136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
import math
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import os
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import sys
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import traceback
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import numpy as np
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from PIL import Image, ImageOps, ImageChops
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from modules import devices
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, state
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import modules.shared as shared
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import modules.processing as processing
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from modules.ui import plaintext_to_html
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import modules.images as images
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import modules.scripts
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def process_batch(p, input_dir, output_dir, args):
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processing.fix_seed(p)
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images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
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print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
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save_normally = output_dir == ''
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p.do_not_save_grid = True
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p.do_not_save_samples = not save_normally
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state.job_count = len(images) * p.n_iter
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for i, image in enumerate(images):
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state.job = f"{i+1} out of {len(images)}"
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if state.interrupted:
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break
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img = Image.open(image)
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p.init_images = [img] * p.batch_size
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proc = modules.scripts.scripts_img2img.run(p, *args)
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if proc is None:
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proc = process_images(p)
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for n, processed_image in enumerate(proc.images):
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filename = os.path.basename(image)
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if n > 0:
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left, right = os.path.splitext(filename)
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filename = f"{left}-{n}{right}"
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if not save_normally:
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processed_image.save(os.path.join(output_dir, filename))
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def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, 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, 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, *args):
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is_inpaint = mode == 1
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is_batch = mode == 2
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if is_inpaint:
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if mask_mode == 0:
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image = init_img_with_mask['image']
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mask = init_img_with_mask['mask']
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alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
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mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
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image = image.convert('RGB')
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else:
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image = init_img_inpaint
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mask = init_mask_inpaint
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else:
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image = init_img
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mask = None
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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p = StableDiffusionProcessingImg2Img(
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sd_model=shared.sd_model,
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outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
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outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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styles=[prompt_style, prompt_style2],
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seed=seed,
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subseed=subseed,
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subseed_strength=subseed_strength,
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seed_resize_from_h=seed_resize_from_h,
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seed_resize_from_w=seed_resize_from_w,
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seed_enable_extras=seed_enable_extras,
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sampler_index=sampler_index,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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restore_faces=restore_faces,
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tiling=tiling,
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init_images=[image],
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mask=mask,
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mask_blur=mask_blur,
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inpainting_fill=inpainting_fill,
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resize_mode=resize_mode,
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denoising_strength=denoising_strength,
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inpaint_full_res=inpaint_full_res,
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inpaint_full_res_padding=inpaint_full_res_padding,
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inpainting_mask_invert=inpainting_mask_invert,
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)
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if shared.cmd_opts.enable_console_prompts:
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print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
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p.extra_generation_params["Mask blur"] = mask_blur
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if is_batch:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, args)
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processed = Processed(p, [], p.seed, "")
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else:
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processed = modules.scripts.scripts_img2img.run(p, *args)
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if processed is None:
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processed = process_images(p)
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shared.total_tqdm.clear()
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generation_info_js = processed.js()
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if opts.samples_log_stdout:
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print(generation_info_js)
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if opts.do_not_show_images:
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processed.images = []
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return processed.images, generation_info_js, plaintext_to_html(processed.info)
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