの集合はリンクを辿る代償。リンクが循環している場合に
|
||||
# スキャンが無限ループするのを防ぎ、同じフォルダに2本のリンクが
|
||||
# 張られている場合の二重スキャンも抑止する。
|
||||
seen_dirs = set()
|
||||
for root, dirs, files in os.walk(folder_path, followlinks=True):
|
||||
real_root = os.path.realpath(root)
|
||||
if real_root in seen_dirs:
|
||||
dirs[:] = []
|
||||
continue
|
||||
seen_dirs.add(real_root)
|
||||
for file in files:
|
||||
if any(file.lower().endswith(ext) for ext in extensions):
|
||||
lora_files.append(os.path.join(root, file))
|
||||
else:
|
||||
for file in os.listdir(folder_path):
|
||||
file_path = os.path.join(folder_path, file)
|
||||
if os.path.isfile(file_path) and any(file.lower().endswith(ext) for ext in extensions):
|
||||
lora_files.append(file_path)
|
||||
|
||||
# ソートして返す。以前は列挙順がファイルシステム任せだったため、
|
||||
# 同じシードでもマシンが変われば選ばれるLoRAが違っていた。
|
||||
# ソートすることでシード指定が環境をまたいで再現するようになる。
|
||||
lora_files = sorted(lora_files)
|
||||
print(f"[RandomLoRALoader] 検出されたLoRA数: {len(lora_files)}")
|
||||
return lora_files
|
||||
|
||||
@@ -246,6 +314,34 @@ class RandomLoRALoader:
|
||||
random.shuffle(selected)
|
||||
return selected
|
||||
|
||||
def _unique_by_filename(self, lora_files, group_name=""):
|
||||
"""
|
||||
ファイル名でユニーク化（重複ファイル名を除外）
|
||||
|
||||
Args:
|
||||
lora_files: LoRAファイルパスのリスト
|
||||
group_name: グループ名（ログ用）
|
||||
|
||||
Returns:
|
||||
list: ファイル名がユニークなファイルパスのリスト
|
||||
"""
|
||||
seen_names = {}
|
||||
unique_files = []
|
||||
|
||||
for file_path in lora_files:
|
||||
filename = os.path.basename(file_path)
|
||||
|
||||
if filename not in seen_names:
|
||||
seen_names[filename] = file_path
|
||||
unique_files.append(file_path)
|
||||
else:
|
||||
# 重複検出時はログ出力
|
||||
print(f"[RandomLoRALoader] {group_name}: Duplicate filename detected: {filename}")
|
||||
print(f" Keeping: {seen_names[filename]}")
|
||||
print(f" Skipping: {file_path}")
|
||||
|
||||
return unique_files
|
||||
|
||||
def _parse_strength(self, strength_str):
|
||||
"""
|
||||
強度文字列をパースして値を返す
|
||||
@@ -255,6 +351,7 @@ class RandomLoRALoader:
|
||||
- "0.55" → 0.55（そのまま）
|
||||
- "0.4-0.8" → 0.4, 0.5, 0.6, 0.7, 0.8からランダム（0.1刻み）
|
||||
- "0.44-0.82" → 0.4, 0.5, 0.6, 0.7, 0.8からランダム（範囲を1桁に丸める）
|
||||
- "-0.8--0.3" → -0.8, -0.7, -0.6, ..., -0.3からランダム（マイナス範囲対応）
|
||||
|
||||
Args:
|
||||
strength_str: 強度文字列
|
||||
@@ -262,18 +359,19 @@ class RandomLoRALoader:
|
||||
Returns:
|
||||
float: 実際に使用する強度値
|
||||
"""
|
||||
import re
|
||||
strength_str = str(strength_str).strip()
|
||||
|
||||
# ハイフンがあればランダム範囲指定
|
||||
if '-' in strength_str:
|
||||
# 範囲指定のパターンマッチ（マイナス値対応）
|
||||
# 例: "0.6-0.9", "-0.8--0.3", "-0.5-0.5", "0.3--0.7"
|
||||
range_pattern = r'^(-?\d+\.?\d*)\s*-\s*(-?\d+\.?\d*)$'
|
||||
match = re.match(range_pattern, strength_str)
|
||||
|
||||
if match:
|
||||
try:
|
||||
parts = strength_str.split('-')
|
||||
if len(parts) != 2:
|
||||
raise ValueError("範囲指定は 'min-max' 形式で入力してください")
|
||||
|
||||
# 範囲の上限下限を1桁に丸める
|
||||
min_val = round(float(parts[0].strip()), 1)
|
||||
max_val = round(float(parts[1].strip()), 1)
|
||||
min_val = round(float(match.group(1)), 1)
