35 lines
1.7 KiB
Markdown
35 lines
1.7 KiB
Markdown
## TT KSampler *TT_KSampler*
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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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Standard sampler node for generating images from latents.
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### Inputs
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| Parameter | Type | Required | Description |
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|----------------|--------------|----------|-----------------------------------------------------------|
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| `model` | MODEL | Yes | The diffusion model to use for sampling |
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| `positive` | CONDITIONING | Yes | Positive conditioning (what to generate) |
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| `negative` | CONDITIONING | Yes | Negative conditioning (what to avoid) |
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| `latent` | LATENT | Yes | Latent image to denoise |
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| `seed` | INT | Yes | Random seed for reproducibility (0 to 2^64-1, default: 0) |
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| `steps` | INT | Yes | Number of sampling steps (1-10000, default: 25) |
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| `cfg` | FLOAT | Yes | Classifier-free guidance scale (0.0-100.0, default: 1.5) |
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| `sampler_name` | COMBO | Yes | Sampler algorithm to use |
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| `scheduler` | COMBO | Yes | Noise schedule to use |
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### Outputs
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| Parameter | Type | Description |
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|-----------|--------|-----------------------|
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| `LATENT` | LATENT | Denoised latent image |
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### Usage
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Standard KSampler for generating images. Accepts all common sampling parameters and returns a denoised latent ready for
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VAE decoding.
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---
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[View on GitHub](https://github.com/tenser-tensor/ComfyUI-TenserTensor)
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