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beta_end (float) — the final beta value.
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beta_schedule (str) —
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the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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linear, scaled_linear, or squaredcos_cap_v2.
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eta (float) —
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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
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1.0 is DDPM scheduler respectively.
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trained_betas (np.ndarray, optional) —
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option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc.
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variance_type (str) —
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options to clip the variance used when adding noise to the denoised sample. Choose from fixed_small,
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fixed_small_log, fixed_large, fixed_large_log, learned or learned_range.
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clip_sample (bool, default True) —
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option to clip predicted sample between -1 and 1 for numerical stability.
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RePaint is a schedule for DDPM inpainting inside a given mask.
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~ConfigMixin takes care of storing all config attributes that are passed in the scheduler’s __init__
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function, such as num_train_timesteps. They can be accessed via scheduler.config.num_train_timesteps.
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SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and
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from_pretrained() functions.
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For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf
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scale_model_input
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<
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source
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>
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(
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sample: FloatTensor
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timestep: typing.Optional[int] = None
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)
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→
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torch.FloatTensor
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Parameters
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sample (torch.FloatTensor) — input sample
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timestep (int, optional) — current timestep
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Returns
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torch.FloatTensor
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scaled input sample
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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step
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<
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source
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>
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(
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model_output: FloatTensor
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timestep: int
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sample: FloatTensor
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original_image: FloatTensor
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mask: FloatTensor
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generator: typing.Optional[torch._C.Generator] = None
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return_dict: bool = True
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)
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→
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~schedulers.scheduling_utils.RePaintSchedulerOutput or tuple
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Parameters
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model_output (torch.FloatTensor) — direct output from learned
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diffusion model.
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timestep (int) — current discrete timestep in the diffusion chain.
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sample (torch.FloatTensor) —
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current instance of sample being created by diffusion process.
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