|
||||
max_val = round(float(match.group(2)), 1)
|
||||
|
||||
# バリデーション
|
||||
if min_val < -10.0 or max_val > 10.0:
|
||||
@@ -320,27 +418,125 @@ class RandomLoRALoader:
|
||||
|
||||
def _load_json_metadata(self, lora_path):
|
||||
"""
|
||||
外部JSONファイルからメタデータを読み込む
|
||||
外部JSONファイルまたはLoRA埋め込みメタデータを読み込む
|
||||
|
||||
優先順位:
|
||||
1. {filename}.metadata.json (ComfyUI Lora Manager形式)
|
||||
2. {filename}.info (Civitai Helper形式)
|
||||
3. LoRA本体ファイルの埋め込みメタデータ
|
||||
|
||||
Args:
|
||||
lora_path: LoRAファイルパス (.safetensors / .pt / .ckpt)
|
||||
|
||||
Returns:
|
||||
dict: JSONデータ（読み込み失敗時はNone）
|
||||
"""
|
||||
# 拡張子を除いたファイル名を取得
|
||||
base_name = os.path.splitext(lora_path)[0]
|
||||
|
||||
# 優先順位1: .metadata.json (ComfyUI Lora Manager)
|
||||
json_path_metadata = f"{base_name}.metadata.json"
|
||||
if os.path.exists(json_path_metadata):
|
||||
try:
|
||||
with open(json_path_metadata, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] JSON読み込みエラー ({json_path_metadata}): {e}")
|
||||
|
||||
# 優先順位2: .info (Civitai Helper)
|
||||
json_path_info = f"{base_name}.info"
|
||||
if os.path.exists(json_path_info):
|
||||
try:
|
||||
with open(json_path_info, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] JSON読み込みエラー ({json_path_info}): {e}")
|
||||
|
||||
# 優先順位3: LoRA本体ファイルの埋め込みメタデータ
|
||||
embedded_data = self._load_embedded_metadata(lora_path)
|
||||
if embedded_data:
|
||||
return embedded_data
|
||||
|
||||
# どれも見つからない
|
||||
print(f"[RandomLoRALoader] メタデータが見つかりません: {base_name}")
|
||||
return None
|
||||
|
||||
def _load_embedded_metadata(self, lora_path):
|
||||
"""
|
||||
LoRA本体ファイルの埋め込みメタデータを読み込む
|
||||
|
||||
Args:
|
||||
lora_path: LoRAファイルパス (.safetensors)
|
||||
|
||||
Returns:
|
||||
dict: JSONデータ（読み込み失敗時はNone）
|
||||
dict: メタデータ（読み込み失敗時はNone）
|
||||
"""
|
||||
# .safetensorsを除いたファイル名を取得
|
||||
base_name = os.path.splitext(lora_path)[0]
|
||||
json_path = f"{base_name}.metadata.json"
|
||||
if not SAFETENSORS_AVAILABLE:
|
||||
return None
|
||||
|
||||
if not os.path.exists(json_path):
|
||||
print(f"[RandomLoRALoader] JSONファイルが見つかりません: {json_path}")
|
||||
# safe_open は .safetensors しか読めない。.pt/.ckpt も候補に含むように
|
||||
# なったため、ここで弾いておかないと選択のたびにエラーログが出る
|
||||
if not lora_path.lower().endswith(".safetensors"):
|
||||
return None
|
||||
|
||||
if not os.path.exists(lora_path):
|
||||
return None
|
||||
|
||||
try:
|
||||
with open(json_path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
with safe_open(lora_path, framework="pt", device="cpu") as f:
|
||||
metadata = f.metadata()
|
||||
|
||||
if not metadata:
|
||||
return None
|
||||
|
||||
# ss_tag_frequency（kohya_ss形式）からトリガーワード抽出
|
||||
if "ss_tag_frequency" in metadata:
|
||||
tag_freq_str = metadata.get("ss_tag_frequency", "{}")
|
||||
try:
|
||||
tag_freq = json.loads(tag_freq_str)
|
||||
# 最も頻度の高いタグセットを取得
|
||||
if tag_freq:
|
||||
# 各データセットのタグを結合
|
||||
all_tags = []
|
||||
for dataset_tags in tag_freq.values():
|
||||
all_tags.extend(dataset_tags.keys())
|
||||
|
||||
# 重複除去
|
||||
unique_tags = list(dict.fromkeys(all_tags))
