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import json
from contextlib import closing
import modules.scripts
from modules import processing, infotext_utils
from modules.infotext_utils import create_override_settings_dict
from modules.shared import opts
import modules.shared as shared
from modules.ui import plaintext_to_html
from PIL import Image
import gradio as gr
def txt2img_create_processing(id_task: str, request: gr.Request, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args, force_enable_hr=False):
override_settings = create_override_settings_dict(override_settings_texts)
if force_enable_hr:
enable_hr = True
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
prompt=prompt,
styles=prompt_styles,
negative_prompt=negative_prompt,
sampler_name=sampler_name,
batch_size=batch_size,
n_iter=n_iter,
steps=steps,
cfg_scale=cfg_scale,
width=width,
height=height,
enable_hr=enable_hr,
denoising_strength=denoising_strength,
hr_scale=hr_scale,
hr_upscaler=hr_upscaler,
hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name,
hr_sampler_name=None if hr_sampler_name == 'Use same sampler' else hr_sampler_name,
hr_prompt=hr_prompt,
hr_negative_prompt=hr_negative_prompt,
override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
p.user = request.username
if shared.opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
return p
def txt2img_upscale(id_task: str, request: gr.Request, gallery, gallery_index, generation_info, *args):
assert len(gallery) > 0, 'No image to upscale'
assert 0 <= gallery_index < len(gallery), f'Bad image index: {gallery_index}'
p = txt2img_create_processing(id_task, request, *args)
p.enable_hr = True
p.batch_size = 1
p.n_iter = 1
geninfo = json.loads(generation_info)
all_seeds = geninfo["all_seeds"]
image_info = gallery[gallery_index] if 0 <= gallery_index < len(gallery) else gallery[0]
p.firstpass_image = infotext_utils.image_from_url_text(image_info)
gallery_index_from_end = len(gallery) - gallery_index
seed = all_seeds[-gallery_index_from_end if gallery_index_from_end < len(all_seeds) + 1 else 0]
p.script_args = modules.scripts.scripts_txt2img.set_named_arg(p.script_args, 'ScriptSeed', 'seed', seed)
with closing(p):
processed = modules.scripts.scripts_txt2img.run(p, *p.script_args)
if processed is None:
processed = processing.process_images(p)
shared.total_tqdm.clear()
new_gallery = []
for i, image in enumerate(gallery):
fake_image = Image.new(mode="RGB", size=(1, 1))
if i == gallery_index:
already_saved_as = getattr(processed.images[0], 'already_saved_as', None)
if already_saved_as is not None:
fake_image.already_saved_as = already_saved_as
else:
fake_image = processed.images[0]
else:
fake_image.already_saved_as = image["name"]
new_gallery.append(fake_image)
geninfo["infotexts"][gallery_index] = processed.info
return new_gallery, json.dumps(geninfo), plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
def txt2img(id_task: str, request: gr.Request, *args):
p = txt2img_create_processing(id_task, request, *args)
with closing(p):
processed = modules.scripts.scripts_txt2img.run(p, *p.script_args)
if processed is None:
processed = processing.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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