REMOVE

        img2img_sampler_name = self.hr_sampler_name or self.sampler_name

        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM

            img2img_sampler_name = 'DDIM'

        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)

        if self.latent_scale_mode is not None:

from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet

from modules.hypernetworks import hypernetwork

from modules.shared import cmd_opts

from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, sd_hijack_inpainting

from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr

import ldm.modules.attention

import ldm.modules.diffusionmodules.model

ldm.util.print = shared.ldm_print

ldm.models.diffusion.ddpm.print = shared.ldm_print

sd_hijack_inpainting.do_inpainting_hijack()

optimizers = []

current_optimizer: sd_hijack_optimizations.SdOptimization = None

import torch

import ldm.models.diffusion.ddpm

import ldm.models.diffusion.ddim

import ldm.models.diffusion.plms

from ldm.models.diffusion.ddim import noise_like

from ldm.models.diffusion.sampling_util import norm_thresholding

@torch.no_grad()

def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,

                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,

                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):

    b, *_, device = *x.shape, x.device

    def get_model_output(x, t):

        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:

            e_t = self.model.apply_model(x, t, c)

        else:

            x_in = torch.cat([x] * 2)

            t_in = torch.cat([t] * 2)

            if isinstance(c, dict):

                assert isinstance(unconditional_conditioning, dict)

                c_in = {}

                for k in c:

                    if isinstance(c[k], list):

                        c_in[k] = [

                            torch.cat([unconditional_conditioning[k][i], c[k][i]])

                            for i in range(len(c[k]))

                        ]

                    else:

                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])

            else:

                c_in = torch.cat([unconditional_conditioning, c])

            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)

            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

        if score_corrector is not None:

            assert self.model.parameterization == "eps"

            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)

        return e_t

    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas

    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev

    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas

    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas

    def get_x_prev_and_pred_x0(e_t, index):

        # select parameters corresponding to the currently considered timestep

        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)

        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)

        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)

        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)

        # current prediction for x_0

        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()

        if quantize_denoised:

            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)

        if dynamic_threshold is not None:

            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)

        # direction pointing to x_t

        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t

        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature

        if noise_dropout > 0.:

            noise = torch.nn.functional.dropout(noise, p=noise_dropout)

        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise

        return x_prev, pred_x0

    e_t = get_model_output(x, t)

    if len(old_eps) == 0:

        # Pseudo Improved Euler (2nd order)

        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)

        e_t_next = get_model_output(x_prev, t_next)

        e_t_prime = (e_t + e_t_next) / 2

    elif len(old_eps) == 1:

        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)

        e_t_prime = (3 * e_t - old_eps[-1]) / 2

    elif len(old_eps) == 2:

        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)

        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12

    elif len(old_eps) >= 3:

        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)

        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24

    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

    return x_prev, pred_x0, e_t

def do_inpainting_hijack():

    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms

from modules import sd_samplers_compvis, sd_samplers_kdiffusion, sd_samplers_timesteps, shared

from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared

# imports for functions that previously were here and are used by other modules

from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  # noqa: F401

all_samplers = [

    *sd_samplers_kdiffusion.samplers_data_k_diffusion,

    *sd_samplers_compvis.samplers_data_compvis,

    *sd_samplers_timesteps.samplers_data_timesteps,

]

all_samplers_map = {x.name: x for x in all_samplers}

    global samplers, samplers_for_img2img

    hidden = set(shared.opts.hide_samplers)

    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])

    samplers = [x for x in all_samplers if x.name not in hidden]

    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]

    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden]

    samplers_map.clear()

    for sampler in all_samplers:

from collections import deque

import torch

from modules import prompt_parser, devices, sd_samplers_common