feat: Add auto-batching support for large LoRA collections

- Add auto-batching functionality to split large LoRA collections into manageable chunks
- Implement batch_size parameter to control number of LoRAs per batch (default: 25)
- Add batch_index parameter to select which batch to process
- Include batch tracking metadata in LORA_PARAMS for visualization
- Display batch info in plot parameters when available
- Update lora_list output to show batch header when auto-batching is enabled
- Add comprehensive tests for batching functionality
- Update documentation with detailed auto-batching usage instructions

This feature prevents UI disconnection issues when processing large numbers of LoRAs
by allowing users to process them in smaller batches sequentially.

.github/workflows/code-quality.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python 3.10
       uses: actions/setup-python@v5
@@ -133,7 +133,7 @@ jobs:
   security:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python 3.10
       uses: actions/setup-python@v5
@@ -164,7 +164,7 @@ jobs:
   architecture:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python 3.10
       uses: actions/setup-python@v5

.github/workflows/publish.yml

@@ -18,7 +18,7 @@ jobs:
     if: ${{ github.repository_owner == 'ComfyAssets' }}
     steps:
       - name: Check out code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
         with:
           submodules: true
       - name: Publish Custom Node

.github/workflows/release.yml

@@ -15,7 +15,7 @@ jobs:
       contents: write
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python 3.10
       uses: actions/setup-python@v5

.github/workflows/tests.yml

@@ -17,7 +17,7 @@ jobs:
         python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
@@ -53,7 +53,7 @@ jobs:
           print('✓ All imports successful')
 
           # Test base node
-          assert ComfyAssetsBaseNode.CATEGORY.startswith('ComfyAssets')
+          assert 'ComfyAssets' in ComfyAssetsBaseNode.CATEGORY
           print('✓ Base node tests passed')
 
           # Test dimension extraction
@@ -168,7 +168,7 @@ jobs:
           assert node.RETURN_TYPES[2] == 'INT'
           assert node.RETURN_TYPES[3] == 'FLOAT'
           assert node.RETURN_NAMES == ('sampler_name', 'scheduler', 'steps', 'cfg')
-          assert node.CATEGORY == 'ComfyAssets/🌀 Samplers'
+          assert node.CATEGORY == '🫶 ComfyAssets/🌀 Samplers'
           print('✓ Sampler Combo return types tests passed')
 
           # Test sampler combo functionality
@@ -217,7 +217,7 @@ jobs:
           # Test return types
           assert node.RETURN_TYPES == ('INT',)
           assert node.RETURN_NAMES == ('seed',)
-          assert node.CATEGORY == 'ComfyAssets/🌱 Seeds'
+          assert node.CATEGORY == '🫶 ComfyAssets/🌱 Seeds'
           print('✓ Seed History return types tests passed')
 
           # Test seed output functionality
@@ -333,7 +333,7 @@ jobs:
 
           assert res_class.RETURN_TYPES == ('INT', 'INT')
           assert res_class.RETURN_NAMES == ('width', 'height')
-          assert res_class.CATEGORY.startswith('ComfyAssets/')
+          assert 'ComfyAssets/' in res_class.CATEGORY
           print('✓ Resolution Calculator ComfyUI integration passed')
 
           # Test Width Height Selector
@@ -354,7 +354,7 @@ jobs:
 
           assert wh_class.RETURN_TYPES == ('INT', 'INT')
           assert wh_class.RETURN_NAMES == ('width', 'height')
-          assert wh_class.CATEGORY.startswith('ComfyAssets/')
+          assert 'ComfyAssets/' in wh_class.CATEGORY
           print('✓ Width Height Selector ComfyUI integration passed')
 
           # Test Sampler Combo
@@ -374,7 +374,7 @@ jobs:
           assert 'steps' in input_types['required']
           assert 'cfg' in input_types['required']
 
-          assert sampler_class.CATEGORY.startswith('ComfyAssets/')
+          assert 'ComfyAssets/' in sampler_class.CATEGORY
           print('✓ Sampler Combo ComfyUI integration passed')
 
           # Test Seed History
@@ -393,7 +393,7 @@ jobs:
 
           assert seed_class.RETURN_TYPES == ('INT',)
           assert seed_class.RETURN_NAMES == ('seed',)
-          assert seed_class.CATEGORY.startswith('ComfyAssets/')
+          assert 'ComfyAssets/' in seed_class.CATEGORY
           print('✓ Seed History ComfyUI integration passed')
 
           print('🎉 All tools ComfyUI integration readiness tests passed!')
@@ -402,7 +402,7 @@ jobs:
   test-package-structure:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python 3.10
         uses: actions/setup-python@v5
@@ -458,7 +458,7 @@ jobs:
   test-documentation:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Test documentation completeness
         run: |

README.md

@@ -8,7 +8,14 @@
 
 > A modular collection of essential custom ComfyUI nodes missing from the standard release.
 
-ComfyUI-KikoTools provides carefully crafted, production-ready nodes grouped under the **"ComfyAssets"** category. Each tool is designed with clean interfaces, comprehensive testing, and optimized performance for SDXL and FLUX workflows.
+ComfyUI-KikoTools provides carefully crafted, production-ready nodes under the "ComfyAssets" category.
+Each tool is built with clean interfaces, thorough testing, and optimized performance for SDXL and FLUX workflows.
+
+This project started out of frustration with keeping ComfyUI up to date and waiting for dozens of custom nodes to update—most of which I didn’t even use. After taking a hard look at my workflow, I realized I only needed one or two features from these nodes, many of which were abandoned or stuck in maintenance mode.
+
+I tried forking, patching, and submitting merge requests, but eventually decided to create my own curated collection of tools—fully supported and maintained by me. That’s how Kiko’s Tools was born.
+
+I’m sharing them here with the community, and I hope you find them as useful as I do.
 
 ## 🚀 Features
 
@@ -26,7 +33,10 @@ ComfyUI-KikoTools provides carefully crafted, production-ready nodes grouped und
 | [🤖 Gemini Prompt Engineer](#-gemini-prompt-engineer) | AI-powered image analysis and prompt generation | 🧠 Prompts |
 | [🔍 Display Any](#-display-any) | Universal debugging tool for any data type | 👁️ Display |
 | [🖼️ Image to Multiple Of](#️-image-to-multiple-of) | Adjust dimensions to multiples for compatibility | 🖼️ Resolution |
-| [🔤 Embedding Autocomplete](#-embedding-autocomplete) | Smart autocomplete for embeddings, LoRAs, and tags | ✍️ Text |
+| [📉 Image Scale Down By](#-image-scale-down-by) | Scale images down by a factor with quality preservation | 🖼️ Resolution |
+| [🎬 Film Grain](#-film-grain) | Add realistic film grain effects to images | 💾 Images |
+| [🔤 Embedding Autocomplete](#-embedding-autocomplete) | Smart autocomplete for embeddings, LoRAs, and tags | 🔧 Utils |
+| [🧹 Kiko Purge VRAM](#-kiko-purge-vram) | Intelligent VRAM management with detailed reporting | 🛠️ Utils |
 
 ### 🧰 xyz-helpers Tools
 
@@ -219,6 +229,41 @@ Adjusts image dimensions to be multiples of a specified value for model compatib
 
 ![Image to Multiple Of Example](examples/workflows/image_to_multiple_of_example.png)
 
+#### 📉 Image Scale Down By
+Efficiently scale images down by a specified factor with quality preservation.
+
+- **Proportional Scaling**: Reduces both width and height by the same factor
+- **Quality Preservation**: Uses bilinear interpolation with antialiasing
+- **Batch Support**: Process multiple images simultaneously
+- **Memory Efficient**: Optimized for large image batches
+- **Flexible Factor**: Scale from 0.01x to 1.0x with 0.01 precision
+
+**Use Cases:**
+- Create thumbnails or preview images
+- Reduce memory usage for large workflows
+- Generate image pyramids for multi-scale processing
+- Quick downsampling for performance optimization
+- Prepare images for web display or transmission
+
+#### 🎬 Film Grain
+Add realistic analog film grain effects to generated images.
+
+- **Realistic Grain Simulation**: Mimics actual film photography characteristics
+- **Grain Size Control**: Fine to coarse grain patterns (0.25x to 2.0x)
+- **Intensity Adjustment**: Variable strength from subtle to pronounced (0-10)
+- **Color Saturation**: Monochrome to full color grain (0-2)
+- **Shadow Lifting (Toe)**: Film-like shadow response curves
+- **Red Multiplier**: Adjust red channel independently for vintage looks
+- **Alpha Preservation**: Maintains transparency when present
+- **ITU-R BT.709 Color Space**: Professional color handling
+
+**Use Cases:**
+- Add vintage film aesthetic to AI-generated images
+- Create cinematic looks with authentic grain patterns
+- Simulate different film stocks (35mm, 16mm, etc.)
+- Add texture to overly smooth AI renders
+- Match grain from reference photography
+
 #### 🎛️ Flux Sampler Params
 FLUX-optimized parameter generator with intelligent batch processing capabilities.
 
@@ -317,9 +362,9 @@ Unified interface for text encoding and sampler parameter management.
 **Intelligent autocomplete for embeddings, LoRAs, and custom tags in text prompts.**
 
 <div align="center">
-  <img src="ac-emb.png" width="30%" alt="Embedding Autocomplete" />
-  <img src="ac-lora.png" width="30%" alt="LoRA Autocomplete" />
-  <img src="ac-tag.png" width="30%" alt="Tag Autocomplete" />
+  <img src="https://github.com/ComfyAssets/ComfyUI-KikoTools/blob/main/examples/ac-emb.png?raw=true" width="30%" alt="Embedding Autocomplete" />
+  <img src="https://github.com/ComfyAssets/ComfyUI-KikoTools/blob/main/examples/ac-lora.png?raw=true" width="30%" alt="LoRA Autocomplete" />
+  <img src="https://github.com/ComfyAssets/ComfyUI-KikoTools/blob/main/examples/ac-tag.png?raw=true" width="30%" alt="Tag Autocomplete" />
 </div>
 
 This feature is an enhanced fork of the autocomplete functionality from [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by pythongosssss. We've modernized the codebase, fixed existing bugs, and added robust security features.
@@ -351,6 +396,46 @@ This feature is an enhanced fork of the autocomplete functionality from [ComfyUI
 - Original autocomplete concept by [pythongosssss](https://github.com/pythongosssss/ComfyUI-Custom-Scripts)
 - Enhanced and modernized by KikoTools team
 
+### 🧹 Kiko Purge VRAM
+**Intelligent GPU memory management with threshold-based triggering and detailed reporting.**
+
+**Key Features:**
+- **4 Purge Modes**: 
+  - `soft`: Basic garbage collection and cache clearing
+  - `aggressive`: Multiple GC passes with full CUDA cache clearing
+  - `models_only`: Unload all models and clear model cache
+  - `cache_only`: Clear CUDA cache without garbage collection
+- **Smart Thresholds**: Only purge when memory usage exceeds specified MB limit
+- **Detailed Reporting**: Shows before/after memory usage, freed MB, and timing
+- **Passthrough Design**: Acts as workflow checkpoint without disrupting data flow
+- **CPU Fallback**: Gracefully handles non-CUDA environments
+
+**Use Cases:**
+- Free memory between heavy processing stages
+- Prevent OOM errors in complex workflows
+- Debug memory usage patterns
+- Optimize multi-model workflows
+- Clean up after batch processing
+
+**Parameters:**
+- **anything**: Any input (passed through unchanged)
+- **mode**: Purge strategy selection
+- **report_memory**: Generate detailed memory statistics
+- **memory_threshold_mb**: Only purge if usage exceeds (0 = always purge)
+
+**Example Output:**
+```
+Memory usage (5000.0 MB) exceeds threshold (4000 MB)
+
+Memory Purge Report
+-------------------
+Mode: soft
+Memory Freed: 2500.0 MB
+Before: 5000.0 MB used (62.5%)
+After: 2500.0 MB used (31.3%)
+Time: 150.0ms
+```
+
 ### 💾 Kiko Save Image Features
 
 **Use Cases:**
@@ -585,6 +670,8 @@ Example workflow available: [xyz_helpers_lora_testing.json](examples/workflows/x
 | **Gemini Prompt Engineer** | AI-powered image analysis with dynamic model refresh | ✅ Complete | [Docs](examples/documentation/gemini_prompt.md) |
 | **Display Any** | Universal debugging tool for any data type or tensor shapes | ✅ Complete | [Docs](examples/documentation/display_any.md) |
 | **Image to Multiple Of** | Adjust image dimensions to multiples for model compatibility | ✅ Complete | [Docs](examples/documentation/image_to_multiple_of.md) |
+| **Image Scale Down By** | Efficiently scale images down by a specified factor | ✅ Complete | [Docs](examples/documentation/image_scale_down_by.md) |
+| **Film Grain** | Add realistic analog film grain effects to images | ✅ Complete | [Docs](examples/documentation/film_grain.md) |
 | **Flux Sampler Params** | FLUX-optimized parameter generator with batch support | ✅ Complete | [Docs](examples/documentation/flux_sampler_params.md) |
 | **LoRA Folder Batch** | Batch process multiple LoRAs from folders | ✅ Complete | [Docs](examples/documentation/lora_folder_batch.md) |
 | **Plot Parameters** | Visualize parameter effects with graphs | ✅ Complete | [Docs](examples/documentation/plot_parameters.md) |
@@ -884,8 +971,9 @@ MIT License - see [LICENSE](LICENSE) file for details.
 
