Parameters

• sigma_min (float, optional, defaults to 0.002) — Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable range is [0, 10].
• sigma_max (float, optional, defaults to 80.0) — Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable range is [0.2, 80.0].
• sigma_data (float, optional, defaults to 0.5) — The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
• sigma_schedule (str, optional, defaults to karras) — Sigma schedule to compute the sigmas. By default, we the schedule introduced in the EDM paper (https://arxiv.org/abs/2206.00364). Other acceptable value is “exponential”. The exponential schedule was incorporated in this model: https://huggingface.co./stabilityai/cosxl.
• num_train_timesteps (int, defaults to 1000) — The number of diffusion steps to train the model.
• 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++ or sde-dpmsolver++. 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.
• 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.

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.