mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-09 07:10:16 +08:00
1467 lines
79 KiB
Python
1467 lines
79 KiB
Python
import datetime
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import json
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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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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 # 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, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts
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from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
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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.generation_parameters_copypaste 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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import modules.images
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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.sd_samplers import samplers, samplers_for_img2img
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from modules.generation_parameters_copypaste import image_from_url_text
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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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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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up_down_symbol = '\u2195\ufe0f' # ↕️
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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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from modules import processing, devices
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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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with devices.autocast():
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p.init([""], [0], [0])
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return f"resize: 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 create_seed_inputs(target_interface):
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with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
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seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
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random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
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reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
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seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
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# Components to show/hide based on the 'Extra' checkbox
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seed_extras = []
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with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
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seed_extras.append(seed_extra_row_1)
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subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
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random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
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reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
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subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
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with FormRow(visible=False) as seed_extra_row_2:
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seed_extras.append(seed_extra_row_2)
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seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
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seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
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random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
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random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
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def change_visibility(show):
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return {comp: gr_show(show) for comp in seed_extras}
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seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
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return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
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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 connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
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""" Connects a 'reuse (sub)seed' button's click event so that it copies last used
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(sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
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was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
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def copy_seed(gen_info_string: str, index):
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res = -1
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try:
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gen_info = json.loads(gen_info_string)
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index -= gen_info.get('index_of_first_image', 0)
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if is_subseed and gen_info.get('subseed_strength', 0) > 0:
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all_subseeds = gen_info.get('all_subseeds', [-1])
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res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
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else:
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all_seeds = gen_info.get('all_seeds', [-1])
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res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
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except json.decoder.JSONDecodeError:
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if gen_info_string:
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errors.report(f"Error parsing JSON generation info: {gen_info_string}")
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return [res, gr_show(False)]
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reuse_seed.click(
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fn=copy_seed,
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_js="(x, y) => [x, selected_gallery_index()]",
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show_progress=False,
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inputs=[generation_info, dummy_component],
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outputs=[seed, dummy_component]
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)
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def update_token_counter(text, steps):
