Repository: huggingface/diffusers
File: src/diffusers/schedulers/scheduling_dpmsolver_multistep.py (Python)

Code before:

class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
    """
    `DPMSolverMultistepScheduler` 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.
        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 (`"linear"`, `"scaled_linear"`, or `"squaredcos_cap_v2"`, defaults to `"linear"`):
            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.
        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 (`"epsilon"`, `"sample"`, `"v_prediction"`, or `"flow_prediction"`, defaults to `"epsilon"`):
            Prediction type of the scheduler function. `epsilon` predicts the noise of the diffusion process, `sample`
            directly predicts the noisy sample, `v_prediction` predicts the velocity (see section 2.4 of [Imagen
            Video](https://imagen.research.google/video/paper.pdf) paper), and `flow_prediction` predicts the flow.
        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 (`"dpmsolver"`, `"dpmsolver++"`, `"sde-dpmsolver"`, or `"sde-dpmsolver++"`, defaults to `"dpmsolver++"`):
            Algorithm type for the solver. 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 (`"midpoint"` or `"heun"`, defaults to `"midpoint"`):
            Solver type for the second-order solver. 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`,

Review comment (suggestion):
```suggestion
            Video](https://huggingface.co/papers/2210.02303) paper), and `flow_prediction` predicts the flow.
```

Code after:

class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
    """
    `DPMSolverMultistepScheduler` 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.
        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 (`"linear"`, `"scaled_linear"`, or `"squaredcos_cap_v2"`, defaults to `"linear"`):
            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.
        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 (`"epsilon"`, `"sample"`, `"v_prediction"`, or `"flow_prediction"`, defaults to `"epsilon"`):
            Prediction type of the scheduler function. `epsilon` predicts the noise of the diffusion process, `sample`
            directly predicts the noisy sample, `v_prediction` predicts the velocity (see section 2.4 of [Imagen
            Video](https://huggingface.co/papers/2210.02303) paper), and `flow_prediction` predicts the flow.
        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 (`"dpmsolver"`, `"dpmsolver++"`, `"sde-dpmsolver"`, or `"sde-dpmsolver++"`, defaults to `"dpmsolver++"`):
            Algorithm type for the solver. 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 (`"midpoint"` or `"heun"`, defaults to `"midpoint"`):
            Solver type for the second-order solver. 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`,