chore: bump version to 1.0.7 in pyproject.toml

style: Add blank lines for better readability

.github/workflows/code-quality.yml

@@ -74,6 +74,23 @@ jobs:
         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')
         "
 
@@ -190,12 +207,77 @@ jobs:
         
         # 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

.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/tests.yml

@@ -78,67 +78,318 @@ 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
+          assert node.RETURN_TYPES == ('SAMPLER', SCHEDULERS, 'INT', 'FLOAT')
+          assert node.RETURN_NAMES == ('sampler_name', 'scheduler', 'steps', 'cfg')
+          assert node.CATEGORY == 'ComfyAssets'
+          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'
+          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 == '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 == '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 == '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 == 'ComfyAssets'
+          print('✓ Seed History ComfyUI integration passed')
+
+          print('🎉 All tools ComfyUI integration readiness tests passed!')
           "
 
   test-package-structure:
@@ -163,14 +414,37 @@ 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 +455,15 @@ 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)

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; \
@@ -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; \

README.md

@@ -44,6 +44,82 @@ 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
+
+#### ⚙️ 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
+
+#### 💾 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
+
+**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
@@ -97,6 +173,58 @@ preset: "1920×1080"   ↘ 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
+
 ### Common Workflows
 
 <details>
@@ -135,6 +263,10 @@ 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) |
 | **Batch Image Processor** | Process multiple images with consistent settings | 🚧 Planned | Coming Soon |
 | **Advanced Prompt Utilities** | Enhanced prompt manipulation and generation | 🚧 Planned | Coming Soon |
 
@@ -178,6 +310,114 @@ 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
@@ -298,12 +538,14 @@ MIT License - see [LICENSE](LICENSE) file for details.
 
 ## 📈 Stats
 
-- **Nodes**: 2 (Resolution Calculator, Width Height Selector)
+- **Nodes**: 6 (Resolution Calculator, Width Height Selector, Seed History, Sampler Combo, Empty Latent Batch, Kiko Save Image)
+- **Format Support**: 3 (PNG, JPEG, WebP with advanced controls)
 - **Presets**: 26 curated resolution presets
-- **Test Coverage**: 100%
+- **Interactive Features**: 4 (Width/Height Swap Button, Seed History UI, Empty Latent Batch Swap Button, Kiko Save Image Popup Viewer)
+- **Test Coverage**: 100% (200+ comprehensive tests)
 - **Python Version**: 3.8+
 - **ComfyUI Compatibility**: Latest
-- **Dependencies**: Minimal (PyTorch, NumPy)
+- **Dependencies**: Minimal (PyTorch, NumPy, Pillow)
 
 ---
 

__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"]

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/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/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/workflows/resolution_calculator_example.json

@@ -1,47 +1,259 @@
 {
-  "last_node_id": 3,
-  "last_link_id": 2,
+  "id": "41469b2d-d616-479d-879a-95cdc6074a37",
+  "revision": 0,
+  "last_node_id": 6,
+  "last_link_id": 5,
   "nodes": [
     {
       "id": 1,
       "type": "ResolutionCalculator",
-      "pos": [100, 100],
-      "size": {"0": 315, "1": 126},
+      "pos": [
+        60,
+        430
+      ],
+      "size": [
+        315,
+        126
+      ],
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+      "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": [
+            1,
+            3
+          ]
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "slot_index": 1,
+          "links": [
+            2,
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "aux_id": "ComfyAssets/ComfyUI-KikoTools",
+        "ver": "965ad60c74d7f25b1acce890d9c06518e46e6d0b",
+        "Node name for S&R": "ResolutionCalculator",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        2.5
+      ]
+    },
+    {
+      "id": 6,
+      "type": "LoadImage",
+      "pos": [
+        -250,
+        430
+      ],
+      "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",
+        "widget_ue_connectable": {},
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "image-2025-06-13-105737.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "Display Int (rgthree)",
+      "pos": [
+        420,
+        360
+      ],
+      "size": [
+        210,
+        88
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "dir": 3,
+          "name": "input",
+          "type": "INT",
+          "link": 3
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "rgthree-comfy",
+        "ver": "1.0.2506081210",
+        "Node name for S&R": "Display Int (rgthree)",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 5,
+      "type": "Display Int (rgthree)",
+      "pos": [
+        430,
+        530
+      ],
+      "size": [
+        210,
+        88
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+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "dir": 3,
+          "name": "input",
+          "type": "INT",
+          "link": 4
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "rgthree-comfy",
+        "ver": "1.0.2506081210",
+        "widget_ue_connectable": {},
+        "Node name for S&R": "Display Int (rgthree)"
+      },
+      "widgets_values": [
+        ""
+      ]
     },
     {
       "id": 2,
       "type": "Note",
-      "pos": [450, 100],
-      "size": {"0": 400, "1": 200},
+      "pos": [
+        -150,
+        170
+      ],
+      "size": [
+        400,
+        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": {
+        "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.",
+        "widget_ue_connectable": {}
+      },
+      "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"
     }
   ],
   "links": [
-    [1, 1, 0, 3, 0, "INT"],
-    [2, 1, 1, 3, 1, "INT"]
+    [
+      1,
+      1,
+      0,
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+      0,
+      "INT"
+    ],
+    [
+      2,
+      1,
+      1,
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+    ],
+    [
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+    [
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+      "INT"
+    ],
+    [
+      5,
+      6,
+      0,
+      1,
+      0,
+      "IMAGE"
+    ]
   ],
   "groups": [],
   "config": {},
-  "extra": {},
+  "extra": {
+    "ue_links": [],
+    "ds": {
+      "scale": 0.7972024500000015,
+      "offset": [
+        709.6391289161842,
+        -90.0127389290854
+      ]
+    },
+    "links_added_by_ue": [],
+    "frontendVersion": "1.21.7",
+    "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,
+  "nodes": [
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        120,
+        390
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "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"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        180,
+        610
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "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": {}
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        863,
+        186
+      ],
+      "size": [
+        315,
+        571
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        },
+        {
+          "name": "steps",
+          "type": "INT",
+          "widget": {
+            "name": "steps"
+          },
+          "link": 12
+        },
+        {
+          "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"
+          },
+          "link": 11
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.40",
+        "Node name for S&R": "KSampler",
+        "widget_ue_connectable": {}
+      },
+      "widgets_values": [
+        555950987049067,
+        "randomize",
+        20,
+        8,
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examples/workflows/seed_history_example.json

@@ -0,0 +1,644 @@
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+}

kikotools/__init__.py

@@ -5,16 +5,30 @@ 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
 
 # ComfyUI node registration mappings
 NODE_CLASS_MAPPINGS = {
     "ResolutionCalculator": ResolutionCalculatorNode,
     "WidthHeightSelector": WidthHeightSelectorNode,
+    "SeedHistory": SeedHistoryNode,
+    "SamplerCombo": SamplerComboNode,
+    "SamplerComboCompact": SamplerComboCompactNode,
+    "EmptyLatentBatch": EmptyLatentBatchNode,
+    "KikoSaveImage": KikoSaveImageNode,
 }
 
 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",
 }
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

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"
+
+    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/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 +1,226 @@
+"""
+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 = ()
+    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)",
                     },
                 ),
             },
@@ -93,7 +94,8 @@ 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 +140,46 @@ 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"
+
+    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"
+
+    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"
+
+    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.",
                     },
                 ),
             }
@@ -75,7 +92,7 @@ class WidthHeightSelectorNode(ComfyAssetsBaseNode):
         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")

pyproject.toml

@@ -0,0 +1,42 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "kikotools"
+description = "Simple tools for ComfyUI"
+version = "1.0.7"
+license = {text = "MIT"}
+dependencies = []
+
+[project.optional-dependencies]
+dev = [
+    # Testing framework
+    "pytest>=7.0.0",
+    "pytest-cov>=4.0.0",
+    "pytest-mock>=3.10.0",
+    # Code quality
+    "black>=23.0.0",
+    "flake8>=6.0.0",
+    "mypy>=1.0.0",
+    # Development utilities
+    "pre-commit>=3.0.0",
+    # ComfyUI testing (mock dependencies for unit tests)
+    "torch>=2.0.0",
+    "numpy>=1.24.0",
+    "pillow>=9.0.0"
+]
+
+[project.urls]
+Repository = "https://github.com/ComfyAssets/ComfyUI-KikoTools"
+#  Used by Comfy Registry https://registry.comfy.org
+
+[tool.setuptools.packages.find]
+include = ["kikotools*"]
+exclude = ["tests*", "web*"]
+
+[tool.comfy]
+PublisherId = "kiko9"
+DisplayName = "ComfyUI-KikoTools"
+Icon = "https://avatars.githubusercontent.com/u/213204677?s=200"
+includes = []

requirements.txt

@@ -0,0 +1,19 @@
+# Development dependencies for ComfyUI-KikoTools
+
+# Testing framework
+pytest>=7.0.0
+pytest-cov>=4.0.0
+pytest-mock>=3.10.0
+
+# Code quality
+black>=23.0.0
+flake8>=6.0.0
+mypy>=1.0.0
+
+# Development utilities
+pre-commit>=3.0.0
+
+# ComfyUI testing (mock dependencies for unit tests)
+torch>=2.0.0
+numpy>=1.24.0
+pillow>=9.0.0

tests/conftest.py

@@ -5,8 +5,6 @@ Provides mock ComfyUI environments and test data
 
 import pytest
 import torch
-import numpy as np
-from typing import Dict, Any
 from unittest.mock import MagicMock
 
 

tests/unit/test_base_node.py

@@ -4,8 +4,7 @@ Tests the shared functionality for all ComfyAssets tools
 """
 
 import pytest
-import logging
-from unittest.mock import patch, MagicMock
+from unittest.mock import patch
 
 from kikotools.base import ComfyAssetsBaseNode
 

tests/unit/tools/test_empty_latent_batch.py

@@ -0,0 +1,219 @@
+"""Tests for Empty Latent Batch node and logic."""
+
+import pytest
+import torch
+
+from kikotools.tools.empty_latent_batch.node import EmptyLatentBatchNode
+from kikotools.tools.empty_latent_batch.logic import (
+    create_empty_latent_batch,
+    validate_dimensions,
+    sanitize_dimensions,
+)
+
+
+class TestEmptyLatentBatchLogic:
+    """Test the logic functions for empty latent batch creation."""
+
+    def test_create_empty_latent_batch_basic(self):
+        """Test basic empty latent creation."""
+        result = create_empty_latent_batch(512, 512, 1)
+
+        assert "samples" in result
+        samples = result["samples"]
+        assert isinstance(samples, torch.Tensor)
+        assert samples.shape == (1, 4, 64, 64)  # 512/8 = 64
+        assert torch.all(samples == 0)  # Should be all zeros
+
+    def test_create_empty_latent_batch_with_batch_size(self):
+        """Test empty latent creation with larger batch size."""
+        batch_size = 4
+        result = create_empty_latent_batch(1024, 768, batch_size)
+
+        assert "samples" in result
+        samples = result["samples"]
+        assert isinstance(samples, torch.Tensor)
+        assert samples.shape == (4, 4, 96, 128)  # 768/8=96, 1024/8=128
+        assert torch.all(samples == 0)
+
+    def test_create_empty_latent_batch_invalid_dimensions(self):
+        """Test error handling for invalid dimensions."""
+        with pytest.raises(ValueError, match="Width and height must be positive"):
+            create_empty_latent_batch(0, 512, 1)
+
+        with pytest.raises(ValueError, match="Width and height must be positive"):
+            create_empty_latent_batch(512, -100, 1)
+
+    def test_create_empty_latent_batch_not_divisible_by_8(self):
+        """Test error handling for dimensions not divisible by 8."""
+        with pytest.raises(ValueError, match="must be divisible by 8"):
+            create_empty_latent_batch(513, 512, 1)
+
+        with pytest.raises(ValueError, match="must be divisible by 8"):
+            create_empty_latent_batch(512, 515, 1)
+
+    def test_create_empty_latent_batch_invalid_batch_size(self):
+        """Test error handling for invalid batch size."""
+        with pytest.raises(ValueError, match="Batch size must be positive"):
+            create_empty_latent_batch(512, 512, 0)
+
+        with pytest.raises(ValueError, match="Batch size must be positive"):
+            create_empty_latent_batch(512, 512, -1)
+
+    def test_validate_dimensions_valid(self):
+        """Test dimension validation with valid inputs."""
+        assert validate_dimensions(512, 512) is True
+        assert validate_dimensions(1024, 768) is True
+        assert validate_dimensions(64, 64) is True  # Minimum size
+        assert validate_dimensions(8192, 8192) is True  # Maximum size
+
+    def test_validate_dimensions_invalid(self):
+        """Test dimension validation with invalid inputs."""
+        assert validate_dimensions(0, 512) is False  # Zero dimension
+        assert validate_dimensions(512, -100) is False  # Negative dimension
+        assert validate_dimensions(513, 512) is False  # Not divisible by 8
+        assert validate_dimensions(32, 32) is False  # Too small
+        assert validate_dimensions(8200, 8200) is False  # Too large
+
+    def test_sanitize_dimensions_basic(self):
+        """Test basic dimension sanitization."""
+        width, height = sanitize_dimensions(512, 512)
+        assert width == 512
+        assert height == 512
+
+    def test_sanitize_dimensions_not_divisible_by_8(self):
+        """Test sanitization of dimensions not divisible by 8."""
+        width, height = sanitize_dimensions(513, 515)
+        assert width == 512  # Rounds down to nearest multiple of 8
+        assert height == 512
+
+        width, height = sanitize_dimensions(517, 519)
+        assert width == 520  # Rounds up to nearest multiple of 8
+        assert height == 520
+
+    def test_sanitize_dimensions_too_small(self):
+        """Test sanitization of dimensions that are too small."""
+        width, height = sanitize_dimensions(32, 16)
+        assert width == 64  # Minimum size
+        assert height == 64
+
+    def test_sanitize_dimensions_too_large(self):
+        """Test sanitization of dimensions that are too large."""
+        width, height = sanitize_dimensions(10000, 9000)
+        assert width == 8192  # Maximum size
+        assert height == 8192
+
+
+class TestEmptyLatentBatchNode:
+    """Test the EmptyLatentBatchNode ComfyUI node."""
+
+    def setup_method(self):
+        """Set up test fixtures."""
+        self.node = EmptyLatentBatchNode()
+
+    def test_input_types_structure(self):
+        """Test that INPUT_TYPES returns proper structure."""
+        input_types = EmptyLatentBatchNode.INPUT_TYPES()
+
+        assert "required" in input_types
+        required = input_types["required"]
+
+        assert "width" in required
+        assert "height" in required
+        assert "batch_size" in required
+
+        # Check width parameter
+        width_spec = required["width"]
+        assert width_spec[0] == "INT"
+        assert width_spec[1]["default"] == 1024
+        assert width_spec[1]["min"] == 64
+        assert width_spec[1]["max"] == 8192
+        assert width_spec[1]["step"] == 8
+
+    def test_node_attributes(self):
+        """Test node class attributes."""
+        assert EmptyLatentBatchNode.RETURN_TYPES == ("LATENT",)
+        assert EmptyLatentBatchNode.RETURN_NAMES == ("latent",)
+        assert EmptyLatentBatchNode.FUNCTION == "create_empty_latent"
+        assert EmptyLatentBatchNode.CATEGORY == "ComfyAssets"
+
+    def test_create_empty_latent_basic(self):
+        """Test basic empty latent creation through node."""
+        result = self.node.create_empty_latent(512, 512, 1)
+
+        assert isinstance(result, tuple)
+        assert len(result) == 1
+
+        latent_dict = result[0]
+        assert isinstance(latent_dict, dict)
+        assert "samples" in latent_dict
+
+        samples = latent_dict["samples"]
+        assert isinstance(samples, torch.Tensor)
+        assert samples.shape == (1, 4, 64, 64)
+
+    def test_create_empty_latent_with_batch(self):
+        """Test empty latent creation with batch size."""
+        batch_size = 3
+        result = self.node.create_empty_latent(1024, 768, batch_size)
+
+        latent_dict = result[0]
+        samples = latent_dict["samples"]
+        assert samples.shape == (3, 4, 96, 128)  # batch=3, 768/8=96, 1024/8=128
+
+    def test_create_empty_latent_dimension_adjustment(self):
+        """Test that dimensions are adjusted when not divisible by 8."""
+        # Input dimensions not divisible by 8
+        result = self.node.create_empty_latent(513, 515, 1)
+
+        latent_dict = result[0]
+        samples = latent_dict["samples"]
+        # Should be adjusted to 512x512 -> 64x64 latent
+        assert samples.shape == (1, 4, 64, 64)
+
+    def test_validate_inputs_valid(self):
+        """Test input validation with valid parameters."""
+        assert self.node.validate_inputs(512, 512, 1) is True
+        assert self.node.validate_inputs(1024, 768, 4) is True
+
+    def test_validate_inputs_invalid_batch_size(self):
+        """Test input validation with invalid batch size."""
+        assert self.node.validate_inputs(512, 512, 0) is False
+        assert self.node.validate_inputs(512, 512, 100) is False  # Too large
+
+    def test_get_latent_info(self):
+        """Test latent info generation."""
+        info = self.node.get_latent_info(512, 512, 2)
+        assert "Empty latent batch" in info
+        assert "2 × 4 × 64 × 64" in info
+        assert "512×512" in info
+
+    def test_get_memory_estimate(self):
+        """Test memory estimation."""
+        estimate = self.node.get_memory_estimate(512, 512, 1)
+        assert "KB" in estimate or "MB" in estimate
+
+        # Larger batch should show larger estimate
+        large_estimate = self.node.get_memory_estimate(1024, 1024, 8)
+        assert "MB" in large_estimate
+
+    def test_node_registration_mappings(self):
+        """Test that node registration mappings are properly defined."""
+        from kikotools.tools.empty_latent_batch.node import (
+            NODE_CLASS_MAPPINGS,
+            NODE_DISPLAY_NAME_MAPPINGS,
+        )
+
+        assert "EmptyLatentBatch" in NODE_CLASS_MAPPINGS
+        assert NODE_CLASS_MAPPINGS["EmptyLatentBatch"] == EmptyLatentBatchNode
+
+        assert "EmptyLatentBatch" in NODE_DISPLAY_NAME_MAPPINGS
+        assert NODE_DISPLAY_NAME_MAPPINGS["EmptyLatentBatch"] == "Empty Latent Batch"
+
+    def test_node_inheritance(self):
+        """Test that node properly inherits from base class."""
+        from kikotools.base.base_node import ComfyAssetsBaseNode
+
+        assert isinstance(self.node, ComfyAssetsBaseNode)
+        assert hasattr(self.node, "handle_error")
+        assert hasattr(self.node, "log_info")
+        assert hasattr(self.node, "validate_inputs")

