Parameters

• num_train_timesteps (int, defaults to 1000) — The number of diffusion steps to train the model.
• beta_start (float, defaults to 0.0001) — The starting beta value of inference.
• beta_end (float, defaults to 0.02) — The final beta value.
• beta_schedule (str, defaults to "linear") — 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) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end.
• solver_order (int, defaults to 2) — The DPMSolver order which can be 1 or 2 or 3. It is recommended to use solver_order=2 for guided sampling, and solver_order=3 for unconditional sampling.
• prediction_type (str, defaults to epsilon, optional) — Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen Video paper).
• thresholding (bool, defaults to False) — Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such as Stable Diffusion.
• dynamic_thresholding_ratio (float, defaults to 0.995) — The ratio for the dynamic thresholding method. Valid only when thresholding=True.
• sample_max_value (float, defaults to 1.0) — The threshold value for dynamic thresholding. Valid only when thresholding=True and algorithm_type="dpmsolver++".
• algorithm_type (str, defaults to dpmsolver++) — Algorithm type for the solver; can be dpmsolver, dpmsolver++, sde-dpmsolver or sde-dpmsolver++. The dpmsolver type implements the algorithms in the DPMSolver paper, and the dpmsolver++ type implements the algorithms in the DPMSolver++ paper. It is recommended to use dpmsolver++ or sde-dpmsolver++ with solver_order=2 for guided sampling like in Stable Diffusion.
• solver_type (str, defaults to midpoint) — Solver type for the second-order solver; can be midpoint or heun. The solver type slightly affects the sample quality, especially for a small number of steps. It is recommended to use midpoint solvers.
• lower_order_final (bool, defaults to True) — Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
• euler_at_final (bool, defaults to False) — Whether to use Euler’s method in the final step. It is a trade-off between numerical stability and detail richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference steps, but sometimes may result in blurring.
• use_karras_sigmas (bool, optional, defaults to False) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True, the sigmas are determined according to a sequence of noise levels {σi}.
• use_exponential_sigmas (bool, optional, defaults to False) — Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
• use_beta_sigmas (bool, optional, defaults to False) — Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to Beta Sampling is All You Need for more information.
• use_lu_lambdas (bool, optional, defaults to False) — Whether to use the uniform-logSNR for step sizes proposed by Lu’s DPM-Solver in the noise schedule during the sampling process. If True, the sigmas and time steps are determined according to a sequence of lambda(t).
• final_sigmas_type (str, defaults to "zero") — The final sigma value for the noise schedule during the sampling process. If "sigma_min", the final sigma is the same as the last sigma in the training schedule. If zero, the final sigma is set to 0.
• lambda_min_clipped (float, defaults to -inf) — Clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for the cosine (squaredcos_cap_v2) noise schedule.
• variance_type (str, optional) — Set to “learned” or “learned_range” for diffusion models that predict variance. If set, the model’s output contains the predicted Gaussian variance.
• timestep_spacing (str, defaults to "linspace") — The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
• steps_offset (int, defaults to 0) — An offset added to the inference steps, as required by some model families.
• rescale_betas_zero_snr (bool, defaults to False) — Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and dark samples instead of limiting it to samples with medium brightness. Loosely related to --offset_noise.

Parameters

• prev_sample (torch.Tensor of shape (batch_size, num_channels, height, width) for images) — Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the denoising loop.