 ## 📈 Stats
 
-- **Nodes**: 16 (10 core tools + 6 xyz-helpers)
-- **Categories**: 8 emoji-based categories for better organization
+- **Nodes**: 19 (13 core tools + 6 xyz-helpers)
+- **Features**: Embedding Autocomplete (settings-based, not a node)
+- **Categories**: 9 emoji-based categories for better organization
 - **Format Support**: 3 (PNG, JPEG, WebP with advanced controls)
 - **Presets**: 26 curated resolution presets
 - **Interactive Features**: 8+ (swap buttons, history UI, popup viewers, parameter visualization)

examples/documentation/lora_folder_batch.md

@@ -13,6 +13,7 @@ This node is based on work from [comfyui-essentials-nodes](https://github.com/cu
 - **Flexible Strength Control**: Single, multiple, or range-based strength values
 - **Batch Modes**: Sequential or combinatorial strength application
 - **Epoch Detection**: Automatically extracts epoch numbers from filenames
+- **Auto-Batching**: Automatically splits large LoRA collections into manageable chunks to prevent UI disconnection
 
 ## Node Properties
 - **Category**: `ComfyAssets/🧰 xyz-helpers`
@@ -33,6 +34,11 @@ This node is based on work from [comfyui-essentials-nodes](https://github.com/cu
 |-----------|------|---------|-------------|
 | `include_pattern` | STRING | "" | Regex pattern to include files |
 | `exclude_pattern` | STRING | "" | Regex pattern to exclude files |
+| `max_loras` | INT | 50 | Maximum LoRAs to process (when auto_batch disabled) |
+| `sort_order` | DROPDOWN | natural | Sorting method [natural, alphabetical, newest, oldest] |
+| `auto_batch` | DROPDOWN | disabled | Enable auto-batching for large collections [disabled, enabled] |
+| `batch_size` | INT | 25 | Number of LoRAs per batch when auto-batching |
+| `batch_index` | INT | 0 | Which batch to output (0-based) when auto-batching |
 
