1225 lines
66 KiB
Python
1225 lines
66 KiB
Python
import datetime
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import mimetypes
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import os
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import sys
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from functools import reduce
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import warnings
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from contextlib import ExitStack
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import gradio as gr
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import gradio.utils
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import numpy as np
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from PIL import Image, PngImagePlugin # noqa: F401
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from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
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from modules import gradio_extensons, sd_schedulers # noqa: F401
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from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
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from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
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from modules.paths import script_path
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from modules.ui_common import create_refresh_button
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from modules.ui_gradio_extensions import reload_javascript
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from modules.shared import opts, cmd_opts
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import modules.infotext_utils as parameters_copypaste
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import modules.hypernetworks.ui as hypernetworks_ui
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import modules.textual_inversion.ui as textual_inversion_ui
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import modules.textual_inversion.textual_inversion as textual_inversion
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import modules.shared as shared
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from modules import prompt_parser
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from modules.sd_hijack import model_hijack
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from modules.infotext_utils import image_from_url_text, PasteField
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create_setting_component = ui_settings.create_setting_component
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warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
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warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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# Likewise, add explicit content-type header for certain missing image types
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mimetypes.add_type('image/webp', '.webp')
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if not cmd_opts.share and not cmd_opts.listen:
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# fix gradio phoning home
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gradio.utils.version_check = lambda: None
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gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
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if cmd_opts.ngrok is not None:
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import modules.ngrok as ngrok
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print('ngrok authtoken detected, trying to connect...')
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ngrok.connect(
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cmd_opts.ngrok,
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cmd_opts.port if cmd_opts.port is not None else 7860,
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cmd_opts.ngrok_options
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)
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def gr_show(visible=True):
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return {"visible": visible, "__type__": "update"}
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
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sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
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# Using constants for these since the variation selector isn't visible.
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# Important that they exactly match script.js for tooltip to work.
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random_symbol = '\U0001f3b2\ufe0f' # 🎲️
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reuse_symbol = '\u267b\ufe0f' # ♻️
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paste_symbol = '\u2199\ufe0f' # ↙
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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apply_style_symbol = '\U0001f4cb' # 📋
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clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
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extra_networks_symbol = '\U0001F3B4' # 🎴
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switch_values_symbol = '\U000021C5' # ⇅
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restore_progress_symbol = '\U0001F300' # 🌀
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detect_image_size_symbol = '\U0001F4D0' # 📐
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plaintext_to_html = ui_common.plaintext_to_html
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def send_gradio_gallery_to_image(x):
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if len(x) == 0:
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return None
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return image_from_url_text(x[0])
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def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
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if not enable:
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return ""
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p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
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p.calculate_target_resolution()
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return f"from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
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def resize_from_to_html(width, height, scale_by):
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target_width = int(width * scale_by)
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target_height = int(height * scale_by)
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if not target_width or not target_height:
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return "no image selected"
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return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
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def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
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if mode in {0, 1, 3, 4}:
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return [interrogation_function(ii_singles[mode]), None]
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elif mode == 2:
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return [interrogation_function(ii_singles[mode]["image"]), None]
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elif mode == 5:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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images = shared.listfiles(ii_input_dir)
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print(f"Will process {len(images)} images.")