tests/unit/tools/test_kiko_save_image.py

@@ -0,0 +1,546 @@
+"""
+Unit tests for KikoSaveImage tool
+Tests image saving functionality with multiple formats and quality settings
+"""
+
+import pytest
+import torch
+import tempfile
+import os
+from PIL import Image
+from unittest.mock import patch
+
+from kikotools.tools.kiko_save_image.node import KikoSaveImageNode
+from kikotools.tools.kiko_save_image.logic import (
+    convert_tensor_to_pil,
+    process_image_batch,
+    validate_save_inputs,
+    save_image_with_format,
+    get_save_image_path,
+    create_png_metadata,
+)
+
+
+class TestKikoSaveImageLogic:
+    """Test core logic functions"""
+
+    def test_convert_tensor_to_pil(self):
+        """Test tensor to PIL conversion"""
+        # Create test tensor [height, width, channels] with values 0-1
+        tensor = torch.rand(64, 64, 3)
+
+        # Convert to PIL
+        pil_image = convert_tensor_to_pil(tensor)
+
+        # Verify conversion
+        assert isinstance(pil_image, Image.Image)
+        assert pil_image.size == (64, 64)  # PIL uses (width, height)
+        assert pil_image.mode in ["RGB", "RGBA"]
+
+    def test_convert_tensor_to_pil_rgba(self):
+        """Test tensor to PIL conversion with alpha channel"""
+        # Create RGBA tensor
+        tensor = torch.rand(32, 32, 4)
+
+        pil_image = convert_tensor_to_pil(tensor)
+
+        assert isinstance(pil_image, Image.Image)
+        assert pil_image.size == (32, 32)
+        assert pil_image.mode == "RGBA"
+
+    def test_get_save_image_path(self):
+        """Test save path generation"""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Test basic path generation
+            full_path, filename = get_save_image_path(
+                "test_prefix", 0, ".png", temp_dir
+            )
+
+            assert full_path.startswith(temp_dir)
+            assert filename.startswith("test_prefix_")
+            assert filename.endswith("_00000.png")
+
+            # Test with empty subfolder (standard behavior)
+            full_path, filename = get_save_image_path("test", 1, ".jpg", temp_dir, "")
+
+            assert full_path.startswith(temp_dir)
+            assert filename.startswith("test_")
+            assert filename.endswith("_00001.jpg")
+
+    def test_create_png_metadata(self):
+        """Test PNG metadata creation"""
+        # Test with no metadata
+        metadata = create_png_metadata()
+        assert metadata is None
+
+        # Test with prompt data
+        prompt_data = {"test": "value"}
+        metadata = create_png_metadata(prompt=prompt_data)
+
+        assert metadata is not None
+        # Check that metadata contains our data (implementation detail)
+        assert hasattr(metadata, "text")
+
+    @patch("kikotools.tools.kiko_save_image.logic.folder_paths")
+    def test_process_image_batch_png(self, mock_folder_paths):
+        """Test batch processing with PNG format"""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            mock_folder_paths.get_output_directory.return_value = temp_dir
+
+            # Create test image batch [batch, height, width, channels]
+            images = torch.rand(2, 32, 32, 3)
+
+            # Process batch
+            results, enhanced_data = process_image_batch(
+                images=images,
+                filename_prefix="test_batch",
+                format_type="PNG",
+                png_compress_level=6,
+            )
+
+            # Verify results (clean data)
+            assert len(results) == 2
+            for i, result in enumerate(results):
+                assert "filename" in result
+                assert "subfolder" in result
+                assert "type" in result
+                assert result["type"] == "output"
+
+            # Verify enhanced data
+            assert len(enhanced_data) == 2
+            for i, enhanced in enumerate(enhanced_data):
+                assert enhanced["format"] == "PNG"
+                assert enhanced["compress_level"] == 6
+                assert enhanced["dimensions"] == "32x32"
+                assert enhanced["popup"] is True  # Default popup value
+                assert "file_size" in enhanced
+
+                # Verify file was saved
+                filepath = os.path.join(temp_dir, enhanced["filename"])
+                assert os.path.exists(filepath)
+
+                # Verify image can be loaded
+                saved_img = Image.open(filepath)
+                assert saved_img.size == (32, 32)
+
+    @patch("kikotools.tools.kiko_save_image.logic.folder_paths")
+    def test_process_image_batch_jpeg(self, mock_folder_paths):
+        """Test batch processing with JPEG format"""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            mock_folder_paths.get_output_directory.return_value = temp_dir
+
+            # Create test image batch
+            images = torch.rand(1, 64, 64, 3)
+
+            # Process batch
+            results, enhanced_data = process_image_batch(
+                images=images,
+                filename_prefix="test_jpeg",
+                format_type="JPEG",
+                quality=85,
+            )
+
+            # Verify results
+            assert len(results) == 1
+            assert len(enhanced_data) == 1
+            enhanced = enhanced_data[0]
+            assert enhanced["format"] == "JPEG"
+            assert enhanced["quality"] == 85
+            assert enhanced["filename"].endswith(".jpg")
+
+            # Verify file exists and can be loaded
+            filepath = os.path.join(temp_dir, results[0]["filename"])
+            assert os.path.exists(filepath)
+
+            saved_img = Image.open(filepath)
+            assert saved_img.size == (64, 64)
+            assert saved_img.mode == "RGB"  # JPEG converts to RGB
+
+    @patch("kikotools.tools.kiko_save_image.logic.folder_paths")
+    def test_process_image_batch_webp(self, mock_folder_paths):
+        """Test batch processing with WebP format"""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            mock_folder_paths.get_output_directory.return_value = temp_dir
+
+            # Create test image batch
+            images = torch.rand(1, 48, 48, 3)
+
+            # Test lossless WebP
+            results = process_image_batch(
+                images=images,
+                filename_prefix="test_webp",
+                format_type="WEBP",
+                quality=90,
+                webp_lossless=True,
+            )
+
+            assert len(results) == 1
+            result = results[0]
+            assert result["format"] == "WEBP"
+            assert result["lossless"] is True
+            assert result["filename"].endswith(".webp")
+
+    def test_validate_save_inputs_valid(self):
+        """Test input validation with valid inputs"""
+        images = torch.rand(2, 64, 64, 3)
+
+        # Should not raise exception
+        validate_save_inputs(images, "PNG", 90, 4)
+        validate_save_inputs(images, "JPEG", 85, 4)
+        validate_save_inputs(images, "WEBP", 95, 6)
+
+    def test_validate_save_inputs_invalid_tensor(self):
+        """Test validation with invalid tensor"""
+        # Wrong tensor dimensions
+        invalid_tensor = torch.rand(64, 64)  # Missing batch and channel dims
+
+        with pytest.raises(ValueError, match="4 dimensions"):
+            validate_save_inputs(invalid_tensor, "PNG", 90, 4)
+
+        # Non-tensor input
+        with pytest.raises(ValueError, match="torch.Tensor"):
+            validate_save_inputs("not_a_tensor", "PNG", 90, 4)
+
+    def test_validate_save_inputs_invalid_format(self):
+        """Test validation with invalid format"""
+        images = torch.rand(1, 32, 32, 3)
+
+        with pytest.raises(ValueError, match="format must be one of"):
+            validate_save_inputs(images, "BMP", 90, 4)
+
+    def test_validate_save_inputs_invalid_quality(self):
+        """Test validation with invalid quality"""
+        images = torch.rand(1, 32, 32, 3)
+
+        # Quality out of range
+        with pytest.raises(
+            ValueError, match="quality must be an integer between 1 and 100"
+        ):
+            validate_save_inputs(images, "JPEG", 0, 4)
+
+        with pytest.raises(
+            ValueError, match="quality must be an integer between 1 and 100"
+        ):
+            validate_save_inputs(images, "JPEG", 101, 4)
+
+    def test_validate_save_inputs_invalid_compress_level(self):
+        """Test validation with invalid PNG compression level"""
+        images = torch.rand(1, 32, 32, 3)
+
+        with pytest.raises(
+            ValueError, match="png_compress_level must be an integer between 0 and 9"
+        ):
+            validate_save_inputs(images, "PNG", 90, -1)
+
+        with pytest.raises(
+            ValueError, match="png_compress_level must be an integer between 0 and 9"
+        ):
+            validate_save_inputs(images, "PNG", 90, 10)
+
+    def test_save_image_with_format_png(self):
+        """Test saving with PNG format"""
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
+            temp_path = temp_file.name
+
+        try:
+            # Create test PIL image
+            img = Image.new("RGB", (32, 32), color="red")
+
+            # Save with PNG format
+            result = save_image_with_format(img, temp_path, "PNG", png_compress_level=8)
+
+            assert result["format"] == "PNG"
+            assert result["compress_level"] == 8
+            assert os.path.exists(temp_path)
+
+            # Verify saved image
+            saved_img = Image.open(temp_path)
+            assert saved_img.size == (32, 32)
+
+        finally:
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+
+    def test_save_image_with_format_jpeg_rgba_conversion(self):
+        """Test JPEG saving with RGBA to RGB conversion"""
+        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as temp_file:
+            temp_path = temp_file.name
+
+        try:
+            # Create RGBA image
+            img = Image.new("RGBA", (32, 32), color=(255, 0, 0, 128))
+
+            # Save as JPEG (should convert to RGB)
+            result = save_image_with_format(img, temp_path, "JPEG", quality=95)
+
+            assert result["format"] == "JPEG"
+            assert result["quality"] == 95
+
+            # Verify saved image is RGB
+            saved_img = Image.open(temp_path)
+            assert saved_img.mode == "RGB"
+
+        finally:
+            if os.path.exists(temp_path):
+                os.unlink(temp_path)
+
+
+class TestKikoSaveImageNode:
+    """Test KikoSaveImageNode class"""
+
+    def setup_method(self):
+        """Setup test fixtures"""
+        self.node = KikoSaveImageNode()
+
+    def test_input_types(self):
+        """Test INPUT_TYPES class method"""
+        input_types = KikoSaveImageNode.INPUT_TYPES()
+
+        # Check required inputs
+        required = input_types["required"]
+        assert "images" in required
+        assert "filename_prefix" in required
+        assert "format" in required
+
+        # Check format options
+        format_options = required["format"][0]
+        assert "PNG" in format_options
+        assert "JPEG" in format_options
+        assert "WEBP" in format_options
+
+        # Check optional inputs
+        optional = input_types["optional"]
+        assert "quality" in optional
+        assert "png_compress_level" in optional
+        assert "webp_lossless" in optional
+        assert "popup" in optional
+
+        # Check hidden inputs
+        hidden = input_types["hidden"]
+        assert "prompt" in hidden
+        assert "extra_pnginfo" in hidden
+
+    def test_node_attributes(self):
+        """Test node class attributes"""
+        assert KikoSaveImageNode.RETURN_TYPES == ()
+        assert KikoSaveImageNode.FUNCTION == "save_images"
+        assert KikoSaveImageNode.OUTPUT_NODE is True
+        assert KikoSaveImageNode.CATEGORY == "ComfyAssets"
+
+    @patch("kikotools.tools.kiko_save_image.node.process_image_batch")
+    def test_save_images_success(self, mock_process):
+        """Test successful image saving"""
+        # Setup mock - new return format (results, enhanced_data)
+        mock_results = [
+            {
+                "filename": "test_00001_00000.png",
+                "subfolder": "",
+                "type": "output",
+            }
+        ]
+        mock_enhanced = [
+            {
+                "filename": "test_00001_00000.png",
+                "popup": True,
+                "type": "output",
+                "format": "PNG",
+                "file_size": 1024,
+                "dimensions": "64x64",
+            }
+        ]
+        mock_process.return_value = (mock_results, mock_enhanced)
+
+        # Create test input
+        images = torch.rand(1, 64, 64, 3)
+
+        # Call save_images
+        result = self.node.save_images(
+            images=images,
+            filename_prefix="test",
+            format="PNG",
+            quality=90,
+            png_compress_level=4,
+        )
+
+        # Verify mock was called
+        mock_process.assert_called_once()
+
+        # Verify result format
+        assert "ui" in result
+        assert "images" in result["ui"]
+        assert "kiko_enhanced" in result["ui"]
+        assert result["ui"]["images"] == mock_results
+        assert result["ui"]["kiko_enhanced"] == mock_enhanced
+
+    def test_validate_inputs_success(self):
+        """Test input validation with valid inputs"""
+        images = torch.rand(1, 32, 32, 3)
+
+        # Should not raise exception
+        self.node.validate_inputs(
+            images=images,
+            format="PNG",
+            quality=90,
+            png_compress_level=4,
+            webp_lossless=False,
+            popup=True,
+        )
+
+    def test_validate_inputs_invalid_webp_lossless(self):
+        """Test validation with invalid webp_lossless type"""
+        images = torch.rand(1, 32, 32, 3)
+
+        with pytest.raises(ValueError, match="webp_lossless must be a boolean"):
+            self.node.validate_inputs(
+                images=images,
+                format="PNG",
+                quality=90,
+                png_compress_level=4,
+                webp_lossless="not_boolean",
+                popup=True,
+            )
+
+    def test_validate_inputs_invalid_popup(self):
+        """Test validation with invalid popup"""
+        images = torch.rand(1, 32, 32, 3)
+
+        # Non-boolean popup
+        with pytest.raises(ValueError, match="popup must be a boolean"):
+            self.node.validate_inputs(
+                images=images,
+                format="PNG",
+                quality=90,
+                png_compress_level=4,
+                webp_lossless=False,
+                popup="not_boolean",
+            )
+
+    @patch("kikotools.tools.kiko_save_image.node.process_image_batch")
+    def test_save_images_error_handling(self, mock_process):
+        """Test error handling in save_images method"""
+        # Setup mock to raise exception
+        mock_process.side_effect = Exception("Test error")
+
+        images = torch.rand(1, 32, 32, 3)
+
+        # Should handle error and re-raise with context
+        with pytest.raises(ValueError, match="Failed to save images"):
+            self.node.save_images(images=images)
+
+    def test_node_info(self):
+        """Test get_node_info method"""
+        info = self.node.get_node_info()
+
+        assert info["class_name"] == "KikoSaveImageNode"
+        assert info["category"] == "ComfyAssets"
+        assert info["function"] == "save_images"
+
+
+class TestNodeRegistration:
+    """Test node registration mappings"""
+
+    def test_node_class_mappings(self):
+        """Test NODE_CLASS_MAPPINGS contains KikoSaveImage"""
+        from kikotools.tools.kiko_save_image.node import NODE_CLASS_MAPPINGS
+
+        assert "KikoSaveImage" in NODE_CLASS_MAPPINGS
+        assert NODE_CLASS_MAPPINGS["KikoSaveImage"] is KikoSaveImageNode
+
+    def test_node_display_name_mappings(self):
+        """Test NODE_DISPLAY_NAME_MAPPINGS contains KikoSaveImage"""
+        from kikotools.tools.kiko_save_image.node import NODE_DISPLAY_NAME_MAPPINGS
+
+        assert "KikoSaveImage" in NODE_DISPLAY_NAME_MAPPINGS
+        assert NODE_DISPLAY_NAME_MAPPINGS["KikoSaveImage"] == "Kiko Save Image"
+
+
+# Integration test fixtures
+@pytest.fixture
+def sample_image_tensor():
+    """Create sample image tensor for testing"""
+    # Create a colorful test image [batch, height, width, channels]
+    batch_size, height, width, channels = 2, 64, 64, 3
+
+    # Create gradient pattern
+    tensor = torch.zeros(batch_size, height, width, channels)
+    for b in range(batch_size):
+        for h in range(height):
+            for w in range(width):
+                # Create RGB gradient pattern
+                tensor[b, h, w, 0] = h / height  # Red gradient
+                tensor[b, h, w, 1] = w / width  # Green gradient
+                tensor[b, h, w, 2] = (b + 1) * 0.5  # Blue varies by batch
+
+    return tensor
+
+
+class TestIntegration:
+    """Integration tests using sample data"""
+
+    @patch("kikotools.tools.kiko_save_image.logic.folder_paths")
+    def test_full_pipeline_png(self, mock_folder_paths, sample_image_tensor):
+        """Test complete pipeline with PNG format"""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            mock_folder_paths.get_output_directory.return_value = temp_dir
+
+            node = KikoSaveImageNode()
+
+            # Save images
+            result = node.save_images(
+                images=sample_image_tensor,
+                filename_prefix="integration_test",
+                format="PNG",
+                png_compress_level=6,
+            )
+
+            # Verify result structure
+            assert "ui" in result
+            assert "images" in result["ui"]
+            assert len(result["ui"]["images"]) == 2
+
+            # Verify files were created
+            for image_info in result["ui"]["images"]:
+                filepath = os.path.join(temp_dir, image_info["filename"])
+                assert os.path.exists(filepath)
+
+                # Verify image properties
+                img = Image.open(filepath)
+                assert img.size == (64, 64)
+                assert img.format == "PNG"
+
+    @patch("kikotools.tools.kiko_save_image.logic.folder_paths")
+    def test_full_pipeline_all_formats(self, mock_folder_paths, sample_image_tensor):
+        """Test complete pipeline with all supported formats"""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            mock_folder_paths.get_output_directory.return_value = temp_dir
+
+            node = KikoSaveImageNode()
+
+            # Test each format
+            formats_to_test = [
+                ("PNG", {"png_compress_level": 8}),
+                ("JPEG", {"quality": 85}),
+                ("WEBP", {"quality": 90, "webp_lossless": False}),
+                ("WEBP", {"quality": 100, "webp_lossless": True}),
+            ]
+
+            for format_type, kwargs in formats_to_test:
+                result = node.save_images(
+                    images=sample_image_tensor,
+                    filename_prefix=f"test_{format_type.lower()}",
+                    format=format_type,
+                    **kwargs,
+                )
+
+                # Verify results
+                assert len(result["ui"]["images"]) == 2
+
+                for image_info in result["ui"]["images"]:
+                    assert image_info["format"] == format_type
+
+                    # Verify file exists and can be opened
+                    filepath = os.path.join(temp_dir, image_info["filename"])
+                    assert os.path.exists(filepath)
+
+                    img = Image.open(filepath)
+                    assert img.size == (64, 64)

