fix(tests): update GitHub Actions tests for emoji categories

- Fix RETURN_TYPES assertion for Sampler Combo (SCHEDULERS is a list)
- Update all CATEGORY assertions to support emoji-based categories
- Change from exact match to startswith('ComfyAssets/') for flexibility
- All tests now properly validate the new category system

.flake8

@@ -0,0 +1,35 @@
+[flake8]
+max-line-length = 127
+max-complexity = 10
+exclude = 
+    .git,
+    __pycache__,
+    .mypy_cache,
+    .pytest_cache,
+    venv,
+    env,
+    build,
+    dist,
+    *.egg-info,
+    .tox
+ignore = 
+    # W503: line break before binary operator (conflicts with Black)
+    W503,
+    # E203: whitespace before ':' (conflicts with Black)
+    E203,
+    # E501: line too long (we use max-line-length)
+    E501
+
+per-file-ignores =
+    # Allow unused imports in __init__.py files
+    __init__.py:F401,F403
+    # Allow assertions in tests
+    tests/*:S101
+    # Allow higher complexity for Gemini prompt module
+    kikotools/tools/gemini_prompt/logic.py:C901
+    kikotools/tools/gemini_prompt/models.py:C901
+    kikotools/tools/gemini_prompt/node.py:C901
+
+# Statistics
+count = True
+statistics = True

.gitattributes

@@ -0,0 +1,41 @@
+# Auto detect text files and perform LF normalization
+* text=auto
+
+# Python files
+*.py text eol=lf
+*.pyi text eol=lf
+
+# Configuration files
+*.json text eol=lf
+*.yaml text eol=lf
+*.yml text eol=lf
+*.toml text eol=lf
+*.ini text eol=lf
+*.cfg text eol=lf
+
+# Documentation
+*.md text eol=lf
+*.rst text eol=lf
+*.txt text eol=lf
+
+# Scripts
+*.sh text eol=lf
+*.bash text eol=lf
+
+# Git files
+.gitignore text eol=lf
+.gitattributes text eol=lf
+
+# ComfyUI specific
+*.workflow text eol=lf
+
+# Binary files
+*.png binary
+*.jpg binary
+*.jpeg binary
+*.gif binary
+*.webp binary
+*.safetensors binary
+*.ckpt binary
+*.pt binary
+*.pth binary

.github/ISSUE_TEMPLATE/bug_report.md

@@ -45,4 +45,4 @@ Paste any error messages or stack traces here
 If possible, attach the ComfyUI workflow file (.json) that reproduces the issue.
 
 **Additional context**
-Add any other context about the problem here.
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -37,7 +37,7 @@ Describe how the tool should process inputs and generate outputs.
 
 **Model Compatibility:**
 - [ ] SDXL optimized
-- [ ] FLUX optimized  
+- [ ] FLUX optimized
 - [ ] General purpose
 - [ ] Specific model requirements: [describe]
 
@@ -64,4 +64,4 @@ Are there existing ComfyUI nodes that do something similar? How would this be di
 - [ ] Yes, I can help with implementation
 - [ ] Yes, I can help with testing
 - [ ] Yes, I can help with documentation
-- [ ] No, but I'd be happy to test it
+- [ ] No, but I'd be happy to test it

.github/dependabot.yml

@@ -0,0 +1,10 @@
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"

.github/workflows/code-quality.yml

@@ -1,4 +1,6 @@
 name: Code Quality
+permissions:
+  contents: read
 
 on:
   push:
@@ -14,12 +16,12 @@ jobs:
     - uses: actions/checkout@v4
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
 
     - name: Cache pip dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pip
         key: ${{ runner.os }}-pip-quality-${{ hashFiles('**/requirements-dev.txt') }}
@@ -59,7 +61,7 @@ jobs:
         import sys
         import os
         sys.path.insert(0, os.getcwd())
-        
+
         # Test that all imports work correctly
         try:
             from kikotools import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
@@ -67,47 +69,64 @@ jobs:
         except ImportError as e:
             print(f'Warning: Package-level imports failed: {e}')
             # This is expected since we don't have ComfyUI installed
-        
+
         # Test individual module imports
         from kikotools.base import ComfyAssetsBaseNode
         from kikotools.tools.resolution_calculator import ResolutionCalculatorNode
         from kikotools.tools.resolution_calculator.logic import extract_dimensions
         from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode as NodeClass
-        
+
+        # Test Width Height Selector imports
+        from kikotools.tools.width_height_selector import WidthHeightSelectorNode
+        from kikotools.tools.width_height_selector.logic import get_preset_dimensions
+        from kikotools.tools.width_height_selector.presets import PRESET_OPTIONS, PRESET_METADATA
+
+        # Test Sampler Combo imports
+        from kikotools.tools.sampler_combo import SamplerComboNode
+        from kikotools.tools.sampler_combo.logic import get_sampler_combo, SAMPLERS, SCHEDULERS
+
+        # Test Seed History imports
+        from kikotools.tools.seed_history import SeedHistoryNode
+        from kikotools.tools.seed_history.logic import generate_random_seed, validate_seed_value
+
+        # Test Kiko Save Image imports
+        from kikotools.tools.kiko_save_image import KikoSaveImageNode
+        from kikotools.tools.kiko_save_image.logic import process_image_batch, validate_save_inputs
+
         print('✓ All module imports successful')
         "
 
     - name: Check code style consistency
       run: |
         echo "Checking code style consistency..."
-        
+
         # Check for consistent naming
         find kikotools/ -name "*.py" -exec grep -l "class.*Node" {} \; | while read file; do
           if ! grep -q "ComfyAssetsBaseNode" "$file" && ! grep -q "class ComfyAssetsBaseNode" "$file"; then
             echo "Checking $file for ComfyUI node inheritance..."
           fi
         done
-        
+
         # Check for proper docstrings
         python -c "
         import ast
         import os
-        
+
         def check_docstrings(filepath):
             with open(filepath, 'r') as f:
                 tree = ast.parse(f.read())
-            
+
             for node in ast.walk(tree):
                 if isinstance(node, (ast.FunctionDef, ast.ClassDef)):
                     if not ast.get_docstring(node) and not node.name.startswith('_'):
                         print(f'Warning: {filepath}:{node.lineno} - {node.name} missing docstring')
-        
+
         for root, dirs, files in os.walk('kikotools'):
             for file in files:
                 if file.endswith('.py') and not file.startswith('__'):
                     filepath = os.path.join(root, file)
                     check_docstrings(filepath)
-        
+
         print('✓ Docstring check completed')
         "
 
@@ -117,7 +136,7 @@ jobs:
     - uses: actions/checkout@v4
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
 
@@ -134,7 +153,7 @@ jobs:
     - name: Check for hardcoded secrets
       run: |
         echo "Checking for potential secrets..."
-        
+
         # Check for common secret patterns
         if grep -r -i "password\|secret\|key\|token" kikotools/ --include="*.py" | grep -v "# " | grep -v "def " | grep -v "class "; then
           echo "Warning: Potential hardcoded secrets found"
@@ -148,7 +167,7 @@ jobs:
     - uses: actions/checkout@v4
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
 
@@ -163,61 +182,126 @@ jobs:
         import sys
         import os
         sys.path.insert(0, os.getcwd())
-        
+
         print('Checking architecture compliance...')
-        
+
         # Test separation of concerns
         from kikotools.tools.resolution_calculator import logic, node
-        
+
         # Logic module should not import node-specific things
         import inspect
         logic_source = inspect.getsource(logic)
-        
+
         if 'ComfyUI' in logic_source and 'INPUT_TYPES' not in logic_source:
             print('⚠️  Warning: Logic module contains ComfyUI-specific code')
         else:
             print('✓ Logic module properly separated')
-        
+
         # Node module should inherit from base
         from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode
         from kikotools.base import ComfyAssetsBaseNode
-        
+
         if issubclass(ResolutionCalculatorNode, ComfyAssetsBaseNode):
             print('✓ Node properly inherits from base class')
         else:
             print('❌ Node does not inherit from base class')
             sys.exit(1)
-        
+
         # Check that nodes have proper ComfyUI interface
         required_attrs = ['INPUT_TYPES', 'RETURN_TYPES', 'RETURN_NAMES', 'FUNCTION', 'CATEGORY']
+
+        # Test Resolution Calculator Node
         for attr in required_attrs:
             if not hasattr(ResolutionCalculatorNode, attr):
-                print(f'❌ Node missing required attribute: {attr}')
+                print(f'❌ ResolutionCalculatorNode missing required attribute: {attr}')
                 sys.exit(1)
-        
-        print('✓ All architecture checks passed')
+
+        # Test Width Height Selector Node
+        from kikotools.tools.width_height_selector.node import WidthHeightSelectorNode
+
+        if issubclass(WidthHeightSelectorNode, ComfyAssetsBaseNode):
+            print('✓ WidthHeightSelectorNode properly inherits from base class')
+        else:
+            print('❌ WidthHeightSelectorNode does not inherit from base class')
+            sys.exit(1)
+
+        for attr in required_attrs:
+            if not hasattr(WidthHeightSelectorNode, attr):
+                print(f'❌ WidthHeightSelectorNode missing required attribute: {attr}')
+                sys.exit(1)
+
+        # Test Sampler Combo Node
+        from kikotools.tools.sampler_combo.node import SamplerComboNode
+
+        if issubclass(SamplerComboNode, ComfyAssetsBaseNode):
+            print('✓ SamplerComboNode properly inherits from base class')
+        else:
+            print('❌ SamplerComboNode does not inherit from base class')
+            sys.exit(1)
+
+        for attr in required_attrs:
+            if not hasattr(SamplerComboNode, attr):
+                print(f'❌ SamplerComboNode missing required attribute: {attr}')
+                sys.exit(1)
+
+        # Test Seed History Node
+        from kikotools.tools.seed_history.node import SeedHistoryNode
+
+        if issubclass(SeedHistoryNode, ComfyAssetsBaseNode):
+            print('✓ SeedHistoryNode properly inherits from base class')
+        else:
+            print('❌ SeedHistoryNode does not inherit from base class')
+            sys.exit(1)
+
+        for attr in required_attrs:
+            if not hasattr(SeedHistoryNode, attr):
+                print(f'❌ SeedHistoryNode missing required attribute: {attr}')
+                sys.exit(1)
+
+        # Test Kiko Save Image Node
+        from kikotools.tools.kiko_save_image.node import KikoSaveImageNode
+
+        if issubclass(KikoSaveImageNode, ComfyAssetsBaseNode):
+            print('✓ KikoSaveImageNode properly inherits from base class')
+        else:
+            print('❌ KikoSaveImageNode does not inherit from base class')
+            sys.exit(1)
+
+        # KikoSaveImage is an output node, so it doesn't have RETURN_TYPES/RETURN_NAMES
+        save_required_attrs = ['INPUT_TYPES', 'FUNCTION', 'CATEGORY']
+        for attr in save_required_attrs:
+            if not hasattr(KikoSaveImageNode, attr):
+                print(f'❌ KikoSaveImageNode missing required attribute: {attr}')
+                sys.exit(1)
+
+        # Check that it's properly marked as an output node
+        if not hasattr(KikoSaveImageNode, 'OUTPUT_NODE') or not KikoSaveImageNode.OUTPUT_NODE:
+            print('❌ KikoSaveImageNode missing OUTPUT_NODE = True')
+            sys.exit(1)
+
+        print('✓ All architecture checks passed for all tools')
         "
 
     - name: Check test coverage expectations
       run: |
         python -c "
         import os
-        
+
         # Count test files vs implementation files
         test_files = 0
         impl_files = 0
-        
+
         for root, dirs, files in os.walk('tests'):
             test_files += len([f for f in files if f.startswith('test_') and f.endswith('.py')])
-        
+
         for root, dirs, files in os.walk('kikotools'):
             impl_files += len([f for f in files if f.endswith('.py') and not f.startswith('__')])
-        
+
         print(f'Implementation files: {impl_files}')
         print(f'Test files: {test_files}')
-        
+
         if test_files >= impl_files * 0.5:  # At least 50% test coverage by file count
             print('✓ Adequate test file coverage')
         else:
             print('⚠️  Warning: Low test file coverage')
-        "
+        "

.github/workflows/publish.yml

@@ -0,0 +1,28 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'ComfyAssets' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.github/workflows/release.yml

@@ -1,5 +1,8 @@
 name: Release
 
+permissions:
+  contents: read
+
 on:
   push:
     tags:
@@ -8,12 +11,14 @@ on:
 jobs:
   create-release:
     runs-on: ubuntu-latest
-    
+    permissions:
+      contents: write
+
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.10'
 
@@ -28,30 +33,30 @@ jobs:
         import sys
         import os
         sys.path.insert(0, os.getcwd())
-        
+
         # Run comprehensive tests before release
         from kikotools.base import ComfyAssetsBaseNode
         from kikotools.tools.resolution_calculator.logic import extract_dimensions, calculate_scaled_dimensions
         from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode
         import torch
-        
+
         print('Running pre-release validation...')
-        
+
         # Test all major functionality
         node = ResolutionCalculatorNode()
-        
+
         # Test various scenarios
         test_cases = [
             (torch.randn(1, 512, 512, 3), 2.0),
             (torch.randn(1, 1024, 1024, 3), 1.5),
             (torch.randn(1, 1216, 832, 3), 1.53),  # User scenario
         ]
-        
+
         for i, (image, scale) in enumerate(test_cases):
             width, height = node.calculate_resolution(scale, image=image)
             print(f'✓ Test case {i+1}: {image.shape[2]}×{image.shape[1]} → {width}×{height} (scale: {scale})')
             assert width % 8 == 0 and height % 8 == 0
-        
+
         print('🎉 All pre-release tests passed!')
         "
 
@@ -64,22 +69,22 @@ jobs:
       run: |
         cat > release_notes.md << 'EOF'
         ## ComfyUI-KikoTools ${{ steps.get_version.outputs.version }}
-        
+
         ### 🎉 What's New
-        
+
         #### Resolution Calculator Tool
         - **Smart Input Handling**: Works with both IMAGE and LATENT tensors
-        - **Model Optimized**: Specific optimizations for SDXL and FLUX models  
+        - **Model Optimized**: Specific optimizations for SDXL and FLUX models
         - **Constraint Enforcement**: Automatically ensures dimensions divisible by 8
         - **Flexible Scaling**: Supports scale factors from 1.0x to 8.0x
-        
+
         ### 📦 Installation
-        
+
         #### ComfyUI Manager
         1. Search for "ComfyUI-KikoTools"
         2. Click Install
         3. Restart ComfyUI
-        
+
         #### Manual Installation
         ```bash
         cd ComfyUI/custom_nodes/
@@ -87,29 +92,29 @@ jobs:
         cd ComfyUI-KikoTools
         pip install -r requirements-dev.txt
         ```
-        
+
         ### 🚀 Quick Start
-        
+
         Look for **ComfyAssets** nodes in your ComfyUI node browser!
-        
+
         ### 📊 Technical Details
-        
+
         - **Nodes**: 1 (Resolution Calculator)
         - **Test Coverage**: 100%
         - **Python Support**: 3.8+
         - **ComfyUI Compatibility**: Latest
-        
+
         ### 🐛 Bug Reports
-        
+
         Found an issue? Please report it [here](https://github.com/ComfyAssets/ComfyUI-KikoTools/issues).
-        
+
         ---
-        
+
         **Full Changelog**: https://github.com/ComfyAssets/ComfyUI-KikoTools/compare/v0.0.0...${{ steps.get_version.outputs.version }}
         EOF
 
     - name: Create GitHub Release
-      uses: softprops/action-gh-release@v1
+      uses: softprops/action-gh-release@v2
       with:
         tag_name: ${{ steps.get_version.outputs.version }}
         name: ComfyUI-KikoTools ${{ steps.get_version.outputs.version }}
@@ -128,18 +133,18 @@ jobs:
     runs-on: ubuntu-latest
     needs: create-release
     if: success()
-    
+
     steps:
     - name: Community notification placeholder
       run: |
         echo "🎉 Release ${{ needs.create-release.outputs.version }} created!"
         echo "Consider posting to:"
         echo "- ComfyUI Discord"
-        echo "- Reddit r/ComfyUI"  
+        echo "- Reddit r/ComfyUI"
         echo "- ComfyUI-Manager database"
         echo ""
         echo "Release includes:"
         echo "- Resolution Calculator tool"
         echo "- Complete documentation"
         echo "- Example workflows"
-        echo "- 100% test coverage"
+        echo "- 100% test coverage"

.github/workflows/tests.yml

@@ -1,5 +1,8 @@
 name: Tests
 
+permissions:
+  contents: read
+
 on:
   push:
     branches: [main, develop]
@@ -17,12 +20,12 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Cache pip dependencies
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pip
           key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements-dev.txt') }}
@@ -50,7 +53,7 @@ jobs:
           print('✓ All imports successful')
 
           # Test base node
-          assert ComfyAssetsBaseNode.CATEGORY == 'ComfyAssets'
+          assert ComfyAssetsBaseNode.CATEGORY.startswith('ComfyAssets')
           print('✓ Base node tests passed')
 
           # Test dimension extraction
@@ -78,67 +81,322 @@ jobs:
           print('🎉 All tests passed!')
           "
 
-      - name: Test error handling
+      - name: Test Width Height Selector
         run: |
           python -c "
           import sys
           import os
           sys.path.insert(0, os.getcwd())
 
-          from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode
+          # Test Width Height Selector imports
+          from kikotools.tools.width_height_selector.node import WidthHeightSelectorNode
+          from kikotools.tools.width_height_selector.presets import PRESET_OPTIONS, PRESET_METADATA
+          from kikotools.tools.width_height_selector.logic import get_preset_dimensions
 
-          node = ResolutionCalculatorNode()
+          print('✓ Width Height Selector imports successful')
 
-          # Test error handling
-          try:
-              node.calculate_resolution(2.0)  # No input provided
-              assert False, 'Should have raised ValueError'
-          except ValueError:
-              print('✓ Error handling test passed')
+          # Test preset structure
+          assert len(PRESET_OPTIONS) > 0
+          assert 'custom' in PRESET_OPTIONS
+          assert len(PRESET_METADATA) > 0
+          print('✓ Preset structure tests passed')
 
-          # Test invalid scale factor
-          try:
-              node.calculate_resolution(0.0)  # Invalid scale
-              assert False, 'Should have raised ValueError'
-          except ValueError:
-              print('✓ Scale factor validation test passed')
+          # Test node interface
+          node = WidthHeightSelectorNode()
+          input_types = node.INPUT_TYPES()
+          assert 'required' in input_types
+          assert 'preset' in input_types['required']
+          assert 'width' in input_types['required']
+          assert 'height' in input_types['required']
+          print('✓ Node interface tests passed')
 
-          print('✓ All error handling tests passed')
+          # Test formatted presets
+          preset_options = input_types['required']['preset'][0]
+          assert 'custom' in preset_options
+          formatted_count = len([opt for opt in preset_options if ' - ' in opt and 'MP' in opt])
+          assert formatted_count > 0
+          print(f'✓ Found {formatted_count} formatted presets')
+
+          # Test dimension calculation
+          result = node.get_dimensions('1024×1024', 512, 512)
+          assert result == (1024, 1024)
+          print('✓ Dimension calculation tests passed')
+
+          # Test formatted preset dimensions
+          formatted_preset = '1024×1024 - 1:1 (1.1MP) - SDXL'
+          result = node.get_dimensions(formatted_preset, 512, 512)
+          assert result == (1024, 1024)
+          print('✓ Formatted preset tests passed')
+
+          # Test preset extraction
+          extracted = node._extract_preset_name(formatted_preset)
+          assert extracted == '1024×1024'
+          print('✓ Preset extraction tests passed')
+
+          print('🎉 All Width Height Selector tests passed!')
           "
 
-      - name: Test ComfyUI integration readiness
+      - name: Test Sampler Combo
         run: |
           python -c "
           import sys
           import os
           sys.path.insert(0, os.getcwd())
 
+          # Test Sampler Combo imports
+          from kikotools.tools.sampler_combo.node import SamplerComboNode
+          from kikotools.tools.sampler_combo.logic import (
+              get_sampler_combo, validate_sampler_settings, SAMPLERS, SCHEDULERS
+          )
+
+          print('✓ Sampler Combo imports successful')
+
+          # Test node interface
+          node = SamplerComboNode()
+          input_types = node.INPUT_TYPES()
+          assert 'required' in input_types
+          assert 'sampler_name' in input_types['required']
+          assert 'scheduler' in input_types['required']
+          assert 'steps' in input_types['required']
+          assert 'cfg' in input_types['required']
+          print('✓ Sampler Combo interface tests passed')
+
+          # Test return types
+          # RETURN_TYPES[1] is the actual SCHEDULERS list
+          assert node.RETURN_TYPES[0] == 'SAMPLER'
+          assert isinstance(node.RETURN_TYPES[1], list)  # SCHEDULERS is a list
+          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'
+          print('✓ Sampler Combo return types tests passed')
+
+          # Test sampler combo functionality
+          result = node.get_sampler_combo('euler', 'normal', 20, 7.0)
+          assert result == ('euler', 'normal', 20, 7.0)
+          print('✓ Sampler combo functionality tests passed')
+
+          # Test validation
+          assert validate_sampler_settings('euler', 'normal', 20, 7.0) == True
+          print('✓ Sampler validation tests passed')
+
+          # Test available samplers and schedulers
+          samplers = node.get_available_samplers()
+          schedulers = node.get_available_schedulers()
+          assert len(samplers) > 0
+          assert len(schedulers) > 0
+          assert 'euler' in samplers
+          assert 'normal' in schedulers
+          print(f'✓ Found {len(samplers)} samplers and {len(schedulers)} schedulers')
+
+          print('🎉 All Sampler Combo tests passed!')
+          "
+
+      - name: Test Seed History
+        run: |
+          python -c "
+          import sys
+          import os
+          sys.path.insert(0, os.getcwd())
+
+          # Test Seed History imports
+          from kikotools.tools.seed_history.node import SeedHistoryNode
+          from kikotools.tools.seed_history.logic import (
+              generate_random_seed, validate_seed_value, sanitize_seed_value
+          )
+
+          print('✓ Seed History imports successful')
+
+          # Test node interface
+          node = SeedHistoryNode()
+          input_types = node.INPUT_TYPES()
+          assert 'required' in input_types
+          assert 'seed' in input_types['required']
+          print('✓ Seed History interface tests passed')
+
+          # Test return types
+          assert node.RETURN_TYPES == ('INT',)
+          assert node.RETURN_NAMES == ('seed',)
+          assert node.CATEGORY == 'ComfyAssets/🌱 Seeds'
+          print('✓ Seed History return types tests passed')
+
+          # Test seed output functionality
+          result = node.output_seed(12345)
+          assert result == (12345,)
+          print('✓ Seed output functionality tests passed')
+
+          # Test seed validation
+          assert validate_seed_value(12345) == True
+          assert validate_seed_value(-1) == False
+          print('✓ Seed validation tests passed')
+
+          # Test seed generation
+          new_seed = generate_random_seed()
+          assert isinstance(new_seed, int)
+          assert validate_seed_value(new_seed) == True
+          print('✓ Seed generation tests passed')
+
+          # Test seed sanitization
+          clean_seed = sanitize_seed_value(12345)
+          assert clean_seed == 12345
+          print('✓ Seed sanitization tests passed')
+
+          # Test node helper methods
+          assert node.is_seed_in_range(12345) == True
+          assert node.is_seed_in_range(-1) == False
+          assert node.get_default_seed() == 12345
+          print('✓ Seed helper methods tests passed')
+
+          print('🎉 All Seed History tests passed!')
+          "
+
+      - name: Test error handling for all tools
+        run: |
+          python -c "
+          import sys
+          import os
+          sys.path.insert(0, os.getcwd())
+
+          print('=== Testing Error Handling for All Tools ===')
+
+          # Test Resolution Calculator error handling
           from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode
+          res_node = ResolutionCalculatorNode()
 
-          # Test ComfyUI interface requirements
-          node_class = ResolutionCalculatorNode
+          try:
+              res_node.calculate_resolution(2.0)  # No input provided
+              assert False, 'Should have raised ValueError'
+          except ValueError:
+              print('✓ Resolution Calculator error handling test passed')
 
-          # Check required class attributes
-          assert hasattr(node_class, 'INPUT_TYPES')
-          assert hasattr(node_class, 'RETURN_TYPES')
-          assert hasattr(node_class, 'RETURN_NAMES')
-          assert hasattr(node_class, 'FUNCTION')
-          assert hasattr(node_class, 'CATEGORY')
+          try:
+              res_node.calculate_resolution(0.0)  # Invalid scale
+              assert False, 'Should have raised ValueError'
+          except ValueError:
+              print('✓ Resolution Calculator scale factor validation test passed')
 
-          # Check INPUT_TYPES structure
-          input_types = node_class.INPUT_TYPES()
+          # Test Width Height Selector error handling
+          from kikotools.tools.width_height_selector.node import WidthHeightSelectorNode
+          wh_node = WidthHeightSelectorNode()
+
+          # Test invalid preset fallback
+          result = wh_node.get_dimensions('invalid_preset', 800, 600)
+          assert result == (800, 600)  # Should fallback to custom dimensions
+          print('✓ Width Height Selector invalid preset handling test passed')
+
+          # Test Sampler Combo error handling
+          from kikotools.tools.sampler_combo.node import SamplerComboNode
+          sampler_node = SamplerComboNode()
+
+          # Test with invalid sampler (should use safe defaults)
+          result = sampler_node.get_sampler_combo('invalid_sampler', 'normal', 20, 7.0)
+          assert result == ('euler', 'normal', 20, 7.0)  # Safe defaults
+          print('✓ Sampler Combo invalid input handling test passed')
+
+          # Test Seed History error handling
+          from kikotools.tools.seed_history.node import SeedHistoryNode
+          seed_node = SeedHistoryNode()
+
+          # Test invalid seed value (should use fallback)
+          result = seed_node.output_seed(-1)  # Invalid negative seed
+          assert result == (12345,)  # Fallback seed
+          print('✓ Seed History invalid seed handling test passed')
+
+          print('🎉 All error handling tests passed for all tools!')
+          "
+
+      - name: Test ComfyUI integration readiness for all tools
+        run: |
+          python -c "
+          import sys
+          import os
+          sys.path.insert(0, os.getcwd())
+
+          print('=== Testing ComfyUI Integration for All Tools ===')
+
+          # Test Resolution Calculator
+          from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode
+          res_class = ResolutionCalculatorNode
+
+          assert hasattr(res_class, 'INPUT_TYPES')
+          assert hasattr(res_class, 'RETURN_TYPES')
+          assert hasattr(res_class, 'RETURN_NAMES')
+          assert hasattr(res_class, 'FUNCTION')
+          assert hasattr(res_class, 'CATEGORY')
+
+          input_types = res_class.INPUT_TYPES()
           assert 'required' in input_types
           assert 'optional' in input_types
           assert 'scale_factor' in input_types['required']
           assert 'image' in input_types['optional']
           assert 'latent' in input_types['optional']
 
-          # Check return types
-          assert node_class.RETURN_TYPES == ('INT', 'INT')
-          assert node_class.RETURN_NAMES == ('width', 'height')
-          assert node_class.CATEGORY == 'ComfyAssets'
+          assert res_class.RETURN_TYPES == ('INT', 'INT')
+          assert res_class.RETURN_NAMES == ('width', 'height')
+          assert res_class.CATEGORY.startswith('ComfyAssets/')
+          print('✓ Resolution Calculator ComfyUI integration passed')
 
-          print('✓ ComfyUI integration readiness tests passed')
+          # Test Width Height Selector
+          from kikotools.tools.width_height_selector.node import WidthHeightSelectorNode
+          wh_class = WidthHeightSelectorNode
+
+          assert hasattr(wh_class, 'INPUT_TYPES')
+          assert hasattr(wh_class, 'RETURN_TYPES')
+          assert hasattr(wh_class, 'RETURN_NAMES')
+          assert hasattr(wh_class, 'FUNCTION')
+          assert hasattr(wh_class, 'CATEGORY')
+
+          input_types = wh_class.INPUT_TYPES()
+          assert 'required' in input_types
+          assert 'preset' in input_types['required']
+          assert 'width' in input_types['required']
+          assert 'height' in input_types['required']
+
+          assert wh_class.RETURN_TYPES == ('INT', 'INT')
+          assert wh_class.RETURN_NAMES == ('width', 'height')
+          assert wh_class.CATEGORY.startswith('ComfyAssets/')
+          print('✓ Width Height Selector ComfyUI integration passed')
+
+          # Test Sampler Combo
+          from kikotools.tools.sampler_combo.node import SamplerComboNode
+          sampler_class = SamplerComboNode
+
+          assert hasattr(sampler_class, 'INPUT_TYPES')
+          assert hasattr(sampler_class, 'RETURN_TYPES')
+          assert hasattr(sampler_class, 'RETURN_NAMES')
+          assert hasattr(sampler_class, 'FUNCTION')
+          assert hasattr(sampler_class, 'CATEGORY')
+
+          input_types = sampler_class.INPUT_TYPES()
+          assert 'required' in input_types
+          assert 'sampler_name' in input_types['required']
+          assert 'scheduler' in input_types['required']
+          assert 'steps' in input_types['required']
+          assert 'cfg' in input_types['required']
+
+          assert sampler_class.CATEGORY.startswith('ComfyAssets/')
+          print('✓ Sampler Combo ComfyUI integration passed')
+
+          # Test Seed History
+          from kikotools.tools.seed_history.node import SeedHistoryNode
+          seed_class = SeedHistoryNode
+
+          assert hasattr(seed_class, 'INPUT_TYPES')
+          assert hasattr(seed_class, 'RETURN_TYPES')
+          assert hasattr(seed_class, 'RETURN_NAMES')
+          assert hasattr(seed_class, 'FUNCTION')
+          assert hasattr(seed_class, 'CATEGORY')
+
+          input_types = seed_class.INPUT_TYPES()
+          assert 'required' in input_types
+          assert 'seed' in input_types['required']
+
+          assert seed_class.RETURN_TYPES == ('INT',)
+          assert seed_class.RETURN_NAMES == ('seed',)
+          assert seed_class.CATEGORY.startswith('ComfyAssets/')
+          print('✓ Seed History ComfyUI integration passed')
+
+          print('🎉 All tools ComfyUI integration readiness tests passed!')
           "
 
   test-package-structure:
@@ -147,7 +405,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
 
@@ -163,14 +421,37 @@ jobs:
           test -d kikotools/base || (echo "kikotools/base directory missing" && exit 1)
           test -d kikotools/tools || (echo "kikotools/tools directory missing" && exit 1)
           test -d kikotools/tools/resolution_calculator || (echo "resolution_calculator directory missing" && exit 1)
+          test -d kikotools/tools/width_height_selector || (echo "width_height_selector directory missing" && exit 1)
+          test -d kikotools/tools/sampler_combo || (echo "sampler_combo directory missing" && exit 1)
+          test -d kikotools/tools/seed_history || (echo "seed_history directory missing" && exit 1)
           test -d tests || (echo "tests directory missing" && exit 1)
           test -d examples || (echo "examples directory missing" && exit 1)
+          test -d web || (echo "web directory missing" && exit 1)
 
           # Check key files
           test -f kikotools/__init__.py || (echo "kikotools/__init__.py missing" && exit 1)
           test -f kikotools/base/base_node.py || (echo "base_node.py missing" && exit 1)
-          test -f kikotools/tools/resolution_calculator/node.py || (echo "node.py missing" && exit 1)
-          test -f kikotools/tools/resolution_calculator/logic.py || (echo "logic.py missing" && exit 1)
+
+          # Resolution Calculator files
+          test -f kikotools/tools/resolution_calculator/node.py || (echo "resolution_calculator node.py missing" && exit 1)
+          test -f kikotools/tools/resolution_calculator/logic.py || (echo "resolution_calculator logic.py missing" && exit 1)
+
+          # Width Height Selector files
+          test -f kikotools/tools/width_height_selector/node.py || (echo "width_height_selector node.py missing" && exit 1)
+          test -f kikotools/tools/width_height_selector/logic.py || (echo "width_height_selector logic.py missing" && exit 1)
+          test -f kikotools/tools/width_height_selector/presets.py || (echo "width_height_selector presets.py missing" && exit 1)
+
+          # Sampler Combo files
+          test -f kikotools/tools/sampler_combo/node.py || (echo "sampler_combo node.py missing" && exit 1)
+          test -f kikotools/tools/sampler_combo/logic.py || (echo "sampler_combo logic.py missing" && exit 1)
+
+          # Seed History files
+          test -f kikotools/tools/seed_history/node.py || (echo "seed_history node.py missing" && exit 1)
+          test -f kikotools/tools/seed_history/logic.py || (echo "seed_history logic.py missing" && exit 1)
+
+          # Web files
+          test -f web/width_height_swap.js || (echo "width_height_swap.js missing" && exit 1)
+          test -f web/seed_history_ui.js || (echo "seed_history_ui.js missing" && exit 1)
 
           echo "✓ Package structure tests passed"
 
@@ -181,9 +462,15 @@ jobs:
 
       - name: Test documentation completeness
         run: |
-          # Check documentation files
+          # Check documentation files for all tools
           test -f examples/documentation/resolution_calculator.md || (echo "Resolution calculator docs missing" && exit 1)
-          test -f examples/workflows/resolution_calculator_example.json || (echo "Example workflow missing" && exit 1)
+          test -f examples/workflows/resolution_calculator_example.json || (echo "Resolution calculator workflow missing" && exit 1)
+          test -f examples/documentation/width_height_selector.md || (echo "Width height selector docs missing" && exit 1)
+          test -f examples/workflows/width_height_selector_example.json || (echo "Width height selector workflow missing" && exit 1)
+          test -f examples/documentation/sampler_combo.md || (echo "Sampler combo docs missing" && exit 1)
+          test -f examples/workflows/sampler_combo_example.json || (echo "Sampler combo workflow missing" && exit 1)
+          test -f examples/documentation/seed_history.md || (echo "Seed history docs missing" && exit 1)
+          test -f examples/workflows/seed_history_example.json || (echo "Seed history workflow missing" && exit 1)
 
           # Check README has key sections
           grep -q "Installation" README.md || (echo "README missing Installation section" && exit 1)

.gitignore

@@ -158,4 +158,7 @@ input/
 test_images/
 test_outputs/
 experiments/
-.claude/
+.claude/
+
+# Gemini model cache
+.gemini_models_cache.json

.pre-commit-config.yaml

@@ -0,0 +1,84 @@
+# Pre-commit hooks configuration for ComfyUI-KikoTools
+# This ensures code quality checks are run before each commit
+
+repos:
+  # Python code formatting with Black
+  - repo: https://github.com/psf/black
+    rev: 25.1.0
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ['--line-length=88']  # Match CI configuration
+
+  # Python linting with flake8
+  - repo: https://github.com/pycqa/flake8
+    rev: 7.3.0
+    hooks:
+      - id: flake8
+        args: ['--max-line-length=88', '--max-complexity=10']
+        exclude: '^tests/'
+
+  # Python type checking with mypy
+  # Note: Mypy is disabled in pre-commit due to package name issue
+  # Run manually with: mypy kikotools/
+  # - repo: https://github.com/pre-commit/mirrors-mypy
+  #   rev: v1.8.0
+  #   hooks:
+  #     - id: mypy
+  #       args: ['--config-file=mypy.ini']
+  #       files: '^kikotools/'
+  #       exclude: '^tests/'
+  #       additional_dependencies: ['types-requests']
+
+  # Security checks with bandit
+  - repo: https://github.com/PyCQA/bandit
+    rev: 1.8.6
+    hooks:
+      - id: bandit
+        args: ['-ll', '-r']
+        files: '^kikotools/'
+
+  # General file checks
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files
+        args: ['--maxkb=1000']
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: check-docstring-first
+      - id: debug-statements
+      - id: mixed-line-ending
+
+  # Check for hardcoded secrets
+  - repo: https://github.com/Yelp/detect-secrets
+    rev: v1.5.0
+    hooks:
+      - id: detect-secrets
+        args: ['--baseline', '.secrets.baseline']
+        exclude: '^(tests/|\.git/)'
+
+# Configuration for specific hooks
+default_language_version:
+  python: python3.10
+
+# Run hooks on all files by default
+fail_fast: false
+
+# Exclude patterns
+exclude: |
+  (?x)^(
+    \.git/|
+    \.mypy_cache/|
+    \.pytest_cache/|
+    __pycache__/|
+    build/|
+    dist/|
+    \.eggs/|
+    .*\.egg-info/|
+    venv/|
+    env/
+  )

.secrets.baseline

@@ -0,0 +1,164 @@
+{
+  "version": "1.5.0",
+  "plugins_used": [
+    {
+      "name": "ArtifactoryDetector"
+    },
+    {
+      "name": "AWSKeyDetector"
+    },
+    {
+      "name": "AzureStorageKeyDetector"
+    },
+    {
+      "name": "Base64HighEntropyString",
+      "limit": 4.5
+    },
+    {
+      "name": "BasicAuthDetector"
+    },
+    {
+      "name": "CloudantDetector"
+    },
+    {
+      "name": "DiscordBotTokenDetector"
+    },
+    {
+      "name": "GitHubTokenDetector"
+    },
+    {
+      "name": "GitLabTokenDetector"
+    },
+    {
+      "name": "HexHighEntropyString",
+      "limit": 3.0
+    },
+    {
+      "name": "IbmCloudIamDetector"
+    },
+    {
+      "name": "IbmCosHmacDetector"
+    },
+    {
+      "name": "IPPublicDetector"
+    },
+    {
+      "name": "JwtTokenDetector"
+    },
+    {
+      "name": "KeywordDetector",
+      "keyword_exclude": ""
+    },
+    {
+      "name": "MailchimpDetector"
+    },
+    {
+      "name": "NpmDetector"
+    },
+    {
+      "name": "OpenAIDetector"
+    },
+    {
+      "name": "PrivateKeyDetector"
+    },
+    {
+      "name": "PypiTokenDetector"
+    },
+    {
+      "name": "SendGridDetector"
+    },
+    {
+      "name": "SlackDetector"
+    },
+    {
+      "name": "SoftlayerDetector"
+    },
+    {
+      "name": "SquareOAuthDetector"
+    },
+    {
+      "name": "StripeDetector"
+    },
+    {
+      "name": "TelegramBotTokenDetector"
+    },
+    {
+      "name": "TwilioKeyDetector"
+    }
+  ],
+  "filters_used": [
+    {
+      "path": "detect_secrets.filters.allowlist.is_line_allowlisted"
+    },
+    {
+      "path": "detect_secrets.filters.common.is_ignored_due_to_verification_policies",
+      "min_level": 2
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_indirect_reference"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_likely_id_string"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_lock_file"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_not_alphanumeric_string"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_potential_uuid"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_prefixed_with_dollar_sign"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_sequential_string"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_swagger_file"
+    },
+    {
+      "path": "detect_secrets.filters.heuristic.is_templated_secret"
+    }
+  ],
+  "results": {
+    "examples/workflows/resolution_calculator_example.json": [
+      {
+        "type": "Hex High Entropy String",
+        "filename": "examples/workflows/resolution_calculator_example.json",
+        "hashed_secret": "5264b0f1a47aeafad88f33511dda3191b32dbf38",
+        "is_verified": false,
+        "line_number": 57
+      }
+    ],
+    "examples/workflows/sampler_combo_example.json": [
+      {
+        "type": "Hex High Entropy String",
+        "filename": "examples/workflows/sampler_combo_example.json",
+        "hashed_secret": "e3c1848dd1141985e412fa39922ac9ba37c4714d",
+        "is_verified": false,
+        "line_number": 348
+      }
+    ],
+    "examples/workflows/seed_history_example.json": [
+      {
+        "type": "Hex High Entropy String",
+        "filename": "examples/workflows/seed_history_example.json",
+        "hashed_secret": "e3c1848dd1141985e412fa39922ac9ba37c4714d",
+        "is_verified": false,
+        "line_number": 408
+      }
+    ],
+    "examples/workflows/width_height_selector_example.json": [
+      {
+        "type": "Hex High Entropy String",
+        "filename": "examples/workflows/width_height_selector_example.json",
+        "hashed_secret": "e3c1848dd1141985e412fa39922ac9ba37c4714d",
+        "is_verified": false,
+        "line_number": 425
+      }
+    ]
+  },
+  "generated_at": "2025-07-31T23:51:20Z"
+}

CLAUDE.md

@@ -0,0 +1,288 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+ComfyUI-KikoTools is a planned modular collection of custom ComfyUI nodes that will provide essential tools missing from the standard ComfyUI release. All nodes will be grouped under "ComfyAssets" in the ComfyUI interface. The project is designed for extensibility, allowing new tools to be added easily while maintaining clean separation of concerns.
+
+**Current Status**: Project is in initial planning phase. Only documentation and licensing files exist.
+
+## Architecture
+
+### Design Principles
+- **Modular Design**: Each tool is a separate, self-contained module
+- **ComfyAssets Grouping**: All nodes appear under the "ComfyAssets" category
+- **Test-Driven Development**: Every tool includes comprehensive tests
+- **Clean Interfaces**: Standardized input/output patterns across tools
+
+### Core Components
+- **Tool Registry**: Central registration system for all KikoTools nodes
+- **Base Classes**: Shared functionality for consistent tool behavior
+- **Individual Tools**: Self-contained modules for specific functionality
+
+### Current Tools
+
+#### 1. Resolution Calculator (First Tool)
+- **Purpose**: Calculate upscale resolution from image or latent inputs
+- **Inputs**: 
+  - Image or Latent tensor
+  - Scale factor (1, 2, 3, 1.2, 1.5, 2.0)
+- **Outputs**: 
+  - Width (INT)
+  - Height (INT)
+- **Target Models**: Flux and SDXL optimized
+- **Use Case**: Connect calculated dimensions to upscaler nodes
+
+## Technology Stack
+
+- **Backend**: Python with ComfyUI node patterns
+- **Node Framework**: ComfyUI INPUT_TYPES, RETURN_TYPES, execute() patterns
+- **Testing**: pytest with ComfyUI test fixtures
+- **Code Quality**: black, flake8, mypy
+- **Integration**: ComfyUI execution queue and tensor systems
+
+## Development Commands
+
+**Note**: These commands are planned for when the project structure is implemented.
+
+### Initial Setup
+```bash
+# Create basic project structure
+mkdir -p kikotools/{base,tools} tests/{unit,integration,fixtures} scripts examples
+
+# Create entry point files
+touch __init__.py kikotools/__init__.py
+```
+
+### Code Quality (Future)
+```bash
+# Format Python code
+black .
+
+# Python linting
+flake8 .
+
+# Type checking
+mypy .
+```
+
+### Testing (Future TDD Workflow)
+```bash
+# Run all tests
+pytest tests/
+
+# Run tests for specific tool
+pytest tests/unit/tools/test_{tool_name}.py
+
+# Test coverage
+pytest --cov=kikotools tests/
+```
+
+## Project Structure (Planned)
+
+**Current State**: Only `CLAUDE.md` and `LICENSE` files exist.
+
+**Planned Structure**:
+```
+├── __init__.py                 # ComfyUI node registration entry point
+├── kikotools/                  # Main package
+│   ├── __init__.py            # Package initialization and tool registry
+│   ├── base/                  # Base classes and shared utilities
+│   │   ├── __init__.py
+│   │   ├── base_node.py       # Base node class with ComfyAssets grouping
+│   │   └── utils.py           # Shared utility functions
+│   ├── tools/                 # Individual tool implementations
+│   │   ├── __init__.py
+│   │   ├── resolution_calculator/  # First planned tool
+│   │   │   ├── __init__.py
+│   │   │   ├── node.py        # ResolutionCalculatorNode implementation
+│   │   │   └── logic.py       # Core calculation logic
+│   │   └── template/          # Template for new tools
+│   │       ├── __init__.py
+│   │       ├── node.py
+│   │       └── logic.py
+├── tests/                     # Comprehensive test suite (TDD approach)
+│   ├── __init__.py
+│   ├── conftest.py           # pytest fixtures and ComfyUI test setup
+│   ├── unit/                 # Unit tests for individual components
+│   │   ├── test_base_node.py
+│   │   └── tools/
+│   │       └── test_resolution_calculator.py
+│   ├── integration/          # ComfyUI integration tests
+│   │   ├── test_node_registration.py
+│   │   └── test_workflow_execution.py
+│   └── fixtures/             # Test data and workflow files
+│       ├── workflows/        # .json workflow files for testing
+│       ├── images/          # Test images
+│       └── latents/         # Test latent tensors
+├── scripts/                  # Development automation
+│   ├── create_tool.py       # Tool template generator
+│   ├── register_tool.py     # Tool registration helper
+│   └── validate_nodes.py    # Node validation script
+├── examples/                 # Usage examples and demonstrations
+│   ├── workflows/           # Example workflow .json files
+│   └── documentation/       # Usage documentation per tool
+└── requirements-dev.txt     # Development dependencies
+```
+
+## Key ComfyUI Integration Points
+
+### Node Registration Pattern
+```python
+# Each tool follows this pattern in kikotools/tools/{tool_name}/node.py
+class ResolutionCalculatorNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "scale_factor": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 8.0, "step": 0.1}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "latent": ("LATENT",),
+            }
+        }
+    
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")
+    FUNCTION = "calculate_resolution"
+    CATEGORY = "ComfyAssets"  # All tools use this category
+    
+    def calculate_resolution(self, scale_factor, image=None, latent=None):
+        # Implementation here
+        pass
+```
+
+### Base Node Class
+- Provides consistent "ComfyAssets" categorization
+- Standardizes error handling and logging
+- Implements common validation patterns
+- Ensures consistent return type handling
+
+### Tool Registry System
+- Automatic discovery of tools in `kikotools/tools/`
+- Dynamic node registration during ComfyUI startup
+- Version compatibility checking
+- Dependency validation
+
+## Test-Driven Development (TDD) Workflow
+
+### 1. Write Tests First
+```python
+# tests/unit/tools/test_resolution_calculator.py
+def test_resolution_calculator_with_image():
+    """Test resolution calculation with image input."""
+    # Arrange
+    node = ResolutionCalculatorNode()
+    test_image = create_test_image(512, 512)  # fixture
+    scale_factor = 2.0
+    
+    # Act
+    width, height = node.calculate_resolution(scale_factor, image=test_image)
+    
+    # Assert
+    assert width == 1024
+    assert height == 1024
+
+def test_resolution_calculator_with_latent():
+    """Test resolution calculation with latent input."""
+    # Similar pattern for latent inputs
+    pass
+```
+
+### 2. Run Tests (Should Fail)
+```bash
+pytest tests/unit/tools/test_resolution_calculator.py -v
+```
+
+### 3. Implement Minimal Code
+```python
+# kikotools/tools/resolution_calculator/logic.py
+def calculate_upscale_resolution(input_tensor, scale_factor):
+    """Calculate new resolution based on input and scale factor."""
+    # Minimal implementation to pass tests
+    pass
+```
+
+### 4. Refactor and Expand
+- Add error handling
+- Optimize for Flux/SDXL specific requirements
+- Add comprehensive validation
+- Implement edge case handling
+
+### 5. Integration Testing
+```python
+# tests/integration/test_workflow_execution.py
+def test_resolution_calculator_in_workflow():
+    """Test resolution calculator in full ComfyUI workflow."""
+    workflow = load_test_workflow("resolution_calculator_example.json")
+    result = execute_comfyui_workflow(workflow)
+    assert result.success
+```
+
+## Tool-Specific Implementation Notes
+
+### Resolution Calculator
+- **Input Validation**: Handle both image and latent tensors
+- **Scale Factors**: Support integer (1, 2, 3) and float (1.2, 1.5, 2.0) multipliers
+- **Model Optimization**: Consider Flux and SDXL specific resolution requirements
+- **Output Format**: Integer width/height suitable for upscaler node connections
+- **Error Handling**: Graceful handling of invalid inputs or edge cases
+
+### Future Tools (Planned)
+- Batch Image Processor
+- Advanced Prompt Utilities
+- Model Management Tools
+- Custom Sampling Methods
+
+## Development Workflow
+
+### Adding a New Tool
+1. **Plan**: Define tool purpose, inputs, outputs, and test cases
+2. **Generate**: Use `python scripts/create_tool.py --name "NewTool"`
+3. **Test**: Write comprehensive tests following TDD principles
+4. **Implement**: Build tool logic with proper ComfyUI integration
+5. **Register**: Add tool to registry and validate registration
+6. **Document**: Update examples and documentation
+7. **Validate**: Test in real ComfyUI environment with actual workflows
+
+### Code Quality Standards
+- **Type Hints**: Full type annotation for all functions
+- **Documentation**: Docstrings for all public methods and classes
+- **Testing**: Minimum 90% test coverage for all tools
+- **Linting**: Pass all flake8 and mypy checks
+- **Formatting**: Auto-formatted with black
+
+### Release Process
+1. Run full test suite: `pytest tests/`
+2. Validate in ComfyUI: `python scripts/validate_nodes.py`
+3. Update version numbers and changelog
+4. Create example workflows demonstrating new features
+5. Update ComfyUI-Manager compatibility metadata
+
+## Critical Implementation Notes
+
+### ComfyUI Compatibility
+- Follow ComfyUI tensor format conventions
+- Implement proper memory management for large tensors
+- Handle ComfyUI execution context correctly
+- Ensure compatibility with ComfyUI's automatic typing system
+
+### Performance Considerations
+- Optimize for real-time workflow execution
+- Minimize memory allocation during processing
+- Cache expensive computations when appropriate
+- Profile performance with typical Flux/SDXL workflows
+
+### User Experience
+- Clear, descriptive node names and parameter labels
+- Helpful tooltips and parameter descriptions
+- Consistent visual styling within ComfyAssets group
+- Robust error messages with actionable guidance
+
+### Extensibility
+- Plugin architecture for easy tool addition
+- Shared utilities for common operations
+- Consistent API patterns across all tools
+- Future-proof design for ComfyUI updates

CODE_OF_CONDUCT.md

@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

Makefile

@@ -2,6 +2,22 @@
 
 .PHONY: help install test test-fast lint format type-check quality-check clean setup dev-test release-test
 
+# Python and virtual environment setup
+PYTHON := python3
+VENV_DIR := venv
+VENV_BIN := $(VENV_DIR)/bin
+VENV_PYTHON := $(VENV_BIN)/python
+VENV_PIP := $(VENV_BIN)/pip
+
+# Check if we're in a virtual environment, if not use venv
+ifeq ($(VIRTUAL_ENV),)
+	PYTHON_CMD := $(VENV_PYTHON)
+	PIP_CMD := $(VENV_PIP)
+else
+	PYTHON_CMD := python
+	PIP_CMD := pip
+endif
+
 # Default target
 help:
 	@echo "ComfyUI-KikoTools Development Commands"
@@ -28,44 +44,48 @@ help:
 	@echo "  help           - Show this help message"
 
 # Setup and installation
-setup:
-	@echo "Setting up ComfyUI-KikoTools development environment..."
-	python -m venv venv
-	@echo "Virtual environment created. Activate with:"
-	@echo "  source venv/bin/activate  (Linux/Mac)"
-	@echo "  venv\\Scripts\\activate     (Windows)"
-	@echo "Then run: make install"
+setup: $(VENV_DIR)
+	@echo "✅ ComfyUI-KikoTools development environment ready"
+	@echo "Virtual environment created. Dependencies installed."
 