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if ii_output_dir != "":
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os.makedirs(ii_output_dir, exist_ok=True)
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else:
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ii_output_dir = ii_input_dir
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for image in images:
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img = Image.open(image)
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filename = os.path.basename(image)
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left, _ = os.path.splitext(filename)
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print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
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return [gr.update(), None]
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def interrogate(image):
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prompt = shared.interrogator.interrogate(image.convert("RGB"))
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return gr.update() if prompt is None else prompt
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def interrogate_deepbooru(image):
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prompt = deepbooru.model.tag(image)
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return gr.update() if prompt is None else prompt
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def connect_clear_prompt(button):
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"""Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
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button.click(
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_js="clear_prompt",
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fn=None,
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inputs=[],
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outputs=[],
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)
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def update_token_counter(text, steps, styles, *, is_positive=True):
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params = script_callbacks.BeforeTokenCounterParams(text, steps, styles, is_positive=is_positive)
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script_callbacks.before_token_counter_callback(params)
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text = params.prompt
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steps = params.steps
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styles = params.styles
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is_positive = params.is_positive
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if shared.opts.include_styles_into_token_counters:
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apply_styles = shared.prompt_styles.apply_styles_to_prompt if is_positive else shared.prompt_styles.apply_negative_styles_to_prompt
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text = apply_styles(text, styles)
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try:
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text, _ = extra_networks.parse_prompt(text)
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if is_positive:
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_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
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else:
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prompt_flat_list = [text]
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prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
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except Exception:
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# a parsing error can happen here during typing, and we don't want to bother the user with
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# messages related to it in console
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prompt_schedules = [[[steps, text]]]
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flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
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prompts = [prompt_text for step, prompt_text in flat_prompts]
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token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
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return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
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def update_negative_prompt_token_counter(*args):
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return update_token_counter(*args, is_positive=False)
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def setup_progressbar(*args, **kwargs):
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pass
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def apply_setting(key, value):
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if value is None:
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return gr.update()
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if shared.cmd_opts.freeze_settings:
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return gr.update()
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# dont allow model to be swapped when model hash exists in prompt
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if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
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return gr.update()
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if key == "sd_model_checkpoint":
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ckpt_info = sd_models.get_closet_checkpoint_match(value)
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if ckpt_info is not None:
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value = ckpt_info.title
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else:
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return gr.update()
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comp_args = opts.data_labels[key].component_args
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if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
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return
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valtype = type(opts.data_labels[key].default)
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oldval = opts.data.get(key, None)
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opts.data[key] = valtype(value) if valtype != type(None) else value
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if oldval != value and opts.data_labels[key].onchange is not None:
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opts.data_labels[key].onchange()
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opts.save(shared.config_filename)
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return getattr(opts, key)
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def create_output_panel(tabname, outdir, toprow=None):
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return ui_common.create_output_panel(tabname, outdir, toprow)
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def ordered_ui_categories():
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user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
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for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
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yield category
