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 DPM-Solver; can be 1 or 2 or 3. 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 algorithm_type=dpmsolver++ 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 algorithm_type="dpmsolver++.
• algorithm_type (str, default dpmsolver++) — the algorithm type for the solver. Either dpmsolver or dpmsolver++ or sde-dpmsolver or sde-dpmsolver++. The dpmsolver type implements the algorithms in https://arxiv.org/abs/2206.00927, and the dpmsolver++ type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use dpmsolver++ or sde-dpmsolver++ with solver_order=2 for guided sampling (e.g. stable-diffusion).
• solver_type (str, default midpoint) — the solver type for the second-order solver. Either midpoint or heun. The solver type slightly affects the sample quality, especially for small number of steps. We empirically find that midpoint solvers are slightly better, so we recommend to use the midpoint type.
• 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.
• 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.
• lambda_min_clipped (float, default -inf) — the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for cosine (squaredcos_cap_v2) noise schedule.
• variance_type (str, optional) — Set to “learned” or “learned_range” for diffusion models that predict variance. For example, OpenAI’s guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model’s output. DPM-Solver only needs the “mean” output because it is based on diffusion ODEs. whether the model’s output contains the predicted Gaussian variance. For example, OpenAI’s guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the Gaussian distribution in the model’s output. DPM-Solver only needs the “mean” output because it is based on diffusion ODEs.