130 lines
4.9 KiB
Python
130 lines
4.9 KiB
Python
import math
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import modules.scripts as scripts
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import gradio as gr
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from PIL import Image, ImageDraw
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from modules import images, processing
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from modules.processing import Processed, process_images
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from modules.shared import opts, cmd_opts, state
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class Script(scripts.Script):
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def title(self):
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return "Poor man's outpainting"
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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if not is_img2img:
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return None
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pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=128, step=8)
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mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
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inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
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direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
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return [pixels, mask_blur, inpainting_fill, direction]
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def run(self, p, pixels, mask_blur, inpainting_fill, direction):
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initial_seed = None
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initial_info = None
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p.mask_blur = mask_blur
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p.inpainting_fill = inpainting_fill
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p.inpaint_full_res = False
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left = pixels if "left" in direction else 0
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right = pixels if "right" in direction else 0
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up = pixels if "up" in direction else 0
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down = pixels if "down" in direction else 0
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init_img = p.init_images[0]
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target_w = math.ceil((init_img.width + left + right) / 64) * 64
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target_h = math.ceil((init_img.height + up + down) / 64) * 64
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if left > 0:
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left = left * (target_w - init_img.width) // (left + right)
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right = target_w - init_img.width - left
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if up > 0:
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up = up * (target_h - init_img.height) // (up + down)
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down = target_h - init_img.height - up
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img = Image.new("RGB", (target_w, target_h))
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img.paste(init_img, (left, up))
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mask = Image.new("L", (img.width, img.height), "white")
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draw = ImageDraw.Draw(mask)
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draw.rectangle((
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left + (mask_blur * 2 if left > 0 else 0),
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up + (mask_blur * 2 if up > 0 else 0),
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mask.width - right - (mask_blur * 2 if right > 0 else 0),
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mask.height - down - (mask_blur * 2 if down > 0 else 0)
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), fill="black")
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latent_mask = Image.new("L", (img.width, img.height), "white")
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latent_draw = ImageDraw.Draw(latent_mask)
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latent_draw.rectangle((left + left//2, up + up//2, mask.width - right - right//2, mask.height - down - down//2), fill="black")
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processing.torch_gc()
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grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
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grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
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grid_latent_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
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p.n_iter = 1
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p.batch_size = 1
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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work = []
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work_mask = []
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work_latent_mask = []
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work_results = []
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for (_, _, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
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for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
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work.append(tiledata[2])
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work_mask.append(tiledata_mask[2])
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work_latent_mask.append(tiledata_latent_mask[2])
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batch_count = len(work)
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print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
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state.job_count = batch_count
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for i in range(batch_count):
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p.init_images = [work[i]]
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p.image_mask = work_mask[i]
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p.latent_mask = work_latent_mask[i]
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state.job = f"Batch {i + 1} out of {batch_count}"
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processed = process_images(p)
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if initial_seed is None:
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initial_seed = processed.seed
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initial_info = processed.info
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p.seed = processed.seed + 1
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work_results += processed.images
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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", (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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if opts.samples_save:
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images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info)
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processed = Processed(p, [combined_image], initial_seed, initial_info)
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return processed
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