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def create_override_settings_dropdown(tabname, row):
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dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
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dropdown.change(
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fn=lambda x: gr.Dropdown.update(visible=bool(x)),
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inputs=[dropdown],
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outputs=[dropdown],
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)
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return dropdown
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def create_ui():
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import modules.img2img
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import modules.txt2img
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reload_javascript()
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parameters_copypaste.reset()
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settings = ui_settings.UiSettings()
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settings.register_settings()
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scripts.scripts_current = scripts.scripts_txt2img
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scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
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with gr.Blocks(analytics_enabled=False) as txt2img_interface:
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toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box)
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dummy_component = gr.Label(visible=False)
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extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs", elem_classes=["extra-networks"])
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extra_tabs.__enter__()
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with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
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with ExitStack() as stack:
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if shared.opts.txt2img_settings_accordion:
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stack.enter_context(gr.Accordion("Open for Settings", open=False))
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stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
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scripts.scripts_txt2img.prepare_ui()
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for category in ordered_ui_categories():
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if category == "prompt":
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toprow.create_inline_toprow_prompts()
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elif category == "dimensions":
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with FormRow():
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with gr.Column(elem_id="txt2img_column_size", scale=4):
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width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
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height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
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with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
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res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height")
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if opts.dimensions_and_batch_together:
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with gr.Column(elem_id="txt2img_column_batch"):
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batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
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elif category == "cfg":
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with gr.Row():
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cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
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elif category == "checkboxes":
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with FormRow(elem_classes="checkboxes-row", variant="compact"):
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pass
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elif category == "accordions":
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with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
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with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
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with enable_hr.extra():
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hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
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with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
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hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
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hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
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with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
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hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
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hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
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hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
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with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
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hr_checkpoint_name = gr.Dropdown(label='Checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
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create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
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hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
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hr_scheduler = gr.Dropdown(label='Hires schedule type', elem_id="hr_scheduler", choices=["Use same scheduler"] + [x.label for x in sd_schedulers.schedulers], value="Use same scheduler")
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with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
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with gr.Column(scale=80):
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with gr.Row():
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hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
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with gr.Column(scale=80):
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with gr.Row():
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hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
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scripts.scripts_txt2img.setup_ui_for_section(category)
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elif category == "batch":
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if not opts.dimensions_and_batch_together:
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with FormRow(elem_id="txt2img_column_batch"):
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batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
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elif category == "override_settings":
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with FormRow(elem_id="txt2img_override_settings_row") as row:
