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trained_betas (np.ndarray, optional) — |
option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc. |
prediction_type (str, default epsilon, optional) — |
prediction type of the scheduler function, one of epsilon (predicting the noise of the diffusion |
process), sample (directly predicting the noisy sample) or v_prediction` (see section 2.4 |
https://imagen.research.google/video/paper.pdf) |
Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson: |
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72 |
~ConfigMixin takes care of storing all config attributes that are passed in the scheduler’s __init__ |
function, such as num_train_timesteps. They can be accessed via scheduler.config.num_train_timesteps. |
SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and |
from_pretrained() functions. |
scale_model_input |
< |
source |
> |
( |
sample: FloatTensor |
timestep: typing.Union[float, torch.FloatTensor] |
) |
→ |
torch.FloatTensor |
Parameters |
sample (torch.FloatTensor) — input sample |
timestep (float or torch.FloatTensor) — the current timestep in the diffusion chain |
Returns |
torch.FloatTensor |
scaled input sample |
Scales the denoising model input by (sigma**2 + 1) ** 0.5 to match the Euler algorithm. |
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. |
device (str or torch.device, optional) — |
the device to which the timesteps should be moved to. If None, the timesteps are not moved. |
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. |
step |
< |
source |
> |
( |
model_output: FloatTensor |
timestep: typing.Union[float, torch.FloatTensor] |
sample: FloatTensor |
generator: typing.Optional[torch._C.Generator] = None |
return_dict: bool = True |
) |
→ |
~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput or tuple |
Parameters |
model_output (torch.FloatTensor) — direct output from learned diffusion model. |
timestep (float) — current timestep in the diffusion chain. |
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