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import gradio as gr
import re
from PIL import Image
import pathlib
import modules.scripts as scripts
from modules import processing
from modules import images
from modules.processing import process_images, Processed
from modules.shared import state
import modules.shared as shared
from modules.shared import opts
from modules.generation_parameters_copypaste import parse_generation_parameters
from modules.extras import run_pnginfo
# github repository -> https://github.com/thundaga/SD-webui-txt2img-script
def int_convert(text: str) -> int:
return int(text)
def float_convert(text: str) -> float:
return float(text)
def boolean_convert(text: str) -> bool:
return True if (text == "true") else False
def hires_resize(p, parsed_text: dict):
# Fix the issue when the values doesn't exist
# Uses the default value (skip the reset part)
if not ('Hires upscale' in parsed_text or parsed_text['Hires resize-1'] != 0 or parsed_text['Hires resize-2'] != 0):
return p
# Reset hr_settings to avoid wrong settings
p.hr_scale = None
p.hr_resize_x = int(0)
p.hr_resize_y = int(0)
if 'Hires upscale' in parsed_text:
p.hr_scale = float(parsed_text['Hires upscale'])
if 'Hires resize-1' in parsed_text:
p.hr_resize_x = int(parsed_text['Hires resize-1'])
if 'Hires resize-2' in parsed_text:
p.hr_resize_y = int(parsed_text['Hires resize-2'])
return p
def override_settings(p, options: list, parsed_text: dict):
if "Checkpoint" in options and 'Model hash' in parsed_text:
p.override_settings['sd_model_checkpoint'] = parsed_text['Model hash']
if "Clip Skip" in options and 'Clip skip' in parsed_text:
p.override_settings['CLIP_stop_at_last_layers'] = int(parsed_text['Clip skip'])
return p
def width_height(p, parsed_text: dict):
if 'Size-1' in parsed_text:
p.width = int(parsed_text['Size-1'])
if 'Size-2' in parsed_text:
p.height = int(parsed_text['Size-2'])
return p
def prompt_modifications(parsed_text: dict, front_tags: str, back_tags: str, remove_tags: str) -> str:
prompt = parsed_text['Prompt']
if remove_tags:
remove_tags = remove_tags.strip("\n")
tags = [x.strip() for x in remove_tags.split(',')]
while("" in tags):
tags.remove("")
text = prompt
for tag in tags:
text = re.sub("\(\(" + tag + "\)\)|\(" + tag + ":.*?\)|<" + tag + ":.*?>|<" + tag + ">", "", text)
text = re.sub(r'\([^\(]*(%s)\S*\)' % tag, '', text)
text = re.sub(r'\[[^\[]*(%s)\S*\]' % tag, '', text)
text = re.sub(r'<[^<]*(%s)\S*>' % tag, '', text)
text = re.sub(r'\b' + tag + r'\b', '', text)
# remove consecutive comma patterns with a coma and space
pattern = re.compile(r'(,\s){2,}')
text = re.sub(pattern, ', ', text)
# remove final comma at start of prompt
text = text.replace(", ", "", 1)
prompt = text
if front_tags:
if front_tags.endswith(' ') == False and front_tags.endswith(',') == False:
front_tags = front_tags + ','
prompt = ''.join([front_tags, prompt])
if back_tags:
if back_tags.startswith(' ') == False and back_tags.startswith(',') == False:
back_tags = ',' + back_tags
prompt = ''.join([prompt, back_tags])
return prompt
# build valid txt and image files e.g (txt(utf-8),img(png)) into valid parsed dictionaries with metadata info
def build_file_list(file, tab_index: int, file_list: list[dict]) -> list[dict]:
file = file.name if tab_index == 0 else file
file_ext = pathlib.Path(file).suffix
filename = pathlib.Path(file).stem
if file_ext == ".txt":
text = open(file, "r", encoding="utf-8").read()
elif run_pnginfo(Image.open(file))[1] != None:
text = run_pnginfo(Image.open(file))[1]
if text != None and text != "":
parsed_text = parse_generation_parameters(text)
parsed_text["filename"] = filename
file_list.append(parsed_text)
return file_list
# key->(option name) : Values->tuple(metadata name, object property, property specific functions)
prompt_options = {
"Checkpoint": ("Model hash", None, override_settings),
"Prompt": ("Prompt", "prompt", prompt_modifications),
"Negative Prompt": ("Negative prompt", "negative_prompt", None),
"Seed": ("Seed", "seed", float_convert),
"Variation Seed": ("Variation seed", "subseed", float_convert),
"Variation Seed Strength": ("Variation seed strength", "subseed_strength", float_convert),
"Sampler": ("Sampler", "sampler_name", None),
"Steps": ("Steps", "steps", int_convert),
"CFG scale": ("CFG scale", "cfg_scale", float_convert),
"Width and Height": (None, None, width_height),
"Upscaler": ("Hires upscaler", "hr_upscaler", None),
"Denoising Strength": ("Denoising strength", "denoising_strength", float_convert),
"Hires Scale or Width and Height": (None, None, hires_resize),
"Clip Skip": ("Clip skip", None, override_settings),
"Face restoration": ("Face restoration", "restore_faces", boolean_convert),
}
class Script(scripts.Script):
def title(self):
return "Process PNG Metadata Info"
def show(self, is_img2img):
return not is_img2img