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override_settings = create_override_settings_dropdown('txt2img', row)
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elif category == "scripts":
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with FormGroup(elem_id="txt2img_script_container"):
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custom_inputs = scripts.scripts_txt2img.setup_ui()
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if category not in {"accordions"}:
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scripts.scripts_txt2img.setup_ui_for_section(category)
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hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
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for component in hr_resolution_preview_inputs:
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event = component.release if isinstance(component, gr.Slider) else component.change
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event(
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fn=calc_resolution_hires,
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inputs=hr_resolution_preview_inputs,
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outputs=[hr_final_resolution],
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show_progress=False,
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)
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event(
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None,
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_js="onCalcResolutionHires",
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inputs=hr_resolution_preview_inputs,
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outputs=[],
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show_progress=False,
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)
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output_panel = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
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txt2img_inputs = [
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dummy_component,
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toprow.prompt,
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toprow.negative_prompt,
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toprow.ui_styles.dropdown,
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batch_count,
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batch_size,
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cfg_scale,
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height,
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width,
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enable_hr,
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denoising_strength,
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hr_scale,
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hr_upscaler,
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hr_second_pass_steps,
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hr_resize_x,
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hr_resize_y,
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hr_checkpoint_name,
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hr_sampler_name,
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hr_scheduler,
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hr_prompt,
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hr_negative_prompt,
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override_settings,
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] + custom_inputs
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txt2img_outputs = [
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output_panel.gallery,
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output_panel.generation_info,
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output_panel.infotext,
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output_panel.html_log,
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]
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txt2img_args = dict(
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fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
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_js="submit",
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inputs=txt2img_inputs,
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outputs=txt2img_outputs,
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show_progress=False,
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)
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toprow.prompt.submit(**txt2img_args)
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toprow.submit.click(**txt2img_args)
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output_panel.button_upscale.click(
|
|
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img_upscale, extra_outputs=[None, '', '']),
|
|
_js="submit_txt2img_upscale",
|
|
inputs=txt2img_inputs[0:1] + [output_panel.gallery, dummy_component, output_panel.generation_info] + txt2img_inputs[1:],
|
|
outputs=txt2img_outputs,
|
|
show_progress=False,
|
|
)
|
|
|
|
res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
|
|
|
|
toprow.restore_progress_button.click(
|
|
fn=progress.restore_progress,
|
|
_js="restoreProgressTxt2img",
|
|
inputs=[dummy_component],
|
|
outputs=[
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
txt2img_paste_fields = [
|
|
PasteField(toprow.prompt, "Prompt", api="prompt"),
|
|
PasteField(toprow.negative_prompt, "Negative prompt", api="negative_prompt"),
|
|
PasteField(cfg_scale, "CFG scale", api="cfg_scale"),
|
|
PasteField(width, "Size-1", api="width"),
|
|
PasteField(height, "Size-2", api="height"),
|
|
PasteField(batch_size, "Batch size", api="batch_size"),
|
|
PasteField(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update(), api="styles"),
|
|
PasteField(denoising_strength, "Denoising strength", api="denoising_strength"),
|
|
PasteField(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d), api="enable_hr"),
|
|
PasteField(hr_scale, "Hires upscale", api="hr_scale"),
|
|
PasteField(hr_upscaler, "Hires upscaler", api="hr_upscaler"),
|
|
PasteField(hr_second_pass_steps, "Hires steps", api="hr_second_pass_steps"),
|
|
PasteField(hr_resize_x, "Hires resize-1", api="hr_resize_x"),
|
|
PasteField(hr_resize_y, "Hires resize-2", api="hr_resize_y"),
|
|
PasteField(hr_checkpoint_name, "Hires checkpoint", api="hr_checkpoint_name"),
|
|
PasteField(hr_sampler_name, sd_samplers.get_hr_sampler_from_infotext, api="hr_sampler_name"),
|
|
PasteField(hr_scheduler, sd_samplers.get_hr_scheduler_from_infotext, api="hr_scheduler"),
|
|
PasteField(hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" or d.get("Hires schedule type", "Use same scheduler") != "Use same scheduler" else gr.update()),
|
|
PasteField(hr_prompt, "Hires prompt", api="hr_prompt"),
|
|
PasteField(hr_negative_prompt, "Hires negative prompt", api="hr_negative_prompt"),
|
|
PasteField(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
|
|
*scripts.scripts_txt2img.infotext_fields
|
|
]
|
|
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
|
|
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
|
paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
|
|
))
|
|
|
|
steps = scripts.scripts_txt2img.script('Sampler').steps
|
|
|
|
txt2img_preview_params = [
|
|
toprow.prompt,
|
|
toprow.negative_prompt,
|
|
steps,
|
|