 ### Strength Format Options
 - **Single**: `"1.0"` - Apply same strength to all LoRAs
@@ -72,6 +78,16 @@ LoRAFolderBatch → Processing Pipeline
     strength: "0.8...1.2+0.1"
 ```
 
+### Auto-Batch Large Collections
+```
+LoRAFolderBatch → FluxSamplerParams → KSampler
+    folder_path: "massive_lora_collection"  # 100+ files
+    strength: "1.0"
+    auto_batch: enabled
+    batch_size: 25
+    batch_index: 0  # Change to 1, 2, 3... for subsequent batches
+```
+
 ## Batch Modes Explained
 
 ### Sequential Mode
@@ -86,6 +102,41 @@ Each LoRA is tested with ALL strength values:
 - LoRA2 → [0.5, 0.75, 1.0]
 - LoRA3 → [0.5, 0.75, 1.0]
 
+## Auto-Batching for Large Collections
+
+### Overview
+When testing large numbers of LoRAs (e.g., 75+ files), ComfyUI can experience UI disconnections or memory issues. Auto-batching solves this by automatically splitting your LoRA collection into smaller, manageable chunks.
+
+### How It Works
+1. **Enable Auto-Batching**: Set `auto_batch` to "enabled"
+2. **Set Batch Size**: Configure `batch_size` (default: 25, range: 5-100)
+3. **Select Batch**: Use `batch_index` to choose which batch to process
+
+### Example: Testing 75 LoRAs
+With 75 LoRAs and batch_size=25, the system creates 3 batches:
+- **Batch 0**: LoRAs 1-25 (set batch_index=0)
+- **Batch 1**: LoRAs 26-50 (set batch_index=1)
+- **Batch 2**: LoRAs 51-75 (set batch_index=2)
+
+Run your workflow 3 times, changing only the `batch_index` each time.
+
+### Visual Feedback
+When auto-batching is enabled, the `lora_list` output includes batch information:
+```
+=== Batch 1/3 (LoRAs 1-25) ===
+
+style-epoch-001
+style-epoch-002
+...
+```
+
+### Best Practices for Auto-Batching
+1. **Start with Default**: Use batch_size=25 for most scenarios
+2. **Adjust for Memory**: Decrease batch_size if you still experience issues
+3. **Combinatorial Mode**: Be extra careful - 25 LoRAs × 3 strengths = 75 combinations
+4. **Save Between Batches**: Save your results after each batch to avoid data loss
+5. **Use Plot Parameters**: The batch info appears in plot visualizations for easy tracking
+
 ## File Naming Patterns
 
 ### Supported Epoch Formats
@@ -207,6 +258,7 @@ batch_mode: sequential
 - **1.0.1**: Added natural sorting for epochs
 - **1.0.2**: Enhanced pattern filtering
 - **1.0.3**: Improved batch modes and strength parsing
+- **1.0.4**: Added auto-batching for large LoRA collections
 
 ## Credits
 Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

kikotools/__init__.py

@@ -11,6 +11,7 @@ from .tools.gemini_prompt import GeminiPromptNode
 from .tools.image_scale_down_by import ImageScaleDownByNode
 from .tools.image_to_multiple_of import ImageToMultipleOfNode
 from .tools.kiko_film_grain import KikoFilmGrainNode
+from .tools.kiko_purge_vram import KikoPurgeVRAM
 from .tools.kiko_save_image import KikoSaveImageNode
 from .tools.resolution_calculator import ResolutionCalculatorNode
 from .tools.sampler_combo import SamplerComboCompactNode, SamplerComboNode
@@ -40,13 +41,15 @@ NODE_CLASS_MAPPINGS = {
     "DisplayAny": DisplayAnyNode,
     "DisplayText": DisplayTextNode,
     "KikoFilmGrain": KikoFilmGrainNode,
+    "KikoPurgeVRAM": KikoPurgeVRAM,
     "SamplerSelectHelper": SamplerSelectHelperNode,
     "SchedulerSelectHelper": SchedulerSelectHelperNode,
     "TextEncodeSamplerParams": TextEncodeSamplerParamsNode,
     "FluxSamplerParams": FluxSamplerParamsNode,
     "PlotParameters+": PlotParametersNode,
     "LoRAFolderBatch": LoRAFolderBatchNode,
-    "KikoEmbeddingAutocomplete": KikoEmbeddingAutocomplete,
+    # Note: KikoEmbeddingAutocomplete is not registered as a node
+    # It's a settings-only feature accessed through ComfyUI settings menu
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -62,14 +65,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "GeminiPrompt": "Gemini Prompt Engineer",
     "DisplayAny": "Display Any",
     "DisplayText": "Display Text",
-    "KikoFilmGrain": "Kiko Film Grain",
+    "KikoFilmGrain": "Film Grain",
+    "KikoPurgeVRAM": "Kiko Purge VRAM",
     "SamplerSelectHelper": "Sampler Select Helper",
     "SchedulerSelectHelper": "Scheduler Select Helper",
     "TextEncodeSamplerParams": "Text Encode for Sampler Params",
     "FluxSamplerParams": "Flux Sampler Parameters",
     "PlotParameters+": "Plot Parameters",
     "LoRAFolderBatch": "LoRA Folder Batch",
-    "KikoEmbeddingAutocomplete": "🫶 Embedding Autocomplete Configuration",
+    # KikoEmbeddingAutocomplete removed - settings only, not a node
 }
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

kikotools/base/any_type.py

@@ -0,0 +1,8 @@
+"""AnyType for wildcard input matching in ComfyUI nodes."""
+
+
+class AnyType(str):
+    """A special type that matches any input type in ComfyUI."""
+
+    def __ne__(self, other):
+        return False

kikotools/base/base_node.py

@@ -20,7 +20,7 @@ class ComfyAssetsBaseNode:
     - Consistent return type handling
     """
 
-    CATEGORY = "ComfyAssets"
+    CATEGORY = "🫶 ComfyAssets"
 
     def validate_inputs(self, **kwargs) -> None:
         """

kikotools/tools/display_any/node.py

@@ -38,7 +38,7 @@ class DisplayAnyNode(ComfyAssetsBaseNode):
         return True
 
     RETURN_TYPES = ("STRING",)
-    CATEGORY = "ComfyAssets/👁️ Display"
+    CATEGORY = "🫶 ComfyAssets/👁️ Display"
     RETURN_NAMES = ("display_text",)
     FUNCTION = "display"
     OUTPUT_NODE = True  # This node displays output in the UI

kikotools/tools/display_text/node.py

@@ -19,7 +19,7 @@ class DisplayTextNode(ComfyAssetsBaseNode):
     RETURN_NAMES = ("text",)
     OUTPUT_NODE = True
     FUNCTION = "display_text"
-    CATEGORY = "ComfyAssets/👁️ Display"
+    CATEGORY = "🫶 ComfyAssets/👁️ Display"
 
     DESCRIPTION = """
 Displays text in the UI with a copy-to-clipboard feature.

kikotools/tools/embedding_autocomplete/node.py

@@ -17,7 +17,7 @@ class KikoEmbeddingAutocomplete:
     """Node that provides embedding autocomplete functionality."""
 
     DISPLAY_NAME = "🫶 Embedding Autocomplete Settings"
-    CATEGORY = "ComfyAssets"
+    CATEGORY = "🫶 ComfyAssets"
 
     # Settings definition for the settings registry
     SETTINGS = {

kikotools/tools/empty_latent_batch/node.py

@@ -96,7 +96,7 @@ class EmptyLatentBatchNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("LATENT", "INT", "INT")
     RETURN_NAMES = ("latent", "width", "height")
     FUNCTION = "create_empty_latent"
-    CATEGORY = "ComfyAssets/📦 Latents"
+    CATEGORY = "🫶 ComfyAssets/📦 Latents"
 
     def create_empty_latent(
         self, preset: str, width: int, height: int, batch_size: int

kikotools/tools/gemini_prompt/node.py

@@ -51,7 +51,7 @@ class GeminiPromptNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("STRING", "STRING")
     RETURN_NAMES = ("prompt", "negative_prompt")
     FUNCTION = "generate_prompt"
-    CATEGORY = "ComfyAssets/🧠 Prompts"
+    CATEGORY = "🫶 ComfyAssets/🧠 Prompts"
 
     DESCRIPTION = """
 Analyzes images using Google's Gemini AI to generate optimized prompts.

kikotools/tools/image_scale_down_by/node.py

@@ -35,7 +35,7 @@ class ImageScaleDownByNode(ComfyAssetsBaseNode):
         }
 
     RETURN_TYPES = ("IMAGE",)
-    CATEGORY = "ComfyAssets/🖼️ Resolution"
+    CATEGORY = "🫶 ComfyAssets/🖼️ Resolution"
     RETURN_NAMES = ("images",)
     FUNCTION = "scale_down"
 

kikotools/tools/image_to_multiple_of/node.py

@@ -36,7 +36,7 @@ class ImageToMultipleOfNode(ComfyAssetsBaseNode):
         }
 
     RETURN_TYPES = ("IMAGE",)
-    CATEGORY = "ComfyAssets/🖼️ Resolution"
+    CATEGORY = "🫶 ComfyAssets/🖼️ Resolution"
     RETURN_NAMES = ("image",)
     FUNCTION = "process"
 

kikotools/tools/kiko_film_grain/node.py

@@ -85,7 +85,7 @@ class KikoFilmGrainNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
     FUNCTION = "apply_grain"
-    CATEGORY = "ComfyAssets/image"
+    CATEGORY = "🫶 ComfyAssets/💾 Images"
     DESCRIPTION = "Apply realistic film grain effect with customizable parameters"
 
     def apply_grain(

kikotools/tools/kiko_purge_vram/__init__.py

@@ -0,0 +1,3 @@
+from .node import KikoPurgeVRAM
+
+__all__ = ["KikoPurgeVRAM"]

kikotools/tools/kiko_purge_vram/logic.py

@@ -0,0 +1,130 @@
+import gc
+from typing import Dict, Tuple
+
+try:
+    import torch
+
+    TORCH_AVAILABLE = True
+except ImportError:
+    TORCH_AVAILABLE = False
+
+try:
+    import comfy.model_management as mm
+
+    COMFY_AVAILABLE = True
+except ImportError:
+    COMFY_AVAILABLE = False
+
+
+def get_memory_stats() -> Dict[str, float]:
+    stats = {
+        "cuda_available": False,
+        "free_mb": 0,
+        "total_mb": 0,
+        "used_mb": 0,
+        "used_percent": 0,
+    }
+
+    if TORCH_AVAILABLE and torch.cuda.is_available():
+        stats["cuda_available"] = True
+        free, total = torch.cuda.mem_get_info()
+        free_mb = free / (1024 * 1024)
+        total_mb = total / (1024 * 1024)
+        used_mb = total_mb - free_mb
+
+        stats["free_mb"] = free_mb
+        stats["total_mb"] = total_mb
+        stats["used_mb"] = used_mb
+        stats["used_percent"] = (used_mb / total_mb) * 100 if total_mb > 0 else 0
+
+    return stats
+
+
+def purge_memory(mode: str = "soft", unload_models: bool = False) -> float:
+    before_stats = get_memory_stats()
+
+    if mode == "soft":
+        # Basic garbage collection and cache clearing
+        gc.collect()
+        if TORCH_AVAILABLE and torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    elif mode == "aggressive":
+        # Multiple passes of garbage collection with full cache clearing
+        gc.collect()
+        gc.collect()
+        if TORCH_AVAILABLE and torch.cuda.is_available():
+            torch.cuda.synchronize()
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+
+    elif mode == "models_only":
+        # Only unload models
+        if COMFY_AVAILABLE:
+            mm.unload_all_models()
+            mm.soft_empty_cache()
+        gc.collect()
+
+    elif mode == "cache_only":
+        # Only clear cache without garbage collection
+        if TORCH_AVAILABLE and torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    # Handle model unloading for non-model-specific modes
+    if unload_models and mode not in ["models_only"]:
+        if COMFY_AVAILABLE:
+            mm.unload_all_models()
+            mm.soft_empty_cache()
+
+    after_stats = get_memory_stats()
+    freed_mb = before_stats["used_mb"] - after_stats["used_mb"]
+
+    return max(0, freed_mb)
+
+
+def format_memory_report(
+    before: Dict[str, float], after: Dict[str, float], mode: str, elapsed_ms: float
+) -> str:
+    if not before.get("cuda_available", True):
+        return (
+            "Memory Purge Report\n"
+            "-------------------\n"
+            "CUDA not available - CPU memory management only\n"
+            f"Mode: {mode}\n"
+            f"Time: {elapsed_ms:.1f}ms"
+        )
+
+    freed_mb = before["used_mb"] - after["used_mb"]
+
+    report = [
+        "Memory Purge Report",
+        "-------------------",
+        f"Mode: {mode}",
+        f"Memory Freed: {freed_mb:.1f} MB",
+        f"Before: {before['used_mb']:.1f} MB used ({before['used_percent']:.1f}%)",
+        f"After: {after['used_mb']:.1f} MB used ({after['used_percent']:.1f}%)",
+        f"Time: {elapsed_ms:.1f}ms",
+    ]
+
+    return "\n".join(report)
+
+
+def should_purge(threshold_mb: int) -> Tuple[bool, str]:
+    if threshold_mb <= 0:
+        return True, ""
+
+    stats = get_memory_stats()
+
+    if not stats["cuda_available"]:
+        return True, "CUDA not available, proceeding with CPU memory management"
+
+    if stats["used_mb"] >= threshold_mb:
+        return (
+            True,
+            f"Memory usage ({stats['used_mb']:.1f} MB) exceeds threshold ({threshold_mb} MB)",
+        )
+    else:
+        return (
+            False,
+            f"Memory usage ({stats['used_mb']:.1f} MB) below threshold ({threshold_mb} MB)",
+        )

kikotools/tools/kiko_purge_vram/node.py

@@ -0,0 +1,102 @@
+import time
+from typing import Any, Dict, Tuple
+
+try:
+    from ...base.base_node import ComfyAssetsBaseNode as BaseNode
+    from ...base.any_type import AnyType
+except ImportError:
+    # Fallback for testing environment
+    from kikotools.base.base_node import ComfyAssetsBaseNode as BaseNode
+    from kikotools.base.any_type import AnyType
+from .logic import get_memory_stats, purge_memory, format_memory_report, should_purge
+
+any_type = AnyType("*")
+
+
+class KikoPurgeVRAM(BaseNode):
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        return {
+            "required": {
+                "anything": (any_type, {}),
+                "mode": (
+                    ["soft", "aggressive", "models_only", "cache_only"],
+                    {
+                        "default": "soft",
+                        "tooltip": "Purge mode: soft (basic), aggressive (thorough), models_only (unload models), cache_only (clear cache)",
+                    },
+                ),
+                "report_memory": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "Generate detailed memory usage report",
+                    },
+                ),
+            },
+            "optional": {
+                "memory_threshold_mb": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 48000,
+                        "step": 100,
+                        "tooltip": "Only purge if memory usage exceeds this threshold (0 = always purge)",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = (any_type, "STRING")
+    RETURN_NAMES = ("passthrough", "memory_report")
+    FUNCTION = "purge_vram"
+    CATEGORY = "🫶 ComfyAssets/🛠️ Utils"
+    OUTPUT_NODE = True
+    DESCRIPTION = "Purge VRAM to free up GPU memory during workflow execution. Passes through any input unchanged."
+
+    def purge_vram(
+        self,
+        anything: Any,
+        mode: str,
+        report_memory: bool,
+        memory_threshold_mb: int = 0,
+    ) -> Tuple[Any, str]:
+        # Check if we should purge based on threshold
+        should_run, threshold_msg = should_purge(memory_threshold_mb)
+
+        if not should_run:
+            if report_memory:
+                return anything, f"Memory purge skipped: {threshold_msg}"
+            else:
+                return anything, ""
+
+        # Get before stats
+        before_stats = get_memory_stats() if report_memory else None
+        start_time = time.time()
+
+        # Determine if we should unload models
+        unload_models = mode in ["models_only", "aggressive"]
+
+        # Perform memory purge
+        purge_memory(mode=mode, unload_models=unload_models)
+
+        # Calculate elapsed time
+        elapsed_ms = (time.time() - start_time) * 1000
+
+        # Generate report if requested
+        if report_memory:
+            after_stats = get_memory_stats()
+            report = format_memory_report(before_stats, after_stats, mode, elapsed_ms)
+            if threshold_msg and memory_threshold_mb > 0:
+                report = f"{threshold_msg}\n\n{report}"
+        else:
+            report = ""
+
+        # Pass through the input unchanged
+        return anything, report
+
+
+NODE_CLASS_MAPPINGS = {"KikoPurgeVRAM": KikoPurgeVRAM}
+
+NODE_DISPLAY_NAME_MAPPINGS = {"KikoPurgeVRAM": "Kiko Purge VRAM"}

kikotools/tools/kiko_save_image/node.py

@@ -95,7 +95,7 @@ class KikoSaveImageNode(ComfyAssetsBaseNode):
         }
 
     RETURN_TYPES = ()
-    CATEGORY = "ComfyAssets/💾 Images"
+    CATEGORY = "🫶 ComfyAssets/💾 Images"
     FUNCTION = "save_images"
     OUTPUT_NODE = True
 

kikotools/tools/resolution_calculator/node.py

@@ -60,7 +60,7 @@ class ResolutionCalculatorNode(ComfyAssetsBaseNode):
         }
 
     RETURN_TYPES = ("INT", "INT")
-    CATEGORY = "ComfyAssets/🖼️ Resolution"
+    CATEGORY = "🫶 ComfyAssets/🖼️ Resolution"
     RETURN_NAMES = ("width", "height")
     FUNCTION = "calculate_resolution"
 

kikotools/tools/sampler_combo/compact_node.py

@@ -53,7 +53,6 @@ class SamplerComboCompactNode(ComfyAssetsBaseNode):
                         "min": 1.0,
                         "max": 15.0,
                         "step": 0.5,
-                        "display": "slider",
                         "tooltip": "CFG",
                     },
                 ),
@@ -63,7 +62,7 @@ class SamplerComboCompactNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("SAMPLER", SCHEDULERS, "INT", "FLOAT")
     RETURN_NAMES = ("sampler", "scheduler", "steps", "cfg")
     FUNCTION = "get_combo"
-    CATEGORY = "ComfyAssets/🌀 Samplers"
+    CATEGORY = "🫶 ComfyAssets/🌀 Samplers"
 
     def get_combo(
         self, sampler: str, sched: str, steps: int, cfg: float

kikotools/tools/sampler_combo/node.py

@@ -58,7 +58,6 @@ class SamplerComboNode(ComfyAssetsBaseNode):
                         "min": 0.0,
                         "max": 20.0,
                         "step": 0.5,
-                        "display": "slider",
                         "tooltip": "CFG scale (0-20)",
                     },
                 ),
@@ -68,7 +67,7 @@ class SamplerComboNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("SAMPLER", SCHEDULERS, "INT", "FLOAT")
     RETURN_NAMES = ("sampler_name", "scheduler", "steps", "cfg")
     FUNCTION = "get_sampler_combo"
-    CATEGORY = "ComfyAssets/🌀 Samplers"
+    CATEGORY = "🫶 ComfyAssets/🌀 Samplers"
 
     def get_sampler_combo(
         self, sampler_name: str, scheduler: str, steps: int, cfg: float

kikotools/tools/seed_history/node.py

@@ -38,7 +38,7 @@ class SeedHistoryNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("INT",)
     RETURN_NAMES = ("seed",)
     FUNCTION = "output_seed"
-    CATEGORY = "ComfyAssets/🌱 Seeds"
+    CATEGORY = "🫶 ComfyAssets/🌱 Seeds"
 
     def output_seed(self, seed: int) -> Tuple[int]:
         """

kikotools/tools/width_height_selector/node.py

@@ -85,7 +85,7 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("INT", "INT")
     RETURN_NAMES = ("width", "height")
     FUNCTION = "get_dimensions"
-    CATEGORY = "ComfyAssets/🖼️ Resolution"
+    CATEGORY = "🫶 ComfyAssets/🖼️ Resolution"
 
     def get_dimensions(self, preset: str, width: int, height: int) -> Tuple[int, int]:
         """

kikotools/tools/xyz_helpers/flux_sampler_params/node.py

@@ -125,7 +125,7 @@ class FluxSamplerParamsNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("LATENT", "SAMPLER_PARAMS")
     RETURN_NAMES = ("latent", "params")
     FUNCTION = "process_batch"
-    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+    CATEGORY = "🫶 ComfyAssets/🧰 xyz-helpers"
 
     def process_batch(
         self,
@@ -352,6 +352,12 @@ class FluxSamplerParamsNode(ComfyAssetsBaseNode):
                                 < len(lora_strength[lora_file_idx])
                                 else 0
                             )
+                            # Add batch info if available
+                            if "batch_info" in loras:
+                                param_record["lora_batch"] = (
+                                    f"Batch {loras['batch_info']['index'] + 1}/"
+                                    f"{loras['batch_info']['total']}"
+                                )
 
                         out_params.append(param_record)
 

kikotools/tools/xyz_helpers/lora_folder_batch/logic.py

@@ -2,8 +2,7 @@
 
 import os
 import re
-from typing import List, Dict, Any, Tuple, Optional
-from pathlib import Path
+from typing import List, Dict, Any
 import logging
 
 logger = logging.getLogger(__name__)
@@ -36,7 +35,7 @@ def get_lora_folders() -> List[str]:
         return [".", "flux", "sdxl", "sd15"]
 
 
-def scan_folder_for_loras(folder_path: str) -> List[str]:
+def scan_folder_for_loras(folder_path: str) -> List[str]:  # noqa: C901
     """
     Scan a folder for LoRA files (.safetensors).
 
@@ -52,70 +51,85 @@ def scan_folder_for_loras(folder_path: str) -> List[str]:
         # Get all LoRA paths from ComfyUI (includes extra_model_paths)
         lora_paths = folder_paths.folder_names_and_paths.get("loras", [[]])[0]
 
+        # Determine the full path and base lora path
+        full_path = None
+        base_lora_path = None
+
         # Check if this is an absolute path
         if os.path.isabs(folder_path):
             full_path = folder_path
 
-            # Try to find which lora base path this belongs to
-            rel_folder = None
+            # Check if this path is inside any of the known lora directories
             for lora_base in lora_paths:
-                try:
-                    potential_rel = os.path.relpath(full_path, lora_base)
-                    if not potential_rel.startswith(".."):
-                        # This path is inside this lora base
-                        rel_folder = potential_rel
-                        break
-                except ValueError:
-                    # Different drives on Windows
-                    continue
+                # Normalize paths for comparison
+                norm_full = os.path.normpath(full_path)
+                norm_base = os.path.normpath(lora_base)
 
-            if rel_folder is None:
-                # Path is outside all known lora directories
-                # Try to extract a relative path that might work
-                # Check if path contains common lora folder structures
-                path_parts = full_path.replace("\\", "/").split("/")
-                if "lora" in path_parts or "loras" in path_parts:
-                    # Find index after lora/loras
+                # Check if full_path starts with this lora_base
+                if norm_full.startswith(norm_base):
+                    base_lora_path = lora_base
+                    break
+
+                # Also check if the path is a subdirectory under lora/loras
+                if "lora" in norm_full.lower():
+                    # Find the lora or loras directory in the path
+                    path_parts = norm_full.replace("\\", "/").split("/")
                     for i, part in enumerate(path_parts):
-                        if part in ["lora", "loras"]:
-                            # Use everything after lora/loras as relative path
-                            rel_folder = "/".join(path_parts[i + 1 :])
-                            break
-
-                if rel_folder is None:
-                    # Last resort: use last two directories as relative path
-                    rel_folder = (
-                        "/".join(path_parts[-2:])
-                        if len(path_parts) >= 2
-                        else path_parts[-1]
-                    )
+                        if part.lower() in ["lora", "loras"]:
+                            # Check if this matches our lora_base
+                            potential_base = "/".join(path_parts[: i + 1])
+                            if os.path.normpath(potential_base) == norm_base:
+                                base_lora_path = lora_base
+                                break
+                    if base_lora_path:
+                        break
         else:
             # Relative path provided
-            full_path = (
-                os.path.join(lora_paths[0], folder_path) if lora_paths else folder_path
-            )
-            rel_folder = folder_path if folder_path != "." else ""
+            base_lora_path = lora_paths[0] if lora_paths else ""
+            full_path = os.path.join(base_lora_path, folder_path)
 
         if not os.path.exists(full_path):
             logger.warning(f"Folder does not exist: {full_path}")
             return []
 
-        # Scan for .safetensors files
+        # Scan for .safetensors files recursively
         lora_files = []
-        for file in os.listdir(full_path):
-            if file.endswith(".safetensors"):
-                # Store relative path from lora base
-                if rel_folder and rel_folder != ".":
-                    lora_files.append(os.path.join(rel_folder, file).replace("\\", "/"))
-                else:
-                    lora_files.append(file)
+        for root, _, files in os.walk(full_path):
+            for file in files:
+                if file.endswith(".safetensors"):
+                    # Get the full path to the file
+                    file_full_path = os.path.join(root, file)
+
+                    # Calculate the correct relative path for ComfyUI
+                    if base_lora_path:
+                        # Path is inside a known lora directory
+                        try:
+                            rel_path = os.path.relpath(file_full_path, base_lora_path)
+                            lora_files.append(rel_path.replace("\\", "/"))
+                        except ValueError:
+                            # Different drives on Windows, use path relative to scan folder
+                            rel_path = os.path.relpath(file_full_path, full_path)
+                            if rel_path == ".":
+                                lora_files.append(file)
+                            else:
+                                lora_files.append(rel_path.replace("\\", "/"))
+                    else:
+                        # Path is outside known lora directories
+                        # Return path relative to the scanned folder
+                        rel_path = os.path.relpath(file_full_path, full_path)
+                        if rel_path == ".":
+                            lora_files.append(file)
+                        else:
+                            lora_files.append(rel_path.replace("\\", "/"))
 
         # Sort naturally (handles epoch numbers properly)
         lora_files = natural_sort(lora_files)
 
-        logger.info(
-            f"Found {len(lora_files)} LoRA files in {folder_path}, returning paths relative to lora base"
-        )
+        logger.info(f"Found {len(lora_files)} LoRA files in {folder_path}")
+        if lora_files and logger.isEnabledFor(logging.DEBUG):
+            logger.debug(f"Base lora path: {base_lora_path}")
+            logger.debug(f"Full scan path: {full_path}")
+            logger.debug(f"First few LoRA paths returned: {lora_files[:3]}")
         return lora_files
 
     except Exception as e:
@@ -123,6 +137,34 @@ def scan_folder_for_loras(folder_path: str) -> List[str]:
         return []
 
 
+def sort_lora_files(lora_files: List[str], sort_order: str) -> List[str]:
+    """
+    Sort LoRA files based on the specified order.
+
+    Args:
+        lora_files: List of LoRA file paths
+        sort_order: Type of sorting ("natural", "alphabetical", "newest", "oldest")
+
+    Returns:
+        Sorted list of LoRA files
+    """
+    if sort_order == "natural":
+        return natural_sort(lora_files)
+    elif sort_order == "alphabetical":
+        return sorted(lora_files)
+    elif sort_order in ["newest", "oldest"]:
+        # For time-based sorting, we need the actual file stats
+        # Since we only have relative paths, we'll sort by name for now
+        # This could be enhanced if we have access to file stats
+        sorted_files = natural_sort(lora_files)
+        if sort_order == "oldest":
+            return sorted_files
+        else:  # newest
+            return sorted_files[::-1]
+    else:
+        return lora_files
+
+
 def natural_sort(items: List[str]) -> List[str]:
     """
     Sort strings naturally, handling numbers properly.
@@ -140,10 +182,17 @@ def natural_sort(items: List[str]) -> List[str]:
 
         # Split on digits and filter out empty strings
         parts = [atoi(c) for c in re.split(r"(\d+)", text) if c]
-        # Put files without numbers first
-        if not any(isinstance(p, int) for p in parts):
-            return [0] + parts
-        return parts
+
+        # Convert to tuple of (type_order, value) to ensure consistent comparison
+        # Integers get type_order 0, strings get type_order 1
+        typed_parts = []
+        for part in parts:
+            if isinstance(part, int):
+                typed_parts.append((0, part))
+            else:
+                typed_parts.append((1, part))
+
+        return typed_parts
 
     return sorted(items, key=natural_key)
 
@@ -183,7 +232,7 @@ def filter_loras_by_pattern(
     return filtered
 
 
-def parse_strength_string(strength_str: str) -> List[float]:
+def parse_strength_string(strength_str: str) -> List[float]:  # noqa: C901
     """
     Parse strength string into list of values.
 
@@ -278,6 +327,57 @@ def create_lora_params(
         return {"loras": lora_files, "strengths": strength_lists}
 
 
+def create_lora_params_batched(
+    lora_files: List[str],
+    strengths: List[float],
+    batch_mode: str = "sequential",
+    batch_size: int = 25,
+) -> List[Dict[str, Any]]:
+    """
+    Create multiple LORA_PARAMS structures for FluxSamplerParams, batched for stability.
+
+    Args:
+        lora_files: List of LoRA file paths
+        strengths: List of strength values to test
+        batch_mode: How to batch ("sequential" or "combinatorial")
+        batch_size: Maximum number of LoRAs per batch
+
+    Returns:
+        List of LORA_PARAMS dictionaries, each with batch info
+    """
+    if not lora_files:
+        logger.warning("No LoRA files provided")
+        return [{"loras": [], "strengths": [], "batch_info": {"index": 0, "total": 0}}]
+
+    # Split lora_files into batches
+    batches = []
+    total_batches = (len(lora_files) + batch_size - 1) // batch_size
+
+    for i in range(0, len(lora_files), batch_size):
+        batch_loras = lora_files[i : i + batch_size]
+        batch_index = i // batch_size
+
+        # Create params for this batch
+        params = create_lora_params(batch_loras, strengths, batch_mode)
+
+        # Add batch tracking info
+        params["batch_info"] = {
+            "index": batch_index,
+            "total": total_batches,
+            "start_idx": i,
+            "end_idx": min(i + batch_size, len(lora_files)),
+            "size": len(batch_loras),
+        }
+
+        batches.append(params)
+
+    logger.info(f"Created {total_batches} batches of LoRAs (batch size: {batch_size})")
