Use settings instead of main interface

    "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",

    "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",

    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",

    "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction.",

    "Custom KDiffusion Scheduler": "Custom noise scheduler to use for KDiffusion. See https://arxiv.org/abs/2206.00364",

    "sigma min": "the minimum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use.",

    "sigma max": "the maximum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use.",

    "rho": "higher will make a more steep noise scheduler (decrease faster). default for karras is 7.0, for polyexponential is 1.0"

    "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."

};

function updateTooltipForSpan(span) {

    ('Conditional mask weight', 'inpainting_mask_weight'),

    ('Model hash', 'sd_model_checkpoint'),

    ('ENSD', 'eta_noise_seed_delta'),

    ('Enable Custom KDiffusion Schedule', 'custom_k_sched'),

    ('KDiffusion Scheduler Type', 'k_sched_type'),

    ('KDiffusion Scheduler sigma_max', 'sigma_max'),

    ('KDiffusion Scheduler sigma_min', 'sigma_min'),

    ('KDiffusion Scheduler rho', 'rho'),

    ('Noise multiplier', 'initial_noise_multiplier'),

    ('Eta', 'eta_ancestral'),

    ('Eta DDIM', 'eta_ddim'),

                processed_image.save(os.path.join(output_dir, filename))

def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, enable_k_sched, k_sched_type, sigma_min, sigma_max, rho, *args):

def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):

    override_settings = create_override_settings_dict(override_settings_texts)

    is_batch = mode == 5

        inpaint_full_res_padding=inpaint_full_res_padding,

        inpainting_mask_invert=inpainting_mask_invert,

        override_settings=override_settings,

        enable_custom_k_sched=enable_k_sched,

        k_sched_type=k_sched_type,

        sigma_min=sigma_min,

        sigma_max=sigma_max,

        rho=rho

    )

    p.scripts = modules.scripts.scripts_img2img

    """

    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 = "karras", sigma_min: float=0.1, sigma_max: float=10.0, rho: float=7.0):

    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, enable_custom_k_sched: bool = False, k_sched_type: str = "", sigma_min: float=0.0, sigma_max: float=0.0, rho: float=0.0):

        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 = enable_custom_k_sched

        self.k_sched_type = k_sched_type

        self.sigma_max = sigma_max

        self.sigma_min = sigma_min

        self.rho = rho

        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

    "inpaint",

    "sampler",

    "checkboxes",

    "kdiffusion_scheduler",

    "hires_fix",

    "dimensions",

    "cfg",

    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

    'custom_k_sched': OptionInfo(False, "Enable Custom KDiffusion Scheduler"),

    'k_sched_type':  OptionInfo("karras", "scheduler type", gr.Dropdown, {"choices": ["karras", "exponential", "polyexponential"]}),

    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("the maximum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),

    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("the minimum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),

    'rho':  OptionInfo(7.0, "rho", gr.Number).info("higher will make a more steep noise scheduler (decrease faster). default for karras is 7.0, for polyexponential is 1.0"),

    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),

    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),

    'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),