scripts.scripts_txt2img.script('Sampler').sampler_name,
|
|
cfg_scale,
|
|
scripts.scripts_txt2img.script('Seed').seed,
|
|
width,
|
|
height,
|
|
]
|
|
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
|
|
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
|
|
ui_extra_networks.setup_ui(extra_networks_ui, output_panel.gallery)
|
|
|
|
extra_tabs.__exit__()
|
|
|
|
scripts.scripts_current = scripts.scripts_img2img
|
|
scripts.scripts_img2img.initialize_scripts(is_img2img=True)
|
|
|
|
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
|
toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
|
|
|
|
extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs", elem_classes=["extra-networks"])
|
|
extra_tabs.__enter__()
|
|
|
|
with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
|
|
with ExitStack() as stack:
|
|
if shared.opts.img2img_settings_accordion:
|
|
stack.enter_context(gr.Accordion("Open for Settings", open=False))
|
|
stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
|
|
|
|
copy_image_buttons = []
|
|
copy_image_destinations = {}
|
|
|
|
def add_copy_image_controls(tab_name, elem):
|
|
with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
|
|
gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
|
|
|
|
for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
|
|
if name == tab_name:
|
|
gr.Button(title, interactive=False)
|
|
copy_image_destinations[name] = elem
|
|
continue
|
|
|
|
button = gr.Button(title)
|
|
copy_image_buttons.append((button, name, elem))
|
|
|
|
scripts.scripts_img2img.prepare_ui()
|
|
|
|
for category in ordered_ui_categories():
|
|
if category == "prompt":
|
|
toprow.create_inline_toprow_prompts()
|
|
|
|
if category == "image":
|
|
with gr.Tabs(elem_id="mode_img2img"):
|
|
img2img_selected_tab = gr.Number(value=0, visible=False)
|
|
|
|
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
|
|
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
|
|
add_copy_image_controls('img2img', init_img)
|
|
|
|
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
|
|
sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
|
|
add_copy_image_controls('sketch', sketch)
|
|
|
|
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
|
|
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
|
|
add_copy_image_controls('inpaint', init_img_with_mask)
|
|
|
|
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
|
|
inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
|
|
inpaint_color_sketch_orig = gr.State(None)
|
|
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
|
|
|
|
def update_orig(image, state):
|
|
if image is not None:
|
|
same_size = state is not None and state.size == image.size
|
|
has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
|
|
edited = same_size and has_exact_match
|
|
return image if not edited or state is None else state
|
|
|
|
inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
|
|
|
|
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
|
|
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
|
|
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
|
|
|
|
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
|
|
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
|
gr.HTML(
|
|
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
|
|
"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
|
|
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
|
|
f"{hidden}</p>"
|
|
)
|
|
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
|
|
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
|
|
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
|
|
with gr.Accordion("PNG info", open=False):
|
|
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
|
|
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
|
|
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
|
|
|
|
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
|
|
|
|
for i, tab in enumerate(img2img_tabs):
|
|
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
|
|
|
|
def copy_image(img):
|
|
if isinstance(img, dict) and 'image' in img:
|
|
return img['image']
|
|
|
|
return img
|
|
|
|
for button, name, elem in copy_image_buttons:
|
|
button.click(
|
|
fn=copy_image,
|
|
inputs=[elem],
|
|
outputs=[copy_image_destinations[name]],
|
|
)
|
|
button.click(
|
|
fn=lambda: None,
|
|
_js=f"switch_to_{name.replace(' ', '_')}",
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
with FormRow():
|
|
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="img2img_column_size", scale=4):
|
|
selected_scale_tab = gr.Number(value=0, visible=False)
|
|
|
|
with gr.Tabs():
|
|
with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
|
|
with FormRow():
|
|
with gr.Column(elem_id="img2img_column_size", scale=4):
|
|
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
|
|
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
|
|
with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
|
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
|
|
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
|
|
|
|
with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
|
|
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
|
|
|
|
with FormRow():
|
|
scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
|
|
gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
|
|
button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
|
|
|
|
on_change_args = dict(
|
|
fn=resize_from_to_html,
|
|
_js="currentImg2imgSourceResolution",
|
|
inputs=[dummy_component, dummy_component, scale_by],
|
|
outputs=scale_by_html,
|
|
show_progress=False,
|
|
)
|
|
|
|
scale_by.release(**on_change_args)
|
|
button_update_resize_to.click(**on_change_args)
|
|
|
|
tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
|
|
tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
|
|
|
|
if opts.dimensions_and_batch_together:
|
|
with gr.Column(elem_id="img2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
|
|
|
elif category == "denoising":
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
|
|
|
|
elif category == "cfg":
|
|
with gr.Row():
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
|
|
image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_classes="checkboxes-row", variant="compact"):
|
|
pass
|
|
|
|
elif category == "accordions":
|
|
with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
|
|
scripts.scripts_img2img.setup_ui_for_section(category)
|
|
|
|
elif category == "batch":
|
|
if not opts.dimensions_and_batch_together:
|
|
with FormRow(elem_id="img2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
|
|
|
elif category == "override_settings":
|
|
with FormRow(elem_id="img2img_override_settings_row") as row:
|
|
override_settings = create_override_settings_dropdown('img2img', row)
|
|
|
|
elif category == "scripts":
|
|
with FormGroup(elem_id="img2img_script_container"):
|
|
custom_inputs = scripts.scripts_img2img.setup_ui()
|
|
|
|
elif category == "inpaint":
|
|
with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
|
|
with FormRow():
|
|
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
|
|
mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
|
|
|
|
with FormRow():
|
|
inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
|
|
|
|
with FormRow():
|
|
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
|
|
|
|
with FormRow():
|
|
with gr.Column():
|
|
inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
|
|
|
|
with gr.Column(scale=4):
|
|
inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
|
|
|
|
if category not in {"accordions"}:
|
|
scripts.scripts_img2img.setup_ui_for_section(category)