|
||||
|
||||
# Civitai形式に変換
|
||||
trigger_words = ", ".join(unique_tags[:20]) # 上位20個
|
||||
|
||||
return {
|
||||
"civitai": {
|
||||
"trainedWords": [trigger_words]
|
||||
}
|
||||
}
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# その他のメタデータフィールドからトリガーワード抽出
|
||||
if "modelspec.trigger_word" in metadata:
|
||||
trigger = metadata["modelspec.trigger_word"]
|
||||
return {
|
||||
"civitai": {
|
||||
"trainedWords": [trigger]
|
||||
}
|
||||
}
|
||||
|
||||
# ss_output_name（モデル名）
|
||||
if "ss_output_name" in metadata:
|
||||
output_name = metadata["ss_output_name"]
|
||||
return {
|
||||
"civitai": {
|
||||
"trainedWords": [output_name]
|
||||
}
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] JSON読み込みエラー ({json_path}): {e}")
|
||||
print(f"[RandomLoRALoader] 埋め込みメタデータ読み込みエラー ({lora_path}): {e}")
|
||||
return None
|
||||
|
||||
def _get_trigger_words_combined(self, lora_path):
|
||||
@@ -434,8 +630,8 @@ class RandomLoRALoader:
|
||||
print(f"[RandomLoRALoader] imagesが見つかりません: {lora_path}")
|
||||
return "", ""
|
||||
|
||||
# metaを持つ画像のみをフィルタ
|
||||
valid_images = [img for img in images if "meta" in img]
|
||||
# metaを持つ画像のみをフィルタ（metaがNoneでないことも確認）
|
||||
valid_images = [img for img in images if "meta" in img and img["meta"] is not None]
|
||||
if not valid_images:
|
||||
print(f"[RandomLoRALoader] metaを持つ画像が見つかりません: {lora_path}")
|
||||
return "", ""
|
||||
@@ -463,6 +659,29 @@ class RandomLoRALoader:
|
||||
|
||||
return positive, negative
|
||||
|
||||
def _get_trigger_words_from_embedded(self, lora_path):
|
||||
"""
|
||||
LoRA埋め込みメタデータから直接トリガーワードを取得
|
||||
外部JSONファイルを無視して、LoRA本体ファイルのみを参照
|
||||
|
||||
Args:
|
||||
lora_path: LoRAファイルパス
|
||||
|
||||
Returns:
|
||||
str: トリガーワード
|
||||
"""
|
||||
embedded_data = self._load_embedded_metadata(lora_path)
|
||||
if not embedded_data:
|
||||
return ""
|
||||
|
||||
# civitai.trainedWordsを取得（_load_embedded_metadataで変換済み）
|
||||
trained_words = embedded_data.get("civitai", {}).get("trainedWords", [])
|
||||
if not trained_words:
|
||||
return ""
|
||||
|
||||
# 最初のパターンを使用（埋め込みデータは通常1つ）
|
||||
return trained_words[0] if trained_words else ""
|
||||
|
||||
def _load_lora(self, model, clip, lora_path, model_strength, clip_strength):
|
||||
"""
|
||||
LoRAをMODELとCLIPに適用
|
||||
@@ -563,13 +782,284 @@ class RandomLoRALoader:
|
||||
text = re.sub(r'<lora:[^>]+>', '', text)
|
||||
|
||||
# 連続カンマを整理
|
||||
text = re.sub(r',\s*,', ',', text)
|
||||
text = re.sub(r',(\s*,)+', ',', text) # 3つ以上連続しても1回で詰める
|
||||
|
||||
# 行頭・行末の空白とカンマを削除
|
||||
text = text.strip().strip(',').strip()
|
||||
|
||||
return text
|
||||
|
||||
def _load_preview_image_as_tensor(self, lora_path):
|
||||
"""
|
||||
プレビュー画像をTensorとして読み込み（部分一致、長辺1240px）
|
||||
|
||||
検索方法:
|
||||
LoRAファイル名（拡張子除く）で始まるファイルを検索
|
||||
優先順位:
|
||||
1. 静止画像 (.png, .jpg, .jpeg, .webp)
|
||||
2. 動画ファイル (.gif, .webp, .mp4, .webm) の1フレーム目
|
||||
|
||||
例: style_anime_v1.safetensors
|
||||
→ style_anime_v1.png
|
||||
→ style_anime_v1_preview.jpg
|
||||
→ style_anime_v1.gif (1フレーム目)
