diff options
Diffstat (limited to 'modules/ui_checkpoint_merger.py')
| -rw-r--r-- | modules/ui_checkpoint_merger.py | 1657 |
1 files changed, 72 insertions, 1585 deletions
diff --git a/modules/ui_checkpoint_merger.py b/modules/ui_checkpoint_merger.py index 07ecee7b..8e72258a 100644 --- a/modules/ui_checkpoint_merger.py +++ b/modules/ui_checkpoint_merger.py @@ -1,1621 +1,108 @@ -import datetime
-import json
-import mimetypes
-import os
-import sys
-from functools import reduce
-import warnings
import gradio as gr
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin # noqa: F401
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path
+from modules import sd_models, sd_vae, errors, extras, call_queue
+from modules.ui_components import FormRow
from modules.ui_common import create_refresh_button
-from modules.ui_gradio_extensions import reload_javascript
-from modules.shared import opts, cmd_opts
+def update_interp_description(value):
+ interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+ interp_descriptions = {
+ "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+ "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+ "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+ }
+ return interp_descriptions[value]
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
-create_setting_component = ui_settings.create_setting_component
-
-warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
-
-# 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
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
- # fix gradio phoning home
- gradio.utils.version_check = lambda: None
- gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok is not None:
- import modules.ngrok as ngrok
- print('ngrok authtoken detected, trying to connect...')
- ngrok.connect(
- cmd_opts.ngrok,
- cmd_opts.port if cmd_opts.port is not None else 7860,
- cmd_opts.ngrok_options
- )
-
-
-def gr_show(visible=True):
- return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f' # 🎲️
-reuse_symbol = '\u267b\ufe0f' # ♻️
-paste_symbol = '\u2199\ufe0f' # ↙
-refresh_symbol = '\U0001f504' # 🔄
-save_style_symbol = '\U0001f4be' # 💾
-apply_style_symbol = '\U0001f4cb' # 📋
-clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
-extra_networks_symbol = '\U0001F3B4' # 🎴
-switch_values_symbol = '\U000021C5' # ⇅
-restore_progress_symbol = '\U0001F300' # 🌀
-detect_image_size_symbol = '\U0001F4D0' # 📐
-up_down_symbol = '\u2195\ufe0f' # ↕️
-
-
-plaintext_to_html = ui_common.plaintext_to_html
-
-
-def send_gradio_gallery_to_image(x):
- if len(x) == 0:
- return None
- return image_from_url_text(x[0])
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
- if name is None:
- return [gr_show() for x in range(4)]
-
- style = modules.styles.PromptStyle(name, prompt, negative_prompt)
- shared.prompt_styles.styles[style.name] = style
- # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
- # reserialize all styles every time we save them
- shared.prompt_styles.save_styles(shared.styles_filename)
-
- return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
-def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
- from modules import processing, devices
-
- if not enable:
- return ""
-
- 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)
-
- with devices.autocast():
- p.init([""], [0], [0])
-
- 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>"
-
-
-def resize_from_to_html(width, height, scale_by):
- target_width = int(width * scale_by)
- target_height = int(height * scale_by)
-
- if not target_width or not target_height:
- return "no image selected"
-
- return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
-
-
-def apply_styles(prompt, prompt_neg, styles):
- prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
- prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
- return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
-def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
- if mode in {0, 1, 3, 4}:
- return [interrogation_function(ii_singles[mode]), None]
- elif mode == 2:
- return [interrogation_function(ii_singles[mode]["image"]), None]
- elif mode == 5:
- assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
- images = shared.listfiles(ii_input_dir)
- print(f"Will process {len(images)} images.")