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try:
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text, _ = extra_networks.parse_prompt(text)
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_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_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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class Toprow:
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"""Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
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def __init__(self, is_img2img):
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id_part = "img2img" if is_img2img else "txt2img"
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self.id_part = id_part
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with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
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with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
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with gr.Row():
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with gr.Column(scale=80):
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with gr.Row():
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self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
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self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
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with gr.Row():
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with gr.Column(scale=80):
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with gr.Row():
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self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
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self.button_interrogate = None
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self.button_deepbooru = None
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if is_img2img:
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with gr.Column(scale=1, elem_classes="interrogate-col"):
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self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
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self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
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with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
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with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
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self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
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self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
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self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
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self.skip.click(
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fn=lambda: shared.state.skip(),
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inputs=[],
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outputs=[],
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)
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self.interrupt.click(
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fn=lambda: shared.state.interrupt(),
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inputs=[],
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outputs=[],
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)
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with gr.Row(elem_id=f"{id_part}_tools"):
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self.paste = ToolButton(value=paste_symbol, elem_id="paste")
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self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
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self.extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
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self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
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self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
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self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
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self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
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self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
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self.clear_prompt_button.click(
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fn=lambda *x: x,
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_js="confirm_clear_prompt",
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inputs=[self.prompt, self.negative_prompt],
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outputs=[self.prompt, self.negative_prompt],
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)
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self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
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self.prompt_img.change(
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fn=modules.images.image_data,
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inputs=[self.prompt_img],
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outputs=[self.prompt, self.prompt_img],
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show_progress=False,
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)
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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):
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return ui_common.create_output_panel(tabname, outdir)
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def create_sampler_and_steps_selection(choices, tabname):
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if opts.samplers_in_dropdown:
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with FormRow(elem_id=f"sampler_selection_{tabname}"):
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sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
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steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
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else:
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with FormGroup(elem_id=f"sampler_selection_{tabname}"):
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steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
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sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
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return steps, sampler_index
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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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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 = Toprow(is_img2img=False)
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dummy_component = gr.Label(visible=False)