|
||||
→ style_anime_v1.mp4 (1フレーム目、opencv-pythonが必要)
|
||||
→ STYLE_ANIME_V1.PNG (大文字小文字無視)
|
||||
|
||||
リサイズ:
|
||||
長辺を1240pxに統一（アスペクト比保持）
|
||||
|
||||
Returns:
|
||||
torch.Tensor: (H, W, C) 形式、見つからない場合はNone
|
||||
"""
|
||||
try:
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import torch
|
||||
except ImportError:
|
||||
return None
|
||||
|
||||
# LoRAファイル名（拡張子なし）
|
||||
base_name = os.path.splitext(os.path.basename(lora_path))[0]
|
||||
folder = os.path.dirname(lora_path)
|
||||
|
||||
# ファイル拡張子（優先順位順）
|
||||
static_image_exts = ('.png', '.jpg', '.jpeg')
|
||||
animated_image_exts = ('.gif', '.webp') # Pillowで対応可能
|
||||
video_exts = ('.mp4', '.webm', '.avi', '.mov') # opencv-python必要
|
||||
|
||||
try:
|
||||
# 同じフォルダ内のファイルを検索
|
||||
files = os.listdir(folder)
|
||||
|
||||
# 部分一致するファイルを収集
|
||||
matched_files = []
|
||||
for file in files:
|
||||
if file.lower().startswith(base_name.lower()):
|
||||
matched_files.append(file)
|
||||
|
||||
if not matched_files:
|
||||
return None
|
||||
|
||||
# 優先順位でソート
|
||||
def get_priority(filename):
|
||||
lower = filename.lower()
|
||||
if any(lower.endswith(ext) for ext in static_image_exts):
|
||||
return 0 # 最優先
|
||||
elif any(lower.endswith(ext) for ext in animated_image_exts):
|
||||
return 1 # 次優先
|
||||
elif any(lower.endswith(ext) for ext in video_exts):
|
||||
return 2 # 最後
|
||||
else:
|
||||
return 999 # その他
|
||||
|
||||
matched_files.sort(key=get_priority)
|
||||
|
||||
# 各ファイルを試す
|
||||
for file in matched_files:
|
||||
preview_path = os.path.join(folder, file)
|
||||
lower_file = file.lower()
|
||||
|
||||
# 静止画像
|
||||
if any(lower_file.endswith(ext) for ext in static_image_exts):
|
||||
img = self._load_static_image(preview_path)
|
||||
if img is not None:
|
||||
return img
|
||||
|
||||
# アニメーション画像（GIF/WebP）
|
||||
elif any(lower_file.endswith(ext) for ext in animated_image_exts):
|
||||
img = self._load_animated_image_first_frame(preview_path)
|
||||
if img is not None:
|
||||
return img
|
||||
|
||||
# 動画ファイル（opencv-python必要）
|
||||
elif any(lower_file.endswith(ext) for ext in video_exts):
|
||||
img = self._load_video_first_frame(preview_path)
|
||||
if img is not None:
|
||||
return img
|
||||
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] Folder read error: {e}")
|
||||
|
||||
return None
|
||||
|
||||
def _load_static_image(self, image_path):
|
||||
"""静止画像を読み込み"""
|
||||
try:
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
img = Image.open(image_path).convert('RGB')
|
||||
return self._resize_and_convert_image(img)
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] Static image load error ({image_path}): {e}")
|
||||
return None
|
||||
|
||||
def _load_animated_image_first_frame(self, image_path):
|
||||
"""アニメーション画像（GIF/WebP）の1フレーム目を読み込み"""
|
||||
try:
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
img = Image.open(image_path)
|
||||
|
||||
# 1フレーム目に移動
|
||||
if hasattr(img, 'seek'):
|
||||
img.seek(0)
|
||||
|
||||
img = img.convert('RGB')
|
||||