tests/unit/tools/test_resolution_calculator.py

@@ -5,7 +5,6 @@ Following TDD principles - these tests define the expected behavior
 
 import pytest
 import torch
-from unittest.mock import patch, MagicMock
 
 # Import the modules we're going to test (they don't exist yet - TDD!)
 from kikotools.tools.resolution_calculator.logic import (
@@ -172,8 +171,6 @@ class TestResolutionCalculatorNode:
 
     def test_node_has_correct_comfyui_attributes(self):
         """Test node has all required ComfyUI attributes"""
-        node = ResolutionCalculatorNode()
-
         # Check class attributes exist
         assert hasattr(ResolutionCalculatorNode, "INPUT_TYPES")
         assert hasattr(ResolutionCalculatorNode, "RETURN_TYPES")

tests/unit/tools/test_sampler_combo.py

@@ -0,0 +1,352 @@
+"""Tests for Sampler Combo node."""
+
+import pytest
+from unittest.mock import patch
+from kikotools.tools.sampler_combo.node import SamplerComboNode
+from kikotools.tools.sampler_combo.logic import (
+    validate_sampler_settings,
+    get_sampler_combo,
+    get_compatible_scheduler_suggestions,
+    get_recommended_steps_range,
+    get_recommended_cfg_range,
+    get_sampler_info,
+    SAMPLERS,
+    SCHEDULERS,
+)
+
+
+class TestSamplerComboLogic:
+    """Test cases for sampler combo logic functions."""
+
+    def test_validate_sampler_settings_valid(self):
+        """Test validation with valid settings."""
+        assert validate_sampler_settings("euler", "normal", 20, 7.0) is True
+        assert validate_sampler_settings("dpmpp_2m", "karras", 15, 8.5) is True
+        assert validate_sampler_settings("ddim", "ddim_uniform", 30, 6.0) is True
+
+    def test_validate_sampler_settings_invalid_sampler(self):
+        """Test validation with invalid sampler."""
+        assert validate_sampler_settings("invalid_sampler", "normal", 20, 7.0) is False
+
+    def test_validate_sampler_settings_invalid_scheduler(self):
+        """Test validation with invalid scheduler."""
+        assert validate_sampler_settings("euler", "invalid_scheduler", 20, 7.0) is False
+
+    def test_validate_sampler_settings_invalid_steps(self):
+        """Test validation with invalid steps."""
+        assert validate_sampler_settings("euler", "normal", 0, 7.0) is False
+        assert validate_sampler_settings("euler", "normal", 1001, 7.0) is False
+        assert validate_sampler_settings("euler", "normal", -5, 7.0) is False
+
+    def test_validate_sampler_settings_invalid_cfg(self):
+        """Test validation with invalid CFG."""
+        assert validate_sampler_settings("euler", "normal", 20, -1.0) is False
+        assert validate_sampler_settings("euler", "normal", 20, 31.0) is False
+
+    def test_get_sampler_combo_valid(self):
+        """Test getting sampler combo with valid inputs."""
+        result = get_sampler_combo("euler", "normal", 20, 7.0)
+        assert result == ("euler", "normal", 20, 7.0)
+
+        result = get_sampler_combo("dpmpp_2m", "karras", 25, 8.5)
+        assert result == ("dpmpp_2m", "karras", 25, 8.5)
+
+    def test_get_sampler_combo_invalid_returns_defaults(self):
+        """Test that invalid inputs return safe defaults."""
+        result = get_sampler_combo("invalid", "normal", 20, 7.0)
+        assert result == ("euler", "normal", 20, 7.0)
+
+        result = get_sampler_combo("euler", "invalid", 20, 7.0)
+        assert result == ("euler", "normal", 20, 7.0)
+
+    def test_get_sampler_combo_sanitizes_values(self):
+        """Test that values are sanitized to valid ranges."""
+        # Test steps clamping
+        result = get_sampler_combo("euler", "normal", 0, 7.0)
+        assert result[2] >= 1  # steps should be at least 1
+
+        result = get_sampler_combo("euler", "normal", 1500, 7.0)
+        assert result[2] <= 1000  # steps should be at most 1000
+
+        # Test CFG clamping
+        result = get_sampler_combo("euler", "normal", 20, -5.0)
+        assert result[3] >= 0.0  # CFG should be at least 0
+
+        result = get_sampler_combo("euler", "normal", 20, 50.0)
+        assert result[3] <= 30.0  # CFG should be at most 30
+
+    def test_get_compatible_scheduler_suggestions(self):
+        """Test getting scheduler suggestions for different samplers."""
+        suggestions = get_compatible_scheduler_suggestions("euler")
+        assert isinstance(suggestions, list)
+        assert len(suggestions) > 0
+        assert "normal" in suggestions
+
+        suggestions = get_compatible_scheduler_suggestions("ddim")
+        assert "ddim_uniform" in suggestions
+
+        # Test unknown sampler returns defaults
+        suggestions = get_compatible_scheduler_suggestions("unknown_sampler")
+        assert "normal" in suggestions
+        assert "karras" in suggestions
+
+    def test_get_recommended_steps_range(self):
+        """Test getting recommended steps range for samplers."""
+        min_steps, max_steps, default_steps = get_recommended_steps_range("euler")
+        assert isinstance(min_steps, int)
+        assert isinstance(max_steps, int)
+        assert isinstance(default_steps, int)
+        assert min_steps <= default_steps <= max_steps
+        assert min_steps > 0
+
+        # Test unknown sampler returns defaults
+        min_steps, max_steps, default_steps = get_recommended_steps_range("unknown")
+        assert min_steps == 10
+        assert max_steps == 50
+        assert default_steps == 20
+
+    def test_get_recommended_cfg_range(self):
+        """Test getting recommended CFG range for samplers."""
+        min_cfg, max_cfg, default_cfg = get_recommended_cfg_range("euler")
+        assert isinstance(min_cfg, float)
+        assert isinstance(max_cfg, float)
+        assert isinstance(default_cfg, float)
+        assert min_cfg <= default_cfg <= max_cfg
+        assert min_cfg >= 0.0
+
+        # Test unknown sampler returns defaults
+        min_cfg, max_cfg, default_cfg = get_recommended_cfg_range("unknown")
+        assert min_cfg == 1.0
+        assert max_cfg == 20.0
+        assert default_cfg == 7.0
+
+    def test_get_sampler_info(self):
+        """Test getting sampler information."""
+        info = get_sampler_info()
+        assert isinstance(info, dict)
+        assert "samplers" in info
+        assert "schedulers" in info
+        assert "sampler_count" in info
+        assert "scheduler_count" in info
+        assert info["sampler_count"] == len(SAMPLERS)
+        assert info["scheduler_count"] == len(SCHEDULERS)
+
+
+class TestSamplerComboNode:
+    """Test cases for SamplerComboNode."""
+
+    def setup_method(self):
+        """Set up test fixtures."""
+        self.node = SamplerComboNode()
+
+    def test_input_types_structure(self):
+        """Test that INPUT_TYPES returns correct structure."""
+        input_types = SamplerComboNode.INPUT_TYPES()
+
+        assert "required" in input_types
+        required = input_types["required"]
+
+        # Check all required inputs are present
+        assert "sampler_name" in required
+        assert "scheduler" in required
+        assert "steps" in required
+        assert "cfg" in required
+
+        # Check sampler input structure
+        sampler_input = required["sampler_name"]
+        assert sampler_input[0] == SAMPLERS
+        assert isinstance(sampler_input[1], dict)
+        assert "default" in sampler_input[1]
+        assert "tooltip" in sampler_input[1]
+
+        # Check scheduler input structure
+        scheduler_input = required["scheduler"]
+        assert scheduler_input[0] == SCHEDULERS
+        assert isinstance(scheduler_input[1], dict)
+
+        # Check steps input structure
+        steps_input = required["steps"]
+        assert steps_input[0] == "INT"
+        assert steps_input[1]["min"] == 1
+        assert steps_input[1]["max"] == 100
+
+        # Check CFG input structure
+        cfg_input = required["cfg"]
+        assert cfg_input[0] == "FLOAT"
+        assert cfg_input[1]["min"] == 0.0
+        assert cfg_input[1]["max"] == 20.0
+
+    def test_return_types_structure(self):
+        """Test that return types are correctly defined."""
+        assert SamplerComboNode.RETURN_TYPES == ("SAMPLER", SCHEDULERS, "INT", "FLOAT")
+        assert SamplerComboNode.RETURN_NAMES == (
+            "sampler_name",
+            "scheduler",
+            "steps",
+            "cfg",
+        )
+        assert SamplerComboNode.FUNCTION == "get_sampler_combo"
+        assert SamplerComboNode.CATEGORY == "ComfyAssets"
+
+    def test_get_sampler_combo_valid_inputs(self):
+        """Test get_sampler_combo with valid inputs."""
+        result = self.node.get_sampler_combo("euler", "normal", 20, 7.0)
+        assert result == ("euler", "normal", 20, 7.0)
+
+        result = self.node.get_sampler_combo("dpmpp_2m", "karras", 15, 8.5)
+        assert result == ("dpmpp_2m", "karras", 15, 8.5)
+
+    def test_get_sampler_combo_invalid_inputs_returns_defaults(self):
+        """Test that invalid inputs return safe defaults."""
+        with patch.object(self.node, "handle_error") as mock_error:
+            mock_error.side_effect = ValueError("Invalid settings")
+
+            try:
+                result = self.node.get_sampler_combo("invalid", "normal", 20, 7.0)
+            except ValueError:
+                pass  # Expected when handle_error raises
+
+        # Test with exception handling bypassed
+        with patch(
+            "kikotools.tools.sampler_combo.node.validate_sampler_settings",
+            return_value=False,
+        ):
+            result = self.node.get_sampler_combo("invalid", "normal", 20, 7.0)
+            assert result == ("euler", "normal", 20, 7.0)
+
+    def test_validate_inputs_valid(self):
+        """Test input validation with valid inputs."""
+        # Should not raise any exception
+        self.node.validate_inputs("euler", "normal", 20, 7.0)
+
+    def test_validate_inputs_invalid(self):
+        """Test input validation with invalid inputs."""
+        with pytest.raises(ValueError):
+            self.node.validate_inputs("invalid", "normal", 20, 7.0)
+
+    def test_get_scheduler_suggestions(self):
+        """Test getting scheduler suggestions."""
+        suggestions = self.node.get_scheduler_suggestions("euler")
+        assert isinstance(suggestions, list)
+        assert len(suggestions) > 0
+
+        suggestions = self.node.get_scheduler_suggestions("ddim")
+        assert "ddim_uniform" in suggestions
+
+    def test_get_steps_recommendation(self):
+        """Test getting steps recommendations."""
+        rec = self.node.get_steps_recommendation("euler")
+        assert isinstance(rec, dict)
+        assert "min" in rec
+        assert "max" in rec
+        assert "default" in rec
+        assert "recommendation" in rec
+
+    def test_get_cfg_recommendation(self):
+        """Test getting CFG recommendations."""
+        rec = self.node.get_cfg_recommendation("euler")
+        assert isinstance(rec, dict)
+        assert "min" in rec
+        assert "max" in rec
+        assert "default" in rec
+        assert "recommendation" in rec
+
+    def test_get_combo_analysis(self):
+        """Test getting combo analysis."""
+        analysis = self.node.get_combo_analysis("euler", "normal", 20, 7.0)
+        assert isinstance(analysis, dict)
+        assert "sampler" in analysis
+        assert "scheduler" in analysis
+        assert "steps" in analysis
+        assert "cfg" in analysis
+        assert "valid" in analysis
+        assert "scheduler_suggestions" in analysis
+        assert "scheduler_compatible" in analysis
+        assert "steps_optimal" in analysis
+        assert "cfg_optimal" in analysis
+
+    def test_get_available_samplers(self):
+        """Test getting available samplers."""
+        samplers = SamplerComboNode.get_available_samplers()
+        assert isinstance(samplers, list)
+        assert len(samplers) > 0
+        assert "euler" in samplers
+
+    def test_get_available_schedulers(self):
+        """Test getting available schedulers."""
+        schedulers = SamplerComboNode.get_available_schedulers()
+        assert isinstance(schedulers, list)
+        assert len(schedulers) > 0
+        assert "normal" in schedulers
+
+    def test_string_representations(self):
+        """Test string representations of the node."""
+        str_repr = str(self.node)
+        assert "SamplerComboNode" in str_repr
+        assert "samplers=" in str_repr
+        assert "schedulers=" in str_repr
+
+        repr_str = repr(self.node)
+        assert "SamplerComboNode" in repr_str
+        assert "category=" in repr_str
+        assert "function=" in repr_str
+
+    def test_node_inheritance(self):
+        """Test that node properly inherits from base class."""
+        from kikotools.base.base_node import ComfyAssetsBaseNode
+
+        assert isinstance(self.node, ComfyAssetsBaseNode)
+        assert hasattr(self.node, "validate_inputs")
+        assert hasattr(self.node, "handle_error")
+        assert hasattr(self.node, "log_info")
+
+
+class TestSamplerComboIntegration:
+    """Integration tests for Sampler Combo functionality."""
+
+    def test_full_workflow_valid_settings(self):
+        """Test complete workflow with valid settings."""
+        node = SamplerComboNode()
+
+        # Test with different sampler/scheduler combinations
+        test_cases = [
+            ("euler", "normal", 20, 7.0),
+            ("dpmpp_2m", "karras", 15, 8.0),
+            ("euler_ancestral", "exponential", 25, 9.0),
+            ("ddim", "ddim_uniform", 30, 6.0),
+        ]
+
+        for sampler, scheduler, steps, cfg in test_cases:
+            result = node.get_sampler_combo(sampler, scheduler, steps, cfg)
+            assert result == (sampler, scheduler, steps, cfg)
+
+    def test_recommendation_compatibility(self):
+        """Test that recommendations are compatible with actual functionality."""
+        node = SamplerComboNode()
+
+        for sampler in SAMPLERS[:5]:  # Test first 5 samplers
+            suggestions = node.get_scheduler_suggestions(sampler)
+            steps_rec = node.get_steps_recommendation(sampler)
+            cfg_rec = node.get_cfg_recommendation(sampler)
+
+            # Test that recommendations work with the node
+            for scheduler in suggestions[:2]:  # Test first 2 suggestions
+                result = node.get_sampler_combo(
+                    sampler, scheduler, steps_rec["default"], cfg_rec["default"]
+                )
+                assert result[0] == sampler
+                assert result[1] == scheduler
+                assert result[2] == steps_rec["default"]
+                assert result[3] == cfg_rec["default"]
+
+    def test_error_recovery(self):
+        """Test error recovery with malformed inputs."""
+        node = SamplerComboNode()
+
+        # These should all return safe defaults due to error handling
+        with patch(
+            "kikotools.tools.sampler_combo.logic.validate_sampler_settings",
+            side_effect=Exception("Simulated error"),
+        ):
+            result = node.get_sampler_combo("euler", "normal", 20, 7.0)
+            assert result == ("euler", "normal", 20, 7.0)  # Safe defaults

