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, scaled_linear, or squaredcos_cap_v2.
• trained_betas (np.ndarray, optional) — option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc.
• solver_order (int, default 2) — the order of UniPC, also the p in UniPC-p; can be any positive integer. Note that the effective order of accuracy is solver_order + 1 due to the UniC. We recommend to use solver_order=2 for guided sampling, and solver_order=3 for unconditional sampling.
• 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)
• thresholding (bool, default False) — whether to use the “dynamic thresholding” method (introduced by Imagen, https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set both predict_x0=True and thresholding=True to use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion).
• dynamic_thresholding_ratio (float, default 0.995) — the ratio for the dynamic thresholding method. Default is 0.995, the same as Imagen (https://arxiv.org/abs/2205.11487).
• sample_max_value (float, default 1.0) — the threshold value for dynamic thresholding. Valid only when thresholding=True and predict_x0=True.
• predict_x0 (bool, default True) — whether to use the updating algrithm on the predicted x0. See https://arxiv.org/abs/2211.01095 for details
• solver_type (str, default bh2) — the solver type of UniPC. We recommend use bh1 for unconditional sampling when steps < 10, and use bh2 otherwise.
• lower_order_final (bool, default True) — whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.
• disable_corrector (list, default []) — decide which step to disable the corrector. For large guidance scale, the misalignment between the epsilon_theta(x_t, c)and epsilon_theta(x_t^c, c) might influence the convergence. This can be mitigated by disable the corrector at the first few steps (e.g., disable_corrector=[0])
• solver_p (SchedulerMixin, default None) — can be any other scheduler. If specified, the algorithm will become solver_p + UniC.