renamed Inpainting strength infotext to Conditional mask weight, made it only...

        'sd_hypernetwork': 'Hypernet',

        'sd_hypernetwork_strength': 'Hypernet strength',

        'CLIP_stop_at_last_layers': 'Clip skip',

        'inpainting_mask_weight': 'Conditional mask weight',

        'sd_model_checkpoint': 'Model hash',

    }

    settings_paste_fields = [

        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.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}

        self.is_using_inpainting_conditioning = False

        if not seed_enable_extras:

            self.subseed = -1

            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

            return x.new_zeros(x.shape[0], 5, 1, 1)

        self.is_using_inpainting_conditioning = True

        height = height or self.height

        width = width or self.width

            # Dummy zero conditioning if we're not using inpainting model.

            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)

        self.is_using_inpainting_conditioning = True

        # Handle the different mask inputs

        if image_mask is not None:

            if torch.is_tensor(image_mask):

        self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]

        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1

        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1

        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning

        self.all_prompts = all_prompts or [self.prompt]

        self.all_seeds = all_seeds or [self.seed]

            "styles": self.styles,

            "job_timestamp": self.job_timestamp,

            "clip_skip": self.clip_skip,

            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,

        }

        return json.dumps(obj)

        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

        "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

        "Denoising strength": getattr(p, 'denoising_strength', None),

        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)),

        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,

        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

        "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,