tests/unit/tools/test_seed_history.py

@@ -0,0 +1,415 @@
+"""Tests for Seed History tool."""
+
+import time
+
+from kikotools.tools.seed_history.node import SeedHistoryNode
+from kikotools.tools.seed_history.logic import (
+    generate_random_seed,
+    validate_seed_value,
+    sanitize_seed_value,
+    create_history_entry,
+    filter_duplicate_seeds,
+    add_seed_to_history,
+    format_time_ago,
+    search_history_by_seed,
+    get_history_statistics,
+    export_history_to_text,
+    import_seeds_from_list,
+)
+
+
+class TestSeedHistoryNode:
+    """Test SeedHistoryNode functionality."""
+
+    def test_node_structure(self):
+        """Test that node has correct ComfyUI structure."""
+        # Test class attributes
+        assert hasattr(SeedHistoryNode, "INPUT_TYPES")
+        assert hasattr(SeedHistoryNode, "RETURN_TYPES")
+        assert hasattr(SeedHistoryNode, "RETURN_NAMES")
+        assert hasattr(SeedHistoryNode, "FUNCTION")
+        assert hasattr(SeedHistoryNode, "CATEGORY")
+
+        # Test input types structure
+        input_types = SeedHistoryNode.INPUT_TYPES()
+        assert "required" in input_types
+        assert "seed" in input_types["required"]
+
+        # Test seed input configuration
+        seed_config = input_types["required"]["seed"]
+        assert seed_config[0] == "INT"
+        assert isinstance(seed_config[1], dict)
+        assert "default" in seed_config[1]
+        assert "min" in seed_config[1]
+        assert "max" in seed_config[1]
+        assert seed_config[1]["min"] == 0
+        assert seed_config[1]["max"] == 0xFFFFFFFFFFFFFFFF
+
+        # Test return types
+        assert SeedHistoryNode.RETURN_TYPES == ("INT",)
+        assert SeedHistoryNode.RETURN_NAMES == ("seed",)
+        assert SeedHistoryNode.FUNCTION == "output_seed"
+        assert SeedHistoryNode.CATEGORY == "ComfyAssets"
+
+    def test_output_seed_valid_input(self):
+        """Test seed output with valid input."""
+        node = SeedHistoryNode()
+
+        # Test various valid seeds
+        test_seeds = [0, 12345, 999999, 0xFFFFFFFFFFFFFFFF]
+
+        for seed in test_seeds:
+            result = node.output_seed(seed)
+            assert isinstance(result, tuple)
+            assert len(result) == 1
+            assert result[0] == seed
+
+    def test_output_seed_invalid_input(self):
+        """Test seed output with invalid input."""
+        node = SeedHistoryNode()
+
+        # Test invalid seeds (negative values)
+        result = node.output_seed(-1)
+        assert result == (12345,)  # Fallback
+
+        # Test seeds too large
+        result = node.output_seed(0xFFFFFFFFFFFFFFFF + 1)
+        assert result == (12345,)  # Fallback
+
+    def test_generate_new_seed(self):
+        """Test random seed generation."""
+        node = SeedHistoryNode()
+
+        # Generate multiple seeds
+        seeds = []
+        for _ in range(10):
+            seed = node.generate_new_seed()
+            seeds.append(seed)
+
+        # Test all seeds are valid
+        for seed in seeds:
+            assert validate_seed_value(seed)
+
+        # Test seeds are different (probabilistically)
+        assert len(set(seeds)) > 5  # Should have some variety
+
+    def test_validate_seed_input(self):
+        """Test seed validation."""
+        node = SeedHistoryNode()
+
+        # Valid seeds
+        assert node.validate_seed_input(0)
+        assert node.validate_seed_input(12345)
+        assert node.validate_seed_input(0xFFFFFFFFFFFFFFFF)
+
+        # Invalid seeds
+        assert not node.validate_seed_input(-1)
+        assert not node.validate_seed_input(0xFFFFFFFFFFFFFFFF + 1)
+        assert not node.validate_seed_input(None)
+
+    def test_get_seed_info(self):
+        """Test seed information generation."""
+        node = SeedHistoryNode()
+
+        # Test various seed types
+        info_zero = node.get_seed_info(0)
+        assert "zero" in info_zero.lower()
+
+        info_default = node.get_seed_info(12345)
+        assert "default" in info_default.lower()
+
+        info_power_of_2 = node.get_seed_info(1024)
+        assert "power of 2" in info_power_of_2.lower()
+
+        # Test invalid seed
+        info_invalid = node.get_seed_info(-1)
+        assert "invalid" in info_invalid.lower()
+
+    def test_seed_range_info(self):
+        """Test seed range information."""
+        node = SeedHistoryNode()
+
+        range_info = node.get_seed_range_info()
+        assert "Valid range" in range_info
+        assert str(0xFFFFFFFFFFFFFFFF) in range_info
+
+    def test_class_methods(self):
+        """Test class methods."""
+        # Test default seed
+        default_seed = SeedHistoryNode.get_default_seed()
+        assert default_seed == 12345
+
+        # Test range checking
+        assert SeedHistoryNode.is_seed_in_range(0)
+        assert SeedHistoryNode.is_seed_in_range(12345)
+        assert SeedHistoryNode.is_seed_in_range(0xFFFFFFFFFFFFFFFF)
+        assert not SeedHistoryNode.is_seed_in_range(-1)
+        assert not SeedHistoryNode.is_seed_in_range(0xFFFFFFFFFFFFFFFF + 1)
+
+
+class TestSeedHistoryLogic:
+    """Test seed history logic functions."""
+
+    def test_generate_random_seed(self):
+        """Test random seed generation."""
+        # Generate multiple seeds
+        seeds = [generate_random_seed() for _ in range(100)]
+
+        # Test all seeds are valid
+        for seed in seeds:
+            assert validate_seed_value(seed)
+
+        # Test seeds have variety
+        assert len(set(seeds)) > 50  # Should have good variety
+
+    def test_validate_seed_value(self):
+        """Test seed validation logic."""
+        # Valid seeds
+        assert validate_seed_value(0)
+        assert validate_seed_value(12345)
+        assert validate_seed_value(0xFFFFFFFFFFFFFFFF)
+
+        # Invalid seeds
+        assert not validate_seed_value(-1)
+        assert not validate_seed_value(0xFFFFFFFFFFFFFFFF + 1)
+        assert not validate_seed_value(None)
+        assert not validate_seed_value("invalid")
+        assert not validate_seed_value([])
+
+    def test_sanitize_seed_value(self):
+        """Test seed sanitization."""
+        # Valid seeds should pass through
+        assert sanitize_seed_value(12345) == 12345
+        assert sanitize_seed_value(0) == 0
+        assert sanitize_seed_value(0xFFFFFFFFFFFFFFFF) == 0xFFFFFFFFFFFFFFFF
+
+        # String numbers should convert
+        assert sanitize_seed_value("12345") == 12345
+        assert sanitize_seed_value("0") == 0
+
+        # Out of range should clamp
+        assert sanitize_seed_value(-100) == 0
+        assert sanitize_seed_value(0xFFFFFFFFFFFFFFFF + 100) == 0xFFFFFFFFFFFFFFFF
+
+        # Invalid should raise
+        try:
+            sanitize_seed_value(None)
+            assert False, "Should have raised ValueError"
+        except ValueError:
+            pass
+
+        try:
+            sanitize_seed_value("invalid")
+            assert False, "Should have raised ValueError"
+        except ValueError:
+            pass
+
+    def test_create_history_entry(self):
+        """Test history entry creation."""
+        seed = 12345
+        timestamp = time.time()
+
+        # With explicit timestamp
+        entry = create_history_entry(seed, timestamp)
+        assert entry["seed"] == seed
+        assert entry["timestamp"] == timestamp
+        assert "dateString" in entry
+
+        # With auto timestamp
+        entry_auto = create_history_entry(seed)
+        assert entry_auto["seed"] == seed
+        assert "timestamp" in entry_auto
+        assert "dateString" in entry_auto
+
+    def test_filter_duplicate_seeds(self):
+        """Test duplicate seed filtering."""
+        seed = 12345
+        current_time = time.time()
+
+        # Empty history should not filter
+        assert not filter_duplicate_seeds([], seed, 500)
+
+        # Recent duplicate should filter
+        recent_entry = create_history_entry(seed, current_time - 0.1)
+        history = [recent_entry]
+        assert filter_duplicate_seeds(history, seed, 500)
+
+        # Old duplicate should not filter
+        old_entry = create_history_entry(seed, current_time - 1.0)
+        history = [old_entry]
+        assert not filter_duplicate_seeds(history, seed, 500)
+
+        # Different seed should not filter
+        different_entry = create_history_entry(54321, current_time - 0.1)
+        history = [different_entry]
+        assert not filter_duplicate_seeds(history, seed, 500)
+
+    def test_add_seed_to_history(self):
+        """Test adding seeds to history."""
+        history = []
+
+        # Add first seed
+        new_history, was_added = add_seed_to_history(history, 12345)
+        assert was_added
+        assert len(new_history) == 1
+        assert new_history[0]["seed"] == 12345
+
+        # Add different seed
+        new_history2, was_added2 = add_seed_to_history(new_history, 54321)
+        assert was_added2
+        assert len(new_history2) == 2
+        assert new_history2[0]["seed"] == 54321  # Most recent first
+
+        # Add duplicate (should remove old and add new)
+        time.sleep(0.6)  # Wait past dedup window
+        new_history3, was_added3 = add_seed_to_history(new_history2, 12345)
+        assert was_added3
+        assert len(new_history3) == 2
+        assert new_history3[0]["seed"] == 12345  # Most recent first
+
+        # Test max history limit
+        history_long = []
+        for i in range(15):
+            history_long, _ = add_seed_to_history(history_long, i, max_history=10)
+            time.sleep(0.001)  # Small delay to avoid dedup
+
+        assert len(history_long) == 10
+
+    def test_format_time_ago(self):
+        """Test time ago formatting."""
+        now = time.time()
+
+        # Recent times
+        assert "s ago" in format_time_ago(now - 30)
+        assert "m ago" in format_time_ago(now - 300)
+        assert "h ago" in format_time_ago(now - 7200)
+        assert "d ago" in format_time_ago(now - 86400)
+
+    def test_search_history_by_seed(self):
+        """Test history search."""
+        history = [
+            create_history_entry(12345),
+            create_history_entry(54321),
+            create_history_entry(99999),
+        ]
+
+        # Found seed
+        result = search_history_by_seed(history, 54321)
+        assert result is not None
+        assert result["seed"] == 54321
+
+        # Not found seed
+        result = search_history_by_seed(history, 11111)
+        assert result is None
+
+    def test_get_history_statistics(self):
+        """Test history statistics."""
+        # Empty history
+        stats = get_history_statistics([])
+        assert stats["total_seeds"] == 0
+        assert stats["unique_seeds"] == 0
+
+        # History with data
+        now = time.time()
+        history = [
+            create_history_entry(12345, now - 3600),
+            create_history_entry(54321, now - 1800),
+            create_history_entry(12345, now),  # Duplicate
+        ]
+
+        stats = get_history_statistics(history)
+        assert stats["total_seeds"] == 3
+        assert stats["unique_seeds"] == 2
+        assert stats["time_span_hours"] == 1.0
+
+    def test_export_history_to_text(self):
+        """Test history export."""
+        # Empty history
+        text = export_history_to_text([])
+        assert "Empty" in text
+
+        # History with data
+        history = [create_history_entry(12345), create_history_entry(54321)]
+
+        text = export_history_to_text(history)
+        assert "12345" in text
+        assert "54321" in text
+        assert "Total seeds: 2" in text
+
+    def test_import_seeds_from_list(self):
+        """Test importing seeds from list."""
+        seed_list = [12345, 54321, 99999]
+
+        history = import_seeds_from_list(seed_list)
+        assert len(history) == 3
+
+        # Check seeds are in correct order (newest first)
+        assert history[0]["seed"] == 12345
+        assert history[1]["seed"] == 54321
+        assert history[2]["seed"] == 99999
+
+        # Check timestamps are spaced
+        assert history[0]["timestamp"] > history[1]["timestamp"]
+        assert history[1]["timestamp"] > history[2]["timestamp"]
+
+
+class TestSeedHistoryIntegration:
+    """Test integration scenarios."""
+
+    def test_complete_workflow(self):
+        """Test complete seed history workflow."""
+        node = SeedHistoryNode()
+
+        # Test basic seed output
+        result = node.output_seed(12345)
+        assert result == (12345,)
+
+        # Test seed generation
+        new_seed = node.generate_new_seed()
+        assert validate_seed_value(new_seed)
+
+        # Test seed info
+        info = node.get_seed_info(new_seed)
+        assert str(new_seed) in info
+
+    def test_history_management(self):
+        """Test history management operations."""
+        history = []
+
+        # Add seeds over time
+        seeds = [12345, 54321, 99999, 11111, 22222]
+        for seed in seeds:
+            history, was_added = add_seed_to_history(history, seed)
+            assert was_added
+            time.sleep(0.001)  # Avoid dedup
+
+        # Check history order (newest first)
+        assert history[0]["seed"] == 22222
+        assert history[-1]["seed"] == 12345
+
+        # Test search
+        found = search_history_by_seed(history, 99999)
+        assert found is not None
+
+        # Test statistics
+        stats = get_history_statistics(history)
+        assert stats["total_seeds"] == 5
+        assert stats["unique_seeds"] == 5
+
+    def test_error_handling(self):
+        """Test error handling scenarios."""
+        node = SeedHistoryNode()
+
+        # Test with invalid seeds
+        result = node.output_seed(-1)
+        assert result == (12345,)  # Fallback
+
+        # Test validation
+        assert not node.validate_seed_input(None)
+        assert not node.validate_seed_input("invalid")
+
+        # Test history with invalid seeds
+        history = []
+        history, was_added = add_seed_to_history(history, -1)
+        assert not was_added
+        assert len(history) == 0

tests/unit/tools/test_width_height_selector.py

@@ -1,7 +1,5 @@
 """Tests for Width Height Selector tool."""
 
-import pytest
-from unittest.mock import Mock
 from kikotools.tools.width_height_selector.node import WidthHeightSelectorNode
 from kikotools.tools.width_height_selector.logic import (
     get_preset_dimensions,
@@ -10,9 +8,12 @@ from kikotools.tools.width_height_selector.logic import (
 )
 from kikotools.tools.width_height_selector.presets import (
     PRESET_OPTIONS,
+    PRESET_METADATA,
     SDXL_PRESETS,
     FLUX_PRESETS,
     ULTRA_WIDE_PRESETS,
+    get_preset_metadata,
+    get_presets_by_model_group,
 )
 
 
@@ -44,7 +45,8 @@ class TestWidthHeightSelectorNode:
         assert result == (1920, 1080)
 
     def test_sdxl_square_preset(self):
-        """Test SDXL square preset."""
+        """Test SDXL square preset (supports both raw and formatted)."""
+        # Test raw preset
         result = self.node.get_dimensions(
             preset="1024×1024",
             width=512,  # Should be ignored
@@ -52,38 +54,91 @@ class TestWidthHeightSelectorNode:
         )
         assert result == (1024, 1024)
 
+        # Test formatted preset
+        result = self.node.get_dimensions(
+            preset="1024×1024 - 1:1 (1.1MP) - SDXL",
+            width=512,  # Should be ignored
+            height=512,  # Should be ignored
+        )
+        assert result == (1024, 1024)
+
     def test_sdxl_portrait_preset(self):
-        """Test SDXL portrait preset."""
+        """Test SDXL portrait preset (supports both raw and formatted)."""
+        # Test raw preset
         result = self.node.get_dimensions(preset="832×1216", width=512, height=512)
         assert result == (832, 1216)
 
+        # Test formatted preset if available
+        formatted_preset = "832×1216 - 13:19 (1.0MP) - SDXL"
+        result = self.node.get_dimensions(
+            preset=formatted_preset, width=512, height=512
+        )
+        assert result == (832, 1216)
+
     def test_sdxl_landscape_preset(self):
-        """Test SDXL landscape preset."""
+        """Test SDXL landscape preset (supports both raw and formatted)."""
+        # Test raw preset
         result = self.node.get_dimensions(preset="1216×832", width=512, height=512)
         assert result == (1216, 832)
 
+        # Test formatted preset if available
+        formatted_preset = "1216×832 - 19:13 (1.0MP) - SDXL"
+        result = self.node.get_dimensions(
+            preset=formatted_preset, width=512, height=512
+        )
+        assert result == (1216, 832)
+
     def test_flux_preset(self):
-        """Test FLUX preset."""
+        """Test FLUX preset (supports both raw and formatted)."""
+        # Test raw preset
         result = self.node.get_dimensions(preset="1920×1080", width=512, height=512)
         assert result == (1920, 1080)
 
+        # Test formatted preset
+        formatted_preset = "1920×1080 - 16:9 (2.1MP) - FLUX"
+        result = self.node.get_dimensions(
+            preset=formatted_preset, width=512, height=512
+        )
+        assert result == (1920, 1080)
+
     def test_ultra_wide_preset(self):
-        """Test ultra-wide preset."""
+        """Test ultra-wide preset (supports both raw and formatted)."""
+        # Test raw preset
         result = self.node.get_dimensions(preset="2560×1080", width=512, height=512)
         assert result == (2560, 1080)
 
+        # Test formatted preset if available
+        formatted_preset = "2560×1080 - 64:27 (2.8MP) - Ultra-Wide"
+        result = self.node.get_dimensions(
+            preset=formatted_preset, width=512, height=512
+        )
+        assert result == (2560, 1080)
+
     def test_all_presets_available(self):
         """Test that all presets are available in INPUT_TYPES."""
         input_types = self.node.INPUT_TYPES()
         available_presets = input_types["required"]["preset"][0]
 
-        # Check that all major preset categories are available
+        # Check that custom is available
         assert "custom" in available_presets
-        assert "1024×1024" in available_presets  # SDXL square
-        assert "832×1216" in available_presets  # SDXL portrait
-        assert "1216×832" in available_presets  # SDXL landscape
-        assert "1920×1080" in available_presets  # FLUX
-        assert "2560×1080" in available_presets  # Ultra-wide
+
+        # Check that formatted presets are available (with metadata)
+        # Extract raw preset names from formatted options
+        raw_presets = []
+        for option in available_presets:
+            if option == "custom":
+                raw_presets.append(option)
+            elif " - " in option:
+                raw_presets.append(option.split(" - ")[0])
+            else:
+                raw_presets.append(option)
+
+        # Check that all major preset categories are available
+        assert "1024×1024" in raw_presets  # SDXL square
+        assert "832×1216" in raw_presets  # SDXL portrait
+        assert "1216×832" in raw_presets  # SDXL landscape
+        assert "1920×1080" in raw_presets  # FLUX
+        assert "2560×1080" in raw_presets  # Ultra-wide
 