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with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
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from modules import ui_extra_networks
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extra_networks_ui = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'txt2img')
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with gr.Row(equal_height=False):
|
|
with gr.Column(variant='compact', elem_id="txt2img_settings"):
|
|
scripts.scripts_txt2img.prepare_ui()
|
|
|
|
for category in ordered_ui_categories():
|
|
if category == "sampler":
|
|
steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="txt2img_column_size", scale=4):
|
|
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
|
|
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
|
|
|
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
|
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
|
|
|
|
if opts.dimensions_and_batch_together:
|
|
with gr.Column(elem_id="txt2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
|
|
|
elif category == "cfg":
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
|
|
|
|
elif category == "seed":
|
|
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_classes="checkboxes-row", variant="compact"):
|
|
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
|
|
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
|
|
enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
|
|
hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
|
|
|
|
elif category == "hires_fix":
|
|
with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
|
|
with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
|
|
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)
|
|
hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
|
|
hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
|
|
hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
|
|
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
|
|
hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
|
|
with gr.Column(scale=80):
|
|
with gr.Row():
|
|
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"])
|
|
with gr.Column(scale=80):
|
|
with gr.Row():
|
|
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"])
|
|
|
|
elif category == "batch":
|
|
if not opts.dimensions_and_batch_together:
|
|
with FormRow(elem_id="txt2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
|
|
|
elif category == "override_settings":
|
|
with FormRow(elem_id="txt2img_override_settings_row") as row:
|
|
override_settings = create_override_settings_dropdown('txt2img', row)
|
|
|
|
elif category == "scripts":
|
|
with FormGroup(elem_id="txt2img_script_container"):
|
|
custom_inputs = scripts.scripts_txt2img.setup_ui()
|
|
|
|
else:
|
|
scripts.scripts_txt2img.setup_ui_for_section(category)
|
|
|
|
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
|
|
|
|
for component in hr_resolution_preview_inputs:
|
|
event = component.release if isinstance(component, gr.Slider) else component.change
|
|
|
|
event(
|
|
fn=calc_resolution_hires,
|
|
inputs=hr_resolution_preview_inputs,
|
|
outputs=[hr_final_resolution],
|
|
show_progress=False,
|
|
)
|
|
event(
|
|
None,
|
|
_js="onCalcResolutionHires",
|
|
inputs=hr_resolution_preview_inputs,
|
|
outputs=[],
|
|
show_progress=False,
|
|
)
|
|
|
|
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
|
|
|
|
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
|
|
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
|
|
|
txt2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
|
|
_js="submit",
|
|
inputs=[
|
|
dummy_component,
|
|
toprow.prompt,
|
|
toprow.negative_prompt,
|
|
toprow.ui_styles.dropdown,
|
|
steps,
|
|
sampler_index,
|
|
restore_faces,
|
|
tiling,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
seed,
|
|
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
|
|
height,
|
|
width,
|
|
enable_hr,
|
|
denoising_strength,
|
|
hr_scale,
|
|
hr_upscaler,
|
|
hr_second_pass_steps,
|
|
hr_resize_x,
|
|
hr_resize_y,
|
|
hr_sampler_index,
|
|
hr_prompt,
|
|
hr_negative_prompt,
|
|
override_settings,
|
|
|
|
] + custom_inputs,
|
|
|
|
outputs=[
|
|
txt2img_gallery,
|
|
generation_info,
|
|
html_info,
|
|
html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
toprow.prompt.submit(**txt2img_args)
|
|
toprow.submit.click(**txt2img_args)
|
|
|
|
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=[
|
|
txt2img_gallery,
|
|
generation_info,
|
|
html_info,
|
|
html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
enable_hr.change(
|
|
fn=lambda x: gr_show(x),
|
|
inputs=[enable_hr],
|
|
outputs=[hr_options],
|
|
show_progress = False,
|
|
)
|
|
|
|
txt2img_paste_fields = [
|
|
(toprow.prompt, "Prompt"),
|
|
(toprow.negative_prompt, "Negative prompt"),
|
|
(steps, "Steps"),
|
|
(sampler_index, "Sampler"),
|
|
(restore_faces, "Face restoration"),
|
|
(cfg_scale, "CFG scale"),
|
|
(seed, "Seed"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
|
|
(subseed, "Variation seed"),
|
|
(subseed_strength, "Variation seed strength"),
|
|
(seed_resize_from_w, "Seed resize from-1"),
|
|
(seed_resize_from_h, "Seed resize from-2"),
|
|
(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
|
|
(denoising_strength, "Denoising strength"),
|
|
(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
|
|
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d))),
|
|
(hr_scale, "Hires upscale"),
|
|
(hr_upscaler, "Hires upscaler"),
|
|
(hr_second_pass_steps, "Hires steps"),
|
|
(hr_resize_x, "Hires resize-1"),
|
|
(hr_resize_y, "Hires resize-2"),
|
|
(hr_sampler_index, "Hires sampler"),
|
|
(hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
|
|
(hr_prompt, "Hires prompt"),
|
|
(hr_negative_prompt, "Hires negative prompt"),
|
|
(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,
|
|
))
|
|
|
|
txt2img_preview_params = [
|
|
toprow.prompt,
|
|
toprow.negative_prompt,
|
|
steps,
|
|
sampler_index,
|
|
cfg_scale,
|
|
seed,
|
|
width,
|
|
height,
|
|
]
|
|
|
|
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
|
|
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
|
|
|
|
ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
|
|
|
|
scripts.scripts_current = scripts.scripts_img2img
|
|
scripts.scripts_img2img.initialize_scripts(is_img2img=True)
|
|
|
|
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
|
toprow = Toprow(is_img2img=True)
|
|
|
|
with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
|
|
from modules import ui_extra_networks
|
|
extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'img2img')
|
|
|
|
with FormRow(equal_height=False):
|
|
with 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))
|
|
|
|
with gr.Tabs(elem_id="mode_img2img"):
|
|
img2img_selected_tab = gr.State(0)
|
|
|
|
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="RGBA", 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="RGBA", 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", 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"], 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")
