230 lines
10 KiB
Python
230 lines
10 KiB
Python
import os
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from contextlib import closing
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from pathlib import Path
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import numpy as np
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from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
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import gradio as gr
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from modules import images as imgutil
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from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
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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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from modules.sd_models import get_closet_checkpoint_match
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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.scripts
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def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
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output_dir = output_dir.strip()
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processing.fix_seed(p)
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images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
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is_inpaint_batch = False
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if inpaint_mask_dir:
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inpaint_masks = shared.listfiles(inpaint_mask_dir)
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is_inpaint_batch = bool(inpaint_masks)
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if is_inpaint_batch:
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print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
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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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state.job_count = len(images) * p.n_iter
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# extract "default" params to use in case getting png info fails
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prompt = p.prompt
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negative_prompt = p.negative_prompt
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seed = p.seed
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cfg_scale = p.cfg_scale
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sampler_name = p.sampler_name
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steps = p.steps
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override_settings = p.override_settings
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sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
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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.skipped:
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state.skipped = False
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if state.interrupted:
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break
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try:
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img = Image.open(image)
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except UnidentifiedImageError as e:
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print(e)
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continue
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# Use the EXIF orientation of photos taken by smartphones.
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img = ImageOps.exif_transpose(img)
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if to_scale:
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p.width = int(img.width * scale_by)
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p.height = int(img.height * scale_by)
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p.init_images = [img] * p.batch_size
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image_path = Path(image)
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if is_inpaint_batch:
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# try to find corresponding mask for an image using simple filename matching
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if len(inpaint_masks) == 1:
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mask_image_path = inpaint_masks[0]
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else:
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# try to find corresponding mask for an image using simple filename matching
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mask_image_dir = Path(inpaint_mask_dir)
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masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
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if len(masks_found) == 0:
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print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
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continue
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# it should contain only 1 matching mask
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# otherwise user has many masks with the same name but different extensions
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mask_image_path = masks_found[0]
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mask_image = Image.open(mask_image_path)
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p.image_mask = mask_image
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if use_png_info:
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try:
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info_img = img
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if png_info_dir:
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info_img_path = os.path.join(png_info_dir, os.path.basename(image))
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info_img = Image.open(info_img_path)
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geninfo, _ = imgutil.read_info_from_image(info_img)
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parsed_parameters = parse_generation_parameters(geninfo)
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parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
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except Exception:
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parsed_parameters = {}
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p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
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p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
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p.seed = int(parsed_parameters.get("Seed", seed))
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p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
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p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
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p.steps = int(parsed_parameters.get("Steps", steps))
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model_info = get_closet_checkpoint_match(parsed_parameters.get("Model hash", None))
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if model_info is not None:
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p.override_settings['sd_model_checkpoint'] = model_info.name
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elif sd_model_checkpoint_override:
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p.override_settings['sd_model_checkpoint'] = sd_model_checkpoint_override
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else:
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p.override_settings.pop("sd_model_checkpoint", None)
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proc = modules.scripts.scripts_img2img.run(p, *args)
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if proc is None:
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if output_dir:
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p.outpath_samples = output_dir
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p.override_settings['save_to_dirs'] = False
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if p.n_iter > 1 or p.batch_size > 1:
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p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
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else:
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p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
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process_images(p)
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def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, 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, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
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override_settings = create_override_settings_dict(override_settings_texts)
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is_batch = mode == 5
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if mode == 0: # img2img
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image = init_img
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mask = None
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elif mode == 1: # img2img sketch
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image = sketch
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mask = None
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elif mode == 2: # inpaint
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image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
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mask = processing.create_binary_mask(mask)
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elif mode == 3: # inpaint sketch
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image = inpaint_color_sketch
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orig = inpaint_color_sketch_orig or inpaint_color_sketch
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pred = np.any(np.array(image) != np.array(orig), axis=-1)
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mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
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mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
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blur = ImageFilter.GaussianBlur(mask_blur)
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image = Image.composite(image.filter(blur), orig, mask.filter(blur))
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elif mode == 4: # inpaint upload mask
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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 = None
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mask = None
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# Use the EXIF orientation of photos taken by smartphones.
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if image is not None:
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image = ImageOps.exif_transpose(image)
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if selected_scale_tab == 1 and not is_batch:
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assert image, "Can't scale by because no image is selected"
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width = int(image.width * scale_by)
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height = int(image.height * scale_by)
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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_styles,
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sampler_name=sampler_name,
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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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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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image_cfg_scale=image_cfg_scale,
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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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override_settings=override_settings,
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)
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p.scripts = modules.scripts.scripts_img2img
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p.script_args = args
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p.user = request.username
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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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if mask:
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p.extra_generation_params["Mask blur"] = mask_blur
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with closing(p):
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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, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
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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), plaintext_to_html(processed.comments, classname="comments")
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