TenserTensor
22c3d146bd
Implement post-production processing nodes: - add_film_grain.py: procedural film grain effect - apply_lut.py: LUT-based color grading - image_enhancer.py: comprehensive image adjustments (brightness, contrast, gamma, hue, saturation, sharpness, posterize, solarize) - postproduction.py: all-in-one pipeline (LUT + film grain + upscaling) - postproduction_advanced.py: extended pipeline with image enhancement controls - quick_image_upscaler.py: simple tiled AI upscaler Add markdown documentation for all post-production nodes
35 lines
1.5 KiB
Markdown
35 lines
1.5 KiB
Markdown
## TT CLIP Text Encode FLUX (Context) *TT_ClipTextEncodeFluxContext*
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*This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.*
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Context-based Flux text encoder that extracts prompts from workflow config.
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### Inputs
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| Parameter | Type | Required | Description |
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|-----------|------------|----------|------------------------------------------------------------------|
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| `context` | TT_CONTEXT | Yes | Context containing CLIP encoder and workflow config with prompts |
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### Outputs
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| Parameter | Type | Description |
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|------------|--------------|-----------------------------------------------------------|
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| `CONTEXT` | TT_CONTEXT | Updated context with positive/negative conditioning added |
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| `POSITIVE` | CONDITIONING | Positive conditioning from both encoders |
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| `NEGATIVE` | CONDITIONING | Negative conditioning from both encoders |
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### Usage
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Simplified encoder for context-based workflows. Automatically extracts CLIP-L and T5-XXL prompts plus guidance from
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workflow_config:
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- `clip_l_positive`, `t5xxl_positive` for positive prompts
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- `clip_l_negative`, `t5xxl_negative` for negative prompts
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- `guidance` for Flux guidance scale
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Updates context with the generated conditioning and also returns them separately for convenient access.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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