Class FlowDPMSolverMultistepScheduler

wan/utils/fm_solvers.py:71–859

`FlowDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.

class FlowDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
    """
    `FlowDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
    methods the library implements for all schedulers such as loading and saving.
    Args:
        num_train_timesteps (`int`, defaults to 1000):
            The number of diffusion steps to train the model. This determines the resolution of the diffusion process.
        solver_order (`int`, defaults to 2):
            The DPMSolver order which can be `1`, `2`, or `3`. It is recommended to use `solver_order=2` for guided
            sampling, and `solver_order=3` for unconditional sampling. This affects the number of model outputs stored
            and used in multistep updates.
        prediction_type (`str`, defaults to "flow_prediction"):
            Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts
            the flow of the diffusion process.
        shift (`float`, *optional*, defaults to 1.0):
            A factor used to adjust the sigmas in the noise schedule. It modifies the step sizes during the sampling
            process.
        use_dynamic_shifting (`bool`, defaults to `False`):
            Whether to apply dynamic shifting to the timesteps based on image resolution. If `True`, the shifting is
            applied on the fly.
        thresholding (`bool`, defaults to `False`):
            Whether to use the "dynamic thresholding" method. This method adjusts the predicted sample to prevent
            saturation and improve photorealism.
        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](https://huggingface.co/papers/2206.00927)
            paper, and the `dpmsolver++` type implements the algorithms in the
            [DPMSolver++](https://huggingface.co/papers/2211.01095) 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&#x27;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`, *optional*, 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&#x27;s output
            contains the predicted Gaussian variance.
    """

    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
    order = 1