Update SD Upscaler to include user selectable Scale Factor

        return is_img2img

    def ui(self, is_img2img):

        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")

        info = gr.HTML(

            "<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")

        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)

        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

        scale_factor = gr.Slider(minimum=0, maximum=4, step=1, label='Scale Factor', value=2)

        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],

                                  value=shared.sd_upscalers[0].name, type="index")

        return [info, overlap, upscaler_index]

        return [info, overlap, upscaler_index, scale_factor]

    def run(self, p, _, overlap, upscaler_index):

    def run(self, p, _, overlap, upscaler_index, scale_factor):

        processing.fix_seed(p)

        upscaler = shared.sd_upscalers[upscaler_index]

        init_img = p.init_images[0]

        if (upscaler.name != "None"):

            img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)

            img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)

        else:

            img = init_img

        batch_count = math.ceil(len(work) / batch_size)

        state.job_count = batch_count * upscale_count

        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")

        print(

            f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")

        result_images = []

        for n in range(upscale_count):

            image_index = 0

            for y, h, row in grid.tiles:

                for tiledata in row:

                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))

                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (

                    p.width, p.height))

                    image_index += 1

            combined_image = images.combine_grid(grid)

            result_images.append(combined_image)

            if opts.samples_save:

                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)

                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format,

                                  info=initial_info, p=p)

        processed = Processed(p, result_images, seed, initial_info)