V 1.0.0 - New readme
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@@ -298,4 +298,153 @@ Remember: Fine-tune these settings based on your specific use case and desired o
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#### Hands, but aesthetic scorer automatically ignored the second:
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<img src="refiner_block_hands2.jpg" width="500px">
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<hr>
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# <ins>DiT Prompt Purifier Node:</ins>
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<img src="prompt_dit_cleaner.jpg" width="500px">
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### Overview:
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The DiT Prompt Purifier node automatically cleans and optimizes prompts for modern Diffusion Transformer (DiT) architectures by removing incompatible elements, weights, and structural components that may interfere with newer models.
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### Key Features:
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- Automatic model version detection
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- Character length limitation
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- Architecture-specific prompt purification
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- Selective cleaning based on model Architecture
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- Incompatible weight and structure removal
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### Cleaning Operations
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#### Removes:
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- Prompt weights (e.g., `(element:1.2)`)
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- Legacy break commands (`BREAK`)
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- Nested parentheses structures
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- Old-style prompt formatting
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#### Controls:
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- `max_length`: Character limit (0 = unlimited)
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- Model-specific purification toggles
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- Automatic architecture detection by loaded checkpoint
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### Architecture Controls:
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#### Standard Models (no need to purify)
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- `purify_sd1`: SD1.x prompts
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- `purify_sd2`: SD2.x prompts
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- `purify_sdxl`: SDXL prompts
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- `purify_sd3`: SD3 prompts
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#### Modern DiT Models (have to purify)
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- `purify_stablecascade`: Stable Cascade
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- `purify_flux`: Flux models
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- `purify_kwaikolors`: KwaiKolors
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- `purify_hunyuan`: Hunyuan
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#### Specialized Models (no need to purify)
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- `purify_turbo`: Turbo models
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- `purify_playground`: Playground
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- `purify_pony`: Pony models
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- `purify_lcm`: LCM variants
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- `purify_lightning`: Lightning
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- `purify_hyper`: Hyper models
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- `purify_pixartsigma`: PixArt-Sigma
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### Use Cases:
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#### Basic Prompt Cleaning:
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```plaintext
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Input: "(high quality:1.2), (detailed:1.4) BREAK (masterpiece)"
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Output: "high quality, detailed, masterpiece"
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```
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#### Length Control:
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```plaintext
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Settings:
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- max_length: 50
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- purify_flux: ON
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Input: "Long prompt with excessive description and weights..."
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Output: "Truncated prompt within 50 characters but keep the whole last word..."
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```
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### Configuration Examples:
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#### Flux Optimization:
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```plaintext
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Settings:
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- purify_flux: ON
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- max_length: 0
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Benefits:
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- DiT-compatible structure
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- Weight removal
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- Clean formatting
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```
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#### Mixed Model Pipeline:
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```plaintext
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Settings:
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- Keep non-target models OFF
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- Enable specific model purification
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- Set appropriate length limit
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Benefits:
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- Model-specific optimization
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- Controlled cleaning
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- Maintained compatibility
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```
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### Benefits:
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#### Compatibility:
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- Ensures DiT architecture compliance
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- Removes problematic structures
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- Maintains old (A1111 compatible) prompt essence
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### Best Practices:
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1. **Model Selection**
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- Enable purification for target model
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- Disable for non-relevant architectures
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- Verify model detection
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2. **Length Management**
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- Set appropriate max_length
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- Use 0 for unlimited length
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- Monitor output for truncation
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3. **Cleaning Strategy**
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- Enable relevant model switches
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- Check debug output
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- Verify cleaned prompt
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### Recommended Settings:
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#### DiT Models:
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```plaintext
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- set purify_<model_type>: ON
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- max_length: 0 (or set if the result too noisy)
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- other model type purify switches: OFF
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```
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#### Legacy Models:
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```plaintext
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Keep settings:
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- purify switches: OFF
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- max_length: 0
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- Maintains original structure
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```
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### Tips for Usage:
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1. Monitor debug output for cleaning verification
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2. Adjust length limits based on model requirements
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3. Enable only relevant DiT model purification
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4. Test cleaned prompts before batch processing
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5. Keep original prompts for reference
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Remember: DiT prompt purification is essential for optimal performance with modern architectures while maintaining prompt effectiveness.
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<hr>
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@@ -395,6 +395,140 @@ This node automatically saves images along with metadata, generated by the full
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<hr>
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# <ins>Network Tag Cleaner Node:</ins>
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<img src="prompt_network_cleaner.jpg" width="250px">
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### Overview:
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The Network Tag Cleaner node provides intelligent management of network adapter tags in prompts, supporting both manual and automatic cleaning modes for various model architectures.