     def test_invalid_preset_fallback(self):
         """Test handling of invalid preset."""
@@ -261,3 +316,243 @@ class TestEdgeCases:
         # Prime number dimensions
         ratio = calculate_aspect_ratio(1920, 1080)
         assert ratio == "16:9"
+
+
+class TestPresetMetadata:
+    """Test preset metadata functionality."""
+
+    def test_preset_metadata_structure(self):
+        """Test that metadata has correct structure."""
+        for preset_name, metadata in PRESET_METADATA.items():
+            assert hasattr(metadata, "width")
+            assert hasattr(metadata, "height")
+            assert hasattr(metadata, "aspect_ratio")
+            assert hasattr(metadata, "aspect_decimal")
+            assert hasattr(metadata, "megapixels")
+            assert hasattr(metadata, "model_group")
+            assert hasattr(metadata, "category")
+            assert hasattr(metadata, "description")
+
+    def test_metadata_aspect_ratios(self):
+        """Test that aspect ratios are correctly calculated."""
+        for preset_name, metadata in PRESET_METADATA.items():
+            expected_decimal = metadata.width / metadata.height
+            assert abs(metadata.aspect_decimal - expected_decimal) < 0.001
+
+            # Common aspect ratios should match expected values
+            if preset_name == "1024×1024":
+                assert metadata.aspect_ratio == "1:1"
+                assert metadata.aspect_decimal == 1.0
+            elif preset_name == "1920×1080":
+                assert metadata.aspect_ratio == "16:9"
+                assert abs(metadata.aspect_decimal - 1.778) < 0.01
+
+    def test_metadata_megapixels(self):
+        """Test that megapixel calculations are correct."""
+        for preset_name, metadata in PRESET_METADATA.items():
+            expected_mp = (metadata.width * metadata.height) / 1_000_000
+            assert abs(metadata.megapixels - expected_mp) < 0.1
+
+    def test_model_groups(self):
+        """Test that model groups are properly assigned."""
+        sdxl_presets = get_presets_by_model_group("SDXL")
+        flux_presets = get_presets_by_model_group("FLUX")
+        ultra_wide_presets = get_presets_by_model_group("Ultra-Wide")
+
+        assert len(sdxl_presets) > 0
+        assert len(flux_presets) > 0
+        assert len(ultra_wide_presets) > 0
+
+        # Check specific presets are in correct groups
+        assert "1024×1024" in [k for k, v in sdxl_presets.items()]
+        assert "1920×1080" in [k for k, v in flux_presets.items()]
+        assert "2560×1080" in [k for k, v in ultra_wide_presets.items()]
+
+    def test_get_preset_metadata_function(self):
+        """Test get_preset_metadata function."""
+        # Valid preset
+        metadata = get_preset_metadata("1024×1024")
+        assert metadata.width == 1024
+        assert metadata.height == 1024
+        assert metadata.model_group == "SDXL"
+
+        # Invalid preset returns default
+        metadata = get_preset_metadata("invalid_preset")
+        assert metadata.width == 0
+        assert metadata.height == 0
+        assert metadata.model_group == "Custom"
+
+
+class TestNodeMetadataIntegration:
+    """Test node integration with metadata."""
+
+    def setup_method(self):
+        """Set up test fixtures."""
+        self.node = WidthHeightSelectorNode()
+
+    def test_get_preset_info_with_metadata(self):
+        """Test that preset info includes metadata."""
+        info = self.node.get_preset_info("1024×1024")
+        assert "1:1" in info  # Aspect ratio
+        assert "1.0MP" in info or "1.1MP" in info  # Megapixels
+        assert "SDXL" in info  # Description
+
+    def test_get_presets_by_model_static(self):
+        """Test static method for getting presets by model."""
+        sdxl_presets = self.node.get_presets_by_model("SDXL")
+        assert isinstance(sdxl_presets, dict)
+        assert len(sdxl_presets) > 0
+
+        # Check that returned values are metadata objects
+        for preset_name, metadata in sdxl_presets.items():
+            assert metadata.model_group == "SDXL"
+
+    def test_get_preset_metadata_static(self):
+        """Test static method for getting preset metadata."""
+        metadata_dict = self.node.get_preset_metadata_static("1920×1080")
+
+        assert metadata_dict["width"] == 1920
+        assert metadata_dict["height"] == 1080
+        assert metadata_dict["aspect_ratio"] == "16:9"
+        assert metadata_dict["model_group"] == "FLUX"
+
+    def test_get_model_groups(self):
+        """Test static method for getting model groups."""
+        groups = self.node.get_model_groups()
+        assert "SDXL" in groups
+        assert "FLUX" in groups
+        assert "Ultra-Wide" in groups
+
+
+class TestMetadataValidation:
+    """Test metadata validation functions."""
+
+    def test_dimensions_validation(self):
+        """Test dimensions validation from metadata."""
+        from kikotools.tools.width_height_selector.presets import (
+            validate_preset_dimensions,
+        )
+
+        assert validate_preset_dimensions() is True
+
+    def test_metadata_consistency_validation(self):
+        """Test metadata consistency validation."""
+        from kikotools.tools.width_height_selector.presets import (
+            validate_metadata_consistency,
+        )
+
+        assert validate_metadata_consistency() is True
+
+
+class TestFormattedPresets:
+    """Test formatted preset functionality."""
+
+    def setup_method(self):
+        """Set up test fixtures."""
+        self.node = WidthHeightSelectorNode()
+
+    def test_formatted_preset_generation(self):
+        """Test that INPUT_TYPES generates formatted presets."""
+        input_types = self.node.INPUT_TYPES()
+        available_presets = input_types["required"]["preset"][0]
+
+        # Should have custom first
+        assert available_presets[0] == "custom"
+
+        # Should have formatted presets with metadata
+        formatted_count = 0
+        for option in available_presets[1:]:  # Skip custom
+            if " - " in option and "MP" in option:
+                formatted_count += 1
+
+        assert formatted_count > 0, "No formatted presets found"
+        assert formatted_count == len(PRESET_METADATA), "Not all presets are formatted"
+
+    def test_preset_name_extraction(self):
+        """Test extraction of raw preset names from formatted strings."""
+        test_cases = [
+            ("custom", "custom"),
+            ("1024×1024 - 1:1 (1.1MP) - SDXL", "1024×1024"),
+            ("1920×1080 - 16:9 (2.1MP) - FLUX", "1920×1080"),
+            ("832×1216 - 13:19 (1.0MP) - SDXL", "832×1216"),
+            ("1024×1024", "1024×1024"),  # Raw preset name
+            ("invalid_preset", "custom"),  # Invalid fallback
+        ]
+
+        for formatted_preset, expected in test_cases:
+            result = self.node._extract_preset_name(formatted_preset)
+            assert (
+                result == expected
+            ), f"Expected {expected}, got {result} for input {formatted_preset}"
+
+    def test_formatted_preset_dimensions(self):
+        """Test that formatted presets return correct dimensions."""
+        # Test with formatted preset string
+        formatted_preset = "1024×1024 - 1:1 (1.1MP) - SDXL"
+        result = self.node.get_dimensions(formatted_preset, 512, 512)
+        assert result == (1024, 1024)
+
+        # Test with FLUX formatted preset
+        formatted_preset = "1920×1080 - 16:9 (2.1MP) - FLUX"
+        result = self.node.get_dimensions(formatted_preset, 512, 512)
+        assert result == (1920, 1080)
+
+    def test_formatted_preset_validation(self):
+        """Test validation of formatted presets."""
+        # Valid formatted preset
+        assert self.node.validate_inputs("1024×1024 - 1:1 (1.1MP) - SDXL", 1024, 1024)
+
+        # Valid raw preset
+        assert self.node.validate_inputs("1024×1024", 1024, 1024)
+
+        # Custom preset
+        assert self.node.validate_inputs("custom", 1024, 1024)
+
+        # Invalid formatted preset should still work (fallback to custom)
+        assert self.node.validate_inputs("invalid - formatted", 1024, 1024)
+
+    def test_backwards_compatibility(self):
+        """Test that raw preset names still work."""
+        # Raw preset names should still work for backwards compatibility
+        raw_presets = ["1024×1024", "1920×1080", "832×1216"]
+
+        for raw_preset in raw_presets:
+            if raw_preset in PRESET_OPTIONS:
+                result = self.node.get_dimensions(raw_preset, 512, 512)
+                expected = PRESET_OPTIONS[raw_preset]
+                assert result == expected, f"Raw preset {raw_preset} failed"
+
+    def test_formatted_preset_metadata_accuracy(self):
+        """Test that formatted presets contain accurate metadata."""
+        input_types = self.node.INPUT_TYPES()
+        formatted_presets = [
+            opt for opt in input_types["required"]["preset"][0] if " - " in opt
+        ]
+
+        for formatted_preset in formatted_presets:
+            # Extract components
+            parts = formatted_preset.split(" - ")
+            assert (
+                len(parts) == 3
+            ), f"Formatted preset should have 3 parts: {formatted_preset}"
+
+            resolution = parts[0]
+            aspect_and_mp = parts[1]
+            model_group = parts[2]
+
+            # Verify resolution exists in metadata
+            assert (
+                resolution in PRESET_METADATA
+            ), f"Resolution {resolution} not in metadata"
+
+            # Verify metadata matches format
+            metadata = PRESET_METADATA[resolution]
+            assert (
+                metadata.model_group == model_group
+            ), f"Model group mismatch for {resolution}"
+            assert (
+                metadata.aspect_ratio in aspect_and_mp
+            ), f"Aspect ratio not in {aspect_and_mp}"
+            assert (
+                f"{metadata.megapixels:.1f}MP" in aspect_and_mp
+            ), f"Megapixels not in {aspect_and_mp}"