- if ii_output_dir != "":
- os.makedirs(ii_output_dir, exist_ok=True)
- else:
- ii_output_dir = ii_input_dir
-
- for image in images:
- img = Image.open(image)
- filename = os.path.basename(image)
- left, _ = os.path.splitext(filename)
- print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
-
- return [gr.update(), None]
-
-
-def interrogate(image):
- prompt = shared.interrogator.interrogate(image.convert("RGB"))
- return gr.update() if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
- prompt = deepbooru.model.tag(image)
- return gr.update() if prompt is None else prompt
-
-
-def create_seed_inputs(target_interface):
- with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
- seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
- seed.style(container=False)
- random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
- reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
-
- seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
-
- # Components to show/hide based on the 'Extra' checkbox
- seed_extras = []
-
- with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
- seed_extras.append(seed_extra_row_1)
- subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
- subseed.style(container=False)
- random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
- reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
- subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
-
- with FormRow(visible=False) as seed_extra_row_2:
- seed_extras.append(seed_extra_row_2)
- 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")
- 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")
-
- random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
- random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
-
- def change_visibility(show):
- return {comp: gr_show(show) for comp in seed_extras}
-
- seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
- return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-
-def connect_clear_prompt(button):
- """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
- button.click(
- _js="clear_prompt",
- fn=None,
- inputs=[],
- outputs=[],
- )
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
- """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
- (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
- was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
- def copy_seed(gen_info_string: str, index):
- res = -1
-
- try:
- gen_info = json.loads(gen_info_string)
- index -= gen_info.get('index_of_first_image', 0)
-
- if is_subseed and gen_info.get('subseed_strength', 0) > 0:
- all_subseeds = gen_info.get('all_subseeds', [-1])
- res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
- else:
- all_seeds = gen_info.get('all_seeds', [-1])
- res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
- except json.decoder.JSONDecodeError:
- if gen_info_string:
- errors.report(f"Error parsing JSON generation info: {gen_info_string}")
-
- return [res, gr_show(False)]
-
- reuse_seed.click(
- fn=copy_seed,
- _js="(x, y) => [x, selected_gallery_index()]",
- show_progress=False,
- inputs=[generation_info, dummy_component],
- outputs=[seed, dummy_component]
- )
-
-
-def update_token_counter(text, steps):
+def modelmerger(*args):
try:
- text, _ = extra_networks.parse_prompt(text)
-
- _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
- prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
- except Exception:
- # a parsing error can happen here during typing, and we don't want to bother the user with
- # messages related to it in console
- prompt_schedules = [[[steps, text]]]
-
- flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
- prompts = [prompt_text for step, prompt_text in flat_prompts]
- token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
- return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
- id_part = "img2img" if is_img2img else "txt2img"
-
- with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
- with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- 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"])
-
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- 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"])
-
- button_interrogate = None
- button_deepbooru = None
- if is_img2img:
- with gr.Column(scale=1, elem_classes="interrogate-col"):
- button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
- button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
- with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
- with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
- submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
- skip.click(
- fn=lambda: shared.state.skip(),
- inputs=[],
- outputs=[],
- )
-
- interrupt.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
-
- with gr.Row(elem_id=f"{id_part}_tools"):
- paste = ToolButton(value=paste_symbol, elem_id="paste")
- clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
- extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
- prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
- save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
- restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
-
- token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
- token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
- negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
- negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
-
- clear_prompt_button.click(
- fn=lambda *x: x,
- _js="confirm_clear_prompt",
- inputs=[prompt, negative_prompt],
- outputs=[prompt, negative_prompt],
- )
-
- with gr.Row(elem_id=f"{id_part}_styles_row"):
- prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
- create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
-
- return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
-
-
-def setup_progressbar(*args, **kwargs):
- pass
-
-
-def apply_setting(key, value):
- if value is None:
- return gr.update()
-
- if shared.cmd_opts.freeze_settings:
- return gr.update()
-
- # dont allow model to be swapped when model hash exists in prompt
- if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
- return gr.update()
-
- if key == "sd_model_checkpoint":
- ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
- if ckpt_info is not None:
- value = ckpt_info.title
- else:
- return gr.update()
-
- comp_args = opts.data_labels[key].component_args
- if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
- return
-
- valtype = type(opts.data_labels[key].default)
- oldval = opts.data.get(key, None)
- opts.data[key] = valtype(value) if valtype != type(None) else value
- if oldval != value and opts.data_labels[key].onchange is not None:
- opts.data_labels[key].onchange()
-
- opts.save(shared.config_filename)
- return getattr(opts, key)
-
-
-def create_output_panel(tabname, outdir):
- return ui_common.create_output_panel(tabname, outdir)
-
-
-def create_sampler_and_steps_selection(choices, tabname):
- if opts.samplers_in_dropdown:
- with FormRow(elem_id=f"sampler_selection_{tabname}"):
- 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")
- steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
- else:
- with FormGroup(elem_id=f"sampler_selection_{tabname}"):
- steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
- 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")
-
- return steps, sampler_index
-
-
-def ordered_ui_categories():
- user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
-
- for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
- yield category
-
-
-def create_override_settings_dropdown(tabname, row):
- dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
-
- dropdown.change(
- fn=lambda x: gr.Dropdown.update(visible=bool(x)),
- inputs=[dropdown],
- outputs=[dropdown],
- )
-
- return dropdown
-
-
-def create_ui():
- import modules.img2img
- import modules.txt2img
-
- reload_javascript()
-
- parameters_copypaste.reset()
-
- modules.scripts.scripts_current = modules.scripts.scripts_txt2img
- modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
- with gr.Blocks(analytics_enabled=False) as txt2img_interface:
- txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
-
- dummy_component = gr.Label(visible=False)
- txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
-
- with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
- from modules import ui_extra_networks
- extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
-
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='compact', elem_id="txt2img_settings"):
- modules.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 = modules.scripts.scripts_txt2img.setup_ui()
-
- else:
- modules.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,
- txt2img_prompt,
- txt2img_negative_prompt,
- txt2img_prompt_styles,
- 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,
- )
-
- txt2img_prompt.submit(**txt2img_args)
- submit.click(**txt2img_args)
-
- res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
-
- 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,
- )
-
- txt_prompt_img.change(
- fn=modules.images.image_data,
- inputs=[
- txt_prompt_img
- ],
- outputs=[
- txt2img_prompt,
- txt_prompt_img
- ],
- show_progress=False,
- )
-
- enable_hr.change(
- fn=lambda x: gr_show(x),
- inputs=[enable_hr],
- outputs=[hr_options],
- show_progress = False,
- )
-
- txt2img_paste_fields = [
- (txt2img_prompt, "Prompt"),
- (txt2img_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"),
- (subseed, "Variation seed"),
- (subseed_strength, "Variation seed strength"),
- (seed_resize_from_w, "Seed resize from-1"),
- (seed_resize_from_h, "Seed resize from-2"),
- (txt2img_prompt_styles, 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),
- (hr_options, lambda d: gr.Row.update(visible="Denoising strength" 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()),
- *modules.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=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
- ))
-
- txt2img_preview_params = [
- txt2img_prompt,
- txt2img_negative_prompt,
- steps,
- sampler_index,
- cfg_scale,
- seed,
- width,
- height,
- ]
-
- token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
- negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
-
- ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
-
- modules.scripts.scripts_current = modules.scripts.scripts_img2img
- modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
- with gr.Blocks(analytics_enabled=False) as img2img_interface:
- img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
-
- img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
-
- 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, extra_networks_button, 'img2img')
-
- with FormRow().style(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").style(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").style(height=opts.img2img_editor_height)
- 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").style(height=opts.img2img_editor_height)
- 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").style(height=opts.img2img_editor_height)
- 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")
-
- modules.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")
+ results = extras.run_modelmerger(*args)
+ except Exception as e:
+ errors.report("Error loading/saving model file", exc_info=True)
+ sd_models.list_models() # to remove the potentially missing models from the list
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+ return results
- 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")
+class UiCheckpointMerger:
+ def __init__(self):
+ with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='compact'):
+ self.interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
- 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,
- )
+ with FormRow(elem_id="modelmerger_models"):
+ self.primary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+ create_refresh_button(self.primary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
- scale_by.release(**on_change_args)
- button_update_resize_to.click(**on_change_args)
+ self.secondary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+ create_refresh_button(self.secondary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
- # 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)
+ self.tertiary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+ create_refresh_button(self.tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
- 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 = modules.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:
- modules.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_prompt_img.change(
- fn=modules.images.image_data,
- inputs=[
- img2img_prompt_img
- ],
- outputs=[
- img2img_prompt,
- img2img_prompt_img
- ],
- show_progress=False,
- )
-
- img2img_args = dict(
- fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
- _js="submit_img2img",
- inputs=[
- dummy_component,
- dummy_component,
- img2img_prompt,
- img2img_negative_prompt,
- img2img_prompt_styles,
- 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=[img2img_prompt, dummy_component],
- )
-
- img2img_prompt.submit(**img2img_args)
- 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,
- )
-
- 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,
- )
-
- img2img_interrogate.click(
- fn=lambda *args: process_interrogate(interrogate, *args),
- **interrogate_args,
- )
-
- img2img_deepbooru.click(
- fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
- **interrogate_args,
- )
-
- prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
- style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
- style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
- for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
- button.click(
- fn=add_style,
- _js="ask_for_style_name",
- # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
- # the same number of parameters, but we only know the style-name after the JavaScript prompt
- inputs=[dummy_component, prompt, negative_prompt],
- outputs=[txt2img_prompt_styles, img2img_prompt_styles],
- )
-
- for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
- button.click(
- fn=apply_styles,
- _js=js_func,
- inputs=[prompt, negative_prompt, styles],
- outputs=[prompt, negative_prompt, styles],
- )
-
- token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
- negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
-
- ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
-
- img2img_paste_fields = [
- (img2img_prompt, "Prompt"),
- (img2img_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"),
- (subseed, "Variation seed"),
- (subseed_strength, "Variation seed strength"),
- (seed_resize_from_w, "Seed resize from-1"),
- (seed_resize_from_h, "Seed resize from-2"),
- (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
- (denoising_strength, "Denoising strength"),
- (mask_blur, "Mask blur"),
- *modules.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=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
- ))
-
- modules.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().style(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],
- )
-
- def update_interp_description(value):
- interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
- interp_descriptions = {
- "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
- "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
- "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
- }
- return interp_descriptions[value]
-
- with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='compact'):
- interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
- with FormRow(elem_id="modelmerger_models"):
- primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
- create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
- secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
- create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
- tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
- create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
- custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
- interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
- interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
- interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
- with FormRow():
- checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
- save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
- save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
-
- with FormRow():
- with gr.Column():
- config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
- with gr.Column():
- with FormRow():
- bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
- create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
- with FormRow():
- discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
- with gr.Row():
- modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
- with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
- with gr.Group(elem_id="modelmerger_results_panel"):
- modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
-
- with gr.Blocks(analytics_enabled=False) as train_interface:
- with gr.Row().style(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").style(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=modules.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)
+ self.custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+ self.interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+ self.interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+ self.interp_method.change(fn=update_interp_description, inputs=[self.interp_method], outputs=[self.interp_description])
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")
+ self.checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+ self.save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+ self.save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
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")
+ with gr.Column():
+ self.config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
- 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 gr.Column():
+ with FormRow():
+ self.bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+ create_refresh_button(self.bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
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")
+ self.discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
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').style(columns=4)
- gr.HTML(elem_id="ti_progress", value="")
- ti_outcome = gr.HTML(elem_id="ti_error", value="")
-
- create_embedding.click(
- fn=modules.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=modules.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(modules.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(modules.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(modules.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_interface, "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()
+ self.modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
- 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)
+ with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+ with gr.Group(elem_id="modelmerger_results_panel"):
+ self.modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=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")
+ self.blocks = modelmerger_interface
- 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])
-
- def modelmerger(*args):
- try:
- results = modules.extras.run_modelmerger(*args)
- except Exception as e:
- errors.report("Error loading/saving model file", exc_info=True)
- modules.sd_models.list_models() # to remove the potentially missing models from the list
- return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
- return results
-
- modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
- modelmerger_merge.click(
- fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+ def setup_ui(self, dummy_component, sd_model_checkpoint_component):
+ self.modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[self.modelmerger_result])
+ self.modelmerger_merge.click(
+ fn=call_queue.wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
_js='modelmerger',
inputs=[
dummy_component,
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- interp_method,
- interp_amount,
- save_as_half,
- custom_name,
- checkpoint_format,
- config_source,
- bake_in_vae,
- discard_weights,
- save_metadata,
+ self.primary_model_name,
+ self.secondary_model_name,
+ self.tertiary_model_name,
+ self.interp_method,
+ self.interp_amount,
+ self.save_as_half,
+ self.custom_name,
+ self.checkpoint_format,
+ self.config_source,
+ self.bake_in_vae,
+ self.discard_weights,
+ self.save_metadata,
],
outputs=[
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- settings.component_dict['sd_model_checkpoint'],
- modelmerger_result,
+ self.primary_model_name,
+ self.secondary_model_name,
+ self.tertiary_model_name,
+ sd_model_checkpoint_component,
+ self.modelmerger_result,
]
)
- loadsave.dump_defaults()
- demo.ui_loadsave = loadsave
-
- # Required as a workaround for change() event not triggering when loading values from ui-config.json
- interp_description.value = update_interp_description(interp_method.value)
-
- 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"])
+ # Required as a workaround for change() event not triggering when loading values from ui-config.json
+ self.interp_description.value = update_interp_description(self.interp_method.value)
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