|
|
|
|
# the code below is meant to update the resolution label after the image in the image selection UI has changed.
|
|
# as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
|
|
# I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
|
|
for component in [init_img, sketch]:
|
|
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
|
|
|
|
def select_img2img_tab(tab):
|
|
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
|
|
|
|
for i, elem in enumerate(img2img_tabs):
|
|
elem.select(
|
|
fn=lambda tab=i: select_img2img_tab(tab),
|
|
inputs=[],
|
|
outputs=[inpaint_controls, mask_alpha],
|
|
)
|
|
|
|
output_panel = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
|
|
|
|
img2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
|
|
_js="submit_img2img",
|
|
inputs=[
|
|
dummy_component,
|
|
dummy_component,
|
|
toprow.prompt,
|
|
toprow.negative_prompt,
|
|
toprow.ui_styles.dropdown,
|
|
init_img,
|
|
sketch,
|
|
init_img_with_mask,
|
|
inpaint_color_sketch,
|
|
inpaint_color_sketch_orig,
|
|
init_img_inpaint,
|
|
init_mask_inpaint,
|
|
mask_blur,
|
|
mask_alpha,
|
|
inpainting_fill,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
image_cfg_scale,
|
|
denoising_strength,
|
|
selected_scale_tab,
|
|
height,
|
|
width,
|
|
scale_by,
|
|
resize_mode,
|
|
inpaint_full_res,
|
|
inpaint_full_res_padding,
|
|
inpainting_mask_invert,
|
|
img2img_batch_input_dir,
|
|
img2img_batch_output_dir,
|
|
img2img_batch_inpaint_mask_dir,
|
|
override_settings,
|
|
img2img_batch_use_png_info,
|
|
img2img_batch_png_info_props,
|
|
img2img_batch_png_info_dir,
|
|
] + custom_inputs,
|
|
outputs=[
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
interrogate_args = dict(
|
|
_js="get_img2img_tab_index",
|
|
inputs=[
|
|
dummy_component,
|
|
img2img_batch_input_dir,
|
|
img2img_batch_output_dir,
|
|
init_img,
|
|
sketch,
|
|
init_img_with_mask,
|
|
inpaint_color_sketch,
|
|
init_img_inpaint,
|
|
],
|
|
outputs=[toprow.prompt, dummy_component],
|
|
)
|
|
|
|
toprow.prompt.submit(**img2img_args)
|
|
toprow.submit.click(**img2img_args)
|
|
|
|
res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
|
|
|
|
detect_image_size_btn.click(
|
|
fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
|
|
_js="currentImg2imgSourceResolution",
|
|
inputs=[dummy_component, dummy_component, dummy_component],
|
|
outputs=[width, height],
|
|
show_progress=False,
|
|
)
|
|
|
|
toprow.restore_progress_button.click(
|
|
fn=progress.restore_progress,
|
|
_js="restoreProgressImg2img",
|
|
inputs=[dummy_component],
|
|
outputs=[
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
toprow.button_interrogate.click(
|
|
fn=lambda *args: process_interrogate(interrogate, *args),
|
|
**interrogate_args,
|
|
)
|
|
|
|
toprow.button_deepbooru.click(
|
|
fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
|
|
**interrogate_args,
|
|
)
|
|
|
|
steps = scripts.scripts_img2img.script('Sampler').steps
|
|
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
|
|
img2img_paste_fields = [
|
|
(toprow.prompt, "Prompt"),
|
|
(toprow.negative_prompt, "Negative prompt"),
|
|
(cfg_scale, "CFG scale"),
|
|
(image_cfg_scale, "Image CFG scale"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
|
|
(denoising_strength, "Denoising strength"),
|
|
(mask_blur, "Mask blur"),
|
|
(inpainting_mask_invert, 'Mask mode'),
|
|
(inpainting_fill, 'Masked content'),
|
|
(inpaint_full_res, 'Inpaint area'),
|
|
(inpaint_full_res_padding, 'Masked area padding'),
|
|
*scripts.scripts_img2img.infotext_fields
|
|
]
|
|
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
|
|
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
|
|
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
|
paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
|
|
))
|
|
|
|
extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
|
|
ui_extra_networks.setup_ui(extra_networks_ui_img2img, output_panel.gallery)
|
|
|
|
extra_tabs.__exit__()
|
|
|
|
scripts.scripts_current = None
|
|
|
|
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
|
ui_postprocessing.create_ui()
|
|
|
|
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
|
|
with ResizeHandleRow(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
|
|
|
|
with gr.Column(variant='panel'):
|
|
html = gr.HTML()
|
|
generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
|
|
html2 = gr.HTML()
|
|
with gr.Row():
|
|
buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
|
|
|
|
for tabname, button in buttons.items():
|
|
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
|
paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
|
|
))
|
|
|
|
image.change(
|
|
fn=wrap_gradio_call(modules.extras.run_pnginfo),
|
|
inputs=[image],
|
|
outputs=[html, generation_info, html2],
|
|
)
|
|
|
|
modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
|
|
|
|
with gr.Blocks(analytics_enabled=False) as train_interface:
|
|