web/empty_latent_batch_swap.js

@@ -0,0 +1,393 @@
+// ComfyUI-KikoTools - Empty Latent Batch with Swap Button
+import { app } from "../../scripts/app.js";
+
+app.registerExtension({
+  name: "comfyassets.EmptyLatentBatch",
+  async beforeRegisterNodeDef(nodeType, nodeData, _app) {
+    if (nodeData.name === "EmptyLatentBatch") {
+      const onNodeCreated = nodeType.prototype.onNodeCreated;
+      nodeType.prototype.onNodeCreated = function () {
+        if (onNodeCreated) onNodeCreated.apply(this, []);
+
+        // Track button click state for visual feedback
+        this.swapButtonPressed = false;
+
+        // Helper function to extract resolution from formatted preset string
+        this.extractResolutionFromPreset = function (presetValue) {
+          if (presetValue === "custom") return null;
+
+          // If it contains formatting metadata, extract the resolution part
+          if (presetValue.includes(" - ")) {
+            // Format is: "1024×1024 - 1:1 (1.0MP) - SDXL"
+            return presetValue.split(" - ")[0];
+          }
+
+          // Otherwise assume it's already a raw resolution
+          return presetValue;
+        };
+
+        // Override preset callback to update width/height widgets when preset changes
+        const presetWidget = this.widgets.find((w) => w.name === "preset");
+        if (presetWidget) {
+          const originalCallback = presetWidget.callback;
+          presetWidget.callback = function (
+            value,
+            graphcanvas,
+            node,
+            pos,
+            event,
+          ) {
+            // Call original callback first
+            if (originalCallback) {
+              originalCallback.call(this, value, graphcanvas, node, pos, event);
+            }
+
+            // Update width/height widgets based on preset
+            const widthWidget = node.widgets.find((w) => w.name === "width");
+            const heightWidget = node.widgets.find((w) => w.name === "height");
+
+            if (widthWidget && heightWidget && value !== "custom") {
+              // Extract raw resolution from formatted preset
+              const rawResolution = node.extractResolutionFromPreset(value);
+
+              // Define all available presets from our preset system
+              const presetDimensions = {
+                // SDXL Presets
+                "1024×1024": [1024, 1024],
+                "896×1152": [896, 1152],
+                "832×1216": [832, 1216],
+                "768×1344": [768, 1344],
+                "640×1536": [640, 1536],
+                "1152×896": [1152, 896],
+                "1216×832": [1216, 832],
+                "1344×768": [1344, 768],
+                "1536×640": [1536, 640],
+                // FLUX Presets
+                "1920×1080": [1920, 1080],
+                "1536×1536": [1536, 1536],
+                "1280×768": [1280, 768],
+                "768×1280": [768, 1280],
+                "1440×1080": [1440, 1080],
+                "1080×1440": [1080, 1440],
+                "1728×1152": [1728, 1152],
+                "1152×1728": [1152, 1728],
+                // Ultra-Wide Presets
+                "2560×1080": [2560, 1080],
+                "2048×768": [2048, 768],
+                "1792×768": [1792, 768],
+                "2304×768": [2304, 768],
+                "1080×2560": [1080, 2560],
+                "768×2048": [768, 2048],
+                "768×1792": [768, 1792],
+                "768×2304": [768, 2304],
+              };
+
+              if (rawResolution && presetDimensions[rawResolution]) {
+                const [w, h] = presetDimensions[rawResolution];
+                widthWidget.value = w;
+                heightWidget.value = h;
+
+                // Trigger widget callbacks to update the UI
+                if (widthWidget.callback) {
+                  widthWidget.callback(w, graphcanvas, node, pos, event);
+                }
+                if (heightWidget.callback) {
+                  heightWidget.callback(h, graphcanvas, node, pos, event);
+                }
+              }
+            }
+          };
+        }
+
+        // Add swap functionality
+        this.swapDimensions = function () {
+          const widthWidget = this.widgets.find((w) => w.name === "width");
+          const heightWidget = this.widgets.find((w) => w.name === "height");
+          const presetWidget = this.widgets.find((w) => w.name === "preset");
+
+          if (widthWidget && heightWidget && presetWidget) {
+            // Handle preset swapping first
+            if (presetWidget.value !== "custom") {
+              const currentPreset = presetWidget.value;
+
+              // Extract raw resolution from formatted preset
+              const rawResolution =
+                this.extractResolutionFromPreset(currentPreset);
+              if (!rawResolution) return;
+
+              // Parse current preset dimensions (handle both × and x separators)
+              let w, h;
+              if (rawResolution.includes("×")) {
+                [w, h] = rawResolution.split("×").map((v) => parseInt(v));
+              } else if (rawResolution.includes("x")) {
+                [w, h] = rawResolution.split("x").map((v) => parseInt(v));
+              } else {
+                return; // Invalid preset format
+              }
+
+              const swappedRawPreset = `${h}×${w}`;
+
+              // Find the formatted version of the swapped preset from available options
+              const availablePresets =
+                presetWidget.options.values || presetWidget.options;
+              let swappedFormattedPreset = null;
+
+              for (const option of availablePresets) {
+                if (option === "custom") continue;
+                const extractedRes = this.extractResolutionFromPreset(option);
+                if (extractedRes === swappedRawPreset) {
+                  swappedFormattedPreset = option;
+                  break;
+                }
+              }
+
+              if (swappedFormattedPreset) {
+                // Swapped preset exists, use the formatted version
+                presetWidget.value = swappedFormattedPreset;
+                widthWidget.value = h;
+                heightWidget.value = w;
+                if (presetWidget.callback) {
+                  presetWidget.callback(
+                    swappedFormattedPreset,
+                    this,
+                    presetWidget,
+                  );
+                }
+                if (widthWidget.callback) {
+                  widthWidget.callback(h, this, widthWidget);
+                }
+                if (heightWidget.callback) {
+                  heightWidget.callback(w, this, heightWidget);
+                }
+              } else {
+                // Swapped preset doesn't exist, switch to custom and swap manual values
+                presetWidget.value = "custom";
+                widthWidget.value = h;
+                heightWidget.value = w;
+
+                if (presetWidget.callback) {
+                  presetWidget.callback("custom", this, presetWidget);
+                }
+                if (widthWidget.callback) {
+                  widthWidget.callback(h, this, widthWidget);
+                }
+                if (heightWidget.callback) {
+                  heightWidget.callback(w, this, heightWidget);
+                }
+              }
+            } else {
+              // Custom preset - just swap the width and height values
+              const tempWidth = widthWidget.value;
+              widthWidget.value = heightWidget.value;
+              heightWidget.value = tempWidth;
+
+              // Trigger widget change events
+              if (widthWidget.callback) {
+                widthWidget.callback(widthWidget.value, this, widthWidget);
+              }
+              if (heightWidget.callback) {
+                heightWidget.callback(heightWidget.value, this, heightWidget);
+              }
+            }
+
+            // Mark the graph as changed
+            this.graph?.setDirtyCanvas(true, true);
+          }
+        };
+
+        // Override onResize to refresh button position
+        const originalOnResize = this.onResize;
+        this.onResize = function (size) {
+          if (originalOnResize) {
+            originalOnResize.call(this, size);
+          }
+          // Force redraw to update button position
+          this.setDirtyCanvas(true, true);
+          // Also mark the graph as dirty
+          if (this.graph) {
+            this.graph.setDirtyCanvas(true, true);
+          }
+        };
+
+        // Override onBounding to ensure proper updates
+        const originalOnBounding = this.onBounding;
+        this.onBounding = function (out) {
+          if (originalOnBounding) {
+            originalOnBounding.call(this, out);
+          }
+          // Force redraw when bounds change
+          this.setDirtyCanvas(true, true);
+        };
+      };
+
+      const onDrawForeground = nodeType.prototype.onDrawForeground;
+      nodeType.prototype.onDrawForeground = function (ctx) {
+        if (onDrawForeground) {
+          onDrawForeground.apply(this, arguments);
+        }
+
+        if (this.flags.collapsed) return;
+
+        // Draw swap button with consistent spacing from widgets
+        const swapButtonSize = 24;
+        const margin = 6;
+        const swapButtonX = this.size[0] - swapButtonSize - margin;
+
+        // Calculate button position based on widget spacing rather than bottom margin
+        // Estimate widget area height and add consistent spacing
+        const estimatedWidgetHeight = 90; // Approximate height for 3 widgets
+        const topMargin = 35; // Space from top to first widget
+        const buttonSpacing = 40; // Space between last widget and button (moved down 5)
+        const swapButtonY = topMargin + estimatedWidgetHeight + buttonSpacing;
+
+        // Button background - change color based on pressed state
+        if (this.swapButtonPressed) {
+          // Darker when pressed
+          ctx.fillStyle = "rgba(30, 120, 200, 0.9)"; // Darker blue when clicked
+        } else {
+          // Normal state
+          ctx.fillStyle = "rgba(66, 165, 245, 0.8)"; // Material blue
+        }
+        ctx.beginPath();
+        ctx.roundRect(
+          swapButtonX,
+          swapButtonY,
+          swapButtonSize,
+          swapButtonSize,
+          4,
+        );
+        ctx.fill();
+
+        // Button border with subtle highlight
+        ctx.strokeStyle = this.swapButtonPressed
+          ? "rgba(20, 100, 180, 1.0)"
+          : "rgba(33, 150, 243, 0.9)";
+        ctx.lineWidth = 1;
+        ctx.stroke();
+
+        // Draw swap icon - modern double arrow design
+        ctx.strokeStyle = "rgba(255, 255, 255, 0.95)";
+        ctx.lineWidth = 2;
+        ctx.lineCap = "round";
+
+        const centerX = swapButtonX + 12;
+        const centerY = swapButtonY + 12;
+
+        // Top arrow (pointing right) - width to height
+        ctx.beginPath();
+        ctx.moveTo(centerX - 7, centerY - 3);
+        ctx.lineTo(centerX + 5, centerY - 3);
+        ctx.stroke();
+
+        // Top arrow head
+        ctx.beginPath();
+        ctx.moveTo(centerX + 5, centerY - 3);
+        ctx.lineTo(centerX + 2, centerY - 5);
+        ctx.moveTo(centerX + 5, centerY - 3);
+        ctx.lineTo(centerX + 2, centerY - 1);
+        ctx.stroke();
+
+        // Bottom arrow (pointing left) - height to width
+        ctx.beginPath();
+        ctx.moveTo(centerX + 5, centerY + 3);
+        ctx.lineTo(centerX - 7, centerY + 3);
+        ctx.stroke();
+
+        // Bottom arrow head
+        ctx.beginPath();
+        ctx.moveTo(centerX - 7, centerY + 3);
+        ctx.lineTo(centerX - 4, centerY + 1);
+        ctx.moveTo(centerX - 7, centerY + 3);
+        ctx.lineTo(centerX - 4, centerY + 5);
+        ctx.stroke();
+      };
+
+      const onMouseDown = nodeType.prototype.onMouseDown;
+      nodeType.prototype.onMouseDown = function (e) {
+        // Check if click is on swap button
+        const swapButtonSize = 24;
+        const margin = 6;
+        const swapButtonX =
+          this.pos[0] + this.size[0] - swapButtonSize - margin;
+
+        // Use same positioning logic as drawing
+        const estimatedWidgetHeight = 90;
+        const topMargin = 35;
+        const buttonSpacing = 40;
+        const swapButtonY =
+          this.pos[1] + topMargin + estimatedWidgetHeight + buttonSpacing;
+
+        if (
+          e.canvasX >= swapButtonX &&
+          e.canvasX <= swapButtonX + swapButtonSize &&
+          e.canvasY >= swapButtonY &&
+          e.canvasY <= swapButtonY + swapButtonSize
+        ) {
+          // Visual feedback - set button as pressed
+          this.swapButtonPressed = true;
+          this.setDirtyCanvas(true, true);
+
+          // Execute swap
+          this.swapDimensions();
+
+          // Reset button state after a short delay for visual feedback
+          setTimeout(() => {
+            this.swapButtonPressed = false;
+            this.setDirtyCanvas(true, true);
+          }, 150);
+
+          return true; // Consume the event
+        }
+
+        // Call original onMouseDown if not clicking swap button
+        if (onMouseDown) {
+          return onMouseDown.apply(this, arguments);
+        }
+      };
+
+      // Optional: Add hover effect for better user feedback
+      const onMouseMove = nodeType.prototype.onMouseMove;
+      nodeType.prototype.onMouseMove = function (e) {
+        // Check if hovering over swap button
+        const swapButtonSize = 24;
+        const margin = 6;
+        const swapButtonX =
+          this.pos[0] + this.size[0] - swapButtonSize - margin;
+
+        // Use same positioning logic as drawing
+        const estimatedWidgetHeight = 90;
+        const topMargin = 35;
+        const buttonSpacing = 40;
+        const swapButtonY =
+          this.pos[1] + topMargin + estimatedWidgetHeight + buttonSpacing;
+
+        const isHovering =
+          e.canvasX >= swapButtonX &&
+          e.canvasX <= swapButtonX + swapButtonSize &&
+          e.canvasY >= swapButtonY &&
+          e.canvasY <= swapButtonY + swapButtonSize;
+
+        // Update cursor style for better UX (safely)
+        if (
+          isHovering &&
+          this.graph &&
+          this.graph.canvas &&
+          this.graph.canvas.canvas
+        ) {
+          this.graph.canvas.canvas.style.cursor = "pointer";
+        } else if (
+          this.graph &&
+          this.graph.canvas &&
+          this.graph.canvas.canvas
+        ) {
+          this.graph.canvas.canvas.style.cursor = "default";
+        }
+
+        // Call original onMouseMove
+        if (onMouseMove) {
+          return onMouseMove.apply(this, arguments);
+        }
+      };
+    }
+  },
+});