+    for i, batch in enumerate(batches):
+        logger.info(f"  Batch {i}: {batch['batch_info']['size']} LoRAs")
+
+    return batches
+
+
 def get_lora_info(lora_file: str) -> Dict[str, Any]:
     """
     Extract information from LoRA filename.
@@ -313,15 +413,25 @@ def validate_folder_path(folder_path: str) -> bool:
     Validate that the folder path exists and is accessible.
 
     Args:
-        folder_path: Folder path to validate
+        folder_path: Folder path to validate (absolute or relative)
 
     Returns:
         True if valid
     """
     try:
+        # Handle absolute paths
+        if os.path.isabs(folder_path):
+            return os.path.exists(folder_path) and os.path.isdir(folder_path)
+
+        # Handle relative paths
         import folder_paths
 
-        lora_base_path = folder_paths.folder_names_and_paths["loras"][0][0]
+        lora_paths = folder_paths.folder_names_and_paths.get("loras", [[]])[0]
+
+        if not lora_paths:
+            return False
+
+        lora_base_path = lora_paths[0]
 
         if folder_path == ".":
             full_path = lora_base_path

kikotools/tools/xyz_helpers/lora_folder_batch/node.py

@@ -1,15 +1,14 @@
 """LoRA Folder Batch node for ComfyUI."""
 
-from typing import Tuple, Any, Dict, List
-import os
+from typing import Tuple, Any, Dict
 import logging
 from ....base.base_node import ComfyAssetsBaseNode
 from .logic import (
-    get_lora_folders,
     scan_folder_for_loras,
     filter_loras_by_pattern,
     parse_strength_string,
     create_lora_params,
+    create_lora_params_batched,
     get_lora_info,
     validate_folder_path,
 )
@@ -74,21 +73,67 @@ class LoRAFolderBatchNode(ComfyAssetsBaseNode):
                         "tooltip": "Regex pattern to exclude files (e.g., 'test|backup')",
                     },
                 ),
+                "max_loras": (
+                    "INT",
+                    {
+                        "default": 50,
+                        "min": 1,
+                        "max": 500,
+                        "tooltip": "Maximum number of LoRAs to process (to prevent UI disconnection)",
+                    },
+                ),
+                "auto_batch": (
+                    ["disabled", "enabled"],
+                    {
+                        "default": "disabled",
+                        "tooltip": "Auto-batch large sets into chunks of 25 LoRAs",
+                    },
+                ),
+                "batch_size": (
+                    "INT",
+                    {
+                        "default": 25,
+                        "min": 5,
+                        "max": 100,
+                        "tooltip": "Number of LoRAs per batch when auto-batching",
+                    },
+                ),
+                "batch_index": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 100,
+                        "tooltip": "Which batch to output (0-based index)",
+                    },
+                ),
+                "sort_order": (
+                    ["natural", "alphabetical", "newest", "oldest"],
+                    {
+                        "default": "natural",
+                        "tooltip": "How to sort the LoRA files",
+                    },
+                ),
             },
         }
 
     RETURN_TYPES = ("LORA_PARAMS", "STRING", "INT")
     RETURN_NAMES = ("lora_params", "lora_list", "lora_count")
     FUNCTION = "batch_loras"
-    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+    CATEGORY = "🫶 ComfyAssets/🧰 xyz-helpers"
 
-    def batch_loras(
+    def batch_loras(  # noqa: C901
         self,
         folder_path: str,
         strength: str,
         batch_mode: str,
         include_pattern: str = "",
         exclude_pattern: str = "",
+        max_loras: int = 50,
+        sort_order: str = "natural",
+        auto_batch: str = "disabled",
+        batch_size: int = 25,
+        batch_index: int = 0,
     ) -> Tuple[Dict[str, Any], str, int]:
         """
         Batch process LoRAs from a folder.
@@ -137,37 +182,98 @@ class LoRAFolderBatchNode(ComfyAssetsBaseNode):
                 self.log_info("No LoRAs left after filtering")
                 return ({"loras": [], "strengths": []}, "", 0)
 
+            # Apply sorting based on sort_order
+            if sort_order != "natural":
+                from .logic import sort_lora_files
+
+                lora_files = sort_lora_files(lora_files, sort_order)
+
+            # Only limit if NOT auto-batching
+            if auto_batch == "disabled" and len(lora_files) > max_loras:
+                self.log_info(
+                    f"⚠️ Limiting to {max_loras} LoRAs (found {len(lora_files)}). "
+                    f"Enable auto_batch or increase max_loras to process more."
+                )
+                lora_files = lora_files[:max_loras]
+
             # Parse strength values
             strengths = parse_strength_string(strength)
             self.log_info(f"Using strength values: {strengths}")
 
-            # Create LORA_PARAMS
-            lora_params = create_lora_params(lora_files, strengths, batch_mode)
+            # Create LORA_PARAMS with auto-batching if enabled
+            if auto_batch == "enabled" and len(lora_files) > batch_size:
+                all_batches = create_lora_params_batched(
+                    lora_files, strengths, batch_mode, batch_size
+                )
 
-            # Create info string
+                # Check if batch_index is valid
+                if batch_index >= len(all_batches):
+                    self.log_info(
+                        f"⚠️ Batch index {batch_index} out of range. "
+                        f"Only {len(all_batches)} batches available. Using batch 0."
+                    )
+                    batch_index = 0
+
+                lora_params = all_batches[batch_index]
+
+                # Update lora_files to only include current batch for list display
+                batch_start = lora_params["batch_info"]["start_idx"]
+                batch_end = lora_params["batch_info"]["end_idx"]
+                lora_files_for_display = lora_files[batch_start:batch_end]
+            else:
+                # Regular single batch mode
+                lora_params = create_lora_params(lora_files, strengths, batch_mode)
+                lora_files_for_display = lora_files
+
+            # Create info string for current batch only
             lora_list = []
-            for lora_file in lora_files:
+            for lora_file in lora_files_for_display:
                 info = get_lora_info(lora_file)
                 if info["epoch"] is not None:
                     lora_list.append(f"{info['name']} (epoch {info['epoch']})")
                 else:
                     lora_list.append(info["name"])
 
-            lora_list_str = "\n".join(lora_list)
-
-            # Calculate total combinations
-            if batch_mode == "combinatorial":
-                total_combos = len(lora_files) * len(strengths)
+            # Add batch info to the list string if auto-batching
+            if auto_batch == "enabled" and "batch_info" in lora_params:
+                batch_header = (
+                    f"=== Batch {batch_index + 1}/{lora_params['batch_info']['total']} "
+                    f"(LoRAs {lora_params['batch_info']['start_idx'] + 1}-"
+                    f"{lora_params['batch_info']['end_idx']}) ===\n\n"
+                )
+                lora_list_str = batch_header + "\n".join(lora_list)
             else:
-                total_combos = len(lora_files)
+                lora_list_str = "\n".join(lora_list)
 
-            self.log_info(
-                f"Created batch with {len(lora_files)} LoRAs, "
-                f"{len(strengths)} strength values, "
-                f"{total_combos} total combinations"
-            )
+            # Calculate total combinations for current batch
+            current_batch_loras = len(lora_files_for_display)
+            if batch_mode == "combinatorial":
+                total_combos = current_batch_loras * len(strengths)
+            else:
+                total_combos = current_batch_loras
 
-            return (lora_params, lora_list_str, len(lora_files))
+            # Warn if generating many combinations
+            if total_combos > 100:
+                self.log_info(
+                    f"⚠️ WARNING: Generating {total_combos} combinations! "
+                    f"This may cause UI disconnection. Consider reducing max_loras or strength values."
+                )
+
+            if auto_batch == "enabled" and "batch_info" in lora_params:
+                self.log_info(
+                    f"Output batch {batch_index + 1}/{lora_params['batch_info']['total']} "
+                    f"with {current_batch_loras} LoRAs, "
+                    f"{len(strengths)} strength values, "
+                    f"{total_combos} total combinations"
+                )
+            else:
+                self.log_info(
+                    f"Created batch with {current_batch_loras} LoRAs, "
+                    f"{len(strengths)} strength values, "
+                    f"{total_combos} total combinations"
+                )
+
+            return (lora_params, lora_list_str, current_batch_loras)
 
         except Exception as e:
             self.handle_error(f"Error creating LoRA batch: {str(e)}", e)

kikotools/tools/xyz_helpers/plot_sampler_params/logic.py

@@ -203,7 +203,11 @@ def format_parameter_text(param: Dict, mode: str = "full") -> str:
         # Optional LoRA line
         if "lora" in param and param["lora"]:
             lora_name = param["lora"][:32] if len(param["lora"]) > 32 else param["lora"]
-            lines.append(f"LoRA: {lora_name}, str: {param.get('lora_strength', 'N/A')}")
+            lora_line = f"LoRA: {lora_name}, str: {param.get('lora_strength', 'N/A')}"
+            # Add batch info if available
+            if "lora_batch" in param:
+                lora_line += f" [{param['lora_batch']}]"
+            lines.append(lora_line)
 
         return "\n".join(lines)
 

kikotools/tools/xyz_helpers/plot_sampler_params/node.py

@@ -113,7 +113,7 @@ class PlotParametersNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
     FUNCTION = "plot_parameters"
-    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+    CATEGORY = "🫶 ComfyAssets/🧰 xyz-helpers"
 
     def plot_parameters(
         self,

kikotools/tools/xyz_helpers/sampler_select_helper/node.py

@@ -30,7 +30,7 @@ class SamplerSelectHelperNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("selected_samplers",)
     FUNCTION = "select_samplers"
-    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+    CATEGORY = "🫶 ComfyAssets/🧰 xyz-helpers"
 
     def select_samplers(self, **sampler_flags) -> Tuple[str]:
         """

kikotools/tools/xyz_helpers/scheduler_select_helper/node.py

@@ -30,7 +30,7 @@ class SchedulerSelectHelperNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("selected_schedulers",)
     FUNCTION = "select_schedulers"
-    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+    CATEGORY = "🫶 ComfyAssets/🧰 xyz-helpers"
 
     def select_schedulers(self, **scheduler_flags) -> Tuple[str]:
         """

kikotools/tools/xyz_helpers/text_encode_sampler_params/node.py

@@ -40,7 +40,7 @@ class TextEncodeSamplerParamsNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("CONDITIONING",)
     RETURN_NAMES = ("conditioning",)
     FUNCTION = "encode_prompts"
-    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+    CATEGORY = "🫶 ComfyAssets/🧰 xyz-helpers"
 
     def encode_prompts(self, text: str, clip: Any) -> Tuple[Any]:
         """

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "kikotools"
 description = "Simple tools for ComfyUI"
-version = "1.0.12"
+version = "1.0.18"
 license = {text = "MIT"}
 dependencies = []
 

tests/test_embedding_autocomplete.py

@@ -12,7 +12,7 @@ def test_import():
     assert (
         KikoEmbeddingAutocomplete.DISPLAY_NAME == "🫶 Embedding Autocomplete Settings"
     )
-    assert KikoEmbeddingAutocomplete.CATEGORY == "ComfyAssets"
+    assert KikoEmbeddingAutocomplete.CATEGORY == "🫶 ComfyAssets"
 
 
 def test_settings_defined():

tests/unit/test_base_node.py

@@ -14,7 +14,7 @@ class TestComfyAssetsBaseNode:
 
     def test_category_is_comfy_assets(self):
         """Test that CATEGORY is set to ComfyAssets"""
-        assert ComfyAssetsBaseNode.CATEGORY == "ComfyAssets"
+        assert ComfyAssetsBaseNode.CATEGORY == "🫶 ComfyAssets"
 
     def test_validate_inputs_default_implementation(self):
         """Test default validate_inputs does nothing"""
@@ -69,7 +69,7 @@ class TestComfyAssetsBaseNode:
 
         assert isinstance(info, dict)
         assert info["class_name"] == "ComfyAssetsBaseNode"
-        assert info["category"] == "ComfyAssets"
+        assert info["category"] == "🫶 ComfyAssets"
         assert info["function"] == "Unknown"  # Base class doesn't have FUNCTION
         assert info["return_types"] == ()
         assert info["return_names"] == ()
@@ -91,14 +91,14 @@ class TestConcreteNodeInheritance:
 
     def test_concrete_node_inherits_category(self):
         """Test concrete node inherits ComfyAssets category"""
-        assert MockConcreteNode.CATEGORY == "ComfyAssets"
+        assert MockConcreteNode.CATEGORY == "🫶 ComfyAssets"
 
     def test_concrete_node_get_info_includes_specific_attributes(self):
         """Test concrete node info includes its specific attributes"""
         info = MockConcreteNode.get_node_info()
 
         assert info["class_name"] == "MockConcreteNode"
-        assert info["category"] == "ComfyAssets"
+        assert info["category"] == "🫶 ComfyAssets"
         assert info["function"] == "mock_function"
         assert info["return_types"] == ("STRING", "INT")
         assert info["return_names"] == ("text", "number")

tests/unit/tools/test_display_any.py

@@ -40,7 +40,7 @@ class TestDisplayAnyNode:
 
     def test_node_properties(self):
         """Test node has correct properties."""
-        assert DisplayAnyNode.CATEGORY == "ComfyAssets/👁️ Display"
+        assert DisplayAnyNode.CATEGORY == "🫶 ComfyAssets/👁️ Display"
         assert DisplayAnyNode.FUNCTION == "display"
         assert DisplayAnyNode.RETURN_TYPES == ("STRING",)
         assert DisplayAnyNode.RETURN_NAMES == ("display_text",)

tests/unit/tools/test_empty_latent_batch.py

@@ -134,7 +134,7 @@ class TestEmptyLatentBatchNode:
         assert EmptyLatentBatchNode.RETURN_TYPES == ("LATENT", "INT", "INT")
         assert EmptyLatentBatchNode.RETURN_NAMES == ("latent", "width", "height")
         assert EmptyLatentBatchNode.FUNCTION == "create_empty_latent"
-        assert EmptyLatentBatchNode.CATEGORY == "ComfyAssets/📦 Latents"
+        assert EmptyLatentBatchNode.CATEGORY == "🫶 ComfyAssets/📦 Latents"
 
     def test_create_empty_latent_basic(self):
         """Test basic empty latent creation through node."""

tests/unit/tools/test_gemini_prompt.py

