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< |
source |
> |
( |
sample: FloatTensor |
sigma: float |
generator: typing.Optional[torch._C.Generator] = None |
) |
Explicit Langevin-like “churn” step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a |
higher noise level sigma_hat = sigma_i + gamma_i*sigma_i. |
TODO Args: |
scale_model_input |
< |
source |
> |
( |
sample: FloatTensor |
timestep: typing.Optional[int] = None |
) |
→ |
torch.FloatTensor |
Parameters |
sample (torch.FloatTensor) — input sample |
timestep (int, optional) — current timestep |
Returns |
torch.FloatTensor |
scaled input sample |
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the |
current timestep. |
set_timesteps |
< |
source |
> |
( |
num_inference_steps: int |
device: typing.Union[str, torch.device] = None |
) |
Parameters |
num_inference_steps (int) — |
the number of diffusion steps used when generating samples with a pre-trained model. |
Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. |
step |
< |
source |
> |
( |
model_output: FloatTensor |
sigma_hat: float |
sigma_prev: float |
sample_hat: FloatTensor |
return_dict: bool = True |
) |
→ |
KarrasVeOutput or tuple |
Parameters |
model_output (torch.FloatTensor) — direct output from learned diffusion model. |
sigma_hat (float) — TODO |
sigma_prev (float) — TODO |
sample_hat (torch.FloatTensor) — TODO |
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