Add UniPC sampler settings

import torch

from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC

from modules import shared

class UniPCSampler(object):

    def __init__(self, model, **kwargs):

            guidance_scale=unconditional_guidance_scale,

        )

        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)

        x = uni_pc.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=3, lower_order_final=True)

        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=shared.opts.uni_pc_thresholding, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)

        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)

        return x.to(device), None

        if method == 'multistep':

            assert steps >= order, "UniPC order must be < sampling steps"

            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)

            print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps")

            print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")

            assert timesteps.shape[0] - 1 == steps

            with torch.no_grad():

                vec_t = timesteps[0].expand((x.shape[0]))

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

    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),

    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),

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

    'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),

    'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 150 - 1, "step": 1}),

    'uni_pc_thresholding': OptionInfo(False, "UniPC thresholding"),

    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),

}))

options_templates.update(options_section(('postprocessing', "Postprocessing"), {

    p.styles.extend(x.split(','))

def apply_uni_pc_order(p, x, xs):

    opts.data["uni_pc_order"] = min(x, p.steps - 1)

def format_value_add_label(p, opt, x):

    if type(x) == float:

        x = round(x, 8)

    AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),

    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),

    AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),

    AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),

]

    def __enter__(self):

        self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers

        self.vae = opts.sd_vae

        self.uni_pc_order = opts.uni_pc_order

    def __exit__(self, exc_type, exc_value, tb):

        opts.data["sd_vae"] = self.vae

        opts.data["uni_pc_order"] = self.uni_pc_order

        modules.sd_models.reload_model_weights()

        modules.sd_vae.reload_vae_weights()