@@ -26,7 +26,7 @@ class TestGeminiPromptNode:
 
     def test_node_properties(self):
         """Test node has correct properties."""
-        assert GeminiPromptNode.CATEGORY == "ComfyAssets/🧠 Prompts"
+        assert GeminiPromptNode.CATEGORY == "🫶 ComfyAssets/🧠 Prompts"
         assert GeminiPromptNode.FUNCTION == "generate_prompt"
         assert GeminiPromptNode.RETURN_TYPES == ("STRING", "STRING")
         assert GeminiPromptNode.RETURN_NAMES == ("prompt", "negative_prompt")

tests/unit/tools/test_image_scale_down_by.py

@@ -145,7 +145,7 @@ class TestImageScaleDownByNode:
 
     def test_category_is_comfyassets(self):
         """Test that the node is in the ComfyAssets category."""
-        assert ImageScaleDownByNode.CATEGORY == "ComfyAssets/🖼️ Resolution"
+        assert ImageScaleDownByNode.CATEGORY == "🫶 ComfyAssets/🖼️ Resolution"
 
     def test_scale_down_with_batch(self, node):
         """Test scaling down with batch of images."""

tests/unit/tools/test_image_to_multiple_of.py

@@ -118,7 +118,7 @@ class TestImageToMultipleOfNode:
         assert ImageToMultipleOfNode.RETURN_TYPES == ("IMAGE",)
         assert ImageToMultipleOfNode.RETURN_NAMES == ("image",)
         assert ImageToMultipleOfNode.FUNCTION == "process"
-        assert ImageToMultipleOfNode.CATEGORY == "ComfyAssets/🖼️ Resolution"
+        assert ImageToMultipleOfNode.CATEGORY == "🫶 ComfyAssets/🖼️ Resolution"
 
     def test_node_process_center_crop(self):
         """Test node processing with center crop."""

tests/unit/tools/test_kiko_purge_vram.py

@@ -0,0 +1,301 @@
+import sys
+from unittest.mock import patch, MagicMock
+
+import pytest
+
+# Mock comfy modules
+mock_mm = MagicMock()
+sys.modules["comfy"] = MagicMock()
+sys.modules["comfy.model_management"] = mock_mm
+
+from kikotools.tools.kiko_purge_vram.logic import (
+    purge_memory,
+    get_memory_stats,
+    format_memory_report,
+)
+
+# Ensure mm is available in the logic module after import
+import kikotools.tools.kiko_purge_vram.logic as logic_module
+
+logic_module.mm = mock_mm
+
+
+class TestMemoryStats:
+    @patch("torch.cuda.is_available")
+    @patch("torch.cuda.mem_get_info")
+    def test_get_memory_stats_with_cuda(self, mock_mem_info, mock_cuda_available):
+        mock_cuda_available.return_value = True
+        mock_mem_info.return_value = (4000000000, 8000000000)  # 4GB free, 8GB total
+
+        stats = get_memory_stats()
+
+        assert stats["cuda_available"] is True
+        assert stats["free_mb"] == pytest.approx(3814.7, rel=0.1)
+        assert stats["total_mb"] == pytest.approx(7629.4, rel=0.1)
+        assert stats["used_mb"] == pytest.approx(3814.7, rel=0.1)
+        assert stats["used_percent"] == pytest.approx(50.0, rel=0.1)
+
+    @patch("torch.cuda.is_available")
+    def test_get_memory_stats_without_cuda(self, mock_cuda_available):
+        mock_cuda_available.return_value = False
+
+        stats = get_memory_stats()
+
+        assert stats["cuda_available"] is False
+        assert stats["free_mb"] == 0
+        assert stats["total_mb"] == 0
+        assert stats["used_mb"] == 0
+        assert stats["used_percent"] == 0
+
+
+class TestMemoryPurge:
+    @patch("torch.cuda.is_available")
+    @patch("torch.cuda.empty_cache")
+    @patch("torch.cuda.ipc_collect")
+    @patch("gc.collect")
+    def test_purge_memory_soft_mode(
+        self, mock_gc, mock_ipc, mock_empty_cache, mock_cuda
+    ):
+        mock_cuda.return_value = True
+
+        with patch(
+            "kikotools.tools.kiko_purge_vram.logic.get_memory_stats"
+        ) as mock_stats:
+            mock_stats.side_effect = [
+                {"used_mb": 4000, "free_mb": 4000},
+                {"used_mb": 2000, "free_mb": 6000},
+            ]
+
+            freed_mb = purge_memory(mode="soft", unload_models=False)
+
+            mock_gc.assert_called_once()
+            mock_empty_cache.assert_called_once()
+            mock_ipc.assert_not_called()
+            assert freed_mb == 2000
+
+    @patch("torch.cuda.is_available")
+    @patch("torch.cuda.empty_cache")
+    @patch("torch.cuda.ipc_collect")
+    @patch("torch.cuda.synchronize")
+    @patch("gc.collect")
+    def test_purge_memory_aggressive_mode(
+        self, mock_gc, mock_sync, mock_ipc, mock_empty_cache, mock_cuda
+    ):
+        mock_cuda.return_value = True
+
+        with patch(
+            "kikotools.tools.kiko_purge_vram.logic.get_memory_stats"
+        ) as mock_stats:
+            mock_stats.side_effect = [
+                {"used_mb": 4000, "free_mb": 4000},
+                {"used_mb": 1500, "free_mb": 6500},
+            ]
+
+            freed_mb = purge_memory(mode="aggressive", unload_models=False)
+
+            assert mock_gc.call_count == 2
+            mock_empty_cache.assert_called()
+            mock_ipc.assert_called_once()
+            mock_sync.assert_called_once()
+            assert freed_mb == 2500
+
+    @patch("kikotools.tools.kiko_purge_vram.logic.COMFY_AVAILABLE", True)
+    @patch("torch.cuda.is_available")
+    @patch("gc.collect")
+    def test_purge_memory_models_only(self, mock_gc, mock_cuda):
+        mock_cuda.return_value = True
+
+        with patch(
+            "kikotools.tools.kiko_purge_vram.logic.get_memory_stats"
+        ) as mock_stats:
+            mock_stats.side_effect = [
+                {"used_mb": 6000, "free_mb": 2000},
+                {"used_mb": 1000, "free_mb": 7000},
+            ]
+
+            freed_mb = purge_memory(mode="models_only", unload_models=True)
+
+            mock_mm.unload_all_models.assert_called_once()
+            mock_mm.soft_empty_cache.assert_called_once()
+            mock_gc.assert_called()
+            assert freed_mb == 5000
+
+    @patch("torch.cuda.is_available")
+    @patch("torch.cuda.empty_cache")
+    @patch("gc.collect")
+    def test_purge_memory_cache_only(self, mock_gc, mock_empty_cache, mock_cuda):
+        mock_cuda.return_value = True
+
+        with patch(
+            "kikotools.tools.kiko_purge_vram.logic.get_memory_stats"
+        ) as mock_stats:
+            mock_stats.side_effect = [
+                {"used_mb": 3000, "free_mb": 5000},
+                {"used_mb": 2500, "free_mb": 5500},
+            ]
+
+            freed_mb = purge_memory(mode="cache_only", unload_models=False)
+
+            mock_gc.assert_not_called()
+            mock_empty_cache.assert_called_once()
+            assert freed_mb == 500
+
+    @patch("torch.cuda.is_available")
+    def test_purge_memory_no_cuda(self, mock_cuda):
+        mock_cuda.return_value = False
+
+        with patch("gc.collect") as mock_gc:
+            freed_mb = purge_memory(mode="soft", unload_models=False)
+
+            mock_gc.assert_called_once()
+            assert freed_mb == 0
+
+
+class TestMemoryReport:
+    def test_format_memory_report_with_improvement(self):
+        before = {
+            "used_mb": 4000,
+            "free_mb": 4000,
+            "total_mb": 8000,
+            "used_percent": 50,
+        }
+        after = {"used_mb": 2000, "free_mb": 6000, "total_mb": 8000, "used_percent": 25}
+
+        report = format_memory_report(before, after, mode="soft", elapsed_ms=150)
+
+        assert "Memory Purge Report" in report
+        assert "Mode: soft" in report
+        assert "Memory Freed: 2000.0 MB" in report
+        assert "Before: 4000.0 MB used (50.0%)" in report
+        assert "After: 2000.0 MB used (25.0%)" in report
+        assert "Time: 150.0ms" in report
+
+    def test_format_memory_report_no_improvement(self):
+        before = {
+            "used_mb": 2000,
+            "free_mb": 6000,
+            "total_mb": 8000,
+            "used_percent": 25,
+        }
+        after = {"used_mb": 2000, "free_mb": 6000, "total_mb": 8000, "used_percent": 25}
+
+        report = format_memory_report(before, after, mode="cache_only", elapsed_ms=50)
+
+        assert "Memory Freed: 0.0 MB" in report
+        assert "Time: 50.0ms" in report
+
+    def test_format_memory_report_no_cuda(self):
+        before = {
+            "used_mb": 0,
+            "free_mb": 0,
+            "total_mb": 0,
+            "used_percent": 0,
+            "cuda_available": False,
+        }
+        after = {
+            "used_mb": 0,
+            "free_mb": 0,
+            "total_mb": 0,
+            "used_percent": 0,
+            "cuda_available": False,
+        }
+
+        report = format_memory_report(before, after, mode="soft", elapsed_ms=10)
+
+        assert "CUDA not available" in report
+
+
+class TestKikoPurgeVRAMNode:
+    @patch("kikotools.tools.kiko_purge_vram.node.format_memory_report")
+    @patch("kikotools.tools.kiko_purge_vram.node.purge_memory")
+    @patch("kikotools.tools.kiko_purge_vram.node.get_memory_stats")
+    @patch("kikotools.tools.kiko_purge_vram.node.should_purge")
+    def test_node_execute_with_threshold(
+        self, mock_should_purge, mock_stats, mock_purge, mock_format
+    ):
+        from kikotools.tools.kiko_purge_vram.node import KikoPurgeVRAM
+
+        mock_should_purge.return_value = (
+            True,
+            "Memory usage (5000.0 MB) exceeds threshold (4000 MB)",
+        )
+        mock_stats.side_effect = [
+            {
+                "used_mb": 5000,
+                "free_mb": 3000,
+                "total_mb": 8000,
+                "used_percent": 62.5,
+                "cuda_available": True,
+            },
+            {
+                "used_mb": 2000,
+                "free_mb": 6000,
+                "total_mb": 8000,
+                "used_percent": 25,
+                "cuda_available": True,
+            },
+        ]
+        mock_purge.return_value = 3000
+        mock_format.return_value = "Memory Purge Report\n-------------------\nMode: soft\nMemory Freed: 3000.0 MB"
+
+        node = KikoPurgeVRAM()
+        test_input = "test_data"
+
+        result, report = node.purge_vram(
+            anything=test_input,
+            mode="soft",
+            report_memory=True,
+            memory_threshold_mb=4000,
+        )
+
+        assert result == test_input
+        assert "Memory Freed: 3000.0 MB" in report
+        mock_purge.assert_called_once_with(mode="soft", unload_models=False)
+
+    @patch("kikotools.tools.kiko_purge_vram.logic.get_memory_stats")
+    def test_node_skip_below_threshold(self, mock_stats):
+        from kikotools.tools.kiko_purge_vram.node import KikoPurgeVRAM
+
+        mock_stats.return_value = {
+            "used_mb": 2000,
+            "free_mb": 6000,
+            "total_mb": 8000,
+            "used_percent": 25,
+            "cuda_available": True,
+        }
+
+        node = KikoPurgeVRAM()
+        test_input = "test_data"
+
+        with patch("kikotools.tools.kiko_purge_vram.logic.purge_memory") as mock_purge:
+            result, report = node.purge_vram(
+                anything=test_input,
+                mode="soft",
+                report_memory=True,
+                memory_threshold_mb=3000,
+            )
+
+            assert result == test_input
+            assert "below threshold" in report.lower()
+            mock_purge.assert_not_called()
+
+    def test_node_input_types(self):
+        from kikotools.tools.kiko_purge_vram.node import KikoPurgeVRAM
+
+        input_types = KikoPurgeVRAM.INPUT_TYPES()
+
+        assert "required" in input_types
+        assert "optional" in input_types
+        assert "anything" in input_types["required"]
+        assert "mode" in input_types["required"]
+        assert "report_memory" in input_types["required"]
+        assert "memory_threshold_mb" in input_types["optional"]
+
+    def test_node_properties(self):
+        from kikotools.tools.kiko_purge_vram.node import KikoPurgeVRAM
+
+        assert KikoPurgeVRAM.FUNCTION == "purge_vram"
+        assert KikoPurgeVRAM.CATEGORY == "🫶 ComfyAssets/🛠️ Utils"
+        assert KikoPurgeVRAM.OUTPUT_NODE is True
+        assert len(KikoPurgeVRAM.RETURN_TYPES) == 2
+        assert KikoPurgeVRAM.RETURN_NAMES == ("passthrough", "memory_report")

tests/unit/tools/test_kiko_save_image.py

@@ -329,7 +329,7 @@ class TestKikoSaveImageNode:
         assert KikoSaveImageNode.RETURN_TYPES == ()
         assert KikoSaveImageNode.FUNCTION == "save_images"
         assert KikoSaveImageNode.OUTPUT_NODE is True
-        assert KikoSaveImageNode.CATEGORY == "ComfyAssets/💾 Images"
+        assert KikoSaveImageNode.CATEGORY == "🫶 ComfyAssets/💾 Images"
 
     @patch("kikotools.tools.kiko_save_image.node.process_image_batch")
     def test_save_images_success(self, mock_process):
@@ -436,7 +436,7 @@ class TestKikoSaveImageNode:
         info = self.node.get_node_info()
 
         assert info["class_name"] == "KikoSaveImageNode"
-        assert info["category"] == "ComfyAssets/💾 Images"
+        assert info["category"] == "🫶 ComfyAssets/💾 Images"
         assert info["function"] == "save_images"
 
 

tests/unit/tools/test_resolution_calculator.py

@@ -179,7 +179,7 @@ class TestResolutionCalculatorNode:
         assert hasattr(ResolutionCalculatorNode, "CATEGORY")
 
         # Check category is correct
-        assert ResolutionCalculatorNode.CATEGORY == "ComfyAssets/🖼️ Resolution"
+        assert ResolutionCalculatorNode.CATEGORY == "🫶 ComfyAssets/🖼️ Resolution"
 
         # Check return types
         assert ResolutionCalculatorNode.RETURN_TYPES == ("INT", "INT")
@@ -281,7 +281,7 @@ class TestResolutionCalculatorNode:
         node = ResolutionCalculatorNode()
         node_info = node.get_node_info()
 
-        assert node_info["category"] == "ComfyAssets/🖼️ Resolution"
+        assert node_info["category"] == "🫶 ComfyAssets/🖼️ Resolution"
         assert node_info["class_name"] == "ResolutionCalculatorNode"
 
 

tests/unit/tools/test_sampler_combo.py

@@ -186,7 +186,7 @@ class TestSamplerComboNode:
             "cfg",
         )
         assert SamplerComboNode.FUNCTION == "get_sampler_combo"
-        assert SamplerComboNode.CATEGORY == "ComfyAssets/🌀 Samplers"
+        assert SamplerComboNode.CATEGORY == "🫶 ComfyAssets/🌀 Samplers"
 
     def test_get_sampler_combo_valid_inputs(self):
         """Test get_sampler_combo with valid inputs."""

tests/unit/tools/test_seed_history.py

@@ -49,7 +49,7 @@ class TestSeedHistoryNode:
         assert SeedHistoryNode.RETURN_TYPES == ("INT",)
         assert SeedHistoryNode.RETURN_NAMES == ("seed",)
         assert SeedHistoryNode.FUNCTION == "output_seed"
-        assert SeedHistoryNode.CATEGORY == "ComfyAssets/🌱 Seeds"
+        assert SeedHistoryNode.CATEGORY == "🫶 ComfyAssets/🌱 Seeds"
 
     def test_output_seed_valid_input(self):
         """Test seed output with valid input."""

tests/unit/tools/test_width_height_selector.py

@@ -37,7 +37,7 @@ class TestWidthHeightSelectorNode:
         assert self.node.RETURN_TYPES == ("INT", "INT")
         assert self.node.RETURN_NAMES == ("width", "height")
         assert self.node.FUNCTION == "get_dimensions"
-        assert self.node.CATEGORY == "ComfyAssets/🖼️ Resolution"
+        assert self.node.CATEGORY == "🫶 ComfyAssets/🖼️ Resolution"
 
