stable-diffusion-webui/modules/postprocessing.py
w-e-w 9d39380705 fix extra batch mode P Transparency
red, green, blue = transparency TypeError: cannot unpack non-iterable int object
2024-04-30 19:17:53 +09:00

164 lines
6.4 KiB
Python

import os
from PIL import Image
from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils
from modules.shared import opts
def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
devices.torch_gc()
shared.state.begin(job="extras")
outputs = []
def get_images(extras_mode, image, image_folder, input_dir):
if extras_mode == 1:
for img in image_folder:
if isinstance(img, Image.Image):
image = images.fix_image(img)
fn = ''
else:
image = images.read(os.path.abspath(img.name))
fn = os.path.splitext(img.orig_name)[0]
yield image, fn
elif extras_mode == 2:
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
assert input_dir, 'input directory not selected'
image_list = shared.listfiles(input_dir)
for filename in image_list:
yield filename, filename
else:
assert image, 'image not selected'
yield image, None
if extras_mode == 2 and output_dir != '':
outpath = output_dir
else:
outpath = opts.outdir_samples or opts.outdir_extras_samples
infotext = ''
data_to_process = list(get_images(extras_mode, image, image_folder, input_dir))
shared.state.job_count = len(data_to_process)
for image_placeholder, name in data_to_process:
image_data: Image.Image
shared.state.nextjob()
shared.state.textinfo = name
shared.state.skipped = False
if shared.state.interrupted:
break
if isinstance(image_placeholder, str):
try:
image_data = images.read(image_placeholder)
except Exception:
continue
else:
image_data = image_placeholder
image_data = image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB")
parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
initial_pp = scripts_postprocessing.PostprocessedImage(image_data)
scripts.scripts_postproc.run(initial_pp, args)
if shared.state.skipped:
continue
used_suffixes = {}
for pp in [initial_pp, *initial_pp.extra_images]:
suffix = pp.get_suffix(used_suffixes)
if opts.use_original_name_batch and name is not None:
basename = os.path.splitext(os.path.basename(name))[0]
forced_filename = basename + suffix
else:
basename = ''
forced_filename = None
infotext = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in pp.info.items() if v is not None])
if opts.enable_pnginfo:
pp.image.info = existing_pnginfo
pp.image.info["postprocessing"] = infotext
shared.state.assign_current_image(pp.image)
if save_output:
fullfn, _ = images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename, suffix=suffix)
if pp.caption:
caption_filename = os.path.splitext(fullfn)[0] + ".txt"
existing_caption = ""
try:
with open(caption_filename, encoding="utf8") as file:
existing_caption = file.read().strip()
except FileNotFoundError:
pass
action = shared.opts.postprocessing_existing_caption_action
if action == 'Prepend' and existing_caption:
caption = f"{existing_caption} {pp.caption}"
elif action == 'Append' and existing_caption:
caption = f"{pp.caption} {existing_caption}"
elif action == 'Keep' and existing_caption:
caption = existing_caption
else:
caption = pp.caption
caption = caption.strip()
if caption:
with open(caption_filename, "w", encoding="utf8") as file:
file.write(caption)
if extras_mode != 2 or show_extras_results:
outputs.append(pp.image)
devices.torch_gc()
shared.state.end()
return outputs, ui_common.plaintext_to_html(infotext), ''
def run_postprocessing_webui(id_task, *args, **kwargs):
return run_postprocessing(*args, **kwargs)
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True, max_side_length: int = 0):
"""old handler for API"""
args = scripts.scripts_postproc.create_args_for_run({
"Upscale": {
"upscale_enabled": True,
"upscale_mode": resize_mode,
"upscale_by": upscaling_resize,
"max_side_length": max_side_length,
"upscale_to_width": upscaling_resize_w,
"upscale_to_height": upscaling_resize_h,
"upscale_crop": upscaling_crop,
"upscaler_1_name": extras_upscaler_1,
"upscaler_2_name": extras_upscaler_2,
"upscaler_2_visibility": extras_upscaler_2_visibility,
},
"GFPGAN": {
"enable": True,
"gfpgan_visibility": gfpgan_visibility,
},
"CodeFormer": {
"enable": True,
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
},
})
return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)