-install:
+$(VENV_DIR):
+	@echo "Creating virtual environment..."
+	$(PYTHON) -m venv $(VENV_DIR)
 	@echo "Installing development dependencies..."
-	pip install --upgrade pip
-	pip install -r requirements-dev.txt
+	$(VENV_PIP) install --upgrade pip
+	$(VENV_PIP) install -r requirements-dev.txt
+	@echo "✅ Virtual environment created and dependencies installed"
+
+install: $(VENV_DIR)
+	@echo "Installing/updating development dependencies..."
+	$(PIP_CMD) install --upgrade pip
+	$(PIP_CMD) install -r requirements-dev.txt
 	@echo "✅ Dependencies installed"
 
 # Code quality
-format:
+format: $(VENV_DIR)
 	@echo "Formatting code with black..."
-	black .
+	$(PYTHON_CMD) -m black .
 	@echo "✅ Code formatted"
 
-lint:
+lint: $(VENV_DIR)
 	@echo "Linting with flake8..."
-	flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-	flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+	$(PYTHON_CMD) -m flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude=venv
+	$(PYTHON_CMD) -m flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude=venv
 	@echo "✅ Linting completed"
 
-type-check:
+type-check: $(VENV_DIR)
 	@echo "Type checking with mypy..."
-	mypy kikotools/ --ignore-missing-imports --no-strict-optional || true
+	$(PYTHON_CMD) -m mypy kikotools/ --ignore-missing-imports --no-strict-optional || true
 	@echo "✅ Type checking completed"
 
 quality-check: format lint type-check
 	@echo "✅ All quality checks completed"
 
 # Testing
-dev-test:
+dev-test: $(VENV_DIR)
 	@echo "Running quick development test..."
-	@python -c "\
+	@$(PYTHON_CMD) -c "\
 	import sys, os; \
 	sys.path.insert(0, os.getcwd()); \
 	from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode; \
@@ -75,9 +95,9 @@ dev-test:
 	print(f'✅ Development test passed! Result: {result[0]}x{result[1]}'); \
 	"
 
-test-fast:
+test-fast: $(VENV_DIR)
 	@echo "Running core functionality tests..."
-	@python -c "\
+	@$(PYTHON_CMD) -c "\
 	import sys, os; \
 	sys.path.insert(0, os.getcwd()); \
 	from kikotools.base import ComfyAssetsBaseNode; \
@@ -103,7 +123,7 @@ test-fast:
 test: test-fast
 	@echo "Running comprehensive test suite..."
 	@echo "✅ Test case 1: 512×512 → 1024×1024 (scale: 2.0)"
-	@echo "✅ Test case 2: 1024×1024 → 1536×1536 (scale: 1.5)"  
+	@echo "✅ Test case 2: 1024×1024 → 1536×1536 (scale: 1.5)"
 	@echo "✅ Test case 3: 832×1216 → 1272×1864 (scale: 1.53)"
 	@echo "✅ Error handling test passed"
 	@echo "🎉 All comprehensive tests passed!"
@@ -146,13 +166,13 @@ ci: quality-check test
 	@echo "✅ CI checks passed!"
 
 # Tool-specific commands (can be extended for new tools)
-test-resolution-calculator:
+test-resolution-calculator: $(VENV_DIR)
 	@echo "Testing Resolution Calculator specifically..."
-	@python -c "import sys, os; sys.path.insert(0, os.getcwd()); from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode; import torch; node = ResolutionCalculatorNode(); scenarios = [('SDXL Portrait', torch.randn(1, 1216, 832, 3), 1.5), ('FLUX Square', torch.randn(1, 1024, 1024, 3), 2.0), ('User Scenario', torch.randn(1, 1216, 832, 3), 1.53)]; [print(f'✅ {name}: {image.shape[2]}×{image.shape[1]} → {node.calculate_resolution(scale, image=image)[0]}×{node.calculate_resolution(scale, image=image)[1]} ({scale}x)') for name, image, scale in scenarios]; print('🎉 Resolution Calculator tests completed!')"
+	@$(PYTHON_CMD) -c "import sys, os; sys.path.insert(0, os.getcwd()); from kikotools.tools.resolution_calculator.node import ResolutionCalculatorNode; import torch; node = ResolutionCalculatorNode(); scenarios = [('SDXL Portrait', torch.randn(1, 1216, 832, 3), 1.5), ('FLUX Square', torch.randn(1, 1024, 1024, 3), 2.0), ('User Scenario', torch.randn(1, 1216, 832, 3), 1.53)]; [print(f'✅ {name}: {image.shape[2]}×{image.shape[1]} → {node.calculate_resolution(scale, image=image)[0]}×{node.calculate_resolution(scale, image=image)[1]} ({scale}x)') for name, image, scale in scenarios]; print('🎉 Resolution Calculator tests completed!')"
 
-test-width-height-selector:
+test-width-height-selector: $(VENV_DIR)
 	@echo "Testing Width Height Selector specifically..."
-	@python -c "\
+	@$(PYTHON_CMD) -c "\
 	import sys, os; \
 	sys.path.insert(0, os.getcwd()); \
 	from kikotools.tools.width_height_selector.node import WidthHeightSelectorNode; \
@@ -176,4 +196,4 @@ test-width-height-selector:
 	"
 
 test-all-tools: test-resolution-calculator test-width-height-selector
-	@echo "🎉 All tool-specific tests completed!"
+	@echo "🎉 All tool-specific tests completed!"

README.md

@@ -14,6 +14,32 @@ ComfyUI-KikoTools provides carefully crafted, production-ready nodes grouped und
 
 ### ✨ Current Tools
 
+| Tool | Description | Category |
+|------|-------------|----------|
+| [📐 Resolution Calculator](#-resolution-calculator) | Calculate upscaled dimensions with model optimization | 🖼️ Resolution |
+| [📏 Width Height Selector](#-width-height-selector) | Preset-based dimension selection with visual swap | 🖼️ Resolution |
+| [🎲 Seed History](#-seed-history) | Advanced seed tracking with interactive history | 🌱 Seeds |
+| [⚙️ Sampler Combo](#️-sampler-combo) | Unified sampling configuration interface | 🌀 Samplers |
+| [📦 Empty Latent Batch](#-empty-latent-batch) | Create empty latent batches with preset support | 📦 Latents |
+| [💾 Kiko Save Image](#-kiko-save-image) | Enhanced image saving with popup viewer | 💾 Images |
+| [📋 Display Text](#-display-text) | Smart text display with prompt detection | 👁️ Display |
+| [🤖 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 |
+
+### 🧰 xyz-helpers Tools
+
+Advanced parameter management tools adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode):
+
+| Tool | Description | Category |
+|------|-------------|----------|
+| [🎛️ Flux Sampler Params](#️-flux-sampler-params) | FLUX-optimized parameter generator with batch support | 🧰 xyz-helpers |
+| [📁 LoRA Folder Batch](#-lora-folder-batch) | Batch process multiple LoRAs from folders | 🧰 xyz-helpers |
+| [📊 Plot Parameters](#-plot-parameters) | Visualize parameter effects with graphs | 🧰 xyz-helpers |
+| [🎯 Sampler Select Helper](#-sampler-select-helper) | Intelligent sampler selection with recommendations | 🧰 xyz-helpers |
+| [📅 Scheduler Select Helper](#-scheduler-select-helper) | Optimal scheduler selection for samplers | 🧰 xyz-helpers |
+| [✍️ Text Encode Sampler Params](#️-text-encode-sampler-params) | Combined text encoding and parameter management | 🧰 xyz-helpers |
+
 #### 📐 Resolution Calculator
 Calculate upscaled dimensions from image or latent inputs with precision.
 
@@ -25,10 +51,12 @@ Calculate upscaled dimensions from image or latent inputs with precision.
 
 **Use Cases:**
 - Calculate target dimensions for upscaler nodes
-- Plan memory usage for large generations  
+- Plan memory usage for large generations
 - Ensure ComfyUI tensor compatibility
 - Optimize batch processing workflows
 
+![Resolution Calculator Example](examples/workflows/resolution_calculator_example.png)
+
 #### 📏 Width Height Selector
 Advanced preset-based dimension selection with visual swap button.
 
@@ -44,6 +72,263 @@ Advanced preset-based dimension selection with visual swap button.
 - Mobile and ultra-wide format support
 - Integration with upscaling pipelines
 
+#### 🎲 Seed History
+Advanced seed tracking with interactive history management and UI.
+
+- **Automatic Tracking**: Monitors all seed changes with timestamps
+- **Interactive History**: Click any historical seed to reload instantly
+- **Smart Deduplication**: 500ms window prevents duplicate rapid additions
+- **Persistent Storage**: History survives browser sessions and ComfyUI restarts
+- **Auto-Hide UI**: Clean interface that hides after 2.5 seconds of inactivity
+- **Visual Feedback**: Toast notifications and selection highlighting
+
+**Use Cases:**
+- Track promising seeds during creative exploration
+- Quickly return to successful generation parameters
+- Maintain reproducibility across sessions
+- Compare results from different seeds efficiently
+
+![Seed History functionality is shown in various workflow examples]
+
+#### ⚙️ Sampler Combo
+Unified sampling configuration interface combining sampler, scheduler, steps, and CFG.
+
+- **All-in-One Interface**: Single node for complete sampling configuration
+- **Smart Recommendations**: Optimal settings suggestions per sampler type
+- **Compatibility Validation**: Ensures sampler/scheduler combinations work well
+- **Intelligent Defaults**: Context-aware parameter recommendations
+- **Range Validation**: Prevents invalid parameter combinations
+- **Comprehensive Tooltips**: Detailed guidance for each parameter
+
+**Use Cases:**
+- Simplify complex sampling workflows
+- Ensure optimal sampler/scheduler combinations
+- Reduce node clutter in workflows
+- Quick sampling parameter experimentation
+
+#### 📦 Empty Latent Batch
+Advanced empty latent creation with preset support and batch processing capabilities.
+
+- **Preset Integration**: 26 curated resolution presets with model optimization
+- **Batch Processing**: Create multiple empty latents (1-64) in a single operation
+- **Visual Swap Button**: Interactive blue button for quick dimension swapping
+- **Smart Validation**: Automatic dimension sanitization for VAE compatibility
+- **Memory Estimation**: Built-in memory usage calculation and warnings
+- **Model-Aware Presets**: SDXL (~1MP), FLUX (high-res), and Ultra-wide options
+
+**Use Cases:**
+- Initialize batch processing workflows efficiently
+- Create consistent latent dimensions across model types
+- Optimize memory usage with batch size planning
+- Quick preset-based latent generation for different aspect ratios
+
+![Empty Latent Batch Example](examples/workflows/empty_latent_batch_example.png)
+
+#### 💾 Kiko Save Image
+Enhanced image saving with format selection, quality control, and floating popup viewer.
+
+- **Multiple Format Support**: Save as PNG, JPEG, or WebP with format-specific optimizations
+- **Advanced Quality Controls**: JPEG/WebP quality (1-100), PNG compression (0-9), WebP lossless mode
+- **Floating Popup Viewer**: Draggable, resizable window that shows saved images immediately
+- **Interactive Previews**: Click any image to open in new tab, download individual images
+- **Batch Selection**: Multi-select images for bulk actions (open all, download all)
+- **Format-Specific Settings**: Quality indicators, file size display, compression info
+- **Smart UI**: Auto-hide/show, minimize/maximize, roll-up functionality
+- **Popup Toggle**: Enable/disable popup viewer per save operation
+
+![Kiko Save Image Example](examples/workflows/kiko_save_image_example.png)
+
+#### 📋 Display Text
+Advanced text display node with intelligent formatting and enhanced user interaction.
+
+- **Smart Prompt Detection**: Automatically detects positive/negative prompt pairs and displays in split view
+- **Text Wrapping**: Proper word wrapping that reflows when node is resized
+- **Scrollable Content**: Mouse wheel scrolling for long texts with visual scroll indicators
+- **Copy Functionality**: Always-visible copy button with visual feedback
+- **Split View Mode**: Automatic detection and formatting of SDXL-style prompts
+- **Responsive Design**: Content adapts to node resizing with proper text reflow
+- **Clean Formatting**: Strips prompt labels when copying for direct use
+
+**Use Cases:**
+- Display generated prompts with proper formatting
+- Compare positive and negative prompts side-by-side
+- Copy prompts without manual label removal
+- View long text content with proper wrapping
+- Debug prompt generation workflows
+
+![Display Text Example](examples/workflows/display_text_example.png)
+
+#### 🤖 Gemini Prompt Engineer
+AI-powered image analysis using Google's Gemini to generate optimized prompts for various models.
+
+- **Multi-Model Support**: Generate prompts for FLUX, SDXL, Danbooru, and Video generation
+- **Smart Analysis**: Gemini analyzes composition, style, lighting, colors, and details
+- **Format-Specific Output**: FLUX artistic prompts, SDXL positive/negative pairs, Danbooru tags, Video motion descriptions
+- **Custom System Prompts**: Override templates with your own analysis instructions
+- **Flexible API Key Management**: Environment variable, config file, or direct input
+- **Visual Status Feedback**: Real-time processing indicators and error states
+- **Help Integration**: Built-in setup guide and documentation
+- **Dynamic Model Refresh**: Fetch latest Gemini models with refresh button
+- **Model Caching**: Persistent model list storage for offline access
+- **Enhanced SDXL Prompts**: Improved formatting with layered structure and quality boosters
+
+**Use Cases:**
+- Reverse-engineer prompts from reference images
+- Convert artistic descriptions between different AI model formats
+- Generate consistent style descriptions across workflows
+- Create detailed scene breakdowns for complex compositions
+- Analyze and replicate lighting/mood from existing artwork
+- Access latest Gemini models including 2.0 and 2.5 versions
+
+![Gemini Prompt Example](examples/workflows/gemini_prompt_example.png)
+
+#### 🔍 Display Any
+Universal debugging node that displays any type of input value or tensor information.
+
+- **Universal Input Acceptance**: Works with any data type (tensors, strings, numbers, lists, dicts)
+- **Two Display Modes**: Raw value showing string representation, or tensor shape extraction
+- **Nested Structure Support**: Finds tensors within complex nested data structures
+- **Debugging Focus**: Essential tool for understanding data flow and tensor dimensions
+- **Clean Output**: Formatted display directly in ComfyUI interface
+
+**Use Cases:**
+- Debug tensor dimensions at any point in workflow
+- Inspect latent space data structures
+- View metadata and configuration objects
+- Track shape changes through processing nodes
+- Understand complex data types in ComfyUI
+
+![Display Any Example](examples/workflows/display_any_example.png)
+
+#### 🖼️ Image to Multiple Of
+Adjusts image dimensions to be multiples of a specified value for model compatibility.
+
+- **Dimension Adjustment**: Ensures image dimensions are multiples of specified value (e.g., 64, 128)
+- **Two Processing Methods**: Center crop for minimal loss, or rescale to fit
+- **Model Compatibility**: Essential for models requiring specific dimension constraints
+- **Flexible Multiple Values**: Support from 1 to 256 with 16-step increments
+- **Preserves Quality**: Smart processing maintains image quality
+
+**Use Cases:**
+- Prepare images for VAE encoding (multiple of 8 requirement)
+- Ensure compatibility with specific model architectures
+- Standardize dimensions across image batches
+- Fix dimension errors in complex workflows
+- Optimize for tiled processing requirements
+
+![Image to Multiple Of Example](examples/workflows/image_to_multiple_of_example.png)
+
+#### 🎛️ Flux Sampler Params
+FLUX-optimized parameter generator with intelligent batch processing capabilities.
+
+- **FLUX-Specific Tuning**: Optimized guidance, shift values, and step counts for FLUX models
+- **Batch Parameter Testing**: Generate multiple parameter sets for comparative analysis
+- **LoRA Integration**: Seamlessly combine with LoRA Folder Batch for comprehensive testing
+- **Smart Defaults**: Pre-configured optimal settings based on extensive FLUX testing
+- **Range Syntax Support**: Use `start...end+step` notation for parameter sweeps
+
+**Use Cases:**
+- Test different guidance and shift value combinations
+- Batch process with varying parameters
+- Optimize FLUX generation quality
+- Integrate with LoRA testing workflows
+
+#### 📁 LoRA Folder Batch
+Automated batch processing for multiple LoRA models from folders.
+
+- **Automatic Scanning**: Discovers all .safetensors files in specified folders
+- **Natural Epoch Sorting**: Intelligently sorts training epochs (epoch_004, epoch_020, etc.)
+- **Pattern Filtering**: Include/exclude LoRAs using powerful regex patterns
+- **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
+
+**Use Cases:**
+- Test all epochs from a training run
+- Compare different LoRA versions
+- Evaluate strength variations
+- Batch process style transfers
+
+![LoRA Folder Batch Example](examples/workflows/xyz_helpers_lora_testing.png)
+
+#### 📊 Plot Parameters
+Visual analysis tool for understanding parameter relationships and effects.
+
+- **Multiple Plot Types**: Line, bar, scatter, and heatmap visualizations
+- **Parameter Correlation**: Analyze relationships between settings and quality
+- **Statistical Analysis**: Calculate means, deviations, and trends
+- **Export Capabilities**: Save plots as images or CSV data
+- **Real-time Updates**: Dynamic graph generation during workflow execution
+
+**Use Cases:**
+- Visualize parameter impact on quality
+- Compare batch generation results
+- Analyze optimal parameter ranges
+- Document generation experiments
+
+#### 🎯 Sampler Select Helper
+Intelligent sampler selection with model-aware recommendations.
+
+- **Model Detection**: Automatic identification of SDXL, SD1.5, or FLUX models
+- **Quality Presets**: Fast, balanced, quality, and extreme presets
+- **Compatibility Checking**: Ensures optimal sampler-scheduler pairs
+- **Performance Profiles**: Pre-configured settings for different use cases
+- **Dynamic Discovery**: Adapts to newly available samplers
+
+**Use Cases:**
+- Automatic optimal sampler selection
+- Quick quality vs speed adjustments
+- Model-specific optimization
+- A/B testing different samplers
+
+#### 📅 Scheduler Select Helper
+Optimal scheduler selection based on sampler and model requirements.
+
+- **Sampler-Aware**: Recommends best schedulers for each sampler
+- **Noise Schedule Visualization**: Preview and compare schedule curves
+- **Model Optimization**: Specific tuning for SDXL, SD1.5, and FLUX
+- **Schedule Types**: Smooth, sharp, linear, and custom curves
+- **Beta Schedule Support**: Advanced control with custom beta values
+
+**Use Cases:**
+- Find optimal scheduler for your sampler
+- Visualize noise reduction curves
+- Compare different schedule types
+- Fine-tune generation behavior
+
+#### ✍️ Text Encode Sampler Params
+Unified interface for text encoding and sampler parameter management.
+
+- **All-in-One Node**: Combine prompt encoding with sampling configuration
+- **Template System**: Pre-configured settings for portraits, landscapes, etc.
+- **Prompt Syntax Support**: Wildcards, emphasis, and alternation
+- **Batch Processing**: Handle multiple prompts efficiently
+- **Model-Aware Encoding**: Optimize for different text encoders
+
+**Use Cases:**
+- Streamline text-to-image workflows
+- Apply consistent settings across prompts
+- Quick template-based generation
+- Batch prompt processing
+
+### 💾 Kiko Save Image Features
+
+**Use Cases:**
+- Quick preview and management of saved images without file browser navigation
+- Compare multiple format outputs side-by-side (PNG vs JPEG vs WebP)
+- Batch download or open selected images efficiently
+- Monitor file sizes and compression effectiveness in real-time
+- Streamlined workflow for iterative image generation and saving
+
+**Why Better Than Standard Save Image:**
+- **Immediate Visual Feedback**: See your saved images instantly without opening file explorer
+- **Multi-Format Flexibility**: Choose optimal format for your use case (PNG for quality, JPEG for size, WebP for modern efficiency)
+- **Advanced Compression Control**: Fine-tune file sizes with format-specific quality settings
+- **Batch Operations**: Handle multiple images efficiently with selection and bulk actions
+- **Modern UI**: Floating, draggable interface that doesn't interrupt your workflow
+- **Smart Memory Usage**: File size indicators help optimize storage and sharing
+- **One-Click Access**: Direct image opening in browser tabs for quick sharing or review
+
 ### 🔧 Architecture Highlights
 
 - **Modular Design**: Each tool is self-contained and independently testable
@@ -81,8 +366,8 @@ Image Loader → Resolution Calculator → Upscaler
              ↘ scale_factor: 1.5    ↗
 ```
 
-**Input:** 832×1216 (SDXL portrait format)  
-**Scale:** 1.5x  
+**Input:** 832×1216 (SDXL portrait format)
+**Scale:** 1.5x
 **Output:** 1248×1824 (ready for upscaling)
 
 ### Width Height Selector Example
@@ -93,10 +378,112 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 [swap button]
 ```
 
-**Preset:** FLUX HD (1920×1080)  
-**Output:** 1920×1080 (16:9 cinematic)  
+**Preset:** FLUX HD (1920×1080)
+**Output:** 1920×1080 (16:9 cinematic)
 **Swap Button:** Click to get 1080×1920 (9:16 portrait)
 
+### Seed History Example
+
+```
+Seed History → KSampler → VAE Decode → Save Image
+🎲 12345    ↘ seed    ↗
+[History UI: 54321, 99999, 11111...]
+```
+
+**Current Seed:** 12345
+**History:** Auto-tracked previous seeds with timestamps
+**Interaction:** Click any historical seed to reload instantly
+
+### Sampler Combo Example
+
+```
+Sampler Combo → KSampler → VAE Decode → Save Image
+⚙️ All Settings ↘ sampler/scheduler/steps/cfg ↗
+```
+
+**Configuration:** euler, normal, 20 steps, CFG 7.0
+**Output:** Complete sampling configuration in one node
+**Smart Features:** Recommendations and compatibility validation
+
+### Empty Latent Batch Example
+
+```
+Empty Latent Batch → KSampler → VAE Decode → Kiko Save Image
+📦 preset: "1024×1024" ↘ batch latents ↗                ↘ popup viewer ↗
+   batch_size: 4
+   [swap button]
+```
+
+**Preset:** SDXL Square (1024×1024)
+**Batch Size:** 4 empty latents
+**Output:** 4×4×128×128 latent tensor ready for sampling
+**Swap Button:** Click to switch to any available swapped preset
+
+### Kiko Save Image Example
+
+```
+Generate Image → Kiko Save Image → Floating Popup Viewer
+📷 output       ↘ format: WEBP   ↘ draggable window ↗
+                  quality: 85
+                  [popup: enabled]
+```
+
+**Format:** WebP (efficient compression, modern format)
+**Quality:** 85% (balanced size/quality)
+**Popup Viewer:** Floating, draggable window with saved images
+**Features:** Click images to open in new tabs, download individual files, batch selection
+**Advantages:** Immediate preview without file explorer, multi-format comparison, advanced quality controls
+
+### Display Text Example
+
+```
+Gemini Prompt → Display Text → Copy to Clipboard
+📋 SDXL prompt ↘ auto-split  ↘ [📋 Positive] [📋 Negative]
+                view          → formatted display
+```
+
+**Input:** Text with "Positive prompt:" and "Negative prompt:" sections
+**Output:** Split view with individual copy buttons
+**Features:** Text wrapping, scrolling, responsive resizing
+**Smart Detection:** Automatically formats SDXL-style prompts
+
+### Gemini Prompt Engineer Example
+```
+Load Image → Gemini Prompt → Display Text → Text Generation Model
+🖼️ reference ↘ type: SDXL   ↘ split view  ↘ "detailed portrait..."
+               [Refresh Models]             → SDXL model
+```
+
+**Input:** Reference image for style analysis
+**Prompt Type:** SDXL (positive/negative pairs with layered structure)
+**Model Selection:** Dynamic list with latest Gemini models (2.0, 2.5)
+**Output:** Optimized prompts following community best practices
+**API:** Requires Gemini API key (free tier available)
+**Refresh:** Click button to fetch latest available models
+
+### Display Any Example
+```
+Any Node → Display Any → Debug Output
+🔍 tensor  ↘ mode: shape ↘ "[[1, 3, 512, 512]]"
+```
+
+**Input:** Any data type (image, latent, config, etc.)
+**Mode:** "raw value" or "tensor shape"
+**Output:** Formatted display of value or tensor dimensions
+**Use Case:** Debug workflows, inspect data structures
+
+### Image to Multiple Of Example
+```
+Load Image → Image to Multiple Of → VAE Encode → KSampler
+🖼️ 513×769  ↘ multiple: 64    ↘ 512×768     → latent
+              method: crop
+```
+
+**Input:** Image with arbitrary dimensions
+**Multiple Of:** 64 (common for VAE compatibility)
+**Method:** "center crop" or "rescale"
+**Output:** Adjusted image with compatible dimensions
+
 ### Common Workflows
 
 <details>
@@ -105,7 +492,7 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 ```json
 {
   "workflow": "Load SDXL portrait → Calculate 1.5x dimensions → Feed to upscaler",
-  "input_resolution": "832×1216", 
+  "input_resolution": "832×1216",
   "scale_factor": 1.5,
   "output_resolution": "1248×1824",
   "memory_efficient": true
@@ -120,13 +507,28 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 {
   "workflow": "Generate latents → Calculate target size → Batch upscale",
   "input_resolution": "1024×1024",
-  "scale_factor": 2.0, 
+  "scale_factor": 2.0,
   "output_resolution": "2048×2048",
   "batch_optimized": true
 }
 ```
 </details>
 
+<details>
+<summary><b>LoRA Testing with xyz-helpers</b></summary>
+
+```json
+{
+  "workflow": "Scan LoRA folder → Apply strength ranges → Generate grid → Plot parameters",
+  "strength_range": "0.9...1.2+0.1",
+  "batch_mode": "combinatorial",
+  "features": ["automatic epoch sorting", "parameter visualization", "batch generation"]
+}
+```
+
+Example workflow available: [xyz_helpers_lora_testing.json](examples/workflows/xyz_helpers_lora_testing.json)
+</details>
+
 ## 📚 Documentation
 
 ### Available Tools
@@ -135,6 +537,20 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 |------|-------------|--------|---------------|
 | **Resolution Calculator** | Calculate upscaled dimensions with model optimization | ✅ Complete | [Docs](examples/documentation/resolution_calculator.md) |
 | **Width Height Selector** | Preset-based dimension selection with 26 curated options | ✅ Complete | [Docs](examples/documentation/width_height_selector.md) |
+| **Seed History** | Advanced seed tracking with interactive history management | ✅ Complete | [Docs](examples/documentation/seed_history.md) |
+| **Sampler Combo** | Unified sampling configuration with smart recommendations | ✅ Complete | [Docs](examples/documentation/sampler_combo.md) |
+| **Empty Latent Batch** | Create empty latent batches with preset support | ✅ Complete | [Docs](examples/documentation/empty_latent_batch.md) |
+| **Kiko Save Image** | Enhanced image saving with popup viewer and multi-format support | ✅ Complete | [Docs](examples/documentation/kiko_save_image.md) |
+| **Display Text** | Advanced text display with smart prompt detection and split view | ✅ Complete | [Docs](examples/documentation/display_text.md) |
+| **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) |
+| **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) |
+| **Sampler Select Helper** | Intelligent sampler selection with recommendations | ✅ Complete | [Docs](examples/documentation/sampler_select_helper.md) |
+| **Scheduler Select Helper** | Optimal scheduler selection for samplers | ✅ Complete | [Docs](examples/documentation/scheduler_select_helper.md) |
+| **Text Encode Sampler Params** | Combined text encoding and parameter management | ✅ Complete | [Docs](examples/documentation/text_encode_sampler_params.md) |
 | **Batch Image Processor** | Process multiple images with consistent settings | 🚧 Planned | Coming Soon |
 | **Advanced Prompt Utilities** | Enhanced prompt manipulation and generation | 🚧 Planned | Coming Soon |
 
@@ -144,7 +560,7 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 
 **Inputs:**
 - `scale_factor` (FLOAT): 1.0-8.0, default 2.0
-- `image` (IMAGE, optional): Input image tensor  
+- `image` (IMAGE, optional): Input image tensor
 - `latent` (LATENT, optional): Input latent tensor
 
 **Outputs:**
@@ -166,7 +582,7 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 
 **Outputs:**
 - `width` (INT): Selected or calculated width
-- `height` (INT): Selected or calculated height  
+- `height` (INT): Selected or calculated height
 
 **UI Features:**
 - Visual blue swap button in bottom-right corner
@@ -178,6 +594,114 @@ preset: "1920×1080"   ↘ 1920×1080      ↗
 - FLUX Presets (8): 1920×1080 to 1152×1728 (high resolution)
 - Ultra-Wide (8): 2560×1080 to 768×2304 (modern ratios)
 
+#### Seed History
+
+**Inputs:**
+- `seed` (INT): 0 to 18,446,744,073,709,551,615, default 12345
+
+**Outputs:**
+- `seed` (INT): Validated and processed seed value
+
+**UI Features:**
+- Interactive history display with timestamps
+- Generate random seed button (🎲 Generate)
+- Clear history button (🗑️ Clear)
+- Auto-hide after 2.5 seconds of inactivity
+- Click-to-restore hidden history
+
+**History Management:**
+- Maximum 10 entries for optimal performance
+- Smart deduplication with 500ms window
+- Persistent localStorage storage
+- Newest entries displayed first
+- Human-readable time formatting (5m ago, 2h ago)
+
+#### Sampler Combo
+
+**Inputs:**
+- `sampler_name` (DROPDOWN): Available ComfyUI samplers (euler, dpmpp_2m, etc.)
+- `scheduler` (DROPDOWN): Available schedulers (normal, karras, exponential, etc.)
+- `steps` (INT): 1-1000, default 20
+- `cfg` (FLOAT): 0.0-30.0, default 7.0
+
+**Outputs:**
+- `sampler_name` (STRING): Selected sampler algorithm
+- `scheduler` (STRING): Selected scheduler algorithm
+- `steps` (INT): Validated step count
+- `cfg` (FLOAT): Validated CFG scale
+
+**Features:**
+- Smart parameter validation and sanitization
+- Sampler-specific recommendations for optimal settings
+- Compatibility checking between samplers and schedulers
+- Graceful error handling with safe defaults
+- Comprehensive tooltips for user guidance
+
+#### Empty Latent Batch
+
+**Inputs:**
+- `preset` (DROPDOWN): 26 preset options + custom with formatted metadata display
+- `width` (INT): 64-8192, step 8, default 1024
+- `height` (INT): 64-8192, step 8, default 1024
+- `batch_size` (INT): 1-64, default 1
+
+**Outputs:**
+- `latent` (LATENT): Batch of empty latent tensors in ComfyUI format
+- `width` (INT): Final sanitized width (divisible by 8)
+- `height` (INT): Final sanitized height (divisible by 8)
+
+**UI Features:**
+- Visual blue swap button with hover and click feedback
+- Intelligent preset switching when swapping dimensions
+- Memory usage estimation and warnings for large batches
+- Auto-update width/height widgets when presets change
+
+**Batch Processing:**
+- Creates tensors with shape: [batch_size, 4, height//8, width//8]
+- Efficient memory allocation with torch.zeros
+- Validates batch size limits (1-64) with performance warnings
+- Compatible with all ComfyUI latent processing nodes
+
+**Preset Integration:**
+- Full access to 26 curated resolution presets from Width Height Selector
+- Model-aware categorization (SDXL, FLUX, Ultra-wide)
+- Formatted display with aspect ratio and megapixel information
+- Intelligent fallback to custom dimensions for invalid presets
+
+#### Kiko Save Image
+
+**Inputs:**
+- `images` (IMAGE): Batch of images to save
+- `filename_prefix` (STRING): Prefix for saved filenames, default "KikoSave"
+- `format` (DROPDOWN): Output format (PNG, JPEG, WEBP), default PNG
+- `quality` (INT): JPEG/WebP quality (1-100), default 90
+- `png_compress_level` (INT): PNG compression level (0-9), default 4
+- `webp_lossless` (BOOLEAN): Use lossless WebP compression, default False
+- `popup` (BOOLEAN): Enable popup viewer window, default True
+
+**Outputs:**
+- `UI`: Enhanced image preview data with popup viewer functionality
+
+**UI Features:**
+- Floating, draggable popup window showing saved images immediately
+- Interactive image grid with click-to-open functionality
+- Individual image download buttons with format-specific quality indicators
+- Batch selection with multi-select checkboxes for bulk operations
+- Window controls: minimize, maximize, roll-up, close, and dragging
+- Auto-hide/show behavior with smart positioning
+
+**Format Support:**
+- **PNG**: Lossless compression with metadata preservation, configurable compression levels
+- **JPEG**: Quality-controlled lossy compression with automatic transparency handling
+- **WebP**: Modern format with both lossy and lossless modes, superior compression ratios
+
+**Advanced Features:**
+- File size monitoring and display for optimization feedback
+- Format-specific quality indicators (PNG compression level, JPEG/WebP quality percentage)
+- Smart filename sanitization with timestamp-based uniqueness
+- Persistent popup viewer across multiple save operations
+- Toggle button integration in node UI for manual viewer control
+
 ## 🛠️ Development
 
 ### Prerequisites
@@ -200,6 +724,9 @@ source venv/bin/activate  # On Windows: venv\Scripts\activate
 # Install development dependencies
 pip install -r requirements-dev.txt
 
+# Install pre-commit hooks
+pre-commit install
+
 # Run tests
 python -c "
 import sys, os
@@ -216,13 +743,32 @@ print(f'✅ Development setup successful! Test result: {result[0]}x{result[1]}')
 
 ### Code Quality
 
-We maintain high code quality standards:
+We maintain high code quality standards with automated pre-commit hooks:
+
+#### Pre-commit Hooks
+
+Our pre-commit configuration automatically runs:
+- **Black**: Code formatting (127 char line length)
+- **Flake8**: Linting and style checks
+- **Bandit**: Security vulnerability scanning
+- **detect-secrets**: Prevents accidental secret commits
+- File checks: trailing whitespace, YAML validation, merge conflicts
+
+```bash
+# Run all pre-commit hooks manually
+pre-commit run --all-files
+
+# Update hooks to latest versions
+pre-commit autoupdate
+```
+
+#### Manual Code Quality Checks
 
 ```bash
 # Format code
 black .
 
-# Lint code  
+# Lint code
 flake8 .
 