web/seed_history_ui.js

@@ -0,0 +1,524 @@
+// ComfyUI-KikoTools - Seed History with Tracking UI
+import { app } from "../../scripts/app.js";
+
+app.registerExtension({
+  name: "comfyassets.SeedHistory",
+
+  async setup() {
+    // Store reference to all SeedHistory nodes
+    window.seedHistoryNodes = window.seedHistoryNodes || [];
+  },
+
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+    if (nodeData.name === "SeedHistory") {
+      const onNodeCreated = nodeType.prototype.onNodeCreated;
+
+      nodeType.prototype.onNodeCreated = function () {
+        if (onNodeCreated) {
+          onNodeCreated.apply(this, arguments);
+        }
+
+        // Initialize seed history
+        this.seedHistory = this.loadSeedHistory();
+        this.hideTimer = null;
+        this.mouseOverHistory = false;
+        
+        // Register this node in global registry
+        window.seedHistoryNodes = window.seedHistoryNodes || [];
+        window.seedHistoryNodes.push(this);
+        
+        // Create UI container
+        const uiContainer = document.createElement("div");
+        uiContainer.style.padding = "8px";
+        uiContainer.style.backgroundColor = "#1e1e1e";
+        uiContainer.style.borderRadius = "6px";
+        uiContainer.style.marginTop = "6px";
+        uiContainer.style.border = "1px solid #404040";
+
+        this.buildSeedInterface(uiContainer);
+
+        // Add as widget
+        this.seedWidget = this.addDOMWidget(
+          "seed_history_ui",
+          "div",
+          uiContainer,
+        );
+
+        // Set node size
+        if (!this.hasBeenResized) {
+          this.size = [280, 320];
+        }
+
+        // Track manual resizing
+        const originalResize = this.onResize;
+        this.onResize = function (size) {
+          this.hasBeenResized = true;
+          if (originalResize) {
+            originalResize.call(this, size);
+          }
+        };
+
+        // Hook into seed widget callbacks to track all changes
+        setTimeout(() => {
+          this.setupSeedWidgetCallbacks();
+        }, 100);
+        
+        // Hook directly into widget value changes
+        const originalOnWidgetChange = this.onWidgetChange;
+        this.onWidgetChange = function(name, value, oldValue, widget) {
+          if (name === "seed" && value !== oldValue) {
+            this.addSeedToHistory(value);
+          }
+          
+          if (originalOnWidgetChange) {
+            return originalOnWidgetChange.call(this, name, value, oldValue, widget);
+          }
+        };
+
+        // Save/load data
+        const originalSerialize = this.serialize;
+        this.serialize = function () {
+          const data = originalSerialize ? originalSerialize.call(this) : {};
+          data.hasBeenResized = this.hasBeenResized;
+          data.seedHistory = this.seedHistory;
+          return data;
+        };
+
+        const originalConfigure = this.configure;
+        this.configure = function (data) {
+          if (originalConfigure) {
+            originalConfigure.call(this, data);
+          }
+          if (data.hasBeenResized) {
+            this.hasBeenResized = data.hasBeenResized;
+          }
+          if (data.seedHistory) {
+            this.seedHistory = data.seedHistory;
+            this.refreshHistoryDisplay();
+          }
+        };
+
+        // Cleanup interval on node removal
+        const originalOnRemoved = this.onRemoved;
+        this.onRemoved = function () {
+          if (this.seedValueWatcher) {
+            clearInterval(this.seedValueWatcher);
+            this.seedValueWatcher = null;
+          }
+          
+          // Clean up deduplication tracking
+          if (this.lastAddedSeed) {
+            this.lastAddedSeed = null;
+          }
+          
+          // Remove from global registry
+          if (window.seedHistoryNodes) {
+            const index = window.seedHistoryNodes.indexOf(this);
+            if (index !== -1) {
+              window.seedHistoryNodes.splice(index, 1);
+            }
+          }
+          
+          if (originalOnRemoved) {
+            originalOnRemoved.call(this);
+          }
+        };
+
+        this.setDirtyCanvas(true, true);
+      };
+
+      // Setup seed widget callbacks to track increment/decrement/randomize
+      nodeType.prototype.setupSeedWidgetCallbacks = function () {
+        // Find the seed widget
+        const seedWidget = this.widgets?.find(w => w.name === "seed");
+        if (!seedWidget) {
+          setTimeout(() => this.setupSeedWidgetCallbacks(), 500);
+          return;
+        }
+
+        // Store the last known seed value to detect changes
+        this.lastSeedValue = seedWidget.value;
+
+        // Monitor for value changes that might not trigger callback
+        this.seedValueWatcher = setInterval(() => {
+          if (seedWidget.value !== this.lastSeedValue) {
+            this.lastSeedValue = seedWidget.value;
+            this.addSeedToHistory(seedWidget.value);
+          }
+        }, 1000);
+      };
+
+      // Build the seed interface
+      nodeType.prototype.buildSeedInterface = function (container) {
+        container.innerHTML = "";
+
+        // Header
+        const headerDiv = document.createElement("div");
+        headerDiv.style.marginBottom = "8px";
+
+        const titleDiv = document.createElement("div");
+        titleDiv.style.fontWeight = "bold";
+        titleDiv.style.color = "#00d4ff";
+        titleDiv.style.textAlign = "center";
+        titleDiv.style.padding = "4px";
+        titleDiv.style.backgroundColor = "rgba(0, 212, 255, 0.1)";
+        titleDiv.style.borderRadius = "4px";
+        titleDiv.style.fontSize = "11px";
+        titleDiv.innerHTML = "🎲 Seed History";
+        headerDiv.appendChild(titleDiv);
+
+        // Action buttons
+        const buttonDiv = document.createElement("div");
+        buttonDiv.style.display = "flex";
+        buttonDiv.style.gap = "4px";
+        buttonDiv.style.marginTop = "6px";
+
+        // Generate button
+        const generateBtn = document.createElement("button");
+        generateBtn.textContent = "🎲 Generate";
+        generateBtn.style.flex = "1";
+        generateBtn.style.padding = "3px 6px";
+        generateBtn.style.backgroundColor = "#0088cc";
+        generateBtn.style.color = "white";
+        generateBtn.style.border = "none";
+        generateBtn.style.borderRadius = "3px";
+        generateBtn.style.cursor = "pointer";
+        generateBtn.style.fontSize = "10px";
+        generateBtn.addEventListener("click", () => this.generateRandomSeed());
+        buttonDiv.appendChild(generateBtn);
+
+        // Clear button
+        const clearBtn = document.createElement("button");
+        clearBtn.textContent = "🗑️ Clear";
+        clearBtn.style.flex = "1";
+        clearBtn.style.padding = "3px 6px";
+        clearBtn.style.backgroundColor = "#cc4444";
+        clearBtn.style.color = "white";
+        clearBtn.style.border = "none";
+        clearBtn.style.borderRadius = "3px";
+        clearBtn.style.cursor = "pointer";
+        clearBtn.style.fontSize = "10px";
+        clearBtn.addEventListener("click", () => this.clearSeedHistory());
+        buttonDiv.appendChild(clearBtn);
+
+        headerDiv.appendChild(buttonDiv);
+        container.appendChild(headerDiv);
+
+        // History display
+        const historyDiv = document.createElement("div");
+        historyDiv.style.maxHeight = "180px";
+        historyDiv.style.overflowY = "auto";
+        historyDiv.style.border = "1px solid #333";
+        historyDiv.style.borderRadius = "4px";
+        historyDiv.style.backgroundColor = "#2a2a2a";
+        historyDiv.style.padding = "6px";
+        historyDiv.style.fontSize = "10px";
+        historyDiv.style.fontFamily = "monospace";
+
+        // Mouse events for auto-hide
+        historyDiv.addEventListener("mouseenter", () => {
+          this.mouseOverHistory = true;
+          this.cancelAutoHide();
+        });
+        
+        historyDiv.addEventListener("mouseleave", () => {
+          this.mouseOverHistory = false;
+          this.startAutoHide();
+        });
+
+        this.historyDisplay = historyDiv;
+        container.appendChild(historyDiv);
+
+        this.refreshHistoryDisplay();
+      };
+
+      // Load history from storage
+      nodeType.prototype.loadSeedHistory = function () {
+        try {
+          const stored = localStorage.getItem('comfyui_kikotools_seed_history');
+          return stored ? JSON.parse(stored) : [];
+        } catch (error) {
+          return [];
+        }
+      };
+
+      // Save history to storage
+      nodeType.prototype.saveSeedHistory = function () {
+        try {
+          localStorage.setItem('comfyui_kikotools_seed_history', JSON.stringify(this.seedHistory));
+        } catch (error) {
+          // Silently handle storage errors
+        }
+      };
+
+      // Add seed to history
+      nodeType.prototype.addSeedToHistory = function (seed) {
+        if (!seed || seed === 0) return;
+
+        const numSeed = typeof seed === 'string' ? parseInt(seed) : seed;
+        const now = Date.now();
+        
+        // Deduplication: prevent adding the same seed within 500ms window
+        if (!this.lastAddedSeed) {
+          this.lastAddedSeed = { seed: null, timestamp: 0 };
+        }
+        
+        const timeSinceLastAdd = now - this.lastAddedSeed.timestamp;
+        const isSameSeed = this.lastAddedSeed.seed === numSeed;
+        const isWithinDupeWindow = timeSinceLastAdd < 500; // 500ms window
+        
+        if (isSameSeed && isWithinDupeWindow) {
+          return;
+        }
+        
+        // Update deduplication tracking
+        this.lastAddedSeed = { seed: numSeed, timestamp: now };
+        
+        // Remove if already exists in history
+        this.seedHistory = this.seedHistory.filter(item => item.seed !== numSeed);
+        
+        // Add to front
+        this.seedHistory.unshift({
+          seed: numSeed,
+          timestamp: now,
+          dateString: new Date().toLocaleString()
+        });
+        
+        // Keep only last 10
+        if (this.seedHistory.length > 10) {
+          this.seedHistory = this.seedHistory.slice(0, 10);
+        }
+        
+        this.saveSeedHistory();
+        this.refreshHistoryDisplay();
+        this.startAutoHide();
+      };
+
+      // Generate new random seed
+      nodeType.prototype.generateRandomSeed = function () {
+        const newSeed = Math.floor(Math.random() * 0xFFFFFFFFFFFFFFFF);
+        
+        const seedWidget = this.widgets?.find(w => w.name === "seed");
+        if (seedWidget) {
+          seedWidget.value = newSeed;
+          if (seedWidget.callback) {
+            seedWidget.callback(newSeed, this, seedWidget);
+          }
+        }
+        
+        this.addSeedToHistory(newSeed);
+        this.setDirtyCanvas(true, true);
+        this.showMessage(`Generated: ${newSeed}`, "success");
+      };
+
+      // Use seed from history
+      nodeType.prototype.useSeedFromHistory = function (historyItem, index) {
+        const seedWidget = this.widgets?.find(w => w.name === "seed");
+        if (seedWidget) {
+          seedWidget.value = historyItem.seed;
+          if (seedWidget.callback) {
+            seedWidget.callback(historyItem.seed, this, seedWidget);
+          }
+        }
+        
+        this.highlightHistoryEntry(index);
+        this.setDirtyCanvas(true, true);
+        this.startAutoHide();
+        this.showMessage(`Loaded: ${historyItem.seed}`, "info");
+      };
+
+      // Clear history
+      nodeType.prototype.clearSeedHistory = function () {
+        this.seedHistory = [];
+        this.saveSeedHistory();
+        this.refreshHistoryDisplay();
+        this.showMessage("History cleared", "info");
+      };
+
+      // Refresh history display