# set up ui to drag and drop the processed images and hold their file info
def ui(self, is_img2img):
tab_index = gr.State(value=0)
with gr.Row().style(equal_height=False, variant='compact'):
with gr.Column(variant='compact'):
with gr.Tabs(elem_id="mode_extras"):
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
upload_files = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id=self.elem_id("files"))
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="Add input folder path", elem_id="files_batch_input_dir")
output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Add output folder path or Leave blank to use default path.", elem_id="files_batch_output_dir")
filename_format = gr.Dropdown(label="Output filename format", choices=["Exact same filename as Input file", "Same filename as Input file but with extrat digits", "Standard - Simple digits"], value="Standard - Simple digits", info="The \"Exact same filename\" option might crash or overwrite file(s) if there are multiple files with the same name in the input directory", interactive=True, elem_id="files_batch_filename_type")
# CheckboxGroup with all parameters assignable from the input image (output is a list with the Name of the Checkbox checked ex: ["Checkpoint", "Prompt"])
options = gr.Dropdown(list(prompt_options.keys()), label="Assign from input image", info="Select are assigned from the input, the rest from UI", multiselect = True)
gr.HTML("<p style=\"margin-bottom:0.75em\">Optional tags to remove or add in front/end of a positive prompt on all images</p>")
front_tags = gr.Textbox(label="Tags to add at the front")
back_tags = gr.Textbox(label="Tags to add at the end")
remove_tags = gr.Textbox(label="Tags to remove")
tab_batch.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
tab_batch_dir.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
return [tab_index,upload_files,front_tags,back_tags,remove_tags,input_dir,output_dir,filename_format,options]
# Files are open as images and the png info is set to the processed class for each iterated process
def run(self,p,tab_index,upload_files,front_tags,back_tags,remove_tags,input_dir,output_dir,filename_format,options):
image_batch = []
# Operation based on current batch process tab
if tab_index == 0:
for file in upload_files:
image_batch = build_file_list(file, tab_index, image_batch)
elif tab_index == 1:
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
assert input_dir, 'input directory not selected'
files_dir = shared.listfiles(input_dir)
for file in files_dir:
image_batch = build_file_list(file, tab_index, image_batch)
if tab_index == 1 and output_dir != '':
p.do_not_save_samples = True
image_count = len(image_batch)
state.job_count = image_count
images_list = []
all_prompts = []
infotexts = []
for parsed_text in image_batch:
state.job = f"{state.job_no + 1} out of {state.job_count}"
metadata, p_property, func = 0, 1, 2
# go through dictionary and commit uniform actions on similar object properties
for option, tuple in prompt_options.items():
match option:
case "Prompt":
if option in options and tuple[metadata] in parsed_text:
setattr(p, tuple[p_property], tuple[func](parsed_text,front_tags,back_tags,remove_tags))
case "Width and Height":
if option in options:
p = tuple[func](p, parsed_text)
case "Hires Scale or Width and Height":
if option in options:
p = tuple[func](p, parsed_text)
case "Checkpoint" | "Clip Skip":
p = tuple[func](p, options, parsed_text)
case _:
if option in options and tuple[metadata] in parsed_text:
if tuple[func] == None:
setattr(p, tuple[p_property], parsed_text[tuple[metadata]])
else:
setattr(p, tuple[p_property], tuple[func](parsed_text[tuple[metadata]]))
proc = process_images(p)
# Reset Hires prompts (else the prompts of the first image will be used as Hires prompt for all the others)
p.hr_prompt = ""
p.hr_negative_prompt = ""
# Reset extra_generation_params as it stores the Hires resize and scale (Avoid having wrong info in the infotext)
p.extra_generation_params = {}
# Modified directory to save generated images in cache
if tab_index == 1 and output_dir != '':
match filename_format:
case "Exact same filename as Input file":
basename = ""
forced_filename = parsed_text["filename"]
case "Same filename as Input file but with extrat digits":
basename = parsed_text["filename"]
forced_filename = None
case "Standard - Simple digits":
basename = ""
forced_filename = None
for n, processed_image in enumerate(proc.images):
images.save_image(image=processed_image, path=output_dir, extension=shared.opts.samples_format, basename=basename, forced_filename=forced_filename, existing_info=processed_image.info)
images_list += proc.images
all_prompts += proc.all_prompts
infotexts += proc.infotexts
processing.fix_seed(p)
return Processed(p, images_list, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)
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