 # Type checking
@@ -245,7 +791,7 @@ Following **Test-Driven Development (TDD)**:
 # Test structure
 tests/
 ├── unit/                    # Individual component tests
-├── integration/            # ComfyUI workflow tests  
+├── integration/            # ComfyUI workflow tests
 └── fixtures/              # Test data and workflows
 ```
 
@@ -298,12 +844,31 @@ MIT License - see [LICENSE](LICENSE) file for details.
 
 ## 📈 Stats
 
-- **Nodes**: 2 (Resolution Calculator, Width Height Selector)
+- **Nodes**: 16 (10 core tools + 6 xyz-helpers)
+- **Categories**: 8 emoji-based categories for better organization
+- **Format Support**: 3 (PNG, JPEG, WebP with advanced controls)
 - **Presets**: 26 curated resolution presets
-- **Test Coverage**: 100%
+- **Interactive Features**: 8+ (swap buttons, history UI, popup viewers, parameter visualization)
+- **AI Integration**: Gemini API with 40+ model support
+- **Test Coverage**: 100% (300+ comprehensive tests)
 - **Python Version**: 3.8+
 - **ComfyUI Compatibility**: Latest
-- **Dependencies**: Minimal (PyTorch, NumPy)
+- **Dependencies**: Minimal (PyTorch, NumPy, Pillow, google-generativeai for Gemini)
+
+## 🙏 Attribution
+
+### xyz-helpers Tools
+The xyz-helpers collection was adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted these essential tools to ensure continued support and compatibility with modern ComfyUI workflows. We're grateful for cubiq's original work and contributions to the ComfyUI community.
+
+The following tools are based on comfyui-essentials-nodes:
+- Flux Sampler Params
+- LoRA Folder Batch
+- Plot Parameters
+- Sampler Select Helper
+- Scheduler Select Helper
+- Text Encode Sampler Params
+
+All adaptations maintain compatibility while adding new features and optimizations for the ComfyAssets ecosystem.
 
 ---
 
@@ -313,4 +878,4 @@ MIT License - see [LICENSE](LICENSE) file for details.
 
 [⭐ Star this repo](https://github.com/ComfyAssets/ComfyUI-KikoTools) • [🐛 Report Bug](https://github.com/ComfyAssets/ComfyUI-KikoTools/issues) • [💡 Request Feature](https://github.com/ComfyAssets/ComfyUI-KikoTools/issues)
 
-</div>
+</div>

SECURITY.md

@@ -0,0 +1,66 @@
+# Security Policy
+
+## Supported Versions
+
+ComfyUI-KikoTools is actively maintained. We provide security updates for the following versions:
+
+| Version | Supported          |
+| ------- | ------------------ |
+| 1.x.x   | :white_check_mark: |
+| < 1.0   | :x:                |
+
+## Reporting a Vulnerability
+
+We take the security of ComfyUI-KikoTools seriously. If you believe you have found a security vulnerability, please report it to us as described below.
+
+### How to Report
+
+Please report security vulnerabilities by [opening a new issue](https://github.com/ComfyAssets/ComfyUI-KikoTools/issues/new) with the following:
+
+- Use the title prefix `[SECURITY]`
+- Provide a clear description of the vulnerability
+- Include steps to reproduce the issue
+- Specify the version(s) affected
+- If possible, suggest a fix or mitigation
+
+### What to Expect
+
+- **Response Time**: We aim to acknowledge receipt within 48 hours
+- **Investigation**: We will investigate and validate the reported vulnerability
+- **Updates**: We will keep you informed about the progress
+- **Resolution**: Once verified, we will work on a fix and release it as soon as possible
+- **Credit**: We will acknowledge your contribution in the release notes (unless you prefer to remain anonymous)
+
+### Scope
+
+Security vulnerabilities in scope include:
+
+- Code execution vulnerabilities in node implementations
+- Path traversal or file system access issues
+- API key or credential exposure
+- Dependency vulnerabilities that affect the project
+- Any issue that could compromise user data or system security
+
+### Out of Scope
+
+The following are generally not considered security vulnerabilities:
+
+- Issues in ComfyUI core (report these to the ComfyUI project)
+- Performance issues
+- Bugs that don't have security implications
+- Feature requests
+
+## Security Best Practices
+
+When using ComfyUI-KikoTools:
+
+- Keep your installation up to date
+- Store API keys (like Gemini API keys) securely using environment variables
+- Review generated files before sharing them
+- Be cautious with custom prompts that might expose sensitive information
+
+## Contact
+
+For urgent security matters, you can also reach out to the maintainers directly through GitHub.
+
+Thank you for helping keep ComfyUI-KikoTools secure!

__init__.py

@@ -3,12 +3,39 @@ ComfyUI-KikoTools: Modular collection of custom ComfyUI nodes
 All nodes are grouped under the "ComfyAssets" category
 """
 
-from .kikotools import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+import re
+from pathlib import Path
+
+try:
+    from .kikotools import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+except ImportError:
+    # Fallback for testing environment
+    from kikotools import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
 # Tell ComfyUI where to find our JavaScript extensions
 WEB_DIRECTORY = "./web"
 
-# Print startup message
-print("\033[94m[ComfyUI-KikoTools] Loaded with swap button support!\033[0m")
+
+def get_version():
+    """Parse version from pyproject.toml"""
+    try:
+        pyproject_path = Path(__file__).parent / "pyproject.toml"
+        if pyproject_path.exists():
+            content = pyproject_path.read_text()
+            match = re.search(r'version\s*=\s*["\']([^"\']+)["\']', content)
+            if match:
+                return match.group(1)
+    except Exception:
+        pass
+    return "unknown"
+
+
+# Print startup message with loaded tools
+print()
+print(f"\033[94m[ComfyUI-KikoTools] Version:\033[0m {get_version()}")
+for node_key, display_name in NODE_DISPLAY_NAME_MAPPINGS.items():
+    print(f"🫶 \033[94mLoaded:\033[0m {display_name}")
+print(f"\033[94mTotal: {len(NODE_CLASS_MAPPINGS)} tools loaded\033[0m")
+print()
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]

example.py

@@ -0,0 +1,366 @@
+import os
+from typing import Tuple
+
+import comfy.sd
+import comfy.utils
+import torch
+import torch.nn.functional as F
+from comfy.sd import CLIP
+from diffusers import ConsistencyDecoderVAE
+from folder_paths import get_folder_paths
+from huggingface_hub import hf_hub_download
+from torch import Tensor
+
+
+def find_or_create_cache():
+    cwd = os.getcwd()
+    if os.path.exists(os.path.join(cwd, "ComfyUI")):
+        cwd = os.path.join(cwd, "ComfyUI")
+    if os.path.exists(os.path.join(cwd, "models")):
+        cwd = os.path.join(cwd, "models")
+    if not os.path.exists(os.path.join(cwd, "huggingface_cache")):
+        print("Creating huggingface_cache directory within comfy")
+        os.mkdir(os.path.join(cwd, "huggingface_cache"))
+
+    return str(os.path.join(cwd, "huggingface_cache"))
+
+
+class ConsistencyDecoder:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"latent": ("LATENT",)}}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+    CATEGORY = "latent"
+
+    def __init__(self):
+        self.vae = (
+            ConsistencyDecoderVAE.from_pretrained(
+                "openai/consistency-decoder",
+                torch_dtype=torch.float16,
+                variant="fp16",
+                use_safetensors=True,
+                cache_dir=find_or_create_cache(),
+            )
+            .eval()
+            .to("cuda")
+        )
+
+    def _decode(self, latent):
+        """Used when patching another vae."""
+        return self.vae.decode(latent.half().cuda()).sample
+
+    def decode(self, latent):
+        """Used for standalone decoding."""
+        sample = self._decode(latent["samples"])
+        sample = sample.clamp(-1, 1).movedim(1, -1).add(1.0).mul(0.5).cpu()
+        return (sample,)
+
+
+class PatchDecoderTiled:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vae": ("VAE",)}}
+
+    RETURN_TYPES = ("VAE",)
+    FUNCTION = "patch"
+    category = "vae"
+
+    def __init__(self):
+        self.vae = ConsistencyDecoder()
+
+    def patch(self, vae):
+        del vae.first_stage_model.decoder
+        vae.first_stage_model.decode = self.vae._decode
+        vae.decode = (
+            lambda x: vae.decode_tiled_(
+                x,
+                tile_x=512,
+                tile_y=512,
+                overlap=64,
+            )
+            .to("cuda")
+            .movedim(1, -1)
+        )
+
+        return (vae,)
+
+
+# quick node to set SDXL-friendly aspect ratios in 1024^2
+# adapted from throttlekitty
+class SDXLAspectRatio:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")
+    FUNCTION = "run"
+    CATEGORY = "image"
+
+    def run(self, image: Tensor) -> Tuple[int, int]:
+        _, height, width, _ = image.shape
+        aspect_ratio = width / height
+
+        aspect_ratios = (
+            (1 / 1, 1024, 1024),
+            (2 / 3, 832, 1216),
+            (3 / 4, 896, 1152),
+            (5 / 8, 768, 1216),
+            (9 / 16, 768, 1344),
+            (9 / 19, 704, 1472),
+            (9 / 21, 640, 1536),
+            (3 / 2, 1216, 832),
+            (4 / 3, 1152, 896),
+            (8 / 5, 1216, 768),
+            (16 / 9, 1344, 768),
+            (19 / 9, 1472, 704),
+            (21 / 9, 1536, 640),
+        )
+
+        # find the closest aspect ratio
+        closest = min(aspect_ratios, key=lambda x: abs(x[0] - aspect_ratio))
+
+        return (closest[1], closest[2])
+
+
+class ImageToMultipleOf:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "multiple_of": (
+                    "INT",
+                    {
+                        "default": 64,
+                        "min": 1,
+                        "max": 256,
+                        "step": 16,
+                        "display": "number",
+                    },
+                ),
+                "method": (["center crop", "rescale"],),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "run"
+    CATEGORY = "image"
+
+    def run(self, image: Tensor, multiple_of: int, method: str) -> Tuple[Tensor]:
+        """Center crop the image to a specific multiple of a number."""
+        _, height, width, _ = image.shape
+
+        new_height = height - (height % multiple_of)
+        new_width = width - (width % multiple_of)
+
+        if method == "rescale":
+            return (
+                F.interpolate(
+                    image.unsqueeze(0),
+                    size=(new_height, new_width),
+                    mode="bilinear",
+                    align_corners=False,
+                ).squeeze(0),
+            )
+        else:
+            top = (height - new_height) // 2
+            left = (width - new_width) // 2
+            bottom = top + new_height
+            right = left + new_width
+            return (image[:, top:bottom, left:right, :],)
+
+
+class HFHubLoraLoader:
+    def __init__(self):
+        self.loaded_lora = None
+        self.loaded_lora_path = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "clip": ("CLIP",),
+                "repo_id": ("STRING", {"default": ""}),
+                "subfolder": ("STRING", {"default": ""}),
+                "filename": ("STRING", {"default": ""}),
+                "strength_model": (
+                    "FLOAT",
+                    {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01},
+                ),
+                "strength_clip": (
+                    "FLOAT",
+                    {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "loaders"
+
+    def load_lora(
+        self,
+        model,
+        clip,
+        repo_id: str,
+        subfolder: str,
+        filename: str,
+        strength_model: float,
+        strength_clip: float,
+    ):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = hf_hub_download(
+            repo_id=repo_id.strip(),
+            subfolder=(
+                None
+                if subfolder is None or subfolder.strip() == ""
+                else subfolder.strip()
+            ),
+            filename=filename.strip(),
+            cache_dir=find_or_create_cache(),
+        )
+
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora_path == lora_path:
+                lora = self.loaded_lora
+            else:
+                self.loaded_lora = None
+                self.loaded_lora_path = None
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = lora
+            self.loaded_lora_path = lora_path
+
+        model_lora, clip_lora = comfy.sd.load_lora_for_models(
+            model, clip, lora, strength_model, strength_clip
+        )
+        return (model_lora, clip_lora)
+
+
+class HFHubEmbeddingLoader:
+    """Load a text model embedding from Huggingface Hub.
+    The connected CLIP model is not manipulated."""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "repo_id": ("STRING", {"default": ""}),
+                "subfolder": ("STRING", {"default": ""}),
+                "filename": ("STRING", {"default": ""}),
+            }
+        }
+
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "download_embedding"
+
+    CATEGORY = "n/a"
+
+    def download_embedding(
+        self,
+        clip: CLIP,  # added to signify it's best put in between nodes
+        repo_id: str,
+        subfolder: str,
+        filename: str,
+    ):
+        hf_hub_download(
+            repo_id=repo_id.strip(),
+            subfolder=(
+                None
+                if subfolder is None or subfolder.strip() == ""
+                else subfolder.strip()
+            ),
+            filename=filename.strip(),
+            local_dir=get_folder_paths("embeddings")[0],
+        )
+
+        return (clip,)
+
+
+class GlifVariable:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "variable": (
+                    [
+                        "",
+                    ],
+                ),
+                "fallback": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "single_line": True,
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "INT", "FLOAT")
+    FUNCTION = "do_it"
+
+    CATEGORY = "glif/variables"
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, variable: str, fallback: str):
+        # Since we populate dynamically, comfy will report invalid inputs. Override to always return True
+        return True
+
+    def do_it(self, variable: str, fallback: str):
+        variable = variable.strip()
+        fallback = fallback.strip()
+        if variable == "" or (variable.startswith("{") and variable.endswith("}")):
+            variable = fallback
+
+        int_val = 0
+        float_val = 0.0
+        string_val = f"{variable}"
+        try:
+            int_val = int(variable)
+        except Exception:
+            pass
+        try:
+            float_val = float(variable)
+        except Exception:
+            pass
+        return (string_val, int_val, float_val)
+
+
+NODE_CLASS_MAPPINGS = {
+    "GlifConsistencyDecoder": ConsistencyDecoder,
+    "GlifPatchConsistencyDecoderTiled": PatchDecoderTiled,
+    "SDXLAspectRatio": SDXLAspectRatio,
+    "ImageToMultipleOf": ImageToMultipleOf,
+    "HFHubLoraLoader": HFHubLoraLoader,
+    "HFHubEmbeddingLoader": HFHubEmbeddingLoader,
+    "GlifVariable": GlifVariable,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "GlifConsistencyDecoder": "Consistency VAE Decoder",
+    "GlifPatchConsistencyDecoderTiled": "Patch Consistency VAE Decoder",
+    "SDXLAspectRatio": "Image to SDXL compatible WH",
+    "ImageToMultipleOf": "Image to Multiple of",
+    "HFHubLoraLoader": "Load HF Lora",
+    "HFHubEmbeddingLoader": "Load HF Embedding",
+    "GlifVariable": "Glif Variable",
+}

examples/documentation/display_any.md

@@ -0,0 +1,120 @@
+# Display Any
+
+The Display Any node is a debugging and inspection tool that can display any type of input value in ComfyUI. It's particularly useful for understanding data structures and tensor shapes during workflow development.
+
+## Features
+
+- **Universal Input**: Accepts any type of input data (tensors, strings, numbers, lists, dictionaries, etc.)
+- **Two Display Modes**:
+  - **Raw Value**: Shows the string representation of the input
+  - **Tensor Shape**: Extracts and displays the shapes of any tensors found in the input
+- **Nested Structure Support**: Can find tensors within nested dictionaries and lists
+- **UI Output**: Displays results directly in the ComfyUI interface
+
+## Inputs
+
+- **input** (*): Any value you want to display or inspect
+- **mode** (DROPDOWN): Display mode selection
+  - `raw value`: Shows the complete string representation of the input
+  - `tensor shape`: Extracts and shows shapes of any tensors in the input
+
+## Outputs
+
+- **display_text** (STRING): The formatted display text
+
+## Usage Examples
+
+### 1. Display Simple Values
+
+Connect any output to see its raw value:
+```
+String Input: "Hello, ComfyUI!"
+Mode: raw value
+Output: "Hello, ComfyUI!"
+```
+
+### 2. Inspect Tensor Shapes
+
+Great for debugging image processing pipelines:
+```
+Image Tensor: [1, 3, 512, 512]
+Mode: tensor shape
+Output: "[[1, 3, 512, 512]]"
+```
+
+### 3. Debug Complex Data Structures
+
+View nested data structures with multiple tensors:
+```python
+Input: {
+    "images": tensor([1, 3, 256, 256]),
+    "masks": [tensor([256, 256]), tensor([256, 256, 1])],
+    "config": {"steps": 20}
+}
+Mode: tensor shape
+Output: "[[1, 3, 256, 256], [256, 256], [256, 256, 1]]"
+```
+
+### 4. Workflow Debugging
+
+Use Display Any nodes at various points in your workflow to understand data flow:
+- After loading images to verify dimensions
+- Before/after processing nodes to track shape changes
+- To inspect conditioning or latent data structures
+- To view metadata or configuration dictionaries
+
+## Use Cases
+
+### Image Pipeline Debugging
+Place Display Any nodes after image loading and processing nodes to track dimension changes:
+```
+Load Image → Display Any (tensor shape) → Resize → Display Any (tensor shape)
+```
+
+### Latent Space Inspection
+Understand latent dimensions in your workflows:
+```
+VAE Encode → Display Any (tensor shape) → KSampler → Display Any (raw value)
+```
+
+### Configuration Verification
+Display complex configuration objects to ensure correct settings:
+```
+Config Node → Display Any (raw value) → Processing Node
+```
+
+## Tips
+
+1. **Multiple Display Nodes**: You can use multiple Display Any nodes in a single workflow to track data at different stages
+
+2. **Tensor Shape Mode**: Particularly useful when working with:
+   - Image batches to verify batch size
+   - Latent tensors to understand dimensions
+   - Mask arrays to check compatibility
+
+3. **Raw Value Mode**: Best for:
+   - String prompts and text
+   - Configuration dictionaries
+   - Debugging node outputs
+   - Understanding data structure
+
+4. **No Tensors Found**: If you see "No tensors found in input" in tensor shape mode, the input doesn't contain any tensor-like objects (numpy arrays, torch tensors, etc.)
+
+## Technical Notes
+
+- The node uses `str()` for raw value display, providing Python's string representation
+- Tensor shape detection works with any object that has a `shape` attribute
+- Nested structure traversal supports dictionaries, lists, and tuples
+- The output is both displayed in the UI and available as a string output for further processing
+
+## Example Workflow Integration
+
+```
+[Load Image] → [Image Processing] → [Display Any (tensor shape)]
+                                           ↓
+                                    "[[1, 3, 512, 512]]"
+                                           ↓
+[Text Multiline] ← [Concatenate] ← "Image dimensions: "
+```
+
+This creates a text output showing the current image dimensions that can be used elsewhere in your workflow.

examples/documentation/display_text.md

@@ -0,0 +1,147 @@
+# Display Text
+
+The Display Text node provides advanced text display capabilities with smart formatting, interactive features, and responsive design for ComfyUI workflows.
+
+## Features
+
+- **Smart Prompt Detection**: Automatically detects and formats SDXL-style positive/negative prompt pairs
+- **Text Wrapping**: Proper word wrapping that reflows when node is resized
+- **Scrollable Content**: Mouse wheel scrolling for long texts with visual indicators
+- **Copy Functionality**: Always-visible copy button with visual feedback
+- **Split View Mode**: Side-by-side display for prompt pairs
+- **Responsive Design**: Content adapts to node resizing
+
+## Inputs
+
+- **text** (STRING): The text to display
+  - Can be a single text block
+  - Can contain "Positive prompt:" and "Negative prompt:" sections for automatic split view
+
+## Outputs
+
+- **text** (STRING): Pass-through of the input text
+
+## Display Modes
+
+### Single Text Mode
+
+When the input is regular text without prompt markers, it displays as a single scrollable text area with:
+- Word wrapping at word boundaries
+- Vertical scrolling for long content
+- Single copy button for the entire text
+
+### Split View Mode
+
+Automatically activated when text contains both "Positive prompt:" and "Negative prompt:" sections:
+- Side-by-side display with 50/50 split
+- Independent scrolling for each section
+- Separate copy buttons for each prompt
+- Labels are stripped when copying (clean prompts)
+
+## Usage Examples
+
+### 1. Display Generated Prompts
+
+```
+Gemini Prompt → Display Text → Copy to workflow
+```
+The node automatically detects SDXL format and shows positive/negative prompts side-by-side.
+
+### 2. Debug Text Processing
+
+```
+Text Processing → Display Text → Further Processing
+```
+View intermediate text processing results with proper formatting.
+
+### 3. Show Long Descriptions
+
+```
+Load Text → Display Text → Review
+```
+Display long text content with scrolling and word wrapping.
+
+## Interactive Features
+
+### Copy Button
+- Always visible in the top-right corner
+- Shows "✓ Copied!" feedback on click
+- In split view: separate buttons for each section
+- Strips prompt labels for clean copying
+
+### Scrolling
+- Mouse wheel scrolling when hovering over text
+- Visual indicators appear when content is scrollable
+- Smooth scrolling with proper boundaries
+- Independent scrolling in split view mode
+
+### Resizing
+- Text reflows when node width changes
+- Maintains readability at different sizes
+- Split view maintains 50/50 proportions
+- Minimum height ensures usability
+
+## Smart Prompt Detection
+
+The node intelligently detects prompt formats:
+
+1. **SDXL Format**: 
+   - Looks for "Positive prompt:" and "Negative prompt:" markers
+   - Case-insensitive detection
+   - Handles various formatting styles
+
+2. **Label Stripping**:
+   - When copying from split view, labels are removed
+   - "Positive prompt: beautiful sunset" → "beautiful sunset"
+   - Clean prompts ready for direct use
+
+## Styling
+
+- **Font**: Monospace for consistent alignment
+- **Colors**: 
+  - Text: Light gray (#ddd) on dark background
+  - Background: Semi-transparent dark (#1a1a1a)
+  - Borders: Subtle gray (#333)
+- **Spacing**: Comfortable padding and line height
+- **Visual Feedback**: Hover effects on interactive elements
+
+## Use Cases
+
+### Prompt Engineering Workflows
+- Display AI-generated prompts with proper formatting
+- Compare positive and negative prompts side-by-side
+- Copy refined prompts without manual cleanup
+
+### Text Processing Pipelines
+- Debug text transformations at each step
+- View formatted outputs from text nodes
+- Monitor prompt construction workflows
+
+### Documentation and Notes
+- Display workflow instructions
+- Show generation parameters
+- Present formatted metadata
+
+## Technical Details
+
+- **Text Processing**: Preserves original text while adding display formatting
+- **Responsive Design**: CSS-based layout adapts to node dimensions
+- **Event Handling**: Proper event propagation for ComfyUI compatibility
+- **Memory Efficient**: Only renders visible text portions
+
+## Tips
+
+1. **For Long Prompts**: The scrolling feature handles texts of any length efficiently
+2. **Quick Copy**: Use the copy buttons to quickly grab prompts for other nodes
+3. **Resizing**: Drag node edges to find optimal display width for your content
+4. **Split View**: Works best with SDXL-format prompts but handles any dual-section text
+
+## Integration Example
+
+```
+[Gemini Prompt Engineer] → [Display Text] → [Copy Button Click]
+         ↓                        ↓                    ↓
+   SDXL Format            Split View Display    Clean Prompts
+```
+
+This creates a seamless workflow from prompt generation to usage, with the Display Text node providing the visual interface for review and interaction.

examples/documentation/empty_latent_batch.md

@@ -0,0 +1,222 @@
+# Empty Latent Batch Documentation
+
+## Overview
+
+The Empty Latent Batch is a ComfyUI node that creates empty latent tensors with batch support and preset integration. It combines the preset functionality of Width Height Selector with efficient batch processing capabilities, making it ideal for batch workflows and optimized generation pipelines.
+
+## Features
+
+### 🎯 **Preset Integration**
+- **26 Curated Presets**: Full access to SDXL, FLUX, and Ultra-wide presets
+- **Formatted Display**: Shows aspect ratio, megapixels, and model group
+- **Smart Fallback**: Automatic fallback to custom dimensions for invalid presets
+- **Model Optimization**: Preset categories optimized for different model types
+
+### 📦 **Batch Processing**
+- **Configurable Batch Size**: Create 1-64 empty latents in single operation
+- **Memory Efficient**: Uses torch.zeros for optimal memory allocation
+- **Batch Validation**: Prevents excessive memory usage with warnings
+- **ComfyUI Compatible**: Standard latent format for seamless integration
+
+### 🔄 **Visual Swap Button**
+- **Interactive UI**: Blue swap button with hover and click feedback
+- **Preset-Aware Swapping**: Intelligent switching between matching presets
+- **Custom Dimension Support**: Simple value swapping for custom inputs
+- **Visual Feedback**: Button state changes during interaction
+
+### ✅ **Smart Validation**
+- **Dimension Sanitization**: Automatic adjustment to divisible-by-8 constraint
+- **Memory Estimation**: Built-in memory usage calculation
+- **Error Handling**: Graceful handling of invalid inputs with helpful messages
+- **Logging**: Detailed operation logging for debugging
+
+## Node Interface
+
+### Inputs
+- **preset**: Dropdown with 26 formatted preset options + custom
+- **width**: Custom width (64-8192, step 8, default 1024)
+- **height**: Custom height (64-8192, step 8, default 1024)
+- **batch_size**: Number of latents to create (1-64, default 1)
+
+### Outputs
+- **latent**: Dictionary containing batch of empty latent tensors
+- **width**: Final sanitized width (guaranteed divisible by 8)
+- **height**: Final sanitized height (guaranteed divisible by 8)
+
+## Preset Reference
+
+The Empty Latent Batch node uses the same 26 curated presets as the Width Height Selector:
+
+### SDXL Presets (~1 Megapixel)
+Optimized for SDXL models with ~1MP resolution constraint.
+
+### FLUX Presets (High Resolution)
+Higher resolution presets optimized for FLUX models with better quality/speed balance.
+
+### Ultra-Wide Presets (Modern Ratios)
+Modern aspect ratios for ultra-wide and panoramic generation.
+
+*For complete preset details, see [Width Height Selector Documentation](width_height_selector.md#preset-reference)*
+
+## Usage Examples
+
+### Basic Empty Latent Creation
+1. **Select Preset**: Choose from dropdown (e.g., "1024×1024 - 1:1 (1.0MP) - SDXL")
+2. **Set Batch Size**: Enter desired number of latents (e.g., 4)
+3. **Connect Output**: Link latent output to KSampler or other processing nodes
+
+### Custom Batch Creation
+1. **Set Preset**: Select "custom"
+2. **Enter Dimensions**: Input width and height manually
+3. **Set Batch Size**: Configure number of latents needed
+4. **Validation**: Automatic sanitization ensures compatibility
+
+### Orientation Swapping
+1. **Choose Preset**: Any preset (e.g., "1920×1080")
+2. **Click Swap Button**: Blue button in bottom-right corner
+3. **Result**: Gets swapped preset if available, or custom dimensions with swapped values
+4. **Widget Update**: Width/height widgets automatically update
+
+### Memory-Aware Batch Processing
+1. **Large Batch**: Set batch_size to 16 or higher
+2. **Memory Warning**: Node provides memory usage estimation
+3. **Optimization**: Choose appropriate resolution preset for available VRAM
+
+## Common Workflows
+
+### Batch Generation Pipeline
+```
+Empty Latent Batch → KSampler → VAE Decode → Save Image
+(batch_size: 4)      ↓         ↓            ↓
+                   4 samples  4 images    4 files
+```
+- Create 4 empty latents at once
+- Process all through sampling
+- Generate 4 images in single operation
+- Efficient for parameter exploration
+
+### Model Comparison Workflow
+```
+Empty Latent Batch → [Multiple KSamplers] → [Multiple VAE Decoders] → Compare Results
+(batch_size: 8)      ↓                      ↓                        ↓
+                   Split batch            Process variants         Side-by-side
+```
+- Create consistent batch of empty latents
+- Split across different samplers/models
+- Compare results with identical starting conditions
+
+### Upscaling Preparation
+```
+Empty Latent Batch → KSampler → VAE Decode → Resolution Calculator → Upscaler
+(832×1216, batch:4)  ↓         ↓            ↓                     ↓
+                   Sample    Decode       Calculate 2x           Upscale batch
+```
+- Generate batch at base resolution
+- Calculate upscale dimensions
+- Process entire batch through upscaler
+
+### Aspect Ratio Exploration
+```
+Empty Latent Batch → [Clone to multiple orientations] → Parallel Processing
+(1920×1080)          ↓                                  ↓
+[Swap Button] →    Portrait & Landscape versions      Compare orientations
+```
+- Start with base preset
+- Use swap button to create orientation variants
+- Process both simultaneously
+
+## Advanced Features
+
+### Memory Estimation
+The node provides built-in memory estimation for batch operations:
+
+```python
+# Example memory calculations
+Batch Size: 4, Resolution: 1024×1024
+Latent Tensor: 4 × 4 × 128 × 128 = 262,144 elements
+Memory Usage: 262,144 × 4 bytes = 1.0 MB per batch
+```
+
+### Intelligent Preset Handling
+- **Formatted Display**: Shows full metadata in dropdown
+- **Original Extraction**: Extracts original preset name from formatted strings
+- **Validation**: Verifies preset exists before processing
+- **Fallback Logic**: Uses custom dimensions if preset is invalid
+
+### Batch Size Optimization
+- **Performance Warnings**: Alerts for large batch sizes
+- **Memory Limits**: Prevents excessive memory allocation
+- **Hardware Awareness**: Considers available system resources
+
+## Tips and Best Practices
+
+### Batch Size Selection
+- **Small Batches (1-4)**: Good for testing and development
+- **Medium Batches (5-16)**: Efficient for most production workflows
+- **Large Batches (17-64)**: Only for high-memory systems and specific use cases
+
+### Preset Selection
+- **SDXL Projects**: Use SDXL presets for memory efficiency
+- **FLUX Projects**: Use FLUX presets for optimal quality
+- **Ultra-wide Projects**: Ensure sufficient VRAM for large resolutions
+- **Custom Projects**: Use custom dimensions for specific requirements
+
+### Memory Management
+- Monitor memory usage with large batches
+- Use appropriate resolution presets for available VRAM
+- Consider splitting very large batches across multiple nodes
+- Clear GPU memory between large batch operations
+
+### Workflow Integration
+- Always connect all three outputs (latent, width, height)
+- Use width/height outputs for downstream dimension calculations
+- Combine with Resolution Calculator for upscaling workflows
+- Leverage batch processing for efficient parameter exploration
+
+## Troubleshooting
+
+### Common Issues
+- **Out of Memory**: Reduce batch_size or use lower resolution presets
+- **Invalid Dimensions**: Node automatically sanitizes to valid values
+- **Preset Not Found**: Falls back to custom dimensions with warning
+- **Swap Button Not Working**: Ensure node is not collapsed and button is visible
+
+### Performance Optimization
+- **Batch Size**: Start with smaller batches and increase as needed
+- **Resolution**: Use appropriate presets for your model and VRAM
+- **Memory Monitoring**: Watch for memory warnings and adjust accordingly
+- **Cleanup**: Clear unused tensors between large batch operations
+
+### Error Handling
+- **Dimension Validation**: Automatic rounding to nearest valid values
+- **Batch Size Limits**: Clamped to 1-64 range with warnings
+- **Memory Allocation**: Graceful handling of insufficient memory
+- **Preset Fallbacks**: Automatic fallback to custom dimensions
+
+## Technical Details
+
+### Latent Tensor Format
+- **Shape**: [batch_size, 4, height//8, width//8]
+- **Data Type**: torch.float32
+- **Initialization**: torch.zeros for clean empty state
+- **Memory Layout**: Contiguous tensor for optimal performance
+
+### Validation Pipeline
+1. **Preset Extraction**: Parse formatted preset strings
+2. **Dimension Calculation**: Get base dimensions from preset or custom
+3. **Sanitization**: Ensure divisible-by-8 constraint
+4. **Batch Validation**: Check batch size limits
+5. **Memory Estimation**: Calculate expected memory usage
+6. **Tensor Creation**: Allocate and initialize latent tensor
+
+### UI Integration
+- **JavaScript Extension**: Custom UI for swap button functionality
+- **Widget Synchronization**: Auto-update width/height when preset changes
+- **Visual Feedback**: Hover effects and click animations
+- **Event Handling**: Proper mouse event management
+
+### Swap Button Implementation
+- **Position Calculation**: Dynamic positioning based on node size
+- **State Management**: Visual feedback for button interactions
+- **Preset Intelligence**: Smart switching between compatible presets
+- **Fallback Logic**: Custom dimension swapping when preset not available

examples/documentation/flux_sampler_params.md

@@ -0,0 +1,152 @@
+# Flux Sampler Params
+
+## Overview
+The **Flux Sampler Params** node provides a specialized parameter generator for FLUX model sampling. This tool was adapted from the excellent [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) project (now in maintenance mode) and enhanced for the ComfyAssets ecosystem.
+
+## Attribution
+This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
+
+## Features
+- **FLUX-Optimized Parameters**: Specifically tuned for FLUX model requirements
+- **Batch Processing Support**: Generate multiple parameter sets for comparative testing
+- **Interactive UI Elements**: Visual controls for quick parameter adjustments
+- **Smart Defaults**: Pre-configured optimal settings for FLUX workflows
+- **Comprehensive Parameter Control**: Fine-tune all aspects of FLUX sampling
+
+## Node Properties
+- **Category**: `ComfyAssets/🧰 xyz-helpers`
+- **Node Name**: `FluxSamplerParams`
+- **Function**: `get_value`
+
+## Inputs
+
+### Required
+| Parameter | Type | Default | Range | Description |
+|-----------|------|---------|-------|-------------|
+| `scheduler` | DROPDOWN | normal | [normal, simple, sgm_uniform] | Scheduler algorithm for sampling |
+| `steps` | INT | 20 | 1-100 | Number of sampling steps |
+| `guidance` | FLOAT | 3.5 | 0.0-100.0 | Guidance scale for conditioning |
+| `max_shift` | FLOAT | 1.0 | 0.0-100.0 | Maximum shift value for FLUX |
+| `base_shift` | FLOAT | 0.5 | 0.0-100.0 | Base shift value for FLUX |
+| `denoise` | FLOAT | 1.0 | 0.0-1.0 | Denoising strength |
+| `batch_mode` | DROPDOWN | single | [single, batch] | Single value or batch processing |
+
+### Optional
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `batch_count` | INT | 1 | Number of batch variations (1-100) |
+| `batch_seed_mode` | DROPDOWN | incremental | Seed generation mode for batches |
+| `variation_seed` | INT | None | Optional seed for variations |
+| `lora_params` | LORA_PARAMS | None | LoRA parameters from LoRAFolderBatch |
+
+## Outputs
+| Name | Type | Description |
+|------|------|-------------|
+| `sampler_params` | SAMPLER_PARAMS | Complete FLUX sampling parameters |
+| `scheduler` | STRING | Selected scheduler algorithm |
+| `steps` | INT | Number of sampling steps |
+| `guidance` | FLOAT | Guidance scale value |
+
+## Usage Examples
+
+### Basic FLUX Sampling
+```
+FluxSamplerParams → KSampler → VAE Decode → Save Image
+    scheduler: normal
+    steps: 20
+    guidance: 3.5
+```
+
+### Batch Parameter Testing
+```
+FluxSamplerParams → KSampler → Image Grid → Save
+    batch_mode: batch
+    batch_count: 5
+    guidance: 2.0...5.0
+```
+
+### With LoRA Integration
+```
+LoRAFolderBatch → FluxSamplerParams → KSampler
+    ↓                    ↓
+    lora_params → Combined parameters
+```
+
+## Best Practices
+
+### FLUX-Specific Settings
+- **Guidance**: FLUX typically works best with lower guidance (2.0-5.0)
+- **Steps**: 15-25 steps usually sufficient for FLUX
+- **Scheduler**: `normal` or `sgm_uniform` recommended for FLUX
+- **Shift Values**: Adjust for different quality/speed tradeoffs
+
+### Batch Testing Workflow
+1. Set `batch_mode` to `batch`
+2. Configure parameter ranges using `...` syntax
+3. Set appropriate `batch_count`
+4. Use with image grid nodes for comparison
+
+### Memory Optimization
+- Start with smaller batch counts for testing
+- Monitor VRAM usage with high batch counts
+- Use incremental seed mode for reproducibility
+
+## Integration with Other Nodes
+
+### Works Well With
+- **LoRA Folder Batch**: Combine multiple LoRAs with FLUX parameters
+- **Plot Parameters**: Visualize parameter effects
+- **Sampler Select Helper**: Dynamic sampler selection
+- **Text Encode Sampler Params**: Add text conditioning
+
+### Common Workflows
+1. **Parameter Sweep**: Test multiple guidance/step combinations
+2. **LoRA Testing**: Evaluate different LoRA strengths with FLUX
+3. **Quality Comparison**: Compare different shift values
+4. **Seed Exploration**: Generate variations with controlled seeds
+
+## Tips and Tricks
+
+### Optimal FLUX Settings
+```python
+# High Quality (Slower)
+scheduler: "sgm_uniform"
+steps: 25
+guidance: 3.5
+max_shift: 1.0
+base_shift: 0.5
+
+# Fast Preview
+scheduler: "simple"
+steps: 12
+guidance: 2.5
+max_shift: 0.8
+base_shift: 0.4
+```
+
+### Batch Parameter Ranges
+- Steps: `15...25+5` (test 15, 20, 25)
+- Guidance: `2.0...5.0+0.5` (test 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0)
+- Denoise: `0.8...1.0+0.1` (test 0.8, 0.9, 1.0)
+
+## Troubleshooting
+
+### Common Issues
+1. **Out of Memory**: Reduce batch_count or image resolution
+2. **Poor Quality**: Increase steps or adjust guidance
+3. **Artifacts**: Check shift values aren't too high
+4. **Slow Generation**: Use `simple` scheduler for previews
+
+### Parameter Guidelines
+- Don't set guidance too high (>10) for FLUX
+- Keep denoise at 1.0 for initial generation
+- Adjust shift values gradually for best results
+
+## Version History
+- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
+- **1.0.1**: Added batch processing support
+- **1.0.2**: Enhanced FLUX-specific optimizations
+- **1.0.3**: Improved UI elements and parameter validation
+
+## Credits
+Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

examples/documentation/gemini_prompt.md

@@ -0,0 +1,209 @@
+# Gemini Prompt Engineer
+
+The Gemini Prompt Engineer node uses Google's Gemini AI to analyze images and generate optimized prompts for various AI image generation models.
+
+## Features
+
+- **Multi-Model Support**: Generate prompts optimized for FLUX, SDXL, Danbooru, and Video generation
+- **Custom Prompts**: Override templates with your own system prompts
+- **Visual Feedback**: UI shows processing status and error states
+- **Flexible API Key Management**: Multiple ways to provide API credentials
+- **Dynamic Model Selection**: Fetch and use latest Gemini models with refresh button
+- **Model Caching**: Persistent storage of available models for offline access
+- **Help Integration**: Built-in setup guide accessible via help button
+
+## Setup
+
+### 1. Get API Key
+
+Get your free Gemini API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
+
+### 2. Install Dependencies
+
+```bash
+pip install google-generativeai
+```
+
+### 3. Configure API Key
+
+Choose one of these methods:
+
+1. **Environment Variable** (Recommended):
+   ```bash
+   export GEMINI_API_KEY="your-api-key-here"
+   ```
+
+2. **Config File**:
+   Create `gemini_config.json` in your ComfyUI root directory:
+   ```json
+   {
+     "api_key": "your-api-key-here"
+   }
+   ```
+
+3. **Node Input**:
+   Enter the API key directly in the node's `api_key` field
+
+## Inputs
+
+- **image** (IMAGE): The image to analyze
+- **prompt_type** (DROPDOWN): Type of prompt to generate
+  - `flux`: Detailed artistic prompts with quality markers
+  - `sdxl`: Positive/negative prompt pairs with weight emphasis
+  - `danbooru`: Anime-style booru tags with underscores
+  - `video`: Motion and temporal descriptions for video generation
+- **model** (DROPDOWN): Gemini model selection
+  - Dynamically populated list of available models
+  - Includes latest models like gemini-2.0-flash-exp
+  - Click refresh button to update model list
+- **api_key** (STRING, optional): Gemini API key if not set elsewhere
+- **custom_prompt** (STRING, optional): Override template with custom system prompt
+
+## Outputs
+
+- **prompt** (STRING): Generated prompt text
+- **negative_prompt** (STRING): Negative prompt (only populated for SDXL format)
+
+## Prompt Type Details
+
+### FLUX Format
+Generates detailed prompts optimized for FLUX models:
+- Starts with main subject and action
+- Includes style and medium descriptors
+- Adds lighting and atmosphere details
+- Uses quality markers like "4K", "highly detailed", "award-winning"
+
+Example output:
+```
+majestic mountain landscape at golden hour, oil painting style, dramatic lighting with sun rays piercing through clouds, wide angle composition, warm color palette with orange and purple hues, highly detailed, 4K resolution, trending on ArtStation, photorealistic rendering
+```
+
+### SDXL Format
+Generates positive and negative prompt pairs with enhanced structure:
+- Layered positive prompts: main subject → style → composition → technical
+- Comprehensive negative prompts to avoid common issues
+- Uses parentheses for emphasis: `(detailed eyes:1.2)`
+- Includes quality boosters and technical specifications
+
+Example output:
+```
+Positive prompt:
+beautiful woman with flowing red hair, elegant pose, (detailed eyes:1.2), serene expression
+oil painting style, renaissance art influence, classical portraiture
+golden hour lighting, warm color palette, soft shadows, dramatic chiaroscuro
+centered composition, rule of thirds, shallow depth of field, bokeh background
+masterpiece, best quality, highly detailed, 8k uhd, professional artwork
+
+Negative prompt:
+low quality, worst quality, blurry, out of focus, pixelated, low resolution
+bad anatomy, deformed features, extra limbs, missing limbs, disconnected limbs
+poorly drawn face, poorly drawn hands, amateur drawing, bad proportions
+oversaturated, overexposed, underexposed, bad lighting, harsh shadows
+jpeg artifacts, watermark, signature, text, cropped, duplicate
+```
+
+### Danbooru Format
+Generates booru-style tags for anime artwork:
+- Uses underscores for multi-word concepts
+- Includes character count descriptors (1girl, 2boys)
+- Orders tags from most to least important
+
+Example output:
+```
+1girl, solo, long_hair, blue_eyes, blonde_hair, school_uniform, serafuku, pleated_skirt, thighhighs, smile, looking_at_viewer, classroom, sitting, desk, window, sunlight, highres, masterpiece
+```
+
+### Video Format
+Generates prompts for video generation models:
+- Describes motion and camera movements
+- Includes temporal markers and transitions
+- Specifies technical details like fps and duration
+
+Example output:
+```
+Aerial shot slowly descending toward a misty forest at dawn, camera smoothly transitions to tracking shot following a deer through the trees, photorealistic style, soft golden hour lighting with fog, 10 second duration, 4K resolution 24fps, ending with close-up of deer looking at camera
+```
+
+## Custom System Prompts
+
+You can override any template by providing your own system prompt. This is useful for:
+- Specialized use cases
+- Different language outputs
+- Custom formatting requirements
+- Integration with specific workflows
+
+Example custom prompt:
+```
+You are an expert at analyzing images and creating simple, concise descriptions.
+Focus only on the main subject and primary colors.
+Keep your response under 50 words.
+```
+
+## Error Handling
+
+The node provides clear error messages for common issues:
+- Missing API key
+- API request failures
+- Invalid image inputs
+- Rate limiting
+
+Errors are displayed in the prompt output for easy debugging.
+
+## Model Selection
+
+### Dynamic Model List
+- Click the refresh button (🔄) next to the model dropdown to fetch latest models
+- Models are fetched from Google's API and include all available versions
+- Common models include:
+  - `gemini-2.0-flash-exp`: Latest experimental flash model
+  - `gemini-1.5-pro`: Advanced model with larger context
+  - `gemini-1.5-flash`: Fast and efficient for most tasks
+
+### Model Caching
+- Available models are cached locally for offline access
+- Cache persists across ComfyUI sessions
+- Refresh button updates the cache with latest models
+
+## UI Features
+
+### Help Button
+- Click the help button (?) for quick setup instructions
+- Shows API key setup methods
+- Links to Google AI Studio for key generation
+
+### Status Indicators
+- Processing spinner during API calls
+- Error messages displayed in red
+- Success feedback when prompt is generated
+
+## Tips
+
+1. **API Usage**: Gemini has generous free tier limits, but be mindful of rate limits
+2. **Image Quality**: Higher resolution images provide better analysis results
+3. **Prompt Refinement**: You can chain multiple Gemini nodes with different custom prompts
+4. **Caching**: Results are not cached, so identical images will make new API calls
+5. **Model Selection**: Use flash models for faster responses, pro models for complex analysis
+
+## Example Workflow
+
+1. Load an image using Load Image node
+2. Connect to Gemini Prompt Engineer
+3. Select appropriate prompt_type for your target model
+4. Connect prompt output to your generation model
+5. For SDXL, connect both prompt and negative_prompt outputs
+
+## Troubleshooting
+
+**"API key not found" error**:
+- Check environment variable is set correctly
+- Verify config file path and JSON format
+- Try entering key directly in node
+
+**"No response generated" error**:
+- Check internet connection
+- Verify API key is valid
+- Image might be too large (resize if needed)
+
+**Import error for google-generativeai**:
+- Run `pip install google-generativeai` in your ComfyUI environment
+- Restart ComfyUI after installation

examples/documentation/image_to_multiple_of.md

@@ -0,0 +1,82 @@
+# Image to Multiple Of
+
+## Overview
+
+The **Image to Multiple Of** node adjusts image dimensions to be multiples of a specified value. This is particularly useful for models that require input dimensions to be multiples of certain values (e.g., 8, 16, 32, 64) for optimal performance or compatibility.
+
+## Purpose
+
+Many AI models, especially diffusion models and VAEs, require input dimensions to be multiples of specific values due to their architecture (e.g., downsampling layers). This node ensures your images meet these requirements without manual calculation.
+
+## Inputs
+
+- **image** (IMAGE, required): The input image to process
+- **multiple_of** (INT, required): The value that dimensions should be multiple of
+  - Default: 64
+  - Range: 1-256
+  - Step: 16
+- **method** (COMBO, required): Processing method
+  - Options: "center crop", "rescale"
+
+## Outputs
+
+- **image** (IMAGE): Processed image with dimensions adjusted to multiples of the specified value
+
+## Processing Methods
+
+### Center Crop
+- Crops the image from the center to achieve the target dimensions
+- Preserves image quality but may lose edge content
+- Best for images where the important content is centered
+
+### Rescale
+- Resizes the image to the target dimensions using bilinear interpolation
+- Keeps all content but may slightly affect image quality
+- Best when you need to preserve all image content
+
+## Usage Examples
+
+### Example 1: Prepare for VAE Encoding
+```
+Load Image → Image to Multiple Of (multiple_of: 64) → VAE Encode
+```
+
+### Example 2: Prepare for Specific Model Requirements
+```
+Load Image → Image to Multiple Of (multiple_of: 32) → Model Processing
+```
+
+### Example 3: Batch Processing
+```
+Load Images → Image to Multiple Of (multiple_of: 16, method: rescale) → Batch Process
+```
+
+## Technical Details
+
+- Supports batch processing (processes all images in a batch)
+- Works with any number of channels (RGB, RGBA, grayscale, etc.)
+- Calculates the largest dimensions that are less than or equal to the original size
+- For center crop: crops equally from all sides to maintain centering
+- For rescale: uses bilinear interpolation with align_corners=False
+
+## Common Use Cases
+
+1. **VAE Preprocessing**: Ensure images are compatible with VAE encoders that require dimensions divisible by 64
+2. **Model Compatibility**: Adjust images for models with specific architectural requirements
+3. **Batch Uniformity**: Ensure all images in a batch have dimensions that meet model requirements
+4. **Performance Optimization**: Some models perform better with dimensions that are powers of 2
+
+## Tips
+
+- Use **center crop** when your subject is centered and you don't mind losing edge details
+- Use **rescale** when you need to preserve all image content
+- Common multiple_of values: 8, 16, 32, 64, 128
+- For Stable Diffusion models, 64 is typically recommended
+- For some upscaling models, 32 or 16 may be sufficient
+
+## Error Handling
+
+The node will raise an error if:
+- The image dimensions are smaller than the specified multiple_of value
+- Invalid input types are provided
+- The resulting dimensions would be 0 or negative

examples/documentation/kiko_save_image.md

@@ -0,0 +1,213 @@
+# Kiko Save Image
+
+Enhanced image saving node with multiple format support, quality controls, and an interactive floating popup viewer for ComfyUI.
+
+## Features
+
+- **Multiple Format Support**: Save as PNG, JPEG, or WebP with format-specific optimizations
+- **Advanced Quality Controls**: Fine-tune compression settings per format
+- **Floating Popup Viewer**: Interactive window showing saved images immediately
+- **Batch Operations**: Multi-select images for bulk actions
+- **File Size Display**: Real-time feedback on compression effectiveness
+- **Smart UI**: Auto-hide, draggable, resizable popup window
+
+## Inputs
+
+- **images** (IMAGE): Batch of images to save
+- **filename_prefix** (STRING): Prefix for saved filenames
+  - Default: "KikoSave"
+  - Supports subfolder paths (e.g., "outputs/renders/final")
+- **format** (DROPDOWN): Output format selection
+  - `PNG`: Lossless compression, best quality
+  - `JPEG`: Lossy compression, smaller files
+  - `WEBP`: Modern format, best compression ratio
+- **quality** (INT): JPEG/WebP quality level
+  - Range: 1-100 (default: 90)
+  - Higher values = better quality, larger files
+- **png_compress_level** (INT): PNG compression level
+  - Range: 0-9 (default: 4)
+  - Higher values = smaller files, slower saving
+- **webp_lossless** (BOOLEAN): Use lossless WebP compression
+  - Default: False (lossy)
+  - True: Lossless compression like PNG
+- **popup** (BOOLEAN): Enable popup viewer window
+  - Default: True
+  - Toggle per save operation
+
+## Outputs
+
+- **UI**: Enhanced preview data with interactive popup viewer
+
+## Popup Viewer Features
+
+### Window Controls
+- **Drag Handle**: Click and drag the header to move window
+- **Minimize Button**: Collapse to title bar only
+- **Maximize Button**: Expand to larger viewing size
+- **Roll-up Button**: Show/hide content area
+- **Close Button**: Hide the popup (can reopen with toggle)
+
+### Image Grid
+- **Thumbnails**: Click any image to open full-size in new tab
+- **File Info**: Shows filename and size for each image
+- **Quality Indicators**: 
+  - PNG: Compression level (0-9)
+  - JPEG/WebP: Quality percentage
+- **Batch Selection**: Checkboxes for multi-select operations
+
+### Bulk Actions
+- **Open All Selected**: Opens selected images in new tabs
+- **Download All Selected**: Downloads selected images as a batch
+- **Individual Downloads**: Download button per image
+
+### Smart Behavior
+- **Auto-positioning**: Appears in convenient screen location
+- **Persistence**: Stays open across multiple saves
+- **Auto-hide**: Can be minimized when not needed
+- **Responsive**: Adapts to different image counts
+
+## Format Details
+
+### PNG Format
+- **Pros**: Lossless quality, transparency support, wide compatibility
+- **Cons**: Larger file sizes
+- **Best for**: Final outputs, images with transparency, archival
+- **Compression**: 0 (none) to 9 (maximum)
+  - Level 4 (default) balances size and speed
+  - Level 9 for maximum compression (slow)
+
+### JPEG Format
+- **Pros**: Smaller files, fast loading, universal support
+- **Cons**: Lossy compression, no transparency
+- **Best for**: Web images, previews, photos
+- **Quality**: 1-100%
+  - 90% (default) excellent quality with good compression
+  - 95%+ for near-lossless quality
+  - 70-85% for web optimization
+
+### WebP Format
+- **Pros**: Best compression ratios, supports transparency, modern
+- **Cons**: Limited software support
+- **Best for**: Web deployment, storage optimization
+- **Modes**:
+  - Lossy (default): Excellent compression with quality control
+  - Lossless: PNG-like quality with better compression
+
+## Usage Examples
+
+### High-Quality Archive
+```
+Format: PNG
+Compression: 0-2
+Use Case: Final renders for portfolio or client delivery
+```
+
+### Web Optimization
+```
+Format: JPEG or WebP
+Quality: 80-85
+Use Case: Website images, social media posts
+```
+
+### Balanced Storage
+```
+Format: WebP
+Quality: 90
+Lossless: False
+Use Case: Large batches with storage constraints
+```
+
+### Transparency Preservation
+```
+Format: PNG or WebP (lossless)
+Use Case: Logos, UI elements, cutout images
+```
+
+## Workflow Integration
+
+### Basic Save
+```
+Generate → Kiko Save Image
+           format: PNG
+           popup: enabled
+```
+
+### Format Comparison
+```
+Generate → Kiko Save Image (PNG)    → Compare file sizes
+        ↘ Kiko Save Image (JPEG)   ↗
+        ↘ Kiko Save Image (WebP)   ↗
+```
+
+### Batch Processing
+```
+Batch Generate → Kiko Save Image → Popup Viewer
+                 ↓                  ↓
+                4 images        Select best results
+```
+
+## Tips and Best Practices
+
+1. **Format Selection**:
+   - Use PNG for maximum quality and transparency
+   - Use JPEG for photographs without transparency
+   - Use WebP for modern web deployment
+
+2. **Quality Settings**:
+   - Start with defaults (90 for JPEG/WebP, 4 for PNG)
+   - Adjust based on file size requirements
+   - Preview results in popup before finalizing
+
+3. **Popup Management**:
+   - Drag to second monitor for larger workspace
+   - Use roll-up to save screen space
+   - Disable popup for automated workflows
+
+4. **Batch Operations**:
+   - Use checkboxes to select multiple images
+   - Open all in tabs for side-by-side comparison
+   - Download all for quick collection
+
+5. **File Organization**:
+   - Use subfolders in filename_prefix
+   - Include descriptive prefixes
+   - Let ComfyUI handle timestamp suffixes
+
+## Advantages Over Standard Save Image
+
+- **Immediate Preview**: No need to navigate file system
+- **Format Flexibility**: Choose optimal format per use case
+- **Quality Control**: Fine-tune compression settings
+- **Batch Management**: Handle multiple images efficiently
+- **Modern UI**: Floating interface doesn't interrupt workflow
+- **File Size Awareness**: See compression effectiveness immediately
+- **Quick Access**: One-click opening and downloading
+
+## Technical Details
+
+- **Image Processing**: Uses Pillow for format conversion
+- **Metadata**: Preserves ComfyUI metadata in saved files
+- **File Naming**: Automatic timestamp and counter suffixes
+- **Memory Efficiency**: Processes images individually
+- **Thread Safety**: Proper handling of concurrent saves
+
+## Troubleshooting
+
+**Popup not appearing**:
+- Check that popup input is enabled
+- Look for minimized window
+- Try toggling the popup button in node
+
+**WebP not working**:
+- Ensure Pillow has WebP support
+- Update Pillow: `pip install --upgrade pillow`
+
+**Large file sizes**:
+- Increase compression (PNG) or reduce quality (JPEG/WebP)
+- Consider switching formats
+- Check image dimensions
+
+**Can't see all images**:
+- Scroll within the popup grid
+- Maximize the popup window
+- Images are shown newest first

examples/documentation/lora_folder_batch.md

@@ -0,0 +1,212 @@
+# LoRA Folder Batch
+
+## Overview
+The **LoRA Folder Batch** node automates the process of testing multiple LoRA models from a folder. This tool was adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode) and enhanced with batch processing capabilities for efficient LoRA evaluation workflows.
+
+## Attribution
+This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
+
+## Features
+- **Automatic Folder Scanning**: Discovers all .safetensors files in specified folders
+- **Natural Sorting**: Intelligently sorts epochs (e.g., epoch_004, epoch_020, epoch_100)
+- **Pattern Filtering**: Include/exclude LoRAs using regex patterns
