Use better way to impl

    """

    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing

    """

    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None, enable_custom_k_sched: bool = False, k_sched_type: str = "", sigma_min: float=0.0, sigma_max: float=0.0, rho: float=0.0):

    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):

        if sampler_index is not None:

            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)

        self.s_tmin = s_tmin or opts.s_tmin

        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option

        self.s_noise = s_noise or opts.s_noise

        self.enable_custom_k_sched = opts.custom_k_sched

        self.k_sched_type = k_sched_type or opts.k_sched_type

        self.sigma_max = sigma_max or opts.sigma_max

        self.sigma_min = sigma_min or opts.sigma_min

        self.rho = rho or opts.rho

        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}

        self.override_settings_restore_afterwards = override_settings_restore_afterwards

        self.is_using_inpainting_conditioning = False

    if uses_ensd:

        uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)

    # avoid loop import

    from modules import sd_samplers_kdiffusion

    use_custom_k_sched = p.enable_custom_k_sched and p.sampler_name in sd_samplers_kdiffusion.k_diffusion_samplers_map

    generation_params = {

        "Steps": p.steps,

        "Sampler": p.sampler_name,

        "Enable Custom KDiffusion Schedule": use_custom_k_sched or None,

        "KDiffusion Scheduler Type": p.k_sched_type if use_custom_k_sched else None,

        "KDiffusion Scheduler sigma_max": p.sigma_max if use_custom_k_sched else None,

        "KDiffusion Scheduler sigma_min": p.sigma_min if use_custom_k_sched else None,

        "KDiffusion Scheduler rho": p.rho if use_custom_k_sched else None,

        "CFG scale": p.cfg_scale,

        "Image CFG scale": getattr(p, 'image_cfg_scale', None),

        "Seed": all_seeds[index],

        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])

        if opts.custom_k_sched:

            p.extra_generation_params["Enable Custom KDiffusion Schedule"] = True

            p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type

            p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max

            p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min

            p.extra_generation_params["KDiffusion Scheduler rho"] = opts.rho

        extra_params_kwargs = {}

        for param_name in self.extra_params:

            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:

        if p.sampler_noise_scheduler_override:

            sigmas = p.sampler_noise_scheduler_override(steps)

        elif p.enable_custom_k_sched:

        elif opts.custom_k_sched:

            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())

            sigmas_func = k_diffusion_scheduler[p.k_sched_type]

            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]

            sigmas_kwargs = {

                'sigma_min': p.sigma_min or sigma_min,

                'sigma_max': p.sigma_max or sigma_max

                'sigma_min': opts.sigma_min or sigma_min,

                'sigma_max': opts.sigma_max or sigma_max

            }

            if p.k_sched_type != 'exponential':

                sigmas_kwargs['rho'] = p.rho

            if opts.k_sched_type != 'exponential':

                sigmas_kwargs['rho'] = opts.rho

            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)

        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':

            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())