return self._resize_and_convert_image(img)
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] Animated image load error ({image_path}): {e}")
|
||||
return None
|
||||
|
||||
def _load_video_first_frame(self, video_path):
|
||||
"""動画ファイルの1フレーム目を読み込み（opencv-python必要）"""
|
||||
try:
|
||||
import cv2
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
# OpenCVで動画を開く
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
|
||||
if not cap.isOpened():
|
||||
return None
|
||||
|
||||
# 1フレーム目を読み込み
|
||||
ret, frame = cap.read()
|
||||
cap.release()
|
||||
|
||||
if not ret:
|
||||
return None
|
||||
|
||||
# BGR → RGB 変換
|
||||
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
# PIL Imageに変換
|
||||
img = Image.fromarray(frame_rgb)
|
||||
|
||||
return self._resize_and_convert_image(img)
|
||||
|
||||
except ImportError:
|
||||
# opencv-pythonがインストールされていない（初回のみ警告）
|
||||
if not RandomLoRALoader._opencv_warning_shown:
|
||||
print("=" * 60)
|
||||
print("[RandomLoRALoader] Video Preview Support")
|
||||
print("=" * 60)
|
||||
print(f"Video file detected: {os.path.basename(video_path)}")
|
||||
print("opencv-python is not installed.")
|
||||
print("")
|
||||
print("To enable video preview support, install:")
|
||||
print(" pip install opencv-python")
|
||||
print("")
|
||||
print("Static images (.png/.jpg) and animated images (.gif/.webp)")
|
||||
print("will continue to work without opencv-python.")
|
||||
print("=" * 60)
|
||||
RandomLoRALoader._opencv_warning_shown = True
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] Video load error ({video_path}): {e}")
|
||||
return None
|
||||
|
||||
def _resize_and_convert_image(self, img):
|
||||
"""画像をリサイズしてTensorに変換"""
|
||||
try:
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
# 長辺を1240pxにリサイズ（アスペクト比保持）
|
||||
width, height = img.size
|
||||
max_size = 1240
|
||||
|
||||
if max(width, height) > max_size:
|
||||
# 長辺が1240pxを超える場合はリサイズ
|
||||
if width > height:
|
||||
new_width = max_size
|
||||
new_height = int(height * (max_size / width))
|
||||
else:
|
||||
new_height = max_size
|
||||
new_width = int(width * (max_size / height))
|
||||
|
||||
img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||||
elif max(width, height) < max_size:
|
||||
# 長辺が1240px未満の場合は拡大
|
||||
if width > height:
|
||||
new_width = max_size
|
||||
new_height = int(height * (max_size / width))
|
||||
else:
|
||||
new_height = max_size
|
||||
new_width = int(width * (max_size / height))
|
||||
|
||||
img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||||
|
||||
# numpy配列に変換 (H, W, C)
|
||||
img_array = np.array(img).astype(np.float32) / 255.0
|
||||
# Tensor化
|
||||
return torch.from_numpy(img_array)
|
||||
|
||||
except Exception as e:
|
||||
print(f"[RandomLoRALoader] Image resize/convert error: {e}")
|
||||
return None
|
||||
|
||||
def _generate_preview_batch(self, preview_images):
|
||||
"""
|
||||
プレビュー画像のバッチを生成（1240pxに統一、パディング）
|
||||
|
||||
Args:
|
||||
preview_images: list of torch.Tensor (各々 H, W, C、長辺1240px)
|
||||
|
||||
Returns:
|
||||
