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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Update SD Upscaler to include user selectable Scale Factor
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parent
0202547696
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91226829f8
@ -17,12 +17,10 @@ class Script(scripts.Script):
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return is_img2img
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def ui(self, is_img2img):
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info = gr.HTML(
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"<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>")
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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>")
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overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
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scale_factor = gr.Slider(minimum=0, maximum=4, step=1, label='Scale Factor', value=2)
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upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],
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value=shared.sd_upscalers[0].name, type="index")
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upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
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return [info, overlap, upscaler_index, scale_factor]
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@ -62,8 +60,7 @@ class Script(scripts.Script):
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batch_count = math.ceil(len(work) / batch_size)
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state.job_count = batch_count * upscale_count
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print(
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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.")
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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.")
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result_images = []
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for n in range(upscale_count):
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@ -87,17 +84,15 @@ class Script(scripts.Script):
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image_index = 0
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for y, h, row in grid.tiles:
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for tiledata in row:
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tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (
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p.width, p.height))
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tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
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image_index += 1
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combined_image = images.combine_grid(grid)
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result_images.append(combined_image)
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if opts.samples_save:
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images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format,
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info=initial_info, p=p)
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images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
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processed = Processed(p, result_images, seed, initial_info)
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return processed
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return processed
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