feat(xyz-helpers): add ComfyUI_essentials nodes adaptation
BREAKING CHANGE: Node categories now use emoji-based organization Add 6 new xyz-helper nodes adapted from comfyui-essentials-nodes: - FluxSamplerParams: FLUX-optimized parameter generator with batch support - LoRAFolderBatch: Batch process multiple LoRAs from folders - PlotParameters: Visualize parameter effects with graphs - SamplerSelectHelper: Intelligent sampler selection with recommendations - SchedulerSelectHelper: Optimal scheduler selection for samplers - TextEncodeSamplerParams: Combined text encoding and parameter management Changes: - Port and enhance nodes from comfyui-essentials (now in maintenance mode) - Add comprehensive documentation with attribution to original author (cubiq) - Create example workflows for xyz-helpers tools - Update all node categories to use emoji-based organization - Fix all unit tests to pass with new category system - Update README with xyz-helpers section and attribution Attribution: xyz-helpers adapted from github.com/cubiq/ComfyUI_essentials All tests passing (318 pass, 2 skip)
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Vito Sansevero
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# Text Encode Sampler Params
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## Overview
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The **Text Encode Sampler Params** node combines text encoding with sampler parameter management, providing a unified interface for prompt processing and sampling configuration. Adapted from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) (now in maintenance mode), this tool streamlines the text-to-image pipeline setup.
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## Attribution
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This node is based on work from [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials) by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.
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## Features
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- **Unified Interface**: Combine text encoding and sampler params in one node
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- **Dynamic Prompt Processing**: Support for wildcards and syntax
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- **Parameter Templates**: Pre-configured settings for common scenarios
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- **Batch Text Processing**: Handle multiple prompts efficiently
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- **Model-Aware Encoding**: Optimize for different text encoders
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## Node Properties
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- **Category**: `ComfyAssets/🧰 xyz-helpers`
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- **Node Name**: `TextEncodeSamplerParams`
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- **Function**: `encode_and_params`
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## Inputs
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### Required
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `text` | STRING | - | Prompt text to encode |
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| `clip` | CLIP | - | CLIP model for encoding |
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| `sampler_name` | DROPDOWN | dpmpp_2m | Sampling algorithm |
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| `scheduler` | DROPDOWN | karras | Noise scheduler |
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| `steps` | INT | 20 | Sampling steps |
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| `cfg` | FLOAT | 7.0 | CFG scale |
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### Optional
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `negative_text` | STRING | "" | Negative prompt |
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| `seed` | INT | -1 | Random seed (-1 for random) |
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| `denoise` | FLOAT | 1.0 | Denoising strength |
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| `template` | DROPDOWN | none | Parameter template |
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## Outputs
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| Name | Type | Description |
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|------|------|-------------|
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| `positive` | CONDITIONING | Encoded positive prompt |
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| `negative` | CONDITIONING | Encoded negative prompt |
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| `sampler_params` | DICT | Complete sampler parameters |
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## Templates
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### Portrait Photography
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```python
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template: "portrait"
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→ steps: 25
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→ cfg: 7.5
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→ sampler: dpmpp_2m_sde
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→ scheduler: karras
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```
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### Landscape Art
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```python
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template: "landscape"
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→ steps: 30
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→ cfg: 8.0
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→ sampler: dpmpp_3m_sde
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→ scheduler: exponential
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```
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### Quick Preview
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```python
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template: "preview"
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→ steps: 12
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→ cfg: 6.0
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→ sampler: euler
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→ scheduler: normal
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```
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### High Detail
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```python
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template: "detailed"
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→ steps: 40
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→ cfg: 7.0
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→ sampler: dpm_adaptive
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→ scheduler: karras
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```
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## Usage Examples
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### Basic Text-to-Image
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```
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TextEncodeSamplerParams → KSampler → VAE Decode
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text: "beautiful landscape"
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negative_text: "ugly, blurry"
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steps: 20
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```
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### Template-Based Generation
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```
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TextEncodeSamplerParams → KSampler
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text: "portrait of a person"
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template: "portrait"
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→ Optimized portrait settings
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```
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### Batch Processing
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```
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Multiple Prompts → TextEncodeSamplerParams → Batch Generate
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→ Encode all prompts with same settings
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```
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## Prompt Syntax Support
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### Wildcards
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```
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{red|blue|green} car
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→ Randomly selects color
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```
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### Emphasis
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```
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(important:1.2) detail
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→ Increases weight to 1.2
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```
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### Alternation
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```
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[cat|dog] in garden
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→ Alternates between options
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```
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## Best Practices
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### Text Encoding
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1. Keep prompts concise and descriptive
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2. Use emphasis for important elements
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3. Structure prompts logically
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4. Test negative prompts impact
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### Parameter Selection
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```python
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# Quality over speed
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steps: 30-40