torch.Tensor: (B, 1240, 1240, C) 形式
|
||||
"""
|
||||
try:
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
except ImportError:
|
||||
return None
|
||||
|
||||
if not preview_images:
|
||||
# プレビュー画像なし → 黒画像1枚（1240x1240）
|
||||
black_image = torch.zeros((1, 1240, 1240, 3), dtype=torch.float32)
|
||||
return black_image
|
||||
|
||||
# 全て1240x1240にパディング
|
||||
padded_images = []
|
||||
target_size = 1240
|
||||
|
||||
for img in preview_images:
|
||||
h, w, c = img.shape
|
||||
|
||||
# パディングが必要か確認
|
||||
if h == target_size and w == target_size:
|
||||
padded_images.append(img)
|
||||
else:
|
||||
# パディング量を計算（中央配置）
|
||||
pad_h = target_size - h
|
||||
pad_w = target_size - w
|
||||
pad_top = pad_h // 2
|
||||
pad_bottom = pad_h - pad_top
|
||||
pad_left = pad_w // 2
|
||||
pad_right = pad_w - pad_left
|
||||
|
||||
# (H, W, C) → (C, H, W) に変換してパディング
|
||||
img_chw = img.permute(2, 0, 1) # (C, H, W)
|
||||
|
||||
# F.pad: (left, right, top, bottom)
|
||||
padded = F.pad(img_chw, (pad_left, pad_right, pad_top, pad_bottom), value=0)
|
||||
|
||||
# (C, H, W) → (H, W, C) に戻す
|
||||
padded = padded.permute(1, 2, 0)
|
||||
padded_images.append(padded)
|
||||
|
||||
# バッチ化 (B, 1240, 1240, C)
|
||||
preview_batch = torch.stack(padded_images, dim=0)
|
||||
return preview_batch
|
||||
|
||||
def _encode_prompt(self, clip, text, token_normalization, weight_interpretation):
|
||||
"""
|
||||
プロンプトをCLIPでエンコードしてCONDITIONINGを生成
|
||||
@@ -607,22 +1097,26 @@ class RandomLoRALoader:
|
||||
clip,
|
||||
token_normalization,
|
||||
weight_interpretation,
|
||||
additional_prompt,
|
||||
additional_prompt_positive,
|
||||
additional_prompt_negative,
|
||||
# グループ1
|
||||
lora_folder_path_1,
|
||||
include_subfolders_1,
|
||||
unique_by_filename_1,
|
||||
model_strength_1,
|
||||
clip_strength_1,
|
||||
num_loras_1,
|
||||
# グループ2
|
||||
lora_folder_path_2,
|
||||
include_subfolders_2,
|
||||
unique_by_filename_2,
|
||||
model_strength_2,
|
||||
clip_strength_2,
|
||||
num_loras_2,
|
||||
# グループ3
|
||||
lora_folder_path_3,
|
||||
include_subfolders_3,
|
||||
unique_by_filename_3,
|
||||
model_strength_3,
|
||||
clip_strength_3,
|
||||
num_loras_3,
|
||||
@@ -634,7 +1128,7 @@ class RandomLoRALoader:
|
||||
メイン処理：ランダムLoRA選択・適用（3グループ対応）
|
||||
|
||||
Returns:
|
||||
tuple: (MODEL, CLIP, positive_text_output, negative_text_output, positive_conditioning, negative_conditioning)
|
||||
tuple: (MODEL, CLIP, lora_text_output, negative_text_output, positive_conditioning, negative_conditioning)
|
||||
"""
|
||||
# seedをそのまま使用（ComfyUIのcontrol_before_generateで制御される）
|
||||
print(f"[RandomLoRALoader] 使用seed: {seed}")
|
||||
@@ -646,14 +1140,17 @@ class RandomLoRALoader:
|
||||
all_positive_parts = []
|
||||
all_negative_parts = []
|
||||
|
||||
# プレビュー画像収集
|
||||
preview_images = []
|
||||
|
||||
# 3つのグループを処理
|
||||
groups = [
|
||||
(lora_folder_path_1, include_subfolders_1, model_strength_1, clip_strength_1, num_loras_1, "Group 1"),
|
||||
(lora_folder_path_2, include_subfolders_2, model_strength_2, clip_strength_2, num_loras_2, "Group 2"),