     def test_custom_dimensions(self):
         """Test custom dimensions."""

tests/unit/tools/xyz_helpers/test_flux_sampler_params.py

@@ -182,7 +182,7 @@ class TestFluxSamplerParamsNode:
 
     def test_node_properties(self):
         """Test node properties."""
-        assert FluxSamplerParamsNode.CATEGORY == "ComfyAssets/🧰 xyz-helpers"
+        assert FluxSamplerParamsNode.CATEGORY == "🫶 ComfyAssets/🧰 xyz-helpers"
         assert FluxSamplerParamsNode.FUNCTION == "process_batch"
         assert FluxSamplerParamsNode.RETURN_TYPES == ("LATENT", "SAMPLER_PARAMS")
         assert FluxSamplerParamsNode.RETURN_NAMES == ("latent", "params")

tests/unit/tools/xyz_helpers/test_lora_folder_batch.py

@@ -1,8 +1,9 @@
 """Tests for LoRA Folder Batch node."""
 
 import pytest
-from unittest.mock import Mock, patch, MagicMock
+from unittest.mock import patch, MagicMock
 import os
+import tempfile
 from kikotools.tools.xyz_helpers.lora_folder_batch import LoRAFolderBatchNode
 from kikotools.tools.xyz_helpers.lora_folder_batch.logic import (
     scan_folder_for_loras,
@@ -10,8 +11,8 @@ from kikotools.tools.xyz_helpers.lora_folder_batch.logic import (
     filter_loras_by_pattern,
     parse_strength_string,
     create_lora_params,
+    create_lora_params_batched,
     get_lora_info,
-    validate_folder_path,
 )
 
 
@@ -35,6 +36,27 @@ class TestLoRAFolderBatchLogic:
         # Base file could be first or last depending on implementation
         assert "model-v1.safetensors" in sorted_files
 
+    def test_natural_sort_with_paths(self):
+        """Test natural sorting with subdirectory paths."""
+        files = [
+            "subdir2/model-10.safetensors",
+            "model-2.safetensors",
+            "subdir1/model-20.safetensors",
+            "subdir1/model-3.safetensors",
+            "model-100.safetensors",
+        ]
+        sorted_files = natural_sort(files)
+
+        # Should handle mixed paths and numbers correctly
+        assert len(sorted_files) == 5
+        # Files with smaller numbers should come first within their directories
+        assert sorted_files.index("model-2.safetensors") < sorted_files.index(
+            "model-100.safetensors"
+        )
+        assert sorted_files.index("subdir1/model-3.safetensors") < sorted_files.index(
+            "subdir1/model-20.safetensors"
+        )
+
     def test_filter_loras_by_pattern(self):
         """Test filtering LoRAs by patterns."""
         files = [
@@ -114,6 +136,59 @@ class TestLoRAFolderBatchLogic:
         assert info["epoch"] is None
         assert info["version"] is None
 
+    def test_scan_folder_recursive(self):
+        """Test recursive scanning of LoRA files in subdirectories."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Create nested directory structure
+            os.makedirs(os.path.join(temp_dir, "flux", "style"))
+            os.makedirs(os.path.join(temp_dir, "flux", "character"))
+            os.makedirs(os.path.join(temp_dir, "sdxl"))
+
+            # Create test files
+            test_files = [
+                os.path.join(temp_dir, "root-lora.safetensors"),
+                os.path.join(temp_dir, "flux", "flux-lora.safetensors"),
+                os.path.join(temp_dir, "flux", "style", "style-lora.safetensors"),
+                os.path.join(temp_dir, "flux", "character", "char-lora.safetensors"),
+                os.path.join(temp_dir, "sdxl", "sdxl-lora.safetensors"),
+                os.path.join(temp_dir, "not-a-lora.txt"),  # Should be ignored
+            ]
+
+            for file_path in test_files:
+                with open(file_path, "w") as f:
+                    f.write("test")
+
+            # Create a mock folder_paths module
+            mock_folder_paths = MagicMock()
+            mock_folder_paths.folder_names_and_paths = {"loras": [[temp_dir]]}
+
+            # Mock the import
+            import sys
+
+            sys.modules["folder_paths"] = mock_folder_paths
+
+            try:
+                # Test scanning from root - should find all .safetensors files
+                results = scan_folder_for_loras(".")
+                assert len(results) == 5
+                assert "root-lora.safetensors" in results
+                assert "flux/flux-lora.safetensors" in results
+                assert "flux/style/style-lora.safetensors" in results
+                assert "flux/character/char-lora.safetensors" in results
+                assert "sdxl/sdxl-lora.safetensors" in results
+                assert "not-a-lora.txt" not in str(results)
+
+                # Test scanning from subdirectory
+                results = scan_folder_for_loras("flux")
+                assert len(results) == 3
+                assert "flux/flux-lora.safetensors" in results
+                assert "flux/style/style-lora.safetensors" in results
+                assert "flux/character/char-lora.safetensors" in results
+            finally:
+                # Clean up the mock
+                if "folder_paths" in sys.modules:
+                    del sys.modules["folder_paths"]
+
 
 class TestLoRAFolderBatchNode:
     """Test the LoRA Folder Batch node."""
@@ -137,6 +212,9 @@ class TestLoRAFolderBatchNode:
         optional = input_types["optional"]
         assert "include_pattern" in optional
         assert "exclude_pattern" in optional
+        assert "auto_batch" in optional
+        assert "batch_size" in optional
+        assert "batch_index" in optional
 
     def test_batch_loras_empty_folder(self, node):
         """Test with empty folder."""
@@ -183,9 +261,13 @@ class TestLoRAFolderBatchNode:
 
     def test_node_properties(self):
         """Test node properties."""
-        assert LoRAFolderBatchNode.CATEGORY == "ComfyAssets/🧰 xyz-helpers"
+        assert LoRAFolderBatchNode.CATEGORY == "🫶 ComfyAssets/🧰 xyz-helpers"
         assert LoRAFolderBatchNode.FUNCTION == "batch_loras"
-        assert LoRAFolderBatchNode.RETURN_TYPES == ("LORA_PARAMS", "STRING", "INT")
+        assert LoRAFolderBatchNode.RETURN_TYPES == (
+            "LORA_PARAMS",
+            "STRING",
+            "INT",
+        )
         assert LoRAFolderBatchNode.RETURN_NAMES == (
             "lora_params",
             "lora_list",
@@ -200,3 +282,90 @@ class TestLoRAFolderBatchNode:
         time.sleep(0.01)
         result2 = LoRAFolderBatchNode.IS_CHANGED()
         assert result1 != result2
+
+    def test_create_lora_params_batched(self):
+        """Test the batched LoRA params creation."""
+        lora_files = [f"lora_{i:03d}.safetensors" for i in range(75)]
+        strengths = [0.5, 1.0]
+
+        # Test with batch size of 25
+        batches = create_lora_params_batched(lora_files, strengths, "sequential", 25)
+
+        assert len(batches) == 3  # 75 / 25 = 3 batches
+
+        # Check first batch
+        assert len(batches[0]["loras"]) == 25
+        assert batches[0]["batch_info"]["index"] == 0
+        assert batches[0]["batch_info"]["total"] == 3
+        assert batches[0]["batch_info"]["start_idx"] == 0
+        assert batches[0]["batch_info"]["end_idx"] == 25
+        assert batches[0]["batch_info"]["size"] == 25
+
+        # Check second batch
+        assert len(batches[1]["loras"]) == 25
+        assert batches[1]["batch_info"]["index"] == 1
+        assert batches[1]["batch_info"]["start_idx"] == 25
+        assert batches[1]["batch_info"]["end_idx"] == 50
+
+        # Check third batch
+        assert len(batches[2]["loras"]) == 25
+        assert batches[2]["batch_info"]["index"] == 2
+        assert batches[2]["batch_info"]["start_idx"] == 50
+        assert batches[2]["batch_info"]["end_idx"] == 75
+
+    def test_auto_batch_node_integration(self, node):
+        """Test auto-batching in the node."""
+        # Create mock LoRA files
+        lora_files = [f"lora_{i:03d}.safetensors" for i in range(75)]
+
+        with patch(
+            "kikotools.tools.xyz_helpers.lora_folder_batch.node.validate_folder_path"
+        ) as mock_validate:
+            with patch(
+                "kikotools.tools.xyz_helpers.lora_folder_batch.node.scan_folder_for_loras"
+            ) as mock_scan:
+                mock_validate.return_value = True
+                mock_scan.return_value = lora_files
+
+                # Test batch 0
+                params, lora_list, count = node.batch_loras(
+                    folder_path="test",
+                    strength="1.0",
+                    batch_mode="sequential",
+                    auto_batch="enabled",
+                    batch_size=25,
+                    batch_index=0,
+                )
+
+                assert count == 25
+                assert "Batch 1/3" in lora_list
+                assert len(params["loras"]) == 25
+                assert params["loras"][0] == "lora_000.safetensors"
+
+                # Test batch 1
+                params, lora_list, count = node.batch_loras(
+                    folder_path="test",
+                    strength="1.0",
+                    batch_mode="sequential",
+                    auto_batch="enabled",
+                    batch_size=25,
+                    batch_index=1,
+                )
+
+                assert count == 25
+                assert "Batch 2/3" in lora_list
+                assert params["loras"][0] == "lora_025.safetensors"
+
+                # Test batch 2
+                params, lora_list, count = node.batch_loras(
+                    folder_path="test",
+                    strength="1.0",
+                    batch_mode="sequential",
+                    auto_batch="enabled",
+                    batch_size=25,
+                    batch_index=2,
+                )
+
+                assert count == 25
+                assert "Batch 3/3" in lora_list
+                assert params["loras"][0] == "lora_050.safetensors"

tests/unit/tools/xyz_helpers/test_plot_parameters.py

@@ -227,7 +227,7 @@ class TestPlotParametersNode:
 
     def test_node_properties(self):
         """Test node properties."""
-        assert PlotParametersNode.CATEGORY == "ComfyAssets/🧰 xyz-helpers"
+        assert PlotParametersNode.CATEGORY == "🫶 ComfyAssets/🧰 xyz-helpers"
         assert PlotParametersNode.FUNCTION == "plot_parameters"
         assert PlotParametersNode.RETURN_TYPES == ("IMAGE",)
         assert PlotParametersNode.RETURN_NAMES == ("image",)

tests/unit/tools/xyz_helpers/test_sampler_select_helper.py

@@ -85,7 +85,7 @@ class TestSamplerSelectHelperNode:
 
     def test_node_properties(self):
         """Test node properties."""
-        assert SamplerSelectHelperNode.CATEGORY == "ComfyAssets/🧰 xyz-helpers"
+        assert SamplerSelectHelperNode.CATEGORY == "🫶 ComfyAssets/🧰 xyz-helpers"
         assert SamplerSelectHelperNode.FUNCTION == "select_samplers"
         assert SamplerSelectHelperNode.RETURN_TYPES == ("STRING",)
         assert SamplerSelectHelperNode.RETURN_NAMES == ("selected_samplers",)

tests/unit/tools/xyz_helpers/test_scheduler_select_helper.py

@@ -99,7 +99,7 @@ class TestSchedulerSelectHelperNode:
 
     def test_node_properties(self):
         """Test node properties."""
-        assert SchedulerSelectHelperNode.CATEGORY == "ComfyAssets/🧰 xyz-helpers"
+        assert SchedulerSelectHelperNode.CATEGORY == "🫶 ComfyAssets/🧰 xyz-helpers"
         assert SchedulerSelectHelperNode.FUNCTION == "select_schedulers"
         assert SchedulerSelectHelperNode.RETURN_TYPES == ("STRING",)
         assert SchedulerSelectHelperNode.RETURN_NAMES == ("selected_schedulers",)

tests/unit/tools/xyz_helpers/test_text_encode_sampler_params.py

@@ -138,7 +138,7 @@ class TestTextEncodeSamplerParamsNode:
 
     def test_node_properties(self):
         """Test node properties."""
-        assert TextEncodeSamplerParamsNode.CATEGORY == "ComfyAssets/🧰 xyz-helpers"
+        assert TextEncodeSamplerParamsNode.CATEGORY == "🫶 ComfyAssets/🧰 xyz-helpers"
         assert TextEncodeSamplerParamsNode.FUNCTION == "encode_prompts"
         assert TextEncodeSamplerParamsNode.RETURN_TYPES == ("CONDITIONING",)
         assert TextEncodeSamplerParamsNode.RETURN_NAMES == ("conditioning",)

web/display_any.js

@@ -58,13 +58,6 @@ app.registerExtension({
                     }
                 };
                 this.addCustomWidget(copyWidget);
-                
-                // Update node title with condensed info
-                const firstLine = text ? text.split('\n')[0] : '';
-                const condensed = firstLine.length > 50 
-                    ? firstLine.substring(0, 50) + "..." 
-                    : firstLine;
-                this.title = `DisplayAny: ${condensed}`;
 
                 requestAnimationFrame(() => {
                     const sz = this.computeSize();

web/kiko_custom_colors.js

@@ -0,0 +1,258 @@
+import { app } from "../../scripts/app.js";
+import { $el } from "../../scripts/ui.js";
+
+// Custom colors feature with extended options based on PR #433
+// Adds custom color pickers for nodes with full, title, and background options
+
+const colorShade = (col, amt) => {
+    col = col.replace(/^#/, "");
+    if (col.length === 3) col = col[0] + col[0] + col[1] + col[1] + col[2] + col[2];
+
+    let [r, g, b] = col.match(/.{2}/g);
+    [r, g, b] = [parseInt(r, 16) + amt, parseInt(g, 16) + amt, parseInt(b, 16) + amt];
+
+    r = Math.max(Math.min(255, r), 0).toString(16);
+    g = Math.max(Math.min(255, g), 0).toString(16);
+    b = Math.max(Math.min(255, b), 0).toString(16);
+
+    const rr = (r.length < 2 ? "0" : "") + r;
+    const gg = (g.length < 2 ? "0" : "") + g;
+    const bb = (b.length < 2 ? "0" : "") + b;
+
+    return `#${rr}${gg}${bb}`;
+};
+
+app.registerExtension({
+    name: "kikotools.customColors",
+    async init() {
+        // Register settings
+        app.ui.settings.addSetting({
+            id: "kikotools.custom_colors.enabled",
+            name: "🫶 Custom Colors: Enable",
+            type: "boolean",
+            defaultValue: false,
+            tooltip: "Enable custom color picker options in node context menu",
+        });
+
+        app.ui.settings.addSetting({
+            id: "kikotools.custom_colors.show_full",
+            name: "🫶 Custom Colors: Show Full Color Option",
+            type: "boolean",
+            defaultValue: true,
+            tooltip: "Show option to change both title and background colors",
+        });
+
+        app.ui.settings.addSetting({
+            id: "kikotools.custom_colors.show_title",
+            name: "🫶 Custom Colors: Show Title Color Option",
+            type: "boolean",
+            defaultValue: true,
+            tooltip: "Show option to change only title color",
+        });
+
+        app.ui.settings.addSetting({
+            id: "kikotools.custom_colors.show_bg",
+            name: "🫶 Custom Colors: Show Background Color Option",
+            type: "boolean",
+            defaultValue: true,
+            tooltip: "Show option to change only background color",
+        });
+
+        app.ui.settings.addSetting({