with gr.Row(equal_height=False):
|
|
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
|
|
|
|
with ResizeHandleRow(variant="compact", equal_height=False):
|
|
with gr.Tabs(elem_id="train_tabs"):
|
|
|
|
with gr.Tab(label="Create embedding", id="create_embedding"):
|
|
new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
|
|
initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
|
|
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
|
|
overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
|
|
|
|
with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
|
|
new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
|
|
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
|
|
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
|
|
new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func")
|
|
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
|
|
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
|
|
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
|
|
new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
|
|
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
|
|
|
|
def get_textual_inversion_template_names():
|
|
return sorted(textual_inversion.textual_inversion_templates)
|
|
|
|
with gr.Tab(label="Train", id="train"):
|
|
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
|
|
with FormRow():
|
|
train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
|
create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
|
|
|
|
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
|
|
create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
|
|
|
|
with FormRow():
|
|
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
|
|
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
|
|
|
|
with FormRow():
|
|
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
|
clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
|
|
|
|
with FormRow():
|
|
batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
|
|
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
|
|
|
|
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
|
|
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
|
|
|
|
with FormRow():
|
|
template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
|
|
create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
|
|
|
|
training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
|
|
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
|
|
varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
|
|
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
|
|
|
|
with FormRow():
|
|
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
|
|
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
|
|
|
|
use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
|
|
|
|
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
|
|
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
|
|
|
|
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
|
|
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
|
|
|
|
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
|
|
|
|
with gr.Row():
|
|
train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
|
|
interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
|
|
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
|
|
|
|
params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
|
|
|
|
script_callbacks.ui_train_tabs_callback(params)
|
|
|
|
with gr.Column(elem_id='ti_gallery_container'):
|
|
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
|
gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4)
|
|
gr.HTML(elem_id="ti_progress", value="")
|
|
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
|
|
|
create_embedding.click(
|
|
fn=textual_inversion_ui.create_embedding,
|
|
inputs=[
|
|
new_embedding_name,
|
|
initialization_text,
|
|
nvpt,
|
|
overwrite_old_embedding,
|
|
],
|
|
outputs=[
|
|
train_embedding_name,
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
create_hypernetwork.click(
|
|
fn=hypernetworks_ui.create_hypernetwork,
|
|
inputs=[
|
|
new_hypernetwork_name,
|
|
new_hypernetwork_sizes,
|
|
overwrite_old_hypernetwork,
|
|
new_hypernetwork_layer_structure,
|
|
new_hypernetwork_activation_func,
|
|
new_hypernetwork_initialization_option,
|
|
new_hypernetwork_add_layer_norm,
|
|
new_hypernetwork_use_dropout,
|
|
new_hypernetwork_dropout_structure
|
|
],
|
|
outputs=[
|
|
train_hypernetwork_name,
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
train_embedding.click(
|
|
fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
dummy_component,
|
|
train_embedding_name,
|
|
embedding_learn_rate,
|
|
batch_size,
|
|
gradient_step,
|
|
dataset_directory,
|
|
log_directory,
|
|
training_width,
|
|
training_height,
|
|
varsize,
|
|
steps,
|
|
clip_grad_mode,
|
|
clip_grad_value,
|
|
shuffle_tags,
|
|
tag_drop_out,
|
|
latent_sampling_method,
|
|
use_weight,
|
|
create_image_every,
|
|
save_embedding_every,