+- **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
+
+## Node Properties
+- **Category**: `ComfyAssets/🧰 xyz-helpers`
+- **Node Name**: `LoRAFolderBatch`
+- **Function**: `batch_loras`
+
+## Inputs
+
+### Required
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `folder_path` | STRING | "." | Folder path relative to models/loras (or absolute) |
+| `strength` | STRING | "1.0" | Strength values (see formats below) |
+| `batch_mode` | DROPDOWN | sequential | [sequential, combinatorial] processing mode |
+
+### Optional
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `include_pattern` | STRING | "" | Regex pattern to include files |
+| `exclude_pattern` | STRING | "" | Regex pattern to exclude files |
+
+### Strength Format Options
+- **Single**: `"1.0"` - Apply same strength to all LoRAs
+- **Multiple**: `"0.5, 0.75, 1.0"` - Comma-separated values
+- **Range**: `"0.5...1.0+0.25"` - Start...End+Step format
+
+## Outputs
+| Name | Type | Description |
+|------|------|-------------|
+| `lora_params` | LORA_PARAMS | Batch parameters for processing |
+| `lora_list` | STRING | List of discovered LoRAs with epoch info |
+| `lora_count` | INT | Number of LoRAs found |
+
+## Usage Examples
+
+### Test All Epochs of a LoRA
+```
+LoRAFolderBatch → FluxSamplerParams → KSampler
+    folder_path: "my_lora_training"
+    strength: "1.0"
+    batch_mode: sequential
+```
+
+### Strength Testing for Each LoRA
+```
+LoRAFolderBatch → KSampler → Image Grid
+    folder_path: "test_loras"
+    strength: "0.5, 0.75, 1.0"
+    batch_mode: combinatorial
+```
+
+### Filter Specific Epochs
+```
+LoRAFolderBatch → Processing Pipeline
+    folder_path: "training_results"
+    include_pattern: "epoch_0[2-5]0"
+    strength: "0.8...1.2+0.1"
+```
+
+## Batch Modes Explained
+
+### Sequential Mode
+Each LoRA gets one strength value in order:
+- LoRA1 → strength[0]
+- LoRA2 → strength[1]
+- LoRA3 → strength[0] (cycles if fewer strengths than LoRAs)
+
+### Combinatorial Mode
+Each LoRA is tested with ALL strength values:
+- LoRA1 → [0.5, 0.75, 1.0]
+- LoRA2 → [0.5, 0.75, 1.0]
+- LoRA3 → [0.5, 0.75, 1.0]
+
+## File Naming Patterns
+
+### Supported Epoch Formats
+- `model-v1-000004.safetensors` → Epoch 4
+- `style_epoch_020.safetensors` → Epoch 20
+- `lora-000100.safetensors` → Epoch 100
+
+### Natural Sorting Examples
+Files are sorted intelligently:
+1. `model-000004.safetensors`
+2. `model-000020.safetensors`
+3. `model-000100.safetensors`
+
+## Best Practices
+
+### Folder Organization
+```
+models/loras/
+├── my_style/
+│   ├── style-000010.safetensors
+│   ├── style-000020.safetensors
+│   └── style-000030.safetensors
+└── character/
+    ├── char-v2-000005.safetensors
+    └── char-v2-000010.safetensors
+```
+
+### Testing Workflows
+1. **Initial Testing**: Use single strength (1.0) to evaluate all epochs
+2. **Fine-tuning**: Use combinatorial mode with multiple strengths
+3. **Final Selection**: Filter to specific epochs and test strength range
+
+### Pattern Filtering Examples
+```python
+# Include only specific versions
+include_pattern: "v2|v3"
+
+# Exclude test/backup files
+exclude_pattern: "test|backup|old"
+
+# Include specific epoch range
+include_pattern: "epoch_0[3-7]0"
+```
+
+## Integration with Other Nodes
+
+### Common Pipelines
+1. **LoRA Comparison Grid**:
+   ```
+   LoRAFolderBatch → KSampler → Image Grid → Save
+   ```
+
+2. **Strength Testing**:
+   ```
+   LoRAFolderBatch → PlotParameters → Graph Display
+   ```
+
+3. **Combined with FLUX**:
+   ```
+   LoRAFolderBatch → FluxSamplerParams → KSampler
+   ```
+
+## Tips and Tricks
+
+### Memory Management
+- Start with fewer LoRAs when testing combinatorial mode
+- Use sequential mode for initial epoch evaluation
+- Clear LoRA cache between large batch runs
+
+### Optimal Strength Ranges
+- **Style LoRAs**: 0.5-1.0
+- **Character LoRAs**: 0.7-1.2
+- **Detail LoRAs**: 0.3-0.7
+
+### Debugging
+- Check `lora_list` output to verify correct files were found
+- Use `lora_count` to confirm expected number of LoRAs
+- Test patterns with include/exclude before full runs
+
+## Troubleshooting
+
+### No LoRAs Found
+- Verify folder path (relative to models/loras or use absolute)
+- Check file extensions (.safetensors)
+- Test without filters first
+
+### Pattern Not Working
+- Patterns use Python regex syntax
+- Test patterns in regex tester first
+- Case-sensitive by default
+
+### Memory Issues
+- Reduce batch_count in combinatorial mode
+- Process LoRAs in smaller groups
+- Use sequential mode for large sets
+
+## Advanced Examples
+
+### Multi-Version Testing
+```python
+# Test different versions at different strengths
+folder_path: "character_loras"
+include_pattern: "v[1-3]"
+strength: "0.6, 0.8, 1.0"
+batch_mode: combinatorial
+```
+
+### Epoch Progression Analysis
+```python
+# Test every 10th epoch
+folder_path: "training_output"
+include_pattern: "0[0-9]0\\.safetensors$"
+strength: "1.0"
+batch_mode: sequential
+```
+
+## Version History
+- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
+- **1.0.1**: Added natural sorting for epochs
+- **1.0.2**: Enhanced pattern filtering
+- **1.0.3**: Improved batch modes and strength parsing
+
+## Credits
+Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

examples/documentation/plot_parameters.md

@@ -0,0 +1,234 @@
+# Plot Parameters
+
+## Overview
+The **Plot Parameters** node creates visual graphs and plots from sampler parameters, enabling data-driven analysis of generation settings. Adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode), this tool helps visualize the relationship between parameters and output quality.
+
+## Attribution
+This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
+
+## Features
+- **Multi-Parameter Plotting**: Visualize multiple parameters simultaneously
+- **Comparison Graphs**: Compare settings across batch runs
+- **Statistical Analysis**: Calculate means, deviations, and trends
+- **Export Capabilities**: Save plots as images or data files
+- **Real-time Updates**: Dynamic graph generation during workflow execution
+
+## Node Properties
+- **Category**: `ComfyAssets/🧰 xyz-helpers`
+- **Node Name**: `PlotParameters`
+- **Function**: `plot`
+
+## Inputs
+
+### Required
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `sampler_params` | SAMPLER_PARAMS | - | Parameters to plot |
+| `plot_type` | DROPDOWN | line | [line, bar, scatter, heatmap] |
+| `x_axis` | DROPDOWN | steps | Parameter for X axis |
+| `y_axis` | DROPDOWN | quality | Metric for Y axis |
+
+### Optional
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `title` | STRING | "Parameter Analysis" | Graph title |
+| `show_grid` | BOOLEAN | True | Display grid lines |
+| `show_legend` | BOOLEAN | True | Display legend |
+| `color_scheme` | DROPDOWN | default | Color palette selection |
+
+## Outputs
+| Name | Type | Description |
+|------|------|-------------|
+| `plot_image` | IMAGE | Generated plot as image |
+| `data_csv` | STRING | Plot data in CSV format |
+| `statistics` | STRING | Statistical summary |
+
+## Usage Examples
+
+### Basic Parameter Visualization
+```
+FluxSamplerParams → PlotParameters → Display Image
+    plot_type: line
+    x_axis: steps
+    y_axis: guidance
+```
+
+### Batch Comparison Plot
+```
+LoRAFolderBatch → PlotParameters → Save Image
+    plot_type: scatter
+    x_axis: lora_strength
+    y_axis: quality_score
+```
+
+### Heatmap Analysis
+```
+Parameter Grid → PlotParameters → Analysis Display
+    plot_type: heatmap
+    x_axis: cfg
+    y_axis: steps
+```
+
+## Plot Types Explained
+
+### Line Plot
+- Best for continuous parameter changes
+- Shows trends and relationships
+- Ideal for time series or progression
+
+### Bar Chart
+- Compares discrete values
+- Good for categorical comparisons
+- Shows distribution clearly
+
+### Scatter Plot
+- Reveals correlations
+- Identifies outliers
+- Best for large datasets
+
+### Heatmap
+- Two-dimensional parameter analysis
+- Color-coded intensity values
+- Perfect for grid searches
+
+## Best Practices
+
+### Parameter Selection
+- Choose related parameters for meaningful plots
+- Use consistent scales for comparison
+- Consider parameter ranges when plotting
+
+### Visual Clarity
+- Limit number of series to 5-7 for readability
+- Use contrasting colors for multiple lines
+- Enable grid for precise value reading
+
+### Data Analysis
+```python
+# Effective parameter combinations
+x_axis: "guidance"
+y_axis: "perceived_quality"
+
+# Step efficiency analysis  
+x_axis: "steps"
+y_axis: "generation_time"
+
+# LoRA impact assessment
+x_axis: "lora_strength"  
+y_axis: "style_adherence"
+```
+
+## Integration Examples
+
+### Complete Analysis Pipeline
+```
+1. Generate with parameters
+2. Plot results
+3. Export data
+4. Statistical analysis
+```
+
+### Multi-Plot Workflow
+```
+Params → Plot1 (steps vs quality)
+      ↘ Plot2 (guidance vs coherence)
+      ↘ Plot3 (strength vs style)
+      → Combined Analysis
+```
+
+## Advanced Features
+
+### Custom Metrics
+- Define custom Y-axis metrics
+- Import external quality scores
+- Calculate derived values
+
+### Export Options
+- PNG/SVG image formats
+- CSV data export
+- JSON statistics export
+
+### Styling Options
+```python
+# Professional presentation
+color_scheme: "scientific"
+show_grid: True
+show_legend: True
+
+# Minimal style
+color_scheme: "minimal"
+show_grid: False
+show_legend: False
+```
+
+## Statistical Analysis
+
+### Available Metrics
+- Mean, Median, Mode
+- Standard Deviation
+- Correlation Coefficients
+- Trend Lines
+- R-squared Values
+
+### Interpretation Guide
+- **Positive Correlation**: Parameters increase together
+- **Negative Correlation**: Inverse relationship
+- **No Correlation**: Independent parameters
+
+## Tips and Tricks
+
+### Optimal Visualization
+1. Start with scatter plots for exploration
+2. Use line plots for trends
+3. Apply heatmaps for 2D parameter spaces
+4. Bar charts for final comparisons
+
+### Data Preparation
+- Normalize scales when comparing different metrics
+- Remove outliers for cleaner plots
+- Group similar parameters
+
+### Performance Tips
+- Cache plot images for repeated viewing
+- Export data for external analysis
+- Use lower resolution for preview plots
+
+## Troubleshooting
+
+### Empty Plots
+- Verify sampler_params contains data
+- Check axis parameter selection
+- Ensure valid parameter ranges
+
+### Scaling Issues
+- Use logarithmic scale for wide ranges
+- Normalize data if needed
+- Adjust plot dimensions
+
+### Export Problems
+- Check file permissions
+- Verify export path exists
+- Ensure sufficient disk space
+
+## Use Cases
+
+### Hyperparameter Optimization
+Track and visualize the effect of different sampling parameters on output quality.
+
+### LoRA Strength Analysis
+Plot the relationship between LoRA strength and style transfer effectiveness.
+
+### Efficiency Studies
+Analyze generation time vs quality trade-offs across different settings.
+
+### Batch Comparison
+Compare multiple generation runs to identify optimal parameters.
+
+## Version History
+- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
+- **1.0.1**: Added heatmap visualization
+- **1.0.2**: Enhanced statistical analysis
+- **1.0.3**: Improved export capabilities
+
+## Credits
+Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

examples/documentation/resolution_calculator.md

@@ -98,4 +98,4 @@ The Resolution Calculator integrates seamlessly with:
 - Standard ComfyUI image loaders
 - VAE encode/decode operations
 - Upscaler nodes (ESRGAN, Real-ESRGAN, etc.)
-- Custom latent processing workflows
+- Custom latent processing workflows

examples/documentation/sampler_combo.md

@@ -0,0 +1,208 @@
+# Sampler Combo Documentation
+
+## Overview
+
+The Sampler Combo is a unified ComfyUI node that combines sampler, scheduler, steps, and CFG settings into a single interface. It reduces workflow complexity while ensuring compatible parameter combinations and providing optimization recommendations.
+
+## Features
+
+### 🎯 **Unified Configuration**
+- Single node for all sampling parameters
+- Compatible sampler + scheduler combinations
+- Optimized steps and CFG recommendations
+- Reduced workflow complexity
+
+### 🧠 **Smart Recommendations**
+- Scheduler suggestions based on selected sampler
+- Optimal steps range for each sampler
+- CFG scale recommendations
+- Compatibility warnings and suggestions
+
+### ✅ **Built-in Validation**
+- Parameter validation and sanitization
+- Error handling with safe defaults
+- Performance optimization hints
+- Real-time compatibility checking
+
+### 📊 **Analysis Tools**
+- Combo configuration analysis
+- Performance assessment
+- Optimization recommendations
+- Compatibility scoring
+
+## Node Interface
+
+### Inputs
+- **sampler_name**: Dropdown with available sampling algorithms
+- **scheduler**: Dropdown with compatible schedulers
+- **steps**: Integer slider (1-100 steps)
+- **cfg**: Float slider (0.0-20.0 CFG scale)
+
+### Outputs
+- **sampler_name**: Selected sampler algorithm
+- **scheduler**: Selected scheduler algorithm
+- **steps**: Number of sampling steps
+- **cfg**: CFG scale value
+
+## Available Samplers
+
+### Primary Samplers
+| Sampler | Type | Speed | Quality | Best For |
+|---------|------|-------|---------|----------|
+| euler | Deterministic | Fast | Good | General use |
+| euler_ancestral | Stochastic | Fast | Good | Creative variation |
+| heun | Higher-order | Medium | Better | Quality focus |
+| dpm_2 | Multi-step | Medium | Good | Balanced |
+| dpm_2_ancestral | Stochastic | Medium | Good | Creative quality |
+| lms | Linear | Fast | Good | Simple scenes |
+| dpm_fast | Optimized | Very Fast | Good | Speed priority |
+| dpm_adaptive | Adaptive | Variable | Best | Automatic tuning |
+
+### Advanced Samplers
+| Sampler | Type | Speed | Quality | Best For |
+|---------|------|-------|---------|----------|
+| dpmpp_2s_ancestral | Advanced | Medium | Better | High quality |
+| dpmpp_2m | Optimized | Fast | Better | Speed + quality |
+| dpmpp_2m_sde | Stochastic | Medium | Best | Maximum quality |
+| dpmpp_3m_sde | Latest | Medium | Best | Cutting edge |
+| ddim | Classic | Fast | Good | Compatibility |
+| uni_pc | Unified | Fast | Better | Efficiency |
+
+## Available Schedulers
+
+### Linear Schedulers
+- **normal**: Standard linear schedule
+- **linear**: Basic linear distribution
+- **sgm_uniform**: Uniform distribution
+
+### Advanced Schedulers
+- **karras**: Karras noise schedule (recommended)
+- **exponential**: Exponential decay
+- **polyexponential**: Polynomial exponential
+- **beta**: Beta distribution schedule
+
+### Specialized Schedulers
+- **cosine**: Cosine annealing schedule
+- **simple**: Simplified schedule
+- **ddim_uniform**: DDIM uniform schedule
+- **laplace**: Laplace distribution
+
+## Optimization Guidelines
+
+### Recommended Combinations
+
+#### Speed Optimized
+```
+Sampler: euler or dpm_fast
+Scheduler: normal or simple
+Steps: 15-25
+CFG: 6.0-8.0
+```
+
+#### Quality Optimized
+```
+Sampler: dpmpp_2m_sde or dpmpp_3m_sde
+Scheduler: karras
+Steps: 25-35
+CFG: 7.0-9.0
+```
+
+#### Balanced
+```
+Sampler: dpmpp_2m or heun
+Scheduler: karras or normal
+Steps: 20-30
+CFG: 7.0-8.5
+```
+
+### Steps Recommendations
+
+| Sampler Type | Min Steps | Optimal | Max Steps |
+|--------------|-----------|---------|-----------|
+| Fast (euler, dpm_fast) | 10 | 20 | 30 |
+| Standard (heun, dpm_2) | 15 | 25 | 40 |
+| Advanced (dpmpp_*) | 20 | 30 | 50 |
+| Adaptive | 10 | 25 | 100 |
+
+### CFG Scale Guidelines
+
+| Content Type | CFG Range | Recommended |
+|--------------|-----------|-------------|
+| Photorealistic | 5.0-8.0 | 7.0 |
+| Artistic/Stylized | 7.0-12.0 | 9.0 |
+| Abstract/Creative | 8.0-15.0 | 11.0 |
+| Text/Details | 10.0-20.0 | 13.0 |
+
+## Usage Examples
+
+### Basic Configuration
+```
+sampler_name: euler
+scheduler: normal
+steps: 20
+cfg: 7.0
+```
+
+### High Quality Setup
+```
+sampler_name: dpmpp_2m_sde
+scheduler: karras
+steps: 30
+cfg: 8.0
+```
+
+### Speed Priority
+```
+sampler_name: dpm_fast
+scheduler: simple
+steps: 15
+cfg: 6.5
+```
+
+## Advanced Features
+
+### Compatibility Analysis
+The node provides real-time analysis of parameter compatibility:
+- Scheduler compatibility with selected sampler
+- Steps optimization for sampler type
+- CFG scale recommendations
+- Performance impact assessment
+
+### Error Handling
+- Invalid samplers default to 'euler'
+- Invalid schedulers default to 'normal'
+- Out-of-range steps clamped to valid range
+- Invalid CFG values sanitized to safe defaults
+
+### Performance Tips
+1. **Use Karras scheduler** for most samplers (better quality)
+2. **Start with 20-30 steps** for most use cases
+3. **Keep CFG 6.0-9.0** for realistic images
+4. **Try dpmpp_2m** for best speed/quality balance
+5. **Use euler** for fastest generation
+
+## Troubleshooting
+
+### Common Issues
+- **Slow generation**: Try euler or dpm_fast samplers
+- **Poor quality**: Increase steps or try dpmpp_2m_sde
+- **Overcooked images**: Lower CFG scale
+- **Underdetailed**: Increase CFG or steps
+- **Artifacts**: Try karras scheduler or different sampler
+
+### Performance Optimization
+- **GPU Memory**: Lower steps if running out of VRAM
+- **Speed**: Use euler + normal + 15-20 steps
+- **Quality**: Use dpmpp_2m_sde + karras + 25-30 steps
+- **Compatibility**: Stick to euler/heun for broad model support
+
+## Integration
+
+The Sampler Combo node outputs are compatible with all standard ComfyUI sampling nodes:
+- KSampler
+- KSamplerAdvanced
+- Custom sampling workflows
+- Upscaling pipelines
+- Img2img workflows
+
+Connect the outputs directly to your sampling node inputs for streamlined configuration.

examples/documentation/sampler_select_helper.md

@@ -0,0 +1,260 @@
+# Sampler Select Helper
+
+## Overview
+The **Sampler Select Helper** node provides intelligent sampler selection with model-specific recommendations and compatibility checking. Adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode), this tool ensures optimal sampler-scheduler combinations for different model architectures.
+
+## Attribution
+This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
+
+## Features
+- **Model-Aware Selection**: Automatic recommendations based on model type
+- **Compatibility Validation**: Ensures sampler-scheduler pairs work well together
+- **Performance Profiles**: Pre-configured settings for quality vs speed
+- **Dynamic Updates**: Adapts to newly available samplers
+- **Batch Support**: Test multiple samplers in sequence
+
+## Node Properties
+- **Category**: `ComfyAssets/🧰 xyz-helpers`
+- **Node Name**: `SamplerSelectHelper`
+- **Function**: `select_sampler`
+
+## Inputs
+
+### Required
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `model_type` | DROPDOWN | auto | [auto, sdxl, sd15, flux, custom] |
+| `quality_preset` | DROPDOWN | balanced | [fast, balanced, quality, extreme] |
+| `sampler_override` | DROPDOWN | auto | Specific sampler selection |
+
+### Optional
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `scheduler_override` | DROPDOWN | auto | Specific scheduler selection |
+| `model_name` | STRING | - | Model name for auto-detection |
+| `custom_rules` | STRING | - | JSON rules for custom selection |
+
+## Outputs
+| Name | Type | Description |
+|------|------|-------------|
+| `sampler_name` | STRING | Selected sampler |
+| `scheduler` | STRING | Selected scheduler |
+| `recommended_steps` | INT | Suggested step count |
+| `recommended_cfg` | FLOAT | Suggested CFG scale |
+
+## Model-Specific Recommendations
+
+### SDXL Models
+```python
+quality_preset: "balanced"
+→ sampler: "dpmpp_2m"
+→ scheduler: "karras"
+→ steps: 25
+→ cfg: 7.0
+```
+
+### SD 1.5 Models
+```python
+quality_preset: "quality"
+→ sampler: "dpmpp_2m_sde"
+→ scheduler: "exponential"
+→ steps: 30
+→ cfg: 7.5
+```
+
+### FLUX Models
+```python
+quality_preset: "fast"
+→ sampler: "euler"
+→ scheduler: "simple"
+→ steps: 15
+→ cfg: 3.5
+```
+
+## Quality Presets Explained
+
+### Fast (Preview)
+- **Goal**: Quick iterations
+- **Steps**: 10-15
+- **Samplers**: euler, dpm_fast
+- **Use Case**: Testing prompts
+
+### Balanced (Default)
+- **Goal**: Good quality/speed ratio
+- **Steps**: 20-25
+- **Samplers**: dpmpp_2m, dpmpp_2m_sde
+- **Use Case**: Regular generation
+
+### Quality
+- **Goal**: Best visual quality
+- **Steps**: 30-40
+- **Samplers**: dpmpp_3m_sde, dpm_adaptive
+- **Use Case**: Final renders
+
+### Extreme
+- **Goal**: Maximum quality
+- **Steps**: 50-100
+- **Samplers**: dpm_adaptive, dpmpp_3m_sde
+- **Use Case**: Hero images
+
+## Usage Examples
+
+### Auto Model Detection
+```
+Load Model → SamplerSelectHelper → KSampler
+    model_type: auto
+    quality_preset: balanced
+```
+
+### Custom Override
+```
+SamplerSelectHelper → KSampler
+    sampler_override: "dpmpp_3m_sde"
+    scheduler_override: "exponential"
+```
+
+### Batch Testing
+```
+SamplerSelectHelper → Batch Process
+    quality_preset: [fast, balanced, quality]
+    → Compare outputs
+```
+
+## Compatibility Matrix
+
+### Recommended Combinations
+| Sampler | Best Schedulers | Avoid |
+|---------|----------------|--------|
+| euler | normal, karras | sgm_uniform |
+| euler_a | normal, karras | simple |
+| dpmpp_2m | karras, exponential | - |
+| dpmpp_2m_sde | karras, exponential | simple |
+| dpmpp_3m_sde | exponential | simple |
+| dpm_adaptive | normal | karras |
+
+## Best Practices
+
+### Model Type Detection
+1. Use `auto` for automatic detection
+2. Override only when necessary
+3. Provide model_name for better accuracy
+
+### Performance Optimization
+```python
+# Quick preview workflow
+quality_preset: "fast"
+→ 10 steps, euler sampler
+
+# Final production
+quality_preset: "quality"
+→ 35 steps, dpmpp_3m_sde
+
+# Experimental/artistic
+quality_preset: "extreme"
+→ 75 steps, dpm_adaptive
+```
+
+### Custom Rules Format
+```json
+{
+  "model_pattern": "anime.*",
+  "sampler": "dpmpp_2m_sde",
+  "scheduler": "karras",
+  "steps": 28,
+  "cfg": 7.0
+}
+```
+
+## Integration with Other Nodes
+
+### Complete Pipeline
+```
+Model Loader → SamplerSelectHelper → KSampler
+             ↘ FluxSamplerParams ↗
+```
+
+### A/B Testing
+```
+SamplerSelectHelper → KSampler → Image A
+    quality: fast
+SamplerSelectHelper → KSampler → Image B
+    quality: quality
+→ Compare Results
+```
+
+## Advanced Features
+
+### Dynamic Sampler Discovery
+- Automatically detects new samplers
+- Updates compatibility matrix
+- Maintains optimal pairings
+
+### Performance Profiling
+- Tracks generation times
+- Suggests optimal settings
+- Adapts to hardware capabilities
+
+### Model Fingerprinting
+- Identifies model architecture
+- Applies specific optimizations
+- Learns from usage patterns
+
+## Tips and Tricks
+
+### Speed vs Quality
+1. Start with "fast" for prompt testing
+2. Move to "balanced" for iteration
+3. Use "quality" for final output
+4. Reserve "extreme" for special cases
+
+### Sampler Selection Logic
+```python
+if model_type == "flux":
+    prefer ["euler", "dpmpp_2m"]
+elif model_type == "sdxl":
+    prefer ["dpmpp_2m_sde", "dpmpp_3m_sde"]
+else:
+    use ["dpmpp_2m", "euler_a"]
+```
+
+### Memory Considerations
+- Fast presets use less memory
+- Extreme presets may require more VRAM
+- Adaptive samplers adjust dynamically
+
+## Troubleshooting
+
+### Wrong Sampler Selected
+- Check model_type setting
+- Verify model detection
+- Use manual override if needed
+
+### Poor Quality Output
+- Increase quality preset
+- Check recommended steps
+- Verify CFG scale
+
+### Performance Issues
+- Start with fast preset
+- Reduce step count
+- Try simpler samplers
+
+## Common Workflows
+
+### Model Comparison
+Test same prompt across different models with optimal settings for each.
+
+### Quality Ladder
+Progress from fast to extreme quality to find optimal balance.
+
+### Sampler Shootout
+Compare all compatible samplers for specific model/prompt combination.
+
+## Version History
+- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
+- **1.0.1**: Added FLUX model support
+- **1.0.2**: Enhanced compatibility matrix
+- **1.0.3**: Improved auto-detection
+
+## Credits
+Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

examples/documentation/scheduler_select_helper.md

@@ -0,0 +1,300 @@
+# Scheduler Select Helper
+
+## Overview
+The **Scheduler Select Helper** node provides intelligent scheduler selection with sampler-aware recommendations and model-specific optimizations. Adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode), this tool ensures optimal scheduler selection for different sampling algorithms and models.
+
+## Attribution
+This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
+
+## Features
+- **Sampler-Aware Selection**: Recommends best schedulers for each sampler
+- **Model Optimization**: Specific scheduler tuning for different models
+- **Noise Schedule Profiles**: Pre-configured curves for various use cases
+- **Visual Feedback**: Preview noise schedules
+- **Batch Testing**: Compare multiple schedulers
+
+## Node Properties
+- **Category**: `ComfyAssets/🧰 xyz-helpers`
+- **Node Name**: `SchedulerSelectHelper`
+- **Function**: `select_scheduler`
+
+## Inputs
+
+### Required
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `sampler_name` | STRING | - | Current sampler being used |
+| `model_type` | DROPDOWN | auto | [auto, sdxl, sd15, flux] |
+| `schedule_type` | DROPDOWN | smooth | [smooth, sharp, linear, custom] |
+
+### Optional
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `override` | DROPDOWN | none | Force specific scheduler |
+| `beta_schedule` | STRING | - | Custom beta schedule values |
+| `visualize` | BOOLEAN | False | Show schedule curve |
+
+## Outputs
+| Name | Type | Description |
+|------|------|-------------|
+| `scheduler` | STRING | Selected scheduler name |
+| `schedule_curve` | IMAGE | Visualization of noise schedule |
+| `beta_values` | FLOAT_ARRAY | Beta schedule values |
+
+## Scheduler Types Explained
+
+### Normal
+- **Curve**: Linear noise reduction
+- **Best For**: General purpose
+- **Samplers**: euler, dpm_fast
+
+### Karras
+- **Curve**: Improved noise schedule
+- **Best For**: High quality
+- **Samplers**: dpmpp_2m, dpmpp_2m_sde
+
+### Exponential
+- **Curve**: Exponential decay
+- **Best For**: Fine details
+- **Samplers**: dpmpp_3m_sde
+
+### Simple
+- **Curve**: Basic linear
+- **Best For**: Fast generation
+- **Samplers**: euler, lcm
+
+### SGM Uniform
+- **Curve**: Uniform distribution
+- **Best For**: FLUX models
+- **Samplers**: euler, dpmpp_2m
+
+## Schedule Types
+
+### Smooth (Default)
+```python
+# Gradual noise reduction
+# Good for most content
+→ karras or exponential
+```
+
+### Sharp
+```python
+# Aggressive early reduction
+# Good for high contrast
+→ normal or simple
+```
+
+### Linear
+```python
+# Constant reduction rate
+# Predictable results
+→ normal
+```
+
+### Custom
+```python
+# User-defined curve
+# Advanced control
+→ based on beta_schedule
+```
+
+## Usage Examples
+
+### Automatic Selection
+```
+KSampler Settings → SchedulerSelectHelper → KSampler
+    sampler_name: "dpmpp_2m_sde"
+    model_type: auto
+    → scheduler: "karras"
+```
+
+### Visual Comparison
+```
+SchedulerSelectHelper → Display
+    visualize: True
+    → Shows noise schedule curve
+```
+
+### Batch Testing
+```
+For each scheduler:
+    SchedulerSelectHelper → KSampler → Save
+    → Compare results
+```
+
+## Sampler-Scheduler Compatibility
+
+### Optimal Pairings
+| Sampler | Best Scheduler | Good Alternatives |
+|---------|---------------|-------------------|
+| euler | normal | karras |
+| euler_a | karras | normal |
+| heun | normal | - |
+| dpm_fast | normal | simple |
+| dpm_adaptive | normal | - |
+| dpmpp_2m | karras | exponential |
+| dpmpp_2m_sde | karras | exponential |
+| dpmpp_3m_sde | exponential | karras |
+| dpmpp_2s_a | karras | normal |
+| lcm | simple | normal |
+
+## Model-Specific Recommendations
+
+### SDXL
+```python
+preferred_schedulers = ["karras", "exponential"]
+# Better convergence for high-res
+```
+
+### SD 1.5
+```python
+preferred_schedulers = ["karras", "normal"]
+# Classic combinations
+```
+
+### FLUX
+```python
+preferred_schedulers = ["simple", "sgm_uniform"]
+# Optimized for FLUX architecture
+```
+
+## Best Practices
+
+### Selection Strategy
+1. Let auto-detection handle defaults
+2. Override for specific artistic goals
+3. Test multiple schedulers for hero images
+4. Use visualization to understand curves
+
+### Performance Tips
+- Simple/normal for quick previews
+- Karras/exponential for quality
+- SGM uniform specifically for FLUX
+- Match scheduler to sampler type
+
+### Testing Workflow
+```python
+schedulers = ["normal", "karras", "exponential"]
+for scheduler in schedulers:
+    generate_image(scheduler)
+    save_with_metadata(scheduler)
+compare_results()
+```
+
+## Advanced Features
+
+### Beta Schedule Customization
+```python
+# Custom exponential curve
+beta_schedule = "0.00085, 0.0012, 0.0018, ..."
+
+# Sharp early reduction
+beta_schedule = "0.001, 0.002, 0.004, 0.006, ..."
+```
+
+### Schedule Visualization
+- Plots noise reduction curve
+- Shows sigma values
+- Compares with standard schedules
+- Exports schedule data
+
+### Adaptive Selection
+- Learns from user preferences
+- Adapts to hardware capabilities
+- Optimizes for generation speed
+
+## Integration Examples
+
+### Complete Pipeline
+```
+Sampler Combo → SchedulerSelectHelper → KSampler
+    ↓                    ↓
+sampler_name → Optimal scheduler selection
+```
+
+### A/B Testing
+```
+Same prompt → Different schedulers → Grid comparison
+    normal vs karras vs exponential
+```
+
+### Noise Schedule Analysis
+```
+SchedulerSelectHelper → Plot Parameters
+    visualize: True
+    → Analyze noise curves
+```
+
+## Tips and Tricks
+
+### Quality Optimization
+```python
+# For maximum quality
+if sampler in ["dpmpp_3m_sde"]:
+    use scheduler="exponential"
+elif sampler in ["dpmpp_2m_sde"]:
+    use scheduler="karras"
+```
+
+### Speed Optimization
+```python
+# For fast generation
+use scheduler="simple" or "normal"
+reduce step count by 20%
+```
+
+### Artistic Effects
+- **Sharp details**: normal scheduler
+- **Smooth gradients**: karras scheduler
+- **Fine textures**: exponential scheduler
+
+## Troubleshooting
+
+### Artifacts or Noise
+- Try different scheduler
+- Check sampler compatibility
+- Adjust step count
+
+### Slow Convergence
+- Switch from simple to karras
+- Increase step count
+- Check model compatibility
+
+### Inconsistent Results
+- Use same scheduler for batch
+- Avoid random scheduler selection
+- Fix seed for testing
+
+## Visual Guide
+
+### Noise Schedule Curves
+```
+Normal:    ████████████████
+           Linear reduction
+
+Karras:    ███████████▓▓▓░░
+           Smooth curve
+
+Exponential: ██████▓▓▓░░░░░
+            Fast early reduction
+```
+
+## Common Workflows
+
+### Scheduler Comparison
+Test same seed with different schedulers to find optimal setting.
+
+### Model Migration
+When switching models, automatically adjust scheduler for best results.
+
+### Quality Ladder
+Progress through schedulers from fast to quality for different use cases.
+
+## Version History
+- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
+- **1.0.1**: Added visualization features
+- **1.0.2**: Enhanced model detection
+- **1.0.3**: Improved compatibility matrix
+
+## Credits
+Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

examples/documentation/seed_history.md

@@ -0,0 +1,167 @@
+# Seed History Tool
+
+The Seed History tool provides advanced seed value tracking with an interactive UI for managing seed history, automatic deduplication, and convenient seed retrieval.
+
+## Overview
+
+The Seed History node functions as both a standard seed input and an intelligent tracking system that automatically monitors seed changes and maintains a searchable history.
+
+## Features
+
+### 🎲 Core Functionality
+- **Seed Output**: Standard ComfyUI seed value output (0 to 18,446,744,073,709,551,615)
+- **History Tracking**: Automatic tracking of all seed changes with timestamps
+- **Deduplication**: Intelligent filtering to prevent duplicate entries within 500ms windows
+- **Persistent Storage**: History persists across ComfyUI sessions using localStorage
+
+### 🎯 Interactive UI
+- **History Display**: Scrollable list showing recent seeds with timestamps
+- **Click to Load**: Click any history entry to instantly load that seed
+- **Generate Button**: Create new random seeds with one click
+- **Clear History**: Remove all tracked seeds when needed
+- **Auto-Hide**: History section automatically hides after 2.5 seconds of inactivity
+
+### ⚡ Smart Features
+- **Real-time Updates**: Tracks seed changes from increment/decrement buttons
+- **Visual Feedback**: Selected seeds are highlighted in green
+- **Time Formatting**: Human-readable "time ago" display (e.g., "5m ago", "2h ago")
+- **Notifications**: Toast messages for actions like generate and clear
+- **Responsive Design**: Adapts to node resizing
+
+## Usage
+
+### Basic Setup
+
+1. **Add Node**: Search for "Seed History" in the ComfyUI node browser
+2. **Connect Output**: Connect the seed output to any node requiring a seed input
+3. **Automatic Tracking**: The node automatically begins tracking seed changes
+
+### Seed Management
+
+```
+🎲 Seed History
+┌─────────────────┐
+│ 🎲 Generate │ 🗑️ Clear │
+├─────────────────┤
+│ 🎲 1,234,567    │ ← Click to load
+│ ⏰ 2m ago       │
+├─────────────────┤
+│ 🎲 9,876,543    │
+│ ⏰ 5m ago       │
+├─────────────────┤
+│ 🎲 5,555,555    │
+│ ⏰ 10m ago      │
+└─────────────────┘
+```
+
+### Workflow Integration
+
+The Seed History node works seamlessly with any ComfyUI workflow:
+
+```
+[Seed History] → [KSampler] → [Image Output]
+     ↓
+[VAE Decode] → [Save Image]
+```
+
+## Advanced Features
+
+### History Management
+- **Maximum Entries**: Keeps the 10 most recent seeds
+- **Smart Deduplication**: Prevents rapid duplicate additions
+- **Timestamp Tracking**: Full date/time information for each seed
+- **Persistent Storage**: History survives ComfyUI restarts
+
+### UI Behavior
+- **Auto-Hide Timer**: History hides after 2.5 seconds of inactivity
+- **Mouse Interaction**: Hovering over history cancels auto-hide
+- **Restore Button**: Click to restore hidden history section
+- **Visual Feedback**: Hover effects and selection highlighting
+
+### Seed Validation
+- **Range Checking**: Ensures seeds are within valid ComfyUI range
+- **Error Handling**: Graceful fallback to default seed (12345) on errors
+- **Sanitization**: Automatic clamping of out-of-range values
+
+## Technical Details
+
+### Input Parameters
+- **seed** (INT): Seed value for generation processes
+  - Range: 0 to 18,446,744,073,709,551,615
+  - Default: 12345
+  - Tooltip: "Seed value for generation processes. History UI tracks all changes automatically."
+
+### Output
+- **seed** (INT): The processed seed value for use in other nodes
+
+### Storage
+- **Key**: `comfyui_kikotools_seed_history`
+- **Format**: JSON array of history entries
+- **Location**: Browser localStorage
+- **Persistence**: Survives browser sessions and ComfyUI restarts
+
+## Use Cases
+
+### 🎨 Creative Workflows
+- **Iteration Tracking**: Keep track of promising seeds during creative exploration
+- **Version Control**: Easily return to previous seeds that produced good results
+- **Experimentation**: Generate and track multiple seed variations
+
+### 🔬 Technical Workflows
+- **Reproducibility**: Maintain exact seed records for reproducing specific outputs
+- **A/B Testing**: Compare results from different seeds with easy switching
+- **Documentation**: Export seed history for technical documentation
+
+### 📊 Batch Processing
+- **Seed Management**: Track seeds across multiple batch runs
+- **Quality Control**: Quickly identify and reuse successful seeds
+- **Workflow Optimization**: Analyze seed performance patterns
+
+## Tips and Best Practices
+
+### Efficient Usage
+1. **Let it Track**: The node automatically tracks all seed changes - no manual intervention needed
+2. **Use Generate**: The generate button is optimized for creating good random seeds
+3. **Regular Clearing**: Clear history periodically to maintain relevant seeds only
+
+### Workflow Integration
+1. **Single Source**: Use one Seed History node per workflow for centralized tracking
+2. **Connect Early**: Place the node early in your workflow chain for complete tracking
+3. **Branch Connections**: Connect to multiple nodes that need the same seed
+
+### History Management
+1. **Review Regularly**: Check history for seeds that produced good results
+2. **Document Success**: Note down particularly successful seeds externally
+3. **Clean Periodically**: Clear history when starting new creative projects
+
+## Troubleshooting
+
+### Common Issues
+
+**History Not Updating**
+- Ensure the node is properly connected to your workflow
+- Check that seed widget is visible and functional
+- Verify browser localStorage is enabled
+
+**UI Not Appearing**
+- Check browser console for JavaScript errors
+- Ensure ComfyUI-KikoTools is properly installed
+- Verify web directory permissions
+
+**Seeds Not Loading**
+- Confirm the seed is within valid range
+- Check that target widgets support the seed value
+- Verify node connections are intact
+
+### Performance Notes
+- History is limited to 10 entries for optimal performance
+- Deduplication prevents excessive storage usage
+- Auto-hide reduces visual clutter during long workflows
+
+## Examples
+
+See the `examples/workflows/` directory for complete workflow examples demonstrating:
+- Basic seed tracking workflow
+- Creative iteration with history
+- Technical reproducibility setup
+- Batch processing with seed management

examples/documentation/text_encode_sampler_params.md

@@ -0,0 +1,310 @@
+# Text Encode Sampler Params
+
+## Overview
+The **Text Encode Sampler Params** node combines text encoding with sampler parameter management, providing a unified interface for prompt processing and sampling configuration. Adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode), this tool streamlines the text-to-image pipeline setup.
+
+## Attribution
+This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
+
+## Features
+- **Unified Interface**: Combine text encoding and sampler params in one node
+- **Dynamic Prompt Processing**: Support for wildcards and syntax
+- **Parameter Templates**: Pre-configured settings for common scenarios
+- **Batch Text Processing**: Handle multiple prompts efficiently
+- **Model-Aware Encoding**: Optimize for different text encoders
+
+## Node Properties
+- **Category**: `ComfyAssets/🧰 xyz-helpers`
+- **Node Name**: `TextEncodeSamplerParams`
+- **Function**: `encode_and_params`
+
+## Inputs
+
+### Required
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `text` | STRING | - | Prompt text to encode |
+| `clip` | CLIP | - | CLIP model for encoding |
+| `sampler_name` | DROPDOWN | dpmpp_2m | Sampling algorithm |
+| `scheduler` | DROPDOWN | karras | Noise scheduler |
+| `steps` | INT | 20 | Sampling steps |
+| `cfg` | FLOAT | 7.0 | CFG scale |
+
+### Optional
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `negative_text` | STRING | "" | Negative prompt |
+| `seed` | INT | -1 | Random seed (-1 for random) |
+| `denoise` | FLOAT | 1.0 | Denoising strength |
+| `template` | DROPDOWN | none | Parameter template |
+
+## Outputs
+| Name | Type | Description |
+|------|------|-------------|
+| `positive` | CONDITIONING | Encoded positive prompt |
+| `negative` | CONDITIONING | Encoded negative prompt |
+| `sampler_params` | DICT | Complete sampler parameters |
+
+## Templates
+
+### Portrait Photography
+```python
+template: "portrait"
+→ steps: 25
+→ cfg: 7.5
+→ sampler: dpmpp_2m_sde
+→ scheduler: karras
+```
+
+### Landscape Art
+```python
+template: "landscape"
+→ steps: 30
+→ cfg: 8.0
+→ sampler: dpmpp_3m_sde
+→ scheduler: exponential
+```
+
+### Quick Preview
+```python
+template: "preview"
+→ steps: 12
+→ cfg: 6.0
+→ sampler: euler
+→ scheduler: normal
+```
+
+### High Detail
+```python
+template: "detailed"
+→ steps: 40
+→ cfg: 7.0
+→ sampler: dpm_adaptive
+→ scheduler: karras
+```
+
+## Usage Examples
+
+### Basic Text-to-Image
+```
+TextEncodeSamplerParams → KSampler → VAE Decode
+    text: "beautiful landscape"
+    negative_text: "ugly, blurry"
+    steps: 20
+```
+
+### Template-Based Generation
+```
+TextEncodeSamplerParams → KSampler
+    text: "portrait of a person"
+    template: "portrait"
+    → Optimized portrait settings
+```
+
+### Batch Processing
+```
+Multiple Prompts → TextEncodeSamplerParams → Batch Generate
+    → Encode all prompts with same settings
+```
+
+## Prompt Syntax Support
+
+### Wildcards
+```
+{red|blue|green} car
+→ Randomly selects color
+```
+
+### Emphasis
+```
+(important:1.2) detail
+→ Increases weight to 1.2
+```
+
+### Alternation
+```
+[cat|dog] in garden
+→ Alternates between options
+```
+
+## Best Practices
+
+### Text Encoding
+1. Keep prompts concise and descriptive
+2. Use emphasis for important elements
+3. Structure prompts logically
+4. Test negative prompts impact
+
+### Parameter Selection
+```python
+# Quality over speed
+steps: 30-40
+cfg: 7-8
+sampler: dpmpp_3m_sde
+
+# Speed over quality  
+steps: 10-15
+cfg: 5-6
+sampler: euler
+```
+
+### Negative Prompts
+```python
+# Common negatives
+"ugly, tiling, poorly drawn, out of frame"
+
+# Style-specific
+"cartoon, anime" (for realism)
+"realistic, photo" (for artwork)
+```
+
+## Integration with Other Nodes
+
+### Complete Pipeline
+```
+TextEncodeSamplerParams → KSampler → VAE Decode
+         ↓                    ↑
+    All parameters      From Model Loader
+```
+
+### With LoRA
+```
+LoRAFolderBatch → TextEncodeSamplerParams → Generate
+    → Apply LoRA to encoded text
+```
+
+### Multi-Pass Processing
+```
+TextEncodeSamplerParams → First Pass (low res)
+                       ↘ Second Pass (high res)
+```
+
+## Advanced Features
+
+### Dynamic Templates
+```python
+# Load template based on prompt content
+if "portrait" in text:
+    use_template("portrait")
+elif "landscape" in text:
+    use_template("landscape")
+```
+
+### Prompt Weighting
+```python
+# Automatic weight calculation
+analyze_prompt_importance()
+apply_semantic_weights()
+```
+
+### CLIP Skip Support
+- Adjust CLIP layers used
+- Model-specific optimization
+- Quality vs style balance
+
+## Tips and Tricks
+
+### Prompt Optimization
+1. Front-load important elements
+2. Use commas for separation
+3. Avoid contradictions
+4. Test with different CFG values
+
+### Performance Tuning
+```python
+# Memory efficient
+encode_in_batches = True
+clear_cache_between = True
+
+# Speed priority
+use_half_precision = True
+minimize_conditioning = True
+```
+
+### Quality Enhancement
+- Higher CFG for prompt adherence
+- Lower CFG for creativity
+- Balance with step count
+
+## Common Workflows
+
+### Style Transfer
+```
+Reference Image → Extract Style
+                ↓
+TextEncodeSamplerParams → Apply Style
+    text: "in the style of [extracted]"
+```
+
+### Prompt Evolution
+```
+Base Prompt → Variations → TextEncodeSamplerParams
+    → Test different phrasings
+```
+
+### A/B Testing
+```
+Same prompt → Different parameters → Compare
+    template A vs template B
+```
+
+## Troubleshooting
+
+### Poor Text Adherence
+- Increase CFG scale
+- Simplify prompt
+- Check CLIP model compatibility
+
+### Over-saturation
+- Reduce CFG scale
+- Adjust negative prompt
+- Check sampler settings
+
+### Encoding Errors
+- Verify CLIP model loaded
+- Check text formatting
+- Remove special characters
+
+## Parameter Guidelines
+
+### CFG Scale Effects
+```
+Low (3-5): Creative, loose interpretation
+Medium (6-8): Balanced adherence
+High (9-12): Strict prompt following
+Very High (13+): Potential artifacts
+```
+
+### Step Count Impact
+```
+Low (10-15): Fast, rough
+Medium (20-30): Good balance
+High (40-50): Maximum quality
+Very High (50+): Diminishing returns
+```
+
+## Model-Specific Settings
+
+### SDXL
+- CFG: 6-8
+- CLIP Skip: 1-2
+- Emphasis: Moderate
+
+### SD 1.5
+- CFG: 7-9
+- CLIP Skip: 1-2
+- Emphasis: Standard
+
+### FLUX
+- CFG: 3-5
+- CLIP Skip: 0
+- Emphasis: Subtle
+
+## Version History
+- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
+- **1.0.1**: Added template system
+- **1.0.2**: Enhanced prompt syntax support
+- **1.0.3**: Improved batch processing
+
+## Credits
+Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.

examples/documentation/width_height_selector.md

@@ -147,7 +147,7 @@ Width Height Selector → EmptyLatentImage → Resolution Calculator → Upscale
 
 ### Aspect Ratio Considerations
 - **Portrait**: 3:4, 2:3, 13:19 work well for people
-- **Landscape**: 16:9, 19:13, 7:4 for scenes and objects  
+- **Landscape**: 16:9, 19:13, 7:4 for scenes and objects
 - **Square**: 1:1 for centered compositions
 - **Ultra-wide**: 21:9+ for panoramic and cinematic shots
 
@@ -192,4 +192,4 @@ Width Height Selector → EmptyLatentImage → Resolution Calculator → Upscale
 ### Preset Organization
 - Categorized by model optimization
 - Sorted by aspect ratio within categories
-- Comprehensive tooltips for each preset
+- Comprehensive tooltips for each preset

examples/workflows/display_any_example.json

@@ -0,0 +1,379 @@
+{
+  "id": "display-any-example",
+  "revision": 0,
+  "last_node_id": 11,
+  "last_link_id": 9,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "DisplayAny",
+      "pos": [
+        400,
+        270
+      ],
+      "size": [
+        210,
+        60
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "input",
+          "type": "*",
+          "link": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "display_text",
+          "type": "STRING",
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayAny"
+      },
+      "widgets_values": [
+        "tensor shape"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "LoadImage",
+      "pos": [
+        50,
+        270
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1,
+            5
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "DisplayAny",
+      "pos": [
+        400,
+        520
+      ],
+      "size": [
+        210,
+        70
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "input",
+          "type": "*",
+          "link": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "display_text",
+          "type": "STRING",
+          "links": [
+            9
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayAny"
+      },
+      "widgets_values": [
+        "raw value"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "VAELoader",
+      "pos": [
+        -250,
+        410
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            6
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "ae.safetensors"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEEncode",
+      "pos": [
+        430,
+        390
+      ],
+      "size": [
+        140,
+        46
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 5
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 6
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "VAEEncode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 9,
+      "type": "DisplayText",
+      "pos": [
+        640,
+        270
+      ],
+      "size": [
+        450,
+        278
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 10,
+      "type": "DisplayText",
+      "pos": [
+        640,
+        610
+      ],
+      "size": [
+        590,
+        278
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 9
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 11,
+      "type": "MarkdownNote",
+      "pos": [
+        -320,
+        530
+      ],
+      "size": [
+        350,
+        270
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Display Any Example\n\nUniversal debugging tool:\n- Accepts ANY input type\n- Two modes: raw value or tensor shape\n- Finds tensors in nested structures\n\nUse cases:\n- Debug tensor dimensions\n- Inspect latent data\n- View config objects\n- Track data flow\n\nConnect anything to see its contents!"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      1,
+      0,
+      "IMAGE"
+    ],
+    [
+      5,
+      2,
+      0,
+      8,
+      0,
+      "IMAGE"
+    ],
+    [
+      6,
+      7,
+      0,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      7,
+      8,
+      0,
+      4,
+      0,
+      "*"
+    ],
+    [
+      8,
+      1,
+      0,
+      9,
+      0,
+      "STRING"
+    ],
+    [
+      9,
+      4,
+      0,
+      10,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Display Any",
+      "bounding": [
+        -400,
+        130,
+        1740,
+        850
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.6115909044841477,
+      "offset": [
+        689.1392030323894,
+        -27.435530779258137
+      ]
+    },
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/display_text_example.json

@@ -0,0 +1,144 @@
+{
+  "id": "display-text-example",
+  "revision": 0,
+  "last_node_id": 6,
+  "last_link_id": 2,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "DisplayText",
+      "pos": [
+        -160,
+        70
+      ],
+      "size": [
+        500,
+        400
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 4,
+      "type": "easy positive",
+      "pos": [
+        -620,
+        70
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "STRING",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfyui-easy-use",
+        "ver": "1.3.1",
+        "Node name for S&R": "easy positive"
+      },
+      "widgets_values": [
+        "Positive prompt:\nbeautiful landscape, mountains in background, sunset lighting, golden hour, professional photography, high resolution, detailed textures, vibrant colors, masterpiece\n\nNegative prompt:\nlow quality, blurry, pixelated, bad composition, oversaturated, underexposed, amateur"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 6,
+      "type": "MarkdownNote",
+      "pos": [
+        -620,
+        330
+      ],
+      "size": [
+        360,
+        250
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Display Text Example\n\nSmart text display with:\n- Auto-detection of SDXL prompt format\n- Split view for positive/negative prompts\n- Text wrapping and scrolling\n- Copy buttons (strips labels)\n- Responsive resizing\n\nTry editing the text to see:\n1. Single text mode (no prompt markers)\n2. Split view mode (with Positive/Negative prompts)\n\nPerfect for displaying Gemini-generated prompts!"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      2,
+      4,
+      0,
+      1,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Display Text",
+      "bounding": [
+        -750,
+        -60,
+        1240,
+        700
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.90909090909091,
+      "offset": [
+        720,
+        30
+      ]
+    },
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/empty_latent_batch_example.json

@@ -0,0 +1,301 @@
+{
+  "id": "empty-latent-batch-example",
+  "revision": 0,
+  "last_node_id": 9,
+  "last_link_id": 8,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "EmptyLatentBatch",
+      "pos": [
+        350,
+        350
+      ],
+      "size": [
+        370,
+        210
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "latent",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": []
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "slot_index": 1,
+          "links": [
+            4
+          ]
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "slot_index": 2,
+          "links": [
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "EmptyLatentBatch"
+      },
+      "widgets_values": [
+        "896×1152 - 7:9 (1.0MP) - SDXL",
+        896,
+        1152,
+        4
+      ]
+    },
+    {
+      "id": 2,
+      "type": "Note",
+      "pos": [
+        -50,
+        320
+      ],
+      "size": [
+        350,
+        250
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {
+        "text": "Empty Latent Batch Example\n\nThis node creates empty latent tensors with:\n- 26 preset resolutions for SDXL/FLUX/Ultra-wide\n- Batch size support (1-64)\n- Interactive swap button\n- Memory usage estimation\n\nConnect to:\n- KSampler for image generation\n- Other latent processing nodes\n\nOutputs sanitized dimensions divisible by 8."
+      },
+      "widgets_values": [
+        "Empty Latent Batch Example\n\nThis node creates empty latent tensors with:\n- 26 preset resolutions for SDXL/FLUX/Ultra-wide\n- Batch size support (1-64)\n- Interactive swap button\n- Memory usage estimation\n\nConnect to:\n- KSampler for image generation\n- Other latent processing nodes\n\nOutputs sanitized dimensions divisible by 8."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5,
+      "type": "DisplayAny",
+      "pos": [
+        760,
+        280
+      ],
+      "size": [
+        210,
+        60
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "input",
+          "type": "*",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "display_text",
+          "type": "STRING",
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayAny"
+      },
+      "widgets_values": [
+        "raw value"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "DisplayAny",
+      "pos": [
+        770,
+        500
+      ],
+      "size": [
+        210,
+        60
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "input",
+          "type": "*",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "display_text",
+          "type": "STRING",
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayAny"
+      },
+      "widgets_values": [
+        "raw value"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "DisplayText",
+      "pos": [
+        1000,
+        280
+      ],
+      "size": [
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+        138
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 9,
+      "type": "DisplayText",
+      "pos": [
+        1010,
+        500
+      ],
+      "size": [
+        210,
+        138
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
+    }
+  ],
+  "links": [
+    [
+      4,
+      1,
+      1,
+      5,
+      0,
+      "*"
+    ],
+    [
+      5,
+      1,
+      2,
+      6,
+      0,
+      "*"
+    ],
+    [
+      7,
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+      0,
+      8,
+      0,
+      "STRING"
+    ],
+    [
+      8,
+      6,
+      0,
+      9,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Empty Latent",
+      "bounding": [
+        -110,
+        120,
+        1440,
+        620
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.8264462809917358,
+      "offset": [
+        156.41790912499965,
+        -2.272547312500285
+      ]
+    },
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/gemini_prompt_example.json

@@ -0,0 +1,207 @@
+{
+  "id": "gemini-prompt-example",
+  "revision": 0,
+  "last_node_id": 6,
+  "last_link_id": 4,
+  "nodes": [
+    {
+      "id": 2,
+      "type": "LoadImage",
+      "pos": [
+        300,
+        30
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            3
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "DisplayText",
+      "pos": [
+        1070,
+        30
+      ],
+      "size": [
+        500,
+        400
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 5,
+      "type": "GeminiPrompt",
+      "pos": [
+        640,
+        30
+      ],
+      "size": [
+        400,
+        276
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "links": [
+            4
+          ]
+        },
+        {
+          "name": "negative_prompt",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "GeminiPrompt"
+      },
+      "widgets_values": [
+        "sdxl",
+        "gemini-2.0-flash",
+        "1234-1234-1234-1234",
+        "",
+        true,
+        null,
+        null,
+        null
+      ]
+    },
+    {
+      "id": 6,
+      "type": "MarkdownNote",
+      "pos": [
+        300,
+        410
+      ],
+      "size": [
+        400,
+        240
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Gemini Prompt Engineer Example\n\nAI-powered image analysis:\n- Analyzes image content and style\n- Generates optimized prompts for FLUX/SDXL/Danbooru/Video\n- Dynamic model selection with refresh button\n- Custom system prompt override\n\nSetup:\n1. Get free API key from Google AI Studio\n2. Set GEMINI_API_KEY env var or use config file\n3. Click refresh button to get latest models\n\nDisplayText automatically shows split view for SDXL prompts!"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      3,
+      2,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ],
+    [
+      4,
+      5,
+      0,
+      3,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Gemini Prompt Engineer",
+      "bounding": [
+        210,
+        -110,
+        1460,
+        850
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.7513148009015777,
+      "offset": [
+        97.5582232217803,
+        133.12148846503663
+      ]
+    },
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/image_to_multiple_of_example.json

@@ -0,0 +1,552 @@
+{
+  "id": "3cd0ac86-8d11-41e5-9d8b-6621bc810ee6",
+  "revision": 0,
+  "last_node_id": 21,
+  "last_link_id": 21,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "LoadImage",
+      "pos": [
+        -80,
+        850
+      ],
+      "size": [
+        315,
+        314.0000305175781
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            13
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "GetNode",
+      "pos": [
+        540,
+        840
+      ],
+      "size": [
+        210,
+        34
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "UPSCALE_MODEL",
+          "type": "UPSCALE_MODEL",
+          "links": []
+        }
+      ],
+      "title": "Get_upscale_skin_model",
+      "properties": {
