Merge pull request #7022 from mezotaken/master

        self.confirm = confirm

        self.cost = cost

        self.choices = choices

        self.is_img2img = False

class AxisOptionImg2Img(AxisOption):

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

        super().__init__(*args, **kwargs)

        self.is_img2img = True

class AxisOptionTxt2Img(AxisOption):

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

        super().__init__(*args, **kwargs)

        self.is_img2img = False

    AxisOption("CFG Scale", float, apply_field("cfg_scale")),

    AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),

    AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),

    AxisOption("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),

    AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),

    AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),

    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),

    AxisOption("Sigma Churn", float, apply_field("s_churn")),

    AxisOption("Sigma min", float, apply_field("s_tmin")),

    AxisOption("Eta", float, apply_field("eta")),

    AxisOption("Clip skip", int, apply_clip_skip),

    AxisOption("Denoising", float, apply_field("denoising_strength")),

    AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [x.name for x in shared.sd_upscalers]),

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

    AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),

    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)),

]

        return "X/Y plot"

    def ui(self, is_img2img):

        current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img and is_img2img]

        current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]

        with gr.Row():

            with gr.Column(scale=19):

        swap_axes_button.click(swap_axes, inputs=swap_args, outputs=swap_args)

        def fill(x_type):

            axis = axis_options[x_type]

            axis = current_axis_options[x_type]

            return ", ".join(axis.choices()) if axis.choices else gr.update()

        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])

        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])

        def select_axis(x_type):

            return gr.Button.update(visible=axis_options[x_type].choices is not None)

            return gr.Button.update(visible=current_axis_options[x_type].choices is not None)

        x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])

        y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])