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
38 lines
2.2 KiB
Markdown
38 lines
2.2 KiB
Markdown
## TT CLIP Text Encode SDXL *TT_ClipTextEncodeSdxl*
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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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SDXL text encoder with separate CLIP-L and CLIP-G prompt inputs and conditioning parameters.
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### Inputs
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| Parameter | Type | Required | Description |
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|-------------------|--------|----------|---------------------------------------------------------|
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| `clip` | CLIP | Yes | CLIP text encoder (dual CLIP-L + CLIP-G) |
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| `clip_l_positive` | STRING | Yes | CLIP-L positive prompt (multiline) |
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| `clip_g_positive` | STRING | Yes | CLIP-G positive prompt (multiline) |
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| `clip_l_negative` | STRING | Yes | CLIP-L negative prompt (multiline) |
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| `clip_g_negative` | STRING | Yes | CLIP-G negative prompt (multiline) |
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| `ascore_positive` | FLOAT | Yes | Aesthetic score for positive (0.0-1000.0, default: 9.0) |
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| `ascore_negative` | FLOAT | Yes | Aesthetic score for negative (0.0-1000.0, default: 6.0) |
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| `width` | INT | Yes | Image width for conditioning (default: 512) |
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| `height` | INT | Yes | Image height for conditioning (default: 512) |
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| `target_width` | INT | Yes | Target width for conditioning (default: 512) |
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| `target_height` | INT | Yes | Target height for conditioning (default: 512) |
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### Outputs
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| Parameter | Type | Description |
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|------------|--------------|-----------------------------------------------------------|
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| `POSITIVE` | CONDITIONING | Positive conditioning with aesthetic score and dimensions |
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| `NEGATIVE` | CONDITIONING | Negative conditioning with aesthetic score and dimensions |
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### Usage
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Encode text prompts for SDXL with separate control over CLIP-L and CLIP-G inputs, aesthetic scores, and target
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dimensions. The aesthetic scores and dimensions are embedded into the conditioning to guide the generation process.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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