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "upscale_skin_model"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "ImageUpscaleWithModel",
+      "pos": [
+        530,
+        810
+      ],
+      "size": [
+        221.98202514648438,
+        60
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "upscale_model",
+          "type": "UPSCALE_MODEL",
+          "link": 16
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            10,
+            21
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "ImageUpscaleWithModel",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 10,
+      "type": "ImageToMultipleOf",
+      "pos": [
+        1000,
+        810
+      ],
+      "size": [
+        230,
+        82
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 6
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            18
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfyui-glifnodes",
+        "ver": "1.0.0",
+        "Node name for S&R": "ImageToMultipleOf",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        65,
+        "center crop"
+      ]
+    },
+    {
+      "id": 13,
+      "type": "ImageScaleBy",
+      "pos": [
+        760,
+        810
+      ],
+      "size": [
+        220,
+        82
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 10
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            6,
+            19
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "ImageScaleBy",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "lanczos",
+        0.5000000000000001
+      ]
+    },
+    {
+      "id": 14,
+      "type": "ImageUpscaleWithModel",
+      "pos": [
+        280,
+        830
+      ],
+      "size": [
+        221.98202514648438,
+        60
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "upscale_model",
+          "type": "UPSCALE_MODEL",
+          "link": 15
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 13
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            5,
+            20
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "ImageUpscaleWithModel",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 15,
+      "type": "UpscaleModelLoader",
+      "pos": [
+        -90,
+        720
+      ],
+      "size": [
+        320,
+        60
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "UPSCALE_MODEL",
+          "type": "UPSCALE_MODEL",
+          "links": [
+            15
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "UpscaleModelLoader",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "4x-ClearRealityV1.pth"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 16,
+      "type": "UpscaleModelLoader",
+      "pos": [
+        140,
+        610
+      ],
+      "size": [
+        320,
+        60
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "UPSCALE_MODEL",
+          "type": "UPSCALE_MODEL",
+          "links": [
+            16
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "UpscaleModelLoader",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "1xSkinContrast-High-SuperUltraCompact.pth"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 18,
+      "type": "PreviewImage",
+      "pos": [
+        1260,
+        810
+      ],
+      "size": [
+        270,
+        280
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 18
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 19,
+      "type": "PreviewImage",
+      "pos": [
+        1010,
+        940
+      ],
+      "size": [
+        210,
+        258
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 19
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 20,
+      "type": "PreviewImage",
+      "pos": [
+        520,
+        940
+      ],
+      "size": [
+        210,
+        258
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 20
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 21,
+      "type": "PreviewImage",
+      "pos": [
+        750,
+        940
+      ],
+      "size": [
+        210,
+        258
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 21
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    }
+  ],
+  "links": [
+    [
+      5,
+      14,
+      0,
+      9,
+      1,
+      "IMAGE"
+    ],
+    [
+      6,
+      13,
+      0,
+      10,
+      0,
+      "IMAGE"
+    ],
+    [
+      10,
+      9,
+      0,
+      13,
+      0,
+      "IMAGE"
+    ],
+    [
+      13,
+      1,
+      0,
+      14,
+      1,
+      "IMAGE"
+    ],
+    [
+      15,
+      15,
+      0,
+      14,
+      0,
+      "UPSCALE_MODEL"
+    ],
+    [
+      16,
+      16,
+      0,
+      9,
+      0,
+      "UPSCALE_MODEL"
+    ],
+    [
+      18,
+      10,
+      0,
+      18,
+      0,
+      "IMAGE"
+    ],
+    [
+      19,
+      13,
+      0,
+      19,
+      0,
+      "IMAGE"
+    ],
+    [
+      20,
+      14,
+      0,
+      20,
+      0,
+      "IMAGE"
+    ],
+    [
+      21,
+      9,
+      0,
+      21,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Image to Multiple of",
+      "bounding": [
+        -180,
+        420,
+        1800,
+        830
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.6655312045216585,
+      "offset": [
+        292.51725658775155,
+        -375.0766486499791
+      ]
+    },
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/kiko_save_image_example.json

@@ -0,0 +1,147 @@
+{
+  "id": "kiko-save-image-example",
+  "revision": 0,
+  "last_node_id": 4,
+  "last_link_id": 1,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "KikoSaveImage",
+      "pos": [
+        390,
+        80
+      ],
+      "size": [
+        400,
+        390
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 1
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "KikoSaveImage"
+      },
+      "widgets_values": [
+        "KikoSave",
+        "PNG",
+        90,
+        4,
+        false,
+        true
+      ]
+    },
+    {
+      "id": 2,
+      "type": "LoadImage",
+      "pos": [
+        -10,
+        80
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.47",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "MarkdownNote",
+      "pos": [
+        -10,
+        460
+      ],
+      "size": [
+        370,
+        270
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Kiko Save Image Example\n\nEnhanced image saving with:\n- Format selection: PNG, JPEG, WebP\n- Quality controls per format\n- Floating popup viewer (draggable)\n- Batch operations support\n- File size display\n\nPopup Features:\n- Click images to open in new tab\n- Download individual or selected images\n- Minimize/maximize/roll-up controls\n- Persistent across saves\n\nTry different formats to compare file sizes!"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      1,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Kiko save Image",
+      "bounding": [
+        -110,
+        -60,
+        1020,
+        870
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.7513148009015777,
+      "offset": [
+        648.1018911400128,
+        110.02838653698085
+      ]
+    },
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/resolution_calculator_example.json

@@ -1,47 +1,355 @@
 {
-  "last_node_id": 3,
-  "last_link_id": 2,
+  "id": "41469b2d-d616-479d-879a-95cdc6074a37",
+  "revision": 0,
+  "last_node_id": 11,
+  "last_link_id": 9,
   "nodes": [
     {
       "id": 1,
       "type": "ResolutionCalculator",
-      "pos": [100, 100],
-      "size": {"0": 315, "1": 126},
+      "pos": [
+        -30,
+        210
+      ],
+      "size": [
+        315,
+        126
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 5
+        },
+        {
+          "name": "latent",
+          "shape": 7,
+          "type": "LATENT",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "width",
+          "type": "INT",
+          "slot_index": 0,
+          "links": [
+            6
+          ]
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "slot_index": 1,
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "965ad60c74d7f25b1acce890d9c06518e46e6d0b",
+        "Node name for S&R": "ResolutionCalculator",
+        "aux_id": "ComfyAssets/ComfyUI-KikoTools",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        2.5
+      ]
+    },
+    {
+      "id": 6,
+      "type": "LoadImage",
+      "pos": [
+        -340,
+        210
+      ],
+      "size": [
+        274.080078125,
+        314
+      ],
       "flags": {},
       "order": 0,
       "mode": 0,
-      "inputs": [
-        {"name": "image", "type": "IMAGE", "link": null},
-        {"name": "latent", "type": "LATENT", "link": null}
-      ],
+      "inputs": [],
       "outputs": [
-        {"name": "width", "type": "INT", "links": [1], "slot_index": 0},
-        {"name": "height", "type": "INT", "links": [2], "slot_index": 1}
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            5
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
       ],
-      "properties": {},
-      "widgets_values": [2.0],
-      "category": "ComfyAssets"
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "LoadImage",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "image-2025-06-13-105737.jpg",
+        "image"
+      ]
     },
     {
-      "id": 2,
-      "type": "Note",
-      "pos": [450, 100],
-      "size": {"0": 400, "1": 200},
+      "id": 7,
+      "type": "MarkdownNote",
+      "pos": [
+        -350,
+        610
+      ],
+      "size": [
+        410,
+        200
+      ],
       "flags": {},
       "order": 1,
       "mode": 0,
       "inputs": [],
       "outputs": [],
-      "properties": {"text": "Resolution Calculator Example\n\nThis node calculates upscaled dimensions from:\n- IMAGE tensors (connect from image loaders)\n- LATENT tensors (connect from VAE encode/generation)\n\nUse cases:\n- Calculate target dimensions for upscalers\n- Plan memory usage for large generations\n- Ensure dimensions are divisible by 8\n\nScale factors optimized for SDXL and FLUX models."},
-      "widgets_values": ["Resolution Calculator Example\n\nThis node calculates upscaled dimensions from:\n- IMAGE tensors (connect from image loaders)\n- LATENT tensors (connect from VAE encode/generation)\n\nUse cases:\n- Calculate target dimensions for upscalers\n- Plan memory usage for large generations\n- Ensure dimensions are divisible by 8\n\nScale factors optimized for SDXL and FLUX models."]
+      "properties": {},
+      "widgets_values": [
+        "Resolution Calculator Example\n\nThis node calculates upscaled dimensions from:\n- IMAGE tensors (connect from image loaders)\n- LATENT tensors (connect from VAE encode/generation)\n\nUse cases:\n- Calculate target dimensions for upscalers\n- Plan memory usage for large generations\n- Ensure dimensions are divisible by 8\n\nScale factors optimized for SDXL and FLUX models."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 8,
+      "type": "DisplayAny",
+      "pos": [
+        330,
+        160
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "input",
+          "type": "*",
+          "link": 6
+        }
+      ],
+      "outputs": [
+        {
+          "name": "display_text",
+          "type": "STRING",
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+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
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+        "raw value"
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "input",
+          "type": "*",
+          "link": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "display_text",
+          "type": "STRING",
+          "links": [
+            9
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
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+        "raw value"
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 9
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "kikotools",
+        "ver": "271cd020c1b2f78e7ee40c1f08e70191fed00012",
+        "Node name for S&R": "DisplayText"
+      },
+      "widgets_values": [
+        null
+      ]
     }
   ],
   "links": [
-    [1, 1, 0, 3, 0, "INT"],
-    [2, 1, 1, 3, 1, "INT"]
+    [
+      5,
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+      1,
+      0,
+      "IMAGE"
+    ],
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+      "*"
+    ],
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+      "*"
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+    [
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+      0,
+      "STRING"
+    ],
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+      9,
+      9,
+      0,
+      11,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Resolution Calculator",
+      "bounding": [
+        -480,
+        20,
+        1480,
+        880
+      ],
+      "color": "#ffffff",
+      "font_size": 24,
+      "flags": {}
+    }
   ],
-  "groups": [],
   "config": {},
-  "extra": {},
+  "extra": {
+    "ue_links": [],
+    "ds": {
+      "scale": 0.45000000000000145,
+      "offset": [
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+        256.0155843113725
+      ]
+    },
+    "links_added_by_ue": [],
+    "frontendVersion": "1.23.4",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
   "version": 0.4
 }

examples/workflows/sampler_combo_example.json

@@ -0,0 +1,537 @@
+{
+  "id": "972425bd-9910-484d-ad09-f142f534fc61",
+  "revision": 0,
+  "last_node_id": 10,
+  "last_link_id": 13,
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+        }
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+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            6
+          ]
+        }
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+        "ver": "0.3.40",
+        "Node name for S&R": "CLIPTextEncode",
+        "widget_ue_connectable": {}
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+      "widgets_values": [
+        "text, watermark"
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+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            2
+          ]
+        }
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+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "EmptyLatentImage",
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+          },
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+          "type": "COMBO",
+          "widget": {
+            "name": "scheduler"
+          },
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+        }
+      ],
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+          "type": "LATENT",
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+          "links": [
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+      "type": "VAEDecode",
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+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            9
+          ]
+        }
+      ],
+      "properties": {
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+        "ver": "0.3.40",
+        "Node name for S&R": "VAEDecode",
+        "widget_ue_connectable": {}
+      },
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+    },
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+      "type": "SaveImage",
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "SaveImage",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "ComfyUI"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        120,
+        180
+      ],
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+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "CLIPTextEncode",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 10,
+      "type": "SamplerCombo",
+      "pos": [
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+        290
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+      "size": [
+        300,
+        200
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "sampler_name",
+          "type": "COMBO",
+          "slot_index": 0,
+          "links": [
+            10
+          ]
+        },
+        {
+          "name": "scheduler",
+          "type": "COMBO",
+          "slot_index": 1,
+          "links": [
+            11
+          ]
+        },
+        {
+          "name": "steps",
+          "type": "INT",
+          "slot_index": 2,
+          "links": [
+            12
+          ]
+        },
+        {
+          "name": "cfg",
+          "type": "FLOAT",
+          "slot_index": 3,
+          "links": [
+            13
+          ]
+        }
+      ],
+      "properties": {
+        "aux_id": "ComfyAssets/ComfyUI-KikoTools",
+        "ver": "dcf2d679c1a1091c54c2aa8cc04724236723227d",
+        "widget_ue_connectable": {},
+        "Node name for S&R": "SamplerCombo"
+      },
+      "widgets_values": [
+        "dpmpp_2m",
+        "karras",
+        25,
+        7.5
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -220,
+        180
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "slot_index": 0,
+          "links": [
+            1
+          ]
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "slot_index": 1,
+          "links": [
+            3,
+            5
+          ]
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "slot_index": 2,
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "CheckpointLoaderSimple",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "sdxl_ckpt/realvisxlV40_v40LightningBakedvae.safetensors"
+      ]
+    }
+  ],
+  "links": [
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+      1,
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+    [
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+      "LATENT"
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+    [
+      8,
+      4,
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+      1,
+      "VAE"
+    ],
+    [
+      9,
+      8,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      10,
+      10,
+      0,
+      3,
+      6,
+      "COMBO"
+    ],
+    [
+      11,
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+      1,
+      3,
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+      "COMBO"
+    ],
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+      4,
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+    ],
+    [
+      13,
+      10,
+      3,
+      3,
+      5,
+      "FLOAT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ue_links": [],
+    "links_added_by_ue": [],
+    "ds": {
+      "scale": 0.9740024562304554,
+      "offset": [
+        75.10666660323399,
+        -252.18018113111526
+      ]
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+    "frontendVersion": "1.21.7",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/seed_history_example.json

@@ -0,0 +1,644 @@
+{
+  "id": "972425bd-9910-484d-ad09-f142f534fc61",
+  "revision": 0,
+  "last_node_id": 11,
+  "last_link_id": 14,
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+        180.6060791015625
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+      "order": 5,
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+      "inputs": [
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+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            6
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "CLIPTextEncode",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
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+      "size": [
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+      "inputs": [],
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+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "EmptyLatentImage",
+        "widget_ue_connectable": {}
+      },
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+        1
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+        {
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+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        },
+        {
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": 14
+        },
+        {
+          "name": "steps",
+          "type": "INT",
+          "widget": {
+            "name": "steps"
+          },
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+        },
+        {
+          "name": "cfg",
+          "type": "FLOAT",
+          "widget": {
+            "name": "cfg"
+          },
+          "link": 13
+        },
+        {
+          "name": "sampler_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "sampler_name"
+          },
+          "link": 10
+        },
+        {
+          "name": "scheduler",
+          "type": "COMBO",
+          "widget": {
+            "name": "scheduler"
+          },
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+        }
+      ],
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+        {
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+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            7
+          ]
+        }
+      ],
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+        "ver": "0.3.40",
+        "Node name for S&R": "KSampler",
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+      },
+      "widgets_values": [
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+        "randomize",
+        20,
+        8,
+        "euler",
+        "normal",
+        1,
+        ""
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
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+        188
+      ],
+      "size": [
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+      "mode": 0,
+      "inputs": [
+        {
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+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            9
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "VAEDecode",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": []
+    },
+    {
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+      "type": "SaveImage",
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+        270
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+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "SaveImage",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "ComfyUI"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        120,
+        180
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "CLIPTextEncode",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -220,
+        180
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "slot_index": 0,
+          "links": [
+            1
+          ]
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "slot_index": 1,
+          "links": [
+            3,
+            5
+          ]
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "slot_index": 2,
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "CheckpointLoaderSimple",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "sdxl_ckpt/realvisxlV40_v40LightningBakedvae.safetensors"
+      ]
+    },
+    {
+      "id": 10,
+      "type": "SamplerCombo",
+      "pos": [
+        550,
+        360
+      ],
+      "size": [
+        300,
+        200
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "sampler_name",
+          "type": "COMBO",
+          "slot_index": 0,
+          "links": [
+            10
+          ]
+        },
+        {
+          "name": "scheduler",
+          "type": "COMBO",
+          "slot_index": 1,
+          "links": [
+            11
+          ]
+        },
+        {
+          "name": "steps",
+          "type": "INT",
+          "slot_index": 2,
+          "links": [
+            12
+          ]
+        },
+        {
+          "name": "cfg",
+          "type": "FLOAT",
+          "slot_index": 3,
+          "links": [
+            13
+          ]
+        }
+      ],
+      "properties": {
+        "aux_id": "ComfyAssets/ComfyUI-KikoTools",
+        "ver": "dcf2d679c1a1091c54c2aa8cc04724236723227d",
+        "Node name for S&R": "SamplerCombo",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        "dpmpp_2m",
+        "karras",
+        25,
+        7.5
+      ]
+    },
+    {
+      "id": 11,
+      "type": "SeedHistory",
+      "pos": [
+        550,
+        100
+      ],
+      "size": [
+        290,
+        220
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "seed",
+          "type": "INT",
+          "links": [
+            14
+          ]
+        }
+      ],
+      "properties": {
+        "aux_id": "ComfyAssets/ComfyUI-KikoTools",
+        "ver": "dcf2d679c1a1091c54c2aa8cc04724236723227d",
+        "widget_ue_connectable": {},
+        "Node name for S&R": "SeedHistory"
+      },
+      "widgets_values": [
+        893082183398485,
+        "randomize",
+        ""
+      ],
+      "color": "#2a363b",
+      "bgcolor": "#3f5159",
+      "hasBeenResized": true,
+      "seedHistory": [
+        {
+          "seed": 893082183398485,
+          "timestamp": 1750032481153,
+          "dateString": "6/15/2025, 5:08:01 PM"
+        },
+        {
+          "seed": 267914687236135,
+          "timestamp": 1750014787315,
+          "dateString": "6/15/2025, 12:13:07 PM"
+        },
+        {
+          "seed": 267914687236134,
+          "timestamp": 1750014721305,
+          "dateString": "6/15/2025, 12:12:01 PM"
+        },
+        {
+          "seed": 267914687236133,
+          "timestamp": 1750012659402,
+          "dateString": "6/15/2025, 11:37:39 AM"
+        },
+        {
+          "seed": 267914687236132,
+          "timestamp": 1750011284415,
+          "dateString": "6/15/2025, 11:14:44 AM"
+        },
+        {
+          "seed": 267914687236131,
+          "timestamp": 1750011223416,
+          "dateString": "6/15/2025, 11:13:43 AM"
+        },
+        {
+          "seed": 267914687236130,
+          "timestamp": 1750011181407,
+          "dateString": "6/15/2025, 11:13:01 AM"
+        },
+        {
+          "seed": 267914687236129,
+          "timestamp": 1750005515384,
+          "dateString": "6/15/2025, 9:38:35 AM"
+        },
+        {
+          "seed": 267914687236128,
+          "timestamp": 1750005462384,
+          "dateString": "6/15/2025, 9:37:42 AM"
+        },
+        {
+          "seed": 267914687236127,
+          "timestamp": 1750005354713,
+          "dateString": "6/15/2025, 9:35:54 AM"
+        }
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      4,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      4,
+      6,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      3,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      4,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      9,
+      8,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      10,
+      10,
+      0,
+      3,
+      7,
+      "COMBO"
+    ],
+    [
+      11,
+      10,
+      1,
+      3,
+      8,
+      "COMBO"
+    ],
+    [
+      12,
+      10,
+      2,
+      3,
+      5,
+      "INT"
+    ],
+    [
+      13,
+      10,
+      3,
+      3,
+      6,
+      "FLOAT"
+    ],
+    [
+      14,
+      11,
+      0,
+      3,
+      4,
+      "INT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ue_links": [],
+    "links_added_by_ue": [],
+    "ds": {
+      "scale": 0.9740024562304554,
+      "offset": [
+        2.9611945935278796,
+        36.50023864466208
+      ]
+    },
+    "frontendVersion": "1.21.7",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/workflows/xyz_helpers_sampler_comparison.json

@@ -0,0 +1,169 @@
+{
+  "name": "Sampler and Scheduler Comparison Workflow",
+  "description": "Compare different sampler and scheduler combinations using xyz_helpers",
+  "nodes": [
+    {
+      "id": "1",
+      "type": "SamplerSelectHelper",
+      "title": "Select Optimal Sampler",
+      "category": "ComfyAssets/🧰 xyz-helpers",
+      "inputs": {
+        "model_type": "auto",
+        "quality_preset": "balanced",
+        "sampler_override": "auto",
+        "model_name": "sdxl_model.safetensors"
+      },
+      "outputs": {
+        "sampler_name": "STRING",
+        "scheduler": "STRING",
+        "recommended_steps": "INT",
+        "recommended_cfg": "FLOAT"
+      },
+      "pos": [100, 100]
+    },
+    {
+      "id": "2",
+      "type": "SchedulerSelectHelper",
+      "title": "Optimize Scheduler",
+      "category": "ComfyAssets/🧰 xyz-helpers",
+      "inputs": {
+        "sampler_name": ["1", "sampler_name"],
+        "model_type": "sdxl",
+        "schedule_type": "smooth",
+        "visualize": true
+      },
+      "outputs": {
+        "scheduler": "STRING",
+        "schedule_curve": "IMAGE"
+      },
+      "pos": [400, 100]
+    },
+    {
+      "id": "3",
+      "type": "TextEncodeSamplerParams",
+      "title": "Setup Text and Params",
+      "category": "ComfyAssets/🧰 xyz-helpers",
+      "inputs": {
+        "text": "a majestic mountain landscape at sunset, highly detailed",
+        "negative_text": "low quality, blurry, artifacts",
+        "clip": ["model", "clip"],
+        "sampler_name": ["1", "sampler_name"],
+        "scheduler": ["2", "scheduler"],
+        "steps": ["1", "recommended_steps"],
+        "cfg": ["1", "recommended_cfg"],
+        "template": "landscape"
+      },
+      "outputs": {
+        "positive": "CONDITIONING",
+        "negative": "CONDITIONING",
+        "sampler_params": "DICT"
+      },
+      "pos": [700, 100]
+    },
+    {
+      "id": "4",
+      "type": "EmptyLatentBatch",
+      "title": "Create Test Latents",
+      "category": "ComfyAssets/📦 Latents",
+      "inputs": {
+        "preset": "1216×832 (SDXL Landscape)",
+        "batch_size": 4
+      },
+      "outputs": {
+        "latent": "LATENT"
+      },
+      "pos": [100, 300]
+    },
+    {
+      "id": "5",
+      "type": "KSampler",
+      "title": "Generate with Optimal Settings",
+      "inputs": {
+        "model": ["model", "model"],
+        "positive": ["3", "positive"],
+        "negative": ["3", "negative"],
+        "latent_image": ["4", "latent"],
+        "sampler_name": ["1", "sampler_name"],
+        "scheduler": ["2", "scheduler"],
+        "steps": ["1", "recommended_steps"],
+        "cfg": ["1", "recommended_cfg"],
+        "seed": 42
+      },
+      "outputs": {
+        "latent": "LATENT"
+      },
+      "pos": [1000, 200]
+    },
+    {
+      "id": "6",
+      "type": "PlotParameters",
+      "title": "Visualize Parameters",
+      "category": "ComfyAssets/🧰 xyz-helpers",
+      "inputs": {
+        "sampler_params": ["3", "sampler_params"],
+        "plot_type": "bar",
+        "x_axis": "parameter_name",
+        "y_axis": "value",
+        "title": "Sampler Configuration Analysis",
+        "show_grid": true
+      },
+      "outputs": {
+        "plot_image": "IMAGE"
+      },
+      "pos": [700, 400]
+    },
+    {
+      "id": "7",
+      "type": "DisplayAny",
+      "title": "Show Schedule Curve",
+      "category": "ComfyAssets/🔍 Debug",
+      "inputs": {
+        "input": ["2", "schedule_curve"],
+        "mode": "tensor shape"
+      },
+      "pos": [400, 400]
+    },
+    {
+      "id": "8",
+      "type": "VAEDecode",
+      "title": "Decode Results",
+      "inputs": {
+        "samples": ["5", "latent"],
+        "vae": ["model", "vae"]
+      },
+      "outputs": {
+        "image": "IMAGE"
+      },
+      "pos": [1300, 200]
+    },
+    {
+      "id": "9",
+      "type": "KikoSaveImage",
+      "title": "Save Comparison",
+      "category": "ComfyAssets/💾 Images",
+      "inputs": {
+        "images": ["8", "image"],
+        "filename_prefix": "sampler_comparison",
+        "format": "WEBP",
+        "quality": 90,
+        "popup": true
+      },
+      "pos": [1600, 200]
+    }
+  ],
+  "workflow_notes": {
+    "purpose": "Compare and optimize sampler/scheduler combinations for best quality",
+    "features": [
+      "Automatic sampler selection based on model",
+      "Scheduler optimization with visualization",
+      "Parameter analysis and plotting",
+      "Batch generation for comparison"
+    ],
+    "tips": [
+      "Try different quality_preset values",
+      "Use visualize=true to see noise schedules",
+      "Compare results across multiple seeds"
+    ],
+    "attribution": "xyz_helpers nodes adapted from comfyui-essentials-nodes"
+  }
+}

kikotools/__init__.py

@@ -5,16 +5,65 @@ Handles automatic discovery and registration of all ComfyAssets tools
 
 from .tools.resolution_calculator import ResolutionCalculatorNode
 from .tools.width_height_selector import WidthHeightSelectorNode
+from .tools.seed_history import SeedHistoryNode
+from .tools.sampler_combo import SamplerComboNode, SamplerComboCompactNode
+from .tools.empty_latent_batch import EmptyLatentBatchNode
+from .tools.kiko_save_image import KikoSaveImageNode
+from .tools.image_to_multiple_of import ImageToMultipleOfNode
+from .tools.image_scale_down_by import ImageScaleDownByNode
+from .tools.gemini_prompt import GeminiPromptNode
+from .tools.display_any import DisplayAnyNode
+from .tools.display_text import DisplayTextNode
+from .tools.xyz_helpers import (
+    SamplerSelectHelperNode,
+    SchedulerSelectHelperNode,
+    TextEncodeSamplerParamsNode,
+    FluxSamplerParamsNode,
+    PlotParametersNode,
+    LoRAFolderBatchNode,
+)
 
 # ComfyUI node registration mappings
 NODE_CLASS_MAPPINGS = {
     "ResolutionCalculator": ResolutionCalculatorNode,
     "WidthHeightSelector": WidthHeightSelectorNode,
+    "SeedHistory": SeedHistoryNode,
+    "SamplerCombo": SamplerComboNode,
+    "SamplerComboCompact": SamplerComboCompactNode,
+    "EmptyLatentBatch": EmptyLatentBatchNode,
+    "KikoSaveImage": KikoSaveImageNode,
+    "ImageToMultipleOf": ImageToMultipleOfNode,
+    "ImageScaleDownBy": ImageScaleDownByNode,
+    "GeminiPrompt": GeminiPromptNode,
+    "DisplayAny": DisplayAnyNode,
+    "DisplayText": DisplayTextNode,
+    "SamplerSelectHelper": SamplerSelectHelperNode,
+    "SchedulerSelectHelper": SchedulerSelectHelperNode,
+    "TextEncodeSamplerParams": TextEncodeSamplerParamsNode,
+    "FluxSamplerParams": FluxSamplerParamsNode,
+    "PlotParameters+": PlotParametersNode,
+    "LoRAFolderBatch": LoRAFolderBatchNode,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "ResolutionCalculator": "Resolution Calculator",
     "WidthHeightSelector": "Width Height Selector",
+    "SeedHistory": "Seed History",
+    "SamplerCombo": "Sampler Combo",
+    "SamplerComboCompact": "Sampler Combo (Compact)",
+    "EmptyLatentBatch": "Empty Latent Batch",
+    "KikoSaveImage": "Kiko Save Image",
+    "ImageToMultipleOf": "Image to Multiple of",
+    "ImageScaleDownBy": "Image Scale Down By",
+    "GeminiPrompt": "Gemini Prompt Engineer",
+    "DisplayAny": "Display Any",
+    "DisplayText": "Display Text",
+    "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",
 }
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

kikotools/tools/display_any/__init__.py

@@ -0,0 +1,5 @@
+"""DisplayAny tool for ComfyUI."""
+
+from .node import DisplayAnyNode
+
+__all__ = ["DisplayAnyNode"]

kikotools/tools/display_any/logic.py

@@ -0,0 +1,73 @@
+"""Logic for DisplayAny node - displays any input value or tensor shape."""
+
+from typing import Any, List, Union
+
+
+def get_tensor_shapes(input_value: Any) -> List[List[int]]:
+    """Extract tensor shapes from nested structures.
+
+    Args:
+        input_value: Any input value that may contain tensors
+
+    Returns:
+        List of tensor shapes found in the input
+    """
+    shapes = []
+
+    def extract_shapes(value: Any) -> None:
+        """Recursively extract shapes from nested structures."""
+        if isinstance(value, dict):
+            for v in value.values():
+                extract_shapes(v)
+        elif isinstance(value, (list, tuple)):
+            for item in value:
+                extract_shapes(item)
+        elif hasattr(value, "shape"):
+            # Handle tensors (numpy arrays, torch tensors, etc.)
+            shapes.append(list(value.shape))
+
+    extract_shapes(input_value)
+    return shapes
+
+
+def format_display_value(input_value: Any, mode: str = "raw value") -> str:
+    """Format input value for display based on selected mode.
+
+    Args:
+        input_value: Any input value to display
+        mode: Display mode - "raw value" or "tensor shape"
+
+    Returns:
+        Formatted string representation of the input
+    """
+    if mode == "tensor shape":
+        shapes = get_tensor_shapes(input_value)
+        if shapes:
+            return str(shapes)
+        else:
+            return "No tensors found in input"
+
+    # Default to raw value display
+    # Try to format as JSON for better readability
+    try:
+        import json
+
+        if isinstance(input_value, (dict, list)):
+            return json.dumps(input_value, indent=2)
+    except:
+        pass
+
+    return str(input_value)
+
+
+def validate_display_mode(mode: str) -> bool:
+    """Validate if the display mode is supported.
+
+    Args:
+        mode: Display mode to validate
+
+    Returns:
+        True if mode is valid, False otherwise
+    """
+    valid_modes = ["raw value", "tensor shape"]
+    return mode in valid_modes

kikotools/tools/display_any/node.py

@@ -0,0 +1,67 @@
+"""DisplayAny node for ComfyUI - displays any input value or tensor information."""
+
+from typing import Any, Dict, Tuple
+
+from ...base import ComfyAssetsBaseNode
+from .logic import format_display_value, validate_display_mode
+
+
+# Define AnyType for wildcard input matching
+class AnyType(str):
+    """A special type that matches any input type in ComfyUI."""
+
+    def __ne__(self, other):
+        return False
+
+
+class DisplayAnyNode(ComfyAssetsBaseNode):
+    """Display any input value or tensor shape information.
+
+    This node can display any type of input in two modes:
+    - Raw value: Shows the string representation of the input
+    - Tensor shape: Extracts and displays shapes of any tensors in the input
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """Define input types for the node."""
+        return {
+            "required": {
+                "input": (AnyType("*"), {}),  # Accept any type of input
+                "mode": (["raw value", "tensor shape"],),
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, **kwargs) -> bool:
+        """Validate inputs - always returns True as we accept any input."""
+        return True
+
+    RETURN_TYPES = ("STRING",)
+    CATEGORY = "ComfyAssets/👁️ Display"
+    RETURN_NAMES = ("display_text",)
+    FUNCTION = "display"
+    OUTPUT_NODE = True  # This node displays output in the UI
+
+    def display(self, input: Any, mode: str = "raw value") -> Dict[str, Any]:
+        """Display the input value according to the selected mode.
+
+        Args:
+            input: Any input value to display
+            mode: Display mode - "raw value" or "tensor shape"
+
+        Returns:
+            Dictionary with UI display and result
+        """
+        # Validate mode
+        if not validate_display_mode(mode):
+            mode = "raw value"  # Default to raw value if invalid
+
+        # Format the display text
+        display_text = format_display_value(input, mode)
+
+        # Return both UI display and result
+        return {
+            "ui": {"text": [display_text]},  # UI expects array
+            "result": (display_text,),
+        }

kikotools/tools/display_text/__init__.py

@@ -0,0 +1,5 @@
+"""Display Text tool for ComfyUI."""
+
+from .node import DisplayTextNode, NODE_DISPLAY_NAME
+
+__all__ = ["DisplayTextNode", "NODE_DISPLAY_NAME"]

kikotools/tools/display_text/node.py

@@ -0,0 +1,48 @@
+"""Display Text node implementation."""
+
+from ...base import ComfyAssetsBaseNode
+
+
+class DisplayTextNode(ComfyAssetsBaseNode):
+    """Displays text in the ComfyUI interface with copy-to-clipboard functionality."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define input types for the node."""
+        return {
+            "required": {
+                "text": ("STRING", {"forceInput": True}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("text",)
+    OUTPUT_NODE = True
+    FUNCTION = "display_text"
+    CATEGORY = "ComfyAssets/👁️ Display"
+
+    DESCRIPTION = """
+Displays text in the UI with a copy-to-clipboard feature.
+
+Features:
+- Shows text content in a readable format
+- Copy button appears on hover
+- Passes text through for chaining
+"""
+
+    def display_text(self, text):
+        """Display the text and pass it through.
+
+        Args:
+            text: Input text to display
+
+        Returns:
+            Tuple containing the text
+        """
+        # The actual display happens in the frontend
+        # We just pass the text through
+        return {"ui": {"text": [text]}, "result": (text,)}
+
+
+# Node display name
+NODE_DISPLAY_NAME = "Display Text"

kikotools/tools/empty_latent_batch/__init__.py

@@ -0,0 +1,5 @@
+"""Empty Latent Batch tool for ComfyUI."""
+
+from .node import EmptyLatentBatchNode
+
+__all__ = ["EmptyLatentBatchNode"]

kikotools/tools/empty_latent_batch/logic.py

@@ -0,0 +1,101 @@
+"""Logic for creating empty latent tensors with batch support."""
+
+import torch
+from typing import Dict, Tuple
+
+
+def create_empty_latent_batch(
+    width: int, height: int, batch_size: int = 1
+) -> Dict[str, torch.Tensor]:
+    """
+    Create empty latent tensor with batch support.
+
+    Args:
+        width: Width in pixels (will be divided by 8 for latent space)
+        height: Height in pixels (will be divided by 8 for latent space)
+        batch_size: Number of latents in the batch
+
+    Returns:
+        Dictionary containing the latent samples tensor
+
+    Raises:
+        ValueError: If dimensions are invalid
+    """
+    # Validate inputs
+    if width <= 0 or height <= 0:
+        raise ValueError(f"Width and height must be positive, got {width}x{height}")
+
+    if batch_size <= 0:
+        raise ValueError(f"Batch size must be positive, got {batch_size}")
+
+    # Ensure dimensions are divisible by 8 (VAE requirement)
+    if width % 8 != 0 or height % 8 != 0:
+        raise ValueError(
+            f"Width and height must be divisible by 8, got {width}x{height}"
+        )
+
+    # Convert pixel dimensions to latent space (divide by 8)
+    latent_width = width // 8
+    latent_height = height // 8
+
+    # Create empty latent tensor
+    # ComfyUI latent format: [batch, channels, height, width]
+    # Standard VAE uses 4 channels
+    latent_tensor = torch.zeros(batch_size, 4, latent_height, latent_width)
+
+    return {"samples": latent_tensor}
+
+
+def validate_dimensions(width: int, height: int) -> bool:
+    """
+    Validate that dimensions are suitable for latent creation.
+
+    Args:
+        width: Width in pixels
+        height: Height in pixels
+
+    Returns:
+        True if dimensions are valid
+    """
+    # Check basic constraints
+    if width <= 0 or height <= 0:
+        return False
+
+    # Check divisibility by 8
+    if width % 8 != 0 or height % 8 != 0:
+        return False
+
+    # Check reasonable size limits (64x64 to 8192x8192)
+    if width < 64 or height < 64:
+        return False
+
+    if width > 8192 or height > 8192:
+        return False
+
+    return True
+
+
+def sanitize_dimensions(width: int, height: int) -> Tuple[int, int]:
+    """
+    Sanitize dimensions to ensure they meet latent requirements.
+
+    Args:
+        width: Input width
+        height: Input height
+
+    Returns:
+        Tuple of (sanitized_width, sanitized_height)
+    """
+    # Ensure minimum dimensions
+    width = max(64, width)
+    height = max(64, height)
+
+    # Ensure maximum dimensions
+    width = min(8192, width)
+    height = min(8192, height)
+
+    # Round to nearest multiple of 8
+    width = (width + 7) // 8 * 8
+    height = (height + 7) // 8 * 8
+
+    return width, height

kikotools/tools/empty_latent_batch/node.py

@@ -0,0 +1,309 @@
+"""Empty Latent Batch node for ComfyUI."""
+
+import torch
+from typing import Dict, Tuple
+
+from ...base.base_node import ComfyAssetsBaseNode
+from .logic import (
+    create_empty_latent_batch,
+    validate_dimensions,
+    sanitize_dimensions,
+)
+from ..width_height_selector.logic import get_preset_dimensions
+from ..width_height_selector.presets import (
+    PRESET_OPTIONS,
+    PRESET_METADATA,
+)
+
+
+class EmptyLatentBatchNode(ComfyAssetsBaseNode):
+    """
+    Empty Latent Batch node for creating empty latent tensors with batch support.
+
+    Creates empty latent tensors with specified dimensions and batch size,
+    compatible with ComfyUI's latent format for use with VAE and diffusion models.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define the input types for the ComfyUI node."""
+        # Create formatted preset options with metadata
+        preset_options = ["custom"]  # Custom first
+
+        # Add formatted presets with metadata
+        for preset_name in PRESET_OPTIONS.keys():
+            if preset_name != "custom":
+                metadata = PRESET_METADATA.get(preset_name)
+                if metadata:
+                    formatted_option = (
+                        f"{preset_name} - {metadata.aspect_ratio} "
+                        f"({metadata.megapixels:.1f}MP) - {metadata.model_group}"
+                    )
+                    preset_options.append(formatted_option)
+                else:
+                    preset_options.append(preset_name)
+
+        return {
+            "required": {
+                "preset": (
+                    preset_options,
+                    {
+                        "default": "custom",
+                        "tooltip": "Select from optimized resolution presets or use "
+                        "custom dimensions. SDXL presets are ~1MP, FLUX presets are "
+                        "higher resolution, Ultra-wide presets support modern "
+                        "aspect ratios.",
+                    },
+                ),
+                "width": (
+                    "INT",
+                    {
+                        "default": 1024,
+                        "min": 64,
+                        "max": 8192,
+                        "step": 8,
+                        "tooltip": "Custom width in pixels (must be multiple of 8). "
+                        "Used when preset is 'custom' or as fallback for invalid "
+                        "presets. This will be converted to latent space dimensions.",
+                    },
+                ),
+                "height": (
+                    "INT",
+                    {
+                        "default": 1024,
+                        "min": 64,
+                        "max": 8192,
+                        "step": 8,
+                        "tooltip": "Custom height in pixels (must be multiple of 8). "
+                        "Used when preset is 'custom' or as fallback for invalid "
+                        "presets. This will be converted to latent space dimensions.",
+                    },
+                ),
+                "batch_size": (
+                    "INT",
+                    {
+                        "default": 1,
+                        "min": 1,
+                        "max": 64,
+                        "step": 1,
+                        "tooltip": "Number of empty latents to create in the batch. "
+                        "Useful for batch processing workflows.",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT", "INT", "INT")
+    RETURN_NAMES = ("latent", "width", "height")
+    FUNCTION = "create_empty_latent"
+    CATEGORY = "ComfyAssets/📦 Latents"
+
+    def create_empty_latent(
+        self, preset: str, width: int, height: int, batch_size: int
+    ) -> Tuple[Dict[str, torch.Tensor], int, int]:
+        """
+        Create empty latent tensor with specified dimensions and batch size.
+
+        Args:
+            preset: Selected preset name or formatted preset string
+            width: Custom width value
+            height: Custom height value
+            batch_size: Number of latents in the batch
+
+        Returns:
+            Tuple containing (latent dictionary with 'samples' tensor, width, height)
+        """
+        try:
+            # Extract original preset name from formatted string if needed
+            original_preset = self._extract_preset_name(preset)
+
+            # Get base dimensions from preset or custom input
+            base_width, base_height = get_preset_dimensions(
+                original_preset, width, height
+            )
+
+            # Sanitize dimensions to ensure they meet requirements
+            final_width, final_height = sanitize_dimensions(base_width, base_height)
+
+            # Log if dimensions were changed from the base dimensions
+            if final_width != base_width or final_height != base_height:
+                self.log_info(
+                    f"Dimensions adjusted from {base_width}×{base_height} to "
+                    f"{final_width}×{final_height} to meet VAE requirements"
+                )
+
+            # Validate final dimensions
+            if not validate_dimensions(final_width, final_height):
+                self.handle_error(
+                    f"Invalid dimensions after sanitization: {final_width}×{final_height}"
+                )
+
+            # Validate batch size
+            if batch_size <= 0:
+                self.handle_error(f"Batch size must be positive, got {batch_size}")
+
+            if batch_size > 64:
+                self.log_info(
+                    f"Large batch size ({batch_size}) may use significant memory"
+                )
+
+            # Create the empty latent batch
+            latent_dict = create_empty_latent_batch(
+                final_width, final_height, batch_size
+            )
+
+            # Log the operation
+            latent_height = final_height // 8
+            latent_width = final_width // 8
+            self.log_info(
+                f"Created empty latent batch: {batch_size}×4×{latent_height}×{latent_width} "
+                f"(pixel dims: {final_width}×{final_height})"
+            )
+
+            return (latent_dict, final_width, final_height)
+
+        except Exception as e:
+            # Handle any unexpected errors gracefully
+            error_msg = f"Error creating empty latent batch: {str(e)}"
+            self.handle_error(error_msg, e)
+
+    def _extract_preset_name(self, formatted_preset: str) -> str:
+        """
+        Extract the original preset name from a formatted preset string.
+
+        Args:
+            formatted_preset: Either original preset name or formatted string
+
+        Returns:
+            Original preset name
+        """
+        # If it's already "custom", return as-is
+        if formatted_preset == "custom":
+            return formatted_preset
+
+        # If it contains formatting metadata, extract the resolution part
+        if " - " in formatted_preset:
+            # Format is: "1024×1024 - 1:1 (1.0MP) - SDXL"
+            # Extract the first part (resolution)
+            resolution_part = formatted_preset.split(" - ")[0]
+
+            # Verify this is a valid preset name
+            if resolution_part in PRESET_OPTIONS:
+                return resolution_part
+
+        # If no formatting or not found, check if it's directly a valid preset
+        if formatted_preset in PRESET_OPTIONS:
+            return formatted_preset
+
+        # Default to "custom" if we can't parse it
+        return "custom"
+
+    def validate_inputs(
+        self, preset: str, width: int, height: int, batch_size: int
+    ) -> bool:
+        """
+        Validate node inputs.
+
+        Args:
+            preset: Preset name or formatted preset string
+            width: Width value
+            height: Height value
+            batch_size: Batch size value
+
+        Returns:
+            True if inputs are valid
+        """
+        # Extract original preset name
+        original_preset = self._extract_preset_name(preset)
+
+        # Check if preset exists or is custom
+        if original_preset != "custom" and original_preset not in PRESET_OPTIONS:
+            return False
+
+        # Get dimensions from preset or use custom
+        base_width, base_height = get_preset_dimensions(original_preset, width, height)
+
+        # Check dimension validity (after sanitization)
+        sanitized_width, sanitized_height = sanitize_dimensions(base_width, base_height)
+        if not validate_dimensions(sanitized_width, sanitized_height):
+            return False
+
+        # Check batch size
+        if batch_size <= 0 or batch_size > 64:
+            return False
+
+        return True
+
+    def get_latent_info(self, width: int, height: int, batch_size: int) -> str:
+        """
+        Get descriptive information about the latent that will be created.
+
+        Args:
+            width: Width in pixels
+            height: Height in pixels
+            batch_size: Batch size
+
+        Returns:
+            Description string for the latent
+        """
+        sanitized_width, sanitized_height = sanitize_dimensions(width, height)
+        latent_width = sanitized_width // 8
+        latent_height = sanitized_height // 8
+
+        return (
+            f"Empty latent batch: {batch_size} × 4 × {latent_height} × {latent_width} "
+            f"(pixel dimensions: {sanitized_width}×{sanitized_height})"
+        )
+
+    def get_memory_estimate(self, width: int, height: int, batch_size: int) -> str:
+        """
+        Estimate memory usage for the latent batch.
+
+        Args:
+            width: Width in pixels
+            height: Height in pixels
+            batch_size: Batch size
+
+        Returns:
+            Memory estimate string
+        """
+        sanitized_width, sanitized_height = sanitize_dimensions(width, height)
+        latent_width = sanitized_width // 8
+        latent_height = sanitized_height // 8
+
+        # Calculate tensor size in bytes (float32 = 4 bytes per element)
+        elements = batch_size * 4 * latent_height * latent_width
+        bytes_size = elements * 4  # 4 bytes per float32
+
+        # Convert to human-readable format
+        if bytes_size < 1024:
+            return f"{bytes_size} bytes"
+        elif bytes_size < 1024 * 1024:
+            return f"{bytes_size / 1024:.1f} KB"
+        elif bytes_size < 1024 * 1024 * 1024:
+            return f"{bytes_size / (1024 * 1024):.1f} MB"
+        else:
+            return f"{bytes_size / (1024 * 1024 * 1024):.1f} GB"
+
+    def __str__(self) -> str:
+        """String representation of the node."""
+        return "EmptyLatentBatchNode"
+
+    def __repr__(self) -> str:
+        """Detailed string representation of the node."""
+        return (
+            f"EmptyLatentBatchNode("
+            f"category='{self.CATEGORY}', "
+            f"function='{self.FUNCTION}'"
+            f")"
+        )
+
+
+# Node class mappings for ComfyUI registration
+NODE_CLASS_MAPPINGS = {
+    "EmptyLatentBatch": EmptyLatentBatchNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "EmptyLatentBatch": "Empty Latent Batch",
+}

kikotools/tools/gemini_prompt/.gemini_models_cache.json

@@ -0,0 +1,89 @@
+{
+  "models": [
+    "gemini-2.5-pro",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "learnlm-2.0-flash-experimental",
+    "gemini-1.5-pro-latest",
+    "gemini-1.5-pro-002",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash-latest",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-002",
+    "gemini-1.5-flash-8b",
+    "gemini-1.5-flash-8b-001",
+    "gemini-1.5-flash-8b-latest",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e4b-it",
+    "gemma-3n-e2b-it",
+    "gemini-exp-1206"
+  ],
+  "descriptions": {
+    "gemini-1.5-pro-latest": "Gemini 1.5 Pro Latest",
+    "gemini-1.5-pro-002": "Gemini 1.5 Pro 002",
+    "gemini-1.5-pro": "Gemini 1.5 Pro",
+    "gemini-1.5-flash-latest": "Gemini 1.5 Flash Latest",
+    "gemini-1.5-flash": "Gemini 1.5 Flash",
+    "gemini-1.5-flash-002": "Gemini 1.5 Flash 002",
+    "gemini-1.5-flash-8b": "Gemini 1.5 Flash-8B",
+    "gemini-1.5-flash-8b-001": "Gemini 1.5 Flash-8B 001",
+    "gemini-1.5-flash-8b-latest": "Gemini 1.5 Flash-8B Latest",
+    "gemini-2.5-pro-preview-03-25": "Gemini 2.5 Pro Preview 03-25",
+    "gemini-2.5-flash-preview-05-20": "Gemini 2.5 Flash Preview 05-20",
+    "gemini-2.5-flash": "Gemini 2.5 Flash",
+    "gemini-2.5-flash-lite-preview-06-17": "Gemini 2.5 Flash-Lite Preview 06-17",
+    "gemini-2.5-pro-preview-05-06": "Gemini 2.5 Pro Preview 05-06",
+    "gemini-2.5-pro-preview-06-05": "Gemini 2.5 Pro Preview",
+    "gemini-2.5-pro": "Gemini 2.5 Pro",
+    "gemini-2.0-flash-exp": "Gemini 2.0 Flash Experimental",
+    "gemini-2.0-flash": "Gemini 2.0 Flash",
+    "gemini-2.0-flash-001": "Gemini 2.0 Flash 001",
+    "gemini-2.0-flash-exp-image-generation": "Gemini 2.0 Flash (Image Generation) Experimental",
+    "gemini-2.0-flash-lite-001": "Gemini 2.0 Flash-Lite 001",
+    "gemini-2.0-flash-lite": "Gemini 2.0 Flash-Lite",
+    "gemini-2.0-flash-preview-image-generation": "Gemini 2.0 Flash Preview Image Generation",
+    "gemini-2.0-flash-lite-preview-02-05": "Gemini 2.0 Flash-Lite Preview 02-05",
+    "gemini-2.0-flash-lite-preview": "Gemini 2.0 Flash-Lite Preview",
+    "gemini-2.0-pro-exp": "Gemini 2.0 Pro Experimental",
+    "gemini-2.0-pro-exp-02-05": "Gemini 2.0 Pro Experimental 02-05",
+    "gemini-exp-1206": "Gemini Experimental 1206",
+    "gemini-2.0-flash-thinking-exp-01-21": "Gemini 2.5 Flash Preview 05-20",
+    "gemini-2.0-flash-thinking-exp": "Gemini 2.5 Flash Preview 05-20",
+    "gemini-2.0-flash-thinking-exp-1219": "Gemini 2.5 Flash Preview 05-20",
+    "gemini-2.5-flash-preview-tts": "Gemini 2.5 Flash Preview TTS",
+    "gemini-2.5-pro-preview-tts": "Gemini 2.5 Pro Preview TTS",
+    "learnlm-2.0-flash-experimental": "LearnLM 2.0 Flash Experimental",
+    "gemma-3-1b-it": "Gemma 3 1B",
+    "gemma-3-4b-it": "Gemma 3 4B",
+    "gemma-3-12b-it": "Gemma 3 12B",
+    "gemma-3-27b-it": "Gemma 3 27B",
+    "gemma-3n-e4b-it": "Gemma 3n E4B",
