Add upscaler to img2img

        res["Hires resize-1"] = 0

        res["Hires resize-2"] = 0

    if "Img2Img Upscale" not in res:

        res["Img2Img Upscale"] = 1

    if "Img2Img upscale" not in res:

        res["Img2Img upscale"] = 1

    restore_old_hires_fix_params(res)

                processed_image.save(os.path.join(output_dir, filename))

def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, scale: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):

def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, scale: float, upscaler: str, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):

    override_settings = create_override_settings_dict(override_settings_texts)

    is_batch = mode == 5

        inpainting_mask_invert=inpainting_mask_invert,

        override_settings=override_settings,

        scale=scale,

        upscaler=upscaler,

    )

    p.scripts = modules.scripts.scripts_txt2img

class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

    sampler = None

    def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs):

    def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs):

        super().__init__(**kwargs)

        self.init_images = init_images

        self.nmask = None

        self.image_conditioning = None

        self.scale = scale

        self.upscaler = upscaler

    def get_final_size(self):

        if self.scale > 1:

        crop_region = None

        if self.scale > 1:

            self.extra_generation_params["Img2Img Upscale"] = self.scale

            self.extra_generation_params["Img2Img upscale"] = self.scale

        # Non-latent upscalers are run before sampling

        # Latent upscalers are run during sampling

        init_upscaler = None

        if self.upscaler is not None:

            self.extra_generation_params["Img2Img upscaler"] = self.upscaler

            if self.upscaler not in shared.latent_upscale_modes:

                assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}"

                init_upscaler = self.upscaler

        self.width, self.height = self.get_final_size()

                image_mask = images.resize_image(2, mask, self.width, self.height)

                self.paste_to = (x1, y1, x2-x1, y2-y1)

            else:

                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)

                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler)

                np_mask = np.array(image_mask)

                np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)

                self.mask_for_overlay = Image.fromarray(np_mask)

            image = images.flatten(img, opts.img2img_background_color)

            if crop_region is None and self.resize_mode != 3:

                image = images.resize_image(self.resize_mode, image, self.width, self.height)

                image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler)

            if image_mask is not None:

                image_masked = Image.new('RGBa', (image.width, image.height))

        self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))

        if self.resize_mode == 3:

            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

        latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")

        if latent_scale_mode is not None:

            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])

        if image_mask is not None:

            init_mask = latent_mask

                    )

                with FormRow():

                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")

                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")

                for category in ordered_ui_categories():

                    if category == "sampler":

                            with FormRow():

                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")

                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")

                            with FormRow():

                                upscaler = gr.Dropdown(label="Upscaler", elem_id="img2img_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)

                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")

                    elif category == "seed":

                    height,

                    width,

                    scale,

                    upscaler,

                    resize_mode,

                    inpaint_full_res,

                    inpaint_full_res_padding,

                (seed, "Seed"),

                (width, "Size-1"),

                (height, "Size-2"),

                (scale, "Img2Img Upscale"),

                (scale, "Img2Img upscale"),

                (upscaler, "Img2Img upscaler"),

                (batch_size, "Batch size"),

                (subseed, "Variation seed"),

                (subseed_strength, "Variation seed strength"),

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

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

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

    AxisOptionImg2Img("Upscaler", str, apply_field("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)),