mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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128 lines
4.8 KiB
Python
128 lines
4.8 KiB
Python
import json
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from contextlib import closing
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import modules.scripts
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from modules import processing, infotext_utils
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from modules.infotext_utils import create_override_settings_dict
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from modules.shared import opts
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import modules.shared as shared
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from modules.ui import plaintext_to_html
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from PIL import Image
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import gradio as gr
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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):
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override_settings = create_override_settings_dict(override_settings_texts)
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if force_enable_hr:
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enable_hr = True
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
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outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
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prompt=prompt,
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styles=prompt_styles,
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negative_prompt=negative_prompt,
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sampler_name=sampler_name,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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enable_hr=enable_hr,
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denoising_strength=denoising_strength,
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hr_scale=hr_scale,
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hr_upscaler=hr_upscaler,
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hr_second_pass_steps=hr_second_pass_steps,
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hr_resize_x=hr_resize_x,
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hr_resize_y=hr_resize_y,
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hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name,
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hr_sampler_name=None if hr_sampler_name == 'Use same sampler' else hr_sampler_name,
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hr_prompt=hr_prompt,
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hr_negative_prompt=hr_negative_prompt,
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override_settings=override_settings,
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)
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p.scripts = modules.scripts.scripts_txt2img
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p.script_args = args
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p.user = request.username
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if shared.opts.enable_console_prompts:
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print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
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return p
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def txt2img_upscale(id_task: str, request: gr.Request, gallery, gallery_index, generation_info, *args):
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assert len(gallery) > 0, 'No image to upscale'
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assert 0 <= gallery_index < len(gallery), f'Bad image index: {gallery_index}'
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p = txt2img_create_processing(id_task, request, *args)
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p.enable_hr = True
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p.batch_size = 1
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p.n_iter = 1
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geninfo = json.loads(generation_info)
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all_seeds = geninfo["all_seeds"]
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all_subseeds = geninfo["all_subseeds"]
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image_info = gallery[gallery_index] if 0 <= gallery_index < len(gallery) else gallery[0]
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p.firstpass_image = infotext_utils.image_from_url_text(image_info)
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gallery_index_from_end = len(gallery) - gallery_index
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seed = all_seeds[-gallery_index_from_end if gallery_index_from_end < len(all_seeds) + 1 else 0]
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subseed = all_subseeds[-gallery_index_from_end if gallery_index_from_end < len(all_seeds) + 1 else 0]
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p.seed = seed
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p.subseed = subseed
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with closing(p):
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processed = modules.scripts.scripts_txt2img.run(p, *p.script_args)
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if processed is None:
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processed = processing.process_images(p)
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shared.total_tqdm.clear()
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new_gallery = []
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for i, image in enumerate(gallery):
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fake_image = Image.new(mode="RGB", size=(1, 1))
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if i == gallery_index:
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already_saved_as = getattr(processed.images[0], 'already_saved_as', None)
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if already_saved_as is not None:
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fake_image.already_saved_as = already_saved_as
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else:
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fake_image = processed.images[0]
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else:
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fake_image.already_saved_as = image["name"]
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new_gallery.append(fake_image)
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geninfo["infotexts"][gallery_index] = processed.info
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return new_gallery, json.dumps(geninfo), plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
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def txt2img(id_task: str, request: gr.Request, *args):
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p = txt2img_create_processing(id_task, request, *args)
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with closing(p):
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processed = modules.scripts.scripts_txt2img.run(p, *p.script_args)
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if processed is None:
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processed = processing.process_images(p)
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shared.total_tqdm.clear()
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generation_info_js = processed.js()
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if opts.samples_log_stdout:
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print(generation_info_js)
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if opts.do_not_show_images:
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processed.images = []
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return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
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