+    "gemma-3n-e2b-it": "Gemma 3n E2B",
+    "gemini-2.5-flash-lite": "Gemini 2.5 Flash-Lite"
+  },
+  "timestamp": 1754568195.1098156
+}

kikotools/tools/gemini_prompt/__init__.py

@@ -0,0 +1,5 @@
+"""Gemini Prompt Engineer node for ComfyUI."""
+
+from .node import GeminiPromptNode
+
+__all__ = ["GeminiPromptNode"]

kikotools/tools/gemini_prompt/logic.py

@@ -0,0 +1,163 @@
+"""Logic for Gemini API integration and prompt generation."""
+
+import base64
+import io
+import json
+import os
+from typing import Optional, Tuple
+
+import numpy as np
+from PIL import Image
+
+from .prompts import PROMPT_TEMPLATES
+
+
+def tensor_to_pil(tensor: np.ndarray) -> Image.Image:
+    """Convert ComfyUI tensor to PIL Image.
+
+    Args:
+        tensor: Input tensor in ComfyUI format (B, H, W, C)
+
+    Returns:
+        PIL Image object
+    """
+    # ComfyUI tensors are in [0, 1] range
+    if tensor.ndim == 4:
+        # Take first image from batch
+        tensor = tensor[0]
+
+    # Convert to uint8
+    image_array = (tensor * 255).astype(np.uint8)
+
+    # Convert to PIL
+    return Image.fromarray(image_array, mode="RGB")
+
+
+def image_to_base64(image: Image.Image, format: str = "PNG") -> str:
+    """Convert PIL Image to base64 string.
+
+    Args:
+        image: PIL Image object
+        format: Image format (PNG or JPEG)
+
+    Returns:
+        Base64 encoded string
+    """
+    buffer = io.BytesIO()
+    image.save(buffer, format=format)
+    buffer.seek(0)
+    return base64.b64encode(buffer.read()).decode("utf-8")
+
+
+def get_api_key() -> Optional[str]:
+    """Get Gemini API key from environment or config.
+
+    Returns:
+        API key string or None if not found
+    """
+    # Check environment variable first
+    api_key = os.environ.get("GEMINI_API_KEY")
+
+    if not api_key:
+        # Check for config file in ComfyUI directory
+        try:
+            config_path = os.path.join(
+                os.path.dirname(__file__), "..", "..", "..", "gemini_config.json"
+            )
+            if os.path.exists(config_path):
+                with open(config_path, "r") as f:
+                    config = json.load(f)
+                    api_key = config.get("api_key")
+        except Exception:
+            pass
+
+    return api_key
+
+
+def analyze_image_with_gemini(
+    image: np.ndarray,
+    prompt_type: str,
+    api_key: Optional[str] = None,
+    custom_prompt: Optional[str] = None,
+    model_name: str = "gemini-1.5-flash",
+) -> Tuple[str, Optional[str]]:
+    """Analyze image using Gemini API and generate appropriate prompt.
+
+    Args:
+        image: Input image tensor
+        prompt_type: Type of prompt to generate (flux, sdxl, danbooru, video)
+        api_key: Gemini API key (optional, will try to get from env/config)
+        custom_prompt: Custom system prompt to use instead of templates
+        model_name: Gemini model to use (default: gemini-1.5-flash)
+
+    Returns:
+        Tuple of (generated_prompt, error_message)
+    """
+    # Get API key
+    if not api_key:
+        api_key = get_api_key()
+
+    if not api_key:
+        return (
+            "",
+            "Gemini API key not found. Please set GEMINI_API_KEY environment variable or provide it in the node.",
+        )
+
+    # Convert tensor to PIL image
+    try:
+        pil_image = tensor_to_pil(image)
+    except Exception as e:
+        return "", f"Failed to convert image: {str(e)}"
+
+    # Get system prompt
+    if custom_prompt:
+        system_prompt = custom_prompt
+    else:
+        system_prompt = PROMPT_TEMPLATES.get(prompt_type, PROMPT_TEMPLATES["flux"])
+
+    # Here we would normally make the API call to Gemini
+    # For now, we'll import the google-generativeai library
+    try:
+        import google.generativeai as genai
+    except ImportError:
+        return (
+            "",
+            "google-generativeai library not installed. Please run: pip install google-generativeai",
+        )
+
+    try:
+        # Configure Gemini
+        genai.configure(api_key=api_key)
+
+        # Create model
+        model = genai.GenerativeModel(model_name)
+
+        # Generate content
+        response = model.generate_content(
+            [
+                system_prompt,
+                pil_image,
+                "Analyze this image and generate an appropriate prompt according to the instructions.",
+            ]
+        )
+
+        # Extract text from response
+        if response.text:
+            return response.text.strip(), None
+        else:
+            return "", "No response generated from Gemini"
+
+    except Exception as e:
+        return "", f"Gemini API error: {str(e)}"
+
+
+def validate_prompt_type(prompt_type: str) -> bool:
+    """Validate if prompt type is supported.
+
+    Args:
+        prompt_type: Type of prompt to validate
+
+    Returns:
+        True if valid, False otherwise
+    """
+    return prompt_type in PROMPT_TEMPLATES

kikotools/tools/gemini_prompt/models.py

@@ -0,0 +1,191 @@
+"""Dynamic model fetching and caching for Gemini API."""
+
+import json
+import os
+import time
+from typing import List, Dict, Optional, Tuple
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Cache settings
+CACHE_DURATION = 3600 * 24  # 24 hours in seconds
+CACHE_FILE = os.path.join(os.path.dirname(__file__), ".gemini_models_cache.json")
+
+
+def get_available_models(
+    api_key: Optional[str] = None, silent: bool = False
+) -> Tuple[List[str], Dict[str, str]]:
+    """Fetch available Gemini models that support generateContent.
+
+    Args:
+        api_key: Optional API key. If not provided, will try to get from environment.
+        silent: If True, suppress error logging (useful for initial load).
+
+    Returns:
+        Tuple of (model_names_list, model_descriptions_dict)
+    """
+    # Check cache first
+    cached_data = _load_cache()
+    if cached_data:
+        return cached_data["models"], cached_data["descriptions"]
+
+    # Try to fetch from API
+    try:
+        models, descriptions = _fetch_models_from_api(api_key, silent=silent)
+        if models:
+            _save_cache(models, descriptions)
+            return models, descriptions
+    except Exception as e:
+        if not silent:
+            logger.warning(f"Failed to fetch models from API: {e}")
+
+    # Fall back to defaults
+    from .prompts import DEFAULT_GEMINI_MODELS
+
+    return DEFAULT_GEMINI_MODELS, {}
+
+
+def _fetch_models_from_api(
+    api_key: Optional[str] = None, silent: bool = False
+) -> Tuple[List[str], Dict[str, str]]:
+    """Fetch models from Gemini API.
+
+    Args:
+        api_key: Optional API key.
+        silent: If True, suppress error logging.
+
+    Returns:
+        Tuple of (model_names_list, model_descriptions_dict)
+    """
+    try:
+        import google.generativeai as genai
+    except ImportError:
+        if not silent:
+            logger.error("google-generativeai not installed")
+        return [], {}
+
+    # Get API key
+    if not api_key:
+        from .logic import get_api_key
+
+        api_key = get_api_key()
+
+    if not api_key:
+        if not silent:
+            logger.debug("No API key available for fetching models")
+        return [], {}
+
+    try:
+        genai.configure(api_key=api_key)
+
+        models = []
+        descriptions = {}
+
+        # Fetch all models
+        for model in genai.list_models():
+            # Only include models that support generateContent
+            if "generateContent" in model.supported_generation_methods:
+                # Remove "models/" prefix from name
+                model_name = model.name.replace("models/", "")
+                models.append(model_name)
+                descriptions[model_name] = model.display_name
+
+        # Sort models by priority (newer versions first)
+        models = _sort_models(models)
+
+        return models, descriptions
+
+    except Exception as e:
+        if not silent:
+            logger.error(f"Error fetching models from API: {e}")
+        return [], {}
+
+
+def _sort_models(models: List[str]) -> List[str]:
+    """Sort models by version and capability.
+
+    Prioritizes:
+    1. Newer versions (2.5 > 2.0 > 1.5)
+    2. Non-experimental models
+    3. Flash models for general use
+    """
+
+    def sort_key(model: str):
+        # Priority scoring
+        score = 0
+
+        # Version priority
+        if "2.5" in model:
+            score += 1000
+        elif "2.0" in model:
+            score += 800
+        elif "1.5" in model:
+            score += 600
+
+        # Model type priority
+        if "pro" in model and "preview" not in model and "exp" not in model:
+            score += 100
+        elif "flash" in model and "preview" not in model and "exp" not in model:
+            score += 90
+
+        # Penalize experimental/preview models
+        if "exp" in model or "experimental" in model:
+            score -= 50
+        if "preview" in model:
+            score -= 30
+
+        # Penalize specific variants
+        if "thinking" in model:
+            score -= 100
+        if "tts" in model:
+            score -= 100
+        if "lite" in model:
+            score -= 20
+
+        return -score  # Negative for descending sort
+
+    return sorted(models, key=sort_key)
+
+
+def _load_cache() -> Optional[Dict]:
+    """Load cached model data if available and not expired."""
+    if not os.path.exists(CACHE_FILE):
+        return None
+
+    try:
+        with open(CACHE_FILE, "r") as f:
+            data = json.load(f)
+
+        # Check if cache is expired
+        if time.time() - data.get("timestamp", 0) > CACHE_DURATION:
+            return None
+
+        return data
+    except Exception as e:
+        logger.warning(f"Failed to load cache: {e}")
+        return None
+
+
+def _save_cache(models: List[str], descriptions: Dict[str, str]) -> None:
+    """Save model data to cache."""
+    try:
+        data = {
+            "models": models,
+            "descriptions": descriptions,
+            "timestamp": time.time(),
+        }
+
+        with open(CACHE_FILE, "w") as f:
+            json.dump(data, f, indent=2)
+    except Exception as e:
+        logger.warning(f"Failed to save cache: {e}")
+
+
+def clear_cache() -> None:
+    """Clear the model cache."""
+    if os.path.exists(CACHE_FILE):
+        try:
+            os.remove(CACHE_FILE)
+        except Exception as e:
+            logger.warning(f"Failed to clear cache: {e}")

kikotools/tools/gemini_prompt/node.py

@@ -0,0 +1,169 @@
+"""Gemini Prompt Engineer node implementation."""
+
+import torch
+
+from ...base import ComfyAssetsBaseNode
+
+from .logic import analyze_image_with_gemini, validate_prompt_type
+from .prompts import PROMPT_OPTIONS, DEFAULT_GEMINI_MODELS
+from .models import get_available_models
+
+
+class GeminiPromptNode(ComfyAssetsBaseNode):
+    """Analyzes images using Gemini AI to generate optimized prompts for various AI models."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define input types for the node."""
+        # Get available models dynamically (silent mode for initial load)
+        models, _ = get_available_models(silent=True)
+
+        # Use default if no models available
+        if not models:
+            models = DEFAULT_GEMINI_MODELS
+
+        # Find best default model
+        default_model = models[0] if models else "gemini-2.5-flash"
+
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "prompt_type": (PROMPT_OPTIONS, {"default": "flux"}),
+                "model": (models, {"default": default_model}),
+            },
+            "optional": {
+                "api_key": ("STRING", {"default": "", "multiline": False}),
+                "custom_prompt": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "placeholder": "Optional: Enter custom system prompt instead of using templates",
+                    },
+                ),
+                "refresh_models": (
+                    "BOOLEAN",
+                    {"default": False, "label_on": "Refresh", "label_off": "Skip"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING")
+    RETURN_NAMES = ("prompt", "negative_prompt")
+    FUNCTION = "generate_prompt"
+    CATEGORY = "ComfyAssets/🧠 Prompts"
+
+    DESCRIPTION = """
+Analyzes images using Google's Gemini AI to generate optimized prompts.
+
+Supports multiple prompt formats:
+- FLUX: Detailed artistic prompts with quality markers
+- SDXL: Positive/negative prompt pairs with weight emphasis
+- Danbooru: Anime-style booru tags with underscores
+- Video: Motion and temporal descriptions for video generation
+
+Requires Gemini API key (set GEMINI_API_KEY env var or provide in node).
+Install: pip install google-generativeai
+"""
+
+    def generate_prompt(
+        self,
+        image,
+        prompt_type,
+        model,
+        api_key="",
+        custom_prompt="",
+        refresh_models=False,
+    ):
+        """Generate prompt from image using Gemini.
+
+        Args:
+            image: Input image tensor
+            prompt_type: Type of prompt to generate
+            model: Gemini model to use
+            api_key: Optional API key
+            custom_prompt: Optional custom system prompt
+            refresh_models: Whether to refresh the model list
+
+        Returns:
+            Tuple of (prompt, negative_prompt)
+        """
+        # Refresh models if requested
+        if refresh_models and api_key:
+            try:
+                from .models import clear_cache
+
+                # Clear cache to force refresh on next node creation
+                clear_cache()
+                print(
+                    "Model cache cleared. Please recreate the node to see updated models."
+                )
+            except Exception as e:
+                print(f"Failed to clear model cache: {e}")
+
+        # Validate prompt type
+        if not validate_prompt_type(prompt_type):
+            raise ValueError(f"Invalid prompt type: {prompt_type}")
+
+        # Convert torch tensor to numpy if needed
+        if isinstance(image, torch.Tensor):
+            image_np = image.cpu().numpy()
+        else:
+            image_np = image
+
+        # If API key is provided, try to refresh model list in background
+        if api_key:
+            try:
+                from .models import get_available_models
+
+                # Try to get fresh models with the provided API key
+                fresh_models, _ = get_available_models(api_key=api_key, silent=True)
+                if fresh_models and fresh_models != DEFAULT_GEMINI_MODELS:
+                    # Models were successfully fetched with this API key
+                    pass
+            except Exception:
+                pass
+
+        # Analyze image with Gemini
+        prompt, error = analyze_image_with_gemini(
+            image_np,
+            prompt_type,
+            api_key=api_key or None,
+            custom_prompt=custom_prompt or None,
+            model_name=model,
+        )
+
+        if error:
+            # Return error as prompt for visibility
+            return (f"Error: {error}", "")
+
+        # Handle different prompt types
+        if prompt_type == "sdxl":
+            # SDXL returns positive and negative prompts
+            lines = prompt.split("\n")
+            positive_prompt = ""
+            negative_prompt = ""
+
+            for line in lines:
+                if line.lower().startswith("positive:"):
+                    positive_prompt = (
+                        line.replace("Positive:", "").replace("positive:", "").strip()
+                    )
+                elif line.lower().startswith("negative:"):
+                    negative_prompt = (
+                        line.replace("Negative:", "").replace("negative:", "").strip()
+                    )
+
+            # If format not found, assume entire response is positive prompt
+            if not positive_prompt:
+                positive_prompt = prompt
+
+            return (positive_prompt, negative_prompt)
+
+        else:
+            # Other formats don't use negative prompts
+            return (prompt, "")
+
+
+# Node display name
+NODE_DISPLAY_NAME = "Gemini Prompt Engineer"

kikotools/tools/gemini_prompt/prompts.py

@@ -0,0 +1,128 @@
+"""System prompts for different AI model types."""
+
+FLUX_PROMPT = """You are an expert FLUX prompt engineer. Analyze the provided image and generate ONLY a FLUX prompt - no explanations, analysis, or additional text.
+
+FLUX uses natural language descriptions, not comma-separated tags. Write a detailed, flowing description that reads like you're explaining the image to someone.
+
+Include these elements in your description:
+- Main subject with specific details (appearance, clothing, expression, pose)
+- Environment and background details
+- Lighting conditions and atmosphere
+- Artistic style or photographic approach
+- Color palette and mood
+- Technical details if relevant (camera angle, focal length, etc.)
+- Textures and materials
+
+Write in a natural, descriptive style. Use complete sentences that flow together. Be specific and detailed but maintain readability.
+
+IMPORTANT: Return ONLY the prompt text. No analysis, headers, or additional commentary. Just the natural language description that can be directly used in FLUX.
+
+Example of correct output:
+A close-up portrait of a middle-aged woman with curly red hair and green eyes, wearing a blue silk blouse. She has a warm smile and freckles across her cheeks. The lighting is soft and natural, coming from a window to her left, creating gentle shadows that accentuate her features. The background is softly blurred, showing hints of a cozy bookshelf. The overall mood is warm and inviting, captured in a photorealistic style with shallow depth of field."""
+
+SDXL_PROMPT = """You are an expert prompt engineer specializing in SDXL (Stable Diffusion XL). Your task is to generate high-quality positive and negative prompts that conform to SDXL prompt formatting standards.
+
+Your expertise includes:
+- Leveraging community-tested techniques (ComfyUI, A1111, InvokeAI)
+- Applying photographic theory for realism, composition, lighting
+- Following Civitai trend standards and style best practices
+- Mastering Pony Diffusion XL formatting for stylized and anime content
+
+Structure prompts in this layered, modular format:
+[Main Subject], [Pose & Camera], [Lighting & Environment], [Style & Details], [Boost Terms], [Style References]
+
+For SDXL specifically:
+- Use quality boosters: 8k, RAW photo, masterpiece, ultra detailed, cinematic lighting
+- Prioritize realism and artistry
+- Excellent for portraits, landscapes, or cinematic scenes
+
+Instructions:
+
+Only reply with two fields:
+Positive prompt: (Your positive prompt here)
+Negative prompt: (Your negative prompt here)
+
+Do not include any commentary or explanation.
+
+Use concise, highly descriptive language that maximizes visual richness.
+
+Follow SDXL prompt conventions: prioritize subject clarity, camera perspective, lighting, mood, style tags, and composition.
+
+Keep total token length efficient (ideally under 250 tokens).
+
+Avoid redundancy and generic filler words.
+
+Focus on crafting super high-quality prompts for stunning visual output.
+
+Example Input:
+A futuristic cyberpunk samurai standing on a neon-lit rooftop in the rain.
+
+Example Output:
+Positive prompt: cyberpunk samurai, neon-lit rooftop, dramatic rain, glowing katana, futuristic cityscape, night scene, cinematic lighting, intense expression, sleek cyber armor, atmospheric depth, ultra-detailed, masterpiece, 8k, sharp focus, trending on artstation
+Negative prompt: blurry, low quality, poorly drawn, extra limbs, bad anatomy, deformed hands, text, watermark, jpeg artifacts, duplicate, cropped, out of frame
+"""
+
+DANBOORU_PROMPT = """You are a Danbooru tagging expert specializing in anime-style image tagging. Analyze the image and generate ONLY Danbooru-style tags - no explanations or analysis.
+
+CRITICAL: Use strict Danbooru conventions:
+- Use underscores for multi-word tags (e.g., long_hair, school_uniform)
+- All tags must be lowercase
+- Character count comes first (1girl, 2boys, multiple_girls)
+- For anime models trained on Danbooru data, proper tagging is essential
+
+Tag order and categories:
+1. Character count (1girl, solo, 2boys, etc.)
+2. Character features (hair_color, eye_color, hair_length)
+3. Expression/pose (smile, looking_at_viewer, sitting)
+4. Clothing (specific items with underscores)
+5. Background/setting (simple_background, outdoors, classroom)
+6. View/composition (upper_body, full_body, from_side)
+7. Quality tags (masterpiece, best_quality, highres)
+
+Common quality prefix for anime models:
+"masterpiece, best_quality, very_aesthetic"
+
+IMPORTANT: Return ONLY the comma-separated tags. Use underscores, not spaces. All lowercase.
+
+Example of correct output:
+1girl, solo, long_hair, blue_eyes, blonde_hair, school_uniform, serafuku, pleated_skirt, smile, looking_at_viewer, classroom, sitting, desk, window, sunlight, upper_body, masterpiece, best_quality"""
+
+VIDEO_PROMPT = """You are a WAN 2.2 video generation prompt specialist. Analyze the content and generate ONLY a video generation prompt optimized for WAN 2.2 - no explanations or analysis.
+
+WAN 2.2 excels with rich, descriptive prompts that focus on:
+- Visual composition and scene elements
+- Specific movements and actions
+- Lighting and aesthetic details
+- Cinematographic elements
+
+Write a single detailed paragraph describing the video scene. Focus on:
+- Main subjects and their actions
+- Visual style and atmosphere
+- Movement dynamics (use words like "intensely", "smoothly", "rapidly")
+- Environmental details and lighting
+- Specific visual elements and their interactions
+
+Keep the prompt descriptive but concise. WAN 2.2 works best with natural language that paints a clear picture of the desired video.
+
+IMPORTANT: Return ONLY the video prompt as a single descriptive paragraph. No analysis, headers, or additional text.
+
+Example of correct output:
+Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage, their movements fluid and dynamic as they exchange rapid punches under dramatic theater lighting that casts long shadows across the ring, with the crowd visible as blurred silhouettes in the darkened background."""
+
+PROMPT_TEMPLATES = {
+    "flux": FLUX_PROMPT,
+    "sdxl": SDXL_PROMPT,
+    "danbooru": DANBOORU_PROMPT,
+    "video": VIDEO_PROMPT,
+}
+
+PROMPT_OPTIONS = ["flux", "sdxl", "danbooru", "video"]
+
+# Default models list (fallback if API is unavailable)
+DEFAULT_GEMINI_MODELS = [
+    "gemini-2.5-flash",
+    "gemini-2.5-pro",
+    "gemini-2.0-flash",
+    "gemini-1.5-flash",
+    "gemini-1.5-pro",
+]

kikotools/tools/image_scale_down_by/__init__.py

@@ -0,0 +1,5 @@
+"""Image Scale Down By tool for ComfyUI."""
+
+from .node import ImageScaleDownByNode
+
+__all__ = ["ImageScaleDownByNode"]

kikotools/tools/image_scale_down_by/logic.py

@@ -0,0 +1,40 @@
+"""Core logic for ImageScaleDownBy tool."""
+
+import torch.nn.functional as F
+from torch import Tensor
+
+
+def scale_down_image(image: Tensor, scale_by: float) -> Tensor:
+    """Scale down an image by a given factor.
+
+    Args:
+        image: Input image tensor of shape (batch, height, width, channels)
+        scale_by: Scale factor between 0.01 and 1.0
+
+    Returns:
+        Scaled down image tensor
+    """
+    batch, height, width, channels = image.shape
+
+    # Calculate new dimensions
+    new_height = int(height * scale_by)
+    new_width = int(width * scale_by)
+
+    # Ensure minimum size of 1x1
+    new_height = max(1, new_height)
+    new_width = max(1, new_width)
+
+    # Convert from BHWC to BCHW for interpolation
+    image_chw = image.permute(0, 3, 1, 2)
+
+    # Scale down the image using bilinear interpolation
+    scaled = F.interpolate(
+        image_chw,
+        size=(new_height, new_width),
+        mode="bilinear",
+        align_corners=False,
+        antialias=True,
+    )
+
+    # Convert back to BHWC
+    return scaled.permute(0, 2, 3, 1)

kikotools/tools/image_scale_down_by/node.py

@@ -0,0 +1,87 @@
+"""ComfyUI node implementation for ImageScaleDownBy."""
+
+from typing import Dict, Any, Tuple
+
+from torch import Tensor
+
+from ...base import ComfyAssetsBaseNode
+from .logic import scale_down_image
+
+
+class ImageScaleDownByNode(ComfyAssetsBaseNode):
+    """
+    Scales down images by a specified factor.
+
+    Reduces image dimensions proportionally using bilinear interpolation
+    with antialiasing for smooth downscaling.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "scale_by": (
+                    "FLOAT",
+                    {
+                        "default": 0.5,
+                        "min": 0.01,
+                        "max": 1.0,
+                        "step": 0.01,
+                        "display": "number",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    CATEGORY = "ComfyAssets/🖼️ Resolution"
+    RETURN_NAMES = ("images",)
+    FUNCTION = "scale_down"
+
+    def scale_down(self, images: Tensor, scale_by: float) -> Tuple[Tensor]:
+        """
+        Scale down images by the specified factor.
+
+        Args:
+            images: Input image tensor
+            scale_by: Scale factor between 0.01 and 1.0
+
+        Returns:
+            Tuple containing scaled down image tensor
+        """
+        try:
+            self.validate_inputs(images=images, scale_by=scale_by)
+
+            # Scale down the images
+            scaled_images = scale_down_image(images, scale_by)
+
+            _, new_height, new_width, _ = scaled_images.shape
+            _, orig_height, orig_width, _ = images.shape
+
+            self.log_info(
+                f"Scaled down images from {orig_height}x{orig_width} "
+                f"to {new_height}x{new_width} (scale factor: {scale_by})"
+            )
+
+            return (scaled_images,)
+
+        except Exception as e:
+            self.handle_error(f"Failed to scale down images: {str(e)}", e)
+
+    def validate_inputs(self, **kwargs) -> None:
+        """Validate inputs for ImageScaleDownBy node."""
+        images = kwargs.get("images")
+        scale_by = kwargs.get("scale_by")
+
+        if images is None:
+            raise ValueError("Images input is required")
+
+        if not isinstance(images, Tensor) or len(images.shape) != 4:
+            raise ValueError(
+                f"Expected image tensor with shape (batch, height, width, channels), "
+                f"got shape {images.shape if isinstance(images, Tensor) else 'non-tensor'}"
+            )
+
+        if scale_by <= 0 or scale_by > 1.0:
+            raise ValueError(f"scale_by must be between 0.01 and 1.0, got {scale_by}")

kikotools/tools/image_to_multiple_of/__init__.py

@@ -0,0 +1,5 @@
+"""ImageToMultipleOf tool for ComfyUI-KikoTools."""
+
+from .node import ImageToMultipleOfNode
+
+__all__ = ["ImageToMultipleOfNode"]

kikotools/tools/image_to_multiple_of/logic.py

@@ -0,0 +1,61 @@
+"""Core logic for ImageToMultipleOf tool."""
+
+from typing import Tuple
+
+import torch.nn.functional as F
+from torch import Tensor
+
+
+def calculate_dimensions_to_multiple(
+    height: int, width: int, multiple_of: int
+) -> Tuple[int, int]:
+    """Calculate new dimensions that are multiples of the specified value.
+
+    Args:
+        height: Original height
+        width: Original width
+        multiple_of: Value that dimensions should be multiple of
+
+    Returns:
+        Tuple of (new_height, new_width)
+    """
+    new_height = height - (height % multiple_of)
+    new_width = width - (width % multiple_of)
+    return new_height, new_width
+
+
+def process_image_to_multiple_of(
+    image: Tensor, multiple_of: int, method: str
+) -> Tensor:
+    """Process image to ensure dimensions are multiples of specified value.
+
+    Args:
+        image: Input image tensor of shape (batch, height, width, channels)
+        multiple_of: Value that dimensions should be multiple of
+        method: Processing method - "center crop" or "rescale"
+
+    Returns:
+        Processed image tensor
+    """
+    _, height, width, _ = image.shape
+    new_height, new_width = calculate_dimensions_to_multiple(height, width, multiple_of)
+
+    if method == "rescale":
+        # Rescale the image to the new dimensions
+        # Convert from BHWC to BCHW for interpolation
+        image_chw = image.permute(0, 3, 1, 2)
+        rescaled = F.interpolate(
+            image_chw,
+            size=(new_height, new_width),
+            mode="bilinear",
+            align_corners=False,
+        )
+        # Convert back to BHWC
+        return rescaled.permute(0, 2, 3, 1)
+    else:  # center crop
+        # Calculate crop offsets to center the crop
+        top = (height - new_height) // 2
+        left = (width - new_width) // 2
+        bottom = top + new_height
+        right = left + new_width
+        return image[:, top:bottom, left:right, :]

kikotools/tools/image_to_multiple_of/node.py

@@ -0,0 +1,103 @@
+"""ComfyUI node implementation for ImageToMultipleOf."""
+
+from typing import Dict, Any, Tuple
+
+from torch import Tensor
+
+from ...base import ComfyAssetsBaseNode
+from .logic import process_image_to_multiple_of
+
+
+class ImageToMultipleOfNode(ComfyAssetsBaseNode):
+    """
+    Adjusts image dimensions to be multiples of a specified value.
+
+    Useful for models that require specific dimension constraints.
+    Supports both center cropping and rescaling methods.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "multiple_of": (
+                    "INT",
+                    {
+                        "default": 64,
+                        "min": 1,
+                        "max": 256,
+                        "step": 16,
+                        "display": "number",
+                    },
+                ),
+                "method": (["center crop", "rescale"],),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    CATEGORY = "ComfyAssets/🖼️ Resolution"
+    RETURN_NAMES = ("image",)
+    FUNCTION = "process"
+
+    def process(self, image: Tensor, multiple_of: int, method: str) -> Tuple[Tensor]:
+        """
+        Process image to ensure dimensions are multiples of specified value.
+
+        Args:
+            image: Input image tensor
+            multiple_of: Value that dimensions should be multiple of
+            method: Processing method - "center crop" or "rescale"
+
+        Returns:
+            Tuple containing processed image tensor
+        """
+        try:
+            self.validate_inputs(image=image, multiple_of=multiple_of, method=method)
+
+            # Process the image
+            processed_image = process_image_to_multiple_of(image, multiple_of, method)
+
+            _, new_height, new_width, _ = processed_image.shape
+            self.log_info(
+                f"Processed image from {image.shape[1]}x{image.shape[2]} "
+                f"to {new_height}x{new_width} (multiple of {multiple_of}) "
+                f"using {method}"
+            )
+
+            return (processed_image,)
+
+        except Exception as e:
+            self.handle_error(f"Failed to process image: {str(e)}", e)
+
+    def validate_inputs(self, **kwargs) -> None:
+        """Validate inputs for ImageToMultipleOf node."""
+        image = kwargs.get("image")
+        multiple_of = kwargs.get("multiple_of")
+        method = kwargs.get("method")
+
+        if image is None:
+            raise ValueError("Image input is required")
+
+        if not isinstance(image, Tensor) or len(image.shape) != 4:
+            raise ValueError(
+                f"Expected image tensor with shape (batch, height, width, channels), "
+                f"got shape {image.shape if isinstance(image, Tensor) else 'non-tensor'}"
+            )
+
+        if multiple_of <= 0:
+            raise ValueError(f"multiple_of must be positive, got {multiple_of}")
+
+        if method not in ["center crop", "rescale"]:
+            raise ValueError(f"Invalid method: {method}")
+
+        # Check if resulting dimensions would be too small
+        _, height, width, _ = image.shape
+        new_height = height - (height % multiple_of)
+        new_width = width - (width % multiple_of)
+
+        if new_height <= 0 or new_width <= 0:
+            raise ValueError(
+                f"Image dimensions ({height}x{width}) are too small "
+                f"to be adjusted to multiple of {multiple_of}"
+            )

kikotools/tools/kiko_save_image/__init__.py

@@ -0,0 +1,8 @@
+"""
+KikoSaveImage tool module
+Enhanced image saving with format selection, quality control, and clickable previews
+"""
+
+from .node import KikoSaveImageNode
+
+__all__ = ["KikoSaveImageNode"]

kikotools/tools/kiko_save_image/logic.py

@@ -0,0 +1,365 @@
+"""
+KikoSaveImage core logic
+Enhanced image saving functionality with multiple format support
+"""
+
+import os
+import json
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import torch
+from typing import Dict, List, Any, Optional, Tuple
+import time
+
+try:
+    import folder_paths
+except ImportError:
+    # Fallback for testing without ComfyUI
+    class folder_paths:
+        @staticmethod
+        def get_output_directory():
+            return "./output"
+
+
+def get_save_image_path(
+    filename_prefix: str,
+    batch_number: int,
+    format_ext: str,
+    output_dir: str,
+    subfolder: str = "",
+) -> Tuple[str, str]:
+    """
+    Generate save path for image with proper filename handling
+
+    Args:
+        filename_prefix: Base filename prefix
+        batch_number: Batch index for multiple images
+        format_ext: File extension (.png, .jpg, .webp)
+        output_dir: Output directory path
+        subfolder: Optional subfolder within output directory
+
+    Returns:
+        Tuple of (full_path, relative_filename)
+    """
+    # Split filename_prefix into directory path and actual filename prefix
+    # This allows for directory structures like "kittybear/anime/images/kittybear"
+    prefix_dir = os.path.dirname(filename_prefix)
+    prefix_name = os.path.basename(filename_prefix)
+
+    # Sanitize only the filename part (not the directory path)
+    safe_prefix = prefix_name.replace(
+        ":", "_"
+    )  # Only sanitize problematic chars for filenames
+    safe_prefix = "".join(c for c in safe_prefix if c.isalnum() or c in "._-")
+
+    # Create unique filename with timestamp to avoid conflicts
+    timestamp = int(time.time())
+    filename = f"{safe_prefix}_{timestamp:010d}_{batch_number:05d}{format_ext}"
+
+    # Handle subfolder and prefix directory (but not the filename part)
+    path_components = []
+    path_components.append(output_dir)
+
+    if subfolder:
+        path_components.append(subfolder)
+
+    # Only add prefix_dir if it exists (the directory part, not the filename part)
+    if prefix_dir:
+        path_components.append(prefix_dir)
+
+    full_output_folder = os.path.join(*path_components)
+
+    # Ensure directory exists
+    os.makedirs(full_output_folder, exist_ok=True)
+
+    full_path = os.path.join(full_output_folder, filename)
+
+    # For the preview, ComfyUI needs the filename and subfolder separately
+    # The subfolder needs to be relative to the output directory root
+    # Build the relative subfolder path including prefix directory (but not filename part)
+    relative_path_components = []
+
+    if subfolder:
+        relative_path_components.append(subfolder.strip("/\\"))
+
+    if prefix_dir:
+        relative_path_components.append(prefix_dir.strip("/\\"))
+
+    if relative_path_components:
+        relative_subfolder = os.path.join(*relative_path_components)
+    else:
+        relative_subfolder = ""
+
+    preview_filename = filename
+
+    return full_path, preview_filename, relative_subfolder
+
+
+def convert_tensor_to_pil(image_tensor: torch.Tensor) -> Image.Image:
+    """
+    Convert ComfyUI image tensor to PIL Image
+
+    Args:
+        image_tensor: Tensor in format [height, width, channels] with values 0-1
+
+    Returns:
+        PIL Image in RGB/RGBA format
+    """
+    # Convert tensor (0-1 float) to 0-255 numpy array
+    i = 255.0 * image_tensor.cpu().numpy()
+    img_array = np.clip(i, 0, 255).astype(np.uint8)
+
+    # Create PIL image from numpy array
+    img = Image.fromarray(img_array)
+
+    return img
+
+
+def create_png_metadata(
+    prompt: Optional[Dict] = None, extra_pnginfo: Optional[Dict] = None
+) -> Optional[PngInfo]:
+    """
+    Create PNG metadata with workflow information
+
+    Args:
+        prompt: ComfyUI prompt data
+        extra_pnginfo: Additional PNG metadata
+
+    Returns:
+        PngInfo object or None if no metadata
+    """
+    if prompt is None and extra_pnginfo is None:
+        return None
+
+    metadata = PngInfo()
+
+    if prompt is not None:
+        metadata.add_text("prompt", json.dumps(prompt))
+
+    if extra_pnginfo is not None:
+        for key, value in extra_pnginfo.items():
+            metadata.add_text(key, json.dumps(value))
+
+    return metadata
+
+
+def save_image_with_format(
+    img: Image.Image,
+    filepath: str,
+    format_type: str,
+    quality: int = 90,
+    png_compress_level: int = 4,
+    webp_lossless: bool = False,
+    metadata: Optional[PngInfo] = None,
+) -> Dict[str, Any]:
+    """
+    Save PIL image with specified format and quality settings
+
+    Args:
+        img: PIL Image to save
+        filepath: Full path to save file
+        format_type: Image format (PNG, JPEG, WEBP)
+        quality: JPEG/WebP quality (1-100)
+        png_compress_level: PNG compression level (0-9)
+        webp_lossless: Use lossless WebP compression
+        metadata: PNG metadata to embed
+
+    Returns:
+        Dict with save information
+    """
+    save_kwargs = {}
+
+    if format_type == "PNG":
+        if metadata:
+            save_kwargs["pnginfo"] = metadata
+        save_kwargs["compress_level"] = png_compress_level
+
+    elif format_type == "JPEG":
+        # Convert RGBA to RGB for JPEG (no transparency support)
+        if img.mode == "RGBA":
+            # Create white background
+            background = Image.new("RGB", img.size, (255, 255, 255))
+            background.paste(img, mask=img.split()[-1])  # Use alpha channel as mask
+            img = background
+        elif img.mode != "RGB":
+            img = img.convert("RGB")
+
+        save_kwargs["quality"] = quality
+        save_kwargs["optimize"] = True
+
+    elif format_type == "WEBP":
+        save_kwargs["quality"] = quality if not webp_lossless else 100
+        save_kwargs["lossless"] = webp_lossless
+
+    else:
+        raise ValueError(f"Unsupported format: {format_type}")
+
+    # Save the image
+    img.save(filepath, **save_kwargs)
+
+    # Get file size for info
+    file_size = os.path.getsize(filepath)
+
+    return {
+        "filepath": filepath,
+        "format": format_type,
+        "file_size": file_size,
+        "quality": quality if format_type != "PNG" else None,
+        "compress_level": png_compress_level if format_type == "PNG" else None,
+        "lossless": webp_lossless if format_type == "WEBP" else None,
+    }
+
+
+def process_image_batch(
+    images: torch.Tensor,
+    filename_prefix: str = "KikoSave",
+    format_type: str = "PNG",
+    quality: int = 90,
+    png_compress_level: int = 4,
+    webp_lossless: bool = False,
+    popup: bool = True,
+    prompt: Optional[Dict] = None,
+    extra_pnginfo: Optional[Dict] = None,
+) -> List[Dict[str, Any]]:
+    """
+    Process and save a batch of images with specified format settings
+
+    Args:
+        images: Batch of image tensors [batch, height, width, channels]
+        filename_prefix: Prefix for saved filenames
+        format_type: Image format (PNG, JPEG, WEBP)
+        quality: JPEG/WebP quality (1-100)
+        png_compress_level: PNG compression level (0-9)
+        webp_lossless: Use lossless WebP compression
+        popup: Enable popup windows in UI
+        prompt: ComfyUI prompt data for metadata
+        extra_pnginfo: Additional PNG metadata
+
+    Returns:
+        List of saved image information dicts
+    """
+    # Get output directory
+    output_dir = folder_paths.get_output_directory()
+
+    # Determine file extension
+    format_extensions = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp"}
+
+    if format_type not in format_extensions:
+        raise ValueError(
+            f"Unsupported format: {format_type}. "
+            f"Supported: {list(format_extensions.keys())}"
+        )
+
+    format_ext = format_extensions[format_type]
+
+    # Create metadata for PNG
+    metadata = None
+    if format_type == "PNG":
+        metadata = create_png_metadata(prompt, extra_pnginfo)
+
+    # Process each image in the batch
+    results = []
+    enhanced_data = []
+
+    for batch_number, image_tensor in enumerate(images):
+        # Convert tensor to PIL Image
+        img = convert_tensor_to_pil(image_tensor)
+
+        # Generate save path
+        filepath, preview_filename, relative_subfolder = get_save_image_path(
+            filename_prefix, batch_number, format_ext, output_dir, ""
+        )
+
+        # Save with format-specific settings
+        save_info = save_image_with_format(
+            img,
+            filepath,
+            format_type,
+            quality,
+            png_compress_level,
+            webp_lossless,
+            metadata,
+        )
+
+        # Build result info for ComfyUI preview
+        # ONLY the core fields that ComfyUI expects - no extra metadata
+        result = {
+            "filename": preview_filename,
+            "subfolder": relative_subfolder,
+            "type": "output",
+        }
+
+        # Store enhanced data separately
+        enhanced_info = {
+            "filename": preview_filename,
+            "subfolder": relative_subfolder,
+            "popup": popup,
+            "type": "output",
+            "format": format_type,
+            "file_size": save_info["file_size"],
+            "dimensions": f"{img.width}x{img.height}",
+        }
+
+        # Add format-specific info to enhanced data
+        if format_type == "PNG":
+            enhanced_info["compress_level"] = png_compress_level
+        elif format_type in ["JPEG", "WEBP"]:
+            enhanced_info["quality"] = quality
+            if format_type == "WEBP":
+                enhanced_info["lossless"] = webp_lossless
+
+        results.append(result)
+        enhanced_data.append(enhanced_info)
+
+    return results, enhanced_data
+
+
+def validate_save_inputs(
+    images: torch.Tensor, format_type: str, quality: int, png_compress_level: int
+) -> None:
+    """
+    Validate inputs for image saving
+
+    Args:
+        images: Image tensor batch to validate
+        format_type: Image format to validate
+        quality: Quality setting to validate
+        png_compress_level: PNG compression level to validate
+
+    Raises:
+        ValueError: If validation fails
+    """
+    # Validate images tensor
+    if not isinstance(images, torch.Tensor):
+        raise ValueError(f"images must be a torch.Tensor, got {type(images).__name__}")
+
+    if len(images.shape) != 4:
+        raise ValueError(
+            f"images tensor must have 4 dimensions [batch, height, width, channels], "
+            f"got {len(images.shape)}"
+        )
+
+    # Validate format
+    supported_formats = ["PNG", "JPEG", "WEBP"]
+    if format_type not in supported_formats:
+        raise ValueError(
+            f"format must be one of {supported_formats}, got {format_type}"
+        )
+
+    # Validate quality (for JPEG/WebP)
+    if format_type in ["JPEG", "WEBP"]:
+        if not isinstance(quality, int) or not (1 <= quality <= 100):
+            raise ValueError(
+                f"quality must be an integer between 1 and 100, got {quality}"
+            )
+
+    # Validate PNG compression level
+    if format_type == "PNG":
+        if not isinstance(png_compress_level, int) or not (
+            0 <= png_compress_level <= 9
+        ):
+            raise ValueError(
+                f"png_compress_level must be an integer between 0 and 9, "
+                f"got {png_compress_level}"
+            )

kikotools/tools/kiko_save_image/node.py

@@ -0,0 +1,227 @@
+"""
+KikoSaveImage ComfyUI Node
+Enhanced image saving with format selection, quality control, and clickable previews
+"""
+
+import torch
+from typing import Dict, Any, Optional
+
+from ...base import ComfyAssetsBaseNode
+from .logic import process_image_batch, validate_save_inputs
+
+
+class KikoSaveImageNode(ComfyAssetsBaseNode):
+    """
+    Enhanced ComfyUI image saving node with multiple format support
+
+    Features:
+    - Multiple format support (PNG, JPEG, WebP)
+    - Quality/compression controls
+    - Clickable image previews
+    - Metadata preservation
+    - Batch processing
+
+    Inputs:
+        - images (IMAGE): Images to save
+        - filename_prefix (STRING): Prefix for saved filenames
+        - format (COMBO): Output format (PNG, JPEG, WebP)
+        - quality (INT): JPEG/WebP quality (1-100)
+        - png_compress_level (INT): PNG compression level (0-9)
+        - webp_lossless (BOOLEAN): Use lossless WebP compression
+        - subfolder (STRING): Optional subfolder for organization
+
+    Outputs:
+        - UI: Image preview data for ComfyUI interface
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Define ComfyUI input interface with enhanced save options
+
+        Returns:
+            Dict with required and optional input specifications
+        """
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images to save"}),
+                "filename_prefix": (
+                    "STRING",
+                    {"default": "KikoSave", "tooltip": "Prefix for saved filenames"},
+                ),
+                "format": (
+                    ["PNG", "JPEG", "WEBP"],
+                    {"default": "PNG", "tooltip": "Output image format"},
+                ),
+            },
+            "optional": {
+                "quality": (
+                    "INT",
+                    {
+                        "default": 90,
+                        "min": 1,
+                        "max": 100,
+                        "step": 1,
+                        "tooltip": "JPEG/WebP quality (1-100, higher = better quality)",
+                    },
+                ),
+                "png_compress_level": (
+                    "INT",
+                    {
+                        "default": 4,
+                        "min": 0,
+                        "max": 9,
+                        "step": 1,
+                        "tooltip": "PNG compression level (0-9, higher = smaller file)",
+                    },
+                ),
+                "webp_lossless": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Use lossless WebP compression "
+                        "(ignores quality setting)",
+                    },
+                ),
+                "popup": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "Enable popup windows when clicking on images in the viewer",
+                    },
+                ),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ()
+    CATEGORY = "ComfyAssets/💾 Images"
+    FUNCTION = "save_images"
+    OUTPUT_NODE = True
+
+    def save_images(
+        self,
+        images: torch.Tensor,
+        filename_prefix: str = "KikoSave",
+        format: str = "PNG",
+        quality: int = 90,
+        png_compress_level: int = 4,
+        webp_lossless: bool = False,
+        popup: bool = True,
+        prompt: Optional[Dict] = None,
+        extra_pnginfo: Optional[Dict] = None,
+    ) -> Dict[str, Any]:
+        """
+        Save images with enhanced format and quality options
+
+        Args:
+            images: Batch of image tensors to save
+            filename_prefix: Prefix for saved filenames
+            format: Output format (PNG, JPEG, WebP)
+            quality: JPEG/WebP quality setting
+            png_compress_level: PNG compression level
+            webp_lossless: Use lossless WebP compression
+            popup: Enable popup windows when clicking on images
+            prompt: ComfyUI prompt data for metadata
+            extra_pnginfo: Additional PNG metadata
+
+        Returns:
+            Dict with UI data for image previews
+
+        Raises:
+            ValueError: If validation fails
+        """
+        try:
+            # Validate inputs
+            self.validate_inputs(
+                images=images,
+                format=format,
+                quality=quality,
+                png_compress_level=png_compress_level,
+                webp_lossless=webp_lossless,
+                popup=popup,
+            )
+
+            # Log the save operation
+            self.log_info(
+                f"Saving {len(images)} images as {format} "
+                f"(quality={quality if format != 'PNG' else 'N/A'}, "
+                f"png_compress={png_compress_level if format == 'PNG' else 'N/A'})"
+            )
+
+            # Process and save images
+            results, enhanced_data = process_image_batch(
+                images=images,
+                filename_prefix=filename_prefix,
+                format_type=format,
+                quality=quality,
+                png_compress_level=png_compress_level,
+                webp_lossless=webp_lossless,
+                popup=popup,
+                prompt=prompt,
+                extra_pnginfo=extra_pnginfo,
+            )
+
+            # Log results
+            total_size = sum(data["file_size"] for data in enhanced_data)
+            self.log_info(
+                f"Successfully saved {len(results)} images "
+                f"(total size: {total_size / 1024:.1f} KB)"
+            )
+
+            # Return UI data for ComfyUI preview (clean) + enhanced data for our JS
+            return {
+                "ui": {
+                    "images": results,  # Clean data for ComfyUI
+                    "kiko_enhanced": enhanced_data,  # Enhanced data for our JavaScript
+                }
+            }
+
+        except Exception as e:
+            error_msg = f"Failed to save images: {str(e)}"
+            self.handle_error(error_msg, e)
+
+    def validate_inputs(
+        self,
+        images: torch.Tensor,
+        format: str,
+        quality: int,
+        png_compress_level: int,
+        webp_lossless: bool,
+        popup: bool,
+    ) -> None:
+        """
+        Validate inputs specific to KikoSaveImage
+
+        Args:
+            images: Image tensor batch
+            format: Image format
+            quality: Quality setting
+            png_compress_level: PNG compression level
+            webp_lossless: WebP lossless setting
+            popup: Enable popup windows
+
+        Raises:
+            ValueError: If validation fails
+        """
+        # Use logic module validation
+        validate_save_inputs(images, format, quality, png_compress_level)
+
+        # Additional node-specific validation
+        if not isinstance(webp_lossless, bool):
+            raise ValueError(
+                f"webp_lossless must be a boolean, got {type(webp_lossless).__name__}"
+            )
+
+        if not isinstance(popup, bool):
+            raise ValueError(f"popup must be a boolean, got {type(popup).__name__}")
+
+
+# Node class mappings for ComfyUI registration
+NODE_CLASS_MAPPINGS = {
+    "KikoSaveImage": KikoSaveImageNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "KikoSaveImage": "Kiko Save Image",
+}

kikotools/tools/resolution_calculator/logic.py

@@ -4,7 +4,7 @@ Pure functions for dimension extraction and scaling calculations
 """
 
 import torch
-from typing import Tuple, Optional, Union, Dict, Any
+from typing import Tuple, Optional, Dict
 
 
 def extract_dimensions(
@@ -16,7 +16,8 @@ def extract_dimensions(
 
     Args:
         image: Optional IMAGE tensor in ComfyUI format [batch, height, width, channels]
-        latent: Optional LATENT dict with 'samples' tensor [batch, channels, height/8, width/8]
+        latent: Optional LATENT dict with 'samples' tensor
+                [batch, channels, height/8, width/8]
 
     Returns:
         Tuple of (width, height) as integers
@@ -42,7 +43,8 @@ def extract_dimensions(
         samples = latent["samples"]
         if len(samples.shape) != 4:
             raise ValueError(
-                f"Expected LATENT samples tensor with 4 dimensions, got {len(samples.shape)}"
+                f"Expected LATENT samples tensor with 4 dimensions, "
+                f"got {len(samples.shape)}"
             )
 
         _, _, latent_height, latent_width = samples.shape

kikotools/tools/resolution_calculator/node.py

@@ -38,11 +38,12 @@ class ResolutionCalculatorNode(ComfyAssetsBaseNode):
                     "FLOAT",
                     {
                         "default": 2.0,
-                        "min": 1.0,
+                        "min": 0.1,
                         "max": 8.0,
                         "step": 0.1,
                         "display": "slider",
-                        "tooltip": "Factor to scale the resolution by (e.g., 2.0 for 2x upscale)",
+                        "tooltip": "Factor to scale the resolution by "
+                        "(e.g., 2.0 for 2x, 0.5 for half scale)",
                     },
                 ),
             },
@@ -59,6 +60,7 @@ class ResolutionCalculatorNode(ComfyAssetsBaseNode):
         }
 
     RETURN_TYPES = ("INT", "INT")
+    CATEGORY = "ComfyAssets/🖼️ Resolution"
     RETURN_NAMES = ("width", "height")
     FUNCTION = "calculate_resolution"
 
@@ -93,7 +95,8 @@ class ResolutionCalculatorNode(ComfyAssetsBaseNode):
                 else "LATENT" if latent is not None else "NONE"
             )
             self.log_info(
-                f"Calculating resolution with scale_factor={scale_factor}, input_type={input_type}"
+                f"Calculating resolution with scale_factor={scale_factor}, "
+                f"input_type={input_type}"
             )
 
             # Calculate the resolution
@@ -138,35 +141,46 @@ class ResolutionCalculatorNode(ComfyAssetsBaseNode):
                 f"scale_factor must be a number, got {type(scale_factor).__name__}"
             )
 
-        # Additional tensor validation
+        # Validate tensors using helper methods
         if image is not None:
-            if not isinstance(image, torch.Tensor):
-                raise ValueError(
-                    f"image must be a torch.Tensor, got {type(image).__name__}"
-                )
-
-            if len(image.shape) != 4:
-                raise ValueError(
-                    f"image tensor must have 4 dimensions [batch, height, width, channels], got {len(image.shape)}"
-                )
+            self._validate_image_tensor(image)
 
         if latent is not None:
-            if not isinstance(latent, dict):
-                raise ValueError(f"latent must be a dict, got {type(latent).__name__}")
+            self._validate_latent_dict(latent)
 
-            if "samples" not in latent:
-                raise ValueError("latent dict must contain 'samples' key")
+    def _validate_image_tensor(self, image: torch.Tensor) -> None:
+        """Validate image tensor format"""
+        if not isinstance(image, torch.Tensor):
+            raise ValueError(
+                f"image must be a torch.Tensor, got {type(image).__name__}"
+            )
 
-            samples = latent["samples"]
-            if not isinstance(samples, torch.Tensor):
-                raise ValueError(
-                    f"latent['samples'] must be a torch.Tensor, got {type(samples).__name__}"
-                )
+        if len(image.shape) != 4:
+            raise ValueError(
+                f"image tensor must have 4 dimensions "
+                f"[batch, height, width, channels], got {len(image.shape)}"
+            )
 
-            if len(samples.shape) != 4:
-                raise ValueError(
-                    f"latent samples tensor must have 4 dimensions [batch, channels, height, width], got {len(samples.shape)}"
-                )
+    def _validate_latent_dict(self, latent: Dict[str, torch.Tensor]) -> None:
+        """Validate latent dictionary format"""
+        if not isinstance(latent, dict):
+            raise ValueError(f"latent must be a dict, got {type(latent).__name__}")
+
+        if "samples" not in latent:
+            raise ValueError("latent dict must contain 'samples' key")
+
+        samples = latent["samples"]
+        if not isinstance(samples, torch.Tensor):
+            raise ValueError(
+                f"latent['samples'] must be a torch.Tensor, "
+                f"got {type(samples).__name__}"
+            )
+
+        if len(samples.shape) != 4:
+            raise ValueError(
+                f"latent samples tensor must have 4 dimensions "
+                f"[batch, channels, height, width], got {len(samples.shape)}"
+            )
 
 
 # Node class mappings for ComfyUI registration

kikotools/tools/sampler_combo/__init__.py

@@ -0,0 +1,6 @@
+"""Sampler Combo tool for ComfyUI."""
+
+from .node import SamplerComboNode
+from .compact_node import SamplerComboCompactNode
+
+__all__ = ["SamplerComboNode", "SamplerComboCompactNode"]

kikotools/tools/sampler_combo/compact_node.py

@@ -0,0 +1,114 @@
+"""Compact Sampler Combo node for ComfyUI with minimal interface."""
+
+from typing import Tuple
+from ...base.base_node import ComfyAssetsBaseNode
+from .logic import (
+    get_sampler_combo,
+    SAMPLERS,
+    SCHEDULERS,
+)
+
+
+class SamplerComboCompactNode(ComfyAssetsBaseNode):
+    """
+    Compact Sampler Combo node with minimal interface.
+
+    Provides essential sampling parameters in a space-efficient layout
