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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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Merge remote-tracking branch 'mk2/outpainting-mk2-batch-out'
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AUTOMATIC
commit
a7aa00d46a
@ -172,54 +172,54 @@ class Script(scripts.Script):
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if down > 0:
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down = target_h - init_img.height - up
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init_image = p.init_images[0]
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state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)
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def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
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def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
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is_horiz = is_left or is_right
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is_vert = is_top or is_bottom
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pixels_horiz = expand_pixels if is_horiz else 0
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pixels_vert = expand_pixels if is_vert else 0
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res_w = init.width + pixels_horiz
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res_h = init.height + pixels_vert
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process_res_w = math.ceil(res_w / 64) * 64
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process_res_h = math.ceil(res_h / 64) * 64
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images_to_process = []
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output_images = []
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for n in range(count):
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res_w = init[n].width + pixels_horiz
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res_h = init[n].height + pixels_vert
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process_res_w = math.ceil(res_w / 64) * 64
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process_res_h = math.ceil(res_h / 64) * 64
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img = Image.new("RGB", (process_res_w, process_res_h))
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img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
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mask = Image.new("RGB", (process_res_w, process_res_h), "white")
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draw = ImageDraw.Draw(mask)
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draw.rectangle((
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expand_pixels + mask_blur if is_left else 0,
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expand_pixels + mask_blur if is_top else 0,
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mask.width - expand_pixels - mask_blur if is_right else res_w,
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mask.height - expand_pixels - mask_blur if is_bottom else res_h,
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), fill="black")
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img = Image.new("RGB", (process_res_w, process_res_h))
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img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
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mask = Image.new("RGB", (process_res_w, process_res_h), "white")
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draw = ImageDraw.Draw(mask)
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draw.rectangle((
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expand_pixels + mask_blur if is_left else 0,
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expand_pixels + mask_blur if is_top else 0,
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mask.width - expand_pixels - mask_blur if is_right else res_w,
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mask.height - expand_pixels - mask_blur if is_bottom else res_h,
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), fill="black")
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np_image = (np.asarray(img) / 255.0).astype(np.float64)
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np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
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noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
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out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
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np_image = (np.asarray(img) / 255.0).astype(np.float64)
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np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
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noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
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output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
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target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
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target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
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target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
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target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
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p.width = target_width if is_horiz else img.width
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p.height = target_height if is_vert else img.height
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crop_region = (
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0 if is_left else out.width - target_width,
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0 if is_top else out.height - target_height,
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target_width if is_left else out.width,
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target_height if is_top else out.height,
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)
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crop_region = (
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0 if is_left else output_images[n].width - target_width,
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0 if is_top else output_images[n].height - target_height,
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target_width if is_left else output_images[n].width,
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target_height if is_top else output_images[n].height,
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)
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mask = mask.crop(crop_region)
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p.image_mask = mask
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image_to_process = out.crop(crop_region)
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mask = mask.crop(crop_region)
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image_to_process = output_images[n].crop(crop_region)
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images_to_process.append(image_to_process)
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p.width = target_width if is_horiz else img.width
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p.height = target_height if is_vert else img.height
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p.init_images = [image_to_process]
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p.image_mask = mask
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p.init_images = images_to_process
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latent_mask = Image.new("RGB", (p.width, p.height), "white")
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draw = ImageDraw.Draw(latent_mask)
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@ -232,31 +232,52 @@ class Script(scripts.Script):
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p.latent_mask = latent_mask
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proc = process_images(p)
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proc_img = proc.images[0]
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if initial_seed_and_info[0] is None:
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initial_seed_and_info[0] = proc.seed
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initial_seed_and_info[1] = proc.info
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out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
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out = out.crop((0, 0, res_w, res_h))
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return out
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for n in range(count):
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output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
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output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
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img = init_image
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return output_images
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if left > 0:
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img = expand(img, left, is_left=True)
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if right > 0:
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img = expand(img, right, is_right=True)
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if up > 0:
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img = expand(img, up, is_top=True)
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if down > 0:
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img = expand(img, down, is_bottom=True)
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batch_count = p.n_iter
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batch_size = p.batch_size
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p.n_iter = 1
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state.job_count = batch_count * batch_size * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
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all_processed_images = []
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res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1])
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for i in range(batch_count):
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imgs = [init_img] * batch_size
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state.job = f"Batch {i + 1} out of {batch_count}"
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if left > 0:
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imgs = expand(imgs, batch_size, left, is_left=True)
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if right > 0:
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imgs = expand(imgs, batch_size, right, is_right=True)
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if up > 0:
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imgs = expand(imgs, batch_size, up, is_top=True)
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if down > 0:
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imgs = expand(imgs, batch_size, down, is_bottom=True)
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all_processed_images += imgs
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all_images = all_processed_images
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combined_grid_image = images.image_grid(all_processed_images)
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unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
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if opts.return_grid and not unwanted_grid_because_of_img_count:
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all_images = [combined_grid_image] + all_processed_images
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res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
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if opts.samples_save:
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images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
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for img in all_processed_images:
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images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
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if opts.grid_save and not unwanted_grid_because_of_img_count:
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images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
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return res
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