TenserTensor
ee88f9be6c
Implement sampling nodes: - ksampler.py: standard sampler with basic parameters - ksampler_advanced.py: advanced sampler with step control and noise settings - ksampler_context.py: context-based sampler extracting parameters from workflow context - ksampler_two_stage.py: automated two-stage sampling (draft + refinement) Add markdown documentation for all sampler nodes
1.7 KiB
1.7 KiB
TT_KSampler
This description was generated with AI assistance. If you spot any errors, please create an issue on GitHub.
Standard sampler node for generating images from latents.
Inputs
| Parameter | Type | Required | Description |
|---|---|---|---|
model |
MODEL | Yes | The diffusion model to use for sampling |
positive |
CONDITIONING | Yes | Positive conditioning (what to generate) |
negative |
CONDITIONING | Yes | Negative conditioning (what to avoid) |
latent |
LATENT | Yes | Latent image to denoise |
seed |
INT | Yes | Random seed for reproducibility (0 to 2^64-1, default: 0) |
steps |
INT | Yes | Number of sampling steps (1-10000, default: 25) |
cfg |
FLOAT | Yes | Classifier-free guidance scale (0.0-100.0, default: 1.5) |
sampler_name |
SAMPLER | Yes | Sampler algorithm to use |
scheduler |
SCHEDULER | Yes | Noise schedule to use |
Outputs
| Parameter | Type | Description |
|---|---|---|
LATENT |
LATENT | Denoised latent image |
Usage
Standard KSampler for generating images. Accepts all common sampling parameters and returns a denoised latent ready for VAE decoding.