|
|
template_file,
|
|
save_image_with_stored_embedding,
|
|
preview_from_txt2img,
|
|
*txt2img_preview_params,
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
train_hypernetwork.click(
|
|
fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
dummy_component,
|
|
train_hypernetwork_name,
|
|
hypernetwork_learn_rate,
|
|
batch_size,
|
|
gradient_step,
|
|
dataset_directory,
|
|
log_directory,
|
|
training_width,
|
|
training_height,
|
|
varsize,
|
|
steps,
|
|
clip_grad_mode,
|
|
clip_grad_value,
|
|
shuffle_tags,
|
|
tag_drop_out,
|
|
latent_sampling_method,
|
|
use_weight,
|
|
create_image_every,
|
|
save_embedding_every,
|
|
template_file,
|
|
preview_from_txt2img,
|
|
*txt2img_preview_params,
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
interrupt_training.click(
|
|
fn=lambda: shared.state.interrupt(),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
|
|
ui_settings_from_file = loadsave.ui_settings.copy()
|
|
|
|
settings.create_ui(loadsave, dummy_component)
|
|
|
|
interfaces = [
|
|
(txt2img_interface, "txt2img", "txt2img"),
|
|
(img2img_interface, "img2img", "img2img"),
|
|
(extras_interface, "Extras", "extras"),
|
|
(pnginfo_interface, "PNG Info", "pnginfo"),
|
|
(modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
|
|
(train_interface, "Train", "train"),
|
|
]
|
|
|
|
interfaces += script_callbacks.ui_tabs_callback()
|
|
interfaces += [(settings.interface, "Settings", "settings")]
|
|
|
|
extensions_interface = ui_extensions.create_ui()
|
|
interfaces += [(extensions_interface, "Extensions", "extensions")]
|
|
|
|
shared.tab_names = []
|
|
for _interface, label, _ifid in interfaces:
|
|
shared.tab_names.append(label)
|
|
|
|
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
|
|
settings.add_quicksettings()
|
|
|
|
parameters_copypaste.connect_paste_params_buttons()
|
|
|
|
with gr.Tabs(elem_id="tabs") as tabs:
|
|
tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
|
|
sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
|
|
|
|
for interface, label, ifid in sorted_interfaces:
|
|
if label in shared.opts.hidden_tabs:
|
|
continue
|
|
with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
|
|
interface.render()
|
|
|
|
if ifid not in ["extensions", "settings"]:
|
|
loadsave.add_block(interface, ifid)
|
|
|
|
loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
|
|
|
|
loadsave.setup_ui()
|
|
|
|
if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio:
|
|
gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
|
|
|
|
footer = shared.html("footer.html")
|
|
footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
|
|
gr.HTML(footer, elem_id="footer")
|
|
|
|
settings.add_functionality(demo)
|
|
|
|
update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
|
|
settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
|
|
demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
|
|
|
|
modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
|
|
|
|
if ui_settings_from_file != loadsave.ui_settings:
|
|
loadsave.dump_defaults()
|
|
demo.ui_loadsave = loadsave
|
|
|
|
return demo
|
|
|
|
|
|
def versions_html():
|
|
import torch
|
|
import launch
|
|
|
|
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
|
|
commit = launch.commit_hash()
|
|
tag = launch.git_tag()
|
|
|
|
if shared.xformers_available:
|
|
import xformers
|
|
xformers_version = xformers.__version__
|
|
else:
|
|
xformers_version = "N/A"
|
|
|
|
return f"""
|
|
version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
|
|
 • 
|
|
python: <span title="{sys.version}">{python_version}</span>
|
|
 • 
|
|
torch: {getattr(torch, '__long_version__',torch.__version__)}
|
|
 • 
|
|
xformers: {xformers_version}
|
|
 • 
|
|
gradio: {gr.__version__}
|
|
 • 
|
|
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
|
|
"""
|
|
|
|
|
|
def setup_ui_api(app):
|
|
from pydantic import BaseModel, Field
|
|
|
|
class QuicksettingsHint(BaseModel):
|
|
name: str = Field(title="Name of the quicksettings field")
|
|
label: str = Field(title="Label of the quicksettings field")
|
|
|
|
def quicksettings_hint():
|
|
return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
|
|
|
|
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
|
|
|
|
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
|
|
|
|
app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
|
|
|
|
def download_sysinfo(attachment=False):
|
|
from fastapi.responses import PlainTextResponse
|
|
|
|
text = sysinfo.get()
|
|
filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
|
|
|
|
return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
|
|
|
|
app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
|
|
app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
|
|
|
|
import fastapi.staticfiles
|
|
app.mount("/webui-assets", fastapi.staticfiles.StaticFiles(directory=launch_utils.repo_dir('stable-diffusion-webui-assets')), name="webui-assets")
|