rework Negative Guidance minimum sigma to work with AND, add infotext and...

    "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",

    "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."

    "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."

}

    ('UniPC order', 'uni_pc_order'),

    ('UniPC lower order final', 'uni_pc_lower_order_final'),

    ('RNG', 'randn_source'),

    ('NGMS', 's_min_uncond'),

]

        "Clip skip": None if clip_skip <= 1 else clip_skip,

        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,

        "Init image hash": getattr(p, 'init_img_hash', None),

        "RNG": (opts.randn_source if opts.randn_source != "GPU" else None)

        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,

        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,

    }

    generation_params.update(p.extra_generation_params)

        sigma_in = denoiser_params.sigma

        tensor = denoiser_params.text_cond

        uncond = denoiser_params.text_uncond

        skip_uncond = False

        if self.step % 2 and s_min_uncond > 0 and not is_edit_model:

        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it

            sigma_threshold = s_min_uncond

            if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):

                uncond = torch.zeros([0,0,uncond.shape[2]])

                x_in=x_in[:x_in.shape[0]//2]

                sigma_in=sigma_in[:sigma_in.shape[0]//2]

        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:

            skip_uncond = True

            x_in = x_in[:-batch_size]

            sigma_in = sigma_in[:-batch_size]

        if tensor.shape[1] == uncond.shape[1]:

            if not is_edit_model:

                cond_in = torch.cat([tensor, uncond])

            else:

        if tensor.shape[1] == uncond.shape[1] or skip_uncond:

            if is_edit_model:

                cond_in = torch.cat([tensor, uncond, uncond])

            elif skip_uncond:

                cond_in = tensor

            else:

                cond_in = torch.cat([tensor, uncond])

            if shared.batch_cond_uncond:

                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))

                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))

            if uncond.shape[0]:

            if not skip_uncond:

                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))

        if skip_uncond:

            #x_out = torch.cat([x_out, x_out[0:batch_size]])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be

            denoised_image_indexes = [x[0][0] for x in conds_list]

            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])

            x_out = torch.cat([x_out, fake_uncond])

        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)

        cfg_denoised_callback(denoised_params)

        elif opts.live_preview_content == "Negative prompt":

            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])

        if not is_edit_model:

            if uncond.shape[0]:

                denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)

            else:

                denoised = x_out

        else:

        if is_edit_model:

            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)

        elif skip_uncond:

            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)

        else:

            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)

        if self.mask is not None:

            denoised = self.init_latent * self.mask + self.nmask * denoised

        self.eta = None

        self.config = None

        self.last_latent = None

        self.s_min_uncond = None

        self.conditioning_key = sd_model.model.conditioning_key