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.
• 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.