|
|
|
|
scripts.scripts_img2img.prepare_ui()
|
|
|
|
for category in ordered_ui_categories():
|
|
if category == "sampler":
|
|
steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="img2img_column_size", scale=4):
|
|
selected_scale_tab = gr.State(value=0)
|
|
|
|
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")
|
|
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
|
|
|
|
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)
|
|
|
|
# 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)
|
|
|
|
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 == "cfg":
|
|
with FormGroup():
|
|
with FormRow():
|
|
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)
|
|
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 == "seed":
|
|
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_classes="checkboxes-row", variant="compact"):
|
|
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
|
|
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
|
|
|
|
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")
|
|
|
|
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],
|
|
)
|
|
else:
|
|
scripts.scripts_img2img.setup_ui_for_section(category)
|
|
|
|
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
|
|
|
|
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
|
|
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
|
|
|
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,
|
|
steps,
|
|
sampler_index,
|
|
mask_blur,
|
|
mask_alpha,
|
|
inpainting_fill,
|
|
restore_faces,
|
|
tiling,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
image_cfg_scale,
|
|
denoising_strength,
|
|
seed,
|
|
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
|
|
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=[
|
|
img2img_gallery,
|
|
generation_info,
|
|
html_info,
|
|
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=[
|
|
img2img_gallery,
|
|
generation_info,
|
|
html_info,
|
|
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,
|
|
)
|
|
|
|
toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
|
|
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
|
|
|
|
ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
|
|
|
|
img2img_paste_fields = [
|
|
(toprow.prompt, "Prompt"),
|
|
(toprow.negative_prompt, "Negative prompt"),
|
|
(steps, "Steps"),
|
|
(sampler_index, "Sampler"),
|
|
(restore_faces, "Face restoration"),
|
|
(cfg_scale, "CFG scale"),
|
|
(image_cfg_scale, "Image CFG scale"),
|
|
(seed, "Seed"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
|
|
(subseed, "Variation seed"),
|
|
(subseed_strength, "Variation seed strength"),
|
|
(seed_resize_from_w, "Seed resize from-1"),
|
|
(seed_resize_from_h, "Seed resize from-2"),
|
|
(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"),
|
|
*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,
|
|
))
|
|
|
|
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 gr.Row(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 gr.Row(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")
|
|
|
|
with gr.Tab(label="Preprocess images", id="preprocess_images"):
|
|
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
|
|
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
|
|
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
|
|
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
|
|
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
|
|
|
|
with gr.Row():
|
|
process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
|
|
process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
|
|
process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
|
|
process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
|
|
process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
|
|
process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
|
|
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
|
|
|
|
with gr.Row(visible=False) as process_split_extra_row:
|
|
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
|
|
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
|
|
|
|
with gr.Row(visible=False) as process_focal_crop_row:
|
|
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
|
|
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
|
|
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
|
|
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
|
|
|
|
with gr.Column(visible=False) as process_multicrop_col:
|
|
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
|
|
with gr.Row():
|
|
process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
|
|
process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
|
|
with gr.Row():
|
|
process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
|
|
process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
|
|
with gr.Row():
|
|
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
|
|
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
with gr.Row():
|
|
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
|
|
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
|
|
|
|
process_split.change(
|
|
fn=lambda show: gr_show(show),
|
|
inputs=[process_split],
|
|
outputs=[process_split_extra_row],
|
|
)
|
|
|
|
process_focal_crop.change(
|
|
fn=lambda show: gr_show(show),
|
|
inputs=[process_focal_crop],
|
|
outputs=[process_focal_crop_row],
|
|
)
|
|
|
|
process_multicrop.change(
|
|
fn=lambda show: gr_show(show),
|
|
inputs=[process_multicrop],
|
|
outputs=[process_multicrop_col],
|
|
)
|
|
|
|
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,
|
|
]
|
|
)
|
|
|
|
run_preprocess.click(
|
|
fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
dummy_component,
|
|
process_src,
|
|
process_dst,
|
|
process_width,
|
|
process_height,
|
|
preprocess_txt_action,
|
|
process_keep_original_size,
|
|
process_flip,
|
|
process_split,
|
|
process_caption,
|
|
process_caption_deepbooru,
|
|
process_split_threshold,
|
|
process_overlap_ratio,
|
|
process_focal_crop,
|
|
process_focal_crop_face_weight,
|
|
process_focal_crop_entropy_weight,
|
|
process_focal_crop_edges_weight,
|
|
process_focal_crop_debug,
|
|
process_multicrop,
|
|
process_multicrop_mindim,
|
|
process_multicrop_maxdim,
|
|
process_multicrop_minarea,
|
|
process_multicrop_maxarea,
|
|
process_multicrop_objective,
|
|
process_multicrop_threshold,
|
|
],
|
|
outputs=[
|
|
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=[],
|
|
)
|
|
|
|
interrupt_preprocessing.click(
|
|
fn=lambda: shared.state.interrupt(),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
|
|
|
|
settings = ui_settings.UiSettings()
|
|
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()
|
|
|
|
for interface, _label, ifid in interfaces:
|
|
if ifid in ["extensions", "settings"]:
|
|
continue
|
|
|
|
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")):
|
|
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'])
|
|
|
|
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
|
|
from typing import List
|
|
|
|
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')}.txt"
|
|
|
|
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"])
|
|
|