Parameters

• num_train_timesteps (int) — number of diffusion steps used to train the model. beta_start (float): the
• starting beta value of inference. 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 or scaled_linear.
• 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).
• clip_sample (bool, default True) — option to clip predicted sample for numerical stability.
• clip_sample_range (float, default 1.0) — the maximum magnitude for sample clipping. Valid only when clip_sample=True.
• use_karras_sigmas (bool, optional, defaults to False) — This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf.
• timestep_spacing (str, default "linspace") — The way the timesteps should be scaled. Refer to Table 2. of Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
• steps_offset (int, default 0) — an offset added to the inference steps. You can use a combination of offset=1 and set_alpha_to_one=False, to make the last step use step 0 for the previous alpha product, as done in stable diffusion.