|
||||
(lora_folder_path_3, include_subfolders_3, model_strength_3, clip_strength_3, num_loras_3, "Group 3"),
|
||||
(lora_folder_path_1, include_subfolders_1, unique_by_filename_1, model_strength_1, clip_strength_1, num_loras_1, "Group 1"),
|
||||
(lora_folder_path_2, include_subfolders_2, unique_by_filename_2, model_strength_2, clip_strength_2, num_loras_2, "Group 2"),
|
||||
(lora_folder_path_3, include_subfolders_3, unique_by_filename_3, model_strength_3, clip_strength_3, num_loras_3, "Group 3"),
|
||||
]
|
||||
|
||||
for folder_path, include_subs, model_str, clip_str, num, group_name in groups:
|
||||
for folder_path, include_subs, unique_by_name, model_str, clip_str, num, group_name in groups:
|
||||
# フォルダパスが空、またはnum_lorasが0の場合はスキップ
|
||||
if not folder_path.strip() or num == 0:
|
||||
print(f"[RandomLoRALoader] {group_name}: スキップ（フォルダ未指定またはnum=0）")
|
||||
@@ -666,6 +1163,13 @@ class RandomLoRALoader:
|
||||
print(f"[RandomLoRALoader] {group_name}: LoRAファイルが見つかりませんでした")
|
||||
continue
|
||||
|
||||
# ファイル名でユニーク化（重複ファイル名を除外）
|
||||
if unique_by_name:
|
||||
lora_files = self._unique_by_filename(lora_files, group_name)
|
||||
if not lora_files:
|
||||
print(f"[RandomLoRALoader] {group_name}: ユニーク化後にファイルがありません")
|
||||
continue
|
||||
|
||||
# ランダムにLoRAを選択（全グループ共通のseedを使用）
|
||||
selected_loras = self._select_random_loras(lora_files, num, seed)
|
||||
|
||||
@@ -702,39 +1206,57 @@ class RandomLoRALoader:
|
||||
all_negative_parts.append(negative_prompt)
|
||||
trigger_display = positive_prompt
|
||||
|
||||
elif trigger_word_source == "metadata":
|
||||
trigger_words = self._get_trigger_words_from_embedded(lora_path)
|
||||
all_positive_parts.append(trigger_words)
|
||||
trigger_display = trigger_words
|
||||
|
||||
# テキスト出力に追加（末尾にカンマを付ける）
|
||||
lora_notation = f"<lora:{lora_name}:{actual_model_str}:{actual_clip_str}>"
|
||||
all_text_parts.append(f"{lora_notation}, {trigger_display},")
|
||||
|
||||
# プレビュー画像を読み込み
|
||||
preview = self._load_preview_image_as_tensor(lora_path)
|
||||
if preview is not None:
|
||||
preview_images.append(preview)
|
||||
|
||||
# テキスト出力を結合（追加プロンプトを先頭に配置、末尾にカンマを付ける）
|
||||
if additional_prompt.strip():
|
||||
if additional_prompt_positive.strip():
|
||||
# 追加プロンプトの末尾にカンマがなければ追加
|
||||
additional_with_comma = additional_prompt.strip()
|
||||
additional_with_comma = additional_prompt_positive.strip()
|
||||
if not additional_with_comma.endswith(','):
|
||||
additional_with_comma += ','
|
||||
if all_text_parts:
|
||||
# LoRAがある場合: 追加プロンプト + LoRA情報
|
||||
positive_text_output = additional_with_comma + "\n" + "\n".join(all_text_parts)
|
||||
lora_text_output = additional_with_comma + "\n" + "\n".join(all_text_parts)
|
||||
else:
|
||||
# LoRAがない場合: 追加プロンプトのみ（末尾カンマなし）
|
||||
positive_text_output = additional_prompt.strip()
|
||||
lora_text_output = additional_prompt_positive.strip()
|
||||
else:
|
||||
# 追加プロンプトなし
|
||||
if all_text_parts:
|
||||
# LoRAのみ
|
||||
positive_text_output = "\n".join(all_text_parts)
|
||||
lora_text_output = "\n".join(all_text_parts)