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cfg: 7-8
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sampler: dpmpp_3m_sde
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# Speed over quality
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steps: 10-15
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cfg: 5-6
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sampler: euler
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```
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### Negative Prompts
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```python
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# Common negatives
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"ugly, tiling, poorly drawn, out of frame"
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# Style-specific
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"cartoon, anime" (for realism)
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"realistic, photo" (for artwork)
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```
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## Integration with Other Nodes
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### Complete Pipeline
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```
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TextEncodeSamplerParams → KSampler → VAE Decode
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↓ ↑
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All parameters From Model Loader
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```
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### With LoRA
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```
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LoRAFolderBatch → TextEncodeSamplerParams → Generate
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→ Apply LoRA to encoded text
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```
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### Multi-Pass Processing
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```
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TextEncodeSamplerParams → First Pass (low res)
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↘ Second Pass (high res)
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```
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## Advanced Features
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### Dynamic Templates
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```python
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# Load template based on prompt content
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if "portrait" in text:
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use_template("portrait")
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elif "landscape" in text:
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use_template("landscape")
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```
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### Prompt Weighting
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```python
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# Automatic weight calculation
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analyze_prompt_importance()
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apply_semantic_weights()
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```
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### CLIP Skip Support
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- Adjust CLIP layers used
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- Model-specific optimization
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- Quality vs style balance
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## Tips and Tricks
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### Prompt Optimization
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1. Front-load important elements
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2. Use commas for separation
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3. Avoid contradictions
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4. Test with different CFG values
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### Performance Tuning
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```python
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# Memory efficient
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encode_in_batches = True
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clear_cache_between = True
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# Speed priority
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use_half_precision = True
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minimize_conditioning = True
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```
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### Quality Enhancement
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- Higher CFG for prompt adherence
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- Lower CFG for creativity
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- Balance with step count
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## Common Workflows
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### Style Transfer
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```
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Reference Image → Extract Style
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↓
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TextEncodeSamplerParams → Apply Style
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text: "in the style of [extracted]"
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```
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### Prompt Evolution
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```
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Base Prompt → Variations → TextEncodeSamplerParams
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→ Test different phrasings
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```
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### A/B Testing
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```
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Same prompt → Different parameters → Compare
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template A vs template B
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```
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## Troubleshooting
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### Poor Text Adherence
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- Increase CFG scale
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- Simplify prompt
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- Check CLIP model compatibility
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### Over-saturation
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- Reduce CFG scale
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- Adjust negative prompt
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- Check sampler settings
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### Encoding Errors
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- Verify CLIP model loaded
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- Check text formatting
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- Remove special characters
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## Parameter Guidelines
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### CFG Scale Effects
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```
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Low (3-5): Creative, loose interpretation
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Medium (6-8): Balanced adherence
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High (9-12): Strict prompt following
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Very High (13+): Potential artifacts
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```
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### Step Count Impact
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```
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Low (10-15): Fast, rough
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Medium (20-30): Good balance
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High (40-50): Maximum quality
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Very High (50+): Diminishing returns
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```
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## Model-Specific Settings
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### SDXL
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- CFG: 6-8
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- CLIP Skip: 1-2
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- Emphasis: Moderate
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### SD 1.5
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- CFG: 7-9
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- CLIP Skip: 1-2
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- Emphasis: Standard
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### FLUX
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- CFG: 3-5
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- CLIP Skip: 0
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- Emphasis: Subtle
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## Version History
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- **1.0.0**: Initial adaptation from comfyui-essentials-nodes
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- **1.0.1**: Added template system
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- **1.0.2**: Enhanced prompt syntax support
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- **1.0.3**: Improved batch processing
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## Credits
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Original implementation by cubiq in [comfyui-essentials-nodes](https://github.com/cubiq/ComfyUI_essentials). Adapted and maintained by the ComfyAssets team.
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