improve code quality

    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),

    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),

]

samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']

samplers_for_img2img.remove(samplers_for_img2img[6])

samplers_for_img2img.remove(samplers_for_img2img[6])

samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]

sampler_extra_params = {

    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

        extra_params_kwargs = self.initialize(p)

        if 'sigma_min' in inspect.signature(self.func).parameters:

            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()

            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()

            if 'n' in inspect.signature(self.func).parameters:

                samples = self.func(self.model_wrap_cfg, x, sigma_min=self.model_wrap.sigmas[0].item(), sigma_max=self.model_wrap.sigmas[-1].item(), n=steps, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

                return samples

            samples = self.func(self.model_wrap_cfg, x, sigma_min=self.model_wrap.sigmas[0].item(), sigma_max=self.model_wrap.sigmas[-1].item(), extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

            return samples

        samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

                extra_params_kwargs['n'] = steps

        else:

            extra_params_kwargs['sigmas'] = sigmas

        samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)

        return samples