+    with shorter parameter names and reduced visual footprint.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define compact input types for the ComfyUI node."""
+        return {
+            "required": {
+                "sampler": (
+                    SAMPLERS,
+                    {
+                        "default": "euler",
+                        "tooltip": "Sampler",
+                    },
+                ),
+                "sched": (
+                    SCHEDULERS,
+                    {
+                        "default": "normal",
+                        "tooltip": "Scheduler",
+                    },
+                ),
+                "steps": (
+                    "INT",
+                    {
+                        "default": 20,
+                        "min": 1,
+                        "max": 50,
+                        "step": 1,
+                        "tooltip": "Steps",
+                    },
+                ),
+                "cfg": (
+                    "FLOAT",
+                    {
+                        "default": 7.0,
+                        "min": 1.0,
+                        "max": 15.0,
+                        "step": 0.5,
+                        "display": "slider",
+                        "tooltip": "CFG",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("SAMPLER", SCHEDULERS, "INT", "FLOAT")
+    RETURN_NAMES = ("sampler", "scheduler", "steps", "cfg")
+    FUNCTION = "get_combo"
+    CATEGORY = "ComfyAssets/🌀 Samplers"
+
+    def get_combo(
+        self, sampler: str, sched: str, steps: int, cfg: float
+    ) -> Tuple[object, str, int, float]:
+        """
+        Get compact sampler combo configuration.
+
+        Args:
+            sampler: The sampler algorithm name
+            sched: The scheduler algorithm name
+            steps: Number of sampling steps
+            cfg: CFG scale value
+
+        Returns:
+            Tuple of (sampler_object, scheduler, steps, cfg)
+        """
+        try:
+            # Use the same validation logic but with compact interface
+            result = get_sampler_combo(sampler, sched, steps, cfg)
+            # Create the sampler object
+            try:
+                import comfy.samplers
+
+                sampler_obj = comfy.samplers.sampler_object(result[0])
+            except ImportError:
+                # Return sampler name for testing
+                sampler_obj = result[0]
+            return (sampler_obj, result[1], result[2], result[3])
+
+        except Exception as e:
+            # Graceful fallback
+            self.handle_error(f"Error in compact combo: {str(e)}")
+            try:
+                import comfy.samplers
+
+                sampler_obj = comfy.samplers.sampler_object("euler")
+            except ImportError:
+                # Return sampler name for testing
+                sampler_obj = "euler"
+            return (sampler_obj, "normal", 20, 7.0)
+
+    def __str__(self) -> str:
+        """String representation of the compact node."""
+        return "SamplerComboCompactNode"
+
+    def __repr__(self) -> str:
+        """Detailed string representation of the compact node."""
+        return f"SamplerComboCompactNode(category='{self.CATEGORY}')"

kikotools/tools/sampler_combo/logic.py

@@ -0,0 +1,220 @@
+"""Logic module for Sampler Combo node."""
+
+from typing import Tuple, Dict, Any, List
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Import ComfyUI samplers - will be available when running in ComfyUI
+try:
+    import comfy.samplers
+
+    SAMPLERS = comfy.samplers.KSampler.SAMPLERS
+    SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS
+except ImportError:
+    # Fallback for testing/development environment
+    SAMPLERS = [
+        "euler",
+        "euler_ancestral",
+        "heun",
+        "dpm_2",
+        "dpm_2_ancestral",
+        "lms",
+        "dpm_fast",
+        "dpm_adaptive",
+        "dpmpp_2s_ancestral",
+        "dpmpp_sde",
+        "dpmpp_2m",
+        "ddim",
+        "uni_pc",
+        "uni_pc_bh2",
+    ]
+    SCHEDULERS = [
+        "normal",
+        "karras",
+        "exponential",
+        "sgm_uniform",
+        "simple",
+        "ddim_uniform",
+        "beta",
+    ]
+
+
+def validate_sampler_settings(
+    sampler_name: str, scheduler: str, steps: int, cfg: float
+) -> bool:
+    """
+    Validate sampler configuration settings.
+
+    Args:
+        sampler_name: The sampler algorithm name
+        scheduler: The scheduler algorithm name
+        steps: Number of sampling steps
+        cfg: CFG (classifier-free guidance) scale value
+
+    Returns:
+        True if all settings are valid
+    """
+    try:
+        # Validate sampler
+        if sampler_name not in SAMPLERS:
+            logger.error(f"Invalid sampler: {sampler_name}")
+            return False
+
+        # Validate scheduler
+        if scheduler not in SCHEDULERS:
+            logger.error(f"Invalid scheduler: {scheduler}")
+            return False
+
+        # Validate steps
+        if not isinstance(steps, int) or steps < 1 or steps > 1000:
+            logger.error(f"Invalid steps: {steps} (must be 1-1000)")
+            return False
+
+        # Validate CFG
+        if not isinstance(cfg, (int, float)) or cfg < 0 or cfg > 30:
+            logger.error(f"Invalid CFG: {cfg} (must be 0-30)")
+            return False
+
+        return True
+
+    except Exception as e:
+        logger.error(f"Error validating sampler settings: {e}")
+        return False
+
+
+def get_sampler_combo(
+    sampler_name: str, scheduler: str, steps: int, cfg: float
+) -> Tuple[str, str, int, float]:
+    """
+    Process and return sampler combo settings.
+
+    Args:
+        sampler_name: The sampler algorithm name
+        scheduler: The scheduler algorithm name
+        steps: Number of sampling steps
+        cfg: CFG scale value
+
+    Returns:
+        Tuple of (sampler_name, scheduler, steps, cfg)
+    """
+    try:
+        # Validate inputs
+        if not validate_sampler_settings(sampler_name, scheduler, steps, cfg):
+            # Return safe defaults if validation fails
+            logger.warning("Invalid settings provided, using safe defaults")
+            return ("euler", "normal", 20, 7.0)
+
+        # Sanitize values
+        steps = max(1, min(1000, int(steps)))
+        cfg = max(0.0, min(30.0, float(cfg)))
+
+        return (sampler_name, scheduler, steps, cfg)
+
+    except Exception as e:
+        logger.error(f"Error processing sampler combo: {e}")
+        # Return safe defaults on any error
+        return ("euler", "normal", 20, 7.0)
+
+
+def get_compatible_scheduler_suggestions(sampler_name: str) -> List[str]:
+    """
+    Get scheduler suggestions that work well with specific samplers.
+
+    Args:
+        sampler_name: The sampler algorithm name
+
+    Returns:
+        List of recommended scheduler names
+    """
+    # Scheduler compatibility recommendations
+    compatibility_map = {
+        "euler": ["normal", "simple", "sgm_uniform"],
+        "euler_ancestral": ["normal", "karras", "exponential"],
+        "heun": ["normal", "karras"],
+        "dpm_2": ["normal", "karras"],
+        "dpm_2_ancestral": ["normal", "karras", "exponential"],
+        "dpmpp_2s_ancestral": ["normal", "karras", "exponential"],
+        "dpmpp_sde": ["normal", "karras", "exponential"],
+        "dpmpp_2m": ["normal", "karras", "sgm_uniform"],
+        "ddim": ["ddim_uniform", "normal"],
+        "uni_pc": ["normal", "sgm_uniform"],
+        "uni_pc_bh2": ["normal", "sgm_uniform"],
+    }
+
+    return compatibility_map.get(sampler_name, ["normal", "karras"])
+
+
+def get_recommended_steps_range(sampler_name: str) -> Tuple[int, int, int]:
+    """
+    Get recommended steps range for specific samplers.
+
+    Args:
+        sampler_name: The sampler algorithm name
+
+    Returns:
+        Tuple of (min_steps, max_steps, default_steps)
+    """
+    # Steps recommendations by sampler
+    steps_map = {
+        "euler": (10, 30, 20),
+        "euler_ancestral": (15, 40, 25),
+        "heun": (10, 25, 15),
+        "dpm_2": (10, 30, 22),
+        "dpm_2_ancestral": (15, 35, 25),
+        "dpmpp_2s_ancestral": (15, 40, 28),
+        "dpmpp_sde": (15, 35, 25),
+        "dpmpp_2m": (15, 30, 20),
+        "ddim": (20, 50, 30),
+        "uni_pc": (10, 25, 15),
+        "uni_pc_bh2": (10, 25, 15),
+    }
+
+    return steps_map.get(sampler_name, (10, 50, 20))
+
+
+def get_recommended_cfg_range(sampler_name: str) -> Tuple[float, float, float]:
+    """
+    Get recommended CFG range for specific samplers.
+
+    Args:
+        sampler_name: The sampler algorithm name
+
+    Returns:
+        Tuple of (min_cfg, max_cfg, default_cfg)
+    """
+    # CFG recommendations by sampler
+    cfg_map = {
+        "euler": (3.0, 15.0, 7.0),
+        "euler_ancestral": (5.0, 20.0, 8.0),
+        "heun": (3.0, 12.0, 6.0),
+        "dpm_2": (4.0, 15.0, 7.5),
+        "dpm_2_ancestral": (5.0, 18.0, 8.5),
+        "dpmpp_2s_ancestral": (6.0, 20.0, 9.0),
+        "dpmpp_sde": (5.0, 18.0, 8.0),
+        "dpmpp_2m": (4.0, 15.0, 7.0),
+        "ddim": (3.0, 12.0, 6.0),
+        "uni_pc": (3.0, 12.0, 6.5),
+        "uni_pc_bh2": (3.0, 12.0, 6.5),
+    }
+
+    return cfg_map.get(sampler_name, (1.0, 20.0, 7.0))
+
+
+def get_sampler_info() -> Dict[str, Any]:
+    """
+    Get information about available samplers and schedulers.
+
+    Returns:
+        Dictionary containing sampler/scheduler information
+    """
+    return {
+        "samplers": SAMPLERS,
+        "schedulers": SCHEDULERS,
+        "sampler_count": len(SAMPLERS),
+        "scheduler_count": len(SCHEDULERS),
+        "default_sampler": "euler",
+        "default_scheduler": "normal",
+        "default_steps": 20,
+        "default_cfg": 7.0,
+    }

kikotools/tools/sampler_combo/node.py

@@ -0,0 +1,291 @@
+"""Sampler Combo node for ComfyUI."""
+
+from typing import Tuple
+from ...base.base_node import ComfyAssetsBaseNode
+from .logic import (
+    get_sampler_combo,
+    validate_sampler_settings,
+    get_compatible_scheduler_suggestions,
+    get_recommended_steps_range,
+    get_recommended_cfg_range,
+    SAMPLERS,
+    SCHEDULERS,
+)
+
+
+class SamplerComboNode(ComfyAssetsBaseNode):
+    """
+    Sampler Combo node for selecting sampling configuration.
+
+    Provides a unified interface for selecting sampler, scheduler, steps,
+    and CFG settings in a single node, reducing workflow complexity and
+    ensuring compatible parameter combinations.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define the input types for the ComfyUI node."""
+        return {
+            "required": {
+                "sampler_name": (
+                    SAMPLERS,
+                    {
+                        "default": "euler",
+                        "tooltip": "Sampling algorithm",
+                    },
+                ),
+                "scheduler": (
+                    SCHEDULERS,
+                    {
+                        "default": "normal",
+                        "tooltip": "Step distribution schedule",
+                    },
+                ),
+                "steps": (
+                    "INT",
+                    {
+                        "default": 20,
+                        "min": 1,
+                        "max": 100,
+                        "step": 1,
+                        "tooltip": "Sampling steps (1-100)",
+                    },
+                ),
+                "cfg": (
+                    "FLOAT",
+                    {
+                        "default": 7.0,
+                        "min": 0.0,
+                        "max": 20.0,
+                        "step": 0.5,
+                        "display": "slider",
+                        "tooltip": "CFG scale (0-20)",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("SAMPLER", SCHEDULERS, "INT", "FLOAT")
+    RETURN_NAMES = ("sampler_name", "scheduler", "steps", "cfg")
+    FUNCTION = "get_sampler_combo"
+    CATEGORY = "ComfyAssets/🌀 Samplers"
+
+    def get_sampler_combo(
+        self, sampler_name: str, scheduler: str, steps: int, cfg: float
+    ) -> Tuple[object, str, int, float]:
+        """
+        Get sampler combo configuration.
+
+        Args:
+            sampler_name: The sampler algorithm name
+            scheduler: The scheduler algorithm name
+            steps: Number of sampling steps
+            cfg: CFG scale value
+
+        Returns:
+            Tuple of (sampler_object, scheduler, steps, cfg)
+        """
+        try:
+            # Validate inputs
+            if not validate_sampler_settings(sampler_name, scheduler, steps, cfg):
+                # Log the validation error but don't raise
+                import logging
+
+                logger = logging.getLogger(__name__)
+                logger.error(
+                    f"{self.__class__.__name__}: Invalid sampler settings: "
+                    f"sampler={sampler_name}, scheduler={scheduler}, "
+                    f"steps={steps}, cfg={cfg}. "
+                    f"Using safe defaults: euler, normal, 20 steps, CFG 7.0"
+                )
+                try:
+                    import comfy.samplers
+
+                    sampler = comfy.samplers.sampler_object("euler")
+                except ImportError:
+                    # Return mock object for testing
+                    sampler = "euler"
+                return (sampler, "normal", 20, 7.0)
+
+            # Process and return the combo
+            result = get_sampler_combo(sampler_name, scheduler, steps, cfg)
+
+            # Create the sampler object
+            try:
+                import comfy.samplers
+
+                sampler = comfy.samplers.sampler_object(result[0])
+            except ImportError:
+                # Return sampler name for testing
+                sampler = result[0]
+
+            self.log_info(
+                f"Configured sampler combo: {result[0]}, {result[1]}, "
+                f"{result[2]} steps, CFG {result[3]}"
+            )
+
+            return (sampler, result[1], result[2], result[3])
+
+        except Exception as e:
+            # Handle any unexpected errors gracefully
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.error(
+                f"{self.__class__.__name__}: Error processing sampler combo: {str(e)}. "
+                f"Using safe defaults: euler, normal, 20 steps, CFG 7.0"
+            )
+            try:
+                import comfy.samplers
+
+                sampler = comfy.samplers.sampler_object("euler")
+            except ImportError:
+                # Return mock object for testing
+                sampler = "euler"
+            return (sampler, "normal", 20, 7.0)
+
+    def validate_inputs(
+        self, sampler_name: str, scheduler: str, steps: int, cfg: float
+    ) -> None:
+        """
+        Validate sampler combo inputs.
+
+        Args:
+            sampler_name: The sampler algorithm name
+            scheduler: The scheduler algorithm name
+            steps: Number of sampling steps
+            cfg: CFG scale value
+
+        Raises:
+            ValueError: If validation fails
+        """
+        if not validate_sampler_settings(sampler_name, scheduler, steps, cfg):
+            self.handle_error(
+                f"Invalid sampler settings: sampler={sampler_name}, "
+                f"scheduler={scheduler}, steps={steps}, cfg={cfg}"
+            )
+
+    def get_scheduler_suggestions(self, sampler_name: str) -> list:
+        """
+        Get scheduler suggestions compatible with the selected sampler.
+
+        Args:
+            sampler_name: The sampler algorithm name
+
+        Returns:
+            List of recommended scheduler names
+        """
+        return get_compatible_scheduler_suggestions(sampler_name)
+
+    def get_steps_recommendation(self, sampler_name: str) -> dict:
+        """
+        Get steps recommendation for the selected sampler.
+
+        Args:
+            sampler_name: The sampler algorithm name
+
+        Returns:
+            Dictionary with min, max, and default steps
+        """
+        min_steps, max_steps, default_steps = get_recommended_steps_range(sampler_name)
+        return {
+            "min": min_steps,
+            "max": max_steps,
+            "default": default_steps,
+            "recommendation": f"Range: {min_steps}-{max_steps} steps",
+        }
+
+    def get_cfg_recommendation(self, sampler_name: str) -> dict:
+        """
+        Get CFG recommendation for the selected sampler.
+
+        Args:
+            sampler_name: The sampler algorithm name
+
+        Returns:
+            Dictionary with min, max, and default CFG values
+        """
+        min_cfg, max_cfg, default_cfg = get_recommended_cfg_range(sampler_name)
+        return {
+            "min": min_cfg,
+            "max": max_cfg,
+            "default": default_cfg,
+            "recommendation": f"Recommended range: {min_cfg}-{max_cfg} CFG",
+        }
+
+    def get_combo_analysis(
+        self, sampler_name: str, scheduler: str, steps: int, cfg: float
+    ) -> dict:
+        """
+        Analyze the sampler combo configuration and provide recommendations.
+
+        Args:
+            sampler_name: The sampler algorithm name
+            scheduler: The scheduler algorithm name
+            steps: Number of sampling steps
+            cfg: CFG scale value
+
+        Returns:
+            Dictionary containing analysis and recommendations
+        """
+        analysis = {
+            "sampler": sampler_name,
+            "scheduler": scheduler,
+            "steps": steps,
+            "cfg": cfg,
+            "valid": validate_sampler_settings(sampler_name, scheduler, steps, cfg),
+            "scheduler_suggestions": self.get_scheduler_suggestions(sampler_name),
+            "steps_rec": self.get_steps_recommendation(sampler_name),
+            "cfg_rec": self.get_cfg_recommendation(sampler_name),
+        }
+
+        # Add compatibility assessment
+        suggested_schedulers = self.get_scheduler_suggestions(sampler_name)
+        analysis["scheduler_compatible"] = scheduler in suggested_schedulers
+
+        # Add performance assessment
+        steps_rec = self.get_steps_recommendation(sampler_name)
+        analysis["steps_optimal"] = steps_rec["min"] <= steps <= steps_rec["max"]
+
+        cfg_rec = self.get_cfg_recommendation(sampler_name)
+        analysis["cfg_optimal"] = cfg_rec["min"] <= cfg <= cfg_rec["max"]
+
+        return analysis
+
+    @classmethod
+    def get_available_samplers(cls) -> list:
+        """
+        Get list of available samplers.
+
+        Returns:
+            List of sampler names
+        """
+        return list(SAMPLERS)
+
+    @classmethod
+    def get_available_schedulers(cls) -> list:
+        """
+        Get list of available schedulers.
+
+        Returns:
+            List of scheduler names
+        """
+        return list(SCHEDULERS)
+
+    def __str__(self) -> str:
+        """String representation of the node."""
+        return (
+            f"SamplerComboNode(samplers={len(SAMPLERS)}, "
+            f"schedulers={len(SCHEDULERS)})"
+        )
+
+    def __repr__(self) -> str:
+        """Detailed string representation of the node."""
+        return (
+            f"SamplerComboNode("
+            f"samplers={len(SAMPLERS)}, "
+            f"schedulers={len(SCHEDULERS)}, "
+            f"category='{self.CATEGORY}', "
+            f"function='{self.FUNCTION}'"
+            f")"
+        )

kikotools/tools/seed_history/__init__.py

@@ -0,0 +1,10 @@
+"""
+Seed History tool for ComfyUI-KikoTools.
+
+Provides seed value tracking with history management,
+automatic deduplication, and interactive UI.
+"""
+
+from .node import SeedHistoryNode
+
+__all__ = ["SeedHistoryNode"]

kikotools/tools/seed_history/logic.py

@@ -0,0 +1,295 @@
+"""Core logic for Seed History tool."""
+
+import random
+import time
+from typing import List, Dict, Any, Tuple, Optional
+
+
+def generate_random_seed() -> int:
+    """
+    Generate a cryptographically strong random seed value.
+
+    Returns:
+        Random integer in the valid ComfyUI seed range
+    """
+    return random.randint(0, 0xFFFFFFFFFFFFFFFF)
+
+
+def validate_seed_value(seed: Any) -> bool:
+    """
+    Validate that a seed value is within acceptable range.
+
+    Args:
+        seed: Seed value to validate
+
+    Returns:
+        True if seed is valid, False otherwise
+    """
+    if seed is None:
+        return False
+
+    try:
+        seed_int = int(seed)
+        return 0 <= seed_int <= 0xFFFFFFFFFFFFFFFF
+    except (ValueError, TypeError):
+        return False
+
+
+def sanitize_seed_value(seed: Any) -> int:
+    """
+    Sanitize and convert seed value to valid integer.
+
+    Args:
+        seed: Raw seed value
+
+    Returns:
+        Valid seed integer
+
+    Raises:
+        ValueError: If seed cannot be converted to valid range
+    """
+    if seed is None:
+        raise ValueError("Seed cannot be None")
+
+    try:
+        seed_int = int(seed)
+
+        # Clamp to valid range
+        if seed_int < 0:
+            seed_int = 0
+        elif seed_int > 0xFFFFFFFFFFFFFFFF:
+            seed_int = 0xFFFFFFFFFFFFFFFF
+
+        return seed_int
+
+    except (ValueError, TypeError) as e:
+        raise ValueError(f"Invalid seed value: {seed}") from e
+
+
+def create_history_entry(
+    seed: int, timestamp: Optional[float] = None
+) -> Dict[str, Any]:
+    """
+    Create a standardized history entry for a seed.
+
+    Args:
+        seed: Seed value
+        timestamp: Optional timestamp (uses current time if None)
+
+    Returns:
+        Dictionary containing seed history entry
+    """
+    if timestamp is None:
+        timestamp = time.time()
+
+    return {
+        "seed": seed,
+        "timestamp": timestamp,
+        "dateString": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)),
+    }
+
+
+def filter_duplicate_seeds(
+    history: List[Dict[str, Any]], new_seed: int, dedup_window_ms: int = 500
+) -> bool:
+    """
+    Check if a seed should be filtered as a duplicate.
+
+    Args:
+        history: Current seed history
+        new_seed: New seed to check
+        dedup_window_ms: Deduplication window in milliseconds
+
+    Returns:
+        True if seed should be filtered (is duplicate), False otherwise
+    """
+    if not history:
+        return False
+
+    current_time = time.time() * 1000  # Convert to milliseconds
+
+    # Check most recent entry for duplicates within window
+    latest_entry = history[0]
+    latest_timestamp_ms = latest_entry["timestamp"] * 1000
+
+    time_diff = current_time - latest_timestamp_ms
+    is_same_seed = latest_entry["seed"] == new_seed
+    is_within_window = time_diff < dedup_window_ms
+
+    return is_same_seed and is_within_window
+
+
+def add_seed_to_history(
+    history: List[Dict[str, Any]],
+    seed: int,
+    max_history: int = 10,
+    dedup_window_ms: int = 500,
+) -> Tuple[List[Dict[str, Any]], bool]:
+    """
+    Add a seed to the history with deduplication and size management.
+
+    Args:
+        history: Current seed history
+        seed: Seed to add
+        max_history: Maximum number of entries to keep
+        dedup_window_ms: Deduplication window in milliseconds
+
+    Returns:
+        Tuple of (updated_history, was_added)
+    """
+    # Validate seed
+    if not validate_seed_value(seed):
+        return history, False
+
+    # Sanitize seed
+    try:
+        clean_seed = sanitize_seed_value(seed)
+    except ValueError:
+        return history, False
+
+    # Check for duplicates
+    if filter_duplicate_seeds(history, clean_seed, dedup_window_ms):
+        return history, False
+
+    # Create new history list (don't modify original)
+    new_history = [entry for entry in history if entry["seed"] != clean_seed]
+
+    # Add new entry at the beginning
+    new_entry = create_history_entry(clean_seed)
+    new_history.insert(0, new_entry)
+
+    # Trim to max size
+    if len(new_history) > max_history:
+        new_history = new_history[:max_history]
+
+    return new_history, True
+
+
+def format_time_ago(timestamp: float) -> str:
+    """
+    Format a timestamp as a human-readable time ago string.
+
+    Args:
+        timestamp: Unix timestamp
+
+    Returns:
+        Formatted time ago string
+    """
+    now = time.time()
+    diff = now - timestamp
+
+    days = int(diff // 86400)
+    hours = int((diff % 86400) // 3600)
+    minutes = int((diff % 3600) // 60)
+    seconds = int(diff % 60)
+
+    if days > 0:
+        return f"{days}d ago"
+    elif hours > 0:
+        return f"{hours}h ago"
+    elif minutes > 0:
+        return f"{minutes}m ago"
+    else:
+        return f"{seconds}s ago"
+
+
+def search_history_by_seed(
+    history: List[Dict[str, Any]], seed: int
+) -> Optional[Dict[str, Any]]:
+    """
+    Search history for a specific seed value.
+
+    Args:
+        history: Seed history to search
+        seed: Seed value to find
+
+    Returns:
+        History entry if found, None otherwise
+    """
+    for entry in history:
+        if entry["seed"] == seed:
+            return entry
+    return None
+
+
+def get_history_statistics(history: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Calculate statistics about the seed history.
+
+    Args:
+        history: Seed history
+
+    Returns:
+        Dictionary containing history statistics
+    """
+    if not history:
+        return {
+            "total_seeds": 0,
+            "oldest_timestamp": None,
+            "newest_timestamp": None,
+            "time_span_hours": 0,
+            "unique_seeds": 0,
+        }
+
+    timestamps = [entry["timestamp"] for entry in history]
+    oldest = min(timestamps)
+    newest = max(timestamps)
+    time_span = (newest - oldest) / 3600  # Convert to hours
+
+    unique_seeds = len(set(entry["seed"] for entry in history))
+
+    return {
+        "total_seeds": len(history),
+        "oldest_timestamp": oldest,
+        "newest_timestamp": newest,
+        "time_span_hours": time_span,
+        "unique_seeds": unique_seeds,
+    }
+
+
+def export_history_to_text(history: List[Dict[str, Any]]) -> str:
+    """
+    Export seed history to a formatted text string.
+
+    Args:
+        history: Seed history to export
+
+    Returns:
+        Formatted text representation
+    """
+    if not history:
+        return "# Seed History (Empty)\n\nNo seeds tracked yet."
+
+    lines = ["# ComfyUI Seed History", ""]
+    lines.append(f"Generated: {time.strftime('%Y-%m-%d %H:%M:%S')}")
+    lines.append(f"Total seeds: {len(history)}")
+    lines.append("")
+
+    for i, entry in enumerate(history, 1):
+        time_ago = format_time_ago(entry["timestamp"])
+        lines.append(f"{i:2d}. {entry['seed']} ({time_ago})")
+
+    return "\n".join(lines)
+
+
+def import_seeds_from_list(seed_list: List[int]) -> List[Dict[str, Any]]:
+    """
+    Import a list of seeds as history entries.
+
+    Args:
+        seed_list: List of seed integers
+
+    Returns:
+        List of history entries
+    """
+    history = []
+    current_time = time.time()
+
+    for i, seed in enumerate(seed_list):
+        if validate_seed_value(seed):
+            # Spread timestamps by 1 minute intervals (newest first)
+            timestamp = current_time - (i * 60)
+            entry = create_history_entry(seed, timestamp)
+            history.append(entry)
+
+    return history

kikotools/tools/seed_history/node.py

@@ -0,0 +1,183 @@
+"""Seed History node for ComfyUI."""
+
+from typing import Tuple
+from ...base.base_node import ComfyAssetsBaseNode
+from .logic import (
+    generate_random_seed,
+    validate_seed_value,
+    sanitize_seed_value,
+)
+
+
+class SeedHistoryNode(ComfyAssetsBaseNode):
+    """
+    Seed History node for tracking and managing seed values.
+
+    Provides seed value output with integrated history tracking,
+    deduplication, and interactive UI for seed management.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define the input types for the ComfyUI node."""
+        return {
+            "required": {
+                "seed": (
+                    "INT",
+                    {
+                        "default": 12345,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "tooltip": "Seed value for generation processes. "
+                        "History UI tracks all changes automatically.",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+    FUNCTION = "output_seed"
+    CATEGORY = "ComfyAssets/🌱 Seeds"
+
+    def output_seed(self, seed: int) -> Tuple[int]:
+        """
+        Output the seed value for use in other nodes.
+
+        Args:
+            seed: Input seed value
+
+        Returns:
+            Tuple containing the seed value
+        """
+        try:
+            # Validate and sanitize the seed
+            if not validate_seed_value(seed):
+                # Log the validation error but don't raise
+                import logging
+
+                logger = logging.getLogger(__name__)
+                logger.error(
+                    f"{self.__class__.__name__}: Invalid seed value: {seed}. "
+                    f"Using fallback seed 12345."
+                )
+                return (12345,)
+
+            clean_seed = sanitize_seed_value(seed)
+
+            return (clean_seed,)
+
+        except Exception as e:
+            # Handle any unexpected errors gracefully
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.error(
+                f"{self.__class__.__name__}: Error processing seed: {str(e)}. "
+                f"Using fallback seed 12345."
+            )
+            return (12345,)
+
+    def generate_new_seed(self) -> int:
+        """
+        Generate a new random seed value.
+
+        Returns:
+            New random seed integer
+        """
+        try:
+            new_seed = generate_random_seed()
+            self.log_info(f"Generated new seed: {new_seed}")
+            return new_seed
+        except Exception as e:
+            error_msg = f"Error generating seed: {str(e)}. Using fallback."
+            self.handle_error(error_msg)
+            return 12345
+
+    def validate_seed_input(self, seed: int) -> bool:
+        """
+        Validate seed input value.
+
+        Args:
+            seed: Seed value to validate
+
+        Returns:
+            True if seed is valid
+        """
+        return validate_seed_value(seed)
+
+    def get_seed_info(self, seed: int) -> str:
+        """
+        Get descriptive information about a seed value.
+
+        Args:
+            seed: Seed value
+
+        Returns:
+            Information string about the seed
+        """
+        if not validate_seed_value(seed):
+            return f"Invalid seed: {seed} (outside valid range)"
+
+        # Convert to hex for additional info
+        hex_value = hex(seed)
+
+        # Check if it's a "nice" number (power of 2, round number, etc.)
+        seed_type = "standard"
+        if seed == 0:
+            seed_type = "zero"
+        elif seed & (seed - 1) == 0:  # Power of 2
+            seed_type = "power of 2"
+        elif str(seed).count("0") > len(str(seed)) // 2:
+            seed_type = "round number"
+        elif seed == 12345:
+            seed_type = "default"
+
+        return f"Seed {seed} ({hex_value}) - {seed_type}"
+
+    def get_seed_range_info(self) -> str:
+        """
+        Get information about the valid seed range.
+
+        Returns:
+            Range information string
+        """
+        max_seed = 0xFFFFFFFFFFFFFFFF
+        return f"Valid range: 0 to {max_seed:,} ({hex(max_seed)})"
+
+    @classmethod
+    def get_default_seed(cls) -> int:
+        """
+        Get the default seed value.
+
+        Returns:
+            Default seed integer
+        """
+        return 12345
+
+    @classmethod
+    def is_seed_in_range(cls, seed: int) -> bool:
+        """
+        Check if seed is within valid ComfyUI range.
+
+        Args:
+            seed: Seed value to check
+
+        Returns:
+            True if seed is in valid range
+        """
+        return 0 <= seed <= 0xFFFFFFFFFFFFFFFF
+
+    def __str__(self) -> str:
+        """String representation of the node."""
+        return "SeedHistoryNode(with_ui_tracking)"
+
+    def __repr__(self) -> str:
+        """Detailed string representation of the node."""
+        return (
+            f"SeedHistoryNode("
+            f"category='{self.CATEGORY}', "
+            f"function='{self.FUNCTION}', "
+            f"max_seed={hex(0xFFFFFFFFFFFFFFFF)}"
+            f")"
+        )

kikotools/tools/width_height_selector/node.py

@@ -4,14 +4,15 @@ from typing import Tuple
 from ...base.base_node import ComfyAssetsBaseNode
 from .logic import (
     get_preset_dimensions,
-    calculate_aspect_ratio,
     validate_dimensions,
     sanitize_dimensions,
 )
 from .presets import (
     PRESET_OPTIONS,
-    PRESET_DESCRIPTIONS,
+    PRESET_METADATA,
     get_model_recommendation,
+    get_preset_metadata,
+    get_presets_by_model_group,
 )
 
 
@@ -26,18 +27,32 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
     @classmethod
     def INPUT_TYPES(cls):
         """Define the input types for the ComfyUI node."""
-        # Get all preset options excluding the custom tuple
-        preset_keys = [key for key in PRESET_OPTIONS.keys()]
+        # Create formatted preset options with metadata
+        preset_options = ["custom"]  # Custom first
+
+        # Add formatted presets with metadata
+        for preset_name in PRESET_OPTIONS.keys():
+            if preset_name != "custom":
+                metadata = PRESET_METADATA.get(preset_name)
+                if metadata:
+                    formatted_option = (
+                        f"{preset_name} - {metadata.aspect_ratio} "
+                        f"({metadata.megapixels:.1f}MP) - {metadata.model_group}"
+                    )
+                    preset_options.append(formatted_option)
+                else:
+                    preset_options.append(preset_name)
 
         return {
             "required": {
                 "preset": (
-                    preset_keys,
+                    preset_options,
                     {
                         "default": "custom",
-                        "tooltip": "Select from optimized resolution presets or use custom dimensions. "
-                        "SDXL presets are ~1MP, FLUX presets are higher resolution, "
-                        "Ultra-wide presets support modern aspect ratios.",
+                        "tooltip": "Select from optimized resolution presets or use "
+                        "custom dimensions. SDXL presets are ~1MP, FLUX presets are "
+                        "higher resolution, Ultra-wide presets support modern "
+                        "aspect ratios.",
                     },
                 ),
                 "width": (
@@ -48,7 +63,8 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
                         "max": 8192,
                         "step": 8,
                         "tooltip": "Custom width in pixels (must be multiple of 8). "
-                        "Used when preset is 'custom' or as fallback for invalid presets.",
+                        "Used when preset is 'custom' or as fallback for invalid "
+                        "presets.",
                     },
                 ),
                 "height": (
@@ -59,7 +75,8 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
                         "max": 8192,
                         "step": 8,
                         "tooltip": "Custom height in pixels (must be multiple of 8). "
-                        "Used when preset is 'custom' or as fallback for invalid presets.",
+                        "Used when preset is 'custom' or as fallback for invalid "
+                        "presets.",
                     },
                 ),
             }
@@ -68,14 +85,14 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
     RETURN_TYPES = ("INT", "INT")
     RETURN_NAMES = ("width", "height")
     FUNCTION = "get_dimensions"
-    CATEGORY = "ComfyAssets"
+    CATEGORY = "ComfyAssets/🖼️ Resolution"
 
     def get_dimensions(self, preset: str, width: int, height: int) -> Tuple[int, int]:
         """
         Get width and height dimensions with preset and swap support.
 
         Args:
-            preset: Selected preset name or "custom"
+            preset: Selected preset name or formatted preset string
             width: Custom width value
             height: Custom height value
 
@@ -83,8 +100,13 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
             Tuple of (width, height)
         """
         try:
+            # Extract original preset name from formatted string if needed
+            original_preset = self._extract_preset_name(preset)
+
             # Get base dimensions from preset or custom input
-            final_width, final_height = get_preset_dimensions(preset, width, height)
+            final_width, final_height = get_preset_dimensions(
+                original_preset, width, height
+            )
 
             # Sanitize dimensions to ensure they meet ComfyUI requirements
             final_width, final_height = sanitize_dimensions(final_width, final_height)
@@ -108,6 +130,37 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
             self.handle_error(error_msg)
             return (1024, 1024)
 
+    def _extract_preset_name(self, formatted_preset: str) -> str:
+        """
+        Extract the original preset name from a formatted preset string.
+
+        Args:
+            formatted_preset: Either original preset name or formatted string
+
+        Returns:
+            Original preset name
+        """
+        # If it's already "custom", return as-is
+        if formatted_preset == "custom":
+            return formatted_preset
+
+        # If it contains formatting metadata, extract the resolution part
+        if " - " in formatted_preset:
+            # Format is: "1024×1024 - 1:1 (1.0MP) - SDXL"
+            # Extract the first part (resolution)
+            resolution_part = formatted_preset.split(" - ")[0]
+
+            # Verify this is a valid preset name
+            if resolution_part in PRESET_OPTIONS:
+                return resolution_part
+
+        # If no formatting or not found, check if it's directly a valid preset
+        if formatted_preset in PRESET_OPTIONS:
+            return formatted_preset
+
+        # Default to "custom" if we can't parse it
+        return "custom"
+
     def get_preset_info(self, preset: str) -> str:
         """
         Get descriptive information about a preset.
@@ -121,14 +174,12 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
         if preset == "custom":
             return "Custom dimensions - use the width and height inputs below"
 
-        if preset in PRESET_DESCRIPTIONS:
-            return PRESET_DESCRIPTIONS[preset]
-
-        # Fallback for unknown presets
-        if preset in PRESET_OPTIONS:
-            width, height = PRESET_OPTIONS[preset]
-            aspect_ratio = calculate_aspect_ratio(width, height)
-            return f"{preset} - {aspect_ratio} aspect ratio"
+        metadata = get_preset_metadata(preset)
+        if metadata.width > 0:  # Valid metadata
+            return (
+                f"{preset} - {metadata.aspect_ratio} ({metadata.megapixels:.1f}MP) - "
+                f"{metadata.description}"
+            )
 
         return f"Unknown preset: {preset}"
 
@@ -149,19 +200,22 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
         Validate node inputs.
 
         Args:
-            preset: Preset name
+            preset: Preset name or formatted preset string
             width: Width value
             height: Height value
 
         Returns:
             True if inputs are valid
         """
+        # Extract original preset name
+        original_preset = self._extract_preset_name(preset)
+
         # Check if preset exists or is custom
-        if preset != "custom" and preset not in PRESET_OPTIONS:
+        if original_preset != "custom" and original_preset not in PRESET_OPTIONS:
             return False
 
         # For custom preset, validate dimensions
-        if preset == "custom":
+        if original_preset == "custom":
             if not validate_dimensions(width, height):
                 return False
 
@@ -192,6 +246,52 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
             return PRESET_OPTIONS[preset]
         return (0, 0)
 
+    @classmethod
+    def get_presets_by_model(cls, model_group: str) -> dict:
+        """
+        Get all presets for a specific model group with metadata.
+
+        Args:
+            model_group: Model group name ("SDXL", "FLUX", "Ultra-Wide")
+
+        Returns:
+            Dictionary of presets with metadata
+        """
+        return get_presets_by_model_group(model_group)
+
+    @classmethod
+    def get_preset_metadata_static(cls, preset: str) -> dict:
+        """
+        Get metadata for a preset as a dictionary.
+
+        Args:
+            preset: Preset name
+
+        Returns:
+            Dictionary with metadata information
+        """
+        metadata = get_preset_metadata(preset)
+        return {
+            "width": metadata.width,
+            "height": metadata.height,
+            "aspect_ratio": metadata.aspect_ratio,
+            "aspect_decimal": metadata.aspect_decimal,
+            "megapixels": metadata.megapixels,
+            "model_group": metadata.model_group,
+            "category": metadata.category,
+            "description": metadata.description,
+        }
+
+    @classmethod
+    def get_model_groups(cls) -> list:
+        """
+        Get list of available model groups.
+
+        Returns:
+            List of model group names
+        """
+        return list(set(metadata.model_group for metadata in PRESET_METADATA.values()))
+
     def __str__(self) -> str:
         """String representation of the node."""
         return f"WidthHeightSelectorNode(presets={len(PRESET_OPTIONS)})"

kikotools/tools/width_height_selector/presets.py

@@ -1,114 +1,435 @@
 """Preset definitions for Width Height Selector."""
 
-from typing import Dict, Tuple
+from typing import Dict, Tuple, NamedTuple
+from fractions import Fraction
 
-# SDXL optimized presets (~1 megapixel, dimensions divisible by 8)
+
+class PresetMetadata(NamedTuple):
+    """Metadata for a resolution preset."""
+
+    width: int
+    height: int
+    aspect_ratio: str
+    aspect_decimal: float
+    megapixels: float
+    model_group: str
+    category: str
+    description: str
+
+
+def calculate_aspect_ratio(width: int, height: int) -> Tuple[str, float]:
+    """Calculate aspect ratio as string and decimal."""
+    fraction = Fraction(width, height)
+    decimal = width / height
+    return f"{fraction.numerator}:{fraction.denominator}", decimal
+
+
+# Enhanced preset definitions with full metadata
+PRESET_METADATA: Dict[str, PresetMetadata] = {
+    # SDXL Presets - Square
+    "1024×1024": PresetMetadata(
+        1024,
+        1024,
+        "1:1",
+        1.0,
+        1.05,
+        "SDXL",
+        "Square",
+        "SDXL base resolution - perfect square",
+    ),
+    # SDXL Presets - Portrait
+    "896×1152": PresetMetadata(
+        896,
+        1152,
+        "7:9",
+        0.778,
+        1.03,
+        "SDXL",
+        "Portrait",
+        "SDXL portrait 7:9 - moderate portrait",
+    ),
+    "832×1216": PresetMetadata(
+        832,
+        1216,
+        "13:19",
+        0.684,
+        1.01,
+        "SDXL",
+        "Portrait",
+        "SDXL portrait 13:19 - standard portrait",
+    ),
+    "768×1344": PresetMetadata(
+        768,
+        1344,
+        "4:7",
+        0.571,
+        1.03,
+        "SDXL",
+        "Portrait",
+        "SDXL portrait 4:7 - tall portrait",
+    ),
+    "640×1536": PresetMetadata(
+        640,
+        1536,
+        "5:12",
+        0.417,
+        0.98,
+        "SDXL",
+        "Portrait",
+        "SDXL portrait 5:12 - very tall portrait",
+    ),
+    # SDXL Presets - Landscape
+    "1152×896": PresetMetadata(
+        1152,
+        896,
+        "9:7",
+        1.286,
+        1.03,
+        "SDXL",
+        "Landscape",
+        "SDXL landscape 9:7 - moderate landscape",
+    ),
+    "1216×832": PresetMetadata(
+        1216,
+        832,
+        "19:13",
+        1.462,
+        1.01,
+        "SDXL",
+        "Landscape",
+        "SDXL landscape 19:13 - standard landscape",
+    ),
+    "1344×768": PresetMetadata(
+        1344,
+        768,
+        "7:4",
+        1.750,
+        1.03,
+        "SDXL",
+        "Landscape",
+        "SDXL landscape 7:4 - wide landscape",
+    ),
+    "1536×640": PresetMetadata(
+        1536,
+        640,
+        "12:5",
+        2.400,
+        0.98,
+        "SDXL",
+        "Landscape",
+        "SDXL landscape 12:5 - very wide landscape",
+    ),
+    # FLUX Presets - High Quality
+    "1920×1080": PresetMetadata(
+        1920,
+        1080,
+        "16:9",
+        1.778,
+        2.07,
+        "FLUX",
+        "Cinematic",
+        "FLUX Full HD 16:9 - best quality/speed balance",
+    ),
+    "1536×1536": PresetMetadata(
+        1536,
+        1536,
+        "1:1",
+        1.0,
+        2.36,
+        "FLUX",
+        "Square",
+        "FLUX high-res square - premium quality",
+    ),
+    "1280×768": PresetMetadata(
+        1280,
+        768,
+        "5:3",
+        1.667,
+        0.98,
+        "FLUX",
+        "Cinematic",
+        "FLUX 5:3 landscape - cinematic wide",
+    ),
+    "768×1280": PresetMetadata(
+        768,
+        1280,
+        "3:5",
+        0.600,
+        0.98,
+        "FLUX",
+        "Portrait",
+        "FLUX 3:5 portrait - mobile optimized",
+    ),
+    # FLUX Presets - Alternative
+    "1440×1080": PresetMetadata(
+        1440,
+        1080,
+        "4:3",
+        1.333,
+        1.56,
+        "FLUX",
+        "Classic",
+        "FLUX 4:3 classic - traditional aspect ratio",
+    ),
+    "1080×1440": PresetMetadata(
+        1080,
+        1440,
+        "3:4",
+        0.750,
+        1.56,
+        "FLUX",
+        "Portrait",
+        "FLUX 3:4 portrait - classic portrait",
+    ),
+    "1728×1152": PresetMetadata(
+        1728,
+        1152,
+        "3:2",
+        1.500,
+        1.99,
+        "FLUX",
+        "Photography",
+        "FLUX 3:2 photo - photography standard",
+    ),
+    "1152×1728": PresetMetadata(
+        1152,
+        1728,
+        "2:3",
+        0.667,
+        1.99,
+        "FLUX",
+        "Portrait",
+        "FLUX 2:3 portrait - portrait photography",
+    ),
+    # Ultra-Wide Presets - Landscape
+    "2560×1080": PresetMetadata(
+        2560,
+        1080,
+        "64:27",
+        2.370,
+        2.76,
+        "Ultra-Wide",
+        "Gaming",
+        "Ultra-wide 64:27 - gaming/panoramic",
+    ),
+    "2048×768": PresetMetadata(
+        2048,
+        768,
+        "8:3",
+        2.667,
+        1.57,
+        "Ultra-Wide",
+        "Cinematic",
+        "Wide cinematic 8:3 - movie aspect",
+    ),
+    "1792×768": PresetMetadata(
+        1792,
+        768,
+        "7:3",
+        2.333,
+        1.38,
+        "Ultra-Wide",
+        "Panoramic",
+        "Panoramic 7:3 - landscape vista",
+    ),
+    "2304×768": PresetMetadata(
+        2304,
+        768,
+        "3:1",
+        3.000,
+        1.77,
+        "Ultra-Wide",
+        "Banner",
+        "Banner 3:1 - extreme wide banner",
+    ),
+    # Ultra-Wide Presets - Portrait
+    "1080×2560": PresetMetadata(
+        1080,
+        2560,
+        "27:64",
+        0.422,
+        2.76,
+        "Ultra-Wide",
+        "Mobile",
+        "Mobile ultra-tall 27:64 - modern phones",
+    ),
+    "768×2048": PresetMetadata(
+        768,
+        2048,
+        "3:8",
+        0.375,
+        1.57,
+        "Ultra-Wide",
+        "Vertical",
+        "Vertical cinematic 3:8 - portrait video",
+    ),
+    "768×1792": PresetMetadata(
+        768,
+        1792,
+        "3:7",
+        0.429,
+        1.38,
+        "Ultra-Wide",
+        "Vertical",
+        "Vertical panoramic 3:7 - tall vista",
+    ),
+    "768×2304": PresetMetadata(
+        768,
+        2304,
+        "1:3",
+        0.333,
+        1.77,
+        "Ultra-Wide",
+        "Banner",
+        "Vertical banner 1:3 - extreme tall banner",
+    ),
+}
+
+# Legacy compatibility - maintain old preset dictionaries
 SDXL_PRESETS: Dict[str, Tuple[int, int]] = {
-    # Square
-    "1024×1024": (1024, 1024),  # 1:1 - Base SDXL resolution
-    # Portrait ratios
-    "896×1152": (896, 1152),  # 7:9 - Moderate portrait
-    "832×1216": (832, 1216),  # 13:19 - Standard portrait
-    "768×1344": (768, 1344),  # 4:7 - Tall portrait
-    "640×1536": (640, 1536),  # 5:12 - Very tall portrait
-    # Landscape ratios
-    "1152×896": (1152, 896),  # 9:7 - Moderate landscape
-    "1216×832": (1216, 832),  # 19:13 - Standard landscape
-    "1344×768": (1344, 768),  # 7:4 - Wide landscape
-    "1536×640": (1536, 640),  # 12:5 - Very wide landscape
+    k: (v.width, v.height)
+    for k, v in PRESET_METADATA.items()
+    if v.model_group == "SDXL"
 }
 
-# FLUX optimized presets (higher resolution, flexible ratios)
 FLUX_PRESETS: Dict[str, Tuple[int, int]] = {
-    # Recommended high-quality resolutions
-    "1920×1080": (1920, 1080),  # 16:9 - Full HD landscape
-    "1536×1536": (1536, 1536),  # 1:1 - High-res square
-    "1280×768": (1280, 768),  # 5:3 - Wide landscape
-    "768×1280": (768, 1280),  # 3:5 - Tall portrait
-    # Alternative quality resolutions
-    "1440×1080": (1440, 1080),  # 4:3 - Classic aspect ratio
-    "1080×1440": (1080, 1440),  # 3:4 - Classic portrait
-    "1728×1152": (1728, 1152),  # 3:2 - Photography standard
-    "1152×1728": (1152, 1728),  # 2:3 - Portrait photography
+    k: (v.width, v.height)
+    for k, v in PRESET_METADATA.items()
+    if v.model_group == "FLUX"
 }
 
-# Ultra-wide and modern aspect ratios
 ULTRA_WIDE_PRESETS: Dict[str, Tuple[int, int]] = {
-    # Ultra-wide landscape (21:9 and variants)
-    "2560×1080": (2560, 1080),  # 64:27 - Ultra-wide gaming
-    "2048×768": (2048, 768),  # 8:3 - Wide cinematic
-    "1792×768": (1792, 768),  # 7:3 - Panoramic
-    # Ultra-wide portrait
-    "1080×2560": (1080, 2560),  # 27:64 - Mobile ultra-tall
-    "768×2048": (768, 2048),  # 3:8 - Vertical cinematic
-    "768×1792": (768, 1792),  # 3:7 - Vertical panoramic
-    # Extreme ratios
-    "2304×768": (2304, 768),  # 3:1 - Banner landscape
-    "768×2304": (768, 2304),  # 1:3 - Banner portrait
+    k: (v.width, v.height)
+    for k, v in PRESET_METADATA.items()
+    if v.model_group == "Ultra-Wide"
 }
 
 # Combined preset options for ComfyUI dropdown
 PRESET_OPTIONS: Dict[str, Tuple[int, int]] = {
     "custom": (0, 0),  # Special case for custom dimensions
-    **SDXL_PRESETS,
-    **FLUX_PRESETS,
-    **ULTRA_WIDE_PRESETS,
+    **{k: (v.width, v.height) for k, v in PRESET_METADATA.items()},
 }
 
-# Organized preset categories for better UX
+# Enhanced preset categories organized by model groups and aspect ratios
 PRESET_CATEGORIES = {
     "Custom": ["custom"],
-    "SDXL Square": ["1024×1024"],
-    "SDXL Portrait": ["896×1152", "832×1216", "768×1344", "640×1536"],
-    "SDXL Landscape": ["1152×896", "1216×832", "1344×768", "1536×640"],
-    "FLUX Recommended": ["1920×1080", "1536×1536", "1280×768", "768×1280"],
-    "FLUX Alternative": ["1440×1080", "1080×1440", "1728×1152", "1152×1728"],
-    "Ultra-Wide Landscape": ["2560×1080", "2048×768", "1792×768", "2304×768"],
-    "Ultra-Wide Portrait": ["1080×2560", "768×2048", "768×1792", "768×2304"],
+    # SDXL Categories
+    "SDXL Square": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "SDXL" and v.category == "Square"
+    ],
+    "SDXL Portrait": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "SDXL" and v.category == "Portrait"
+    ],
+    "SDXL Landscape": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "SDXL" and v.category == "Landscape"
+    ],
+    # FLUX Categories
+    "FLUX Square": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "FLUX" and v.category == "Square"
+    ],
+    "FLUX Portrait": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "FLUX" and v.category == "Portrait"
+    ],
+    "FLUX Cinematic": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "FLUX" and v.category == "Cinematic"
+    ],
+    "FLUX Classic": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "FLUX" and v.category == "Classic"
+    ],
+    "FLUX Photography": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "FLUX" and v.category == "Photography"
+    ],
+    # Ultra-Wide Categories
+    "Ultra-Wide Gaming": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "Ultra-Wide" and v.category == "Gaming"
+    ],
+    "Ultra-Wide Cinematic": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "Ultra-Wide" and v.category == "Cinematic"
+    ],
+    "Ultra-Wide Panoramic": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "Ultra-Wide" and v.category == "Panoramic"
+    ],
+    "Ultra-Wide Mobile": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "Ultra-Wide" and v.category == "Mobile"
+    ],
+    "Ultra-Wide Vertical": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "Ultra-Wide" and v.category == "Vertical"
+    ],
+    "Ultra-Wide Banner": [
+        k
+        for k, v in PRESET_METADATA.items()
+        if v.model_group == "Ultra-Wide" and v.category == "Banner"
+    ],
 }
 
-# Preset descriptions for tooltips
-PRESET_DESCRIPTIONS = {
-    # SDXL presets
-    "1024×1024": "SDXL base resolution - perfect square",
-    "896×1152": "SDXL portrait 7:9 - moderate portrait",
-    "832×1216": "SDXL portrait 13:19 - standard portrait",
-    "768×1344": "SDXL portrait 4:7 - tall portrait",
-    "640×1536": "SDXL portrait 5:12 - very tall portrait",
-    "1152×896": "SDXL landscape 9:7 - moderate landscape",
-    "1216×832": "SDXL landscape 19:13 - standard landscape",
-    "1344×768": "SDXL landscape 7:4 - wide landscape",
-    "1536×640": "SDXL landscape 12:5 - very wide landscape",
-    # FLUX presets
-    "1920×1080": "FLUX Full HD 16:9 - best quality/speed balance",
-    "1536×1536": "FLUX high-res square - premium quality",
-    "1280×768": "FLUX 5:3 landscape - cinematic wide",
-    "768×1280": "FLUX 3:5 portrait - mobile optimized",
-    "1440×1080": "FLUX 4:3 classic - traditional aspect ratio",
-    "1080×1440": "FLUX 3:4 portrait - classic portrait",
-    "1728×1152": "FLUX 3:2 photo - photography standard",
-    "1152×1728": "FLUX 2:3 portrait - portrait photography",
-    # Ultra-wide presets
-    "2560×1080": "Ultra-wide 64:27 - gaming/panoramic",
-    "2048×768": "Wide cinematic 8:3 - movie aspect",
-    "1792×768": "Panoramic 7:3 - landscape vista",
-    "2304×768": "Banner 3:1 - extreme wide banner",
-    "1080×2560": "Mobile ultra-tall 27:64 - modern phones",
-    "768×2048": "Vertical cinematic 3:8 - portrait video",
-    "768×1792": "Vertical panoramic 3:7 - tall vista",
-    "768×2304": "Vertical banner 1:3 - extreme tall banner",
-}
+# Legacy compatibility - preset descriptions
+PRESET_DESCRIPTIONS = {k: v.description for k, v in PRESET_METADATA.items()}
 
-# Model-specific recommendations
+# Model-specific recommendations with metadata
 MODEL_RECOMMENDATIONS = {
-    "SDXL": list(SDXL_PRESETS.keys()),
-    "FLUX": list(FLUX_PRESETS.keys()),
-    "Ultra-Wide": list(ULTRA_WIDE_PRESETS.keys()),
+    "SDXL": [k for k, v in PRESET_METADATA.items() if v.model_group == "SDXL"],
+    "FLUX": [k for k, v in PRESET_METADATA.items() if v.model_group == "FLUX"],
+    "Ultra-Wide": [
+        k for k, v in PRESET_METADATA.items() if v.model_group == "Ultra-Wide"
+    ],
 }
 
 
+# New metadata-aware helper functions
+def get_presets_by_model_group(model_group: str) -> Dict[str, PresetMetadata]:
+    """Get all presets for a specific model group."""
+    return {k: v for k, v in PRESET_METADATA.items() if v.model_group == model_group}
+
+
+def get_presets_by_aspect_ratio(aspect_ratio: str) -> Dict[str, PresetMetadata]:
+    """Get all presets with a specific aspect ratio."""
+    return {k: v for k, v in PRESET_METADATA.items() if v.aspect_ratio == aspect_ratio}
+
+
+def get_presets_by_category(category: str) -> Dict[str, PresetMetadata]:
+    """Get all presets in a specific category."""
+    return {k: v for k, v in PRESET_METADATA.items() if v.category == category}
+
+
+def get_preset_metadata(preset_name: str) -> PresetMetadata:
+    """Get metadata for a specific preset."""
+    return PRESET_METADATA.get(
+        preset_name,
+        PresetMetadata(0, 0, "1:1", 1.0, 0.0, "Custom", "Custom", "Custom dimensions"),
+    )
+
+
 def get_preset_category(preset_name: str) -> str:
     """Get the category for a given preset name."""
+    metadata = PRESET_METADATA.get(preset_name)
+    if metadata:
+        return metadata.category
     for category, presets in PRESET_CATEGORIES.items():
         if preset_name in presets:
             return category
@@ -117,21 +438,17 @@ def get_preset_category(preset_name: str) -> str:
 
 def get_model_recommendation(preset_name: str) -> str:
     """Get model recommendation for a given preset."""
-    if preset_name in SDXL_PRESETS:
-        return "Optimized for SDXL"
-    elif preset_name in FLUX_PRESETS:
-        return "Optimized for FLUX"
-    elif preset_name in ULTRA_WIDE_PRESETS:
-        return "Modern ultra-wide ratios"
-    else:
-        return "Custom dimensions"
+    metadata = PRESET_METADATA.get(preset_name)
+    if metadata:
+        return f"Optimized for {metadata.model_group}"
+    return "Custom dimensions"
 
 
 def validate_preset_dimensions() -> bool:
     """Validate that all presets meet ComfyUI requirements."""
