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beta_end (float) — the final beta value. |
beta_schedule (str) — |
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from |
linear, scaled_linear, or squaredcos_cap_v2. |
eta (float) — |
The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and |
1.0 is DDPM scheduler respectively. |
trained_betas (np.ndarray, optional) — |
option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc. |
variance_type (str) — |
options to clip the variance used when adding noise to the denoised sample. Choose from fixed_small, |
fixed_small_log, fixed_large, fixed_large_log, learned or learned_range. |
clip_sample (bool, default True) — |
option to clip predicted sample between -1 and 1 for numerical stability. |
RePaint is a schedule for DDPM inpainting inside a given mask. |
~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. |
For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf |
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. |
step |
< |
source |
> |
( |
model_output: FloatTensor |
timestep: int |
sample: FloatTensor |
original_image: FloatTensor |
mask: FloatTensor |
generator: typing.Optional[torch._C.Generator] = None |
return_dict: bool = True |
) |
→ |
~schedulers.scheduling_utils.RePaintSchedulerOutput or tuple |
Parameters |
model_output (torch.FloatTensor) — direct output from learned |
diffusion model. |
timestep (int) — current discrete timestep in the diffusion chain. |
sample (torch.FloatTensor) — |
current instance of sample being created by diffusion process. |
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