The SamplerDPMAdaptative node implements an adaptive DPM (Diffusion Probabilistic Model) sampler that automatically adjusts step sizes during the sampling process. It uses tolerance-based error control to determine optimal step sizes, balancing computational efficiency with sampling accuracy. This adaptive approach helps maintain quality while potentially reducing the number of steps needed.

## Inputs

| Parameter | Description | Data Type | Required | Range |
| --- | --- | --- | --- | --- |
| `order` | The order of the sampler method (default: 3) | INT | Yes | 2-3 |
| `rtol` | Relative tolerance for error control (default: 0.05) | FLOAT | Yes | 0.0-100.0 |
| `atol` | Absolute tolerance for error control (default: 0.0078) | FLOAT | Yes | 0.0-100.0 |
| `h_init` | Initial step size (default: 0.05) | FLOAT | Yes | 0.0-100.0 |
| `pcoeff` | Proportional coefficient for step size control (default: 0.0) | FLOAT | Yes | 0.0-100.0 |
| `icoeff` | Integral coefficient for step size control (default: 1.0) | FLOAT | Yes | 0.0-100.0 |
| `dcoeff` | Derivative coefficient for step size control (default: 0.0) | FLOAT | Yes | 0.0-100.0 |
| `accept_safety` | Safety factor for step acceptance (default: 0.81) | FLOAT | Yes | 0.0-100.0 |
| `eta` | Stochasticity parameter (default: 0.0) | FLOAT | Yes | 0.0-100.0 |
| `s_noise` | Noise scaling factor (default: 1.0) | FLOAT | Yes | 0.0-100.0 |

## Outputs

| Output Name | Description | Data Type |
| --- | --- | --- |
| `sampler` | Returns a configured DPM adaptive sampler instance | SAMPLER |

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