+      nodeType.prototype.refreshHistoryDisplay = function () {
+        if (!this.historyDisplay) return;
+
+        if (!this.seedHistory || this.seedHistory.length === 0) {
+          this.historyDisplay.innerHTML = 
+            '<div style="color: #888; text-align: center; padding: 15px;">No seeds tracked<br><small>Generate seeds to build history</small></div>';
+          return;
+        }
+
+        this.historyDisplay.innerHTML = "";
+
+        this.seedHistory.forEach((item, index) => {
+          const entryDiv = document.createElement("div");
+          entryDiv.style.padding = "4px";
+          entryDiv.style.marginBottom = "3px";
+          entryDiv.style.backgroundColor = "#333";
+          entryDiv.style.borderRadius = "2px";
+          entryDiv.style.cursor = "pointer";
+          entryDiv.style.border = "1px solid transparent";
+          entryDiv.style.lineHeight = "1.2";
+
+          entryDiv.addEventListener("mouseenter", () => {
+            entryDiv.style.backgroundColor = "#444";
+            entryDiv.style.border = "1px solid #555";
+          });
+          entryDiv.addEventListener("mouseleave", () => {
+            entryDiv.style.backgroundColor = "#333";
+            entryDiv.style.border = "1px solid transparent";
+          });
+
+          entryDiv.addEventListener("click", () => {
+            this.useSeedFromHistory(item, index);
+          });
+
+          const timeAgo = this.formatTimeAgo(item.timestamp);
+          entryDiv.innerHTML = `
+            <div style="color: #fff; font-weight: bold; margin-bottom: 1px;">
+              🎲 ${item.seed}
+            </div>
+            <div style="color: #999; font-size: 8px;">
+              ⏰ ${timeAgo}
+            </div>
+          `;
+
+          this.historyDisplay.appendChild(entryDiv);
+        });
+        
+        this.startAutoHide();
+      };
+
+      // Highlight selected entry
+      nodeType.prototype.highlightHistoryEntry = function (index) {
+        const entries = this.historyDisplay.querySelectorAll('div[style*="cursor: pointer"]');
+        entries.forEach((entry, i) => {
+          if (i === index) {
+            entry.style.backgroundColor = "#006600";
+            entry.style.border = "1px solid #00aa00";
+          } else {
+            entry.style.backgroundColor = "#333";
+            entry.style.border = "1px solid transparent";
+          }
+        });
+      };
+
+      // Auto-hide functionality
+      nodeType.prototype.startAutoHide = function () {
+        this.cancelAutoHide();
+        if (!this.mouseOverHistory) {
+          this.hideTimer = setTimeout(() => {
+            this.hideHistorySection();
+          }, 2500);
+        }
+      };
+
+      nodeType.prototype.cancelAutoHide = function () {
+        if (this.hideTimer) {
+          clearTimeout(this.hideTimer);
+          this.hideTimer = null;
+        }
+      };
+
+      nodeType.prototype.hideHistorySection = function () {
+        if (this.historyDisplay && !this.mouseOverHistory) {
+          this.historyDisplay.style.display = "none";
+          
+          if (!this.restoreButton) {
+            const restoreDiv = document.createElement("div");
+            restoreDiv.style.padding = "10px";
+            restoreDiv.style.backgroundColor = "#2a2a2a";
+            restoreDiv.style.border = "1px solid #333";
+            restoreDiv.style.borderRadius = "4px";
+            restoreDiv.style.textAlign = "center";
+            restoreDiv.style.cursor = "pointer";
+            restoreDiv.style.color = "#888";
+            restoreDiv.style.fontSize = "10px";
+            restoreDiv.innerHTML = "🎲 History auto-hidden<br><small>Click to show</small>";
+
+            restoreDiv.addEventListener("mouseenter", () => {
+              restoreDiv.style.backgroundColor = "#333";
+              restoreDiv.style.color = "#bbb";
+            });
+            restoreDiv.addEventListener("mouseleave", () => {
+              restoreDiv.style.backgroundColor = "#2a2a2a";
+              restoreDiv.style.color = "#888";
+            });
+
+            restoreDiv.addEventListener("click", () => {
+              this.showHistorySection();
+            });
+
+            this.restoreButton = restoreDiv;
+            this.historyDisplay.parentNode.insertBefore(
+              restoreDiv,
+              this.historyDisplay.nextSibling
+            );
+          }
+        }
+      };
+
+      nodeType.prototype.showHistorySection = function () {
+        if (this.historyDisplay) {
+          this.historyDisplay.style.display = "block";
+          
+          if (this.restoreButton && this.restoreButton.parentNode) {
+            this.restoreButton.parentNode.removeChild(this.restoreButton);
+            this.restoreButton = null;
+          }
+          
+          this.startAutoHide();
+        }
+      };
+
+      // Format time ago
+      nodeType.prototype.formatTimeAgo = function (timestamp) {
+        const now = Date.now();
+        const diff = now - timestamp;
+        const seconds = Math.floor(diff / 1000);
+        const minutes = Math.floor(seconds / 60);
+        const hours = Math.floor(minutes / 60);
+        const days = Math.floor(hours / 24);
+
+        if (days > 0) return `${days}d ago`;
+        if (hours > 0) return `${hours}h ago`;
+        if (minutes > 0) return `${minutes}m ago`;
+        return `${seconds}s ago`;
+      };
+
+      // Show messages
+      nodeType.prototype.showMessage = function (message, type = "info") {
+        const notification = document.createElement("div");
+        notification.style.position = "fixed";
+        notification.style.top = "15px";
+        notification.style.right = "15px";
+        notification.style.padding = "6px 10px";
+        notification.style.borderRadius = "3px";
+        notification.style.color = "white";
+        notification.style.fontSize = "10px";
+        notification.style.zIndex = "10000";
+        notification.style.maxWidth = "200px";
+        notification.textContent = message;
+
+        switch (type) {
+          case "success":
+            notification.style.backgroundColor = "#28a745";
+            break;
+          case "error":
+            notification.style.backgroundColor = "#dc3545";
+            break;
+          case "warning":
+            notification.style.backgroundColor = "#ffc107";
+            break;
+          default:
+            notification.style.backgroundColor = "#17a2b8";
+        }
+
+        document.body.appendChild(notification);
+
+        setTimeout(() => {
+          if (notification.parentNode) {
+            document.body.removeChild(notification);
+          }
+        }, 1800);
+      };
+    }
+  },
+});

web/width_height_swap.js

@@ -12,6 +12,20 @@ app.registerExtension({
         // Track button click state for visual feedback
         this.swapButtonPressed = false;
         
+        // Helper function to extract resolution from formatted preset string
+        this.extractResolutionFromPreset = function(presetValue) {
+          if (presetValue === "custom") return null;
+          
+          // If it contains formatting metadata, extract the resolution part
+          if (presetValue.includes(" - ")) {
+            // Format is: "1024×1024 - 1:1 (1.0MP) - SDXL"
+            return presetValue.split(" - ")[0];
+          }
+          
+          // Otherwise assume it's already a raw resolution
+          return presetValue;
+        };
+        
         // Override preset callback to update width/height widgets when preset changes
         const presetWidget = this.widgets.find(w => w.name === "preset");
         if (presetWidget) {
@@ -27,6 +41,9 @@ app.registerExtension({
             const heightWidget = node.widgets.find(w => w.name === "height");
             
             if (widthWidget && heightWidget && value !== "custom") {
+              // Extract raw resolution from formatted preset
+              const rawResolution = node.extractResolutionFromPreset(value);
+              
               // Define all available presets from our preset system
               const presetDimensions = {
                 // SDXL Presets
@@ -43,8 +60,8 @@ app.registerExtension({
                 "768×1792": [768, 1792], "768×2304": [768, 2304]
               };
               
-              if (presetDimensions[value]) {
-                const [w, h] = presetDimensions[value];
+              if (rawResolution && presetDimensions[rawResolution]) {
+                const [w, h] = presetDimensions[rawResolution];
                 widthWidget.value = w;
                 heightWidget.value = h;
                 
@@ -71,39 +88,42 @@ app.registerExtension({
             if (presetWidget.value !== "custom") {
               const currentPreset = presetWidget.value;
               
+              // Extract raw resolution from formatted preset
+              const rawResolution = this.extractResolutionFromPreset(currentPreset);
+              if (!rawResolution) return;
+              
               // Parse current preset dimensions (handle both × and x separators)
               let w, h;
-              if (currentPreset.includes('×')) {
-                [w, h] = currentPreset.split('×').map(v => parseInt(v));
-              } else if (currentPreset.includes('x')) {
-                [w, h] = currentPreset.split('x').map(v => parseInt(v));
+              if (rawResolution.includes('×')) {
+                [w, h] = rawResolution.split('×').map(v => parseInt(v));
+              } else if (rawResolution.includes('x')) {
+                [w, h] = rawResolution.split('x').map(v => parseInt(v));
               } else {
                 return; // Invalid preset format
               }
               
-              const swappedPreset = `${h}×${w}`;
+              const swappedRawPreset = `${h}×${w}`;
               
-              // Define all available presets from our preset system
-              const availablePresets = [
-                "custom",
-                // SDXL Presets
-                "1024×1024", "896×1152", "832×1216", "768×1344", "640×1536",
-                "1152×896", "1216×832", "1344×768", "1536×640",
-                // FLUX Presets  
-                "1920×1080", "1536×1536", "1280×768", "768×1280",
-                "1440×1080", "1080×1440", "1728×1152", "1152×1728",
-                // Ultra-Wide Presets
-                "2560×1080", "2048×768", "1792×768", "2304×768",
-                "1080×2560", "768×2048", "768×1792", "768×2304"
-              ];
+              // Find the formatted version of the swapped preset from available options
+              const availablePresets = presetWidget.options.values || presetWidget.options;
+              let swappedFormattedPreset = null;
               
-              if (availablePresets.includes(swappedPreset)) {
-                // Swapped preset exists, use it
-                presetWidget.value = swappedPreset;
+              for (const option of availablePresets) {
+                if (option === "custom") continue;
+                const extractedRes = this.extractResolutionFromPreset(option);
+                if (extractedRes === swappedRawPreset) {
+                  swappedFormattedPreset = option;
+                  break;
+                }
+              }
+              
+              if (swappedFormattedPreset) {
+                // Swapped preset exists, use the formatted version
+                presetWidget.value = swappedFormattedPreset;
                 widthWidget.value = h;
                 heightWidget.value = w;
                 if (presetWidget.callback) {
-                  presetWidget.callback(swappedPreset, this, presetWidget);
+                  presetWidget.callback(swappedFormattedPreset, this, presetWidget);
                 }
                 if (widthWidget.callback) {
                   widthWidget.callback(h, this, widthWidget);