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952 B
952 B
SDTurboScheduler
Category: sampling/custom_sampling/schedulers
Description
SDTurboScheduler is designed to generate a sequence of sigma values for image sampling, adjusting the sequence based on the denoise level and the number of steps specified. It leverages a specific model's sampling capabilities to produce these sigma values, which are crucial for controlling the denoising process during image generation.
Inputs
| Parameter | Type | Default | Description |
|---|---|---|---|
| model | Model | — | |
| steps | Int | 1 | |
| denoise | Float | 1.0 |
Outputs
| Output | Type |
|---|---|
| )] | |
| ) |
@classmethod
def execute(cls, model, steps, denoise) -> io.NodeOutput:
start_step = 10 - int(10 * denoise)
timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,) | Sigmas |
Source: comfy_extras/nodes_custom_sampler.py
Used in 1 template(s)