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import os
from contextlib import closing
from pathlib import Path
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
import gradio as gr
from modules import images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
import modules.scripts
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):
output_dir = output_dir.strip()
processing.fix_seed(p)
images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
is_inpaint_batch = False
if inpaint_mask_dir:
inpaint_masks = shared.listfiles(inpaint_mask_dir)
is_inpaint_batch = bool(inpaint_masks)
if is_inpaint_batch:
print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
state.job_count = len(images) * p.n_iter
# extract "default" params to use in case getting png info fails
prompt = p.prompt
negative_prompt = p.negative_prompt
seed = p.seed
cfg_scale = p.cfg_scale
sampler_name = p.sampler_name
steps = p.steps
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
state.skipped = False
if state.interrupted:
break
try:
img = Image.open(image)
except UnidentifiedImageError as e:
print(e)
continue
# Use the EXIF orientation of photos taken by smartphones.
img = ImageOps.exif_transpose(img)
if to_scale:
p.width = int(img.width * scale_by)
p.height = int(img.height * scale_by)
p.init_images = [img] * p.batch_size
image_path = Path(image)
if is_inpaint_batch:
# try to find corresponding mask for an image using simple filename matching
if len(inpaint_masks) == 1:
mask_image_path = inpaint_masks[0]
else:
# try to find corresponding mask for an image using simple filename matching
mask_image_dir = Path(inpaint_mask_dir)
masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
if len(masks_found) == 0:
print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
continue
# it should contain only 1 matching mask
# otherwise user has many masks with the same name but different extensions
mask_image_path = masks_found[0]
mask_image = Image.open(mask_image_path)
p.image_mask = mask_image
if use_png_info:
try:
info_img = img
if png_info_dir:
info_img_path = os.path.join(png_info_dir, os.path.basename(image))
info_img = Image.open(info_img_path)
geninfo, _ = imgutil.read_info_from_image(info_img)
parsed_parameters = parse_generation_parameters(geninfo)
parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
except Exception:
parsed_parameters = {}
p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
p.seed = int(parsed_parameters.get("Seed", seed))
p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
p.steps = int(parsed_parameters.get("Steps", steps))
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
if output_dir:
p.outpath_samples = output_dir
p.override_settings['save_to_dirs'] = False
if p.n_iter > 1 or p.batch_size > 1:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
else:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
process_images(p)
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):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
if mode == 0: # img2img
image = init_img
mask = None
elif mode == 1: # img2img sketch
image = sketch
mask = None
elif mode == 2: # inpaint
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
mask = processing.create_binary_mask(mask)
elif mode == 3: # inpaint sketch
image = inpaint_color_sketch
orig = inpaint_color_sketch_orig or inpaint_color_sketch
pred = np.any(np.array(image) != np.array(orig), axis=-1)
mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
blur = ImageFilter.GaussianBlur(mask_blur)
image = Image.composite(image.filter(blur), orig, mask.filter(blur))
elif mode == 4: # inpaint upload mask
image = init_img_inpaint
mask = init_mask_inpaint
else:
image = None
mask = None
# Use the EXIF orientation of photos taken by smartphones.
if image is not None:
image = ImageOps.exif_transpose(image)
if selected_scale_tab == 1 and not is_batch:
assert image, "Can't scale by because no image is selected"
width = int(image.width * scale_by)
height = int(image.height * scale_by)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
prompt=prompt,
negative_prompt=negative_prompt,
styles=prompt_styles,
sampler_name=sampler_name,
batch_size=batch_size,
n_iter=n_iter,
steps=steps,
cfg_scale=cfg_scale,
width=width,
height=height,
init_images=[image],
mask=mask,
mask_blur=mask_blur,
inpainting_fill=inpainting_fill,
resize_mode=resize_mode,
denoising_strength=denoising_strength,
image_cfg_scale=image_cfg_scale,
inpaint_full_res=inpaint_full_res,
inpaint_full_res_padding=inpaint_full_res_padding,
inpainting_mask_invert=inpainting_mask_invert,
override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_img2img
p.script_args = args
p.user = request.username
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
if mask:
p.extra_generation_params["Mask blur"] = mask_blur
with closing(p):
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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)
processed = Processed(p, [], p.seed, "")
else:
processed = modules.scripts.scripts_img2img.run(p, *args)
if processed is None:
processed = process_images(p)
shared.total_tqdm.clear()
generation_info_js = processed.js()
if opts.samples_log_stdout:
print(generation_info_js)
if opts.do_not_show_images:
processed.images = []
return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
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