+            id: "kikotools.custom_colors.auto_shade",
+            name: "🫶 Custom Colors: Auto-shade Title",
+            type: "boolean",
+            defaultValue: true,
+            tooltip: "Automatically apply shading to title color for better contrast",
+        });
+    },
+
+    setup() {
+        let pickerFull, pickerTitle, pickerBG;
+        let activeNode;
+        
+        // Check if feature is enabled
+        const isEnabled = () => {
+            const setting = app.ui.settings.getSettingValue("kikotools.custom_colors.enabled");
+            return setting !== undefined ? setting : false;
+        };
+
+        const getSettings = () => ({
+            showFull: app.ui.settings.getSettingValue("kikotools.custom_colors.show_full") !== false,
+            showTitle: app.ui.settings.getSettingValue("kikotools.custom_colors.show_title") !== false,
+            showBG: app.ui.settings.getSettingValue("kikotools.custom_colors.show_bg") !== false,
+            autoShade: app.ui.settings.getSettingValue("kikotools.custom_colors.auto_shade") !== false,
+        });
+
+        // Helper function to apply color to node(s)
+        const applyColorToNodes = (colorValue, colorType, node) => {
+            const settings = getSettings();
+            const graphcanvas = LGraphCanvas.active_canvas;
+            const nodes = (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) 
+                ? [node] 
+                : Object.values(graphcanvas.selected_nodes);
+
+            nodes.forEach(n => {
+                if (colorValue) {
+                    if (n.constructor === LiteGraph.LGraphGroup) {
+                        // For groups, only set the main color
+                        if (colorType === 'full' || colorType === 'bg') {
+                            n.color = colorValue;
+                        }
+                    } else {
+                        // For regular nodes
+                        switch(colorType) {
+                            case 'full':
+                                n.color = settings.autoShade ? colorShade(colorValue, 20) : colorValue;
+                                n.bgcolor = colorValue;
+                                break;
+                            case 'title':
+                                n.color = colorValue;
+                                break;
+                            case 'bg':
+                                n.bgcolor = colorValue;
+                                break;
+                        }
+                    }
+                }
+            });
+
+            node.setDirtyCanvas(true, true);
+        };
+
+        // Create color picker input if not exists
+        const createPicker = (type) => {
+            const picker = $el("input", {
+                type: "color",
+                parent: document.body,
+                style: {
+                    display: "none",
+                },
+            });
+            
+            picker.onchange = () => {
+                if (activeNode) {
+                    applyColorToNodes(picker.value, type, activeNode);
+                }
+            };
+            
+            return picker;
+        };
+
+        // Hook into the node colors menu
+        const onMenuNodeColors = LGraphCanvas.onMenuNodeColors;
+        LGraphCanvas.onMenuNodeColors = function (value, options, e, menu, node) {
+            const r = onMenuNodeColors.apply(this, arguments);
+            
+            // Only add custom options if enabled
+            if (!isEnabled()) return r;
+            
+            const settings = getSettings();
+            
+            requestAnimationFrame(() => {
+                const menus = document.querySelectorAll(".litecontextmenu");
+                for (let i = menus.length - 1; i >= 0; i--) {
+                    if (menus[i].firstElementChild.textContent.includes("No color") || 
+                        menus[i].firstElementChild.value?.content?.includes("No color")) {
+                        
+                        // Add Custom Full option
+                        if (settings.showFull) {
+                            $el(
+                                "div.litemenu-entry.submenu",
+                                {
+                                    parent: menus[i],
+                                    $: (el) => {
+                                        el.onclick = () => {
+                                            LiteGraph.closeAllContextMenus();
+                                            if (!pickerFull) {
+                                                pickerFull = createPicker('full');
+                                            }
+                                            activeNode = null;
+                                            pickerFull.value = node.bgcolor || "#000000";
+                                            activeNode = node;
+                                            pickerFull.click();
+                                        };
+                                    },
+                                },
+                                [
+                                    $el("span", {
+                                        style: {
+                                            paddingLeft: "4px",
+                                            display: "block",
+                                        },
+                                        textContent: "🫶 Custom Full",
+                                    }),
+                                ]
+                            );
+                        }
+
+                        // Add Custom Title option
+                        if (settings.showTitle) {
+                            $el(
+                                "div.litemenu-entry.submenu",
+                                {
+                                    parent: menus[i],
+                                    $: (el) => {
+                                        el.onclick = () => {
+                                            LiteGraph.closeAllContextMenus();
+                                            if (!pickerTitle) {
+                                                pickerTitle = createPicker('title');
+                                            }
+                                            activeNode = null;
+                                            pickerTitle.value = node.color || "#000000";
+                                            activeNode = node;
+                                            pickerTitle.click();
+                                        };
+                                    },
+                                },
+                                [
+                                    $el("span", {
+                                        style: {
+                                            paddingLeft: "4px",
+                                            display: "block",
+                                        },
+                                        textContent: "🫶 Custom Title",
+                                    }),
+                                ]
+                            );
+                        }
+
+                        // Add Custom BG option
+                        if (settings.showBG) {
+                            $el(
+                                "div.litemenu-entry.submenu",
+                                {
+                                    parent: menus[i],
+                                    $: (el) => {
+                                        el.onclick = () => {
+                                            LiteGraph.closeAllContextMenus();
+                                            if (!pickerBG) {
+                                                pickerBG = createPicker('bg');
+                                            }
+                                            activeNode = null;
+                                            pickerBG.value = node.bgcolor || "#000000";
+                                            activeNode = node;
+                                            pickerBG.click();
+                                        };
+                                    },
+                                },
+                                [
+                                    $el("span", {
+                                        style: {
+                                            paddingLeft: "4px",
+                                            display: "block",
+                                        },
+                                        textContent: "🫶 Custom BG",
+                                    }),
+                                ]
+                            );
+                        }
+                        
+                        break;
+                    }
+                }
+            });
+            return r;
+        };
+    },
+});

web/kiko_follow_execution.js

@@ -0,0 +1,157 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+// Adds follow execution feature when enabled in settings
+// Adds menu options to toggle follow execution and go to executing node
+
+app.registerExtension({
+    name: "kikotools.followExecution",
+    async init() {
+        // Register settings in ComfyUI's settings panel
+        app.ui.settings.addSetting({
+            id: "kikotools.follow_execution.enabled",
+            name: "🫶 Follow Execution: Enable",
+            type: "boolean",
+            defaultValue: false,
+            tooltip: "Enable follow execution feature in canvas right-click menu",
+        });
+
+        app.ui.settings.addSetting({
+            id: "kikotools.follow_execution.show_goto_node",
+            name: "🫶 Follow Execution: Show 'Go to node' menu",
+            type: "boolean",
+            defaultValue: true,
+            tooltip: "Show 'Go to node' submenu in canvas menu",
+        });
+
+        app.ui.settings.addSetting({
+            id: "kikotools.follow_execution.auto_start",
+            name: "🫶 Follow Execution: Auto-start",
+            type: "boolean",
+            defaultValue: false,
+            tooltip: "Automatically start following execution when workflow starts",
+        });
+    },
+    
+    async setup() {
+        let followExecution = false;
+        let isEnabled = false;
+
+        // Check if the feature is enabled in settings
+        const checkEnabled = () => {
+            const setting = app.ui.settings.getSettingValue("kikotools.follow_execution.enabled");
+            isEnabled = setting !== undefined ? setting : false;
+            
+            // If disabled, turn off follow execution
+            if (!isEnabled && followExecution) {
+                followExecution = false;
+            }
+        };
+
+        // Check for auto-start setting
+        const checkAutoStart = () => {
+            const autoStart = app.ui.settings.getSettingValue("kikotools.follow_execution.auto_start");
+            if (autoStart && isEnabled) {
+                followExecution = true;
+            }
+        };
+
+        // Initialize settings on startup
+        checkEnabled();
+        checkAutoStart();
+
+        // Center on the executing node
+        const centerNode = (id) => {
+            if (!followExecution || !id || !isEnabled) return;
+            const node = app.graph.getNodeById(id);
+            if (!node) return;
+            app.canvas.centerOnNode(node);
+        };
+
+        // Listen for execution events
+        api.addEventListener("executing", ({ detail }) => centerNode(detail));
+
+        // Listen for execution start to handle auto-start
+        api.addEventListener("execution_start", () => {
+            checkEnabled();
+            checkAutoStart();
+        });
+
+        // Extend canvas menu options
+        const orig = LGraphCanvas.prototype.getCanvasMenuOptions;
+        LGraphCanvas.prototype.getCanvasMenuOptions = function () {
+            const options = orig.apply(this, arguments);
+            
+            // Check if feature is enabled before adding menu items
+            checkEnabled();
+            if (!isEnabled) return options;
+
+            // Add separator
+            options.push(null);
+            
+            // Add follow execution toggle
+            options.push({
+                content: followExecution ? "🫶 Stop following execution" : "🫶 Follow execution",
+                callback: () => {
+                    followExecution = !followExecution;
+                    if (followExecution) {
+                        centerNode(app.runningNodeId);
+                    }
+                },
+            });
+
+            // Add go to executing node option if a node is currently executing
+            if (app.runningNodeId) {
+                options.push({
+                    content: "🫶 Show executing node",
+                    callback: () => {
+                        const node = app.graph.getNodeById(app.runningNodeId);
+                        if (!node) return;
+                        app.canvas.centerOnNode(node);
+                    },
+                });
+            }
+
+            // Add go to node by type submenu
+            const showGoToNode = app.ui.settings.getSettingValue("kikotools.follow_execution.show_goto_node");
+            if (showGoToNode !== false) {  // Default to true if not set
+                const nodes = app.graph._nodes;
+                const types = nodes.reduce((p, n) => {
+                    if (n.type in p) {
+                        p[n.type].push(n);
+                    } else {
+                        p[n.type] = [n];
+                    }
+                    return p;
+                }, {});
+
+                options.push({
+                    content: "🫶 Go to node",
+                    has_submenu: true,
+                    submenu: {
+                        options: Object.keys(types)
+                            .sort()
+                            .map((t) => ({
+                                content: t,
+                                has_submenu: true,
+                                submenu: {
+                                    options: types[t]
+                                        .sort((a, b) => {
+                                            return a.pos[0] - b.pos[0];
+                                        })
+                                        .map((n) => ({
+                                            content: `${n.getTitle()} - #${n.id} (${Math.round(n.pos[0])}, ${Math.round(n.pos[1])})`,
+                                            callback: () => {
+                                                app.canvas.centerOnNode(n);
+                                            },
+                                        })),
+                                },
+                            })),
+                    },
+                });
+            }
+
+            return options;
+        };
+    },
+});