|
||||
else:
|
||||
# 何もない
|
||||
positive_text_output = ""
|
||||
lora_text_output = ""
|
||||
|
||||
# negativeテキスト出力（json_sample_promptの場合のみ内容あり）
|
||||
negative_text_output = ", ".join([n for n in all_negative_parts if n])
|
||||
# negativeテキスト出力
|
||||
# 1. json_sample_promptからのnegative
|
||||
sample_negative = ", ".join([n for n in all_negative_parts if n])
|
||||
# 2. additional_prompt_negativeと結合
|
||||
negative_parts = []
|
||||
if additional_prompt_negative.strip():
|
||||
negative_parts.append(additional_prompt_negative.strip())
|
||||
if sample_negative:
|
||||
negative_parts.append(sample_negative)
|
||||
negative_text_output = ", ".join(negative_parts)
|
||||
|
||||
# positiveプロンプトを結合してCONDITIONINGを生成（追加プロンプトを含める）
|
||||
final_positive_parts = []
|
||||
if additional_prompt.strip():
|
||||
if additional_prompt_positive.strip():
|
||||
# LoRA構文を削除してクリーンなプロンプトにする
|
||||
cleaned_prompt = self._remove_lora_syntax(additional_prompt.strip())
|
||||
cleaned_prompt = self._remove_lora_syntax(additional_prompt_positive.strip())
|
||||
if cleaned_prompt: # 削除後に空でなければ追加
|
||||
final_positive_parts.append(cleaned_prompt)
|
||||
final_positive_parts.extend([p for p in all_positive_parts if p])
|
||||
@@ -744,7 +1266,13 @@ class RandomLoRALoader:
|
||||
)
|
||||
|
||||
# negativeプロンプトを結合してCONDITIONINGを生成
|
||||
negative_text = ", ".join([n for n in all_negative_parts if n])
|
||||
final_negative_parts = []
|
||||
if additional_prompt_negative.strip():
|
||||
cleaned_negative = self._remove_lora_syntax(additional_prompt_negative.strip())
|
||||
if cleaned_negative:
|
||||
final_negative_parts.append(cleaned_negative)
|
||||
final_negative_parts.extend([n for n in all_negative_parts if n])
|
||||
negative_text = ", ".join(final_negative_parts)
|
||||
negative_conditioning = self._encode_prompt(
|
||||
clip, negative_text, token_normalization, weight_interpretation
|
||||
)
|
||||
@@ -752,19 +1280,14 @@ class RandomLoRALoader:
|
||||
total_loras = len(all_text_parts)
|
||||
if total_loras > 0:
|
||||
print(f"[RandomLoRALoader] 適用完了: 合計{total_loras}個のLoRA")
|
||||
elif additional_prompt.strip():
|
||||
elif additional_prompt_positive.strip():
|
||||
print(f"[RandomLoRALoader] 適用完了: LoRAなし、追加プロンプトのみ使用")
|
||||
else:
|
||||
print(f"[RandomLoRALoader] 適用完了: LoRAなし、プロンプトなし")
|
||||
|
||||
return (model, clip, positive_text_output, negative_text_output, positive_conditioning, negative_conditioning)
|
||||
|
||||
|
||||
# ComfyUIへのノード登録
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"RandomLoRALoader": RandomLoRALoader
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"RandomLoRALoader": "Random LoRA Loader"
|
||||
}
|
||||
# プレビュー画像バッチ生成
|
||||
preview_batch = self._generate_preview_batch(preview_images)
|
||||
|
||||
# positive_text = positive にエンコードした文字列そのもの（<lora:...> なし）
|
||||
return (model, clip, positive_text, negative_text_output, positive_conditioning,
|
||||
negative_conditioning, preview_batch, lora_text_output)
|
||||
|
||||
Reference in New Issue
Block a user