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### Supported Network Types:
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- Embeddings (`embedding:name`)
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- LoRA (`<lora:name>`)
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- LyCORIS (`<lycoris:name>`)
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- Hypernetworks (`<hypernetwork:name>`)
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### Operation Modes:
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#### Manual Mode:
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When `auto_remover` is set to OFF:
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- Full control over tag removal
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- Selective cleaning based on model architecture
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- Requires manual configuration
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#### Auto Mode:
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When `auto_remover` is set to ON:
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- Intelligent compatibility checking
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- Automatic removal of only incompatible tags
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- Preserves all matching architecture tags
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### Control Switches:
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#### Network Type Controls:
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- `remove_embedding`: Toggle embedding removal
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- `remove_lora`: Toggle LoRA tag removal
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- `remove_lycoris`: Toggle LyCORIS tag removal
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- `remove_hypernetwork`: Toggle hypernetwork removal
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#### Architecture-Specific Controls:
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- `remove_only_sd1`: SD1.x network tags
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- `remove_only_sd2`: SD2.x network tags
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- `remove_only_sdxl`: SDXL network tags
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- `remove_only_sd3`: SD3 network tags
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- `remove_only_stablecascade`: Stable Cascade tags
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- `remove_only_turbo`: Turbo model tags
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- `remove_only_flux`: Flux model tags
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- `remove_only_kwaikolors`: KwaiKolors tags
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- `remove_only_hunyuan`: Hunyuan tags
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- `remove_only_playground`: Playground tags
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- `remove_only_pony`: Pony tags
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- `remove_only_lcm`: LCM tags
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- `remove_only_lightning`: Lightning tags
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- `remove_only_hyper`: Hyper tags
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- `remove_only_pixartsigma`: PixArt-Sigma tags
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### Use Cases:
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#### Manual Cleaning Mode:
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```plaintext
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Scenario 1: SDXL-Only Workflow
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Settings:
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- auto_remover: OFF
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- remove_lora: ON
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- remove_only_sd1: ON
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- remove_only_sd2: ON
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- remove_only_sdxl: OFF
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Result: Removes all SD1 and SD2 LoRA tags while keeping SDXL LoRAs
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```
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#### Auto Cleaning Mode:
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```plaintext
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Scenario 1: Using Flux Checkpoint
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Settings:
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- auto_remover: ON
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- remove_lora: ON
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- remove_lycoris: ON
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Result:
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- Keeps Flux-compatible network tags
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- Removes SD1/SD2/SDXL and all other non-flux network tags
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- Preserves model-specific optimizations
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```
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```plaintext
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Scenario 2: Using SDXL Checkpoint
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Settings:
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- auto_remover: ON
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- remove_lora: ON
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Result:
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- Maintains SDXL LoRA tags
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- Removes all other incompatible network tags
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- Ensures workflow compatibility
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```
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### Benefits:
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#### Workflow Optimization:
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- Prevents incompatible network usage
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- Reduces generation errors
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- Streamlines prompt management
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#### Error Prevention:
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- Eliminates architecture mismatches
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- Prevents memory issues
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- Reduces failed generations
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#### User Convenience:
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- Automatic compatibility checking
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- Simple toggle controls
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- Clear visual feedback
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#### Advanced Usage:
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```plaintext
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Settings:
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- auto_remover: ON
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- All network types enabled
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- Model-specific switches as needed
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Benefits:
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- Comprehensive tag management
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- Maximum compatibility
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- Automated workflow optimization
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```
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### Recommendations:
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1. Start with auto mode for general use
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2. Switch to manual mode for specific needs
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3. Regular prompt verification
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4. Keep model architecture in mind
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Remember: The cleaner node ensures optimal network compatibility while maintaining prompt integrity and workflow efficiency.
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<hr>
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# <ins>Network Tag Loader:</ins>
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<img src="networks_loader.jpg" width="250px">
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@@ -431,4 +565,57 @@ This node manages additional networks such as Lora, Lycoris, and hypernetworks f
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- `Enhanced Control`: Allows fine-tuning of model behavior using additional networks
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- `Prompt Customization`: Adjusts prompts dynamically using Lora and Lycoris keywords for more refined image generation
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- `Network Compatibility`: Supports multiple network types, providing flexibility in mixing styles and behaviors
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- `Workflow Integration`: Seamlessly integrates with existing models and workflows in ComfyUI for consistent results
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- `Workflow Integration`: Seamlessly integrates with existing models and workflows in ComfyUI for consistent results
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<hr>
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# <ins>Primere Embedding Handler:</ins>
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<img src="prompt_embedding.jpg" width="500px">
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A ComfyUI node that automatically converts Automatic1111-style textual inversion embedding syntax to ComfyUI-compatible format.
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### Description:
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This node allows you to use Automatic1111-style embedding syntax in your prompts while working in ComfyUI. It automatically detects embedding keywords and converts them to the proper ComfyUI format.
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### How It Works:
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#### Input Format (Automatic1111 style):
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- Simple keywords like `Autumn` or `Summer`
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- No special prefix needed
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- Must match exactly with embedding filename (case-sensitive) without extension
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#### Output Format (ComfyUI style):
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- Converted to `embedding:Autumn` or `embedding:Summer`
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- Properly formatted for ComfyUI processing
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- Only converts if matching embedding file exists
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### Example:
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Input prompt:
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```
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This prompt contains Autumn. The embedding name is case sensitive: summer just a word, but Summer is embedding.
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```
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Output prompt:
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```
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This prompt contains embedding:Autumn. The embedding name is case sensitive: summer just a word, but embedding:Summer is embedding.
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```
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### Features:
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- Seamless conversion between Automatic1111 and ComfyUI embedding formats
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- Case-sensitive matching to prevent unwanted conversions
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- Only converts existing embeddings
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- Works with both positive and negative prompts
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- Real-time conversion during workflow execution
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### Benefits:
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- Use Automatic1111 prompts directly in ComfyUI without manual conversion
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- Maintain compatibility with existing prompt collections
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- Reduce errors from incorrect embedding syntax
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- Save time on prompt formatting
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<hr>
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