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"] all_subseeds = geninfo["all_subseeds"] 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] subseed = all_subseeds[-gallery_index_from_end if gallery_index_from_end < len(all_seeds) + 1 else 0] p.seed = seed p.subseed = subseed 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")