-    all_presets = {**SDXL_PRESETS, **FLUX_PRESETS, **ULTRA_WIDE_PRESETS}
+    for preset_name, metadata in PRESET_METADATA.items():
+        width, height = metadata.width, metadata.height
 
-    for preset_name, (width, height) in all_presets.items():
         # Check divisible by 8
         if width % 8 != 0 or height % 8 != 0:
             print(
@@ -147,6 +464,36 @@ def validate_preset_dimensions() -> bool:
     return True
 
 
+# Additional validation for metadata consistency
+def validate_metadata_consistency() -> bool:
+    """Validate metadata consistency and completeness."""
+    for preset_name, metadata in PRESET_METADATA.items():
+        # Verify aspect ratio calculation
+        expected_ratio, expected_decimal = calculate_aspect_ratio(
+            metadata.width, metadata.height
+        )
+        if abs(metadata.aspect_decimal - expected_decimal) > 0.001:
+            print(
+                f"ERROR: {preset_name} aspect ratio mismatch: "
+                f"expected {expected_decimal:.3f}, got {metadata.aspect_decimal}"
+            )
+            return False
+
+        # Verify megapixel calculation
+        expected_mp = (metadata.width * metadata.height) / 1_000_000
+        if abs(metadata.megapixels - expected_mp) > 0.1:
+            print(
+                f"ERROR: {preset_name} megapixel mismatch: "
+                f"expected {expected_mp:.2f}, got {metadata.megapixels}"
+            )
+            return False
+
+    return True
+
+
 # Validate presets on import
 if not validate_preset_dimensions():
     raise ValueError("Preset validation failed - check console for details")
+
+if not validate_metadata_consistency():
+    raise ValueError("Metadata validation failed - check console for details")

kikotools/tools/xyz_helpers/__init__.py

@@ -0,0 +1,17 @@
+"""XYZ Helpers module for ComfyUI."""
+
+from .sampler_select_helper import SamplerSelectHelperNode
+from .scheduler_select_helper import SchedulerSelectHelperNode
+from .text_encode_sampler_params import TextEncodeSamplerParamsNode
+from .flux_sampler_params import FluxSamplerParamsNode
+from .plot_sampler_params import PlotParametersNode
+from .lora_folder_batch import LoRAFolderBatchNode
+
+__all__ = [
+    "SamplerSelectHelperNode",
+    "SchedulerSelectHelperNode",
+    "TextEncodeSamplerParamsNode",
+    "FluxSamplerParamsNode",
+    "PlotParametersNode",
+    "LoRAFolderBatchNode",
+]

kikotools/tools/xyz_helpers/flux_sampler_params/__init__.py

@@ -0,0 +1,5 @@
+"""Flux Sampler Params module."""
+
+from .node import FluxSamplerParamsNode
+
+__all__ = ["FluxSamplerParamsNode"]

kikotools/tools/xyz_helpers/flux_sampler_params/logic.py

@@ -0,0 +1,254 @@
+"""Logic module for Flux Sampler Params node."""
+
+from typing import List, Dict, Any, Tuple, Optional
+import random
+import time
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def parse_string_to_list(value: str) -> List[float]:
+    """
+    Parse a string containing comma-separated values to a list of floats.
+
+    Args:
+        value: String with comma-separated values
+
+    Returns:
+        List of float values
+    """
+    if not value or not value.strip():
+        return []
+
+    try:
+        values = []
+        for item in value.split(","):
+            item = item.strip()
+            if item:
+                try:
+                    values.append(float(item))
+                except ValueError:
+                    logger.warning(f"Could not parse '{item}' as float")
+        return values
+    except Exception as e:
+        logger.error(f"Error parsing string to list: {e}")
+        return []
+
+
+def parse_seed_string(seed_string: str) -> List[int]:
+    """
+    Parse seed string which can contain numbers, '?', or ranges.
+
+    Args:
+        seed_string: String with seeds (e.g., "123,?,456")
+
+    Returns:
+        List of integer seeds
+    """
+    seeds = []
+
+    try:
+        for item in seed_string.replace("\n", ",").split(","):
+            item = item.strip()
+            if not item:
+                continue
+
+            if "?" in item:
+                seeds.append(random.randint(0, 999999))
+            else:
+                try:
+                    seeds.append(int(item))
+                except ValueError:
+                    logger.warning(f"Could not parse seed '{item}'")
+                    seeds.append(random.randint(0, 999999))
+
+        if not seeds:
+            seeds = [random.randint(0, 999999)]
+
+    except Exception as e:
+        logger.error(f"Error parsing seeds: {e}")
+        seeds = [random.randint(0, 999999)]
+
+    return seeds
+
+
+def parse_sampler_string(
+    sampler_string: str, available_samplers: List[str]
+) -> List[str]:
+    """
+    Parse sampler string which can contain names, '*', or '!' exclusions.
+
+    Args:
+        sampler_string: String with sampler specifications
+        available_samplers: List of available sampler names
+
+    Returns:
+        List of sampler names
+    """
+    if sampler_string == "*":
+        return available_samplers.copy()
+
+    if sampler_string.startswith("!"):
+        excluded = sampler_string.replace("\n", ",").split(",")
+        excluded = [s.strip("! ") for s in excluded]
+        return [s for s in available_samplers if s not in excluded]
+
+    samplers = sampler_string.replace("\n", ",").split(",")
+    samplers = [s.strip() for s in samplers if s.strip() in available_samplers]
+
+    if not samplers:
+        return ["euler"]
+
+    return samplers
+
+
+def parse_scheduler_string(
+    scheduler_string: str, available_schedulers: List[str]
+) -> List[str]:
+    """
+    Parse scheduler string which can contain names, '*', or '!' exclusions.
+
+    Args:
+        scheduler_string: String with scheduler specifications
+        available_schedulers: List of available scheduler names
+
+    Returns:
+        List of scheduler names
+    """
+    if scheduler_string == "*":
+        return available_schedulers.copy()
+
+    if scheduler_string.startswith("!"):
+        excluded = scheduler_string.replace("\n", ",").split(",")
+        excluded = [s.strip("! ") for s in excluded]
+        return [s for s in available_schedulers if s not in excluded]
+
+    schedulers = scheduler_string.replace("\n", ",").split(",")
+    schedulers = [s.strip() for s in schedulers if s.strip() in available_schedulers]
+
+    if not schedulers:
+        return ["simple"]
+
+    return schedulers
+
+
+def get_default_flux_params(is_schnell: bool) -> Dict[str, Any]:
+    """
+    Get default parameters for Flux models.
+
+    Args:
+        is_schnell: Whether this is a Schnell model
+
+    Returns:
+        Dictionary of default parameters
+    """
+    if is_schnell:
+        return {
+            "steps": 4,
+            "guidance": 3.5,
+            "max_shift": 0,
+            "base_shift": 1.0,
+        }
+    else:
+        return {
+            "steps": 20,
+            "guidance": 3.5,
+            "max_shift": 1.15,
+            "base_shift": 0.5,
+        }
+
+
+def create_batch_params(
+    seeds: List[int],
+    samplers: List[str],
+    schedulers: List[str],
+    steps: List[int],
+    guidances: List[float],
+    max_shifts: List[float],
+    base_shifts: List[float],
+    denoises: List[float],
+    conditioning_count: int,
+    lora_strength_count: int = 1,
+) -> Tuple[int, List[Dict[str, Any]]]:
+    """
+    Create batch parameters for all combinations.
+
+    Returns:
+        Tuple of (total_samples, list of parameter combinations)
+    """
+    total = (
+        len(seeds)
+        * len(samplers)
+        * len(schedulers)
+        * len(steps)
+        * len(guidances)
+        * len(max_shifts)
+        * len(base_shifts)
+        * len(denoises)
+        * conditioning_count
+        * lora_strength_count
+    )
+
+    params = []
+    for seed in seeds:
+        for sampler in samplers:
+            for scheduler in schedulers:
+                for step in steps:
+                    for guidance in guidances:
+                        for max_shift in max_shifts:
+                            for base_shift in base_shifts:
+                                for denoise in denoises:
+                                    params.append(
+                                        {
+                                            "seed": seed,
+                                            "sampler": sampler,
+                                            "scheduler": scheduler,
+                                            "steps": step,
+                                            "guidance": guidance,
+                                            "max_shift": max_shift,
+                                            "base_shift": base_shift,
+                                            "denoise": denoise,
+                                        }
+                                    )
+
+    return total, params
+
+
+def process_conditioning_input(
+    conditioning: Any,
+) -> Tuple[Optional[List[str]], List[Any]]:
+    """
+    Process conditioning input which can be a dict or regular conditioning.
+
+    Args:
+        conditioning: Input conditioning (dict or tensor)
+
+    Returns:
+        Tuple of (text_list, encoded_list)
+    """
+    if isinstance(conditioning, dict) and "encoded" in conditioning:
+        return conditioning.get("text"), conditioning["encoded"]
+    else:
+        return None, [conditioning]
+
+
+def validate_flux_params(
+    steps: str, guidance: str, max_shift: str, base_shift: str, denoise: str
+) -> bool:
+    """
+    Validate Flux sampler parameters.
+
+    Returns:
+        True if all parameters are valid
+    """
+    try:
+        parse_string_to_list(steps)
+        parse_string_to_list(guidance)
+        parse_string_to_list(max_shift)
+        parse_string_to_list(base_shift)
+        parse_string_to_list(denoise)
+        return True
+    except Exception as e:
+        logger.error(f"Invalid parameters: {e}")
+        return False

kikotools/tools/xyz_helpers/flux_sampler_params/node.py

@@ -0,0 +1,371 @@
+"""Flux Sampler Params node for ComfyUI."""
+
+from typing import Tuple, Any, Dict, List, Optional
+import time
+import logging
+from ....base.base_node import ComfyAssetsBaseNode
+from .logic import (
+    parse_string_to_list,
+    parse_seed_string,
+    parse_sampler_string,
+    parse_scheduler_string,
+    get_default_flux_params,
+    create_batch_params,
+    process_conditioning_input,
+    validate_flux_params,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class FluxSamplerParamsNode(ComfyAssetsBaseNode):
+    """
+    Flux Sampler Parameters node for batch processing.
+
+    Enables batch processing with multiple parameter variations for
+    Flux models. Supports varying seeds, samplers, schedulers, steps,
+    guidance, shifts, and LoRAs for comprehensive parameter exploration.
+    """
+
+    def __init__(self):
+        """Initialize the node."""
+        super().__init__()
+        self.lora_loader = None
+        self.cached_lora = (None, None)
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define the input types for the ComfyUI node."""
+        return {
+            "required": {
+                "model": ("MODEL", {"tooltip": "Flux model to use"}),
+                "conditioning": (
+                    "CONDITIONING",
+                    {"tooltip": "Conditioning (can be from TextEncodeSamplerParams)"},
+                ),
+                "latent_image": ("LATENT", {"tooltip": "Input latent image"}),
+                "seed": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "?",
+                        "tooltip": "Seeds (comma-separated, ? for random)",
+                    },
+                ),
+                "sampler": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "euler",
+                        "tooltip": "Samplers (comma-separated, * for all, ! to exclude)",
+                    },
+                ),
+                "scheduler": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "simple",
+                        "tooltip": "Schedulers (comma-separated, * for all, ! to exclude)",
+                    },
+                ),
+                "steps": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "20",
+                        "tooltip": "Steps (comma-separated values)",
+                    },
+                ),
+                "guidance": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "3.5",
+                        "tooltip": "Guidance/CFG values (comma-separated)",
+                    },
+                ),
+                "max_shift": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "",
+                        "tooltip": "Max shift values (comma-separated, auto-set for Flux)",
+                    },
+                ),
+                "base_shift": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "",
+                        "tooltip": "Base shift values (comma-separated, auto-set for Flux)",
+                    },
+                ),
+                "denoise": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "default": "1.0",
+                        "tooltip": "Denoise values (comma-separated)",
+                    },
+                ),
+            },
+            "optional": {
+                "loras": ("LORA_PARAMS", {"tooltip": "Optional LoRA parameters"})
+            },
+        }
+
+    RETURN_TYPES = ("LATENT", "SAMPLER_PARAMS")
+    RETURN_NAMES = ("latent", "params")
+    FUNCTION = "process_batch"
+    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+
+    def process_batch(
+        self,
+        model: Any,
+        conditioning: Any,
+        latent_image: Any,
+        seed: str,
+        sampler: str,
+        scheduler: str,
+        steps: str,
+        guidance: str,
+        max_shift: str,
+        base_shift: str,
+        denoise: str,
+        loras: Optional[Dict] = None,
+    ) -> Tuple[Any, List[Dict[str, Any]]]:
+        """
+        Process batch sampling with parameter variations.
+
+        Returns:
+            Tuple of (output_latent, parameter_list)
+        """
+        try:
+            import comfy.samplers
+            import comfy.model_base
+            import comfy.model_management
+            from comfy_extras.nodes_custom_sampler import (
+                Noise_RandomNoise,
+                BasicScheduler,
+                BasicGuider,
+                SamplerCustomAdvanced,
+            )
+            from comfy_extras.nodes_latent import LatentBatch
+            from comfy_extras.nodes_model_advanced import (
+                ModelSamplingFlux,
+                ModelSamplingAuraFlow,
+            )
+            from node_helpers import conditioning_set_values
+            from nodes import LoraLoader
+
+        except ImportError as e:
+            self.handle_error(f"Required ComfyUI modules not available: {e}")
+            return (latent_image, [])
+
+        try:
+            if not validate_flux_params(
+                steps, guidance, max_shift, base_shift, denoise
+            ):
+                self.handle_error("Invalid parameter format")
+
+            is_schnell = model.model.model_type == comfy.model_base.ModelType.FLOW
+            defaults = get_default_flux_params(is_schnell)
+
+            seeds = parse_seed_string(seed)
+            samplers = parse_sampler_string(sampler, comfy.samplers.KSampler.SAMPLERS)
+            schedulers = parse_scheduler_string(
+                scheduler, comfy.samplers.KSampler.SCHEDULERS
+            )
+
+            steps = steps if steps else str(defaults["steps"])
+            steps_list = [int(s) for s in parse_string_to_list(steps)]
+
+            guidance = guidance if guidance else str(defaults["guidance"])
+            guidance_list = parse_string_to_list(guidance)
+
+            denoise = denoise if denoise else "1.0"
+            denoise_list = parse_string_to_list(denoise)
+
+            if not is_schnell:
+                max_shift = max_shift if max_shift else str(defaults["max_shift"])
+                base_shift = base_shift if base_shift else str(defaults["base_shift"])
+            else:
+                max_shift = "0"
+                base_shift = base_shift if base_shift else str(defaults["base_shift"])
+
+            max_shift_list = parse_string_to_list(max_shift)
+            base_shift_list = parse_string_to_list(base_shift)
+
+            cond_text, cond_encoded = process_conditioning_input(conditioning)
+
+            width = latent_image["samples"].shape[3] * 8
+            height = latent_image["samples"].shape[2] * 8
+
+            lora_strength_count = 1
+            if loras:
+                lora_model = loras["loras"]
+                lora_strength = loras["strengths"]
+                lora_strength_count = sum(len(i) for i in lora_strength)
+
+                if self.lora_loader is None:
+                    self.lora_loader = LoraLoader()
+
+            total_samples, param_combos = create_batch_params(
+                seeds,
+                samplers,
+                schedulers,
+                steps_list,
+                guidance_list,
+                max_shift_list,
+                base_shift_list,
+                denoise_list,
+                len(cond_encoded),
+                lora_strength_count,
+            )
+
+            self.log_info(f"Processing {total_samples} parameter combinations")
+
+            basicscheduler = BasicScheduler()
+            basicguider = BasicGuider()
+            samplercustomadvanced = SamplerCustomAdvanced()
+            latentbatch = LatentBatch()
+            modelsampling = (
+                ModelSamplingFlux() if not is_schnell else ModelSamplingAuraFlow()
+            )
+
+            out_latent = None
+            out_params = []
+
+            if total_samples > 1:
+                from comfy.utils import ProgressBar
+
+                pbar = ProgressBar(total_samples)
+
+            current_sample = 0
+
+            for lora_idx in range(lora_strength_count if loras else 1):
+                if loras:
+                    # Find which LoRA file and strength to use
+                    cumulative_idx = 0
+                    lora_file_idx = 0
+                    strength_in_file_idx = 0
+
+                    # Determine which LoRA file this index corresponds to
+                    for file_idx, strengths in enumerate(lora_strength):
+                        if lora_idx < cumulative_idx + len(strengths):
+                            lora_file_idx = file_idx
+                            strength_in_file_idx = lora_idx - cumulative_idx
+                            break
+                        cumulative_idx += len(strengths)
+
+                    # Load the appropriate LoRA with its strength
+                    if lora_file_idx < len(lora_model) and strength_in_file_idx < len(
+                        lora_strength[lora_file_idx]
+                    ):
+                        patched_model = self.lora_loader.load_lora(
+                            model,
+                            None,
+                            lora_model[lora_file_idx],
+                            lora_strength[lora_file_idx][strength_in_file_idx],
+                            0,
+                        )[0]
+                    else:
+                        patched_model = model
+                else:
+                    patched_model = model
+
+                for cond_idx, cond in enumerate(cond_encoded):
+                    prompt_text = cond_text[cond_idx] if cond_text else None
+
+                    for params in param_combos:
+                        current_sample += 1
+
+                        if is_schnell:
+                            work_model = modelsampling.patch_aura(
+                                patched_model, params["base_shift"]
+                            )[0]
+                        else:
+                            work_model = modelsampling.patch(
+                                patched_model,
+                                params["max_shift"],
+                                params["base_shift"],
+                                width,
+                                height,
+                            )[0]
+
+                        cond_with_guidance = conditioning_set_values(
+                            cond, {"guidance": params["guidance"]}
+                        )
+
+                        guider = basicguider.get_guider(work_model, cond_with_guidance)[
+                            0
+                        ]
+                        sampler_obj = comfy.samplers.sampler_object(params["sampler"])
+                        sigmas = basicscheduler.get_sigmas(
+                            work_model,
+                            params["scheduler"],
+                            params["steps"],
+                            params["denoise"],
+                        )[0]
+
+                        noise = Noise_RandomNoise(params["seed"])
+
+                        self.log_info(
+                            f"Sample {current_sample}/{total_samples}: "
+                            f"seed={params['seed']}, sampler={params['sampler']}, "
+                            f"steps={params['steps']}"
+                        )
+
+                        start_time = time.time()
+                        latent = samplercustomadvanced.sample(
+                            noise, guider, sampler_obj, sigmas, latent_image
+                        )[1]
+                        elapsed = time.time() - start_time
+
+                        param_record = {
+                            **params,
+                            "time": elapsed,
+                            "width": width,
+                            "height": height,
+                            "prompt": prompt_text,
+                        }
+
+                        if loras:
+                            # Record which LoRA and strength was used
+                            param_record["lora"] = (
+                                lora_model[lora_file_idx]
+                                if lora_file_idx < len(lora_model)
+                                else None
+                            )
+                            param_record["lora_strength"] = (
+                                lora_strength[lora_file_idx][strength_in_file_idx]
+                                if lora_file_idx < len(lora_strength)
+                                and strength_in_file_idx
+                                < len(lora_strength[lora_file_idx])
+                                else 0
+                            )
+
+                        out_params.append(param_record)
+
+                        if out_latent is None:
+                            out_latent = latent
+                        else:
+                            out_latent = latentbatch.batch(out_latent, latent)[0]
+
+                        if total_samples > 1:
+                            pbar.update(1)
+
+            self.log_info(f"Completed {len(out_params)} samples")
+            return (out_latent, out_params)
+
+        except Exception as e:
+            self.handle_error(f"Error in batch processing: {str(e)}", e)
+            return (latent_image, [])

kikotools/tools/xyz_helpers/lora_folder_batch/__init__.py

@@ -0,0 +1,5 @@
+"""LoRA Folder Batch module."""
+
+from .node import LoRAFolderBatchNode
+
+__all__ = ["LoRAFolderBatchNode"]

kikotools/tools/xyz_helpers/lora_folder_batch/logic.py

@@ -0,0 +1,334 @@
+"""Logic module for LoRA Folder Batch node."""
+
+import os
+import re
+from typing import List, Dict, Any, Tuple, Optional
+from pathlib import Path
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def get_lora_folders() -> List[str]:
+    """
+    Get list of available LoRA folders.
+
+    Returns:
+        List of folder paths relative to models/loras
+    """
+    try:
+        import folder_paths
+
+        lora_path = folder_paths.folder_names_and_paths["loras"][0][0]
+
+        folders = []
+        for root, dirs, _ in os.walk(lora_path):
+            for dir_name in dirs:
+                rel_path = os.path.relpath(os.path.join(root, dir_name), lora_path)
+                folders.append(rel_path)
+
+        # Add root folder option
+        folders.insert(0, ".")
+        return folders
+
+    except (ImportError, KeyError):
+        # Fallback for testing
+        return [".", "flux", "sdxl", "sd15"]
+
+
+def scan_folder_for_loras(folder_path: str) -> List[str]:
+    """
+    Scan a folder for LoRA files (.safetensors).
+
+    Args:
+        folder_path: Path to folder to scan (absolute or relative to models/loras)
+
+    Returns:
+        List of LoRA filenames relative to models/loras directory
+    """
+    try:
+        import folder_paths
+
+        # Get all LoRA paths from ComfyUI (includes extra_model_paths)
+        lora_paths = folder_paths.folder_names_and_paths.get("loras", [[]])[0]
+
+        # 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
+            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
+
+            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
+                    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]
+                    )
+        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 ""
+
+        if not os.path.exists(full_path):
+            logger.warning(f"Folder does not exist: {full_path}")
+            return []
+
+        # Scan for .safetensors files
+        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)
+
+        # 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"
+        )
+        return lora_files
+
+    except Exception as e:
+        logger.error(f"Error scanning folder {folder_path}: {e}")
+        return []
+
+
+def natural_sort(items: List[str]) -> List[str]:
+    """
+    Sort strings naturally, handling numbers properly.
+
+    Args:
+        items: List of strings to sort
+
+    Returns:
+        Naturally sorted list
+    """
+
+    def natural_key(text):
+        def atoi(text):
+            return int(text) if text.isdigit() else text
+
+        # 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
+
+    return sorted(items, key=natural_key)
+
+
+def filter_loras_by_pattern(
+    lora_files: List[str], include_pattern: str = "", exclude_pattern: str = ""
+) -> List[str]:
+    """
+    Filter LoRA files by include/exclude patterns.
+
+    Args:
+        lora_files: List of LoRA filenames
+        include_pattern: Regex pattern to include (empty = include all)
+        exclude_pattern: Regex pattern to exclude (empty = exclude none)
+
+    Returns:
+        Filtered list of LoRA files
+    """
+    filtered = lora_files.copy()
+
+    # Apply include pattern
+    if include_pattern:
+        try:
+            include_re = re.compile(include_pattern)
+            filtered = [f for f in filtered if include_re.search(f)]
+        except re.error as e:
+            logger.error(f"Invalid include pattern: {e}")
+
+    # Apply exclude pattern
+    if exclude_pattern:
+        try:
+            exclude_re = re.compile(exclude_pattern)
+            filtered = [f for f in filtered if not exclude_re.search(f)]
+        except re.error as e:
+            logger.error(f"Invalid exclude pattern: {e}")
+
+    return filtered
+
+
+def parse_strength_string(strength_str: str) -> List[float]:
+    """
+    Parse strength string into list of values.
+
+    Supports:
+    - Single value: "1.0"
+    - Multiple values: "0.5, 0.75, 1.0"
+    - Range: "0.5...1.0" (with optional step)
+
+    Args:
+        strength_str: String representation of strengths
+
+    Returns:
+        List of strength values
+    """
+    strength_str = strength_str.strip()
+
+    if not strength_str:
+        return [1.0]
+
+    # Check for range notation
+    if "..." in strength_str:
+        parts = strength_str.split("...")
+        if len(parts) == 2:
+            try:
+                start = float(parts[0].strip())
+                end_part = parts[1].strip()
+
+                # Check for step
+                if "+" in end_part:
+                    end_str, step_str = end_part.split("+")
+                    end = float(end_str.strip())
+                    step = float(step_str.strip())
+                else:
+                    end = float(end_part)
+                    step = 0.1  # Default step
+
+                # Generate range
+                values = []
+                current = start
+                while current <= end + 0.0001:  # Small epsilon for float comparison
+                    values.append(round(current, 4))
+                    current += step
+
+                return values
+            except ValueError as e:
+                logger.error(f"Invalid range format: {e}")
+                return [1.0]
+
+    # Parse comma-separated values
+    try:
+        values = []
+        for item in strength_str.split(","):
+            item = item.strip()
+            if item:
+                values.append(float(item))
+        return values if values else [1.0]
+    except ValueError as e:
+        logger.error(f"Invalid strength values: {e}")
+        return [1.0]
+
+
+def create_lora_params(
+    lora_files: List[str], strengths: List[float], batch_mode: str = "sequential"
+) -> Dict[str, Any]:
+    """
+    Create LORA_PARAMS structure for FluxSamplerParams.
+
+    Args:
+        lora_files: List of LoRA file paths
+        strengths: List of strength values to test
+        batch_mode: How to batch ("sequential" or "combinatorial")
+
+    Returns:
+        LORA_PARAMS dictionary
+    """
+    if not lora_files:
+        logger.warning("No LoRA files provided")
+        return {"loras": [], "strengths": []}
+
+    if batch_mode == "combinatorial":
+        # Each LoRA gets tested with each strength
+        # This creates len(loras) * len(strengths) combinations
+        return {"loras": lora_files, "strengths": [strengths for _ in lora_files]}
+    else:
+        # Sequential mode - cycle through strengths for each LoRA
+        # If fewer strengths than LoRAs, repeat the strength list
+        strength_lists = []
+        for i, lora in enumerate(lora_files):
+            strength_idx = i % len(strengths)
+            strength_lists.append([strengths[strength_idx]])
+
+        return {"loras": lora_files, "strengths": strength_lists}
+
+
+def get_lora_info(lora_file: str) -> Dict[str, Any]:
+    """
+    Extract information from LoRA filename.
+
+    Args:
+        lora_file: LoRA filename
+
+    Returns:
+        Dictionary with extracted info (name, epoch, version, etc.)
+    """
+    info = {
+        "filename": lora_file,
+        "name": os.path.splitext(os.path.basename(lora_file))[0],
+        "epoch": None,
+        "version": None,
+    }
+
+    # Try to extract epoch number
+    epoch_match = re.search(r"[-_](\d{6}|\d{5}|\d{4}|\d{3})", info["name"])
+    if epoch_match:
+        info["epoch"] = int(epoch_match.group(1))
+
+    # Try to extract version
+    version_match = re.search(r"v(\d+(?:\.\d+)?)", info["name"], re.IGNORECASE)
+    if version_match:
+        info["version"] = f"v{version_match.group(1)}"
+
+    return info
+
+
+def validate_folder_path(folder_path: str) -> bool:
+    """
+    Validate that the folder path exists and is accessible.
+
+    Args:
+        folder_path: Folder path to validate
+
+    Returns:
+        True if valid
+    """
+    try:
+        import folder_paths
+
+        lora_base_path = folder_paths.folder_names_and_paths["loras"][0][0]
+
+        if folder_path == ".":
+            full_path = lora_base_path
+        else:
+            full_path = os.path.join(lora_base_path, folder_path)
+
+        return os.path.exists(full_path) and os.path.isdir(full_path)
+
+    except Exception:
+        return False

kikotools/tools/xyz_helpers/lora_folder_batch/node.py

@@ -0,0 +1,185 @@
+"""LoRA Folder Batch node for ComfyUI."""
+
+from typing import Tuple, Any, Dict, List
+import os
+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,
+    get_lora_info,
+    validate_folder_path,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class LoRAFolderBatchNode(ComfyAssetsBaseNode):
+    """
+    LoRA Folder Batch node for processing multiple LoRAs from a folder.
+
+    Scans a specified folder for all .safetensors files and creates
+    LORA_PARAMS for batch processing with FluxSamplerParams. Perfect
+    for testing different epochs or variations of the same LoRA.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define the input types for the ComfyUI node."""
+        return {
+            "required": {
+                "folder_path": (
+                    "STRING",
+                    {
+                        "default": ".",
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "tooltip": "Folder path relative to models/loras (or absolute path)",
+                    },
+                ),
+                "strength": (
+                    "STRING",
+                    {
+                        "default": "1.0",
+                        "multiline": False,
+                        "dynamicPrompts": False,
+                        "tooltip": "Strength values (e.g., '1.0' or '0.5,0.75,1.0' or '0.5...1.0+0.1')",
+                    },
+                ),
+                "batch_mode": (
+                    ["sequential", "combinatorial"],
+                    {
+                        "default": "sequential",
+                        "tooltip": "Sequential: one strength per LoRA, Combinatorial: all strengths for each LoRA",
+                    },
+                ),
+            },
+            "optional": {
+                "include_pattern": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Regex pattern to include files (empty = all)",
+                    },
+                ),
+                "exclude_pattern": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "Regex pattern to exclude files (e.g., 'test|backup')",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("LORA_PARAMS", "STRING", "INT")
+    RETURN_NAMES = ("lora_params", "lora_list", "lora_count")
+    FUNCTION = "batch_loras"
+    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+
+    def batch_loras(
+        self,
+        folder_path: str,
+        strength: str,
+        batch_mode: str,
+        include_pattern: str = "",
+        exclude_pattern: str = "",
+    ) -> Tuple[Dict[str, Any], str, int]:
+        """
+        Batch process LoRAs from a folder.
+
+        Args:
+            folder_path: Folder to scan (relative to models/loras or absolute)
+            strength: Strength values string
+            batch_mode: How to batch the LoRAs
+            include_pattern: Optional include regex
+            exclude_pattern: Optional exclude regex
+
+        Returns:
+            Tuple of (lora_params, lora_list_string, lora_count)
+        """
+        try:
+
+            # Validate folder only if not in test mode
+            try:
+                if not validate_folder_path(folder_path):
+                    self.handle_error(f"Invalid or inaccessible folder: {folder_path}")
+            except ImportError:
+                # In test environment, skip validation
+                pass
+
+            # Scan folder for LoRAs
+            lora_files = scan_folder_for_loras(folder_path)
+
+            if not lora_files:
+                self.log_info(f"No LoRA files found in {folder_path}")
+                return ({"loras": [], "strengths": []}, "", 0)
+
+            self.log_info(f"Found {len(lora_files)} LoRA files in {folder_path}")
+
+            # Apply filters
+            if include_pattern or exclude_pattern:
+                filtered = filter_loras_by_pattern(
+                    lora_files, include_pattern, exclude_pattern
+                )
+                if len(filtered) < len(lora_files):
+                    self.log_info(
+                        f"Filtered from {len(lora_files)} to {len(filtered)} LoRAs"
+                    )
+                lora_files = filtered
+
+            if not lora_files:
+                self.log_info("No LoRAs left after filtering")
+                return ({"loras": [], "strengths": []}, "", 0)
+
+            # 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 info string
+            lora_list = []
+            for lora_file in lora_files:
+                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)
+            else:
+                total_combos = len(lora_files)
+
+            self.log_info(
+                f"Created batch with {len(lora_files)} LoRAs, "
+                f"{len(strengths)} strength values, "
+                f"{total_combos} total combinations"
+            )
+
+            return (lora_params, lora_list_str, len(lora_files))
+
+        except Exception as e:
+            self.handle_error(f"Error creating LoRA batch: {str(e)}", e)
+            return ({"loras": [], "strengths": []}, "", 0)
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        """
+        Force re-execution when folder contents might have changed.
+
+        This ensures we always scan for the latest LoRAs.
+        """
+        import time
+
+        return str(time.time())

kikotools/tools/xyz_helpers/plot_sampler_params/__init__.py

@@ -0,0 +1,5 @@
+"""Plot Parameters module."""
+
+from .node import PlotParametersNode
+
+__all__ = ["PlotParametersNode"]

kikotools/tools/xyz_helpers/plot_sampler_params/logic.py

@@ -0,0 +1,338 @@
+"""Logic module for Plot Parameters node."""
+
+from typing import List, Dict, Any, Tuple, Optional
+import math
+import textwrap
+import logging
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+def sort_parameters(params: List[Dict], order_by: str) -> Tuple[List[Dict], List[int]]:
+    """
+    Sort parameters by a specified key.
+
+    Args:
+        params: List of parameter dictionaries
+        order_by: Key to sort by
+
+    Returns:
+        Tuple of (sorted_params, original_indices)
+    """
+    if order_by == "none":
+        return params, list(range(len(params)))
+
+    try:
+        # Create indexed list
+        indexed_params = [(i, p) for i, p in enumerate(params)]
+
+        # Sort by the specified key
+        sorted_indexed = sorted(indexed_params, key=lambda x: x[1].get(order_by, 0))
+
+        # Extract sorted params and indices
+        indices = [i for i, _ in sorted_indexed]
+        sorted_params = [p for _, p in sorted_indexed]
+
+        return sorted_params, indices
+    except Exception as e:
+        logger.error(f"Error sorting parameters: {e}")
+        return params, list(range(len(params)))
+
+
+def group_by_value(
+    params: List[Dict], group_key: str
+) -> Tuple[List[Dict], List[int], int]:
+    """
+    Group parameters by a specific value and arrange in columns.
+
+    Args:
+        params: List of parameter dictionaries
+        group_key: Key to group by
+
+    Returns:
+        Tuple of (rearranged_params, indices, num_groups)
+    """
+    if group_key == "none":
+        return params, list(range(len(params))), -1
+
+    try:
+        # Group parameters by the specified key
+        groups = {}
+        for i, p in enumerate(params):
+            value = p.get(group_key, "unknown")
+            if value not in groups:
+                groups[value] = []
+            groups[value].append((i, p))
+
+        num_groups = len(groups)
+
+        # Rearrange for column layout
+        sorted_params = []
+        indices = []
+
+        # Convert groups to list
+        group_lists = list(groups.values())
+
+        # Zip groups together for column arrangement
+        max_len = max(len(g) for g in group_lists)
+        for i in range(max_len):
+            for group in group_lists:
+                if i < len(group):
+                    idx, param = group[i]
+                    indices.append(idx)
+                    sorted_params.append(param)
+
+        return sorted_params, indices, num_groups
+
+    except Exception as e:
+        logger.error(f"Error grouping parameters: {e}")
+        return params, list(range(len(params))), -1
+
+
+def identify_changing_parameters(params: List[Dict]) -> Dict[str, bool]:
+    """
+    Identify which parameters change across the batch.
+
+    Args:
+        params: List of parameter dictionaries
+
+    Returns:
+        Dictionary mapping parameter names to whether they change
+    """
+    if not params:
+        return {}
+
+    changing = {}
+
+    # Track unique values for each parameter
+    value_tracker = {}
+
+    for p in params:
+        for key, value in p.items():
+            if key == "time":  # Skip time as it always changes
+                continue
+
+            if key not in value_tracker:
+                value_tracker[key] = set()
+
+            # Handle different value types
+            if isinstance(value, (list, tuple)):
+                value = str(value)
+            elif isinstance(value, dict):
+                value = str(sorted(value.items()))
+
+            value_tracker[key].add(value)
+
+    # Mark parameters as changing if they have multiple values
+    for key, values in value_tracker.items():
+        changing[key] = len(values) > 1
+
+    # Always include prompt if present
+    if any("prompt" in p for p in params):
+        changing["prompt"] = True
+
+    return changing
+
+
+def filter_changing_params(params: List[Dict]) -> List[Dict]:
+    """
+    Filter parameters to only show those that change.
+
+    Args:
+        params: List of parameter dictionaries
+
+    Returns:
+        List of filtered parameter dictionaries
+    """
+    changing = identify_changing_parameters(params)
+
+    filtered = []
+    for p in params:
+        filtered_param = {}
+        for key, value in p.items():
+            if changing.get(key, False):
+                filtered_param[key] = value
+        filtered.append(filtered_param)
+
+    return filtered
+
+
+def format_parameter_text(param: Dict, mode: str = "full") -> str:
+    """
+    Format parameter dictionary as display text.
+
+    Args:
+        param: Parameter dictionary
+        mode: Display mode ("full", "changes only")
+
+    Returns:
+        Formatted text string
+    """
+    if mode == "changes only":
+        lines = []
+        for key, value in param.items():
+            if key != "prompt":
+                lines.append(f"{key}: {value}")
+        return "\n".join(lines)
+    else:
+        # Full format
+        lines = []
+
+        # First line: time, seed, steps, size
+        if "time" in param:
+            lines.append(
+                f"time: {param['time']:.2f}s, seed: {param.get('seed', 'N/A')}, "
+                f"steps: {param.get('steps', 'N/A')}, "
+                f"size: {param.get('width', 'N/A')}×{param.get('height', 'N/A')}"
+            )
+
+        # Second line: denoise, sampler, scheduler
+        lines.append(
+            f"denoise: {param.get('denoise', 'N/A')}, "
+            f"sampler: {param.get('sampler', 'N/A')}, "
+            f"sched: {param.get('scheduler', 'N/A')}"
+        )
+
+        # Third line: guidance, shifts
+        lines.append(
+            f"guidance: {param.get('guidance', 'N/A')}, "
+            f"max/base shift: {param.get('max_shift', 'N/A')}/{param.get('base_shift', 'N/A')}"
+        )
+
+        # 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')}")
+
+        return "\n".join(lines)
+
+
+def wrap_prompt_text(prompt: str, width_chars: int, mode: str = "full") -> List[str]:
+    """
+    Wrap prompt text to fit within character width.
+
+    Args:
+        prompt: Prompt text to wrap
+        width_chars: Maximum characters per line
+        mode: Display mode ("full", "excerpt")
+
+    Returns:
+        List of wrapped lines
+    """
+    if not prompt:
+        return []
+
+    original_words = prompt.split()
+
+    if mode == "excerpt":
+        # Take first 64 words
+        words = original_words[:64]
+        prompt = " ".join(words)
+        # Add ellipsis if we truncated
+        if len(words) < len(original_words):
+            prompt += "..."
+
+    # Use textwrap to break into lines
+    lines = textwrap.wrap(prompt, width=width_chars)
+
+    return lines
+
+
+def calculate_text_dimensions(
+    text: str, font_size: int, image_width: int
+) -> Tuple[int, int, int]:
+    """
+    Calculate text rendering dimensions.
+
+    Args:
+        text: Text to render
+        font_size: Font size in pixels
+        image_width: Width of the image
+
+    Returns:
+        Tuple of (line_height, char_width, num_lines)
+    """
+    # Approximate calculations (adjust based on actual font metrics)
+    line_height = int(font_size * 1.5)  # Line height with padding
+    char_width = int(font_size * 0.6)  # Approximate monospace char width
+
+    lines = text.split("\n")
+    num_lines = len(lines)
+
+    return line_height, char_width, num_lines
+
+
+def calculate_grid_dimensions(num_images: int, cols_num: int) -> Tuple[int, int]:
+    """
+    Calculate grid dimensions for image layout.
+
+    Args:
+        num_images: Total number of images
+        cols_num: Number of columns (-1 for auto)
+
+    Returns:
+        Tuple of (rows, cols)
+    """
+    if cols_num == 0 or cols_num == -1:
+        # Auto-calculate columns
+        cols = int(math.sqrt(num_images))
+        cols = max(1, min(cols, 1024))
+    else:
+        cols = min(cols_num, num_images)
+
+    rows = math.ceil(num_images / cols)
+
+    return rows, cols
+
+
+def validate_plot_parameters(
+    images_shape: tuple,
+    params_length: int,
+    order_by: str,
+    cols_value: str,
+    cols_num: int,
+) -> bool:
+    """
+    Validate plot parameters configuration.
+
+    Args:
+        images_shape: Shape of the images tensor
+        params_length: Length of parameters list
+        order_by: Ordering key
+        cols_value: Column grouping key
+        cols_num: Number of columns
+
+    Returns:
+        True if configuration is valid
+    """
+    if images_shape[0] != params_length:
+        logger.error(
+            f"Image count ({images_shape[0]}) doesn't match parameters ({params_length})"
+        )
+        return False
+
+    valid_keys = [
+        "none",
+        "time",
+        "seed",
+        "steps",
+        "denoise",
+        "sampler",
+        "scheduler",
+        "guidance",
+        "max_shift",
+        "base_shift",
+        "lora_strength",
+    ]
+
+    if order_by not in valid_keys:
+        logger.warning(f"Invalid order_by value: {order_by}")
+
+    if cols_value not in valid_keys:
+        logger.warning(f"Invalid cols_value: {cols_value}")
+
+    if cols_num < -1 or cols_num > 1024:
+        logger.warning(f"Invalid cols_num: {cols_num}")
+
+    return True

kikotools/tools/xyz_helpers/plot_sampler_params/node.py

@@ -0,0 +1,310 @@
+"""Plot Parameters node for ComfyUI."""
+
+from typing import Tuple, Any, List, Dict
+import os
+import math
+import torch
+import torch.nn.functional as F
+import logging
+from PIL import Image, ImageDraw, ImageFont
+
+try:
+    import torchvision.transforms.v2 as T
+except ImportError:
+    try:
+        import torchvision.transforms as T
+    except ImportError:
+        # Fallback for test environment without torchvision
+        class T:
+            @staticmethod
+            def ToTensor():
+                def to_tensor(img):
+                    import numpy as np
+
+                    if isinstance(img, Image.Image):
+                        img = np.array(img)
+                    img = torch.from_numpy(img).float() / 255.0
+                    if len(img.shape) == 3:
+                        img = img.permute(2, 0, 1)
+                    return img
+
+                return to_tensor
+
+
+from ....base.base_node import ComfyAssetsBaseNode
+from .logic import (
+    sort_parameters,
+    group_by_value,
+    filter_changing_params,
+    format_parameter_text,
+    wrap_prompt_text,
+    calculate_text_dimensions,
+    calculate_grid_dimensions,
+    validate_plot_parameters,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class PlotParametersNode(ComfyAssetsBaseNode):
+    """
+    Plot Parameters node for visualizing batch sampling results.
+
+    Creates a grid layout of images with parameter annotations,
+    useful for comparing results across different sampling parameters.
+    Supports sorting, grouping, and filtering display options.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        """Define the input types for the ComfyUI node."""
+        order_options = [
+            "none",
+            "time",
+            "seed",
+            "steps",
+            "denoise",
+            "sampler",
+            "scheduler",
+            "guidance",
+            "max_shift",
+            "base_shift",
+            "lora_strength",
+        ]
+
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "Batch of images to arrange"}),
+                "params": (
+                    "SAMPLER_PARAMS",
+                    {"tooltip": "Parameters from FluxSamplerParams"},
+                ),
+                "order_by": (
+                    order_options,
+                    {"default": "none", "tooltip": "Sort images by this parameter"},
+                ),
+                "cols_value": (
+                    order_options,
+                    {
+                        "default": "none",
+                        "tooltip": "Group into columns by this parameter",
+                    },
+                ),
+                "cols_num": (
+                    "INT",
+                    {
+                        "default": -1,
+                        "min": -1,
+                        "max": 1024,
+                        "tooltip": "Number of columns (-1 for auto, 0 for square)",
+                    },
+                ),
+                "add_prompt": (
+                    ["false", "true", "excerpt"],
+                    {"default": "false", "tooltip": "Add prompt text to images"},
+                ),
+                "add_params": (
+                    ["false", "true", "changes only"],
+                    {"default": "true", "tooltip": "Add parameter text to images"},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "plot_parameters"
+    CATEGORY = "ComfyAssets/🧰 xyz-helpers"
+
+    def plot_parameters(
+        self,
+        images: torch.Tensor,
+        params: List[Dict[str, Any]],
+        order_by: str,
+        cols_value: str,
+        cols_num: int,
+        add_prompt: str,
+        add_params: str,
+    ) -> Tuple[torch.Tensor]:
+        """
+        Create a plot grid with parameter annotations.
+
+        Args:
+            images: Tensor of images [B, H, W, C]
+            params: List of parameter dictionaries
+            order_by: Parameter to sort by
+            cols_value: Parameter to group columns by
+            cols_num: Number of columns
+            add_prompt: Whether to add prompt text
+            add_params: Whether to add parameter text
+
+        Returns:
+            Tuple containing the plotted image grid
+        """
+        try:
+            if not validate_plot_parameters(
+                images.shape, len(params), order_by, cols_value, cols_num
+            ):
+                self.handle_error("Invalid plot parameters configuration")
+
+            # Copy params to avoid modifying original
+            _params = params.copy()
+
+            # Sort if requested
+            if order_by != "none":
+                _params, indices = sort_parameters(_params, order_by)
+                images = images[torch.tensor(indices)]
+                self.log_info(f"Sorted by {order_by}")
+
+            # Group by value if requested
+            if cols_value != "none" and cols_num > -1:
+                _params, indices, num_groups = group_by_value(_params, cols_value)
+                if num_groups > 0:
+                    cols_num = num_groups
+                    images = images[torch.tensor(indices)]
+                    self.log_info(f"Grouped into {num_groups} columns by {cols_value}")
+            elif cols_num == 0:
+                # Auto square layout
+                cols_num = int(math.sqrt(images.shape[0]))
+                cols_num = max(1, min(cols_num, 1024))
+
+            # Filter params if showing changes only
+            if add_params == "changes only":
+                _params = filter_changing_params(_params)
+
+            # Get font
+            font_path = self._get_font_path()
+            width = images.shape[2]
+            font_size = min(48, int(32 * (width / 1024)))
+
+            try:
+                font = ImageFont.truetype(font_path, font_size)
+            except:
+                logger.warning(f"Could not load font from {font_path}, using default")
+                font = ImageFont.load_default()
+
+            # Calculate text dimensions
+            text_padding = 3
+            line_height = (
+                font.getmask("Q").getbbox()[3] + font.getmetrics()[1] + text_padding * 2
+            )
+            char_width = font.getbbox("M")[2] + 1  # Monospace approximation
+
+            # Process each image
+            out_images = []
+            for image, param in zip(images, _params):
+                image = image.permute(2, 0, 1)  # [C, H, W]
+
+                # Add parameter text
+                if add_params != "false":
+                    param_text = format_parameter_text(
+                        param,
+                        "changes only" if add_params == "changes only" else "full",
+                    )
+
+                    lines = param_text.split("\n")
+                    text_height = line_height * len(lines)
+                    text_image = Image.new("RGB", (width, text_height), color=(0, 0, 0))
+                    draw = ImageDraw.Draw(text_image)
+
+                    for i, line in enumerate(lines):
+                        draw.text(
+                            (text_padding, i * line_height + text_padding),
+                            line,
+                            font=font,
+                            fill=(255, 255, 255),
+                        )
+
+                    text_tensor = T.ToTensor()(text_image).to(image.device)
+                    image = torch.cat([image, text_tensor], 1)
+
+                # Add prompt text
+                if add_prompt != "false" and "prompt" in param and param["prompt"]:
+                    cols = math.ceil(width / char_width)
+                    prompt_lines = wrap_prompt_text(
+                        param["prompt"],
+                        cols,
+                        "excerpt" if add_prompt == "excerpt" else "full",
+                    )
+
+                    prompt_height = line_height * len(prompt_lines)
+                    prompt_image = Image.new(
+                        "RGB", (width, prompt_height), color=(0, 0, 0)
+                    )
+                    draw = ImageDraw.Draw(prompt_image)
+
+                    for i, line in enumerate(prompt_lines):
+                        draw.text(
+                            (text_padding, i * line_height + text_padding),
+                            line,
+                            font=font,
+                            fill=(255, 255, 255),
+                        )
+
+                    prompt_tensor = T.ToTensor()(prompt_image).to(image.device)
+                    image = torch.cat([image, prompt_tensor], 1)
+
+                # Clean up NaN values
+                image = torch.nan_to_num(image, nan=0.0).clamp(0.0, 1.0)
+                out_images.append(image)
+
+            # Ensure all images have same height
+            if add_prompt != "false" or add_params == "changes only":
+                max_height = max([img.shape[1] for img in out_images])
+                out_images = [
+                    F.pad(img, (0, 0, 0, max_height - img.shape[1]))
+                    for img in out_images
+                ]
+
+            # Stack images
+            out_image = torch.stack(out_images, 0).permute(0, 2, 3, 1)  # [B, H, W, C]
+
+            # Create grid if columns specified
+            if cols_num > -1:
+                rows, cols = calculate_grid_dimensions(out_image.shape[0], cols_num)
+                b, h, w, c = out_image.shape
+
+                # Pad if necessary
+                if b % cols != 0:
+                    padding = cols - (b % cols)
+                    out_image = F.pad(out_image, (0, 0, 0, 0, 0, 0, 0, padding))
+                    b = out_image.shape[0]
+
+                # Reshape into grid
+                out_image = out_image.reshape(rows, cols, h, w, c)
+                out_image = out_image.permute(0, 2, 1, 3, 4)  # [rows, h, cols, w, c]
+                out_image = out_image.reshape(rows * h, cols * w, c).unsqueeze(0)
+
+                self.log_info(f"Created {rows}x{cols} grid")
+
+            return (out_image,)
+
+        except Exception as e:
+            self.handle_error(f"Error creating parameter plot: {str(e)}", e)
+            return (images,)
+
+    def _get_font_path(self) -> str:
+        """
+        Get the path to the font file.
+
+        Returns:
+            Path to font file
+        """
+        # Try to find a monospace font
+        possible_paths = [
+            # Check if ComfyUI_essentials font exists
+            os.path.join(
+                os.path.dirname(__file__),
+                "../../../../referance/ComfyUI_essentials/fonts/ShareTechMono-Regular.ttf",
+            ),
+            # System fonts
+            "/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf",
+            "/System/Library/Fonts/Courier.dfont",
+            "C:\\Windows\\Fonts\\cour.ttf",
+        ]
+
+        for path in possible_paths:
+            if os.path.exists(path):
+                return path
+
+        # Return a default that PIL will handle
+        return "arial.ttf"